National Scale Modeling of Air Toxics 

for the Mobile Source Air Toxics Rule; 

Technical Support Document 
United States Office of Air Quality Planning and Standards Publication No

Environmental Protection Emissions, Monitoring and Analysis Division January 2006 Agency Research Triangle Park, NC 
TECHNICAL REPORT DATA 

(Please read Instructions on reverse before completing) 

1. REPORT NO.

 EPA-454/R-06-002 2. 3. RECIPIENT'S ACCESSION NO. 

4. TITLE AND SUBTITLE 

National Scale Modeling of Air Toxics for the Mobile 

Source Air Toxics Rule; Technical Support Document 

5. REPORT DATE

 January 2006 

6. PERFORMING ORGANIZATION CODE 

7. AUTHOR(S) 8. PERFORMING ORGANIZATION REPORT NO. 

9. PERFORMING ORGANIZATION NAME AND ADDRESS 10. PROGRAM ELEMENT NO. 

11. CONTRACT/GRANT NO. EPA Contract 

No.IAG47939482-01 (CSC) 

12. SPONSORING AGENCY NAME AND ADDRESS

 U.S. Environmental Protection Agency

 Office of Air Quality Planning and Standards

 Emissions, Monitoring & Analysis Division

 Research Triangle Park, NC 27711 

13. TYPE OF REPORT AND PERIOD COVERED

 Final Report 

14. SPONSORING AGENCY CODE 

15. SUPPLEMENTARY NOTES

 EPA Work Assignment Manager: Madeleine Strum 

16. ABSTRACT 

The purpose of the work described in this technical document was to project emissions 

for mobile source hazardous air pollutants (HAPs) to 2007, 2010, 2015, 2020, and 2030 

from the 1999 National Emissions Inventory Version 3 (NEI), conduct air quality and 

exposure modeling, and estimate cancer and non-cancer risk for select future years. 

Air quality modeling utilized the Assessment System for Population Exposure Nationwide 

(ASPEN) model. Exposure modeling utilized the Hazardous Air Pollutant Exposure Model, 

Version 5 (HAPEM5). Modeling was done for reference cases, which included programs 

currently planned and in place, as well as control scenarios that evaluated potential 

impacts of additional control programs. This work was done to support regulatory needs 

related to the 2006 proposed mobile source air toxics rule. Intermediate year 

inventories for 2002 through 2010, inclusive, were also developed to support other 

program needs in the Office of Air and Radiation. 

17. KEY WORDS AND DOCUMENT ANALYSIS 

a. DESCRIPTORS b. IDENTIFIERS/OPEN ENDED TERMS c. COSATI Field/Group

 Air Pollution

 Air Quality Dispersion Models 

Meteorology 

Air Toxics 

Urban Area Modeling 

18. DISTRIBUTION STATEMENT

 Release Unlimited 

19. SECURITY CLASS (Report)

 Unclassified 21. NO. OF PAGES 222 

20. SECURITY CLASS (Page)

 Unclassified 22. PRICE 

EPA Form 2220-1 (Rev. 4-77) PREVIOUS EDITION IS OBSOLETE

This page intentionally blank 
EPA-454/ R-06-002 

January 2006 

National Scale Modeling of Air Toxics for the Mobile Source Air Toxics Rule; 

Technical Support Document 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Emissions, Monitoring and Analysis Division 

Research Triangle Park, North Carolina 
Disclaimer 

The information in this document has been reviewed in accordance with the U.S. EPA 

administrative review policies and approved for publication. Mention of trade names or 

commercial products does not constitute endorsement or recommendation for their use. 

The following trademarks appear in this document: 

UNIX is a registered trademark of AT&T Bell Laboratories. 

Linux is a registered trademark of Red Hat 

SAS® is a registered trademark of SAS Institute 

SUN is a registered trademark of Sun Microsystems, Inc. 
Table of Contents 

1. Purpose of Work .........................................................................................................................1 

2. 1999 base inventories .................................................................................................................5 

2.1 1999 HAP inventories........................................................................................................... 5 

2.2 1999 Precursor inventories ................................................................................................... 9 

3. Development of Future Year Mobile and Mobile-Related Emissions .....................................11 

3.1 Locomotive and commercial marine vessels ...................................................................... 11 

3.2 Aircraft and Aviation gasoline............................................................................................ 19 

3.3 Projection of onroad and nonroad categories using NMIM................................................ 25 

3.3.1 Description of NMIM .................................................................................................. 25 

3.3.2 Onroad projections using NMIM................................................................................. 25 

3.3.3 Nonroad projections using NMIM (excluding aircraft, locomotives, and commercial 

marine vessels)...................................................................................................................... 32 

3.3.4 Projection of onroad refueling emissions .................................................................... 41 

3.4 Projection of HAP Precursor Emissions from Mobile Sources.......................................... 44 

3.4.1 Locomotive and Commercial Marine Vessel Precursor Emissions............................. 44 

3.4.2 Aircraft Precursor Emissions ....................................................................................... 44 

3.4.3 Onroad Precursor Emissions........................................................................................ 45 

3.4.4 Nonroad Precursor Emissions (excluding aircraft, locomotives, and commercial 

marine vessels)...................................................................................................................... 45 

4. Development of Future Year Stationary Source Emissions .....................................................47 

4.1 Growth factors .................................................................................................................... 47 

4.1.1 MACT based growth factors........................................................................................ 47 

4.1.2 SIC based growth factors............................................................................................. 51 

4.1.3 SCC based growth factors............................................................................................ 52 

4.2 Reduction factors ................................................................................................................ 53 

4.3 Application of growth and reductions to project stationary source emissions ................... 55 

5. EMS-HAP Processing for HAPs ..............................................................................................57

5.1 Point sources ....................................................................................................................... 57 

5.2 Non-point sources ............................................................................................................... 58 

5.3 Onroad sources.................................................................................................................... 58 

5.4 Nonroad sources.................................................................................................................. 59 

5.4.1 Aircraft sources................................................................................................................ 59 

5.4.2 Airport Support Equipment.............................................................................................. 59

5.4.3 Remaining nonroad sources............................................................................................. 59 

5.5 EMS-HAP for precursors.................................................................................................... 60 

6. ASPEN Processing ...................................................................................................................63 

6.1 MSAT HAPs....................................................................................................................... 63 

6.2 Precursors............................................................................................................................ 63 

6.3 Post-processing of ASPEN concentrations......................................................................... 67 

7. HAPEM5 Model and Post-Processing......................................................................................77 

7.1 HAPEM model.................................................................................................................... 77 

7.2 Summaries of annual HAPEM5 output .............................................................................. 79 

8. Cancer and non-cancer risk calculations ..................................................................................83

8.1 Cancer risk calculations ...................................................................................................... 84 

i 
Table of Contents 

8.2 Non-cancer risk calculations............................................................................................... 85 

8.3 Cancer and non-cancer risk population statistics using 2000 and projected population .... 88 

8.3.1 Allocation of future county level populations to tract level ........................................ 88 

8.3.2 Population statistic calculations for cancer risk........................................................... 89 

8.3.3 Population statistic calculations for non-cancer respiratory hazard index................... 90 

8.3.4 Population statistic calculations using 2000 population for all years.......................... 92 

9. Background concentration sensitivity analysis.........................................................................93 

10. Benzene Control Scenario .......................................................................................................97 

10.1 Stationary gasoline distribution and vehicle gasoline refueling inventory....................... 97 

10.2 Highway gasoline vehicle inventory............................................................................... 107 

10.3 Nonroad gasoline inventory............................................................................................ 108 

10.4 EMS-HAP Processing..................................................................................................... 109

10.5 ASPEN Processing and Post-Processing ........................................................................ 110 

10.6 HAPEM Processing and Post-Processing....................................................................... 111 

10.7 Cancer and Non-cancer Calculations.............................................................................. 112 

10.7.1 Cancer ...................................................................................................................... 113 

10.7.2 Non-cancer............................................................................................................... 114 

10.7.3 Population statistics ................................................................................................. 115 

References....................................................................................................................................119 

Appendix A: Documentation of NMIM Runs Used to Develop Inventories for MSAT Rule Air 

Quality Modeling........................................................................................................................ A-1 

Appendix B: Steps and Example calculations of onroad projections.........................................B-1 

Appendix C: Example calculations of nonroad projections .......................................................C-1 

Appendix D: Risk Calculations ................................................................................................. D-1 

Appendix E: Control of stationary refueling and gasoline marketing emissions ........................E-1 

Appendix F: Control of onroad gasoline emissions ...................................................................F-1 

Appendix G: Development of controlled nonroad inventory .................................................... G-1 

ii 
List of Tables 

Table 1. Pollutants of interest in MSAT study. CAS numbers in italics are in the stationary 

inventories only; otherwise they are in mobile and stationary inventories............................. 2 

Table 2. Changes made to the 1999 NEI HAP inventories prior to processing for 1999 NATA or 

projections............................................................................................................................... 6 

Table 3. Emissions (tons) for MSAT HAPs in the 1999 NEI inventories. Totals include Puerto 

Rico and the Virgin Islands..................................................................................................... 8

Table 4. Non-HAP precursors for the MSAT secondary HAPs with source sector emissions for 

1999. Totals include Puerto Rico and the Virgin Islands. ................................................... 10 

Table 5. Locomotive SCC codes in the 1999 NEI nonroad inventory. ....................................... 11 

Table 6. Locomotive 50-State annual emissions trends (tons per year) and future year ratios ... 12 

Table 7. Locomotive HAPs. HAPs not in bold are not emphasized in the MSAT study but are 

projected................................................................................................................................ 13 

Table 8. Commercial marine vessel 50-State annual emissions trends (tons per year) and future 

year ratios used as projection factors.................................................................................... 14 

Table 9. Commercial marine vessel SCC codes, HAPs, and basis of projection factors. HAPs in 

bold are emphasized in the MSAT study.............................................................................. 15 

Table 10. National locomotive emissions (rounded) by SCC for selected HAPs and across all HAPs. 17 

Table 11. National commercial marine vessel emissions (rounded) by SCC for selected HAPs 

and across all HAPs. ............................................................................................................. 18 

Table 12. TAF landing and take-off data for 2002 through 2020, 2015, and 2020..................... 19 

Table 13. Aircraft growth factors for MSAT study years............................................................ 20 

Table 14. Airport related SCC codes and assigned growth factor basis...................................... 21 

Table 15. Airport related emissions (excluding airport support equipment) for selected HAPs 

and all HAPs by SCC. Non-point SCC emissions for 2030 are set equal to 2020. ............. 24 

Table 16. HDDV SCC codes used to calculate HDDV emissions for NEI projections. ............. 28 

Table 17. Motorcycle (MC) SCC codes not in NMIM output for Alpine, Modoc, and Sierra 

Counties California. .............................................................................................................. 29 

Table 18. National summary of projected onroad emissions by vehicle type for 1999, 2007, 

2010, 2015, 2020, and 2030 across all HAPs and for 1,3-butadiene, acetaldehyde, acrolein, 

benzene, formaldehyde, and naphthalene. ............................................................................ 30 

Table 19. SCC codes in the 1999 NEI inventory and not in the NMIM inventory. .................... 33 

Table 20. National engine emissions for selected HAPs and total MSAT HAPs for 1999, 2007, 

2010, 2015, 2020, and 2030.................................................................................................. 35 

Table 21. National equipment emissions for selected HAPs and all MSAT HAPs for 1999, 2007, 

2010, 2015, 2020, and 2030.................................................................................................. 37 

Table 22. National engine/equipment emissions for MSAT HAPs............................................. 40 

Table 23. Onroad refueling SCC codes. ...................................................................................... 42 

Table 24. Onroad refueling emissions by SCC for 1999, 2007, 2010, 2015 and 2020. .............. 43 

The precursors from nonroad emission categories covered by the NONROAD model were 

processed using a similar methodology as the emissions for HAPs. However, instead of 

HAP specific projection ratios, we used VOC ratios from NMIM.Table 25. HDDV SCC 

codes used to calculate HDDV emissions in the precursor inventory.................................. 45 

Table 25. HDDV SCC codes used to calculate HDDV emissions in the precursor inventory.... 46 

Table 26. National level MACT growth factors for 2015 and 2020............................................ 49 

iii 
List of Tables 

Table 27. Utility Boilers: Coal (MACT=1808-1) state level growth factors for 2015 and 2020.50 

Table 28. SIC codes changed due to unrealistic growth factors.................................................. 51 

Table 29. Summary of Categories for which reductions were applied in EMS-HAP. ................ 54 

Table 30. 1999 and projected stationary emissions for selected HAPs and total MSAT HAPs... 56 

Table 31. ASPEN emission groups for MSAT for future years .................................................. 60 

Table 32. Reactivity classes for MSAT HAPs and precursors. ................................................... 64 

Table 33. Description of emissions files for the stationary and mobile divisions used for ASPEN 

simulations. ........................................................................................................................... 65 

Table 34. National average background, stationary, and mobile ASPEN concentrations (μg m-3) 

for each MSAT HAP for 2015, 2020, and 2030................................................................... 70 

Table 35. National average stationary and mobile HAPEM concentrations (μg m-3) for 2015, 

2020, and 2030 by HAP........................................................................................................ 81

Table 36. MSAT HAPs carcinogenic class, URE, non-cancer target organ systems, and Rfc. 

N/A denotes HAP is neither a cancer or non-cancer HAP ................................................... 83 

Table 37. National average inhalation cancer risks for stationary and mobile sources for MSAT 

HAPs, each carcinogenic class and total risk (all MSAT HAPs). ........................................ 85 

Table 38. National average non-cancer hazard quotient (HQ) for MSAT HAPs and hazard index 

(HI) for organ systems for stationary and mobile sources.................................................... 88 

Table 39. Population risk classes for mobile total risk for 2015, 2020, and 2030 using projected 

populations for each year...................................................................................................... 90 

Table 40. Population respiratory HI classes for mobile sources for 2015, 2020, and 2030 using 

projected populations for each year. ..................................................................................... 92 

Table 41. Total benzene emissions of counties within 300 km of Wake County, NC for 1999, 

2015, 2020 and 2030, 1999 background benzene concentration for Wake County, and 

scaled background concentrations for Wake County for 2015, 2020, and 2030. ................. 94 

Table 42. National average 1999 background and scaled backgrounds for 1,3-butadiene, 

acetaldehyde, benzene, formaldehyde, and xylenes. ............................................................ 95 

Table 43. National average total concentrations (all sources and background) for 2015, 2020, 

and 2030 using both the 1999 background and the scaled backgrounds. ............................. 95 

Table 44. Benzene gasoline marketing and distribution SCC codes to be controlled. ................ 98 

Table 45. Change in Average Fuel Benzene Level (Volume Percent) by PADD with 

Implementation of Proposed Fuel Benzene Standard (CG – Conventional Gasoline; RFG – 

Reformulated Gasoline)...................................................................................................... 104

Table 46. Benzene stationary emissions (tons) before and after applying controls to reference 

case gasoline marketing and distribution emissions (non refueling gasoline) and vehicle 

refueling emissions. Also shown are the percent differences (control-reference). 1999 NEI 

emissions are shown for comparison. ................................................................................. 105 

Table 47. National MSAT reference and controlled emissions (nearest ton) for gasoline powered 

vehicles by HAP for 2015, 2020, and 2030........................................................................ 108 

Table 48. 2015, 2020, and 2030 reference and controlled emissions for the five HAPS for 

nonroad gasoline categories................................................................................................ 109 

Table 49. National average 1999 and future year reference and controlled benzene stationary 

concentrations. .................................................................................................................... 110 

iv 
List of Tables 

Table 50. National average reference and controlled onroad gasoline concentrations for the five 

HAPs for 2015, 2020, and 2030.......................................................................................... 111 

Table 51. National average reference and controlled nonroad gasoline concentrations for the five 

HAPs for 2015, 2020, and 2030.......................................................................................... 111 

Table 52. National average 1999 and future reference and controlled benzene HAPEM 

stationary concentrations. ................................................................................................... 112 

Table 53. National average reference and controlled HAPEM onroad gasoline concentrations for 

the five HAPs for 2015, 2020, and 2030. ........................................................................... 112 

Table 54. National average reference and controlled HAPEM nonroad gasoline concentrations 

for the five HAPs for 2015, 2020, and 2030....................................................................... 112 

Table 55. National average risks from stationary sources for 1999 and future year reference and 

controlled benzene, carcinogen class A, and total (all MSAT HAPs)................................ 113 

Table 56. Reference and controlled HAPEM onroad gasoline risks for 2015, 2020, and 2030 for 

individual HAPs and carcinogen classes A, B1, and B2 and total risk (all MSAT HAPs, 

including HAPs not controlled). ......................................................................................... 113 

Table 57. Reference and controlled HAPEM nonroad gasoline risks for 2015, 2020, and 2030 

for individual HAPs and carcinogen classes A, B1, and B2 and total risk (all MSAT HAPs, 

including HAPs not controlled). ......................................................................................... 114 

Table 58. 1999 and future year reference and controlled stationary benzene hazard quotients and 

immune system hazard indices for MSAT HAPs for 2015 and 2020................................. 114 

Table 59. Reference and controlled HAPEM onroad gasoline HQ for controlled HAPs and HI 

for immune, reproductive, and respiratory systems (including MSAT HAPs not controlled) 

for 2015, 2020, and 2030. ................................................................................................... 115

Table 60. Reference and controlled HAPEM nonroad gasoline HQ for controlled MSAT HAPs 

and HI for immune, reproductive, and respiratory systems (from MSAT HAPs including 

those HAPs not controlled) for 2015, 2020, and 2030. ...................................................... 115 

Table 61. Population risk classes for stationary and mobile total risk for 2015, 2020, and 2030 

for reference and controlled risks from MSAT HAPs using projected populations for each 

year. The total category includes background contributions. ............................................ 116 

Table 62. Population HI classes for mobile and total respiratory HI for 2015, 2020, and 2030 for 

reference and controlled risks using projected populations for each year. The total category 

includes background contributions. .................................................................................... 117 

v 
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List of Figures 

Figure 1. Format of the aircraft growth factor file. 2010 growth factors shown as example. .... 22 

Figure 2. Flowchart of aircraft and aviation gasoline emissions projections. 

Figure 5. Box and whisker plots of ratios of stationary secondary contributions to total 

............................. 23 

Figure 3. 2015 county level refueling projection factors............................................................. 41 

Figure 4. Annual emissions by source sector at the national level. ............................................. 56 

concentrations (white boxes) and ratios of mobile secondary contributions to total 

concentrations (gray boxes) for 1999 acetaldehyde and formaldehyde concentrations. Dots 

represent the national mean ratios......................................................................................... 61 

Figure 6. Stationary source emission input files and ASPEN output files for each reactivity class 

for MSAT HAPs. .................................................................................................................. 65 

Figure 7. Mobile source emission input files and ASPEN output files for each reactivity class for 

Figure 8. Mobile source emission input files and ASPEN output files for each reactivity class for 

Figure 9. 1999 County level median total (all sources and background) concentrations (μg m-3) 

Figure 10. 2015 County level median total (all sources and background) concentrations (μg m-3) 

Figure 11. 2020 County level median total (all sources and background) concentrations (μg m-3) 

Figure 12. 2030 County level median total (all sources and background) concentrations (μg m-3) 

MSAT HAPs......................................................................................................................... 66 

MSAT precursors.................................................................................................................. 67 

for benzene............................................................................................................................ 71 

for benzene............................................................................................................................ 72 

for benzene............................................................................................................................ 73 

for benzene............................................................................................................................ 74 

Figure 13. Sample records of the Run1 2015 HAPEM input air quality file con45201_run1.txt 

for benzene. Note that each set of concentrations for a tract is one record. More records 

appear due to of “wrapping” of text in word processor........................................................ 75 

Figure 14. Sample records of the Run2 2015 HAPEM input air quality file con45201_run2.txt 

for benzene............................................................................................................................ 75 

Figure 15. Sample records showing HAPEM5 output for Benzene Run1. Filename is 

2015_45201_run1.HAPEM5-TRACT.txt. Variables are FIPS, tract id, major, area & other, 

onroad gasoline, nonroad gasoline, total and background concentrations............................ 79 

Figure 16. Sample records showing HAPEM5 output for Benzene Run2. Filename is 

2015_45201_run2.HAPEM5-TRACT.txt. Variables are FIPS, tract id, major, area and 

other, onroad diesel, nonroad other, total and background concentrations. ......................... 79 

Figure 17. 2015 HAPEM county median total concentrations (all sources) for benzene............ 82 

Figure 18. County median total inhalation cancer risks for all MSAT HAPs for 2015. Risk is 

Figure 19. County median total (all sources) non-cancer hazard index for MSAT HAPs affecting 

Figure 20. Counties within 300 km of the centroid of Wake County, North Carolina (county in 

Figure 21. Benzene background concentrations (μg m-3) for a) 1999 background, b) 2015 scaled 

Figure 22. Xylenes background concentrations (μg m-3) for a) 1999 background, b) 2015 scaled 

characterized as N in a million.............................................................................................. 85

the respiratory system. .......................................................................................................... 87 

gray). Dots represent county centroids. ............................................................................... 94 

background c) 2020 scaled background and d) 2030 scaled background............................. 96 

background c) 2020 scaled background and d) 2030 scaled background............................. 96 

vi 
List of Figures 

Figure 23. PADD regions for the U.S........................................................................................ 103

Figure 24. RFG counties (dark gray) for the U.S. ..................................................................... 104 

vii 
List of Acronyms 

AEO Annual Energy Outlook 

ASPEN Assessment System for Population Exposure Nationwide 

BEA Bureau of Economic Analysis 

CAS Chemical Abstract Service 

EGAS Economic Growth Analysis System 

EMS-HAP The Emissions Modeling System for Hazardous Air Pollutants 

EPA United States Environmental Protection Agency 

HAP Hazardous Air Pollutant 

HAPEM5 Hazardous Air Pollutant Exposure Model, Version 5 

HI non-cancer Hazard Index for a target organ system 

HQ non-cancer Hazard Quotient for an individual HAP 

MACT Maximum Available Control Technology standards for HAP, established under 

Section 112 of the Clean Air Act 

MSAT Mobile Source Air Toxics 

NATA National Air Toxics Assessment 

NEI EPA’s National Emission Inventory 

NMIM National Mobile Inventory Model 

OAQPS EPA’s Office of Air Quality Planning and Standards 

OTAQ EPA’s Office of Transportation and Air Quality 

REMI Regional Economic Model, Inc. 

SAROAD Air pollution chemical species classification system used in EPA’s initial data 

base for “Storage and Retrieval of Aerometric Data” 

SCC Source Classification Code 

SIC Standard Industrial Classification code used for Federal economic statistics 

TAF Terminal Area Forecast 

URE Unit risk estimate for cancer risk 

vi 
List of files referenced in document 

File Description Section 

onroad_0923.xls Excel workbook of onroad emissions by vehicle type for state and 

national level 

3.3.2 

onroad_pivot.xls Excel workbook containing pivot table of onroad emissions by 

vehicle type for state and national 

3.3.2 

nonroad_0923.xls Excel workbook of nonroad emissions by engine, equipment, and 

engine/equipement type for state and national level 

3.3.3 

nonroad_pivot.xls Excel workbook containing pivot table of nonroad emissions by 

engine, equipment, and engine/equipment type for state and 

national level 

3.3.3 

mwi.sas SAS® program to substitute 2002 MWI point emissions in the 

1999 point inventory 

4.3 

concentrations.xls Excel workbook of national and state mean concentrations and 

concentration distribution for ASPEN results 

6.3 

ASPEN_medians.ppt PowerPoint file containing national maps of county median total 

ASPEN concentrations 

6.3 

hapem_concentrations.xls Excel workbook of national and state mean concentrations and 

concentration distribution for HAPEM results 

7.2 

HAPEM_medians.ppt PowerPoint file containing national maps of county median total 

HAPEM concentrations 

7.2 

hapem_risks.xls Excel workbook of national and state mean risks and risk 

distribution for HAPEM based results 

8.1 

risk_030305.ppt PowerPoint file containing national maps of county median total 

HAPEM based risks 

8.1 

hapem_hq.xls Excel workbook of national and state mean HQ and HI and HQ 

and HI distribution for HAPEM based results 

8.2 

hq_030305.ppt PowerPoint file containing national maps of county median total 

HAPEM based HQ and respiratory HI 

8.2 

pop_stats_risk.xls Excel workbook of population statistics using both 2000 and 

projected populations for cancer risk 

8.3 

pop_stats_hi_respiratory.xls Excel workbook of population statistics using both 2000 and 

projected populations for respiratory HI 

8.3 

cty_cntr99.sas7bdat SAS® dataset of county centroids 9 

background_test.xls Excel workbook containing national and county mean 

concentrations using 1999 background and scaled backgrounds 

9 

background_acetaldehyde_0111.ppt PowerPoint file containing county maps of concentrations using 

1999 and scaled backgrounds for acetaldehyde. 

9 

background_butadiene_0111.ppt PowerPoint file containing county maps of concentrations using 

1999 and scaled backgrounds for 1,3-butadiene. 

9 

background_test_0111.ppt PowerPoint file containing county maps of concentrations using 

1999 and scaled backgrounds for benzene. 

9 

background_formaldehyde_0111.ppt Powerpoint file containing county maps of concentrations using 

1999 and scaled backgrounds for formaldehyde. 

9 

background_xylenes_0111.ppt PowerPoint file containing county maps of concentrations using 

1999 and scaled backgrounds for xylenes. 

9 

benzene_gas_scc.xls Excel workbook containing benzene gasoline distribution 

reference and controlled emissions by SCC 

10.1 

onroad_controls.xls Excel workbook of controlled onroad emissions by vehicle type 

for state and national level 

10.2 

onroad_controls_pivot.xls Excel workbook containing pivot table of controlled onroad 

emissions by vehicle type for state and national 

10.2 

vii 
List of files referenced in document 

File Description Section 

nonroad_ controls.xls Excel workbook of controlled nonroad emissions by engine, 

equipment, and engine/equipment type for state and national level 

10.3 

nonroad_pivot_ controls.xls Excel workbook containing pivot table of controlled onroad 

emissions by engine, equipment, and engine/equipment type for 

state and national level 

10.3 

aspen_conc_controls.xls Excel workbook of national and state mean controlled 

concentrations and concentration distribution for ASPEN results 

10.5 

ASPEN_median_cntrl.ppt PowerPoint file containing national maps of county median total 

ASPEN controlled concentrations 

10.5 

hapem_concentrations_cntrl.xls Excel workbook of national and state mean concentrations and 

concentration distribution for HAPEM controlled results 

10.6 

HAPEM_median_cntrl.ppt PowerPoint file containing national maps of county median total 

HAPEM controlled concentrations 

10.6 

hapem_risks_control.xls Excel workbook of national and state mean risks and risk 

distribution for controlled HAPEM based results 

10.7.1 

risk_cntrll.ppt PowerPoint file containing national maps of county median total 

controlled HAPEM based risks 

10.7.1 

hapem_hq_control.xls Excel workbook of national and state mean HQ and HI and HQ 

and HI distribution for controlled HAPEM based results 

10.7.2 

hq_cntrll.ppt PowerPoint file containing national maps of county median total 

controlled HAPEM based HQ and respiratory HI 

10.7.2 

pop_stats_risk_cntrl.xls Excel workbook of population statistics using both 2000 and 

projected populations for controlled cancer risk 

10.7.3 

pop_stats_hi_resp_cntrl.xls Excel workbook of population statistics using both 2000 and 

projected populations for controlled respiratory HI 

10.7.3 

onroad.sas SAS® program to project 1999 onroad emissions to future years App. B 

loco_marine.sas SAS® program to create locomotive and CMV projection factor 

files 

App. C 

marine_locomotive_growth.sas SAS® program to project 1999 locomotive and CMV emissions to 

future years. 

App. C 

nonroad.sas SAS® program to project nonroad emissions (excluding aircraft, 

locomotives, and CMV) to future years 

App. C 

cancer_risk.sas SAS® program to calculate risk estimates from HAPEM results App. D 

noncancer.sas SAS® program to calculate non-cancer estimates from HAPEM 

results 

App. D 

project_stationary_benz.sas SAS® program to apply controls to projected benzene gasoline 

distribution emissions 

App. E 

control_onroad.sas SAS® program to apply controls to projected onroad emissions App. F 

calc_factors.sas SAS® program to calculate exhaust and evaporative factors for 

use in controlling projected nonroad emissions 

App. G 

control_nonroad.sas SAS® program to control projected nonroad emissions App. G 

viii 
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1. Purpose of Work 

The purpose of the work described in this technical document was to project emissions for 

mobile source hazardous air pollutants (HAPs) to 2007, 2010, 2015, 2020, and 2030 from the 

1999 National Emissions Inventory Version 3 (NEI) (U. S. EPA, 2004a), conduct air quality and 

exposure modeling, and estimate cancer and non-cancer risk for select future years. Air quality 

modeling utilized the Assessment System for Population Exposure Nationwide (ASPEN) model 

(U. S. EPA, 2000). Exposure modeling utilized the Hazardous Air Pollutant Exposure Model, 

Version 5 (HAPEM5) (U.S. EPA, 2005d). Cancer risk and non-cancer risk were estimated for 

2015, 2020, and 2030. Modeling was done for reference cases, which included programs 

currently planned and in place, as well as control scenarios that evaluated potential impacts of 

additional control programs. This work was done to support regulatory needs related to the 2006 

proposed mobile source air toxics rule. Intermediate year inventories for 2002 through 2010, 

inclusive, were also developed to support other program needs in the Office of Air and 

Radiation. 

The pollutants modeled in this study, in support of the mobile source air toxics rule, are shown in 

Table 1. They are referenced in the document as MSAT HAPs. These pollutants are all included 

in the NEI and are on EPA’s list of hazardous pollutants in Section 112 of the Clean Air Act. 

They are also emitted by mobile sources. In this assessment, projected inventories were 

developed for both the mobile and stationary emission sources in the 1999 NEI. There are 

additional hazardous air pollutants in the 1999 NEI with a mobile source emissions estimate that 

are not included in Table 1. Some of these were pollutants found only in data submitted by 

individual States. Others were generated by EPA through the use of speciation factors obtained 

from a non-mobile source process (e.g., commercial marine vessels, residual oil). More 

information on the 1999 NEI development can be found at www.epa.gov/ttn/chief/. 

Emission projection methods for other HAPs (non-MSAT HAPs) are also discussed in several 

places in this document. These projections were done to support other OAR program needs. 

After inventory projection, these pollutants were modeled in ASPEN and HAPEM5, following 

the same general methods used in the 1999 National Air Toxics Assessment 

(www.epa.gov/ttn/nata99). 

The remainder of this document describes the methodology used for the inventory projections 

and subsequent air quality modeling. Section 2 describes the 1999 base HAP and precursor 

inventories, Section 3 describes the development of the projected mobile inventories and 

refueling projection factors. Section 4 describes the development of the projected stationary 

inventories. Sections 5, 6, and 7 describe the emissions processing, air quality modeling and 

exposure modeling. Section 8 describes the calculation of cancer risk and non-cancer risk 

(hazard quotients and hazard indices). Section 9 describes the methodology to adjust future year 

background concentrations based on projected emissions, and Section 10 describes the 

methodology to develop future year inventories that incorporate the benzene control scenario. 

Appendices also follow describing the calculations in more detail, and providing sample 

calculations and additional supporting data. 

1 
Table 1. Pollutants of interest in MSAT study. CAS numbers in italics are in the stationary 

inventories only; otherwise they are in mobile and stationary inventories. 

HAP CAS or pollutant code in 1999 NEI SAROAD(s) 

Organic gaseous HAPs (excluding those assessed as POM group) 

1,3-Butadiene 106990 43218 

2,2,4-Trimethylpentane 540841 43250 

Acetaldehyde 75070 43503 

Acrolein 107028 43505 

Benzene 71432 45201 

Ethyl Benzene 100414 45203 

Formaldehyde 50000 43502 

Hexane 110543 43231 

Methyl tert-butyl ether (MTBE) 1634044 43376 

Naphthalene 91203 46701, 46702 

Propionaldehyde 123386 43504 

Styrene 100425 45220 

Toluene 108883 45202 

Xylenes 106423, 108383, 1330207, 95476 45102 

Metal HAPs 

Chromium III 10060125, 12018018, 1308389, 136, 16065831, 21679312, 

7440473 

59992, 59993 

Chromium VI 10294403, 10588019, 11103869, 11115745, 1308130, 

1333820, 13530659, 136, 13765190, 14307358, 18454121, 

18540299, 7440473, 7738945, 7758976, 7775113, 

7778509, 7789006, 7789062 

69992, 69993 

Manganese 10101505, 1313139, 1317346, 1317357, 198, 7439965, 

7722647, 7783166, 7785877 

80196, 80396 

Nickel 10101970, 12054487, 13138459, 1313991, 1314063, 

13462889, 13463393, 13770893, 226, 373024, 7440020, 

7718549, 7786814, NY059280 

80216, 80316 

HAPs grouped as POM 

Acenaphthene 83329 72002 

Acenaphthylene 208968 72002 

Anthracene 120127 72002 

Benzo(g,h,i)perylene 191242 72002 

Fluoranthene 206440 72002 

Fluorene 86737 72002 

Phenanthrene 85018 72002 

Pyrene 129000 72002 

Benzo(a)pyrene 50328 75002 

Dibenzo(a,h)anthracene 53703 75002 

Benz(a)anthracene 56553 76002 

Benzo(b)fluoranthene 205992 76002 

Benzo(k)fluoranthene 207089 76002 

Indeno(1,2,3,c,d)-pyrene 193395 76002 

Chrysene 218019 77002 

Description of POM groups by SAROAD 

72002: POM, Group 2: no URE data 

75002: POM, Group 5: 5.0E-4 < URE ≤ 5.0E-3 

76002: POM, Group 6: 5.0E-5 < URE ≤ 5.0E-4 

77002: POM, Group 7: 5.0E-6 < URE ≤ 5.0E-5 

2 
The following notes apply to Table 1: 

• Although designated as SAROAD code in the EMS-HAP User’s Guide (U.S. EPA, 

2004b) and ASPEN User’s Guide (U.S. EPA, 2000), the SAROAD code value in Table 1 

is not the actual SAROAD code for the HAP. Rather, it is a 5-digit code used by ASPEN 

and EMS-HAP to represent the specific pollutant or pollutant group that is modeled in 

ASPEN. 

• For HAPs with two SAROAD codes, the lower numbered code represents the fine 

particle mode and the other number represents the coarse particle mode. For 

naphthalene, the lower numbered code represents the gas mode while the higher number 

represents the fine particle mode. For chromium III and chromium VI, CAS numbers 

136 and 7440473 are used for both HAPs. These two CAS numbers represent nonspeciated chromium. During emissions processing, the non-speciated chromium is 

speciated to chromium III and chromium VI. For mobile sources, eighteen percent of the 

chromium was assumed to be hexavalent, based on combustion data from stationary 

combustion turbines that burn diesel fuel (Taylor, 2003). 

• The NEI contains additional POM pollutants including unspeciated POM groups such as 

7-PAH or other specific POM that are not on the MSAT list but are emitted from 

stationary sources and were thus modeled as a POM group. These other POM pollutants, 

with CAS in parentheses, are listed below along with the POM group that they fall in. 

 POM group 71002: total PAH (234), POM (246), 16-PAH – 7-PAH (75040)1, 

16-PAH (40), Benz(a)Anthracene/Chrysene (103) 

 POM group 72002: Benzo(e)pyrene (192972), Perylene (198550), 2­

Methylnaphthalene (91576), Benzofluoranthenes (56832736), 2­

Chloronaphthalene (91587), Methylanthracene (26914181), Methylchrysene 

(248), 12-Methylbenz(a)Anthracene (2422799), 1-Methylpyrene (2381217), 1­

Methylphenanthrene (832699), Methylbenzopyrenes (247), 9­

Methylbenz(a)Anthracene (779022), Benzo(a)fluoranthene (203338), 

Benzo(g,h,i)Fluoranthene (203123), Benzo(c)phenanthrene (195197) 

 POM group 73002: 7,12-Dimethylbenz(a)anthracene (57976) 

 POM group 74002: Dibenzo(a,i)pyrene (189559), D(a,h)pyrene (189640), 3­

Methylcholanthrene (56495) 

 POM group 75002: D(a,e)pyrene (192654), 5-Methylchrysene (3697243) 

 POM group 76002: B(j)fluoranthene (205823), D(a,j)acridine (224420), 

Benzo(b+k)fluoranthene (102) 

 POM group 78002: 7-PAH (75) 

1

 See Table 2 for explanation of CAS 75040. 

3 
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4 
2. 1999 base inventories 

2.1 1999 HAP inventories 

The inventories used in development of the future mobile and stationary inventories are from the 

1999 National Emissions Inventory (NEI) Version 3 

(http://www.epa.gov/ttn/chief/net/1999inventory.html); this is the inventory used for the 1999 

NATA. The HAP emissions are provided for the following four inventory sectors: point, nonpoint, onroad mobile, and nonroad mobile. Point and non-point inventories contain the 

stationary source emissions, and onroad and nonroad contain the mobile emissions. For details 

about each inventory see 

http://www.epa.gov/ttn/chief/net/1999inventory.html. 

For the 1999 NATA, emissions, concentrations and risks are also summarized by emission 

source sector: major, area & other, onroad and nonroad. The inventory sectors onroad and 

nonroad map directly to the corresponding NATA source sectors. The stationary sources (point 

and non-point) map as follows: point sources contain both major and area sources; non-point 

sources contain area and area & other sources. 

Before processing the inventories for the 1999 NATA and/or the future year projections, some 

changes (or fixes) were made to the inventories before processing in EMS-HAP. Table 2 lists 

these changes. 

5 
Table 2. Changes made to the 1999 NEI HAP inventories prior to processing for 1999 NATA or projections. 

Inventory Change 

Changed stack diameter for siteid 4200300899 to 0.67 ft Corrected emissions from pounds to tons for siteid 31109-0217 for methylene chloride Convert stack parameters from English units to metric units Removed dashes from SCC code and convert all lowercase characters in SCC code to uppercase. Also if SCC is 0, 0000“NONE”, or “N/A” make SCC blank If SIC code is “NONE” or “XXXX” make SIC blank 

Point If Maximum Achievable Control Technology (MACT) code is “NONE” make MACT blank 

For sources defaulted to county centroids (DEFAULT_LOC_FLAG=’CNTYCENT’) make the location coordinates equmissing so that EMS-HAP will default location to tracts. If MACT code = 723 then MACT = 0723 If MACT code = 724 then MACT = 0724 Remove mercury emissions Remove all emissions for Puerto Rico and Virgin Islands since we conducted the MSAT analysis for the 50 states 

Replace 1999 MACT 1801 emissions with 2002 draft 1801 emissions 

Remove all emissions for Puerto Rico and Virgin Islands since we conducted the MSAT analysis for the 50 states. Change MACT 1801 emissions to 0 for projections. Remove mercury emissions (note onroad and nonroad inventories contain no mercury) 

Non-point For FIPS/SCC/ combinations where there were both 16-PAH emissions (CAS=40) and 7-PAH emissions (CAS=75) and 

PAH emissions are larger than the 7-PAH emissions, subtract the 7-PAH emissions from the 16-PAH emissions and assiCAS 75040 to the emissions. For the FIPS/SCC combinations being changed delete the 16-PAH emissions but retain themissions. For FIPS/SCC combinations that have both 16-PAH and 7-PAH, but 7-PAH emissions are larger than 16-PAemissions, make no changes. Also make no changes where there are 7-PAH emissions but no 16-PAH and vice versa. 

Remove all emissions for Puerto Rico and Virgin Islands since we conducted the MSAT analysis for the 50 states 

Onroad Change Chromium III and Chromium VI CAS numbers to the unspeciated Chromium CAS (7440473). Once making th

sum up the chromium emissions by FIPS/SCC. The chromium was summed so that EMS-HAP would use an 82/18 chroIII/chromium VI split. Before the summation, the chromium III/chromium VI split was not 82/18% as desired. 

6 
Table 2. Continued. 

Inventory Change 

Remove all emissions for Puerto Rico and Virgin Islands since we conducted the MSAT analysis for the 50 states 

Nonroad 

For FIPS/SCC/ combinations where there were both 16-PAH emissions (CAS=40) and 7-PAH emissions (CAS=75) and PAH emissions are larger than the 7-PAH emissions, subtract the 7-PAH emissions from the 16-PAH emissions and assiCAS 75040 to the emissions. For the FIPS/SCC combinations being changed delete the 16-PAH emissions but retain themissions. For FIPS/SCC combinations that have both 16-PAH and 7-PAH, but 7-PAH emissions are larger than 16-PAemissions, make no changes. Also make no changes where there are 7-PAH emissions but no 16-PAH and vice versa. Change Chromium III and Chromium VI CAS numbers to the unspeciated Chromium CAS (7440473). Once making thsum up the chromium emissions by FIPS/SCC. The chromium was summed so that EMS-HAP would use an 82/18 chroIII/chromium VI split. Before the summation, the chromium III/chromium VI split was not 82/18% as desired. 

Corrected FIPS for aircraft emissions in a few counties in which we found the underlying NEI geographic data to be erroSection C.2.1 of the EMS-HAP V3 User's Guide provides details, in particular, see Table C-4. 

7 
Table 3 lists the inventory emissions for each of the MSAT HAPs prior to EMS-HAP processing. 

Table 3. Emissions (tons) for MSAT HAPs in the 1999 NEI inventories. Totals include Puerto 

Rico and the Virgin Islands. 

HAP Inventory Total 

Point Non-point Onroad Nonroad 

1,3-butadiene 2,068 22,133 23,785 9,923 57,909 

2,2,4-Trimethylpentane 7,211 7,076 167,576 94,546 276,409 

Acetaldehyde 12,141 26,474 30,068 24,099 92,782 

Acrolein 959 21,083 4,013 3,183 29,238 

Benzene 12,529 98,961 171,644 67,642 350,776 

Ethyl Benzene 12,026 27,805 70,075 44,137 154,043 

Formaldehyde 35,571 121,738 81,081 57,520 295,910 

Hexane 46,698 94,755 65,898 30,063 237,414 

MTBE 4,398 13,915 82,777 24,338 125,428 

Naphthalene 2,652 11,433 4,008 1,257 19,350 

Propionaldehyde 1,964 3,480 4,231 4,833 14,508 

Styrene 40,713 9,673 13,266 4,319 67,971 

Toluene 93,912 231,196 460,240 211,095 996,443 

Xylenes 62,588 181,248 269,500 198,748 712,084 

Chromium (total) 846 47 21 22 936 

Manganese 2,844 356 16 6 3,222 

Nickel 1,304 176 16 34 1,530 

Acenaphthene 40 310 26 26 402 

Acenaphthylene 1 1,745 139 66 1,951 

Anthracene 38 333 32 15 418 

Benzo(g,h,i)perylene 2 284 9 10 305 

Fluoranthene 232 524 33 30 819 

Fluorene 60 254 55 51 420 

Phenanthrene 175 1,085 91 101 1,452 

Pyrene 399 617 46 35 1097 

Benzo(a)pyrene 16 1,074 5 3 1,098 

Dibenzo(a,h)anthracene 1 7 0.001 0.07 8.071 

Benz(a)anthracene 109 434 8 5 556 

Benzo(b)fluoranthene 5 86 5 3 99 

Benzo(k)fluoranthene 2 144 5 2 153 

Indeno(1,2,3,c,d)-pyrene 0.4 178 3 3 184.4 

Chrysene 30 388 4 3 425 

7-PAH* 66 40 0 1 107 

16-PAH* 13 320 0 1 334 

16-PAH – 7-PAH* 0 132 0 0 132 

Total PAH* 55 977 0 0 1032 

Total POM* 3,315 644 0 0 3,959 

*Some portion of these could be MSAT HAPs but are not sufficiently speciated in the inventory to determine what 

portion is MSAT POM HAP 

One change made to modeled concentrations for NATA (and this effort) after EMS-HAP and 

ASPEN was to the POM group 75002 concentrations for Oregon for area & other sources. The 

area & other emissions for benzo(a)pyrene were incorrect in the 1999 NEI for Oregon. In order 

to alleviate the problem, the national median area & other concentration (excluding Oregon) was 

substituted for Oregon’s area & other tract level concentrations. 

8 
2.2 1999 Precursor inventories 

In order to calculate secondary concentrations for acetaldehyde, acrolein, formaldehyde, and 

propionaldehyde after ASPEN simulations for the primary concentrations, the emissions for the 

precursors also had to be processed through EMS-HAP and subsequently ASPEN for later 

secondary contribution calculations. For those precursors that are not HAPs themselves (nonHAP precursors) a separate precursor inventory was used. The precursor inventory used was the 

same as that used for the 1999 NATA and is Version 2 of the NEI for VOC. Precursor emissions 

were obtained by speciating VOC emissions from Version 2 of the NEI. The speciation profiles 

are the same as those used for the 1996 NATA (see Section D.1.2 in EMS-HAP Version 2 User’s 

Guide, [U.S. EPA, 2002]). All point sources in the precursor inventory were treated as major 

sources. Table 4 lists the non-HAP precursors for the four secondary HAPs being modeled in 

this study. 

9 
Table 4. Non-HAP precursors for the MSAT secondary HAPs with source sector emissions for 

1999. Totals include Puerto Rico and the Virgin Islands. 

Precursor Precursor for Inventory Total 

Point Non-point Onroad Nonroad 

1-Butene Formaldehyde, 5,337 24,245 41,481 12,503 83,566 

1-2,3-Dimethyl butene Formaldehyde 110 3,004 1,902 0 5,016 

1-2-Ethyl butene Formaldehyde 0.0005 0 0 255 255.0005 

1-2-Methyl butene Formaldehyde 104 4,750 30,647 11,898 47,399 

1-3-Methyl butene Formaldehyde 81 7,527 4,625 2,947 15,180 

2-Butene Acetaldehyde 2,170 7,503 42,270 7,521 59,464 

2-2-Methyl butene Acetaldehyde 78 4,750 89,651 19,722 114,201 

1-Decene Formaldehyde 762 0 0 166 928 

Ethanol Acetaldehyde 43,907 201,811 22,820 330 268,868 

Ethene Formaldehyde 24,481 349,992 411,045 165,071 950,589 

1-Heptene Formaldehyde 602 177 1,524 1,805 4,108 

2-Heptene Acetaldehyde 579 177 2,822 2,196 5,774 

1-Hexene Formaldehyde 1,248 1,332 15,498 6,507 24,585 

2-Hexene Acetaldehyde 94 1,332 13,399 10,077 24,902 

3-Hexene Propionaldehyde 516 1,332 5,370 4,370 11,588 

Isoprene Formaldehyde 316 401 6,280 5,289 12,286 

1-Nonene Formaldehyde 56 301 35,806 7,082 43,245 

2-Nonene Acetaldehyde 2 0 0 82 84 

1-Octene Formaldehyde 37 0.3 1,961 1,828 3,826.3 

2-Octene Acetaldehyde 18 0.3 1,961 837 2,816.3 

1-Pentene Formaldehyde 2,693 18,199 32,313 8,959 62,164 

1-2,4,4-Trimethyl Formaldehyde 27 0 21,064 0 21,091 

1-2-Methyl pentene Formaldehyde 124 1,784 37,937 4,780 44,625 

1-3-Methyl pentene Formaldehyde 87 1,782 55,197 4,127 61,193 

1-4-Methyl pentene Formaldehyde 96 1,782 6,072 2,275 10,225 

2-Pentene Acetaldehyde, Propionaldehyde 2,699 4,550 70,835 22,234 100,318 

2-3-Methyl pentene Acetaldehyde 66 1,782 14,328 10,916 27,092 

2-4-Methyl pentene Acetaldehyde 132 1,782 37,937 8,950 48,801 

Propene Acetaldehyde, Formaldehyde 15,705 64,853 171,468 66,043 318,069 

2-Methyl propene Formaldehyde 602 8,896 93,943 19,171 122,612 

10 
3. Development of Future Year Mobile and Mobile-Related Emissions 

3.1 Locomotive and commercial marine vessels 

Emissions from locomotive and commercial marine vessels were projected similarly; using ratios 

computed from previously projected, multi-year, national-level, criteria pollutant emission data. 

Because these previously projected emissions account for both activity growth and reductions 

due to control programs, the term “projection factor” is used rather than “growth factor” to 

describe the factor used to multiply base year emissions to obtain future year emissions. Table 5 

shows the eight locomotive SCC codes in the 1999 NEI. 

Table 5. Locomotive SCC codes in the 1999 NEI nonroad inventory. 

SCC Description 

2285000000 Mobile Sources, Railroad Equipment, All Fuels, Total 

2285002000 Mobile Sources, Railroad Equipment, Diesel, Total 

2285002005 Mobile Sources, Railroad Equipment, Diesel, Line Haul Locomotives 

2285002006 Mobile Sources, Railroad Equipment, Diesel, Line Haul Locomotives: Class I operations 

2285002007 Mobile Sources, Railroad Equipment, Diesel, Line Haul Locomotives: Class II/III operations 

2285002008 Mobile Sources, Railroad Equipment, Diesel, Line Haul Locomotives: Passenger Trains 

(Amtrak) 

2285002009 Mobile Sources, Railroad Equipment, Diesel, Line Haul Locomotives: Commuter lines 

2285002010 Mobile Sources, Railroad Equipment, Diesel, Yard Locomotives 

Projection factors for the locomotive emissions, which account for both growth and reductions 

due to control programs, were developed from the VOC and PM10 projected emissions shown in 

Table 6. These were derived as part of the EPA’s 2004 Clean Air Nonroad Diesel Rule (U.S. 

EPA, 2004d). 

11 
Table 6. Locomotive 50-State annual emissions trends (tons per year) and future year ratios 

Year VOC 

emissions 

VOC ratio 

 (future year/1999) 

PM10 

emissions 

PM10 ratio 

(future year/1999) 

1999 34,579 1.0000 20,869 1.0000 

2007 32,646 0.9441 17,657 0.8461 

2010 31,559 0.9127 15,109 0.7240 

2015 31,072 0.8986 14,461 0.6929 

2020 30,170 0.8725 13,652 0.6542 

2030 28,622 0.8277 12,061 0.5779 

In that they were computed using 50-state total emission sums, the projection factors were 

national level. In addition, they were applied to each pollutant across all SCC codes. That is, all 

locomotive SCC codes with pollutants deemed VOC received the same projection factor. The 

pollutants associated with locomotive emissions are shown in Table 7 with their assigned 

projection factor for locomotives. 

12 
Table 7. Locomotive HAPs. HAPs not in bold are not emphasized in the MSAT study but are 

projected. 

HAP Growth factor basis HAP Growth factor basis 

1,3-Butadiene VOC ratios Hexane VOC ratios 

2,2,4­

Trimethylpentane 

VOC ratios Lead Metals (projection factors = 1.0) 

Acetaldehyde VOC ratios Manganese Metals (growth factors = 1.0) 

Acrolein VOC ratios Methanol VOC ratios 

Antimony Metals (projection factors = 1.0) Methyl ethyl 

ketone 

VOC ratios 

Benzene VOC ratios Naphthalene PM ratios 

Beryllium Metals (projection factors = 1.0) Nickel Metals (projection factors = 1.0) 

Cadmium Metals (projection factors = 1.0) POM (excluding 

Naphthalene) 

PM ratios 

Chlorine VOC ratios Phosphorus PM ratios 

Chromium Metals (projection factors = 1.0) Propionaldehyde VOC ratios 

Cobalt Metals (projection factors = 1.0) Selenium Metals (projection factors = 1.0) 

Cumene VOC ratios Styrene VOC ratios 

Ethyl benzene VOC ratios Toluene VOC ratios 

Formaldehyde VOC ratios Xylene VOC ratios 

Metals were set to no growth (projection factor = 1.0, metals remain at 1999 levels) because little 

activity change is expected in locomotives in the future. This is because metal emissions were 

most likely the result of impurities in fuel and engine oil, and from engine wear, and it is not 

known how these emissions would be impacted by controls, if it all. Several of the metals were 

estimated using emission factor (EF) x Activity, and several were estimated as fractions of PM 

emissions. 

Projection factors for commercial marine vessels (CMV) were computed similarly to locomotive 

projection factors, using 50-state emission summaries for various future years that were 

developed as part of the EPA’s 2004 Clean Air Nonroad Diesel Rule (U.S. EPA, 2004d). These 

emissions summaries are shown in Table 8. 

One difference, however, is that the projection factors for CMV were specific to both SCC 

(diesel, residual, or no fuel information) and pollutant specific (VOC or PM). The SCC 

dependence on the projection factor was based on whether the SCC was related to diesel 

emissions or residual emissions. There were three SCC codes used to assign the basis of the 

13 
projection factor for the SCC. Within the SCC, the projection factor used was dependent on 

whether the HAP was VOC or PM. Table 8 lists the projection factors computed from the 50­

state total emission summaries commercial marine vessels. 

Projection factors computed for the SCC codes in Table 8 were assigned to the five SCC codes 

corresponding to the commercial marine vessels in the 1999 NEI. Each HAP within the SCC 

category was then assigned the projection factor for VOC or PM. Table 9 lists the SCC codes 

and HAPs associated with the commercial marine vessel emissions in the 1999 NEI. 

Table 8. Commercial marine vessel 50-State annual emissions trends (tons per year) and future 

year ratios used as projection factors 

SCC Description Year VOC 

emissions 

VOC ratio 

(year/ 1999) 

PM10 

emissions 

PM10 ratio 

(year/ 1999) 

2280000000 Mobile Sources, 1999 32,133 1.0000 39,012 1.0000 

Marine Vessels, 2007 35,951 1.1188 43,247 1.1086 

Commercial, All 2010 36,990 1.1511 43,717 1.1206 

Fuels, Total, All 2015 39,543 1.2306 47,456 1.2165 

Vessel Types 2020 43,395 1.3505 53,496 1.3713 

2030 55,083 1.7142 72,489 1.8581 

2280002000 Mobile Sources, 1999 23,403 1.0000 19,927 1.0000 

Marine Vessels, 2007 24,530 1.0482 19,133 0.9601 

Commercial, Diesel, 2010 24,568 1.0498 17,721 0.8893 

Total, All Vessel 2015 24,695 1.0552 16,900 0.8481 

Types 2020 25,268 1.0797 16,795 0.8428 

2030 27,546 1.7700 18,258 1.9162 

228003000 Mobile Sources, 

Marine Vessels, 

Commercial, 

Residual, Total, All 

Vessel Types 

1999 8,730 1.0000 19,085 1.0000 

2007 11,421 1.3083 24,115 1.2635 

2010 12,421 1.4229 25,996 1.3621 

2015 14,848 1.7009 30,556 1.6011 

2020 18,127 2.0765 36,701 1.9230 

2030 27,537 3.1544 54,231 2.8416 

14 
Table 9. Commercial marine vessel SCC codes, HAPs, and basis of projection factors. HAPs in 

bold are emphasized in the MSAT study. 

SCC Description VOC and PM HAPs Projection factor basis 

2280000000 Mobile Sources, Marine 

Vessels, Commercial, All 

Fuels, Total, All Vessel Types 

1,3-Butadiene, 2,2,4­

Trimethylpentane, Acetaldehyde, 

Benzene, Chlorine, Cumene, Ethyl 

Benzene, Formaldehyde, Hexane, 

Methanol, Methyl Ethyl Ketone, 

Propionaldehyde, Styrene, 

Toluene, Xylenes 

VOC ratios for 

2280000000 (all fuels) 

Antimony, Cadmium, Chromium, 

Cobalt, Lead, Manganese, 

Naphthalene, Nickel, Phosphorus, 

POM, Selenium 

PM ratios for 

2280000000 (all fuels) 

2280002100 Mobile Sources, Marine 

Vessels, Commercial, Diesel, 

Diesel- port emissions 

1,3-Butadiene, 2,2,4­

Trimethylpentane, Acetaldehyde, 

Acrolein, Benzene, Chlorine, 

Cumene, Ethyl Benzene, 

Formaldehyde, Hexane, Methanol, 

Methyl Ethyl Ketone, 

Propionaldehyde, Styrene, 

Toluene, Xylenes 

VOC ratios for 

2280002000 (diesel) 

Antimony, Cadmium, Chromium, 

Cobalt, Lead, Manganese, 

Naphthalene, Nickel, Phosphorus, 

POM, Selenium 

PM ratios for 

2280002000 (diesel) 

2280002200 Mobile Sources, Marine 

Vessels, Commercial, Diesel, 

Diesel- underway emissions 

2,2,4-Trimethylpentane, 

Acetaldehyde, Acrolein, Benzene, 

Ethyl Benzene, Formaldehyde, 

Hexane, Propionaldehyde, 

Styrene, Toluene, Xylenes 

VOC ratios for 

2280002000 (diesel) 

Chromium, Lead, Manganese, 

Naphthalene, Nickel, POM PM ratios for 

2280002000 (diesel) 

2280003100 Mobile Sources, Marine 

Vessels, Commercial, 

Residual, Residual - port 

emissions 

2,2,4-Trimethylpentane, 

Acetaldehyde, Acrolein, Benzene, 

Chlorobenzene, Ethyl Benzene, 

Formaldehyde, Hexane, 

Propionaldehyde, Styrene, 

Toluene, Xylenes 

VOC ratios for 

2280003000 (residual) 

Beryllium, Cadmium, Chromium, 

Lead, Manganese, Naphthalene, 

Nickel, POM, Selenium 

PM ratios for 

2280003000 (residual) 

2280003200 Mobile Sources, Marine 

Vessels, Commercial, 

Residual, Residual -underway 

emissions 

2,2,4-Trimethylpentane, 

Acetaldehyde, Acrolein, Benzene, 

Ethyl Benzene, Formaldehyde, 

Hexane, Propionaldehyde, 

Styrene, Toluene, Xylenes 

VOC ratios for 

2280003000 (residual) 

Beryllium, Cadmium, Chromium, 

Lead, Manganese, Naphthalene, 

Nickel, POM, Selenium 

PM ratios for 

2280003000 (residual) 

15 
As can be seen from Tables 7 and 9, there were other HAPs being projected other than those of 

interest for the MSAT study. As mentioned previously, these were HAPs found only in data 

submitted by States or in surrogate profiles for vessels running on residual fuel. These HAPs 

were not removed from the inventories for MSAT because these HAPs would need to be 

projected for other projections work; by not removing pollutants there were fewer datasets to 

manage. 

Appendix C (C.1.1) describes the steps involved in the development of the projection factor files 

used for the locomotives and commercial marine vessels emission projections and (C.1.2) 

the steps taken to apply the factors and produce projected emissions. 

Tables 10 and 11 present the nationwide 1999 and projected emissions for locomotives and 

commercial marine vessels, respectively. “All HAPs” refer to the sum of MSAT and non-MSAT 

HAPs. 

16 
Table 10. National locomotive emissions (rounded) by SCC for selected HAPs and across all HAPs. 

SCC HAP 

Emissions (tons/yr) 

1999 2007 2010 2015 2020 2030 

2285000000 Acrolein 24 23 22 22 21 20 

All HAPs 151 143 138 136 132 125 

2285002000 

Acetaldehyde 1 1 1 1 1 1 

Acrolein 0.3 0.3 0.3 0.3 0.3 0.3 

Formaldehyde 2 2 2 2 2 1 

All HAPs 5 5 5 5 5 4 

2285002005 

1,3-Butadiene 5 4 4 4 4 4 

Acetaldehyde 174 165 159 157 152 144 

Benzene 47 45 43 43 41 39 

Formaldehyde 349 330 319 314 305 289 

Naphthalene 2 2 2 1 1 1 

All HAPs 717 677 654 644 625 593 

2285002006 

1,3-Butadiene 90 85 82 81 78 74 

Acetaldehyde 519 490 474 466 453 429 

Acrolein 87 83 80 79 76 72 

Benzene 71 67 65 64 62 59 

Formaldehyde 1,196 1,129 1,091 1,074 1,043 990 

Naphthalene 48 40 35 33 31 28 

All HAPs 2,709 2,550 2,458 2,419 2,347 2,223 

2285002007 

1,3-Butadiene 6 6 5 5 5 5 

Acetaldehyde 35 33 32 31 30 29 

Acrolein 6 6 6 6 5 5 

Benzene 5 5 4 4 4 4 

Formaldehyde 80 75 73 72 70 66 

Naphthalene 3 3 2 2 2 2 

All HAPs 184 173 167 164 160 151 

2285002008 

1,3-Butadiene 2 2 2 2 2 2 

Acetaldehyde 11 11 10 10 10 9 

Acrolein 2 2 2 2 2 2 

Benzene 2 1 1 1 1 1 

Formaldehyde 26 24 24 23 23 21 

Naphthalene 1 1 1 1 1 1 

All HAPs 59 55 53 53 51 48 

2285002009 

1,3-Butadiene 2 2 2 2 1 1 

Acetaldehyde 10 9 9 9 8 8 

Acrolein 1 1 1 1 1 1 

Benzene 1 1 1 1 1 1 

Formaldehyde 22 21 20 20 20 19 

Naphthalene 1 1 1 1 1 1 

All HAPs 49 46 45 44 43 40 

2285002010 

1,3-Butadiene 8 7 7 7 7 6 

Acetaldehyde 66 62 60 59 57 54 

Acrolein 7 6 6 6 6 5 

Benzene 12 12 11 11 11 10 

Formaldehyde 143 135 131 129 125 118 

Naphthalene 5 5 4 4 3 3 

All HAPs 371 350 337 332 322 305 

Total 

Locomotive 

1,3-Butadiene 111 105 102 100 97 92 

Acetaldehyde 815 770 744 732 711 675 

Acrolein 128 121 117 115 112 106 

Benzene 139 131 127 125 121 115 

Formaldehyde 1,817 1,716 1,659 1,634 1,586 1,505 

Naphthalene 60 51 44 42 39 35 

All HAPs 4,246 4,000 3,858 3,796 3,684 3,491 

17 
Table 11. National commercial marine vessel emissions (rounded) by SCC for selected HAPs 

and across all HAPs. 

SCC HAP 

Emissions (tons/yr) 

1999 2007 2010 2015 2020 2030 

1,3-Butadiene 0.1 0.2 0.2 0.2 0.2 0.2 

Acetaldehyde 5 6 6 6 7 9 

2280000000 Benzene 1 2 2 2 2 2 

Formaldehyde 10 12 12 13 14 18 

Naphthalene 0.1 0.1 0.1 0.1 0.1 0.1 

All HAPs 21 24 25 26 29 37 

1,3-Butadiene 6 6 6 6 6 7 

Acetaldehyde 1,478 1,549 1,551 1,559 1,595 1,739 

Acrolein 54 56 56 57 58 63 

2280002100 Benzene 404 424 424 427 437 476 

Formaldehyde 2,973 3,116 3,121 3,136 3,210 3,499 

Naphthalene 33 31 29 28 27 30 

All HAPs 5,478 5,736 5,740 5,766 5,898 6,430 

Acetaldehyde 459 481 482 485 496 540 

Acrolein 22 23 23 23 23 25 

2280002200 Benzene 126 132 132 133 136 148 

Formaldehyde 925 969 971 976 998 1,088 

Naphthalene 11 10 10 9 9 10 

All HAPs 1,694 1,774 1,776 1,785 1,826 1,990 

Acetaldehyde 300 392 426 510 622 945 

Acrolein 17 23 24 29 36 54 

2280003100 Benzene 80 104 113 135 165 251 

Formaldehyde 561 733 798 954 1,164 1,768 

Naphthalene 16 20 22 26 31 46 

All HAPs 1,126 1,467 1,594 1,901 2,316 3,506 

Acetaldehyde 122 160 174 208 254 386 

Acrolein 6 8 8 10 12 18 

2280003200 Benzene 33 44 48 57 69 105 

Formaldehyde 247 323 351 420 513 779 

Naphthalene 6 7 8 9 11 16 

All HAPs 466 607 660 787 959 1,451 

1,3-Butadiene 6 6 6 6 6 7 

Acetaldehyde 2364 2588 2639 2768 2974 3619 

Acrolein 98 109 112 118 129 161 

Total CMV Benzene 644 705 719 753 809 982 

Formaldehyde 4715 5153 5252 5499 5899 7152 

Naphthalene 65 69 68 72 79 102 

All HAPs 8,786 9,609 9,795 10,266 11,028 13,414 

18 
3.2 Aircraft and Aviation gasoline 

Aircraft emissions were projected by using growth factors based on activity growth. These 

growth factors were also used to project aviation gasoline source categories that are inventoried 

in the NEI as stationary sources. Note that the projection of airport support equipment source 

categories did not use this approach; they were projected using the National Mobile Inventory 

Model (NMIM) as described in Section 3.3.3. 

Aircraft growth factors were developed using data on itinerant (landing and take-off) operations 

from the Terminal Area Forecast System (TAF) (FAA, 2004), http://www.apo.data.faa.gov/. 

These data were accessed from the website in February 2004. 

The TAF model provides itinerant activity for commercial aircraft, general aviation, air taxis, 

and military aircraft. The four categories map directly to inventory categories for aircraft 

emissions. We used the growth factors for general aviation for aviation gasoline emissions since 

most aircraft gasoline is used with general aviation aircraft. Although the TAF model provides 

activity at individual airports, the TAF data were summed to create growth factors at the national 

level. This was done to smooth out the large-scale year-to-year changes in individual airport 

itinerant data that were questionable. The same approach was used in the modeling for the Clean 

Air Interstate (CAIR) rule (EPA, 2005b). Table 12 provides the nationally aggregated TAF 

intinerant data for 2002-2010, inclusive, 2015 and 2020. Note that the “all operations” data is 

simply the sum of commercial aircraft, air taxi, general aviation, and military operations. 

Table 12. TAF landing and take-off data for 2002 through 2020, 2015, and 2020. 

Year Commercial Air Taxi 

General 

Aviation Military 

All 

Operations 

1999 14,769,055 14,177,496 44,413,777 3,977,646 77,337,974 

2000 15,239,632 14,474,434 45,075,619 4,023,672 78,813,357 

2001 14,806,610 14,559,287 44,199,554 4,145,459 77,710,910 

2002 13,823,811 13,976,494 44,096,192 4,168,042 76,064,539 

2003 12,877,845 15,192,330 42,946,739 4,141,986 75,158,900 

2004 13,144,840 15,812,801 43,302,577 4,148,717 76,408,935 

2005 13,587,336 16,177,234 43,672,459 4,155,567 77,592,596 

2006 13,931,296 16,478,077 44,041,238 4,155,999 78,606,610 

2007 14,244,149 16,754,880 44,409,867 4,156,432 79,565,328 

2008 14,559,169 17,022,228 44,778,567 4,156,864 80,516,828 

2009 14,875,408 17,292,401 45,147,517 4,157,297 81,472,623 

2010 15,199,253 17,566,653 45,516,733 4,157,730 82,440,369 

2011 15,516,407 17,839,971 45,886,238 4,158,162 83,400,778 

2012 15,837,083 18,113,540 46,255,950 4,158,595 84,365,168 

2013 16,167,397 18,388,892 46,626,125 4,159,028 85,341,442 

2014 16,503,853 18,666,510 46,996,330 4,159,460 86,326,153 

2015 16,844,216 18,943,304 47366,817 4,159,893 87,314,230 

2020 18,584,876 20,347,985 49,223,017 4,162,058 92,317,936 

19 
Growth factors were computed for 2002-2010, inclusive, 2015 and 2020 by dividing each year’s 

TAF data by the TAF data for 1999. The TAF data did not cover 2030; growth factor for 2030 

was calculated by using the same rate of growth between 2015 and 2020 and extrapolating to 

2030 using Equation 1: 

GF2030 = GF2020 + ((2030 − 2020) × (GF2020 − GF2015 ) ÷ (2020 − 2015)) (1) 

where GF is the growth factor for the respective years. 

The growth factors for the MSAT study years are shown in Table 13. The growth factor 

assignments for each of the airport related SCC codes are shown in Table 14. 

Table 13. Aircraft growth factors for MSAT study years. 

Aviation type 

Growth Factors 

2007 2010 2015 2020 2030 

Commercial 0.9645 1.0291 1.1405 1.2584 1.4941 

Air Taxi 1.1818 1.2391 1.3362 1.4352 1.6334 

General 0.9999 1.0248 1.0665 1.1083 1.1919 

Military 1.0449 1.0453 1.0458 1.0464 1.0475 

All operations 1.0288 1.0660 1.1290 1.1937 1.3231 

20 
Table 14. Airport related SCC codes and assigned growth factor basis. 

SCC Description Growth factor basis 

2265008000 Airport Support Equipment, Total, Off-highway 4­

stroke 

No factor. Projected emissions in NMIM 

(see 3.3.3) 

2265008005 Airport Support Equipment, Off-highway 4-stroke No factor. Projected emissions in NMIM 

(see 3.3.3) 

2267008000 Airport Ground Support Equipment, All, LPG No factor. Projected emissions in NMIM 

(see 3.3.3) 

2267008005 Airport Ground Support Equipment, LPG No factor. Projected emissions in NMIM 

(see 3.3.3) 

2268008000 Airport Ground Support Equipment, CNG, All No factor. Projected emissions in NMIM 

(see 3.3.3) 

2270008000 Airport Service Equipment, Total, Off-highway 

Diesel 

No factor. Projected emissions in NMIM 

(see 3.3.3) 

2270008005 Airport Service Equipment, Airport Support 

Equipment, Off-highway Diesel 

No factor. Projected emissions in NMIM 

(see 3.3.3) 

2275000000 All Aircraft Types and Operations Growth factor for All operations 

2275060000 Air Taxi, Total Growth factor for Air Taxi 

2275020000 Commercial Aircraft, Total Growth factor for Commercial Aviation 

2275070000 Aircraft Auxiliary Power Units, Total Growth factor for Commercial Aviation 

2275050000 General Aircraft, Total Growth factor for General Aviation 

2275900000 Aircraft Refueling: All Fuels, All Processes Growth factor for General Aviation 

2501080000# Aviation Gasoline Distribution: Stage 1 & II Growth factor for General Aviation 

2501080050# Aviation Gasoline Storage -Stage I Growth factor for General Aviation 

2501080100# Aviation Gasoline Storage -Stage II Growth factor for General Aviation 

2275001000 Military Aircraft, Total Growth factor for Military Aviation 

# Stationary sources in the non-point inventory. All others are nonroad sources. 

Growth factor files were created for each year, 2002-2010 inclusive, 2015, 2020, and 2030, using 

the SCC growth factor file format for EMS-HAP Version 3.0 described in Appendix B of the 

EMS-HAP Version 3.0 User’s Guide (U.S. EPA, 2004b). For this format, each SCC was 

assigned a code describing its growth method, basically the “growth factor basis” column in 

Table 14. The format of the file is shown in Figure 1. The naming convention of the aircraft and 

aviation gasoline growth factor files is gf99scca_XX.txt where XX is the two-digit year for 2002 

through 2010 inclusive, 2015, 2020, and 2030. 

21 
1999 Base Year EGAS SCC Growth Factors for 2010, Created 12APRIL04 BEGIN SCC-REMI XREF on line 3. 

GROWTH FACTORS BEGIN ON LINE 18. 

2265008000 N/A projected emissions will be supplied with NMIM 

2265008005 N/A projected emissions will be supplied with NMIM 

2267008000 N/A projected emissions will be supplied with NMIM 

2267008005 N/A projected emissions will be supplied with NMIM 

2268008000 N/A projected emissions will be supplied with NMIM 

2270008000 N/A projected emissions will be supplied with NMIM 

2270008005 N/A projected emissions will be supplied with NMIM 

2275000000 TAF for ALL OPERATIONS (p_tot) 

2275060000 TAF for Air Taxi 

2275020000 TAF for Commercial Aviation 

2275070000 TAF for Commercial Aviation 

2275050000 TAF for General Aviation 

2275900000 TAF for General Aviation 

2501080000 TAF for General Aviation 

2501080050 TAF for General Aviation 

2501080100 TAF for General Aviation 

2275001000 TAF for Military Aviation 

00 000 1.0000 N/A projected emissions will be supplied with NMIM 

00 000 1.0660 TAF for ALL OPERATIONS (p_tot) 

00 000 1.2391 TAF for Air Taxi 

00 000 1.0291 TAF for Commercial Aviation 

00 000 1.0248 TAF for General Aviation 

00 000 1.0453 TAF for Military Aviation 

Figure 1. Format of the aircraft growth factor file. 2010 growth factors shown as example. 

EMS-HAP V3 was used to apply the growth factors to the aircraft and aviation gasoline sources. 

Aircraft emissions were projected by first subsetting the nonroad airport-related emissions to 

exclude the airport support equipment emissions, which were projected using NMIM future 

emissions data as described in Section 3.3.3. The subsetting of the data was done on the 

temporally allocated 1999 NEI emissions for NATA (National Air Toxics Assessment). These 

emissions had previously been processed through the appropriate EMS-HAP programs, COPAX, 

PtDataProc, PtModelProc, and PtTemporal for the 1999 NATA (see EMS-HAP User’s Guide for 

details, (U.S. EPA, 2004b)). After the subsetting was completed, the emissions were processed 

through the EMS-HAP program PtGrowCntl for 2002 through 2010, 2015, 2020, and 2030, 

using the TAF-derived growth factors described above. 

Aviation gasoline emissions (SCCs shown in Table 14 with # footnotes) that had been processed 

through the appropriate EMS-HAP programs for the 1999 NATA, were projected using the 

EMS-HAP program PtGrowCntl for 2002 through 2010, 2015 and 2020, using the TAF-derived 

growth factors described above. Aviation gasoline emissions were projected to 2030 but it was 

decided to use 2020 projected emissions for 2030 for all stationary sources because of 

uncertainty in the 2030 projection and growth factors. 

A flowchart of the projection processing is shown in Figure 2. 

22 
Airports (nonroad) 

PtTemporal output 

(temporal) 

Subset inventory not include airport support 

equipment (SCC: 2265008000, 

2265008005, 2267008000, 2267008005, 

2268008000, 2270008000, 2270008005) 

Temporal_no_supp 

PtGrowCntl 

Airports (nonpoint) 

PtTemporal output 

(temporal) 

PtGrowCntl 2007 

inventory 

2020 

inventory 

2015 

inventory 

2010 

inventory 

2002 

inventory 

2003 

inventory 

2004 

inventory 

2005 

inventory 

2006 

inventory 

2008 

inventory 

2009 

inventory 

2007 

inventory 

2030 

inventory 

2020 

inventory 

2015 

inventory 

2010 

inventory 

2002 

inventory 

2003 

inventory 

2004 

inventory 

2005 

inventory 

2006 

inventory 

2008 

inventory 

2009 

inventory 

Figure 2. Flowchart of aircraft and aviation gasoline emissions projections. 

Projected airport related emissions (excluding airport support equipment) by SCC are shown in 

Table 15. 

23 
Table 15. Airport related emissions (excluding airport support equipment) for selected HAPs 

and all HAPs by SCC. Non-point SCC emissions for 2030 are set equal to 2020. 

SCC HAP 

Emissions (tons/yr) 

1999 2007 2010 2015 2020 2030 

2275000000 

1,3-Butadiene 6 6 6 6 7 7 

Acetaldehyde 12 12 13 14 14 16 

Acrolein 6 6 6 6 7 7 

Benzene 10 10 10 11 12 13 

Formaldehyde 40 41 43 45 48 53 

Naphthalene 1 1 1 1 2 2 

All HAPs 116 119 123 131 138 153 

2275001000 

1,3-Butadiene 193 202 202 202 202 202 

Acetaldehyde 506 529 529 529 530 530 

Acrolein 247 258 258 258 258 258 

Benzene 213 223 223 223 223 223 

Formaldehyde 1,635 1,708 1,709 1,710 1,711 1,712 

Naphthalene 62 64 64 64 64 64 

All HAPs 3,171 3,313 3,315 3,316 3,318 3,322 

2275020000 

1,3-Butadiene 525 506 540 599 661 784 

Acetaldehyde 1,357 1,309 1,397 1,548 1,708 2028 

Acrolein 662 638 681 754 832 988 

Benzene 574 554 591 655 722 858 

Formaldehyde 4,383 4,227 4,510 4,998 5,515 6548 

Naphthalene 164 158 169 187 206 245 

All HAPs 8,455 8,155 8,701 9,643 10,640 12,633 

2275050000 

1,3-Butadiene 60 60 61 64 66 71 

Acetaldehyde 85 85 87 91 95 102 

Acrolein 33 33 34 35 36 39 

Benzene 186 186 191 199 206 222 

Formaldehyde 285 285 293 304 316 340 

Naphthalene 109 109 111 116 120 129 

All HAPs 2,053 2,053 2,104 2,190 2,275 2,447 

2275060000 

1,3-Butadiene 40 47 49 53 57 65 

Acetaldehyde 58 68 72 77 83 94 

Acrolein 22 26 27 29 31 35 

Benzene 119 141 147 159 171 194 

Formaldehyde 206 243 255 275 296 336 

Benzene 121 143 150 162 174 198 

All HAPs 1,077 1,272 1,334 1,439 1,545 1,758 

2501080000 

Benzene 0.04 0.04 0.04 0.04 0.05 0.05 

Naphthalene 0.01 0.01 0.01 0.01 0.01 0.01 

All HAPs 1 1 1 1 1 1 

2501080050 

Benzene 287 287 294 306 318 318 

Naphthalene 16 16 16 17 18 18 

All HAPs 1,676 1,676 1,718 1,788 1,858 1,858 

2501080100 

Benzene 20 20 20 21 22 22 

Naphthalene 1 1 1 1 1 1 

All HAPs 117 117 119 124 129 129 

Total 

1,3-Butadiene 824 821 859 924 993 1,131 

Acetaldehyde 2,019 2,004 2,098 2,259 2,430 2,770 

Acrolein 968 960 1,005 1,083 1,165 1,329 

Benzene 1,409 1,420 1,477 1,574 1,674 1,850 

Formaldehyde 6,549 6,505 6,809 7,333 7,885 8,990 

Naphthalene 473 492 513 548 585 657 

All HAPs 16,666 16,706 17,415 18,632 19,904 22,301 

24 
3.3 Projection of onroad and nonroad categories using NMIM 

3.3.1 Description of NMIM 

For all mobile source categories except commercial marine vessels, locomotives, and aircraft 

(Sections 3.1 and 3.2), EPA’s Office of Transportation and Air Quality’s (OTAQ) new emissions 

inventory modeling system for highway and nonroad sources, the National Mobile Inventory 

Model (NMIM) (Michaels et al. 2005; Cook et al. 2004) was used to generate emission data for 

projections. NMIM develops county level inventories using MOBILE6.2, NONROAD, and 

model inputs stored in data files. The version of NMIM used in this assessment includes 

NONROAD2004, which was also used in the recent Clean Air Nonroad Diesel Rule (U.S. EPA, 

2004d). More details on the inputs and data files used in the modeling can be found in Appendix 

A. In addition to criteria pollutants, NMIM can currently produce 13 gaseous hydrocarbons, 16 

polycyclic aromatic hydrocarbons, 4 metal compounds and 17 dioxin and furan congeners, for 

any calendar year 1999 through 2050. 

Future year MOBILE6.2 and NONROAD inputs include future year vehicle miles traveled 

(VMT) and fuel parameters, and future year equipment populations. Future year VMT for years 

2010, 2020 and 2030 were developed at the county-level using data from the Energy Information 

Administration’s National Energy Modeling System (NEMS) Transportation Model and 

Regional Economic Models Inc. population growth (Mullen and Neumann, 2004). VMT for 

intermediate years were interpolated, using 1999 as the base year. This same approach and 

projected VMT were used for the CAIR rule. Projection year fuel parameters were developed 

using results of several refinery modeling analyses conducted to assess impacts of fuel control 

programs on fuel properties (MathPro, 1998; 1999a, 1999b). The projection year fuel parameters 

were calculated by applying adjustment factors to the base year parameters (Eastern Research 

Group, 2003). In addition, NMIM uses monthly rather than seasonal fuel parameters, and 

parameters for spring and fall months were estimated by interpolating from summer and winter 

data. Documentation of the fuel parameters used in NMIM was compiled in 2003 (Eastern 

Research Group, 2003) and then subsequently, a number of changes were made, based on 

comments from States. These changes are documented in the change log for NMIM, dated May 

14, 2004. This change log is included in the docket for this rule (EPA-HQ-OAR-2005-0036), 

along with the original documentation. In general, multiplicative adjustment factors were used 

to calculate future year gasoline parameters (i.e., future year parameter = base year parameter x 

adjustment factor). However, additive adjustment factors were used to calculate future year 

parameters for E200, E300, and oxygenate market shares (i.e., future year parameter = base year 

parameter + adjustment factor). The database used for this assessment assumes no Federal ban 

on MTBE, but does include State bans. Also, it did not include the renewable fuels mandate in 

the recent Energy Policy Act. 

3.3.2 Onroad projections using NMIM 

The 1999 NEI, which contains some State reported data for California and Texas, served as the 

base year inventory for the emission projections. In order to preserve the State reported data, it 

25 
was decided to compute projection factors from NMIM output for 1999 and each future year of 

interest. 

Equation 2 shows the computation of the projection factor 

E NMIM ,20 XX PF20 XX = (2) E NMIM ,1999 

where PF20XX is the projection factor for 2007, 2010, 2015, 2020, or 2030, E20XX is the emissions 

for the corresponding year and E1999 is the 1999 emissions. 

Under this approach, the projection factor is computed at the detailed inventory level, for each 

FIPS/SCC/CAS combination where 1999 emissions are nonzero. The FIPS represents the 

specific state and county of the emission; the SCC is the source category code and the CAS is the 

particular HAP emitted. 

Before calculating the projection factors, the NMIM emissions for each year had to be summed 

by FIPS/SCC/CAS to remove the emissions type from the NMIM SCC. This was because the 

NMIM onroad emissions SCCs were broken out by exhaust and evaporative (non refueling) 

emissions for several of the HAPs. The NEI emissions were not broken out into exhaust and 

evaporative emissions. Once 1999 and future years’ NMIM results were summed by 

FIPS/SCC/CAS for each year, then the projection factors were calculated using Equation 2. 

 The projection factors were then applied to the same FIPS/SCC/CAS combinations in the 1999 

NEI. Only combinations in the 1999 NEI were kept. However, before the projection factors 

could be applied, the NMIM output needed to be processed because for some situations, the 

NMIM FIPS/SCC/CAS combinations did not match the NEI FIPS/SCC/CAS combinations. The 

bullets below describe the necessary processing. More details on the programming steps and 

example calculations are provided in Appendix B (B.1). 

• Since the NEI contained the CAS for chromium, 7440473, the NMIM chromium III and 

chromium VI emissions were summed for each FIPS/SCC to give a total chromium 

number. New projection factors were calculated for the summed chromium and the CAS 

7440473 was assigned to each record. This was done for all FIPS/SCC combinations 

with chromium III or chromium VI in NMIM. 

• For xylenes, manganese, and nickel, NMIM results and projection factors were assigned 

to the CAS associated with total xylenes (1330207) and elemental manganese (7439965) 

and nickel (7440020). The 1999 NEI used CAS numbers 106423, 108383, and 95476 

for p-xylene, m-xylene, and o-xylene respectively. In addition to 7439965 and 7440020 

for manganese and nickel, the NEI also reported emissions using 198 as a manganese 

CAS number and 226 as a nickel CAS number. NMIM xylenes, manganese, and nickel 

observations were copied to observations with the same FIPS/SCC and emissions but 

26 
replacing the CAS numbers with one of the other xylenes, manganese, or nickel CAS 

numbers while still retaining the original NMIM emissions. 

• The 1999 NEI contained emissions for SCC codes 2230070YYY where YYY is the 3­

digit road type descriptor (110, 130, 150, 170, 190, 210, 230, 250, 270, 290, 310, and 

330). These SCC codes were for heavy duty diesel vehicles (HDDV). In NMIM, there 

were no SCC codes with 2230070 as the first seven numbers. NMIM contained 

emissions for SCC codes beginning with 2230071, 2230072, 2230073, 2230074, and 

2230075. In order to calculate a projection factor for SCC codes beginning with 

2330070 in the NEI, the emissions for 2230071YYY, 2230072YYY, 2230073YYY, 

2230074YYY, and 2230075YYY were summed together for each road type YYY (as 

described above) for each FIPS/CAS across the HDDV SCC emissions. The summed 

emissions were assigned to an SCC code 223007XYYY where YYY is the road type. 

Table 16 shows the NMIM SCC codes used to create each of the 223007XYYY SCC 

emissions. Emissions were assigned to an SCC code 223007X instead of 2230070 for 

ease of visual QA of the emissions, given the quantity of data being processed for the 

onroad emissions. Once the 223007XYYY emissions were created from the NMIM 

results (for 1999 and future years), a projection factor was calculated using Equation 2. 

In this case, ENMIM,20XX represents the sum of the 5 HDDV types by road type for each 

FIPS/CAS for a future year and ENMIM,1999 represents the sum of the 5 HDDV types by 

road type for each FIPS/CAS for 1999. For example, for SCC 2230070130 for benzene 

in Autauga County, AL for 2007 the projection factor used would be: 

E + E + E2230073130 2007 + E , 2230071130 , 2007 2230072130 , 2007 , 2230074130 2007 + E2230075130 ,2007 

= PF2230070130 , 2007 E + E + E2230073130 1999 + E , + E2230075130 1999 

(3) 2230071130 , 1999 2230072130 , 1999 , 2230074130 1999 , 

where E represents the benzene emissions. For examples of the calculations see Table B2 in Appendix B. 

• After preliminary processing, it was found that three counties in California, had data for 

motorcycle SCC codes, 2201080YYY, (where YYY is the road type as described in the 

above HDDV discussion) in the 1999 NEI which were not in NMIM and thus had no 

FIPS/SCC/CAS projection factor. These counties were Alpine County (06003), Modoc 

County (06049), and Sierra County (06091). The SCC codes are shown in Table 17. To 

project the 1999 NEI emissions in these counties, the future year onroad emissions were 

summed across all SCC codes for each FIPS/CAS, resulting in a county-HAP specific 

emissions number. The same was done for 1999. To calculate a county level HAPspecific projection factor the summed future year emissions were divided by the summed 

1999 emissions. The county level HAP specific projection factors were then applied to 

the 1999 NEI motorcycle emissions for the appropriate HAPs. 

27 
Table 16. HDDV SCC codes used to calculate HDDV emissions for NEI projections. 

HDDV type 

(First 7 characters of SCC code) 

Road types 

(last 3 digits of SCC code) 

SCC in NEI which 

projections are applied 

2230071, 2230072, 2230073, 2230074, 2230075 110 (Rural Interstate) 2230070110 

2230071, 2230072, 2230073, 2230074, 2230075 130 (Other Principal Arterial) 2230070130 

2230071, 2230072, 2230073, 2230074, 2230075 150 (Rural Minor Arterial) 2230070150 

2230071, 2230072, 2230073, 2230074, 2230075 170 (Rural Major Collector) 2230070170 

2230071, 2230072, 2230073, 2230074, 2230075 190 (Rural Minor Collector) 2230070190 

2230071, 2230072, 2230073, 2230074, 2230075 210 (Rural Local) 2230070210 

2230071, 2230072, 2230073, 2230074, 2230075 230 (Urban Interstate) 2230070230 

2230071, 2230072, 2230073, 2230074, 2230075 250 (Urban Other Freeways and 

Expressways) 

2230070250 

2230071, 2230072, 2230073, 2230074, 2230075 270 (Urban Other Principal 

Arterial) 

2230070270 

2230071, 2230072, 2230073, 2230074, 2230075 290 (Urban Minor Arterial) 2230070290 

2230071, 2230072, 2230073, 2230074, 2230075 310 (Urban Collector) 2230070310 

2230071, 2230072, 2230073, 2230074, 2230075 330 (Urban Local) 2230070330 

HDDV descriptions: 

2230070: HDDV 

2230071: HDDV Class 2B 

2230072: HDDV Class 3, 4, and 5 

2230073: HDDV Class 6 and 7 

2230074: HDDV Class 8A and 8B 

2230075: HDDV Buses (School and Transit) 

28 
Table 17. Motorcycle (MC) SCC codes not in NMIM output for Alpine, Modoc, and Sierra 

Counties California. 

SCC Description 

2201080110 Mobile Sources, Highway Vehicles - Gasoline, Motorcycles (MC), Rural Interstate: Total 

2201080130 Mobile Sources, Highway Vehicles - Gasoline, Motorcycles (MC), Rural Other Principal 

Arterial: Total 

2201080150 Mobile Sources, Highway Vehicles - Gasoline, Motorcycles (MC), Rural Minor Arterial: Total 

2201080170 Mobile Sources, Highway Vehicles - Gasoline, Motorcycles (MC), Rural Major Collector: Total 

2201080190 Mobile Sources, Highway Vehicles - Gasoline, Motorcycles (MC), Rural Minor Collector: Total 

2201080210 Mobile Sources, Highway Vehicles - Gasoline, Motorcycles (MC), Rural Local: Total 

2201080330 Mobile Sources, Highway Vehicles - Gasoline, Motorcycles (MC), Urban Local: Total 

A summary of national-level onroad projected emissions is provided in Table 18. Summary 

emissions were calculated nationwide by vehicle type. The vehicle types summarized are: 1) 

heavy duty gasoline vehicles (HDGV), 2) heavy duty diesel vehicles (HDDV), 3) light duty 

diesel trucks (LDDT), 4) light duty diesel vehicles (LDDV), 5) light duty gasoline trucks 1 

(LDGT1), 6) light duty gasoline trucks 2 (LDGT2), 7) light duty gasoline vehicles (LDGV) and 

8) motorcycles (MC). 

More detailed summaries of onroad projected emissions can be found in the MSAT rule docket: 

EPA-HQ-OAR-2005-0036. The State and HAP specific summaries can be found in 

onroad_0923.xls and as a pivot table in onroad_pivot.xls 

29 
Table 18. National summary of projected onroad emissions by vehicle type for 1999, 2007, 

2010, 2015, 2020, and 2030 across all HAPs and for 1,3-butadiene, acetaldehyde, acrolein, 

benzene, formaldehyde, and naphthalene. 

Vehicle Emissions (tons/yr) 

Type HAP 1999 2007 2010 2015 2020 2030 

1,3-Butadiene 1,431 995 877 760 755 859 

Acetaldehyde 7,568 5,310 4,682 4,071 4,049 4,633 

Acrolein 807 561 494 429 425 483 

HDDV Benzene 2,674 1,872 1,650 1,434 1,426 1,629 

Formaldehyde 19,887 13,921 12,272 10,663 10,601 12,109 

Naphthalene 172 98 67 33 20 16 

All HAPs 38,534 26,923 23,707 20,570 20,435 23,336 

1,3-Butadiene 1,507 483 260 130 103 84 

Acetaldehyde 1,569 722 465 297 245 209 

Acrolein 714 177 79 25 18 12 

HDGV Benzene 7,967 4,041 2,970 2,152 1,760 1,539 

Formaldehyde 6,648 2,242 1,309 741 599 498 

Naphthalene 752 540 388 241 189 170 

All HAPs 80,227 35,096 24,838 17,342 13,666 12,023 

1,3-Butadiene 64 38 31 29 26 23 

Acetaldehyde 200 120 96 84 73 57 

Acrolein 24 14 12 11 10 9 

LDDT Benzene 167 100 82 74 67 57 

Formaldehyde 495 297 283 211 186 148 

Naphthalene 6 3 2 1 1 1 

All HAPs 1,279 766 617 552 491 402 

1,3-Butadiene 44 6 3 1 1 1 

Acetaldehyde 164 24 10 6 4 4 

Acrolein 16 2 1 1 0.4 0.4 

LDDV Benzene 120 17 7 4 3 3 

Formaldehyde 391 56 24 14 9 9 

Naphthalene 7 1 0.3 0.2 0. 1 0.1 

All HAPs 977 139 60 34 23 22 

1,3-Butadiene 5,132 3,218 2,801 2,307 2,291 2,447 

Acetaldehyde 5,766 3,947 3,265 2,714 2,682 2,899 

Acrolein 661 434 368 306 302 326 

LDGT1 Benzene 42,433 30,773 27,498 23,835 23,346 24,856 

Formaldehyde 14,907 8,540 6,787 5,572 5,516 5,975 

Naphthalene 766 612 4,164 702 774 906 

All HAPs 342,839 239,534 208,636 177,486 170,855 179,122 

1,3-Butadiene 3,483 1,919 1,735 1,524 1,503 1,486 

Acetaldehyde 3,433 2,411 2,023 1,789 1,726 1,710 

Acrolein 357 255 222 198 191 188 

LDGT2 Benzene 20,638 17,701 16,805 15,694 14,897 14,505 

Formaldehyde 9,809 5,264 4,164 3,628 3,513 3,509 

Naphthalene 491 260 268 274 281 316 

All HAPs 186,078 139,447 126,396 114,204 105,843 102,085 

30 
Table 18. Continued. 

Vehicle 

Type HAP 

Emissions (tons/yr) 

1999 2007 2010 2015 2020 2030 

LDGV 

1,3-Butadiene 11,743 3,983 2,855 1,895 1,500 1,614 

Acetaldehyde 11,057 4,311 3,155 2,123 1,690 1,831 

Acrolein 1,396 511 374 251 199 215 

Benzene 955,951 40,478 29,722 19,835 15,643 16,895 

Formaldehyde 27,957 9,239 6,811 4,628 3,705 4,028 

Naphthalene 1,757 950 831 726 678 807 

All HAPs 778,772 317,021 232,547 153,050 118,762 128,305 

MC 

1,3-Butadiene 220 234 244 266 288 350 

Acetaldehyde 171 204 214 233 253 309 

Acrolein 18 19 20 22 24 29 

Benzene 764 784 817 892 967 1177 

Formaldehyde 582 609 635 693 751 912 

Naphthalene 26 27 28 30 33 40 

All HAPs 8,826 8,691 9,035 9,854 10,673 12,957 

Total 

Onroad 

1,3-Butadiene 23,623 10,876 8,807 6,913 6,468 6,864 

Acetaldehyde 29,928 17,049 13,909 11,317 10,721 11,651 

Acrolein 3,993 1,974 1,570 1,242 1,170 1,263 

Benzene 170,355 95,766 79,550 63,920 58,109 60,660 

Formaldehyde 80,677 40,168 32,240 26,150 24,879 27,188 

Naphthalene 3,978 2,490 2,229 2,007 1,976 2,255 

All HAPs 1,437,532 767,617 625,836 493,092 440,748 458,252 

HDDV: Heavy Duty Diesel Vehicles; HDGV: Heavy Duty Gasoline Vehicles 

LDDT: Light Duty Diesel Trucks; LDDV: Light Duty Diesel Vehicles 

LDGT1: Light Duty Gasoline Trucks 1; LDGT2: Light Duty Gasoline Trucks 2 

LDGV: Light Duty Gasoline Vehicles; MC: Motorcycles 

Once the onroad inventory had been projected, emissions for intermediate years not included in 

assessments done for the MSAT rule, 2002 through 2006, inclusive, 2008, and 2009 were 

interpolated from the MSAT projections and 1999 base emissions for each FIPS/SCC/CAS. For 

years between 1999 and 2007, the following formula was used to interpolate the projection 

factors for non-MSAT years: 

PF = 1+ ((20 XX − 1999) × (PF − 1) (2007 − 1999)) (4) 20 XX 2007 

where PF20XX is the interpolated projection factor for 2002 through 2006, 20XX is the year 2002 

through 2006, PF2007 is the projection factor for 2007 calculated from Equation 2 and 1 is the 

projection factor for 1999 (base year, no growth PF=1). 

For 2008 and 2009, 2010 replaces 2007, and 2007 replaces 1999. 

PF = 1+ ((20 XX − 2007) × (PF − PF ) (2010 − 2007)) (5) 20 XX 2010 2007 

31 
3.3.3 Nonroad projections using NMIM (excluding aircraft, locomotives, and commercial 

marine vessels) 

The projection of the portion of the nonroad inventory that is developed using the NONROAD 

model followed a similar methodology as for the onroad. Projection factors (FIPS/SCC/CAS 

specific) were developed using the 1999 and future year NMIM runs using equation (2) above, 

and were applied to nonroad categories in the 1999 NEI. Similar to onroad, some processing 

took place, as described by the bullets below, to create FIPS/SCC/CAS projection factors to map 

to the 1999 NEI. 

• The same processing was done for the nonroad as for onroad to create summed 

chromium, xylenes, manganese, and nickel projection factors. See first two bullets of 

Section 3.3.2. 

• NMIM SCC emissions were summed to a “Total” or aggregated category (first 7 digits 

of SCC followed by 3 zeros) for each FIPS/HAP/SCC since numerous emission records 

in the 1999 NEI contained these aggregated categories and thus needed NMIM-based 

projection ratios. See Table 19 for a list. 

• NMIM pleasure craft emissions, SCC codes beginning with 2282, were summed to 

provide a projection factor for the SCC code 2282000000 for each FIPS/CAS. 

• For remaining FIPS/SCC/CAS combinations that did not match the NMIM emissions, 

county-level HAP specific projection factors were created based on engine/fuel type by 

summing emissions for 1999 NMIM and future year NMIM for each 

FIPS/CAS/engine/fuel type. These were applied to all SCC codes with the relevant 

engine/fuel type by HAP and by county. The engine fuel types were 2-stroke gasoline, 

4-stroke gasoline, diesel, LPG, CNG, and miscellaneous. 

• For CNG and LPG emissions in California and Texas (SCC codes beginning with 

226800, 226801, and 226700) from the 1999 NEI without an NMIM based 

FIPS/SCC/CAS specific projection factor, used the VOC or PM county level ratios for 

CNG and LPG as fuel types for the HAPs in the inventory. Particulate HAPs received 

the PM ratios, and gaseous HAPs received the VOC ratios. 

 Appendix C provides the steps used to develop the projected emissions and contains sample 

calculations. 

32 
Table 19. SCC codes in the 1999 NEI inventory and not in the NMIM inventory. 

SCC Description SCC Description 

2260001000 

Mobile Sources, Off-highway Vehicle 

Gasoline, 2-Stroke, Recreational Equipment, 

Total 

2265008000 

Airport Support Equipment, Total, Offhighway 4-stroke 

2260002000 

Mobile Sources, Off-highway Vehicle 

Gasoline, 2-Stroke, Construction and Mining 

Equipment, Total 

2265010000 

Mobile Sources, Off-highway Vehicle 

Gasoline, 4-Stroke, Industrial Equipment, All 

2260003000 Mobile Sources, Off-highway Vehicle 

Gasoline, 2-Stroke, Industrial Equipment, Total 2267001000 Mobile Sources, LPG, Recreational 

Equipment, All 

2260004000 

Mobile Sources, Off-highway Vehicle 

Gasoline, 2-Stroke, Lawn and Garden 

Equipment, All 

2267002000 

Mobile Sources, LPG, Construction and 

Mining Equipment, All 

2260005000 

Mobile Sources, Off-highway Vehicle 

Gasoline, 2-Stroke, Agricultural Equipment, 

Total 

2267003000 

Mobile Sources, LPG, Industrial Equipment, 

All 

2260006000 

Mobile Sources, Off-highway Vehicle 

Gasoline, 2-Stroke, Commercial Equipment, 

Total 

2267004000 

Mobile Sources, LPG, Lawn and Garden 

Equipment, All 

2260007000 Mobile Sources, Off-highway Vehicle 

Gasoline, 2-Stroke, Logging Equipment, Total 2267005000 Mobile Sources, LPG, Agricultural Equipment, All 

2265001000 

Mobile Sources, Off-highway Vehicle 

Gasoline, 4-Stroke, Recreational Equipment, 

Total 

2267006000 

Mobile Sources, LPG, Commercial Equipment, 

All 

2265001020 

Mobile Sources, Off-highway Vehicle 

Gasoline, 4-Stroke, Recreational Equipment, 

Snowmobiles 

2267008000 

Airport Ground Support Equipment, All, LPG 

2265002000 

Mobile Sources, Off-highway Vehicle 

Gasoline, 4-Stroke, Construction and Mining 

Equipment, Total 

2268002000 

Mobile Sources, CNG, Construction and 

Mining Equipment, All 

2265003000 Mobile Sources, Off-highway Vehicle 

Gasoline, 4-Stroke, Industrial Equipment, Total 2268003000 Mobile Sources, CNG, Industrial Equipment, 

All 

2265004000 

Mobile Sources, Off-highway Vehicle 

Gasoline, 4-Stroke, Lawn and Garden 

Equipment, All 

2268005000 

Mobile Sources, CNG, Agricultural 

Equipment, All 

2265005000 

Mobile Sources, Off-highway Vehicle 

Gasoline, 4-Stroke, Agricultural Equipment, 

Total 

2268006000 

Mobile Sources, CNG, Commercial 

Equipment, All 

2265006000 

Mobile Sources, Off-highway Vehicle 

Gasoline, 4-Stroke, Commercial Equipment, 

Total 

2268008000 

Airport Ground Support Equipment, CNG, All 

2265007000 Mobile Sources, Off-highway Vehicle 

Gasoline, 4-Stroke, Logging Equipment, Total 2268010000 Mobile Sources, CNG, Industrial Equipment, 

All 

2270001000 Mobile Sources, Off-highway Vehicle Diesel, 

Recreational Equipment, Total 2270009000 Mobile Sources, Off-highway Vehicle Diesel, 

Underground Mining Equipment, All 

2270002000 Mobile Sources, Off-highway Vehicle Diesel, 

Construction and Mining Equipment, Total 2270010000 Mobile Sources, Off-highway Vehicle Diesel, Industrial Equipment, All 

2270003000 Mobile Sources, Off-highway Vehicle Diesel, 

Industrial Equipment, Total 2282000000 Mobile Sources, Pleasure Craft, All Fuels, Total, All Vessel Types 

2270004000 Mobile Sources, Off-highway Vehicle Diesel, 

Lawn and Garden Equipment, All 2282005000 Mobile Sources, Pleasure Craft, Gasoline 2­

Stroke, Total 

2270005000 Mobile Sources, Off-highway Vehicle Diesel, 

Agricultural Equipment, Total 2282010000 Mobile Sources, Pleasure Craft, Gasoline 4­Stroke, Total 

2270006000 Mobile Sources, Off-highway Vehicle Diesel, 

Commercial Equipment, Total 2282020000 Mobile Sources, Pleasure Craft, Diesel, Total 

2270007000 Mobile Sources, Off-highway Vehicle Diesel, 

Logging Equipment, Total 2270008000 Airport Service Equipment, Total, Offhighway Diesel 

33 
In addition to the MSAT HAPs, there were several other HAPs in the 1999 NEI nonroad 

inventory. These HAPs were also projected for other projections work and the processing is 

described in Appendix C. 

Summaries of national-level nonroad projected emissions for the MSAT HAPS by engine, 

equipment, and engine/equipment type are provided in Tables 20, 21 and 22, respectively. 

Engine types include 4-stroke gasoline, 2-stroke gasoline, diesel, aircraft, CNG (natural gas), 

LPG (liquid propane), miscellaneous, and residual fuel. Equipment types projected include 

agriculture, aircraft, airport support, commercial, commercial marine vessel, construction, 

industrial, lawn & garden, logging, pleasure craft, railroad, recreation, and underground mining. 

Engine and equipment type definitions were based on the NMIM definitions found in the NMIM 

tables. These tables include the nonroad categories (locomotives, commercial marine and 

aircraft) that did not utilize NMIM for projections; these were discussed in Sections 3.1 and 3.2. 

More detailed summaries of nonroad projected emissions can be found in the MSAT rule docket: 

EPA-HQ-OAR-2005-0036. The State and HAP specific summaries, including non-MSAT 

HAPs, can be found in nonroad_0923.xls and nonroad_pivot.xls. 

34 
Table 20. National engine emissions for selected HAPs and total MSAT HAPs for 1999, 2007, 

2010, 2015, 2020, and 2030. 

Engine type HAP 

Emissions (tons/yr) 

1999 2007 2010 2015 2020 2030 

1,3-Butadiene 3,182 2,513 2,240 1,847 1,604 1,595 

Acetaldehyde 2,560 1,974 1,755 1,467 1,293 1,296 

2-stroke gas 

Acrolein 559 472 426 344 292 291 

Benzene 29,998 25,165 22,545 18,582 16,287 16,457 

Formaldehyde 6,701 5,235 4,728 3,928 3,415 3,414 

Naphthalene 82 72 69 71 74 80 

MSAT HAPs 501,265 437,667 395,319 318,999 270,889 270,706 

1,3-Butadiene 5,157 4,148 3,330 3,224 3,379 3,805 

Acetaldehyde 2,968 2,848 2,309 2,196 2,265 2,511 

Acrolein 458 367 300 289 300 334 

4-stroke gas Benzene 28,713 23,717 19,531 19,165 20,153 22,705 

Formaldehyde 8,759 7,043 5,736 5,495 5,688 6,326 

Naphthalene 487 493 470 497 531 597 

MSAT HAPs 171,323 139,306 115,264 113,151 118,789 133,591 

1,3-Butadiene 824 821 859 924 993 1131 

Acetaldehyde 2,019 2,004 2,098 2,259 2,430 2,770 

Acrolein 968 960 1,005 1,083 1,165 1,329 

Aircraft Benzene 1,102 1,114 1,163 1,247 1,335 1,511 

Formaldehyde 6,549 6,505 6,809 7,333 7,885 8,990 

Naphthalene 456 475 496 530 566 638 

MSAT HAPs 14,276 14,315 14,965 16,081 17,256 19,603 

1,3-Butadiene 8 2 1 1 1 1 

Acetaldehyde 5 2 1 0.5 0.4 0.3 

CNG Acrolein 1 0.2 0.1 0.1 0.1 0.05 

Benzene 64 20 11 6 5 4 

Formaldehyde 38 17 10 5 4 3 

MSAT HAPs 373 115 61 33 28 24 

1,3-Butadiene 526 404 359 299 259 231 

Acetaldehyde 15,472 11,926 10,604 8,766 7,633 7,059 

Acrolein 1,048 801 709 583 504 459 

Diesel Benzene 5,190 3,930 3,467 2,812 2,410 2,199 

Formaldehyde 33,311 25,598 22,729 18,739 16,274 15,000 

Naphthalene 207 168 149 125 105 87 

MSAT HAPs 67,113 51,410 45,569 37,457 32,414 29,718 

1,3-Butadiene 22 17 9 3 2 2 

Acetaldehyde 12 9 5 2 1 1 

LPG Acrolein 2 1 1 0.3 0.2 0.2 

Benzene 174 134 72 23 16 15 

Formaldehyde 45 34 19 6 4 4 

MSAT HAPs 957 697 376 121 83 77 

1,3-Butadiene 1 0.4 0.4 0.4 0.4 0.4 

Acetaldehyde 22 19 19 18 18 20 

Acrolein 24 23 22 22 21 20 

Miscellaneous Benzene 6 5 4 4 4 4 

Formaldehyde 44 37 34 32 32 36 

Naphthalene 0.3 0.3 0.3 0.3 0.3 0.3 

MSAT HAPs 238 215 205 200 196 196 

35 
Table 20. Continued. 

Engine type HAP 

Emissions (tons/yr) 

1999 2007 2010 2015 2020 2030 

Acetaldehyde 422 552 600 717 876 1330 

Acrolein 23 30 33 39 47 72 

Residual Oil 

Benzene 113 148 161 192 235 356 

Formaldehyde 807 1,056 1,149 1,373 1,677 2,547 

Naphthalene 22 28 30 35 42 62 

MSAT HAPs 1,591 2,074 2,253 2,687 3,274 4,956 

36 
Table 21. National equipment emissions for selected HAPs and all MSAT HAPs for 1999, 

2007, 2010, 2015, 2020, and 2030. 

Equipment type HAP 

Emissions (tons/yr) 

1999 2007 2010 2015 2020 2030 

1,3-Butadiene 243 176 148 120 101 85 

Acetaldehyde 4,493 3,058 2,581 1,966 1,542 1,260 

Acrolein 285 194 164 125 98 81 

Agriculture Benzene 2,203 1,569 1,323 1,058 877 744 

Formaldehyde 9,816 6,671 5,630 4,288 3,363 2,749 

Naphthalene 49 36 32 26 21 15 

MSAT HAPs 23,098 15,954 13,476 10,546 8,530 7,129 

1,3-Butadiene 824 821 859 924 993 1131 

Acetaldehyde 2,019 2,004 2,098 2,259 2,430 2,770 

Acrolein 968 960 1,005 1,083 1,165 1,329 

Aircraft Benzene 1,102 1,114 1,163 1,247 1,335 1,511 

Formaldehyde 6,549 6,505 6,809 7,333 7,885 8,990 

Naphthalene 456 475 496 530 566 638 

MSAT HAPs 14,276 14,315 14,965 16,081 17,256 19,603 

1,3-Butadiene 7 5 3 3 3 3 

Acetaldehyde 63 49 42 33 29 30 

Acrolein 6 4 4 3 3 3 

Airport Support Benzene 44 33 26 21 20 22 

Formaldehyde 139 105 90 71 63 65 

Naphthalene 1 1 1 1 1 1 

MSAT HAPs 421 311 251 206 191 205 

1,3-Butadiene 1,140 892 683 738 813 972 

Acetaldehyde 1,400 1,270 1,071 975 920 906 

Acrolein 156 127 105 99 98 102 

Commercial Benzene 6,809 5,323 4,206 4,529 4964 5,906 

Formaldehyde 3,418 2,907 2,435 2,236 2131 2,128 

Naphthalene 98 106 98 108 119 142 

MSAT HAPs 46,990 33,732 27,281 29,004 31,451 36,981 

1,3-Butadiene 6 6 6 6 6 7 

Acetaldehyde 2,364 2,588 2,639 2,768 2,974 3,619 

Commercial 

Marine Vessel 

Acrolein 98 109 112 118 129 161 

Benzene 644 705 719 753 809 982 

Formaldehyde 4,715 5,153 5,252 5,499 5,899 7,152 

Naphthalene 65 69 68 72 79 102 

MSAT HAPs 8,736 9,557 9,742 10,213 10,973 13,354 

1,3-Butadiene 407 259 214 182 165 156 

Acetaldehyde 5,723 4,138 3,578 2,745 2,210 1,883 

Acrolein 392 280 241 186 151 130 

Construction Benzene 3,601 2,310 1,957 1,639 1,450 1,348 

Formaldehyde 12,417 8,958 7,742 5,937 4,779 4,074 

Naphthalene 61 46 42 32 23 16 

MSAT HAPs 39,675 25,138 21,702 17,937 15,609 14,303 

37 
Table 21. Continued. 

Equipment type HAP 

Emissions (tons/yr) 

1999 2007 2010 2015 2020 2030 

1,3-Butadiene 302 143 88 50 39 33 

Acetaldehyde 1,350 857 676 459 389 381 

Acrolein 119 71 55 38 33 34 

Industrial Benzene 1,976 986 633 368 291 258 

Formaldehyde 3,046 1,790 1,404 963 832 837 

Naphthalene 30 18 15 9 6 4 

MSAT HAPs 14,559 7,456 5,114 3,157 2,573 2,382 

1,3-Butadiene 3,423 2,445 1,933 1,887 2,030 2,342 

Acetaldehyde 2,478 1,920 1,548 1,480 1,546 1,748 

Acrolein 388 252 207 201 212 241 

Lawn/Garden Benzene 20,451 14,729 12,112 12,039 12,960 14,941 

Formaldehyde 6,867 4,727 3,830 3,678 3,856 4,371 

Naphthalene 261 245 224 232 251 289 

MSAT HAPs 196,257 115,652 99,485 101,535 109,328 125,823 

1,3-Butadiene 44 29 29 29 31 36 

Acetaldehyde 176 102 85 62 55 55 

Acrolein 16 9 8 7 7 8 

Logging Benzene 267 185 180 177 187 221 

Formaldehyde 432 248 214 167 155 163 

Naphthalene 4 4 4 4 4 5 

MSAT HAPs 3,816 2,325 2,339 2,394 2,562 3,054 

1,3-Butadiene 2071 1423 1201 1018 928 895 

Acetaldehyde 1703 1179 1002 854 782 757 

Acrolein 316 212 179 152 139 134 

Pleasure Craft Benzene 20304 14177 13113 10507 9787 9598 

Formaldehyde 4136 2848 2447 2105 1932 1879 

Naphthalene 112 103 100 101 104 110 

MSAT HAPs 258,190 172,930 144,245 122,057 111,936 108,260 

1,3-Butadiene 114 107 104 102 99 94 

Acetaldehyde 853 805 776 758 731 686 

Acrolein 131 124 120 117 113 107 

Railroad Benzene 162 150 144 140 134 125 

Formaldehyde 1,901 1,793 1,730 1,690 1,629 1,529 

Naphthalene 61 51 44 42 40 35 

MSAT HAPs 4,416 4,143 3,984 3,896 3,758 3,531 

1,3-Butadiene 1,136 1,600 1,530 1,238 1,029 1,009 

Acetaldehyde 820 1,330 1,264 1,041 886 870 

Acrolein 206 312 295 228 180 176 

Recreational Benzene 7,781 12,938 12,365 9,544 7,622 7,587 

Formaldehyde 2,731 3,743 3,562 2,890 2,404 2,333 

Naphthalene 56 81 90 101 105 109 

MSAT HAPs 146,526 244,129 231,291 171,593 128,661 124,142 

38 
Table 21. Continued. 

Equipment type HAP 

Emissions (tons/yr) 

1999 2007 2010 2015 2020 2030 

1,3-Butadiene 1 1 1 1 1 1 

Acetaldehyde 39 34 31 25 22 23 

Underground 

Mining 

Acrolein 2 2 2 1 1 1 

Benzene 15 13 12 10 9 9 

Formaldehyde 87 77 68 55 50 51 

Naphthalene 0.2 0.2 0.2 0.1 0.1 0.1 

MSAT HAPs 176 155 138 112 100 104 

39 
Table 22. National engine/equipment emissions for MSAT HAPs 

Engine Type Equipment Type Emissions (ton/yr) 

1999 2007 2010 2015 2020 2030 

2-stroke gas Agriculture 358 81 79 85 91 102 

2-stroke gas Commercial 8,758 2,330 2,487 2,769 3,072 3,698 

2-stroke gas Construction 9,852 4,001 3,774 3,825 3,877 3,980 

2-stroke gas Industrial 127 17 14 8 4 0.002 

2-stroke gas Lawn/Garden 107,470 45,085 43,643 47,298 51,110 58,731 

2-stroke gas Logging 2,753 1,629 1,736 1,882 2,051 2,483 

2-stroke gas Pleasure Craft 241,175 158,105 129,809 108,270 98,520 94,557 

2-stroke gas Recreational 130,772 226,419 213,777 154,861 112,163 107,154 

4-stroke gas Agriculture 3,186 2,620 2,210 1,967 1,803 1,621 

4-stroke gas Airport Support 162 116 81 74 77 88 

4-stroke gas Commercial 34,652 28,153 21,883 23,931 26,534 31,956 

4-stroke gas Construction 5,327 3,366 2,537 2,374 2,350 2,384 

4-stroke gas Industrial 8,571 3,695 2,251 1,269 920 669 

4-stroke gas Lawn/Garden 86,647 68,917 54,462 53,090 57,199 66,074 

4-stroke gas Logging 418 340 319 331 366 444 

4-stroke gas Pleasure Craft 16,724 14,515 14,131 13,491 13,129 13,407 

4-stroke gas Railroad 51 33 28 29 30 33 

4-stroke gas Recreational 15,595 17,551 17,361 16,595 16,383 16,915 

Aircraft Aircraft 14,276 14,315 14,965 16,081 17,256 19,603 

CNG Agriculture 10 3 1 1 1 1 

CNG Airport Support 0.21 0.11 0.07 0.03 0.03 0.02 

CNG Commercial 55 20 11 6 5 4 

CNG Construction .04 0.02 0.01 0.005 0.004 0.003 

CNG Industrial 307 93 48 27 23 20 

Diesel Agriculture 19,553 13,249 11,184 8,493 6,636 5,405 

Diesel Airport Support 257 193 169 131 114 117 

Diesel Commercial 3,485 3,201 2,884 2,292 1,836 1,320 

Diesel Commercial Marine Vessel 7,125 7,460 7,466 7,500 7,672 8,363 

Diesel Construction 24,479 17,758 15,384 11,735 9,381 7,939 

Diesel Industrial 4,671 3,009 2,455 1,742 1,550 1,622 

Diesel Lawn/Garden 2,127 1,640 1,374 1,145 1,018 1,017 

Diesel Logging 645 356 284 181 145 127 

Diesel Pleasure Craft 225 262 262 257 250 260 

Diesel Railroad 4,213 3,967 3,818 3,732 3,596 3,373 

Diesel Recreational 159 159 153 136 115 72 

Diesel Underground Mining 176 155 138 112 100 104 

LPG Agriculture 0.24 0.19 0.12 0.05 0.04 0.02 

LPG Airport Support 2 2 1 0.31 0.21 0.19 

LPG Commercial 39 29 16 5 4 3 

LPG Construction 18 13 7 2 2 1 

LPG Industrial 883 642 346 111 76 71 

LPG Lawn/Garden 14 10 5 2 1 1 

LPG Railroad 0.04 0.03 0.02 0.02 0.02 0.02 

LPG Recreational 1 0.40 0.22 0.08 0.05 0.05 

Miscellaneous Commercial Marine Vessel 20 23 23 25 27 35 

Miscellaneous Pleasure Craft 66 49 44 39 36 36 

Miscellaneous Railroad 151 143 138 136 132 125 

Residual Commercial Marine Vessel 1,591 2,074 2,253 2,687 3,274 4,956 

Total nonroad 757,136 645,797 574,012 488,730 442,928 458,871 

40 
3.3.4 Projection of onroad refueling emissions 

Onroad refueling emissions are inventoried as stationary sources, although the emissions are 

related to mobile sources, and can be estimated using NMIM. As such, the onroad refueling 

emissions were projected using ratios developed from 1999, 2007, 2010, 2015, 2020, and 2030 

refueling emissions developed from NMIM (Michaels et. al, 2005). More details on the NMIM 

refueling runs can be found in Appendix A. The ratios, used as projection factors, were 

calculated by dividing the future year NMIM onroad refueling emissions (2007 and beyond) by 

1999 NMIM onroad refueling emissions in each county. These factors were then assigned to the 

onroad refueling SCC codes in the 1999 point and non-point inventory shown in Table 23. A 

map of the county-level 2015 growth factors is shown as an example in Figure 3. As with the 

aviation refueling emissions, the 2020 projected emissions were used for 2030 as well because of 

uncertainty in other stationary source projection information for 2030. The onroad refueling 

projection factors were included in the SCC growth factor files described in Section 4.1.3, and 

the onroad refueling emissions were projected at the same time as the other stationary sources as 

described in Section 4.3. Results are presented here however, since the projection factors were 

derived from NMIM. 

Projection factors 

0.1000 - 0.2012 

0.2013 - 0.2961 

0.2962 - 0.4370 

0.4371 - 0.6639 

0.6640 - 1.0647 

1.0648 - 2.1957 

Figure 3. 2015 county level refueling projection factors. 

41 
Table 23. Onroad refueling SCC codes. 

SCC Description 

2501060000 Storage and Transport, Petroleum and Petroleum Product Storage, Gasoline Service Stations, 

Total: All Gasoline/All Processes 

2501060100 Storage and Transport, Petroleum and Petroleum Product Storage, Gasoline Service Stations, 

Stage 2: Total 

2501060101 Storage and Transport, Petroleum and Petroleum Product Storage, Gasoline Service Stations, 

Stage 2: Displacement Loss/Uncontrolled 

2501060102 Storage and Transport, Petroleum and Petroleum Product Storage, Gasoline Service Stations, 

Stage 2: Displacement Loss/Controlled 

2501060103 Storage and Transport, Petroleum and Petroleum Product Storage, Gasoline Service Stations, 

Stage 2: Spillage 

40600401 Petroleum and Solvent Evaporation, Transportation and Marketing of Petroleum Products, Filling 

Vehicle Gas Tanks - Stage II, Vapor Loss w/o Controls 

40600402 Petroleum and Solvent Evaporation, Transportation and Marketing of Petroleum Products, Filling 

Vehicle Gas Tanks - Stage II, Liquid Spill Loss w/o Controls 

40600403 Petroleum and Solvent Evaporation, Transportation and Marketing of Petroleum Products, Filling 

Vehicle Gas Tanks - Stage II, Vapor Loss w/o Controls 

40600499 Petroleum and Solvent Evaporation, Transportation and Marketing of Petroleum Products, Filling 

Vehicle Gas Tanks - Stage II, Not Classified 

40600601 Petroleum and Solvent Evaporation, Transportation and Marketing of Petroleum Products, 

Consumer (Corporate) Fleet Refueling - Stage II, Vapor Loss w/o Controls 

40600602 Petroleum and Solvent Evaporation, Transportation and Marketing of Petroleum Products, 

Consumer (Corporate) Fleet Refueling - Stage II, Liquid Spill Loss w/o Controls 

40600603 Petroleum and Solvent Evaporation, Transportation and Marketing of Petroleum Products, 

Consumer (Corporate) Fleet Refueling - Stage II, Vapor Loss w/controls 

A national summary of onroad refueling emissions by SCC is shown in Table 24. 

42 
Table 24. Onroad refueling emissions by SCC for 1999, 2007, 2010, 2015 and 2020. 

SCC 

Emissions (tons) 

HAP 1999 2007 2010 2015 2020 

2501060000 

1,3-Butadiene 3 2 2 1 1 

Benzene 93 65 54 46 45 

Naphthalene 3 2 2 2 2 

All HAPs 1,329 905 747 633 629 

2501060100 

Benzene 993 08 452 322 291 

Naphthalene 151 94 73 57 54 

All HAPs 10,882 6,775 5,146 3,821 3,544 

2501060101 

Benzene 223 140 105 75 67 

Naphthalene 7 4 3 2 2 

All HAPs 1,336 839 625 445 401 

2501060102 

Benzene 21 17 16 16 17 

All HAPs 1,171 998 940 942 1,017 

2501060103 

Benzene 138 112 103 102 109 

Naphthalene 24 19 18 18 19 

All HAPs 4,672 3,838 3,585 3,565 3,825 

40600401 

Benzene 84 109 119 142 164 

Naphthalene 0.001 0.002 0.003 0.003 0.003 

All HAPs 111 144 156 185 214 

40600402 

Benzene 6 8 8 10 12 

All HAPs 9 11 12 14 16 

40600403 

Benzene 8 10 11 13 14 

Naphthalene 0.2 0.2 0.3 0.3 0.3 

All HAPs 215 281 295 342 386 

40600499 

Benzene 0.01 0.01 0.01 0.01 0.01 

Naphthalene 0.002 0.002 0.002 0.002 0.002 

All HAPs 0.23 0.30 0.31 0.33 0.36 

40600601 

Benzene 0.004 0.004 0.004 0.005 0.005 

All HAPs 0.021 0.021 0.022 0.025 0.028 

40600602 

Benzene 0.001 0.001 0.001 0.002 0.002 

All HAPs 0.006 0.006 0.007 0.007 0.008 

40600603 

Benzene 0.1 0.1 0.2 0.2 0.2 

All HAPs 0.36 0.46 0.52 0.61 0.70 

43 
3.4 Projection of HAP Precursor Emissions from Mobile Sources 

In order to calculate secondary concentrations for acetaldehyde, acrolein, formaldehyde, and 

propionaldehyde after ASPEN simulations for the primary concentrations for those HAPs, the 

emissions for the precursors also had to be projected to 2015, 2020, and 2030 (see Table 4 for 

non-HAP precursors). The total number of precursors is thirty-four. The precursor inventory 

used was the same as that used for the 1999 NATA and was derived from Version 2 of the NEI 

for VOC. In addition to the non-HAP precursors listed in Table 4 there are four HAPs that are 

precursors as well: 1,3-butadiene, acetaldehyde, MTBE, and methanol. The first three were 

HAPs already in the projected nonroad, aircraft, and locomotive/commercial marine inventories. 

Methanol (a HAP, but not an MSAT HAP) was projected as described in Appendix C. The 

precursors which themselves are HAPs were projected separately from the other non-HAP 

precursors and were appended to the remaining non-HAP precursors’ inventories prior to 

processing through EMS-HAP. Following is the methodology used to project the mobile nonHAP precursors for locomotive and commercial marine vessels, aircraft, onroad, and remaining 

nonroad sources. 

3.4.1 Locomotive and Commercial Marine Vessel Precursor Emissions 

Locomotive and commercial marine vessel precursor emissions were projected in the same way 

as the HAP locomotive and commercial marine vessel emissions. For locomotives, the VOC 

ratios shown in Table 6 were applied to each precursor. For commercial marine vessels, the 

VOC ratios (Table 8) were applied to the same SCC codes shown in Table 9. In addition to the 

SCC codes in Table 9, there were two other SCC codes in the precursor inventory, 2280001000 

(commercial marine vessels, coal) and 2283002000 (military marine vessels, diesel). The coal 

fueled marine vessel emissions were projected using the VOC ratio for all vessel types (VOC 

factors for 2280000000). The military marine vessel emissions were projected using the VOC 

ratios for diesel (VOC factors for 2280002000). 

After the precursor emissions were assigned ratios and projected to 2015, 2020, and 2030, the 

locomotive and commercial marine 1,3-butadiene, acetaldehyde, MTBE, and methanol air toxics 

emissions from the projections were appended to the projected precursor locomotive and 

commercial marine inventory. 

3.4.2 Aircraft Precursor Emissions 

Aircraft precursor emissions were projected using the same methodology and growth factors as 

discussed in Section 3.2. The temporally allocated nonroad airport precursor emissions output 

from PtTemporal for 1999 NATA were subset to: 1) include only the pollutants shown in Table 

4, other than the precursors that were MSAT HAPs (1,3-Butadiene, acetaldehyde, MTBE, and 

methanol); and 2) exclude airport support equipment SCC codes. They were then projected for 

2015, 2020, and 2030 using PtGrowCntl. 

44 
Non-point airport-related precursor emissions (i.e., aviation gasoline) were not projected. This is 

because these are stationary source emissions and it was decided to use the 1999 NATA 

precursor secondary concentrations for all future year stationary precursor concentrations, except 

for acrolein, which utilizes 1,3-butadiene as the sole precursor. Since acrolein’s precursor is an 

MSAT HAP, its secondary formation could be reasonably calculated with some confidence. 

There were several reasons for not projecting the other stationary precursors: projection data 

were not readily available for the stationary precursors as they were for the mobile precursors 

and the approach for estimating secondary concentrations is approximate and generally shows 

secondary concentrations from stationary sources to be a small portion of the total concentration 

as discussed in Section 5.5. 

3.4.3 Onroad Precursor Emissions 

Onroad emissions for the precursors were projected using the ratio of VOC emissions for each 

FIPS/SCC of 2015, 2020, or 2030 to 1999 NMIM results, in a similar fashion to that done for the 

MSAT HAPs. The precursor inventory’s SCC codes were classified as either exhaust or 

evaporative emissions, i.e., HDDV emissions for rural interstates were divided into exhaust and 

evaporative emissions. The NMIM results also were divided by exhaust or evaporative 

emissions. It was decided to calculate VOC projection ratios for exhaust and evaporation 

separately for each FIPS/SCC. 

As with the onroad processing for MSAT HAPs, the heavy-duty diesel vehicle emissions in 

NMIM were summed to create a total HDDV emission number for each FIPS/road type/exhaust 

or evaporative emission type. New ratios were calculated and were applied to SCC codes 

beginning with 2230070 for each FIPS/CAS in the 1999 NEI precursor inventory for the same 

road type and exhaust/evaporation emission type. Table 25 lists the HDDV SCC codes in the 

precursor inventory. 

3.4.4 Nonroad Precursor Emissions (excluding aircraft, locomotives, and commercial marine 

vessels) 

The precursors from nonroad emission categories covered by the NONROAD model were 

processed using a similar methodology as the emissions for HAPs. However, instead of HAP 

specific projection ratios, we used VOC ratios from NMIM. 

45 
Table 25. HDDV SCC codes used to calculate HDDV emissions in the precursor inventory. 

HDDV type 

(First 7 characters of 

SCC code) 

Road types 

(last 3 digits of SCC code) 

SCC in precursor 

inventory which 

projections are 

applied 

2230071, 2230072, 

2230073, 2230074, 

2230075 

11X (Rural Interstate, Exhaust) 

11V (Rural Interstate, Evaporation) 

223007011X 

223007011V 

2230071, 2230072, 

2230073, 2230074, 

2230075 

13X (Other Principal Arterial, Exhaust) 

13V (Other Principal Arterial, Evaporation) 

223007013X 

223007013V 

2230071, 2230072, 

2230073, 2230074, 

2230075 

15X (Rural Minor Arterial, Exhaust) 

15V (Rural Minor Arterial, Evaporation) 

223007015X 

223007015V 

2230071, 2230072, 

2230073, 2230074, 

2230075 

17X (Rural Major Collector, Exhaust) 

17V (Rural Major Collector, Evaporation) 

223007017X 

223007017V 

2230071, 2230072, 

2230073, 2230074, 

2230075 

19X (Rural Minor Collector, Exhaust) 

19V (Rural Minor Collector, Evaporation) 

223007019X 

223007019V 

2230071, 2230072, 

2230073, 2230074, 

2230075 

21X (Rural Local, Exhaust) 

21V (Rural Local, Evaporation) 

223007021X 

223007021V 

2230071, 2230072, 

2230073, 2230074, 

2230075 

23X (Urban Interstate, Exhaust) 

23V (Urban Interstate, Evaporation) 

223007023X 

223007023V 

2230071, 2230072, 

2230073, 2230074, 

2230075 

25X (Urban Other Freeways and Expressways, Exhaust) 

25V (Urban Other Freeways and Expressways, Evaporation) 

223007025X 

223007025V 

2230071, 2230072, 

2230073, 2230074, 

2230075 

27X (Urban Other Principal Arterial, Exhaust) 

27V (Urban Other Principal Arterial, Evaporation) 

223007027X 

223007027V 

2230071, 2230072, 

2230073, 2230074, 

2230075 

29X (Urban Minor Arterial, Exhaust) 

29V (Urban Minor Arterial, Evaporation) 

223007029X 

223007029V 

2230071, 2230072, 

2230073, 2230074, 

2230075 

31X (Urban Collector, Exhaust) 

31V (Urban Collector, Evaporation) 

223007031X 

223007031V 

2230071, 2230072, 

2230073, 2230074, 

2230075 

33X (Urban Local, Exhaust) 

33V (Urban Local, Evaporation) 

223007033X 

223007033V 

46 
4. Development of Future Year Stationary Source Emissions 

This section describes the methodology used to develop growth factors, reduction factors, and 

other inventory changes used to project the stationary (point and non-point inventories) to 

various future years, including 2015 and 2020, which are the MSAT years of interest. As 

previously noted, 1999 stationary source emissions were not projected to 2030 because of 

uncertainty in 2030 projection information; 2020 stationary emissions were used for both 2020 

and 2030. 

The general approach was to develop growth and reduction factors, and apply them using EMSHAP Version 3.0. For one category (medical waste incineration), however, a draft 2002 

emission inventory was used to represent emissions for all future years (Section 4.3). 

4.1 Growth factors 

Three sets of growth factors (GFs) were developed for input into EMS-HAP for use in growing 

stationary source emissions: Maximum Achievable Control Technology (MACT)-based GFs, 

Standard Industrial Classification (SIC)-based GFs and SCC-based GFs. Depending upon the 

particular code (i.e., MACT, SCC, SIC), the GFs were national, state-level or county level. 

EMS-HAP uses the most specific level of data (county) available within a particular GF file. 

Thus, if a SIC-based GF file contained state and county GFs for the same SIC, and if the county 

in the GF file matched the county in the inventory, EMS-HAP would apply the county SIC-based 

GF. Also, in EMS-HAP, if an inventory record matches to GFs in multiple files, the MACTbased GF overrides the SIC-based GF, which overrides the SCC-based one. 

For stationary sources, growth factors were developed using three sources of information: 

• Regional Economic Model, Inc. (REMI) Policy Insight® model, version 5.5 (REMI, 

2004; Fan et al., 2000), 

• Regional and National fuel-use forecast data from the U.S. Department of Energy, 

Annual Energy Outlook for the years 2004, 2001 and 2002 (Energy Information 

Administration, 2005), and 

• Rule development leads or economists who had obtained economic information in the 

process of rule development. 

The first two sources of information were also used in projecting criteria pollutant emissions for 

the Clean Air Interstate Rule (U.S. EPA, 2005b). Earlier versions of REMI and AEO were used 

to develop the EGAS 4.0, which provides growth factors from 1996 up to 2020 (E.H. Pechan and 

Associates, 2001). 

4.1.1 MACT based growth factors 

The MACT-based growth factors used in the projections are shown in Tables 26 (national level 

growth factors) and Table 27 (state level growth factors for utility boilers, coal, which is 

47 
MACT=1808-1). Most growth factors were based on data from rule development project leads. 

Some leads estimated that particular categories were not expected to experience any growth, and 

were assigned growth factors of 1.0. Some leads provided a per year rate, which resulted in a 

formula of raising a percent growth to a power, where the power was the number of years 

between the future year and 1999. In one case, for primary aluminum production 

(MACT=0201), year-specific growth factors based on a 1996 base year were provided; we 

determined the 1999 base year growth factors as the ratio of the future year’s growth factor and 

1999 growth factor from the 1996 base year information (Table 26). All MACT-based growth 

factors in the files were national level growth factors with the exception of 1808-1 (coal burning 

utility boilers). These growth factors were developed at the state level, using Integrated Planning 

Model (IPM) run results from the IAQR proposal (http://www.epa.gov/airmarkets/epaipm/iaqr.html) (U. S. EPA, 2004c). The IPM data were available for 2010 and 2015; thus 

growth factors for 1808-1 for other years were computed using interpolation, with 2020 being set 

equal to 2015. For years prior to 2010 the interpolation formula was: 

GFX = 1 + (( X − 1999) × (GF 2010 − 1) / ( 2010 − 1999)) (6) 

where X is 2015 or 2020. 

48 
Table 26. National level MACT growth factors for 2015 and 2020. 

MACT Description Methodology* Equation 

Growth Factors 

2015 2020 

0101-2 Rocket Engine Test 

Firing 

no growth GF = 1 1.0000 1.0000 

0105 Stationary RICE 5% growth per year GF=1.05(year-1999) 2.1829 2.7860 

0108 Stationary 

Combustion Turbines 

0.8% growth per year GF=1.008(year-1999) 1.1360 1.1821 

0201 Primary Aluminum 

Production 

Future year’s 1996 based 

growth factor divided by 1999 

growth factor based on 1996 

GF=GF1996/(1999 GF1996) 

1999 GF1996 = 0.832 

2015 GF1996 = 1.025 

2020 GF1996 = 1.11 

1.2320 1.3341 

0302 Coke Ovens: 

Charging, Top Side, 

and Door Leaks 

4% decline per year GF=0.96(year-1999) 0.5204 0.4243 

0303 Coke Ovens: 

Pushing, Quenching, 

& Battery Stacks 

4% decline per year GF=0.96(year-1999) 0.5204 0.4243 

0409 Mineral Wool 

Production 

no growth GF = 1 1.0000 1.0000 

0412 Wool Fiberglass 

Manufacturing 

no growth GF = 1 1.0000 1.0000 

0415 Clay Ceramics 

Manufacturing 

no growth GF = 1 1.0000 1.0000 

0705 Magnetic Tapes 

(Surface Coating) 

no growth GF = 1 1.0000 1.0000 

0707 Metal Can (Surface 

Coating) 

no growth GF = 1 1.0000 1.0000 

0802 Municipal Landfills no growth GF = 1 1.0000 1.0000 

1001 Acrylic/Modacrylic 

Fibers Production 

no growth before 2007, 1% 

growth after 2007 

GF=1 before 2007 

GF=1.01(year-2007) 

1.0829 1.1381 

1101 Manufacture of 

Nutritional Yeast 

growth factors based on 2020 

GF=1.14 

1.006258947(year-1999) 1.1045 1.1400 

1609 Commercial 

Sterilization Facilities 

0.5% growth per year 1.005(year-1999) 1.0831 1.1104 

1614 Halogenated Solvent 

Cleaners 

no growth GF = 1 1.0000 1.0000 

1621 Paint Stripping 

Operations 

decline by 40% from 1999 to 

2010 Keep same growth factor 

as 2010 for all future years 

thereafter. 

GF=0.954623 (year-1999) 

GF=0.6 for 2010 and 

beyond 

0.6 0.6 

1631 Rubber Tire 

Production 

increase by 2% per year from 

1999 to 2020. 

1.02(year-1999) 1.3728 1.5157 

1643 Dry Cleaning: 

Perchloroethylene 

no growth GF = 1 1.0000 1.0000 

1801 Medical Waste 

Incinerators 

no growth; future set to 2002 

emissions. See Section 4.3 

GF = 1 1.0000 1.0000 

1802 Municipal Waste 

Combustors 

no growth GF = 1 1.0000 1.0000 

1808-2 Utility Boilers: 

Natural Gas 

no growth GF = 1 1.0000 1.0000 

1808-3 Utility Boilers: Oil no growth GF = 1 1.0000 1.0000 

* growth factor methodologies provided by project leads 

49 
Table 27. Utility Boilers: Coal (MACT=1808-1) state level growth factors for 2015 and 2020. 

State 

FIPS 

State Growth 

Factor 

State 

FIPS 

State Growth 

Factor 

State 

FIPS 

State Growth 

Factor 

01 Alabama 1.0124 21 Kentucky 1.0061 38 North Dakota 0.8446 

02 Alaska 1.0291 22 Louisiana 0.7512 39 Ohio 1.1332 

04 Arizona 0.8722 23 Maine 0.8222 40 Oklahoma 0.9677 

05 Arkansas 1.0505 24 Maryland 0.8925 41 Oregon 0.9657 

06 California 1.1607 25 Massachusetts 0.6548 42 Pennsylvania 1.1294 

08 Colorado 0.9969 26 Michigan 1.0635 44 Rhode Island 1.0000 

09 Connecticut 2.9294 27 Minnesota 1.0894 45 South Carolina 1.1315 

10 Delaware 1.1898 28 Mississippi 1.1299 46 South Dakota 0.9049 

11 District of 

Columbia 

1.0000 29 Missouri 1.1095 47 Tennessee 1.0324 

12 Florida 0.9407 30 Montana 0.9568 48 Texas 0.8056 

13 Georgia 1.1779 31 Nebraska 1.1353 49 Utah 0.8566 

15 Hawaii 1.0291 32 Nevada 1.1310 50 Vermont 1.0000 

16 Idaho 1.0000 33 New 

Hampshire 

0.9262 51 Virginia 0.9378 

17 Illinois 1.1783 34 New Jersey 1.3554 53 Washington 1.0034 

18 Indiana 1.0211 35 New Mexico 0.9538 54 West Virginia 1.0764 

19 Iowa 0.9547 36 New York 1.1976 55 Wisconsin 1.2966 

20 Kansas 1.1285 37 North Carolina 1.1753 56 Wyoming 0.8366 

In Table 27, Alaska and Hawaii were set equal to the average 48 state growth factor. 

Note, MACT codes in the NEI that are not listed in Tables 26 and 27 were not assigned a 

MACT-based growth factor. Instead growth for sources with those MACT codes were grown 

using the SIC or SCC based growth factors, described in the next sections. 

The actual MACT-based growth factors files containing the data described above are provided 

with the EMS-HAP version 3.0 projection-related ancillary files, at 

http://www.epa.gov/ttn/chief/emch/projection/emshap30.html and also in the MSAT rule docket 

(EPA-HQ-OAR-2005-0036). 

50 
4.1.2 SIC based growth factors 

State-specific SIC-based growth factors, for specific standard industrial codes (SIC) were 

developed using the Regional Economic Model, Inc. (REMI) Policy Insight® model, version 5.5 

(being used in the development of the Economic Growth Analysis System (EGAS), version 5.0, 

(U.S. EPA, 2005c)). The REMI model forecasts economic activity by region and for individual 

sectors of the economy. By making assumptions about which economic indicators can represent 

emissions growth, growth factors can be developed for projecting emission inventories. A 

review of these growth factors for the development of the Clean Air Interstate Rule (U.S. EPA, 

2005b) projected inventories, led to changes to about thirty SIC-based growth factors where they 

were unrealistic or highly uncertain (U.S. EPA 2005b). They were replaced with data (nationallevel) from industry forecasts, bureau of labor statistics (BLS) projections and Bureau of 

Economic Analysis (BEA) historical growth from 1986 – 2002 (U. S. EPA, 2005b). These SIC 

codes are shown in Table 28. Also SIC 1041 (Mining of gold ores) was set to no growth 

(GF=1.0). Growth factors for 3322 (Malleable iron foundries) and 3324 (Steel investment 

foundries) were set equal to the growth factors for SIC 3321. 

Table 28. SIC codes changed due to unrealistic growth factors. 

SIC Description 

1311 Oil And Gas Extraction, Crude Petroleum And Natural Gas, Crude petroleum and natural gas 

1321 Oil And Gas Extraction, Natural Gas Liquids, Natural gas liquids 

2821 Chemicals And Allied Products, Plastics Materials and Synthetics, Plastics materials and resins 

2822 Chemicals And Allied Products, Plastics Materials and Synthetics, Synthetic rubber 

2823 Chemicals And Allied Products, Plastics Materials and Synthetics, Cellulosic manmade fibers 

2851 Chemicals And Allied Products, Paints and Allied Products, Paints and allied products 

2873 Chemicals And Allied Products, Agricultural Chemicals, Nitrogenous fertilizers 

2874 Chemicals And Allied Products, Agricultural Chemicals, Phosphatic fertilizers 

2895 Chemicals And Allied Products, Miscellaneous Chemical Products, Carbon black 

3011 Rubber And Misc. Plastics Products, Tires and Inner Tubes, Tires and inner tubes 

3211 Stone, Clay, And Glass Products, Flat Glass, Flat glass 

3221 Stone, Clay, And Glass Products, Glass and Glassware, Pressed Or Blown, Glass containers 

3229 Stone, Clay, And Glass Products, Glass and Glassware, Pressed Or Blown, Pressed and blown glass, nec 

3241 Stone, Clay, And Glass Products, Cement, Hydraulic, Cement, hydraulic 

3321 Primary Metal Industries, Iron and Steel Foundries, Gray and ductile iron foundries 

3325 Primary Metal Industries, Iron and Steel Foundries, Steel foundries, nec 

3331 Primary Metal Industries, Primary Nonferrous Metals, Primary copper 

3334 Primary Metal Industries, Primary Nonferrous Metals, Primary aluminum 

3339 Primary Metal Industries, Primary Nonferrous Metals, Primary nonferrous metals, nec 

3411 Fabricated Metal Products, Metal Cans and Shipping Containers, Metal cans 

3441 Fabricated Metal Products, Fabricated Structural Metal Products, Fabricated structural metal 

3471 Fabricated Metal Products, Metal Services, Nec, Plating and polishing 

3479 Fabricated Metal Products, Metal Services, Nec, Metal coating and allied services 

3497 Fabricated Metal Products, Misc. Fabricated Metal Products, Metal foil & leaf 

3499 Fabricated Metal Products, Misc. Fabricated Metal Products, Fabricated metal products, nec 

3711 Transportation Equipment, Motor Vehicles and Equipment, Motor vehicles and car bodies 

3713 Transportation Equipment, Motor Vehicles and Equipment, Truck and bus bodies 

3714 Transportation Equipment, Motor Vehicles and Equipment, Motor vehicle parts and accessories 

3715 Transportation Equipment, Motor Vehicles and Equipment, Truck trailers 

51 
The actual SIC-based growth factors files containing the data described above are provided with 

the EMS-HAP version 3.0 projection-related ancillary files, at 

http://www.epa.gov/ttn/chief/emch/projection/emshap30.html and in the MSAT rule docket 

(EPA-HQ-OAR-2005-0036). 

4.1.3 SCC based growth factors 

SCC based growth factors for stationary sources were derived from four sources: 1) REMI 

model, 2) Energy Information Administration’s National Energy Modeling System (Energy 

Information Administration, 2005), and 3) NMIM derived onroad refueling future-to-1999 

emission ratios. The REMI model is discussed in Section 4.1.2 and the onroad refueling factors 

are discussed in Section 2.3; and 4) aviation gasoline emissions (discussed in Section 3.2). The 

National Energy Modeling system was used to calculate growth factors for emission sources 

related to energy use such as residential heating. The data are provided at a division level, with 

the country divided into nine divisions, for some sectors (e.g., residential fuel use), and at the 

national level for more detailed industrial sectors (e.g., paper). Growth factors were developed 

at the most detailed geographic scale (e.g., developed State-level growth factors from the 

division information) and sectors available. The AEO data were then mapped to SCC codes 

(Bollman, 2004). 

In addition to the three sources of data above, emissions for fires (wild and prescribed) were 

assumed to remain flat, i.e. no. 

For all SCC codes, with the exception of the onroad refueling SCC codes, growth factors were at 

national or state level. The refueling factors were at county level. 

In the growth factor files that are input into EMS-HAP, instead of listing growth factors by SCC, 

each SCC is assigned a growth indicator group. These groups consist of related SCC codes that 

shared common growth factors. For example, for the onroad refueling SCC codes, instead of 

listing the growth factor for each of the 12 SCC codes by FIPS, the onroad refueling SCC codes 

are assigned the growth indicator group “NMIM Refueling” and the growth factors crossreferenced by growth indicator group instead of SCC. This cuts down on the number of records 

in the SCC-based growth factor files. Example records showing the SCC based growth factor 

file format are shown in Figure 1 in Section 3.2. 

The actual SCC-based growth factors files containing the data described above are provided with 

the EMS-HAP version 3.0 projection-related ancillary files, at 

http://www.epa.gov/ttn/chief/emch/projection/emshap30.html and in the MSAT rule docket 

(EPA-HQ-OAR-2005-0036). 

52 
4.2 Reduction factors 

Not only does EMS-HAP allow the user to specify the growth factors for emissions sources, 

EMS-HAP also allows for reduction of emissions. Reduction factors were applied to the grown 

stationary source emissions to account for regulatory impacts and plant closures. 

The percent reductions were primarily based on estimates of national average reductions for 

specific HAPs or for groups of HAPs from a source category or subcategory as a result of 

regulatory efforts. These efforts are primarily the MACT and Section 129 standards, mandated 

in Title III of the 1990 Clean Air Act Amendments. Percent reductions were determined by, as 

well as information on applicability and compliance dates, whether they apply to “major” only or 

both “major” and “area” sources. With regards to applicability it was necessary to gather 

information for the various rules from rule preambles, fact sheets and through the project leads 

(questionnaire and phone calls). A major source is defined as any stationary source or group of 

stationary sources located within a contiguous area and under common control that has the 

potential to emit, considering controls, in the aggregate, 10 tons per year or more of any 

hazardous air pollutant or 25 tons per year or more of any combination of hazardous air 

pollutants; the status of a point source as “major” is indicated in the NEI by the field called 

“FACILITY CATEGORY”. For some rules, percent reductions were provided for specific 

HAPs or groups of HAPs (e.g., all metals, or all volatiles) rather than a single number for all 

HAPs in the categories. Information was also received on plant closures for several categories 

such as coke ovens and municipal waste combustors. For the “utility boilers coal” category, it 

was assumed that the acid gases (hydrochloric acid, hydrogen fluoride and chlorine) would be 

reduced by the same amount as SO2 due to co-benefits of potential controls. State-level SO2 

reductions were calculated using SO2 projected emissions from the Integrated Planning Model 

(IPM) runs done for proposed CAIR (U. S. EPA, 2004c) and applied these reductions to the acid 

gas emissions. At the time of the projections, the IPM runs for the final CAIR rule were not 

available. 

Emission reductions were applied in EMS-HAP by MACT code; some were HAP and MACT 

specific, some were SCC and MACT specific. Site specific reductions such as plant closures or 

estimations of reductions expected from particular facilities in the source category, were applied 

by the EMS-HAP site id; process specific, site specific reductions used the SCC as well. 

A list of the source categories to which reductions were applied in EMS-HAP, either to facilities 

in the category or the entire category, is presented in Table 29. Note that this does not include the 

impacts of all of the rules, only those for which HAP emission reductions were able to be 

estimated and for which the compliance date was later than 1999, or for which information on 

closures was obtained. In addition, if the inventory did not have emissions for which the rule 

was expected to impact, then that was also left out of the table. It also does not include 

reductions from MWI, as discussed in the next section. 

The actual reduction information for these source categories is provided with the EMS-HAP 

version 3.0 projection-related ancillary files, at 

http://www.epa.gov/ttn/chief/emch/projection/emshap30.html along with more detailed 

53 
descriptions and summaries of the data. The reduction information and detailed summaries and 

descriptions can also be found in the MSAT rule docket (EPA-HQ-OAR-2005-0036). 

Table 29. Summary of Categories for which reductions were applied in EMS-HAP. 

Category Category 

Amino/Phenolic Resins Production: POLYMERS & 

RESINS III 

Ammonium Sulfate - Caprolactam By-Product Plants: 

THE MON 

Asphalt roofing and Processing 

Boat Manufacturing 

Brick and Structural Clay Products Manufacturing 

Carbon Black Production 

Carbonyl Sulfide (COS) Production 

Cellulose products manufacturing 

Commercial/Industrial Solid Waste Incineration 

(CISWI)Coke Ovens: Charging, Topside and Door 

Leaks Coke Ovens: Pushing, Quenching, & Battery 

Stacks Cyanide Chemicals Manufacturing Ethylene 

Processes Flexible Polyurethane Foam Production 

Friction Products Manufacturing 

Hazardous Waste Incineration and its subcategories: 

Commercial Haz. Waste Incinerators, On-Site Haz. 

Waste Incinerators, Cement Kilns, Lightweight 

Aggregate Kilns 

Industrial/Commercial/ Institutional Boilers & Process 

Heaters 

Industrial/Commercial/ Institutional Boilers & Process 

Heaters (Coal) 

Integrated Iron & Steel Manufacturing 

Iron Foundries 

Leather Tanning & Finishing Operations 

Lime Manufacturing 

Manufacturing of Nutritional Yeast 

Mineral Wool Production 

Municipal Solid Waste Landfills 

Miscellaneous Organic Chemical Products & Processes 

Miscellaneous Coatings Manufacturing 

Municipal Waste Combustors 

Primary Aluminum Production 

Primary Copper Smelting 

Primary Magnesium Refining 

Secondary Aluminum Production 

Stationary Reciprocating Internal Combustion Engines 

Natural Gas Transmission & Storage 

Off-Site Waste and Recovery Operations 

Oil & Natural Gas Production 

Organic Liquids Distribution (Non-Gasoline) 

Pesticide Active Ingredient Production 

Petroleum Refineries - Catalytic Cracking, Catalytic 

Reforming, & Sulfur Plant Units (10 yr) 

Petroleum Refineries - Other Sources Not Distinctly 

Listed (4yr) 

Pharmaceuticals Production 

Reinforced Plastic Composites Production 

Phosphate Fertilizers Production& Phosphoric Acid 

Manufacturing 

Plywood and Composite Wood Products 

Polyether Polyols Production 

Portland Cement Manufacturing 

Pulp & Paper Production – Combustion & 

Noncombustion. 

Refractories Products Manufacturing 

Rubber Tire Production 

Secondary Aluminum Production 

Secondary Lead Smelting 

Site Remediation 

Solvent Extraction for Vegetable Oil Production 

Stationary Reciprocating Internal Combustion Engines 

Surface coating related categories: 

• Auto & Light Duty Truck 

• Wood Building Products 

• Large Appliances 

• Metal Can 

• Metal Coil 

• Metal Furniture 

• Miscellaneous Metal Parts 

• Paper & Other Webs 

• Plastic Parts & Products 

• Fabric Coating Dying and Printing 

• Printing/Publishing 

Steel Pickling - HCL Process 

Taconite Iron Ore Processing 

Viscose Process Manufacturing 

Wet-Formed Fiberglass Mat Production 

Wool Fiberglass Manufacturing 

Utility Boilers: Coal 

54 
4.3 Application of growth and reductions to project stationary source emissions 

For stationary sources, EMS-HAP was used to project the emissions, including onroad refueling, 

with the lone exception of Medical Waste Incinerator (MWI, MACT=1801) emissions which 

utilized draft 2002 MWI emissions as advised by the MWI project lead. For this category, it was 

expected that emissions would remain at 2002 levels into the future. 

For point sources, the PtTemporal output from the 1999 NATA EMS-HAP run was adjusted (via 

a program called mwi.sas, which is available in the docket for this rule [EPA-HQ-OAR-2005­

0036] to change the 1999 medical waste incineration (MWI) emissions to 2002 emissions (U.S. 

EPA, 2005a). The adjusted emissions then processed through PtGrowCntl, using the growth and 

reduction factors described in Sections 3.3.5, 4.1 and 4.2, to project the inventory to 2002 

through 2010 inclusive, 2015, and 2020. 

The substitution of the 2002 MWI emissions for the 1999 emissions resulted in a change from 

727 tons to 31.5 tons. 

Note that the aviation gasoline point sources were run separately through EMS-HAP, using the 

aircraft growth factors as described in Section 3.2. 

For the non-point inventory, the EMS-HAP program CountyProc was run using the growth and 

reduction factors described in Sections 3.3.4, 4.1 and 4.2, to project the inventory to 2002 

through 2010 inclusive, 2015, and 2020. For all non-point projection years except for 2015 and 

2020, ASPEN ready files for the non-point inventory were not needed so the GCFLAG variable 

in CountyProc was set to 0, creating projected emissions without the other ASPEN-specific 

steps. This was done to decrease run time. 

There were no 2002 MWI emissions in the non-point inventory, so 1999 MWI non-point 

emissions were removed. The amount of emissions removed was 220 tons. 

Summaries of major and area & other emissions for 1999, 2007, 2010, 2015, and 2020 for 

selected MSAT HAPs and the sum across all MSAT HAPs are shown in Table 30. For all 

MSAT HAPs, major source emissions initially decrease from 1999 to 2007 but then increase 

with time to 2020. Area & other source emissions increase with all years. 

55 
Table 30. 1999 and projected stationary emissions for selected HAPs and total MSAT HAPs. 

HAP 

Year 

1999 2007 2010 2015 2020 

Major Area & 

other 

Major Area & 

other 

Major Area & 

other 

Major Area & 

other 

Major Area & 

other 

1,3-butadiene 1,982 22,164 1,731 22,819 1,805 22,961 2,011 23,068 2,247 23,212 

Acetaldehyde 11,578 26,990 9,299 28,277 9,225 28,715 10,695 29,419 12,111 30,142 

Acrolein 899 21,097 763 21,808 731 21,896 819 21,990 904 22,088 

Benzene 9,820 101,362 7,671 108,123 7,877 109,628 8,696 111,634 9,634 114,161 

Formaldehyde 30,611 126,365 30,857 131,649 30,970 133,283 35,367 136,008 40,657 139,095 

Naphthalene 2,245 11,831 1,850 13,162 1,919 13,570 2,146 14,314 2,398 15,137 

ALL MSAT 

HAPs* 

290,498 925,042 230,800 1,020,953 242,641 1,067,558 277,173 1,143,706 313,831 1,225,530 

* POM groups 2, 5, 6, and 7 may include emissions of HAPs that are not MSAT HAPs but part of those POM 

groups in the stationary inventories. Non MSAT HAPs may be included due to processing in EMS-HAP when 

POM HAPs are grouped into POM groups. 

Figure 4 shows the comparison of stationary and mobile emissions, nationwide, after all 

projections for 1999, 2007, 2010, 2015, 2020, and 2030. With all source groups considered, it 

can be seen that total MSAT HAP emissions were projected to decrease with time from 1999 to 

2030 with a slight increase between 2020 and 2030, due to mobile sources. It can also be seen 

that non-gasoline mobile emissions are a very small part of the total emissions for all years. 

E

missi

o

ns (millio

ns o

f to

n

s)

4 

3.5 

3 

2.5 

2 

1.5 

1 

0.5 

0 

1999 2007 2010 2015 2020 2030 

Year 

Major 

Area & Other 

Onroad Diesel 

Onroad Gasoline 

Other Nonroad 

Nonroad Gasoline 

Figure 4. Annual emissions by source sector at the national level. 

56 
5. EMS-HAP Processing for HAPs 

Prior to conducting air quality modeling using the ASPEN model, the emissions were processed 

in the Emissions Modeling System for Hazardous Air Pollutants (EMS-HAP) Version 3 (U.S. 

EPA, 2004b). EMS-HAP creates the emissions input files that are used by ASPEN to calculate 

the air quality concentrations. Following are brief descriptions of the EMS-HAP processing. 

The reader is referred to the EMS-HAP User’s Guide (U.S. EPA, 2004b) for more details. 

5.1 Point sources 

Point sources (including major and area sources) are processed through four EMS-HAP 

programs to create ASPEN ready files: PtDataProc, PtModelProc, PtTemporal, and 

PtFinal_ASPEN. A fifth point source program, PtGrowCntl is used to apply growth factors and 

reduction information to a base year inventory to develop future year emissions inventories. 

This program is run between PtTemporal and PtFinal_ASPEN. 

For the MSAT study, the point inventory had already been processed through PtDataProc, 

PtModelProc and PtTemporal for the 1999 National Air Toxics Assessment (NATA). 

Geographic locations and stack parameters’ quality assurance was done in PtDataProc. See Ch. 

3, EMS-HAP User’s Guide for details. 

In PtModelProc, the individual POM HAPs were grouped into eight POM groups, based on 

cancer risk (See Section C.4.2 in Appendix C of the EMS-HAP User’s Guide for POM 

groupings). Also in PtModelProc, the metals (chromium, nickel, and manganese) were split into 

fine and coarse particle emissions. Also, unspeciated chromium was speciated into chromium III 

and chromium VI based on MACT codes. For naphthalene, emissions were split into gaseous 

and particle mode. For descriptions of these two processes see Ch. 4, EMS-HAP User’s Guide. 

Urban/rural dispersion parameters, vent type, and building parameters are also assigned in 

PtModelProc. 

PtTemporal allocated the annual emissions to eight 3-hour time blocks based on the category of 

the emissions. PtTemporal output was adjusted to change the 1999 medical waste incineration 

(MWI) emissions to 2002 emissions (see Section 4.3) which were used as the projected MWI 

emissions for all future years. 

As discussed in 4.3, PtGrowCntl was run to project the inventory to 2002 through 2010 

inclusive, 2015, and 2020. For 2015 and 2020, the PtGrowCntl output was subset to MSAT 

HAPs and then processed through PtFinal_ASPEN to create ASPEN ready emissions files 

(including reactivity/particle size information) for the point inventory. EMS-HAP also allows 

for grouping of the emissions so that the contribution of different source groups can be 

quantified when calculating concentrations in ASPEN. As for the 1999 NATA, the point sources 

were binned into two groups, major (group=0) and area & other sources (group=1). Source 

groupings for stationary and mobile sources can be seen in Table 31. 

57 
5.2 Non-point sources 

For the 1999 NATA, the non-point emissions inventory was first processed through the EMSHAP COPAX program to separate the airport related emissions from other non-point emissions 

(see Table 14 for airport related SCC codes). COPAX allocated the airport related emissions to 

point source locations at the airports (See Ch. 2 in the EMS-HAP User’s Guide). The airport 

related emissions were then processed through the same programs as the point source inventory. 

The growth factors used for PtGrowCntl are documented in Section 2.2. 

For the remaining non-point inventory, after removing the MWI (MACT=1801) emissions, the 

emissions were projected to 2002 through 2010 inclusive, 2015, and 2020 using the EMS-HAP 

program CountyProc. This program also spatially allocated county level emissions to census 

tracts, temporally allocated emissions to 3-hour time blocks, assigned urban/rural dispersion 

parameters, assigned reactivity classes/particle size information for ASPEN, and grouped certain 

pollutants together such as the POM groups, and metals (See Ch. 9 of EMS-HAP User’s Guide). 

For 2015 and 2020, not all HAPs were needed for ASPEN files. Therefore, the 1999 inventory 

was subset to MSAT HAPs and all POM HAPs only and CountyProc run again to project 

emissions, this time with the GCFLAG set to 1, resulting in projected ASPEN-ready emissions. 

With GCFLAG=1, ASPEN ready files are created with the projected emissions. 

For both the non-point airport related emissions and remaining non-point sources, the emissions 

were grouped into area & other sources (group=1). 

5.3 Onroad sources 

The emission inventories for 2015, 2020, and 2030 were projected outside of EMS-HAP using 

the methodology in Section 2.4.2. Therefore, EMS-HAP was only used to create the ASPEN 

ready files. For the onroad inventory, the CountyProc program was used to create the ASPEN 

ready files. As with the non-point inventory, CountyProc spatially allocated county level 

emissions to census tracts, temporally allocated emissions to 3-hour time blocks, assigned 

urban/rural dispersion parameters, assigned reactivity classes/particle size information for 

ASPEN, and grouped certain pollutants together such as the POM groups, and metals (See Ch. 9 

of EMS-HAP User’s Guide for details). Onroad emissions were grouped into two onroad 

groups: onroad gasoline emissions (group=2) and onroad diesel emissions (group=4). SCC 

codes beginning with 2201 were assigned to group 2 and SCC codes beginning with 2230 were 

assigned to group 4. 

58 
5.4 Nonroad sources 

5.4.1 Aircraft sources 

Aircraft emissions had been previously extracted from the 1999 inventory for NATA using 

COPAX in order to be modeled in ASPEN as point sources. The projected aircraft emissions 

were processed in PtFinal_ASPEN to create ASPEN ready files. Aircraft emissions, SCC codes 

beginning with 2275, were grouped into non-gasoline nonroad emissions (group=3). 

5.4.2 Airport Support Equipment 

The projected nonroad inventories discussed in Section 3.3.3 contained emissions related to 

airport support equipment. Therefore, the projected nonroad inventories were processed through 

COPAX to separate the airport related emissions from the remaining nonroad emissions. See 

Table 14 for airport support equipment SCC codes (those denoted as being projected in NMIM). 

After the COPAX program, the airport support equipment emissions were processed through the 

point source programs PtDataProc, PtmodelProc, PtTemporal, and PtFinal_ASPEN. Note that 

unlike the non-point airport emissions and aircraft emissions, the airport support equipment 

emissions were not processed through the PtGrowCntl program since emissions had already been 

projected outside of EMS-HAP. 

5.4.3 Remaining nonroad sources 

The remaining nonroad emissions were processed through CountyProc in a similar fashion to the 

onroad emissions. 

Both airport support equipment emissions and remaining nonroad emissions were binned into 

two groups, non-gasoline nonroad emissions (group=3) and nonroad gasoline (group=5) (Table 

31). SCC codes beginning with 2267, 2268, 2270, 2280, and 2285 were assigned to group 3 and 

SCC codes beginning with 2260, 2265, and 2282 were assigned to group 5. The exceptions to 

this were SCC codes 22882020000, 2282020005, and 2282020010, which are diesel pleasure 

craft emissions. These codes were assigned to group 5 by mistake and should have been 

assigned to group 3. This mistake was found after EMS-HAP and ASPEN modeling. It was 

determined however, that these emissions were small when compared to the nonroad emissions 

and changes were not made, and EMS-HAP and ASPEN were not rerun. 

59 
Table 31. ASPEN emission groups for MSAT for future years2 

Group Source Sector Description Inventories

# 

0 Major sources Any stationary source or group of stationary 

sources located within a contiguous area and 

under common control that emits or has the 

potential to emit considering controls, in the 

aggregate, 10 tons per year or more of any 

hazardous air pollutant or 25 tons per year or 

more of any combination of hazardous air 

pollutants 

Point 

1 Area & other sources Any stationary source of hazardous air 

pollutants that is not a major source. Does not 

include motor vehicles or nonroad vehicles. 

Point, and 

non-point 

2 Onroad gasoline sources Onroad vehicles burning gasoline Onroad 

3 Non-gasoline nonroad 

sources 

Nonroad vehicles burning fuels other than 

gasoline such as diesel, natural gas, aviation 

fuel, LP gas, residual oils, and miscellaneous 

fuel sources. 

Nonroad 

4 Onroad diesel sources Onroad vehicles burning diesel Onroad 

5 Nonroad gasoline 

sources 

Nonroad vehicles burning gasoline Nonroad 

#

Non-point and nonroad include airport related emissions. 

5.5 EMS-HAP for precursors 

EMS-HAP was run for 2015, 2020, and 2030 for the precursor emissions from the mobile 

inventory only (i.e. not stationary sources) with the exception of 1,3 butadiene, which is both a 

HAP and a precursor to acrolein, and was thus projected and run for both stationary and mobile 

sources. Mobile EMS-HAP processing followed the same steps as described in Sections 5.3 and 

5.4 

Secondary concentrations for stationary sources for all HAPs other than acrolein were taken as 

secondary concentrations from the 1999 NATA for all years. Stationary precursors were not 

projected due to the small contribution of stationary secondary contributions to the total 

concentrations for acetaldehyde and formaldehyde. An analysis of the secondary contributions 

of the 1999 precursor concentrations for acetaldehyde and formaldehyde revealed that stationary 

secondary contributions were small when compared to the total concentrations (secondary and 

background included). Figure 5 shows box and whisker plots for acetaldehyde and 

formaldehyde for ratios of tract level stationary secondary concentrations to total concentrations 

(white boxes) and ratios of tract level mobile secondary concentrations to total concentrations 

(gray boxes) for 1999. The ratios for the stationary secondary contributions are much less than 

the mobile ratios, since acetaldehyde and formaldehyde are mobile dominant. Note that even 

though propionaldehyde is an MSAT HAP, it has no cancer or non-cancer risks associated with it 

and was not included in the analysis of the secondary concentrations. 

2

 1999 NATA source groups were: 0=major, 1=area & other, 2=all onroad mobile, and 3=all nonroad mobile. 

60 
5th percentile 

25th percentile 

75th percentile 

95th percentile 

Median 

Figure 5. Box and whisker plots of ratios of stationary secondary contributions to total 

concentrations (white boxes) and ratios of mobile secondary contributions to total concentrations 

(gray boxes) for 1999 acetaldehyde and formaldehyde concentrations. Dots represent the 

national mean ratios. 

61 
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62 
6. ASPEN Processing 

6.1 MSAT HAPs 

Once the emissions were processed, they were input into ASPEN (U.S. EPA, 2000) to calculate 

ambient air quality concentrations. In addition to the emissions, ASPEN needs meteorological 

parameters, and census tract centroid locations for concentration calculations. For the MSAT 

years, 2015, 2020, and 2030, the 1999 meteorology and year 2000 census tract locations were 

used as for the 1999 NATA. 

In EMS-HAP, emissions are divided into nine files, one for each HAP reactivity class, 1-9, as 

defined for ASPEN (Reactivity classes 6 and 8 are not used for HAPs) based on decay rates or 

particulate sizes (See ASPEN User’s Guide [U.S. EPA, 2000] for details). For example, the 

emissions file for reactivity class 1 would contain the emissions information (location, emissions, 

stack parameters, etc.) for all of the HAPs processed through EMS-HAP with reactivity class 1. 

The reactivity classes for each MSAT HAP are listed in Table 32. 

ASPEN runs were set up such that the stationary and mobile concentrations were calculated in 

two separate runs, one for stationary and one for mobile. Table 33 shows how the separate sets 

of emission files (one file for each reactivity class) are provided to ASPEN. 

ASPEN is composed of two modules, ASPENA and ASPENB. ASPENA calculates 

concentrations at receptors arranged in rings around an emission source up to 50 km away. 

ASPENB then reads the ASPENA output and interpolates the concentrations to census tract 

centroids. ASPEN is run for each reactivity class for mobile sources and for each reactivity class 

for stationary sources. The output from ASPEN is a binary file for each SAROAD code in the 

emissions input file (see Table 1 for MSAT HAPs’ SAROAD codes). Figures 6 and 7 

graphically show the input/output for each reactivity class for stationary and mobile sources 

respectively, including which HAPS are in each reactivity class. 

Once ASPEN has been run, the programs AVGDAT and EXTRAVG were used to convert the 

binary output from ASPEN to ASCII text. Concentrations are annual average concentrations for 

each source sector and are at the census tract level. For details of the two programs see the 

ASPEN User’s Guide (U.S. EPA, 2000). 

6.2 Precursors 

Precursor emissions were processed through ASPEN in the same manner as for the HAPs but in 

a separate model run. Reactivity classes for precursors are also shown in Table 32 with 

input/output files shown in Figure 8 for mobile sources. As discussed in Section 5.5, the 1999 

stationary secondary concentrations were used for 2015 and 2020 secondary concentrations, 

excluding acrolein whose precursor emissions were projected to 2030 for both stationary and 

mobile sources. 

63 
Table 32. Reactivity classes for MSAT HAPs and precursors. 

Pollutant SAROAD Reactivity Pollutant SAROAD Reactivity 

1,3-Butadiene 43218 7 Propionaldehyde, primary 43505 5 

2,2,4-Trimethylpentane 43250 1 Styrene 45220 7 

Acetaldehyde, primary 43503 5 Toluene 45202 4 

Acrolein, primary 43505 5 Xylenes 45102 5 

Benzene 45201 1 POM 1 71002 2 

Chromium III, fine 59992 2 POM 2 72002 2 

Chromium III, coarse 59993 3 POM 3 73002 2 

Chromium VI, fine 69992 2 POM 4 74002 2 

Chromium VI, coarse 69993 3 POM 5 75002 2 

Ethyl Benzene 45203 4 POM 6 76002 2 

Formaldehyde, primary 43502 5 POM 7 77002 2 

Hexane 43231 9 POM 8 78002 2 

Manganese, fine 80196 2 Acetaldehyde precursors, 

reactive 

80100 7 

Manganese, coarse 80396 3 Formaldehyde precursors, 

reactive 

80180 6 

MTBE 43376 1 Propionaldehyde precursors, 

reactive 

80234 7 

Naphthalene, gas 46701 5 Acetaldehyde precursors, 

inert 

80301 1 

Naphthalene, fine PM 46702 2 Acrolein precursor, inert 80302 1 

Nickel, fine 80216 2 Formaldehyde precursors, 

inert 

80303 1 

Nickel, coarse 80316 3 Propionaldehyde precursors, 

inert 

80305 1 

POM 1: POM, Group 1: Unspeciated 

POM 2: POM, Group 2: no URE data 

POM 3: POM, Group 3: 5.0E-2 < URE <= 5.0E-1 

POM 4: POM, Group 4: 5.0E-3 < URE <= 5.0E-2 

POM 5: POM, Group 5: 5.0E-4 < URE <= 5.0E-3 

POM 6: POM, Group 6: 5.0E-5 < URE <= 5.0E-4 

POM 7: POM, Group 7: 5.0E-6 < URE <= 5.0E-5 

POM 8: POM, Group 8: Unspeciated (7-PAH only) 

REACTIVITY CLASSES: 

1 non reactive 

2 fine particulate (2.5 microns and less) 

3 coarse particulate (2.5 to 10 microns) 

4 medium low reactivity 

5 medium reactivity 

6 medium high reactivity 

7 very high reactivity 

8 high reactivity 

9 low reactivity 

64 
Reactivity 1 Reactivity 2 

Point 

reactivity 1 

input file 

Non-point 

reactivity 1 

input file 

Airport 

reactivity 1 

input file 

ASPEN 

43250.exp 

43376.exp 45201.exp

80302.exp 

ASPEN. 

75002.exp 

76002.exp 

74002.exp 

77002.exp 

46702.exp 59992.exp 71002.exp 

72002.exp 73002.exp 

Reactivity 3 

ASPEN 

69993.exp 80316.exp 

59993.exp 80396.exp 

Reactivity 4 

ASPEN 

45202.exp 45203.exp

Reactivity 5 

ASPEN 46701.exp 

43505.exp 

43502.exp 

43503.exp 43504.exp 45102.exp 

Reactivity 7 

43218.exp ASPEN 45220.exp 

Reactivity 9 

ASPEN 43231.exp 

Point 

reactivity2 

input file 

Non-point 

reactivity 2 

input file 

Airport 

reactivity 2 

input file 

Point 

reactivity 4 

input file 

Non-point 

reactivity 4 

input file 

Airport 

reactivity 4 

input file 

Point 

reactivity 5 

input file 

Non-point 

reactivity 5 

input file 

Airport 

reactivity 5 

input file 

Point 

reactivity 7 

input file 

Non-point 

reactivity 7 

input file 

Airport 

reactivity 7 

input file 

Point 

reactivity 9 

input file 

Non-point 

reactivity 9 

input file 

Airport 

reactivity 9 

input file 

69992.exp 

78002.exp 80196.exp 80216.exp

Point 

reactivity 3 

input file 

Non-point 

reactivity 3 

input file 

Airport 

reactivity 3 

input file 

Figure 6. Stationary source emission input files and ASPEN output files for each reactivity class 

for MSAT HAPs. 

Table 33. Description of emissions files for the stationary and mobile divisions used for ASPEN 

simulations. 

Division Emissions type 

Stationary point sources (major and area & other) 

non-point airport emissions (i.e., aviation gasoline categories) assigned to 

point sources by COPAX 

non-point emissions (excluding non-point airport emissions) 

Mobile Aircraft emissions assigned to point sources by COPAX 

Airport support equipment emissions assigned to point sources by COPAX. 

Onroad mobile sources 

Nonroad mobile sources (excluding aircraft and airport support equipment) 

65 
Reactivity 1 Reactivity 2 

Onroad reactivity 1 

input file 

Remaining nonroad 

reactivity 1 

input file 

Aircraft reactivity 1 

input file 

Airport support 

reactivity 1 

input file 

ASPEN 

43250.exp 45201.exp 

43376.exp 

Onroad reactivity 2 

input file 

Aircraft reactivity 2 

input file 

Airport support 

reactivity 2 

input file 

ASPEN. 

72002.exp 75002.exp 76002.exp 

77002.exp 

46702.exp 

80196.exp 80216.exp 

Reactivity 3 

Onroad reactivity 3 

input file 

Remaining nonroad 

reactivity 3 

input file 

Aircraft reactivity 3 

input file 

Airport support 

reactivity 4 

input file 

ASPEN 

69993.exp 

59993.exp 80316.exp 

80396.exp 

Reactivity 4 

Onroad reactivity 4 

input file 

Remaining nonroad 

reactivity 4 

input file 

Aircraft reactivity 4 

input file 

Airport support 

reactivity 4 

input file 

ASPEN 

45202.exp 45203.exp 

Reactivity 5 

Onroad reactivity 5 

input file 

Remaining nonroad 

reactivity 5 

input file 

Aircraft reactivity 5 

input file 

Airport support 

reactivity 5 

input file 

ASPEN 46701.exp 

43502.exp 43503.exp 43504.exp 43505..exp 

45102.exp 

Reactivity 7 

Onroad reactivity 7 

input file 

Remaining nonroad 

reactivity 7 

input file 

Aircraft reactivity 7 

input file 

Airport support 

reactivity 7 

input file 

43218.exp ASPEN 45220.exp 

Reactivity 9 

Onroad reactivity 9 

input file 

Remaining nonroad 

reactivity 9 

input file 

Aircraft reactivity 9 

input file 

Airport support 

reactivity 9 

input file 

ASPEN 

43231.exp 

80302.exp 

Remaining nonroad 

reactivity 2 

input file 

59992.exp 

69992.exp 

Figure 7. Mobile source emission input files and ASPEN output files for each reactivity class 

for MSAT HAPs. 

66 
Reactivity 1 

Onroad reactivity 1 

input file 

Remaining nonroad 

reactivity 1 

input file 

Aircraft reactivity 1 

input file 

Airport support 

reactivity 1 

input file 

ASPEN 

80301.exp 80303.exp 80305.exp 

Reactivity 6 

Onroad reactivity 6 

input file 

Remaining nonroad 

reactivity 6 

input file 

Aircraft reactivity 6 

input file 

Airport support 

reactivity 6 

input file 

ASPEN 

80180.exp 

Reactivity 7 

Onroad reactivity 7 

input file 

Remaining nonroad 

reactivity 7 

input file 

Aircraft reactivity 7 

input file 

Airport support 

reactivity 7 

input file 

ASPEN 

80100.exp 80234.exp 

Figure 8. Mobile source emission input files and ASPEN output files for each reactivity class 

for MSAT precursors. 

6.3 Post-processing of ASPEN concentrations 

ASPEN output concentrations were calculated for each SAROAD associated with the MSAT 

HAPs (see Table 1 for SAROADs). Post-processing of the ASPEN concentrations for each year 

included the following: 

• Adjusting the SAROAD 75002 (POM Group 5) area & other concentrations in Oregon as 

described in Section 2.1. 

• Merging the stationary and mobile concentrations together at tract level. For 2030, 2020 

stationary concentrations were used. 

• Summing the fine and coarse metal concentrations (i.e., fine and coarse nickel) at census 

tract level for each source sector. 

67 
• Summing the particle and gas modes of naphthalene at census tract level for each source 

category. 

• Adding secondary concentrations for each source category using the appropriate 

precursor concentrations for acetaldehyde, acrolein, formaldehyde, and propionaldehyde 

at the census tract level. Total concentrations were calculated by adding the primary 

concentrations (SAROADS in Table 1) and secondary concentrations. Secondary 

concentrations are computing by subtracting the reactive component of the precursor 

from the inert component of the precursor, and multiplying by a factor (if needed). The 

following equations show how the ASPEN-modeled3 concentrations for each of the 

HAPs with secondary components were calculated: 

Χ acetaldehyde = Χ 43503 + Χ 80301 − Χ 80100 (7) 

Χ acrolein = Χ 43503 + 104 Χ − Χ 80100 .( ) (8) 80301 

Χ formaldehyde = Χ 43502 + Χ 80303 − Χ 80180 (9) 

Χ propionaldehyde = Χ 43504 + Χ 80305 − Χ 80234 (10) 

 Where: 

Χacetaldehyde = Acetaldehyde concentrations with secondary contributions included. 

Χ43503 = Primary acetaldehyde concentrations due to directly emitted acetaldehyde. 

Χ80301 = Inert precursor concentrations for acetaldehyde (reactivity class 1). 

Χ80100 = Reactive precursor concentrations for acetaldehyde (reactivity class 7). 

Χacrolein = Acrolein concentrations with secondary contributions included. 

Χ43505 = Primary acrolein concentrations due to directly emitted acrolein. 

Χ80302 = Inert precursor concentrations for acrolein (reactivity class 1)- note that 1,3 

butadiene is the sole precursor for acrolein and that 80302 represents 1,3 butadiene, inert. 

Χ43218 = Reactive precursor concentrations for acrolein (reactivity class 7))- note that 1,3 

butadiene is the sole precursor for acrolein and that SAROAD=43218 represents 1,3 

butadiene. 

Χformaldehyde = Formaldehyde concentrations with secondary contributions included. 

Χ43502 = Primary formaldehyde concentrations due to directly emitted formaldehyde. 

Χ80303 = Inert precursor concentrations for formaldehyde (reactivity class 1). 

Χ80180 = Reactive precursor concentrations for formaldehyde (reactivity class 6). 

Χpropionaldehyde = Propionaldehyde concentrations with secondary contributions included. 

3

 These equations provide the ASPEN-modeled concentrations prior to the addition of the background concentration, 

discussed in the next bullet. 

68 
Χ43504 = Primary propionaldehyde concentrations due to directly emitted 

propionaldehyde. 

Χ80305 = Inert precursor concentrations for propionaldehyde (reactivity class 1). 

Χ80234 = Reactive precursor concentrations for propionaldehyde (reactivity class 6). 

• Adding county level background concentrations to total concentrations (all sources) for 

HAPs with background. The MSAT HAPs with nonzero background are: 1,3-butadiene, 

acetaldehyde, benzene, formaldehyde, and xylenes. Each of the three model years used 

1999 background. For details about the 1999 background see 

http://www.epa.gov/ttn/atw/nata1999/background.html or Batelle (2003). Because it 

would be expected that background levels would likely change in the future due to 

emissions changes, a sensitivity analysis was done to evaluate the potential impact of 

changing the constant background assumption. This analysis is detailed in Section 9. 

After post-processing of the concentrations, summary statistics for the concentrations for each 

year, including 1999 were calculated. They included: 

• Average concentrations for major, area & other, onroad gasoline, onroad diesel, nonroad 

gasoline, remaining nonroad, background, and total at the county, state, state urban/rural, 

state RFG/non-RFG, national, national urban/rural, and national RFG/non-RFG levels. 

• Distributions (5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles) for total concentrations 

at the county, state, state urban/rural, state RFG/non-RFG levels. 

• Maps of county median concentrations for 1,3-butadiene, acetaldehyde, acrolein, 

benzene, formaldehyde, and naphthalene were generated for 1999, 2015, 2020, and 2030. 

National level average concentrations for 1999, 2015, 2020, and 2030 are shown in Table 34 and 

county maps for the same years for benzene are in Figures 9 through 12. 

The complete summaries and maps described above are in concentrations.xls and 

ASPEN_medians.ppt respectively in the MSAT rule docket EPA-HQ-OAR-2005-0036. Note 

that in Table 34 and in concentrations.xls, the national average background concentration differs 

from the national average background concentration for NATA even though the same county 

level background concentrations were used for all years. This is because Puerto Rico and the 

Virgin Islands were not included in the 2015, 2020, and 2030 analyses but were included in the 

NATA analysis. 

69 
Table 34. National average background, stationary, and mobile ASPEN concentrations (μg m-3) 

for each MSAT HAP for 2015, 2020, and 2030. 

HAP Back­ 2015 2020 2030 

ground Stationary Mobile Stationary Mobile Stationary Mobile 

1,3-Butadiene 5.11×10-2 2.27×10-2 2.44×10-2 2.28×10-2 2.38×10-2 2.28×10-2 2.63×10-2 

2,2,4­

Trimethylpentane 

0 4.19×10-2 2.86×10-1 4.44×10-2 2.59×10-1 4.44×10-2 2.75×10-1 

Acetaldehyde 5.17×10-1 8.67×10-2 3.62×10-1 8.93×10-2 3.29×10-1 8.93×10-2 3.50×10-1 

Acrolein 0 2.97×10-2 3.41×10-2 2.93×10-2 3.37×10-2 2.93×10-2 3.74×10-2 

Benzene 3.94×10-1 2.04×10-1 3.16×10-1 2.13×10-1 2.95×10-1 2.13×10-1 3.18×10-1 

Chromium III 0 1.66×10-3 2.11×10-4 1.86×10-3 2.30×10-4 1.87×10-3 2.73×10-4 

Chromium VI 0 4.08×10-4 4.64×10-5 4.61×10-4 5.05×10-5 4.61×10-4 6.00×10-5 

Ethyl Benzene 0 1.24×10-1 1.33×10-1 1.37×10-1 1.23×10-1 1.37×10-1 1.32×10-1 

Formaldehyde 7.62×10-1 1.48×10-1 3.13×10-1 1.60×10-1 3.03×10-1 1.60×10-1 3.31×10-1 

Hexane 0 5.91×10-1 1.35×10-1 6.38×10-1 1.18×10-1 6.38×10-1 1.23×10-1 

MTBE 0 7.04×10-2 1.26×10-1 7.41×10-2 1.05×10-1 7.41×10-2 1.05×10-1 

Manganese 0 6.14×10-3 1.85×10-4 6.80×10-3 2.08×10-4 6.80×10-3 2.61×10-4 

Naphthalene 0 6.15×10-2 1.24×10-2 6.57×10-2 1.26×10-2 6.57×10-2 1.46×10-2 

Nickel 0 2.50×10-3 2.50×10-4 2.74×10-3 2.71×10-4 2.74×10-3 3.19×10-4 

POM* 0 2.25×10-2 1.47×10-3 2.34×10-2 1.50×10-3 2.34×10-2 1.71×10-3 

Propionaldehyde 0 3.32×10-2 9.20×10-2 3.39×10-2 8.21×10-2 3.39×10-2 8.62×10-2 

Styrene 0 4.90×10-2 1.23×10-2 5.53×10-2 1.19×10-2 5.53×10-2 1.29×10-2 

Toluene 0 1.19 7.24×10-1 1.31 6.63×10-1 1.31 7.07×10-1 

Xylenes 1.70×10-1 8.62×10-1 5.22×10-1 9.52×10-1 4.87×10-1 9.52×10-1 5.24×10-1 

*POM is the sum of all POM groups. 

70 
Concentrations 

0.063 - 0.338 

0.339 - 0.567 

0.568 - 0.880 

0.881 - 1.316 

1.317 - 2.114 

2.115 - 4.929 

Figure 9. 1999 County level median total (all sources and background) concentrations (μg m-3) 

for benzene. 

71 
Concentrations 

0.063 - 0.338 

0.339 - 0.567 

0.568 - 0.880 

0.881 - 1.316 

1.317 - 2.114 

2.115 - 4.929 

Figure 10. 2015 County level median total (all sources and background) concentrations (μg m-3) 

for benzene. 

72 
Concentrations 

0.063 - 0.338 

0.339 - 0.567 

0.568 - 0.880 

0.881 - 1.316 

1.317 - 2.114 

2.115 - 4.929 

Figure 11. 2020 County level median total (all sources and background) concentrations (μg m-3) 

for benzene. 

73 
Concentrations 

0.064 - 0.338 

0.339 - 0.567 

0.568 - 0.880 

0.881 - 1.316 

1.317 - 2.114 

2.115 - 4.929 

Figure 12. 2030 County level median total (all sources and background) concentrations (μg m-3) 

for benzene. 

ASPEN results were also processed for input to HAPEM5. ASPEN outputs annual average 

tract-level concentrations for eight 3-hour time blocks. The concentrations were extracted from 

the binary ASPEN output, .exp files, using the AVGDAT program and written to an ASCII text 

file. The concentrations were then processed in a similar fashion as for the annual average 

concentrations: stationary and mobile concentrations merged together, fine and coarse 

components of the metals added together, gas and particulate phases of naphthalene added 

together and secondary concentrations added to the secondary HAPs. Once these steps were 

done there were eight 3-hour concentrations for major, area & other, onroad gasoline, onroad 

diesel, nonroad gasoline and, nonroad other. Background was also added for each tract. 

Normally HAPEM input files, also called air quality files, contain major, area & other, onroad, 

nonroad, and background concentrations, as done for the 1999 NATA. For MSAT, the onroad 

and nonroad concentrations were broken into two categories each, resulting in a total of seven 

concentration categories. In order to avoid recoding the FORTRAN programs of HAPEM, the 

HAPEM input files were split into two different files, called “run1” and “run2.” Run1 files 

contained major, area & other, onroad gasoline, nonroad gasoline and background. Run2 files 

contained placeholders (values of zero concentration) for major, area & other and background. 

The onroad diesel and nonroad other concentrations were added to the run2 files. Figures 13 and 

14 show sample records for the run1 and run2 HAPEM input files. 

74 
Annual average concentrations in ug/m3 by time blocks

Pollutant species: Benzene

Source category: ALL 

FIPS Tract Conc_block1 Conc_block2 Conc_block3 Conc_block4 Conc_block5 

Conc_block6 Conc_block7 Conc_block8 Conc_block1 Conc_block2 Conc_block3 

Conc_block4 Conc_block5 Conc_block6 Conc_block7 Conc_block8 Conc_block1 

Conc_block2 Conc_block3 Conc_block4 Conc_block5 Conc_block6 Conc_block7 

Conc_block8 Conc_block1 Conc_block2 Conc_block3 Conc_block4 Conc_block5 

Conc_block6 Conc_block7 Conc_block8 Bconc_block8 

01001 020100 1.014290E-02 7.904850E-03 2.722090E-03 1.851860E-03 2.127090E-03 

4.262630E-03 1.014880E-02 1.014940E-02 6.959740E-02 1.451020E-01 1.125670E-01 

7.934730E-02 8.579260E-02 1.807510E-01 2.245080E-01 6.572720E-02 9.880010E-02 

7.547470E-02 8.572060E-02 6.858460E-02 8.586170E-02 2.021050E-01 2.630900E-01 

1.726230E-01 7.336690E-03 6.009230E-03 3.060390E-02 3.728000E-02 4.081490E-02 

8.620350E-02 4.950200E-02 7.228590E-03 3.212943E-01 

01001 020200 1.926930E-02 1.436350E-02 4.269420E-03 3.102840E-03 3.331600E-03 

6.697180E-03 1.928040E-02 1.928210E-02 1.090870E-01 2.961870E-01 4.099370E-01 

3.081070E-01 3.301660E-01 5.106280E-01 4.794080E-01 1.037620E-01 1.762320E-01 

1.409060E-01 2.053260E-01 1.727360E-01 2.178950E-01 4.099920E-01 4.509380E-01 

3.009450E-01 7.639370E-03 6.252210E-03 1.075850E-01 1.475200E-01 1.605220E-01 

2.436150E-01 9.773740E-02 7.526810E-03 3.212943E-01 

Figure 13. Sample records of the Run1 2015 HAPEM input air quality file con45201_run1.txt 

for benzene. Note that each set of concentrations for a tract is one record. More records appear 

due to of “wrapping” of text in word processor. 

Annual average concentrations in ug/m3 by time blocks

Pollutant species: Benzene

Source category: ALL 

FIPS Tract Conc_block1 Conc_block2 Conc_block3 Conc_block4 Conc_block5 

Conc_block6 Conc_block7 Conc_block8 Conc_block1 Conc_block2 Conc_block3 

Conc_block4 Conc_block5 Conc_block6 Conc_block7 Conc_block8 Conc_block1 

Conc_block2 Conc_block3 Conc_block4 Conc_block5 Conc_block6 Conc_block7 

Conc_block8 Conc_block1 Conc_block2 Conc_block3 Conc_block4 Conc_block5 

Conc_block6 Conc_block7 Conc_block8 Bconc_block8 

01001 020100 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00 

0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00 

0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00 5.437490E-04 

7.163460E-04 2.433580E-03 2.331080E-03 2.200300E-03 5.139870E-03 2.213920E-03 

9.321060E-04 3.162220E-04 2.568330E-04 1.723930E-03 1.608990E-03 1.761980E-03 

3.808400E-03 3.525590E-03 1.210110E-03 0.000000E+00 

01001 020200 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00 

0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00 

0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00 9.786940E-04 

1.364310E-03 6.234640E-03 6.259710E-03 5.902090E-03 1.110560E-02 3.984490E-03 

1.677710E-03 4.114270E-04 3.463900E-04 4.707650E-03 4.879280E-03 5.317610E-03 

8.468020E-03 5.789880E-03 1.999970E-03 0.000000E+00 

Figure 14. Sample records of the Run2 2015 HAPEM input air quality file con45201_run2.txt 

for benzene. 

75 
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76 
7. HAPEM5 Model and Post-Processing 

7.1 HAPEM model 

The HAPEM5 (U.S. EPA, 2005d) model was originally compiled to run on a DOS PC, one 

pollutant (HAP) at a time. In early 2004, HAPEM5 was compiled on a Linux cluster in order to 

run the model for multiple pollutants simultaneously (using multiple computer processors), 

thereby reducing the overall time required to run HAPEM5 for a long list of pollutants. The 

post-processing FORTRAN code that converts HAPEM5 output into the input for the risk 

calculations was also compiled for Linux. 

The HAPEM model consists of 5 modules, to be run in order: 1) DURAV5, 2) INDEXPOP5, 3) 

COMMUTE5, 4) AIRQUAL5 and 5) HAPEM5. The first three modules only need to be run 

once for the whole domain, while the last two modules must be run for each HAP (generally 

distinguished by SAROAD code). Refer to the HAPEM5 User’s Guide for more detailed 

information about the model and formats of input and output files (U.S. EPA, 2005e). 

For NATA (and thus 1999 results), as noted in Section 6.3, the emissions source categories were 

major, area & other, onroad, nonroad and background. However, as discussed previously in 5.3, 

for the future year MSAT analyses described here, the onroad and nonroad mobile source 

categories were split into two categories each (highway gasoline, highway diesel, nonroad 

gasoline and remaining nonroad), making the total number of non-background source categories 

equal to six. Because HAPEM is limited to a maximum of five source categories per run, the 

runs for each projection year were split into two runs per HAP. In other words, there were two 

runs (2) for each year (3) and for each HAP (26), a grand total of 156 individual HAPEM runs. 

The first run for each HAP for each year (“run1”) included ASPEN-derived air quality input data 

for the major, area & other, highway gasoline, and nonroad gasoline groups and background, 

while “run2” only included highway diesel and remaining nonroad (no background). The output 

for the two onroad groups were added together after post-processing and prior to the risk 

calculations, as were the two nonroad groups. 

The HAPEM5 runs for MSAT used 2000 census data, 1990 commuting data adjusted to reflect 

the 2000 census tract designations, and activity pattern data from the Consolidated Human 

Activity Database (CHAD) (Glen et al. 1997). HAPEM5 output for the first three modules from 

a previous set of HAPEM5 runs done for the 1999 NATA were included, so only the HAPspecific modules (AIRQUAL5 and HAPEM5) were run for MSAT.

 The input files were the same for all pollutants, except for: 

• a) the HAP-specific parameter file used for the AIRQUAL5 and HAPEM5 modules 

(p2_MSAT_XXXXX_YYYY_runZ.txt, where XXXXX is the 5-digit pollutant 

SAROAD code or text identifier, YYYY is the 4-digit projection year and Z is the run 

number “1” or “2”), 

• b) the ASPEN-derived air quality (AQ) concentration file (conXXXXX_runZ.txt, for 

each pollutant, located in the appropriate year- and run-specific run directories), and 

77 
• c) the microenvironmental factors file used (gas, particulate or mixed). 

There are three microenvironments factor files provided with the HAPEM5 model: gas, 

particulate and mixed. The factor file used for each HAP is determined from a HAP factors 

lookup file also provided with the model. The factor file is listed in each HAP-specific 

parameter file (p2 files) for the AIRQUAL5 and HAPEM5 modules. 

The final output files generated by HAPEM5 are in the form of XXXXX.YY.dat, where 

XXXXX is the 5-digit SAROAD (for gaseous HAPs excluding secondary HAPs) or text HAP 

identifier (for metals or secondary HAPs) and YY is the 2-digit state FIPS code. There are 53 

output files for each SAROAD (HAP), one for each state FIPS code. This includes Puerto Rico 

and Virgin Islands even though those areas were not projected. Puerto Rico and the Virgin 

Islands are included because HAPEM5 was run as set up for the 1999 NATA. Given that there 

would be many files to edit (because of the number of HAPs and years) to exclude Puerto Rico 

and the Virgin Islands, and that runtimes would not be significantly decreased if they were 

excluded, they remained in the setup files for AIRQUAL and ignored after post-processing. 

The raw HAPEM5 output had to be post-processed prior to making the risk calculations. The 

post-processing code we used basically takes the exposure concentrations for each demographic 

group (10 demographic groups, 5 age groups x 2 genders) and builds a lifetime exposure (about 

70 years) for an individual at that tract for each HAP. At each tract and demographic group, 

there are 30 replicates. For the total exposure (all sources), for each tract and demographic 

group, the median exposure is used to adjust the mean exposure at each tract for the difference 

source categories (major, area & other, etc.) by dividing the mean source category exposure 

concentration for the tract by the median total concentration. This operation causes stationary 

exposure concentrations for 2020 and 2030 to differ even though the same input concentrations 

were used. The mobile concentrations between the two years changed, causing the total 

concentrations to change, changing the median total concentrations at the tract level. This in 

turn, changed the adjusted stationary source exposure concentrations. The final output file is in 

the form XXXX_SAROAD_runY.HAPEM5-TRACT.txt where XXXX is 2015, 2020, or 2030, 

SAROAD is the SAROAD or HAP name, and Y is 1 or 2 for run1 or run2. Sample records of 

post-processed HAPEM5 output for 2015 Benzene run1 and run2 are shown in Figures 15 and 

16 respectively. 

78 
 01001 020100 5.592E-03 1.027E-01 1.715E-01 3.346E-02 5.653E-01 2.521E-01 

01001 020200 1.002E-02 2.417E-01 2.838E-01 7.260E-02 8.586E-01 2.505E-01 

01001 020300 1.158E-02 1.763E-01 2.403E-01 5.643E-02 7.346E-01 2.500E-01 

01001 020400 1.597E-02 1.413E-01 2.582E-01 4.394E-02 7.132E-01 2.538E-01 

01001 020500 1.446E-02 1.044E-01 2.155E-01 3.762E-02 6.250E-01 2.531E-01 

01001 020600 4.544E-02 8.300E-02 2.014E-01 3.387E-02 6.139E-01 2.502E-01 

01001 020700 2.747E-02 7.944E-02 1.601E-01 3.409E-02 5.496E-01 2.485E-01 

01001 020800 3.978E-03 5.112E-02 9.521E-02 2.191E-02 4.208E-01 2.486E-01 

01001 020900 1.757E-03 4.429E-02 6.416E-02 1.158E-02 3.648E-01 2.430E-01 

01001 021000 1.300E-03 4.273E-02 5.122E-02 1.045E-02 3.525E-01 2.468E-01 

01001 021100 3.073E-03 4.065E-02 6.345E-02 1.440E-02 3.736E-01 2.520E-01 

01003 010100 3.347E-03 3.120E-02 4.427E-02 1.870E-02 3.471E-01 2.496E-01 

01003 010200 1.019E-02 5.030E-02 6.172E-02 2.341E-02 3.949E-01 2.493E-01 

01003 010300 9.414E-03 4.720E-02 6.464E-02 3.340E-02 4.035E-01 2.488E-01 

01003 010400 4.961E-03 7.028E-02 6.688E-02 2.827E-02 4.210E-01 2.506E-01 

01003 010500 5.667E-03 1.279E-01 7.744E-02 4.564E-02 5.071E-01 2.504E-01 

01003 010600 6.178E-03 1.192E-01 7.016E-02 4.180E-02 4.884E-01 2.511E-01 

01003 010701 1.852E-02 4.337E-02 8.418E-02 5.977E-02 4.595E-01 2.537E-01 

01003 010703 9.308E-03 4.769E-02 9.990E-02 5.949E-02 4.656E-01 2.492E-01 

Figure 15. Sample records showing HAPEM5 output for Benzene Run1. Filename is 

2015_45201_run1.HAPEM5-TRACT.txt. Variables are FIPS, tract id, major, area & other, 

onroad gasoline, nonroad gasoline, total and background concentrations. 

01001 020100 0.000E+00 0.000E+00 2.836E-03 1.629E-03 4.465E-03 0.000E+00 

01001 020200 0.000E+00 0.000E+00 5.176E-03 3.097E-03 8.273E-03 0.000E+00 

01001 020300 0.000E+00 0.000E+00 4.209E-03 2.422E-03 6.631E-03 0.000E+00 

01001 020400 0.000E+00 0.000E+00 4.552E-03 1.975E-03 6.527E-03 0.000E+00 

01001 020500 0.000E+00 0.000E+00 3.569E-03 1.984E-03 5.554E-03 0.000E+00 

01001 020600 0.000E+00 0.000E+00 3.553E-03 1.895E-03 5.448E-03 0.000E+00 

01001 020700 0.000E+00 0.000E+00 2.575E-03 1.941E-03 4.516E-03 0.000E+00 

01001 020800 0.000E+00 0.000E+00 1.513E-03 1.089E-03 2.602E-03 0.000E+00 

01001 020900 0.000E+00 0.000E+00 9.994E-04 6.306E-04 1.630E-03 0.000E+00 

01001 021000 0.000E+00 0.000E+00 7.444E-04 4.991E-04 1.243E-03 0.000E+00 

01001 021100 0.000E+00 0.000E+00 1.016E-03 7.317E-04 1.748E-03 0.000E+00 

01003 010100 0.000E+00 0.000E+00 3.194E-04 7.389E-04 1.058E-03 0.000E+00 

01003 010200 0.000E+00 0.000E+00 6.738E-04 1.490E-03 2.164E-03 0.000E+00 

01003 010300 0.000E+00 0.000E+00 8.175E-04 2.639E-03 3.456E-03 0.000E+00 

01003 010400 0.000E+00 0.000E+00 6.943E-04 2.991E-03 3.685E-03 0.000E+00 

01003 010500 0.000E+00 0.000E+00 1.069E-03 2.462E-03 3.531E-03 0.000E+00 

01003 010600 0.000E+00 0.000E+00 8.959E-04 2.692E-03 3.588E-03 0.000E+00 

01003 010701 0.000E+00 0.000E+00 1.063E-03 7.130E-03 8.193E-03 0.000E+00 

01003 010703 0.000E+00 0.000E+00 1.256E-03 9.177E-03 1.043E-02 0.000E+00 

Figure 16. Sample records showing HAPEM5 output for Benzene Run2. Filename is 

2015_45201_run2.HAPEM5-TRACT.txt. Variables are FIPS, tract id, major, area and other, 

onroad diesel, nonroad other, total and background concentrations. 

7.2 Summaries of annual HAPEM5 output 

After post-processing of the HAPEM5 concentrations, summary statistics for the concentrations 

for each year, including 1999 were calculated as done for ASPEN results. The statistics 

included: 

79 
• Average concentrations for major, area & other, onroad gasoline, onroad diesel, nonroad 

gasoline, remaining nonroad, background, and total at the county, state, state urban/rural, 

state RFG/non-RFG, national, national urban/rural, and national RFG/non-RFG levels. 

• Distributions (5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles) for total concentrations 

at the county, state, state urban/rural, state RFG/non-RFG levels. 

• Maps of county median concentrations for 1,3-butadiene, acetaldehyde, acrolein, 

benzene, formaldehyde, and naphthalene were generated for 1999, 2015, 2020, and 2030. 

Table 35 lists national average HAPEM concentrations for each MSAT HAP for stationary and 

mobile sources for each year. Figure 17 shows the county median total concentration for 

Benzene for 2015. 

The summaries and maps described above can be found in the MSAT rule docket: EPA-HQOAR-2005-0036 in the excel file hapem_concentrations.xls. County median maps are also in the 

docket; the file name is: HAPEM_medians.ppt. 

80 
Table 35. National average stationary and mobile HAPEM concentrations (μg m-3) for 2015, 2020, and 2030 by 

HAP 2015 2020 

Stationary Mobile Background Stationary Mobile Background Stationary 

1,3-Butadiene 1.91×10-2 2.77×10-2 3.90×10-2 1.92×10-2 2.68×10-2 3.90×10-2 1.92×10-2 2,2,4­Trimethylpentane 

3.61×10-2 3.16×10-1 0 3.83×10-2 2.86×10-1 0 3.83×10-2 

Acetaldehyde 7.40×10-2 3.84×10-1 4.00×10-1 7.62×10-2 3.47×10-1 4.00×10-1 7.62×10-2 Acrolein 2.53×10-2 3.40×10-2 0 2.50×10-2 3.33×10-2 0 2.50×10-2 Benzene 1.78×10-1 3.54×10-1 3.01×10-1 1.86×10-1 3.29×10-1 3.01×10-1 1.86×10-1 Chromium III 6.68×10-4 1.21×10-4 0 7.51×10-4 1.33×10-4 0 7.51×10-4 Chromium VI 1.687×10-4 2.70×10-5 0 1.90×10-4 2.96×10-5 0 1.90×10-4 Ethyl Benzene 1.08×10-1 1.44×10-1 0 1.19×10-1 1.32×10-1 0 1.19×10-1 Formaldehyde 1.27×10-1 3.25×10-1 5.91×10-1 1.38×10-1 3.12×10-1 5.91×10-1 1.38×10-1 Hexane 5.12×10-1 1.50×10-1 0 5.53×10-1 1.30×10-1 0 5.53×10-1 MTBE 5.95×10-2 1.40×10-1 0 6.27×10-2 1.14×10-1 0 6.27×10-2 Manganese 2.48×10-3 1.23×10-4 0 2.74×10-3 1.39×10-4 0 2.74×10-3 Naphthalene 5.25×10-2 1.34×10-2 0 5.61×10-2 1.36×10-2 0 5.61×10-2 Nickel 1.03×10-3 1.35×10-4 0 1.12×10-3 1.48×10-4 0 1.12×10-3 POM 1.40×10-2 1.12×10-3 0 1.46×10-2 1.14×10-3 0 1.46×10-2 Propionaldehyde 2.80×10-2 9.68×10-2 0 2.86×10-2 8.58×10-2 0 2.86×10-2 Styrene 4.09×10-2 1.36×10-2 0 4.61×10-2 1.30×10-2 0 4.61×10-2 Toluene 1.03 8.06×10-1 0 1.14 7.35×10-1 0 1.14 Xylenes 7.52×10-1 5.61×10-1 1.28×10-1 8.31×10-1 5.20×10-1 1.27×10-1 8.31×10-1 

81 
Note that even though the 2020 and 2030 stationary concentrations input into HAPEM were 

identical, the output stationary concentrations for the two years were not exactly the same. This 

is due to the post-processing of the raw HAPEM output. The average tract-level source category 

concentrations are adjusted by dividing by the tract-level median total concentration. Between 

2020 and 2030, the mobile concentrations change, therefore changing the total concentrations. 

Therefore, the stationary HAPEM output concentrations change, even though the ASPEN 

concentrations are no different between the two years. 

Concentrations 

0.000 - 0.293 

0.294 - 0.510 

0.511 - 0.825 

0.826 - 1.310 

1.311 - 2.206 

2.207 - 4.784 

Figure 17. 2015 HAPEM county median total concentrations (all sources) for benzene. 

82 
8. Cancer and non-cancer risk calculations 

Once HAPEM runs were completed, cancer and non-cancer risks were calculated for each of the 

MSAT HAPs. Table 36 lists the MSAT HAPS with their respective unit risk estimates (URE) 

for cancer calculations and non-cancer reference concentrations (Rfc) for non-cancer 

calculations, resulting from long term (chronic) inhalation exposure to these HAPS. Also listed 

are the HAPs appropriate carcinogenic class and target organ system(s) for non-cancer effects. 

Table 36. MSAT HAPs carcinogenic class, URE, non-cancer target organ systems, and Rfc. 

N/A denotes HAP is neither a cancer or non-cancer HAP 

HAP Carcinogen 

Class 

URE Organ systems Rfc 

1,3-Butadiene A 3.0x10-5 Reproductive 2.0x10-3 

2,2,4-Trimethylpentane N/A N/A N/A N/A 

Acetaldehyde B2 2.2x10-6 Respiratory 9.0x10-3 

Acrolein N/A Respiratory 2.0x10-5 

Benzene A 7.8x10-6 Immune 3.0x10-2 

Chromium III N/A N/A N/A N/A 

Chromium VI A 1.2x10-2 Respiratory 1.0x10-4 

Ethyl Benzene N/A Developmental 1.0 

Formaldehyde B 5.5x10-9 Respiratory 9.8x10-3 

Hexane N/A Respiratory, Neurological 2.0x10-1 

Manganese N/A Neurological 5.0x10-5 

MTBE N/A Liver, Kidney, Ocular 3.0 

Naphthalene C 3.4x10-5 Respiratory 3.0x10-3 

Nickel A 1.6x10-4 Respiratory, Immune 6.5x10-5 

Propionaldehyde N/A N/A N/A N/A 

POM1 B2 5.5x10-5 N/A 

POM2 B2 5.5x10-5 N/A 

POM3 B2 1.0x10-1 N/A 

POM4 B2 1.0x10-2 N/A 

POM5 B2 1.0x10-3 N/A 

POM6 B2 1.0x10-4 N/A 

POM7 B2 1.0x10-5 N/A 

POM8 B2 2.0x10-4 N/A 

Styrene N/A Neurological 1.0 

Toluene N/A Respiratory, Neurological 4.0x10-1 

Xylenes N/A Neurological 1.0x10-1 

URE and Rfc estimates were obtained from hazard and dose-response information that EPA’s 

Office of Air Quality Planning and Standards posts on the internet (“OAQPS Toxicity Values”) 

at the following link: www.epa.gov/ttn/fera. This information is updated as new data become 

available; the version of the table used for this paper is the same as used for the 1999 NATA (U. 

S. EPA, 2005d). 

Prior to computing risks, the HAPEM results from run1 and run2 were combined into a single 

data set containing the major, area & other, onroad gasoline, onroad diesel, nonroad gasoline, 

nonroad other, background, and total (all sources) exposure concentrations for each census tract. 

For each modeling year, each HAP was contained in its own dataset. 

83 
In that the 1999 NATA approach involved the use of ACCESS to store exposure results and 

compute risks, and the MSAT approach involved the use of a series of SAS® programs, quality 

assurance was performed on the SAS® programs to ensure the same results were obtained as 

would have been under the ACCESS approach. 

8.1 Cancer risk calculations 

To calculate cancer risks and summary statistics for 1999, 2015, 2020, and 2030, the HAPEM 

concentrations for each HAP were multiplied by the corresponding URE for the HAP. This was 

done for each source sector (and background) in each census tract to produce tract-level, source 

sector level cancer risks. Appendix D (D.1) provides the programming steps. 

After calculating the risks, the tract level risks for each HAP, risk for each carcinogen class, and 

risk across all HAPs were summarized at the same levels as done for the ASPEN and HAPEM5 

outputs. Once statistics were calculated for each year and level (county, state, etc.) they were 

merged together. Maps of county median risk were generated for several HAPs and total risk. 

The total risk map for 2015 is shown in Figure 18. Table 37 lists national and stationary and 

mobile risks for the cancer risk HAPs, carcinogen class, and total risk across all HAPs. 

More detailed summaries can be found in the MSAT rule docket: EPA-HQ-OAR-2005-0036 in 

the excel file named hapem_risks.xls. County median risk maps are also in the docket; the file 

name is: risk_030305.ppt. 

84 
HAP Carcinogen 2015 2020 2030

Class stationary mobile stationary mobile stationary mobile 

1,3-Butadiene A 5.73×10-7 8.30×10-7 5.76×10-7 8.03×10-7 5.76×10-7 8.82×10-7 

Benzene A 1.38×10-6 2.76×10-6 1.45×10-6 2.57×10-6 1.45×10-6 2.75×10-6 

Chromium VI A 2.01×10-6 3.24×10-7 2.28×10-6 3.56×10-7 2.28×10-6 4.29×10-7 

Nickel A 1.64×10-7 2.17×10-8 1.80×10-7 2.37×10-8 1.80×10-7 2.83×10-8 

Class A A 4.14×10-6 3.94×10-6 4.48×10-6 3.75×10-6 4.48×10-6 4.09×10-6 

Formaldehyde B1 7.01×10-10 1.79×10-9 7.57×10-10 1.73×10-9 7.57×10-10 1.87×10-9 

Class B1 B1 7.01×10-10 1.79×10-9 7.57×10-10 1.73×10-9 7.57×10-10 1.87×10-9 

Acetaldehyde B2 1.63×10-7 8.45×10-7 1.68×10-7 7.64×10-7 1.68×10-7 8.08×10-7 

POM B2 1.31×10-6 7.29×10-8 1.36×10-6 7.44×10-8 1.36×10-6 8.51×10-8 

Class B2 B2 1.47×10-6 9.18×10-7 1.53×10-6 8.38×10-7 1.53×10-6 8.931×10-7 

Naphthalene C 1.79×10-6 4.57×10-7 1.91×10-6 4.63×10-7 1.91×10-6 6.35×10-7 

Class C C 1.79×10-6 4.57×10-7 1.91×10-6 4.63×10-7 1.91×10-6 6.35×10-7 

Total Risk All 7.39×10-6 5.31×10-6 7.92×10-6 5.05×10-6 7.92×10-6 5.52×10-6 

Risk ( N in a million) 

0.0 - 5.5 

5.6 - 10.3 

10.4 - 17.4 

17.5 - 27.9 

28.0 - 47.3 

47.4 - 96.3 

Figure 18. County median total inhalation cancer risks for all MSAT HAPs for 2015. Risk is 

characterized as N in a million. 

Table 37. National average inhalation cancer risks for stationary and mobile sources for MSAT 

HAPs, each carcinogenic class and total risk (all MSAT HAPs). 

8.2 Non-cancer risk calculations 

Tract level non-cancer hazard quotients (HQ) for each HAP were calculated by dividing, for 

each HAP and each source sector, the exposure concentration by the Rfc. A hazard index was 

85 
computed for each organ system by summing the HQs over all HAPs that share the same target 

organ system. Appendix D (D.2) describes the programming steps. 

The tract level HQ and HI estimates for each HAP and organ system respectively, were 

summarized at the same levels as done for the ASPEN and HAPEM5 outputs. Once statistics 

were calculated for each year and level (county, state, etc.) they were merged together. Maps of 

county median HQ for several HAPs and respiratory HI were generated. The respiratory system 

HI map for 2015 is shown in Figure 19. Table 38 lists national and stationary and mobile HI for 

the non-cancer HAPs, and the organ systems affected by the MSAT HAPs. 

More detailed summaries can be found in the MSAT rule docket: EPA-HQ-OAR-2005-0036 in 

the excel file named hapem_hq.xls. County median non-cancer risk maps are also in the docket; 

the file name is hq_030305.ppt. 

86 
Hazard Index 

0.000 - 0.883 

0.884 - 2.175 

2.176 - 5.099 

5.100 - 10.182 

10.183 - 16.707 

16.708 - 31.635 

Figure 19. County median total (all sources) non-cancer hazard index for MSAT HAPs 

affecting the respiratory system. 

87 
Table 38. National average non-cancer hazard quotient (HQ) for MSAT HAPs and hazard index 

(HI) for organ systems for stationary and mobile sources. 

HAP 2015 2020 2030 

stationary mobile stationary mobile stationary mobile 

1,3-Butadiene 9.55×10-3 1.38×10-2 9.60×10-3 1.39×10-2 9.60×10-3 1.47×10-2 

Acetaldehyde 8.22×10-3 4.27×10-2 8.47×10-3 3.86×10-2 8.47×10-3 4.08×10-2 

Acrolein 1.27 1.70 1.25 1.67 1.25 1.84 

Benzene 5.92×10-3 1.18×10-2 6.19×10-3 1.10×10-2 6.19×10-3 1.18×10-2 

Chromium VI 1.68×10-3 2.70×10-4 1.90×10-3 2.97×10-4 1.90×10-3 3.57×10-4 

Ethyl Benzene 1.08×10-4 1.44×10-4 1.19×10-4 1.32×10-4 1.19×10-4 1.41×10-4 

Formaldehyde 1.30×10-2 3.32×10-2 1.40×10-2 3.19×10-2 1.40×10-2 3.47×10-2 

Hexane 2.56×10-3 7.52×10-4 2.77×10-3 6.51×10-4 2.77×10-3 6.80×10-4 

MTBE 1.98×10-5 4.67×10-5 2.09×10-5 3.82×10-5 2.09×10-5 3.80×10-5 

Manganese 4.95×10-2 2.46×10-3 5.48×10-2 2.77×10-3 5.48×10-2 3.47×10-3 

Naphthalene 1.75×10-2 4.48×10-3 1.87×10-2 4.54×10-3 1.87×10-2 5.24×10-3 

Nickel 1.58×10-2 2.08×10-3 1.73×10-2 2.28×10-3 1.73×10-2 2.72×10-3 

Styrene 4.09×10-5 1.36×10-5 4.61×10-5 1.30×10-5 4.61×10-5 1.42×10-5 

Toluene 2.58×10-3 2.01×10-3 2.84×10-3 1.84×10-3 2.84×10-3 1.96×10-3 

Xylenes 7.52×10-3 5.61×10-3 8.31×10-3 5.20×10-3 8.31×10-3 5.59×10-3 

Organ 2015 2020 2030 

Systems stationary mobile stationary mobile stationary mobile 

Developmental 1.08×10-4 1.44×10-4 1.19×10-4 1.32×10-4 1.19×10-4 1.41×10-4 

Immunological 2.17×10-2 1.39×10-2 2.35×10-2 1.32×10-2 2.35×10-2 1.45×10-2 

Kidney 1.98×10-5 4.67×10-5 2.09×10-5 3.82×10-5 2.09×10-5 3.80×10-5 

Liver 1.98×10-5 4.67×10-5 2.09×10-5 3.82×10-5 2.09×10-5 3.80×10-5 

Neurological 6.22×10-2 1.09×10-2 6.87×10-2 1.05×10-2 6.87×10-2 1.17×10-2 

Ocular 1.98×10-5 4.67×10-5 2.09×10-5 3.82×10-5 2.09×10-5 3.80×10-5 

Reproductive 9.55×10-3 1.38×10-2 9.60×10-3 1.39×10-2 9.60×10-3 1.47×10-2 

Respiratory 1.33 1.79 1.31 1.75 1.31 1.93 

8.3 Cancer and non-cancer risk population statistics using 2000 and projected population 

Tract level population and risks were used to develop population statistics for base and future 

years. We used the same county-level projected population data as is used in BenMAP (Abt, 

2005) for 2015, 2020, and 2030, which originated from Woods and Poole 

(www.woodsandpoole.com). The projected population data was for the contiguous 48 states, not 

Alaska and Hawaii. Therefore, the statistics were for the contiguous 48 state region. 

Also, populations statistics were calculated using 2000 census population for all years’ future 

year risks. Following is the methodology used for the population statistic calculations. 

8.3.1 Allocation of future county level populations to tract level 

As previously noted, the projected populations for 2015, 2020, and 2030 were at the county 

level. Since the calculated risks and non-cancer HQ or HI estimates were at tract level, the 

county level future year populations were allocated to the tracts using the 2000 census based 

tract-level to county-level population ratio (this is also spatial surrogate code 100 [population] 

used by EMS-HAP Version 3.0). Each tract’s ratio was applied to the county’s projected 

88 
population to calculate a future year’s tract population. For example, for Wake County, NC 

(FIPS=37183), the 2000 county population was 627,846. The 2000 population of tract 050100 

was 1,847, resulting in a ratio of 0.003. The projected 2015 population of Wake County was 

867,680.75. Applying the 2000 population ratio to the 2015 county population resulted in a 

projected tract population of 2,552.55. This was done for each tract in the 48 contiguous U.S. 

states. 

8.3.2 Population statistic calculations for cancer risk 

Once the 2015, 2020, and 2030 populations had been allocated to tract level, the populations 

were then merged by FIPS and tract with the tract level total risk (across all MSAT HAPs) 

estimates. Next, for each year and source sector, population totals were calculated based on 

three risk benchmark values: 1x10-4, 1x10-5, and 1x10-6. 

For example, for major source total risk for 2015, each tract level major risk was checked to see 

how it compared to the three values listed above. The following formulas show the checks and 

the population calculations: 

If tract major source total risk ≥ 10-4 then 

major_4 = major _4 + poptract (11) 

If tract major source total risk ≥ 10-5 then 

major_5 = major _5 + poptract (12) 

If tract major source total risk ≥ 10-6 then 

major_6 = major _6 + poptract (13) 

If tract major source total risk < 10-6 then 

major_7 = major _7 + poptract (14) 

where major_4, major_5, major_6, and major_7 are the running totals and poptract is the tract 

population for 2015. Note, initially, before any checks of the tracts, major_4, major_5, major_6, 

and major_7 are initialized to zero. 

From the logic, a tract's 2015 population was added to each major_4, major_5, and major_6. For 

example if the major source risk was 1.1 then the tract’s population was added to major_4, 

major_5, and major_6. After all tracts in the contiguous 48 states had been checked, each 

running total represented a national population affected by each of the three risk classes listed. 

These populations were then binned so that they became mutually exclusive, i.e., no overlap, and 

were plotted on charts or summarized. The following formulas were used to bin the populations: 

For population affected by risk ≥ 10-5 but < 10-4

major_5a m= ajor_5 − major _4 (15) 

89 
For population affected by risk ≥ 10-6 but < 10-5

major_6a = major _6 − major _5 (16) 

where major_5a is the population affected by risk ≥ 10-5 but < 10-4 and major_6a is the 

population affected by risk ≥ 10-6 but < 10-5. Note that major_4 and major_7 do not need to be 

modified. 

The steps described in this section were performed for each source sector, major, area & other, 

onroad gasoline, onroad diesel, total onroad, nonroad gasoline, non-gasoline nonroad, total 

nonroad, background, and total risk (all source sectors). Table 39 shows the results for onroad 

nonroad for 1999, 2015, 2020, and 2030. Note that Alaska and Hawaii populations are not 

included. 

Table 39. Population risk classes for mobile total risk for 2015, 2020, and 2030 using projected 

populations for each year. 

Source 

Category 

Population Class 

Populations 

Year 

1999 2015 2020 2030 

Onroad 

Risk ≥ 10-4 208,150 0 0 0 

10-5 ≤ Risk <10-4 112,848,474 19,596,469 16,703,891 21,839,016 

10-6 ≤ Risk <10-5 145,060,999 241,185,986 249,373,492 269,464,226 

Risk <10-6 21,465,809 56,122,217 63,615,359 64,592,322 

Total Population 279,583,432 316,904,672 329,692,742 355,895,564 

Nonroad 

Risk ≥ 10-4 22,272 23,710 25,123 27,986 

10-5 ≤ Risk <10-4 2,630,188 1,365,537 1,584,116 2,215,401 

10-6 ≤ Risk <10-5 180,439,149 150,013,784 159,142,708 18,553,8098 

Risk <10-6 96,491,823 165,501,640 168,940,795 168,114,078 

Total Population 279,583,432 316,904,672 329,692,742 355,895,564 

8.3.3 Population statistic calculations for non-cancer respiratory hazard index 

A similar procedure was used for the respiratory system hazard index. The threshold HI values 

used for binning purposes were 10, 1, and 0.1. As described above, each respiratory HI for each 

source sector and year were compared against these values and a running total kept for three 

populations. For example for 2015 for major source HI values, the following conditions and 

equations are used: 

If tract major source respiratory HI ≥ 10 then 

major_10 = major _10 + poptract (17) 

If tract major source respiratory HI ≥ 1 then 

major_1 = major _1+ poptract (18) 

90 
 If tract major source respiratory HI ≥ 0.1 then 

major_01 = major _01+ poptract (19) 

If tract major source respiratory HI < 0.1 then 

major_0 = major _0 + poptract (20) 

where major_10, major_1, major_01, and major_0 are the running totals and poptract is the tract 

population for 2015. Note, initially the four running totals are set to zero before any checks. 

As with the risk calculations, from the logic, a tract's 2015 population can be added to each 

major_10, major_1, and major_01. For example if the major source respiratory HI was 11 then 

the tract’s population would be added to major_10, major_1, and major_01. After all tracts in 

the contiguous 48 states have been checked, each running total represented a national population 

affected by each of the three HI classes listed. These populations were then binned so that they 

become mutually exclusive, i.e., no overlap. The following formulas were used to bin the 

populations: 

For population affected by HI ≥ 1 but < 10 

major_1a = major _1− major _10 (21) 

For population affected by HI ≥ 0.1 but < 1 

major_01a = major _01− major_1 (22) 

where major_1a is the population affected by HI ≥ 1 but < 10 and major_01a is the population 

affected by HI ≥ 0.1 but < 1. Major_10 and major_0 were not modified. 

The steps described in this section were performed for each source sector, major, area & other, 

onroad gasoline, onroad diesel, total onroad, nonroad gasoline, non-gasoline nonroad, total 

nonroad, background, and total HI (all source sectors). Table 40 shows the results for onroad 

nonroad for 1999, 2015, 2020, and 2030. Note that Alaska and Hawaii populations are not 

included. 

91 
Table 40. Population respiratory HI classes for mobile sources for 2015, 2020, and 2030 using 

projected populations for each year. 

Source Population Class 

Populations 

Year 

Category 1999 2015 2020 2030 

HI ≥ 10 17,567,623 37,998 16,297 30,134 

Onroad 

1 ≤ HI < 10 200,171,904 114,954,321 105,041,326 121,633,149 

0.1 ≤ HI <1 58,288,431 184,163,448 204,853,751 214,512,775 

HI < 0.1 3,555,474 17,748,905 19,781,369 19,719,506 

Total Population 279,583,432 316,904,672 329,692,742 355,895,564 

HI ≥ 10 1,116,086 1,280,756 1,449,660 1,989,107 

Nonroad 

1 ≤ HI < 10 85,670,356 65,585,394 70,546,734 85,032,069 

0.1 ≤ HI <1 161,073,537 189,650,787 192,548,911 202,958,366 

HI < 0.1 31,723,453 60,387,735 65,147,437 65,916,022 

Total Population 279,583,432 316,904,672 329,692,742 355,895,564 

8.3.4 Population statistic calculations using 2000 population for all years 

A similar program as described above was used to calculate populations using the 2000 census 

population for all years. Basically the 2015, 2020, and 2030 projected populations were replaced 

with the year 2000 population. Population statistics calculated using 2000 population for all 

years allowed the differences in risks or HI to be evaluated separately from changes in 

population. 

Detailed summaries of population statistics for 2000 and projected year populations can be found 

in the MSAT rule docket: EPA-HQ-OAR-2005-0036 in the excel files pop_stats_risks.xls and 

pop_stats_hi_respiratory.xls, for cancer and non-cancer respiratory risks, respectively. 

92 
9. Background concentration sensitivity analysis 

For the air quality modeling, background concentrations that were added to the ASPEN-modeled 

concentrations for subsequent HAPEM modeling (Section 7) and risk calculations (Section 8) 

were assumed to remain the same across the modeled future years, 2015, 2020, and 2030. The 

values used were the same background values used for the 1999 NATA background 

concentrations. The background concentrations were at the county level so for a given HAP with 

a background value, all tracts in a county received the same background. Details about the 

development of the 1999 background concentrations can be found in Batelle (2003). 

Because background concentrations added are assumed to account for medium-to-long range 

transport of emissions, it is expected that they would decrease or increase as emissions increased 

or decreased. A sensitivity analysis to determine the potential magnitude of such background 

concentration changes was made for 1,3-butadiene, acetaldehyde, benzene, formaldehyde, and 

xylenes for the following years: 2015, 2020, and 2030. The sensitivity analysis included 

adjusting the 1999 background concentrations using the change in emissions between 1999 and 

these future years. Following is the methodology and results of the analysis. 

For each county in the U.S. (excluding Puerto Rico and Virgin Islands), the emissions from all 

sources in the county were summed together for each year, 1999, 2015, 2020 and 2030. Next, 

the emissions for that county and the surrounding counties whose county centroids (supplied 

from the EMS-HAP ancillary file cty_cntr99.sas7bdat, found in the MSAT rule docket EPA-HQOAR-2005-0036) were within 300 km of the county were summed together for each year, 

resulting in emission totals for 1999, 2015, 2020, and 2030. These summed emissions were 

assigned to the county being analyzed. After summation, for each year, 2015, 2020, and 2030, 

the emissions were divided by the 1999 emissions to create a scaling factor to multiply with the 

1999 background concentration. 

As an example, consider Wake County, NC (FIPS=37183). Figure 20 shows the 209 counties 

(including Wake County) whose centroids are within 300 km of Wake County’s centroid. These 

counties cover most of North Carolina with some in Virginia and South Carolina. The total 

benzene emissions for 1999, 2015, 2020, and 2030 can be seen in Table 41. The ratio to apply to 

the 1999 background was calculated by dividing the future year's emissions (2015, 2020, or 

2030) by the 1999 emissions. This ratio was then applied to the 1999 background, and the new 

scaled background was added to the total model concentrations at each tract. Table 41 also 

shows the scaled backgrounds used for 2015, 2020, and 2030 for benzene. 

93 
0 75 150 300 Kilometers 

Figure 20. Counties within 300 km of the centroid of Wake County, North Carolina (county in 

gray). Dots represent county centroids. 

Table 41. Total benzene emissions of counties within 300 km of Wake County, NC for 1999, 

2015, 2020 and 2030, 1999 background benzene concentration for Wake County, and scaled 

background concentrations for Wake County for 2015, 2020, and 2030. 

Year Emissions 

(tons) 

Emissions Ratios 

(Future year/1999) 

Background 

Concentration (μg m-3) 

(1999 background × Ratio) 

1999 15,692.404 1 0.403794 

2015 9,364.748 0.59677 0.242117 

2020 9,225.224 0.58788 0.238277 

2030 9,552.035 0.608704 0.246566 

Table 42 shows national average background concentrations for 1999 and future years. Table 43 

shows total concentrations (all sources plus background) using both the 1999 background for 

each year and using the scaled background for each year for the five HAPs studied in the 

analysis. The analysis showed how much the scaling affected background concentrations but 

also showed how little background changed between 2015, 2020, and 2030. This can be seen 

both for one county in Table 41 and for the whole country in Table 42. In Figure 21, the changes 

for the benzene background between the four years can be seen. The 1999 background 

concentrations are generally higher than 2015, 2020, and 2030. One outcome of the analysis was 

a change in spatial variability of background for xylenes. For 1999, the entire country received 

the same background for xylenes (0.17 μg m-3). Scaling background by emissions created a 

spatial variability of xylenes background (Figure 22). 

94 
Detailed summaries of scaled background concentrations for the five HAPs summaries can be 

found in the MSAT rule docket: EPA-HQ-OAR-2005-0036 in the excel file named 

background_test.xls with maps for the HAPs in background_acetaldehyde_0111.ppt, 

background_butadiene_0111.ppt, background_test_0111.ppt (for benzene), 

background_formaldehyde_0111.ppt, and background_xylenes_0111.ppt. 

Table 42. National average 1999 background and scaled backgrounds for 1,3-butadiene, 

acetaldehyde, benzene, formaldehyde, and xylenes. 

HAP 

Background concentrations (μg m-3) 

1999 2015 2020 2030 

1,3-Butadiene 5.12×10-2 2.86×10-2 2.83×10-2 2.95×10-2 

Acetaldehyde 5.17×10-1 3.29×10-1 3.28×10-1 3.36×10-1 

Benzene 3.94×10-1 2.38×10-1 2.32×10-1 2.40×10-1 

Formaldehyde 7.62×10-1 4.96×10-1 5.05×10-1 5.21×10-1 

Xylenes 1.70×10-1 1.15×10-1 1.17×10-1 1.20×10-1 

Table 43. National average total concentrations (all sources and background) for 2015, 2020, 

and 2030 using both the 1999 background and the scaled backgrounds. 

HAP 

Total concentrations (μg m-3) using 

1999 background 

Total concentrations (μg m-3) using 

scaled background concentrations 

2015 2020 2030 2015 2020 2030 

1,3-Butadiene 9.81×10-2 9.77×10-2 1.00×10-1 7.58×10-2 7.50×10-2 7.86×10-2 

Acetaldehyde 9.66×10-1 9.36×10-1 9.56×10-1 7.77×10-1 7.47×10-1 7.78×10-1 

Benzene 9.13×10-1 9.02×10-1 9.24×10-1 7.57×10-1 7.40×10-1 7.71×10-1 

Formaldehyde 1.22 1.22 1.25 9.57×10-1 9.68×10-1 1.01 

Xylenes 1.55 1.61 1.65 1.50 1.56 1.60 

95 
Background Background 

0.032 - 0.164 0.028 - 0.164 

0.164 - 0.226 0.164 - 0.226 

0.227 - 0.275 0.226 - 0.275 

0.275 - 0.324 0.275 - 0.324 

a 0.325 - 0.384 b 0.325 - 0.384 

0.384 - 0.578 0.388 - 0.578 

Background Background 

0.028 - 0.164 0.028 - 0.164 

0.164 - 0.226 0.164 - 0.226 

0.226 - 0.275 0.226 - 0.275

c 0.276 - 0.324 d 0.275 - 0.324

0.324 - 0.384 0.324 - 0.384 

0.386 - 0.578 0.387 - 0.578 

Figure 21. Benzene background concentrations (μg m-3) for a) 1999 background, b) 2015 scaled 

background c) 2020 scaled background and d) 2030 scaled background. 

Background 

0.082 - 0.114 

0.115 - 0.125 

0.126 - 0.141 

0.142 - 0.177 

a Background b 0.178 - 0.240 

0.17 0.241 - 0.298 

Background Background 

0.075 - 0.114 0.073 - 0.114 

0.115 - 0.125 0.115 - 0.125 

0.126 - 0.141 0.126 - 0.141 

c 0.142 - 0.177 d 0.142 - 0.177 

0.178 - 0.240 0.178 - 0.240 

0.241 - 0.298 0.241 - 0.298 

Figure 22. Xylenes background concentrations (μg m-3) for a) 1999 background, b) 2015 scaled 

background c) 2020 scaled background and d) 2030 scaled background. 

While background scaling did not change background concentrations much between 2015, 2020, 

and 2030, concentrations did differ between the three future years and 1999. Scaling the 

background using the projected emissions can add a spatial variation to background 

concentration. 

96 
10. Benzene Control Scenario 

This section details the methodology used to develop the controlled inventories for 2015, 2020 

and 2030 for benzene, formaldehyde, acetaldehyde, acrolein, and 1,3-butadiene as part of the 

benzene control scenario. Controls were applied to gasoline marketing and distribution 

emissions, onroad gasoline refueling emissions, onroad gasoline emissions, and nonroad gasoline 

emissions. 

10.1 Stationary gasoline distribution and vehicle gasoline refueling inventory 

For the stationary inventories, controls were applied to benzene emissions only for gasoline 

marketing and distribution and onroad gasoline refueling for 2015 and 2020. For 2030, these 

emissions (along with all other stationary source emissions for the reference case) were set equal 

to 2020 emissions. Table 44 lists the gasoline distribution SCC codes contained in the reference 

case inventories, for which controlled emissions were estimated. Onroad gasoline refueling SCC 

codes can be found in Table 23. 

Gasoline marketing and distribution emissions were estimated for the control scenario by 

applying a county specific control ratio based on the change in average fuel benzene level for the 

control and reference case. Average fuel benzene level for the control case was determined from 

refinery modeling done for the rule. As part of the refinery modeling, average fuel properties for 

each Petroleum Administration for Defense District (PADD) under the new standards were 

estimated. Average fuel benzene levels for conventional gasoline (CG) and reformulated 

gasoline (RFG) in each PADD before and after implementation of the proposed standards were 

used to develop multiplicative factors. These multiplicative factors were used as control ratios 

for estimating the controlled gasoline marketing and distribution emissions. They were also 

applied to the reference case fuel benzene levels for each county in the NMIM database to use 

for generating the NMIM controlled case emissions, which were used to develop control 

inventories for the other categories discussed in this section. 

The multiplicative factors (control ratios for gasoline marketing and distribution emissions) are 

shown in Table 45. Although California is part of PADD5, it was treated separately since 

California has its own reformulated gasoline program. PADD regions are shown in Figure 23. 

To apply the control ratios to the gasoline marketing and distribution SCCs, it was necessary to 

distinguish between the counties in each PADD using RFG versus CG. Figure 24 shows which 

counties are RFG counties. 

Onroad gasoline refueling emissions were estimated for the control scenario by calculating a 

county specific ratio of control to reference case NMIM refueling emissions for benzene for 

2015, 2020 and 2030. NMIM was rerun for refueling emissions for the control case with revised 

gasoline input parameters as described in 10.2. 

Appendix E describes the steps used to develop the controlled benzene emissions for gasoline 

marketing and distribution and onroad gasoline refueling. Sample calculations are also provided. 

97 
Table 44. Benzene gasoline marketing and distribution SCC codes to be controlled. 

SCC Description SCC Description 

2501000000 Storage and Transport; Petroleum and Petroleum Product 

Storage; All Storage Types: Breathing Loss; Total: All Products 

2501050120 Storage and Transport; Petroleum an

Storage; Bulk Stations/Terminals: Br

2501060050 Storage and Transport; Petroleum and Petroleum Product 

Storage; Gasoline Service Stations; Stage 1: Total 

2501060051 Storage and Transport; Petroleum an

Storage; Gasoline Service Stations; SFilling 

2501060052 Storage and Transport; Petroleum and Petroleum Product 

Storage; Gasoline Service Stations; Stage 1: Splash Filling 

2501060053 Storage and Transport; Petroleum an

Storage; Gasoline Service Stations; SSubmerged Filling 

2501060200 Storage and Transport; Petroleum and Petroleum Product 

Storage; Gasoline Service Stations; Underground Tank: Total 

2501060201 Storage and Transport; Petroleum an

Storage; Gasoline Service Stations; UBreathing and Emptying 

2501080000 Aviation Gasoline Distribution: Stage 1 & II 2501080050 Aviation Gasoline Storage -Stage I 2501080100 Aviation Gasoline Storage -Stage II 2501995120 Storage and Transport; Petroleum an

Storage; All Storage Types: Working 

2505000000 Storage and Transport; Petroleum and Petroleum Product 

Transport; All Transport Types; Total: All Products 

2505000120 Storage and Transport; Petroleum an

Transport; All Transport Types; Gaso

2505010000 Storage and Transport; Petroleum and Petroleum Product 

Transport; Rail Tank Car; Total: All Products 

2505020000 Storage and Transport; Petroleum an

Transport; Marine Vessel; Total: All 

2505020120 Storage and Transport; Petroleum and Petroleum Product 

Transport; Marine Vessel; Gasoline 

2505020121 Marine Vessel Operations - Barge Ha

2505030120 Storage and Transport; Petroleum and Petroleum Product 

Transport; Truck; Gasoline 

40400101 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminBreathing Loss (67000 Bbl Capacity) 

40400102 Petroleum and Solvent Evaporation; Petroleum Liquids 

Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank 

40400103 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminBreathing Loss (67000 Bbl. Capacity

40400104 Petroleum and Solvent Evaporation; Petroleum Liquids 

Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Breathing Loss (250000 Bbl Capacity)-Fixed Roof Tank 

40400105 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminBreathing Loss (250000 Bbl Capacity

40400106 Petroleum and Solvent Evaporation; Petroleum Liquids 

Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Breathing Loss (250000 Bbl Capacity) - Fixed Roof Tank 

40400107 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminWorking Loss (Diam. Independent) - 

40400108 Petroleum and Solvent Evaporation; Petroleum Liquids 

Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Working Loss (Diameter Independent) - Fixed Roof Tank 

40400109 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminWorking Loss (Diameter Independen

98 
Table 44. Continued. 

SCC Description SCC Description 

40400110 Petroleum and Solvent Evaporation; Petroleum Liquids 

Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss (67000 Bbl Capacity)-Floating Roof Tank 

40400111 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminStanding Loss (67000 Bbl Capacity)-

40400112 Petroleum and Solvent Evaporation; Petroleum Liquids 

Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss (67000 Bbl Capacity)- Floating Roof Tank 

40400113 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminStanding Loss (250000 Bbl Cap.) - F

40400114 Petroleum and Solvent Evaporation; Petroleum Liquids 

Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss (250000 Bbl Cap.) - Floating Roof Tank 

40400115 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminStanding Loss (250000 Bbl Cap.) - F

40400116 Petroleum and Solvent Evaporation; Petroleum Liquids 

Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13/10/7: Withdrawal Loss (67000 Bbl Cap.) - Float Rf Tnk 

40400117 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk Termin13/10/7: Withdrawal Loss (250000 B

40400118 Petroleum and Solvent Evaporation; Petroleum Liquids 

Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space 

40400119 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminFilling Loss (10500 Bbl Cap.) - Varia

40400120 Petroleum and Solvent Evaporation; Petroleum Liquids 

Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space 

40400131 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminStanding Loss - Ext. Floating Roof w

40400132 Petroleum and Solvent Evaporation; Petroleum Liquids 

Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Primary Seal 

40400141 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminStanding Loss - Ext. Floating Roof w

40400142 Petroleum and Solvent Evaporation; Petroleum Liquids 

Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss - Ext. Floating Roof w/ Secondary Seal 

40400143 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminStanding Loss - Ext. Floating Roof w

40400148 Petroleum and Solvent Evaporation; Petroleum Liquids 

Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13/10/7: Withdrawal Loss - Ext. Float Roof (Pri/Sec Seal) 

40400150 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminLosses/Leaks: Loading Racks 

40400151 Petroleum and Solvent Evaporation; Petroleum Liquids 

Storage (non-Refinery); Bulk Terminals; Valves, Flanges, and Pumps 

40400152 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminLosses 

40400153 Petroleum and Solvent Evaporation; Petroleum Liquids 

Storage (non-Refinery); Bulk Terminals; Vapor Control Unit Losses 

40400154 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminLeaks 

40400161 Petroleum and Solvent Evaporation; Petroleum Liquids 

Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss - Int. Floating Roof w/ Primary Seal 

40400162 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminStanding Loss - Int. Floating Roof w/ 

99 
Table 44. Continued. 

SCC Description SCC Description 

40400163 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss - Internal Floating Roof w/ Primary Seal 

40400171 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminStanding Loss - Int. Floating Roof w/ 

40400172 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Secondary Seal 

40400173 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk TerminStanding Loss - Int. Floating Roof w/ 

40400178 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Bulk Terminals; Gasoline RVP 13/10/7: Withdrawal Loss - Int. Float Roof (Pri/Sec Seal) 

40400201 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk Plants; Breathing Loss (67000 Bbl Capacity) 

40400202 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Bulk Plants; Gasoline RVP 10: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank 

40400203 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk Plants; Breathing Loss (67000 Bbl. Capacity

40400204 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Bulk Plants; Gasoline RVP 13: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank 

40400205 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk Plants; Working Loss (67000 Bbl. Capacity) 

40400206 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Bulk Plants; Gasoline RVP 7: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank 

40400207 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk Plants; Standing Loss (67000 Bbl Cap.) - Flo

40400208 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Bulk Plants; Gasoline RVP 10: Standing Loss (67000 Bbl Cap.) - Floating Roof Tank 

40400209 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk Plants; Standing Loss (67000 Bbl Cap.) - Flo

40400210 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Bulk Plants; Gasoline RVP 13/10/7: Withdrawal Loss (67000 Bbl Cap.) - Float Rf Tnk 

40400212 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk Plants; Filling Loss (10500 Bbl Cap.) - Varia

40400213 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Bulk Plants; Gasoline RVP 7: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space 

40400231 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk Plants; Standing Loss - Ext. Floating Roof w

40400241 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Bulk Plants; Gasoline RVP 13: Standing Loss - Ext. Floating Roof w/ Secondary Seal 

40400242 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk Plants; Standing Loss - Ext. Floating Roof w

40400250 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Bulk Plants; Loading Racks 

40400251 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk Plants; Pumps 

40400252 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Bulk Plants; Miscellaneous Losses/Leaks: Vapor Collection Losses 

40400253 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk Plants; Losses/Leaks: Vapor Control Unit Lo

100 
Table 44. Continued. 

SCC Description SCC Description 

40400254 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Bulk Plants; Tank Truck Vapor Losses 

40400261 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk Plants; Standing Loss - Int. Floating Roof w/ 

40400262 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Bulk Plants; Gasoline RVP 10: Standing Loss - Int. Floating Roof w/ Primary Seal 

40400263 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Bulk Plants; Standing Loss - Internal Floating Roo

40400278 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Bulk Plants; Gasoline RVP 10/13/7: Withdrawal Loss - Int. Float Roof (Pri/Sec Seal) 

40400401 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Petroleum PTanks; Gasoline RVP 13: Breathing 

40400402 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Petroleum Products - Underground Tanks; Gasoline RVP 13: Working Loss 

40400403 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Petroleum PTanks; Gasoline RVP 10: Breathing 

40400404 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Petroleum Products - Underground Tanks; Gasoline RVP 10: Working Loss 

40400405 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Petroleum PTanks; Gasoline RVP 7: Breathing L

40400406 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Petroleum Products - Underground Tanks; Gasoline RVP 7: Working Loss 

40400497 Petroleum and Solvent Evaporation; 

Storage (non-Refinery); Petroleum PTanks; Specify Liquid: Breathing Lo

40400498 Petroleum and Solvent Evaporation; Petroleum Liquids Storage 

(non-Refinery); Petroleum Products - Underground Tanks; Specify Liquid: Working Loss 

406001 Petroleum and Solvent Evaporation; 

Marketing of Petroleum Products; Ta

40600101 Petroleum and Solvent Evaporation; Transportation and 

Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Splash Loading ** 

40600126 Petroleum and Solvent Evaporation; 

Marketing of Petroleum Products; TaGasoline: Submerged Loading ** 

40600131 Petroleum and Solvent Evaporation; Transportation and 

Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Submerged Loading (Normal Service) 

40600136 Petroleum and Solvent Evaporation; 

Marketing of Petroleum Products; TaGasoline: Splash Loading (Normal S

40600141 Petroleum and Solvent Evaporation; Transportation and 

Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Submerged Loading (Balanced Service) 

40600144 Petroleum and Solvent Evaporation; 

Marketing of Petroleum Products; TaGasoline: Splash Loading (Balanced 

40600147 Petroleum and Solvent Evaporation; Transportation and 

Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Submerged Loading (Clean Tanks) 

40600162 Petroleum and Solvent Evaporation; 

Marketing of Petroleum Products; TaGasoline: Loaded with Fuel (Transit 

40600163 Petroleum and Solvent Evaporation; Transportation and 

Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Return with Vapor (Transit Losses) 

406002 Petroleum and Solvent Evaporation; 

Marketing of Petroleum Products; M

101 
Table 44. Continued. 

SCC Description SCC Description 

40600231 Petroleum and Solvent Evaporation; Transportation and 

Marketing of Petroleum Products; Marine Vessels; Gasoline: Ship Loading - Cleaned and Vapor Free Tanks 

40600232 Petroleum and Solvent Evaporation; 

Marketing of Petroleum Products; MOcean Barges Loading 

40600233 Petroleum and Solvent Evaporation; Transportation and 

Marketing of Petroleum Products; Marine Vessels; Gasoline: Barge Loading - Cleaned and Vapor Free Tanks 

40600234 Petroleum and Solvent Evaporation; 

Marketing of Petroleum Products; MShip Loading - Ballasted Tank 

40600236 Petroleum and Solvent Evaporation; Transportation and 

Marketing of Petroleum Products; Marine Vessels; Gasoline: Ship Loading - Uncleaned Tanks 

40600237 Petroleum and Solvent Evaporation; 

Marketing of Petroleum Products; MOcean Barges Loading - Uncleaned T

40600238 Petroleum and Solvent Evaporation; Transportation and 

Marketing of Petroleum Products; Marine Vessels; Gasoline: Barges Loading - Uncleaned Tanks 

40600239 Petroleum and Solvent Evaporation; 

Marketing of Petroleum Products; MTanker Ship - Ballasted Tank Condit

40600240 Petroleum and Solvent Evaporation; Transportation and 

Marketing of Petroleum Products; Marine Vessels; Gasoline: Barge Loading - Average Tank Condition 

40600241 Petroleum and Solvent Evaporation; 

Marketing of Petroleum Products; MTanker Ship - Ballasting 

40600242 Petroleum and Solvent Evaporation; Transportation and 

Marketing of Petroleum Products; Marine Vessels; Gasoline: Transit Loss 

40600301 Petroleum and Solvent Evaporation; 

Marketing of Petroleum Products; Ga- Stage I; Splash Filling 

40600302 Petroleum and Solvent Evaporation; Transportation and 

Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; Submerged Filling w/o Controls 

40600305 Petroleum and Solvent Evaporation; 

Marketing of Petroleum Products; Ga- Stage I; Unloading ** 

40600306 Petroleum and Solvent Evaporation; Transportation and 

Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; Balanced Submerged Filling 

40600307 Petroleum and Solvent Evaporation; 

Marketing of Petroleum Products; Ga- Stage I; Underground Tank Breathi

40600399 Petroleum and Solvent Evaporation; Transportation and 

Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; Not Classified ** 

40600706 Petroleum and Solvent Evaporation; 

Marketing of Petroleum Products; CoFleet Refueling - Stage I; Balanced S

40600707 Petroleum and Solvent Evaporation; Transportation and 

Marketing of Petroleum Products; Consumer (Corporate) Fleet Refueling - Stage I; Underground Tank Breathing and Emptying 

40688801 Petroleum and Solvent Evaporation; 

Marketing of Petroleum Products; FuSpecify in Comments Field 

40688802 Petroleum and Solvent Evaporation; Transportation and 

Marketing of Petroleum Products; Fugitive Emissions; Specify in Comments Field 

102 
Legend 

CA 

PADD 1 

PADD 2 

Legend 

CA 

PADD1 

PADD2 

PADD3 

PADD4 

PADD5 

Figure 23. PADD regions for the U.S. 

103 
Figure 24. RFG counties (dark gray) for the U.S. 

Table 45. Change in Average Fuel Benzene Level (Volume Percent) by PADD with 

Implementation of Proposed Fuel Benzene Standard (CG – Conventional Gasoline; RFG – 

Reformulated Gasoline). 

Gasoline 

Type 

Region 

PADD 1 PADD 2 PADD 3 PADD 4 PADD 5 Calif. 

Reference CG 0.91 % 1.26% 0.95% 1.47% 1.42% 0.62% 

Case RFG 0.59% 0.80% 0.57% 1.05% 0.65% 0.62% 

Control Case CG 0.55% 0.68% 0.54% 0.93% 0.85% 0.61% 

RFG 0.54% 0.71% 0.55% 0.62% 0.60% 0.61% 

Multiplicative 

Factor 

CG 0.60 0.54 0.57 0.63 0.60 0.98 

RFG 0.92 0.89 0.96 0.59 0.92 0.98 

Table 46 shows the reference (no controls) and controlled stationary benzene emissions after 

applying the controls to 2015 and 2020 emissions by SCC. Detailed summaries can be found in 

benzene_gas_scc.xls in the MSAT rule docket EPA-HQ-OAR-2005-0036 

104 
Table 46. Benzene stationary emissions (tons) before and after applying controls to reference case gasoline mar

distribution emissions (non refueling gasoline) and vehicle refueling emissions. Also shown are the percent differeference). 1999 NEI emissions are shown for comparison. 

Emissions type SCC codes 1999 2015 

Reference Control % Re

Diff. 

All storage types and all products: total breathing loss 

2501000000 8 12 7 -40 

Bulk stations/terminals: gasoline breathing loss: gasoline 

2501050120 1,535 1,579 952 -40 

Stage 1 Filling 2501060050, 2501060051, 2501060052, 2501060053 1,785 1,826 1,176 -36 Underground Tanks: Gasoline Service 

2501060200, 2501060201 86 88 62 -25 

Aviation gasoline distribution 

2501080050, 2501080000, 2501080100 307 327 226 -31 

Transport 2501995120, 2505000000, 2505000120, 2505010000, 

2505020000, 2505020120, 2505020121, 2505030120 

915 1,110 908 -18 

Bulk terminals 40400101, 40400102, 40400103, 40400104, 40400105, 

40400106, 40400107, 40400108, 40400109, 40400110, 

69 107 71 -33 

40400111, 40400112, 40400113, 40400114, 40400115, 40400116, 40400117, 40400118, 40400119, 40400120, 40400131, 40400132, 40400141, 40400142, 40400143, 40400148, 40400150, 40400151, 40400152, 40400153, 40400154, 40400161, 40400162, 40400163, 40400171, 40400172, 40400173, 40400178 

Bulk plants 40400201, 40400202, 40400203, 40400204, 40400205, 

40400206, 40400207, 40400208, 40400209, 40400210, 40400212, 40400213, 40400231, 40400241, 40400242, 40400250, 40400251, 40400252, 40400253, 40400254, 40400261, 40400262, 40400263, 40400278, 

40 61 41 -32 

Underground tanks 40400401, 40400402, 40400403, 40400404, 40400405, 

40400406, 40400497, 40400498 

3 4 3 -23 

105 
Table 46. Continued. 

Emissions type SCC codes 1999 2015 

Reference Control % Ref

Diff. 

Tank cars and trucks 406001, 40600101, 40600126, 40600131, 40600136, 

40600141, 40600144, 40600147, 40600162, 40600163 

149 192 127 -34 

Marine vessels 406002, 40600231, 40600232, 40600233, 40600234, 17 24 17 -30 

40600236, 40600237, 40600238, 40600239, 40600240, 40600241, 40600242 

Stage 1 Evaporation Retail 

40600301, 40600302, 40600305, 40600306, 40600307, 40600399 

22 35 22 -37 

Stage 1 Evaporation Fleet 

40600706, 40600707, 40688801, 40688802 35 54 49 -10 

Total non refueling gasoline 4,970 5,419 3,663 -32 Vehicle refueling 1,566 724 459 -37 Other stationary sources 104,645 114,186 114,186 0 1Total 111,181 120,329 118,308 -2 1

106 
10.2 Highway gasoline vehicle inventory 

To develop the highway vehicle inventories, NMIM was rerun for the controlled case, using 

revised gasoline fuel parameter inputs for fuel benzene and aromatics levels. The revised fuel 

benzene inputs were described in Section 10.1 (see Table 45). The refinery modeling also 

indicated that the reduction in fuel benzene levels would result in small decreases in aromatics 

levels as well. Thus, aromatics levels were adjusted using the additive factors calculated as 

follows: 

Additive Factor = 0.7*(Benzenecontrol - Benzenereference) (23) 

A pollutant, county and SCC specific projection factor was computed from the controlled and 

reference case NMIM- based emissions as follows: 

E NMIMcontrol ,20 XX PF20 XX = (24) E NMIMreference ,20 XX 

This factor was then applied to the reference case inventories for 2015, 2020 and 2030, at the 

county and SCC and pollutant level, for the 1,3-butadiene, benzene, acetaldehyde, acrolein, and 

formaldehyde. 

Details on the computation of the projection factor and the application to the reference case 

inventories are provided in Appendix F. Sample calculations are also provided. Summaries are 

shown in Table 47. The complete summaries can be found in onroad_controls.xls or 

onroad_controls_pivot.xls in the MSAT rule docket, EPA-HQ-OAR-2005-0036. 

107 
Table 47. National MSAT reference and controlled emissions (nearest ton) for gasoline 

powered vehicles by HAP for 2015, 2020, and 2030. 

Vehicle HAP 2015 emissions 2020 emissions 2030 emissions 

Base Controlled Base Controlled Base Controlled 

HDGV 

1,3-Butadiene 130 130 103 103 84 84 

Acetaldehyde 297 297 245 245 209 209 

Acrolein 25 25 18 18 12 12 

Benzene 2,152 1,890 1,760 1,557 1,539 1,359 

Formaldehyde 741 741 599 599 498 498 

LDGT1 

1,3-Butadiene 2,307 2,312 2,291 2,297 2,447 2,453 

Acetaldehyde 2,714 2,721 2,682 2,690 2,899 2,907 

Acrolein 306 306 302 302 326 326 

Benzene 23,835 21,060 23,346 20,700 24,856 22,116 

Formaldehyde 5,572 5,591 5,516 5,534 5,975 5,994 

LDGT2 

1,3-Butadiene 1,524 1,528 1,503 1,506 1,486 1,489 

Acetaldehyde 1,789 1,793 1,726 1,730 1,710 1,714 

Acrolein 198 198 191 191 188 188 

Benzene 15,694 13,940 14,897 13,329 14,505 13,060 

Formaldehyde 3,628 3,639 3,513 3,524 3,509 3,519 

LDGV 

1,3-Butadiene 1,895 1,899 1,500 1,503 1,614 1,618 

Acetaldehyde 2,123 2,130 1,690 1,695 1,831 1,837 

Acrolein 251 251 199 199 215 215 

Benzene 19,835 17,424 15,643 13,794 16,895 14,914 

Formaldehyde 4,628 4,643 3,705 3,717 4,028 4,041 

MC 

1,3-Butadiene 266 266 288 288 350 350 

Acetaldehyde 233 233 253 253 309 309 

Acrolein 22 22 24 24 29 29 

Benzene 892 770 967 835 1,177 1,017 

Formaldehyde 693 693 751 751 912 912 

HDGV: Heavy Duty Gasoline Vehicles 

LDGT1: Light Duty Gasoline Trucks 1 

LDGT2: Light Duty Gasoline Trucks 2 

LDGV: Light Duty Gasoline Vehicles 

MC: Motorcycles 

10.3 Nonroad gasoline inventory 

The approach used to compute controlled inventories for 2015, 2020 and 2030 for all nonroad 

gasoline source categories (excluding planes, trains and ships) was to use projection factors 

based on NMIM results for the controlled case and reference case, and apply them to the 

reference inventories. 

Exhaust and evaporative projection factors for each year, 2015, 2020, and 2030 were obtained 

from the NMIM light duty gasoline vehicle reference and control case inventories. We assumed 

that changes in county level exhaust emissions of nonroad gasoline equipment were proportional 

to changes in highway light duty gasoline vehicle exhaust emissions, and changes in county level 

evaporative emissions of nonroad gasoline equipment were proportional to changes in highway 

light duty gasoline vehicle evaporative (refueling and non-refueling) emissions: 

108 
ELDGVexhaust NMIM Control20XX PF nonroad exhaust20XX = (25) ELDGVExhaust NMIM Reference20XX 

ELDGVevap NMIMControl20XX PF nonroad evap20XX = (26) ELDGVEvap NMIMReference20XX 

The steps taken to compute the projection factors, along with example calculations, are shown in 

Appendix G. 

Once the projection factors were developed, the reference case nonroad emissions were projected 

using the factors. For benzene, the reference MSAT emissions were broken out by exhaust and 

evaporative emissions in NMIM, with each type being multiplied by the appropriate projection 

factor, exhaust or evaporative. For the other HAPs, the exhaust projection factor was applied to 

the reference MSAT emissions, with no exhaust or evaporative breakout of the emissions 

because those HAPs did not have an evaporative component. 

Appendix G describes the steps taken to develop of the controlled nonroad gasoline emissions. 

Example calculations are also provided. 

Table 48 summarizes the 2-stroke and 4-stroke emissions for the reference and controlled case 

inventories for 2015, 2020, and 2030. Complete nonroad summaries, including emissions not 

affected by the controls for the five HAPs are in nonroad_controls.xls and 

nonroad_pivot_controls.xls in the MSAT rule docket EPA-HQ-OAR-2005-0036. 

Table 48. 2015, 2020, and 2030 reference and controlled emissions for the five HAPS for 

nonroad gasoline categories. 

Engine HAP 2015 emissions 2020 emissions 2030 emissions 

Reference Controlled Reference Controlled Reference Controlled 

1,3-Butadiene 1,847 1,852 1,604 1,608 1,595 1,599 

Acetaldehyde 1,467 1,471 1,293 1,297 1,296 1,300 

2-stroke Acrolein 344 344 292 292 291 291 

Benzene 18,582 16,295 16,287 14,198 16,457 14,311 

Formaldehyde 3,928 3,942 3,415 3,427 3,414 3,426 

5 HAP total 26,168 23,904 22,890 20,821 23,054 20,927 

1,3-Butadiene 3,224 3,231 3,379 3,386 3,805 3,814 

Acetaldehyde 2,196 2,201 2,265 2,271 2,511 2,517 

4-stroke Acrolein 289 289 300 300 334 334 

Benzene 19,165 16,852 20,153 17,820 22,705 20,089 

Formaldehyde 5,495 5,510 5,688 5,704 6,326 6,344 

5 HAP total 30,368 28,183 31,784 29,480 35,682 33,098 

10.4 EMS-HAP Processing 

EMS-HAP processing for stationary sources followed that as described in Section 5.1 for point 

sources and 5.2 for non-point sources. For onroad emissions, processing followed that as 

109 
described in Section 5.3, while the nonroad processing followed that as described in Section 

5.4.2 for airport support equipment and Section 5.4.3 for remaining nonroad emissions. Aircraft 

emissions were unaffected, and EMS-HAP was not rerun. Note that the point, non-point, 

onroad, airport support equipment, and remaining nonroad emissions (no aircraft) contained only 

the five HAPS being emphasized. The aircraft emissions files input into ASPEN contained other 

HAPs. 

10.5 ASPEN Processing and Post-Processing 

ASPEN processing followed that as described in Section 6.1. For the stationary sources, only 

benzene was modeled for the controlled case, as that was the only HAP affected by the benzene 

control scenario as described in Section 10.1. For the mobile sources, 1,3-butadiene, benzene, 

acetaldehyde, acrolein, and formaldehyde were modeled. 

ASPEN post-processing followed that as described in Section 6.3, using the same “run1” and 

“run2” file organization as described in that section. For the creation of the HAPEM input files, 

only the run1 file was affected since it contains the stationary, onroad gasoline, and nonroad 

gasoline concentrations. The run2 files contain the onroad diesel and non-gasoline nonroad 

concentrations and zeros for major, area & other, and background. The non-gasoline mobile 

concentrations were unaffected by the controls on the emissions; thus, the run2 files did not need 

to be rerun through HAPEM. 

Table 49 presents the national average 1999 and projected reference and controlled stationary 

source concentrations for benzene. 

Table 49. National average 1999 and future year reference and controlled benzene stationary 

concentrations. 

Year Concentration Benzene Concentrations (μg m-3) 

Type Major Area & other 

1999 Base 2.24x10-2 1.63x10-1 

2015 Reference 1.60x10-2 1.88x10-1 

Controlled 1.58x10-2 1.81x10-1 

2020 Reference 1.75x10-2 1.95x10-1 

Controlled 1.73x10-2 1.88x10-1 

Tables 50 and 51 present the national average reference and controlled concentrations for onroad 

gasoline and nonroad gasoline, respectively. 

110 
Table 50. National average reference and controlled onroad gasoline concentrations for the five 

HAPs for 2015, 2020, and 2030. 

HAP 

Onroad Gasoline Concentrations (μg m-3) 

2015 2020 2030 

Reference Controlled Reference Controlled Reference Controlled 

1,3-Butadiene 1.33x10-2 1.33x10-2 1.22x10-2 1.22x10-2 1.31x10-2 1.31x10-2 

Acetaldehyde 2.38x10-1 2.38x10-1 2.05x10-1 2.05x10-1 2.13x10-1 2.13x10-1 

Acrolein 1.44x10-2 1.44x10-2 1.31x10-2 1.31x10-2 1.41x10-2 1.41x10-2 

Benzene 2.21x10-1 2.00x10-1 1.98x10-1 1.80x10-1 2.09x10-1 1.91x10-1 

Formaldehyde 1.12x10-1 1.12x10-1 1.01x10-1 1.01x10-1 1.08x10-1 1.08x10-1 

Table 51. National average reference and controlled nonroad gasoline concentrations for the 

five HAPs for 2015, 2020, and 2030. 

HAP 

Nonroad Gasoline Concentrations (μg m-3) 

2015 2020 2030 

Reference Controlled Reference Controlled Reference Controlled 

1,3-Butadiene 7.44x10-3 7.45x10-3 7.87x10-3 7.88x10-3 9.01x10-3 9.02x10-3 

Acetaldehyde 4.89x10-2 4.89x10-2 4.97x10-2 4.97x10-2 5.53x10-2 5.54x10-2 

Acrolein 5.37x10-3 5.37x10-3 5.53x10-3 5.53x10-3 6.18x10-3 6.19x10-3 

Benzene 7.40x10-2 6.76x10-2 7.68x10-2 7.00x10-2 8.67x10-2 7.91x10-2 

Formaldehyde 5.31x10-2 5.31x10-2 5.55x10-2 5.56x10-2 6.29x10-2 6.30x10-2 

More detailed summaries can be found in the MSAT rule docket EPA-HQ-OAR-2005-0036 in 

the excel file named aspen_conc_control.xls. County median concentration maps are also in the 

docket; the file name is: ASPEN_median_cntrl.ppt. 

10.6 HAPEM Processing and Post-Processing 

HAPEM runs were made for 2015, 2020, and 2030 for the five HAPs modeled in the control 

case. Only the run1 file was needed, since it contained the stationary and the mobile gasoline 

ASPEN concentrations and background concentrations. Run2 files contained zeros for stationary 

and background and the non-gasoline nonroad concentrations and onroad diesel concentrations. 

After the HAPEM runs, summaries were calculated for the five HAPs. Due to the adjustment to 

exposure concentrations in HAPEM (documented in Section 7) by the median total concentration 

at each tract, the stationary source concentrations of 1,3-butadiene, acetaldehyde, acrolein, and 

formaldehyde stationary concentrations were different from the reference case, even though the 

stationary input concentrations into HAPEM were unchanged. This is because they were 

contained in the run1 file along with the onroad and nonroad gasoline concentrations, which did 

change. Since the control case does not impact the stationary source contribution for these 

HAPs, we replaced the HAPEM control case concentrations for these four HAPs with the 

reference case concentrations. This was not done for benzene, since stationary concentrations 

were expected to change resulting from the changes to the stationary benzene gasoline inventory 

described in Section 10.1. 

111 
Table 52 presents the national average 1999 and projected reference and controlled stationary 

source concentrations for benzene. 

Table 52. National average 1999 and future reference and controlled benzene HAPEM 

stationary concentrations. 

Year Concentration 

Type 

Benzene Concentrations (μg m-3) 

Major Area & other 

1999 Base 1.88x10-2 1.42x10-1 

2015 Reference 1.35x10-2 1.64x10-1 

Controlled 1.34x10-2 1.58x10-1 

2020 Reference 1.48x10-2 1.71x10-1 

Controlled 1.47x10-2 1.65x10-1 

Tables 53 and 54 present the national average reference and controlled concentrations for the 

five modeled HAPs for onroad gasoline and nonroad gasoline, respectively. 

More detailed summaries can be found in the MSAT rule docket EPA-HQ-OAR-2005-0036. in 

the excel file named hapem_concentrations_cntrl.xls. County median concentration maps are 

also in the docket; the file name is: HAPEM_median_cntrl.ppt. 

Table 53. National average reference and controlled HAPEM onroad gasoline concentrations 

for the five HAPs for 2015, 2020, and 2030. 

HAP 

Onroad Gasoline Concentrations (μg m-3) 

2015 2020 2030 

Reference Controlled Reference Controlled Reference Controlled 

1,3-Butadiene 1.68x10-2 1.68x10-2 1.54x10-2 1.54x10-2 1.65x10-2 1.65x10-2 

Acetaldehyde 2.73x10-1 2.73x10-1 2.35x10-1 2.35x10-1 2.44x10-1 2.44x10-1 

Acrolein 1.65x10-2 1.66x10-2 1.51x10-2 1.51x10-2 1.61x10-2 1.62x10-2 

Benzene 2.66x10-1 2.41x10-1 2.38x10-1 2.17x10-1 2.51x10-1 2.30x10-1 

Formaldehyde 1.40x10-1 1.40x10-1 1.26x10-1 1.27x10-1 1.35x10-1 1.35x10-1 

Table 54. National average reference and controlled HAPEM nonroad gasoline concentrations 

for the five HAPs for 2015, 2020, and 2030. 

HAP 

Nonroad Gasoline Concentrations (μg m-3) 

2015 2020 2030 

Reference Controlled Reference Controlled Reference Controlled 

1,3-Butadiene 7.28x10-3 7.29x10-3 7.73x10-3 7.75x10-3 8.88x10-3 8.89x10-3 

Acetaldehyde 4.26x10-2 4.26x10-2 4.35x10-2 4.36x10-2 4.86x10-2 4.86x10-2 

Acrolein 4.67x10-3 4.68x10-3 4.82x10-3 4.82x10-3 5.40x10-3 5.40x10-3 

Benzene 6.86x10-2 6.28x10-2 7.15x10-2 6.54x10-2 8.11x10-2 7.42x10-2 

Formaldehyde 4.73x10-2 4.74x10-2 4.96x10-2 4.96x10-2 5.63x10-2 5.63x10-2 

10.7 Cancer and Non-cancer Calculations 

The cancer and non-cancer risk calculations followed the same general methodology as 

discussed in Section 8 with some minor differences in that HAP specific calculations were made 

112 
for only the five HAPs being modeled. When calculating total risk or organ specific non-cancer 

estimates, MSAT results for the other MSAT HAPs were used. Following are the results of the 

calculations. 

10.7.1 Cancer 

Cancer risk estimates for 1,3-butadiene, acetaldehyde, benzene, and formaldehyde were 

calculated based on the controlled HAPEM results. Total risks (across all MSAT HAPs) and 

risks by carcinogen classes were also recalculated using the newly calculated risks for the four 

above HAPs and the other carcinogenic MSAT HAPs reference case MSAT risks. 

Table 55 lists the stationary risks for benzene, MSAT HAPs in carcinogen class A (benzene’s 

carcinogen class), and total risk from MSAT HAPs. Table 56 lists the onroad gasoline risks for 

1,3-butadiene, acetaldehyde, benzene, formaldehyde and MSAT HAPs in carcinogenic classes 

A, B, B2 and total MSAT HAP risk. Table 57 lists the nonroad gasoline risks for the same 

HAPs, carcinogen classes, and total risk. 

More detailed summaries can be found in the MSAT rule docket EPA-HQ-OAR-2005-0036 in 

the excel file named hapem_risks_control.xls. County median risk maps are also in the docket; 

the file name is: risk_cntrll.ppt. 

Table 55. National average risks from stationary sources for 1999 and future year reference and 

controlled benzene, carcinogen class A, and total (all MSAT HAPs). 

Year 

Risk Type Stationary Risks 

Benzene Carcinogen Class A Total Risk (All HAPs) 

Major 

Area & 

other Major 

Area & 

other Major 

Area & 

other 

1999 Base 1.47E-07 1.11E-06 7.71E-07 2.70E-06 1.14E-06 5.19E-06 

2015 Reference 1.05E-07 1.28E-06 8.85E-07 3.25E-06 1.20E-06 6.19E-06 

Controlled 1.04E-07 1.23E-06 8.85E-07 3.20E-06 1.20E-06 6.15E-06 

2020 Reference 1.15E-07 1.33E-06 9.98E-07 3.49E-06 1.34E-06 6.57E-06 

Controlled 1.14E-07 1.28E-06 9.97E-07 3.44E-06 1.34E-06 6.53E-06 

Table 56. Reference and controlled HAPEM onroad gasoline risks for 2015, 2020, and 2030 for 

individual HAPs and carcinogen classes A, B1, and B2 and total risk (all MSAT HAPs, 

including HAPs not controlled). 

HAP 

Onroad Gasoline Risks 

2015 2020 2030 

Reference Controlled Reference Controlled Reference Controlled 

1,3-Butadiene 5.04x10-7 5.05x10-7 4.62x10-7 4.63x10-7 4.94x10-7 4.94x10-7 

Acetaldehyde 6.00x10-7 6.00x10-7 5.18x10-7 5.18x10-7 5.38x10-7 5.38x10-7 

Benzene 2.07x10-6 1.88x10-6 1.86x10-6 1.69x10-6 1.96x10-6 1.79x10-6 

Formaldehyde 7.71x10-10 7.72x10-10 6.96x10-10 6.96x10-10 7.42x10-10 7.43x10-10 

Class A MSAT 2.84x10-6 2.64x10-6 2.61x10-6 2.44x10-6 2.81x10-6 2.64x10-6 

Class B1 MSAT 7.71x10-10 7.72x10-10 6.96x10-10 6.96x10-10 7.42x10-10 7.43x10-10 

Class B2 MSAT 6.38x10-7 6.38x10-7 5.57x10-7 5.58x10-7 5.84x10-7 5.85x10-7 

Total MSAT Risk 3.79x10-6 3.60x10-6 3.48x10-6 3.32x10-6 3.76x10-6 3.60x10-6 

113 
Table 57. Reference and controlled HAPEM nonroad gasoline risks for 2015, 2020, and 2030 

for individual HAPs and carcinogen classes A, B1, and B2 and total risk (all MSAT HAPs, 

including HAPs not controlled). 

HAP 

Nonroad Gasoline Risks 

2015 2020 2030 

Reference Controlled Reference Controlled Reference Controlled 

1,3-Butadiene 2.18x10-7 2.19x10-7 2.32x10-7 2.32x10-7 2.66x10-7 2.67x10-7 

Acetaldehyde 9.38x10-8 9.38x10-8 9.58x10-8 9.58x10-8 1.07x10-7 1.07x10-7 

Benzene 5.35x10-7 4.90x10-7 5.58x10-7 5.10x10-7 6.32x10-7 5.78x10-7 

Formaldehyde 2.60x10-10 2.60x10-10 2.73x10-10 2.73x10-10 3.10x10-10 3.10x10-10 

Class A MSAT 7.63x10-7 7.18x10-7 8.00x10-7 7.52x10-7 9.10x10-7 8.56x10-7 

Class B1 MSAT 2.60x10-10 2.60x10-10 2.73x10-10 2.73x10-10 3.10x10-10 3.10x10-10 

Class B2 MSAT 1.11x10-7 1.11x10-7 1.14x10-7 1.15x10-7 1.28x10-7 1.28x10-7 

Total MSAT Risk 9.23x10-7 8.78x10-7 9.67x10-7 9.19x10-7 1.10x10-6 1.04x10-6 

10.7.2 Non-cancer 

Non-cancer hazard quotient estimates for 1,3-butadiene, acetaldehyde, acrolein, benzene, and 

formaldehyde were calculated based on the controlled HAPEM results. Hazard indices by organ 

system (across all MSAT HAPs) were also recalculated using the newly calculated risks for the 

five above HAPs and the other non-cancer MSAT HAPs reference case risks. 

Table 58 lists the stationary HQ for benzene and stationary HI for the immune system. Table 59 

lists the onroad gasoline HQ for 1,3-butadiene, acetaldehyde, benzene, formaldehyde and HI for 

immune, reproductive and respiratory systems. Table 60 lists the nonroad gasoline HQ for 1,3­

butadiene, acetaldehyde, benzene, formaldehyde and HI for immune, reproductive and 

respiratory systems. 

More detailed summaries can be found in the MSAT rule docket EPA-HQ-OAR-2005-0036 in 

the excel file named: hapem_hq_control.xls. County median HQ or HI maps are also in the 

docket; the file name is: hq_cntrll.ppt. 

Table 58. 1999 and future year reference and controlled stationary benzene hazard quotients and 

immune system hazard indices for MSAT HAPs for 2015 and 2020. 

Stationary 

Year 

Non-cancer 

estimate type 

Benzene Immune System 

Major 

Area & 

other Major Area & other 

1999 Base 6.27x10-4 4.72x10-3 5.42x10-3 1.39x10-2 

2015 Reference 4.49x10-4 5.47x10-3 5.95x10-3 1.57x10-2 

Controlled 4.46x10-4 5.27x10-3 5.95x10-3 1.55x10-2 

2020 Reference 4.93x10-4 5.70x10-3 6.48x10-3 1.70x10-2 

Controlled 4.89x10-4 5.49x10-3 6.47x10-3 1.68x10-2 

114 
Table 59. Reference and controlled HAPEM onroad gasoline HQ for controlled HAPs and HI 

for immune, reproductive, and respiratory systems (including MSAT HAPs not controlled) for 

2015, 2020, and 2030. 

HAP 

Onroad Gasoline 

2015 2020 2030 

Reference Controlled Reference Controlled Reference Controlled 

1,3-Butadiene 8.40x10-3 8.42x10-3 7.70x10-3 7.71x10-3 8.23x10-3 8.24x10-3 

Acetaldehyde 3.03x10-2 3.03x10-2 2.61x10-2 2.62x10-2 2.72x10-2 2.72x10-2 

Acrolein 8.27x10-1 8.27x10-1 7.54x10-1 7.54x10-1 8.07x10-1 8.08x10-1 

Benzene 8.87x10-3 8.05x10-3 7.94x10-3 7.24x10-3 8.37x10-3 7.65x10-3 

Formaldehyde 1.43x10-2 1.43x10-2 1.29x10-2 1.29x10-2 1.38x10-2 1.38x10-2 

Immune System 1.01x10-2 9.29x10-3 9.34x10-3 8.63x10-3 1.01x10-2 9.40x10-3 

Reproductive 

System 8.40x10-3 8.42x10-3 7.70x10-3 7.71x10-3 8.23x10-3 8.24x10-3 

Respiratory System 8.78x10-1 8.79x10-1 8.00x10-1 8.00x10-1 8.56x10-1 8.57x10-1 

Table 60. Reference and controlled HAPEM nonroad gasoline HQ for controlled MSAT HAPs 

and HI for immune, reproductive, and respiratory systems (from MSAT HAPs including those 

HAPs not controlled) for 2015, 2020, and 2030. 

HAP 

Nonroad Gasoline 

2015 2020 2030 

Reference Controlled Reference Controlled Reference Controlled 

1,3-Butadiene 3.64x10-3 3.64x10-3 3.87x10-3 3.87x10-3 4.44x10-3 4.44x10-3 

Acetaldehyde 4.74x10-3 4.74x10-3 4.84x10-3 4.84x10-3 5.40x10-3 5.40x10-3 

Acrolein 2.34x10-1 2.34x10-1 2.41x10-1 2.41x10-1 2.70x10-1 2.70x10-1 

Benzene 2.29x10-3 2.09x10-3 2.38x10-3 2.18x10-3 2.70x10-3 2.47x10-3 

Formaldehyde 4.83x10-3 4.83x10-3 5.06x10-3 5.06x10-3 5.74x10-3 5.74x10-3 

Immune System 2.34x10-3 2.15x10-3 2.44x10-3 2.24x10-3 2.77x10-3 2.54x10-3 

Reproductive 

System 3.64x10-3 3.64x10-3 3.87x10-3 3.87x10-3 4.44x10-3 4.44x10-3 

Respiratory 

System 2.44x10-1 2.44x10-1 2.52x10-1 2.52x10-1 2.82x10-1 2.82x10-1 

10.7.3 Population statistics 

Population statistics were calculated for total risk across MSAT HAPs and respiratory HI across 

MSAT HAPs as documented in Section 8.3. Table 61 lists the total risk populations for 1999, 

and for the future years 2015, 2020, and 2030 for reference and control cases. Differences 

between reference and control case are also shown. Table 62 lists the respiratory HI populations 

for 1999, and for the future years 2015, 2020, and 2030 for reference and control cases as well as 

the differences. Major and area & other statistics are not shown for the HI statistics because 

benzene is the only stationary source HAP impacted by the controls and is not a respiratory 

HAP. Full summaries can be found in pop_stats_risk_cntrl.xls and pop_stats_hi_resp_cntrl.xls 

for cancer and non-cancer respectively in the MSAT rule docket EPA-HQ-OAR-2005-0036. 

115 
Table 61. Population risk classes for stationary and mobile total risk for 2015, 2020, and 2030 for reference and 

MSAT HAPs using projected populations for each year. The total category includes background contributions. 

Source 

Populations 

Year 

Category Population Class 1999 2015 2020 

Base Reference Controlled Reference Controlled Refe

Risk ≥ 10-4 168,437 192,100 192,100 254,904 254,904 210-5 ≤ Risk <10-4 3,537,717 4,244,119 4,244,119 5,215,260 5,215,260 5

Major 10-6 ≤ Risk <10-5 52,027,786 52,174,340 52,019,015 57,544,083 57,423,740 61

Risk <10-6 223,849,492 260,294,114 260,449,439 266,678,495 266,798,839 288

Total Population 279,583,432 316,904,672 316,904,672 329,692,742 329,692,742 355,

Risk ≥ 10-4 433,665 636,991 636,991 739,981 739,981 7

Area & 

Other 

10-5 ≤ Risk <10-4 28,874,198 39,345,554 39,086,924 4,417,331 43,749,847 4610-6 ≤ Risk <10-5 210,220,920 245,736,898 245,293,550 254,558,606 254,305,389 276Risk <10-6 40,054,649 31,185,230 31,887,208 30,276,824 30,987,525 32

Total Population 279,583,432 316,904,672 316,904,672 329,692,742 329,692,742 355,

Risk ≥ 10-4 208,150 0 0 0 0 

Onroad 

10-5 ≤ Risk <10-4 112,848,474 19,596,469 17,860,243 16,703,891 15,240,789 2110-6 ≤ Risk <10-5 145,060,999 241,185,986 238,194,479 249,373,492 245,938,812 269Risk <10-6 21,465,809 56,122,217 60,849,950 63,615,359 68,513,141 64

Total Population 279,583,432 316,904,672 316,904,672 329,692,742 329,692,742 355,

Nonroad 

Risk ≥ 10-4 22,272 23,710 23,710 25,123 25,123 10-5 ≤ Risk <10-4 2,630,188 1,365,537 1,335,534 1,584,116 1,548,720 210-6 ≤ Risk <10-5 180,439,149 150,013,784 143,285,777 159,142,708 151,982,761 18,5Risk <10-6 96,491,823 165,501,640 172,259,651 168,940,795 176,136,139 168

Total Population 279,583,432 316,904,672 316,904,672 329,692,742 329,692,742 355,

Total 

Risk ≥ 10-4 2,035,482 1,303,148 1,253,210 1,547,121 1,489,062 110-5 ≤ Risk <10-4 211,743,744 210,880,893 207,704,745 219,257,053 216,237,537 23910-6 ≤ Risk <10-5 64,760,978 104,720,624 107,946,710 108,888,561 111,966,135 114Risk <10-6 10,243,228 7 7 7 7 

Total Population 279,583,432 316,904,672 316,904,672 329,692,742 329,692,742 355,

116 
Table 62. Population HI classes for mobile and total respiratory HI for 2015, 2020, and 2030 for reference and c

projected populations for each year. The total category includes background contributions. 

Source 

Populations 

Year 

Category Population Class 1999 2015 2020 

Base Reference Controlled Reference Controlled Refe

HI ≥ 10 17,567,623 37,998 0 16,297 0 1 ≤ HI < 10 200,171,904 114,954,321 17,860,243 105,041,326 15,240,789 121

Onroad 0.1 ≤ HI <1 58,288,431 184,163,448 238,194,479 204,853,751 245,938,812 214

HI < 0.1 3,555,474 17,748,905 60,849,950 19,781,369 68,513,141 19

Total Population 279,583,432 316,904,672 316,904,672 329,692,742 329,692,742 355,

Nonroad 

HI ≥ 10 1,116,086 1,280,756 23,710 1,449,660 25,123 11 ≤ HI < 10 85,670,356 65,585,394 1,335,534 70,546,734 1,548,720 850.1 ≤ HI <1 161,073,537 189,650,787 143,285,777 192,548,911 151,982,761 202HI < 0.1 31,723,453 60,387,735 172,259,651 65,147,437 176,136,139 65

Total Population 279,583,432 316,904,672 316,904,672 329,692,742 329,692,742 355,

Total 

HI ≥ 10 44,450,141 19,252,802 19,267,037 19,134,918 19,134,918 211 ≤ HI < 10 208,040,954 223,718,621 223,762,189 230,402,091 230,458,518 2510.1 ≤ HI <1 26,972,148 73,579,914 73,522,111 79,779,745 79,723,317 82HI < 0.1 120,189 353,336 353,336 375,989 375,989 

Total Population 279,583,432 316,904,672 316,904,672 329,692,742 329,692,742 355,

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Appendix A: Documentation of NMIM Runs Used to Develop Inventories for MSAT Rule 

Air Quality Modeling 

Harvey Michaels, Rich Cook and David Brzezinski, 

Office of Transportation and Air Quality 

A-1
Mobile source hazardous air pollutants (HAP) inventories at a county-level resolution were 

produced for the Mobile Sources Air Toxics (MSAT) rule analysis by running the National 

Mobile Inventory Model (NMIM) for all 50 States and the District of Columbia. Simulations 

were made for calendar years 1999, 2007, 2010, 2015, 2020 and 2030. The same temperature 

and humidity inputs were used for all calendar years; the determination of these values is 

explained in more detail below. Although monthly inputs were used in the model, results were 

provided (summed up) to an annual temporal resolution for all calendar years. Resulting 

inventories were used to develop future year to 1999 inventory ratios, which were then applied to 

the final 1999 National Emissions Inventory (NEI). Thus, the emission trends for projection 

years were consistent with the 1999 NEI, the inventory used in the 1999 NATA assessment. The 

onroad sources were run separately, with and without refueling emissions for all inventory years. 

This allowed the refueling runs to be used to develop future year to 1999 inventory ratios that 

could be applied for refueling emissions which are contained in the 1999 NEI as non-point 

sources. 

 Modeling air toxics requires specific fuel parameter inputs, as discussed in NMIM 

documentation. The sources of the fuel parameter inputs used in this modeling, and the methods 

used to develop the fuel parameter database are described in the technical document, “Draft 

National Mobile Inventory Model (NMIM) Base and Future Year County Database 

Documentation and Quality Assurance Procedures,” prepared by Eastern Research Group for U. 

S. EPA, Office of Transportation and Air Quality, 15 April, 2003. However, the gasoline 

parameters as delivered from the contractor do not address the phase out of gasoline containing 

MTBE in California, Arizona, New York and Connecticut properly. The difference is important 

when calculating HAPs. Thus, a new set of gasoline parameters was derived for areas in these 

States using reformulated gasoline with MTBE, for summer and winter in 2004, 2005 and 2006. 

An interpolated set of parameters was derived from the summer/winter values for use in 

spring/fall months. The 2006 gasoline parameters were used for all 2007 and later calendar years 

as well. More information on these revisions can be found in the change log for the NMIM 

database. Both the technical document and change log described above can be found in the 

docket for the rulemaking. 

Three versions of the NMIM code and two versions of the NMIM County database were used to 

generate onroad and nonroad inventories. The three code versions represent fixes to minor 

problems in the computer code and added features that do not affect the output results. The three 

NMIM code versions are equivalent in terms of the emission results they produce. 

Two versions of the NMIM County database were used. The May 14, 2004, database is identical 

to the April 12, 2004, version of the database, but includes the 1999 National Emission Inventory 

(NEI) list of counties with Stage 2 refueling programs for estimating the effects of local control 

programs on refueling emissions. Stage 2 refueling programs only affect onroad inventories for 

refueling emissions. The Stage 2 used counties are listed in Appendix E-2 County Level 

Allocation Values Used for Allocation Schemes 18, 22 and 27 (Stage 2 Control) in the report, 

"Documentation for the Final 1999 Nonpoint Area Source National Emission Inventory for 

A-2
Hazardous Air Pollutants (Version 3)," August 26, 2003. This document is available on the EPA 

web site at: http://www.epa.gov/ttn/chief/net/1999inventory.html 

Multiple runs were necessary for the refueling runs due to computer problems during the runs 

that resulted in incomplete results. The additional runs (labeled "a" and "b" in the table below) 

were needed to fill in for the missing data in the original runs. The entire nationwide run was not 

redone to save running time. 

All onroad results are based on runs of the MOBILE6.2.03 version of MOBILE6. All nonroad 

results are based on runs of the NR2003a version of the NONROAD model. 

Output databases have been named to be the same as the run identification, except for 

recreational marine, which goes in the same database as the other nonroad output. All run results 

aggregate by emission type and power class. 

Every run has associated with it a RunSpec and a batch file with the run name and extensions 

“nrs” (for NMIM RunSpec) and “bat,” respectively. RunSpecs and batch files have been 

archived along with copies of the output results from the simulations. 

Table A-1 describes the specific version of the NMIM code and the version of the input 

parameter database used for the analysis. The codes following the table are useful in 

understanding the names used in the table. 

A-3
Table A-1. Run summary for MSAT mobile source inventories. 

Run ID Output Database Description 

MSATOH1999c24d13 MSATOH1999c24d13 Onroad HAPS 1999 

MSATOH2007c24d13 MSATOH2007c24d13 Onroad HAPS 2007 

OH2010c22d11 OH2010c22d11 Onroad HAPS 2010 

MSATOH2015c24d13 MSATOH2015c24d13 Onroad HAPS 2015 

MSATOH2020c24d13 MSATOH2020c24d13 Onroad HAPS 2020 

MSATOH2030c24d13 MSATOH2030c24d13 Onroad HAPS 2030 

NH1999c22d11 NH1999c22d11 Nonroad HAPS 1999 

NH2007c22d11 NH2007c22d11 Nonroad HAPS 2007 

NH2010c22d11 NH2010c22d11 Nonroad HAPS 2010 

NH2015c22d11 NH2015c22d11 Nonroad HAPS 2015 

NH2020c22d11 NH2020c22d11 Nonroad HAPS 2020 

NH2030c22d11 NH2030c22d11 Nonroad HAPS 2030 

RH1999c22d11 NH1999c22d11 Recreational Marine HAPS 1999 

RH2007c22d11 NH2007c22d11 Recreational Marine HAPS 2007 

RH2010c22d11 NH2010c22d11 Recreational Marine HAPS 2010 

RH2015c22d11 NH2015c22d11 Recreational Marine HAPS 2015 

RH2020c22d11 NH2020c22d11 Recreational Marine HAPS 2020 

RH2030c22d11 NH2030c22d11 Recreational Marine HAPS 2030 

MSATOR1999c24rd13 MSATOR1999c24rd13 Onroad Refueling 1999 

MSATOR1999c24rd13a MSATOR1999c24rd13 Onroad Refueling 1999 

MSATOR2001c24rd13 MSATOR2001c24rd13 Onroad Refueling 2001 

MSATOR2001c24rd13a MSATOR2001c24rd13 Onroad Refueling 2001 

MSATOR2007c24rd13 MSATOR2007c24rd13 Onroad Refueling 2007 

MSATOR2010c24rd13 MSATOR2010c24rd13 Onroad Refueling 2010 

MSATOR2015c24rd13 MSATOR2015c24rd13 Onroad Refueling 2015 

MSATOR2015c24rd13a MSATOR2015c24rd13 Onroad Refueling 2015 

MSATOR2015c24rd13b MSATOR2015c24rd13 Onroad Refueling 2015 

MSATOR2020c24rd13 MSATOR2020c24rd13 Onroad Refueling 2020 

MSATOR2030c24rd13 MSATOR2030c24rd13 Onroad Refueling 2030 

NMIM Code Version: 

c22 = NMIMSource20040415 

c24 = NMIMSource20040512 

c24r = NMIMSource20040512 altered for refueling emissions only output. 

NMIM County Database Version: 

d11 = County20040412 

d13 = County20040514 

Run codes: 

NH = Nonroad except diesel recreational marine HAPS 

RH = Diesel recreational marine HAPS 

OH = Onroad HAPS 

OR = Onroad Refueling 

A-4
Methodology Used to Compute By-County, By-Month, By-Hour Temperature and Relative 

Humidity Tables 

Both onroad and nonroad emission inventories are affected by changes in temperature. In 

addition, onroad estimates for NOx from gasoline fueled vehicles are affected by humidity. A 

detailed analysis of climate data was done to produce an estimate for the average hourly 

temperatures and humidity (over an approximately 20 year period) to use for each county in the 

nation. The results of this analysis are found in the NMIM County database. Below is a brief 

discussion of how the county specific average hourly temperatures were determined for each 

month. 

1) Hourly temperature and dew point data, as well as location (latitude and longitude), for all 1st 

Order weather stations across the United States were obtained from the National Climatic 

Data Center (NCDC). (Note: Automated weather stations began being installed in 1996. 

Data from these 2nd order stations were used for the more recent, shorter analysis periods.) 

2) For each station, an inventory was made as to the number of hours with joint temperature and 

dew point data. In order to be included in the 1981-2000 analysis, each station had to have at 

least 50% data recovery for each hour of each month, and at least 75% data recovery over the 

entire 20 years. (This cutoff was raised to 75% for the 5-year analysis, and to 90% for oneyear analyses). (Note: Climatological averages are usually based on a 30-year period. The 

20-year period of 1981-2000 was selected due to hourly data availability constraints. Prior to 

1981, limited computer technology forced the NCDC to only store observations for every 3rd 

hour. Attempts to interpolate the 3-hour data showed biases and errors (for example dew 

point exceeding temperature.) 

3) For each station passing the data availability filter, the average temperature and dew point for 

each hour of each month over the 20-year period was computed. (Note: Relative humidity 

data should never be averaged. Since it depends on the associated temperature and dew point, 

relative humidity is not a conservative property of the atmosphere.) 

4) Population centroids (latitude and longitude) for each county were obtained from the 2000 

United States Census. Population, rather than geographic, centroids were used to provide the 

best estimate where the county’s VMT would occur. (Note: This selection proved 

particularly important for those counties near mountainous or desert areas.) 

5) From each county’s centroid, the distance and direction to each weather station was 

calculated. The shortest distance was computed using the standard great circle navigation 

method and the constant course direction was computed using the standard rhumb line 

method. 

6) Based on the computed directions, the stations were assigned to an octant, as follows: Octant 

1: 0°<Dir≤45°, Octant 2: 45°<Dir≤90°, Octant 3: 90°<Dir≤135°, Octant 4: 135°<Dir≤180°, 

Octant 5: 180°<Dir≤225°, Octant 6: 225°<Dir≤270°, Octant 7: 270°<Dir≤315°, Octant 8: 

315°<Dir≤360°. 

A-5
7) For each octant, the stations were sorted by distance. The station closest to the centroid for 

each octant was chosen for further processing. If the closest station was more than 200 miles 

away, that octant is ignored. (Such situations occurred near the oceans and the along the 

Canadian and Mexican borders.) 

8) To remove the effects of differing time zones between counties and stations, the temperature 

and dew point data from each octant station were synchronized to the same local hour. Thus, 

noon EST is matched up with noon CST, with noon MST with noon PST, etc. 

9) The octant (8 or less) temperature and dew point values were merged together using inverse 

distance-squared weighting. 

10) The corresponding relative humidity was then computed from the weighted temperature and 

dew point values. (Note: In keeping with standard meteorological practices, the relative 

humidity was always computed with respect to water, even if the temperature was below 

freezing.) 

11) The above process was repeated for each hour, for each month, and for each county centroid. 

As a final check, the results from different times and months were plotted on maps and 

contoured. 

A-6
Appendix B: Steps and Example calculations of onroad projections 

B.1 Onroad HAPs (Section 3.3.2) 

The following steps summarizes the SAS® program, onroad.sas (found in the MSAT rule docket 

EPA-HQ-OAR-2005-0036) used to project the onroad inventory with sample calculations for 

2015 for Autauga County, AL and Modoc County, CA. A detailed flow chart is shown in Figure 

B-1. 

1. Read the 1999 NEI onroad inventory and retained only the MSAT HAPs listed in Table 

1, Section 1. It was found that all HAPs in the onroad NEI were MSAT HAPs. Also, 

NMIM emissions were initially provided by FIPS/SCC/CAS where SCC categories were 

broken out by evaporative and exhaust components. For each FIPS/SCC/CAS, the 

exhaust and evaporative components were summed together to give one emission 

number. 

Table B-1. Partial listing of emissions after merger of 1999 NEI emissions after merger 

with MSAT CAS numbers. CAS 71432 is benzene, 1330207 is xylenes, 7440473 is 

chromium, and CAS 226 and 7440020 are nickel. 

FIPS SCC CAS emis 

01001 2201001130 226 0.00007 

01001 2201001130 71432 1.15235 

01001 2201001130 7440473 0.0001 

01001 2201080130 71432 0.009925 

01001 2230070130 226 5E-6 

01001 2230070130 71432 0.02079 

06049 2201001130 71432 1.075615 

06049 2201001130 7440020 0.000225 

06049 2201001130 7440473 0.00032 

06049 2201080130 71432 0.015485 

2. Read in NMIM emissions and summed heavy-duty diesel vehicle emissions to create a 

total HDDV emission number for each FIPS/CAS/ road type where road type is 

represented by the last 3 characters of SCC code and calculate new projection factors 

based on the summed emissions for 1999 NMIM and future year NMIM results. These 

factors were applied to SCC codes beginning with 2230070 for each FIPS/CAS in the 

1999 NEI in step 13. These SCC codes are shown in Table 16 (Section 3.3.2). Output 

dataset was named hddv_nmim. The temporary SCC code in the NMIM output was 

223007XYYY where YYY is road type. 

B-1
Table B-2. Partial listing of Autauga County emissions after creating total HDDV SCC 

from NMIM results. Emis99 are the 1999 NMIM emissions, emis15 are 2015 emissions, 

and ratio_15 is the ratio of 2015 emissions to the 1999 emissions. 

FIPS SCC CAS emis99 emis15 ratio_15 

01001 223007X130 71432 0.0009132375 0.000441322 0.4832499782 

01001 223007X130 7440020 7.8660057E-7 9.3792589E-7 1.1923788539 

01001 223007X130 71432 0.0006728924 0.0004738843 0.7042497321 

01001 223007X130 7440020 4.798758E-7 7.5895458E-7 1.5815645908 

01001 223007X130 71432 0.0029419617 0.0017220468 0.5853396615 

01001 223007X130 7440020 1.1361038E-6 1.7165236E-6 1.510886278 

01001 223007X130 71432 0.0155974124 0.0072218833 0.4630180423 

01001 223007X130 7440020 4.0112847E-6 5.4976188E-6 1.3705381898 

01001 223007X130 71432 0.0006618121 0.0004315654 0.6520966767 

01001 223007X130 7440020 1.7606117E-7 3.0090454E-7 1.7090908974 

Table B-3. Partial listing of Autuaga County HDDV emissions after summing by 

FIPS/SCC/CAS with recalculated ratio. 

FIPS SCC CAS emis99 emis15 ratio_15 

01001 223007X130 71432 0.020787316 0.0102907019 0.4950471688 

01001 223007X130 7440020 6.589926E-6 9.2119274E-6 1.3978802578 

3. Recombined step 2 output with the original NMIM dataset. 

Table B-4. Partial listing of emissions after concatenating HDDV emissions with 

original NMIM emissions. 

FIPS SCC CAS emis99 emis15 ratio 

01001 223007X130 71432 0.020787316 0.0102907019 0.4950471688 

01001 223007X130 7440020 6.589926E-6 9.2119274E-6 1.3978802578 

01001 2201001130 16065831 0.0000593097 0.0000494437 0.8336535061 

01001 2201001130 18540299 0.0000395398 0.0000329625 0.8336535004 

01001 2201001130 71432 1.1247759168 0.2370964659 0.2107944012 

01001 2201001130 7440020 0.0000718905 0.0000599318 0.8336535267 

01001 2201080130 16065831 5.9322425E-7 6.6164804E-7 1.1153422083 

01001 2201080130 18540299 3.9548283E-7 4.410987E-7 1.1153422209 

01001 2201080130 71432 0.00979517 0.0104629781 1.0681772804 

01001 2201080130 7440020 7.1905967E-7 8.0199761E-7 1.1153422226 

06049 2201001130 16065831 0.0000394923 0.0000369164 0.9347752652 

06049 2201001130 18540299 0.0000263282 0.0000246109 0.9347752577 

06049 2201001130 71432 0.506942138 0.136686063 0.2696285292 

06049 2201001130 7440020 0.0000478695 0.0000447472 0.9347752469 

B-2
4. The NMIM Chromium III and Chromium VI emissions were summed for each FIPS/SCC 

to give a total Chromium number. New projection factors were calculated for the 

summed chromium and the CAS 7440473 was assigned to each record. This was done 

for all FIPS/SCC combinations with Chromium III or Chromium VI. 

Table B-5. Partial listing of NMIM chromium emissions (Section 3.4.2, step 4). 

FIPS SCC CAS emis99 emis15 ratio 

01001 2201001130 16065831 0.0000593097 0.0000494437 0.8336535061 

01001 2201001130 18540299 0.0000395398 0.0000329625 0.8336535004 

01001 2201080130 16065831 5.9322425E-7 6.6164804E-7 1.1153422083 

01001 2201080130 18540299 3.9548283E-7 4.410987E-7 1.1153422209 

06049 2201001130 16065831 0.0000394923 0.0000369164 0.9347752652 

06049 2201001130 18540299 0.0000263282 0.0000246109 0.9347752577 

Table B-6. Partial listing of NMIM chromium after summing by FIPS/SCC, assigning a 

CAS, and calculating a ratio. 

FIPS SCC CAS emis99 emis15 ratio 

01001 2201001130 7440473 0.0000988494 0.0000824062 0.8336535038 

01001 2201080130 7440473 9.8870708E-7 1.1027467E-6 1.1153422134 

06049 2201001130 7440473 0.0000658205 0.0000615274 0.9347752622 

5. Combined the chromium data with the original NMIM data. 

Table B-7. Partial listing of NMIM emissions after concatenating with chromium 

emissions. 

FIPS SCC CAS emis99 emis15 ratio 

01001 223007X130 71432 0.020787316 0.0102907019 0.4950471688 

01001 223007X130 7440020 6.589926E-6 9.2119274E-6 1.3978802578 

01001 2201001130 16065831 0.0000593097 0.0000494437 0.8336535061 

01001 2201001130 18540299 0.0000395398 0.0000329625 0.8336535004 

01001 2201001130 71432 1.1247759168 0.2370964659 0.2107944012 

01001 2201001130 7440020 0.0000718905 0.0000599318 0.8336535267 

01001 2201080130 16065831 5.9322425E-7 6.6164804E-7 1.1153422083 

01001 2201080130 18540299 3.9548283E-7 4.410987E-7 1.1153422209 

01001 2201080130 71432 0.00979517 0.0104629781 1.0681772804 

01001 2201080130 7440020 7.1905967E-7 8.0199761E-7 1.1153422226 

06049 2201001130 16065831 0.0000394923 0.0000369164 0.9347752652 

06049 2201001130 18540299 0.0000263282 0.0000246109 0.9347752577 

06049 2201001130 71432 0.506942138 0.136686063 0.2696285292 

06049 2201001130 7440020 0.0000478695 0.0000447472 0.9347752469 

01001 2201001130 7440473 0.0000988494 0.0000824062 0.8336535038 

01001 2201080130 7440473 9.8870708E-7 1.1027467E-6 1.1153422134 

06049 2201001130 7440473 0.0000658205 0.0000615274 0.9347752622 

B-3
6. Extracted the NMIM xylenes, manganese, and nickel observations from the NMIM 

results in preparation for work described in step 7. 

Table B-8. Partial list of emissions for nickel. 

FIPS SCC CAS emis99 emis15 ratio 

01001 223007X130 7440020 6.589926E-6 9.2119274E-6 1.3978802578 

01001 2201001130 7440020 0.0000718905 0.0000599318 0.8336535267 

01001 2201080130 7440020 7.1905967E-7 8.0199761E-7 1.1153422226 

06049 2201001130 7440020 0.0000478695 0.0000447472 0.9347752469 

7. Copied the xylenes, manganese, and nickel observations to new observations with new 

CAS numbers. 

Table B-9. Partial list of nickel emissions after copying observations to duplicate records 

and assigning CAS number to 226. 

FIPS SCC CAS emis99 emis15 ratio 

01001 223007X130 226 6.589926E-6 9.2119274E-6 1.3978802578 

01001 2201001130 226 0.0000718905 0.0000599318 0.8336535267 

01001 2201080130 226 7.1905967E-7 8.0199761E-7 1.1153422226 

06049 2201001130 226 0.0000478695 0.0000447472 0.9347752469 

8. Appended output from step 7 to output from step 5. 

Table B-10. Partial list of emissions after concatening duplicate nickel records with 

original data and sorted by FIPS/SCC/CAS. 

FIPS SCC CAS emis99 emis15 ratio 

01001 2201001130 16065831 0.0000593097 0.0000494437 0.8336535061 

01001 2201001130 18540299 0.0000395398 0.0000329625 0.8336535004 

01001 2201001130 226 0.0000718905 0.0000599318 0.8336535267 

01001 2201001130 71432 1.1247759168 0.2370964659 0.2107944012 

01001 2201001130 7440020 0.0000718905 0.0000599318 0.8336535267 

01001 2201001130 7440473 0.0000988494 0.0000824062 0.8336535038 

01001 2201080130 16065831 5.9322425E-7 6.6164804E-7 1.1153422083 

01001 2201080130 18540299 3.9548283E-7 4.410987E-7 1.1153422209 

01001 2201080130 226 7.1905967E-7 8.0199761E-7 1.1153422226 

01001 2201080130 71432 0.00979517 0.0104629781 1.0681772804 

01001 2201080130 7440020 7.1905967E-7 8.0199761E-7 1.1153422226 

01001 2201080130 7440473 9.8870708E-7 1.1027467E-6 1.1153422134 

01001 223007X130 226 6.589926E-6 9.2119274E-6 1.3978802578 

01001 223007X130 71432 0.020787316 0.0102907019 0.4950471688 

01001 223007X130 7440020 6.589926E-6 9.2119274E-6 1.3978802578 

06049 2201001130 16065831 0.0000394923 0.0000369164 0.9347752652 

06049 2201001130 18540299 0.0000263282 0.0000246109 0.9347752577 

06049 2201001130 226 0.0000478695 0.0000447472 0.9347752469 

06049 2201001130 71432 0.506942138 0.136686063 0.2696285292 

06049 2201001130 7440020 0.0000478695 0.0000447472 0.9347752469 

06049 2201001130 7440473 0.0000658205 0.0000615274 0.9347752622 

B-4
9. After making changes to NMIM for total chromium, xylenes, manganese, and nickel, the 

NEI and NMIM data were merged by FIPS/SCC/CAS keeping all records from the NEI 

inventory. Keep all NEI observations and output data to merged1. 

Table B-11. Merged NEI and NMIM emissions by FIPS/SCC/CAS. Emis_nei is the 

1999 NEI emissions variable. 

FIPS SCC CAS emis emis99 emis15 ratio 

01001 2201001130 226 0.00007 0.0000718905 0.0000599318 0.8336535267 

01001 2201001130 71432 1.15235 1.1247759168 0.2370964659 0.2107944012 

01001 2201001130 7440473 0.0001 0.0000988494 0.0000824062 0.8336535038 

01001 2201080130 71432 0.009925 0.00979517 0.0104629781 1.0681772804 

01001 2230070130 226 5E-6 . . . 

01001 2230070130 71432 0.02079 . . . 

06049 2201001130 71432 1.075615 0.506942138 0.136686063 0.2696285292 

06049 2201001130 7440020 0.000225 0.0000478695 0.0000447472 0.9347752469 

06049 2201001130 7440473 0.00032 0.0000658205 0.0000615274 0.9347752622 

06049 2201080130 71432 0.015485 . . . 

10. Calculated the projection factors for motorcycles by first summing across all SCC codes 

for each FIPS/CAS for the future year and 1999 and dividing the future year summed 

emissions by the 1999 summed emissions. 

Table B-12. County emissions for benzene for Modoc County. Emissions total includes 

SCC emissions not shown in tables. 

FIPS CAS emis99 emis15 ratio1 

06049 71432 4.0984894367 2.560901195 0.6248402575 

11. Merged output from step 10 to output from step 9 for the observations without a 

projection factor. Did not do this for the HDDV emissions (1999 NEI SCC beginning 

with 2230070). 

Table B-13. Merged NEI and NMIM emissions with Modoc County benzene county 

ratio reset to county ratio (ratio1). 

FIPS SCC CAS emis emis99 emis15 ratio ratio1 

01001 2201001130 226 0.00007 0.0000718905 0.0000599318 0.8336535267 

01001 2201001130 71432 1.15235 1.1247759168 0.2370964659 0.2107944012 

01001 2201001130 7440473 0.0001 0.0000988494 0.0000824062 0.8336535038 

01001 2201080130 71432 0.009925 0.00979517 0.0104629781 1.0681772804 

01001 2230070130 226 5E-6 . . . 

01001 2230070130 71432 0.02079 . . . 

06049 2201001130 71432 1.075615 0.506942138 0.136686063 0.2696285292 

06049 2201001130 7440020 0.000225 0.0000478695 0.0000447472 0.9347752469 

06049 2201001130 7440473 0.00032 0.0000658205 0.0000615274 0.9347752622 

06049 2201080130 71432 0.015485 . . 0.6248402575 0.6248402575 

B-5
12. Subset HDDV emissions, 223007XYYY from the NMIM data. 

Table B-14. Subsetted HDDV emissions. Ratio has been renamed ratio_07 and SCC to 

SCC1. 

FIPS SCC1 CAS emis99 emis15 ratio_07 

01001 223007X130 226 6.589926E-6 9.2119274E-6 1.3978802578 

01001 223007X130 71432 0.020787316 0.0102907019 0.4950471688 

01001 223007X130 7440020 6.589926E-6 9.2119274E-6 1.3978802578 

13. Merged the subsetted HDDV emissions by FIPS, CAS, and where the NEI SCC began 

with 2230070 and the NMIM derived SCC began with 223007X. 

Table B-15. Merged NEI and NMIM emissions merged with HDDV data by FIPS/CAS 

where SCC=SCC1. SCC1, ratio1, emis99 and emis15 not shown. For HDDV SCC 

emissions, ratio has been set equal to ratio_07 

FIPS SCC CAS emis_nei ratio ratio_07 

01001 2201001130 226 0.00007 0.8336535267 

01001 2201001130 71432 1.15235 0.2107944012 

01001 2201001130 7440473 0.0001 0.8336535038 

01001 2201080130 71432 0.009925 1.0681772804 

01001 2230070130 226 5E-6 1.3978802578 1.3978802578 

01001 2230070130 71432 0.02079 0.4950471688 0.4950471688 

06049 2201001130 71432 1.075615 0.2696285292 

06049 2201001130 7440020 0.000225 0.9347752469 

06049 2201001130 7440473 0.00032 0.9347752622 

06049 2201080130 71432 0.015485 0.6248402575 0.6248402575 

14. Output to a permanent dataset. 

Table B-16. Output to permanent dataset and create 2015 projected emissions by 

multiplying the ratio by emis_nei, creating a new variable called emis. 

FIPS SCC CAS emis ratio emis_nei 

01001 2201001130 226 0.0000583557 0.8336535267 0.00007 

01001 2201001130 71432 0.2429089282 0.2107944012 1.15235 

01001 2201001130 7440473 0.0000833654 0.8336535038 0.0001 

01001 2201080130 71432 0.0106016595 1.0681772804 0.009925 

01001 2230070130 226 6.9894013E-6 1.3978802578 5E-6 

01001 2230070130 71432 0.0102920306 0.4950471688 0.02079 

06049 2201001130 71432 0.2900164904 0.2696285292 1.075615 

06049 2201001130 7440020 0.0002103244 0.9347752469 0.000225 

06049 2201001130 7440473 0.0002991281 0.9347752622 0.00032 

06049 2201080130 71432 0.0096926381 0.6248402575 0.015485 

B-6
1999 NEI onroad inventory 

on99_sept04.sas7bdat 

NMIM output 

Onroad_20xx.sas7bdat 

(xx=05,07,10,15, 20, 30) 

Sum up HDDV emissions by FIPS/CAS. 

Create new termporary SCC 

223007XYYY where last 3 Y’s 

are road type 

Recombine NMIM output with 

summed HDDV emssions 

Sum up Chromium III and 

ChromiumVI for each 

FIIPS/SCC 

and calculate chromium 

projection ratios. 

Copy xylenes, nickel, and 

manganese observations to 

new 

observations for the other 

xylene, nickel, and 

manganese CAS 

In the NEI. 

merged3 

Merge with NEI 

By FIPS/SCC/CAS. 

Output matching observations 

and observations from NMIM 

not in NEI to separate datasets. 

merged1 

Summarize 

emissions by 

FIPS/CAS 

and calculate 

new projection 

ratios 

Cnty_sum 

Subset out HDDV summed emissions 

(SCC=223007XYYY) 

nmim_hddv1 

merged2 

Merge with HDDV ratios and apply 

Ratios to FIPS/CAS with SCC 2230070 

Onroad_20xx 

1 3 

4 

6 7 

hddv_nmim 

2 

nmim_chrom 

nmim_dat 

concatenate data nmim_dat 

5 

extract xylenes, 

manganese, and 

nickel 

observations 

xy_ni_mn 

xynimn_sum 

nmim_dat concatenate data

8 

9 

10 

Output to permanent dataset 

Merge by 

FIPS/CAS 

where SCC 

does not begin 

with 2230070 

11 

12 

13 

14 

Figure B-1. Projection of the 1999 NEI onroad inventory to 2007, 2010, 2015, 2020, and 2030. 

B-7
B.2 Onroad Precursor Emissions (see 3.4.3) 

Following are the steps used to project the onroad precursor emissions: 

1. Read in the precursor HAP table used for EMS-HAP and subset the data to the 

acetaldehyde, acrolein, formaldehyde, and propionaldehyde precursors, with the 

exception of 1,3-butadiene, acetaldehyde, and MTBE. 

2. The precursor onroad inventory was subset to the acetaldehyde, acrolein, formaldehyde, 

and propionaldehyde precursors by merging with the output of step 1 by CAS. Also 

created a variable called CAS1 that is set to a value of “VOC”. This was to be used for 

merging with the NMIM inventory. 

3. The NMIM data was split into non-HDDV emissions and HDDV emissions. Summed up 

the heavy-duty diesel vehicle emissions in NMIM to create a total HDDV emission 

number for each FIPS/ HDDV road type (last 3 characters of SCC code) and exhaust or 

evaporative type. Created a new SCC, by replacing the seventh digit of the SCC with a 

zero. Output dataset for HDDV emissions was hddv and for non-HDDV emissions, voc. 

For both datasets created a new variable, CAS1 which was set to “VOC”. 

4. Summed up the HDDV emissions with the new SCC by FIPS. Calculated new projection 

factors for each FIPS/SCC using Equation 2 (Section 3.3.2). These factors would be 

applied to SCC codes beginning with 2230070YY# for each FIPS/CAS in the 1999 

precursor inventory. These SCC codes are shown in Table 25. 

5. Concatenated the HDDV and non-HDDV data with projection factors. 

6. Merged step 5 output with the 1999 precursor inventory by FIPS/SCC/CAS1. Output all 

observations for 1999 precursor inventory. Some 1999 observations did not have a 

matching observation by FIPS/SCC/CAS1 in the NMIM data. 

7. Extracted the emissions where the SCC code did not contain X or V, i.e. the total SCC 

emissions (exhaust + evaporative). 

8. To provide projection factors for the non-matching data, summed all the emissions in 

each county across all SCC codes and calculated a new projection factor using Equation 

2. 

9. Merged the output from step 7 with the output from step 6 by FIPS. 

10. Applied the projection factors to each FIPS/SCC/CAS. 

11. Extracted the onroad emissions for 1,3-butadiene, acetaldehyde, and MTBE from the 

MSAT onroad inventory. 

B-8
12. Appended output from step 10 to output from step 9. 

13. If emissions were 1,3-butadiene, acetaldehyde, or MTBE, set the emis variable 

(emissions variable for EMS-HAP) equal to the appropriate year emissions (emis_xx 

where xx is 15, 20, or 30). Otherwise, projected the emissions from 1999 by multiplying 

the 1999 emissions by the projection factor. 

The flowchart of the projection processing is shown in Figure B-2. 

1999 precursor onroad inventory 

on99pre_out.sas7bdat 

Precursor onroad HAP table 

Retain only CAS numbers of 

MSAT HAP rprecursors 

(except for precursors which 

are MSAT HAPs) 

cas_saroad 

Merge by CAS and exclude 

Puerto Rico and the Virgin 

Islands and create new 

variable CAS1 with value of 

“VOC.” 

pre1 

NMIM VOC emissions 

by FIPS/SCC containing 

projection facotrs. For 

each FIPS/SCC, the 

dataset contain s the total 

SCC emissions as well as 

the evaporative and 

exhaust components 

(Nmim_203_30.sas7bdat) 

Split data into non HDDV 

emissions and HDDV 

emisions.For HDDV 

emisisons create a new SCC 

by changing the seventh digit 

of the SCC to a 0. Create a 

new variable CAS1 with 

value “VOC.” 

voc hddv 

Sum up HDDV 

emissions by 

FIPS/SCC/CAS 

1. Calculate 

new projection 

factors 

hddv_sum 

Concatenate 

Merge 1999 

precursor 

emissions and 

NMIM emissions 

by 

FIPS/SCC/CAS1 

voc1 match 

Retain FIPS/SCC where 

SCC is the total 

emissions for the SCC. 

voc2 

Sum by FIPS/CAS1 

and calcul.ate new 

projection factors 

voc_sum 

Merge by 

FIPS/CAS 

match1 

If no projection 

factor from first 

merge, then use 

new factor from 

voc_sum 

match2 

Projected emissions 

from MSAT 

(onroad_proj.sas7bdat) 

Subset to 1,3-Butadiene, 

Acetaldehyde, and 

MTBE 

msat_haps 

Concatenate 

datasets 

match3 

If an MSAT HAP, set the 

emis variable equal to the 

appropriate year’s 

emissions. Otherwise, apply 

projection factors to 1999 

precursor emissions. Output 

to permanent dataset. 

onroad_20XX 

where XX is 

15, 20, or 30. 

1 

2 

3 

4 

5 

6 

8 

9 

10 

11 

13 

12 

7 

Figure B-2. Precursor onroad inventory projection processing. 

B-9
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B-10
Appendix C: Example calculations of nonroad projections 

C.1 Locomotive and commercial marine vessel emissions 

C.1.1 Development of Projection Factor Files for locomotives and commercial marine vessels 

emission projections 

The development of the projection factor files included the following steps performed in a SAS® 

program called loco_marine.sas which can be found in the docket for the MSAT rule (EPA-HQOAR-2005-0036): 

1. Read the 1999 NEI nonroad inventory, nonroad_fixed_airports.sas7bdat (found in the 

MSAT rule docket EPA-HQ-OAR-2005-0036), and extracted the HAP emissions by 

CAS for the locomotive and commercial marine vessel SCC codes. Emissions were 

summed by CAS and SCC. HAP names were then assigned by CAS numbers. 

Emissions were then summed by HAP name and SCC. This was done because some 

HAPs such as Chromium III or Chromium VI were composed of several CAS numbers. 

This step was done as a matter of convenience for the user. The summations were done 

for the entire U.S. excluding Puerto Rico and the Virgin Islands. 

. 

2. The resulting emissions from step 1 were transferred to a PC where they were imported 

into an Excel spreadsheet where the projection factors for the various years were added to 

the emissions based on the criteria in Tables 4 and 6. The spreadsheet name is 

loco_marine.xls 

3. The Excel spreadsheet was then imported into SAS® where SAROAD codes were added 

based on the HAP name in the SAS® program loco_marine.sas. For HAPs with two 

SAROAD codes, i.e. the metals and naphthalene, both SAROAD codes were assigned 

and the projection factors were associated with each SAROAD. 

4. Output the SAROAD/SCC/projection factors to text files, locomotive_gf.txt for 

locomotives and marine_cv.txt for commercial marine vessels. The files contained the 

growth factors for all MSAT years. 

Figures C-1 and C-2 show sample records of the projection factor files for locomotive and 

commercial marine vessels. 

C-1
2285000000 43231 0.9962 0.9441 0.9127 0.8986 0.8725 0.8277 

2285000000 43504 0.9962 0.9441 0.9127 0.8986 0.8725 0.8277 

2285000000 59992 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 

2285000000 59993 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 

Figure C-1. Sample records of the locomotive_gf.txt file. Variables are in the following order: 

SCC, SAROAD, 2001 projection factor, 2007 projection factor, 2010 projection factor, 2015 

projection factor, 2020 projection factor, and 2030 projection factor. Note that the 2001 

projection factor is not used. 

2280000000 43218 1.0352 1.1188 1.1511 1.2306 1.3505 1.7142 

2280000000 43231 1.0352 1.1188 1.1511 1.2306 1.3505 1.7142 

2280000000 59992 1.0181 1.0743 1.1036 1.1541 1.2070 1.3202 

2280000000 59993 1.0181 1.0743 1.1036 1.1541 1.2070 1.3202 

Figure C-2. As for Figure 1, except for the commercial marine vessel projection factor file, 

marine_cv.txt. 

C.1.2. Projection of the 1999 locomotive and commercial marine vessel emissions 

Projection of the 1999 locomotive and commercial marine vessel emissions was performed in 

marine_locomotive_growth.sas (found in the MSAT rule docket EPA-HQ-OAR-2005-0036) and 

included the following steps. The methodology of the program is shown in Figure C-3 as a 

flowchart with example calculations shown with each step: 

1. Combined the commercial marine vessels and locomotive SAROAD/SCC/projection 

factor data into one file. 

Table C-1. Partial listing of locomotive and commercial marine vessel growth factors by 

SAROAD after reading in growth factor files, concatenating, and sorting. Benzene and 

unspeciated chromium growth factors are shown. 

SCC SAROAD gf99_01 gf99_07 gf99_10 gf99_15 gf99_20 gf99_30 

2280000000 45201 1.0352 1.1188 1.1511 1.2306 1.3505 1.7142 

2280000000 80141 1.0181 1.0743 1.1036 1.1541 1.207 1.3202 

2280000000 80341 1.0181 1.0743 1.1036 1.1541 1.207 1.3202 

2280002200 45201 1.0196 1.0482 1.0498 1.0552 1.0797 1.177 

2280002200 80141 1.0181 1.0743 1.1036 1.1541 1.207 1.3202 

2280002200 80341 1.0181 1.0743 1.1036 1.1541 1.207 1.3202 

2285002005 45201 0.9962 0.9441 0.9127 0.8986 0.8725 0.8277 

2285002005 80141 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 

2285002005 80341 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 

2285002010 45201 0.9962 0.9441 0.9127 0.8986 0.8725 0.8277 

2285002010 80141 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 

2285002010 80341 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 

C-2
2. Read in the text file, haptabl_nonroadGEN_toxwt.txt to get the CAS/SAROAD cross 

reference for nonroad HAPs. The data was sorted to eliminate duplicate CAS numbers. 

For metals, this usually meant the CAS became associated with the fine particle 

SAROAD or the lowest numbered SAROAD for the metal. 

Table C-2. CAS/SAROAD cross reference for benzene and unspeciated chromium after 

sorting by CAS/SAROAD. 

CAS SAROAD 

136 80141 

136 80341 

71432 45201 

7440473 80141 

7440473 80341 

3. Merged the output from steps 1 and 2 together so that now the projection factors were 

associated with CAS and SCC codes instead of SAROAD and SCC codes. Metals were 

associated with the fine SAROAD codes usually but duplicate records were put into the 

projection factor text files for the coarse SAROAD codes as a precaution. 

Table C-3. Partial listing merged CAS/SAROAD cross-reference with growth factors 

after sorting by SCC/CAS, eliminating duplicate CAS observations. 

SCC SAROAD gf99_01 gf99_07 gf99_10 gf99_15 gf99_20 gf99_30 CAS 

2280000000 80141 1.0181 1.0743 1.1036 1.1541 1.207 1.3202 136 

2280000000 45201 1.0352 1.1188 1.1511 1.2306 1.3505 1.7142 71432 

2280000000 80141 1.0181 1.0743 1.1036 1.1541 1.207 1.3202 7440473 

2280002200 80141 1.0181 1.0743 1.1036 1.1541 1.207 1.3202 136 

2280002200 45201 1.0196 1.0482 1.0498 1.0552 1.0797 1.177 71432 

2280002200 80141 1.0181 1.0743 1.1036 1.1541 1.207 1.3202 7440473 

2285002005 80141 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 136 

2285002005 45201 0.9962 0.9441 0.9127 0.8986 0.8725 0.8277 71432 

2285002005 80141 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 7440473 

2285002010 80141 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 136 

2285002010 45201 0.9962 0.9441 0.9127 0.8986 0.8725 0.8277 71432 

2285002010 80141 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 7440473 

4. Summed the locomotive and commercial marine vessel emissions from the 1999 NEI 

nonroad inventory for QA purposes. 

Total locomotive and commercial marine vessels before processing among all HAPs is 

13,085.96 tons 

C-3
5. Merged the nonroad emissions with the projection factors by CAS/SCC across all FIPS, retaining only th

commercial marine vessel HAP and SCC emissions. Summed the 1999 emissions again to check against from step 4. These emissions should be the same. Table C-4. Partial listing of merged emissions for Los Angeles County, CA (FIPS=06037) after merging with growth factors. 

SCC FIPS CAS emis gf99_01 gf99_07 gf99_10 gf99_15 gf99_20 gf99_30 

2280000000 06037 71432 0.764 1.0352 1.1188 1.1511 1.2306 1.3505 1.7142 2280002200 06037 71432 0.0100924896 1.0196 1.0482 1.0498 1.0552 1.0797 1.177 2280002200 06037 7440473 2.1818725E-6 1.0181 1.0743 1.1036 1.1541 1.207 1.3202 2285002005 06037 71432 5.956 0.9962 0.9441 0.9127 0.8986 0.8725 0.8277 2285002005 06037 7440473 0.001143 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 2285002010 06037 71432 1.336 0.9962 0.9441 0.9127 0.8986 0.8725 0.8277 2285002010 06037 7440473 0.0002790113 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 

Summed emissions of 1999 emissions after merger is 13,085.96. 

6. Projected the 1999 emissions to future years at the FIPS/SCC/CAS level by multiplying each future year’

1999 emissions. 

7. Output to a SAS® dataset for later use with the other nonroad emissions for projection to future years. 

Table C-5. Partial listing of projected emissions (steps 6 and 7). 1999 base emissions and growth factor

SCC FIPS CAS emis_01 emis_07 emis_10 emis_15 em

2280000000 06037 71432 0.7908928 0.8547632 0.8794404 0.9401784 1.032280002200 06037 71432 0.0102903024 0.0105789476 0.0105950955 0.010649595 0.010892280002200 06037 7440473 2.2213644E-6 2.3439856E-6 2.4079145E-6 2.518099E-6 2.6335202285002005 06037 71432 5.9333672 5.6230596 5.4360412 5.3520616 5.12285002005 06037 7440473 0.001143 0.001143 0.001143 0.001143 0.002285002010 06037 71432 1.3309232 1.2613176 1.2193672 1.2005296 1.12285002010 06037 7440473 0.0002790113 0.0002790113 0.0002790113 0.0002790113 0.000279

8. Output first two records of projected dataset to manually QA calculations. 

C-4 
haptable_nonroadGEN_toxwt..txt 

(cross reference of saroad 

and CAS numbers) 

Commercial marine 

vessel projection factors 

by saroad and SCC 

(marine_cv_gf.txt) 

locomotive projection factors 

by saroad and SCC 

(locomotive_gf.txt) 

Cross reference saroad 

and CAS numbers 

Projection factors by 

CAS/SCC 

(loco_marine_cas) 

1999 NEI nonroad inventory 

(non99_oct03_fixed_airports) 

non_haps loco_marine 

Combine emissions with projection factors 

by SCC and CAS for each county 

loco_marine_emis 

Calculate total locomotive and 

marine vessel emissions for 

QA purposes. Output to lst file 

Calculate total locomotive and 

marine vessel emissions for 

QA purposes. Output to lst file 

Visually compare in lst file 

(should be equal) 

Apply projection factors for each 

Year to emissions (gf*emis) 

growth 

Output first 2 observations to 

manually QA results growth_qa 

Output to permament 

dataset loco_marine_growth 

1 

2 

3 

4 

5 

6 

7 

8 

Figure C-3. Projection of 1999 locomotive and commercial marine vessel emissions to 2002, 

2007, 2010, 2015, 2020, and 2030. Data is represented by solid boxes with processes or steps 

denoted by dashed boxes. Input and final output data denoted by heavy solid boxes. Steps 

outlined in text are shown as circled numbers. Unless otherwise denoted, data represents SAS® 

datasets. 

C-5
C.2 Remaining nonroad emissions (excluding aircraft, locomotives, and commercial marine 

vessels) 

The following summarizes the steps used in the SAS® program nonroad.sas (found in the MSAT 

rule docket EPA-HQ-OAR-2005-0036) with example calculations for Alameda County, CA 

(FIPS=06001). A detailed flow chart is shown in Figure C-4. 

1. The 1999 NEI nonroad inventory was subsetted to the MSAT HAPs, excluding aircraft, 

marine commercial vessels, and locomotive emissions. These were projected separately 

from the other nonroad emissions as documented in Section 3.1. 

Table C-6. Partial listing of 1999 NEI nonroad emissions for Alameda County after 

subsetting inventory to MSAT HAPs and excluding aircraft, locomotive, and commercial 

marine vessel SCC emissions. SCC descriptions are listed for informational purposes. 

Emis_nei is the emissions variable and polldesc is the pollutant description. 

FIPS SCC CAS emis_nei polldesc SCC description 

06001 2260001010 1330207 14.317113886 Xylenes (mixture of o, 

m, and p isomers) 

Mobile Sources,Off-highway 

Vehicle Gasoline, 2­

Stroke,Recreational 

Equipment,Motorcycles: Off-road 

06001 2260001010 71432 2.3263908964 Benzene 

06001 2260001010 7440473 0.0000122711 Chromium 

06001 2260002000 108383 0.30705 m-Xylene Mobile Sources, Off-highway 

Vehicle Gasoline, 2­

Stroke,Construction and Mining 

Equipment, Total 

06001 2260002000 71432 0.23736 Benzene 

06001 2260002000 7440473 0.00015 Chromium 

06001 2260002000 95476 0.10695 o-Xylene 

06001 2265008000 108383 0.6141 m-Xylene 

Airport Support Equipment, Total, 

Off-highway 4-stroke 

06001 2265008000 71432 0.47472 Benzene 

06001 2265008000 7440473 0.00015 Chromium 

06001 2265008000 95476 0.2139 o-Xylene 

06001 2268003000 108383 0.01102 m-Xylene 

Mobile Sources,CNG,Industrial 

Equipment, All 

06001 2268003000 71432 0.12122 Benzene 

06001 2268003000 7440473 0.0013 Chromium 

06001 2268003000 95476 0.01102 o-Xylene 

06001 2282000000 106423 0.0015 p-Xylene 

Mobile Sources, Pleasure Craft, All 

Fuels, Total, All Vessel Types 

06001 2282000000 108383 0.00915 m-Xylene 

06001 2282000000 71432 0.03 Benzene 

06001 2282000000 7440473 2E-6 Chromium 

06001 2282000000 95476 0.0051 o-Xylene 

2. NMIM SCC emissions were summed to a “Total” category for each SCC category (first 7 

digits of SCC followed by 3 zeros) for each FIPS/HAP/SCC. These SCC codes were 

found in a separate SAS® program missing_scc.sas (found in the MSAT rule docket 

EPA-HQ-OAR-2005-0036) that was run before any nonroad processing. See Table 19 

for list. 

C-6
Table C-7. Partial listing of NMIM 1999 and 2015 emissions for Alameda County after 

summing SCC emissions to total SCC category for each HAP. 

FIPS SCC CAS emis99 emis15 

06001 2260002000 1330207 14.632461572 5.9177500529 

06001 2260002000 16065831 9.9206348E-6 7.218985E-6 

06001 2260002000 18540299 5.1106299E-6 3.718871E-6 

06001 2260002000 71432 3.3503477212 1.3761635963 

3. NMIM pleasure craft emissions, first four SCC digits 2282, were summed and assigned 

SCC 2282000000 for each FIPS/SCC. 

Table C-8. Partial listing of NMIM 1999 and 2015 emissions for Alameda County after 

summing pleasure craft emissions into one SCC for each HAP (Section 3.4.3, step 3). 

FIPS SCC CAS emis99 emis15 

06001 2282000000 1330207 45.980996386 18.529284389 

06001 2282000000 16065831 0.0000759904 0.0000757439 

06001 2282000000 18540299 0.0000391466 0.0000390196 

06001 2282000000 71432 9.8014022292 3.6067584954 

4. Concatenated the total SCC emissions from step 2 and pleasure craft emissions from step 

3. 

Table C-9. Partial listing of NMIM emissions and 2015 to 1999 ratios for Alameda 

County after concatenating the total SCC emissions and the total pleasure craft emissions. 

Ratio_15 has been renamed ratio. 

FIPS SCC CAS emis99 emis15 ratio 

06001 2260002000 1330207 14.632461572 5.9177500529 0.4044261469 

06001 2260002000 16065831 9.9206348E-6 7.218985E-6 0.7276737009 

06001 2260002000 18540299 5.1106299E-6 3.718871E-6 0.727673714 

06001 2260002000 71432 3.3503477212 1.3761635963 0.4107524683 

06001 2282000000 1330207 45.980996386 18.529284389 0.4029770089 

06001 2282000000 16065831 0.0000759904 0.0000757439 0.9967563592 

06001 2282000000 18540299 0.0000391466 0.0000390196 0.9967563665 

06001 2282000000 71432 9.8014022292 3.6067584954 0.3679839283 

5. Concatenated the data from step 4 with the original NMIM inventory. 

C-7
Table C-10. Partial listing of NMIM emissions after concatenating the total SCC and pleasure 

craft emissions with the original NMIM emissions. 

FIPS SCC CAS emis99 emis15 ratio 

06001 2260002000 1330207 14.632461572 5.9177500529 0.4044261469 

06001 2260002000 16065831 9.9206348E-6 7.218985E-6 0.7276737009 

06001 2260002000 18540299 5.1106299E-6 3.718871E-6 0.727673714 

06001 2260002000 71432 3.3503477212 1.3761635963 0.4107524683 

06001 2282000000 1330207 45.980996386 18.529284389 0.4029770089 

06001 2282000000 16065831 0.0000759904 0.0000757439 0.9967563592 

06001 2282000000 18540299 0.0000391466 0.0000390196 0.9967563665 

06001 2282000000 71432 9.8014022292 3.6067584954 0.3679839283 

06001 2260001010 1330207 9.711512804 14.214579582 1.4636833487 

06001 2260001010 16065831 5.1118629E-6 0.0000109192 2.1360424041 

06001 2260001010 18540299 2.6333838E-6 5.6250196E-6 2.1360424142 

06001 2260001010 71432 1.5561215132 2.2015727758 1.4147820444 

6. Extracted chromium III and chromium VI emissions from the output of step 5. 

Table C-11. Partial listing of extracted chromium emissions. 

FIPS SCC CAS emis99 emis15 ratio 

06001 2260001010 16065831 5.1118629E-6 0.0000109192 2.1360424041 

06001 2260001010 18540299 2.6333838E-6 5.6250196E-6 2.1360424142 

06001 2260002000 16065831 9.9206348E-6 7.218985E-6 0.7276737009 

06001 2260002000 18540299 5.1106299E-6 3.718871E-6 0.727673714 

06001 2282000000 16065831 0.0000759904 0.0000757439 0.9967563592 

06001 2282000000 18540299 0.0000391466 0.0000390196 0.9967563665 

7. As with the onroad summed up NMIM chromium III and chromium VI emissions to 

create total chromium. For the NEI nonroad inventory, chromium was reported with 

either CAS 136 or CAS 74404734

. To make sure all FIPS/SCC/CAS combinations are 

covered, the summed chromium III and chromium VI emissions were assigned to both 

Chromium CAS numbers. Therefore temporarily, chromium emissions were double 

counted while processing the NMIM output. 

 In the 1999 NEI nonroad inventory (just like in the onroad inventory), chromium was speciated as chromium III 

and chromium VI. The emissions were summed and re-speciated by EMS-HAP to use a speciation factor of 18% of 

total chromium is chromium VI. 

C-8

4
Table C-12. Partial listing of chromium emissions after summing by FIPS/SCC and 

assigning unspeciated chromium CAS numbers and calculating a new ratio. 

FIPS SCC CAS emis99 emis15 ratio 

06001 2260001010 136 7.7452467E-6 0.0000165442 2.1360424076 

06001 2260001010 7440473 7.7452467E-6 0.0000165442 2.1360424076 

06001 2260002000 136 0.0000150313 0.0000109379 0.7276737054 

06001 2260002000 7440473 0.0000150313 0.0000109379 0.7276737054 

06001 2282000000 136 0.000115137 0.0001147635 0.9967563617 

06001 2282000000 7440473 0.000115137 0.0001147635 0.9967563617 

8. Concatenated step 7 output with step 5 output. 

Table C-13. Partial listing of NMIM emissions after concatenating chromium emissions 

with NMIM data. 

FIPS SCC CAS emis99 emis15 ratio 

06001 2260002000 1330207 14.632461572 5.9177500529 0.4044261469 

06001 2260002000 16065831 9.9206348E-6 7.218985E-6 0.7276737009 

06001 2260002000 18540299 5.1106299E-6 3.718871E-6 0.727673714 

06001 2260002000 71432 3.3503477212 1.3761635963 0.4107524683 

06001 2282000000 1330207 45.980996386 18.529284389 0.4029770089 

06001 2282000000 16065831 0.0000759904 0.0000757439 0.9967563592 

06001 2282000000 18540299 0.0000391466 0.0000390196 0.9967563665 

06001 2282000000 71432 9.8014022292 3.6067584954 0.3679839283 

06001 2260001010 1330207 9.711512804 14.214579582 1.4636833487 

06001 2260001010 16065831 5.1118629E-6 0.0000109192 2.1360424041 

06001 2260001010 18540299 2.6333838E-6 5.6250196E-6 2.1360424142 

06001 2260001010 71432 1.5561215132 2.2015727758 1.4147820444 

06001 2260001010 136 7.7452467E-6 0.0000165442 2.1360424076 

06001 2260001010 7440473 7.7452467E-6 0.0000165442 2.1360424076 

06001 2260002000 136 0.0000150313 0.0000109379 0.7276737054 

06001 2260002000 7440473 0.0000150313 0.0000109379 0.7276737054 

06001 2282000000 136 0.000115137 0.0001147635 0.9967563617 

06001 2282000000 7440473 0.000115137 0.0001147635 0.9967563617 

9. Extracted xylenes, nickel, and manganese observations from step 8 output. 

Table C-14. Extracted xylenes emissions. 

FIPS SCC CAS emis99 emis15 ratio 

06001 2260002000 1330207 14.632461572 5.9177500529 0.4044261469 

06001 2282000000 1330207 45.980996386 18.529284389 0.4029770089 

06001 2600010210 1330207 9.711512804 14.214579582 1.4636833487 

10. As with the onroad processing, copied the NMIM xylenes, nickel, and manganese NMIM 

observations to duplicate observations with the other xylenes, nickel, and manganese 

CAS numbers. 

C-9
Table C-15. Xylenes emissions after copying records to duplicate records and changing 

CAS numbers to 106423, 108383, and 95476. 

FIPS SCC CAS emis99 emis15 ratio 

06001 2260002000 106423 14.632461572 5.9177500529 0.4044261469 

06001 2260002000 108383 14.632461572 5.9177500529 0.4044261469 

06001 2260002000 95476 14.632461572 5.9177500529 0.4044261469 

06001 2282000000 106423 45.980996386 18.529284389 0.4029770089 

06001 2282000000 108383 45.980996386 18.529284389 0.4029770089 

06001 2282000000 95476 45.980996386 18.529284389 0.4029770089 

06001 2600010210 106423 9.711512804 14.214579582 1.4636833487 

06001 2600010210 108383 9.711512804 14.214579582 1.4636833487 

06001 2600010210 95476 9.711512804 14.214579582 1.4636833487 

11. Concatenated step 10 output with step 8 output. 

Table C-16. Concatenated NMIM emissions and duplicate xylenes emissions. 

FIPS SCC CAS emis99 emis15 ratio 

06001 2260002000 1330207 14.632461572 5.9177500529 0.4044261469 

06001 2260002000 16065831 9.9206348E-6 7.218985E-6 0.7276737009 

06001 2260002000 18540299 5.1106299E-6 3.718871E-6 0.727673714 

06001 2260002000 71432 3.3503477212 1.3761635963 0.4107524683 

06001 2282000000 1330207 45.980996386 18.529284389 0.4029770089 

06001 2282000000 16065831 0.0000759904 0.0000757439 0.9967563592 

06001 2282000000 18540299 0.0000391466 0.0000390196 0.9967563665 

06001 2282000000 71432 9.8014022292 3.6067584954 0.3679839283 

06001 2260001010 1330207 9.711512804 14.214579582 1.4636833487 

06001 2260001010 16065831 5.1118629E-6 0.0000109192 2.1360424041 

06001 2260001010 18540299 2.6333838E-6 5.6250196E-6 2.1360424142 

06001 2260001010 71432 1.5561215132 2.2015727758 1.4147820444 

06001 2260001010 136 7.7452467E-6 0.0000165442 2.1360424076 

06001 2260001010 7440473 7.7452467E-6 0.0000165442 2.1360424076 

06001 2260002000 136 0.0000150313 0.0000109379 0.7276737054 

06001 2260002000 7440473 0.0000150313 0.0000109379 0.7276737054 

06001 2282000000 136 0.000115137 0.0001147635 0.9967563617 

06001 2282000000 7440473 0.000115137 0.0001147635 0.9967563617 

06001 2260002000 106423 14.632461572 5.9177500529 0.4044261469 

06001 2260002000 108383 14.632461572 5.9177500529 0.4044261469 

06001 2260002000 95476 14.632461572 5.9177500529 0.4044261469 

06001 2282000000 106423 45.980996386 18.529284389 0.4029770089 

06001 2282000000 108383 45.980996386 18.529284389 0.4029770089 

06001 2282000000 95476 45.980996386 18.529284389 0.4029770089 

06001 2600010210 106423 9.711512804 14.214579582 1.4636833487 

06001 2600010210 108383 9.711512804 14.214579582 1.4636833487 

06001 2600010210 95476 9.711512804 14.214579582 1.4636833487 

C-10
12. Merged the NEI and NMIM output, nei_dat and nmim_dat, by FIPS/SCC/CAS, retaining 

all NEI observations. Split the data into a dataset that matched (called okay), i.e. has a 

projection factor, and into dataset that did not matched (called need_ratio), i.e. no 

projection factors. 

Table C-17. Partial listing of merged NEI and NMIM emissions, with all NEI emissions 

retained. 

FIPS SCC CAS emis_nei emis99 emis15 ratio 

06001 2260001010 1330207 14.317113886 9.711512804 14.214579582 1.4636833487 

06001 2260001010 71432 2.3263908964 1.5561215132 2.2015727758 1.4147820444 

06001 2260001010 7440473 0.0000122711 7.7452467E-6 0.0000165442 2.1360424076 

06001 2260002000 108383 0.30705 14.632461572 5.9177500529 0.4044261469 

06001 2260002000 71432 0.23736 3.3503477212 1.3761635963 0.4107524683 

06001 2260002000 7440473 0.00015 0.0000150313 0.0000109379 0.7276737054 

06001 2260002000 95476 0.10695 14.632461572 5.9177500529 0.4044261469 

06001 2265008000 108383 0.6141 . . . 

06001 2265008000 71432 0.47472 . . . 

06001 2265008000 7440473 0.00015 . . . 

06001 2265008000 95476 0.2139 . . . 

06001 2268003000 108383 0.01102 . . . 

06001 2268003000 71432 0.12122 . . . 

06001 2268003000 7440473 0.0013 . . . 

06001 2268003000 95476 0.01102 . . . 

06001 2282000000 106423 0.0015 45.980996386 18.529284389 0.4029770089 

06001 2282000000 108383 0.00915 45.980996386 18.529284389 0.4029770089 

06001 2282000000 71432 0.03 9.8014022292 3.6067584954 0.3679839283 

06001 2282000000 7440473 2E-6 0.000115137 0.0001147635 0.9967563617 

06001 2282000000 95476 0.0051 45.980996386 18.529284389 0.4029770089 

Table C-18. Listing of emissions still needing a ratio with six digit SCC, scc6. 

FIPS scc6 SCC CAS emis_nei emis99 emis15 ratio 

06001 226500 2265008000 108383 0.6141 . . . 

06001 226500 2265008000 71432 0.47472 . . . 

06001 226500 2265008000 7440473 0.00015 . . . 

06001 226500 2265008000 95476 0.2139 . . . 

06001 226800 2268003000 108383 0.01102 . . . 

06001 226800 2268003000 71432 0.12122 . . . 

06001 226800 2268003000 7440473 0.0013 . . . 

06001 226800 2268003000 95476 0.01102 . . . 

C-11
Table C-19. Listing of emissions assigned a ratio with projected emissions, emis. 

FIPS SCC CAS emis_nei emis99 emis15 ratio emis 

06001 2260001010 1330207 14.317113886 9.711512804 14.214579582 1.4636833487 20.955721197 

06001 2260001010 71432 2.3263908964 1.5561215132 2.2015727758 1.4147820444 3.2913360685 

06001 2260001010 7440473 0.0000122711 7.7452467E-6 0.0000165442 2.1360424076 0.0000262116 

06001 2260002000 108383 0.30705 14.632461572 5.9177500529 0.4044261469 0.1241790484 

06001 2260002000 71432 0.23736 3.3503477212 1.3761635963 0.4107524683 0.0974962059 

06001 2260002000 7440473 0.00015 0.0000150313 0.0000109379 0.7276737054 0.0001091511 

06001 2260002000 95476 0.10695 14.632461572 5.9177500529 0.4044261469 0.0432533764 

06001 2282000000 106423 0.0015 45.980996386 18.529284389 0.4029770089 0.0006044655 

06001 2282000000 108383 0.00915 45.980996386 18.529284389 0.4029770089 0.0036872396 

06001 2282000000 71432 0.03 9.8014022292 3.6067584954 0.3679839283 0.0110395178 

06001 2282000000 7440473 2E-6 0.000115137 0.0001147635 0.9967563617 1.9935127E-6 

06001 2282000000 95476 0.0051 45.980996386 18.529284389 0.4029770089 0.0020551827 

13. For remaining FIPS/SCC/CAS combinations in the 1999 NEI that did not match the 

NMIM results, created county-level HAP specific projection factors based on engine/fuel 

type by summing emissions for 1999 NMIM and future year NMIM for each 

FIPS/CAS/engine/fuel type. These were applied to all SCC codes with the relevant 

engine/fuel type by HAP and by county. The engine fuel types were 2-stroke gasoline, 4­

stroke gasoline, diesel, LPG, CNG, and miscellaneous. 

Table C-20. Partial listing of Alameda County emissions for SCC6 of 226500. 

FIPS scc6 SCC CAS emis99 emis15 ratio 

06001 226500 2265008000 108383 428.80604172 253.71038624 0.5916670046 

06001 226500 2265008000 71432 324.73697506 197.05535819 0.6068152792 

06001 226500 2265008000 7440473 0.0056123247 0.0066134549 1.1783806775 

06001 226500 2265008000 95476 428.80604172 253.71038624 0.5916670046 

14. Merged the output from step 13, cnty_sum, with the need_ratio dataset from step 12. 

Separated data into two datasets, observations with a projection factor (fill_data2) and 

those without a projection factor (need_data2). 

Table C-21. Alameda County emissions where a ratio was applied from cnty_sums and 

applied to NEI emissions to calculate emis variable. 

FIPS scc6 SCC CAS emis_nei emis99 emis15 ratio emis 

06001 226500 2265008000 108383 0.6141 . . 0.5916670046 0.3633427075 

06001 226500 2265008000 71432 0.47472 . . 0.6068152792 0.2880673493 

06001 226500 2265008000 7440473 0.00015 . . 1.1783806775 0.0001767571 

06001 226500 2265008000 95476 0.2139 . . 0.5916670046 0.1265575723 

C-12
Table C-22. Alameda County emissions still needing a ratio with a CAS1 variable 

assigned. 

FIPS scc6 SCC CAS CAS1 emis_nei emis99 emis15 ratio 

06001 226800 2268003000 108383 VOC 0.01102 . . . 

06001 226800 2268003000 71432 VOC 0.12122 . . . 

06001 226800 2268003000 7440473 PM10-PRI 0.0013 . . . 

06001 226800 2268003000 95476 VOC 0.01102 . . . 

15. Even after the above step, there remained CNG and LPG emissions for California and 

Texas (SCC codes beginning with 226800, 226801, and 226700) from the 1999 NEI 

without an NMIM based projection factor. Per discussion with Madeleine Strum and 

Rich Cook, the VOC or PM county level ratios for CNG and LPG as fuel types were 

calculated and used for the HAPs in the inventory. Particulate HAPs received the PM 

ratios and gaseous HAPs received the VOC ratios. Calculated county level projection 

factors by summing VOC or PM emissions across all SCC codes that used CNG and LPG 

as fuel types for 1999 NMIM and future year NMIM output and dividing the future year 

summed emissions by the 1999 summed emissions for each county. 

Table C-23. Summed VOC and PM10-PRI NMIM emissions for Alameda County by 

six digit SCC code emissions with ratio. 

FIPS scc6 CAS1 emis99 emis15 ratio_15 

06001 226800 PM10-PRI 0.6706211131 1.0020067495 1.4941473358 

06001 226800 VOC 2.2227693134 0.3633906096 0.1634855257 

16. Merged the projection factors from step 15 (ca_tx_sum) with the need_data2 output from 

step 14 and apply factors. Output dataset was fill_data3. 

Table C-24. Projected emissions for Alameda County using the county sums for LPG 

and CNG . 

FIPS scc6 SCC CAS CAS1 emis_nei emis ratio 

06001 226800 2268003000 108383 VOC 0.01102 0.0018016105 0.1634855257 

06001 226800 2268003000 71432 VOC 0.12122 0.0198177154 0.1634855257 

06001 226800 2268003000 7440473 PM10-PRI 0.0013 0.0019423915 1.4941473358. 

06001 226800 2268003000 95476 VOC 0.01102 0.0018016105 0.1634855257 

17. Concatenated datasets okay, fill_data2, and fill_data3. Output was merged2. 

18. Appended the locomotive and commercial marine vessel projected emissions to step 17 

output and output data to permanent dataset, nonroad_20xx where xx is 07, 10, 15, 20, or 

30. 

C-13
Table C-25. Projected nonroad emissions with appended locomotive and commercial 

marine vessels after sorting by FIPS/SCC/CAS (steps 17 and 18). 

FIPS SCC CAS emis_nei ratio emis 

06001 2260001010 1330207 14.317113886 1.4636833487 20.955721197 

06001 2260001010 71432 2.3263908964 1.4147820444 3.2913360685 

06001 2260001010 7440473 0.0000122711 2.1360424076 0.0000262116 

06001 2260002000 108383 0.30705 0.4044261469 0.1241790484 

06001 2260002000 71432 0.23736 0.4107524683 0.0974962059 

06001 2260002000 7440473 0.00015 0.7276737054 0.0001091511 

06001 2260002000 95476 0.10695 0.4044261469 0.0432533764 

06001 2265008000 108383 0.6141 0.5916670046 0.3633427075 

06001 2265008000 71432 0.47472 0.6068152792 0.2880673493 

06001 2265008000 7440473 0.00015 1.1783806775 0.0001767571 

06001 2265008000 95476 0.2139 0.5916670046 0.1265575723 

06001 2268003000 108383 0.01102 0.1634855257 0.0018016105 

06001 2268003000 71432 0.12122 0.1634855257 0.0198177154 

06001 2268003000 7440473 0.0013 1.4941473358. 0.0019423915 

06001 2268003000 95476 0.01102 0.1634855257 0.0018016105 

06001 2280000000 106423 0.0045 1.2306 0.0055377 

06001 2280000000 108383 0.02745 1.2306 0.03377997 

06001 2280000000 71432 0.09 1.2306 0.110754 

06001 2280000000 95476 0.0153 1.2306 0.01882818 

06001 2282000000 106423 0.0015 0.4029770089 0.0006044655 

06001 2282000000 108383 0.00915 0.4029770089 0.0036872396 

06001 2282000000 71432 0.03 0.3679839283 0.0110395178 

06001 2282000000 7440473 2E-6 0.9967563617 1.9935127E-6 

06001 2282000000 95476 0.0051 0.4029770089 0.0020551827 

06001 2285000000 106423 0.038 0.8986 0.0341468 

06001 2285000000 108383 0.2318 0.8986 0.20829548 

06001 2285000000 71432 0.76 0.8986 0.682936 

06001 2285000000 7440473 0.000192 1.0000 0.000192 

06001 2285000000 95476 0.1292 0.8986 0.11609912 

C-14
1999 NEI onroad inventory 

non99_oct03_fixed_airports.sas7bdat 

Extract MSAT HAP emissions Excluding aircraft, marine vessel, and locomotive emissions. 

nei

NMIM output nonroad_20xx.sas7bdat (xx=05,07,10,15, 20, 30) 

Sum up SCCs beginning with 2282 for each FIPS/CAS. 

Sum up Chromium III and Chromium VI for each FIPS/SCC. 

nmim

_

dat 

Merge with NEI By FIPS/SCC/CAS. Output all observations from NEI Set future year MTBE emissions and projection factors to zero. Separate observations with a projection factor from those that do not. Project emissions to future year in okay dataset 

need_ratio okay

Summarize emissions by FIPS/engine type/CAS and calculate new ratios. 

cnty_sums 

Merge by FIPS/engine type/CAS 

fill_data1 

Separate observations with a projection factor from those that do not. Assign a VOC or PM flag to the need_data2 observations and project emissions in fill_data 

need_data2 fill_data2 

Concatenate datasets 

merged2 

Projected locomotive & marine commercial vessel emissions. loco

_

marine_growth.sas7bdat 

NMIM VOC and PM emissions by FIPS/SCC( voc_pm.sas7bdat) 

Extract and summarize CA and TX CNG and LPG emissions by FIPS and VOC or PM. Calculate projection factors. 

ca_tx_sum 

Merge by FIPS/engine type/VOC/PM flag and project emissions. Engine type refers to CNG or LPG 

fill_data3 

Concatenate datasets 

nonroad_20xx 

2 3 

4 

5 

13 

14 

16 

10 

17 

18 

Sum up first 7 digit SCC level emissions for each FIPS/CAS 

nmim_non1 pleas_craft 

Concatenate 

recombine Concatenate nmim

_

dat 

extract chromium 6 

chrom 

Copy xylenes, nickel, and manganese emissions to duplicate observations using other CAS for xylenes, manganese, and nickel 

7 

nmim_chrom 

Concatenate 

8 

extract xylenes, nickel, and manganese 

9 

xy_ni_mn 

xylenes_ni_mn 

10 

Concatenate 

nmim_dat 

11 

15 

Figure C-4. Flowchart of nonroad projections. Box types as for Figure C-3. 

C-15 
C.3 Nonroad Precursor Emissions 

The following contains the steps used for projecting precursors from nonroad emission 

categories covered by the NONROAD model . 

1. Subset 1999 precursor nonroad inventory to the precursors for acetaldehyde, acrolein, 

formaldehyde, and propionaldehyde using the precursor HAP table from EMS-HAP to 

get the CAS numbers associated with the appropriate precursors. 

2. Excluded locomotive, commercial marine vessel, and aircraft emissions. Also subset 

data to exclude Puerto Rico and the Virgin Islands. Create a variable called CAS1 with 

value “VOC.” 

3. Summed NMIM VOC SCC emissions to a “Total” category for each SCC category (first 

7 digits of SCC) for each FIPS/ SCC. 

4. Summed up NMIM VOC pleasure craft emissions, first four SCC digits 2282, and 

assigned SCC 2282000000 for each FIPS/SCC. 

5. Combined output from steps 3 and 4 with original NMIM output and calculated new 

projection factors for the total SCC codes and pleasure craft emissions. 

6. Merged the 1999 precursor inventory and NMIM output, by FIPS/SCC/CAS1, retaining 

all NEI observations. 

7. For remaining non-matching FIPS/SCC/CAS combinations, created a county level HAP 

specific projection factor based on engine/fuel type by summing emissions for 1999 and 

future year NMIM VOC for each FIPS and calculate a county level projection factor. 

These were then assigned to all FIPS/SCC codes for each pollutants based on engine 

type. The engine fuel types were: 2-stroke gasoline, 4-stroke gasoline, diesel, LPG, 

CNG, residual, and miscellaneous. 

8. Merged the output from step 6,with output from step 7. 

9. Appended output from step 8 to the matched data from step 6 and applied projection 

factors to create 2015, 2020, and 2030 emissions. 

10. Extracted precursor locomotive and commercial marine vessel emissions from the 

precursor locomotive and commercial marine vessel projected inventory. 

11. Appended output from step 10 to step 9 output. 

12. Extracted 1,3-butadiene, acetaldehyde, MTBE, and methanol nonroad emissions 

(excluding aircraft) from the interpolated nonroad inventory (see Appendix B) that 

contains MSAT and non-MSAT HAPs. 

C-16
13. Appended output from step 12 to output from step 11. 

14. Split data into separate datasets for 2015, 2020, and 2030. 

The flowchart of the processing is shown in Figure C-5. 

C-17
1999 precursor nonroad inventory 

Non99pre_out.sas7bdat 

Exclude aircraft, locomotive, and commercial marine vessel emissions 

Precursor nonroad HAP table haptable_precursor.txt 

Retain only CAS numbers of MSAT HAP rprecursors (except for precursors which are MSAT HAPs) 

cas_saroad 

Subset inventory to CAS in cas_sarroad and exclude Puerto Rico and the Virgin Islands and create new variable CAS1 with value of “VOC.” pre1 

pre_msat 

NMIM VOC emisions (voc_pm.sas7bdat) 

Keep only VOC emissions and create new CAS1 variable 

voc

_pm Create “Total” SCC for 

each FIPS/SCC 

Create total pleasure craft SCC voc_pm1 

pleasure_craft 

Sum by FIPS/SCC 

Sum by FIPS/SCC 

voc

_pm2 

craft 

Concatenate data voc

_pm Merge by 

FIPS/SCC/CAS 

merged1 

Split into data 

that has a 

projection factor 

and that does not 

no_match match 

Create county 

level engine/fuel 

types 

vocpm_eng 

sum by FIPS/engine type and calculate projection factors 

engine 

merge by FIPS/engine type/CAS1. Use first 6 digits of no_match SCC for engine type 

fixed 

Concatenate data 

and apply 

projection factors 

recombined 

precursor_marine_loco 

Extract 1,3-Butadiene, Acetaldehyde, MTBE, and Methanol 

trains_ships 

Concatenate data 

recombined1 

nonroad_proj 

Extract 1,3-Butadiene, Acetaldehyde, MTBE, and Methanol 

msat

_

haps 

Concatenate data 

and apply ratios 

Split data all_precursors 

non_2015 non_2020 non_2030 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

Figure C-5. Nonroad projection processing for precursors. 

C-18 
C.4 Non-MSAT HAPs non-road processing 

The remaining nonroad inventory contained HAPs not covered by the NMIM results. These 

included Antimony, Beryllium, Cadmium, Chlorine, Cobalt, Cumene, Lead, Methanol, Methyl 

Ethyl Ketone, Phenol, Phosphorus, and Selenium. In order to project these HAPs for the GPRA 

project, NMIM VOC and PM emissions would be used to calculate the projection factors instead 

of the actual HAP emissions as done for the MSAT HAPs. The metals, Antimony, Beryllium, 

Cadmium, Cobalt, Lead, and Selenium would use the PM emissions for projection and all the 

other HAPs would use the VOC emissions. 

The processing of the nonroad inventory for non MSAT HAPs followed a very similar procedure 

as the nonroad processing for MSAT HAPs: 

1. Subset 1999 NEI nonroad inventory to the non MSAT HAPs, excluding aircraft, marine 

commercial vessels, and locomotive emissions. These were projected separately from the 

other nonroad emissions as documented in Section 3.1. Assign a variable, CAS1 

denoting whether the HAP is to use VOC or PM projection factors. CAS1 = VOC for 

HAPs using VOC and CAS1 = PM for HAPs using PM factors. 

2. Assigned a CAS1 flag to the NMIM output to be used for merger with the NEI data. 

CAS1=CAS. 

3. Summed NMIM SCC emissions to a “Total” category for each SCC category (first 7 

digits of SCC) for each FIPS/CAS1/SCC. 

4. Summed up NMIM pleasure craft emissions by FIPS/CAS1, first four SCC digits 2282, 

and assign SCC 2282000000 to these emissions. Combine these emissions and the 

emissions from step 3 to the original NMIM output. 

5. Merged the NEI and NMIM output, by FIPS/SCC/CAS1, retaining all NEI observations. 

Split the data into a dataset that matched i.e. has a projection factor, and into dataset that 

did not matched, i.e. no projection factors. 

6. For remaining non-matching FIPS/CAS/HAP combinations, created a county level VOC 

or PM specific projection factor based on engine/fuel type by summing emissions for 

1999 and future year NMIM for each FIPS/HAP where HAP is VOC and PM and 

calculate a county level projection factor. These were then assigned to all SCC codes for 

each CAS based on the CAS1 value. The engine fuel types were: 2-stroke gasoline, 4­

stroke gasoline, diesel, LPG, CNG, and miscellaneous. 

C-19
7. Merged the output from step 6, cnty_sum, with the nonmatched dataset from step 5. 

Separate data into two datasets, observations with a projection factor (fill_data2) and 

those without a projection factor (need_data2). 

8. Concatenated output datasets from step 7 and the matched dataset from step 5. 

9. Appended the non-MSAT locomotive and commercial marine vessel emissions and 

aircraft projected emissions to the output from step 8. 

10. Projected the emissions to non-MSAT years using Equations 3 and 4. 

After projecting the non-MSAT emissions, the MSAT HAPs were appended to the non-MSAT 

projections. This included the locomotive and commercial marine vessel emissions. Also 

MSAT projected aircraft emissions were appended to the data. MSAT HAPs were then 

projected to non-MSAT years using Equations 3 and 4. 

C-20
Appendix D: Risk Calculations 

D.1 Cancer risk calculation methodology 

The following steps detail the cancer risk calculations in cancer_risk.sas (found in the MSAT 

rule docket EPA-HQ-OAR-2005-0036). 

1. Read in a sorted a SAS® dataset of census tracts and retained FIPS, state name, county 

name, tract ID, and tract population. 

2. Read in the URE and carcinogenic class for each MSAT HAP from the SAS® dataset 

msat_haps_tox_factors.sas7bdat (found in the MSAT rule docket EPA-HQ-OAR-2005­

0036), keeping only HAPs where the URE is nonzero and nonmissing. The dataset, 

msat_haps_tox_factors.sas7bdat was created from the ACCESS table, 0Toxicity in 

Master.mdb from Roy Smith. 

3. From the output of step 2, created a list of HAPs by SAROAD to be read in by a SAS® 

macro for further processing, beginning with step 4. 

4. Read in the tract level HAPEM5 output. 

5. Merged the output from step 4 with the URE data from step 2 using PROC SQL. 

6. Merged the tract population data from step 1 with the output from step 5. 

7. For each source sector in each tract, multiplied the tract level source sector concentration 

by the URE. 

8. Output the tract level risk estimates to a permanent dataset for the HAP. 

9. After computing risk estimates for each HAP, appended the HAP risk estimates to a 

master dataset containing risk estimates for all HAPs. 

Steps 5 through 9 were executed in the SAS® macro calc_risk. 

10. Repeated steps 4 through 9 for each HAP in the MACRO calc_risk. 

11. Sorted the master dataset by carcinogen class and FIPS/tract and summed the source 

category risks within carcinogen classes for each FIPS and tract. For example, for each 

tract, summed the risks for 1,3-butadiene, benzene, and nickel for Class A carcinogens. 

12. From the output of step 11, output permanent datasets for each carcinogen class. 

13. Sorted the master dataset by FIPS/TRACT and calculate a total risk (across all HAPs) for 

each source sector at the tract level. 

D-1
14. Output the total risk estimates to a permanent dataset. 

15. Calculated national risk distributions (percentiles and median) for each source category 

for each carcinogen class across all census tracts. 

16. Calculated a national risk distribution for each source category across all tracts and 

carcinogen class, i.e. total risk. 

17. Concatenated outputs from step 15 and step 16. 

18. Output to a permanent dataset. 

All 18 steps are done in the SAS® MACRO main for each modeling year and 1999 where 

the argument for the macro is the year and the flowchart of the program is shown in 

Figure D-1. 

D-2
URE and Rfc factors by 

SAROAD msat_haps_tox_factors.sas7bdat 

Retain only HAPs with nonzero or nonmissing URE. Keep SAROAD, URE, and carcinogen class 

2 

haps convert the SAROAD codes to a list of macro 

variables 

3 

_null_ 

Read in tract level HAPEM5 output for individual HAP 

4 

scratch 

Merge with URE data by SAROAD 

5 census tract data 

(tractdat_merged.sas7bdat) 1 

Sort and retain FIPS, state name, county name, tract ID, and population 

counties 

scratch1 

Merge by FIPS and tract ID 

6 scratch_pop 

Calculate tract level risk for each source category by multiplying the source category concentration by the URE 

7 

hap_risk Output to permanent dataset 

8 

risk_SAROAD 

Append to master dataset 

all

_

haps_risk 

Repeat steps 4 through 9 in MACRO calc

_

risk 10 

Calculate carcinogen class risk for each source category and tract 

risk

_

class 

Output to permanent datasets 

risk

_

a risk

_

b1 risk

_

b2 ris

Calculate total risk across all HAPs for each source category and tract 

risk

_

total 

Output to permanent dataset 

total_risk 

14 13

15 

Calculate national distribution of total risk (all sources) for each carcinogen class class_dist 

Calculate national distribution of total risk (all sources) 

16 

total_dist 

Concatenate 

17 

combined_dist 

Output to permanent dataset 

distributions 

18 

Figure D-1. Flowchart of the cancer risk calculations in cancer.sas. 

D-3
D.2 Non-cancer Risk Calculation methodology 

The following steps were used to calculate hazard quotients and hazard indices and summary 

statistics for 1999, 2015, 2020, and 2030 in the program noncancer.sas (found in the MSAT rule 

docket EPA-HQ-OAR-2005-0036): 

1. Read in and sorted a SAS® dataset of census tracts retain FIPS, state name, county name, 

tract ID, and tract population. 

2. Read in the Rfc and target organ system(s) for each MSAT HAP from the SAS® dataset 

msat_haps_tox_factors.sas7bdat, keeping only HAPs where the Rfc was non-zero and 

non-missing. 

3. From the output of step 1, created a list of HAPs by SAROAD to be read in by a SAS® 

macro for further processing, beginning with step 4. 

4. The tract level HAPEM5 output was read into a dataset. 

5. Merged the output from step 4 with the Rfc data from step 2 using PROC SQL. 

6. Merged the tract population data from step 1 with the output from step 5. 

7. For each source category, multiplied the tract level source category concentrations by 

0.001 and then divided by the Rfc to calculate the HAP's hazard quotient (HQ). 

8. Output the tract level HQ estimates to a permanent dataset for the HAP. 

9. Appended the HAP HQ estimates to a master dataset. 

Steps 4 through 9 were executed in the SAS® MACRO calc_hq. 

10. Repeated steps 4 through 9 for each HAP in the MACRO calc_hq. 

11. After performing steps 4 through 9 for each HAP, the master dataset was separated into 

multiple datasets, one for each target organ system. If a HAP affected more than one 

organ system, such as hexane, its HQ estimates would go to both the datasets for 

respiratory and neurological organ systems. 

12. Sorted each organ system dataset by FIPS/tract and calculated a hazard index (HI) for the 

organ system by summing the individual HQ estimates at the FIPS/tract level. This was 

done for each source category (major, area, onroad gasoline, etc.). 

13. Output each organ system's HI tract level estimates to a permanent dataset. 

D-4
14. Calculated a national distribution of HI estimates for each source category for the organ 

systems across all census tracts. 

15. Output distribution to dataset named for organ system. 

16. Repeat steps 12 through 15 in the MACRO stats. 

17. Once the HI and distributions had been calculated for each organ system, concatenated all 

the datasets into one dataset. 

18. Sorted step 17 output by organ system and output to a permanent dataset. 

All 18 steps were done for each modeling year and 1999 in the SAS® macro main with 

the macro's argument as the year and the processing is shown in Figure D-2. 

D-5
URE and Rfc factors by SAROAD 

msat_haps_tox_factors.sas7bdat 

Retain only HAPs with nonzero or nonmissing Rfc. Keep SAROAD, Rfc, and target organ systems 

2 

haps 

convert the SAROAD codes to a list of macro variables 

3 

_

null_ 

Read in tract level HAPEM5 output for individual HAP 

4 

Merge with Rfc data by SAROAD scratch 

5 

census tract data (tractdat_merged.sas7bdat) 

1 

Sort and retain FIPS, state name, county name, tract ID, and population counties 

scratch1 

Merge by FIPS and tract ID 

6 

scratch_pop 

Calculate tract level HQ for each source category by multiplying the source category concentration by 0.001 and dividing by Rfc 

7 

hap_hq 

Output to permanent dataset 

8 

hq_SAROAD.sas7bdat 

Append to master dataset 

all

_

haps_hq 

Repeat steps 4 through 9 iMACRO calc

_

risk 

10 

Break up data into separate datasets for each organ system 11 

MACRO STATS 

Calculate hazard index for each source category at tract level 

Output to permanent datasets 

hq_liver 

13 

sums 

Calculate national distribution of total HI (all sources). 

14 

total

_

dist 

Output to dataset named for organ system 

15 

Repeat steps 12 through 15 for each organ system 

16 

hq_dist_ocular, 

hq_dist_development, 

hq_dist_liver, 

hq_dist_immune, 

hq_dist_respiratory, 

hq_dist_kidney, 

hq_dist_reproductive, 

hq_dist_neurological 

Concatenate 

Sort and output 

permanent 

dataset 

17 

18 

hq_all_systems.sas7bdat 

all

_

systems 

liver kidney ocular development reproductive respiratory immune neurological 

hq_dist_liver 

hq_dist_kidney 

hq_dist_ocular 

hq_dist_development 

hq_dist_reproductive 

hq_dist_respiratory 

hq_dist_immune 

hq_dist_neurological 

hq_kidney hq_ocular hq_development hq_reproductive hq_respiratory 

hq_immune 

hq_neurological 

Figure D-2. Flowchart of the HQ and HI calculations in noncancer.sas. 

D-6 
Appendix E: Control of stationary refueling and gasoline marketing emissions 

Steps used in project_stationary_benz.sas (found in the MSAT rule docket EPA-HQ-OAR-2005­

0036) to develop the controlled gasoline inventories for benzene are listed below with example 

calculations for 2015 for Imperial County, CA (FIPS=06025) and Denver County, CO 

(FIPS=08031). 

1. Read the comma delimited county level refueling emissions for benzene and VOC and 

convert the integer state and county FIPS codes to character and combining into one code 

for the state/county. Retain records for benzene only. 

Perform step 1 for 2015 control emissions, 2015 base emissions, 2020 control emissions, 

and 2020 base emissions, resulting in step 1 being executed four times. 

2. Once the 2015 and 2020 control and base cases have been read into SAS®, merged the 

emissions by FIPS/CAS so that the control and base for both years are in one dataset. 

Table E-1. Partial listing of 2015 and 2020 base and controlled refueling emissions after 

merger (Steps 1 and 2). 

FIPS CAS refuel_15_base refuel_15_control refuel_20_base refuel_20_control 

06025 71432 0.123459112 0.0740812439 0.1337206137 0.0802398396 

08031 71432 1.0662192751 0.6716807497 1.1037468356 0.695301335 

3. Calculated 2015 projection factor by dividing the 2015 control refueling emissions by the 

2015 base refueling emissions and calculated 2020 projection factor by dividing the 2020 

control refueling emissions by the 2020 base refueling emissions. 

Table E-2. Partial listing of refueling projection factors after dividing control emissions 

by base emissions. 

FIPS CAS pf15_refuel pf20_refuel 

06025 71432 0.6000467907 0.6000558732 

08031 71432 0.6299649288 0.6299463904 

4. Read in a text file containing the FIPS codes for the 3,141 counties in the U.S. with their 

RFG status. Create a flag denoting the county as CG or RFG, rfg_status. If a county is 

an RFG county, rfg_status='RFG', otherwise rfg_status='CG.' 

Table E-3. Rfg status of Imperial and Denver counties. RFG=reformulated gasoline, 

CG=conventional gasoline. 

FIPS rfg_status 

06025 RFG 

08031 CG 

E-1
5. Sorted a SAS® dataset of all 66,300 tracts by FIPS, eliminating double values of FIPS 

and Puerto Rico and the Virgin Islands. Retain the FIPS and 2 letter state abbreviation. 

6. Assign the PADD region to the counties based on 2-letter state abbreviation. 

Table E-4. Partial listing of counties after assign PADD region (Steps 5 and 6). 

Region FIPS state 

CA 06025 CA 

PADD4 08031 CO 

7. Sorted the output of step 4, the rfg status data, by FIPS. 

Table E-5. Rfg status of Imperial and Denver counties after sorting. 

FIPS rfg_status 

06025 RFG 

08031 CG 

8. Merged the rfg status data (step 4 output) with the PADD/county data (output of step 5) 

and the refueling projection factors (step 3 output) by FIPS, retaining matching 

observations. 

Table E-6. Partial listing of counties after merging counties with rfg status and refueling 

projection factors. 

Region FIPS state rfg_status CAS pf15_refuel pf20_refuel 

CA 06025 CA RFG 71432 0.6000467907 0.6000558732 

PADD4 08031 CO CG 71432 0.6299649288 0.6299463904 

9. Create a dataset containing PADD region identifiers and emissions to develop projection 

factors for the gasoline marketing and distribution emissions (excluding refueling). 

10. Calculate the projection factors for the PADD regions output from step 9. 

Table E-7. PADD regions and percentages (Steps 9 and 10). 

Region rfg_status start end pf 

PADD1 CG 0.91 0.55 0.6043956044 

RFG 0.59 0.54 0.9152542373 

PADD2 CG 1.26 0.68 0.5396825397 

RFG 0.80 0.71 0.8875 

PADD3 CG 0.95 0.54 0.5684210526 

RFG 0.57 0.55 0.9649122807 

PADD4 CG 1.47 0.93 0.6326530612 

RFG 1.05 0.62 0.5904761905 

PADD5 CG 1.42 0.85 0.5985915493 

RFG 0.65 0.60 0.9230769231 

CA CG 0.62 0.61 0.9838709677 

RFG 0.62 0.61 0.9838709677 

E-2
11. Merged the output of step 8 with the step 10 output by FIPS using PROC SQL so that 

each county was assigned a PADD region and projection factor for gasoline marketing 

and distribution. 

Table E-8. Refueling projection factors and gasoline marketing projection factors after 

merging the county dataset with the PADD regions dataset. 

Region FIPS state rfg_status CAS pf15_refuel pf20_refuel pf 

CA 06025 CA RFG 71432 0.6000467907 0.6000558732 0.9838709677 

PADD4 08031 CO CG 71432 0.6299649288 0.6299463904 0.6326530612 

12. Read in a text file of SCC codes pertaining to gasoline marketing and distribution. 

Created a variable called gas_flag and gave it a value of 1 to help identify these codes in 

the inventory later. 

Table E-9. Partial listing of gasoline marketing and distribution SCC codes with flag 

indicating them as marketing/distribution SCC codes. SCC descriptions added for 

reference only. 

SCC gas_flag Description 

2501000000 1 Storage and Transport; Petroleum and Petroleum Product Storage; All 

Storage Types: Breathing Loss; Total: All Products 

2501050120 1 Storage and Transport; Petroleum and Petroleum Product Storage; All 

Storage Types: Breathing Loss; Total: All Products 

2501060050 1 Storage and Transport; Petroleum and Petroleum Product Storage; 

Gasoline Service Stations; Stage 1: Total 

2501060051 1 Storage and Transport; Petroleum and Petroleum Product Storage; 

Gasoline Service Stations; Stage 1: Submerged Filling 

2501060052 1 Storage and Transport; Petroleum and Petroleum Product Storage; 

Gasoline Service Stations; Stage 1: Splash Filling 

E-3
Steps 13 through 20 are performed in the MACRO point for the following cases: 2015 point inventory

airport inventory, 2020 point inventory, and 2020 non-point airport inventory. 13. From the projected point or airport inventory for 2015 or 2020, pull the benzene emissions, based on SAR

from the inventory. Calculate a variable, gcemis which is the average of the eight temporally allocated empurposes. Table E-10. Partial listing of benzene point source emissions with key variables. 

FIPS site_id emrelpid src_type SCC temis1 temis2 temis3 temis4 temis5 

06025 06025-13151144 111-11-1 AREA 40688801 0.00033 0.00033 0.00033 0.00033 0.00033 06025 06025-13151177 3081-308-1 AREA 40600403 0.01006 0.01006 0.01006 0.01006 0.01006 06025 06025-13151115 2M-3-2 AREA 10300601 0.02940 0.02940 0.02940 0.02940 0.02940 08031 08031-1194 001-001-01 AREA 40400401 0.01398 0.01398 0.01398 0.01398 0.01398 08031 08031-1713 001-001-03 AREA 40600401 0.07731 0.07731 0.07731 0.07731 0.07731 08031 08031-13388 69583-55912­

69185 

AREA 10100601 0.00253 0.00252 0.00254 0.00255 0.00267 

14. Using PROC SQL, m

erge the benzene emissions from step 13 with the SCC list created in step 12 by SC

observations and data from the benzene inventory and the gas_flag variable. Emissions that are not gasolm

arketing/distribution will have a missing value for the gas_flag and emissions that are gasoline m

arketin

have a value of 1 for the gas_flag. Table E-11. Partial listing of benzene point sources after m

erging with gasoline m

arketing/distribution S

FIPS site_id emrelpid src_type SCC temis1 temis2 temis3 temis4 temis5 temis6 

06025 06025­

13151144 

111-11-1 AREA 40688801 0.00033 0.00033 0.00033 0.00033 0.00033 0.00033 

06025 06025­

13151177 

3081-308-1 AREA 40600403 0.01006 0.01006 0.01006 0.01006 0.01006 0.01006 

06025 06025­

13151115 

2M-3-2 AREA 10300601 0.02940 0.02940 0.02940 0.02940 0.02940 0.02940 

08031 08031-1194 001-001-01 AREA 40400401 0.01398 0.01398 0.01398 0.01398 0.01398 0.01398 08031 08031-1713 001-001-03 AREA 40600401 0.07731 0.07731 0.07731 0.07731 0.07731 0.07731 08031 08031-13388 69583­

55912­69185 

AREA 10100601 0.00253 0.00252 0.00254 0.00255 0.00267 0.00254 

E-4
15. Split the benzene inventory into two datasets: gasoline and others. Output observations to the gasoline d

value of 1 for the gas_flag OR they are a vehicle refueling SCC (shown in Table 23). Otherwise output tThe others dataset contains non-gasoline marketing/distribution or vehicle refueling emissions. Table E-12. Partial listing of gasoline related emissions after splitting gasoline related emissions and noemissions. 

FIPS site_id emrelpid src_type SCC temis1 temis2 temis3 temis4 temis5 temis6 

06025 06025­

13151144 

111-11-1 AREA 40688801 0.00033 0.00033 0.00033 0.00033 0.00033 0.00033 

06025 06025­

13151177 

3081-308-1 AREA 40600403 0.01006 0.01006 0.01006 0.01006 0.01006 0.01006 

08031 08031-1194 001-001-01 AREA 40400401 0.01398 0.01398 0.01398 0.01398 0.01398 0.01398 08031 08031-1713 001-001-03 AREA 40600401 0.07731 0.07731 0.07731 0.07731 0.07731 0.07731 

Table E-13. Partial listing of non-gasoline related emissions after splitting gasoline related emissions anemissions. 

FIPS site_id emrelpid src_type SCC temis1 temis2 temis3 temis4 temis5 temis6 

06025 06025­ 2M-3-2 AREA 10300601 0.02940 0.02940 0.02940 0.02940 0.02940 0.02940 0

13151115 

08031 08031­ 69583­ AREA 10100601 0.00253 0.00252 0.00254 0.00255 0.00267 0.00254 0

13388 55912­

69185 

16. Using PROC SQL, merge the gasoline dataset from step 15 with the projection factor data from step 11 bTable E-14. Partial listing of gasoline related emissions with projection factors. Note not all variables show

FIPS site_id SCC temis1 temis2 temis3 temis4 temis5 temis6 temis7 temis8 gas_f

06025 06025­

13151144 

40688801 0.00033 0.00033 0.00033 0.00033 0.00033 0.00033 0.00033 0.00033 

06025 06025­

13151177 

40600403 0.01006 0.01006 0.01006 0.01006 0.01006 0.01006 0.01006 0.01006 

08031 08031-1194 40400401 0.01398 0.01398 0.01398 0.01398 0.01398 0.01398 0.01398 0.01398 08031 08031-1713 40600401 0.07731 0.07731 0.07731 0.07731 0.07731 0.07731 0.07731 0.07731 

E-5
17. Apply the projection factors to each of the eight temporally allocated projected emissions. If a gasoline m

SCC emission (gas_flag=1), apply the projection factor based on the PADD data from step 10. Otherwistemporally allocated projected emissions by the appropriate year's projection factor for vehicle refueling. Table E-15. Partial listing of gasoline related emissions after applying appropriate projection factors to ewith gas_flag=1 use the pf number while the others use the pf15_refuel number. 

FIPS site_id SCC temis1 temis2 temis3 temis4 temis5 temis6 temis7 temis8 gas_f

06025 06025­

13151144 

40688801 0.00033 0.00033 0.00033 0.00033 0.00033 0.00033 0.00033 0.00033 

06025 06025­

13151177 

40600403 0.00604 0.00604 0.00604 0.00604 0.00604 0.00604 0.00604 0.00604 

08031 08031-1194 40400401 0.00885 0.00885 0.00885 0.00885 0.00885 0.00885 0.00885 0.00885 08031 08031-1713 40600401 0.04870 0.04870 0.04870 0.04870 0.04870 0.04870 0.04870 0.04870 18. Concatenate the output of step 17 with the others dataset created in step 15. Note the projected and contro

step 17 have the same variable name as the projected only emissions from the dataset others. This is to kPtFinal_ASPEN. 

19. Sort the concatenated data from step 18 by FIPS site id emrelpid SAROAD MACT SIC and SCC and out

dataset. Table E-16. Partial listing of point emissions after concatenating controlled emissions with the non-gasosorting, and output to a permanent dataset (Steps 18 and 19). 

FIPS site_id emrelpid src_type SCC temis1 temis2 temis3 temis4 temis5 temi

06025 06025­ 2M-3-2 AREA 10300601 0.02940 0.02940 0.02940 0.02940 0.02940 0.029

13151115 

06025 06025­

13151144 

111-11-1 AREA 40688801 0.00033 0.00033 0.00033 0.00033 0.00033 0.000

06025 06025­

13151177 

3081-308-1 AREA 40600403 0.00604 0.00604 0.00604 0.00604 0.00604 0.006

08031 08031-1194 001-001-01 AREA 40400401 0.00885 0.00885 0.00885 0.00885 0.00885 0.00808031 08031-13388 69583­ AREA 10100601 0.00253 0.00252 0.00254 0.00255 0.00267 0.002

55912­69185 

08031 08031-1713 001-001-03 AREA 40600401 0.04870 0.04870 0.04870 0.04870 0.04870 0.048

E-6
20. Sort the 1999 non-point COPAX output for non-airport emissions by SCC to get the 

surrogate codes used in EMS-HAP. Sort only where the CAS = 71432 (benzene). This 

data is needed to merge with the controlled non-point emissions for EMS-HAP input. 

Table E-17. Partial listing of non-point SCC codes and surrogate codes. SCC and 

surrogate descriptions added for informational purposes only. 

SCC spatsurr SCC description Surrogate 

description 

2102004000 505 Stationary Source Fuel Combustion; Industrial; Distillate Oil; 

Total: Boilers and IC Engines 

Industrial land 

2501050120 650 Storage and Transport; Petroleum and Petroleum Product 

Storage; Bulk Stations/Terminals: Breathing Loss; Gasoline 

Refineries and 

tank farms 

2501060102 600 Storage and Transport; Petroleum and Petroleum Product 

Storage; Gasoline Service Stations; Stage 2: Displacement 

Loss/Controlled 

Gas stations 

Steps 21-28 are performed in the MACRO nonpoint for the following cases: 2015 nonpoint (excluding airport emissions) and 2020 non-point (excluding airport emissions). 

21. Extract the benzene emissions from the projected non-point inventory and assign the 

benzene CAS number to the observations. 

Table E-18. Partial listing of benzene non-point emissions with key variables. 

FIPS CAS SCC emisgc 

06025 71432 2102004000 1.3460706 

06025 71432 2501050120 0.08181288 

06025 71432 2501060102 0.067603788 

22. Using PROC SQL, merge the benzene emissions from step 22 with the gasoline 

marketing/distribution SCC list from step 12, retaining all observations and data from the 

benzene inventory and the gas_flag variable. Emissions that are not gasoline 

marketing/distribution will have a missing value for the gas_flag and emissions that are 

gasoline marketing/distribution will have a value of 1 for the gas_flag. 

Table E-19. Partial listing of benzene emissions after merging with the gasoline 

marketing/distribution SCC list. 

FIPS CAS SCC emisgc gas_flag 

06025 71432 2102004000 1.3460706 . 

06025 71432 2501050120 0.08181288 1 

06025 71432 2501060102 0.067603788 . 

23. Split the benzene inventory into two datasets: gasoline and others. Output observations 

to the gasoline dataset if they have a value of 1 for the gas_flag OR they are a vehicle 

refueling SCC (shown in Table X). Otherwise output to the others dataset. The others 

dataset contains non-gasoline marketing/distribution or vehicle refueling emissions. 

E-7 
Table E-20. Partial listing of benzene gasoline related emissions after splitting gasoline 

and non-gasoline emissions. 

FIPS CAS SCC emisgc gas_flag 

06025 71432 2501050120 0.08181288 1 

06025 71432 2501060102 0.067603788 . 

Table E-21. Partial listing of benzene non-gasoline related emissions after splitting 

gasoline and non-gasoline emissions. 

FIPS CAS SCC emisgc gas_flag 

06025 71432 2102004000 1.3460706 . 

24. Using PROC SQL, merge the gasoline dataset from step 24 with the projection factor 

data from step 11 by FIPS. 

Table E-22. Partial listing of benzene gasoline emissions after merging with projection 

factors. 

FIPS CAS SCC emisgc gas_flag pf pf15_refuel 

06025 71432 2501050120 0.08181288 1 0.98387096777 0.6000467907 

06025 71432 2501060102 0.067603788 . 0.98387096777 0.6000467907 

25. Apply the projection factors to the annual projected emissions. If a gasoline 

marketing/distribution SCC emission (gas_flag=1), apply the projection factor based on 

the PADD data from step 10. Otherwise, multiply the annual projected emissions by the 

appropriate year's projection factor for vehicle refueling. 

Table E-23. Partial listing of benzene gasoline related emissions after applying 

appropriate projection factors. If gas_flag =1 apply pf, otherwise apply pf15_refuel to 

emissions. 

FIPS CAS SCC emisgc gas_flag pf pf15_refuel 

06025 71432 2501050120 0.0804933174 1 0.98387096777 0.6000467907 

06025 71432 2501060102 0.040565436 . 0.98387096777 0.6000467907 

26. Concatenate the output of step 26 with the others dataset created in step 24. 

Table E-24. Partial listing of benzene emissions after concatenating controlled emissions 

with non-gasoline emissions. 

FIPS CAS SCC emisgc 

06025 71432 2501050120 0.0804933174 

06025 71432 2501060102 0.040565436 

06025 71432 2102004000 1.3460706 

27. Using PROC SQL, merge the emissions with the SCC/surrogate cross reference created 

in step 21 by SCC. This is to assign surrogate codes to the emissions, which is needed for 

CountyProc for non-point sources. 

E-8
Table E-25. Partial listing of benzene emissions after merging the emissions with the 

spatial surrogate codes. 

FIPS CAS SCC emisgc spatsurr 

06025 71432 2501050120 0.0804933174 650 

06025 71432 2501060102 0.040565436 600 

06025 71432 2102004000 1.3460706 505 

28. Sort step 27 output by FIPS MACT SIC SCC and CAS and output to permanent dataset. 

Table E-26. Partial listing of benzene emissions after sorting by FIPS/SCC/CAS and 

renaming the emissions variable emisgc to emis and output to permanent dataset. 

FIPS CAS SCC emis spatsurr 

06025 71432 2102004000 1.3460706 505 

06025 71432 2501050120 0.0804933174 650 

06025 71432 2501060102 0.040565436 600 

Figure E-1 shows the first 19 steps of the program and Figure E-2 shows the non-point steps. 

MSATBenzR2015_control.csv 

MSATBenzR2015_base.csv 

MSATBenzR2020_control.csv 

MSATBenzR2020_base.csv 

Read refueling comma 

delimited files and retain 

only benenze. Repeat 

this step for each of the 

files 1 

refuel_15_base refuel_15_control refuel_20_base refuel_20_control 

Merge by FIPS/CAS 

2 

merged_refuel calculate projection 

factors 

merged_refuel 

3 

rfg_counties.txt tractdat_merged.sas7bdat 

read in RFG status of 

counties 

sort by FIPS, 

eliminating duplicate 

observations 

4 

5 

rfg 

sort by FIPS 

rfg 

counties 

Assign PADD region 

7 6 

8 

counties 

rfg_counties Merge by FIPS Create a dataset of 

factors by PADD 

9 

factors 

Calculate projection 

factors 10 factors Merge by PADD and 

RFG status cnty_factors 

11 

gasoline_scc.txt read in SCC list and 

create flag variable 

12 

scc_list 

proj_cp_pt15.sas7bdat 

ap_np_gair15.sas7bdat 

proj_cp_pt20.sas7bdat 

ap_np_gair20.sas7bdat 

Extract benzene. do for point and 

airports for each year. 

13 

point Merge by SCC 

14 

gasoline 

others 

point1 Split data 

15 

Merge by FIPS gasoline_factors 

16 

17 

Apply control 

factors to emissions 

19 18

gasoline_fac 

all Concatenate 

Sort and output to permanent 

dataset 

point_2015.sas7bdat 

point_2020.sas7bdat 

ap_pt_2015.sas7bdat 

ap_pt_2020.sas7bdat 

Figure E-1. Steps in point gasoline inventory control program. 

E-9
scc_list 

nonpt99_aspen_ap.sas7bdat 

arnonpt_proj_np15.sas7bdat 

arnonpt_proj_np20.sas7bdat 

Extract benzene. 

do for each year. 

nonpoint 

Merge by 

SCC 

nonpoint1 

Split data 

Merge by gasoline others 

FIPS 

gasoline_factors 

cnty_factors 

20 

Sort by SCC where CAS =71432, 

eliminating duplicate 

observations 

nonpt_scc 

21 

Apply control 

factors to 

emissions 

gasoline_fac 

Concatenate all 

Sort and output to 

permanent dataset 

Merge by 

SCC 

all2 

22 

23 

24 

25 

26 

27 

28 

nonpoint_2015.sas7bdat nonpoint_2020.sas7bdat 

Figure E-2. Non-point steps of the stationary gasoline controls program. 

E-10
Appendix F: Control of onroad gasoline emissions 

Following are the steps and examples for Alpine County, CA (FIPS=06003) for benzene for the 

year 2015 in applying controls to the onroad gasoline emissions. Example calculations are also 

shown below. Controls are done in the SAS® program control_onroad.sas (found in the MSAT 

rule docket EPA-HQ-OAR-2005-0036). All steps take place within the SAS® MACRO control 

where the argument for control is the four digit year. Steps 1 and 2 are performed in the SAS® 

MACRO control where the argument for control is the four digit year. 

1. Read in the projected MSAT emissions (output from onroad.sas in Section 3.3.2) and 

subset the emissions to the five HAPs. 

Table F-1. Partial listing of 2015 projected benzene emissions for Alpine County. Note 

that SCC descriptions are listed here for information purposes only. The variables emis, 

ratio, and emis_nei are the 2015 projected emissions, the projection factor for 2015, and 

the 1999 NEI emissions respectively. 

FIPS SCC SCC description CAS emis ratio emis_nei 

06003 2201001130 Mobile Sources, Highway 

Vehicles - Gasoline, Light 

Duty Gasoline Vehicles 

(LDGV), Rural Other 

Principal Arterial: Total 

71432 0.0578743127 0.269621769 0.21465 

06003 2201001150 Mobile Sources, Highway 

Vehicles - Gasoline, Light 

Duty Gasoline Vehicles 

(LDGV), Rural Minor 

Arterial: Total 

71432 0.0330683928 0.2681076114 0.12334 

06003 2201001170 Mobile Sources, Highway 

Vehicles - Gasoline, Light 

Duty Gasoline Vehicles 

(LDGV), Rural Major 

Collector: Total 

71432 0.0323135532 0.2666134753 0.1212 

06003 2201080130 Mobile Sources, Highway 

Vehicles - Gasoline, 

Motorcycles (MC),Rural 

Other Principal Arterial: Total 

71432 0.0041931539 0.6348454049 0.006605 

06003 2201080150 Mobile Sources, Highway 

Vehicles - Gasoline, 

Motorcycles (MC),Rural 

Minor Arterial: Total 

71432 0.0024060641 0.6348454049 0.00379 

06003 2230001130 Mobile Sources, Highway 

Vehicles - Diesel, Light Duty 

Diesel Vehicles 

(LDDV),Rural Other Principal 

Arterial: Total 

71432 0.0011643267 0.377415447 0.003085 

06003 2230001150 Mobile Sources, Highway 

Vehicles - Diesel, Light Duty 

Diesel Vehicles (LDDV), 

Rural Minor Arterial: Total 

71432 0.0006597704 0.3706575353 0.00178 

F-1
2. Split the output from step 1 into two datasets, gasoline and diesel. Gasoline contained 

gasoline emissions and diesel contained diesel emissions. The first four characters of the 

SCC code were used to determine if the observation being read was gasoline or diesel. If 

the first four characters were 2201 then the observation was gasoline, otherwise it was 

diesel. 

Table F-2. Partial listing of Alpine County gasoline emissions after splitting the gasoline 

and diesel emissions. The variables ratio and emis_nei have been dropped. SCC 

description has also been dropped. 

FIPS SCC CAS emis 

06003 2201001130 71432 0.0578743127 

06003 2201001150 71432 0.0330683928 

06003 2201001170 71432 0.0323135532 

06003 2201080130 71432 0.0041931539 

06003 2201080150 71432 0.0024060641 

Table F-3. Partial listing of Alpine County diesel emissions after splitting the gasoline 

and diesel emissions. The variables ratio and emis_nei have been dropped. SCC 

description has also been dropped. 

FIPS SCC CAS emis 

06003 2230001130 71432 0.0011643267 

06003 2230001150 71432 0.0006597704 

3. Create a dataset of motorcycle SCC codes to be used later. 

Table F-4. Dataset of motorcycle SCC codes. SCC description added for information 

purposes. 

SCC Description 

2201080110 Mobile Sources, Highway Vehicles - Gasoline, Motorcycles (MC), Rural Interstate: 

Total 

2201080130 Mobile Sources, Highway Vehicles - Gasoline, Motorcycles (MC), Rural Other 

Principal Arterial: Total 

2201080150 Mobile Sources, Highway Vehicles - Gasoline, Motorcycles (MC), Rural Minor 

Arterial: Total 

2201080170 Mobile Sources, Highway Vehicles - Gasoline, Motorcycles (MC), Rural Major 

Collector: Total 

2201080190 Mobile Sources, Highway Vehicles - Gasoline, Motorcycles (MC), Rural Minor 

Collector: Total 

2201080210 Mobile Sources, Highway Vehicles - Gasoline, Motorcycles (MC), Rural Local: Total 

2201080230 Mobile Sources, Highway Vehicles - Gasoline, Motorcycles (MC), Urban Interstate: 

Total 

2201080330 Mobile Sources, Highway Vehicles - Gasoline, Motorcycles (MC), Urban Local: Total 

F-2
Steps 4 through 18 are performed in the SAS® MACRO read_nmim. 

4. Read in the NMIM emissions for MSAT and subset to the five HAPs and gasoline 

emissions only. The first four digits of the SCC code were used to determine if the 

emissions were gasoline as in step 2 above. 

5. Sorted output from step 4 by FIPS, SCC, and CAS. 

Table F-5. Partial listing of Alpine County base MSAT NMIM onroad gasoline 

emissions for benzene after sorting. (Steps 4 and 5). The variable nmim_msat is the 

NMIM emissions. 

FIPS SCC CAS nmim_msat 

06003 2201001130 71432 0.0223670533 

06003 2201001150 71432 0.0129730606 

06003 2201001170 71432 0.0128725145 

6. Read the NMIM control emissions comma delimited file and subset to the five HAPs and 

gasoline emissions. Create FIPS variable from the integer state and county FIPS 

variables. 

7. Sorted the output from step 6 by FIPS, SCC, and CAS. 

Table F-6. Partial listing of the control NMIM output after sorting (Steps 6 and 7). Note 

that each SCC is listed twice, one entry for exhaust emissions and the other for 

evaporative emissions. Emissions type, exhaust or evaporative, was not retained. 

FIPS SCC CAS emis 

06003 2201001130 71432 0.019649687 

06003 2201001130 71432 0.0009109781 

06003 2201001150 71432 0.0113615684 

06003 2201001150 71432 0.0005509424 

06003 2201001170 71432 0.0112324624 

06003 2201001170 71432 0.0005728415 

06003 2201080130 71432 0 

06003 2201080130 71432 0 

06003 2201080150 71432 0 

06003 2201080150 71432 0 

F-3
8. Summed the output from step 7 by FIPS, SCC, and CAS. This was done because the 

emissions were broken down by evaporative and exhaust types. 

Table F-7. Partial listing of the control NMIM emissions after summing exhaust and 

evaporative components. 

FIPS SCC CAS nmim_cntrl 

06003 2201001130 71432 0.0205606651 

06003 2201001150 71432 0.0119125108 

06003 2201001170 71432 0.0118053039 

06003 2201080130 71432 0 

06003 2201080150 71432 0 

9. Merged the output of step 8 with the output of step 4 by FIPS, SCC, and CAS. Output 

datasets were merged, no_msat, and no_cntrl. The dataset merged contained matching 

observations, no_msat contained observations from step 8 output ? are you sure ? I 

thought no_msat had no step 8 (controlled NMIM) emissions, but did have the reference 

NMIM emissions? that were not in the original MSAT NMIM emissions, and no_cntrl 

contained observations where there were original MSAT emissions but no matching 

observations in the step 8 output (this dataset was always empty). The dataset no_msat 

contained observations where the control emissions were 0. 

Table F-8. Partial listing of merged base MSAT NMIM data and control NMIM data 

with matching observations (merged). 

FIPS SCC CAS nmim_cntrl nmim_msat 

06003 2201001130 71432 0.0205606651 0.0223670533 

06003 2201001150 71432 0.0119125108 0.0129730606 

06003 2201001170 71432 0.0118053039 0.0128725145 

Table F-9. Partial listing of emissions where there were no base MSAT NMIM 

observations but there were control NMIM emissions (no_msat). The control emissions 

are zero in this dataset. 

FIPS SCC CAS nmim_cntrl nmim_msat 

06003 2201080130 71432 0 . 

06003 2201080150 71432 0 . 

10. Created a dataset from no_msat where the control emissions were nonzero as a second 

check. This dataset was always empty. 

11. Subset the output from step 8 to the three California counties (Modoc, Sierra, and Alpine) 

that did not have NMIM motorcycle emissions. Note, there are observations for these in 

the NMIM data for both the MSAT and control runs, but they are zero for all years. 

F-4
12. Sorted the output of step 11 by FIPS and CAS. 

Table F-9. Partial listing of emissions after subsetting to California and sorting by CAS 

(Steps 11 and 12). 

FIPS SCC CAS nmim_cntrl nmim_msat 

06003 2201001130 71432 0.0205606651 0.0223670533 

06003 2201001150 71432 0.0119125108 0.0129730606 

06003 2201001170 71432 0.0118053039 0.0128725145 

13. Summed the output of step 12 by FIPS and CAS to get county level HAP emissions. 

Table F-10. Summed emissions for Alpine County. 

FIPS CAS nmim_cntrl nmim_msat 

06003 71432 0.3708404994 0.403293093 

14. Merged the output of step 13 with the motorcycle SCC data from step 13. This would 

basically expand the output of step 13 8 times. 

Table F-11. Partial listing of emissions after merging county emissions with motorcycle 

SCC codes. 

FIPS CAS nmim_cntrl nmim_msat SCC 

06003 71432 0.3708404994 0.403293093 2201080110 

06003 71432 0.3708404994 0.403293093 2201080130 

06003 71432 0.3708404994 0.403293093 2201080150 

06003 71432 0.3708404994 0.403293093 2201080170 

06003 71432 0.3708404994 0.403293093 2201080190 

06003 71432 0.3708404994 0.403293093 2201080210 

06003 71432 0.3708404994 0.403293093 2201080230 

06003 71432 0.3708404994 0.403293093 2201080330 

15. Concatenated the output of step 9 and step 14. 

16. Sorted the output of step 15 by FIPS, SCC, and CAS. 

Table F-12. Partial listing of concatenated data shown in Table F-8 and F-11 after 

sorting (Steps 15 and 16). 

FIPS SCC CAS nmim_cntrl nmim_msat 

06003 2201001130 71432 0.0205606651 0.0223670533 

06003 2201001150 71432 0.0119125108 0.0129730606 

06003 2201001170 71432 0.0118053039 0.0128725145 

06003 2201080110 71432 0.3708404994 0.403293093 

06003 2201080130 71432 0.3708404994 0.403293093 

06003 2201080150 71432 0.3708404994 0.403293093 

06003 2201080170 71432 0.3708404994 0.403293093 

06003 2201080190 71432 0.3708404994 0.403293093 

06003 2201080210 71432 0.3708404994 0.403293093 

06003 2201080230 71432 0.3708404994 0.403293093 

06003 2201080330 71432 0.3708404994 0.403293093 

F-5
17. Created a projection or control factor by dividing the control NMIM emissions by the 

MSAT NMIM emissions for each FIPS, SCC, and CAS. 

Table F-13. Partial listing of emissions and projection factors. 

FIPS SCC CAS nmim_cntrl nmim_msat pf 

06003 2201001130 71432 0.0205606651 0.0223670533 0.9192388834 

06003 2201001150 71432 0.0119125108 0.0129730606 0.9182498384 

06003 2201001170 71432 0.0118053039 0.0128725145 0.9170938514 

06003 2201080110 71432 0.3708404994 0.403293093 0.9195309959 

06003 2201080130 71432 0.3708404994 0.403293093 0.9195309959 

06003 2201080150 71432 0.3708404994 0.403293093 0.9195309959 

06003 2201080170 71432 0.3708404994 0.403293093 0.9195309959 

06003 2201080190 71432 0.3708404994 0.403293093 0.9195309959 

06003 2201080210 71432 0.3708404994 0.403293093 0.9195309959 

06003 2201080230 71432 0.3708404994 0.403293093 0.9195309959 

06003 2201080330 71432 0.3708404994 0.403293093 0.9195309959 

18. Created a dataset of the first five observations of step 17 output for QA purposes to 

visually check calculations. 

19. Merged the gasoline dataset from step 2 with the output of step 17 by FIPS, SCC, and 

CAS. Two datasets were created, merged_gas, containing matching observations and 

no_nmim, which contained observations from gasoline but not in step 17 output. This 

dataset was empty. 

Table F-14. Partial listing of emissions after merging the projected emissions shown in 

Table F-1 with the factors shown in Table F-13. 

FIPS SCC CAS emis nmim_cntrl nmim_msat pf 

06003 2201001130 71432 0.0578743127 0.0205606651 0.0223670533 0.9192388834 

06003 2201001150 71432 0.0330683928 0.0119125108 0.0129730606 0.9182498384 

06003 2201001170 71432 0.0323135532 0.0118053039 0.0128725145 0.9170938514 

06003 2201080130 71432 0.0041931539 0.3708404994 0.403293093 0.9195309959 

06003 2201080150 71432 0.0024060641 0.3708404994 0.403293093 0.9195309959 

20. Applied the factors calculated in step 17 to the projected MSAT emissions. Created a 

variable emis_msat, which was the original MSAT projected emissions. 

Table F-15. Partial listing of emissions after applying factor to projected 2015 

emissions. Note nmim_cntrl and nmim_msat not shown but are in dataset. 

FIPS SCC CAS emis pf emis_msat 

06003 2201001130 71432 0.0532003187 0.9192388834 0.0578743127 

06003 2201001150 71432 0.0303650464 0.9182498384 0.0330683928 

06003 2201001170 71432 0.0296345609 0.9170938514 0.0323135532 

06003 2201080130 71432 0.003855735 0.9195309959 0.0041931539 

06003 2201080150 71432 0.0022124505 0.9195309959 0.0024060641 

F-6
21. Concatenated step 20 output with the diesel data from step 2. 

22. For the diesel emissions, set the variable emis_msat equal to the diesel emissions for QA 

purposes. 

23. Sorted the step 22 output by FIPS, SCC, and CAS to a permanent dataset with the year in 

the dataset name. 

Table F-16. Partial listing of emissions after concatenating controlled emissions with 

diesel emissions and set the variable emis_msat for diesel equal to the emis variable for 

diesel, sorting and creating a permanent dataset (Steps 21 through 23). 

FIPS SCC CAS emis pf emis_msat 

06003 2201001130 71432 0.0532003187 0.9192388834 0.0578743127 

06003 2201001150 71432 0.0303650464 0.9182498384 0.0330683928 

06003 2201001170 71432 0.0296345609 0.9170938514 0.0323135532 

06003 2201080130 71432 0.003855735 0.9195309959 0.0041931539 

06003 2201080150 71432 0.0022124505 0.9195309959 0.0024060641 

06003 2230001130 71432 0.0011643267 . 0.0011643267 

06003 2230001150 71432 0.0006597704 . 0.0006597704 

24. Created a dataset of the first five observations of step 23 output to visually check 

calculations. 

Figure F-1 shows the steps of the program. 

F-7
onroad_20XX.sas7bdat where 

XX is 15, 20, or 30. Subset to the 5 HAPs scratch 

1 

Split into gasoline & 

diesel emissions 

gasoline 

diesel 

2 

Create a dataset of 

motorcycle SCC codes mc 

3 

ratios_on.sas7bdat where XX 

is 15, 20, or 30. 

MSATBenzO20XX.csv where 

XX is 15, 20, or 30. 

Subset base NMIM 

emissions to the 5 HAPs 

and gasoline emissions 

Read in the control 

NMIM emissions and 

subset to 5 HAPs and 

gasoline emissions. 

4 

msat_nmim 

6 

on1 

Sort by 

FIPS/SCC/CAS 

5 

msat_nmim 

Sort by FIPS/SCC/CAS 

7 

Sum by 

FIPS/SCC/CAS 

on1 

8 on2 

Merge by FIPS/SCC/CAS 

9 

merged 

no_cntrl no_msat 

Output observations 

where control 

emissions are 

nonzero 

10 

check 

Subset to Modoc, Alpine, and 

Sierra Counties, CA 

11 

ca_counties 

Sort by FIPS/CAS ca_counties 

12 13 Sum by FIPS/CAS 

ca_cnty_sum 

Merge 

ca_mc 

14 

Concatenate 

15 

all 

Sort by FIPS/SCC/CAS 

16 

all 

Calculate 

factors 

17 

factors 

Output observation 

for QA 

18 

junk 

Merge by 

FIPS/SCC/CAS 

19 

no_nmim 

merged_gas 

apply 

factors 

merged_gas 

20 

Concatenate 

21 

onroad 

set base and control 

emissions equal for 

diesel. 

22 

onroad 

Sort by 

FIPS/SCC/CAS onroad_control_20XX.sas7bdat 

23 

Output observations 

for QA 

junk 

24 

Figure F-1. Steps in the onroad gasoline controls program. 

F-8
Appendix G: Development of controlled nonroad inventory 

G.1. Calculation of exhaust and evaporative fractions 

Following are the steps taken in the SAS® program calc_factors.sas (found in the MSAT rule 

docket EPA-HQ-OAR-2005-0036) to develop the exhaust and evaporative fractions for use in 

developing the controlled nonroad inventory. Example calculations for Autauga County, AL are 

shown. 

1. Read in the base onroad NMIM emissions comma delimited file and subset emissions to 

the five HAPs (by CAS) and LDGV emissions (first seven characters of SCC code = 

2201001). Emissions were by emtype (exhaust or evaporative). 

2. Sorted by FIPS/CAS/emtype. 

Table G-1. Partial listing of NMIM base LDGV emissions after sorting by FIPS, CAS, 

and emtype. (Steps 1 and 2). Emtype is exh for exhaust and eva is for evaporative. 

FIPS SCC CAS emtype emis 

01001 2201001110 106990 Exh 0.1329114838 

01001 2201001130 106990 Exh 0.0224262833 

01001 2201001150 106990 Exh 0.0274003661 

01001 2201001110 107028 Exh 0.0149620306 

01001 2201001130 107028 Exh 0.0025289852 

01001 2201001150 107028 Exh 0.0030914513 

01001 2201001110 50000 Exh 0.2919313181 

01001 2201001130 50000 Exh 0.0491281133 

01001 2201001150 50000 Exh 0.0599782676 

01001 2201001110 71432 Eva 0.1188655905 

01001 2201001130 71432 Eva 0.0226015763 

01001 2201001150 71432 Eva 0.0290840303 

01001 2201001110 71432 Exh 1.2735248953 

01001 2201001130 71432 Exh 0.2144948896 

01001 2201001150 71432 Exh 0.2619329412 

01001 2201001110 75070 Exh 0.1059866981 

01001 2201001130 75070 Exh 0.0178493559 

01001 2201001150 75070 Exh 0.0217964613 

3. Summarized emissions by FIPS/CAS/type to give county level LDGV emissions for each 

CAS. 

G-1
Table G-2. Total base LDGV emissions by CAS and emtype for Autauga County. Note 

that total emissions include emissions not shown in Table G-1. 

FIPS CAS emtype emis 

01001 106990 Exh 0.3896782826 

01001 107028 Exh 0.0442493623 

01001 50000 Exh 0.8536461103 

01001 71432 Eva 0.4343823954 

01001 71432 Exh 3.7298372318 

01001 75070 Exh 0.3102395715 

4. Transposed the output of step 3 by FIPS/CAS so that the exhaust and evaporative 

emissions were on the same row. 

5. Created a dataset called ldgv_base and renamed the exhaust and evaporative emissions to 

exhaust_base and ldgv_evap_base respectively. 

Table G-3. Autauga County reference LDGV emissions after transposing the data by 

FIPS and CAS and renaming the exhaust and evaporative emissions (Steps 4 and 5). 

FIPS CAS exhaust_base ldgv_evap_base 

01001 106990 0.3896782826 . 

01001 107028 0.0442493623 . 

01001 50000 0.8536461103 . 

01001 71432 3.7298372318 0.4343823954 

01001 75070 0.3102395715 . 

6. Repeated steps 1 through 5 for the control case with output file called ldgv_control and 

emissions called exhaust_control and ldgv_evap_control. 

Table G-4. Partial listing of NMIM control LDGV emissions after sorting by FIPS, 

CAS, and emtype. (Steps 1 and 2). Emtype is exh for exhaust and eva is for evaporative. 

FIPS SCC CAS emtype emis 

01001 2201001110 106990 Exh 0.1332807094 

01001 2201001130 106990 Exh 0.0224884795 

01001 2201001150 106990 Exh 0.0274765282 

01001 2201001110 107028 Exh 0.0149620306 

01001 2201001130 107028 Exh 0.0025289852 

01001 2201001150 107028 Exh 0.0030914513 

01001 2201001110 50000 Exh 0.2930203266 

01001 2201001130 50000 Exh 0.0493116695 

01001 2201001150 50000 Exh 0.0602024053 

01001 2201001110 71432 Eva 0.071318306 

01001 2201001130 71432 Eva 0.0135608203 

01001 2201001150 71432 Eva 0.0174501906 

01001 2201001110 71432 Exh 1.1610455513 

01001 2201001130 71432 Exh 0.1955535179 

01001 2201001150 71432 Exh 0.2388030291 

01001 2201001110 75070 Exh 0.1063300385 

01001 2201001130 75070 Exh 0.0179073763 

01001 2201001150 75070 Exh 0.0218667063 

G-2
Table G-5. Total control LDGV emissions by CAS and emtype for Autauga County. 

Note that total emissions include emissions not shown in Table G-4. 

FIPS CAS emtype emis 

01001 106990 Exh 0.390761545 

01001 107028 Exh 0.0442493623 

01001 50000 Exh 0.8568307054 

01001 71432 Eva 0.2606270021 

01001 71432 Exh 3.4006572287 

01001 75070 Exh 0.3112453358 

Table G-6. Autauga County control LDGV emissions after transposing the data by FIPS 

and CAS and renaming the exhaust and evaporative emissions (Steps 4 and 5). 

FIPS CAS exhaust_control ldgv_evap_control 

01001 106990 0.390761545 . 

01001 107028 0.0442493623 . 

01001 50000 0.8568307054 . 

01001 71432 3.4006572287 0.2606270021 

01001 75070 0.3112453358 . 

Table G-7. Autauga County base refueling emissions for 2015. 

FIPS CAS refuel_base 

01001 71432 0.1618722891 

Steps 1 through 5 are performed in the SAS® MACRO read_ldgv. 

7. Read the county level refueling emissions for benzene and VOC from comma delimited 

file and output to dataset called refuel_base with emissions called refuel_base. Create 5 

character FIPS variable and retain only benzene emissions. 

8. Repeated step 7 for the control case, with output file called refuel_control and emissions 

called refuel_control. 

FIPS CAS refuel_control 

01001 71432 0.0971250232 

Table G-8. Autauga County control refueling emissions for 2015. 

Step 7 is performed in the SAS® MACRO refuel. 

9. Merged the ldgv_base and ldgv_control datasets by FIPS/CAS. 

G-3
Table G-9. Autauga County LDGV emissions after merging base and control emissions 

by FIPS and CAS. 

FIPS CAS exhaust_base ldgv_evap_base exhaust_control ldgv_evap_control 

01001 106990 0.3896782826 . 0.390761545 . 

01001 107028 0.0442493623 . 0.0442493623 . 

01001 50000 0.8536461103 . 0.8568307054 . 

01001 71432 3.7298372318 0.4343823954 3.4006572287 0.2606270021 

01001 75070 0.3102395715 . 0.3112453358 . 

10. Merged refuel_base and refuel_control by FIPS/CAS. 

Table G-10. Autauga County refueling emissions after merging base and control 

emissions by FIPS and CAS. 

FIPS CAS refuel_control refuel_base 

01001 71432 0.0971250232 0.1618722891 

G-4
11. Merged output of step 10 and step 11 by FIPS/CAS using PROC SQL retaining all observations from step 

Table G-11. Autauga County LDGV and refueling emissions after merging the two datasets together by retaining all LDGV emissions. 

FIPS CAS exhaust_base ldgv_evap_base exhaust_control ldgv_evap_control refuel_control 

01001 106990 0.3896782826 . 0.390761545 . 01001 107028 0.0442493623 . 0.0442493623 . 01001 50000 0.8536461103 . 0.8568307054 . 01001 71432 3.7298372318 0.4343823954 3.4006572287 0.2606270021 0.0971250232 01001 75070 0.3102395715 . 0.3112453358 . 

12. Calculated projection factors for exhaust type for all HAPs by dividing exhaust_control by exhaust base. 

calculated projection factors for evaporation type by dividing the sum of ldgv_evap_control and refuel_cldgv_evap_base and refuel_base. Table G-12. Autauga County emissions with projection factors. Projection factors rounded for visual pu

FIPS CAS exhaust_base ldgv_evap_base exhaust_control ldgv_evap_control refuel_control re

01001 106990 0.3896782826 . 0.390761545 . . 01001 107028 0.0442493623 . 0.0442493623 . . 01001 50000 0.8536461103 . 0.8568307054 . . 01001 71432 3.7298372318 0.4343823954 3.4006572287 0.2606270021 0.0971250232 0.01001 75070 0.3102395715 . 0.3112453358 . . 

13. Sorted the output of step 12 by FIPS and CAS and output to a permanent dataset, retaining the FIPS, CAS

factor and evaporative projection factor. Table G-13. Autauga County projection factors after sorting and outputting to a permanent dataset. 

FIPS CAS pf_exh pf_evap 

01001 106990 1.00278 . 01001 107028 1 . 01001 50000 1.00373 . 01001 71432 0.91174 0.59999 01001 75070 1.00324 . 

G-5
Figure G-1 shows the steps of the projection factor development program. 

MSATOH20XX.csv, 

base NMIM onroad 

emssions 

MSATBenzO20XX.csv, 

control onroad NMIM 

emissions 

Read in files and subset to LDGV 

emissions and the 5 HAPs 

1 

nmim Sort by FIPS/CAS/emtype 

nmim 

2 

Sum by 

FIPS/CAS/emtype 

nmim1 

3 

Transpose by 

FIPS/CAS 

4 

transdat 

rename exhaust and 

evaporative emissions 

Repeat steps 1 through 4 

for control case 

5 

ldgv_base ldgv_control 

6 

MSATBenzR2015base.csv, 

base refueling emssions 

MSATBenzR2015control.csv, 

control refueling emssions 

Read in files and subset to benzene 

7 

Repeat for control case 

8 

refuel_base 

refuel_control 

Merge by FIPS/CAS 

Merge by FIPS/CAS 

9 

ldgv 

10 

refuel 

Merge by 

FIPS/CAS, 

retaining all ldgv 

observations 

11 

ldgv_refuel 

12 Calculate exhaust factors for all HAPs. 

Calculate evaporative factors for 

benzene by dividing the sum of control 

LDGV evap and control refuel 

emissions by the sum of base LDGV 

evap and base refuel emissions. 

Sort by FIPS/CAS and 

output to permanent dataset 

13 

nonroad_factors_20XX.sas7bdat 

Figure G-1. Steps of the nonroad projection factor development program. 

G-6
G.2 Development of controlled nonroad inventories 

The following describes the steps taken to apply the projection factors to the nonroad controlled 

inventory in the program control_nonroad.sas (found in the MSAT rules docket EPA-HQ-OAR2005-0036) with examples from California and Texas. 

1. Read in the SCC/ASPEN source group cross reference text file and retain only the 

nonroad group SCC codes. Corrected the airport support equipment group to nonroad 

gasoline and corrected pleasure craft SCC codes to other nonroad. 

Table G-14. Partial listing of SCC codes with bins after correcting airport support 

equipment and pleasure craft bins. 

SCC grp 

2260000000 5 

2265008000 5 

2265008005 5 

2282000000 3 

2282020000 3 

2282020005 3 

2282020010 3 

2. Read in the NMIM base nonroad exhaust and evaporative emissions for benzene and 

VOC. Retained only benzene. 

3. Sorted step 2 output by FIPS/SCC/CAS/emtype where emtype is exh for exhaust and eva 

for evaporative emissions. 

G-7
Table G-15. Partial listing of NMIM base nonroad exhaust and evaporative emissions 

for benzene after sorting by FIPS, SCC, and CAS (Steps 2 and 3). 

FIPS SCC CAS emtype emis 

06001 2260001010 71432 Eva 0.027924 

06001 2260001010 71432 Exh 2.173649 

06001 2260001030 71432 Eva 0.0332 

06001 2260001030 71432 Exh 1.412159 

06001 2260002006 71432 Eva 0.003535 

06001 2260002006 71432 Exh 0.36573 

06001 2265001010 71432 Eva 0.0106649406 

06001 2265001010 71432 Exh 0.1943388665 

06001 2265007015 71432 Eva 0 

06001 2265007015 71432 Exh 0 

06021 2260001010 71432 Eva 0 

06021 2260001010 71432 Exh 0 

06021 2260001030 71432 Eva 0 

06021 2260001030 71432 Exh 0 

06021 2260002006 71432 Eva 2.81E-05 

06021 2260002006 71432 Exh 0.002958 

06021 2265001050 71432 Eva 0.0012738279 

06021 2265001050 71432 Exh 0.0632532043 

06021 2265007015 71432 Eva 0 

06021 2265007015 71432 Exh 0 

48001 2260001010 71432 Eva 0 

48001 2260001010 71432 Exh 0 

48001 2260001030 71432 Eva 0 

48001 2260001030 71432 Exh 0 

48001 2260002006 71432 Eva 0.00016 

48001 2260002006 71432 Exh 0.006782 

48001 2265007015 71432 Eva 2.63E-05 

48001 2265007015 71432 Exh 0.000281 

4. Transposed step 3 output by FIPS/SCC/CAS so that the exhaust and evaporative 

emissions are now on the same observation or row instead of multiple rows. 

G-8
Table G-16. Partial listing of NMIM base emissions after transposing by FIPS, SCC, 

and CAS. 

FIPS SCC CAS Eva Exh 

06001 2260001010 71432 0.027924 2.173649 

06001 2260001030 71432 0.0332 1.412159 

06001 2260002006 71432 0.003535 0.36573 

06001 2265001010 71432 0.0106649406 0.1943388665 

06001 2265007015 71432 0 0 

06021 2260001010 71432 0 0 

06021 2260001030 71432 0 0 

06021 2260002006 71432 2.81E-05 0.002958 

06021 2265001050 71432 0.0012738279 0.0632532043 

06021 2265007015 71432 0 0 

48001 2260001010 71432 0 0 

48001 2260001030 71432 0 0 

48001 2260002006 71432 0.00016 0.006782 

48001 2265007015 71432 2.63E-05 0.000281 

5. From step 4 output, created a new value for the SCC codes but changing the last three 

characters to 000 to create "Total" level SCC codes. Output to new dataset. 

6. Sorted step 5 output by FIPS/SCC/CAS. 

Table G-17. Partial listing of NMIM base emissions after changing last three characters 

of SCC codes to 000 and sorting by FIPS, SCC, and CAS (Steps 5 and 6). 

FIPS SCC CAS Eva Exh 

06001 2260001000 71432 0.027924 2.173649 

06001 2260001000 71432 0.0332 1.412159 

06001 2260002000 71432 0.003535 0.36573 

06001 2265001000 71432 0.0106649406 0.1943388665 

06001 2265007000 71432 0 0 

06021 2260001000 71432 0 0 

06021 2260001000 71432 0 0 

06021 2260002000 71432 2.81E-05 0.002958 

06021 2265001000 71432 0.0012738279 0.0632532043 

06021 2265007000 71432 0 0 

48001 2260001000 71432 0 0 

48001 2260001000 71432 0 0 

48001 2260002000 71432 0.00016 0.006782 

48001 2265007000 71432 2.63E-05 0.000281 

7. Summed the exhaust and evaporative emissions by FIPS/SCC/CAS of the step 6 output. 

This created "Total" level SCC emissions for exhaust and evaporative components. 

G-9
Table G-18. Partial listing of NMIM base emissions after summing “Total” level SCC 

codes by FIPS and CAS. Note emissions include SCC codes not listed in Table G-17. 

FIPS SCC CAS Exh Eva 

06001 2260001000 71432 3.6161273849 0.065301121 

06001 2260002000 71432 1.3666668567 0.0094967323 

06001 2265001000 71432 2.1453478962 0.1289871176 

06001 2265007000 71432 0 0 

06021 2260001000 71432 0 0 

06021 2260002000 71432 0.0110537204 0.000076092 

06021 2265001000 71432 0.0632532043 0.0012738279 

06021 2265007000 71432 0 0 

48001 2260001000 71432 0 0 

48001 2260002000 71432 0.0253426696 0.000416299 

48001 2265007000 71432 0.015208714 0.008868335 

8. Created a dataset of snow mobile emissions (SCC 265001020) for California from step 7 

output by changing the SCC code 2265001000 to 2265001020. This was done because 

counties in California had snow mobile emissions but they were not in the NMIM output. 

Table G-19. Partial listing of NMIM base emissions after changing SCC code 

2265001000 to 2265001020 for California. 

FIPS SCC CAS Exh Eva 

06001 2265001020 71432 2.1453478962 0.1289871176 

06001 2265001020 71432 0.0632532043 0.0012738279 

9. Created a dataset by concatenating step 4, step 7, and step 8 output. 

10. Created macro variables for the exhaust and evaporative emissions for SCC 2265001000 

for FIPS 06021. 

11. For FIPS 06021 and SCC = 2265001030 set the exhaust and evaporative emissions equal 

to the exhaust and evaporative emission macro variables created in step 10. 

G-10
Table G-20. Partial listing of NMIM base emissions after concatenating base NMIM 

output, total SCC emissions, and snow mobile emissions and after substituting 

2265001000 emissions in FIPS 06021 (Steps 9, 10, and 11). 

FIPS SCC CAS Eva Exh 

06001 2260001010 71432 0.027924 2.173649 

06001 2260001030 71432 0.0332 1.412159 

06001 2260002006 71432 0.003535 0.36573 

06001 2265001010 71432 0.0106649406 0.1943388665 

06001 2265007015 71432 0 0 

06021 2260001010 71432 0 0 

06021 2260002006 71432 2.81E-05 0.002958 

06021 2265001050 71432 0.0012738279 0.0632532043 

06021 2265007015 71432 0 0 

48001 2260001010 71432 0 0 

48001 2260001030 71432 0 0 

48001 2260002006 71432 0.00016 0.006782 

48001 2265007015 71432 2.63E-05 0.000281 

06001 2260001000 71432 0.065301121 3.6161273849 

06001 2260002000 71432 0.0094967323 1.3666668567 

06001 2265001000 71432 0.1289871176 2.1453478962 

06001 2265007000 71432 0 0 

06021 2260001000 71432 0 0 

06021 2260002000 71432 0.000076092 0.0110537204 

06021 2265001000 71432 0.0012738279 0.0632532043 

06021 2265001030 71432 0.0012738279 0.0632532043 

06021 2265007000 71432 0 0 

48001 2260001000 71432 0 0 

48001 2260002000 71432 0.000416299 0.0253426696 

48001 2265007000 71432 0.008868335 0.015208714 

06001 2265001020 71432 0.1289871176 2.1453478962 

06001 2265001020 71432 0.0012738279 0.0632532043 

12. Created a dataset for California and Texas with the engine designation for 2 and 4 stroke 

emissions based on the first six characters of the SCC code. This was only done with 

SCC codes where the last three characters were not 000 (total level) to avoid double 

counts of emissions. 

13. Sorted step 12 output by FIPS/CAS/eng where eng = 2 for 2-stroke gasoline and eng=4 

for 4-stroke gasoline emissions. 

G-11
Table G-22. Partial listing of NMIM base emissions after adding engine type variable 

and sorting by engine type (Steps 12 and 13). 

FIPS SCC CAS Eva Exh eng 

06001 2260001010 71432 0.027924 2.173649 2 

06001 2260001030 71432 0.0332 1.412159 2 

06001 2260002006 71432 0.003535 0.36573 2 

06001 2265001010 71432 0.0106649406 0.1943388665 4 

06001 2265001020 71432 0.1289871176 2.1453478962 4 

06001 2265007015 71432 0 0 4 

06021 2260001010 71432 0 0 2 

06021 2260002006 71432 2.81E-05 0.002958 2 

06001 2265001020 71432 0.0012738279 0.0632532043 4 

06021 2260001030 71432 0.0012738279 0.0632532043 4 

06021 2265001050 71432 0.0012738279 0.0632532043 4 

06021 2265007015 71432 0 0 4 

48001 2260001010 71432 0 0 2 

48001 2260001030 71432 0 0 2 

48001 2260002006 71432 0.00016 0.006782 2 

48001 2265007015 71432 2.63E-05 0.000281 4 

14. Summed the exhaust and summed the evaporative emissions for by FIPS/CAS/eng. 

Table G-23. Partial listing of emissions after summing by FIPS, CAS, and engine type. 

(Section. 

FIPS CAS eng exh1 eva1 

06001 71432 2 20.824142764 3.1043091582 

06001 71432 4 93.924771216 6.8976984165 

06021 71432 2 0.0570237128 0.0113984838 

06021 71432 4 0.9153963121 0.0600273041 

48001 71432 2 0.1681255378 0.0664583016 

48001 71432 4 1.4996708652 0.2811232142 

15. Merged the step 14 output and step 9 output by FIPS and CAS and where the first six 

digits of the SCC were 226501 or 226500 and eng=4 or where the first six digits of the 

SCC code were 226000 and eng=2. Retain all observations of the step 9 dataset and the 

FIPS/CAS/eng emissions from step 14. 

G-12
Table G-24. Partial listing of emissions after merging with the engine emissions by 

FIPS, CAS, and engine type. 

FIPS SCC CAS Eva Exh eng exh1 eva1 

06001 2260001000 71432 0.065301121 3.6161273849 2 20.824142764 3.1043091582 

06001 2260001010 71432 0.027924 2.173649 2 20.824142764 3.1043091582 

06001 2260001030 71432 0.0332 1.412159 2 20.824142764 3.1043091582 

06001 2260002000 71432 0.0094967323 1.3666668567 2 20.824142764 3.1043091582 

06001 2260002006 71432 0.003535 0.36573 2 20.824142764 3.1043091582 

06001 2265001000 71432 0.1289871176 2.1453478962 4 93.924771216 6.8976984165 

06001 2265001010 71432 0.0106649406 0.1943388665 4 93.924771216 6.8976984165 

06001 2265001020 71432 0.1289871176 2.1453478962 4 93.924771216 6.8976984165 

06001 2265007000 71432 0 0 4 93.924771216 6.8976984165 

06001 2265007015 71432 0 0 4 93.924771216 6.8976984165 

06021 2260001000 71432 0 0 2 0.0570237128 0.0113984838 

06021 2260001010 71432 0 0 2 0.0570237128 0.0113984838 

06021 2260002000 71432 0.000076092 0.0110537204 2 0.0570237128 0.0113984838 

06021 2260002006 71432 2.81E-05 0.002958 2 0.0570237128 0.0113984838 

06021 2265001000 71432 0.0012738279 0.0632532043 4 0.9153963121 0.0600273041 

06001 2265001020 71432 0.0012738279 0.0632532043 4 0.9153963121 0.0600273041 

06021 2265001030 71432 0.0012738279 0.0632532043 4 0.9153963121 0.0600273041 

06021 2265001050 71432 0.0012738279 0.0632532043 4 0.9153963121 0.0600273041 

06021 2265007000 71432 0 0 4 0.9153963121 0.0600273041 

06021 2265007015 71432 0 0 4 0.9153963121 0.0600273041 

48001 2260001000 71432 0 0 2 0.1681255378 0.0664583016 

48001 2260001010 71432 0 0 2 0.1681255378 0.0664583016 

48001 2260001030 71432 0 0 2 0.1681255378 0.0664583016 

48001 2260002000 71432 0.000416299 0.0253426696 2 0.1681255378 0.0664583016 

48001 2260002006 71432 0.00016 0.006782 2 0.1681255378 0.0664583016 

48001 2265007000 71432 0.008868335 0.015208714 4 1.4996708652 0.2811232142 

48001 2265007015 71432 2.63E-05 0.000281 4 1.4996708652 0.2811232142 

16. Corrected the exhaust and evaporative emissions for SCC codes where the eng variable 

was 2 or 4 and the exhaust and evaporative emissions were 0 and the emissions were 

California or Texas. They were replaced with exhaust and evaporative emissions for the 

eng type as calculated in step 14. This was done because the MSAT emissions for 

several SCC codes were not zero in 1999 but were not available in 2015 NMIM output 

for base MSAT. The same was true with the exhaust and evaporative emissions. 

Therefore county level engine emissions for exhaust and evaporative emissions were 

calculated to be used for the fraction calculations in step 17. 

G-13
Table G-25. Emissions after correcting zero exhaust and evaporative emissions with 

engine type exhaust and evaporative emissions. 

flag FIPS SCC CAS Eva Exh eng exh1 eva1 

06001 2260001000 71432 0.065301121 3.6161273849 2 20.824142764 3.1043091582 

06001 2260001010 71432 0.027924 2.173649 2 20.824142764 3.1043091582 

06001 2260001030 71432 0.0332 1.412159 2 20.824142764 3.1043091582 

06001 2260002000 71432 0.0094967323 1.3666668567 2 20.824142764 3.1043091582 

06001 2260002006 71432 0.003535 0.36573 2 20.824142764 3.1043091582 

06001 2265001000 71432 0.1289871176 2.1453478962 4 93.924771216 6.8976984165 

06001 2265001010 71432 0.0106649406 0.1943388665 4 93.924771216 6.8976984165 

06001 2265001020 71432 0.1289871176 2.1453478962 4 93.924771216 6.8976984165 

C 06001 2265007000 71432 6.8976984165 93.924771216 4 93.924771216 6.8976984165 

C 06001 2265007015 71432 6.8976984165 93.924771216 4 93.924771216 6.8976984165 

C 06021 2260001000 71432 0.0113984838 0.0570237128 2 0.0570237128 0.0113984838 

C 06021 2260001010 71432 0.0113984838 0.0570237128 2 0.0570237128 0.0113984838 

06021 2260002000 71432 0.000076092 0.0110537204 2 0.0570237128 0.0113984838 

06021 2260002006 71432 2.81E-05 0.002958 2 0.0570237128 0.0113984838 

06021 2265001000 71432 0.0012738279 0.0632532043 4 0.9153963121 0.0600273041 

06001 2265001020 71432 0.0012738279 0.0632532043 4 0.9153963121 0.0600273041 

06021 2265001030 71432 0.0012738279 0.0632532043 4 0.9153963121 0.0600273041 

06021 2265001050 71432 0.0012738279 0.0632532043 4 0.9153963121 0.0600273041 

C 06021 2265007000 71432 0.0600273041 0.9153963121 4 0.9153963121 0.0600273041 

C 06021 2265007015 71432 0.0600273041 0.9153963121 4 0.9153963121 0.0600273041 

C 48001 2260001000 71432 0.1681255378 0.0664583016 2 0.1681255378 0.0664583016 

C 48001 2260001010 71432 0.1681255378 0.0664583016 2 0.1681255378 0.0664583016 

C 48001 2260001030 71432 0.1681255378 0.0664583016 2 0.1681255378 0.0664583016 

48001 2260002000 71432 0.000416299 0.0253426696 2 0.1681255378 0.0664583016 

48001 2260002006 71432 0.00016 0.006782 2 0.1681255378 0.0664583016 

48001 2265007000 71432 0.008868335 0.015208714 4 1.4996708652 0.2811232142 

48001 2265007015 71432 2.63E-05 0.000281 4 1.4996708652 0.2811232142 

17. Calculated exhaust emission fractions by dividing the exhaust emissions by the sum of 

the exhaust and evaporative emissions for each FIPS/SCC/CAS from step 16 output. 

Calculate the evaporative emissions fractions by dividing the evaporative emissions by 

the sum of the exhaust and evaporative emissions for each FIPS/SCC/CAS. Do this only 

where both are not zero. One of them could be zero but not both. If both were zero, then 

set the fractions equal to 0. 

18. Sort step 17 output by FIPS/SCC/CAS. 

G-14
Table G-26. Partial listing of emissions after calculating projection factors and sorting by FIPS, 

SCC, and CAS (Steps 17 and 18). 

flag FIPS SCC CAS Eva Exh eng exh_frac eva_frac 

06001 2260001000 71432 0.065301121 3.6161273849 2 0.9822620157107 0.01773798428934 

06001 2260001010 71432 0.027924 2.173649 2 0.987316341543 0.012683658457 

06001 2260001030 71432 0.0332 1.412159 2 0.977029928205 0.0229700718 

06001 2260002000 71432 0.0094967323 1.3666668567 2 0.9930991254413 0.006900874558744 

06001 2260002006 71432 0.003535 0.36573 2 0.99042692917 0.00957307083 

06001 2265001000 71432 0.1289871176 2.1453478962 4 0.9432857882338 0.05671421176623 

06001 2265001010 71432 0.0106649406 0.1943388665 4 0.9479768656452 0.05202313435476 

06001 2265001020 71432 0.1289871176 2.1453478962 4 0.9432857882338 0.05671421176623 

C 06001 2265007000 71432 6.8976984165 93.924771216 4 0.931585702655 0.06841429734505 

C 06001 2265007015 71432 6.8976984165 93.924771216 4 0.931585702655 0.06841429734505 

C 06021 2260001000 71432 0.0113984838 0.0570237128 2 0.833409560546 0.166590439454 

C 06021 2260001010 71432 0.0113984838 0.0570237128 2 0.833409560546 0.166590439454 

06021 2260002000 71432 0.000076092 0.0110537204 2 0.9931632270819 0.006836772918113 

06021 2260002006 71432 2.81E-05 0.002958 2 0.990589732427 0.00941027 

06021 2265001000 71432 0.0012738279 0.0632532043 4 0.9802590037606 0.0197409962394 

06001 2265001020 71432 0.0012738279 0.0632532043 4 0.9802590037606 0.0197409962394 

06021 2265001030 71432 0.0012738279 0.0632532043 4 0.9802590037606 0.0197409962394 

06021 2265001050 71432 0.0012738279 0.0632532043 4 0.9802590037606 0.0197409962394 

C 06021 2265007000 71432 0.0600273041 0.9153963121 4 0.9384602719239 0.06153972807615 

C 06021 2265007015 71432 0.0600273041 0.9153963121 4 0.9384602719239 0.06153972807615 

C 48001 2260001000 71432 0.0664583016 0.1681255378 2 0.7166970164271 0.2833029835729 

C 48001 2260001010 71432 0.0664583016 0.1681255378 2 0.7166970164271 0.2833029835729 

C 48001 2260001030 71432 0.0664583016 0.1681255378 2 0.7166970164271 0.2833029835729 

48001 2260002000 71432 0.000416299 0.0253426696 2 0.9838386774539 0.01616132254612 

48001 2260002006 71432 0.00016 0.006782 2 0.976951887064 0.02304811294 

48001 2265007000 71432 0.008868335 0.015208714 4 0.6316685238295 0.3683314761705 

48001 2265007015 71432 2.63E-05 0.000281 4 0.914415880247 0.08558412 

Steps 2 through 18 were performed in the SAS® MACRO exhaust_evap. 

19. Read the projected MSAT emissions (output from Section 3.3.3) and subset to 1,3­

butadiene, acetaldehyde, acrolein, benzene, and formaldehyde based on CAS. 

Table G-27. Partial listing of reference MSAT emissions after subsetting to the five 

HAPs. 

FIPS SCC CAS emis 

06001 2260001010 106990 0.219016 

06001 2260001010 107028 0.065565 

06001 2260001010 50000 0.438031 

06001 2260001010 71432 3.291336 

06001 2260001010 75070 0.131409 

06001 2260001030 106990 0.095558 

06001 2260001030 107028 0.028607 

06001 2260001030 50000 0.191117 

06001 2260001030 71432 1.454391 

06001 2260001030 75070 0.057335 

06001 2260002000 106990 0.022212 

06001 2260002000 107028 0.004665 

G-15
Table 27. Continued. 

FIPS SCC CAS emis 

06001 2260002000 50000 0.090633 

06001 2260002000 71432 0.097496 

06001 2260002000 75070 0.041693 

06001 2265001010 106990 0.039095 

06001 2265001010 107028 0.002868 

06001 2265001010 50000 0.067717 

06001 2265001010 71432 0.187604 

06001 2265001010 75070 0.034401 

06001 2270005045 107028 0.000112 

06021 2260002006 106990 0.00028 

06021 2260002006 107028 3.84E-05 

06021 2260002006 50000 0.000398 

06021 2260002006 71432 0.003259 

06021 2260002006 75070 0.000457 

06021 2265001020 106990 0.319786 

06021 2265001020 107028 0.066658 

06021 2265001020 50000 1.220082 

06021 2265001020 71432 1.522297 

06021 2265001020 75070 0.608339 

06021 2270005045 107028 0.001556 

48001 2260001000 106990 0.032812 

48001 2260001000 107028 0.006512 

48001 2260001000 50000 0.058605 

48001 2260001000 71432 0.70733 

48001 2260001000 75070 0.035782 

48001 2260002000 106990 0.001097 

48001 2260002000 107028 0.000219 

48001 2260002000 50000 0.001857 

48001 2260002000 71432 0.018899 

48001 2260002000 75070 0.001217 

48001 2265001000 106990 0.034624 

48001 2265001000 107028 0.003068 

48001 2265001000 50000 0.075164 

48001 2265001000 71432 0.397382 

48001 2265001000 75070 0.017972 

48001 2265007015 106990 5.26E-05 

48001 2265007015 107028 3.86E-06 

48001 2265007015 50000 6.47E-05 

48001 2265007015 71432 0.000304 

48001 2265007015 75070 2.26E-05 

48001 2270001000 106990 0.000114 

48001 2270001000 107028 0.000843 

48001 2270001000 50000 0.010962 

48001 2270001000 71432 0.00152 

48001 2270001000 75070 0.005444 

G-16
20. Merged the output of step 19 with step 1 output using PROC SQL by SCC, retaining all 

observations from the step 19 output and the ASPEN source group from step 1 output. 

21. Based on the source group, output source group=5 emissions to a gasoline dataset and all 

other emissions to a non-gasoline dataset for later use. 

Table G-28. MSAT gasoline emissions after merging with SCC/bin cross reference and 

separate gasoline emissions and non-gasoline emissions (Section 10.3.2, steps 20 and 21). 

FIPS SCC CAS emis grp 

06001 2260001010 106990 0.219016 5 

06001 2260001010 107028 0.065565 5 

06001 2260001010 50000 0.438031 5 

06001 2260001010 71432 3.291336 5 

06001 2260001010 75070 0.131409 5 

06001 2260001030 106990 0.095558 5 

06001 2260001030 107028 0.028607 5 

06001 2260001030 50000 0.191117 5 

06001 2260001030 71432 1.454391 5 

06001 2260001030 75070 0.057335 5 

06001 2260002000 106990 0.022212 5 

06001 2260002000 107028 0.004665 5 

06001 2260002000 50000 0.090633 5 

06001 2260002000 71432 0.097496 5 

06001 2260002000 75070 0.041693 5 

06001 2265001010 106990 0.039095 5 

06001 2265001010 107028 0.002868 5 

06001 2265001010 50000 0.067717 5 

06001 2265001010 71432 0.187604 5 

06001 2265001010 75070 0.034401 5 

06021 2260002006 106990 0.00028 5 

06021 2260002006 107028 3.84E-05 5 

06021 2260002006 50000 0.000398 5 

06021 2260002006 71432 0.003259 5 

06021 2260002006 75070 0.000457 5 

06021 2265001020 106990 0.319786 5 

06021 2265001020 107028 0.066658 5 

06021 2265001020 50000 1.220082 5 

06021 2265001020 71432 1.522297 5 

06021 2265001020 75070 0.608339 5 

48001 2260001000 106990 0.032812 5 

48001 2260001000 107028 0.006512 5 

48001 2260001000 50000 0.058605 5 

48001 2260001000 71432 0.70733 5 

48001 2260001000 75070 0.035782 5 

48001 2260002000 106990 0.001097 5 

48001 2260002000 107028 0.000219 5 

48001 2260002000 50000 0.001857 5 

48001 2260002000 71432 0.018899 5 

48001 2260002000 75070 0.001217 5 

48001 2265001000 106990 0.034624 5 

G-17
Table G-28. Continued. 

FIPS SCC CAS emis grp 

48001 2265001000 107028 0.003068 5 

48001 2265001000 50000 0.075164 5 

48001 2265001000 71432 0.397382 5 

48001 2265001000 75070 0.017972 5 

48001 2265007015 106990 5.26E-05 5 

48001 2265007015 107028 3.86E-06 5 

48001 2265001000 106990 0.034624 5 

48001 2265001000 107028 0.003068 5 

48001 2265001000 50000 0.075164 5 

48001 2265001000 71432 0.397382 5 

48001 2265001000 75070 0.017972 5 

48001 2265007015 106990 5.26E-05 5 

48001 2265007015 107028 3.86E-06 5 

Table G-29. Partial listing of non-gasoline emissions. 

FIPS SCC CAS emis grp 

06001 2270005045 107028 0.000112 3 

06021 2270005045 107028 0.001556 3 

48001 2270001000 106990 0.000114 3 

48001 2270001000 107028 0.000843 3 

48001 2270001000 50000 0.010962 3 

48001 2270001000 71432 0.00152 3 

48001 2270001000 75070 0.005444 3 

G-18
22. From the gasoline dataset created in step 21, merged the emissions with the fractions created in step 18 b

Output all observations from the dataset created in step 21 to the dataset. 

23. Multiplied the emissions by fractions. If the CAS = 71432 (benzene) multiplied the emissions by the exh

exhaust emissions and multiply the emissions by the evaporative fraction to get evaporative emissions. Icalculations were not done. Table G-30. Partial listing of gasoline emissions with projection factors and exhaust and evaporative em(Steps 22 and 23). 

FIPS SCC CAS emis exh_frac eva_frac emis_exh e

06001 2260001010 106990 0.219016 . . . 06001 2260001010 107028 0.065565 . .

 . 

06001 2260001010 50000 0.438031 . . . 06001 2260001010 71432 3.291336 0.987316341543 0.012683658457 3.249589818309 0.0406001 2260001010 75070 0.131409 . . . 06021 2260002006 106990 0.00028 . . . 06021 2260002006 107028 3.84E-05 . . . 06021 2260002006 50000 0.000398 . . . 06021 2260002006 71432 0.003259 0.990589732427 0.00941027 0.003228331938 006021 2260002006 75070 0.000457 . . . 48001 2260001000 106990 0.032812 . . . 48001 2260001000 107028 0.006512 . . . 48001 2260001000 50000 0.058605 . . . 48001 2260001000 71432 0.70733 0.7166970164271 0.2833029835729 0.5069413006294 0.20048001 2260001000 75070 0.035782 . . . Steps 19 through 23 were performed in the SAS® MACRO read_msat. 

G-19
24. Merged the output of step 23 with the projection factors calculated in calc_factors.sas by FIPS/CAS using 

all observations from step 23 output. Table G-31. Partial listing of gasoline emissions with projection factors. 

FIPS SCC CAS emis emis_exh emis_eva pf_exh pf_ev

06001 2260001010 106990 0.219016 . . 1.0012384215 06001 2260001010 107028 0.065565 . .

 1

06001 2260001010 50000 0.438031 . . 1.0018241077 06001 2260001010 71432 3.291336 3.249589818309 0.041746181691 0.944296577 0.599906001 2260001010 75070 0.131409 . . 1.0015537589 06021 2260002006 106990 0.00028 . . 1.0012845671 06021 2260002006 107028 3.84E-05 . . 1 06021 2260002006 50000 0.000398 . . 1.0018887224 06021 2260002006 71432 0.003259 0.003228331938 0.00003067 0.9424016216 0.5999906021 2260002006 75070 0.000457 . . 1.0016036635 48001 2260001000 106990 0.032812 . . 1.0028720014 48001 2260001000 107028 0.006512 . . 1 48001 2260001000 50000 0.058605 . . 1.0038495361 48001 2260001000 71432 0.70733 0.5069413006294 0.2003886993706 0.9104735847 0.570001548001 2260001000 75070 0.035782 . . 1.0033413338 

25. Calculated projected or controlled emissions for step 24 output. For benzene, the projected emissions we

exhaust emissions multiplied by the exhaust projection factor (calculated in calc_factors.sas) and the evapmultiplied by the evaporative projection factor (calculated in calc_factors.sas) (Equation 23). Otherwise multiplied by the exhaust projection factor (Equation 24). 

G-20
Table G-32. Partial listing of gasoline emissions after calculating controlled emissions. The variable em

emissions and emis_orig is the reference projected emissions for MSAT. 

FIPS SCC CAS emis emis_exh emis_eva pf_exh pf_

06001 2260001010 106990 0.2192872341 . . 1.0012384215 06001 2260001010 107028 0.065565 . . 1 06001 2260001010 50000 0.438031 . . 1.0018241077 06001 2260001010 71432 0.4388300157 3.249589818309 0.041746181691 0.944296577 0.5906001 2260001010 75070 3.093623204868 . . 1.0015537589 06021 2260002006 106990 0.1316131779 . . 1.0012845671 06021 2260002006 107028 0.0002803597 . . 1 06021 2260002006 50000 0.0003987517 . . 1.0018887224 06021 2260002006 71432 0.003060787025 0.003228331938 0.00003067 0.9424016216 0.599906021 2260002006 75070 0.0004577329 . . 1.0016036635 48001 2260001000 106990 0.0329062361 . . 1.0028720014 48001 2260001000 107028 0.006512 . . 1 48001 2260001000 50000 0.0588306021 . . 1.0038495361 48001 2260001000 71432 0.5757785228416 0.5069413006294 0.2003886993706 0.9104735847 0.5700048001 2260001000 75070 0.0359015596 . . 1.0033413338 

26. Concatenated the output of step 25 with the non-gasoline emissions created in step 21. 27. Sorted step 27 output by FIPS/SCC/CAS and output to a permanent dataset. 

G-21
Table G-33. Partial listing of nonroad emissions after concatenating controlled nonroad gasoline emissio

nonroad emissions, sorting by FIPS, SCC, and CAS and output to a permanent dataset with only needed vand 27). 

FIPS CAS SCC emis emis_exh emis_eva flag emis_orig 

06001 106990 2260001010 0.2192872341 . . 0.219016 06001 107028 2260001010 0.065565 . . 0.065565 06001 50000 2260001010 0.438031 . . 0.438031 06001 71432 2260001010 0.4388300157 3.249589818309 0.041746181691 3.291336 06001 75070 2260001010 3.093623204868 . . 0.131409 06001 107028 2270005045 0.000112 . . 06021 106990 2260002006 0.1316131779 . . 0.00028 06021 107028 2260002006 0.0002803597 . . 3.84E-05 06021 50000 2260002006 0.0003987517 . . 0.000398 06021 71432 2260002006 0.003060787025 0.003228331938 0.00003067 0.003259 06021 75070 2260002006 0.0004577329 . . 0.000457 06001 107028 2270005045 0.001556 . . 48001 106990 2260001000 0.0329062361 . . 0.032812 48001 107028 2260001000 0.006512 . . 0.006512 48001 50000 2260001000 0.0588306021 . . 0.058605 48001 71432 2260001000 0.5757785228416 0.5069413006294 0.2003886993706 C 0.70733 48001 75070 2260001000 0.0359015596 . . 0.035782 48001 106990 2270001000 0.000114 . . 48001 107028 2270001000 0.000843 . . 48001 50000 2270001000 0.010962 . . 48001 71432 2270001000 0.00152 . . 

G-22
Steps 24 through 27 were performed in the SAS® MACRO apply_pf. 

All steps were performed in the SAS® MACRO control with the four digit year, 2015, 

2020, or 2030 as the argument. 

Figure G-2 shows the steps to calculate the exhaust and evaporative fractions and Figure G-3 

shows the steps of the application of the exhaust and evaporative fractions and projection factors. 

am_grp_MSAT.txt 

Read SCC/grouping cross 

reference file, retaining 

nonroad. Correct SCC 

codes with wrong group 

1 

MSATBenzN20XX.csv 

where XX is 2 digit 

year 

Read nonroad NMIM 

emissions broken out into 

exhaust and evaporative 

emissions for VOC and 

benzene. Retain benzene. 

2 

nmim 

Sort by 

FIPS/SCC/CAS/emissions 

type 

3 

4 

5 

6 

7 

8 

9 

10 

11 12 

14 13

transdat Transpose by FIPS/SCC/CAS nmim 

Change last 3 characters of 

SCC to 000 

tot_scc 

scc_bin 

Sort by 

FIPS/SCC/CAS tot_scc 

total_scc Sum by FIPS/SCC/CAS 

For CA, copy 2265001000 

emissions and change SCC to 

2265001020 

snow 

Concatenate 

all_scc 

Create macro variables of the exhaust 

and evaporative emissions for SCC 

2265001000 in FIPS 06021 

_null_ 

Correct SCC 2265001030 in FIPS 

06021 emissions using macro variables all_scc 

Extract emissions for 2 and 4 stroke 

emissions, assigning engine type. 

Keep only SCC codes where 3 last 3 

characters not 000. 

engine 

Sort by 

FIPS/CAS/engine 

Sum by 

FIPS/CAS/engine engine_sum 15 

16 

17 

18 

Merge by FIPS/CAS and 

where emissions are 2 or 4 

stroke 

all_scc1 all_scc2 Correct emissions where 

necessary 

Calculate exhaust and 

evaporative fractions 

Sort by 

FIPS/SCC/CAS fractions 

fractions 

Figure G-2. Steps in the calculation of the exhaust and evaporative emissions fractions (steps 2­

18 in control_nonroad.sas (found in the MSAT rule docket EPA-HQ-OAR-2005-0036). 

G-23
scc_bin 

fractions 

nonroad_20XX.sas7bdat 

MSAT nonroad inventory 

Extract the 5 HAPs 

scratch 

Merge by 

SCC with 

scc_bin 

from step 1 

scratch1 

19 

20 

21 

22 

23 

24 

25 

26 

27 

Split into gasoline and 

non-gasoline 

emissions based on 

group 

Merge by gasoline non_gas 

FIPS/SCC/CAS 

with fractions 

from step 18 

no_msat msat_exh_evap 

Calculate exhaust and 

evaporative components of 

benzene emissions 

msat_exh_evap 

Merge with projection 

factors by FIPS/CAS nonroad_factors_20XX.sas7bdat base_emis 

Apply projection factors to 

emissions 

proj 

Concatenate 

nonroad 

Sort by FIPS/SCC/CAS and 

output to permanent dataset 

nonroad_20XX.sas7bdat 

Figure G-3. Steps in calculating controlled nonroad emissions (steps 19-27 of 

control_nonroad.sas). 

G-24