Appendix C.4c

White Paper on Mobile Source 

SMOKE Modeling
Processing Mobile Emissions in SMOKE: Is it worth simulating everyday onroad

mobile emissions to support 8-hr ozone modeling?

Mike Abraczinskas

North Carolina Department of Environment and Natural Resources

Division of Air Quality

1641 Mail Service Center

Raleigh, NC 27699-1641

(919) 715-3743

Michael.Abraczinskas@ncmail.net

Cynthia Loomis

Alpine Geophysics, LLC

7341 Poppy Way

Arvada, CO 80007

(303) 421-2211

cfl@alpinegeophysics.com 

Gregory Stella

Alpine Geophysics, LLC

387 Pollard Mine Road

Burnsville, NC 28714

(828) 675-9045

gms@alpinegeophysics.com

ABSTRACT

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

Association for Southeastern Integrated Planning (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 VISTAS/ASIP modeling schedules. 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. 

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, PM and regional haze prediction.
INTRODUCTION

On December 17, 2004, EPA made fine particle (PM2.5) nonattainment determinations for at least one

area in seven of the states participating in the Visibility Improvement State and Tribal Association of the

Southeast (VISTAS) regional haze project. They are Alabama, Georgia, North Carolina, Kentucky,

Tennessee, Virginia, and West Virginia. In addition, South Carolina has one three-county area that was

designated as unclassifiable in the same action. EPA’s Clean Air Interstate Rule (CAIR) modeling

indicated that certain nonattainment areas may still be in nonattainment after full implementation of

CAIR. These areas include Jefferson County, Alabama and Clayton and Fulton Counties in Georgia.

The PM2.5 compliance date is April 2010 unless a state demonstrates that more time is necessary in

which case up to five additional years may be granted. The nonattainment designations triggered the

requirement for development of state implementation plans (SIPs) that will be due in April 2008. The

draft guidance from EPA indicates that a significant requirement of PM2.5 SIPs will be attainment

demonstrations using, at least in part, modeling analyses to define effective emissions control strategies

and confirm that attainment can be achieved after implementation of the strategies. 2009 is the modeling

year for the PM2.5 attainment demonstration and also is an interim analysis year for the VISTAS regional

haze demonstration. 

In April of 2004, EPA determined areas that were not meeting the 8-hour ozone standard. States having

one or more 8-hour ozone nonattainment areas in the Southeast are Alabama, Georgia, Kentucky, North

Carolina, South Carolina, Tennessee, Virginia, and West Virginia. EPA will require attainment of the 8-

hour ozone standard in basic nonattainment areas by June 15, 2009 and in moderate nonattainment areas

by June 15, 2010. This will require states with basic 8-hour ozone nonattainment areas to model 2008 as

the SIP modeling demonstration year while moderate nonattainment areas will require 2009 as the

modeling year. Given that North Carolina and Virginia have two year SIP approval processes, there is an

immediate need to complete an analysis of ozone attainment using air quality modeling.

The states participating in the VISTAS project (the SESARM EPA Region 4 states plus Virginia and

West Virginia from Region 3) have concluded that a collaborative process will be the most efficient

approach for the collective states to develop information upon which to base the PM2.5 and 8-hour ozone

attainment demonstrations. The local air regulatory agencies for Jefferson County, AL, Jefferson

County, KY, Mecklenburg County, NC, Forsyth County, NC, Knox County, TN, and Shelby County,

TN have also become signatory parties to this collaborative effort. SESARM will coordinate among

participating agencies and oversee the performance of the inventory and modeling tasks in parallel with

the VISTAS regional haze project tasks. 

The name of this collaborative effort is the Association for Southeastern Integrated Planning (ASIP).

SESARM was awarded a grant from EPA on February 8, 2005 to conduct what was originally called the

fine particle SIP development support project but is now known as ASIP. 

These states need to submit their 8-hour ozone State Implementation Plans (SIPs) to EPA by June 2007;

the PM2.5 SIPs are due by April 2008. Some of the states involved in the ASIP ozone/PM modeling have

two-year legislative review processes. Thus, the definition of the SIP control plans is needed in early

2006. Consequently, the ASIP regional ozone and PM modeling has an aggressive schedule.
Figure 1. PM2.5 nonattainment counties designed by EPA in December 2004.

Figure 2. 8-hour ozone nonattainment counties in the US designated by EPA in April 2004.
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

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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