By far the most computationally limiting step in emissions modeling is typically the generation of 

onroad mobile sources. Motor vehicle emissions are influenced by meteorological variability and the 

processing requirements for daily motor vehicle emissions have been determined to be rate limiting 

under most modeling schedules. Rather than utilizing averaged meteorological data or pre-calculated 

motor vehicle emissions, the VISTAS and ASIP modeling team developed an emissions processing 

approach that models a representative week for each month of the year in order to make the SMOKE 

processing time more manageable and consistent with modeling schedule1

. This representative week 

was selected from mid-month, to try to best represent the average temperature ranges for the month, and 

also adjusted to exclude holidays that would require atypical processing. 

Based on the findings in the VISTAS Phase I and II modeling activities, ASIP selected the following 

models for use in modeling 8-hour ozone and particulate matter (PM) of size of 2.5 microns or less 

(PM2.5): 

3⁄4 MM52,3: The Pennsylvania State University/National Center for Atmospheric Research 

(PSU/NCAR) Mesoscale Meteorological Model (MM5) is a nonhydrostatic, prognostic 

meteorological model routinely used for urban- and regional-scale photochemical, fine 

particulate and regional haze regulatory modeling studies. 

 

3⁄4 SMOKE4

: The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is an 

emissions modeling system that generates hourly gridded speciated emission inputs of mobile, 

nonroad, area, point, fire and biogenic emission sources for photochemical grid models. 

3⁄4 CMAQ5,6: EPA’s Models-3/Community Multiscale Air Quality (CMAQ) modeling system is a 

‘One-Atmosphere’ photochemical grid model capable of addressing ozone, particulate matter 

(PM), visibility and acid deposition at regional scale for periods up to one year. 

The purpose of this paper is to describe processing options for onroad mobile source emissions using the 

MOBILE module of the SMOKE emissions processor and to determine, based on air quality predictions 

and time and resource expenditure, benefits of simulating everyday for onroad mobile emissions to 

support 8-hr ozone modeling. We will present 12km evaluations of everyday vs. representative week 

emissions and associated air quality for a number of domains and discuss the benefits and limitations of 

the various methods relative to ozone and regional haze prediction. 

MOBILE6 / SMOKE PREPARATION 

For the VISTAS/ASIP 2009 annual emissions inventory modeling, SMOKE was configured to generate 

point, area, nonroad, highway, and biogenic source emissions. In addition, certain subcategories, such as 

fires and EGUs were maintained in separate source category files in order to allow maximum flexibility 

in producing alternate strategies. With the exception of biogenic and highway mobile source emissions 

that are generated using the BEIS and MOBILE6 modules in SMOKE, pre-computed annual emissions 

will be processed using the month, day, and hour specific temporal profiles of the SMOKE model. Area, 

nonroad, and point sources were modeled as a block of Thursday, Friday, Saturday, Sunday, Monday 

one per month (total of 60 days modeled). Biogenics were modeled for each day of the episode. 

For this investigation, the onroad mobile source emissions were produced using two approaches: 

1) Modeling every day of the annual episode, using the MM5 meteorology files for each model day. 

When full annual runs were executed, holidays were modeled as Sundays. 
2) Modeling selected weeks (seven days) of each month and using these days as representative of the 

entire month. This selection criterion allows for the representation of day-of-the-week variability in 

the onroad motor vehicles, and models a representation of the meteorological variability in each 

month. The modeled weeks were selected from mid-month, avoiding inclusion of major holidays. 

The parameters for the SMOKE runs are as follows: 

Episodes: 

2002 Initial Base Year, and 

2009 Future year, using 2009 inventory and modeled using the same meteorology and 

episode days as 2002. 

Episode represented by the following weeks per month:

 January 15-21 

 February 12-18 

 March 12-18 

 April16-22 

 May 14-20 

 June 11-17 

 July 16-22 

August 13-19 

September 17-23 

October 15-21 

November 12-18 

December 17-23 

Days modeled as holidays for annual run:

 New Year’s Day - January 1 

 Good Friday – March 29 

 Memorial Day – May 27 

 July 4th 

 Labor Day – September 2 

 Thanksgiving Day – November 28, 29 

 Christmas Eve – December 24 

 Christmas Day – December 25 

Output time zone: 

Greenwich Mean Time (zone 0) 

Projection: 

Lambert Conformal with Alpha=33, Beta=45, Gamma=-97, and center at (-97, 40). 

Domain: 

36 Kilometer Grid: Origin at (-2736, -2088) kilometers with 148 rows by 112 columns 

and 36-km square grid cells. 

12 Kilometer Grid: Origin at (108, -1620) kilometers with 168 rows by 177 columns and 

12-km square grid cells. 

Layer structure: 

The CMAQ layer structure will be 19 layers, with specific layer positions defined in the 

meteorology files to be provided by VISTAS meteorological contractor. 
CMAQ model species: 

The CMAQ configuration will be for CB-IV with PM. The model species will be: CO, 

NO, NO2, ALD2, ETH, FORM, ISOP, NR, OLE, PAR, TERPB, TOL, XYL, NH3, SO2, 

SULF, PEC, PMFINE, PNO3, POA, PSO4, and PMC. 

Meteorology data: 

Daily (25-hour). SMOKE requires the following five types of MCIP outputs: (1) Grid 

cross 2-d, (2) Grid cross 3-d, (3) Met cross 2-d, (4) Met cross 3-d, and (5), Met dot 3-d. 

Elevated sources: 

All sources will be treated by SMOKE as potentially elevated. No plume-in-grid sources 

will be modeled. Wildfire emissions will be handled as point sources. 

Figure 3. 36-km national unified RPO domain and VISTAS 12-km domain. 
DEVELOPMENT OF ONROAD MOTOR VEHICLE SOURCE EMISSIONS 

The MOBILE6 module of SMOKE was used to develop the onroad mobile source emissions estimates 

for CO, NOX, PM, and VOC emissions. The MOBILE6 parameters, vehicle fleet descriptions, and 

VMT estimates are combined with gridded, episode-specific temperature data to calculate the gridded, 

temporalized emission estimates. The MOBILE6 emissions factors are based on episode-specific 

temperatures predicted by the meteorological model. Further, the MOBILE6 emissions factors model 

accounts for the following: 

• Hourly and daily minimum/maximum temperatures; 

• Facility speeds; 

• Locale-specific inspection/maintenance (I/M) control programs, if any; 

• Adjustments for running losses; 

• Splitting of evaporative and exhaust emissions into separate source categories; 

• VMT, fleet turnover, and changes in fuel composition and Reid vapor pressure (RVP). 

The primary input to MOBILE6 is the MOBILE shell file. The MOBILE shell contains the various 

options (e.g. type of inspection and maintenance program in effect, type of oxygenated fuel program in 

effect, alternative vehicle mix profiles, RVP of in-use fuel, operating mode) that direct the calculation of 

the MOBILE6 emissions factors. The shells used in these runs were based on VISTAS/ASIP BaseF 

modeling inputs7

. The options for all MOBILE6 parameters were held constant between the annual and 

representative week runs. 

Daily results of these model runs for a winter (January 17) and summer (July 18) day are represented in 

Figures 4 through 6 below. These data provide a comparison of the magnitude difference between ozone 

and particulate matter precursor species for each of these seasonally different episodes. As can be seen 

in these figures, the variable inputs (temperature, VMT, seasonal fuels) associated with each month’s 

run have an impact on the overall emissions generated for the onroad mobile source category. It is 

through modeling these differences with CMAQ for both ozone and PM that we have based our 

conclusions. 

Each of the onroad mobile source emissions runs conducted with the MOBILE6 module of SMOKE 

were performed on a dual Athlon MP 2600+ with 1.5 G RAM. With this configuration, the modeling 

team experienced run times of approximately sixty-three (63) minutes per run day on the 12km domain. 

Using this estimate, the representative week processing would require a total of 5,292 minutes (12 

months x 7 days x 63 minutes per run day) or about 88.2 hours (3.5 days) of CPU runtime to generate 

the files necessary to simulate the annual episode. In comparison, actually running each day’s onroad 

mobile source emissions using the same configuration would require 22,995 minutes (365 days x 63 

minutes per run day) or about 383.25 hours (16 days) of CPU run time. 
Figure 4. Daily VOC emissions as generated with the MOBILE6 module of SMOKE for a winter 

(January 17) and summer (July 18) episode day. 

Figure 5. Daily NOx emissions as generated with the MOBILE6 module of SMOKE for a winter 

(January 17) and summer (July 18) episode day. 
Figure 6. Daily PM-fine emissions as generated with the MOBILE6 module of SMOKE for a winter 

(January 17) and summer (July 18) episode day. 

EMISSIONS SUMMARY 

The reconstructed emissions based on the representative week run were calculated by mapping each day 

of week (Mon, Tue, Wed, etc.) from the modeled month to the same day of week generated in the 

representative week run. In the case of holidays, these days were mapped to representative week 

Sundays. An example of this mapping for the January episode is presented in Table 1. Note that 

although the emissions were generated for calendar year 2009, the meteorology is based on 2002. Table 

2 presents a comparison of January emissions as generated using the everyday MOBILE6 module run 

for each VISTAS/ASIP State and these emissions as reconstructed from the representative week 

MOBILE6 module runs. In comparison, Table 3 presents these emissions for the month of July. 

Table 1. Representative day mapping for January episode (Highlighted representative week). 

Modeled Representative Modeled Representative Modeled Representative 

Date Day Date Day Date Day 

1/1/2002* 1/20/2002 1/11/2002 1/18/2002 1/22/2002 1/15/2002 

1/2/2002 1/16/2002 1/12/2002 1/19/2002 1/23/2002 1/16/2002 

1/3/2002 1/17/2002 1/13/2002 1/20/2002 1/24/2002 1/17/2002 

1/4/2002 1/18/2002 1/14/2002 1/21/2002 1/25/2002 1/18/2002 

1/5/2002 1/19/2002 1/15/2002 1/15/2002 1/26/2002 1/19/2002 

1/6/2002 1/20/2002 1/16/2002 1/16/2002 1/27/2002 1/20/2002 

1/7/2002 1/21/2002 1/17/2002 1/17/2002 1/28/2002 1/21/2002 

1/8/2002 1/15/2002 1/18/2002 1/18/2002 1/29/2002 1/15/2002 

1/9/2002 1/16/2002 1/19/2002 1/19/2002 1/30/2002 1/16/2002 

1/10/2002 1/17/2002 1/20/2002 1/20/2002 1/31/2002 1/17/2002 

 1/21/2002 1/21/2002 

* Modeled holiday 
Table 2. January 2009 onroad mobile emissions comparison. 

State VOC NOx CO SO2 PM-10 PM-2.5 NH3

Alabama 6,567 8,774 105,011 51 266 174 500

Florida 28,354 26,686 336,541 171 834 529 1,736

Georgia 15,558 17,935 224,920 100 509 328 999

Kentucky 5,321 8,618 102,603 47 250 165 453

Mississippi 3,928 5,999 61,323 31 191 130 312

North Carolina 13,590 18,406 231,897 104 489 311 988

South Carolina 5,372 7,934 92,169 44 240 159 429

Tennessee 8,729 12,954 142,906 67 356 238 609

Virginia 7,377 11,708 156,617 72 311 190 716

West Virginia 2,025 3,177 41,742 18 91 59 168

96,821 122,190 1,495,728 705 3,536 2,283 6,910

State VOC NOx CO SO2 PM-10 PM-2.5 NH3

Alabama 6,394 8,765 102,800 51 266 174 500

Florida 28,852 26,476 333,248 171 833 529 1,736

Georgia 15,337 17,867 218,990 100 509 328 999

Kentucky 5,023 8,679 104,247 47 250 165 453

Mississippi 3,710 6,012 60,454 31 191 130 312

North Carolina 12,605 18,383 225,563 104 489 311 988

South Carolina 5,226 7,911 89,001 44 240 159 430

Tennessee 8,011 13,000 141,962 67 356 238 609

Virginia 7,005 11,735 155,321 72 311 190 715

West Virginia 1,941 3,194 42,096 18 91 59 168

94,104 122,021 1,473,682 705 3,536 2,283 6,909

State VOC NOx CO SO2 PM-10 PM-2.5 NH3

Alabama -2.6% -0.1% -2.1% 0.0% 0.0% 0.0% 0.0%

Florida 1.8% -0.8% -1.0% 0.0% 0.0% 0.0% 0.0%

Georgia -1.4% -0.4% -2.6% 0.0% 0.0% 0.0% 0.0%

Kentucky -5.6% 0.7% 1.6% 0.0% 0.0% 0.0% 0.0%

Mississippi -5.5% 0.2% -1.4% 0.0% 0.0% 0.0% 0.0%

North Carolina -7.2% -0.1% -2.7% 0.0% 0.0% 0.0% 0.0%

South Carolina -2.7% -0.3% -3.4% 0.0% 0.0% 0.0% 0.0%

Tennessee -8.2% 0.4% -0.7% 0.0% 0.0% 0.0% 0.0%

Virginia -5.0% 0.2% -0.8% 0.0% 0.0% 0.0% 0.0%

West Virginia -4.1% 0.6% 0.8% 0.0% 0.0% 0.0% 0.0%

-2.8% -0.1% -1.5% 0.0% 0.0% 0.0% 0.0%

January 2009 Emissions (Difference as Percent)

January 2009 Emissions (Everyday Calculation)

January 2009 Emissions (Representative Day Calculation)
Table 3. July 2009 onroad mobile emissions comparison. 

State VOC NOx CO SO2 PM-10 PM-2.5 NH3

Alabama 5,968 8,654 61,362 58 278 175 584

Florida 21,715 27,067 208,947 190 864 531 1,971

Georgia 15,833 17,965 133,828 114 533 332 1,162

Kentucky 5,289 8,196 56,333 53 262 166 537

Mississippi 3,934 6,013 38,674 36 200 130 376

North Carolina 12,975 17,340 130,042 120 512 311 1,171

South Carolina 5,316 7,859 57,163 51 251 160 512

Tennessee 8,797 12,446 81,289 75 368 237 712

Virginia 7,064 11,221 87,946 82 331 195 832

West Virginia 2,038 3,006 23,429 21 96 61 205

88,930 119,768 879,013 800 3,695 2,299 8,063

State VOC NOx CO SO2 PM-10 PM-2.5 NH3

Alabama 6,017 8,682 61,581 58 278 175 585

Florida 22,006 27,217 210,901 190 864 531 1,971

Georgia 16,252 18,091 135,119 114 533 332 1,163

Kentucky 5,274 8,196 56,184 53 262 167 537

Mississippi 3,960 6,023 38,911 36 200 130 376

North Carolina 13,160 17,394 130,728 120 512 311 1,171

South Carolina 5,449 7,903 57,867 51 251 160 512

Tennessee 8,798 12,454 81,930 75 368 237 712

Virginia 7,104 11,248 87,523 82 331 195 832

West Virginia 2,047 3,010 23,419 21 96 61 205

90,068 120,218 884,162 800 3,695 2,299 8,063

State VOC NOx CO SO2 PM-10 PM-2.5 NH3

Alabama 0.8% 0.3% 0.4% 0.0% 0.0% 0.0% 0.0%

Florida 1.3% 0.6% 0.9% 0.0% 0.0% 0.0% 0.0%

Georgia 2.6% 0.7% 1.0% 0.0% 0.0% 0.0% 0.0%

Kentucky -0.3% 0.0% -0.3% 0.0% 0.0% 0.0% 0.0%

Mississippi 0.7% 0.2% 0.6% 0.0% 0.0% 0.0% 0.0%

North Carolina 1.4% 0.3% 0.5% 0.0% 0.0% 0.0% 0.0%

South Carolina 2.5% 0.6% 1.2% 0.0% 0.0% 0.0% 0.0%

Tennessee 0.0% 0.1% 0.8% 0.0% 0.0% 0.0% 0.0%

Virginia 0.6% 0.2% -0.5% 0.0% 0.0% 0.0% 0.0%

West Virginia 0.5% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0%

1.3% 0.4% 0.6% 0.0% 0.0% 0.0% 0.0%

July 2009 Emissions (Difference as Percent)

July 2009 Emissions (Everyday Calculation)

July 2009 Emissions (Representative Day Calculation)
These aggregate emission summaries would lead one to believe that on an extended episode scale (like 

those required for PM or regional haze modeling), the use of representative week onroad mobile source 

emissions would be appropriate. However, in modeling either 1-hr or 8-hr ozone, there is enough of a 

temperature variability and therefore apparent ozone precursor emissions delta on an hour-to-hour basis 

that this same assumption could not be made without accompanying air quality simulations. 

AIR QUALITY MODELING 

The VISTAS/ASIP modeling team has applied the CMAQ Version 4.5 O3/PM2.5 photochemical grid 

modeling system. The VISTAS/ASIP modeling team implemented a comprehensive evaluation of the 

meteorological8

, emissions and air quality models. The CMAQ model performance evaluation indicated 

an underestimation of 8-hour ozone maximums during the summer. The model demonstrated reasonably 

good performance for sulfate, winter overestimation bias and summer underestimation bias for nitrate 

and reasonably good performance for elemental carbon (EC), albeit with lots of scatter and low 

correlation. However, organic carbon (OC) was underestimated with the summer OC underestimation 

bias being quite severe. After an intense focused analysis of the OC underestimation issue, the 

VISTAS/ASIP modeling team identified processes important to the formation of secondary organic 

aerosols (SOA) that were not included in the CMAQ SOA module that may be important to OC in the 

Southeastern U.S.9

 Consequently, VISTAS/ASIP enhanced the CMAQ SOA module by adding several 

new processes. This enhancement, called “SOAmods”, was implemented in CMAQ Version 4.5 and 

exhibited much improved OC model performance over the standard CMAQ SOA treatment10. A 

complete description of the modeling methods, configurations and performance are described 

elsewhere1,7. 

CMAQ was applied using both of the mobile emissions modeling methods described above. Recall, all 

emissions and air quality model inputs and configurations were held constant, with the exception of the 

mobile source emissions. This will allow us to isolate the air quality impacts of using the representative 

week mobile emissions versus the “actual” daily modeled mobile emissions. While the VISTAS/ASIP 

modeling is conducted on both 36-km National RPO and 12-km “VISTAS/ASIP” modeling domains as 

shown in Figure 3, this study focuses on evaluations of the 12-km air quality modeling results only. 

Using each of the mobile emissions databases (daily and the representative week) generated for the 

January and July study periods, we performed future-year air quality simulations for 2009 using CMAQ. 

We then post-processed the air quality model results to qualitatively evaluate the magnitude, location, 

and spatial extent of the differences in predicted ozone and PM2.5 concentrations due to the different 

mobile emissions modeling methodologies. Spatial plots were generated for each day simulated, 

including: 

1) daily maximum 8-hour ozone difference plots; 

2) maximum 1-hour ozone maximum difference plots; and, 

3) daily PM2.5 difference plots. 
RESULTS 

Our examination of the two air quality simulations began with the daily differences in PM2.5 

concentrations. Figures 7 and 8 represent the percent difference in the daily PM2.5 concentrations 

between the air quality simulations with representative week mobile emissions and the daily mobile 

emissions for one winter day (January 22nd) and one summer day (July 9th). No change is seen in either 

plot indicating daily PM2.5 concentrations changed less than one percent. Absolute differences in daily 

PM2.5 concentrations are shown in Figures 9 and 10 for the same two days (January 22nd and July 9th). 

Again, no change is seen in either plot indicating daily PM2.5 concentrations changed less than 0.2 

μg/m3

. In fact, all of the fourteen days modeled (seven winter days and seven summer days) show no 

differences as high as 0.2 μg/m3

. 

We next examined the results of the two air quality simulations with the daily differences in maximum 

8-hour ozone concentrations. Figures 11 and 12 present the percent difference in the daily maximum 8-

hour ozone concentrations for one winter day (January 28th) and one summer day (July 15th). In most 

areas for the winter day, daily maximum 8-hour ozone concentrations changed less than one percent. In 

a few urban corridors, namely, near Chicago, IL, Atlanta, GA and Baltimore, MD, changes of one 

percent are noted. Near Philadelphia, PA, changes of up to two percent are noted. However, this was the 

only day of the seven wintertime days simulated that showed a daily maximum 8-hour ozone difference 

as high as one percent anywhere in the modeling domain. It should also be noted that predicting 

wintertime ozone concentrations is not usually an interest because most, if not all high ozone events in 

the middle latitudes of the northern hemisphere occur during the summertime. Therefore the remainder 

of the ozone analysis will focus on summertime differences. On the summer day, July 15th, presented in 

Figure 12, no changes are seen indicating daily maximum 8-hour ozone concentrations changed less 

than one percent. In fact, all seven of the summer days modeled showed no changes as high as one 

percent. 

Absolute differences in daily maximum 8-hour ozone concentrations are shown in Figure 13 for July 

15th. Again, no change as high as 0.5 ppb (0.0005 ppm) was noted on this day or any of the seven 

summer days modeled. In addition to the 8-hour ozone metrics discussed above, differences in 1-hour 

ozone maximums were examined. As shown in Figure14 and 15, only two days during the seven day 

summertime period simulated showed differences in 1-hour ozone maximums as high as 0.5 ppb (0.0005 

ppm). 
Figure 7. Percent difference in 24-hour PM2.5 concentrations for January 22nd (Representative week 

mobile emissions versus daily mobile emissions). 

Figure 8. Percent difference in 24-hour PM2.5 concentrations for July 9th (Representative week mobile 

emissions versus daily mobile emissions). 
Figure 9. Absolute differences in 24-hour PM2.5 concentrations for January (Representative week 

mobile emissions versus daily mobile emissions). 

Figure 10. Absolute differences in 24-hour PM2.5 concentrations for July 9th (Representative week 

mobile emissions versus daily mobile emissions). 
Figure 11. Percent differences in daily 8-hour maximum ozone concentrations for January 28th 

(Representative week mobile emissions versus daily mobile emissions). 

Figure 12. Percent differences in daily 8-hour maximum ozone concentrations for July 15th

(Representative week mobile emissions versus daily mobile emissions). 
Figure 13. Absolute differences in daily 8-hour maximum ozone concentrations for July 15th

(Representative week mobile emissions versus daily mobile emissions). 
Figure 14. Absolute differences in 1-hour maximum ozone concentrations for July 12th. 

Figure 15. Absolute differences in 1-hour maximum ozone concentrations for July 14th. 
CONCLUSIONS 

U.S. EPA attainment demonstration modeling guidance11,12 notes that in some cases it may be useful to 

evaluate how the response of an air quality model to emissions changes varies as a function of 

alternative model inputs or model algorithms. These types of tests can be used to assess the robustness 

of a base case or control strategy modeling evaluation. As an example, EPA remarks that States/Tribes 

could consider the effects of assumed boundary conditions on predicted effectiveness of a control 

strategy. If the model response does not differ greatly over a variety of alternative plausible 

configurations, this increases confidence in the model results. 

The parameters for these sensitivity tests can include, but are not limited to: different chemical 

mechanisms, finer or coarser grid resolution, meteorological inputs from alternative, credible 

meteorological model(s), different initial/boundary conditions, and multiple sets of reasonable emission 

projections. Sensitivity tests can and should be applied throughout the modeling process, not just when 

model performance is being evaluated. 

The modeling team’s research in using reasonable alternate sets of onroad emission projections has 

determined that the use of representative week onroad mobile emissions for each month of our episodes 

within our 12km modeling domain predicts ozone and particulate matter concentration differences from 

annual, everyday onroad mobile modeling which could be considered insignificant from an air quality 

modeling standpoint. The small differences in the air quality results in combination with the length of 

time necessary to conduct daily onroad mobile runs using the MOBILE6 module of SMOKE has 

resulted in the project team’s recommendation that representative week onroad mobile emissions 

methodology be carried forward in the VISTAS regional haze modeling and the ASIP PM2.5 and 8-hour 

ozone modeling. 

REFERENCES 

1. “Draft Modeling Protocol For Association for Southeastern Integrated Planning (ASIP) – 

Emissions and Air Quality Modeling to Address 8-Hour Ozone and PM2.5 Nonattainment in the 

Southeastern United States”; Prepared for ASIP by ENVIRON, Alpine Geophysics and 

University of California, Riverside, January 31, 2006. 

2. Dudhia, J. "A Non-hydrostatic Version of the Penn State/NCAR Mesoscale Model: Validation 

Tests and Simulation of an Atlantic Cyclone and Cold Front", Mon. Wea. Rev., 1993, Vol. 121. 

pp. 1493-1513. 

3. Grell, G. A., J. Dudhia, and D. R. Stauffer. "A Description of the Fifth Generation Penn 

State/NCAR Mesoscale Model (MM5). NCAR Tech. Note, 1994. NCAR TN-398-STR, 138 pp. 

4. University of North Carolina at Chapel Hill, Sparse Matrix Operator Kernel Emissions 

(SMOKE) Modeling System, http://cf.unc.edu/cep/empd/products/smoke/index.cfm. 

5. Byun, D.W., and J.K.S. Ching. “Science Algorithms of the EPA Models-3 Community 

Multiscale Air Quality (CMAQ) Modeling System”, EPA/600/R-99/030. 1999. 

6. Pleim J. et al. “New Developments in CMAQ Model Physics”, Presented at the 4th Annual 

CMAS Models-3 User’s Conference, September 26-28, 2005, Friday Center Chapel Hill North 

Carolina. 
7. VISTAS Phase II Modeling website, http://pah.cert.ucr.edu/vistas/vistas2/ . 

8. Abraczinskas, M.A., Olerud, D.T., Sims, A.P. “Characterizing Annual Meteorological Modeling 

Performance for Visibility Improvement Strategy Modeling in the Southeastern U.S.”, Presented 

at the 3th AMS Joint Conference on the Applications of Air Pollution Meteorology with the Air 

and Waste Management Association, Vancouver, BC, 2004. 

9. Morris, R.E., McNally, D.E., Tesche, T.W., Tonnesen, G., Boylan, J.W., Brewer, P. 

“Preliminary Evaluation of the Community Multiscale Air Quality Model for 2002 over the 

Southeastern United States”. 2005. J. Air & Waste Manag. Assoc. 55:1694-1708 

10. Morris, R.E.; Koo, B.; Yarwood, G.; McNally, D.E.; Tesche, T.W.; Tonnesen, G.S.; Boylan, J.; 

Brewer, P. “Model Sensitivity Evaluation for Organic Carbon Using Two Multi-Pollutant Air 

Quality Models that Simulate Regional Haze in the Southeastern United States”, Atmos. 

Environ. Submitted for publication. 

11. Guidance on the Use of Models and Other Analyses in Attainment Demonstrations for the 8-hour 

Ozone NAAQS, U.S. Environmental Protection Agency Office of Air Quality Planning and 

Standards Emissions, Monitoring, and Analysis Division Air Quality Modeling Group Research 

Triangle Park, North Carolina, 2005; EPA-454/R-05-002. 

12. Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and Regional Haze, 

Draft Report, U.S. Environmental Protection Agency, Research Triangle Park, NC. 2001. 

ACKNOWLEDGEMENTS 

VISTAS and ASIP are funded by grants to the Southeastern States Air Resource Managers, Inc. from 

the United States Environmental Protection Agency and the VISTAS/ASIP state and local agencies. The 

authors of this paper would like to acknowledge VISTAS and ASIP State, local and Tribal air quality 

agencies, participating stakeholders, and associated contractors for their contribution and input in the 

emission inventory development and modeling. Additionally, the authors would like to recognize the 

support of the North Carolina Department of Environment and Natural Resources, Division of Air 

Quality and Dennis McNally, Alpine Geophysics for his time in preparing the CMAQ runs used in this 

analysis. 
KEYWORDS 

ASIP 

MOBILE 

Modeling 

Onroad Mobile 

Ozone 

Particulate Matter 

Regional Haze 

Regional Planning Organization 

SMOKE 

VISTAS 
Appendix H.4 

Emissions Modeling 

Deviation From Defaults 
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Table Of Contents 

1. Introduction................................................................................................................................. 1 

2. Deviation From Everyday Modeling .......................................................................................... 1 

3. Point Source Deviation ............................................................................................................... 2 

List Of Tables 

Table 1. Representative Time Period for Emissions Modeling ..................................................... 2 
 

1. Introduction 

The emissions modeling for the three Kentucky PM2.5 nonattainment areas were performed in 

conjunction with the regional haze modeling being done by the Southeast Regional Planning 

Organization, Visibility Improvement State and Tribal Association of the Southeast (VISTAS) 

and the fine particulate matter (PM2.5) and ozone modeling being done by the Association of 

Southeastern Integrated Planning (ASIP). VISTAS and ASIP are run by the ten Southeast states 

(Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee, 

Virginia and West Virginia. The emissions preprocessing model used to ready the emissions for 

input into the air quality model was the Spare Matrix Operator Kernel Emissions (SMOKE) 

modeling system. 

2. Deviation From Everyday Modeling

The VISTAS/ASIP modeling was an annual simulation. It would be too resource intensive to 

process all sources sectors for everyday of the year. Therefore, to produce an emissions 

inventory to support the annual modeling, representative time periods were selected and 

modeled. 

The area and nonroad mobile sources were modeled as a block of Thursday, Friday, Saturday, 

Sunday, Monday, one per month (total of 60 days modeled for the annual simulation). Similarly, 

the on-road mobile sources were represented by an entire single week for each month. This 

select criteria allows for the representation of day-of-the-week variability in the on-road motor 

vehicles, and models a representation of the meteorological variability in each month. 

The stationary point sources, which include the electric generating units (EGUs) and Non-EGUs, 

were modeled everyday of the annual simulation. This was due to the plume rise calculations 

used to parse the emissions into the various layers of the model is different everyday depending 

on the meteorological inputs. Additionally, VISTAS/ASIP modeled large wild land fires as day 

specific sources with plume rise incorporated into the emissions modeling. Similar to the 

stationary point sources, the biogenic emissions were modeled everyday of the annual simulation 

since the amount of volatile organic compounds emitted is significantly impacted by temperature 

and solar radiation. 

For the area, nonroad mobile and on-road mobile sources, the holidays were modeled as a 

Sunday. Table 1 describes the representative time period for all source categories. 
 

Table 1. Representative Time Period for Emissions Modeling 

Source Category Emission Modeling frequency of run 

Area Sources 5 days per month 

Biogenic Everyday 

Canada_area 5 days per month 

Canada_point 5 days per month 

Dust 5 days per month 

EGU Everyday 

Fire_cenrap Everyday 

Hi_file_typ Everyday 

Lo_file_typ 5 days per month 

Mexico_area 5 days per month 

Mexico_point 5 days per month 

mms_area 5 days per month 

mms_point Everyday 

Non-EGU Everyday 

Nonroad Mobile 5 days per month 

On-road Mobile 7 days per month 

3. Point Source Deviation 

The VISTAS/ASIP emissions modeling used results from the Integrated Planning Model (IPM) 

to generate future year emissions for the EGU source sector. 

Another point source deviation was the temporal profiles used for the typical emissions for the 

EGU source sector. Instead of using the 2002 continuous emissions monitoring (CEM) profiles 

for the EGUs, a typical temporal profile was created using data from 2000 through 2004. How 

the typical temporal profiles were generated is discussed in detail in Appendix H.2