PM2.5 SIP 

Appendix K 

CMAQ Modeling Documentation 

K-1: CMAQ Model Performance and Assessment 

K-2: Meterological Modeling using MM5 

K-3: Processing of 2002 Anthropogenic Emissions 

K-4: PM2.5 Modeling using SMOKE/CMAQ 
{This page left blank for printing purposes} 
K-1: CMAQ Model Performance and Assessment 
 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 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 g m-3: 

RMSE

N

P O i i   



 

  

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

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 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-3 to +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

3/2/2002

4/1/2002

5/1/2002

5/31/2002

6/30/2002

7/30/2002

8/29/2002

9/28/2002

10/28/2002

11/27/2002

12/27/2002

PM2.5 (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

PM 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

SO4 (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

SO4 (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

NO3 (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

NO3 (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

NH 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

EC ( 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

EC ( 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

OM (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

OM (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

OM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

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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

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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

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

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

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OBS PRED
K-2: Meterological Modeling using MM5 
 TSD-1a 

Meteorological Modeling of 2002 using Penn State/NCAR 5th

Generation Mesoscale Model (MM5) 

Bureau of Air Quality Analysis and Research 

Division of Air Resources 

New York State Department of Environmental Conservation 

Albany, NY 12233 

March 19, 2006 
2 

Meteorological Modeling using Penn State/NCAR 5th Generation Mesoscale Model 

(MM5)

Version 3.6 of MM5 was used to generate annual 2002 meteorology for the OTC 

modeling work. Prof. Dalin Zhang of the University of Maryland performed the MM5 

simulations in consultation with NYSDEC staff. The model was applied in Lambert 

conformal map projection and utilized MPP Version developed for clusters. The two-way 

nested domain consisted of coarse (36km) and fine (12km) mesh corresponding to 

149x129 and 175x175 grids, respectively, in this application (see Figure 1). 

The Lambert projection used in this work followed the Regional Planning Organization 

(RPO) national domain setup with the center at (40oN, 97oW) and parallels at 33oN and 

45oN. Map projection parameters in reference to the projection center point are as 

follows: Southwest corner for the 36 km grid is at (-2664km, -2304km) and the northeast 

corner at (2664km, 2304km). In the case of the 12km grid, the southwest corner is at 

(252km, -900km) and the northeast corner at (2340km, 1188km). In the vertical direction, 

the terrain following σ-coordinate system was used with the pressure at each σ-level 

determined from a reference state that is estimated using the hydrostatic equation from a 

given sea-level pressure and temperature with a standard lapse rate. There are 30 

unevenly spaced σ levels, giving 29 vertical layers, with higher resolution within the 

planetary boundary layer (PBL). The σ levels are: 

1.0000, 0.9974, 0.9940, 0.8980, 0.9820, 0.9720, 0.9590, 0.9430, 0.9230, 0.8990, 

0.8710, 0.8390, 0.8030, 0.7630, 0.7180, 0.6680, 0.6180, 0.5680, 0.5180, 0.4680, 

0.3680, 0.3180, 0.2680, 0.2180, 0.1680, 0.1230, 0.0800, 0.0400, 0.0000 

The surface layer was set at about 10m, the level at which surface winds were typically 

observed, and the model top was set at 50hPa with a radiative top boundary condition. 

The time steps for the 36km and 12km domains were 75 and 25 seconds, respectively. 

The important model physics options used for this MM5 simulation include: 

 Kain-Fritsch (1993) convective scheme for both 36- and 12-km domains 

 Explicit moisture scheme (without the mixed phase) containing prognostic 

equations for cloud water (ice) and rainwater (snow) (Dudhia 1989; Zhang 1989) 

 Modified version of the Blackadar planetary boundary layer (PBL) scheme 

(Zhang and Anthes 1982; Zhang and Zheng 2004) 

 Simple radiative cooling scheme (Grell et al. 1994) 

 Multi-layer soil model to predict land surface temperatures using the surface 

energy budget equation (Dudhia 1996) 

Note that the Blackadar PBL scheme has been modified in order to correct the phase shift 

of surface wind speed and temperature diurnal cycle, following a study that compared 

five different PBL schemes: the Gayno-Seaman TKE scheme (Shafran et al. 2000), Burk-
3 

Thompson (1989), Blackadar (Zhang and Anthes 1982), MRF (Hong and Pan 1996), and 

Mellor-Yamada-Jajic (Mellor and Yamada 1974; Jajic 1990, 1994). The details of the 

study can be found at Zhang and Zheng (2004). 

Nudging Processes 

The MM5 provides options for nudging observations for each domain during the model 

integration process (Stauffer and Seaman, 1990; Stauffer et al. 1991). The Eta analyses of 

upper-air winds, temperature and water-vapor mixing ratio as well as their associated surface 

fields were used for nudging every 6 hours, and the Eta surface wind fields blended with 

surface wind observations were used to nudge every 3 hours. While only the surface winds 

were nudged, their influences could extend into the PBL as well (see Stauffer et al. 1991). 

Based on UMD’s prior experience in numerical experiments, the following nudging 

coefficients have been used: 

 Upper-air wind fields: 5. 0E-4s-1 for Domain 1 (36km), and 2. 5E-4s-1 for Domain 2 

(12km); 

 Upper-air temperature fields: 1.0E-5s-1 for both Domains; 

 Surface winds: 5. 0s-1E-4s-1 for Domain 1, and 2.5E-4s-1 for Domain 2; and 

 Surface temperature and moisture: not nudged due to instability consideration. 

ASSESSMENT

This assessment covers the period of May through September 2002. 

National Weather Service (NWS) and CASTNet data – Surface temperature, Wind 

Speed, and Humidity 

NWS (TDL) and CASTNet (www.epa.gov/castnet/) surface measurements of 

temperature, wind speed, and humidity (note there were no humidity measurements for 

CASTNet) were used to compare with the MM5 outputs. The evaluation was performed 

with METSTAT program developed by Environ Corporation 

(www.camx.com/files/metstat.15feb05.tar.gz) When comparing to NWS data, the 

METSTAT interpolates the first layer MM5 (at 10m height) temperature and humidity 

data to a height of 2m, the level that corresponds to the NWS measurement of these 

parameters. However, no such interpolation was made for wind speed and direction. In 

the case of CASTNet surface measurements, no such changes were needed as CASTNet 

data were reported at a height of 10m. In this analysis, no exclusion was made for calm 

conditions. The reported calm winds (zero wind speed measured) were treated as is in 

this evaluation effort. The METSTAT calculated standard statistical measures – 

average, bias, error and index of agreement between the measured and predicted 

parameters. 

 Figure 2 displays the temperature and wind speed comparison of MM5 and measured 

data from NWS and CASTNet networks for August 2002. MM5 performance for both in 

magnitude and diurnal timing, temperature can be considered to be quite good for both 

NWS and CASTNet data, while MM5 underpredicted NWS and overpredicted CASTNet 
4 

daytime wind speed, respectively. It should be pointed out that there are differences in 

how the meteorological information is collected and reported by the two networks as well 

as in MM5. The CASTNet measurements are based on hourly averaged wind speed while 

NWS reports 2min average at 10min before the hour, whereas MM5 predictions are 

reflective of the last time-step of the hour of computation. Interestingly, MM5 appears to 

track quite well the nighttime minimum wind speed for both networks. In the case of 

humidity (not shown), MM5 tracks the NWS observed humidity trend well, but MM5 

missed the observed semi-diurnal cycles. Comparisons for the five months including bias 

and root mean square error from both NWS and CASTNet are available on request from 

NYSDEC. 

The above assessment is based on domain-wide averages to provide an overall response 

of the model over the five months. Another way of assessing the model is to examine the 

degree of correlation between the measured and predicted parameters. Figures 3a and 3b 

displays such a comparison for wind speed and temperature, respectively, for the NWS 

hourly data covering the period of May through September 2002. For the NWS data, the 

correlations are in the range from 0.7 to 0.8 for wind speed, above 0.96 for temperature, 

and in the range of 0.8 to 0.9 for humidity. CASTNet data (not shown) also exhibit 

similar correlation. These correlations indicate that MM5 simulation has captured both 

the diurnal and synoptic scale variations. Detailed plots of this comparison are available 

on request from NYSDEC. 

Vertical Profiler – Winds 

The Wind-Profiler network measurements along the U. S. East Coast (www.madisfsl.org/cap) were used to evaluate the vertical profiles from MM5. There are twelve 

wind-profiler measurement stations from which data were available for comparison. For 

convenience of comparison, the wind-profiler measurements were interpolated to the 

MM5 vertical levels. The approach used was simple interpolation between two adjacent 

wind-profiler layers to the MM5 vertical level, and was limited to that reported by the 

profiler measurement. The focus of the comparison was to assess if MM5 was able to 

capture the measured vertical structure, and for this we used the observed Low Level Jet 

(LLJ) as an indicator. The comparison was performed for June, July and August 2002. In 

general it is found that MM5 captures the profiler measured vertical wind field structure 

reasonably well. Figure 4 displays an example of the MM5 and wind profiler comparison 

for the August 2002 episode at Richmond, VA and Concord, NH. MM5 predicted weaker 

LLJ winds compared to those based on the wind-profiler measurements. The detailed 

plots of this comparison are available on request from NYSDEC. 

Cloud Cover – Satellite cloud image 

Cloud information derived from satellite image data 

(www.atmos.umd.edu/~srb/gcip/webgcip.htm )were used to assess the MM5 prediction 

of cloud cover. The 0.5o

 by 0.5o resolution of the satellite data were interpolated into the 

12km MM5 grid for comparison. The MM5 total cloud fraction was estimated by MCIP 

based on the MM5’s low cloud, middle cloud and high cloud predictions. In general, 
5 

MM5 captured the satellite cloud pattern well but underestimates the satellite cloud 

fraction (see Figure 5 as an example). Part of problem may due to the coarse resolution of 

the satellite cloud data. 

Precipitation comparison 

The monthly total observed precipitation data were constructed from 1/8-degree daily 

precipitation analysis data (http://data.eol.ucar.edu/codiac/dss/id=21.093 produced by 

Climate Prediction Center, based on 7,000-8,000 hourly/6-hourly gauge reports and 

radar). The MM5 monthly total precipitation was estimated from the MM5 predicted 

convective and non-convective rainfall and summed up for each month. In general, MM5 

captured the observed spatial patterns in May and September, but no so well for June, 

July and August (See Figure 6), perhaps reflective of the summertime convective rain 

activities not captured by MM5. Detailed plots of this comparison are available on 

request from NYSDEC. 

Calm Conditions 

Calm conditions are defined as observed wind speed of zero knots and wind direction as 

0o

. It would be useful to assess how MM5 performs under observed calm conditions, 

because of potential pollutant buildup that could occur under such conditions. Table 1 

lists the summary of the percentage of calm condition at each hour for the August 2002 

from the NWS data within the 12km domain. It is apparent from the Table that the calm 

conditions occur primarily during the night and early morning hours, from 23Z (7 p.m. 

EDT) to 15Z (11 a.m. EDT) with a peak at 10Z (6 a.m. EDT). To assess MM5 

performance, the observed and MM5 predicted wind speeds were divided into calm and 

non-calm according to observed wind speed. Figure 7 displays such a comparison of the 

MM5 predicted wind speed to the observed wind speed under the calm and non-calm 

conditions for the month of August 2002. For the “calm” group, the average wind speed 

for MM5 varies from 1 m/s during the night and early morning hours and over 1.5 m/s 

during the day. MM5 is over-predicting during observed calm wind conditions. There 

are local minima every 3 hours, due to the surface observed wind speed nudging in MM5. 

In contrast under the non-calm conditions, MM5 underpredicts by about 0.5 m/s for all 

hours with noticeable local maximum happening at the nudging hours. The MM5 

nudging process would pull predictions toward the measured data, while the 

underprediction of MM5 for the non-calm conditions may due to the adopted PBL 

scheme in this simulation. 

Summary 

In this study, we performed an assessment of the MM5 simulation to real-world data, 

both at the surface level as well as in the vertical. While there are no specific 

recommended procedures identified for this assessment, similar approaches have been 

used elsewhere (Dolwick 2005, Baker 2004, and Johnson 2004). Traditionally, the NWS 

surface measurements are used for such a comparison. Since NWS data had been used 

through nudging processes in developing the MM5 simulation, the comparisons should 
6 

not be far removed from each other. In this study, we extended the evaluation by using 

CASTNet measurements that were not used in the MM5 simulations. Thus comparison 

with CASTNet data provides for an independent assessment and should complement the 

comparison with NWS data. We also compared the MM5 results with the wind profiler 

data and cloud data derived from satellite images to diagnose if the MM5 simulation is 

yielding the right type of dynamics in the vertical. The analyses shows that in general, the 

performance of the MM5 is reasonable both at the surface and in the vertical, thereby 

providing confidence in the use of these data in the CMAQ simulations. 

 

References 

Baker, K. 2004: www.ladco.org/tech/photo/photochemical.html

Burk, S. D. and W. T. Thompson, 1989: A vertically nested regional numerical weather 

prediction model with second-order closure physics. Mon. Wea. Rev., 117, 2305–2324. 

Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon 

experiments using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 3077–3107. 

Dudhia, J., 1996: A multi-layer soil temperature model for MM5. Preprints, 6th Annual 

MM5 Users Workshop, Boulder, CO. 

Dolwick, P. 2005: 

http://cleanairinfo.com/modelingworkshop/presentations/MPE_Dolwick.pdf

Grell, G. A., J. Dudhia , and D. R. Stauffer 1994: A description of the fifth-generation 

Penn State/NCAR Mesoscale Model (MM5). NCAR Tech.Note NCAR/TN-398 1 STR, 

122 pp. 

Hong, S.-H., and H.-L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a 

medium-range forecast model. Mon. Wea. Rev., 124, 2322–2339. 

Jajic, Z. I., 1990: The step-mountain coordinate: Physical package. Mon. Wea. Rev., 

118, 1429-1443. 

Jajic, Z. I., 1994: The step-mountain Eta coordinate model: Further development of the 

convection, viscous sublayer and turbulent closure schemes. Mon. Wea. Rev., 122, 927-

945. 

Johnson, M. 2004: www.ladco.org/tech/photo/photochemical.html

Kain, J.S., and J.M. Fritsch, 1993: Convective parameterization for mesoscale models: 

The Kain-Fritsch scheme. Cumulus Parameterization. Meteor. Monogr., 46, Amer. 

Meteor. Soc., 165-170. 

Mellor, G. L., and T. Yamada, 1974: A hierarchy of turbulence closure models for 

planetary boundary layers. J. Atmos. Sci., 31, 1791–1806. 
7 

Shafran, P.C., N.L. Seaman, and G. A. Gayno, 2000: Evaluation of numerical 

predictions of boundary layer structure during the Lake Michigan ozone study. J. Appl. 

Meteor., 39, 412-426. 

Stauffer, D. R., N. L. Seaman and F. S. Binkowski 1991: Use of four-dimensional data 

assimilation in a limited-area mesoscale model. Part II: Effects of data assimilation 

within the planetary boundary layer. Mon. Wea. Rev., 119, 734-754. 

Stauffer, D. R. and N. L. Seaman, 1990: Use of four-dimensional data assimilation in a 

limited-area mesoscale model. Part I: Experiments with synoptic-scale data. Mon. 

Wea. Rev., 118, 1250-1277. 

Zhang, D.-L., 1989: The effect of parameterized ice microphysics on the 

simulation of vortex circulation with a mesoscale hydrostatic model. Tellus, 41A, 132-

147. 

Zhang, D.-L, and R. A. Anthes, 1982: A high-resolution model of the planetary 

boundary layer-sensitivity tests and comparisons with SESAME-79 data. J. Appl. 

Meteor., 21, 1594–1609. 

Zhang, D.-L, and W.-Z. Zheng, 2004: Diurnal cycles of surface winds and temperatures 

as simulated by five boundary-layer parameterizations. J. Appl. Meteor., 43, 157-169. 
8 

Table 1 Measured calm and non-calm occurrences over the modeling domain 

during August 2002 based on NWS data 

Hour 

#NonCalm #Calm #Total % Calm 

00Z 18209 3924 22133 17.7 

01Z 16531 6026 22557 26.7 

02Z 15604 6929 22533 30.8 

03Z 14983 7245 22228 32.6 

04Z 14309 7540 21849 34.5 

05z 14073 7735 21808 35.5 

06Z 13934 7949 21883 36.3 

07Z 13792 8040 21832 36.8 

08Z 13542 8273 21815 37.9 

09Z 13542 8385 21927 38.2 

10Z 13708 8591 22299 38.5 

11Z 14139 8693 22832 38.1 

12Z 15297 7690 22987 33.5 

13Z 17336 5192 22528 23 

14Z 18522 3439 21961 15.7 

15Z 18755 2617 21372 12.2 

16Z 19169 2015 21184 9.5 

17Z 19555 1617 21172 7.6 

18Z 19982 1430 21412 6.7 

19Z 20149 1389 21538 6.4 

20Z 20565 1288 21853 5.9 

21Z 20518 1383 21901 6.3 

22Z 20672 1556 22228 7 

23Z 20231 2292 22523 10.2 
9 

Figure 1: OTC MM5 modeling domain with areal extent of 12km and 36km grids 
10 

Figure 2: Temperature and Wind speed comparisons for August 2002. In each case the 

upper panel corresponds to comparison between MM5 and NWS data and the lower 

panel between MM5 and CASTNet data. 

O b se rve d /P re d icte d T e m p e ra tu re

275

280

285

290

295

300

305

 8/ 1 8/ 2 8/ 3 8/ 4 8/ 5 8/ 6 8/ 7 8/ 8 8/ 9

 8/10 8/11 8/12 8/13 8/14 8/15 8/16 8/17 8/18 8/19 8/20 8/21 8/22 8/23 8/24 8/25 8/26 8/27 8/28 8/29 8/30 8/31

K

O b sTe m p P rdTem p 

O b se rve d /P re d icte d T e m p e ra tu re

275

280

285

290

295

300

305

 8/ 1 8/ 2 8/ 3 8/ 4 8/ 5 8/ 6 8/ 7 8/ 8 8/ 9

 8/10 8/11 8/12 8/13 8/14 8/15 8/16 8/17 8/18 8/19 8/20 8/21 8/22 8/23 8/24 8/25 8/26 8/27 8/28 8/29 8/30 8/31

K

O b sTe m p P rdTem p 

Observed/Predicted Windspeed

1

2

3

4

5

6

7

 8/ 1 8/ 2 8/ 3 8/ 4 8/ 5 8/ 6 8/ 7 8/ 8 8/ 9

 8/10 8/11 8/12 8/13 8/14 8/15 8/16 8/17 8/18 8/19 8/20 8/21 8/22 8/23 8/24 8/25 8/26 8/27 8/28 8/29 8/30 8/31

m/s

ObsWndSpd PrdWndSpd 

Observed/Predicted Windspeed

1

2

3

4

5

6

7

 8/ 1 8/ 2 8/ 3 8/ 4 8/ 5 8/ 6 8/ 7 8/ 8 8/ 9

 8/10 8/11 8/12 8/13 8/14 8/15 8/16 8/17 8/18 8/19 8/20 8/21 8/22 8/23 8/24 8/25 8/26 8/27 8/28 8/29 8/30 8/31

m/s

ObsWndSpd PrdWndSpd 
11 

Figure 3a: Spatial correlation estimates between MM5 and NWS data for wind speed 

 from May to September 2002 
12 

Figure 3b: Spatial distribution of correlation coefficients for Temperature between 

MM5 and NWS data from May to September 2002. 
13 

Richmond, VA 

Concord, NH 

Figure 4: MM5 and Wind profiler comparison for August 6 to 17, 2002 at Richmond, VA 

and Concord, NH. The upper and lower panes at each station are for MM5 and profiler, 

respectively. The abcissa represents day and the ordinate the height (m). 
14 

Figure 5: MM5 and Satellite cloud images for August 14, 2002 at 0700 EST 
15 

Figure 6: MM5 predicted and measured precipitation over the domain for the month of 

August 2002 
16 

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z

TIME

WIND SPEED (m/s)

TDLAvgNC

MM5AvgNC

MM5AvgC

Figure 7: Comparison of averaged wind speed between MM5 and observed under calm 

(C) and non-calm (NC) conditions. 
K-3: Processing of 2002 Anthropogenic Emissions 
 TSD-2b

Processing of 2002 Anthropogenic Emissions: 

 OTC Regional and Urban 12km Base Year Simulation 

Bureau of Air Quality Analysis and Research 

Division of Air Resources 

New York State Department of Environmental Conservation 

Albany, NY 12233 

March 19, 2007 
2

Overview 

All emissions processing for the revised 2002 OTC regional and urban 12 km 

base case simulations was performed with SMOKE2.1 compiled on a Red Hat 9.0 Linux 

operating system with the Portland group fortran compiler version 5.1. The emissions 

processing was performed on a month-by-month and RPO-by-RPO basis, i.e. SMOKE 

processing was performed for each month for each of the RPOs (MANE-VU, VISTAS, 

CENRAP, MRPO) individually as well as for Canada. For each month/RPO combination, 

a separate SMOKE ASSIGNS file was created, and the length of the episode in each of 

these ASSIGNS files was set to the entire month. Also, as discussed in Section 3, there 

was no difference between “episode-average” temperatures and “monthly-average” 

temperatures for the Mobile6 simulations that used the option of temperature averaging. 

This document is structured as follows: A listing of all emission inventories is 

given in Section 2, organized by RPO and source category. Section 3 discusses the 

Mobile6 processing approach employed for the different RPOs, while Section 4 describes 

the processing of biogenic emissions with BEIS3.12. Finally, Sections 5 through7 

describe the temporal allocation, speciation, and spatial allocation of the emissions 

inventories, respectively. 

1. Emission Inventories 

1.1 MANE-VU 

Version 3 of the MANE_VU inventory was utilized to generate CMAQ-ready 

emissions. This emissions inventory data were obtained from the MANEVU archive in 

April 2006. 

1.1.1 Area Sources

 Files: 

MANEVU_AREA_SMOKE_INPUT_ANNUAL_SUMMERDAY_040606.txt 

and MANEVU_AREA_SMOKE_INPUT_ANNUAL_WINTERDAY_040606.txt 

prepared by PECHAN, downloaded from ftp.marama.org (username mane-vu, 

password exchange) 

 Fugitive dust correction: This was applied as county-specific correction factors 

for SCC’s listed at http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; the 

correction factor file gcntl.xportfrac.txt was obtained from EPA’s CAIR NODA 

ftp site http://www.airmodelingftp.com (password protected).; this adjustment 

was performed using the SMOKE programs cntlmat and grwinven to generate an 

adjusted IDA inventory file used for subsequent SMOKE processing 

1.1.2 Nonroad Sources

 File: MANEVU_NRD2002_SMOKE_030306 prepared by PECHAN; 

downloaded from ftp.marama.org (username mane-vu, password exchange) 
3

1.1.3 Mobile Sources

 VMT/Speed: MANEVU_2002_mbinv_02022006_addCT.txt prepared by 

PECHAN and NESCAUM; downloaded from 

http://bronze.nescaum.org/Private/junghun/MANEVU/onroad_ver3_update/MANEVU_V3_update.tar 

1.1.4 Point Sources

 Files: 

MANEVU_Point_SMOKE_INPUT_ANNUAL_SUMMERDAY_041006.txt and 

MANEVU_Point_SMOKE_INPUT_ANNUAL_WINTERDAY_041006.txt 

prepared by PECHAN were downloaded from ftp.marama.org (username manevu, password exchange) 

 Fugitive dust correction: This was applied as county-specific correction factors 

for SCC’s listed at http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; the 

correction factor file gcntl.xportfrac.txt was obtained from EPA’s CAIR NODA 

ftp site http://www.airmodelingftp.com (password protected).; this adjustment 

was performed using the SMOKE programs cntlmat and grwinven to generate an 

adjusted IDA inventory file used for subsequent SMOKE processing 

 Corrected the omission of 2,100 tons/year VOC emissions from several point 

sources in NJ. NJDEP provided updated IDA files on June 30 that were used for 

modeling. 

1.2 CENRAP 

The inventory data were obtained from the CENRAP ftp site in March 2006 and 

reflect version BaseB of the CENRAP inventory. 

1.2.1 Area Sources

 Files: 

o CENRAP_AREA_SMOKE_INPUT_ANN_STATES_081705.txt 

o CENRAP_AREA_MISC_SMOKE_INPUT_ANN_STATE_071905.txt 

o CENRAP_AREA_BURNING_SMOKE_INPUT_ANN_TX_ 

NELI_071905.txt 

o CENRAP_AREA_MISC_SMOKE_INPUT_NH3_MONTH_{MMM} 

_072805.txt where {MMM} is JAN, FEB, ... DEC 

o CENRAP_AREA_SMOKE_INPUT_NH3_MONTH_{MMM} 

_071905.txt where {MMM} is JAN, FEB, ... DEC 

 Fugitive dust correction: This was applied as county-specific correction factors 

for SCC’s listed at http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; the 

correction factor file gcntl.xportfrac.txt was obtained from EPA’s CAIR NODA 

ftp site http://www.airmodelingftp.com (password protected).; this adjustment 

was performed using the SMOKE programs cntlmat and grwinven to generate an 

adjusted IDA inventory file used for subsequent SMOKE processing 

 Note about area and nonroad source SMOKE processing for the CENRAP region: 

All area source inventories (both annual and month-specific) were processed in 
4

one step through SMOKE. SMK_AVEDAY_YN was set to N, so seasonal 

profiles were used to apportion the annual inventories numbers by month. This 

setting was also used for the nonroad processing performed in a separate step. 

This was necessary since the month-specific files had zero in their ‘average-day’ 

column and the annual total column reflects the “monthly emissions as annual 

totals” as per header line. Therefore, seasonal profiles are used to apportion both 

the annual and month-specific files. As described below, we utilized the temporal 

profiles and cross-reference files generated by CENRAP. However, we did not 

verify that this approach indeed leads to the intended monthly allocation of 

ammonia and nonroad emissions. 

1.2.2 Nonroad Sources

 Files: 

o CENRAP_NONROAD_SMOKE_INPUT_ANN_071305.txt 

o CENRAP_NONROAD_SMOKE_INPUT_MONTH_{MMM}_071305.txt 

where {MMM} is JAN, FEB, ... DEC 

1.2.3 Mobile Sources

 VMT/Speed files: 

o mbinv02_vmt_cenrap_ce.ida 

o mbinv02_vmt_cenrap_no.ida 

o mbinv02_vmt_cenrap_so.ida 

o mbinv02_vmt_cenrap_we.ida 

1.2.4 Point Sources

 File: CENRAP_POINT_SMOKE_INPUT_ANNUAL_DAILY_072505.txt 

 Fugitive dust correction: This was applied as county-specific correction factors 

for SCC’s listed at http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; the 

correction factor file gcntl.xportfrac.txt was obtained from EPA’s CAIR NODA 

ftp site http://www.airmodelingftp.com (password protected).; this adjustment 

was performed using the SMOKE programs cntlmat and grwinven to generate an 

adjusted IDA inventory file used for subsequent SMOKE processing. 

1.3 VISTAS 

All VISTAS emission files were obtained from the Alpine Geophysics ftp site. They 

reflect version BaseG of the VISTAS inventory with the exception of fire emissions 

which reflect BaseF and BaseD. These files were downloaded between February and 

August, 2006. 

1.3.1 Area Sources

 Files: 
5

o arinv_vistas_2002g_2453922_w_pmfac.txt 

o ida_ar_fire_2002_vistaonly_basef.ida 

 Note: the header lines of these files indicate that the fugitive dust correction was 

already applied, so no further correction was performed. 

1.3.2 Nonroad Sources

 Files: 

o nrinv_vistas_2002g_2453908.txt 

o marinv_vistas_2002g_2453972.txt 

1.3.3 Mobile Sources

 VMT/Speed file: mbinv_vistas_02g_vmt_12jun06.txt 

1.3.4 Point Sources

 Files: 

o Annual: 

 egu_ptinv_vistas_2002typ_baseg_2453909.txt 

 negu_ptinv_vistas_2002typ_baseg_2453909.txt 

 ptinv_fires_{MM}_typ.vistas.ida where {MM} is 01, 02, 03, etc. 

depending on the month; these annual point fire files were 

generated as part of the VISTAS BaseD inventory and were 

obtained in January 2005 

o Hour-specific: 

 pthour_2002typ_baseg_{MMM}_28jun2006.ems where {MMM} 

is jan, feb, mar, etc. 

 pthour_fires_{MM}_typ.vistas.ida where {MM} is 01, 02, 03, etc. 

depending on the month; these hourly point fire files were 

generated as part of the VISTAS BaseD inventory and were 

obtained in January 2005 

 Note: No fugitive dust correction was performed for these files. 

1.4 MRPO 

MRPO emissions for SMOKE modeling were generated by Alpine Geophysics through a 

contract from MARAMA to convert the MRPO BaseK inventory from NIF to IDA 

format. The files were downloaded from the MARAMA ftp site ftp.marama.org

(username mane-vu, password exchange) between April and June 2006. 

1.4.1 Area Sources

 Files: 

o Annual: 

 arinv_mar_mrpok_2002_27apr2006.txt 

 arinv_other_mrpok_2002_20jun2006.txt 

o Month-specific: 
6

 arinv_nh3_2002_mrpok_{mmm}_3may2006.txt where {mmm} is 

jan, feb, etc. 

 dustinv_2002_mrpok_{mmm}_23may2006.txt where {mmm} is 

jan, feb, etc. 

 Fugitive dust correction: This correction was performed only to the 

arinv_other_mrpok_2002_20jun2006.txt file using county-specific correction 

factors for SCC’s listed at 

http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; the correction factor 

file gcntl.xportfrac.txt was obtained from EPA’s CAIR NODA ftp site 

http://www.airmodelingftp.com (password protected).; this adjustment was 

performed using the SMOKE programs cntlmat and grwinven to generate an 

adjusted IDA inventory file used for subsequent SMOKE processing. 

 Note about area source SMOKE processing: SMOKE processing was performed 

separately for the annual and month-specific files. For the annual inventory 

processing, SMK_AVEDAY_YN was set to N, so seasonal profiles were used to 

apportion the annual inventories numbers by month. For the month-specific 

inventory processing, this variable was set to Y so that no seasonal profiles would 

be applied and the inventory numbers in the ‘average day’ column would be used. 

To save a SMOKE processing step, the annual “marine” inventory 

“arinv_mar_mrpok_2002_27apr2006.txt” was processed together with the annual 

“other area source” inventory “arinv_other_mrpok_2002_20jun2006.txt” even 

though it technically is part of the nonroad inventory. 

1.4.2 Nonroad Sources

 Files: nrinv_2002_mrpok_{mmm}_3may2006.txt where {mmm} is jan, feb, etc. 

1.4.3 Mobile Sources

 VMT/Speed file: mbinv_mrpo_02f_vmt_02may06.txt 

1.4.4 Point Sources

 Files: ptinv_egu_negu_2002_mrpok_1may2006.txt 

 Fugitive dust correction: This correction was performed only to the 

arinv_other_mrpok_2002_20jun2006.txt file using county-specific correction 

factors for SCC’s listed at 

http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; the correction factor 

file gcntl.xportfrac.txt was obtained from EPA’s CAIR NODA ftp site 

http://www.airmodelingftp.com (password protected).; this adjustment was 

performed using the SMOKE programs cntlmat and grwinven to generate an 

adjusted IDA inventory file used for subsequent SMOKE processing. 
7

1.5 Canada 

1.5.1 Area Sources

 File: AS2000_SMOKEready.txt obtained from 

ftp://ftp.epa.gov/EmisInventory/canada_2000inventory

 Fugitive dust correction: We applied “divide-by-four” correction for SCC’s listed 

at http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; this adjustment was 

performed outside SMOKE with in-house Fortran programs. No county/provincespecific correction factors were available for Canada 

1.5.2 Nonroad Sources

 File: NONROAD2000_SMOKEready.txt obtained from 

ftp://ftp.epa.gov/EmisInventory/canada_2000inventory

1.5.3 Mobile Sources

 File: MOBILE2000_SMOKEready.txt obtained from 

ftp://ftp.epa.gov/EmisInventory/canada_2000inventory

 Fugitive dust correction: applied “divide-by-four” correction for SCC’s listed at 

http://www.epa.gov/ttn/chief/emch/invent/index.html#dust; this adjustment was 

performed outside of SMOKE with in-house Fortran programs. No 

county/province-specific correction factors were available for Canada. 

1.5.4 Point Sources

There has long been difficulty in obtaining an up-to-date Canadian criteria 

emissions inventory for point sources. This is due largely to confidentiality rights 

afforded to Canadian facilities. Thus far, the most recent inventory of Canadian point 

sources is rooted in the 1985 NAPAP data and is close to two decades old. Because there 

are a number of high emitting industrial facilities in southern Canada it is of particular 

importance to have a reasonably accurate inventory of these sources especially when 

modeling air quality over the Northeast and Midwest United States. Toward this end, an 

effort was made to obtain more recent Canadian point source data and incorporate it into 

an inventory database, which could then be used for the 2002 OTC air quality modeling. 

Perhaps the most accurate and publicly accessible source of Canadian pollutant 

data is now available from the National Pollutant Release Inventory (NPRI) database. 

This database contains 268 substances. Facilities that manufacture, process or otherwise 

use one of these substances and that meet reporting thresholds are required to report these 

emissions to Environment Canada on an annual basis. The NPRI data are available at 

Environment Canada’s website and can be found at the link 

http://www.ec.gc.ca/pdb/npri/npri_home_e.cfm. The page hosts an on-line search engine 

where one can locate emissions by pollutant or location. In addition, the entire database is 

available for download as an MS Access or Excel file. The NPRI database contains 
8

numerous pages with a rather comprehensive list of information. Detailed information is 

available about each facility, including location, activity and annual emissions. In 

addition, facilities having stacks with a height of 50 meters or more are required to report 

stack parameters. 

Unfortunately, one of the limitations of the NPRI database for modeling purposes 

is that the data are only available at the facility level. Emissions models require process 

level information, so in order to use this data, a few generalizations had to be made. Each 

facility has a Standard Industrial Classification (SIC) code associated with it; however, 

emissions models require Source Classification Codes (SCC’s). SCC’s are of critical 

importance as the emissions models use these codes for assignment of temporal and 

speciation profiles. SIC codes describe the general activity of a facility while SCC codes 

describe specific processes taking place at each facility. While no direct relationship 

exists between these two codes, a general albeit subjective association can be made. 

For the purposes of creating a model-ready inventory file it was necessary to obtain the 

whole NPRI database. After merging all the necessary components from the NPRI 

database required in the SMOKE inventory file, the SIC code from each facility was 

examined and assigned an SCC code. In most cases, only a SCC3 level code was 

assigned with confidence. While this is admittedly a less than desirable process, it does 

allow for the use of the most recent emissions from the NPRI database to be used in 

modeling. Furthermore, having some level of SCC associated with these emissions will 

ensure that they will be assigned a temporal and speciation profile by the model, other 

than the default. Once the model-ready inventory file was developed, it was processed 

through SMOKE. 

2. Mobile6 Processing 

2.1 MANE-VU 

2.1.1 Mobile6 input files

 Month-specific input files were prepared by PECHAN and NESCAUM and were 

downloaded from http://bronze.nescaum.org/Private/junghun/MANEVU/onroad_ver3_update/MANEVU_V3_update.tar 

 Added the line “REBUILD EFFECTS :0.10” to each file before the 

SCENARIO record to override the Mobile6 default setting of 0.9 (90%) for the 

“chip reflash” effectiveness 

2.1.2 SMOKE/Mobile6 auxiliary files

 SMOKE/Mobile6 auxiliary files were prepared by PECHAN and NESCAUM and 

were downloaded from http://bronze.nescaum.org/Private/junghun/MANEVU/onroad_ver3_update/MANEVU_V3_update.tar 
9

2.1.3 Temperature averaging

 Following the setting in the MANEVU_2002_mvref.txt files, the following 

procedures were used by SMOKE for temporal and spatial temperature averaging 

in the calculation of emission factors: 

o Spatial averaging: temperatures were averaged over all counties that share 

a common reference county (i.e. Mobile6 input file) 

o Temporal averaging for May – September emissions processing: no 

temporal averaging was used, i.e. day-specific temperatures were used to 

calculate emission factors for each day. 

o Temporal averaging for non-summer-months emissions processing: 

Temporal averaging over the duration of the episode (i.e. the entire month, 

see introduction) was used, i.e. monthly average temperatures were used 

to calculate the emission factors. 

2.2 CENRAP 

2.2.1 Mobile6 input files

 Mobile6 input files for the CENRAP region for January and July were contained 

in the files central_M6_{MMM}.zip, north_M6_{MMM}.zip, 

south_M6_{MMM}.zip, west_M6_{MMM}.zip where {MMM} is either jan or 

jul. July input files were used for April – September processing, while January 

input files were used for the remaining months 

 All files were downloaded from the CENRAP ftp site in March 2006. 

2.2.2 SMOKE/Mobile6 auxiliary files

 SMOKE/Mobile6 auxiliary files were contained in the files central_M6_RD.zip, 

north_M6_RD.zip, south_M6_RD.zip, and west_M6_RD.zip. The SMOKE 

MCREF, MVREF, and MCODES files were contained in the file 

MOBILESMOKE_Inputs.zip. The MCREF and MVREF files were combined for 

the different regions (“central”, “east”, “west”, “north”) 

 All files were downloaded from the CENRAP ftp site in March 2006. 

2.2.3 Temperature averaging

 The following procedures were used by SMOKE for temporal and spatial 

temperature averaging in the calculation of emission factors according to the 

setting in the mvref files: 

o Spatial averaging: no spatial averaging of temperatures, i.e. the 

temperatures for the reference county is used to calculate emission factors 

for all counties that share this reference county (i.e. Mobile6 input file) 

o Temporal averaging: Temporal averaging over the duration of the episode 

(i.e. the entire month, see introduction) was used, i.e. monthly average 

temperatures were used to calculate the emission factors. 
10

2.3 VISTAS 

2.3.1 Mobile6 input files

 Month-specific Mobile6 input files were obtained from the Alpine Geophysics ftp 

site in July 2006. They reflect version BaseG of the VISTAS inventory. 

2.3.2 SMOKE/Mobile6 auxiliary files

 SMOKE/Mobile6 auxiliary files utilized were obtained from the Alpine 

Geophysics ftp site in July 2006. They reflect version BaseG of the VISTAS 

inventory. 

2.3.3 Temperature averaging

 The following procedures were used by SMOKE for the temporal and spatial 

temperature averaging in the calculation of emission factors according to the 

setting in the mvref_baseg.36k.ag.txt file: 

o Spatial averaging: temperatures averaged over all counties that share a 

common reference county (i.e. Mobile6 input file) 

o Temporal averaging: Temporal averaging over the duration of the episode 

(i.e. the entire month, see introduction) was used, i.e. monthly average 

temperatures were used to calculate the emission factors. 

2.4 MRPO 

2.4.1 Mobile6 input files

 Month-specific Mobile6 input files for SMOKE modeling were generated by 

Alpine Geophysics through a contract from MARAMA. They are based on 

version BaseK of the MRPO inventory. The files were downloaded from the 

MARAMA ftp site ftp.marama.org (username mane-vu, password exchange) in 

May 2006. 

2.4.2 SMOKE/Mobile6 auxiliary files

 SMOKE/Mobile6 auxiliary files for SMOKE modeling were generated by Alpine 

Geophysics through a contract from MARAMA. They are based on version 

BaseK of the MRPO inventory. The files were downloaded from the MARAMA 

ftp site ftp.marama.org (username mane-vu, password exchange) in May 2006. 

2.4.3 Temperature averaging

 The following procedures were used by SMOKE for the temporal and spatial 

temperature averaging in the calculation of emission factors according to the 

setting in the mvreg_mrpo_basek.txt file: 

o Spatial averaging: temperatures averaged over all counties that share a 

common reference county (i.e. Mobile6 input file) 
11

o Temporal averaging: Temporal averaging over the duration of the episode 

(i.e. the entire month, see introduction) was used, i.e. monthly average 

temperatures were used to calculate the emission factors. 

3. Biogenic Emission Processing 

Hourly gridded biogenic emissions for the 12 km and 36 km modeling domains 

were calculated by BEIS3.12 through SMOKE, using MCIP-processed MM5 fields for 

temperature (“TA”, layer-1 temperature), solar radiation (“RGRND”), surface pressure 

(“PRES”), and precipitation (“RN” and “RC”). A ‘seasonal switch’ file was generated by 

the SMOKE utility metscan to determine whether winter or summer emission factors 

should be used for any given grid cell on any given day. Winter emission factors are used 

from January 1st through the date of the last frost and again from the data of the first frost 

in fall through December 31st. Summer emission factors are used for the time period in 

between. This calculation is performed separately for each grid cell. 

4. Temporal Allocation 

4.1 MANE-VU 

4.1.1 Area and nonroad sources

 Generated as part of the MANE-VU version 1 inventory 

 amptpro.m3.us+can.manevu.030205.txt 

 amptref.m3.manevu.012405.txt 

 downloaded from ftp.marama.org (username mane-vu, password exchange) in 

January 2005 

4.1.2 Mobile sources

 MANEVU_2002_mtpro_02022006_addCT.txt 

 MANEVU_2002_mtref_02022006_addCT.txt 

 prepared by PECHAN and NESCAUM and downloaded from 

http://bronze.nescaum.org/Private/junghun/MANEVU/onroad_ver3_update/MANEVU_V3_update.tar 

4.1.3 Point Sources

 Based on the same files as for the MANE-VU area and nonroad temporal files 

listed above, but added the CEM-based 2002 state-specific temporal profiles and 

cross-references for EGU sources for the MANE-VU states that were generated 

by VISTAS for their BaseD modeling and obtained in February 2005. 

 No CEM-based hour-specific EGU emissions were utilized 

4.2 CENRAP 

The following temporal profiles and cross-reference files were used: 
12

 Area and nonroad sources: 

o amptpro.m3.us+can.cenrap.010605_incl_nrd.txt 

o amptref.m3.cenrap.010605_add_nh3_and_nrd.txt 

 Mobile sources: 

o mtpro.cenrap.v3.txt 

o mtref.cenrap.v3.txt 

 Point sources: 

o ptpro.{QQ}.cenrap_egus_cem.00-03avg.121205.txt where {QQ} is Q1 for 

January/February/March, Q2 for April/May/June, etc. 

o ptref.{QQ}.cenrap_egus_cem.00-03avg.121205.txt where {QQ} is Q1 for 

January/February/March, Q2 for April/May/June, etc. 

 All files were downloaded from the CENRAP ftp site in March 2006. 

4.3 VISTAS 

The following month-specific temporal profiles and cross-reference files were used: 

 Area and nonroad sources: 

o atpro_vistas_basef_15jul05.txt 

o atref_vistas_basef_15jul05.txt 

 Mobile sources: 

o mtpro_vistas_basef_04jul05.txt 

o mtref_us_can_vistas_basef_04jul05.txt 

 Point sources: 

o ptpro_typ_{MMM}_vistasg_28jun2006.txt where {MMM} is jan, feb, 

mar, etc. 

o ptref_typ_vistas_baseg_28jun2006.txt 

 These files were obtained from the Alpine Geophysics ftp site. They reflect 

version BaseG of the VISTAS inventory for the point source allocation files and 

version BaseF for the area, nonroad, and mobile source allocation files. These 

files were downloaded between February and July, 2006. 

4.4 MRPO 

The following month-specific temporal profiles and cross-reference files were used for 

all source categories: 

 amptpro_typ_us_can_{MMM}_vistas_27nov04.txt where {MMM} is jan, feb, 

mar, etc. 

 amptref_2002_us_can_vistas_17dec04.txt 

 These files were obtained from VISTAS in January 2005 and reflect their BaseD 

modeling. No updated temporal profiles or cross-reference files were developed 

for use with the MRPO BaseK inventory. 

4.5 Canada 

For Canada, the SMOKE2.1 default temporal profiles and cross-reference files 

(amptpro.m3.us+can.txt and amptref.m3.us+can.txt) were utilized. 
13

5. Speciation 

The same speciation profiles (gspro.cmaq.cb4p25.txt) and cross-references 

(gsref.cmaq.cb4p25.txt) were utilized for all regions and all source categories. Different 

versions of these files were obtained (SMOKE2.1 default, EPA-CAIR modeling, 

VISTAS, CENRAP and MANE-VU) and compared. After comparing the creation dates 

and header lines of these files, it was determined that the EPA-CAIR and MANE-VU 

files had the most recent updates, and consequently the final speciation profile and crossreference files used for all regions and source categories was based on the EPA-CAIR 

files with the addition of MANE-VU specific updates. 

6. Spatial Allocation 

6.1 U.S. 

The spatial surrogates for the 12km domain were extracted from the national grid 12km 

U.S. gridding surrogates posted at EPA’s website at 

http://www.epa.gov/ttn/chief/emch/spatial/newsurrogate.html

The gridding cross-references were also obtained from this website, but for the 

processing of MANE-VU area source emissions, MANE-VU specific cross-reference 

entries posted on the MARAMA ftp site were added. 

6.2 Canada 

The spatial surrogates for Canadian emissions for the 12km domain were extracted from 

the national grid 12km Canadian gridding surrogates posted at EPA’s website at 

http://www.epa.gov/ttn/chief/emch/spatial/newsurrogate.html

The gridding cross-references were also obtained from this website. 

Reference: 

Pechan: (2006) Technical Support document for 2002 MANE-VU SIP Modeling 

inventories, version 3. Prepared by E. H. Pechan & Associates, Inc. 3622 Lyckan 

Parkway, Suite 2005, Durham, NC 27707. 
K-4: PM2.5 Modeling using SMOKE/CMAQ 
 TSD-2c 

PM2.5 modeling using the SMOKE/CMAQ system over the 

Ozone Transport Region (OTR) 

Bureau of Air Quality Analysis and Research 

Division of Air Resources 

New York State Department of Environmental Conservation 

Albany, NY 12233 

February 1, 2006 
2

Air Quality Modeling Domain 

The modeling domain utilized in this application represented a sub-set of the inter-RPO’s 

continental modeling domain that covered the entire 48-state region with emphasis on the 

Ozone Transport Region. The OTC modeling domain at 12km horizontal mesh is 

displayed in Figure 1 is part of the 36km continental domain that is designed to provide 

boundary conditions (BCs). The particulars of the two modeling domains are: 

 The 36km domain covered the continental US by a 149 by 129 mesh in the east-west and 

north-south directions, respectively. The domain is based on Lambert Conformal 

Projection with the center at (97oW 40oN) and parallels at 33oN and 45oN. As evident 

from Figure 1, the 12km domain utilized in this analysis covers most areas of the eastern 

US and has 172 by 172 mesh in the horizontal. Both domains utilize 22 layers in the 

vertical extending to about 16km with 16 layers placed within the lower 3km. 

Photochemical Modeling -- CMAQ

The CMAQ (version 4.5.1) with CB4 chemistry, aerosol module for PM2.5 and RADM 

cloud scheme was utilized in this study. Photochemical modeling was performed with the 

CCTM software that is part of the CMAQ modeling package. Version 4.5.1 of this 

modeling software was obtained from the CMAS modeling center at 

http://www.cmascenter.org. The following module options were used in compiling the 

CCTM executable: 

 Horizontal advection: yamo 

 Vertical advection: yamo 

 Horizontal diffusion: multiscale 

 Vertical diffusion: eddy 

 Plume-in-Grid: non operational 

 Gas phase chemical mechanism: CB-4 

 Chemical solver: EBI 

 Aerosol module: aero3 

 Process analysis: non operational 

The following computational choices were made during compilation: 

 Compiler version: PGI 6.0 

 Fortran compiler flags:-Mfixed -Mextend -Bstatic -O2 -module ${MODLOC} -I. 

 C compiler flags: -v -O2 -I${MPICH}/include 

 IOAPI library: version 3.0 

 NETCDF library: version 3.6.0 

 Parallel processing library version: mpich 1.2.6 

 Static compilation on 32-bit system 
3

The following choices were made for running the executable: 

 Number of processors: 8 

 Domain decomposition for parallel processing: 4 columns, 2 rows 

 Number of species written to the layer-1 hourly-average concentration output 

(ACONC) file: 39 (O3, NO, CO, NO2, HNO3, N2O5, HONO, PNA, PAN, NTR, 

NH3, SO2, FORM, ALD2, PAR, OLE, ETH, TOL, XYL, ISOP, ASO4I, ASO4J, 

ANO3I, ANO3J, ANH4I, ANH4J, AORGAI, AORGAJ, AORGPAI, AORGPAJ, 

AORGBI, AORGBJ, AECI, AECJ, A25I, A25J, ACORS, ASEAS, ASOIL) 

 Each daily simulation was performed for 24 hours starting at 05:00 GMT (00:00 

EST) 

The following postprocessing steps were performed using utility tools from the “ioapi” 

software package obtained from 

http://www.baronams.com/products/ioapi/AA.html#tools: 

 Extract and combine the following species for each hour for the first 16 model 

layers from the full 3-D instantaneous concentration output file: O3, CO, NO, 

NO2, NOY_1 (=NO + NO2 + PAN + HNO3), NOY_2 (=NO + NO2 + PAN + 

HNO3 + HONO + N2O5 + NO3 + PNA + NTR), HOX (=OH + HO2), VOC 

(=2*ALD2 + 2*ETH + FORM + 5*ISOP + 2*OLE + PAR + 7*TOL + 8*XYL), 

ISOP, PM2.5 (=ASO4I + ASO4J + ANO3I + ANO3J + ANH4I + ANH4J + 

AORGAI + AORGAJ + 1.167*AORGPAI + 1.167*AORGPAJ + AORGBI + 

AORGBJ + AECI + AECJ + A25I + A25J), PM_SULF (=ASO4I + ASO4J), 

PM_NITR (=ANO3I + ANO3J), PM_AMM (=ANH4I + ANH4J), PM_ORG_SA 

(=AORGAI + AORGAJ), PM_ORG_PA (=1.167*AORGPAI + 

1.167*AORGPAJ), PM_ORG_SB(=AORGBI + AORGBJ), PM_ORG_TOT 

(=AORGAI + AORGAJ + 1.167*AORGPAI + 1.167*AORGPAJ + AORGBI + 

AORGBJ), PM_EC (=AECI + AECJ), PM_OTH (=A25I + A25J), PM_COARS 

(=ACORS + ASEAS + ASOIL), SO2, HNO3, NH3, H2O2 

 Extract all species for all model layers for the last hour of each daily 

instantaneous concentration output file to enable “hot” restarts of modeling 

simulations 

 Create daily files of hourly running-average 8-hr ozone concentrations with time 

stamps assigned to the first hour of the averaging interval 

The following files are archived on LTO2 computer tapes (each tape holds approximately 

200 Gb of data) for each day: 

 Aerosol/visibility file 

 Layer-1 hourly-average concentration output file (contains 39 species) 

 Dry deposition file 

 Wet deposition file 

 Extracted 16-layer species file 

 Restart file (last hour of full 3-D instantaneous concentration file) 

 Hourly 8-hr concentration file 
4

Photolysis Rates

One of the inputs to CMAQ is the photolysis rates. In this study, photolysis rate lookup 

tables were generated for each day of 2002 with the JPROC software that is part of the 

CMAQ modeling package. This software was obtained from the CMAS modeling center 

at http://www.cmascenter.org. Rather than using climatological ozone column data, daily 

ozone column measurements from the NASA Earthprobe TOMS instrument were 

downloaded from ftp://toms.gsfc.nasa.gov/pub/eptoms/data/ozone/Y2002/ and used as 

input to the JPROC processor. It should be noted that TOMS data were missing for the 

time period from August 3 – 11, 2002. The missing period was filled as follows-- TOMS 

data file for August 2 was used as JPROC input for August 3rd through August 7th, and 

the TOMS data file for August 12th was used as JPROC input for August 8th through 

August 11th. 

Boundary Conditions (BCs)

The boundary conditions for the 12km grid were extracted from the 36km CMAQ 

simulation. The 36km simulation utilized boundary conditions that were based on a oneway nest approach to GEOS-CHEM global model outputs (Moon and Byun 2004, Baker 

2005). As stated above, the intent of the 36km CMAQ simulation was to provide the 

BCs for the 12km model that would be more reflective of the emissions and meteorology 

rather than to use either clean or arbitrary pollutant fields. Also, in this study the CMAQ 

simulations utilized a 15-day ramp-up period, thereby minimizing the propagation of the 

boundary fields into the areas of concern. A report on the setup and application of the 

36km CMAQ and the extraction of the BCs is available from NYSDEC. 

Meteorological data

The meteorological data for this study was based on MM5 modeling (see Meteorological 

Modeling, 2007). The MM5 fields are then processed by MCIP version 3.0, a utility 

available as part of the CCTM software from CMAS Modeling Center (see 

http://www.cmascenter.org) to provide CMAQ model-ready inputs. 

Emissions

 

The emissions data for 2002 were generated by individual states within the OTR and 

were assembled and processed through the Mid Atlantic Northeast Visibility Union 

(MANE-VU), a Regional Planning Organization (RPO). These emissions were then 

processed by NYSDEC using SMOKE processor to provide CMAQ compatible inputs 

(Anthro-Emissions 2006). The 2002 emissions for the non-OTR areas within the 

modeling domain were obtained from the corresponding RPOs and were processed using 

SMOKE, in a manner similar to that of the OTR.emissions. Details of this processing are 

outlined in the report (Pechan 2007), and the hourly biogenic emissions (Bio-Emissions, 

2006) 
5

CMAQ simulations

CMAQ simulations were performed using the one-way nesting approach in which we 

perform the continental CMAQ simulation at 36km grid spacing. For this simulation we 

utilized clean initial conditions with boundary conditions extracted from the simulation of 

GEOS-CHEM global chemical model. The interface program used in this application was 

developed by University of Huston (Moon and Byun 2004), which was applied to obtain 

hourly 36km boundary concentrations from GEOS-CHEM outputs. The CMAQ 36km 

simulation was initiated from December 15, 2001 with the first 15 days as spin up period 

and terminated on December 31, 2002. The simulation utilized the 2002 emissions data 

available from the RPOs and 2002 MM5 meteorological fields developed by the 

University of Maryland (TSD-1a). The hourly boundary fields for the 12km CMAQ 

domain were obtained by application of BCON program to the 3-D concentration fields 

generated by the 36km CMAQ simulation. 

The 12km simulations for both base and future year were assigned the boundary 

conditions based on the 36km CMAQ simulation and clean initial conditions. The annual 

simulation was parsed out between different member states or their contractors of the 

OTR, so as to expedite the process of completing the simulation in a limited time. The 

approach used is as follows: The annual simulation was parsed out into five parts and 

each modeling center identified below initiated and completed the simulation, extracted 

the outputs which were then combined to provide the annual simulation. There was 

considerable exchange of information in the setup and execution of the modeling system 

between the centers using benchmark runs to ensure consistency and uniformity between 

the centers. The process was followed both for the base year 2002 and for the future year 

2009. Details on CMAQ setup and run scripts are available from NYSDEC. 

Modeling Center Simulation period Analysis period 

MDE/UMD Dec 15, 2001 to Feb 28, 2002 Jan 01, 2002 to Feb 28, 2002 

NJDEP/Rutgers Feb 15, 2002 to May 14, 2002 Mar 01, 2002 to May 14, 2002 

NYSDEC May 01, 2002 to Sep 30, 2002 May 15, 2002 to Sep 30, 2002 

VA DEQ Sep 15, 2002 to Oct 30, 2002 Oct 01, 2002 to Oct 30, 2002 

NESCAUM Oct 15, 2002 to Dec 31, 2002 Nov 01, 2002 to Dec 31, 2002 

References 

Baker, K.: (2005) http://www.ladco.org/tech/photo/present/ozone.pdf

Moon, N. and D. Byun: (2004) A simple user’s guide for “geos2cmaq”code: Linking 

CMAQ with GEOS-CHEM. Version 1.0. Institute for Multidimensional Air quality 

Studies (IMAQS), University of Houston, Houston TX. 
6

Meteorological Modeling: (2007) Meteorological Modeling of 2002 using Penn 

State/NCAR 5th Generation Mesoscale Model (MM5). TSD-1a 

Pechan: (2006) Technical Support document for 2002 MANE-VU SIP Modeling 

inventories, version 3. Prepared by E. H. Pechan & Associates, Inc. 3622 Lyckan 

Parkway, Suite 2005, Durham, NC 27707. 

Bio-Emissions: (2006) Processing of Biogenic Emissions for OTC/MANE-VU 

Modeling. TSD-1b 

Anthro-Emissions: (2006) Emission Processing for the Revised 2002 OTC Regional and 

Urban 12 km Base Case Simulations. TSD-1c 

 Figure 1 Display of 36- and 12km air quality modeling domains.