## 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

Ai r 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 sy stem that has been utilized to predict future air qual ity over the region of interest. As part of the attainment demonstration, the SMOKE/CMAQ modeling s ys tem was applied to simulate the pollutant concentration fields for the base year 2002 emissions with the corresponding meteorological information. The mode ling databases for meteorology using MM5 (TSD 1a), 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 simulatio n. In the following sections a comparison between the measured and predicted concentrations is performed and results are presented, demonstrating on an overall basis the utility of the modeling s ys tem 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 b y request from the New York State Department of Environmental Conservation. Summary of measured d ata 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 ys tem (AQS)  EPA fine particulate S peciation Trends N etwork (STN)  EPA C l ean A ir S tatus & Trends N etwork (CASTNet)  Interagency M onitoring of PROtected V isual Environments (IMPROVE)  Pinnacle State Park, NY operated b y Atmospheric Science Research Center, University

at Albany , Albany , NY  Harvard Forest, Petersham, MA operated b y Harvard University, Boston, MA  Atmospheric Investigation, Regional M odeling, Analysis and P rediction (AIRMAP) operated b y 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 b y the University of Maryland, College Park MD  Wet deposition data from the N ational Atmospheric D eposition P rogram/ N ational Trends N etwork (NADP/NTN), Atmospheric Integrated Research Mo nitoring N etwork (A IRMoN), and the N ew Y ork S tate D epartment of Environmental 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 correspo nding to the monitor location; no interpolation 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/aqswebhome.html ). Hourl y O 3 

## concentrations from the Harvard Forest Environmental 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 generall y rural locations 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 database, primaril y 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 - OPS site in Philadelphi a, 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), a nd 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 every 3 rd 

## or 6 th 

## day . Data from 49 STN si tes, generall y in urban areas and often collocated with FRM monitors, and 21 IMPROVE sites (including Dolly Sods, WV) were used in this analy sis. Organic mass is assumed to equal 1.8×OC, and soil/crustal matter is assumed to consist of oxides of Al, Ca, F e, Si, and Ti. The STN OC data are blank- corrected by removing a monitor- specific, constant blank, and these values are available from http://www.epa.gov/airtrends/aqtrnd03/pdfs/2_chemspec0fpm25.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 sites 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 hydro carbons While there are several dozen hydrocarbon species measured routinel y, for this model evaluation database the focus was on Carbon Bond IV species groups that consist of a single primary

species. For this reason onl y 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 Monitoring Stations (PAMS) sites and 24 - hour average HCHO from 18 air toxics sites are included in this database. University

of M aryl and 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 database, 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) collects wet deposition samples across the US, through the NTN and the AIRMoN. We ekl y wet deposition samples are collected by the NTN, while dail y or event - based samples were collected by the AIRMoN. The NYSDEC (http://www.dec.state.ny.us ) also collects weekly wet deposition samples indepen dentl y 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 information for the above referenced pollutants over the OTR portion of the 12 - km modeling domain. The statistical formulations that have been computed for each species are as follows: Pi 

## 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 size. Observed average, in ppb: O N Oi 

##  

#  1 

## Predicted average, in ppb (only use P i 

## when O i 

## is valid): P N P

i 

##  

#  1

## 5

Correlation coefficient, R2 

## : 

#   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 dail y maximum 8 - hour average O 3 

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

## concentration was at least 85 ppb, are presented here for all sites across 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 b y large forest fires in Quebec. 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 dail y maximum 8 - hour O 3 

## concentratio n exceeded 40 ppb. Second, the statistics were computed using only those days when the observed dail y maximum 8 - hour O 3 

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

## days. Figures 1 - 4 display time series of observed and predicted dail y m aximum 8 - hour O 3 

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

## concentrations track each other rather well, although there was fairl y substantial underprediction 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 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 special moni tors are displayed here. In general, the model performance was better in the vicinity 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 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 alway s 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 - 2 9 to +23% for the 40 ppb threshold, and ranged from

- 40 to +22% for the 60 ppb threshold. Adding the special sites did not affect the statistics substantially .

## 7

Diurnal variations of gases Figures 9 - 17 display

the composite diurnal variations of the species reported hourly

– O 3 

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

## , CO, NO, NO 2 

## , SO 2 

## , ethene, and isoprene. The average diurnal variations 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 - hour averages, with hours denoting the start of the 8 - hour block. The number of monitors used to compute each composite diurnal variation is shown in each figure . For O 3 

## , the composite diurnal pattern predicted by CMAQ is fairly

similar to that observed, especiall y at the more urban SLAMS/NAMS monitors. However, on average CMAQ predicts the dail y 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 variation. On the other hand, primary pollut ants 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 concentrations of CO, NO, NO 2 

## , SO 2 

## , C 2 

## H 4 

## , HCHO, and C 5 

## H 8 

## acro ss 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 hourl y averages, and onl y those days having at least 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 the 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 thi s anal ys is is the July 6 - 9 period when many

sites across the eastern US were affected b y 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 this analy sis. The value s 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 sh ould 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 anal ys is. The data cover the period May 15

– September 30, and again the July 6 - 9 period was excluded, when numerous sites in the eastern US were affected

## 8

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

## data are shown every day , s ince there are ample daily FRM sites across the OTR. The speciation data included here are daily

averages every

third day , and consist of the largel y urban EPA STN and the largel y rural IMPROVE network. The 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 this analy sis. 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 valid data. The best q ualitative agreement between observed and modeled concentrations is exhibited for PM 2.5 

## and SO 4 

## . Note that in the case of crustal mass, the data from July 4 are also not included since this day is greatl y affected b y fireworks. On July 4, the composite average observed and predicted crustal concentrations were 4.59 μg m - 3 

## and 1.74 μg m - 3 

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

## and 0.99 μg 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 si tes, although some of this discrepancy may be attributed to the fact that OM is operationall y defined and is highl y dependent on the blank correction 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; 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 o f SO 4 

## , NO 3 

## , and NH 4 

## were compared over the period May

14

– September 30. For this anal ys is, weekl y 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 al l 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 corresponding CMAQ predictions also include this period. Table 4 lists the model evaluation statistics 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 weekl y values relative to the 1:1 line. Overall CMAQ tended to overpredict wet deposition of 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, especiall y during the summer months when rainfall can vary tremendously

ov er 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 2 002. On 26 day s from May- August meteorological, trace gas, and particle scattering/absorption data were collected during ascent or descent spirals over 41 regional airports. In all, 144 spirals were performed from near the surface to about 3 km above gro und 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 increasingl y thick away

from the surface; the surface 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). Figure s 43 - 51 display

the observed and predicted composite vertical profiles of O 3 

## , CO, and SO 2 

## for the three time periods. In terms of profile shape, CMAQ was in good qualitative agreement for all three species above the surface during the afternoon hours. Fo r CO, the model tends to greatly underpredict 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 presented here for a variety of gaseous and aerosol species in addition to O 3 

## . In general, the CMAQ results were best for dail y 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 large biases on a composite basis. It is important to demonstrate that the model performs reasonabl y 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 the ground level.

## 10

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

## using the 40 ppb and 60 ppb observed thresholds. The v alues using onl y S LAMS/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 bias (%) for dail y 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 dail y 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% +3 4% 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 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, R2 

## 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. Obser ved and Pred ic te d aver ag es 208 SLA MS /N AMS s it es; 4 0 ppb th re sh ol d

0 10 20 30 40 50 60 70 80 90 100

2002 0515 2002 0523 2002 0531 2002 0608 2002 0616 2002 0624 2002 0702 2002 0710 2002 0718 2002 0726 2002 0803 2002 0811 2002 0819 2002 0827 2002 0904 2002 0912 2002 0920 2002 0928ppb 

### obs av

g, ppb CM AQ a vg , pp b



## Figure 2. Obser ved and Pred ic te d aver ag es 208 SLA MS /N AMS s it es; 6 0 ppb th re sh ol d

0 10 20 30 40 50 60 70 80 90 100

2002 0515 2002 0523 2002 0531 2002 0608 2002 0616 2002 0624 2002 0702 2002 0710 2002 0718 2002 0726 2002 0803 2002 0811 2002 0819 2002 0827 2002 0904 2002 0912 2002 0920 2002 0928ppb 

### obs av

g, ppb CM AQ a vg , pp b

## 13

Figure 3. Obser ved and Pred ic te d aver ag es 28 speci al m oni to rs ; 40 ppb th re shol d

0 10 20 30 40 50 60 70 80 90 100

2002 0515 2002 0523 2002 0531 2002 0608 2002 0616 2002 0624 2002 0702 2002 0710 2002 0718 2002 0726 2002 0803 2002 0811 2002 0819 2002 0827 2002 0904 2002 0912 2002 0920 2002 0928ppb 

### obs av

g, ppb CM AQ a vg , pp b



## Figure 4. Ob ser ved and Predi ct ed a ver ag es 28 speci al s ite s; 60 ppb th re shol d

0 10 20 30 40 50 60 70 80 90 100

2002 0515 2002 0523 2002 0531 2002 0608 2002 0616 2002 0624 2002 0702 2002 0710 2002 0718 2002 0726 2002 0803 2002 0811 2002 0819 2002 0827 2002 0904 2002 0912 2002 0920 2002 0928ppb 

### obs av

g, ppb CM AQ a vg , pp b

## 14

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# # # # # # # # #

# # # # # # # # # #

# # # # # # # # # #

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

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

# # #

# # #

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# # # # #

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

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

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

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

# #

# # # # # #

#

#
# #
#

#
# #

# # # # # # # # # #

3 0 0 0 3 0 0 6 0 0 M i l e s N E W S 

### M F B , 6 0 p p b c u t o f

f # 

### - 42 - -3 0 % # 

### -3 0 - - 1 8 % # 

### - 1 8 - -6 % # 

### -6 - 6 % # 

### 6 - 1 8 % # 

### 1 8 - 3 0 %

## 16 Figure 9. Oz one (S LA MS /N AM S) - 2 08 si te s

0. 0 10. 0 20. 0 30. 0 40. 0 50. 0 60. 0

0 4 8 12 16 20 24O3(ppb) Obs erv ed Predic te d



## Figure 10. Oz one (C AS TN et& o th ers) - 2 8 si te s

0. 0 10. 0 20. 0 30. 0 40. 0 50. 0 60. 0

0 4 8 12 16 20 24O3(ppb) Obs erv ed Predic te d

## 17

Figure 11. Cont in uous PM 2.5

### m ass - 5 7 si te s

0. 00 5. 00 10. 00 15. 00 20. 00 25. 00 30. 00

0 4 8 12 16 20 24PM2.5(ug/m3) Obs erv ed Predic te d



## Figure 12. Ca rb on monoxi de - 9 7 si te s

0. 0 100. 0 200. 0 300. 0 400. 0 500. 0 600. 0

0 4 8 12 16 20 24CO (ppb) Obs erv ed Predic te d

## 18

Figure 13. Ni tric o xi de - 7 5 si te s

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) Obs erv ed Predic te d



## Figure 14. Ni tro gen di oxi de - 9 7 si te s

0. 00 5. 00 10. 00 15. 00 20. 00 25. 00 30. 00

0 4 8 12 16 20 24NO2(ppb) Obs erv ed Predic te d

## 19

Figure 15. Su lfu r d io xi de - 1 34 si te s

0. 00 1. 00 2. 00 3. 00 4. 00 5. 00 6. 00 7. 00

0 5 10 15 20 25SO2(ppb) Obs erv ed Predic te d



## Figure 16. Et hene - 1 9 si te s

0. 00 0. 50 1. 00 1. 50 2. 00 2. 50 3. 00

0 4 8 12 16 20 24ethene (ppb) Obs erv ed Predic te d

## 20

Figure 17. Is oprene - 1 9 si te s

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) Obs erv ed Predic te d



## Figure 18. Ob ser ved and Pred ic te d average CO (9 7 si te s)

0 100 200 300 400 500 600 700 800

2002 0515 2002 0523 2002 0531 2002 0608 2002 0616 2002 0624 2002 0702 2002 0710 2002 0718 2002 0726 2002 0803 2002 0811 2002 0819 2002 0827 2002 0904 2002 0912 2002 0920 2002 0928ppb 

### obs a vg , pp b

CM AQ a vg , p pb

## 21

Figure 19. Ob ser ved and Pred ic te d average NO (7 5 si te s)

0 2 4 6 8 10 12 14 16 18

2002 0515 2002 0523 2002 0531 2002 0608 2002 0616 2002 0624 2002 0702 2002 0710 2002 0718 2002 0726 2002 0803 2002 0811 2002 0819 2002 0827 2002 0904 2002 0912 2002 0920 2002 0928ppb 

### obs a vg , pp b

CM AQ a vg , p pb



## Figure 20. Ob ser ved and Pred ic te d average NO 2 

## ( 97 si te s)

0 5 10 15 20 25 30 35

2002 0515 2002 0523 2002 0531 2002 0608 2002 0616 2002 0624 2002 0702 2002 0710 2002 0718 2002 0726 2002 0803 2002 0811 2002 0819 2002 0827 2002 0904 2002 0912 2002 0920 2002 0928ppb 

### obs a vg , pp b

CM AQ a vg , p pb

## 22

Figure 21. Ob served and Predi ct ed a verag e SO 2 

## (1 34 si te s)

0 1 2 3 4 5 6 7 8

2002 0515 2002 0523 2002 0531 2002 0608 2002 0616 2002 0624 2002 0702 2002 0710 2002 0718 2002 0726 2002 0803 2002 0811 2002 0819 2002 0827 2002 0904 2002 0912 2002 0920 2002 0928ppb 

### obs a vg , pp b

CM AQ a vg , p pb



## Figure 22. Ob ser ved an d Pred ic te d C 2 

### H 4

### ( 19 si te s)

0 0. 5 1 1. 5 2 2. 5

2002 0601 2002 0606 2002 0611 2002 0616 2002 0621 2002 0626 2002 0701 2002 0706 2002 0711 2002 0716 2002 0721 2002 0726 2002 0731 2002 0805 2002 0810 2002 0815 2002 0820 2002 0825 2002 0830ppb obs a vg , pp b

CM AQ a vg , p pb

## 23

Figure 23. Ob served and Predi ct ed HCHO (1 8 si te s)

0 1 2 3 4 5 6 7 8 9

2002 0601 2002 0607 2002 0613 2002 0619 2002 0625 2002 0701 2002 0707 2002 0713 2002 0719 2002 0725 2002 0731 2002 0806 2002 0812 2002 0818 2002 0824 2002 0830ppb obs a vg , pp b

CM AQ a vg , p pb



## Figure 24. Ob ser ved an d Pred ic te d C 5 

### H 8 

### ( 19 si te s)

0 0. 5 1 1. 5 2 2. 5 3

2002 0601 2002 0606 2002 0611 2002 0616 2002 0621 2002 0626 2002 0701 2002 0706 2002 0711 2002 0716 2002 0721 2002 0726 2002 0731 2002 0805 2002 0810 2002 0815 2002 0820 2002 0825 2002 0830ppb obs a vg , pp b

CM AQ a vg , p pb

## 24

Figure 25. Ob ser ved and Pred ic te d PM 2.5

### (F RM ; 257 si te s)

0 5 10 15 20 25 30 35 40 45

2002 0515 2002 0523 2002 0531 2002 0608 2002 0616 2002 0624 2002 0702 2002 0710 2002 0718 2002 0726 2002 0803 2002 0811 2002 0819 2002 0827 2002 0904 2002 0912 2002 0920 2002 0928g m-3 obs a vg , u g/ m3

CM AQ av

g, ug/m 3



## Figure 26. Ob ser

ved and Pred ic te d PM 2.5

### (c ont in uous; 57 si te s)

0 5 10 15 20 25 30 35 40 45

2002 0515 2002 0523 2002 0531 2002 0608 2002 0616 2002 0624 2002 0702 2002 0710 2002 0718 2002 0726 2002 0803 2002 0811 2002 0819 2002 0827 2002 0904 2002 0912 2002 0920 2002 0928g m-3 obs a vg , u g/ m3

CM AQ av

g, ug/m 3

## 25

Figure 27. Ob served an d Predi ct ed P M 2.5 

### (S TN; 49 si te s)

0 5 10 15 20 25 30 35 40

2002 0517 2002 0526 2002 0604 2002 0613 2002 0622 2002 0701 2002 0710 2002 0719 2002 0728 2002 0806 2002 0815 2002 0824 2002 0902 2002 0911 2002 0920 2002 0929g m-3 obs a vg , u g/ m3

CM AQ av

g, ug/m 3



## Figure 28. Ob served an d Predi cted PM 2.5

### (IM PRO VE; 21 si te s)

0 5 10 15 20 25 30

2002 0517 2002 0526 2002 0604 2002 0613 2002 0622 2002 0701 2002 0710 2002 0719 2002 0728 2002 0806 2002 0815 2002 0824 2002 0902 2002 0911 2002 0920 2002 0929g m-3 obs a vg , ug /m3

CM AQ a vg , ug /m3

## 26

Figure 29. Ob served and Predi ct ed S O 4

### (S TN; 49 si te s)

0 5 10 15 20 25

2002 0517 2002 0526 2002 0604 2002 0613 2002 0622 2002 0701 2002 0710 2002 0719 2002 0728 2002 0806 2002 0815 2002 0824 2002 0902 2002 0911 2002 0920 2002 0929g m-3 obs a vg , u g/ m3

CM AQ av

g, ug/m 3



## Figure 30. Ob served a nd Predi ct ed S O 4

### ( IM PRO VE; 21 si te s)

0 2 4 6 8 10 12 14 16 18

2002 0517 2002 0526 2002 0604 2002 0613 2002 0622 2002 0701 2002 0710 2002 0719 2002 0728 2002 0806 2002 0815 2002 0824 2002 0902 2002 0911 2002 0920 2002 0929mg m-3 obs a vg , ug /m3

CM AQ a vg , ug /m3

## 27

Figure 31. Ob served and Predi ct ed N O 3 

### (S TN; 49 si te s)

0 0. 5 1 1. 5 2 2. 5 3 3. 5

2002 0517 2002 0526 2002 0604 2002 0613 2002 0622 2002 0701 2002 0710 2002 0719 2002 0728 2002 0806 2002 0815 2002 0824 2002 0902 2002 0911 2002 0920 2002 0929g m-3 obs a vg , u g/ m3

CM AQ av

g, ug/m 3



## Figure 32. Ob served a nd Predi ct ed N O 3 

### ( IM PRO VE; 21 si te s)

0 0. 2 0. 4 0. 6 0. 8 1 1. 2 1. 4 1. 6 1. 8 2

2002 0517 2002 0526 2002 0604 2002 0613 2002 0622 2002 0701 2002 0710 2002 0719 2002 0728 2002 0806 2002 0815 2002 0824 2002 0902 2002 0911 2002 0920 2002 0929g m-3 obs a vg , ug /m3

CM AQ a vg , ug /m3

## 28

Figure 33. Ob served and Predi ct ed N H 4

### (S TN; 49 si te s)

0 1 2 3 4 5 6 7

2002 0517 2002 0526 2002 0604 2002 0613 2002 0622 2002 0701 2002 0710 2002 0719 2002 0728 2002 0806 2002 0815 2002 0824 2002 0902 2002 0911 2002 0920 2002 0929g m-3 obs a vg , u g/ m3

CM AQ av

g, ug/m 3



## Figure 34. Ob served and Predi cted EC (S TN; 49 si te s)

0 0. 2 0. 4 0. 6 0. 8 1 1. 2 1. 4 1. 6 1. 8

2002 0517 2002 0526 2002 0604 2002 0613 2002 0622 2002 0701 2002 0710 2002 0719 2002 0728 2002 0806 2002 0815 2002 0824 2002 0902 2002 0911 2002 0920 2002 0929g m-3 obs a vg , u g/ m3

CM AQ av

g, ug/m 3

## 29

Figure 35. Ob ser ved and Pred ic te d EC ( IM PR OV E; 21 si te s)

0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8

2002 0517 2002 0526 2002 0604 2002 0613 2002 0622 2002 0701 2002 0710 2002 0719 2002 0728 2002 0806 2002 0815 2002 0824 2002 0902 2002 0911 2002 0920 2002 0929g m-3 obs a vg , ug /m3

CM AQ a vg , ug /m3



## Figure 36. Ob served and Predi cted OM (S TN; 49 si te s)

0 2 4 6 8 10 12 14

2002 0517 2002 0526 2002 0604 2002 0613 2002 0622 2002 0701 2002 0710 2002 0719 2002 0728 2002 0806 2002 0815 2002 0824 2002 0902 2002 0911 2002 0920 2002 0929g m-3 obs a vg , u g/ m3

CM AQ av

g, ug/m 3

## 30

Figure 37. Ob ser ved and Predi ct ed OM ( IM PRO VE; 21 si te s)

0 1 2 3 4 5 6 7 8 9 10

2002 0517 2002 0526 2002 0604 2002 0613 2002 0622 2002 0701 2002 0710 2002 0719 2002 0728 2002 0806 2002 0815 2002 0824 2002 0902 2002 0911 2002 0920 2002 0929g m-3 obs a vg , ug /m3

CM AQ a vg , ug /m3



## Figure 38. Ob ser ved and Pred ic te d cr ustal (S TN ; 49 si te s)

0 0. 5 1 1. 5 2 2. 5 3

2002 0517 2002 0526 2002 0604 2002 0613 2002 0622 2002 0701 2002 0710 2002 0719 2002 0728 2002 0806 2002 0815 2002 0824 2002 0902 2002 0911 2002 0920 2002 0929g m-3 obs a vg , u g/ m3

CM AQ av

g, ug/m 3

## 31

Figure 39. Ob ser

ved and Pred ic te d cr us ta l (IM PR OV E; 21 si te s)

0 0. 2 0. 4 0. 6 0. 8 1 1. 2 1. 4 1. 6 1. 8

2002 0517 2002 0526 2002 0604 2002 0613 2002 0622 2002 0701 2002 0710 2002 0719 2002 0728 2002 0806 2002 0815 2002 0824 2002 0902 2002 0911 2002 0920 2002 0929g m-3 obs a vg , ug /m3

CM AQ a vg , ug /m3



## Figure 40. WetS O 4

### D ep osi tio n

0 500 1000 1500 2000 2500

0 500 1000 1500 2000 2500 Observe d ( mg /m 2 )Predicted (mg/m2)

## 32

Figure 41. WetN O 3 

### D ep osi tio n

0 200 400 600 800 1000 1200 1400 1600

0 200 400 600 800 1000 1200 1400 1600 Observe d ( mg /m 2 )Predicted (mg/m2)



## Figure 42. WetN H 4

### D ep osi tio n

0 50 100 150 200 250 300 350 400 450 500

0 50 100 150 200 250 300 350 400 450 500 Observe d ( mg /m 2 )Predicted (mg/m2)

## 33

Figure 43.

Figure 44. Mo rni ng (08-11 E ST )

0 500 1000 1500 2000 2500 3000

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

obs erved CMA Q

Afte rn oon (12-16 E

ST )

0 500 1000 1500 2000 2500 3000

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

obs erved CMA Q

## 34

Figure 45.

Figure 46. Ev eni ng (17-19 E ST )

0 500 1000 1500 2000 2500 3000

40 50 60 70 80 90 O3 (p pb)Z (m)

obs erved CMA Q

Mo rni ng (08-11 E

ST )

0 500 1000 1500 2000 2500 3000

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

obs erved CMA Q

## 35

Figure 47.

Figure 48. Afte rn oon (12-16 E ST )

0 500 1000 1500 2000 2500 3000

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

obs erved CMA Q

Ev en in g (17-19 E

ST )

0 500 1000 1500 2000 2500 3000

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

obs erv ed CMA Q

## 36

Figure 49.

Figure 50. Mo rni ng (08-11 E ST )

0 500 1000 1500 2000 2500 3000

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

obs erved CMA Q

Afte rn ooon (12-16 E

ST)

0 500 1000 1500 2000 2500 3000

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

obs erved CMA Q

## 37

Figure 51. Ev en in g (17-19 E ST )

0 500 1000 1500 2000 2500 3000

0 1 2 3 4 5 SO2 (p pb)Z (m)

obs erv ed CMA Q