Final Report

Final Report
Texas Environmental Research Consortium Project H-3
Emissions Inventory Associated with Forest, Grassland, and Agricultural Burning
during the Texas Air Quality Study
Submitted by:
David Allen
Anil Katamreddy
Victoria Junquera
Jan Decabooter
Center for Energy and Environmental Resources
The University of Texas
Austin, Texas
Submitted to:
Anne Brun
Texas Environmental Research Consortium
FINAL REPORT
December 15, 2002
EXECUTIVE SUMMARY
An inventory of emissions of total particulate matter (PM), particulate matter smaller
than 10 microns in aerodynamic diameter (PM10), particulate matter smaller than 2.5
microns in aerodynamic diameter (PM2.5), nitrogen oxides (NOX), non-methane
hydrocarbons (NMHC), carbon monoxide (CO), carbon dioxide (CO2), methane (CH4)
and ammonia (NH3) from outdoor fires was performed for the period of August 1 to
September 30, 2000. The domain over which fire emissions were estimated encompassed
multiple states and was centered on the Houston/Galveston-Beaumont/Port Arthur
(HGBPA) region.
The methodology employed in this study was based on the work of Dennis (2002).
Information on fire location and acreage burned was gathered from various state and
federal agencies for each day of the study period. Fuel loadings (in tons/acre) were
calculated for each fire, based on land use data specific for the state of Texas, as well as
comprehensive national sources of data. Emission factors (in pollutant emission rate per
ton of fuel burned) were based on literature data.
Wildfires were the main source of emissions during the study period. Agricultural, slash
and prescribed burns were found to contribute relatively little to emissions during the
study period, due to severe drought conditions during the summer of 2000. The emissions
exhibited considerable variability. On a day of moderate fire activity, such as August 31,
emissions from wildfires were estimated to be 1300, 92, 120, and 11 short tons of CO,
NMHC, PM2.5, and NOX, respectively. On one of the days with the highest fire activity,
September 4, wildfire emissions were estimated to be 3709, 255, 334, and 48 short tons
of CO, NMHC, PM2.5, and NOX, respectively. These emissions were significant
compared to other emission sources in the region. For example, emissions of CO,
NMHC from wildfires exceeded emissions from light duty gasoline vehicles in the 8county Houston/Galveston area on several days between August 31 and September 8,
2000.
The ultimate goal of this study was to generate an emissions input file suitable for use in
the photochemical model used by the State of Texas to evaluate regulatory policies (the
Comprehensive Air Quality Model, with extensions, CAMx). For the model-ready input
file, NMHC emissions were classified according to Carbon Bond IV (CB-IV) reaction
classes, and the plume rises of the fires were based on calculations using the CALPUFF
and FIREPLUME models.
ii
Table of Contents
1. Introduction..................................................................................................................... 1
2. Collection and Assembly of Fire History Data............................................................... 3
2.1 Overview of Approach.............................................................................................. 3
2.2 Wildfires ................................................................................................................... 3
2.3 Agricultural, Prescribed Range, and Slash Burning ................................................. 5
2.3.1 Agricultural Burns ............................................................................................. 5
2.3.2 Prescribed Range Burning ................................................................................. 6
2.3.3 Slash Burning..................................................................................................... 6
2.3.3.1 Logging debris ............................................................................................ 7
2.3.3.2 Land Clearing Debris.................................................................................. 7
3. Estimation of Fuel Loading and Consumption Factors for Wildfires............................. 7
3.1 Overview of Approach.............................................................................................. 7
3.2 State of Texas............................................................................................................ 8
3.3 Rest of the Modeling Domain................................................................................. 10
4. Estimation of Emission Factors and Composite Emission Factors for Wildfires......... 12
4.1 Overview of Approach............................................................................................ 12
4.2 Estimation of Emission Factors .............................................................................. 12
4.3 Calculation of Composite Emission Factors for the State of Texas ....................... 13
4.4 Calculation of Composite Emission Factors for the Rest of the Modeling Domain
....................................................................................................................................... 14
4.4.1 Use of FOFEM Emission Factors and NFDRS Fuel Loading Factors ............ 14
4.4.2 Spatial Correlation of NFDRS and FOFEM Fuel Models............................... 15
5. Results........................................................................................................................... 16
5.1 Overview................................................................................................................. 16
5.2 Total Acreage Burned in Texas in 2000 ................................................................. 17
5.3 Total Acreage Burned in Texas during August to September, 2000 ...................... 17
5.4 Total Wildfire Acreage Burned in HGBPA Subdomain......................................... 17
5.5 Wildfire Emissions in HGBPA Subdomain............................................................ 18
5.6 Observational Evidence: TEOM Results ................................................................ 21
6. Preparation of a CAMx-Ready Input File..................................................................... 24
6.1 Overview................................................................................................................. 24
6.2 AFS File .................................................................................................................. 24
6.3 Chemical Speciation of NMHCs ............................................................................ 24
6.4 Calculating fire plume rise...................................................................................... 27
6.4.1 Existing Plume Rise Models............................................................................ 28
6.4.1.1 FIREPLUME model ................................................................................. 28
6.4.1.2 CALPUFF Model ..................................................................................... 30
6.4.1.3 Obtaining Input Data................................................................................. 32
6.4.2 Using Models ................................................................................................... 35
6.4.2.1 FIREPLUME Results................................................................................ 36
6.4.2.2 CALPUFF Calculations ............................................................................ 40
6.4.2.3 Comparison between FIREPLUME and CALPUFF results..................... 43
6.4.2.4 Conclusions and Recommendations ......................................................... 44
7. References..................................................................................................................... 45
iii
Appendix A: Land Cover Code Descriptions ................................................................... 47
Appendix B: Daily Plots of Fires and 32-Hour Wind Back-Trajectories........................ 49
Appendix B-1: Daily Plots of Fires onto the LULC Map............................................ 49
Appendix B-2: Daily Plots of Fires and 32-Hour Wind Back-Trajectories............... 112
Appendix C: Contact Summary ...................................................................................... 172
iv
List of Tables
Table 1: Fuel consumption values (ton/acre) assigned to fuel categories and wildland
cover types. ................................................................................................................. 9
Table 2: Dead fuel categories as the time scale at which moisture content varies and by
diameter (Dennis, 2000).............................................................................................. 9
Table 3: Fuel loading values (ton/acre) assigned to fuel categories and vegetation cover
types in the NFDRS. ................................................................................................. 11
Table 4: Relationship between NFDRS and FOFEM fuel categories. ............................. 12
Table 5: Emission factors (EFs) (lb/ton) for wildfire burns. These factors were calculated
under dry conditions for each fuel component using combustion efficiency
information from FOFEM 4.0 and EF algorithms developed by Ward et al. (1993).
EFs for NOX and NH3 were calculated with relations by Dignon and Penner (1991)
and Yokelson et al. (1997). ....................................................................................... 13
Table 6: Composite emission factors (CEFs) (lb/acre) assigned to wildland cover types.
CEFs were calculated from the dry conditions and emission efficiencies given by the
FOFEM program and FOFEM fuel consumption values (Tables 1 and 5). ............. 14
Table 7: Calculated composite emission factors (lb/acre) assigned to wildland cover types
for the domain excluding Texas. The emission factors were calculated from the dry
conditions and emission efficiencies given by the FOFEM program and NFDRS fuel
loading values (Table 3)............................................................................................ 15
Table 8: Mass Percents (%) of Total gaseous NMHC of different chemical species in
Loblolly pine............................................................................................................. 25
Table 9: CB-IV Speciation Profile for NMHCs .............................................................. 27
Table 10: Input Data for FIREPLUME and CALPUFF Models ...................................... 32
Table 11: Access database of selected Forest Fires .......................................................... 35
Table A- 1: Description of FOFEM land cover codes..................................................... 47
Table A- 2: Description of NFDRS LULC codes............................................................. 48
Table B- 1: Description of land use categories and associated composite emission factors
for PM2.5, NOX, and NMHC. .................................................................................... 50
Table C- 1: Contact Information..................................................................................... 172
v
List of Figures
Figure 1: Details of CAMx Modeling Domain for the Houston-Galveston modeling
episode, August 25 – September 1, 2000. Regional Domain (dark blue), East Texas
Subdomain (green) HGBPA Subdomain (red), and HG Subdomain (light blue)....... 2
Figure 2: Acreage burned in different types of fires in Texas in August – September
2000........................................................................................................................... 17
Figure 3: Acres burned in wildfires in the HGBPA subdomain. During August –
September, 2000. ...................................................................................................... 18
Figure 4: Emissions of CO, NMHC, PM2.5, and NOX from fires in the HGBPA
Subdomain during August – September, 2000. ........................................................ 20
Figure 5: Comparison of LDGV emissions of VOC and NOX in the 8-county HG area
(first four columns) with wildfire emissions of NMHC and NOX in the HGBPA
domain (last four columns). ...................................................................................... 20
Figure 6: Details of TEOM Domain (black line), TEOM monitors (colored points), and
HGBPA Subdomain (red line). ................................................................................. 21
Figure 7: Time series of TEOM monitors PM2.5 measurements for August 22 September 7, 2000. ................................................................................................... 22
Figure 8: (a) Interpolated TEOM monitor data of PM2.5 concentrations for September 4,
2000 at 5:00 p.m. within the TEOM Domain. (b) Fires (red points) and 32-hour
wind-back trajectories starting at 5:00 p.m. for September 4, 2000. The different
back-trajectory colors represent the wind’s height above the ground at the starting
point (La Porte): green: 10 meters above ground, brown: 500 m, blue: 1000 m.
However, thorough wind mixing occurred on that day, so that, except at the starting
point, the different wind trajectories cannot be allocated onto a vertical layer. ....... 23
Figure 9: Plume rise calculations with velocity data of different CAMx layers ............. 37
Figure 10: Plume rise with vertical CAMx structure (with wind velocity-layer 3)......... 38
Figure 11: Plume rise of fire ID 1033 and wind velocity on Aug 30, 2000 .................... 39
Figure 12: Plume rise calculations with different fire temperatures. Fire ID 1033, 1085,
and 1094 are the fire events occurred in Aug 30, Aug 31, and Sep 1, respectively. 41
Figure 13: CALPUFF Plume rise calculations with CAMx vertical layers..................... 42
Figure 14 (a) Plume rise for low wind day showing a gradual change during the day;
(b) Plume rise on a near neutral atmospheric stability in the afternoon. .................. 43
Figure B- 1: Legend. The red dots indicate the size of the fire. The colors correspond to
vegetation cover codes. Table B-1 contains the description of the cover codes...... 49
Figure B- 2: Legend. The values in meters (green, brown, and blue dots) correspond to
the height above the ground of the wind trajectory at the starting hour (La Porte
site). The dots in red correspond to different fire sizes.......................................... 112
vi
1. Introduction
Outdoor fires, including wildfires, prescribed rangeland burns, slash burns, and
agricultural field burns, can emit substantial amounts of particulate matter, carbon
monoxide, hydrocarbons, nitrogen oxides, and ammonia into the atmosphere. The
purpose of this project is to calculate the magnitude of emissions and the spatial and
temporal distribution of emissions from outdoor fires for the period of the Texas Air
Quality Study (TexAQS), August 1 to September 30, 2000. Emissions were estimated
over the Super-COAST (Coastal Oxidant Assessment for Southeast Texas) CAMx
(Comprehensive Air Quality Model with Extensions) Regional Domain, as well as for the
Houston/Galveston-Beaumont/Port Arthur (HGBPA) Subdomain, which is embedded in
the Super-Coast domain. The modeling domain is shown in Figure 1.
The data required to calculate emissions of CO, CO2, CH4, non-methane hydrocarbons
(NMHC), particulate matter (PM), PM less than 2.5 micrometers in diameter (PM2.5), PM
less than 10 micrometers in diameter (PM10), ammonia (NH3), and nitrogen oxides (NOX)
are as follows:
• The acreage burned (acres/day)
• The location and duration of the fire
• The fuel consumption factors of the burned vegetation (tons/acre)
• The emission factors for each pollutant and fuel type (lbs/ton), and
• The emission efficiency (unitless).
The methods used to estimate acreage burned, fire location and duration, fuel loadings,
and emission factors are described in the next three sections. The final sections of the
report describe the results and methods used to develop CAMx input files.
1
The CAMx modeling domain is defined with the Lambert Conformal Conic map
projection:
First True Latitude (Alpha)
30°N
Second True Latitude (Beta)
60°N
Central Longitude (Gamma)
100°W
Projection Origin
(100°W, 40°N)
Spheroid
Perfect Sphere, Radius = 3670 km
Domain Name
Regional Domain
East Texas Subdomain
HGBPA Subdomain
HG Subdomain
Range (km)
Easting
(-108,1512)
(-12,1056)
(356,688)
(431,505)
Northing
(-1584,72)
(-1488,-420)
(-1228,-968)
(-1153,-1079)
Number of Cells
Cell Size (km)
Easting
Northing
Easting
Northing
45
89
83
74
46
89
65
74
36
12
4
1
36
12
4
1
Figure 1: Details of CAMx Modeling Domain for the Houston-Galveston modeling episode, August
25 – September 1, 2000. Regional Domain (dark blue), East Texas Subdomain (green) HGBPA
Subdomain (red), and HG Subdomain (light blue).
2
2. Collection and Assembly of Fire History Data
2.1 Overview of Approach
To create a spatially and temporally resolved inventory of outdoor fire emissions over the
Super-COAST CAMx modeling domain, data on fire location and burned acreage were
collected from several data sources for each fire, on a daily basis for the period of August
1, 2000-September 30, 2000. The data sources for wildfire activity data include the
National Interagency Fire Management Integrated Database (NIFMID), the Texas
Interagency Coordination Center (TICC), the Louisiana Interagency Coordination Center,
the United States Department of Agriculture (USDA) Forest Service, and the Oklahoma
Department of Agriculture, Food and Forestry. After contacting several agencies, it was
found that there are no reliable data sources for prescribed rangeland, slash, and
agricultural burns. Thus, data for this type of fire will be extracted from the work of
Dennis (2000).
2.2 Wildfires
Due to the number and extent of wildfires during the summer of 2000, the collection of
wildfire data was the most difficult and important task in this inventory development.
Wildfire activity data were collected for the period of August 1, 2000 – September 30,
2000 over the Super-COAST CAMx modeling domain from the following data sources:
a) National Interagency Fire Management Integrated Database (NIFMID): This database
is maintained by the National Interagency Coordination Center (NICC) and captures
information pertaining to large wildfire incidents (i.e., more than 100 acres in timber or
more than 300 acres in grass/shrub fuel types) occurring on federal and private ownership
lands. Several state forest departments and federal agencies, including the United States
Department of Agriculture (USDA) Forest Service, the Fish & Wildlife Service (FWS),
the National Park Service (NPS), the Bureau of Land Management (BLM), the Bureau of
Indian Affairs (BIA), and the Department of Defense (DOD), report fire incident
information to the NICC. NIFMID information for the period of interest was
downloaded in a MS Access format from http://famweb.nwcg.gov. Most of the wildfire
records include information on fire location in latitude/longitude coordinates, the date on
which the fire occurred, the burned area in acres, the fire incident name, and the type of
vegetation burned. The fire records with missing latitude/longitude coordinates included
information on the closest city to the fire location.
b) Texas Interagency Coordination Center (TICC): In the TICC, a Texas Forest Service
(TFS) representative maintains the wildfire records for state and private lands and a
USDA Forest Service representative maintains the wildfire records for federal ownership
lands. The records of wildfires that occurred on state, private and federal ownership
lands were obtained in spreadsheet formats. Most of the wildfire records include
information on the fire location, the date on which fire occurred, the burned area in acres,
the fire incident name, the date on which the fire was controlled, and the name of the
3
county where the fire was located. In this database, the fire location was reported in the
TFS Block & Grid format for wildfires in East Texas and the county in which the fire had
occurred was reported for the fires in West Texas. The location of the fires in East Texas
was converted to latitude/lomgitude coordinates using TFS conversion software and a
TFS Block & Grid Map.
c) Louisiana Interagency Coordination Center: The records of wildfires that occurred on
Kisatchie National Forest lands (federal ownership) in Louisiana were obtained from this
agency. Information on other federal lands was not available. The records include
information on the fire location in Township/Range/Section format, the date on which
fire occurred, the burned area in acres, and the district information. The fire location data
were manually converted to latitude/longitude coordinates using hard copy maps of
Louisiana with Township/Range and latitude/longitude coordinates.
d) USDA Forest Service: The records of wildfires that occurred on federal ownership
lands in the United States for the year 2000 were obtained from the USDA Forest
Service. The records include information on the fire location in latitude/longitude
coordinates, the date on which the fire occurred, the burned acreage in acres, the type of
vegetation burned, and the state in which the fire occurred.
e) Oklahoma Department of Agriculture, Food & Forestry: The records of wildfires that
occurred on state and private lands in the state of Oklahoma were obtained. The records
include information on the fire location in Township/Range/Section format, the date on
which fire occurred, the burned area in acres, the type of vegetation burned, and the
county in which the fire occurred. The fire location data in Township/Range/Section
format were converted to latitude/longitude coordinates using TRS2LL version
11/26/2001. This program was downloaded from http://www.esg.montana.edu/gl/trsdata.html and converts between Township/Range/Section data and latitude/longitude
coordinates. At present this program covers 17 states (AR, AZ, CA, CO, ID, KS, MT,
ND, NE, NM, NV, OK, OR, SD, UT, WA and WY).
All the fire records in the above data sources were integrated into a single database in
spreadsheet format and duplicated fires were identified through common parameters,
such as the fire incident name, the fire location, and the reporting agency. Most of the
duplicate fire records in NIFMID and TFS databases were identified with the fire incident
name. Only fires greater than 10 acres were analyzed for duplications in this study, since
most of the smaller fires lacked complete information and common parameters in the
databases. It was assumed that the duplicated fires under 10 acres would not add
significantly to the error in the calculation of emissions.
The databases used in this study have varying degrees of completeness. The most
complete databases were the TFS database, the NIFMID, and USDA Forest Service
database. In the TFS database, fire records include additional information on the duration
of the burning. For the fires with this information, the burned acreage was uniformly
distributed over the duration of burning. The USDA Forest Service database reports
wildfire incidents occurring on federal ownership lands in all the states in the Super-
4
COAST CAMx modeling domain. The NIFMID database is a complete database for the
large wildfire incidents occurring on federal and private ownership lands in all the states
of the Regional Domain. The least complete database was collected from the Oklahoma
Dept. of Agriculture, Food & Forestry. This database provides the records for wildfire
incidents occurring in eastern Oklahoma private ownership lands only.
2.3 Agricultural, Prescribed Range, and Slash Burning
Agricultural, prescribed range, and slash burns were found not to contribute substantially
to emissions and thus they were not used in the final fire inventory and emissions
calculations. Detailed below is a description of how the acreage burned was estimated for
these types of fires.
2.3.1 Agricultural Burns
County agricultural extension agents, TNRCC field investigators and the Texas Dept. of
Agriculture were surveyed by phone to determine the extent to which crop residue
burning is currently practiced in Texas. Questions about the extent and frequency of
cropland burning were included in an email survey of 58 randomly selected Agricultural
Extension Agents representative of the 12 Extension Districts of Texas. The response
rate to the survey was only 14%; however, the responses were consistent. Responses
indicated that very little (<5%) of cropland is commonly burned after harvest and that the
practice was probably more rare in the August/September 2000 period due to drought
conditions. Responses also indicated that, regardless of weather conditions, burning of
crop residues is losing favor among farmers because crop residues aid moisture retention
in soils. One agricultural extension agent noted that crop residue is only burned in his
area when the residue is excessively dense; this is not a condition that occurs in drought
season like summer 2000. No reporting of this type of burn is required and no records of
such burns were found.
Responses to surveys and phone calls were consistent with previous findings (Dennis,
2000) about the extent of cropland burning activities in 1996-1997. Some Agricultural
Extension Agents indicated that the practice has become less common in the last decade,
but all of these statements were anecdotal. No agents indicated that burning crop residue
is becoming more common or favored among farmers.
Dennis (2000) produced estimations of emissions from cropland burning by using reports
from local officials, known crop distributions, and fuel loading factors. The results of
these calculations are emissions values for each county on a monthly basis. Though the
estimates are not point specific or date specific, they may represent the best resolution
currently possible given the paucity of specific records and they were used this in this
study to predict emissions from these types of fires. Though anecdotal reports suggest
that cropland burning is gradually declining, there is not enough information to assign a
value to this trend.
5
2.3.2 Prescribed Range Burning
Researchers associated with Texas A&M University who study prescribed rangeland
burns were contacted concerning the extent of the practice of burning rangelands. Dr.
Butch Taylor, of the TAMU Agricultural Research Station Sonora, who leads the
Edwards Plateau Prescribed Burning Association, reports that, though there is increasing
interest in the practice, drought conditions have made it unfeasible for the past few years.
He also indicated that much of the increased interest in prescribed burning has been in
Central Texas, where fire is used to control Ashe Juniper (cedar) populations.
Conversations with Dr. Taylor and Donnie Frels of the Kerr Wildlife Management Area
(which researches prescribed burning of rangeland) indicate that hot summer burns that
could have impact on the TexAQS data should not be performed in conditions like those
in August and September of 2000. These statements are consistent with published
rangeland burning practices found at http://www.rw.ttu.edu/fec/burnweather.htm and
http://texnat.tamu.edu/ranchref/guide/rwabc.htm.
Questions about the extent and frequency of rangeland burning were included in an email
survey of 58 randomly selected Agricultural Extension Agents representative of the 12
Extension Districts of Texas. The response rate to the survey was only 14%; however,
the responses about frequency and extent of rangeland burning were consistent with
statements by Dr.Taylor and Mr. Frels.
The only finding in this survey that was not included in Dennis (2000) is that coastal
marsh grasses are sometimes burned to increase the palatable grasses for cattle.
According to Kelly Boult, Agricultural Extension Agent for Jefferson Co., the marsh
grasses that are burned this way grow within 15 miles of the coast. Local agricultural
extension agents report that the practice usually takes place in winter and that the practice
is uncommon; however, since ranchers are not required to report these fires, no records
are available for burning of coastal marsh grasses.
Dennis (2000) produced estimations of emissions from rangeland burning in the same
way that she estimated cropland burning, by using reports from local officials, known
land use patterns and fuel loading factors. The results of these calculations are emissions
values for each county on a monthly basis. Though the estimates are not point specific or
date specific, they may represent the best resolution currently possible given the paucity
of specific records and they were used this in this study to predict emissions from these
types of fires. Though anecdotal reports suggest that rangeland burning is becoming a
more popular method the same reports indicate that, during TexAQS, prescribed
rangeland burning would have been minimal due to unfavorable meteorological
conditions. There was not enough information to support any modification of the
emission values in Dennis (2000).
2.3.3 Slash Burning
In Texas, two types of slash burning activities were identified by Dennis (2000) as
sources of outdoor burning emissions: residue of logging operations burned as part of site
preparation processes, and burning of debris from land clearing operations. Dennis’
6
(2000) procedure to estimate the burned acreage was followed in this study and is
outlined below.
2.3.3.1 Logging debris
Dennis (2000) estimated the burned acreage of logging residues from information on the
total industrial harvest volume (in cubic feet) in the year of interest (published by the
Harvest Trends Report, an annual TFS publication). Dennis (2000) also assumed that all
counties experienced the most intense harvesting reported by the Harvest Trends Report,
80 cubic feet per acre. The total harvest volume in 2000 was 728,500,000 cubic feet
(Texas Forest Service, 2000), which implies that over 9.1 million acres were harvested.
2.3.3.2 Land Clearing Debris
The state of Texas has no record keeping system for the burning of land clearing debris.
Dennis (2000) calculated a first estimate of the acres cleared in land clearing operations
by multiplying the fraction of population growth by the acres of urban area. Dennis
(2000) used the composite database created by Wiedinmyer et al. (2000) to estimate the
total urban area in each county, which totaled 2,677,124 acres distributed in 75 counties.
Census data for 1999 (US Census Bureau, 2000) and 2000 populations (US Census
Bureau, 2001) by county were assembled for this study. Applying the growth fractions to
the urban area of each county, an estimated total of 231,230 acres were cleared in 2000
from the 75 counties. An estimated 10 percent of the cleared acres were burned (Dennis,
2000), totaling 23,123 acres burned in land clearing activities. This number is not listed
in the results section, since slash burns were deemed negligible and there is a high
uncertainty associated with the calculations.
3. Estimation of Fuel Loading and Consumption Factors for Wildfires
Fuel loading values (ton/acre) refer to the tons of fuel available for combustion per acre,
and the fuel consumption factors (ton/acre) represent the tons of fuel that are actually
combusted in a fire. The vegetation comprising the total fuel available to a fire is made
of several components, generally broken down into dead woody fuels, live fuels, duff,
and litter, and dead fuel is subcategorized based on the diameter of the wood. Fuel
loading and consumption factors were broken down into these categories for each
vegetation type.
3.1 Overview of Approach
Fuel consumption factors for prescribed range burns and slash burns are not included in
the report, since emissions from these types of fires were assumed to be negligible. Fuel
loading and consumption values for wildfires were based on the First Order Fire Effects
Model version 4.0 (FOFEM 4.0) default values for wildfires in Texas. For the domain
outside of Texas, the National Fire Danger Ranting System (NFDRS) database was used
7
to spatially allocate fuel loading factors (fuel consumption factors are not available within
this database).
Fuel consumption values (tons of fuel per acre) were spatially allocated within the
modeling domain. A Geographic Information System (GIS) framework was used to
record and manipulate the data; ArcInfo 8.1, and ArcView 3.2a were used.
3.2 State of Texas
FOFEM 4.0 contains default fuel consumption factors for vegetation cover codes in the
contiguous United States, but it does not include information on the spatial distribution of
these cover types. Dennis (2000) selected 15 vegetation cover codes, corresponding to
vegetation types in the Regional Domain, and cross-referenced them with vegetation
types allocated onto a database of the state of Texas developed by Wiedinmyer et al.
(2000). As a result, a composite land use-land cover (LULC) database with spatial
FOFEM cover types was created for Texas. Dennis (2000) modified the fuel
consumption factors associated with the cover codes where additional information was
available. The fuel consumption factors were then spatially allocated onto the LULC
database with ArcInfo.
The fuel consumption factors in FOFEM are broken down into fuel categories. Ward et
al. (1993) gave a general definition of fuel categories:
• Dead woody fuels: include branches, logs, stumps, and limbs. These fuels are
broken down into sub-categories based on diameter: wood 0-1’’, wood 1-3’’, and
wood 3+’’.
• Live fuels: include grass, low, shrubs, ferns, seedling, and other small herbaceous
plants.
• Duff: matted layers of partially decomposed organic matter and high organic content
soils such as humus or peat.
• Litter: fallen leaves and needles, twigs, bark, cones, and small branches that have not
decayed to the extent of loosing their identity.
The FOFEM fuel categories are as follows:
• Litter
• Wood 0-1’’, wood 1-3’’, and wood 3+’’
• Herb
• Shrub
• Duff
• Canopy fuels.
Table 1 shows the fuel consumption values assigned to FOFEM 4.0 vegetation cover
types, and broken down into fuel categories. A description of the vegetation cover types
can be found in Table A-1 of the Appendix.
8
Table 1: Fuel consumption values (ton/acre) assigned to fuel categories and wildland cover types.
Cover
Code
Litter
Wood
0-1’’
Consumption Factor (ton/acre)
Wood Wood
Herb Shrub Duff
1-3’’
3+’’
Canopy
Fuels
Total
0
1
2
0.3
0.3
3
6
7
18
21
22
23
100a
147a
147b
147c
151a
211
293
999
0.6
2.4
4.5
0.4
0.1
0.1
1.0
0.6
0.6
2.4
4.5
2.2
2.0
3.1
5.2
0.6
4.7
6.3
3.0
6.6
9.8
6.7
0
0.1
0.1
0.1
0.1
0.5
0.6
0.5
0.7
2.0
1.4
1.0
0.5
0.6
1.7
1.8
2.9
3.7
1.3
1.7
0.5
0.4
2.5
3.3
2.0
0.1
0.2
0.2
4.2
1.0
0.2
0.8
4.0
2.6
4.7
0.1
Source: Dennis (2000)
The 15 cover codes assigned by Dennis (2000) to the composite LULC database are
characterized by different numbers and are not broken down into further subcategories, as
in FOFEM 4.0 (i.e., only 147 exists, not 147a, b, c); in this study, fuel consumption
factors for 147a were assigned to the code 147.
Table 2 shows two different ways of measuring the fuel diameter. The time scale
reported in Table 2 is an historical definition and it represents the length of time that it
takes for the fuel to respond to within 63.2% of a new equilibrium moisture content
during drying or wetting processes. Larger diameter fuels have longer time lags, because
they respond more slowly to changes in environmental conditions.
Table 2: Dead fuel categories as the time scale at which moisture content varies and by diameter
(Dennis, 2000).
Time Scale
1 hr
10 hr
100 hr
1000 hr
Fuel Diameter
0 – 0.25’’
0.25 – 1’’
1 – 3’’
3 – 6’’
Source: Dennis (2000)
9
3.3 Rest of the Modeling Domain
The rest of the modeling domain was assigned fuel loading factors based on NFDRS fuel
models. The NFDRS was developed as a method to rate wildfire danger by the USDA
Forest Service. The NFDRS fuel models have several disadvantages relative to the
FOFEM fuel models: the fuel categories do not include canopy, duff, or litter loadings,
and only fuel loading values (not fuel consumption values) are included in the model,
whereas, consumption equations are embedded in FOFEM. Although FOFEM 4.0 and
the newer version 5.0 contain vegetation cover types and fuel consumption values for the
contiguous United States, the spatial allocation of these vegetation types –such as the
cross-referencing work of Dennis (2000) done for the State of Texas– was outside the
scope of this project. Therefore, the readily available, spatially allocated NFDRS fuel
models were used for the domain outside of Texas.
For this
purpose, the file nfdrfuel.asc was downloaded by anonymous ftp from
ftp://www.fs.fed.us/pub/wfas/fuels and imported into ArcInfo. The file assigns 24
different vegetation cover types to the contiguous United States. The modeling domain
was clipped out, and fuel loading values corresponding to each cover type (Burgan, 1988)
were projected onto the file with ArcInfo and ArcView. NFDRS fuel models do not
include fuel loading values for agricultural lands.
In the NFDRS models, fuel is broken down into the following categories:
• 1 hr
• 10 hr
• 100 hr
• 1000 hr
• Woody
• Herbaceous
• Drought
The drought fuel category accounts for the deep drying of litter and duff. As deep drying
occurs, it is assumed that more fuel becomes available for consumption within the
flaming front of a fire (Burgan, 1988). Table 3 lists the NFDRS fuel loading factors
broken down into fuel categories and vegetation cover types. A description of the
vegetation cover types can be found in Table A-2 of the Appendix.
A relationship between the NFDRS fuel categories and the FOFEM fuel categories will
be necessary for the estimation of emission factors outside Texas. Hardy et al. (1996)
related the NFDRS fuel categories (except drought and canopy fuels) with the FOFEM
fuel categories. The NFDRS drought fuel category was set equivalent to the duff fuel
category with a “very dry” moisture option in FOFEM, since the drought fuel category
accounts for the deep drying of litter and duff (Burgan, 1988). The NFDRS does not
account for the burning of canopy fuels. Hardy et al. (1996) did not establish a
relationship between the FOFEM fuel categories litter, duff, shrubs, and regen and the
NFDRS fuel categories. In this study, litter and wood 0 – 1’’ were assumed to be
10
equivalent, and shrubs and herbs were assumed to be equivalent. Table 4 summarizes
these relationships.
Table 3: Fuel loading values (ton/acre) assigned to fuel categories and vegetation cover types in the
NFDRS.
Vegetation
code
Fuel Load (ton/acre)
Total
1hr
10hr
100hr
1000hr
Woody
0
0
1
0.7
2
23
3
4.8
4
8.5
5
4.75
6
16.5
7
27.5
8
9.5
9
58
10
32.5
11
12
12
1
13
0
14
7
15
20.5
16
4.5
17
19.3
18
3
19
4.5
20
5.5
21
7
22
0
23
0
24
0
Source: Burgan (1988)
0
0.2
3.5
0.4
2
1
2.5
2.5
1.5
12
7
2.5
0.25
0
1.5
2
1
2.5
0.5
0.5
1
1.5
0
0
0
0
0
4
1
1
0.5
2
2
1
12
7
2.5
0
0
1.5
3
1
5.4
0.5
0.5
0.5
1.5
0
0
0
0
0
0.5
0
0
0.25
1.5
5
2
10
6
2
0
0
0
3
0.5
2.9
0.5
0.5
0
1
0
0
0
0
0
0
0
0
0
0
12
2
12
5.5
2.5
0
0
0
2
0
1
0
0.5
0
0
0
0
0
0
0
11.5
0.8
3
1
7
0.5
0.5
0
0
0
0
0
2
7
0.5
3
0.5
0.5
2.5
0.5
0
0
0
11
Herba
ceous
0
0.3
0
0.8
1
0.5
1
0.5
0.5
0
0
0
0.5
0
0
0
0.5
1
0.5
0.5
0.5
0.5
0
0
0
Drought
0
0.2
3.5
1.8
1.5
1.5
2.5
5
2
12
7
2.5
0.25
0
2
3.5
1
3.5
0.5
1.5
1
2
0
0
0
Table 4: Relationship between NFDRS and FOFEM fuel categories.
Fuel Component
Model
Litter
1 hr (0 – 0.25’’)
10 hr (0.25 – 1’’)
100 hr (1 – 3’’)
1000 hr (3 – 9’’)
Woody (9+’’)
Duff
Herbs
Shrubs
Regen.
Canopy
NFDRS
1 hr
1 hr
10 hr
100 hr
1000 hr
Woody
--Herbaceous
Herbaceous
-----
Drought
Drought
FOFEM
Litter
Wood 0 – 1’’
Wood 0 – 1’’
Wood 1 – 3’’
Wood 3+’’
Wood 3+’’
Duff
Herbs
Shrubs
Regen
Canopy
Duff (“very dry” moisture
option)
4. Estimation of Emission Factors and Composite Emission Factors for Wildfires
4.1 Overview of Approach
Emission factors (lb/ton) represent the pounds of pollutant emitted per ton of burned fuel.
Emission factors for wildfires are based on combustion efficiency algorithms and fuel
components, and were estimated with the assistance of the computer model FOFEM 4.0.
Composite emission factors (lb/acre) reflect the pounds of pollutant emitted per area
burned.
4.2 Estimation of Emission Factors
FOFEM 4.0 was selected to assist in the determination of emission factors and
efficiencies for wildfire. Emission factors were calculated with algorithms developed by
Ward et al. (1993) for PM, PM2.5, PM10, CO, CH4, and NMHC, which required the use of
combustion efficiencies. Combustion efficiencies are defined in FOFEM for six fuel
categories (wood 0-1’’, wood 1-3’’, wood 3+’’, herb and shrub, duff, and canopy fuel),
four different fuel moistures (very dry, dry, moderate, and wet), and different stages of
the fire (flaming and smoldering). The default ‘dry’ fuel moisture was selected for all
calculations, and the efficiencies for the flaming and smoldering phase were averaged.
Emission factors were then calculated for each fuel category and pollutant. To calculate
the NOX emission factor, its relationship with percent fuel nitrogen was used (Clements
and McMahon, 1980; as referenced by Dignon and Penner, 1991), where 70 was the
percent fuel nitrogen. A molar ratio of NH3 to NOX was used to calculate the NH3
emission factor (Yokelson et al., 1997). This procedure is outlined with more detail in
Dennis (2000). The emission factors are listed in Table 5.
12
Emission factors were then spatially allocated onto the modeling domain. This was done
by calculating composite emission factors in lb/acre for each vegetation cover type and
fuel category. Composite emission factors have different units from emission factors and
need only be multiplied by the area of a fire to obtain emissions in lbs.
Table 5: Emission factors (EFs) (lb/ton) for wildfire burns. These factors were calculated under dry
conditions for each fuel component using combustion efficiency information from FOFEM 4.0 and
EF algorithms developed by Ward et al. (1993). EFs for NOX and NH3 were calculated with relations
by Dignon and Penner (1991) and Yokelson et al. (1997).
Average
Fuel
Combustion
Component
Efficiency
Wood 00.95
1’’
Wood 10.92
3’’
Wood 3+’’
0.89
Herb and
0.85
Shrub
Emission factors (lb/ton)
CO
CH4
NMHC
PM
PM2.5
PM10
NOX
NH3
52
3
6
15
8
9
2.5
0.5
111
6
9
20
12
14
2.5
1.1
174
9
12
26
16
19
2.5
1.7
249
12
16
33
21
25
2.5
2.6
Duff
0.82
316
15
20
39
26
30
2.5
3.2
Canopy
Fuels
0.85
249
12
16
33
21
25
2.5
2.6
Source: Dennis (2000)
4.3 Calculation of Composite Emission Factors for the State of Texas
For each pollutant and vegetation cover type, composite emission factors were obtained
by multiplying the emission factors of the pollutant broken down into fuel categories by
the fuel consumption factors of the vegetation cover type, which are broken down into
the same fuel categories. The calculation is shown in the example below and Table 6
shows the results. The composite emission factors were then allocated spatially onto the
state of Texas.
Example:
Calculation of a composite emission factor (CEF) for the land cover type 2 and the
pollutant CO (see Table 1 for the fuel loading in ton per acre for land cover 2, and Table
5 for the emission factors in lb/ton):
CEF = (52 lb/ton)×(0 ton/acre) + (111 lb/ton) × (0 ton/acre) + (174 lb/ton) × (0 ton/acre)
+ (249 lb/ton) × (0.3 ton/acre) + (316 lb/ton) × (0 ton/acre) + (249 lb/ton) ×(0 ton/acre) =
74.7 lb/ton ≈ 75 lb/acre.
Since the LULC GIS vegetation database created by Dennis (2000) does not include
appendices “a”, “b”, “c”, or “d” in the cover codes, the cover code 147 in the database
was assigned the composite emission factors of 147a in Table 6.
13
Table 6: Composite emission factors (CEFs) (lb/acre) assigned to wildland cover types. CEFs were
calculated from the dry conditions and emission efficiencies given by the FOFEM program and
FOFEM fuel consumption values (Tables 1 and 5).
Code
1
2
3
6
7
18
21
22
23
100a
147a
147b
147c
151a
211
293
999
CO
0
75
157
586
1121
534
481
753
1299
157
1033
1378
748
1389
1803
1750
0
CH4
0
4
8
28
54
26
23
36
62
8
50
67
36
68
89
84
0
Composite Emission Factor (lb/acre)
NMHC
PM
PM10
PM2.5
0
0
0
0
5
10
8
6
10
21
16
13
38
77
59
50
73
147
113
96
35
71
54
46
32
64
49
41
49
100
76
65
85
170
131
111
10
21
16
13
69
141
107
90
92
188
142
121
49
98
75
64
93
193
145
123
125
262
194
165
113
227
174
148
0
0
0
0
NOX
0
0.7
1.5
5.8
11.1
5.5
4.9
7.6
12.8
1.5
11.6
15
7
16.2
23.9
16.5
0
NH3
0
0.3
0.6
2.2
4.2
2.1
1.9
2.9
4.8
0.6
4.4
14
8
6.2
9.3
6.2
0
4.4 Calculation of Composite Emission Factors for the Rest of the Modeling Domain
Two methods were devised to allocate emission factors spatially onto the rest of the
modeling domain.
4.4.1 Use of FOFEM Emission Factors and NFDRS Fuel Loading Factors
NFDRS fuel loading factors and emission factors, broken down into fuel categories, were
used to calculate the new emission factors for each NFDRS vegetation type. The NFDRS
and FOFEM fuel categories were correlated as outlined in Table 4 to assign the emission
factors calculated with the FOFEM program (Table 5) to the fuel subcategories in the
NFDRS. The results are shown in Table 7. The values were calculated as outlined in the
example above.
A drawback of this method resides in the failure of the NFDRS fuel models to account
for fuel consumption equations and canopy, duff, or litter fuel components. Yet, the
NFDRS fuel models include high fuel loadings for the drought fuel component, which
has a compensating effect. A source of uncertainty associated with this method is the
error underlying the relationship established between the fuel types in FOFEM and in the
NFDRS (Table 4).
14
Table 7: Calculated composite emission factors (lb/acre) assigned to wildland cover types for the
domain excluding Texas. The emission factors were calculated from the dry conditions and emission
efficiencies given by the FOFEM program and NFDRS fuel loading values (Table 3).
Code
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
CO
0
148.3
3552.5
980
1401
878.25
2657.5
4668.5
1543.5
8238
4563
1707
216.5
0
1136
3265
687
2783.7
477
880
953.5
1110.5
0
0
0
CH4
0
7.2
181.5
48
70.5
43.5
135
237
78
420
232.5
87
10.5
0
57
166.5
34.5
141.6
24
43.5
48
55.5
0
0
0
Composite Emission Factor (lb/acre)
NMHC
PM
PM2.5
PM10
0
0
0
0
10
20.7
13.1
15.3
257.5
558
341
398
66.8
138.4
87.6
101.8
100
214.5
132
154
61.25
128.5
80.5
93.5
190.5
410
252
294.5
330
704
436.5
510.5
111
237
146.5
170.5
618
1340
816
944
344
746
454
524.5
128
277.5
169
195.5
14.5
30
19
22.25
0
0
0
0
82
175
108
125
235
505.5
311
363
50.5
108.5
66.5
77
207.5
450
274
317.7
34.5
74
45.5
53
60.5
126
79.5
92.5
67
143
88.5
103.5
81
172.5
106.5
123
0
0
0
0
0
0
0
0
0
0
0
0
NOX
0
1.75
57.5
12
21.25
11.875
41.25
68.75
23.75
145
81.25
30
2.5
0
17.5
51.25
11.25
48.25
7.5
11.25
13.75
17.5
0
0
0
NH3
0
1.52
35.05
9.9
14
8.825
26.4
46.3
15.4
81.8
45.35
16.95
2.225
0
11.3
32.3
6.9
27.74
4.8
8.85
9.5
11.15
0
0
0
4.4.2 Spatial Correlation of NFDRS and FOFEM Fuel Models
The LULC fuel model map created by Dennis (2000) and the NFDRS fuel model map
were superimposed and the vegetation cover codes in Texas were correlated with the
NFRDS categories. In principle, if NFRDS categories could be assigned to the land
covers used by Dennis, the land covers could then, in turn, be related to FOFEM
categories.
Unfortunately, this method is rather inaccurate, since species are
agglomerated differently in both databases and thus the NFDRS and the FOFEM 4.0
cover codes cannot be easily related. For instance, in attempting to build correlations, the
NFDRS cover codes 22, 23, and 24 were assigned composite emission factors greater
than zero, even though they represent land covers that do not emit pollutants when
burned or cannot get burned (i.e., water, barren). This illustrates the uncertainty
associated with this methodology. Furthermore, the NFDRS cover code 15 is not present
in Texas, but exists in the South of Louisiana; an emission factor for this cover code
would need to be calculated by making an educated guess.
15
After reviewing the two methods outlined above, the former, consisting of calculating
composite emission factors for each NFDRS cover code, was chosen, since the
uncertainty associated with it is most likely smaller than the uncertainty associated with
the latter method. Table B-1 in the Appendix shows the land cover codes used in the
modeling domain (FOFEM for Texas and NFDRS for the domain that excludes Texas)
and the composite emission factors for PM2.5, NOX, and NMHC for each land cover type.
5. Results
Plots of fire locations for August and September 2000 are attached in Appendix B-1. The
fires are plotted onto the land use/land cover map of the modeling domain, each color
representing a different land use type. Presented in the Appendix B-2 are fire locations
and wind back-trajectory plots for the same days. The wind back trajectories were
computed for each day in the months of August and September 2000 using the HYSPLIT
(Hybrid Single-Particle Lagrangian Integrated Trajectory) model available at
http://www.arl.noaa.gov/ready/hysplit4.html and the Trajectory Analysis for
TexAQS2000 model available from the Air Quality Modeling group at the University of
Houston (http://www.uh.edu/aqm/). The University of Houston trajectories were used
whenever they were available (for the dates of August 22 – September 01) since they are
based on a richer observational data set. The HYSPLIT data were used for all other
dates. The EDAS (Eta Data Assimilation System) and MM5 (Mesoscale Model)
meteorological data sets are used in the HYSPLIT and Trajectory Analysis models,
respectively. Each trajectory had a run time of 32 hrs. and starting heights of 10m, 500m
and 1000m. The wind back trajectories are 32-hr back trajectories starting at the La Porte
site. The trajectories start at 5 p.m., each dot representing one hour. The three different
colors of the back trajectories represent aboveground heights of the wind: green, brown,
and blue represent 10, 500,and 1000 meters above the ground, respectively.
5.1 Overview
Emissions from wildfires were calculated by projecting the daily fire information
(location and acreage) onto a GIS map with spatial composite emission factors.
Composite emission factors at the location of a fire were multiplied by the acreage of the
fire to obtain daily pounds of emissions of each pollutant (recall that, within the same
cover code, each pollutant has a different composite emission factor.) Since fires are
treated as points, only one land cover code is assigned to each fire, which will not be
entirely accurate if a fire is large enough to encompass two or more different land cover
codes. However, since few cover types and emission factors are used to characterize the
vegetation of the modeling domain, relatively large geographical areas (sometimes
covering up to 10 million acres, or one tenth of the Texas territory) are characterized by a
single emission factor. As the largest fires range from 60,000 to 70,000 acres, using a
single land cover type for each fire should be a minor source of uncertainty.
16
5.2 Total Acreage Burned in Texas in 2000
The wildfire activity in Texas during the year 2000 was two times larger than the activity
in all of 1996 and almost four times larger than in all of 1997. 219,000 acres were burned
in wildfires in the year 2000, as opposed to 109,000 and 56,000 in 1996 and 1997,
respectively.
5.3 Total Acreage Burned in Texas during August to September, 2000
From August to September 2000, 121,000 acres were burned in wildfires in Texas and
most of the fires occurred in East Texas. Estimates for the years 1996-97 based on
Dennis (2000) attribute 28,000, 13,000, and 30,000 acres to agricultural, slash, and
prescribed range burning activities in August and September, respectively. These last
three estimates were possibly smaller in the year 2000 due to drought conditions. Figure
2 shows that wildfire burns exceeded other types of burns.
140000
120000
Acres
100000
80000
60000
40000
20000
0
Wildfires
Agricultural
Slash
Prescribed
range
Type of fire
Figure 2: Acreage burned in different types of fires in Texas in August – September 2000.
5.4 Total Wildfire Acreage Burned in HGBPA Subdomain
The acres burned by wildfires in the HGBPA Subdomain during August – September
2000 are shown in the figure below. The period of most intense fires ranged from August
31 to September 8. The days of highest fire activity were September 4 and 6.
17
14000
Acres Burned (Acres)
12000
10000
8000
6000
4000
2000
0
9/26/2000
9/19/2000
9/12/2000
9/5/2000
8/29/2000
8/22/2000
8/15/2000
8/8/2000
8/1/2000
Fire Date
Figure 3: Acres burned in wildfires in the HGBPA subdomain. During August – September, 2000.
5.5 Wildfire Emissions in HGBPA Subdomain
The estimated emissions of CO, NMHC, PM2.5, and NOX in the HGBPA Subdomain are
shown in the graphs of Figure 4. These graphs also include the daily average emissions
of CO, NMHC, and NOX from light duty gasoline vehicles (LDGV) in the HoustonGalveston area, a category that excludes diesel fuel vehicles (Harley, personal
communication). Figure 4 shows that (1) emissions of pollutants from fires exceeded at
times emissions from LDGV, and (2) the period of highest emissions from fires falls
between August 31 and September 8 for all emissions. The highest emissions during this
short period were approximately 3700 short tons/day, 250 short tons/day, 340 short
tons/day, and 50 short tons/day for CO, NMHC, PM2.5, and NOX, respectively.
18
9/14/2000
9/18/2000
9/22/2000
9/26/2000
9/30/2000
9/10/2000
9/14/2000
9/18/2000
9/22/2000
9/26/2000
9/30/2000
9/10/2000
9/14/2000
9/18/2000
9/22/2000
9/26/2000
9/30/2000
Fire Date
19
9/10/2000
8/1/2000
8/5/2000
8/9/2000
8/13/2000
8/17/2000
8/21/2000
8/25/2000
8/29/2000
9/2/2000
9/6/2000
8/1/2000
8/5/2000
8/9/2000
8/13/2000
8/17/2000
8/21/2000
8/25/2000
8/29/2000
9/2/2000
9/6/2000
9/6/2000
9/2/2000
8/29/2000
8/25/2000
8/21/2000
8/17/2000
8/13/2000
150 short tons/day (LDGV)
200
8/9/2000
250
150
100
50
NMHC (Short Tons)
1200 short tons/day (LDGV)
CO Emissions (short tons)
4000
3500
3000
2500
2000
1500
1000
500
0
8/5/2000
8/1/2000
400
350
300
250
200
150
100
50
0
PM 2.5 (Short Tons)
CO Time Series
Fire Date
NMHC Time Series
300
0
Fire Date
PM 2.5 Time Series
NOx (Short Tons)
NOx Time Series
150
135
120
105
90
75
60
45
30
15
0
146 short tons/day (LDGV)
9/30/2000
9/26/2000
9/22/2000
9/18/2000
9/14/2000
9/10/2000
9/6/2000
9/2/2000
8/29/2000
8/25/2000
8/21/2000
8/17/2000
8/13/2000
8/9/2000
8/5/2000
8/1/2000
Fire Date
Figure 4: Emissions of CO, NMHC, PM2.5, and NOX from fires in the HGBPA Subdomain during
August – September, 2000.
Emissions (short tons)
In Figure 5, wildfire NMHC and NOX emission magnitudes on August 31, 2000 and
September 4, 2000 in the HGBPA Subdomain (last four columns in Figure 5) are
compared with VOC and NOX emissions from LDGV on September 8,1993 and with the
daily average of VOC and NOX emissions in 1996 in the 8-county Houston-Galveston
(HG) area (first four columns). The figure indicates that NMHC emissions from
wildfires are considerable compared to VOC emissions from LDGV.
450
400
350
300
250
200
150
100
50
0
VOC/NMHC (t/d)
NOx (t/d)
9/8/1993 (8- 1996 Daily Avg.
County HG Area) (8-County HG
Area)
8/31/2000
9/4/2000
Figure 5: Comparison of LDGV emissions of VOC and NOX in the 8-county HG area (first four
columns) with wildfire emissions of NMHC and NOX in the HGBPA domain (last four columns).
20
5.6 Observational Evidence: TEOM Results
The temporal distribution of wildfire emissions, predicted with the inventory developed
in this work, can be compared to ambient measurements of fine particulate ,matter, which
can be an effective tracer for fire activity. Six monitors based on Tapered Element
Oscillating Microscale (TEOM) technology recorded PM2.5 concentrations within the
HGBPA area. The TEOM Domain and the location of the monitors are shown in Figure
6. The observational TEOM data and the findings of this study are qualitatively in
agreement. An hourly time series of PM2.5 concentration at the monitor sites during
August 22 – September 7, 2000 is shown in Figure 7. The figure shows PM2.5
concentration peaks on September 4 and September 6, and these two days are
characterized by the largest fire activity in the HGBPA Subdomain. In Figure 8, the
PM2.5 concentrations obtained by interpolating the TEOM observational data for
September 4 (Figure 8a) is contrasted with the fire and 32-hour wind back-trajectory data
found in this study (Figure 8b). The starting point and time of the wind trajectories is La
Porte, 5:00 p.m. The different colors represent the wind’s height above ground at the
starting point (La Porte).
3390089
2010026
2450022
2011034
2011039
1670014
Figure 6: Details of TEOM Domain (black line), TEOM monitors (colored points), and HGBPA
Subdomain (red line).
21
PM2.5 Concentration ( µg/m3)
200
1670014
2010026
2011034
2011039
2450022
3390089
175
150
125
100
75
50
25
Date and Time
Figure 7: Time series of TEOM monitors PM2.5 measurements for August 22 - September 7, 2000.
Figure 8b shows that on that particular day, wind from the Northeast of Houston could
have carried particulate matter from the large fires in East Texas to the location of the
monitors in the Houston area. Figure 8a shows a steep concentration gradient possibly
caused by fire emissions. The interpolation performed for Figure 8a was done for each
hour during August 22 – September 7, 2000; the results have a high uncertainty because
only six data points (corresponding to the six TEOM monitors) were available for the
interpolation.
22
9/07 00:00
9/06 00:00
9/05 00:00
9/04 00:00
9/03 00:00
9/02 00:00
9/01 00:00
8/31 00:00
8/30 00:00
8/29 00:00
8/28 00:00
8/27 00:00
8/26 00:00
8/25 00:00
8/24 00:00
8/23 00:00
8/22 00:00
0
(a)
(b)
Figure 8: (a) Interpolated TEOM monitor data of PM2.5 concentrations for September 4, 2000 at
5:00 p.m. within the TEOM Domain. (b) Fires (red points) and 32-hour wind-back trajectories
starting at 5:00 p.m. for September 4, 2000. The different back-trajectory colors represent the
wind’s height above the ground at the starting point (La Porte): green: 10 meters above ground,
brown: 500 m, blue: 1000 m. However, thorough wind mixing occurred on that day, so that, except
at the starting point, the different wind trajectories cannot be allocated onto a vertical layer.
23
6. Preparation of a CAMx-Ready Input File
The final goal of this study is to prepare a fire emissions input file suitable for use in the
Comprehensive Air Quality Model with extensions (CAMx).
The Emissions
Preprocessor System, version 2.0 (EPS2.0) was used to create a CAMx-ready input file.
In addition, fire plume rise was calculated. The CAMx-ready input file covers the period
of August 25 to September 1, 2000, which is the 2000 CAMx Modeling Episode.
6.1 Overview
The fire emissions inventory was preprocessed using the Urban Airshed Model (UAM)
EPS2.0. The wildfires were modeled as point sources with no plume-in-grid, which
allocates the emissions of a point onto an entire grid cell. EPS2.0 input includes:
• A data file in Aerometric Information Retrieval System (AIRS) Facility Subsystem
(AFS) format
• A chemical split factor file (CHEMSPLIT), and
• A temporal split file (TMPRL).
6.2 AFS File
The AFS file contains information on the location of the fires and the emissions
registered during each fire event. The AFS file was prepared using the results of this
study.
6.3 Chemical Speciation of NMHCs
EPS2.0 recognizes only six types of emissions: CO, VOC, PM, PM10, and NOX. The
VOC category is equivalent to the NMHC category in this study, and it is broken down
into Carbon Bond-IV (CB-IV) species. CB-IV species are groups of organic compounds
or fragments of compounds that have similar reactivities, and NMHCs input into EPS2.0
must be broken down into CB-IV groups. CHEMSPLIT is the file that contains this
information.
The speciation of NMHCs was based on data collected on the combustion of loblolly pine
vegetation, shown in Table 8 (Hays et al., 2002). The table indicates that ethylene,
ethane and propylene are the predominant species, comprising as much as 46% of the
total NMHC emissions.
24
Table 8: Mass Percents (%) of Total gaseous NMHC of different chemical species in Loblolly pine.
Chemical Species
n-alkanes
ethane
propane
butane
pentane
hexane
heptane
octane
nonane
decane
undecane
dodecane
alkenes
ethene
propene
isobutene/1-butene
trans-2-butene
cis-2-butene
1-pentene
trans-2-pentene
cis-2-pentene
1-hexene
1-heptene
1-octene
1-nonene
1-undecene
1-dodecene
isoprene
1,3-butadiene
alkynes
ethyne
branched alkanes
isobutane
2,2-dimethylbutane
2-methylpentane
Mass Percent (%) of
Total gaseous NMHC in
Loblolly pine
12.71
4.43
1.60
0.70
0.38
0.28
0.25
0.31
0.31
2.08
21.24
12.22
2.79
1.08
0.82
0.85
0.53
0.28
1.68
0.97
0.78
0.44
0.42
0.20
3.44
2.27
2.82
0.39
0.50
0.71
25
2,4-dimethylpentane
2,2,4-trimethylpentane
2,3,4-trimethylpentane
2-methylhexane
3-methylhexane
2-methylheptane
3-methylheptane
branched alkenes
3-methyl-1-butene
2-methyl-1-butene
2-methyl-2-butene
4-methyl-1-pentene
cyclic compounds
cyclopentane
cyclopentene
methylcyclohexane
α-pinene
β-pinene
aromatic compounds
benzene
toluene
ethylbenzene
m-, p-xylene
styrene
o-xylene
isopropylbenzene
n-propylbenzene
m-ethyltoluene
p-ethyltoluene
1,3,5-trimethylbenzene
o-ethyltoluene
1,2,4-trimethylbenzene
1,2,3-trimethylbenzene
m-diethylbenzene
p-diethylbenzene
0.01
0.13
0.15
0.17
0.43
0.05
0.13
0.51
0.74
1.37
0.10
0.06
0.63
0.15
0.24
0.04
5.15
5.52
0.71
2.52
0.66
0.75
0.10
0.14
0.48
0.22
0.12
0.12
0.80
0.30
0.02
0.01
Source: (Hays et al., 2002)
26
The NMHCs listed in Table 8 are further classified into CB-IV species. A spreadsheet
program is available at http://pah.cert.ucr.edu/~carter/emitdb/ to classify NMHCs into
CB-IV species. The input to this program includes Chemical Abstracts Service (CAS)
number and mass fraction of the chemical species. The results of the chemical split of
NMHC into CB-IV species are shown in Table 9.
Table 9: CB-IV Speciation Profile for NMHCs
CB-IV Species
Moles of CB-IV
species/gram of
Total NMHC
PAR [Paraffin carbon
bond (C-C)]
OLE [Olefinic carbon
bond]
TOL [Toluene]
XYL [Xylene]
7.501*10-4
4.807*10-4
FORM [Formaldehyde]
1.061*10-4
ALD2 [High molecular
weight aldehydes]
1.314*10-3
ETH [Ethene]
7.57*10-3
ISOP [Isoprene]
MEOH [Methanol]
ETOH [Ethanol]
UNR [Unreactive
Species]
5.046*10-4
0.0000
0.0000
1.879*10-2
4.931*10-3
1.339*10-2
6.4 Calculating fire plume rise
To accurately model the emission inventory, a method to determine the correct vertical
placement of the emissions must be defined. The placement of the emissions is a function
of the plume rise. CAMx has its own algorithm to calculate plume rise. TUPOS
(Turbulence Profile Sigmas) model for elevated buoyant releases is implemented as a
sub-routine in Fortran Language in CAMx called plumerise.f (ENVIRON, 2000).
Equations recommended by Briggs (1984) and Turner et.al. (1986) are used in this model
to calculate the plume rise layer-by-layer so that the effects of changing vertical structure
of wind and temperature could be included (Turner et.al., 1986).
TUPOS model is recommended for calculating the plume rise for stack emissions, which
have high exit velocities. Unfortunately, this model has no provision to calculate the
plume rise for area source emissions such as fire emissions, since it does not take buoyant
rise into account.
27
6.4.1 Existing Plume Rise Models
The FIREPLUME and CALPUFF models were used to perform plume rise calculations.
The results were then input into CAMx using “dummy” parameters.
6.4.1.1 FIREPLUME model
The FIREPLUME model was developed (Brown et al., 1999) to simulate atmospheric
dispersion and air quality impacts of fire plumes and other smoke sources. The
FIREPLUME model predicts the ground-level concentration resulting from chemicals
emitted from or within 1) instantaneously discharged thermals or explosive discharges, 2)
fires that generate hot continuous plumes 3) smoldering or decaying fires. The
FIREPLUME model is an extension of a Lagrangian particle model (MCLDM). The
ability to treat buoyant plumes was added to MCLDM by Brown et al. (1996).
In this model, plume rise of fires can be calculated according to the Brigg’s two-thirds
law, which is applicable in cases where the buoyancy source has low initial momentum.
Fires clearly fall into this category.
  3r x 2   r  3 
o
+ o  
∆h =  
  4 β 2 F 2 K 3   β  


1/ 3
−
ro
(1)
β
where ∆h is the plume rise, x is the downward distance, ro is the fire radius [m], K is the
velocity ratio (U/wo), β is the entrainment coefficient and F is the Froude number.
1/ 2
 wo 2 ρ a 

F =
(2)
 2∆ρr g 
o 

where wo is the initial vertical velocity [m/s], ρ is the air density [kg/m3]and ∆ρ is the
initial density difference between ambient air and the fire plume.
Final rise
Due to the effects of entrainment into the plume and thermal stratification of the
atmosphere, the rise of the fire plume is limited. It can be calculated depending on
stability class (stable, neutral, unstable).
For stable conditions, plume rise is limited by thermal stratification. Based on a survey of
field data Briggs (1984) suggests that the final rise in stably stratified air is
1/ 3
 rU2 
∆h f = 2.1 2o 2 3 
N F K 
(3)
where ∆hf is the final rise in [m] and N is the Brunt Vaisala frequency.
1/ 2
g ∂θ 
N can be calculated as N = 
 , (Arya, 1999)
 θ ∂z 
28
For neutral conditions, ambient turbulence limits the rise by breaking up the plume. The
final rise in this case is the level at which the internal turbulent dissipation rate of the
plume matches the ambient turbulent dissipation rate.
 rU2 
(4)
∆h f = 0.76 2o 2 3 
 u* F K 
where u* is the friction velocity [m/s]. In this study, this value is approximated to be 10%
of wind velocity at 10 meters.
In unstable conditions, plume rise is also limited by turbulence. However, the turbulent
dissipation of the downdrafts is equated with the plume dissipation rate since downdrafts
are responsible for bringing elevated material to ground level.
 r U2z 2/3 
∆h f = 4.5 o 2 2i 3 
 4w * F K 
3/5
(5)
where zi is the inversion height [m] and w* is the convective velocity scale [m/s].
g
(6)
w* = [ ( w'θ ' ) s z i ]1 / 3
θv
where ( w'θ ' ) s is the surface heat flux [K.m/s] and θv is the mean virtual potential
temperature which can be approximated as actual temperature at that height.
In the literature (Brown et al., 1999), the power of inversion height in equation (5) is one,
making the unit of final rise unmatched with [m]. According to Weil J.C (1988), power of
inversion height is 2/3 and the unit of final rise is matched with [m].
In practice, the neutral plume rise relationship serves as a limiting case since both the
stable and unstable limiting rise relationships go to infinity as neutral conditions are
approached. The literature suggests using the lesser of the stable or unstable final rise
estimate (which ever is applicable) and the neutral estimate. Therefore, in unstable
conditions the final rise is given by:
∆h f = min{∆h f [Eq.(3)], ∆h f [Eq.(4)]
whereas for stable conditions
∆h f = min{∆h f [Eq.(5)], ∆h f [Eq.(4)]
The basic theory of plume rise (based on the so-called Brigg's formulas) is limited to
assumptions of uniform wind field and constant stability in the atmospheric layers
through which the plume rises. It does not account layer-by-layer calculation with
residual flux as TUPOS does in CAMx. It calculates only final plume rise. This may
create an issue of changing in vertical structure of wind and temperature.
29
Meteorological data, including U, ρ, and ∆ρ, are inputs to the equations. Meteorological
data stratified in vertical layers are available in CAMx simulations on limited numbers of
days during the Texas Air Quality Study and from the National Oceanic and Atmospheric
Administration (NOAA).
6.4.1.2 CALPUFF Model
CALPUFF is a non-steady state air quality modeling system for a variety of air quality
modeling studies, including buoyant area emission sources such as forest fires. This
system consists of three main components: CALMET (a diagnostic 3-D meteorological
model), CALPUFF (the transport and dispersion model), and CALPOST (a postprocessing package). The area source plume model in CALPUFF calculates the plume
rise and is applicable to large forest fires.
Unlike the FIREPLUME Model, the plume rise model in CALPUFF is formulated on the
basis of mass, momentum and energy balances. The resulting time dependency to the
plume rise can be calculated assuming that the plume motion is quasi-steady state so the
time-derivatives in the governing equations are neglected. This assumption is reasonable
because the time scale for plume rise is much shorter than that of the fire life span.
The derivation of the governing equations are given below:
The mass conservation equation
d
(ρU sc r 2 ) = 2rαρ a | U sc − U a cos ϕ | +2rβρ a | U a cos ϕ |
ds
where α=0.11 and β=0.6 are the entrainment parameters corresponding to the differences
of velocity components between the wind and the plume in directions parallel and normal
to the plume centerline respectively
Ua = the ambient horizontal wind speed
Usc = u 2 + w 2
ρ , ρa = the plume density and air density, respectively
s
= length of the plume centerline measured from the emission source
ϕ
= centerline inclination
The momentum equation in the wind direction
dU a
d
ρU sc r 2 (u − U a ) = −r 2 ρw
ds
dz
(
)
(7)
The momentum equation in the vertical direction is a balance among inertial acceleration,
entrained momentum and buoyancy:
30
d
(ρU sc r 2 w ) = gr 2 (ρ a − ρ)
ds
(8)
g = gravitational acceleration
The energy equation
 dT
d
g 
4
ρwr 2 − R p r (T 4 − Ta )
ρU sc r 2 (T − Ta ) = − a +


ds
 dz C p 
(
)
(9)
2εσ
Rp = /Cp = 9.1 x 10-11 kg/ m2K3s
σ = Stefan-Boltzmann constant = 5.67 x 10-8 w/m2K4
ε = emissivity = 0.8
To close the equation set, two geometric relations are needed:
dz
w
=
= sin ϕ
ds U sc
dx
u
=
= cos ϕ
ds U sc
In order to utilize this model, a sub-routine in CALPUFF called “numrise” is extracted.
Inputs needed for this sub-routine are:
Effective Temperature of release (K)
Effective radius of release (m)
Effective exit velocity (m/s)
Number of points in trajectory passed
EPM (Emission Production Model) and EPM2BAEM
The Emission Production Model, EPM, is the emission source strength model for fires in
widespread use. EPM has been used satisfactorily as input to models of long and shortrange plume dispersion. Applicability of an Emission Production Model is to predict air
pollutant source strength, heat release rate, and plume buoyancy from all fire
environments and all fuel types.
CALPUFF has a conversion utility called “EPM2BAEM”, which is the interface between
EPM output and CALPUFF input. By using this utility, with our emission inventory and
heat release rate, Effective Temperature of release (Plume temperature) and Effective exit
velocity (vertical velocity) can be estimated.
31
6.4.1.3 Obtaining Input Data
The input parameters for FIREPLUME and CALPUFF models are given in Table 10.
Meteorological data were extracted from CAMx input files and also from MM5 output.
The hourly modeling data files and related documents for the TexAQS 2000 period were
downloaded from Texas Commission on Environmental Quality (TCEQ) website
(www.trnrcc.state.tx.us).
Table 10: Input Data for FIREPLUME and CALPUFF Models
Input
FIREPLUME Model
CALPUFF Model
CAMx Input Files
Layer 1-2 in CAMx stucture
CAMx Input Files
Layer 1-14 in CAMx
stucture
CAMx Input Files
Layer 3 in CAMx stucture
CAMx Input Files
Layer 1-14 in CAMx
stucture
Meteorological data
Temperature
Horizontal Wind
Pressure
CAMx Input Files
Layer 1-14 in CAMx
stucture
Mixing Height
MM5 Output
Heat Flux at
Surface
Plume
Characteristics
Temperature of
release
Exit vertical
velocity
Heat Release
Acres Burned
MM5 Output
900 – 1600 K
900 – 1600 K
CALPUFF Methodology
CALPUFF Methodology
Emissions Inventory
Emissions Inventory
Emissions Inventory
Emissions Inventory
32
Fortran codes were written to extract the metrological data from CAMx input files. The
following are the fortran codes to extract wind, temperature and pressure data.
Wind File
Hour, idate
Loop from 1 to nlay layers:
((uwind(i,j,k), i=1,nx), j=1, ny)
((vwind(i,j,k), i=1,nx), j=1, ny)
((dummy, i=1,nx), j=1, ny)
Temperature File
Hour, idate, ((temps(i,j), i=1,nx), j=1, ny)
Loop from 1 to nlay layers:
Hour, idate, ((temps(i,j), i=1,nx), j=1, ny)
Pressure File
Loop from 1 to nlay layers:
Hour, idate, ((height(i,j), i=1,nx), j=1, ny)
Hour, idate, ((press(i,j), i=1,nx), j=1, ny)
Mixing height and surface heat flux
MM5 output reports meteorological data, including mixing height and surface heat flux,
which are used in FIREPLUME model. To extract this information into a Microsoft
Access database, the existing fortran code was modified and used for this task. The
original
Fortran
code
for
extracting
MM5
can
be
found
at
http://mailman.ucar.edu/pipermail/mm5-users/2002/000073.html. A visual basic code
was created to revise Greenwich time information used in the MM5 model to standard
time (Greenwich Time – 6 hours).
Initial vertical velocity
Both CALPUFF and FIREPLUME models require initial velocity of fire plume to
calculate the plume rise. The calculation procedure (Chang et al., 1999) for initial vertical
velocity used in CALPUFF is as follows:
33
Once the area of a fire is specified, the initial plume radius rp can be readily approximated
by
A
rp =
(10)
π
The plume buoyancy flux Fb [m4/s3] is calculated based on the following equation:
Fb = g
Tp − Ta
Tp
w p rp
2
(11)
where Tp, Ta are the initial plume temperature and the ambient temperature [K],
respectively. wp is the initial plume vertical speed [m/s]. From Briggs (1975), Fb is
related to the heat emission, Qh [J/s] as
Fb =
gQ h
πc p ρ a Ta
(12)
Cp = 1004 J/kgK, ρa is the ambient air density [km/m3]
From ideal gas law, the above equation can be written as
P
(13)
Fb = 8.8x10 −6 s Q h
Pa
where Ps is the standard sea level pressure (=101325 Pa), and Pa is the ambient pressure
(Pa). Assuming Pa~Ps, then equating equation (11) and (13) gives
wp =
8.8x10 −6 Q h x Tp
g(Tp − Ta )rp
(14)
2
Fire plume temperature
The plume temperature and other source characteristics are source-specific.
Unfortunately, the fire plume temperature information is not available in the literature
and not reported by the fire agencies. Therefore a typical plume temperature range of
900-1600 K is assumed.
Heat release and Acres burnt
The daily acreage burned is uniformly distributed for 24 hours. To run the FIREPLUME
model, all input parameters are stored in a Microsoft Access database for easy access. A
34
Visual Basic programming program is used to extract data from Access database and
perform the calculations. For CALPUFF, plume characteristics were specified in an
Excel file. A Fortran code “main.f” was created to link this fire plume characteristics and
meteorological data to the extracted CALPUFF subroutines and further details are given
in Section 6.4.2.
Sample of Fire Events
The focus of this study is limited to HG-BPA (Houston-Galveston, Beaumont-Port
Arthur) sub-domain since most of the fire events during TexAQS 2000 occurred in this
domain. The metrological data are available for the TexAQS modeling episode days
(August 25 – September 1, 2000). To observe the performance of plume rise model, only
large fire events that burnt more than 500 acres were used. This limited the analysis to
three fires, with fire ids 1085, 1094 and 1033. The x-y coordinates of each fire, (X1,Y1
and X2,Y2, which are the coordinates on CAMx and MM5 domains, respectively) are
shown in Table 11.
Table 11: Access database of selected Forest Fires
Fire ID
1033
1085
1094
Fire Date/Start
Date
30-Aug-00
31-Aug-00
01-Sep-00
LatDD
30.2590
30.5210
30.9019
LongDD
-95.0060
-93.0480
-93.5894
Total Acres Heat_Release X1 Y1 X2
Burned
(Btu)/day
8.42E+10
31 45 66
1000
2.15E+11
77 56 112
4000
6.68E+10
64 65 99
793.2
Y2
77
88
97
6.4.2 Using Models
FIREPLUME:
In the FIREPLUME model, uniform wind field and constant stability in the atmospheric
layers through which the plume rises are assumed. Only wind data of layer 1 and layer 3
were used. Lapse rate, which represents the atmospheric stability conditions, was
calculated based on temperature data for the first two layers.
CALPUFF:
To use CALPUFF model for plume rise calculations, several sub-routines were extracted.
The subroutines are “numrise.f”, “numpr1.f”, “param.puf”, “numparam.puf”, “rate.f”,
“marching.f”, “zmet.f”, “unlump.f”, “ambient.puf”, and “ar2.puf”.
To link meteorological data, fire data, and all other sub-routines each other, main code
“main.f” was created using Fortran. Main.f defined the fourteen vertical layers used in
CAMx model, so that ambient temperatures, ambient pressures, and ambient wind speeds
in each different layer were used to calculate the fire plume trajectory step by step.
35
Inputs needed for “main.f” are:
1. Meteorological data
2. Fire data
3. Non-variable conditions such as defining layers
Fire data in “main.f” is the input for CALPUFF sub-routine “numrise.f”, and it is
described as follows:
1. Release height (m)
2. Effective temperature of release (K)
3. Effective radius of release (m)
4. Effective exit velocity (m/s)
5. Number of points in trajectory passed back to calling program (final point is
“final rise”)
6.4.2.1 FIREPLUME Results
Sensitivity runs and model observations indicate that plume temperature has negligible
effect the plume rise calculations. Only heat release (J/s) parameter has significant effect
on the plume rise. Though the radius of fire does not explicitly contribute to plume rise, it
is implicitly included in heat release value.
As the first step of calculation, wind data of layer 1 was used to observe the
characteristics of plume rise as shown in Figure 9. Horizontal lines represent top height
of layer 3, 4 and 5 of the CAMx grid structure. Note that most of the plumes rise values
are above layer 3. Thus, choosing layer 3 for wind velocity data may be a good
approximation. All further calculations in this work are based on wind data of layer 3.
Well mixed conditions are assumed in all the grids CAMx model. Therefore plume
heights falling within a particular layer were treated to be of same height as the top of
that layer. Figure 10 shows the plume rise with vertical CAMx structure. All the three fire
events show similar trend of plume rise with low height at night and peak during the late
afternoon. From nighttime till 10 am in the morning, stable atmospheric conditions occur
indicating low turbulence in the atmosphere. On the other hand, unstable conditions occur
during the day with the maximum mixing height during late afternoon. This condition
leads to a vertical convective transport, resulting in high plume rise in the afternoon. The
maximum plume rise is in the range of 800-1300 m.
36
Plume rise calculations using different layers
for velocity data--AUG 30, 2000 Fire ID 1033
3500
plume rise (m.)
3000
2500
2000
z= 1
1500
z= 3
1000
500
0
0
2
4
6
8
10 12 14 16 18 20 22 24
time
Plume rise calculations using different layers
for velocity data--AUG 31, 2000 Fire ID 1085
plume rise (m.)
1800
1600
1400
1200
1000
800
z= 1
z= 3
600
400
200
0
0
2
4
6
8
10 12 14 16 18 20 22 24
time
plume rise (m.)
Plume rise calculations using different layers
for velocity data--Sep 1, 2000 Fire ID 1094
1800
1600
1400
1200
1000
800
600
400
200
0
z =1
z =3
0
2
4
6
8
10 12 14 16 18 20 22 24
time
Figure 9: Plume rise calculations with velocity data of different CAMx layers
37
Fireplume calculations
Aug 30, Fire ID 1033 , 1000 acres
1600
plume rise (m.)
1400
11
1200
10
1000
9
800
8
600
7
6
400
5
4
200
3
0
0
2
4
6
8
10
12
time
14
16
18
20
22
Fireplume calculations
Aug 31, Fire ID 1085 , 4000 acres
1600
plume rise (m.)
1400
11
1200
10
1000
9
800
8
600
7
6
400
5
4
200
3
0
0
2
4
6
8
10
12
time
14
16
18
20
22
18
20
22
Fireplume calculations
Sep 1, Fire ID 1094 , 800 acres
1600
plume rise (m.)
1400
11
1200
1000
800
600
400
6
5
4
200
3
2
0
0
2
4
6
8
10
12
time
14
16
Figure 10: Plume rise with vertical CAMx structure (with wind velocity-layer 3)
38
As shown in Figure 10, Fire ID 1033 does not have a peak at any specific time of the day
but it tends to have high plume rise during many hours in the afternoon. The gradual
change in the plume rise during the afternoon of this fire event is due to the low wind
velocity as shown in Figure 11. Further the low wind velocity favors vertical
displacement rather than horizontal dispersion, resulting in high plume rise.
Peaks in the plume rise behavior occurred on Aug 31 at 5 pm and on Sep 1 at 6 pm.
Lapse rates at these two times are 0.008915 and 0.009605 degree/m., respectively. These
are the only values that are close to adiabatic lapse rate, which is 0.0098 degree/m.,
representing stable near neutral atmospheric condition. Sensitivity runs were performed
to investigate the plume rise behavior for neutral conditions. For neutral conditions, the
plume rise values are very high compared to stable and unstable modes.
plume rise
plume rise (m)
Plume rise for Fire ID 1033
Aug 30 ,2000
wind
1000
10.00
800
8.00
600
6.00
400
4.00
200
2.00
0
0.00
0
2
4
6
8
10
12 14 16 18
20 22 24
time
Figure 11: Plume rise of fire ID 1033 and wind velocity on Aug 30, 2000
39
6.4.2.2 CALPUFF Calculations
One of the input variables for the CALPUFF calculations, with the largest uncertainty, is
fire temperature. Accurate temperatures for the fires of interest are not available, so a
typical temperature range of 900 - 1200K was assumed. Analyses were done at three
different temperatures- 900K, 1200K, and 1600K. Figure 12 represents fire plume rise in
meters for each fire event. As shown in the Figure, temperature has negligible effect on
the plume rise. Similar trends in plume rise are observed for the three fire events. For fire
ID 1033 on August 30 and fire ID 1094 on September 1, plume rise value is around 400
m during the nighttime period (between midnight and 10 am) and 9 pm through midnight.
During the daytime, plume rise increases rapidly with mixing height and reaches a
maximum plume height in the range of 1000 – 2000 m from noon to 5pm. As mentioned
above, each grid and layer in CAMx domain is assumed to be well mixed. Therefore, a
plume that reaches any point in a layer will be considered to at the top of that layer.
Figure 13 shows the maximum plume rise results.
40
Fire ID 1033
3000
12
2000
11
1500
Temp 900K, 42 acre/hr
Temp1200K, 42 acre/hr
1000
Temp 1600K, 42 acre/hr
2200
2000
1800
800
600
400
200
0
8
7
10
9
1600
4
0
5
1400
6
1200
500
1000
Plume rise(m)
2500
Hour
Fire ID 1085
Plume rise(m)
2500
12
2000
Temp 900K, 167 acre/hr
1500
1000
500
7
6
11
10
9
8
Temp1200K, 167 acre/hr
Temp 1600K, 167 acre/hr
80
0
10
00
12
00
14
00
16
00
18
00
20
00
22
00
60
0
40
0
0
20
0
0
Hour
Fire ID 1094
1200
10
800
9
600
400
Temp 900K, 33acre/hr
8
Temp1200K, 33 acre/hr
Temp 1600K, 33 acre/hr
2200
2000
1800
1600
1400
1200
1000
800
3
600
200
0
0
12
7
6
5
4
200
400
Plume rise(m)
1000
Hour
Figure 12: Plume rise calculations with different fire temperatures. Fire ID 1033, 1085, and 1094 are
the fire events occurred in Aug 30, Aug 31, and Sep 1, respectively.
41
3500
3000
2500
2000
1500
1000
500
0
Temp 900K, 42 acre/hr
13
12
2200
2000
1800
1400
1200
1000
1600
10
9
8
800
600
400
200
7
6
5
0
Plume rise(m)
Fire ID 1033
Hour
3500
3000
2500
2000
1500
1000
500
0
Temp 900K, 166 acre/hr
13
12
0
22
0
0
20
0
0
18
0
0
16
0
0
14
0
0
12
0
0
11
10
9
10
0
80
0
60
0
40
0
20
0
8
7
6
0
Plume rise(m)
Fire ID 1085
Hour
Fire ID 1094
1200
10
9
800
8
600
400
Hour
Figure 13: CALPUFF Plume rise calculations with CAMx vertical layers
42
2200
2000
1800
1600
1400
1200
1000
800
3
600
0
12
400
200
7
6
5
4
200
Plume rise(m)
1000
0
Temp 900K, 33acre/hr
6.4.2.3 Comparison between FIREPLUME and CALPUFF results
For the purposes of regional modeling, a simple profile of plume rise, combining the
results of CALPUFF and FIREPLUME will be assumed. Combining the results of
FIREPLUME and CALPUFF models give two proposed profiles as shown in Figure 14.
Figure 14. (a) represents a low wind day in the afternoon and Figure 14. (b) indicates near
neutral condition during the late afternoon. Both models, FIREPLUME and CALPUFF,
agree well at night time (stable condition) between 10 pm at night through 10 am in the
morning. Average plume rise is within level 5 of the CAMx vertical structure. They both
also give the same trend of peak value during mid-late afternoon. Peaks plume heights
range from layers 11 to layer 14. Top of level 12 is chosen to represent this high peak in
the afternoon time.
Proposed plume rise profile of low wind day
plan A.
plume rise (m.)
2500
12
2000
1500
11
10
1000
9
7
500
5
3
0
0
2
4
6
8
10
12
time
14
16
18
20
22
Proposed plume rise profile with high peaks in the
afternoon- plan B.
plume rise (m.)
2500
12
2000
1500
1000
500
5
0
0
2
4
6
8
10
12
time
14
16
18
20
Figure 14 (a) Plume rise for low wind day showing a gradual change during the day;
on a near neutral atmospheric stability in the afternoon.
43
22
(b) Plume rise
6.4.2.4 Conclusions and Recommendations
CALPUFF and FIREPLUME model were selected as the appropriate models for
estimating plume rise for wild fires, and their plume rise results were compared.
Although the two models show some differences in the magnitude and temporal variation
of the plume rise, the model results are in reasonable agreement for the purposes of
regional modeling. To integrate these results into CAMx, simple plume rise profiles were
chosen. The proposed profiles suggest low plume rise at night time and high plume rises
during late afternoon.
44
7. References
Arya, S. P (1999). Air Pollution Meteorology and Dispersion. Oxford University Press,
NewYork.
Briggs, G. A. (1975). Plume rise predictions. In: Lectures on Air Pollution and
Environmental Impact Analysis, D. A. Haugen (ed.), American Meteorological Society,
Boston. 59-111.
Briggs, G.A. (1984). Plume Rise and Buoyancy Effects, in Atmospheric Science and
Power Production, D. Randerson (ed.), DOE/TIC – 27601, Weather Service Nuclear
Support Office, National Oceanic and Atmospheric Administration, United States
Department of Commerce, 327-366.
Burgan, R. (1988). 1988 Revision to the 1978 National Fire Danger Rating System,
Research Paper SE-273. Asheville, NC: USDA Forest Service, Southeastern Region.
Dennis, A. (2000). Inventory of Air Pollutant Emissions Associated with Forest,
Grassland and Agricultural Burning in Texas. Masters of Science Thesis, The University
of Texas at Austin, Austin, Texas.
Dennis, A., M. Fraser, S. Anderson, and D. T. Allen, “Air Pollutant Emissions
Associated with Forest, Grassland and Agricultural Burning in Texas,” Atmospheric
Environment, 36, 3779-3792 (2002).
Brown, D., Dunn, W., Lazaro, M., Policastro, A. (1999): The FIREPLUME model: Tool
for Eventual Application to Prescribed Burns and Wildland Fires, Proc. from The Joint
Fire Science Conference and Workshop.
http://www.nifc.gov/joint_fire_sci/conferenceproc/
Chang, J. C., Scire, J. S. (1999). User’s Guide to the EPM2BAEM Interface Program.
Earth Tech, Inc. Concord, Massachusetts.
ENVIRON (2000). User’s Guide Comprehensive Air Quality Model with Extensions
(CAMx) version 3.10. ENVIRON International Corporation. Novato, California.
EPA. (1992). Open Burning. Compilation of Air Pollution Emission Factors, Volume I:
Stationary Point and Area Sources, Fifth Edition, Chapter 2.5, AP-42 (GPO 055-00000500-1). US Environmental Protection Agency. Research Triangle Park, North
Carolina.
Harley, R. Dept. of Civil and Environmental Engineering, University of California at
Berkeley. Personal communications with Dr. David Allen, Center for Energy and
Environmental Resources, The University of Texas at Austin.
45
Jenkins, B. M. et al. (1996). Atmospheric Pollutant Emission Factors from Open
Burning of Agricultural and Forest Biomass By Wind Tunnel Simulations. Volume 2,
Results. Cereal Crop Residues. California Air Resources Board (CARB) Project No.
A932-126. Reproduced by the National Technical Information Service, PB97-126940.
McMahon, C. K. (1983). Characteristics of Forest Fuels, Fires, and Emissions,
Presented at the 76th Annual Meeting of the Air Pollution Control Association, Atlanta,
GA.
Scire, J. S., Strimaitism, D. G., Yamartino R. J. (2000). A User’s Guide for the
CALPUFF Dispersion Model v. 5. Earth Tech, Inc. Concord, Massachusetts.
Texas Forest Service, 2000: Highlights. Texas Forest Resource Harvest Trends 2000.
http://txforestservice.tamu.edu/forest%5Fmanagement/pdf/ht2000.pdf
Turner, D.B., Chico, T., Catalano, J.A. (1986). TUPOS-P - A Program for Reducing
Hourly and Partial Concentration Files Produced by TUPOS: User's Guide. EPA-600/886/012. U.S. Environmental Protection Agency. Research Triangle Park, North Carolina.
Turner, D. B., Chico, T., and Catalano, J. A. (1986). TUPOSCA Multiple Source
Gaussian Dispersion Algorithm Using On-Site Turbulence Data. U.S. Environmental
Protection Agency. Research Triangle Park, North Carolina (EPA-600/8-86/010).
US Census Bureau, 2000. County Population Estimates and Demographic Components
of Population Change: Annual Time Series, July 1, 1990 to July 1, 1999.
http://eire.census.gov/popest/archives/county/co_99_8.php
US Census Bureau, 2001. Population Change for Counties.
http://eire.census.gov/popest/data/counties/tables/CO-EST2001-08.php
Ward, D.E. and Hardy, C.C. (1991). Smoke Emissions from Wildland Fires. Envrionment
International, Vol. 17, pp. 117-134.
Ward, D., Peterson, J., Hao, W. (1993). An Inventory of Particulate Matter and Air Toxic
Emissions from Prescribed Fires in the USA for 1989. Report A1299, USDA Forest
Service, Intermountain Research Station, Missoula, MT.
Weil, J.C. (1988). Plume Rise, in Lectures on Air Pollution Modeling, A. Venkatram and
J.C.Wyngaard (eds.), American Meteorological Society, Boston. 119-157
Wiedinmyer, C., Strange, I., Estes M. Yarwood, G., and Allen, D., 2000: Biogenic
Hydrocarbon Emission Estimates for North Central Texas, Atmos. Environ. 34, 3419 –
3435.
Zonato, C., Vidili, A., Pastorino, R., De Faveri, D.M., 1993: Plume Rise of Smoke
Coming from Free Burning Fires, J. Hazardous Materials. 34, 69 – 79.
46
Appendix A: Land Cover Code Descriptions
Table A- 1: Description of FOFEM land cover codes.
Source Category
Logging Slash
Code
100b
100c
100d
100e
147d
Description
Corn Residue
Hay/Pasture Grasses
Sugarcane residue
Wheat Residue
Pine-Hardwood Slash Debris
Prescribed Range, Landclearing Slash
151b
Other Forests/Woods Range
Agricultural
Prescribed Range, Wildland, Land Clearing
Slash
Wildland
Urban
Water/Barren
2
Desert Grasslands
3
Plains Grasslands
6
Wet Grasslands
7
Prairie - Tall Grass
18
Shinnery - Moderate Coverage
21 SW Shrub Steppe - Moderate Coverage
22
SW Shrub Steppe - High Coverage
23
Texas Savannah
147c
Piney Woods Non-Forest
293
Pinyon - Juniper
100a
Cropland Mosaic
147a
Pine-Hardwood Natural Forest
147b
Pine-Hardwood Plantation
151a
Other Forests/Woods Natural
211
Ponderosa Pine Natural
1
Urban
999
Water/Barren
47
Table A- 2: Description of NFDRS LULC codes.
NFDRS Code NFDRS Model
Vegetation Represented
1
A
Western annual grasses
2
B
California mixed chaparral
3
C
Pine grass savanna
4
D
Southern rough
5
E
Hardwoods (winter)
6
F
Intermediate brush
7
G
Short needle conifers with heavy dead load
8
H
Short needle conifers with normal dead load
9
I
Heavy logging slash
10
J
Intermediate logging slash
11
K
Light logging slash
12
L
Western perennial grasses
13
M
Agricultural land
14
N
Sawgrass or other thick stemmed grasses
15
O
High pocosin
16
P
Southern pine plantation
17
Q
Alaskan black spruce
18
R
Hardwoods (summer)
19
S
Alpine tundra
20
T
Sagebrush-grass mixture
21
U
Western long-needle conifer
22
V
Water
23
W
Barren
24
X
Water
48
Appendix B: Daily Plots of Fires and 32-Hour Wind Back-Trajectories
Appendix B-1: Daily Plots of Fires onto the LULC Map
Figure B- 1: Legend. The red dots indicate the size of the fire. The colors correspond to vegetation
cover codes. Table B-1 contains the description of the cover codes.
#
#
#
#
#
#
#
0.1 - 250 acres
250 - 500 acr es
500 - 1000 acres
1000 - 5000 acr es
5000 - 10000 acres
10000 - 15000 acr es
15000 - 20000 acr es
Wat er/Barren
FOFEM_1
FOFEM_3
FOFEM_6
FOFEM_7
FOFEM_18
FOFEM_21
FOFEM_22
FOFEM_23
FOFEM_100
FOFEM_147
FOFEM_151
FOFEM_293
NFDRS_3
NFDRS_4
NFDRS_6
NFDRS_12
NFDRS_13
NFDRS_14
NFDRS_15
NFDRS_16
NFDRS_18
NFDRS_20
49
Table B- 1: Description of land use categories and associated composite emission factors for PM2.5,
NOX, and NMHC.
Legend
Water/Barren
FOFEM 1
FOFEM 2
FOFEM 3
FOFEM 6
FOFEM 7
FOFEM 18
FOFEM 21
FOFEM 22
FOFEM 23
FOFEM 100
FOFEM 147
FOFEM_151
FOFEM_211
FOFEM_293
NFDRS_1
NFDRS_2
NFDRS_3
NFDRS_4
NFDRS_5
NFDRS_6
NFDRS_7
NFDRS_8
NFDRS_9
NFDRS_10
NFDRS_11
NFDRS_12
NFDRS_13
NFDRS_14
NFDRS_15
NFDRS_16
NFDRS_17
NFDRS_18
NFDRS_19
NFDRS_20
NFDRS_21
Composite
emission
Description
Factor PM2.5
(lbs/acre)
Water/Barren
0
Urban
0
Desert Grasslands
6.00
Plains Grasslands
13.00
Wet Grasslands
50.00
Prairie - Tall Grass
96.00
Shinnery - Moderate Coverage
46.00
SW Shrub Steppe - Moderate Coverage
41.00
SW Shrub Steppe - High Coverage
65.00
Texas Savannah
111.00
Cropland Mosaic
13.00
Pine-Hardwood Natural Forest
90.00
Other Forests/Woods Natural
123.00
Ponderosa Pine Natural
165.00
Pinyon - Juniper
148.00
Western annual grasses
13.1
California mixed chaparral
341
Pine grass savanna
87.6
Southern rough
132
Hardwoods (winter)
80.5
Intermediate brush
252
Short needle conifers with heavy dead load
436.5
Short needle conifers with normal dead load
146.5
Heavy logging slash
816
Intermediate logging slash
454
Light logging slash
169
Western perennial grasses
19
Agricultural land
0
Sawgrass or other thick stemmed grasses
108
High pocosin
311
Southern pine plantation
66.5
Alaskan black spruce
274
Hardwoods (summer)
45.5
Alpine tundra
79.5
Sagebrush-grass mixture
88.5
Western long-needle conifer
106.5
50
Composite
Composite
emission
emission
Factor NOX Factor NMHC
(lbs/acre)
(lbs/acre)
0
0
0
0
0.70
5.00
1.50
10.00
5.80
38.00
11.10
73.00
5.50
35.00
4.90
32.00
7.60
49.00
12.80
85.00
1.50
10.00
11.60
69.00
16.20
93.00
23.90
125.00
16.50
113.00
1.75
10
57.5
257.5
12
66.8
21.25
100
11.875
61.25
41.25
190.5
68.75
330
23.75
111
145
618
81.25
344
30
128
2.5
14.5
0
0
17.5
82
51.25
235
11.25
50.5
48.25
207.5
7.5
34.5
11.25
60.5
13.75
67
17.5
81
August 1
51
August 2
52
August 3
53
August 4
54
August 5
55
August 6
56
August 7
57
August 8
58
August 9
59
August 10
60
August 11
61
August 12
62
August 13
63
August 14
64
August 15
65
August 16
66
August 17
67
August 18
68
August 19
69
August 20
70
August 21
71
August 22
72
August 23
73
August 24
74
August 25
75
August 26
76
August 27
77
August 28
78
August 29
79
August 30
80
August 31
81
September 1
82
September 2
83
September 3
84
September 4
85
September 5
86
September 6
87
September 7
88
September 8
89
September 9
90
September 10
91
September 11
92
September 12
93
September 13
94
September 14
95
September 15
96
September 16
97
September 17
98
September 18
99
September 19
100
September 20
101
September 21
102
September 22
103
September 23
104
September 24
105
September 25
106
September 26
107
September 27
108
September 28
109
September 29
110
September 30
111
Appendix B-2: Daily Plots of Fires and 32-Hour Wind Back-Trajectories
Figure B- 2: Legend. The values in meters (green, brown, and blue dots) correspond to the height
above the ground of the wind trajectory at the starting hour (La Porte site). The dots in red
correspond to different fire sizes.
112
August 1
113
August 2
114
August 3
115
August 4
116
August 5
117
August 6
118
August 7
119
August 8
120
August 9
121
August 10
122
August 11
123
August 12
124
August 13
125
August 14
126
August 15
127
August 17
128
August 18
129
August 19
130
August 20
131
August 21
132
August 23
133
August 24
134
August 25
135
August 26
136
August 27
137
August 28
138
August 29
139
August 30
140
August 31
141
September 1
142
September 2
143
September 3
144
September 4
145
September 5
146
September 6
147
September 7
148
September 8
149
September 9
150
September 10
151
September 11
152
September 12
153
September 13
154
September 14
155
September 15
156
September 16
157
September 17
158
September 18
159
September 19
160
September 20
161
September 21
162
September 22
163
September 23
164
September 24
165
September 25
166
September 26
167
September 27
168
September 28
169
September 29
170
September 30
171
Appendix C: Contact Summary
Table C- 1: Contact Information
Institution
Texas
Interagency
Cooridination
Center
(TICC)
Contact
Gill Hodges
Phone
Fax
936-875-4786 936-875-4812
Texas Forest
Curt Stripling 936-435-0852
Service (TFS)
Louisiana
Interagency
Corrdination
Center
Texas Fire
Incident
Reporting
System
(TEXFIRS)
National
Interagency
Fire
Management
Integrated
Database
(NIFMID)
Sheryl Roach 318-473-7152 318-473-7172
E-mail
Message
Website Address
[email protected]
Gill Hodges maintains the database of
wildfires that occur on Texas State and
Private Ownership Lands. Database of
wildfires that occurred on the Texas State
and Private Ownership Lands for year
2000 was received from her.
http://www.usda.gov/nass/
[email protected] ,
[email protected]
[email protected]
Written request by E-mail was submitted
to TEXFIRS to obtain wildfires records
for year 2000 from Texas State Fire
Marshal's Office. Records were not yet
received.
Virginia Garza 512-305-7950 512-305-7910
Daniel Ervin 208-387-5288
He sent the Texas Forest Service
Conversion Software to convert the Block
and Grid Format to Latitude/Longitude
Coordinates
She maintains the records of wildfires that
occur on Federal Ownership Lands in
Louisiana. The records of wildfires that
occurred on the Federal Lands in
Louisiana for Year 2000 was received
from her.
[email protected]
172
He maintains the National Interagency
Fire Management Integrated Database
(NIFMID). NIFMID for year 2000 can be
downloaded from
http://famweb.nwcg.gov/ and the user's
guide for the database can be downloaded
from
www.fs.fed.us/fire/planning/nist/sit.htm
Texas
Interagency
Cooridination
Center
(TICC)
Glenn
Hammond
936-875-4812
Louisiana
Department of
Louis Heaton 225-925-4500
Agriculture and
Forestry
United States Dellora Gauger
Department of
(Northern
406-329-3256 406-329-3359
Agriculture
Region)
(USDA) Forest
Service (FOIA
Marianne
Coordinators) Frazier (Rocky
303-275-5292 303-275-5299
Mountain)
[email protected]
Glenn Hammond maintains the database
of wildfires that occur on National Forests
in the state of Texas. He is the USDA
Forest Service's representative at TICC.
Database of wildfires that occurred on the
National Forests in Texas in year 2000
was received from him.
He is the concerned person to contact for
wildfire records in the Louisina Dept. of
Agriculture and Forestry (LDAF). LDAF
has no computer records for wildfires that
[email protected]
occurred in year 2000 due to their
computer system failure. Personal visit to
various districts was recommended to
obtain the hardcopies of the wildfire
records for year 2000.
[email protected]
[email protected]
Joe Sedillo
505-842-3383 505-842-3111
(Southwestern)
[email protected]
Rochelle Jelaco
801-625-5354 801-625-5240
(Intermountain)
[email protected]
173
Freedom of Information Act (FOIA)
request letters were submitted by E-mail
to all the USDA regional offices
requesting wildfire records that occurred
on federal ownership in the year 2000.
The completed database of the wildfire
records that occurred on federal
ownership in the United States for year
2000 was received from the Fire &
Aviation Management, Forest Service,
USDA (14th & Independence, SW,
P.O.Box 96090, Washington, D.C. 200906090)
Juanita Cuevas
(Pacific
707-562-8768 707-562-9041
Southwest)
Patty Brandt
(Pacific
Northwest)
503-808-2268 503-808-2255
[email protected]
Andrea Csergei
404-347-7310 404-347-5401
(Southern)
[email protected]
Paul Witte
(Eastern)
414-297-3403 414-297-3808
Cherie Shelley
907-586-8855 907-586-7852
(Alaska)
Arkansas
Forestry
Commission
Oklahoma
Department of
Agriculture,
Food &
Forestry
Alabama
Forestry
Commission
[email protected]
Sherry Russell 501-332-2000
Patrick Mc
Dowell
Lou Hyman
405-522-6146
334-240-9354
[email protected]
[email protected]
Written request by E-mail was submitted
to this person to obtain wildfires records
[email protected]
for year 2000 in the state of Arkansas.
Records were not yet received.
[email protected]
He is the concerned person to contact for
wildfire records in the Oklahoma Dept. of
Agriculture and Forestry. Received
wildfire records that occurred on the lands
under the protection of Oklahoma Dept. of
Agriculture and Forestry during AugustSeptember 2000.
He is the concerned person to contact for
wildfire records in the Alabama Forestry
Commission. Received records for
[email protected]
wildfires and prescribed fires that
occurred on the state and private lands in
Alabama for year 2000.
174
Mississippi
Randal Romedy 601-359-2823
Forestry
Commission
Florida
Division of
Forestry
Sue McLellan 850-414-8554
Southern Area
Coordination Randy Dzialo 770-458-2464 770-458-6308
Center
[email protected]
This is the person to contact for wildfire
records in the Mississippi Forestry
Commission. Received information that
records for wildfires that occurred both on
state and federal lands in the state of
Mississippi in the year 2000 are available
in GIS format. Submitted written request
for wildfire records for year 2000 and
records were not yet received.
[email protected]
This is the person to contact for wildfire
records in the Florida Division of
Forestry. Submitted written request for
wildfire records for year 2000 and records
were not yet received.
[email protected]
This is the person to contact for wildfire
records in the Southern Area Coordination
Center (SACC). This center reports the
wildfire incidents in 13 southern states,
Puerto Rico and Virgin Islands to the
National Interagency Coordination Center
(NICC). For more information on SACC,
http://www.southernregion.fs.fed.us/sacc/.
This person informed that this center
doesn't maintain detailed wildfire records
on a daily basis and can provide only the
total number of fires and total acreage
burned for year 2000. He recommended to
contact the individual state forest
departments, USDA Forest Service, and
NICC.
175
U.S.
Department of
Agriculture
National
Agricultural
Statistics
Service
Mark Miller
He confirmed that the databases at NASS
do not include information about cropland
burning. NASS databases list crop types
by county
202-720-7621
Texas
Agricultural Burt Williams, 806-659-4130
Extension Hansford Co.
Agents
(contacted by Mike Bragg, 806-244-4434
Dallam Co.
phone)
Vince
Mannino,
409-835-8461
Jefferson Co.
Mike McCown,
409-267-8347
Chambers Co.
[email protected]
He reports that <5% of irrigated wheat
fields in this area are burned and that
burning is unlikely on non-irrigated fields.
[email protected]
[email protected]
He reports that buring of crop residues is
uncommon in his area and occurs in
winter.
[email protected]
He reports that there is minimal marsh
burning near the coast in February and
early spring. In the same period no more
that 10% of hay fields are burned and that
no rice fields are burned. The Anahuac
Wildlife preserve sometimes burns coastal
grasses to improve wildlife habitat. In the
last 5 years the practice of burning crop
residue has been decreasing due to
changes in accepted practices and drought
conditions.
176
[email protected]
He reports that very little marsh grass is
burned in his area and set the approximate
amount at 5%. The marsh grasses that are
burned are no more than 10-15 miles from
the coast. He also said that no croplands
are burned in his area.
Brent M
Batchelor, 979-245-4100
Matagorda Co.
[email protected]
He reports that crop burning is very rare
and that coastal marsh grasses are
sometimes burned, mainly in winter, most
commonly in February.
Ismaro
Cardenas, Live 361-449-2733
Oak Co.
[email protected]
Donnie
Montemayor, 361-362-3280
Bee Co.
[email protected]
Kelly R Boldt,
409-835-8461
Jefferson Co.
Region 1 Panhandle
(Amarillo)
Texas
Agricultural Armstrong
Extension
Offices
Deaf Smith
(contacted in
Hemphill
email
Oldham
survey)
Wheeler
Region 2 South Plains
(Lubbock)
Bailey
Dawson
Hockley
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
177
Parmer
Yoakum
Region 3 Rolling Plains
(Vernon)
Archer
Dickens
Jack
Motley
[email protected]
[email protected]
Throckmorton
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
Region 4 North (Dallas)
Bowie
[email protected]
Dallas
Grayson
Morris
Titus
Region 5 - East
(Overton)
[email protected]
[email protected]
[email protected]
[email protected]
Anderson
[email protected]
Henderson
[email protected]
Newton
[email protected]
San Augustine
[email protected]
Upher
[email protected]
178
Responded to email questionnaire by
telephone. Indicated that about 4% of
crop residue from wheat fields is burned,
usually in June, and that <1% of
rangelands are burned in the county.
Region 6 - Far
West (Ft.
Stockton)
Andrews
Ector
[email protected]
[email protected]
Jeff Davis
[email protected]
Presidio
Val Verde
Region 7 West Central
Brown
Concho
Llano
Nolan
Sterling
Region 8 Cenral
Bell
Elleis
[email protected]
[email protected]
Hill Country
[email protected]
McLellan
[email protected]
Willaimson
[email protected]
Region 9 Southeast
(Bryan)
Brazos
Grimes
Liberty
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
179
Walker
Region 10 Southwest
(Uvalde)
Aransas
Caldwell
[email protected]
[email protected]
[email protected]
Guadalupe
[email protected]
Kinney
Uvalde
[email protected]
[email protected]
Regioin 11 Coastal Bend
(Corpus
Christi)
Texas
Department of
Agriculture
Bee
Goliad
Nueces
Wharton
Region 12 South
(Weslaco)
[email protected]
[email protected]
[email protected]
[email protected]
Atascosa
[email protected]
Frio
Kleberg
Starr
Zavala
[email protected]
[email protected]
[email protected]
[email protected]
The Texas Department of Agriculture
keeps no records of cropland or rangeland
burning.
512-339-2929
180
Texas
Department of
Agriculture
TNRCC
Regional
Affairs
TAMU
Academy for
Ranch
Management
TAMU
Agricultural
Research
Station Sonora
Kerr WMA
The Texas Department of Agriculture
keeps no records of cropland or rangeland
burning.
512-339-2929
He oversees a 10 county area and reports
that he is not aware of any cropland
burning in his area. He mentioned the
rangeland burning of salt marshes near the
Gulf Coast in winter.
Patrick
Derek Eads
409-898-3838
He reports that cropland and rangeland
burning is very rare.
Dr. Ray
Hinnant
979-845-5580
Texas A&M researcher recommended
contacting Dr. Butch Taylor for
information about rangeland burning.
915-387-3168
Dr. Taylor is a proponent of rangeland
burning and leads the Edwards Plateau
Prescribed Burning Association. He
reports that the interest in prescribed
rangeland burnig has increased, but that
drought conditions have prevented its
practice from becoming much more
common. There has been some increase
in burning for Ashe Juniper control on the
Edwards Plateau.
Dr. Butch
Taylor
He reports that most prescribed burns
occur in the winter (Dec. - Feb.); hot
summer burns are rare and should not
occur during drought conditions.
Donnie Freds
Texas A&M
University
Reference
Guide for Texas
Ranchers
Website outlines accepted practices for http://texnat.tamu.edu/ranchref/guid
rangeland prescribes burns.
e/rwabc.htm
181
Newspapers
Contacted for
Fire
Information
Lake Charles
American
Press, LA
The Advocate Baton Rouge,
LA
The Daily
Advertiser Lafayett, LA
Deridder Daily
News Deridder. LA
The Shreveport
Times Shreveport, LA
Austin
American
Statesman Austin, TX
Beaumont
Enterprise Beaumont, TX
The Examiner Beaumont, TX
Dallas Morning
News - Dallas,
TX
Houston
Chronicle Houston, TX
Houston
Chronicle -
http://www.americanpress.com/
http://www.theadvocate.com/
http://www.theadvertiser.com/news
http://www.deridderdailynews.com/
http://www.nwlouisiana.com/
http://www.austin360.com/aas/
http://www.southeasttexaslive.com/
site/news.asp?brd=2287
http://www.theexaminer.com/
http://web.lexis-nexis.com/universe
http://www.chron.com/
http://web.lexis-nexis.com/universe
182
Houston, TX
Times-Picayune
- New Orleans,
LA
The Daily
Sentinal Nacogdoces,
TX
http://web.lexis-nexis.com/universe
http://www.dailysentinel.com/
183