# Appendix D.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 th e generation of onroad
mobile sources. Motor vehicle emissions are influenced by meteorological variab ility and the processing requirements for daily motor vehicle emissions have been determined to be rate limiting under most modeling schedules. Rather than utili zing averaged meteorological data or pre-calculated motor vehicle emissions, the Visibility Improvement State and Tr ibal Association of th e Southeast (VISTAS) / Association for Southeastern Integrated Planni ng (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 consis tent 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 exclud e holidays that would requ ire 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 si mulating everyday for onroad mobile emissions to support 8-hr ozone modeling. We will present 12km ev aluations of everyday vs. representative week emissions and associated air quality for a number of domains and disc uss the benefits and limitations of the various methods relative to ozone, PM and regional haze prediction.INTRODUCTION

On December 17, 2004, EPA ma de fine particle (PM 2.5 ) nonattainment determina tions for at least one
area in seven of the states participating in the Visibility Impr ovement State and Tribal Association of the Southeast (VISTAS) regional haze project. They are Alabama, Georgia, No rth Carolina, Kentucky, Tennessee, Virginia, and West Virginia. In additi on, South Carolina has one three-county area that was designated as unclassifiable in the same action. EP A’s Clean Air Interstate Rule (CAIR) modeling
indicated that certain n onattainment areas may still be in nonatta inment after full implementation of CAIR. These areas include Jefferson County, Alabam a and Clayton and Fulton Counties in Georgia.

The PM 2.5 compliance date is April 2010 unless a state dem onstrates that more time is necessary in which case up to five additional years may be gran ted. 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 th at a significant requirement of PM 2.5 SIPs will be attainment demonstrations using, at least in part, modeling analys es to define effective em issions control strategies
and confirm that attainment can be achieved after implementation of the strategies. 2009 is the modeling year for the PM 2.5 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, Ge orgia, Kentucky, North Carolina, South Carolina, Te nnessee, 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 basi c 8-hour ozone nonattainment areas to model 2008 as the SIP modeling demonstration year while moderate nonattainment areas wi ll 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 (t he SESARM EPA Region 4 st ates 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 de velop information upon which to base the PM 2.5 and 8-hour ozone
attainment demonstrations. The local air regulatory agencies for Jefferson County, AL, Jefferson County, KY, Mecklenburg County, NC, Forsyth C ounty, NC, Knox County, TN, and Shelby County, TN have also become signatory pa rties to this collaborative effo rt. SESARM will coordinate among participating agencies and oversee the performance of the inventory and modeling tasks in parallel with the VISTAS regional ha ze project tasks.

The name of this collaborative effort is the Asso ciation for Southeastern In tegrated Planning (ASIP). SESARM was awarded a grant from EPA on February 8, 2005 to conduct what wa s originally called the fine particle SIP development support project but is now known as ASIP.

These states need to submit their 8-hour ozone St ate Implementation Plans (SIPs) to EPA by June 2007;
the PM 2.5 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 defi nition of the SIP control pl ans is needed in early 2006. Consequently, the ASIP regional ozone an d PM modeling has an aggressive schedule.Figure 1. PM 2.5 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 modeli ng is typically the generation of
onroad mobile sources. Motor vehicle emissions ar e influenced by meteorologi cal variability and the processing requirements for daily mo tor vehicle emissions have been determined to be rate limiting under most modeling schedules. Rather than utilizing averaged meteor ological data or pre-calculated motor vehicle emissions, the VISTAS and ASIP m odeling 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 a nd consistent with modeling schedule 1 . This representative week was selected from mid-month, to tr y to best represent the average te mperature ranges for the month, and also adjusted to exclude holidays th at would require at ypical 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 partic ulate matter (PM) of size of 2.5 microns or less (PM 2.5 ):

3⁄4 MM5 2,3 : The Pennsylvania State University/Na tional Center for Atmospheric Research (PSU/NCAR) Mesoscale Meteorological M odel (MM5) is a no nhydrostatic, prognostic meteorological model routinely used for urba n- and regional-scale photochemical, fine particulate and regional haze regulatory modeling studies.

3⁄4 SMOKE 4 : The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is an emissions modeling system that generates hourly gridded speciated emissi on inputs of mobile, nonroad, area, point, fire and biogenic emissi on sources for photochemical grid models.

3⁄4 CMAQ 5,6 : EPA’s Models-3/Community Multiscale Ai r Quality (CMAQ) modeling system is a ‘One-Atmosphere’ photochemical grid model cap able 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 si mulating everyday for onroad mobile emissions to support 8-hr ozone modeling. We will present 12km ev aluations of everyday vs. representative week emissions and associated air quality for a number of domains and disc uss 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 inventor y modeling, SMOKE was configured to generate point, area, nonroad, highway, and bioge nic 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. W ith 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 specifi c temporal profiles of the SMOKE model. Area, nonroad, and point sources were modeled as a bloc k 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 onroa d mobile source emissions were produced using two approaches:

1) Modeling every day of the annual episode, usin g 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 mont h and using these days as representative of the
entire month. This selection criterion allows for th e representation of day-of -the-week variability in the onroad motor vehicles, and models a representa tion of the meteorological variability in each
month. The modeled weeks were se lected 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 4 th 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) kilome ters 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 la yer 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, PNO 3, POA, PSO4, and PMC.

Meteorology data: Daily (25-hour). SMOKE requires the followi ng 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 poten tially elevated. No plume-in-grid sources will be modeled. Wildfire emissions will be handled as point sources.

Figure 3. 36-km national unified RPO doma in and VISTAS 12-km domain.DEVELOPMENT OF ONROAD MOTO R VEHICLE SOURCE EMISSIONS

The MOBILE6 module of SMOKE was used to devel op the onroad mobile sour ce emissions estimates for CO, NOX, PM, and VOC emissions. The MOBILE6 parameters, vehicle fleet descriptions, and VMT estimates are combined with gridded, episode-sp ecific temperature data to calculate the gridded, temporalized emission estimates. The MOBILE6 em issions 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 emi ssions 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 she ll file. The MOBILE shell contains the various options (e.g. type of inspection a nd maintenance program in effect, t ype of oxygenated fuel program in effect, alternative vehicle mix profile s, RVP of in-use fuel, operating mode) that direct the calculation of
the MOBILE6 emissions factors. The shells used in these runs were ba sed on VISTAS/ASIP BaseF modeling inputs 7 . The options for all MOBILE6 parameters we re held constant between the annual and representative week runs.

Daily results of these model runs fo r a winter (January 17) and summer (July 18) day are represented in Figures 4 through 6 below. These data provide a co mparison 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 fo r both ozone and PM that we have based our
conclusions.

Each of the onroad mobile source emissions r uns conducted with the MOBILE6 module of SMOKE were performed on a dual Athlon MP 2600+ with 1.5 G RAM. With th is configurati on, 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 pr ocessing 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 confi guration would require 22,995 minutes (365 days x 63 minutes per run day) or about 383.2 5 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 representati ve week run were calculated by mapping each day of week (Mon, Tue, Wed, etc.) from the modeled m onth 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 Janua ry 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 th e representative week MOBILE6 module runs. In comparison, Table 3 presen ts these emissions for the month of July.

Table 1. Representative day mapping for January ep isode (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 holidayTable 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 exte nded 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 oz one, there is enough of a temperature variability a nd therefore apparent ozone precursor em issions 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 ha s applied the CMAQ Version 4.5 O 3 /PM 2.5 photochemical grid modeling system. The VISTAS/ASIP modeling team implemented a comprehensive evaluation of the meteorological 8 , emissions and air quality models. The CM AQ 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 underestim ation bias for nitrate and reasonably good performance for elemental carbon (EC), albeit with lots of scatter and low correlation. However, organic carbon (OC) was unde restimated with the su mmer OC underestimation bias being quite severe. After an intense focuse d analysis of the OC underestimation issue, the VISTAS/ASIP modeling team identified processes im portant to the formation of secondary organic aerosols (SOA) that were not included in the CMAQ SOA module that may be im portant to OC in the
Southeastern U.S. 9 Consequently, VISTAS/ASIP enhanced th e CMAQ SOA module by adding several new processes. This enhancement, called “SOAm ods”, was implemented in CMAQ Version 4.5 and exhibited much improved OC model perfor mance over the standard CMAQ SOA treatment 10 . A complete description of the modeling methods, configurations and perf ormance are described
elsewhere 1,7

.

CMAQ was applied using both of the mobile emissi ons modeling methods described above. Recall, all emissions and air quality model inputs and configurati ons were held constant, with the exception of the
mobile source emissions. This will allow us to isolat e 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 “VIS TAS/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 (da ily 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 differe nces in predicted ozone and PM 2.5 concentrations due to the different mobile emissions modeling methodologies. Spatial plots were generated for each day simulated, including:

1) daily maximum 8-hour oz one difference plots; 2) maximum 1-hour ozone maxi mum difference plots; and, 3) daily PM 2.5 difference plots.RESULTS

Our examination of the two air quality simula tions began with the daily differences in PM 2.5 concentrations. Figures 7 and 8 represen t the percent difference in the daily PM 2.5 concentrations between the air quality simulations with representative week mobile emissions and the daily mobile emissions for one winter day (January 22 nd ) and one summer day (July 9 th ). No change is seen in either plot indicating daily PM 2.5 concentrations changed less than one pe rcent. Absolute differences in daily

PM 2.5 concentrations are shown in Figures 9 and 10 for the same two days (January 22 nd and July 9 th ). Again, no change is seen in e ither plot indicating daily PM 2.5 concentrations cha nged less than 0.2 μg/m 3 . In fact, all of the fourteen days modeled (s even winter days and se ven summer days) show no differences as high as 0.2 μg/m 3

.

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 presen t the percent difference in the daily maximum 8-
hour ozone concentrations for one winter day (January 28 th ) and one summer day (July 15 th ). In most areas for the winter day, daily ma ximum 8-hour ozone concen trations changed less than one percent. In a few urban corridors, namely, near Chicago, IL, Atlanta, GA and Baltimor e, 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 inte rest because most, if not all high ozone events in the middle latitudes of the northern hemisphere o ccur during the summertime. Therefore the remainder of the ozone analysis will focus on summertim e differences. On the summer day, July 15 th , presented in Figure 12, no changes are seen indicating daily maxi mum 8-hour ozone concentr ations changed less than one percent. In fa ct, all seven of the summer days modele d showed no changes as high as one
percent.

Absolute differences in daily maximum 8-hour ozone concentrations are show n in Figure 13 for July
15 th . 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 Fi gure14 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 PM 2.5 concentrations for Januar y 22nd (Representative week mobile emissions versus daily mobile emissions).

Figure 8. Percent difference in 24-hour PM 2.5 concentrations for July 9 th (Representative week mobile emissions versus daily mobile emissions).Figure 9. Absolute differences in 24-hour PM 2.5 concentrations for January (Representative week mobile emissions versus daily mobile emissions).

Figure 10. Absolute differences in 24-hour PM 2.5 concentrations for July 9 th (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 15 th (Representative week mobile emissions versus daily mobile emissions).Figure 13. Absolute differences in daily 8-hour ma ximum ozone concentrations for July 15 th (Representative week mobile emissions versus daily mobile emissions).Figure 14. Absolute differences in 1-hour maximu m ozone concentrations for July 12 th

.

Figure 15. Absolute differences in 1-hour maxi mum ozone concentrations for July 14 th

.CONCLUSIONS

U.S. EPA attainment demonstration modeling guidance 11,12 notes that in some cases it may be useful to evaluate how the response of an air quality model to emissions ch anges varies as a function of alternative model inputs or model al gorithms. 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 predic ted effectiveness of a control strategy. If the model response doe s not differ greatly over a vari ety of alternative plausible configurations, this increases confidence in the model results.

The parameters for these sensitivity tests can in clude, but are not limited to: different chemical mechanisms, finer or coarser grid resolution, me teorological 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 th roughout 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 onr oad mobile emissions for each month of our episodes within our 12km modeling domain pr edicts ozone and particulate matte r 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 combin ation 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 PM 2.5 and 8-hour ozone modeling.

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ACKNOWLEDGEMENTS

VISTAS and ASIP are funded by gran ts 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 Stat e, local and Trib al air quality agencies, participating stakeholders , and associated contractors for th eir contribution and input in the emission inventory development and modeling. Add itionally, the authors woul d like to recognize the support of the North Carolina Department of E nvironment and Natural Re sources, 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