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 415.899.0700February 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 tec hnical 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 Was hington Department of Ecology and John Kowalczyk, now retired, formerly of the Oregon Department of Environmental Quality. We also acknowledge the assistan ce and input of many people from air quality planning agencies, too numerous to list here.February 2004

G:\WRAP MSEI\Reporti ng\Final\TOC.doc 

### TABLE OF CONTENTS

Page

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

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

Regulatory Framework for Tribal Vi sibility Implementation Plans ....................... iii

1.0 INTROD UCTION...............................................................................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 MO DELS 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 th e Base Emission Rates..........................................2-8 2.2.2.1 Oxygena ted Gasoline .........................................................2-8 2.2.2.2 Temperat ure Corrections ....................................................2-8 2.2.2.3 Sulfur Effects ..................................................................2-9 2.2.2.4 HDDV Altit ude Adjustment.................................................2-9 2.2.3 Non-Exhaus t PM Emissions........................................................2-9 2.2.4 Particle Si ze Distributions ........................................................ 2-10 2.2.5 Elemental Carbon-Organic Carbon Splits...................................... 2-10 2.2.6 Ammoni a Emissions............................................................... 2-11 2.2.7 Fleet Characteristic Data.......................................................... 2-13 2.3 NONR OAD............................................................................. 2-13 2.3.1 Comparison of NON-ROAD2000 and NON-ROAD2003.................... 2-14 2.3.2 NONROAD Emissi ons Calculations............................................. 2-17 2.4 California Models...................................................................... 2-18February 2004

G:\WRAP MSEI\Reporti ng\Final\TOC.doc 

### 3.0 1996 BASE YEAR ON-ROAD EMISSIONS METHODOLOGY ....................3-1

3.1 VMT Es timates ..........................................................................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 Ca lifornia 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 Emi ssions Methodology......................................................4-2 4.2.1 Airports a nd 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 To wered Airports .....................................................4-7 4.2.2.3 Non-To wered Airports.......................................................4-7 4.3 Commercial Marine Emissions Methodology.......................................4-8 4.3.1 Emissi on Factors.....................................................................4-8 4.3.2 Ocean-G oing Vessels.............................................................. 4-11 4.3.2.1 Port of Coos Bay............................................................ 4-11 4.3.2.2 Puge t Sound Ports .......................................................... 4-13 4.3.2.3 Columbia River Ports ...................................................... 4-17 4.3.3 Tugs.................................................................................. 4-18 4.3.3.1 Puge t Sound Area........................................................... 4-18 4.3.3.2 Columbia and Snake River Traffic....................................... 4-18 4.3.3.3 Or egon 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 Fishi ng 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 Emissi ons Methodology ............................................... 4-25 4.5 California OFFR OAD Emissions ................................................... 4-26 4.6 Generations of SMOKE Files........................................................ 4-27February 2004

G:\WRAP MSEI\Reporti ng\Final\TOC.doc 

### 5.0 1996 RE SULTS ..................................................................................5-1

5.1 On-Road Emissi on Inventories ........................................................5-1 5.2 Off-Road Emissi on Inventories...................................................... 5-13

6.0 FUTURE YEARS O NROAD EMISSIONS................................................6-1

6.1 Vehicle Miles Travel ed (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 Emissi on Calculat ions .......................................................6-7 6.4 Processing of Ca lifornia Data .........................................................6-7 6.5 Generation of SMOKE Files...........................................................6-7

7.0 FUTURE YEARS OF F-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 Agricultu ral 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 Commercia l Marine Vessels ................................................7-3 7.1.1.5 Co nstruction ...................................................................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 Fie ld Equipment ..........................................................7-5 7.1.1.10 Pleas ure Craft ................................................................7-5 7.1.1.11 Recreati onal 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 Emissi ons Projections ..................................................... 7-25 7.4 Commercial Marine Em issions Projections ....................................... 7-25 7.4.1 International Regulations ......................................................... 7-26 7.4.2 EPA Emissi on Standard s ......................................................... 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......................................................... 7-29February 2004

G:\WRAP MSEI\Reporti ng\Final\TOC.doc 

### 8.0 MOBILE SOURCE EMISSION S TRENDS, 1996 TO 2018...........................8-1

8.1 On-road Mobile Sour ce Emissions Trends ..........................................8-1 8.2 Off-road Mobile Sour ce Emissions Trends........................................ 8-10

9.0 PAVED AND UNPAVED RO AD DUST EMISSIONS..................................9-1

9.1 2018 Road Dust Emi ssions-AP-42 Approach .......................................9-1 9.1.1 VMT Estimates for Pa ved and Unpaved Roads.................................9-1 9.1.1.1 Unpaved Ro ad 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 Allocati on 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 Emissi ons-Revised Estimates......................................9-9 9.2.1 Revisions to St ate 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 REFERE NCES................................................................................. 10-1

APPENDICES

Appendix A: Default 1996 Fuel Sulfur Levels

TABLES

Table 2-1. LDGV and LDGT PM ba se emission rates (g/mi) for calendar year 1996 ......................................................................2-6 Table 2-2. LDGV and LDGT PM ba se 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 temp erature correction factors. .................................2-9February 2004

G:\WRAP MSEI\Reporti ng\Final\TOC.doc 

### Table 2-6. Brake wear PM emission rates (mg/mi)............................................ 2-10 Table 2-7. Revised PM 2.5 

### and PM 10 

### 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. Oxygenate d 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 cat egories. EPA specifies standards for each category a nd subcate gory. .......................................................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: AR CADIS, 1999. ........................................................... 4-13 Table 4-8. 1995 Puget Sound ports tr ips totals and allocation to specific ports by vessel t ype. Source: AR CADIS, 1999 ....................... 4-16 Table 4-9. Emission estimates (ton s/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 ARCA DIS (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 Rive r 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 (t ons/year) from ferries in the Puget Sound ............................................................................ 4-22 Table 4-17. Dredging activity a nd emissions estimates......................................... 4-23 Table 4-18. Estimated emissions (ton s/year) from commercial fishing vessels in Washi ngton and Oregon.................................................. 4-23 Table 4-19. Washington and Oregon fi shing ports allocation. Source NMFS (2001)........................................................................... 4-23 Table 4-20. Emissions estimates (ton s/year) for Puget Sound commercial marine vessels.......................................................................... 4-24February 2004

G:\WRAP MSEI\Reporti ng\Final\TOC.doc 

### 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 us ed in SMOKE files .............................................. 4-27 Table 4-24. Methods for estimating NO, SO4, EC and OC for off-road gasoline a nd 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 to tal on-road emissions by state and fuel type (TPD) .....................................................................5-3 Table 5-3. 1996 Western states to tal off-road emissions by state (TPD), based on June 2000 NONROAD model.................................. 5-13 Table 5-4. 1996 Western states tota l off-road emissions by state and fuel type (TPD), based on June 2000 NONROAD model ...................... 5-20 Table 5-5. 1996 Western states tota l off-road emissions by state and fuel type (TPD), based on NONROAD2002 model.............................. 5-22 Table 5-6. Change in 1996 Western stat es 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 fact ors from 1996 ..............................................................6-2 Table 6-2 Seasonal VMT (million miles per day) for 1996 and projec tion year s .....................................................................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 mari ne primary exhaust emission standards (g/kW-hr). .................................................................. 7-27 Table 7-4 Fraction of emissions (HC, NOx, PM) by engine type associated with harbor ve ssels. ...................................................... 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 tota l off-road emissions by state and fuel type (TPD), based on the June 2000 NONROAD model.................. 8-11 Table 8-2b. 2008 Western states tota l off-road emissions by state and fuel type (TPD), based on the June 2000 NONROAD model.................. 8-12 Table 8-2c. 2013 Western states tota l off-road emissions by state and fuel type (TPD), based on the June 2000 NONROAD model.................. 8-13 Table 8-2d. 2018 Western states tota l off-road emissions by state and fuel type (TPD), based on the June 2000 NONROAD model.................. 8-14 Table 8-3a. 2003 Western states tota l off-road emissions by state and fuel type (TPD), based on the NONROAD2002 model ......................... 8-19February 2004

G:\WRAP MSEI\Reporti ng\Final\TOC.doc 

### Table 8-3b. 2008 Western states to tal 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 to tal 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 to tal 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 si lt content by state........................................................9-4 Table 9-3 Monthly average preci pitation data by state (from a representative rural monitoring station) used to estimate unpaved road dust emissions...........................................................9-5 Table 9-4 Monthly average preci pitation 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 IS WS database (p ercent)..................................... 9-10 Table 9-6 Comparison of NET 96 and revised AD TV values ............................... 9-12 Table 9-7 Comparison of Wes tern states 2018 unpaved road dust PM10 emissions from NET96-based approach to the revised approach us ing 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 NO X 

### emission factors. Source: B eardsley, 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 e missions from NONROAD2000 to NONROAD2002 by sour ce category and fuel .................................... 2-16

Figure 4-1. Dependence of commercia l marine PM emissions on fuel sulfur levels. Da ta 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 aver age 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.............5-7February 2004

G:\WRAP MSEI\Reporti ng\Final\TOC.doc 

### Figure 5-1e. 1996 Western states annual average on-road total PM2.5 emissions............5-8 Figure 5-1f. 1996 Western states annual aver age 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 aver age annual on-road VOC emissions by vehicle cla ss and fuel type........................................................ 5-11 Figure 5-2b. 1996 Western states aver age annual on-road NOx emissions by vehicle cla ss and fuel type........................................................ 5-11 Figure 5-2c. 1996 Western states aver age annual on-road PM10 emissions by vehicle cla ss and fuel type........................................................ 5-12 Figure 5-2d. 1996 Western states aver age annual on-road SO2 emissions by vehicle cla ss 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 av erage 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 ta ke-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 inde x for air transportation SIC............................. 7-13 Figure 7-4b General aviation growth rates by state ............................................. 7-13 Figure 7-5a Gross state product inde x for Federal military SIC .............................. 7-14 Figure 7-5b Military aviation growth rates by state............................................. 7-14 Figure 7-6a Total gross stat e product index by state............................................ 7-15 Figure 7-6b Commercial and industrial equipment growth rates by state and defau lt PSR growth rate................................................... 7-15 Figure 7-7a Commercial marine annu al 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 equipmen t growth rates by state and PSR default growth rate .................................................................... 7-17 Figure 7-9a Human popul ation by state ........................................................... 7-18February 2004

G:\WRAP MSEI\Reporti ng\Final\TOC.doc 

### Figure 7-9b Lawn and garden equipment growth rates by state and default PS R 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 PS R growth rate ......................................................... 7-19 Figure 7-11a Softwood supply by re gion........................................................... 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 gr owth rates by state and default PSR grow th rate ....................................................................... 7-22 Figure 7-15a Mining GSP index by state........................................................... 7-23 Figure 7-15b Underground mining equipm ent growth rates by state and default PSR growth rate. ............................................................. 7-23

Figure 8-1a. GCVTC States annual averag e 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 averag e 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 th e 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 estima ted 2018 Western states unpaved road dust emissions, ba sed 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 Stat es DRAFT Transport Fractions......................... 9-18February 2004

G:\WRAP MSEI\Reporting\Final\Acronyms.doc i 

### 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 Di spersion 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-dut y diesel vehicle LPG Liquid Propane Gas LTO Landing and take-offFebruary 2004

G:\WRAP MSEI\Reporting\Final\Acronyms.doc ii 

### MARPOL International Convention for th e Prevention of Pollution from Ships MHDDV Medium heavy- duty diesel vehicle MPO Metropolitan Planning Organization MSEF Mobile Sources Emissions Forum NAPAP National Acid Precip itation Assessment Program NCDC National Climatic Data Center NEI National Emissions Inventory NET National Emissions Trends NFS National Forest Service NH 3 

### 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 PM 10 

### Particulate matter less than or equal to 10 micrometers PM 2.5 

### 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 SO 2 

### Sulfur dioxide SO 4 

### Sulfate SO X 

### 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 PartnershipFebruary 2004

G:\WRAP MSEI\Reporting\Fi nal\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 princi ples 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 th e following framework:

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

2. Federally recognized tribes may apply for an d receive delegation of federal authority to implement CAA programs, including visibility re gulation, 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 severa ble 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 re gulatory 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 impl ement §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 tr ibes 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 throu gh a TIP, EPA, as necessary and appropriate, will promulgate a Federal Implementation Plan (F IP) 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 m obile source emissions described in this report will prove useful to tribes, whether they choos e 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 prin ciples of tribal sovereignty and autonomy as reflected in the above framework. Any inc onsistency with this framework is strictly inadvertent and not an attemp t 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 Com mittees but are not eligible for the Board.February 2004

G:\WRAP MSEI\Reporting\Fi nal\Tribal language.doc 

### iv Despite this equality of representation on the Boar d, 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 ma kes full participation impossible. Moreover, many tribes are faced with pressing environmen tal, 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 necessari ly limit the level of tribal input into and endorsement of WRAP products.

The tribal participants in the WRAP, in cluding 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, fede ral, 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 ma ny areas of the United States, especially in scenic national parks and wilderness areas. Vi sibility 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 cal ls for state and federal agencies to work together to improve visibility in 156 national parks a nd wilderness areas (referred to as Class I areas). The rule requires the states, in c oordination 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 impair ment. 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 Part nership (WRAP), which is made up of Western states, tribes, and federal agencies. The WR AP 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 visi bility in 16 national parks and wilderness areas on the Colorado Plateau.

The WRAP is implementing regional planning pro cesses to improve visibility in all Western Class I areas by providing the technical and poli cy 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 emis sions, and provides the resulting emissions estimates.

1.2 EMISSIONS INVENTOR IED UNDER MOBILE SOURCES

Mobile sources include on-road and off-road vehi cles and engines. On-road mobile sources include vehicles certified for highway use – car s, trucks, and motorcycles. For reporting onroad mobile source emissions, vehicles are di vided 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. H eavy-duty vehicles are trucks of more than 8500 lbs GVW.

Off-road mobile equipment encompasses a wide va riety of equipment types that either move under their own power or are capable of being moved from site to si te. Off-road mobile equipment sources are defined as those that move or are move d within a 12-month period and are covered under the EPA’s emissions regula tions for nonroad mobile sources. Off-road mobile sources are vehicles and en gines in the following categories:

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

• Aircraft, jet and piston engines;February 2004

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

### 1-2 • Airport ground support equipment, such as terminal tractors;

• Commercial and industrial equipment, su ch 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 sh redders 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 MO BILE SOURCES INVENTORY

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

Geographic domain : Emissions were estimated by c ounty for all counties in 13 states: Arizona, California, Colorado, Idaho, Mont ana, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah , Washington, and Wyoming. Alaska was not a WRAP member at the time the emissions were estima ted, 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; su mmer is June thr ough 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 (PM 10 

### and PM 2.5 

### ), nitrogen oxides (NO x 

### ), sulfur oxides (SO x 

### ), volatile organic compounds (VOCs), carbon monoxide (CO), ammonia (NH 3 

### ), 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 SO URCE 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 deri ved from the EPA MOBILE6 and PART5 models (the latter modified for this project). Activ ity for on-road mobile sources is vehicle miles traveled (VMT). States were provided defaul t modeling inputs and VMT levels for base andFebruary 2004

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

### 1-3 future years for review and update; several stat es 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, commerc ial 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 latt er modified for this project). Activity for on-road mobile sources is vehicle miles tr aveled (VMT). EPA’s NONROAD model was used to estimate off-r oad sources except for aircraft, commercial marine, and locomotives. This section provi des information on the versions of each model that were used in estimating emissions.

2.1 MOBILE6

The MOBILE model is EPA’s regulatory model fo r estimating on-road mobile source emission factors for VOC, NO X 

### , and CO. For many years, MOBILE5, released in 1993, was the regulatory model. MOBILE5 was still the re gulatory model when the on-road emissions estimation work for this project was initiate d. However, EPA was many years into the development of MOBILE6, and released a conf idential 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 pub lic release version of MOBILE6 was finally released in January 2002.

For WRAP on-road emissions estimates, the late st available version of draft MOBILE6 that was available at the time was used. Specifi cally, 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 y ears. Differences among these model versions are discussed below.

The MOBILE6 model includes the effects of al l 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, be ginning with model year 2005, with low sulfur gasoline beginning in the summer of 2004;

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

• Heavy-duty vehicle standards (with low sulf ur diesel), beginning with model year 2007.

2.1.1 Comparison of MOBILE5 and MOBILE6

MOBILE5 and MOBILE6 both estimate emissi on factors for on-road vehicles for NO X 

### , VOC, and CO. Exhaust, evaporative, and refueling e mission factors are estimated in units of grams per mile. The MOBILE6 emission factors are then multiplied by an estimate of vehicle milesFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 

### 2-2 traveled (VMT) to estimate total on-road emis sions. MOBILE5 estimated emission factors for the following eight vehicle classes:

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

• Light-duty gas trucks (pick-ups, minivans, pa ssenger vans, and spor t-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 num erically 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), vehi cles of 8501 lb and higher GVW equipped with heavy-duty diesel engines

• Motorcycles (MC, all of which are gasolin e powered; highway-certified motorcycles only are included in the model, off-road moto rcycles 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 deteri oration rates for light-duty vehicles;

• Incorporation of gasoline sulfur impact s 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 NO X 

### impacts for heavy-duty diesel ve hicles as a result of fueling strategies that optimize fuel economy (i.e., the “defeat device” issue);

• Low NOx rebuilds for heavy-duty diesel vehicl es 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 standa rds 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 vehi cle emission standards (2004 and 2007); andFebruary 2004

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 technica l 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 emissi on factors are higher than MOBILE5 in past years, and lower than MOBILE6 in future ye ars. Figures 2-1 and 2-2 show a general comparison of MOBILE5 and MOBI LE6 for a national fleet for NO X 

### and VOC, respectively.

Figure 2-1. Comparison of MOBILE5 and MOBILE6 NO X 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 Yeargrams per mile MOBILE5

MOBILE6February 2004

G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 

### 2-4 Figure 2-2. Comparison of MOBILE5 and MOBILE6 VO C (exhaust + evaporative) emission factors. Source: Beardsley, 2001.



### 2.1.2 Comparison of MOBILE6 Draf t 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 versi on was used for all future years. To check for differences between the models, both draft ve rsions were run for Maricopa County, AZ. Comparisons were made by vehicle class, em issions 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 MOBI LE6, 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 emis sion estimates (direction and magnitude differ by vehicle class).

• Fixed a bug in running loss emissions calcula tion (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 bene fits 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 Yeargrams per mile MOBILE5

MOBILE6 with Tier 2 & HD ruleFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 

### 2-5 • Fixed a typo in diesel school bus NO X 

### emission rates for model years 1988, 1989, 1992, and 1993; very small effect.

Without doing many draft vs. fi nal 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 PA RT5 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 ma ny 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 th e revisions that were made to update PART5 with new data. Changes were made to both light-duty vehicl e and heavy-duty vehicle exhaust emission factors, and some of the non-exhaust processe s (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, mil eage 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 organi c 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 gasoli ne 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 Univ ersity of California, Riverside (College of Engineering-Center for Environmental Resear ch and Technology, CE-CERT) for CARB (CECERT, 1998a), while the high-altitude factors we re 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 be cause 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 compil ed 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). TheFebruary 2004

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 incor porated 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 ve hicle age) in the low-a ltitude 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 m ean high-emitter emission rate was 126 mg/mi. For 1991 and newer vehicles, this was combined with the normal-emi tter emission rate of 3.1 mg/mi. For the 1986-1990 model year gr oup, the normal-emitter emission rate was determined to be 7.3 mg/mi. To illustrate th e calculation procedure, it was determined that the 8-12 year old group contained 9% high emit ters. 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 th e high-altitude emission rates, the differences observed between the low- and high-altitude em ission 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 theFebruary 2004

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 rate s developed by EPA for a 1999 air toxics study (Sierra, 1999) were used; these reflect the imp lementation of tighter PM emission standards under the Tier 2 rule. Table 2-3 summarizes th e 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 PA RT5 were also modified for this project. The revised emission rates were based on da ta 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 measur e 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 testi ng, 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 segregat ed 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 m odel 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 emis sion factors by the ratio of emi ssion 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).February 2004

G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 

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

Model Year LHDDV a 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 ge nerated, 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 c ontaining 3.5% by weight of oxygen. The results of that testing indicat ed a 34% reduction in exhaust PM with the oxygenated fuel. Based on those results, we as sumed 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 rate s 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 60 E F and at 35 E F on the same group of vehicles. Those data were used to generate a multiplicative correction factor at a temperature of 35 E F. In this study, it was assumed that no temperature correc tion was needed until the ambient temperature was below 60 E F, and it reached a maximum at 35 E F. At temperatures between 35 E F and 60 E F, the correction factor was linearly interpolated. The 35 E F temperature correction factors developed for this project are provided in Table 2-5.February 2004

G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 

### 2-9 Table 2-5. LDV exhaust PM temperature correction factors.

Correction Factor Base Emission Rate (g/mi) 60 E F 35 E F

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 SO 2 

### emission rates calculated by PART5, but also the mass of exhaus t 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 le vels are a required input for each area being modeled. Second, the default split between sulfate and SO 2 

### 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 fr action 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 altit ude 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 w ear emission factors fo r PART5. Brake wear PM emissions were reported as a function of vehicle type:February 2004

G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 

### 2-10 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 mappe d to the PART5 vehicle classes on the basis of vehicle weight (i.e., it was assumed that brak e wear is proportional to the kinetic energy dissipated during a stop, 1⁄2mv 2 

### ). For example, assuming that class 8B trucks (80,000 lbs. GVWR) would have an average loaded/unloade d weight of 55,000 lbs ., emissions would be approximately 10 times that of a large pick-up tr uck (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 PM 10 

### and PM 2.5 

### in this study. Those estimates are calculated in PART5 by applying an appropria te particle size distribution to the total PM emission rates for the various emi ssion processes. For example, if it is found that 96% of the total PM mass from gasoline exhaust is from par ticles with a diameter less than or equal to 10 F m, then the total exhaust PM is multiplied by 0.96 to generate PM 10 

### 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 PM 2.5 and PM 10 fractions.

Process/Pollutant PM 2.5 PM 10 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, esti mates of elemental carbon (EC) a nd organic carbon (OC) had to be made. Thus, PART5 was modified to include ca lculations of EC and OC. These calculationsFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 

### 2-11 were performed by applying an EC/OC fraction to the PM 10 

### and PM 2.5 

### emissions estimated by the model. The EC/OC splits used for thes e 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 pr ofiles 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 de veloped from emissions data reported by the College of Engineering—Center for Environm ental Research and Technology (CE-CERT) at the University of California—Riverside (Norbeck, 1998).

DRI reported that mobile source profiles are av ailable 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 composi tion 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 in to 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 PM 2.5 

### to its various components, using enhanced organic spec iation 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 se nsitivity testing. Thes e profiles consistently produced the best goodness-of-fit parameters wh en used with ambient NFRAQS data. CECERT profiles (Norbeck, 1998) were also r ecommended for inclusion in SPECIATE since they represent the only detailed source profiles that characterize hot-stabilized and high emitting light-duty ga soline powered vehicles from a comple tely different sample of vehicles, with the same speciation as the NFRAQS pr ofiles. 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 areFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 

### 2-12 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 vehicl es were highly variable (ranging from 0 to 216 mg/mi on the FTP) and very sensit ive 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 regulati ons are in place. Clearly, more work needs to be done to better characterize ammonia emissions from late-m odel 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+ 87 a CE-CERT, 2001
LDGV/T – Tier 1 and LEV 1987+ 79 a 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 a nd newer light-duty vehi cle ammonia emission factors to account for sulfur levels ranging from 30 ppm (m inimum 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 toFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 

### 2-13 330 ppm. For intermediate values, the correction factor was linearly interpolated (i.e., a 10% decrease in ammonia per 100 ppm increase in ga soline sulfur content was assumed, up to a maximum of 30%).

2.2.7 Fleet Characteristic Data

The following default fleet character istics data contained in PART 5 block data statements were updated to reflect revisions planned for MOBI LE6, 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 ve hicle classes (commonly called the “VMT Mix”). The default PART5 counts were replaced with MOBILE6 estimates. The primary difference between the baseline PA RT5 estimates and the revised MOBILE6 estimates is in terms of the split betw een 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 SO 2 

### . Fuel economy esti mates for HDGVs and HDDVs were updated based on M6.HDE.002 (EPA, 2002).

2.3 NONROAD

Off-road mobile equipment encompasses a wide va riety of equipment types that either move under their own power or are capable of being moved from site to si te. Off-road mobile equipment sources are defined as those that move or are move d 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 draf t 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 al so in SIP modeling efforts that have been accepted by EPA. The June 2000 draft model was used to estimate off-road emissions forFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 

### 2-14 WRAP states for the 1996 and 2018 air quality modeling. The off-road equipment emissions estimates in the WRAP Techni cal Support Document (TSD) were estimated with a later version of the model, NONROAD2002. Differen ces between these two versions of the model are discussed below.

The NONROAD model includes both emission fact ors 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 appr oach can be found in Pollack and Lindhjem (1997); complete and detailed technical descript ions 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 stratifi es 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 ga rden equipment, such as leaf and snowblowers;

• logging equipment, such as sh redders and large chain saws;

• recreational marine vessels, such as power boats;

• underground mining equipment; and

• oil field equipment.

The NONROAD model does not include commerc ial marine, locomotive, and aircraft emissions. Emissions for these three source cat egories 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), NO X 

### , carbon monoxide (CO), carbon dioxide (CO 2 

### ), sulfur oxides (SO X 

### ), and PM. The user selects among five different types for reporting HC — as total hydrocarbons (THC), total organic gases (TOG), non-methane organi c gases (NMOG), non-methane hydrocarbons (NMHC), or volatile organic compounds (VOC). Particulate matter can be reported as total PM, PM of 10μ or less (PM 10 

### ), or PM of 2.5μ or less (PM 2.5 

### ). The model also estimates emissions of non-exhaust HC for six modes — ho t 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:February 2004

G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 

### 2-15 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 spar k-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 engi nes 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 quali ty 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 repor ts. The NONROAD2002 model incorporates updated population data, load factors, and medi an life; increased PM deterioration rates; revised PM and SOx calculations (correcting co ding in the prior versio n); revised equipment growth and scrappage; revised activity data for all terrain vehicles; and revised algorithms for allocating national to county-leve l equipment populations (Harvey, Carey, and Warila, 2003).

With all of these changes, the emission inventory estimate s from the NONROAD2002 model are substantially different from the June 2000 dr aft version. Total U. S. emissions for 1996 are 13 percent lower for VOC in NONROAD2002, 35 percent lower for NO X 

### , 28 percent lower for PM2.5, and 72 percent lower for SO 2 

### . 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 NO X 

### , PM2.5, and SO 2 

### . 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 lifetim es 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. to tal comparisons. Comparisons for individual states or counties will be different because of changes in geographical allocation and differences in fuel properties. Th e differences between NONROAD2000 and NONROAD2002 over time will also vary. Compar isons of the two models for U.S. total emissions for years 2000, 2010, 2020, and 2030 ma y be found in an EPA memo released along with the confidential December 2002 model release (Harvey, 2002).February 2004

G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 

### 2-16 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) spar k-ignition equipment, r ecreational equipment, and compression-ignition pleasure craft a bove 50 hp. ENVIRON obtained updated NONROAD2002 model input files from EPA that incorporate these new exhaust standards (the effects of the evaporative standards are notyet 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 f actors, but they are based on a confidential and undocumented data source, and they are na tional-level only. ENVIRON developed statelevel growth factors so that state-to-state gr owth 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 sect or 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 DieselFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 

### 2-17 Affairs (BEA), Census population data, or other a ppropriate 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 ne ed to be reduced to 15 ppm to enable the PM and NO X 

### emission control technology. Nonroad dies el sulfur is proposed to be 500 ppm in 2008 and 15 ppm in 2010.

The NONROAD2002 modeling for the TSD was comp leted prior to the release of these proposed new standards. While the NONROA D2002 emissions modeling did not include the anticipated emissions effects of th e Tier 4 technology, the diesel fu el sulfur levels were set to 15 ppm in the modeling for years 2008, 2013, a nd 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 es timated and multiplied by the average power, activity, and emission factors. National averag e 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 calcu late the national yearly emissions. Equipment population by county is estimated in the model by geographically allocating national engine population through the use of econometric indicator s, such as construc tion valuation. The manner in which the geographic allocat ion is performed is as follows:

(County Population) i 

### /(National Population) I 

### = (County Indicator) i 

### /(National Indicator ) i



### where i is an equipment application li ke construction or agriculture.

Activity is temporally allocated using a similar equation as for geographic allocation through the use of monthly, and day of w eek fractions of yearly activity.February 2004

G:\WRAP MSEI\Reporting\Final\Sec2_emissions_models.doc 

### 2-18 The NONROAD model has default estimates for all variables and factors used in the calculations. All of these estima tes are in model input files, and can be changed by the user if data more appropriate to the local area are avai lable. 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 EM FAC2002; 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 th eir off-road equipment regulations. An internal working version of the OFFROAD were used to generate emissions for traditional off-road emissions source categories.February 2004

G:\WRAP MSEI\Reporting\Final\Sec3_1996on-road.doc 

### 3-1 3.0 1996 BASE YEAR ON-ROA D 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 mode l 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 poi nt 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/EmisInve ntory/net_96/). These VMT data were at the county and SCC level of detail (vehicle t ype and roadway type) for the eight MOBILE5 vehicle classes. For more information on th e 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 classificati on 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 co unties 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 s easonal 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 Departme nt of Transportation (WSDOT), by roadway class.

Since the VMT data that were provided by the st ate or local agencies did not include vehicle type breakdowns, the data were distributed w ithin a given county and roadway class using the distribution by vehicle type for that county/roadway type with in 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 count y in the NET, then the VMT provided by aFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec3_1996on-road.doc 

### 3-2 state agency for urban interstates within th at 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 th e NET (that vary by vehicle type) and by daily factors that vary by vehicle type and road way 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 ve hicle refueling. The modified PART5 model was used to calculate emission factors for SO 2 

### , 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 th e Trends Procedures Document (EPA, 2001). The sections below discuss thes e 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 mode l emission factor calculations. For the draft version of MOBILE6, speed was input throug h external files accessed by the “SPEED VMT” command. These files are matr ices showing the fraction of VMT accruing within each of 14 speed bins. Separate sets of data must be pr ovided 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 sp eed 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 speed s to MOBILE6 freeway speeds and then provides the fractions that should be used fo r the speed bin above and below the converted MOBILE6 speed. The speed distributions did not vary by hour.February 2004

G:\WRAP MSEI\Reporting\Final\Sec3_1996on-road.doc 

### 3-3 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 ra mp). 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 th e VMT by speed distributio ns to match the speeds by roadway type and vehicle type shown in Tabl e 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 distribu tion 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 co llectors. 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 lo cal 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 rema ining counties), Utah (one set of speeds by roadway type for the rural counties and county-sp ecific speeds by roadway type for each of the four urban counties), and Clark and Washoe Coun ties, Nevada (with a different set of speeds by roadway type for these two counties and the de faults used for all other counties in Nevada).February 2004

G:\WRAP MSEI\Reporting\Final\Sec3_1996on-road.doc 

### 3-4 3.2.2 Registration Distributions

The registration distribution is an input to MOBI LE6 that defines the age mix of each vehicle type. This distribution lists th e fraction of vehicles registered from each of 25 model years for each vehicle type. This distribu tion controls the fleet turnover rates within the model. The default distribution used in the WRAP modelin g was a national 1996 registration distribution, based on the NET 1996 MOBILE5b national regist ration distribution, converted to the MOBILE6 format following the guidance in the Us er’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 Count y, Nevada. All of these dist ributions were supplied in the MOBILE5 format and were converted to MOBI LE6 format in the same manner that the national default registration distribution was converted.

3.2.3 Seasonal Temperatures

The starting point for the de fault temperature inputs used in the WRAP MOBILE6 modeling were the average daily minimum and maximum m onthly temperatures for each state that were used in the NET 1996. The monthly temperat ure data used in the NET were actual 1996 temperature data, from a central weather sta tion 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 defau lt 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 (i ndicating the volatility of gasoline) used in the WRAP MOBILE6 modeling were the RVP values fr om 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 monthl y values for the month in the middle of each season, rather than the seasonal averages. In ad dition to RVP, gasoline fuel sulfur levels are also required as an input to MOBILE6, and gasolin e and diesel fuel sulfur levels are required as input to the modified PART5 model. The fu el specification data used to construct default summer and winter fuel sulfur levels were ex tracted from city-specific fuel survey data published by the American Automobile Manuf acturers 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 com posite gasoline specification for each area was estimated by volume-weighting the three grad es 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 us ed for spring and summer emissions estimates, and winter sulfur levels were used for fall and winter emissions es timates. Appendix A provides the default on-highway gasoline and diesel fuel sulfur levels by county.February 2004

G:\WRAP MSEI\Reporting\Final\Sec3_1996on-road.doc 

### 3-5 Arizona, Colorado, Nevada (Clark and Was hoe Counties), Oregon, Utah, and Washington provided updates to the default RVP data for thei r States. No states provided on-road fuel sulfur level updates.

3.2.5 Altitude

In the NET 1996, each state was identified as e ither 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 update s 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 in spection and maintenance (I/M) programs, oxygenated fuel programs, and Stage II (at-the-pum p) vehicle refueling controls. Note that reformulated gasoline is not included in this list because none of the WRAP states had implemented a reformulated gasoline progr am by 1996. The default control program parameters were those in the 1996 NET. Thes e were updated by the state and local areas in some cases. As described in Section 2, fede ral 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 countie s 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 modele d with oxygenated fuels and the inputs used to model these programs. The info rmation in this table include s updated information on these programs provided by the states.

3.2.6.3 Stage II Refueling Controls

Stage II controls were applied in the followi ng counties: Maricopa County, AZ; Clark and Washoe Counties, NV; Multnomah County, OR; a nd Clark, King, and Pierce Counties, WA. The Oregon and Washington counties were modeled with a 95 percent Stage II controlFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec3_1996on-road.doc 

### 3-6 efficiency for light-duty gasoline vehicles a nd trucks and an 80 percent Stage II control efficiency for heavy-duty gaso line vehicles. Maricopa Co unty, Clark County (NV), and Washoe County were modeled with a 50 percen t control efficiency, 95 percent control efficiency, and 85 percent control efficiency, re spectively, 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 fi les 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 repr esenting 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.February 2004

G:\WRAP MSEI\Reporting\Final\Sec3_1996on-road.doc 

### 3-7 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 estima tes from EMFAC2000 model runs by vehicle class, county, and season. EC/OC e missions were estimated from the PM 10 

### and PM 2.5 

### 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 emissi ons by county, roadway type, and vehicle type.February 2004

G:\WRAP MSEI\Reporting\Final\Sec3_1996on-road.doc 

### 3-8 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 roadwa y 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, NO X 

### , CO, NH3, SO 2 

### , 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 Fr eeways 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 InterstateFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec3_1996on-road.doc 

### 3-9 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 Fr eeways 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 InterstateFebruary 2004

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 MCFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

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

This section describes the methods for esti mating 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 monoxi de (CO), volatile organic compounds (VOC), nitrogen oxide (NO), nitrogen dioxide (NO 2 

### ), sulfur oxides (SOx), coarse and fine particulates (PM 10 

### and PM 2.5 

### ), ammonia (NH 3 

### ), 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 draf t NONROAD model was us ed 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 publis hed by the National Institute for Petroleum andFebruary 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 fu el sulfur levels; the Maricopa County off-road nonroad diesel sulfur le vel was set to the same as on-road diesel sulfur level per local regulation 1



### . Seasonal differences in the parameters that af fect 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 program s 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 wee kday 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 desc ribed 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 SOx 2 

### at airports employs the use of the FAA’s Emissions and Dispersion Modeling Syst em (EDMS). The EDMS model combines specified aircraft and activity levels with defa ult emissions factors in order to estimate annual inventories for a specific airport. Aircraft activ ity 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. Defa ult values for the amount of time a specific aircraft spends in each mode, or the time-in-m odes (TIMs), are coded in to EDMS, but may be updated with airport-specific numbers where available. In addition, the model also includes updateable default settings for th e mixing height and aircraft engi ne 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 au tomate 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 st atistics from the FAA indi cated that the aircraft model-specific activity data need ed to support a complete analysis of emissions from all categories of aircraft operated in the WRAP st ates are currently not available. Aircraft

1 Arizona DEQ originally indicated that this regulation took effect in early 1996. Ho wever, after all of the modeling and analys is was completed, ADEQ staff indicated that the rule ac tually did not go into effect until October of 1996.
2 The EDMS model also has the ability to estimate PM 10 emissions; however, it currently contains PM 10 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 fo r the other aircraft cate gories. 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 pi ston-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 a nd 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” ai rports) and used fleet-av erage emission factors for the rest of the analysis. A similar aggr egate method of analysis has been employed by EPA in the past 3 

### to estimate aircraft emission invent ories 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, PM 2.5 

### , PM 10 

### and ammonia), other available emission factors for aircraft a nd non-aircraft turbine and spark- ignition engines were utilized and reasonable inventory estimates for the ot her pollutants were made. More detailed discussions of the data collected and the analys is 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 iden tified 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 gran ts, and includes all but those airfields with very nominal activity. About 62% of all air ports open to the public are included in the NPIAS; however, these airports account for 100% of commercial aircra ft activity and 92% of all general aviation flights in the U.S. The rema ining 38% of airports are not included in the NPIAS because they do not meet the minimum crite ria of 10 based aircraft (activity measure), are not within 20 miles of a NPIA S airport (accessibility measure ), or are located at sites that cannot be improved for efficiency and safety.

3 In the EPA analysis, the FAA Airc raft Engine Emission Database (FA EED), which preceded EDMS, was used as the source for aircraft emission factors.February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-4 The aircraft category-specific fi scal 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 fo recasted aviation activity data for all NPIAS airports and is used mainly by the FAA fo r 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 emis sion inventories, the mo nthly 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-cont racted traffic control towers only. The towered airports included in the ATADS make up all of the higher-traffic airports throughout the U.S. Th is constituted about 68 of th e 565 airports identified by the NPIAS in the 12 states being considered. Th e 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 analys is, 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 In ternational (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, ai rframe model-specific data were obtained for the large-hub airports from the Bureau of Transportation Statis tics (BTS)(BTS, 2001). Commercial air carrier operators managing aircraft with over 60 seats are required to report their airframe model-specific act ivity 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, wh ich 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 invent ory 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 cla ssified 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 inventorie s from the airport activity data collected are described for the three categories of airports de fined 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 mini mized the input time n ecessary for using EDMS and, at the same time, provided better estima tes of the emission inventories for the larger airports that account for the bulk of the total ai rcraft 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-speci fic activity levels were obt ained for air carriers at the large-hub airports, EDMS was used to estima te the air carrier CO, HC, NOx, and SOx inventories. For all of the la rge hubs, the EDMS default mixing height of 3,000 feet and the default TIMs, with the exception of the taxi a nd 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 TA F total operations da ta 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 NO 2 

### were assumed to be 90% and 10%, respectively, which are the default speciations in EPA’s Em ission Preprocessor System Version 2 (EPS2) model. It was assumed that no NH 3 

### is emitted from air carrier turbine engines, which normally run lean.

In order to estimate the amount of SO 2 

### and SO 4



### . 7H 2 

### O formed from fuel-bound sulfur, a sulfur conversion to SO 4



### . 7H 2 

### O of 6.5% was used based on obser vations of distillate oil-powered stationary turbine engines (Carlson, 1993). Data for stationary turbine engines were used as substitutes for aircraft turbine engines sin ce limited data on emissi on factors and emission speciation currently exist for aircraft engines. The rest of the fuel-bound sulfur was assumed to form SO 2 

### in the engine exhaust.

Due to the lack of other, more recent sources fo r aircraft particulate emission factors, the total suspended particulate (TSP) emissions from th e air carriers were estimated using a commercial fleet-average emission factor from EPA’ s 1985 National Acid Precipitation Assessment Program (NAPAP)(Alliance, 1988). The percenta ge of the TSP emissions consisting of PM 2.5 

### and PM 10 

### was estimated using stationary turbine engi ne data in EPA’s AP-42. According to the AP-42, 100% of the particulate matter em itted from distillate oil-powered stationary turbine engines is PM 2.5 

### , and, consequently, also represented as PM 10 

### (EPA, 1995). No sourceFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-6 for particulate size distribution is currently av ailable 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 aircra ft 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 e ngines have diameters less than 2.5 μ m, 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 fo r 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 we re 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 de fault. 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 dist ribution 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 th e total flights or LTOs taken in 1996.

As with the air carriers, 90% of the NOx e missions were assumed to be NO and 10% NO 2 

### , per the EPS2. Although it was assumed that turbine aircraft do not have NH 3 

### emissions, emissions of NH 3 

### for the piston aircraft in the fleet were estimated using the NH 3 

### emissions from non-catalyzed motor vehicle engine s, which are about 0.14 grams of NH 3 

### per gallon of fuel use (Harvey et. al., 1983). The air taxi an d 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 NH 3 

### emission factor to estimate the piston aircraft NH 3 

### inventory.

Data from distillate oil-powered stationary turbin e engines were again used to estimate the fuel sulfur conversion to SO 2 

### and SO 4



### . 7H 2 

### O for the turbine-powered airc raft. For piston aircraft, data from EPA’s NONROAD model on spark-i gnition engines, which show 97% conversion to SO 2 

### , were used. The rest of the sulfur in the fuel was assumed to be emitted as SO 4



### . 7H 2 

### O.

The particulate emissions from the turbine-pow ered 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 TS P emissions, the civil aircraft fleet-average emission factor from the 1985 NAPAP data was us ed. The particle size distribution for the piston aircraft was based on the particulate size distribution f ound in EPA’s PART5 model for non-catalyzed motor vehicle emissions. According to PART5, 64% of the particulates emitted from these engines are PM 10 

### and 43% have diameter less than 2.0 μ m. The percentage for PM 2.5 

### was interpolated between the two percentage dist ributions. The amount of EC10, EC2.5, OC10,February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-7 and OC2.5 emitted from the piston aircraft were approximated using numbers for piston aviation gasoline or AvGas from the CARB SC AB analysis (Gray, 1986) and data from a particulate motor vehicle exhaust study conducte d 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 th e 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 est imate fleet-average emis sion 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 estimati ng the corresponding inventories. The VOC inventories were converted from the HC inve ntories using conversion factors from EPA’s “Procedures for Emission Inventory Prepara tion” (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% NO 2 

### per the EPS2 model. The SOx speciation a nd the emissions of particulates, NH 3 

### , EC, and OC were all estimated using the same procedure used fo r 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-averag e emission factors, along with the conversion factors, emission factors, and speciation profiles app lied 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 fis cal year total activity le vels, are the only data obtained for the non-towered airports or airfield s, 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.February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-8 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 assumpti ons or estimates have been made to either provide new information or use existing information to est imate 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 (suc h as day tour boats), supply vessels (noted in some general data only for the Port of Everet t), 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 emissi ons 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 emis sion 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 refere nce 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).February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-9 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 usi ng 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 aver age fuel efficiency of 304 g-fuel/kW-hr of power output was calculated from the fuel c onsumption calculation in BAH (1991) for the vessels in the Puget Sound.

EPA has revised motorship emission factor esti mates in the Regulatory Impact Analysis (RIA) and published a rulemaking for commercial mari ne vessels (EPA, 1999a and 1999b). In the absence of revised official EPA guidance for de termining emissions from commercial marine vessels, the emission factors used in the EP A’s RIA were used as the best available information. The emission factors were separa ted 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. Cate gory 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, dre dges, 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 th e 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 or der to convert the Lloyds emission factors fromFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-10 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 deter mined from Lloyds; however, Lloyds (1995) found that PM emissions from marine engines are highly dependent on the fuel sulfur. In order to take advant age 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 dur ing transit modes) of 3% sulfur and light fuel at 0.33% sulfur used during hotel ling (berthing or at dock) modes.February 2004

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 a nd to allocate activity to i ndividual 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 fo r 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 ve ssel calls were obtained from the local Marine Exchange and are listed in Table 4-5. The crui se was considered to begin 25 miles out from shore to the breakwater, where the reduced spee d zone (RSZ) of 6.5 knots was used to near the docks where vessels slow to 4 knots for ma neuvering. Any vessel shifting between docks was captured within the maneuvering time estimated for each port.February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-12 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 acco rding 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 horsepow er-hours per year for the port. The emission factor in horsepower-hours was associated with th e emission factor for either slow speed (2- stroke) or medium speed (4-stroke) engine t ypes. Most vessels use 4-stroke on-board generators running on low sulfur diesel fuel on ce 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 po int starting either during the reduced speed zone or Maneuvering modes, so particulate emissions may have been overestimated for these modes using the high su lfur 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.February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-13 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 show n in Table 4-7. As reported by ARCADIS (1999), Grays Harbor data were included in th e 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 1.8 321.5February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-14 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 1.9 103.4February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-15 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, lo ad in mode, and time in mode. The emission factors were the same with the addition of a nother 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 (ARC ADIS, 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 tr ips (Trips are individual vessel movements with at minimum of 2 per vessel call but may also incl ude 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.February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-16 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 associ ated 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 c onsidered to occur in Grays Harbor County. For all Puget Sound vessel calls, the cruise em issions were assumed to occur in Clallum County. For all Puget Sound vessel calls, th e 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 consider ed 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 theFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-17 ships pass west of Vashon Island), Pierce, and Thurston counties; and port calls to Bellingham included reduced speed zone transit through Cl allum, 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 de termined with a similar port, in this case, the Puget Sound totals. Adjustments were made to the actual vessel act ivity such as reduced speed zone load and time in mode base d 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 ke pt 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 Columb ia 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 th e 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 di rectly 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. Re duced 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 visiti ng 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 (cruis e and RSZ) emissions was to the Washington counties (Pacific for cruise and some RSZ, Wahkiakum, Cowlitz, or Clark) below each portFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-18 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 includ ing towing (or push boats on the Columbia) and vessel assist. For the Puget Sound and Oregon Co ast, the population and power level of tugs was used along with estimates of annual hours of us e and average load fact or to estimate total emissions. For the Columbia River system, a different methodology was used that combines freight movements to estimate push boat activit y, 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 base d 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 wa s 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 regist ry 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 Snak e River tug activity by tonnage of material through each lock; USACE (1996) determined t ug activity for domestic coast-wise shipping for the mouth of the Columbia system, total sh ipping at the mouth of the Snake, and river traffic above Vancouver. The esti mates indicated that river tra ffic 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.February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-19 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 syst em. 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 wa s available from a MAR AD (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 Cate gory 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 st eamships and foreign ve ssel emission subtotalswere 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 estima tes (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 ri ver) traffic and river mileage was supplied, so some assumptions were used to estimate th e 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.February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-20 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 averag e of the two locks. For traf fic downstream of Vancouver, only the tonnage at the mouth of the Columbia was us ed to determine ton-miles assuming that most of the tonnage was loaded on ocean-going vesse ls. 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 vesse ls, 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 Vancouve r may indeed be coast-wise towboats. This method was also employed for othe r ports not covered under ARCADIS (1999) documentation, including Grays Harbor and Coeu r 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 Whitma n, Franklin, Benton, Klickitat, Skamania, Clark, Cowlitz, Wahkiakum, and Pacific, although ri ver 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 es timated 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 hom e ports. Estimated Oreg on 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 vessel s in and out of harbors. The Puget Sound and Oregon Coast tugs were chronicled in gr eat detail (ARCADIS, 1999), but the data were not distinguished between assist and tow activ ity, so it was assumed th at 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 averageFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-21 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 possi ble that other tug activity exists either on the Columbia or elsewhere on the ocean coast or ot her rivers (such as the Willamette), but there was no data for freight or other activity availabl e. The tug assist activity was expected to occur in the Washington or Oregon C ounty 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 Washi ngton 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 services 6 

### were not estimated. The load during ea ch 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 e ngines are not appreciably different from those for Category 2. The allocation of idle emi ssions was to each dock, and running emissions between each stop. Most ferry runs occur acros s 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 RiverFebruary 2004

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 Running 21.9 1052.1 202.3 24.3 Seattle-Bainbridge 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 Running 1.9 68.8 10.3 2.1 Vashon-Seattle (passenger only) Idle 0.1 5.2 0.8 0.2 Running 1.3 60.7 11.7 1.4 Port Defiance- Tahlequah Idle 0.2 7.6 1.5 0.2 Running 0.7 25.3 3.8 0.8 Port Townsend- 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 Running 19.5 937.4 180.3 21.6 Anacortes-San Juan Island-Sidney, B.C. Idle 0.3 15.5 3.0 0.4 Running 0.5 18.7 2.80 0.56 Anderson Island/ Ketron Island/ Steilacoom Service 1 Idle 0.1 4.2 0.62 0.12 Running 0.9 41.2 7.92 0.95 Port Angeles / Victoria B.C. Service 2 Idle 0.2 7.7 1.48 0.18 Running 0.1 2.4 0.4 0.1 Port Angeles –Victoria 3 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 materi al was taken from Starcrest (2000) for the Houston-Galveston area and shown in the table be low. 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 cont racts and Army Corps-operated dredges for 1996. This data includes Pacific Coast, Colu mbia-Snake River, and Puget Sound activity. The USACE (2001b) dredging activity was locate d by longitude and latitude making the county allocation of the dredging activity stra ightforward. The activity and emissions estimates are provided in Table 4-17.February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-23 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 accounti ng 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 Stat e 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 (10 6 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 severa l 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 c ounties 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 unreasonabl y 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 es timate emissions was very similar to BAH (1991) with revised act ivity and emission factor information.

A comparison of the emissions es timated in this work compared to the BAH (1991) estimates is shown in Table 4-20. The most striking di fference 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 emissi ons 25 miles out from the entrance to the Strait of San Juan de Fuca or roughly double the averag e transit time. Growth in overseas shipping from 1990 to 1996 may also be responsible for in creased emissions from ocean-going vessels.

Table 4-20. Emissions estimates (tons/year) for Puget Sound commercial marine vessels. Region (Study) HC NOx CO PMOceanGoing (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 1721990 SeattleTacoma (BAH, 1991) Total 2,499 18,513 32,133 1,216OceanGoing (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 1681996 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 emi ssions became available after the commercial marine emission inventory was prepared for the WRAP modeling. Corbett (2001) and TVA (2002) provide alternative fuel consumption a nd efficiency estimates for the Columbia and Snake River system.February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-25 The national average efficiency estimate for fu el 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 Co lumbia 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 commercia l fishing was not considered significant in the Columbia River. Summary data for all Columbia Rive r traffic for the WRAP report (with a revised towboat efficiency estimate) in dicated 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 invent ory for 1996 as part of the U.S. EPA’s National Emission Trends (NET) project (EPA, 1998). To define locomotive activ ity levels the annual estimates of railroad locomotive fuel consumpt ion 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 provi ded all California emissions estimates.

Emission inventories for VOC, CO , NOx, and PM were comput ed 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 consider ing all PM emitted by locomotives to be PM 10



### . Consistent with the methodology us ed for the 1996 NET project, PM 2.5 

### is assumed to account for 92% of PM 10 

### 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. (l bs/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



### SO 2 

### and sulfate emissions were calculated using the methodology outlined in EPA’s NONROAD model based on average sulfur conten t 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 base d on NIPER’s 1996 fuel pr operty survey (NIPER, 1996). In keeping with EPA’s NONROAD mode l assumptions, we have assumed 2.2% of fuel sulfur is converted to sulf ate in the exhaust and that the sulfate in the locomotive exhaust stream is hydrated. In acc ounting 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 PM 10 

### into elemental carbon and organic carbon was performed using the same basic speciation fractions that are applied to on-r oad heavy-duty diesel engines, expressed as a percentage of the total PM 10 

### emissions. However, since these fractions were developed for

engines using low sulfur (500 ppm maximum) di esel 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 a nd organic carbon fractions were applied to the remaining PM emissions.

The final issue with respect to the locomotive methodology is th e 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 activ ity allocation fractions were found, and the 1995 national activity allocations published by EPA in its Decem ber 1997 “Emission Factors for Locomotives” update were used to apportion the county-specif ic 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 (s till 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 inte rvals. CARB provided 1995 CEFS emissions estimates for locomotives, ai rcraft, 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, a nd LPG engines from the NONROAD model were all lumped together in one fuel category, th e 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 equipmen t, 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 2285002005February 2004

G:\WRAP MSEI\Reporting\Final\Sec4_1996off-road.doc 

### 4-28 CARB follows a slightly different SCC convent ion and does not model all the same off-road categories as EPA. The California source categ ories 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 assigne d 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 assu mptions 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 SO 4, 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 emis sions estimates for 1996 on-road and off-road mobile source emissions, developed using the methodologies desc ribed 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 w eekday 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 emissi ons 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 equi pment 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 sh owing the relative contributions by vehicle class and fuel type. Light-duty gasoline vehi cles dominate VOC emissions, and also SO2 emissions, except in California. Light-duty ga soline 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 emissi ons 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 2004G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc5-3Table 5-2.1996 Western states total on-road emissions by state and fuel type (TPD).VOCNOxCOPM10+PM2.5+SO2NH3StateDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineArizona * 15 396 139 245 71 4,008 9.5 4.8 9.1 3.5 2.2 5.8 0.2 8.7California * 49 1,743 6951,465 216 17,220 28.0 32.6 24.8 19.0 6.3 5.7 0.1 58.3Colorado * 10 288 160 215 60 3,749 10.9 5.9 10.4 4.6 2.4 7.3 0.2 7.8Idaho * 5 106 55 75 21 1,348 3.4 1.7 3.2 1.3 0.9 2.5 0.1 2.5Montana 3 81 43 58 15 1,141 2.6 1.3 2.5 1.0 0.7 1.7 0.1 1.9Nevada * 6 102 50 68 29 1,115 3.4 1.7 3.2 1.3 0.7 1.3 0.1 3.0New Mexico * 13 181 91 117 67 2,262 5.5 2.8 5.2 2.2 1.4 4.3 0.1 4.2North Dakota 2 62 30 42 11 880 1.9 0.9 1.8 0.7 0.5 1.2 0.0 1.3Oregon * 10 305 114 199 43 3,408 6.8 3.7 6.5 2.8 1.6 7.6 0.2 5.5South Dakota 3 68 36 48 12 973 2.2 1.0 2.1 0.8 0.6 1.4 0.0 1.5Utah * 9 163 110 108 56 2,242 6.8 2.9 6.5 2.3 1.5 2.9 0.1 4.1Washington 21 569 178 377 79 6,696 11.0 7.0 10.5 5.4 2.5 12.2 0.2 9.3Wyoming * 3 64 33 45 11 898 2.0 1.0 1.9 0.8 0.5 1.4 0.0 1.5* 9 GCVTC States 119 3,347 1,447 2,537 574 36,249 76 57 71 3818 39 1 9613 WRAP States 149 4,128 1,735 3,062 691 45,938 94 67 88 4622 56 1 110+PM emissions include exhaust, brake wear, and tire wear emissions.February 2004G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc5-4Figure 5-1a.1996 Western states annual average on-road VOC emissions.gDate Generated: 6/19/2001Emissions (TPD)0.0 to 1.01.0 to 10.010.0 to 100.0100.0 to 200.0200.0 to 500.0zeroDenverLos AngelesSan FranciscoBoiseLas VegasAlbuquerqueSalt Lake CityPortlandSioux FallsFargoSeattleCheyennePhoenixBillingsSpokaneBismarckRapid CityRenoTucsonFebruary 2004G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc5-5Figure 5-1b.1996 Western states annual average on-road NOx emissions.Date Generated: 6/19/2001Emissions (TPD)0.0 to 1.01.0 to 10.010.0 to 100.0100.0 to 200.0200.0 to 600.0zeroDenverLos AngelesSan FranciscoBoiseLas VegasAlbuquerqueSalt Lake CityPortlandSioux FallsFargoSeattleCheyennePhoenixBillingsSpokaneBismarckRapid CityRenoTucsonFebruary 2004G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc5-6Figure 5-1c.1996 Western states annual average on-road CO emissions.Date Generated: 6/19/2001Emissions (TPD)0.0 to 10.010.0 to 100.0100.0 to 500.0500.0 to 1000.01000.0 to 5000.0zeroDenverLos AngelesSan FranciscoBoiseLas VegasAlbuquerqueSalt Lake CityPortlandSioux FallsFargoSeattleCheyennePhoenixBillingsSpokaneBismarckRapid CityRenoTucsonFebruary 2004G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc5-7Figure 5-1d.1996 Western states annual average on-road PM10 emissions.Date Generated: 6/19/2001Emissions (TPD)0.0 to 0.10.1 to 1.01.0 to 3.03.0 to 10.010.0 to 20.0zeroDenverLos AngelesSan FranciscoBoiseLas VegasAlbuquerqueSalt Lake CityPortlandSioux FallsFargoSeattleCheyennePhoenixBillingsSpokaneBismarckRapid CityRenoTucsonFebruary 2004G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc5-8Figure 5-1e.1996 Western states annual average on-road PM2.5 emissions.Date Generated: 6/19/2001Emissions (TPD)0.0 to 0.10.1 to 1.01.0 to 3.03.0 to 10.010.0 to 15.0zeroDenverLos AngelesSan FranciscoBoiseLas VegasAlbuquerqueSalt Lake CityPortlandSioux FallsFargoSeattleCheyennePhoenixBillingsSpokaneBismarckRapid CityRenoTucsonFebruary 2004G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc5-9Figure 5-1f.1996 Western states annual average on-road SO2 emissions.Date Generated: 6/20/2001Emissions (TPD)0.0 to 0.10.1 to 0.50.5 to 1.01.0 to 2.02.0 to 5.0zeroDenverLos AngelesSan FranciscoBoiseLas VegasAlbuquerqueSalt Lake CityPortlandSioux FallsFargoSeattleCheyennePhoenixBillingsSpokaneBismarckRapid CityRenoTucsonFebruary 2004G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc5-10Figure 5-1g.1996 Western states annual average on-road NH3 emissions.Date Generated: 6/25/2001Emissions (TPD)0.0 to 0.10.1 to 1.01.0 to 5.05.0 to 10.010.0 to 20.0zeroDenverLos AngelesSan FranciscoBoiseLas VegasAlbuquerqueSalt Lake CityPortlandSioux FallsFargoSeattleCheyennePhoenixBillingsSpokaneBismarckRapid CityRenoTucsonFebruary 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 WYTons 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 WYTons per Day Light Duty Gasoline Light Duty Diesel Heavy Duty Gasoline Heavy Duty DieselFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc 

### 5-12 Figure 5-2c. 1996 Western states average annual on-r oad 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 WYTons 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 WYTons per Day Light Duty Gasoline Light Duty Diesel Heavy Duty Gasoline Heavy Duty DieselFebruary 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 emissi ons 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 emission s, while gasoline equipment dominates offroad VOC and CO emissions.February 2004G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc5-14Figure 5-3a.1996 Western states annual average off-road mobile VOC emissions, based on June 2000 NONROAD model.Date Generated: 5/25/2001Emissions (TPD)0.0 to 1.01.0 to 5.05.0 to 10.010.0 to 50.050.0 to 100.0zeroDenverLos AngelesSan FranciscoBoiseLas VegasAlbuquerqueSalt Lake CityPortlandSioux FallsFargoSeattleCheyennePhoenixBillingsSpokaneBismarckRapid CityRenoTucsonFebruary 2004G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc5-15Figure 5-3b.1996 Western states annual average off-road mobile NOx emissions, based on June 2000 NONROAD model.Date Generated: 5/25/2001Emissions (TPD)0.0 to 1.01.0 to 10.010.0 to 50.050.0 to 100.0100.0 to 200.0zeroDenverLos AngelesSan FranciscoBoiseLas VegasAlbuquerqueSalt Lake CityPortlandSioux FallsFargoSeattleCheyennePhoenixBillingsSpokaneBismarckRapid CityRenoTucsonFebruary 2004G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc5-16Figure 5-3c.1996 Western states annual average off-road mobile CO emissions, based on June 2000 NONROAD model.Date Generated: 5/25/2001Emissions (TPD)0.0 to 1.01.0 to 10.010.0 to 100.0100.0 to 500.0500.0 to 900.0zeroDenverLos AngelesSan FranciscoBoiseLas VegasAlbuquerqueSalt Lake CityPortlandSioux FallsFargoSeattleCheyennePhoenixBillingsSpokaneBismarckRapid CityRenoTucsonFebruary 2004G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc5-17Figure 5-3d.1996 Western states annual average off-road mobile PM2.5 emissions, based on June 2000 NONROAD model.Date Generated: 5/25/2001Emissions (TPD)0.0 to 0.10.1 to 1.01.0 to 5.05.0 to 10.010.0 to 15.0zeroDenverLos AngelesSan FranciscoBoiseLas VegasAlbuquerqueSalt Lake CityPortlandSioux FallsFargoSeattleCheyennePhoenixBillingsSpokaneBismarckRapid CityRenoTucsonFebruary 2004G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc5-18Figure 5-3e.1996 Western states annual average off-road mobileSO2 emissions, based on June 2000 NONROAD model.Date Generated: 5/25/2001Emissions (TPD)0.0 to 1.01.0 to 5.05.0 to 10.010.0 to 20.020.0 to 40.0zeroDenverLos AngelesSan FranciscoBoiseLas VegasAlbuquerqueSalt Lake CityPortlandSioux FallsFargoSeattleCheyennePhoenixBillingsSpokaneBismarckRapid CityRenoTucsonFebruary 2004G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc5-19Figure 5-3f.1996 Western states annual average off-road mobile NH3 emissions, based on June 2000 NONROAD model.Date Generated: 5/25/2001Emissions (TPD)0.0 to 0.010.01 to 0.10.1 to 0.50.5 to 1.01.0 to 1.5zeroDenverLos AngelesSan FranciscoBoiseLas VegasAlbuquerqueSalt Lake CityPortlandSioux FallsFargoSeattleCheyennePhoenixBillingsSpokaneBismarckRapid CityRenoTucsonFebruary 2004G:\WRAP MSEI\Reporting\Final\Sec5_1996results.doc5-20Table 5-4.1996 Western states total off-road emissions by state and fuel type (TPD), based on June 2000 NONROAD model.VOCNOXCOPM10PM2.5SO2NH3StateDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineArizona * 36 105 257 13 132 1,179 23.8 2.5 22.2 2.3 24.7 0.5 0.4 0.5California * 130 349 911 110 478 2,724 59.1 9.4 54.4 8.6 142.5 2.4 1.3 3.9Colorado * 35 87 322 13 121 962 24.6 2.0 22.8 1.8 60.6 0.7 0.5 0.5Idaho * 12 72 103 5 38 417 8.3 2.8 7.7 2.5 21.2 0.4 0.2 0.2Montana 17 40 198 3 38 245 11.5 1.4 10.7 1.3 30.9 0.3 0.3 0.1Nevada * 21 41 163 3 82 407 14.2 1.0 13.2 0.9 29.1 0.1 0.2 0.1New Mexico * 8 23 48 3 26 285 6.0 0.5 5.6 0.5 11.8 0.2 0.1 0.1North Dakota 25 23 245 3 46 197 16.0 0.5 14.7 0.5 46.7 0.2 0.4 0.1Oregon * 39 209 460 14 125 1,179 20.1 8.4 18.6 7.7 49.3 0.9 0.7 0.5South Dakota 19 19 157 3 35 191 12.1 0.5 11.1 0.4 34.2 0.2 0.2 0.1Utah * 17 58 136 7 63 432 11.8 1.4 10.9 1.3 28.2 0.3 0.2 0.3Washington 43 245 421 19 175 1,599 28.8 9.4 26.6 8.6 83.8 1.3 0.6 0.7Wyoming * 8 16 123 1 20 112 5.0 0.4 4.6 0.4 14.8 0.1 0.2 0.0* 9 GCVTC States 307 960 2,522 169 1,085 7,698 173 28 160 26382 6 4 613 WRAP States 411 1,288 3,543 197 1,379 9,930 241 40 223 37578 8 5 7February 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, a nd pleasure craft; these categorie s are mostly small gasoline engines. Lawn and garden equipment also contributes the largest portion of off-road CO emissions. The largest contributors to NO x, 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 NONR OAD2002 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 emi ssions, for reasons discussed in Section 2.3.1. The reductions vary across states for a given po llutant because of changes in several of the population allocation methods fr om the June 2000 model to the NONROAD2002 model, and also changes in emissions by source cate gory (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 NH3Percent 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 EquipmentFebruary 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%February 2004

G:\WRAP MSEI\Reporting\Final\Sec6_FYon-road.doc 

### 6-1 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 TRAV ELED (VMT) ESTIMATES

To calculate on-road emissions for the WR AP States for the years 2003, 2008, 2013, and 2018, the 1996 VMT data used in the WRAP base year modeling were proj ected 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 estima tes. 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 provide d 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: Davi s 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 fact ors were not calculated for light-duty diesel vehicles (LDDVs), light-duty diesel trucks (L DDTs), and motorcycles; the growth factors applied for these three vehicle types in these fo ur counties were the default values calculated based on the HDV analysis as discussed above.

The resulting annual VMT estimates are listed at th e State level in Table 6-1. Table 6-1 also lists the resulting State-level VMT growth fact ors from 1996 to each of the four projection years; these growth rates are compared graphica lly 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 multiply ing the same set of growth f actors 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.February 2004

G:\WRAP MSEI\Reporting\Final\Sec6_FYon-road.doc 

### 6-2 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 2016VMT Growth Rate Relative to 1996 AZ
CO

ID

MT

NV

NM

ND
OR
SD
UT

WA

WYFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec6_FYon-road.doc 

### 6-3 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 28.02February 2004

G:\WRAP MSEI\Reporting\Final\Sec6_FYon-road.doc 

### 6-4 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 ve hicle refueling. The modified version of EPA’s PART5 model (descr ibed 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, EC 10, EC2.5, OC10, and OC2.5.

Default inputs for the MOBILE6 and PART5 m odeling 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 th e following: registrati on 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 gaso line 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 we re 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 registrati on distributions were those used in the WRAP 1996 base year modeling. The MOBILE6 defaults (which are based on national 1996 regi stration data) were used in the counties for which no updates were provided. The State or county-specific registration distributions app lied in the 1996 modeling for Colorado, Arizona, Oregon, and Clark County, Nevada were al so applied in all of the pr ojection years. An updated registration distribution was pr ovided 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 fo r all four projection years, and replaced the State-specific registration distribu tion provided by Washington for 1996.

6.2.3 Seasonal Temperatures

The seasonal temperatures modeled in the proj ection years were the same as the seasonal temperatures modeled in the 1996 base year on-ro ad modeling, with no changes. The default 1996 temperature data were derived by averag ing 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 Stat e. 1996 temperature data were provided byFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec6_FYon-road.doc 

### 6-5 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 Re id vapor pressure (RVP), gasoline fuel sulfur content, and diesel fuel sulfur content. All of these parame ters 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 Washingt on for the counties of King, Pierce, and Snohomish. In each of these counties, the su mmer 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 remain ing 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 fu el sulfur content was set to 15 ppm in the 2008, 2013, and 2018 projection years to reflect the dies el 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 season s. 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 th e same in the projection years as in the 1996 base year modeling.

6.2.6 Control Programs

The effects of Federal on-road control program s 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 ar e: National Low Emission Vehicle (NLEV) emission standards starting with the 2001 model year; Tier 2 e mission 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; onboa rd diagnostics; and the Supplemental Federal Test Pr ocedure (SFTP) rule. As discussed above, the low sulfur gasoline fuel corresponding with the Tier 2 emis sion standards and the low sulfur diesel fuel corresponding with the heavy-duty vehicle 200 7 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 e missions through early pullahead of the 2004 heavy-duty diesel emission st andards and through low emission rebuilds ofFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec6_FYon-road.doc 

### 6-6 existing engines. All of these control progr ams 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 St age 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 Tabl e 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 oxyge nated fuel in Oregon, the oxygenated fuel program was eliminated from the 2008, 2013, and 2018 projection years. In Clark, King, Pierce, and Snohomish Counties, Washi ngton, 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: Maric opa County, AZ; Clark and Washoe Counties, NV; Multnomah County, OR; and Clark, Ki ng, 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 tw o 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 a nd trucks and an 80 percent Stage II control efficiency for heavy-duty gasoline vehicles.February 2004

G:\WRAP MSEI\Reporting\Final\Sec6_FYon-road.doc 

### 6-7 6.3 SEASONAL EMISSION CALCULATIONS

Using the inputs discussed above, MOBILE6 a nd 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 st ate. 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 countie s in Wyoming. Most of th e other States fell somewhere between these two extremes, with several input f iles needed to account for the different control programs and inputs throughout the State. Input f iles were created separately for each of the four projection years. Within each MOBILE6 i nput 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 ei ght vehicle types. MOBI LE6 emission factors for VOC, NOx, CO, and refueling VOC were extracted for each scenario. The modified PART5 model provided PM10, PM2.5, SO2, NH3, elem ental 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 season al 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 emission s were then aggregated by light- and heavy-duty vehicles, and by diesel and gasoline fueled vehi cle 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 fr om EMFAC2000 model runs for all four future years by vehicle class, county, and season. EC/OC emissions were estimated from the PM 10 

### and PM 2.5 

### 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 f iles were generated in the same manner as the 1996 on-road SMOKE files as described in S ection 3.5. The SMOKE files for the twelve non-California states include emissions by count y, 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.February 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 

### 7-1 7.0 FUTURE YEARS WRAP OFF-ROAD INVENTORY

This section describes the methods for estimati ng 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 (NO 2 

### ), sulfur oxides (SOx), coarse and fine particulates (PM 10 

### and PM 2.5 

### ), ammonia (NH 3 

### ), 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 dr aft 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, th e updated NONROAD2002 model (with revisions as discussed in Section 2.3.1) was used to es timate emissions for these source categories; the off-road equipment emissions es timates 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 Res ources Board (CARB) provided all nonroad emissions estimates for all source ca tegories 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 EP A’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 st ate and source category. The indicators used to estimate the growth factors follow the E mission Inventory Improvement Program projectionFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 

### 7-2 guidance (EIIP, 1999). The default and alternativ e 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 cu rrently used in the NONROAD model. These are the same as in the most recent EGAS pr ojection 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 usi ng 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 proprie tary 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 hi gh 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 fact ors 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 productio n is a more direct ac tivity indicator for oil field equipment).

7.1.1.1 Agricultural Equipment

The EIIP projection guidance suggests agricu ltural equipment populatio n, 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 Un iversity) and Bureau of Economic Analysis (BEA) web sites ( http://govinfo.library.or st.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 declin es for most states and slight growth (<0.5% per year) in the remaining states. The availabl e 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 fa il 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 u pon linear regressions of state-specific data from 1977 through 1999. Use of this indicator i nherently leads to slig htly 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 resemb le the population trend and are close to theFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 

### 7-3 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 equipm ent (GSE), the EIIP pr escribes 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 di rectly, 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 an d compares these to the NONROAD default, which were based upon PSR GSE population estima tes. 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 correspondi ng 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 an d Industrial Equipment

For these groups of equipment, total GSP data (fr om BEA) were used to derive growth rates. The EIIP advocates the use of equipment popula tion, industrial/commercial GSP, earnings, or employment. Total GSP was used because from the available data, it wa s 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 fa ct 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 accordan ce with EIIP guidance. Only three of the WRAP states (apart from California) have a ppreciable 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 se lected. However these data required careful consideration to prevent double counting. In gene ral, the total for a state was determined by adding tonnage at each port within that stat e to tonnage reported by specific waterway segments either within or bordering the state. The totals were checked against the stateFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 

### 7-4 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 th e 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 Id aho was derived from the 1996-1999 state summaries. However, there is very little commer cial 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 th e BEA served as the basis for construction equipment growth factor estimates; these ar e shown in Figure 7-8a. These data, covering 1977 through 1999, were analyzed using linear re gressions. The resulting growth rates, shown in Figure 7-8b, are slightly above or s lightly below PSR-based growth. These future year growth rates are influenced by the hi gh 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, No rth Dakota, and Montana) that show rapidly decreasing construction activity. Figure 7-8a shows the underl ying historical data, which suggest that these states’ re sults are strongly influenced by data in the early 1980s. We suspect that these data show such high fluctuat ions because these states’ real construction GSP is small and that a change would produce signi ficant effects on a percentage basis. The construction emissions in these states are rela tively small, and the moderate growth trend associated with the average of the other west ern 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 av ailable 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 tre nds closely match human population. Since the full 2000 Census results we re not available at the time this work was performed, projected population data from th e Census were used, along with 1990 Census data; these populations by state ar e shown in Figure 7-9a. Linear regressions using these data yielded the trends shown in Figure 7-9b. Thes e projections are lower than that predicted by the PSR equipment population data in the far fu ture 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 railro ad equipment. We exa mined national railroad fuel consumption data available from the Association of American Railroads (AAR), and also converted projected freight ton-miles data fr om the Energy Informati on Administration (EIA) to fuel consumption (Figure 7-10a). The fo rmer data set covered 1990 through 2000, and the latter provided the estimates for 2000 through 2018. The national fuel consumption was thenFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 

### 7-5 allocated to the states using ra tios derived from 2000 EIA state-sp ecific fuel sales data. The resulting growth rates by st ate 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 re flect the production and consumption of lumber products. As a result, softwood production da ta 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. Th e 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. Fo r 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 ba se-year value was obtained from a separate EIA report found at http://www.eia.doe.gov/pub/oil_gas/petroleum/ data_publications/petro leum_supply_monthly/h istorical/html/details.html . The projected values were from the EIA’s National Energy Modeling System and are not linear. The result ing 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 po pulations, the EIIP suggests using equipment population or human population. Recreational marine registration data were initially evaluated. Registration data were obtained by state from th e 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 Recreati onal 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 grow th rates than the national PSR-based growth for some states. This may just be a more deta iled geographic distribution of the national data. Of more concern is that the resulting grow th 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 th at the resulting trend is not representative ofFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 

### 7-6 long-term growth. For that reason, human popu lation 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 grow th rates are greater than the PSR default pleasure craft growth rate.

7.1.1.11 Recreational Equipment

Human population is recommended as the su rrogate for activity of recreational equipment/vehicles. These indi cators were derived as discussed in the lawn and garden section above. For this category, human popul ation forecasts are on average higher than the growth rates based on PSR equipment popul ations, as shown in Figure 7-14.

7.1.1.12 Underground Mining Equipment

For this category, BEA mining GSP forecasts we re used. These data; shown in Figure 7-15a, were obtained from the BEA and cover the y ears 1977 through 1999. Linear regressions were performed on the data for all states except No rth Dakota; the results are shown in Figure 7- 15b. Examination of the raw GSP data for No rth 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 th an 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 GSPFebruary 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 Ariz ona 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 Ariz ona 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 I daho 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 Ar izona 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 1.454February 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 Ariz ona 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 0.169February 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 Ar izona 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 Arizon a 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 1.519February 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
YearGrowth Factor GSE
Construction
Farm
Industrial
Lawn & Garden Light Commercial Logging Railway
Recreational
Oil FieldFebruary 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
YearGrowth 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

YearGSP index (1996=100) Arizona
Colorado
Idaho
Montana
Nevada
New Mexico
North Dakota Oregon
South Dakota
Utah W ashington W yomingFebruary 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
YearGrowth 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

YearLTO AZ
CO
ID
MT
ND
NM
NV
OR
SD
UT
WA
WYFebruary 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

YearGSP 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
YearGrowth Factor (relative to 1996) Arizona
Colorado
Idaho
Montana
Nevada
New Mexico
North Dakota Oregon
South Dakota
Utah Washington WyomingFebruary 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

YearGSP 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
YearGrowth Factor (relative to 1996) Arizona
Colorado
Idaho
Montana Nevada

New Mexico
North Dakota Oregon
South Dakota
Utah Washington WyomingFebruary 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

YearGSP 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 YearGrowth 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 commercialFebruary 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

YearFreight (tons) Idaho Oregon Washington

0.8 1.3 1.8 2.3 2.8

1995 2000 2005 2010 2015 2020
YearGrowth Factor (relative to 1996) Idaho

Oregon

WashingtonFebruary 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

YearGSP 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
YearGrowth Factor (relative to 1996) Arizona Colorado Idaho Montana Nevada New Mexico North Dakota Oregon South Dakota Utah Washington Wyoming PSR NationalFebruary 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

YearPopulation 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
YearGrowth Factor (relative to 1996) Arizona Colorado Idaho Montana Nevada New Mexico North Dakota Oregon South Dakota Utah Washington Wyoming PSR NationalFebruary 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
YearGrowth 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

YearFuel Consumption (Gallons) AZ
CO

ID

MT

ND

NM

NV
OR

SD

UT

WA

WYFebruary 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

YearSupply (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

YearGrowth Factor Coast Region (OR, WA)

Inland Region (AR, CO, ID, MT, NV, NM, ND, SD, UT, WY)

PSR NationalFebruary 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

YearProduction (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 YearGrowth Factor (relative to 1996) Rocky Mountain (AZ,CO,ID,MT,ND,NM,NV,SD,UT,WY) West Coast (OR,WA) PSR NationalFebruary 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
YearGrowth 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
YearGrowth Factor (relative to 1996) Arizona Colorado Idaho Montana Nevada New Mexico North Dakota Oregon South Dakota Utah Washington Wyoming PSR NationalFebruary 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

YearGSP 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 YearGrowth Factor (relative to 1996) Arizona Colorado Idaho Montana Nevada New Mexico North Dakota Oregon South Dakota Utah Washington Wyoming PSR NationalFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 

### 7-24 7.2. NONROAD MODEL INPUTS

The future year modeling using EPA’s NO NROAD 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 seas on in the years 2003, 2008, 2013, and 2018.

No State or local agencies provided updates to substitute for NONR OAD default inputs for equipment population or activity data. Fuel and temperatures parameters used in the future year MOBILE6 modeling, as described in Secti on 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 Federa l Tier 2/low sulfur rulemaking. Seasonal differences in the parameters that affect emis sions from off-road equipment were taken into account. These parameters include temperatures and fuel specifications (vapor pressure and oxygenate content). The NONROAD model wa s run to obtain seasonal average weekday emissions for spring, summer, wint er, 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 seas on 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 obtaine d from regional survey data published by the National Institute for Petroleum and Ener gy 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 afte r 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 de fault diesel fuel sulfur levels; the future year Maricopa County off-road nonroad diesel sulf ur 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 wa s set mistakenly set to 15 ppm for year 2018 only.

For all future year NONROAD modeling, the m odel was run with zero growth rates, and the growth rates as described above were inst ead 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 equi pment 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. Emissi ons were estimated for three types of aircraft: commercial aircraft, general aviation, and mi litary aviation. Emission projections were generated for the twelve WRAP; CARB provided California aircraft emissions projections for all future years.

Future year emissions projections were estima ted by applying the state-level growth factors (discussed in Section 7.1.1.2) to the 1996 aircra ft 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 Or ganization (ICAO) promulgated NO x 

### 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 covere d by the CO standard and the first-stage NO x 

### standard currently meet the standard. A second-stage NO x 

### standard is 20 percent lower than the first-stage standard. NO x 

### 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 we re assumed for commercial aircraft.

The base year aircraft emissions were availa ble in two forms: by fuel type and by aviation type. The emissions by fuel type were used in the base year emis sions inventory. The emissions by aviation type involved a less ex act methodology and were developed for the purpose of growing the emissions based on commer cial, military, and general aviation growth factors. Because of complications in methodo logy these two base year aircraft databases yielded different emissions tota ls and could not be combined.

First, future year emissions by aviation type we re estimated, using the appropriate growth factors. Then, in order to estimate future y ear 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-pow ered (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 a ll new international and relatively small-sized US flagged commercial marine engines. Inte rnational standards were developed for all international ships under the MARPOL agreements (MARPOL, 1997). EPA has finalized emission standards for new US flagged compressi on-ignition (i.e. diesel) marine engines with displacements of under 30 liters per cylinder (EPA, 1999a). These engines are generally noFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec7_FYoff-road.doc 

### 7-26 larger than locomotive engines and typically ra ted 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 devel oped under the aegis of the International Maritime Organization (IMO). Under these re gulations, engines are tested under three different loads and the three tests are averag ed 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 dom estic 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 e ngines in 2004; these standards are shown in Table 7-3. The international standards, if im plemented, are considered ‘Tier 1’ emission controls. Regardless of international implementat ion, 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 primar ily use engines with displacements above 30 l/cylinder, there are three types of engine s used for propulsion (slow speed [<200 rpm], medium [~550 rpm], and steamships boilers driv ing 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 th e engine speed) when all ships operating meet the standard because the baseline emission rate was assu med 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 sp eed 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 replacem ent may lead to higher NOx emissions.

For harbor vessels (tugs, ferries, dredges, a nd fishing vessels), emi ssions reductions depend on the type of the engine used for propulsion and auxiliary power. EPA defined three categories of engines: Category 1 for engine wi th displacements under 5 l/cylinder, Category 2 for engines of 5-30 l/cylinder displacement, an d 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 Categor y 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 Caterp illar 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 0.50February 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 Category 2 Category 1 Vessel Types < 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 emission s 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% NOx 0% 5% 16% 31% 33% Category 1 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 emis sion factors data to estimate the emissions. These growth factors, listed in Table 7-2 and shown in Fi gure 7-10b, are based on railroad fuel consumption data.

The Regulatory Support Document (RSD) for loco motive emission standard s contains national emission projections up to the year 2040 for HC, NO x 

### , CO and PM. For their emission reduction analysis, EPA assumed th at future year fuel consump tion would remain constant at 1996 levels. It was thought that improvements in the fuel effi ciency 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 fu ture, phased-in controls that will primarily reduce NO x 

### and PM emissions, and HC emissions as well. Because no growth was assumed, emissions reductions can be esti mated based on the percent change in emissions from the base year to a given projection year. The 1996 base year emission factors for NO x 

### , HC, and PM 10 

### 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. PM 2.5 

### emissions were estimated as 92 percent of PM 10 

### emissions.

7.6 CALIFORNIA DATA PROCESSING

CARB provided future year projections fo r 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 estima ted as the average of the summer and winter emissions.

CARB also provided an emissions data set de rived from the California Emission Forecasting System (CEFS) for recreational equipment, locomotives, aircraft, and commercial marine source categories. The years with availa ble data were 2000, 2005, 2010, 2015, and 2020. Emissions years 2003, 2008, 2013, and 2018 were es timated using linear interpolation. The CEFS data also only included winter, summer, and annual e missions. Spring and autumn emissions were estimated as the average of th e 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 fuel s. 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; th is 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 emis sions. CARB estimated that the use of low sulfur diesel fuel would result in a four pe rcent reduction in PM emissions (Agrawal, 2001); this factor was thus app lied to all relevant emissi ons for 2008, 2013, and 2018.

7.7 GENERATION OF SMOKE FILES

SMOKE files were created for year 2018 off-r oad emissions for input into air quality modeling. The SMOKE files were generated on ly 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 NO x 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 23.6February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc 

### 8-1 8.0 MOBILE SOURCE EMIS SIONS TRENDS, 1996 TO 2018

This section shows trends in emissions from th e 1996 base year to the four future years (2003, 2008, 2013, and 2018) for on-road and off-road mob ile 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 deta iled spreadsheets are available that tabulate onroad and off-road emissions for all states fo r 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 estimate d annual average weekday on-road emissions by state and fuel type for the four future y ears 2003, 2008, 2013, and 2018, respectively. The relatively small emissions from CNG and LPG fu eled vehicles have been included with gasoline vehicle emissions in these tables. Th e 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 equi pment dominates off-road VOC, CO, SO2, and NH3 emissions.

Figures 8-1a through 8-1d show the changes over time in VOC, NO X 

### , SO 2 

### , 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, NO X 

### , and PM2.5 emissions in the 9-state region are projected to decline cons istently 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 SO 2 

### 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 SO 2 

### emissions will increase as the fuel sulfur levels stay constant but fuel consumption increases.February 2004G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc8-2Table 8-1a.2003 Western states total on-road emissions by state and fuel type (TPD).VOCNOxCOPM10^PM2.5^SO2NH3StateDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineArizona * 9 327 125 221 52 3,492 8.0 4.5 7.5 3.1 2.6 6.9 0.2 10.1California * 41 1,108 604990 164 10,695 19.3 40.2 16.8 24.4 6.3 4.4 0.1 67.0Colorado * 7 203 147 185 40 3,057 8.9 4.8 8.3 3.5 2.8 8.3 0.3 8.4Idaho * 2 76 45 63 13 1,052 2.9 1.5 2.7 1.1 1.2 3.5 0.1 2.6Montana 2 55 35 48 9 875 2.2 1.2 2.1 0.9 0.8 3.3 0.1 1.7Nevada * 3 84 45 68 20 1,080 3.4 1.5 3.2 1.1 0.9 1.2 0.1 3.7New Mexico * 7 130 76 103 41 1,736 6.2 2.5 5.9 1.8 1.6 6.0 0.2 4.4North Dakota 1 41 23 34 6 653 1.5 0.8 1.4 0.6 0.5 1.5 0.1 1.3Oregon * 6 228 98 163 34 2,778 5.5 3.5 5.1 2.5 1.9 8.6 0.2 6.1South Dakota 1 46 29 40 7 751 1.8 1.0 1.7 0.7 0.7 1.8 0.1 1.6Utah * 5 123 103 97 38 1,811 6.4 2.7 6.1 2.0 1.9 3.6 0.2 4.6Washington 10 447 127 298 49 5,456 7.8 6.5 7.3 4.8 2.5 13.0 0.3 10.2Wyoming * 1 42 27 37 7 672 1.7 0.9 1.6 0.7 0.7 1.9 0.1 1.4* 9 GCVTC States 82 2,321 1,270 1,928 408 26,373 62 62 57 40 20 45 1 10813 WRAP States 97 2,911 1,485 2,348 479 34,109 76 72 70 4724 64 2 123^PM emissions include exhaust, brake wear, and tire wear emissions.February 2004G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc8-3Table 8-1b.2008 Western states total on-road emissions by state and fuel type (TPD).VOCNOxCOPM10^PM2.5^SO2NH3StateDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineArizona * 8 234 96 161 45 2,484 7.5 5.1 6.9 3.5 0.1 1.5 0.3 13.5California * 34 801 465723 141 7,569 14.3 45.3 12.1 27.8 0.7 4.6 0.1 73.7Colorado * 6 151 103 148 33 2,433 9.1 5.3 8.5 3.9 0.2 1.2 0.3 11.6Idaho * 2 52 33 46 10 802 2.7 1.7 2.5 1.3 0.1 0.4 0.1 3.9Montana 2 37 25 35 7 672 2.1 1.3 1.9 1.0 0.0 0.3 0.1 2.7Nevada * 3 63 35 51 17 786 3.3 1.8 3.0 1.2 0.1 0.5 0.1 4.7New Mexico * 6 89 55 77 35 1,317 5.9 2.8 5.5 2.0 0.1 0.7 0.2 6.6North Dakota 1 27 16 24 5 490 1.4 0.9 1.3 0.7 0.0 0.2 0.1 1.8Oregon * 5 156 77 115 25 2,050 4.9 4.1 4.5 3.0 0.1 1.0 0.2 9.2South Dakota 1 31 21 29 6 575 1.7 1.1 1.6 0.8 0.0 0.3 0.1 2.2Utah * 5 84 77 70 32 1,357 6.0 3.0 5.6 2.2 0.1 0.7 0.2 6.2Washington 8 295 93 203 40 3,855 6.6 7.1 6.0 5.3 0.1 1.5 0.3 14.9Wyoming * 1 28 19 26 5 507 1.5 1.0 1.4 0.7 0.0 0.2 0.1 2.1* 9 GCVTC States 69 1,658 961 1,418 344 19,305 55 70 5046 1 11 2 13113 WRAP States 81 2,048 1,116 1,709 402 24,897 67 81 61 532 13 2 153^PM emissions include exhaust, brake wear, and tire wear emissions.February 2004G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc8-4Table 8-1c.2013 Western states total on-road emissions by state and fuel type (TPD).VOCNOxCOPM10^PM2.5^SO2NH3StateDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineArizona * 7 180 61 124 23 2,120 5.1 5.5 4.5 3.7 0.2 1.7 0.3 14.8California * 25 579 306 525 126 5,303 9.6 50.0 7.8 30.8 0.8 4.9 0.1 80.2Colorado * 5 108 54 105 17 2,214 4.7 5.7 4.1 4.2 0.2 1.4 0.4 12.5Idaho * 2 37 19 32 5 709 1.8 1.9 1.6 1.3 0.1 0.5 0.1 4.2Montana 1 27 15 25 4 598 1.4 1.4 1.2 1.0 0.0 0.3 0.1 3.0Nevada * 2 46 22 38 9 696 2.0 1.9 1.8 1.3 0.1 0.6 0.1 5.2New Mexico * 5 65 33 55 18 1,187 3.8 3.0 3.4 2.2 0.1 0.8 0.2 7.2North Dakota 1 19 9 17 2 428 0.9 1.0 0.8 0.7 0.0 0.2 0.1 1.9Oregon * 4 117 50 82 14 1,788 3.5 4.4 3.0 3.1 0.1 1.1 0.3 10.0South Dakota 1 22 12 21 3 511 1.1 1.2 1.0 0.9 0.0 0.3 0.1 2.4Utah * 4 62 46 50 16 1,254 3.9 3.3 3.5 2.4 0.1 0.8 0.2 6.9Washington 5 213 59 154 18 3,252 4.1 7.3 3.6 5.4 0.1 1.6 0.3 15.8Wyoming * 1 20 11 19 3 447 1.0 1.1 0.9 0.8 0.0 0.3 0.1 2.2* 9 GCVTC States 54 1,213 602 1,030 231 15,719 36 77 3150 2 12 2 14313 WRAP States 63 1,494 697 1,246 258 20,509 43 88 37 582 14 2 166^PM emissions include exhaust, brake wear, and tire wear emissions.February 2004G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc8-5Table 8-1d.2018 Western states total on-road emissions by state and fuel type (TPD).VOCNOxCOPM10^PM2.5^SO2NH3StateDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineArizona * 6 146 38 92 15 1,989 3.9 6.0 3.3 4.0 0.2 1.9 0.4 16.3California * 17 437 181 397 120 3,820 6.5 54.4 4.8 33.7 0.8 5.2 0.1 86.3Colorado * 5 80 25 76 9 2,125 3.3 6.3 2.7 4.6 0.2 1.6 0.4 13.6Idaho * 2 30 11 24 3 673 1.4 2.0 1.1 1.5 0.1 0.5 0.1 4.6Montana 1 22 8 19 2 567 1.0 1.5 0.9 1.1 0.1 0.4 0.1 3.2Nevada * 2 36 13 28 6 668 1.5 2.2 1.3 1.5 0.1 0.7 0.1 5.8New Mexico * 5 54 18 41 11 1,144 2.8 3.3 2.4 2.4 0.1 0.9 0.2 7.9North Dakota 1 15 5 13 1 401 0.7 1.0 0.5 0.8 0.0 0.2 0.1 2.1Oregon * 4 88 32 61 9 1,678 2.6 4.7 2.1 3.4 0.1 1.3 0.3 10.9South Dakota 1 18 7 16 2 485 0.9 1.3 0.7 0.9 0.0 0.3 0.1 2.6Utah * 4 51 25 36 10 1,220 2.8 3.7 2.4 2.7 0.1 0.9 0.2 7.7Washington 4 171 36 110 12 2,957 3.0 7.7 2.4 5.7 0.2 1.7 0.3 16.7Wyoming * 1 17 6 14 2 421 0.8 1.1 0.6 0.8 0.0 0.3 0.1 2.4* 9 GCVTC States 45 939 347 770 185 13,741 26 84 2154 2 13 2 15513 WRAP States 53 1,165 402 927 203 18,151 31 95 25 63 2 16 3 180^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 3500Annual 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
W y omin g 66 44 29 21 17 1996 2003 2008 2013 2018February 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 4000Annual 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
W y omin g 78 64 45 30 20 1996 2003 2008 2013 2018February 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 70Annual 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
W y omin g 1.9 2.6 0.3 0.3 0.3 1996 2003 2008 2013 2018February 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 100Annual 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
W y omin g 2.4 1.9 1.8 1.2 1.0 1996 2003 2008 2013 2018February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc 

### 8-10 8.2 OFF-ROAD MOBILE SOURC E 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 releas ed 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 larg e (> 25 hp) spark-ignition equipment, recreational equipment, and compression-ignition pleasure craft above 50 hp; ENVIRON obtained updated NONROAD2002 model input file s from EPA that incorporate these new exhaust standards (the effects of the evapora tive 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 wa s 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 in clude 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 pro posed rulemaking. For the Technical Support Document, WRAP staff adjusted the emissions esti mates to back out the effects of low sulfur diesel based on state average di esel fuel sulfur relative to the 15 ppm level input to the NONROAD2002 model.

Tables 8-2a through 8-2d show the estimate d 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 to tals 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 a nd 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 th rough 2018 as equipment populations and fuel consumption increase.February 2004G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc8-11Table 8-2a.2003 Western states total off-road emissions by state and fuel type (TPD), based on the June 2000 NONROAD model.VOCNOXCOPM10^PM2.5^SO2NH3StateDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineArizona * 28 77 212 17 110 1,273 19.9 2.5 18.7 2.3 26.1 0.6 0.3 0.6California * 124 299 833118 435 2,706 57.4 12.5 52.8 11.5 156.3 1.5 1.2 3.5Colorado * 26 69 256 17 99 1,069 20.6 2.0 19.1 1.8 68.0 0.8 0.4 0.6Idaho * 10 58 105 6 33 421 7.5 2.5 7.0 2.2 26.5 0.4 0.2 0.2Montana 14 33 199 3 36 241 10.9 1.3 10.1 1.2 37.3 0.4 0.3 0.1Nevada * 20 31 219 4 79 411 14.5 1.0 13.4 0.9 40.7 0.1 0.4 0.2New Mexico * 6 18 39 4 20 305 5.0 0.5 4.7 0.5 13.1 0.2 0.1 0.2North Dakota 19 20 232 3 42 206 13.5 0.5 12.5 0.5 54.2 0.2 0.4 0.1Oregon * 32 205 460 18 112 1,253 18.6 10.4 17.2 9.6 60.7 1.0 0.7 0.7South Dakota 14 17 141 4 30 207 10.0 0.5 9.2 0.4 40.4 0.2 0.2 0.2Utah * 14 55 130 9 55 496 10.8 1.5 9.9 1.4 34.8 0.4 0.2 0.3Washington 30 240 35627 147 1,767 24.2 11.8 22.3 10.8 89.1 1.5 0.6 1.0Wyoming * 9 15 153 2 25 119 6.0 0.4 5.5 0.4 21.0 0.1 0.3 0.1* 9 GCVTC States 267 827 2,406 197 968 8,052 160 33 14831 447 5 4 613 WRAP States 345 1,137 3,333 235 1,223 10,473 219 48 202 43668 7 5 8^PM emissions include exhaust, brake wear, and tire wear emissions.February 2004G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc8-12Table 8-2b.2008 Western states total off-road emissions by state and fuel type (TPD), based on the June 2000 NONROAD model.VOCNOXCOPM10^PM2.5^SO2NH3StateDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineArizona * 24 52 181 18 110 1,388 18.4 2.6 17.3 2.3 28.9 0.3 0.3 0.6California * 111 260 716104 420 2,647 50.5 15.5 46.4 14.3 82.3 0.5 1.0 3.1Colorado * 21 49 212 18 96 1,147 18.7 2.0 17.3 1.8 74.8 0.2 0.4 0.6Idaho * 8 35 87 6 32 423 6.9 2.1 6.4 1.9 29.2 0.1 0.2 0.2Montana 12 23 161 3 35 242 10.1 1.1 9.4 1.0 40.6 0.1 0.3 0.1Nevada * 19 26 198 5 89 514 15.1 1.1 14.0 1.0 50.6 0.1 0.4 0.2New Mexico * 5 13 32 4 20 332 4.6 0.5 4.4 0.5 14.2 0.1 0.0 0.2North Dakota 15 17 185 3 39 215 11.8 0.5 10.9 0.5 57.0 0.0 0.3 0.1Oregon * 28 114 39118 114 1,313 17.0 11.4 15.7 10.4 66.8 0.3 0.7 0.7South Dakota 10 14 118 4 28 223 9.1 0.4 8.4 0.4 44.5 0.1 0.2 0.2Utah * 12 46 110 10 55 538 10.0 1.5 9.2 1.3 38.9 0.1 0.2 0.4Washington 25 138 30228 144 1,852 21.8 12.8 20.2 11.7 93.7 0.4 0.5 1.0Wyoming * 8 13 123 2 25 124 5.7 0.4 5.2 0.4 23.0 0.0 0.3 0.1* 9 GCVTC States 235 608 2,049 185 960 8,428 147 37 13634 409 2 3 613 WRAP States 297 801 2,816 224 1,206 10,959 200 52 185 47645 2 5 7^PM emissions include exhaust, brake wear, and tire wear emissions.February 2004G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc8-13Table 8-2c.2013 Western states total off-road emissions by state and fuel type (TPD), based on the June 2000 NONROAD model.VOCNOXCOPM10^PM2.5^SO2NH3StateDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineArizona * 22 48 162 19 119 1,492 18.5 2.7 17.4 2.4 31.6 0.3 0.3 0.7California * 98 212 59490 410 2,504 44.1 16.5 40.6 15.2 86.6 0.5 0.8 2.6Colorado * 17 45 185 19 102 1,221 18.5 2.0 17.2 1.8 81.3 0.3 0.3 0.7Idaho * 7 32 76 7 34 436 6.8 1.7 6.3 1.5 31.7 0.1 0.1 0.2Montana 10 21 145 3 37 247 9.8 1.0 9.1 0.9 43.7 0.1 0.3 0.1Nevada * 18 24 187 5 99 546 15.8 1.1 14.6 1.0 56.9 0.1 0.4 0.2New Mexico * 4 12 27 5 20 359 4.6 0.6 4.3 0.5 15.3 0.1 0.0 0.2North Dakota 12 16 154 4 39 224 11.0 0.5 10.2 0.4 59.7 0.0 0.3 0.1Oregon * 24 118 347 20 121 1,427 16.9 12.5 15.6 11.5 72.6 0.3 0.7 0.7South Dakota 8 12 97 5 29 238 8.8 0.4 8.1 0.4 48.5 0.1 0.2 0.2Utah * 10 44 97 11 59 580 10.1 1.4 9.3 1.3 42.9 0.2 0.2 0.4Washington 20 140 25930 148 2,011 21.4 13.9 19.8 12.8 98.1 0.4 0.4 1.1Wyoming * 8 13 116 2 27 131 5.5 0.4 5.1 0.4 24.8 0.0 0.3 0.1* 9 GCVTC States 207 549 1,791 177 991 8,697 141 39 13036 444 2 3 613 WRAP States 258 738 2,446 219 1,244 11,416 192 55 178 50694 2 4 7^PM emissions include exhaust, brake wear, and tire wear emissions.February 2004G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc8-14Table 8-2d.2018 Western states total off-road emissions by state and fuel type (TPD), based on the June 2000 NONROAD model.VOCNOXCOPM10^PM2.5^SO2NH3StateDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineArizona * 22 52 160 22 129 1,619 19.3 2.9 18.1 2.6 34.3 0.3 0.3 0.8California * 89 177 52181 405 2,286 39.0 17.7 35.9 16.3 90.8 0.5 0.7 2.4Colorado * 16 47 175 20 109 1,295 19.2 2.1 17.8 1.9 87.8 0.3 0.3 0.7Idaho * 6 30 71 7 36 448 7.0 1.3 6.4 1.2 34.3 0.1 0.1 0.3Montana 10 20 136 4 39 252 9.8 0.8 9.2 0.7 46.8 0.1 0.3 0.1Nevada * 19 25 189 6 111 583 17.0 1.2 15.7 1.1 63.1 0.1 0.4 0.2New Mexico * 4 12 24 5 21 388 4.7 0.6 4.4 0.5 16.3 0.1 0.0 0.2North Dakota 10 16 136 4 41 233 10.8 0.5 9.9 0.4 62.3 0.0 0.3 0.1Oregon * 22 126 395 21 132 1,537 17.9 13.6 16.6 12.5 79.0 0.3 1.0 0.8South Dakota 7 12 84 5 31 253 8.9 0.4 8.2 0.4 52.5 0.1 0.1 0.2Utah * 10 45 95 12 65 619 10.6 1.5 9.9 1.3 46.9 0.2 0.2 0.4Washington 18 148 28432 162 2,165 22.3 15.2 20.7 13.9 103.5 0.5 0.6 1.2Wyoming * 8 13 114 2 29 139 5.6 0.4 5.1 0.4 26.7 0.0 0.3 0.1* 9 GCVTC States 195 528 1,743 177 1,036 8,912 140 41 130 38 479 2 3 613 WRAP States 239 724 2,382 221 1,308 11,815 192 58 178 53744 3 5 7^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 3000Annual 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 2018February 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 250Annual 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 2018February 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 600Annual 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 2018February 2004

G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc 

### 8-18 Tables 8-3a through 8-3d show the estimate d 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 year s 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 equi pment VOC, NOx, PM10, and PM2.5 emissions based on NONROAD2002 to be continually dec lining 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 leve ls 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 2004G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc8-19Table 8-3a.2003 Western states total off-road emissions by state and fuel type (TPD), based on the NONROAD2002 model.VOCNOXCOPM10PM2.5SO2NH3StateDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineArizona* 16 96 129 21 58 1,424 12.0 3.1 11.4 2.8 11.0 0.3 0.2 0.7California* 124 299 833118 435 2,706 57.4 12.5 52.8 11.5 156.3 1.5 1.2 3.5Colorado* 16 76 174 19 68 1,098 13.4 2.2 12.4 2.0 26.4 0.3 0.3 0.7Idaho* 6 41 71 6 28 374 5.8 1.1 5.4 1.0 11.1 0.2 0.1 0.2Montana 12 19 164 3 46 210 11.3 0.5 10.5 0.5 21.9 0.1 0.3 0.1Nevada* 12 37 168 6 44 493 8.6 1.2 8.0 1.1 20.5 0.1 0.3 0.2New Mexico* 4 22 27 5 17 318 3.9 0.6 3.7 0.6 5.2 0.1 0.0 0.2North Dakota 17 17 178 4 75 224 17.1 0.4 15.7 0.4 28.4 0.1 0.3 0.1Oregon* 26 73 401 19 83 906 13.5 2.2 12.5 2.0 34.0 0.3 0.7 0.7South Dakota 11 17 87 4 52 218 11.6 0.4 10.7 0.4 16.9 0.1 0.1 0.2Utah* 12 49 121 10 50 527 9.5 1.3 8.8 1.2 20.4 0.1 0.2 0.4Washington 22 108 275 29 111 1,419 17.3 3.5 16.0 3.1 52.3 0.5 0.4 1.0Wyoming* 9 16 148 2 27 137 6.3 0.4 5.8 0.4 17.2 0.1 0.3 0.1Total - 13 WRAP States 288 871 2,776246 1,092 10,055 187.7 29.4 173.7 26.7 421.6 3.9 4.4 8.0*Total - 9 GCVTC States226 710 2,073 206809 7,984 130.4 24.5 120.7 22.4 302.2 3.0 3.3 6.6February 2004G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc8-20Table 8-3b.2008 Western states total off-road emissions by state and fuel type (TPD), based on the NONROAD2002 model with dieselsulfur set to 15 ppm.VOCNOXCOPM10PM2.5SO2NH3StateDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineArizona* 14 64 109 15 54 1,467 10.5 3.3 10.0 2.9 8.3 0.1 0.2 0.5California* 111 260 716104 420 2,647 50.5 15.5 46.4 14.3 82.3 0.5 1.0 3.1Colorado* 14 52 147 13 62 1,115 10.6 2.3 9.8 2.1 14.1 0.1 0.3 0.5Idaho* 6 32 61 5 25 381 4.5 1.2 4.2 1.1 5.7 0.0 0.1 0.2Montana 11 15 135 2 42 206 9.2 0.6 8.6 0.5 14.9 0.0 0.3 0.1Nevada* 11 24 141 4 43 508 7.4 1.3 6.9 1.1 15.9 0.0 0.3 0.2New Mexico* 4 15 23 3 15 329 3.2 0.7 3.0 0.6 1.6 0.0 0.0 0.1North Dakota 14 13 149 3 61 211 12.3 0.4 11.4 0.4 12.4 0.0 0.3 0.1Oregon* 23 51 345 13 80 948 11.2 2.4 10.4 2.1 25.6 0.1 0.6 0.5South Dakota 9 13 78 3 43 211 8.4 0.5 7.8 0.4 4.2 0.0 0.1 0.1Utah* 10 36 103 7 45 539 7.4 1.5 6.8 1.3 9.3 0.0 0.2 0.3Washington 19 75 240 20 102 1,456 14.0 3.6 13.0 3.3 38.1 0.1 0.4 0.7Wyoming* 8 13 120 1 27 137 5.5 0.5 5.1 0.4 15.6 0.0 0.3 0.0Total - 13 WRAP States 253 662 2,367193 1,019 10,155 154.7 33.7 143.4 30.7 248.1 1.0 4.1 6.3*Total - 9 GCVTC States 202 547 1,765165 771 8,071 110.7 28.6 102.6 26.0 178.5 0.9 3.0 5.3February 2004G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc8-21Table 8-3c.2013 Western states total off-road emissions by state and fuel type (TPD), based on the NONROAD2002 model with dieselsulfur set to 15 ppm.VOCNOXCOPM10PM2.5SO2NH3StateDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineArizona* 13 57 101 11 55 1,531 10.0 3.4 9.6 3.0 7.6 0.1 0.2 0.4California* 98 212 59490 410 2,504 44.1 16.5 40.6 15.2 86.6 0.5 0.8 2.6Colorado* 13 46 135 9 60 1,145 9.4 2.3 8.8 2.1 10.3 0.1 0.3 0.3Idaho* 5 27 55 3 24 386 3.9 1.1 3.6 1.0 4.1 0.0 0.1 0.1Montana 9 12 126 2 39 207 8.1 0.6 7.6 0.5 13.2 0.0 0.3 0.1Nevada* 10 22 134 3 45 527 7.0 1.3 6.5 1.1 14.7 0.0 0.3 0.1New Mexico* 3 14 20 3 14 346 2.8 0.7 2.7 0.6 0.3 0.0 0.0 0.1North Dakota 11 11 131 2 52 209 9.6 0.4 8.9 0.4 7.9 0.0 0.3 0.1Oregon* 21 46 313 8 79 979 10.3 2.4 9.6 2.1 23.1 0.1 0.6 0.3South Dakota 7 11 68 2 37 213 6.6 0.4 6.1 0.4 0.1 0.0 0.1 0.1Utah* 9 32 93 5 44 554 6.6 1.5 6.1 1.3 5.5 0.0 0.2 0.2Washington 16 67 212 13 98 1,508 12.6 3.7 11.7 3.3 32.4 0.1 0.4 0.5Wyoming* 8 11 114 1 27 139 5.1 0.4 4.7 0.4 15.6 0.0 0.3 0.0Total - 13 WRAP States 223 567 2,097152 985 10,247 136.3 34.7 126.4 31.5 221.5 1.1 3.8 4.9*Total - 9 GCVTC States 180 467 1,560133 759 8,110 99.3 29.6 92.1 26.9 167.8 0.9 2.8 4.2February 2004G:\WRAP MSEI\Reporting\Final\Sec8_FYresults.doc8-22Table 8-3d.2018 Western states total off-road emissions by state and fuel type (TPD), based on the NONROAD2002 model with dieselsulfur set to 15 ppm.VOCNOXCOPM10PM2.5SO2NH3StateDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineDieselGasolineArizona* 13 57 98 10 56 1,615 10.0 3.5 9.6 3.1 8.1 0.1 0.2 0.3California* 89 177 52181 405 2,286 39.0 17.7 35.9 16.3 90.8 0.5 0.7 2.4Colorado* 12 44 130 8 62 1,192 9.1 2.3 8.5 2.1 11.0 0.1 0.3 0.3Idaho* 4 23 53 3 24 396 3.7 1.1 3.4 1.0 4.4 0.0 0.1 0.1Montana 9 10 121 2 39 211 7.7 0.6 7.2 0.5 14.2 0.0 0.3 0.1Nevada* 10 22 133 3 48 551 7.1 1.3 6.6 1.2 15.8 0.0 0.3 0.1New Mexico* 3 13 18 2 14 368 2.8 0.8 2.7 0.7 0.4 0.0 0.0 0.1North Dakota 9 9 119 2 49 211 8.5 0.4 7.8 0.4 8.5 0.0 0.3 0.1Oregon* 20 45 363 7 84 1,025 10.6 2.4 9.8 2.2 24.8 0.1 1.0 0.3South Dakota 6 9 61 2 35 218 5.9 0.4 5.5 0.4 0.2 0.0 0.1 0.1Utah* 9 29 90 4 46 576 6.6 1.4 6.2 1.3 5.9 0.0 0.2 0.1Washington 15 64 242 12 105 1,583 12.7 3.8 11.7 3.4 32.6 0.1 0.6 0.4Wyoming* 7 9 112 1 28 142 5.0 0.4 4.6 0.4 16.8 0.0 0.3 0.0Total - 13 WRAP States 205 513 2,063137 995 10,374 128.8 36.1 119.6 32.8 233.4 1.1 4.4 4.4*Total - 9 GCVTC States 167 420 1,520120 767 8,151 93.9 31.0 87.3 28.2 178.0 0.9 3.1 3.8February 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 1200Annual 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 2018February 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 2500Annual 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 2018February 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 160Annual 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 2018February 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 350Annual 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 2018February 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 m odeling, 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 a nd 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 emis sions from unpaved and paved roadways is to estimate the activity level. Fo r these two categories, the activity is the VMT on the unpaved or paved roads, respectively. Activity data for unp aved roads is almost always unavailable, since neither state, regional, or local transportati on agencies, nor government land managers, track VMT for unpaved roads. The general procedure fo llowed here for these calculations was to first estimate unpaved road VMT by state and roadway t ype, 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 road way type level as the starting point. These base year unpaved road VMT estimates were mu ltiplied 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 proj ected 2018 population divided by the 1996 population. The second factor was a regional factor that was developed based on histor ical 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 a pplied 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 tr aveled (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 A labama 1.161 East 0.6186 0.718 A laska 1.245 West 0.5088 0.634 A rizona 1.371 West 0.5088 0.697 A rkansas 1.175 Central 0.9136 1.073 California 1.283 We st 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 Ea st 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 Cent ral 0.9136 1.062 Kentucky 1.153 Ea st 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 C entral 0.9136 1.076 Mississippi 1.134 East 0.6186 0.701 Missouri 1.173 Central 0.9136 1.072 Montana 1.241 Cent ral 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 Ea st 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 1.079February 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 discu ssed above, from total 2018 VMT at the state and roadway type level. The calculation of 201 8 total VMT for the WRAP states was described in Section 6.1. Year 2018 VMT projections for th e 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 stat e 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 a nd 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 unpa ved and paved VMT to estimate seasonal unpaved and paved VMT.

9.1.2 Emission Factor Developm ent 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:

E

ext



### = [ k * ( s /12) a 

### * ( W /3) b 

### / ( M dry 

### /0.2) c 

### ] * [(365 – ( p *12))/365]

where

E ext 

### = monthly PM10 emission factor extra polated 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) M dry 

### = 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 precipitationFebruary 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 documen tation for travel on publicly accessible unpaved roads. A value of 1.0 percent was applied natio nally for the surface material moisture content under dry, uncontrolled conditions as a conservative estimate based on available data.

Average state-level, unpaved road silt conten t values developed as part of the 1985 NAPAP Inventory, were obtained from th e 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 sample s from all states was s ubstituted. 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, th e equation uses the number of days simply to calculate a percentage of rain days. The a bove 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 byFebruary 2004

G:\WRAP MSEI\Reporting\ Final\Sec9_Road_dust.doc5 

### 9-5 state; these averages are for a me teorological 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 A labama 11.2 10.2 10.9 8.5 9.6 9.6 10.2 7.7 8. 8 8.6 9.3 10.9 A laska 12.8 8.6 12.2 6.9 12.5 11. 6 12.6 16.2 16.4 14.4 13.2 14.0 A rizona 4.2 3.4 4.0 1.8 2.4 1.4 3.5 5.5 2. 9 2.6 2.7 3.8 A rkansas 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 4.8February 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 averag e 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 partic les of less than 10 microns in diameter from paved road dust (7.3 g/mi for PM10) PVSILT = road surface silt loading (g/m 2 

### ) WEIGHT = average weight of all vehicle types combined (tons)

Paved road silt loadings were assigned to each of the twelve functional road way classifications (six urban and six rural) based on the average annual tr affic volume of each functional system by state. One of three values was assigned to each of these road classes: 1 (gm/m 2 

### ) was assigned to local functional class roads; 0.20 gm/m 2 

### was assigned to non-local road types that had an estimated average daily traffic volume (ADTV) less th an 5,000 vehicles per day; and 0.04 gm/m 2 

### was assigned to non-local road types that had an es timated ADTV greater than or equal to 5,000 vehicles per day. ADTV was calculated by divi ding 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 acc ount for the number of days with a sufficient amount of precipitation to prevent road dust re suspension. The PART5 emission factors were multiplied by the fraction of days in a month wi th less than 0.01 inches of precipitation, reduced by 50 percent (i.e., the rain corr ection factor is calculated as: [365 –( p * 12 * 0.5)] / 365 , where
p represents the number of days in a given month w ith 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 mete orological station in each stat e. (These differ from the PAVED = PSDPVD * ( PVSILT /2) 0.65 

### * ( WEIGHT /3) 1.5February 2004

G:\WRAP MSEI\Reporting\ Final\Sec9_Road_dust.doc7 

### 9-7 precipitation data used in the unpaved road emi ssion factor calculations, which were taken from stations representative of rural areas in each st ate.) 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 A labama 10.4 10.1 9.0 8.4 8.8 10.1 10.9 8.4 9.1 7.1 9.3 10.4 A laska 10.5 8.4 5.3 2.9 7.5 11. 0 10.0 12.6 13.0 8.5 11.1 12.1 A rizona 6.3 6.6 7.1 2.8 3.3 2.7 6.9 9.4 5.3 4.5 4.3 6.5 A rkansas 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 6.8February 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 fact ors for each state. Seasonal paved and unpaved road fugitive dust emissions were estimated fo r 2018 at the state and roadway type level by multiplying the state/roadway type-specifi c average seasonal emission factors by the corresponding VMT estimate, at the same level of de tail. These state-level fugitive dust emissions were then allocated to the county leve l 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 fo r 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 road way 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 emis sions by state and roadway type to county and roadway type was similar to that used for allo cating 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 rath er than total VMT. Thus, the estimates of unpaved road emissions by state and roadway type we re multiplied by the ratio of a county’s rural population to the state’s rural population to deri ve 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 pave d and unpaved road fugitive dust emissions. An EPA-sponsored evaluation of particle size distri bution information indica ted 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 roadwa y type by 0.25 for paved roads and by 0.15 for unpaved roads.

9.1.2.2.3 Application of F ugitive 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 m easures in PM nonattainment areas. The control assumed for paved roads was vacuum sweeping twi ce 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 classifi cation (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 co ntrol was paving the unpaved road s. This control was applied with a control efficiency of 96 percent and a pene tration rate of 50 percent. On rural roads in serious PM nonattainment ar eas, 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 inventorie s for air quality mode ling are not required to be delineated by roadway type. Therefore, th e 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-leve l unpaved road dust emissions. The map shows unexpected inconsistencies in these estimates de veloped 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 variat ion 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 EMISSION S – REVISED ESTIMATES

Because of the inconsistencies seen in the Weste rn states unpaved road dust emissions estimates developed using the standard AP-42 approach s hown 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 produc t of which yields the emission estimates.

9.2.1 Revisions to State Average Silt Content

The region-specific parameters governing the es timated unpaved road dust emissions from unpaved roads include precipitation/dust moisture cont ent and silt content. Updated silt content data were obtained from the Illinois State Wate r Survey (ISWS) (Stensland, 2002). These data showed that the variability in the state-level emission factors is largel y attributable to variability in the assumed state-level silt content values. The stat e-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 th an three measurements), ra ther than the national average of 3.9 that EPA uses. Second, the larges t value (17.99, an outlier) was deleted from the Montana average, which reduces the Montana average significantly.February 2004

G:\WRAP MSEI\Reporting\Fi nal\Sec9_Road_dust.doc10 

### 9-10 Table 9-5 shows the assumed unpaved road silt cont ents 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 adjacen t within the Colorado Plateau group, former estimates placed the silt c ontents from 1.5 to 7.1. This resulte d 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 av erage 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 tr affic 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 relie d on road owners/managers (usually Federal agencies). Data were reported separately for urba n 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 PM 10 

### State Implementation Plan (SIP) (Clark County, 2001) and traffi c 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 we re based upon those in the Clark County, NV June 2001 PM 10 

### 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 firstFebruary 2004

G:\WRAP MSEI\Reporting\Fi nal\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 we re based on 1990 U.S. Census Bureau population and land area data (the 2000 Census was not availabl e 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 ex amined in this study. Given this information, it seemed prudent that rural volume estimates shoul d specifically take into account th e activity under this agency’s jurisdiction; i.e. activity in nati onal forests. The counties that ha ve 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 NF S 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 yiel ds accurate local estimates, an enormous amount of geospatial data is required for its applicati on. In order to apply this methodology within the available time and budget resources, the visits we re spread over all NFS roads and over the entire year at the state level. The first part of the ap proach 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 pe rcent are spatially. The second portion is because seasonal allocation of VMT is done separately from this analysis.

For this approach, state level (1996) forest visi tation data were obtained from the NFS web site ( http://www.fs.fed.us/recreati on/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 pe rformed 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 numbe r 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 th e same roads twice to get in and out of the forest. These state average NFS ADTV were app lied 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 coun ties without National Fo rests. 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 ADTV s, 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 theFebruary 2004

G:\WRAP MSEI\Reporting\Fi nal\Sec9_Road_dust.doc12 

### 9-12 adjustments for NFS visitation and population dens ity. The incorporation of population density into the calculations also yielded results that are reflective of the rela tive state populations. Previously, states such as Idaho, New Mexico , and Wyoming, all of which have relative low population densities, were inordinately high wi th 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 revi sed road dust emission estimates were multiplied by a factor to account for deposition and other rem oval mechanisms that tend to lo wer 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 ro ad dust emissions by a factor of four to account for these effects. For this proj ect, county-specific transport frac tions (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 fracti ons in each county. The transport fractions for each vegetation type ar e as follows: barren and water, 97%; agricultural, 85%; grasses, 70%; urban, 40%; scrub and spar se wooded, 30%; and forested, 5%. The county-level transport fractions are shown in Figure 9-3.

9.2.4 Final Revised 2018 Unpaved R oad Dust Emissions Estimates

Revision of the silt loading valu es as described above resulted in a 15 percent reduction in 2018 unpaved road dust emissions across the 13 Western st ates. 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 prev ious inventory. The final emissions by state are shown in Table 9-7 in comparison to the or iginal inventory; Figure 9-2 shows the revisedFebruary 2004

G:\WRAP MSEI\Reporting\Fi nal\Sec9_Road_dust.doc13 

### 9-13 county-level emissions estimates for unpaved road s in 2018. In comparison to the original unpaved road dust emissions shown in Figure 9- 1, Figure 9-3 shows th at the significant inconsistencies in the original road dust emis sions between adjacent counties and states have disappeared in the revised inventory. The revi sed emissions distribution shows more consistency among the counties in the Colorado Plateau States.

Table 9-7. Comparison of Western states 2018 unpav ed 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 – REVI SED ESTIMATES AND COMPARISON WITH 2018.

Unpaved road dust emissions for 1996 were revi sed using the same methodology as applied to develop the 2018 revised unpaved road dust emissi ons described above. Paved and unpaved road dust PM 10 

### 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 cha nge in paved and unpaved road dust emissions by state from 1996 to 2018. Paved road dust emis sions 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 PM 10 

### emissions in 1996, and about 65% of road dust PM 10 

### emissions in 2018. Overall, road dust PM 10 

### 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\Fi nal\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 64,404February 2004

G:\WRAP MSEI\Reporting\Fi nal\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 2003G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc9-16Figure 9-1.Distribution of estimated 2018 Western statesunpaved road dust emissions, based on EPA AP-42 methods.g pDate Generated: 5/23/2002Emissions (TPD)0.0 to 5.05.0 to 10.010.0 to 50.050.0 to 100.0100.0 to 200.0DenverLos AngelesSan FranciscoBoiseLas VegasAlbuquerqueSalt Lake CityPortlandSioux FallsFargoSeattleCheyennePhoenixBillingsSpokaneBismarckRapid CityRenoTucsonNovember 2003G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc9-17Figure 9-2.Distribution ofrevised 2018 Western states unpaved road dust emissions.g pDate Generated: 5/23/2002Emissions (TPD)0.0 to 0.50.5 to 1.01.0 to 5.05.0 to 10.010.0 to 20.0DenverLos AngelesSan FranciscoBoiseLas VegasAlbuquerqueSalt Lake CityPortlandSioux FallsFargoSeattleCheyennePhoenixBillingsSpokaneBismarckRapid CityRenoTucsonNovember 2003G:\WRAP MSEI\Reporting\Final\Sec9_Road_dust.doc9-18Figure 9-3.Continental United States DRAFT Transport Fractions.Date Created: 5/28/02Transport0.0 to 0.20.2 to 0.40.4 to 0.60.6 to 0.80.8 to 1.0DenverLos AngelesSan FranciscoBoiseLas VegasAlbuquerqueSalt Lake CityPortlandSioux FallsFargoSeattleCheyennePhoenixBillingsSpokaneBismarckRapid CityRenoTucsonFebruary 2004

G:\WRAP MSEI\Reporting\Final\Sec10_refs.doc 

### 10-1 10.0 REFERENCES

AAMA. 1996. “Fuel Volatility Survey 1996,” American Automobile Manufacturers Association, Washington, DC.

Acurex. 1996. “Marine Vessel Emissions Invent ory and Control Strategies.” Prepared for the South Coast Air Quality Management District, December.

Agrawal, Archana. 2001. Fax from Archana Agrawal, California Air Resources Board, to Alison Pollack. November.

Alliance. 1988. “Area Source Documentation for the 1985 National Acid Precipitation Assessment Program Inventory,” Final Report, EPA 600/8-88-106, Prepared by Alliance Technologies Corporation for the O ffice of Research and Development, U.S. EPA, December.

ARCADIS. 1999. “Commercial Marine Activit y for Deep Sea Ports in the United States.” Prepared for EPA by ARCADIS Geraghty & Miller, Inc., EPA420-R-99-020 September.

Automotove News, 1991. “1991 Market Data Book”. Crain Communications, Inc. May.

BAH. 1991. “Commercial Marine Vessel Cont ributions to Emission Inventories.” Final Report prepared for EPA, Booz-Allen & Hamilton. October.

Beardsley, M. 2001. “MOBILE6-EPA’s Highwa y Vehicle Emissions Model.” Presented at North American Vehicle Emission Contro l Conference, Atlanta, GA. April.

BTS. 2001. Personal communication with Buzz Ri fe, Office of Airline Information, Bureau of Transportation Statistics, February - March.

Brzezinski, D. 2002. Email communication to Alison Pollack on changes in MOBILE6 from September 2001 draft to January 2002 public release. February.

Cadle, S.H. et al. 1999a. “In-Use Light-Duty Vehicle Partic ulate Matter Emissions on the FTP, REP05, and UC Cycles,” Prepared for the Coordinating Research Council (Project E-46), June.

Cadle, S.H. et al. 1999b. “Light-Duty Motor Vehicle Exhaust Particulate Matter Measurement in the Denver, Colorado, Area,” Journal of the Air & Waste Management Association , Vol. 49, September.

CARB. 2001a. Personal communication, Kim Heroy-Rogalski, California Air Resources Board, April.

CARB. 2001b. Personal communication, Edie Chang, California Air Resources Board, August.February 2004

G:\WRAP MSEI\Reporting\Final\Sec10_refs.doc 

### 10-2 CARB. 2000. “Public Meeting to Consider A pproval of Revisions to the State’s On-Road Motor Vehicle Emissions Inventory – Tec hnical Support Document,” California Air Resources Board. May.

Carlson, A. 1993. Memorandum from Amy Ca rlson, Black & Veatch Engineering, to Shirley Rivera, San Diego Gas & Electric (SDGE), August.

CE-CERT. 1998a. “Characterization of Pa rticulate Emissions from Gasoline-Fueled Vehicles.” September.

CE-CERT. 1998b. “Measurement of Primary Pa rticulate Matter Emissions from Light-Duty Motor Vehicles,” CRC Project No. E-24-2. December.

CE-CERT. 2001. Unpublished ammonia data provided by Thomas Durbin, March.

Census. 1990. “Truck Inventory and Us e Survey – United States” TC87-T-52, US Department of Commerce, Bureau of Census. August.

Corbett, J.J. 2001. “Commercial Marine Vessel Inventory Review and Preparation for the Northwest U.S.”. Prepared for the Washington Department of Ecology, 22 May.

Corbett, J. J., P. S. Fishbeck. 1998. “C ommercial Marine Emissions Inventory for EPA Category 2 and 3 Compression Ignition Marine Engines in the United States Continental and Inland Waterways.” Prep ared for the Environmental Protection Agency. EPA420-R-98-020. August.

Cowheard, Chatten. 1996. Midwest Research Institute memorandum to Bill Kuykendal of the US EPA, Emission Factors and Inventories Gr oup, and W.R. Barnard of E.H. Pechan and Asociates, Inc., September.

Dickson, C.L., G.P. Sturm. 1996. “D iesel Fuel Oils, 1996” NIPER, October.

Environment Canada. 1999. “Gaseous and Part iculate Matter Emissions from in-Use Light Duty Gasoline Motor Vehicles,” ERMD Report #99-67.

EPA. 2002. “Update Heavy-Duty Engine Emission Conversion Factors for MOBILE6. Analysis of Fuel Economy, Non-Engine Fuel Economy Improvements and Fuel Densities.” EPA420-R-02-006. U.S. Envir onmental Protection Agency, Office of Air and Radiation. January.

EPA. 2001. “Fleet Charact erization Data for MOBILE6: Development and Use of Age Distributions, Average Annual Mileage Accu mulation Rates, and Projected Vehicle Counts for Use in MOBILE6.” EPA420-R- 01-047. U.S. Environmental Protection Agency, Office of Air and Radiation. September.

EPA. 2001. “Draft User’s Guide to MOBILE6 .0: Mobile Source Emission Factor Model” EPA420-D-01-003. U.S. Environmental Protection Agency, Office of Air and Radiation. August.February 2004

G:\WRAP MSEI\Reporting\Final\Sec10_refs.doc 

### 10-3 EPA. 2001. “Procedures Document for Nationa l Emission Inventory, Criteria Air Pollutants 1985-1999.” EPA-454/R-01-006. U.S. Envir onmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC. March.

EPA. 2001. Personal communication, Joe Some rs, U.S. Environmenta l Protection Agency, February.

EPA. 2000. Personal communication with John Mueller, January.

EPA. 2000. “Procedures for Developing Base Year and Future Year Mass and Modeling Inventories for the Heavy-Duty Engine a nd Vehicle Standards and Highway Diesel Fuel (HDD) Rulemaking.” EPA420-R-00- 020. U.S. Environmental Protection Agency, Office of Air and Radiation. October.

EPA. 2000. ‘Phase 2 Emission Standards for New Nonhandheld Engines At or Below 19 kW,’ Available Online: http://www.epa.gov/ oms/equip-ld.htm#regs , Signed March.

EPA. 1999. ‘Phase 2 Emission Standards fo r New Spark-Ignition Handheld Engines At or Below 19 kW,’ Available Online: http://www.epa.gov/oms/equip-ld.htm#regs , Signed March.

EPA. 1999b. “Final Regulatory Impact Analys is: Control of Emissions from CompressionIgnition Marine Engines”. EPA420-R-99-026. November.

EPA. 1999a. “Control Of Air Pollution From Marine Compression-Ignition Engines.” Part 94, Code of Federal Regulations. Publis hed in the Federal Register December.

EPA. 1999. “Update of Heavy-Duty Emissi on Levels (Model Years 1988-2004+) for Use in MOBILE6.” EPA420-R-99-010. U.S. Envi ronmental Protection Agency, Office of Air and Radiation. April.

EPA. 1998. “National Air Pollutant Emi ssion Trends, Procedures Document 1900-1996,” EPA-454/R-98-008. U.S. Environmental Pr otection Agency, Office of Air and Radiation. May.

EPA. 1998, ‘Control of Emissions of Air Po llution From Nonroad Diesel Engines; Final Rule,’ Available Online; http://www.epa. gov/oms/nonroad.htm, Federal Register v.63 n. 205, October.

EPA. 1997. “Emission Factors for Locomo tives,” EPA420-97-051. U.S. Environmental Protection Agency, Office of Ai r and Radiation. December.

EPA. 1997. “Locomotive Emission Standa rds - Regulatory Support Document (RSD).” U.S. Environmental Protection Agency, O ffice of Mobile Sources, Ann Arbor, MI. April.

EPA. 1996. “Control of Air Pollution; Final Rule for New Gasoline Spark-Ignition Marine Engines; Exemptions for New Nonroad Comp ression-Ignition Engines at or Above 37February 2004

G:\WRAP MSEI\Reporting\Final\Sec10_refs.doc 

### 10-4 Kilowatts and New Nonroad Spark-Ignition En gines at or Below 19 Kilowatts,” Code of Federal Regulations, Federal Register, October.

EPA. 1995. “Stationary Point and Area Sources.” AP-42 5 th 

### Edition, Volume 1, January.

EPA. 1995. ‘Control of Air Pollution; Emission Regulations for New Nonroad Spark-ignition Engines At or Below 19 Kilo watts; Final Rule,’ Federal Register Page 34582, Signed July.

EPA. 1994. “Draft User’s Guide to PART5: A Program for Calculating Particle Emissions from Motor Vehicles” EPA-AA-AQAB-94-2, US Environmental Protection Agency, Office of Mobile Sources, Ann Arbor, IM, July.

EPA. 1994. ‘Determination of Significance fo r Nonroad Sources and Emission Standards for New Nonroad Compression-Ignition Engines At or above 37 Kilowatts; Final Rule,’ Federal Register, v. 59, n. 116, June.

EPA. 1992. “Procedures for Emission Inventor y Preparation Volume IV: Mobile Sources,” Emission Planning and Strategies Division, Office of Mobile Sources and Technical Support Division, Office of Air Qua lity and Planning Standards, U.S.

EPA. 1992. “Procedures for Emission Inventory Preparation, Volume IV: Mobile Sources.” EPA-450/4-81-026d (Revised).

ERG. 2000. “Documentation for the 1996 Base Y ear National Toxics Inventory for Aircraft Sources,” Eastern Research Group, Inc ., Emissions, Monitoring and Analysis Division, U.S. EPA. June.

FAA. 2001a. Terminal Area Forecast (TAF ) System, Federal Aviation Administration website, http://www.apo.data.faa.g ov/faatafall.HTM, March.

FAA. 2001b. Air Traffic Activity Data Syst em (ATADS), Federal Aviation Administration website, http://www.apo.data.faa.gov/faaatadsall.HTM, March.

FAA. 2001c. Consolidated Operations and Dela y Analysis System (CODAS) taxi time data, EDMS website, http://www.aee.faa. gov/aee-100/aee-120/EDMS, March.

FAA. 1999. “National Plan of Integrat ed Airport Systems (1998-2002),” Report to Congress, Federal Aviation Administration, Washington, DC, March.

FAA. 1998a. “General Aviation and Air Taxi Activity Survey,” Office of Aviation Policy, Planning, and Internationa l Aviation, available at http://www.api.faa.gov/apo_pubs.htm#ANCHOR98 2. April

FAA. 1998b. “Economic Values for Eval uation of Federal Aviation Administration Investment and Regulatory Program,” Offi ce of Aviation Policy and Plans. June.February 2004

G:\WRAP MSEI\Reporting\Final\Sec10_refs.doc 

### 10-5 Fujita, E., J.G. Chow, N.F. Robinson, L.W. Richards, and N. Kumar. 1998. “Northern Front Range Air Quality Study, Volume C: Source Apportionment and Simulation Methods and Evaluation .” Prepared for Colorado State University, Cooperative Institute for Research in the Atmosp here, by Desert Research Institute.

Garg, B.D. et al. 2000. “Brake Wear Particulate Matter Emissions,” Environmental Science and Technology , Vol. 34, No. 21.

Gillies, J.A. and A.W. Gertler. 2000. “Com parison and Evaluation of Chemically Speciated Mobile Source PM2.5 Partic ulate Matter Profiles.” Journal of the Air & Waste Management Association , Vol. 50, August.

Gray, H. 1986. “Control of Atmospheric Fine Primary Carbon Particle Concentrations.” Environmental Quality Laboratory, Calif ornia Institute of Technology, for the California Air Resources Board. February.

Harvey, C., P. Carey, J. Warila. 2003. “E PA’s Newest Draft Nonroad Emission Inventory Model”. Prepared for the 12 th 

### International Emission Inve ntory Conference ‘Emission Inventories – Applying New Technologi es’. San Diego, CA. April-May.

Harvey, C.A., et al. 1983. “A Study of the Potential Impact of Some Unregulated Motor Vehicle Emissions.” Society of Automotiv e Engineers Technical Paper Series, Paper No. 830987.

ICAO. 1998c. “Committee on Aviation Envir onmental Protection – Fourth Meeting – Report,” Doc 9720, CAEP/4, Montreal, 6-8 April 1998.

Koupal, J. and R. Rykowski. 1998. “”Method ology for Modifying MOBILE5b in the Tier 2 Study,” U.S. Environmental Protection Agency, EPA420-R-98-004. April.

Lloyds. 1995. “Marine Exhaust Emissions Res earch Programme,” “Steady-state Operation,” 1990; “Slow Speed Addendum,” 1991; “M arine Exhaust Emissions Research Programme”. 1995. Lloyds Register Engin eering Services, Croyden, Lloyds Register of Shipping, London.

MARAD. 2000. “Environmental Advantages of Inland Barge Transportation.” Maritime Administration, US Department of Transportation, June.

MARPOL. 1997. “Consideration And Adoption Of The Protocol Of 1997 To Amend The International Convention For The Preven tion Of Pollution From Ships, 1973, As Modified By The Protocol Of 1978 Relating Th ereto: Text of the Protocol of 1997 and Annex VI to the International Convention fo r the prevention of Pollution from Ships, 1973, as modified by the Protocol of 1978 relating thereto (MARPOL 73/78),” October.

MVMA. 1991. “Motor Vehicl e Facts and Gifures’91.” Motor Vehicle Manufacturers Association, Detroit, MI.February 2004

G:\WRAP MSEI\Reporting\Final\Sec10_refs.doc 

### 10-6 NCC. 1996. National Climatic Center, 1996 meteor ological data files sent electronically to E.H. Pechan & Associates, Inc., Asheville, NC, 1995.

NESCAUM. 1999. “Heavy-Duty Nonroad Engine Activity and Emissions in the Northeast,” Final Report, Northeast States for C ooperative Air Use Management, August.

NIPER. 1997. “Aviation Turbine Fuels, 1996,” National Institute for Petroleum and Energy Research, April.

NMFS. 2001. “Annual Landings”. National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Department of Commerce, Available Online at: http://www.st.nmfs.gov/st1/c ommercial/landings/annual_ landings.html. March.

Norbeck, J.M., T.D. Durbin, and T. Truex. 1998. Measurement of Primary Particulate Matter Emissions from Li ght-Duty Motor Vehicles , Prepared for Coordinating Research Council and South Coast Air Quality Management District, by Center for Environmental Research and Technology, Co llege of Engineering, University of California, Riverside, CA.

NPRA/API. 1997. “Final Report 1996 Americ an Petroleum Institute /National Petroleum Refiners Association Survey of Refining Operations and Product Quality,” National Petroleum Refiners Associ ation and American Petroleum Institute. July.

NREL. 1998. “Statistical Analysis of Par ticulate Matter from Light-Duty and Heavy-Duty Diesel Vehicles – Final Report to the Northern Front Range Air Quality Study,” Prepared by the National Renewable Energy Laboratory for the Office of the Vice President for Research and Information Tec hnology (Colorado State University). July.

Pechan. 2001. Personal communi cation with Jim Wilson, January.

Pechan-Avanti Group. 1999. “Emission Projections” Volume X. Prepared for Projections Committee; Emission Inventory Impr ovement Program. Available at http://www.epa.gov/ttn/chie f/eiip/techreport/volume10/ December.

Pechan. 1997. “Documentation of Mobile S ource Inventories Used in OAQPS Trends Report – Task 2/3 Report,” Prepared by E.H. P echan and Associates for U.S. EPA, September.

Pollack, A.K., C. Lindhjem. 1997. “NONRO AD MOBILE Emissions Modeling,” presented at The Emission Inventory: Planning for the Future. Research Triangle Park, North Carolina. October.

Radian. 1988. “Air Emissions Species Manua l. 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

G:\WRAP MSEI\Reporting\Final\Sec10_refs.doc 

### 10-7 Sierra. 1999. “Analysis of the Impacts of Control Programs on Motor Vehicle Emissions and Exposure in Urban Areas and Nationwide,” Prepared by Sierra Research for the U.S. Environmental Protection Agency, Sie rra 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.

Stensland, G. 1989. Gary Stensland, Illi nois State Water Survey, personal communication with W. Barnard of E.H. Pechan & Associates, Inc., Durham, NC.

TRC. 1989. ‘Ship Emissions Control Study for the Ports of Long Beach and Los Angeles, 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 Va lley Authority, Transportation Research Board, TR News, And personal communica tion with Mr. Dager July – August.

Ubanwa, B., A. Burnette, S. Kishan, S. Fritz. 2000. “An Analysis of Exhaust PM Emission Factors for In-Use Motor Vehicles,” Presented at the 10 th 

### CRC On-Road Vehicle Emissions Workshop, March.

US Army Corps of Engineers. 1996. “Water borne Commerce of the United States Calendar Year 1996; Part 4 Pacific Coast, Alaska , and Hawaii.” Water Resources Support Center.

US Army Corps of Engineers. 2001a. “US Waterway Data, Lock Statistics.” Available Online at http://www.wrsc.usace.arm y.mil/ndc/lpms.htm. March.

US Army Corps of Engineers. 2001b. “U S Waterway Data, Dredging Information System” Available Online at http://www.wrsc.usace.ar my.mil/ndc/datadrgsel.htm. March.

Watson, J.G., et al. 1990. “Chemical Source Profiles for Particulate Motor Vehicle Exhaust Under Cold and High Altitude Operating Conditions.” No. 93, The Science of the Total Environment.

### Appendix A

Default 1996 Fuel Sulfur LevelsTable 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 19Summer 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 167Summer 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 167Summer 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 252Summer 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 342Summer 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 361Summer 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 342Summer 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 338Summer 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 230Summer 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 230Summer 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 252Summer 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 252Summer 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 252Table 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 1101996 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 AAM A-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 AAM A-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 41001996 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 AAM A-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 41001996 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 41001996 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 41001996 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 41001996 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 41001996 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 34001996 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 42001996 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 42001996 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 34001996 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 41001996 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