# 1 TSD-1e 

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

Bureau of Air Quality Analysis and Research Division of Air Resources New York State Department of Environmental Conservation Albany, NY 12233

February 23, 2006

# 2 Air quality model evaluation and assessment One of the tasks that is required as part of demonstrating 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 over the region of interest. As part of the attainment demonstration, the SMOKE/CMAQ m odeling system was applied to simulate the pollutant concentration fields for the ba se year 2002 emissions with the correspondingmeteorological 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 are compared to measurements, in order to establish the credibility of the simu lation. In the following sections a comparison between the measured and predicted concen trations is performed and results are presented, demonstrating on an overall basis th e utility of the modeling system in this application. The results presented here should serve as an illustration of some of the evaluation and assessment performed on the base 2002 CMAQ simulation. Additional information can be made available by request from the New York State Department of Environmental Conservation. Summary of measured data The ambient air quality data, both gaseous and aerosol 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 Atmo spheric Science Research Center, University at Albany, Albany, NY • Harvard Forest, Petersham, MA operate d by Harvard University, Boston, MA • A tmospheric I nvestigation, R egional M odeling, A nalysis and P rediction (AIRMAP) operated by University of New Hampshire, Durham, 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 Univer sity of Maryland, 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-ba sed data were obtained at the grid-cell corresponding to the mon itor location; no interpol ation 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/air saqs/aqsweb/aqswebhome.html ). Hourly O 3 

# concentrations from the Harvard Forest Envi ronmental 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 included in this database. The EPA CASTNet program collects hourly O 3 

# at generally rural lo cations across the US ( http://www.epa.gov/castnet ); data from 22 sites, including two from West Virginia, 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 extracted from AQS (257 sites). Hourly PM 2.5 

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

# mass were also taken from the Thompson Farm, NH AIRMAP site, Pinnacle State Park, and the NE-O PS site in Philadelphia, 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 mass (OC/OM), and soil/crustal matter) from Class I areas acro ss 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 ne twork are collected every 3 rd 

# or 6 th 

# day. Data from 49 STN sites, generally in urban areas and often collocated with FRM monitors, and 21 IMPROVE sites (including Dolly Sods, WV) were used in this analysis. Organic mass is assumed to equal 1.8×OC, a nd soil/crustal matter is assumed to consist of oxides of Al, Ca, Fe, Si, and Ti. The ST N OC data are blank-corrected by removing a monitor-specific, constant blank, an d these values ar e available from http://www.epa.gov/airtrends/aqt rnd03/pdfs/2_chemspec0fpm25.pdf ; the IMPROVE OC blanks are assumed to equal zero. Criteria gaseous pollutants Hourly carbon monoxide (CO; 97 sites), ni tric 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 sites are SLAMS/NAMS monitors located primarily in urban in suburban areas, but da ta 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 measured routinely, for this model evaluation database the focus was on Ca rbon Bond 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 from AQS. Hourly C 2 

# H 4 

# and C 5 

# H 8 

# data from 19 Photochemical Assessment M onitoring Stations (PAMS) sites and 24-hour average HCHO from 18 air toxics site s are included in this database. University of Maryland aircraft data The University of Maryland performed 144 aircraft spirals 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 of O 3 

# , CO, and SO 2 

# from these spirals were included in this databa se, and help provide a semi- quantitative evaluation of CMAQ performance above the ground surface. Minute average aircraft data were compared to the nearest instantaneous 3-dimensional CMAQ output. Wet deposition The NADP (http://nadp.sws.uiuc.edu) colle cts wet deposition samples across the US, through the NTN and the AIRMoN. Weekly wet deposition samples are collected by the NTN, while daily or event-based sa mples were collected 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, a nd 19 NYSDEC sites are included in this model evaluation database. Evaluation of CMAQ predictions The following sections provide model ev aluation information for the above referenced pollutants over the OTR por tion of the 12-km modeling domain. The statistical formulations that have been computed for 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 con centrations, respectively; 

# P and 

# O are the average concentrations, respectively, and N is the sample size. 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 PO O PP OO 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 obs ervations, or (2) fewer than 18 valid observations but the obs erved daily maximum O 3 

# concentration was at least 85 ppb, are presented here for all sites acr oss the OTR and all of VA. The data cover the period May 15 through September 29, excluding July 6-9, when many sites across the eastern US were affected by large forest fires in Qu ebec. There are 208 SLAMS/NAMS sites and 28 special sites. These model evaluation statistics were computed using 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-hour O 3 

# concentration exceeded 40 ppb. Second, the statistics were computed us ing only those days when the observed daily maximum 8-hour O 3 

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

# days. Figures 1-4 display time series of obser ved and predicted daily maximum 8-hour O 3 

# concentrations averaged over all sites acr oss the OTR, at SLAMS/NAMS and special sites and for the daily maximum two thresholds . These averages were computed for each day considering all sites that met the corres ponding threshold criteria. In general the observed and predicted composite average O 3 

# concentrations track each other rather well, although there was fairly subs tantial underprediction duri ng 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 frac tional error and mean fractionalized bias for the two threshold levels. At each site the statistics were computed over the entire modeling season. Both the SLAMS/NAMS and sp ecial monitors are displayed here. In general, the model performan ce was better in the vicinity of urban areas and along the northeastern corridor, compared to the perfor mance 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 frac tional error, 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 always less than 32% for the 40 ppb threshold, and less than 40% for the 60 ppb threshold. 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 th e special sites did not affect the statistics substantially.

# 7 Diurnal variations of gases Figures 9-17 display the composite diur nal variations of the species reported hourly – O 3 

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

# , CO, NO, NO 2 

# , SO 2 

# , ethene, and isoprene. The aver age diurnal variations are for the period of May 15-September 30 – again excl uding July 6-9 – considering all sites in the OTR. Note that the O 3 

# diurnal variations were computed from running 8-hour averages, with hours denoting the start of the 8-hour block. The number of monitors used to compute each composite diurnal va riation is shown in each figure. For O 3 

# , the composite diurnal pattern predicted by CMAQ is fairly similar to that observed, especially at the more urban SL AMS/NAMS monitors. However, on average CMAQ predicts the daily maximum about an hour earlier 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 vari ation. On the other hand, primary pollutants like CO, NO, and ethane, CMAQ exhibits qual itative agreement with the observations. Daily average concentrations of co-pollutant trace gases Composite daily average pr edicted and observed concen trations 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. Daily 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 le ast 12 hours of valid observed data were considered here. The HCHO data shown here are based on 24-hour average values every 6 th 

# day. The criteria gas data cover th e period May 15 – 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 fractionalized bias calculated at each site over the season used in th is analysis. The values listed in Table 2 were computed at each site over the entire seas on. 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 s hould be noted that these species can vary substantially from day to day, and days with very low modeled or observed values can contribute to high MFB. PM 2.5 

# mass and speciation Composite daily average predicted and observed concentrations 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-corrected 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 cove r the period May 15 – September 30, and again the July 6-9 period was excluded, when numerou s sites in the eastern US were affected

# 8 by large forest fires in Queb ec. The continuous and FRM PM 2.5 

# data are shown every day, since there are ample daily FRM sites acros s the OTR. The speciation data included here are daily averages every third day, a nd consist of the largely urban EPA STN and the largely rural IMPROVE network. Th e two speciation networks collect PM 2.5 

# , SO 4 

# , NO 3 

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

# at a sufficient number of locations. Table 3 lists the median and range in mean fractionalized bias calculated at each site over the season used in th is analysis. The values listed in Table 3 were computed at each site over the entire season. Figures 25- 39 display time series of composite average observed and predicted daily concentrations; in these figures, for each day the statistics were computed using all monitors with va lid data. The best qualitative agreement between observed and modeled conc entrations is exhibited for PM 2.5 

# and SO 4 

# . Note that in the case of crustal mass, the data from Ju ly 4 are also not included since this day is greatly affected by fireworks. On July 4, the composite 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, th ere 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 site s. CMAQ tends to overpredict NO 3 

# , more so at the IMPROVE sites. CMAQ also tends to underpre dict OM at both urban and rural sites, although some of this discrepancy may be attrib uted to the fact that OM is operationally defined and is highly depende nt on the blank correction a nd multiplier to account for other components of OM not directly measur ed. CMAQ tends to overpredict both EC and crustal mass, especially at urban sites; similar to OM, the crustal mass overprediction is related to the fact that this parameter is operationally 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 th is analysis, weekly or event-based wet deposition amounts from the NADP/NTN (43 sites), NADP/AIRMoN (7 sites), and New York State DEC (19 sites) covering the entire OTR plus all of VA and WV were integrated over the four-and-a-half months. Because the observed weekly wet deposition samples did include July 6-9, the corres ponding CMAQ predictions also include this period. Table 4 lists the model evaluation st atistics 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 line. Overall CMAQ tended to overpredict we t deposition of these ions. On a percentage basis, the overp rediction was least for SO 4 

# and highest for NO 3 

# . The NME, MNGE, MNB, and NMB were less than 50% for the three ions. Give n that precipitation is very difficult to predict, especially duri ng 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 seasona l 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-Augus t meteorological, trace gas, and particle scattering/absorption data were collected dur ing ascent 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 ob served concentrations were aggregated into layer averages, which correspond to the lowest 15 model layers. Model layers are increasingly thick away from the surface; th e surface layer is about 20 m thick while the 15 th 

# layer is about 500 m thick (and cent ered about 2.8 km above the ground). Figures 43-51 display the observed and pred icted composite verti cal profiles of O 3 

# , CO, and SO 2 

# for the three time periods. In te rms of profile shape, CMAQ was in good qualitative agreement for all three species above the surface during the afternoon hours. For CO, the model tends to greatly underpre dict observed levels near the surface, whereas the predicted O 3 

# and SO 2 

# concentrations are closer to the respective observed values. Summary Various model evaluation statistics are pr esented 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 fr om day to day, and small model over- or underprediction at low concentrations can lead to large biases on a composite basis. It is important to demonstrate that the model perf orms reasonably over th e diurnal cycle, not just in terms of daily maximum or average va lues. Also, it is important to demonstrate that the model can reproduce concentrations above the ground level.

# 10 Table 1. Median and range in fractional er ror (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 fracti onalized bias (%) 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 bias (%) 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 site s) -18 to +163% +38%

Table 4. Model evaluation statistics for integrated 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
20020515 20

0 2 0 5 23 200205 3 1 2 0 020608

20

0 2 0 6 1 6 2 0 020624

20

0 2 0 7 02 200207 1 0 2 0 020718

20

0 2 0 7 2 6 2 0 020803

20

0 2 0 8 11 20020819 2 0 020827

20

0 209

0 4 2 0 020912

20

0 2 0 9 20 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 0 020515

20

0 2 0 5 23 200205 3 1 2 0 020608

20

0 2 0 6 1 6 2 0 020624

20

0 2 0 7 0 2 200207 1 0 2 0 020718

20

0 2 0 7 2 6 2 0 020803

20

0 2 0 8 1 1 20020819 2 0 020

8 27 20

0 209

0 4 2 0 020912

20

0 2 0 9 2 0 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
20020515 20

0 2 0 5 23 200205 3 1 2 0 020608

20

0 2 0 6 1 6 2 0 020624

20

0 2 0 7 02 200207 1 0 2 0 020718

20

0 2 0 7 2 6 2 0 020803

20

0 2 0 8 11 20020819 2 0 020827

20

0 209

0 4 2 0 020912

20

0 2 0 9 20 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 0 020515

20

0 2 0 5 23 200205 3 1 2 0 020608

20

0 2 0 6 1 6 2 0 020624

20

0 2 0 7 0 2 200207 1 0 2 0 020718

20

0 2 0 7 2 6 2 0 020803

20

0 2 0 8 1 1 20020819 2 0 020

8 27 20

0 209

0 4 2 0 020912

20

0 2 0 9 2 0 20020928ppb 

# obs avg, ppb CMAQ avg, ppb

# 14 Figure 5.

# # # # # # # # # #

#

# # # # #

# # # # # # # # # # # # #
# # # # #

# # # # # #

# # # # # # #

# # # # #
# # # # # # # # # # # # # # #

# # # #
#

#

# # # # # #

# #

# #
# # # #
# #

# # # # # # # #

# # # # # # # # #
# # #

# # # # #

# # # # #

# # #

# # # # #

#

# # # # # # # # # # # # # # # #
# # #
# # # #

# # # #

# # # # # # # #

# # # # # # # # #

# # # # # # # # # # #

# #
# # # #

# # # # #

# # # # # #

# # #

# #

# # # # #

# #

# # # # # #

#

#

# #

#

#

# #

#

# # # # # #

# # #



### 300 0 300 600 Miles 

## N
E W S 

# FE, 40 ppb cutoff

# 

# 10-15%
# 

# 15-20%
# 

# 20-25%
# 

# 25-30%
# 

# 30-35%



# Figure 6.

# # # # # # # # # #

#

# # # # #

# # # # # # # # # # # # #
# # # # #

# # # # # #

# # # # # # #

# # # # #
# # # # # # # # # # # # # # #

# # # #
#

#

# # # # # #

# #

# #
# # # #
# #

# # # # # # # #

# # # # # # # # #
# # #

# # # # #

# # # # #

# # #

# # # # #

#

# # # # # # # # # # # # # # # #
# # #
# # # #

# # # #

# # # # # # # #

# # # # # # # # #

# # # # # # # # # # #

# #
# # # #

# # # # #

# # # # # #

# # #

# #

# # # # #

# #

# # # # # #

#

#

# #

#

#

# #

#

# # # # # #

# # # 

# FE, 60 ppb cutoff
# 

# 7-14%
# 

# 14-21%
# 

# 21-28%
# 

# 28-35%
# 

# 35-42%

300 0 300 600 Miles 

## N
E W S

# 15 Figure 7.

# # # # # # # # # #

#

# # # # #

# # # # # # # # # # # # #
# # # # #

# # # # # #

# # # # # # #

# # # # #
# # # # # # # # # # # # # # #

# # # #
#

#

# # # # # #

# #

# #
# # # #
# #

# # # # # # # #

# # # # # # # # #
# # #

# # # # #

# # # # #

# # #

# # # # #

#

# # # # # # # # # # # # # # # #
# # #
# # # #

# # # #

# # # # # # # #

# # # # # # # # #

# # # # # # # # # # #

# #
# # # #

# # # #

# # # # # #

# # #

# #

# # # # #

# #

# # # # # #

#

#

# #

#

#

# #

#

# # # # # #

# # # 

# MFB, 40 ppb cutoff
# 

# -35 - -25%
# 

# -25 - -15%
# 

# -15 - -5%
# 

# -5 - 5%
# 

# 5-15%
# 

# 15-25%

300 0 300 600 Miles 

### N
E W S 

# Figure 8.

# # # # # # # # # #

#

# # # # #

# # # # # # # # # # # # #
# # # # #

# # # # # #

# # # # # # #

# # # # #
# # # # # # # # # # # # # # #

# # # #
#

#

# # # # # #

# #

# #
# # # #
# #

# # # # # # # #

# # # # # # # # #
# # #

# # # # #

# # # # #

# # #

# # # # #

#

# # # # # # # # # # # # # # # #
# # #
# # # #

# # # #

# # # # # # # #

# # # # # # # # #

# # # # # # # # # # #

# #
# # # #

# # # #

# # # # # #

# # #

# #

# # # # #

# #

# # # # # #

#

#

# #

#

#

# #

#

# # # # # #

# # #



### 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 0 20

5 1 5 2 0 0 20

5 2 3 2 0 0 20

5 3 1 20020608 20020616 20020624 2 0 0 20

7 0 2 2 0 0 20

7 1 0 2 0 0 20

7 1 8 20020726 20020803 20020811 2 0 0 20

8 1 9 2 0 0 20

8 2 7 2 0 02

0 9 0 4 20020912 20020920 20020928ppb 

# 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 0 20

5 1 5 2 0 0 20

5 2 3 2 0 0 20

5 3 1 20020608 20020616 20020624 2 0 0 20

7 0 2 2 0 0 20

7 1 0 2 0 0 20

7 1 8 20020726 20020803 20020811 2 0 0 20

8 1 9 2 0 0 20

8 2 7 2 0 02

0 9 0 4 20020912 20020920 20020928ppb 

# 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 0 20

5 1 5 2 0 0 20

5 2 3 2 0 0 20

5 3 1 20020608 20020616 20020624 2 0 0 20

7 0 2 2 0 0 20

7 1 0 2 0 0 20

7 1 8 20020726 20020803 20020811 2 0 0 20

8 1 9 2 0 0 20

8 2 7 2 0 02

0 9 0 4 20020912 20020920 20020928ppb 

# 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 0 20

5 1 5 2 0 0 20

5 2 3 2 0 0 20

5 3 1 20020608 20020616 20020624 2 0 0 20

7 0 2 2 0 0 20

7 1 0 2 0 0 20

7 1 8 20020726 20020803 20020811 2 0 0 20

8 1 9 2 0 0 20

8 2 7 2 0 02

0 9 0 4 20020912 20020920 20020928ppb 

# 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

0 20

6 01 20

02

0 6 06 20

02

0 6 11 2 0 02

0 61

6 2 0 02

0 62

1 2 0 02

0 62

6 2002 0 701 20020706 20

0 20711

20

0 20716

20

0 20721

20

02

0 7 26 20

02

0 7 31 2 0 02

0 80

5 2 0 02

0 81

0 2002 0 815 2002 0 820 2002 0 825 20

0 20830ppb 

# 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

0 20

6 01 2002 0 607 2 0 02

0 61

3 20

0 20619

20020625 2 0 02

0 70

1 20

0 20

7 07 2002 0 713 2 0 02

0 71

9 20

0 20725

2002 0 731 20

02

0 8 06 20

0 20812

2002 0 818 2 0 02

0 82

4 20

0 20830ppb 

# 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

0 20

6 01 20

02

0 6 06 20

02

0 6 11 2 0 02

0 61

6 2 0 02

0 62

1 2 0 02

0 62

6 2002 0 701 20020706 20

0 20711

20

0 20716

20

0 20721

20

02

0 7 26 20

02

0 7 31 2 0 02

0 80

5 2 0 02

0 81

0 2002 0 815 2002 0 820 2002 0 825 20

0 20830ppb 

# 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 0 0 20

5 1 5 2 0 0 20

5 2 3 2 0 0 20

5 3 1 2 0020608 20020616 200206 2 4 2 0 0 20

7 0 2 2 0 0 20

7 1 0 2 0 0 20

7 1 8 20020726 20020803 200208 1 1 2 0 0 20

8 1 9 2 0 0 20

8 2 7 2 0 0 20

9 04 20020912 20020920 200209 2 8μg 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 0 0 20

5 1 5 2 0 0 20

5 2 3 2 0 0 20

5 3 1 2 0020608 20020616 200206 2 4 2 0 0 20

7 0 2 2 0 0 20

7 1 0 2 0 0 20

7 1 8 20020726 20020803 200208 1 1 2 0 0 20

8 1 9 2 0 0 20

8 2 7 2 0 0 20

9 04 20020912 20020920 200209 2 8μg 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 0 0 20

5 1 7 200

20

5 2 6 20020604 20020613 20020622 20020701 2 0020710 2 0020719 2 0 0 20

7 2 8 2 0 0 20

8 0 6 2 0 0 20

8 1 5 2 0 0 20

8 2 4 200

20

9 0 2 200209 1 1 20020920 20020929μg 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 0020526 20020604 2 0020613 20020622 2 002070 1 2 0020710 2 002071 9 2 0020728 2 002080 6 2 002081 5 2 002082 4 2 002090 2 2 002091 1 2 002092 0 2 002

0 92

9μg 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 0 0 20

5 1 7 200

20

5 2 6 20020604 20020613 20020622 20020701 2 0020710 2 0020719 2 0 0 20

7 2 8 2 0 0 20

8 0 6 2 0 0 20

8 1 5 2 0 0 20

8 2 4 200

20

9 0 2 200209 1 1 20020920 20020929μg 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 0020526 20020604 2 0020613 20020622 2 002070 1 2 0020710 2 002071 9 2 0020728 2 002080 6 2 002081 5 2 002082 4 2 002090 2 2 002091 1 2 002092 0 2 002

0 92

9mg 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 0 0 20

5 1 7 200

20

5 2 6 20020604 20020613 20020622 20020701 2 0020710 2 0020719 2 0 0 20

7 2 8 2 0 0 20

8 0 6 2 0 0 20

8 1 5 2 0 0 20

8 2 4 200

20

9 0 2 200209 1 1 20020920 20020929μg 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 0020526 20020604 2 0020613 20020622 2 002070 1 2 0020710 2 002071 9 2 0020728 2 002080 6 2 002081 5 2 002082 4 2 002090 2 2 002091 1 2 002092 0 2 002

0 92

9μg 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 0 0 20

5 1 7 200

20

5 2 6 20020604 20020613 20020622 20020701 2 0020710 2 0020719 2 0 0 20

7 2 8 2 0 0 20

8 0 6 2 0 0 20

8 1 5 2 0 0 20

8 2 4 200

20

9 0 2 200209 1 1 20020920 20020929μg 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 0 0 20

5 1 7 200

20

5 2 6 20020604 20020613 20020622 20020701 2 0020710 2 0020719 2 0 0 20

7 2 8 2 0 0 20

8 0 6 2 0 0 20

8 1 5 2 0 0 20

8 2 4 200

20

9 0 2 200209 1 1 20020920 20020929μg 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

2 002051 7 20020526 2 002060 4 20020613 2 002062 2 20020701 20020710 20020719 20020728 20020806 20020815 2002 0 824 20020902 2002 0 911 2002 0 920 2002 0 929μg 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 0 0 20

5 1 7 200

20

5 2 6 20020604 20020613 20020622 20020701 2 0 0 20710

2 0 0 20719

2 0 0 20

7 2 8 2 0 0 20

8 0 6 2 0 0 20

8 1 5 2 0 0 20

8 2 4 200

20

9 0 2 20020911 20020920 20020929μg 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

2 002051 7 20020526 2 002060 4 20020613 2 002062 2 20020701 20020710 20020719 20020728 20020806 20020815 2002 0 824 20020902 2002 0 911 2002 0 920 2002 0 929μg 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 0 0 20

5 1 7 200

20

5 2 6 20020604 20020613 20020622 20020701 2 0 0 20710

2 0 0 20719

2 0 0 20

7 2 8 2 0 0 20

8 0 6 2 0 0 20

8 1 5 2 0 0 20

8 2 4 200

20

9 0 2 20020911 20020920 20020929μg 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

2 002051 7 20020526 2 002060 4 20020613 2 002062 2 20020701 20020710 20020719 20020728 20020806 20020815 2002 0 824 20020902 2002 0 911 2002 0 920 2002 0 929μg 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

0123456 SO2 (ppb)Z (m)



### observed CMAQ

Afternooon (12-16 EST)

0 500 1000 1500 2000 2500 3000

0123456 SO2 (ppb)Z (m)



### observed CMAQ

# 37 Figure 51. Evening (17-19 EST)

0 500 1000 1500 2000 2500 3000

012345 SO2 (ppb)Z (m)



### observed CMAQ