Central California Ozone Study

Transcription

Central California Ozone Study
(CCOS)
Volume I
Field Study Plan
Version 3
November 24, 1999
CCOS Field Study Plan
Version 3 – 11/24/99
Central California Ozone Study – Volume I
Field Study Plan
Prepared by:
Eric Fujita, Robert Keislar, William Stockwell,
Hans Moosmuller, David DuBois, Darko Koracin,
and Barbara Zielinska
Division of Atmospheric Science
Desert Research Institute
2215 Raggio Parkway
Reno, NV 89512
Saffet Tanrikulu and Andrew Ranzieri
Planning and Technical Support Division
California Air Resources Board
2020 L Street
Sacramento, CA 95812
www.arb.ca.gov
www.arb.ca.gov/ccaqs/ccos/ccos.htm
The CCOS Plan was prepared with extensive input from the CCOS Technical Committee, Scientific
Advisory Work Group, Meteorological Work Group, and Emission Inventory Coordination Group.
Technical Committee
(Planning Subcommittee)
Emission Inventory Coordination Group
Linda Murchison, California Air Resources Board
Dale Shimp, California Air Resources Board
Cheryl Taylor, California Air Resources Board
Dennis Wade, California Air Resources Board
Michael Benjamin, California Air Resources Board
Phil Martien, Bay Area AQMD
Toch Mangat, Bay Area AQMD
Bruce Katayama, Sacramento Metropolitan AQMD
Brigette Tollstrup, Sacramento Metropolitan AQMD
Hazel Hoffmann, San Joaquin Valley Unified APCD
Dave Jones, San Joaquin Valley Unified APCD
Tom Jordon, San Joaquin Valley Unified APCD
Scott Nestor, San Joaquin Valley Unified APCD
Stephen Shaw, San Joaquin Valley Unified APCD
Gretchen Bennitt, Northern Sierra AQMD
Dick Johnson, Placer County APCD
Tom Roemer, San Luis Obispo County APCD
Larry Green, Yolo-Solano AQMD
Nancy O’Connor, Yolo-Solano AQMD
Dave Smith, Yolo-Solano AQMD
Gordon Garry, Sacramento Area CoG
Guido Franco, California Energy Commission
Morris Goldberg, U.S. EPA
Andrew Ranzieri, California Air Resources Board
Saffet Tanrikulu, California Air Resources Board
Rob DeMandel, Bay Area AQMD
Evan Shipp, San Joaquin Valley Unified APCD
Phil Roth, Envair
Jim Sweet, San Joaquin Valley Unified APCD
Steve Ziman, Chevron Research and Technology
Scientific Advisory Work Group
Dan Chang, University of California, Davis
Dennis Fitz, UCR, CE-CERT
Robert Harley, University of California, Berkeley
Mike Kleeman, University of California, Davis
Gail Tonnesen, University of California, Riverside
Meteorological Work Group
Saffet Tanrikulu, California Air Resources Board
Bob Keislar, Desert Research Institute
David Fairley, Bay Area AQMD
Tom Umeda, Bay Area AQMD
Evan Shipp, San Joaquin Valley Unified APCD
Steve Gouze, California Air Resources Board
Bob Noon, Monterey Bay Unified AQMD
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TABLE OF CONTENTS
Page
List of Tables ................................................................................................................................. vi
List of Figures .............................................................................................................................viii
1. 0 STUDY PERSPECTIVE AND SUMMARY......................................................................1-1
1.1 Introduction .................................................................................................................1-1
1.2 CCOS Goals and Technical Objectives.......................................................................1-3
1.2.1 Objectives A - Planning and Preparation for the CCOS Field Study..............1-3
1.2.2 Objectives B - Emission Inventory Development ...........................................1-6
1.2.3 Objectives C - Preparation, Execution and Evaluation of Air Quality
Simulation Modeling System ..........................................................................1-6
1.2.4 Objectives D - Data Analysis ..........................................................................1-8
1.3 Basis for CCOS Field Measurement Program...........................................................1-10
1.3.1 Analysis of Past Ozone Data .........................................................................1-11
1.3.2 Cluster Analysis of High Ozone Days...........................................................1-11
1.3.3 Meteorological Scenarios by Weather Map Analysis ...................................1-12
1.3.4 Episode Forecasting and Selection................................................................1-21
1.4 Overview of CCOS Field Measurements ..................................................................1-22
1.4.1 Surface Meteorological and Air Quality Measurements ...............................1-23
1.4.2 Aloft Meteorology and Air Quality Measurements.......................................1-26
1.4.3 Complementary Measurement Programs ......................................................1-30
1.4.4 Consideration of Measurement Alternatives .................................................1-31
1.5 Applications of CCOS Data for Evaluation of Air Quality Modeling Systems........1-31
1.5.1 Meteorological Models..................................................................................1-32
1.5.2 Emission Inventories .....................................................................................1-33
1.5.3 Air Quality Models........................................................................................1-34
1.5.4 Plume in Grid Module Evaluation.................................................................1-35
1.6 Corroborative Data Analysis .....................................................................................1-36
1.6.1 Contribution of Transported Pollutants to Ozone Violations in Downwind
Areas..............................................................................................................1-36
1.6.2 VOC Versus NOx Sensitivity........................................................................1-37
1.7 Funding......................................................................................................................1-38
2.0 BASIS FOR THE FIELD STUDY PLAN ..........................................................................2-1
2.1 CCOS Study Area........................................................................................................2-1
2.2 Ozone Air Quality Standards and SIP Requirements..................................................2-3
2.3 Ambient Trends in Ozone and Precursor Gases..........................................................2-5
2.3.1 Trends in Ozone Exceedances.........................................................................2-5
2.3.2 Spatial and Temporal Patterns of Ozone Precursors .......................................2-7
2.4 Emissions and Source Contributions...........................................................................2-7
2.5 Central California Summer Meteorology and Ozone Climatology.............................2-8
2.5.1 Typical Large-Scale Meteorological Features.................................................2-8
2.5.2 Mesoscale Meteorological Features in the CCOS Study Region....................2-9
2.5.3 Major Transport Couples in Central California.............................................2-12
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TABLE OF CONTENTS (continued)
Page
2.5.4 Meteorological Scenarios Associated with Ozone Exceedances ..................2-14
2.6 Atmospheric Transformation and Deposition ...........................................................2-21
2.6.1 Ozone Formation ...........................................................................................2-21
2.6.2 Heterogeneous Reactions and Ozone - Secondary Aerosol Formation.........2-32
2.6.3 Role of Ozone Precursors from Natural Sources ..........................................2-36
2.6.4 Relative Effectiveness of VOC and NOx Controls .......................................2-36
2.6.5 Atmospheric Deposition................................................................................2-37
2.7 Conceptual Model of Ozone Episodes and Transport Scenarios of Interest .............2-37
2.7.1 Current Conceptual Model of Ozone Formation and Transport ...................2-37
2.7.2 Implications of Change in Federal Ozone Standard on Conceptual Model ..2-38
2.8 Review of previous SAQM studies and implications for CCOS measurement
sites............................................................................................................................2-39
3.0 REQUIREMENTS FOR AIR QUALITY MODELING SYSTEMS AND DATA
ANALYSIS..........................................................................................................................3-1
3.1 Modeling System Inputs..............................................................................................3-1
3.1.1 Meteorological Modeling ................................................................................3-1
3.1.2 Emission Inventory Development ...................................................................3-4
3.1.3 Air Quality Modeling ....................................................................................3-12
3.2 Model Evaluation ......................................................................................................3-14
3.2.1 Evaluation of Meteorological Modeling .......................................................3-14
3.2.2 Evaluation of Emission Inventory Estimates ................................................3-15
3.2.3 Air Quality Model Evaluation .......................................................................3-20
3.3 Data Analysis Methods..............................................................................................3-21
3.3.1 Accuracy, Precision, Validity, and Equivalence of Field Measurements .....3-22
3.3.2 Spatial, Temporal, and Statistical Distributions of Air Quality
Measurements................................................................................................3-24
3.3.3 Meteorological Transport Phenomena ..........................................................3-26
3.3.4 Meteorological Dispersion Processes............................................................3-27
3.3.5 Characterize Pollutant Fluxes........................................................................3-28
3.3.6 Characterize Chemical and Physical Interactions .........................................3-28
3.3.7 Characterize Episodes ...................................................................................3-31
3.3.8 Observation-Driven Methods ........................................................................3-31
Areas..............................................................................................................3-32
3.3.10 Contributions of Elevated NOx Sources to Downwind O3 ...........................3-32
3.3.11 Deposition Studies.........................................................................................3-33
3.3.12 Reformulate the Conceptual Model...............................................................3-34
4.0 CCOS FIELD MEASUREMENT PROGRAM ..................................................................4-1
4.1 Study Design Principles ..............................................................................................4-1
4.2 Study Domain..............................................................................................................4-2
4.3 Study Period ................................................................................................................4-2
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Page
4.4 Surface Meteorological Measurements .......................................................................4-3
4.5 Surface Air Quality Measurements .............................................................................4-4
4.5.1 CCOS Type 0 Supplemental Monitoring Sites ...............................................4-5
4.5.2 CCOS Type 1 Supplemental Monitoring Sites ...............................................4-5
4.5.3 CCOS Type 2 Supplemental Monitoring Sites: ..............................................4-6
4.5.4 CCOS Research Sites ......................................................................................4-7
4.6 Upper Air Meteorological Network ............................................................................4-8
4.7 In-Situ Aircraft Measurements ....................................................................................4-9
4.8 Consideration of Alternative Vertical Ozone Measurements....................................4-11
4.9 Measurements for Special Studies.............................................................................4-12
5.0 BUDGET ESTIMATES .......................................................................................................5-1
6.0 PROGRAM MANAGEMENT PLAN AND SCHEDULE.................................................6-1
6.1 CCOS Management Structure .....................................................................................6-1
6.1.1 Policy Committee ............................................................................................6-1
6.1.2 Technical Committee.......................................................................................6-1
6.1.3 Principal Investigators .....................................................................................6-2
6.1.4 Meteorological Working Group ......................................................................6-3
6.1.5 Emissions InventoryWorking Group...............................................................6-3
6.1.6 Field Coordinator.............................................................................................6-3
6.1.7 Quality Assurance Officer...............................................................................6-7
6.1.8 Data Manager ..................................................................................................6-7
6.1.9 Measurement Investigators..............................................................................6-8
6.1.10 Data Analysis and Modeling Investigators......................................................6-8
6.2 Schedule ........................................................................................................................6-9
7.0 REFERENCES ....................................................................................................................7-1
A.
MEASUREMENT METHODS
A.1 Surface Meteorology................................................................................................... A-1
A.1.1 Surface Wind Speed and Direction ................................................................ A-1
A.1.2 Surface Relative Humidity ............................................................................. A-2
A.1.3 Surface Temperature ...................................................................................... A-3
A.1.4 Surface Pressure ............................................................................................. A-3
A.1.5 Solar Radiation ............................................................................................... A-3
A.2 Upper-Air Wind Speed, Wind Direction, and Temperature....................................... A-3
A.2.1 Radar Wind Profilers...................................................................................... A-3
A.2.2 Radio-Acoustic Sounding Systems (RASS)................................................... A-4
A.2.3 Acoustic Sounders (Sodars) ........................................................................... A-4
A.2.4 Radiosondes.................................................................................................... A-5
A.2.5 Tethered Balloons........................................................................................... A-5
A.2.6 Sonic Anemometers........................................................................................ A-6
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Page
A.3 Routine Surface Air Quality....................................................................................... A-6
A.3.1 Ozone.............................................................................................................. A-7
A.3.2 Nitrogen Oxides ............................................................................................. A-8
A.3.3 Photochemical Assessment Monitoring Stations (PAMS) Program .............. A-9
A.4 Supplemental Measurements of Ozone .................................................................... A-10
A.4.1 Ozonesondes................................................................................................. A-10
A.4.2 Ozone Aloft  Lidar Measurements ........................................................... A-11
A.5 Supplemental Measurements of Oxidized Nitrogen Species ................................... A-16
A.5.1 NO2 and PAN by Gas Chromatography with Luminol Chemiluminescence
Detection....................................................................................................... A-17
A.5.2 NO2 and HNO3 by Tunable Diode Laser Absorption Spectroscopy........... A-18
A.5.3 NO2, HONO, and NO3 Radical by Differential Optical Absorption
Spectroscopy (DOAS) .................................................................................. A-18
A.5.4 Automated Particle Nitrate Monitor............................................................. A-18
A.6 In-Situ Instrumented Aircrafts.................................................................................. A-20
A.7 Supplemental Measurements of Volatile Organic Compounds ............................... A-21
A.7.1 Collection and Analysis of Hydrocarbons and Oxygenated Species ........... A-21
A.7.2 Carbonyl Compounds................................................................................... A-23
A.7.3 C8-C20 Hydrocarbons by Tenax Sampling and Analysis by GC/FID or
GC/MS.......................................................................................................... A-24
A.7.4 Continuous Formaldehyde by Fluorescent Detection of Dihydrolutidine
Derivative ..................................................................................................... A-24
A.7.5 Continuos GC/MS System ........................................................................... A-25
A1 MASTER VOC PARAMETER LIST .............................................................................. A1-1
B.
QUALITY ASSURANCE.................................................................................................. B-1
B.1 Quality Assurance Overview...................................................................................... B-3
B.2 Data Quality Objectives ............................................................................................. B-3
B.3 Systems Audits ........................................................................................................... B-4
B.3.1 Field Systems Audits for Surface Monitoring Sites ....................................... B-4
B.3.2 Field Systems Audits for Aircraft Platforms .................................................. B-5
B.3.3 Laboratory Systems Audits ............................................................................ B-5
B.4 Performance Audits .................................................................................................... B-5
B.4.1 Field Performance Audits of Surface Monitors.............................................. B-5
B.4.2 Field Performance Audits for Surface Meteorological Measurements .......... B-6
B.4.3 Field Performance Audits for Upper-Air Meteorology.................................. B-7
B.4.4 Field Performance Audits for Aircraft Platforms........................................... B-7
B.4.5 Laboratory Performance Audits for Chemical Analysis ................................ B-7
C.
DATA MANAGEMENT ................................................................................................... C-1
C.1 Data Specifications..................................................................................................... C-1
C.2 Data Formats .............................................................................................................. C-2
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Page
C.3
C.4
C.5
C.6
C.7
C.8
D.
File Names.................................................................................................................. C-3
Validation Flags.......................................................................................................... C-3
Data Validation Levels ............................................................................................... C-3
Internet Server ............................................................................................................ C-5
Directory Structure ..................................................................................................... C-5
Data Processing .......................................................................................................... C-6
Elevated Plume Research Plan ........................................................................................... D-1
D.1 Study Objectives......................................................................................................... D-1
D.2 Scientific Background ................................................................................................ D-1
D.3 Measurement Plan ...................................................................................................... D-6
D.4 Data Analysis and Model Evaluation ....................................................................... D-13
D.5 References ................................................................................................................ D-14
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LIST OF TABLES (continued)
Table No.
Page No.
Table 1.4-1
Supplemental Surface Air Quality and Meteorological Measurements.............1-25
Table 1.4-2
CCOS Upper-Air Meteorological Measurements ..............................................1-27
Table 2.1-1
Populations and Areas for Central California Metropolitan Statistical Areas ...2-42
Table 2.3-1a Linked Master Ozone Monitoring Site List .......................................................2-43
Table 2.3-1b Ozone Monitoring Sites Linked in Time for Longer Period of Record .............2-47
Table 2.3-2
Summary of Trends in Daily 1hr and 8hr Ozone Maxima during MayOctober, 1990-98 ...............................................................................................2-48
Table 2.3-3
Annual Maximum of Daily Maximum Ozone Concentrations in Central
California During May to October, 1990-98......................................................2-50
Table 2.3-4
Annual Exceedances of the 1-hr and 8-hr Ozone Standards in Central
California During May to October, 1990-98......................................................2-55
Table 2.3-5
Average Annual Exceedances of the 1-hr and 8-hr Ozone Standards in
Central California by Month May to October, 1990-98.....................................2-60
Table 2.3-6
Average Annual Exceedances of the 1-hr and 8-hr Ozone Standards in
Central California by Day-of-the-Week.............................................................2-65
Table 2.4-1
1996 Daily Average ROG Emissions by Air Basins in the CCOS Domain ......2-70
Table 2.4-2
1996 Daily Average NOx Emissions by Air Basins in the CCOS Domain.......2-72
Table 2.5-1
May-October Climate Summaries for Selected Central California Cities
(1961-1990 means).............................................................................................2-74
Table 2.5-2
Meteorological Scenarios by Weather Map Inspection .....................................2-75
Table 2.5-3
Basin and Subbasin Exceedance Frequencies by Meteorological Scenario ......2-76
Table 2.5-4
High-Ozone Episodes Identified by the Local Districts for Ozone Seasons
1996-98...............................................................................................................2-77
Table 2.5-5
Cluster Analysis Days by Cluster with Subjective Scenario Type ....................2-81
Table 2.5-6
Descriptive Statistics of Meteorological Parameters for Identified Clusters .....2-83
Table 2.7-1
Number of Exceedances and Ratios of 8-Hour and 1-Hour Parameters............2-84
Table 3.1-1
Emission Inventory Roles and Responsibilities .................................................3-37
Table 3.1-2
Required Meteorological Files. ..........................................................................3-38
Table 3.1-3
Typical Chemical Species in an Emission Inventory for Modeling...................3-38
Table 4.5-1
CCOS Supplemental Surface Measurements.....................................................4-16
Table 4.5-2
CCOS Supplemental Surface and Air Quality and Meteorological
Measurements.....................................................................................................4-17
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LIST OF TABLES (continued)
Table No.
Page No.
Table 4.6-1
Upper-Air Meteorological Measurement for CCOS..........................................5-18
Table 5-1
Summary of Cost Estimates for the CCOS Field Measurement Program ......... 5-2
Table 6.2-1
CCOS Schedule of Milestones...........................................................................6-10
Table A.1-1
Meteorological Sensor Specifications .............................................................. A-26
Table A.1-2
Air Quality Monitoring Site in Northern and Central California ..................... A-27
Table A.1-3
PAMS Sites in the CCOS Area ........................................................................ A-32
Table A.1-4
PAMS Target Species ...................................................................................... A-33
Table A.6.1
UCD Instrumentation ....................................................................................... A-34
Table A.6.2
STI Instrumentation .......................................................................................... A-35
Table D-1
Flight characteristics and instrumentation of Bell 205 Helicopter:
TVA Environmental Research Center................................................................. D-8
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LIST OF FIGURES
Figure No.
Figure 1.3-1
Page No.
Mean basin maximum 1-hr ozone for each cluster grouped by cluster
and grouped by air basin ...................................................................................1-13
Figure 1.3.2
Maximum 1-hr ozone for 126 monitoring stations on 8/12/98, a cluster 1 day. 1-14
Figure 1.3-3
Figure 1.3-4
Figure 1.3-5
Figure 1.3-6
Figure 1.3-7
Figure 2.1-1
Overall study domain with major landmarks, mountains and passes. ...............2-85
Figure 2.1-2
Major political boundaries and air basins within central California. .................2-86
Figure 2.1-3
Major population centers within central California. ..........................................2-87
Figure 2.1-4
Land use within central California from the U.S. Geological Survey. ..............2-88
Figure 2.1-5
Major highway routes in central California. ......................................................2-89
Figure 2.3-1
Annual basin maximum of 8-hour daily maximum ozone trends by
location type ......................................................................................................2- 90
Figure 2.3-2
Average 8-hour daily maximum ozone trends in central California by
location type . .................................................................................................... 2-91
Figure 2.3-3
Weekend/weekday effect on average 1-hour daily maximum ozone in
central California by location type .....................................................................2-92
Figure 2.6-1
Overview of ozone production in the troposphere. ............................................2-93
Figure 2.6-2. (A) Rate constant for the O3 + NO reaction with upper and lower bounds.
(B) The uncertainty factor, f(T)..........................................................................2-94
Figure 2.6-3
(A) Rate constant for the CH3O2 + HO2 reaction with upper and lower
bounds. (B) The uncertainty factor, f(T). ..........................................................2-95
Figure 2.6-4
Atmospheric lifetimes of selected organic compounds with respect to a
hydroxyl radical concentration of 7.5 x 106 molecules cm-3.............................2-96
Figure 2.6-5
Uncertainties in rate parameters for HO radical reactions with alkenes. ...........2-97
Figure 2.6-6
Relative sensitivity of ozone to reaction rate constants. ....................................2-98
Figure 2.6-7
Mean values and 1 σ uncertainties of maximum incremental reactivity
values for selected hydrocarbons determined from Monte Carlo
simulations. ........................................................................................................2-99
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LIST OF FIGURES (continued)
Figure No.
Page No.
Figure 2.8-2
Plot of 1-hr ozone concentrations in central California at midnight after
two simulated days ..........................................................................................2-100
Figure 2.8-2
Plot of 1-hr ozone concentrations in central California at midnight after
two simulated days ..........................................................................................2-101
Figure 2.8-3
Plot of 1-hr ozone concentrations in central California at noon on the third
simulated day ...................................................................................................2-102
Figure 2.8-4
Plot of 1-hr ozone concentrations in the vertical along a north – south
cross section through the center of central California .....................................2-103
Figure 2.8-5
Plot of 1-hr ozone concentrations in the vertical along a west – east cross
section through central California along a line running from the
San Francisco Bay Area through Sacramento to the front range ....................2-104
Figure 2.8-6
Plot of formaldehyde concentrations at midnight after two simulated days. ...2-105
Figure 2.8-7
Plot of NO concentrations at 1:00 PM on the second simulated day ..............2-106
Figure 3.1-1
File typically required by photochemical air quality model. .............................3-39
Figure 3.2-1
Average source contribution estimates of ambient hydrocarbons at
Rider College, NJ during summer, 1995 by time of day. Source:
Fujita and Lu, 1998 ...........................................................................................3-40
Figure 3.2-3
Wind directional dependence of source contributions by time of the day at
Rider College, NJ during summer, 1995............................................................3-41
Figure 4.4-1
Central California surface meteorological networks and measurement
locations. ............................................................................................................4-23
Figure 4.4-2
Surface meteorological observables measured in the combined central
California meteorological network.....................................................................4-24
Figure 4.5-1
Existing routine O3 and NOx monitoring sites..................................................4-25
Figure 4.5-2
CCOS supplemental air quality and meteorological monitoring sites and
Photochemical Assessment Monitoring Stations ...............................................4-26
Figure 4.6-1
Upper air meteorological measurements during the summer campaign,
including annual average study sites and NEXRAD (WSR-88D) profilers. .....4-27
Figure 4.6-2
Upper air meteorological measurement network indicating operating
agency.................................................................................................................4-28
Figure A.1-1 Ultraviolet Absorption Spectra of Ozone, Sulfur Dioxide, and Nitrogen
Dioxide ............................................................................................................. A-36
Figure A.1-2 Schematic diagram of NOAA’s airborne ozone lidar. ..................................... A-37
Figure D-1
Typical ozone isopleth. The maximum ozone concentrations have been
xi
Version 3 – 11/24/99
removed to allow the structure at lower NOx and VOC concentrations
to be seen............................................................................................................. D-3
Figure D-2
Ozone efficiency in terms of moles of ozone produced per moles of NOx
converted to NOy. ............................................................................................... D-4
Figure D-3
Bell 205 Helicopter: TVA Environmental Research Center............................... D-5
Figure D-4
Proposed flight plans for plume measurement experiments. ............................ D-12
Figure D-5
Estimated NOx concentrations for the plume from the Pittsburgh electric power
generating station. The NOx concentrations were calculated from a simple
dispersion model. .............................................................................................. D-13
xii
1.0
STUDY PERSPECTIVE AND SUMMARY
Central California is a complex region from an air quality and meteorological
perspective, owing to its proximity to the Pacific Ocean, its diversity of climates, and its complex
terrain. As a result of progressively more stringent controls on emissions of reactive organic
gases and oxides of nitrogen, the frequency and intensity of excessive ozone concentrations in
central and northern California have been significantly reduced despite rapid increases in
population, commercial activities and vehicle miles traveled. For example, the average annual
maximum hourly ozone concentrations have declined by 40, 21, and 30 percent from 1980 to
1997 in the San Francisco Bay Area, Sacramento Valley, and San Joaquin Valley air basins,
respectively. The average annual exceedances of the federal 1-hour ozone standard (0.12 ppm) in
the San Francisco Bay Area declined from 11 in 1980 to 4 in 1997, from 21 to 10 in the
Sacramento Valley Air Basin and from 58 to 41 in the San Joaquin Valley Air Basin. While
progress has been made toward attainment of the 1-hour ozone standard, it continues to be
exceeded frequently in central California, and the prediction of attainment for the San Joaquin
Valley by 1999, based on modeled forecast of emissions in 1994 (SJVUAPCD, 1994), has not
been acheived.
Retrospective analyses of ozone (O3) data (details in Section 2) have also shown varying
progress toward attainment of ozone standards within northern and central California with
greater reductions in ozone within coastal urban areas than in the Central Valley. In addition, the
data show larger downward trends in 1-hour-average peak O3 concentrations 1 and less progress
in reducing the frequency of exceedances of the state 1-hour standard (0.09 ppm) and the
pending federal 8-hour ozone standard 2. In addition to areas that are currently in nonattainment
of the 1-hour ozone standard, several areas in central and southern Sierra Foothills and northern
Sacramento Valley that are now in compliance of the 1-hour standard are also expected to
become nonattainment for the 8-hour standard. The state 1-hour and pending federal 8-hour
ozone standards will require a reappraisal of past strategies that have focused primarily on
addressing the urban/suburban ozone problem to one that considers the problem in a more
regional context. Although the recent court action prohibits EPA from enforcing the 8-hour
ozone standard, the ruling did not remove the standard. Pending the likely appeal by EPA and its
ultimate outcome, the current 1-hour ozone standard continues to apply in areas that have not
attained the standard.
1.1
Introduction
The Central California Ozone Study (CCOS) is intended to provide another milestone in
the understanding of relationships among emissions, transport, and ozone standard exceedances,
as well as to facilitate planning for further emission reductions needed to attain state and federal
ozone standards. The CCOS is being proposed to gather aerometric and emissions databases for
modeling and to apply air quality models for the attainment demonstration portion of the SIP for
the federal 8-hour and state 1-hour ozone standards. CCOS is an integrated effort that includes
air quality and meteorological field measurements, emissions characterization, data analysis and
1
In this report, the term concentration refers to mixing ratios by volume.
The new standard will be attained when the 3-year average of the annual 4th highest daily maximum 8-hour
concentration is less than or equal to 0.08 ppm.
2
1-1
air quality modeling. The modeling domain for CCOS will cover all of central California and
most of northern California, extending from the Pacific Ocean to east of the Sierra Nevada and
from Redding to the Mojave Desert. The selection of this study area reflects the regional nature
of the state 1-hour and federal 8-hour ozone exceedances, increasing urbanization of traditionally
rural areas, and a need to include all of the major flow features that affect air quality in central
California in the modeling domain. The CCOS field measurement program will be conducted in
the summer of 2000 in conjunction with the California Regional PM10/PM2.5 Air Quality Study
(CRPAQS), a major study of the origin, nature and extent of excessive levels of fine particles in
central California (Watson et al., 1998).
The CCOS is directed by a technical committee that comprises staff from the California
Air Resources Board (ARB), U.S. Environmental Protection Agency (EPA), the California
Energy Commission (CEC), local air pollution control agencies, industry, and other sponsoring
organizations with technical input from a consortium of university researchers in California and
the Desert Research Institute (DRI). The CCOS plan consists of this field study plan (Volume I)
and a field operations plan and protocol (Volume II), which will be available by April, 2000.
These documents correspond to the two phases of the planning process for CCOS. Parallel
efforts are also underway to develop a broad conceptual model of ozone formation in central
California based on SARMAP3 and other relevant studies and a “comprehensive” study plan that
addresses the long-term research needs related to the ozone problem in central California (Roth,
1999a and 1999b).
This CCOS field study plan describes the goals and technical objectives that will be
addressed by the study and describes alternative experimental, modeling, and data analysis
approaches for addressing the study objectives. This introductory section provides an overview
of the study, which includes the background and rationale for the proposed study, statements of
study goals and technical objectives, and a summary of the proposed field measurements and
subsequent modeling and data analysis. Chapter 2 presents a summary of the current knowledge
of the relationship among meteorology, emissions, chemical and physical transformation, and
ozone concentrations in northern and central California. It also reviews the results from prior
SAQM4 modeling by ARB, and identifies the remaining uncertainties and their implications for
the design of the CCOS field measurement program. Section 3 describes the requirements for
the modeling and data analysis approaches that are proposed to address CCOS technical
objectives. Section 4 specifies the CCOS measurements that are recommended to meet the
requirements of modeling and data analysis. It considers the merits of alternative measurement
approaches and explains the rationale and criteria for measurement decisions. Details of the
measurements are contained in three appendices. Appendix A describes the existing
meteorological and air quality measurement networks and details of the supplemental
measurements proposed for CCOS (e.g., measurement method, precision, and accuracy).
Appendix A1 provides a list of volatile organic compounds that are recommended for
identification and quantification as part of the chemical characterization of ozone precursors.
Appendices B and C describe the required quality assurance and data management activities for
the study, respectively. Appendix D describes a special study of ozone formation in elevated
3
San Joaquin Valley Air Quality Study and Atmospheric Utility Signatures, Predictions and Experiments Regional
Model Adaptation Program
4
SARMAP Air Quality Model
1-2
plumes. Section 5 provides the corresponding budget estimates and Section 6 defines the
program management and schedule.
This third version of the CCOS field study plan is a product of regular meetings of the
CCOS Technical Committee and Working Groups over the past six months with input from local
air pollution control districts, academia, and industry reviewers on two earlier versions of the
plan (released on 6/11/99 and 9/7/99). The current plan serves as the basis for contracts with
measurement groups and for developing the operations plan and protocols for the field study in
summer 2000.
1.2
CCOS Goals and Technical Objectives
The CCOS has the following goals.
1. Obtain suitable aerometric and emission databases to update, evaluate, and improve
model applications for representing urban and regional-scale ozone episodes in central
and northern California to meet the regulatory requirements for the state 1-hour and
pending federal 8-hour ozone standards.
2. Determine the contributions of transported and locally generated ozone and the relative
benefits of volatile (VOC) and nitrogen oxide (NOx) emission controls in upwind and
downwind areas. Assess the relative contributions of ozone generated from emissions in
one air basin to federal and state exceedances in neighboring air basins.
These goals are to be met through a process that includes analysis of existing data;
execution of a large-scale field study to acquire a comprehensive database to support modeling
and data analysis; analysis of the data collected during the field study; and the development,
evaluation, and application of an air quality simulation model for northern and central California.
Although air quality simulation modeling may be used to address both CCOS goals, past
experience has demonstrated the need for thorough diagnostic analyses and corroborative data
analysis to assess the reliability of model outputs.
The study design and experimental approach for CCOS are defined in terms of specific
technical objectives. These objectives fall into four categories: (A) planning and preparation; (B)
emission inventory development, (C) modeling, and (D) data analysis. A number of tasks are
required to meet each of the technical objectives. These tasks are listed in this section and are
described in greater detail in Section 3 (data requirements) and Section 4 (field measurement
program).
1.2.1
Objectives A - Planning and Preparation for the CCOS Field Study
Objective A-1. Develop a technically and scientifically defensible experimental design based on
the goals and objectives of the sponsoring organizations and current conceptual understanding of
the chemical and meteorological conditions that are conducive to violations of the state 1-hour
and federal 8-hour ozone standards in central California.
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a. Update current bibliography, review and summarize the literature on emissions,
meteorology, ozone formation, and transport in central California, and identify gaps in
current knowledge with respect to the questions posed.
b. Summarize existing monitoring data in central California with respect to locations and
frequency of exceedances of the threshold value for the state 1-hour and pending federal
8-hour ozone standards.
c. Review and evaluate past SIP modeling projections of air quality for the San Francisco
Bay Area, the San Joaquin Valley, and the Sacramento Valley to ascertain the causes of
biases in estimates of air quality improvements.
d. Formulate specific questions to be answered by the CCOS field measurement program.
Prioritize these questions according to their importance in meeting the objectives of
compiling a database for grid-based modeling and improving understanding of the
atmospheric processes that affect surface and aloft ozone concentrations.
e. Identify meteorological scenarios related to exceedances of the 8-hour ozone standard
and specify the characteristics of episodes for which monitoring would be sought as part
of CCOS, and outline a strategy for forecasting these episodes based on existing data
acquisition.
f. Survey existing and planned quantitative modeling and data analysis approaches and
determine the additional information needed to improve, evaluate, and apply them.
g. Summarize and evaluate existing, long-term measurement networks in central California.
Consider the need for supplemental measurements of surface and aloft concentrations of
ozone, ozone precursors and reaction products and reactive intermediates to adequately
characterize their three-dimensional distribution within the CCOS modeling domain.
Consider alternative measurement methods with respect to temporal and spatial
resolution, precision and accuracy, and cost.
h. Review and determine the applicability of existing surface and upper-air meteorological
measurements (e.g., ARB, APCD, and military) and planned CRPAQS meteorological
measurements toward achieving the technical objectives of CCOS. Consider
supplemental surface and upper-air meteorological measurements needed to adequately
characterize the main air flow patterns in the CCOS domain.
i. Evaluate potential measurement methods with respect to required averaging times,
duration, detection limits, accuracy, precision, validity, and cost-effectiveness.
Determine which observables can be measured continuously and which require integrated
samples that are submitted to off-site laboratory analysis. Relate each measurement to its
intended use in a modeling and/or data analysis activity.
j. Define steps for appropriate quality control/quality assurance in order to produce data of
specified completeness, validity, accuracy and precision. Articulate the appropriate levels
of funding required for the necessary components of quality control/quality assurance
(QC/QA), database management and data archival.
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k. Estimate costs for each element of the conceptual plan and summarize the rationale and
justification for their inclusion in the final network design in order to facilitate
consideration by the CCOS Technical Committee of program options and tradeoffs.
l. Prepare and post the program plan, in PDF format, to the CCOS web site.
Objective A-2. Prepare the CCOS operational plan and field operations protocol, make
contractual arrangements with measurement groups, and conduct preliminary evaluations of
measurement methods and forecast protocol.
a. Prepare and issue requests for proposals for CCOS field measurements, as necessary, and
make contractual arrangements with measurement groups. Identify coordinators for
quality assurance and data management.
b. Evaluate the accuracy, precision and validity of measurement methods that have not been
sufficiently tested under either laboratory or field conditions. Such methods include
continuous NO2 and PAN measurement by gas chromatography (GC)/Luminol and
continuous HCHO measurements. Assemble and evaluate the accuracy and precision of
an automated GC/ion trap mass spectrometry (MS) and develop a calibration library for
identification and quantitation of VOC species of interest.
c. Arrange for the acquisition of new air quality measurement sites. Ensure compliance with
applicable siting criteria in the network design plan. Document the site with GPS data,
digital images, and perform a video survey of each new site and enter site information
into the field operations protocol. Determine the needs and costs for permits,
indemnification/insurance requirements, compliance with environmental and safety laws,
water, power, air conditioning, and sanitary facilities, and additional structures to
accommodate added sampling equipment.
d. Set up a forecast system to identify potential study days that meet preselected criteria and
maintain daily contact with the study management for selection of sampling days. Design
a protocol for handling the scheduling of intensive measurement periods. Specify the
episode forecasting method that will be implemented and identify how go/no go decisions
will be made and disseminated to project participants.
e. Prepare an operational program plan and field operations protocol that specifies the
details of the field measurement program that will allow the study plan to be executed
with available resources. It identifies measurement locations, observables, and
monitoring methods. It specifies data management and reporting conventions and
outlines the activities needed to ensure data quality. The field operations protocol is a
short document that serves as the guide for those in the field. It is a concise overview of
the field study, enumerating the most pertinent information needed by those conducting
the measurements. Post the operational plan and field operations protocol, in PDF format,
to the CCOS web site by April 2000.
f. Conduct a workshop in May 2000 in Sacramento with ARB and district staff,
measurement contractors, and other study participants to orient them to the elements in
the plan and their responsibilities as members of the project team. Review the draft field
1-5
operations protocol with the project team, and reconcile any discrepancies between the
protocol and measurements planned by study participants. Ensure that potential crosscontamination between measurements is eliminated. Confirm schedules and protocols,
and identify potential logistical problems and develop appropriate action plans for their
resolution.
1.2.2
Objectives B - Emission Inventory Development
Objective B-1. Prepare day-specific, hourly, gridded emission inventories that cover each day of
the ozone episodes captured during the CCOS field study.
a. Develop a “fast-track” spatially and temporally resolved inventory of emission estimates
of ROG, NOx, and CO for the CCOS domain for summer 2000 for preliminary modeling.
b. Update the “fast-track” with day-specific information collected during the CCOS study
(see Objective B-2).
c. Project the effects of future activity and alternative controls on emission estimates and
develop a spatially and temporally resolved inventory for future year simulation.
Objective B-2. Collect day-specific emissions data to update the “fast-track” inventory to more
accurately determine the spatial and temporal distribution of emissions from elevated point
sources, motor vehicles and other area sources.
a. Integrate transportation data for CCOS domain and run DTIM for entire modeling
domain. Create gridded, hourly emission estimates of NOx, TOG, CO and PM for onroad mobile sources.
b. Develop base year and future year gridding surrogates for spatial distribution of
stationary area source categories.
c. Compile biomass and emission factor data for major plant species in the Central Valley
and Bay Area for use in GIS-based biogenic inventory for the CCOS modeling domain.
d. Collect day-specific emission data for wildfires, controlled burns, and agricultural burns.
e. Develop point and area source emission inventories for smaller air pollution control
districts.
f. Obtain hourly estimates of NOx emissions from major point sources to supplement the
“fast-track” inventory during potential modeling episodes.
1.2.3
Objectives C - Preparation, Execution and Evaluation of Air Quality Simulation
Modeling System
Objective C-1. Apply air quality models for the attainment demonstration portion of the SIP for
the proposed 8-hour and state 1-hour ozone standards.
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a. Select the most suitable modeling systems (for meteorology, emissions, and air quality)
for representing photochemical air pollution in central and northern California. At least
one of the models selected should have the ability to simulate both ozone and fine
particles. Simulations with that model could be applied to both CRPAQS and CCOS.
This will facilitate integration of results across both studies.
b. Analyze the data collected during the CCOS field measurement program and select a
minimum of three ozone episodes to simulate.
c. Specify model domain boundaries, boundary conditions, initial conditions, chemical
mechanisms, aerosol module, plume-in-grid module, grid sizes, layers, and surface
characteristics.
d. Identify performance evaluation methods and performance measures, and specify
methods by which these will be applied.
e. Evaluate the results of the meteorological model (see Section 3.2.1 for list of evaluation
checks).
f. Perform operational evaluation of the air quality model (see Section 3.2.3)
g. Evaluate and improve the performance of emissions, meteorological, and air quality
simulations. Apply simulation methods to estimate ozone concentrations at receptor sites
and to test potential emissions reduction strategies.
h. Identify the limiting precursors of ozone and assess the extent to which reductions in
volatile organic compounds and nitrogen oxides, would be effective in reducing ozone
concentrations.
Objective C-2. Determine the relative contributions of ozone generated from emissions in one
basin to federal and state exceedances in neighboring air basin.
a. Characterize the structure and evolution of the boundary layer and the nature of regional
circulation patterns that determine the transport and diffusion of ozone and its precursors
in northern and central California.
b. Identify and describe transport pathways between neighboring air basins, and estimate the
fluxes of ozone and precursors transported at ground level and aloft under differing
meteorological conditions. Reconcile results with flux plane measurements.
c. Determine the contribution of ozone generated from emissions in one basin to federal and
state exceedances in neighboring air basin through emission reduction sensitivity
analysis.
Objective C-3. Provide improve understanding of the role of thermal power plant plumes in
contributing to regional air quality problems in central California.
1-7
a. Conduct measurements during CCOS Intensive Operational Periods (IOPs) in plumes
from one or more power plants to allow reactive plume or plume-in-grid model to be
evaluated.
b. Provide operational evaluation of a state-of-the-science reactive plume model component
using data collected during the CCOS field study.
c. Using the modeling system developed by CCOS, provide a technical analysis appropriate
to support the development of interbasin/intrabasin as well as interpollutant offset rules
that could be applied to the central California region.
d. Run model simulations to estimate the air quality implications of different energy
policies.
Objective C-4. Assess the reliability associated with air quality model inputs and formulation,
and reconcile model results with observation-based and other data analysis methods.
a. Perform diagnostic evaluation of model results (see Section 3.2.3)
b. Quantify the uncertainty of emissions rates, chemical compositions, locations, and timing
of ozone precursors that are estimated by emission models.
c. Quantify the uncertainty of meteorological and air quality models in simulating
atmospheric transport, transformation, and deposition.
1.2.4
Objectives D - Data Analysis
Objective D-1. Determine the accuracy, precision, validity, and equivalence of CCOS field
measurements.
a. Evaluate the precision, accuracy, and validity of criteria pollutant data from routine
monitoring stations.
b. Evaluate the precision, accuracy, and validity of PAMS and CCOS supplemental VOC
measurements and nitrogenous species measurements.
c. Evaluate the precision, accuracy, validity, and equivalence of routine network and CCOS
supplemental surface and upper-air meteorological data.
d. Evaluate the precision, accuracy, validity of data from instrumented aircraft and
radiosonde/ozonesonde releases.
e. Evaluate the precision, accuracy, validity, and equivalence of routine and supplemental
solar radiation data.
Objective D-2. Determine the spatial, temporal, and statistical distributions of air quality
measurements to provide a guide to the database and aid in the formulation of hypotheses to be
tested by more detailed analyses.
1-8
a. Examine average diurnal changes of surface concentration data during episodic and nonepisodic periods.
b. Examine spatial distributions of surface concentration data.
c. Examine statistical distributions and relationships among surface air quality
measurements.
d. Examine vertical distribution of concentrations from airborne measurements.
e. Examine the spatial and temporal distribution of solar radiation.
Objective D-3. Characterize meteorological transport phenomena and dispersion processes.
a. Examine the mechanisms for the movement of air into, out of, and between the different
air basins in both horizontal and vertical directions.
b. Determine occurrence, spatial extent, intensity, and variability of phenomena affecting
horizontal transport (low-level jet, slope flows, eddies, coastal meteorology and flow
bifurcation) and vertical transport (convergence and divergence zones).
c. Characterize the depth, intensity, and temporal changes of the mixed layer and
characterize mixing of elevated and surface emissions.
Objective D-4. Reconcile emissions inventory estimates with ambient measurements and “realworld” source measurements.
a. Compare proportions of species measured in ambient air and those estimated by emission
inventories for reactive and nonreactive species.
b. Estimate source contributions by Chemical Mass Balance using measured and predicted
ambient VOC composition.
c. Conduct on-road remote sensing measurements of CO, HC, NOx and (PM if available) in
Sacramento, Fresno, and the San Francisco Bay Area, and evaluate the effects of coldstarts, grade and geographic distribution of high-emitting vehicles.
d. Reconcile on-road motor vehicle emission inventory estimates and fuel-based emissions
estimates.
e. Determine effects of meteorological variables on emissions rates.
f. Determine the detectability of day-specific emissions (e.g., fires) and variations in
emissions between weekday and weekend.
Objective D-5. Characterize pollutant fluxes between upwind and receptor areas. Examine the
orientations, dimensions, and locations of flux planes by using aircraft spiral and traverse data,
and ground-based concentration data for VOCs, NOx, and O3 coupled with average wind speeds
that are perpendicular to the chosen flux planes.
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a. Define the orientations, dimensions, and locations of flux planes.
b. Estimate the fluxes and total quantities of selected pollutants transported across flux
planes. Test following hypotheses: 1) whether there is significant local generation of
pollutants; 2) whether there is significant dilution within or turbulent exchange through
the top of the mixed layer; and 3) whether there is substantial transport or dilution owing
to eddies, nocturnal jets, and upslope/downslope flows.
Objective D-6. Characterize chemical and physical interactions in the formation of ozone.
a. Examine VOC and nitrogen budgets as functions of location and time of day.
b. Reconcile the spatial, temporal, and chemical variations in ozone, precursor, and endproduct concentrations with expectations from chemical theory.
c. Apply observation-based methods to determine where and when ozone concentrations are
limited by the availability of NOx or VOC.
d. Identify the composition and location of one or more power plant plumes and their
surrounding air aloft so that the dispersion and chemical evolution of the plume(s) can be
inferred.
Objective D-7. Characterize episodes in terms of emissions, meteorology, and air quality and
determine the degree to which each intensive episode is a valid representation of commonly
occurring conditions and its suitability for control strategy development.
a. Describe each intensive episode in terms of emissions, meteorology, and air quality.
b. Examine continuous meteorological and air quality data acquired for the entire study
period, and determine the frequency of occurrence of days which have transport and
transformation potential similar to those of the intensive study days. Generalize this
frequency to previous years, using existing information for those years.
Objective D-8. Reformulate the conceptual model of ozone formation in the study domain using
the results yielded by the foregoing data analyses and modeling. Examine the formulation,
assumptions, and parameters in mathematical modules in the air quality model with respect to
their consistency with reality.
1.3
Basis for CCOS Field Measurement Program
In the development of this study plan, new analysis has been undertaken to complement
past studies addressed in Section 2. In this summary, three investigations are described that were
initiated to further understand the meteorological conditions that foster ozone formation and that
influence the spatial distribution of ozone in central California (details in Section 2). First, nine
years (1990-98) of ozone data from selected sites were compiled and examined for 1-hr and 8-hr
ozone trends and to identify important features of the diurnal and hebdomadal (weekly) cycles of
ozone. Second, a bottom-up statistical cluster analysis of daily-maximum ozone data was
completed to objectively group high ozone days selected by local districts, and then to search for
1-10
statistically significant differences in meteorological parameters among the clusters. Third, a topdown approach based on subjective classification of daily weather maps was compared to the
observed air quality. Toward this end, a meteorological working group has been formed to
generate, at a minimum, a small set of qualitative scenarios to aid in the development of the
CCOS study plan, and to link these scenarios to objective analysis. These scenarios can then be
used to guide forecasting of episodes, and to help distinguish between the types of episodes that
may enhance ozone formation in specific sub-regions or different air basins within the proposed
study area.
1.3.1
Analysis of Past Ozone Data
A 126-site, 9-year (1990-98) subset of ozone data was selected from the ARB database as
described in Section 2.3.1. Breakdowns by site and air basin were examined for 1-hour and 8hour annual maximum of daily maxima, number of exceedance days per year, seasonal
occurrences by month (May-Oct), and the hebdomadal cycle of exceedances. As expected, sites
downwind of metropolitan areas have the greatest number of exceedances per year. These are
Folsom, Auburn, Cool, and Placerville downwind of Sacramento, Arvin and Edison downwind
of Bakersfield, Parlier and Maricopa downwind of Fresno, and Livermore downwind of the
major Bay Area cities. Overall, southern San Joaquin Valley has the worst air quality in the
CCOS region. The CCOS surface sites are located to measure the chemical transformations
taking place with downwind transport.
Seasonal occurrences of ozone exceedances were dominated by July and August with the
lower numbers in June and September being approximately equal to each other. The July-August
time frame for CCOS intensives is justified. A weekend/weekday effect was found for rural,
suburban and urban sites, although rigorous statistical significance of the effect was not pursued
and is part of other on-going efforts.
1.3.2
Cluster Analysis of High Ozone Days
Ozone data from ozone seasons 1996-98 are employed to determine groupings of spatial
patterns on several very high ozone days selected by the local air quality districts. Despite some
differences in the 8-hour and 1-hour results that are still being examined in the Meteorological
Working Group, three clusters are found for both 1-hour and 8-hour exceedances:
Cluster 1 - The San Francisco Bay Area (SFBA) has its highest basin-wide ozone values,
though still less in absolute magnitude than San Joaquin Valley. This cluster is characterized by
the weakest sea breeze (lowest west-to-east component through Carquinez Strait), and the lowest
Oakland inversion base heights. Among the cluster days, North Central Coast ozone is also
highest during Cluster 1.
Cluster 2 - The San Joaquin Valley (SJV) has its highest basin-wide values while the Bay
Area and Sacramento Valley are relatively cleaner. A stronger sea breeze, relatively to Cluster 1,
keeps the pollutants moving through the Bay Area and the Sacramento Valley, but may increase
transport into the SJV. Among the cluster days, Mountain Counties ozone is lowest during
Cluster 2
1-11
Cluster 3 – Sacramento Valley (SV) has its highest basin-wide ozone values, as does the
Mountain Counties Air Basin. As with Cluster 2, a stronger sea breeze is present, relative to
Cluster 1, but surface temperatures in Sacramento Valley are significantly higher, indicating less
and/or later intrusion of the sea breeze, allowing more time for photochemistry before evening
transport to the Mountain Counties.
Figure 1.3-1 shows the average ozone for each cluster and for all 42 cluster days (Cluster
1- 22 days, Cluster 2 – 12 days, and Cluster 3 – 8 days). Days were selected from 1996-98 highozone episodes identified by local districts (see Section 2.5.4.3). Differences in average ozone
concentrations among the three clusters are statistically significant (at 95% confidence level) for
both SFBA and SV. However, for SJV, none of the clusters are significantly different due to less
marine air intrusion into SJV. This analysis shows the importance of the sea breeze in
determining spatial distribution of ozone accumulation. When the sea breeze is inhibited, higher
ozone levels occur throughout the study area, including the coastal regions. The San Joaquin
Valley shows the least variation among the clusters owing to the combined effect of topography
and greater distance from the coast, while Sacramento Valley shows more variation due to
potential for greater influence of the marine intrusion.
Figures 1.3-2 through 1.3-7 illustrate the differences in the spatial patterns of ozone
concentrations for these three clusters, for both 1hr (Figures 1.3-2, 4 and 6) and 8hr (Figures 1.33, 5, and 7) ozone maximums. Each figure displays the daily ozone maximum from 126
monitoring stations throughout the entire study region. More discussion is presented in Sections
2.3, 2.5 and 2.7, but some features are noted here.
•
The SFBA had ozone on a par with the SJV (147 ppb at Concord and 145 ppb at Clovis in
the SJV) on August 12, 1998, a Cluster 1 day. The hexagon and large circles represent
exceedances of the 1hr standard. The upper Sacramento Valley was relatively clean with no
state 1-hr or Federal 8-hr exceedances.
•
On August 30, 1996, a Cluster 2 day, the SFBA and Sacramento Valley (SV) were relatively
clean, but San Andreas registered a 138 ppb on the southeastern edge of the clean zone,
whereas the northern Mountain Counties were clean like the SV. Arvin and Edison
experienced 156 and 163 ppb ozone, respectively.
•
An interesting feature on August 14, 1998, a Cluster 3 day, is the high 8-hr ozone values in
the north Sacramento Valley. There were 8-hr exceedances downwind of Sacramento, but
even higher 8-hr values of 110 ppb were present in Redding.
1.3.3
Meteorological Scenarios by Weather Map Analysis
The development of a conceptual model for ozone formation is aided by identification of
meteorological scenarios that foster the formation, accumulation and transport of ozone (see
Sections 2.3, 2.5 and 2.7). An idealized set of meteorological scenarios would be distinct from
one another. Each scenario should be linked to a set of commonly measured observables, like
routine meteorological and air quality data, to increase the success rate of go/no-go decisions
based on weather forecasts.
1-12
Mean Basin Maximum 1hr Ozone for Each Cluster Grouped by Cluster
180
SFBA
Ozone (ppb)
150
SV
120
SJV
90
60
30
0
C1_mean
C2_mean
C3_mean
All_mean
Mean Basin Maximum 1hr Ozone for Clusters Grouped by Air Basin
Ozone (ppb)
180
C1_mean
150
C2_mean
120
C3_mean
All_mean
90
60
30
0
SFBA
SV
SJV
Figure 1.3-1. Mean basin maximum5 1-hour ozone for each cluster grouped by cluster and
grouped by air basin.
5
Average ozone for clusters and for all 42 cluster days (Cluster 1- 22 days, Cluster 2 – 12 days, and Cluster 3 – 8
days). Days were selected from 1996-98 high-ozone episodes identified by local districts (see Section 2.5.4.3).
Differences for SFBA are statistically significant for all three clusters and each is different from the mean.
Differences for SV are statistically significant for all three clusters, but only 2 and 3 differ from the mean. None of
the clusters are significant for SJV due to less sea breeze penetration over the surrounding topography.
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Figure 1.3-2. Maximum 1hr ozone for 126 monitoring stations on 8/12/98, a Cluster 1 Day.
1-14
1-15
1-16
1-17
1-18
1-19
The identification of meteorological scenarios is an on-going task of the CCOS
Meteorological Working Group. Ozone season days, May 1 – October 31, have been classified
for three recent seasons, 1996-1998, using two complementary approaches. This effort is a topdown approach using inspection of 500-mb weather maps to establish gross features and to
classify all 552 subject days into eight categories, including 2-4 subcategories for six of the eight
scenarios. These scenarios are presented here in the approximate order of decreasing ozone
impact:
1. Western U.S. High – Upper-level high pressure center located over the Western U.S.
2. Eastern Pacific High – Upper-level high pressure center off the Western U.S. coast
3. Monsoonal Flow – Upper-level high pressure center in the south-western U.S. or in
northern Mexico such that southerly flow brings moisture north
4. Zonal Flow – West-to-East
5. Pre-Frontal – Front approaching from northwest brings southwesterly flow
6. Trough Passage – Upper-level trough moves through California, usually ventilating
Central California.
7. Continental High – Northerly wind with no marine component, more typical in winter
8. El Nino Cut-off Low – A special class for 1997 where a cut-off low sat just of the
southern California coast for several days.
The Western U.S. high accounts for proportionately the greatest number of exceedances.
As shown in Table 2.5-3, 97% of days with highs centered over southern California have 8-hour
exceedances in the Central San Joaquin Valley, where the frequency of exceedances on all 199698 ozone season days is 42%. The Western U.S. High contributes to stagnation conditions
throughout central California by fostering an off-shore gradient which weakens (and in some
cases even reverses) the usual sea breeze. Compared to a more vigorous sea breeze scenario like
the Eastern Pacific High, this tends to keep pollutants longer within respective source regions,
although some transport can still occur with the delayed and/or weakened sea breeze. The SFBA
has 8-hour exceedances on 29% of days when the Western U.S. High is centered over the Pacific
Northwest, while the frequency of SFBA 8-hour exceedances on all 1996-98 ozone season days
is only about 4%. This scenario also provides abundant sunlight and the greatest subsidence
inversion to reduce mixing heights and trap pollutants vertically all over central California.
Monsoonal flow can have both mitigating and exacerbating impacts on SJV air quality. Some
monsoonal days are identified (e.g., 9/3/98), where SJV ozone is lower relative to the rest of
Central California, but on other days, the southerly monsoonal flow can weaken the SJV exit
flow through Tehachapi Pass. Zonal flow tends to increase synoptic forcing, and less ozone
impact is seen in the northern portion of the study area, but the topography surrounding the SJV
helps decouple the valley from the upper level winds, and many exceedances are observed in
SJV during zonal flow. The last four scenarios all help ventilate the Central Valley. There were
no 1-hour exceedances during any of the 142 out of 552 days studied (26%), although some 8-
1-20
hour exceedances are observed in the SJV and the Mountain Counties even with a trough
passage.
While these results are conceptually helpful, interpretation and application of the topdown approach is limited due to the large scale of the eight classes that does not allow for
adequate consideration of day-to-day atmospheric variability and of smaller mesoscale effects.
Additionally, during the Western U.S. High, when synoptic forcing over central California is
weakest, ozone concentrations are greatest, and mesoscale features which re-distribute ozone and
precursors become most important. Subtle synoptic differences, fostering the formation and/or
amplification of these features, are difficult to identify and classify.
1.3.4
Episode Forecasting and Selection
The broad goal of the meteorological working group is to develop protocols for
forecasting and selecting episodes of interest for intensive study. The group has undertaken four
efforts, each with a task leader, to accomplish this goal. These efforts will be described and the
protocols documented in the operational plan.
a.
Define regional mesoscale flow features – Create a document with mesoscale flow
features identified. Define the air quality impact of each feature. Develop operational
definitions to define the presence or absence of each feature. Evaluate locations of
existing and proposed stations to adequately define each feature. These features include:
•
•
•
•
•
•
•
•
•
•
•
•
•
•
b.
Sea breeze
Marine air intrusion
Coastal Windflow – off-shore flow, north or south along coast
Marine fog and stratus
San Joaquin Valley/Sacramento Valley Bifurcation Zone – location and relative
proportions of air moving north, east, and south.
Pacheco Anti-Cyclone – possible impact on SJV to San Louis Obispo area transport.
Fresno Eddy – degree to which pollutants are trapped and re-entrained in central SJV
with the damming of air against the Tehachapi mountains and subsequent
recirculation.
Schultz Eddy – impacts of transport to northern mountain counties and upper SV
Redding Eddy – Discuss any evidence for and or possible importance to air quality in
the Redding area.
Upper/Lower SV Convergence zone
Upper/Lower SJV Convergence zone
Upslope/Downslope
Up-valley/Down-valley
Compensation Flow/Re-entrainment
Investigate the use of forecast models – Evaluate the use of daily runs of mesoscale
models for local forecasts. This is currently done in other areas. For example, the
University of Utah performs daily forecast runs for Salt Lake City and the surrounding
area. The Naval Post-Graduate School is involved in this activity in California and CCOS
may coordinate these efforts.
1-21
c.
Establish website tool for forecast – Create a web site with near real-time display of
selected meteorological and air quality data. This would include consolidated information
from NWS, ARB, military sites, local districts and other sources.
d.
Develop forecast team protocol – Define the composition and operations of the forecast
team. Develop criteria for go/no-go decision and selection of episodes. This will likely be
modeled after previous successful forecast team efforts like the SARMAP 1990 and
SCOS97 effort.
1.4
Overview of CCOS Field Measurements
Data requirements for CCOS are determined by the need to drive and evaluate the
performance of modeling systems, which include three components. A meteorological model
provides winds fields, vertical profiles of temperature and humidity, and other physical
parameters in a gridded structure. Emissions inventory and supporting models provide gridded
emissions for anthropogenic area and point sources and natural emissions. An air quality model
simulates the chemical and physical processes involved in the formation and accumulation of
ozone. In evaluating modeling system performance, the primary concern is replicating the
physical and chemical processes associated with actual ozone episodes. This necessitates the
collection of suitable meteorological, emissions, and air quality data that pertain to these
episodes. The data requirements of CCOS are also driven by a need for complementary,
independent and corroborative data analysis so that modeling results can be compared to current
conceptual understanding of the phenomena replicated by the model. Data requirements for
CCOS are summarized in Section 1.5 (details in Section 3).
The CCOS field measurement program will be conducted during a four-month period
from 6/1/00 to 9/30/00. A network of upper-air meteorological monitoring stations will
supplement the existing routine meteorological and air quality monitoring network in order to
identify and characterize meteorological scenarios that are conducive to ozone formation during
the ozone season. Supplemental air quality measurements will be made during a three-month
period from 6/15/00 to 9/15/00 (study period), which corresponds to the majority of elevated
ozone levels observed in northern and central California during previous years. Continuous
surface and upper-air meteorological measurements and surface air quality measurements of O3
NO, NOx or NOy6 will be made hourly throughout the study period in order to provide sufficient
input data to model any day during the study period. These measurements are made in order to
assess the representativeness of the episode days, to provide information on the meteorology and
air quality conditions on days leading up to the episodes, and to assess the meteorological
regimes and transport patterns which lead to ozone episodes.
Additional continuous surface air quality measurements will be made at several sites
during a shorter two-month study period from 7/6/00 to 9/2/00 (primary study period). These
measurements include nitrogen dioxide (NO2), peroxyacetylnitrate (PAN) and other
peroxyacylnitrates (PAcN), particulate nitrate (NO3-), formaldehyde (HCHO), and speciated
6
Reactive oxidized nitrogen (NOy) include nitric oxide (NO), nitrogen dioxide (NO2), nitrous acid (HONO),
peroxynitric acid (HNO4), nitrate radical (NO3), nitrate aerosol (NO3-), dinitrogen pentoxide (N2O5), nitric acid
(HNO3), peroxyacetylnitrate (PAN) and other PAN analogues, and organic nitrates (ORNI).
1-22
volatile organic compounds. These measurements allow detailed examination of the diurnal,
day-to-day, and day-of-the-week variations in carbon and nitrogen chemistry at transport
corridors and at locations downwind of the San Francisco Bay Area, Sacramento, Fresno, and
Bakersfield where ozone formation may be either VOC- or NOx-limited depending upon time of
day and pattern of pollutant transport. These data support operational and diagnostic model
evaluations, evaluations of emission inventories, and corroborative observation-based data
analyses.
Additional data will be collected during ozone episodes (intensive operational periods,
IOP) to better understand the dynamics and chemistry of the formation of high ozone
concentrations. The budget for CCOS allows for up to 15 days total for episodic measurements.
With an average episode of three to four days, four to five episodes are likely. These
measurements include instrumented aircraft, speciated VOC, and radiosonde measurements,
which are labor intensive and require costly expendables or laboratory analyses. IOPs will be
forecasted during periods that correspond to categories of meteorological conditions called
scenarios, which are associated with ozone episodes and ozone transport in northern and central
California. These intensive measurements will be made on days leading up to and during ozone
episodes and during specific ozone transport scenarios. The additional measurements are needed
for operational and diagnostic model evaluation, to improve our conceptual understanding of the
causes of ozone episodes in the study region and the contribution of transport to exceedances of
federal and state ozone standards in downwind areas.
1.4.1
Surface Meteorological and Air Quality Measurements
The existing meteorological network in central California is extensive, but uncoordinated
among the different agencies. Surface meteorological monitoring networks includes those
operated by the Air Resources Board (ARB), the Bay Area Air Quality Management District
(BAAQMD), the National Oceanic and Atmospheric Administration (NOAA), the California
Irrigation Management Information Service (CIMIS), Interagency Monitoring of Protected
Visual Environments (IMPROVE), the National Weather Service (NWS), Pacific Gas and
Electric Company (PG&E), the U.S. Coast Guard, Remote Automated Weather Stations
(RAWS) for fire fighting, and a few miscellaneous monitors. Wind speed and direction,
temperature, and relative humidity are the most common measurements. The network of surface
pressure and solar radiation measurements is also extensive. Three sites measure ultraviolet
radiation in the Sacramento Valley, in the San Joaquin Valley, and along the south coast in Santa
Barbara County.
The California Air Resources Board and local air pollution control districts currently
operate 185 air quality monitoring stations throughout northern and central California. Of the
active sites, 130 measure ozone and 76 measure NOx. Carbon monoxide and hydrocarbons are
measured at 57 and 11 sites, respectively. Data from these sites are routinely acquired and
archived by the ARB and Districts. This extensive surface air quality monitoring network
provides a substantial database for setting initial condition for the model, and for operational
evaluation of model outputs.
The existing meteorological network will be augmented with the CCOS supplemental
sites described below. Ten meter meteorological towers at each of newly established CCOS
1-23
supplemental sites will be equipped with low threshold (~0.3 m/s) wind sensors and high
sensitivity relative humidity sensors. Supplemental air quality measurement are required at
several existing monitoring sites to increase the extent of chemical speciation and in key areas of
the modeling domain where routine monitoring stations do not currently exist. Measurements of
documented quality and adequate sensitivity are needed along the western boundary of the
modeling domain to adequately characterize the temporal and spatial distributions of ambient
background levels of ozone precursors because boundary conditions can significantly affected
model outputs. Background sites intend to measure concentrations that are not influenced by
northern and central California emissions. Interbasin transport sites are intended to evaluate
concentrations along established or potential transport pathways between basins, including the
Bay Area, the North Central Coast Air Basin, the Sacramento Valley, the San Joaquin Valley,
Mountain counties, the South Central Coast Air Basin, and the Mojave desert. Intrabasin gradient
sites are located in non-urban areas between routine network sites. They are intended to evaluate
the extent to which one urban area affects ozone concentrations in another urban area, as well as
the extent to which urban contributions arrive at suburban and rural locations. The CCOS field
measurement program consists of four categories of supplemental measurement sites with
increasing levels of chemical speciation and time resolution – Type 0, 1, and 2 “supplemental”
(S) sites and “research” (R) sites. Table 1.4-1 lists the measurements (described in Appendix A)
to be made at each of type of supplemental monitoring sites along averaging times and operating
schedules.
Type 0 supplemental monitoring sites (S0) are intended to fill in key areas of the
modeling domain where ozone and nitrogen oxides are not currently measured. Proposed sites
include McKittrick and Kettleman City (both along the western side of the San Joaquin Valley),
Shasta (downwind of Redding), and Carizo Plain (along transport route between San Luis
Obispo and the southern San Joaquin Valley). In addition NO/NOy analyzers will be added at
several existing monitoring sites that currently measure only ozone. Three of these sites are
located along pollutant transport routes (Vacaville, San Martin, and Walnut Grove Tower at two
elevations). Yosemite (Turtleback Dome) is proposed in order to monitor NO/NOy at a site
where formation of ozone is expected to be always NOx limited.
Type 1 supplemental monitoring sites (S1) are intended to establish boundary and initial
conditions for input into air quality models. These sites are needed at the upwind boundaries of
the modeling domain, in the urban center (initial conditions) and at downwind locations
(boundary conditions). With the exception of NOy measurements, S1 sites are equivalent to
Photochemical Assessment Monitoring Stations (PAMS) sites. Measurements of speciated
volatile organic compounds (VOC) made under CCOS (four 3-hour samples on 15 IOP days)
supplement the 11 existing PAMS sites in the study area (four in Sacramento, four in Fresno, and
three in Bakersfield). The ozone episodic samples that will be collected under PAMS will
coincide with the CCOS IOP days. S1 sites are proposed for Bodega Head and along the south
central coast north of Morro Bay to obtain background data near the western boundary of the
CCOS modeling domain. Sutter Buttes and Turlock provide characterization of ambient air
transported into the upper Sacramento Valley and into the northern San Joaquin Valley,
respectively, as a function of the nature of the flow bifurcation downwind of the San Francisco
Bay Area. Measurements at Anderson (located south of Redding) are designed to determine
whether ozone precursors immediately upwind of Redding are largely transported or are
1-24
Table 1.4-1
CCOS Supplemental Surface Measurements
Observable and Method
Meteorology and Radiation
Meteorology (WS,WD, T and RH) at 10 m
Radiation (JNO2 and JO1D)
Period
Avg Time
Type of Sites
6/15/00 to 9/15/00
5-minute
S0, S1, S2 and R
6/15/00 to 9/15/00
5-minute
R
6/15/00 to 9/15/00
5-minute
S0, S1, S2 and R
15 IOP days
10-minute
Oxidants
Ozone (ultraviolet absorption monitor)
H2O2 (TDLAS)
R
(1)
Nitrogen Species
NO, NOx (chemiluminescent monitor)
6/15/00 to 9/15/00
5-minute
S2, R
NO, NOy (high sensitivity chemiluminescent
monitor with external converter)
NOy, NOy-HNO3 (high sensitivity
chemiluminescent monitor with dual converters
w/ & w/o NaCl impregnated fiber denuder)
6/15/00 to 9/15/00
5-minute
S0, S1
6/15/00 to 9/15/00
10-minute
S2, R
NO2, PAcNs (GC - Luminol)
7/2/00 to 9/2/00
30-minute
S2, R
7/2/00 to 9/2/00
10-minute
R
15 IOP days
10-minute
15 IOP days
4 x 3-hr
S1, S2, R
15 IOP days
4 x 3-hr
S1, S2, R
HCHO (dihydrolutinine derivative/fluorescent
detection)
C8-C20 hydrocarbons (Tenax GC-FID, MSD)
7/2/00 to 9/2/00
10-minute
S2, R
15 IOP days
4 x 3-hr
R
VOC (Automated-GC/ion trap mass
spectrometer)
HCHO (TDLAS)
7/2/00 to 9/2/00
hourly
R
15 IOP days
10-minute
15 IOP days
hourly,
-
NO (flash vaporization)
NO2, HNO3 (TDLAS)
R
(1)
Carbon Species
CO, CO2, CH4, C2-C12 hydrocarbons
(canister/GC-FID)
C1-C7 carbonyls( DNPH-HPLC/UV)
Hydroxy carbonyls
R
(1)
(2)
CO (nondispersive infrared)
6/15/00 to 9/15/00
5-minute
R
R
CO2 (nondispersive infrared)
6/15/00 to 9/15/00
5-minute
R
PM2.5 light absorption (aethalometer)
6/15/00 to 9/15/00
5-minute
R
PM2.5 light scattering (portable nephelometer)
6/15/00 to 9/15/00
5-minute
R
PM/Visibility
(1) At the Fresno research site only.
(2) At the Sacramento research site only.
1-25
attributable to local sources. Similar transport issues will be addressed by measurements in the
foothill communities near Grass Valley and San Andreas. Type S1 measurements are also
proposed for the CRPAQS Anchor site at Angiola. The Bay Area AQMD will operate the
existing San Jose and San Leandro monitoring sites as S1 sites during CCOS.
Type 2 supplemental monitoring sites (S2) are located at the interbasin transport and
intrabasin gradient sites, and near the downwind edge of the urban center where ozone formation
may either be VOC or NOx limited depending upon time of day and pattern of pollutant
transport. S2 sites also provide data for initial conditions and operation evaluations and some
diagnostic evaluation of model outputs. Measurements at S2 sites include those at S1 sites plus
continuous NOy* (NOy minus nitric acid), nitrogen dioxide (NO2), peroxyacetylnitrate (PAN)
and formaldehyde (HCHO). The measurements allow more detailed assessments of VOC- and
NOx-limitation by observation-driven methods during the entire two-month primary study
period. S2 sites are proposed along the three main passes connecting the Bay Area and the
Central Valley (Bethel Island, Altamont Pass, and Pacheco Pass. S2 measurements are also
proposed downwind of Fresno at the Mouth of the Kings River and downwind of Bakersfield at
Edison. One additional Type S2 site will be located east of Sacramento.
Research sites (R) have the same site requirements as S2 sites. The sites are intended to
measure a representative urban mix of pollutants, and must be carefully selected to minimize the
potential influence of local emission sources. As with S2 sites, research sites are located where
ozone formation may either be VOC or NOx limited depending upon time of day and pattern of
pollutant transport. Research site are intended to provide the maximum extent of high-quality,
time-resolved chemical and other aerometric data for rigorous diagnostic evaluation of air quality
model simulations and emission inventory estimates. VOC speciation will be obtained at
research sites hourly rather than four 3-hour samples at S1 and S2 sites. Other continuous
measurements carbon monoxide, carbon dioxide, photolytic rate parameters, light adsorption and
scattering. Data for these measurements will be collected during the primary study period of two
months. Other measurements that will be made during ozone episodes include NO2, HNO3,
HCHO, and hydrogen peroxide (H2O2) by tunable diode laser absorption spectroscopy (TDLAS),
C8 to C18 hydrocarbons and hydroxy carbonyl compounds. Research sites are proposed
downwind of Sacramento and Fresno, and near Dublin between Oakland and Livermore.
1.4.2
Aloft Meteorology and Air Quality Measurements
Table 1.4-2 describes the upper air sites, their measurements and operators. Radar
profilers, doppler sodars, and RASS are used at most sites because they acquire hourly average
wind speed, wind direction, and temperature by remote sensing without constant operator
intervention. Sodars are collocated with profilers at several locations because they provide
greater vertical resolution in the first 100 m agl. Several radar profilers are being installed to
acquire a multi-year database, and one of the important functions of the CCOS/CRPAQS
supplements to this network is to relate these relatively sparse measurements to the detailed
meteorological patterns determined during CCOS. The ARB operates two profilers (with RASS)
in the San Joaquin Valley, and the San Joaquin Valley Unified APCD and Sacramento
Metropolitan AQMD operate one profiler/RASS each as part of their PAMS monitoring
1-26
Table 1.4-2
CCOS Upper-Air Meteorological Measurements
ID
Name
Purpose
Operatora
Contractor
Radarb
RASSb
ABK
Arbuckle
Intrabasin Transport
ETL/NOAA
SC
SC
ABU
Upslope/Downslope Flow, Downwind of Major
Source Area
Upslope/Downslope Flow
ETL/NOAA
SC
SC
ACP
N. of Auburn, S.
of Grass Valley
Angel's Camp
CCOS/
CRPAQS
CCOS
ETL/NOAA
ANGI
Angiola
Intrabasin Transport, Vertical Mixing,
CRG
Corning
Northern Valley Barrier and Convergence Cone
EDI
EDW
Edison
Edwards AFB
CCOS/
CRPAQS
CCOS/
CRPAQS
CCOS/
CRPAQS
ARB
EAF
CCOS/
CRPAQS
ETL/NOAA
CCOS/
CRPAQS
TBD
HUR
LGR
LHL
LIV
Interbasin Transport through Tehachapi Pass
Interbasin Transport through Tehachapi Pass, Desert
Mixed Layer, Synoptic Conditons
Fresno Air
Intrabasin Transport, Fresno Eddy, Major Soure
Terminal
Area
Fresno-First Street Urban Heat Island, Intrabasin Transport, Synoptic
Conditons. Major Source Area
Huron
Intrabasin Transport, SJV Nocturnal Jet
Lagrange
Upslope/Downslope Flow, Mariposa River Valley
Lost Hills
Intra&Interbasin Transport across Carizo Plain
Livingston
Intrabasin Transport, mid-SJV Valley Axis Flow
MJD
MKR
Mojave Desert
Mouth Kings
Interbasin Transport, Downwind of southern SJV
MON
NTD
OAK
RIC
Monterey
Point Mugu USN
Oakland airport
Richmond
Onshore/Offshore Transport
Onshore/Offshore Transport, Synoptic Conditions
Onshore/Offshore Transport, Synoptic Conditions
FAT
FSF
CRPAQS
CRPAQS
ARB
CCOS/
CRPAQS
CRPAQS
CRPAQS/
CCOS
USNPGS
USN
NWS
BAAQMD/
CCOS
SMAPCD
CCOS
Sodarb,c
SC
ETL/NOAA
AC
AC
ETL/NOAA
SC
SC
AC
AC
SC
AS
ETL/NOAA
SC
SC
SC
SE
AC
SC
AC
AC
AC
SC
AC
AC
AC
AC
AC
AC
AC
AC
SC
SC
AS
AS
ETL/NOAA
SC
SC
AC
AC
AC
Sacramento
AC
AC
Santa Nella, E of I- Interbasin Transport through Pacheco Pass
5 toward Los
Banos
TAF
AC
TRA
Travis AFB
Interbasin Transport between Valley and Bay Area
CCOS
STI
SC
SC
TRC
Tracy, W of
Interbasin Transport through Altamont Pass
Tracy, S of I-205,
W of I-580
VAF
AC
VBG
Vandenberg AFB Onshore/Offshore Transport, Synoptic Conditions
SJVUAPCD
AC
AC
VIS
Visalia
CCOS
STI
SC
SC
Pt. Reyes
CCOS
STI
SC
SC
LVR
Livermore
Intrabasin Transport from Oakland to Livermore
CCOS
STI
SC
SC
San Martin
Interbasin Transport from Bay Area to North Central
Coast
CCOS
ARL/NOAA
SC
SC
SC
CAR
Carizo Plain
Interbasin Transport from San Joaquin Valley to
South Central Coast Air Basin
CCOS
ETL/NOAA
SC
SC
PLE
Plesant Grove
Interbasin Transport from Sacramento to Upper
Sacramento Valley
a
CCOS=Central California Ozone Study (this study), ARB=Air Resoures Board, BAAQMD=Bay Area Air Quality Management District;
USNPGS=U.S. Navy Post Graduate School; SJVUAPCD=SJV Unified Air Pollution Control District, NWS=National Weather Service;
SMAQMD=Sacramento Metropolitan Air Quality Management District, CRPAQS=California Regional PM10/PM2.5 Air Quality Study;
VAF=Vandenberg Air Force Base, TAF=Travis Air Force Base, EAF=Edwards Air Force Base, USN=U.S. Navy.
b
AC=Annual continuous measurements; AS=Annual sporadic measurements, SC=Summer continuous, 6/1/2000-9/30/2000;
SE=Summer episodic measurements on forecasted days.
c
Summer campaign sodars added at some sites as part of CRPAQS/CCOS except at RIC.
SAC
SNA
d
Balloon launch on episode days. Frequency should be 4-8 times per day but include 0700 and 1900 PST.
1-27
Sondeb,d
SE
AS
program. Military facilities with operational profilers include Travis AFB, Vandenberg AFB, and
the Naval Post Graduate School in Monterey. As part of CRPAQS, NOAA will upgrade existing
equipment, as required, at these facilities, and coordinate data collection to ensure compatibility
with the CRPAQS/CCOS upper-air database. Because these profilers are operated by different
entities, equivalent methods of data evaluation and reporting need to be established among these
entities prior to CCOS field study. Six profiles/RASS will be installed and operational during
summer 2000 as part of the CRPAQS. In addition, nine profilers/RASS and 5 sodars will be
installed for the CCOS summer 2000 field study.
Another radar/RASS profiler will be located in the vicinity of the power plant stacks
selected for study to ensure that the local 3D winds are well defined for accurate model
simulation during the crucial early stages of plume dispersion. Measurements are planned for the
power plants at Moss Landing and at Pittsburgh and the radar/RASS profiler will be moved as
necessary. Radiosondes are needed to determine changes in relative humidity and to quantify
conditions at elevations above ~2000 m agl. They are also the only practical means of acquiring
upper air measurements in cities where the noise and siting requirements of remote sensing
devices make them difficult to operate. Radiosondes are routinely launched through the year at
0400 and 1600 PST from Oakland, with additional launches at Vandenberg, Edwards, and Pt.
Mugu according to military mission requirements. None of these locations are within the Central
Valley, so these will be supplemented by launches at Sacramento and in the southern San
Joaquin Valley on 15 episodic days during summer with six radiosondes (with ozonesonde)
releases per day. The 490 MHz RWP will be placed in the Fresno area to provide higher vertical
soundings in the central San Joaquin Valley.
In addition to ozonesondes mentioned in the previous section, aloft air quality
measurements are available from fixed platforms that are part of the routine monitoring network
(e.g., Walnut Grove radio tower and Sutter Buttes). CCOS will add NOy measurements at
Walnut Grove and Sutter Buttes to provide additional information on oxidants available as carryover to mix-down on the following day.
Four aircraft are proposed for the CCOS field study. Instrumented aircraft will be used to
measure the three dimensional distribution of ozone, ozone precursors, and meteorological
variables. For CCOS, aircraft data have five specific uses:
•
Aloft boundary and initial conditions – direct input to model.
•
Definition of temporal and spatial ozone patterns in layers aloft – model evaluation.
•
Direct measurement of mixing depth during spirals – model evaluation and many
corroborative studies.
•
Flux plane estimation of transport - model evaluation and corroborative transport
assessment.
•
Reconciliation of aloft data with the conceptual model.
1-28
Three small air quality aircraft are needed to document the vertical and horizontal
gradients of ozone, NOx, ROG, temperature, and humidity in the study region. One aircraft is
needed for the Bay Area, a second aircraft for the northern boundary, Sacramento Valley and
northern Sierra Nevada, and a third aircraft for the San Joaquin Valley and the central and
southern Sierra Nevada. Onboard air quality instruments should have high sensitivity and fast
response (e.g., modified TEI 42S for NO and NOy). The small aircraft will make one flight in
the early morning (0500 to 0900 PDT) to document the morning precursors and the carryover
from the day before and a second flight in mid-afternoon (1300 to 1700 PDT) to document the
resulting ozone distribution. An occasional third flight might be considered during the night to
characterize the nocturnal transport regime and pollutant layers. Flights last between three to
four hours and may consist of a series of spirals (over fixed points on the ground) and traverses
(at constant altitude from one point to another) throughout the mixed layer. One of these aircraft
will also participate in characterizing flux-planes. All aircraft will have the capability to measure
wind direction and speeds.
A larger multi-engine aircraft will be used to document the horizontal and vertical
gradients along the offshore boundaries of the modeling domain, Bay Area and the North Central
Coast. This plane will carry the same instrumentation as the smaller planes. This long-range
aircraft will make two flights per day, one in the early morning and one in mid-afternoon. The
flights will take about four hours and will likely consist of a series of dolphin patterns (slow
climbs and descents along the flight path) and traverses. During one leg of the morning flight of
the first day of an IOP, this aircraft will measure the concentrations at the western, overwater
boundary of the study area. On the return leg, the aircraft will document the concentrations and
fluxes across the shoreline. VOC samples are collected during constant-altitude traverses for the
overwater boundary and during several spirals for the shoreline legs. Boundary measurements
will be made during both non-episode and episode days. This plane will also participate in flux
plane measurements.
Hydrocarbon samples are collected in stainless steel canisters and carbonyl samples are
collected in Tedlar bags and transferred to dinitrophenyl hydrazine impregnated cartridges on the
ground at the conclusion of the flight. Hydrocarbon samples are subsequently analyzed in the
laboratory by gas chromatography with flame ionization detection and carbonyl samples are
analyzed in the laboratory by HPLC with UV detection. The budget allows for collection and
analysis of three or four sets of hydrocarbon and carbonyl samples per flight. The specific flight
plans will be developed over the next several months for the four aircraft under different
meteorological scenarios. The above general description of flight patterns and objectives of each
flight will be specified in the operational program plan.
Helicopter based measurements of power plant plume will be used to evaluate the plumein-grid (PiG) parameterizations used in air quality models. With PiG parameterizations plume
emissions are simulated in a Lagrangian reference frame superimposed on the Eulerian reference
frame of the host grid model. Ozone formation rates in the plume of an elevated point source will
be different from the ambient air because of their very different VOC/NOx ratios. CCOS
measurements of ozone, VOC and nitrogenous compounds in plumes and in the surrounding air
will be compared with the simulations of models using the PiG approach. The plume study is
described in Addendix D.
1-29
In addition to the instrumented aircraft, both airborne and ground-based lidars, and
network of ozonesondes have been used in previous studies to obtain vertical ozone
measurements. The CCOS Technical Committee discussed the merits of these alternative
approaches and reached the consensus that a small fleet of instrumented aircraft would provide
the most cost-effective approach given the tradeoffs between temporal and spatial information
and requirements for pollutant flux and plume measurements. The rationale for the Committee’s
recommendation is explained further in Section 4.8. The CCOS field measurement program is
described in detail in Section 4.
1.4.3
Complementary Measurement Programs
The current study emphasizes collection of data that are needed to model transport
between air basins. Because a Photochemical Assessment Monitoring Station (PAMS) network
does not exist in the Bay Area, the density of air quality measurements (e.g., NO/NOy and VOC)
in the Bay Area is lower than is needed to reliably understand and simulate the conditions that
result in days exceeding the ozone standard in the Bay Area alone. The Bay Area AQMD will
sponsor additional measurements during CCOS to obtain the information needed to develop a
Bay Area specific plan.
The supplemental Bay Area measurements are primarily designed to increase the
understanding of high ozone in the Livermore Valley, where the Bay Area’s highest ozone levels
and most frequent exceedances of the standards occur. For this purpose, four types of
measurements are proposed: 1) Monitoring of pollutant levels aloft on selected episode days with
instrumented aircraft flights within and upwind of the Livermore Valley; 2) surface monitoring
of ozone and precursor gases in the gaps through which air enters and exits the Livermore
Valley; 3) continuous aloft measurements of wind and temperature, using Doppler acoustic
sounders and radar profilers, above the Livermore Valley and its entrance and exit gaps; and 4)
remote sensing of on-road vehicle exhaust to verify that the emissions that are input to the
photochemical model are accurate.
The CCOS field measurement program will be conducted in conjunction with the
California Regional PM10/PM2.5 Air Quality Study (CRPAQS). The CRPAQS includes air
quality and meteorological field measurements, emissions characterization, data analysis and air
quality modeling. The CRPAQS field study will consist of a long-term campaign from 12/1/99
through 1/31/01, a winter intensive study within the period of 11/15/00 through 1/31/01, and a
fall intensive study within the period of 9/1/00 through 10/31/00. Several experiments will be
conducted during the summer period of 7/1/00 through 8/31/00. These include chemical
characterization of PM2.5 at the Fresno site to estimate the fraction of fine particles that is
attributable to secondary organic aerosol and source contributions of directly-emitted fine
particles. Other experiments will examine the timing and intensity of light extinction in the San
Joaquin Valley and the Mohave Desert. The baseline measurements for CRPAQS, which will
begin in December 1999 and continue to the end of the study, are incorporated and leveraged
into the CCOS field program. These include six upper-air meteorological measurement sites
within the CCOS domain. Opportunities for cost sharing also exist for purchases of instruments
that are needed for both CCOS and the CRPAQS winter intensive study (e.g., NO2, PAN, and
NOy and continuous particulate nitrate). CCOS will also benefit from measurements that are
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available throughout the study region from existing federal, state, local, and private air quality
and meteorological monitoring programs.
1.4.4
Consideration of Measurement Alternatives
An investigation of the various processes that are important for ozone formation would
require extensive measurements for each process. This detailed investigation for each process
goes beyond the resources of CCOS. Although this is a potential limitation, the corroborative
analysis of other detailed process studies can be used to evaluate the conceptual model and
identify data needs that can be met by future studies.
During the California Ozone Deposition Experiment (CODE) in 1991, aircraft and towerbased flux measurements were taken over different types of San Joaquin Valley crops, irrigated
and non-irrigated fields, and over dry grass. Estimates of ozone deposition velocities are 0.7-1.0
cm/s (Pederson et al. 1995). Order of magnitude calculations by Pun et al. show that dry
deposition can be a few percent (~3-5%) of the total ozone budget in the San Joaquin Valley.
However, modeling studies (Glen Cass, personal communication) have shown that dry
deposition can play a more significant role in the budget of an important ozone precursor, NO2.
Three alternative deposition studies were considered. Two are tower-based and could take
advantage of the 100-m tower at Angiola planned for CRPAQS. The third is an aircraft flux
measurement and could be used for a variety of different terrain types. The consensus view of
the CCOS Technical Committee was that a proper study of atmospheric deposition would require
far more funds than available within CCOS. Rather than dilute the CCOS effort, the Committee
recommended that separate funding be sought for a comprehensive deposition study in the year
2001.
Other tests of atmospheric chemistry such as the measurements made at the University of
Michigan Biological Research Station (UMBS) near Pellston, Michigan, under the Program for
Research on Oxidants: PHotochemistry, Emissions and Transport (PROPHET) (Carroll et al.,
1998 http://aoss.engin.umich.edu/PROPHET/) should be analyzed as part of the corroborative
analysis. Data were collected in summer 1997 and 1998, and a third field study is planned for
2000. The data set includes: continuous measurements of meteorological parameters, ultraviolet
radiation, ozone, CO, peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN),
peroxymethacrylic nitric anhydride nitrate (MPAN); simultaneous measurements of nitrogenous
species, NOx, NOy, HONO, HNO3 and organic nitrates; measurements of volatile organic
compounds and organic peroxy radicals (ROx); HO and HO2 radicals; peroxides, and the
physical properties and chemical composition of aerosol (Carroll et al., 1998). The PROPHET
study provides a very complete data set from one site that can be used to develop and evaluate
the chemical mechanisms used air quality models with a level of detail that will not be obtained
under CCOS.
1.5
Applications of CCOS Data for Evaluation of Air Quality Modeling Systems
Air quality modeling systems are composed of three major components: 1) the
meteorological model, 2) the emissions inventory and 3) the air quality model. Each of these
components must be evaluated on its performance in simulating the physical and chemical
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processes associated with actual ozone episodes. An overview of the application of CCOS data
to this evaluation is given below. One of the main objectives of CCOS is to evaluate the
performance of air quality modeling systems. The CCOS data will be used to assess the
performance in operational and diagnostic evaluations. Operational evaluations involve
comparing model simulations with ambient measurements. Diagnostic evaluations assess a
model’s representation of the physical and chemical processes to determine if the model
estimates the correct ozone concentrations for the correct reasons.
1.5.1
Meteorological Models
An air quality model requires the meteorological fields that drive the transport and
dispersion of atmospheric pollutants. The meteorological situation in central California is
difficult to model due to its complex topography. The larger scale meteorological flows in
northern and central California are channeled by the Sacramento and San Joaquin Valley. On a
smaller scale the concentrations of air pollutants are affected by land-sea breezes, urban
circulations, local flows (slope and drainage), and diurnal variation of thermal stability and wind
shear. The mixing depth for both convective and stable conditions is very important to model but
the treatment of the atmospheric boundary layer (ABL) is difficult to characterize for the stable
and stagnant conditions associated with episodes of high ozone. The depth of the stable
atmospheric boundary layer (ABL) may be of the order of 100 m, and a number of local effects
such as urban land use, vegetation, soil properties, and small-scale topographic features, can
significantly influence ABL characteristics. All of these factors contribute to a challenging
meteorological situation for the models.
The meteorological models will use surface network measurements of wind speed and
direction, temperature, humidity, pressure, solar radiation, and precipitation available from
routine aloft network measurements. This routine data set will be augmented by the CCOS data
that will include: additional surface sites for the routine measurements listed above, a number of
airborne and remote sensing upper-air measurements, turbulence by sonic anemometer, actinic
flux for photolysis of key species, and soil moisture for key land use.
These data will be used as input to the Mesoscale Meteorological Model, version 5
(MM5) and it will be run in both fully predictive and data assimilation modes. Four-Dimensional
Data Assimilation (4DDA) involves the use of observations to “nudge” the prognostic
calculations of a meteorological model back to the observed meteorological situation at regular
intervals. A meteorological model outputs winds fields, vertical profiles of temperature and
humidity, and other physical parameters in a gridded structure that are physically self consistent
and consistent with observations.
Comparisons of the model results with the meteorological observations will be made as
part of the operational evaluation of the meteorological model. The simulated meteorological
fields by MM5 in its fully predictive model will be compared with the results of MM5
simulations made with 4DDA and with the observations. The CCOS airborne and remote sensing
upper-air measurements will be important for this comparison.
The modeled flow patterns for northern and central California will be compared with
observations. Here it will be important to determine the optimum horizontal and vertical
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resolutions for air quality modeling applications. Aircraft observations will allow the elevated
layers with specific stability and dynamics to be determined and compared with the stability and
dynamics of the modeled layers.
On a smaller scale the detailed measurements of the vertical wind and temperature
structure of the atmospheric boundary layer and the spatial characteristics of mixing depth for
both convective and stable conditions will be compared with modeling results. The observed
properties of land-sea breezes, urban circulations, local flows (slope and drainage), and diurnal
variation of thermal stability and shear will be compared with model calculations.
Diagostic tests will include sensitivity tests of the input parameters, for example,
topographic resolution, model grid, synoptic fields vs. radiosonde network, range and variation
of sea/surface temperature, urban effects/roughness, sinks and sources of heat. This will allow an
estimation of the relative importance of various meteorological processes and the uncertainties in
model results.
1.5.2
Emission Inventories
The development of the emission inventory for air quality modeling of central California
will be a major effort. The study domain is large and the required emission inventory should be
highly detailed for CCOS modeling. The emission inventory needed to support the CCOS
modeling will be a series of day-specific, hourly, gridded emission inventories that cover each
day of the ozone episodes captured during the field study. There are about 30 districts in the
CCOS modeling domain. Each local air district in the state updates a portion of the emission
inventory for their area. To help coordinate this effort, the Emission Inventory Coordination
Group (EICG) has been established to determine the process for preparing the emission
inventories needed to support air quality modeling for CCOS. Participants in the group include
many local air districts, several local councils of government, Caltrans, California Energy
Commission, and the ARB. Local air districts participating to date include San Joaquin Valley
Unified APCD, Bay Area AQMD, Sacramento Metropolitan AQMD, Mendocino County
AQMD, Northern Sierra AQMD, Yolo-Solano AQMD, Placer County APCD, San Luis Obispo
County APCD, and Monterey Bay Unified APCD. Other local air districts will also be
participating. See Section 3.1.2 for details.
Evaluations of emission inventory estimates are an essential part of model performance
evaluations. The application of continuous speciated VOC data in source apportionment offers
insights regarding the temporal variations in source contributions that are difficult to discern
from a limited number of canister samples that are integrated over a period of 3 hours or more.
The diurnal and day-of-the-week variations in the measured relative and absolute levels of ozone
precursors (NOx, CO and VOC) can be compared to corresponding values estimated by model
simulations. Other related approaches for evaluation of emission inventories include: 1)
performance evaluations of air quality simulation models; 2) spatial and temporal comparisons of
ambient and emission inventory non-methane organic gas speciation profiles and pollutant ratios
(e.g., CO/NOx and VOC/NOx); 3) comparisons of long-term trends in ambient pollutant
concentrations and concentration ratios with emission inventory trends; 4) comparisons of onroad measurements with motor vehicle emission models; and 5) fuel-based inventory based on
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regional gasoline sales and fleet-averaged, fuel-based emission factors from remote sensing
measurements. Details are in Section 3.2.2.
1.5.3
Air Quality Models
ARB, in coordination with Districts and the CCOS Technical Committee, will analyze
the data collected during the CCOS field measurement program and will select a minimum of
three ozone episodes to simulate. Assistance will be sought from air districts to develop dayspecific emission inventory as well as additional simulation days. See Section 3.2 for details.
Air quality models consist of parameterizations of atmospheric chemical mechanisms,
advection, photolysis, deposition and other atmospheric processes that affect the concentrations
of air pollutants. The air quality model uses the output of the meteorological model and the
emissions inventory as its input. The evaluation of the meteorological model and the emissions
inventory is therefore important for the evaluation of the air quality model as discussed above.
Despite efforts to improve models during the past two decades, significant questions
remain in both model formulation and input data. Uncertainties in the estimation of emissions
are believed to one of the major limitations to producing reliable air quality model results.
Studies during the past twelve years have shown that on-road reactive organic gases (ROG) and
CO emissions have been historically underestimated (e.g., Ingalls, 1989; Pierson et al., 1990;
Fujita et al., 1992). Sensitivity studies also showed that model performance was greatly
improved when the base on-road motor vehicle ROG emissions were increased by substantial
margins (Wagner and Wheeler, 1993; Chico et al., 1993; Harley et al., 1993). Gaps in model
formulation that fail to treat certain chemical and physical processes adequately are additional
limitations. Boundary-layer parameterizations that determine the rates of dilution and mixing,
and the origin and evolution of ozone layers aloft are examples of such gaps. Other limitations
include insufficient spatial and temporal data to adequately specify boundary conditions and
photolytic rate parameters. Despite the limitations imposed by model uncertainties, air quality
models remain the only acceptable tools available for quantitatively estimating the effect of
control measures on future air quality.
The reliability of model outputs is assessed through operational and diagnostic
evaluations and application of alternative diagnostic tools. Operational evaluations consist of
comparing concentration estimates from the model to ambient measurements. The level of
confidence that can be developed from this type of evaluation increases with the number and
variety of episodes and chemical species that are examined. Measurements of the threedimensional variations in ozone and ozone precursors also enhance the utility of the evaluations.
Diagnostic evaluations determine if the model is estimating ozone concentrations correctly for
the right reasons by assessing whether the physical and chemical processes within the model are
simulated correctly. Examples of diagnostic tests include examining ratios of chemical species
that are sensitive to specific processes within the model such as O3/NOy, O3/ NOz, examining
the flux of ozone and ozone precursor across interbasin transport corridors, and comparing
concentration changes from weekdays to weekends.
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Operational Evaluation
The operational model evaluation will focus on the determination of the extent of
agreement between simulated and measured concentrations of ozone and its precursors in their
spatial distribution and timing. Model simulations will be compared with airborne measurements
of VOCs (total VOC, homologous groups, and lumped VOC classes), along boundaries, above
the mixed layer, and at the surface with measurements made at the research sites. A similar
comparison will be made for the measurements of nitrogenous species concentrations. The
temporal and spatial variability of the initial and boundary conditions will be evaluated to
determine their effect on model output.
Diagnostic Evaluations
The diagnostic evaluation must evaluate a model’s representations of gas-phase
chemistry, photolysis rates, treatment of advection, deposition rates and the treatment of subgrid
scale processes. The treatment of photolytic rate parameters will be evaluated by comparing the
model default values with measurements. Comparisons of calculated secondary chemical product
concentrations with measurements will be used to assess the chemical mechanisms. Other
examples of chemical diagnostic tests include examining ratios of chemical species that are
sensitive to specific processes within the model such as O3/NOy, O3/ NOz and comparing
concentration changes from weekdays to weekends.
The process analysis of Jeffries and Tonnesen (1994) should be used to make a detailed
mass balance for each simulation. This will allow the determination of the effect of each process
on the concentrations of ozone and other air pollutants. Although it is not possible to use
measurements to develop an independent, comprehensive mass balance throughout the study
region, the measurements can be used to check key parameters within simulations. For example,
the concentration ratio of NO to NO2 or the concentration ratio of a highly reactive VOC to a less
reactive VOC could be compared with the model results. Alternatively model calculated
parameters such as HO could be used along with measured concentrations of VOC to estimate
VOC to NOx ratios. Both approaches should provide strong tests of the conceptual model.
Diagnostic evaluation of the transport is more difficult but comparisons of the horizontal
and vertical distributions of ozone and its precursors will be required. Another test to evaluate
the transport would be to compare measured and modeled fluxes of ozone and ozone precursors
across interbasin transport corridors.
1.5.4
Plume in Grid Module Evaluation
One of the most important issues for air quality modeling in CCOS is the treatment of
plumes from elevated point sources containing high concentrations of NOx. Ozone formation
rate in the plume of an elevated point source will be different from the ambient air because of
their very different VOC/NOx ratios. Although many models ignore the effect of plumes by
simply mixing the plume emissions into typical grid cell volumes, some air quality models use a
plume-in-grid (PiG) approach. In the PiG approach a Gaussian-shaped plume is simulated in a
Lagrangian reference frame that moves with the local wind vector, superimposed on the Eulerian
reference frame of the host grid model, and at some suitable time period the contents of the
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plume are mixed into the grid cells. Most current approaches are overly dispersive and do not
treat the effect of turbulence on chemistry and they ignore the effect of wind shear on plume
separation.
A more advanced approach was developed under EPRI sponsorship, the Second order
Closure Integrated PUFF model (SCIPUFF) with a chemistry module (SCICHEM). Their
improved parameterizations of turbulent chemistry makes simulations more realistic than models
using simpler approaches, particularly within the first few kilometers downwind of the
smokestack, where plume confinement and turbulent chemistry have the greatest effect on local
as well as overall ozone production. The SCICHEM was evaluated favorably as part of the 1995
Nashville/Middle Tennessee Ozone Study. Since the composition of the plumes will be different
for central California, these new modules should be evaluate using the CCOS database.
1.6
Corroborative Data Analysis
Data analysis is an essential part of the database and model development components of
CCOS. Measurements, by themselves, say nothing about the causes of air pollution and the
likely effects of emission reductions. It is only when these measurements are interpreted that
relationships can be observed and conclusions can be drawn. Similarly, mathematical models
cannot be expected to explain phenomena that are not conceptually defined. "Conceptual
models" of pollutant emissions, transport, chemical transformation, and deposition must be
formed so that the best mathematical formulations can be selected to describe them. Section 3.3
provides details of tasks that address data analysis objectives.
The corroborative data analysis will determine if the conceptual model of ozone
formation in central California and the models derived from it is sufficiently valid for policy
development. It will involve all aspects of CCOS diagnostic data analysis and model evaluation.
Corroborative data analysis will be used to reinforce current understanding, identify gaps and to
improve the conceptual model of ozone formation. The corroborative data analysis will be used
to provide a comprehensive picture of ozone formation that will be used to determine if
measurement and modeling results are consistent with the conceptual model as revised by
CCOS.
1.6.1
Contribution of Transported Pollutants to Ozone Violations in Downwind Areas
In principle, well-performing air quality modeling systems have the ability to quantify
local and transported contributions to ozone exceedances in a receptor area. However, many of
the interbasin transport couples in the CCOS study region involve complex flow patterns with
strong terrain influences that are difficult to realistically simulate. The CCOS field campaign
provides routine and supplemental measurements at locations where transport can occur. The
proposed upper air network provides a “flux plane” method by which quantitative estimate is
possible, with suitable assumptions. In combination with modeling, data analyses can improve
the evaluation of modeling results and provide additional quantification of transport
contributions.
Methods that should be applied include timing of ozone where morning peaks indicate
fumigation of carry-over aloft, peaks near solar noon indicate possible local contributions, and
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delayed peaks indicate transport, with later times corresponding to sites further downwind along
the transport path. Other methods include back trajectory wind analysis and examination of ratios
of species (i.e., xylenes-to-benzene ratio). In addition, vertical planes intersecting the profiler
sites downwind of and perpendicular to the transport path can be defined and provide estimates
of transport through likely transport corridors. The analyses use surface and aircraft
measurements of pollutant concentrations and surface, wind profiler, and aircraft meteorological
data for volume flux estimations.
Tracer techniques, both active (intentional release) and passive (tracers of opportunity),
were considered very early in the study, but were dismissed due to relatively high costs.
However, source apportionment of hydrocarbons can be applied as a passive tracer technique,
where potential hydrocarbon “fingerprints” are the tracers of opportunity. This method is also
supported by the CCOS study plan, and corroboration with modeling results will be evaluated.
1.6.2
VOC Versus NOx Sensitivity
One of the most important questions for ozone control strategies is whether to focus on
NOx or VOC control. To choose the proper control strategy it is necessary to know if ozone
production is limited in a location more by the NOx or VOC available. Models and
measurements have been applied to answer this question. One of the methods would be to
determine ozone isopleths from measurements for each site in an airshed but usually not enough
data are available. Therefore indicators for VOC or NOx limitation include ratios such as
HNO3/H2O2 and other ratios have been applied to both measurements and models. Within
CCOS, NOx and VOC limitation will be investigated through the use of measurements,
indicators and modeling analysis.
One of the most important tests of model simulations is the ability to simulate accurately
weekday-weekend differences in precursors and ozone. One of the most important applications
of this test is that it helps to probe the relative sensitivity of ozone concentrations to VOC and
NOx. Since the mid-1970’s it has been documented that ozone levels in California’s South
Coast Air Basin (SoCAB) are higher on weekends than on weekdays, in spite of the fact that
ozone pollutant precursors are lower on weekends than on weekdays (Elkus and Wilson, 1977;
Horie et al., 1979; Levitts and Chock, 1975; Zeldin et al., 1989; Blier and Winer, 1998; and
Austin and Tran, 1999). Similar effects have been observed in San Francisco (Altshuler et al.,
1995) and in the northeastern cities of Washington D.C., Philadelphia, and New York (SAIC,
1997). While a substantial “weekend effect” has been observed in these cities, the effect is less
pronounced in Sacramento (Austin and Tran, 1999), and is often reversed in Atlanta (Walker,
1993) where VOC/NOx ratios are typically higher. Several of the above studies show that the
“weekend effect” is generally less pronounced in downwind locations where ambient VOC/NOx
ratios are higher.
Understanding the response of ozone levels to specific changes in VOC or NOx
emissions is a fundamental prerequisite to developing a cost-effective ozone abatement strategy.
The varying emissions that occur between weekday and weekend periods provide a natural test
case for air quality simulation models. At the same time, the model performance and evaluation
must be accompanied by an evaluation of the accuracy of the temporal and spatial patterns of
precursor emissions. With the questions that still remain regarding the accuracy of emission
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inventories, the “weekend effect” emphasizes the need for observation-based data analysis to
examine the relationship between ambient O3 and precursor emissions. The results of
corroborative data analyses need to be reconciled with model outputs taking into consideration
the various sources of uncertainties associated with both approaches.
The current conceptual model must be revisited and refined using the results yielded by
the foregoing data analyses. New phenomena, if they are observed, must be conceptualized so
that a mathematical model to describe them may be formulated and tested. The formulation,
assumptions, and parameters in mathematical modules that will be included in the integrated air
quality model must be examined with respect to their consistency with reality.
1.7
Funding
Cost estimates have been prepared for each element of the base and optional programs
based on a consensus of the CCOS Technical Committee. Cost estimates are summarized in
Table 5-1 for major components of the proposed measurement plan. The total contract costs for
CCOS is $7,200,000.
In addition to the above contract costs, the ARB and the local APCD’s are committing inkind resources for planning and execution of CCOS. In-kind costs include additional efforts that
are specifically required during CCOS (e.g., project management, episode forecast, site
operations, quality assurance, emission inventory development, and data management) and does
not include data collection and analysis associated with normal daily operations (e.g., PAMS and
other aerometric monitoring programs). The in-kind costs for the field study portion of CCOS
total $1,400,000, which does not include substantial leveraging of resources that will be available
from CRPAQS. Additional in-kind resources will be committed for modeling and data analysis.
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2.
Chapter 2: Basis for Study
BASIS FOR THE FIELD STUDY PLAN
This section describes the central California study area, the magnitudes and locations of
ozone concentrations and their chemical components, emissions sources, meteorology that
affects ozone levels, and applicable transformation chemistry of the study area. It integrates this
knowledge into a “conceptual model” of the phenomena that should be reproduced by the
regulatory ozone models.
2.1
CCOS Study Area
Central California is a complex region for air pollution, owing to its proximity to the
Pacific Ocean, its diversity of climates, and its complex terrain. Figure 2.1-1 shows the overall
study domain with major landmarks, mountains and passes. Figure 2.1-2 shows major political
boundaries, including cities, counties, air quality planning districts, roads, Class 1 (pristine)
areas, and military facilities. The Bay Area, southern Sacramento Valley, San Joaquin Valley,
central portion of the Mountain Counties Air Basin (MCAB), and the Mojave Desert are
currently classified as nonattainment for the federal 1-hour ozone NAS. With the exception of
Plumas and Sierra Counties in the MCAB, Lake County, and the North Coast, the entire study
domain is currently nonattainment for the state 1-hour ozone standard. The Mojave Desert
inherits poor air quality generated in the other parts of central and southern California.
The Bay Area Air Quality Management District (BAAQMD) encompasses an area of
more than 14,000 km2 of which 1,450 km2 are the San Francisco and San Pablo Bays, 300 km2
are the Sacramento and San Joaquin river deltas, 9,750 km2 are mountainous or rural, and 2,500
km2 are urbanized. The Bay Area is bounded on the west by the Pacific Ocean, on the east by
the Mt. Hamilton and Mt. Diablo ranges, on the south by the Santa Cruz Mountains, and on the
north by the northern reaches of the Sonoma and Napa Valleys. The San Joaquin Valley lies to
the east of the BAAQMD, and major airflows between the two air basins occur at the
Sacramento delta, the Carquinez Strait, and Altamont Pass (elevation 304 m). The coastal
mountains have nominal elevations of 500 m, although major peaks are much higher (Mt.
Diablo, 1,173 m; Mt. Tamalpais, 783 m; Mt. Hamilton, 1,328 m). Bays and inland valleys
punctuate the coastal mountains, including San Pablo Bay, San Francisco Bay, San Ramon
Valley, Napa Valley, Sonoma Valley, and Livermore Valley. Many of these valleys and the
shorelines of the bays are densely populated. The Santa Clara, Bear, and Salinas Valleys lie to
the south of the BAAQMD, containing lower population densities and larger amounts of
agriculture.
The BAAQMD manages air quality in Alameda, Contra Costa, Marin, San Francisco,
San Mateo, Santa Clara, and Napa counties, in the southern part of Sonoma County, and in the
southwestern portion of Solano County. More than six million people, approximately 20% of
California’s population, reside within this jurisdiction. The Bay Area contains some of
California’s most densely populated incorporated cities, including San Francisco (pop.
~724,000), San Jose (pop. ~782,000), Fremont (pop. ~173,000), Oakland (pop. ~372,000), and
Berkeley (pop. ~103,000). In total, over 100 incorporated cities lie within the jurisdiction of the
BAAQMD.
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Major industries and areas of employment in the Bay Area include tourism,
government/defense, electronics manufacturing, software development, agriculture (vineyards,
orchards, and livestock), petroleum-refining, power generation, and steel manufacturing.
BAAQMD residences are often distant from employment locations. More than 1,800 km of
major controlled-access highways and bridges accommodate approximately 148 million vehicle
miles traveled on a typical weekday. The Bay Area includes a diverse mixture of income levels,
ethnic heritages, and lifestyles.
The Sacramento Valley Air Basin is administered by five air pollution control districts
(Butte County, Colusa County, Glenn County, Placer County, and Tehama County) and four air
quality management districts (Feather River, Sacramento Metropolitan, Shasta County, and
Yolo/Solano Counties). About 1.5 million people reside in the four-county Sacramento
Metropolitan Statistical Area. Sacramento is an important highway, rail and river hub, the state
capitol, and the marketing center for the rich agricultural region.
The counties of Amador, Calaveras, El Dorado, Mariposa, Placer, Tuolumne, Nevada,
Plumas, and Sierra comprise the Mountain Counties Air Basin. The later three counties merged
to form the Northern Sierra Air Quality Management District. Mountain Counties are generally
sparsely populated consisting of many small foothill communities, and local pollutant emissions
are comparatively low.
The San Joaquin Valley, administered by the San Joaquin Valley Unified Air Pollution
Control District (SJVUAPCD), is much larger than the Bay Area but with a lower population. It
encompasses nearly 64,000 km2 and contains a population in excess of three million people, with
a much lower density than that of the Bay Area. The majority of this population is centered in
the large urban areas of Bakersfield (pop. ~175,000), Fresno (pop. ~355,000), Modesto (pop.
~165,000), and Stockton (pop. ~211,000). There are nearly 100 smaller communities in the
region and many isolated residences surrounded by farmland.
The SJV is bordered on the west by the coastal mountain range, rising to 1,530 meters
(m) above sea level (ASL), and on the east by the Sierra Nevada range with peaks exceeding
4,300 m ASL. These ranges converge at the Tehachapi Mountains in the southernmost end of
the valley with mountain passes to the Los Angeles basin (Tejon Pass, 1,256 m ASL) and to the
Mojave Desert (Tehachapi Pass, 1,225 m ASL, Walker Pass, 1609 m ASL). Agriculture of all
types is the major industry in the SJV. Oil and gas production, refining, waste incineration,
electrical co-generation, transportation, commerce, local government and light manufacturing
constitute the remainder of SJV the economy. Cotton, alfalfa, corn, safflower, grapes, and
tomatoes are the major crops. Cattle feedlots, dairies, chickens, and turkeys constitute most of
the animal husbandry in the region.
Other areas in the study region include the North Coast, Lake County, North Central
Coast, South Central Coast and Mojave Desert air basins.
Figure 2.1-3 shows the major population centers in central California, while Table 2.1-1
summarizes populations for Metropolitan Statistical Areas (MSA). Figure 2.1-4 shows land use
within central California. There are substantial tracts of grazed and ungrazed forest and
woodland along the Pacific coast and in the Sierra Nevada. Cropland with grazing and irrigated
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cropland dominate land use in the San Joaquin Valley, while desert scrubland is the dominant
land use east of Tehachapi Pass. Tanner et al. (1992) show the various vegetation classes
determined from satellite imagery. The central portion of the SJV is intensively farmed; the
periphery consists of open pasture into the foothills of the coastal ranges and the Sierra Nevada.
As elevations increase above 400 m, the vegetation progresses through chaparral to deciduous
and coniferous trees.
Central California contains the state’s major transportation routes, as shown in Figure
2.1-5. Interstate 5 and State Route 99 traverse the western and central lengths of the SJV. U.S.
Highway 101 is aligned with the south central coast, then through the Salinas Valley, through the
Bay Area and further north. These are the major arteries for both local and long-distance
passenger and commercial traffic. Major east-west routes include I 80 and SR 120, 152, 198,
46, and 58. Many smaller arteries, both paved and unpaved, cross the SJV on its east side,
although there are few of these small roads on the western side. The major cities contain a
mixture of expressways, surface connectors, and residential streets. Farmland throughout the
region contains private lanes for the passage of off-road implements and large trucks that
transport agricultural products to market.
2.2
Ozone Air Quality Standards and SIP Requirements
In November 1990, Congress enacted a series of amendments to the Clean Air Act
(CAA) intended to intensify air pollution control efforts across the nation. One of the primary
goals of the 1990 amendments was an overhaul of the planning provisions for those areas do not
meet the National Ambient Air Quality Standard (NAAQS). The NAAQS for ozone is exceeded
when the daily maximum hourly average concentration exceeds 0.12 ppm more than once per
year on average during a three-year period. The California State standard is more stringent: no
hourly average ozone concentration is to exceed 0.09 ppm. The CAA identifies specific
emission reduction goals, requires both a demonstration of reasonable further progress and
attainment, and incorporates more stringent sanctions for failure to attain the ozone NAAQS or
to meet interim milestones.
The 1990 CAA established a classification structure for ozone nonattainment areas based
on the area’s fourth worst exceedance during a three-year period (“design value”). These
classifications are marginal (0.120-0.138 ppm), moderate (0.138-0.160), serious (0.160-0.180),
severe-1 (0.180-0.190), severe-2 (0.190-0.280) and extreme (0.280 +). Each nonattainment area
is assigned a statutory deadline for achieving the national ozone standard. Serious areas must
attain the NAAQS by the end of 1999, severe areas by 2005 or 2007 (depending on their peak
ozone concentrations), and extreme areas by 2010. The lower Sacramento Valley is classified as
severe. The San Joaquin Valley is currently classified as serious but re-classification by EPA to
severe is expected next year. This re-classification would impose additional requirements but
would also extend the attainment deadline by six year to 2005. EPA designated the Bay Area in
attainment of the national ozone standard on May 22, 1995. However, as a result of exceedances
during the summers of 1995 and 1996, EPA redesignated the area in August 1998 from a
“maintenance” area to an “unclassified nonattainment” area. This action required the Bay Area
to submit an inventory of VOC and NOx emissions (1995), assess emission reductions needed to
attain the national ozone standard by 2000, and develop and implement control strategies. The
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CAA prescribes minimum control measures for each ozone nonattainment area with more
stringent controls required for greater degrees of nonattainment.
Emission reduction plans for ozone precursors in serious, severe, and extreme
nonattainment areas were submitted to the U.S. Environmental Protection Agency (EPA) on
November 15, 1994, as a revision to the California State Implementation Plan (SIP). Each ozone
plan contained an emissions inventory, plans for enhanced monitoring of ozone and ozone
precursors, and estimation of future ozone concentrations based on photochemical modeling. To
ensure a minimum rate of progress, each plan shows a 15 percent reduction in emissions of
reactive organic gases (ROG) between 1990 and 1996, an additional 9 percent reduction in ROG
by 1999, and 3 percent reductions per year thereafter, quantified at three year intervals to the
attainment date.
In July 1997, United States Environmental Protection Agency (EPA) announced that it
will phase out and replace the 1-hour primary ozone standard (health-based) of 0.12 parts per
million (ppm) with a new 8-hour standard to protect against longer exposure periods. The new
standard would be attained when the 3-year average of the annual 4th-highest daily maximum 8hour concentration is less than or equal to 0.08 ppm. The implementation of this standard is
currently on hold. On May 14, 1999, a three-judge panel of the U.S. Court of Appeals for the
District of Columbia set aside EPA’s new air quality standards for ozone and fine particles. The
court action prohibits EPA from enforcing the standard, but did not remove the standard. EPA
intends to recommend an appeal to the Department of Justice and is currently reviewing its
options. The previously existing one-hour ozone standard continues to apply in areas that have
not attained the standard. In addition to areas that are currently in nonattainment of the 1-hour
ozone standard, several areas in central and southern Sierra Foothills and northern Sacramento
Valley that are now in compliance of the 1-hour standard are also expected to become
nonattainment for the 8-hour standard.
At the state level, pollutant transport is a recognized cause of air quality degradation.
The California Clean Air Act of 1988 requires the California Air Resources Board (ARB) to
assess the relative contributions of upwind pollutants to violations of the state ozone standard in
downwind areas. The California Health and Safety Code, Division 26, paragraph 39610(b) states
"The state board shall, in cooperation with the districts, assess the relative contribution of
upwind emissions to downwind ozone ambient pollutant levels to the extent permitted by
available data, and shall establish mitigation requirements commensurate with the level of
contribution" (California Air Pollution Control Laws, 1992 edition, p. 14). Previous studies in
California have demonstrated pollutant transport between air basins on specific days, but few
studies have quantified the contribution of transported pollutants to ozone violations in
downwind areas.
The implication of the state 1-hour ozone standard and the new federal 8-hour ozone
standard is that they require a reappraisal of past strategies that have focused primarily on
addressing the urban/suburban ozone problem to one that considers the problem in a more
regional context. Retrospective analysis of the O3 data for northern and central California during
the 1990’s show larger downward trends in 1-hour-average peak O3 concentrations than in 8hour averages. This will require further departure from the local emission-control approach to
ozone attainment and the development of region-wide management approaches. The Central
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California Ozone Study is intended to provide another milestone in the understanding of
relationships between emissions, transport, and ozone standard exceedances, as well as to
facilitate planning for further emission reductions needed to attain state and federal standards.
2.3
Ambient Trends in Ozone and Precursor Gases
Recent work has shown modest progress in reducing ozone exposure (concentration *
time) in central California (Wittig et al. 1999) between 1987-1997. Wittig et al. used a variety of
statistical approaches for the analysis, drawing conclusions only when there was general
consensus among the statistical indicators. However, downward ozone trends in central
California are less obvious during the 1990s, when the relatively high ozone years in the late
1980s are dropped and 1998, another relatively high ozone year, is added. This is in contrast to
reductions in exposure in southern California, i.e., the South Coast and San Diego Air Basins,
where graphically presented conclusions by Wittig et al. for 1990-1997 show a more marked
downward trend. After analysis of ozone at PAMS Type 2 and Type 3 sites, Wittig et al.
conclude:
“In no case did all the indicators reveal consistent trends in ozone . . . [which is]
not surprising given the variability in atmospheric and meteorological parameters
. . . and the complex relationship [among] these parameters, emissions, and ozone
formation. More intricate methods that address the known variability . . . were
also investigated. The adjustment methods . . . in general clarified the observed
trends . . . [but in] some cases were not sensitive or robust enough to fully discern
trends even when some variability was removed. ”
Wittig et al. also found slight but never statistically significant decreases in ambient
morning NOx over their study period for all California metropolitan areas examined, including
Sacramento, Bakersfield and Fresno. The same was true for total VOCs, except that a significant
change in composition, led by a marked decrease in benzene, was observed during years 19941997.
A new analysis of ozone trends over the CCOS study region was undertaken to examine
the changes in mean and maximum daily ozone concentration and the frequency of occurrence of
exceedances of the Federal 1-hr and, in particular, the proposed 8-hr ozone standards over the
years 1990-1998. Consistent with the findings of Wittig et al., no significant trends for NOx or
VOCs were observed for the 1990s, with the exception of 1997 being a very clean year for VOCs
at the Sacramento PAMS sites.
2.3.1
Trends in Ozone Exceedances
A database was obtained from all stations reporting ozone measurements listed in the
California Air Resources Board Aerometric Data Analysis and Management (ADAM) System
for 1990-1998. (Data supplied courtesy of Dwight Oda, ARB). For each available day during the
nine ozone seasons, defined as May-October for this investigation, the 1-hour and 8-hour
maximum ozone concentrations and the start-time of the 1-hr and 8-hour peak ozone were
compiled. From the 207 sites in the ADAM database, a subset of 153 sites was selected based on
period of record, the acceptability of linking nearby sites to gain a longer period of record, the
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data recovery rate during the 1996-1998 ozone seasons, and the expected continuation of
monitoring at that site into the summer 2000 CCOS field study period. Twenty-seven sites,
satisfying the criteria of close proximity and similar ozone temporal patterns, were linked in time
to an in-service site, providing 126 “linked ADAM” sites (assigned a “LADAM#” equivalent to
the ARB ADAM# number of the most recent site). This linked master list is shown in Table 2.31a. Included in Table 2.3-1a is information on the three functional site location types, Urban/City
Center, Suburban, or Rural. Documentation and details of linking these sites is provided in Table
2.3-1b.
Table 2.3-2 gives a summary of 1-hour and 8-hour annual maximum and annual mean
daily maximum ozone for the years 1990-1998 by air basin. All reporting sites for the air basin in
each year were used to compile the mean of daily 1-hour and 8-hour maxima and the basin-wide
average of daily 1-hr and 8-hr maxima. Three annual groupings are also provided for 1990-1995,
1996-1998, and the entire nine-year period, but only years where a site achieves >75% data
recovery are included in the group means. Figures 2.3-1 and 2.3-2 summarize maximum and
mean, respectively, 8-hr ozone trends by basin and by location type, respectively. Figure 2.3-3
shows average ozone maxima by weekday across all basins. Figure 2.3-3 is not a rigorous
statistical treatment, since the distribution of daily ozone maxima across basins are skewed
somewhat from a normal distribution. However, the large number of cases (over 9000 for Rural
and Suburban locations, and over 6000 for Urban/City) in each average provides compelling
evidence of the effect in consideration of the error bars of ± 2 standard errors.
Tables 2.3-3 through 2.3-6 give a breakdown by site, in each air basin, of 1hr and 8hr
annual maximum of daily maxima, number of exceedance days per year (also giving site-specific
annual trends), seasonal occurrences by month (May-Oct), and the hebdomadal cycle of
exceedances, respectively. Several features are evident from the tables:
•
Sites downwind of Sacramento have the greatest number of exceedances per year in the SV
and MC air basins, i.e., Folsom and Auburn in SV, and Cool and Placerville in MC.
•
Sites downwind of Bakersfield (Arvin and Edison) and Fresno (Parlier and Maricopa) have
the greatest number of exceedances per year in the SJV air basin, with more exceedances per
season in the south SJV basin. Southern SJV has the worst air quality in the CCOS region.
•
Healdsburg, Livermore, Pinnacles National Monument, and Simi Valley, have the highest
exceedances per season of both the 1-hr and 8-hr standards for NC, SFBA, NCC, and SCC
air basins, respectively. Simi Valley is under the influence of the South Coast Air Basin and
is of less interest for this study.
•
Air quality is in attainment in the LC and NEP basins, and northern portions of the NC meet
the standards.
•
The El Nino event during 1997 significantly lowered the number of exceedances of both the
1hr and 8hr standards in the NCC, SV, and SFBA basins. In fact, no 1hr or 8hr exceedances
occurred in SFBA during 1997. However, this El Nino effect is not evident for all sites in the
SJV and SCC basins, particularly for sites closer to the South Coast air basin.
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•
By inspection of Table 2.3-5, July and August are approximately equal in the number of
exceedances per month, for both the 1hr and 8hr standards, at most sites in the study domain.
Similarly, June and September are approximately equal. Notable exceptions include more
September than June exceedances in the southern SJV and more June than September
exceedances at all MD sites.
•
Subtle weekday/weekend differences are not immediately obvious by inspection of Table
2.3-6. Figure 2.3-2 provides more detail, but a rigorous statistical analysis is beyond the
scope of this conceptual plan.
2.3.2
Spatial and Temporal Patterns of Ozone and Precursors
The complex relationship between chaotic atmospheric forcing, changes in emissions,
and the non-linear ozone formation process provide endless variations in the spatial pattern of
ozone, across space (central California) and time (1990-1998 for this study, with focus on 199698). Temporal patterns of ozone follow the well-known timing of near solar noon in source
regions, with subsequently delayed peaks in downwind areas (e.g., Smith et al. 1983; Roberts et
al. 1992). Precursors spatial patterns are more predictable than ozone, although weather, day-ofweek, and seasonal considerations can change emissions. Three typical ozone patterns were
shown in Figures 1.5-1 through 1.5-6. A typical NOx pattern from modeling of the 1990 effort is
given in Figure 2.8-7. The following section address precursor emissions by air basin.
2.4
Emissions and Source Contributions
Section 39607(b) of the California Health and Safety Code requires the California Air
Resources Board (ARB) to inventory sources of air pollution within the 14 air basins of the state
and to determine the kinds and quantities of pollutants that come from those sources. The
pollutants inventoried are total organic gases (TOG), reactive organic gases (ROG), carbon
monoxide (CO), oxides of nitrogen (NOx), oxides of sulfur (SOx), and particulate matter with an
aerodynamic diameter of 10 micrometers or smaller (PM10). TOG consist of hydrocarbons
including methane, aldehydes, ketones, organic acids, alcohol, esters, ethers, and other
compounds containing hydrogen and carbon in combination with one or more other elements.
ROG include all organic gases except methane and a number of organic compounds such as low
molecular weight halogenated compounds that have been identified by the U.S. Environmental
Protection Agency (EPA) as essentially non-reactive. For ROG and PM10, the emission
estimates are calculated from TOG and PM, respectively, using reactive organic fractions and
particle size fractions. Emission sources are categorized as on-road mobile sources, non-road
mobile sources, stationary point sources, stationary area sources, and natural sources.
The emission inventory for 1996 is the most recent compilation published by the
California Air Resources Board. Point source emission estimates in the inventory were provided
by the air pollution control districts and the air quality management districts. Area source
emission estimates were made by either the districts or the ARB staffs. The ARB staff made onroad motor vehicle emission estimates. The emission estimates are in tons per average day,
determined by dividing annual emissions by 365. The estimates have been rounded off to two
significant figures.
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Tables 2.4-1 and 2.4-2 show the daily averages by air basin for ROG and NOx emissions,
respectively (California Air Resources Board, 1998). Emissions in the CCOS area in 1996 total
1575 and 1545 tons/day for ROG and NOx, respectively, with 80 and 84 percent of those
pollutants emitted within the three major air basins, Bay Area, Sacramento Valley and San
Joaquin Valley. Stationary and area sources, together, contribute equally to ROG emissions, as
do mobile sources, while mobile sources account for the majority of NOx emissions (74 percent
from mobile).
2.5
Central California Summer Meteorology and Ozone Climatology
Given the primary emissions within central California, it is the local climate of California
that fosters generation of ozone, a secondary pollutant. High ozone concentrations most
frequently occur during the “ozone season,” spanning late spring, summer, and early fall when
sunlight is most abundant. Meteorology is the dominant factor controlling the change in ozone
air quality from one day to the next. Synoptic and mesoscale meteorological features govern the
transport of emissions between sources and receptors, affecting the dilution and dispersion of
pollutants during transport and the time available during which pollutants can react with one
another to form ozone. These features are important to transport studies and modeling efforts
owing to their influence on reactive components and ozone formation and deposition. This
subsection provides a summary of meteorological features affecting central California air quality,
and provides a brief overview of the regulatory response to inter-basin transport, i.e., the
identification of “transport couples” and the characterization of the effect of transport on air
quality in the receptor air basin. Specific transport studies are discussed in greater detail with the
introduction of transport scenarios of interest.
2.5.1
Typical Large-Scale Meteorological Features
General descriptions of meteorological effects on California air quality abound in the
literature. For the San Joaquin Valley, the 1990 SJVAQS/AUSPEX/SARMAP bibliography
prepared by Solomon et al (1997) is comprehensive. Briefly, the summer climatology of central
California is generally dominated by the semi-permanent Eastern Pacific High-Pressure System.
This synoptic feature is manifest as a dome of warm air (a maximum in the 500-mb geopotential
height field) with a surrounding anticyclonic circulation (clockwise in the Northern Hemisphere).
Therefore, surface winds blow clockwise and outward from the high, a motion associated with
low-level divergence, and therefore sinking motion aloft and fair weather. This sinking motion
also gives rise to adiabatic heating and therefore warm temperatures aloft. A key indicator of this
warm, capping subsidence inversion in California is the temperature of the 850-mb pressure
surface from the Oakland soundings. This single meteorological variable from the 0400 PST
sounding is perhaps best correlated with surface ozone concentrations in the central valley (e.g.,
Smith et al. 1984; Smith 1994; Fairley and De Mandel 1996, Ship and McIntosh 1999. The shape
of the 500-mb height contours (at 5500-m elevation) over the Eastern Pacific is broad and flat
and can extend inland for 100s of km.
Accompanying the warm temperatures aloft, are warm temperatures on the central valley
floor. Table 2.5-1 presents a summer surface climatology for the cities of Redding, Sacramento,
San Francisco, Fresno, Santa Maria, and Bakersfield. The coastal cities of San Francisco and
Santa Maria have mean daily maximum temperatures in the low- to mid-70s (deg F) while
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Sacramento averages about 20 F warmer. The northern and southern ends of the Central Valley,
represented by Redding and Bakersfield, average an additional 5 F warmer than Sacramento.
This heating causes an inland thermal low-pressure trough as evidenced by the lower station
pressures at Redding and Bakersfield. The pressure gradient enhances the movement of the
thermally generated sea breeze through the Carquinez Straight, through other gaps in the coastal
range to the north and south of the San Francisco (SF) Bay, and sometimes over the coastal range
altogether. Pollutants from the SF Bay Area source region are carried with the breeze to receptor
regions within the Central Valley. With the abundant sunlight accompanying this fair weather
pattern fair weather, the transported pollutants and the Sacramento Valley and San Joaquin
Valley emissions cause frequent exceedances of the 1hr and 8hr standards at several sites in the
interior of the Central Valley.
This typical scenario is observed on most summer afternoons. For the SF Bay Area,
Hayes et al. (1984), in the now-famous “California Surface Wind Climatology,” assign a
frequency of 77% to sea breeze conditions matching average surface wind streamlines at 1600
PST. They give a frequency of 75% for the Sacramento Valley. However, the high pressure
system can migrate with changes in the planetary weather (Rossby wave) pattern. The center of
the pressure cell can move ashore, causing a decrease and even a reversal in the mean pressure
gradients observed in Table 2.5-1 (Lehrman et al. 1994; Pun et al. 1998). The sea breeze is
weakened, and its inland extent can become limited, leading to stagnation conditions fostering
higher ozone concentrations in many areas. The high can also move east all together, followed
by a trough that ventilates the valley. The high pressure is not always dominant. Neff et al.
(1994) classified synoptic patterns during summer 1994 and found approximately one-third of
the days to be “normal” Pacific highs, one-third to be inland highs, and one-third to be troughs.
Therefore, the mesoscale sea breeze surface pattern, with 77% frequency, must exist in more
than one synoptic regime. Unfortunately, the link between synoptic and mesoscale patterns is not
one-to-one. Mesoscale features must be considered in any discussion of ozone climatology.
2.5.2
Mesoscale Meteorological Features in the CCOS Study Region
Several mesoscale flow features in Central California can have significant air quality
impacts by transporting or blocking transport of ozone and precursors between important sourcereceptor couples.
The Sea Breeze and Marine Air Intrusion
Differential heating between the land and ocean causes a pressure gradient between the
relatively cooler denser air over ocean and the warmer air over the land. The marine air mass
comes ashore. However, this heating takes time to occur and may be impeded if a cloud cover
prevents direct insolation of the land. A further complication may be provided by any additional
surface pressure gradients due to synoptic conditions that can enhance, hinder, or overwhelm this
thermal effect. The actual time of onset of a sea breeze can be difficult to forecast with overnight
fog or coastal status. Typically, with calm coastal mornings, rush hour pollutants can accumulate
in the coastal source region. Then, as the sea breeze is established (often by late-morning, usually
by mid-day), maximum ozone production can occur after pollutants leave the coastal areas. It is
well-known that maximum ozone occurs downwind of respective source areas (e.g., Livermore
downwind of the SF Bay communities.) As marine air penetrates the mainland, it is modified and
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can become entrained in a different thermal flow, e.g., an upvalley or upslope flow. Studies of
sea breeze and marine air intrusion impacts on Central California air quality include that by
Stoeckenius et al, (1994), who present an objective classification scheme.
Nocturnal Jets and Eddies
A low-level nocturnal wind maximum can arise as the nocturnal inversion forms and
effectively reduces boundary layer friction. Wind friction can be represented (crudely) as a force
that is directly opposed to the wind (termed the "antitriptic wind" by Schaefer and Doswell
1980). The overall direction of flow is determined by the vector balance among horizontal
pressure gradient, Coriolis, and frictional forces. However, in the evening, with the establishment
of a surface-based nocturnal inversion, the friction is "turned off.” The flow is no longer in
balance, and there is a component of the pressure gradient force that is directed along the wind,
increasing wind speed, which increases the Coriolis force. Since Coriolis is always 90o to the
right of the wind (in the northern hemisphere), this means that the wind must veer. In the SJV,
the rapidly moving jet (7-30 m/s) may veer toward the western valley but is channeled by the
topography and soon encounters the Tehachapi range. While the nocturnal jet may be present in
other seasons, it has been observed during the ozone season (Smith et al. 1981; Blumenthal
1985; Thuillier et al. 1994). It is believed to be a transport mechanism during the summer
months, and the CCOS study plan includes instrumented aircraft, which can provide
measurements for estimating the impact of the transport. Depending on the temperature structure
of the valley, the jet may not be able to exit through Tehachapi Pass (~1400 m), as it can during
the neutral stability of daytime convective heating. The air is forced to turn north along the Sierra
foothills at the southeastern edge of the SJV. Smith et al. (1981) mapped the Fresno eddy with
pibals and described an unusual case where it extended as far north as Modesto. During the
Southern San Joaquin Ozone Study, Blumenthal et al. (1985) measured the Fresno eddy
extending above 900 m AGL about 50% of the time. Neff et al. (1991) have measured the eddy
using radar wind profilers during SJVAQS/AUSPEX. The impact of these jets and eddies is to
redistribute pollutants within an air basin. The SJV nocturnal jet can bring pollutants form the
north SJV to the south overnight. Ozone created in the south SJV can then be redistributed to the
central SJV and/or can be transported into layers aloft by the eddy. The Schultz eddy forms when
westerly marine air flow in the south SV valley (which may become a jet with the evening
boundary layer) impacts the Sierra and turns north. It can redistribute pollutants to Sutter Buttes
and points north and east (or west after a half-circulation) of Sacramento (Schultz, 1975; ARB,
1989).
Bifurcation and Convergence Zones
Marine air entering the Sacramento River Delta region from the west has a “choice”, SJV
to the south or SV the north. The position of this zone may move north and south based on flow
entering the SV from the north or on the infrequent but sometimes observed southerly flow
coming up the SJV axis flow. The relative position of the bifurcation zone may affect the
proportion of SFBA pollutants transported to each downwind basin. But the dynamics governing
the position of the bifurcation zone are currently not well understood. On the other hand,
convergence zones can prevent transport between air basins. In the SFBA-NCC couple,
pollutants from the south bay communities (e.g., San Jose) are transported by northwesterly
winds through the Santa Clara valley to the south. This flow impacts Gilroy and can continue
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down the Santa Clara Valley to Pinnacles National Monument if the northwesterly winds
crossing inland at Moss Landing continue through Pacheco pass without turning north. However,
under perhaps subtly different conditions, some of this onshore flow at Moss Landing will turn
north, damming the southerly flow coming from Gilroy in the Santa Clara Valley. Another
example of the effect of convergence zones on air quality is provided by Blumenthal et al.
(1985). They hypothesize that the increase in mixing heights (~200 m higher than in the north
SJV) at the southern end of the SJV is due to damming of the northerly flow against the
Tehachapi mountains at the south end. Without this damming effect, the mixing levels over
Bakersfield, Arvin and Edison would be even lower, and O3 concentrations may be higher.
The increased daytime heating in mountain canyons and valleys with a topographic
amplification factor (i.e., heating less air volume when compared to flat land; see White, 1991)
causes significant upslope flows during the afternoons in the San Joaquin and Sacramento
Valleys. This can act as a removal mechanism, and can lift mixing heights on edges of the
valleys, relative to the mixing heights at valley center. Myrup et al. (1989) studied transport of
aerosols from the SJV valley into Sequoia National Park. They found a net up flow of most
species. The return flow can bring pollutants back down. Smith et al. (1981) from tracer mass
budgets during tracer releases has estimated pollutant budgets due to slope flow fluxes (and other
ventilation mechanisms). Smith et al. caution that less polluted air at higher elevations is
entrained in the slope flow, thus diluting SJV air and removing less pollutants. From the tracer
mass balance, they found that northwesterly flow was a more effective dilution mechanism, and
the benefits of slope flow removal by upslope flows would be confined to the edges of the valley.
Slope fluxes have also been modeled by Moore et al. (1987) with acceptable agreement
between observed and modeled winds during maximum heating, but less agreement during
morning and evening transition hours. In general, Whiteman and McKee (1979) first proposed
slope flows as a pollutant removal mechanism, but Vergeiner and Dreiseitl (1987) showed it not
to be that effective.
Up-Valley/Down-Valley Flow
This is the big brother of upslope/downslope flow. Up-valley flow draws air south in the
SJV and north in the SV during the day, while down-valley drainage winds tend to ventilate both
valleys at night. Hayes et al. (1984) has both regimes for both valleys in the “California Surface
Wind Climatology” although with a bit different terminology.
Compensation Flow/Re-Entrainment
This proposed mechanism should be distinguished from the observed nocturnal
downslope flow. Rather, this mesoscale circulation is a direct result of mass balance and the
necessarily simultaneous compensating for mass loss due to upslope flow. In a closed system,
there would be a one-to-one correspondence between pollutant flow upslope and pollutant return
in a compensation flow. As air was removed from the valley floor, there would be subsidence
motion to replace the air, and finally, a compensation flow of air from the top of the Sierra crest
would return to replace the vertically descending air. However, the San Joaquin Valley is not a
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closed system in many ways. Air to replace that removed by slope flows could be come from the
relatively clean western boundary, and need not recirculate pollutants at all.
Given a valid emissions inventory, the interplay of these mesoscale features with the
synoptic pattern leads to a conceptual model of ozone formation in the study region. It is
desirable to objectively classify the wind patterns into distinct scenarios, where the
meteorological impacts on air quality are understood in the context of the model. In addition, the
MetWG is undertaking objective, empirical techniques to assess the frequency of formation, the
links to synoptic patterns, and the air quality impacts of the following mesoscale features:
•
•
•
•
•
•
•
•
•
•
•
•
•
•
2.5.3
Sea breeze
Marine air intrusion
Coastal Windflow – off-shore flow, north or south along coast
Marine fog and stratus
San Joaquin Valley/Sacramento Valley Bifurcation Zone – location and relative
proportions of air moving north, east, and south.
Pacheco Anti-Cyclone – possible impact on SJV to San Louis Obispo area transport.
Fresno Eddy – degree to which pollutants are trapped and re-entrained in central SJV
with the damming of air against the Tehachapi mountains and subsequent
recirculation.
Schultz Eddy – impacts of transport to northern mountain counties and upper SV
Redding Eddy – Discuss any evidence for and or possible importance to air quality in
the Redding area.
Upper/Lower SV Convergence zone
Upper/Lower SJV Convergence zone
Upslope/Downslope
Up-valley/Down-valley
Compensation Flow/Re-entrainment
Major Transport Couples in Central California
In accordance with the 1988 California Clean Air Act, the California Air Resources
Board has identified transport couples within the state where “transported air pollutants from
upwind areas outside a district can cause or contribute to violations of the state ambient air
quality standard for ozone in a downwind district.” (CARB, 1989). Since then, CARB has issued
triennial assessments of the impacts of transported pollutants on ozone concentrations (CARB,
1990, 1993).
Realizing the limitations of the state of the art in quantifying transport, ARB staff chose
to characterize transport as overwhelming, significant, or inconsequential. Operational
definitions of these characterizations have been developed (MDAPTC, 1995). Overwhelming
transport denotes a situation where an ozone exceedance can occur in the downwind basin due to
upwind emissions even in the absence of any downwind emissions. Significant transport means
that both upwind and downwind basin pollutants are necessary to cause an exceedance.
Inconsequential transport means that downwind emissions alone are sufficient to cause an
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exceedance with little or no transport of upwind emissions. In identification of transport couples,
ARB staff performed analyses using meteorological methods, air quality methods (ARB, 1989,
1990, 1993) and a combination of the two approaches as outlined by Roberts et al. (1992). These
methods and others that are relevant to the current study are summarized in Section 3.7.
The following Central California transport couples and their transport characterization are
relevant to the CCOS study (CARB, 1993):
San Francisco Bay Area (SFBA) to Sacramento Valley (SV) – Overwhelming, significant and
inconsequential. Hayes et al. 1984 found the sea breeze pattern in the SV with a frequency of
75%, and Roberts et al. (1992) characterized impacts in the Upper Sacramento Valley as
overwhelming.
SFBA to San Joaquin Valley (SJV) – Overwhelming, significant and inconsequential. The sea
breeze is the effective transport mechanism. Using air quality methods, Douglas et al. (1991)
found that transport from the SFBA affected the northern SJV 37% of the time.
1. SFBA to Mountain Counties (MC) – Significant.
2. SFBA to North Central Coast (NCC) – Overwhelming and significant.
3. SV to SJV– Significant and inconsequential.
4. SV to SFBA – Significant and inconsequential. Two possibilities for this infrequent
event are discussed in CARB (1989). During stagnation events, or the rare occurrence
of the Hayes et al. (1984) northeasterly scenario in the Sacramento Valley (1%
frequency at 1600 PST), a north easterly wind brings SV pollutants in the reverse
direction through the Carquinez Straight in to the SFBA. A case of this pattern was
observed by Stoeckenius et al. (1994) in their comparison of observed wind stream
patterns to the Hayes et al. cases. The other scenario of compensation flow is
discussed in Section 2.4.3.
5. SJV to SV – Significant and inconsequential. The Hayes et al. (1984) southeasterly
scenario occurs only 2-3% of the time in the morning hours during summer, but it
could transport pollutants, within the SJV from the previous day, into the SV.
6. SV to MC – Overwhelming.
7. SJV to MC – Overwhelming.
8. SJV to South Central Coast (SCC) – Overwhelming, significant and inconsequential.
In addition to these inter-basin transport couples, other source-receptor areas of interest
include:
•
Intra-basin transport due to nocturnal eddies, i.e., the Fresno eddy within the SJV and
the Schultz eddy north of Sacramento in the SV.
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2.5.4
•
Intra-basin source-receptor couples such as Sacramento-Folsom, Sacramento-Auburn,
or Sacramento-Redding and other upper SV receptor sites.
•
California coastal waters to SCC and CC.
•
SJV to Great Basin Valleys – Flux estimates of pollutants that escape from the SJV
valley as opposed to returning to the valley in downslope flows. Will aid in modeling
boundary conditions.
•
SJV to Mojave Desert (specifically the MOP site in Mojave, CA.) – Flux estimates of
what leaves the SJV valley through Tehachapi Pass. Will aid in modeling boundary
conditions. Roberts et al. (1992) have documented transport to Mojave through
Tehachapi, and Smith et al. (1997) have demonstrated the correlation between
southern SJV and Mojave ozone concentrations.
Meteorological Scenarios Associated with Ozone Exceedances
The development of a conceptual model for ozone formation is aided by identification of
meteorological scenarios of interest that foster mesoscale processes that dominate physical
transport and dispersion of pollutants. An idealized, robust set of meteorological scenarios would
be distinct from one another, provide enough “wiggle-room” within each scenario to
accommodate the natural variability in the turbulent atmosphere, and transition smoothly
between scenarios in a temporally varying atmosphere. Secondly, a viable set of meteorological
scenarios can increase human forecast skill (and possibly go/no-go decision accuracy) and
enhance the physical intuition of the investigators in interpreting results. Finally, each scenario
should be linked to a set of commonly measured observables, like routine meteorological and air
quality data, to increase the capture rate of episodes useful in modeling.
This section discusses the identification and classification of meteorological scenarios.
Previous classification efforts are reviewed, and two classification approaches undertaken during
the CCOS planning effort are presented as works in progress. The first is a top-down approach
using inspection of 500-mb weather maps from 1996-98 ozone seasons, and the second is a
cluster analysis performed for a select group of days from these three seasons. After a brief
overview of coastal meteorology, following a summary by Rogers et al. (1995), a possible link
between the semi-cyclic coastal fog patterns and air pollution scenarios is also discussed. Finally,
some forecast ideas are presented, based on district input.
2.5.4.1
Previous Classification Studies in Central California
Previous studies have classified California weather and wind flow patterns. Objective
classification is possible for air quality in the San Joaquin Valley and the San Francisco (SF) Bay
Area as the studies summarized below demonstrate.
Hayes et al (1984)
Grouped surface wind patterns for SF Bay Area (6 primary scenarios), San Joaquin
Valley (4 primary scenarios), and Sacramento Valley (8 primary scenarios). This document is
cited in many of the documents in the included review.
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Fairley and DeMandel (1996)
Performed cluster analysis to group ozone episode days ([O3] ≥15 pphm) for 1985-89.
For each cluster, the average peak ozone level in the San Joaquin Valley exceeds 120 ppb.
•
•
•
•
Cluster 1: high in San Joaquin Valley, low-moderate in Sacramento Valley, and low
in Bay Area
Cluster 2: high in San Joaquin Valley, moderate in Sacramento Valley, and moderate
in Bay Area
Cluster 3: high in northern San Joaquin Valley, high in Sacramento Valley, and
moderate Bay Area
Cluster 4: high in Bay Area, varies over other areas.
Performed CART analysis on 31 surface and upper-air variables to determine which
variables best distinguish between episode and non-episode days:
•
•
Stockton daily maximum temperature
900 mb temperature from the 00Z (1600 PST) Oakland sounding
and to distinguish between clusters:
•
•
•
Sacramento daily maximum temperature
v-component (north-south) of Sacramento afternoon winds
product of Pittsburg 1500 PST u-component (east-west) wind with Oakland inversion
base height from the 00Z (1600 PST) sounding
Meteorological features of clusters are identified and discussed, but “ozone levels within
clusters vary considerably and . . . clusters could only roughly be predicted with the
meteorological variables.” Noted that Cluster 4 days are not characterized by a SARMAP
intensive, as with other clusters.
Roberts, P.T., C.G. Lindsey, and T.B. Smith. (1994)
For 1981-89 ozone exceedance days, performed individual and multiple linear
regressions between ozone and various meteorological parameters:
•
•
•
850-mb and 950-mb temperatures, and inversion height from the 00Z (1600 PST)
Oakland sounding
Daily maximum temperatures from Bakersfield, Fresno, and Modesto
12Z (0400 PST) surface pressure gradients from San Franciso to Reno and from San
Francisco to Las Vegas
Performed a cluster analysis between Fresno ozone and these meteorological parameters.
Estimated mean meteorological parameters on ozone exceedance days. Results are presented in
tabular form. Clusters are not specifically identified nor definitively associated with surface flow
patterns. One cluster is discussed for which a trough can pass through the northern SJV (and
presumably the Sacramento Valley) without materially affecting the southern SJV.
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Used paired-station correlations for ozone, Pittsburg v. Bethel Island, and Bethel Island v.
Stockton, to follow the transport route west from SF Bay Area (Pittsburg to Bethel Island) and
into the San Joaquin Valley (Bethel Island to Stockton). More detail is available in Roberts et al
(1990).
T.B. Smith (1994)
Defined two criteria based on Fresno and Edison maximum ozone concentrations:
•
•
Stringent criteria - O3>130 ppb at both sites on at least 1 of 2 consecutive days with
one of the two sites >140 ppb
Marginal criteria - O3>130 ppb at either site on 2 consecutive days but with no value
>130 ppb at the other site.
Stringent criteria days were then subjected to a cluster analysis. Two clusters were found
that did not differ in main synoptic characteristics, but only by the intensity or development of
the synoptic pattern.
Meteorological scenarios associated with SJV episodes include:
•
•
•
•
•
•
•
Warm temperature aloft
Offshore surface pressure gradients (From Reno to SF)
High maximum temperatures in SJV
Extensive high-pressure in the western U.S. centered near Four Corners
Western edge of high should extend to the west coast
Low pressure trough off-shore
Southerly winds aloft prevalent along the west coast
Ludwig, Jiang, and Chen (1995)
Performed cluster and empirical orthogonal function (EOF) analysis for ozone violation
in Pinnacles national Monument. Found that Pinnacles is often located within the subsidence
inversion of the EPHPS. Did not conclude the source region of pollutants or the mechanism
responsible for trapping pollutants in the inversion.
Stoeckenius, Roberts, and Chinkin (1994)
Performed streamline analysis and matching of patterns to the Hayes et al. Surface flow
patterns. Backtrajectories and forward trajectories were also computed and examined. Generated
six source-receptor scenarios with six additional classifications to describe coastal winds. The
main scenarios are:
•
•
•
•
•
•
Northwest
Northeast
Bay Outflow
Calm
Southerly
Northwest-South
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They performed a cluster analysis with 17 meteorological variables, and were able to
approximately match the Hayes et al. flow patterns. Concluded with an 85% success rate in
“forecasting” observed ozone exceedances, but found that four variables were almost as
successful. Conditions for potential ozone days, i.e., days with the potential to exceed the federal
1-hr standard, are:
•
•
•
Oakland 850-mb temperature ≥ 17.5 oC
Sacramento and Fresno surface temperature ≥ 85 oF
San Francisco to Reno sea-level surface pressure difference ≤ 10 mb
2.5.4.2 Preliminary Subjective Analysis: Inspection of Daily Weather Maps
As introduced in Section 1.4, ozone season days, May 1 – October 31, have been
classified for three recent seasons, 1996-1998, using inspection of 500-mb weather maps to
establish gross features. All 552 subject days have been classified into eight categories, arranged
approximately in order of decreasing ozone impact:
1. Western U.S. Hi – Upper-level high centered over the Western U.S.
2. Eastern Pacific Hi – Upper-level high centered off the Western U.S. coast
3. Monsoonal Flow – Upper-level high centered in the south-western U.S. or in northern
Mexico such that southerly flow brings moisture north
4. Zonal Flow – West-to-East
5. Pre-Frontal – Front approaching from northwest brings southwesterly flow
6. Trough Passage – Upper-level trough moves through California, usually ventilating
Central California.
7. Continental Hi – Northerly wind with no marine component, more typical in winter
8. El Nino Cut-off Lo – A special class for 1997 where a cut-off low sat just of the
southern California coast for several days.
Table 2.5-2 provides descriptions and the frequencies of occurrence of these eight
synoptic types and subtypes for the 1996-98 seasons. Ozone impacts for the 8hr standard, by
basin and proposed sub-basin, are described in Table 2.5-3 as a frequency (percentage) of days
of each type and subtype for which an 8hr exceedance is observed. It is possible to define impact
levels in terms severity of maximum and/or mean ozone concentration as well. It is intended to
link this top down approach with previous cluster analysis, in particular that of Fairley and
DeMandel (1996), and, if possible, to the Hayes et al (1984) surface wind climatology. It is also
proposed to develop an objective classification scheme with ARB and district meteorologists as
part of the on-going Meteorology Working Group (MetWG).
The Western U.S. High accounts for proportionately the greatest number of exceedances.
As shown in Table 2.5-3, 97% of days with highs centered over southern California have 8hr
exceedances in the Central San Joaquin Valley, where the frequency of exceedances on all 199698 ozone season days is 42%. Similarly, 90% of these days have Southern SJV exceedances,
where the overall ozone season frequency is 38%. The Western U.S. High contributes to
stagnation conditions throughout central California by fostering an off-shore gradient which
weakens the marine air intrusion and in some cases even reverses the usual sea breeze.
Compared to a more vigorous sea breeze scenario like the Eastern Pacific High, this tends to
keep pollutants longer within respective source regions, although some transport can still occur
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with the delayed and/or weakened sea breeze, the reversed flow, or other thermally driven
mesoscale features. The SFBA has 8hr exceedances on 29% of days when the Western U.S. High
is centered over the Pacific Northwest, while the frequency of 8hr exceedances on all 1996-98
ozone season days is only about 4%. This scenario also provides abundant sunlight and the
greatest subsidence inversion to reduce mixing heights and trap pollutants vertically all over
central California. Monsoonal flow can have both mitigating and exacerbating impacts on SJV
air quality. Some monsoonal days are identified (e.g., 9/3/98), where SJV air quality is lower
relative to the rest of Central California, but on other days, the southerly monsoonal flow can
weaken the SJV exit flow through Tehachapi Pass. Zonal flow tends to increase synoptic forcing,
and less ozone impact is seen in the northern portion of the study area, but the topography
surrounding the SJV helps decouple the valley from the upper level winds, and many
exceedances are observed in SJV for this scenario. The last four scenarios all help ventilate the
Central Valley. There were no 1hr exceedances during any of the 142 out of 552 days studied
(26%), although some 8hr exceedances are observed in the SJV and the Mountain Counties
(MC) with a trough passage. (Troughs are weaker in the summer.)
There are, however, important limitations to this top-down approach. The eight, largescale classes do not allow for adequate consideration of day-to-day atmospheric variability and
of smaller mesoscale effects. Additionally, during the Western U.S. High scenario, when
synoptic forcing over central California is weakest but ozone concentrations are greatest,
mesoscale features become most important, and subtle synoptic differences fostering the
formation and/or amplification of these features are even more difficult to classify. The
following cluster analysis describes a more objective classification method to complement the
top-down approach.
2.5.4.3 Cluster Analysis of High Ozone Days
This second approach uses cluster analysis of ozone data from the same three ozone
seasons to determine groupings of spatial patterns on several very high ozone days selected by
the local air quality districts as being of interest for air quality modeling. Work on cluster
analysis continues among members of the Meteorology Working Group. The goal is to arrive at
objectively classified scenarios linked to upper level flow, to the most likely “phase” of the
synoptic quasi-cycle, to the surface flow, and ultimately to the spatial and temporal pattern of
ozone concentrations in Central California. This work is planned to be complete for the CCOS
Operational Plan. A description of progress to date follows.
Local districts were asked by the MetWG to provide a list of the top-ten most interesting
ozone episodes from 1996-98 to model. The response is presented in Table 2.5-4, and will be
updated as more district input is received. There were 125 total days spanning 20 episodes from
district responses. Considerable overlap was observed, particularly during the months of July and
August. This implies that CCOS can capture a single episode that may be of interest to more than
one district. A subset of these episode days was selected if the maximum 1-hour ozone exceeded
a threshold value for any of the three major basins, SJV, SV or SFBA. The thresholds, called
target values, selected by the Met WG were 128 ppb for SFBA, 129 ppb for SV, and 145 ppb for
SJV. The selection was based on the highest days for each episode for each of the three, and then
by adding any other day that is higher than the lowest episode maximum. Ideally, the design
value would be most appropriate selection criteria, but the occurrence of these days is by
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definition rare, and the state-of-the-art in ozone forecasting is such that lower target values will
be used.
Using the target criteria, there were 43 days from the 125 that were selected for cluster
analysis, as shown in Table 2.5-5. As can be seen from the Subjective Scenario column in Table
2.5-5, a Western U.S. High dominated 34 of the 43 days. There is a “Cluster 0” in Table 2.5-5
representing an outlier day where SJV ozone values were relatively much lower than either SV
or SFBA.
The metric used to cluster groups was 1-r, where r is the correlation coefficient between
the spatial pattern of ozone values on any two days. (If two days are perfectly correlated in ozone
spatial distribution, r would be one and the distance between them would be zero.) Three clusters
are found for both 1-hour and 8-hour exceedances, although the correspondence between 1hr and
8hr days is not one-to-one. Statistical values of meteorological parameters for all 43 days and for
each cluster are presented as a work in progress in Table 2.5-6. The remaining clusters are:
Cluster 1 – 22 of 43 days. The San Francisco Bay Area has its highest basin-wide ozone
values, though still less in absolute magnitude than San Joaquin Valley. This cluster is
characterized by the weakest sea breeze (lowest west-to-east component through Carquinez
Strait as represented by Bethel Island winds in Table 2.5-6). It also has the lowest Oakland
inversion base heights. Among the cluster days, North Central Coast ozone is also highest during
Cluster 1. Cluster 1 days also have a statistically lower gradient between San Francisco and
Redding.
Cluster 2 – 12 of 43 days. The San Joaquin Valley (SJV) has its highest basin-wide
values while the Bay Area and Sacramento Valley are relatively cleaner. A stronger sea breeze,
relative to Cluster 1, keeps the pollutants moving through the Bay Area and the Sacramento
Valley, but may increase transport into the SJV. Among the cluster days, Mountain Counties
ozone is lowest during Cluster 2.
Cluster 3 – 8 of 43 days. Sacramento Valley has its highest basin-wide ozone values, as
does the Mountain Counties Air Basin. As with Cluster 2, a stronger sea breeze is present,
relative to Cluster 1, but surface temperatures in Sacramento Valley are higher, indicating less
and/or later intrusion of the sea breeze, allowing more time for photochemistry before evening
transport to the Mountain Counties.
As discussed in Section 1.3 and presented in Figure 1.3-1, the difference among the
clusters is greatest for the Bay Area. Differences in mean ozone concentration among clusters are
statistically significant (at the 95%-confidnece level) for both SFBA and SV. However, none of
the clusters are significant different from one another for SJV due to less marine air intrusion
over the surrounding topography.
2.5.4.4
Coastal Meteorology and the Fog Cycle
Coastal meteorology directly influences the mesoscale from about 100 km offshore to
100 km inland (Rogers et al. 1995, concise overview article). The coastal zone includes
interaction of the marine and land atmospheric boundary layers, air-sea thermal exchange, and
large-scale atmospheric dynamics linked to fog formation by Leipper (1994, 1995) and others as
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summarized by Leipper (1994). The atmospheric physics of the sea breeze, discussed in Section
2.4, is well understood, but there is currently better understanding of a homogeneous layer,
marine layer out at sea or a convective boundary layer over land, than for all the effects of the
land-sea interface. The implications for inland air quality are complicated by the inherent
variability in a boundary layer depth which can vary from 100--200 m at the shore to several
kilometers inland (McElroy and Smith, 1991). Nevertheless, coastal meteorology has significant
impacts on inland air quality.
For example, major ozone episodes in the vicinity of Santa Barbara, California are often
associated with the storage of ozone precursors in the shallow marine layer over the Santa
Barbara Channel (Moore et al. 1991) and the onshore flow of marine air as a miniature cold
front (McElroy and Smith 1991). A coherent marine layer, with an imbedded thermal internal
boundary layer that forms at the shoreline, can propagate inland for distances of 20 to 50 km.
In addition to the heterogeneity of the coastal zone, the California coastal environment is
modified by considerable coastal topography, which can accelerate the wind while constraining
the flow parallel to the coast. Rogers et al. (1995) describe the problem as characterized by two
free parameters:
•
•
the Froude number Fr, defined by U/(Nh), and
the Rossby number Ro, defined by U/(fl),
where U is the speed of the air stream, h is the height of the barrier, f is the Coriolis parameter, l
is the half width of the barrier, and N is the Brunt-Vaisala frequency of oscillation of gravity
waves. Generally blocking of the air flow occurs when Fr is < 1 which can occur with elevations
as low as 100 m. Thus, localized regions of high or low pressure generated in the coastal zone
can become trapped and propagate along the coastline for days. These features may have length
scales of 1000 km in the alongshore direction and 100-300 km across-shore and can cause
significant changes in the local weather in the coastal zone. For example, a southerly surge is
defined as the advection of a narrow band of stratus northward along the coast, with a rapid
transition from northerly to southerly flow at coastal buoys. It can replace clear skies with clouds
and fog, and cause intensification and reversals of the wind field (e.g., Dorman, 1985, 1987;
Mass and Albright, 1987). It typically covers 500-1000 km of coastline, propagates at an
average speed of 7-9 m/s, and lasts 24-36 hours (Archer and Reynolds, 1996).
However, every summer, southerly winds develop along the coast one or two times a
month that are related to synoptic scale flow. The transition from northerly to southerly winds
often corresponds to the end of a coastal heat wave and the abrupt onset of stratus. Leipper
(1994, 1995) has documented a cycle in coastal fog, with periodic clearing of large areas of
clouds off the coast of as warm, offshore flow spread out over the ocean. Leipper (and Kloesel,
1992) have shown that synoptic-to-mesoscale clearing episodes are correlated with ridging of the
Pacific subtropical anticyclone in the United States Pacific Northwest region that results in these
offshore flows.
Leipper (1994) has identified four phases of fog formation:
•
•
•
Phase 1: Initial conditions
Phase 2: Fog Formation
Phase 3: Fog Growth and Extension
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•
Phase 4: Stratus
Furthermore, Leipper (1995) has linked the four phases to properties of the Oakland
soundings called Leipper Inversion Based Statistics (LIBS). These parameters include the 850mb temperature and the height of inversion base and top, thickness of the inversion layer, and
strength of the inversion, which have been employed by the previously summarized studies. But
Leipper also separates the inversion into sublayers of 0-250m, 250-400 m and 400-800 m and
looks at wind statistics of these layers and wind direction. He has developed frequency
distribution charts, one per phase per U.S. west coast city, linking fog local climatology
measures of frequency and intensity (visibility-based) to the four synoptic phases. Such is being
investigated for possible applications to Central California ozone forecasting. There may also be
a connection in the phases of fog formation to air quality, with an approximate 90-180o lag in the
fog phase relative to the air quality climatology phase. The fog cycle “resets” with the initial
conditions (Leipper’s Phase 1) when there is off-shore flow, which may correspond to the
Western U.S. High scenario which brings generally the worst ozone impacts to Central
California.
The applicability of LIBS in forecasting ozone events is under evaluation by the MetWG.
As part of the CCOS Operational Plan, the MetWG plans to develop these ideas and make
recommendations for the use of LIBS in the objective classification and forecasting of
meteorological scenarios.
2.6
Atmospheric Transformation and Deposition
Much of the difficulty in addressing the ozone problem is related to ozone’s complex
photochemistry. The rate of O3 production is a non-linear function of the mixture of VOC and
NOx in the atmosphere. Depending upon the relative concentration of VOC and NOx and the
specific mix of VOC present, the rate of O3 formation can be most sensitive to changes in VOC
alone or to changes in NOx alone or to simultaneous changes in both VOC and NOx.
Understanding the response of ozone levels to specific changes in VOC or NOx emissions is the
fundamental prerequisite to developing a cost-effective ozone abatement strategy, and is the
principal goal of CCOS.
2.6.1
Ozone Formation
Photochemical production of O3 in the troposphere was considered to be important only
in highly polluted urban regions until the 1970s. It was believed that transport of stratospheric
O3 was the main source of tropospheric O3 (Junge, 1963). The results of Crutzen (1973),
Fishman et al. (1979), for example, show that photochemical production of O3 from nitrogen
oxides and volatile organic compounds is a major source of O3 in the troposphere (Warneck,
1988). Recent calculations suggest that about 50% of tropospheric O3 is due to in-situ
production (Müller and Brasseur, 1995). At remote sites tropospheric production accounts for
observations of O3 concentrations that are greater than 25 ppb (Derwent and Kay, 1988).
Haagen-Smit (1952) was the first to determine that photochemistry was responsible for
the production of O3 in the highly polluted Los Angeles basin. Air pollution research has
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determined the overall reaction mechanism for the production of tropospheric ozone. But many
important aspects of the organic chemistry are unknown are topics of current research and these
uncertainties are discussed below. Figure 2.6-1. Gives an overview of ozone production in the
troposphere.
In the troposphere O3 is produced through the photolysis of nitrogen dioxide to produce
ground state oxygen atoms, O(3P), Reaction (1). The ground state oxygen atoms react with
molecular oxygen to produce O3, Reaction (2), (where M is a third body such as N2 or O2).
NO2 + hν → NO + O(3P)
O(3P) + O2 + M → O3 + M
(1)
(2)
When nitrogen oxides are present, O3 reacts with NO to reproduce NO2.
O3 + NO → NO2 + O2
(3)
Reactions (1-3) by themselves, in the absence of CO or organic compounds, do not produce O3
because these reactions only recycle O3 and NOx.
O3 concentrations are determined by the "NO-photostationary state equation", Equation
(4).
J1[NO2]
[O3] = k [NO]
3
(4)
where J1 is the photolysis frequency of Reaction (1), k3 is the rate constant for Reaction (3),
[NO2] is the concentration of nitrogen dioxide and [NO] is the concentration of nitric oxide.
Reactions of NO with HO2 and organic peroxy radicals, produced through the atmospheric
degradation of CO or organic compounds, are required to produce ozone. Tropospheric O3
formation is a highly nonlinear process (i.e. Dodge, 1984; Liu et al., 1987; Lin et al., 1988).
A fraction of O3 photolyzes to produce an excited oxygen atom, O(1D).
O3 + hν → O(1D) + O2
(5)
A fraction of these react with water to produce HO radicals.
O(1D) + H2O → 2 HO
(6)
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The HO radicals react with CO or organic compounds (RH) to produce peroxy radicals (HO2 or
RO2). The peroxy radicals react with NO to produce NO2 which photolyzes to produce
additional O3:
CO + HO (+O2)
RH + HO
R + O2 + M
RO2 + NO
RO + O2
HO2 + NO
→
→
→
→
→
→
CO2 + HO2
R + H2O
RO2 + M
RO + NO2
HO2 + CARB (11)
HO + NO2
(7)
(8)
(9)
(10)
(12)
The net reaction is the sum of Reactions (8) through (12) plus twice Reactions (1) and (2):
RH + 4 O2 + 2 hν
→
CARB + H2O + 2 O3
(13)
where CARB is either a carbonyl species, either an aldehyde (R'CHO) or a ketone (R'CR''O).
The carbonyl compounds may further react with HO or they may photolyze to produce additional
peroxy radicals that react with NO to produce NO2 (Seinfeld, 1986; Finlayson-Pitts and Pitts,
1986). Peroxy radical reactions with NO reduce the concentration of NO and increase the
concentration of NO2. This reduces the rate of Reaction (3), which destroys O3 and increases the
rate of reaction (1) that produces ozone. The increase in the ratio of [NO2] / [NO] leads to
higher O3 concentration according to Equation (4).
Reaction (13) suggests that NOx is a catalyst for the production of O3. The O3
production efficiency of NOx can be defined as the ratio of the rate at which NO molecules are
converted to NO2 to the total rate of NOx lost through conversion to nitric acid, organic nitrates
or its loss through deposition (Liu et al., 1987; Lin et al., 1988; Hov, 1989). Under most
atmospheric conditions the O3 production efficiency of NOx is inversely related to the NOx
concentration.
In the lower troposphere the formation of HNO3 is a major sink of NOx because HNO3
reacts slowly in the lower troposphere and it is rapidly removed due to dry and wet deposition.
In the gas-phase NO2 reacts with HO to form nitric acid by a relatively well understood process.
HO + NO2 → HNO3
(14)
During the night a significant amount of NOx can be removed through heterogeneous reactions
of N2O5 on water coated aerosol particles. Nitrate radical (NO3) is produced by the reaction of
NO2 with O3, Reaction (15). The NO3 produced may react with NO2 to produce N2O5,
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Reaction (16). Finally the N2O5 reacts with liquid water to produce HNO3, Reaction (17)
(DeMore et al., 1997).
NO2 + O3 → NO3 + O2
(15)
(16)
(17)
NO3 + NO2 → N2O5
N2O5 + H2O(l) → 2 HNO3
The rate of Reaction (17) can be fast during the night-time but its rate constant is very difficult to
correctly characterize (Leaitch et al., 1988; DeMore et al., 1997). There also may be a gas-phase
reaction of N2O5 with water but it is relatively small (Mentel et al., 1996).
Uncertainties in rate parameters
The tropospheric chemistry mechanisms are developed using chemical kinetics data from
laboratory experiments and these data have uncertainties associated with them. In addition there
are difficulties in extrapolating from the laboratory to the real atmosphere. Review panels under
the auspices of the International Union of Pure and Applied Chemistry (IUPAC) (Atkinson et al.,
1997) and the U.S. National Aeronautics and Space Administration (NASA) (DeMore et al.,
1997) evaluate rate constants for use in atmospheric chemistry models. Both groups report not
only a recommended value but also uncertainty factors. For example, the NASA review panel
gives the nominal rate constants for thermal reactions as:
(( ) ( ))
ko (T ) = A × exp − E a R × 1 T
(18)
where ko(T) is the temperature dependent rate constant, A is the Arrhenius factor, R is the gas
constant, Ea is the activation energy and T is the temperature (K). The NASA panel assigns an
uncertainty factor for 298 K, f(298) and an uncertainty factor for the activation energy, • E.
Using f(298) and • E the NASA panel recommends the following expression for the temperature
dependent uncertainty factor:
f ( T) = f (298) × exp
∆E 1
× T − 1 298
R
(
)
(19)
The temperature dependent uncertainty factor is used to calculate the upper and lower bounds to
the rate constant:
T
Lower_Bound = ko ( ) f T
( )
(20)
Upper_ Bound = k o (T ) × f ( T)
(21)
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The upper and lower bounds correspond to approximately one standard deviation:
σk ~
k(T) ∞ f(T) - k(T) / f(T)
2
(22)
However, the upper and lower bounds are not completely symmetric as defined by the NASA
panel and the asymmetry becomes more apparent for larger values of f(T) as discussed below.
Rate constants are most accurately known near 298 K and become more uncertain as the
temperature becomes lower, for this reason, chemical mechanisms become more uncertain in the
upper troposphere. To illustrate this point we calculated the effect of temperature on the
uncertainty of rate constants by using the U.S. standard atmosphere (NOAA, 1976). The
pressure decreases exponentially with altitude while the temperature decreases with a constant
lapse rate with altitude until the tropopause is reached.
Figure 2.6-2 shows the variation of the rate constant with altitude for the reaction of
ozone with nitric oxide. The rate constant decreases with increasing altitude due to the
temperature decrease. In curve A, the upper and lower bounds of the rate constant are shown as
dashed lines. The relative uncertainty in the rate constant increases for lower temperatures but
the absolute uncertainty decreases for the O3 + NO reaction. The rate constant for the CH3O2 +
HO2 reaction is much less well measured than the rate constant for the O3 + NO reaction, Figure
2.6-3. The uncertainty factor is greater than 2.0 even at 298 K. Both the absolute and the
relative uncertainty increase with altitude for this reaction. For this reaction the NASA
recommendations give highly asymmetric error limits. However, the asymmetry is probably
more of a defect in the NASA recommendation than a real asymmetry in the uncertainty limits.
This is very important because in order to evaluate the combined effect of sensitivities and
uncertainties on the reliability of model predictions it is necessary to know the nature of the
uncertainty distribution (Thompson and Stewart 1991a,b; Yang et al., 1995; Gao et al., 1995,
1996).
The rate parameters for the reactions of HO radical with many organic compounds have
been measured. The rate constant of organic compounds span a wide range of values. If an
average HO radical concentration of 7.5 × 106 molecules cm-3 is assumed than the atmospheric
lifetime of typical organic compounds range from less than an hour to several weeks to over two
years for methane, Figure 2.6-4.
Figure 2.6-5 shows nominal rate constants and uncertainty estimates for the reaction of
HO with selected alkenes. The uncertainties in these reaction rates are typically ± 20 to 30 % of
the recommended value (Atkinson, 1986). The absolute uncertainty is greatest for the most
reactive compounds and the rate constants are known best for temperatures near 298 K. More
research is needed to better characterize rate constants over a wider range of temperatures.
Uncertainties and deficiencies in atmospheric chemical mechanisms
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The chemistry of organic compounds is a major source of uncertainty in the chemical
mechanisms. There is a wide variety of organic compounds emitted into the atmosphere, for
example Graedel (1979) has inventoried over 350 organic compounds that are emitted from
vegetation. In rural and background environments biogenic organic compounds, especially
isoprene and terpenes, are often the dominate organic species (Trainer et al., 1987; Chameidies et
al., 1988; Blake et al., 1993). Reactive alkenes and aromatic hydrocarbons of anthropogenic
origin are often the most important organic precursors of O3 in urban locations (Leone and
Seinfeld, 1985).
Even the predictions of highly detailed explicit mechanisms derived completely from first
principles are extremely uncertain because, in spite of extensive recent research [DeMore, 1997;
Le Bras, 1997; Atkinson et . al, 1997] there is a substantial lack of data. New research,
especially on the chemistry of organic compounds, is needed to improve the chemical
mechanisms (Gao et al., 1995, 1996; Stockwell et al. 1997). Although the rate constants for the
primary reactions of HO, O3 and NO3 with many organic compounds have been measured, there
have been relatively few product yield studies or studies aimed at understanding the chemistry of
the reaction products.
Especially the chemistry of higher molecular weight organic compounds and their
photooxidation products is highly uncertain (Gao et al., 1995, 1996; Stockwell et al. 1997).
There is little available data on the chemistry of compounds with carbon numbers greater than 3
or 4 and most of the chemistry for these compounds is based upon extrapolating experimental
studies of the reactions of lower molecular weight compounds. For example, for alkenes, there
is a lack of mechanistic and product yield data even for the reaction of HO with propene. In
development of chemical mechanisms it is usually assumed that 65% of the HO radicals add to
the terminal carbon for primary alkenes based upon the work of Cvetanovic (1976) for propene,
but there has been little confirmation of these results. For the higher molecular weight alkenes
this uncertainty may affect the estimated organic product yields.
The chemistry of intermediate oxidation products including aldehydes, ketones, alcohols,
ethers, etc. requires additional study. The nature, yield and fate of most carbonyl products
produced from the high molecular weight alkanes, alkenes and other compounds are unknown
(Gao et al., 1995, 1996; Stockwell et al. 1997). For photolysis reactions the quantum yields,
absorption cross sections and product yields for C4 and higher aldehydes, ketones, alcohols,
dicarbonyls, hydroxycarbonyls and ketoacids are not well known. For the reactions of C4 and
higher carbonyl compounds with HO and NO3 the rate constants and product yields need to be
measured better.
The reactions of ozone with alkenes and the products are not well characterized
(Atkinson, 1994; Stockwell et al. 1997). The fate of Criegee biradicals and their reaction
products may be incorrectly described by the mechanisms; especially for any Criegee biradicals
beyond those produced from ethene and propene. These uncertainties include the nature and
yield of radicals and organic acids. More data are required on the nature of the products of NO3
- alkene addition reactions. The relative importance of unimolecular decomposition, reaction
with oxygen and isomerization reactions of higher alkoxy radicals is unknown and this may
effect ozone production rates.
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This lack of understanding of alkene chemistry is particularly significant for biogenics
like isoprene and terpenes (Stockwell et al. 1997). The photochemical oxidation of biogenic
compounds yields a wide variety of organic compounds including peroxyacetyl nitrate (PAN),
methyl vinyl ketone, methacrolein and 3-methylfuran, organic aerosols and it may produce
additional O3 if NOx is present (Paulson et al., 1992a,b). Isoprene and terpenes react rapidly
with HO and O3. The lifetime of isoprene with respect to its reaction with HO is estimated to be
24 minutes and the lifetime of d-limonene is about 3.2 hours if the rate constants provided by
Atkinson (1994) and an HO concentration of 5 × 106 are used. The terpenes react very rapidly
with O3; α-Pinene and d-limonene have a lifetime of 2.2 hours and 54 minutes, respectively,
with respect to reaction with O3 assuming the rate constants of Atkinson (1994) and an O3
mixing ratio of 60 ppb. Although the outlines of the atmospheric chemistry of isoprene are
known much more research is needed before terpene oxidation mechanisms are understood.
The uncertainties in aromatic chemistry are very high (Yang et al., 1995; Gao et al., 1995,
1996; Stockwell et al., 1997). The nature of all the products have not been characterized. The
initial fate of the HO - aromatic adduct is not known. Cresol formation, which has been
observed by a number of groups, may be an experimental artifact or its formation may occur in
the real atmosphere. At some point during the oxidation cycle the aromatic ring breaks but for
most aromatic compounds it is not known at what reaction step or ring location. Most
operational mechanisms use parameterizations based upon smog chamber data, which possibly
are inappropriate for the real atmosphere.
The reactions of peroxy radicals (RO2) are important under night time conditions when
nitric oxide concentrations are low. The RO2 - RO2 reactions and NO3 - RO2 reactions strongly
affect PAN and organic peroxide concentrations through their impact on nighttime chemistry
(Stockwell et al., 1995; Kirchner and Stockwell, 1996, 1997). These reactions need to be better
characterized.
Photolysis frequencies are also uncertain due to uncertainties in quantum yields,
absorption cross sections and actinic flux. The photolysis frequencies of even the most
important air pollutants, O3 and NO2, remain uncertain because of uncertainties in the measured
absorption cross sections and quantum yields. For these compounds the combined uncertainties
in the absorption cross sections and quantum yields is between 20 to 30% (Yang et al., 1995;
Gao et al., 1995, 1996). Furthermore although the actinic flux is not a direct component of a
mechanism, it is important to note that the actinic fluxes used in experimental measurements can
be very different than typical atmospheric conditions. Mechanisms based on experiments that
used actinic fluxes that are very different from the real atmosphere may incorporate
inappropriate chemistry.
Sensitivities, uncertainties and model predictions
Uncertainty estimates given by NASA, IUPAC and other reviews are now being used to
determine the reliability of model calculations. There are uncertainties in direct measurements of
rate constants and product yields including both systematic and random errors in the data. These
uncertainties are easiest to quantify if the measurements have been performed in a number of
different laboratories. If there are a reasonable number of experiments, in principle error bounds
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can be estimated from the experimental standard deviations. The most important problem is that
usually there are only a few measurements, the measurements are often of variable quality, and a
complete error analysis is not always made of the measurement data. The NASA and IUPAC
panel estimates are described as corresponding to + 1σ but the intention of the reviewers is not
always clear. The values are subjectively estimated, and generally not based on detailed
statistical analysis. The review panels need to give much greater attention to uncertainty
assignments. Improved uncertainty assignments are especially important because uncertainty
assignments are now being used for calculations to help determine the reliability of
photochemical model predictions as described below.
The sensitivity of chemical concentrations to small variations in rate parameters is one
measure (but not the only measure) of the relative importance of a reaction. The direct
decoupled method (McCroskey and McRae 1987; Dunker 1984) was used to determine
sensitivity coefficients for a continental case (Stockwell et al., 1995). Figure 2.6-6 gives the
relative sensitivities of the calculated concentrations of ozone to rate parameters. The
concentrations of O3 are sensitive to the photolysis rates of NO2, and O3 because these are
important sources of ozone and HO radicals. The concentrations were also very sensitive to the
formation and decomposition rates of PAN. The O3 concentration is sensitive to the rates for the
reaction of peroxy radicals with NO and to the reaction rates of HO with CO and organic
compounds because these reactions are sources of peroxy radicals. The O3 concentration is
sensitive to the reaction rates of HO2 with methyl peroxy radical and acetyl peroxy radical
(Stockwell et al., 1995).
Gao et al. (1995) calculated local sensitivity coefficients for the concentrations of O3,
HCHO, H2O2, PAN, and HNO3 to the values of 157 rate constants and 126 stoichiometric
coefficients of the RADM2 gas-phase mechanism (Stockwell et al., 1990). Gao (1995) found
that the RADM2 mechanism exhibits similar sensitivities to input parameters as do other widely
used mechanisms (Gery et al., 1989; Carter, 1990). Thus the uncertainty estimates presented for
RADM2 mechanism should be generally representative of mechanisms used in current
atmospheric chemistry models. Gao et al.'s sensitivity analysis was combined with estimates of
the uncertainty in each parameter in the RADM mechanism, to produce a local measure of its
contribution to the uncertainty in the outputs. They used several different sets of simulation
conditions that represented summertime surface conditions for urban and nonurban areas. The
analysis identified the most influential rate parameters to be those for PAN chemistry, formation
of HNO3, and photolysis of HCHO, NO2, O3 and products (DCB) of the oxidation of aromatics.
Rate parameters for the conversion of NO to NO2 (such as O3 + NO, HO2 + NO, and organic
radical + NO), and the product yields of XYLP (organic peroxy radical) in the reaction of xylene
+ HO and DCB in the reaction XYLP + NO, are also relatively influential.
When Gao et al. (1995) compared the parameters to which ozone is most sensitive to
those parameters contributing to the most uncertainty it was found that seven to nine of the same
parameters were in "top ten" of both lists for each case examined. However, the rankings of the
most influential rate parameters differ between the sensitivity results and the uncertainty results
because some parameters are substantially more uncertain than others. Some of the parameters
that contribute relatively large uncertainties, e.g., rate parameters for the reactions HO + NO2
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and O3 + NO, are already well studied, with small uncertainties to which concentrations of some
products are highly sensitive.
Monte Carlo calculations examining uncertainties in chemical parameters have been
performed previously for descriptions of gas-phase chemistry in the stratosphere (Stolarski et al.,
1978; Ehhalt et al., 1979) and clean troposphere (Thompson and Stewart, 1991a,b). Thompson
and Stewart used the Monte Carlo method to investigate how uncertainties in the rate coefficients
of a 72-reaction mechanism translate into uncertainties in output concentrations of key
tropospheric species for conditions representing clean continental air at mid-latitudes. The
mechanism studied includes methane, ethane and the oxidation products of these two
hydrocarbons. Uncertainties of approximately 20 % and 40 % were found for surface O3 and
H2O2 concentrations, respectively, due to the rate uncertainties.
This work was extended by Gao et al. (1996) who performed Monte Carlo analysis with
Latin hypercube sampling. Gao et al. showed that the rate parameter for the reaction HO + NO2
→ HNO3 was highly influential due to its role in removing NOx and radicals from participation
in gas-phase chemistry. The rate parameter for the reaction HCHO + hν → 2HO2 + CO was
highly influential as a source of radicals. Rate parameters for O3 photolysis and production of
HO from O1D, and parameters for XYL oxidation were also relatively influential because these
reactions were net sources of radicals. Rate parameters for NO2 photolysis, the reaction of O3 +
NO, and PAN chemistry were substantially more important for absolute ozone concentrations
than for responses to reductions in emissions.
Gao et al. (1996) showed that the uncertainties in peak O3 concentrations predicted with
the RADM2 mechanism for 12-hour simulations range from about ±20 to 50%. Relative
uncertainties in O3 are highest for simulations with low initial ratios of reactive organic
compounds to NOx. Uncertainties for predicted concentrations of other key species ranged from
15 to 30% for HNO3, from 20 to 30% for HCHO, and from 40 to 70% for PAN. Uncertainties in
final H2O2 concentrations for cases with ratios of reactive organic compounds to NOx of 24:1 or
higher range from 30 to 45%.
Monte Carlo analysis has also been applied to ozone forming potentials and used to
quantify their uncertainty. The total O3 production induced by an organic compound is related to
the number of NO-to-NO2 conversions affected by the compound and its decomposition
products over compound's entire degradation cycle. The greater the number of NO-to-NO2
conversions affected by the compound, the greater the amount of O3 produced (Leone and
Seinfeld, 1985). This ozone formation potential can be quantified as an incremental reactivity,
Equation (23).
 R(HC j + ∆HC j ) − R(HC j ) 
IRj = lim 
=
∆HC j


∂R
∂HCj
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where R(HCj) is the maximum value of ([O3] - [NO]) calculated from a base case simulation and
R(HCj + ∆HCj) is the maximum value of ([O3] - [NO]) calculated from a second simulation in
which a small amount, ∆HCj, of an organic compound, j, has been added (Carter and Atkinson,
1989). Maximum incremental reactivities (MIR) are defined as the incremental reactivities for
ozone determined under conditions that maximize the overall incremental reactivity of a base
organic mixture.
Calculated incremental reactivities are dependent upon simulation conditions and the
chemical mechanism used to make the calculations (Chang and Rudy, 1990; Dunker, 1990;
Derwent and Jenkin, 1991; Milford et al., 1992; Carter 1994; Yang et al., 1995). Yang et al.
(1995) performed calculations with a single-cell trajectory model employing a detailed chemical
mechanism (Carter, 1990). They calculated the MIRs for a number of compounds (Yang et al.,
1995). Furthermore (Yang et al., 1995) estimated the uncertainty of the mechanism rate
parameters and product yields. Monte Carlo calculations were performed to estimate the
uncertainty in the incremental reactivities.
Figure 2.6-7 shows MIRs of 26 organic compounds and their uncertainties (1σ) as
calculated from Monte Carlo simulations (Yang et al., 1995). The uncertainties ranged from
27% of the mean estimate, for 2-methyl-1-butene, to 68% of the mean, for ethanol. The
relatively unreactive compounds tended to have higher uncertainties than more reactive
compounds. The impact of the more reactive compounds on O3 was not affected by small
changes in their primary oxidation rates because these compounds reacted completely over the
10-hr simulation period used by Yang et al.
Yang et al. (1995) used regression analysis to identify the rate constants that have the
strongest influence on the calculated MIRs. Their results showed that the same rate constants
that are influential for predicting O3 concentrations are also influential for MIRs. For the most
of the reactive organic compounds the rate constant for its primary oxidation reaction was
influential on the MIR. The rate parameters for the reactions of secondary chemical products
were more important for highly reactive compounds because the primary species reacted
completely. Compounds that react relatively slowly with HO show a high sensitivity to the
uncertainties in the rate of HO production by the photolysis of O3 photolysis and reactions of
O1D. Rate constants for the reactions of the products are also influential for most compounds.
The MIRs for alcohols and olefins were sensitive to uncertainties in the associated aldehyde
photolysis rates, and those of aromatics to uncertainties in the photolysis rates of higher
molecular weight dicarbonyls and similar aromatic oxidation products (represented by AFG1 and
AFG2). Although Yang et al. used a box model, the 3-d simulations by Russell et al. (1995)
yielded similar results. The sensitivity analysis by Yang et al. underscores the need to improve
the organic component in tropospheric chemistry mechanisms.
Gas-Phase Mechanism Evaluation
Chemical mechanisms need to be evaluated and tested before they are widely used but,
unfortunately, there are no generally accepted methods. Ideally field measurements should be
used but these are typically limited to only a few species and interpretation is greatly
complicated by varying meteorological conditions. Environmental chamber experiments are an
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alternative approach for the testing condensed chemical mechanisms. Typically the differences
between modeled and measured ozone concentrations are about 30%. Better environmental
chamber data is required because the data now available suffers from several limitations; these
include very high initial concentrations, wall effects and uncertainties in photolysis rates
(Stockwell et al., 1990; Kuhn et al., 1997).
Many species are more sensitive to the details of the chemical schemes than ozone and
comparisons between models and measurements for these may help in evaluating the
mechanisms (Kuhn et al., 1997). For example, routine measurements of both reactive
hydrocarbons and carbonyl compounds may provide a data-base, which should be valuable for
testing model predictions (Solberg et al., 1995). Comparison of measurements and simulation
results for H2O2 (Slemr and Tremmel, 1994) or nitrogen species can also provide a good test for
chemical mechanisms (Stockwell, 1986; Stockwell et al., 1995). Given that differences in the
carbonyl predictions of different chemical mechanisms are often larger than a factor of two, such
measurements are much more useful than ozone in discriminating between mechanisms (Kuhn et
al., 1997).
The intercomparison of field measurements and model simulations for HO can be used to
test the fast photochemistry in the troposphere. Model simulations for HO are only slightly
affected by transport, since the lifetime of HO is in the order of a few seconds. Therefore the
concentration of HO is determined by the concentrations of the precursors. Poppe et al (1994)
presented a comparison between model simulations based on the RADM2 chemical mechanism
(Stockwell et al., 1990) and measurements for rural and moderately polluted sites in Germany.
They showed that the measured and modeled HO concentrations for rural environments correlate
well with a coefficient of correlation r=0.73 while the model over predicts HO by about 20%.
Under more polluted conditions the correlation coefficient between experimental and modeled
data is significant smaller (r=0.61) and the model over predicts HO by about 15%. Poppe et al.
(1994) concluded that the deviations between model simulations and measurements are well
within the systematic uncertainties of the measured and calculated HO due to uncertain rate
constants. In contrast to these results McKeen et al. (1997) failed to simulate measured
concentrations of HO with a photochemical model. Their model consistently over predicted
observed HO from the Tropospheric OH Photochemistry Experiment (TOHPE) by about 50%.
Agreement between the gas-phase mechanisms
The intercomparison of chemical mechanisms is one way to assess the degree of
consensus among the mechanism builders. A decision about which chemical mechanism
performs the best cannot be made on the basis of model intercomparisons and the fact that a
model produces results in the central range of the other models is not a proof of correctness
(Dodge, 1989). Only comparisons with real measurements made in the atmosphere provide the
final assessment of the performance of a chemical mechanism (Kuhn et al., 1997).
Olson et al. (1997) and Kuhn et al. (1997) compared the predictions of gas-phase
chemical mechanisms now in wide use in atmospheric chemistry models.
The
Intergovernmental Panel on Climate Change (IPCC) compared simulations made with several
chemical schemes used in global modeling in a study named PhotoComp (Olson et al., 1997). In
PhotoComp each participant used their own photolysis frequencies. Many of the differences
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between the simulations made with the mechanisms could be attributed to differences in
photolysis frequencies, especially for O3, NO2, HCHO and H2O2. The mechanisms predicted
different HO2 concentrations and these differences were attributed to inconsistencies in the rate
constants for the conversion of HO2 to H2O2 and differences in the photolysis rates of HCHO
and H2O2 (Olson et al., 1997).
The intercomparison by Kuhn et al. (1997) extended the IPCC exercise to more polluted
scenarios that are more typical for the regional scale. The cases used in this study were a
representative set of atmospheric conditions for regional scale atmospheric modeling over
Europe. In contrast to PhotoComp, the most polluted case included emissions. Photolysis
frequencies were prescribed for the study of Kuhn et al. (1997) to ensure that the differences in
results from different mechanisms are due to gas phase chemistry rather than radiative transfer
modeling.
Most of chemical mechanisms yielded similar O3 concentrations (Kuhn et al., 1997).
This is not unexpected, since many of the schemes were designed to model ozone and therefore
were selected for their ability to model the results of environmental chamber experiments.
However, looking at the deviation of the tendencies rather than the final concentrations, the
differences were substantial: these ranged from 15 to 38% depending upon the conditions. For
the HO radical the noon time differences between mechanisms ranged from 10 to 19% and for
the NO3 radical the night time differences ranged from 16 to 40%. Calculated concentrations of
other longer-lived species like H2O2 and PAN differed considerably between the mechanisms.
For H2O2 the rms errors of the tendencies ranged from 30 to 76%. This confirms earlier findings
by Stockwell (1986), Hough (1988) and Dodge (1989). The differences in H2O2 can partly be
explained by the incorrect use of the HO2+HO2 rate constant (Stockwell, 1995) and by
differences in the treatment of the peroxy radical interactions. Large differences between
mechanisms are observed for higher organic peroxides and higher aldehydes with a rms error of
around 50% for the final concentration in the most polluted case.
2.6.2
Heterogeneous Reactions and Ozone - Secondary Aerosol Formation
Reactions occurring on aerosol particles or in cloud water droplets may have a large
affect on the constituents of the troposphere (Baker, 1997; Andreae and Crutzen, 1997;
Ravishankara, 1997). Heterogeneous reactions may be defined as those reactions that occur on
the surfaces of solid aerosol particles while multiphase reactions are reactions that occur in a
bulk liquid such as cloud water or water coated aerosol particles (Ravishankara, 1997). Particles
may affect gas-phase tropospheric concentrations through both chemical and physical processes.
Sedimentation of aerosol particles or rain out removes soluble species from the gas-phase
leaving behind the relatively insoluble species. Cloud water or water coated aerosols may
scavenge soluble reactive species such as HO2, acetyl peroxy radicals, H2O2 and HCHO (Jacob
1986; Mozurkewich et al. 1987). Ozone formation is suppressed by the removal of HO2 radicals
and highly reactive stable species such as HCHO.
The loss of HO2 and H2O2 influences the hydrogen cycle in the gas phase (Jacob 1986)
and furthermore HO2 is lost in the liquid phase through its reactions with copper ions, ozone and
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by its self reaction (Walcek et al., 1996). These effects reduce the concentrations of HO and
HO2 radicals in clouds. Jacob (1986) estimated a decrease of approximate 25% while Lelieveld
and Crutzen (1991) estimated a decrease of 20 to 90% although Lelieveld and Crutzen's gasphase chemical mechanism would be expected to over estimate gas-phase HO2 concentrations in
clear air (Stockwell, 1994). The reduction of HOx concentrations reduced ozone concentrations
(Lelieveld and Crutzen 1991). In relatively clean areas with NOx concentrations smaller than
100 ppt, the ozone destruction rate is increased by clouds by a factor of 1.7 to 3.7 (Lelieveld and
Crutzen, 1991). Jonson and Isaksen (1993) also found that the clouds are most effective in
reducing ozone concentrations under clean conditions and they calculated a reduction between
10 and 30% for clean conditions.
Lelieveld and Crutzen (1991) and Jonson and Isaksen (1993) did not consider the effects
of the reactions of dissolved transition metals in their calculations. If models include reactions
involving copper, iron and manganese a different result is obtained. The ozone destruction rate
is decreased by clouds by 45 to 70% for clean conditions (Matthijsen et al. 1995, Walcek et al.
1996). In polluted areas with high NOX concentration the photochemical formation rate of ozone
is also decreased. Lelieveld and Crutzen (1991) found a decrease in the photochemical
formation rate of ozone by 40 to 50% and Walcek et al. (1996) found a decrease by 30 to 90%.
Matthijsen et al. (1995) came to the result that the reaction between Fe(II) and ozone was
especially important and it increases the ozone destruction rate in polluted areas by a factor of 2
to 20 depending on the iron concentration. Peroxy acetyl nitrate (PAN) concentrations may be
converted to NOx through the scavenging of acetyl peroxy radicals by cloud water even though
PAN itself is not very soluble (Villalta et al., 1996). This process would tend to increase ozone
concentrations.
Heterogeneous, multiphase and gas-phase reactions may have comparable effects on the
concentrations of tropospheric species (Ravishankara, 1997). Although heterogeneous and
multiphase reactions typically affect the same species as gas-phase reactions the overall result
may be very different. Sulfur dioxide may be oxidized to sulfate by all three reaction types but
only the gas-phase oxidation of SO2 leads to the production of new particles.
Tropospheric multiphase reactions have received recent attention because of their role in
tropospheric acid deposition (Calvert, 1984) and more recently in stratospheric ozone depletion
(WMO, 1994). Aqueous phase reactions occurring in cloud water are very important examples
of multiphase reactions. Many theoretical studies suggest that clouds affect tropospheric
chemistry on the global scale (Chameides, 1986; Crutzen, 1996; Chameides and Davis, 1982;
Chameides, 1984; Chameides and Stelson, 1992). Clouds cover more than 50% of the Earth's
surface and occupy about 7% of the volume of the troposphere under average conditions
(Pruppacher and Jaenicke, 1995; Ravishankara, 1997). Clouds strongly affect the actinic flux
reaching reactive species. Depending upon conditions and location in the atmosphere, clouds
can increase or decrease actinic flux (Madronich, 1987). Photolysis rates within droplets may be
high because of multiple reflections. The conversion of SO2 to sulfate by H2O2 or O3 in cloud
water is an important example of a multiphase chemical process (Penkett et al., 1979; Gervat et
al., 1988; Chandler et al., 1988). Another potentially important process is suggested by recent
studies that show that the photolysis of rainwater containing dissolved organic compounds may
produce H2O2 at a rapid rate (Gunz and Hoffmann, 1990; Faust, 1994).
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Many multiphase reactions may be much faster than their corresponding gas-phase
counterparts. The most important multiphase reactant is often water and many hydrolysis
reactions have a somewhat heterogeneous character because the reactions are so fast that they are
completed at or very near to the water surface (Hanson and Ravishankara, 1994). N2O5 rapidly
hydrolyzes in the presence of liquid water but this reaction is extremely slow in the gas-phase
(W. B. DeMore et al., 1997). The oxidation of SO2 by H2O2 in cloud water is very fast but it
does not occur in the gas-phase (Calvert and Stockwell, 1984). Even very slow hydrolysis
reactions may be the dominate reactions because of overwhelming water concentrations. Other
multiphase reactions will be important only if hydrolysis is extremely slow (Ravishankara,
1997).
There is often no gas-phase counterpart to the aqueous-phase reaction. Tropospheric
sulfate aerosol may participate in many multiphase reactions that are acid catalyzed. Sulfate
aerosols may reduce HNO3 to NOx through reactions involving aldehydes, alcohols and biogenic
emissions and they may convert of CH3OH to CH3ONO2 making sulfate aerosol the dominate
source of CH3ONO2 in the troposphere (Tolbert et al., 1993; Chatfield, 1994; Ravishankara
1997).
Gas-phase sources of halides in the troposphere are small and on a global basis sea-salt
aerosols are a major source. Halogens are strong oxidants, they may affect ozone concentrations
in the Arctic and are therefore potentially very important (Barrie et al., 1988). The marine
boundary contains large numbers of sea-salt aerosols that contain high concentrations of halides.
Halides and their compounds may be released from the sea-salt aerosols through the scavenging
and reactions of compounds such as NO3, N2O5 and HOBr (Finlayson-Pitts et al., 1989; Vogt et
al., 1996; Ravishankara, 1997). The water content of the sea-salt aerosol may be an important
variable for characterizing the chemistry of these aerosols because they may react differently if
they are above the deliquescence point then if they are below (Rood et al., 1987, Ravishankara
1997).
Multiphase reactions involving organic compounds may be important as mentioned
above. The characteristics of organic containing aerosols are not well known but they are
formed from emissions of organic compounds from biogenic and anthropogenic sources
(Mazurek et al., 1991; Odum et al., 1997). The oxidation of organic compounds leads to the
production of a wide variety of highly oxygenated compounds including, aldehydes, ketones,
dicarbonyls and alcohols that are condensable or water soluble. For example, in rural regions
biogenically emitted organics such as α-pinene react with ozone produce aerosols (FinlaysonPitts and Pitts, 1986) and in urban areas the photochemical oxidation of gasoline may be an
important organic continuing aerosol source (Odum et al., 1997).
Examples of important heterogeneous reactions include those occurring on ice, wind
blown dust, fly ash and soot. Heterogeneous reactions on cirrus clouds involving ice, N2O5 and
other species may be important in the upper troposphere. These reactions could remove reactive
nitrogen from the upper troposphere. These processes could play in important role in
determining the impact of aircraft on this part of the atmosphere (Ravishankara, 1997).
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Fly ash and wind blown dust typically contain silicates and metals such as Al, Fe, Mn
and Cu (Ramsden and Shibaoka, 1982; Ravishankara 1997). Photochemically induced catalysis
may oxidize SO2 to sulfate or produce oxidants such as H2O2 (Gunz and Hoffmann, 1990). On
the other hand, soot contains mostly carbon and trace substances. The understanding of soot is a
very important because it may affect the Earth's radiation balance through its strong radiation
absorbing properties. Soot may reduce HNO3 to NOx and otherwise react as a reducing agent
(Hauglustaine et al., 1996; Rogaski et al., 1997; Lary et al., 1997). It is difficult to estimate the
importance of heterogeneous reactions because changes to the aerosol's surface will strongly
affect its chemical properties. For example, the surface of soot particles may become oxidized or
coated by condensable species (Ravishankara 1997).
Multiphase reactions are better understood than heterogeneous reactions but in neither
case is the understanding satisfactory. Unfortunately particle microphysics is insufficiently well
understood to estimate the rates of heterogeneous and multiphase reactions with an accuracy
similar to the gas-phase (Baker, 1997; Ravishankara, 1997). These required particle
characteristics may include surface area, volume, composition, phase, Henry's law coefficients,
accommodation coefficients and reaction probabilities, depending upon the reaction.
For many atmospheric chemistry modeling applications it would be adequate to consider
the gas-phase chemistry as primary and to represent the effect of heterogeneous and multiphase
reactions as first order loss reactions of gas-phase species (Ravishankara 1997). Walcek et al.
(1996) have used this general approach to couple gas and aqueous-phase chemistry mechanisms.
One approach to multiphase reaction rates is to calculate them from a knowledge of mass
transport rates, diffusion constants, solubilities and liquid-phase reaction rate constants and
correct them to apply to small atmospheric droplets (Danckwerts, 1951, 1970; Schwartz, 1986).
Alternatively, another approach for describing the overall reactive uptake coefficients of
gas-phase species for multiphase reactions is to treat them as a network of resistances as has been
done for the stratosphere (Hanson et al., 1994, Kolb et al.; 1995). This same approach may be
applied to tropospheric multiphase reactions (Ravishankara, 1997). The first resistance is due to
diffusion of molecules through the gas-phase to a droplet surface. The mass transfer rate is
calculated from the equations of Fuchs and Sutugin (1970; 1971) and the droplet size spectrum.
The transfer of a molecule from the gas-phase to the liquid-phase is the second resistance. The
probability of this process is described by a mass accommodation coefficient (Ravishankara,
1997). Once the molecule enters the liquid-phase it must diffuse through the liquid before it
reacts. The liquid-phase reaction rate is described by a rate coefficient. More measurements of
mass accommodation coefficients or the means to reliably calculate them for many species are
required along with measurements of liquid-phase reaction rate constants. The methods of
Hanson et al. (1994) and by Schwartz (1986) provide the same results but the approach of
Schwartz may be more convenient to use for clouds while the approach of Hanson et al. may be
easier to use for fine particles.
The mechanisms of heterogeneous reactions are too poorly known to produce anything
like the schemes for multiphase reactions described above. The mechanisms for surface
diffusion and dissociation are unknown (Ravishankara, 1997). Heterogeneous reaction rates are
typically more sensitive than multiphase reactions to the mass transfer to a surface and therefore
these rates are more dependent on the total surface area. Heterogeneous reactions may be more
2-35
Version 3 – 11/24/99
or less important in the troposphere than in the stratosphere depending on whether the reactants
are physisorbed or chemisorbed on to the solid surface. Heterogeneous reactions involving two
molecules physisorbed on to a solid surface would be expected to be much slower due to the
greater temperatures in the troposphere but the reaction rate of species that are chemisorbed on a
surface are probably not as sensitive to temperature (Ravishankara, 1997). If there is some
energy barrier to the scavenging of one of the reactants, such as dissociation on the particle
surface, than the heterogeneous reaction rate may even increase with temperature. However if
water is a reactant, the rate of a heterogeneous reaction may be more sensitive to the relative
humidity than temperature.
Determination of the chemical and microphysical parameters required to estimate the
effect of heterogeneous and multiphase reactions should be a research priority. However, field
measurements of the composition, surface characteristics, phase, number, size spectra, time
trends and other characteristics of atmospheric particles may be even more important. Field
measurements may provide the most reliable assessment of the rates and relative importance of
heterogeneous and multiphase reactions in the troposphere during the next few years.
(Ravishankara, 1997).
2.6.3
Role of Ozone Precursors from Natural Sources
A variety of organic compounds are emitted by vegetation. Most biogenic compounds
are either alkenes or cycloalkenes. Because of the presence of carbon double bond, these
molecules are susceptible to attack by O3 and NO3, in addition to reaction with OH radicals.
The atmospheric lifetimes of biogenic hydrocarbons are relatively short compared to those of
other organic species. The OH radical and ozone reactions are of comparable importance during
the day, and the NO3 radical reaction is more important at night.
Isoprene is generally the most abundant biogenic hydrocarbon except where conifers are
the dominant plant species. Isoprene reacts with OH radical, NO3 radicals, and O3. The OHisoprene reaction proceeds almost entirely by the addition of OH radical to the C=C double bond.
Formaldehyde, methacrolein, and methyl vinyl ketone are the major products of the OH-isoprene
reaction.
The O3-isoprene reaction proceeds by initial addition of O3 to the C=C double bonds to
form two primary ozonides, each of which decomposes to two sets of carbonyl pus biradical
products. Formaldehyde, methacrolein, and methyl vinyl ketone are the major products of the
O3-isoprene reaction.
2.6.4
Relative Effectiveness of VOC and NOx Controls
The relative behavior of VOCs and NOx in ozone formation can be understood in terms
of competition for the hydroxyl radical. When the instantaneous VOC-to-NO2 ratio is less than
about 5.5:1, OH reacts predominantly with NO2, removing radicals and retarding O3 formation.
Under these conditions, a decrease in NOx concentration favors O3 formation. At a sufficiently
low concentration of NOx, or a sufficiently high VOC-to NO2 ratio, a further decrease in NOx
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Version 3 – 11/24/99
favor peroxy-peroxy reactions, which retard O3 formation by removing free radicals from the
system.
In general, increasing VOC produces more ozone. Increasing NOx may lead to either
more or less ozone depending on the prevailing VOC/NOx ratio. At a given level of VOC, there
exists a NOx mixing ratio, at which a maximum amount of ozone is produced, an optimum
VOC/NOx ratio. For ratios less than this optimum ratio, increasing NOx decreases ozone. This
situation occurs more commonly in urban centers and in plumes immediately downwind of NO
sources. Rural environments tend to have high VOC/NOx ratios because of the relatively rapid
removal of NOx compared to that of VOCs.
2.6.5
Atmospheric Deposition
Dry deposition is the transport of gaseous and particulate species from the atmosphere
onto surfaces in the absence of precipitation. The factors that govern the dry deposition of a
gaseous species or particle are the level of atmospheric turbulence, the chemical properties of the
depositing species, and the nature of the surface itself. The level of turbulence in the atmosphere
governs the rate at which species are delivered down to the surface. For gases, solubility and
chemical reactivity may affect uptake at the surface. The surface itself is a factor in dry
deposition. A non-reactive surface may not permit absorption or adsorption of certain gases; a
smooth surface may lead to particle bounce-off. Natural surfaces, such as vegetation generally
promote dry deposition.
Eddy correlation is the most direct micrometeorological technique. In this technique, the
vertical flux of an atmospheric trace constituent is represented by the covariance of the vertical
velocity and the trace constituent concentration. Fluxes are determined by measuring the vertical
wind velocity with respect to the Earth and appropriate scalar quantities (gas concentrations,
temperature, etc). These measurements can be combined over a period of time at a single
location (such as measurements made at a stationary location from a tower) or over a wide area
(as with measurements made with aircraft) to yield a better understanding of the role surface
characteristics, such as vegetation, play in the exchange of mass, momentum, and energy at the
Earth’s surface.
2.7
Conceptual Model of Ozone Episodes and Transport Scenarios of Interest
This section starts with a brief summary of the current conceptual model as recorded by
Pun et al. (1998). Modifications to the model are introduced with discussion of a possible link
between coastal meteorology (specifically fog formation) and central valley air quality, with
possible applications to air quality forecasting. The optimal forecast would be 72 hours prior to
the high ozone day, to begin intensive field measurements at least one day before the high ozone
day.
2.7.1
Current Conceptual Model of Ozone Formation and Transport
During a typical summer day, airflow over the Pacific Ocean is dominated by the Eastern
Pacific High-Pressure System (EPHPS). Off the coast of central California, outflow from the
anticyclone becomes westerly and penetrates the Central Valley through various gaps along the
coastal ranges. The largest gap in the coastal ranges is located in the San Francisco Bay Area.
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Airflow reaching the Central Valley through the Carquinez Straits is directed Northward into the
Sacramento Valley, southward into the San Joaquin Valley (SJV), and eastward into the
Mountain Counties. The amount of air entering into these regions and the north-south
bifurcation of the flow depends on the location, extent, and strength of the EPHPS and on the
surface weather pattern. As the low-level divergence from the EPHPS continues to rotate in the
clockwise direction, the high can migrate with the planetary wave pattern from west to east. If
the EPHPS is approaching the coast of California, it will reinforce on-shore surface gradients and
increase the amount of air entering the Sacramento Valley. However, if the center of the EPHPS
comes on-shore over central California, the normal surface gradient is diminished and (or can
even be reversed). The amount of air entering the Central Valley is reduced. If the southern end
of the EPHPS is over the Delta region, the amount of air entering the Central Valley will be
blocked, and, in rare cases may even reverse direction through the Carquinez Straits (summer
frequency of northeasterly pattern is ~1%, according to Hayes et al. 1984).
This complex feature of airflow, unique to a region from the Pacific Ocean to the Sierra
Nevada, and from Yuba City to Modesto, contributes to various types of ozone episodes in the
SJV, Sacramento Valley, Mountain Counties and the Bay Area. Both local and transport ozone
episodes are observed in the SJV as well as the Sacramento area depending upon the nature of
the airflow in the region. In the Bay Area, ozone concentrations are elevated when airflow from
the Bay Area to the Central Valley is limited. Elevated ozone concentrations are observed in the
Mountain Counties due mostly to transported pollutants. Transport of pollutants from the
northern SJV to the central and southern SJV is accelerated at night due to the “low-level jet” (an
airflow that develops at night and moves from the north to south along the SJV with speeds of
10-15 m/sec). Air also rotates in the counterclockwise direction around Fresno (Fresno Eddy) in
the morning hours, limiting the ventilation of air out of the SJV. During the day, pollutants are
transported from the SJV to the Mojave Desert via the Tehachapi Pass. Occasionally, an outflow
from the SJV to the San Luis Obispo area is observed.
The above conceptual model is an oversimplification, but it purports to describe the
typical summer pattern. The movement of the EPHPS on-shore often corresponds to the 2-3 day
ozone episodes bringing some of the worst air quality to the Central Valley.
2.7.2
Implications of Change in Federal Ozone Standard on Conceptual Model
The implication of the state 1-hour ozone standard and the new federal 8-hour ozone
standard is that they require a reappraisal of past strategies that have focused primarily on
addressing the urban/suburban ozone problem. The lower concentration standards necessarily
require a more regional context for at least two reasons: the larger spatial extent of a more dilute
plume, and greater downwind distances required for pollutant dispersion. While there is some
benefit required in the longer time requirement of 8 hours on the federal standard, in areas with
relatively closed topography, like the southern San Joaquin Valley, stagnation and intra-basin redistribution prolong carry-over.
Table 2.7-1 provides a brief summary of 8hr and 1hr exceedance days during the threeyear period 1996-98 and the full nine-year analysis period 1990-98, based on the data in Tables
2.3-3 and 2.3-4. Two new quantities are presented for each basin, the average of each annual
maximum 1hr to maximum 8hr concentrations, and the ratio of total 8hr exceedance days to total
2-38
Version 3 – 11/24/99
1hr exceedance days. The SFBA has the greatest concentration ratio (1.34) and lowest
exceedance day ratio ( 1.4 for 1996-98 and 1.8 for 1990-98) as pollutants generally move
through before having a chance to accumulate, thus making the 8hr exceedance less likely. Areas
which typically experience greater transport, i.e., the Mountain Counties, North Coast, and South
Central have lower concentration ratios and higher exceedance day ratios. An extreme case is
provided by the Mojave Desert site (1.17 and 25, respectively), which is highly influenced by
transport from SJV. It is these downwind areas and the source regions with less ventilation that
will be impacted most by the change in standards.
2.8
Review of previous SAQM studies and implications for CCOS measurement sites.
Thuillier and Ranzieri (1995, 1997a,b) have described evaluation of the SARMAP effort.
(SARMAP acronym: San Joaquin Valley Air Quality Study and Atmospheric Utility Signatures,
Predictions and Experiments Regional Model Adaptation Program.). They conclude that, in
general, the model represented the relative magnitude and distribution of ozone. However, they
cite deficiencies from SARMAP modeling of the August 3-6, 1990 episode. The following is a
partial list from Thuillier and Ranzieri (1995), and sections from this CCOS plan where these
issues are addressed are given in parentheses.
•
Corrections/adjustments needed in emissions modeling.
•
Adjustment of hydrocarbon inventory needed.
•
Better characterization of nitrogen oxide emission from soils needed.
•
Meteorological modeling was deficient due to lack of input data in critical areas.
•
Air quality modeling tended to underestimate surface ozone as well as ozone and
ozone precursors aloft.
•
Air Quality modeling tended to overestimate surface ozone at night.
•
Modeled boundary conditions significantly departed from concentrations measured
aloft by aircraft.
•
Domain not large enough to adequately capture slope flows in some areas.
•
A “plume-in-grid” algorithm may be needed to refine treatment of major point
sources.
Roth et al. (1998) provided a critical review of 18 recent air quality modeling efforts
including SARMAP. For 20 key areas of inquiry, they assigned “indicators”, two of which
represent major deficiencies or an absence or omission of a key area of inquiry in a particular
study. The SARMAP effort scored well overall, but it received indicators of major deficiency in
8 out of the 20 areas. The following three areas are relevant to planning the CCOS field
measurement program, and paragraphs from this CCOS plan where these issues are addressed
are given in parentheses.
2-39
Version 3 – 11/24/99
•
Insufficient extent of corroborative studies
•
Insufficient effort to estimate uncertainties
•
Insufficient effort to evaluate soundness of emissions projections
As summarized in Section 1 (and further discussed in additional sections referenced in
the 12 bullets above), the proposed CCOS plan addresses as many of the deficiencies found by
Thuillier and Ranzieri (1995) and Roth et al. (1998) as possible, given budget limitations. With
the high-time resolution and detailed chemical speciation, the proposed CCOS measurements can
support many independent approaches to evaluate and corroborate model results and
uncertainties. The following discussion of SARMAP results highlights key features for the
CCOS measurement network design.
The SARMAP Air Quality Model (SAQM – Chang et al. 1997), applied to central
California for August 3-6, 1990, shows maximum 1-hr ozone concentrations near 120 ppb. The
maximum ozone concentrations occur roughly along two lines: one line running from the San
Francisco Bay Area through Sacramento to the Sierra front range and the other line along eastern
side of the San Joaquin Valley axis. For example, Figure 2.8-1 shows a plot of ozone
concentrations over central California at noon on the second simulated day. The area directly
west of the San Francisco Bay Area has ozone concentrations near 120 ppb while high ozone
concentrations are found along and directly west of the San Joaquin valley. The surface ozone
concentrations show strong diurnal variation. Figure 2.8-2 shows that at midnight the ozone
concentrations drop to near 40 ppb or less at the western boundary and over the San Joaquin
Valley.
For model evaluation, it is desirable to have measurement stations along the line running
from the San Francisco Bay Area through Sacramento to the front range and along the San
Joaquin valley. The model predicts that ozone concentrations show strong variations over this
region due to transport, deposition and chemistry. Measurement stations to measure ozone
concentrations and the concentrations of precursors and other photochemical products placed
along the west–east line will help to determine if the models represent a scientifically reasonable
description of ozone formation and transport. The same can be said of an array of measurements
through the San Joaquin valley.
It is also important to have measurement stations to the north and south of the
Sacramento area because of the bifurcation in typical meteorological situations, where the flow
fields from the west diverges toward the north and south. The ozone concentrations shown in
Figure 2.8-3 shows several ozone hot spots one directly east of the San Francisco Bay Area with
two more directly to the north and south. Measurements to the north and south in this area will
help determine if the model predicted patterns are real. Figure 2.8-3 also shows high ozone
concentrations along and directly east and south of the San Joaquin valley. Measurement stations
are required in this area as well.
The SAQM model predicts a rather complex vertical structure in ozone concentrations.
For the simulations that were analyzed here, the vertical structure along a north–south cross
section through the center of central California near Sacramento is shown in Figure 2.8-4. The
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Version 3 – 11/24/99
vertical coordinate is linear in pressure-following coordinates and this exaggerates the apparent
height. At noon on the second day the ozone concentrations in the Sacramento area are very
high. The model calculates that ozone is lost and transported to the north and south leaving an
arc of ozone that extends aloft and to the east and west by midnight. Ozonesondes and possibly
LIDAR measurements would be very helpful in the observation of this complex vertical
structure. It should be positioned to observe the diurnal variations to the north and south of
Sacramento.
The vertical structure of the ozone concentrations the west–east cross section through
central California along the line running from the San Francisco Bay Area through Sacramento
to the front range is similar in its overall behavior to the north–south line, Figure 2.8-5. The plots
show evidence of transport of ozone and NOx from the west to the east. Ozone is lost in the
lower levels and remains relatively high aloft as the time progresses from noon to midnight.
Some of the parcels of high ozone are seen to travel from west to east in this sequence.
Ozonesondes near San Francisco and Sacramento and aircraft measurements of ozone over the
front range and the western boundary are required to observe this complex ozone structure
predicted by the models.
Some measurements of NOx, formaldehyde and other VOCs need to be made at the
western boundary to better define the boundary conditions for modeling. In the SAQM
simulations that we examined, the western boundary generated 5 ppb of HCHO, as shown in
Figure 2.8-6. These high HCHO concentrations are a potential problem since HCHO is
extremely reactive in producing ozone, and subsequent studies (e.g., SCOS97) found much lower
average values along the coastal regions. The same is true for NOx concentrations. Figure 2.8-7
shows that the western boundary has over 0.5 ppb of NO. These moderate NO concentrations
over the ocean appear to be due to transport from the model’s boundary. More measurements to
better define the boundary conditions, especially at the western boundary are required to
constrain the modeling activities.
2-41
Version 3 – 11/24/99
Table 2.1-1.
Populations and Areas for Central California Metropolitan Statistical Areas
State
Metropolitan Area
CA Bakersfield, CA
CA Chico-Paradise, CA
CA Fresno, CA
TYPE
MSA
MSA
MSA
CA
Riverside-San Bernardino, CA
PMSA
CA
CA
CA
CA
Ventura, CA
Merced, CA
Modesto, CA
Sacramento, CA
PMSA
MSA
MSA
PMSA
CA
CA
CA
Yolo, CA
Salinas, CA
Oakland, CA
PMSA
MSA
PMSA
CA
Sacramento-Yolo, CA
CMSA
CA
San Francisco, CA
PMSA
CA
San Francisco-Oakland-San Jose, CA
CMSA
CA
CA
CA
CA
San Jose, CA
Santa Cruz-Watsonville, CA
Santa Rosa, CA
Vallejo-Fairfield-Napa, CA
PMSA
PMSA
PMSA
PMSA
CA
CA
CA
CA
San Luis Obispo-Atascadero-Paso Robles, CA
Santa Barbara-Santa Maria-Lompoc, CA
Stockton-Lodi, CA
Visalia-Tulare-Porterville, CA
MSA
MSA
MSA
MSA
Counties
Kern County
Butte County
Fresno County
Madera County
Riverside County
San Bernardino County
Ventura County
Merced County
Stanislaus County
El Dorado County
Placer County
Sacramento County
Yolo County
Monterey County
Alameda County
Contra Costa County
El Dorado County
Placer County
Sacramento County
Yolo County
Marin County
San Francisco County
San Mateo County
Alameda County
Contra Costa County
Marin County
San Francisco County
San Mateo County
Santa Clara County
Santa Cruz County
Sonoma County
Napa County
Solano County
Santa Clara County
Santa Cruz County
Sonoma County
Napa County
Solano County
San Luis Obispo County
Santa Barbara County
San Joaquin County
Tulare County
2-42
1990
Population
543,477
182,120
755,580
1995 Est.
Population
617,528
192,880
844,293
1995 pop
density
(km-2)
29.3
45.4
40.2
Area
(km2)
21086.7
4246.6
20983.3
2,588,793
2,949,387
41.8
70629.2
669,016
178,403
370,522
1,340,010
710,018
194,407
410,870
1,456,955
148.5
38.9
106.1
137.8
4781.0
4995.8
3870.9
10571.3
141,092
355,660
2,082,914
147,769
348,841
2,195,411
56.4
40.5
581.5
2622.2
8603.8
3775.7
1,481,220
1,604,724
121.1
13250.4
1,603,678
1,645,815
625.7
2630.4
6,249,881
6,539,602
341.1
19173.7
1,497,577
229,734
388,222
451,186
1,565,253
236,669
414,569
481,885
468.0
205.0
101.6
117.6
3344.3
1154.6
4082.4
4097.5
217,162
369,608
480,628
311,921
226,071
381,401
523,969
346,843
26.4
53.8
144.6
27.8
8558.6
7092.6
3624.5
12495.0
2-43
LADAM
#
3033
3010
3116
2752
2914
3196
3017
3144
3161
3126
2993
3018
3164
2968
3002
3157
2208
3020
3187
2891
3117
3008
2829
2731
2956
2123
3197
2958
2977
3032
3155
3249
2848
2143
3011
2744
3158
3137
2115
2972
Air Basin
North Coast
North Coast
North Coast
Northeast Plateau
Lake County
Mountain Counties
Mountain Counties
Mountain Counties
Mountain Counties
Mountain Counties
Mountain Counties
Mountain Counties
Mountain Counties
Mountain Counties
Mountain Counties
Mountain Counties
Mountain Counties
Mountain Counties
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
Sacramento Valley
County
Sonoma
Mendocino
Mendocino
Siskiyou
Lake
El Dorado
El Dorado
Calaveras
Mariposa
Nevada
Amador
Mariposa
Tuolumne
Tuolumne
Placer
Nevada
Nevada
Plumas
Sacramento
Placer
Sutter
Placer
Shasta
Sacramento
Placer
Sacramento
Tehama
Sutter
Sacramento
Shasta
Solano
Yolo
Sutter
Yolo
Sacramento
Colusa
Tehama
Glenn
Butte
Shasta
Location
Data Record
Link
Elevation
Type
Begin
End
# Latitude Longitude (msl)
Rural
5/1/92 10/31/98
38.6536 -122.9006
30
Suburban 9/30/92 10/31/98
39.1447 -123.2065
194
Suburban 6/30/93 10/31/98
39.4030 -123.3491
1377
Suburban
5/1/90 9/30/98
41.7293 -122.6354
800
Suburban
5/1/90 10/31/98
39.0330 -122.9219
405
Rural
5/31/96 9/30/98
38.8905 -121.0000
0
Suburban
5/1/92 10/31/98
38.7247 -120.8220
585
Rural
5/1/94 5/31/98
38.2000 -120.6670
Rural
7/18/95 10/31/98
37.5500 -119.8436
0
Suburban 9/30/93 10/31/98 3
39.2334 -121.0555
853
Suburban 5/14/92 5/31/98
38.3421 -120.7641
377
Rural
8/31/90 10/31/98
37.7135 -119.7055
1605
Rural
8/31/95 7/31/98
38.0505 -120.2997
0
Urban/CC 7/31/92 7/31/98
37.9816 -120.3786
571
Rural
5/1/92 10/31/97
39.0998 -120.9542
768
Rural
6/2/95 9/30/98
39.3166 -120.8444
1302
Urban/CC 10/31/92 10/31/98
39.3302 -120.1808
1676
Urban/CC 10/31/92 10/31/98
39.9381 -120.9413
1067
Suburban 8/31/96 10/31/98 10 38.6838 -121.1627
98
Suburban
5/1/90 10/10/97
38.9395 -121.1054
433
Rural
6/30/93 10/31/98
39.1583 -121.7500
640
Rural
5/1/91 9/30/98 8
38.7890 -121.2070
100
Suburban
5/1/90 10/31/98
40.5503 -122.3802
143
Suburban
5/1/90 10/31/98
38.6141 -121.3669
25
Suburban
5/1/93 9/30/98 9
38.7500 -121.2640
161
Suburban
5/1/90 10/31/98
38.7122 -121.3810
27
Urban/CC 7/31/96 9/30/98 11 40.1763 -122.2374
98
Suburban
5/1/90 10/31/98
39.1388 -121.6191
20
Rural
5/1/93 10/31/98
38.3028 -121.4207
6
Suburban 5/31/93 10/31/98
40.4653 -122.2973
498
Suburban
5/1/95 10/31/98
38.3519 -121.9631
55
Suburban 5/31/98 10/31/98 12 38.6619 -121.7278
Rural
5/1/90 9/30/98
38.7658 -121.5191
50
Rural
5/1/90 10/31/98
38.5352 -121.7746
16
Urban/CC
5/1/90 10/31/98
38.5679 -121.4931
7
Rural
7/18/96 10/31/98 6
39.1886 -121.9989
17
Rural
6/1/95 9/30/98
40.2617 -122.0911
568
Suburban 6/16/94 10/31/98 7
39.5170 -122.1895
41
Suburban
5/1/90 10/31/98
39.7569 -121.8595
61
Rural
5/1/90 10/31/98
40.5397 -121.5816
1788
Table 2.3-1a
Linked Master Ozone Monitoring Site List
Site
AIRS Site Moniker
Short Name
060971003 HDM Healdsb-Aprt
060450008 UKG Ukiah-EGobbi
060450009 WLM Willits-Main
060932001 YRE Yreka-Fthill
060333001 LKL Lakepor-Lake
060170020 CUS Cool-Hwy193
060170010 PGN Plcrvll-Gold
060090001 SGS San_Andreas
060430006
JSD Jerseydale
060570005 GVL Grs_Vly-Litn
060050002 JAC Jackson-ClRd
060430003 YOT Yos_NP-Trtle
061090006 FML Sonora-OakRd
061090005 SNB Sonora-Brret
060610004 CXC Colfax-CtyHl
060570007 WCM White_Cld_Mt
060571001 TRU Truckee-Fire
060631006 QUC Quincy-NChrc
060670012 FLN Folsom-Ntma
060610002 AUB Auburn-DwttC
061010004 SUT Sutter_Butte
060613001 ROC Rocklin
060890004 RDH Redding-HDrf
060670006 SDP Sacto-DelPas
060610006 ROS Rosevil-NSun
060670002 SNH N_High-Blckf
061030005 RBO Red_Blf-Oak
061010003 YAS Yuba_Cty-Alm
060670011 ELK Elk_Grv-Brcv
060890007 ADN Anderson-Nth
060953002 VEL Vacavil-Alli
061131003 WLG Woodland-GibsonRd
061010002 PGV Plsnt_Grv4mi
061130004 DVS Davis-UCD
060670010
S13
Sacto-T_Strt
060111002 CSS Colusa-Sunrs
061030004 TSB Tuscan Butte
060210002 WLW Willows-ELau
060070002 CHM Chico-Manznt
060893003 LNP Lasn_Vlcn_NP
Version 3 – 11/24/99
2-44
LADAM
#
2372
3140
2613
2804
2831
2320
2397
2969
2102
2293
2413
2806
2973
2655
2125
2070
2410
2225
3207
2622
2373
2105
2983
3133
2628
2894
2016
2789
2985
3200
2939
2329
Site
AIRS Site Moniker
Short Name
060010003 LVF Livrmor-Old1
060852006 SMM San_Martin
060851001 LGS Los Gatos
060131002
BTI Bethel_Is_Rd
060130002 CCD Concord-2975
060850002 GRY Gilroy-9th
060950002 FFD Fairfld-AQMD
060852005
SJD San_Jose-935
060133001 PBG Pittsbg-10th
060011001 FCW Fremont-Chpl
060850004
SJ4
San_Jose-4th
060012001 HLM Hayward
060010006 SEH Sn_Lndro-Hos
060550003
NJS Napa-Jffrsn
060811001 RED Redwood_City
060851002 MVC Mtn_View-Cst
060950004 VJO Vallejo-304T
060010005 OKA Oakland-Alic
060131003
SPE San Pablo
060410001 SRL San Rafael
060750005 SFA S_F-Arkansas
060970003
SRF S_Rosa-5th
060690003
PIN Pinn_Nat_Mon
060870006 SVD Scotts_V-Drv
060690002 HST Hollistr-Frv
060530005 KCM King_Cty-Mtz
060530002 CMV Carm_Val-Frd
060531002
SL2 Salinas-Ntvd
060870004 WAA Watsonvll-AP
060870007 SCQ S_Cruz-Soqul
060530006 MON Montery-SlvC
060870003 DVP Davenport
Version 3 – 11/24/99
Air Basin
San Francisco Bay Area
North Central Coast
North Central Coast
North Central Coast
North Central Coast
North Central Coast
North Central Coast
North Central Coast
North Central Coast
North Central Coast
North Central Coast
County
Alameda
Santa Clara
Santa Clara
Contra Costa
Contra Costa
Santa Clara
Solano
Santa Clara
Contra Costa
Alameda
Santa Clara
Alameda
Alameda
Napa
San Mateo
Santa Clara
Solano
Alameda
Contra Costa
Marin
San Francisco
Sonoma
San Benito
Santa Cruz
San Benito
Monterey
Monterey
Monterey
Santa Cruz
Santa Cruz
Monterey
Santa Cruz
Location
Type
Urban/CC
Rural
Urban/CC
Rural
Suburban
Suburban
Urban/CC
Rural
Urban/CC
Suburban
Urban/CC
Rural
Suburban
Urban/CC
Suburban
Suburban
Urban/CC
Urban/CC
Urban/CC
Urban/CC
Urban/CC
Urban/CC
Rural
Rural
Rural
Rural
Suburban
Suburban
Suburban
Suburban
Rural
Rural
Data Record
Link
Elevation
Begin
End
5/1/90 10/31/98
37.6849 -121.7657
146
5/1/94 10/31/98
37.0793 -121.6001
87
5/1/90 10/31/98
37.2279 -121.9792
183
5/1/90 10/31/98
38.0067 -121.6414
0
5/1/90 10/31/98
37.9391 -122.0247
26
5/1/90 10/31/98
36.9999 -121.5752
55
5/1/90 10/31/98
38.2368 -122.0561
3
8/24/92 10/31/98
37.3913 -121.8420
63
5/1/90 10/31/98
38.0296 -121.8969
2
5/1/90 10/31/98
37.5357 -121.9618
18
5/1/90 10/31/98
37.3400 -121.8875
24
5/1/90 10/31/98
37.6542 -122.0305
287
8/2/90 10/31/98
37.7098 -122.1164
36
5/1/90 10/31/98
38.3115 -122.2942
12
5/1/90 10/31/98
37.4823 -122.2034
5
5/1/90 10/31/98
37.3724 -122.0767
24
5/1/90 10/31/98
38.1029 -122.2369
23
5/1/90 10/31/98
37.8014 -122.2672
7
5/9/97 10/31/98 13 37.9500 -122.3561
15
5/1/90 10/31/98
37.9728 -122.5184
1
5/1/90 10/31/98
37.7661 -122.3978
5
5/1/90 10/31/98
38.4436 -122.7092
49
5/1/90 10/31/98
36.4850 -120.8444
335
6/30/94 10/31/98 5
37.0514 -122.0148
122
5/1/90 10/31/98
36.8432 -121.3620
126
6/30/90 9/30/98
36.2269 -121.1153
116
5/1/90 10/31/98
36.4815 -121.7329
131
5/1/90 10/31/98
36.6986 -121.6354
13
7/31/92 10/31/98
36.9337 -121.7816
67
9/24/96 10/31/98 4
36.9858 -121.9930
78
5/1/92 10/31/98
36.5722 -121.8117
73
5/1/90 10/31/98
37.0120 -122.1929
0
Table 2.3-1a (continued)
2-45
LADAM
#
2941
2312
3026
3009
2919
2114
3146
3036
2032
2844
2772
3022
3145
2981
2013
3129
3160
3211
2996
2833
3159
2553
2094
Site
AIRS Site Moniker
Short Name
060295001 ARV Arvin-Br_Mtn
060290007 EDS Edison
060195001 CLO Clovis
060190008
FSF Fresno-1st
060290008 MCS Maricopa-Stn
060194001 PLR Parlier
060290014 BKA Baker-5558Ca
061070006 SEK Seq_NP-Kawea
061072002 VCS Visalia-NChu
060190242
FSS Fresno-Sky#2
060290232 OLD Oildale-3311
060470003 MRA Merced-SCofe
060290010 BGS Baker-GS_Hwy
060296001 SHA Shafter-Wlkr
060190007 FSD Fresno-Drmnd
060311004
HIR Hanford-Irwn
060190010 FNP ShavLk-PerRd
060390004 M29 Madera
060990006 TSM Turlock-SMin
060990005 M14 Modesto-14th
060773003
TPP Tracy-Patt#2
060770009 SOM Stockton-EMr
060771002 SOH Stockton-Haz
Version 3 – 11/24/99
Air Basin
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
San Joaquin Valley
Kern
Kern
Fresno
Fresno
Kern
Fresno
Kern
Tulare
Tulare
Fresno
Kern
Merced
Kern
Kern
Fresno
Kings
Fresno
Madera
Stanislaus
Stanislaus
San Joaquin
San Joaquin
San Joaquin
County
Location
Type
Rural
Rural
Urban/CC
Suburban
Suburban
Rural
Urban/CC
Rural
Urban/CC
Suburban
Suburban
Rural
Urban/CC
Suburban
Suburban
Suburban
Rural
Rural
Suburban
Urban/CC
Rural
Urban/CC
Urban/CC
Data Record
Link
Elevation
Begin
End
5/1/90 10/31/98
35.2087 -118.7763
145
5/1/90 10/31/98
35.3452 -118.8521
128
9/5/90 9/30/98
36.8194 -119.7165
86
5/1/90 10/31/98
36.7816 -119.7732
96
5/1/90 9/30/98
35.0519 -119.4037
289
5/1/90 9/30/98
36.5967 -119.5042
166
5/1/94 10/31/98 14 35.3561 -119.0402
120
7/31/96 10/31/98 19 36.5640 -118.7730
1901
5/1/90 10/31/98
36.3328 -119.2907
92
7/29/91 9/30/98
36.8411 -119.8764
98
5/1/90 10/31/98
35.4385 -119.0168
180
10/9/91 9/30/98
37.2819 -120.4334
86
7/6/94 9/30/98
35.3855 -119.0147
123
5/1/90 10/31/98
35.5033 -119.2721
126
5/1/90 9/30/98
36.7019 -119.7391
162
5/1/94 9/30/98 15 36.3149 -119.6431
99
8/31/95 10/31/98
37.1383 -119.2666
0
8/21/97 9/30/98 16 36.8669 -120.0100
5/1/92 9/30/98 18 37.4883 -120.8355
56
5/1/90 10/31/98
37.6424 -120.9936
27
6/14/95 9/30/98 17 37.7363 -121.5335
31
5/1/90 9/30/98
37.9056 -121.1461
17
5/1/90 9/30/98
37.9508 -121.2692
13
2-46
LADAM
#
2880
3172
2984
2702
2756
2957
3101
2955
2991
3003
2088
2954
3029
3153
2965
2008
2500
2992
3023
2593
2979
2321
2671
2709
2360
2161
3177
3152
3121
2923
3215
Site
AIRS Site Moniker
Short Name
061112002 SIM Simi_V-CchrS
061111004 OJO Ojai-OjaiAve
061110007 THM 1000_Oaks-Mr
061110004
PIR
Piru-2mi_SW
061110005 VTA WCasitasPass
060831014 LPD Los_PadresNF
060831025 CA1 Capitan-LF#1
060790005
PRF Pas_Rob-Snta
061113001 ELM El_Rio-Sch#2
060831021 CRP Carpint-Gbrn
061112003 VTE Emma_Wood_SB
060831018 GVB Gaviota-GT#B
060831020 SBU S_Barbr-UCSB
060832011 GNF Goleta-NFrvw
060798001 ATL Atascadero
060830008 ECP El_Capitan_B
060830010 SBC S_Barbr-WCrl
060831013 LHS Lompoc-HS&P1
060834003 VBS Van_AFB-STSP
060833001 SYN S_Ynez-Airpt
060792004 NGR Nipomo-Gudlp
060793001 MBP Morro Bay
060792001 GCL Grover_City
060792002 SLM S_L_O-Marsh
060832004 LOM Lompoc-S_HSt
060831007 SMY S_Maria-SBrd
060832012
SRI
SRosaIsland
060719002
JSN Josh_Tr-Mnmt
060290011 MOP Mojave-Poole
060710001 BSW Barstow
060711234 TRT Trona-Telegraph
Version 3 – 11/24/99
Air Basin
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
South Central Coast
Mojave Desert
Mojave Desert
Mojave Desert
Mojave Desert
County
Ventura
Ventura
Ventura
Ventura
Ventura
Santa Barbara
Santa Barbara
San Luis Obispo
Ventura
Santa Barbara
Ventura
Santa Barbara
Santa Barbara
Santa Barbara
San Luis Obispo
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
Santa Barbara
San Luis Obispo
San Luis Obispo
San Luis Obispo
San Luis Obispo
Santa Barbara
Santa Barbara
Santa Barbara
San Bernardino
Kern
San Bernardino
San Bernardino
Location
Type
Suburban
Suburban
Suburban
Rural
Rural
Rural
Rural
Suburban
Rural
Rural
Suburban
Rural
Rural
Suburban
Suburban
Rural
Urban/CC
Rural
Rural
Rural
Rural
Urban/CC
Suburban
Urban/CC
Urban/CC
Suburban
Rural
Rural
Rural
Urban/CC
Rural
Data Record
Link
Elevation
Begin
End
5/1/90 10/31/98
34.2775 -118.6847
310
5/1/96 10/31/98 25 34.4166 -119.2458
262
5/1/92 10/31/98 23 34.2198 -118.8671
232
5/1/90 10/31/98
34.4025 -118.8244
182
5/1/90 10/31/98
34.3842 -119.4145
319
5/2/90 10/31/98
34.5416 -119.7913
547
5/1/90 10/31/98
34.4897 -120.0458
0
8/31/91 10/31/98 21 35.6316 -120.6900
100
5/1/92 10/31/98 24 34.2520 -119.1545
34
5/1/90 10/31/98
34.4030 -119.4580
152
5/1/90 10/31/98
34.2804 -119.3153
3
5/1/90 10/31/98
34.5275 -120.1964
305
5/1/90 7/15/98
34.4147 -119.8788
9
5/1/94 10/31/98 22 34.4455 -119.8287
50
5/1/90 10/31/98
35.4919 -120.6681
262
5/1/90 10/31/98
34.4624 -120.0245
30
5/1/90 10/31/98
34.4208 -119.7007
16
5/1/90 10/31/98
34.7251 -120.4284
244
5/1/90 10/31/98
34.5961 -120.6327
100
5/1/90 10/31/98
34.6078 -120.0734
204
6/6/91 10/31/98 20 35.0202 -120.5614
60
5/1/90 10/31/98
35.3668 -120.8450
18
5/1/90 10/31/98
35.1241 -120.6322
4
5/1/90 10/31/98
35.2826 -120.6546
66
5/16/90 10/31/98
34.6360 -120.4541
24
5/1/90 10/31/98
34.9507 -120.4341
76
5/1/96 10/31/98
34.0166 -120.0500
0
9/30/93 10/31/98 1
34.0713 -116.3905
1244
8/1/93 10/31/98
35.0500 -118.1479
853
5/1/90 10/31/98
34.8950 -117.0236
692
5/1/97 10/31/98 2
35.7639 -117.3961
2-47
L#
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Retained Site
Josh_Tr-Mnmt
Trona-Telegraph
Grs_Vly-Litn
S_Cruz-Soqul
Scotts_V-Drv
Colusa-Sunrs
Willows-ELau
Rocklin
Rosevil-NSun
Folsom-Ntma
Red_Blf-Oak
Woodland-GibsonRd
San Pablo
Baker-5558Ca
Hanford-Irwn
Madera
Tracy-Patt#2
Turlock-SMin
Seq_NP-Kawea
Nipomo-Gudlp
Pas_Rob-Snta
Goleta-NFrvw
1000_Oaks-Mr
El_Rio-Sch#2
Ojai-OjaiAve
Version 3 – 11/24/99
StartDate1
9/30/93
5/1/97
9/30/93
9/24/96
6/30/94
7/18/96
6/16/94
5/1/91
5/1/93
8/31/96
7/31/96
5/31/98
5/9/97
5/1/94
5/1/94
8/21/97
6/14/95
5/1/92
7/31/96
6/6/91
8/31/91
5/1/94
5/1/92
5/1/92
5/1/96
Linked Site #2 Preceding Linked Site #1
Linked Site #1 Preceding Retained Sited
LSite 1
StartDate1 StopDate1 Dist1_km LSite 2
StartDate2 StopDate2 Dist2_km
Josh_Tr-LHrs
5/1/90
9/22/93
19.5
Trona-Market
5/1/90 10/31/93
1.8 Trona-Athol
5/1/93 10/31/96
2.7
Nvda_Cty-Wll
5/31/90
9/30/92
5.6
S_Cruz-Bstwc
5/1/90
9/12/96
0.5
Scotts_V-Vin
8/12/92 10/31/94
2.3
Colusa-FairG
10/3/91 10/15/96
2.2
Willows-NVil
7/24/90
6/2/94
1.7
Rocklin-Sier
5/1/90 10/31/90
0.3
Citrus_Hghts
5/1/90 10/31/92
5.7
Folsom-CityC
5/1/90 10/31/96
2.2
Red_Blf-Wlnt
7/6/90 10/31/95
1.6
Woodlnd-Main
5/1/90
8/30/91
5.4 Woodlnd-Sutr
5/1/92 10/31/97
7.7
Richmnd-13th
5/1/90
5/6/97
0.1
Baker-Chestr
5/1/90 10/31/93
1.9
Hanford
5/1/90
7/30/93
2.3
Madera-HD#2
5/1/90
9/30/96
10.1
Tracy-Pattrs
8/18/94
6/13/95
0.4
Turlock-MV#1
5/1/90
8/31/92
4.7
Seq_NP-Giant
5/1/90
7/31/96
0.6
Nipomo-Eclps
6/12/90
5/23/91
2.5
Pas_Rob-10th
5/1/90
9/17/90
0.2
Goleta
5/1/90 10/31/93
0.0
1000_Oaks-Wn
5/1/90 10/31/91
2.9
El Rio
5/1/90 10/31/91
1.1
Ojai-1768Mar
5/1/90 10/31/95
3.5
Table 2.3-1b
Ozone Monitoring Sites Linked in Time for Longer Period of Record
2-48
Sacramento Valley
Mountain Counties
Lake County
8hr
1hr
8hr
1hr
8hr
1hr
8hr
1hr
8hr
1hr
Standard
Variable
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
1990 1991 1992
0.09
0.045
0
0 215
0.072
0.038
0
0 216
0.07 0.05 0.08
0.042 0.033 0.048
107
21 113
0.068 0.046 0.073
0.036 0.025 0.043
108
23 113
0.09 0.08 0.07
0.040 0.047 0.043
184 183
91
0.063 0.066 0.057
0.034 0.041 0.037
184 183
92
0.15 0.11 0.13
0.066 0.074 0.071
212 293 849
0.115 0.102 0.112
0.059 0.066 0.063
214 294 852
0.15 0.19 0.17
0.061 0.065 0.067
2713 2546 2880
0.127 0.14 0.122
0.051 0.054 0.056
2713 2547 2880
1993
0.09
0.040
487
0.073
0.033
490
0.07
0.041
175
0.07
0.036
174
0.08
0.039
184
0.072
0.033
184
0.12
0.064
1217
0.111
0.056
1218
0.15
0.059
3460
0.12
0.050
3463
Year
1994
0.1
0.043
540
0.08
0.035
545
0.08
0.050
181
0.068
0.044
181
0.09
0.053
184
0.075
0.046
184
0.13
0.068
1568
0.108
0.061
1572
0.145
0.066
3426
0.121
0.056
3428
1995
0.1
0.041
543
0.09
0.034
542
0.07
0.044
184
0.062
0.038
184
0.07
0.037
184
0.063
0.031
184
0.146
0.067
1670
0.113
0.060
1674
0.156
0.062
3938
0.128
0.052
3940
1996
0.08
0.039
264
0.071
0.037
240
0.07
0.047
105
0.063
0.040
106
0.09
0.041
184
0.07
0.035
184
0.138
0.069
2322
0.113
0.062
2324
0.157
0.065
3876
0.126
0.055
3885
1997
0.1
0.039
545
0.091
0.033
544
0.082
0.050
182
0.074
0.044
182
0.08
0.039
184
0.065
0.033
184
0.145
0.064
2178
0.112
0.057
2181
0.125
0.058
3993
0.107
0.049
3993
1998
0.13
0.042
548
0.106
0.035
541
0.078
0.053
146
0.071
0.046
146
0.08
0.039
184
0.076
0.034
184
0.163
0.062
1549
0.127
0.056
1554
0.16
0.063
3550
0.137
0.054
3556
Group Annual Meansa
1990-95 1996-98 1990-98
0.095
0.103
0.099
0.042
0.040
0.041
1785
1357
3142
0.079
0.089
0.083
0.035
0.035
0.035
1793
1325
3118
0.074
0.077
0.075
0.045
0.050
0.047
781
433
1214
0.068
0.069
0.069
0.039
0.044
0.041
783
434
1217
0.082
0.083
0.083
0.043
0.040
0.042
1010
552
1562
0.068
0.070
0.069
0.037
0.034
0.036
1011
552
1563
0.131
0.149
0.137
0.067
0.065
0.066
5809
6049
11858
0.110
0.117
0.113
0.060
0.058
0.059
5824
6059
11883
0.160
0.147
0.156
0.063
0.062
0.062
18963
11419
30382
0.126
0.123
0.125
0.053
0.053
0.053
18971
11434
30405
Table 2.3-2
Summary of Trends in Daily 1hr and 8hr Ozone Maxima during May-October, 1990-98
Northeast Plateau
North Coast
Basin
Version 3 – 11/24/99
2-49
8hr
1hr
8hr
1hr
8hr
1hr
8hr
Variable
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
Max Daily Max
Avg Daily Max
Count
1990
0.13
0.041
3561
0.105
0.033
3562
0.12
0.046
1187
0.095
0.039
1186
0.17
0.074
3074
0.123
0.063
3066
0.17
0.055
4592
0.143
0.047
4584
0.13
0.072
436
0.102
0.062
435
1991
0.14
0.042
3657
0.108
0.034
3658
0.14
0.048
1273
0.108
0.042
1272
0.18
0.078
3378
0.13
0.066
3370
0.17
0.056
4525
0.14
0.049
4520
0.13
0.072
475
0.115
0.063
475
1992
0.13
0.043
3722
0.101
0.035
3722
0.11
0.045
1560
0.09
0.039
1565
0.16
0.076
3390
0.121
0.064
3391
0.15
0.054
4699
0.125
0.047
4697
0.12
0.069
338
0.097
0.059
339
1993
0.13
0.044
3854
0.112
0.035
3851
0.11
0.046
1795
0.087
0.040
1790
0.16
0.076
3508
0.125
0.065
3508
0.146
0.053
4769
0.129
0.046
4766
0.14
0.072
561
0.114
0.063
562
Year
1994
0.13
0.042
4043
0.097
0.034
4043
0.101
0.043
1814
0.092
0.037
1810
0.175
0.075
3828
0.129
0.064
3827
0.164
0.056
4565
0.132
0.048
4559
0.165
0.079
723
0.127
0.069
727
1995
0.155
0.047
4037
0.115
0.037
4034
0.138
0.045
1825
0.102
0.039
1825
0.173
0.075
4051
0.134
0.064
4052
0.169
0.056
4681
0.144
0.048
4678
0.151
0.072
714
0.106
0.063
713
1996
0.138
0.046
3923
0.112
0.037
3924
0.12
0.047
1802
0.101
0.040
1800
0.165
0.079
4165
0.137
0.068
4167
0.158
0.057
4918
0.127
0.050
4913
0.146
0.077
726
0.117
0.068
726
1997
0.114
0.040
4039
0.084
0.033
4039
0.112
0.043
1825
0.091
0.037
1825
0.147
0.071
4083
0.127
0.061
4084
0.137
0.053
4923
0.114
0.047
4915
0.149
0.072
711
0.122
0.064
711
Totals by Annual Grouping
1998 1990-95 1996-98 1990-98
0.147
0.136
0.133
0.135
0.044
0.043
0.043
0.043
3961
22874
11923
34797
0.111
0.106
0.102
0.105
0.035
0.035
0.035
0.035
3961
22870
11924
34794
0.124
0.120
0.119
0.119
0.045
0.045
0.045
0.045
1795
9454
5422
14876
0.097
0.096
0.096
0.096
0.039
0.039
0.039
0.039
1794
9448
5419
14867
0.169
0.170
0.160
0.167
0.076
0.075
0.075
0.075
3549
21229
11797
33026
0.136
0.127
0.133
0.129
0.066
0.064
0.065
0.064
3552
21214
11803
33017
0.174
0.162
0.156
0.160
0.053
0.055
0.054
0.055
4831
27831
14672
42503
0.151
0.144
0.151
0.151
0.046
0.076
0.048
0.065
4828
27804
14656
42460
0.142
0.139
0.146
0.141
0.072
0.073
0.074
0.073
692
3247
2129
5376
0.123
0.110
0.121
0.114
0.064
0.063
0.065
0.064
685
3251
2122
5373
a. For years with greater than 75% data capture. Some records for 1998 were not complete at time of compilation.
Mojave Desert
South Central Coast
San Joaquin Valley
North Central Coast
1hr
8hr
1hr
Standard
Table 2.3-2 (cont.)
Summary of Trends in Daily 1hr and 8hr Ozone Maxima during May-October, 1990-98
Basin
Version 3 – 11/24/99
2-50
Short_Name
Healdsb-Aprt
Ukiah-EGobbi
Willits-Main
Yreka-Fthill
Lakepor-Lake
Cool-Hwy193
Plcrvll-Gold
San_Andreas
Jerseydale
Grs_Vly-Litn
Jackson-ClRd
Yos_NP-Trtle
Sonora-OakRd
Sonora-Brret
Colfax-CtyHl
White_Cld_Mt
Truckee-Fire
Quincy-NChrc
Folsom-Ntma
Auburn-DwttC
Sutter_Butte
Rocklin
Redding-HDrf
Sacto-DelPas
Rosevil-NSun
Basin
NC
NC
NC
NEP
LC
MC
MC
MC
MC
MC
MC
MC
MC
MC
MC
MC
MC
MC
SV
SV
SV
SV
SV
SV
SV
Rural
Suburban
Suburban
Suburban
Suburban
Rural
Suburban
Rural
Rural
Suburban
Suburban
Rural
Rural
Urban/CC
Rural
Rural
Urban/CC
Urban/CC
Suburban
Rural
Rural
Rural
Suburban
Suburban
Suburban
0.15
0.13
0.15
0.15
0.13
0.11
0.18
0.15
0.11 0.19
0.15
0.07
0.09 0.09
0.15 0.15 0.14
0.14 0.12 0.13
0.12
0.17 0.15 0.13
0.11
0.11
0.13 0.15 0.15
0.13 0.15 0.12
0.12 0.11
0.13 0.12
0.15 0.11 0.11
0.11
0.12 0.11 0.12
0.12 0.11
0.12 0.12 0.13
0.12
0.09 0.09 0.1
0.08 0.09
0.07
0.07 0.08
0.09 0.08
0.08 0.09
Ave
8-hour Exceedances
1993 1994 1995 1996
1997
1998
0.072 0.073 0.08 0.09 0.071 0.091 0.106 0.083
0.065 0.06 0.07
0.061 0.071 0.065
0.06 0.05
0.058 0.052 0.055
0.07 0.07 0.06
0.074 0.071 0.069
0.07
0.072 0.08 0.06 0.07 0.065 0.076 0.069
0.113 0.106 0.125 0.115
0.112 0.108 0.1 0.11 0.108 0.102 0.127 0.111
0.11 0.11 0.112 0.112
0.110
0.107 0.105 0.103 0.105
0.1 0.087
0.11
0.104 0.101 0.098 0.101
0.105 0.09 0.1 0.11 0.106 0.104
0.104
0.111 0.1 0.1 0.094 0.1 0.099 0.102
0.108 0.107
0.108
0.088 0.09 0.11 0.094 0.103
0.097
0.098 0.097
0.1 0.091 0.097
0.097
0.09 0.092 0.087 0.099 0.093
0.07 0.06 0.065 0.054 0.066 0.064
0.076 0.08
0.08
0.074 0.078
0.14 0.122 0.118 0.12 0.13 0.126 0.101 0.137 0.120
0.122 0.107 0.12 0.12 0.11 0.089
0.113
0.1 0.1 0.102 0.092 0.102 0.100
0.1 0.122 0.12 0.11 0.11 0.11 0.096 0.119 0.111
0.1 0.091
0.11 0.08 0.1 0.107 0.126 0.102
0.12 0.108 0.103 0.11 0.12 0.11 0.094 0.13 0.111
0.12 0.102 0.11 0.1 0.1 0.108 0.091 0.117 0.106
1990 1991 1992
0.1 0.08 0.1 0.13 0.099
0.08
0.07 0.09 0.082
0.06
0.07 0.06 0.066
0.07
0.08 0.08 0.076
0.07 0.09 0.08 0.08 0.083 0.06
0.14 0.15 0.16 0.148
0.13 0.13 0.11 0.14 0.126
0.15 0.14 0.14
0.136
0.11 0.12 0.11 0.115
0.11 0.11 0.11 0.116 0.12
0.15 0.13 0.14
0.127
0.11 0.11 0.11 0.11 0.112
0.11 0.11
0.113
0.14 0.12 0.12
0.120
0.13 0.11 0.1
0.118
0.1 0.11 0.1 0.12 0.105
0.09 0.06 0.08 0.075
0.09
0.09 0.090
0.16 0.16 0.13 0.15 0.148 0.09
0.15 0.13 0.11
0.132 0.13
0.11 0.12 0.11 0.12 0.114
0.15 0.13 0.11 0.14 0.140 0.11
0.1 0.11 0.12 0.14 0.116 0.11
0.15 0.15 0.11 0.16 0.147 0.11
0.14 0.14 0.11 0.15 0.138 0.11
Location
1-hour Exceedances
Type
1990 1991 1992 1993 1994 1995 1996 1997 1998
Ave
Table 2.3-3
Annual Maximum of Daily Maximum Ozone Concentrations in Central California During May to October, 1990-98a
Version 3 – 11/24/99
2-51
Short_Name
N_High-Blckf
Red_Blf-Oak
Yuba_Cty-Alm
Elk_Grv-Brcv
Anderson-Nth
Vacavil-Alli
Woodland-GibRd
Plsnt_Grv4mi
Davis-UCD
Sacto-T_Strt
Colusa-Sunrs
Tuscan Butte
Willows-ELau
Chico-Manznt
Lasn_Vlcn_NP
Livrmor-Old1
San_Martin
Los Gatos
Bethel_Is_Rd
Concord-2975
Gilroy-9th
Fairfld-AQMD
San_Jose-935
Pittsbg-10th
Fremont-Chpl
San_Jose-4th
Basin
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
Suburban
Urban/CC
Suburban
Rural
Suburban
Suburban
Suburban
Rural
Rural
Urban/CC
Rural
Rural
Suburban
Suburban
Rural
Urban/CC
Rural
Urban/CC
Rural
Suburban
Suburban
Urban/CC
Rural
Urban/CC
Suburban
Urban/CC
0.11
0.12
0.12
0.12
0.11
0.12
0.14 0.1
0.13 0.1
0.13 0.11
0.1 0.11
0.1 0.11 0.1 0.1
0.13 0.1 0.09 0.1 0.1
0.1 0.08
0.08 0.09
0.13 0.14 0.11 0.13 0.13
0.13
0.12 0.11 0.13 0.13 0.12
0.12 0.11 0.11 0.11 0.11
0.11 0.11 0.11 0.13 0.12
0.12 0.13 0.12 0.11 0.1
0.1 0.1 0.1 0.13 0.11
0.11 0.12
0.11 0.08 0.11 0.13 0.11
0.13 0.12 0.12 0.13 0.12
0.12 0.1 0.12 0.11 0.11
0.11
0.11 0.1
0.11
0.13 0.14
0.12 0.13 0.12 0.11 0.12
0.11 0.1 0.1 0.1
0.1 0.11 0.12 0.1 0.11
0.1 0.11
0.09
0.13
0.11
0.11
0.12
0.1
0.12
0.11
0.13
0.11
0.13
0.11
0.1
0.1
0.11
0.09
0.16
0.13
0.14
0.13
0.15
0.13
0.13
0.15
0.12
0.15
0.13
0.11
0.12
0.1
0.13
0.12
0.1
0.12
0.12
0.11
0.11
0.1
0.11
0.09
0.14
0.12
0.13
0.14
0.13
0.12
0.11
0.12
0.12
0.1
0.11
0.13
0.1
0.1
0.09
0.12
0.1
0.11
0.1
0.1
0.1
0.09
0.09
0.1
0.1
0.09
0.08
0.11
0.09
0.1
0.1
0.1
0.1
0.09
0.1
0.09
0.11
0.09
0.1
0.11
0.09
0.15
0.14
0.13
0.12
0.15
0.14
0.12
0.13
0.1
0.12
0.15
0.15
0.12
0.12
0.15
0.12
0.14
0.11
0.11
0.12
0.14
0.1
Location
1-hour Exceedances
Type
1990 1991 1992 1993 1994 1995 1996 1997 1998
0.123
0.106
0.108
0.120
0.101
0.121
0.112
0.111
0.114
0.123
0.104
0.103
0.101
0.103
0.089
0.132
0.122
0.123
0.117
0.123
0.118
0.110
0.119
0.108
0.122
0.116
Ave
0.08
0.08
0.08
0.09
0.08 0.088 0.083 0.09
0.1 0.09 0.077 0.083 0.09
0.09 0.07
0.072 0.09
0.11 0.09 0.091 0.095 0.09
0.1
0.09 0.09 0.101 0.112 0.09
0.11 0.08 0.087 0.09 0.09
0.08 0.09 0.092 0.096 0.09
0.1 0.11 0.091 0.086 0.09
0.09 0.09 0.086 0.096 0.08
0.093 0.07
0.09 0.07 0.092 0.086 0.1
0.08 0.08 0.081 0.102 0.08
0.09 0.08 0.083 0.087 0.07
0.09
0.096 0.097
0.08 0.08 0.108 0.108
0.09
0.092 0.088
0.09 0.09 0.091 0.093
0.092 0.085
0.11
0.1
0.09
0.11
0.08
0.09
0.09
0.1
0.09
0.1
0.09
0.09
0.09
0.09
0.08
0.12
0.1
0.1
0.1
0.11
0.1
0.1
0.11
0.1
0.11
0.11
1997
0.103 0.084
0.088
0.09 0.079
0.106 0.091
0.088 0.09
0.101 0.083
0.09 0.082
0.091 0.082
0.104 0.086
0.097 0.085
0.091 0.081
0.084 0.093
0.081 0.08
0.084 0.072
0.085 0.076
0.112 0.084
0.097 0.074
0.103 0.08
0.1 0.081
0.099 0.081
0.103 0.076
0.095 0.072
0.083 0.071
0.093 0.067
0.079 0.078
0.082 0.068
8-hour Exceedances
1993 1994 1995 1996
0.1 0.098 0.092 0.09
0.09 0.091 0.093 0.09
0.08 0.1 0.095 0.086 0.09
0.082 0.09
0.083
0.09
1990 1991 1992
0.088
0.09
0.086
0.11
0.107
0.095
0.096
0.109
0.111
0.097
0.082
0.089
0.077
0.091
0.112
0.111
0.097
0.11
0.107
0.101
0.102
0.088
0.095
0.098
0.088
1998
0.098
0.094
0.090
0.097
0.090
0.094
0.092
0.092
0.090
0.091
0.088
0.089
0.084
0.085
0.080
0.100
0.095
0.096
0.092
0.095
0.095
0.089
0.085
0.087
0.085
0.084
Ave
Table 2.3-3 (cont.)
Version 3 – 11/24/99
2-52
Short_Name
Hayward
Sn_Lndro-Hos
Napa-Jffrsn
Redwood_City
Mtn_View-Cst
Vallejo-304T
Oakland-Alic
San Pablo
San Rafael
S_F-Arkansas
S_Rosa-5th
Pinn_Nat_Mon
Scotts_V-Drv
Hollistr-Frv
King_Cty-Mtz
Carm_Val-Frd
Salinas-Ntvd
Watsonvll-AP
S_Cruz-Soqul
Montery-SlvC
Davenport
Arvin-Br_Mtn
Edison
Clovis
Fresno-1st
Maricopa-Stn
Basin
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
NCC
NCC
NCC
NCC
NCC
NCC
NCC
NCC
NCC
NCC
SJV
SJV
SJV
SJV
SJV
Rural
Suburban
Urban/CC
Suburban
Suburban
Urban/CC
Urban/CC
Urban/CC
Urban/CC
Urban/CC
Urban/CC
Rural
Rural
Rural
Rural
Suburban
Suburban
Suburban
Suburban
Rural
Rural
Rural
Rural
Urban/CC
Suburban
Suburban
0.1
0.12
0.11
0.08
0.12
0.11
0.06
0.05
0.08
0.05
0.09
0.14
0.13
0.11
0.09
0.09
0.11
0.1
0.08
0.08
0.07
0.08
0.08
0.11
0.07 0.12 0.07
0.09
0.07 0.07 0.08
0.17 0.16 0.15
0.15 0.16 0.14
0.13 0.15
0.15 0.18 0.14
0.12 0.11 0.11
0.1 0.1
0.09 0.08
0.09 0.1 0.08
0.06 0.07 0.06
0.11
0.09
0.08
0.1
0.11
0.05
0.06
0.06
0.05
0.07
0.12
0.08
0.09
0.12
0.12
0.1
0.11
0.11
0.11
0.12
0.08
0.08
0.08
0.11
0.1
0.1
0.09
0.11
0.09
0.1
0.09
0.11
0.08
0.15
0.16
0.14
0.16
0.1
0.09
0.09
0.08
0.08
0.1
0.06
0.09
0.09
0.06
0.08
0.1
0.09
0.1
0.09
0.09
0.06
0.08
0.07
0.08
0.08
0.15
0.18
0.14
0.14
0.12
0.15
0.15
0.13
0.14
0.12
0.13
0.11
0.09
0.09
0.09
0.1
0.14
0.1
0.1
0.09
0.09
0.06
0.09
0.08
0.08
0.06
0.15
0.17
0.15
0.17
0.13
0.11
0.09
0.1
0.11
0.11
0.09
0.08
0.11
0.07
0.08
0.12
0.11
0.1
0.09
0.09
0.07
0.09
0.09
0.08
0.08
0.16
0.17
0.15
0.15
0.12
0.11
0.11
0.08
0.09
0.11
0.1
0.08
0.11
0.11
0.07
0.09
0.11
0.09
0.08
0.07
0.08
0.06
0.09
0.09
0.09
0.06
0.13
0.15
0.15
0.13
0.12
0.06
0.07
0.05
0.07
0.12
0.11
0.11
0.09
0.08
0.06
0.07
0.08
0.07
0.06
0.15
0.17
0.17
0.15
0.12
0.11
0.13
0.07
0.1
0.12
Location
1-hour Exceedances
Type
1990 1991 1992 1993 1994 1995 1996 1997 1998
0.109
0.115
0.103
0.092
0.106
0.111
0.081
0.083
0.084
0.066
0.083
0.119
0.099
0.100
0.087
0.091
0.066
0.085
0.084
0.084
0.070
0.153
0.159
0.147
0.152
0.117
Ave
0.086
0.077
0.07
0.065
0.085
0.07
0.051
0.07
0.053
0.052
0.061
0.09
0.06 0.06
0.12 0.12
0.11 0.12
0.1
0.12 0.13
0.11 0.1
0.06 0.08
0.07
0.077
0.067
0.115
0.111
0.115
0.111
0.102
0.09 0.08 0.078
0.08 0.067
0.07 0.08 0.068
0.05 0.07 0.06
0.06 0.08
0.08
0.07 0.08
0.05 0.06
0.06 0.08
0.08 0.08
0.04 0.05
0.05 0.05
0.05 0.05
0.04 0.05
0.06 0.07
0.1 0.11
1990 1991 1992
0.072
0.088
0.083
0.076
0.076
0.086
0.063
0.077
0.061
0.052
0.058
0.087
0.086
0.082
0.068
0.083
0.07
0.078
0.072
0.077
0.063
0.12
0.125
0.111
0.12
0.07
0.08
0.08
0.07
0.06
0.08
0.05
0.07
0.06
0.05
0.07
0.08
0.08
0.08
0.09
0.08
0.05
0.06
0.06
0.06
0.06
0.12
0.13
0.1
0.11
0.11
0.11
0.11
0.1
0.1
0.09
0.08
0.08
0.07
0.07
0.07
0.08
0.1
0.07
0.08
0.08
0.08
0.05
0.07
0.06
0.06
0.06
0.12
0.13
0.11
0.13
0.12
0.076
0.075
0.067
0.081
0.084
0.059
0.058
0.081
0.05
0.063
0.101
0.088
0.087
0.081
0.08
0.058
0.072
0.07
0.066
0.06
0.137
0.131
0.122
0.123
0.115
8-hour Exceedances
1993 1994 1995 1996
0.077
0.075
0.071
0.073
0.084
0.083
0.062
0.079
0.073
0.059
0.08
0.091
0.071
0.073
0.06
0.072
0.048
0.063
0.063
0.076
0.058
0.112
0.118
0.127
0.107
0.104
1997
0.054
0.058
0.042
0.054
0.097
0.077
0.085
0.076
0.069
0.049
0.058
0.063
0.056
0.054
0.123
0.136
0.13
0.118
0.074
0.066
0.099
0.047
0.062
0.083
1998
0.079
0.080
0.080
0.066
0.075
0.080
0.055
0.065
0.062
0.050
0.065
0.095
0.078
0.082
0.075
0.075
0.056
0.066
0.066
0.068
0.060
0.122
0.124
0.115
0.118
0.108
Ave
Table 2.3-3 (cont.)
Version 3 – 11/24/99
2-53
Short_Name
Parlier
Baker-5558Ca
Seq_NP-Kawea
Visalia-NChu
Fresno-Sky#2
Oildale-3311
Merced-SCofe
Baker-GS_Hwy
Shafter-Wlkr
Fresno-Drmnd
Hanford-Irwn
ShavLk-PerRd
Madera
Turlock-SMin
Modesto-14th
Tracy-Patt#2
Stockton-EMr
Stockton-Haz
Simi_V-CchrS
Ojai-OjaiAve
1000_Oaks-Mr
Piru-2mi_SW
WCasitasPass
Los_PadresNF
Capitan-LF#1
Pas_Rob-Snta
Basin
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
Suburban
Urban/CC
Rural
Urban/CC
Suburban
Suburban
Rural
Urban/CC
Suburban
Suburban
Suburban
Rural
Rural
Suburban
Urban/CC
Rural
Suburban
Urban/CC
Suburban
Suburban
Suburban
Rural
Rural
Rural
Rural
Suburban
0.13 0.12 0.15 0.1 0.12
0.12 0.12 0.12 0.13 0.11 0.13
0.13 0.11 0.11 0.12 0.12 0.13
0.12
0.13 0.12 0.11 0.13 0.12 0.13
0.12 0.11 0.11 0.11 0.13 0.13
0.16 0.17 0.14 0.15 0.16 0.17
0.14 0.14 0.15 0.14 0.13 0.14
0.17 0.12 0.12 0.13 0.14 0.15
0.14 0.15 0.11 0.11 0.14 0.13
0.14 0.13 0.12 0.12 0.15 0.13
0.12 0.13 0.11 0.11 0.11 0.12
0.15 0.12 0.14 0.12 0.14 0.14
0.08
0.09 0.09 0.1 0.11
0.16 0.16 0.16 0.13 0.14
0.15 0.13 0.14 0.12 0.13
0.12
0.13 0.13 0.12
0.13 0.13 0.15 0.15 0.13
0.13 0.15 0.13 0.15
0.14 0.13 0.12 0.12 0.12 0.13
0.12 0.13 0.12 0.13
0.13
0.12 0.12 0.1 0.11 0.12 0.11
0.15 0.15 0.14 0.15 0.11 0.12
0.1 0.11 0.1
0.12 0.1
0.14
0.12
0.12
0.14
0.15
0.13
0.12
0.14
0.14
0.13
0.13
0.13
0.12
0.15
0.14
0.14
0.13
0.13
0.13
0.14
0.11
0.12
0.16
0.14
0.14
0.12
0.13
0.11
0.13
0.14
0.16
0.12
0.13
0.15
0.16
0.12
0.14
0.12
0.15
0.14
0.12
0.14
0.12 0.15
0.12 0.13
0.12 0.12
0.1
0.1
0.13 0.17
0.11 0.11
0.12 0.13
0.11 0.13
0.11 0.13
0.1 0.12
0.14 0.11
0.08 0.13
0.14
0.12
0.11
0.13
0.13
0.11
0.09
0.12
0.11
0.13
0.13
0.14
Location
1-hour Exceedances
Type
1990 1991 1992 1993 1994 1995 1996 1997 1998
0.149
0.130
0.122
0.139
0.139
0.124
0.124
0.124
0.114
0.140
0.117
0.132
0.128
0.126
0.122
0.125
0.119
0.116
0.157
0.133
0.134
0.125
0.130
0.113
0.132
0.103
Ave
0.1
0.09
0.14
0.13
0.13
0.12
0.12
0.11
0.14
0.07
0.11
0.1
0.1
0.09
0.14
0.11
0.1
0.12
0.11
0.11
0.09
0.1
0.1
0.1
0.097 0.11 0.08 0.1
0.102 0.108 0.1 0.11
0.092 0.105 0.1 0.1
0.1
0.09 0.097 0.1 0.11
0.087 0.086 0.09 0.1
0.115 0.129 0.13 0.14
0.117 0.101 0.11 0.11
0.103 0.1
0.1 0.1
0.101 0.079 0.11 0.1
0.11 0.103 0.11 0.11
0.098 0.093 0.09 0.09
0.125 0.096 0.12 0.12
0.08 0.077 0.09 0.09
0.11
0.11
0.1
0.11
0.12
0.11
0.11
0.11
0.11 0.11 0.097 0.101 0.11 0.1
0.12 0.12 0.1 0.107 0.09 0.1
0.09 0.09 0.078
0.1 0.09
0.112
0.12
0.108
0.111
0.109
0.116
0.116
0.116
0.109
0.122
0.121
0.102
0.111
0.111
0.102
0.096
0.083
0.094
0.127
0.125
0.11
0.095
0.1
0.1
0.122
0.117
8-hour Exceedances
1993 1994 1995 1996
0.11 0.12 0.121 0.108 0.1
0.1 0.11 0.105 0.121 0.12
0.1 0.1
0.116 0.12
0.12 0.11 0.105 0.125 0.12
0.11 0.121 0.1
0.11 0.11 0.105 0.106 0.11
0.107 0.11 0.11
1990 1991 1992
0.12
0.11
0.103
0.122
0.134
0.11
0.129
1998
0.102
0.115
0.113
0.093
0.116
0.1 0.125
0.091 0.119
0.099 0.094
0.083
0.082
0.114 0.151
0.097 0.103
0.093 0.101
0.089 0.118
0.097 0.115
0.088 0.097
0.108 0.092
0.076 0.113
0.112
0.109
0.1
0.104
0.102
0.103
0.079
0.104
0.101
0.099
0.106
0.106
1997
0.112
0.112
0.105
0.114
0.114
0.109
0.109
0.108
0.103
0.107
0.099
0.100
0.103
0.107
0.101
0.097
0.095
0.090
0.133
0.112
0.105
0.102
0.108
0.098
0.112
0.088
Ave
Table 2.3-3 (cont.)
Version 3 – 11/24/99
2-54
0.11
0.11
0.11
0.12
0.11
0.12
0.1
0.1
0.1
0.1
0.14
0.1
0.1
0.1
0.11
0.07
0.11
0.08
0.14
0.12
0.14
0.14
0.12
0.11
0.1
0.11
0.09
0.12
0.1
0.1
0.1
0.08
0.08
0.09
0.11
0.07
0.06
0.07
0.07
0.1
0.07
0.12
0.13
0.1
0.12
0.09
0.13
0.1
0.11
0.12
0.1
0.09
0.1
0.15
0.12
0.12
0.09
0.12
0.12
0.12
0.1
0.12
0.12
0.1
0.14
0.12
0.09
0.11
0.1
0.08
0.07
0.08
0.08
0.1
0.07
0.12
0.13
0.12
0.13
0.11
0.13
0.11
0.12
0.12
0.1
0.1
0.1
0.1
0.06
0.07
0.08
0.08
0.1
0.1
0.15
0.13
0.13
0.1
0.1
0.11
0.11
0.1
0.09
0.09
0.09
0.09
0.1
0.09
0.09
0.1
0.07
0.06
0.07
0.07
0.08
0.07
0.08
0.15
0.11
0.11
0.1
0.09
0.1
0.07
0.09
0.08
0.08
0.1
0.07
0.07
0.06
0.07
0.07
0.06
0.08
0.14
0.13
0.11
0.11
0.11
0.11
0.09
0.09
0.117
0.120
0.112
0.116
0.106
0.111
0.100
0.106
0.109
0.096
0.102
0.099
0.084
0.069
0.075
0.075
0.090
0.071
0.087
0.145
0.125
0.123
0.103
Ave
0.1
0.1
0.09
0.09
0.08
0.1
0.09
0.09
0.08
0.1
0.09
0.08
0.08
0.08
0.07
0.06
0.07
0.07
0.07
0.05
0.087
0.09
0.088
0.097
0.095
0.107
0.083
0.083
0.092
0.071
0.102
0.088
0.09
0.086
0.098
0.055
0.077
0.063
0.1
0.09
0.112
0.099
0.104
0.095
0.079
0.093
0.082
0.099
0.088
0.079
0.088
0.066
0.064
0.076
0.071
0.063
0.09
0.1
0.09
0.08
0.08
0.1
0.09
0.08
0.09
0.09
0.08
0.08
0.1
0.1
0.1
0.09
0.11
0.1
0.08
0.11
0.08
0.09
0.09
0.09
0.06
0.06
0.06
0.07
0.08
0.06
8-hour Exceedances
1993 1994 1995 1996
0.096
0.104
0.096
0.093
0.076
0.107
0.098
0.106
0.092
0.093
0.089
0.092
0.067
0.06
0.053
0.06
0.059
0.07
0.071
0.06
0.068
0.06
0.08
0.079
0.12
0.13 0.11 0.117
0.11 0.11 0.109
0.097 0.114 0.11 0.1 0.109
0.1 0.085 0.093 0.09 0.08 0.093
0.11
0.1
0.11
0.09
0.09
0.08
0.1
0.08
0.08
0.07
0.09
0.08
0.08
0.06
0.06
0.06
0.07
0.05
1990 1991 1992
0.17
0.12
0.13
0.12 0.13 0.13
0.12 0.1 0.1 0.1
0.12
0.12
0.13
0.13
0.11
0.1
0.11
0.11
0.1
0.08
0.11
0.1
0.08
0.06
0.07
0.07
0.09
0.05
0.13 0.13
0.12
0.13
0.09
0.13
0.1
0.11
0.1
0.11
0.14
0.1
0.1
0.09
0.08
0.07
0.07
0.08
0.08
0.06
Rural
Rural
Suburban
Rural
Rural
Suburban
Suburban
Rural
Urban/CC
Rural
Rural
Rural
Rural
Urban/CC
Suburban
Urban/CC
Urban/CC
Suburban
Rural
Rural
Rural
Urban/CC
Rural
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
MD
MD
MD
MD
El_Rio-Sch#2
Carpint-Gbrn
Emma_Wood_SB
Gaviota-GT#B
S_Barbr-UCSB
Goleta-NFrvw
Atascadero
El_Capitan_B
S_Barbr-WCrl
Lompoc-HS&P1
Van_AFB-STSP
S_Ynez-Airpt
Nipomo-Gudlp
Morro Bay
Grover_City
S_L_O-Marsh
Lompoc-S_HSt
S_Maria-SBrd
SRosaIsland
Josh_Tr-Mnmt
Mojave-Poole
Barstow
Trona-Telegraph
Location
1-hour Exceedances
Type
1990 1991 1992 1993 1994 1995 1996 1997 1998
Basin Short_Name
0.081
0.091
0.081
0.08
0.079
0.08
0.077
0.076
0.079
0.084
0.08
0.073
0.06
0.056
0.066
0.06
0.069
0.06
0.074
0.122
0.096
0.106
0.083
1997
0.068
0.09
0.062
0.066
0.07
0.072
0.084
0.06
0.057
0.055
0.062
0.059
0.055
0.075
0.123
0.117
0.09
0.102
0.084
0.078
0.075
0.071
1998
0.093
0.093
0.092
0.089
0.090
0.092
0.087
0.087
0.083
0.082
0.085
0.083
0.072
0.061
0.066
0.065
0.069
0.060
0.076
0.116
0.107
0.104
0.091
Ave
Table 2.3-3 (cont.)
Version 3 – 11/24/99
2-55
12
2
0
5
6
4
1
4
4
0
2
0
9
0
0
7
0
2
1
9
3
1
0
0
0
0
3
3
3
0
3
0
0
0
0
0
0
0
0
0
0
0
0
6
4
0
1
0
1
0
0
0
0
0
2
0
0
0
0
0
0
7
2
0
3
0
7
2
1
1
0
1
0
1
1
0
0
0
0
0
0
2
1
3
0
0
2
0
0
0
0
0
0
0
7
1
0
1
0
3
2
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
1
0
0
0
0
0
0
0
3
3
5
5
0
0
0
10
0
0
0
1
0
0
0
0
5
1
Rural
Suburban
Suburban
Suburban
Suburban
Rural
Suburban
Rural
Rural
Suburban
Suburban
Rural
Rural
Urban/CC
Rural
Rural
Urban/CC
Urban/CC
Suburban
Rural
Rural
Rural
Suburban
Suburban
Suburban
NC
NC
NC
NEP
LC
MC
MC
MC
MC
MC
MC
MC
MC
MC
MC
MC
MC
MC
SV
SV
SV
SV
SV
SV
SV
Healdsb-Aprt
Ukiah-EGobbi
Willits-Main
Yreka-Fthill
Lakepor-Lake
Cool-Hwy193
Plcrvll-Gold
San_Andreas
Jerseydale
Grs_Vly-Litn
Jackson-ClRd
Yos_NP-Trtle
Sonora-OakRd
Sonora-Brret
Colfax-CtyHl
White_Cld_Mt
Truckee-Fire
Quincy-NChrc
Folsom-Ntma
Auburn-DwttC
Sutter_Butte
Rocklin
Redding-HDrf
Sacto-DelPas
Rosevil-NSun
Location
1-hour Exceedances
Type
1990 1991 1992 1993 1994 1995 1996 1997 1998
Basin SHORT_NAME
0.14
0.00
0.00
0.00
0.00
2.67
0.71
1.25
0.00
0.29
0.67
0.00
0.00
0.20
0.40
0.00
0.00
0.00
6.11
2.71
0.00
2.67
0.50
3.33
2.56
Ave
13
12
17
11
1
41
9
0
12
11
14
10
40
7
0
24
10
14
9
29
26
12
5
11
29
0
1990 1991 1992
6
7
9
0
13
15
6
4
5
22
12
0
0
0
0
0
0
22
25
18
19
7
5
8
7
9
15
11
22
34
0
0
0
0
0
27
18
15
17
0
23
8
10
11
4
0
18
11
31
19
1
0
0
0
0
0
30
27
18
30
28
14
10
16
13
5
6
0
0
23
17
26
20
13
13
12
0
8-hour Exceedances
1993 1994 1995 1996
8
1
3
4
6
1
2
1
0
0
0
0
10
13
4
7
17
3
3
5
6
2
1
0
1997
13
12
45
10
12
14
0
0
26
9
14
16
5
0
0
0
0
25
3
1998
1.00
0.00
0.00
0.00
0.00
21.67
19.57
18.75
17.00
13.00
11.00
11.00
10.50
8.40
6.80
6.25
0.00
0.00
21.00
20.43
15.00
14.44
13.00
11.44
8.78
Ave
Table 2.3-4
Annual Exceedances of the 1-hr and 8-hr Ozone Standards in Central California During May to October, 1990-98 a
Version 3 – 11/24/99
2-56
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
0
0
0
1
0
0
0
0
1
0
1
0
0
0
0
0
0
1
0
1
0
1
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2
0
1
0
1
1
0
0
0
0
1
0
2
1
1
0
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
1
0
1
0
0
0
0
0
7
1
4
1
3
1
1
3
0
2
1
0
0
0
1
0
0
0
0
0
0
0
0
0
8
0
1
1
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
3
1
0
2
2
0
1
0
0
1
3
0
0
1
0
2
0
0
0
1
0
Location
1-hour Exceedances
Type
1990 1991 1992 1993 1994 1995 1996 1997 1998
N_High-Blckf
Suburban
Red_Blf-Oak
Urban/CC
Yuba_Cty-Alm
Suburban
Elk_Grv-Brcv
Rural
Anderson-Nth
Suburban
Vacavil-Alli
Suburban
Woodland-GibsonRSuburban
Plsnt_Grv4mi
Rural
Davis-UCD
Rural
Sacto-T_Strt
Urban/CC
Colusa-Sunrs
Rural
Tuscan Butte
Rural
Willows-ELau
Suburban
Chico-Manznt
Suburban
Lasn_Vlcn_NP
Rural
Livrmor-Old1
Urban/CC
San_Martin
Rural
Los Gatos
Urban/CC
Bethel_Is_Rd
Rural
Concord-2975
Suburban
Gilroy-9th
Suburban
Fairfld-AQMD
Urban/CC
San_Jose-935
Rural
Pittsbg-10th
Urban/CC
Fremont-Chpl
Suburban
San_Jose-4th
Urban/CC
Basin SHORT_NAME
0.78
0.00
0.00
0.17
0.00
0.75
0.00
0.33
0.13
0.56
0.00
0.00
0.00
0.11
0.00
2.89
1.00
0.89
0.22
0.89
0.44
0.22
0.67
0.11
0.44
0.22
Ave
1
0
1
2
5
0
2
1
1
1
4
2
0
3
2
0
4
0
0
0
4
0
2
1
1
0
1
0
2
2
0
5
2
2
1
0
0
2
2
1
1
2
3
4
0
6
4
4
2
3
5
9
12
1990 1991 1992
3
2
2
2
1
2
1
3
1
0
0
0
3
1
2
1
1
2
3
4
2
0
0
2
2
1
3
1
1
2
1
1
0
0
1
0
0
0
0
0
2
6
3
6
3
10
9
4
4
0
3
3
7
2
3
2
4
1
1
0
11
8
5
3
6
5
5
6
3
4
3
4
9
3
2
2
5
4
3
4
0
0
0
1
10
5
5
3
3
3
3
0
2
0
0
15
8-hour Exceedances
1993 1994 1995 1996
0
1
0
3
2
0
0
0
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1997
1
1
1
10
6
2
5
6
4
3
0
1
0
1
9
10
5
4
10
7
4
4
4
4
1
1998
6.33
5.43
3.89
3.83
3.00
3.00
2.57
2.44
2.38
2.00
2.00
1.67
1.00
0.67
0.50
5.11
4.00
2.67
2.44
2.33
2.11
1.78
1.33
1.11
0.78
0.67
Ave
Table 2.3-4 (cont.)
Version 3 – 11/24/99
2-57
0
28
22
3
27
0
0
0
23
22
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
3
17
12
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
28
13
11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
17
31
9
7
0
2
3
1
1
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
19
34
7
14
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
37
25
16
15
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
3
9
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12
22
26
15
0
0
1
0
0
0
Rural
Suburban
Urban/CC
Suburban
Suburban
Urban/CC
Urban/CC
Urban/CC
Urban/CC
Urban/CC
Urban/CC
Rural
Rural
Rural
Rural
Suburban
Suburban
Suburban
Suburban
Rural
Rural
Rural
Rural
Urban/CC
Suburban
Suburban
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
NCC
NCC
NCC
NCC
NCC
NCC
NCC
NCC
NCC
NCC
SJV
SJV
SJV
SJV
SJV
Hayward
Sn_Lndro-Hos
Napa-Jffrsn
Redwood_City
Mtn_View-Cst
Vallejo-304T
Oakland-Alic
San Pablo
San Rafael
S_F-Arkansas
S_Rosa-5th
Pinn_Nat_Mon
Scotts_V-Drv
Hollistr-Frv
King_Cty-Mtz
Carm_Val-Frd
Salinas-Ntvd
Watsonvll-AP
S_Cruz-Soqul
Montery-SlvC
Davenport
Arvin-Br_Mtn
Edison
Clovis
Fresno-1st
Maricopa-Stn
Location
1-hour Exceedances
Type
1990 1991 1992 1993 1994 1995 1996 1997 1998
Basin SHORT_NAME
0.38
0.38
0.22
0.11
0.00
0.11
0.00
0.00
0.00
0.00
0.00
0.22
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
18.33
21.11
12.50
12.11
0.14
Ave
29
29
0
77
64
0
0
0
2
0
0
0
0
0
0
0
0
0
4
0
0
98
74
18
71
40
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
84
16
58
41
33
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
3
1990 1991 1992
0
1
0
0
0
1
0
0
0
0
0
2
2
0
0
0
0
0
0
0
0
76
86
29
54
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
76
88
33
50
23
4
3
1
2
1
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
80
86
44
53
67
0
0
0
0
0
0
0
0
0
0
9
2
1
0
0
0
0
0
0
0
104
77
60
49
78
8-hour Exceedances
1993 1994 1995 1996
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
46
30
67
23
35
1997
0
0
0
0
5
0
1
0
0
0
0
0
0
0
64
61
61
43
0
0
1
0
0
0
1998
0.63
0.50
0.22
0.22
0.22
0.11
0.00
0.00
0.00
0.00
0.00
3.33
0.67
0.44
0.13
0.00
0.00
0.00
0.00
0.00
0.00
78.33
64.67
46.25
45.89
43.57
Ave
Table 2.3-4 (cont.)
Version 3 – 11/24/99
2-58
0
8
0
0
6
0
1
0
13
2
2
3
4
0
4
0
0
1
1
1
0
0
30
3
0
3
1
3
0
2
0
0
14
3
0
1
5
0
0
1
0
0
3
2
0
0
0
0
1
0
0
0
0
0
6
0
2
3
0
0
12
1
1
0
8
1
4
0
0
0
0
0
6
2
0
0
5
10
7
2
9
6
0
1
0
1
15
2
2
2
4
0
2
0
0
0
0
0
0
0
3
0
1
10
3
0
0
0
2
2
0
2
1
21
2
1
1
1
0
3
0
9
2
0
2
3
1
3
1
0
0
0
17
3
0
4
5
2
1
3
0
8
8
3
2
1
2
2
0
0
13
2
4
0
2
0
3
1
0
0
0
0
0
2
0
0
0
0
0
1
0
9
0
0
1
1
0
0
0
0
1
2
1
4
0
1
1
1
0
0
1
0
8
3
0
2
4
3
0
13
0
1
6
13
0
3
Suburban
Urban/CC
Rural
Urban/CC
Suburban
Suburban
Rural
Urban/CC
Suburban
Suburban
Suburban
Rural
Rural
Suburban
Urban/CC
Rural
Suburban
Urban/CC
Suburban
Suburban
Suburban
Rural
Rural
Rural
Rural
Suburban
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
Parlier
Baker-5558Ca
Seq_NP-Kawea
Visalia-NChu
Fresno-Sky#2
Oildale-3311
Merced-SCofe
Baker-GS_Hwy
Shafter-Wlkr
Fresno-Drmnd
Hanford-Irwn
ShavLk-PerRd
Madera
Turlock-SMin
Modesto-14th
Tracy-Patt#2
Stockton-EMr
Stockton-Haz
Simi_V-CchrS
Ojai-OjaiAve
1000_Oaks-Mr
Piru-2mi_SW
WCasitasPass
Los_PadresNF
Capitan-LF#1
Pas_Rob-Snta
Location
1-hour Exceedances
Type
1990 1991 1992 1993 1994 1995 1996 1997 1998
Basin SHORT_NAME
10.22
1.78
0.50
4.00
4.86
0.56
1.14
1.33
0.00
4.67
1.63
1.33
1.71
1.00
0.89
0.50
0.50
0.25
12.11
1.56
1.56
1.11
1.44
0.33
1.56
0.25
Ave
5
3
56
18
6
20
15
11
12
0
12
7
16
28
3
29
37
22
29
32
9
3
77
21
9
22
11
14
2
17
11
5
31
34
9
48
49
48
32
23
8
2
38
21
10
8
5
6
3
0
12
11
2
12
29
0
13
42
29
40
44
19
1990 1991 1992
4
1
31
15
16
0
5
4
4
0
26
11
7
24
17
43
44
54
50
25
24
19
5
4
61
12
14
10
7
3
9
1
0
10
9
23
6
12
8
33
58
50
31
28
26
15
18
14
7
3
4
60
19
14
11
10
4
10
1
25
57
18
40
39
37
35
25
26
9
1
58
67
49
43
36
51
44
47
49
34
80
29
28
19
15
14
0
2
55
37
14
13
9
11
9
9
8-hour Exceedances
1993 1994 1995 1996
7
2
3
0
0
39
8
7
3
3
2
1
0
48
23
26
17
15
5
0
11
4
11
26
11
1997
28
9
11
4
1
3
1
20
27
41
31
12
13
29
13
5
54
38
26
45
50
44
35
1998
40.67
39.00
36.50
34.78
34.00
32.78
28.43
27.67
23.56
23.22
20.25
17.33
15.86
14.22
8.22
7.25
4.25
2.38
49.44
17.78
11.22
10.11
7.33
6.44
5.67
3.88
Ave
Table 2.3-4 (cont.)
Annual Exceedances of the 1-hr and 8-hr Ozone Standards in Central California During May to October, 1990-98
Version 3 – 11/24/99
2-59
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
1
0
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
20
0
1
0
0
0
0
0
0
0
3
0
0
0
1
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
5
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
2
0
0
0
0
0
0
0.11
0.56
0.33
0.44
0.00
0.22
0.00
0.22
0.11
0.00
0.11
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
7.71
0.80
0.50
0.00
Ave
20
15
2
1
0
3
1
2
0
1
2
0
0
0
0
0
0
0
0
0
3
36
12
3
5
1
3
0
1
0
0
0
1
0
0
0
0
0
0
0
16
1
4
1
2
1
1
1
0
0
1
0
1
1
1
1
1
0
0
0
1990 1991 1992
21
7
2
2
2
3
1
1
0
1
0
2
1
0
1
0
0
0
0
0
62
45
14
5
0
0
0
0
0
3
4
1
0
0
2
1
0
1
1
0
0
26
30
1
0
3
3
4
1
3
2
0
3
0
1
1
1
0
0
0
0
0
0
3
3
1
4
0
1
5
2
1
1
1
2
0
0
0
0
0
0
0
47
42
9
8
8-hour Exceedances
1993 1994 1995 1996
0
6
1
1
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
Rural
Rural
Suburban
Rural
Rural
Suburban
Suburban
Rural
Urban/CC
Rural
Rural
Rural
Rural
Urban/CC
Suburban
Urban/CC
Urban/CC
Suburban
Rural
Rural
Rural
Urban/CC
Rural
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
MD
MD
MD
MD
El_Rio-Sch#2
Carpint-Gbrn
Emma_Wood_SB
Gaviota-GT#B
S_Barbr-UCSB
Goleta-NFrvw
Atascadero
El_Capitan_B
S_Barbr-WCrl
Lompoc-HS&P1
Van_AFB-STSP
S_Ynez-Airpt
Nipomo-Gudlp
Morro Bay
Grover_City
S_L_O-Marsh
Lompoc-S_HSt
S_Maria-SBrd
SRosaIsland
Josh_Tr-Mnmt
Mojave-Poole
Barstow
Trona-Telegraph
Location
1-hour Exceedances
Type
1990 1991 1992 1993 1994 1995 1996 1997 1998
Basin SHORT_NAME
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
41
18
11
0
1997
0
1
0
0
0
0
0
0
0
0
0
0
0
0
19
40
5
4
0
0
0
0
1998
3.22
2.00
1.67
1.44
1.13
1.00
0.89
0.78
0.56
0.56
0.56
0.44
0.25
0.11
0.11
0.00
0.00
0.00
0.00
35.14
35.00
12.13
3.50
Ave
Table 2.3-4 (cont.)
Annual Exceedances of the 1-hr and 8-hr Ozone Standards in Central California During May to October, 1990-98a
Version 3 – 11/24/99
2-60
Short_Name
Healdsb-Aprt
Ukiah-EGobbi
Willits-Main
Yreka-Fthill
Lakepor-Lake
Cool-Hwy193
Plcrvll-Gold
San_Andreas
Jerseydale
Grs_Vly-Litn
Jackson-ClRd
Yos_NP-Trtle
Sonora-OakRd
Sonora-Brret
Colfax-CtyHl
White_Cld_Mt
Truckee-Fire
Quincy-NChrc
Folsom-Ntma
Auburn-DwttC
Sutter_Butte
Rocklin
Redding-HDrf
Sacto-DelPas
Rosevil-NSun
Basin
NC
NC
NC
NEP
LC
MC
MC
MC
MC
MC
MC
MC
MC
MC
MC
MC
MC
MC
SV
SV
SV
SV
SV
SV
SV
Rural
Suburban
Suburban
Suburban
Suburban
Rural
Suburban
Rural
Rural
Suburban
Suburban
Rural
Rural
Urban/CC
Rural
Rural
Urban/CC
Urban/CC
Suburban
Rural
Rural
Rural
Suburban
Suburban
Suburban
Location
Type
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.3
0.1
0.0
0.2
0.0
0.0
0.1
May
0.0
0.0
0.0
0.0
0.0
0.0
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.0
0.0
0.0
0.7
0.4
0.0
0.5
0.0
0.5
0.3
Jun
0.0
0.0
0.0
0.0
0.0
0.3
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2.2
0.8
0.0
0.6
0.0
1.2
0.8
0.0
0.0
0.0
0.0
0.0
2.0
0.0
1.3
0.0
0.3
0.7
0.0
0.0
0.2
0.2
0.0
0.0
0.0
1.7
1.3
0.0
0.9
0.2
1.5
1.0
0.1
0.0
0.0
0.0
0.0
0.4
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.0
0.2
0.0
0.5
0.2
0.2
0.2
1-hour Exceedances
Jul
Aug
Sep
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.4
0.0
0.0
0.1
0.0
0.0
0.1
Oct
0.0
0.0
0.0
0.0
0.0
0.5
1.3
0.2
0.0
0.4
0.3
0.0
0.0
0.0
0.2
0.0
0.0
0.0
1.3
0.6
0.6
0.4
0.3
0.5
0.2
May
0.0
0.0
0.0
0.0
0.0
1.7
2.7
2.1
1.0
1.8
1.6
0.6
2.5
0.4
1.0
0.5
0.0
0.2
3.1
3.1
1.1
2.6
1.2
1.8
1.5
Jun
0.2
0.0
0.0
0.0
0.0
9.0
5.0
5.5
3.2
5.9
3.2
4.0
3.7
4.1
2.8
2.0
0.0
0.2
6.2
7.5
4.3
5.1
4.8
3.1
3.3
0.0
0.0
0.0
0.0
0.0
8.7
6.0
6.8
7.8
3.7
4.1
5.7
3.7
3.0
2.7
2.0
0.0
0.0
5.5
6.3
3.7
3.8
5.1
3.7
2.3
0.9
0.0
0.0
0.0
0.0
1.8
4.0
3.9
4.9
1.0
1.9
3.2
2.8
0.9
1.7
1.3
0.0
0.0
4.6
4.2
3.8
2.2
1.4
2.0
1.3
8-hour Exceedances
Jul
Aug
Sep
0.0
0.0
0.0
0.0
0.0
0.5
1.1
0.5
2.3
0.5
0.2
1.7
0.7
0.2
0.2
0.7
0.0
0.0
1.1
1.0
0.9
0.5
0.0
0.6
0.3
Oct
Table 2.3-5
Average Annual Exceedances of the 1-hr and 8-hr Ozone Standards in Central California by Month May to October, 1990-98
Version 3 – 11/24/99
2-61
Short_Name
N_High-Blckf
Red_Blf-Oak
Yuba_Cty-Alm
Elk_Grv-Brcv
Anderson-Nth
Vacavil-Alli
Woodland-GibsonRd
Plsnt_Grv4mi
Davis-UCD
Sacto-T_Strt
Colusa-Sunrs
Tuscan Butte
Willows-ELau
Chico-Manznt
Lasn_Vlcn_NP
Livrmor-Old1
San_Martin
Los Gatos
Bethel_Is_Rd
Concord-2975
Gilroy-9th
Fairfld-AQMD
San_Jose-935
Pittsbg-10th
Fremont-Chpl
San_Jose-4th
Basin
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
Suburban
Urban/CC
Suburban
Rural
Suburban
Suburban
Suburban
Rural
Rural
Urban/CC
Rural
Rural
Suburban
Suburban
Rural
Urban/CC
Rural
Urban/CC
Rural
Suburban
Suburban
Urban/CC
Rural
Urban/CC
Suburban
Urban/CC
Location
Type
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
May
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.6
0.0
0.2
0.0
0.2
0.0
0.1
0.3
0.1
0.1
0.0
Jun
0.1
0.0
0.0
0.0
0.0
0.3
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.8
0.4
0.6
0.1
0.2
0.3
0.0
0.3
0.0
0.3
0.2
0.3
0.0
0.0
0.2
0.0
0.3
0.0
0.1
0.1
0.3
0.0
0.0
0.0
0.1
0.0
1.3
0.6
0.0
0.0
0.3
0.1
0.1
0.0
0.0
0.0
0.0
0.3
0.0
0.0
0.0
0.0
0.3
0.0
0.1
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.2
0.0
0.1
0.1
0.1
0.0
0.0
0.1
0.0
0.0
0.0
1-hour Exceedances
Jul
Aug
Sep
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Oct
0.1
0.2
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.2
0.1
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
May
1.2
0.0
0.4
0.5
0.0
0.0
0.1
0.7
0.1
0.1
0.1
0.0
0.1
0.0
0.1
0.9
1.2
0.5
0.2
0.5
0.7
0.5
0.5
0.2
0.3
0.3
Jun
1.8
2.3
1.1
0.7
1.6
0.5
0.6
0.2
0.5
0.6
0.6
0.3
0.0
0.2
0.0
1.4
0.8
1.1
0.6
0.8
0.6
0.6
0.7
0.4
0.3
0.3
2.2
2.2
1.8
2.1
0.8
1.5
1.0
0.8
1.3
0.9
0.6
1.0
0.4
0.1
0.3
1.5
1.6
0.3
0.9
0.6
0.7
0.4
0.2
0.2
0.1
0.0
1.2
1.4
0.8
0.7
0.4
1.0
0.6
0.8
0.2
0.5
0.7
1.8
0.5
0.2
0.1
1.3
0.2
0.7
0.7
0.6
0.1
0.3
0.3
0.2
0.0
0.0
8-hour Exceedances
Jul
Aug
Sep
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Oct
Table 2.3-5 (cont.)
Version 3 – 11/24/99
2-62
Short_Name
Hayward
Sn_Lndro-Hos
Napa-Jffrsn
Redwood_City
Mtn_View-Cst
Vallejo-304T
Oakland-Alic
San Pablo
San Rafael
S_F-Arkansas
S_Rosa-5th
Pinn_Nat_Mon
Scotts_V-Drv
Hollistr-Frv
King_Cty-Mtz
Carm_Val-Frd
Salinas-Ntvd
Watsonvll-AP
S_Cruz-Soqul
Montery-SlvC
Davenport
Arvin-Br_Mtn
Edison
Clovis
Fresno-1st
Maricopa-Stn
Basin
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
SFB
NCC
NCC
NCC
NCC
NCC
NCC
NCC
NCC
NCC
NCC
SJV
SJV
SJV
SJV
SJV
Rural
Suburban
Urban/CC
Suburban
Suburban
Urban/CC
Urban/CC
Urban/CC
Urban/CC
Urban/CC
Urban/CC
Rural
Rural
Rural
Rural
Suburban
Suburban
Suburban
Suburban
Rural
Rural
Rural
Rural
Urban/CC
Suburban
Suburban
Location
Type
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.0
0.2
0.0
May
0.1
0.3
0.1
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
3.1
2.3
1.3
1.6
0.0
Jun
0.1
0.1
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5.2
5.0
3.5
2.5
0.5
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6.8
8.5
5.3
4.2
0.4
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2.5
4.1
1.9
2.3
0.1
1-hour Exceedances
Jul
Aug
Sep
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.1
1.4
0.6
1.4
0.0
Oct
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.2
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
3.8
2.9
1.1
1.3
1.3
May
0.3
0.3
0.1
0.1
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.6
0.3
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
13.9
10.3
7.3
7.3
7.5
Jun
0.3
0.3
0.0
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
1.0
0.2
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
19.7
16.2
12.9
13.0
12.6
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.2
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
20.5
17.9
11.7
11.8
10.2
0.1
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.3
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
15.2
12.9
10.5
10.1
8.2
8-hour Exceedances
Jul
Aug
Sep
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
7.6
5.9
3.6
3.4
7.7
Oct
Table 2.3-5 (cont.)
Version 3 – 11/24/99
2-63
Short_Name
Parlier
Baker-5558Ca
Seq_NP-Kawea
Visalia-NChu
Fresno-Sky#2
Oildale-3311
Merced-SCofe
Baker-GS_Hwy
Shafter-Wlkr
Fresno-Drmnd
Hanford-Irwn
ShavLk-PerRd
Madera
Turlock-SMin
Modesto-14th
Tracy-Patt#2
Stockton-EMr
Stockton-Haz
Simi_V-CchrS
Ojai-OjaiAve
1000_Oaks-Mr
Piru-2mi_SW
WCasitasPass
Los_PadresNF
Capitan-LF#1
Pas_Rob-Snta
Basin
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
Suburban
Urban/CC
Rural
Urban/CC
Suburban
Suburban
Rural
Urban/CC
Suburban
Suburban
Suburban
Rural
Rural
Suburban
Urban/CC
Rural
Suburban
Urban/CC
Suburban
Suburban
Suburban
Rural
Rural
Rural
Rural
Suburban
Location
Type
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.4
0.1
0.2
0.0
0.0
0.0
0.3
0.0
May
1.2
0.0
0.0
0.7
0.0
0.0
0.2
0.0
0.0
0.2
0.2
0.0
0.0
0.1
0.1
0.0
0.1
0.0
1.5
0.2
0.2
0.2
0.3
0.0
0.1
0.0
Jun
2.5
0.8
0.3
0.9
0.6
0.0
0.4
0.2
0.0
0.7
0.1
0.7
0.1
0.1
0.2
0.3
0.1
0.1
2.4
0.3
0.1
0.2
0.2
0.2
0.3
0.1
3.9
0.6
0.0
1.7
2.1
0.3
0.4
0.8
0.0
1.9
1.1
0.7
0.4
0.7
0.4
0.2
0.1
0.1
4.0
0.3
0.4
0.2
0.6
0.0
0.1
0.1
2.2
0.5
0.3
0.7
1.6
0.2
0.1
0.0
0.0
0.9
0.0
0.0
0.9
0.1
0.1
0.0
0.1
0.1
2.5
0.2
0.5
0.3
0.2
0.0
0.2
0.0
1-hour Exceedances
Jul
Aug
Sep
0.6
0.0
0.0
0.1
0.9
0.0
0.3
0.0
0.0
1.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
1.3
0.3
0.1
0.1
0.1
0.1
0.4
0.0
Oct
1.5
1.5
0.0
1.6
1.3
0.8
1.6
0.7
0.5
1.0
0.9
0.0
0.1
0.6
0.1
0.0
0.1
0.0
3.8
1.2
1.0
0.6
0.2
0.2
0.4
0.3
May
6.2
6.6
8.4
7.6
5.5
5.1
3.9
4.3
3.8
2.8
3.8
3.1
2.3
2.3
1.4
0.8
1.0
0.6
7.9
3.4
2.3
2.0
1.3
0.9
0.9
0.1
Jun
11.1
10.5
13.4
10.4
8.1
6.9
9.1
8.7
6.6
5.8
5.0
7.0
3.9
4.0
3.1
2.0
1.1
0.6
11.4
2.9
1.5
2.2
1.0
2.0
0.8
0.8
11.0
10.8
10.8
8.9
8.4
8.2
7.4
11.0
6.5
7.7
5.9
6.3
4.9
5.1
2.7
2.9
1.2
0.7
14.4
6.9
2.6
3.2
1.5
1.6
1.0
2.0
8.2
7.5
4.7
5.4
8.7
7.8
5.8
3.7
4.5
4.6
3.0
1.3
3.6
2.0
1.0
1.2
0.6
0.7
8.0
2.0
2.8
1.5
1.9
0.8
1.4
0.7
8-hour Exceedances
Jul
Aug
Sep
4.0
3.1
1.0
1.4
4.6
4.7
2.8
0.8
2.1
2.1
1.3
0.0
1.4
0.6
0.0
0.0
0.4
0.1
4.3
1.6
1.3
0.8
1.7
1.0
1.2
0.1
Oct
Table 2.3-5 (cont.)
Version 3 – 11/24/99
2-64
Short_Name
El_Rio-Sch#2
Carpint-Gbrn
Emma_Wood_SB
Gaviota-GT#B
S_Barbr-UCSB
Goleta-NFrvw
Atascadero
El_Capitan_B
S_Barbr-WCrl
Lompoc-HS&P1
Van_AFB-STSP
S_Ynez-Airpt
Nipomo-Gudlp
Morro Bay
Grover_City
S_L_O-Marsh
Lompoc-S_HSt
S_Maria-SBrd
SRosaIsland
Josh_Tr-Mnmt
Mojave-Poole
Barstow
Trona-Telegraph
Basin
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
MD
MD
MD
MD
Rural
Rural
Suburban
Rural
Rural
Suburban
Suburban
Rural
Urban/CC
Rural
Rural
Rural
Rural
Urban/CC
Suburban
Urban/CC
Urban/CC
Suburban
Rural
Rural
Rural
Urban/CC
Rural
Location
Type
0.1
0.1
0.1
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.8
0.2
0.0
0.0
May
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.8
0.0
0.0
0.0
Jun
0.0
0.1
0.0
0.1
0.0
0.0
0.0
0.0
0.1
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
3.6
0.2
0.7
0.0
0.0
0.1
0.1
0.1
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.6
0.5
0.0
0.0
0.0
0.1
0.1
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.3
0.2
0.0
0.0
1-hour Exceedances
Jul
Aug
Sep
0.0
0.0
0.0
0.2
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Oct
0.4
0.3
0.1
0.2
0.1
0.2
0.1
0.2
0.1
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
6.7
2.8
0.8
0.1
May
0.5
0.3
0.0
0.1
0.1
0.1
0.1
0.1
0.0
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
11.0
8.6
4.1
1.5
Jun
0.2
0.2
0.0
0.2
0.1
0.2
0.3
0.0
0.3
0.0
0.1
0.0
0.1
0.1
0.1
0.0
0.0
0.0
0.0
10.6
14.1
5.4
1.5
0.4
0.2
0.2
0.2
0.0
0.2
0.3
0.1
0.1
0.1
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
7.2
7.9
2.6
0.4
0.3
0.7
0.6
0.2
0.4
0.1
0.0
0.1
0.0
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.2
1.7
0.3
0.3
8-hour Exceedances
Jul
Aug
Sep
1.3
0.2
0.8
0.4
0.4
0.1
0.0
0.2
0.0
0.2
0.2
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.8
1.2
0.0
0.0
Oct
Table 2.3-5 (cont.)
Version 3 – 11/24/99
2-65
Short_Name
1000_Oaks-Mr
Anderson-Nth
Arvin-Br_Mtn
Atascadero
Auburn-DwttC
Baker-5558Ca
Baker-GS_Hwy
Barstow
Bethel_Is_Rd
Capitan-LF#1
Carm_Val-Frd
Carpint-Gbrn
Chico-Manznt
Clovis
Colfax-CtyHl
Colusa-Sunrs
Concord-2975
Cool-Hwy193
Davenport
Davis-UCD
Edison
El_Capitan_B
El_Rio-Sch#2
Elk_Grv-Brcv
Emma_Wood_SB
Basin
SCC
SV
SJV
SCC
SV
SJV
SJV
MD
SFB
SCC
NCC
SCC
SV
SJV
MC
SV
SFB
MC
NCC
SV
SJV
SCC
SCC
SV
SCC
Suburban
Suburban
Rural
Suburban
Not Available
Urban / Center City
Urban / Center City
Urban / Center City
Rural
Rural
Suburban
Rural
Suburban
Urban / Center City
Not Available
Rural
Suburban
Rural
Rural
Rural
Rural
Rural
Rural
Rural
Suburban
Location Type
0.0
0.0
2.1
0.0
0.4
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
1.7
0.0
0.0
0.2
0.7
0.0
0.1
3.0
0.0
0.0
0.0
0.1
Mon
0.0
0.0
3.1
0.0
0.4
0.1
0.0
0.1
0.0
0.2
0.0
0.0
0.0
2.1
0.0
0.0
0.0
0.4
0.0
0.0
2.7
0.0
0.0
0.2
0.0
Tue
0.1
0.0
2.1
0.0
0.4
0.2
0.0
0.1
0.0
0.4
0.0
0.2
0.0
2.1
0.0
0.0
0.1
0.7
0.0
0.0
2.8
0.2
0.0
0.0
0.0
0.3
0.0
3.6
0.0
0.7
0.4
0.2
0.4
0.1
0.4
0.0
0.0
0.1
1.4
0.2
0.0
0.2
0.4
0.0
0.0
3.2
0.0
0.0
0.0
0.0
0.4
0.0
3.8
0.0
0.7
0.7
0.5
0.0
0.0
0.2
0.0
0.2
0.0
2.1
0.2
0.0
0.1
0.8
0.0
0.0
3.6
0.0
0.0
0.0
0.0
1-hour Exceedances
Wed
Thu
Fri
0.2
0.0
2.6
0.0
0.3
0.2
0.2
0.1
0.0
0.1
0.0
0.1
0.0
1.5
0.0
0.0
0.0
0.0
0.0
0.0
3.1
0.0
0.0
0.0
0.1
Sat
0.5
0.0
1.1
0.0
0.0
0.1
0.2
0.0
0.0
0.1
0.0
0.0
0.0
1.6
0.0
0.0
0.2
0.0
0.0
0.0
2.8
0.0
0.1
0.0
0.1
Sun
0.8
0.2
11.6
0.0
3.6
5.6
3.6
1.1
0.4
0.3
0.0
0.2
0.0
6.9
1.1
0.0
0.4
3.0
0.0
0.6
9.0
0.1
0.2
0.5
0.3
Mon
0.4
0.4
11.2
0.0
3.1
5.5
3.4
1.4
0.2
0.6
0.0
0.1
0.0
6.5
1.3
0.1
0.4
4.6
0.0
0.2
9.5
0.0
0.2
0.8
0.1
Tue
1.4
0.6
10.7
0.1
3.9
5.3
4.1
1.1
0.4
1.1
0.0
0.4
0.2
6.7
1.3
0.6
0.2
3.0
0.0
0.6
9.6
0.3
0.3
0.5
0.3
1.6
0.4
10.9
0.0
4.5
4.8
4.3
2.9
0.2
1.0
0.0
0.3
0.3
6.6
1.5
0.7
0.2
4.5
0.0
0.5
8.1
0.0
0.2
0.7
0.2
2.1
1.0
12.1
0.3
4.0
5.9
3.9
2.5
0.3
1.2
0.0
0.2
0.1
6.3
2.5
0.4
0.2
4.1
0.0
0.1
9.7
0.2
0.4
0.2
0.0
8-hour Exceedances
Wed
Thu
Fri
1.7
0.4
11.4
0.2
2.5
6.5
5.9
2.0
0.3
0.7
0.0
0.3
0.0
7.4
0.7
0.1
0.2
2.7
0.0
0.1
9.7
0.0
0.8
0.8
0.5
Sat
3.3
0.0
11.5
0.2
1.8
5.8
6.9
1.9
0.4
0.8
0.0
0.3
0.0
6.4
0.0
0.0
0.6
2.7
0.0
0.1
9.6
0.1
1.0
0.3
0.2
Sun
Table 2.3-6
Average Annual Exceedances of the 1-hr and 8-hr Ozone Standards in Central California by Day-of-the-Week
May to October, 1990-98 (1=Monday)
Version 3 – 11/24/99
2-66
Short_Name
Fairfld-AQMD
Folsom-Ntma
Fremont-Chpl
Fresno-1st
Fresno-Drmnd
Fresno-Sky#2
Gaviota-GT#B
Gilroy-9th
Goleta-NFrvw
Grover_City
Grs_Vly-Litn
Hanford-Irwn
Hayward
Healdsb-Aprt
Hollistr-Frv
Jackson-ClRd
Jerseydale
Josh_Tr-Mnmt
King_Cty-Mtz
Lakepor-Lake
Lasn_Vlcn_NP
Livrmor-Old1
Lompoc-HS&P1
Lompoc-S_HSt
Los Gatos
Los_PadresNF
Basin
SFB
SV
SFB
SJV
SJV
SJV
SCC
SFB
SCC
SCC
MC
SJV
SFB
NC
NCC
MC
MC
MD
NCC
LC
SV
SFB
SCC
SCC
SFB
SCC
Urban / Center City
Suburban
Suburban
Suburban
Suburban
Suburban
Rural
Suburban
Suburban
Suburban
Suburban
Suburban
Rural
Rural
Rural
Suburban
Rural
Rural
Rural
Suburban
Rural
Urban / Center City
Rural
Urban / Center City
Urban / Center City
Rural
Location Type
0.0
1.3
0.0
1.8
0.5
0.5
0.0
0.2
0.0
0.0
0.1
0.1
0.1
0.0
0.0
0.2
0.0
1.0
0.0
0.0
0.0
0.4
0.0
0.0
0.2
0.0
Mon
0.0
1.0
0.1
2.1
0.7
1.0
0.0
0.0
0.0
0.0
0.1
0.2
0.0
0.0
0.0
0.2
0.0
0.9
0.0
0.0
0.0
0.2
0.0
0.0
0.0
0.1
Tue
0.0
1.3
0.0
1.6
0.3
0.7
0.1
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.0
1.5
0.0
0.0
0.0
0.3
0.0
0.0
0.0
0.2
0.0
1.1
0.0
1.3
0.6
1.0
0.1
0.0
0.0
0.0
0.0
0.4
0.0
0.1
0.0
0.2
0.0
1.8
0.0
0.0
0.0
0.2
0.0
0.0
0.1
0.0
0.0
0.8
0.0
1.6
0.9
0.4
0.0
0.1
0.0
0.0
0.0
0.2
0.0
0.0
0.0
0.0
0.0
1.4
0.0
0.0
0.0
0.3
0.0
0.0
0.1
0.0
1-hour Exceedances
Wed
Thu
Fri
0.0
0.6
0.3
2.1
0.8
0.8
0.1
0.1
0.1
0.0
0.0
0.4
0.2
0.0
0.0
0.0
0.0
0.7
0.0
0.0
0.0
0.7
0.0
0.0
0.4
0.0
Sat
0.2
0.2
0.0
1.7
1.0
1.0
0.1
0.0
0.0
0.0
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.3
0.0
0.0
0.0
0.7
0.0
0.0
0.0
0.0
Sun
0.3
3.2
0.0
6.8
2.9
5.0
0.1
0.3
0.0
0.0
1.8
2.1
0.1
0.1
0.1
1.3
2.0
4.6
0.0
0.0
0.1
0.9
0.0
0.0
0.3
0.6
Mon
0.4
3.3
0.0
6.6
3.2
5.0
0.1
0.2
0.0
0.0
1.9
2.9
0.0
0.0
0.0
2.0
3.3
4.9
0.1
0.0
0.0
0.4
0.0
0.0
0.3
0.7
Tue
0.2
3.5
0.0
6.1
2.8
5.0
0.3
0.0
0.3
0.0
1.6
2.6
0.0
0.1
0.1
1.8
4.5
5.0
0.0
0.0
0.0
0.4
0.1
0.0
0.1
0.8
0.1
3.0
0.0
5.5
2.3
5.2
0.1
0.3
0.2
0.1
2.6
2.5
0.0
0.3
0.0
2.6
3.6
6.4
0.0
0.0
0.1
0.3
0.0
0.0
0.2
1.3
0.0
2.8
0.1
5.8
2.7
4.6
0.1
0.2
0.2
0.0
2.7
3.0
0.0
0.1
0.1
1.6
3.3
4.6
0.0
0.0
0.1
0.6
0.1
0.0
0.1
1.2
8-hour Exceedances
Wed
Thu
Fri
0.2
2.6
0.3
7.1
4.9
5.5
0.7
0.9
0.1
0.0
1.3
4.0
0.2
0.1
0.1
0.7
2.2
5.9
0.0
0.0
0.0
0.7
0.2
0.0
0.9
1.3
Sat
0.4
3.4
0.3
8.3
5.1
6.2
0.0
0.1
0.1
0.0
1.3
2.8
0.2
0.1
0.0
0.8
1.4
4.6
0.0
0.0
0.1
1.8
0.1
0.0
0.7
0.6
Sun
Table 2.3-6 (cont.)
Version 3 – 11/24/99
2-67
Short_Name
Madera
Maricopa-Stn
Merced-SCofe
Modesto-14th
Mojave-Poole
Montery-SlvC
Morro Bay
Mtn_View-Cst
N_High-Blckf
Napa-Jffrsn
Nipomo-Gudlp
Oakland-Alic
Oildale-3311
Ojai-OjaiAve
Parlier
Pas_Rob-Snta
Pinn_Nat_Mon
Piru-2mi_SW
Pittsbg-10th
Plcrvll-Gold
Plsnt_Grv4mi
Quincy-NChrc
Red_Blf-Oak
Redding-HDrf
Redwood_City
Rocklin
Basin
SJV
SJV
SJV
SJV
MD
NCC
SCC
SFB
SV
SFB
SCC
SFB
SJV
SCC
SJV
SCC
NCC
SCC
SFB
MC
SV
MC
SV
SV
SFB
SV
Rural
Suburban
Rural
Urban / Center City
Rural
Rural
Urban / Center City
Suburban
Suburban
Urban / Center City
Rural
Urban / Center City
Suburban
Suburban
Not Available
Suburban
Rural
Rural
Urban / Center City
Suburban
Rural
Urban / Center City
Urban / Center City
Suburban
Suburban
Rural
Location Type
0.0
0.1
0.4
0.0
0.2
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.1
0.2
1.7
0.0
0.0
0.0
0.0
0.1
0.1
0.0
0.0
0.0
0.0
0.6
Mon
0.5
0.1
0.3
0.2
0.0
0.0
0.0
0.0
0.1
0.1
0.0
0.0
0.0
0.1
1.6
0.0
0.1
0.0
0.0
0.1
0.0
0.0
0.0
0.1
0.0
0.6
Tue
0.1
0.0
0.1
0.2
0.0
0.0
0.0
0.0
0.3
0.0
0.0
0.0
0.1
0.2
1.9
0.0
0.0
0.1
0.0
0.3
0.1
0.0
0.0
0.1
0.0
0.6
0.4
0.2
0.0
0.0
0.2
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.1
0.3
2.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.5
0.1
0.4
0.4
0.2
0.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.3
1.4
0.0
0.0
0.4
0.1
0.1
0.1
0.0
0.0
0.1
0.0
0.3
1-hour Exceedances
Wed
Thu
Fri
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.1
1.0
0.1
0.1
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
Sat
0.3
0.1
0.0
0.1
0.2
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.2
0.9
0.1
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
Sun
1.8
6.1
3.8
0.8
3.4
0.0
0.0
0.0
0.9
0.0
0.0
0.0
4.5
1.6
6.3
0.5
0.6
0.8
0.2
2.1
0.5
0.2
0.3
1.4
0.0
2.2
Mon
2.6
6.8
4.1
1.3
4.9
0.0
0.0
0.0
1.2
0.1
0.0
0.0
4.6
1.8
6.2
0.4
0.5
0.6
0.3
3.5
0.6
0.0
1.0
2.0
0.0
2.4
Tue
2.2
7.4
3.6
0.7
5.2
0.0
0.0
0.0
1.3
0.0
0.0
0.0
4.4
2.2
5.6
0.2
0.6
1.7
0.1
3.7
0.3
0.0
0.4
1.7
0.0
2.5
2.8
6.4
4.6
0.8
5.1
0.0
0.1
0.0
1.0
0.0
0.1
0.0
4.5
3.3
5.3
0.2
0.3
1.3
0.1
3.9
0.5
0.0
1.8
2.1
0.0
2.5
1.5
6.8
4.5
0.9
5.8
0.0
0.0
0.0
1.0
0.0
0.0
0.0
4.5
3.4
5.8
0.7
0.5
1.3
0.2
2.6
0.6
0.0
1.3
2.1
0.0
2.6
8-hour Exceedances
Wed
Thu
Fri
2.6
8.3
5.5
1.8
5.4
0.0
0.0
0.2
0.6
0.0
0.0
0.0
5.3
3.4
7.0
0.9
0.8
3.3
0.0
2.2
0.0
0.2
0.9
1.8
0.2
1.7
Sat
2.8
5.8
4.2
2.2
4.6
0.0
0.0
0.0
0.6
0.1
0.1
0.0
5.2
2.0
6.1
1.0
0.2
1.1
0.1
2.1
0.1
0.0
0.7
1.4
0.0
0.9
Sun
Table 2.3-6 (cont.)
Version 3 – 11/24/99
2-68
Short_Name
Rosevil-NSun
S_Barbr-UCSB
S_Barbr-WCrl
S_Cruz-Soqul
S_F-Arkansas
S_L_O-Marsh
S_Maria-SBrd
S_Rosa-5th
S_Ynez-Airpt
Sacto-DelPas
Sacto-T_Strt
Salinas-Ntvd
San Pablo
San Rafael
San_Andreas
San_Jose-4th
San_Jose-935
San_Martin
Scotts_V-Drv
Seq_NP-Kawea
Shafter-Wlkr
ShavLk-PerRd
Simi_V-CchrS
Sn_Lndro-Hos
Sonora-Brret
Sonora-OakRd
Basin
SV
SCC
SCC
NCC
SFB
SCC
SCC
SFB
SCC
SV
SV
NCC
SFB
SFB
MC
SFB
SFB
SFB
NCC
SJV
SJV
SJV
SCC
SFB
MC
MC
Suburban
Rural
Urban / Center City
Suburban
Urban / Center City
Urban / Center City
Suburban
Urban / Center City
Rural
Suburban
Urban / Center City
Suburban
Urban / Center City
Urban / Center City
Rural
Urban / Center City
Rural
Rural
Rural
Rural
Suburban
Rural
Suburban
Suburban
Urban / Center City
Rural
Location Type
0.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.6
0.1
0.0
0.0
0.0
0.2
0.0
0.0
0.2
0.0
0.0
0.0
0.0
1.0
0.0
0.0
0.0
Mon
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.8
0.3
0.0
0.0
0.0
0.2
0.0
0.0
0.2
0.0
0.0
0.0
0.0
1.0
0.0
0.2
0.0
Tue
0.7
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.4
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.3
1.8
0.0
0.0
0.0
0.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.6
0.0
0.0
0.0
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.6
1.9
0.0
0.0
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.6
0.0
0.0
0.0
0.0
0.5
0.0
0.2
0.2
0.0
0.1
0.0
0.3
2.5
0.0
0.0
0.0
1-hour Exceedances
Wed
Thu
Fri
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.2
0.6
0.4
0.0
0.3
0.0
0.0
2.3
0.2
0.0
0.0
Sat
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.6
0.1
0.0
0.0
Sun
0.9
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.6
0.3
0.0
0.0
0.0
2.9
0.0
0.2
0.2
0.0
3.8
2.8
2.6
5.3
0.0
1.0
1.5
Mon
1.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.7
0.6
0.0
0.0
0.0
2.9
0.1
0.0
0.6
0.0
5.2
2.9
2.6
5.9
0.0
1.0
1.1
Tue
2.0
0.4
0.2
0.0
0.0
0.0
0.0
0.0
0.2
1.6
0.2
0.0
0.0
0.0
2.6
0.0
0.0
0.2
0.0
6.4
3.2
3.6
6.7
0.0
1.4
2.3
1.7
0.2
0.2
0.0
0.0
0.0
0.0
0.0
0.0
2.0
0.3
0.0
0.0
0.0
4.1
0.0
0.0
0.2
0.2
5.4
4.2
2.5
6.5
0.0
1.4
1.8
1.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.1
1.3
0.2
0.0
0.0
0.0
2.6
0.1
0.2
0.6
0.0
6.6
3.9
2.5
8.0
0.0
2.5
3.3
8-hour Exceedances
Wed
Thu
Fri
0.7
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.1
1.7
0.0
0.0
0.0
0.0
1.4
0.3
0.8
1.2
0.5
5.9
3.7
1.6
9.2
0.4
0.9
1.5
Sat
1.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.7
0.3
0.0
0.0
0.0
1.4
0.1
0.5
1.0
0.0
4.4
3.0
1.9
7.7
0.1
0.5
0.8
Sun
Table 2.3-6 (cont.)
Version 3 – 11/24/99
2-69
Short_Name
SRosaIsland
Stockton-EMr
Stockton-Haz
Sutter_Butte
Tracy-Patt#2
Trona-Telegraph
Truckee-Fire
Turlock-SMin
Tuscan Butte
Ukiah-EGobbi
Vacavil-Alli
Vallejo-304T
Van_AFB-STSP
Visalia-NChu
Watsonvll-AP
WCasitasPass
White_Cld_Mt
Willits-Main
Willows-ELau
Woodland-GibsonRd
Yos_NP-Trtle
Yreka-Fthill
Yuba_Cty-Alm
Basin
SCC
SJV
SJV
SV
SJV
MD
MC
SJV
SV
NC
SV
SFB
SCC
SJV
NCC
SCC
MC
NC
SV
SV
MC
NEP
SV
Rural
Not Available
Urban / Center City
Not Available
Rural
Rural
Urban / Center City
Suburban
Not Available
Suburban
Suburban
Urban / Center City
Rural
Urban / Center City
Suburban
Rural
Rural
Suburban
Suburban
Suburban
Rural
Suburban
Suburban
Location Type
0.0
0.1
0.0
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.3
0.0
0.0
0.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Mon
0.0
0.1
0.1
0.0
0.0
0.0
0.0
0.2
0.0
0.0
0.3
0.0
0.0
0.6
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Tue
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.0
0.0
0.3
0.0
0.0
1.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.1
0.8
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.0
0.0
0.0
0.0
0.3
0.0
0.0
0.0
0.0
0.0
0.4
0.0
0.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1-hour Exceedances
Wed
Thu
Fri
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.7
0.0
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Sat
0.0
0.0
0.0
0.0
0.2
0.0
0.0
0.1
0.0
0.0
0.0
0.1
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Sun
0.0
0.8
0.4
1.6
1.2
0.1
0.0
1.9
0.3
0.0
0.8
0.0
0.0
4.3
0.0
0.5
0.8
0.0
0.0
0.5
1.5
0.0
0.5
Mon
0.0
1.2
0.4
2.0
1.2
0.2
0.0
2.4
0.6
0.0
1.0
0.0
0.0
4.9
0.0
0.8
0.8
0.0
0.2
0.4
2.1
0.0
1.3
Tue
0.0
0.6
0.7
2.7
1.2
0.2
0.0
1.4
0.3
0.0
0.5
0.0
0.1
4.7
0.0
1.5
1.1
0.0
0.4
0.5
2.7
0.0
0.6
0.0
0.5
0.2
3.0
1.2
0.7
0.0
1.7
1.1
0.0
0.5
0.0
0.1
4.7
0.0
0.7
2.2
0.0
0.2
0.4
1.7
0.0
0.7
0.0
0.4
0.2
2.7
0.7
1.0
0.0
1.8
1.2
0.0
0.3
0.1
0.1
4.7
0.0
1.1
1.4
0.0
0.0
0.3
2.8
0.0
0.5
8-hour Exceedances
Wed
Thu
Fri
0.0
0.4
0.6
1.6
0.2
0.6
0.0
3.0
0.0
0.0
0.0
0.0
0.0
5.9
0.0
1.6
0.3
0.0
0.1
0.1
2.7
0.0
0.4
Sat
0.0
0.5
0.2
1.3
1.4
0.6
0.0
2.5
0.0
0.0
0.0
0.0
0.2
5.8
0.0
1.4
0.3
0.0
0.1
0.3
1.4
0.0
0.2
Sun
Table 2.3-6 (cont.)
Version 3 – 11/24/99
Version 3 – 11/24/99
Table 2.4-1
1996 Daily Average ROG Emissions by Air Basins in the CCOS Domain
SOURCE CATEGORIES
STATIONARY
FUEL COMBUSTION
electric utilities
cogeneration
oil and gas production (combustion)
petroleum refining (combustion)
manufacturing and industrial
food and agricultural processing
service and commercial
other (fuel combustion)
WASTE DISPOSAL
sewage treatment
landfills
incinerators
soil remediation
other (waste disposal)
CLEANING AND SURFACE COATINGS
laundering
degreasing
coatings and related process solvents
printing
other (cleaning and surface coatings)
PETROLEUM PRODUCTION AND MARKETING
oil and gas production
petroleum refining
petroleum marketing
other (petroleum production and marketing)
INDUSTRIAL PROCESSES
chemical
food and agriculture
mineral processes
metal processes
wood and paper
glass and related products
electronics
other (industrial processes)
AREA-WIDE
SOLVENT EVAPORATION
consumer products
architectural coatings and related solvents
pesticides/fertilizers
asphalt paving
refrigerants
other (solvent evaporation)
MISCELLANEOUS PROCESSES
residential fuel combustion
farming operations
construction and demolition
paved road dust
unpaved road dust
fugitive windblown dust
fires
waste burning and disposal
utility equipment
other (miscellaneous processes)
Mountain
Counties
Bay
Area
Sacramento
Valley
0.1
0.4
0.0
0.0
0.4
0.0
0.0
0.0
0.1
0.2
0.0
0.5
0.7
0.0
0.7
0.5
0.1
0.1
0.3
0.0
0.3
0.0
0.3
0.2
0.0
1.0
5.3
0.0
0.2
2.4
2.2
0.1
0.1
0.4
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.5
0.0
0.1
0.2
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.2
5.0
0.0
0.0
0.0
0.0
0.8
0.0
0.0
0.0
0.0
5.0
0.0
0.0
1.3
0.0
2.3
0.0
0.0
0.0
0.0
1.0
0.0
0.0
0.0
0.4
1.0
2.5
0.0
0.4
3.8
5.8
26.3
6.0
11.3
1.7
5.0
16.6
1.3
2.6
0.6
7.0
16.1
1.3
4.0
0.9
1.8
5.8
0.2
0.6
0.3
3.5
6.1
1.0
2.2
0.0
0.0
0.8
0.0
0.2
19.2
22.6
5.5
10.0
0.0
5.6
0.0
51.8
1.3
6.6
0.3
0.9
0.0
1.3
0.0
6.8
0.5
2.4
0.0
0.0
0.0
0.0
0.0
0.5
0.0
0.0
0.5
2.1
1.7
0.6
0.1
0.0
0.0
0.0
7.1
3.2
0.8
1.0
0.0
0.9
0.0
0.0
0.4
1.5
9.0
0.2
0.1
0.0
0.1
0.0
0.0
0.1
0.3
0.0
0.0
0.0
0.0
0.1
0.0
0.1
0.2
0.0
0.0
0.1
0.0
0.0
0.1
3.0
2.2
0.5
3.8
0.0
0.0
47.0
23.7
4.9
0.2
0.0
0.0
16.0
10.0
7.5
7.5
0.0
0.0
21.0
10.6
46.3
1.1
0.0
0.9
4.5
2.6
10.9
2.2
0.0
0.0
10.1
5.1
6.5
0.8
0.0
0.2
7.3
0.0
0.0
0.0
0.0
0.0
0.0
4.8
6.3
0.0
8.8
3.8
0.0
0.0
0.0
0.0
0.2
0.7
8.5
0.9
8.2
2.1
0.0
0.0
0.0
0.0
0.1
15.8
4.7
1.0
5.6
70.1
0.0
0.0
0.0
0.0
0.2
17.0
4.9
0.4
1.7
0.0
0.0
0.0
0.0
0.0
0.0
1.3
1.3
0.1
2.0
0.0
0.0
0.0
0.0
0.0
0.0
3.5
1.9
0.4
2-70
San Joaquin North Central South Central
Valley
Coast
Coast
Version 3 – 11/24/99
Table 2.4-1 (continued)
1996 Daily Average ROG Emissions by Air Basins in the CCOS Domain
SOURCE CATEGORIES
MOBILE
ON-ROAD MOTOR VEHICLES
light duty passenger
light and medium duty trucks
light duty trucks
medium duty trucks
heavy duty gas trucks (all)
light heavy duty gas trucks
medium heavy duty gas trucks
heavy duty diesel trucks (all)
light heavy duty diesel trucks
medium heavy duty diesel trucks
heavy heavy duty diesel trucks
motorcycles
heavy duty diesel urban buses
other (on-road motor vehicles)
OTHER MOBILE SOURCES
aircraft
trains
ships and commercial boats
recreational boats
off-road recreational vehicles
commercial/industrial mobile equipment
farm equipment
other (other mobile sources)
NATURAL (NON-ANTHROPOGENIC)
geogenic sources
wildfires
windblown dust
other (natural sources)
TOTALS
Stationary
Area-Wide
On-Road Motor Vehicles
Other Mobile Sources
Natural
TOTAL
Mountain
Counties
Bay
Area
Sacramento
Valley
13.5
0.0
9.4
1.2
0.0
0.3
0.2
0.0
0.1
0.2
0.5
0.2
0.0
0.0
158.3
0.0
66.7
8.5
0.0
2.1
1.0
0.0
0.6
1.4
3.9
1.8
0.5
0.0
68.5
0.0
38.2
4.8
0.0
1.5
0.7
0.0
0.5
1.2
3.4
0.7
0.1
0.0
82.9
0.0
54.3
6.7
0.0
2.7
1.2
0.0
0.7
1.6
4.5
1.2
0.1
0.0
15.5
0.0
8.0
1.0
0.0
0.3
0.2
0.0
0.1
0.3
0.8
0.2
0.0
0.0
32.5
0.0
16.1
2.1
0.0
0.5
0.3
0.0
0.2
0.4
1.1
0.5
0.0
0.0
0.1
0.2
0.0
10.0
17.8
0.6
0.6
0.0
10.3
0.5
0.7
11.3
2.3
15.4
0.8
0.0
2.4
0.7
0.1
10.7
5.2
2.3
2.6
0.0
10.0
0.9
0.1
7.7
5.7
4.7
5.2
0.0
0.3
0.1
0.0
2.3
0.6
0.9
1.0
0.0
1.1
0.2
0.8
3.2
1.2
1.6
1.5
0.0
0.0
2.3
0.0
0.0
0.0
0.2
0.0
0.0
0.1
3.0
0.0
0.0
0.3
3.5
0.0
0.0
0.0
1.3
0.0
0.0
20.8
4.9
0.0
0.0
7.0
27.9
25.6
29.3
2.3
92.1
120.2
98.7
244.8
41.3
0.2
505.2
51.2
72.9
119.6
24.0
3.1
270.8
117.4
178.1
155.9
34.3
3.8
489.5
14.8
24.6
26.4
5.2
1.3
72.3
25.4
30.5
53.7
9.6
25.7
144.9
2-71
Valley
Coast
Coast
Version 3 – 11/24/99
Table 2.4-2
1996 Daily Average NOx Emissions by Air Basins in the CCOS Domain
SOURCE CATEGORIES
STATIONARY
FUEL COMBUSTION
electric utilities
cogeneration
oil and gas production (combustion)
petroleum refining (combustion)
manufacturing and industrial
food and agricultural processing
service and commercial
other (fuel combustion)
WASTE DISPOSAL
sewage treatment
landfills
incinerators
soil remediation
other (waste disposal)
CLEANING AND SURFACE COATINGS
laundering
degreasing
coatings and related process solvents
printing
other (cleaning and surface coatings)
PETROLEUM PRODUCTION AND MARKETING
oil and gas production
petroleum refining
petroleum marketing
other (petroleum production and marketing)
INDUSTRIAL PROCESSES
chemical
food and agriculture
mineral processes
metal processes
wood and paper
glass and related products
electronics
other (industrial processes)
AREA-WIDE
SOLVENT EVAPORATION
consumer products
architectural coatings and related solvents
pesticides/fertilizers
asphalt paving
refrigerants
other (solvent evaporation)
MISCELLANEOUS PROCESSES
residential fuel combustion
farming operations
construction and demolition
paved road dust
unpaved road dust
fugitive windblown dust
fires
waste burning and disposal
utility equipment
other (miscellaneous processes)
Mountain
Counties
Bay
Area
Sacramento
Valley
0.8
2.0
0.0
0.0
2.3
0.0
0.5
0.2
11.8
9.5
0.3
32.5
25.5
0.6
9.5
1.9
2.0
2.3
3.6
0.0
5.4
1.6
7.1
1.0
2.0
17.9
49.8
2.5
26.9
37.3
25.6
0.9
7.1
0.5
0.4
0.0
8.1
0.1
1.2
0.1
2.4
0.7
4.0
0.3
1.9
1.6
3.6
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.2
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
8.2
0.0
0.0
2.4
0.0
0.0
0.0
0.2
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.5
0.0
0.8
0.0
0.0
0.0
0.0
0.2
0.1
0.0
2.1
0.0
0.5
0.0
0.0
0.0
0.1
9.3
1.5
0.0
0.0
10.0
0.0
0.0
0.0
0.0
2.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
19.0
0.0
0.0
0.0
0.0
0.0
0.1
0.9
0.4
0.1
7.0
0.0
0.0
0.0
0.0
0.0
0.0
0.4
0.0
0.0
7.7
0.0
0.0
0.0
0.0
0.0
0.1
4.6
0.1
0.0
2.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
3.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2-72
Valley
Coast
Coast
Version 3 – 11/24/99
1996 Daily Average NOx Emissions by Air Basins in the CCOS Domain
SOURCE CATEGORIES
MOBILE
ON-ROAD MOTOR VEHICLES
light duty passenger
light and medium duty trucks
light duty trucks
medium duty trucks
heavy duty gas trucks (all)
light heavy duty gas trucks
medium heavy duty gas trucks
heavy duty diesel trucks (all)
light heavy duty diesel trucks
medium heavy duty diesel trucks
heavy heavy duty diesel trucks
motorcycles
heavy duty diesel urban buses
other (on-road motor vehicles)
OTHER MOBILE SOURCES
aircraft
trains
ships and commercial boats
recreational boats
off-road recreational vehicles
commercial/industrial mobile equipment
farm equipment
other (other mobile sources)
NATURAL (NON-ANTHROPOGENIC)
geogenic sources
wildfires
windblown dust
other (natural sources)
TOTALS
Stationary
Area-Wide
On-Road Motor Vehicles
Other Mobile Sources
Natural
TOTAL
Mountain
Counties
Bay
Area
Sacramento
Valley
11.4
0.0
11.0
1.7
0.0
1.7
0.5
0.0
0.6
1.3
4.2
0.1
0.0
0.0
134.8
0.0
79.1
12.0
0.0
10.7
3.6
0.0
5.0
11.3
37.0
1.0
5.2
0.0
53.7
0.0
42.4
6.4
0.0
8.5
2.8
0.0
4.1
9.1
29.9
0.4
0.7
0.0
71.2
0.0
65.4
9.8
0.0
16.5
5.4
0.0
5.7
12.8
42.0
0.7
0.8
0.0
13.0
0.0
9.5
1.4
0.0
1.7
0.6
0.0
0.8
1.9
6.2
0.1
0.4
0.0
29.8
0.0
20.8
3.2
0.0
3.1
1.0
0.0
1.3
3.0
9.8
0.3
0.3
0.0
0.0
4.8
0.0
0.5
1.1
5.1
3.6
0.0
22.0
11.5
11.4
0.3
0.1
66.9
4.4
0.0
2.1
20.0
0.2
0.9
0.4
15.6
16.9
0.0
3.1
19.8
0.3
0.9
0.4
21.6
30.3
0.0
0.3
2.7
0.1
0.1
0.0
4.6
6.6
0.0
0.5
5.2
4.2
0.4
0.1
9.9
9.4
0.0
0.0
0.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.8
0.0
0.0
0.0
0.9
0.0
0.0
0.0
0.4
0.0
0.0
0.0
1.2
0.0
0.0
5.8
2.3
32.5
15.1
0.6
56.3
102.6
20.5
299.7
116.6
0.0
539.4
28.2
7.4
158.0
56.1
0.8
250.5
184.1
12.5
230.3
76.4
0.9
504.2
20.2
2.1
35.6
14.4
0.4
72.7
14.8
3.4
72.6
29.7
1.2
121.7
2-73
Valley
Coast
Coast
2-74
MAY
JUN
JUL
REDDING
AUG
SEP
OCT
MAY
JUN
JUL
SACRAMENTO
AUG
SEP
OCT
MAY
JUN
SAN
JUL
FRANCISCO AUG
SEP
OCT
MAY
JUN
JUL
FRESNO
AUG
SEP
OCT
MAY
JUN
JUL
SANTA MARIA
AUG
SEP
OCT
MAY
JUN
JUL
BAKERSFIELD
AUG
SEP
OCT
MON
SKY COVER
PRES RELATIVE HUMIDITY (%) PRECIP
TEMP(Deg. F)
WIND
Daily Max Daily Min (tenths;Sunrise-set) (mb) 04PST 10PST 16PST 22PST (in.) Speed (mph) Direction
81
52
5
995.6 73
44
33
58
1.27
7.7
*
90
62
4
994.5 64
37
25
48
0.56
8.1
*
98
65
2
994.1 58
32
19
40
0.17
7.4
*
96
63
2
994.0 59
32
18
40
0.46
6.7
*
89
59
2
995.1 61
34
22
45
0.91
6.3
*
78
49
4
997.7 68
42
30
56
2.24
6.5
*
80
50
4
1012.5 82
51
36
70
0.27
9.1
SW
88
55
2
1011.2 79
47
31
65
0.12
9.7
SW
93
58
1
1011.0 77
47
29
62
0.05
8.9
SSW
92
58
1
1011.2 78
50
29
64
0.07
8.5
SW
87
56
2
1011.3 78
50
31
65
0.37
7.4
SW
78
50
3
1014.5 80
57
38
70
1.08
6.4
SW
67
50
5
1015.0 84
64
60
79
0.19
13.4
W
70
53
4
1014.3 85
63
58
80
0.11
14.0
W
72
54
3
1014.2 86
65
59
82
0.03
13.6
NW
72
55
3
1013.9 87
67
61
83
0.05
12.8
NW
74
55
3
1013.6 84
66
59
80
0.20
11.1
NW
70
52
4
1015.8 82
68
59
77
1.22
9.4
WNW
84
54
3
1001.6 72
43
26
50
0.30
8.1
NW
93
60
2
1000.5 65
39
23
44
0.08
8.3
NW
99
65
1
1000.4 61
38
22
41
0.01
7.4
NW
97
64
1
1000.4 66
41
25
46
0.03
6.8
NW
90
59
2
1000.8 72
45
28
52
0.24
6.1
NW
80
51
3
1003.9 78
52
34
64
0.53
5.2
NW
68
46
4
*
91
60
61
87
0.20
8.3
WNW
71
50
4
*
92
61
60
88
0.03
7.9
WNW
73
53
3
*
88
63
61
86
0.01
6.5
WNW
74
54
4
*
93
65
62
91
0.05
6.2
W
75
52
4
*
91
63
63
89
0.30
5.9
W
74
48
4
*
85
57
62
85
0.49
6.2
W
85
57
3
995.4 55
39
26
40
0.20
7.9
NW
92
64
2
994.8 50
35
23
34
0.10
7.9
NW
99
70
1
994.7 48
33
22
33
0.01
7.2
NW
97
69
1
994.7 53
37
24
38
0.09
6.8
NW
90
64
2
995.0 57
41
28
144
0.17
6.2
WNW
81
55
3
998.4 63
46
33
52
0.29
5.5
NW
Table 2.5-1
May-October Climate Summaries for Selected Central California Cities (1961-1990 means)
Version 3 – 11/24/99
2-75
N wind, no marine air, more typical in winter
Persistent Lo off coast of SoCal or Mex
Hits SV and/or N SJV; not S SJV
Off the Southern California Coast
Trough moves thru CA, NW-erlies follow
Includes Hi over Mexico
Includes Lo over Mexico
Trough moving on-shore
West-to-East flow
Broad ridge centered off-shore, sea breeze
Off-shore Hi North of LA
Off-shore Hi on or below latitude of LA
Can have a cut-off Lo, but check monsoonal
Southeast flow brings gulf moisture
Description
(Comments on subtypes if needed)
Ridge, off-shore gradient, weaker sea breeze
Includes Hi over Northern California
Day
May-Oct Month Most
Count Frequency
Likely
113
20.5%
7
1.3%
Sep
29
5.3%
Aug
46
8.3%
Aug
31
5.6%
Aug
49
8.9%
8
1.4%
Oct
19
3.4%
Jun
22
4.0%
Oct
31
5.6%
30
5.4%
Jul
1
0.2% Jul (1case)
85
15.4%
29
5.3%
May
43
7.8%
June
13
2.4%
May
102
18.5%
39
7.1%
Jun
53
9.6%
Aug
10
1.8%
May
142
25.7%
91
16.5%
May
15
2.7%
June
31
5.6%
Oct
5
0.9%
Sep
0.5%
Sep
3
4.9%
Aug
27
Table 2.5-2
Meteorological Scenarios by Weather Map Inspection
Meteorological Scenario
Type
Name
I
Western U.S. Hi
Pacific NW Hi
Ia
Great Basin Hi
Ib
Four Corners Hi
Ic
Central or SoCal Hi
Id
II
Eastern Pacific Hi
North Hi (North of LA)
IIa
South Hi (LA or below)
IIb
w/ Cut-Off Lo to S
IIc
III
Monsoonal Flow
IIIa Cut-Off Lo
IIIb No Cut-Off Lo
IV
Zonal
IVa Whole CA Coast
IVb Hi in SE Pac or Mex
IVc Lo in SE Pac or Mex
V
Pre-Frontal
Whole CA Coast
Va
North CA Coast
Vb
Cut-off Lo
Vc
VI
Trough Passage
VIa Whole CA Coast
VIb North CA Coast
VIc NW-erlies after trough
VId Cut-off Lo
VII Continental High
VIII El Nino Cut-Off Lo
Version 3 – 11/24/99
2-76
NC
MC
*
*
7%
*
*
*
*
*
*
*
3%
*
3%
*
8%
*
2%
*
*
2%
*
*
*
*
*
*
*
8%
*
*
*
*
3%
9%
*
3%
7%
*
*
*
*
*
*
*
17% 17% 3%
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
5%
*
N
MC
S
N
S
29%
14%
7%
13%
7%
9%
*
*
5%
8%
*
2%
*
3%
9%
8%
*
5%
*
71%
59%
24%
61%
N
86%
83%
83%
97%
SJV
C
71%
86%
70%
90%
S
14% *
21% 45%
11% 39%
23% 65%
SCC MD
*
*
*
*
*
*
*
*
*
8% 13% *
5%
9% 11% 13% 2% 2%
* 10% 50% 10% *
3% 10% 10% *
7%
14% 56% 40% 2% 19%
15% 46% 46% 8% 15%
43% 83% 77% 7% 47%
(1) (1) (1)
*
(1)
*
*
*
*
*
*
1% 3%
*
*
*
*
*
*
*
7% 33% 27% *
7%
10% *
*
*
3%
* 26% 23% *
*
40% 40% *
*
*
*
*
*
*
*
33% *
*
*
* 33% 67% 67% *
*
11% 7% 7%
*
*
4% 63% 44% *
7%
18% 12% 14% 4.5% 2.7% 17% 42% 38% 5.4% 18%
*
*
*
*
*
*
*
9% 8% 4% 8%
*
20% * 10% 10% * 10%
*
*
8%
14%
7%
7%
13%
SFB NCC
38% *
* 38% 25% 25% 63% 63% 63% 13% 25%
16% 11% 5% 21% 16% 11% 16% 68% 53% 16% 32%
23% 36% 32% 14% 5%
* 23% 55% 45% 5% 23%
43%
48%
26%
58%
SV
3% 47% 50% 40% 17% 10%
*
(1) (1) (1) (1)
*
*
*
*
(Sub)Basin Frequency of 8hr Exceedance Days
* 14% 71% 57% 29%
3%
* 62% 45% 31%
*
* 39% 41% 35%
6%
* 61% 61% 29%
SCC MD NC
14% *
55% *
24% 2%
84% *
S
13% 25% 13%
* 11% 32%
* 18% 9%
57%
59%
35%
61%
SJV
C
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
7%
*
*
*
*
*
*
*
*
*
3%
*
*
*
*
*
*
*
*
* 20%
*
*
*
*
*
*
*
*
* 67%
*
*
*
* 11% 4%
*
*
4% 26%
3.4% 2.9% 0.0% 2.0% 14% 14% 0.4% 0.7% 1.1% 20%
*
*
*
*
*
*
*
*
*
25% 13%
*
5%
*
*
14%
21%
2%
3%
*
*
*
*
14%
17%
2%
19%
29%
10%
9%
10%
N
SFB NCC
S
SV
(Sub)Basin Frequency of 1hr Exceedance Days
Table 2.5-3
Basin and Subbasin Exceedance Frequencies by Meteorological Scenario
Type
Name
N
S
N
I
Western U.S. Hi
Pacific NW Hi
* 14% *
*
Ia
Great Basin Hi
* 7% 3% 3%
Ib
Four Corners Hi
*
4%
*
*
Ic
Central or SoCal Hi
* 13% *
*
Id
II
Eastern Pacific Hi
*
*
*
*
IIa North Hi
*
*
*
*
IIb South Hi
*
*
*
*
IIc w/ Cut-Off Lo to S
III Monsoonal Flow
3%
*
7% 7%
IIIa Cut-Off Lo
*
*
*
*
IIIb No Cut-Off Lo
IV Zonal
*
*
*
*
IVa Whole CA Coast
*
*
*
*
IVb Hi in SE Pac
*
*
*
*
IVc Lo in SE Pac
V
Pre-Frontal
*
*
*
*
Va Whole CA Coast
*
*
*
*
Vb North CA Coast
*
*
*
*
Vc Cut-off Lo
VI Trough Passage
*
*
*
*
VIa Whole CA Coast
*
*
*
*
VIb North CA Coast
*
*
*
*
VIc NW-erlies after trough
*
*
*
*
VId Cut-off Lo
*
*
*
*
VII Continental High
*
*
*
*
VIII El Nino Cut-Off Lo
Exceedance Frequency (all) 0.2% 1.6% 0.5% 0.5%
Scenario
Version 3 – 11/24/99
2-77
Table 2.5-4
High-Ozone Episodes Identified by the Local Districts for Ozone Seasons 1996-98
Episode
Bay Area
SJV
Sacto
Northern Sierra
O3 Rank month day year O3 Rank
#
month day year O3 Rank month day year O3 Rank month day year O3 Rank month day year
6
2 96 121
9
1
6
3 96 128
6
4 96 99
96 0.105
2
2
6 10
6 11
96
96
6 12
96
6 13
96
6 14
96
6 15
96
6 16
96
6 17
6 29
96 121
6
3
6 30
96 131
7
1 96 137
7
2 96 72
5 96 0.111
1
4
8
7
7
6 96 0.14
7
6 96
7
7 96
7
7 96
7
8 96
7
8 96
7
9 96
7
9 96
7 10
96
7 10
96
96
7 11
96
7 12
96
7 13
96
7 14
96
7 15
96
7 16
96
7 17
10
7 21
96 0.106
3
5
7
22
96 125
7 22
96
96 132
7 23
96
7 23
96 128
7 24
96
7 24
96 137
7 25
96
7 25 96 109
5
7 25
7
26
96 145
7 26
96
7 26 96
96 116
7 27
96
7 27
96
7 28
Version 3 – 11/24/99
2-78
11
94
130
150
144
148
133
154
151
125
106
152
165
157
131
126
162
163
151
129
5
4
7
8 29
8 30
8 31
96 0.13
96
96
96 0.16
96
96
96
96
96
8 12
8 13
8 14
8 22
8 23
8 24
96 0.15
96
96
96
8 7
8 8
8 9
8 10
9
3
7
6
6
6
6
6
6
6
7
7
7
7
7
7
8
8
8
8
16
17
18
19
20
21
22
1
2
3
4
5
6
21
22
23
24
97 0.101
97
97
97
97
97
97
97 0.108
97
97
97
97
97
96 0.104
96
96
96
9
5
4
8 12
8 13
8 14
8 8
8 9
8 10
96
96
96
96
96
96
141
1
Sacto
Northern Sierra
O3 Rank month day year O3 Rank month day year
Table 2.5-4 (cont.)
Episode
Bay Area
SJV
#
month day year O3 Rank month day year
6
7 27 96 83
8
7 28 96 129
7 29 96 124
7
5
8
6 96
8 7 96 81
8
7
96
8 8 96 133
8
8 96
8 9 96 138
8
9 96
8 10 96 137
8 10 96
8 11 96 117
8 11 96
8 12 96
8 13 96
8 14 96
8
8 21
96
8 22 96
8 23 96
8 24 96
8 25 96
9
8 28 96
8 29 96
8 30 96
8 31 96
9
1 96
10
Version 3 – 11/24/99
2-79
16
15
14
13
2
3
4
5
8 11
8 12
8 13
8
8
8
8
7 17
7 18
7 19
110
142
144
104
98 120
98 147
98 106
98
98
98
98
98 78
98 147
98 114
1
3
2
8
8
8
8
8
8
8
8
8
8
8
7
7
7
7
7
7
10
11
12
13
14
2
3
4
5
6
7
16
17
18
19
20
21
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
Sacto
Northern Sierra
9
8 4 97 0.15
6
0.108
10
126
8 5 97
8 5 97
141
8 6 97
8 6 97
146
8 7 97
8 7 97
147
8 8 97
8 8 97
103
8 9 97
8 10 97
7 7 98 105
7
7 8 98
133
3
7 16 98 0.15
1
129
2
165
7 17 98
7 17 98
158
7 18 98
7 18 98
147
7 19 98
7 19 98
145
119
6
7 31 98 0.16
2
0.101
6
114
3
8 1 98
8 1 98
121
8 2 98
8 2 98
8 2 98
143
8 3 98
8 3 98
8 3 98
153
8 4 98
8 4 98
8 4 98
162
8 5 98
8 5 98
8 5 98
156
8 6 98
8 6 98
127
8 7 98
8 8 98
2
8 9 98 0.15
5
0.109
8
108
6
120
8 10 98
130
8 11 98
8 11 98
8 11 98
145
8 12 98
8 12 98
8 12 98
167
8 13 98
8 13 98
123
8 14 98
8 14 98
8 15 98
8 16 98
Table 2.5-4 (cont.)
Episode
Bay Area
SJV
#
12
8
5 97
8
6 97
8
7 97
8
8 97
8
9 97
Version 3 – 11/24/99
2-80
a
Ozone is expressed in the original units from the districts, either ppb or ppm.
9
9
9
9
9
9
20
21
22
23
24
25
98 0.115
98
98
98
98
98
7
9 11
9 12
9 13
98 100
98
98
8
Sacto
Northern Sierra
112
8
143
135
150
127
132
1
8 27 98 0.15
4
8 27 98 114
4
150
8 28 98
8 28 98
154
8 29 98
8 29 98
161
8 30 98
8 30 98
156
8 31 98
169
9 1 98
9 1 98
153
9 2 98
9 2 98
119
9 3 98
9 3 98
Table 2.5-4 (cont.)
Episode
Bay Area
SJV
#
17
8 21 98
8 22 98
8 23 98
8 24 98
8 25 98
18
4
8 27 98
8 28 98 101
8 28 98
8 29 98 131
8 29 98
8 30 98 76
8 30 98
8 31 98
9 1 98 117
9
1 98
9 2 98 139
9
2 98
9 3 98 130
9
3 98
9 4 98 75
19
7
9 12 98 116
9 13 98 136
9 14 98 63
20
Version 3 – 11/24/99
Version 3 – 11/24/99
Table 2.5-5
Cluster Analysis Days by Cluster with Subjective Scenario Type
Date
9/3/1998
9/2/1998
9/13/1998
6/3/1996
8/12/1996
8/4/1998
8/12/1998
8/8/1996
8/9/1996
8/10/1996
8/13/1996
8/29/1998
7/1/1996
7/6/1996
8/29/1996
8/6/1997
8/7/1997
7/18/1998
8/3/1998
8/31/1998
6/30/1996
7/28/1996
7/19/1998
8/22/1996
9/1/1998
8/24/1996
8/28/1998
7/26/1996
8/30/1996
8/31/1996
8/8/1997
8/6/1998
8/24/1998
8/30/1998
7/20/1998
SFBA
130
139
136
128
113
144
147
133
138
137
116
131
137
102
116
111
114
147
142
117
131
129
114
87
117
63
101
99
89
93
71
78
93
76
73
Basin/County Ozone (ppb)
Subjective
SV
SJV
MC
NCC SLO
Type
149
119
116
76
104
3a
145
153
144
87
102
1a
127
124
110
91
99
1a
113
126
105
114
91
1b
124
154
129
86
95
1b
148
153
126
101
114
1b
130
145
124
102
108
1b
110
150
121
118
95
1c
150
144
130
107
107
1c
113
148
103
94
141
1c
125
151
136
82
96
1c
111
154
124
103
101
1c
126
143
116
95
92
1d
129
129
119
85
88
1d
129
162
116
111
90
1d
143
141
118
89
90
1d
136
146
140
112
81
1d
104
158
112
124
103
1d
151
143
163
110
99
1d
133
156
122
79
94
2a
114
137
95
91
103
2b
93
121
94
72
77
3a
124
147
103
93
129
3a
102
152
117
85
95
1a
127
169
134
107
96
1a
118
157
122
81
77
1b
100
150
114
109
114
1b
100
145
106
76
109
1c
108
163
138
86
94
1d
102
151
95
81
70
1d
112
147
145
69
54
1d
122
156
114
44
93
1d
113
150
110
78
105
1d
118
161
114
90
108
1d
100
145
96
76
88
3a
2-81
Objective
Cluster
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
Version 3 – 11/24/99
Table 2.5-5 (cont.)
Cluster Analysis Days by Cluster with Subjective Scenario Type
Date
8/23/1996
8/5/1998
8/13/1998
7/17/1998
8/14/1998
7/10/1996
9/4/1998
7/13/1996
SFBA
96
104
106
78
80
83
75
74
Basin/County Ozone (ppb)
Subjective
SV
SJV
MC
NCC SLO
Type
157
165
120
95
91
1b
160
164
143
74
99
1b
154
167
134
76
91
1b
154
165
124
88
113
1d
142
130
125
73
72
1d
130
134
120
86
77
3a
140
120
105
69
77
3a
135
126
120
74
72
4a
Mean O3
Std. Error O3
109.0
3.8
126.1
2.8
147.0
2.1
120.0
2.3
89.3
2.4
95.2
2.4
C1_mean
C1_stderr
128.3
2.7
127.3
3.3
143.7
2.6
120.3
3.3
96.6
2.9
100.0
2.9
C2_mean
C2_stderr
86.7
4.4
110.2
2.8
153.8
2.2
117.1
4.5
81.8
4.9
91.9
5.1
C3_mean
C3_stderr
87.0
4.6
146.5
3.9
146.4
7.3
123.9
3.9
79.4
3.2
86.5
5.2
2-82
Objective
Cluster
3
3
3
3
3
3
3
3
Version 3 – 11/24/99
Table 2.5-6
Descriptive Statistics of Meteorological Parameters for Identified Clusters
(Note: Table is work in progress, 9/7/99)
Variable
Missing Medians for Cluster p-Value Sig.
Values
1
2
3
Temperature (F)
SFO
0
77 71.5 68.5
0.002 **
SAC
0
103
99
100
0 ***
SCK
18
104
96
100
0.002 **
RDD
FAT
BFL
BI
SM
LI
850a
850 p
Wind Speed (mph)
SM
BI
BFL
107 103.5
103
104.5
103
103
103 102.5 101.5
98.5
92 91.5
100
102
78
78
92.5
93.5
77.5
76
93.5
94
78
76.5
3
9.6
7.5
9.7
9.1
11.7
10.7
7.3
12.5
15
SAC
6
6.8
15.7
12.7
SFO
RDD
SCK
850 a
850 p
SUU
3
3
22
1
1
14.1
7.2
13.1
7.2
15.1
6.2
5.5
5
5.9
7
10
11.8
7
7
12.5
FAT
3
6.3
6.9
16.6
1000ft
10
3
Wind Direction
1000ft
10
260
850a
1 187.5
850p
1
235
Pressure Grad. (mb)
sf-sac
20
1.7
sf-bfl
7
2.1
sf-rdd
5
1.5
4.5
5
2.6
2.4
2.1
Cluster 1 > Clust 2,3. Clust 1 contains all high temps.
Many missing values
Cluster 1 > Clust 2,3
0.062
0.254
0.496
0 *** Cluster 1 > Clust 2,3. Clust 1 contains all high temps.
This temp variable gives best separation between
Cluster 1 and Clusters 2 and 3.
0.696
Clusters very similar
0.212
0.052 *
Cluster 3 < Clusters 1 or 2
0 *** Cluster 1 < Clusters 2 or 3
0.207
Although not sig., Cluster 3's winds are mainly high,
while Clusters 1 and 2 are generally lower.
0.66
Although not sig., Cluster 1's winds are generally
much lower than for 2 or 3.
0.226
0.594
0.388
0.016 *
Cluster 1 < Cluster 3. Cluster 2 in middle
0 *** Cluster 1 < Clusters 2 or 3. Cluster 1 contains all ws
< 6 mph.
0.492
Cluster 3 has much higher median winds but all have
wide range
0.158
Cluster 3 has consistently higher winds
280
265
180 132.5
220
210
2.5
2.3
2.6
Comments
(Sig. is significance: *=>95%; **>99%; ***>99.9%)
Cluster 3 very concentrated in westerly direction
All with predominantly easterly component
All clusters similar
0.206
0.006 **
2-83
Too many missing values
Cluster 1 contains all the gradients <= 1 mb
Cluster 1 < Cluster 2 or 3
Version 3 – 11/24/99
Table 2.7-1
Number of Exceedances and Ratios of 8-Hour and 1-Hour Parameters
NC
#_Stations reporting exceedances
1996-98 #_8HrExDays
1990-98 #_8HrExDays
1996-98 #_1HrExDays
1990-98 #_1HrExDays
1996-98 #_8hrExDays/#_1hrExDays Ratio
1990-98 #_8hrExDays/#_1hrExDays Ratio
1996-98 1hrMax_O3/8hrMax_O3 Ratio
1990-98 1hrMax_O3/8hrMax_O3 Ratio
1
6
7
1
1
6.0
7.0
1.15
1.19
MC
9
72
204
9
15
8.0
13.6
1.16
1.17
SV
22
79
256
18
64
4.4
4.0
1.19
1.21
Air Basin
SFB
NCC
17
4
20
15
49
31
14
0
28
2
1.4
*
1.8
*
1.34
1.21
1.34
1.23
Footnotes: SCC includes only San Lois Obispo stations and Atascadero, and Paso Robles.
MD includes only Mojave–Pool St. station.
2-84
SJV
25
235
754
72
233
3.3
3.2
1.20
1.22
SCC
6
29
37
2
4
14.5
9.3
1.15
1.15
MD
1
100
175
4
4
25.0
43.8
1.17
1.17
Figure 2.1-1. Overall study domain with major landmarks, mountains and passes.
Version 3 – 11/24/99
2-85
Version 3 – 11/24/99
Figure 2.1-2. Major political boundaries and air basins within central California.
2-86
Version 3 – 11/24/99
Figure 2.1-3. Major population centers within central California.
2-87
Version 3 – 11/24/99
Figure 2.1-4. Land use within central California from the U.S. Geological Survey.
2-88
Version 3 – 11/24/99
Figure 2.1-5. Major highway routes in central California.
2-89
2-90
0.07
0.08
0.09
0.1
0.11
0.12
0.13
0.14
0.15
0.16
0.17
1991
1992
1993
1994
SVAB
SJVAB
NCC
1995
SFBA
MCAB
SCC
1996
1997
Ozone Trends - Annual Maximum of Daily 8hr Maxima
1998
Figure 2.3-1. Annual basin maximum of 8-hour daily maximum ozone trends by location type.
1990
Version 3 – 11/24/99
Ozone 4th Hi in 3-year (ppm)
2-91
0.03
0.035
0.04
0.045
0.05
0.055
0.06
0.065
1990
1991
1992
1993
1994
Rural
Suburban
Urban/City
1995
1996
Ozone Trends in Central California by Location Type
Average 8-Hr Daily Ozone Maxima
1997
1998
Figure 2.3-2. Average 8-hour daily maximum ozone trends in central California by location type.
Version 3 – 11/24/99
Ozone Concentration (ppm)
2-92
Mon
Rural
Tue
Wed
Suburban
Thurs
Fri
Urban/City Center
Sat
(Error bars shown are plus and minus two standard errors for all observations, 1990-1998.)
Weekend/Weekday Effect in Central California, 1990-1998
Average Daily 1-Hour Ozone Maxima by Location Type
Sun
Figure 2.3-3. Weekend/weekday effect on average 1-hour daily maximum ozone in central California by location type.
0.045
0.05
0.055
0.06
0.065
Version 3 – 11/24/99
Average Ozone Concentration (ppm)
Version 3 – 11/24/99
HCHO, RCHO
O3
NO2
PAN
CO, VOC
RO2
HO
NO2
HO2
HNO3
NO2
NO
hν
O3
Figure 2.6-1. Overview of ozone production in the troposphere.
2-93
H2O2
ROOH
Version 3 – 11/24/99
15000
A
Z(m)
10000
5000
0
0.0
1 × 10-14
k cm3 molecule-1 s-1
15000
2 × 10-14
B
Z(m)
10000
5000
0
1.1
1.2
1.3
1.4
Uncertainty Factor
1.5
Figure 2.6-2. (A) Rate constant for the O3 + NO reaction with upper and lower bounds. (B)
The uncertainty factor, f(T). Data are from DeMore et al. (1997).
2-94
Version 3 – 11/24/99
15000
A
Z(m)
10000
5000
0
0.0
4 × 10-11
2 × 10-11
k cm3 molecule-1 s-1
15000
6 × 10-11
B
Z(m)
10000
5000
0
2.0
2.5
3.0
Uncertainty Factor
3.5
Figure 2.6-3. (A) Rate constant for the CH3O2 + HO2 reaction with upper and lower bounds.
(B) The uncertainty factor, f(T). Data from DeMore et al. (1997).
2-95
2-96
VOC
Figure 2.6-4. Atmospheric lifetimes of selected organic compounds with respect to a hydroxyl radical concentration of 7.5 × 106
molecules cm-3.
0.1
1
10
100
1000
10000
Version 3 – 11/24/99
Lifetime (hrs)
methane
ethane
propane
i-butane
n-butane
n-pentane
3-methylpentane
2,3 dimethylbutane
n-propylbenzene
2-methylhexane
n-heptane
ethene
n-octane
methyloctanes
n-decane
benzaldehyde
p-ethyltoluene
n-undecane
n-dodecane
acetaldehyde
proprionaldehyde
m-xylene
i-butyraldehyde
1-butene
butene isomers
3-methyl-1-butene
1,2,4-trimethylbenzene
2-methyl-1-butene
2-methyl-2-butene
Version 3 – 11/24/99
1.5 × 10 -10
A
298 K
1.5 × 10 -10
216 K
Isoprene
1,3 Butadiene
2 Methyl - 1 - Butene
Propene
0.0
2 Methyl - 2 - Butene
5.0 × 10 -11
Ethene
k HO, cm3 molecules-1 s-1
1.0 × 10 -10
B
1.0 × 10 -10
5.0 × 10 -11
0.0
Figure 2.6-5.
Uncertainties in rate parameters for HO radical reactions with alkenes. The
closed circles represent the nominal value while the crosses represent the
approximate 1σ; (A) 298 K; (B) 216 K.
2-97
2-98
CH3O2 + NO
O1D + O2
HO2 + MO2
O1D + H2O
O3 + HO2
O1D + N2
CO + HO
ALD + HO
Reaction
CHO + hv -> HO2
HO + KET
HO2 + CH3CO3
HO + HC5
HO + HC3
HO + RNO3
HO + HCHO
CH3CO3 + NO
O3 + hv -> O1D
HO + CH4
CH3CO3 + NO2
HO2 + NO
O3 + NO
HO + NO2
PAN ->
NO2 + hv
Figure 2.6-6. Relative sensitivity of ozone to reaction rate constants. Initial total reactive nitrogen concentration is 2 ppb and total
initial organic compound concentration is 50 ppbC (Stockwell et al. 1995).
0%
5%
10%
[Ozone] Relative Sensitivity
15%
20%
HO + HC8
Version 3 – 11/24/99
HO + CH3CO3H
Other Reactions
0.0
Formaldehyde
1,3-Butadiene
Propene
Isoprene
Propionaldehyde
Acetaldehyde
1,2,4-TMB
Ethene
3-M-cyclopentene
2-M-2-Butene
m,p-Xylene
o-Xylene
2-M-1-Butene
M-cyclopentane
Toluene
Ethylbenzene
Ethanol
MEK
2-M-pentane
Methanol
Butane
2,2,4-Tri-M-pentane
MTBE
Benzene
Ethane
Methane
MIR (ppm O3 /ppm C)
Version 3 – 11/24/99
6.0
5.0
4.0
3.0
2.0
1.0
Figure 2.6-7. Mean values and 1σ uncertainties of maximum incremental reactivity values for
selected hydrocarbons determined from Monte Carlo simulations (Yang et al., 1995).
2-99
Version 3 – 11/24/99
Figure 2.8-1. Plot of 1-hr ozone concentrations over central California at noon on the second
simulated day. The area directly west of the San Francisco Bay Area has ozone
concentrations near 120 ppb while high ozone concentrations are found along and
directly west of the San Joaquin valley.
0
40
80
O3 ppb
2-100
120
Version 3 – 11/24/99
Figure 2.8-2. Plot of 1-hr ozone concentrations in central California at midnight after two
simulated days. Note that the western boundary and the San Joaquin Valley have
ozone concentrations near 40 ppb or less.
0
40
80
O3 ppb
2-101
120
Version 3 – 11/24/99
Figure 2.8-3. Plot of 1-hr ozone concentrations in central California at noon on the third
simulated day. The high ozone pattern is more complicated with several ozone hot spots one
directly west of the San Francisco Bay Area with two more directly to the north and south. High
ozone concentrations are found also along and directly west and south of the San Joaquin valley.
0
40
80
O3 ppb
2-102
120
Version 3 – 11/24/99
Figure 2.8-4. Plot of 1-hr ozone concentrations in the vertical along a north – south cross
section through the center of central California. The vertical coordinate is linear in pressure
following coordinates which exaggerates the apparent height. Plot A shows the ozone
concentrations at noon on the second day and Plot B shows the ozone concentrations at midnight
after two simulated days. The high ozone concentrations at noon, Plot A, and the low ozone
concentrations at midnight, Plot B, are found over the Sacramento area.
A
North
South
B
North
South
0
40
80
O3 ppb
2-103
120
Version 3 – 11/24/99
Figure 2.8-5. Plot of 1-hr ozone concentrations in the vertical along a west – east cross section
through central California along a line running from the San Francisco Bay Area through
Sacramento to the front range. The vertical coordinate is linear in pressure following coordinates
which exaggerates the apparent height. Plot A shows the ozone concentrations at noon on the
second day, Plot B shows them at 6:00 PM on the second day and Plot C shows them at midnight
after two simulated days. The plots show evidence of transport of ozone and NOx from the west
to the east.
A
West
East
West
East
West
East
B
C
0
40
80
O3 ppb
2-104
120
Version 3 – 11/24/99
Figure 2.8-6. Plot of formaldehyde concentrations at midnight after two simulated days. Note
that the western boundary has around 5 ppb of HCHO. These high HCHO
concentrations appear to be due to transport from the model’s boundary.
0
2
4
HCHO ppb
2-105
6
Version 3 – 11/24/99
Figure 2.8-7. Plot of NO concentrations at 1:00 PM on the second simulated day. Note
that the western boundary has over 0.5 ppb of NO. These moderate NO concentrations
for over the ocean appear to be due to transport from the model’s boundary.
0
1
NO ppb
2-106
2
Version 3 – 11/24/99
3.0
Chapter 3: Data Requirements
REQUIREMENTS FOR AIR QUALITY MODELING SYSTEMS AND
DATA ANALYSIS
Data requirements for CCOS are determined by the need to drive and evaluate the
performance of modeling systems, which include the following components.
1) Meteorological model, which provides winds fields, vertical profiles of temperature and
humidity, and other physical parameters in a gridded structure.
2) Emissions inventory and supporting models that provide gridded emissions for area
sources such as on- and off- road motor vehicles and vegetation.
3) Air quality model, which simulates the chemical and physical processes involved in the
formation and accumulation of ozone.
In evaluating modeling system performance, the primary concern is replicating the
physical and chemical processes associated with actual ozone episodes. This necessitates the
collection of suitable meteorological, emissions, and air quality data that pertain to these
episodes. The data requirements of CCOS are also driven by a need for complementary,
independent and corroborative data analysis so that modeling results can be compared to current
conceptual understanding of the phenomena replicated by the model.
This section describes the data requirements for meteorological and air quality models
and for developing temporally and spatially resolved emissions inventories of reactive organic
compounds and nitrogen oxides. The input data required for each modeling component (Section
3.1) are described separately from the data required to evaluate model performance and validity
of modeling results (Section 3.2). Section 3.3 describes the potential approaches and methods
for observation-based data analysis and verification of emission inventories.
3.1
Modeling System Inputs
The output of the meteorological model and gridded emission inventory serve as inputs to
the air quality model. Meteorological modeling and emission inventory development are
discussed first in Sections 3.1.1 and 3.1.2, and the output of these first two components of the
modeling system are discussed in the context of inputs to the air quality model in Section 3.1.3.
3.1.1
Meteorological Modeling
The specification of the meteorological fields that drive the transport and dispersion of
atmospheric pollutants is the critical component in mesoscale air quality modeling. The primary
objective is to obtain wind fields over the model grid and determine mechanical and convective
mixing depths. The simplest way is to use field measurements and interpolate the values over
the entire domain. However, field measurements are generally spatially and temporarily sparse,
and can be especially inadequate in areas with complex terrain and land-sea interactions. Another
way is to use a diagnostic meteorological model to estimate meteorological fields from existing
data and then to adjust these fields with a prognostic model containing dynamically consistent
parameterizations of physical processes. And best results are often achieved when the methods
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are combined such that interpolated or gridded field measurements are used as input to a
prognostic model at regular time intervals in the model simulation in a process called FourDimensional Data Assimilation (4DDA). After a general description of this process and a few of
the models involved, specific data requirements for setup of and input to the meteorological
model are listed.
Transport and the dynamics and thermodynamics of the atmospheric boundary layer
(ABL) govern dispersion of atmospheric pollutants. The main difficulties in dispersion estimates
arise with topographic complexity and increasing atmospheric stability. Turbulence in the ABL
is created by wind shear and destroyed by buoyancy and dissipation. Since these effects are
nearly balanced in the stable ABL, turbulence intensities are usually low and intermittent. In
some cases of stagnant stable conditions the horizontal diffusion of the plume can be of the same
magnitude or larger than the actual transport. Moreover, turbulent velocities are frequently
affected by gravity waves and the stable ABL undergoes non-stationary evolution. Additional
dispersion due to wave phenomena also needs to be resolved. During stable conditions the ABL
flow usually decouples from the synoptic winds and the local circulations dominate its flows. In
some cases a low-level jet can develop at the top of the surface stable layer and the fate of
pollutants at various elevated layers can be completely different over a very small vertical
separation. In contrast, during stable and stagnant conditions the winds close to the surface are
weak and sometimes below the detection limit of usual instrumentation.
Radiation and advection can also cause fog and cloud formation and significantly change
the rate of chemical reactions for some species and deposition processes. The depth of the stable
ABL is of the order of 100 m, and radiation processes, as well as local effects such as urban
effects, vegetation, soil properties, and small-scale topographic features, can significantly
influence ABL characteristics. Plume meandering is frequently observed during stable
conditions in topographically complex terrain, and use of data from limited measurement sites
can yield erroneous conclusions. All these effects significantly modify transport and dispersion
as well as removal of pollutants. Consequently, an extensive measurement network is necessary
in order to capture the spatial and temporal structure of the ABL in a mesoscale domain. The
success in any type of dispersion calculation will be limited to appropriate capture and input of
atmospheric parameters.
One of the diagnostic models recommended for this study is CALMET. Wind fields in
CALMET are calculated in a user specified number of vertical levels by taking into account the
influence of terrain on the atmospheric flow and applying an inverse weighting scheme. The
initial terrain-adjusted domain mean horizontal components of the wind at each grid point are
modified to obtain the final interpolated wind components. CALMET can calculate a spatially
variable initial guess field using objective analysis of the measurements. Moreover, CALMET
allows use of gridded wind fields created by a prognostic atmospheric model, such as the Penn
State University Meteorological Model (MM4, MM5), as “initial guess” fields or as substitutes
for observations. CALMET has detailed algorithms for the depth of the convective layer as a
function of the potential temperature lapse rate in the layer above the mixing depth, the time step,
and the temperature discontinuity at the top of the mixed layer. The daytime mechanical mixing
depth is determined from the Coriolis parameter, the friction velocity and the Brunt-Vaisala
frequency in the stable layer above the mixed layer. The nighttime depth of mechanical mixing
is determined from the friction velocity. CALMET uses an upwind-positioned averaging scheme
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to smooth out the mixing depths through use of determined weighting factors. Since CALMET
has detailed algorithms for wind fields and mixing depth, and furthermore allows initialization
with the output from the prognostic atmospheric model, it is an optimum tool for obtaining the
initial meteorological fields necessary for estimation of transport, dispersion, and chemical
transformation of atmospheric pollutants.
Based on the complexity of terrain in northern and central California, the MM5 model
developed by Penn State University and the National Center for Atmospheric Research (NCAR)
represents an appropriate tool for resolving dynamics and thermodynamics if used on the scale of
1-2 km horizontal resolution with nesting capabilities. It should include a nonhydrostatic option
and full parameterization of physical processes including turbulent transfer. MM5 uses an
advanced four-dimensional data assimilation scheme connected to either measurements or
synoptic fields. However, uncertainty exists as to the extent to which MM5 can be used to infer
turbulence properties in complex terrain.
Some advantages in using a prognostic model such as MM5 are:
•
High resolution in horizontal and vertical directions.
•
Topography with resolution of 30 seconds embedded within the model structure.
•
Detailed prognostic fields of meteorological parameters (wind, temperature, humidity,
turbulence, radiation, and clouds).
•
Physically-based estimate of mixing depth in both convective and stable cases, with full
spatial and temporal variability.
•
Detailed structure of the small-scale local flows that cannot be resolved through
simplified parameterization.
•
Detailed vertical structure of meteorological parameters and stability, which is especially
important near sources and receptors and along the transport path.
Some disadvantages in using a prognostic modeling approach are:
•
The models are fairly complex and expensive to run. Usually they are limited to certain
case studies.
•
The models have assumptions in simplification of basic differential equations and
numerical techniques and in the parameterizations of physical processes.
Meteorological modeling for the CCOS must focus on the characterization of the origin
and fate of atmospheric pollutants in central California. The main difficulties are due to complex
terrain and a number of significant sources within the large region and its surroundings.
Integration with dispersion models may also be a problem. It is also desirable that atmospheric
modeling adequately treat formation and evolution of fog and clouds, which are essential
determinants for liquid-phase chemistry, that may be important in coastal regions or during
precipitation events that may clean the air after ozone episodes (e.g., monsoonal flow in the San
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Joaquin Valley). However, these processes are often not well-represented over a large scale
domain like that planned for CCOS. Despite these difficulties, atmospheric models are useful
tools in understanding the structure and evolution of boundary layer dynamics and providing
meteorological fields as input for dispersion models. The results from wind field modeling will
be used as input for dispersion and chemical modeling of relevant pollutants also for the entire
intensive study period.
Necessary decisions and data requirements for the setup of the meteorological model
include:
•
Domain size (coordinates in latitude and longitude or UTM).
•
Topographic information for the orography of the domain.
•
Spatial resolution, i.e., grid size, and any nesting of grids of different sizes.
•
Land use per surface grid square with information on soil moisture, albedo, roughness
height, and vegetation.
Typical land use categories include high rise urban, residential, deciduous forestland and
evergreen forest categories. Surface resistances and deposition velocities are a function of land
use and vegetation data. Measurements of deposition for important species over representative
categories of land use and vegetation type are planned for a subsequent follow-on study after
summer 2000.
Necessary and/or highly desired field measurements for input to the meteorological
model include:
•
Routine surface network measurements of wind speed and direction, temperature,
humidity, pressure, solar radiation, and precipitation.
•
Routine aloft network measurements of wind speed and direction by radiosonde and
existing radar wind profilers (RWP), and sodars; of temperature by RWP with Radio
Acoustic Sounding Systems (RASS), of humidity and pressure by radiosonde, and of
precipitation by WSR-88D (NEXRAD), which can also provide some wind information
as a radial component of the Doppler radar beam).
•
Supplemental measurements as part of the CCOS and CRPAQS study design. These may
include: additional surface and upper sites making routine measurements listed above,
turbulence by sonic anemometer (CRPAQS 100-m tower at Angiola), actinic flux for
photolysis of key species, and soil moisture for key land use types.
3.1.2
Emission Inventory Development
For a study covering as large of an area as CCOS, the development of the emission
inventory will be a major effort. In addition to the size of the study domain, the emission
inventory required for modeling is more detailed than the routine summer seasonal emission
inventory. The emission inventory (EI) needed to support the CCOS modeling will be a series of
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day-specific, hourly, gridded emission inventories that cover each day of the ozone episodes
captured during the field study. To help coordinate the EI development effort, the Emission
Inventory Coordination Group (EICG) has been established to determine the process for
preparing the emission inventories needed to support air quality modeling for CCOS.
Participants in the group include many local air districts, several local councils of government,
Caltrans, California Energy Commission, and the ARB. Local air districts participating to date
include San Joaquin Valley Unified APCD, Bay Area AQMD, Sacramento Metropolitan
AQMD, Mendocino County AQMD, Northern Sierra AQMD, Yolo-Solano AQMD, Placer
County APCD, San Luis Obispo County APCD, and Monterey Bay Unified APCD. Other local
air districts will also be participating.
There are about 30 districts in the CCOS modeling domain. Each local air district in the
state updates a portion of the emission inventory for their area. An emission inventory is needed
to estimate emissions for each day that will be modeled in the summer of 2000. Table 3.1-1
provides a preliminary outline of the proposed roles and responsibilities of the various agencies
and groups involved in the development of the emission inventory. Special effort is being made
to assist smaller districts that have limited staffing resources. The following subsections discuss
point, area, on-road, and biogenic sources; and emissions forecasts, a two-track approach for the
modeling schedule, quality controls, and proposed studies for the EI development effort
3.1.2.1
Point Sources
The local air districts have primary responsibility for controlling stationary sources of air
pollution within their jurisdictions. In fulfilling this responsibility, the districts issue permits,
inspect and evaluate point sources, and estimate emissions for these sources. Point source
emissions occur at facilities that can be identified by name and location. Generally, facilities
with emissions greater than 10 tons per year of any pollutant are reported as point sources.
Districts may include smaller facilities in their point source inventory if they choose. The
emissions from smaller facilities that fall below the point source reporting threshold are
estimated as part of the area source emission inventory.
Districts gather information from their point sources and generally update the inventory
annually. Point sources are estimated using a “bottom up” approach. Point source activity levels
usually relate to a facility-specific process rate. Emission factors are derived from tests that
relate emissions to the process causing the emissions. The districts then pass the emissions and
other related information to ARB. The statewide point source emission estimates are stored and
maintained in the California Emission Inventory Development and Reporting System
(CEIDARS).
Modeling inventories require more data than are needed to calculate seasonal or annual
average emissions. For point sources, the location in UTM coordinates is used to place the
emissions within a grid cell. Temporal data are used to adjust the annual emissions for a
particular month, day and hour. Stack data such as height, diameter, exit temperature and
velocity are used to estimate the plume rise from the stack to place the emissions in the correct
vertical cell of the model.
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The local air districts and, if needed, private contractors will gather day-specific point
source data. The local districts will gather information from large point sources, particularly
those that have significant seasonal variations and the one(s) selected for plume characterization
studies. The CCOS EICG will agree upon a standard emission reporting threshold for collecting
day-specific, and when available hourly specific, point source emission information for all
districts in the CCOS domain. Air districts will have the option to report day-specific data below
the standard threshold.
For the remaining point sources for which day-specific data are not collected, the ARB
will use facility-specific information contained in CEIDARS. The emissions are adjusted to
represent a specific month and a specific day of the week (a Tuesday in August, for example).
Daily emissions are distributed over each hour. These adjustments are based on temporal
profiles that are stored in CEIDARS. Each district assigns temporal profiles using survey data or
knowledge of the source. The local districts and the ARB will work through the EICG to
develop comprehensive point source updates for 1999 and 2000 emission inventories.
3.1.2.2
Area Sources
There are currently approximately 360 emission source categories for which area source
methods are used to estimate emissions. These categories include both stationary and off-road
mobile sources. The source categories are divided into four types of emission sources.
Aggregated point sources are many small point sources, or facilities, that are not inventoried
individually but are estimated as a group and reported as a single source category. Examples
include gas stations and dry cleaners. Area-wide sources include source categories associated
with human activity and emissions take place over a wide geographic area. The emissions data
are developed only at the aggregated level. Consumer products and agricultural operations are
examples of area-wide sources. Natural (non-anthropogenic) sources generally include source
categories with naturally occurring emissions such as geogenic sources and wildfires. Off-road
mobile sources include categories such as farm equipment, off-road recreational vehicles, and
aircraft. Collectively, these types of sources are referred to as area sources.
Emissions from area sources are generally estimated using a “top down” approach. This
approach generally uses a process rate or amount of a substance being used multiplied by an
emission factor. An example of a process rate for dry cleaners would be the amount of
perchloroethylene used in a county. Emissions are estimated for each county. ARB and the
districts generally update the emissions from area sources once every three years. Updates are
based on the latest data available including surveys and the results of research projects. The
statewide area source emission estimates are stored and maintained in CEIDARS.
The ARB staff is responsible for developing methods and estimating emissions from over
260 categories. The local air districts are responsible for developing methods and estimating
emissions for the remaining categories. The local districts also have the prerogative to use their
own methods for area source categories that are normally the responsibility of ARB. ARB’s
Mobile Source Division is developing an off-road mobile source emission inventory model,
OFFROAD, which will be used to calculate the emissions from off-road mobile sources. The
ARB and the local districts will work together through the CCOS EICG to revise and/or update
all major area source categories for use in preparing the 1999 and 2000 emission inventories.
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To grid area sources, emissions for a particular source category are spatially distributed
within a county through the use of surrogates. In this context, a surrogate is an economic or
demographic parameter, which is correlated to emissions for a particular source category and has
a known spatial distribution. Some common surrogates used for this purpose are population,
housing units, and employment. As with point sources, temporal data are used to adjust the
annual emissions for a particular month, day and hour. Annual emissions from all sources are
checked for reasonableness and completeness. The gridding surrogates are planned for update
as part of the CCOS emission inventory development process.
Day-specific data can also be collected for area source categories. For this study, data
will be collected for wildfires, agricultural burns and prescribed burns. ARB will gather
information needed to estimate wildfire emissions including the date, time, and location of the
burn along with the amount and type of material burned. Either the local districts or a contractor
will collect similar information for agricultural and prescribed burns.
3.1.2.3
On-Road Motor Vehicle Sources
The ARB has primary responsibility for developing emission estimates for on-road
mobile sources. On-road mobile sources are motor vehicles that travel on public roads. This
category consists of gasoline-powered and diesel-powered passenger cars, light-duty trucks
(6,000 lbs. gross vehicle weight [GVW] or less), medium-duty trucks (6,001-8,500 lbs. GVW),
heavy-duty trucks (over 8,500 lbs. GVW), urban buses, and motorcycles. Emissions from motor
vehicles include exhaust, evaporative, crankcase, and tire-wear emissions. The Mobile Source
Division of the ARB develops emission factors for California. The Emission Inventory Branch
of the Planning and Technical Support Division of the ARB has the responsibility for developing
the motor vehicle activity components such as vehicle miles traveled (VMT), starts, and vehicle
population, which are used by the models.
The emission factor model to be used for the on-road motor vehicle inventory is
EMFAC99. This model, which is expected to be approved by the ARB in October 1999, is an
integrated, modular system of programs. These modules perform the functions of the computer
programs CALIMFAC, WEIGHT, EMFAC and BURDEN that were used in previous versions
of the model. Although the EMFAC99 modules have been integrated into a single program, the
functions of EMFAC99 will be discussed in terms of the individual modules to make it easier to
understand the model.
The “CALifornia Inspection/Maintenance emission FACtor” (CALIMFAC) module
computes base emission rates for each technology group, with and without inspection and
maintenance (I/M) benefits. The base emission rates consist of a zero mile rate and a
deterioration rate for each model year for each pollutant. The “EMission FACtor” (EMFAC)
module computes fleet composite emission factors by vehicle class, speed, ambient temperature,
emissions process and technology for a calendar year. The WEIGHT module provides EMFAC
with activity weighting fractions for individual model years so that the fleet composite emission
factors can be produced. The WEIGHT module also provides the accumulated mileage by
model year for any particular calendar year in order to calculate the “deteriorated” emission rate
for a model year. The BURDEN module calculates the emission estimates (in tons per day) by
multiplying the composite emission factors from EMFAC by activity estimates. Vehicle miles
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traveled, vehicle type distribution, the number of registered vehicles, and speed distributions,
which are the input data for the BURDEN module, are obtained from the California Department
of Motor Vehicles (DMV) and the California Department of Transportation (Caltrans).
Transportation data also are provided by the local councils of government (COGs). The
BURDEN module selects the proper emission rate according to the speed, ambient temperature,
relative humidity, and heavy-duty I/M benefits of a county or sub-region of a county.
Motor vehicle emission rates consist of exhaust emissions of TOG, CO and NOx from
running and continuous starts and TOG emissions from hot soak, diurnal, running and resting
evaporative processes. Running exhaust emissions include emissions from the tailpipe and
through the crankcase after the vehicle has warmed up and has reached a stabilized mode of
operation. Running exhaust emissions also include exhaust particulate matter and particulate
matter from tire wear. Exhaust emissions from continuous starts result from operation of the
vehicle before the engine has reached the stabilized mode of operation. Continuous starts
replace the concept of hot and cold starts used in older versions of the model. The continuous
start rate is a function of the engine time off. This is estimated in increments of 1, 5, 10, 20, 40,
60, 90…720 minutes, with the 1 minute rate being very close to a hot stabilized value, and the
rate for 720 minutes being essentially a cold start. Hot soak emissions result from gasoline
vaporization from elevated engine and exhaust temperatures after the engine is turned off at the
end of a trip. Emissions for the running exhaust and running evaporative processes are assumed
to be proportional to vehicle miles traveled. The emission rates for hot soaks and continuous
starts are expressed in terms of grams per start rather than as grams per vehicle mile traveled.
Diurnal evaporative emissions are expressed in terms of grams per vehicle and result from the
daily changes in the ambient temperature due to expansion of the air-fuel mixture in a partially
filled fuel tank. Running evaporative losses are releases of gasoline vapor from the fuel system
during vehicle operation. Resting loss evaporative emissions are due to fuel line hose or fuel
tank permeation.
There are two ways of characterizing inventories available from the ARB: daily average
and planning. The average daily emissions are expressed as an emission rate in tons per average
day, determined by dividing annual emissions by 365. Countywide and basinwide totals are
provided by source categories. This inventory is updated and published annually by the ARB.
EMFAC99 can produce planning inventories that take into account the effects of diurnal and
seasonal variations in temperature and activity patterns. The planning inventories from
EMFAC99 will provide emission estimates for various periods specified by the user. Hourly
resolution will be possible during an average summer day, winter day, season, month or annual
average.
The Direct Travel Impact Model (DTIM) is the analog to EMFAC99’s BURDEN module
for gridded inventories. DTIM was developed jointly by Caltrans and the ARB. DTIM is
designed to use the output of travel demand models as its activity input for vehicle miles
traveled, trips (or starts) and speed distributions. These activity estimates from the travel
demand model are at the individual link or zone level and therefore provide spatial allocations
for motor vehicle emissions. DTIM applies the same fleet composite emission factors to the
motor vehicle activity, as does the BURDEN module. The user defines an input file of
parameters such as hourly temperatures by grid cell to calculate and distribute emissions into the
grid system. Data are provided by the COGs, particularly the numbers of trips by zone, volumes
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of vehicles by link, and link-specific speed estimates. Factors to allocate trip and VMT estimates
from the model to individual hours are also an input to DTIM.
3.1.2.4
Biogenic Sources
Many studies suggest that VOC emissions from plants may play a role in the formation of
ozone and fine particulate matter. Unfortunately, biogenic hydrocarbons remain a potentially
large and yet not well understood source of emissions. The ARB has developed a GIS-based
biogenic hydrocarbon emissions inventory model (BEIGIS) for use in California. BEIGIS has
been applied in southern California and can be applied in central California if region-specific
databases are available. BEIGIS has four primary data inputs, which are discussed below. Once
these inputs are assembled, ARB will generate a speciated, gridded emission inventory for
biogenic hydrocarbons for the CCOS domain.
The primary data inputs for BEIGIS are: 1) maps showing the distribution of vegetation
species, 2) vegetation biomass distribution maps, 3) leaf mass constants, and 4) plant-specific
emission rates. For the first input, all currently available GIS coverages of the distribution of
plant species for agricultural, natural and urban areas will be compiled and meshed into a
seamless coverage for the CCOS domain. A list of the plant species ranked by acreage will be
developed. This ranking will assist in identifying those key species to be targeted for leaf mass
constants and emission rate measurements and thus focusing on the region-specific data needed
for the CCOS domain. For the second input, biomass can be estimated using the leaf area index
(LAI) in forested areas where species distributions are relatively homogeneous. LAI can be
thought of as the number of leaf layers directly above a given ground surface area. ARB
currently has a 1 km resolution LAI database for California based on satellite imagery. As part
of this study, it is hoped that a 30 meter resolution database can be developed using satellite
imagery. The third input listed is leaf mass constants. Biomass cannot be accurately estimated
using indirect methods such as LAI in areas that contain heterogeneous vegetation species
distributions, such as urban areas. In these areas, biomass is most accurately determined directly
using ground-based botanical surveys. In this methodology, biomass is estimated using the
canopy volume (cubic meters) for each tree surveyed in the field, as well as leaf mass constants
(grams per cubic meter) for those trees. However, leaf mass constants have only been
determined for less than 5% of the 6000 tree species in California. It is hoped for this study that
leaf mass constants can be developed for the most important vegetation species in the CCOS
domain. The final input listed, plant-specific emission rates will be compiled in a database for
the most commonly occurring species in the CCOS domain. At a minimum, the database will
include emission rates for isoprene, monoterpenes, and oxygenated compounds. For those
species for which emission rate measurements are not available, it is hoped during this study to
assign emission rates using a taxonomic method.
3.1.2.5
Emission Forecasts
Emission backcasts and forecasts are made by the ARB using base year emissions in
conjunction with estimates of growth and emission control effectiveness. Emission growth is
based upon available projections of socio-economic trends. Local districts supply growth data,
often using information from local planning organizations. When local data are not available
ARB uses default values from research projects that use surrogates based on business activity.
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Emission reductions are based upon adopted regulations and control measures. Backcasts and
forecasts are also available for planning inventories.
3.1.2.6
“Fast Track” and Final Modeling Inventory Development Process
A gridded emission inventory is a critical input to photochemical air quality models.
Historically, modeling emission inventories have been criticized for using incomplete dayspecific data and for lagging behind the development of other data such as meteorological and air
quality data that are collected in air quality studies. This subsection describes a two-track
approach.
To make a modeling inventory available in a shorter time frame, the EICG is taking a
two-tier approach to the development of the modeling emission inventory that will provide
modelers with a “fast track” inventory for preliminary modeling. This inventory will be
followed by a more detailed modeling inventory that will incorporate all of the day-specific
information collected during the field study.
The districts and ARB will prepare a seasonal emissions inventory for 1999 during
calendar year 2000. The 1999 annual emissions for point sources will be forecasted to 2000 and
then spatially and temporally distributed to create a modeling inventory for a summer weekday.
Area source emissions for 1999 will also be forecasted to 2000. The forecasted emissions will
be spatially distributed using the spatial surrogates used in the 1994 Ozone State Implementation
Plans and temporally distributed to represent a summer weekday. On-road mobile sources will
also be gridded based on the 1994 Plans. Modelers will use this “fast track” inventory to begin
testing the performance of the photochemical model. When the 2000 annual average inventory
is ready, it will be gridded to replace the forecasted 2000 inventory. Day-specific data will
replace the gridded annual average inventory. Results from contracts will be incorporated into
the annual average inventory and/or the day-specific inventories.
3.1.2.7
Requirements for Quality Control and Quality Assurance of Emission Inventory
The EICG is placing special emphasis on ensuring consistency in emission inventory
preparation procedures between all the districts in the modeling domain. The Air Resources
Board will take the lead in coordinating and ensuring quality control and quality assurance
standards for the emission inventory portion of the study. Specific guidelines will be outlined for
assisting state and local agencies for implementing more uniform and systematic approaches to
collecting, compiling (formats and accuracy), and reporting emission inventory data. Also, to
ensure good quality control practices, the EICG will be developing a comprehensive quality
control and quality assurance plan. The plan will include items such as:
•
Quality control checks for collecting non-emission data, updating activity data, and
using appropriate emission factors for calculating emissions;
•
Emission calculation methodology;
•
Updating or revising categories to ensure that the latest methodology is used;
•
Data submittal;
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3.1.2.8
•
Data correction;
•
Documentation; and
•
Quality review of the entire inventory.
Proposed Contractor Studies for Emission Inventory Development
The collection of most of the emission inventory data discussed in the previous sections
will be provided by public agencies as in-kind contributions to the CCOS study. However, there
are some critical elements of the CCOS emission inventory that the members of the EICG do not
have sufficient resources and /or expertise to provide. In general, these needs relate to the spatial
and temporal distribution of emissions from motor vehicles and other area sources. The
emission inventory elements, which will need to be supported by the CCOS study, are listed
below.
Day-Specific Traffic Count Information
Day-specific traffic count information will be gathered during the field study. These
traffic counts are used to create factors to adjust the travel demand volumes to hourly vehicle
volumes. A scoping study is planned to review the availability of traffic data and develop a
sampling plan using statistical methods for the placement of traffic counters for CCOS. Based
on the results of the scoping study, a field sampling plan will be developed to collect and analyze
hourly traffic count information for the CCOS domain. The day-specific data will be
incorporated into the transportation model.
Gridding of On-Road Mobile Source Emissions
The first step needed to create an on-road motor vehicle inventory is to integrate the
transportation data and run DTIM (Direct Travel Impact Model) for the entire CCOS domain.
After compilation of the necessary transportation data from the local COGs who have
transportation planning responsibilities, approximately thirty transportation networks can be
meshed into a single uniform network for CCOS. Once a uniform mesh is created, the original
VMT and speed data will be reassigned to the uniform mesh. ARB’s MVEI (Motor Vehicle
Emission Inventory) models and DTIM can then create gridded, hourly emission estimates of
NOx, TOG, CO and PM for on-road mobile sources.
Gridding of Area Source Emissions
This project is to develop base year and future year gridding surrogates for spatially
distributing area source and off-road emission source categories. The emissions from these
categories are estimated at the county level. For use in photochemical models, area and off-road
emissions are spatially distributed within a county through the use of surrogates. In this context,
a surrogate is an economic or demographic parameter that is correlated to the emissions for a
specific area or off-road source emissions and has a known spatial distribution. Some common
surrogates used for this purpose are population, housing units, and zoning designations. The last
update of the spatial surrogates for area and off-road sources was done for the 1994 plans for
attaining the one-hour ozone standard and was based on 1990 data. These surrogates need to be
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updated to reflect the actual conditions in the year 2000 using the most recent data available.
Surrogates will also need to be projected for future years for use in attainment demonstrations.
Biogenic Emission Model Expansion
The ARB is working to develop a statewide biogenic hydrocarbon inventory. A GISbased biogenic inventory has been created for southern California. ARB is extending the
biogenic inventory further north. However, data on some of the major plant species are missing
for the Central Valley and the Bay Area. Biomass and emission factor data will be needed for
these species.
Day-Specific Information on Agricultural and Controlled Burns
This project would be the collection of day-specific data for controlled burns and
agricultural burns. Local air districts track some information about controlled and agricultural
burns. Because the burn may not occur on the day that the permit is issued, there is some
concern about the accuracy of the data for day-specific use. This research project would review
available data and collect information needed to estimate emissions daily during the study period
from controlled and agricultural burns.
3.1.3
Air Quality Modeling
This section discusses commonly used air quality models and their chemical mechanisms,
advection schemes, grid setup, and boundary and initial conditions. Specific data needs for input
into the air quality model are then presented. These are grouped by meteorological files (output
from the meteorological model), gridded emission inventory files, and air quality files (for
boundary and initial conditions).
There are several air quality models that are well established and within the state-of-theart. The models include CIT, CALGRID, UAM-V, CAMx, CALPUF, MAQSIP, and MODELS3. State-of-the-art air quality models can be applied to simulate photochemical air pollution and
aerosol particles. Many of the models can be used to investigate air pollution from urban to
regional scales. The models are grid models and some allow two-way nesting so that the model
can simulate air pollution on a larger regional scale and on a more highly resolved urban scales
simultaneously.
Three different chemical mechanisms are in wide use; carbon bond IV (CBM-IV),
SAPRC 1997 and RADM2, all of these mechanisms were validated by extensive testing against
environmental chamber data. The chemistry solvers used in the models are relatively fast but
more work is needed to make them more general so that it is easier to make changes in the
chemical mechanisms. Aerosol formation is not yet understood on a fundamental level. The
formation of aerosol particles from the reactions of NOx, SO2 and from volatile organic
compounds (VOC) are often treated by an empirical thermodynamic approach.
The advection of trace gases has seen some improvement in recent years. There are
several possible horizontal advection solvers. The Bott advection solver and the Smolarkiewicz
solver are two typical choices. The Bott solver is becoming more popular than the
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Smolarkiewicz solver because Bott is faster and more accurate with less numerical diffusion than
the Smolarkiewicz solver.
The typical files required by an air quality model are shown in Figure 3.1-1. Gridded data
files should be available with resolution of at least 1 by 1 km if possible. An air quality model
requires files with gridded land use and surface cover over the modeling domain. Typical land
use files divide the land use into at least 11 categories (such as urban, rocky, industrial, etc.). A
file of the UV surface albedos over the modeling domain is also required.
Meteorological Files for Input to Air Quality Model
Typical meteorological data files for air quality modeling are listed in Table 3.1-2. As
discussed in Section 3.1.1, these gridded fields may be generated through the use of objective
analysis of observations or through the use of a meteorological model. The height of the
modeling grid must be greater than the mixing height field. The mixing height must be specified
relative to the ground surface because the grid follows the local ground elevation. Nighttime
conditions for neutral and unstable conditions are directly calculated by the model. The hourly
wind components (x, y, z) must satisfy Equation (3-1) so that the flow fields are mass consistent.
∇ •u = 0
(3-1)
The rate of chemical reactions is highly dependent upon the temperature. The surface
temperature field is used by the chemical solver to determine the correct chemical rate constants.
The absolute humidity field is needed by the chemical solver because many reactions, especially
the generation and loss of HOx radicals, are humidity dependent. The solar radiation scaling
field is needed to calculate the atmospheric stability. The ultraviolet radiation scaling field is
required to calculate photolysis rates. Both the solar radiation scaling field and ultraviolet
radiation scaling field are affected by the presence of clouds and this effect should be
incorporated into the data files for these quantities.
Emissions Inventory Input to Air Quality Model
A great amount of care must be taking in preparing the emissions inventory. Without a
reasonably accurate emissions inventory, it is not possible to accurately model the photochemical
formation of ozone and other pollutants. There are two required emissions files; the area source
emissions inventory and the elevated point source emissions inventory. Table 3.1-3 shows
typical chemicals that need to be included in the emissions inventory. However, the organic
chemical emissions should be given in as much detail as possible, rather than just alkane, alkenes
etc, and should address biogenic sources.
Area source emissions may be considered as part of the boundary conditions (McRae,
1992). The area source file requires that the emissions be given in units of ppm meter / min.
The height of the surface grid box is used to calculate the emissions in units of ppm / min, which
is often called an “emissions frequency” since it has the same units of inverse time.
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Point sources are assumed to be volume sources in the model filling an entire grid square.
If the emissions are vented through a stack, the stack height, diameter, exhaust temperature, flow
rate and source strength are needed to calculate the plume rise height and emissions rate.
Air Quality Input to Air Quality Model
Air quality files of initial conditions and boundary conditions are required. Each grid
square and each layer in the modeling domain starts with a concentration for each chemical
species. One of the difficulties of determining the initial conditions is that there are not enough
measurements available to determine the three dimensional chemical fields for starting the
simulations, thus these initial concentrations must be estimated from a sparse network of ambient
measurements. It will be necessary to interpolate surface concentrations to estimate vertical
distributions. If there are even a few measurements of vertical chemical distributions, e.g., from
aircraft, the relative changes in chemical concentration with height could be used for this
interpolation. Otherwise, typical values for other urban areas will have to be used (Milford et al.,
1989). The CCOS modeling team should examine the different methods by which initial
conditions are estimated from surface and airborne measurements, and state the equations,
assumptions, inputs, and uncertainties for these methods. The team should then determine which
methods are most applicable to the single point and aircraft measurements, and determine which
methods are most useful for approximating initial conditions for integrated air quality modeling.
For the boundary conditions it would be desirable to set the boundaries at locations with
relatively low emission rates. VOC concentrations are usually assumed to be constant or
negligible at the western and northern boundaries and at the top of the study domain. As shown
in Section 2.8, boundary conditions, particularly for formaldehyde, significantly affected model
outputs in the SARMAP modeling, resulting in over-prediction of ozone levels in the Bay Area.
Thus, sufficient field measurements of VOC must be made to realistically represent boundary
conditions in the air quality model. Aircraft measurements of the western boundary are deemed
essential.
3.2
Model Evaluation
comparing concentration estimates from the model to ambient measurements. Diagnostic
evaluations determine if the model is estimating ozone concentrations correctly for the right
reasons by assessing whether the physical and chemical processes within the model are
simulated correctly. This broad task also involves reconciliation of data analysis and
observation-based results with modeling results. It includes the evaluation of modeling
uncertainties, processes, and assumptions and their effect on observed differences among model
results, measurements, and data analysis results.
3.2.1
Evaluation of Meteorological Modeling
Components of meteorological model evaluation include collection of sufficient
measurements to support the following generic evaluation tasks:
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1. Compare the simulated atmospheric processes using MM5 in both fully predictive and
data assimilation modes. Since a number of airborne and remote sensing upper-air
measurements will be available, it is desirable to place emphasis on the data assimilation
mode for the entire field program period. The fully prognostic mode may be desirable for
understanding basic characteristics of specific weather episodes with high pollutant
concentrations, but results from the two approaches, with and without 4DDA, should be
compared.
2. Determine and compare modeled and observed flow patterns in northern and central
California with acceptable horizontal and vertical resolution, using physical
parameterization of the main atmospheric processes (radiation, moisture, clouds, and
fog).
3. Compare detailed information on the vertical wind and temperature structure of the
atmospheric boundary layer with modeled results during the case studies. From aircraft
observations, determine elevated layers with specific stability and dynamics, and
compare to concentrations, stability and dynamics in modeled layers. The vertical
structure is essential for estimates of transport and dispersion of atmospheric pollutants,
as well as for determination of the amount of decoupling of local flows from the air aloft.
Determine to what extent differences are attributable to lack of vertical resolution in the
model.
4. Determine properties of land-sea breezes, urban circulations, local flows (slope and
drainage), and diurnal variation of thermal stability and shear, and compare modeled
results to any available observation-based estimates of these features.
5. Determine spatial characteristics of mixing depth for both convective and stable
conditions and compare to aircraft spirals or other observation-based methods of
determining mixing heights (e.g., profiler return amplitude data).
6. Estimate properties of turbulence transfer and associated vertical fluxes in the boundary
layer.
7. Conduct sensitivity tests of the input parameters (topographic resolution, model grid,
synoptic fields vs. radiosonde network, range and variation of sea/surface temperature,
urban effects/roughness, sinks and sources of heat) to estimate uncertainties in model
results.
8. Quantify differences between the predicted and observed wind fields and stability
parameters for specific case studies.
3.2.2
Evaluation of Emission Inventory Estimates
Section 3.1.2 described the planned development of the CCOS emission inventory data.
Uncertainties in precursor emissions are largely viewed as one of the weaker links in the air
quality modeling process. Independent evaluations of emission inventory estimates must be an
integral component of air quality modeling studies. If ambient measurements and emission data
are correct, consistent relationships between the spatial, temporal, and meteorological variability
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in emissions and ambient measurements should be found. The tasks associated with this
objective will examine these relationships and will explain why they are observed or are not
observed.
Approaches for evaluation of emission inventories include: 1) performance evaluations of
air quality simulation models; 2) source apportionment by receptor modeling; 3) spatial and
temporal comparisons of ambient and emission inventory non-methane organic gas speciation
profiles and pollutant ratios (e.g., CO/NOx and VOC/NOx); 4) comparisons of long-term trends
in ambient pollutant concentrations and concentration ratios with emission inventory trends; 5)
comparisons of on-road measurements with motor vehicle emission models; and 6) fuel-based
inventory based on regional gasoline sales and fleet-averaged, fuel-based emission factors from
remote sensing measurements. These approaches are described below.
Compare Proportions of Species
Comparisons of the ambient pollutant ratios with corresponding ratios derived from
emission inventory estimates have been widely used to determine consistency between emission
inventory estimates and ambient measurements. This approach has been used in the Southern
California Air Quality Study (Fujita et al., 1992), the San Joaquin Valley Air Quality Study
(Fujita et al., 1994), the Lake Michigan Ozone Study (Korc et al., 1993) and by the ARB in
response to requirements of SB 2174 (ARB, 1997). The premise of this evaluation approach is
that in areas with high emission rates, early morning NMOG/NOx and CO/NOx ratios and
speciation profiles in emission estimates should compare well with observed ambient ratios and
speciation profiles for the same time period. Issues that are important to address in this type of
comparison include: 1) the importance of ground level versus elevated plumes and their
influence on ground level measurements; 2) the location of ambient monitoring sites (at urban
locations, nearly all sites are located adjacent to roadways); 3) pollution carry-over from the
previous day(s); 4) the reliance upon NOx as the reference species for the "top-down" ratio
comparison, including the assumption that NOx emissions are reasonably accurate; 5)
hydrocarbon speciation differences between ambient data and the inventory; and 6)
representativeness of the monitoring site in characterizing a grid cell's emissions and ambient
concentrations.
Calculate the average VOC/NOx and CO/NOx ratios and ratios of selected species (e.g.,
acetylene/benzene, ethylene/acetylene, benzene/xylenes, carbon monoxide/selected VOCs, total
aromatics/total VOC, total biogenic species/total VOC, >C10/total VOC, benzene/VOC,
MTBE/VOC) in source-dominated areas for samples which are expected to be dominated by
fresh, local emissions (e.g., morning samples). Compare these ratios with those in emission
inventory grid squares in the vicinity of the sampling site and with ratios in speciated profiles.
Calculate the average ratios of VOC/NOx and ratios of selected species (e.g. homologous
groups/total VOC, oxygenates/total VOC, methyl vinyl ketone and methyl acrolein/isoprene,
other daughter products/precursors) for samples that are expected to contain end products of
photochemical reactions. Compare these ratios with those in emissions inventory grid squares
in the vicinity of the sampling site and with ratios in speciated profiles to determine the extent to
which primary emissions and secondary products contribute to the entire VOC loading. Explain
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differences between inventoried and measured VOC/NOx ratios in terms of chemical reactions,
if possible.
Source Apportionment by Chemical Mass Balance Receptor Modeling
The Chemical Mass Balance (CMB) model (Friedlander, 1973; Cooper and Watson,
1980; Gordon, 1980, 1988; Watson, 1984; Watson et al., 1984; 1990; 1991; Hidy and
Venkataraman, 1996) consists of a least-squares solution to a set of linear equations which
expresses each receptor concentration of a chemical species as a linear sum of products of source
profile species and source contributions. The source profile species (the fractional amount of the
species in the NMOG emissions from each source type) and the receptor concentrations, each
with uncertainty estimates, serve as input data to the CMB model. The output consists of the
contributions for each source type to the total ambient NMOG as well as to individual NMOG
concentrations. The model calculates values for contributions from each source and the
uncertainties of those values. Input data uncertainties are used both to weight the relative
importance of the input data to the model solution and to estimate uncertainties of the source
contributions. CMB software currently in use (Watson et al., 1990) applies the effective
variance solution developed and tested by Watson et al. (1984) because: 1) it calculates realistic
uncertainties of source contributions from both the source and receptor uncertainties; and 2)
chemical species measured more precisely in both source and receptor samples are given greater
influence in the solution than are less precisely measured species. The software also
incorporates collinearity measures (Henry, 1982, 1992) to assess the effects of source profile
similarity on source contribution estimates and their standard errors. The software is interactive,
allowing many sensitivity and assumptions-testing sets to be performed rapidly.
In addition to developing the CMB software, DRI investigators have developed and
formalized the protocol for applying and validating the CMB model (Pace and Watson, 1987;
Watson et al., 1991; 1998) for apportioning particles (Watson et al., 1994) and gaseous organic
compounds (Fujita et al., 1994). In the past six years, DRI investigators have applied the CMB
receptor model to PAMS or PAMS-type speciated NMOG data from southern California (Fujita
et al., 1994), San Joaquin Valley, CA (Fujita et al., 1995a), southeast Texas (Fujita et al., 1995b),
northeastern U.S. (Fujita and Lu, 1998a), Phoenix, AZ (Fujita and Lu, 1997a), western
Washington (Fujita et al., 1997b), El Paso, TX (Fujita, 1998), and Austin, TX (Fujita et al.,
1999).
The application of continuous speciated VOC data in source apportionment offers
additional insights regarding the temporal variations in source contributions that are difficult to
discern from a limited number of canister samples that are integrated over a period of 3 hours or
more. The following graphical display show examples of CMB results obtained from hourly
speciated hydrocarbon data.
• Average source contribution estimates of ambient hydrocarbons by hour of day (e.g.,
Figure 3.2-1)
• Diurnal plots of the average CMB source contribution estimates by site for each day of
the week (e.g., Figure 3.2-2).
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• CMB source contributions and residual hydrocarbon concentrations and graphical
displays of residuals by wind direction and time of day (e.g., Figure 3.2-3).
Another useful graphical display that can also be applied to time-integrated canister
samples show relationships between source contribution estimates (in particular the residual
unexplained mass) and extent of reaction of the ambient air sample (estimated by a ratio of
reactive to unreactive hydrocarbon species. Scatterplots of CMB-predicted versus measured
concentrations for reactive species by site and time of day are also useful in examining the
photochemical age of the ambient hydrocarbons.
For CCOS, we propose comparing the ambient source apportionment at selected
measurement sites with a model and emission inventory based CMB analysis. A dispersion
model could be used to estimate the concentrations of the various species at measurement sites
based upon the meteorology and emission inventory. This calculation should be made for
specific chemical species and not lumped species. First order loss chemistry based upon rate
constants and concentrations of HO and O3 could be included in the dispersion models for each
reactive VOC. The HO concentrations would be calculated from 3-d model such as CAMx. The
dispersion model estimated concentrations at a specific measurement site would be subjected to
CMB analysis. Comparison of the observed source apportionment with the model derived
source apportionment will provide an excellent test of the emission inventory. Comparison of the
model derived source profiles with and without chemistry should provide a useful check of the
CMB approach. Specifically compare ozone precursor, NOx, CO and VOC on a diurnal and
weekday basis as estimated by model with the measurements should also be included.
Remote Sensing of Vehicle Exhaust Emissions and Fuel-Based Inventories
With initial support from the Colorado Office of Energy Conservation in 1987, the
University of Denver (DU) developed an infra-red (IR) remote monitoring system for automobile
CO exhaust emissions. The current instrument measures CO, HC, NO and smoke opacity. The
remote sensing detector (RSD) has been widely used to identify high-emitting vehicles and to
characterize the emissions distributions for large fleets of on-road motor vehicles.
The instrument was designed to emulate the results one would obtain using a
conventional non-dispersive infra-red (NDIR) exhaust gas analyzer. Thus, the RSD is also based
on NDIR (NDUV for NO). An interference filter that transmits infra-red (IR) light of a
wavelength known to be absorbed by the molecule of interest is placed in front of a detector.
Reduction in the signal caused by absorption of light by the molecules of interest produces a
reduction in the detector's voltage output. One way of conceptualizing the instrument is to
imagine a typical garage exhaust NDIR instrument in which the separation of the IR source and
detector is increased from 10 cm to 20-40 feet. Instead of pumping exhaust gas through a flow
cell, a car now drives between source and detector. The light source, across the road, now
contains a deuterium lamp, which is mounted in such a manner that the net result from the source
is a collimated beam of UV and IR light. Because the effective plume path length and amount of
plume seen depends on turbulence and wind, one can only look at ratios of CO, HC, of NO to
CO2. These ratios are termed Q for CO/CO2, Q' for HC/CO2, and Q'' for NO/CO2 and are
constant for a given exhaust plume. By themselves, Q and Q' are useful parameters with which
to describe the combustion system.
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With the aid of a fundamental knowledge of combustion chemistry, many parameters of
the vehicle's operating characteristics can be determined including the instantaneous air/fuel
ratio, the %CO, %HC, or %NO which would be read by a tailpipe probe, and the grams CO, HC,
or NO emitted per gallon of gasoline (gCO/gallon or gHC/gallon) emissions (Bishop and
Stedman 1996). The following formulas assume a fuel density of 750 g/L and 0.86 g carbon per
gram of fuel (Stedman 1998).
gCO/gal = 5671 * Q/( 1+ Q + 3*Q')
gHC/gal = 8911 * Q'/( 1+ Q + 3*Q')
gNO/gal = 6076 * Q''/( 1+ Q + 3*Q')
All pollutants except HC are a specific gas, which can unambiguously be measured and
calibrated. Exhaust HC is a very complex mixture of hydrocarbons and oxygenated organic
compounds. The filter chosen measures carbon-hydrogen stretching vibrations that are present,
but not equally in all HC compounds. This system is entirely adequate for gross polluter
detection. However, adjustments are necessary when remote sensing values are applied to fuelbased emission inventory estimates. Singer et al. (1998) showed that C-H infrared absorption
measurements miss about half of the hydrocarbon mass (especially for aromatics and low
molecular weight alkenes). A scaling factor of 8911 should be replace with 17900 to obtain a
more accurate estimate of mass of hydrocarbons emitted. Also it is conventional to report NOx
emissions as NO2 equivalent, even though most of the direct NOx emissions are in the form of
NO. The g/gal estimate for NO should be increase by the ratio of the molecular weights of NO2
to NO (i.e., 46/30).
Remote sensing has been shown to give accurate readings for CO by means of doubleblind studies of vehicles both on the road and on dynamometers (Lawson et al., 1990; Stedman
and Bishop, 1990). EPA has shown that the readings are closely comparable to laboratory
readings from a vehicle on a dynamometer (Stedman and Bishop, 1990). Lawson and coworkers
used a vehicle with variable emissions under passenger control to show the correctness of the onroad readings (Lawson et al., 1990). Independent studies (Ashbaugh et al., 1992) show that the
CO readings are correct within ±5% and HC within ±15%. Recent work (Stedman et al., 1997)
has shown that fleet average on-road emissions by model year correlate with IM240 readings
with r2 greater than 0.95 for CO, HC and NO.
The fact that remote on-road readings are well correlated with more complex tests is also
illustrated by a study in California (CARB, 1994 and Knapp 1992) in which the remote sensing
readings were used in California immediately to pull over apparently gross polluting vehicles
which were then tested by a team of Smog-Check engineers, and then brought to a dynamometer
and subjected to the EPA IM240 test. Of 79 vehicles tested on IM240, 76 failed and the three
which passed had all failed the previous Smog-Check.
The remote sensor is accompanied by a video system when vehicle identification
information is required. The video camera is coupled directly into the data analysis computer so
that the image of each passing vehicle is frozen onto the video screen. The computer writes the
date, time, and the CO, NO, HC and CO2 concentrations at the bottom of the image. These
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images are then stored on videotape. The digital database also contains uncertainty limits and
opacity data. The height of the sensing beam is typically set at 20-30 cm above the road surface
optimally to observe exhaust plumes from light duty vehicles, including gasoline and dieselpowered vehicles, as long as the exhaust plume exits the vehicle within a few feet of the ground.
The remote sensor is effective across single or multiple traffic lanes of up to 40 feet in
width. However, if one wishes to positively identify and video each vehicle with its exhaust, it is
optimally efficient when used across a single lane of traffic. FEAT operates most effectively on
dry pavement. Rain, snow, and vehicle spray from very wet pavement cause interferences with
the optical beam. These interferences do not cause incorrect readings, rather they cause the
frequency of invalid readings to increase, ultimately to the point that all data are rejected as
being contaminated by too much "noise". At suitable locations exhaust can be monitored from
over one thousand vehicles per hour.
Determine the Effects of Meteorological Variables on Emissions Rates
Select representative sampling sites from major source regions (e.g., urban, industrial,
biogenic, etc.) and examine concentrations of "marker" species for sources as functions of
temperature, relative humidity, wind speed, and other environmental variables which are used to
adjust emission factors. If possible, draw conclusions concerning the efficacy of current
emission factors to respond accurately to changes in meteorological variables. Compare VOC
source contributions when intermittent sources are known to operate and when they are known
not to operate. Attempt to detect effects of different wind speeds, temperatures, solar radiation
levels, and relative humidities on biogenic and industrial emissions.
Determine the Detectability of Day-Specific Emissions at Receptors
Examine day-specific emissions and identify sampling locations and times that
correspond to fires, pesticide applications, spills, etc. From previous measurements of emission
compositions, identify chemical species that are likely to be contributed by that source. Examine
transport and dispersion patterns to determine the likelihood of influence at nearby sites.
Compare the ambient concentrations at likely impact sites with concentrations at that site when
day-specific emissions do not exist. Examine nearby sites and draw conclusions about the region
of influence of intermittent emitters. Draw conclusions regarding the importance of intermittent
emissions for ozone formation.
3.2.3
Air Quality Model Evaluation
comparing concentration estimates from the model to ambient measurements. The key question
in an operational model evaluation is to determine the extent of agreement between simulated
and measured concentrations of ozone and its precursors. The measured and simulated ozone
and its precursor concentrations should agree in their spatial extent and in their timing. Typical
statistics for model evaluation: the comparisons of predicted and observed daily maximum 1-hr
observed ozone concentrations, comparison of 90th percentile concentrations, mean bias (ppb)
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and mean normalized bias (%) and the mean error (ppb) and mean normalized error (%)
(Lurmann et al., 1998).
Examples of other operational evaluation include comparison of model predictions with
airborne measurements of VOCs (total VOC, homologous groups, and lumped VOC classes)
along boundaries and above the mixed layer and the magnitude and constancy of their
concentrations in space and time. List assumptions and input data requirements for VOC
boundary conditions, and estimate the effects of uncertainties caused by insufficient data on the
ability of the model to represent reality. Nitrogenous species concentrations are usually assumed
to be constant or negligible at the western and northern boundaries and at the top of the study
domain. Plot airborne measurements of NO, and NOy along boundaries and above the mixed
layer, then examine the magnitude and constancy of their concentrations in space and time.
Specify the model assumptions for boundary conditions of nitrogenous species, and estimate the
effect if deviations from those assumptions on chemical concentrations.
Diagnostic evaluations are required to determine if the model is estimating ozone
concentrations for the right reasons. The emissions, chemistry and transport are assessed to
determine if these are treated correctly within the model. The emissions may be assessed through
process analysis and mass balance analysis. The chemistry may be assessed through the
measurement of predicted secondary chemical products. The photolytic rate parameters used in
models should be evaluated by comparing parameters estimated by measurements versus model
default values. The transport might be evaluated through comparisons of the spatial and vertical
distributions of ozone and its precursors. This broad task also involves reconciliation of data
analysis and observation-based results with modeling results. It includes the evaluation of
modeling uncertainties, processes, and the assumptions, and their effect on observed differences
among model results, measurements, and data analysis results. A key finding of a diagnostic
evaluation should be to determine the physical and chemical reasons for the concentration
differences between those predicted by models and the measurements. It would also be highly
desirable if the likely model bias induced by a model’s deficiencies could be identified.
Examples of diagnostic tests include examining ratios of chemical species that are sensitive to
specific processes within the model as O3/NOy, O3/NOz, examining the flux of ozone and ozone
precursor across interbasin transport corridors, and comparing concentration changes from
weekdays to weekends.
Jeffries and Tonnesen (1994) have developed methods to modify air quality models so
that the rate of each chemical and physical process that affects a chemical concentration is
integrated and saved in external files. Post processors are used to analyze the integrated rates
(masses determined for each process), to make a detailed mass balance for a simulation. From
the mass balance calculations it is possible to determine quantitatively the effect of each process
on the concentrations of ozone or any air pollutant. Although this process analysis works well in
models it is difficult to apply directly to field measurements due to the lack of data in any
reasonable field campaign.
Due to inherent limitations of measurement techniques and budget considerations, it is
not possible to develop an independent, comprehensive mass balance throughout the study
region. However, an alternative that should be applied to CCOS is a “model assisted mass
balance.” In this approach, measurements are used to check key parameters within simulations
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made for process analysis without the assistance of a model. For example, the concentration ratio
of NO to NO2 or the concentration ratio of a highly reactive VOC to a less reactive VOC could
be compared with the model results. Alternatively model calculated parameters such as HO
could be used along with measured concentrations of VOC to estimate VOC to NOx ratios. Both
approaches should provide strong tests of the conceptual model.
3.3
Data Analysis Methods
As discussed in Section 1.2, data analysis is an essential part of any large field study and
will complement and enhance the model development component of CCOS. The major goals of
CCOS data analysis are to:
•
Evaluate field measurements for applicability for model input, parameterization,
evaluation, and verification.
•
Describe the air quality and meteorology during the field study period and determine
the degree to which these measurements represent other summertime pollution levels.
•
Further develop conceptual models of physical and chemical processes which affect
ozone formation and transport in southern California.
While these data analysis goals provide guidance, a critical outcome of the CCOS
planning process is to specify the:
•
Data analysis objectives and hypotheses to be tested.
•
Data analysis methods and work elements by which objectives can be met and
hypotheses can be tested.
•
Required field measurements to collect sufficient data to apply specified data analysis
methods and work elements.
With these three planning carried out and integrated into publications and reports to meet
data analysis objectives. Data analysis activities are defined here within the following topic
areas: 1) measurement evaluation; 2) spatial, temporal, and statistical descriptions; 3) qualitative
transport characterization; 4) dispersion characterization; 5) emission characterization; 6)
quantitative transport characterization (pollutant fluxes); 7) ozone chemistry; 8) episode
characterization; and 9) refinement of conceptual models. Activities are detailed in the following
sub-sections.
3.3.1
Accuracy, Precision, Validity, and Equivalence of Field Measurements
There has never been a field study to date that did not require substantial examination and
investigation of the measurements prior to their further use in data analysis and modeling. This
first topic area is essential to all subsequent data analysis efforts, and needs to be performed as
data are received into the database. Major concerns focus on: 1) accuracy of VOC peak
identification, and proper, consistent quantification of total nonmethane hydrocarbons; 2)
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representativeness of meteorological measurements, especially those drawn from existing
networks; 3) accuracy and precision of low-concentration measurements, especially NOy and
PAN; and 4) comparability of measurements taken from different networks with different
procedures.
Evaluate the Precision, Accuracy, and Validity of Light and Heavy Hydrocarbon and Aldehyde
Measurements
For sites and sampling periods which represent different expected mixtures of VOCs
(e.g., background, fresh urban, aged urban, regional, forested, industrial), create scatterplots of
the sum of measured species vs. total nonmethane VOC. Calculate slopes, intercepts and
correlations and compare these among different sites and sampling times. Estimate the range of
values for unaccounted hydrocarbons and the degree to which these differ at different sites.
Compare the sum of identified VOC concentrations to total nonmethane VOCs and identify
significant differences among sampling sites and sampling periods. Plot hydrocarbon profiles
(bar charts of percent composition for each carbon number and for the lumped parameter classes
used in photochemical models) and compare these among different sampling periods and
sampling sites.
Compare the detailed speciation of quality assurance samples with the routine speciation
from normal network samples. Ascertain which species are not typically identified in normal
network samples. Estimate the feasibility and effort required to reduce existing chromatograms
with unidentified peaks for more complete speciation of normal network samples. Examine a
selection of chromatograms from routine analyses and determine the feasibility and value of reprocessing these chromatograms for more complete speciation. Use collocated and replicate
analysis results to determine an overall precision for hydrocarbon measurements. Examine the
discrepancies between different analyses as a function of aging time in the canisters and attempt
to quantify the extent to which the gaseous contents change with time.
Evaluate sampling and analysis methods for aldehyde measurements by comparing
collocated and replicate measurements. Determine the extent to which reactions take place in
sampling bags used in aircraft from collocated measurements of bag and cartridge sampling. See
Fung et al. (1993) for examples.
Evaluate the Precision, Accuracy, and Validity of Nitrogenous Species Measurements
Create scatterplots and calculate slopes, intercepts, and correlation coefficients for normal
sensitivity chemiluminescent NOx, high sensitivity chemiluminescent NOy measurements sites,
and spectroscopic measurements (e.g., TDLAS). Determine the equivalency of these different
measurement methods by evaluating these plots and statistics. Examine differences among sites
(along with calibration and performance test data) to attribute differences to instrument
differences or to interferents in the sampled air streams (e.g. HNO3 detected by
chemiluminescence). Create scatterplots and statistics of collocated PANalyzer values. Explain
differences in terms of measurement methods or environmental variables (e.g., interferents).
Compare differences to propagated uncertainty intervals derived from performance tests and
extrapolate the collocated uncertainties to other luminol PAN sampling sites in the network.
Reconcile laboratory comparison data with field comparison data.
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Evaluate the Precision, Accuracy, Validity, and Equivalence of Meteorological Data
Devise methods to compare upper air measurements of wind speed and direction from
profilers, acoustic sounders, and surface wind towers and with corresponding measurements
from radiosondes. Determine equivalent averaging layers and averaging times. Determine times
of day (e.g., early morning, mid-day, and late-day) when these measurements are similar and
when they are not. Draw conclusions regarding the equivalence of these methods. Compare
surface measurements of wind speed and direction with the lowest elevation values from
collocated profile, sonde, and sounder measurements. Stratify comparisons by time of day to
obtain well-mixed and layered vertical structures. Determine when surface measurements are an
adequate estimate of upper air winds and when they are not, both with respect to elevation above
ground level and time of day.
Evaluate the Precision, Accuracy, Validity, and Equivalence of Solar Radiation Data
Compare nearby measurements from the spectral radiation, ultraviolet radiation, and total
solar radiation instruments. Determine the extent to which total solar radiation and spectral
radiation are correlated with the ultraviolet wavelengths which are most influential in
photochemistry. Determine the equivalence or difference between measurements taken in the
CCOS and existing networks.
3.3.2
Spatial, Temporal, and Statistical Distributions of Air Quality Measurements
More data will be produced by field monitoring than can be examined by any data
analysis plan. Summaries need to be created which can serve as a guide to the database and for
the formulation of hypotheses to be tested by more detailed analyses. These summaries may be
examples drawn from a data display package (such as that which was developed to display data
in real-time during the study). The database and display software could then be used in other data
interpretation projects to provide support for their findings.
Examine Average Diurnal Changes of Surface Concentration Data
Create diurnal box plots (which graphically show quartiles, median, mean, and extrema)
for the entire sampling period and for each hour at each site of ozone, NO, NO2, and PAN
concentrations. Note differences in the timing and intensity of peak values as a function of
sampling site, episode, and stratification. Group sites for which diurnal variations behave in a
similar manner. Compare these with plots from selected sites in prior summers where ozone and
NOx data are available. Evaluate the extent to which the summer of 2000 is similar to or
different from prior summers.
Create diurnal box plots for each hour at each site of ozone, NO2, and PAN (where
available) for each episode and the stratified intensive sampling days. Superimpose diurnal
average total VOC, total aldehyde, nitric acid, and particulate nitrate boxes for specified
sampling periods. Note differences in the timing and intensity of peak values as a function of
sampling site and how these are similar to or different from those of the longer-term averages.
Create diurnal box plots of surface temperature, relative humidity, insolation, sigma
theta, and scalar wind speed for each hour at each site for the entire monitoring period and for
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the intensive episodes. Identify similarities and differences among sites and between the
intensive episodes and the entire study period.
Examine Spatial Distributions of Surface Concentration Data
Plot spatial isopleths corresponding to each intensive episode sample for total VOCs,
selected VOC surrogates (selected to represent reactivity class, sources of precursors, or end
products), NOx, NO2, PAN, HCHO, NO/NO2, VOC/NOx, and O3. Note similarities and
differences in patterns with time of day, pollutant, and from episode to episode.
Examine Statistical Distributions and Relationships Among Surface Air Quality Measurements
Combine hourly averages of air quality measurements into sample averages
corresponding to VOC and aerosol samples. For each site, calculate averages, standard
deviations, first, second, third and fourth maxima (with date and time of occurrence), and
minima (with date and time of occurrence) concentrations for each species measured. Identify
differences between morning, afternoon and nighttime, sampling locations, episodes, and
chemical observable. Give special attention to differences between rural vs. urban areas, central
California vs. other air basins, morning vs. afternoon vs. nighttime.
Combine hourly averages of continuous measurements into sampling periods
corresponding to VOC and aerosol samples. For each site, calculate the temporal correlation
coefficients for all measured variables. Identify those variables that are highly correlated
(negatively or positively) with each other and identify observables which might be represented
by a single surrogate at each site. Combine these surrogates with meteorological variables and
note positive or negative correlations among them.
Perform Principal Component Analysis (PCA) on VOC and nitrogenous species.
Calculate eigenvectors of the correlation matrix and perform a varimax rotation to identify
empirical factors that explain the variability in the data. Describe these factors in terms of
physical phenomena, and examine factor scores to determine when each factor has much greater
than or much less than average influence at each site.
Using selected species concentrations, calculate spatial correlations for the intensive
episode samples. Examine correlations and identify which sites are highly correlated (positively
or negatively) with each other. Calculate eigenvectors of this correlation matrix, perform a
varimax rotation, and plot the empirical orthogonal functions that are deemed significant. Use
these to select surrogate sites which can be used to represent neighboring sites for different
observables (the surrogates and the area which they represent may not be the same for all
observables).
Plot surface wind roses for all data at selected times of the day on a single map. Identify
when flow reversals take place and when wind speeds increase or decrease. Note which areas
have the highest frequencies of calms and when they occur. Perform these analyses for
meteorologically stratified cases. Perform analyses for stations near sea-level as well as those
located on higher terrain, and compare and explain differences.
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Examine Vertical Distribution of Concentrations from Airborne Measurements
Plot VOC (total and selected species, aldehydes (total and selected species), NO2, NO,
O3, and bscat as a function of altitude for each spiral. Note similarities and differences with
respect to location, time of day, and chemical species. Give special attention to VOC speciation
in morning samples (i.e., local emissions) over urban (motor vehicle), non-urban (biogenic), and
industrial (oil-field) areas. Compare canister and cartridge samples of VOC and aldehydes taken
in spirals with those taken in circles at the top of the spiral.
Plot spatial distributions of VOC (total and selected species, aldehydes (total and selected
species), NO2, NO, O3, and bscat along aircraft traverses (within the mixed layer). Note
similarities and differences with respect to location, time of day, chemical species, and the
spatial distributions derived from surface-based measurements. Compare concentrations along
boundaries (long-range aircraft) with those measured at locations within the study domain and
note similarities and differences.
Examine the Spatial and Temporal Distribution of Solar Radiation
From the radiometer data, estimate the photosynthetically active radiation (needed for
deposition and biogenic emissions), direct beam solar radiation, diffuse solar radiation, visible
radiation (for light extinction), incident flux, and actinic flux (at frequencies relevant to
photochemical reactions. Create spatial and temporal plots of these observables. Describe
differences between sites and time-of-day in terms of measurement uncertainty, meteorological
and air quality parameters. Note the effects of clouds and smoke on different types of radiation.
3.3.3
Meteorological Transport Phenomena
This topic addresses the major mechanisms for the movement of air into, out of, and
between the different air basins in both horizontal and vertical directions. This requires an
examination of conditions near sea level and also just below, within, and above the inversion
layer.
Determine Horizontal Transport Patterns and Intensities Into, Out of, and Within the Air Basins
Plot 0400, 1000, 1600, and 2200 PST horizontal windfields at three different heights
(surface, within the mixed layer, and above the mixed layer) using continuous and radiosonde
data. Examine the consistency of these flow vectors with those predicted from synoptic weather
maps and pressure gradients. Note similarities and differences with respect to time of day,
elevation, and episode. Associate the directions with the expected phenomena of low-level jet,
slope flows, eddies, coastal meteorology and bifurcation. Examine aircraft data to further
describe the evolution of these phenomena. Determine the time of occurrence, spatial extent,
intensity, and variability of these phenomena.
Plot detailed horizontal wind vectors as a function of height for the Carquinez, Altamont
Pass, Pacheco Pass. Examine these to determine the intensity and duration of transport from the
the Bay Area to the Sacramento and San Joaquin Valleys. Note similarities and differences with
respect to time of day, elevation, and episode.
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Plot back-trajectories for critical receptors (e.g., ozone hot-spots, forested sites, and
transport corridors) for each intensive episode at three elevations. Start trajectories at the time of
maximum ozone concentration. Identify general areas over which air masses might have passed
to reach these receptors. Classify sampling periods into categories that are likely to be
influenced by different types of source areas (e.g., industry, traffic, forest). Track horizontal
pollution movements (e.g., visibility analyses) and determine if ozone values at stations
downwind can be traced to photochemical changes on precursors upwind.
Determine Vertical Transport Patterns and Intensities within the Modeling Domain
Examine wind flow patterns to identify convergence and divergence zones. Examine
acoustic sounder, profiler, and aircraft meteorological data for evidence of vertical exchanges in
these regions. Determine the extent to which surface air is transported above the mixed layer, or
air above is transported into the mixed layer at these locations. Verify this with vertical profiles
of pollutant concentrations from onboard aircraft measurements. Examine and describe the
intensity and duration of upslope flows to estimate the amount and frequency with which
pollutants might be transported above the mixed layer. Verify this with onboard aircraft
pollutant measurements. Plot the vertical velocity structure as a function of time. Examine
monostatic sounder data and profiler data to determine the degree of layering in the atmosphere,
especially during the morning. Identify the locations of wind shears and their effect on layering.
Note the differences in layers at different locations throughout the study area.
3.3.4
Meteorological Dispersion Processes
Dispersion processes address the mixing of pollutants within the mixing layer, especially
elevated and ground-level emissions, dispersion within and between modeling grid cells, and
transport to the surface where deposition of pollutants may occur.
Characterize the Depth, Intensity, and Temporal Changes of the Mixed Layer and Characterize
Mixing of Elevated and Surface Emissions
Plot the spatial distribution of expected mixing depths derived from temperature
soundings at 0400, 1000, 1600, and 2200 PST. Examine sounding, aircraft, and profiler data to
determine the accuracy of interpolations of mixing depths between the 4 per day soundings.
Examine aircraft and profiler data for evidence of other layers within the mixed layer, their time
of formation and dissipation, and their typical duration and intensity. Describe the changes in
layers as a function of time, especially during the morning when rapid changes are taking place
with heating. Associate changes in layers with changes in surface temperature and solar
radiation.
Examine vertical mixing as a function of location and time of day using aircraft data and
continuous profiler and acoustic sounder measurements. Estimate the times of day on which
pollutants emitted from stacks, and pollutants carried over from the previous day above the
mixed layer, will combine with pollutants emitted at the surface. Verify this by examining aloft
and surface level concentrations which are associated with aged emissions.
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3.3.5
Characterize Pollutant Fluxes
A "flux plane" is a rectangular cross-section that is perpendicular to the prevailing
horizontal wind direction at a location between major emissions areas. The major transport
pathways that are suspected of passing through these flux planes were specified in Section 2.
Define the Orientations, Dimensions, and Locations of Flux Planes
Examine wind fields to identify areas in air that is transported across a boundary at
vertical levels, especially at entry and exit points to the Central Valley. Specify the horizontal
and vertical coordinates of these flux planes. Examine the usefulness of different conceptual
definitions of flux (i.e., mass/unit area/time, mass/time, upwind/downwind concentrations along
a wind vector). Estimate uncertainties due to: 1) mis-specifying the portion of ozone flux
attributable to background vs. that generated in the upwind source area; 2) the effects of vertical
and horizontal wind shears or reversals on the definition of the flux plane; 3) variations in wind
speed and direction between measurements; 4) mis-specification of the boundary plane height;
and 5) effects of wind speed and direction variability on flux estimates.
Estimate the Fluxes and Total Quantities of Selected Pollutants Transported Across Flux Planes
Using aircraft spiral and traverse data, lidar data, and ground-based concentration data
for VOCs, NOx, and O3 coupled with average wind speeds that are perpendicular to the chosen
flux planes, calculate the mg/m2-sec of each pollutant which crosses each plane as a function of
time of day. Compare the fluxes for the different planes and assign downwind fluxes to a
combination of fresh pollutant generation and contributions from the upwind flux plane.
Examine fluxes at different layers, especially at night, if major differences are observed in
vertical concentrations and wind speeds. Plot vertical cross-sections of concentrations, wind
speed, and direction.
Compare the magnitudes of inflow to and outflow from regions which are bordered by
flux planes. Advance explanations for major differences between inflow and outflow. Using all
relevant field study data, test the hypotheses that: 1) there is significant local generation of
pollutants; 2) there is significant venting through the mixed layer; and 3) there is substantial
reverse or lateral transport owing to eddies, nocturnal jets, and upslope/downslope flows.
3.3.6
Characterize Chemical and Physical Interactions
Ozone, several nitrogenous species, and significant portions of the VOCs found in the
study domain are not emitted directly from sources, but form from precursors. In particular, it is
necessary to determine where or when ozone concentrations are limited by the availability of
NOX or VOC. These issues are addressed within this topic.
State the equations, assumptions, and input data for ozone formation. Identify those
species in these models which were measured during CCOS. Evaluate the uncertainties
introduced by non-continuous, 2-hour VOC measurements, variations in solar radiation, and
uncertainties in boundary and initial conditions.
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VOC and Nitrogen Budgets
Plot pie charts of VOC speciation (with species expressed as mg/m3 carbon categorized
into homologous groupings such as paraffins, olefins, aromatics, formaldehyde, other aldehydes,
heavy hydrocarbons, unidentified peaks, and total nonmethane VOC minus sum of peaks and as
average carbon number for <C10 and >C10) maps for morning, afternoon, and evening sampling
periods, with the size of the pie proportional to the total VOC. Select relatively inert compounds
(e.g., ethane and acetylene) as well as reactive species to help identify sources. Determine the
extent to which total VOCs are accounted for as a function of location and time of day. Advance
hypotheses regarding the compositions of the unidentified peaks and the unaccounted-for VOC.
Test these hypotheses by examination of example chromatograms from source-oriented and
receptor-oriented sampling sites and from direct samples of source emissions. Draw conclusions
regarding the effects of these unidentified and unknown fractions on chemical mechanisms used
in air quality models.
Plot pie charts of the gaseous and particulate nitrogen (NO, NO2, HNO3, PAN, HONO,
and NO3-), and carbon (heavy and light VOCs, aldehydes, and organic aerosol carbon) as a
function of location. Make the radius of each pie proportional to the total number of N, S, or C
atoms, and make each wedge proportional to the number of N, S, or C atoms contributed by each
species. Examine the distributions of these species among gaseous and particulate phases as a
function of time and location.
Reconcile Spatial, Temporal, and Chemical Variations in Ozone, Precursor, and End-Product
Concentrations with Expectations from Chemical Theory
Calculate pseudo-steady-state values of ozone using NO/NO2 ratios (from maps
generated in work element 2.2.1) and assumed photolysis rates. Compare the spatial distribution
of these calculated values with the measured values plotted in earlier data analysis activities.
Explain possible reasons for differences. Compare NO2 plots to PAN plots to determine when
NO2 is less than PAN.
Calculate average VOC-OH reactivity and determine VOC vs. NOx limitations. Estimate
(from literature) typical VOC-OH rate constants for individual VOC species. Calculate an
average VOC reactivity by weighting each rate constant by the proportion of total VOC
represented by its corresponding species. Compare the resulting average rate constants at
different types of sites (urban, rural, oilfield) and sampling times (morning, day, night) with the
VOC-OH reactivity of a standard urban VOC mixture (~3500 ppm-1min-1). Stratify data near
elevated point sources to separate periods when plumes are above the mixed layer from periods
for which elevated plumes are within the mixed layer. Note similarities and differences with
respect to site-type and time of day. Calculate adjusted VOC/NOX ratios by multiplying a
reference value of VOC/NOX (for typical urban areas) by the ratio of the adjusted reference rate
constant. Relate the results to bands of demarcation on an EKMA-type diagram between regions
of VOC, combined, and NOX limitation in producing ozone. Use the results to classify areas of
the study domain into ones for which the ozone-forming potential is limited by VOC or NOX.
Simulate ozone-producing potential of ambient VOC samples using an accepted chemical
mechanism (e.g., CBM-IV, RADM, SAPRC) in a simple box using a range of diurnal radiation
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profiles from the radiometers. Artificially add NOx concentrations and calculate time to
formation of peak ozone and the amount of ambient carbon carried over or available for reaction
the next day for an assumed radiation intensity and time. Define an "ozone-forming-potential"
figure of merit and map these as a function of location, time, and VOC level. Estimate the
effects of VOC, radiation, and other measurement uncertainties on ozone-forming potential.
Identify differences in potential owing to changes in the model mechanisms (e.g., compare
results from CBM-IV with SAPRC and RADM).
Stratify episodes by high and low photochemical potential days, and compare
photochemical products along trajectories estimated by other data analyses. Further stratify
these episodes by VOC/NOx ratios in the western and northern parts of the study domain and
estimate the extent to which this ratio affects the maximum ozone levels in the eastern and
southern portions of the study domain. Performing the same analyses as above for ozone levels
in central California air basins. Recalculate VOC/NOx ratios for specific reactivity classes,
especially aromatics, and examine receptor area ozone concentrations for cases of high and low
ratios in the source areas. Examine the emission maps to compare the quantity of fresh
hydrocarbon and NOx injected along the trajectories, and determine the degree to which this
might interfere with the conclusions drawn from the VOC/NOx ratios in the source areas.
Examine aircraft traverses and compare ozone, NOx, and VOC levels to simple
equilibrium calculations. Examine nighttime concentrations of O3 and precursors above the
mixed layer and off the coastline to determine the degree of carryover from the previous day.
Compare VOC/NOx ratios above the mixed layer with those calculated from localized emissions
grid squares near the northern and western boundaries of the study domain. Identify potential
causes of discrepancies, if they are found.
Compare day-to-day changes in emission patterns (using day-specific inventories) with
O3 and VOC concentrations for otherwise similar meteorological conditions. Compare oxidant
values in cases with high ambient aromatic VOC concentrations to values obtained when
aromatic VOCs are low.
Apply and Evaluate Ozone Receptor Models to Determine VOC and NOx Limitations
Several receptor-oriented models have been developed and applied to determining
relationships between oxides of nitrogen, VOC, and ozone levels. In each case, evaluate and
determine the applicability of these models to CCOS by evaluating their fundamental
assumptions as part of applying them to appropriate measurements from the database.
Compute correlations and regression relationships between ozone, NOx and NOy for
measurement locations with fresh and aged emissions. Determine the extent to which ozone
increases in photochemically aged air when NOy is less than 1 ppb and when it is higher than 10
ppb (e.g., Trainer et al., 1993; Jacob et al., 1995). Calculate Integrated Empirical Rates (e.g.,
Johnson, 1984, Blanchard et al., 1994; Chang et al., 1995) to determine at what downwind
distances from major source regions NOx reductions or VOC reductions would most affect
ozone concentrations. Compare these distances with those determined by other methods.
Examine ratios among ozone and nitrogenous species (e.g., Sillman et al., 1990; Sillman, 1995;
Milford et al., 1994; Jacob et al., 1995; Watkins et al., 1995) to estimate when VOC and NOx
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limitations might apply.
Chameides (1995).
3.3.7
Apply the observation-based model (OBM) of Cardelino and
Characterize Episodes
Each of the episodes of two- to four-day duration has similarities and differences with
respect to emissions, meteorology, transformation, deposition, and air quality levels. These
episodes may be high for similar or for different reasons. Information derived the proceeding
activities is synthesized to provide an anatomy of each episode. Conclusions are drawn with
respect to which episodes are, for all practical purposes, the same, and which ones are
substantially different.
Describe Each Intensive Episode in Terms of Emissions, Meteorology, and Air Quality
Prepare written overviews of each intensive sampling period. Describe the synoptic
meteorology leading up the episode and summarize the forecasting rationale. Illustrate, with
plots generated in other work elements, the general wind flows for the duration of the period and
any deviations from these generalizations. Identify major emissions events, identified as
significant in other work elements, which affected pollutant concentrations. Summarize the key
pollutant concentrations at key times and key locations in the study domain. Summarize the
completeness and validity of the data set from each episode with respect to modeling of ozone.
Identify the transport and transformation mechanisms that are likely to be dominant in each
episode. Evaluate each episode for its potential use in model testing and control strategy
evaluation.
Determine the Degree to which Each Intensive Episode is a Valid Representation of Commonly
Occurring Conditions and its Suitability for Control Strategy Development
Examine continuous meteorological and air quality data acquired for the entire study
period, and determine the frequency of occurrence of days which have transport and
transformation potential similar to those of the intensive study days. Generalize this frequency
to previous years, using existing information for those years.
3.3.8
Observation-Driven Methods
Observation-based approaches use high-quality ambient measurements of O3, its
precursors, and/or secondary products of the photochemical mechanism to diagnose the
underlying relationship between O3 production and sources of O3 precursors. The methods range
from those that rely solely on analysis of chemical measurements to more complex methods that
rely on photochemical models as well as observations to diagnose VOC- or NOx-limitation.
The following are four such methods.
1. The correlation between ozone and NOy or NOz (e.g., Trainer et al., 1993; Jacob et al.,
1995)
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2. The Integrated Empirical Rate (IER) model of Johnson (1984), as revised by Blanchard et
al., (1994) and Chang et al., (1995). This method is also known as Smog Production (SP)
algorithms.
3. The use of indicator species and ratios (e.g., NOy, NOz, O3/NOy, O3/HNO3,
HCHO/NOy, H2O2/HNO3, H2O2/NOy, and H2O2/NOz) (Sillman et al., 1990; Milford et
al., 1994; Sillman, 1995; Watkins et al., 1995; Jacob et al., 1995), and
4. The obervation-based model (OBM) of Cardelino and Chameides (1995).
Observation-based methods provide procedures for deriving O3 precursor relationships
that are independent of emission inventories and other inherently uncertain inputs. However,
these methods are not without their own limitations and uncertainties, and are best used in
conjunction with air quality simulation models.
3.3.9
Although past transport studies have documented pollutant transport on specific days,
they have not always quantified the contribution of transported pollutants to ozone violations in
the downwind area. Quantitative estimates of the contribution of transported pollutants to ozone
violations in the downwind area can be accomplished by photochemical grid modeling and by
advanced data analysis techniques such as “flux planes” measured by aircraft which traverse a
vertical plane perpendicular to a suspected transport corridor at different elevations.
In principle, well-performing grid models have the ability to quantify transport
contributions. However, many of the interbasin transport problems involve complex flow
patterns with strong terrain influences that are difficult and expensive to model. Upper-air
meteorological and air quality data in critical transport locations is generally required in order to
properly evaluate and use grid models for quantifying transport contributions. In combination
with modeling, data analyses can improve the evaluation of modeling results and provide
additional quantification of transport contributions.
In order to quantify pollutant transport and to provide data for modeling and data
analyses, surface and aloft measurements are needed at locations where transport can occur and
at the times when transport is occurring. These monitoring locations include in and near
mountain passes, along coastlines, offshore, and at various locations in the downwind air basin.
Previous studies (e.g., Roberts et al., 1993) have used aircraft measurements to calculate
transport across flux planes. Vertical planes, intersecting the profiler sites downwind of and
perpendicular to the transport path, can be defined and provide estimates of transport through
these passes using surface and aircraft measurements of pollutant concentrations and surface and
wind profiler data for volume flux estimations.
3.3.10 Contributions of Elevated NOx Sources to Downwind O3
Power plants and other sources with tall stacks are significant sources of NOx, which in
the presence of NMHC can lead to catalytic formation of ozone downwind of the source.
However, close to the stack there is a temporary decrease in ozone levels due to “titration” by
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high levels of NO in the near field of the plume. Further downstream, ozone levels above the
local background indicate net ozone production due to the reaction of plume NOx with NMHC
that are entrained into the plume in the dilution process. However, question remain as to how
much ozone is actually produced in the plume, how the ozone production efficiency depends on
the chemical composition of the plume, and what the relative contributions of power plants are to
high ozone episodes ozone in downwind areas. Therefore one of the most important issues for air
quality modeling in CCOS is the treatment of plumes from elevated point sources containing
high concentrations of NOx.
As discussed in section 1.5.4 there are relatively simple Gaussian treatments of PiG and
these along with the more detailed SCICHEM will be evaluated using CCOS data as discussed in
Appendix D. SCICHEM was evaluated as part of the 1995 Nashville/Middle Tennessee Ozone
Study but since the composition of the plumes differs in central California, CCOS data should be
used to evaluate the new modules.
Models use plume-in-grid to represent the chemistry and dispersion of large elevated
point source plumes. Typically the individual sub-grid scale plumes are simulated in a
Lagrangian mode. The plumes are assumed to have a Gaussian distribution that can be treated
analytically and these plumes may extend 10 to 20 km from the point source. The chemistry is
often simplified to account only for the high NOx concentrations found within the plumes and the
effect of NOx on ozone concentrations while ignoring VOC chemistry. The plumes disperse and
undergo chemical reaction until the spatial extent of the plume, and the pollutant concentrations
reach levels that can be adequately represented within a 3-d model grid. Criteria for terminating
the plume, i.e., mixing its contents into the regular grid, include plume size relative to the size of
the grid cell and the age of the plume.
It is not clear that the treatment of plumes by state-of-the-art models is adequate. Vertical
transport (e.g., plume rise and fall the plume during downwind transport) may not be adequately
described. Recent power plant plumes studies (Senff et al., 1998) utilized airborne ozone and
aerosol lidar in conjunction with other instrumented aircraft. Because of its ability to
characterize the two dimensional structure of ozone and aersols below the aircraft, the airborne
lidar is well suited to document the evolution of the size and shape of the power plant plume as
well as its impact on ozone concentration levels as the plume is advected downwind. This
aircraft was considered as a study option, but budget constraints will prevent its use during
CCOS. However, aircraft measurements of NOx, ozone and VOC concentrations made in plumes
are required to test the validity of the treatment of plume dispersion and chemistry and the
procedures for terminating the plume into the regular model grid by plume-in-grid
parameterizations.
3.3.11 Deposition Studies
During the California Ozone Deposition Experiment (CODE) in 1991, aircraft and towerbased flux measurements were taken over different types of San Joaquin crops, irrigated and
non-irragated fields, and over dry grass. Results are briefly summarized in Pun et al. (1998) and
include estimates of ozone deposition velocities of 0.7-1.0 cm/s (Pederson et al. 1995). Vertical
fluxes (deposition rates) can be calculated if a vertical gradient is known (assumed or measured).
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Order of magnitude calculations by Pun et al. show that dry deposition can be a few percent (~35%) of the total ozone budget in the San Joaquin Valley.
Reasons for further flux and deposition measurements during the CCOS study, with its
expanded geographic scope, are at least three-fold:
1. Further consideration of NO sources. While NO was not considered to be a problem at
three sites reported on by Pederson et al. (1995), Mahrt et al. (1995) found that rapid
titration of O3 by NO did affect aircraft-based flux O3 surface flux measurments.
Controlled experiments that employ a spatially diverse array of NOx monitors upwind or
surrounding a flux measurement site could help quantify this effect.
2. Further consideration of relative humidity effects. The Sacramento River Delta region
and coastal regions are part of the CCOS domain. Ozone is not highly water soluble, but
McLaughlin and Taylor (1981) report that ozone deposition to plants can increase by a
factor of 2-3 when relatively humidity changes from 35% to 75%. Based on this, Mahrt et
al. (1995) suggest that ozone deposition may show a more complex spatial and/or
temporal pattern than heat and moisture fluxes. Deposition studies for CCOS could
include measurement of ozone fluxes over more varied types of terrain within the CCOS
study area, including the higher humidity areas of delta or the coastal areas. Desjardins et
al. (1995) report that aircraft flux measurements compared well with tower-based flux
measurement at two instrumented vineyard sites during CODE. Aircraft could be used to
expand the diversity of sites measured.
3. Precurser deposition rates. Fluxes of NOx and VOC were not measured during CODE.
Specify the equations, assumptions, input data, and uncertainties for the deposition
model. From the examination of micrometeorological data and vertical flux measurements,
determine the extent to which these equations represent reality, and the degree to which
assumptions are complied with. Evaluate the effects of input data uncertainties on deposition
estimates.
3.3.12 Reformulate the Conceptual Model
The conceptual model described in Section 2 must be revisited and refined using the
results yielded by the foregoing data analyses. New phenomena, if they are observed, must be
conceptualized so that a mathematical model to describe them may be formulated and tested.
The formulation, assumptions, and parameters in mathematical modules which will be included
in the integrated air quality model must be examined with respect to their consistency with
reality.
3.3.12.1
Refine Conceptual Models of Pollutant Emissions
Specify motor vehicle emission model equations, assumptions, input data, and
uncertainties. Reconcile the ambient species ratios found in ambient data (as studied in prior
work elements) with ratios determined from emissions models in terms of model or measurement
biases. By stratifying samples, estimate the effects of different meteorological variables on
emission rates. Pay special attention to the validity of models regarding vehicle age,
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maintenance, effects of hot and cold operating conditions, and vehicle-type distributions.
Recommend improvements to emission models based on these observations.
Specify biogenic emission model assumptions, input data, and uncertainties. Reconcile
ambient hydrocarbon species ratios at sites located in agricultural and forested areas with those
determined from emission models in terms of model or measurement biases. By stratifying
samples, estimate the effects of different meteorological variables, especially wind speed, on
emission rates. Examine chemical speciation as a function of vegetation type. Examine total
ammonia concentrations as a function of nearby soil types and fertilizer applications.
Recommend improvements to emission models based on these observations.
Specify oilfield and refinery emission model assumptions, input data, and uncertainties
for cogeneration systems, diesel-powered internal combustion engines associated with pumps,
natural gas plants, drilling rigs, remote operations, spills, leaks from valves and flanges,
evaporation from storage tanks, cyclic and non-cyclic well head vents, sumps, measuring
stations, evaporation from tanker trucks and loading racks, pumping stations, and vacuum trucks,
and gasoline stations. Evaluate how well these emissions estimates relate to reality, and which
variables are not included in current methodology.
Reconcile ambient hydrocarbon species ratios at sites located near sewage plants and
feedlots with those determined from emission models in terms of model or measurement biases.
By stratifying samples, estimate the effects of different meteorological variables, especially
temperature and relative humidity, on emission rates. Recommend improvements to emission
models based on these observations.
Intermittent events include fires, entertainment and sporting events, and industrial upsets.
Specify the models that treat intermittent events, their assumptions, and input data. Examine the
magnitude of emissions from intermittent events with respect to other emissions to determine
whether or not these emissions are significant. Recommend improvements to emission models
based on these observations.
Examine the variability in emissions for intensive analysis days. Compare this variability
to that assumed by point source models. Recommend improvements in emission models based
on these observations.
3.3.12.2
Refine Models of Pollutant Transport and Dispersion
Thermal lows are caused by surface heating over the Mojave Desert and the Central
Valley and are one of the major causes of flow between the Bay Area and the central coast to the
Central Valley. Specify meteorological model assumptions and input data relevant to calculating
the vertical profile of this transport pathway. For unidirectional flows, the mathematical
formulation should show a minimum in turbulence or laminar flow occurring at the height of the
wind maximum, and the width of the jet should be adequately estimated. The model formulation
should allow this region of minimal turbulence to intensify the nighttime inversion in the vicinity
of the flow and to inhibit vertical transport. Reconcile the model formulation with the location,
dimensions, intensity, duration, and frequency of the thermal low transport. Quantify and
compare measurement and model uncertainties.
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Upslope flows in the Sierra Nevada result from heating of the mountain sidewalls by the
afternoon. The downslope flows commence after sunset when the slopes cool. When the
intensity of these up-slope winds is large, pollutants are vented from the Central Valley into the
air above the mixed layer, and possibly into neighboring air basins. Examine the assumptions
and input data of the meteorological model, which relate to slope flows. Determine whether or
not the intensity and timing of these flows corresponds to those observed in other data analysis
work elements. Identify those cases in which upslope flows vent pollutants above the mixed
layer or over the summit, and determine the extent to which the mathematical formulation can
represent these cases. Identify areas of uncertainty in the modeling and measurement processes
and attempt to quantify these uncertainties.
Marine airflows mix, age, and transport pollutants from coastal air basin to the Central
Valley. These airflows develop primarily from strong coast-to-inland pressure gradients. The
most visible artifact of these flows is the marine stratus that forms along the coastal areas. A
stronger inversion, a feature of the subtropical high, is usually present above the marine stratus
layer and may extend to heights of 100 to 1000 m ASL, sufficiently deep to extend over the
coastal mountains. Examine the results of windflow analysis to determine where and when this
transport phenomenon occurs. Determine the presence or absence of fogs and high humidity at
night, with the intent of understanding whether or not NOx transported off the coast can be
rapidly transformed to particulate nitrate that could be advected on-shore during the next day.
Examine meteorological model formulation for those features that will describe these flows.
Identify uncertainty in the modeling and measurement processes and attempt to quantify these
uncertainties. Compare different pollutants with diurnal/spatial variations in humidity, visibility,
cloud cover, and solar radiation for the same air mass. Evaluate episodes using Leipper
Inversion Based Statistics (LIBS) discussed in Section 2.6.
Identify the occurrence and thickness of different atmospheric layers from other data
analysis work elements. Determine the modeled layer structure. Examine the extent to which
layers can be assumed to be uniform, or must vary in depth as a function of location and time.
Evaluate the uncertainty introduced to the modeling process by anticipated deviations from
layering assumptions.
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Table 3.1-1. Emission Inventory Roles and Responsibilities
Agency
Program Responsibilities
Stationary
Sources
Area
On-road
Sources - Mobile
Agricul- Sources
tural and
Controlled
Burns
'
Air
Resources
Board
Local Air
Districts
Area
Sources
'
'
'
State and
Federal
Agencies
NonAnthropogenic
Sources
–
Biogenic
QC
&
QA
Checks
'
'
'
'
'
'
'
Councils
of
Government
Contractor
NonAnthropogenic
Sources
–
Wildland
Fires
'
'
'
'
'
'
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'
'
Version 3 – 11/24/99
Table 3.1-2. Required meteorological files.
Mixing depth field
Three dimensional wind field
Surface temperature field
Absolute humidity field
Solar radiation scaling field
Ultraviolet radiation scaling field
Table 3.1-3. Typical chemical species in an emission inventory for modeling.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
Nitrogen dioxide
Nitric oxide
Sulfur dioxide
Carbon monoxide
Methane
Ethane
Higher alkanes
Ethene
Terminal alkenes
Internal alkenes
Isoprene
Benzene
Toluene
Xylene and more reactive aromatics
Carbonyls
Formaldehyde
Acetaldehyde and higher aldehydes
Ketones
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Air Quality
Geographic
Emissions
Air Quality
Model
Photolysis
3-d Concentration
Fields of O3, PM10 ...
GIS
Graphical Display and Analysis
Figure 3.1-1. Files typically required by photochemical air quality model.
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Version 3 – 11/24/99
Rider College, NJ
60
Contributions (µg/m3)
50
40
30
20
10
0
-10
00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23
(EDT)
HD Exhaust
LD Exhaust
Liquid Gasoline
Gasoline Vapor
CNG
LPG
Biogenic
Unexplained
Figure 3.2-1. Average source contribution estimates of ambient hydrocarbons at Rider College,
NJ during summer, 1995 by time of day. Source: Fujita and Lu, 1998.
Rider College - LD Exhaust
20
µg/m
3
30
10
0
00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Monday
Friday
Tuesday
Saturday
Wednesday
Sunday
Thursday
Figure 3.2-2. Average source contribution estimates of hydrocarbons at Rider College, NJ
during summer, 1995 (EDT) by day of the week. Source: Fujita and Lu, 1998.
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Rider College (00-06 EDT)
N
N
30
30
HD Exhaust
25
NW
Liq. Gas.
15
W
SW
Liq. Gas.
W
SE
CNG
Biogenic
E
0
SW
SE
S
S
N
NW
HD Exhaust
30
25
LD Exhaust
25
NE
NW
Liq. Gas.
HD Exhaust
LD Exhaust
NE
20
Liq. Gas.
15
Gaso. Vap.
15
Gaso. Vap.
10
CNG
10
CNG
Biogenic
5
W
N
30
20
W
E
0
SW
SE
W
W
W
Biogenic
5
E
0
SW
W
W
Gaso. Vap.
5
Biogenic
E
0
LD Exhaust
10
CNG
5
NE
20
15
Gaso. Vap.
10
HD Exhaust
25
NW
LD Exhaust
NE
20
SE
S
S
Figure 3.2-3. Wind directional dependence of source contributions by time of the day at Rider
College, NJ during summer, 1995.
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3. 0 REQUIREMENTS FOR AIR QUALITY MODELING SYSTEMS AND DATA
ANALYSIS.....................................................................................................................3-1
3.1 Modeling System Inputs ..........................................................................................3-1
3.1.1 Meteorological Modeling...............................................................................3-1
3.1.2 Emission Inventory Development..................................................................3-4
3.1.3 Air Quality Modeling ...................................................................................3-12
3.2 Model Evaluation...................................................................................................3-14
3.2.1 Evaluation of Meteorological Modeling ......................................................3-14
3.2.2 Evaluation of Emission Inventory Estimates ...............................................3-15
3.2.3 Air Quality Model Evaluation......................................................................3-20
3.3 Data Analysis Methods ..........................................................................................3-22
3.3.1 Accuracy, Precision, Validity, and Equivalence of Field Measurements ....3-22
3.3.2 Spatial, Temporal, and Statistical Distributions of Air Quality
Measurements...............................................................................................3-24
3.3.3 Meteorological Transport Phenomena .........................................................3-26
3.3.4 Meteorological Dispersion Processes...........................................................3-27
3.3.5 Characterize Pollutant Fluxes ......................................................................3-28
3.3.6 Characterize Chemical and Physical Interactions ........................................3-28
3.3.7 Characterize Episodes ..................................................................................3-31
3.3.8 Observation-Driven Methods.......................................................................3-31
Areas.............................................................................................................3-32
3.3.10 Contributions of Elevated NOx Sources to Downwind O3........................3-32
3.3.11 Deposition Studies .....................................................................................3-33
3.3.12 Reformulate the Conceptual Model ...........................................................3-34
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4.0
Chapter 4: CCOS Field Measurements
CCOS FIELD MEASUREMENT PROGRAM
This section summarizes the major components of the field measurement program with
consideration of alternatives, options, and tradeoffs. Cost estimates are itemized in Section 5.
Measurement methods are described in Appendix A and requirements for quality assurance and
data management are specified in Appendices B and C, respectively.
4.1
Study Design Principles
The proposed measurement program is designed to meet the goals and technical
objectives specified in Section 1 and incorporates the following design guidelines that combine
technical, logistical and cost considerations, and lessons learned from similar studies.
1. CCOS is designed to provide the aerometric and emission databases needed to apply and
evaluate atmospheric and air quality simulation models, and to quantify the contributions
of upwind and downwind air basins to exceedances of the federal 8-hour and state 1-hour
ozone standards in northern and central California. While urban-scale and regional
model applications are emphasized in this study, the CCOS database is also designed to
support the data requirements of both modelers and data analysts. Air quality models
require initial and boundary measurements for chemical concentrations. Meteorological
models require sufficient three-dimensional wind, temperature, and relative humidity
measurements for data assimilation. Data analysts require sufficient three-dimensional
air quality and meteorological data within the study region to resolve the main features of
the flows and the spatial and temporal pollutant distributions. The data acquired for
analyses are used for diagnostic purposes to help identify problems with and to improve
models.
2. Since episodes are caused by changes in meteorology, it is useful to document both the
meteorology and air quality on non-episode days. For this reason, surface and upper air
meteorological data as well as surface air quality data for NOx and ozone will be
continuously collected during the entire summer of 2000. The database will be adequate
for modeling and a network of radar profilers will allow increased confidence in
assigning qualitative transport characterization (i.e., overwhelming, significant, or
inconsequential) throughout the study period.
3. Many of the transport phenomena and important reservoirs of ozone and ozone
precursors are found aloft. CCOS is designed to include extensive three-dimensional
measurements and simulations because the terrain in the study area is complex and
because the flow field is likely to be strongly influenced by land-ocean interactions.
Several upper air meteorological measurements are proposed at strategic locations to
elucidate this flow field.
4. Although specific advances have been made in characterizing emissions from major
sources of precursor emissions, the accuracy of emission estimates for mobile, biogenic
and other area sources at any given place and time remains poorly quantified. Ambient
and source measurements, with sufficient temporal and chemical resolution, are required
to identify and evaluate potential biases in emission inventory estimates.
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5. Past studies document that the differences in temporal and spatial distributions of
precursor emissions on weekdays and weekends alter the magnitude and distribution of
peak ozone levels. Measurements are needed to evaluate model performance during
periods that include weekends.
6. Boundary conditions, particularly for formaldehyde, significantly affected model outputs
in the SARMAP modeling resulting in over-prediction of ozone levels in the Bay Area.
Measurements of documented quality and adequate sensitivity are needed along the
western boundary of the modeling domain to adequately characterize the temporal and
spatial distributions of ambient background levels of ozone precursors.
7. The measurements should be designed such that no single measurement system or
individual measurement is critical to the success of the program. The measurement
network should be dense enough that the loss of any one instrument or sampler will not
substantially change analysis or modeling results. The study should be designed such
that a greater number of intensive days than minimally necessary for modeling are
included. This helps minimize the influence of atypical weather during the field program
and decreases the probability of equipment being broken or unavailable on a day selected
for modeling. Most measurements should be consistent in location and time for all
intensive study days and during the entire study period (i.e., no movement of
measurements). In this way, one day can be compared to another. Continuous
measurements should be designed to make use of the existing monitoring networks to the
extent possible.
4.2
Study Domain
The study domain includes most of northern California and all of central California. The
northern boundary extends through Redding and provides representation of the entire Central
Valley of California. The western boundary extends approximately 200 km west of San
Francisco and allows the meteorological model to use mid-oceanic values for boundary
conditions. The southern boundary extends below Santa Barbara and into the South Coast Air
Basin. The eastern boundary extends past Barstow and includes a large part of the Mojave
Desert and all of the southern Sierra Nevada.
4.3
Study Period
The CCOS field measurement program will be conducted during a three-month period
from 6/15/00 to 9/15/00 (study period). This period corresponds to the majority of elevated
ozone levels observed in northern and central California during previous years. Continuous
surface and upper-air meteorological measurements and surface air quality measurements of O3
NO, NOx or NOy will be made hourly throughout the study period in order to provide sufficient
input data to model any day during the study period. These measurements are made in order to
assess the representativeness of the episode days, to provide information on the meteorology and
air quality conditions on days leading up to the episodes, and to assess the meteorological
regimes and transport patterns which lead to ozone episodes.
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Additional continuous surface air quality measurements will be made at several sites
during a shorter two-month study period from 7/6/00 to 9/2/00 (primary study period). These
measurements include nitrogen dioxide (NO2), peroxyacetylnitrate (PAN) and other
peroxyacylnitrates (PAcN), particulate nitrate (NO3-), formaldehyde (HCHO), and speciated
volatile organic compounds. These measurements allow detailed examination of the diurnal,
day-to-day, and day-of-the-week variations in carbon and nitrogen chemistry at transport
corridors and at locations downwind of the San Francisco Bay Area, Sacramento, Fresno, and
Bakersfield where ozone formation may be either VOC- or NOx-limited depending upon time of
day and pattern of pollutant transport. These data support operational and diagnostic model
evaluations, evaluations of emission inventories, and corroborative observation-based data
analyses.
Additional data will be collected during ozone episodes (intensive operational periods,
IOP) to better understand the dynamics and chemistry of the formation of high ozone
concentrations. The budget for CCOS allows for up to 15 days total for episodic measurements.
With an average episode of three to four days, four to five episodes are likely. These
measurements include instrumented aircraft, speciated VOC, and radiosonde measurements,
which are labor intensive and require costly expendables or laboratory analyses. IOPs will be
forecasted during periods that correspond to categories of meteorological conditions called
scenarios, which are associated with ozone episodes and ozone transport in northern and central
California. These intensive measurements will be made on days leading up to and during ozone
episodes and during specific ozone transport scenarios. The additional measurements are needed
for operational and diagnostic model evaluation, to improve our conceptual understanding of the
causes of ozone episodes in the study region and the contribution of transport to exceedances of
federal and state ozone standards in downwind areas.
4.4
Surface Meteorological Measurements
Field monitoring includes continuous measurements over several months and intensive
studies that are performed on a forecast basis during selected periods when episodes are most
likely to occur. This section describes the existing routine air quality and meteorological
monitoring network in northern and central California, and the options for continuous and
intensive air quality and meteorological measurements (surface and aloft) to be made during
CCOS.
The existing meteorological network in central California is extensive, but uncoordinated
among the different agencies. Figure 4.4-1 shows the locations of surface meteorological
monitoring sites operated by the Air Resources Board (ARB), the Bay Area Air Quality
Management District (BAAQMD), the National Oceanic and Atmospheric Administration
(NOAA), the California Irrigation Management Information Service (CIMIS), Interagency
Monitoring of PROtected Visual Environments (IMPROVE), the National Weather Service
(NWS), Pacific Gas and Electric Company (PG&E), the U.S. Coast Guard, Remote Automated
Weather Stations (RAWS) for fire fighting, and a few miscellaneous monitors. Wind speed and
direction, temperature, and relative humidity are the most common measurements. The network
of surface pressure and solar radiation measurements is also extensive. Three sites measure
ultraviolet radiation in the Sacramento Valley, in the San Joaquin Valley, and along the south
coast in Santa Barbara County.
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Figure 4.4-2 shows the surface meteorological observables measured at each monitoring
location, regardless of the network from which they are derived. Wind speed and direction,
temperature, and relative humidity are the most common measurements. The network or surface
pressure and solar radiation measurements is also extensive. Three sites measure ultraviolet
radiation in the Sacramento Valley, in the San Joaquin Valley, and along the south coast in Santa
Barbara County. The specific measurements at each site and the networks they belong to are
documented in Appendix C of Watson et al., (1998).
Thuillier et al. (1994) document the methods used to acquire and report data in most of
these networks with their similarities and differences. Wind speed measurements are taken at
heights ranging from 2 m to 10 m agl at most sites and temperatures are measured by aspirated
and unaspirated thermometers. The major limitations of existing network instrumentation are: 1)
wind thresholds of ~1 m/s for most instruments, which is adequate for non-winter periods, but
not for low winds in the surface layer during winter; 2) relative humidity sensors that are
inaccurate at high (<90%) humidities; and 3) insufficient temporal resolution (i.e. on the order of
minutes) to detect wind gusts that might suspend dust.
The existing meteorological network will be augmented with the CCOS supplemental
sites described below. Ten meter meteorological towers at each of these sites will be equipped
with low threshold (~0.3 m/s) wind sensors and high sensitivity relative humidity sensors.
Section 10 describes the monitors available for these measurements. Five-minute average
measurements will be acquired so that the data can be interpreted with respect to wind gusts that
might raise dust, short-term shifts and wind direction that might correspond to pulses measured
by continuous particle monitors, and short duration clouds and fogs that cause rapid changes in
the 90% to 100% RH interval. With these supplemental measurements and surface
measurements at the upper air sites, the existing surface monitoring network provides adequate
coverage for the northern and central California study domain.
4.5
Surface Air Quality Measurements
The California Air Resources Board and local air pollution control districts currently
operate 185 air quality monitoring stations throughout northern and central California. Of the
active sites, 130 measure ozone and 76 measure NOx. Carbon monoxide and hydrocarbons are
measured at 57 and 11 sites, respectively. Data from these sites are routinely acquired and
archived by the ARB and Districts. This extensive surface air quality monitoring network
provides a substantial database for setting initial condition for the model, and for operational
evaluation of model outputs. ARB, in collaboration with the California air quality management
districts, is establishing the PM2.5 monitoring sites. The PM10 acquires filter samples every sixth
day. Several of the PM10 sites have continuous monitors that measure hourly PM10 everyday.
Watson et al. (1998) describes the PM measurement network.
Supplemental air quality measurement are required at several existing monitoring sites to
increase the extent of chemical speciation and in key areas of the modeling domain where
routine monitoring stations do not currently exist. Measurements of documented quality and
adequate sensitivity are needed along the western boundary of the modeling domain to
adequately characterize the temporal and spatial distributions of ambient background levels of
ozone precursors because boundary conditions can significantly affected model outputs.
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Background sites intend to measure concentrations that are not influenced by northern and
central California emissions. Interbasin transport sites are intended to evaluate concentrations
along established or potential transport pathways between basins, including the Bay Area, the
North Central Coast, the Sacramento Valley, the San Joaquin Valley, Mountain counties, the
South Central Coast Air Basin, and the Mojave desert. Intrabasin gradient sites are located in
non-urban areas between routine network sites. They are intended to evaluate the extent to
which one urban area affects ozone concentrations in another urban area, as well as the extent to
which urban contributions arrive at suburban and rural locations.
The CCOS field measurement program consists of four categories of supplemental
measurement sites with increasing levels of chemical speciation and time resolution – Type 0, 1,
and 2 “supplemental” (S) sites and “research” (R) sites. The instruments to be used in the
supplemental network are listed in Table 4.5-1. Appendix A describes the various types of
instruments and measurements methods. Table 4.5-2 shows the instrument configuration at each
of the CCOS supplemental air quality monitoring sites, using the letter designations introduced
in Table 4.5-1. Figure 4.5-1 shows the locations of the existing monitoring stations measuring
ozone and NOx. Figure 4.5-2 shows the locations of existing monitoring stations measuring
carbon monoxide and speciated hydrocarbons and carbonyl compounds in relation to proposed
CCOS supplemental monitoring sites.
4.5.1
CCOS Type 0 Supplemental Monitoring Sites (S0)
S0 sites are intended to fill in key areas of the modeling domain where ozone, nitrogen
oxides, and surface meteorology are not currently measured. Proposed sites include McKittrick
and Kettleman City (both along the western side of the San Joaquin Valley), Shasta (downwind
of Redding), and Carizo Plain (along transport route between San Luis Obispo and the southern
San Joaquin Valley). In addition NO/NOy analyzers will be added at several existing monitoring
sites that currently measure only ozone. Three of these sites are located along pollutant transport
routes (Vacaville, San Martin, and Walnut Grove Tower at two elevations). Yosemite
(Turtleback Dome) is proposed in order to monitor NO/NOy at one site where formation of
ozone is expected to be always NOx limited.
4.5.2
S1 sites are intended to establish boundary and initial conditions for input into air quality
models. These sites are needed at the upwind boundaries of the modeling domain, in the urban
center and at downwind locations. The following aerometric parameters are measured at S1
supplemental monitoring sites.
1. Continuous surface wind speed and direction and temperature during study period.
2. Continuous ozone during study period.
3. Continuous NO and NOy during study period by high sensitivity chemiluminescence
analyzer (e.g., TEI42S or equivalent) with the converter near the sample inlet.
4. Four 3-hour canister samples for up to 15 IOP days (with option for 2 additional days) for
analysis of CO, CO2, methane by gas chromatography, reduction of CO and CO2 to CH4,
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Version 3 – 11/24/99
and analysis by flame ionization detection; and C2-C12 hydrocarbons and MTBE by gas
chromatography with flame ionization detection.
5. Four 3-hour DNPH cartridge samples for up to 15 IOP days (with option for 2 additional
days) for C1-C7 carbonyl compounds by HPLC with UV detection.
With the exception of NOy measurements, S1 sites are equivalent to Photochemical
Assessment Monitoring Stations (PAMS) sites. Measurements of speciated volatile organic
compounds (VOC) made under CCOS (four 3-hour samples on 15 IOP days) supplement the 11
existing PAMS sites in the study area (four in Sacramento, four in Fresno, and three in
Bakersfield. The ozone episodic samples that will be collected under PAMS will coincide with
the CCOS IOP days. S1 sites are proposed for Bodega Head and along the south central coast
north of Morro Bay at or near Piedras Blancas Point to obtain background data near the western
boundary of the CCOS modeling domain. Sutter Buttes and Turlock provide characterization of
ambient air transported into the upper Sacramento Valley and into the northern San Joaquin
Valley, respectively, as a function of the nature of the flow bifurcation downwind of the San
Francisco Bay Area. Measurements at Anderson (located south of Redding) are designed to
determine whether ozone precursors immediately upwind of Redding are largely transported or
are attributable to local sources. Similar transport issues are address by measurements in the
foothill communities near Grass Valley and San Andreas. Type S1 measurements are also
proposed for the CRPAQS Anchor site at Angiola. The Bay Area AQMD will operate the
existing San Jose and San Leandro monitoring sites as S1 sites during CCOS.
4.5.3
S2 sites are located at the interbasin transport and intrabasin gradient sites, and near the
downwind edge of the urban center where ozone formation may either be VOC or NOx limited
depending upon time of day and pattern of pollutant transport. S2 sites also provide data for
initial conditions and operation evaluations and some diagnostic evaluation of model outputs.
The following aerometric parameters are measured at Type 2 supplemental monitoring sites.
1. Continuous surface wind speed and direction and temperature during study period.
2. Continuous O3 during study period.
3. Continuous NO and NOy during study period by a high sensitivity chemiluminescence
analyzer (e.g., TEI 42S) for new sites or NOy and NOy* (NOy minus HNO3) with
TEI42CY for existing monitoring sites with a NO/NOx analyzer. Nitric acid can be
estimated by difference between the signals with and without a NaCl impregnated fiber
denuder.
4. Continuous NO2 and peroxyacylnitrate (PAcN) during primary study period by gas
chromatography with Luminol detector. A second estimate of HNO3 is obtained by the
difference between NOy and the sum of NO, NO2, and PAcN. This second estimate is an
upper-limit because NOy also includes other organic nitrates and particulate ammonium
nitrate.
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5. Continuous formaldehyde (HCHO) during primary study period by an instrument that
continuously measures the fluorescent, dihydrolutidine derivative formed by the reaction
of formaldehyde with 1,3-cyclohexanedione and ammonium ion (Dong and Dasgupta,
1994; Fan and Dasgupta, 1994).
6. Four 3-hour canister samples for up to 15 IOP days (with option for 2 additional days) for
analysis of CO, CO2, methane by gas chromatography, reduction of CO and CO2 to CH4,
and analysis by flame ionization detection; and C2-C12 hydrocarbons and MTBE by gas
chromatography with flame ionization detection.
7. Four 3-hour DNPH cartridge samples for up to 15 IOP days (with option for 2 additional
days) for C1-C7 carbonyl compounds by HPLC with UV detection.
Measurements at S2 sites include those at S1 sites plus continuous NOy*, nitrogen
dioxide (NO2), peroxyacetylnitrate (PAN) and formaldehyde (HCHO). The measurements allow
more detailed assessments of VOC- and NOx-limitation by observation-driven methods during
the entire two-month primary study period. S2 sites are proposed along the three main passes
connecting the Bay Area and the Central Valley (Bethel Island, Altamont Pass, and Pacheco
Pass. S2 measurements are also proposed downwind of Fresno at the Mouth of the Kings River
and downwind of Bakersfield at Edison. One additional S2 site is proposed at a location
northeast of Sacramento.
4.5.4
CCOS Research Sites
Research sites have the same site requirements as S2 sites. The sites are intended to
measure a representative urban mix of pollutants, and must be carefully selected to minimize the
potential influence of local emission sources. As with S2 sites, research sites are located where
ozone formation may either be VOC or NOx limited depending upon time of day and pattern of
pollutant transport. Research site are intended to provide the maximum extent of high-quality,
time-resolved chemical and other aerometric data for rigorous diagnostic evaluation of air quality
model simulations and emission inventory estimates. The following aerometric parameters are
measured at Research monitoring sites.
1. Continuous surface wind speed and direction and temperature during study period;
2. Continuous ozone during study period;
3. Continuous NO/NOx analyzer during study period.
4. Continuous NOy and NOy* during study period with TEI42CY. Nitric acid estimated by
difference between the signals with and without a NaCl impregnated fiber denuder.
5. Continuous NO2 and PAcN during primary study period by gas chromatography with
Luminol detector. A second estimate of HNO3 is obtained by the difference between NOy
and the sum of NO, NO2, and PAcN. This second estimate is an upper-limit because
NOy also includes organic nitrates and particulate ammonium nitrate.
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6. Continuous formaldehyde during primary study period by an instrument that
continuously measures the fluorescent, dihydrolutidine derivative formed by the reaction
of formaldehyde with 1,3-cyclohexanedione and ammonium ion (Dong and Dasgupta,
1994; Fan and Dasgupta, 1994).
7. Semi-continuous hourly organic compound speciation data during primary study period
by gas chromatography with mass spectrometry. VOC speciation includes C2 and higher
volatile hydrocarbons, carbonyl and halogenated compounds. Collect up to 10 sets of
canister and DNPH samples for measurement comparisons with GC/MS and continuous
HCHO analyzer.
8. Continuous CO by TEI 48C or equivalent and continuos CO2 by TEI 41C or equivalent
during study period.
9. Continuous NO2 and O3 photolysis rates during study period by filter radiometer.
10. Semi-continuous particulate nitrate during primary study period by Aerosol Dynamics
Inc. (ADI) automated particle nitrate monitor. The monitor uses an integrated collection
and vaporization cell whereby particles are collected by a humidified impaction process,
and analyzed in place by flash vaporization with quantitation of the evolved gases by
chemiluminescent analyzer. A commercial unit is anticipated by next summer.
11. Continuos total light absorption by aethalometer and total light scattering by ambient
integrating nephelometer during primary study period.
12. Continuous NO2, HNO3, HCHO and H2O2 on twenty IOP days by dual tunable diode
laser absorption spectrometers at the Fresno research site.
13. Semi-continuous measurements of multi-functional carbonyl compounds on 15 IOP days
by derivatization and analysis by GC/MS at the Sacramento research site.
14. Continuous HONO by Differential Optical Absorption Spectroscopy on 15 IOP days at
one research site. (Contingent upon available funding).
15. Four 3-hour Tenax cartridge samples for up to 15 IOP days (with option for 2 additional
days) for analysis C8-C20 hydrocarbons.
Research sites are proposed downwind of Sacramento and Fresno, and near Dublin
between Oakland and Livermore.
4.6
Upper Air Meteorological Network
Figure 4.6-1 and 4.6-2 show the locations of types of upper air meteorological monitors
to be deployed for the summer 2000 field study. Table 4.5-1 describes the upper air sites, their
measurements and operators. Radar profilers, doppler sodars, and RASS are used at most sites
because they acquire hourly average wind speed, wind direction, and temperature by remote
sensing without constant operator intervention. Sodars are collocated with profilers at several
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locations because they provide greater vertical resolution in the first 100 m agl.
especially important near terrain features and during winter.
This is
Several radar profilers are being installed to acquire a multi-year database, and one of the
important functions of the CCOS/CRPAQS supplements to this network is to relate these
relatively sparse measurements to the detailed meteorological patterns determined during CCOS.
The ARB operates two profilers (with RASS) in the San Joaquin Valley, and the San Joaquin
Unified APCD and Sacramento Metropolitan AQMD operate one profiler/RASS each as part of
their PAMS monitoring program. Military facilities with operational profilers include Travis
AFB, Vandenberg AFB, and the Naval Post Graduate School in Monterey. Because these
profilers are operated by different entities, equivalent methods of data evaluation and reporting
need to be established among these entities prior to CCOS field study. Six profiles/RASS will be
installed and operational during summer 2000 as part of the CRPAQS. In addition, nine
profilers/RASS and 5 sodars will be installed for the CCOS summer 2000 field study.
Radiosondes are needed to determine changes in relative humidity and to quantify
conditions at elevations above ~2000 m agl. They are also the only practical means of acquiring
upper air measurements in cities where the noise and siting requirements of remote sensing
devices make them difficult to operate. Radiosondes are routinely launched through the year at
0400 and 1600 PST from Oakland, with additional launches at Vandenberg, Edwards, and Pt.
Mugu according to military mission requirements. None of these locations are within the Central
Valley, so these will be supplemented by launches at Sacramento and in the southern San
Joaquin Valley on 20 episodic days during summer with six radiosondes (with ozonesonde)
releases per day. The 490 MHz RWP will be place in the Fresno area to provide higher vertical
sounding in the southern portion of the Central Valley.
In addition to ozonesondes mentioned in the previous section, aloft air quality
measurements are available from fixed platforms that are part of the routine monitoring network
(e.g., Walnut Grove radio tower and Sutter Buttes). CCOS will add NOy measurements at
Walnut Grove and Sutter Buttes to provide additional information on oxidants available as carryover to mix-down on the following day.
4.7
In-Situ Aircraft Measurements
Instrumented aircraft will be used to measure the three dimensional distribution of ozone,
ozone precursors, and meteorological variables. The aircraft will provide information at the
boundaries and will document the vertical gradients, the mixed layer depth, and nature of
elevated pollutant layers. The concentrations and (in conjunction with upper air wind soundings)
the transport of pollutants across selected vertical planes will be measured to document transport
of pollutants and precursors between offshore and onshore and between air basins. Redundancy
and operational cross-checks can be built into the aircraft measurements by including
overlapping flight plans for the various types of aircraft and by doing aircraft measurements near
the ground over air quality monitoring sites. Three aircraft are included in the program and one
additional aircraft is equipped with an ozone lidar.
Instrumented aircraft will be used to measure the three dimensional distribution of ozone,
ozone precursors, and meteorological variables. The aircraft will provide information about
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boundary concentrations, vertical concentration gradients, the mixed layer depth, and the spatial
extent of some elevated pollutant layers. The concentrations and (in conjunction with upper air
wind soundings) the transport of pollutants across selected vertical planes will be measured to
document transport of pollutants and precursors between offshore and onshore and between air
basins. Four aircraft are included in the base program.
Three small air quality aircraft are needed to document the vertical and horizontal
gradients of ozone, NOx, ROG, temperature, and humidity in the study region. One aircraft is
needed for the Bay Area and the northern San Joaquin Valley, a second aircraft for the
Sacramento Valley and northern Sierra Nevada, and a third aircraft the San Joaquin Valley and
southern Sierra Nevada. Onboard air quality instruments should have high sensitivity and fast
response (e.g., modified TEI 42S for NO and NOy). The small aircraft will make one flight in
the early morning (0500 to 0900 PDT) to document the morning precursors and the carryover
from the day before and a second flight in mid-afternoon (1300 to 1700 PDT) to document the
resulting ozone distribution. An occasional third flight might be considered during the night to
characterize the nocturnal transport regime and pollutant layers. Flights last between three to
four hours and may consist of a series of spirals (over fixed points on the ground) and traverses
(at constant altitude from one point to another) throughout the mixed layer. One of these aircraft
will also participate in characterizing flux-planes. This aircraft should have the capability to
measure wind direction and speed.
VOC samples are collected in the morning during downward spirals between 200 and
600 m AGL in order to characterize carryover of VOC from the previous day. VOC samples in
the afternoon are collected in the mixed layer in the bottom 350 m of the downward spiral.
Sample durations in these layers are approximately two minutes. Hydrocarbon samples are
collected in stainless steel canisters and carbonyl samples are collected in Tedlar bags and
transferred to dinitrophenyl hydrazine impregnated C18 cartridges on the ground at the
conclusion of the flight. Hydrocarbon samples are subsequently analyzed in the laboratory by
gas chromatography with flame ionization detection per EPA Method TO-14 and carbonyl
samples are analyzed in the laboratory by HPLC with UV detection per Method TO-11. The
budget allows for collection and analysis of three sets of hydrocarbon and carbonyl samples per
flight. Analytical laboratories must demonstrate the capability to achieve detection limits that
are anticipated for these samples .
A larger multi-engine aircraft is needed to document the horizontal and vertical gradients
along the offshore boundaries of the modeling domain. This plane carries the same
instrumentation as the smaller planes with the capability to measure wind direction and speed.
This long-range aircraft will make two flights per day, one in the early morning and one in midafternoon. The flights will take about four hours and will likely consist of a series of dolphin
patterns (slow climbs and descents along the flight path) and traverses. During one leg of the
morning flight of the first day of an IOP, this aircraft will measure the concentrations at the
western, overwater boundary of the study area. On the return leg, the aircraft will document the
concentrations and fluxes across the shoreline. VOC samples are collected during constantaltitude traverses for the overwater boundary and during several spirals for the shoreline legs.
Boundary measurements will be made during both non-episode and episode days. This plane
will also participate in flux plane measurements.
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This long-range aircraft will make two flights per day, one in the early morning and one
in mid-afternoon. The flights will take about four hours and will consist of a series of dolphin
patterns (slow climbs and descents along the flight path) and traverses. During one leg of the
morning flight of the first day of an IOP, this aircraft will measure the concentrations at the
overwater (western) boundary of the study area. On the return leg, the aircraft will document the
concentrations and fluxes across the shoreline. VOC samples are collected during constantaltitude traverses for the overwater boundary and channel legs and during several spirals for the
shoreline legs. The specific flight plans will need to be developed over the next year for this
aircraft under different meteorological scenarios.
The specific flight plans will be developed over the next several months for the four
aircraft under different meteorological scenarios. The above general description of flight patterns
and objectives of each flight will be specified in the operational program plan. Aircraft that are
available during the summer 2000 field study include two single-engine Cessna from UCD, twinengine Aztec and up to two additional single engine aircraft by STI. The capabilities of these
aircraft and associated personnel vary with each group.
4.8
Consideration of Alternative Vertical Ozone Measurements
In addition to the instrumented aircraft described in Section 4.7, both airborne and
ground-based lidars, and ozonesondes have been used in previous studies to obtain vertical ozone
measurements. The CCOS Technical Committee discussed the merits of these alternative
approaches and reached the consensus that a small fleet of instrumented aircraft would provide
the most cost-effective approach given the tradeoffs between temporal and spatial information
and requirements for pollutant flux and plume measurements. The following summary provides
the rationale for the Committee’s recommendation.
The Atmospheric Lidar Division of NOAA’s Environmental Technology Laboratory in
Boulder operates an airborne, downlooking UV-DIAL, which was originally developed by EPA's
Environmental Monitoring Systems Laboratory – Las Vegas. This system is capable of
measuring range resolved ozone concentrations and aerosol, nadir looking from its airborne
platform to near the ground level. The current measurement range for ozone is from about 0.8
km to 2.5–3 km, with the lower limit corresponding to the complete overlap of laser beams with
the field of view of the telescope. The lower limit might be reduced somewhat in the future by
applying the overlap correction in the data analysis and/or different alignment of the hardware.
Generally, the DIAL data are analyzed for ozone concentrations down to about 90–150 m above
ground. Accuracy of the DIAL data is 4 ppbv from comparisons with in situ instruments.
Precision is 3 ppbv at 1.5 km range from the lidar to 11 ppbv at 2.5 km range with 500 m
horizontal resolution (8 seconds at 65 m/s flight speed) and 90 m vertical resolution (Alvarez,
1999).
Cost estimate for NOAA's airborne ozone lidar including preliminary on-site data
processing is $300,000 for 1 month deployment and 80 flight hours and $70,000 for one
additional week deployment with additional 20 flight hours. Including $150,000 for data
processing, the total cost is $520,000 for 100 flight hours. The airborne system is currently
scheduled to participate in the Texas 2000 experiment from 8/15/00 through 9/15/00, and will be
available only during the first five weeks of the CCOS primary study period.
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An airborne lidar system provides the advantage of spatial coverage, but the disadvantage
of limited temporal information. These attributes are reversed for a ground-based lidar system.
The Atmospheric Lidar Division of NOAA’s Environmental Technology Laboratory in Boulder
has developed a transportable ozone and aerosol lidar specifically for the measurement of ozone
in the boundary layer and the lower free troposphere. Ozone measurements can be obtained for a
range of up to 3 km under moderate to high surface ozone concentrations (< 150 ppb) while, for
extremely high concentrations, a range of 2 km can still be achieved. Aerosol profiles for a
maximum range of about 10 km can be obtained with a range resolution of 15 m. The lower
range limit is very good (≈ 50 m) due to the use of an innovative technique for the compression
of the lidar dynamic range (Zhao et al., 1992). Using the 266/289 nm wavelengths pair,
averaging 600-1200 pulses (5-10 min at 2 Hz or 1-2 min at 10 Hz), the retrieval of ozone
concentrations has a range resolution from a few tens of meters in the lower boundary layer to
150-200 m at about 3 km. Range resolution decreases with height because the signal-to-noise
ratio decreases with distance.
The NOAA ground-based lidar has the necessary range to make useful measurements
further inland where the boundary layer height is larger. The measurement direction of the lidar
system can be scanned in one dimension from 30o to 150o yielding a two dimensional ozone
measurement. The vertical scanning capability provides a valuable internal system check,
frequent calibration, and was desired for both monitoring and modeling studies. This system is
being modified to add a new wavelength at 299 nm, to provide a longer maximum range of
ozone measurements in a thick boundary layer. Cost estimate for NOAA's ground based ozone
lidar (OPAL) is $230,000 for one month deployment with 150 hours of operation.
The ground-based lidar could be located at up to two sites (with one move during the
field study). The ground-base lidar could serve as an anchor site within a network of ozonesonde
launch locations arrayed along the following transport routes: 1) west-east transport path
between the Bay Area, Sacramento, and the Sierra Nevada foothills; and 2) north-south transport
up the San Joaquin Valley. Ozonesondes have the disadvantages of being labor intensive and
expensive. In addition, ozonesonde data is difficult to interpret in region where ozone is not
spatially uniform.
4.9
Measurements for Special Studies
In addition to the measurement described above, data are also needed to develop dayspecific emissions data and to evaluate the validity of emission estimates as described under
CCOS technical objectives B-2 and D-4, respectively. The following experiments are also
required to address specific technical issues that cannot be fully address by the proposed CCOS
monitoring program.
In principle, well-performing grid models have the ability to quantify transport
contributions. However, many of the interbasin transport problems involve complex flow
patterns with strong terrain influences that are difficult and expensive to model. Upper-air
meteorological and air quality data in critical transport locations is generally required in order to
properly evaluate and use grid models for quantifying transport contributions. In combination
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with modeling, data analyses can improve the evaluation of modeling results and provide
additional quantification of transport contributions.
In order to quantify pollutant transport and to provide data for modeling and data
analyses, surface and aloft measurements are needed at locations where transport can occur and
at the times when transport is occurring. These monitoring locations include in and near
mountain passes, along coastlines, offshore, and at various locations in the downwind air basin.
Aloft measurements made by instrumented aircraft are used to calculate transport across flux
planes. Vertical planes, intersecting the profiler sites downwind of and perpendicular to the
transport path, can be defined and provide estimates of transport through these passes using
surface and aircraft measurements of pollutant concentrations and surface and wind profiler data
for volume flux estimations.
Contributions of Elevated NOx Sources to Downwind Ozone
Power plants and other sources with tall stacks are significant sources of NOx, which in
the presence of NMHC can lead to catalytic formation of ozone downwind of the source.
However, close to the stack there is a temporary decrease in ozone levels due to “titration” by
high levels of NO in the near field of the plume. Further downstream, ozone levels above the
local background indicate net ozone production due to the reaction of plume NOx with NMHC
that are entrained into the plume in the dilution process. However, question remain as to how
much ozone is actually produced in the plume, how the ozone production efficiency depends on
the chemical composition of the plume, and what the relative contributions of power plants are to
high ozone episodes ozone in downwind areas.
It is not clear that the treatment of plumes by state-of-the-art models is adequate. Vertical
transport (e.g., plume rise and fall the plume during downwind transport) may not be adequately
described. Recent power plant plumes studies (Senff et al., 1998) utilized airborne ozone and
aerosol lidar in conjunction with other instrumented aircraft. Because of its ability to
characterize the two dimensional structure of ozone and aerosols below the aircraft, the airborne
lidar is well suited to document the evolution of the size and shape of the power plant plume as
well as its impact on ozone concentration levels as the plume is advected downwind. This
aircraft was considered as a study option, but budget constraints will prevent its use during
CCOS. However, aircraft measurements of NOx, ozone and VOC concentrations made in
plumes are required to test the validity of the treatment of plume dispersion and chemistry and
the procedures for terminating the plume into the regular model grid by plume-in-grid
parameterizations.
Helicopter based measurements of power plant plume will be used to evaluate the plumein-grid (PiG) parameterizations used in air quality models. With PiG parameterizations plume
emissions are simulated in a Lagrangian reference frame superimposed on the Eulerian reference
frame of the host grid model. Ozone formation rates in the plume of an elevated point source will
be different from the ambient air because of their very different VOC/NOx ratios. CCOS
measurements of ozone, VOC and nitrogenous compounds in plumes and in the surrounding air
will be compared with the simulations of models using the PiG approach. The plume study is
described in Addendix D.
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Deposition Studies
During the California Ozone Deposition Experiment (CODE) in 1991, aircraft and towerbased flux measurements were taken over different types of San Joaquin Valley crops, irrigated
and non-irrigated fields, and over dry grass. Estimates of ozone deposition velocities are 0.7-1.0
cm/s (Pederson et al. 1995). Order of magnitude calculations by Pun et al. show that dry
deposition can be a few percent (~3-5%) of the total ozone budget in the San Joaquin Valley.
However, modeling studies (Glen Cass, personal communication) have shown that dry
deposition can play a more significant role in the budget of an important ozone precursor, NO2.
Three alternative deposition studies are described in this conceptual plan. Two are tower-based
and could take advantage of the 100-m tower at Angiola planned for CRPAQS. The third is an
aircraft flux measurement and could be used for a variety of different terrain types.
As part of CRPAQS, a 100 m, walk-up, scaffold tower will be constructed and
maintained at the Angiola site to support year-long micrometeorological measurements as well
as other vertical experiments. For the long-term measurements, the tower will be instrumented at
five elevations with high precision anemometers, relative humidity, and temperature
measurements and will record five minute averages of wind speed, wind direction, temperature,
and relative humidity as well as average cross-products in the vertical and horizontal directions.
These micrometeorological measurements will be used to create a diurnal and seasonal
climatology for surface layer evolution, describe turbulent mixing and dispersion at the sub-grid
scale level, and to determine micrometeorological conditions near the surface that affect
suspension and deposition of dust, gases, and fine particles.
A tower-based experiment using a DOAS lidar at three to five different heights could be
employed to measure vertical O3, NO2, HONO, NO3, and HCHO concentration gradients.
Optical fibers would distribute the laser pulse to each height, and a multi-pass cells could be used
to increase the path length and thereby the accuracy. The O3 and NO2 measurement has an
estimated accuracy on the order of 1.5 ppb for a five-minute averaging period over a 100-m
pathlength (5 m folded 20 times). (Accuracy for any other species measured may be part of the
investigation.) To get fluxes from the lidar gradient measurement, either an assumed form of
eddy diffusivity would be required, or a modified Bowen ratio approach could be employed. The
modified Bowen ratio and a direct eddy correlation measurement both require fast-response
sonic anemometry (on the order of 10 Hz) to measure turbulent perturbations in the vertical
velocity. With direct eddy correlation techniques, the species of interest must also be measured at
the same rate to find the covariance with the vertical turbulent perturbations. This is the approach
used in aircraft flux measurements. With the modified Bowen ratio technique, fast response
measurements of a surrogate species (typically CO2 or H2O) are made and are then related to the
species of interest via the ratio of vertical concentrations. Since fast response instruments are
available for ozone (chemi-fluorescence) and for NOx (chemi-luminescence), the two techniques
could be directly compared, giving greater confidence in the HONO, NO3, and HCHO flux
estimates.
Instrumented aircraft can also be a part of a wider study to investigate more divers land
types. For example, the NOAA Long-EZ operated by the NOAA Air Resources Lab is already
instrumented with fast response O3 and NOx analyzers and has a wind probe providing 2 cm/s
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accuracy in turbulent perturbations. As was done in CODE, the aircraft could be periodically
flown near the tower for crosscheck and QA purposes.
The consensus view of the CCOS Technical Committee was that a proper study of
atmospheric deposition would require far more funds than available within CCOS. Rather than
dilute the CCOS effort, the Committee recommended that separate funding be sought for a
comprehensive deposition study in the year 2001.
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Table 4.5-1
CCOS Supplemental Surface Measurements
Observable and Method
Meteorology and Radiation
Meteorology (WS,WD, T and RH) at 10 m
Radiation (JNO2 and JO1D)
Oxidants
Ozone (ultraviolet absorption monitor)
H2O2 (TDLAS)
Nitrogen Species
NO, NOx (chemiluminescent monitor)
NO, NOy (high sensitivity chemiluminescent
monitor with external converter)
NOy, NOy-HNO3 (high sensitivity
chemiluminescent monitor with dual converters
w/ & w/o NaCl impregnated fiber denuder)
NO2, PAcNs (GC - Luminol)
NO - (flash vaporization)
NO2, HNO3 (TDLAS)
Carbon Species
CO, CO2, CH4, C2-C12 hydrocarbons
(canister/GC-FID)
C1-C7 carbonyls( DNPH-HPLC/UV)
HCHO (dihydrolutinine derivative/fluorescent
detection)
C8-C20 hydrocarbons (Tenax GC-FID, MSD)
VOC (Automated-GC/ion trap mass
spectrometer)
HCHO (TDLAS)
Hydroxy carbonyls
CO (nondispersive infrared)
CO2 (nondispersive infrared)
PM/Visibility
PM2.5 light absorption (aethalometer)
PM2.5 light scattering (portable nephelometer)
(1) At the Fresno research site only.
(2) At the Sacramento research site only.
Code
Period
Avg Time
Type of Sites
A
T
6/15/00 to 9/15/00
6/15/00 to 9/15/00
5-minute
5-minute
S0, S1, S2 and R
R
B
Q
6/15/00 to 9/15/00
15 IOP days
5-minute
10-minute
S0, S1, S2 and R
C
D
6/15/00 to 9/15/00
6/15/00 to 9/15/00
5-minute
5-minute
S2, R
S0, S1
E
6/15/00 to 9/15/00
10-minute
S2, R
F
G
P
7/2/00 to 9/2/00
7/2/00 to 9/2/00
15 IOP days
30-minute
10-minute
10-minute
S2, R
R
J
15 IOP days
4 x 3-hr
S1, S2, R
N
M
15 IOP days
7/2/00 to 9/2/00
4 x 3-hr
10-minute
S1, S2, R
S2, R
K
L
15 IOP days
7/2/00 to 9/2/00
4 x 3-hr
hourly
R
R
Q
O
H
I
15 IOP days
15 IOP days
6/15/00 to 9/15/00
6/15/00 to 9/15/00
10-minute
hourly,
5-minute
5-minute
R
(2)
R
R
R
R
S
6/15/00 to 9/15/00
6/15/00 to 9/15/00
5-minute
5-minute
R
R
4-16
R (1)
R (1)
(1)
4-17
Fresno
Downwind of Fresno
R
R
R
S2
S2
S2
S2
S2
S2
S1+
S1
S1
S1
S1
S1
S1
S1
S1
S1
S0
S0
S0
S0
NOy
NOy
NOy
NOy
Type
New - CCOS
SMAQMD
CRPAQS
ARB
CRPAQS
SMAQMD
CRPAQS
CRPAQS
BAAQMD, CRPAQS
CRPAQS
New - CCOS
SJVUAPCD
ARB
BAAQMD
BAAQMD
CRPAQS
ARB
ARB
Shasta Co.
CRPAQS
New - CCOS
CRPAQS
New - CCOS
NPS, CRPAQS
BAAQMD
Yolo-Solano
SMAQMD
Site Acquisition
(1) CRPAQS Annual Site, 24-hour canister sample every 6th day.
(2) Bay Area component of CCOS, samples collected and analyzed by BAAQMD.
(3) 10-m meteorological tower located nearby.
Sacramento
Sloughhouse
Merced
Pacheco Pass
Alameda
Alameda
Altamont
Kern
Contra Costa
Bethel Island
Dublin (Nextel)
Tulare
Angiola
Edison
San Luis Obispo
South Central Coast
Sacramento
Stanislaus
Turlock
Fresno
Calaveras
San Andreas
Trimmer (Forest Service)
Santa Clara
San Jose 4th Street
Northeast Sacramento
Sonoma
White Cloud
Alameda
Nevada
Sutter Buttes
San Leandro
Sutter
Anderson
Bodega Bay
San Luis Obispo
Shasta
Carizo Plain (California
Kern
McKittrick
Mariposa
Yosemite - Turtleback
Shasta
Santa Clara
San Martin
Kings
Solano
Vacaville - El Mira Rd
Kettleman City
Sacramento
Walnut Grove Tower
Shasta
County
CCOS
CCOS
CCOS
ARB/CCOS
CCOS
CCOS
CCOS
CCOS
CCOS
CCOS
CCOS
CCOS
CCOS
BAAQMD
BAAQMD
CCOS
CCOS
ARB
CCOS
SLOAPCD
SJVUAPCD
SJVUAPCD
CCOS
CCOS
BAAQMD
Yolo-Solano
CCOS
Operation
--
ABC
--
ABC
--
ABC
--
--
ABCH
--
--
ABCH
(2)
(2)
ABCHJ N
ABH
(2)
ABCJ N
(2)
--
AB
AB
AB
--
--
--
--
AB
AB
AB
AB
Existing
(1)
--
--
S
(3)
A S
--
AS
ASU
ABDJ SRUVW
S
(1)
--
--
--
S
RSJ
--
--
--
--
S
--
S
SJ
--
(1)
--
--
S
CRPAQS
EFJMN
BDFJMN
EFJMN
BCEFJMN
BCEFJMN
JMN
FJMN
ABDJN
EJN
DJN
--
--
ABDJN
DJN
DJN
DJN
ABD
ABD
ABD
ABD
D
D
D
2-D
ABCEFGHI JKLMNPQRST
EFGHI JKLMNORST
ABCEFGHI JKLMNRST
Measurements
CCOS
Table 4.5-2
Supplemental Surface Air Quality and Meteorological Measurements
Site
Version 3 – 11/24/99
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Angel's Camp
Angiola
Beale AFB-Oro Northern boundary transport, Fulfill needs of National Weather Service and
Dam Blvd West synoptic conditions
Beale AFB flight operations; existing long-term
site.
Humboldt
County
Carizo Plain
Corning
Sacramento
Edison
Edwards AFB
ACP
ANGI
BBX
BHX
CAR
CRG
DAX
EDI
EDW
Intrabasin transport
Interbasin transport through
Tehachapi Pass. Downwind
of major source.
Nothern Valley barrier,
characterize Northern SV
convergence zone.
Interbasin tranport.
Onshore/offshore transport
Intrabasin transport, vertical
mixing, micrometeorology
Existing long term site. Transport through
Tehachapi Pass, desert mixed layer, synoptic
conditions.
Site to observe possible divergence flow at
southern end of the valley, low level jet flow,
and eddy flows. Data from SJVAQS/AUSPEX
taken at Oildale supports these observations.
Downwind of Bakersfield area source.
Fulfill needs of National Weather Service;
existing long-term site
To observe southerly barrier winds along the
Sierra Nevada which may be a transport
mechanism. May characterize extent of northerly
flow into SV for some scenarios.
Monitor tranport between San Joaquin Valley
and South Central Coast Air Basins.
Positioned to monitor transport up the valley,
low level nocturnal jet flow, and Fresno eddy
flow patterns. Collocated with tall tower.
Upslope/downslope flow,
Served as site to capture eddy flow, mixing,
complex terrain for
vertical temperature structure, model input and
challenging model evaluation evaluation data during SJVAQS/AUSPEX
Site to monitor possible summer eddy flow,
vertical temperature structure evolution, model
input and evaluation data. Downwind of
Sacramento area source.
N. of Auburn, S. Upslope/downslope flow,
of Grass Valley downwind of major area
source
Location provides coverage of predominant
summer flow through Sacramento Valley.
Justification
ABU
Purpose
Arbuckle
Name
NOAA-ETL
CRPAQSrwp, CCOSsodar
NOAA-ETL
CCOS
EAFB
ARB
NWS
NOAA-ARL
CCOS
AC
SC
SC
AC
AC
SC
SC
AC
SC
SC
SC
SC
AS SE
Nexrad
AC
AC
NOAA-ETL
CCOS
SC
SC
Sodarb,c Sondeb,d
NWS
NOAA-ETL
CCOS
SC
RASSb
AC
NOAA-ETL
CCOS
Radarb
BAFB
Contractor
Operatora
Table 4.6-1
Upper Air Meteorological Measurements for CCOS
ABK
Site ID
Version 3 – 11/24/99
4-19
Fresno-First
Street
Hanford-edge of Intrabasin Transport
town between
fairgrounds and
municipal
Huron
Lagrange
Lost Hills
Livingston
Livermore
FSF
HNX
HUR
LGR
LHL
LIV
LVR
Intra&interbasin transport
across Carizo Plain
Upslope/downslope flow
Fresno Air
Terminal
FAT
Justification
Monitor flow through Castro Valley between
San Leandro/Oakland and Livermore.
Representative of mid SJV flow since variation
in flow is small along the valley's central axis.
Situated east of the coastal range and represents
uniform flow aloft at 1000m as opposed to a site
on the Tremblor Range. Good position to detect
the direction of flow between the Carrizo Plain
and the SJV
This site represents valley/Sierra interactiion in
northern SJV. Monitor possible upslope flow
transport of pollutants during day and possible
recirculation via Mariposa River Valley exit jet
by night. Also completes the west to east
transect across SJV from SNA to LIV sites.
This is to monitor daily transport from north to
south with average surface winds during
afternoons and early evening and the low level
nocturnal jet on the western side of the SJV;
models should do well with topographic
existing long-term site.
T&B
CCOS
STI
CCOS
SC
AC
NOAA_ETL
CRPAQSrwp, CCOS-
SC
AC
AC
NOAA_ETL
CCOS
b
SC-449
Radar
SC
AC
AC
SC
AC
SC
b
RASS
SC
SC
b,c
Sodar
SE
Sonde
b,d
AC
AC
Nexrad
ARB or
NOAA
NOAA-ETL
or ARB
CRPAQS or
ARB
NWS
NOAA_ETL
Contractor
CCOS
EAFB
Operator
a
Fulfill needs of National Weather Service and
Edwards AFB flight operations; existing longterm site.
Capture the Fresno eddy, characterize urban
mixing heights, transport from major Fresno area
source.
Urban Heat Island, Intrabasin Site to monitor possible summer eddy flow,
Transport, Synoptic
vertical temperature structure evolution, model
Conditons. Characterize
input and evaluation data. Flow out of Fresno.
Purpose
Edwards AFB
Name
EYX
Site ID
Version 3 – 11/24/99
4-20
Pt. Reyes
Reno National Northern boundary transport, Fulfill needs of National Weather Service;
Weather Service synoptic conditions
Office
Washoe County- Northern boundary transport, Fulfill needs of National Weather Service;
Virginia Peak synoptic conditions
Richmond
Onshore/offshore transport. Monitor possible deeper mixed layer.
POR
REV
RGX
RIC
Plesant Grove
On-shore flow, along coast
flow
Coastal meteorology impacts air quality not only
in coastal regions but by modulating the
strength, and intrusion extent of the sea breeze.
Intra- and interbasin tranport. Monitor transport between Sacramento and
Upper Sacramento Valley and North Mountain
Existing long term site
PLE
Onshore/offshore transport,
synoptic conditions.
Oakland airport Onshore/offshore transport,
Existing long term site. Transport through
Tehachapi Pass, desert mixed layer, synoptic
conditions.
OAK
Interbasin transport
Onshore/offshore transport
The current suspicion is that the mountain exit
jets flow along the axis of the valley over
Trimmer. A site between Academy and
Humphrey's Station is more likely to observe the
flow than a site at Piedra.
Point Mugu
USN
Monterey
MON
Upslope/downslope flow
Chosen to monitor interbasin flow out of the San
Joaquin Valley to the desert via Tehachapi Pass.
Previous monitoring studies have shown a clear
exit jet out of the SJV in this region. The exact
site is to be determined.
NTD
Mouth Kings
River
MKR
Interbasin transport
Justification
Santa Clara
Mojave Desert
MJD
Purpose
NOAA_ETL
NOAA_ETL
CRPAQS
CRPAQS
CCOS-p,
CCOS-sodar
NWS
NOAA-ETL
STI
CCOS
NWS
NOAA-ETL
CCOS
SC
SC
SC
SC
AS
AS SE
AC
Sodarb,c Sondeb,d
NWS
SC
AC
AC
AC
RASSb
AS SE
SC
AC
AC
AC
Radarb
AC
AC
Nexrad
USN
NWS
USNPGS
Contractor
Operatora
MUX
Name
Site ID
Version 3 – 11/24/99
4-21
Tracy, W of
Interbasin transport through
Tracy, S of IAltamont Pass.
205, W of I-580
Vandenberg
AFB
Orcutt Oil field- Onshore/offshore transport.
Vandenberg
Visalia
Ventura County Intrabasin transportonshore/offshore transport.
TRC
VBG
VBX
VIS
VTX
Plume Study
Plume Study
Pittsburg
Moss Landing
Intrabasin transport.
Onshore/offshore transport,
Fulfill needs of National Weather Service and
Vandenberg operations; existing long-term site.
Monitor flow through Altamont Pass for San
Francisco Bay Area to SJV transport in p.m.;
also help monitor less frequent off-shore flow.
Interbasin transport between Existing long term site
San Joaquin Valley and Bay
Travis AFB
San Martin
SNM
Monitor flow at the northern end of the
Sacramento Valley. Eddy flows.
PG&E
PG&E
PG&E
PG&E
NWS
SJVUAPCD
VAFB
SC
SC
AC
AC
SC
CCOS
VAFB
AC
STI
SC
STI
TAFB
NWS
AC
NOAA-ETL
or ARB
SC
NOAA-ETL
CCOS
Radarb
AC
Contractor
SC
SC
AC
SC
SC
AC
SC
AC
RASSb
WC
AS SE
SE
Sodarb,c Sondeb,d
AC
AC
AC
Nexrad
SMUAPCD/A
RB
Operatora
CRPAQS or
May represents flow through Pacheco pass
ARB
during some coastal valley intrusions; represents
along-valley flow on western side at other times.
Models should handle channeled, along-valley
flow well at this point.
CCOS
Intra- and interbasin
Monitor transport from SFBA to NCC via Santa
transport, flow through Santa Clara Valley south of San Jose.
Interbasin transport from
Pacheco Pass, model QA.
TRA
Santa Nella, E
of I-5 toward
Los Banos
SNA
Orange County Onshore/offshore transport.
Shasta
SHA
Justification
Intra and interbasin transport Monitor N-S flow within Sacramento Valley,
afternoon sea breeze intrusion, and flow from
San Francisco Bay Area; help resolve northern
boundary of SV/SJV divergence zone.
Purpose
SOX
Sacramento
Name
SAC
Site ID
Version 3 – 11/24/99
4-22
Totals by Operator:
26
TOTALS
Balloon launch on episode days. Frequency should be 4-8 times per day but include 0700 and 1900 PST.
d
a
Radarb
13
6
4
3
Contractor
24
4
1
RASSb
13
6
6
1
6
4
Sodarb,c Sondeb,d
5
2
10
10
Nexrad
ARB/Districts
Military/U.S.
Operatora
CCOS
CRPAQS
Footnotes
CCOS=Central California Ozone Study (this study), ARB=Air Resoures Board, BAAQMD=Bay Area Air Quality Management District;
USNPGS=U.S. Navy Post Graduate School; SJVUAPCD=SJV Unified Air Pollution Control District, NWS=National Weather Service;
SMAQMD=Sacramento Metropolitan Air Quality Management District, CRPAQS=California Regional PM10/PM2.5 Air Quality Study;
VAF=Vandenberg Air Force Base, TAF=Travis Air Force Base, EAF=Edwards Air Force Base, USN=U.S. Navy.
b
AC=Annual continuous measurements; AS=Annual sporadic measurements, SC=Summer continuous, 6/1/2000-9/30/2000;
SE=Summer episodic measurements on forecasted days.
c
Summer campaign sodars added at some sites as part of CRPAQS/CCOS except at RIC.
Version 3 – 11/24/99
Version 3 – 11/24/99
Figure 4.4-1. Central California surface meteorological networks and measurement locations.
4-23
Version 3 – 11/24/99
Figure 4.4-2. Surface meteorological observables measured in the combined central California
meteorological network.
4-24
Version 3 – 11/24/99
Figure 4.5-1. Existing routine O3 and NOx monitoring sites
4-25
Version 3 – 11/24/99
Figure 4.5-2. CCOS supplemental air quality and meteorological monitoring sites and
Photochemical Assessment Monitoring Stations
4-26
Version 3 – 11/24/99
Figure 4.6-1. Upper air meteorological measurements during the summer campaign, including
annual average study sites and NEXRAD (WSR-88D) profilers.
4-27
Version 3 – 11/24/99
Figure 4.6-2. Upper air meteorological measurement network indicating operating agency.
4-28
Version 3 – 11/24/99
5.0
Chapter 5: Budget Estimates
BUDGET ESTIMATES
Cost estimates have been prepared for each element of the base and optional programs
based on a consensus of the CCOS Technical Committee. Cost estimates are summarized in
Table 5-1 for major components of the proposed measurement plan. These cost estimates are
supported by detailed itemized costs, which vary in detail and accuracy. Some cost are based on
contracts already in place, some on unit costs for measurements that are being made in current
ARB extramural contracts or cost quotations from vendors and contractors. These detailed
itemized costs were provided to the CCOS Technical Committee for the purposes of considering
alternative measurements and tradeoffs and are not presented here. The total contract costs for
CCOS is $5,800,000 for the base program and $1,400,000 for optional elements for a total
combined contract costs of $7,200,000.
In addition to the above contract costs, the ARB and the local APCD’s are committing inkind resources for planning and execution of CCOS. Estimates of in-kind costs are estimated by
multiplying the projected level of effort (in hours) by a nominal hourly rate. In-kind costs
include additional efforts that are specifically required during CCOS and does not include data
collection and analysis associated with normal daily operations (e.g., PAMS and other
aerometric monitoring programs). The in-kind costs total $1,369,000, which does not include
substantial leveraging of resources that will be available from CRPAQS.
5-1
5-2
$
$
$
$
H2O2, HCHO by TDLAS (includes operation) 1
Multi-functional carbonyls (includes operation) 1
Total Surface AQ & Met
$
$
NO2, HNO3 by TDLAS (includes operation) 1
Subtotal of supplemental measurements
$
$
Auto GC/Ion Trap MS at Research Sites
-
-
-
$
$
$
$
$
$
Tenax Samples at Research Sites
-
$
$
$
$
$
$
$
$
$
Canisters Samples at S1 and Research Sites
-
$
$
Carbonyls Samples at S1 and Research Sites
Supplemental Measurements
-
$
$
Operations
Subtotal Research Sites
-
-
-
$
$
Site acquisition and setup
Equipment purchase
Research Sites
-
$
$
Operations
Subtotal Type 2 Sites
-
-
-
$
$
Equipment purchase
Type 2 Supplemental Sites (5 + 1 optional)
-
$
$
Operations
Subtotal Type 1 Sites
-
-
$
$
Equipment purchase
$
2,708,000
1,336,000
-
20,000
50,000
550,000
99,000
142,000
475,000
600,000
160,000
390,000
50,000
490,000
190,000
260,000
40,000
282,000
124,000
114,000
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
2,708,000
1,336,000
-
20,000
50,000
550,000
99,000
142,000
475,000
600,000
160,000
390,000
50,000
490,000
190,000
260,000
40,000
282,000
124,000
114,000
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
-
-
-
-
-
-
-
-
-
-
-
-
-
-
39,200
39,200
-
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
Type 1 Supplemental sites (7 + 1 optional)
77,200
$
Operations
Subtotal Type 0 and NO/NOy Sites
602,000
169,000
120,000
-
-
-
-
11,000
38,000
-
-
-
-
121,700
39,200
65,500
17,000
-
-
-
-
310,950
188,000
45,750
$0
$
44,000
$0
Cost ($)
Contracts
$
$
$202,000
Cost ($)
In-Kind
44,000
Total
Cost ($)
Optional
Equipment purchase
Type 0 (4) and Supplemental NO/NOy (5)
-
$130,000
$72,000
Preliminary Studies
Supplemental Surface Measurements
Cost ($)
Contracts
Cost ($)
In-Kind
Base Study
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
602,000
169,000
120,000
-
-
-
-
11,000
38,000
-
-
-
-
121,700
39,200
65,500
17,000
39,200
39,200
-
-
310,950
77,200
188,000
45,750
$0
Cost ($)
Total
Table 5-1
Summary of Cost Estimates for the CCOS Field Measurement Program
Elements
Version 3 – 11/24/99
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
-
-
-
-
-
-
-
-
-
-
-
-
-
-
39,200
39,200
-
-
$72,000
Cost ($)
In-Kind
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
3,310,000
1,505,000
120,000
20,000
50,000
550,000
99,000
153,000
513,000
600,000
160,000
390,000
50,000
612,000
229,000
325,500
57,000
282,000
124,000
114,000
44,000
310,950
77,200
188,000
45,750
$130,000
Cost ($)
Contracts
Total
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
3,310,000
1,505,000
120,000
20,000
50,000
550,000
99,000
153,000
513,000
600,000
160,000
390,000
50,000
611,700
229,200
325,500
57,000
321,200
163,200
114,000
44,000
310,950
77,200
188,000
45,750
$202,000
Cost ($)
Project
5-3
$
$
Continuous RWP/Rass and SODARs
$
132,000
$
$
$
$
$
$
Nitrogen Species
VOC data validation
Aloft air quality data validation
Surface meteorology data validation
Upper-Air Meteorology validation
Total Quality Assurance
-
$
Surface and upper-air meteorology audits
12,000
-
-
-
-
-
80,000
$
$
40,000
Ozone and NOx (2 persons x 10 sites x 32 hrs)
$
QA Coordination (1 person x 500 hrs)
VOC Audits (1 person x 160 hrs)
$
QA Plan
-
203,000
$
Field Operations and Management
Quality Assurance
20,000
$
Total Upper-Air Meteorology and Air Quality
Aircraft Carbonyl
Aircraft Canisters
Aloft VOC
65,000
$
$
$
$
$
$
$
$
$
$
$
$
310,000
50,000
-
40,000
40,000
30,000
50,000
50,000
-
-
50,000
190,000
1,970,000
$
$
170,000
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
442,000
50,000
12,000
40,000
40,000
30,000
50,000
50,000
80,000
40,000
50,000
393,000
1,990,000
65,000
170,000
200,000
265,000
350,000
$
-
$
$
-
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
200,000
$
$
730,000
210,000
Cost ($)
In-Kind
AQ aircraft #6 (for plume study)
$
AQ aircraft #4
265,000
350,000
$
$
Cost ($)
Total
$
$
190,000
730,000
Cost ($)
Contracts
Base Study
AQ aircraft #5
$
$
AQ aircraft #3 overwater
20,000
-
Cost ($)
In-Kind
AQ aircraft (UCD #1 and UCD #2)
Instumented Aircrafts
Table 5-1 (continued)
-
-
-
-
-
-
-
-
-
-
-
600,000
10,000
20,000
300,000
-
270,000
Cost ($)
Contracts
Optional
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
-
-
-
-
-
-
-
-
-
-
-
300,000
10,000
20,000
270,000
Cost ($)
Total
IOP Radiosondes/Ozonesondes
Upper-Air Meteorology and Air Quality
Elements
Version 3 – 11/24/99
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
132,000
-
12,000
-
-
-
-
-
80,000
40,000
-
203,000
20,000
-
-
-
-
-
-
-
20,000
-
Cost ($)
In-Kind
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
310,000
50,000
-
40,000
40,000
30,000
50,000
50,000
-
-
50,000
190,000
2,570,000
75,000
190,000
300,000
-
200,000
265,000
350,000
190,000
1,000,000
Cost ($)
Contracts
Total
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
442,000
50,000
12,000
40,000
40,000
30,000
50,000
50,000
80,000
40,000
50,000
393,000
2,290,000
75,000
190,000
-
-
200,000
265,000
350,000
210,000
1,000,000
Cost ($)
Project
5-4
Measurements made at one site
TOTAL
1
$1,094,000
595,000
$
Emission Inventory Development
Contingency
72,000
$
Cost ($)
In-Kind
50,000
$
$5,788,000
330,000
100,000
$
$
Cost ($)
Contracts
Base Study
$
$
$
$
6,882,000
50,000
925,000
172,000
Cost ($)
Total
$0
Cost ($)
In-Kind
$
$
$
$1,387,000
50,000
135,000
-
Cost ($)
Contracts
Optional
$
$
$
$
1,387,000
50,000
135,000
-
Cost ($)
Total
Table 5-1 (continued)
Data Management
Elements
Version 3 – 11/24/99
$
$
$
$1,094,000
-
595,000
72,000
Cost ($)
In-Kind
$
$
$
$
7,175,000
100,000
465,000
100,000
Cost ($)
Contracts
Total
$
$
$
$
8,269,000
100,000
1,060,000
172,000
Cost ($)
Project
Version 3 – 11/24/99
6.0
Chapter 6: Management and Schedule
PROGRAM MANAGEMENT PLAN AND SCHEDULE
This section specifies the management structure and the roles of CCOS committees and
working groups. It also specifies the responsibilities of field study participants and schedule of
milestones for the CCOS field study and related activities.
6.1
CCOS Management Struture
The CCOS is a large-scale program involving many sponsors and participants. Three
entities are involved in the overall management of the Study. The San Joaquin Valleywide Air
Pollution Study Agency, a joint powers agency (JPA) formed by the nine counties in the Valley,
directs the fund-raising and contracting aspects of the Study. A Policy Committee comprised of
four voting blocks: State, local, and federal government, and the private sector, provides
guidance on the Study objectives and funding levels. The Policy Committee approves all
proposal requests, contracts and reports. A Technical Committee parallels the Policy Committee
in membership and provides overall technical guidance on proposal requests, direction and
progress of work, contract work statements, and reviews of all technical reports produced from
the study. The leadership roles in the CCOS field study include the principal investigators, the
field coordinator, the quality assurance officer, the data manager, the measurement investigators,
the CCOS/CRPAQS technical committee, and the CCOS/CRPAQS policy committee. On a dayto-day basis, the California Air Resources Board (ARB) is responsible for management of the
Study under the direction of the Program Manager, Chief of the Modeling and Meteorology
Branch, in the ARB’s Planning & Technical Support Division.
6.1.1
Policy Committee
The policy committee is composed of senior management representatives from local,
state, and federal regulatory agencies and private sector stakeholders. It provides overall policy
and financial direction to the project. Specifically, the Policy Committee will:
•
Evaluate CCOS technical components for their relevance to future policy decisions and
assure an adequate balance among those components.
•
Monitor CCOS budgets, schedules, and product quality to assure fiduciary responsibility
and fulfillment of commitments.
•
Request, justify, and obtain sufficient sponsorship to complete defined activities that will
achieve CCOS objectives.
•
Review and evaluate policy-relevant CCOS findings, provide constructive
recommendations for their expression, and convey these findings to stakeholders.
6.1.2
Technical Committee
The CCOS is directed by a technical committee that comprises staff from the California
Air Resources Board (ARB), the California Energy Commission (CEC), local air pollution
control agencies, industry, and other sponsoring organizations with technical input from a
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consortium of university researchers in California and the Desert Research Institute (DRI).
Members are listed on the title page of this conceptual program plan. This committee provides
overall technical direction to the project. Specifically, the technical committee has the following
role.
•
Reviews each of the plans and recommends the final technical scope of each of these
program elements.
•
Prepare and disseminate requests for proposals for necessary work elements and evaluate
the proposals received.
•
Commission and evaluate CCOS technical reports and publications, including those
relevant to field studies.
•
Create and monitor overall project schedules and budgets, evaluate compromises between
project needs and budget allocations, and institute remedial actions where needed.
•
Institute, facilitate, and coordinate CCOS research activities with complementary
activities sponsored by the local air quality management districts (i.e. BAAQMD),
California Air Resources Board, U.S. Environmental Protection Agency (Region IX,
ORD, and OAQPS), the Department of Defense (Naval Weapons Center), Department of
Agriculture, and industrial research sponsors (EPRI, CRC, WSPA, API).
•
Participate in, to the extent possible, CCOS data acquisition, data analysis, and modeling
activities.
•
Disseminate CCOS scientific findings in scientific forums, national guidance documents,
and peer-reviewed publications.
6.1.3
Principal Investigators
The principal investigators provide scientific and technical guidance for the Central
California Ozone Study. Specifically, the principal investigators will:
•
Create initial study protocols for data analysis, modeling, emissions characterization, and
field studies, and modify them in response to external evaluation and technical support
study results.
•
Assist in the preparation of work statements for complex project components.
•
Critically review CCOS technical products and provide constructive recommendations
for improvement and focus on CCOS goals and objectives.
•
Keep informed of developments and findings in PM programs external to CCOS and
incorporate that knowledge into CCOS planning and execution.
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•
Devise and refine conceptual models of elevated PM2.5 and PM10 in central California
and assess the extent to which mathematical models simulate the physical and chemical
mechanisms embodied in these concepts.
•
Periodically integrate CCOS findings into summary reports and publications that are
scientifically rigorous and policy-relevant.
•
Disseminate CCOS scientific findings in scientific forums, national guidance documents,
and peer-reviewed publications. Promote and facilitate the dissemination of specific
research projects among other CCOS participants.
6.1.4
Meteorological Working Group
A Meteorological Working Group has been established to update information regarding
the meteorological scenarios associated with ozone exceedances in various areas of the modeling
domain. This group will evolve into the formation of the CCOS forecast team. The forecast
team develops the Forecast Plan in conjunction with the field manager, reviews meteorological
data, and provides consensus forecasts to program management. The forecast team also
documents the daily meteorological conditions during 1997. This team includes representatives
from the ARB, BAAQMD, SMAQMD, and SJVUAPCD.
6.1.5
Emissions InventoryWorking Group
There are about 30 districts in the CCOS modeling domain. Each local air district in the
state updates a portion of the emission inventory for their area. To help coordinate this effort,
the Emission Inventory Coordination Group (EICG) has been established to determine the
process for preparing the emission inventories needed to support air quality modeling for CCOS.
Participants in the group include many local air districts, several local councils of government,
Caltrans, California Energy Commission, and the ARB. Local air districts participating to date
include San Joaquin Valley Unified APCD, Bay Area AQMD, Sacramento Metropolitan
AQMD, Mendocino County AQMD, Northern Sierra AQMD, Yolo-Solano AQMD, Placer
County APCD, San Luis Obispo County APCD, and Monterey Bay Unified APCD. Other local
air districts will also be participating.
6.1.6
Field Coordinator
The implementation of the experimental phase of the program plan will be the
responsibility of the field coordinator. Success of the field measurement program depends on
proper preparation, effective communications, and timely documentation. When handled
properly, most field management is done before the beginning of the study. Communication
channels are established, siting is arranged, and procedures and schedules are formalized.
Coordination among participating groups is initiated through a field study protocol that sets
suitable schedules to maximize the benefits of coordinated measurements while minimizing the
potential interferences among measurements. During the study, oversight by the FM ensures the
quality of the data collected. The field coordinator is responsible for seeing that the planned
quality assurance program is implemented, measurement plans are followed, and corrective
action is taken immediately as problems arise. The field coordinator also is responsible for
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documenting measurement activities and presenting a description of the field program activities
in a formal report.
The most important aspects of the field management effort are: field operations protocol;
siting; communications; oversight of measurements and QA activities; and documentation of
measurements and activities. The field operations protocol is a short document that serves as the
guide for those in the field. It should be a concise overview of the field study, enumerating the
most pertinent information needed by those conducting the measurements. The contents would
include: (1) a brief summary of the study objectives; (2) a list of the measurements; (3) a
schedule of measurements; (4) a roster of participants; (5) a description of sites; (6) a description
of communications channels; (7) protocols for determining and announcing intensive
measurement periods; (8) an outline of deployment and operational logistics; (9) an outline of
on-site quality assurance activities; (10) basic rules regarding site access and operations; and (11)
reporting procedures. This document lists telephone numbers and addresses for sites, update
lines, and field contacts for the participants.
Several planning activities by the field coordinator are needed for the preparation of the
field operations protocol. The field coordinator must determine the optimum schedules for
coordinating measurements, taking into account both program objectives and logistical
considerations. These decisions will be made in consultation and close cooperation with those
who formulate program study design. Additionally, the field coordinator has the task of ensuring
that potential cross-contamination between measurements is eliminated.
As part of the preparation of the field operations protocol, the field coordinator would be
required to design an effective protocol for handling the scheduling of intensive measurement
periods. Clean, effective handling of the forecasting and intensive scheduling can reduce the
cost of the study and improve the data quality. Participants must be given a window of time
during which intensive studies will be scheduled, and the announcement of those intensive
studies must be done as soon as possible, based on daily forecasts by meteorologists assigned to
this task.
Siting of the measurements, including access, security, and electrical power, as well as
location, is extremely important. In addition to the selection of the general location desired for
the study measurements, siting criteria must include maintaining distance from immediate
sources, such as streets, paint shops, or dry cleaners. Existing monitoring sites are desirable
because such siting criteria have been met, because of the existing ozone, nitrogen oxides, and
meteorological measurements at these sites, and because of the historical record of pollutant
concentrations. Whenever possible, the field coordinator will work in cooperation with the ARB
and AQMDs. The necessary lead time can become quite lengthy and must be anticipated in the
planning. Indeed, securing sites becomes the most immediate priority, and this should begin
even before the program plan is approved.
In addition to finding suitable study sites, the role of siting also includes obtaining all
necessary permits, meeting indemnification and insurance requirements for access, ensuring
compliance with local and state environmental and safety regulations, and arranging for study
needs at the site, including power and telephone. The direct costs of these items (such as
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insurance premiums, electrical contracting etc.) should be the responsibility of the sponsors.
However, the field coordinator will act in the role of facilitator.
Complete and clear communications before, during, and after the field study are key to a
successfully coordinated field study program. Before the study, participants must be clearly
informed of their roles and of the study protocols. The field study protocol document referred to
above is an important communications instrument. In the weeks just prior to the field
deployment, a communications center will need to be established in the study region. A
telephone message machine will communicate the daily status of the study. Participants will be
instructed to call in each day and to leave a message on the telephone tape machine briefly
stating their operational status. In this way, the project manager knows exactly who has been
informed and can respond to problems or follow up with those who have not reported. The phone
machine system has the advantages that it can be accessed from any public phone, and that
parties can "report in" at the time they receive their daily update. Additionally, a network
communications tree should be established, such that the field coordinator will be in direct
contact with a designated representative of each of the measurement groups. For intensive
studies, daily contact with the major participating groups is anticipated. A computer bulletin
board or WEB page may be established but the telephone answering machine will to be the
primary means of disseminating information. Communications at the conclusion of the study are
important for complete and accurate documentation of study activities and for initiating data
reporting procedures.
During the field measurement phase of the study, the field coordinator is responsible for
coordinating activities among the participating groups, and for ensuring that measurement and
quality assurance activities are completed as planned. As problems arise, they must be dealt with
in a timely fashion. The field coordinator is responsible for monitoring accomplishments against
the milestones of the program plan, identifying impediments to attaining those milestones and
creating liaisons among project participants in order to remove those impediments. Additionally,
the field coordinator will maintain a log of measurement and quality assurance activities, as well
as a record of all daily forecasts and special events.
At the end of the study, the field coordinator must coordinate a smooth evacuation of the
sites, ensure that sites are left in order and that owners of the sites are thanked in writing for their
assistance to the project. The field coordinator must follow up with each of the study
participants to verify the record of measurements made at the site, and to disseminate
information regarding the reporting of data. A data manager should be selected before the
completion of the study, and the field coordinator will work with that person to disseminate
information regarding data reporting before the end of the study. Additionally, as soon after the
study as possible the field coordinator will supply to the data manager and project directors a
summary list of measurements, including schedules on which they were performed and by
whom, as well as a list of the quality assurance audits performed in the field.
Finally, the field coordinator will compile this information into a report of field
measurement activities. This report will include site descriptions and a list of participants and
measurement activities, as well as a daily log of events during intensive studies. It will
incorporate the relevant information from the field protocol document, as well as the pertinent
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information from the field project log including but not limited to daily forecasts, and a daily log
of measurement activities.
Specifically, the field coordinator will:
•
Assist in field study planning, specifically keeping current the measurement locations,
measurement methods, and quality assurance, schedule, and budget sections of this plan.
•
Prepare work statements for different components of the field measurement program.
•
Procure information needed to evaluate measurement method feasibility, practicality, and
costs and make judgements concerning the costs vs. benefits of new measurement
technology.
•
Select sampling sites based on the monitoring purposes and criteria stated in this plan and
document those sites with respect to their location and surroundings.
•
Specify siting, electrical, communications and environmental requirements for each
sampling site and procure the necessary permits, power, and security to operate those
sites.
•
Recruit, train (in collaboration with measurement investigators) and direct site operators.
Monitor operator quality, in collaboration with measurement investigators, and take
remedial actions where needed.
•
Establish an efficient communications network, including a project roster of names,
addresses, phone numbers, fax numbers, and e-mail addresses among other field study
participants and apply that network to convey needed information among the participants.
•
Participate in the evaluation of technical proposals from measurement investigators and
provide perspective on the likelihood that different measurement alternatives to
accomplish objectives.
•
Identify and resolve communications difficulties among measurement investigators, the
data manager, and the quality assurance officer to assure that audit findings are corrected
and documented and that data flow efficiently and smoothly into the project database.
•
Organize a forecasting team to determine the initiation and end of intensive operating
periods during winter, develop a forecasting protocol, and implement it during the winter
study. Develop a protocol to disseminate go/nogo and abort decisions.
•
Document field study activities and accomplishments after its completion.
•
Prepare a site report
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6.1.7
Quality Assurance Officer
The quality assurance officer provides direction, coordination, and documentation of
measurement accuracy, precision, and validity. Specifically, the quality assurance officer will:
•
Prepare a quality assurance program plan that specifies systems and performance auditing
methods, intercomparison methods, primary and transfer standards, schedules, audit
reporting formats, feedback to measurement investigators, and methods to verify that
remedial actions have been taken.
•
Assemble and direct a quality assurance team with specialized expertise and equipment in
evaluating precision, accuracy, and validity of established and developing measurement
methods. Deploy this team to conduct systems and performance audits, assemble the
results, provide feedback to measurement investigators, and verify that remedial actions
have been implemented.
•
Analyze data from collocated or nearby air quality and meteorological measurement
systems to estimate precision, equivalence, and predictability among measurements.
Identify environmental and atmospheric composition conditions under which values for
these attributes are acceptable and unacceptable.
•
Identify, obtain, and evaluate standard operating procedures for methods to be applied
during field studies.
•
Evaluate data qualification statements prepared by measurement investigators.
6.1.8
Data Manager
•
The data manager will establish and maintain computer-based data archives and
communications. Specifically, the data manager will:
•
Review and revise the data management section of this plan as changes are made to data
reporting, structure, and retrieval conventions.
•
Define and procure hardware and software needed to successfully complete the data
management tasks.
•
Prepare statements of work and direct data management support investigators to assist in
the accomplishment of data management tasks.
•
Establish a CCOS web page with links to: 1) long-term data archives; 2) electronic copies
of CCOS reports and documents (electronic form); 3) CCOS participants’ roster, roles,
and websites (where available); and 4) standard operating procedures; 5) data receipt
archives; 6) CCOS field study data archives; and 7) measurement sites and observables.
•
Develop, apply, and document efficient and modern methods for receiving, processing,
and delivering data from and to measurement, data analysis, and modeling investigators.
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•
Perform Level 1B validation tests. Apply Level II and III validation adjustments and
flags.
•
Transmit the final data base to the NARSTO data archive.
6.1.9
Measurement Investigators
Measurement investigators are responsible for specialized CCOS measurements.
Specifically, measurement investigators will:
•
Specify and procure (or construct) monitoring devices and verify their adequacy for
operation under conditions anticipated during CCOS field monitoring.
•
Specify for the field coordinator space, power, sample presentation, supplies, storage,
shipping/receiving, environmental, and communications requirements for each
monitoring site at which the investigator's measurements will be acquired.
•
Develop or adapt standard operating procedures and checklists and participate in training
of site operators, or supply specially-trained site operators as needed.
•
Install and calibrate instruments at the measurement sites and remove them after study
completion.
•
Supply expendables needed to maintain measurement schedules.
•
Monitor network operations, in collaboration with the field coordinator, identify and
remediate deficiencies.
•
Evaluate and remediate system and performance audit findings and document that
remediation.
•
Regularly acquire raw measurements to evaluate monitor performance. Periodically
reduce data, validate it at Level 1A, and deliver it in the prescribed formats to the data
manager.
•
Prepare a summary of monitoring methods, data and validation results, and measurement
activities when measurements have been completed. Prepare data quality statements that
evaluate the accuracy, precision, validity, and completeness of the measurements
acquired during each phase of the field study.
6.1.10 Data Analysis and Modeling Investigators
Data analysis and modeling investigators will use the integrated data set to perform Level
2 and Level 3 data validation, to further answer the questions posed in Section 3, and to develop,
evaluate, and apply source and receptor models. Several of the data analysis activities,
especially those involved with determining Level 2 validation status of measurements and
descriptive analysis of the measurements, are best performed by the measurement investigators.
Specifically, data analysis and modeling investigators will:
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6.2
•
Evaluate the consistency of measurements, i.e. Level 2 validation.
•
Describe statistical, temporal, and spatial distributions of measurements on average and
during episodes.
•
Interpret data to answer questions in Section 3.
•
Recommend revisions to the conceptual models in Section 3, elaborating on the
dominance and validity of chemical and physical mechanisms and the importance of
different pollution sources.
•
Use field measurements in meteorological and air quality source and receptor models to
evaluate the extent to which they represent the identified mechanisms and emissions.
•
Use field measurements to establish base cases for annual average and several different
episodes of maximum concentration to evaluate the effects of different emissions
reduction strategies.
•
Identify data deficiencies and initiate Level 3 validation checks and corrections with the
data manager.
Schedule
Table 6.2-1 provides milestones for CCOS field study and related activities. This
schedule should be integrated with the master project schedule that includes emissions and
modeling activities. This schedule will be maintained by the field coordinator and made more
specific as planning progresses. The overall time scale shown is four years. To meet this
schedule, however, will require careful planning, fast turn-around by the sponsors on requests for
proposals and contracts, and much front-end work by the Technical Committee. In the early
stages of the program, several tasks must be performed in parallel, and the technical committee
and working groups must meet frequently and coordinate closely. Once the field study is
underway, less effort is required of the sponsors and committees. The quality assurance and data
managers should be selected as soon as the general scope of the field study is defined.
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Table 6.2-1 CCOS Schedule of Milestones
Activity/Milestone
Completion Date
Planning
Preparation of preliminary scope of work by ARB.
1/31/99
Technical Committee (TC) meeting in Sacramento – upper air network.
3/26/99
TC meeting in Sacramento – emission inventory development.
4/14/99
TC meeting in Sacramento – review of past studies and conceptual model.
5/3/99
TC and SAW meeting in Rocklin – DRI present draft Conceptual Program Plan 6/11/99
Revised draft of Conceptual Program Plan available on CCOS web site
9/7/99
Release requests for proposals for field operations
11/15/99
Final Field Study Plan available on CCOS web site
11/30/99
Selection of supplemental monitoring sites
12/15/99
Contractor selection
1/7/00
Executable Contracts
2/15/00
CCOS Field Study Operational Plan and Protocol
4/27/00
CCOS Field Measurement
Field coordination meeting in Sacramento
Field measurements begin
Field measurements end
Draft documentation of field activities by DRI
Final summary of field monitoring activities by DRI
mid-May/00
6/15/00
9/15/00
10/15/00
12/15/00
Data Validation, Data Analysis and Modeling
Quality audit report by QA Contractor
Level 1 Data Archive
Level 2 Data Validation
12/15/00
4/31/01
6/30/01
Data Analysis/Emissions/Modeling
Data analysis
Emission inventory development
Model improvements
Base-case simulations (model performance, diagnostic/sensitivity testing)
12/31/01
9/31/01
9/31/01
3/31/02
Future year emission inventory and sensitivity simulations
9/30/02
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7.0
Chapter 7: References
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APPENDIX A
MEASUREMENT METHODS
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A.
Appendix A: Measurement Approach
MEASUREMENT APPROACH
Field monitoring includes continuous measurements over several months and intensive
studies that are performed on a forecast basis during selected periods when episodes are most
likely to occur. The continuous measurements are made in order to assess the representativeness
of the intensive study days, to provide information on the meteorology and air quality conditions
on days leading up to the episodes, and to assess the meteorological regimes and transport
patterns which lead to ozone episodes. The intensive study components are designed to provide
a detailed aerometric database which, along with the emission estimates and continuous
monitoring data, can be used to improve our understanding of the causes of pollutant episodes in
the study region and to provide data for input to the models and for model evaluation. This
section describes the options for continuous and intensive air quality and meteorological
measurements (surface and aloft) to be made during CCOS.
A.1
Surface Meteorology
The types of surface meteorological measurements needed for this study include
measurements of wind speed, wind direction, and temperature. Measurements of humidity,
pressure, visibility, solar radiation, and ultraviolet radiation are also available at some sites.
Within the study area (modeling domain), these measurements are provided by several existing
networks of sites operated by several organizations including:
•
Surface Airways Observations sites from the National Weather Service (NWS),
operating in conjunction with the Federal Aviation Administration
•
California Irrigation Management Information System sites from the California
Department of Agriculture
•
Remote Automated Weather Station sites from the Bureau of Land Management,
California Department of Forestry, National Park Service, and U.S. Forest Service
•
APCD/AQMD Aerometric Monitoring Station sites from the Bay Area AQMD, Kern
County APCD, San Joaquin Valley Unified Control District, Santa Barbara APCD,
Ventura County APCD, Mojave Desert AQMD, and California Air Resources Board
•
Military installations
•
Private companies
•
Public organizations such as universities
A.1.1 Surface Wind Speed and Direction
Wind speeds are measured with anemometers at 10 m above ground to minimize the
effects of nearby structures and vegetation. Anemometers, both cup and propeller type, work on
the principal that the rate of rotation about a vertical or horizontal shaft is proportional to the
speed of the air flow past the sensor. Anemometers used for scientific monitoring should have a
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quick response to changes in wind speed as well as a low threshold for rotation for conditions of
very light winds. Acceptable precision levels for anemometers are ±0.5 m/s for most air quality
studies (e.g., Thullier et al., 1993).
Wind direction measurements are made with wind vanes that orient themselves with the
direction of the wind. High quality bearings are used for sensitivity and direction is resolved
with an internal potentiometer that has a reference location of zero which must be physically
aligned with true north when in position on the tower. The position of true north is determined
with a sensitive compass or solar reference and the north position of the wind vane is then
aligned in this direction. Care must be taken in the alignment procedure or in subsequent
resolution of the wind direction vectors to account for the magnetic declination at the observation
site.
Averaging can be done over intervals of a few points to the entire data record. The
interval must be chosen with respect to the phenomena being observed. As shorter averaging
intervals are chosen, periodic motion may be revealed when the period is significantly greater
than the averaging period. The averaging process attenuates small-scale fluctuations due to
turbulence. The averaging interval must contain sufficient data points to constitute a reasonable
average, eliminating the small scale turbulent fluctuations and instrument derived errors such as
response sensitivity and anemometer run-up and run-down. Five minutes is a reasonable
compromise between the need to acquire a statistically valid number of samples and the nature
of phenomena such as wind gusts and dispersion.
The time average of horizontal wind direction standard deviation (σθ) can be calculated as
from one second observations over a period of 5 minutes or less (Greeley and Iversen, 1985).
This minimizes the influence of the wind flow which may be non-stationary in both speed and
direction over longer averaging periods.
Table A.1-1 provides specifications for CCOS/CRPAQS wind sensors. Instruments
meeting these specifications were used in the Southern California Ozone Study (SCOS97NARSTO) (Fujita et al., 1997) and the Northern Front Range Air Quality Study (NFRAQS)
(Chow et al., 1998). SCOS97 performance audits were satisfactory with ±0.25 m/s for wind
speeds between 0 and 5 m/s, and ±5% for wind speeds greater than 5 m/s. The NFRAQS
intercomparison of meteorological sensors concluded that from collocated instruments were
comparable to each other at the 95% confidence level when sampling the same environment.
NFRAQS and SCOS97 also demonstrated a high level of instrument functionality, with few
episodes of missing data at any of the seven monitoring sites.
A.1.2 Surface Relative Humidity
Humidity indicates the amount of water vapor in the air. The most common reference for
this property of the air is the relative humidity (RH), the ratio of the amount of water vapor
actually in the air to the amount the air could hold at a given temperature and pressure, that is, the
ratio of the actual to the saturated vapor pressure.
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Accurate measurements at high RH (>85%) is important to atmospheric transformations
and to visibility reduction. An accuracy of 2-3% RH is needed to adequately estimate aerosol
properties. A 2-3% accuracy for RH over the 90-100% RH is attainable by several RH sensors
(e.g. Vaisala, Rotronics) and corresponds to ±0.5°C when expressed as dewpoint temperature.
Motor-aspirated radiation shields ensure adequate ventilation and exposure rates (U.S. EPA,
1989).
A.1.3 Surface Temperature
Equivalent resistance temperature detectors (RTD (e.g., Vaisala, Rotronics, Climatronics,
Campbell Scientific, etc.) are used at the meteorological sites. Motor-aspirated radiation shields
ensure adequate ventilation and exposure rates, according to U.S. EPA quality assurance
requirements (U.S. EPA, 1989). The accuracy (~0.15-0.5 °C) and functional precision (0.1°C) of
these sensors are sufficiently good and they can be used to infer lapse rates and thus stability
(e.g., estimates of delta-T) when several sensors are used on a tower. Temperature sensors are
normally located at 5 m above ground level. Table 10.1-1 presents specifications for the Vaisala
and Campbell Scientific (CV) temperature (• C) and relative humidity (%) sensors
A.1.4 Surface Pressure
Pressure is routinely measured by the National Weather Service in terms of altimeter
setting or sea level pressure. The altimeter setting is the pressure value to which an aircraft
altimeter scale is set so it will indicate the altitude above mean-sea-level of the aircraft on the
ground at the location the pressure value was determined. The sea level pressure is the
atmospheric pressure at mean sea level. It is either measured directly or obtained by the
empirical reduction of station pressure to sea level. When the earth's surface is above sea level, it
is assumed that the atmosphere extends to sea level below the station and that the properties of
the fictitious air column are related to conditions observed at the station.
A.1.5 Solar Radiation
Solar radiation sensors are operated at several existing ARB sites throughout the region. The
measurements employ a solar pyranometer that collects direct solar beam and diffuse sky
radiation passing though a horizontal beam.
A.2
Upper-Air Wind Speed, Wind Direction, and Temperature
The types of upper-air meteorological measurements needed include wind speed, wind
direction, temperature, and mixing height. Measurements of humidity are also useful where they
are available at some sites. A comprehensive discussion of the accuracy and methodology of
upper air measurements is provided in the SJVAQS/AUSPEX Meteorological Field
Measurement Program report (Thuillier, 1994).
A.2.1 Radar Wind Profilers
Radar wind profilers (RWPs) provide continuous remote measurements of wind speed
and direction to altitudes of ~3 km AGL. The profilers emit short pulses of generally 915 MHz
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electromagnetic radiation which scatter off inhomogeneities in the index of refraction of an air
mass. By emitting energy directionally in three orthogonal components, the received scattered
radiation provides estimates of wind speed and direction. Because of the finite length of the
emitted pulse train, vertical resolution is limited to approximately 100 m. Echoes received
during the detector downtime cannot be processed and this raises the threshold height to
approximately 100 m above ground level. The typical five-degree beam width also limits the
resolution of wind direction in a height dependent manner. There are general trade-offs between
maximum range and vertical resolution and between averaging time and accuracy.
Radar wind profiler data are generally reported as 15-minute up to hourly averaged data
and are representative of a three-dimensional cell. Because wind vectors may vary significantly
over the course of an hour, the near-instantaneous radiosonde measurements may not compare
well with radar wind profiler measurements on a case-by-case basis. However, if radiosondes
and wind profilers are deployed in areas with similar winds, the statistical distribution of winds
may be expected to be reasonably similar. The RWP is highly sensitive to stray electromagnetic
signals and stationary or solid objects in the beam paths or their side lobes. Thus, it should be
sited away from large objects such as buildings and trees and away from frequent overflights by
aircraft.
A.2.2 Radio-Acoustic Sounding Systems (RASS)
Temperature aloft up to 500-700 m can be characterized using a RASS. The RASS
essentially uses collocated radio wave and acoustic sources such that radio waves are scattered
off the acoustic wavefronts. The propagation velocity of the acoustic wave fronts is then
measured, which can be related to the (virtual) temperature of the air. This effort could install
one radar profiler with RASS at critical locations along major transport paths, such as mountain
passes or the coastal shoreline, for example, near Cajon and Banning Passes and along the
downwind shorelines in San Diego and Santa Barbara. There is considerably less accuracy in the
RASS temperature profiles compared to the those obtainable from tethersonde measurements,
particularly in areas of complex terrain where radar side lobes may present a problem. However,
the RASS temperature data may be compared to QA radiosonde or tethersonde temperature data
because the virtual temperature can also be calculated from the sonde measurements of ambient
temperature, dew point or relative humidity, and pressure. A favorable comparison under
representative conditions would increase confidence in the RASS data at these sites.
A.2.3 Acoustic Sounders (Sodars)
The sodar uses an observational process that is similar to the RWP except that the sodar
uses pulses of acoustic instead of electromagnetic energy. The sodar then detects returned
acoustic energy scattered from turbulent density fluctuation (instead of index of refraction
fluctuations). It provides hourly averaged wind speed and direction up to a 500-600 m maximum
range with a threshold height of approximately 50-60 m. The sodar can operate routinely with
little or no on-site care, but compared to radiosondes it does not measure humidity, and the
equipment is expensive. The sodar is highly sensitive to extraneous sources of sound. Thus, it
should be sited away from substantial and continuous noise sources such as heavily traveled
roads. The sodar acoustic emissions are audible and may disturb nearby residents, which can be
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a siting disadvantage compared with the RWP. With the lower vertical range, sodars should be
deployed in areas of lower expected mixing heights, such as in marine layers.
A.2.4 Radiosondes
Upper-air wind speed, wind direction, and temperature can be collected by in-situ systems
or by remote sensing instruments. The most commonly used in-situ systems rely on a balloonborne sensor (radiosonde), carried aloft by a freely ascending weather balloon to measure
atmospheric pressure, temperature, and moisture (relative humidity or wet bulb temperature).
These thermodynamic variables are used to compute the altitude of the balloon. Wind speed and
direction aloft are determined by measuring the position of the balloon as it ascends. The
position data can be obtained by tracking the balloon from a fixed ground station using radio
direction finding techniques (RDF) or optical tracking, or they can be obtained using one of the
radio navigation (NAVAID) networks, such as loran or Global Positioning System (GPS). By
measuring the position of the balloon with respect to time and altitude, horizontal wind vectors
can be computed that represent the layer-averaged wind speed and wind direction for successive
layers. Balloons can also measure relative humidity, which is not detected by proven remote
sensing methods.
The vertical resolution of the wind data is typically 50-200 m (depending on altitude and
the type of sounding system used), and the range is from about 100 m above ground level (AGL)
to altitudes as high as 10,000 m altitude or higher. Radiosondes provide wind data that are
averaged over short vertical distances and are thus nearly instantaneous measurements at a
particular location. Although the radiosonde directly measures temperature and humidity during
the short balloon flight, it is only a "snapshot" at each level of the atmosphere. As the radiosonde
rises, it is transported downwind, so measurements are not made above a fixed location, as
within a wind profiler, for example.
For air pollution studies such as this, balloon sounding systems can provide relatively
complete profiles of winds, temperature, and moisture in the boundary layer and lower
troposphere. Balloon soundings usually can be made two to eight times per day on selected
sampling days within reasonable budget contraints, but they are not cost-effective for long-term
monitoring.
A.2.5 Tethered Balloons
Tethered balloons employ an instrument package, called a tethersonde, which provides in
situ measurements of wind speed and direction, temperature, dew point, and pressure. The
package is deployed on a blimp-shaped helium balloon that is tied by light cord to a mechanical
winch system. The altitude range is usually limited to less than 1 km; wind speeds over 10
meters per second may cause loss of the balloon. The tethersonde transmits the information back
to a ground station, with values taken every few seconds corresponding to one value every 2-3
meters apart for average ascent and descent speeds. The accuracy of the measurements compares
to that of any surface station, since the sonde package is akin to a miniature surface station
carried aloft by the balloon. Advantages of the tethersonde include much better vertical
resolution with no threshold height compared to the RWP or sodar. Once the system is set up
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and manned, soundings can be taken through the surface layer as often as every 15 minutes.
However, disadvantages of the tethersonde include site setup/breakdown time, the need for
continual manning during operations, and the difficulty in obtaining FAA permits to operate
within 8 km of any airport.
A.2.6 Sonic Anemometers
Sonic anemometers deployed at surface sites would provide estimates of turbulent fluxes
for the modeling effort. The variations in sound propagation over a short (30 cm) path are
typically sampled at 10 Hz, providing an estimate of turbulent fluctuations over that path. Onedimensional sonic anemometers measure vertical turbulent flux, while three-dimensional sonic
anemometers include turbulent fluxes in the two horizontal dimensions. These data provide
boundary layer characterization, representative of the type of terrain over which the measurement
is taken. Urban effects could be better incorporated in the mesoscale model. The measurement
could provide better dispersion estimates for models run in the data assimilation mode, and
provide real-world measurements against which model estimates of turbulent flux are compared.
A.3
Routine Surface Air Quality
The California Air Resources Board and local air pollution control districts operate a
network of sampling sites that measure ambient pollutant levels. This network consists of three
types of surface air quality monitoring stations. The National Air Monitoring Stations (NAMS)
were established to ensure a long term national network for urban area-oriented ambient
monitoring and to provide a systematic, consistent database for air quality comparisons and trend
analysis. The State and Local Air Monitoring Stations (SLAMS) allow state and local
governments to develop networks tailored to their immediate monitoring needs. Special purpose
monitors (SPM) fulfill very specific or short-term monitoring goals. SPMs are typically used as
source-oriented monitors rather than monitors which reflect the overall urban air quality. Data
from all three types are submitted by state and local agencies to EPA’s Aerometric Information
Retrieval System (AIRS), which serves as the national repository for air quality, meteorological
and emissions data.
Under Title I, Section 182, of the 1990 Amendments to the Federal Clean Air Act, the
EPA proposed a rule to revise the current ambient air quality surveillance regulations. The rule
requires implementing a national network of enhanced ambient air monitoring stations (Federal
Register, 1993). States with areas classified as serious, severe, or extreme for ozone
nonattainment are required to establish photochemical assessment monitoring stations (PAMS)
as part of their State Implementation Plan (SIP). In the CCOS area, PAMS are required in
Sacramento, Fresno and Bakerfield metropolitan areas. Each station measures speciated
hydrocarbons and carbonyl compounds, ozone, oxides of nitrogen, and surface meteorological
data. Additionally, each area must monitor upper air meteorology at one representative site. The
program was phased in over a five-year schedule, beginning in 1994, at a rate of at least one
station per area per year. Intended applications for the PAMS database include ozone and
precursor trends, emission inventory reconciliation and verification, population exposure
analyses, photochemical modeling support, and control strategy evaluation.
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There are 185 active monitoring stations in northern and central California. Table A.1-2
contains a list of the monitoring sites and the air quality parameters measured at each site. Of the
active sites, 130 measure ozone and 76 measure NOX. Carbon monoxide and total hydrocarbons
are measured at 57 and 14 sites, respectively. Data from these sites are routinely acquired and
archived by the ARB and Districts. The following is a brief description of the measurement
methods that are commonly used at routine air quality monitoring stations.
A.3.1 Ozone
Ozone is continuously measured either by ultraviolet absorption photometry or by gasphase ethylene-ozone chemiluminescence. In the ultraviolet analyzer, a mercury vapor lamp is
used to produce ultraviolet radiation at 254 nm which is absorbed by the ozone in the air sample.
The ozone signal is determined by the difference between ambient air containing ozone and
ambient air with the ozone removed or scrubbed. The ultraviolet analyzer is calibrated by
comparison with an ozone photometer which is certified as a transfer standard. The transfer
standard is certified against absolute ozone photometers located at the California Air Resources
Board test and laboratory facilities. The minimum detectable level of UV monitors is about 2-5
ppbv. Accuracies and precisions are on the order of 10-15 percent or 2-5 ppbv, whichever is
larger. Interferences with the UV measurement method include any gas or fine particle that
absorbs or scatters light at 254 nm. Gaseous inorganic compounds normally found in the
atmosphere, including NO2 and SO2, do not interfere, and particles are largely removed by a prefilter. The most likely interferent is gaseous hydrocarbon compounds that are strong absorbers at
254 nm and are either partially or completely absorbed onto the scrubber. Examples are aromatic
compounds, such as benzene and substituted benzenes. Interferences from hydrocarbons can
account for a positive bias in the UV measurement for ozone of up to 40 ppb based on the
concentration of the interferences occurring during peak ozone periods (Leston and Ollison,
1992). Kleindienst et al. (1993) observed about a 3 percent interference with ozone
measurements under hydrocarbon loadings typical of ambient smoggy conditions. Water vapor
may also interfere with the UV method when water vapor concentrations are high and variable.
These interferences appear to be due to the condensation of water vapor on imperfect absorption
cell windows.
The ozone/ethylene chemiluminescence method (ECL) is a Federal Reference Method for
measuring ozone in ambient air (EPA, 1971). The ECL method is based on the reaction of ozone
with ethylene, which produces formaldehyde in an electrically excited state and, on transition to
the ground state, emits light in the visible range. This reaction is rapid and specific to ozone and
takes place in a chamber coupled to a sensitive photomultiplier tube. Under controlled
conditions, the signal produced by the ozone-ethylene reaction is proportional to the ozone
concentration in the reaction chamber and with proper calibration it is proportional to ambient
ozone concentrations. The chemiluminescence analyzer is calibrated in the same manner as the
ultraviolet analyzers. The minimum detection level of commercial ECL monitors is about 2-5
ppbv. Accuracies and precisions are on the order of 10-15 percent or 2-5 ppbv, whichever is
larger. The only major interference for measuring ozone by the ECL method is water vapor. The
positive interference ranges from 3-12 percent. However, this interference can be adjusted for by
calibrating the ECL monitors at humidities expected during peak ozone periods. In practice, this
is rarely done.
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The Air Pollution Control Districts measure ambient ozone concentrations with
instruments made by several different manufacturers. All analyzers employ the UV photometric
technique to determine ozone concentration. All analyzers have been designated as EPA
Equivalent Methods. The following analyzers are deployed in the networks:
Thermo Environmental Inc., model 49
Dasibi Environmental, model 1003
Advanced Pollution Instrumentation, Inc., model 400
A.3.2 Nitrogen Oxides
Nitric oxide (NO) is continuously measured by the chemiluminescence nitric oxide-ozone
method (OCM). This method is based on the gas-phase chemical reaction of NO with ozone. In
this method an ambient air is mixed with a high concentration of ozone so that any NO in the air
sample will react and thereby produce light. The light intensity is measured with a
photomultiplier and converted into an electronic signal which is proportional to the NO
concentration. To measure NOx concentrations, the sum of NO and NO2 (nitrogen dioxide), the
air sample is first reduced to NO, either by a heated catalyst (molybdenum or gold in the presence
of CO) or chemically using FeSO4, adding to the NO already present in the sample, then into the
reaction chamber for measurement as described above. The NO2 concentration is derived by
subtracting the NO concentration measurement from the NOx concentration measurements.
Standard sensitivity instruments have detection limits of about 0.5 to 3 ppb (60 sec
averaging times) and are suitable for air quality monitoring in urban and suburban areas. Thermo
Environmental Instruments, Inc. (TEI) Model 42C and Monitor Labs 8440 and 8840 are
examples of this type of instrument. These and similar instruments from Columbia Scientific
and Bendix have been used widely by federal, state, and local agencies for routine monitoring of
NO and NO2 (actually NOx minus NO plus other interfering nitrogen oxides). Trace level
instruments, such as the TEI Model 42C-TL have detection limits of about 50 ppt (120 sec
averaging times) and are better suited in rural and background sites, and onboard instrumented
aircrafts.
The reduction of NO2 to NO by these methods is not specific and a number of other
nitrogen-containing species are reduced to NO that can interfere with the measurement of NO2
(e.g., HNO3, PAN, N2O5, HONO, and NO3). Therefore the thermal catalytic method is used to
measure NO, and then NO plus other nitrogen oxides as a group. If the group is not well defined,
it is referred commonly as NOx, since the species included in the group depend on factors such as
inlet and line losses and environmental factors. HNO3 is most prone to line losses. Placing the
converter as close to the sample inlet as possible minimizes these losses. Chemiluminescence
analyzers that are configured in this manner are commonly known as NOy analyzers. NOy, or
reactive nitrogen oxides, consists of a variety of species, the most abundant of which are NO,
NO2, PAN and HNO3. TEI Model 42CY is configured with dual converters, which allows
estimates of HNO3 by difference between the signals with and without an in-line nylon filter or
NaCl impregnated fiber denuder.
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A.3.3 Photochemical Assessment Monitoring Stations (PAMS) Program
A maximum of five PAMS sites are required in affected nonattainment areas, depending
on the population of the Metropolitan Statistical Area/Consolidated Metropolitan Statistical Area
(MSA/CMSA) or nonattainment area, whichever is larger. PAMS networks are based on
selection of an array of site locations relative to ozone precursor source areas and predominant
wind directions associated with high ozone events. Specific monitoring objectives associated
with each of these sites result in four distinct site types.
Type 1 sites are established to characterize upwind background and transported ozone and
its precursor concentrations entering the area and to identify those areas which are subjected to
overwhelming transport. Type 1 sites are located in the predominant morning upwind direction
from the local area of maximum precursor emissions during the ozone season. Typically, Type 1
sites will be located near the edge of the photochemical grid model domain in the predominant
upwind direction from the city limits or fringe of the urbanized area.
Type 2 sites are established to monitor the magnitude and type of precursor emissions in
the area where maximum precursor emissions are expected. These sites also are suited for the
monitoring of urban air toxic pollutants. Type 2 sites are located immediately downwind of the
area of maximum precursor emissions and are typically placed near the downwind boundary of
the central business district. Additionally, a second Type 2 site may be required depending on
the size of the area, and will be placed in the second-most predominant morning wind direction.
Type 3 sites are intended to monitor maximum ozone concentrations occurring downwind
from the area of maximum precursor emissions. Typically, Type 3 sites will be located 10 to 30
miles downwind from the fringe of the urban area.
Type 4 sites are established to characterize the extreme downwind transported ozone and
its precursor concentrations exiting the area and identify those areas that are potentially
contributing to overwhelming transport in other areas. Type 4 sites are located in the
predominant afternoon downwind direction, as determined for the Type 3 site, from the local area
of maximum precursor emissions during the ozone season. Typically, Type 4 sites are located
near the downwind edge of the photochemical grid model domain.
The current status of the implementation of PAMS by local air pollution control districts
in central California is outlined in Table A.1-3. PAMS precursor monitoring is conducted
annually in California during the peak ozone season (July, August and September) according to
the schedule shown in Table A.1-3. Eleven PAMS sites will be in operation during summer
2000 (four in Sacramento County, four in Fresno County, and three in Kern County). The table
includes minimum network requirements that are specified in the EPA rule and the alternative
network specifications submitted to EPA by ARB on behalf of the affected districts in the state.
The California Alternative Plan was deemed by EPA to satisfy the PAMS monitoring objectives
and has been approved for use by districts in California.
EPA methods TO-14 and TO-11 are specified by the EPA for sampling and analysis of
speciated hydrocarbons and carbonyl compounds, respectively (EPA, 1991). Table A.1-
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4contains the minimum list of targeted hydrocarbon species. For carbonyl compounds, state and
local agencies are currently required to report only formaldehyde, acetaldehyde and acetone. The
ARB and districts laboratories may be able to quantify and report several C3 to C7 carbonyl
compounds that appear in the HPLC chromatograms. The EPA rule requires eight 3-hour
hydrocarbon samples (midnight-3 am, 3-6 am, 6-9 am, 9-noon, noon-3 pm, 3-6 pm, 6-9 pm, and
9-midnight PDT) every day at Type 2 sites and every third day at all other PAMS sites.
Sampling for carbonyl compounds is required at Type 2 sites only. In addition, one 24-hour
sample is required every sixth day year-round at Type 2 sites and during the summer monitoring
period at all other sites. Under the California Alternative Plan, four 3-hour samples (0-3 am, 6-9
am, 1-4 pm, and 5-8 pm, PDT) are collected every third day during the monitoring period at all
PAMS sites for speciated hydrocarbons and at Type 2 sites only for carbonyl compounds. In
addition to the regularly scheduled measurements, samples are collected on a forecast basis
during up to five high-ozone episodes of at least two consecutive days. Episodic measurements
consist of four samples per day (6-9 am, 9-noon, 1-4 pm and 5-8 pm, PDT) for speciated
hydrocarbons at all PAMS sites and for carbonyl compounds at Type 2 sites.
Total nonmethane hydrocarbon (NMHC) concentrations are also measured by analysis of
the canister samples by preconcentration direct injection flame ionization detection (PDFID).
Total NMHC is measured by passing the air sample through a chromatographic column to
separate methane from other hydrocarbons and analyzing the bulk hydrocarbon sample by FID.
In the FID, sample air is burned in a hydrogen flame creating a quantity of ions from the
hydrogen molecules in the air sample. The ions conduct a small electrical current which is
measured by an electrometer, which in turn produces an electronic signal proportional to the
number of ions collected. Thus, the total hydrocarbon data are reported as parts per billion
carbon (ppbC). Total nonmethane hydrocarbon (NMHC) concentrations are monitored at some
PAMS sites by automated-Preconcentration Direct Injection Flame Ionization Detection (PDFID)
(e.g., Xontech 850).
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Supplemental Measurements of Ozone
Supplemental air quality measurements are needed during intensive operational periods
(IOPs) in order to examine the three-dimensional distribution of ozone in the study area and to
quantify some of the important species, other than those routinely monitored, which participate in
ozone photochemistry.
A.4.1 Ozonesondes
In addition to atmospheric pressure, temperature, and moisture (relative humidity or wet
bulb temperature), balloon-borne radiosondes can be equipped with instrumentation to measure
the vertical distributions of ozone mixing ratios. Ozonesondes have lower quantifiable limit of
less than 15 ppb with precision of ± 5 ppb or ± 10%.
Ozonesondes provide ozone data that are averaged over short vertical distances and are
thus nearly instantaneous measurements at a particular location. The data are only "snapshots" at
each level of the atmosphere. As the radiosonde rises, it can be transported downwind, so
measurements are not made above a fixed location. The typical response time of the instrument
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(> 80% of step change in 1 minute) may affect accuracy during ascent through sharp gradients in
ozone mixing ratios. Balloon soundings usually can be made two to eight times per day on
selected sampling days within reasonable budget contraints, but they are not cost-effective for
long-term monitoring. Use of ozonesondes are most cost-effective when coupled to existing
radiosonde releases.
A.4.2 Ozone Aloft  Lidar Measurements
In the more than 30 years since the invention of the laser, the evolution of lidar (light
detection and ranging) technology has led to great advances in atmospheric remote sensing
(Measures, 1984). A particular form of lidar, the Differential Absorption Lidar (DIAL) has made
it possible to measure the concentration distributions of many tropospheric trace gases and
pollutants (Rothe et al., 1974a; Measures, 1984; Grant et al., 1992).
A basic lidar system consists of a transmitter and a receiver located next to each other.
The transmitter, typically a pulsed laser, sends a short pulse of collimated light into the
atmosphere. A small part of this light pulse is scattered back into the receiver by atmospheric
particles and gas molecules. Light scattered at a distance r will arrive at the receiver after a round
trip time t = 2r/c, where c is the speed of light. The lidar return signal S as a function of distance
r is described by the lidar equation (Measures, 1984).
S (r , λ) = A(r , λ )C (λ)
β (r , λ )
 r

exp
−2 ∫ σ (x , λ)dx 

2
 0

r
,
where A is the overlap integral between the transmitted laser beam and the field of view of the
telescope, C is the system constant, ß is the backscattering coefficient, σ is the volume extinction
coefficient, and λ is the wavelength of the laser. While the overlap integral A can be determined
from measurements in a homogeneous atmosphere, the system constant C is generally unknown
and for each measured signal S(r,λ) there are two atmospheric parameters ß(r,λ) and σ(r,λ) one
would like to determine. The lidar equation is therefore under-determined and cannot be solved
without additional assumptions or data. To obtain at least semi-quantitative information about
the range-resolved atmospheric extinction, one generally assumes an empirical relationship
between the backscattering coefficient ß(r,λ) and the extinction coefficient σ(r,λ) (Gibson, 1994),
together with an estimate of a boundary condition for the extinction coefficient within the
measurement range (Fernald et al., 1972; Klett, 1981). The mathematical technique used for this
semi-quantitative analysis is based on Hitschfeld’s work for the analysis of radar data (Hitschfeld
and Bordan, 1954) and is often referred to as Klett inversion (Klett, 1981).
Differential absorption lidar (DIAL) is a multi-wavelength lidar that uses the wavelengthdependent absorption of atmospheric constituents to measure their range resolved concentration
(Measures, 1984). For this purpose, the extinction coefficient σ at a distance r and wavelength λi
may be written as
σ (r, λi ) = σ i (r ) + α i n(r) ,
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where σi is the extinction coefficient due to scattering at λi, αi denotes the absorption coefficient
at λi, and n is the concentration of the absorbing gas. If the lidar equation is applied at two
different wavelengths λi and λj with substantially different absorption coefficients, the
concentration n at range r can be calculated from the ratio of the lidar signals at the two
wavelengths as
n (r ) =
1
1 d  β i (r ) 
1 d  Ai (r ) 
d  S j (r )  ∆σij (r )
 ln
+
 ln
,
+
 ln
−
2∆α ij dr  Si (r ) 
2∆α ij dr  β j (r )  2∆α ij dr  Aj (r ) 
∆α ij
where αi,j=αi-αj and σi,j=σi-σj. The laser line (wavelength) with the larger (smaller) absorption
coefficient is referred to as “on-line” (“off-line”). The first term in this equation is the main
term, while the others are correction terms; the second term and the third term, respectively, are
corrections for differential aerosol extinction [Ei,j] and backscatter [Bi,j] between the two
wavelengths, whereas the fourth term is a correction for the incomplete overlap [Oi,j]. The
extinction correction vanishes ([Ei,j]=0) if the extinction due to scattering is identical for λi and
λj, the backscatter correction vanishes ([Bi,j]=0) if the ratio of the backscattering coefficients for
λi and λj is independent of distance, and the overlap correction vanishes ([Oi,j]=0) if the ratio of
the overlap integrals for λi and λj is independent of distance. In contrast to extinction and
backscattering corrections, the overlap correction is a pure system parameter, independent of
atmospheric conditions, requiring only an initial system calibration.
Several criteria pollutants (i.e., ozone (O3) (Molina and Molina, 1986), sulfur dioxide
(SO2) (Manatt and Lane, 1993), and nitrogen dioxide (NO2) (Davidson et al., 1988)) have
absorption features in the middle ultraviolet (shown in Figure A.1-1) where an absence of
disturbing interference from other atmospheric gases, large backscattering cross-sections for both
particles and gases, low solar background, and the commercial availability of both tunable and
fixed frequency lasers as radiation sources make the development of appropriate active remote
sensors feasible.
In particular, SO2 and NO2 have enough fine structure in their spectra so that their
concentration can be measured with DIAL utilizing two closely (≈ 1 nm) spaced wavelengths
(Rothe et al., 1974b; Browell, 1982; Galle et al., 1988). In this case differential extinction and
backscattering corrections are negligible and the systematic errors of the DIAL measurement
become independent of aerosol loading. However, to utilize the spectral fine structure
efficiently, tunable lasers have to be used.
In contrast, the O3 absorption spectrum is quite smooth, necessitating a wider spacing
between the two wavelengths, on the order of 20 nm. Therefore, differential extinction and
backscattering corrections become important. While the extinction correction is generally
negative (i.e., the measured concentration is too high without correction) and on the order of a
few ppb, the backscattering correction can become much larger in regions with large gradients of
aerosol concentration. For small aerosol loading the backscattering is dominated by Rayleigh
scattering with its λ-4 wavelength dependence, while for large aerosol loading particle scattering
with its approximately λ-1 wavelength dependence contributes most of the backscattering.
Therefore, a rapid change with distance r from polluted to clear air, as encompassed for example
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in inversion layers, requires a positive backscattering correction, while the opposite change
requires a negative correction. In the case of thin aerosol layers this can lead to a dispersion
shape of the required correction or of the uncorrected, measured ozone concentration.
To calculate the backscattering correction one needs range resolved data for the aerosol
backscatter coefficient at both DIAL wavelength. Generally, one can guess the wavelength
dependence of the backscattering coefficient (Charlson, 1972) and may try to obtain the
backscattering coefficient at the “off” wavelength from a lidar inversion (Klett, 1981). However,
even this task is quite difficult in the near ultraviolet, especially in the lower troposphere
(Kovalev and Moosmüller, 1994).
Ozone DIAL measurements can also be influenced by the simultaneous optical absorption
by SO2 and to a far lesser degree by NO2. Generally this effect becomes only problematic in
power plant plumes and can be minimized by proper selection of laser wavelengths (Moosmüller
et al., 1994).
While DIAL systems have been used for the remote measurement of atmospheric
pollutants for more than two decades (Rothe et al., 1974a), their hardware, use, and data analysis
procedures have not become routine. They still require close attention, skilled personnel and
considerable resources. Of particular importance are well designed, implemented, and tested
hardware and data analysis algorithms. Especially the interdependence between hardware and
data analysis algorithms has to be stressed. Generally, their performance evolves, and hopefully
improves, together in an iterative fashion. The successful use of these systems is highly
dependent on skilled, experienced, and dedicated operators and developers, which are often
identical.
It should be noted that range, precision, spatial, and temporal resolution of DIAL
measurements are closely related and fair comparisons between different systems can only be
made if all of these parameters are given. Lower and upper range limits are of different
importance for zenith looking ground based systems and nadir looking airborne systems. For
ground based systems the lower range limit is of particular importance as it is identical to the
height above ground at which measurements start for a zenith looking operation. For airborne
systems, the flight altitude can generally be chosen so that the upper range limit approximates the
height above ground.
The precision of the DIAL measurements is generally best around the distance from the
system where complete overlap between laser beams and field of view of the telescope is
obtained. With increasing distance from the system the signal level and consequently the signalto-noise ratio and precision of the DIAL measurement deteriorate. For ground based systems this
trend is frequently reduced by decreasing the range resolution with increasing distance. This is a
sensible approach, as the scale of atmospheric structure generally increases with increasing
altitude. However, for airborne, downlooking lidar systems the opposite is true  the finer scale
atmospheric structure is generally encountered towards the far end of their measurement range,
close to the ground where the measurement precision is the lowest. In addition, these systems are
moving, with a typical averaging time of 15 s corresponding to about 1 km of horizontal
movement. If atmospheric structure smaller than the distance covered in the averaging time (e.g.,
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1 km) is encountered, the data averaging becomes rather problematic. Both these problems can
make measurements close to the ground difficult for airborne, downlooking lidar systems.
Aerosol extinction and backscattering corrections have not been implemented in the
routine data analysis for any of the following systems. Its inclusion would be a worthy goal for
the analysis of CCOS data.
Ground-Based Ozone Profiling Atmospheric Lidar (OPAL)
Boulder has developed a transportable ozone and aerosol lidar specifically for the measurement
of ozone in the boundary layer and the lower free troposphere. This lidar has been employed in
several field experiments:
•
July 1993, Intercomparison Experiment in Davis, CA, sponsored by ARB (Zhao et
al., 1994)
•
September 1993, LAFRS Experiment in Claremont, CA, sponsored by ARB (Zhao et
al., 1994)
•
August, 1995, Ozone Transport Experiment in Victorville, CA, sponsored by ARB
•
October–November 1995, Table Mountain Vertical Ozone
Intercomparison Experiment in Boulder, CO, sponsored by NOAA.
•
June-August 1997, Southern California Ozone Study (SCOS97-NARSTO) (Fujita et
al., 1998)
•
Summer 1999, Southern Oxidants Study (SOS99) in Nashville and in Atlanta.
Transport
and
OPAL is based on a solid state laser, the Nd:YAG laser with a fundamental wavelength
of 1064 nm and a pulse repetition rate of up to 10 Hz. The third harmonic of this wavelength
(i.e., 355 nm) with an operating pulse energy of 7–10 mJ is used for aerosol profiling with a
range of about 9 km. The fourth harmonic of the fundamental (i.e., 266 nm) with an operating
pulse energy of 20–30 mJ is used as “on-line” for the ozone measurement. The “off-line” for the
ozone measurement is generated by Raman shifting the second harmonic (i.e., 532 nm) by the
vibrational frequency of the deuterium molecule (i.e., 2987 cm-1) to 632.5 nm, and subsequent
sum-frequency mixing of 532 nm and 632.5 nm, yielding an “off-line” at 289 nm. This process
takes place in a specially designed Raman cell, yielding a pulse energy of 1–2 mJ. This process
utilizes the laser energy better than the more direct Raman shifting of the fourth harmonics
(Ancellet et al., 1989; Zhao et al., 1994), while yielding the same wavelength.
The receiver section utilizes an 8”-diameter telescope to collect the backscattered light.
Dichroic beamsplitters separate the light from the different laser lines for the detection by
photomultiplier tubes. The signals are digitized by 12 bit A/D converters for the subsequent
analysis.
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Ozone measurements can be obtained for a range of up to 3 km under moderate to high
surface ozone concentrations (< 150 ppb) while, for extremely high concentrations, a range of 2
km can still be achieved. Aerosol profiles for a maximum range of about 10 km can be obtained
with a range resolution of 15 m. The lower range limit is very good (≈ 50 m) due to the use of an
innovative technique for the compression of the lidar dynamic range (Zhao et al., 1992). Using
the 266/289 nm wavelengths pair, averaging 600-1200 pulses (5-10 min at 2 Hz or 1-2 min at 10
Hz), the retrieval of ozone concentrations has a range resolution from a few tens of meters in the
lower boundary layer to 150-200 m at about 3 km. Range resolution decreases with height
because the signal-to-noise ratio decreases with distance.
The measurement direction of the lidar system can be scanned in one dimension from 30o
to 150o yielding a two dimensional ozone measurement. The vertical scanning capability
provides a valuable internal system check, frequent calibration, and was desired for both
monitoring and modeling studies. This system is being modified to add a new wavelength at 299
nm, to provide a longer maximum range of ozone measurements in a thick boundary layer. The
modified system will be deployed in the SOS99 experiment in Nashville and in Atlanta this
summer.
Cost Estimate: Cost estimate for NOAA's ground based ozone lidar (OPAL):
1 month deployment including 150 hours of operation
$230k
Aircraft Ozone Lidar
Boulder is operating an airborne, downlooking UV-DIAL, which was originally developed by
EPA's Environmental Monitoring Systems Laboratory – Las Vegas. This system is capable of
measuring range resolved ozone concentrations and aerosol, nadir looking from its airborne
platform to near the ground level. It has been tested and employed in several field experiments:
•
July 1991, Initial Ground Tests at the Lake Mead National Recreation Area, Nevada
(Moosmüller et al., 1992)
•
May 1992, Initial Airborne Tests in Southeastern Michigan (Moosmüller et al., 1993;
McElroy et al., 1994; Moosmüller et al., 1994)
•
July–August 1993, COAST Study in Southeastern Texas (McElroy et al., 1994;
Moosmüller, 1994)
•
June–July 1995, Southern Oxidant Study in Tennessee
•
October–November 1995, Table Mountain Vertical Ozone Transport
Intercomparison Experiment in Boulder, CO, Ground Based Operation.
and
This system is based on a KrF excimer laser, which generates 700 mJ, 20 ns-long pulses
at 248 nm with a maximum pulse repetition rate of 20 Hz. Raman cells produce transmitted laser
beams at frequencies shifted from the KrF fundamental by integral multiples of the vibrational
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frequencies of hydrogen (4160 cm-1) and deuterium (2987 cm-1). The pulse power of the five
utilized laser lines ranges from 5–12 mJ.
The receiver section consists of a 20”-diameter downlooking telescope for collection of
the backscattered light and a spectrograph/detector system for the simultaneous detection of five
individual laser lines (i.e., 276.9 nm, 291.6 nm, 312.9 nm, 319.4 nm, and 359.4 nm). Normally
the 276.9 nm and 312.9 nm wavelengths are used as on- and off- lines for the ozone analysis.
The 359.4 nm line is well suited for the measurement of aerosol profiles. The PMT signals are
digitized by 12 bit A/D converters with a spatial resolution of 30 m. The resulting data together
with the OMA spectra are stored on magnetic tape and simultaneously displayed on a monitor in
real time to allow for optimization and control of the system operation.
The current measurement range for ozone is from about 0.8 km to 2.5–3 km, with the
lower limit corresponding to the complete overlap of laser beams with the field of view of the
telescope. The lower limit might be reduced somewhat in the future by applying the overlap
correction in the data analysis and/or different alignment of the hardware. Generally, the DIAL
data are analyzed for ozone concentrations down to about 90–150 m above ground.
Cost Estimate: Cost estimate for NOAA's airborne ozone lidar including preliminary onsite data processing. Final data processing is included as separate item:
1 month deployment and 80 flight hours
$300k
Additional week including additional 20 flight hours
$ 70k
Final data processing, approximately
$150k
Possible Scheduling Conflicts:
The airborne system is currently scheduled to participate in
the SOS 2000 experiment (Houston) from August 15 through September 15, 2000.
A.5
Supplemental Measurements of Oxidized Nitrogen Species
Accurate measurements of NO and NO2 in the atmosphere are of considerable importance
because of the role of these compounds in the production of ozone and other photochemically
derived air pollutants, such as peroxyacetyl nitrate (PAN), the nitrate radical (NO3), and nitric
acid (HNO3). Proper evaluation and exercise of photochemical models require low detection
limits (<1 ppbv) and interference-free measurements of these species. Routine monitoring
techniques for measurement of NO and NO2 and other oxides of nitrogen do not meet these
requirements, thus, the photochemical models are unable to reproduce the measured atmospheric
concentrations of these species accurately. Recent improvements in routine monitors and the
development of ultrasensitive and specific methods for measuring the nitrogen oxide species of
interest (e.g., NO, NO2, NOX, NOy, HONO, HNO3, NO3, and PAN) provide more accurate and
reliable input data for photochemical models. Most of these methods were evaluated recently by
Solomon (1995) during the SJVAQS/AUSPEX/SARMAP air quality study. Measurement of
oxidized nitrogen species during CCOS will be made primarily by the commercially available
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instruments described below. Redundant measurements by alternative spectroscopic method
such as a tunable diode laser absorption spectroscopy (TDLAS) or differential optical absorption
spectroscopy (DOAS) would be desirable if sufficient funds are available.
A.5.1 NO2 and PAN by Gas Chromatography with Luminol Chemiluminescence Detection
The Luminol NO2 analyzer operates on the principle that gaseous NO2 undergoes a
surface reaction with a specially formulated solution containing water, luminol, sodium sulfate,
sodium hydroxide, and alcohol (“Luminol II” solution). The luminol is oxidized and the product
chemiluminesces in the 425 nm region. The luminol solution is presented to the air stream on a
wick which is replenished with solution from a reservoir. The solution is introduced at the top of
the wick and removed to a waste container by a two channel peristaltic pump. A 250 ml
reservoir holds sufficient solution for about 3 days of operation. The light emitted by the
chemiluminescence reaction is detected by a photomultiplier tube, amplified and output to a chart
recorder and data logger. The signal is very sensitive, with a detection limit of 5 pptv if zeroed
every 30 minutes or 50 ppt if zeroed daily.
Although luminol can produce chemiluminescence with other oxidants, these reactions
usually require the presence of metal ion catalyst. Use of deionized water in the solution
formulation prevents chemiluminescence from other oxidizers such as hydrogen peroxide. Only
O3 and PAN were found to produce luminescence, and addition of other substances to the
solution, such as sodium sulfite, make the response to O3 negligible for NO2 mixing ratios above
1 ppbv. The interference by PAN is a constant fraction of the PAN mixing ratio, although the
fraction may depend on the formulation, batch, and age of the luminol solution.
The
The LCM method has be adapted to measure PAN as well as NO2.
Unisearch/Scintrex Model LPA-4 is the only commercial instrument for measuring PAN in the
atmosphere. In this method, PAN is separated from NO2 and other organonitrates by gas
chromatography, thermally reduced to NO2, and measured using the same luminol detector
described above for the luminol chemiluminescence measurement of NO2. The more reactive
oxides of nitrogen, such as HNO3, HONO, NO3, and other reactive interfering species such as
ozone are retained on the column. NO, while passing through the GC column, is not detected by
the luminol detector. One major advantage of this method is that the instrument can be
calibrated in the field with NO2 rather than the thermally unstable PAN, which is required for the
GC/electron capture detector method.
As a further modification to the LPA-4, the LNC-3 converter/sequencer can be used to
enable the measurement of NOX as well as NOy. The NOy converter in the model LNC-3M
converter/sequencer consists of a hot stainless steel tube operated at 400 oC. At this temperature,
two otherwise difficult to measure NOy species, namely HNO3 and isopropyl nitrate, have been
shown to convert at efficiencies approaching 100 percent. The resulting NOX (NO + NO2) is
then measured by using a CrO3 converter upstream of the LMA3 Luminox NO2 analyzer. The
cycle time between species is one minute each for NO2, NOX and NOy, and five minutes for
PAN. Detection limit for PAN is 30 ppt and 50 ppt for the other nitrogen species. NO and NOz
(NOy - NOX) are obtained from the difference. NOz minus PAN yields an upper estimate for
nitric acid. Because of the cycle time the modified LPA-4 cannot be used on-board an aircraft.
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A TEI 42S which has been modified to measure NOy is now commonly used by contractors that
perform airborne air quality measurements because of its high sensitivity and fast response (see
Section 5.4 for additional details).
A.5.2 NO2 and HNO3 by Tunable Diode Laser Absorption Spectroscopy
The TDLAS method takes advantage of the high monochromaticity and rapid tunability
of a Pb salt diode laser to measure absorptions from single rotational-vibrational lines in the
middle infrared spectrum of a molecule. Almost all gases absorb radiation in this spectral region.
However, since many gases absorb in this region, very high spectral resolution is required to
prevent interferences from other gases in the sampled air. The atmospheric sample is pumped
rapidly at the reduced pressure through a White cell, which also provides the long optical path
lengths required to achieve the desired detection limits. The tunable diode laser is a small Pb
crystal with variable amounts of Sn, Se, Te or S. The wavelength region at which the laser emits
radiation is governed by the proportions of the three elements in the crystal. Techniques of
measuring NO, NO2 and HNO3 by TDLAS has been described by Hasties (1983) and Mackay
(1993) and measuring H2O2 and HCHO by MacKay (1994).
The precision of the measurements is experimentally found to be better than ± 1 percent.
The accuracy depends on the ability to accurately measure the various flows and on the ability to
determine the mixing ratio of the calibration standard. The computed accuracy for H2O2, HCHO
and HNO3 is ± 15 percent (MacKay, 1994).
A.5.3 NO2, HONO, and NO3 Radical by Differential Optical Absorption Spectroscopy
(DOAS)
In this method, in situ atmospheric concentrations are determined by measuring the
absorption of the species of interest in the ultraviolet/visible wavelength region. The DOAS is
based on measuring the difference between the absorbance at a wavelength where the species of
interest has a distinct peak, and another wavelength on either side of the peak. High sensitivities
are obtained by combining the detector with a long-pathlength, multiple-reflectance system to
yield optical pathlengths up to 10 km. Concentrations are determined from the pathlength and
from absorption coefficients. Therefore, this is an absolute, highly specific, and nearly
interference-free measurement that can be considered as a reference method.
The DOAS has been used to measure several pollutants in the atmosphere, including
NO2, HONO, NO3, HCHO, SO2, O3 and OH. Several species can be monitored simultaneously.
Detection limits as low as about 1 ppbv for NO2, 0.020 ppbv for NO3, 3.3 ppbv for HCHO, and
0.6 ppbv for HONO have been reported for pathlengths of 0.5 to 8 km (Finlayson-Pitts and Pitts,
1986; Plane, 1989; Winer and Biermann, 1989; Plane and Nien, 1991).
A.5.4 Automated Particle Nitrate Monitor
The Aerosol Dynamics Inc. (ADI) automated particle nitrate monitor (Aerosol Dynamics
Inc., Berkeley, CA) is a new method being developed to provide automated, high-time-resolution
measurements of fine particle nitrate concentration. The ADI Automated Particle Nitrate
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monitor uses an integrated collection and vaporization cell whereby particles are collected by a
humidified impaction process, and analyzed in place by flash vaporization. The measurements
for the Northern Front Range Air Quality Study (NFRAQS) in the Denver area represent the first
deployment of the system (Chow et al., 1998). Fine particle nitrate concentrations were
measured at the Brighton site with five measurements per hour during a three-to-four day
pollution episode between 01/16/97 and 01/20/97. During the winter 1997 field campaign, the
system operated over two, one-week periods yielding concentrations of fine particle nitrate with
five determinations per hour. The automated particle nitrate monitor captured the time
variability in fine particle nitrate concentrations with a time resolution of 12 minutes and a
precision of approximately 0.5 µg/m3. The absolute accuracy of the nitrate concentrations, as
indicated through comparison with nitric-acid-denuded particulate nitrate measurements on filter
packs, showed agreement within ±0.6 µg/m3 for most measurement periods, but exhibited a
discrepancy of a factor of two for the highest nitrate concentration measurement periods. The
reason for this difference is not known, and additional data is needed to qualify the accuracy and
precision of the method.
The approach for continuous nitrate measurements is similar to the manual method used
for more than 20 years to measure the size distribution of sulfate aerosols (Hering and
Friedlander, 1982). The difference is that the particle collection and analysis has been combined
into a single cell, allowing the system to be automated. Particles are humidified, and collected
onto a metal strip by means of impaction. The humidification essentially eliminates the rebound
of particles from the collection surface without the use of grease (Winkler, 1974; Stein et al.,
1994). Interference from vapors such as nitric acid is minimized by using a nitric acid denuder
upstream of the humidifier. At the end of the 10-minute particle sampling period, the valving is
switched to stop particle collection and to pass a nitrogen carrier gas through the cell and into a
gas analyzer. The deposited particles were analyzed for nitrate by flash-vaporization in a
nitrogen carrier gas, with quantitation of the evolved gases by a chemiluminscent analyzer
operated in NOx mode (Yamamota and Kousaka, 1992). The flow system is configured such
that there are no valves on the aerosol sampling line. Details of the method are given by Hering
(1997).
Field calibration and validation procedures include on-line checks of particle collection
efficiency, calibration of analysis step with aqueous standards applied directly to the collection
substrate, and determination of blanks by measurements of filtered, ambient air. Particle
collection efficiencies were checked by means of an optical particle counter which operated
between the collection cell and the pump. The analysis step of the monitor was calibrated by
application of aqueous standards of sodium nitrate and ammonium nitrate applied directly to the
metal collection substrate. To ensure that the system did not respond to ammonium ion,
standards of ammonium sulfate were also applied. These calibrations were done in the field at
the beginning and end of each intensive sampling period. Field blanks were determined by
placing a Teflon-membrane filter at the inlet of the system, collecting for the 10-minute sampling
period, and then analyzing the strip exactly as done for a normal sample.
The data are reduced based on the response of the system to aqueous nitrate standards
which were applied at four concentration levels, corresponding to deposits of 0, 40, 80 and 160
ng of nitrate. Standards data from the beginning and end of the sampling episode are combined
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to generate a calibration curve in the form of “y=axn”. This method forces the least squares
regression line to pass through the zero-nanogram level response. For the Denver data set the
regression is nearly linear, with n=0.92. Additionally, the data are corrected for field blank based
on the average reading obtained when sampling through a Teflon-membrane filter. The field
blank corresponded to approximately 0.84 µg/m3 of fine-particle nitrate. This value is
approximately 10 times higher than the analysis blank, presumably due to interference from a
gaseous constituent. The data are corrected for 9% particle losses in the inlet, measured in the
laboratory using monodisperse aerosol. These losses were observed to be independent of particle
size in the range from 0.1 to 0.8 µm.
Level 1A data validation included a review of all logged system parameters and daily log
sheets, and an inspection of time series display of the reduced data. System parameters that are
automatically logged by the data acquisition system include temperature and relative humidity of
the sample collection, and capacitor voltage prior to each vaporization step. Parameters recorded
manually on the daily log sheets include flow and pressure meter readings such as the purge gas
rotameter readings and the differential pressure at the inlet of the NOx analyzer. The reduced
data are displayed as a time series to inspect for possible outliers. Potential level II validation
checks include comparison to dried particle light scattering using a nephelometer, and
comparisons to filter nitrate acquired on a quartz-fiber/sodium-chloride-impregnated cellulosefiber filter pack in a PM2.5 sequential filter sampler equipped with an upstream nitric acid
denuder.
A.6
In-Situ Instrumented Aircrafts
Instrumented aircraft will be used to measure the three-dimensional distribution of ozone,
ozone precursors and meteorological variables. The aircraft will provide information at the
boundaries of the modeling domain and will document the vertical gradients, the mixed layer
depth, and nature of the polluted layers aloft. In addition to the NOAA lidar aircraft, there are
five aircraft that are available to CCOS during the summer of 2000. The University of
California, Davis single-engine Cessna 182 can be used to provide initial condition in the central
portion of the modeling domain and provide data to determine the presence of pollutant transport
between the Bay Area/Sacramento region to the upper Sacramento Valley, the San Joaquin
Valley and the Sierra Nevada.. It can also provide data to validate the ground-based lidar
measurements by NOAA. UCD may have a second, similarly equipped aircraft that can be used
to extend coverage towards the southern end of the San Joaquin Valley or up to the northern end
of the Sacramento Valley. The Sonoma Technology twin-engine Piper Aztec can provide
boundary and initial conditions in the western (over-water) and northern portion of the study
domain. It can also provide data to characterize ozone and NOY fluxes through the passes along
the coastal range. An overview of the UCD and STI instrumentation complete with data quality
objectives, i.e., accuracy is given in Table A.6-1 and A.6.2, respectively.
The NOAA Long-EZ N3R is a research aircraft measures turbulent exchange of
momentum, energy and trace gases, such as CO2, H2O, and O3, in flights as close as 10 m to the
surface. The Best Aircraft Turbulence (BAT) probe measures mean and turbulent wind
parameters with fidelity. Many remote sensing and in situ instruments are available. The nominal
sensor suite includes the BAT probe, an infrared H2O and CO2 gas analyzer, a net radiometer,
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upward and downward PAR (photosynthetically active radiation) radiometers, and sensors for
dew point, and surface temperature. Other sensors can be carried including lasers, laser arrays,
Ka-band radar, visible light cameras, and a multi-band radiometer.
The NOAA twin otter is an advance atmospheric research platform. It is specially suited
for measuring eddy fluxes and concentration gradients through the mixed layer. It offers unique
capabilities to explore the rates of exchange of atmospheric properties through the lower
atmosphere, and especially between the atmosphere and the surface. The aircraft measures air
temperature, dew point, pressure, 3-D winds, and radiation variables. Atmospheric chemistry
sampling uses a flow-through air inlet system for measuring NO, NOx, NOy, SO2, O3, CO and
reactive hydrocarbons.
A.7
Supplemental Measurements of Volatile Organic Compounds
A.7.1 Collection and Analysis of Hydrocarbons and Oxygenated Species
Canister Sampling
Stainless steel SUMMA™-polished canisters of 6-L capacity are customarily employed for
volatile hydrocarbon (C2-C12) collection. These canister samples are to be analyzed for speciated
hydrocarbons by a method consistent with EPA Method TO-14, as well as for CO, CO2, methane,
and oxygenated species. Prior to sampling, the canisters are to be cleaned by repeated evacuation
and pressurization with humidified zero air, and certified as described in the EPA document
"Technical Assistance Document for Sampling and Analysis of Ozone Precursors" (October 1991,
EPA/600-8-91/215).
The sampling procedure should essentially follow the pressurized sampling method
described by EPA Methods TO-12 and TO-14 and the EPA document "Technical Assistance
Document for Sampling and Analysis of Ozone Precursors" (October 1991, EPA/600-8-91/215). A
stainless steel Viton pump draws in ambient air from the sampling manifold to fill and pressurize
the sample canisters. A flow control device maintains a constant flow into the canisters over the
desired sample period. This flow rate is preset to fill the canisters to about 1 atm above ambient
pressure at the end of the sampling period (as described by EPA Method TO-14). For automatic
operation, the timer starts and stops the pump at the appropriate time. The timer also opens the
solenoid valve when the pump starts and closes it when the pump stops. The use of the solenoid
manifold valves permits the automatic selection of preloaded canisters. The multiple-event
sampling systems, allowing unattended collection of up to six canister samples are recommended
for this study.
After sampling, an identification tag should be attached to each canister with the canister
serial number, sample number, and sampling location, date, and time recorded on this tag. In
addition a field sampling form and chain-of-custody form should be filled out giving all pertinent
information on the collection of the sample.
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GC/FID Analysis
Gas chromatography with flame ionization detector is the established technique for
monitoring volatile hydrocarbons, ozone precursors, in ambient air. An air sample is taken from
the canister and passed through the sample concentration system. This system usually consists of a
freeze-out loop, made from chromatographic-grade stainless steel tubing packed with 60/80 mesh
deactivated glass beads, and a 10-port sampling valve. First, the sample is transferred from the
canister through the loop immersed in liquid oxygen to the volume transfer measurement apparatus.
The C2 and heavier hydrocarbons are cryogenically trapped inside the loop when air is transferred to
an evacuated flask of known volume. From the difference in pressure inside the flask, the volume
of the air sample can be calculated, based on the ideal gas law. When a sufficient volume of the air
sample has been transferred from the canister to the concentration system, the 10-port valve is
switched to position 2, the liquid oxygen is replaced with boiling water, and the contents of the trap
are injected into a chromatographic column where separation of the C2-C12 hydrocarbons takes
place. No Perma-Pure permeable membrane or other moisture-removal device should be used prior
to concentration, since the use of such drying devices results in the loss of certain volatile organic
compounds of interest (all polar compounds and some olefins and aromatics). It can also
introduce contaminants into the system and it lowers the total NMHC by 10-20% (Sagebiel and
Zielinska, 1994).
The chromatographic column used for the C2-C12 hydrocarbon analysis is usually a 60 m
long DB-1 fused silica capillary column (or equivalent) with a 0.32 mm inside diameter and 1 µm
phase thickness. However, the DB-1 column does not provide complete separation of the light C2
and some important C4 hydrocarbons. Therefore, a separate analysis of the canister sample is
recommended to obtain accurate concentrations for ethane, ethylene, acetylene, 1-butene, and
isobutylene. The chromatographic column used for this analysis is usually GS-Alumina PLOT
fused silica capillary column (or equivalent) with an internal diameter of 0.53 mm and a length of
30 m. A separate gas chromatograph or two-dimensional (2D) chromatography could be used for
this analysis.
The GC/FID response is calibrated in ppbC, using primary calibration standards traceable to
the National Institute of Standards and Technology (NIST) Standard Reference Materials (SRM).
The NIST SRM 1805 (254 ppb of benzene in nitrogen) is used for calibrating the analytical system
for C2-C12 hydrocarbon analysis, whereas 1 ppm propane in a nitrogen standard (Scott Specialty
Gases), periodically traced to SRM 1805, is used for calibrating the light hydrocarbon analytical
system. Based on the uniform carbon response of the FID to hydrocarbons, the response factors
determined from these calibration standards are used to convert area counts into concentration units
(ppbC) for every peak in the chromatogram.
Identification of individual compounds in an air sample is usually based on the comparison
of linear retention indices (RI) with those RI values of authentic standard compounds, as well as
with the RI values obtained by other laboratories performing the same type of analysis using the
same chromatographic conditions. Appendix A-1 lists target species, together with other pertinent
information. Note that all PAMS target compounds listed in the EPA document "Technical
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Assistance Document for Sampling and Analysis of Ozone Precursors" (October 1991, EPA/600-891/215) are also on this list.
The gas chromatographs should be connected to a data acquisition system. The software
performs data acquisition, peak integration and identification, hardcopy output, post-run
calculations, calibrations, peak re-integration, and user program interfacing. Typically, over 85%
of total detectable C2-C12 hydrocarbon mass is identified and quantified. The detection limit for
hydrocarbon VOC is approximately 0.1 ppbC for each compound.
Methane (CH4), carbon monoxide (CO), and carbon dioxide (CO2) are to be measured
from the canister samples using GC/FID. Since the FID does not respond to CO and CO2, these
species are to be converted to methane by a methanator, positioned after the GC column, but
ahead of the FID. The minimum detection limit for both CO and CH4 should be < 20 ppbv,
whereas for CO2 < 3 ppmv. The precision of measurements should be generally better than 10%.
Methyl t-butyl ether should be quantified from canister samples, using the method of
analysis for C3-C12 hydrocarbons. The individual response factor should be determined for
MTBE and its concentration reported in ppbC.
A.7.2 Carbonyl Compounds
Formaldehyde and other volatile carbonyl compounds are to be collected, utilizing solid
adsorbent cartridges coated with 2,4-dinitrophenylhydrazine (DNPH) reagents, by the method
consistent with the EPA document "Technical Assistance Document for Sampling and Analysis of
Ozone Precursors" (October 1991, EPA/600-8-91/215). The method is based on the specific
reaction of organic carbonyl compounds with DNPH deposited on silica gel or C18 bonded SepPak
cartridges in the presence of an acid to form stable derivatives, hydrazones, which are subsequently
analyzed by high performance liquid chromatography (HPLC).
A carbonyl sampling system consists of a diaphragm pump capable of maintaining air flow
through the cartridges of 500 - 1500 ml/min, flowmeter, six-port solenoid manifold allowing
unattended collection of up to six carbonyl samples, needle valves for flow rate regulation, and
check valves to protect cartridges from outside air when air is not being sampled through a given
cartridge. For automatic operation, the timer starts and stops the pump at the appropriate time. The
timer also opens the six-port solenoid valve when the pump starts and closes it when the pump
stops. A charcoal filter is attached to the pump outlet in order to remove traces of acetonitrile from
DNPH cartridges.
Carbonyl compounds collected in the cartridges (as hydrazones) are eluted with HPLC
grade acetonitrile and analyzed by HPLC with UV detection at 360 nm. A reverse phase HPLC
column is used. Identifications are made based on matching the HPLC retention times with those
of authentic standards. A three-level calibration curve (plus blank) is constructed for each
quantified hydrazone. C1-C7 carbonyl compounds are recommended for quantification as
follows: formaldehyde, acetaldehyde, acetone, acrolein, propionaldehyde, crotonaldehyde,
methyl ethyl ketone, methacrolein, butyraldehyde, benzaldehyde, valeraldehyde, tolualdehyde,
and hexanaldehyde.
A-23
Version 3 – 11/24/99
A.7.3 C8-C20 Hydrocarbons by Tenax Sampling and Analysis by GC/FID or GC/MS
Volatile hydrocarbons in the range of C8-C20, are usually collected using Tenax solid
adsorbent. The Tenax sampling unit draws two parallel streams of air allowing for the collection
of duplicate samples, with the pump downstream of the Tenax. The flow rates are controlled by a
flow controller or are measured before and after each run using a calibrated mass flow meter and
the mean value is used to calculate volumes of air sampled. Prior to use, the Tenax solid
adsorbent is cleaned by Soxhlet extraction with hexane/acetone mixture, packed into Pyrex glass
tubes and thermally conditioned for four hours by heating at 300 °C under nitrogen purge.
Approximately 10% of the precleaned Tenax cartridges are tested by GC/FID for purity prior to
sampling. After sampling, the Tenax cartridges are capped tightly using clean Swagelok caps
(brass) with graphite/vespel ferrules, and placed in metal containers with activated charcoal on
the bottom.
Tenax samples are usually analyzed by the thermal desorption-cryogenic preconcentration
method, followed by quantification by high resolution gas chromatography with flame ionization
detection (GC/FID) or mass spectrometric detection (GC/MSD) of individual hydrocarbons. In
some cases, analysis may also include identification by Fourier transform infrared/mass
spectrometric detection (GC/IRD/MSD). A 60 m (0.32 mm i.d., 0.25 mm film thickness) DB-1
capillary column (or equivalent) is usually used. For analytical system calibration, a set of
standard Tenax cartridges is prepared by spiking the cartridges with a methanol solution of
standard SVHC, prepared from high-purity commercially available C9-C20 aliphatic and aromatic
hydrocarbons. For the GC/FID system, the calibration is based on the uniform response of FID
detector to carbon atoms. The FID response factor per one nanomole of carbon is determined
experimentally, using n-dodecane and 1,3,5-trimethylbenzene deposited on Tenax cartridges.
Three different concentrations (plus one blank) are used to construct calibration curves. The
response factor per one nanomole of carbon for each compound used for calibration is averaged
to give one uniform response factor for all hydrocarbons (both aliphatic and aromatic). For
GC/MS, calibration is based on the response factors of authentic standards. In addition, Tenax
cartridges spiked with paraffinic and aromatic hydrocarbons are periodically analyzed by GC/FID
or GC/MS to verify quantitative recovery of these hydrocarbons from the cartridges.
A.7.4 Continuous Formaldehyde by Fluorescent Detection of Dihydrolutidine Derivative
Formaldehyde is measured by an instrument that continuously measures the fluorescent,
dihydrolutidine derivative formed by the reaction of formaldehyde with 1,3-cyclohexanedione
(CHD) and ammonium ion (Dong and Dasgupta, 1994; Fan and Dasgupta, 1994). The present
instrument from AnalTech (Lubbock, TX) is a fully automated measurement system. The
instrument is designed to operate continuously or more typically, alternate between a sample gas
and zero air. The instrument is also configured to measure aqueous HCHO.
The product with CHD is substantially more sensitive and is used in the present
instrument. The reaction with CHD proceeds at a rate that is first order in CHD concentration
and first order in ammonium ion concentration. When a dilute formaldehyde solution is mixed
with an equal volume of a reagent containing 10 mg% CHD and 1.3 M CH3COONH4 at pH 4.9
A-24
Version 3 – 11/24/99
and heated to 95oC, the reaction is completed in under 3 min. The excitation and emission
maximum of the fluorescent product are broadly centered at 395 and 465 nm, respectively.
For gas-phase formaldehyde measurements, the system is configured as a gas phase flow
injection analyzer in which the gaseous analyte is continuously transferred to an aqueous stream
by a Nafion membrane based diffusion scrubber (DS) and the resulting aqueous stream is
continuously analyzed by the liquid phase analysis system.
A.7.5 Automated GC/MS System
A continuous gas chromatography – mass spectrometry (GC/MS) system with an initial
sample preconcentration is proposed for the analysis of VOC in ambient air during Central
California Ozone Study (CCOS). This system would allow for measurement of hydrocarbons,
oxygenated hydrocarbons, halogenated compounds, and other volatile organic compound
concentrations with a temporal resolution of approximately 1-hour. GC/MS system would allow
for identification and quantification of much broader classes of organic compound as compared
with GC/FID system.
Preconcentration System. The analysis of VOC at levels found in ambient air requires an
initial sample preconcentration to reach the detection limit on the order of 0.1 – 0.2 ppbv.
Commercially available automatic preconcentration systems are usually based on combination of
multisorbent traps, such as XonTech Model 930 organic vapor concentrator or Model 940
cryofocusing trap. The multisorbent traps usually contain a combination of Tenax, Carbotrap and
Carbosieve S-III solid adsorbents in various amount and proportions. These sorbents are
relatively strong and they may introduce some artifact when more reactive organic compounds
are required to be quantified.
As an alternative, the automatic preconcentration trap could be custom build, utilizing
cryofocusing loops filled with very week, inert adsorbent, such as glass beads. Such systems are
being currently developed by the University of Miami (Rod Zika, personal communication),
NCAR (Eric Apel, personal communication) and NOAA (Paul Goldan).
GC/MS Systems. Commercially available GC/MS system could be used for this project.
A new Hewlett Packard GC/MSD system offers enhanced sensitivity over older HP5970 MSD.
An ion trap Varian Saturn GC/MS is an alternative to a conventional quadrupole MS and offers
very good sensitivity; 0.1 – 0.2 ppbv detection limit is a standard for most of the analytes.
To obtain data for C2 hydrocarbons without using excessive amount of liquid N2, a dual
capillary column (2-D) chromatography is proposed. A PLOT type column (such as Al2O3 or
GasPro GSC) would allow for separation of light C2-C4 hydrocarbons, whereas narrow bore DB1 or equivalent capillary column assures separation of higher boiling organic compounds.
Additional FID detector could be used in parallel with MS detector for the quantification of light
(C2-C4) hydrocarbons.
A-25
Version 3 – 11/24/99
A.8
Photolysis Frequencies and Rates
As discussed in Section 2.5.1, the formation of photochemical air pollution depends on
the photolysis of NO2, O3, HONO, HCHO, aldehydes and ketones and other compounds. the two
most important photolysis rates are those for NO2 and ozone. The rate of ozone formation is
controlled through the photolysis of NO2.
NO2 + hν (+O2) → O3 + NO
(1)
The magnitude of ozone concentrations is related to the NO2 photolysis frequency, J, and NO2 to
NO concentration ratio.
[O3] = J [NO2] / k [NO]
(2)
The photolysis of O3 to make excited oxygen atoms, O1D, produces ozone by making HO
radicals that react with VOC to make HO2 and organic peroxy radicals.
O3 + hν → O1D + O2
O1D + H2O → 2 HO
(3)
(4)
A compound’s photolysis frequency, J, is determined by the product of the spherically integrated
photon flux (actinic flux), I(λ), the compound’s absorption cross sections, σ(λ), and its quantum
yields, φ(λ), all integrated over the range of available wavelengths.
J = ∫I(λ) × σ(λ) × φ(λ) dλ
(5)
The quantity J should be considered a frequency because its dimension is time-1. Photolysis rates
are the product of the photolysis frequency and the photolysis species concentration.
Although photolysis frequencies are often a major cause of differences between modeling
studies they are usually not accurately measured in field studies. Photolysis frequencies are often
calculated for assumed sky conditions or they are estimated from Epply and UV radiometer
measurements. These procedures may yield photolysis frequencies that are uncertain by as much
as 30 to 40% (Madronich, 1987).
Chemical actinometers where the photolysis frequencies are measured directly provide
the most accurate J values in principle. However, chemical actinometers are expensive and
difficult to use under field conditions. The use of spectrally resolving radiometers to measure
actinic flux and the calculation of photolysis frequencies from Equation (5) is more practical. An
advantage of using spectral radiometer data is that any photolysis frequency may be calculated
from this data in the future. A significant disadvantage of spectral radiometers is their relatively
high cost.
A-26
Version 3 – 11/24/99
Radiometers have been developed that mimic the response of O3 and NO2 to photolysis.
If calibrated against chemical actinometers these radiometers provide a relatively inexpensive
way of measuring the photolysis frequencies of O3 and NO2. The measured photolysis
frequencies for O3 and NO2 should be used to provide an important constraint on the application
of photochemical air quality models to modeling the CCOS measurements.
A-27
Version 3 – 11/24/99
Table A.1-1
Meteorological Sensor Specifications
Sensor
Accuracy
Range
Response
Time
Sensitivity
AIR db
Pr
±0.7 mb
600-1100 mb
ms-range
.01 mb
CS207
T
±0.4 °C
-33 - +48°C
<0.1 °C
linear
error
CS207
RH
±5%
0 - 100 %
<±3.0 %
CS500
T
±0.4
±0.6
-40 - +60 °C
CS500
RH
±2% @ 0-90%
±3% @ 90-100%
0 - 100 %
Li-Cor
200SA
Solar
Within 95-98% of
Eppley standard
.15-3.0 µm
10 µs
.25-60 µm
30 s
REBS
Net Rad
Stability
Error
±1%/yr
80 µA per
1000 Wm-2
RM Young
Speed
±.25 m/s,
ws: 0-5 m/s
±5%, ws >5
m/s
0-60 m/s
100 m/s
(peak gust)
0.9 m/s
RM Young
Direction
±5 Deg
0-355 Deg
electrical
1 m/s @
10° displ.
Vaisala
T
±0.4 °C
-35 - +50 °C
Vaisala RH
±2% @ 0-90%
±3% @ 90-100%
0 - 100 %
15 s
A-28
<±2%/yr
1%/yr
Abs error:
±5% of
Eppley
SDP
SEW
WSA
S13
SST
SBR
DVS
SLU
VEL
ELK
Site ID
CCY
CCY2
EU6
WEV
FTB
WLF
WLM
UKC
UKG
YRE
LVB
ALW
MSO
RDH
LNP
ADN
RBL
RBO
CHM
CHL
WLW
CSS
YAS
PGV
SNH
FLN
WSS
Air Basin
NC
NC
NC
NC
NC
NC
NC
NC
NC
NEP
NEP
NEP
NEP
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
SV
City
Site Address
Crescent City
9th & H St Crescent City - Ch Annex
Crescent City
880 Northcrest Drive
Eureka
Health Dept-6th & I St
Weaverville
Main Street
Fort Bragg
416 N. Franklin St.
Willits
Fire Stn-Commercial & Humboldt
Willits
899 So Main Street
Ukiah
Library-105 N Main St.
Ukiah
306 E. Gobbi Street
Yreka
528 Foothill Dr.
Lava Beds
P.O. Box 867 Lava Beds
Alturas
202 W. Fourth Street
Mount Shasta
3 North Old Stage Road
Redding
Hlth Ctr-2630 Hospital Ln
Lassen Volcanic Nat Pa Manzanita Lake Rs
Anderson
2220 North Street
Red Bluff
Messer Drive
Red Bluff
502 Oak Street
Chico
468 Manzanita Ave
Chico
101 Salem St.
Willows
420 E. Laurel St.
Colusa
100 Sunrise Blvd.
Yuba City
773 Almond St.
Pleasant Grove
4sw-7310 Pacific Ave
North Highlands
7823 Blackfoot Way
Folsom
50 Natoma Street
Woodland
40 Sutter Street
Woodland
41929 E. Gibson Road
Sacramento
3801 Airport Road
Sacramento
Del Paso-2701 Avalon Dr
Sacramento
3535 El Camino & Watt
West Sacramento
132 15th St.
Sacramento
1309 T St.
Sacramento
Hlth Ctr-2221 Stockton Blvd
Sacramento
3711 Branch Center Rd.
Davis
Uc Davis-Campus
Sloughhouse
7520 Sloughouse Road
Vacaville
1001 Allison Drive
Elk Grove
12490 Bruceville Rd
A-29
Land Use
Residential
Residential
Commercial
Residential
Commercial
Not Available
Mobile
Commercial
Mobile
Commercial
Agricultural
Commercial
Residential
Commercial
Forest
Industrial
Industrial
Mobile
Commercial
Mobile
Mobile
Commercial
Commercial
Agricultural
Residential
Residential
Agricultural
Residential
Commercial
Residential
Commercial
Industrial
Residential
Commercial
Residential
Agricultural
Agricultural
Residential
Agricultural
Location Type
Urban / Center City
Urban / Center City
Urban / Center City
Urban / Center City
Urban / Center City
Not Available
Suburban
Suburban
Suburban
Suburban
Rural
Urban / Center City
Suburban
Suburban
Rural
Suburban
Suburban
Urban / Center City
Suburban
Urban / Center City
Suburban
Rural
Suburban
Rural
Suburban
Suburban
Urban / Center City
Suburban
Suburban
Suburban
Suburban
Urban / Center City
Urban / Center City
Urban / Center City
Suburban
Rural
Rural
Suburban
Rural
25
18
13
7
8
20
16
58
55
6
18
600
14
955
1377
192
194
800
1451
1334
0
143
1788
498
98
98
61
61
41
17
20
50
27
0
17
EL_MSL
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
NO2
1
1
HC
1
1
NMHC Carb
Parameters Measured
1
CO
Table A.1-2
Air Quality Monitoring Site in Northern and Central California
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
O3
1
3
2
1
2
3
1
1
2
2
2
2
1
1
1
1
1
1
1
1
PM10
1
1
1
1
1
1
Site ID
FFD
LKL
CHE
QUC
POL
LOY
GFS
TRU
WCM
GVL
GVH
LTY
LTS
CUS
ROC
ROS
PGN
JAC
SGS
FML
SNB
YOY
YOT
JSD
CLV
HDM
HDB
GUE
SRF
VAC
NJS
VJO
CKT
PBG
MTZ
BTI
SRL
SPE
Air Basin
SV
LC
MtC
MtC
MtC
MtC
MtC
MtC
MtC
MtC
MtC
MtC
MtC
MtC
MtC
MtC
MtC
MtC
MtC
MtC
MtC
MtC
MtC
MtC
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
City
Fairfield
Lakeport
Chester
Quincy-East Quincy
Portola
Loyalton
Truckee
Truckee
Nevada City
Grass Valley
Grass Valley
South Lake Tahoe
South Lake Tahoe
Cool
Rocklin
Roseville
Placerville
Jackson
San Andreas
Sonora
Sonora
Yosemite Nat Park
Yosemite Nat Park
Jerseydale
Cloverdale
Healdsburg
Healdsburg
Guerneville
Santa Rosa
Vacaville
Napa
Vallejo
Crockett
Pittsburg
Martinez
Bethel Island
San Rafael
San Pablo
Site Address
Baapcd-401 Gregory St
905 Lakeport Blvd.
222 First Avenue
267 N. Church St.
220 Commercial St
309 W. Third Street
Glenshire Fs-10900 Manchester
Fs-10049 Donner Pass Rd
26533 State Hwy 20-White Cloud Mtn
200 Litton Dr.
420 Henderson St
3337 Sandy Way
Stateline-4045 Hwy 50
1400 American River Trail
5000 Rocklin Road
151 No Sunrise Blvd
3111 Gold Nugget Way
201 Clinton Road
501 Gold Strike Road
15700 Old Oak Ranch Road
251 S Barretta
Visitor Ctr-Yosemite Village
Turtleback Dome
6440 Jerseydale Road
100 Washington St.
Airport-200a Heidelberg Way
133 Matheson St.
Church And First Streets
837 5th St.
650 Merchant St.
2552 Jefferson Ave.
304 Tuolumne St.
Kendall Ave.
583 W. 10th St.
521 Jones St.
5551 Bethel Island Rd
534 4th Street
Unit 759 El Portal Shopping Center
A-30
Land Use
Residential
Mobile
Residential
Commercial
Residential
Residential
Residential
Residential
Forest
Residential
Residential
Commercial
Commercial
Agricultural
Residential
Mobile
Residential
Commercial
Agricultural
Forest
Residential
Not Available
Forest
Forest
Commercial
Commercial
Commercial
Mobile
Commercial
Commercial
Commercial
Commercial
Industrial
Residential
Residential
Agricultural
Commercial
Commercial
Location Type
Urban / Center City
Suburban
Urban / Center City
Urban / Center City
Urban / Center City
Rural
Suburban
Urban / Center City
Rural
Suburban
Urban / Center City
Urban / Center City
Urban / Center City
Rural
Rural
Suburban
Suburban
Suburban
Rural
Rural
Urban / Center City
Not Available
Rural
Rural
Urban / Center City
Rural
Urban / Center City
Urban / Center City
Urban / Center City
Urban / Center City
Urban / Center City
Urban / Center City
Suburban
Urban / Center City
Urban / Center City
Rural
Urban / Center City
Urban / Center City
EL_MSL
3
405
1403
1067
1480
1505
1811
1676
0
853
735
0
1911
0
100
161
0
377
0
0
0
1216
1605
0
91
30
31
17
49
59
12
23
63
2
8
0
1
15
Table A.1-2 (continued)
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
NO2
1
HC
Parameters Measured
NMHC Carb
1
1
CO
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
O3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
2
1
1
1
2
PM10
M14
TSM
MRA
FNP
M29
FSS
CLO
FIS
FSF
FSD
PLR
SLK
LMK
Site ID
RMD
CCD
OKA
SFE
SFA
SEH
LVF
HLM
FCW
RED
SJD
MVC
SJ4
SJK
SJT
LGS
SMM
GRY
SOW
SOC
SOH
SOM
TPP
MIS
Air Basin
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SFBA
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
City
Richmond
Concord
Oakland
San Francisco
San Francisco
San Leandro
Livermore
Hayward
Fremont
Redwood City
San Jose
Mountain View
San Jose
San Jose
San Jose
Los Gatos
San Martin
Gilroy
Stockton
Stockton
Stockton
Stockton
Tracy
Modesto
Modesto
Modesto
Turlock
Merced
Shaver Lake
Madera
Fresno
Clovis
Fresno
Fresno
Fresno
Parlier
Sequoia Nat Park
Site Address
1065 7th St.
2975 Treat Blvd
822 Alice St.
939 Ellis St.
10 Arkansas St.
Hospital-15400 Foothill Blvd
2614 Old 1st St.
3466 La Mesa Dr.
40733 Chapel Way.
897 Barron Ave.
935 Piedmont Road
160 Cuesta Dr.
120b N 4th St
Hlth Ctr-2220 Moorpark Ave
528 Tully Rd.
306 University Ave.
13030 Murphy Ave.
9th & Princeville
8778 Brattle Place Stockton-Wagner Holt
4310 Claremont
Hazelton-Hd
13521 E. Mariposa
24371 Patterson Pass Road
1100 I St
814 14th St-Modesto Rover
814 14th St.
900 S Minaret Street
385 S. Coffee Avenue
North Perimeter Road
Rd. 29 1/2 No. Of Ave 8
Sierra Skypark#2-Blythe & Chnnlt
908 N Villa Ave
1145 Fisher Street
3425 N First St
4706 E. Drummond St.
9240 S. Riverbend
Lower Kaweah-Sequoia NP
Lookout Point-Mineral King Road
A-31
Land Use
Location Type
Industrial
Suburban
Residential
Suburban
Commercial
Urban / Center City
Commercial
Urban / Center City
Industrial
Urban / Center City
Residential
Suburban
Commercial
Urban / Center City
Residential
Rural
Residential
Suburban
Industrial
Suburban
Residential
Rural
Residential
Suburban
Residential
Urban / Center City
Residential
Suburban
Industrial
Suburban
Residential
Urban / Center City
Residential
Rural
Residential
Suburban
Residential
Suburban
Commercial
Suburban
Residential
Urban / Center City
Not Available
Not Available
Agricultural
Rural
Other Unknown ResideUrban / Center City
Commercial
Urban / Center City
Commercial
Urban / Center City
Residential
Suburban
Agricultural
Rural
Forest
Rural
Agricultural
Rural
Residential
Suburban
Residential
Urban / Center City
Commercial
Urban / Center City
Residential
Suburban
Commercial
Suburban
Not Available
Not Available
Forest
Rural
Forest
Rural
27
56
86
0
0
98
86
90
96
162
166
1890
0
EL_MSL
6
26
7
38
5
36
146
287
18
5
0
24
24
360
38
183
87
55
7
13
13
17
31
27
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
O3
1
1
NO2
1
HC
Parameters Measured
NMHC Carb
1
1
1
1
CO
3
1
1
2
1
2
3
1
3
1
1
1
1
1
1
1
2
1
PM10
Site ID
VCS
HIR
COP
COV
SHA
OLD
BGS
BKA
EDS
ARV
TAC
MCS
MLK
LEE
MAG
LPE
DVL
KCG
OLW
COS
CJN
SVD
DVP
SCQ
WAA
HST
MLS
SL2
MON
CMV
PIN
KCM
PRF
ATL
MBP
SLM
GCL
ARR
Air Basin
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
SJV
GBV
GBV
GBV
GBV
GBV
GBV
GBV
GBV
GBV
NCC
NCC
NCC
NCC
NCC
NCC
NCC
NCC
NCC
NCC
NCC
SCC
SCC
SCC
SCC
SCC
SCC
City
Site Address
Visalia
310 N Church St
Hanford
807 South Irwin Street
Corcoran
1520 Patterson Ave.
Corcoran
Van Dorsten Ave.
Shafter
548 Walker Street
Oildale
3311 Manor St.
Bakersfield
1128 Golden State Highway
Bakersfield
5558 California Ave
Edison
Johnson Farm
Arvin
20401 Bear Mtn Blvd
Taft
College-29 Emmons Park Dr.
Maricopa
School-755 Stanislaus St.
Mono Lake
Simus Res-Hiwy 167
Lee Vining
Sms-Hwy 395
Mammoth Lakes
Gateway Hc.
Lone Pine
501 E. Locust St.
Death Valley
Death Valley NP
Keeler
190 Cerro Gordo Road
Olancha
131 Walker Creek Rd.
Coso Junction
Rest Area On Hiwy 395
Coso Junction
10 Mi E Of Coso Junction
Scotts Valley
4859 Scotts Valley Dr #E
Davenport
Fire Dept.
Santa Cruz
2544 Soquel Avenue
Watsonville
444 Airport Blvd
Hollister
1979 Fairview Rd.
Moss Landing
7539 Sandholt Road
Salinas
Ii-1270 Natividad Rd
Monterey
24580 Silver Cloud Ct.
Carmel Valley
35 Ford Rd-Tularcito Sch
Pinnacles National MonNe Entrance
King City
750 Metz Road
Paso Robles
235 Santa Fe Avenue
Atascadero
6005 Lewis Avenue
Morro Bay
Morro Bay Blvd & Kern Ave
San Luis Obispo
1160 Marsh St.
Grover City
9 Le Sage Dr.
Arroyo Grande
000 Ralcoa Way
A-32
Land Use
Commercial
Residential
Residential
Not Available
Commercial
Industrial
Commercial
Mobile
Agricultural
Agricultural
Commercial
Residential
Not Available
Not Available
Commercial
Residential
Desert
Residential
Desert
Not Available
Desert
Commercial
Residential
Commercial
Commercial
Residential
Commercial
Residential
Commercial
Residential
Forest
Industrial
Residential
Commercial
Commercial
Commercial
Residential
Commercial
Location Type
Urban / Center City
Suburban
Suburban
Not Available
Suburban
Suburban
Urban / Center City
Urban / Center City
Rural
Rural
Suburban
Suburban
Rural
Not Available
Urban / Center City
Suburban
Rural
Rural
Rural
Not Available
Rural
Rural
Rural
Suburban
Suburban
Rural
Rural
Suburban
Rural
Suburban
Rural
Rural
Suburban
Suburban
Urban / Center City
Urban / Center City
Suburban
Rural
EL_MSL
92
99
0
61
126
180
123
120
128
145
292
289
1948
2071
2396
1128
125
1097
1100
1010
1315
122
0
78
67
126
10
13
0
131
335
116
100
262
18
66
4
300
1
1
1
1
1
1
1
1
1
1
1
1
1
NO2
1
1
1
1
1
1
1
1
1
HC
Parameters Measured
NMHC Carb
1
1
1
CO
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
O3
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
4
2
2
1
1
1
1
2
3
1
1
5
PM10
3
1
2
3
Site ID
NGR
STL
SMY
LHS
LOM
SYN
VBS
LPD
GVB
CA1
CA2
GVW
GVE
GVC
CA3
ECP
PCN
GNF
SBC
OJO
SBU
CRP
PIR
VTA
VTE
SIM
ELM
THM
SRI
CLK
MOP
Air Basin
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
SCC
MD
MD
City
Site Address
Nipomo
1300 Guadalupe Rd.
Santa Maria
Library-420 S Broadway
Santa Maria
500 S Broadway
Lompoc
Hs & P Facility-500 M Sw
Lompoc
128 S H St.
Santa Ynez
Airport Rd.
Vandenberg Afb
Sts Power Plant
Los Padres National ForParadise Rd
Gaviota
Gtc B-Hwy 101-Near Nojoqui Pass
Capitan
Lfc #1-Las Flores Canyon
Capitan
Gaviota
Gaviota West-Nw Of Chevron Plant
Gaviota
Gaviota East-N Of Chevron Plant
Gaviota
Gtc C-1 Mi E Of Plant
Capitan
Capitan
El Capitan St Prk Hwy 101
Concepcion
Point Conception Lighthouse
Goleta
380 N Fairview Avenue
Santa Barbara
3 W. Carrillo St.
Ojai
1201 Ojai Avenue
Isla Vista
Ucsb West Campus-Arco Tank
Carpinteria
Gobernador Rd
Piru
2sw 2815 Telegraph Rd
Oak View
5500 Casitas Pass Rd-Near Oak View
Ventura
Emma Wood State Beach
Simi Valley
5400 Cochran Street
El Rio
Rio Mesa School
Thousand Oaks
9 2323 Moorpark Road
Channel Islands NationaSanta Rosa Island-Becher's Bay
China Lake
Powerline Rd.
Mojave
923 Poole Street
A-33
Land Use
Industrial
Commercial
Commercial
Agricultural
Commercial
Agricultural
Agricultural
Agricultural
Agricultural
Agricultural
Agricultural
Agricultural
Agricultural
Agricultural
Agricultural
Not Available
Agricultural
Residential
Mobile
Agricultural
Agricultural
Agricultural
Agricultural
Agricultural
Mobile
Residential
Agricultural
Residential
Forest
Not Available
Mobile
Location Type
Rural
Urban / Center City
Suburban
Rural
Urban / Center City
Rural
Rural
Rural
Rural
Rural
Rural
Rural
Rural
Rural
Rural
Rural
Rural
Suburban
Urban / Center City
Suburban
Rural
Rural
Rural
Rural
Suburban
Suburban
Rural
Suburban
Rural
Rural
Rural
EL_MSL
60
152
76
244
24
204
100
547
305
0
0
91
94
70
0
30
40
50
16
262
9
152
182
319
3
310
34
232
0
697
853
1
1
1
1
1
1
1
CO
1
1
1
1
HC
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
O3
1
NO2
Parameters Measured
NMHC Carb
1
1
2
1
1
1
1
1
2
2
2
1
2
1
1
2
2
1
PM10
2
1
1
Table A.1-3
PAMS Sites in the CCOS Area
Measurement Method
Type of Site
Hydrocarbons
Elk Grove-Bruceville
PAMS - 1
Canister/GC-FID a
Sacramento-Airport Rd.
PAMS - 2
Canister/GC-FID a
PAMS - 2A
Canister/GC-FID a
Site
Carbonyl Compounds
Sacramento
Sacramento-Del Paso
PAMS - 3
Canister/GC-FID
PAMS - 3/1
Canister/GC-FID a
Clovis Villa
PAMS - 2
Canister/GC-FID a
Fresno-1st Street
PAMS - 2
Canister/GC-FID a
PAMS - 3
Canister/GC-FID a
PAMS - 2
Canister/GC-FID a
PAMS - 3
PAMS - 2
Canister/GC-FID a
Emma Wood Beach
PAMS - 1
Canister/GC-FID a
El Rio
Simi Valley
PAMS - 2
PAMS - 3
Canister/GC-FID a
Folsom-50 Natoma Street
Fresno
Madera
Parlier
Bakersfield
Bakersfield-Golden State Highway
Arvin
Shafter
Ventura
DNPH/HPLC b
a
Canister/GC-FID a
DNPH/HPLC b
DNPH/HPLC b
DNPH/HPLC b
DNPH/HPLC b
Canister/GC-FID a
Type 1 - Upwind background.
Type 2 - Maximum precursor emissions (typically located immediately downwind of the central business district).
Type 3 - Maximum ozone concentration.
Type 4 - Extreme downwind transported ozone area that may contribute to overwhelming transport in other areas.
a - Canisters collected every third day (four 3-hr samples beginning at 0000, 0600, 1300, and 1700, PDT).
Analyzed for PAMS species and total by PDFID.
b - DNPH cartridge samples collected every third day (four 3-hr samples beginning at 0000, 0600, 1300, and 1700, PDT).
A-34
Table A.1-4
PAMS Target Species
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
Ethylene
Acetylene
Ethane
Propene
Propane
Isobutane
1-Butene
n-Butane
trans-2-Butene
cis-2-Butene
Isopentane
1-Pentene
n-Pentane
Isoprene
trans-2-Pentene
cis-2-Pentene
2,2-Dimethylbutane
Cyclopentane
2,3-Dimethylbutane
2-Methylpentane
3-Methylpentane
2-Methyl-1-Pentene
n-Hexane
Methylcyclopentane
2,4-Dimethylpentane
Benzene
Cyclohexane
2-Methylhexane
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
A-35
2,3-Dimethylpentane
3-Methylhexane
2,2,4-Trimethylpentane
n-Heptane
Methylcyclohexane
2,3,4-Trimethylpentane
Toluene
2-Methylheptane
3-Methylheptane
n-Octane
Ethylbenzene
m&p-Xylene
Styrene
o-Xylene
n-Nonane
Isopropylbenzene
n-Propylbenzene
1-ethyl 3-methylbenzene
1,3,5-Trimethylbenzene
n-decane
m-diethylbenzene
p-diethylbenzene
n-undecane
Total NMOC
Table A.6-1
UCD Instrumentation
Parameter
Measured
Technique
Manufacturer
Time
Response
Measurement Range
Accuracy
Pressure
(Altitude)
Capacitive
Setra
1s-3s
-30 m - 3700 m
± 0.3 mB
±3m
Temperature
Platinum RTD
Omega
1s-3s
-20°C - 50°C
± 0.2°C
Relative
Humidity
Capacitive
Qualimetrics
1s-3s
10% - 98%
± 3%
Air Speed
Thermal
Anemometer
T.S.I.
1s-3s
15 m/s - 75 m/s
± 0.4 m/s
Heading
Electronic
Compass
Precision
Navigation
1s-3s
0° - 359°
± 2°
Position
GPS
Garmin
10 s
Lat. - Long.
± 15 m
Particle
Concentration
Optical Counter
Climet
10 s
2 channels: d > 0.3 µm
& d > 3 µm
± 2%
NO, NO2
Concentration
O3 Titration
Chemilumin.
Monitor Labs.
10 s - 15 s
0 ppmv - 20 ppmv
± 0.5 ppbv
Ozone
Concentration
UV Absorption
Dasibi 1008
10 s - 15 s
0 ppbv - 999 ppbv
± 3 ppbv
A-36
Table A.6-2
STI Instrumentation
Parameter
Measured
Technique
Manufacturer
Time
Response
Measurement
Range(s)
Accuracya
(Full Range)
NO/NOy
Concentration
Chemilumin.
Thermo Env.
Model 42S
< 20 s
50 ppb, 100 ppb,
200 ppb
± 10%
Ozone
Concentration
Chemilumin.
Monitor Labs.
8410E
12 s
200 ppb, 500 ppb
± 10%
bscat
Integrating
Nephelometer
MRI 1560 Series
1s
100 Mm-1,
1000 Mm-1
± 10%
Dew Point
Cooled Mirror
Cambridge
Systems 137-C
0.5 s/°C
-50°C - 50°C
± 10%
Altitude
Altitude Encoder
II-Morrow
1s
0 m - 5000 m
± 10%
Altitude (backup)
Pressure Transducer
Validyne P24
<1s
0 m - 5000 m
± 10%
Temperature
Bead Thermistor/
Vortex Housing
YSI/MRI
5s
-30°C - 50°C
± 10%
Temperature
(backup)
Platinum Resistance
Rosemont 102
AV/AF
1s
-50°C - 50°C
± 10%
Position
GPS
II-Morrow
<1s
Lat. - Long.
± 50 m
Data Logger
(includes time)
Dual Floppy
Acquisition
STI 486 System
1s
± 9.99 VDC
± 10%
NO/NOwb
Chemilumin.
Thermo Env.
Model 42S
< 20 s
50 ppb, 100 ppb,
200 ppb
± 10%
SO2b
Pulsed Fluorescence
Thermo Env.
Model 43S
15 s
1 ppb, 5ppb, 50
ppb,200 ppb
± 10%
COb
Gas Filter
Correlation
Thermo Env.
Model 48S
< 20 s
1 ppm, 2 ppm, 5
ppm, 10 ppm
± 10%
a
b
For values between 10% and 90% of full scale
Without modifying the aircraft for additional power, only one of these three instruments can be operated.
A-37
10.000
Ozone (100 ppb)
1.000
Nitrogen Dioxide
(100 ppb)
0.100
Sulfur Dioxide
(100 ppb)
Rayleigh Scattering
(300 K, 1 bar)
0.010
0.001
250
300
350
400
450
Wavelength (nm)
Figure A.1-1. Ultraviolet Absorption Spectra of Ozone, Sulfur Dioxide, and Nitrogen Dioxide.
A-38
Figure A.1-2. Schematic diagram of NOAA’s airborne ozone lidar.
A-39
APPENDIX A1
MASTER VOC PARAMETER LIST
Version 3 – 11/24/99
Appendix A1
Appendix A1
Master VOC Parameter List
#
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
Mnemonic
METHAN
CO2PPM
PAMS
OTHER
UNID
TNMHC
CO_PPM
IDNMHC_d
UNID_d
IDOXY_D
CARB
HALO
TENAX11
ETHENE
ACETYL
ETHANE
PROPE
N_PROP
I_BUTA
LBUT1E
N_BUTA
T2BUTE
C2BUTE
IPENTA
PENTE1
N_PENT
I_PREN
T2PENE
C2PENE
BU22DM
CPENTA
BU23DM
PENA2M
PENA3M
P1E2ME
N_HEX
MCYPNA
PEN24M
BENZE
CYHEXA
HEXA2M
PEN23M
HEXA3M
PA224M
N_HEPT
MECYHX
PA234M
TOLUE
HEP2ME
HEP3ME
N_OCT
ETBZ
MP_XYL
STYR
O_XYL
N_NON
IPRBZ
N_PRBZ
M_ETOL
P_ETOL
BZ135M
O_ETOL
BZ124M
N_DEC
BZ123M
DETBZ1
DETBZ2
N_UNDE
LIBUTE
BUDI13
METOH
BUTYN
Compound Name a
methane
carbon dioxide
sum of PAMS species
other identified to undecane
unidentified to undecane
total NMHC to undecane
carbon monoxide
TO14-FID identified NMHC
TO14-FID unidentified
TO14-FID oxygenates
sum of carbonyls
sum of halocarbons
sum of tenax >undecane
ethene
acetylene
ethane
propene
propane
isobutane
1-butene
n-butane
t-2-butene
c-2-butene
isopentane
1-pentene
n-pentane
isoprene
t-2-pentene
c-2-pentene
2,2-dimethylbutane
cyclopentane
2,3-dimethylbutane
2-methylpentane
3-methylpentane
2-methyl-1-pentene
n-hexane
methylcyclopentane
2,4-dimethylpentane
benzene
cyclohexane
2-methylhexane
2,3-dimethylpentane
3-methylhexane
2,2,4-trimethylpentane
n-heptane
methylcyclohexane
2,3,4-trimethylpentane
toluene
2-methylheptane
3-methylheptane
n-octane
ethylbenzene
m- & p-xylene
styrene
o-xylene
n-nonane
isopropylbenzene
n-propylbenzene
m-ethyltoluene
p-ethyltoluene
o-ethyltoluene
n-decane
1,3-diethylbenzene
1,4-diethylbenzene
n-undecane
iso-butene
1,3-butadiene
methanol
1&2-butyne
Method
1
2
3
4
5
6
7
8
9
10
12
13
14
a002
a003
a001
a006
a007
a010
a004
a014
a016
a018
a024
a026
a028
a030
a031
a032
a037
a043
a044
a046
a050
a051
a054
a063
a064
a068
a070
a072
a073
a075
a080
a083
a088
a093
a094
a096
a099
a104
a113
a115
a119
a120
a123
a127
a133
a134
a135
a136
a138
a142
a143
a148
a153
a155
a165
a005
a013
a015
a017
Sort Codes
PAMS
CMB
P00
P01
P02
P03
P04
P05
P06
P07
P08
P09
P10
P11
P12
P13
P14
P15
P16
P17
P18
P19
P20
P21
P22
P23
P24
P25
P26
P27
P28
P29
P30
P31
P32
P33
P34
P35
P36
P37
P38
P39
P40
P41
P42
P43
P44
P45
P46
P47
P48
P49
P50
P51
P52
P53
P54
P55
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
x
x
Conversion
to ug/m3
656.036
1800.009
Formula
CH4
CO2
Units
ppmv
ppmv
CO
C1H1.85
ppmv
ppbC
ppbC
1145.609
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
0.5736
0.5325
0.6149
0.5737
0.6012
0.5943
0.5737
0.5943
0.5737
0.5737
0.5902
0.5737
0.5902
0.5571
0.5737
0.5737
0.5874
0.5737
0.5874
0.5874
0.5874
0.5737
0.5874
0.5737
0.5855
0.5324
0.5737
0.5737
0.5855
0.5855
0.584
0.5855
0.5737
0.584
0.5384
0.5829
0.584
0.584
0.5427
0.5427
0.5324
0.5428
0.5829
0.5462
0.5462
0.5462
0.5462
0.5462
0.5462
0.5462
0.582
0.5462
C2H4
C2H2
C2H6
C3H6
C3H8
C4H10
C4H8
C4H10
C4H8
C4H8
C5H12
C5H10
C5H12
C5H8
C5H10
C5H10
C6H14
C5H10
C6H14
C6H14
C6H14
C6H12
C6H14
C6H12
C7H16
C6H6
C6H12
C7H16
C7H16
C7H16
C8H18
C7H16
C7H14
C8H18
C7H8
C8H18
C8H18
C8H18
C8H10
C8H10
C8H8
C8H10
C9H20
C9H12
C9H12
C9H12
C9H12
C9H12
C9H12
C9H12
C10H22
C9H12
C10H14
C10H14
C11H24
C4H8
C4H6
CH3OH
C4H6
A1-1
0.5665
0.5737
0.5531
1.3104
0.5531
C_no
1
1
mw
16.04
44.01
1
1
1
28.01
13.85
13.85
2
2
2
3
3
4
4
4
4
4
5
5
5
5
5
5
6
5
6
6
6
6
6
6
7
6
6
7
7
7
8
7
7
8
7
9
8
8
8
8
8
8
9
9
9
9
9
9
9
9
10
9
10
10
11
4
4
0.58
4
28.05
26.04
30.07
42.08
44.1
58.12
56.11
58.12
56.11
56.11
72.15
70.13
72.15
68.11
70.13
70.13
86.17
70.13
86.17
86.17
86.17
84.16
86.17
84.16
100.2
78.11
84.16
98.19
100.2
100.2
114.23
100.2
98.19
114.23
92.14
114.23
114.23
114.23
106.16
106.16
104.14
106.17
128.26
120.2
120.2
120.2
120.2
120.2
120.2
120.2
142.29
120.2
134.22
134.22
156.3
56.11
54.09
32.04
54.09
Group
P
Method
Canister/GC-FID
Canister/GC-FID
Sum(P01..P55)
idnmhc-pams-sum(aa166.aa177)
0.5*UNID
pams + other + unid
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
O
Y
P
O
P
P
O
P
O
O
P
O
P
O
O
O
P
P
P
P
P
O
P
P
P
A
P
P
P
P
P
P
P
P
A
P
P
P
A
A
A
A
P
A
A
A
A
A
A
A
P
A
A
A
P
O
O
OH
Y
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID/Tenax
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID. Tenax
Canister/GC-FID, Tenax
Canister/GC-FID/Tenax
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Version 3 – 11/24/99
Appendix A1
Appendix A1 (continued)
#
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
Mnemonic
B1E2M
pr2oh
B2E2M
PRAL2M
CPENTE
P1E4ME
P1E3ME
MTBE
PEN22M
HEX1E
T3HEXE
T2HEXE
P2E2ME
P2E3MC
C3HEXE
C2HEXE
P2E3MT
BU223M
CPENE1
PEN33M
HEXE4M
CYHEXE
c7ole1
CPA13M
PA3ET
hept1e
c7ole2
T3HEPE
c8ole1
c8ole2
c8ole3
P1E244
c8pa1
HEX25M
HEX24M
c8pa2
HX23DM
HEP4ME
c8pa3
HEX225
OCT1E
CHX11M
HEX235
HEP24D
c9ole2
HEP44D
HEP26D
HEP25D
HEP33D
c9ole1
c9ole3
OCT2ME
OCT3ME
c9par1
none1
c9par2
c9par3
c9ole4
c9par4
IPCYHX
OCT26D
A_PINE
OCT36M
c10p_a
OCTAL
B_PINE
dec1e
c10ar1
I_BUBZ
S_BUBZ
c10ol2
c10p_c
Compound Name a
2-methyl-1-butene
2-methyl-2-butene
2-methylpropanal
cyclopentene
4-methyl-1-pentene
3-methyl-1-pentene
MTBE
2,2-dimethylpentane
1-hexene
t-3-hexene
t-2-hexene
2-methyl-2-pentene
cis-3-methyl-2-pentene
c-3-hexene
c-2-hexene
trans-3-methyl-2-pentene
2,2,3-trimethylbutane
1-methylcyclopentene
3,3-dimethylpentane
4-methylhexene
cyclohexene
C7 olefin-1
1,3-dimethylcyclopentane
3-ethylpentane
1-heptene
C7 olefin-2
t-3-heptene
C8 olefin-1
C8 olefin-2
C8 olefin-3
2,4,4-trimethyl-1-pentene
C8 paraffin-1
2,5-diemthylhexane
2,4-diemthylhexane
C8 paraffin-2
2,3-dimethylhexane
4-methylheptane
C8 paraffin-3
2,2,5-trimethylhexane
octene-1
1,1-dimethylcyclohexane
2,3,5-trimethylhexane
2,4-dimethylheptane
C9 olefin-2
4,4-dimethylheptane
2,6-dimethylheptane
2,5-dimethylheptane
3,3-dimethylheptane
C9 olefin-1
C9 olefin-3
2-methyloctane
3-methyloctane
C9 paraffin-1
1-nonene
C9 paraffin-2
C9 paraffin-3
C9 olefin-4
C9 paraffin-4
isopropylcyclohexane
2,6-dimethyloctane
alpha-pinene
3,6-dimethyloctane
C10 paraffin-a
octanal
beta-pinene
1-decene
C10 aromatic-1
isobutylbenzene
sec-butylbenzene
C10 olefin-2
C10 paraffin-c
Method
a027
a029
a034
a038
a039
a041
a042
a045
a049
a052
a056
a057
a058
a059
a060
a061
a062
a066
a067
a069
a071
a074
a076
a077
a078
a079
a081
a082
a084
a085
a086
a087
a089
a090
a091
a092
a095
a097
a098
a101
a102
a103
a105
a106
a107
a108
a109
a110
a111
a112
a114
a116
a117
a118
a121
a122
a124
a125
a126
a128
a130
a131
a132
a137
a139
a140
a141
a144
a145
a146
a147
a149
Sort Codes
PAMS
CMB
x
x
x
x
Formula
C5H10
Units
ppbC
Conversion
to ug/m3
0.5737
C_no
5
mw
70.13
Group
O
C5H10
C3H7CHO
C5H8
C6H12
C6H12
C5H12O
C7H16
C6H12
C6H12
C6H12
C6H12
C6H12
C6H12
C6H12
C6H12
C7H16
C6H7
C7H16
C7H16
C6H10
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
0.5737
0.7369
0.5571
0.5737
0.5737
3.6049
0.5855
0.5737
0.5737
0.5737
0.5737
0.5737
0.5737
0.5737
0.5737
0.5855
0.56
0.5855
0.5737
0.56
5
4
5
6
6
4.37
7
6
6
6
6
6
6
6
6
7
6
7
7
6
70.13
72.09
68.11
84.16
84.16
88.14
100.2
84.16
84.16
84.16
84.16
84.16
84.16
84.16
84.16
100.2
82.15
100.2
98.19
82.15
C7H14
C7H16
ppbC
ppbC
0.5737
0.584
7
8
98.19
100.2
O
AL
O
O
O
E
P
O
O
O
O
O
O
O
O
A
O
P
P
O
O
A
P
C7H14
ppbC
0.5737
7
98.19
C8H16
ppbC
0.5737
8
112.21
C8H18
C8H18
ppbC
ppbC
0.584
0.584
8
8
114.23
114.23
C8H18
C8H18
ppbC
ppbC
0.584
0.5829
8
9
114.23
114.23
C9H20
C8H16
C8H16
C9H20
C9H20
ppbC
ppbC
ppbC
ppbC
ppbC
0.5829
0.5737
0.5737
0.5829
0.5829
9
8
8
9
9
128.26
112.21
112.21
128.26
128.26
C9H20
C9H20
C9H20
C9H20
ppbC
ppbC
ppbC
ppbC
0.5829
0.5829
0.5829
0.5829
9
9
9
9
128.26
128.26
128.26
128.26
C9H20
C9H20
ppbC
ppbC
0.5829
0.5829
9
9
128.26
128.26
C9H18
C10H22
C10H16
C10H22
ppbC
ppbC
ppbC
ppbC
0.5737
0.582
0.5572
0.582
9
10
10
10
126.28
142.29
136.23
142.29
C9H16O
C10H16
ppbC
ppbC
0.6544
0.5572
8
10
128.22
136.23
C10H14
C10H14
ppbC
ppbC
0.549
0.549
10
10
134.22
134.22
A1-2
O
O
O
O
O
O
P
P
P
P
P
P
P
P
O
P
P
P
O
P
P
P
P
O
O
P
P
P
P
P
O
P
P
P
O
P
P
AL
O
A
A
A
O
P
Method
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Version 3 – 11/24/99
Appendix A1
#
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
Mnemonic
c10ar2
N_BUBZ
DETBZ3
c10ar3
BZDME
c10ar4
c10ar5
IPRTOL
NONAL
unde1e
c10ar6
c11p_a
BZ1245
BZ1235
BZ1234
IND_2M
IND_1M
c11ar1
c11ar3
dode1e
NAPHTH
n_dode
F12
F114
MEBR
F11
VINECL
MECL2
F113
T12DCE
C12DCE
CCL3
ETDC12
MECCL3
CCL4
DBRME
TCE112
t13dcp
C13DCP
tce112
CLDBRM
ETDB12
PERC
CLBZ
TCENE
MDCBZ
PDCBZ
ODCBZ
FORMAL
ACETAL
ACETO
ACROLN
PROAL
CROTON
MEK
acrolx
MEACRO
BUAL
BENZAL
glyoxl
VALAL
TOLUAL
HEXAL
PHENOL
ACPHONE
NONE1
MEOCT
DMETBZ
NAP_1M
NAP_2M
DMOCT
INDDM1
Compound Name a
C10 aromatic-2
n-butylbenzene
1,2-diethylbenzene
C10 aromatic-3
1,3-dimethyl-4-ethylbenzene
C10 aromatic-4
C10 aromatic-5
isopropyltoluene
nonanal
1-undecene
C10 aromatic-6
C11 paraffin-a
1,2,4,5-tetramethylbenzene
2-methylindan
1-methylindan
c11 aromatic-1
c11 aromatic-3
1-dodecene
naphthalene
n-dodecane
freon 12
freon 114
methylbromide
freon 11
vinylidenechloride
methylene chloride
F113
trans-1,2-dichloroethylene
cis-1,2,-dichloroethylene
chloroform
1,2-dichloroethane
methyl chloroform
carbon tetrachloride
dibromomethane
1,1,2-trichloroethane
cis-1,3-dichloropropene
chlorodibromomethane
1,2-dibromoethane
perchloroethylene
chlororbenzene
trichloroethylene
m-dichlorobenzene
p-dichlorobenzene
o-dichlorobenzene
formaldehyde
acetaldehyde
acetone
acrolein
propionaldehyde
crotonaldehyde
methyl ethyl ketone
methacrolein
butanal
benzaldehyde
valeraldehyde
tolualdehyde
hexanal
phenol
acetophenone
1-nonene
methyloctane
dimethylethylbenzene
1-methylnaphthalene
2-methylnaphthalene
dimethyloctane
dimethylindan
Method
a154
a156
a157
a158
a159
a160
a161
a162
a163
a164
aa166
aa167
aa168
aa169
aa170
aa171
aa172
aa173
aa174
aa175
aa176
aa177
b01
b02
b03
b04
b05
b06
b07
b08
b09
b10
b11
b12
b13
b14
b15
b16
b17
b18
b19
b20
b21
b22
b23
b24
b25
b26
c01
c02
c03
c04
c05
c06
c07
c08
c09
c10
c11
c12
c13
c14
c15
Sort Codes
PAMS
CMB
x
Conversion
to ug/m3
Formula
Units
C_no
mw
C10H14
C10H14
ppbC
ppbC
0.549
10
10
134.22
134.22
C10H14
ppbC
0.549
10
134.22
C10H14
C9H18O
ppbC
ppbC
0.549
10
9
134.22
142.24
Group
A
A
A
A
A
A
A
A
AL
C10H14
C10H14
C10H14
C10H12
C10H12
ppbC
ppbC
ppbC
ppbC
ppbC
0.549
10
10
10
10
10
134.22
134.22
134.22
132.21
132.21
C10H8
ppbC
0.5242
10
128.16
CF2Cl2
C2F4Cl2
CH3BR
CFCl3
C2H2Cl2
CH2CL2
C2F3Cl3
C2H2Cl2
C2H2Cl2
CHCl3
C2H4Cl2
C2H3Cl3
CCl4
CH2Br2
C2H3Cl3
ppbv
ppbv
ppbv
ppbv
ppbv
ppbv
ppbC
ppbv
ppbv
ppbv
ppbv
ppbv
ppbv
ppbv
ppbv
1
2
1
1
2
1
2
2
2
1
2
2
1
1
2
120.91
170.91
94.94
137.37
96.94
84.93
187.38
96.94
96.94
119.38
98.96
133.41
153.82
173.85
133.41
C3H4Cl2
ppbv
3
98.96
x
CHClBr2
C2H4Br2
C2Cl4
C6H5Cl
C2HCl3
C6H4Cl2
C6H4Cl2
C6H4Cl2
HCHO
CH3CHO
C3H6O
C3H4O
C2H5CHO
C3H5CHO
C4H8O
ppbv
ppbv
ppbv
ppbv
ppbv
ppbv
ppbv
ppbv
ppbv
ppbC
ppbC
ppbv
ppbv
ppbv
ppbC
210
0.9008
0.7918
236
238
250
0.7374
1
2
2
6
2
6
6
6
1
2
3
3
3
4
4
208.28
187.87
165.83
112.55
131.39
147.01
147
147.01
33.03
44.05
58.08
56.07
58.08
70.09
72.09
A
P
A
A
A
A
A
A
A
O
A
P
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
A
X
X
X
X
AL
AL
K
AL
AL
AL
K
x
x
x
C4H5CHO
C3H7CHO
C7H6O
ppbC
ppbC
ppbv
0.5736
0.7374
286
2
4
7
70.09
72.09
106.13
AL
AL
AL
C4H9CHO
C8H8O
C5H11CHO
C6H5OH
C8H8O
C9H18
C9H20
C10H14
C11H10
C11H10
C10H22
C11H14
ppbv
ppbv
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
266
300
0.6828
5
8
6
6
8
9
9
10
11
11
10
11
86.14
120.16
100.16
94.11
120.16
126.28
128.26
134.22
142.2
142.2
142.29
146.23
AL
AL
AL
AL
K
O
P
A
A
A
P
A
x
x
x
x
x
x
x
x
x
x
A1-3
3.8319
Method
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
Tenax, Canister/GC-FID
Canister/GC-FID
Canister/GC-FID
tenax/Canister/GC-FID
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
Canister/GC-ECD
DNPH/HPLC-UV
NPH/HPLC-UV/ Canister/GC-FID
DNPH/HPLC-UV
DNPH/HPLC-UV
DNPH/HPLC-UV
DNPH/HPLC-UV
H/HPLC-UV, Canister/GC-FID, Te
DNPH/HPLC-UV
DNPH/HPLC-UV
DNPH/HPLC-UV
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Version 3 – 11/24/99
Appendix A1
#
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
Mnemonic
ACENPE
DMN12
DMN13
DMN14
DMN15
DMN18
DMN23
DMN26
DMN27
NAP1ET
NAP2ET
N_DODE
N_TRID
N_TETD
N_PEND
N_HEXD
N_HEPD
N_OCTD
N_NOND
N_EICO
N_HENE
Compound Name a
acenaphthene
1,2-dimethylnaphthalene
1-ethylnaphthalene
2-ethylnaphthalene
n-dodecane
n-tridecane
n-tetradecane
n-pentadecane
n-hexadecane
n-heptadecane
n-octadecane
n-nonadecane
n-eicosane
n-heneicosane
Method
Sort Codes
PAMS
CMB
x
x
x
x
x
x
x
x
x
x
x
x
Formula
C12H10
C12H12
C12H12
C12H12
C12H12
C12H12
C12H12
C12H12
C12H12
C12H12
C12H12
C12H26
C13H28
C14H30
C15H32
C16H34
C17H36
C18H38
C19H40
C20H42
C21H44
Units
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
ppbC
Conversion
to ug/m3
C_no
12
12
12
12
12
12
12
12
12
12
12
12
13
14
15
16
17
18
19
20
21
mw
154.23
156.23
156.23
156.23
156.23
156.23
156.23
156.23
156.23
156.23
156.23
170.34
184.37
198.4
212.42
226.45
240.48
254.5
268.53
282.56
296.58
A = aromatic, AL = Aldehyde, O = alkene (olefin), P = parafin, Y = alkyne, K = ketone, E = ether, X = haogenated, OH = alcohol
A1-4
Group
A
A
A
A
A
A
A
A
A
A
A
P
P
P
P
P
P
P
P
P
P
Method
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
Tenax/GC-FID
APPENDIX B
QUALITY ASSURANCE
Version 3 – 11/24/99
B.
Appendix B: Quality Assurance
QUALITY ASSURANCE
Purpose is to provide a quantitative estimate of the uncertainty of the measurements
through estimates of the precision, accuracy (or bias) and validity. QA ensures that the
procedures and sampling methods used in the study are well documented and are capable of
producing the data which meet the specifications for the study.
Quality assurance will be under the overall direction of the QA manager who will
coordinate a QA team. The team will consist of the QA manager and sponsoring agencies that
have the necessary expertise. The QA manager and team will be responsible for developing a
QA plan for the study, reviewing standard operating procedures, performing systems and
performance audits, reviewing and validating study data processing procedures and data, and
estimating the uncertainties in the data. The QA team will work closely with the data manager,
the field manager, and investigators. The QA manager should be selected early and should be an
integral member of the planning team. The QA manager should coordinate with and be assisted
by the audit staff members of the air quality agencies in the study area to assure that
measurements are based on the same standards.
Once the sampling has been completed and the investigators have provided the data
management contractor with a clean data set, the QA team helps validate the data in two stages,
Level 1 (univariate checks such as maxima and minima, rates of change, and diurnal variations)
and Level 2 (multivariate consistency tests based on known physical, spatial, or temporal
relationships). The QA team also makes the final estimates of the precision and accuracy of the
data with the help of the investigators and the data manager.
Quality auditing tasks can be performed both by contractors and by the QA staff of the
sponsoring organizations. The QA manager bears overall responsibility for ensuring that the
quality auditing tasks are performed by members of the QA team. The major tasks are
summarized below.
Overall:
•
Manage the overall QA activities. Interact with the field manager and the principal
investigator and provide feedback to them concerning the status of unresolved QA
problems and the potential for their resolution.
Before field operations begin:
•
Work with investigators to determine whether each measurement method is likely to
meet its specifications for accuracy (or bias) and precision.
•
Review the SOPs for each measurement and verify the assumptions on which they are
based.
•
Prepare systems audit procedures.
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•
Perform preliminary systems audits at investigators' analytical laboratories, paying
particular attention to calibration methods.
•
Develop performance audit procedures for measurements for either accuracy (i.e.,
compared to established standards) or, where that is not possible, for bias (i.e.,
compared to another co-equal laboratory's measurements).
•
Arrange for investigators' calibration standards to be checked against EPA, ARB or
other standards.
•
Audit transfer standards.
•
If not all field sites are to be audited, develop a priority system indicating which field
sites and which laboratories should be audited during or just prior to field
measurements.
•
Immediately prior to field operations, carry out performance audits of the continuous
gas analyzers and flow measuring instruments at the chosen field sites.
During field operations:
•
Perform systems audits on field and laboratory measurements and data processing
procedures. Field air quality measurement sites, sampling aircraft, upper air sites, and
meteorology sites should be audited.
•
Perform systems audits for the data processing and data management operations of
the measurement and data management contractors.
•
Coordinate performance audits on routine measurements. Arrange for or perform
those audits not done by the sponsoring organizations.
After field operations:
•
Prepare reports of the audits.
•
Work interactively with the data management contractor in the on-going Level 1 and
Level 2 validation of the data.
•
Work with investigators to determine the accuracy (or bias) and precision for each
measurement value and prepare a report summarizing the uncertainties in the study
data.
A quality assurance plan specifies the activities associated with the CCOS quality
assurance program, schedules, and responsibilities. The following sections describe elements of
the Quality Assurance Plan.
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B.1
Quality Assurance Overview
The primary purpose of the quality assurance (QA) tasks is to provide a quantitative
estimate of the uncertainty of the measurements through estimates of the precision, accuracy (or
bias), and validity. In addition, QA ensures that the procedures and sampling methods used in
the study are well documented and are capable of producing data which meet the specifications
of the study. Quality assurance is intimately connected with data management. Before sampling
starts, the QA team assists the investigators and the data management contractor to develop the
format of the database; the QA team also reviews the investigators' standard operating
procedures (SOPs) and makes estimates of the precision and accuracy that might be expected
from the measurement systems. Prior to or during sampling the QA team carries out quality
audits and helps resolve any problems.
The quality assurance program includes two types of activities: quality control (QC), and
quality auditing (QA). The QC activities are on-going activities of the measurement and data
processing personnel. QC activities consist of written standard operating procedures to be
followed during sample collection, sample analysis, and data processing. These procedures
define schedules for periodic calibrations and performance tests (including blank and replicate
analyses). They specify pre-defined tolerances which are not to be exceeded by performance
tests and the actions to be taken when they are exceeded. The QC activities also include
equipment maintenance, and acceptance testing, and operator training, supervision, and support.
Quality auditing consists of two components: systems audits and performance audits.
Systems audits include a review of the operational and quality control (QC) procedures to assess
whether they are adequate to assure valid data which meet the specified level of accuracy and
precision. After reviewing the procedures, the auditor examines phases of the measurement or
data processing activity to determine whether the procedures are being followed and that the
operating personnel are properly trained. The system audit is a cooperative assessment resulting
in improved data. Performance audits establish whether the predetermined specifications for
accuracy are being achieved in practice. For measurements, the performance audit involves
challenging the measurement/analysis system with a known standard sample that is traceable to a
primary standard. Performance audits of data processing involve independently processing
samples of raw data and comparing the results with reports generated by routine data processing.
The specialized nature of some measurements (e.g., hydrocarbon speciation, carbonyl
compounds, PAN, NOy, ozone lidar, upper air meteorology) preclude simple performance audits
for these measurements.
Intercomparison studies are typically used to assess the
representativeness, accuracy, and precision of these measurements.
B.2
Data Quality Objectives
Data quality objectives should be specified prior to the study to ensure that all measured
data meet the end-use requirements for air quality and meteorological model input and
evaluation, data analyses, and monitoring the success of meeting data quality objectives.
Precision and accuracy goals are identified for measurement variables. Many methods and
procedures employed in CCOS are routinely measured variables for which expected precision
and accuracy are known. Other measurements are experimental and target objectives can only
be estimated.
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In evaluating precision and accuracy objectives, it is important to consider the methods
used to determine the values. For example, a greater deviation may occur between replicates
with real samples with complex matrices than with replicates of standards in simple matrices.
Synthetic mixtures of hydrocarbons that are used in the Photochemical Assessment Monitoring
Station Program is an example. An ambient sample yields a more complex chromatogram and
greater potential for inconsistent identification. Analysis of a standard mixture of hydrazones
does not address potential sampling artifacts that may be associated with carbonyl compound
measurements using the DNPH derivitization method.
Precision and accuracy targets are commonly based on relative percent differences.
Precision is either based on a relative percent difference between replicates (analytical precision)
or duplicate samples (method precision) as follows:
100 * (rep1 - rep2)/(rep1 + rep2)/2
The standard deviation of the average of a group of replicate (or duplicate) pairs
represents the precision for a measurement parameter. For accuracy, percent difference is
determined relative to a known or target value and is as follows:
100 * (observed - target)/target
The objective may be a standard of known concentration or an audit value independently
obtained or prepared by the QA team. For some parameters, standards of known concentration
are not easily obtained or cannot be accurately prepared for use in the field, and accuracy can
only be checked against an independent method that is believed to be either without bias, has a
known bias that can be accounted for, or a method that has been used historically. Accuracy
determined in this manner is considered a test of equivalency and not true accuracy.
After an audit, data flags are reported immediately to the field operations manager and to
the appropriate contractor to ensure rapid implementation of corrective action by the
measurement group. Tasks Performed by the Quality Assurance Manager
Specify tasks to be performed by the QA manager before, during and after the field study
B.3
Systems Audits
B.3.1 Field Systems Audits for Surface Monitoring Sites
Prior to the start of the field study, the auditing team obtains pertinent forms and
documents, their latest revisions, and information needed to perform the audits. These forms and
documents include SOPs, instrument manuals, logbooks, chain-of-custody records, data sheets,
control charts, and maintenance records. The auditor verifies that each of these forms and
documents is available at the field site. If out-of-date documents are identified at the field site,
recommendations for replacement are made in the systems audit report. Calibration records,
performance test tracking charts, and maintenance records are examined to determine that the
tasks were being performed on the schedules specified in the SOP. Contents of logbooks and
checklists are examined to determine that the field documentation procedures were being
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followed. The auditor examines the site description, field documentation, SOPs, spare parts, and
supplies, and performs a general instrument inspection. The QA manager coordinates review of
the latest revision of field SOPs and ensures that each auditor has the most recent version prior to
systems audits. The auditor independently evaluates the siting of measurement platforms to
document relevant characteristics that might affect the measurement at a particular location. An
inspection of measurement devices and evaluation of their condition with respect to obtaining a
quantitative measurement is also part of each system audit. The audit examines the relationship
among different instruments and their conformity with requirements at each site. The instrument
serial numbers and model numbers are compared with those recorded in the project records as
being present at each site. Sample lines are examined for dirt or obstruction. Leads from each
instrument to data acquisition system are examined to ensure that they are connected to the
proper channels. Inconsistencies with project records are reported, and recommendations for site
modification are made by the QA manager.
B.3.2 Field Systems Audits for Aircraft Platforms
Aircraft systems audits are similar to surface systems audits. A systems audit
questionnaire is completed by the auditor for each aircraft. The auditor reviews the type of
measurements that are actually being performed relative to those specified in the most recent
work plan. The level of documentation of quality control checks, instrument logs, etc., are
examined. Results of calibration records and performance tests are evaluated. The auditor
records pertinent information relative to the sampling system to form an independent
documentation of conditions as they existed during the audit.
B.3.3 Laboratory Systems Audits
Laboratory analysis and data processing activities are audited in this procedure. The
laboratory systems audit examines the procurement and acceptance testing of sampling
substrates, laboratory documentation, SOPs, laboratory instrumentation, spare parts, and
supplies. A traceability audit randomly selects a single data value for each observable from a
recent data report and tracks the documentation and traceability to standards associated with that
value. This traceability audit determines how well each of the individual procedures was
integrated to produce valid data values.
B.4
Performance Audits
B.4.1 Field Performance Audits of Surface Monitors
Quantitative transfer standards are used during field performance audits to determine the
percent difference between the field measurements and the standard (i.e., to estimate the
accuracy of the measurement). The difference should meet the acceptance criteria defined by the
quality assurance objectives. Otherwise, reasons for exceeding acceptable levels are sought, and
recommendations are made for eliminating the problem and adjusting or flagging data as
necessary.
Ozone. A calibrated transfer standard with an internal ozone generator is used to
generate five standard ozone concentrations and one zero level concentration to audit the
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instruments. Corresponding concentrations are recorded from each instrument and compared. A
linear regression of measured versus audit results is calculated to determine baseline offsets and
linearity of response. The in-station performance test gases are verified against the certified
NIST standards. The audit includes a comparison of values taken from the instrument display,
the strip chart recorder, and the data acquisition system.
Standard and High-sensitivity NO/NOx and NO/NOy. A calibrated audit system used to
challenge the standard sensitivity instruments consists of zero air, NIST-traceable NO gas in a
cylinder, and an ozone generator. At least three NO concentrations and a zero are introduced to
the instrument, and the response of the data acquisition system and the instrument are recorded.
Audit NO2 is produced by gas-phase titration and introduced to the analyzer for at least five
different concentrations. Audit versus site differences are determined, and a linear regression of
site versus audit results is calculated to determine baseline offsets and linearity of response. In
addition, site test gases are verified against the audit standard.
PAN and NO2. This audit involves the use of a calibrated audit system consisting of zero
air and a NIST-traceable low concentration NO2 gas cylinder. These standards can be unstable
with time, and precautions need to taken into account for any degradation that occurs during the
audit process. At least three NO2 concentrations and a zero are introduced to the instrument, and
the response of the data acquisition system and the instrument is recorded. Audit versus site
differences are determined, and a linear regression of site versus audit results is calculated to
determine baseline offsets and linearity of response. In addition, site test gases are verified
against the audit standard. Since NIST transfer standards do not exist for PAN, collocated
measurements using a gas chromatograph with electron capture detection (GC/ECD) may be
used for comparison. PAN is thermally unstable, even at room temperature, and thus difficult to
calibrate. This difficulty can lead to discrepancies between field measurements that are difficult
to resolve without further laboratory studies. One advantage of the LPA-4 PAN analyzer is that
the instrument can be calibrated in the field with NO2 rather than the thermally unstable PAN.
Level 2 validation of the PAN data includes correlation and time series of PAN values compared
to NOy, NOz, and NO/NO2 ratios.
B.4.2 Field Performance Audits for Surface Meteorological Measurements
This audit includes the variables of wind direction, wind speed, temperature, relative
humidity, and solar radiation. These procedures generally are performed by both auditor and site
operator, since several of these procedures require readings to be made in the instrument shelter
while someone is on the meteorological tower. Safety considerations also require the presence
of an additional person whenever someone ascends the tower. Audit values are compared with
instrument displays (when available), stripchart output, and data acquisition system output. In
this way, the entire measurement system is audited, and the causes of exceedances of the
acceptance criteria can be isolated. The following paragraphs summarize the audit procedures.
Wind Direction. Distance sighting targets are determined for each site. Where possible,
these targets are measured with a stable sighting compass on a nonmagnetic tripod and corrected
for declination. The operator ascends or cranks down the tower and aligns the point and tail of
the wind vane toward these targets while the auditor records the output in the shelter.
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Differences between true and measured direction are recorded. Vane starting thresholds are
checked using a starting torque watch.
Wind Speed. The anemometer cups are temporarily replaced by synchronous motors,
and the equivalent wind speed displayed by the anemometer is compared with the speed
corresponding to the rotation rate as supplied by the manufacturer. Anemometer starting
thresholds are checked from a torque measurement using a gram scale applied at a measured
distance from the axis of rotation.
Temperature. An aspirated thermometer traceable to standards from the NIST is placed
adjacent to each temperature-sensing device, and the two readings are compared. The resistance
of temperature-sensing units is compared to the NIST-traceable thermometer. When feasible,
two sets of readings are taken to cover a wide range of readings.
Relative Humidity. An aspirated psychrometer using NIST-traceable thermometers is
operated at the level of the relative humidity sensor. Relative humidity based on the
psychrometer readings is determined and compared to the instrument value.
Solar Radiation. An audit pyranometer is zeroed and readings are taken with the audit
instrument placed next to the station pyranometer. A comparison is made between the hourly
average readings of the two instruments.
B.4.3 Field Performance Audits for Upper-Air Meteorology
Field audits for upper-air meteorological measurements form surface-based platforms are
particularly challenging, and special techniques are needed. For systems, ground truthing of setup conditions (surface wind, pressure, and temperature) is performed similar to the standard
surface meteorology audit procedures described above. In addition, for Doppler acoustic
sounders and radar profilers, performance audits are accomplished using collocated audit
tethersondes, radiosondes, and/or instrumented aircraft (flying nearby spirals). The Quality
Assurance Plan for CCOS will need to provide more specific quality assessment and data
validation procedures.
B.4.4 Field Performance Audits for Aircraft Platforms
Quantitative transfer standards, similar to those used for the performance audits of the
surface-based monitors, are used to challenge each measurement system aboard the aircraft
platform. Results of each audit are compared to acceptance criteria and values that exceed the
criteria are flagged. Immediately following the audit, the auditors provide a verbal report of the
audit to the appropriate aircraft supervisor. During the verbal report, values and equipment
problems are discussed in terms of possible reasons for the discrepancies and corrective action to
be taken. If corrective action is implemented, the audit is repeated.
B.4.5 Laboratory Performance Audits for Chemical Analysis
Laboratory performance audits for CCOS will consist of the submission of blind
performance evaluation samples of known concentrations and/or interlaboratory comparisons of
samples for measurements of individual and total hydrocarbons and carbonyl compounds.
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Experiences from previous field studies demonstrate that measurements of ambient
hydrocarbon speciation are not routine, and that the quality and completeness of measurements
vary among different laboratories using essentially the same samplers and analytical
instrumentation (Fujita et al., 1994). Potential problems include: positive and negative artifacts
due to effects of sampler and sampling media; incomplete resolution or loss of C2-C3
hydrocarbons due to introduction of excessive moisture in the column or improper sample
loading and injection; underreporting of true concentrations due to selection of incorrect
integration threshold; loss of material in the analytical system due to poor chromatographic
technique (particularly for very light and heavy hydrocarbons) or prolonged storage in canisters
prior to analysis (especially for olefins and some aromatics); incorrect or incomplete peak
identification due to limitation of peak identification software, especially for compounds that
exist in lower concentrations and elute in a crowded segment of the chromatogram; systematic
bias due to calibration problems; and variable measurement of true total NMHC among
laboratories due to variation in analytical method and data processing (e.g., use of Nafion®
dryer, inclusion or exclusion of oxygenated compounds in total NMHC). Interlaboratory
comparisons using ambient samples are required to fully assess these problems. Also the need to
measure VOC species that are not currently quantified in the PAMS program (semi-volatile
hydrocarbons and oxygenated compounds) should be evaluated with respect to the goals of
CCOS. For example, MTBE is a major component of ambient VOC in areas where this
compound is the primary oxygenated compound in reformulated gasoline (RFG) and higher
molecular weight carbonyls are relatively more abundant in downwind receptor areas. MTBE
may serve as a useful marker for motor vehicle emissions and higher carbonyls have important
implications for photochemical modeling.
The sampling and analytical parameters that affect the accuracy and validity of
measurements of carbonyl compounds by the 2,4-dinitrophenylhydrazine-impregnated cartridge
technique (TO-11) are not completely resolved and are currently under extensive study and
scrutiny by EPA and the scientific community (NARSTO-NE: field comparison in Agawam,
MA, SOS: formaldehyde intercomparison at Boulder, CO, field measurement comparison in
Nashville, TN). Relevant parameters include the substrate (type, DNPH loadings, blank levels,
and variability), sampling conditions (ambient ozone concentrations, temperature, relative
humidity, sample volume measurements, breakthrough, type of sampling line and ozone
scrubber), sample storage, and handling (exposure to light and heat, type of storage and duration
of storage), sample preparation and analysis (extraction efficiency and instrument calibration,
peak resolution).
Each of the air pollution control districts in central California that have PAMS networks
participate in performance audit programs run by the EPA and by the California Air Resources
Board. Both the federal and state performance audits for hydrocarbons involve analysis of a
standard mixtures of target compounds on an annual basis during the PAMS measurement
season. Carbonyl audits involve laboratory analysis of standard extracts of selected hydrazones.
While these audits can document possible systematic calibration biases, they do not address a
number of other potential problems that can affect the accuracy of analytical results.
The following quality assessment tasks should be completed prior to the CCOS field
study in order to resolve these issues.
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•
The ARB should conduct expanded performance audits of the district hydrocarbon
measurements during the summer of 1999. In addition to the synthetic audit
mixtures, the expanded performance audit should also include a set of ambient
samples consisting of both urban, mobile source dominated samples and downwind,
aged air samples. In addition to the ARB laboratory, one other laboratory (possibly
EPA-AREAL) should participate in the interlaboratory comparison. A similar
interlaboratory comparison should be conducted in 2000 prior to the CCOS episodes.
This comparison should include the contractor that is selected to provide
supplemental VOC measurements.
•
Review and summarize the results of past EPA and ARB performance audits of the
PAMS programs in southern California. Summarize the problems that were
identified and corrective actions that have been taken.
•
Review the results of on-going hydrocarbon interlaboratory and performance audit
programs during NARSTO-Northeast and SCOS97-NARSTO
•
Review recently completed and on-going laboratory evaluations and intercomparisons
for carbonyl measurements by Method TO-11.
•
Incorporate these tasks in the quality assurance plan for CCOS and include results in
the final QA report for the CCOS field measurement program.
B-9
APPENDIX C
DATA MANAGEMENT
Version 3 – 11/24/99
C.
Appendix C: Data Management
DATA MANAGEMENT
The CCOS data base will be compiled, documented, evaluated, and distributed by the
Technical Support Division of the Air Resources Board (ARB). Common data management and
validation conventions need to be assembled in a consistent and efficient manner. These
conventions are described in this section. This section will be updated with more specific
conventions and reporting structures after CCOS measurement investigators have been
commissioned and provided their suggestions and recommendations. To the greatest extent
possible, CCOS field data structures, processing, validation, and delivery procedures will be
consistent with those established for the long-term data base and other ARB data sets from recent
air quality studies (e.g. Fujita et al., 1997).
A data manager should be appoint at least one year prior to the field program. The data
manager will be responsible for establishing and maintaining computer-based data archives and
managing the overall data storage and validation activities. The data manager will interact with
the field manager and the program management and provide feedback to them concerning the
status of unresolved data management problems and potential for resolution.
C.1
Data Specifications
CCOS data management conventions and methods build on experience from and
development supported by the 1990 San Joaquin Valley Air Quality Study (SJVAQS)
(Blumenthal et al., 1993), the 1995 Integrated Monitoring Study (IMS-95) (Solomon and
Magliano, 1997), and the 1997 Southern California Oxidant Study (SCOS-97) (Fujita et al.,
1997). The following specifications are maintained by the data manager and are available to all
project participants via the internet:
•
Measurement locations: Each measurement location is identified with a unique
alphanumeric site ID accompanied by its name and address, coordinates, elevation, its
primary operator, and a summary of measurements taken at the site for different
monitoring periods. Appendices A-D summarizes existing and proposed field study
measurement locations. Coordinates and elevations are verified by the field manager
with a Global Positioning System (GPS), pressure-based altimeter, and topographical
maps. Immediate surroundings are recorded with a digital camera and a video tape of
the area surrounding the site is available and catalogued. The make and model of
instruments used to acquire measurements at each site is recorded with its threshold,
range, expected accuracy and precision.
•
Variable definitions: Each variable is assigned a unique code that is accompanied
by its definition, units, averaging time, applicable temperature and pressure
adjustments, and data reporting format.
•
Data validation flags: Flags specific to each measurement investigator are translated
into a common set of validation flags that are carried with each data point. These are
currently being defined by EPA for its speciation program, and this will be a starting
point for CCOS data validation flags.
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C.2
•
Data files: Basic data files are constructed in normalized formats that have the same
structure for different types of data. These files will be transparent to most users.
•
Timing conventions: Times are expressed in Pacific Standard Time (PST), hour or
minute beginning. All dates as MM/DD/YYYY (note that years are four-digit codes:.
1999, 2000, 2001).
•
Missing data conventions: Missing or invalid measurements are replaced by a –99
or a “NULL” value if the data management software permits.
•
Investigator code: Each measurement investigator or network operator is assigned a
unique two-character code that is used to identify the source of the data. Data
transmittals will carry this code as part of the filename when they are loaded into a
raw data sub-directory, and a separate sub-directory on the CCOS internet server.
Data Formats
Data acquired at set intervals are submitted as comma delimited text files via file transfer
protocol to the sub-directory set up for each measurement investigator. File naming conventions
are given below. Raw data file naming conventions include the investigator code, a
measurement type code, and an indicator for the period of data acquisition. Formats are:
•
Continuous sequential measurements: SSSS, MM/DD/YY, HHMM, TIME,
DURATION, PARAM1, RESULT, QCFLAG, PARAM2, RESULT, QCFLAG,
•
Surface particle measurements: SSSS, MM/DD/YY, HHHMM, TIME,
DURATION, SIZE, PARAM1, RESULT, QCFLAG, PARAM2 ,RESULT,
QCFLAG
•
Upper air measurements: SSSS, MM/DD/YY, HHMM, TIME, EV_MSL,
PARAM1, RESULT, QCFLAG, PARAM2, RESULT, QCFLAG
The mnemonics have the following definitions:
–
SSSS = 3 - 4 character project site ID code
–
MM/DD/YYYY = date specification
–
HHMM=standard sample start time, begin hour and minutes PDT (0000-2355)
–
TIME = actual sample start time, HHMMSS
–
DURATION = sampling in total minutes
–
EV_MSL = elevation in meters above mean sea level
–
PARAM = project parameter code
–
SIZE = 1 letter particle size category (e.g. T = PM10 (0-10 µm); F = Fine(0-2.5
µm); C =Coarse (2.5-10 µm); S=sum of Fine+Coarse ~ PM10; P = TSP (0-30 µm),
and other particle size ranges as needed)
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C.3
–
RESULT = data value in project specified significant digits
–
QCFLAG = 1 character project QC code indicating quality of data point
File Names
File names are of the form CDDMMYYA.PLL
C.4
–
C=investigator code (to be defined)
–
DD =data type code (to be defined)
–
MM = month code (JA,FE,MR,AP,MY,JN,JL,AU,SE,OC,NO,DE)
–
YYYY = Year code
–
A = Averaging interval codes: (A = 3 hour; B = 6 hour; C = 12 hour; D= 24 hour
;H = 1 hour; J = jumps (every other hour); V = hourly but varies, possibly less
than 24 measurements per day; I = Instantaneous ( < 1 min); F=5 minute; T=10
minute; M = 15 minute; N=30 minute; P = Partial hour samples (< 60 min)
–
P = Measurement platform code (S = surface, U = upper air)
–
LL = Two character data validation level code (OA,OB,1A,1B,2A,2B,3A).
Validation Flags
Procedure- and investigator-specific validation flags will be maintained in a separate
validation file. These must be translated into the common flags listed below. A translation table
will be established as part of the database that associates each investigator flag with one of the
following flags: 0=valid; 1 = estimated; 2=calibration; 3=instrument failure; 4=off-scale
reading; 5 = interpolated; 6=below detection limits; 7=suspect; 8=invalid; 9=missing; a=hourly
avg (45 <->60 minutes); b=hourly avg (<45 minutes); d=averaged data; e=zero mode; and
f=blank sample
C.5
Data Validation Levels
Mueller (1980), Mueller et al., (1983), and Watson et al. (1983, 1989, 1995) define a
three-level data validation process that should be mandatory in any environmental measurement
study. Data records are designated as having passed these levels by entries in the VAL column
of each data file. These levels, and the validation codes that designate them, are defined as
follows:
•
Level 0 (0): These data are obtained directly from the data loggers that acquire data
in the field. Averaging times represent the minimum intervals recorded by the data
logger, which do not necessarily correspond to the averaging periods specified for the
data base files. Level 0 data have not been edited for instrument downtime, nor have
procedural adjustments for baseline and span changes been applied. Level 0 data are
not contained in the CCOS database, although they are consulted on a regular basis to
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ascertain instrument functionality and to identify potential episodes prior to receipt of
Level 1A data.
•
Level 1A (1A): These data have passed several validation tests applied by the
measurement investigator prior to data submission. The general features of Level 1A
are: 1) removal of data values and replacement with -99 when monitoring
instruments did not function within procedural tolerances; 2) flagging measurements
when significant deviations from measurement assumptions have occurred; 3)
verifying computer file entries against data sheets; 4) replacement of data from a
backup data acquisition system in the event of failure of the primary system; 5)
adjustment of measurement values for quantifiable baseline and span or interference
biases; and 6) identification, investigation, and flagging of data that are beyond
reasonable bounds or that are unrepresentative of the variable being measured (e.g. high
light scattering associated with adverse weather).
•
Level 1B (1B): Pre-programmed consistency and reasonability tests are applied by
the data manager prior to integration into the CCOS data base. Consistency tests
verify that file naming conventions, data formats, site codes, variable names,
reporting units, validation flags, and missing value codes are consistent with project
conventions. Discrepancies are reported to the measurement investigator for
remediation. When the received files are consistent, reasonability tests are applied
that include: 1) identification of data values outside of a specified minimum or
maximum value; 2) values that change by more than a specified amount from one
sample to the next; and 3) values that do not change over a specified period. Data
identified by these filters are individually examined and verified with the data
supplier. Obvious outliers (e.g. high solar radiation at midnight, 300 °C temperature)
are invalidated. Others may be invalidated or flagged based on the results of the
investigation. The bounds used in these tests will be determined in cooperation with
measurement investigators and network operators..
•
Level 2 (2): Level 2 data validation takes place after data from various measurement
methods have been assembled in the master database. Level 2 validation is the first
step in data analysis. Level 2A tests involve the testing of measurement assumptions
(e.g. internal nephelometer temperatures do not significantly exceed ambient
temperatures), comparisons of collocated measurements (e.g. filter and continuous
sulfate and absorption), and internal consistency tests (e.g. the sum of measured
aerosol species does not exceed measured mass concentrations). Level 2 tests also
involve the testing of measurement assumptions, comparisons of collocated
measurements, and internal consistency tests.
• Level 3 (3): Level 3 is applied during the model reconciliation process, when the results
from different modeling and data analysis approaches are compared with each other and
with measurements. The first assumption upon finding a measurement which is
inconsistent with physical expectations is that the unusual value is due to a measurement
error. If, upon tracing the path of the measurement, nothing unusual is found, the value
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can be assumed to be a valid result of an environmental cause. The Level 3 designation
is applied only to those variables that have undergone this re-examination after the
completion of data analysis and modeling. Level 3 validation continues for as long as the
database is maintained.
A higher validation level assigned to a data record indicates that those data have gone
through, and passed, a greater level of scrutiny than data at a lower level. All data in the CCOS
data set will achieve Level 1B status prior to use in data analysis and modeling. The validation
tests passed by Level 1B data are stringent by the standards of most air quality and
meteorological networks, and few changes are made in elevating the status of a data record from
Level 1B to Level 2. Since some analyses are applied to episodes rather than to all samples,
some data records in a file will achieve Level 2 designation while the remaining records will
remain at Level 1B. Only a few data records will be designated as Level 3 to identify that they
have undergone additional investigation. Data designated as Levels 2 or 3 validations are not
necessarily “better” than data designated at Level 1B. The level only signifies that they have
undergone additional scrutiny as a result of the tests described above.
C.6
Internet Server
CCOS/CRPAQS data and communications and will be received and made available on the
Internet server http://sparc2.baaqmd.gov/centralca/ maintained by the Bay Area Air Quality
Management District. This server currently contains project documentation and will be
developed to contain project status during field monitoring and project data as it becomes
available.
C.7
Directory Structure
Data and communications files are organized into several sub-directories within the
CCOS/CRPAQS server. Each of these contains additional sub-directories to further organize the
information. This organization will be transparent to most users who will access information
through links available through browsing software. All directories, with exception of the TEMP
directory and its sub-directories, have read-only privileges for most users to avoid the inadvertent
erasure of information. Files may be uploaded to investigator-specific sub-directories in the
TEMP directory for later placement in the appropriate read-only directory by the data manager.
These CCOS directories are:
• TEMP: This is a temporary location where files are uploaded by project participants
prior to their transfer to their designated parent directory. An e-mail message should be
sent to the data manager indicating the uploaded file name and its desired directory
location. The TEMP directory is also used to allow the transfer of non-archived scratch
files among project participants.
• REPORTS: This directory contains files related to project reports, memoranda, and
minutes. Sub-directories are:
-- PROGPLN for the latest draft of the program and management plans
-- MEMO for project memoranda
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-----
MINUTES for minutes of discussions
NOTICES for meeting notices
PRGREP for progress reports
RFP for final versions of requests for proposals
• RAWDATA: This directory contains data files in the form they are received from the
data source. These files are in several different formats, cover different time periods, and
do not necessarily conform to the units and variable naming conventions adopted for
CCOS field studies. To conserve disk space, these files are usually backed up onto
storage media after they have been processed; they can be re-loaded upon request to the
data manager. They are located in directories specific to each data supplier, as specified
in the next section.
This directory contains validated ambient measurement data in data
• DATA:
management formats that have been converted to common units and variable names.
• QA: This directory contains quality assurance results from audits, performance tests, and
collocated measurements.
• TABLES: This directory contains tables of processed results, including statistical
summaries of data, frequency distributions, and data capture rates. These are made
available via html links through the CCOS home page.
• FIGURES: This directory contains figures of processed results, including time series,
spatial isopleth plots, cumulative frequency plots, and scatterplot comparisons. These are
made available via html links through the CCOS home page.
• MAPS: This directory contains base maps of terrain, highways, population centers,
political boundaries, land use, and surface characteristics in formats that are deemed
useful for different analyses.
• UTILITIES: This directory contains software created for the project, software available
for distribution for which licenses have been obtained, and commonly used shareware.
These include data conversion, format conversion, file compression, and data display
programs. These are made available via html links through the CCOS home page.
Other directories and sub-directories are created as needed to organize the information
produced by CCOS field studies.
C.8
Data Processing
Data are submitted by each investigator using the defined variable naming conventions
and units All values are to be at Level 1A when submitted. These are passed through the Level
1B tests described above, and discrepancies are resolved with the measurement investigator and
corrected prior to designation as Level 1B. Data are added to the master data files as they are
received.
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Measurements from other ongoing networks described in Section 4 are acquired in
formats and units specific to those networks. Data processing functions are specific to these
networks and have been established during the compilation of the multi-year database for central
California. First, these files are manually edited, when needed, because they sometimes contain
minor variations in format that confound the data conversion programs. After editing, a networkspecific data conversion program reads the data and converts it to an xBase file format with a
single record for each measurement. Variable names in these intermediate file differ from those
in CCOS files by designating the unit used for each measurement in the specified network. For
example, field name “TA__F” indicates ambient temperature in degrees Fahrenheit as found in
the NWS database rather than the “TA” field name for ambient temperature in degrees Celsius
that is used in the CCOS data files.
The next step converts measurement units to the CCOS common units. Conversion
factors are accessed from a file that maps one unit into another based on the specification of the
input and output variable names. In addition to changing units, the conversion program maps
times, dates, missing values, and validation flags into the CCOS conventions described above.
A data validation log is kept to document all changes made to the data files, including
changes in the data validation level. This includes a record of the data changed, the reason for
the change, and the date of the change. All data as originally submitted to the database are retain
C-7
APPENDIX D
ELEVATED PLUME RESEARCH PLAN
Version 3 – 11/24/99
D.
Appendix D: Elevated Plume Research Plan
ELEVATED PLUME RESEARCH PLAN
Study Objective
An integrated assessment of ozone formation in central California is being performed under the
Central California Ozone Study (CCOS). The objective of the work proposed in this report is to
estimate the impact of power plant emissions on formation ozone in central California. An
important aspect of this study is to link the ground based air quality monitoring network of the
CCOS with aircraft based measurements of ozone and ozone precursors in and around power
plant plumes.
The data collected will be used in an analysis of the physical and chemical processes occurring
in the plume that contribute to ozone formation over central California. The issues to be resolved
include evaluating the effect of transport and mixing processes on ozone formation in and
around power plant plumes, the impact of power plant emissions on the relative importance of
VOCs and NOx emissions in limiting ozone production in central California and the evaluation
of air quality models and their parameterizations of the power plant plumes (such as plume in
grid).
Specifically the following four questions will be addressed in this study.
1. What are the rates of ozone formation and NOx conversion in power plant plumes?
2. What are the characteristics of dilution and mixing downwind of a plume for different
conditions of atmospheric stability?
3. What is the effect of plume-in-grid parameterizations (PiG) on model predictions and do
PiGs allow better fitting of the observations?
4. How well do alternative model treatments of power plant plumes perform?
5. Can the data serve as a basis for future plume model or parameterization development?
Scientific Background
The most significant emissions from power plants are nitrogen oxides (NOx = NO + NO2) and
particles from an air quality perspective. The nitrogen oxide emissions are important because
they lead to the formation of ozone when they react photochemically with volatile organic
compounds (VOC) emitted from biological and anthropogenic sources.
Initially the power plant plume contains nitric oxide, CO2 CO and little VOC. The combustion in
power plants is extremely efficient so they emit little VOC but the high temperatures that
contribute to the efficient combustion lead to the production of NOx. Under these conditions
there is no real production of ozone; ozone mixing ratios are controlled by the ozone/NOx
photochemical stationary state. In the absence of VOC reactions (1) and (3) control the
concentrations of ozone and NOx and their concentrations are described by equation (4).
NO2 + hν → NO + O(3P)
O(3P) + O2 + M → O3 + M
O3 + NO → NO2 + O2
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(2)
(3)
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[O3] = J[NO2] / k[NO]
(4)
where O(3P) are ground state oxygen atoms, hν represents ultraviolet radiation, M is either a
nitrogen or oxygen molecule, [O3], [NO2] and [NO] are the mixing ratios of ozone, NO2 and NO,
respectively, and J is the photolysis rate parameter of NO2 and k is the rate constant for the
reaction of O3 with NO.
If there is no CO or VOC there is no formation of ozone because reactions 1 through 3 only
recycle O3. To produce ozone the power plant plume must be diluted and mixed with VOC
containing air. Biogenic sources located along the trajectory of the plume may be one important
source of VOC to the plume. Another important source of VOC and other ozone precursors, such
as CO and PAN, is mixing with urban plumes.
Ozone is produced through reactions that cycle NO back to NO2 as the plume mixes with VOC
containing air. The process begins with the photolysis of ozone that produces an excited oxygen
1
atom, O( D).
O3 + hν → O( D) + O2
1
(5)
1
A fraction of theO( D) reacts with water to produce hydroxyl radicals (HO).
O( D) + H2O → 2 HO
1
(6)
Hydroxyl radicals react with CO and VOC (represented structurally as RH below) to produce
peroxy radicals (HO2 or RO2). Peroxy radicals react with NO to convert it back to NO2 which
photolyzes to produce additional O3.
CO + HO (+O2)
HO2 + NO
RH + HO (+O2)
→
→
→
CO2 + HO2
NO2 + HO
RO2 + H2O
(7)
(8)
(9)
RO2 + NO
→
NO2 + Organic Products
(10)
The organic products of reaction (10) may react with HO or photolyze to produce more ozone if
there is sufficient NOx (Seinfeld, 1986; Finlayson-Pitts and Pitts, 1986).
The ozone formation chain reactions are terminated under high NOx conditions when HO reacts
with NO2 to form nitric acid.
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HO + NO2 → HNO3
(11)
The chemistry of O3 formation is a highly nonlinear process (i.e. Dodge, 1984; Liu et al., 1987;
Lin et al., 1988). Figure 1 is a three dimensional plot of ozone concentrations produced from the
given initial concentrations of NO2 and VOC. The catalytic production efficiency of NOx is
defined as the ratio of the rate at which NO molecules are converted to NO2 to the total rate of
the NOx loss through conversion of NOx to nitric acid, organic nitrates or its loss through
deposition (Liu et al., 1987; Lin et al., 1988; Hov, 1989). The O3 production efficiency is
inversely related to the NOx concentration for most atmospheric conditions. The O3 production
efficiency varies over a wide range of values because in the continental boundary layer, NOx
concentrations vary over a range of three orders of magnitude but it is relatively independent of
the VOC concentrations, Figure 2. For these reasons the formation of ozone in a plume depends
on the characteristics of its dilution and the flights must be used to determine these
characteristics.
O3
200.0
100.0
3.00
2.00
1.00
LOG(NOx, ppb)
0.00
-1.00
-2.00
Ozone, ppb
4.00
3.00
2.00
1.00
LOG(VOC, ppbC)
0.00
-1.00
Figure 1. Typical ozone isopleth. The maximum ozone concentrations have been removed
to allow the structure at lower NOx and VOC concentrations to be seen.
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Ozone_Efficiency
120.0
80.0
40.0
4.0
3.0
3.0
2.0
1.0
1.0
LOG(NOx. ppb)
0.0
2.0
LOG(VOC, ppbC)
0.0
-1.0
-2.0
-1.0
Figure 2. Ozone efficiency in terms of moles of ozone produced per moles of NOx converted
to NOy.
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Reflecting the knowledge that ozone production in elevated point source plumes
containing high concentrations of NOx should be quite different than when plume
contents are diluted into typical grid cell volumes --because of vastly different VOC/NOx
ratios, some air quality models use a plume-in-grid (PiG) approach. In this approach, a
gaussian-shaped plume is generally simulated in a Lagrangian reference frame that
moves with the local wind vector, superimposed on the Eulerian reference frame of the
host grid model. After a period of time, the contents of the plume are released into the
grid cell or cells the plume occupies at the time of release, determined usually by the
relative sizes of the plume and grid cell. Confining the plume for this period of time is
thought to be a better way of simulating the plume chemistry and dispersion than
immediately diluting the plume contents into a grid cell volume upon emission. Some
drawbacks in the way this approach is currently implemented in air quality models are
that:
•
•
•
The schemes used to simulate plume dispersion are based on time-averaged
plumes rather than instantaneous plumes and thus are overly dispersive with
respect to simulating the chemistry accurately.
The effect of turbulence on chemistry is usually not simulated.
The effect of wind shear on plume separation is not simulated
These drawbacks are sufficient to compromise the realism of these simpler PiG
approaches.
Under EPRI sponsorship a more advanced approach has been developed. It relies on a
Lagrangian puff dispersion model (SCIPUFF -- Second order Closure Integrated PUFF
model) that uses a second order closure treatment of turbulent mixing. This gives it the
ability to simulate instantaneous concentrations of plume constituents. It also can
accommodate wind shear by separating puffs and can merge puffs when they converge.
The version of SCIPUFF that includes chemistry is called SCICHEM and can be used as
a stand-alone reactive plume model. Its parameterization of turbulent chemistry makes
simulations more realistic than simpler approaches, particularly within the first few
kilometers downwind of the smokestack, where plume confinement and turbulent
chemistry have the greatest effect on local as well as overall ozone production.
SCICHEM has been evaluated favorably against the extensive plume data acquired using
the TVA instrumented helicopter during the 1995 Nashville/Middle Tennessee Ozone
Study.
In an environment where the plume encounters concentration gradients in the background
air caused by heterogeneous sources and sinks, plume chemical evolution is best handled
by embedding SCICHEM in a gridded Eulerian air quality model. With this
arrangement, the Eulerian model supplies SCICHEM with time-and-space-varying
background concentrations and SCICHEM supplies the Eulerian model with a more
realistic sub-grid-scale treatment of plume chemistry and dispersion. Based on early
results, the outcome is less ozone produced per unit of NOx emitted from elevated point
sources than if the plume NOx is dispersed more rapidly, consistent with plume
observations during the Nashville study
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The first implementation of SCICHEM in a PiG was using MAQSIP as the host model.
This marriage of models is referred to as MAQSIP-APT -- for Advanced Plume
Treatment. SCICHEM has been implemented into MAQSIP as a flexibly interfaced
module so that it should be relatively straightforward to implement it in any other
modeling system. In fact, EPRI is currently implementing SCICHEM into Models3/CMAQ. SCICHEM could be embedded in whichever model(s) is (are) selected by the
Technical Committee.
If any PiG treatment is to be used to model elevated point sources in the central
California domain, the temporal and spatial evolution of one or more such plumes must
be sufficiently characterized to form an accurate conceptual model of plume evolution as
well as provide an observational data set against which to evaluate the PiG model.
Without such data, it would be very difficult to demonstrate whether any differences seen
among different plume treatments are real and if they exert a real and significant impact
on ozone production from elevated point source NOx emissions. A good candidate for
such a study would be the Pittsburg electric generating station. A second candidate is the
electric generating station at Moss Landing.
Measurement Plan
Our approach relies on the use of the Tennessee Valley Authority Bell 205 helicopter, Figure 3.
The low speed and high maneuverability of a helicopter makes it an ideal platform for this study.
The Tennessee Valley Authority Bell 205 helicopter is equipped with instrumentation for the
measurement of ozone, carbon monoxide, sulfur dioxide, nitric oxide, nitrogen dioxide, total
*
nitrogen oxides (NOy), NO y (NOy - HNO3), canister measurements for VOC, aerosol particle size
and distribution and meteorological parameters. It will be used to characterize the VOC
concentrations at selected locations in the background air. A detailed list of the instrumentation
is given in Table 1.
This aircraft would make a series of plume transects at successively greater distances from the
stack, timed to sample approximately the same air parcel in a quasi-Lagrangian experiment. A
series of transects will made under a variety of meteorological conditions.
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Figure 3. Bell 205 Helicopter: TVA Environmental Research Center
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Table 1.
Flight characteristics and instrumentation of Bell 205 Helicopter:
TVA Environmental Research Center
Endurance
2 hrs
Payload
500 kg
Ceiling
2.5 km
Research Speed
40-50 m/s
Aircraft Instrument Package for the TVA Bell 205 Helicopter
Parameter
Time Resolution Method
Det. Limit
Ozone (O3)
1s
NO
2 ppb
Chemiluminescence
Carbon Monoxide
NDIR or HgO
(CO)
reduction
Sulfur Dioxide (SO2)
5s
UV Pulsed
0.5 ppb
Fluorescence
1 ppb
Nitric Oxide (NO)
1s
NO/O3
Chemiluminescence
Nitrogen Dioxide
5s
Photolysis, NO/O3
1 ppb
(NO2)
Chem.
Total Nitrogen
1s
Au Converter, NO/O3
1 ppb
Oxides (NOy)
Chem.
NOy*
1s
NOy detection +
1 ppb
Nylasorb Filter
Canister VOCs
1 min
Canister Sampling,
GC/FID
bscat
5s
Nephelometer
< 10-6 m-1
Aerosol Size
1s
PCASP
(0.17 - 3µm)
Distribution
Particle Composition
variable
Filter Pack, IC
analysis
Particle Composition
variable
Anderson Cascade
by Size
Impactor
Air Temperature
5s
Platinum Thermistor
Dewpoint
5s
Capacitance Sensor
Altitude
5s
Barometric
Position
5s
GPS
Air Speed
5s
Pitot- Static Pressure
2 m/s
Heading
5s
Flux Gate Compass
0.5 deg.
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The instrumentation given in Table 1 was used in flights made for the Southern Oxidant Study
(SOS). The minimum research speed of 40 m/s must be maintained to avoid contamination
problems associated with emissions form the helicopter and the downwash of it propeller.
Although the 1999 SOS Field Study Science Plan for Nashville indicated that particle
measurements, CO, CO2 and SO2 could be used as a tracers of opportunity for power plant
plumes it appears that these tracers are not feasible because of their low concentrations in the
power plant plumes of central California. However, high measurements of CO might be used to
identify those urban plumes that may mix with the power plant plumes.
It would be highly desirable if a filter radiometer for the measurement of the photolysis rate
parameter of NO2 (JNO2) could be added to the instrumentation. Measurement of JNO2 would allow
a more direct calculation of the ozone - NOx photostationary state as described above. Without
this measurement JNO2 must be calculated from a radiative transfer model. Given that ozone
formation occurs only during the daytime, safety considerations and the limited budget the plume
measurement flights should be restricted to the daylight hours.
The O3 and NOx measurements should be sufficient to track the plume. A SODAR in the vicinity
of the power plant stack or aircraft based LIDAR system or the release of tracers is highly
desirable for a complete study. Since a LIDAR system will to expensive for the CCOS study a
radar/RASS profiler will be located in the vicinity of the power plant stacks selected for study to
ensure that the local 3D winds are well defined for accurate model simulation during the crucial
early stages of plume dispersion. The radar/RASS profiler will be moved as necessary for the
measurements planned for the power plants at Moss Landing and at Pittsburgh.
It may be possible that the measurement of aerosols by the TVA helicopter will allow some
evaluation of the plume in grid models for their ability to describe aerosols but aerosol
concentrations may be too low in the plumes measured by CCOS.
It is crucial that the helicopter measure background concentrations to evaluate the plume in grid
model. Karamchandani and Seigneur (1999) have shown that indicates that the measurement of
background concentrations of O3, VOC, and PAN are crucial for the correct representation of the
formation of nitrate and sulfates inside power plant plumes. They are also important to simulate
ozone formation inside power plant plumes and for the evaluation of the host grid model.
Another reason to measure the background O3 and NOx is that the formation of ozone in the
plume has been characterized by the quantity “excess ozone”. Excess ozone is defined as the
difference between the ambient ozone and the ozone within the plume (Luria et al., 1999).
Therefore it will be necessary to sample the air upwind of the power plant stack and to sample
the air surrounding the plume.
Both horizontal flight paths made at constant altitude and “vertical” plume characterization
flights are needed to characterize the plume. Figure 4. The horizontal flights start with
measurements made upwind of the stack. Next the aircraft flies across the plume as close to the
stack as possible within the limits imposed by safety. The aircraft flies out of the plume and then
returns to fly through the plume at a greater distance than the first path. This flight path is
continued as long as a plume can be identified by NOx and aerosol measurements.
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The flight strategy is similar for the “vertical” plume flights. The flights should begin upwind of
the stack. Next the aircraft should fly down as near as possible to the stack and through the
plume. When the aircraft reaches the bottom of its trajectory it should fly upward through the
plume. As with the horizontal flights this flight path is continued as long as a plume can be
identified by NOx and aerosol measurements.
During the 1995 SOS Nashville study of power plant plume study it was possible to follow the
plume for about 5 to 6 hours or between 50 and 100 kilometers downwind of the stack (Luria et
al., 1999). The power plants that affect central California have NOx photostationary state as
described above. Without this measurement JNO2 must be ca emission rates that are about a factor
of 10 lower than those around Nashville. These lower emission rates will reduce the distance
over which the plumes can be tracked. However, since lower NOx concentrations may lead to
greater ozone production efficiencies enhanced ozone mixing ratios might be observed relatively
nearer the stack for the central California power plant plumes.
Luria et al. (1999) also showed the importance of making measurements of VOC during the
plume measurements. About 8 measurements were made during each flight. One of the VOC
canister samples should be collected upwind of the stack, 6 should be made within the plume and
1 sample should be collected outside the stack but downwind of the stack.
The flights need to be coordinated with the ground-based CCOS air quality monitoring stations.
One of the most important uses of the stations will be to provide the meteorological conditions to
help determine the aircraft flight plans. The meteorological conditions and the air quality
measurements provided by the ground stations will be extremely important for the interpretation
of the aircraft measurements. While it is clear that measurements should be made during
episodes of high ozone measurements should also be made during periods of average or low
ozone. A valid model must be able to predict both peak and average ozone concentrations.
The power plant plumes to be investigated in California include those from Pittsburgh and the
Moss Landing. The Pittsburgh, CA, power plant plume has the advantage in that it often interacts
strongly with the urban plume from the San Francisco Bay area and it would be expected to have
a significant impact on air quality. Observations of Pittsburgh plume would allow its
contributions to formation of ozone in the urban plume to be evaluated. On the other hand since
the Pittsburgh power plant plume is in a much more polluted area it would be harder to follow.
Figure 5 shows estimated NOx concentrations for the plume from the Pittsburgh electric power
generating station. The NOx concentrations were calculated from a simple dispersion model.
Assuming an ambient background NOx concentration of 15 ppb it should be possible to follow
the plume for at least 15 km under the most stable conditions. The calculation suggests that 20 to
30 km will be more typical.
Alternatively the Moss Landing power plant would be easier to follow because it is in proximity
to the coast with cleaner and more stable air conditions. However given its location the plume
will contribute less to ozone formation in central California than the Pittsburgh plume. Given the
choice between Pittsburgh and the Moss Landing more flight hours should be devoted to the
Pittsburgh power plant plume.
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To get a more complete description of the effect of meteorological conditions on the plume it
will be necessary to make some flights during the morning hours when the mixing height is low
and rising. To capture the effect of the plume on the photochemistry of ozone formation it will be
necessary to make flights during the afternoon. A reasonable split between the morning and
afternoon flight hours would be a ratio of one to two.
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Stack Location
Power Plant Plume
Flight Path
Horizontal Plume Characterization Flights
"Vertical" Plume Characterization Flights
z
Figure 4. Proposed flight plans for plume measurement experiments.
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100
Stability Class A
Stability Class B
Stability Class C
NOx (ppb as NO)
75
Stability Class D
Stability Class E
Stability Class F
50
25
0
0
10
20
30
40
50
km
Figure 5.
Estimated NOx concentrations for the plume from the Pittsburgh electric power
generating station. The NOx concentrations were calculated from a simple
dispersion model.
.
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Data Analysis and Model Evaluation
Models use plume-in-grid to represent the chemistry and dispersion of large point source plumes.
Typically the individual sub-grid scale plumes are simulated in a Lagrangian mode. The plumes
are assumed to have a Gaussian distribution that can be treated analytically and these plumes
may extend 10 to 20 km from the point source. The chemistry is often simplified to account only
for the high NOx concentrations found within the plumes and the effect of NOx on ozone
concentrations while ignoring VOC chemistry. The plumes disperse and undergo chemical
reaction until the spatial extent of the plume and the pollutant concentrations reach levels that
can be adequately represented within a 3-d model grid. The size of the plumes relative to the size
of the grid cell is one criteria while another criteria is the age of the plume for terminating the
plume and mixing its contents in to the regular grid.
It is not clear that the treatment of plumes by state-of-the-art models is adequate. Aircraft
measurements of NOx, ozone and VOC concentrations made in plumes are required to test the
validity of the treatment of plume dispersion and chemistry and the procedures for terminating
the plume in to the regular model grid by plume-in-grid parameterizations.
Model simulations are required to compare with the measurements to evaluate the models. The
reliability of model outputs is assessed through operational and diagnostic evaluations and
application of alternative diagnostic tools. Operational evaluations consist of comparing
concentration estimates from the model to ambient measurements. The key question in an
operational model evaluation is to determine the extent of agreement between simulated and
measured concentrations of ozone and its precursors? The measured and simulated ozone and its
precursor concentrations should agree in their spatial extent and in their timing. Typical statistics
for model evaluation: the comparisons of predicted and observed 1-hr observed ozone
concentrations, comparison of 90th percentile concentrations, mean bias (ppb) and mean
normalized bias (%) and the mean error (ppb) and mean normalized error (%) (Lurmann et al.,
1998).
Diagnostic evaluations are required to determine if the model is estimating ozone concentrations
for the right reasons. The emissions, chemistry and transport are assessed to determine if these
are treated correctly within the model. The emissions may be assessed through process analysis
and mass balance analysis. The chemistry may be assessed through the measurement of predicted
secondary chemical products. The transport might be evaluated through comparisons of the
spatial and vertical distributions of ozone and its precursors. This broad task also involves
reconciliation of data analysis and observation-based results with modeling results. It includes
the evaluation of modeling uncertainties, processes, and the assumptions, and their effect on
observed differences among model results, measurements, and data analysis results. A key
finding of a diagnostic evaluation should be to determine the physical and chemical reasons for
the concentration differences between those predicted by models and the measurements. It would
also be highly desirable if the likely model bias induced by a model’s deficiencies could be
identified.
Particular attention needs to be devoted to the Lagrangian puff dispersion models, SCIPUFF and
SCICHEM, that were developed under EPRI sponsorship. These models rely on a second order
closure treatment of turbulent mixing and are expected to provide more realistic simulations than
simpler approaches. The improvement may be greatest nearest the stack. It will be important to
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Version 3 – 11/24/99
compare the evaluation of SCICHEM with CCOS data to its evaluation made with data from the
1995 Nashville/Middle Tennessee Ozone Study.
References
Dickerson, The Impact of Aerosols on Solar Ultraviolet Radiation and Photochemical Smog"
Science. Vol. 278, 1997.
Hov Ø., Changes in tropospheric ozone: A simple model experiment. In: Bojkov RD and Fabian
P (eds) Ozone in the Atmosphere, Proceedings of the Quadrennial Ozone Symposium 1988
and Tropospheric Ozone Workshop. Deepak, Hampton, Virginia, pp 557-560, 1989.
Karamchandani and Seigneur, Simulation of Sulfate and Nitrate Chemistry in Power Plant
Plumes. Journal of the A&WMA 49, 175-181, 1999.
Lin X, Trainer M, Liu SC On the nonlinearity of the tropospheric ozone production. Journal
Geophysical Research 93:15879-15888, 1988.
Liu SC, Trainer M, Fehsenfeld FC, Parrish DD, Williams EJ, Fahey DW, Hübler G, Murphy PC
Ozone production in the rural troposphere and the implications for regional and global ozone
distributions. Journal Geophysical Research 92:4191-4207, 1987.
Lurmann, F.W. and N. Kumar, R. Londergan, G. Moore, Evaluation of the UAM-V Model
Performance in the Northeast Region for OTAG Episodes, Ozone Transport Assessment
Group, 1998.
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