**TSD -**  **3**


## CMAQ Model Performance and Assessment **for Base Year 2002 :PM**  **2.5** 

## Mass and Speciation

**Bureau of Air Quality Analysis and Researc h** 

**Division of Air Resources New York State Department of Environmental Conservation** 

**Albany, NY 12233**

**December 14**  **, 200 7** **Introduction** One of the required tasks for attainment demonstration for the PM 2.5 National Ambient Air Quality

Standard ( NAAQS ) is the evaluation and assessment of the air quality modeling sy stem that has been utilized to predict future air qualit y over the region of interest. As part of the attainment demonstration , the MM5/ SMOKE/CMAQ modeling sy stem was applied to simula te concentration fields of PM 2.5 mass and major species for the base ye ar 2002 emissions with the corresponding meteorological information. The modeling databases for meteorology using MM5 (TSD

- 1, 2007) , the emissions using SMOKE (TSD

- 2a, TSD

- 2b, 2006) , an d application of the SMOKE/ CMAQ sy stem (TSD

2c , 2007) provide the simulated pollutant fields which are compared to measurements to establish the credibility

of the simulation. I n the following sections a comparison between the measured and predicted concen trations 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 serve as an illustration of some of the model evaluation and assessment , as outlined in t he EPA modeling guidance document (US EPA, 2007), performed on the B ase Year 2002 CMAQ simulation. Additional information can be made available b y request from the New York State Department of Environmental Conservation. **Summary of m easured data**  The amb ient air quality

data for annual 2002 simulation were obtained from the following sources:

 EPA A ir Q uality S ys tem (AQS)  EPA fine particulate S peciation T rend s N etwork (STN)  I nteragency M onitoring of PR O tected V isual E nvironments (IMPROVE)  Pinnacle State Park in Addison , NY operated b y Atmospheric Science Research Center, Universit y at Albany, Alban y, NY  Harvard Forest, Petersham, MA operated b y Harvard Universit y, Boston, MA  A tmospheric I nvestigation, R egional M odeling, A naly sis and P rediction ( AI RMAP ) o perated b y University of New Hampshire, Durham, NH  N orth E ast O zone & F ine P article S tudy (NE

- OPS), led by

Penn State University and other research groups in Philadelphia, PA Measured data from sites within the Ozone Transport Region (OTR) plus the rest of Virginia were included here , which hereafter will be referred to as the “OT R +” region

. The model

- based data were obtained at the grid

- cell corresponding to the monitor location and no interpolation was performed. The anal ys is examines the model response t o the OTR+ region with emphasis on the New York City non - attainment area (NYC NAA), which consists of two counties in C onnecticut (Fairfield and New Haven) and 10 counties each in New Jersey (Bergen, Essex, Hudson, Mercer, Middlesex, Monmouth, Morris, Pass aic, Somerset, and Union) and N ew York (Bronx, Kings, Nassau, New York, Orange, Queens, Richmond, Rockland, Suffolk, and Westchester)

.Fine particulate (PM 2.5 ) mass The 24

- hour average Federal Reference Method (FRM) PM 2.5 mass data collected routinely a t SLAMS/NAMS sites across the OTR+ region were extracted from AQS for 2 25 sites ( see http://www.epa.gov/ttn/airs/airsweb/aqsweb/aqswebhome.html ). Although 39 of these locations had duplicate monitors in 2002, only data from the primary monitors are presented here. Fift y six of these FRM monitors

– 45 unique locations and 11 collocated monitors

– are located within the NYC NAA. Hourl y PM 2.5 mass data from 62 sites across the OTR+ r egion were also included in this analy sis

. Twenty
- eight of these hourl y sites are located within the NYC NAA. Hourl y PM 2.5 mass were also obtained from th e AI RMAP Program ( http://airmap.unh.edu ) at Thompson Farm, NH ; Pinnacle State Park ( http://www.asrc.cestm.alban y.edu ); and the NE

- OPS site in Philadelphia, PA ( http://lidar1.ee.psu.edu ). In addition to examining the daily av erages based on the PM 2.5 data, composite and site

- specific diurnal variations are also presented here. Figures 1 and 2 show the locations of the FRM and hourly PM 2.5 monitors across the OT R + region. Fine particulate speciation The 24

- hour average PM 2.5 and fine particulate speciation data , consisting of sulfate (SO 4 ), nitrate (NO 3 ), elemental carbon (EC), organic carbon/organic mass (OC/OM), and soil/crustal matter from Class I areas across the OTR + region , collected every third day , were obtained from the IMPROVE web site ( http://vista.cira.colostate.edu/I MPROVE/Default.htm ) for 2 1 IMPROVE sites (which includes Dolly

Sods, WV , even though this site is not in the OTR+ region . In addit ion 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 third or sixth day

. The STN data f rom 50 sites in the OTR+ region 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, Fe, Si, and Ti. The STN OC data were first corrected by subtracting a monitor

- specific constant blank concentration , and these STN blank data are available from http://www.epa.gov/airtrends/aqtrnd03/pdfs/2_chemspecofpm25.pdf . T he IMPROVE OC blanks are assumed to equal zero

. Nine of the STN sites are located within the NYC NAA. Figure 3 shows the locations of the STN and IMPROVE locations across the OTR+ region. In addition to these daily average speciation data, a preliminary examination of diurnal variation s using continuous hourly SO 4 and NO 3 data from the New York State Department of Environmental Conservation ( http://www.dec.n y. gov ) site I S52 (Bronx) is also presented here. **Evaluation of CMAQ** The following section s provide information on model performance 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: P i and O i are the individual dail y aver age predicted and observed concentrations, respectivel y; Pand O are the average concentrations, respectivel y, and N is the sample size. These

statistical measures are listed in the EPA modeling guidance document (USEPA, 2007). Each statistic is computed two way s. First, each statistic is computed at each site over the entire year and each quarter; second, each statistic is computed on each day using all available sites. Note that the July 6
- 9, 2002 pe riod was excluded from this anal ys is since the observed PM 2.5 and OM data at many sites were greatl y affected b y Canadian forest fires. Additionally , those day s on which the observed and predicted averages differed b y more than a factor of 25 were not inc luded in this analysis, since they can greatl y affect some of the normalized metrics. Observed average, in  g m - 3 : O N O i  

##  1 Predicted average, in  g m - 3 (only use P i when O i is valid): P N P

i  

##  1 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 m ean square error (RMSE), in  g m - 3 : RMSE N P O i i      

  

##  1 2 1 2 ( ) /

Mean f ractional error ( M FE), in %:

MFE N P O

P O i i i i  

  

#  2 100% Mean absolute gross error (MAGE), in  g m - 3 : 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  g m - 3 : 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% Daily PM 2.5 mass Model performance statistics, based on dail y average PM 2.5 levels , are presented in this section across the OTR+ region

. Figure 4 display s the time series of observed and predicted PM 2.5 mass averaged across all FRM monitors. As stated earlier the July 6
- 9 period was not included in this analy sis since many monitors across the region were substantially

impacted b y Canadian wildfires. Overall, the mean bias (predicted minus observed) was about 3. 8  g m - 3 , ranging from

- 17. 7  g m - 3 to +24. 0  g m - 3 . The highest overprediction tended to occur during the colder months, quarters 1 and 4, whereas the day s on which the model tended to underpredict PM 2.5 were more likel y to occur during the summer months. Figures 5 and 6 display the mean fractional error (MFE) and mean fractionalized bias (MFB), respectivel y, at each FRM monitor across the OTR+ region over the entire ye ar . Boy lan and Russell (2006) propose that these two metrics are the most useful for m odel evaluation since they set upper bounds on error/bias, are not likel y dominated by a few “outlier” points, and do not assume that observations are absolute truth. Bo yl an and Russell (2006) further point out that MFE<75% and MFB<60% define a level of a ccuracy close to the best a model can be expected to achieve in terms of acceptable model performance. The anal ys is shows that a t 2 2 1 of the 2 25 FRM sites the MFE was below 75%, while at 2 1 5 sites the MFB was below 60% in the OTR + region

. However, such i s not the case for the NYC NAA, a region with high emissions density and complex meteorology

. While the MFE and MFB criteria are met at each of the 10 primary FRM monitors in CT and 1 4 primary FRM monitors in NJ, the MFE criteria was met at 17 ofthe 2 1 pr imary FRM monitors in NY and the MFB criteria was onl y met at 12 of the

primary NY FRM monitors. Hence, on an overall basis the model predicts PM 2.5 mass reasonabl y well, although not consistently at all locations and all day s in the NY C NAA
. Tables 1 an d 2 list the median and range in MFE and MFB for the OTR+ region for the entire ye ar excluding July 6
- 9 that are associated with Canadian fires

. The statistical estimates are based on comparing predicted with measurements from FRM, continuous hourly , STN, and IMPROVE locations. I n Table 1, the MFE and MFB values are computed at each site, while in Table 2 the MFE and MFB are computed on each day using the data from all available sites. The median MFE values are about 40

- 50%, ranging from <20% to about 100 %. The median MFB values are about 30%, ranging within ±100% (substantial underprediction to large overprediction)

. Figure 7 display s the quarterl y average PM 2.5 mass at all OTR+ monitors and also at those monitors within the NYC NAA. Note that, not su rprisingl y, the observed average PM 2.5 mass tends to be higher in the NYC NAA than across the entire OTR+. CMAQ tends to overpredict PM 2.5 at these locations, especiall y during the colder months. On the other hand, CMAQ performs well during the July

- Sept ember period on a composite average basis

. PM 2.5 speciation Composite d aily

average predicted and observed concentrations of major PM 2.5 species

– SO 4 , NO 3 , NH 4 , EC , OM ( defined here operationall y as 1.8×blank

- corrected organic carbon), and crustal mass (sum of oxides of Al, Ca, Fe, Si, and Ti)

– across the OTR + region were compared in this anal ys is. The time series of these species at the 50 STN and 21 IMPROVE locations are shown in Figures 8

- 18. The data display ed are for every third day , correspondin g to the nominal sampling schedule . The se two speciation networks collect SO 4 , NO 3 , EC, OM, and crustal mass, while only the STN reports NH 4 at a number of locations. The NH 4 data from the IMPROVE network are not included here, since i n the OTR+ region, onl y the IMPROVE sites of Dolly Sods, WV; James River Face Wilderness, VA; and Shenandoah National Park, VA reported NH 4 data in 2002. As before, the July 6

- 9 period was excluded. The model exhibits good agreement for SO 4 at both the urban STN (Figure 8) and rural IMPROVE (Figure 9) sites, both in terms of absolute concentrations and the seasonal variation. During the summer months, photochemical production of SO 4 is higher than at other times of the y ear, which the model seems to capture. Figures 10 an d 11 suggest that the model qualitatively predicts the seasonal variation of NO 3 fairly well, although the observed and modeled concentrations during the cold season, quarters 1 and 4 (when volatilization of NO 3 is lowest), differ by several  g m - 3 . The s easonal variation in NH 4 (Figure 1 2 ) is more complicated, but appears to be consistent with SO 4 and NO 3 . During the summer, most NH 4 is tied to SO 4 (which the model tends to predict well), whereas during the winter a substantial fraction of NH 4 is tied to NO 3 (which the model appears to predict poorl y ). Both EC (Figures 13 and 14) and crustal mass (Figure 17 and 18) are predominantl y primary emissions, and the model tends to overpredict bothspecies

– particularl y at urban locations and during the colder months. Note, too, the

effects of emissions from the July 4 th fireworks on crustal mass are obvious in the observed data at both the STN and IMPROVE sites , but are not included in the model emissions inventory . The measured crustal mass on this day excee ds 4  g m - 3 , wh ile it is generall y closer to ~0.5

- 1.0  g m - 3 only

. The model appears to also have difficult y predicting the seasonal variation of OM (Figures 15 and 16). During the summer months a substantial fraction of OM is secondary in nature, while during the colder months much of the OM may be in the form of primary

emissions. To illustrate this, Figure 19 display s the quarterl y variation in observed OM, as well as the CMAQ predictions of the three components of modeled OM

– anthropogenic primary

organic aerosol (POA), anthropogenic secondary organic aerosol (SOA), and biogenic SOA. Note that CMAQ predicts that >80% of the modeled OM is anthropogenic POA (with a wintertime maximum), and only predicts slight increases in the SOA fraction during the summer . On the other hand, the observed OM has a summertime maximum when secondary formation is highest

. Table 3 lists the median and range in MFE and MFB calculated at each site over the season used in this analy sis. For SO 4 , which contributes ~30% to the to tal PM 2.5 mass, the MFE at both STN and IMPROVE locations is roughl y 40%, and there is a general tendency

to underpredict SO 4 (MFB < 0). At all 71 locations (STN and IMPROVE) , the MFE and MFB criteria suggested by Boy lan and Russell (2006) are met. This is not surprising, since SO 2 emissions are generall y well

- characterized and ambient SO 4 levels tend to be somewhat spatially uniform compared to other PM 2.5 species

. The range in MFE and MFB tends to be lower for SO 4 compared to the other species as well. The urban

- rural differences for the other species are much more evident than for SO 4 . Note that the degree of overprediction of NO 3 , NH 4 , and EC is considerably higher at the STN locations compared to I MPROVE. CMAQ tends to underpredict OM as well, alt hough 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. Similarly , the predicted crustal mass tends to be much higher than that observed. This, too, is due in part to relating the CMAQ predictions of unspeciated, primary particulates to the measured crustal mass which is operationall y defined as the sum of major metal oxides. For these and other reasons, Bo yl an and Russell (2006) state that for the non - SO 4 or less abundant PM 2.5 species, the MFE and MFB guidelines should be relaxed

. Diurnal variations PM 2.5 , SO 4 , and NO 3 The hourl y PM 2.5 mass and speciation data allow for examination of the mod el predictions with high temporal resolution. Figure 20 display s the composite diurnal variation of PM 2.5 mass averaged across the 28 locations in the NYC NAA, over the entire year (with the exception of July 6
- 9) . For comparison, the average diurnal pro files during quarters 1 and 3 are shown in Figures 21 and 22, respectivel y. Quarter 1 exhibits the largest discrepancy

between observed and predicted PM 2.5 , while q uarter 3 exhibits the smallest discrepancy

between observed and predicted PM 2.5 mass. Rega rdless of timeof y ear, CMAQ predicts a pronounced double peak, with maxima during the morning and

late afternoon/earl y evening time periods, likel y corresponding to average commuting patterns. During quarter 1, there is a double peak in the observed diur nal profile, albeit smaller in amplitude. The double peak is not nearly as evident during quarter 3, when during the middle of the day the observed PM 2.5 levels are actually slightly higher than the predicted levels. There were onl y a handful of location s in 2002 where hourl y SO 4 and/or NO 3 were measured. One such location was IS52 in Bronx, NY. To illustrate how the model performed over the course of a day at this single location, Figures 2 3 and 2 4 display the average diurnal variation of SO 4 and NO 3 , respectively . On an average basis, there is little variation in SO 4 at this site over the day (see Figure 23), and the observed and predicted SO 4 levels generall y are within 10

- 20% of each other. The observed SO 4 levels on average do exhibit a small peak during the summer months, but the lack of a strong diurnal indicates the regional nature of this pollutant. I n contrast to SO 4 , there is a more pronounced diurnal variation in NO 3 at this location (see Figure 24), with a morning peak related to NO x emiss ions and a still shallow boundary lay er, and an afternoon minimum related to a deep boundary lay er, deposition, and the thermal instability of ammonium nitrate (NH 4 NO 3 ). Similar to PM 2.5 mass, the model overprediction tends to be highest during the nightt ime and earl y morning hours. **Summary** Various model evaluation statistics are presented here for PM 2.5 mass and major species over the entire 2002 CMAQ simulation . In general, the CMAQ results were best for dail y average PM 2.5 and SO 4 mass. By comparis on, CMAQ does not appear to be able to reproduce the day

- to
- day or seasonal variation in other species like NO 3 , OM, EC, or crustal mass. The model appears to meet model performance criteria for PM 2.5 reasonabl y well over the OTR+ region as a whole, but n ot as well if one focuses on an emissions

- dense , complex urban setting such as the NY C NAA
. **References** TSD

- 1 (2006) Meteorological Modeling of 2002 using Penn State/NCAR 5 th Generation Mesoscale Model (MM5) TSD

- 2a (2006) Processing of 2002 Biogenic Emissi ons for OTC / MANE

- VU Regional and Urban Modeling

. TSD

- 2b (2006) Processing of 2002 Anthropogenic Emissions: OTC Regional and Urban 12km Base Year Simulation

. TSD

- 2c (2006) PM 2.5 modeling using the SMOKE/CMAQ sy stem over the Ozone T ransport Region (OTR)Bo yl an, J. W., and Russell, A. G., 2006. PM and light extinction model performance

metrics, goals, and criteria for three

- dimensional air quality models. Atmospheric Environment 40, 4946

- 4959. United States Environmental Protection Agency (USEPA), 2007. Guidance on the use of models and other analy ses for demonstrating attainment of air qualit y goals for ozone, PM 2.5 , and regional haze. EPA

- 454/B

- 07
- 002, Research Triangle Park, NC.**Table 1.** The median and range in mean fractional error (MFE) and mean fr actionalized

bias (MFB) values for daily PM 2.5 mass at FRM, hourly , and speciation sites across the OTR+ region. These statistics were computed at each site over the entire year. **Median MFE (minimum to maximum MFE)**  **Median MFB (minimum to maximum MFB)**  **FR M (2**  **25 sites)**  4 2
. 9 % ( 28.1 to 109 %) 2 2
. 6 % (

- 72 to 109 %) **Hourly (62 sites)** 49.3% (17.4 to 88%) 35.3% (

- 41.9 to 134%) **STN (50 sites)** 40.1% (28.1 to 83%) 12.3% (

- 27.3 to 81%) **IMPROVE (21 sites)** 53.1% (35.3 to 73%) 30.1% (

- 12.0 to 64%)
**Table 2.** Same as T able 1, except that the statistics were computed on each day using all available sites. **Median MFE (minimum to maximum MFE)**  **Median MFB (minimum to maximum MFB)**  **FRM (2 25**  **sites)** 42. 5 % ( 17.6 to 87 %) 2 6
. 4 % (

- 83 to 88 %) **Hourly (62 sites)** 50.0% (18.6 to 99%) 35.9% (

- 68 to 99%) **STN (50 sites)** 42.1% (17.9 to 84%) 27.0% (

- 80 to 75%) **IMPROVE (21 sites)** 48.4% (22.9 to 102%) 30.3% (

- 101 to 101%)
**Table 3.** Same as Table 1, except for dail y average spec ies concentrations at the 50 STN and 21 IMPROVE sites. **Media n MFE (minimum**  **to maximum MFE) Median MFB (minimum**  **to maximum MFB) STN SO**  **4** 39.8% (27.2 to 60%)

- 11.1% (

- 40.9 to 52%) **IMPROVE SO 4**  42.4% (34.5 to 60%)

- 22.6% (

- 54 to 2.2%) **STN NO 3**  80% (57 to 123%) 25.3% (

- 76 to 118%) **IMPROVE NO 3**  104% (86 to 125%) 58% (

- 2 .4 to 122%) **STN NH 4**  43.0% (27.8 to 83%) 27.8% (

- 13.2 to 81%) **STN EC** 48.8% (29.4 to 106%) 29.5% (

- 43.2 to 102%) **IMPROVE EC** 57% (41.3 to 87%) 7.2% (

- 64 to 85%) **STN OM** 62% (33.7 to 106%)

- 34.9% (

- 94 to 64%) **IMPROVE OM** 54% (40.1 to 83%)

- 4.0% (

- 82 to 43.8 %) **STN crustal** 120% (72 to 159%) 116% (56 to 159%) **IMPROVE crustal** 101% (65 to 126%) 97% (36.0 to 124%)**Figure 1.** Lo cations of the FRM monitors across the OTR+ (open squares) and NYC

NAA (filled squares). Forty five of the 2 25 monitors are within the NYC NAA.

**Figure 2.** Lo cations of the hourly TEOM monitors across the OTR+ (open squares) and NYC NAA (filled squares). Twent y - eight of the 62 monitors are within the NYC NAA.**Figure 3.** Lo cations of the speciation monitors across the OTR+ (open s quares denote

STN, open triangles denote I MPROVE) and the NYC NAA (filled squares denote STN). Nine of the 50 STN monitors are within the NYC NAA. There are 21 IMPROVE monitors across the OTR+ region

.

**Figure 4.** Time series of PM 2.5 mass based on the composite average of all 2 25 FRM monitors across the OTR+ region. The observed values are denoted with the black line, the model predictions are denoted with a thick gray line and asterixes.

0 5 10 15 20 25 30 35 40 45

1/1/2 002 1/31/ 2002 3/2/2 002 4/1/2 002 5/1/2 002 5/31/ 2002 6/30/ 2002 7/30/ 2002 8/29/ 2002 9/28/ 2002 10/28 /2002 11/27 /2002 12/27 /2002PM2.5(gm-3)

OBS PR ED**Figure 5.** Mean fractional error (MFE, %) at each FR M locatio n over the entire year:

blue, <30%; gr een, 30
- 45%; orange, 45

- 60%; re d, 60

- 75%; pink, >75%.

**Figure 6**  **.** Mean fractionalized bias (MFB, %) at each FR M location over the entire ye ar: gray , < - 45%; blue,

- 45 to
- 15%; green,

- 15 to 15%; orange, 15

- 45%; red, 4 5
- 75%; pink, >75%.**Figure 7.** Observed and predicted quarterl y average PM 2.5 mass at FRM locations, for

the entire OTR+ region and for the NYC NAA.

0 3 6 9 12 15 18 21 24 27

Q1 Q2 Q3 Q4PM2.5(gm-3)

Ob s, OTR + Pre d, OTR + Ob s, N YC Pre d, N YC **Figure 8.** Time series of SO 4 mass based on the composite average of all 50 STN monitors across the OT R+ region. The observed values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 4 8 12 16 20 24SO4(gm-3)

OBS PR ED**Figure 9.** Time series of SO 4 mass based on the composite average of all 21 I MPROVE

monitors across the OTR+ region. The observed values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 2 4 6 8 10 12 14 16 18SO4(gm-3)

OBS PR ED **Figure 10.** Time series of NO 3 mass based on the composite average of all 50 STN monitors across the OTR+ region. The observed values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 2 4 6 8 10 12 14 16NO3(gm-3)

OBS PR ED**Figure 11.** Time series of NO 3 mass based on the composite average of all 21

IMPROVE monitors across the OTR+ region. The observed values are denoted with the black diamonds, the mode l predictions are denoted with gray squares.

0 2 4 6 8 10 12NO3(gm-3)

OBS PR ED **Figure 12.** Time series of NH 4 mass based on the composite average of all 50 STN monitors across the OTR+ region. The observed values are denoted with the black diamonds, the model predictions are denoted wi th gray squares.

0 1 2 3 4 5 6 7NH4(gm-3)

OBS PR ED**Figure 1 3**  **.** Time series of EC mass based on the composite average of all 50 STN

monitors across the OTR+ region. The observed values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 0. 5 1 1. 5 2 2. 5 3EC(gm-3)

OBS PR ED **Figure 1 4**  **.** Time series of EC mass based on the composite average of all 21 I MPROVE monitors across the OTR+ region. The observed values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 0. 2 0. 4 0. 6 0. 8 1 1. 2 1. 4EC(gm-3)

OBS PR ED**Figure 1 5**  **.** Time series of OM mas s based on the composite average of all 50 STN

monitors across the OTR+ region. The observed values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 2 4 6 8 10 12 14 16OM(gm-3)

OBS PR ED **Figure 1 6**  **.** Time series of OM mass based on the composite average of all 21 I MPROVE monitors across the OTR+ region. The observed values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 1 2 3 4 5 6 7 8 9 10OM(gm-3)

OBS PR ED**Figure 1 7**  **.** Time series of crustal mass based on the composite average of all 50 STN

monitors across the OTR+ region. The observed values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 1 2 3 4 5 6 7 8crustal(gm-3)

OBS PR ED **Figure 1 8**  **.** Time series of crustal mass based on the compos ite average of all 21 IMPROVE monitors across the OTR+ region. The observed values are denoted with the black diamonds, the model predictions are denoted with gray squares.

0 0. 5 1 1. 5 2 2. 5 3 3. 5 4 4. 5 5crustal(gm-3)

OBS PR ED**Figure 19.** Quarterly variation in observed OM and predicted components of OM

–

anthropogenic primary organic aerosol (POA), anthropogenic secondary organic aerosol (SOA), and biogenic SOA

– averaged over sites in the NYC NAA.

0 1 2 3 4 5 6 7 8 9 10

OBS PR ED OBS PR ED OBS PR ED OBS PREDOMg/m3)

ant hr o SOA biog SOA ant hr o POA obs er ve d Q2 Q1
Q3 Q4

**Figure 20.** Composite average diurnal variation in observed (black line) and predicted (gray line) PM 2.5 mass over the 28 hourl y monitors in the NYC NAA, entire year.

0 5 10 15 20 25 30 35

0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 Ti m e ( EST )PM2.5(gm-3)

OBS PR ED**Figure 2 1**  **.** Composite average diurnal variation in observed (black line) and predicted

(gray line) PM 2.5 mass over the 28 hourl y monitors in the NYC NAA, quarter 1.

0 5 10 15 20 25 30 35 40

0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 Ti m e ( EST )PM2.5(gm-3)

OBS PR ED
**Figure 2 2**  **.** Composite average diurnal variation in observed (black line) and predicted (gray line) PM 2.5 mass over the 28 hourl y monitors in the NYC NAA, quarter 3.

0 5 10 15 20 25 30 35 40

0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 Ti m e ( EST )PM2.5(gm-3)

OBS PR ED**Figure 23.** Average diurnal variation at IS52 (Bronx, NY) of SO 4 , entire y ear.

0 1 2 3 4 5 6 7

0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 Tim e (EST)SO4(gm-3)

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**Figure 24.** Same as Figur e 23, except for NO 3 .

0 1 2 3 4 5 6

0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 Tim e (EST)NO3(gm-3)

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