By far the most computationally limiting step in emissions modeling is typically the generation of onroad mobile sources. Motor vehicle emissions ar e influenced by meteorological variability and the processing requirements for daily motor vehicle emi ssions 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 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 and consistent with modeling schedule 1 . This representative week was selected from mid-month, to tr y 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 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 Model (MM5) is a nonhydrostatic, prognostic meteorological model routinely used for ur ban- and regional-scale photochemical, fine particulate and regional haze regulatory modeling studies.

3⁄4 SMOKE 4 : The Sparse Matrix Operator Kernel Em issions (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 CMAQ 5,6 : EPA’s Models-3/Community Multiscale Ai r Quality (CMAQ) modeling system is a ‘One-Atmosphere’ photochemical grid model capab le 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 a nd 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 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 inventor y modeling, SMOKE was configured to generate point, area, nonroad, highway, and biogenic source emissi ons. 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 excepti on 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 speci fic 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 onroad mobile source emissions were produced using two approaches:

1) Modeling every day of the annual episode, us ing 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 m onth 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 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 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) 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, O LE, 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 MOTO R VEHICLE SOURCE EMISSIONS

The MOBILE6 module of SMOKE was used to devel op the onroad mobile source emissions estimates for CO, NOX, PM, and VOC emissi ons. 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 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 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 she ll file. The MOBILE shell contains the various options (e.g. type of inspection and maintenance progr am 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 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 for a winter (Ja nuary 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 genera ted 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 r uns 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 sixt y-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 383.25 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 represen tative 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 January episode is presented in Table 1. Note that although the emissions were generated for calenda r 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 Repres entative 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 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 modeli ng 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 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 duri ng 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 undere stimated with the summer 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 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 “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 performance 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 “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 (da ily and the representative week) generated for the January and July study periods, we performed futu re-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 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 ozone difference plots; 2) maximum 1-hour ozone maximum difference plots; and, 3) daily PM 2.5 difference plots.RESULTS

Our examination of the two air quality simulati ons began with the daily differences in PM 2.5 concentrations. Figures 7 and 8 represent the percent difference in the daily PM 2.5 concentrations between the air quality simulations with representa tive 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 percent. 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 either plot indicating daily PM 2.5 concentrations changed less than 0.2 μg/m 3 . In fact, all of the fourteen days modeled (seven winter days and seven 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 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 th at 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 occu r during the summertime. Therefore the remainder of the ozone analysis will focus on summer time differences. On the summer day, July 15 th , presented in Figure 12, no changes are seen indicating daily ma ximum 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 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 January 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 maximu m ozone concentrations for January 28th (Representative week mobile emissions versus daily mobile emissions).

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

.

Figure 15. Absolute differences in 1-hour maximum 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 changes varies as a function of alternative model inputs or model algorithms. These t ypes of tests can be used to assess the robustness of a base case or control strategy modeling evaluati on. As an example, EPA remarks that States/Tribes could consider the effects of assumed boundary c onditions on predicted effectiveness of a control strategy. If the model response does not differ gr eatly over a variety 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 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 VIST AS regional haze modeling and the ASIP PM 2.5 and 8-hour ozone modeling.

REFERENCES

1. “Draft Modeling Protocol For Association fo r Southeastern Integrated Planning (ASIP) – Emissions and Air Quality Modeling to Address 8-Hour Ozone and PM2.5 Nonattainment in the Southeastern United States”; Prepared fo r ASIP by ENVIRON, Alpine Geophysics and University of California, Riverside, January 31, 2006.

2. Dudhia, J. "A Non-hydrostatic Version of th e 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. Stauffe r. "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 Al gorithms 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 4 th Annual CMAS Models-3 User’s Conference, Septembe r 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 M odeling in the Southeastern U.S.”, Presented at the 3 th 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 PM 2.5 and Regional Haze

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

ACKNOWLEDGEMENTS

VISTAS and ASIP are funded by grants to the South eastern 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 VIST AS 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. Additi onally, the authors would like to recognize the support of the North Carolina Department of Envi ronment 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

### [This page intentionally left blank.]

### 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 be ing done by the Southeas t Regional Planning Organization, Visibility Improvement State and Tr ibal Association of the Southeast (VISTAS) and the fine particulate matte r (PM2.5) and ozone modeling be ing done by the Association of Southeastern Integrated Planning (ASIP). VISTAS and ASIP are run by the ten Southeast states (Alabama, Florida, Georgia, Ke ntucky, Mississippi, North Carolin a, South Carolina, Tennessee, Virginia and West Virginia. The emissions prep rocessing model used to ready the emissions for input into the air quality model was the Spar e Matrix Operator Kernel Emissions (SMOKE) modeling system.

2. Deviation From Everyday Modeling

The VISTAS/ASIP modeling was an annual simulati on. It would be too resource intensive to process all sources sectors for everyday of the year. Theref ore, to produce an emissions inventory to support the annual modeling, representative time periods were selected and modeled.

The area and nonroad mobile sour ces were modeled as a block of Thursday, Frid ay, Saturday, Sunday, Monday, one per month (total of 60 days m odeled 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 th e 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 mode l is different everyday depending on the meteorological inputs. A dditionally, VISTAS/ASIP modeled la rge 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 emissi ons for the EGU source sector.

Another point source deviation wa s the temporal profiles used fo r 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 generate d is discussed in detail in Appendix H.2