International Corporation Air Sciences

Final Report

DEVELOPMENT OF WRAP 

MOBILE SOURCE EMISSION INVENTORIES 

Prepared for 

Western Governors’ Association 

1515 Cleveland Place, Suite 200 

Denver, CO 80202 

Prepared by 

Alison K. Pollack, Rosa Chi, Chris Lindhjem 

Cuong Tran, Priya Chandraker 

ENVIRON International Corporation 

101 Rowland Way, Suite 220 

Novato, CA 94945 

Phil Heirigs, Lori Williams, Siona S. Delaney 

Sierra Research 

1801 J Street 

Sacramento, CA 95814 

Maureen A. Mullen, Kristin B. Thesing 

E.H. Pechan & Associates, Inc. 

5528-B Hempstead Way 

Springfield, VA 22151 

February 9, 2004 

 101 Rowland Way, Suite 220, Novato, CA 94945 415.899.0700 
February 2004 

G:\WRAP MSEI\Reporting\Final\ACKNOWLEDGEMENTS.doc 

ACKNOWLEDGEMENTS 

The authors acknowledge the contributions of many people who provided input and advice on 

the work described in this report. The technical work reported herein was guided by the 

WRAP Mobile Sources Forum (MSF). We wish in particular to acknowledge the extensive 

involvement and significant input from Mike Boyer of the Washington Department of Ecology 

and John Kowalczyk, now retired, formerly of the Oregon Department of Environmental 

Quality. We also acknowledge the assistance and input of many people from air quality 

planning agencies, too numerous to list here. 
 February 2004 

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TABLE OF CONTENTS 

Page 

ACRONYMS ................................................................................................. i 

PREFACE ............................................................................................... iii 

 

 Regulatory Framework for Tribal Visibility Implementation Plans ....................... iii

1.0 INTRODUCTION...............................................................................1-1 

1.1 Background ...............................................................................1-1 

1.2 Emissions Inventoried under Mobile Sources.......................................1-1 

1.3 Scope of the Mobile Sources Inventory ..............................................1-2 

1.4 General Approach for Estimating On-Road and 

 Off-Road Mobile Source Emissions..............................................1-2 

2.0 EMISSIONS MODELS USED ...............................................................2-1 

2.1 MOBILE6..................................................................................2-1 

 2.1.1 Comparison of MOBILE5 and MOBILE6.......................................2-1 

 2.1.2 Comparison of MOBILE6 Draft an Public Release Version..................2-4 

2.2 PART5 ....................................................................................2-5 

2.2.1 Exhaust PM Emission Rates.......................................................2-5 

 2.2.1.1 Light-duty Gasoline Vehicles ...............................................2-5 

 2.2.1.2 Light-duty Diesel Vehicles ..................................................2-6 

 2.2.1.3 Heavy-duty Diesel Vehicles.................................................2-7 

 2.2.2 Corrections to the Base Emission Rates..........................................2-8 

 2.2.2.1 Oxygenated Gasoline .........................................................2-8 

 2.2.2.2 Temperature Corrections ....................................................2-8 

 2.2.2.3 Sulfur Effects ..................................................................2-9 

 2.2.2.4 HDDV Altitude Adjustment.................................................2-9 

 2.2.3 Non-Exhaust PM Emissions........................................................2-9 

 2.2.4 Particle Size Distributions ........................................................ 2-10 

 2.2.5 Elemental Carbon-Organic Carbon Splits...................................... 2-10 

 2.2.6 Ammonia Emissions............................................................... 2-11 

 2.2.7 Fleet Characteristic Data.......................................................... 2-13 

2.3 NONROAD............................................................................. 2-13 

 2.3.1 Comparison of NON-ROAD2000 and NON-ROAD2003.................... 2-14 

 2.3.2 NONROAD Emissions Calculations............................................. 2-17 

2.4 California Models...................................................................... 2-18 
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3.0 1996 BASE YEAR ON-ROAD EMISSIONS METHODOLOGY ....................3-1 

3.1 VMT Estimates ..........................................................................3-1 

3.2 MOBILE6 and PART5 Inputs .........................................................3-2 

 3.2.1 Speed...................................................................................3-2 

 3.2.2 Registration Distributions...........................................................3-4 

 3.2.3 Seasonal Temperatures..............................................................3-4 

 3.2.4 Seasonal Fuel Data...................................................................3-4 

 3.2.5 Altitude ................................................................................3-5 

 3.2.6 Control Programs ....................................................................3-5 

 3.2.6.1 Inspection and Maintenance (I/M) Program ..............................3-5 

 3.2.6.2 Oxygenated Fuel ..............................................................3-5 

 3.2.6.3 Stage II Refueling Controls..................................................3-5 

3.3 Seasonal Emissions Calculations ......................................................3-6 

3.4 Processing of California Data .........................................................3-7 

3.5 Generation of SMOKE Files...........................................................3-7 

4.0 1996 BASE YEAR OFF-ROAD EMISSIONS METHODOLOGY...................4-1 

4.1 NONROAD Modeling ..................................................................4-1 

4.2 Aircraft Emissions Methodology......................................................4-2 

 4.2.1 Airports and Activity Data .........................................................4-3 

 4.2.2 Estimation Methods..................................................................4-5 

 4.2.2.1 Large-Hub Airports...........................................................4-5 

 4.2.2.2 Other Towered Airports .....................................................4-7 

 4.2.2.3 Non-Towered Airports.......................................................4-7 

4.3 Commercial Marine Emissions Methodology.......................................4-8 

 4.3.1 Emission Factors.....................................................................4-8 

 4.3.2 Ocean-Going Vessels.............................................................. 4-11 

 4.3.2.1 Port of Coos Bay............................................................ 4-11 

 4.3.2.2 Puget Sound Ports .......................................................... 4-13 

 4.3.2.3 Columbia River Ports ...................................................... 4-17 

 4.3.3 Tugs.................................................................................. 4-18 

 4.3.3.1 Puget Sound Area........................................................... 4-18 

 4.3.3.2 Columbia and Snake River Traffic....................................... 4-18 

 4.3.3.3 Oregon Coast ................................................................ 4-20 

 4.3.3.4 Other Tug Activity.......................................................... 4-20 

 4.3.4 Ferries ............................................................................... 4-21 

 4.3.5 Dredges.............................................................................. 4-22 

 4.3.6 Fishing Vessels..................................................................... 4-23 

4.3.7 Comparison with BAH (1991) Estimates for Puget Sound ................. 4-24 

4.3.8 Recent Columbia River Emission Estimates .................................. 4-24 

4.4 Locomotive Emissions Methodology ............................................... 4-25 

4.5 California OFFROAD Emissions ................................................... 4-26 

4.6 Generations of SMOKE Files........................................................ 4-27 
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5.0 1996 RESULTS ..................................................................................5-1 

5.1 On-Road Emission Inventories ........................................................5-1 

5.2 Off-Road Emission Inventories...................................................... 5-13 

6.0 FUTURE YEARS ONROAD EMISSIONS................................................6-1 

6.1 Vehicle Miles Traveled (VMT) Estimates...........................................6-1 

6.2 MOBILE6 and PART5 Inputs .........................................................6-4 

 6.2.1 Speed...................................................................................6-4 

 6.2.2 Registration Distributions...........................................................6-4 

 6.2.3 Seasonal Temperatures..............................................................6-4 

 6.2.4 Seasonal Fuel Data...................................................................6-5 

 6.2.5 Altitude ................................................................................6-5 

 6.2.6 Control Programs ....................................................................6-5 

6.3 Seasonal Emission Calculations .......................................................6-7 

6.4 Processing of California Data .........................................................6-7 

6.5 Generation of SMOKE Files...........................................................6-7 

7.0 FUTURE YEARS OFF-ROAD EMISSIONS .............................................7-1 

7.1 Development of Population Growth Factors for 

 OFFROAD Source Categories ........................................................7-1 

 7.1.1 Nonroad Model Default Activity Growth Factors ..............................7-2 

 7.1.1.1 Agricultural Equipment ......................................................7-2 

 7.1.1.2 Aircraft and Ground Support Equipment (GSE) .........................7-3 

 7.1.1.3 Commercial and Industrial Equipment ....................................7-3 

 7.1.1.4 Commercial Marine Vessels ................................................7-3 

 7.1.1.5 Construction ...................................................................7-4 

 7.1.1.6 Lawn and Garden Equipment ...............................................7-4 

 7.1.1.7 Locomotives and Railroad Equipment.....................................7-4 

 7.1.1.8 Logging Equipment...........................................................7-5 

 7.1.1.9 Oil Field Equipment ..........................................................7-5 

 7.1.1.10 Pleasure Craft ................................................................7-5 

 7.1.1.11 Recreational Equipment ....................................................7-6 

 7.1.1.12 Underground Mining Equipment..........................................7-6 

 7.1.1.13 Summary of Growth Factors ..............................................7-6 

7.2 NONROAD Model Inputs............................................................ 7-24 

7.3 Aircraft Emissions Projections ..................................................... 7-25 

7.4 Commercial Marine Emissions Projections ....................................... 7-25 

 7.4.1 International Regulations ......................................................... 7-26 

 7.4.2 EPA Emission Standards ......................................................... 7-26 

 7.4.3 Incorporating Emission Standards............................................... 7-27 

7.5 Locomotive Emissions Projections.................................................. 7-28 

7.6 California Data Processing........................................................... 7-29 

7.7 Generation of SMOKE Files......................................................... 7-29 
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8.0 MOBILE SOURCE EMISSIONS TRENDS, 1996 TO 2018...........................8-1 

8.1 On-road Mobile Source Emissions Trends ..........................................8-1 

8.2 Off-road Mobile Source Emissions Trends........................................ 8-10 

9.0 PAVED AND UNPAVED ROAD DUST EMISSIONS..................................9-1 

9.1 2018 Road Dust Emissions-AP-42 Approach .......................................9-1 

 9.1.1 VMT Estimates for Paved and Unpaved Roads.................................9-1 

 9.1.1.1 Unpaved Road VMT Projections...........................................9-1 

 9.1.1.2 Paved Road VMT Projections ..............................................9-3 

 9.1.1.3 Seasonal VMT Allocations ..................................................9-3 

 9.1.2 Emission Factor Development and Emissions Calculations...................9-3 

 9.1.2.1 Emission Factors..............................................................9-3 

 9.1.2.1.1 Unpaved Roads...........................................................9-3 

 9.1.2.1.2 Paved Road Emission Factors .........................................9-6 

 9.1.2.2 Calculation of Paved and Unpaved Road Emissions ....................9-8 

 9.1.2.2.1 Allocation of Emissions by County ...................................9-8 

 9.1.2.2.2 Calculation of PM2.5 Emissions ......................................9-8 

 9.1.2.2.3 Application of Fugitive Dust Control Measures ....................9-8 

 9.1.2.2.4 Final Emission Estimation..............................................9-9 

 9.2 2018 Road Dust Emissions-Revised Estimates......................................9-9 

 9.2.1 Revisions to State Average Silt Content..........................................9-9 

 9.2.2 Revisions to Average Daily Traffic Volume (ADTV) Assumptions....... 9-10 

9.2.3 Application of Transport Fractions ............................................. 9-12 

9.2.4 Final Revised 2018 Unpaved Road Dust Emissions Estimates ............. 9-12 

 9-3 1996 Road Dust Emissions–Revised Estimates And Comparison With 2018 9-13 

10.0 REFERENCES................................................................................. 10-1 

APPENDICES 

Appendix A: Default 1996 Fuel Sulfur Levels 

TABLES 

Table 2-1. LDGV and LDGT PM base emission rates (g/mi) for 

 calendar year 1996 ......................................................................2-6 

Table 2-2. LDGV and LDGT PM base emission rates (g/mi) for 

 forecast years.............................................................................2-6 

Table 2-3. LDDV and LDDT PM base emission rates (g/mi).................................2-7 

Table 2-4. HDDV PM base emission rates (g/mi)...............................................2-8 

Table 2-5. LDV exhaust PM temperature correction factors. .................................2-9 
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Table 2-6. Brake wear PM emission rates (mg/mi)............................................ 2-10 

Table 2-7. Revised PM2.5 and PM10 fractions................................................... 2-10 

Table 2-8. Elemental carbon/organic carbon fractions........................................ 2-11 

Table 2-9. Ammonia emission factors ........................................................... 2-12 

Table 3-1. Default NET 1996 speeds by road type and vehicle type (mph). ................3-3 

Table 3-2. Counties modeled with an inspection and maintenance 

 program in 1996 .........................................................................3-6 

Table 3-3. Oxygenated fuel inputs..................................................................3-7 

Table 3-4. SCC codes for on-road emissions in SMOKE files 

 (non-California states). .................................................................3-8 

Table 3-5. SCC codes for California on-road emissions in SMOKE files. ................ 3-10 

Table 4-1. EPA (1992) emission factors (kg/tonne) for large ships 

 propulsion power ........................................................................4-9 

Table 4-2. EPA definition of engine categories. EPA specifies standards for 

 each category and subcategory. .......................................................4-9 

Table 4-3. Baseline emission factors for Category 1 marine engines....................... 4-10 

Table 4-4. Baseline emission factors for Category 2 and 3 engines ........................ 4-10 

Table 4-5. 1996 Vessel traffic to Coos Bay. Source: ARCADIS, 1999................... 4-12 

Table 4-6. Emission estimates (tons/year) for Coos Bay 

 ocean-going vessels in 1996.......................................................... 4-13 

Table 4-7. 1996 Ocean-going vessel activity for Puget Sound ports. 

 Source: ARCADIS, 1999. ........................................................... 4-13 

Table 4-8. 1995 Puget Sound ports trips totals and allocation to 

 specific ports by vessel type. Source: ARCADIS, 1999 ....................... 4-16 

Table 4-9. Emission estimates (tons/year) by mode for Puget Sound 

 (including Grays Harbor) ocean-going vessels in 1996.......................... 4-16 

Table 4-10. Port activity totals as presented by ARCADIS (1999)........................... 4-17 

Table 4-11. Emission estimates (tons/year) for Columbia River 

 ocean-going vessels.................................................................... 4-18 

Table 4-12. 1996 Tug activity along the Columbia River System. 

 Source: USACE (2001a), USACE (1996)......................................... 4-19 

Table 4-13. Comparison of alternative emission estimates (g/ton-mile) 

 for tug activity by ton-mile.......................................................... 4-19 

Table 4-14. Columbia and Snake River emission estimates (tons/year) 

 for tug freight activity................................................................. 4-20 

Table 4-15. Emission estimates (tons/year) for Columbia River 

 assist tug activity....................................................................... 4-21 

Table 4-16. Estimated emissions (tons/year) from ferries in the 

 Puget Sound ............................................................................ 4-22 

Table 4-17. Dredging activity and emissions estimates......................................... 4-23 

Table 4-18. Estimated emissions (tons/year) from commercial fishing 

 vessels in Washington and Oregon.................................................. 4-23 

Table 4-19. Washington and Oregon fishing ports allocation. Source 

 NMFS (2001)........................................................................... 4-23 

Table 4-20. Emissions estimates (tons/year) for Puget Sound commercial 

 marine vessels.......................................................................... 4-24 
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Table 4-21. Locomotive emission factors. (lbs/1000 gallons of fuel consumed)........... 4-25 

Table 4-22. Growth factors for estimating 1996 emissions from 

 CARB 1995 CEFS data............................................................... 4-27 

Table 4-23. Off-road SCCs used in SMOKE files .............................................. 4-27 

Table 4-24. Methods for estimating NO, SO4, EC and OC for 

 off-road gasoline and diesel equipment ............................................ 4-28 

Table 5-1. 1996 Western states total on-road emissions by state (TPD) .....................5-2 

Table 5-2. 1996 Western states total on-road emissions by state 

 and fuel type (TPD) .....................................................................5-3 

Table 5-3. 1996 Western states total off-road emissions by state 

 (TPD), based on June 2000 NONROAD model.................................. 5-13 

Table 5-4. 1996 Western states total off-road emissions by state and 

 fuel type (TPD), based on June 2000 NONROAD model ...................... 5-20 

Table 5-5. 1996 Western states total off-road emissions by state and 

 fuel type (TPD), based on NONROAD2002 model.............................. 5-22 

Table 5-6. Change in 1996 Western states total off-road emissions by state, 

 June 2000 NONROAD model to NONROAD2002 model...................... 5-22 

Table 6-1 Annual VMT for 1996 and projection years with 

 growth factors from 1996 ..............................................................6-2 

Table 6-2 Seasonal VMT (million miles per day) for 1996 

 and projection years .....................................................................6-3 

Table 7-1 Recommended growth indicators......................................................7-6 

Table 7-2 Activity growth factors by state and source category..............................7-7 

Table 7-3 EPA commercial marine primary exhaust emission 

 standards (g/kW-hr). .................................................................. 7-27 

Table 7-4 Fraction of emissions (HC, NOx, PM) by engine type 

 associated with harbor vessels. ...................................................... 7-28 

Table 7-5 Emission reductions by engine type (from EPA, 1999b)........................ 7-28 

Table 7-6 Percent reduction applied to 1996 base year 

 locomotive emission facto............................................................ 7-29 

Table 8-1a. 2003 Western states total on-road emissions by state and fuel type (TPD).....8-2 

Table 8-1b. 2008 Western states total on-road emissions by state and fuel type (TPD).....8-3 

Table 8-1c. 2013 Western states total on-road emissions by state and fuel type (TPD).....8-4 

Table 8-1d. 2018 Western states total on-road emissions by state and fuel type (TPD).....8-5 

Table 8-2a. 2003 Western states total off-road emissions by state and 

 fuel type (TPD), based on the June 2000 NONROAD model.................. 8-11 

Table 8-2b. 2008 Western states total off-road emissions by state and 

 fuel type (TPD), based on the June 2000 NONROAD model.................. 8-12 

Table 8-2c. 2013 Western states total off-road emissions by state and 

 fuel type (TPD), based on the June 2000 NONROAD model.................. 8-13 

Table 8-2d. 2018 Western states total off-road emissions by state and 

 fuel type (TPD), based on the June 2000 NONROAD model.................. 8-14 

Table 8-3a. 2003 Western states total off-road emissions by state and 

 fuel type (TPD), based on the NONROAD2002 model ......................... 8-19 
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Table 8-3b. 2008 Western states total off-road emissions by state 

 and fuel type (TPD), based on the NONROAD2002 model 

 with diesel sulfur set to 15 ppm. .................................................... 8-20 

Table 8-3c. 2013 Western states total off-road emissions by state 

 and fuel type (TPD), based on the NONROAD2002 model 

 with diesel sulfur set to 15 ppm. .................................................... 8-21 

Table 8-3d. 2018 Western states total off-road emissions by state 

 and fuel type (TPD), based on the NONROAD2002 model 

 with diesel sulfur set to 15 ppm. .................................................... 8-22 

Table 9-1 Calculation of unpaved road vehicle miles traveled 

 (VMT) growth factors by state ........................................................9-2 

Table 9-2 Paved road silt content by state........................................................9-4 

Table 9-3 Monthly average precipitation data by state (from a 

 representative rural monitoring station) used to estimate 

 unpaved road dust emissions...........................................................9-5 

Table 9-4 Monthly average precipitation data by state (from a 

 representative urban monitoring station) used to estimate 

 paved road dust emissions..............................................................9-7 

Table 9-5 Unpaved road silt content averages used in EPA NET96 and 

 derived from recent ISWS database (percent)..................................... 9-10 

Table 9-6 Comparison of NET96 and revised ADTV values ............................... 9-12 

Table 9-7 Comparison of Western states 2018 unpaved road 

 dust PM10 emissions from NET96-based approach 

 to the revised approach using ISWS silt content, updated 

 activity, and transport fractions. .................................................... 9-13 

Table 9-8 1996 WRAP states paved and unpaved road dust emissions (TPY). .......... 9-14 

Table 9-9 2018 WRAP states paved and unpaved road dust emissions (TPY). .......... 9-14 

Table 9-10 Change in Western states paved and unpaved road dust 

 emissions from 1996 to 2018 (%)................................................... 9-15 

FIGURES 

Figure 2-1. Comparison of MOBILE5 and MOBILE6 NOX emission factors. 

 Source: Beardsley, 2001................................................................2-3 

Figure 2-2. Comparison of MOBILE5 and MOBILE6 VOC 

 (exhaust + evaporative) emission factors. Source: Beardsley, 2001...........2-4 

Figure 2-3. Changes in 1996 US emissions from NONROAD2000 to 

 NONROAD2002 by source category and fuel .................................... 2-16 

Figure 4-1. Dependence of commercial marine PM emissions on fuel 

 sulfur levels. Data source: Lloyds, 1995. ........................................ 4-11 

Figure 5-1a. 1996 Western states annual average on-road total VOC emissions..............5-4 

Figure 5-1b. 1996 Western states annual average on-road total NOx emissions. .............5-5 

Figure 5-1c. 1996 Western states annual average on-road total CO emissions................5-6 

Figure 5-1d. 1996 Western states annual average on-road total PM10 emissions.............5-7 
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Figure 5-1e. 1996 Western states annual average on-road total PM2.5 emissions............5-8 

Figure 5-1f. 1996 Western states annual average on-road total SO2 emissions...............5-9 

Figure 5-1g. 1996 Western states annual average on-road total NH3 emissions ............ 5-10 

Figure 5-2a. 1996 Western states average annual on-road VOC emissions 

 by vehicle class and fuel type........................................................ 5-11 

Figure 5-2b. 1996 Western states average annual on-road NOx emissions 

 by vehicle class and fuel type........................................................ 5-11 

Figure 5-2c. 1996 Western states average annual on-road PM10 emissions 

 by vehicle class and fuel type........................................................ 5-12 

Figure 5-2d. 1996 Western states average annual on-road SO2 emissions 

 by vehicle class and fuel type........................................................ 5-12 

Figure 5-3a. 1996 Western states annual average off-road mobile 

 VOC emissions, based on June 2000 NONROAD model....................... 5-14 

Figure 5-3b. 1996 Western states annual average off-road mobile 

 NOx emissions, based on June 2000 NONROAD model ....................... 5-15 

Figure 5-3c. 1996 Western states annual average off-road mobile 

 CO emissions, based on June 2000 NONROAD model......................... 5-16 

Figure 5-3d. 1996 Western states annual average off-road mobile 

 PM2.5 emissions, based on June 2000 NONROAD model..................... 5-17 

Figure 5-3e. 1996 Western states annual average off-road mobile 

 SO2 emissions, based on June 2000 NONROAD model........................ 5-18 

Figure 5-3f. 1996 Western states annual average off-road mobile 

 NH3 emissions, based on June 2000 NONROAD model ....................... 5-19 

Figure 5-4. 1996 Western state annual average weekday off-road emissions 

 by source category, based on the June 2000 NONROAD model .............. 5-21 

Figure 6-1 Vehicle miles traveled (VMT) growth rates by state, 1996 to 2018.............6-2 

Figure 7-1 EPA NONROAD model default growth factors.................................. 7-10 

Figure 7-2a Farm Gross State Product index by state .......................................... 7-11 

Figure 7-2b Agricultural equipment growth rates by state and 

 default PSR growth rate .............................................................. 7-11 

Figure 7-3a FAA total landing and take-off (LTO) data by state ............................. 7-12 

Figure 7-3b Commercial aircraft and ground support equipment (GSE) 

 growth rates by state and default PSR GSE growth rate ........................ 7-12 

Figure 7-4a Gross state product index for air transportation SIC............................. 7-13 

Figure 7-4b General aviation growth rates by state ............................................. 7-13 

Figure 7-5a Gross state product index for Federal military SIC .............................. 7-14 

Figure 7-5b Military aviation growth rates by state............................................. 7-14 

Figure 7-6a Total gross state product index by state............................................ 7-15 

Figure 7-6b Commercial and industrial equipment growth rates by 

 state and default PSR growth rate................................................... 7-15 

Figure 7-7a Commercial marine annual freight tonnage by state ............................. 7-16 

Figure 7-7b Commercial marine growth rates by state ......................................... 7-16 

Figure 7-8a Construction GSP index by state .................................................... 7-17 

Figure 7-8b Construction equipment growth rates by state and PSR 

 default growth rate .................................................................... 7-17 

Figure 7-9a Human population by state ........................................................... 7-18 
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Figure 7-9b Lawn and garden equipment growth rates by state 

 and default PSR growth rate ......................................................... 7-18 

Figure 7-10a Railroad fuel consumption by stat................................................... 7-19 

Figure 7-10b Locomotive and railroad equipment growth rates by state 

 and default PSR growth rate ......................................................... 7-19 

Figure 7-11a Softwood supply by region........................................................... 7-20 

Figure 7-11b Logging equipment growth rates by state and default PSR 

 growth rate.............................................................................. 7-20 

Figure 7-12a Domestic crude oil production by region .......................................... 7-21 

Figure 7-12b Oil field equipment growth rates by state and default PSR 

 growth rate.............................................................................. 7-21 

Figure 7-13 Pleasure craft growth rates by state and default PSR growth rate ............. 7-22 

Figure 7-14 Recreational equipment growth rates by state and default 

 PSR growth rate ....................................................................... 7-22 

Figure 7-15a Mining GSP index by state........................................................... 7-23 

Figure 7-15b Underground mining equipment growth rates by state and 

 default PSR growth rate. ............................................................. 7-23 

Figure 8-1a. GCVTC States annual average on-road VOC emissions, 1996 - 2018..........8-6 

Figure 8-1b. GCVTC States annual average on-road NOx emissions, 1996 - 2018 ..........8-7 

Figure 8-1c. GCVTC States annual average on-road SO2 emissions, 1996 - 2018...........8-8 

Figure 8-1d. GCVTC States annual average on-road PM2.5 emissions, 1996 - 2018........8-9 

Figure 8-2a. GCVTC States annual average off-road NOx emissions, 

 1996-2018, based on the June 2000 NONROAD model ........................ 8-15 

Figure 8-2b. GCVTC States annual average off-road PM10 emissions, 

 1996-2018, based on the June 2000 NONROAD model ........................ 8-16 

Figure 8-2c. GCVTC States annual average off-road SO2 emissions, 

 1996-2018, based on the June 2000 NONROAD model ........................ 8-17 

Figure 8-3a. GCVTC States annual average off-road VOC emissions, 

 1996-2018, based on the NONROAD2002 model ............................... 8-23 

Figure 8-3b. GCVTC States annual average off-road NOx emissions, 

 1996-2018, based on the NONROAD2002 model 

 with diesel sulfur set to 15 ppm ..................................................... 8-24 

Figure 8-3c. GCVTC States annual average off-road PM2.5 emissions, 

 1996-2018, based on the NONROAD2002 model 

 with diesel sulfur set to 15 ppm ..................................................... 8-25 

Figure 8-3d. GCVTC States annual average off-road SO2 emissions, 

 1996-2018, based on the NONROAD2002 model 

 with diesel sulfur set to 15 ppm ..................................................... 8-26 

Figure 9-1 Distribution of estimated 2018 Western states unpaved 

 road dust emissions, based on EPA AP-42 methods............................. 9-16 

Figure 9-2 Distribution of revised 2018 Western states 

 unpaved road dust emissions......................................................... 9-17 

Figure 9-3 Continental United States DRAFT Transport Fractions......................... 9-18 
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ACRONYMS 

ADTV Average daily traffic volume 

ATADS Air Traffic Activity Data System (FAA) 

BEA Bureau of Economic Affairs 

BTS Bureau of Transportation Statistics 

CARB California Air Resources Board 

CE-CERT College of Engineering-Center for Environmental Research and Technology 

CEFS California Emissions Forecasting System 

CMV Commercial marine vessel 

CNG Compresses natural gas 

CO Carbon monoxide 

CODAS Consolidated Operations and Delay Analysis System 

COG Council of Governments 

DOE Department of Energy 

EC Elemental carbon 

EDMS Emissions and Dispersion Modeling System 

EIA Energy Information Administration 

EIIP Emission Inventory Improvement Program 

EPA Environmental Protection Agency 

EPS2 Emissions Processing System version 2 

FAA Federal Aviation Administration 

FTP Federal Test Procedure 

GCVTC Grand Canyon Visibility Transport Commission 

GSE Ground support equipment 

GSP Gross State Product 

GVWR Gross vehicle weight rating 

HC Hydrocarbon 

HDDV Heavy-duty diesel vehicle 

HDGT Heavy-duty gasoline truck 

HDV Heavy-duty vehicle 

HHDDV Heavy heavy-duty diesel vehicle 

HP Horsepower 

HPMS Highway Performance Monitoring System 

ICAO International Civil Aviation Organization 

I/M Inspection and Maintenance Program 

ISWS Illinois State Water Survey 

LDDT Light-duty diesel truck 

LDDV Light-duty diesel vehicle 

LDGT Light-duty gasoline truck 

LDGV Light-duty gasoline vehicle 

LDT Light-duty truck 

LDV Light-duty vehicle 

LEV Low Emission Vehicle 

LHDDV Light heavy-duty diesel vehicle 

LPG Liquid Propane Gas 

LTO Landing and take-off 
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MARPOL International Convention for the Prevention of Pollution from Ships 

MHDDV Medium heavy-duty diesel vehicle 

MPO Metropolitan Planning Organization 

MSEF Mobile Sources Emissions Forum 

NAPAP National Acid Precipitation Assessment Program 

NCDC National Climatic Data Center 

NEI National Emissions Inventory 

NET National Emissions Trends 

NFS National Forest Service 

NH3 Ammonia 

NIPER National Institute for Petroleum and Energy Research 

NPIAS National Plan of Integrated Airport System 

NOx Nitrogen oxides 

NREL National Renewable Energy Laboratory 

OC Organic carbon 

PM Particulate matter 

PM10 Particulate matter less than or equal to 10 micrometers 

PM2.5 Particulate matter less than or equal to 2.5 micrometers 

RIA Regulatory Impact Analysis 

RSD Regulatory Support Document 

SCC Source Classification Codes 

SFTP Supplemental Federal Test Procedure 

SIC Standard Industrial Classification 

SIP State Implementation Plan 

SMOKE Sparse Matrix Operator Kernel Emissions 

SO2 Sulfur dioxide 

SO4 Sulfate 

SOX Sulfur oxides 

TAF Terminal Area Forecast (FAA) 

THC Total hydrocarbons 

TIM Time in mode 

TSD Technical Support Document 

TWC Three-way catalyst 

VMT Vehicle miles traveled 

VOC Volatile organic compound 

WGA Western Governor’s Association 

WRAP Western Regional Air Partnership 
February 2004 

G:\WRAP MSEI\Reporting\Final\Tribal language.doc iii

PREFACE 

Regulatory Framework for Tribal Visibility Implementation Plans 

The Regional Haze Rule explicitly recognizes the authority of tribes to implement the 

provisions of the Rule, in accordance with principles of Federal Indian law, and as provided 

by the Clean Air Act §301(d) and the Tribal Authority Rule (TAR) (40 CFR §§49.1– .11). 

Those provisions create the following framework: 

1. Absent special circumstances, reservation lands are not subject to state jurisdiction. 

2. Federally recognized tribes may apply for and receive delegation of federal authority to 

implement CAA programs, including visibility regulation, or "reasonably severable" elements 

of such programs (40 CFR §§49.3, 49.7). The mechanism for this delegation is a Tribal 

Implementation Plan (TIP). A reasonably severable element is one that is not integrally 

related to program elements that are not included in the plan submittal, and is consistent with 

applicable statutory and regulatory requirements. 

3. The Regional Haze Rule expressly provides that tribal visibility programs are “not 

dependent on the strategies selected by the state or states in which the tribe is located” (64. 

Fed. Reg. 35756), and that the authority to implement §309 TIPs extends to all tribes within 

the GCVTC region (40 CFR §51.309(d)(12). 

4. The EPA has indicated that under the TAR tribes are not required to submit §309 TIPs by 

the end of 2003; rather they may choose to opt-in to §309 programs at a later date (67 Fed. 

Reg. 30439). 

5. Where a tribe does not seek delegation through a TIP, EPA, as necessary and appropriate, 

will promulgate a Federal Implementation Plan (FIP) within reasonable timeframes to protect 

air quality in Indian country (40 CFR §49.11). EPA is committed to consulting with tribes on 

a government to government basis in developing tribe-specific or generally applicable TIPs 

where necessary (See, e.g., 63 Fed. Reg.7263-64). 

 

It is our hope that the methods for estimating mobile source emissions described in this report 

will prove useful to tribes, whether they choose to submit full or partial 308 or 309 TIPs, or 

work with EPA to develop FIPs. The amount of modification necessary will vary 

considerably from tribe to tribe. The authors have striven to ensure that all references to 

tribes in the document are consistent with principles of tribal sovereignty and autonomy as 

reflected in the above framework. Any inconsistency with this framework is strictly 

inadvertent and not an attempt to impose requirements on tribes which are not present under 

existing law. 

Tribes, along with states and federal agencies, are full partners in the WRAP, having equal 

representation on the WRAP Board as states. Whether Board members or not, it must be 

remembered that all tribes are governments, as distinguished from the “stakeholders” (private 

interest) which participate on Forums and Committees but are not eligible for the Board. 
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G:\WRAP MSEI\Reporting\Final\Tribal language.doc iv

Despite this equality of representation on the Board, tribes are very differently situated than 

states. There are over four hundred federally recognized tribes in the WRAP region, 

including Alaska. The sheer number of tribes makes full participation impossible. Moreover, 

many tribes are faced with pressing environmental, economic, and social issues, and do not 

have the resources to participate in an effort such as the WRAP, however important its goals 

may be. These factors necessarily limit the level of tribal input into and endorsement of 

WRAP products. 

The tribal participants in the WRAP, including Board members Forum and Committee 

members and co-chairs, make their best effort to ensure that WRAP products are in the best 

interest of the tribes, the environment, and the public. One interest is to ensure that WRAP 

policies, as implemented by states and tribes, will not constrain the future options of tribes 

who are not involved in the WRAP. With these considerations and limitations in mind, the 

tribal participants have joined the state, federal, and private stakeholder interests in approving 

this report as a consensus document. 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec1_Intro.doc 1-1 

1.0 INTRODUCTION 

1.1 BACKGROUND 

Visibility impairment is a growing concern in many areas of the United States, especially in 

scenic national parks and wilderness areas. Visibility is impaired as a result of haze, a 

condition in which sunlight is absorbed and/or scattered when it encounters airborne particles 

from both natural and anthropogenic pollution. In 1999 the U.S. Environmental Protection 

Agency announced the Regional Haze Rule, which calls for state and federal agencies to work 

together to improve visibility in 156 national parks and wilderness areas (referred to as Class I 

areas). The rule requires the states, in coordination with the Environmental Protection 

Agency, the National Park Service, U.S. Fish and Wildlife Service, the U.S. Forest Service, 

and other interested parties, to develop and implement air quality protection plans to reduce 

the pollution that causes visibility impairment. Five multi-state regional planning 

organizations (RPOs) are working to develop the technical basis for these plans. The largest 

of these RPOs is the Western Regional Air Partnership (WRAP), which is made up of Western 

states, tribes, and federal agencies. The WRAP was formed in 1997 as the successor to the 

Grand Canyon Visibility Transport Commission (GCVTC), which made over 70 

recommendations in June 1996 for improving visibility in 16 national parks and wilderness 

areas on the Colorado Plateau. 

The WRAP is implementing regional planning processes to improve visibility in all Western 

Class I areas by providing the technical and policy tools needed by states and tribes to 

implement the federal regional haze rule. The rule requires the development of mobile source 

emissions inventories. WRAP contracted with ENVIRON to develop approaches for and to 

estimate mobile source emissions for the Western states. This report describes in detail the 

methods used to estimate mobile source emissions, and provides the resulting emissions 

estimates. 

1.2 EMISSIONS INVENTORIED UNDER MOBILE SOURCES 

Mobile sources include on-road and off-road vehicles and engines. On-road mobile sources 

include vehicles certified for highway use – cars, trucks, and motorcycles. For reporting onroad mobile source emissions, vehicles are divided into two major classes – light-duty and 

heavy-duty. Light-duty vehicles include passenger cars, light-duty trucks (up to 8500 lbs 

gross vehicle weight [GVW]), and motorcycles. Heavy-duty vehicles are trucks of more than 

8500 lbs GVW. 

Off-road mobile equipment encompasses a wide variety of equipment types that either move 

under their own power or are capable of being moved from site to site. Off-road mobile 

equipment sources are defined as those that move or are moved within a 12-month period and 

are covered under the EPA’s emissions regulations for nonroad mobile sources. Off-road 

mobile sources are vehicles and engines in the following categories: 

• Agricultural equipment, such as tractors, combines, and balers; 

• Aircraft, jet and piston engines; 
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G:\WRAP MSEI\Reporting\Final\Sec1_Intro.doc 1-2 

• Airport ground support equipment, such as terminal tractors; 

• Commercial and industrial equipment, such as fork lifts and sweepers; 

• Construction and mining equipment, such as graders and back hoes; 

• Lawn and garden equipment, such as leaf and snow blowers; 

• Locomotives, switching and line-haul trains; 

• Logging equipment, such as shredders and large chain saws; 

• Pleasure craft, such as power boats and personal watercraft; 

• Railway maintenance equipment, such as rail straighteners; 

• Recreational equipment, such as all-terrain vehicles and off-road motorcycles; and 

• Underground mining and oil field equipment, such as mechanical drilling engines. 

Road dust emissions estimates are also included as part of the mobile source inventory and are 

described in this report. 

1.3 SCOPE OF THE MOBILE SOURCES INVENTORY 

The scope of the WRAP mobile sources emission inventories is as follows: 

Geographic domain: Emissions were estimated by county for all counties in 13 states: 

Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, North Dakota, 

Oregon, South Dakota, Utah, Washington, and Wyoming. Alaska was not a WRAP 

member at the time the emissions were estimated, and so is not currently included in 

the mobile sources emission inventories. 

Temporal resolution: Emissions were estimated for an average weekday in each of the 

four seasons, and for an average annual weekday. Seasons are defined as three-month 

periods: spring is March through May; summer is June through August; fall is 

September through November; and winter is December through February. Emissions 

were estimated for the 1996 base year and for four future years – 2003, 2008, 2013, 

and 2018. 

Pollutants: Emissions were estimated for primary particulate matter (PM10 and PM2.5), 

nitrogen oxides (NOx), sulfur oxides (SOx), volatile organic compounds (VOCs), 

carbon monoxide (CO), ammonia (NH3), elemental and organic carbon (EC/OC), and 

sulfate (SO4). For all pollutants, emissions were estimated separately for gasoline and 

diesel-fueled engines. 

1.4 GENERAL APPROACH FOR ESTIMATING ON-ROAD AND 

OFF-ROAD MOBILE SOURCE EMISSIONS 

As with most emissions sources, on-road and off-road mobile source emissions are estimated 

as the products of emission factors and activity estimates. Except for California, the on-road 

mobile sources emission factors were derived from the EPA MOBILE6 and PART5 models 

(the latter modified for this project). Activity for on-road mobile sources is vehicle miles 

traveled (VMT). States were provided default modeling inputs and VMT levels for base and 
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G:\WRAP MSEI\Reporting\Final\Sec1_Intro.doc 1-3 

future years for review and update; several states provided revised data. California provided 

on-road emissions estimates by county directly. 

EPA’s NONROAD model was used to estimate so-called traditional off-road sources, all 

sources listed above except aircraft, commercial marine, and locomotives. The NONROAD 

model includes estimates of emission factors, activity levels, and growth factors for all 

traditional off-road sources. The default activity levels were provided to state agencies for 

input and update; however, no state provided updated off-road activity data. Emissions 

estimation methods for aircraft, commercial marine, and locomotives were similar to 

approaches EPA has recently used in developing national emission inventories. California also 

provided off-road emissions estimates by county directly.
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 2-1 

2.0 EMISSIONS MODELS USED

On-road and off-road mobile source emissions are estimated as the products of emission 

factors and activity estimates. Except for California, the on-road mobile sources emission 

factors were derived from the EPA MOBILE6 and PART5 models (the latter modified for this 

project). Activity for on-road mobile sources is vehicle miles traveled (VMT). EPA’s 

NONROAD model was used to estimate off-road sources except for aircraft, commercial 

marine, and locomotives. This section provides information on the versions of each model 

that were used in estimating emissions. 

2.1 MOBILE6

The MOBILE model is EPA’s regulatory model for estimating on-road mobile source emission 

factors for VOC, NOX, and CO. For many years, MOBILE5, released in 1993, was the 

regulatory model. MOBILE5 was still the regulatory model when the on-road emissions 

estimation work for this project was initiated. However, EPA was many years into the 

development of MOBILE6, and released a confidential draft version of MOBILE6 in March 

2001 to air quality planning agencies for their review. In August 2001, EPA released an 

updated draft version of the model. The public release version of MOBILE6 was finally 

released in January 2002. 

For WRAP on-road emissions estimates, the latest available version of draft MOBILE6 that 

was available at the time was used. Specifically, the March 2001 draft version was used for 

estimating 1996 base year on-road emissions, and the September 2001 draft version was used 

for estimating on-road emissions for all future years. Differences among these model versions 

are discussed below. 

The MOBILE6 model includes the effects of all Federal motor vehicle control programs: 

• Tier 1 light-duty vehicle standards, beginning with the 1996 model year; 

• National Low Emission Vehicle (NLEV) standards for light-duty vehicles, 

beginning with model year 2001; 

• Tier 2 light-duty vehicle standards, beginning with model year 2005, with low 

sulfur gasoline beginning in the summer of 2004; 

• Heavy-duty vehicle standards, beginning with model year 2004; and 

• Heavy-duty vehicle standards (with low sulfur diesel), beginning with model year 

2007. 

2.1.1 Comparison of MOBILE5 and MOBILE6 

MOBILE5 and MOBILE6 both estimate emission factors for on-road vehicles for NOX, VOC, 

and CO. Exhaust, evaporative, and refueling emission factors are estimated in units of grams 

per mile. The MOBILE6 emission factors are then multiplied by an estimate of vehicle miles 
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G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 2-2 

traveled (VMT) to estimate total on-road emissions. MOBILE5 estimated emission factors for 

the following eight vehicle classes: 

• Light-duty gas vehicles (LDGV – passenger cars), up to 6000 lb gross vehicle weight 

(GVW) 

• Light-duty gas trucks (pick-ups, minivans, passenger vans, and sport-utility vehicles), 

up to 6000 lb GVW (LDGT1) 

• Light-duty gas trucks of 6001-8500 lb GVW (heavier versions of LDGT1s; the 

categories are modeled separately because numerically different emission standards are 

established under the Clean Air Act for LDGT1s and LDGT2s) 

• Light-duty diesel vehicles (LDDV – passenger cars), up to 6000 lb GVW 

• Light-duty diesel trucks (LDDT), up to 8500 lb GVW (unlike gasoline powered LDTs, 

the same emission standards are applicable to all diesel LDTs up to 8500 lb GVW) 

• Heavy-duty gas vehicles, 8501 lb and higher GVW, that are equipped with heavy-duty 

gas engines (HDGV) 

• Heavy-duty diesel vehicles (HDDV), vehicles of 8501 lb and higher GVW equipped 

with heavy-duty diesel engines 

• Motorcycles (MC, all of which are gasoline powered; highway-certified motorcycles 

only are included in the model, off-road motorcycles such as "dirt bikes" are modeled 

as a nonroad mobile source) 

Significant updates in MOBILE6 from the MOBILE5 model include: 

• Update of base emission rate equations to account for new data and analytical methods 

that better characterize in-use deterioration rates for light-duty vehicles; 

• Incorporation of gasoline sulfur impacts and revised gasoline oxygenate impacts; 

• Revisions to speed correction factors to better reflect off-cycle operation for light-duty 

vehicles; 

• Revisions to the air conditioning algorithm to reflect data and analytical methodologies 

developed in the last several years for light-duty vehicles; 

• Revisions to vehicle activity estimates (e.g., annual mileage accrual rates, VMT 

distributions by vehicle class, etc.) to better reflect more recent data; 

• Addition of off-cycle NOX impacts for heavy-duty diesel vehicles as a result of fueling 

strategies that optimize fuel economy (i.e., the “defeat device” issue); 

• Low NOx rebuilds for heavy-duty diesel vehicles to correct the “defeat device” issue; 

• Updated I/M algorithms, including emissions impacts of the second-generation onboard diagnostics (OBD II) regulations; 

• Incorporation of new engine, vehicle, and fuel standards; 

• National Low Emission Vehicle (NLEV) standards; 

• Tier 2 exhaust emission standards for cars and light-duty trucks; 

• Tier 2 evaporative emission standards for cars and light-duty trucks; 

• Tier 2 gasoline sulfur limits; 

• Supplemental Federal Test Procedure (SFTP) requirements (i.e., control of “off-cycle” 

and air conditioning impacts); 

• New heavy-duty Diesel and gasoline vehicle emission standards (2004 and 2007); and 
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G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 2-3 

• Wholesale revision of evaporative emission factor estimates, using real-time 

evaporative emissions testing data from several major test programs conducted by both 

EPA and industry. 

The public release version of MOBILE6, along with detailed technical documentation on the 

MOBILE6 updates, may be found on the EPA MOBILE6 web site at 

http://www.epa.gov/OMSWWW/m6.htm. MOBILE6 has significantly different emissions 

from MOBILE5. In general, MOBILE6 emission factors are higher than MOBILE5 in past 

years, and lower than MOBILE6 in future years. Figures 2-1 and 2-2 show a general 

comparison of MOBILE5 and MOBILE6 for a national fleet for NOX and VOC, respectively. 

Figure 2-1. Comparison of MOBILE5 and MOBILE6 NOX emission factors. Source: Beardsley, 

2001.

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

1990 1995 2000 2005 2010 2015 2020 2025 2030

Calendar Year

grams per mile

MOBILE5

MOBILE6
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G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 2-4 

Figure 2-2. Comparison of MOBILE5 and MOBILE6 VOC (exhaust + evaporative) emission 

factors. Source: Beardsley, 2001. 

2.1.2 Comparison of MOBILE6 Draft and Public Release Versions 

As stated above, the March 2001 draft version of MOBILE6 was used to estimate 1996 onroad emissions, and the September 2001 draft version was used for all future years. To check 

for differences between the models, both draft versions were run for Maricopa County, AZ. 

Comparisons were made by vehicle class, emissions mode, and season. Except for VOC 

refueling emissions, all changes were less than one percent. Refueling VOC emissions in 

MOBILE6 differ from MOBILE5 by about three percent. 

The January 2002 public release version of MOBILE6, not used in any WRAP modeling, has 

several changes from the September 2001 draft version, some with significant emissions 

effects. Per EPA (Brzezinski, 2002), these changes and their effects are as follows: 

• Updated fuel economy estimates for gasoline vehicles and added estimates for other 

vehicle classes; significant changes to refueling emission estimates (direction and 

magnitude differ by vehicle class). 

• Fixed a bug in running loss emissions calculation (default trip length data); reduces 

running VOC emissions significantly (amount depends on temperature and speed) in 

runs where the default trip lengths are used. 

• Fixed a bug in the Defeat Device rebuild calculation; slightly increases HDDV NOx 

for 2002 and 2003. 

• Fixed Inspection and Maintenance (I/M) bugs; changes the I/M benefits slightly, and 

speeds up the model run-time significantly (for I/M runs). 

0

1

2

3

4

5

6

1990 1995 2000 2005 2010 2015 2020 2025 2030

Calendar Year

grams per mile

MOBILE5

MOBILE6 with

Tier 2 & HD rule
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G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 2-5 

• Fixed a typo in diesel school bus NOX emission rates for model years 1988, 1989, 

1992, and 1993; very small effect. 

 

Without doing many draft vs. final MOBILE6 comparison runs, it is not clear what the overall 

effects are of these corrections on the WRAP modeling. Based on a limited comparison and 

analysis, the most significant effects, though, are likely to be in VOC refueling and running 

loss emissions.

2.2 PART5 

At the time of inventory development, the PART5 model was the only publicly available tool 

from EPA with which to generate particulate matter estimates for on-road motor vehicles. 

PART5 was last updated in February 1995, but many of the emission and correction factors in 

the model are based on data collected over 10 years ago. EPA is currently working on 

integrating PART5 emission rates with MOBILE6, and will release this as MOBILE6.1. As a 

result, it was necessary as part of this study to incorporate a number of changes to PART5 to 

update the model with more recent data. 

The discussion below highlights the revisions that were made to update PART5 with new data. 

Changes were made to both light-duty vehicle and heavy-duty vehicle exhaust emission 

factors, and some of the non-exhaust processes (e.g., brake wear) were updated as well. 

Where new data existed, particle size distributions were also updated. Fleet characteristic data 

(e.g., registration fractions, mileage accumulation rates, etc.) were updated to be consistent 

with EPA’s plans for MOBILE6. Finally, the model was modified to calculate ammonia 

emissions from all vehicle classes, and organic carbon/elemental carbon fractions were applied 

to the PM estimates. 

2.2.1 Exhaust PM Emission Rates 

2.2.1.1 Light-Duty Gasoline Vehicles

The PM base emission rates for light-duty gasoline vehicles (i.e., passenger cars and light 

trucks) were generated separately for low-altitude and high-altitude regions. The low-altitude 

factors were based on testing performed by the University of California, Riverside (College of 

Engineering-Center for Environmental Research and Technology, CE-CERT) for CARB (CECERT, 1998a), while the high-altitude factors were based on testing performed by General 

Motors and the Colorado Department of Public Health and Environment (Cadle et al, 1999b). 

These databases were used for this analysis because the vehicles were randomly selected from 

the in-use fleet. 

Emissions data from the above programs (which were collected based on the Federal Test 

Procedure for light-duty vehicles) were compiled into model year groups, and mean emission 

rates for each group were determined. Because testing was performed with tank fuel that 

contained oxygenates, the mean emission levels were adjusted to remove the impacts of 

oxygenates on PM emissions (using the correction factors described later in this section). The 
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G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 2-6 

mean PM emission rates for low-altitude and high-altitude regions for calendar year 1996 are 

shown in Table 2-1. 

Table 2-1. LDGV and LDGT PM base emission rates (g/mi) for calendar year 1996. 

Model Year Group Low-Altitude High-Altitude 

Pre-1981 0.0423 0.0908 

1981 - 1985 0.0613 0.0505 

1986 - 1990 0.0180 0.0342 

1991+ 0.0031 0.0037 

The emission rates shown in Table 2-1 were used directly for the 1996 base year inventory 

estimates. However, for the forecast years (i.e., 2003, 2008, 2013, and 2018), emission 

control system deterioration effects were incorporated into the analysis for the 1986 and newer 

model year groups. This was accomplished by determining the fraction of high PM emitters 

as a function of model year group (and therefore vehicle age) in the low-altitude database used 

to construct Table 2-1. For this analysis, a high emitter was defined as a vehicle with PM 

emissions greater than 40 mg/mi, and the mean high-emitter emission rate was 126 mg/mi. 

For 1991 and newer vehicles, this was combined with the normal-emitter emission rate of 

3.1 mg/mi. For the 1986-1990 model year group, the normal-emitter emission rate was 

determined to be 7.3 mg/mi. To illustrate the calculation procedure, it was determined that 

the 8-12 year old group contained 9% high emitters. Applying the normal and high emitter 

emission rates to this distribution resulted in: 

 1991+ MY(8-12 yrs old) = 3.1 mg/mi * 0.91 + 126 mg/mi * 0.09 = 14.2 mg/mi 

A similar procedure was used for the remaining age groups, and the resulting PM base 

emission rates are given in Table 2-2. For the high-altitude emission rates, the differences 

observed between the low- and high-altitude emission rates in Table 2-1 were applied to 

generate the high-altitude rates in Table 2-2. 

Table 2-2. LDGV and LDGT PM base emission rates (g/mi) for forecast years. 

Model Year/Age Group Low-Altitude High-Altitude 

1986-1990 / 8-12 years 0.0358 0.0505 

1991+ / 13+ years 0.0326 0.0388 

1991+ / 8-12 years 0.0142 0.0169 

1991+ / 1-7 years 0.0031 0.0037 

2.2.1.2 Light-Duty Diesel Vehicles

The light-duty diesel vehicle (i.e., cars and light trucks) emission rates for 1996 and older 

model year vehicles were based on data collected by CE-CERT under contract to the 

Coordinating Research Council (CE-CERT, 1998b), as well as data collected as part of the 
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G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 2-7 

Northern Front Range Study (NREL, 1998). The data for those programs were combined and 

mean emission rates by model year groups were determined. For 1997 and newer model year 

vehicles, LDDV and LDDT PM emission rates developed by EPA for a 1999 air toxics study 

(Sierra, 1999) were used; these reflect the implementation of tighter PM emission standards 

under the Tier 2 rule. Table 2-3 summarizes the LDDV and LDDT PM emission rates used in 

this study. 

Table 2-3. LDDV and LDDT PM base emission rates (g/mi). 

Model Year LDDV LDDT

Pre-1981 0.588 0.588 

1982 - 1984 0.474 0.474 

1985 - 1996 0.123 0.123 

1997 - 2003 0.100 0.109 

2004 0.065 0.056 

2005 0.030 0.043 

2006 0.011 0.033 

2007 0.011 0.022 

2008 0.011 0.016 

2009+ 0.011 0.011 

2.2.1.3 Heavy-Duty Diesel Vehicles

Heavy-duty diesel vehicle PM emission rates in PART5 were also modified for this project. 

The revised emission rates were based on data compiled by CARB for its EMFAC2000 model 

(CARB, 2000). Of significance with respect to those data is that the testing was based on 

chassis dynamometer test procedures that measure emissions directly in terms of grams per 

mile. Previous versions of CARB and EPA emissions models based heavy-duty vehicle 

emissions estimates on engine dynamometer testing, which requires conversion of the emission 

results (reported in grams per brake horsepower-hour) into units that are consistent with travel 

data (i.e., grams per mile). 

The HDDV data compiled by CARB were segregated into weight categories and model year 

groups. The weight categories followed the categories used in PART5 (i.e., 2BHDDV, 

LHDDV, MHDDV, HHDDV, and buses), while the model year groups typically were based 

on changes in certification standards. Mean emission rates were then generated for each 

weight class and model year group combination. The results of this analysis are given in 

Table 2-4. Note that the recently adopted 2007 and later emission standards were accounted 

for by simply scaling the 1998 emission factors by the ratio of emission standards. (Note that 

the 2007 chassis-based standard for LHDDVs is 0.02 g/mi; that was used in place of the 

scaled 1998 estimates). 
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Table 2-4. HDDV PM base emission rates (g/mi). 

Model Year LHDDVa MHDDV HHDDV Buses 

Pre-1988 0.44 1.47 2.57 1.73 

1988 - 1990 0.38 1.03 1.98 1.33 

1991 - 1992 0.19 0.86 0.64 0.96 

1993 0.19 0.86 0.64 1.26 

1994 - 1995 0.084 0.61 0.62 1.26 

1996 - 1997 0.084 0.61 0.62 1.75 

1998 - 2006 0.084 0.61 0.62 1.75 

2007+ 0.02 0.061 0.062 0.25 

a The LHDDV emission rates were also applied to the class 2B HDDVs. 

2.2.2 Corrections to the Base Emission Rates 

Once the exhaust base emission factors were generated, a number of corrections to those 

factors were developed. Those are described below. 

2.2.2.1 Oxygenated Gasoline

CRC project E-46 (Cadle et al, 1999a) included testing of light-duty gasoline vehicles 

conducted with non-oxygenated fuel and with an ethanol blend containing 3.5% by weight of 

oxygen. The results of that testing indicated a 34% reduction in exhaust PM with the 

oxygenated fuel. Based on those results, we assumed that the use of oxygenated gasoline 

would result in a 10% reduction in PM for each one percentage point (by weight) of oxygen in 

the gasoline. 

2.2.2.2 Temperature Corrections

Testing conducted during the winter in Colorado (Cadle et al, 1999b) indicated that light-duty 

vehicle exhaust PM emission rates are a function of ambient temperature. Further, it appears 

that the temperature effect is also a function of the base emission rate. In the test program 

cited above, testing was conducted at 60EF and at 35EF on the same group of vehicles. Those 

data were used to generate a multiplicative correction factor at a temperature of 35EF. In this 

study, it was assumed that no temperature correction was needed until the ambient temperature 

was below 60EF, and it reached a maximum at 35EF. At temperatures between 35EF and 

60EF, the correction factor was linearly interpolated. The 35EF temperature correction factors 

developed for this project are provided in Table 2-5.
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Table 2-5. LDV exhaust PM temperature correction factors.

Correction Factor 

Base Emission Rate (g/mi) 60EF 35EF

0.004 1.0 6.7 

0.012 1.0 2.4 

0.046 1.0 1.4 

0.395 1.0 1.1 

Diesel 1.0 1.1 

2.2.2.3 Sulfur Effects

Gasoline and diesel fuel sulfur content affects not only the directly emitted SO2 emission rates 

calculated by PART5, but also the mass of exhaust PM attributable to sulfate. Two primary 

revisions were made to PART5 with respect to sulfur. First, the source code was modified 

such that gasoline and diesel fuel sulfur levels are a required input for each area being 

modeled. Second, the default split between sulfate and SO2 was modified for light-duty 

gasoline vehicles with relatively low emission rates (i.e., below 10 mg/mi), based on data 

collected by Environment Canada (1999). This revision was made because the data that were 

used for PART5 are very old (i.e., collected 15 to 20 years ago), and it was felt that newly 

collected data are more appropriate (particularly for late-model vehicles). Based on an 

analysis of the Environment Canada data, the fraction of sulfur converted to sulfate was found 

to be a function of the PM emission rate as follows: 

Sulfate Fraction = 0.0022 + 0.0004*PM (mg/mi) 

This equation was used for PM emission rates of less than 10 mg/mi; for emission rates 

greater than 10 mg/mi, the default PART5 fractions were used. 

2.2.2.4 HDDV Altitude Adjustment

Consistent with EPA’s plans for MOBILE6, the HDDV exhaust PM emission rates were 

adjusted to reflect high altitude for those areas modeled as high-altitude areas. The high 

altitude multiplicative correction factor used in this analysis was 1.47, which was presented in 

the EPA technical report, M6.HDE.001 (EPA, 1999). This factor was also applied to lightduty diesel emission factors. 

2.2.3 Non-Exhaust PM Emissions 

In addition to exhaust PM emission rates, the PART5 model also estimates PM emissions from 

non-exhaust sources, i.e., brake wear and tire wear. For this analysis, the tire wear estimates 

were not revised. However, new data on brake wear estimates were available (Garg et al, 

2000) and were used to develop revised brake wear emission factors for PART5. Brake wear 

PM emissions were reported as a function of vehicle type: 
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 Small car = 5.3 mg/mi 

 Large car = 8.4 mg/mi 

 Large pick-up = 14.1 mg/mi 

For this study, the above estimates were mapped to the PART5 vehicle classes on the basis of 

vehicle weight (i.e., it was assumed that brake wear is proportional to the kinetic energy 

dissipated during a stop, 1⁄2mv2

). For example, assuming that class 8B trucks (80,000 lbs. 

GVWR) would have an average loaded/unloaded weight of 55,000 lbs., emissions would be 

approximately 10 times that of a large pick-up truck (i.e., 141 mg/mi). This approach resulted 

in the estimates that are summarized in Table 2-6. 

Table 2-6. Brake wear PM emission rates (mg/mi). 

Vehicle Class Brake Wear Emissions 

LDGV/LDDV 6.9 

LDGT1/LDDT 11.3 

LDGT2 14.1 

HDGV 14.1 

2BHDDV 14.1 

LHDDV 35 

MHDDV 70 

HHDDV 141 

Buses 70 

2.2.4 Particle Size Distributions 

PM emissions estimates were prepared for both PM10 and PM2.5 in this study. Those estimates 

are calculated in PART5 by applying an appropriate particle size distribution to the total PM 

emission rates for the various emission processes. For example, if it is found that 96% of the 

total PM mass from gasoline exhaust is from particles with a diameter less than or equal to 10 

Fm, then the total exhaust PM is multiplied by 0.96 to generate PM10 estimates. To the extent 

that more recent data were available, the particle size distributions in PART5 were updated for 

this project. A summary of the revised fractions and data sources is contained in Table 2-7. 

Table 2-7. Revised PM2.5 and PM10 fractions. 

Process/Pollutant PM2.5 PM10 Source 

Gasoline Exhaust – Non-catalyst 0.904 0.962 CE-CERT, 1998a 

Gasoline Exhaust – Catalyst 0.912 0.961 CE-CERT, 1998a 

Diesel Exhaust 0.980 1.000 Ubanwa et al, 2000 

Brake Wear 0.630 0.860 Garg et al, 2000 

2.2.5 Elemental Carbon-Organic Carbon Splits 

As part of this project, estimates of elemental carbon (EC) and organic carbon (OC) had to be 

made. Thus, PART5 was modified to include calculations of EC and OC. These calculations 
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were performed by applying an EC/OC fraction to the PM10 and PM2.5 emissions estimated by 

the model. The EC/OC splits used for these calculations are summarized in Table 2-8. 

Table 2-8. Elemental carbon/organic carbon fractions. 

Process/Pollutant EC OC Source 

Gasoline Exhaust 23.9% 51.8% Gillies and Gertler, 2000 

Light-Duty Diesel Exhaust 61.3% 30.3% Gillies and Gertler, 2000 

Heavy-Duty Diesel Exhaust 75.0% 18.9% Gillies and Gertler, 2000 

Tire Wear 60.9% 21.75% Radian, 1988 

Brake Wear 2.8% 97.2% Garg et al, 2000 

Most of the data used to develop these profiles can be found in a 1999 Desert Research 

Institute (Gillies and Gertler, 2000) that was prepared for the U.S. EPA. The DRI study 

evaluated profiles contained in four databases. These four databases were EPA’s SPECIATE 

database, the DRI source profile library, and the Northern Front Range Air Quality Study 

(NFRAQS). In addition, source profiles were developed from emissions data reported by the 

College of Engineering—Center for Environmental Research and Technology (CE-CERT) at 

the University of California—Riverside (Norbeck, 1998). 

DRI reported that mobile source profiles are available from various sources for both gaseous 

and PM emissions. These profiles, in the form of weight fraction by chemical species type, 

characterize and quantify the chemical composition of the emissions from different mobile 

source types (e.g., diesel versus gasoline fueled vehicles). The weight fraction format is 

commonly used since it is required for input into the Chemical Mass Balance receptor model, 

which is one of the most common methods of estimating source attribution in ambient PM. 

DRI recommended that for future studies that attempt to apportion mobile source PM2.5 to its 

various components, using enhanced organic speciation measurements, that a selection of new 

source profiles be included in the SPECIATE database. Recommended profiles were drawn 

from Fujita (1998) and were selected based on sensitivity testing. These profiles consistently 

produced the best goodness-of-fit parameters when used with ambient NFRAQS data. CECERT profiles (Norbeck, 1998) were also recommended for inclusion in SPECIATE since 

they represent the only detailed source profiles that characterize hot-stabilized and high 

emitting light-duty gasoline powered vehicles from a completely different sample of vehicles, 

with the same speciation as the NFRAQS profiles. The PM speciation used for WRAP 

emissions estimates for vehicle exhaust emissions are from the DRI report recommendations. 

2.2.6 Ammonia Emissions 

In its standard configuration, PART5 does not calculate the ammonia emission factors that 

were needed for this effort. As a result, an algorithm was developed to generate ammonia 

emission factors for the revised PART5 model used in this study. Over the past several years, 

EPA has relied on emission factors from SAE paper 830987 (Harvey et al, 1983) to develop 

on-road motor vehicle ammonia emissions inventories (EPA, 2001). Because those data are 
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somewhat dated, and because new data were available on late-model light-duty vehicles, the 

data published in 1983 were supplemented with data recently collected by CE-CERT (2001). 

The CE-CERT data showed the following important characteristics: 

• Ammonia emissions increased with lower gasoline sulfur levels; 

• Ammonia emissions increased during aggressive driving behavior; and 

• Ammonia emissions for LEV-category vehicles were highly variable (ranging from 

0 to 216 mg/mi on the FTP) and very sensitive to sulfur and aggressive driving 

behavior. 

As a result of the uncertainty surrounding LEV-category emission rates, data from 1991 and 

newer Tier 0 and Tier 1 vehicles were used to generate revised ammonia emission rates (FTP 

basis). The results from the Tier 0 vehicles were used to reflect 1987 and newer vehicles in 

the model, and the Tier 1 results were used to reflect Tier 1 and LEV-category vehicle 

emission rates for the future-year forecasts. It was felt that this was a more conservative 

approach given the large variability in LEV emission rates and the uncertainty surrounding 

how those vehicles will react once SFTP regulations are in place. Clearly, more work needs 

to be done to better characterize ammonia emissions from late-model cars and trucks, 

particularly under aggressive driving conditions. 

A summary of the ammonia emission rates used in the revised PART5 model developed for 

this study is contained in Table 2-9. 

Table 2-9. Ammonia emission factors. 

Vehicle Type/ 

Technology 

Model 

Year 

Ammonia 

(mg/mi) Source 

LDGV/T – Non-catalyst pre-1987 11.3 Harvey et al, 1983 

LDGV/T – Oxidation catalyst pre-1987 15.1 Harvey et al, 1983 

LDGV/T – Three-way catalyst pre-1987 101.8 Harvey et al, 1983 

LDGV/T – Tier 0 1987+ 87a CE-CERT, 2001 

LDGV/T – Tier 1 and LEV 1987+ 79a CE-CERT, 2001 

HDGVs All 45.1 Harvey et al, 1983 

LDDV/LDDT All 6.8 Harvey et al, 1983 

HDDV All 27.1 Harvey et al, 1983 

MC All 11.3 

Assumed from 

LDGV/T Non-Cat a

 Based on 30 ppm sulfur gasoline. 

A correction was also applied to the 1987 and newer light-duty vehicle ammonia emission 

factors to account for sulfur levels ranging from 30 ppm (minimum value tested) to 330 ppm 

(maximum value tested). The CE-CERT data on Tier 0 and Tier 1 vehicles showed an 

approximate 30% reduction in ammonia emission rates when sulfur was increased from 30 to 
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330 ppm. For intermediate values, the correction factor was linearly interpolated (i.e., a 10% 

decrease in ammonia per 100 ppm increase in gasoline sulfur content was assumed, up to a 

maximum of 30%). 

2.2.7 Fleet Characteristic Data 

The following default fleet characteristics data contained in PART5 block data statements were 

updated to reflect revisions planned for MOBILE6, as described in the MOBILE6 technical 

report M6.FLT.007 (EPA, 2001): 

• Registration Fractions – The default registration fractions (i.e., for each vehicle class, 

the fraction of the fleet as a function of vehicle age) were updated with the MOBILE6 

estimates. 

• Mileage Accumulation Rates – The default PART5 mileage accumulation rates were 

replaced with the MOBILE6 estimates. 

• Vehicle Counts – Vehicle count estimates are used in the model to calculate the fraction 

of VMT attributable to the different vehicle classes (commonly called the “VMT 

Mix”). The default PART5 counts were replaced with MOBILE6 estimates. The 

primary difference between the baseline PART5 estimates and the revised MOBILE6 

estimates is in terms of the split between passenger cars and light-duty trucks 

(MOBILE6 has a higher fraction of LDT). 

• Gasoline/Diesel Splits – The fraction of gasoline versus Diesel vehicles by vehicle type 

and model year was also updated based on MOBILE6 estimates. 

• HDGV and HDDV Fuel Economy Estimates – Fuel economy estimates are used within 

PART5 to generate emission factors for SO2. Fuel economy estimates for HDGVs and 

HDDVs were updated based on M6.HDE.002 (EPA, 2002). 

2.3 NONROAD 

Off-road mobile equipment encompasses a wide variety of equipment types that either move 

under their own power or are capable of being moved from site to site. Off-road mobile 

equipment sources are defined as those that move or are moved within a 12-month period and 

are covered under the EPA’s emissions regulations for nonroad mobile sources. Emissions for 

so-called traditional nonroad sources are estimated by EPA in their NONROAD emissions 

model, currently available in draft form on the NONROAD web page at 

http://www.epa.gov/otaq/nonrdmdl.htm. 

At the time that the off-road equipment emissions were estimated for use in WRAP air quality 

modeling, the NONROAD model was in draft form and was still evolving. It had been used 

to develop the NEI99 emissions estimates and also in SIP modeling efforts that have been 

accepted by EPA. The June 2000 draft model was used to estimate off-road emissions for 
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WRAP states for the 1996 and 2018 air quality modeling. The off-road equipment emissions 

estimates in the WRAP Technical Support Document (TSD) were estimated with a later 

version of the model, NONROAD2002. Differences between these two versions of the model 

are discussed below. 

The NONROAD model includes both emission factors and default county-level population and 

activity data, and so estimates not just emission factors but also emissions. The original model 

development and overview of the technical approach can be found in Pollack and Lindhjem 

(1997); complete and detailed technical descriptions of all aspects of the model can be found 

on the EPA NONROAD web page. 

The NONROAD model includes more than 80 basic and 260 specific types of nonroad 

equipment, and further stratifies equipment types by horsepower rating and fuel type, in the 

following categories: 

• airport ground support, such as terminal tractors; 

• agricultural equipment, such as tractors, combines, and balers; 

• construction equipment, such as graders and back hoes; 

• industrial and commercial equipment, such as fork lifts and sweepers; 

• recreational vehicles, such as all-terrain vehicles and off-road motorcycles; 

• residential and commercial lawn and garden equipment, such as leaf and 

snowblowers; 

• logging equipment, such as shredders and large chain saws; 

• recreational marine vessels, such as power boats; 

• underground mining equipment; and 

• oil field equipment. 

The NONROAD model does not include commercial marine, locomotive, and aircraft 

emissions. Emissions for these three source categories are estimated using other EPA methods 

and guidance documents (described in Section 4). However, support equipment for aircraft, 

locomotive, and commercial marine operations and facilities are included in the NONROAD 

model. 

The NONROAD model estimates emissions for six exhaust pollutants: hydrocarbons (HC), 

NOX, carbon monoxide (CO), carbon dioxide (CO2), sulfur oxides (SOX), and PM. The user 

selects among five different types for reporting HC — as total hydrocarbons (THC), total 

organic gases (TOG), non-methane organic gases (NMOG), non-methane hydrocarbons 

(NMHC), or volatile organic compounds (VOC). Particulate matter can be reported as total 

PM, PM of 10μ or less (PM10), or PM of 2.5μ or less (PM2.5). The model also estimates 

emissions of non-exhaust HC for six modes — hot soak, diurnal, refueling, resting loss, 

running loss, and crankcase emissions. 

2.3.1 Comparison of NONROAD2000 and NONROAD2002 

The June 2000 NONROAD model incorporates the effects of off-road equipment regulations 

that were in effect at the time: 
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Gasoline engines: 

$ Emission standards for new nonroad spark-ignition engines at or below 19 kilowatts 

(EPA, 1995) 

$ Phase 2 emission standards for new spark-ignition hand-held engines at or below 19 

kilowatts (EPA, 1999) 

$ Phase 2 emission standards for new spark-ignition nonhandheld engines at or below 19 

kilowatts (EPA, 2000) 

$ Emission standards for new gasoline spark-ignition marine engines (EPA, 1996) 

Diesel engines: 

$ Tier 1 emission standards for new nonroad compression-ignition engines at or above 37 

kilowatts (50 HP) (EPA, 1994) 

$ Tier 1 and Tier 2 emission standards for new nonroad compression-ignition engines 

below 50 hp including recreational marine engines less than 50 hp, and Tier 2 and Tier 

3 standards for engines of 50 hp and greater not including recreational marine engines 

greater than 50 hp (EPA, 1998) 

In December 2002, EPA confidentially released an updated version of the model, 

NONROAD2002, to WRAP and other air quality planning agencies. The NONROAD2002 

model was publicly released on the NONROAD model website in late June 2003, along with 

an updated User Guide and technical reports. The NONROAD2002 model incorporates 

updated population data, load factors, and median life; increased PM deterioration rates; 

revised PM and SOx calculations (correcting coding in the prior version); revised equipment 

growth and scrappage; revised activity data for all terrain vehicles; and revised algorithms for 

allocating national to county-level equipment populations (Harvey, Carey, and Warila, 2003). 

With all of these changes, the emission inventory estimates from the NONROAD2002 model 

are substantially different from the June 2000 draft version. Total U.S. emissions for 1996 are 

13 percent lower for VOC in NONROAD2002, 35 percent lower for NOX, 28 percent lower 

for PM2.5, and 72 percent lower for SO2. The percent changes vary by equipment type and 

fuel, as shown in Figure 2-3. Construction and mining equipment are the largest source of 

emissions for NOX, PM2.5, and SO2. Diesel emissions for construction and mining and other 

equipment types are reduced in NONROAD2002 because the population numbers are lower, 

and also because lower average equipment lifetimes result in the age distribution with more 

newer equipment. Pleasure craft are the largest source of VOC emissions. Pleasure craft and 

other SI equipment are on average lower by about 10 percent in NONROAD2002, though 

recreational equipment VOC emissions increased primarily because the activity rates and 

increased. Note that these are 1996 U.S. total comparisons. Comparisons for individual 

states or counties will be different because of changes in geographical allocation and 

differences in fuel properties. The differences between NONROAD2000 and 

NONROAD2002 over time will also vary. Comparisons of the two models for U.S. total 

emissions for years 2000, 2010, 2020, and 2030 may be found in an EPA memo released 

along with the confidential December 2002 model release (Harvey, 2002).
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Figure 2-3. Changes in 1996 US emissions from NONROAD2000 to NONROAD2002 by 

source category and fuel. 

The NONROAD2002 model does not incorporate the effects of the September 2002 off-road 

equipment regulations for large (> 25 hp) spark-ignition equipment, recreational equipment, 

and compression-ignition pleasure craft above 50 hp. ENVIRON obtained updated 

NONROAD2002 model input files from EPA that incorporate these new exhaust standards 

(the effects of the evaporative standards are not yet modeled). Emissions for traditional offroad equipment were estimated with the NONROAD2002 model, with the updates for the 

September 2002 rulemaking. 

The NONROAD2002 emission factors for future years incorporate the effects of all 

promulgated off-road equipment regulations (except as noted above). The NONROAD2002 

model also includes equipment population growth factors, but they are based on a confidential 

and undocumented data source, and they are national-level only. ENVIRON developed statelevel growth factors so that state-to-state growth differences could be taken into account for 

estimating future year emissions. The growth factors were based on analyses of historical 

Gross State Product (GSP) data by economic sector available from the Bureau of Economic 

0 200,000 400,000 600,000 800,000 1,000,000

Agricultural Equipment

Airport Equipment

Commercial Equipment

Construction and Mining

Equipment

Industrial Equipment

Lawn and Garden

Equipment (Com)

Lawn and Garden

Equipment (Res)

Logging Equipment

Pleasure Craft

Railroad Equipment

Recreational Equipment

1996 US VOC Emissions (tons)

NONROAD2000 SI

NONROAD2000 Diesel

NONROAD2002 SI

NONROAD2002 Diesel

0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000

Agricultural Equipment

Airport Equipment

Commercial Equipment

Construction and Mining

Equipment

Industrial Equipment

Lawn and Garden

Equipment (Com)

Lawn and Garden

Equipment (Res)

Logging Equipment

Pleasure Craft

Railroad Equipment

Recreational Equipment

1996 US NOx Emissions (tons)

NONROAD2000 SI

NONROAD2000 Diesel

NONROAD2002 SI

NONROAD2002 Diesel

0 30,000 60,000 90,000 120,000 150,000

Agricultural Equipment

Airport Equipment

Commercial Equipment

Construction and Mining

Equipment

Industrial Equipment

Lawn and Garden

Equipment (Com)

Lawn and Garden

Equipment (Res)

Logging Equipment

Pleasure Craft

Railroad Equipment

Recreational Equipment

1996 US PM2.5 Emissions (tons)

NONROAD2000 SI

NONROAD2000 Diesel

NONROAD2002 SI

NONROAD2002 Diesel

0 50,000 100,000 150,000 200,000 250,000 300,000

Agricultural Equipment

Airport Equipment

Commercial Equipment

Construction and Mining

Equipment

Industrial Equipment

Lawn and Garden

Equipment (Com)

Lawn and Garden

Equipment (Res)

Logging Equipment

Pleasure Craft

Railroad Equipment

Recreational Equipment

1996 US SO2 Emissions (tons)

NONROAD2000 SI

NONROAD2000 Diesel

NONROAD2002 SI

NONROAD2002 Diesel
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Affairs (BEA), Census population data, or other appropriate activity trend data; full details are 

in the ENVIRON (2003) report. 

In April 2003, EPA proposed new standards for off-road compression-ignition equipment. 

The proposal is for new engine standards to phase in between 2008 and 2013, with smaller 

engine standards phasing in earlier than for larger engines. Emissions standards for the largest 

engines, between 175 and 750 horsepower, phase in from 2011 to 2013. With these so-called 

Tier 4 emission standards, fuel sulfur levels need to be reduced to 15 ppm to enable the PM 

and NOX emission control technology. Nonroad diesel sulfur is proposed to be 500 ppm in 

2008 and 15 ppm in 2010. 

The NONROAD2002 modeling for the TSD was completed prior to the release of these 

proposed new standards. While the NONROAD2002 emissions modeling did not include the 

anticipated emissions effects of the Tier 4 technology, the diesel fuel sulfur levels were set to 

15 ppm in the modeling for years 2008, 2013, and 2018. For the TSD, WRAP staff adjusted 

the emissions estimates to back out the effects of low sulfur diesel by applying the known state 

averages of diesel fuel sulfur to the 15 ppm level input to NONROAD2002. 

2.3.2 NONROAD Emissions Calculations 

The NONROAD model provides emission estimates at the national, state, and county level. 

The basic equation for estimating emissions in the NONROAD model is as follows: 

 Emissions = (Pop)(Power)(LF)(A)(EF) 

where 

Pop = Engine Population 

Power = Average Power (hp) 

LF = Load Factor (fraction of available power) 

A = Activity (hrs/yr) 

EF = Emission Factor (g/hp-hr) 

The national or state engine population is estimated and multiplied by the average power, 

activity, and emission factors. National average engine power, load factor (the relative 

fraction of maximum available power that engine use on average), annual activity, and 

emission factors are estimated and used to calculate the national yearly emissions. Equipment 

population by county is estimated in the model by geographically allocating national engine 

population through the use of econometric indicators, such as construction valuation. The 

manner in which the geographic allocation is performed is as follows: 

(County Population)i /(National Population)I = (County Indicator)i /(National Indicator)i 

where 

 i is an equipment application like construction or agriculture.

Activity is temporally allocated using a similar equation as for geographic allocation through 

the use of monthly, and day of week fractions of yearly activity. 
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The NONROAD model has default estimates for all variables and factors used in the 

calculations. All of these estimates are in model input files, and can be changed by the user if 

data more appropriate to the local area are available. For example, if construction equipment 

surveys are used to gather local population and activity estimates, these can be input to the 

model for improved local emissions estimates. 

2.4 CALIFORNIA MODELS 

The California Air Resources Board (CARB) has developed their own models for on-road and 

off-road emissions estimation. CARB’s on-road model is referred to as EMFAC; the version 

of the model that was used to generate the on-road emissions was EMFAC2000. The 

EMFAC2000 model has since been replaced by EMFAC2002; that version of the model and 

associated documentation is on CARB’s web page at http://arbis.arb.ca.gov/msei/onroad/latest_version.htm. 

For many years, CARB has been developing their own off-road emissions model, called 

OFFROAD. The model has not yet been released, though CARB has developed many of the 

inputs as part of their analyses in support of their off-road equipment regulations. An internal 

working version of the OFFROAD were used to generate emissions for traditional off-road 

emissions source categories. 
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3.0 1996 BASE YEAR ON-ROAD EMISSIONS METHODOLOGY 

This section describes the emissions model inputs and calculations performed for estimating 

1996 on-road emissions. The generation of SMOKE files for input into the air quality model 

is also described. Defaults were established for all mobile source model input parameters, and 

also for vehicle miles traveled (VMT) estimates by vehicle class and roadway type; these are 

described below. The default model inputs and VMT estimates were provided to mobile 

source emission inventory contacts at state air quality planning agencies and metropolitan 

planning organizations (MPOs), who were asked to provide updated data. Updated data and 

model inputs provided by state and local contacts are described below. 

3.1 VMT ESTIMATES 

The default VMT data used as the starting point in this analysis were the annual VMT 

estimates from the EPA 1996 National Emissions Trends (NET) emission inventory, Version 

3.00 (available at ftp://ftp.epa.gov/pub/EmisInventory/net_96/). These VMT data were at the 

county and SCC level of detail (vehicle type and roadway type) for the eight MOBILE5 

vehicle classes. For more information on the development of these default VMT, see the 

Trends Procedures Document (EPA, 2001). These data were provided in a spreadsheet format 

to the WRAP state and local contacts for review and updating. The spreadsheet tables showed 

the NET 1996 VMT data by roadway classification and vehicle type for each county in the 

given state and also for major metropolitan areas (if applicable). 

The states of Arizona, Colorado, Nevada, New Mexico, Utah, and Washington provided 

updates to the default NET 1996 VMT data. For both Arizona and New Mexico, the only 

change made to the VMT was to split one county in each of these two states into two separate 

counties: For Arizona, Yuma County VMT was divided between La Paz County (which was 

not included in the NET 1996 VMT) and Yuma County. For New Mexico, Valencia County 

VMT was split between Cibola County (which was not included in the NET 1996 VMT) and 

Valencia County. For Nevada, updated VMT data were provided for Washoe County. 

Colorado provided updated VMT for all counties in the state, broken down by roadway type. 

The VMT data for counties within the Denver Regional Council of Governments was based on 

network VMT while the data for the remaining counties in Colorado were HPMS-based. 

VMT data updates were also provided for all counties in Utah. These data were provided by 

county and by three roadway types. The Utah data also included seasonal VMT adjustment 

factors that were used to override the default seasonal factors (discussed below in Section 3.3). 

Revised VMT estimates were also provided and incorporated for all counties in Washington, 

estimated by the Washington State Department of Transportation (WSDOT), by roadway 

class. 

Since the VMT data that were provided by the state or local agencies did not include vehicle 

type breakdowns, the data were distributed within a given county and roadway class using the 

distribution by vehicle type for that county/roadway type within the NET 1996 VMT database. 

For example, if light-duty gasoline vehicles (LDGVs) accounted for 65 percent of the total 

VMT on urban interstates within a given county in the NET, then the VMT provided by a 
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state agency for urban interstates within that same county would be multiplied by 0.65 to 

estimate urban interstate LDGV VMT in that county. 

Weekday average VMT for each of the four seasons was calculated for each VMT record in 

the database. These seasonal VMT values were calculated by multiplying the annual VMT by 

the seasonal VMT adjustment factors used in the NET (that vary by vehicle type) and by daily 

factors that vary by vehicle type and roadway type. Seasonal VMT adjustment factors 

provided for Utah and Washington replaced the default seasonal VMT adjustment factors for 

these two states 

3.2 MOBILE6 AND PART5 INPUTS 

The March 2001 draft version of EPA’s MOBILE6 model was used to calculate 1996 VOC, 

CO, and NOx emission factors, as well as VOC emission factors from vehicle refueling. The 

modified PART5 model was used to calculate emission factors for SO2, NH3, and exhaust, 

brake wear, and tire wear emission factor components of PM10, PM2.5, EC10, EC2.5, 

OC10, and OC2.5. 

As with the VMT, default inputs for the MOBILE6 and PART5 models were sent to the states 

and local contacts for review and updating. These default inputs were from the EPA NET 

1996 inventory, and are also documented in the Trends Procedures Document (EPA, 2001). 

The sections below discuss these inputs and updates made by the states for use in the WRAP 

modeling. 

3.2.1 Speed 

Speed is a critical input to the MOBILE6 model emission factor calculations. For the draft 

version of MOBILE6, speed was input through external files accessed by the “SPEED VMT” 

command. These files are matrices showing the fraction of VMT accruing within each of 14 

speed bins. Separate sets of data must be provided for arterial and freeway roadway types and 

for each hour of the day. For the default speeds in this analysis, we attempted to represent the 

speeds that had been modeled nationally in the 1996 NET (which used MOBILE5b). These 

average speeds by roadway type and vehicle type are shown in Table 3-1. To accomplish this, 

an external speed VMT file was developed for each of the speeds included in Table 3-1. 

Following the guidance in the MOBILE6 User’s Guide, the VMT fractions were set to 1 in the 

speed bins containing the speed to be modeled in the arterial roadway section of the file and all 

other VMT fractions were set to 0. The VMT speed distributions for the freeway sections of 

the speed VMT files were filled following the MOBILE6 User’s Guide guidance which first 

provides a mapping of MOBILE5 freeway speeds to MOBILE6 freeway speeds and then 

provides the fractions that should be used for the speed bin above and below the converted 

MOBILE6 speed. The speed distributions did not vary by hour. 
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Table 3-1. Default NET 1996 speeds by road type and vehicle type (mph). 

Urban 

Interstate 

Other Freeways 

& Expressways 

Principal 

Arterial 

Minor 

Arterial Collector Local 

LDV 45 45 20 20 20 20 

LDT 45 45 20 20 20 20 

HDV 35 35 15 15 15 15 

Rural 

Interstate 

Principal 

Arterial 

Minor 

Arterial 

Major 

Collector 

Minor 

Collector Local 

LDV 60 45 40 35 30 30 

LDT 55 45 40 35 30 30 

HDV 40 35 30 25 25 25 

The VMT BY FACILITY command was used in conjunction with the SPEED VMT command 

to uniquely classify each MOBILE6 scenario by roadway type and speed. This input 

represents the distribution of travel among the four different MOBILE6 roadway classes 

(freeway, arterial, local, and ramp). The distributions must be provided for each hour of the 

day and for each of the 28 MOBILE6 vehicle classes. Again, to maintain consistency with the 

1996 NET, three distributions were used: one representing 100 percent travel on freeways, 

one representing 100 percent travel on arterial roadways, and one representing 100 percent 

travel on local roadways. When the MOBILE6 scenarios were developed, these VMT by 

facility distributions were combined with the VMT by speed distributions to match the speeds 

by roadway type and vehicle type shown in Table 3-1. For the local roadway types, no speed 

adjustments are applied within MOBILE6, so no VMT by speed table is used in the MOBILE6 

scenarios with the VMT by facility distribution representing 100 percent travel on local 

roadways. The twelve roadway types shown in Table 3-1 were assigned one of these three 

VMT by roadway type distributions based on EPA guidance. The MOBILE6 freeway 

roadway type was assigned to rural interstates, urban interstates, and urban other freeways and 

expressways. The MOBILE6 arterial roadway type was assigned to rural arterials, rural 

minor arterials, rural major collectors, rural minor collectors, rural locals, urban principal 

arterials, urban minor arterials, and urban collectors. The MOBILE6 local roadway type was 

assigned to urban locals. 

Several state and local agencies provided information on average speeds within their 

jurisdictions. These speed data were converted to SPEED VMT files in the same manner that 

the default speeds were converted. State or local speeds replaced the defaults for the following 

areas: Colorado (county-specific speeds by roadway type for the Denver COG counties and 

one set of speeds by roadway type for the remaining counties), Utah (one set of speeds by 

roadway type for the rural counties and county-specific speeds by roadway type for each of the 

four urban counties), and Clark and Washoe Counties, Nevada (with a different set of speeds 

by roadway type for these two counties and the defaults used for all other counties in Nevada). 
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3.2.2 Registration Distributions 

The registration distribution is an input to MOBILE6 that defines the age mix of each vehicle 

type. This distribution lists the fraction of vehicles registered from each of 25 model years for 

each vehicle type. This distribution controls the fleet turnover rates within the model. The 

default distribution used in the WRAP modeling was a national 1996 registration distribution, 

based on the NET 1996 MOBILE5b national registration distribution, converted to the 

MOBILE6 format following the guidance in the User’s Guide. This national distribution was 

overridden by statewide distributions provided by Colorado and Washington, county-specific 

distributions provided for Oregon (LDV and LDT only) and Arizona, and a registration 

distribution specific to Clark County, Nevada. All of these distributions were supplied in the 

MOBILE5 format and were converted to MOBILE6 format in the same manner that the 

national default registration distribution was converted. 

3.2.3 Seasonal Temperatures 

The starting point for the default temperature inputs used in the WRAP MOBILE6 modeling 

were the average daily minimum and maximum monthly temperatures for each state that were 

used in the NET 1996. The monthly temperature data used in the NET were actual 1996 

temperature data, from a central weather station for each state (NCC, 1996). These monthly 

minimum and maximum temperatures were averaged together by season as the defaults for the 

WRAP modeling. Many of the WRAP states provided 1996 seasonal temperatures (either 

statewide or county-specific) to replace the default temperatures for their states. These states 

are: Arizona, Colorado, New Mexico, Oregon, Utah, and Washington. 

3.2.4 Seasonal Fuel Data 

The default Reid vapor pressure (RVP) values (indicating the volatility of gasoline) used in the 

WRAP MOBILE6 modeling were the RVP values from January, April, July, and October that 

were used in the 1996 NET. The RVP values used in the NET were based on AAMA RVP 

survey data, which were then allocated by month and state (AAMA, 1996). Unlike the 

temperature inputs, these values were the monthly values for the month in the middle of each 

season, rather than the seasonal averages. In addition to RVP, gasoline fuel sulfur levels are 

also required as an input to MOBILE6, and gasoline and diesel fuel sulfur levels are required 

as input to the modified PART5 model. The fuel specification data used to construct default 

summer and winter fuel sulfur levels were extracted from city-specific fuel survey data 

published by the American Automobile Manufacturers Association (AAMA) and regional 

survey data published by the National Institute for Petroleum and Energy Research (NIPER) 

(Dickson and Sturm, 1996). Because the gasoline data were generally reported separately for 

regular, mid-grade, and premium blends, a composite gasoline specification for each area was 

estimated by volume-weighting the three grades of gasoline (73% regular, 10% mid-grade, 

and 17% premium, based on national sales data published by the Energy Information 

Administration). Summer sulfur levels were used for spring and summer emissions estimates, 

and winter sulfur levels were used for fall and winter emissions estimates. Appendix A 

provides the default on-highway gasoline and diesel fuel sulfur levels by county. 
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Arizona, Colorado, Nevada (Clark and Washoe Counties), Oregon, Utah, and Washington 

provided updates to the default RVP data for their States. No states provided on-road fuel 

sulfur level updates. 

3.2.5 Altitude 

In the NET 1996, each state was identified as either high or low altitude, with the same 

designation for all counties in the state. With these defaults, the States of Colorado, Nevada, 

New Mexico, and Utah were identified as high altitude for the purposes of MOBILE6 

modeling. County-specific updates to the altitude information were provided by Arizona, New 

Mexico, and Utah. 

3.2.6 Control Programs 

In addition to the above inputs, MOBILE6/PART5 inputs related to several on-road control 

programs were also included in the modeling. These control programs are area-specific (i.e., 

not applied nationally or regionwide), generally based on an area’s ozone or CO nonattainment 

status. These programs include vehicle inspection and maintenance (I/M) programs, 

oxygenated fuel programs, and Stage II (at-the-pump) vehicle refueling controls. Note that 

reformulated gasoline is not included in this list because none of the WRAP states had 

implemented a reformulated gasoline program by 1996. The default control program 

parameters were those in the 1996 NET. These were updated by the state and local areas in 

some cases. As described in Section 2, federal control programs are included in MOBILE6 

and no additional inputs are needed to model these programs. 

3.2.6.1 Inspection and Maintenance (I/M) Program

I/M program inputs are specific to each state or area implementing such a program. The 

default I/M program inputs were those from the 1996 NET, converted to MOBILE6 input 

format, along with the county coverage of these programs in the 1996 NET. Updated 

information on these programs was provided by Arizona, Colorado, Nevada, Oregon, Utah, 

and Washington. Table 3-2 lists the counties modeled with an I/M program in place. 

3.2.6.2 Oxygenated Fuel

For the WRAP modeling, the program in place in each of the mid-months of the seasons was 

used (i.e., the program in place in January for the November to February winter season). 

Table 3-3 lists the counties that were modeled with oxygenated fuels and the inputs used to 

model these programs. The information in this table includes updated information on these 

programs provided by the states. 

3.2.6.3 Stage II Refueling Controls

Stage II controls were applied in the following counties: Maricopa County, AZ; Clark and 

Washoe Counties, NV; Multnomah County, OR; and Clark, King, and Pierce Counties, WA. 

The Oregon and Washington counties were modeled with a 95 percent Stage II control 
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efficiency for light-duty gasoline vehicles and trucks and an 80 percent Stage II control 

efficiency for heavy-duty gasoline vehicles. Maricopa County, Clark County (NV), and 

Washoe County were modeled with a 50 percent control efficiency, 95 percent control 

efficiency, and 85 percent control efficiency, respectively, applied to both light and heavy 

vehicles. 

Table 3-2. Counties modeled with an inspection and maintenance program in 1996. 

State County 

AZ Maricopa 

AZ Pima 

CO Adams 

CO Arapahoe 

CO Boulder 

CO Douglas 

CO Jefferson 

CO Denver 

CO El Paso 

CO Larimer 

CO Weld 

ID Ada 

NM Bernalillo 

NV Clark 

NV Washoe 

OR Clackamas 

OR Jackson 

OR Multnomah 

OR Washington 

UT Davis 

UT Salt Lake 

UT Weber 

UT Utah 

WA Clark 

WA King 

WA Snohomish 

WA Spokane 

WA Pierce 

3.3 SEASONAL EMISSIONS CALCULATIONS 

After creating MOBILE6 and PART5 input files using the inputs discussed above, the 

MOBILE6 and modified PART5 models were run. The output seasonal emission factors were 

used to calculate seasonal emissions. These emission calculations were performed at the SCC 

level; i.e., for each seasonal VMT value representing a given county, vehicle type, and 

roadway type, the corresponding emission factor and multiplied by the VMT value. The 

speed and MOBILE6 roadway type were used to determine the appropriate roadway type of 

the emission factors. Emissions were then aggregated by light- and heavy-duty vehicles and 

by diesel and gasoline fuel types for each county and season. 
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Table 3-3. Oxygenated fuel inputs. 

 January Oxygenated Fuel Inputs October Oxygenated Fuel Inputs

Market Share 

(%) 

Oxygen Content 

(%) 

Market Share 

(%) 

Oxygen Content 

(%) 

State County 

Ether 

Blend 

Alcohol 

Blend 

Ether 

Blend

Alcohol 

Blend 

Ether 

Blend

Alcohol 

Blend 

Ether 

Blend

Alcohol 

Blend 

AZ Maricopa 17 83 2.7 3.5 17 83 2.7 3.5 

AZ Pima * 17 83 2.7 3.5 17 83 2.7 3.5 

CO Adams 25 75 2.7 3.3 

CO Arapahoe 25 75 2.7 3.3 

CO Boulder 25 75 2.7 3.3 

CO Denver 25 75 2.7 3.3 

CO Douglas 25 75 2.7 3.3 

CO El Paso 0 100 2.7 2.7 0 100 2.7 2.7 

CO Jefferson 25 75 2.7 3.3 

CO Larimer 0 100 2.7 2.7 0 100 2.7 2.7 

CO Weld 25 75 2.7 3.3 

MT Missoula 0 100 2.7 3.5 0 100 2.7 3.5 

NV Clark 24 76 2.7 3.5 24 76 2.7 3.5 

NV Washoe 95 5 2.7 3.5 95 5 2.7 3.5 

NM Bernalillo 15 85 2.7 3.5 15 85 2.7 3.5 

OR Clackamas 0 100 0 3.5 0 100 0 3.5 

OR Jackson 0 100 0 3.5 0 100 0 3.5 

OR Josephine 0 100 0 3.5 0 100 0 3.5 

OR Klamath 0 100 0 3.5 0 100 0 3.5 

OR Multnomah 0 100 0 3.5 0 100 0 3.5 

OR Washington 0 100 0 3.5 0 100 0 3.5 

OR Yamhill 0 100 0 3.5 0 100 0 3.5 

UT Utah 0 100 0 3.5 0 100 0 3.5 

WA Clark 0 100 0 2.7 

WA King 0 100 0 2.7 

WA Pierce 0 100 0 2.7 

WA Snohomish 0 100 0 2.7 

WA Spokane 0 100 0 3.2 0 100 0 3.5 

* Pima County inputs were provided by ADEQ. Actual Pima County winter oxygenate is 1.8% rather than 

3.5%.

3.4 PROCESSING OF CALIFORNIA DATA 

CARB provided 1996 on-road emissions estimates from EMFAC2000 model runs by vehicle 

class, county, and season. EC/OC emissions were estimated from the PM10 and PM2.5

emissions using the methods described in Section 2.2.5. 

3.5 GENERATION OF SMOKE FILES 

SMOKE files were generated for the 1996 on-road emission inventory. The SMOKE files for 

the twelve non-California states include emissions by county, roadway type, and vehicle type. 
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Table 3-4 shows the source classification codes (SCC) for the on-road emissions categorized 

by vehicle type and HPMS roadway classification. 

CARB did not provide emissions by roadway type. The SMOKE files generated for 

California emissions therefore are categorized by county and vehicle type only. The SCCs in 

the SMOKE files for the California on-road emissions are shown in Table 3-5. 

The pollutants included in the SMOKE files are VOC, NOX, CO, NH3, SO2, SO4(2.5), coarse 

PM (PMC), EC2.5, OC2.5, EC10, OC10, NO, NO2, PM10, PM2.5, and SO4(10). The 

following equations were used in the generation of the PM emissions estimates: 

PMC = PM10 – PM2.5 

SO4(2.5) = PM2.5 – EC2.5 – OC2.5 

SO4(10) = PM10 - EC10 - OC10 

Table 3-4. SCC codes for on-road emissions in SMOKE files (non-California states). 

SCC Vehicle Class Facility Type 

2201001110 LDGV Rural Interstate 

2201001130 LDGV Rural Other Principal Arterial 

2201001150 LDGV Rural Minor Arterial 

2201001170 LDGV Rural Major Collector 

2201001190 LDGV Rural Minor Collector 

2201001210 LDGV Rural Local 

2201001230 LDGV Urban Interstate 

2201001250 LDGV Urban Other Freeways and Expressways 

2201001270 LDGV Urban Other Principal Arterial 

2201001290 LDGV Urban Minor Arterial 

2201001310 LDGV Urban Collector 

2201001330 LDGV Urban Local 

2201020110 LDGT1 Rural Interstate 

2201020130 LDGT1 Rural Other Principal Arterial 

2201020150 LDGT1 Rural Minor Arterial 

2201020170 LDGT1 Rural Major Collector 

2201020190 LDGT1 Rural Minor Collector 

2201020210 LDGT1 Rural Local 

2201020230 LDGT1 Urban Interstate 

2201020250 LDGT1 Urban Other Freeways and Expressways 

2201020270 LDGT1 Urban Other Principal Arterial 

2201020290 LDGT1 Urban Minor Arterial 

2201020310 LDGT1 Urban Collector 

2201020330 LDGT1 Urban Local 

2201040110 LDGT2 Rural Interstate 

2201040130 LDGT2 Rural Other Principal Arterial 

2201040150 LDGT2 Rural Minor Arterial 

2201040170 LDGT2 Rural Major Collector 

2201040190 LDGT2 Rural Minor Collector 

2201040210 LDGT2 Rural Local 

2201040230 LDGT2 Urban Interstate 
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Table 3-4. SCC codes for on-road emissions in SMOKE files (non-California states) 

(continued).

SCC Vehicle Class Facility Type 

2201040250 LDGT2 Urban Other Freeways and Expressways 

2201040270 LDGT2 Urban Other Principal Arterial 

2201040290 LDGT2 Urban Minor Arterial 

2201040310 LDGT2 Urban Collector 

2201040330 LDGT2 Urban Local 

2201070110 HDGV Rural Interstate 

2201070130 HDGV Rural Other Principal Arterial 

2201070150 HDGV Rural Minor Arterial 

2201070170 HDGV Rural Major Collector 

2201070190 HDGV Rural Minor Collector 

2201070210 HDGV Rural Local 

2201070230 HDGV Urban Interstate 

2201070250 HDGV Urban Other Freeways and Expressways 

2201070270 HDGV Urban Other Principal Arterial 

2201070290 HDGV Urban Minor Arterial 

2201070310 HDGV Urban Collector 

2201070330 HDGV Urban Local 

2201080110 MC Rural Interstate 

2201080130 MC Rural Other Principal Arterial 

2201080150 MC Rural Minor Arterial 

2201080170 MC Rural Major Collector 

2201080190 MC Rural Minor Collector 

2201080210 MC Rural Local 

2201080230 MC Urban Interstate 

2201080250 MC Urban Other Freeways and Expressways 

2201080270 MC Urban Other Principal Arterial 

2201080290 MC Urban Minor Arterial 

2201080310 MC Urban Collector 

2201080330 MC Urban Local 

2230001110 LDDV Rural Interstate 

2230001130 LDDV Rural Other Principal Arterial 

2230001150 LDDV Rural Minor Arterial 

2230001170 LDDV Rural Major Collector 

2230001190 LDDV Rural Minor Collector 

2230001210 LDDV Rural Local 

2230001230 LDDV Urban Interstate 

2230001250 LDDV Urban Other Freeways and Expressways 

2230001270 LDDV Urban Other Principal Arterial 

2230001290 LDDV Urban Minor Arterial 

2230001310 LDDV Urban Collector 

2230001330 LDDV Urban Local 

2230060110 LDDT Rural Interstate 

2230060130 LDDT Rural Other Principal Arterial 

2230060150 LDDT Rural Minor Arterial 

2230060170 LDDT Rural Major Collector 

2230060190 LDDT Rural Minor Collector 

2230060210 LDDT Rural Local 

2230060230 LDDT Urban Interstate 
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G:\WRAP MSEI\Reporting\Final\Sec3_1996on-road.doc 3-10

Table 3-4. SCC codes for on-road emissions in SMOKE files (non-California states) 

(concluded).

SCC Vehicle Class Facility Type 

2230060250 LDDT Urban Other Freeways and Expressways 

2230060270 LDDT Urban Other Principal Arterial 

2230060290 LDDT Urban Minor Arterial 

2230060310 LDDT Urban Collector 

2230060330 LDDT Urban Local 

2230070110 HDDV Rural Interstate 

2230070130 HDDV Rural Other Principal Arterial 

2230070150 HDDV Rural Minor Arterial 

2230070170 HDDV Rural Major Collector 

2230070190 HDDV Rural Minor Collector 

2230070210 HDDV Rural Local 

2230070230 HDDV Urban Interstate 

2230070250 HDDV Urban Other Freeways and Expressways 

2230070270 HDDV Urban Other Principal Arterial 

2230070290 HDDV Urban Minor Arterial 

2230070310 HDDV Urban Collector 

2230070330 HDDV Urban Local 

Table 3-5. SCC codes for California on-road emissions in SMOKE files. 

SCC Vehicle Class 

2230070000 HDDV 

2230060000 LDDV 

2230000000 LDDV 

2201070000 HDGV 

2201020000 LDGT1 

2201040000 LDGT2 

2201000000 LDGV 

2201080000 MC 
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4.0 1996 BASE YEAR OFF-ROAD EMISSIONS METHODOLOGY 

This section describes the methods for estimating off-road equipment emissions for the 1996 

base year. Seasonal average weekday emissions were estimated for the twelve non-California 

western states (Arizona, Colorado, Idaho, Montana, North Dakota, New Mexico, Nevada, 

Oregon, South Dakota, Utah, Washington, and Wyoming), and the California Air Resources 

Board (CARB) provided all off-road emissions estimates for California. The pollutants 

included in this analysis are carbon monoxide (CO), volatile organic compounds (VOC), 

nitrogen oxide (NO), nitrogen dioxide (NO2), sulfur oxides (SOx), coarse and fine particulates 

(PM10 and PM2.5), ammonia (NH3), and elemental and organic carbon in coarse and fine 

particulate emissions (EC10, EC2.5, OC10, and OC2.5). 

EPA’s NONROAD model was used to estimate emissions from the following source 

categories:

• Agricultural Equipment 

• Airport Ground Support Equipment 

• Commercial Equipment 

• Construction and Mining Equipment 

• Industrial Equipment 

• Lawn and Garden Equipment 

• Locomotives 

• Logging Equipment 

• Oil Field Equipment 

• Pleasure Craft 

• Railroad Equipment 

• Recreational Equipment 

• Underground Mining Equipment 

As discussed in Section 2, the June 2000 draft NONROAD model was used to estimate offroad emissions for WRAP states for the 1996 and 2018 air quality modeling. The off-road 

equipment emissions estimates in the WRAP Technical Support Document (TSD) were 

estimated with a later version of the model, NONROAD2002. 

Aircraft, locomotive, and commercial marine emissions were estimated separately using EPA 

accepted methodology. 

4.1. NONROAD MODELING

EPA’s NONROAD model was run for each county for each season in the 1996 base year. No 

State or local agencies provided updates to substitute for NONROAD default inputs for 

equipment population or activity data. Fuel and temperatures parameters used in the 

MOBILE6 modeling, as described in Section 3, were used in the NONROAD runs. Off-road 

diesel sulfur levels are required for NONROAD. The default off-road diesel fuel sulfur levels 

were obtained from regional survey data published by the National Institute for Petroleum and 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 4-2

Energy Research (NIPER) (Dickson and Sturm, 1996); these are listed by county in Appendix 

A. Only Maricopa County provided an update to the default diesel fuel sulfur levels; the 

Maricopa County off-road nonroad diesel sulfur level was set to the same as on-road diesel 

sulfur level per local regulation1

. 

Seasonal differences in the parameters that affect emissions from off-road equipment were 

taken into account. These parameters include temperatures and fuel specifications (vapor 

pressure and oxygenate content). The NONROAD model was run to obtain seasonal average 

weekday emissions for spring, summer, winter, and fall for most counties. However, for 

counties that have fuel-based control programs that start mid-season, the model was run 

separately for each of the three months in the season and the monthly results were then 

averaged to derive the seasonal average weekday emissions. The counties subject to this 

approach are shown in Table 3-3. 

The NONROAD model estimates county-level emissions of VOC, NOx, CO, PM10, PM2.5, 

and SO2; other pollutants were estimated as described in Section 4.6 below. Emissions were 

summed across equipment types by source category and by either gasoline or diesel fuel 

groupings. CNG and LPG-powered equipment, which account for only a small fraction of 

emissions, were lumped into the gasoline category.

4.2. AIRCRAFT EMISSIONS METHODOLOGY 

The method currently required by the Federal Aviation Administration (FAA) for estimating 

aircraft emission inventories of CO, HC, NOx and SOx2 at airports employs the use of the 

FAA’s Emissions and Dispersion Modeling System (EDMS). The EDMS model combines 

specified aircraft and activity levels with default emissions factors in order to estimate annual 

inventories for a specific airport. Aircraft activity levels in EDMS are expressed in terms of 

landing and take-off cycles (LTOs), which consist of the four aircraft operating modes: taxi 

and queue, take-off, climb-out, and landing. Default values for the amount of time a specific 

aircraft spends in each mode, or the time-in-modes (TIMs), are coded into EDMS, but may be 

updated with airport-specific numbers where available. In addition, the model also includes 

updateable default settings for the mixing height and aircraft engine assignments. In order to 

use EDMS, a separate setup and model run for each airport is required, and each combination 

of aircraft model, engine type, and activity level to be considered in the modeling scenario 

must be explicitly entered. Due to this input-intensive procedure, and because the current 

version of the model lacks the capability to automate the setup for each model run, it was not 

possible given the available resources to use EDMS to estimate emissions for all of the airports 

in WRAP states. 

In addition, a review of available air traffic statistics from the FAA indicated that the aircraft 

model-specific activity data needed to support a complete analysis of emissions from all 

categories of aircraft operated in the WRAP states are currently not available. Aircraft 

 

1 Arizona DEQ originally indicated that this regulation took effect in early 1996. However, after all of the modeling and analysis 

was completed, ADEQ staff indicated that the rule actually did not go into effect until October of 1996. 2

 The EDMS model also has the ability to estimate PM10 emissions; however, it currently contains PM10 emission 

factors for only a limited number of aircraft engines, mostly older and less represented ones. A rework and an 

update of the PM emission factors in the EDMS model are currently underway. 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 4-3

activity data in varying levels of detail may be obtained for all aircraft categories at airports 

with FAA managed traffic control towers, which are required to keep detailed activity records 

on air carrier traffic and less detailed records for the other aircraft categories. The different 

aircraft categories include: 

• Air carriers, which are larger turbine-powered commercial aircraft with at least 60 seats 

or 18,000 lbs payload capacity; 

• Air taxis, which are commercial turbine or piston-powered aircraft with less than 60 

seats or 18,000 lbs payload capacity; 

• General aviation aircraft, which are small piston-powered, non-commercial aircraft; and 

• Military aircraft. 

Currently available activity data are inadequate to run the EDMS model for air taxis and 

military aircraft for which little detail is kept in control tower records, and for general aviation 

aircraft flights, which mostly occur at non-towered facilities. Non-towered facilities tend to 

have very limited information on activity levels and do not, as normal practice, keep detailed 

records on airframe models flying in and out of the facility. 

In order to address the data inadequacies mentioned above, an aggregate methodology was 

used in developing the aircraft CO, HC, NOx and SOx emission inventories in this analysis. 

The methodology employed the EDMS model for air carriers at airports with higher levels of 

aircraft activity (referred to as “large hub” airports) and used fleet-average emission factors 

for the rest of the analysis. A similar aggregate method of analysis has been employed by 

EPA in the past3

 to estimate aircraft emission inventories from a number of airports, most 

recently in developing the 1996 National Toxics Inventory (NTI)(ERG, 2000). 

Moreover, because EDMS does not currently address all of the pollutants included in the study 

(i.e., VOC, SOx and NOx speciation, EC, OC, PM2.5, PM10 and ammonia), other available 

emission factors for aircraft and non-aircraft turbine and spark-ignition engines were utilized 

and reasonable inventory estimates for the other pollutants were made. More detailed 

discussions of the data collected and the analysis methods employed in generating the complete 

inventory estimates for the WRAP airports are provided below. 

4.2.1 Airports and Activity Data 

Airports included in the analysis were identified based on the FAA’s “National Plan of 

Integrated Airport Systems” (NPIAS)(FAA,1999). The NPIAS serves to identify all airports 

that qualify for Federal airport improvement grants, and includes all but those airfields with 

very nominal activity. About 62% of all airports open to the public are included in the 

NPIAS; however, these airports account for 100% of commercial aircraft activity and 92% of 

all general aviation flights in the U.S. The remaining 38% of airports are not included in the 

NPIAS because they do not meet the minimum criteria of 10 based aircraft (activity measure), 

are not within 20 miles of a NPIAS airport (accessibility measure), or are located at sites that 

cannot be improved for efficiency and safety. 

 

3

 In the EPA analysis, the FAA Aircraft Engine Emission Database (FAEED), which preceded EDMS, was used 

as the source for aircraft emission factors. 
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The aircraft category-specific fiscal year 1996 activity levels were generated for the airports 

identified in the NPIAS using the FAA’s Terminal Area Forecast (TAF) System (FAA, 

2001a). The TAF contains both historical and forecasted aviation activity data for all NPIAS 

airports and is used mainly by the FAA for budget and planning purposes. A total of 565 

airports were identified from the 12 states being considered. Since no more comprehensive 

source of airport activity data exists for a majority of the airports, activity levels for fiscal year 

1996 were used to estimate the calendar year 1996 activity levels.

In order to estimate seasonal county-level emission inventories, the monthly distribution of 

activity for each airport was needed. For this, the FAA’s Air Traffic Activity Data System 

(ATADS) was used (FAA, 2001b). The ATADS is the official source for historical monthly 

or annual air traffic statistics for airports with FAA-operated or FAA-contracted traffic control 

towers only. The towered airports included in the ATADS make up all of the higher-traffic 

airports throughout the U.S. This constituted about 68 of the 565 airports identified by the 

NPIAS in the 12 states being considered. The average seasonal distribution found in the 

ATADS data for each of the states was used to estimate the seasonal activity levels for the 

other non-towered NPIAS airports in the same state. 

To better estimate the aircraft emission inventories, the busier “large hub” airports were 

identified from the ATADS data. For this analysis, an airport was classified as a large hub if 

there were more than 50,000 large commercial aircraft operations reported for calendar year 

1996. 4 Based on this criterion, the following airports were classified as large hubs: 

• Albuquerque International (ABQ), 

• Colorado Springs Municipal (COS), 

• Denver Stapleton International (DEN), 

• Las Vegas McCarran International (LAS), 

• Phoenix Sky Harbor International (PHX), 

• Portland International (PDX), 

• Reno Cannon International (RNO), 

• Salt Lake City International (SLC), and 

• Seattle Tacoma International (SEA).

In addition to the ATADS monthly traffic data, airframe model-specific data were obtained for 

the large-hub airports from the Bureau of Transportation Statistics (BTS)(BTS, 2001). 

Commercial air carrier operators managing aircraft with over 60 seats are required to report 

their airframe model-specific activity data in T-100 forms to the BTS on an annual basis. 

Having the BTS airframe model-specific data enabled the use of EDMS to provide better 

estimates of emissions from air carriers, which account for the bulk of the total aircraft 

emission inventory at the large-hub airports. 

After all activity data were compiled for the airports in the 12 WRAP states (CARB provided 

CA aircraft emission inventories), the inventory estimates were generated for each airport 

using the procedures described in more detail below. The airport inventories were then 

 

4

 FAA defines a “large hub” as an airport that accounts for at least 1% of total U.S. passenger enplanements. Only some of 

the airports classified here as large hubs were also classified as such by the FAA for 1996 (DEN, LAS, PHX, SEA, and 

SLC), according to the National Plan of Integrated Airport Systems. 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 4-5

summed over all the counties for each season to obtain the seasonal county-level inventories 

for all the states. 

4.2.2 Estimation Methods 

The methods used to estimate emission inventories from the airport activity data collected are 

described for the three categories of airports defined earlier: large-hub airports, other towered 

airports, and non-towered airports. 

4.2.2.1 Large-Hub Airports

Since airframe model-specific activity levels were obtained for the large-hub airports, they 

were analyzed in greater depth. This minimized the input time necessary for using EDMS 

and, at the same time, provided better estimates of the emission inventories for the larger 

airports that account for the bulk of the total aircraft inventory. The procedures used in 

estimating the emission inventories for large-hub airports were different for the different 

aircraft categories. 

Air Carriers - Since airframe model-specific activity levels were obtained for air carriers at the 

large-hub airports, EDMS was used to estimate the air carrier CO, HC, NOx, and SOx 

inventories. For all of the large hubs, the EDMS default mixing height of 3,000 feet and the 

default TIMs, with the exception of the taxi and delay time, were used. The 1996 total taxi 

and delay time for each large-hub airport was estimated from 1999 CODAS taxi/delay data 

(FAA, 2001c) using the ratio of the 1996 TAF total operations data to the 1999 TAF 

operations. 

The HC inventories were converted to VOC using the conversion factor for commercial 

aircraft in EPA’s “Procedures for Emission Inventory Preparation” (EPA, 1992). The NOx 

inventory speciation values for NO and NO2 were assumed to be 90% and 10%, respectively, 

which are the default speciations in EPA’s Emission Preprocessor System Version 2 (EPS2) 

model. It was assumed that no NH3 is emitted from air carrier turbine engines, which 

normally run lean. 

In order to estimate the amount of SO2 and SO4.7H2O formed from fuel-bound sulfur, a sulfur 

conversion to SO4.7H2O of 6.5% was used based on observations of distillate oil-powered 

stationary turbine engines (Carlson, 1993). Data for stationary turbine engines were used as 

substitutes for aircraft turbine engines since limited data on emission factors and emission 

speciation currently exist for aircraft engines. The rest of the fuel-bound sulfur was assumed 

to form SO2 in the engine exhaust. 

Due to the lack of other, more recent sources for aircraft particulate emission factors, the total 

suspended particulate (TSP) emissions from the air carriers were estimated using a commercial 

fleet-average emission factor from EPA’s 1985 National Acid Precipitation Assessment 

Program (NAPAP)(Alliance, 1988). The percentage of the TSP emissions consisting of PM2.5

and PM10 was estimated using stationary turbine engine data in EPA’s AP-42. According to 

the AP-42, 100% of the particulate matter emitted from distillate oil-powered stationary 

turbine engines is PM2.5, and, consequently, also represented as PM10 (EPA, 1995). No source 
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for particulate size distribution is currently available specifically for aircraft particulate 

emissions. 

Particulate carbon emissions from the turbine air carriers were partitioned for EC and OC 

based on partition estimates found for jet aircraft in the South Coast Air Basin (SCAB) in 

1982. According to the report analyzing the SCAB data for CARB, about 74% of the jet 

aircraft particulate emissions is EC and about 22% is OC (Gray, 1986). Since all of the 

particulates emitted by the turbine engines have diameters less than 2.5 μm, the EC emissions 

are represented as both EC10 and EC2.5, and the OC emissions as both OC10 and OC2.5. 

Air Taxi and General Aviation Aircraft - For air taxi and general aviation aircraft, fleetaverage emission factors were available for CO, HC, NOx, and SOx from the EPA’s 

“Procedures for Emission Inventory Preparation” (1992). This was done after the aircraft fuel 

sulfur levels used in the EPA procedures were compared to 1996 surveys of aircraft fuels 

performed by the National Institute for Petroleum and Energy Research (NIPER, 1997) and by 

the American Petroleum Institute (API) and the National Petroleum Refiners Association 

(NPRA/API, 1997). The fleet-average emission factors in the EPA procedures assumed a 

default mixing height of 3,000 feet, the same as with the EDMS default. Also included in the 

EPA report are factors for converting HC to VOC, with separate factors available for piston 

and turbine aircraft. In order to estimate the engine type distribution of the air taxi activity, 

total flight hours for 1996 were obtained from the FAA’s “General Aviation and Air Taxi 

Activity Survey” (FAA, 1998a). It was assumed in the analysis that the total flight hours for 

each engine type were proportional to the total flights or LTOs taken in 1996. 

As with the air carriers, 90% of the NOx emissions were assumed to be NO and 10% NO2, 

per the EPS2. Although it was assumed that turbine aircraft do not have NH3 emissions, 

emissions of NH3 for the piston aircraft in the fleet were estimated using the NH3 emissions 

from non-catalyzed motor vehicle engines, which are about 0.14 grams of NH3 per gallon of 

fuel use (Harvey et. al., 1983). The air taxi and general aviation piston aircraft fuel usage 

data for 1996 were taken from the 1996 “General Aviation and Air Taxi Activity Survey,” 

then used with the motor vehicle NH3 emission factor to estimate the piston aircraft NH3

inventory. 

Data from distillate oil-powered stationary turbine engines were again used to estimate the fuel 

sulfur conversion to SO2 and SO4.7H2O for the turbine-powered aircraft. For piston aircraft, 

data from EPA’s NONROAD model on spark-ignition engines, which show 97% conversion 

to SO2, were used. The rest of the sulfur in the fuel was assumed to be emitted as SO4.7H2O.

The particulate emissions from the turbine-powered air taxi were estimated using the same 

fleet-average commercial aircraft emission factors used in estimating the particulate emissions 

from air carriers, along with the same particle size distribution. For piston-powered air taxi 

and general aviation aircraft TSP emissions, the civil aircraft fleet-average emission factor 

from the 1985 NAPAP data was used. The particle size distribution for the piston aircraft was 

based on the particulate size distribution found in EPA’s PART5 model for non-catalyzed 

motor vehicle emissions. According to PART5, 64% of the particulates emitted from these 

engines are PM10 and 43% have diameter less than 2.0 μm. The percentage for PM2.5 was 

interpolated between the two percentage distributions. The amount of EC10, EC2.5, OC10, 
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and OC2.5 emitted from the piston aircraft were approximated using numbers for piston 

aviation gasoline or AvGas from the CARB SCAB analysis (Gray, 1986) and data from a 

particulate motor vehicle exhaust study conducted in Colorado (Watson et. al., 1990). Due to 

the lack of data for the elemental and organic carbon contents of coarse particulates from 

piston engines, they were assumed using the same distribution of 20% EC and 65% OC 

derived from the data on fine particulates. 

Military Aircraft - The EDMS model contains CO, HC, NOx, and SOx emission factors for a 

number of military aircraft. In order to estimate fleet-average emission factors that best 

represent the fleet mix of aircraft, the national military aircraft engine class distribution for 

1996 was used (FAA, 1998b).5 The known emission factors in EDMS were distributed 

according to the fleet engine mix, then combined to result in fleet-average CO, HC, NOx, and 

SOx emission factors for use in estimating the corresponding inventories. The VOC 

inventories were converted from the HC inventories using conversion factors from EPA’s 

“Procedures for Emission Inventory Preparation” (1992). EPA’s procedures include a 

conversion factor for military turbine engines, which was used with the general aviation piston 

engine conversion factor for the piston-powered military aircraft in the fleet. 

As with the other aircraft categories, NOx was assumed to be 90% NO and 10% NO2 per the 

EPS2 model. The SOx speciation and the emissions of particulates, NH3, EC, and OC were 

all estimated using the same procedure used for the turbine and piston-powered air taxi and 

general aviation aircraft. 

4.2.2.2 Other Towered Airports

With the exception of the air carriers, the emission inventories of the air taxi, general aviation, 

and military aircraft flying in and out of the smaller-hub, towered airports were estimated 

using the same procedures as for the large-hub airports. However, since no aircraft modelspecific data were obtained for the air carriers at the other towered airports, the fleet-average 

emission factors for HC, CO, NOx, and SOx were generated using the results from the air 

carriers at all of the large-hub airports. The air carrier fleet-average emission factors, along 

with the conversion factors, emission factors, and speciation profiles applied to the air carriers 

at the large hubs, were then used to estimate the emission inventories for air carriers at the 

smaller-hub, towered airports. 

4.2.2.3 Non-Towered Airports

Because the TAF system data, which generate fiscal year total activity levels, are the only data 

obtained for the non-towered airports or airfields, the total 1996 activities were distributed by 

season to generate the seasonal inventories. To do this, the seasonal activity distribution was 

generated for each of the 12 states using the monthly data obtained from the ATADS for the 

towered airports. The resulting state-specific seasonal activity distribution was applied to the 

TAF total activity data on the non-towered airports in each state. After the seasonal activity 

levels were obtained for each airport, the same procedures used in estimating the emission 

inventories for the small-hub, towered airports were used. 

 

5 The different engine classes include turbojets or turbofans with three or more engines, fighter turbojets or 

turbofans, other turbojets or turbofans, turboprops, piston engine aircraft, and rotary engine aircraft. 
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4.3 COMMERCIAL MARINE EMISSIONS METHODOLOGY 

This subsection describes the methods used to estimate emissions from commercial marine 

activity for the States of Washington and Oregon. Commercial marine emissions for 

California were provided by CARB. 

The emissions for commercial marine vessels are categorized into five separate segments of 

vessels: ocean-going, tugs, ferries, dredges, and fishing vessel types. Within each segment, a 

number of simplifying assumptions or estimates have been made to either provide new 

information or use existing information to estimate overall emissions. Ocean-going vessels 

represent the large ships used in overseas freight traffic. Tugs are used in a variety of 

activities including assisting in docking ocean-going vessels and push or towing barges. 

Some commercial vessel activity has not been chronicled. This study does not include oceangoing vessels heading to and from Vancouver, B.C. and other Canadian traffic that may pass 

into or near US waters. The emissions from vessels in the Pacific 25 miles out from the ocean 

coasts either headed toward Washington and Oregon ports or past the west coast (such as 

between Alaska and California) were not estimated in this work. Also, there was no readily 

available data on commercial excursion craft (such as day tour boats), supply vessels (noted in 

some general data only for the Port of Everett), other work boats (such as fire boats), and 

auxiliary marine equipment (such as barge pumps) for Oregon or Washington. Emissions 

from these vessel types were considered very small relative to vessels for which data are 

available. 

The allocation of the emission totals for commercial marine was not well suited to county 

allocation because main shipping lines often straddle county, state, and international 

boundaries. For ocean-going vessels, transit lanes to several ports overlap, such as vessels 

moving near and past Seattle toward or from Tacoma. For most ocean going vessel and tug 

activity it is understood that transit emissions are by and large along standard shipping 

channels or in the Columbia and Snake River channels. County allocations are described in 

more detail below with the emission estimates by vessel type. 

4.3.1 Emission Factors 

EPA has detailed the emission factors for the shipboard and propulsion engines in several 

documents all of which are based on a Booze, Allen, and Hamilton study (BAH, 1991). These 

emission factors and estimates represent the official guidance for emission inventory 

preparation and are provided here as a reference in Table 4-1 (EPA, 1992). However, the 

data on which these EPA emission factors were derived was not referenced, and a number of 

studies determining emissions rates have been completed since the time the EPA guidance was 

written. Some of the more recent data have been incorporated into new emission inventories 

for other ports, which are currently used for ozone modeling and planning for attainment 

demonstration (Acurex, 1996). 
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Table 4-1. EPA (1992) emission factors (kg/tonne) for large ships propulsion power. 

Propulsion Type NOx HC CO PM 

Motorships 69.6 3.0 7.7 4.2 

Steamships (full power) 8.1 0.2 0.9 7.2 

Steamships (maneuvering) 7.1 0.1 0.4 2.5 

Steamships (hotelling) 4.6 0.4 0.5 1.3 

Emission estimates converted from lb/1000 gallons using 7.9 lb/gallon fuel density for heavy fuel oil.

The steamship estimates needed to be converted to g/hp-hr units to associate with the vessel 

power information for each port call. An average fuel efficiency of 304 g-fuel/kW-hr of 

power output was calculated from the fuel consumption calculation in BAH (1991) for the 

vessels in the Puget Sound. 

EPA has revised motorship emission factor estimates in the Regulatory Impact Analysis (RIA) 

and published a rulemaking for commercial marine vessels (EPA, 1999a and 1999b). In the 

absence of revised official EPA guidance for determining emissions from commercial marine 

vessels, the emission factors used in the EPA’s RIA were used as the best available 

information. The emission factors were separated into the three EPA-defined categories of 

marine engines shown in Table 4-2 (EPA, 1999b). The Category 3 engines are nearly 

exclusively used in ocean-going vessels. Category 2 engines are used in smaller ocean-going 

vessels, and larger ferries, the largest tugs, and perhaps in some dredges. Category 1 engines 

are used in smaller ferries, tugs, dredges, fishing, and other watercraft. 

Table 4-2. EPA definition of engine categories. EPA specifies standards for each category and 

subcategory.

Category 

Displacement 

(liters/cylinder) 

Power ≤ 37 kW 

And disp. <0.9 

0.9 ≤ disp. < 1.2 

1.2 ≤ disp. < 2.5 

Category 1 

2.5 ≤ disp. < 5.0 

5.0 ≤ disp. < 15 

15 ≤ disp. < 20 

Power <3300 kW 

15 ≤ disp. < 20 

Power 3300 kW 

20 ≤ disp. < 25 

Category 2 

25 ≤ disp. < 30 

Category 3 Disp.30 

In the RIA (EPA, 1999b), EPA estimated the emission factor in accordance with these defined 

engine categories. Tables 4-3 and 4-4 show the base emission factors for marine engines for 

each engine category. These emission factors apply to both propulsion and auxiliary engines. 

For Category 2, EPA estimated the Category 1 emission rates. For Category 3 engines, EPA 

relied on base emission estimates by Corbett and Fischbeck (1998), who in turn used emission 

factors determined from Lloyds (1995). In order to convert the Lloyds emission factors from 
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kilogram per tonne of fuel consumed to gram per kilowatt-hr, the fuel consumption estimates 

in Lloyds (1995) of 195 g/kW-hr for slow speed engines and 210 g/kW-hr for medium speed 

engines were used.

Table 4-3. Baseline emission factors for Category 1 marine engines. 

Power Range 

[kW] 

HC 

[g/kW-hr] 

NOx 

[g/kW-hr] 

CO 

[g/kW-hr] 

PM 

[g/kW-hr] 

37-75 0.27 11 2.0 0.90 

75-130 0.27 10 1.7 0.40 

130-225 0.27 10 1.5 0.40 

225-450 0.27 10 1.5 0.30 

450-560 0.27 10 1.5 0.30 

560-1000 0.27 10 1.5 0.30 

1000+ 0.27 13 2.5 0.30 

Table 4-4. Baseline emission factors for Category 2 and 3 engines. 

Engine Category HC 

[g/kW-hr] 

NOx 

[g/kW-hr] 

CO 

[g/kW-hr] 

PM 

[g/kW-hr] 

Category 2 

(5-30 l/cylinder) 0.134 13.36 2.48 0.32 

Category 3 Medium Speed 

(> 300 rpm) 

(> 30 l/cylinder) 

0.5 12 1.6 1.61 

(0.24)* 

Category 3 Slow Speed 

(< 200 rpm) 

(> 30 l/cylinder) 

0.5 17 1.6 1.49 

* Using low (0.33 wt.%) sulfur diesel instead of bunker oil at 3 wt.% sulfur. 

EPA (1998) used one average emission factor for particulate determined from Lloyds; 

however, Lloyds (1995) found that PM emissions from marine engines are highly dependent 

on the fuel sulfur. In order to take advantage of this functional dependence, the Lloyds 

information was incorporated into an equation as shown in Figure 4-1 using the specific fuel 

consumption estimates above. By determining the sulfur level of the fuel currently used in and 

around a given port, a much closer estimate of actual PM emissions could be made. Average 

sulfur values were used for heavy fuel (used during transit modes) of 3% sulfur and light fuel 

at 0.33% sulfur used during hotelling (berthing or at dock) modes.
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G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 4-11

y = 0.9016 * e(0.7238x)

R2 = 0.9306

0

2

4

6

8

10

12

0 0.5 1 1.5 2 2.5 3 3.5

Fuel Sulfur (wt.%)

PM Emissions (kg/tonne)

Figure 4-1. Dependence of commercial marine PM emissions on fuel sulfur levels. Data 

source: Lloyds, 1995. 

4.3.2 Ocean-Going Vessels 

ARCADIS (1999) reviewed estimates of the ocean-going vessel activity for Coos Bay and 

Puget Sound ports. In this document, a method is also described to extrapolate ocean-going 

vessel activity for other ports and to allocate activity to individual Puget Sound ports. Two 

data sources existed for the ARCADIS (1999) report, one of which gathered general 

information about the total number of trips by vessel type for the top 95 US ports, and the 

other gathered more specific information for several ports including the Puget Sound ports 

totals and Coos Bay. The more general information was used to allocate the more specific 

activity information to each of the Puget Sound ports and to extrapolate an estimate of the 

activity of the Columbia River ports. 

4.3.2.1 Port of Coos Bay

For the Port of Coos Bay, specific data on vessel calls were obtained from the local Marine 

Exchange and are listed in Table 4-5. The cruise was considered to begin 25 miles out from 

shore to the breakwater, where the reduced speed zone (RSZ) of 6.5 knots was used to near 

the docks where vessels slow to 4 knots for maneuvering. Any vessel shifting between docks 

was captured within the maneuvering time estimated for each port. 
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Table 4-5. 1996 Vessel traffic to Coos Bay. Source: ARCADIS, 1999. 

Time in Mode 

Ship 

Type 

Engine 

Type 

Dead 

Weight 

Tonnage 

Range Calls 

DWT 

(tonnes)

Power 

(HP) 

Vessel 

Speed 

(knots)

Cruise 

(hr/call)

RSZ 

(hr/call)

Maneuver 

(hr/call) 

Hotel 

(hr/call)

2 < 25,000 26 22,978 7,007 14 3.6 3.6 0.6 64.0 

2 

25,000 -

35,000 39 30,108 9,756 15 3.4 3.4 0.6 69.4 

2 

35,000 -

45,000 60 42,436 9,136 14 3.5 3.0 0.6 58.9 

2 > 45,000 28 46,825 10,249 14 3.5 3.5 0.6 94.8 

Bulk 

Carrier 

 

 

 

 2 (blank) 2 4.0 0.3 179.7 

2 < 25,000 10 20,800 11,770 16 3.1 3.6 0.6 65.3 

2 

25,000 -

35,000 18 30,068 8,040 14 3.5 3.6 0.6 52.5 

2 

35,000 -

45,000 20 42,857 13,010 15 3.2 2.7 0.6 56.5 

2 > 45,000 5 46,547 12,300 16 3.1 4.0 0.6 128.5 

General 

Cargo 

 

 

 

 4 < 25,000 1 23,168 7,800 15 3.3 3.0 0.6 67.4 

Misc. 4 (blank) 1 36,189 9,612 15 3 4.0 0.3 128.7 

Each vessel call was associated with the time in mode shown in Table 4-5 along with the 

vessel power. The vessel power was assumed to have a load factor of 10% for hotelling, 80% 

at cruise (based on the EPA guidance), and according to the equation below for intermediate 

speeds based on fundamental laws of fluid dynamics. 

Load factor = 0.1 + 0.7 * (actual speed/cruise speed)3

In this manner the vessel calls were multiplied by the power for each vessel and the load and 

time in each mode to estimate total horsepower-hours per year for the port. The emission 

factor in horsepower-hours was associated with the emission factor for either slow speed (2-

stroke) or medium speed (4-stroke) engine types. Most vessels use 4-stroke on-board 

generators running on low sulfur diesel fuel once in port, so medium speed emission factors 

were used for hotelling emissions on each vessel. Harbor and River Pilots for many ports 

have indicated that most vessels used low sulfur diesel fuel at some point starting either during 

the reduced speed zone or Maneuvering modes, so particulate emissions may have been 

overestimated for these modes using the high sulfur fuel emission rates. The resulting 

emissions by mode for ocean-going vessels from calls to the Port of Coos Bay in 1996 are 

shown in Table 4-6; these emissions were assigned to Coos County. 
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Table 4-6. Emission estimates (tons/year) for Coos Bay ocean-going vessels in 1996. 

Mode HC CO NOx PM 

Cruise (25 miles to 

shore) 2.2 7.2 76.7 6.7 

Reduced Speed Zone 0.4 1.4 14.9 1.3 

Maneuvering 0.1 0.2 1.9 0.2 

Hotelling 5.7 18.4 138.3 2.8 

Total 8.5 27.2 231.8 11.0 

4.3.2.2 Puget Sound Ports

For Puget Sound ports, the procedure describe above for Coos Bay was used with the Puget 

Sound vessel calls and time in mode data shown in Table 4-7. As reported by ARCADIS 

(1999), Grays Harbor data were included in the Puget Sound data although Grays Harbor is 

not actually located within the Puget Sound; however, it represents less than 4% of reported 

total Puget Sound vessel calls. 

Table 4-7. 1996 Ocean-going vessel activity for Puget Sound ports. Source: ARCADIS, 1999).

Time in Mode 

Ship Type 

– Manip 

Stroke 

Type 

Dead Weight 

Tonnage Range Calls Shifts 

DWT 

(tonnnes)

Power 

(HP) 

Vessel 

Speed 

(knots)

Cruise 

(hr/call)

RSZ 

(hr/call) 

Maneuver 

(hr/call) 

Hotel 

(hr/call)

2 <25,000 165 42 22,130 7,073 14 3.6 15.4 1.4 70.6 

2 25,000 - 35,000 306 114 27,887 8,155 14 3.5 16.5 1.5 128.1 

2 35,000 - 45,000 167 69 40,489 10,752 15 3.4 16.3 1.6 88.1 

2 > 45,000 216 165 66,419 12,646 15 3.5 15.8 2.0 154.1 

4 <25,000 8 9 6,436 3,625 13 4.0 17.7 5.1 84.4 

4 25,000 - 35,000 2 1 32,019 10,400 15 3.5 14.6 1.4 96.8 

4 35,000 - 45,000 2 ND 41,642 10,943 14 3.6 13.4 0.9 64.3 

4 > 45,000 13 8 63,029 14,806 15 3.4 16.6 2.1 177.9 

Bulk 

Carrier 

Steam 

Turbine > 45,000 13 6 82,035 17,111 16 3.1 21.5 1.4 58.8 

2 <25,000 184 18 19,019 18,365 19 2.7 17.9 1.0 34.3 

2 25,000 - 35,000 135 2 31,480 26,364 20 2.5 16.6 0.9 25.9 

2 35,000 - 45,000 363 22 40,261 31,808 22 2.3 16.4 1.0 30.9 

2 > 45,000 276 9 56,958 51,033 23 2.2 16.0 0.9 28.4 

4 <25,000 8 3 19,987 12,405 19 2.7 16.0 1.1 59.5 

Steam 

Turbine <25,000 3 2 19,800 26,797 21 2.4 18.2 1.5 138.7 

Steam 

Turbine 25,000 - 35,000 93 2 28,628 30,080 20 2.5 16.5 0.9 40.4 

Steam 

Turbine 35,000 - 45,000 64 ND 38,988 31,565 21 2.4 15.5 0.9 17.8 

Container 

Ship 

Steam 

Turbine > 45,000 24 1 47,851 80,006 23 2.2 16.2 0.9 30.3 

Fishing 2 <1500 12 18 789 1,897 12 4.3 21.1 5.9 1291.8 

 2 1,500 - 3,000 3 ND 1,883 3,626 14 3.5 15.9 1.8 33.4 

 2 3,000 - 4,500 1 ND 4,500 10,768 18 2.8 16.7 0.9 1432.0 

 2 > 4,500 2 1 9,360 10,331 18 2.8 16.0 3.7 654.3 

 4 <1500 20 19 698 1,702 12 4.3 20.1 4.3 915.6 

 4 1,500 - 3,000 10 9 1,861 5,159 16 3.1 13.2 1.8 321.5 
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Table 4-7. 1996 Ocean-going vessel activity for Puget Sound ports. Source: ARCADIS, 1999) 

(continued).

Time in Mode

Ship Type 

– Manip 

Stroke 

Type 

Dead Weight 

Tonnage Range Calls Shifts 

DWT 

(tonnnes)

Power 

(HP) 

Vessel 

Speed 

(knots)

Cruise 

(hr/call)

RSZ 

(hr/call) 

Maneuver 

(hr/call) 

Hotel 

(hr/call)

 4 > 4,500 27 27 5,805 8,048 14 3.6 14.8 4.3 399.2 

Steam 

Turbine > 4,500 4 5 19,286 37,976 20 2.5 13.0 4.1 534.6 

2 <15,000 7 2 3,540 3,647 12 4.3 17.9 2.8 65.2 

2 15,000 – 30,000 73 16 21,745 11,495 16 3.1 18.4 1.5 52.4 

General 

Cargo 

2 30,000 – 45,000 52 1 41,323 12,006 15 3.3 13.3 1.0 32.0 

 2 > 45,000 77 5 45,539 10,164 15 3.3 16.2 1.1 23.2 

 4 <15,000 21 7 9,063 9,493 15 3.5 17.9 2.1 353.6 

 4 15,000 – 30,000 32 21 20,039 20,164 18 2.9 17.6 2.1 112.5 

Steam 

Turbine <15,000 1 ND 14,897 15,289 19 2.6 18.3 0.9 163.9 

Misc. 2 (blank) 3 3 7,900 9,387 14 3.6 19.3 2.4 2189.6 

 2 (blank) 1 ND 1,200 1,860 12 4.2 22.4 0.9 472.8 

 4 (blank) 4 2 761 3,486 13 3.8 16.1 3.2 300.1 

Steam 

Turbine (blank) 3 ND 3,988 8,483 15 3.3 15.4 0.9 49.0 

Passenger 2 <5,000 3 1 4,226 29,370 21 2.4 14.5 1.4 67.1 

 2 5,000 – 10,000 1 ND 5,340 32,350 19 2.6 17.5 0.9 7.7 

 4 <5,000 4 ND 850 9,906 16 3.2 17.1 0.9 9.5 

 4 5,000 – 10,000 3 1 7,089 45,589 21 2.4 16.2 0.9 54.6 

Steam 

Turbine 5,000 – 10,000 2 ND 8,706 25,154 23 2.2 17.6 0.9 67.5 

Reefer 2 <5,000 17 17 3,307 4,767 15 3.3 15.6 3.9 315.7 

 2 5,000 – 10,000 21 17 6,642 6,945 17 3.0 14.1 3.4 163.2 

 2 10,000 – 15,000 7 7 11,746 11,969 20 2.6 15.6 2.4 201.5 

 4 <5,000 7 3 2,004 1,730 11 4.7 22.1 1.5 259.8 

 4 5,000 – 10,000 8 1 5,804 5,676 16 3.1 16.7 1.0 51.5 

2 <10,000 11 2 7,976 6,738 16 3.3 22.1 1.3 30.1 

2 10,000 – 20,000 16 ND 11,346 8,004 16 3.3 19.9 0.9 24.4 

2 20,000 – 30,000 16 1 26,787 18,649 19 2.6 17.9 0.9 25.0 

Roll 

On/Roll Off 

2 > 30,000 4 ND 41,856 15,136 14 3.5 14.2 0.9 10.3 

Steam 

Turbine 10,000 – 20,000 121 14 17,084 29,764 25 2.0 17.0 1.4 73.8 

Tanker 2 <30,000 66 49 19,629 9,104 15 3.5 19.0 4.3 43.7 

 2 30,000 – 60,000 79 68 46,934 12,451 15 3.4 15.9 4.0 67.6 

 2 60,000 – 90,000 18 13 71,315 15,262 15 3.3 16.0 3.3 45.2 

 2 90,000 – 120,000 20 20 100,679 14,738 14 3.5 15.8 2.6 64.0 

 2 

120,000 – 

150,000 26 46 123,742 26,146 16 3.1 14.5 7.2 63.9 

 4 <30,000 12 ND 10,056 4,864 13 4.0 15.6 0.9 16.2 

 4 30,000 – 60,000 1 1 37,350 11,700 14 3.6 17.0 4.2 84.7 

Steam 

Turbine <30,000 18 15 19,992 14,795 18 2.8 14.4 3.8 59.7 

Steam 

Turbine 30,000 – 60,000 35 24 39,541 13,809 16 3.1 15.4 3.5 45.3 

Steam 

Turbine 60,000 – 90,000 125 95 71,997 19,286 17 3.0 16.6 3.4 62.4 

Steam 

Turbine 90,000 – 120,000 29 30 91,915 23,095 16 3.2 15.9 5.3 52.1 

Steam 

Turbine 

120,000 – 

150,000 119 145 122,732 26,360 16 3.1 14.7 6.2 62.8 

Steam 

Turbine > 150,000 5 1 189,978 27,620 14 3.5 11.1 1.9 103.4 
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Table 4-7. 1996 Ocean-going vessel activity for Puget Sound ports. Source: ARCADIS, 1999) 

(concluded).

Time in Mode

Ship Type 

– Manip 

Stroke 

Type 

Dead Weight 

Tonnage Range Calls Shifts 

DWT 

(tonnnes)

Power 

(HP) 

Vessel 

Speed 

(knots)

Cruise 

(hr/call)

RSZ 

(hr/call) 

Maneuver 

(hr/call) 

Hotel 

(hr/call)

2 12,500 - 15,000 33 5 13,709 14,049 18 2.7 18.6 1.2 19.0 

2 15,000 - 17,500 49 23 16,272 14,023 18 2.8 17.3 1.5 19.6 

2 > 17,500 7 2 19,783 15,501 18 2.7 18.4 1.7 19.9 

 4 <12,500 19 1 10,981 13,118 18 2.7 20.2 0.9 20.2 

 4 12,500 - 15,000 4 ND 12,917 13,600 19 2.6 20.2 0.9 13.5 

 4 15,000 - 17,500 2 ND 17,224 16,880 19 2.6 18.9 0.9 22.0 

 4 > 17,500 1 1 19,712 16,880 18 2.8 19.4 3.1 21.1 

Cruising was assumed to occur from 25 miles out from the entrance point of the Strait of San 

Juan de Fuca. The vessels were then assumed to slow to 15 knots until the pilot boards near 

Port Angeles, and then to continue on at 10 – 13 knots to near the docks of each port. An 

average of 13 knots was used to determine the load factors for the reduced speed zone because 

this was the estimated speed during most of this mode to all ports and typical of the busiest 

ports. 

The same calculation method described above for Coos Bay was used for the Puget Sound 

ports by associating vessel calls, power level, load in mode, and time in mode. The emission 

factors were the same with the addition of another power type, steam turbines, occurring for 

some vessels calling within the Puget Sound ports. The total emissions for the ports is shown 

in Table 4-8. 

Using available Puget Sound activity totals (ARCADIS, 1999), it was necessary to allocate the 

activity to each specific port. The allocation for each vessel type at each port, shown in Table 

4-8, was a simple ratio of the individual port trips (Trips are individual vessel movements with 

at minimum of 2 per vessel call but may also include shifts between docks.) by the total for the 

Puget Sound region. The reduced speed zone times were estimated for each port for traffic 

from the entrance of the Strait of San Juan de Fuca.
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Table 4-8. 1995 Puget Sound ports trips totals and allocation to specific ports by vessel type. 

Source: ARCADIS, 1999).

Port 

RSZ 

(hrs) 

Bulk 

Carrier 

Container

Ship 

General 

Cargo Other Passenger Reefer 

Roll 

On/ 

Roll 

Off Tanker 

Vehicle 

Carrier 

Puget Sound 

Total 1551 2703 474 65 87 116 714 1000 919 

Seattle 

Harbor, WA 18.1 455 1713 307 19 63 61 246 209 72 

Tacoma 

Harbor, WA 21.1 521 725 41 2 1 10 396 282 125 

Anacortes 

Harbor, WA 16.3 54 39 9 0 4 2 8 394 688 

Everett 

Harbor, WA 18.1 120 9 2 7 0 10 6 0 1 

Port Angeles 

Harbor, WA 9.7 62 13 5 22 0 8 18 66 2 

Grays Harbor, 

WA 4.4 175 21 25 0 6 3 3 0 3 

Bellingham 

Harbor, WA 18.3 155 181 85 11 10 23 37 49 27 

Olympia 

Harbor, WA 25.5 8 1 0 4 2 0 0 0 1 

The Puget Sound ports (including Grays Harbor) total emissions by mode are listed in Table 4-

9. Each activity estimate was also associated with each port using the allocation method 

described above. Except for Grays Harbor, all of the cruise and much of the reduced speed 

zone activity occurs in the same physical place in the Strait of Juan de Fuca and into the Puget 

Sound. 

Table 4-9. Emission estimates (tons/year) by mode for Puget Sound (including Grays Harbor) 

ocean-going vessels in 1996.

Estimate HC CO NOx PM 

Cruise (25 miles to 

entrance of the Strait) 45 148 1,541 196 

Reduced Speed Zone 137 451 4,687 646 

Maneuvering 3 10 109 12 

Hotelling 112 343 2,597 80 

Total 298 951 8,935 935 

The geographic allocation of the emissions for each port was done for three segments: cruise, 

reduced speed zone, and maneuvering and hotelling/dwelling. For emissions associated with 

Grays Harbor vessel calls, all emissions were considered to occur in Grays Harbor County. 

For all Puget Sound vessel calls, the cruise emissions were assumed to occur in Clallum 

County. For all Puget Sound vessel calls, the maneuvering and hotelling emissions were 

allocated to the county of the port of interest. The emissions for the reduced speed zone were 

allocated to the counties along what was considered the primary shipping channel according to 

rough estimates of the fraction of time spent in each county. For example, port calls to 

Olympia included reduced speed zone emissions in Clallum, Jefferson, Kitsap (assuming the 
 February 2004

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ships pass west of Vashon Island), Pierce, and Thurston counties; and port calls to Bellingham 

included reduced speed zone transit through Clallum, San Juan, and Whatcom counties. 

4.3.2.3 Columbia River Ports

Vessel activity for Columbia River ports was estimated according to the procedure described 

in ARCADIS (1999) where a scaling factor was determined with a similar port, in this case, 

the Puget Sound totals. Adjustments were made to the actual vessel activity such as reduced 

speed zone load and time in mode based on discussions with the River Pilots 

(http://www.colrip.com/) for the Columbia River ports. Other factors, such as cruise, 

maneuvering, and hotelling time and load, were kept the same with the adjusted number of 

vessel calls. Vessels arriving near the mouth of the Columbia River are guided by the Bar 

pilots (http://www.columbiariverbarpilots.com/) across the Columbia Bar to Astoria 

(approximately 14 nautical miles), where river pilots begin piloting ships to their destination. 

The river pilots estimate that 12 knots is a typical average speed for ships once pilots take 

command. 

There were five major ports in the Columbia River for which ARCADIS (1999) estimated 

total vessel visits. These total vessel visits are compared with the total activity for Puget 

Sound ports (including Grays Harbor) in Table 4-10. The number of individual vessel visits 

by type of vessel for each Columbia River port was divided by the Puget Sound total and 

multiplied by the more detailed estimate of the Puget Sound port totals to produce an estimate 

of vessel activity for each of the Columbia River ports. By using the ratio of total visits, the 

emissions for the Columbia River ports were directly calculated from the Puget Sound totals 

for cruise, maneuvering, and hotelling emissions. Cruise conditions were assumed in this 

report to begin 14 miles out from Astoria. Reduced speed zone emissions used the ratio of 

total visits and the ratios of load and time in mode for each Columbia River port. 

Table 4-10. Port activity totals as presented by ARCADIS (1999).

Port 

RSZ 

(Nautical 

Miles) 

Bulk 

Carrier 

Container 

Ship 

General 

Cargo Other Passenger Reefer 

Roll 

On/Roll 

Off Tanker 

Vehicle 

Carrier 

Puget Sound 

ports 

(ARCADIS, 

1999) 1551 2703 474 65 87 116 714 1000 919 

Port of 

Portland, OR 93 684 532 68 0 781 10 124 333 244 

Port of Kalama, 

WA 

69 

374 0 1 0 0 0 0 24 0 

Port of 

Vancouver, WA 

94 

440 7 110 0 20 0 7 37 13 

Port of 

Longview, WA 

59 

541 0 44 0 5 13 15 11 0 

Port of Astoria, 

OR 

14 

397 0 5 1 9 0 4 0 0 

Overall estimated emissions for vessels visiting each port are shown in Table 4-11. However, 

transit emissions occur in the Columbia River below each port rather than in the port area. 

The geographic allocation for the transit (cruise and RSZ) emissions was to the Washington 

counties (Pacific for cruise and some RSZ, Wahkiakum, Cowlitz, or Clark) below each port 
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according to rough estimates of the fraction of time spent in each county. Maneuvering and 

hotelling emissions were allocated to the county of the port in either Washington or Oregon. 

Table 4-11. Emission estimates (tons/year) for Columbia River ocean-going vessels. 

Port HC CO NOx PM

Port of Astoria, OR 14.8 47.4 393.8 17.7 

Port of Kalama, WA 18.4 58.9 521.1 33.3 

Port of Longview, WA 27.1 86.9 760.2 45.5 

Port of Portland, OR 87.6 277.8 2520.8 259.5 

Port of Vancouver, 

WA 29.6 94.9 851.9 60.7 

4.3.3 Tugs 

Tugs operate in a variety of activities including towing (or push boats on the Columbia) and 

vessel assist. For the Puget Sound and Oregon Coast, the population and power level of tugs 

was used along with estimates of annual hours of use and average load factor to estimate total 

emissions. For the Columbia River system, a different methodology was used that combines 

freight movements to estimate push boat activity, and relative ocean-going vessel calls for 

assist tug activity for lower Columbia ports. 

4.3.3.1 Puget Sound Area

ARCADIS (1999) indicated that 135 tugs are based in the Puget Sound and listed the installed 

power for each. As instructed by ARCADIS (1999), the average load factor was assumed to 

be 0.47, and annual activity in hours per year was assumed to be 2,000 for vessels with power 

levels of 500 hp or below and 3,000 for larger tugs. The emission factors were taken from the 

EPA (1999b) estimates for Category 1 engines, assuming two engines combined for the total 

installed power on each tug. This estimate of activity was assumed to include freight towing, 

ocean-going vessel assist, and other activity only within the Puget Sound, but may have 

include towing activity outside of the Puget Sound. However it did not include tug activity for 

vessels based outside of Puget Sound ports, such as Vancouver, Alaska or other west coast 

ports not elsewhere estimated in this work. The resulting emissions estimates for Puget Sound 

tug activity were 118 tons/year (tpy) HC, 5359 tpy NOx, 984 tpy CO, and 133 tpy PM. The 

tug activity was allocated to the port of registry for each tug. This was a rough estimate 

because most activity occurs between ports. 

4.3.3.2 Columbia and Snake River Traffic

USACE (2001a) determined Columbia and Snake River tug activity by tonnage of material 

through each lock; USACE (1996) determined tug activity for domestic coast-wise shipping 

for the mouth of the Columbia system, total shipping at the mouth of the Snake, and river 

traffic above Vancouver. The estimates indicated that river traffic in terms of freight tons 

grew for each lock moving down river until near Vancouver, where the freight was most 

likely transferred to larger ocean-going vessels. These estimates are shown in Table 4-12. 
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Table 4-12. 1996 Tug activity along the Columbia River System. Source: USACE (2001a), 

USACE (1996). 

River Point 

Tonnage 

(1000 

tons) 

Mileage 

Mouth of Columbia 4,781 0 

Portland-Vancouver 10,733 107 

Bonneville Dam 9,737 145.3 

The Dalles Dam 9,357 191.7 

John Day Dam 9,053 216.5 

McNary Dam 7,886 292 

Mouth of Snake 5,707 322 

Ice Harbor Dam (Snake) 3,565 9.7 

(from Snake mouth) 

Lower Monumental Dam 

(Snake) 

3,099 41.6 

Little Goose Dam (Snake) 2,912 70.3 

Lower Granite (Snake) 1,771 107.5 

This information afforded an opportunity to use the activity indicator of ton-miles of transport 

of material along the river system. This method was similar to the method employed by EPA 

(1998) for inland rivers but reported only as total emissions and total ton-miles. An alternate 

fuel consumption estimate per ton-mile was available from a MARAD (2000) study, which 

states that 514 ton-miles of traffic consumes one gallon of fuel. By converting the EPA 

(1999b) emission factor in g/kW-hr for Category 1 engines above 1,000 kW to g/gallon 

through an average fuel consumption figure of 210 g/kW-hr and a fuel density of 7.1 

pounds/gallon, the MARAD (2000) information can be compared with the EPA (1998) 

estimate as shown in Table 4-13. Because steamships and foreign vessel emission subtotals 

were included in the inland rivers emission totals in EPA (1998), it was suspected that oceangoing vessels were included in the inland river traffic, so the MARAD estimates were used for 

this work as more reflective of Columbia tug (tow and push boat) emission rates. 

Table 4-13. Comparison of alternative emission estimates (g/ton-mile) for tug activity by tonmile. 

Source HC NOx CO PM 

EPA (1998) 0.0091 0.256 0.0281 0.0129 

MARAD (2000) 0.0081 0.389 0.0746 0.0090 

For each lock, only total tonnage (up and down river) traffic and river mileage was supplied, 

so some assumptions were used to estimate the ton-miles of traffic. For the uppermost dam 

used by river traffic on each river, assumptions were made on the mileage to the terminus: 

~25 miles from the Lower Granite Dam on the Snake to the Lewiston, ID terminus and ~50 

miles from the McNary Dam on the Columbia to the Richland, WA terminus on the 

Columbia. 
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The tonnage at each lock decreases at each lock up river, so the tonnage at the uppermost lock 

was assumed to proceed from the up river terminus down through the lock and to the next 

lock. Between each lock, the mileage for added traffic (difference in tonnage between the two 

locks) was an arithmetic average of the two locks. For traffic downstream of Vancouver, only 

the tonnage at the mouth of the Columbia was used to determine ton-miles assuming that most 

of the tonnage was loaded on ocean-going vessels. This method may have missed internal 

traffic between Vancouver and the mouth of the Columbia. Some of the domestic coastwise 

shipping may be handled by ocean-going vessels, so could be double-counted. However 

domestic coastwise (assumed to be primarily tow boat activity) to and from PortlandVancouver harbors was roughly equivalent to the totals for the mouth of the Columbia 

(~4,000 ktons), so the tug activity below Vancouver may indeed be coast-wise towboats. 

This method was also employed for other ports not covered under ARCADIS (1999) 

documentation, including Grays Harbor and Coeur D'Alene Lake commercial traffic with tug 

and tow activity. The resulting estimates for tug emissions are shown in table 4-14 with 

Columbia-Snake River emission totals. The emissions for Columbia River traffic were 

allocated to the Washington counties of Whitman, Franklin, Benton, Klickitat, Skamania, 

Clark, Cowlitz, Wahkiakum, and Pacific, although river traffic occurs at the very edge of each 

of these counties and state boundaries. 

Table 4-14. Columbia and Snake River emission estimates (tons/year) for tug freight activity. 

Area HC NOx CO PM 

Columbia-Snake 25.5 1228.5 313.3 28.4 

Grays Harbor 0.06 2.7 0.53 0.06 

Coeur D'Alene Lake 0.08 4.0 0.77 0.09 

4.3.3.3 Oregon Coast

ARCADIS (1999) gathered tug population and estimated activity for tugs based along the 

Oregon Coast. As instructed by ARCADIS (1999), the average load factor was assumed to be 

0.47, and annual activity in hours per year was assumed to be 2,000 for vessels with power 

levels of 500 hp or below and 3,000 for larger tugs. This method likely includes activity off 

the coast of Oregon as well as within the home ports. Estimated Oregon Coast tug emissions 

are 0.2 tpy HC, 5.8 tpy NOx, 0.9 tpy CO, and 0.3 tpy PM. The emissions were allocated to 

the port of registry for each of the tug and tow boat operators. 

4.3.3.4 Other Tug Activity

Many tugs are used to support ocean-going vessels in and out of harbors. The Puget Sound 

and Oregon Coast tugs were chronicled in great detail (ARCADIS, 1999), but the data were 

not distinguished between assist and tow activity, so it was assumed that this data was a 

complete estimate of all tug activity. 

For the Columbia River ports, however, the freight tug activity was estimated uniquely, so a 

separate estimate was made for tugs engaged in assisting ocean-going vessels. The Columbia 

River Pilots indicated that typical tug activity assisting ships consisted of two tugs operating 

one hour idle (assumed to be 10% load) and 15 minutes loaded maneuvering (assumed to be 

90% load) per trip, for an average load of 26% for 2.5 hours per vessel call. The average 
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power for Puget Sound tugs was determined to be 2,821 hp, and was used to estimate the 

power of the assist tugs. The vessel calls for each Columbia River ports was taken from the 

general information provided by ARCADIS (1999) for 1995. The resulting emissions 

estimates are provided in Table 4-15. It is possible that other tug activity exists either on the 

Columbia or elsewhere on the ocean coast or other rivers (such as the Willamette), but there 

was no data for freight or other activity available. The tug assist activity was expected to 

occur in the Washington or Oregon County in which each port is located. 

Table 4-15. Emission estimates (tons/year) for Columbia River assist tug activity. 

Port Calls HC NOx CO PM 

Port of Portland, OR 1,389 1.1 54.3 10.4 1.3 

Port of Kalama, WA 200 0.2 7.8 1.5 0.2 

Port of Vancouver, WA 316 0.3 12.4 2.4 0.3 

Port of Longview, WA 315 0.3 12.3 2.4 0.3 

Port of Astoria, OR 209 0.2 8.2 1.6 0.2 

4.3.4 Ferries 

ARCADIS (1999) estimated the activity for Washington State ferries; this includes most of the 

ferry activity within the Puget Sound. Other ferry runs do occur, however, and some were 

estimated in this work. Because of project resource limitations, emissions for smaller ferry 

services6

 were not estimated. The load during each ferry run was assumed to be 80%, and 

during the idle time (while loading passengers and vehicles) was assumed to be 10%. The 

emission rates for Category 1 were used for ferries; although many Category 2 engines are 

used, the emissions rates for large Category 1 engines are not appreciably different from those 

for Category 2. The allocation of idle emissions was to each dock, and running emissions 

between each stop. Most ferry runs occur across county boundaries. For those ferries that run 

into Canada, only the emissions occurring in US waters were estimated. The resulting 

emissions estimates are shown in Table 4-16. 

The largest of the ferry systems not estimated was that associated with the Alaska Marine 

Highway Service with large ferries such as the M/V KENNICOTT, which is powered by 2 

engines each rated at 6,690 hp. This could be a significant source of emissions for the 

Bellingham area, but the time required to determine the fleet and activity arriving in 

Bellingham was outside the scope of this work. 

 

6

 Lummi Island Service 

Bremerton/Port Orchard/Annapolis Service 

Bellingham/San Juan Island Service 

Bellingham - Victoria B.C. and San Juan Islands 

Bellingham/Juneau, Alaska Service (Alaska Marine Highway Service) 

Seattle/Victoria B.C. Service (Victoria Clipper) 

Port Townsend/Friday Harbor Service 

Puget Island Ferry on the Columbia River
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G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 4-22

Table 4-16. Estimated emissions (tons/year) from ferries in the Puget Sound. 

Route Mode HC NOx CO PM 

Seattle-Bainbridge Running 21.9 1052.1 202.3 24.3 

Island Idle 1.2 56.4 10.8 1.3 

Running 11.6 557.2 107.2 12.9 

Seattle-Bremerton Idle 0.4 18.6 3.6 0.4 

Running 9.3 446.4 85.8 10.3 

Edmonds-Kingston Idle 0.6 27.9 5.4 0.6 

Running 3.4 165.6 31.8 3.8 

Fauntleroy-Vashon Idle 0.4 20.7 4.0 0.5 

Running 6.8 326.1 62.7 7.5 

Fauntleroy-Southworth Idle 0.4 17.5 3.4 0.4 

Running 1.6 75.9 14.6 1.8 

Southworth-Vashon Idle 0.3 14.2 2.7 0.3 

Vashon-Seattle Running 1.9 68.8 10.3 2.1 

(passenger only) Idle 0.1 5.2 0.8 0.2 

Port Defiance- Running 1.3 60.7 11.7 1.4 

Tahlequah Idle 0.2 7.6 1.5 0.2 

Port Townsend- Running 0.7 25.3 3.8 0.8 

Keystone Idle 0.0 1.6 0.2 0.0 

Running 6.4 310.5 59.7 7.2 

Mukilteo-Clinton Idle 0.4 19.4 3.7 0.4 

Anacortes-San Juan Running 19.5 937.4 180.3 21.6 

Island-Sidney, B.C. Idle 0.3 15.5 3.0 0.4 

Anderson Island/ Ketron Running 0.5 18.7 2.80 0.56 

Island/ Steilacoom 

Service1

 Idle 0.1 4.2 0.62 0.12 

Port Angeles / Victoria Running 0.9 41.2 7.92 0.95 

B.C. Service2 Idle 0.2 7.7 1.48 0.18 

Running 0.1 2.4 0.4 0.1 

Port Angeles –Victoria3 Idle 0.0 0.4 0.1 0.0 

Puget Sound Totals 90 4,305 823 100.2 

1 - Pierce County Ferry (addition from ARCADIS (1999)) 

2 – Black Ball Transport (addition from ARCADIS (1999)) – Only US half of activity estimated 

3 – Victoria Express (addition from ARCADIS (1999)) – Only US half of activity estimated 

4.3.5 Dredges 

Emission estimates by tonnage of dredged material was taken from Starcrest (2000) for the 

Houston-Galveston area and shown in the table below. The dredge activity (vessel activity and 

dredged material) was surveyed and combined with emission rates consistent with the 

ARCADIS (1999) estimates. Dredge activity for Washington and Oregon was taken from 

USACE (2001b) information of dredging contracts and Army Corps-operated dredges for 

1996. This data includes Pacific Coast, Columbia-Snake River, and Puget Sound activity. 

The USACE (2001b) dredging activity was located by longitude and latitude making the 

county allocation of the dredging activity straightforward. The activity and emissions 

estimates are provided in Table 4-17. 
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Table 4-17. Dredging activity and emissions estimates (tons/year). 

Area 

Tonnage 

(cubic yards) 

HC NOx CO PM 

Houston 5,667,000 1.8 143.3 25.7 3.6 

1996 Washington 2,811,404 0.9 71.1 12.7 1.8 

1996 Oregon 12,728,740 4.0 321.9 57.6 8.1 

4.3.6 Fishing Vessels 

It was out of the scope of the present work to revise the estimate of emissions from fishing 

vessels because it would require a complete accounting of vessels and a survey of their use. In 

order to revise the estimates available in BAH (1991), annual landings in tonnage of catch 

(NMFS, 2001) were used to scale the emission estimates for the State of Washington in 1990 

to 1996 for Washington and Oregon. The ratio of tonnage catch for Washington in 1990 to 

1996 for Washington and Oregon was multiplied by the emissions estimated by BAH. The 

resulting 1996 activity and emissions estimates are provided ion Table 4-18. 

Table 4-18. Estimated emissions (tons/year) from commercial fishing vessels in Washington 

and Oregon. 

State Total 

Catch 

(106 lbs) HC NOx CO PM SOx 

1990 Totals 

Washington 

(BAH, 1991) 151.6 2122 4670 42437 242.4 1339 

1996 Totals 

Washington 136.5 1911 4205 38210 218.3 1205.6 

1996 Totals 

Oregon 262.7 3678 8092 73537 420.0 2320.3 

The state total emissions were allocated to several of the largest ports for fish catch using data 

obtained from NMFS (2001), shown in Table 4-19. It was assumed that all emissions were 

allocated to these ports, though many other ports serve as homeports and much of the fishing 

activity occurs across several counties and in the open ocean. 

Table 4-19. Washington and Oregon fishing ports allocation. Source NMFS (2001). 

County City % of State Totals

King Seattle, WA 69% 

Lincoln Newport, OR 44% 

Clatsop Astoria, OR 41% 

Grays Harbor Westport, WA 13% 

Coos Coos Bay-Charleston, OR 9% 

Pacific Ilwaco-Chinook, WA 7% 

Whatcom Bellingham, WA 6% 

Curry Brookings, OR 5% 

Kitsap Poulsbo, WA 2% 

Clallum Neah Bay, WA 2% 

Mason Shelton, WA 1% 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 4-24

ARCADIS (1999) also estimated fishing vessel activity for the Puget Sound, but the total 

vessel calls, 81, was considered to be unreasonably small for this region. This may represent 

only the larger vessels and may ignore vessels that do not leave the Puget Sound. So this 

activity estimate was not included in this work. 

4.3.7 Comparison with BAH (1991) Estimates for Puget Sound 

In summary, this work represents a complete revision to the BAH (1991) report prepared for 

the official inventory guidance from EPA (1992). The method used to estimate emissions was 

very similar to BAH (1991) with revised activity and emission factor information. 

A comparison of the emissions estimated in this work compared to the BAH (1991) estimates 

is shown in Table 4-20. The most striking difference is in the transit emissions for oceangoing vessels, but this is explainable because BAH (1991) estimated only emissions within 

Port Angeles, while this work estimates emissions 25 miles out from the entrance to the Strait 

of San Juan de Fuca or roughly double the average transit time. Growth in overseas shipping 

from 1990 to 1996 may also be responsible for increased emissions from ocean-going vessels. 

Table 4-20. Emissions estimates (tons/year) for Puget Sound commercial marine vessels. 

Region 

(Study) HC NOx CO PM 

OceanGoing 

(underway) 122 2,841 315 273 

OceanGoing 

(Hotelling) 301 1,111 178 88 

Harbor 568 11,241 1,481 683 

Fishing 1,508 3,319 30,160 172 

1990 SeattleTacoma 

(BAH, 1991) Total 2,499 18,513 32,133 1,216 

OceanGoing 

(underway) 183 6,239 599 845 

OceanGoing 

(Hotelling) 107 2,461 325 78 

Harbor* 209 9,673 1,808 233 

Fishing 1,471 3,238 29,422 168 

1996 SeattleTacoma 

(this work) Total 1,970 21,611 32,154 1,324 

* Tug, ferry, and dredge totals. 

4.3.8 Recent Columbia River Emission Estimates 

Two data sources for commercial marine emissions became available after the commercial 

marine emission inventory was prepared for the WRAP modeling. Corbett (2001) and TVA 

(2002) provide alternative fuel consumption and efficiency estimates for the Columbia and 

Snake River system. 
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G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 4-25

The national average efficiency estimate for fuel consumption by towboats used in the WRAP 

work was estimated to be 514 ton-miles per gallon, derived from a national estimate available 

from MARAD (2000). TVA (2002) presented the Columbia River efficiency at 208 ton-miles 

per gallon above McNary Dam, and 250 ton-miles per gallon below McNary Dam. The 

revised fuel efficiency estimate would bring the WRAP estimate in line with the Corbett 

(2001) estimate for towboat traffic. 

Corbett (2001) also used TVA fuel consumption estimates to estimate all commercial marine 

emissions along the Columbia River system. The TVA data is reported to include all 

commercial marine traffic including ocean going, vessel assist tugs, commercial fishing, and 

dredging activity, though commercial fishing was not considered significant in the Columbia 

River. Summary data for all Columbia River traffic for the WRAP report (with a revised 

towboat efficiency estimate) indicated an estimate of NOx emissions of 7,933 tons for 1996 

compared with the Corbett estimate of 7,762 tons for 1999 for towboats and ocean-going 

vessel emissions. 

4.4 LOCOMOTIVE EMISSIONS METHODOLOGY 

The methodology used to estimate emissions from railroad locomotives is, in general, based 

on that used to generate the locomotive inventory for 1996 as part of the U.S. EPA’s National 

Emission Trends (NET) project (EPA, 1998). To define locomotive activity levels the annual 

estimates of railroad locomotive fuel consumption generated as part of the 1996 NET project 

were used. These estimates were available by county for each of the WRAP states with the 

exception of California, as CARB provided all California emissions estimates. 

Emission inventories for VOC, CO, NOx, and PM were computed using the activity data 

referenced above and emission factors for uncontrolled line-haul and switch locomotives 

published by the U.S. EPA (EPA, 1997). These emission factors are shown in Table 4-21. 

For purposes of this project, we are considering all PM emitted by locomotives to be PM10. 

Consistent with the methodology used for the 1996 NET project, PM2.5 is assumed to account 

for 92% of PM10 emissions from locomotives. Ammonia emissions for locomotives were 

estimated using the NET emission factor of 0.95 pounds per 1,000 gallons of fuel. 

Table 4-21. Locomotive emission factors. (lbs/1000 gallons of fuel consumed). 

HC CO NOx PM10 

Line Haul 22.0 58.6 595.2 14.8 

Switch 46.3 83.8 798.1 20.3 

SO2 and sulfate emissions were calculated using the methodology outlined in EPA’s 

NONROAD model based on average sulfur content of 3,900 ppm for diesel fuel used in 

locomotive applications. This value represents an average of the values shown for non-road 

diesel fuel sulfur for the WRAP states based on NIPER’s 1996 fuel property survey (NIPER, 

1996). In keeping with EPA’s NONROAD model assumptions, we have assumed 2.2% of 

fuel sulfur is converted to sulfate in the exhaust and that the sulfate in the locomotive exhaust 

stream is hydrated. In accounting for hydration we used the molecular weight of hydrated 

sulfate specified by EPA in the NONROAD model. 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 4-26

Speciation of PM10 into elemental carbon and organic carbon was performed using the same 

basic speciation fractions that are applied to on-road heavy-duty diesel engines, expressed as a 

percentage of the total PM10 emissions. However, since these fractions were developed for 

engines using low sulfur (500 ppm maximum) diesel fuel, adjustment was required to account 

for the larger amounts of sulfate expected in PM emissions from use of higher sulfur fuels. In 

order to accomplish this, the estimated mass of sulfate was subtracted from the estimated 

PM10 and PM2.5 mass and normalized elemental and organic carbon fractions were applied to 

the remaining PM emissions. 

The final issue with respect to the locomotive methodology is the allocation of line-haul and 

switch engine activity within each county. In the NET 1996 inventory, weighted emission 

factors based on nationwide fuel consumption in line-haul and switch engines are used. No 

state- or county-specific activity allocation fractions were found, and the 1995 national activity 

allocations published by EPA in its December 1997 “Emission Factors for Locomotives” 

update were used to apportion the county-specific activity estimates into line-haul vs. switch 

activity. 

4.5. CALIFORNIA OFF-ROAD EMISSIONS 

The California Air Resources Board (CARB) provided two types of off-road emissions data. 

First, they provided 1996 seasonal off-road emissions estimates based on assumptions and data 

being developed for CARB’s OFFROAD model (still in development) for the following offroad source categories: 

Agricultural equipment 

Airport ground support equipment 

Construction equipment 

Dredging equipment 

Industrial equipment 

Lawn and garden equipment 

Light-duty commercial equipment 

Logging equipment 

Military tactical equipment 

Miscellaneous portable equipment 

Oil drilling equipment 

Pleasure craft 

Transport refrigeration. 

The second type of off-road emissions data received from CARB was derived from the 

California Emission Forecasting System (CEFS), an on-line database of county-level emissions 

for all source categories at five-year intervals. CARB provided 1995 CEFS emissions 

estimates for locomotives, aircraft, and commercial marine source categories. The 1995 

emissions were grown to 1996 using the growth factors shown in Table 4-22. The CEFS data 

included winter, summer, and annual emissions. Spring and autumn emissions were estimated 

by averaging the winter and summer values for each source category. 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 4-27

Table 4-22. Growth factors for estimating 1996 emissions from CARB 1995 CEFS data. 

Category 

Growth 

Factor Growth Factor Basis 

Aircraft 0.9958 CA Aircraft Landing and Take Offs 

Locomotives 0.9864 Bureau of Economic Analysis (BEA) CA earnings for 

SIC Code 40-Railroad Transportation 

Commercial Marine 1.0054 BEA CA Earnings for SIC Code 44-Water 

Transportation 

4.6. GENERATION OF SMOKE FILES 

In order to create SMOKE files for the non-road emissions, several adjustments had to be 

made. Because 2-stroke, 4-stroke, CNG, and LPG engines from the NONROAD model were 

all lumped together in one fuel category, the SCC used for the SMOKE files are not always 

exact matches for EPA convention. Thus, EPA SCCs falling under 2260******, 

2265******, 2267******, and 2268****** were combined and designated with 2200****** 

codes. For pleasure craft and railroad equipment, the 4-stroke SCC was chosen to represent 

the 2-stroke, 4-stroke, CNG, and LPG category. The source categories and SCCs used in the 

SMOKE files are listed in Table 4-23.

Table 4-23. Off-road SCCs used in SMOKE files. 

Source Category Fuel Assigned SCC 

Agricultural Equipment Gasoline 2200005000 

Agricultural Equipment Diesel 2270005000 

Airport Ground Support Equipment Gasoline 2200008005 

Airport Ground Support Equipment Diesel 2270008005 

Commercial Equipment Gasoline 2200006000 

Commercial Equipment Diesel 2270006000 

Construction and Mining Equipment Gasoline 2200002000 

Construction and Mining Equipment Diesel 2270002000 

Industrial Equipment Gasoline 2200003000 

Industrial Equipment Diesel 2270003000 

Lawn and Garden Equipment Gasoline 2200004000 

Lawn and Garden Equipment Diesel 2270004000 

Logging Equipment Gasoline 2200007000 

Logging Equipment Diesel 2270007000 

Oil Field Equipment Gasoline 2200010000 

Oil Field Equipment Diesel 2270010000 

Pleasure Craft Gasoline 2282010005 

Pleasure Craft Diesel 2282020000 

Railroad Equipment Gasoline 2285004015 

Railroad Equipment Diesel 2285002015 

Recreational Equipment Gasoline 2200001000 

Recreational Equipment Diesel 2270001000 

Underground Mining Equipment Gasoline 2200009000 

Underground Mining Equipment Diesel 2270009000 

Aircraft Gasoline 2200008000 

Aircraft Diesel 2270008000 

Commercial Marine Diesel 2280002000 

Locomotives Diesel 2285002005 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 4-28

CARB follows a slightly different SCC convention and does not model all the same off-road 

categories as EPA. The California source categories were assigned the EPA-like SCCs shown 

in Table 4-23. Dredging Equipment was lumped into the Commercial Marine source 

category, and Transport Refrigeration was lumped with Industrial Equipment. However, two 

CARB categories did not fall under any of the standard EPA categories. Military Tactical 

Equipment (all diesel), not modeled by EPA, was assigned SCC 2290000000. Miscellaneous 

Portable Equipment (also all diesel) was assigned the most general EPA diesel non-road code 

(2270000000). 

The NONROAD and OFFROAD models do not estimate NO, NO2, SO4, EC10, EC2.5, 

OC10, OC2.5, or NH3 emissions. In generating the SMOKE input files, these pollutants were 

calculated using the methods and assumptions shown in Table 4-24. 

Table 4-24. Methods for estimating NO, SO4, EC and OC for off-road gasoline and diesel 

equipment. 

Pollutant Equation Used Methodology Source 

NO 0.90 * NOx EPS2* default NOx speciation 

NO2 0.10 * NOx EPS2* default NOx speciation 

SO4 PM10-EC10-OC10 Assumption that SO4, EC, and OC comprise all of PM 

Gasoline EC 0.239 * PM On-road light duty gas vehicle PM speciation 

Diesel EC 0.75 * PM On-road heavy duty diesel vehicle PM speciation 

Gasoline OC 0.518 * PM On-road light duty gas vehicle PM speciation 

Diesel OC 0.189 * PM On-road heavy duty diesel vehicle PM speciation 

Gasoline NH3 0.0361 * NOx On-road light duty gas vehicle NH3 to NOx emissions ratio 

Diesel NH3 0.001556 * NOx Locomotives NH3 to NOx emissions ratio 

* EPS2 is the Emissions Processing System (version 2). 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc 5-1 

5.0 1996 RESULTS 

This section provides the emissions estimates for 1996 on-road and off-road mobile source 

emissions, developed using the methodologies described in Sections 3 and 4, respectively. 

The results are presented in a series of tables and graphs; more detailed emissions estimates by 

state, county, and source category are available in spreadsheets. 

5.1 ON-ROAD EMISSION INVENTORIES 

Table 5-1 shows the estimated annual average weekday on-road emissions by state. Figures 5-

1a through 5-1g compare the emissions by county for each of the pollutants. For all pollutants 

except SO2, California has the highest emissions of all of the states; California on-road SO2 

emissions are lower because of regulations requiring low sulfur fuels. The county-level 

emissions maps clearly show the majority of on-road emissions in urban areas; Los Angeles, 

Phoenix, and Seattle urban counties have the highest emissions for all pollutants. 

Table 5-2 shows the estimated annual average weekday on-road emissions by state and fuel 

type; the relatively small emissions from CNG and LPG fueled vehicles have been included 

with gasoline vehicle emissions. This table shows that diesel equipment dominates on-road 

NOx, and PM emissions; while gasoline equipment dominates off-road VOC, CO, SO2, and 

NH3 emissions. 

Figures 5-2a through 5-2d show 1996 weekday on-road emissions by state and vehicle 

class/fuel type, for VOC, NOx, PM10, and SO2, respectively. These graphs allow a visual 

comparison of emissions by state, as well as showing the relative contributions by vehicle class 

and fuel type. Light-duty gasoline vehicles dominate VOC emissions, and also SO2 

emissions, except in California. Light-duty gasoline vehicles account for a little more than 

half of the on-road NOx emissions, with most of the rest of the NOx emissions coming from 

heavy-duty diesel vehicles. For PM10 emissions the opposite is true – heavy-duty diesel 

vehicles account for more than half, with most of the rest coming from light-duty gasoline 

vehicles. 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc 5-2 

Table 5-1. 1996 Western states total on-road emissions by state (TPD). 

State VOC NOx CO PM10 + PM2.5 + SO2 NH3 

Arizona* 411 384 4,079 14.3 12.6 8.1 8.9 

California* 1,792 2,160 17,435 60.6 43.8 12.0 58.4 

Colorado* 298 374 3,809 16.8 15.1 9.6 8.0 

Idaho* 111 130 1,368 5.1 4.6 3.4 2.6 

Montana 85 101 1,155 3.9 3.5 2.4 1.9 

Nevada* 108 118 1,144 5.1 4.5 2.0 3.1 

New Mexico* 194 208 2,328 8.3 7.4 5.7 4.4 

North Dakota 65 72 891 2.8 2.5 1.7 1.4 

Oregon* 316 313 3,451 10.5 9.2 9.2 5.7 

South Dakota 71 84 986 3.3 2.9 2.0 1.6 

Utah* 172 218 2,298 9.6 8.7 4.4 4.2 

Washington 591 556 6,775 18.0 15.9 14.7 9.5 

Wyoming* 66 78 910 3.0 2.7 1.9 1.5 

* 9 GCVTC States 3,467 3,984 36,823 133 109 56 97 

13 WRAP States 4,278 4,797 46,629 161 133 77 111 

+

 PM emissions include exhaust, brake wear, and tire wear emissions.
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc Table 5-2. 1996 Western states total on-road emissions by state and fuel type (TPD). 

VOC NOx CO PM10 + PM2.5 + 

State 

Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diese

Arizona * 15 396 139 245 71 4,008 9.5 4.8 9.1 3.5 2California * 49 1,743 695 1,465 216 17,220 28.0 32.6 24.8 19.0 6Colorado * 10 288 160 215 60 3,749 10.9 5.9 10.4 4.6 2Idaho * 5 106 55 75 21 1,348 3.4 1.7 3.2 1.3 0Montana 3 81 43 58 15 1,141 2.6 1.3 2.5 1.0 0Nevada * 6 102 50 68 29 1,115 3.4 1.7 3.2 1.3 0New Mexico * 13 181 91 117 67 2,262 5.5 2.8 5.2 2.2 1North Dakota 2 62 30 42 11 880 1.9 0.9 1.8 0.7 0Oregon * 10 305 114 199 43 3,408 6.8 3.7 6.5 2.8 1South Dakota 3 68 36 48 12 973 2.2 1.0 2.1 0.8 0Utah * 9 163 110 108 56 2,242 6.8 2.9 6.5 2.3 1Washington 21 569 178 377 79 6,696 11.0 7.0 10.5 5.4 2Wyoming * 3 64 33 45 11 898 2.0 1.0 1.9 0.8 0* 9 GCVTC States 119 3,347 1,447 2,537 574 36,249 76 57 71 38 113 WRAP States 149 4,128 1,735 3,062 691 45,938 94 67 88 46 2+ PM emissions include exhaust, brake wear, and tire wear emissions.
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc Figure 5-1a. 1996 Western states annual average on-road VOC emissions. 

g

Date Generated: 6/19/2001

Emissions (TPD)

0.0 to 1.01.0 to 10.010.0 to 10100.0 to 2200.0 to 5

zero

Denver

Los Angeles

San Francisco

Boise

Las Vegas

Albuquerque

Salt Lake City

Portland

Seattle

Cheyenne

Phoenix

Billings

Spokane

Bismarck

Rapid City

Reno

Tucson
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc Figure 5-1b. 1996 Western states annual average on-road NOx emissions.

Date Generated: 6/19/2001

Emissions 

0.0 1.0 101020

zer

Denver

Los Angeles

San Francisco

Boise

Las Vegas

Albuquerque

Salt Lake City

Portland

Seattle

Cheyenne

Phoenix

Billings

Spokane

Bisma

Rapid City

Reno

Tucson
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc Figure 5-1c. 1996 Western states annual average on-road CO emissions.

Date Generated: 6/19/2001

Emissions (TPD

0.0 to 110.0 to 100.0 to 500.0 to 1000.0 

zero

Denver

Los Angeles

San Francisco

Boise

Las Vegas

Albuquerque

Salt Lake City

Portland

Seattle

Cheyenne

Phoenix

Billings

Spokane

Bismarck

Rapid City

Reno

Tucson
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc Figure 5-1d. 1996 Western states annual average on-road PM10 emissions.

Date Generated: 6/19/2001

Emissions (TPD

0.0 to 00.1 to 11.0 to 33.0 to 110.0 to 

zero

Denver

Los Angeles

San Francisco

Boise

Las Vegas

Albuquerque

Salt Lake City

Portland

Seattle

Cheyenne

Phoenix

Billings

Spokane

Bismarck

Rapid City

Reno

Tucson
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc Figure 5-1e. 1996 Western states annual average on-road PM2.5 emissions.

Date Generated: 6/19/2001

Emissions (TPD

0.0 to 00.1 to 11.0 to 33.0 to 110.0 to 

zero

Denver

Los Angeles

San Francisco

Boise

Las Vegas

Albuquerque

Salt Lake City

Portland

Seattle

Cheyenne

Phoenix

Billings

Spokane

Bismarck

Rapid City

Reno

Tucson
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc Figure 5-1f. 1996 Western states annual average on-road SO2 emissions.

Date Generated: 6/20/2001

Emissions (TPD

0.0 to 00.1 to 00.5 to 11.0 to 22.0 to 5

zero

Denver

Los Angeles

San Francisco

Boise

Las Vegas

Albuquerque

Salt Lake City

Portland

Seattle

Cheyenne

Phoenix

Billings

Spokane

Bismarck

Rapid City

Reno

Tucson
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc Figure 5-1g. 1996 Western states annual average on-road NH3 emissions.

Date Generated: 6/25/2001

Emissions (TPD)

0.0 to 0.10.1 to 1.01.0 to 5.05.0 to 10.010.0 to 20

zero

Denver

Los Angeles

San Francisco

Boise

Las Vegas

Albuquerque

Salt Lake City

Portland

Seattle

Cheyenne

Phoenix

Billings

Spokane

Bismarck

Rapid City

Reno

Tucson
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc 5-11 

Figure 5-2a. 1996 Western states average annual on-road VOC emissions by vehicle class 

and fuel type. 

Figure 5-2b. 1996 Western states average annual on-road NOx emissions by vehicle class 

and fuel type. 

0

200

400

600

800

1000

1200

1400

1600

1800

AZ CA CO ID MT NV NM ND OR SD UT WA WY

Tons per Day

Light Duty Gasoline

Light Duty Diesel

Heavy Duty Gasoline

Heavy Duty Diesel

0

500

1000

1500

2000

AZ CA CO ID MT NV NM ND OR SD UT WA WY

Tons per Day

Light Duty Gasoline

Light Duty Diesel

Heavy Duty Gasoline

Heavy Duty Diesel
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc 5-12 

Figure 5-2c. 1996 Western states average annual on-road PM10 emissions by vehicle class 

and fuel type. 

Figure 5-2d. 1996 Western states average annual on-road SO2 emissions by vehicle class 

and fuel type.

0

10

20

30

40

50

AZ CA CO ID MT NV NM ND OR SD UT WA WY

Tons per Day

Light Duty Gasoline

Light Duty Diesel

Heavy Duty Gasoline

Heavy Duty Diesel

0

2

4

6

8

10

12

14

AZ CA CO ID MT NV NM ND OR SD UT WA WY

Tons per Day

Light Duty Gasoline

Light Duty Diesel

Heavy Duty Gasoline

Heavy Duty Diesel
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc 5-13 

5.2 OFF-ROAD EMISSION INVENTORIES 

Table 5-3 shows the estimated annual average weekday off-road emissions by state, with 

emissions for traditional off-road sources estimated by the June 2000 NONROAD model. 

Figures 5-3a through 5-3f compare these emissions by county for each of the pollutants. For 

all pollutants, California has the highest off-road emissions of all of the Western states. As for 

on-road emissions, the county-level emissions maps clearly show the highest on-road 

emissions in urban areas. 

Table 5-3. 1996 Western states total off-road emissions by state (TPD), based on June 2000 

NONROAD model. 

State VOC NOx CO PM10 PM2.5 SO2 NH3 

Arizona * 141 270 1,311 26.3 24.5 25.2 0.8 

California * 479 1,021 3,202 68.5 62.9 144.9 5.3 

Colorado * 122 334 1,082 26.6 24.5 61.3 0.9 

Idaho * 85 107 455 11.1 10.2 21.6 0.3 

Montana 57 201 283 13.0 12.0 31.2 0.4 

Nevada * 62 166 489 15.2 14.0 29.2 0.4 

New Mexico * 31 51 312 6.5 6.1 12.1 0.2 

North Dakota 48 248 243 16.5 15.2 46.9 0.5 

Oregon * 248 474 1,304 28.5 26.3 50.2 1.2 

South Dakota 38 160 227 12.5 11.6 34.4 0.4 

Utah * 75 143 496 13.2 12.2 28.5 0.5 

Washington 288 440 1,773 38.2 35.2 85.1 1.3 

Wyoming * 24 124 132 5.4 5.0 15.0 0.2 

* 9 GCVTC States 1,267 2,691 8,782 201 186 388 10 

13 WRAP States 1,699 3,740 11,309 282 260 586 12 

Table 5-4 shows the estimated annual average weekday off-road emissions by state and fuel 

type, based on the June 2000 NONROAD model. This table shows that diesel equipment 

dominates off-road NOx, PM, and SO2 emissions, while gasoline equipment dominates offroad VOC and CO emissions. 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc Figure 5-3a. 1996 Western states annual average off-road mobile VOC emissions, based on June 2000 NONR

Date Generated: 5/25/2001

Emissions (TPD)

0.0 to 1.01.0 to 5.05.0 to 10.010.0 to 5050.0 to 10

zero

Denver

Los Angeles

San Francisco

Boise

Las Vegas

Albuquerque

Salt Lake City

Portland

Seattle

Cheyenne

Phoenix

Billings

Spokane

Bismarck

Rapid City

Reno

Tucson
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc Figure 5-3b. 1996 Western states annual average off-road mobile NOx emissions, based on June 2000 NONR

Date Generated: 5/25/2001

Emissions (TPD)

0.0 to 1.01.0 to 10.010.0 to 50.050.0 to 100.0100.0 to 200.0zero

Denver

Los Angeles

San Francisco

Boise

Las Vegas

Albuquerque

Salt Lake City

Portland

Seattle

Cheyenne

Phoenix

Billings

Spokane

Bismarck

Rapid City

Reno

Tucson
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc Figure 5-3c. 1996 Western states annual average off-road mobile CO emissions, based on June 2000 NONRO

Date Generated: 5/25/2001

Emissions (TPD

0.0 to 11.0 to 110.0 to 100.0 to 500.0 to 

zero

Denver

Los Angeles

San Francisco

Boise

Las Vegas

Albuquerque

Salt Lake City

Portland

Seattle

Cheyenne

Phoenix

Billings

Spokane

Bismarck

Rapid City

Reno

Tucson
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc Figure 5-3d. 1996 Western states annual average off-road mobile PM2.5 emissions, based on June 2000 NON

Date Generated: 5/25/2001

Emissions (TPD)

0.0 to 0.10.1 to 1.01.0 to 5.05.0 to 10.010.0 to 15

zero

Denver

Los Angeles

San Francisco

Boise

Las Vegas

Albuquerque

Salt Lake City

Portland

Seattle

Cheyenne

Phoenix

Billings

Spokane

Bismarck

Rapid City

Reno

Tucson
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc Figure 5-3e. 1996 Western states annual average off-road mobile SO2 emissions, based on June 2000 NONR

Date Generated: 5/25/2001

Emissions (TPD)

0.0 to 1.01.0 to 5.05.0 to 10.010.0 to 2020.0 to 40

zero

Denver

Los Angeles

San Francisco

Boise

Las Vegas

Albuquerque

Salt Lake City

Portland

Seattle

Cheyenne

Phoenix

Billings

Spokane

Bismarck

Rapid City

Reno

Tucson
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc Figure 5-3f. 1996 Western states annual average off-road mobile NH3 emissions, based on June 2000 NONR

Date Generated: 5/25/2001

Emissions (TPD

0.0 to 00.01 to 0.1 to 00.5 to 11.0 to 1

zero

Denver

Los Angeles

San Francisco

Boise

Las Vegas

Albuquerque

Salt Lake City

Portland

Seattle

Cheyenne

Phoenix

Billings

Spokane

Bismarck

Rapid City

Reno

Tucson
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc Table 5-4. 1996 Western states total off-road emissions by state and fuel type (TPD), based on June 2000 NO

VOC NOX CO PM10 PM2.5 

State 

Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Dies

Arizona * 36 105 257 13 132 1,179 23.8 2.5 22.2 2.3 24California * 130 349 911 110 478 2,724 59.1 9.4 54.4 8.6 142Colorado * 35 87 322 13 121 962 24.6 2.0 22.8 1.8 60Idaho * 12 72 103 5 38 417 8.3 2.8 7.7 2.5 2Montana 17 40 198 3 38 245 11.5 1.4 10.7 1.3 30Nevada * 21 41 163 3 82 407 14.2 1.0 13.2 0.9 29New Mexico * 8 23 48 3 26 285 6.0 0.5 5.6 0.5 11North Dakota 25 23 245 3 46 197 16.0 0.5 14.7 0.5 46Oregon * 39 209 460 14 125 1,179 20.1 8.4 18.6 7.7 49South Dakota 19 19 157 3 35 191 12.1 0.5 11.1 0.4 34Utah * 17 58 136 7 63 432 11.8 1.4 10.9 1.3 28Washington 43 245 421 19 175 1,599 28.8 9.4 26.6 8.6 83Wyoming * 8 16 123 1 20 112 5.0 0.4 4.6 0.4 14* 9 GCVTC States 307 960 2,522 169 1,085 7,698 173 28 160 26 3813 WRAP States 411 1,288 3,543 197 1,379 9,930 241 40 223 37 57
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc 5-21 

Figure 5-4 shows the relative contribution of each off-road source category to 1996 emissions 

for each pollutant. The largest contributors to VOC emissions are lawn and garden 

equipment, logging equipment, and pleasure craft; these categories are mostly small gasoline 

engines. Lawn and garden equipment also contributes the largest portion of off-road CO 

emissions. The largest contributors to NOx, PM, and SO2 emissions are agricultural 

equipment and construction and mining equipment. Recreational equipment also accounts for 

a sizeable fraction of off-road NOx emissions. 

Figure 5-4. 1996 Western state annual average weekday off-road emissions by source 

category, based on the June 2000 NONROAD model. 

Table 5-5 lists 1996 weekday off-road emissions by state, based on the updated 

NONROAD2002 model. Emissions changes by state and pollutant from the June 2000 

NONROAD model to the NONROAD2002 model are shown in Table 5-6. (The June 2000 

model was used to estimate off-road emissions for WRAP states for the 1996 and 2018 air 

quality modeling; the off-road estimates in the WRAP TSD were estimated with the 2002 

model). The largest reductions are in SO2 emissions, for reasons discussed in Section 2.3.1. 

The reductions vary across states for a given pollutant because of changes in several of the 

population allocation methods from the June 2000 model to the NONROAD2002 model, and 

also changes in emissions by source category (as discussed in Section 2.3.1). 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

VOC NOx CO PM10 PM2.5 SO2 NH3

Percent of Total Emissions

Underground Mining Equipment

Recreational Equipment

Railroad Equipment

Pleasure Craft

Oil Field Equipment

Misc. Portable Equip

Military Tactical Equipment

Logging Equipment

Locomotives

Lawn and Garden Equipment

Industrial Equipment

Construction and Mining

Equipment

Commercial Marine

Commercial Equipment

Airport Ground Support Equipment

Aircraft

Agricultural Equipment
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc 5-22 

Table 5-5. 1996 Western states total off-road emissions by state and fuel type (TPD), based on 

NONROAD2002 model. 

State VOC NOx CO PM10 PM2.5 SO2 NH3 

Arizona * 135.04 182.13 1328.37 16.76 15.71 12.49 0.80 

California * 478.95 1021.14 3202.08 68.53 62.95 144.89 5.26 

Colorado * 113.29 248.32 1082.89 18.60 17.21 27.69 0.89 

Idaho * 48.73 70.51 358.92 7.54 6.95 8.83 0.28 

Montana 34.96 160.39 249.85 13.15 12.17 18.08 0.34 

Nevada * 52.13 111.23 473.63 8.28 7.66 11.60 0.31 

New Mexico * 33.53 38.77 324.73 5.53 5.18 5.25 0.19 

North Dakota 40.28 175.61 296.08 20.90 19.24 23.93 0.38 

Oregon * 113.39 412.01 922.12 16.02 14.78 28.41 1.14 

South Dakota 33.56 97.91 265.65 15.34 14.12 14.77 0.28 

Utah * 66.21 137.73 525.05 11.96 11.04 17.62 0.48 

Washington 156.96 357.82 1436.25 24.27 22.36 55.11 1.34 

Wyoming * 23.94 118.30 151.85 5.76 5.30 11.84 0.24 

* 9 GCVTC States 1,065 2,340 8,370 159 147 269 10 

13 WRAP States 1,331 3,132 10,617 233 215 381 12 

Table 5-6. Change in 1996 Western states total off-road emissions by state, June 2000 

NONROAD model to NONROAD2002 model. 

 VOC NOx CO PM10 PM2.5 SO2 NH3 

State Total Total Total Total Total Total Total 

Arizona -4% -32% 1% -36% -36% -50% -5% 

California 

Colorado -7% -26% 0% -30% -30% -55% -6% 

Idaho -42% -34% -21% -32% -32% -59% -15% 

Montana -39% -20% -12% 2% 2% -42% -16% 

Nevada -15% -33% -3% -46% -45% -60% -16% 

New Mexico 7% -24% 4% -15% -15% -56% 1% 

North Dakota -17% -29% 22% 26% 26% -49% -22% 

Oregon -54% -13% -29% -44% -44% -43% -5% 

South Dakota -12% -39% 17% 22% 22% -57% -24% 

Utah -12% -4% 6% -9% -9% -38% 5% 

Washington -46% -19% -19% -36% -36% -35% -1% 

Wyoming 0% -5% 15% 6% 6% -21% -2% 
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6.0 FUTURE YEARS ON-ROAD EMISSIONS 

This section describes the methods and MOBILE6 modeling inputs used to estimate on-road 

mobile source emissions for the future years 2003, 2008, 2013, and 2018. MOBILE6 

modeling by season was performed using the 1996 base year inputs, with updates as provided 

by State and local agencies. Vehicle miles traveled (VMT) growth rates were provided by 

most States. As for the 1996 base year, the California Air Resources Board provided seasonal 

outputs from EMFAC2000 modeling for all future years for all counties in California. 

6.1 VEHICLE MILES TRAVELED (VMT) ESTIMATES 

To calculate on-road emissions for the WRAP States for the years 2003, 2008, 2013, and 

2018, the 1996 VMT data used in the WRAP base year modeling were projected to these years 

using VMT growth factors developed from EPA’s Heavy Duty Vehicle (HDV) Rulemaking 

(EPA, 2000). In the HDV analysis, the base year 1996 VMT were projected to 2007, 2020, 

and 2030. For the WRAP work, VMT growth factors for 2003 were calculated using the 

HDV 1996 and 2007 VMT estimates; the 2008, 2013, and 2018 VMT growth factors were 

calculated using the HDV 2007 and 2020 VMT estimates. To accomplish this, the VMT data 

for the HDV analysis were interpolated to the years 2003, 2008, 2013, and 2018 at the SCC 

level by county. Growth factors for each of the four WRAP projection years were then 

calculated by dividing the projection year VMT (interpolated from the HDV analysis) at the 

county/SCC level by the 1996 VMT at the county/SCC level from the HDV analysis. These 

growth factors varied by State, metropolitan area, and vehicle type. These growth factors 

were then multiplied by the 1996 WRAP VMT data at the county/SCC level to project VMT 

to 2003, 2008, 2013, and 2018. 

Spreadsheets containing these growth factors by county and vehicle type were provided to the 

States for review. Two States updated these default data and provided their own growth 

factors for projecting VMT. Washington provided county-specific VMT growth factors from 

1996 to each of the projection years. Utah also provided VMT growth factors that varied by 

vehicle type; one set of factors was provided for all rural counties and one set was provided 

for each of the following urban counties: Davis County, Salt Lake County, Utah County, and 

Weber County. Utah did not provide growth factor data for Weber County for 2018, so the 

defaults calculated as discussed above were applied to Weber County in 2018. In addition, for 

these four Utah urban counties, growth factors were not calculated for light-duty diesel 

vehicles (LDDVs), light-duty diesel trucks (LDDTs), and motorcycles; the growth factors 

applied for these three vehicle types in these four counties were the default values calculated 

based on the HDV analysis as discussed above. 

The resulting annual VMT estimates are listed at the State level in Table 6-1. Table 6-1 also 

lists the resulting State-level VMT growth factors from 1996 to each of the four projection 

years; these growth rates are compared graphically in Figure 6-1. Overall, VMT growth rates 

range from about two to four percent per year. The seasonal VMT values for the projection 

years were calculated by multiplying the same set of growth factors as applied to the annual 

VMT to the 1996 seasonal WRAP VMT values. Table 6-2 shows the resulting seasonal VMT 

values for each State. 
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Table 6-1. Annual VMT for 1996 and projection years with growth factors from 1996. 

 Annual VMT (million miles) 

VMT Growth Factor from 

1996 to: 

State 1996 2003 2008 2013 2018 2003 2008 2013 2018 

Arizona 41,641 49,511 57,837 63,947 70,928 1.189 1.389 1.536 1.703

Colorado 38,653 44,668 51,091 55,949 61,468 1.156 1.322 1.447 1.590

Idaho 12,924 15,001 17,227 18,817 20,615 1.161 1.333 1.456 1.595

Montana 9,419 10,818 12,333 13,396 14,595 1.149 1.309 1.422 1.549

Nevada 13,666 16,915 20,281 22,751 25,608 1.238 1.484 1.665 1.874

New Mexico 21,450 25,186 29,162 32,091 35,425 1.174 1.360 1.496 1.651

North Dakota 6,723 7,402 8,214 8,799 9,453 1.101 1.222 1.309 1.406

Oregon 30,235 34,930 40,039 43,815 48,094 1.155 1.324 1.449 1.591

South Dakota 7,796 8,899 10,111 11,003 12,009 1.141 1.297 1.411 1.540

Utah 19,447 23,663 27,295 30,879 34,576 1.217 1.404 1.588 1.778

Washington 49,771 56,529 62,783 67,502 71,833 1.136 1.261 1.356 1.443

Wyoming 7,339 8,224 9,233 9,963 10,783 1.121 1.258 1.358 1.469

Figure 6-1. Vehicle miles traveled (VMT) growth rates by state, 1996 to 2018

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

1996 2000 2004 2008 2012 2016

VMT Growth Rate Relative to 1996

AZ

CO

ID

MT

NV

NM

ND

OR

SD

UT

WA

WY
 February 2004

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Table 6-2. Seasonal VMT (million miles per day) for 1996 and projection years. 

Season State 1996 2003 2008 2013 2018 

Spring Arizona 119.03 141.53 165.31 182.74 202.65

Spring Colorado 109.96 127.00 145.21 158.98 174.61

Spring Idaho 35.86 41.61 47.78 52.19 57.17 

Spring Montana 26.00 29.84 33.99 36.92 40.21 

Spring Nevada 38.94 48.23 57.85 64.89 73.04 

Spring New Mexico 60.01 70.41 81.47 89.64 98.93 

Spring North Dakota 18.54 20.40 22.64 24.25 26.05 

Spring Oregon 85.29 98.52 112.91 123.54 135.59

Spring South Dakota 21.47 24.50 27.83 30.28 33.05 

Spring Utah 55.37 67.13 77.37 87.48 97.91 

Spring Washington 144.12 163.70 181.80 195.48 208.01

Spring Wyoming 20.36 22.79 25.55 27.56 29.82 

Summer Arizona 129.59 153.94 179.74 198.61 220.18

Summer Colorado 120.48 139.01 158.88 173.86 190.87

Summer Idaho 40.51 46.94 53.86 58.78 64.36 

Summer Montana 29.69 34.01 38.71 42.01 45.72 

Summer Nevada 42.47 52.54 62.98 70.61 79.44 

Summer New Mexico 67.45 79.01 91.35 100.44 110.78

Summer North Dakota 21.13 23.21 25.73 27.54 29.56 

Summer Oregon 94.44 108.96 124.81 136.49 149.72

Summer South Dakota 24.56 27.96 31.73 34.49 37.61 

Summer Utah 60.10 73.19 84.41 95.49 106.93

Summer Washington 155.00 176.06 195.53 210.24 223.72

Summer Wyoming 23.20 25.92 29.05 31.31 33.85 

Fall Arizona 119.34 141.90 165.75 183.23 203.20

Fall Colorado 110.87 128.07 146.45 160.34 176.11

Fall Idaho 37.02 42.94 49.29 53.82 58.95 

Fall Montana 27.08 31.06 35.37 38.40 41.82 

Fall Nevada 39.09 48.40 58.04 65.11 73.28 

Fall New Mexico 61.69 72.35 83.70 92.06 101.59

Fall North Dakota 19.28 21.21 23.52 25.18 27.05 

Fall Oregon 86.68 100.11 114.71 125.50 137.72

Fall South Dakota 22.39 25.53 28.98 31.53 34.40 

Fall Utah 55.69 67.76 78.15 88.40 98.98 

Fall Washington 142.83 162.24 180.18 193.73 206.15

Fall Wyoming 21.16 23.66 26.54 28.62 30.96 

Winter Arizona 111.52 132.72 155.08 171.49 190.24

Winter Colorado 103.21 119.33 136.50 149.49 164.26

Winter Idaho 33.60 39.02 44.80 48.95 53.64 

Winter Montana 24.39 28.02 31.93 34.68 37.79 

Winter Nevada 36.48 45.23 54.27 60.89 68.56 

Winter New Mexico 56.25 66.05 76.44 84.13 92.88 

Winter North Dakota 17.38 19.15 21.25 22.77 24.47 

Winter Oregon 79.99 92.48 106.01 116.02 127.37

Winter South Dakota 20.14 23.01 26.14 28.46 31.07 

Winter Utah 51.82 62.86 72.46 81.92 91.69 

Winter Washington 132.36 150.34 166.97 179.53 191.04

Winter Wyoming 19.10 21.39 23.99 25.89 28.02 
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6.2 MOBILE6 AND PART5 INPUTS 

The September 10, 2001 draft version of EPA’s MOBILE6 model was used to calculate VOC, 

CO, and NOx emission factors, as well as VOC emission factors from vehicle refueling. The 

modified version of EPA’s PART5 model (described in Section 2) was used to calculate 

emission factors of SO2, NH3, and exhaust, brake wear, and tire wear emission factor 

components of PM10, PM2.5, EC10, EC2.5, OC10, and OC2.5. 

Default inputs for the MOBILE6 and PART5 modeling were based on input parameters used 

in EPA’s HDV Rulemaking and, where appropriate, included updates from the WRAP 1996 

on-road modeling. These parameters included the following: registration distribution files, 

Stage 2 inputs, Inspection and Maintenance (I/M) program inputs, anti-tampering program 

(ATP) program inputs, oxygenated fuel inputs, altitude designation, seasonal temperatures, 

seasonal RVP values, and seasonal diesel and gasoline fuel sulfur inputs. The default inputs 

were sent to the states for review and update. 

6.2.1 Speed 

The speeds modeled for the projection years in both the MOBILE6 and the PART5 input files 

were the same as those modeled for the WRAP States in the 1996 base year. These speeds 

modeled in both 1996 and the projection years were national defaults by roadway type and 

vehicle type (as shown in Table 3-1), with area-specific speed inputs provided for the States of 

Colorado and Utah and the counties of Clark and Washoe in Nevada. 

6.2.2 Registration Distributions

The default inputs for registration distributions were those used in the WRAP 1996 base year 

modeling. The MOBILE6 defaults (which are based on national 1996 registration data) were 

used in the counties for which no updates were provided. The State or county-specific 

registration distributions applied in the 1996 modeling for Colorado, Arizona, Oregon, and 

Clark County, Nevada were also applied in all of the projection years. An updated 

registration distribution was provided for modeling the State of Washington in the projection 

years; this updated registration distribution was based on registration data from 2000, with a 

single distribution applied to the entire State for all four projection years, and replaced the 

State-specific registration distribution provided by Washington for 1996. 

6.2.3 Seasonal Temperatures 

The seasonal temperatures modeled in the projection years were the same as the seasonal 

temperatures modeled in the 1996 base year on-road modeling, with no changes. The default 

1996 temperature data were derived by averaging the maximum and minimum average daily 

temperatures for each of the three months within a season using actual 1996 climatic data 

taken from a single station within each State. 1996 temperature data were provided by 
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Arizona, Colorado, New Mexico, Oregon, Utah, and Washington for the base year modeling, 

and these temperatures were also used in the projection year modeling. 

6.2.4 Seasonal Fuel Data 

The seasonal fuel data inputs include gasoline Reid vapor pressure (RVP), gasoline fuel sulfur 

content, and diesel fuel sulfur content. All of these parameters were modeled using inputs 

representative of the conditions in the month at the midpoint of each season (i.e., January, 

April, July, and October). The RVP values modeled in 1996 were the default RVP values for 

the projection years. These included State-provided 1996 RVP inputs for Arizona; Colorado; 

Clark and Washoe Counties, Nevada; Oregon; Utah; and Washington. For the projection 

years, RVP updates were provided by Washington for the counties of King, Pierce, and 

Snohomish. In each of these counties, the summer RVP was changed from the base year 

value of 8.5 psi to 7.8 psi. 

No changes were made to the default diesel and gasoline fuel sulfur contents in the 2003 

projection year. However, in each of the remaining projection years, the gasoline fuel sulfur 

content was set to 30 ppm in all counties to reflect the sulfur levels of the Federal Tier 2/low 

sulfur rulemaking. In addition, the diesel fuel sulfur content was set to 15 ppm in the 2008, 

2013, and 2018 projection years to reflect the diesel fuel sulfur levels of the Federal HDV 

rulemaking. These gasoline and diesel fuel sulfur contents for the 2008, 2013, and 2018 

projection years were applied in all four seasons. Both the gasoline fuel sulfur content and 

diesel fuel sulfur content are inputs to the revised PART5 model used for this analysis. The 

gasoline fuel sulfur content is also an input to the MOBILE6 model. 

6.2.5 Altitude 

Altitude inputs to the MOBILE6 modeling were the same in the projection years as in the 1996 

base year modeling. 

6.2.6 Control Programs 

The effects of Federal on-road control programs are included in the MOBILE6 and modified 

PART5 models. The Federal control programs that started in or after 1996 that are treated as 

defaults in the MOBILE6/PART5 modeling are: National Low Emission Vehicle (NLEV) 

emission standards starting with the 2001 model year; Tier 2 emission standards starting with 

the 2004 model year; two phases of new heavy duty vehicle emission standards—one starting 

in the 2004 model year and the other starting in the 2007 model year; onboard diagnostics; and 

the Supplemental Federal Test Procedure (SFTP) rule. As discussed above, the low sulfur 

gasoline fuel corresponding with the Tier 2 emission standards and the low sulfur diesel fuel 

corresponding with the heavy-duty vehicle 2007 emission standards were also modeled 

throughout the WRAP region. Also modeled as part of the default conditions in MOBILE6 

are estimates of excess NOx emissions resulting from the use of defeat devices in heavy-duty 

diesel vehicles as well as the provisions to offset these excess emissions through early pullahead of the 2004 heavy-duty diesel emission standards and through low emission rebuilds of 
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existing engines. All of these control programs were modeled using the MOBILE6 defaults 

and the modified PART5 model defaults, with no additional user input. 

In addition to the national on-road control programs, several area-specific control programs 

were included in the MOBILE6 modeling for the projection years. These include I/M and 

ATP programs, oxygenated fuel programs, and Stage II refueling control programs. These 

were modeled as follows: 

• I/M and ATP Programs – County coverage of the I/M and ATP programs did not 

change from the 1996 base year modeling to the projection years. The counties with 

I/M and/or ATP programs are listed in Table 3-2. The States of Colorado, Oregon, 

Utah, and Washington provided updates to the I/M or ATP program inputs for the 

projection years. For the remaining States with I/M or ATP programs modeled in the 

1996 base year modeling (Arizona, Idaho, New Mexico, and Nevada), the same I/M 

and ATP program inputs were modeled in the projection years. It should be noted, 

however, that these programs did already include projection years in the inputs, with 

OBD testing starting with the 1996 model year. In both the base year modeling and 

the projection year modeling, the I/M programs in Washington were only applied to a 

fraction of the VMT in each of the five counties with an I/M program. These fractions 

that the I/M emission factors apply to were provided by Washington, and emission 

factors without I/M programs applied were modeled for the remainder of the VMT in 

each of these counties. 

• Oxygenated Fuel Programs – Table 3-3 lists the counties that were modeled with 

oxygenated fuel in the 1996 base year, as well as the corresponding inputs used to 

model the oxygenated fuel program in each county with MOBILE6. Several changes 

were made to these base year oxygenated fuel inputs for the projection years. For Utah 

County, Utah, the oxygen content of the oxygenated fuel was changed from 3.5 percent 

to 2.7 percent. For the counties with oxygenated fuel in Oregon, the oxygenated fuel 

program was eliminated from the 2008, 2013, and 2018 projection years. In Clark, 

King, Pierce, and Snohomish Counties, Washington, the oxygenated fuel program was 

discontinued after 1996, so no oxygenated fuel was modeled for these counties in any 

of the projection years. 

• Stage II Refueling Controls – In the 1996 base year modeling, Stage II controls were 

applied in the following counties: Maricopa County, AZ; Clark and Washoe Counties, 

NV; Multnomah County, OR; and Clark, King, and Pierce Counties, WA. The only 

changes made for the projection year modeling were to add Stage II controls in 

Clackamas County and Washington Counties, in Oregon. The MOBILE6 inputs for 

modeling Stage II controls applied to these two counties were the same as those applied 

to Multnomah County in the 1996 base year modeling—a 95 percent Stage II control 

efficiency for light-duty gasoline vehicles and trucks and an 80 percent Stage II control 

efficiency for heavy-duty gasoline vehicles. 
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6.3 SEASONAL EMISSION CALCULATIONS 

Using the inputs discussed above, MOBILE6 and modified PART5 model input files were 

created for each unique combination of inputs within a given State. In some cases, a separate 

input file was created for each county in the state. One such example is Arizona, where a 

separate registration distribution was provided for each county. On the other extreme, only 

one input file was created for the entire State of Wyoming, as there were no variations in 

inputs among the various counties in Wyoming. Most of the other States fell somewhere 

between these two extremes, with several input files needed to account for the different control 

programs and inputs throughout the State. Input files were created separately for each of the 

four projection years. Within each MOBILE6 input file, separate scenarios were created to 

model each of the four seasonal temperature and fuel conditions at each of the required speeds. 

Once all of these input files were created, the MOBILE6 and modified PART5 models were 

run to obtain emission factor outputs. Emission factor outputs from MOBILE6 were obtained 

at the daily level of detail for the standard eight vehicle types. MOBILE6 emission factors for 

VOC, NOx, CO, and refueling VOC were extracted for each scenario. The modified PART5 

model provided PM10, PM2.5, SO2, NH3, elemental carbon (EC), and organic carbon (OC) 

emission factors. The emission factors for PM10, PM2.5, EC, and OC were generated 

separately for each of: exhaust, brake wear, and tire wear. 

Emissions for all of the above pollutants were then calculated by matching the VMT at the 

county, vehicle type, roadway type, and seasonal level of detail to the emission factor 

representing the same conditions. Once emissions were calculated for all counties, seasons, 

vehicle types, and roadway types, the emissions were then aggregated by light- and heavy-duty 

vehicles, and by diesel and gasoline fueled vehicle types, for each county and season in each 

of the four projection years. 

6.4 PROCESSING OF CALIFORNIA DATA 

CARB provided on-road emissions estimates from EMFAC2000 model runs for all four future 

years by vehicle class, county, and season. EC/OC emissions were estimated from the PM10

and PM2.5 emissions using the methods described in Section 2.2.5. 

6.5 GENERATION OF SMOKE FILES 

SMOKE files were generated for the 2018 future year on-road emissions only for input to 

visibility modeling. The 2018 on-road SMOKE files were generated in the same manner as 

the 1996 on-road SMOKE files as described in Section 3.5. The SMOKE files for the twelve 

non-California states include emissions by county, roadway type, and vehicle type, using the 

SCCs listed in Table 3-4. 

As was the case for 1996, CARB did not provide emissions by roadway type for the future 

years, and the SMOKE files generated for California on-road emissions are therefore 

categorized by county and vehicle type only. The SCCs in the SMOKE files for the California 

on-road emissions are shown in Table 3-5. 
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7.0 FUTURE YEARS WRAP OFF-ROAD INVENTORY 

This section describes the methods for estimating off-road equipment emissions inventories for 

the future years 2003, 2008, 2013, and 2018. Seasonal average weekday emissions were 

estimated for carbon monoxide (CO), volatile organic compounds (VOC), nitrogen oxide 

(NO), nitrogen dioxide (NO2), sulfur oxides (SOx), coarse and fine particulates (PM10 and 

PM2.5), ammonia (NH3), and elemental and organic carbon in coarse and fine particulate 

emissions (EC10, EC2.5, OC10, and OC2.5). 

EPA’s NONROAD model was used to estimate emissions from the following source 

categories:

• Agricultural Equipment 

• Airport Ground Support Equipment 

• Commercial Equipment 

• Construction and Mining Equipment 

• Industrial Equipment 

• Lawn and Garden Equipment 

• Locomotives 

• Logging Equipment 

• Oil Field Equipment 

• Pleasure Craft 

• Railroad Equipment 

• Recreational Equipment 

• Underground Mining Equipment 

As discussed in Section 2, the June 2000 draft NONROAD model was originally used to 

estimate future year off-road emissions for the WRAP states; these estimates were used in the 

2018 air quality modeling. Subsequently, the updated NONROAD2002 model (with revisions 

as discussed in Section 2.3.1) was used to estimate emissions for these source categories; the 

off-road equipment emissions estimates in the WRAP Technical Support Document (TSD) are 

from these updated NONROAD2002 modeling runs. 

Aircraft, locomotive, and commercial marine emissions were estimated separately using EPA 

accepted methodology. The California Air Resources Board (CARB) provided all nonroad 

emissions estimates for all source categories for all future years. 

7.1 DEVELOPMENT OF POPULATION GROWTH FACTORS FOR OFF-ROAD 

SOURCE CATEGORIES 

Off-road equipment activity growth factors were developed for all non-California states, rather 

than using the default growth factors in EPA’s NONROAD model. We first discuss the 

reasons why the default NONROAD growth factors were not used, and then provide the data 

used to develop alternative growth factors by state and source category. The indicators used 

to estimate the growth factors follow the Emission Inventory Improvement Program projection 
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guidance (EIIP, 1999). The default and alternative growth factors are compared graphically 

and in tabular format. Growth factors are also presented for the three categories of equipment 

not included in the NONROAD model – aircraft, commercial marine, and locomotive. All 

growth factors are provided as relative to the 1996 base year. 

7.1.1 NONROAD Model Default Activity Growth Factors 

Figure 7-1 shows the default growth factors currently used in the NONROAD model. These 

are the same as in the most recent EGAS projection model, EGAS4. The NONROAD growth 

rates are very high for some categories, especially for projecting well into the future. The 

NONROAD growth rates were developed using a small number of consecutive years of 

national equipment populations from Power Systems Research (PSR); the methods used by 

PSR to estimate equipment populations are proprietary and therefore not available. For earlier 

draft versions of the NONROAD model, EPA had developed exponential growth rates from 

the PSR population data, leading to unreasonably high growth especially in longer-term future 

years. More recently, however, EPA revised their methodology to using linear rather than 

exponential growth regimes. While the latest growth rates are more reasonable, they are still 

based on undocumented data. In addition, the PSR population data are national-level and 

therefore lead to national-level growth factors by equipment category, so they will not 

accurately reflect state-to-state growth differences. The PSR-based growth rates are based on 

anticipated sales in equipment populations, and equipment population is not always the best 

indicator of activity or use (e.g., crude production is a more direct activity indicator for oil 

field equipment). 

7.1.1.1 Agricultural Equipment

The EIIP projection guidance suggests agricultural equipment population, land use, Gross 

State Product (GSP), earnings, or employment as possible indicators of activity in this 

category. We investigated both cropland acreage and farm GSP data found at the Government 

Information Sharing Project (Oregon State University) and Bureau of Economic Analysis 

(BEA) web sites (http://govinfo.library.orst.edu/ag-stateis.html and 

http://www.bea.doc.gov/bea/regional/gsp/, respectively). Note that GSP in the discussion 

below refers to ‘chained’ GSP, which has been adjusted for inflation by normalizing to the 

1996 ‘current dollar’ GSP. 

Analysis of the cropland data resulted in declines for most states and slight growth (<0.5% 

per year) in the remaining states. The available data were at the state level for only three 

years: 1987, 1992, and 1997. Activity growth in this category should approximately parallel 

that of human population. The cropland data fail to reflect this because they do not account 

for increases in productivity due to advances in horticulture, emergence of agricultural 

corporations, etc. 

From GSP data, trends were developed based upon linear regressions of state-specific data 

from 1977 through 1999. Use of this indicator inherently leads to slightly high growth in 

equipment activity because GSP does not take into account such things as government 

subsidies, costs, and commodity prices. They show approximately ~2-4% growth annually. 

Nonetheless, these data more closely resemble the population trend and are close to the 
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NONROAD default, except for North Dakota. Figures 7-2a and 7-2b show the raw data and 

the resulting growth factors, respectively. 

7.1.1.2 Aircraft and Ground Support Equipment (GSE)

For commercial aircraft and ground support equipment (GSE), the EIIP prescribes the use of 

landing and takeoff (LTO) data as the activity indicator. LTO data by state are available on 

the Federal Aviation Administration (FAA) web page at 

http://www.apo.data.faa.gov\faatafall.HTM. Both historic (pre-2000) and FAA-projected 

data are available. The latter were used directly, along with 1996 values to estimate future 

year growth factors; the resulting growth rates are therefore not linear between 1996 and 

2003. 

Figure 7-3a shows the historical FAA LTO data by state, annually from 1976 to 1999. Figure 

7-3b shows the LTO growth factors by state and compares these to the NONROAD default, 

which were based upon PSR GSE population estimates. Clearly, there is a significant 

difference, with the NONROAD national growth rate significantly higher than the FAA LTO 

forecast growth rates. 

For general and military aviation, the corresponding GSP data from the BEA were used. The 

available data covered the calendar years 1977 through 1999. Linear regressions were 

performed using these data with the results as shown in Figures 7-4b and 7-4b and Figures 7-

5a and 7-5b. 

7.1.1.3 Commercial and Industrial Equipment

For these groups of equipment, total GSP data (from BEA) were used to derive growth rates. 

The EIIP advocates the use of equipment population, industrial/commercial GSP, earnings, or 

employment. Total GSP was used because from the available data, it was difficult to deduce 

the fractions of commercial vs. industrial equipment. Figure 7-6a shows these 

industrial/commercial GSP trends. Data were available for 1977 through 1999, and all years 

were used in the regressions. The resulting growth factors are shown in Figure 7-6b. For 

most states, the total GSP growth rates by state are similar to the PSR-based growth factors, 

notably more so with the industrial trend; the fact that they agree lends credibility to both. 

7.1.1.4 Commercial Marine Vessels

Freight tonnage data from the US Army Corps of Engineers (available at 

http://www.iwr.usace.army.mil/ndc/wcsc/wcsc.htm) were used to determine the growth 

factors for commercial marine vessels, in accordance with EIIP guidance. Only three of the 

WRAP states (apart from California) have appreciable commercial marine activity: Oregon, 

Washington, and Idaho. The data available cover the period from 1990 through 1999 and are 

provided by port as well as by waterway segment. Summaries by state were also available, 

but these data only extend from 1996 to 1999. It was deemed better to use more data in the 

regressions so the detailed data set was selected. However these data required careful 

consideration to prevent double counting. In general, the total for a state was determined by 

adding tonnage at each port within that state to tonnage reported by specific waterway 

segments either within or bordering the state. The totals were checked against the state 
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summaries for years for which these are available. From this, the segments to be included in 

the sum were ascertained. Figure 7-7a shows the annual freight tonnage for each of the three 

states, and Figure 7-7b shows the estimated growth factors. 

Idaho presented a problem in that the detailed data did not agree with the state totals. The 

growth trend shown in Figure 7-7b for Idaho was derived from the 1996-1999 state 

summaries. However, there is very little commercial marine activity in Idaho, so the size of 

the growth rate is unimportant in terms of overall commercial marine emissions. 

7.1.1.5 Construction

Construction gross state product data from the BEA served as the basis for construction 

equipment growth factor estimates; these are shown in Figure 7-8a. These data, covering 

1977 through 1999, were analyzed using linear regressions. The resulting growth rates, 

shown in Figure 7-8b, are slightly above or slightly below PSR-based growth. These future 

year growth rates are influenced by the high growth in construction in the 1990s. The 

construction industry is strongly influenced by such cycles so the use of these growth 

projections seems reasonable. 

However, there are three states (Wyoming, North Dakota, and Montana) that show rapidly 

decreasing construction activity. Figure 7-8a shows the underlying historical data, which 

suggest that these states’ results are strongly influenced by data in the early 1980s. We 

suspect that these data show such high fluctuations because these states’ real construction GSP 

is small and that a change would produce significant effects on a percentage basis. The 

construction emissions in these states are relatively small, and the moderate growth trend 

associated with the average of the other western states was used to avoid having negative 

growth factors. 

7.1.1.6 Lawn and Garden Equipment

The EIIP states that human population can be used as an activity indicator for lawn and garden 

equipment. Human population data are available on-line from the Census Bureau 

(http://www.census.gov/population/projections/state/stpjpop.txt). Single-family and double 

housing units can also be used as an activity indicator, but those trends closely match human 

population. Since the full 2000 Census results were not available at the time this work was 

performed, projected population data from the Census were used, along with 1990 Census 

data; these populations by state are shown in Figure 7-9a. Linear regressions using these data 

yielded the trends shown in Figure 7-9b. These projections are lower than that predicted by 

the PSR equipment population data in the far future though not by considerably large amounts. 

7.1.1.7 Locomotives and Railroad Equipment

According to the EIIP, fuel consumption and/or rail ton-miles are the preferred surrogates for 

locomotive emissions and emissions from railroad equipment. We examined national railroad 

fuel consumption data available from the Association of American Railroads (AAR), and also 

converted projected freight ton-miles data from the Energy Information Administration (EIA) 

to fuel consumption (Figure 7-10a). The former data set covered 1990 through 2000, and the 

latter provided the estimates for 2000 through 2018. The national fuel consumption was then 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-5 

allocated to the states using ratios derived from 2000 EIA state-specific fuel sales data. The 

resulting growth rates by state are shown in Figure 7-10b. 

7.1.1.8 Logging Equipment

For logging equipment, BEA GSP data for lumber and wood products were evaluated. These 

data were available for 1977 through 1999 and show sustained high activity during the early 

1980s through early 1990s. These GSP data are related to public land management, and were 

high during the Republican administrations of Reagan and Bush and conservative during the 

Clinton tenure. Thus GSP may not directly reflect the production and consumption of lumber 

products. As a result, softwood production data by region located at the Western Wood 

Products Association’s website (http://www.wwpa.org/) were used as an alternative indicator; 

these data are shown in Figure 7-11a. Use of these data requires the assignment of each state 

to one of the regions. Washington and Oregon were assigned to the Coast Region; and the 

Inland Region was used for other states. The resulting regional trends are shown in 

Figure 7-11b in comparison to the PSR logging equipment growth rate. 

7.1.1.9 Oil Field Equipment

Domestic crude oil production volumes were used to derive the growth factors for oil field 

equipment. The data were obtained from the Department of Energy’s (DOE) Energy 

Information Administration’s (EIA) web site 

(http://www.eia.doe.gov/oiaf/aeo/supplement/index/html). The values were reported by region 

with Washington and Oregon in the West Coast region and the other ten states in the Rocky 

Mountain region, as shown in Figure 7-12a. For this reason, only two trends were derived 

and each is applicable to all states in the region. Data for the years 1999 to 2020 were 

projected/estimated by the EIA, while the 1996 base-year value was obtained from a separate 

EIA report found at 

http://www.eia.doe.gov/pub/oil_gas/petroleum/data_publications/petroleum_supply_monthly/h

istorical/html/details.html. The projected values were from the EIA’s National Energy 

Modeling System and are not linear. The resulting growth factors are shown in Figure 7-12b. 

Oil and gas GSP was also examined as a potential indicator, but the data were highly variable 

and the regression fits were poor. 

7.1.1.10 Pleasure Craft

For projecting recreational marine equipment populations, the EIIP suggests using equipment 

population or human population. Recreational marine registration data were initially 

evaluated. Registration data were obtained by state from the National Marine Manufacturer 

Association (http://www.nmma.org/facts/index.asp?bhcp=1) for the years 1995 through 1999. 

We also obtained 1990 data from a Recreational Boating and Fishing Foundation report (at 

http://www.rbff.org/research/Lit-Review-Final.pdf). Although these data basically account 

for equipment population, they show higher growth rates than the national PSR-based growth 

for some states. This may just be a more detailed geographic distribution of the national data. 

Of more concern is that the resulting growth in this category was higher than human 

population in some instances. Given that the data are only available for five years, mostly in 

the booming latter half of the 1990s, we believe that the resulting trend is not representative of 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-6 

long-term growth. For that reason, human population was used as the growth indicator for 

pleasure craft; these growth rates shown in Figure 7-13 along with the PSR default growth rate 

for pleasure craft. For most states, the human population growth rates are greater than the 

PSR default pleasure craft growth rate. 

7.1.1.11 Recreational Equipment

Human population is recommended as the surrogate for activity of recreational 

equipment/vehicles. These indicators were derived as discussed in the lawn and garden 

section above. For this category, human population forecasts are on average higher than the 

growth rates based on PSR equipment populations, as shown in Figure 7-14. 

7.1.1.12 Underground Mining Equipment

For this category, BEA mining GSP forecasts were used. These data; shown in Figure 7-15a, 

were obtained from the BEA and cover the years 1977 through 1999. Linear regressions were 

performed on the data for all states except North Dakota; the results are shown in Figure 7-

15b. Examination of the raw GSP data for North Dakota (Figure 15a) showed a high level of 

sustained activity during the 1980s, an immediate sharp drop, and more recently, a growth 

trend comparable with those of the other states. For North Dakota, the average of the other 

states was used to forecast underground mining equipment population growth. The result, as 

shown in Figure 7-15b, is slightly higher than the PSR-based growth rate estimate. 

7.1.1.13 Summary of growth factors

Table 7-1 lists the surrogates used to project activity in each off-road equipment category; 

Table 7-2 lists the growth factors by source category and state. 

Table 7-1. Recommended growth indicators. 

Source Category Growth Indicator 

Agricultural Agricultural GSP 

Aircraft/GSE 

LTO, Air Transportation GSP, 

Military GSP 

Commercial & Industrial Total GSP 

Commercial Marine Cargo Tonnage 

Construction Construction GSP 

Lawn & Garden Human Population 

Locomotives & Railroad Railroad Fuel Consumption 

Logging Softwood Production 

Oil Field Domestic Crude Oil Production 

Pleasure Craft Human Population 

Recreational Human Population 

Underground Mining Mining GSP 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-7 

Table 7-2. Activity growth factors by state and source category. 

Source Category State 2003/1996 2008/1996 2013/1996 2018/1996 

Agricultural Equipment Arizona 1.194 1.333 1.472 1.610 

 Colorado 1.189 1.324 1.459 1.594 

 Idaho 1.200 1.343 1.486 1.629 

 Montana 1.146 1.249 1.353 1.457 

 Nevada 1.210 1.361 1.511 1.661 

 New Mexico 1.265 1.455 1.644 1.834 

 North Dakota 1.056 1.096 1.136 1.177 

 Oregon 1.232 1.397 1.562 1.728 

 South Dakota 1.164 1.281 1.398 1.514 

 Utah 1.236 1.405 1.573 1.742 

 Washington 1.231 1.396 1.561 1.725 

 Wyoming 1.192 1.329 1.466 1.603 

 PSR National 1.179 1.307 1.435 1.562 

Commercial Aircraft & GSE Arizona 1.206 1.326 1.450 1.571 

 Colorado 1.220 1.313 1.405 1.498 

 Idaho 1.138 1.267 1.395 1.523 

 Montana 1.158 1.203 1.248 1.294 

 Nevada 1.117 1.244 1.372 1.499 

 New Mexico 1.108 1.167 1.226 1.285 

 North Dakota 1.209 1.272 1.337 1.399 

 Oregon 1.111 1.214 1.317 1.420 

 South Dakota 1.107 1.134 1.160 1.186 

 Utah 1.113 1.210 1.306 1.402 

 Washington 1.151 1.209 1.268 1.326 

 Wyoming 1.406 1.439 1.472 1.506 

 PSR National - GSE 1.431 1.743 2.056 2.369 

General Aviation Arizona 1.401 1.687 1.973 2.259 

 Colorado 1.261 1.447 1.633 1.820 

 Idaho 1.387 1.664 1.940 2.217 

 Montana 1.325 1.557 1.788 2.020 

 Nevada 1.375 1.643 1.912 2.180 

 New Mexico 1.372 1.637 1.903 2.168 

 North Dakota 1.321 1.550 1.779 2.008 

 Oregon 1.399 1.684 1.968 2.253 

 South Dakota 1.370 1.634 1.899 2.163 

 Utah 1.396 1.680 1.963 2.246 

 Washington 1.303 1.520 1.737 1.954 

 Wyoming 1.314 1.538 1.761 1.985 

Military Aviation Arizona 1.014 1.025 1.035 1.045 

 Colorado 1.014 1.025 1.035 1.046 

 Idaho 0.976 0.959 0.942 0.925 

 Montana 1.027 1.046 1.065 1.084 

 Nevada 0.992 0.987 0.981 0.975 

 New Mexico 1.012 1.020 1.028 1.037 

 North Dakota 0.988 0.980 0.971 0.962 

 Oregon 1.046 1.079 1.111 1.144 

 South Dakota 0.948 0.911 0.874 0.837 

 Utah 1.083 1.142 1.201 1.261 

 Washington 1.094 1.162 1.229 1.297 

 Wyoming 1.070 1.120 1.170 1.219 

Commercial Marine Idaho 1.622 2.066 2.510 2.954 

 Oregon 0.970 0.948 0.927 0.905 

 Washington 0.939 0.896 0.853 0.809 

Construction Equipment Arizona 1.117 1.200 1.284 1.367 

 Colorado 1.132 1.227 1.322 1.416 

 Idaho 1.198 1.339 1.480 1.621 

 Montana 1.145 1.248 1.351 1.454 

 Nevada 1.295 1.506 1.717 1.927 

 New Mexico 1.034 1.059 1.084 1.108 

 North Dakota 1.145 1.248 1.351 1.454 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-8 

Table 7-2. Activity growth factors by state and source category (continued).

Source Category State 2003/1996 2008/1996 2013/1996 2018/1996 

 Oregon 1.195 1.335 1.474 1.613 

 South Dakota 1.044 1.075 1.106 1.137 

 Utah 1.186 1.319 1.452 1.585 

 Washington 1.100 1.171 1.243 1.314 

 Wyoming 1.145 1.248 1.351 1.454 

PSR National – 

Construction 1.165 1.283 1.401 1.519 

Commercial & Industrial 

Equipment Arizona 1.268 1.459 1.650 1.841 

 Colorado 1.205 1.352 1.499 1.646 

 Idaho 1.218 1.374 1.530 1.686 

 Montana 1.082 1.141 1.199 1.258 

 Nevada 1.266 1.456 1.646 1.836 

 New Mexico 1.199 1.341 1.483 1.625 

 North Dakota 1.084 1.144 1.204 1.264 

 Oregon 1.195 1.335 1.475 1.614 

 South Dakota 1.169 1.289 1.409 1.530 

 Utah 1.221 1.379 1.537 1.694 

 Washington 1.191 1.328 1.464 1.600 

 Wyoming 1.075 1.129 1.183 1.236 

PSR National – 

Industrial 1.198 1.339 1.480 1.621 

PSR National - Light 

commercial 1.289 1.496 1.703 1.910 

Lawn & Garden Equipment Arizona 1.167 1.247 1.313 1.382 

 Colorado 1.135 1.195 1.242 1.289 

 Idaho 1.189 1.269 1.328 1.381 

 Montana 1.110 1.157 1.192 1.224 

 Nevada 1.245 1.316 1.350 1.388 

 New Mexico 1.136 1.222 1.304 1.392 

 North Dakota 1.040 1.062 1.083 1.103 

 Oregon 1.105 1.167 1.227 1.284 

 South Dakota 1.079 1.109 1.129 1.148 

 Utah 1.163 1.243 1.308 1.365 

 Washington 1.105 1.178 1.250 1.320 

 Wyoming 1.126 1.206 1.281 1.343 

 PSR National 1.169 1.290 1.411 1.532 

Locomotive & Railroad Equipment Arizona 0.766 0.834 0.890 0.956 

 Colorado 0.707 0.771 0.822 0.883 

 Idaho 1.600 1.744 1.861 1.999 

 Montana 1.317 1.435 1.532 1.645 

 Nevada 2.519 2.745 2.930 3.147 

 New Mexico 0.451 0.492 0.525 0.564 

 North Dakota 1.589 1.731 1.848 1.984 

 Oregon 1.393 1.518 1.620 1.740 

 South Dakota 1.931 2.104 2.245 2.412 

 Utah 1.079 1.176 1.255 1.348 

 Washington 0.565 0.616 0.658 0.706 

 Wyoming 1.579 1.721 1.837 1.973 

 PSR National - Railway 1.174 1.298 1.423 1.547 

Logging Equipment Arizona 0.736 0.547 0.358 0.169 

 Colorado 0.736 0.547 0.358 0.169 

 Idaho 0.736 0.547 0.358 0.169 

 Montana 0.736 0.547 0.358 0.169 

 Nevada 0.736 0.547 0.358 0.169 

 New Mexico 0.736 0.547 0.358 0.169 

 North Dakota 0.736 0.547 0.358 0.169 

 Oregon 1.204 1.349 1.495 1.640 

 South Dakota 0.736 0.547 0.358 0.169 

 Utah 0.736 0.547 0.358 0.169 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-9 

Table 7-2. Activity growth factors by state and source category (concluded).

Source Category State 2003/1996 2008/1996 2013/1996 2018/1996 

 Washington 1.204 1.349 1.495 1.640 

 Wyoming 0.736 0.547 0.358 0.169 

 PSR National 1.308 1.527 1.747 1.967 

Oil Field Equipment Arizona 1.035 0.872 0.845 0.954 

 Colorado 1.035 0.872 0.845 0.954 

 Idaho 1.035 0.872 0.845 0.954 

 Montana 1.035 0.872 0.845 0.954 

 Nevada 1.035 0.872 0.845 0.954 

 New Mexico 1.035 0.872 0.845 0.954 

 North Dakota 1.035 0.872 0.845 0.954 

 Oregon 0.868 0.894 0.984 1.036 

 South Dakota 1.035 0.872 0.845 0.954 

 Utah 1.035 0.872 0.845 0.954 

 Washington 0.868 0.894 0.984 1.036 

 Wyoming 1.035 0.872 0.845 0.954 

 PSR National 1.016 1.027 1.039 1.050 

Pleasure Craft Arizona 1.167 1.247 1.313 1.382 

 Colorado 1.135 1.195 1.242 1.289 

 Idaho 1.189 1.269 1.328 1.381 

 Montana 1.110 1.157 1.192 1.224 

 Nevada 1.245 1.316 1.350 1.388 

 New Mexico 1.136 1.222 1.304 1.392 

 North Dakota 1.040 1.062 1.083 1.103 

 Oregon 1.105 1.167 1.227 1.284 

 South Dakota 1.079 1.109 1.129 1.148 

 Utah 1.163 1.243 1.308 1.365 

 Washington 1.105 1.178 1.250 1.320 

 Wyoming 1.126 1.206 1.281 1.343 

 PSR National 1.058 1.099 1.141 1.182 

Recreational Equipment Arizona 1.167 1.247 1.313 1.382 

 Colorado 1.135 1.195 1.242 1.289 

 Idaho 1.189 1.269 1.328 1.381 

 Montana 1.110 1.157 1.192 1.224 

 Nevada 1.245 1.316 1.350 1.388 

 New Mexico 1.136 1.222 1.304 1.392 

 North Dakota 1.040 1.062 1.083 1.103 

 Oregon 1.105 1.167 1.227 1.284 

 South Dakota 1.079 1.109 1.129 1.148 

 Utah 1.163 1.243 1.308 1.365 

 Washington 1.105 1.178 1.250 1.320 

 Wyoming 1.126 1.206 1.281 1.343 

 PSR National 1.058 1.099 1.141 1.182 

Underground Mining Equipment Arizona 1.304 1.521 1.738 1.955 

 Colorado 1.157 1.270 1.382 1.495 

 Idaho 1.055 1.094 1.134 1.173 

 Montana 1.062 1.106 1.151 1.195 

 Nevada 1.376 1.645 1.914 2.183 

 New Mexico 1.104 1.178 1.253 1.327 

 North Dakota 1.180 1.308 1.437 1.565 

 Oregon 1.112 1.192 1.273 1.353 

 South Dakota 1.159 1.272 1.386 1.499 

 Utah 1.208 1.356 1.504 1.653 

 Washington 1.331 1.567 1.803 2.039 

 Wyoming 1.111 1.190 1.269 1.348 

 PSR National 1.165 1.283 1.401 1.519 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-10 

Figure 7-1. EPA NONROAD model default growth factors.

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

1995 2000 2005 2010 2015 2020

Year

Growth Factor

GSE

Construction

Farm

Industrial

Lawn & Garden

Light Commercial

Logging

Railway

Recreational

Oil Field
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-11 

Figure 7-2a. Farm Gross State Product index by state. 

Figure 7-2b. Agricultural equipment growth rates by state and default PSR growth rate. 

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

1995 2000 2005 2010 2015 2020

Year

Growth Factor (relative to 1996)

Arizona

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming

PSR National

0

20

40

60

80

100

120

140

160

180

200

1975 1980 1985 1990 1995 2000

Year

GSP index (1996=100)

Arizona

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

W ashington

W yoming
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-12 

Figure 7-3a. FAA total landing and take-off (LTO) data by state.

Figure 7-3b. Commercial aircraft and ground support equipment (GSE) growth rates by state 

and default PSR GSE growth rate. 

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

1995 2000 2005 2010 2015 2020

Year

Growth Factor (relative to 1996)

Arizona

Colordao

Idaho

Montana

North Dakota

New Mexico

Nevada

Oregon

South Dakota

Utah

Washington

Wyoming

PSR National

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

4000000

4500000

1975 1980 1985 1990 1995 2000

Year

LTO

AZ

CO

ID

MT

ND

NM

NV

OR

SD

UT

WA

WY
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-13 

Figure 7-4a. Gross state product index for air transportation SIC. 

Figure 7-4b. General aviation growth rates by state. 

0

20

40

60

80

100

120

140

160

180

1975 1980 1985 1990 1995 2000

Year

GSP index (1996=100)

Arizona

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

1995 2000 2005 2010 2015 2020

Year

Growth Factor (relative to 1996)

Arizona

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-14 

Figure 7-5a. Gross state product index for Federal military SIC. 

Figure 7-5b. Military aviation growth rates by state. 

0

20

40

60

80

100

120

140

160

180

1975 1980 1985 1990 1995 2000

Year

GSP index (1996=100)

Arizona

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1995 2000 2005 2010 2015 2020

Year

Growth Factor (relative to 1996)

Arizona

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-15 

Figure 7-6a. Total gross state product index by state. 

Figure 7-6b. Commercial and industrial equipment growth rates by state and default PSR 

growth rate. 

0

20

40

60

80

100

120

140

1975 1980 1985 1990 1995 2000

Year

GSP index (1996=100)

Arizona

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming

1.0

1.2

1.4

1.6

1.8

2.0

2.2

1995 2000 2005 2010 2015 2020

Year

Growth Factor (relative to 1996)

Arizona

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming

PSR National - Industrial

PSR National - Light commercial
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-16 

Figure 7-7a. Commercial marine annual freight tonnage by state. 

Figure 7-7b. Commercial marine growth rates by state. 

-

20,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

140,000,000

1989 1991 1993 1995 1997 1999

Year

Freight (tons)

Idaho

Oregon

Washington

0.8

1.3

1.8

2.3

2.8

1995 2000 2005 2010 2015 2020

Year

Growth Factor (relative to 1996)

Idaho

Oregon

Washington
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-17 

Figure 7-8a. Construction GSP index by state. 

Figure 7-8b. Construction equipment growth rates by state and PSR default growth rate. 

0

50

100

150

200

250

1975 1980 1985 1990 1995 2000

Year

GSP index (1996=100)

US

Arizona

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming

Growth in Construction Activity

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2.0

1995 2000 2005 2010 2015 2020

Year

Growth Factor (relative to 1996)

Arizona

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming

PSR National
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-18 

Figure 7-9a. Human population by state. 

Figure 7-9b. Lawn and garden equipment growth rates by state and default PSR growth rate. 

-

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

6,000,000

7,000,000

8,000,000

9,000,000

1985 1990 1995 2000 2005 2010 2015 2020 2025 2030

Year

Population

Arizona

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1995 2000 2005 2010 2015 2020

Year

Growth Factor (relative to 1996)

Arizona

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming

PSR National
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-19 

Figure 7-10a. Railroad fuel consumption by state. 

Figure 7-10b. Locomotive and railroad equipment growth rates by state and default PSR 

growth rate. 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

1995 2000 2005 2010 2015 2020

Year

Growth Factor (relative to 1996)

Arizona

Colorado

Idaho

Montana

North Dakota

New Mexico

Nevada

Oregon

South Dakota

Utah

Washington

Wyoming

PSR Railway

Railroad Fuel Consumption by State

-

20,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

140,000,000

160,000,000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Fuel Consumption (Gallons)

AZ

CO

ID

MT

ND

NM

NV

OR

SD

UT

WA

WY
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-20 

Figure 7-11a. Softwood supply by region. 

Figure 7-11b. Logging equipment growth rates by state and default PSR growth rate. 

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Supply (Million Board Feet)

Coast Region

Inland Region

California Redwood Region

Southern Pine Region

Other U.S.

Total

0.0

0.5

1.0

1.5

2.0

2.5

1995 2000 2005 2010 2015 2020

Year

Growth Factor

Coast Region (OR, WA)

Inland Region (AR, CO, ID, MT, NV, NM, ND, SD, UT, WY)

PSR National
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-21 

Figure 7-12a. Domestic crude oil production by region.

Figure 7-12b. Oil field equipment growth rates by state and default PSR growth rate. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1995 2000 2005 2010 2015 2020

Year

Production (million barrels/day)

Rocky Mountain

West Coast

0.80

0.85

0.90

0.95

1.00

1.05

1.10

1995 2000 2005 2010 2015 2020

Year

Growth Factor (relative to 1996)

Rocky Mountain (AZ,CO,ID,MT,ND,NM,NV,SD,UT,WY)

West Coast (OR,WA)

PSR National
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-22 

Figure 7-13. Pleasure craft growth rates by state and default PSR growth rate. 

Figure 7-14. Recreational equipment growth rates by state and default PSR growth rate.

1.00

1.05

1.10

1.15

1.20

1.25

1.30

1.35

1.40

1.45

1995 2000 2005 2010 2015 2020

Year

Growth Factor (relative to 1996)

Arizona

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming

PSR National

1.0

1.1

1.1

1.2

1.2

1.3

1.3

1.4

1.4

1.5

1995 2000 2005 2010 2015 2020

Year

Growth Factor (relative to 1996)

Arizona

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming

PSR National
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-23 

Figure 7-15a. Mining GSP index by state. 

Figure 7-15b. Underground mining equipment growth rates by state and default PSR growth 

rate. 

0

50

100

150

200

250

1975 1980 1985 1990 1995 2000

Year

GSP index (1996=100)

Arizona

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

1995 2000 2005 2010 2015 2020

Year

Growth Factor (relative to 1996)

Arizona

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming

PSR National
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-24 

7.2. NONROAD MODEL INPUTS 

The future year modeling using EPA’s NONROAD model followed the same methodology as 

for the 1996 base year. Both the June 2000 NONROAD model and the later NONROAD2002 

model were run for each county for each season in the years 2003, 2008, 2013, and 2018. 

No State or local agencies provided updates to substitute for NONROAD default inputs for 

equipment population or activity data. Fuel and temperatures parameters used in the future 

year MOBILE6 modeling, as described in Section 6, were used in the NONROAD runs. This 

included setting gasoline sulfur levels to 30 ppm for all counties in years 2008, 2013, and 

2018 to reflect the requirements of the Federal Tier 2/low sulfur rulemaking. Seasonal 

differences in the parameters that affect emissions from off-road equipment were taken into 

account. These parameters include temperatures and fuel specifications (vapor pressure and 

oxygenate content). The NONROAD model was run to obtain seasonal average weekday 

emissions for spring, summer, winter, and fall for most counties. However, for counties that 

have fuel-based control programs that start mid-season, the model was run separately for each 

of the three months in the season and the monthly results were then averaged to derive the 

seasonal average weekday emissions. 

Future year off-road diesel fuel sulfur levels are also required for NONROAD modeling. For 

all of the future year June 2000 version NONROAD runs, and for the NONROAD2002 model 

runs for year 2003 only, the off-road diesel fuel sulfur levels were the same as those used in 

the 1996 NONROAD modeling; these were obtained from regional survey data published by 

the National Institute for Petroleum and Energy Research (NIPER) (Dickson and Sturm, 1996) 

and are listed by county in Appendix A. For the NONROAD2002 runs for future years 2008, 

2013, and 2018, all diesel fuel sulfur levels were set to 15 ppm at the request of WRAP staff, 

in anticipation of the proposed standards for off-road compression-ignition equipment and low 

sulfur fuel, which were not released until after the modeling was completed. Note that 

although the NONROAD2002 model runs include low sulfur off-road diesel fuel, they do not 

include emissions reductions corresponding to the new standards that were proposed in April 

2003. In addition, the proposed rule (referred to as the Tier 4 emissions standards) is for fuel 

sulfur levels to be reduced to 500 ppm in 2008 and then 15 ppm in 2010. 

Only Maricopa County provided an update to the default diesel fuel sulfur levels; the future 

year Maricopa County off-road nonroad diesel sulfur levels were set to the same as on-road 

diesel sulfur level per local regulation. However, in the June 2000 NONROAD model runs, 

Maricopa County off-road diesel fuel sulfur was set mistakenly set to 15 ppm for year 2018 

only. 

For all future year NONROAD modeling, the model was run with zero growth rates, and the 

growth rates as described above were instead applied during post-processing steps. 

The NONROAD model estimates county-level emissions of VOC, NOx, CO, PM10, PM2.5, 

and SO2. Emissions were summed across equipment types by source category and by either 

gasoline or diesel fuel groupings. CNG and LPG-powered equipment, which account for only 

a small fraction of emissions, were lumped into the gasoline category.
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-25 

7.3 AIRCRAFT EMISSIONS PROJECTIONS

Future year aircraft emissions for 2003, 2008, 2013, and 2018 were estimated based on 1996 

base year emissions by type of aircraft. Emissions were estimated for three types of aircraft: 

commercial aircraft, general aviation, and military aviation. Emission projections were 

generated for the twelve WRAP; CARB provided California aircraft emissions projections for 

all future years. 

Future year emissions projections were estimated by applying the state-level growth factors 

(discussed in Section 7.1.1.2) to the 1996 aircraft emissions data for each of the four future 

years. These state-level growth factors are shown in Figures 7-3b to 7-5b for commercial 

aircraft, general, and military aviation, respectively; the numeric values are listed in Table 7-

2. 

The International Civil Aviation Organization (ICAO) promulgated NOx and CO emission 

standards for commercial aircraft, exempting general aviation and military engines from the 

rule (ICAO, 1998c). The Regulatory Support Document (RSD) for the ICAO standard 

indicates that all applicable engines covered by the CO standard and the first-stage NOx

standard currently meet the standard. A second-stage NOx standard is 20 percent lower than 

the first-stage standard. NOx emission reductions for the second-stage standard are small, and 

the majority of engines are already meeting this standard. As such, no reductions in emission 

factors from 1996 base year values were assumed for commercial aircraft. 

The base year aircraft emissions were available in two forms: by fuel type and by aviation 

type. The emissions by fuel type were used in the base year emissions inventory. The 

emissions by aviation type involved a less exact methodology and were developed for the 

purpose of growing the emissions based on commercial, military, and general aviation growth 

factors. Because of complications in methodology these two base year aircraft databases 

yielded different emissions totals and could not be combined. 

First, future year emissions by aviation type were estimated, using the appropriate growth 

factors. Then, in order to estimate future year emissions by fuel type, several assumptions 

were made. Commercial and military aviation emissions were assumed to be entirely Jet-A 

and JP-8 fuel-based (considered equivalent of diesel fuel in this work). General aviation 

emissions were assumed to be entirely AvGas-powered (gasoline equivalent in this work). 

Using the base and future year emissions by aviation type summed across these assumed fueltype categories, new growth factors were calculated for each county and pollutant. These new 

growth factors were applied to the base year emissions by fuel type to obtain the appropriate 

future year estimates. 

7.4 COMMERCIAL MARINE EMISSIONS PROJECTIONS 

Emission standards have been developed for all new international and relatively small-sized 

US flagged commercial marine engines. International standards were developed for all 

international ships under the MARPOL agreements (MARPOL, 1997). EPA has finalized 

emission standards for new US flagged compression-ignition (i.e. diesel) marine engines with 

displacements of under 30 liters per cylinder (EPA, 1999a). These engines are generally no 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-26 

larger than locomotive engines and typically rated at or less than 5,000 hp individually, though 

multiple engines can be used on a given vessel. The international and EPA federal regulations 

cover propulsion and auxiliary marine engines. 

7.4.1 International Regulations 

The proposed MARPOL regulations were developed under the aegis of the International 

Maritime Organization (IMO). Under these regulations, engines are tested under three 

different loads and the three tests are averaged to compare with an overall NOx emission 

standard. For new vessels constructed after January 1, 2000, the NOx emission standard is 

related to the rated engine speed through the following relationship: 

Engine Speed <130 rpm; 17.0 g/kW-hr 

130 rpm  Engine Speed < 2,000 rpm; 45 * n-0.2 g/kW-hr 

Engine Speed  2,000 rpm; 9.8 g/kW-hr 

However, these emission regulations have not been ratified, and at this time it is difficult to 

predict if the regulations will be in practice for new ships built after 2000. EPA is currently 

assuming that these regulations are effective with the 2000 build year, so the benefits were 

counted in this work. 

7.4.2 EPA Emission Standards 

EPA has promulgated emission standards for domestic ships with engines smaller than 30 

liters per cylinder that incorporate the international standards and have lower emission 

standards beginning with some types of new engines in 2004; these standards are shown in 

Table 7-3. The international standards, if implemented, are considered ‘Tier 1’ emission 

controls. Regardless of international implementation, EPA is assuming that US flagged vessels 

will comply with the international regulations as proposed. 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-27 

Table 7-3. EPA commercial marine primary exhaust emission standards (g/kW-hr).

7.4.3 Incorporating Emission Standards 

For ocean-going merchant vessels, which primarily use engines with displacements above 30 

l/cylinder, there are three types of engines used for propulsion (slow speed [<200 rpm], 

medium [~550 rpm], and steamships boilers driving steam turbines) and two types used for 

auxiliary generation (medium speed [500 –1000 rpm] and steamships). The international 

emissions regulations for slow speed engines used in ocean-going vessels may produce small 

NOx emission reductions (~5% depending upon the engine speed) when all ships operating 

meet the standard because the baseline emission rate was assumed to be 17 g/kW-hr, but no 

emission reduction was estimated in this work. No emission reduction is expected for larger 

medium speed engines because these medium speed engines were estimated to already produce 

lower emissions on average than the standard. Steamships are gradually being replaced 

because motorships are more energy efficient, and are estimated to have lower NOx emissions 

levels than motorships, so their replacement may lead to higher NOx emissions. 

For harbor vessels (tugs, ferries, dredges, and fishing vessels), emissions reductions depend 

on the type of the engine used for propulsion and auxiliary power. EPA defined three 

categories of engines: Category 1 for engine with displacements under 5 l/cylinder, Category 2 

for engines of 5-30 l/cylinder displacement, and Category 3 for engines above 30 l/cylinder. 

However, it was not entirely clear what type of engine (as defined by EPA) were used in these 

different vessel categories, and these different types of engines can be used for the same 

purpose. The Category 3 engines are likely only used for larger ocean-going merchant vessels. 

The harbor vessel activity data included only the total installed power for vessels, so an 

estimate of the fraction of emissions by Category made using the total installed power of 3,996 

per vessel (or 1490 kW per engine with two engines assumed to be on-board each vessel using 

the power level distinction between Caterpillar 3500 [Category 1] and 3600 [Category 2] 

series engines); these estimates are shown in Table 7-4. Engine types used in fishing, dredge, 

and Columbia tug vessels were assumed as shown in Table 7-4. 

Tier 

Model 

Year* 

THC + NOx 

g/kW-hr 

CO 

G/kW-hr 

PM 

g/kW-hr 

Power  37 kW Tier 2 2005 7.5 5.0 0.40 

0.9  disp. < 1.2 Tier 2 2004 7.2 5.0 0.30 

1.2  disp. < 2.5 Tier 2 2004 7.2 5.0 0.20 

2.5  disp. < 5.0 Tier 2 2007 7.2 5.0 0.20 

5.0  disp. < 15 Tier 2 2007 7.8 5.0 0.27 

15  disp. < 20 Tier 2 2007 8.7 5.0 0.50 

15  disp. < 20 Tier 2 2007 9.8 5.0 0.50 

20 disp. < 25 Tier 2 2007 9.8 5.0 0.50 

25  disp. < 30 Tier 2 2007 11.0 5.0 0.50 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-28 

Table 7-4. Fraction of emissions (HC, NOx, PM) by engine type associated with harbor 

vessels.

Engine Type 

Vessel Types Category 2 Category 1 

 < 3996 hp per vessel 

Fishing 1 

Dredges 1 

Columbia Tugs 1 

Ferry 0.79, 0.80, 0.79 0.21, 0.20, 0.21 

Puget Tugs 1 

EPA (1999b) provided estimates of the emissions reduction by vessel type; these are shown in 

Table 7-5. Intermediate years were determined by linear interpolation. 

Table 7-5. Emission reductions by engine type (from EPA, 1999b).

Engine Type Pollutant 2000 2005 2010 2020 2030 

Category 2 NOx 0.5% - 9.5% 23.2% 32.4% 

HC 0% 3% 12% 24% 26% 

Category 1 NOx 0% 5% 16% 31% 33% 

PM 0% 4% 16% 34% 37% 

7.5 LOCOMOTIVE EMISSIONS PROJECTIONS 

Future year locomotive emissions for 2003, 2008, 2013, and 2018 were estimated based on 

1996 base year emission estimates. State-level growth factors for each of the four years were 

applied to the 1996 locomotive emission factors data to estimate the emissions. These growth 

factors, listed in Table 7-2 and shown in Figure 7-10b, are based on railroad fuel consumption 

data.

The Regulatory Support Document (RSD) for locomotive emission standards contains national 

emission projections up to the year 2040 for HC, NOx, CO and PM. For their emission 

reduction analysis, EPA assumed that future year fuel consumption would remain constant at 

1996 levels. It was thought that improvements in the fuel efficiency of locomotives would 

offset the increase in the amount of freight hauled; therefore, no growth was assumed (EPA, 

1997). 

The national emissions in the RSD account for future, phased-in controls that will primarily 

reduce NOx and PM emissions, and HC emissions as well. Because no growth was assumed, 

emissions reductions can be estimated based on the percent change in emissions from the base 

year to a given projection year. The 1996 base year emission factors for NOx, HC, and PM10

were reduced by the percentages for each projection year shown in Table 7-6. 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 7-29 

Table 7-6. Percent reduction applied to 1996 base year locomotive emission factor.

VOC emissions were estimated by applying 1.053 to the HC emissions. PM2.5 emissions were 

estimated as 92 percent of PM10 emissions. 

7.6 CALIFORNIA DATA PROCESSING 

CARB provided future year projections for all off-road source categories by county and by 

season. Two sets of data files were provided. First, CARB generated summer, winter, and 

annual emissions for 2003, 2008, 2013, and 2018 based on their unreleased OFFROAD 

model. Spring and autumn emissions were estimated as the average of the summer and winter 

emissions. 

CARB also provided an emissions data set derived from the California Emission Forecasting 

System (CEFS) for recreational equipment, locomotives, aircraft, and commercial marine 

source categories. The years with available data were 2000, 2005, 2010, 2015, and 2020. 

Emissions years 2003, 2008, 2013, and 2018 were estimated using linear interpolation. The 

CEFS data also only included winter, summer, and annual emissions. Spring and autumn 

emissions were estimated as the average of the winter and summer emissions for each source 

category. 

An additional correction had to be applied to all California diesel equipment, per instructions 

from CARB. Federal regulations require the use of low sulfur diesel fuels starting in 2006 

for on-road vehicles. However, unlike other states, California off-road diesel fuels are 

required to meet the same standards as on-road fuels. CARB instructed us to apply a factor of 

0.11 to all off-road diesel equipment SO2 emissions for calendar years 2008, 2013, and 2018 

to account for the use of low-sulfur diesel fuel starting in 2006; this factor is the ratio of the 

mandated low sulfur level (15 ppm) to the current diesel fuel sulfur level of 141ppm (Agrawal, 

2001). Low sulfur diesel also affects PM emissions. CARB estimated that the use of low 

sulfur diesel fuel would result in a four percent reduction in PM emissions (Agrawal, 2001); 

this factor was thus applied to all relevant emissions for 2008, 2013, and 2018. 

7.7 GENERATION OF SMOKE FILES 

SMOKE files were created for year 2018 off-road emissions for input into air quality 

modeling. The SMOKE files were generated only for the 2018 emissions estimates based on 

the June 2000 NONROAD model runs, following the same methodology as was used for the 

base year, described in Section 4.6. 

Projection 

Year

NOx PM HC 

2003 15.3 0 0.2 

2008 37.9 11.9 11 

2013 43.5 19.6 18 

2018 47.7 25.8 23.6 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc 8-1 

8.0 MOBILE SOURCE EMISSIONS TRENDS, 1996 TO 2018 

This section shows trends in emissions from the 1996 base year to the four future years (2003, 

2008, 2013, and 2018) for on-road and off-road mobile source emissions, developed using the 

methodologies described in the previous sections. A large number of tables and graphs were 

prepared for each year and are available separately; in this section we provide only the key 

summary tables and trends graphs. More detailed spreadsheets are available that tabulate onroad and off-road emissions for all states for all years, and by state, county, and source 

category. The projection year inventories include all promulgated on-road and off-road mobile 

source controls, as discussed in Section 2. 

8.1 ON-ROAD MOBILE SOURCE EMISSIONS TRENDS 

Tables 8-1a through 8-1d show the estimated annual average weekday on-road emissions by 

state and fuel type for the four future years 2003, 2008, 2013, and 2018, respectively. The 

relatively small emissions from CNG and LPG fueled vehicles have been included with 

gasoline vehicle emissions in these tables. The same information for the 1996 base year was 

provided in Table 5-1. As was the case for the base year, diesel equipment dominates off-road 

NOx, and PM emissions; while gasoline equipment dominates off-road VOC, CO, SO2, and 

NH3 emissions. 

Figures 8-1a through 8-1d show the changes over time in VOC, NOX, SO2, and PM2.5 onroad mobile source emissions, respectively, by state and in total for the GCVTC 9-state 

region, for the 1996 through 2018 timeframe. On-road VOC, NOX, and PM2.5 emissions in 

the 9-state region are projected to decline consistently from 1996 to 2018 as a result of fleet 

turnover; i.e., older vehicles will be scrapped and replaced with newer vehicles that meet 

tighter emissions standards (for both light-duty and heavy-duty vehicles). On-road SO2

emissions will decrease significantly between 2003 and 2008 with the introduction of low 

sulfur gasoline in 2004 and low sulfur diesel fuel in 2006; from 2008 onward SO2 emissions 

will increase as the fuel sulfur levels stay constant but fuel consumption increases.
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc Table 8-1a. 2003 Western states total on-road emissions by state and fuel type (TPD). 

VOC NOx CO PM10

^

PM2.5

^

S

State 

Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel 

Arizona * 9 327 125 221 52 3,492 8.0 4.5 7.5 3.1 2.6 California * 41 1,108 604 990 164 10,695 19.3 40.2 16.8 24.4 6.3 Colorado * 7 203 147 185 40 3,057 8.9 4.8 8.3 3.5 2.8 Idaho * 2 76 45 63 13 1,052 2.9 1.5 2.7 1.1 1.2 Montana 2 55 35 48 9 875 2.2 1.2 2.1 0.9 0.8 Nevada * 3 84 45 68 20 1,080 3.4 1.5 3.2 1.1 0.9 New Mexico * 7 130 76 103 41 1,736 6.2 2.5 5.9 1.8 1.6 North Dakota 1 41 23 34 6 653 1.5 0.8 1.4 0.6 0.5 Oregon * 6 228 98 163 34 2,778 5.5 3.5 5.1 2.5 1.9 South Dakota 1 46 29 40 7 751 1.8 1.0 1.7 0.7 0.7 Utah * 5 123 103 97 38 1,811 6.4 2.7 6.1 2.0 1.9 Washington 10 447 127 298 49 5,456 7.8 6.5 7.3 4.8 2.5 Wyoming * 1 42 27 37 7 672 1.7 0.9 1.6 0.7 0.7 * 9 GCVTC States 82 2,321 1,270 1,928 408 26,373 62 62 57 40 20 13 WRAP States 97 2,911 1,485 2,348 479 34,109 76 72 70 47 24 

^

 PM emissions include exhaust, brake wear, and tire wear emissions.
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc Table 8-1b. 2008 Western states total on-road emissions by state and fuel type (TPD). 

VOC NOx CO PM10

^

PM2.5

^

State 

Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel 

Arizona * 8 234 96 161 45 2,484 7.5 5.1 6.9 3.5 0.California * 34 801 465 723 141 7,569 14.3 45.3 12.1 27.8 0.Colorado * 6 151 103 148 33 2,433 9.1 5.3 8.5 3.9 0.Idaho * 2 52 33 46 10 802 2.7 1.7 2.5 1.3 0.Montana 2 37 25 35 7 672 2.1 1.3 1.9 1.0 0.Nevada * 3 63 35 51 17 786 3.3 1.8 3.0 1.2 0.New Mexico * 6 89 55 77 35 1,317 5.9 2.8 5.5 2.0 0.North Dakota 1 27 16 24 5 490 1.4 0.9 1.3 0.7 0.Oregon * 5 156 77 115 25 2,050 4.9 4.1 4.5 3.0 0.South Dakota 1 31 21 29 6 575 1.7 1.1 1.6 0.8 0.Utah * 5 84 77 70 32 1,357 6.0 3.0 5.6 2.2 0.Washington 8 295 93 203 40 3,855 6.6 7.1 6.0 5.3 0.Wyoming * 1 28 19 26 5 507 1.5 1.0 1.4 0.7 0.* 9 GCVTC States 69 1,658 961 1,418 344 19,305 55 70 50 46 1 13 WRAP States 81 2,048 1,116 1,709 402 24,897 67 81 61 53 2 ^

 PM emissions include exhaust, brake wear, and tire wear emissions.
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc Table 8-1c. 2013 Western states total on-road emissions by state and fuel type (TPD). 

VOC NOx CO PM10

^

PM2.5

^

State 

Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Dies

Arizona * 7 180 61 124 23 2,120 5.1 5.5 4.5 3.7 California * 25 579 306 525 126 5,303 9.6 50.0 7.8 30.8 Colorado * 5 108 54 105 17 2,214 4.7 5.7 4.1 4.2 Idaho * 2 37 19 32 5 709 1.8 1.9 1.6 1.3 Montana 1 27 15 25 4 598 1.4 1.4 1.2 1.0 Nevada * 2 46 22 38 9 696 2.0 1.9 1.8 1.3 New Mexico * 5 65 33 55 18 1,187 3.8 3.0 3.4 2.2 North Dakota 1 19 9 17 2 428 0.9 1.0 0.8 0.7 Oregon * 4 117 50 82 14 1,788 3.5 4.4 3.0 3.1 South Dakota 1 22 12 21 3 511 1.1 1.2 1.0 0.9 Utah * 4 62 46 50 16 1,254 3.9 3.3 3.5 2.4 Washington 5 213 59 154 18 3,252 4.1 7.3 3.6 5.4 Wyoming * 1 20 11 19 3 447 1.0 1.1 0.9 0.8 * 9 GCVTC States 54 1,213 602 1,030 231 15,719 36 77 31 50 13 WRAP States 63 1,494 697 1,246 258 20,509 43 88 37 58 

^

 PM emissions include exhaust, brake wear, and tire wear emissions.
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc Table 8-1d. 2018 Western states total on-road emissions by state and fuel type (TPD).

VOC NOx CO PM10

^

PM2.5

^

State 

Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Dies

Arizona * 6 146 38 92 15 1,989 3.9 6.0 3.3 4.0 California * 17 437 181 397 120 3,820 6.5 54.4 4.8 33.7 Colorado * 5 80 25 76 9 2,125 3.3 6.3 2.7 4.6 Idaho * 2 30 11 24 3 673 1.4 2.0 1.1 1.5 Montana 1 22 8 19 2 567 1.0 1.5 0.9 1.1 Nevada * 2 36 13 28 6 668 1.5 2.2 1.3 1.5 New Mexico * 5 54 18 41 11 1,144 2.8 3.3 2.4 2.4 North Dakota 1 15 5 13 1 401 0.7 1.0 0.5 0.8 Oregon * 4 88 32 61 9 1,678 2.6 4.7 2.1 3.4 South Dakota 1 18 7 16 2 485 0.9 1.3 0.7 0.9 Utah * 4 51 25 36 10 1,220 2.8 3.7 2.4 2.7 Washington 4 171 36 110 12 2,957 3.0 7.7 2.4 5.7 Wyoming * 1 17 6 14 2 421 0.8 1.1 0.6 0.8 * 9 GCVTC States 45 939 347 770 185 13,741 26 84 21 54 13 WRAP States 53 1,165 402 927 203 18,151 31 95 25 63 

^

 PM emissions include exhaust, brake wear, and tire wear emissions.
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc 8-6 

Figure 8-1a. GCVTC States annual average on-road VOC emissions, 1996 - 2018. 

0

500

1000

1500

2000

2500

3000

3500

Annual Average Day (Tons Per Day)

Arizona 411 337 242 187 152 

California 1,792 1,149 834 604 454 

Colorado 298 210 157 113 85 

Idaho 111 78 54 39 32 

Nevada 108 87 66 48 38 

New Mexico 194 137 95 70 59 

Oregon 316 234 161 121 92 

Utah 172 128 88 65 54 

Wyoming 66 44 29 21 17 

1996 2003 2008 2013 2018
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc 8-7 

Figure 8-1b. GCVTC States annual average on-road NOx emissions, 1996 - 2018. 

0

500

1000

1500

2000

2500

3000

3500

4000

Annual Average Day (Tons Per Day)

Arizona 384 346 257 186 129 

California 2,159 1,594 1,188 831 578 

Colorado 374 332 252 159 101 

Idaho 130 109 79 52 35 

Nevada 118 113 86 60 40 

New Mexico 208 179 132 87 60 

Oregon 313 261 192 132 93 

Utah 218 200 147 96 61 

Wyoming 78 64 45 30 20 

1996 2003 2008 2013 2018
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc 8-8 

Figure 8-1c. GCVTC States annual average on-road SO2 emissions, 1996 - 2018. 

0

10

20

30

40

50

60

70

Annual Average Day (Tons Per Day)

Arizona 8.0 9.6 1.6 1.8 2.1 

California 12.0 10.7 5.3 5.6 6.0 

Colorado 9.6 11.1 1.4 1.6 1.8 

Idaho 3.4 4.8 0.5 0.5 0.6 

Nevada 2.0 2.0 0.6 0.6 0.7 

New Mexico 5.7 7.6 0.8 0.9 1.0 

Oregon 9.2 10.5 1.1 1.3 1.4 

Utah 4.4 5.5 0.8 0.9 1.0 

Wyoming 1.9 2.6 0.3 0.3 0.3 

1996 2003 2008 2013 2018
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc 8-9 

Figure 8-1d. GCVTC States annual average on-road PM2.5 emissions, 1996 - 2018. 

0

10

20

30

40

50

60

70

80

90

100

Annual Average Day (Tons Per Day)

Arizona 11.2 8.8 8.2 5.8 4.6 

California 36.7 32.7 30.6 28.4 27.5 

Colorado 13.7 10.2 10.4 6.1 4.8 

Idaho 4.1 3.2 3.1 2.1 1.7 

Nevada 4.1 3.7 3.5 2.3 1.8 

New Mexico 6.6 6.7 6.3 4.3 3.3 

Oregon 8.2 6.3 5.9 4.4 3.6 

Utah 8.0 7.1 6.7 4.7 3.7 

Wyoming 2.4 1.9 1.8 1.2 1.0 

1996 2003 2008 2013 2018
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc 8-10 

8.2 OFF-ROAD MOBILE SOURCE EMISSIONS TRENDS 

As described in Section 2, off-road emissions trends were estimated using two versions of 

EPA’s NONROAD model. The June 2000 draft NONROAD model was the latest version at 

the time this work was initiated, and emissions were originally estimated using that model. In 

December 2002, EPA confidentially released an updated version of the model, 

NONROAD2002, to WRAP and other air quality planning agencies; that model was publicly 

released in June 2003. This model update does not incorporate the effects of the September 

2002 off-road equipment regulations for large (> 25 hp) spark-ignition equipment, 

recreational equipment, and compression-ignition pleasure craft above 50 hp; ENVIRON 

obtained updated NONROAD2002 model input files from EPA that incorporate these new 

exhaust standards (the effects of the evaporative standards are not yet modeled). Emissions 

for traditional off-road equipment were then estimated with the NONROAD2002 model, with 

the updates for the September 2002 rulemaking. 

In April 2003, EPA proposed further (Tier 4)emissions controls for large off-road diesel 

equipment, along with a requirement for low sulfur off-road diesel fuel to enable the emissions 

control technology. Nonroad diesel sulfur was proposed to be 500 ppm in 2008 and 15 ppm in 

2010. The NONROAD2002 modeling was completed prior to the release of these proposed 

new standards. While the modeling did not include the anticipated emissions effects of the 

Tier 4 technology, the diesel fuel sulfur levels were set to 15 ppm in the modeling for years 

2008, 2013, and 2018, in anticipation of the proposed rulemaking. For the Technical Support 

Document, WRAP staff adjusted the emissions estimates to back out the effects of low sulfur 

diesel based on state average diesel fuel sulfur relative to the 15 ppm level input to the 

NONROAD2002 model. 

Tables 8-2a through 8-2d show the estimated annual average weekday off-road emissions by 

state and fuel type for each of the four future years, based on the June 2000 NONROAD 

model. Comparable emissions totals for the 1996 base year were shown in Table 5-4. As was 

the case for the base year, diesel equipment dominates off-road NOx, PM, and SO2 emissions, 

while gasoline equipment dominates off-road VOC and CO emissions. The individual and 

cumulative state total off-road emissions trends from 1996 to 2018 for NOx, PM10, and SO2 

for the nine GCVTC states are shown in Figures 8-2a through 8-2c, respectively. These tables 

and plots show that off-road equipment NOx and VOC emissions are predicted to continually 

decline from 1996 to 2018 despite growth in equipment populations. PM10 and PM2.5 

emissions are decreasing from 1996 though 2013, and relatively flat from 2013 to 2018. SO2 

emissions increase from 1996 to 2003, then drop in 2008 because of the introduction of low 

sulfur gasoline in 2004, then increase through 2018 as equipment populations and fuel 

consumption increase. 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc Table 8-2a. 2003 Western states total off-road emissions by state and fuel type (TPD), based on the June 2000 N

VOC NOX CO PM10

^

PM2.5

^

State 

Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Dies

Arizona * 28 77 212 17 110 1,273 19.9 2.5 18.7 2.3 2California * 124 299 833 118 435 2,706 57.4 12.5 52.8 11.5 15Colorado * 26 69 256 17 99 1,069 20.6 2.0 19.1 1.8 6Idaho * 10 58 105 6 33 421 7.5 2.5 7.0 2.2 2Montana 14 33 199 3 36 241 10.9 1.3 10.1 1.2 3Nevada * 20 31 219 4 79 411 14.5 1.0 13.4 0.9 4New Mexico * 6 18 39 4 20 305 5.0 0.5 4.7 0.5 1North Dakota 19 20 232 3 42 206 13.5 0.5 12.5 0.5 5Oregon * 32 205 460 18 112 1,253 18.6 10.4 17.2 9.6 6South Dakota 14 17 141 4 30 207 10.0 0.5 9.2 0.4 4Utah * 14 55 130 9 55 496 10.8 1.5 9.9 1.4 3Washington 30 240 356 27 147 1,767 24.2 11.8 22.3 10.8 8Wyoming * 9 15 153 2 25 119 6.0 0.4 5.5 0.4 2* 9 GCVTC States 267 827 2,406 197 968 8,052 160 33 148 31 4413 WRAP States 345 1,137 3,333 235 1,223 10,473 219 48 202 43 66^

PM emissions include exhaust, brake wear, and tire wear emissions.
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc Table 8-2b. 2008 Western states total off-road emissions by state and fuel type (TPD), based on the June 2000 N

VOC NOX CO PM10

^

PM2.5

^

State 

Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel 

Arizona * 24 52 181 18 110 1,388 18.4 2.6 17.3 2.3 28California * 111 260 716 104 420 2,647 50.5 15.5 46.4 14.3 82.Colorado * 21 49 212 18 96 1,147 18.7 2.0 17.3 1.8 74Idaho * 8 35 87 6 32 423 6.9 2.1 6.4 1.9 29Montana 12 23 161 3 35 242 10.1 1.1 9.4 1.0 40Nevada * 19 26 198 5 89 514 15.1 1.1 14.0 1.0 50New Mexico * 5 13 32 4 20 332 4.6 0.5 4.4 0.5 14North Dakota 15 17 185 3 39 215 11.8 0.5 10.9 0.5 57Oregon * 28 114 391 18 114 1,313 17.0 11.4 15.7 10.4 66.South Dakota 10 14 118 4 28 223 9.1 0.4 8.4 0.4 44Utah * 12 46 110 10 55 538 10.0 1.5 9.2 1.3 38Washington 25 138 302 28 144 1,852 21.8 12.8 20.2 11.7 93.Wyoming * 8 13 123 2 25 124 5.7 0.4 5.2 0.4 23* 9 GCVTC States 235 608 2,049 185 960 8,428 147 37 136 34 409 13 WRAP States 297 801 2,816 224 1,206 10,959 200 52 185 47 645 ^

PM emissions include exhaust, brake wear, and tire wear emissions.
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc Table 8-2c. 2013 Western states total off-road emissions by state and fuel type (TPD), based on the June 2000 N

VOC NOX CO PM10

^

PM2.5

^

State 

Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diese

Arizona * 22 48 162 19 119 1,492 18.5 2.7 17.4 2.4 31California * 98 212 594 90 410 2,504 44.1 16.5 40.6 15.2 86Colorado * 17 45 185 19 102 1,221 18.5 2.0 17.2 1.8 81Idaho * 7 32 76 7 34 436 6.8 1.7 6.3 1.5 31Montana 10 21 145 3 37 247 9.8 1.0 9.1 0.9 43Nevada * 18 24 187 5 99 546 15.8 1.1 14.6 1.0 56New Mexico * 4 12 27 5 20 359 4.6 0.6 4.3 0.5 15North Dakota 12 16 154 4 39 224 11.0 0.5 10.2 0.4 59Oregon * 24 118 347 20 121 1,427 16.9 12.5 15.6 11.5 72South Dakota 8 12 97 5 29 238 8.8 0.4 8.1 0.4 48Utah * 10 44 97 11 59 580 10.1 1.4 9.3 1.3 42Washington 20 140 259 30 148 2,011 21.4 13.9 19.8 12.8 98Wyoming * 8 13 116 2 27 131 5.5 0.4 5.1 0.4 24* 9 GCVTC States 207 549 1,791 177 991 8,697 141 39 130 36 444 13 WRAP States 258 738 2,446 219 1,244 11,416 192 55 178 50 694 ^

PM emissions include exhaust, brake wear, and tire wear emissions.
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc Table 8-2d. 2018 Western states total off-road emissions by state and fuel type (TPD), based on the June 2000 N

VOC NOX CO PM10

^

PM2.5

^

State 

Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diese

Arizona * 22 52 160 22 129 1,619 19.3 2.9 18.1 2.6 34California * 89 177 521 81 405 2,286 39.0 17.7 35.9 16.3 90Colorado * 16 47 175 20 109 1,295 19.2 2.1 17.8 1.9 87Idaho * 6 30 71 7 36 448 7.0 1.3 6.4 1.2 34Montana 10 20 136 4 39 252 9.8 0.8 9.2 0.7 46Nevada * 19 25 189 6 111 583 17.0 1.2 15.7 1.1 63New Mexico * 4 12 24 5 21 388 4.7 0.6 4.4 0.5 16North Dakota 10 16 136 4 41 233 10.8 0.5 9.9 0.4 62Oregon * 22 126 395 21 132 1,537 17.9 13.6 16.6 12.5 79South Dakota 7 12 84 5 31 253 8.9 0.4 8.2 0.4 52Utah * 10 45 95 12 65 619 10.6 1.5 9.9 1.3 46Washington 18 148 284 32 162 2,165 22.3 15.2 20.7 13.9 103Wyoming * 8 13 114 2 29 139 5.6 0.4 5.1 0.4 26* 9 GCVTC States 195 528 1,743 177 1,036 8,912 140 41 130 38 4713 WRAP States 239 724 2,382 221 1,308 11,815 192 58 178 53 744 ^

PM emissions include exhaust, brake wear, and tire wear emissions.
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc 8-15 

Figure 8-2a. GCVTC States annual average off-road NOx emissions, 1996-2018, based on the 

June 2000 NONROAD model. 

g y

0

500

1000

1500

2000

2500

3000

Annual Average Day (Tons Per Day)

Arizona 270 228 198 181 182 

California 1,021 952 820 684 602 

Colorado 334 273 230 203 195 

Idaho 107 112 94 83 78 

Nevada 166 223 204 193 195 

New Mexico 51 43 37 31 30 

Oregon 474 478 409 367 416 

Utah 143 139 120 108 106 

Wyoming 124 155 125 118 116 

1996 2003 2008 2013 2018
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc 8-16 

Figure 8-2b. GCVTC States annual average off-road PM10 emissions, 1996-2018, based on 

the June 2000 NONROAD model. 

0

50

100

150

200

250

Annual Average Day (Tons Per Day)

Arizona 26.3 22.5 20.9 21.1 22.2 

California 68.5 69.9 66.0 60.6 56.7 

Colorado 26.6 22.7 20.7 20.5 21.3 

Idaho 11.1 10.0 8.9 8.5 8.3 

Nevada 15.2 15.5 16.2 16.9 18.2 

New Mexico 6.5 5.6 5.2 5.2 5.3 

Oregon 28.5 29.0 28.4 29.4 31.6 

Utah 13.2 12.3 11.4 11.5 12.1 

Wyoming 5.4 6.4 6.1 5.9 6.0 

1996 2003 2008 2013 2018
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc 8-17 

Figure 8-2c. GCVTC States annual average off-road SO2 emissions, 1996-2018, based on the 

June 2000 NONROAD model.

g y

0

100

200

300

400

500

600

Annual Average Day (Tons Per Day)

Arizona 25.2 26.6 29.1 31.9 34.6 

California 144.9 157.8 82.9 87.1 91.3 

Colorado 61.3 68.7 75.0 81.6 88.0 

Idaho 21.6 26.9 29.3 31.9 34.4 

Nevada 29.2 40.9 50.7 56.9 63.2 

New Mexico 12.1 13.3 14.3 15.4 16.4 

Oregon 50.2 61.7 67.0 72.9 79.4 

Utah 28.5 35.2 39.1 43.1 47.0 

Wyoming 15.0 21.2 23.0 24.8 26.7 

1996 2003 2008 2013 2018
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc 8-18 

Tables 8-3a through 8-3d show the estimated annual average weekday off-road emissions by 

state and fuel type for each of the four future years, based on the NONROAD2002 model, 

with diesel sulfur set at 15ppm for years 2008, 2013, and 2018. The individual and 

cumulative state total off-road emissions trends from 1996 to 2018 for NOx, PM10, and SO2 

for the nine GCVTC states are shown in Figures 8-3a through 8-3c, respectively. These tables 

and plots show that predicted off-road equipment VOC, NOx, PM10, and PM2.5 emissions 

based on NONROAD2002 to be continually declining from 1996 to 2018. Implementation of 

the proposed Tier 4 diesel equipment standards will result in even further reductions in years 

2008 and later. With diesel fuel sulfur levels set to 15 ppm beginning in 2008, these tables 

and plots show SO2 off-road SO2 emissions increasing from 1996 to 2003, then dropping in 

2008 with the lower diesel fuel sulfur levels. If the proposed Tier 4 regulations are adopted, 

then the SO2 emissions will be higher than shown for year 2008 – the proposal is for 500 ppm 

sulfur while this modeling used 15 ppm in 2008. 
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc Table 8-3a. 2003 Western states total off-road emissions by state and fuel type (TPD), based on the NONROAD2

 VOC NOX CO PM10 PM2.5 

State 

 Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Dies

Arizona* 16 96 129 21 58 1,424 12.0 3.1 11.4 2.8 1California* 124 299 833 118 435 2,706 57.4 12.5 52.8 11.5 15Colorado* 16 76 174 19 68 1,098 13.4 2.2 12.4 2.0 2Idaho* 6 41 71 6 28 374 5.8 1.1 5.4 1.0 1Montana 12 19 164 3 46 210 11.3 0.5 10.5 0.5 2Nevada* 12 37 168 6 44 493 8.6 1.2 8.0 1.1 2New Mexico* 4 22 27 5 17 318 3.9 0.6 3.7 0.6 North Dakota 17 17 178 4 75 224 17.1 0.4 15.7 0.4 2Oregon* 26 73 401 19 83 906 13.5 2.2 12.5 2.0 3South Dakota 11 17 87 4 52 218 11.6 0.4 10.7 0.4 1Utah* 12 49 121 10 50 527 9.5 1.3 8.8 1.2 2Washington 22 108 275 29 111 1,419 17.3 3.5 16.0 3.1 5Wyoming* 9 16 148 2 27 137 6.3 0.4 5.8 0.4 1Total - 13 WRAP States 288 871 2,776 246 1,092 10,055 187.7 29.4 173.7 26.7 42*Total - 9 GCVTC States 226 710 2,073 206 809 7,984 130.4 24.5 120.7 22.4 30
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc Table 8-3b. 2008 Western states total off-road emissions by state and fuel type (TPD), based on the NONROAD2sulfur set to 15 ppm.

 VOC NOX CO PM10 PM2.5 

State 

 Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Dies

Arizona* 14 64 109 15 54 1,467 10.5 3.3 10.0 2.9 California* 111 260 716 104 420 2,647 50.5 15.5 46.4 14.3 8Colorado* 14 52 147 13 62 1,115 10.6 2.3 9.8 2.1 Idaho* 6 32 61 5 25 381 4.5 1.2 4.2 1.1 Montana 11 15 135 2 42 206 9.2 0.6 8.6 0.5 Nevada* 11 24 141 4 43 508 7.4 1.3 6.9 1.1 New Mexico* 4 15 23 3 15 329 3.2 0.7 3.0 0.6 North Dakota 14 13 149 3 61 211 12.3 0.4 11.4 0.4 Oregon* 23 51 345 13 80 948 11.2 2.4 10.4 2.1 2South Dakota 9 13 78 3 43 211 8.4 0.5 7.8 0.4 Utah* 10 36 103 7 45 539 7.4 1.5 6.8 1.3 Washington 19 75 240 20 102 1,456 14.0 3.6 13.0 3.3 3Wyoming* 8 13 120 1 27 137 5.5 0.5 5.1 0.4 Total - 13 WRAP States 253 662 2,367 193 1,019 10,155 154.7 33.7 143.4 30.7 24*Total - 9 GCVTC States 202 547 1,765 165 771 8,071 110.7 28.6 102.6 26.0 17
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc Table 8-3c. 2013 Western states total off-road emissions by state and fuel type (TPD), based on the NONROAD2sulfur set to 15 ppm.

 VOC NOX CO PM10 PM2.5 

State 

 Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Dies

Arizona* 13 57 101 11 55 1,531 10.0 3.4 9.6 3.0 California* 98 212 594 90 410 2,504 44.1 16.5 40.6 15.2 8Colorado* 13 46 135 9 60 1,145 9.4 2.3 8.8 2.1 Idaho* 5 27 55 3 24 386 3.9 1.1 3.6 1.0 Montana 9 12 126 2 39 207 8.1 0.6 7.6 0.5 Nevada* 10 22 134 3 45 527 7.0 1.3 6.5 1.1 New Mexico* 3 14 20 3 14 346 2.8 0.7 2.7 0.6 North Dakota 11 11 131 2 52 209 9.6 0.4 8.9 0.4 Oregon* 21 46 313 8 79 979 10.3 2.4 9.6 2.1 2South Dakota 7 11 68 2 37 213 6.6 0.4 6.1 0.4 Utah* 9 32 93 5 44 554 6.6 1.5 6.1 1.3 Washington 16 67 212 13 98 1,508 12.6 3.7 11.7 3.3 3Wyoming* 8 11 114 1 27 139 5.1 0.4 4.7 0.4 Total - 13 WRAP States 223 567 2,097 152 985 10,247 136.3 34.7 126.4 31.5 22*Total - 9 GCVTC States 180 467 1,560 133 759 8,110 99.3 29.6 92.1 26.9 16
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc Table 8-3d. 2018 Western states total off-road emissions by state and fuel type (TPD), based on the NONROAD2sulfur set to 15 ppm. 

 VOC NOX CO PM10 PM2.5 

State 

 Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diese

 Arizona* 13 57 98 10 56 1,615 10.0 3.5 9.6 3.1 8 California* 89 177 521 81 405 2,286 39.0 17.7 35.9 16.3 90. Colorado* 12 44 130 8 62 1,192 9.1 2.3 8.5 2.1 11 Idaho* 4 23 53 3 24 396 3.7 1.1 3.4 1.0 4 Montana 9 10 121 2 39 211 7.7 0.6 7.2 0.5 14 Nevada* 10 22 133 3 48 551 7.1 1.3 6.6 1.2 15 New Mexico* 3 13 18 2 14 368 2.8 0.8 2.7 0.7 0 North Dakota 9 9 119 2 49 211 8.5 0.4 7.8 0.4 8 Oregon* 20 45 363 7 84 1,025 10.6 2.4 9.8 2.2 24 South Dakota 6 9 61 2 35 218 5.9 0.4 5.5 0.4 0 Utah* 9 29 90 4 46 576 6.6 1.4 6.2 1.3 5 Washington 15 64 242 12 105 1,583 12.7 3.8 11.7 3.4 32 Wyoming* 7 9 112 1 28 142 5.0 0.4 4.6 0.4 16 Total - 13 WRAP States 205 513 2,063 137 995 10,374 128.8 36.1 119.6 32.8 233. *Total - 9 GCVTC States 167 420 1,520 120 767 8,151 93.9 31.0 87.3 28.2 178.
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc 8-23 

Figure 8-3a. GCVTC States annual average off-road VOC emissions, 1996-2018, based on the 

NONROAD2002 model. 

g y

0

200

400

600

800

1000

1200

Annual Average Day (Tons Per Day)

Arizona 135 112 78 70 70 

California 479 423 371 310 266 

Colorado 113 93 66 58 56 

Idaho 49 47 37 32 28 

Nevada 52 49 35 32 31 

New Mexico 34 27 19 17 17 

Oregon 113 99 75 67 65 

Utah 66 61 46 41 38 

Wyoming 24 25 21 19 17 

1996 2003 2008 2013 2018
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc 8-24 

Figure 8-3b. GCVTC States annual average off-road NOx emissions, 1996-2018, based on the 

NONROAD2002 model with diesel sulfur set to 15 ppm. 

g y

0

500

1000

1500

2000

2500

Annual Average Day (Tons Per Day)

Arizona 182 150 124 111 108 

California 1,021 952 820 684 602 

Colorado 248 193 160 144 138 

Idaho 71 78 65 59 56 

Nevada 111 174 146 138 137 

New Mexico 39 32 27 22 21 

Oregon 412 420 358 321 370 

Utah 138 131 110 98 95 

Wyoming 118 150 121 115 113 

1996 2003 2008 2013 2018
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc 8-25 

Figure 8-3c. GCVTC States annual average off-road PM2.5 emissions, 1996-2018, based on 

the NONROAD2002 model with diesel sulfur set to 15 ppm. 

g y

0

20

40

60

80

100

120

140

160

Annual Average Day (Tons Per Day)

Arizona 15.7 14.2 13.0 12.6 12.7 

California 62.9 64.3 60.7 55.8 52.2 

Colorado 17.2 14.4 11.9 10.8 10.6 

Idaho 7.0 6.3 5.3 4.6 4.4 

Nevada 7.7 9.0 8.0 7.6 7.8 

New Mexico 5.2 4.3 3.7 3.4 3.3 

Oregon 14.8 14.5 12.5 11.7 12.0 

Utah 11.0 10.0 8.2 7.4 7.5 

Wyoming 5.3 6.1 5.5 5.1 4.9 

1996 2003 2008 2013 2018
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc 8-26 

Figure 8-3d. GCVTC States annual average off-road SO2 emissions, 1996-2018, based on the 

NONROAD2002 model with diesel sulfur set to 15 ppm. 

g y

0

50

100

150

200

250

300

350

Annual Average Day (Tons Per Day)

Arizona 12.5 11.3 8.4 7.7 8.2 

California 144.9 157.8 82.9 87.1 91.3 

Colorado 27.7 26.7 14.1 10.3 11.1 

Idaho 8.8 11.3 5.7 4.1 4.4 

Nevada 11.6 20.5 16.0 14.7 15.8 

New Mexico 5.3 5.3 1.7 0.4 0.4 

Oregon 28.4 34.4 25.7 23.2 24.8 

Utah 17.6 20.6 9.3 5.5 5.9 

Wyoming 11.8 17.3 15.6 15.6 16.8 

1996 2003 2008 2013 2018
 February 2004 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc1 9-1 

9.0 PAVED AND UNPAVED ROAD DUST EMISSIONS 

Fugitive dust from paved and unpaved roads is a significant source of particulate matter (PM) 

emissions. For the 1996 base case WRAP modeling, road dust estimates from EPA’s 1996 

National Emissions Trends (NET96) were used (EPA, 1998); these are based on EPA AP-42 road 

dust emission factors. For the first round of 2018 air quality 2018 modeling, road dust emissions 

estimates were generated using these same EPA’s AP-42 methods, with growth in vehicle miles 

traveled (VMT) and controls applied. However, significant inconsistencies in the resulting road 

dust emissions were observed between states, and revised road dust emissions were estimated. 

This section describes both the original and revised 2018 road dust calculations, and provides 

graphical and tabular comparisons. The revised 1996 and 2018 road dust emissions were used in 

later rounds of air quality modeling. 

9.1 2018 ROAD DUST EMISSIONS – AP-42 APPROACH 

9.1.1 VMT Estimates for Paved and Unpaved Roads

The first step in estimating fugitive dust emissions from unpaved and paved roadways is to 

estimate the activity level. For these two categories, the activity is the VMT on the unpaved or 

paved roads, respectively. Activity data for unpaved roads is almost always unavailable, since 

neither state, regional, or local transportation agencies, nor government land managers, track 

VMT for unpaved roads. The general procedure followed here for these calculations was to first 

estimate unpaved road VMT by state and roadway type, and then to calculate paved road VMT at 

the same level of detail by subtracting the unpaved road VMT estimates from total VMT 

projections for 2018, also at the state and roadway type level of detail. 

9.1.1.1 Unpaved Road VMT Projections 

The 2018 unpaved road VMT estimates were calculated using 1996 unpaved road VMT estimates 

from EPA’s NET96 inventory at the state and roadway type level as the starting point. These 

base year unpaved road VMT estimates were multiplied by two factors to account for changes in 

unpaved road VMT from 1996 to 2018. The first factor was a population growth factor. This 

was calculated at the state level as the projected 2018 population divided by the 1996 population. 

The second factor was a regional factor that was developed based on historical trends in unpaved 

roadway mileage. This second factor, developed by EPA, accounts for regional differences in the 

counter-acting trends of increasing unpaved road mileage in some areas while paving over existing 

unpaved roads in other areas. Table 9-1 shows the state-level population growth factors, the 

regional unpaved roadway mileage growth factor applied to each state, and the resulting factor 

that was multiplied by each state’s 1996 unpaved road VMT at the roadway type level to estimate 

2018 unpaved road VMT. 
 February 2004 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc2 9-2 

Table 9-1. Calculation of unpaved road vehicle miles traveled (VMT) growth factors by state.

State 

Population 

Growth Factor--

1996 to 2018 

Region Used for 

Unpaved Mileage 

Factor 

Regional Unpaved 

Roadway Mileage 

Growth Factor— 

1996 to 2018 

Population Growth Factor 

Multiplied by Regional 

Unpaved 

VMT Growth Factor 

Alabama 1.161 East 0.6186 0.718 

Alaska 1.245 West 0.5088 0.634 

Arizona 1.371 West 0.5088 0.697 

Arkansas 1.175 Central 0.9136 1.073 

California 1.283 West 0.5088 0.653 

Colorado 1.322 Central 0.9136 1.208 

Connecticut 1.179 East 0.6186 0.730 

Delaware 1.233 East 0.6186 0.763 

DC 0.991 East 0.6186 0.613 

Florida 1.384 East 0.6186 0.856 

Georgia 1.269 East 0.6186 0.785 

Hawaii 1.272 West 0.5088 0.647 

Idaho 1.248 West 0.5088 0.635 

Illinois 1.159 Central 0.9136 1.059 

Indiana 1.149 Central 0.9136 1.050 

Iowa 1.104 Central 0.9136 1.009 

Kansas 1.163 Central 0.9136 1.062 

Kentucky 1.153 East 0.6186 0.713 

Louisiana 1.143 Central 0.9136 1.044 

Maine 1.182 East 0.6186 0.731 

Maryland 1.219 East 0.6186 0.754 

Massachusetts 1.168 East 0.6186 0.723 

Michigan 1.107 Central 0.9136 1.011 

Minnesota 1.178 Central 0.9136 1.076 

Mississippi 1.134 East 0.6186 0.701 

Missouri 1.173 Central 0.9136 1.072 

Montana 1.241 Central 0.9136 1.134 

Nebraska 1.146 Central 0.9136 1.047 

Nevada 1.495 West 0.5088 0.761 

New Hampshire 1.227 East 0.6186 0.759 

New Jersey 1.177 East 0.6186 0.728 

New Mexico 1.281 Central 0.9136 1.170 

New York 1.064 East 0.6186 0.658 

North Carolina 1.257 East 0.6186 0.777 

North Dakota 1.089 Central 0.9136 0.995 

Ohio 1.115 Central 0.9136 1.018 

Oklahoma 1.163 Central 0.9136 1.063 

Oregon 1.252 West 0.5088 0.637 

Pennsylvania 1.123 East 0.6186 0.695 

Rhode Island 1.159 East 0.6186 0.717 

South Carolina 1.236 East 0.6186 0.764 

South Dakota 1.190 Central 0.9136 1.087 

Tennessee 1.220 East 0.6186 0.755 

Texas 1.240 Central 0.9136 1.133 

Utah 1.398 Central 0.9136 1.277 

Vermont 1.213 East 0.6186 0.751 

Virginia 1.230 East 0.6186 0.761 

Washington 1.315 West 0.5088 0.669 

West Virginia 1.103 East 0.6186 0.682 

Wisconsin 1.175 Central 0.9136 1.073 

Wyoming 1.181 Central 0.9136 1.079 
 February 2004 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc3 9-3 

9.1.1.2 Paved Road VMT Projections

Paved road VMT values were calculated at the state and roadway type level by subtracting the 

projected 2018 unpaved VMT, calculated as discussed above, from total 2018 VMT at the state 

and roadway type level. The calculation of 2018 total VMT for the WRAP states was described 

in Section 6.1. Year 2018 VMT projections for the remaining states was calculated in the same 

manner, with no state or local VMT or VMT growth factor adjustments. 

9.1.1.3 Seasonal VMT Allocations

After annual VMT on unpaved and paved roads were estimated by state and roadway type, 

seasonal (winter, spring, summer, and fall) daily VMT at the state and roadway type level were 

estimated. To do this, the total daily seasonal VMT from the WRAP states used in the onroad 

emission calculations (paved and unpaved combined) for each season were summed at the state 

and roadway type level. The ratio of each state/roadway type’s seasonal to annual VMT ratio was 

then multiplied by the state/roadway type unpaved and paved VMT to estimate seasonal unpaved 

and paved VMT. 

9.1.2 Emission Factor Development and Emissions Calculations 

9.1.2.1 Emission Factors

9.1.2.1.1 Unpaved Road Emission Factors 

The following equation, from AP-42, was used to calculate emission factors for unpaved road dust 

at a monthly level: 

Eext = [k * (s/12)a

 * (W/3)b / (Mdry/0.2)c] * [(365 – (p*12))/365] 

 where 

 Eext = monthly PM10 emission factor extrapolated for natural mitigation (lb/mile) 

 k = empirical constant (2.6 lb/mile) 

 2000 = conversion factor, number of pounds per ton 

 s = surface material silt content (%) 

 a = empirical constant (0.8) 

 W = mean vehicle weight (tons) 

 b = empirical constant (0.4) 

 Mdry = surface material moisture content under dry, uncontrolled conditions (%) 

 c = empirical constant (0.3) 

 p = number of days in a given month with greater than 0.01 inches of 

precipitation 
 February 2004 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc4 9-4 

The above equation is representative of a fleet average emission factor rather than a vehiclespecific emission factor. A default value of 2.2 tons was used nationally as the mean vehicle 

weight, as recommended in the AP-42 documentation for travel on publicly accessible unpaved 

roads. A value of 1.0 percent was applied nationally for the surface material moisture content 

under dry, uncontrolled conditions as a conservative estimate based on available data. 

Average state-level, unpaved road silt content values developed as part of the 1985 NAPAP 

Inventory, were obtained from the Illinois State Water Survey (Stensland, 1989). Silt contents of 

over 200 unpaved roads from over 30 states were obtained. Average silt contents of unpaved 

roads were calculated for each state that had three or more samples. For states that did not have 

three or more samples, the average for all samples from all states was substituted. Table 9-2 

shows the unpaved road silt content values used by state. 

Table 9-2. Paved road silt content by state.

State Unpaved Road Silt Content (%)

Alabama 3.9 

Alaska 3.8 

Arizona 3.0 

Arkansas 3.9 

California 2.6 

Colorado 1.5 

Connecticut 3.9 

Delaware 3.9 

DC 0.0 

Florida 3.9 

Georgia 3.9 

Hawaii 3.8 

Idaho 3.9 

Illinois 2.6 

Indiana 2.6 

Iowa 2.5 

Kansas 3.9 

Kentucky 3.9 

Louisiana 3.9 

Maine 3.9 

Maryland 3.9 

Massachusetts 3.9 

Michigan 2.6 

Minnesota 2.7 

Mississippi 3.9 

Missouri 6.5 

State Unpaved Road Silt Content (%)

Montana 6.6 

Nebraska 4.2 

Nevada 1.7 

New Hampshire 3.9 

New Jersey 3.9 

New Mexico 4.3 

New York 4.7 

North Carolina 5.1 

North Dakota 3.9 

Ohio 3.1 

Oklahoma 4.4 

Oregon 7.2 

Pennsylvania 3.3 

Rhode Island 3.9 

South Carolina 3.9 

South Dakota 3.1 

Tennessee 2.0 

Texas 5.6 

Utah 3.9 

Vermont 3.9 

Virginia 3.2 

Washington 3.9 

West Virginia 3.9 

Wisconsin 4.2 

Wyoming 7.1 

Precipitation data for the original AP-42 emission factor equation is required as total number of 

days with precipitation in the year. However, the equation uses the number of days simply to 

calculate a percentage of rain days. The above AP-42 equation was modified to calculate a 

monthly emission factor for each state. Monthly precipitation data for the period 1985-1996 from 

the National Climatic Data Center (NCDC) were used, with measurable daily precipitation defined 

as more than 0.01 inches of rain. Table 9-3 shows the average monthly precipitation data by 
 February 2004 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc5 9-5 

state; these averages are for a meteorological station in each state selected to be representative of 

rural areas within the state. 

Table 9-3. Monthly average precipitation data by state (from a representative rural monitoring 

station) used to estimate unpaved road dust emissions.

Number of days with More Than 0.01 Inches of Precipitation 

State Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Alabama 11.2 10.2 10.9 8.5 9.6 9.6 10.2 7.7 8.8 8.6 9.3 10.9 

Alaska 12.8 8.6 12.2 6.9 12.5 11.6 12.6 16.2 16.4 14.4 13.2 14.0 

Arizona 4.2 3.4 4.0 1.8 2.4 1.4 3.5 5.5 2.9 2.6 2.7 3.8 

Arkansas 7.5 8.8 9.6 9.6 11.1 8.7 6.6 6.7 8.5 7.3 7.7 7.6 

California 6.5 6.5 7.5 3.5 2.8 1.5 0.6 0.6 1.8 2.3 4.4 5.5 

Colorado 4.5 4.7 6.7 6.7 7.1 4.7 6.7 8.3 6.6 5.1 6.3 5.2 

Connecticut 10.3 9.9 11.6 11.7 12.0 11.5 10.5 10.0 9.8 9.4 11.0 10.4 

Delaware 10.5 8.8 10.5 10.7 10.0 8.6 9.6 8.1 8.8 7.2 9.1 9.7 

DC 10.5 9.5 11.4 9.8 10.4 9.0 10.9 8.7 8.7 7.2 9.6 9.9 

Florida 8.6 6.6 7.6 5.4 6.7 13.2 16.1 15.8 12.0 6.7 6.4 5.8 

Georgia 10.7 9.3 9.1 7.3 8.3 10.4 11.2 10.7 8.0 6.9 8.4 9.1 

Hawaii 15.6 12.3 11.7 13.8 19.5 14.7 18.8 20.2 15.6 15.9 15.8 20.3 

Idaho 8.0 7.7 9.7 9.9 10.2 9.6 6.4 4.1 5.1 5.6 12.0 8.7 

Illinois 9.2 8.1 10.5 11.4 9.4 8.7 10.4 9.3 9.4 9.3 12.0 9.4 

Indiana 12.4 11.2 13.7 12.9 10.3 9.5 10.7 10.0 9.4 10.3 13.6 12.7 

Iowa 7.6 6.5 9.1 10.9 10.2 10.1 10.6 9.3 9.8 7.2 10.3 8.5 

Kansas 3.9 5.1 6.9 7.1 10.2 10.2 9.2 8.8 6.8 5.7 5.4 5.7 

Kentucky 13.6 12.5 13.9 11.2 13.0 12.0 12.3 10.2 9.6 8.4 11.6 13.3 

Louisiana 11.2 9.6 7.8 8.0 9.3 12.1 12.0 10.4 10.5 6.6 8.2 10.1 

Maine 15.1 11.2 12.0 12.7 13.8 14.1 13.0 13.4 12.4 14.3 15.9 13.4 

Maryland 10.9 9.1 10.4 10.0 10.6 8.9 10.2 8.8 8.9 7.5 9.3 9.7 

Massachusetts 11.8 11.2 13.6 12.6 12.8 12.7 11.0 9.9 11.0 10.0 12.4 12.1 

Michigan 14.0 11.3 11.5 12.1 9.9 9.5 10.5 10.9 12.2 12.9 14.7 14.1 

Minnesota 9.2 7.3 8.4 9.3 11.2 11.5 11.1 10.5 11.0 8.2 10.6 9.9 

Mississippi 10.4 9.4 9.3 8.7 11.4 10.1 8.6 8.3 7.4 8.0 8.0 11.1 

Missouri 7.0 8.3 10.3 10.2 10.9 8.3 10.0 8.6 8.2 8.6 11.3 8.5 

Montana 6.8 5.5 7.4 7.1 10.3 10.1 7.7 7.3 6.5 5.3 6.8 6.7 

Nebraska 6.0 6.6 7.8 8.0 10.3 8.9 9.5 7.8 7.9 5.9 6.8 5.0 

Nevada 6.8 8.1 7.9 7.7 7.3 4.8 4.7 5.1 4.1 5.2 8.7 6.0 

New Hampshire 9.8 8.7 11.0 12.1 11.2 11.5 10.9 9.7 9.4 10.5 10.8 10.9 

New Jersey 9.8 8.9 9.5 10.0 9.1 8.0 9.8 9.1 8.6 6.6 8.0 9.3 

New Mexico 5.4 5.0 6.1 4.4 9.3 7.4 7.3 10.6 7.6 4.6 5.3 5.8 

New York 13.9 12.0 13.0 13.5 12.0 11.5 12.1 11.7 11.8 10.6 12.8 13.2 

North Carolina 10.6 9.3 9.9 8.5 10.1 9.7 11.5 13.6 8.2 7.4 9.6 9.5 

North Dakota 7.4 6.4 7.9 8.3 10.8 11.3 9.6 8.5 8.0 5.9 7.6 8.9 

Ohio 13.0 11.3 13.2 12.6 11.6 10.6 10.3 9.9 8.5 9.4 12.6 12.7 

Oklahoma 6.5 6.1 8.8 8.0 10.4 7.8 6.2 7.0 9.0 7.6 7.0 7.1 

Oregon 12.4 10.7 11.7 10.7 10.7 7.5 5.1 3.0 5.1 6.8 14.3 11.7 

Pennsylvania 9.9 9.8 12.1 12.4 11.5 10.5 11.5 10.1 10.4 8.6 10.5 10.2 

Rhode Island 11.1 9.6 12.6 11.5 10.8 10.1 10.9 8.8 9.4 9.0 9.9 10.7 

South Carolina 10.1 9.0 9.1 7.2 8.4 9.2 10.6 12.1 6.6 7.5 8.1 8.8 

South Dakota 6.3 8.0 7.3 8.4 8.5 10.3 11.7 9.8 7.9 5.2 8.3 6.0 

Tennessee 9.8 9.2 10.7 8.6 11.0 9.8 10.2 9.7 7.9 7.8 8.7 10.9 

Texas 6.9 6.7 6.1 5.9 9.0 8.4 5.7 6.0 7.3 4.8 6.0 7.4 

Utah 8.8 8.2 9.2 8.6 7.8 4.3 4.6 7.1 4.6 6.3 8.2 7.5 

Vermont 14.9 11.3 13.0 12.5 15.4 12.6 11.2 12.8 11.9 13.5 13.7 13.5 

Virginia 9.1 10.0 11.0 9.9 12.0 9.1 11.5 9.8 8.9 7.2 8.4 9.2 

Washington 14.9 11.9 13.5 12.5 11.6 9.5 7.2 5.4 5.8 10.5 15.3 14.4 

West Virginia 18.6 16.1 16.3 15.5 13.8 14.5 14.5 12.5 12.5 10.5 13.5 17.8 

Wisconsin 8.9 7.1 9.0 11.5 10.1 10.1 10.9 10.1 10.4 8.3 11.7 9.4 

Wyoming 4.2 5.4 7.0 6.7 7.9 6.1 7.5 5.5 5.5 5.7 6.9 4.8 
 February 2004 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc6 9-6 

9.1.2.1.2 Paved Road Emission Factors 

PM10 emission factors for reentrained road dust from paved roads were calculated using EPA’s 

PART5 model (EPA, 1994). PART5 reentrained road dust emission factors for paved roads 

depend on the road surface silt loading and the average weight of all of the vehicles traveling on 

paved roadways. The equation used in PART5 to calculate PM emission factors from reentrained 

road dust on paved roads is a generic paved road dust calculation from AP-42: 

 where 

 PAVED = paved road dust emission factor for all vehicle classes combined 

(grams per mile) 

 PSDPVD = base emission factor for particles of less than 10 microns in diameter 

from paved road dust (7.3 g/mi for PM10) 

 PVSILT = road surface silt loading (g/m2

) 

 WEIGHT = average weight of all vehicle types combined (tons) 

Paved road silt loadings were assigned to each of the twelve functional roadway classifications (six 

urban and six rural) based on the average annual traffic volume of each functional system by state. 

One of three values was assigned to each of these road classes: 1 (gm/m2) was assigned to local 

functional class roads; 0.20 gm/m2

 was assigned to non-local road types that had an estimated 

average daily traffic volume (ADTV) less than 5,000 vehicles per day; and 0.04 gm/m2 was 

assigned to non-local road types that had an estimated ADTV greater than or equal to 5,000 

vehicles per day. ADTV was calculated by dividing annual VMT by state and functional class by 

state-specific functional class roadway mileage. 

The PM emission factor equation for paved roads is representative of a fleet average emission 

factor rather than a vehicle-specific emission factor, and it includes particulate matter from 

tailpipe exhaust, brake wear, tire wear, and ambient background particulate concentrations. 

Therefore, the PART5 fleet average PM emission factors for the tailpipe, tire wear, and brake 

wear components were subtracted from these paved road fugitive dust emission factors. Estimates 

of average vehicle weight over the entire vehicle fleet on paved road were based on data provided 

in the 1987 Truck Inventory and Use Survey (Census, 1990), the Motor Vehicle Manufacturers 

Association Motor Vehicle Facts and Figures ’91(MVMA, 1991), and the 1991 Market Data Book

(Automotive News, 1991). Using these data sources, a fleet average vehicle weight was estimated 

to be 6,360 pounds. 

The PART5 emission factors were modified to account for the number of days with a sufficient 

amount of precipitation to prevent road dust resuspension. The PART5 emission factors were 

multiplied by the fraction of days in a month with less than 0.01 inches of precipitation, reduced 

by 50 percent (i.e., the rain correction factor is calculated as: [365 –( p * 12 * 0.5)] / 365, where 

p represents the number of days in a given month with greater than 0.01 inches of precipitation). 

Table 9-4 shows the paved road precipitation data; these are monthly averages for the period 1985 

to 1996 from a selected urban meteorological station in each state. (These differ from the 

PAVED = PSDPVD * (PVSILT/2)0.65 * (WEIGHT/3)1.5
 February 2004 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc7 9-7 

precipitation data used in the unpaved road emission factor calculations, which were taken from 

stations representative of rural areas in each state.) These emission factors were developed by 

month at the state and road type level for the average vehicle fleet. 

Table 9-4. Monthly average precipitation data by state (from a representative urban monitoring 

station) used to estimate paved road dust emissions.

Number of days with More Than 0.01 Inches of Precipitation 

State Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Alabama 10.4 10.1 9.0 8.4 8.8 10.1 10.9 8.4 9.1 7.1 9.3 10.4 

Alaska 10.5 8.4 5.3 2.9 7.5 11.0 10.0 12.6 13.0 8.5 11.1 12.1 

Arizona 6.3 6.6 7.1 2.8 3.3 2.7 6.9 9.4 5.3 4.5 4.3 6.5 

Arkansas 8.4 9.7 9.6 10.6 11.8 9.7 7.8 7.2 9.3 8.2 8.0 9.8 

California 10.1 9.0 8.7 4.5 3.7 1.6 0.4 0.5 1.5 2.5 4.8 7.1 

Colorado 5.1 4.8 8.4 8.5 9.1 7.8 11.7 11.5 8.0 6.0 7.7 5.3 

Connecticut 10.2 10.1 11.7 11.9 12.2 11.5 10.4 10.1 9.7 9.3 10.7 10.4 

Delaware 10.3 10.1 10.0 11.1 9.5 8.3 9.3 8.2 9.0 7.1 9.0 9.9 

DC 10.1 10.8 10.7 10.7 9.8 8.8 11.0 8.6 8.5 7.0 9.7 10.0 

Florida 9.5 7.3 8.0 5.3 7.5 13.4 15.5 15.8 10.9 5.8 6.4 6.4 

Georgia 10.7 9.8 10.1 7.0 9.0 9.7 11.8 11.1 8.7 6.9 9.4 11.0 

Hawaii 8.7 8.0 9.2 6.1 6.7 5.1 6.2 2.6 6.4 7.8 10.1 12.1 

Idaho 8.7 8.5 7.9 8.1 7.5 5.6 3.7 2.9 4.1 3.8 9.8 8.7 

Illinois 8.9 8.8 10.3 11.8 10.1 9.1 10.6 8.3 10.5 9.1 12.0 9.2 

Indiana 12.3 11.2 12.3 11.5 10.8 10.1 11.1 8.8 10.3 9.7 12.1 11.0 

Iowa 7.9 7.3 8.7 11.8 10.3 11.6 11.8 9.5 10.2 7.6 9.7 8.8 

Kansas 4.6 5.8 9.1 9.0 11.9 11.9 11.0 9.8 8.7 7.3 6.8 6.1 

Kentucky 11.9 11.3 11.7 10.8 11.8 9.2 11.3 8.9 9.4 7.1 10.6 11.9 

Louisiana 10.1 8.6 7.3 7.8 10.3 13.0 13.0 11.3 10.8 6.7 8.0 10.3 

Maine 11.7 8.7 11.4 10.5 11.5 11.0 10.5 11.0 10.7 11.0 12.9 11.1 

Maryland 10.7 10.7 9.8 10.5 10.2 8.3 9.8 8.9 8.8 7.4 9.5 9.7 

Massachusetts 10.8 10.0 12.4 10.8 11.6 11.4 9.4 9.3 9.8 9.6 10.6 10.8 

Michigan 15.7 11.5 12.9 12.6 10.3 10.1 10.5 11.1 13.2 14.2 14.0 15.2 

Minnesota 9.2 7.6 8.5 10.5 10.7 11.1 12.6 10.4 11.4 8.1 10.8 10.1 

Mississippi 9.1 9.9 7.9 8.0 10.6 10.5 8.9 9.9 7.1 7.1 9.7 10.5 

Missouri 6.8 7.4 9.5 10.7 10.9 8.0 10.5 8.6 9.4 9.0 10.1 8.8 

Montana 6.2 7.0 8.7 9.3 11.9 10.2 8.2 7.6 7.3 5.5 5.5 6.6 

Nebraska 6.6 6.9 9.4 8.2 11.1 9.6 11.2 8.9 8.1 5.7 6.9 5.6 

Nevada 5.0 6.5 5.4 3.8 4.4 2.7 2.3 2.5 2.3 2.9 4.7 3.8 

New Hampshire 9.7 8.5 11.3 12.5 11.7 11.3 11.0 10.3 9.6 10.7 10.4 10.7 

New Jersey 9.6 11.2 9.2 12.3 9.4 10.2 11.4 11.3 10.5 7.4 9.0 11.0 

New Mexico 4.1 4.6 5.2 2.7 7.7 5.9 7.5 10.4 6.8 4.1 4.6 5.6 

New York 15.3 14.2 14.2 14.1 12.2 11.7 11.9 11.9 12.2 11.6 13.9 14.0 

North Carolina 10.5 9.2 10.5 9.5 10.8 10.2 10.5 13.2 8.2 7.4 10.1 10.3 

North Dakota 6.4 7.0 8.1 7.3 9.3 10.1 11.1 6.4 8.5 4.8 7.7 6.8 

Ohio 13.0 12.9 13.0 13.1 12.3 11.3 10.9 10.7 9.7 9.2 12.0 12.8 

Oklahoma 5.4 6.4 8.8 8.9 10.7 8.3 5.7 7.0 10.0 7.7 7.5 7.1 

Oregon 16.4 13.6 14.3 13.8 12.2 8.3 4.5 3.3 5.9 8.2 16.7 14.6 

Pennsylvania 11.2 11.6 12.1 12.3 11.6 11.4 11.4 10.6 11.0 8.8 10.7 11.0 

Rhode Island 10.9 10.0 12.5 11.8 10.5 10.0 10.9 9.3 9.1 9.5 9.6 10.8 

South Carolina 10.1 9.5 10.6 8.1 8.6 9.5 11.0 12.3 8.4 7.9 9.5 9.9 

South Dakota 5.0 7.2 6.3 8.9 7.9 10.2 11.0 8.7 8.2 4.2 6.9 4.6 

Tennessee 8.7 9.1 9.8 9.2 11.5 10.2 10.4 10.0 8.7 7.5 8.8 11.3 

Texas 7.2 6.7 6.2 6.2 9.9 9.8 6.2 7.6 7.0 5.7 6.3 8.8 

Utah 8.9 8.8 10.3 9.6 8.0 4.6 5.3 9.5 5.4 6.1 8.1 7.2 

Vermont 14.6 10.9 13.4 12.2 15.5 12.4 11.7 13.1 12.2 14.0 13.5 12.9 

Virginia 9.2 9.8 10.0 9.9 10.8 8.0 11.8 10.0 9.4 6.7 8.6 9.8 

Washington 15.0 11.4 12.6 12.0 10.7 7.8 6.9 4.3 4.9 8.5 14.7 13.8 

West Virginia 14.8 14.9 14.1 13.5 11.3 12.2 11.7 11.0 10.8 9.9 11.9 14.1 

Wisconsin 11.2 8.7 10.1 12.7 8.8 11.2 11.3 10.8 12.1 10.3 12.7 8.7 

Wyoming 4.7 8.3 8.9 10.2 10.3 8.4 9.1 7.4 6.1 8.2 8.0 6.8 
 February 2004 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc8 9-8 

9.1.2.2 Calculation of Paved and Unpaved Road Emissions

Seasonal unpaved and paved road fugitive dust emission factors were calculated for each state by 

averaging across the three monthly emission factors for each state. Seasonal paved and unpaved 

road fugitive dust emissions were estimated for 2018 at the state and roadway type level by 

multiplying the state/roadway type-specific average seasonal emission factors by the 

corresponding VMT estimate, at the same level of detail. These state-level fugitive dust emissions 

were then allocated to the county level using the procedure discussed below. 

9.1.2.2.1 Allocation of Emissions by County 

Paved road emissions were distributed from the state/roadway type level to the county/roadway 

type level in three steps. First, the total VMT (from paved and unpaved roads combined) was 

summed by county and roadway type and by state and roadway type. Next, the ratio of county to 

state total VMT by roadway type was calculated for each county and roadway type combination. 

Finally, the paved road emissions at the state and roadway type level were multiplied by the ratio 

of the county to state total VMT for the given roadway type. The result of this calculation was a 

database of paved road emissions by county and roadway type each season. 

The procedure for allocating the unpaved road emissions by state and roadway type to county and 

roadway type was similar to that used for allocating the paved road emissions. However, the 

surrogate for distributing the unpaved road emissions from the state to counties was the ratio of 

county rural population to state rural population rather than total VMT. Thus, the estimates of 

unpaved road emissions by state and roadway type were multiplied by the ratio of a county’s rural 

population to the state’s rural population to derive unpaved road emissions by county and roadway 

type. 

9.1.2.2.2 Calculation of PM2.5 Emissions

All of the discussion above applied to PM10 paved and unpaved road fugitive dust emissions. An 

EPA-sponsored evaluation of particle size distribution information indicated that the PM2.5/PM10 

ratio for reentrained road dust from paved and unpaved roads should be reduced from the older 

AP-42 particle size multipliers (Cowherd, 1996). Following the recommendations of this study, , 

all PM2.5 emissions from paved and unpaved roads were calculated by multiplying the 

corresponding PM10 emissions by county and roadway type by 0.25 for paved roads and by 0.15 

for unpaved roads. 

9.1.2.2.3 Application of Fugitive Dust Control Measures

The final step in estimating PM emissions from fugitive dust from paved and unpaved roads was 

the application of adjustments for PM control measures in PM nonattainment areas. The control 

assumed for paved roads was vacuum sweeping twice per month to achieve a control level of 79 

percent. This control was applied to urban and rural roads in serious PM nonattainment areas and 

to urban roads in moderate PM nonattainment areas. The penetration factor used varied by road 

type and nonattainment area classification (serious or moderate). 
 February 2004 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc9 9-9 

For unpaved roads, the control measure and level of control assumed varied by PM nonattainment 

area classification and by rural and urban areas. On urban unpaved roads in moderate PM 

nonattainment areas, the assumed control was paving the unpaved roads. This control was applied 

with a control efficiency of 96 percent and a penetration rate of 50 percent. On rural roads in 

serious PM nonattainment areas, chemical stabilization was the assumed control; this control was 

applied with an assumed control efficiency of 75 percent and penetration rate of 50 percent. On 

urban unpaved roads in serious PM nonattainment areas, paving and chemical stabilization were 

the assumed control measures, with an assumed overall control efficiency of 90 percent and 

penetration rate of 75 percent. 

9.1.2.2.4 Final Emissions Estimates

The fugitive dust emissions used in emission inventories for air quality modeling are not required 

to be delineated by roadway type. Therefore, the paved and unpaved road fugitive dust emissions 

were separately totaled across the roadway types within each county, for both PM10 and PM2.5 

emissions and for each of the four seasons. 

Figure 1 is a map portraying the 2018 county-level unpaved road dust emissions. The map shows 

unexpected inconsistencies in these estimates developed using the standard AP-42 approach. 

There is large variation in the emission estimates from state to state, and in adjacent counties. In 

particular, there is unexpectedly large variation in the unpaved road dust emissions among the 

Colorado Plateau states. For that reason, the calculations and underlying data described above 

were reviewed and the unpaved road dust emission estimates were revised. 

9.2 2018 ROAD DUST EMISSIONS – REVISED ESTIMATES 

Because of the inconsistencies seen in the Western states unpaved road dust emissions estimates 

developed using the standard AP-42 approach shown in Figure 1, the underlying data used to 

estimate the road dust emissions were reviewed. Significant variation was found in both the 

emission factors and the VMT activity data, the product of which yields the emission estimates. 

9.2.1 Revisions to State Average Silt Content 

The region-specific parameters governing the estimated unpaved road dust emissions from 

unpaved roads include precipitation/dust moisture content and silt content. Updated silt content 

data were obtained from the Illinois State Water Survey (ISWS) (Stensland, 2002). These data 

showed that the variability in the state-level emission factors is largely attributable to variability in 

the assumed state-level silt content values. The state-specific silt contents in the ISWS data set for 

the Western states were used, with two changes. First, the Western states average of 3.2 was 

applied in states with inadequate data (less than three measurements), rather than the national 

average of 3.9 that EPA uses. Second, the largest value (17.99, an outlier) was deleted from the 

Montana average, which reduces the Montana average significantly. 
 February 2004 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc10 9-10 

Table 9-5 shows the assumed unpaved road silt contents from both the NET96 and this revision. 

It can be seen immediately that the updated ISWS data tend to homogenize the silt content values 

among the states. Where variation still exists, they are supported by at least three samples 

throughout the state. Perhaps one of the most striking changes occurred in Colorado, Utah, and 

Wyoming. Although these states are all adjacent within the Colorado Plateau group, former 

estimates placed the silt contents from 1.5 to 7.1. This resulted in large step changes in emissions 

at the borders among these states (shown in Figure 9-1), a fact not easily reconciled within the 

dispersion model. The new values place the average for all three states at 3.2 percent.

Table 9-5. Unpaved road silt content averages used in EPA NET96 and 

derived from recent ISWS database (percent). 

State EPA NET96 ISWS 

Arizona 3.0 3.0 

California 2.6 2.6 

Colorado 1.5 3.2 

Idaho 3.9 3.2 

Montana 6.6 7.1 

Nevada 1.7 1.6 

New Mexico 4.3 4.3 

North Dakota 3.9 3.2 

Oregon 7.2 3.2 

South Dakota 3.1 3.2 

Utah 3.9 3.2 

Washington 3.9 3.2 

Wyoming 7.1 3.2 

9.2.2 Revisions to Average Daily Traffic Volume (ADTV) Assumptions 

Changes were also made to the average daily traffic volume (ADTV) assumptions. Unpaved road 

VMT is commonly estimated as the product of the road mileage and the assumed ADTV. The 

1996 Federal Highway Administration’s Highway Statistics Report (FHWA, 1996) was the source 

for the mileage estimate used in the inventory described above. The FHWA data were submitted 

by each state, with varying data collection methods across states and counties — some 

jurisdictions performed surveys; others relied on road owners/managers (usually Federal 

agencies). Data were reported separately for urban and rural roads. While these facts question the 

consistency of the unpaved road length data, they were not revised since no alternative source was 

available to justify or facilitate their revision. However, the ADTV estimates were revised based 

on survey work done for the Clark County, NV June 2001 PM10 State Implementation Plan (SIP) 

(Clark County, 2001) and traffic volume estimates for National Forest Service unpaved roads 

(NFS is the largest manager of unpaved roads in the west). 

The ADTV estimates for urban unpaved roads were based upon those in the Clark County, NV 

June 2001 PM10 SIP (Clark County, 2001). VMT estimates on urban unpaved roads were 

estimated using the average ADTV value for Clark County (69.2), with adjustments for 

differences in population density. Urban unpaved road ADTV values for all counties were first 
 February 2004 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc11 9-11 

estimated using the average Clark County ADTV, and then adjusted with the ratio of county 

population densities. Population densities were based on 1990 U.S. Census Bureau population 

and land area data (the 2000 Census was not available at the time this work was performed). The 

final ADTV for each state was estimated as the arithmetic average ADTV across all counties in 

the state. 

Rural unpaved roads warranted a more detailed examination. Roads managed by the National 

Forest Service account for between a few percent to over ninety percent of unpaved road mileage 

in the thirteen western states examined in this study. Given this information, it seemed prudent 

that rural volume estimates should specifically take into account the activity under this agency’s 

jurisdiction; i.e. activity in national forests. The counties that have rural unpaved roads were first 

categorized into those with National Forests and those without. For the latter group, the Clark 

County-derived ADTV described above was assumed. 

For the counties with NFS land (forests), the approach was based upon a methodology outlined by 

a NFS contact (Bell, 2002). Briefly, the approach is to spread a percentage of the annual number 

of forest visits upon a certain fraction of the NFS roads and over a certain portion of the year. 

This recommendation is consistent with the fact that 80 percent of their traffic occurs on about 15 

percent of their roads. While this approach yields accurate local estimates, an enormous amount 

of geospatial data is required for its application. In order to apply this methodology within the 

available time and budget resources, the visits were spread over all NFS roads and over the entire 

year at the state level. The first part of the approach is in recognition that the NFS portion in the 

FHWA mileage estimates does not distinguish those that have high and low activity level; i.e., 

insufficient detail exists to tell where the 15 percent are spatially. The second portion is because 

seasonal allocation of VMT is done separately from this analysis. 

For this approach, state level (1996) forest visitation data were obtained from the NFS web site 

(http://www.fs.fed.us/recreation/recinfo/recuse_93-96.shtml). County level unpaved road 

mileage was obtained from NFS staff (Lesser, 2002). The average number of occupants per 

vehicle was estimated from surveys performed at various forests by the NFS 

(http://www.fs.fed.us/recreation/recuse/recuse.shtml). This number was used to convert the 

number of visitors to the number of vehicles. The result was then divided by the NFS unpaved 

mileage and by 365 to obtain the ADTV. The ADTV was multiplied by a factor of two according 

to the assumption that people will drive through the same roads twice to get in and out of the 

forest. These state average NFS ADTV were applied only to counties with National Forests (i.e., 

have NFS unpaved road mileage). Once the NFS estimates were obtained for each county, they 

were used in conjunction with the Clark County-based volumes as follows. The average of the 

NFS and Clark County-based ADTV were assumed for counties with National Forests, while the 

Clark County values alone were applied to counties without National Forests. Finally, these 

county-level ADTVs were combined to obtain an average value for the state using rural population 

as weighting factors. 

Table 9-6 shows the state-level unadjusted ADTVs, and ADTVs adjusted using the Clark County 

survey data adjusted for population density with and without a further adjustment based on the 

NFS visitation data. For the revised inventory, the Clark County/NFS ADTVs were used. In 

general, these revised ADTVs are lower than the previous estimates, largely because of the 
 February 2004 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc12 9-12 

adjustments for NFS visitation and population density. The incorporation of population density 

into the calculations also yielded results that are reflective of the relative state populations. 

Previously, states such as Idaho, New Mexico, and Wyoming, all of which have relative low 

population densities, were inordinately high with respect to unpaved road ADTV. After the 

revision, these states’ ADTVs are considerably lower. 

Table 9-6. Comparison of NET96 and revised ADTV values. 

State 

Current 

Inventory 

Clark County 

(urban) 

Clark County 

adjusted 

with NFS data 

Arizona 43 22 23 

California 459 417 138 

Colorado 64 64 38 

Idaho 119 16 24 

Montana 25 5 6 

Nevada 45 10 14 

New Mexico 187 20 16 

North Dakota 15 6 9 

Oregon 64 60 45 

South Dakota 14 9 15 

Utah 44 56 26 

Washington 42 68 93 

Wyoming 133 4 4 

9.2.3 Application of Transport Fractions 

For air quality modeling, the revised road dust emission estimates were multiplied by a factor to 

account for deposition and other removal mechanisms that tend to lower the amount of dust that is 

transported on a regional basis (i.e., across the 36 km grid cells in the WRAP modeling domain). 

In the past, EPA and others have reduced the road dust emissions by a factor of four to account 

for these effects. For this project, county-specific transport fractions (Pace, 2002) were applied. 

The transport fractions depend on the vegetative characteristics of each county, and were 

calculated as the weighted average of vegetation-specific transport fractions in each county. The 

transport fractions for each vegetation type are as follows: barren and water, 97%; agricultural, 

85%; grasses, 70%; urban, 40%; scrub and sparse wooded, 30%; and forested, 5%. The 

county-level transport fractions are shown in Figure 9-3. 

9.2.4 Final Revised 2018 Unpaved Road Dust Emissions Estimates 

Revision of the silt loading values as described above resulted in a 15 percent reduction in 2018 

unpaved road dust emissions across the 13 Western states. Revisions to the ADTV data resulted 

in a further 68 percent reduction, and application of the transport fractions resulted in a further 60 

percent reduction. The overall effect of all of these changes was an 89 percent reduction in 2018 

unpaved road dust emissions estimates from the previous inventory. The final emissions by state 

are shown in Table 9-7 in comparison to the original inventory; Figure 9-2 shows the revised 
 February 2004 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc13 9-13 

county-level emissions estimates for unpaved roads in 2018. In comparison to the original 

unpaved road dust emissions shown in Figure 9-1, Figure 9-3 shows that the significant 

inconsistencies in the original road dust emissions between adjacent counties and states have 

disappeared in the revised inventory. The revised emissions distribution shows more consistency 

among the counties in the Colorado Plateau States. 

Table 9-7. Comparison of Western states 2018 unpaved road dust PM10 emissions from NET96-

based approach to the revised approach using ISWS silt content, updated activity, and transport 

fractions. 

State 

Previous Inventory 

(TPY) 

Updated Inventory 

(TPY) 

Reduction 

(%) 

Arizona 47,712 12,976 -72.8 

California 471,927 56,447 -88.0 

Colorado 160,378 36,956 -77.0 

Idaho 228,731 17,976 -92.1 

Montana 179,909 17,906 -90.0 

Nevada 43,404 7,198 -83.4 

New Mexico 769,123 32,730 -95.7 

North Dakota 77,822 34,255 -55.9 

Oregon 226,706 12,158 -94.6 

South Dakota 65,013 50,239 -22.7 

Utah 108,530 20,487 -81.1 

Washington 52,472 22,991 -56.2 

Wyoming 431,935 3,719 -99.1 

Total 2,863,662 326,040 -88.6 

9.3 1996 ROAD DUST EMISSIONS – REVISED ESTIMATES AND COMPARISON 

WITH 2018. 

Unpaved road dust emissions for 1996 were revised using the same methodology as applied to 

develop the 2018 revised unpaved road dust emissions described above. Paved and unpaved road 

dust PM10 and PM2.5 emissions by state for 1996 and 2018 are listed by state in Tables 9-8 and 

9-9, respectively. Table 9-10 shows the change in paved and unpaved road dust emissions by 

state from 1996 to 2018. Paved road dust emissions increase by about three percent per year 

from 1996 to 2018, per the increase in vehicle miles traveled. The increase varies by state, with 

the largest projected growth in vehicle travel in Washington, Idaho, and Utah. Unpaved road dust 

emissions are projected to decrease between 1996 and 2018, by about 0.75% per year, because of 

reductions in unpaved road mileage over time as more roads are paved. As a result, unpaved road 

dust emissions are about 80% of road dust PM10 emissions in 1996, and about 65% of road dust 

PM10 emissions in 2018. Overall, road dust PM10 emissions increase by about 6% from 1996 to 

2018. 

Table 9-8. 1996 WRAP states paved and unpaved road dust emissions (TPY). 
 February 2004 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc14 9-14 

Paved Road Dust Unpaved Road 

Dust 

Total Road Dust 

State 

PM10 PM2.5 PM10 PM2.5 PM10 PM2.5 

Arizona * 7,318 1,830 18,605 2,791 25,923 4,620 

California * 35,643 8,911 86,474 14,090 122,117 23,001 

Colorado * 7,897 1,974 30,601 4,590 38,498 6,564 

Idaho * 3,502 876 28,304 4,246 31,806 5,121 

Montana 2,909 727 15,796 2,369 18,705 3,097 

Nevada * 2,528 632 9,460 1,449 11,988 2,081 

New Mexico * 5,395 1,349 27,972 4,196 33,367 5,545 

North Dakota 2,461 615 34,419 5,163 36,880 5,778 

Oregon * 8,067 2,017 19,078 2,862 27,144 4,878 

South Dakota 2,296 574 46,199 6,930 48,495 7,504 

Utah * 3,680 920 16,040 2,406 19,721 3,326 

Washington 7,804 1,951 34,365 5,155 42,169 7,106 

Wyoming * 1,823 456 3,449 517 5,271 973 

13 states total 91,322 22,831 370,762 56,763 462,084 79,594 

* 9 GCVTC States 75,853 18,963 239,983 37,146 315,835 56,109 

Table 9-9. 2018 WRAP states paved and unpaved road dust emissions (TPY). 

Paved Road Dust Unpaved Road 

Dust 

Total Road Dust 

State 

PM10 PM2.5 PM10 PM2.5 PM10 PM2.5 

Arizona * 12,618 3,154 12,976 1,945 25,594 5,099 

California * 59,794 14,950 56,447 9,198 116,242 24,148 

Colorado * 13,771 3,442 36,956 5,544 50,728 8,986 

Idaho * 7,548 1,887 17,976 2,697 25,524 4,584 

Montana 4,796 1,201 17,907 2,686 22,703 3,887 

Nevada * 5,019 1,256 7,198 1,102 12,217 2,358 

New Mexico * 10,198 2,551 32,730 4,909 42,928 7,461 

North Dakota 3,723 931 34,255 5,139 37,978 6,070 

Oregon * 14,684 3,672 12,158 1,825 26,842 5,497 

South Dakota 3,723 931 50,239 7,537 53,962 8,468 

Utah * 7,541 1,883 20,487 3,073 28,028 4,957 

Washington 18,681 4,668 22,991 3,449 41,672 8,118 

Wyoming * 3,019 756 3,719 558 6,738 1,314 

13 states total 165,115 41,282 326,040 49,666 491,155 90,947 

* 9 GCVTC States 134,192 33,551 200,648 30,853 334,840 64,404 
 February 2004 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc15 9-15 

Table 9-10. Change in Western states paved and unpaved road dust emissions from 

1996 to 2018 (%).

Paved Road Dust

Unpaved Road 

Dust Total Road Dust 

State PM10 PM2.5 PM10 PM2.5 PM10 PM2.5 

Arizona * 72% 72% -30% -30% -1% 10%

California * 68% 68% -35% -35% -5% 5%

Colorado * 74% 74% 21% 21% 32% 37%

Idaho * 116% 116% -36% -36% -20% -10%

Montana 65% 65% 13% 13% 21% 26%

Nevada * 99% 99% -24% -24% 2% 13%

New Mexico * 89% 89% 17% 17% 29% 35%

North Dakota 51% 51% 0% 0% 3% 5%

Oregon * 82% 82% -36% -36% -1% 13%

South Dakota 62% 62% 9% 9% 11% 13%

Utah * 105% 105% 28% 28% 42% 49%

Washington 139% 139% -33% -33% -1% 14%

Wyoming * 66% 66% 8% 8% 28% 35%

13 states total 81% 81% -12% -13% 6% 14%

* 9 GCVTC States 77% 77% -16% -17% 6% 15%
November 2003 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc Figure 9-1. Distribution of estimated 2018 Western states unpaved road dust emissions, based on EPA AP-42 

g p

Date Generated: 5/23/2002

Emissions (TPD)

0.0 to 5.05.0 to 10.010.0 to 50.050.0 to 100.100.0 to 200

Denver

Los Angeles

San Francisco

Boise

Las Vegas

Albuquerque

Salt Lake City

Portland

Seattle

Cheyenne

Phoenix

Billings

Spokane

Bismarck

Rapid City

Reno

Tucson
November 2003 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc Figure 9-2. Distribution of revised 2018 Western states unpaved road dust emissions. 

g p

Date Generated: 5/23/2002

Emissions (

0.0 t0.5 t1.0 t5.0 t10.0 

Denver

Los Angeles

San Francisco

Boise

Las Vegas

Albuquerque

Salt Lake City

Portland

Seattle

Cheyenne

Phoenix

Billings

Spokane

Bismarc

Rapid City

Reno

Tucson
November 2003 

G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc Figure 9-3. Continental United States DRAFT Transport Fractions.

Date Created: 5/28/02

Denver

Los Angeles

San Francisco

Boise

Las Vegas

Albuquerque

Salt Lake City

Portland

Sioux Falls

Fargo Seattle

Cheyenne

Phoenix

Billings

Spokane

Bismarck

Rapid City

Reno

Tucson
 February 2004

G:\WRAP MSEI\Reporting\Final\Sec10_refs.doc 10-1 

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– Task 2/3 Report,” Prepared by E.H. Pechan and Associates for U.S. EPA, 

September. 

Pollack, A.K., C. Lindhjem. 1997. “NONROAD MOBILE Emissions Modeling,” presented 

at The Emission Inventory: Planning for the Future. Research Triangle Park, North 

Carolina. October. 

Radian. 1988. “Air Emissions Species Manual. Volume II. Particulate Matter Species 

Manual,” Prepared by Radian Corporation for the U.S. Environmental Protection 

Agency, EPA-45/2-88-003b. April. 
 February 2004

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Exposure in Urban Areas and Nationwide,” Prepared by Sierra Research for the U.S. 

Environmental Protection Agency, Sierra Report No. SR99-11-02. November. 

Starcrest. 2000. “Houston-Galveston Area Vessel Emissions Inventory.” Prepared by 

Starcrest Consulting for the Port of Houston and the Texas Natural Resource 

Conservation Commission. November 2000. Also Appendix C of HGA Post-1999 

ROP/Attainment Demonstration SIP, Texas Natural Resources Conservation 

Commission. December. 

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with W. Barnard of E.H. Pechan & Associates, Inc., Durham, NC. 

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Volume I Marine Vessel Emissions While Hotelling in Port.” Prepared for the Ports 

of Long Beach and Los Angeles, South Coast Air Quality Management District, and 

Western States Petroleum Association. December. 

TVA. Bray, L. G, Dager, C.A.,Henry, R.L., Koroa, M.C. 2002. “River Efficiency, Fuel 

Taxes, and Modal Shifts”. Tennessee Valley Authority, Transportation Research 

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Under Cold and High Altitude Operating Conditions.” No. 93, The Science of the 

Total Environment. 
Appendix A 

Default 1996 Fuel Sulfur Levels
Table A-1. Default 1996 gasoline sulfur levels. 

Summer 1996 Gasoline Winter 1996 Gasoline 

State County Data Sulfur Data Sulfur 

FIPS No. No. State County Source (ppm) Source (ppm) 

04001 04 001 AZ Apache NIPER-11 263 NIPER-11 361 

04003 04 003 AZ Cochise NIPER-11 263 NIPER-11 361 

04005 04 005 AZ Coconino AAMA-PX 115 AAMA-PX 179 

04007 04 007 AZ Gila AAMA-PX 115 AAMA-PX 179 

04009 04 009 AZ Graham NIPER-11 263 NIPER-11 361 

04011 04 011 AZ Greenlee NIPER-11 263 NIPER-11 361 

04012 04 012 AZ La Paz AAMA-PX 115 AAMA-PX 179 

04013 04 013 AZ Maricopa AAMA-PX 115 AAMA-PX 179 

04015 04 015 AZ Mohave AAMA-PX 115 AAMA-PX 179 

04017 04 017 AZ Navajo NIPER-11 263 NIPER-11 361 

04019 04 019 AZ Pima AAMA-PX 115 AAMA-PX 179 

04021 04 021 AZ Pinal AAMA-PX 115 AAMA-PX 179 

04023 04 023 AZ Santa Cruz NIPER-11 263 NIPER-11 361 

04025 04 025 AZ Yavapai AAMA-PX 115 AAMA-PX 179 

04027 04 027 AZ Yuma AAMA-PX 115 AAMA-PX 179 

06001 06 001 CA Alameda AAMA-SF 26 AAMA-SF 19 

06003 06 003 CA Alpine AAMA-SF 26 AAMA-SF 19 

06005 06 005 CA Amador AAMA-SF 26 AAMA-SF 19 

06007 06 007 CA Butte AAMA-SF 26 AAMA-SF 19 

06009 06 009 CA Calaveras AAMA-SF 26 AAMA-SF 19 

06011 06 011 CA Colusa AAMA-SF 26 AAMA-SF 19 

06013 06 013 CA Contra Costa AAMA-SF 26 AAMA-SF 19 

06015 06 015 CA Del Norte AAMA-SF 26 AAMA-SF 19 

06017 06 017 CA El Dorado AAMA-SF 26 AAMA-SF 19 

06019 06 019 CA Fresno AAMA-SF 26 AAMA-SF 19 

06021 06 021 CA Glenn AAMA-SF 26 AAMA-SF 19 

06023 06 023 CA Humboldt AAMA-SF 26 AAMA-SF 19 

06025 06 025 CA Imperial AAMA-LA 20 AAMA-LA 29 

06027 06 027 CA Inyo AAMA-LA 20 AAMA-LA 29 

06029 06 029 CA Kern AAMA-SF 26 AAMA-SF 19 

06031 06 031 CA Kings AAMA-SF 26 AAMA-SF 19 

06033 06 033 CA Lake AAMA-SF 26 AAMA-SF 19 

06035 06 035 CA Lassen AAMA-SF 26 AAMA-SF 19 

06037 06 037 CA Los Angeles AAMA-LA 20 AAMA-LA 29 

06039 06 039 CA Madera AAMA-SF 26 AAMA-SF 19 

06041 06 041 CA Marin AAMA-SF 26 AAMA-SF 19 

06043 06 043 CA Mariposa AAMA-SF 26 AAMA-SF 19 

06045 06 045 CA Mendocino AAMA-SF 26 AAMA-SF 19 

06047 06 047 CA Merced AAMA-SF 26 AAMA-SF 19 

06049 06 049 CA Modoc AAMA-SF 26 AAMA-SF 19 

06051 06 051 CA Mono AAMA-SF 26 AAMA-SF 19 

06053 06 053 CA Monterey AAMA-SF 26 AAMA-SF 19 
Summer 1996 Gasoline Winter 1996 Gasoline 

State County Data Sulfur Data Sulfur 

FIPS No. No. State County Source (ppm) Source (ppm) 

06055 06 055 CA Napa AAMA-SF 26 AAMA-SF 19 

06057 06 057 CA Nevada AAMA-SF 26 AAMA-SF 19 

06059 06 059 CA Orange AAMA-LA 20 AAMA-LA 29 

06061 06 061 CA Placer AAMA-SF 26 AAMA-SF 19 

06063 06 063 CA Plumas AAMA-SF 26 AAMA-SF 19 

06065 06 065 CA Riverside AAMA-LA 20 AAMA-LA 29 

06067 06 067 CA Sacramento AAMA-SF 26 AAMA-SF 19 

06069 06 069 CA San Benito AAMA-SF 26 AAMA-SF 19 

06071 06 071 CA San Bernardino AAMA-LA 20 AAMA-LA 29 

06073 06 073 CA San Diego AAMA-LA 20 AAMA-LA 29 

06075 06 075 CA San Francisco AAMA-SF 26 AAMA-SF 19 

06077 06 077 CA San Joaquin AAMA-SF 26 AAMA-SF 19 

06079 06 079 CA San Luis Obispo AAMA-SF 26 AAMA-SF 19 

06081 06 081 CA San Mateo AAMA-SF 26 AAMA-SF 19 

06083 06 083 CA Santa Barbara AAMA-LA 20 AAMA-LA 29 

06085 06 085 CA Santa Clara AAMA-SF 26 AAMA-SF 19 

06087 06 087 CA Santa Cruz AAMA-SF 26 AAMA-SF 19 

06089 06 089 CA Shasta AAMA-SF 26 AAMA-SF 19 

06091 06 091 CA Sierra AAMA-SF 26 AAMA-SF 19 

06093 06 093 CA Siskiyou AAMA-SF 26 AAMA-SF 19 

06095 06 095 CA Solano AAMA-SF 26 AAMA-SF 19 

06097 06 097 CA Sonoma AAMA-SF 26 AAMA-SF 19 

06099 06 099 CA Stanislaus AAMA-SF 26 AAMA-SF 19 

06101 06 101 CA Sutter AAMA-SF 26 AAMA-SF 19 

06103 06 103 CA Tehama AAMA-SF 26 AAMA-SF 19 

06105 06 105 CA Trinity AAMA-SF 26 AAMA-SF 19 

06107 06 107 CA Tulare AAMA-SF 26 AAMA-SF 19 

06109 06 109 CA Tuolumne AAMA-SF 26 AAMA-SF 19 

06111 06 111 CA Ventura AAMA-LA 20 AAMA-LA 29 

06113 06 113 CA Yolo AAMA-SF 26 AAMA-SF 19 

06115 06 115 CA Yuba AAMA-SF 26 AAMA-SF 19 

08001 08 001 CO Adams AAMA-DN 298 AAMA-DN 198 

08003 08 003 CO Alamosa NIPER-10 207 NIPER-10 167 

08005 08 005 CO Arapahoe AAMA-DN 298 AAMA-DN 198 

08007 08 007 CO Archuleta NIPER-10 207 NIPER-10 167 

08009 08 009 CO Baca NIPER-10 207 NIPER-10 167 

08011 08 011 CO Bent NIPER-10 207 NIPER-10 167 

08013 08 013 CO Boulder AAMA-DN 298 AAMA-DN 198 

08015 08 015 CO Chaffee NIPER-10 207 NIPER-10 167 

08017 08 017 CO Cheyenne NIPER-10 207 NIPER-10 167 

08019 08 019 CO Clear Creek NIPER-10 207 NIPER-10 167 

08021 08 021 CO Conejos NIPER-10 207 NIPER-10 167 

08023 08 023 CO Costilla NIPER-10 207 NIPER-10 167 
Summer 1996 Gasoline Winter 1996 Gasoline 

State County Data Sulfur Data Sulfur 

FIPS No. No. State County Source (ppm) Source (ppm) 

08025 08 025 CO Crowley NIPER-10 207 NIPER-10 167 

08027 08 027 CO Custer NIPER-10 207 NIPER-10 167 

08029 08 029 CO Delta NIPER-10 207 NIPER-10 167 

08031 08 031 CO Denver AAMA-DN 298 AAMA-DN 198 

08033 08 033 CO Dolores NIPER-10 207 NIPER-10 167 

08035 08 035 CO Douglas AAMA-DN 298 AAMA-DN 198 

08037 08 037 CO Eagle NIPER-10 207 NIPER-10 167 

08039 08 039 CO Elbert NIPER-10 207 NIPER-10 167 

08041 08 041 CO El Paso AAMA-DN 298 AAMA-DN 198 

08043 08 043 CO Fremont NIPER-10 207 NIPER-10 167 

08045 08 045 CO Garfield NIPER-10 207 NIPER-10 167 

08047 08 047 CO Gilpin NIPER-10 207 NIPER-10 167 

08049 08 049 CO Grand NIPER-10 207 NIPER-10 167 

08051 08 051 CO Gunnison NIPER-10 207 NIPER-10 167 

08053 08 053 CO Hinsdale NIPER-10 207 NIPER-10 167 

08055 08 055 CO Huerfano NIPER-10 207 NIPER-10 167 

08057 08 057 CO Jackson NIPER-10 207 NIPER-10 167 

08059 08 059 CO Jefferson AAMA-DN 298 AAMA-DN 198 

08061 08 061 CO Kiowa NIPER-10 207 NIPER-10 167 

08063 08 063 CO Kit Carson NIPER-10 207 NIPER-10 167 

08065 08 065 CO Lake NIPER-10 207 NIPER-10 167 

08067 08 067 CO La Plata NIPER-10 207 NIPER-10 167 

08069 08 069 CO Larimer AAMA-DN 298 AAMA-DN 198 

08071 08 071 CO Las Animas NIPER-10 207 NIPER-10 167 

08073 08 073 CO Lincoln NIPER-10 207 NIPER-10 167 

08075 08 075 CO Logan NIPER-10 207 NIPER-10 167 

08077 08 077 CO Mesa NIPER-10 207 NIPER-10 167 

08079 08 079 CO Mineral NIPER-10 207 NIPER-10 167 

08081 08 081 CO Moffat NIPER-10 207 NIPER-10 167 

08083 08 083 CO Montezuma NIPER-10 207 NIPER-10 167 

08085 08 085 CO Montrose NIPER-10 207 NIPER-10 167 

08087 08 087 CO Morgan NIPER-10 207 NIPER-10 167 

08089 08 089 CO Otero NIPER-10 207 NIPER-10 167 

08091 08 091 CO Ouray NIPER-10 207 NIPER-10 167 

08093 08 093 CO Park NIPER-10 207 NIPER-10 167 

08095 08 095 CO Phillips NIPER-10 207 NIPER-10 167 

08097 08 097 CO Pitkin NIPER-10 207 NIPER-10 167 

08099 08 099 CO Prowers NIPER-10 207 NIPER-10 167 

08101 08 101 CO Pueblo NIPER-10 207 NIPER-10 167 

08103 08 103 CO Rio Blanco NIPER-10 207 NIPER-10 167 

08105 08 105 CO Rio Grande NIPER-10 207 NIPER-10 167 

08107 08 107 CO Routt NIPER-10 207 NIPER-10 167 

08109 08 109 CO Saguache NIPER-10 207 NIPER-10 167 
Summer 1996 Gasoline Winter 1996 Gasoline 

State County Data Sulfur Data Sulfur 

FIPS No. No. State County Source (ppm) Source (ppm) 

08111 08 111 CO San Juan NIPER-10 207 NIPER-10 167 

08113 08 113 CO San Miguel NIPER-10 207 NIPER-10 167 

08115 08 115 CO Sedgwick NIPER-10 207 NIPER-10 167 

08117 08 117 CO Summit NIPER-10 207 NIPER-10 167 

08119 08 119 CO Teller NIPER-10 207 NIPER-10 167 

08121 08 121 CO Washington NIPER-10 207 NIPER-10 167 

08123 08 123 CO Weld NIPER-10 207 NIPER-10 167 

08125 08 125 CO Yuma NIPER-10 207 NIPER-10 167 

16001 16 001 ID Ada NIPER-9 318 NIPER-9 252 

16003 16 003 ID Adams NIPER-9 318 NIPER-9 252 

16005 16 005 ID Bannock NIPER-9 318 NIPER-9 252 

16007 16 007 ID Bear Lake NIPER-9 318 NIPER-9 252 

16009 16 009 ID Benewah NIPER-9 318 NIPER-9 252 

16011 16 011 ID Bingham NIPER-9 318 NIPER-9 252 

16013 16 013 ID Blaine NIPER-9 318 NIPER-9 252 

16015 16 015 ID Boise NIPER-9 318 NIPER-9 252 

16017 16 017 ID Bonner NIPER-9 318 NIPER-9 252 

16019 16 019 ID Bonneville NIPER-9 318 NIPER-9 252 

16021 16 021 ID Boundary NIPER-9 318 NIPER-9 252 

16023 16 023 ID Butte NIPER-9 318 NIPER-9 252 

16025 16 025 ID Camas NIPER-9 318 NIPER-9 252 

16027 16 027 ID Canyon NIPER-9 318 NIPER-9 252 

16029 16 029 ID Caribou NIPER-9 318 NIPER-9 252 

16031 16 031 ID Cassia NIPER-9 318 NIPER-9 252 

16033 16 033 ID Clark NIPER-9 318 NIPER-9 252 

16035 16 035 ID Clearwater NIPER-9 318 NIPER-9 252 

16037 16 037 ID Custer NIPER-9 318 NIPER-9 252 

16039 16 039 ID Elmore NIPER-9 318 NIPER-9 252 

16041 16 041 ID Franklin NIPER-9 318 NIPER-9 252 

16043 16 043 ID Fremont NIPER-9 318 NIPER-9 252 

16045 16 045 ID Gem NIPER-9 318 NIPER-9 252 

16047 16 047 ID Gooding NIPER-9 318 NIPER-9 252 

16049 16 049 ID Idaho NIPER-9 318 NIPER-9 252 

16051 16 051 ID Jefferson NIPER-9 318 NIPER-9 252 

16053 16 053 ID Jerome NIPER-9 318 NIPER-9 252 

16055 16 055 ID Kootenai NIPER-9 318 NIPER-9 252 

16057 16 057 ID Latah NIPER-9 318 NIPER-9 252 

16059 16 059 ID Lemhi NIPER-9 318 NIPER-9 252 

16061 16 061 ID Lewis NIPER-9 318 NIPER-9 252 

16063 16 063 ID Lincoln NIPER-9 318 NIPER-9 252 

16065 16 065 ID Madison NIPER-9 318 NIPER-9 252 

16067 16 067 ID Minidoka NIPER-9 318 NIPER-9 252 

16069 16 069 ID Nez Perce NIPER-9 318 NIPER-9 252 
Summer 1996 Gasoline Winter 1996 Gasoline 

State County Data Sulfur Data Sulfur 

FIPS No. No. State County Source (ppm) Source (ppm) 

16071 16 071 ID Oneida NIPER-9 318 NIPER-9 252 

16073 16 073 ID Owyhee NIPER-9 318 NIPER-9 252 

16075 16 075 ID Payette NIPER-9 318 NIPER-9 252 

16077 16 077 ID Power NIPER-9 318 NIPER-9 252 

16079 16 079 ID Shoshone NIPER-9 318 NIPER-9 252 

16081 16 081 ID Teton NIPER-9 318 NIPER-9 252 

16083 16 083 ID Twin Falls NIPER-9 318 NIPER-9 252 

16085 16 085 ID Valley NIPER-9 318 NIPER-9 252 

16087 16 087 ID Washington NIPER-9 318 NIPER-9 252 

30001 30 001 MT Beaverhead AAMA-BL 407 AAMA-BL 342 

30003 30 003 MT Big Horn AAMA-BL 407 AAMA-BL 342 

30005 30 005 MT Blaine AAMA-BL 407 AAMA-BL 342 

30007 30 007 MT Broadwater AAMA-BL 407 AAMA-BL 342 

30009 30 009 MT Carbon AAMA-BL 407 AAMA-BL 342 

30011 30 011 MT Carter AAMA-BL 407 AAMA-BL 342 

30013 30 013 MT Cascade AAMA-BL 407 AAMA-BL 342 

30015 30 015 MT Chouteau AAMA-BL 407 AAMA-BL 342 

30017 30 017 MT Custer AAMA-BL 407 AAMA-BL 342 

30019 30 019 MT Daniels AAMA-BL 407 AAMA-BL 342 

30021 30 021 MT Dawson AAMA-BL 407 AAMA-BL 342 

30023 30 023 MT Deer Lodge AAMA-BL 407 AAMA-BL 342 

30025 30 025 MT Fallon AAMA-BL 407 AAMA-BL 342 

30027 30 027 MT Fergus AAMA-BL 407 AAMA-BL 342 

30029 30 029 MT Flat Head AAMA-BL 407 AAMA-BL 342 

30031 30 031 MT Gallatin AAMA-BL 407 AAMA-BL 342 

30033 30 033 MT Garfield AAMA-BL 407 AAMA-BL 342 

30035 30 035 MT Glacier AAMA-BL 407 AAMA-BL 342 

30037 30 037 MT Golden Valley AAMA-BL 407 AAMA-BL 342 

30039 30 039 MT Granite AAMA-BL 407 AAMA-BL 342 

30041 30 041 MT Hill AAMA-BL 407 AAMA-BL 342 

30043 30 043 MT Jefferson AAMA-BL 407 AAMA-BL 342 

30045 30 045 MT Judith Basin AAMA-BL 407 AAMA-BL 342 

30047 30 047 MT Lake AAMA-BL 407 AAMA-BL 342 

30049 30 049 MT Lewis and Clark AAMA-BL 407 AAMA-BL 342 

30051 30 051 MT Liberty AAMA-BL 407 AAMA-BL 342 

30053 30 053 MT Lincoln AAMA-BL 407 AAMA-BL 342 

30055 30 055 MT Mc Cone AAMA-BL 407 AAMA-BL 342 

30057 30 057 MT Madison AAMA-BL 407 AAMA-BL 342 

30059 30 059 MT Meagher AAMA-BL 407 AAMA-BL 342 

30061 30 061 MT Mineral AAMA-BL 407 AAMA-BL 342 

30063 30 063 MT Missoula AAMA-BL 407 AAMA-BL 342 

30065 30 065 MT Musselshell AAMA-BL 407 AAMA-BL 342 

30067 30 067 MT Park AAMA-BL 407 AAMA-BL 342 
Summer 1996 Gasoline Winter 1996 Gasoline 

State County Data Sulfur Data Sulfur 

FIPS No. No. State County Source (ppm) Source (ppm) 

30069 30 069 MT Petroleum AAMA-BL 407 AAMA-BL 342 

30071 30 071 MT Phillips AAMA-BL 407 AAMA-BL 342 

30073 30 073 MT Pondera AAMA-BL 407 AAMA-BL 342 

30075 30 075 MT Powder River AAMA-BL 407 AAMA-BL 342 

30077 30 077 MT Powell AAMA-BL 407 AAMA-BL 342 

30079 30 079 MT Prairie AAMA-BL 407 AAMA-BL 342 

30081 30 081 MT Ravalli AAMA-BL 407 AAMA-BL 342 

30083 30 083 MT Richland AAMA-BL 407 AAMA-BL 342 

30085 30 085 MT Roosevelt AAMA-BL 407 AAMA-BL 342 

30087 30 087 MT Rosebud AAMA-BL 407 AAMA-BL 342 

30089 30 089 MT Sanders AAMA-BL 407 AAMA-BL 342 

30091 30 091 MT Sheridan AAMA-BL 407 AAMA-BL 342 

30093 30 093 MT Silver Bow AAMA-BL 407 AAMA-BL 342 

30095 30 095 MT Stillwater AAMA-BL 407 AAMA-BL 342 

30097 30 097 MT Sweet Grass AAMA-BL 407 AAMA-BL 342 

30099 30 099 MT Teton AAMA-BL 407 AAMA-BL 342 

30101 30 101 MT Toole AAMA-BL 407 AAMA-BL 342 

30103 30 103 MT Treasure AAMA-BL 407 AAMA-BL 342 

30105 30 105 MT Valley AAMA-BL 407 AAMA-BL 342 

30107 30 107 MT Wheatland AAMA-BL 407 AAMA-BL 342 

30109 30 109 MT Wibaux AAMA-BL 407 AAMA-BL 342 

30111 30 111 MT Yellowstone AAMA-BL 407 AAMA-BL 342 

30113 30 113 MT Yellowstone Park AAMA-BL 407 AAMA-BL 342 

32001 32 001 NV Churchill NIPER-14 61 NIPER-14 134 

32003 32 003 NV Clark AAMA-LV 62 AAMA-LV 90 

32005 32 005 NV Douglas NIPER-14 61 NIPER-14 134 

32007 32 007 NV Elko NIPER-14 61 NIPER-14 134 

32009 32 009 NV Esmeralda AAMA-LV 62 AAMA-LV 90 

32011 32 011 NV Eureka NIPER-14 61 NIPER-14 134 

32013 32 013 NV Humboldt NIPER-14 61 NIPER-14 134 

32015 32 015 NV Lander NIPER-14 61 NIPER-14 134 

32017 32 017 NV Lincoln AAMA-LV 62 AAMA-LV 90 

32019 32 019 NV Lyon NIPER-14 61 NIPER-14 134 

32021 32 021 NV Mineral AAMA-LV 62 AAMA-LV 90 

32023 32 023 NV Nye AAMA-LV 62 AAMA-LV 90 

32027 32 027 NV Pershing NIPER-14 61 NIPER-14 134 

32029 32 029 NV Storey NIPER-14 61 NIPER-14 134 

32031 32 031 NV Washoe NIPER-14 61 NIPER-14 134 

32033 32 033 NV White Pine NIPER-14 61 NIPER-14 134 

32510 32 510 NV Carson City NIPER-14 61 NIPER-14 134 

35001 35 001 NM Bernalillo AAMA-ALB 290 AAMA-ALB 240 

35003 35 003 NM Catron NIPER-11 263 NIPER-11 361 

35005 35 005 NM Chaves NIPER-11 263 NIPER-11 361 
Summer 1996 Gasoline Winter 1996 Gasoline 

State County Data Sulfur Data Sulfur 

FIPS No. No. State County Source (ppm) Source (ppm) 

35006 35 006 NM Cibola NIPER-11 263 NIPER-11 361 

35007 35 007 NM Colfax NIPER-11 263 NIPER-11 361 

35009 35 009 NM Curry NIPER-11 263 NIPER-11 361 

35011 35 011 NM De Baca NIPER-11 263 NIPER-11 361 

35013 35 013 NM Dona Ana NIPER-11 263 NIPER-11 361 

35015 35 015 NM Eddy NIPER-11 263 NIPER-11 361 

35017 35 017 NM Grant NIPER-11 263 NIPER-11 361 

35019 35 019 NM Guadalupe NIPER-11 263 NIPER-11 361 

35021 35 021 NM Harding NIPER-11 263 NIPER-11 361 

35023 35 023 NM Hidalgo NIPER-11 263 NIPER-11 361 

35025 35 025 NM Lea NIPER-11 263 NIPER-11 361 

35027 35 027 NM Lincoln NIPER-11 263 NIPER-11 361 

35028 35 028 NM Los Alamos AAMA-ALB 290 AAMA-ALB 240 

35029 35 029 NM Luna NIPER-11 263 NIPER-11 361 

35031 35 031 NM Mc Kinley NIPER-11 263 NIPER-11 361 

35033 35 033 NM Mora NIPER-11 263 NIPER-11 361 

35035 35 035 NM Otero NIPER-11 263 NIPER-11 361 

35037 35 037 NM Quay NIPER-11 263 NIPER-11 361 

35039 35 039 NM Rio Arriba NIPER-11 263 NIPER-11 361 

35041 35 041 NM Roosevelt NIPER-11 263 NIPER-11 361 

35043 35 043 NM Sandoval AAMA-ALB 290 AAMA-ALB 240 

35045 35 045 NM San Juan NIPER-11 263 NIPER-11 361 

35047 35 047 NM San Miguel NIPER-11 263 NIPER-11 361 

35049 35 049 NM Santa Fe AAMA-ALB 290 AAMA-ALB 240 

35051 35 051 NM Sierra NIPER-11 263 NIPER-11 361 

35053 35 053 NM Socorro NIPER-11 263 NIPER-11 361 

35055 35 055 NM Taos NIPER-11 263 NIPER-11 361 

35057 35 057 NM Torrance AAMA-ALB 290 AAMA-ALB 240 

35059 35 059 NM Union NIPER-11 263 NIPER-11 361 

35061 35 061 NM Valencia AAMA-ALB 290 AAMA-ALB 240 

38001 38 001 ND Adams AAMA-BL 407 AAMA-BL 342 

38003 38 003 ND Barnes NIPER-7 230 NIPER-7 230 

38005 38 005 ND Benson NIPER-7 230 NIPER-7 230 

38007 38 007 ND Billings AAMA-BL 407 AAMA-BL 342 

38009 38 009 ND Bottineau NIPER-7 230 NIPER-7 230 

38011 38 011 ND Bowman AAMA-BL 407 AAMA-BL 342 

38013 38 013 ND Burke AAMA-BL 407 AAMA-BL 342 

38015 38 015 ND Burleigh NIPER-7 230 NIPER-7 230 

38017 38 017 ND Cass NIPER-7 230 NIPER-7 230 

38019 38 019 ND Cavalier NIPER-7 230 NIPER-7 230 

38021 38 021 ND Dickey NIPER-7 230 NIPER-7 230 

38023 38 023 ND Divide AAMA-BL 407 AAMA-BL 342 

38025 38 025 ND Dunn AAMA-BL 407 AAMA-BL 342 
Summer 1996 Gasoline Winter 1996 Gasoline 

State County Data Sulfur Data Sulfur 

FIPS No. No. State County Source (ppm) Source (ppm) 

38027 38 027 ND Eddy NIPER-7 230 NIPER-7 230 

38029 38 029 ND Emmons NIPER-7 230 NIPER-7 230 

38031 38 031 ND Foster NIPER-7 230 NIPER-7 230 

38033 38 033 ND Golden Valley AAMA-BL 407 AAMA-BL 342 

38035 38 035 ND Grand Forks NIPER-7 230 NIPER-7 230 

38037 38 037 ND Grant NIPER-7 230 NIPER-7 230 

38039 38 039 ND Griggs NIPER-7 230 NIPER-7 230 

38041 38 041 ND Hettinger AAMA-BL 407 AAMA-BL 342 

38043 38 043 ND Kidder NIPER-7 230 NIPER-7 230 

38045 38 045 ND La Moure NIPER-7 230 NIPER-7 230 

38047 38 047 ND Logan NIPER-7 230 NIPER-7 230 

38049 38 049 ND Mc Henry NIPER-7 230 NIPER-7 230 

38051 38 051 ND Mc Intosh NIPER-7 230 NIPER-7 230 

38053 38 053 ND Mc Kenzie AAMA-BL 407 AAMA-BL 342 

38055 38 055 ND Mc Lean NIPER-7 230 NIPER-7 230 

38057 38 057 ND Mercer NIPER-7 230 NIPER-7 230 

38059 38 059 ND Morton NIPER-7 230 NIPER-7 230 

38061 38 061 ND Mountrail AAMA-BL 407 AAMA-BL 342 

38063 38 063 ND Nelson NIPER-7 230 NIPER-7 230 

38065 38 065 ND Oliver NIPER-7 230 NIPER-7 230 

38067 38 067 ND Pembina NIPER-7 230 NIPER-7 230 

38069 38 069 ND Pierce NIPER-7 230 NIPER-7 230 

38071 38 071 ND Ramsey NIPER-7 230 NIPER-7 230 

38073 38 073 ND Ransom NIPER-7 230 NIPER-7 230 

38075 38 075 ND Renville NIPER-7 230 NIPER-7 230 

38077 38 077 ND Richland NIPER-7 230 NIPER-7 230 

38079 38 079 ND Rolette NIPER-7 230 NIPER-7 230 

38081 38 081 ND Sargent NIPER-7 230 NIPER-7 230 

38083 38 083 ND Sheridan NIPER-7 230 NIPER-7 230 

38085 38 085 ND Sioux NIPER-7 230 NIPER-7 230 

38087 38 087 ND Slope AAMA-BL 407 AAMA-BL 342 

38089 38 089 ND Stark AAMA-BL 407 AAMA-BL 342 

38091 38 091 ND Steele NIPER-7 230 NIPER-7 230 

38093 38 093 ND Stutsman NIPER-7 230 NIPER-7 230 

38095 38 095 ND Towner NIPER-7 230 NIPER-7 230 

38097 38 097 ND Traill NIPER-7 230 NIPER-7 230 

38099 38 099 ND Walsh NIPER-7 230 NIPER-7 230 

38101 38 101 ND Ward NIPER-7 230 NIPER-7 230 

38103 38 103 ND Wells NIPER-7 230 NIPER-7 230 

38105 38 105 ND Williams AAMA-BL 407 AAMA-BL 342 

41001 41 001 OR Baker NIPER-9 318 NIPER-9 252 

41003 41 003 OR Benton NIPER-13 256 NIPER-13 346 

41005 41 005 OR Clackamas NIPER-13 256 NIPER-13 338 
Summer 1996 Gasoline Winter 1996 Gasoline 

State County Data Sulfur Data Sulfur 

FIPS No. No. State County Source (ppm) Source (ppm) 

41007 41 007 OR Clatsop NIPER-13 256 NIPER-13 346 

41009 41 009 OR Columbia NIPER-13 256 NIPER-13 346 

41011 41 011 OR Coos NIPER-13 256 NIPER-13 346 

41013 41 013 OR Crook NIPER-9 318 NIPER-9 252 

41015 41 015 OR Curry NIPER-13 256 NIPER-13 346 

41017 41 017 OR Deschutes NIPER-9 318 NIPER-9 252 

41019 41 019 OR Douglas NIPER-13 256 NIPER-13 346 

41021 41 021 OR Gilliam NIPER-9 318 NIPER-9 252 

41023 41 023 OR Grant NIPER-9 318 NIPER-9 252 

41025 41 025 OR Harney NIPER-9 318 NIPER-9 252 

41027 41 027 OR Hood River NIPER-9 318 NIPER-9 252 

41029 41 029 OR Jackson NIPER-13 256 NIPER-13 338 

41031 41 031 OR Jefferson NIPER-9 318 NIPER-9 252 

41033 41 033 OR Josephine NIPER-13 256 NIPER-13 338 

41035 41 035 OR Klamath NIPER-13 256 NIPER-13 338 

41037 41 037 OR Lake NIPER-9 318 NIPER-9 252 

41039 41 039 OR Lane NIPER-13 256 NIPER-13 346 

41041 41 041 OR Lincoln NIPER-13 256 NIPER-13 346 

41043 41 043 OR Linn NIPER-13 256 NIPER-13 346 

41045 41 045 OR Malheur NIPER-9 318 NIPER-9 252 

41047 41 047 OR Marion NIPER-13 256 NIPER-13 346 

41049 41 049 OR Morrow NIPER-9 318 NIPER-9 252 

41051 41 051 OR Multnomah NIPER-13 256 NIPER-13 338 

41053 41 053 OR Polk NIPER-13 256 NIPER-13 346 

41055 41 055 OR Sherman NIPER-9 318 NIPER-9 252 

41057 41 057 OR Tillamook NIPER-13 256 NIPER-13 346 

41059 41 059 OR Umatilla NIPER-9 318 NIPER-9 252 

41061 41 061 OR Union NIPER-9 318 NIPER-9 252 

41063 41 063 OR Wallowa NIPER-9 318 NIPER-9 252 

41065 41 065 OR Wasco NIPER-9 318 NIPER-9 252 

41067 41 067 OR Washington NIPER-13 256 NIPER-13 338 

41069 41 069 OR Wheeler NIPER-9 318 NIPER-9 252 

41071 41 071 OR Yamhill NIPER-13 256 NIPER-13 338 

46003 46 003 SD Aurora NIPER-7 230 NIPER-7 230 

46005 46 005 SD Beadle NIPER-7 230 NIPER-7 230 

46007 46 007 SD Bennett NIPER-7 230 NIPER-7 230 

46009 46 009 SD Bon Homme NIPER-7 230 NIPER-7 230 

46011 46 011 SD Brookings NIPER-7 230 NIPER-7 230 

46013 46 013 SD Brown NIPER-7 230 NIPER-7 230 

46015 46 015 SD Brule NIPER-7 230 NIPER-7 230 

46017 46 017 SD Buffalo NIPER-7 230 NIPER-7 230 

46019 46 019 SD Butte NIPER-9 318 NIPER-9 252 

46021 46 021 SD Campbell NIPER-7 230 NIPER-7 230 
Summer 1996 Gasoline Winter 1996 Gasoline 

State County Data Sulfur Data Sulfur 

FIPS No. No. State County Source (ppm) Source (ppm) 

46023 46 023 SD Charles Mix NIPER-7 230 NIPER-7 230 

46025 46 025 SD Clark NIPER-7 230 NIPER-7 230 

46027 46 027 SD Clay NIPER-7 230 NIPER-7 230 

46029 46 029 SD Codington NIPER-7 230 NIPER-7 230 

46031 46 031 SD Corson NIPER-7 230 NIPER-7 230 

46033 46 033 SD Custer NIPER-9 318 NIPER-9 252 

46035 46 035 SD Davison NIPER-7 230 NIPER-7 230 

46037 46 037 SD Day NIPER-7 230 NIPER-7 230 

46039 46 039 SD Deuel NIPER-7 230 NIPER-7 230 

46041 46 041 SD Dewey NIPER-7 230 NIPER-7 230 

46043 46 043 SD Douglas NIPER-7 230 NIPER-7 230 

46045 46 045 SD Edmunds NIPER-7 230 NIPER-7 230 

46047 46 047 SD Fall River NIPER-9 318 NIPER-9 252 

46049 46 049 SD Faulk NIPER-7 230 NIPER-7 230 

46051 46 051 SD Grant NIPER-7 230 NIPER-7 230 

46053 46 053 SD Gregory NIPER-7 230 NIPER-7 230 

46055 46 055 SD Haakon NIPER-7 230 NIPER-7 230 

46057 46 057 SD Hamlin NIPER-7 230 NIPER-7 230 

46059 46 059 SD Hand NIPER-7 230 NIPER-7 230 

46061 46 061 SD Hanson NIPER-7 230 NIPER-7 230 

46063 46 063 SD Harding NIPER-9 318 NIPER-9 252 

46065 46 065 SD Hughes NIPER-7 230 NIPER-7 230 

46067 46 067 SD Hutchinson NIPER-7 230 NIPER-7 230 

46069 46 069 SD Hyde NIPER-7 230 NIPER-7 230 

46071 46 071 SD Jackson NIPER-7 230 NIPER-7 230 

46073 46 073 SD Jerauld NIPER-7 230 NIPER-7 230 

46075 46 075 SD Jones NIPER-7 230 NIPER-7 230 

46077 46 077 SD Kingsbury NIPER-7 230 NIPER-7 230 

46079 46 079 SD Lake NIPER-7 230 NIPER-7 230 

46081 46 081 SD Lawrence NIPER-9 318 NIPER-9 252 

46083 46 083 SD Lincoln NIPER-7 230 NIPER-7 230 

46085 46 085 SD Lyman NIPER-7 230 NIPER-7 230 

46087 46 087 SD Mc Cook NIPER-7 230 NIPER-7 230 

46089 46 089 SD Mc Pherson NIPER-7 230 NIPER-7 230 

46091 46 091 SD Marshall NIPER-7 230 NIPER-7 230 

46093 46 093 SD Meade NIPER-9 318 NIPER-9 252 

46095 46 095 SD Mellette NIPER-7 230 NIPER-7 230 

46097 46 097 SD Miner NIPER-7 230 NIPER-7 230 

46099 46 099 SD Minnehaha NIPER-7 230 NIPER-7 230 

46101 46 101 SD Moody NIPER-7 230 NIPER-7 230 

46103 46 103 SD Pennington NIPER-9 318 NIPER-9 252 

46105 46 105 SD Perkins NIPER-9 318 NIPER-9 252 

46107 46 107 SD Potter NIPER-7 230 NIPER-7 230 
Summer 1996 Gasoline Winter 1996 Gasoline 

State County Data Sulfur Data Sulfur 

FIPS No. No. State County Source (ppm) Source (ppm) 

46109 46 109 SD Roberts NIPER-7 230 NIPER-7 230 

46111 46 111 SD Sanborn NIPER-7 230 NIPER-7 230 

46113 46 113 SD Shannon NIPER-9 318 NIPER-9 252 

46115 46 115 SD Spink NIPER-7 230 NIPER-7 230 

46117 46 117 SD Stanley NIPER-7 230 NIPER-7 230 

46119 46 119 SD Sully NIPER-7 230 NIPER-7 230 

46121 46 121 SD Todd NIPER-7 230 NIPER-7 230 

46123 46 123 SD Tripp NIPER-7 230 NIPER-7 230 

46125 46 125 SD Turner NIPER-7 230 NIPER-7 230 

46127 46 127 SD Union NIPER-7 230 NIPER-7 230 

46129 46 129 SD Walworth NIPER-7 230 NIPER-7 230 

46135 46 135 SD Yankton NIPER-7 230 NIPER-7 230 

46137 46 137 SD Ziebach NIPER-7 230 NIPER-7 230 

49001 49 001 UT Beaver NIPER-10 207 NIPER-10 167 

49003 49 003 UT Box Elder NIPER-10 207 NIPER-10 167 

49005 49 005 UT Cache NIPER-10 207 NIPER-10 167 

49007 49 007 UT Carbon NIPER-10 207 NIPER-10 167 

49009 49 009 UT Daggett NIPER-10 207 NIPER-10 167 

49011 49 011 UT Davis NIPER-10 207 NIPER-10 167 

49013 49 013 UT Duchesne NIPER-10 207 NIPER-10 167 

49015 49 015 UT Emery NIPER-10 207 NIPER-10 167 

49017 49 017 UT Garfield NIPER-10 207 NIPER-10 167 

49019 49 019 UT Grand NIPER-10 207 NIPER-10 167 

49021 49 021 UT Iron NIPER-10 207 NIPER-10 167 

49023 49 023 UT Juab NIPER-10 207 NIPER-10 167 

49025 49 025 UT Kane NIPER-10 207 NIPER-10 167 

49027 49 027 UT Millard NIPER-10 207 NIPER-10 167 

49029 49 029 UT Morgan NIPER-10 207 NIPER-10 167 

49031 49 031 UT Piute NIPER-10 207 NIPER-10 167 

49033 49 033 UT Rich NIPER-10 207 NIPER-10 167 

49035 49 035 UT Salt Lake NIPER-10 207 NIPER-10 167 

49037 49 037 UT San Juan NIPER-10 207 NIPER-10 167 

49039 49 039 UT Sanpete NIPER-10 207 NIPER-10 167 

49041 49 041 UT Sevier NIPER-10 207 NIPER-10 167 

49043 49 043 UT Summit NIPER-10 207 NIPER-10 167 

49045 49 045 UT Tooele NIPER-10 207 NIPER-10 167 

49047 49 047 UT Uintah NIPER-10 207 NIPER-10 167 

49049 49 049 UT Utah NIPER-10 207 NIPER-10 106 

49051 49 051 UT Wasatch NIPER-10 207 NIPER-10 167 

49053 49 053 UT Washington NIPER-10 207 NIPER-10 167 

49055 49 055 UT Wayne NIPER-10 207 NIPER-10 167 

49057 49 057 UT Weber NIPER-10 207 NIPER-10 167 

53001 53 001 WA Adams NIPER-9 318 NIPER-9 252 
Summer 1996 Gasoline Winter 1996 Gasoline 

State County Data Sulfur Data Sulfur 

FIPS No. No. State County Source (ppm) Source (ppm) 

53003 53 003 WA Asotin NIPER-9 318 NIPER-9 252 

53005 53 005 WA Benton NIPER-9 318 NIPER-9 252 

53007 53 007 WA Chelan NIPER-9 318 NIPER-9 252 

53009 53 009 WA Clallam NIPER-9 318 NIPER-9 252 

53011 53 011 WA Clark AAMA-SE 310 AAMA-SE 224 

53013 53 013 WA Columbia NIPER-9 318 NIPER-9 252 

53015 53 015 WA Cowlitz AAMA-SE 310 AAMA-SE 244 

53017 53 017 WA Douglas NIPER-9 318 NIPER-9 252 

53019 53 019 WA Ferry NIPER-9 318 NIPER-9 252 

53021 53 021 WA Franklin NIPER-9 318 NIPER-9 252 

53023 53 023 WA Garfield NIPER-9 318 NIPER-9 252 

53025 53 025 WA Grant NIPER-9 318 NIPER-9 252 

53027 53 027 WA Grays Harbor AAMA-SE 310 AAMA-SE 244 

53029 53 029 WA Island AAMA-SE 310 AAMA-SE 244 

53031 53 031 WA Jefferson AAMA-SE 310 AAMA-SE 244 

53033 53 033 WA King AAMA-SE 310 AAMA-SE 244 

53035 53 035 WA Kitsap AAMA-SE 310 AAMA-SE 244 

53037 53 037 WA Kittitas NIPER-9 318 NIPER-9 252 

53039 53 039 WA Klickitat NIPER-9 318 NIPER-9 252 

53041 53 041 WA Lewis AAMA-SE 310 AAMA-SE 244 

53043 53 043 WA Lincoln NIPER-9 318 NIPER-9 252 

53045 53 045 WA Mason AAMA-SE 310 AAMA-SE 244 

53047 53 047 WA Okanogan NIPER-9 318 NIPER-9 252 

53049 53 049 WA Pacific AAMA-SE 310 AAMA-SE 244 

53051 53 051 WA Pend Oreille NIPER-9 318 NIPER-9 252 

53053 53 053 WA Pierce AAMA-SE 310 AAMA-SE 244 

53055 53 055 WA San Juan AAMA-SE 310 AAMA-SE 244 

53057 53 057 WA Skagit AAMA-SE 310 AAMA-SE 244 

53059 53 059 WA Skamania AAMA-SE 310 AAMA-SE 244 

53061 53 061 WA Snohomish AAMA-SE 310 AAMA-SE 244 

53063 53 063 WA Spokane NIPER-9 318 NIPER-9 227 

53065 53 065 WA Stevens NIPER-9 318 NIPER-9 252 

53067 53 067 WA Thurston AAMA-SE 310 AAMA-SE 244 

53069 53 069 WA Wahkiakum AAMA-SE 310 AAMA-SE 244 

53071 53 071 WA Walla Walla NIPER-9 318 NIPER-9 252 

53073 53 073 WA Whatcom AAMA-SE 310 AAMA-SE 244 

53075 53 075 WA Whitman NIPER-9 318 NIPER-9 252 

53077 53 077 WA Yakima NIPER-9 318 NIPER-9 252 

56001 56 001 WY Albany NIPER-9 318 NIPER-9 252 

56003 56 003 WY Big Horn NIPER-9 318 NIPER-9 252 

56005 56 005 WY Campbell NIPER-9 318 NIPER-9 252 

56007 56 007 WY Carbon NIPER-9 318 NIPER-9 252 

56009 56 009 WY Converse NIPER-9 318 NIPER-9 252 
Summer 1996 Gasoline Winter 1996 Gasoline 

State County Data Sulfur Data Sulfur 

FIPS No. No. State County Source (ppm) Source (ppm) 

56011 56 011 WY Crook NIPER-9 318 NIPER-9 252 

56013 56 013 WY Fremont NIPER-9 318 NIPER-9 252 

56015 56 015 WY Goshen NIPER-9 318 NIPER-9 252 

56017 56 017 WY Hot Springs NIPER-9 318 NIPER-9 252 

56019 56 019 WY Johnson NIPER-9 318 NIPER-9 252 

56021 56 021 WY Laramie NIPER-9 318 NIPER-9 252 

56023 56 023 WY Lincoln NIPER-9 318 NIPER-9 252 

56025 56 025 WY Natrona NIPER-9 318 NIPER-9 252 

56027 56 027 WY Niobrara NIPER-9 318 NIPER-9 252 

56029 56 029 WY Park NIPER-9 318 NIPER-9 252 

56031 56 031 WY Platte NIPER-9 318 NIPER-9 252 

56033 56 033 WY Sheridan NIPER-9 318 NIPER-9 252 

56035 56 035 WY Sublette NIPER-9 318 NIPER-9 252 

56037 56 037 WY Sweetwater NIPER-9 318 NIPER-9 252 

56039 56 039 WY Teton NIPER-9 318 NIPER-9 252 

56041 56 041 WY Uinta NIPER-9 318 NIPER-9 252 

56043 56 043 WY Washakie NIPER-9 318 NIPER-9 252 

56045 56 045 WY Weston NIPER-9 318 NIPER-9 252 
Table A-2. . Default 1996 diesel fuel sulfur levels. 

1996 Diesel Fuel Sulfur Levels (ppm) 

State County Data On-Highway Data Off-Highway 

FIPS No. No. State County Source Summer Winter Source Summer Winter 

04001 04 001 AZ Apache NIPER-RM 380 380 NIPER-RM 4100 4100 

04003 04 003 AZ Cochise NIPER-RM 380 380 NIPER-RM 4100 4100 

04005 04 005 AZ Coconino NIPER-W 310 310 NIPER-W 3400 3400 

04007 04 007 AZ Gila NIPER-W 310 310 NIPER-W 3400 3400 

04009 04 009 AZ Graham NIPER-RM 380 380 NIPER-RM 4100 4100 

04011 04 011 AZ Greenlee NIPER-RM 380 380 NIPER-RM 4100 4100 

04012 04 012 AZ La Paz NIPER-W 310 310 NIPER-W 3400 3400 

04013 04 013 AZ Maricopa NIPER-W 310 310 NIPER-W 3400 3400 

04015 04 015 AZ Mohave NIPER-W 310 310 NIPER-W 3400 3400 

04017 04 017 AZ Navajo NIPER-RM 380 380 NIPER-RM 4100 4100 

04019 04 019 AZ Pima NIPER-W 310 310 NIPER-W 3400 3400 

04021 04 021 AZ Pinal NIPER-W 310 310 NIPER-W 3400 3400 

04023 04 023 AZ Santa Cruz NIPER-RM 380 380 NIPER-RM 4100 4100 

04025 04 025 AZ Yavapai NIPER-W 310 310 NIPER-W 3400 3400 

04027 04 027 AZ Yuma NIPER-W 310 310 NIPER-W 3400 3400 

06001 06 001 CA Alameda AAMA-LA 160 110 AAMA-LA 160 110 

06003 06 003 CA Alpine AAMA-LA 160 110 AAMA-LA 160 110 

06005 06 005 CA Amador AAMA-LA 160 110 AAMA-LA 160 110 

06007 06 007 CA Butte AAMA-LA 160 110 AAMA-LA 160 110 

06009 06 009 CA Calaveras AAMA-LA 160 110 AAMA-LA 160 110 

06011 06 011 CA Colusa AAMA-LA 160 110 AAMA-LA 160 110 

06013 06 013 CA Contra Costa AAMA-LA 160 110 AAMA-LA 160 110 

06015 06 015 CA Del Norte AAMA-LA 160 110 AAMA-LA 160 110 

06017 06 017 CA El Dorado AAMA-LA 160 110 AAMA-LA 160 110 

06019 06 019 CA Fresno AAMA-LA 160 110 AAMA-LA 160 110 

06021 06 021 CA Glenn AAMA-LA 160 110 AAMA-LA 160 110 

06023 06 023 CA Humboldt AAMA-LA 160 110 AAMA-LA 160 110 

06025 06 025 CA Imperial AAMA-LA 160 110 AAMA-LA 160 110 

06027 06 027 CA Inyo AAMA-LA 160 110 AAMA-LA 160 110 

06029 06 029 CA Kern AAMA-LA 160 110 AAMA-LA 160 110 

06031 06 031 CA Kings AAMA-LA 160 110 AAMA-LA 160 110 

06033 06 033 CA Lake AAMA-LA 160 110 AAMA-LA 160 110 

06035 06 035 CA Lassen AAMA-LA 160 110 AAMA-LA 160 110 

06037 06 037 CA Los Angeles AAMA-LA 160 110 AAMA-LA 160 110 

06039 06 039 CA Madera AAMA-LA 160 110 AAMA-LA 160 110 

06041 06 041 CA Marin AAMA-LA 160 110 AAMA-LA 160 110 

06043 06 043 CA Mariposa AAMA-LA 160 110 AAMA-LA 160 110 

06045 06 045 CA Mendocino AAMA-LA 160 110 AAMA-LA 160 110 

06047 06 047 CA Merced AAMA-LA 160 110 AAMA-LA 160 110 

06049 06 049 CA Modoc AAMA-LA 160 110 AAMA-LA 160 110 

06051 06 051 CA Mono AAMA-LA 160 110 AAMA-LA 160 110 

06053 06 053 CA Monterey AAMA-LA 160 110 AAMA-LA 160 110 
1996 Diesel Fuel Sulfur Levels (ppm) 

State County Data On-Highway Data Off-Highway 

FIPS No. No. State County Source Summer Winter Source Summer Winter 

06055 06 055 CA Napa AAMA-LA 160 110 AAMA-LA 160 110 

06057 06 057 CA Nevada AAMA-LA 160 110 AAMA-LA 160 110 

06059 06 059 CA Orange AAMA-LA 160 110 AAMA-LA 160 110 

06061 06 061 CA Placer AAMA-LA 160 110 AAMA-LA 160 110 

06063 06 063 CA Plumas AAMA-LA 160 110 AAMA-LA 160 110 

06065 06 065 CA Riverside AAMA-LA 160 110 AAMA-LA 160 110 

06067 06 067 CA Sacramento AAMA-LA 160 110 AAMA-LA 160 110 

06069 06 069 CA San Benito AAMA-LA 160 110 AAMA-LA 160 110 

06071 06 071 CA San Bernardino AAMA-LA 160 110 AAMA-LA 160 110 

06073 06 073 CA San Diego AAMA-LA 160 110 AAMA-LA 160 110 

06075 06 075 CA San Francisco AAMA-LA 160 110 AAMA-LA 160 110 

06077 06 077 CA San Joaquin AAMA-LA 160 110 AAMA-LA 160 110 

06079 06 079 CA San Luis Obispo AAMA-LA 160 110 AAMA-LA 160 110 

06081 06 081 CA San Mateo AAMA-LA 160 110 AAMA-LA 160 110 

06083 06 083 CA Santa Barbara AAMA-LA 160 110 AAMA-LA 160 110 

06085 06 085 CA Santa Clara AAMA-LA 160 110 AAMA-LA 160 110 

06087 06 087 CA Santa Cruz AAMA-LA 160 110 AAMA-LA 160 110 

06089 06 089 CA Shasta AAMA-LA 160 110 AAMA-LA 160 110 

06091 06 091 CA Sierra AAMA-LA 160 110 AAMA-LA 160 110 

06093 06 093 CA Siskiyou AAMA-LA 160 110 AAMA-LA 160 110 

06095 06 095 CA Solano AAMA-LA 160 110 AAMA-LA 160 110 

06097 06 097 CA Sonoma AAMA-LA 160 110 AAMA-LA 160 110 

06099 06 099 CA Stanislaus AAMA-LA 160 110 AAMA-LA 160 110 

06101 06 101 CA Sutter AAMA-LA 160 110 AAMA-LA 160 110 

06103 06 103 CA Tehama AAMA-LA 160 110 AAMA-LA 160 110 

06105 06 105 CA Trinity AAMA-LA 160 110 AAMA-LA 160 110 

06107 06 107 CA Tulare AAMA-LA 160 110 AAMA-LA 160 110 

06109 06 109 CA Tuolumne AAMA-LA 160 110 AAMA-LA 160 110 

06111 06 111 CA Ventura AAMA-LA 160 110 AAMA-LA 160 110 

06113 06 113 CA Yolo AAMA-LA 160 110 AAMA-LA 160 110 

06115 06 115 CA Yuba AAMA-LA 160 110 AAMA-LA 160 110 

08001 08 001 CO Adams AAMA-DN 340 330 NIPER-RM 4100 4100 

08003 08 003 CO Alamosa AAMA-DN 340 330 NIPER-RM 4100 4100 

08005 08 005 CO Arapahoe AAMA-DN 340 330 NIPER-RM 4100 4100 

08007 08 007 CO Archuleta AAMA-DN 340 330 NIPER-RM 4100 4100 

08009 08 009 CO Baca AAMA-DN 340 330 NIPER-RM 4100 4100 

08011 08 011 CO Bent AAMA-DN 340 330 NIPER-RM 4100 4100 

08013 08 013 CO Boulder AAMA-DN 340 330 NIPER-RM 4100 4100 

08015 08 015 CO Chaffee AAMA-DN 340 330 NIPER-RM 4100 4100 

08017 08 017 CO Cheyenne AAMA-DN 340 330 NIPER-RM 4100 4100 

08019 08 019 CO Clear Creek AAMA-DN 340 330 NIPER-RM 4100 4100 

08021 08 021 CO Conejos AAMA-DN 340 330 NIPER-RM 4100 4100 

08023 08 023 CO Costilla AAMA-DN 340 330 NIPER-RM 4100 4100 
1996 Diesel Fuel Sulfur Levels (ppm) 

State County Data On-Highway Data Off-Highway 

FIPS No. No. State County Source Summer Winter Source Summer Winter 

08025 08 025 CO Crowley AAMA-DN 340 330 NIPER-RM 4100 4100 

08027 08 027 CO Custer AAMA-DN 340 330 NIPER-RM 4100 4100 

08029 08 029 CO Delta AAMA-DN 340 330 NIPER-RM 4100 4100 

08031 08 031 CO Denver AAMA-DN 340 330 NIPER-RM 4100 4100 

08033 08 033 CO Dolores AAMA-DN 340 330 NIPER-RM 4100 4100 

08035 08 035 CO Douglas AAMA-DN 340 330 NIPER-RM 4100 4100 

08037 08 037 CO Eagle AAMA-DN 340 330 NIPER-RM 4100 4100 

08039 08 039 CO Elbert AAMA-DN 340 330 NIPER-RM 4100 4100 

08041 08 041 CO El Paso AAMA-DN 340 330 NIPER-RM 4100 4100 

08043 08 043 CO Fremont AAMA-DN 340 330 NIPER-RM 4100 4100 

08045 08 045 CO Garfield AAMA-DN 340 330 NIPER-RM 4100 4100 

08047 08 047 CO Gilpin AAMA-DN 340 330 NIPER-RM 4100 4100 

08049 08 049 CO Grand AAMA-DN 340 330 NIPER-RM 4100 4100 

08051 08 051 CO Gunnison AAMA-DN 340 330 NIPER-RM 4100 4100 

08053 08 053 CO Hinsdale AAMA-DN 340 330 NIPER-RM 4100 4100 

08055 08 055 CO Huerfano AAMA-DN 340 330 NIPER-RM 4100 4100 

08057 08 057 CO Jackson AAMA-DN 340 330 NIPER-RM 4100 4100 

08059 08 059 CO Jefferson AAMA-DN 340 330 NIPER-RM 4100 4100 

08061 08 061 CO Kiowa AAMA-DN 340 330 NIPER-RM 4100 4100 

08063 08 063 CO Kit Carson AAMA-DN 340 330 NIPER-RM 4100 4100 

08065 08 065 CO Lake AAMA-DN 340 330 NIPER-RM 4100 4100 

08067 08 067 CO La Plata AAMA-DN 340 330 NIPER-RM 4100 4100 

08069 08 069 CO Larimer AAMA-DN 340 330 NIPER-RM 4100 4100 

08071 08 071 CO Las Animas AAMA-DN 340 330 NIPER-RM 4100 4100 

08073 08 073 CO Lincoln AAMA-DN 340 330 NIPER-RM 4100 4100 

08075 08 075 CO Logan AAMA-DN 340 330 NIPER-RM 4100 4100 

08077 08 077 CO Mesa AAMA-DN 340 330 NIPER-RM 4100 4100 

08079 08 079 CO Mineral AAMA-DN 340 330 NIPER-RM 4100 4100 

08081 08 081 CO Moffat AAMA-DN 340 330 NIPER-RM 4100 4100 

08083 08 083 CO Montezuma AAMA-DN 340 330 NIPER-RM 4100 4100 

08085 08 085 CO Montrose AAMA-DN 340 330 NIPER-RM 4100 4100 

08087 08 087 CO Morgan AAMA-DN 340 330 NIPER-RM 4100 4100 

08089 08 089 CO Otero AAMA-DN 340 330 NIPER-RM 4100 4100 

08091 08 091 CO Ouray AAMA-DN 340 330 NIPER-RM 4100 4100 

08093 08 093 CO Park AAMA-DN 340 330 NIPER-RM 4100 4100 

08095 08 095 CO Phillips AAMA-DN 340 330 NIPER-RM 4100 4100 

08097 08 097 CO Pitkin AAMA-DN 340 330 NIPER-RM 4100 4100 

08099 08 099 CO Prowers AAMA-DN 340 330 NIPER-RM 4100 4100 

08101 08 101 CO Pueblo AAMA-DN 340 330 NIPER-RM 4100 4100 

08103 08 103 CO Rio Blanco AAMA-DN 340 330 NIPER-RM 4100 4100 

08105 08 105 CO Rio Grande AAMA-DN 340 330 NIPER-RM 4100 4100 

08107 08 107 CO Routt AAMA-DN 340 330 NIPER-RM 4100 4100 

08109 08 109 CO Saguache AAMA-DN 340 330 NIPER-RM 4100 4100 
1996 Diesel Fuel Sulfur Levels (ppm) 

State County Data On-Highway Data Off-Highway 

FIPS No. No. State County Source Summer Winter Source Summer Winter 

08111 08 111 CO San Juan AAMA-DN 340 330 NIPER-RM 4100 4100 

08113 08 113 CO San Miguel AAMA-DN 340 330 NIPER-RM 4100 4100 

08115 08 115 CO Sedgwick AAMA-DN 340 330 NIPER-RM 4100 4100 

08117 08 117 CO Summit AAMA-DN 340 330 NIPER-RM 4100 4100 

08119 08 119 CO Teller AAMA-DN 340 330 NIPER-RM 4100 4100 

08121 08 121 CO Washington AAMA-DN 340 330 NIPER-RM 4100 4100 

08123 08 123 CO Weld AAMA-DN 340 330 NIPER-RM 4100 4100 

08125 08 125 CO Yuma AAMA-DN 340 330 NIPER-RM 4100 4100 

16001 16 001 ID Ada NIPER-RM 380 380 NIPER-RM 4100 4100 

16003 16 003 ID Adams NIPER-RM 380 380 NIPER-RM 4100 4100 

16005 16 005 ID Bannock NIPER-RM 380 380 NIPER-RM 4100 4100 

16007 16 007 ID Bear Lake NIPER-RM 380 380 NIPER-RM 4100 4100 

16009 16 009 ID Benewah NIPER-RM 380 380 NIPER-RM 4100 4100 

16011 16 011 ID Bingham NIPER-RM 380 380 NIPER-RM 4100 4100 

16013 16 013 ID Blaine NIPER-RM 380 380 NIPER-RM 4100 4100 

16015 16 015 ID Boise NIPER-RM 380 380 NIPER-RM 4100 4100 

16017 16 017 ID Bonner NIPER-RM 380 380 NIPER-RM 4100 4100 

16019 16 019 ID Bonneville NIPER-RM 380 380 NIPER-RM 4100 4100 

16021 16 021 ID Boundary NIPER-RM 380 380 NIPER-RM 4100 4100 

16023 16 023 ID Butte NIPER-RM 380 380 NIPER-RM 4100 4100 

16025 16 025 ID Camas NIPER-RM 380 380 NIPER-RM 4100 4100 

16027 16 027 ID Canyon NIPER-RM 380 380 NIPER-RM 4100 4100 

16029 16 029 ID Caribou NIPER-RM 380 380 NIPER-RM 4100 4100 

16031 16 031 ID Cassia NIPER-RM 380 380 NIPER-RM 4100 4100 

16033 16 033 ID Clark NIPER-RM 380 380 NIPER-RM 4100 4100 

16035 16 035 ID Clearwater NIPER-RM 380 380 NIPER-RM 4100 4100 

16037 16 037 ID Custer NIPER-RM 380 380 NIPER-RM 4100 4100 

16039 16 039 ID Elmore NIPER-RM 380 380 NIPER-RM 4100 4100 

16041 16 041 ID Franklin NIPER-RM 380 380 NIPER-RM 4100 4100 

16043 16 043 ID Fremont NIPER-RM 380 380 NIPER-RM 4100 4100 

16045 16 045 ID Gem NIPER-RM 380 380 NIPER-RM 4100 4100 

16047 16 047 ID Gooding NIPER-RM 380 380 NIPER-RM 4100 4100 

16049 16 049 ID Idaho NIPER-RM 380 380 NIPER-RM 4100 4100 

16051 16 051 ID Jefferson NIPER-RM 380 380 NIPER-RM 4100 4100 

16053 16 053 ID Jerome NIPER-RM 380 380 NIPER-RM 4100 4100 

16055 16 055 ID Kootenai NIPER-RM 380 380 NIPER-RM 4100 4100 

16057 16 057 ID Latah NIPER-RM 380 380 NIPER-RM 4100 4100 

16059 16 059 ID Lemhi NIPER-RM 380 380 NIPER-RM 4100 4100 

16061 16 061 ID Lewis NIPER-RM 380 380 NIPER-RM 4100 4100 

16063 16 063 ID Lincoln NIPER-RM 380 380 NIPER-RM 4100 4100 

16065 16 065 ID Madison NIPER-RM 380 380 NIPER-RM 4100 4100 

16067 16 067 ID Minidoka NIPER-RM 380 380 NIPER-RM 4100 4100 

16069 16 069 ID Nez Perce NIPER-RM 380 380 NIPER-RM 4100 4100 
1996 Diesel Fuel Sulfur Levels (ppm) 

State County Data On-Highway Data Off-Highway 

FIPS No. No. State County Source Summer Winter Source Summer Winter 

16071 16 071 ID Oneida NIPER-RM 380 380 NIPER-RM 4100 4100 

16073 16 073 ID Owyhee NIPER-RM 380 380 NIPER-RM 4100 4100 

16075 16 075 ID Payette NIPER-RM 380 380 NIPER-RM 4100 4100 

16077 16 077 ID Power NIPER-RM 380 380 NIPER-RM 4100 4100 

16079 16 079 ID Shoshone NIPER-RM 380 380 NIPER-RM 4100 4100 

16081 16 081 ID Teton NIPER-RM 380 380 NIPER-RM 4100 4100 

16083 16 083 ID Twin Falls NIPER-RM 380 380 NIPER-RM 4100 4100 

16085 16 085 ID Valley NIPER-RM 380 380 NIPER-RM 4100 4100 

16087 16 087 ID Washington NIPER-RM 380 380 NIPER-RM 4100 4100 

30001 30 001 MT Beaverhead AAMA-BL 310 330 NIPER-RM 4100 4100 

30003 30 003 MT Big Horn AAMA-BL 310 330 NIPER-RM 4100 4100 

30005 30 005 MT Blaine AAMA-BL 310 330 NIPER-RM 4100 4100 

30007 30 007 MT Broadwater AAMA-BL 310 330 NIPER-RM 4100 4100 

30009 30 009 MT Carbon AAMA-BL 310 330 NIPER-RM 4100 4100 

30011 30 011 MT Carter AAMA-BL 310 330 NIPER-RM 4100 4100 

30013 30 013 MT Cascade AAMA-BL 310 330 NIPER-RM 4100 4100 

30015 30 015 MT Chouteau AAMA-BL 310 330 NIPER-RM 4100 4100 

30017 30 017 MT Custer AAMA-BL 310 330 NIPER-RM 4100 4100 

30019 30 019 MT Daniels AAMA-BL 310 330 NIPER-RM 4100 4100 

30021 30 021 MT Dawson AAMA-BL 310 330 NIPER-RM 4100 4100 

30023 30 023 MT Deer Lodge AAMA-BL 310 330 NIPER-RM 4100 4100 

30025 30 025 MT Fallon AAMA-BL 310 330 NIPER-RM 4100 4100 

30027 30 027 MT Fergus AAMA-BL 310 330 NIPER-RM 4100 4100 

30029 30 029 MT Flat Head AAMA-BL 310 330 NIPER-RM 4100 4100 

30031 30 031 MT Gallatin AAMA-BL 310 330 NIPER-RM 4100 4100 

30033 30 033 MT Garfield AAMA-BL 310 330 NIPER-RM 4100 4100 

30035 30 035 MT Glacier AAMA-BL 310 330 NIPER-RM 4100 4100 

30037 30 037 MT Golden Valley AAMA-BL 310 330 NIPER-RM 4100 4100 

30039 30 039 MT Granite AAMA-BL 310 330 NIPER-RM 4100 4100 

30041 30 041 MT Hill AAMA-BL 310 330 NIPER-RM 4100 4100 

30043 30 043 MT Jefferson AAMA-BL 310 330 NIPER-RM 4100 4100 

30045 30 045 MT Judith Basin AAMA-BL 310 330 NIPER-RM 4100 4100 

30047 30 047 MT Lake AAMA-BL 310 330 NIPER-RM 4100 4100 

30049 30 049 MT Lewis and Clark AAMA-BL 310 330 NIPER-RM 4100 4100 

30051 30 051 MT Liberty AAMA-BL 310 330 NIPER-RM 4100 4100 

30053 30 053 MT Lincoln AAMA-BL 310 330 NIPER-RM 4100 4100 

30055 30 055 MT Mc Cone AAMA-BL 310 330 NIPER-RM 4100 4100 

30057 30 057 MT Madison AAMA-BL 310 330 NIPER-RM 4100 4100 

30059 30 059 MT Meagher AAMA-BL 310 330 NIPER-RM 4100 4100 

30061 30 061 MT Mineral AAMA-BL 310 330 NIPER-RM 4100 4100 

30063 30 063 MT Missoula AAMA-BL 310 330 NIPER-RM 4100 4100 

30065 30 065 MT Musselshell AAMA-BL 310 330 NIPER-RM 4100 4100 

30067 30 067 MT Park AAMA-BL 310 330 NIPER-RM 4100 4100 
1996 Diesel Fuel Sulfur Levels (ppm) 

State County Data On-Highway Data Off-Highway 

FIPS No. No. State County Source Summer Winter Source Summer Winter 

30069 30 069 MT Petroleum AAMA-BL 310 330 NIPER-RM 4100 4100 

30071 30 071 MT Phillips AAMA-BL 310 330 NIPER-RM 4100 4100 

30073 30 073 MT Pondera AAMA-BL 310 330 NIPER-RM 4100 4100 

30075 30 075 MT Powder River AAMA-BL 310 330 NIPER-RM 4100 4100 

30077 30 077 MT Powell AAMA-BL 310 330 NIPER-RM 4100 4100 

30079 30 079 MT Prairie AAMA-BL 310 330 NIPER-RM 4100 4100 

30081 30 081 MT Ravalli AAMA-BL 310 330 NIPER-RM 4100 4100 

30083 30 083 MT Richland AAMA-BL 310 330 NIPER-RM 4100 4100 

30085 30 085 MT Roosevelt AAMA-BL 310 330 NIPER-RM 4100 4100 

30087 30 087 MT Rosebud AAMA-BL 310 330 NIPER-RM 4100 4100 

30089 30 089 MT Sanders AAMA-BL 310 330 NIPER-RM 4100 4100 

30091 30 091 MT Sheridan AAMA-BL 310 330 NIPER-RM 4100 4100 

30093 30 093 MT Silver Bow AAMA-BL 310 330 NIPER-RM 4100 4100 

30095 30 095 MT Stillwater AAMA-BL 310 330 NIPER-RM 4100 4100 

30097 30 097 MT Sweet Grass AAMA-BL 310 330 NIPER-RM 4100 4100 

30099 30 099 MT Teton AAMA-BL 310 330 NIPER-RM 4100 4100 

30101 30 101 MT Toole AAMA-BL 310 330 NIPER-RM 4100 4100 

30103 30 103 MT Treasure AAMA-BL 310 330 NIPER-RM 4100 4100 

30105 30 105 MT Valley AAMA-BL 310 330 NIPER-RM 4100 4100 

30107 30 107 MT Wheatland AAMA-BL 310 330 NIPER-RM 4100 4100 

30109 30 109 MT Wibaux AAMA-BL 310 330 NIPER-RM 4100 4100 

30111 30 111 MT Yellowstone AAMA-BL 310 330 NIPER-RM 4100 4100 

30113 30 113 MT 

Yellowstone 

Park AAMA-BL 310 330 NIPER-RM 4100 4100 

32001 32 001 NV Churchill NIPER-W 310 310 NIPER-W 3400 3400 

32003 32 003 NV Clark NIPER-W 310 310 NIPER-W 3400 3400 

32005 32 005 NV Douglas NIPER-W 310 310 NIPER-W 3400 3400 

32007 32 007 NV Elko NIPER-W 310 310 NIPER-W 3400 3400 

32009 32 009 NV Esmeralda NIPER-W 310 310 NIPER-W 3400 3400 

32011 32 011 NV Eureka NIPER-W 310 310 NIPER-W 3400 3400 

32013 32 013 NV Humboldt NIPER-W 310 310 NIPER-W 3400 3400 

32015 32 015 NV Lander NIPER-W 310 310 NIPER-W 3400 3400 

32017 32 017 NV Lincoln NIPER-W 310 310 NIPER-W 3400 3400 

32019 32 019 NV Lyon NIPER-W 310 310 NIPER-W 3400 3400 

32021 32 021 NV Mineral NIPER-W 310 310 NIPER-W 3400 3400 

32023 32 023 NV Nye NIPER-W 310 310 NIPER-W 3400 3400 

32027 32 027 NV Pershing NIPER-W 310 310 NIPER-W 3400 3400 

32029 32 029 NV Storey NIPER-W 310 310 NIPER-W 3400 3400 

32031 32 031 NV Washoe NIPER-W 310 310 NIPER-W 3400 3400 

32033 32 033 NV White Pine NIPER-W 310 310 NIPER-W 3400 3400 

32510 32 510 NV Carson City NIPER-W 310 310 NIPER-W 3400 3400 

35001 35 001 NM Bernalillo AAMA-ALB 300 320 NIPER-RM 4100 4100 

35003 35 003 NM Catron AAMA-ALB 300 320 NIPER-RM 4100 4100 

35005 35 005 NM Chaves AAMA-ALB 300 320 NIPER-RM 4100 4100 
1996 Diesel Fuel Sulfur Levels (ppm) 

State County Data On-Highway Data Off-Highway 

FIPS No. No. State County Source Summer Winter Source Summer Winter 

35006 35 006 NM Cibola AAMA-ALB 300 320 NIPER-RM 4100 4100 

35007 35 007 NM Colfax AAMA-ALB 300 320 NIPER-RM 4100 4100 

35009 35 009 NM Curry AAMA-ALB 300 320 NIPER-RM 4100 4100 

35011 35 011 NM De Baca AAMA-ALB 300 320 NIPER-RM 4100 4100 

35013 35 013 NM Dona Ana AAMA-ALB 300 320 NIPER-RM 4100 4100 

35015 35 015 NM Eddy AAMA-ALB 300 320 NIPER-RM 4100 4100 

35017 35 017 NM Grant AAMA-ALB 300 320 NIPER-RM 4100 4100 

35019 35 019 NM Guadalupe AAMA-ALB 300 320 NIPER-RM 4100 4100 

35021 35 021 NM Harding AAMA-ALB 300 320 NIPER-RM 4100 4100 

35023 35 023 NM Hidalgo AAMA-ALB 300 320 NIPER-RM 4100 4100 

35025 35 025 NM Lea AAMA-ALB 300 320 NIPER-RM 4100 4100 

35027 35 027 NM Lincoln AAMA-ALB 300 320 NIPER-RM 4100 4100 

35028 35 028 NM Los Alamos AAMA-ALB 300 320 NIPER-RM 4100 4100 

35029 35 029 NM Luna AAMA-ALB 300 320 NIPER-RM 4100 4100 

35031 35 031 NM Mc Kinley AAMA-ALB 300 320 NIPER-RM 4100 4100 

35033 35 033 NM Mora AAMA-ALB 300 320 NIPER-RM 4100 4100 

35035 35 035 NM Otero AAMA-ALB 300 320 NIPER-RM 4100 4100 

35037 35 037 NM Quay AAMA-ALB 300 320 NIPER-RM 4100 4100 

35039 35 039 NM Rio Arriba AAMA-ALB 300 320 NIPER-RM 4100 4100 

35041 35 041 NM Roosevelt AAMA-ALB 300 320 NIPER-RM 4100 4100 

35043 35 043 NM Sandoval AAMA-ALB 300 320 NIPER-RM 4100 4100 

35045 35 045 NM San Juan AAMA-ALB 300 320 NIPER-RM 4100 4100 

35047 35 047 NM San Miguel AAMA-ALB 300 320 NIPER-RM 4100 4100 

35049 35 049 NM Santa Fe AAMA-ALB 300 320 NIPER-RM 4100 4100 

35051 35 051 NM Sierra AAMA-ALB 300 320 NIPER-RM 4100 4100 

35053 35 053 NM Socorro AAMA-ALB 300 320 NIPER-RM 4100 4100 

35055 35 055 NM Taos AAMA-ALB 300 320 NIPER-RM 4100 4100 

35057 35 057 NM Torrance AAMA-ALB 300 320 NIPER-RM 4100 4100 

35059 35 059 NM Union AAMA-ALB 300 320 NIPER-RM 4100 4100 

35061 35 061 NM Valencia AAMA-ALB 300 320 NIPER-RM 4100 4100 

38001 38 001 ND Adams AAMA-BL 310 330 NIPER-RM 4100 4100 

38003 38 003 ND Barnes NIPER-F 310 310 NIPER-F 4200 4200 

38005 38 005 ND Benson NIPER-F 310 310 NIPER-F 4200 4200 

38007 38 007 ND Billings AAMA-BL 310 330 NIPER-RM 4100 4100 

38009 38 009 ND Bottineau NIPER-F 310 310 NIPER-F 4200 4200 

38011 38 011 ND Bowman AAMA-BL 310 330 NIPER-RM 4100 4100 

38013 38 013 ND Burke AAMA-BL 310 330 NIPER-RM 4100 4100 

38015 38 015 ND Burleigh NIPER-F 310 310 NIPER-F 4200 4200 

38017 38 017 ND Cass NIPER-F 310 310 NIPER-F 4200 4200 

38019 38 019 ND Cavalier NIPER-F 310 310 NIPER-F 4200 4200 

38021 38 021 ND Dickey NIPER-F 310 310 NIPER-F 4200 4200 

38023 38 023 ND Divide AAMA-BL 310 330 NIPER-RM 4100 4100 

38025 38 025 ND Dunn AAMA-BL 310 330 NIPER-RM 4100 4100 
1996 Diesel Fuel Sulfur Levels (ppm) 

State County Data On-Highway Data Off-Highway 

FIPS No. No. State County Source Summer Winter Source Summer Winter 

38027 38 027 ND Eddy NIPER-F 310 310 NIPER-F 4200 4200 

38029 38 029 ND Emmons NIPER-F 310 310 NIPER-F 4200 4200 

38031 38 031 ND Foster NIPER-F 310 310 NIPER-F 4200 4200 

38033 38 033 ND Golden Valley AAMA-BL 310 330 NIPER-RM 4100 4100 

38035 38 035 ND Grand Forks NIPER-F 310 310 NIPER-F 4200 4200 

38037 38 037 ND Grant NIPER-F 310 310 NIPER-F 4200 4200 

38039 38 039 ND Griggs NIPER-F 310 310 NIPER-F 4200 4200 

38041 38 041 ND Hettinger AAMA-BL 310 330 NIPER-RM 4100 4100 

38043 38 043 ND Kidder NIPER-F 310 310 NIPER-F 4200 4200 

38045 38 045 ND La Moure NIPER-F 310 310 NIPER-F 4200 4200 

38047 38 047 ND Logan NIPER-F 310 310 NIPER-F 4200 4200 

38049 38 049 ND Mc Henry NIPER-F 310 310 NIPER-F 4200 4200 

38051 38 051 ND Mc Intosh NIPER-F 310 310 NIPER-F 4200 4200 

38053 38 053 ND Mc Kenzie AAMA-BL 310 330 NIPER-RM 4100 4100 

38055 38 055 ND Mc Lean NIPER-F 310 310 NIPER-F 4200 4200 

38057 38 057 ND Mercer NIPER-F 310 310 NIPER-F 4200 4200 

38059 38 059 ND Morton NIPER-F 310 310 NIPER-F 4200 4200 

38061 38 061 ND Mountrail AAMA-BL 310 330 NIPER-RM 4100 4100 

38063 38 063 ND Nelson NIPER-F 310 310 NIPER-F 4200 4200 

38065 38 065 ND Oliver NIPER-F 310 310 NIPER-F 4200 4200 

38067 38 067 ND Pembina NIPER-F 310 310 NIPER-F 4200 4200 

38069 38 069 ND Pierce NIPER-F 310 310 NIPER-F 4200 4200 

38071 38 071 ND Ramsey NIPER-F 310 310 NIPER-F 4200 4200 

38073 38 073 ND Ransom NIPER-F 310 310 NIPER-F 4200 4200 

38075 38 075 ND Renville NIPER-F 310 310 NIPER-F 4200 4200 

38077 38 077 ND Richland NIPER-F 310 310 NIPER-F 4200 4200 

38079 38 079 ND Rolette NIPER-F 310 310 NIPER-F 4200 4200 

38081 38 081 ND Sargent NIPER-F 310 310 NIPER-F 4200 4200 

38083 38 083 ND Sheridan NIPER-F 310 310 NIPER-F 4200 4200 

38085 38 085 ND Sioux NIPER-F 310 310 NIPER-F 4200 4200 

38087 38 087 ND Slope AAMA-BL 310 330 NIPER-RM 4100 4100 

38089 38 089 ND Stark AAMA-BL 310 330 NIPER-RM 4100 4100 

38091 38 091 ND Steele NIPER-F 310 310 NIPER-F 4200 4200 

38093 38 093 ND Stutsman NIPER-F 310 310 NIPER-F 4200 4200 

38095 38 095 ND Towner NIPER-F 310 310 NIPER-F 4200 4200 

38097 38 097 ND Traill NIPER-F 310 310 NIPER-F 4200 4200 

38099 38 099 ND Walsh NIPER-F 310 310 NIPER-F 4200 4200 

38101 38 101 ND Ward NIPER-F 310 310 NIPER-F 4200 4200 

38103 38 103 ND Wells NIPER-F 310 310 NIPER-F 4200 4200 

38105 38 105 ND Williams AAMA-BL 310 330 NIPER-RM 4100 4100 

41001 41 001 OR Baker NIPER-W 310 310 NIPER-W 3400 3400 

41003 41 003 OR Benton AAMA-SE 260 320 NIPER-W 3400 3400 

41005 41 005 OR Clackamas AAMA-SE 260 320 NIPER-W 3400 3400 
1996 Diesel Fuel Sulfur Levels (ppm) 

State County Data On-Highway Data Off-Highway 

FIPS No. No. State County Source Summer Winter Source Summer Winter 

41007 41 007 OR Clatsop AAMA-SE 260 320 NIPER-W 3400 3400 

41009 41 009 OR Columbia AAMA-SE 260 320 NIPER-W 3400 3400 

41011 41 011 OR Coos AAMA-SE 260 320 NIPER-W 3400 3400 

41013 41 013 OR Crook NIPER-W 310 310 NIPER-W 3400 3400 

41015 41 015 OR Curry AAMA-SE 260 320 NIPER-W 3400 3400 

41017 41 017 OR Deschutes NIPER-W 310 310 NIPER-W 3400 3400 

41019 41 019 OR Douglas AAMA-SE 260 320 NIPER-W 3400 3400 

41021 41 021 OR Gilliam NIPER-W 310 310 NIPER-W 3400 3400 

41023 41 023 OR Grant NIPER-W 310 310 NIPER-W 3400 3400 

41025 41 025 OR Harney NIPER-W 310 310 NIPER-W 3400 3400 

41027 41 027 OR Hood River NIPER-W 310 310 NIPER-W 3400 3400 

41029 41 029 OR Jackson AAMA-SE 260 320 NIPER-W 3400 3400 

41031 41 031 OR Jefferson NIPER-W 310 310 NIPER-W 3400 3400 

41033 41 033 OR Josephine AAMA-SE 260 320 NIPER-W 3400 3400 

41035 41 035 OR Klamath AAMA-SE 260 320 NIPER-W 3400 3400 

41037 41 037 OR Lake NIPER-W 310 310 NIPER-W 3400 3400 

41039 41 039 OR Lane AAMA-SE 260 320 NIPER-W 3400 3400 

41041 41 041 OR Lincoln AAMA-SE 260 320 NIPER-W 3400 3400 

41043 41 043 OR Linn AAMA-SE 260 320 NIPER-W 3400 3400 

41045 41 045 OR Malheur NIPER-W 310 310 NIPER-W 3400 3400 

41047 41 047 OR Marion AAMA-SE 260 320 NIPER-W 3400 3400 

41049 41 049 OR Morrow NIPER-W 310 310 NIPER-W 3400 3400 

41051 41 051 OR Multnomah AAMA-SE 260 320 NIPER-W 3400 3400 

41053 41 053 OR Polk AAMA-SE 260 320 NIPER-W 3400 3400 

41055 41 055 OR Sherman NIPER-W 310 310 NIPER-W 3400 3400 

41057 41 057 OR Tillamook AAMA-SE 260 320 NIPER-W 3400 3400 

41059 41 059 OR Umatilla NIPER-W 310 310 NIPER-W 3400 3400 

41061 41 061 OR Union NIPER-W 310 310 NIPER-W 3400 3400 

41063 41 063 OR Wallowa NIPER-W 310 310 NIPER-W 3400 3400 

41065 41 065 OR Wasco NIPER-W 310 310 NIPER-W 3400 3400 

41067 41 067 OR Washington AAMA-SE 260 320 NIPER-W 3400 3400 

41069 41 069 OR Wheeler NIPER-W 310 310 NIPER-W 3400 3400 

41071 41 071 OR Yamhill AAMA-SE 260 320 NIPER-W 3400 3400 

46003 46 003 SD Aurora NIPER-F 310 310 NIPER-F 4200 4200 

46005 46 005 SD Beadle NIPER-F 310 310 NIPER-F 4200 4200 

46007 46 007 SD Bennett NIPER-F 310 310 NIPER-F 4200 4200 

46009 46 009 SD Bon Homme NIPER-F 310 310 NIPER-F 4200 4200 

46011 46 011 SD Brookings NIPER-F 310 310 NIPER-F 4200 4200 

46013 46 013 SD Brown NIPER-F 310 310 NIPER-F 4200 4200 

46015 46 015 SD Brule NIPER-F 310 310 NIPER-F 4200 4200 

46017 46 017 SD Buffalo NIPER-F 310 310 NIPER-F 4200 4200 

46019 46 019 SD Butte NIPER-RM 380 380 NIPER-RM 4100 4100 

46021 46 021 SD Campbell NIPER-F 310 310 NIPER-F 4200 4200 
1996 Diesel Fuel Sulfur Levels (ppm) 

State County Data On-Highway Data Off-Highway 

FIPS No. No. State County Source Summer Winter Source Summer Winter 

46023 46 023 SD Charles Mix NIPER-F 310 310 NIPER-F 4200 4200 

46025 46 025 SD Clark NIPER-F 310 310 NIPER-F 4200 4200 

46027 46 027 SD Clay NIPER-F 310 310 NIPER-F 4200 4200 

46029 46 029 SD Codington NIPER-F 310 310 NIPER-F 4200 4200 

46031 46 031 SD Corson NIPER-F 310 310 NIPER-F 4200 4200 

46033 46 033 SD Custer NIPER-RM 380 380 NIPER-RM 4100 4100 

46035 46 035 SD Davison NIPER-F 310 310 NIPER-F 4200 4200 

46037 46 037 SD Day NIPER-F 310 310 NIPER-F 4200 4200 

46039 46 039 SD Deuel NIPER-F 310 310 NIPER-F 4200 4200 

46041 46 041 SD Dewey NIPER-F 310 310 NIPER-F 4200 4200 

46043 46 043 SD Douglas NIPER-F 310 310 NIPER-F 4200 4200 

46045 46 045 SD Edmunds NIPER-F 310 310 NIPER-F 4200 4200 

46047 46 047 SD Fall River NIPER-RM 380 380 NIPER-RM 4100 4100 

46049 46 049 SD Faulk NIPER-F 310 310 NIPER-F 4200 4200 

46051 46 051 SD Grant NIPER-F 310 310 NIPER-F 4200 4200 

46053 46 053 SD Gregory NIPER-F 310 310 NIPER-F 4200 4200 

46055 46 055 SD Haakon NIPER-F 310 310 NIPER-F 4200 4200 

46057 46 057 SD Hamlin NIPER-F 310 310 NIPER-F 4200 4200 

46059 46 059 SD Hand NIPER-F 310 310 NIPER-F 4200 4200 

46061 46 061 SD Hanson NIPER-F 310 310 NIPER-F 4200 4200 

46063 46 063 SD Harding NIPER-RM 380 380 NIPER-RM 4100 4100 

46065 46 065 SD Hughes NIPER-F 310 310 NIPER-F 4200 4200 

46067 46 067 SD Hutchinson NIPER-F 310 310 NIPER-F 4200 4200 

46069 46 069 SD Hyde NIPER-F 310 310 NIPER-F 4200 4200 

46071 46 071 SD Jackson NIPER-F 310 310 NIPER-F 4200 4200 

46073 46 073 SD Jerauld NIPER-F 310 310 NIPER-F 4200 4200 

46075 46 075 SD Jones NIPER-F 310 310 NIPER-F 4200 4200 

46077 46 077 SD Kingsbury NIPER-F 310 310 NIPER-F 4200 4200 

46079 46 079 SD Lake NIPER-F 310 310 NIPER-F 4200 4200 

46081 46 081 SD Lawrence NIPER-RM 380 380 NIPER-RM 4100 4100 

46083 46 083 SD Lincoln NIPER-F 310 310 NIPER-F 4200 4200 

46085 46 085 SD Lyman NIPER-F 310 310 NIPER-F 4200 4200 

46087 46 087 SD Mc Cook NIPER-F 310 310 NIPER-F 4200 4200 

46089 46 089 SD Mc Pherson NIPER-F 310 310 NIPER-F 4200 4200 

46091 46 091 SD Marshall NIPER-F 310 310 NIPER-F 4200 4200 

46093 46 093 SD Meade NIPER-RM 380 380 NIPER-RM 4100 4100 

46095 46 095 SD Mellette NIPER-F 310 310 NIPER-F 4200 4200 

46097 46 097 SD Miner NIPER-F 310 310 NIPER-F 4200 4200 

46099 46 099 SD Minnehaha NIPER-F 310 310 NIPER-F 4200 4200 

46101 46 101 SD Moody NIPER-F 310 310 NIPER-F 4200 4200 

46103 46 103 SD Pennington NIPER-RM 380 380 NIPER-RM 4100 4100 

46105 46 105 SD Perkins NIPER-RM 380 380 NIPER-RM 4100 4100 

46107 46 107 SD Potter NIPER-F 310 310 NIPER-F 4200 4200 
1996 Diesel Fuel Sulfur Levels (ppm) 

State County Data On-Highway Data Off-Highway 

FIPS No. No. State County Source Summer Winter Source Summer Winter 

46109 46 109 SD Roberts NIPER-F 310 310 NIPER-F 4200 4200 

46111 46 111 SD Sanborn NIPER-F 310 310 NIPER-F 4200 4200 

46113 46 113 SD Shannon NIPER-RM 380 380 NIPER-RM 4100 4100 

46115 46 115 SD Spink NIPER-F 310 310 NIPER-F 4200 4200 

46117 46 117 SD Stanley NIPER-F 310 310 NIPER-F 4200 4200 

46119 46 119 SD Sully NIPER-F 310 310 NIPER-F 4200 4200 

46121 46 121 SD Todd NIPER-F 310 310 NIPER-F 4200 4200 

46123 46 123 SD Tripp NIPER-F 310 310 NIPER-F 4200 4200 

46125 46 125 SD Turner NIPER-F 310 310 NIPER-F 4200 4200 

46127 46 127 SD Union NIPER-F 310 310 NIPER-F 4200 4200 

46129 46 129 SD Walworth NIPER-F 310 310 NIPER-F 4200 4200 

46135 46 135 SD Yankton NIPER-F 310 310 NIPER-F 4200 4200 

46137 46 137 SD Ziebach NIPER-F 310 310 NIPER-F 4200 4200 

49001 49 001 UT Beaver NIPER-W 310 310 NIPER-W 3400 3400 

49003 49 003 UT Box Elder NIPER-RM 380 380 NIPER-RM 4100 4100 

49005 49 005 UT Cache NIPER-RM 380 380 NIPER-RM 4100 4100 

49007 49 007 UT Carbon NIPER-RM 380 380 NIPER-RM 4100 4100 

49009 49 009 UT Daggett NIPER-RM 380 380 NIPER-RM 4100 4100 

49011 49 011 UT Davis NIPER-RM 380 380 NIPER-RM 4100 4100 

49013 49 013 UT Duchesne NIPER-RM 380 380 NIPER-RM 4100 4100 

49015 49 015 UT Emery NIPER-RM 380 380 NIPER-RM 4100 4100 

49017 49 017 UT Garfield NIPER-W 310 310 NIPER-W 3400 3400 

49019 49 019 UT Grand NIPER-RM 380 380 NIPER-RM 4100 4100 

49021 49 021 UT Iron NIPER-W 310 310 NIPER-W 3400 3400 

49023 49 023 UT Juab NIPER-RM 380 380 NIPER-RM 4100 4100 

49025 49 025 UT Kane NIPER-W 310 310 NIPER-W 3400 3400 

49027 49 027 UT Millard NIPER-RM 380 380 NIPER-RM 4100 4100 

49029 49 029 UT Morgan NIPER-RM 380 380 NIPER-RM 4100 4100 

49031 49 031 UT Piute NIPER-W 310 310 NIPER-W 3400 3400 

49033 49 033 UT Rich NIPER-RM 380 380 NIPER-RM 4100 4100 

49035 49 035 UT Salt Lake NIPER-RM 380 380 NIPER-RM 4100 4100 

49037 49 037 UT San Juan NIPER-RM 380 380 NIPER-RM 4100 4100 

49039 49 039 UT Sanpete NIPER-RM 380 380 NIPER-RM 4100 4100 

49041 49 041 UT Sevier NIPER-RM 380 380 NIPER-RM 4100 4100 

49043 49 043 UT Summit NIPER-RM 380 380 NIPER-RM 4100 4100 

49045 49 045 UT Tooele NIPER-RM 380 380 NIPER-RM 4100 4100 

49047 49 047 UT Uintah NIPER-RM 380 380 NIPER-RM 4100 4100 

49049 49 049 UT Utah NIPER-RM 380 380 NIPER-RM 4100 4100 

49051 49 051 UT Wasatch NIPER-RM 380 380 NIPER-RM 4100 4100 

49053 49 053 UT Washington NIPER-W 310 310 NIPER-W 3400 3400 

49055 49 055 UT Wayne NIPER-RM 380 380 NIPER-RM 4100 4100 

49057 49 057 UT Weber NIPER-RM 380 380 NIPER-RM 4100 4100 

53001 53 001 WA Adams NIPER-W 310 310 NIPER-W 3400 3400 
1996 Diesel Fuel Sulfur Levels (ppm) 

State County Data On-Highway Data Off-Highway 

FIPS No. No. State County Source Summer Winter Source Summer Winter 

53003 53 003 WA Asotin NIPER-W 310 310 NIPER-W 3400 3400 

53005 53 005 WA Benton NIPER-W 310 310 NIPER-W 3400 3400 

53007 53 007 WA Chelan NIPER-W 310 310 NIPER-W 3400 3400 

53009 53 009 WA Clallam NIPER-W 310 310 NIPER-W 3400 3400 

53011 53 011 WA Clark AAMA-SE 260 320 NIPER-W 3400 3400 

53013 53 013 WA Columbia NIPER-W 310 310 NIPER-W 3400 3400 

53015 53 015 WA Cowlitz AAMA-SE 260 320 NIPER-W 3400 3400 

53017 53 017 WA Douglas NIPER-W 310 310 NIPER-W 3400 3400 

53019 53 019 WA Ferry NIPER-W 310 310 NIPER-W 3400 3400 

53021 53 021 WA Franklin NIPER-W 310 310 NIPER-W 3400 3400 

53023 53 023 WA Garfield NIPER-W 310 310 NIPER-W 3400 3400 

53025 53 025 WA Grant NIPER-W 310 310 NIPER-W 3400 3400 

53027 53 027 WA Grays Harbor AAMA-SE 260 320 NIPER-W 3400 3400 

53029 53 029 WA Island AAMA-SE 260 320 NIPER-W 3400 3400 

53031 53 031 WA Jefferson AAMA-SE 260 320 NIPER-W 3400 3400 

53033 53 033 WA King AAMA-SE 260 320 NIPER-W 3400 3400 

53035 53 035 WA Kitsap AAMA-SE 260 320 NIPER-W 3400 3400 

53037 53 037 WA Kittitas NIPER-W 310 310 NIPER-W 3400 3400 

53039 53 039 WA Klickitat NIPER-W 310 310 NIPER-W 3400 3400 

53041 53 041 WA Lewis AAMA-SE 260 320 NIPER-W 3400 3400 

53043 53 043 WA Lincoln NIPER-W 310 310 NIPER-W 3400 3400 

53045 53 045 WA Mason AAMA-SE 260 320 NIPER-W 3400 3400 

53047 53 047 WA Okanogan NIPER-W 310 310 NIPER-W 3400 3400 

53049 53 049 WA Pacific AAMA-SE 260 320 NIPER-W 3400 3400 

53051 53 051 WA Pend Oreille NIPER-W 310 310 NIPER-W 3400 3400 

53053 53 053 WA Pierce AAMA-SE 260 320 NIPER-W 3400 3400 

53055 53 055 WA San Juan AAMA-SE 260 320 NIPER-W 3400 3400 

53057 53 057 WA Skagit AAMA-SE 260 320 NIPER-W 3400 3400 

53059 53 059 WA Skamania AAMA-SE 260 320 NIPER-W 3400 3400 

53061 53 061 WA Snohomish AAMA-SE 260 320 NIPER-W 3400 3400 

53063 53 063 WA Spokane NIPER-W 310 310 NIPER-W 3400 3400 

53065 53 065 WA Stevens NIPER-W 310 310 NIPER-W 3400 3400 

53067 53 067 WA Thurston AAMA-SE 260 320 NIPER-W 3400 3400 

53069 53 069 WA Wahkiakum AAMA-SE 260 320 NIPER-W 3400 3400 

53071 53 071 WA Walla Walla NIPER-W 310 310 NIPER-W 3400 3400 

53073 53 073 WA Whatcom AAMA-SE 260 320 NIPER-W 3400 3400 

53075 53 075 WA Whitman NIPER-W 310 310 NIPER-W 3400 3400 

53077 53 077 WA Yakima NIPER-W 310 310 NIPER-W 3400 3400 

56001 56 001 WY Albany NIPER-RM 380 380 NIPER-RM 4100 4100 

56003 56 003 WY Big Horn NIPER-RM 380 380 NIPER-RM 4100 4100 

56005 56 005 WY Campbell NIPER-RM 380 380 NIPER-RM 4100 4100 

56007 56 007 WY Carbon NIPER-RM 380 380 NIPER-RM 4100 4100 

56009 56 009 WY Converse NIPER-RM 380 380 NIPER-RM 4100 4100 
1996 Diesel Fuel Sulfur Levels (ppm) 

State County Data On-Highway Data Off-Highway 

FIPS No. No. State County Source Summer Winter Source Summer Winter 

56011 56 011 WY Crook NIPER-RM 380 380 NIPER-RM 4100 4100 

56013 56 013 WY Fremont NIPER-RM 380 380 NIPER-RM 4100 4100 

56015 56 015 WY Goshen NIPER-RM 380 380 NIPER-RM 4100 4100 

56017 56 017 WY Hot Springs NIPER-RM 380 380 NIPER-RM 4100 4100 

56019 56 019 WY Johnson NIPER-RM 380 380 NIPER-RM 4100 4100 

56021 56 021 WY Laramie NIPER-RM 380 380 NIPER-RM 4100 4100 

56023 56 023 WY Lincoln NIPER-RM 380 380 NIPER-RM 4100 4100 

56025 56 025 WY Natrona NIPER-RM 380 380 NIPER-RM 4100 4100 

56027 56 027 WY Niobrara NIPER-RM 380 380 NIPER-RM 4100 4100 

56029 56 029 WY Park NIPER-RM 380 380 NIPER-RM 4100 4100 

56031 56 031 WY Platte NIPER-RM 380 380 NIPER-RM 4100 4100 

56033 56 033 WY Sheridan NIPER-RM 380 380 NIPER-RM 4100 4100 

56035 56 035 WY Sublette NIPER-RM 380 380 NIPER-RM 4100 4100 

56037 56 037 WY Sweetwater NIPER-RM 380 380 NIPER-RM 4100 4100 

56039 56 039 WY Teton NIPER-RM 380 380 NIPER-RM 4100 4100 

56041 56 041 WY Uinta NIPER-RM 380 380 NIPER-RM 4100 4100 

56043 56 043 WY Washakie NIPER-RM 380 380 NIPER-RM 4100 4100 

56045 56 045 WY Weston NIPER-RM 380 380 NIPER-RM 4100 4100