TSD-3 

CMAQ Model Performance and Assessment

for Base Year 2002:PM2.5 Mass and Speciation

Bureau of Air Quality Analysis and Research

Division of Air Resources

New York State Department of Environmental Conservation

Albany, NY 12233

December 14, 2007
Introduction

One of the required tasks for attainment demonstration for the PM2.5 National 

Ambient Air Quality Standard (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 MM5/SMOKE/CMAQ modeling 

system was applied to simulate concentration fields of PM2.5 mass and major species for 

the base year 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), and application of the SMOKE/CMAQ system (TSD2c, 2007) provide the simulated pollutant fields which are compared to measurements to 

establish the credibility of the simulation. 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 system in this application.

The results presented here serve as an illustration of some of the model evaluation 

and assessment, as outlined in the EPA modeling guidance document (US EPA, 2007),

performed on the Base Year 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 for annual 2002 simulation were obtained from the 

following sources:

 EPA Air Quality System (AQS)

 EPA fine particulate Speciation Trends Network (STN)

 Interagency Monitoring of PROtected Visual Environments (IMPROVE) 

 Pinnacle State Park in Addison, NY operated by Atmospheric Science Research 

Center, University at Albany, Albany, NY

 Harvard Forest, Petersham, MA operated by Harvard University, Boston, MA

 Atmospheric Investigation, Regional Modeling, Analysis and Prediction 

(AIRMAP) operated by University of New Hampshire, Durham, NH

 NorthEast Ozone & Fine Particle Study (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 “OTR+” region.

The model-based data were obtained at the grid-cell corresponding to the monitor 

location and no interpolation was performed. The analysis examines the model response 

to the OTR+ region with emphasis on the New York City non-attainment area (NYC 

NAA), which consists of two counties in Connecticut (Fairfield and New Haven) and 10 

counties each in New Jersey (Bergen, Essex, Hudson, Mercer, Middlesex, Monmouth, 

Morris, Passaic, Somerset, and Union) and New York (Bronx, Kings, Nassau, New York, 

Orange, Queens, Richmond, Rockland, Suffolk, and Westchester). 
Fine particulate (PM2.5) mass

The 24-hour average Federal Reference Method (FRM) PM2.5 mass data collected 

routinely at SLAMS/NAMS sites across the OTR+ region were extracted from AQS for 

225 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. Fifty six of these FRM monitors – 45 unique locations and 

11 collocated monitors – are located within the NYC NAA. Hourly PM2.5 mass data 

from 62 sites across the OTR+ region were also included in this analysis. Twenty-eight 

of these hourly sites are located within the NYC NAA. Hourly PM2.5 mass were also 

obtained from the AIRMAP Program (http://airmap.unh.edu) at Thompson Farm, NH;

Pinnacle State Park (http://www.asrc.cestm.albany.edu); and the NE-OPS site in 

Philadelphia, PA (http://lidar1.ee.psu.edu). In addition to examining the daily averages 

based on the PM2.5 data, composite and site-specific diurnal variations are also presented 

here. Figures 1 and 2 show the locations of the FRM and hourly PM2.5 monitors across 

the OTR+ region.

Fine particulate speciation

The 24-hour average PM2.5 and fine particulate speciation data, consisting of

sulfate (SO4), nitrate (NO3), 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/IMPROVE/Default.htm) for 21 IMPROVE sites (which 

includes Dolly Sods, WV, even though this site is not in the OTR+ region. In addition to 

these parameters, the EPA STN (http://www.epa.gov/ttn/amtic/speciepg.html) also 

reports ammonium (NH4) to AQS; data from this network are collected every third or 

sixth day. The STN data from 50 sites in the OTR+ region were used in this analysis. 

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. The 

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 variations using continuous hourly SO4 and NO3 data from the 

New York State Department of Environmental Conservation (http://www.dec.ny.gov) 

site IS52 (Bronx) is also presented here.

Evaluation of CMAQ 

The following sections 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: Pi and Oi

are the individual daily average predicted and observed concentrations, respectively; P
and O are the average concentrations, respectively, and N is the sample size. These 

statistical measures are listed in the EPA modeling guidance document (USEPA, 2007).

Each statistic is computed two ways. 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 period was excluded from this 

analysis since the observed PM2.5 and OM data at many sites were greatly affected by 

Canadian forest fires. Additionally, those days on which the observed and predicted 

averages differed by more than a factor of 25 were not included in this analysis, since 

they can greatly affect some of the normalized metrics.

Observed average, in g m-3:

O

N

  Oi

1

Predicted average, in g m-3(only use Pi when Oi is valid):

P

N

  Pi

1

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 g m-3:

RMSE

N

Pi Oi  











 

1 2

1 2

( )

/

Mean fractional error (MFE), in %:

MFE

N

P O

P O

i i

i i





  

2

100%

Mean absolute gross error (MAGE), in g m-3:

MAGE

N

Pi Oi   

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

Pi Oi   

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 PM2.5 mass

Model performance statistics, based on daily average PM2.5 levels, are presented 

in this section across the OTR+ region. Figure 4 displays the time series of observed and 

predicted PM2.5 mass averaged across all FRM monitors. As stated earlier the July 6-9 

period was not included in this analysis since many monitors across the region were 

substantially impacted by Canadian wildfires. Overall, the mean bias (predicted minus 

observed) was about 3.8 g m-3, ranging from -17.7 g m-3to +24.0 g m-3. The highest 

overprediction tended to occur during the colder months, quarters 1 and 4, whereas the 

days on which the model tended to underpredict PM2.5 were more likely to occur during 

the summer months.

Figures 5 and 6 display the mean fractional error (MFE) and mean fractionalized 

bias (MFB), respectively, at each FRM monitor across the OTR+ region over the entire 

year. Boylan and Russell (2006) propose that these two metrics are the most useful for 

model evaluation since they set upper bounds on error/bias, are not likely dominated by a 

few “outlier” points, and do not assume that observations are absolute truth. Boylan and 

Russell (2006) further point out that MFE<75% and MFB<60% define a level of 

accuracy close to the best a model can be expected to achieve in terms of acceptable 

model performance. The analysis shows that at 221 of the 225 FRM sites the MFE was 

below 75%, while at 215 sites the MFB was below 60% in the OTR + region. However, 

such is 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 14 primary FRM monitors in NJ, the MFE criteria was met at 17 of 
the 21 primary FRM monitors in NY and the MFB criteria was only met at 12 of the 

primary NY FRM monitors. Hence, on an overall basis the model predicts PM2.5 mass 

reasonably well, although not consistently at all locations and all days in the NYC NAA.

Tables 1 and 2 list the median and range in MFE and MFB for the OTR+ region 

for the entire year 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. In 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 displays the quarterly average PM2.5 mass at all OTR+ monitors and also 

at those monitors within the NYC NAA. Note that, not surprisingly, the observed 

average PM2.5 mass tends to be higher in the NYC NAA than across the entire OTR+. 

CMAQ tends to overpredict PM2.5 at these locations, especially during the colder months. 

On the other hand, CMAQ performs well during the July-September period on a

composite average basis.

PM2.5 speciation

Composite daily average predicted and observed concentrations of major PM2.5

species – SO4, NO3, NH4, 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+ region were compared in this analysis. The time series of these species at the 50 

STN and 21 IMPROVE locations are shown in Figures 8-18. The data displayed are for

every third day, corresponding to the nominal sampling schedule. These two speciation 

networks collect SO4, NO3, EC, OM, and crustal mass, while only the STN reports NH4

at a number of locations. The NH4 data from the IMPROVE network are not included 

here, since in the OTR+ region, only the IMPROVE sites of Dolly Sods, WV; James 

River Face Wilderness, VA; and Shenandoah National Park, VA reported NH4 data in 

2002. As before, the July 6-9 period was excluded.

The model exhibits good agreement for SO4 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 SO4 is 

higher than at other times of the year, which the model seems to capture. Figures 10 and 

11 suggest that the model qualitatively predicts the seasonal variation of NO3 fairly well, 

although the observed and modeled concentrations during the cold season, quarters 1 and 

4 (when volatilization of NO3 is lowest), differ by several g m-3. The seasonal variation 

in NH4 (Figure 12) is more complicated, but appears to be consistent with SO4 and NO3. 

During the summer, most NH4 is tied to SO4 (which the model tends to predict well), 

whereas during the winter a substantial fraction of NH4 is tied to NO3 (which the model 

appears to predict poorly). Both EC (Figures 13 and 14) and crustal mass (Figure 17 and 

18) are predominantly primary emissions, and the model tends to overpredict both 
species – particularly at urban locations and during the colder months. Note, too, the 

effects of emissions from the July 4th 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 exceeds 4 g m-3, while it is

generally closer to ~0.5-1.0 g m-3only.

The model appears to also have difficulty 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 displays the quarterly 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 analysis. For SO4, which contributes ~30% to the total PM2.5

mass, the MFE at both STN and IMPROVE locations is roughly 40%, and there is a 

general tendency to underpredict SO4 (MFB < 0). At all 71 locations (STN and

IMPROVE), the MFE and MFB criteria suggested by Boylan and Russell (2006) are met. 

This is not surprising, since SO2 emissions are generally well-characterized and ambient 

SO4 levels tend to be somewhat spatially uniform compared to other PM2.5 species. The 

range in MFE and MFB tends to be lower for SO4 compared to the other species as well. 

The urban-rural differences for the other species are much more evident than for SO4. 

Note that the degree of overprediction of NO3, NH4, and EC is considerably higher at the 

STN locations compared to IMPROVE. CMAQ tends to underpredict OM as well, 

although some of this discrepancy may be attributed to the fact that OM is operationally 

defined and is highly 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 operationally defined as the sum of major metal oxides. For these and other 

reasons, Boylan and Russell (2006) state that for the non-SO4 or less abundant PM2.5

species, the MFE and MFB guidelines should be relaxed.

Diurnal variations PM2.5, SO4, and NO3

The hourly PM2.5 mass and speciation data allow for examination of the model 

predictions with high temporal resolution. Figure 20 displays the composite diurnal 

variation of PM2.5 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 profiles 

during quarters 1 and 3 are shown in Figures 21 and 22, respectively. Quarter 1 exhibits 

the largest discrepancy between observed and predicted PM2.5, while quarter 3 exhibits 

the smallest discrepancy between observed and predicted PM2.5 mass. Regardless of time 
of year, CMAQ predicts a pronounced double peak, with maxima during the morning and 

late afternoon/early evening time periods, likely corresponding to average commuting 

patterns. During quarter 1, there is a double peak in the observed diurnal 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 PM2.5 levels are actually slightly higher than 

the predicted levels.

There were only a handful of locations in 2002 where hourly SO4 and/or NO3

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 23 and 24 display the 

average diurnal variation of SO4 and NO3, respectively. On an average basis, there is 

little variation in SO4 at this site over the day (see Figure 23), and the observed and 

predicted SO4 levels generally are within 10-20% of each other. The observed SO4 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. In contrast to SO4, there is a more 

pronounced diurnal variation in NO3 at this location (see Figure 24), with a morning peak 

related to NOx emissions and a still shallow boundary layer, and an afternoon minimum 

related to a deep boundary layer, deposition, and the thermal instability of ammonium 

nitrate (NH4NO3). Similar to PM2.5 mass, the model overprediction tends to be highest 

during the nighttime and early morning hours.

Summary

Various model evaluation statistics are presented here for PM2.5 mass and major 

species over the entire 2002 CMAQ simulation. In general, the CMAQ results were best 

for daily average PM2.5 and SO4 mass. By comparison, CMAQ does not appear to be 

able to reproduce the day-to-day or seasonal variation in other species like NO3, OM, EC, 

or crustal mass. The model appears to meet model performance criteria for PM2.5

reasonably well over the OTR+ region as a whole, but not as well if one focuses on an 

emissions-dense, complex urban setting such as the NYC NAA.

References

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

Mesoscale Model (MM5)

TSD-2a (2006) Processing of 2002 Biogenic Emissions 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) PM2.5 modeling using the SMOKE/CMAQ system over the Ozone 

Transport Region (OTR)
Boylan, 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 analyses for demonstrating attainment of air quality goals for ozone, 

PM2.5, 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 fractionalized 

bias (MFB) values for daily PM2.5 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)

FRM (225 sites) 42.9% (28.1 to 109%) 22.6% (-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 Table 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 (225 sites) 42.5% (17.6 to 87%) 26.4% (-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 daily average species concentrations at the 50 STN

and 21 IMPROVE sites.

Median MFE (minimum 

to maximum MFE)

Median MFB (minimum 

to maximum MFB)

STN SO4 39.8% (27.2 to 60%) -11.1% (-40.9 to 52%)

IMPROVE SO4 42.4% (34.5 to 60%) -22.6% (-54 to 2.2%)

STN NO3 80% (57 to 123%) 25.3% (-76 to 118%)

IMPROVE NO3 104% (86 to 125%) 58% (-2.4 to 122%)

STN NH4 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. Locations of the FRM monitors across the OTR+ (open squares) and NYC 

NAA (filled squares). Forty five of the 225 monitors are within the NYC NAA.

Figure 2. Locations of the hourly TEOM monitors across the OTR+ (open squares) and 

NYC NAA (filled squares). Twenty-eight of the 62 monitors are within the NYC NAA.
Figure 3. Locations of the speciation monitors across the OTR+ (open squares denote 

STN, open triangles denote IMPROVE) 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 PM2.5 mass based on the composite average of all 225 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

1/1/2002

1/31/2002

3/2/20024/1/20025/1/2002

5/31/20026/30/20027/30/20028/29/20029/28/2002

10/28/200211/27/200212/27/2002

P

M2.5 (g m

-3

)

OBS PRED
Figure 5. Mean fractional error (MFE, %) at each FRM location over the entire year: 

blue, <30%; green, 30-45%; orange, 45-60%; red, 60-75%; pink, >75%.

Figure 6. Mean fractionalized bias (MFB, %) at each FRM location over the entire year: 

gray, <-45%; blue, -45 to -15%; green, -15 to 15%; orange, 15-45%; red, 45-75%; pink, 

>75%.
Figure 7. Observed and predicted quarterly average PM2.5 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 Q4

P

M 2.5 ( g m

-3

)

Obs, OTR+ Pred, OTR+ Obs, NYC Pred, NYC

Figure 8. Time series of SO4 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

4

8

12

16

20

24

S

O4 (g m

-3

)

OBS PRED
Figure 9. Time series of SO4 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 model predictions are denoted with gray squares.

0

2

4

6

8

10

12

14

16

18

S

O4 (g m

-3

)

OBS PRED

Figure 10. Time series of NO3 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

16

N

O 3 (g m

-3

)

OBS PRED
Figure 11. Time series of NO3 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 model predictions are denoted with gray squares.

0

2

4

6

8

10

12

N

O 3 (g m

-3

)

OBS PRED

Figure 12. Time series of NH4 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

N

H 4 (g m

-3

)

OBS PRED
Figure 13. 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

3

E

C ( g m

-3

)

OBS PRED

Figure 14. Time series of EC 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 model predictions are denoted with gray squares.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

E

C ( g m

-3

)

OBS PRED
Figure 15. Time series of OM 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

16

O

M (g m

-3

)

OBS PRED

Figure 16. Time series of OM 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 model predictions are denoted with gray squares.

0

1

2

3

4

5

6

7

8

9

10

O

M (g m

-3

)

OBS PRED
Figure 17. 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

8

crustal ( g m

-3

)

OBS PRED

Figure 18. Time series of crustal 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 model predictions are denoted with gray squares.

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

crustal ( g m

-3

)

OBS PRED
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 PRED OBS PRED OBS PRED OBS PRED

O

M  g/m

3

)

anthro SOA biog SOA anthro POA observed

Q2

Q1

Q3 Q4

Figure 20. Composite average diurnal variation in observed (black line) and predicted 

(gray line) PM2.5 mass over the 28 hourly monitors in the NYC NAA, entire year.

0

5

10

15

20

25

30

35

0.51.52.53.54.55.56.57.58.59.5

10.511.512.513.514.515.516.517.518.519.520.521.522.523.5

Tim e (EST)

P

M2.5 (g m

-3

)

OBS PRED
Figure 21. Composite average diurnal variation in observed (black line) and predicted 

(gray line) PM2.5 mass over the 28 hourly monitors in the NYC NAA, quarter 1.

0

5

10

15

20

25

30

35

40

0.51.52.53.54.55.56.57.58.59.5

10.511.512.513.514.515.516.517.518.519.520.521.522.523.5

Tim e (EST)

P

M2.5 (g m

-3

)

OBS PRED

Figure 22. Composite average diurnal variation in observed (black line) and predicted 

(gray line) PM2.5 mass over the 28 hourly monitors in the NYC NAA, quarter 3.

0

5

10

15

20

25

30

35

40

0.51.52.53.54.55.56.57.58.59.5

10.511.512.513.514.515.516.517.518.519.520.521.522.523.5

Tim e (EST)

P

M2.5 (g m

-3

)

OBS PRED
Figure 23. Average diurnal variation at IS52 (Bronx, NY) of SO4, entire year.

0

1

2

3

4

5

6

7

0.51.52.53.54.55.56.57.58.59.5

10.511.512.513.514.515.516.517.518.519.520.521.522.523.5

Time (EST)

S

O4 (g m

-3

)

OBS PRED

Figure 24. Same as Figure 23, except for NO3.

0

1

2

3

4

5

6

0.51.52.53.54.55.56.57.58.59.5

10.511.512.513.514.515.516.517.518.519.520.521.522.523.5

Time (EST)

N

O 3 (g m

-3

)

OBS PRED