# TSD-1e



# CMAQ Model Performance and Assessment 8-h OTC Ozone Modeling

Bureau of Air Quality Analysis and Research

Division of Air Resources New York State Department of Environmental Conserva tion

Albany, NY 12233

March 19, 2006

# - 2

Air quality model evaluation and assessment One of the tasks that is required as part of demons trating attainment for the 8-hr ozone NAAQS is the evaluation and assessment of the air quality modeling system that has been utilized to predict future air quality ove r the region of interest. As part of the attainment demonstration, the SMOKE/CMAQ modeling s ystem was applied to simulate the pollutant concentration fields for the base yea r 2002 emissions with the corresponding meteorological information. The modeling databases for meteorology using MM5 (TSD1a), the emissions using SMOKE (TSD-1b and TSD-1c), and application of CMAQ (TSD-1d) provides simulated pollutant fields that a re compared to measurements, in order to establish the credibility of the simulatio n. In the following sections a comparison between the measured and predicted concentrations i s performed and results are presented, demonstrating on an overall basis the ut ility of the modeling system in this application. The results presented here should serve as an illus tration of some of the evaluation and assessment performed on the base 200 2 CMAQ simulation. Additional information can be made available by request from t he New York State Department of Environmental Conservation. Summary of measured data The ambient air quality data, both gaseous and aero sol species, for the simulation period of May through September 2002 were obtained from the following sources:
• EPA A ir Q uality S ystem (AQS) • EPA fine particulate S peciation T rends N etwork (STN) • EPA C lean A ir S tatus & T rends N etwork (CASTNet) • I nteragency M onitoring of PR Otected V isual E nvironments (IMPROVE) • Pinnacle State Park, NY operated by Atmospheric Sci ence Research Center, University at Albany, Albany, NY • Harvard Forest, Petersham, MA operated by Harvard U niversity, Boston, MA • A tmospheric I nvestigation, R egional M odeling, A nalysis and P rediction (AIRMAP) operated by University of New Hampshire, D urham, NH • N orthE ast O zone & F ine P article S tudy (NE-OPS), led by Penn State University and other research groups in Philadelphia, PA • Aircraft data obtained by the University of Marylan d, College Park MD • Wet deposition data from the N ational A tmospheric D eposition P rogram/N ational T rends N etwork (NADP/NTN), A tmospheric I ntegrated R esearch Mo nitoring N etwork (AIRMoN), and the N ew Y ork S tate D epartment of E nvironmental C onservation (NYSDEC) Measured data from sites within the Ozone Transport Region (OTR) plus the rest of Virginia were included here. The model-based data were obtained at the grid-cell corresponding to the monitor location; no interpola tion was performed.

# - 3

Ozone (O 3 

# ) Hourly O 3 

# is measured at a large number of State, Local, and National Air Monitoring Stations (SLAMS/NAMS) across the US on a routine basis, and the data from 208 sites were extracted from the AQS database ( http://www.epa.gov/ttn/airs/airsaqs/aqsweb/aqswebho me.html ). Hourly O 3 

# concentrations from the Harvard Forest Environmenta l Management Site in Petersham, MA ( http://www.as.harvard.edu/data/nigec-data.html ); Pinnacle State Park in Addison, NY ( http://www.asrc.cestm.albany.edu ); and the four University of New Hampshire AIRMAP sites (http://airmap.unh.edu) were also incl uded in this database. The EPA CASTNet program collects hourly O 3 

# at generally rural locations across the US ( http://www.epa.gov/castnet ); data from 22 sites, including two from West Virg inia, were used in the model evaluation. Fine particulate matter (PM 2.5 

# ) The 24-hour average Federal Reference Method (FRM) PM 2.5 

# mass data collected routinely at SLAMS/NAMS sites across the US were ex tracted from AQS (257 sites). Hourly PM 2.5 

# mass was also included in this database, primarily extracted from AQS (54 sites). Hourly PM 2.5 

# mass were also taken from the Thompson Farm, NH AI RMAP site, Pinnacle State Park, and the NE-OPS site in Philade lphia, PA ( http://lidar1.ee.psu.edu ). Fine particulate speciation The 24-hour average PM 2.5 

# and fine particulate speciation (sulfate (SO 4 

# ), nitrate (NO 3 

# ), elemental carbon (EC), organic carbon/organic ma ss (OC/OM), and soil/crustal matter) from Class I areas across the US, collected every 3 rd 

# day, were obtained from the IMPROVE web site ( http://vista.cira.colostate.edu/IMPROVE/Default.htm ). In addition to these parameters, the EPA STN ( http://www.epa.gov/ttn/amtic/speciepg.html ) also reports ammonium (NH 4 

# ) to AQS; data from this network are collected ever y 3 rd 

# or 6 th 

# day. Data from 49 STN sites, generally in urban are as and often collocated with FRM monitors, and 21 IMPROVE sites (including Dolly Sod s, WV) were used in this analysis. Organic mass is assumed to equal 1.8×OC, and soil/c rustal matter is assumed to consist of oxides of Al, Ca, Fe, Si, and Ti. The STN OC da ta are blank-corrected by removing a monitor-specific, constant blank, and these values are available from http://www.epa.gov/airtrends/aqtrnd03/pdfs/2_chemsp ec0fpm25.pdf ; the IMPROVE OC blanks are assumed to equal zero. Criteria gaseous pollutants Hourly carbon monoxide (CO; 97 sites), nitric oxide (NO; 75 sites), nitrogen dioxide (NO 2 

# ; 97 sites) and sulfur dioxide (SO 2 

# ; 134 sites) are also included in this model evaluation database. A large majority of these sit es are SLAMS/NAMS monitors located primarily in urban in suburban areas, but data from the Harvard Forest, Pinnacle State Park, and AIRMAP sites are also included here.

# - 4

Non-methane hydrocarbons While there are several dozen hydrocarbon species m easured routinely, for this model evaluation database the focus was on Carbon B ond IV species groups that consist of a single primary species. For this reason only ethene (C 2 

# H 4 

# ), isoprene (C 5 

# H 8 

# ), and formaldehyde (HCHO) concentrations were extracted f rom AQS. Hourly C 2 

# H 4 

# and C 5 

# H 8 

# data from 19 Photochemical Assessment Monitoring St ations (PAMS) sites and 24-hour average HCHO from 18 air toxics sites are included in this database. University of Maryland aircraft data The University of Maryland performed 144 aircraft s pirals at 41 regional airport locations over 26 days from May-August 2002 ( http://www.atmos.umd.edu/~RAMMPP ). Spirals are approximately 20-45 minutes in duration , over which time the atmosphere from about 0-3 km is sampled. The concentrations o f O 3 

# , CO, and SO 2 

# from these spirals were included in this database, and help provide a semi-quantitative evaluation of CMAQ performance above the ground surface. Minute avera ge aircraft data were compared to the nearest instantaneous 3-dimensional CMAQ output
. Wet deposition The NADP (http://nadp.sws.uiuc.edu) collects wet de position samples across the US, through the NTN and the AIRMoN. Weekly wet depo sition samples are collected by the NTN, while daily or event-based samples were c ollected by the AIRMoN. The NYSDEC ( http://www.dec.state.ny.us ) also collects weekly wet deposition samples independently from the NADP. The wet deposition of SO 4 2- 

# , NO 3 - 

# , and NH 4 + 

# from 43 NADP/NTN sites, 7 NADP/AIRMoN sites, and 19 NYSDEC sites are included in this model evaluation database. Evaluation of CMAQ predictions The following sections provide model evaluation inf ormation for the above referenced pollutants over the OTR portion of the 1 2-km modeling domain. The statistical formulations that have been computed fo r each species are as follows: P i 

# and O i 

# are the individual (daily maximum 8-hour O 3 

# or daily average for the other species) predicted and observed concentrations, respectively ; P 

# and O 

# are the average concentrations, respectively, and N is the sample s ize. Observed average, in ppb: O N O i 

# = 

# ∑ 1 

# Predicted average, in ppb (only use P i 

# when O i 

# is valid): P N P i 

# = 

# ∑ 1

# - 5

Correlation coefficient, R 2 

# : 

# [ ] R P P O O P P O O i i i i 2 2
2 2 

# = − −

− − 

# ∑ ∑ ∑ ( )( ) ( ) ( ) Normalized mean error (NME), in %:
NME P O

O i i i 

# = − × 

# ∑ ∑ | | 100% 

# Root mean square error (RMSE), in ppb: RMSE N P O i i 

# = −    

  

# ∑ 1 2 1 2 

# ( ) /


# Fractional error (FE), in %: 

# FE N P O P O i i i i 

# = −

+ × 

# ∑ 2 100% Mean absolute gross error (MAGE), in ppb: MAGE N P O i i 

# = − 

# ∑ 1 

# Mean normalized gross error (MNGE), in %: MNGE N P O

O i i i 

# = − × 

# ∑ 1 100% Mean bias (MB), in ppb: MB N P O i i 

# = − 

# ∑ 1 ( ) 

# Mean normalized bias (MNB), in %: MNB N P O

O i i i 

# = − × 

# ∑ 1 100% ( )


# Mean fractionalized bias (MFB), in %: 

# MFB N P O

P O i i i i 

# = −

+       × 

# ∑ 2 100% Normalized mean bias (NMB), in %: NMB P O

O i i i 

# = − × 

# ∑ ∑ ( ) 100%

# - 6

Daily maximum 8-hour O 3 

# concentrations Model evaluation statistics, based on daily maximum 8-hour average O 3 

# levels on those days having (1) at least 18 valid observation s, or (2) fewer than 18 valid observations but the observed daily maximum O 3 

# concentration was at least 85 ppb, are presented here for all sites across the OTR and all of VA. The data covered the period May 15 through September 29, excluding July 6-9, wh en many sites across the eastern US were affected by large forest fires in Quebec. There are 208 SLAMS/NAMS sites and 28 special sites. These model evaluation statistics were computed usi ng two different threshold values for observed daily maximum 8-hour O 3 

# . First, the statistics were computed using only those days when the observed daily maximum 8-h our O 3 

# concentration exceeded 40 ppb. Second, the statistics were computed using on ly those days when the observed daily maximum 8-hour O 3 

# exceeded 60 ppb. This latter method focuses on th e highest O 3 

# days. Figures 1-4 display time series of observed and pre dicted daily maximum 8-hour O 3 

# concentrations averaged over all sites across the OTR, at SLAMS/NAMS and special sites and for the daily maximum two thresholds. Th ese averages were computed for each day considering all sites that met the correspondin g threshold criteria. In general the observed and predicted composite average O 3 

# concentrations track each other rather well, although there was fairly substantial underpredicti on during the mid-August period. Also, the model performance tends to be better when the lower cutoff (40 ppb) was considered. Figures 5-8 display spatial maps of fractional erro r and mean fractionalized bias for the two threshold levels. At each site the sta tistics were computed over the entire modeling season. Both the SLAMS/NAMS and special m onitors are displayed here. In general, the model performance was better in the vi cinity of urban areas and along the northeastern corridor, compared to the performance in rural areas where the model tended to underpredict daily maximum concentrations. The other statistical metrics yielded similar results to FE and MFB. Table 1 lists the median and range in fractional er ror, and the mean fractionalized bias of daily maximum 8-hour O 3 

# calculated at each site over the season, for both observed thresholds (40 and 60 ppb), as well as all sites versus just the SLAMS/NAMS sites. Considering just SLAMS/NAMS sites, FE was a lways less than 32% for the 40 ppb threshold, and less than 40% for the 60 ppb thr eshold. Similarly, the MFB at SLAMS/NAMS sites ranged from -29 to +23% for the 40 ppb threshold, and ranged from -40 to +22% for the 60 ppb threshold. Adding the s pecial sites did not affect the statistics substantially.

# - 7

Diurnal variations of gases Figures 9-17 display the composite diurnal variatio ns of the species reported hourly – O 3 

# (SLAMS/NAMS and other/special sites, displayed sep arately), continuous PM 2.5 

# , CO, NO, NO 2 

# , SO 2 

# , ethene, and isoprene. The average diurnal variat ions are for the period of May 15-September 30 – again excluding July 6-9 – considering all sites in the OTR. Note that the O 3 

# diurnal variations were computed from running 8-ho ur averages, with hours denoting the start of the 8-ho ur block. The number of monitors used to compute each composite diurnal variation is show n in each figure. For O 3 

# , the composite diurnal pattern predicted by CMAQ i s fairly similar to that observed, especially at the more urban SLAMS/NAMS m onitors. However, on average CMAQ predicts the daily maximum about an hour earli er than observed. For most of the other species presented here, CMAQ tends to predict two daily peaks, one morning and one late afternoon. For some species, such as PM 2.5 

# mass the observed concentration on a composite basis has very little diurnal variation . On the other hand, primary pollutants like CO, NO, and ethane, CMAQ exhibits qualitative agreement with the observations. Daily average concentrations of co-pollutant trace gases Composite daily average predicted and observed conc entrations of CO, NO, NO 2 

# , SO 2 

# , C 2 

# H 4 

# , HCHO, and C 5 

# H 8 

# across the OTR are displayed in Figures 18-24. Da ily average concentrations of the criteria gases, C 2 

# H 4 

# and C 5 

# H 8 

# were computed from hourly averages, and only those days having at least 12 ho urs of valid observed data were considered here. The HCHO data shown here are base d on 24-hour average values every 6 th 

# day. The criteria gas data cover the period May 1 5 – September 30, whereas the NMHC data only cover the June 1 – August 31 period, since these data are predominantly PAMS data; however, excluded from this analysis is the July 6-9 period when many sites across the eastern US were affected by large forest fires in Quebec. Table 2 lists the median and range in mean fraction alized bias calculated at each site over the season used in this analysis. The va lues listed in Table 2 were computed at each site over the entire season. While the range in MFB is rather large for each species across all sites, the median MFB was below 50% for all species except C 2 

# H 4 

# , which is substantially overpredicted by CMAQ. It should be noted that these species can vary substantially from day to day, and days with very l ow modeled or observed values can contribute to high MFB. PM 2.5 

# mass and speciation Composite daily average predicted and observed conc entrations of PM 2.5 

# mass (both daily average FRM data and continuous data), as well as major speciation –SO 4 

# , NO 3 

# , NH 4 

# , EC, OM (defined here operationally as 1.8×blank-c orrected organic carbon), and crustal mass (sum of oxides of Al, Ca, Fe, Si, and Ti) – across the OTR were compared in this analysis. The data cover the perio d May 15 – September 30, and again the July 6-9 period was excluded, when numerous sit es in the eastern US were affected

# - 8

by large forest fires in Quebec. The continuous an d FRM PM 2.5 

# data are shown every day, since there are ample daily FRM sites across t he OTR. The speciation data included here are daily averages every third day, and consis t of the largely urban EPA STN and the largely rural IMPROVE network. The two speciat ion networks collect PM 2.5 

# , SO 4 

# , NO 3 

# , EC, OM, and crustal mass, while only the STN repo rts NH 4 

# at a sufficient number of locations. Table 3 lists the median and range in mean fraction alized bias calculated at each site over the season used in this analysis. The va lues listed in Table 3 were computed at each site over the entire season. Figures 25-39 di splay time series of composite average observed and predicted daily concentrations; in the se figures, for each day the statistics were computed using all monitors with valid data. The best qualitative agreement between observed and modeled concentrations is exhi bited for PM 2.5 

# and SO 4 

# . Note that in the case of crustal mass, the data from July 4 a re also not included since this day is greatly affected by fireworks. On July 4, the comp osite average observed and predicted crustal concentrations were 4.59 μ g m -3 

# and 1.74 μ g m -3 

# , respectively at the STN monitors, and 4.46 μ g m -3 

# and 0.99 μ g m -3 

# , respectively at the IMPROVE monitors. As with the gaseous co-pollutant data, there is a substantial spread in MFB across the sites. However, the median MFB for PM 2.5 

# mass and SO 4 

# was generally small (<12%) for both urban and rural sites. CMAQ tends to overpredict NO 3 

# , more so at the IMPROVE sites. CMAQ also tends to underpredict OM at both urban and rural sites, although some of this discrepancy may be attributed to the fact that OM is operationally defined and is highly dependent on the blank correc tion and multiplier to account for other components of OM not directly measured. CMAQ tends to overpredict both EC and crustal mass, especially at urban sites; simila r to OM, the crustal mass overprediction is related to the fact that this parameter is opera tionally defined. Wet deposition of sulfate, nitrate, and ammonium Observed and predicted wet deposition of SO 4 

# , NO 3 

# , and NH 4 

# were compared over the period May 14 – September 30. For this an alysis, weekly or event-based wet deposition amounts from the NADP/NTN (43 sites), NA DP/AIRMoN (7 sites), and New York State DEC (19 sites) covering the entire OTR p lus all of VA and WV were integrated over the four-and-a-half months. Becaus e the observed weekly wet deposition samples did include July 6-9, the corresponding CMA Q predictions also include this period. Table 4 lists the model evaluation statist ics for integrated wet deposition of SO 4 

# , NO 3 

# , and NH 4 

# at each site over the season, while Figures 40-42 compare the observed and predicted weekly values relative to the 1:1 lin e. Overall CMAQ tended to overpredict wet deposition o f these ions. On a percentage basis, the overprediction was least for SO 4 

# and highest for NO 3 

# . The NME, MNGE, MNB, and NMB were less than 50% for the three ions. Given that precipitation is very difficult to predict, especially during the summer months when rainfall can vary tremendously over a 12 km by 12 km area represented by this model grid, CMAQ did a rather good job reproducing seasonal wet deposition over the OTR.

# - 9

Upper-air O 3 

# , CO, and SO 2 

# data The University of Maryland operated an instrumented light aircraft during the summer of 2002. On 26 days from May-August meteoro logical, trace gas, and particle scattering/absorption data were collected during as cent or descent spirals over 41 regional airports. In all, 144 spirals were performed from near the surface to about 3 km above ground level. For this analysis, composite average profiles of O 3 

# , CO, and SO 2 

# were created over three time periods: “morning” (08-11 EST), “afternoon” (12-16 EST), and “evening” (17-19 EST). The minute average observed concentrations were aggregated into layer averages, which correspond to the lowest 15 model layers. Model layers are increasingly thick away from the surface; the surfa ce layer is about 20 m thick while the 15 th 

# layer is about 500 m thick (and centered about 2.8 km above the ground). Figures 43-51 display the observed and predicted co mposite vertical profiles of O 3 

# , CO, and SO 2 

# for the three time periods. In terms of profile s hape, CMAQ was in good qualitative agreement for all three species above t he surface during the afternoon hours. For CO, the model tends to greatly underpredict obs erved levels near the surface, whereas the predicted O 3 

# and SO 2 

# concentrations are closer to the respective observ ed values. Summary Various model evaluation statistics are presented here for a variety of gaseous and aerosol species in addition to O 3 

# . In general, the CMAQ results were best for daily maximum O 3 

# and daily average PM 2.5 

# and SO 4 

# mass. Many other species vary tremendously over the course of a day, or from day to day, and small model over- or underprediction at low concentrations can lead to l arge biases on a composite basis. It is important to demonstrate that the model performs re asonably over the diurnal cycle, not just in terms of daily maximum or average values. Also, it is important to demonstrate that the model can reproduce concentrations above t he ground level.

# - 10

Table 1. Median and range in fractional error (FE, %) and mean fractionalized bias (MFB, %) for daily maximum 8-hour O 3 

# using the 40 ppb and 60 ppb observed thresholds. The values using only SLAMS/NAMS sites are boldfaced, the values using all sites are in regular font. Metric, threshold Range (%) Median (%) FE, 40 ppb +10 to +34% +10 to +32% +15% +15% MFB, 40 ppb -34 to +23% -29 to +23% -6% -6% FE, 60 ppb +9 to +40% +9 to +40% +15% +15% MFB, 60 ppb -40 to +22% -40 to +22% -12% -11%
Table 2. Median and range in mean fractionalized b ias (%) for daily average CO, NO, NO 2 

# , SO 2 

# , C 2 

# H 4 

# , HCHO, and C 5 

# H8. Pollutant Range in MFB (%) Median MFB (%) CO (97 sites) -128 to +144% -10% NO (75 sites) -182 to +116% -46% NO 2 

# (97 sites) -125 to +107% +13% SO 2 

# (134 sites) -139 to 140% +3% C 2 

# H 4 

# (19 sites) +28 to +168% +86% HCHO (18 sites) -66 to +96% -13% C 5 

# H 8 

# (19 sites) -54 to +165% +43%

# - 11

Table 3. Median and range in mean fractionalized b ias (%) for daily average PM 2.5 

# , SO 4 

# , NO 3 

# , NH 4 

# , EC, and OM.

Pollutant Range in MFB (%) Median MFB (%) PM 2.5 

# (FRM; 257 sites) -59 to +119% -4% PM 2.5 

# (continuous; 57 sites) -39 to +85% +5% STN PM 2.5 

# (49 sites) -45 to +102% -9% IMPROVE PM 2.5 

# (21 sites) -36 to +19% -10% STN SO 4 

# (49 sites) -21 to +60% +12% IMPROVE SO 4 

# (21 sites) -26 to +16% -7% STN NO 3 

# (49 sites) -73 to +406% +25% IMPROVE NO 3 

# (21 sites) -57 to +358% +64% STN NH 4 

# (49 sites) -36 to +112% +16% STN EC (49 sites) -42 to +269% +34% IMPROVE EC (21 sites) -60 to +146% -27% STN OM (49 sites) -82 to -25% -58% IMPROVE OM (21 sites) -60 to +7% -40% STN crustal (49 sites) +2 to +546% +182% IMPROVE crustal (21 sites) -18 to +163% +38%
Table 4. Model evaluation statistics for integrate d wet deposition of SO 4 

# , NO 3 

# , and NH 4



# . Parameter SO 4 

# NO 3 

# NH 4 

# Observed average, mg m -2 

# 1063 704 185 Predicted average, mg m -2 

# 946 367 117 Correlation coefficient, R 2 

# 0.17 0.22 0.12 NME, % 34 49 48 RMSE, mg m -2 

# 490 417 109 FE, % 36 62 57 MAGE, mg m -2 

# 365 344 89 MNGE, % 36 45 46 MB, mg m -2 

# -118 -337 -68 MNB, % -3 -44 -28 MFB, % -13 -61 -44 NMB, % -11 -48 -37

# - 12

Figure 1. Observed and Predicted averages

208 SLAMS/NAMS sites; 40 ppb threshold

0 10 20 30 40 50 60 70 80 90 100

2 002051 5 2 00205 23 2 00205 31 2 00 2 0 608 2 0 020616 20020624 2 002070 2 2 00207 10 2 00207 18 2 00 2 0 726 2 0 020803 20020811 2 002081 9 2 002082 7 2 00209 04 2 00 20912 2 0 020920 20020928ppb 

# obs avg, ppb

CMAQ avg, ppb



# Figure 2. Observed and Predicted averages

208 SLAMS/NAMS sites; 60 ppb threshold

0 10 20 30 40 50 60 70 80 90 100

2 002051 5 2 00205 23 2 00205 31 2 00 2 0 608 2 0 020616 20020624 2 002070 2 2 00207 10 2 00207 18 2 00 2 0 726 2 0 020803 20020811 2 002081 9 2 002082 7 2 00209 04 2 00 20912 2 0 020920 20020928ppb 

# obs avg, ppb

CMAQ avg, ppb

# - 13

Figure 3. Observed and Predicted averages

28 special monitors; 40 ppb threshold

0 10 20 30 40 50 60 70 80 90 100

2 002051 5 2 00205 23 2 00205 31 2 00 2 0 608 2 0 020616 20020624 2 002070 2 2 00207 10 2 00207 18 2 00 2 0 726 2 0 020803 20020811 2 002081 9 2 002082 7 2 00209 04 2 00 20912 2 0 020920 20020928ppb 

# obs avg, ppb

CMAQ avg, ppb



# Figure 4. Observed and Predicted averages 28 special sites; 60 ppb threshold

0 10 20 30 40 50 60 70 80 90 100

2 002051 5 2 00205 23 2 00205 31 2 00 2 0 608 2 0 020616 20020624 2 002070 2 2 00207 10 2 00207 18 2 00 2 0 726 2 0 020803 20020811 2 002081 9 2 002082 7 2 00209 04 2 00 20912 2 0 020920 20020928ppb 

# obs avg, ppb

CMAQ avg, ppb

# - 14

Figure 5.

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### 300 0 300 600 Miles 

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# FE, 40 ppb cutoff
# 

# 10-15%
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# - 15

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### 300 0 300 600 Miles 

### N
E W S 

# MFB, 60 ppb cutoff
# 

# -42 - -30%
# 

# -30 - -18%
# 

# -18 - -6%
# 

# -6 - 6%
# 

# 6-18%
# 

# 18-30%

# - 16 Figure 9. Ozone (SLAMS/NAMS) - 208 sites

0.0 10.0 20.0 30.0 40.0 50.0 60.0

0 4 8 12 16 20 24O3(ppb) 

# Observed Predicted



# Figure 10. Ozone (CASTNet & others) - 28 sites

0.0 10.0 20.0 30.0 40.0 50.0 60.0

0 4 8 12 16 20 24O3(ppb) 

# Observed Predicted

# - 17

Figure 11. Continuous PM 2.5 

# mass - 57 sites

0.00 5.00 10.00 15.00 20.00 25.00 30.00

0 4 8 12 16 20 24PM2.5(ug/m3) 

# Observed Predicted



# Figure 12. Carbon monoxide - 97 sites

0.0 100.0 200.0 300.0 400.0 500.0 600.0

0 4 8 12 16 20 24CO (ppb) 

# Observed Predicted

# - 18

Figure 13. Nitric oxide - 75 sites

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

0 4 8 12 16 20 24NO (ppb) 

# Observed Predicted



# Figure 14. Nitrogen dioxide - 97 sites

0.00 5.00 10.00 15.00 20.00 25.00 30.00

0 4 8 12 16 20 24NO2(ppb) 

# Observed Predicted

# - 19

Figure 15. Sulfur dioxide - 134 sites

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00

0 5 10 15 20 25SO2(ppb) 

# Observed Predicted



# Figure 16. Ethene - 19 sites

0.00 0.50 1.00 1.50 2.00 2.50 3.00

0 4 8 12 16 20 24ethene (ppb) 

# Observed Predicted

# - 20

Figure 17. Isoprene - 19 sites

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00

0 4 8 12 16 20 24isoprene (ppb) 

# Observed Predicted



# Figure 18. Observed and Predicted average CO (97 sites)

0 100 200 300 400 500 600 700 800

2 0 0205 1 5 20 0 2052 3 2 0 0205 3 1 20020608 20 020 61 6 20020624 20 0 2070 2 2 0 0207 1 0 20 0 2071 8 2 0 0207 2 6 20020803 20 020 81 1 20020819 20 0 2082 7 2 0 0209 0 4 20 0 2091 2 2 0 0209 2 0 20 0 2092 8ppb 

# obs avg, ppb

CMAQ avg, ppb

# - 21

Figure 19. Observed and Predicted average NO (75 sites)

0 2 4 6 8 10 12 14 16 18

2 0 0205 1 5 20 0 2052 3 2 0 0205 3 1 20020608 20 020 61 6 20020624 20 0 2070 2 2 0 0207 1 0 20 0 2071 8 2 0 0207 2 6 20020803 20 020 81 1 20020819 20 0 2082 7 2 0 0209 0 4 20 0 2091 2 2 0 0209 2 0 20 0 2092 8ppb 

# obs avg, ppb

CMAQ avg, ppb



# Figure 20. Observed and Predicted average NO 2 

# (97 sites)

0 5 10 15 20 25 30 35

2 0 0205 1 5 20 0 2052 3 2 0 0205 3 1 20020608 20 020 61 6 20020624 20 0 2070 2 2 0 0207 1 0 20 0 2071 8 2 0 0207 2 6 20020803 20 020 81 1 20020819 20 0 2082 7 2 0 0209 0 4 20 0 2091 2 2 0 0209 2 0 20 0 2092 8ppb 

# obs avg, ppb

CMAQ avg, ppb

# - 22

Figure 21. Observed and Predicted average SO 2 

# (134 sites)

0 1 2 3 4 5 6 7 8

2 0 0205 1 5 20 0 2052 3 2 0 0205 3 1 20020608 20 020 61 6 20020624 20 0 2070 2 2 0 0207 1 0 20 0 2071 8 2 0 0207 2 6 20020803 20 020 81 1 20020819 20 0 2082 7 2 0 0209 0 4 20 0 2091 2 2 0 0209 2 0 20 0 2092 8ppb 

# obs avg, ppb

CMAQ avg, ppb



# Figure 22. Observed and Predicted C 2 

# H 4 

# (19 sites)

0 0.5 1 1.5 2 2.5

20 0206 01 2002 06 06 20 0206 11 2002 06 16 200 206 21 20020 6 26 2002 07 01 20020706 2002 07 11 2 00207 1 6 2002 07 21 20 0207 26 2002 07 31 20 0208 05 20020 8 10 2002 08 15 20020820 2002 08 25 2 00208 3 0ppb 

# obs avg, ppb

CMAQ avg, ppb

# - 23

Figure 23. Observed and Predicted HCHO (18 sites)

0 1 2 3 4 5 6 7 8 9

20 0206 01 20020607 20020 6 13 2002 06 19 2002 06 25 20 0207 01 2 00207 0 7 20020713 20020 7 19 2002 07 25 2002 07 31 20 0208 06 2 00208 1 2 20020818 2002 08 24 2002 08 30ppb 

# obs avg, ppb

CMAQ avg, ppb



# Figure 24. Observed and Predicted C 5 

# H 8 

# (19 sites)

0 0.5 1 1.5 2 2.5 3

20 0206 01 2002 06 06 20 0206 11 2002 06 16 200 206 21 20020 6 26 2002 07 01 20020706 2002 07 11 2 00207 1 6 2002 07 21 20 0207 26 2002 07 31 20 0208 05 20020 8 10 2002 08 15 20020820 2002 08 25 2 00208 3 0ppb 

# obs avg, ppb

CMAQ avg, ppb

# - 24

Figure 25. Observed and Predicted PM 2.5 

# (FRM; 257 sites)

0 5 10 15 20 25 30 35 40 45

2 0020515 2 00 2 05 2 3 2 00 2 05 3 1 2 00 2 06 0 8 2 0 02 0 61 6 2 0 02 0 62 4 2002070 2 20020710 2 0020718 2 0020726 2 00 2 08 0 3 2 00 2 08 1 1 2 0 02 0 81 9 2 0 02 0 82 7 2002090 4 2002091 2 2 0020920 2 0020928μg m-3 

# obs avg, ug/m3

CMAQ avg, ug/m3



# Figure 26. Observed and Predicted PM 2.5 

# (continuous; 57 sites)

0 5 10 15 20 25 30 35 40 45

2 0020515 2 00 2 05 2 3 2 00 2 05 3 1 2 00 2 06 0 8 2 0 02 0 61 6 2 0 02 0 62 4 2002070 2 20020710 2 0020718 2 0020726 2 00 2 08 0 3 2 00 2 08 1 1 2 0 02 0 81 9 2 0 02 0 82 7 2002090 4 2002091 2 2 0020920 2 0020928μg m-3 

# obs avg, ug/m3

CMAQ avg, ug/m3

# - 25

Figure 27. Observed and Predicted PM 2.5 

# (STN; 49 sites)

0 5 10 15 20 25 30 35 40

2 002051 7 2 002052 6 2 002060 4 2 002061 3 2 002062 2 2 002070 1 2 002071 0 2 002071 9 2 002072 8 2 002080 6 2 002081 5 2 002082 4 2 002090 2 2 002091 1 2 002092 0 2 002092 9μg m-3 

# obs avg, ug/m3

CMAQ avg, ug/m3



# Figure 28. Observed and Predicted PM 2.5 

# (IMPROVE; 21 sites)

0 5 10 15 20 25 30

20020517 2 002 05 26 20020604 2 002 0 613 20020622 2 002 0 701 20020710 2 002 0 719 200 2 072 8 2002 0 806 200 2 081 5 20020824 200 2 090 2 20020911 20 02 092 0 20020929μg m-3 

# obs avg, ug/m3

CMAQ avg, ug/m3

# - 26

Figure 29. Observed and Predicted SO 4 

# (STN; 49 sites)

0 5 10 15 20 25

2 002051 7 2 002052 6 2 002060 4 2 002061 3 2 002062 2 2 002070 1 2 002071 0 2 002071 9 2 002072 8 2 002080 6 2 002081 5 2 002082 4 2 002090 2 2 002091 1 2 002092 0 2 002092 9μg m-3 

# obs avg, ug/m3

CMAQ avg, ug/m3



# Figure 30. Observed and Predicted SO 4 

# (IMPROVE; 21 sites)

0 2 4 6 8 10 12 14 16 18

20020517 2 002 05 26 20020604 2 002 0 613 20020622 2 002 0 701 20020710 2 002 0 719 200 2 072 8 2002 0 806 200 2 081 5 20020824 200 2 090 2 20020911 20 02 092 0 20020929mg m-3 

# obs avg, ug/m3

CMAQ avg, ug/m3

# - 27

Figure 31. Observed and Predicted NO 3 

# (STN; 49 sites)

0 0.5 1 1.5 2 2.5 3 3.5

2 002051 7 2 002052 6 2 002060 4 2 002061 3 2 002062 2 2 002070 1 2 002071 0 2 002071 9 2 002072 8 2 002080 6 2 002081 5 2 002082 4 2 002090 2 2 002091 1 2 002092 0 2 002092 9μg m-3 

# obs avg, ug/m3

CMAQ avg, ug/m3



# Figure 32. Observed and Predicted NO 3 

# (IMPROVE; 21 sites)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

20020517 2 002 05 26 20020604 2 002 0 613 20020622 2 002 0 701 20020710 2 002 0 719 200 2 072 8 2002 0 806 200 2 081 5 20020824 200 2 090 2 20020911 20 02 092 0 20020929μg m-3 

# obs avg, ug/m3

CMAQ avg, ug/m3

# - 28

Figure 33. Observed and Predicted NH 4 

# (STN; 49 sites)

0 1 2 3 4 5 6 7

2 002051 7 2 002052 6 2 002060 4 2 002061 3 2 002062 2 2 002070 1 2 002071 0 2 002071 9 2 002072 8 2 002080 6 2 002081 5 2 002082 4 2 002090 2 2 002091 1 2 002092 0 2 002092 9μg m-3 

# obs avg, ug/m3

CMAQ avg, ug/m3



# Figure 34. Observed and Predicted EC (STN; 49 sites)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

2 002051 7 2 002052 6 2 002060 4 2 002061 3 2 002062 2 2 002070 1 2 002071 0 2 002071 9 2 002072 8 2 002080 6 2 002081 5 2 002082 4 2 002090 2 2 002091 1 2 002092 0 2 002092 9μg m-3 

# obs avg, ug/m3

CMAQ avg, ug/m3

# - 29

Figure 35. Observed and Predicted EC (IMPROVE; 21 sites)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

20020517 2 002 05 26 20020604 2 002 0 613 20020622 2 002 0 701 20020710 2 002 0 719 200 2 072 8 2002 0 806 200 2 081 5 20020824 200 2 090 2 20020911 20 02 092 0 20020929μg m-3 

# obs avg, ug/m3

CMAQ avg, ug/m3



# Figure 36. Observed and Predicted OM (STN; 49 sites)

0 2 4 6 8 10 12 14

2 002051 7 2 002052 6 2 002060 4 2 002061 3 2 002062 2 2 002070 1 2 002071 0 2 002071 9 2 002072 8 2 002080 6 2 002081 5 2 002082 4 2 002090 2 2 002091 1 2 002092 0 2 002092 9μg m-3 

# obs avg, ug/m3

CMAQ avg, ug/m3

# - 30

Figure 37. Observed and Predicted OM (IMPROVE; 21 sites)

0 1 2 3 4 5 6 7 8 9 10

20020517 2 002 05 26 20020604 2 002 0 613 20020622 2 002 0 701 20020710 2 002 0 719 200 2 072 8 2002 0 806 200 2 081 5 20020824 200 2 090 2 20020911 20 02 092 0 20020929μg m-3 

# obs avg, ug/m3

CMAQ avg, ug/m3



# Figure 38. Observed and Predicted crustal (STN; 49 sites)

0 0.5 1 1.5 2 2.5 3

2 002051 7 2 002052 6 2 002060 4 2 002061 3 2 002062 2 2 002070 1 2 002071 0 2 002071 9 2 002072 8 2 002080 6 2 002081 5 2 002082 4 2 002090 2 2 002091 1 2 002092 0 2 002092 9μg m-3 

# obs avg, ug/m3

CMAQ avg, ug/m3

# - 31

Figure 39. Observed and Predicted crustal (IMPROVE; 21 sites)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

20020517 2 002 05 26 20020604 2 002 0 613 20020622 2 002 0 701 20020710 2 002 0 719 200 2 072 8 2002 0 806 200 2 081 5 20020824 200 2 090 2 20020911 20 02 092 0 20020929μg m-3 

# obs avg, ug/m3

CMAQ avg, ug/m3



# Figure 40. Wet SO 4 

# Deposition

0 500 1000 1500 2000 2500

0 500 1000 1500 2000 2500 Observed (mg/m 2 

# )Predicted (mg/m2)

# - 32

Figure 41. Wet NO 3 

# Deposition

0 200 400 600 800 1000 1200 1400 1600

0 200 400 600 800 1000 1200 1400 1600 Observed (mg/m 2 

# )Predicted (mg/m2)



# Figure 42. Wet NH 4 

# Deposition

0 50 100 150 200 250 300 350 400 450 500

0 50 100 150 200 250 300 350 400 450 500 Observed (mg/m 2 

# )Predicted (mg/m2)

# - 33

Figure 43.

Figure 44. Morning (08-11 EST)

0 500 1000 1500 2000 2500 3000

40 45 50 55 60 65 70 O3 (ppb)Z (m)



### observed CMAQ

Afternoon (12-16 EST)

0 500 1000 1500 2000 2500 3000

50 60 70 80 90 O3 (ppb)Z (m)



### observed CMAQ

# - 34

Figure 45.

Figure 46. Evening (17-19 EST)

0 500 1000 1500 2000 2500 3000

40 50 60 70 80 90 O3 (ppb)Z (m)



### observed CMAQ

Morning (08-11 EST)

0 500 1000 1500 2000 2500 3000

0 100 200 300 400 500 CO (ppb)Z (m)



### observed CMAQ

# - 35

Figure 47.

Figure 48. Afternoon (12-16 EST)

0 500 1000 1500 2000 2500 3000

0 100 200 300 400 500 CO (ppb)Z (m)



### observed CMAQ

Evening (17-19 EST)

0 500 1000 1500 2000 2500 3000

0 100 200 300 400 500 600 CO (ppb)Z (m)



### observed CMAQ

# - 36

Figure 49.

Figure 50. Morning (08-11 EST)

0 500 1000 1500 2000 2500 3000

0 1 2 3 4 5 6 SO2 (ppb)Z (m)



### observed CMAQ

Afternooon (12-16 EST)

0 500 1000 1500 2000 2500 3000

0 1 2 3 4 5 6 SO2 (ppb)Z (m)



### observed CMAQ

# - 37

Figure 51. Evening (17-19 EST)

0 500 1000 1500 2000 2500 3000

0 1 2 3 4 5 SO2 (ppb)Z (m)



### observed CMAQ