Accepted Manuscript

Accepted Manuscript
Title: Performance Evaluation of an Air Quality Forecast Modeling System for a
Summer and Winter Season - Photochemical Oxidants and Their Precursors
Authors: Chenxia Cai, Christian Hogrefe, Petros Katsafados, George Kallos, Mark
Beauharnois, James. J. Schwab, Xinrong Ren, William H. Brune, Xianliang Zhou,
Yi He, Kenneth L. Demerjian
PII:
DOI:
Reference:
S1352-2310(08)00793-0
10.1016/j.atmosenv.2008.08.029
AEA 8545
To appear in:
Atmospheric Environment
Received Date: 10 April 2008
Revised Date: 15 August 2008
Accepted Date: 19 August 2008
Please cite this article as: Cai, C., Hogrefe, C., Katsafados, P., Kallos, G., Beauharnois, M., Schwab,
J.J., Ren, X., Brune, W.H., Zhou, X., He, Y., Demerjian, K.L. Performance Evaluation of an Air Quality
Forecast Modeling System for a Summer and Winter Season - Photochemical Oxidants and Their
Precursors, Atmospheric Environment (2008), doi: 10.1016/j.atmosenv.2008.08.029
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ARTICLE IN PRESS
Performance Evaluation of an Air Quality Forecast Modeling System for a Summer and
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Winter Season - Photochemical Oxidants and Their Precursors
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Chenxia Cai1,a, Christian Hogrefe1, Petros Katsafados2,b, George Kallos2, Mark Beauharnois1,
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James. J. Schwab1, Xinrong Ren3,c, William H. Brune3, Xianliang Zhou4,5 Yi He4,5, Kenneth L.
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Demerjian1,*
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Atmospheric Sciences Research Center, State University of New York at Albany, Albany, NY
12203, USA
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School of Physics, University of Athens, Athens, 15784, Greece
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Department of Meteorology, Pennsylvania State University, University Park, PA 16802, USA
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Department of Environmental Health and Toxicology, University at Albany, State University of
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Wadsworth Center, New York State Department of Health, Albany, NY 12201, USA
*
Corresponding author: K. L. Demerjian
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New York, Albany, NY 12201, USA
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Email: [email protected]
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Address: 251 Fuller Road ASRC, Albany, NY, USA 12203
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Phone: +1-518-4378705
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Fax: +1-518-4378711
a
Now at California Air Resources Board, Sacramento, California, 95814, USA
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b
Now at Department of Geography, Harokopio University of Athens, Athens, 17671,
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Greece.
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c
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USA
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Now at Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL 33149,
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Submitted to Atmospheric Environment (August, 2008)
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Abstract
The predictions of O3, precursor species and the key transit species from an Air Quality
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Forecast Modeling System (AQFMS) are evaluated for July 2001 and January 2004 utilizing an
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extensive measurement data set from the PMTACS-NY “Supersite” program. The AQFMS,
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consisting of a chemical transport model coupled with a mesoscale meteorological forecasting
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model, operated routinely over the course of the six year study. The domain wide 8-hour average
7
O3 predictions in the summer season have an average mean normalized bias (MNB) of 8.6%.
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The AQFMS captured the day-to-day variations of O3, CO, NOx (NOy) and SO2 at the Queens
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College (urban) and Pinnacle State Park (rural) sites during both summer and winter. During July
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2001, the linear regressions of CO vs. NOx at Queens College and CO vs. NOy at Pinnacle State
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Park are in reasonable agreement with observations. However, during January 2004 the slopes of
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the linear regressions are significantly overestimated suggesting more uncertainties with the
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winter emission inventories. The Ozone Production Efficiency (OPE) is under predicted by 45%
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at Pinnacle State Park during July 2001 which may be caused by the underestimation of NOz
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removal and/or the underestimation of OH concentrations. Concentrations of HONO, a key
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transient specie for OH production are significantly under predicted by the AQFMS indicating
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deficiencies in the chemical mechanism in the AQFMS. The underestimation of OH
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concentrations is much more significant during January 2004 which suggests larger uncertainties
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with chemical mechanism for winter conditions.
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Keywords:
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Air quality forecast model, Ozone, Precursor gases, Hydroxyl radical, Nitrous acid
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1. Introduction
Ozone and particulate matter (PM) air quality remain of great concern to the public health
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and regulatory communities, as these pollutants continue to show deleterious health outcomes in
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exposed populations, most significantly those sensitive populations at high risk (NRC, 1991;
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Laden et al., 2000). The availability of a real time air quality forecast and pollution warning
6
system capable of providing accurate air quality forecasts in a timely manner would be of
7
significant value to the health effects, health care and regulatory communities as well as the
8
public at large. During the past several years, there has been an increasing interest in the
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development and application of numerical 3-D air quality modeling systems to forecast the local
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or regional air quality in real time (Cai et al., 2006; McHenry et al., 2004; Cope et al., 2004;
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Mckeen et al., 2005). The evaluation of these modeling systems has mainly focused on O3
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predictions for the summer season. However, an AQFMS must have the ability to simulate
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atmospheric processes/air quality throughout the year, since particulate matter, unlike O3, is not a
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warm season only pollutant, but has significant contributions under cold weather conditions. The
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successful model must not only accurately predict ozone and particulate matter in space and
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time, but also their associated precursors (Dennis et al., 2000). In addition, HOx (OH and HO2)
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radicals and the key chemical constituents that drive their production and termination are critical
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in determining the oxidative capacity of the atmosphere. Thus, the opportunity to evaluate the
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accuracy of predictions of the key transient species that drive atmospheric oxidation processes in
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model reaction mechanisms is highly desirable.
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An Air Quality Forecast Modeling System (AQFMS) has been developed and operated in
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the Atmospheric Sciences Research Center (ASRC), State University of New York at Albany
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since July 2001 to evaluate the adequacy and reliability of a photochemical air quality model
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system in supporting real-time 24-hr air quality forecasts for ozone, PM2.5 and the precursors for
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the Northeastern United States. In this paper, the predictions of gaseous species including O3 and
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precursor species (CO, NOx/NOy, SO2, VOC) as well as two key transient species: HONO and
4
HOx radical for a summer (July 2001) and a winter month (January 2004) are evaluated. During
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these two months, intensive field campaigns of PM2.5 Technology Assessment and
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Characterization Study-New York (PMTACS-NY), one of EPA’s Supersite programs (Solomon
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and Hopke, 2008), were conducted at Queens College, New York City. A variety of
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measurements utilizing advanced instrument technologies performed during the two campaigns
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provide a detailed chemical characterization of the gas and particle phase composition of the
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atmosphere (Drewnick et al., 2004; Ren et al., 2003; 2006; Weimer et al., 2006) and provide a
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unique opportunity to evaluate the performance of modeling systems like the AQFMS.
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Measurements of O3, CO, SO2, NOx and NOy performed at Pinnacle State Park in Addison, NY,
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a regional/rural site located in the southern tier along the New York - Pennsylvania border, are
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also used in the model evaluations, providing a unique opportunity to compare and contrast
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urban-rural and seasonal differences.
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(HONO) measurements also provide a unique opportunity to evaluate the chemical mechanism
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within the AQFMS and identify sources of model uncertainty.
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The availability of HOx (OH and HO2) and nitrous acid
2. Operational AQFMS
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The three core models in the AQFMS are the SKIRON/Eta meteorology model, the
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Sparse Matrix Operator Kernel Emissions (SMOKE) processing system and the Comprehensive
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Air Quality Model with Extensions (CAMx). The ETA model which is fully operational in the
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National Weather Service providing weather forecasting in United States (Black, 1994) has been
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further developed at the University of Athens for project SKIRON (Kallos et al., 1997). The
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statistical assessment of SKIRON/Eta for a 12-month period show satisfactory predictions of
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wind speed/direction, air temperature and mean sea surface pressure (Papadopoulos et al., 2001).
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The AQFMS used SMOKE processed emissions from the National Emission Inventory 1999
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(NEI99), the most up-to-date inventory at the time of this application. Biogenic emissions are
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estimated by the Biogenic Emission Inventory System (BEIS2) module incorporated in SMOKE
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and are affected by both temperature and radiation. It should be noted that differences between
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1999 and 2001 emissions are not expected to be significant, as major emission changes with
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respect to the NOx SIP Call (USEPA,1998) did not get underway until 2002. In addition, as
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pointed out in Frost et al. (2006), winter emissions are not significantly affected by the NOx SIP
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Call which is geared to address summertime emissions impacting ozone exceedences and thus,
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not likely to introduce significant changes to 2004 winter emission. The Comprehensive Air
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Quality Model with Extensions (CAMx) version 4.0 (Environ, 2003) used in the AQFMS, is a
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“state of the science” Eulerian photochemical grid model that allows for integrated assessment of
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gaseous and particulate air-pollution with many notable features. In the operational air quality
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forecast, the CBM4 mechanism with revised radical-radical termination reactions and updated
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isoprene chemistry based on (Carter, 1996) is used for gas phase chemistry. Aqueous phase
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chemistry follows the approach developed for RADM (Chang et al., 1987)
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The domains of SKIRON/Eta and CAMx are illustrated in Figure 1. Nested grids are
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used in the CAMx model in order to provide air quality forecasts at finer resolution in the region
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of greatest interest (in this case, the northeastern USA) while simulating the regional air masses
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outside this domain at coarser resolution. The coarse CAMx domain covers the area from (26°N,
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99.0°W) to (47°N, 67°W) with 1/2 deg longitude by 1/3 deg latitude horizontal grid (roughly 36
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km) increment and the number of grid points x-y plane is 64 x 63. The fine CAMx domain
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covers the area from (31.9°N, 92.2°W) to (44.1°N, 69.3°W) with 1/6 deg longitude by 1/9 deg
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latitude (roughly 12 km) horizontal grid increment and the number of grid points x-y plane is 137
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x 110. There are 14 vertical model layers for both fine and coarse domains extending from the
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surface to about 4000 m aloft. A time-invariant boundary condition with ozone background
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concentration at 35 ppb was prescribed for the CAMx coarse domain for all the heights. The
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nested grid allows the fine domain to use the predicted pollutant concentrations from coarse
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domain as the boundary condition, reducing the impact of the artificial boundary condition set
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for the coarse domain. 35 ppb of O3 is also used as the top boundary of the entire model grid.
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The contribution of the free tropospheric O3 above 4000 m is treated as an upper boundary
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condition
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(http://croc.gsfc.nasa.gov/intexb/ions06.html) show that ozone at 4 km is more in the range of
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50-70 ppb over northeastern U.S, our sensitivity studies indicate less than a 5% increase in
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surface layer O3 concentrations with the application of 60 ppb as the top boundary condition.
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Since PBL heights > 4 km are rare in the northeast, this also suggests that the exchange between
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free troposphere and boundary layer does not significantly impact the forecast for surface level
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for our study time periods. The left lower and right upper corners of the SKIRON/Eta domain are
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located at (23.6°N, 106.7°W) and (50.4°N, 59.5°W) respectively. The horizontal grid increment
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is 0.12 x 0.12 deg and there are 32 vertical layers with the top layer at 15.8 km. The
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meteorological parameters generated by SKIRON/Eta are interpolated to the coarse and fine
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CAMx grids before they are applied as the input for CAMx. Initial conditions for the CAMx air
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quality simulation are obtained from the previous day’s forecast. If the previous day’s forecast is
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not available, a two-day spin-up simulation starting from nominal initial conditions is performed
the
model.
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the
recently
released
ozonesonde
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prior to the forecast simulation. These include initial and boundary conditions of 35 ppb for
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ozone and based on the analyses of CO measurements at multiple rural sites, 100 ppb and 180
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ppb as boundary and initial condition for CO during summer and winter respectively. The
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forecast results presented in this paper are all from the fine domain simulation.
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3. Observation Data
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The evaluation analyses for the domain-wide predictions of ozone during summer month
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draw upon the observations from EPA’s AQS. These data are available online from
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(http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm).
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Detailed
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observations from the two PMTACS-NY Supersite monitoring sites located at Queens College in
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Queens, NY (urban, 40.7°N, 74.0°W) and Pinnacle State Park in Addison, NY (rural, 42.1°N,
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72.2°W) are used to assess the model’s capabilities in capturing urban and rural air mass
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characteristics over different seasons. The locations of the two sites are shown in Figure 1. In
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addition to routine air quality parameters (SO2, NO, NO2, O3, CO, NMHC), transient species
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(e.g., HOx(OH, HO2) and HONO) were also measured during PMTACS-NY Queens College
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July 2001 and January/February 2004 field intensive. This compliment of data is used to evaluate
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the overall performance of the AQFMS and most uniquely its ability to predict radical chain
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lengths and the oxidative capacity of the sampled air masses, both critical in understanding the
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production of secondary pollutants.
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4. Meteorology Forecasts
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Meteorological inputs are essential to air quality modeling and the accuracy of
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meteorological forecasts will largely affect the accuracy of air quality prediction (Seaman, 2000).
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It should be noted that regulatory applications of air quality simulation models are performed
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using meteorological hindcast data with observational data assimilation. Such applications
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should under most circumstances produce more accurate meteorological input parameters than
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that associated with real-time unperturbed meteorological forecasts, which do not have the
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benefit of such data nudging and therefore face a great challenge in forecasting air quality
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concentration fields. Mean Bias Error (MBE) a mod ( x, t ) − a obs ( x, t ) (Hogrefe et al., 2001) for
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SKIRON/Eta predictions of temperature at a height of 2 meters and wind speed at a height of 10
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meters are calculated using the observation data for measurement sites within the fine modeling
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domain. The data are retrieved from the Data Support Section at the National Center for
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Atmospheric Research (NCAR-DSS). For July 2001, SKIRON/Eta has an average positive bias
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of 0.97 ºC for 2 m temperature and a positive bias of 0.48 m/s for 10m wind speed prediction.
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For January 2004, SKIRON/Eta has a negative bias of -1.31 ºC for 2 m temperature and a
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positive bias of 1.2 m/s for 10 m wind speed. These values are comparable with those from MM5
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and RAMS3b predictions (Hogrefe et al., 2001) showing similar levels of performance between
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the respective models. We also provide the time series and diurnal comparisons for relative
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humidity and temperature at Queens College for July 2001 and January 2004 in Figure 2. The
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comparisons consider the post-processed predictions as inputs for the first model layer of CAMx
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(the middle of the first layer is 25 m) with the surface measurements. The July 2001 time series
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data indicate that the SKIRON/Eta performs reasonably well in predicting RH and temperature.
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There is slight systematic under prediction of temperature and over prediction of RH for both
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seasons. The biases may be partly due to the fact that the model grid containing the Queens
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College monitor site is adjacent to water and the grid resolution does not fully capture the
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sea-land interaction.
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5. Air Quality Forecast Results
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5.1 Statistical Evaluation for Domain Wide O3 during Summer
One of the principal goals of this AQFMS is to alert the public of impending unhealthful
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O3 and PM2.5 air quality. Figure 3 shows a map of the calculated mean normalized bias (MNB)
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of
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1 n ⎛ ( Modeli − Obsi ) ⎞
⎟⎟ × 100% is computed using the observations from EPA’s AQS
MNB = ∑ ⎜⎜
n i =1 ⎝
Obsi
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database at 574 monitoring stations within the fine domain. The MNB calculated with a cutoff
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value of 60 ppb applied to the observations is recommended by the EPA for the assessment of
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ozone predictions for the concentrations at or above the 8-hour standard of 85 ppb (USEPA,
maximum
8-hour
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2005).
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with model over prediction greater than 35% present for some sites along the Ohio Valley region
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and eastern Kentucky and Alabama. The over predictions within these regions are consistent
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with the findings from McKeen et al. (2005). The MNBs for urban sites in the northeastern
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corridor are mostly in the +5% to -15% range consistent with U.S. EPA performance objectives
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for episodic days.
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Figure 3 shows that the MNB typically range from -15% to 25% for the whole domain
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5.2 Predicted and Observed Hourly Concentrations for Gaseous Species at Queens College
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(urban) and Pinnacle State Park (rural) during July 2001 and January 2004
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5.2.1 Summer Evaluation Results
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Figure 4 shows hourly time series of model predictions and observations for O3, CO,
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NOx, SO2 and total non-methane hydrocarbon (NMHC) at Queens College during 14-31 July
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2001 (all days begin at 0:00am EST). The AQFMS predicted O3 concentrations show very
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consistent day-to-day and diurnal variations with the observations. The predicted CO, NOx, SO2
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and NMHC concentrations match the observations well for the entire time period of July 2001,
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with the exception that the model missed the high CO, NOx and NMHC concentration events
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which took place during the nights of 20-21 and 30-31 July. Both the observations and
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predictions show strong correlation between CO and NOx, while the correlation between NOx
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and SO2 are much less significant. Northerly winds during these nights indicate that the site was
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impacted by mobile source emissions from the Long Island Expressway a few hundred meters to
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the north. It is also evident that there is a build up of O3 from 14 to 17 July which is consistent
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with the build up of the temperature. The low O3 concentrations on the 14 July correspond to the
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post-passage of a cold front from the north which swept the polluted air mass to southeast and
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finally to the ocean. From 16th, a subtropical high pressure system started to build up over most
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part of the eastern US and persist during the following day. A strong southwesterly flow brought
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an O3 rich air mass from Ohio Valley and southeast region to the northeast. As indicated in
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Figure 3, the positive biases of O3 are mostly found in the Ohio Valley, so the over predictions of
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O3 at Queens College during 16-17 July are likely influenced by the O3 over predictions in these
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upwind regions. These over predictions do not show up at Pinnacle State Park on 16 July (Figure
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5) since the site was still under the impact of northerly flow during the day. The enhancements in
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precursor species during 16-17 July are also evident and successfully captured by the AQFMS
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with some biases. The spatial distribution maps suggest that the enhancement of SO2 is due to
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the transport from Ohio Valley while the enhancements of CO, NOx and NMHC are more likely
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from the lower part of the northeastern urban corridor.
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The observed and predicted hourly time series of O3, CO, NOx, and SO2 at Pinnacle State
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Park during July 2001 are shown in Figure 5. The measurements for total nitrogen, denoted as
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NOy (NOy = NOx+HNO3+HONO+NO3+N2O5+RNO3) and HNO3 are also available for this
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rural site providing opportunity to track the model prediction of the total nitrogen budget of aged
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air masses as well. NOy is conserved in terms of chemical transformations, but can be depleted
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by physical removal processes (Parrish et al., 1991). HNO3 is typically a major component of
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the NOy budget and the reaction of OH+NO2 to form HNO3 is the dominant sink for OH in
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polluted atmospheres. It is shown in Figure 5 that the predicted concentrations for O3, CO, NOy,
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NOx, SO2 and HNO3 at Pinnacle State Park all track the temporal variations of the observations
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well. Elevated NOx concentrations observed during many nighttime periods over the July period
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suggest the presence of elevated plumes, likely trapped in stable layers, entraining on the
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monitoring site. The treatment of the nocturnal boundary layers being a well known shortcoming
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on mesoscale meteorological models. Enhanced HNO3 concentrations as well as low NOx/NOy
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ratios, when HNO3 measurements are not available, are suggestive of aged air masses that have
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not encountered precipitation. The O3 over predictions during 17-18 July are associated with the
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over predictions of NOy, HNO3 and SO2. With its long life time (~2-3 months), CO can serve as
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a good indicator for transport. The CO predictions are in excellent agreement with the
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observations during the entire time period including 17 and 18 July indicating that transport from
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upwind urban centers is adequately predicted by the model. The over predictions of SO2 and
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NOy, are likely due to point source emissions in the upwind regions and/or potential uncertainties
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with the aqueous phase chemistry in the model since both SKIRON/Eta and measurement show
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the existence of cloud over Pinnacle State Park during the two days. Our calculation shows that
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the mean NOz/NOy ratio (an indicator for the age of the air mass) from AQFMS is 0.75, slightly
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higher than the ratio of 0.60 from the observations.
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5.2.2 Winter Evaluation Results
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Time series data for O3 and precursor gas measurements and predictions for Queens
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College during the time period from 8-31 January, 2004 are shown in Figure 6. NMHC
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measurement data are not available for this time period, but CO can serve as a reasonable
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surrogate for this component. As expected, O3 concentrations in winter are much lower than in
5
summer at both sites due mainly to reduced solar insolation and its impact on photochemical
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activity. Winter predictions and observations show a general increase in concentrations
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compared to summer of 40% and 25% on average for NOx and 20% and 10% on average for CO
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respectively, while SO2 concentrations were 50% and 100% higher on average. These findings
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are consistent with the lower PBL heights and increased wintertime emissions of the respective
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species. At Queens College the O3 concentrations vary from zero to ~30ppb. Though O3 data are
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missing during the hours when peak NOx concentration are observed during 12, 17 January, low
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O3 concentrations are observed during 22, 28 January 2004 with correspondingly high
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concentrations of NOx indicating strong titration events. The temporal characteristics of these
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events are captured by the AQFMS. The CO predictions are generally biased high during midday
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and in better agreement with observations during nighttime hours. NOx concentrations are under
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predicted for most days, in contrast to the CO predictions, but are in better agreement with
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observations for the daytime hours than nighttime hours. The predicted SO2 concentrations
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overall show good agreement with observations during this winter month, with some plume
19
structure missed by the model, typically during nighttime hours. The three episodes at Queens
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College during the periods of 11-13 January, 17-18 January and 21-22 January, each features
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high concentrations of CO, NOx and SO2 from both observations and predictions, all correspond
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to air mass changes associated with relatively high temperature and relative humidity (Figure 2).
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The time series of predicted and observed gas species at Pinnacle State Park during January
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2004 presented in Figure 7 show strong ozone depletion events associated with increased
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concentrations in CO, NOx and SO2 on 12, 18, 22, 24, and 28 January, features well captured in
4
the model predictions. For the days when O3 concentrations are not perturbed by the fresh
5
polluted air, predicted and observed O3 concentrations at Pinnacle State Park are about 35 ppb, a
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value associated with regional background. NOy is under predicted for most of the days and
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consistent with the under prediction of NOx, though the under prediction is counteracted partly
8
by the over prediction of HNO3 which is possibly due to the too active NOx transformation
9
processes and/or the low removal process in the model. The average ratio of NOz/NOy is 0.68
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and 0.30 from prediction and observation respectively suggesting the air mass is more aged in
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the model. SO2 predictions show good agreement with observations for the entire period with the
12
exception of the last few hours of 21 January and early morning of 22 January when the model
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under predicted high SO2 observed concentrations (up to 30 ppb) and NOy concentrations. The
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event is likely the result of a point source plume impacting the monitoring site that was missed
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by the model due to errors in transport or limited grid resolution.
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5.3 Correlations of Gaseous Species
Emission sources of CO and NOx are dominated by anthropogenic activities.
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Comparisons of predicted and observed correlations of NOx (or NOy) and CO provides an
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indication of the basic performance of the model, the quality of the emission inventory and
21
precursor chemistry (Cardenas et al., 1998; Parrish et al., 1991). Queens College, a site strongly
22
impacted by the fresh emissions as evidenced by night plumes from the Long Island Expressway
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(e.g. July 21 and July31) and diurnal traffic emissions patterns in CO and NOx observations, is
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contrasted with Pinnacle State Park, a site typically influenced by more aged air masses. Pinnacle
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State Park measurements provide another useful indicator for assessing regional photochemistry
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through the correlation of O3 and NOz (NOy–NOx). The slope of the linear portion of the O3 vs.
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NOz correlation is defined as the O3 production efficiency (OPE), a measure of the number of O3
5
molecules produced per NOx molecule consumed (Trainer et al., 1993). Such comparisons
6
provide basic understanding of the model’s performance relative to the O3 - NOx-VOC precursor
7
relationship, which is of fundamental importance in air quality management. Linear regression of
8
O3 vs. NOz concentrations at Pinnacle State Park considered measurements during the daytime
9
hours from 9:00am to 5:00pm when the boundary layer is well developed (Trainer et al., 1993)
10
for both July 2001 and January 2004. It should be noted that other indicators of precursor
11
relationships have been suggested (Sillman, 1995; Sillman and He,2002; Tonnesen and Dennis
12
2000), but most are limited by the availability of measurement data.
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5.3.1 Summer Correlations
The linear regression of observed and predicted CO vs. NOx at Queens College shown in
16
Figure 8 for July 2001 has an intercept of 141 ppb and a slope of 7.7 with R2 equal 0.79 from
17
observations and an intercept of 189 ppb and a slope of 10.4 with R2 equal 0.87 based on model
18
predictions, suggesting a strong correlation between these precursor species. Since the Queens
19
College measurement site is located in the vicinity of two major highways (≤ 1 km) and
20
predicted CO and NOx concentrations are averaged over the 12 km grid element, the higher slope
21
of CO vs. NOx from model predictions is consistent with NOx’s relatively shorter lifetime
22
compared to that of CO. The AQFMS also accurately predicts the NMHC/NOx ratio which plays
23
key role in NOx-VOC-O3 relationship and O3 formation (NARSTO, 2000). The linear regression
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of NMHC vs. NOx model predictions has an intercept of 23 ppbC and a slope of 2.8 with an
2
R2=0.81, both the intercept and slope are consistent with the observed linear regression having an
3
intercept of 35 ppbC, a slope of 2.6 with an R2=0.6.
At Pinnacle State Park, the forecasted CO vs. NOy linear regression has a slope of 13.9
5
and an intercept of 89 ppb, in very good agreement with observations which have a slope of 14.0
6
and intercept of 104 ppb. These results suggest that the model performs well in capturing the
7
characteristics of the regional precursor emissions, transformation and transport. The R2 for the
8
linear regression of predictions is 0.80 and that for observations, 0.48, suggest much greater
9
variability in the observations due to temporal and spatial smoothing in the model as a result of
10
the 12 km grid resolution of the model. The results reported for the O3 vs. NOz linear regression
11
at Pinnacle State Park indicate a model-predicted slope of 4.8 (R2=0.94), about 55% of the
12
observed slope of 8.9 (R2=0.75). Possible explanations for the difference in OPE include: 1) the
13
overall reactivity of the emissions is underestimated,
14
may be underestimated by the AQFMS, 3) plumes are diluted, leading to more efficient NOz
15
production, or 4) the chemical mechanism in the model does not adequately captured the
16
photochemical cycles associated with ozone production. The latter explanation is further
17
investigated in the later section.
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2) the removal process of NOz species
5.3.2 Winter Correlations
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During the winter month of January 2004, the linear regression analysis of observed CO
21
vs. NOx at Queens College indicates a slope of 4.2 with R2=0.48, much lower than prediction
22
data which reports a slope of 11.7 and R2=0.95 (see Figure 8, right column). Given the low
23
photochemical reactivity during the winter season, the observed CO vs. NOx slope at this site
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should mostly reflect the emission ratio from mobile sources. In fact, this ratio is consistent with
2
measurements from a roadside site in New York City (PS-59) which report a CO vs. NOx slope
3
of 4.7 for the same period of time. The discrepancy between the predicted and the observed
4
slopes suggest that NEI99 mobile emission inventories remain significantly uncertain at this
5
particular urban site. The intercepts for the linear regressions are 200 ppb and 263 ppb for
6
observation and prediction respectively, and are higher than those in July 2001 campaign. These
7
differences reflect the general increase in the winter CO background concentration resulting from
8
its increased chemical lifetime, increased emissions and lower mixing layer heights.
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At Pinnacle State Park in January 2004, the linear regression of predicted CO vs. NOy has
10
a slope of 21.5 and an intercept of 188 ppb while the slope for the observation data is 6.9 with an
11
intercept of 185 ppb. The over prediction of the slope of CO vs. NOy reflects the overall under
12
prediction on NOx during winter month, and also partly caused by the under prediction of NOy
13
for the events when the measurement site is mostly likely under impact of point source plume.
14
This is consistent with the over prediction of the slope of CO vs. NOx correlation at Queens
15
College during the same month. For winter O3 vs. NOz linear regression, the AQFMS predicted
16
data show a slope of -2.2 and an intercept of 33 ppb very close to the values from the observation
17
data which show a slope of -2.4 and an intercept of 32.6 ppb. However, the R2 of the regression
18
between O3 and NOz from both the observations and predictions are only about 0.1. O3
19
concentrations in winter season are dominated by the background O3 boundary condition with
20
little O3 production from local photochemistry. The negative slopes with low correlation
21
coefficients for observations and predictions at Pinnacle State Park during January 2004 suggest
22
that the variation of O3 during winter is mainly due to titration from NO emissions in the region.
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It is interesting that winter regression slopes of CO vs. NOx observations at Queens College
2
and CO vs. NOy at Pinnacle State Park are both about half the summer slopes while the predicted
3
data show higher winter slopes than summer slopes. The higher predicted winter slopes are
4
consistent with the higher winter mole emission ratio from the inventory. (i.e. average emission
5
ratio for the 9 grids centered at Queens College is 8.4 in winter and 7.2 in summer). The
6
discrepancies between the model prediction and the observation suggest larger uncertainties in
7
the winter emission inventory.
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5.4 Predicted and Observed Hourly Concentrations of HOx Radicals and HONO at Queens
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College during July 2001 and January 2004
11
5.4.1 HOx Comparison
The OPE is closely tied to the model’s ability to capture the chain length of atmospheric
13
photochemical oxidation cycles. Hydroxyl radical (OH) and HOx chemistry overall are key to
14
understanding the photochemical oxidation capacity of the troposphere. The OH radical initiates
15
oxidation processes of most atmospheric trace gases especially NMHC and regulates the net
16
chemical production of ozone (Poppe et al., 1994).
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In the air quality forecast modeling system, the chemical mechanism must accurately
18
reproduce radical processes and related precursor and product concentration relationships, if it is
19
to have creditability as an air quality management tool for regulatory guidance. OH and HO2
20
radical concentrations measured by a laser-induced fluorescence instrument during the
21
PMTACS-NY intensive campaign in Queens College in July 2001 and January 2004 (Ren et al.,
22
2003; 2006), provide unique information in the evaluation of the performance of AQFMS
23
especially as it relates to the simulation of the oxidative capacity of the atmosphere.
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The time series data and diurnal patterns of predicted and observed OH concentrations at
2
Queens College for the period from 14 to 31 July are illustrated in Figure 9. For the 18 days,
3
both the predicted and observed OH concentrations vary mostly from ~ 0 to 0.5 ppt. The
4
predicted OH concentrations show reasonable agreement with observations and indicate a
5
moderate bias for under prediction with the exception of 25 July 2001. During 18 July when the
6
largest discrepancies between prediction and observation occur, the observed solar radiation at
7
Queens College on that day shows the pattern of clear sky, while the modeled actinic flux
8
reflects significant attenuation from an incorrect prediction of cloud cover over the Queens
9
College area. The general tendency of OH under prediction for this summer month is also
10
evident in the diurnal plot. The diurnal patterns shown in Figure 9 are the means of the days from
11
14 to 31 July. The observations show that OH peaks at 14:00 EST while the predicted data show
12
daily maximum OH concentration at 13:00 EST. The average predicted OH concentrations are
13
about 0.05 ppt lower than the observed OH concentrations for all hours. The observations also
14
show that OH concentrations are sustained at a relatively high level of about 0.05 ppt during
15
night while the predicted OH concentrations approach zero.
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The time series and diurnal patterns of predicted and observed OH concentrations at Queens
College for the period from 14 to 31 January 2004 are illustrated in Figure 10.
During the 18
18
days, the observed OH concentrations vary from ~0 to 0.09 ppt, about 1/5 of those for the
19
summer month. The observed OH concentrations are significantly underestimated by AQFMS
20
for this winter month with the predicted concentrations varying from 0 to 0.018ppt. The OH
21
concentration reaches maximum between 12:00-13:00 EST for the winter month from both
22
observation and prediction. The HO2 prediction (not shown here) also biased low to a significant
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degree during day time in both summer and winter which is very consistent with the OH
2
prediction.
3
5.4.2 HONO Comparison
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It is evident that the OH concentrations are under predicted in AQFMS at Queens College
6
for both summer and winter season with more significant underestimation in winter. The
7
discrepancies in OH predictions versus observations raise concerns regarding the radical
8
production and loss in the chemical model used in the AQFMS. The initial sources of OH radical
9
include the photolysis of ozone followed by the reaction of electronically excited O(1D) with
10
H2O; photolysis of nitrous acid; and the reaction of alkenes with ozone. Many studies (Martinez
11
et al., 2003; Tan et al., 2001) have found that the formation of O(1D) from the photolysis of O3
12
and its subsequent reaction with water O(1D)+H2O→2OH dominates the new OH production in
13
the late morning and afternoon. Ren et al. (2003) use measured concentrations of precursor
14
species and meteorology data collected at July 2001 Queens College field campaign as input to a
15
box model. The budget studies from their box model indicate that apart from
16
HO2+NO→OH+NO2, the most important OH source is daytime photolysis of HONO for the
17
Queens College during July 10 to Aug 2, 2001. The production and loss rates from the AQFMS
18
are derived from the predicted precursor concentrations, which are emission inventory based, and
19
their reaction products. Thus, any bias in the predictions of the precursor species or products, e.g.
20
HONO, will certainly cause a bias in the prediction of OH.
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The comparisons of observed and predicted HONO at Queens College for the period of
22
19-28 July 2001 and 17-31 January 2004 are provided in Figure 11. During the 10 days in July
23
2001, the observed HONO concentrations vary from 0.17 ppb to 4.1 ppb with a mean
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concentration of 0.8 ppb, the highest HONO concentration of 4.1 ppb was observed during the
2
early morning of July 21. The predicted HONO concentrations from the AQFMS range from 0
3
ppb to 0.19 ppb for this study period, with a mean concentration of 0.035 ppb. During the 15
4
winter days, the observed HONO concentrations vary from 0.16ppb to 1.76ppb with a mean
5
concentration of 0.55 ppb. The predicted HONO concentrations range from 0 to 0.04 ppb for the
6
winter study period, with a mean concentration of 0.007 ppb. The model predicted HONO
7
concentrations are systematically under predicted suggesting that the CBM4 chemical
8
mechanism has chemical deficiencies and is not capturing the full extent of the chemistry of
9
HONO in the atmosphere. In another paper (Cai
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et al., 2008, to be submitted), HONO
formation mechanisms associated with heterogeneous surface based reactions added to the
11
standard CBM4 mechanism in CAMx lead to significant improvements in the prediction of
12
HONO and OH radical concentrations. These improvements in predictions, in turn, increase the
13
model predicted OPE, bring it closer to observation. These results suggest that the model
14
projected ozone attainment strategies may have significantly uncertainties as a result of
15
deficiencies in model’s chemical mechanism.
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The larger discrepancies of OH predictions in winter than in summer also suggest that
17
there may be more uncertainties in chemical mechanism under winter conditions. It is worth
18
noting that the chemical mechanisms used in all chemical transport models have been developed
19
under conditions associated with high photochemical oxidation (i.e. typical summertime solar
20
and temperature conditions), therefore it is not particularly surprising that they may not capture
21
the details of chemical transformations under low light, low temperature conditions. Although
22
these winter time condition are not conducive to high ozone production events, winter time OH
23
plays an important role in secondary production of PM and therefore must be predicted with
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improved accuracy, to assure the credible application of air quality models to the development of
2
mitigation strategies for the attainment of PM2.5 National Ambient Air Quality Standard.
3
6. Summary
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The predictions of O3 and related precursor species and key transient species from an
7
AQFMS are evaluated for a summer and winter month, July 2001 and January 2004 respectively.
8
The overall mean normalized bias (MNB) for daily maximum 8-hour average ozone
9
concentrations higher than 60 ppb for the model domain during the summer month of July 2001
10
is 8.6%. The AQFMS successfully captured the temporal patterns of O3, CO, NOx, SO2 at the
11
urban site (Queens College, NY) and O3, CO, NOy, SO2 at the rural site (Pinnacle State Park,
12
NY) for both summer month and winter month. The AQFMS captures the temporal variations of
13
these species for both the two month. However, the AQFMS does not fully capture the variations
14
of precursor species at Queens College when this urban site was impacted by local mobile
15
emission and at Pinnacle State Park when the site was impacted by plumes from point sources.
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The linear regression of model-predicted CO vs. NOx at Queens College and CO vs. NOy
17
at Pinnacle State Park during July 2001 are very comparable with those derived from
18
observations indicating that the emissions of precursor species as well as transport are well
19
simulated in the model during the summer season. However the predicted O3 production
20
efficiency (OPE) at Pinnacle State Park is only 55% of that from observation which suggests that
21
the removal process of NOz species during transport might be underestimated by the model
22
and/or the chemical mechanisms in the model does not adequately capture the photochemical
23
cycles associated with ozone production. Analysis of Queens College data suggests that OH
24
concentration a key indicator of the oxidative capacity of an air mass, is generally under
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predicted in the model and the under prediction of HONO concentrations is likely a significant
2
contributing factor to OH under prediction. Ongoing studies suggest the need to revisit primary
3
HONO emissions and chemistry in the model. Measurement of a more complete set of species to
4
be able to reconstruct NOy from the ground up are also necessary to better address the OPE
5
discrepancy. The CO/NOx ratio at Queens College and CO/NOy ratio at Pinnacle State Park are
6
significantly overestimated during winter season. The discrepancies between the observed and
7
predicted ratios might reflect more emission uncertainties during the winter season. Also, the OH
8
concentrations are significantly underestimated during January 2004 suggesting more
9
uncertainties with the chemical mechanism in capturing the details of chemical transformations
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under low light, low temperature conditions.
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Acknowledgments. Much of the measurement data used in this study was collected under the
13
PMTACS-NY PM Supersite program and supported in part by the U.S. Environmental
14
Protection Agency (EPA) cooperative agreement # R828060010, the New York State Energy
15
Research and Development Authority (NYSERDA), contract # 4918ERTERES99, and New
16
York State Department of Environmental Conservation (NYS DEC), contract # C004210.
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Tonnesen, G. S and R. L. Dennis (2000), Analysis of radical propagation efficiency to assess
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Measurements during the PMTACS-NY 2004 Winter Intensive in New York City, J.
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Geophys. Res., 111, D18205, doi10.1029/2006JD007215.
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Figure Captions:
2
Figure 1. AQFMS model domains and the locations of Pinnacle State Park (PSP) and Queens
3
College (QC).
4
Figure 2. Time Series and diurnal patterns for relative humidity and temperature from AQFMS
5
predictions and observations at Queens College during July 2001 and January 2004.
6
Figure 3. Mean Normalized Bias (MNB) for the predictions of daily maximum 8-hr averaged
7
ozone during 3-31 July 2001 (60ppb cutoff value applied to observations).
8
Figure 4. Hourly time series data for AQFMS predicted and observed O3, CO, NOx, SO2 and
9
NMHC at Queens College during July 2001.
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Figure 5. Hourly time series data for AQFMS predicted and observed O3, CO, NOx, NOy,
11
HNO3 and SO2 at Pinnacle State Park during July 2001.
12
Figure 6. Hourly time series data for AQFMS predicted and observed O3, CO, NOx and SO2 at
13
Queens College during January 2004.
14
Figure 7. Hourly time series data for AQFMS predicted and observed O3, CO, NOx, NOy
15
HNO3 and SO2 at Pinnacle State Park during January 2004.
16
Figure 8. Linear regression for CO vs. NOx at Queens College, CO vs. NOy at Pinnacle State
17
Park and O3 vs. NOz at Pinncle State Park from AQFMS predictions (“A”) and observations
18
(“O”) during July 2001 (left column) and January 2004 (right column).
19
Figure 9. Predicted and observed hourly OH concentrations at Queens College during 14-31
20
July 2001, top: hourly time series data; bottom: mean diurnal patterns.
21
Figure 10. Predicted and observed hourly OH concentrations at Queens College during 14-31
22
January 2004, top: hourly time series data; bottom: mean diurnal patterns.
AC
CE
PT
ED
10
28
ARTICLE IN PRESS
Figure 11. Predicted and observed hourly HONO concentrations at Queens College during
2
19-31 July 2001 (top) and 17-31 January 2004 (bottom).
AC
CE
PT
ED
M
AN
U
SC
R
IP
T
1
29
AC
CE
PT
ED
M
AN
U
SC
R
IP
T
ARTICLE IN PRESS
120
40
0
20
25
Temperature (Co)
15
20
25
30
35
0
10
15
Hour of Day (EST)
20
25
0
5
10
15
Hour of Day (EST)
20
25
0
5
10
15
Hour of Day (EST)
20
25
20
RH (%)
40
60
80
100 120
5
-15
Temperature (Co)
-10
-5
0
5
AC
Temperature (Co)
-20 -15 -10 -5
0
5
0
0
CE
PT
ED
120
100
40
20
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Date of January 2004
10
10
15
Hour of Day (EST)
10
M
AN
U
Temperature (Co)
10
20
30
40
11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Date of July 2001
RH (%)
60
80
5
SC
R
50
40
11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Date of July 2001
IP
T
0
AQFMS
Observation
20
20
40
RH (%)
60
80
100
100 120
RH (%)
60 80
ARTICLE IN PRESS
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Date of January 2004
AC
CE
PT
ED
M
AN
U
SC
R
IP
T
ARTICLE IN PRESS
100
AQFMS
Observation
0
20
O3 ppb
60
ARTICLE IN PRESS
14
15
16
17
18
19
20
21
22
23
24
18
19
20
21
22
23
24
15
16
17
CE
PT
ED
16
17
29
30
31
IP
T
NOx ppb
150
50
0
15
40
14
28
18
25
26
27
28
29
30
31
M
AN
U
14
27
SC
R
1500
CO ppb
500
0
250
26
AQFMS
Observation
AQFMS
Observation
19
20
21
22
23
24
25
26
27
28
29
30
31
18
19
20
21
22
23
24
25
26
27
28
29
30
31
18
19
20
21
22
23
24
25
26
27
28
29
30
31
14
15
AC
0
10
SO2 ppb
20
30
AQFMS
Observation
16
17
AQFMS
Observation
0
NMHC ppbC
200 400 600
800
25
14
15
16
17
Date of July 2001
120
O3 ppb
40
80
AQFMS
Observation
0
ARTICLE IN PRESS
11
12
13
14
15
16
17
18
19
20
21
22
23
16
17
18
19
20
21
22
23
16
17
18
19
20
24
25
26
27
28
29
30
31
12
13
14
15
10
11
12
13
14
15
20
11
12
13
14
10
11
21
CE
PT
ED
0
5
NOy ppb
10 15
AQFMS
Observation
15
16
24
M
AN
U
0
2
NOx ppb
4 6 8
AQFMS
Observation
SC
R
0
IP
T
CO ppb
300
100
AQFMS
Observation
25
26
27
28
29
30
31
22
23
24
25
26
27
28
29
30
31
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
0
2
AC
HNO3 ppb
4 6 8
AQFMS
Observation
12
13
14
15
20
11
0
5
SO2 ppb
10 15
AQFMS
Observation
11
12
13
14
15
Date of July 2001
50
ARTICLE IN PRESS
8
9
10 11
12 13
14
15
16 17
18 19
14
15
16 17
18 19
20
21
9
10 11
NOx ppb
150
50
0
10 11
12 13
27
28
29 30
31
20
21
22
23 24
25 26
27
28
29 30
31
14
15
16 17
18 19
20
21
22
23 24
25 26
27
28
29 30
31
14
15
16 17
18 19
20
21
22
23 24
25 26
27
28
29 30
31
AC
9
40
8
25 26
SC
R
12 13
AQFMS
Observation
23 24
M
AN
U
8
CE
PT
ED
250
0
500
CO ppb
1500
AQFMS
Observation
22
IP
T
0
10
O3 ppb
20 30
40
AQFMS
Observation
0
10
SO2 ppb
20
30
AQFMS
Observation
8
9
10 11
12 13
Date of January 2004
0 10 20 30 40 50 60
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
5 10 15 20 25 30
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
0
AQFMS
Observation
5 10 15 20 25 30
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
9
5
8
CE
PT
ED
AQFMS
Observation
0
NOy ppb
8
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
2
AC
3
4
AQFMS
Observation
0
1
HNO3 ppb
SC
R
IP
T
AQFMS
Observation
8
NOx ppb
9
100 200 300 400 500
CO ppb
8
ARTICLE IN PRESS
M
AN
U
O3 ppb
AQFMS
Observation
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
10
20
AQFMS
Observation
0
SO2 ppb
30
8
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Date of January 2004
January 2004
OO
O
O
O
OO
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O
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400
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O
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4
6
8
NOy (ppb)
O
O
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A
O
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O
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A
40
O3 (ppb)
50
60
O
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A
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A
2
4
30
A AA
A
0
14
A
A
A
A
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6
8
NOz (ppb)
10
NOy ppb
O
O
AQFMS
y=28.24+4.73*x
R-square=0.95
10
12
OO
A
O
O
O
O
15
O
0
Observation
y=32.56-2.36*x
R-square=0.10
A
AO
A
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1
20
A
PSP
-1
O
AQFMS
y=187.02+22.47*x
R-square=0.61
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150
Observation
y=184.76+6.91*x
R-square=0.69
5
A
PSP
0
12
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AC
70
Observation
y=24.23+8.97*x
R-square=0.78 O
10
15
2
O
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100
NOx (ppb)
PSP
O3 (ppb)
20
25
100
PSP
0
80
AQFMS
y=89.37+13.94*x
R-square=0.8
CE
PT
ED
150
CO (ppb)
200
O
50
A
350
A
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200
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120
250
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100
200
250
Observation
y=104.37+13.97*x
R-square=0.48
60
80
NOx (ppb)
35
40
O
O
AQFMS
y=233.2+11.75*x
R-square=0.95
30
20
CO ppb
0
O
O
QC
M
AN
U
200
AQFMS
y=189.42+10.44*x
R-square=0.87
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AA
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Observation
AAAA
A
A
A
O
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y=200.41+4.25*x A AOAAAAA
A
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ARTICLE IN PRESS
R-square=0.48 AAAAAAAAAAAAAA AA
1000
Observation
y=140.73+7.65*x
R-square=0.79
CO (ppb)
600
800
1000 1200
July2001
2
3
NOz (ppb)
AQFMS
y=33.24-2.19*x
R-square=0.14
4
5
OH Concentration ppt
0.0 0.1 0.2 0.3 0.4 0.5 0.6
ARTICLE IN PRESS
M
AN
U
SC
R
IP
T
AQFMS
Observation
CE
PT
ED
0.3
14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Date of July 2001
0.0
AC
OH Concentration ppt
0.1
0.2
AQFMS
Observation
0
5
10
15
Hour of day (EST)
20
25
0.10
0.08
IP
T
0.06
SC
R
0.04
14
15
16
17
18
19
20
21
M
AN
U
0.02
0.00
OH Concentration ppt
ARTICLE IN PRESS
AQFMS
Observation
22
23
24
25
26
27
28
29
30
31
32
CE
PT
ED
OH Concentration ppt
0.0
0.02
0.04
0.06
Date of January 2004
AC
AQFMS
Observation
0
5
10
15
Hour of Day (EST)
20
25
4
ARTICLE IN PRESS
19
20
21
22
23
M
AN
U
0
1
SC
R
IP
T
HONO ppb
2
3
AQFMS
Observation
24
25
26
27
28
29
AQFMS
Observation
AC
CE
PT
ED
HONO ppb
0.0 0.5 1.0 1.5 2.0 2.5
Date of July 2001
17
18
19
20
21
22
23
24
25
26
Date of January 2004
27
28
29
30
31