International Corporation Air Sciences

Final Report 

WRAP MOBILE SOURCE EMISSION INVENTORIES UPDATE 

Prepared for: 

Western Governors’ Association 

1515 Cleveland Place, Suite 200 

Denver, CO 80202 

Prepared by: 

Alison K. Pollack, Lit Chan, Priya Chandraker, John Grant, 

Chris Lindhjem, Sunja Rao, James Russell, Cuong Tran 

ENVIRON International Corporation 

101 Rowland Way, Suite 220 

Novato, CA 94945-5010 

May 2006 

 101 Rowland Way, Suite 220, Novato, CA 94945 415.899.0700 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\ACKNOWLEDGEMENTS.doc 

ACKNOWLEDGEMENTS 

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

the work described in this report: 

• Mr. Tom Moore, WRAP technical liaison for this project, served as the primary contact 

for this work, and assisted in aspects of the work too numerous to mention. 

• The WRAP Emissions Forum actively participated in the work, advising and guiding. 

• State and local air quality planning agency staff reviewed and provided updated modeling 

inputs and activity data. 

• Jim Lyons and Phil Heirigs of Sierra Research performed the fuel survey data analyses 

described in Section 2 

To all these individuals the authors express their sincere appreciation. 
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TABLE OF CONTENTS 

Page 

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

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

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

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

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

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

 1.4 Overview of Approach for Estimating On-Road and 

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

 1.3 Mobile Source Emission Inventory Work Products ............................................ 1-3 

 

2.0 EMISSIONS MODELS USED AND ADDITIONAL CALCULATIONS 

 FOR AIR QUALITY MODELING ....................................................................... 2-1 

2.1 EPA MOBILE6 Model ........................................................................................ 2-1 

2.2 EPA NONROAD Model...................................................................................... 2-2 

 2.3 California Models ................................................................................................ 2-4 

 2.4 Pollutants Added for Air Quality Modeling ........................................................ 2-4 

 2.5 Temporal Profiles................................................................................................. 2-5 

 

3.0 ON-ROAD VEHICLE EMISSIONS METHODOLOGY.......................................... 3-1 

 

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

3.2 MOBILE6 Inputs ................................................................................................. 3-2 

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

 3.2.2 Meteorological and Altitude Inputs ................................................................. 3-2 

 3.2.3 Control Programs............................................................................................. 3-3 

 3.2.4 External Files ................................................................................................... 3-4 

 3.2.5 On-Road Fuel Properties.................................................................................. 3-4 

3.3 Seasonal Emissions Calculations......................................................................... 3-5 

3.4 Future Year Emissions......................................................................................... 3-5 

 3.4.1 VMT Growth Factors....................................................................................... 3-5 

 3.4.2 MOBILE6 Inputs ............................................................................................. 3-6 

3.5 Processing of California Emissions ..................................................................... 3-7 

3.6 Generation of SMOKE and NIF Files ................................................................. 3-7 

4.0 OFF-ROAD EQUIPMENT EMISSIONS METHODOLOGY ................................. 4-1 

 4.1. Nonroad Modeling............................................................................................... 4-1 

 4.2. Processing of California Emissions ..................................................................... 4-2 

 4.3. Generation of SMOKE and NIF Files ................................................................. 4-3 
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5.0 LOCOMOTIVE EMISSIONS METHODOLOGY.................................................... 5-1 

 5.1 2002 Locomotive Emissions................................................................................ 5-1 

 5.2 Projected Future Year Locomotive Emissions .................................................... 5-4 

 5.3 California Locomotive Emissions........................................................................ 5-6 

 5.4 Generation of SMOKE and NIF Files ................................................................. 5-6 

6.0 2002 AIRCRAFT EMISSIONS METHODOLOGY .................................................. 6-1 

 

 6.1 2002 Aircraft Emissions ...................................................................................... 6-1 

 6.1.1 NEI2002 Aircraft Emissions Estimates and Additional Pollutants ................. 6-1 

 6.1.2 State Updates ................................................................................................... 6-2 

 6.1.3 Seasonal Emissions Estimates ......................................................................... 6-3 

 6.2 Future Year Aircraft Emissions ........................................................................... 6-3 

 6.3 California Aircraft Emissions .............................................................................. 6-3 

 6.4 Generation of SMOKE and NIF Files ................................................................. 6-4

7.0 COMMERCIAL MARINE EMISSIONS METHODOLOGY ................................. 7-1 

 7.1 Off-shore Emissions Estimates............................................................................ 7-1 

 7.2 In-shore Port Revisions to Emissions (Including Larger Alaskan Freight Ports) 7-4 

 7.2.1 Emission Factors Revision............................................................................... 7-4 

 7.2.2 Revised Estimates for Ocean-Going Vessels................................................... 7-6 

 7.2.3 Columbia River Ports....................................................................................... 7-8 

 7.2.4 Alaskan Ports ................................................................................................... 7-9 

 7.2.5 California Coastal Transit and Ports.............................................................. 7-10 

 7.3 Future Year Commercial Marine Projections.................................................... 7-12 

 7.4 Recent Goods Movement Regulatory Activity.................................................. 7-13 

 

8.0 ROAD DUST EMISSIONS METHODOLOGY......................................................... 8-1 

8.1 Road Dust Survey ................................................................................................ 8-1 

 8.1.1 Updates to Paved and Unpaved Road VMT.................................................... 8-2 

 8.1.2 Updates to Road Dust Controls........................................................................ 8-3 

 8.2 Road Dust Emissions Calculations ...................................................................... 8-3 

 8.3 Generation of SMOKE and NIF Files ................................................................. 8-4 

9.0 RESULTS ....................................................................................................................... 9-1 

9.1 Overview of WRAP Emission Inventories.......................................................... 9-1 

9.2 On-Road Emissions ............................................................................................. 9-1 

9.3 Off-Road Emissions............................................................................................. 9-2 

9.4 Total Mobile Source Emissions........................................................................... 9-2 

9.5 Road Dust Emissions ........................................................................................... 9-3 

 

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

Appendix A: Development of Calendar Year 2002 County-Level 

Fuel Specification Data 

Appendix B: Geographic Characterization of Ship Emissions for the 

U.S. West Coast - Final Task 1 Report – Corbett and Wang, 2005 

TABLES 

Table 2-1. MOBILE5 vehicle classes for which emissions were estimated ......................... 2-2 

Table 2-2. Elemental carbon/organic carbon fractions ......................................................... 2-5 

Table 3-1. Default speeds by road type and vehicle type (mph), from NEI2002 ................. 3-2 

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

Table 3-3. SCC codes for on-road emissions in SMOKE files (non-California states)........ 3-8 

Table 3-4. SCC codes for California on-road emissions in SMOKE files.......................... 3-10 

Table 5-1. Track segment density ranges used for allocation to counties............................. 5-2 

Table 5-2. National fleet average emission factors (gram per gallon) from EPA (1997) ..... 5-3 

Table 5-3. Locomotive activity growth projection for this work.......................................... 5-5 

Table 5-4. Locomotive emission rate projections ................................................................. 5-6 

Table 7-1. Commercial vessel types...................................................................................... 7-1 

Table 7-2. 2002 Large ocean-going ship emissions by location (tons/year)......................... 7-3 

Table 7-3. US EPA (1999a, 2003) baseline emission factors for marine engines ................ 7-4 

Table 7-4. Emission factors for marine engines in the Port of Los Angeles 

 emission inventory report .................................................................................... 7-5 

Table 7-5. Emission factors found in the IVL 2004 report for average 1999 conditions ..... 7-5 

Table 7-6. Emission estimates for Puget Sound (excluding Grays Harbor) 

 ocean-going vessels in 2002 ................................................................................ 7-6 

Table 7-7. Freight tonnage from 1996 to 2002 by port......................................................... 7-7 

Table 7-8. Port activity totals as presented by EPA (1999b) for 1995.................................. 7-8 

Table 7-9. Emission estimates for Columbia River ocean-going vessels in 1996 ................ 7-9 

Table 7-10. Emission estimates for Columbia River ocean-going vessels in 2002 ................ 7-9 

Table 7-11. Port activity totals as presented by EPA (1999b) compared 

 with Puget Sound Ports or Coos Bay................................................................... 7-9 

Table 7-12. Emission estimates for Alaskan port ocean-going vessels in 1996 ................... 7-10 

Table 7-13. Emission estimates for Alaskan port ocean-going vessels in 2002 ................... 7-10 

Table 7-14. Projection factors for ocean-going vessels. ....................................................... 7-11 

Table 9-1. 2002 Western states total on-road emissions by fuel type................................. 9-33 

Table 9-2. 2008 Western states total on-road emissions by fuel type................................. 9-34 

Table 9-3. 2013 Western states total on-road emissions by fuel type................................. 9-35 

Table 9-4. 2018 Western states total on-road emissions by fuel type................................. 9-36 

Table 9-5. 2002 Western states total nonroad annual emissions by fuel type..................... 9-37 

Table 9-6. 2008 Western states total nonroad annual emissions by fuel type..................... 9-38 

Table 9-7. 2013 Western states total nonroad annual emissions by fuel type..................... 9-39 

Table 9-8. 2018 Western states total nonroad annual emissions by fuel type..................... 9-40 

Table 9-9. West coast 2002 NOx and SO2 emissions......................................................... 9-41 

Table 9-10. West coast 2002 NOx and SO2 emissions......................................................... 9-41 

Table 9-11. 2002 Paved road dust PM emissions ................................................................. 9-42 

Table 9-12. 2002 Unpaved road dust PM emissions............................................................. 9-43 

Table 9-13. 2018 Paved road dust PM emissions ................................................................. 9-44 

Table 9-14. 2018 Unpaved road dust PM emissions............................................................. 9-45 
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FIGURES 

Figure 2-1. Day of week profiles by vehicle class.................................................................. 2-6 

Figure 2-2. Weekday hour of day profiles by vehicle class ................................................... 2-7 

Figure 2-3. Day of week profiles by vehicle class.................................................................. 2-7 

Figure 3-1. Statewide VMT growth rates, 2002 to 2018........................................................ 3-6 

Figure 5-1. County level rail activity in the WRAP states ..................................................... 5-2 

Figure 5-2. County level rail NOx emissions (tons per year) in the WRAP states ................ 5-4 

Figure 5-3. Trends in historical rail fuel consumption by railroad......................................... 5-5 

Figure 7-1. Raw offshore emission estimates and with near port emissions 

 substituted (Blue grids indicate no emissions over water)................................... 7-2 

Figure 7-2. Offshore emissions by state and grid cells replaced with near port data............. 7-3 

Figure 7-3. Grid assignment of California in-transit (green) and 

 port/inshore (red) emissions............................................................................... 7-11 

Figure 7-4. Distinct zones included in the WRAP commercial marine 

 emissions inventory. .......................................................................................... 7-12 

Figure 7-5. Commercial marine installed power trend and analysis ................................... .7-13 

Figure 8-1. Unpaved road dust coarse PM emissions, with transport 

 fractions applied, from previous and current WRAP inventories........................ 8-4 

Figure 9-1. 2000 – 2004 WRAP region annual emissions contributions 

 by source category. Source: Adelman et al., 2006.............................................. 9-4 

Figure 9-2. 2018 WRAP region Base Case annual emissions contributions 

 by source category. Source: Adelman et al., 2006.............................................. 9-5 

Figure 9-3. 2002 Western states on-road emissions (% by vehicle class 

 and fuel type). ...................................................................................................... 9-6 

Figure 9-4. 2002 Western states average annual on-road NOx emissions 

 by vehicle class and fuel type. ............................................................................. 9-6 

Figure 9-5. 2002 Western states average annual on-road VOC emissions by 

 vehicle class and fuel type. .................................................................................. 9-7 

Figure 9-6. 2002 Western states average annual on-road PM10 emissions 

 by vehicle class and fuel type. ............................................................................. 9-7 

Figure 9-7. 2002 Western states average annual on-road SO2 emissions 

 by vehicle class and fuel type. ............................................................................. 9-8 

Figure 9-8. 2002 Western states average annual on-road CO emissions by 

 vehicle class and fuel type ................................................................................... 9-8 

Figure 9-9. Spatial distribution of all RPO on-road NO emissions, 2002 July weekday ...... 9-9 

Figure 9-10. Western states on-road annual average daily NOx emissions, 2002 – 2018 ..... 9-10 

Figure 9-11. Western states on-road annual average daily VOC emissions, 2002 – 2018..... 9-11 

Figure 9-12. Western states on-road annual average daily PM10 emissions, 2002 – 2018 ... 9-12 

Figure 9-13. Western states on-road annual average daily PM2.5 emissions, 2002 – 2018 .. 9-13 

Figure 9-14. Western states on-road annual average daily SO2 emissions, 2002 – 2018...... 9-14 

Figure 9-15. Western states on-road annual average daily CO emissions, 2002 – 2018 ....... 9-15 

Figure 9-16. Western states 2002 off-road emissions ............................................................ 9-16 

Figure 9-17. 2002 Western states average annual off-road NOx emissions 

 by source category. ............................................................................................ 9-16 

Figure 9-18. 2002 western states average annual off-road VOC emissions 

 by source category. ............................................................................................ 9-17 

Figure 9-19. 2002 western states average annual off-road PM10 emissions 

 by source category. ............................................................................................ 9-17 
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Figure 9-20. 2002 western states average annual off-road SO2 emissions 

 by source category ............................................................................................. 9-18 

Figure 9-21. 2002 western states average annual off-road CO emissions 

 by source category ............................................................................................ 9-18 

Figure 9-22. Spatial distribution of all RPO off-road NO emissions, 2002 July weekday .... 9-19 

Figure 9-23. Western states off-road annual average daily NOx 

 emissions, 2002 – 2018...................................................................................... 9-20 

Figure 9-24. Western states off-road annual average daily VOC 

 emissions, 2002 – 2018...................................................................................... 9-21 

Figure 9-25. Western states off-road annual average daily PM10 

 emissions, 2002 – 2018...................................................................................... 9-22 

Figure 9-26. Western states off-road annual average daily PM2.5 

 emissions, 2002 – 2018...................................................................................... 9-23 

Figure 9-27. Western states off-road annual average daily SO2 emissions, 

 2002 – 2018........................................................................................................ 9-24 

Figure 9-28. Western states off-road annual average daily CO emissions, 

 2002 – 2018........................................................................................................ 9-25 

Figure 9-29. Western states total mobile annual average daily NOx 

 emissions, 2002 – 2018...................................................................................... 9-26 

Figure 9-30. Western states total mobile annual average daily VOC 

 emissions, 2002 – 2018...................................................................................... 9-27 

Figure 9-31. Western states total mobile annual average daily PM10 

 emissions, 2002 – 2018...................................................................................... 9-28 

Figure 9-32. Western states total mobile annual average daily PM2.5 

 emissions, 2002 – 2018...................................................................................... 9-29 

Figure 9-33. Western states total mobile annual average daily SO2 

 emissions, 2002 – 2018...................................................................................... 9-30 

Figure 9-34. Western states total mobile annual average daily CO 

 emissions, 2002 – 2018...................................................................................... 9-31 

Figure 9-35. Spatial distribution of all RPO road dust coarse 

 PM emissions, 2002 July weekday .................................................................... 9-32 
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ACRONYMS 

ADTV Average daily traffic volume 

ATADS Air Traffic Activity Data System (of the FAA) 

ATP Anti-Tampering Program 

BEA Bureau of Economic Affairs 

BTS Bureau of Transportation Statistics 

CARB California Air Resources Board 

CEFS California Emissions Forecasting System 

CERR Consolidate Emissions Reporting Rule 

CI Compression ignition 

CMV Commercial marine vessel 

CNG Compressed natural gas 

CO Carbon monoxide 

CODAS Consolidated Operations and Delay Analysis System (of the FAA) 

COG Council of Governments 

DOE Department of Energy 

EC Elemental carbon 

EDMS Emissions and Dispersion Modeling System (of the FAA) 

EDMS WRAP’s Emissions Data Management System 

EIA Energy Information Administration 

EIIP Emission Inventory Improvement Program 

EPA Environmental Protection Agency 

EPS2 Emissions Processing System version 2 

FAA Federal Aviation Administration 

FHWA Federal Highway Administration 

FTP Federal Test Procedure 

GCVTC Grand Canyon Visibility Transport Commission 

GSE Ground support equipment 

GSP Gross State Product 

GVW Gross vehicle weight 

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 

I/M Inspection and Maintenance 

LDDT Light-duty diesel truck 

LDDV Light-duty diesel vehicle 

LDGT Light-duty gasoline truck 

LDGV Light-duty gasoline vehicle 

LDT Light-duty truck 

LDV Light-duty vehicle 

LEV Low Emission Vehicle 

LHDDV Light heavy-duty diesel vehicle 

LPG Liquid Propane Gas 
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LTO Landing and take-off 

MARPOL International Convention for the Prevention of Pollution from Ships 

MHDDV Medium heavy-duty diesel vehicle 

MPO Metropolitan Planning Organization 

MSF Mobile Sources Forum 

MY Model year 

NAPAP National Acid Precipitation Assessment Program 

NCDC National Climatic Data Center 

NEI National Emissions Inventory 

NET National Emissions Trends 

NFS National Forest Service 

NH3 Ammonia 

NIPER National Institute for Petroleum and Energy Research 

NPIAS National Plan of Integrated Airport System 

NOx Nitrogen oxides 

NO2 Nitrogen dioxide 

NREL National Renewable Energy Laboratory 

OC Organic carbon 

PM Particulate matter 

PM10 Particulate matter less than or equal to 10 micrometers 

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

QA Quality assurance 

RIA Regulatory Impact Analysis 

RMC Regional Modeling Center 

RPO Regional Planning Organization 

RSD Regulatory Support Document 

SCC Source Classification Code 

SI Spark ignition 

SIC Standard Industrial Classification 

SIP State Implementation Plan 

SMOKE Sparse Matrix Operator Kernel Emissions 

SO2 Sulfur dioxide 

SO4 Sulfate 

SOX Sulfur oxides 

TAF Terminal Area Forecast (of the FAA) 

THC Total hydrocarbons 

TIM Time in mode 

TPD Tons per day 

TPY Tons per year 

TSD Technical Support Document 

TWC Three-way catalyst 

VMT Vehicle miles traveled 

VOC Volatile organic compound 

WGA Western Governor’s Association 

WRAP Western Regional Air Partnership 
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PREFACE 

Regulatory Framework for Tribal Visibility Implementation Plans 

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

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

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

create the following framework: 

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

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

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

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

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

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

statutory and regulatory requirements. 

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

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

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

region (40 CFR §51.309(d)(12). 

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

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

30439). 

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

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

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

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

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

 

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

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

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

tribe to tribe. The authors have striven to ensure that all references to tribes in the document are 

consistent with principles of tribal sovereignty and autonomy as reflected in the above 

framework. Any inconsistency with this framework is strictly inadvertent and not an attempt to 

impose requirements on tribes which are not present under existing law. 

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

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

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

interest) which participate on Forums and Committees but are not eligible for the Board. 

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

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

Alaska. The sheer number of tribes makes full participation impossible. Moreover, many tribes 
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are faced with pressing environmental, economic, and social issues, and do not have the 

resources to participate in an effort such as the WRAP, however important its goals may be. 

These factors necessarily limit the level of tribal input into and endorsement of WRAP products. 

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

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

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

implemented by states and tribes, will not constrain the future options of tribes who are not 

involved in the WRAP. With these considerations and limitations in mind, the tribal participants 

have joined the state, federal, and private stakeholder interests in approving this report as a 

consensus document. 
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1.0 INTRODUCTION 

1.1 BACKGROUND 

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

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

which sunlight is absorbed and/or scattered when it encounters airborne particles from both 

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

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

improve visibility in 156 national parks and wilderness areas (referred to as Class I areas). The 

rule requires the states, in coordination with the Environmental Protection Agency, the National 

Park Service, U.S. Fish and Wildlife Service, the U.S. Forest Service, and other interested 

parties, to develop and implement air quality protection plans to reduce the pollution that causes 

visibility impairment. Five multi-state regional planning organizations (RPOs) are working to 

develop the technical basis for these plans. The largest of these RPOs is the Western Regional 

Air Partnership (WRAP), which is made up of Western states, tribes, and federal agencies. The 

WRAP was formed in 1997 as the successor to the Grand Canyon Visibility Transport 

Commission (GCVTC), which made over 70 recommendations in June 1996 for improving 

visibility in 16 national parks and wilderness areas on the Colorado Plateau. 

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

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

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

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

mobile source emissions for the Western states. The first set of mobile source emissions use in 

WRAP air quality modeling were developed several years ago (Pollack et al., 2004). 

The purpose of this project was to update the previously developed WRAP mobile source 

emission inventories. Specifically, the goals of this effort were to survey state and local air 

quality planning agencies to obtain the most up-to-date activity data and modeling inputs, to 

account for all of the “on the books” mobile source control measures, and to use the latest 

modeling tools available. This report describes in detail the methods used to estimate these 

revised mobile source emissions, and provides the resulting emissions estimates. 

1.2 EMISSIONS INVENTORIED UNDER MOBILE SOURCES 

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

include vehicles certified for highway use – cars, trucks, and motorcycles. For reporting on-road 

mobile source emissions, vehicles are divided into two major classes – light-duty and heavyduty. Light-duty vehicles include passenger cars, light-duty trucks (up to 8500 lbs gross vehicle 

weight [GVW]), and motorcycles. Heavy-duty vehicles are trucks of more than 8500 lbs GVW. 

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

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

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

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

sources are vehicles and engines in the following categories: 
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• Agricultural equipment, such as tractors, combines, and balers; 

• Aircraft, jet and piston engines; 

• Airport ground support equipment, such as terminal tractors; 

• Commercial marine vessels, such as ocean-going deep draft vessels; 

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

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

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

• Locomotives, switching and line-haul trains; 

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

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

• Railway maintenance equipment, such as rail straighteners; 

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

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

Road dust emissions estimates were also updated in this work are described in this report. 

1.3 SCOPE OF THE MOBILE SOURCES INVENTORY 

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

Geographic domain: Emissions were estimated by county for all counties in 14 states: 

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

Dakota, Oregon, South Dakota, Utah, Washington, and Wyoming. 

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

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

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

through November; and winter is December through February. Emissions were estimated 

for the 2002 base year and for three future years – 2008, 2013, and 2018. 

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

nitrogen oxides (NOx), sulfur dioxide (SO2), volatile organic compounds (VOCs), carbon 

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

(SO4). 

Sources: For all pollutants, emissions were estimated separately by vehicle class for onroad sources and by equipment type/engine type for off-road sources. Emissions were 

summarized for gasoline and diesel-fueled engines. 

1.4 OVERVIEW OF APPROACH FOR ESTIMATING ON-ROAD AND 

OFF-ROAD MOBILE SOURCE EMISSIONS 

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

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

mobile sources emission factors were derived from EPA’s MOBILE6 model, available at 
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http://www.epa.gov/OMSWWW/m6.htm. Activity for on-road mobile sources is vehicle miles 

traveled (VMT). State and local agencies were provided default modeling inputs and VMT 

levels for base and future years for review and update; all states and several agencies provided 

updated. The California Air Resources Board (CARB) provided on-road emissions estimates by 

county and vehicle class directly; these were based on CARB’s in-house version of their 

EMFAC model. 

For all states except California, EPA’s draft NONROAD2004 model was used to estimate socalled traditional off-road sources1

, all sources listed above except aircraft, commercial marine, 

and locomotives. The NONROAD model includes estimates of emission factors, activity levels, 

and growth factors for all traditional off-road sources. The default activity levels were provided 

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

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

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

CARB provided off-road emissions estimates by source category and county directly. 

1.5 MOBILE SOURCE EMISSION INVENTORY WORK PRODUCTS 

The following types emission of emission inventory data files were prepared for the on-road 

emissions, off-road emissions, and road dust emissions: 

• SMOKE files for processing the emissions for use in air quality modeling. SMOKE 

takes the county-level emissions results and performs three primary operations: temporal 

allocation to days of the week and hours of the day, spatial allocation to the 36km grid 

cells used in the modeling, and application of speciation profiles to generate the model 

species needed for CMAQ modeling. 

• NIF files for input to the WRAP Emissions Data Management System (EDMS), the online repository of all WRAP emissions data 

(http://www.wrapedms.org/default_login.asp). 

• Summary tables and graphs, available on the WRAP Mobile Sources Emission Inventory 

Update project web page at http://wrapair.org/forums/ef/UMSI/index.html. 

Section 8 of this report provides summary tables and graphs of the estimated on-road and offroad emissions by state for the base and future years. 

 

1

 The final version of NONROAD (NONROAD2005, available at http://www.epa.gov/otaq/nonrdmdl.htm) was 

released after the work in this project was completed. 
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2.0 EMISSIONS MODELS USED AND ADDITIONAL CALCULATIONS 

FOR AIR QUALITY MODELING

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 model. Activity for on-road mobile sources is vehicle miles 

traveled (VMT). EPA’s NONROAD2004 model was used to estimate emissions from off-road 

mobile sources except for aircraft, commercial marine, and locomotives. This section provides 

information on the versions of each model that were used in estimating emissions. 

This section also provides the assumptions and data used to calculate additional pollutants that 

are not calculated in these models but are required for air quality modeling. Lastly, this section 

discusses the development of the temporal profiles used for allocation of the average day 

emissions to day of week and hour of day for use in air quality modeling. 

2.1 EPA MOBILE6 MODEL

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

mile emission factors for VOC (exhaust and evaporative), NOX, CO, PM, NH3, and SO2. The 

current regulatory version of the model is MOBILE6, released in 2002. The model and 

supporting documentation may be found on EPA’s web site at 

http://www.epa.gov/OMSWWW/m6.htm. 

The MOBILE6 model includes the effects of all of the following “on the books” Federal 

regulations for on-road motor vehicles: 

• Tier 1 light-duty vehicle standards, beginning with, beginning MY 1996; 

• National Low Emission Vehicle (NLEV) standards, beginning MY 2001; 

• Tier 2 light-duty vehicle standards beginning MY 2005, with low sulfur gasoline 

beginning summer 2004; 

• Heavy-duty vehicle standards beginning MY 2004; and 

• Heavy-duty vehicle standards beginning MY 2007, with low sulfur diesel beginning 

summer 2006. 

MOBILE6 estimates emissions by vehicle class, for 28 vehicle classes. For the WRAP 

modeling, the emissions were estimated for eight vehicle classes, which are combined from these 

28. The eight vehicle classes are those that were modeled in the prior generation of the mode, 

MOBILE5, as shown in Table 2-1. 
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Table 2-1. MOBILE5 vehicle classes for which emissions were estimated. 

Vehicle Class MOBILE 

Code 

Weight Description 

Light-duty gasoline vehicles 

(passenger cars) 

LDGV Up to 6000 lb gross vehicle weight (GVW) 

Light-duty gasoline trucks LDGT1 Up to 6000 lb GVW 1 

(pick-ups, minivans, passenger 

vans, and sport-utility vehicles) LDGT2 6001-8500 lb GVW 

Heavy-duty gasoline vehicles HDGV 8501 lb and higher GVW equipped with 

heavy-duty gasoline engines 

Light-duty diesel vehicles 

(passenger cars) 

LDDV Up to 6000 lb GVW 

Light-duty diesel trucks LDDT Up to 8500 lb GVW 

Heavy-duty diesel vehicles HDDV 8501 lb and higher GVW 

Motorcycles2 MC 

1 Emissions for light-duty trucks are modeled separately for two weight classes with different emissions standards in the Clean Air 

Act 

2

 Highway-certified motorcycles only are included in the model. Off-road motorcycles, such as dirt bikes, are modeled as a no-road 

mobile source in EPA’s NONROAD model.

The particulate matter emission factors in MOBILE6 are from an earlier EPA particulates 

emission factor model called PART5. The tire and brake wear estimates from PART5 used in 

MOBILE6 are dated, and newer brake wear estimates were available (Garg et al,) and were used 

to develop revised brake wear emission factors, the same as used in the previous WRAP mobile 

sources emission inventory (Pollack et al., 2004). 

2.2 EPA NONROAD MODEL 

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

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

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

covered under the EPA’s emissions regulations for nonroad mobile sources. Emissions for socalled traditional nonroad sources are estimated by EPA in their NONROAD emissions model, 

available on the NONROAD web page at http://www.epa.gov/otaq/nonrdmdl.htm. 

At the time that the off-road emissions were estimated for this project, the latest version of the 

model was draft NONROAD2004. In December of 2005 final NONROAD2005 was released. 

The web page above provides now only the NONROAD2005 final model.

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

activity data. The model therefore estimates not just emission factors but also emissions. 

Technical documentation of all aspects of the model can be found on the EPA NONROAD web 

page. 

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

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

following categories: 
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G:\WRAP MSEI2\Reporting\Final report\Sec2_emissions_models.doc 2-3 

• airport ground support, such as terminal tractors; 

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

• construction equipment, such as graders and back hoes; 

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

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

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

snowblowers; 

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

• recreational marine vessels, such as power boats; 

• underground mining equipment; and 

• oil field equipment. 

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

Emissions for these three source categories are estimated using other EPA methods and guidance 

documents (described in Sections 5-7). However, support equipment for aircraft, locomotive, 

and commercial marine operations and facilities are included in the NONROAD model. 

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

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

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

loss, running loss, and crankcase emissions. 

The NONROAD model used in this study incorporates the effects of all of the following “on the 

books” Federal nonroad equipment regulations: 

$ Emission standards for new nonroad spark-ignition engines below 25 hp; 

$ Phase 2 emission standards for new spark-ignition hand-held engines below 25 hp; 

$ Phase 2 emission standards for new spark-ignition nonhandheld engines below 25 hp; 

$ Emission standards for new gasoline spark-ignition marine engines; 

$ Tier 1 emission standards for new nonroad compression-ignition engines above 50 hp; 

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

below 50 hp including recreational marine engines; 

$ Tier 2 and Tier 3 standards for new nonroad compression-ignition engines of 50 hp and 

greater not including recreational marine engines greater than 50 hp; and 

$ Tier 4 emissions standards for new nonroad compression-ignition engines above 50 hp, 

and reduced nonroad diesel fuel sulfur levels. 

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) 
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The national or state engine population is estimated and multiplied by the average power, 

activity, and emission factors. Equipment population by county is estimated in the model by 

geographically allocating national engine population through the use of econometric indicators, 

such as construction valuation. The manner in which the geographic allocation is performed is 

as follows: 

(County Population)i /(National Population)I = (County Indicator)i /(National Indicator)i 

where 

 i is an equipment application like construction or agriculture.

Activity is temporally allocated through the use of monthly, and day of week fractions of yearly 

activity. 

The NONROAD model has default estimates for all variables and factors used in the 

calculations. All of these estimates are in model input files, and can be changed by the user if 

data more appropriate to the local area are available. 

2.3 CALIFORNIA MODELS 

The California Air Resources Board (CARB) provided on-road and off-road emissions data for 

base and future years for use in this project. 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 CARB on-road emissions was EMFAC2002 

(available at http://www.arb.ca.gov/msei/on-road/latest_version.htm), with internal updates for 

some of the activity data that were not publicly available. 

For many years, CARB has been developing its own off-road emissions model, called 

OFFROAD. Although CARB has developed most of the model inputs as part of their analyses 

in support of their off-road equipment regulations, the model has never been publicly released. 

For all California emissions, CARB provided their emissions estimates for the base and future 

years. Emissions data only were proved, not activity data and emission factors. Details on the 

data that CARB provided for each source category are provided in Sections 3 through 7. 

2.4 POLLUTANTS ADDED FOR AIR QUALITY MODELING 

For CMAQ modeling, additional model species are required beyond what is estimated in 

MOBILE, NONROAD, EMFAC, and OFFROAD. Specifically, particulate matter needed to be 

split into elemental carbon (EC), organic carbon (OC), and sulfate (SO4); and NOX needed to be 

split into NO and NO2. 

EC and OC were estimated by applying EC/OC fractions to the PM10 and PM2.5 emissions 

estimates. The EC/OC splits used for these calculations are summarized in Table 2-2. These are 

the same EC/OC fractions used in the previous WRAP mobile sources emissions estimates; their 

derivation is described in Pollack et al., 2004. Sulfate was then estimated as PM – EC – OC, for 

both PM10 and PM2.5. Coarse PM is calculated as PM10 – PM2.5 
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G:\WRAP MSEI2\Reporting\Final report\Sec2_emissions_models.doc 2-5 

Table 2-2. 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 

While there have been several studies and reviews of particulate composition (e.g. EPA, 2001 

and Turpin and Lim, 2000) since the time of the work referenced in Table 2-2, there has not been 

a comparable comprehensive evaluation of particulate composition. Many particulate 

source/receptor statistical modeling efforts have been attempted, but all used source profiles that 

predate those listed in Table 2-2. A comprehensive evaluation of source profiles needs to 

include the effect of the proper age distribution and maintenance history of in-use vehicles. No 

recent studies have investigated the source profiles using such an evaluation, and so could not be 

used for this work. In addition, the default EPA resource for compositional estimates of 

emissions, SPECIATE, has not provided any revised profiles since October 1999. 

The ratio of NO to NO2 for NOx emissions from mobile sources is a result of the chemical 

equilibrium formed during internal combustion with NO the primary constituent of NOx. 

Aftertreatment devices may begin to perturb the ratio of NO and NO2 as NOx and particulate 

control are applied to diesel engines (Tonkyn, 2001, Herndon, 2002, and Chatterjee, 2004). 

However, these systems have not yet been widely employed, so it is not possible to judge what 

the proportion of NOx that NO and NO2 will be in the future. For this work the EPA default 

proportions of NO and NO2 (90/10) were used to apportion the NOx emission estimates. 

2.5 TEMPORAL PROFILES 

The on-road and off-road emissions are estimated as average day, per season. For use in air 

quality modeling, these average day emissions must be temporally allocated to the 24 hours of 

the day for each day of the week. This temporal allocation is done in the SMOKE emissions 

processing system. The EPA temporal profiles for on-road and off-road emissions were 

reviewed and found to be deficient for on-road sources. The EPA defaults for on-road temporal 

profiles vary only by weekday vs. weekend; for both weekdays and weekends the 24-hour 

profiles do not vary by vehicle class. And there are only two day of week profiles – one for 

light-duty gasoline vehicles and one for all vehicle classes. 

ENVIRON has analyzed an extremely large database of detailed traffic counter data by vehicle 

class, roadway type, and state under contract to EPA (Lindhjem, 2004). From this work using 

national databases of vehicle activity maintained by the Federal Highway Administration 

(FHWA), revised temporal profiles for on-road sources were developed. The databases used 

were the FHWA Traffic Volume Trends (http://www.fhwa.dot.gov/policy/ohpi/travel/index.htm) 

for temporal activity of vehicles, and the FHWA Vehicle Travel Information System (VTRIS) 

(http://www.fhwa.dot.gov/ohim/ohimvtis.htm) that identifies individual vehicle classes to 

estimate temporal variation in the vehicle mix. Three sets of profiles were developed: day of 
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G:\WRAP MSEI2\Reporting\Final report\Sec2_emissions_models.doc 2-6 

week profiles by vehicle class (Figure 2-1); hour of day profiles for weekdays, by vehicle class 

(Figure 2-2); and hour of day profiles for weekends, by vehicle class (Figure 2-3). These 

temporal profiles show important differences in vehicle activity by vehicle class across the days 

of the week and the hours of the day. 

y

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

0.160

0.180

0.200

S M T W Th F Sat

LDGV

LDGT1

LDGT2

HDGV

LDDV

LDDT

HDDV

MC

Figure 2-1. Day of week profiles by vehicle class. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec2_emissions_models.doc 2-7 

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

LDGV

LDGT1

LDGT2

HDGV

LDDV

LDDT

HDDV

MC

Figure 2-2. Weekday hour of day profiles by vehicle class. 

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

LDGV

LDGT1

LDGT2

HDGV

LDDV

LDDT

HDDV

MC

Figure 2-3. Day of week profiles by vehicle class. 
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G:\WRAP MSEI2\Reporting\Final report\Sec3_On-road.doc 3-1

3.0 ON-ROAD VEHICLE EMISSIONS METHODOLOGY 

This section describes the emissions model inputs and calculations performed for estimating 

2002 base year and 2008/2013/2018 future year on-road emissions, using EPA’s MOBILE6 

model. The generation of SMOKE files for input into the air quality model is also described. To 

estimate on-road emissions, defaults were established for all mobile source model input 

parameters, and also for vehicle miles traveled (VMT) estimates. These default inputs were sent 

to state and local air quality planning agencies in the WRAP states (except for California), and 

they were requested to provide the most up-to-date modeling and VMT inputs. 

Updated data and model inputs provided by state and local contacts are described below. All 

state agencies responded to the survey requests for base and future year modeling inputs. In 

addition, survey responses were received from the local agencies for the following counties: 

• Arizona – Pima and Maricopa 

• Colorado – Denver nonattainment area counties 

• Nevada – Clark and Washoe 

• New Mexico – Bernalillo 

For California, the Air Resources Board provided emissions estimates directly for all years, 

estimated using an internal version of the EMFAC2002 model, with updated activity data for 

some areas. 

3.1 VMT ESTIMATES 

The default VMT data used as the starting point in this analysis were the annual VMT estimates 

that EPA had compiled for the 2002 National Emission Inventory (NEI2002), in the county 

database for the National Mobile Inventory Model (NMIM, available at 

http://www.epa.gov/OMSWWW/nmim.htm). EPA had derived the VMT estimates from 

Highway Performance Monitoring System (HPMS) data from the Federal Highway 

Administration (FHWA). State and local data provided to EPA as part of the June 2004 

Consolidated Emissions Reporting Rule (CERR) submittal were incorporated into the default 

VMT estimates. These default VMT estimates, by county, vehicle type, and roadway type, were 

posted to the WRAP Mobile Sources Update Project web page, and a survey form was sent to 

state and local air quality planners asking them to review the estimates and provide updates 

where available. By default the annual VMT were assumed to be allocated evenly to each 

season; two states (WA and UT) had provided seasonal VMT allocation data that was included 

in the default modeling inputs files posted. 

Most of the state and local agencies that responded to the survey provided updated VMT 

estimates. The areas for which updates were not provided and the state or local agency accepted 

the NMIM defaults were: Arizona - all counties except Maricopa; Denver area; North Dakota; 

South Dakota; and Washington. In most of these cases, the VMT estimates had been previously 

provided to EPA under the CERR and had already been incorporated into the NMIM database. 
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3.2 MOBILE6 INPUTS 

EPA’s MOBILE6 model was used to estimate on-road VOC, CO, NOx, PM, SO2, and NH3 

emission factors. Default MOBILE6 inputs were sent to the state and local agencies for review 

and updating. The default inputs were primarily derived from the NMIM database as discussed 

above. For fuel inputs, however, fuel survey data were analyzed to develop default modeling 

inputs. The subsections below discuss these inputs and updates made by the state and local 

agencies for use in the WRAP MOBILE6 modeling. 

3.2.1 Speed 

Speed is an important input to the MOBILE6 model emission factor calculations, as MOBILE6 

has significant non-linear speed correction factors by roadway type. The default speeds were 

those used by EPA in the NEI2002 (EPA/Pechan, 2005), as shown in Table 3-1. Local speed 

data were provided by Utah, Wyoming, Idaho, and Maricopa Co (AZ). Except for Utah, the 

AVERAGE SPEED command was used to provide average speeds by roadway type for each 

vehicle class. Utah provided more detailed speed distribution information, and the SPEED VMT 

command was used for MOBIL6 runs for all Utah counties. 

Table 3-1. Default speeds by road type and vehicle type (mph), from NEI2002. 

Urban 

Interstate 

Other Freeways 

& Expressways 

Principal 

Arterial 

Minor 

Arterial Collector Local 

Light-duty vehicles 45 45 20 20 20 20 

Light-duty trucks 45 45 20 20 20 20 

Heavy-duty vehicles 35 35 15 15 15 15 

Rural 

Interstate Principal Arterial 

Minor 

Arterial 

Major 

Collector 

Minor 

Collector Local 

Light-duty vehicles 60 45 40 35 30 30 

Light-duty trucks 55 45 40 35 30 30 

Heavy-duty vehicles 40 35 30 25 25 25 

The twelve HPMS roadway types shown in Table 3-1 were assigned to the MOBILE6 roadway 

types. MOBILE6 freeway roadway type was assigned to rural interstates, urban interstates, and 

urban other freeways and expressways. The MOBILE6 arterial roadway type was assigned to 

rural arterials, rural minor arterials, rural major collectors, rural minor collectors, rural locals, 

urban principal arterials, urban minor arterials, and urban collectors. The MOBILE6 local 

roadway type was assigned to urban locals. 

3.2.2 Meteorological and Altitude Inputs

The default temperature and humidity inputs were actual 2002 seasonal temperatures, derived 

from the EPA NMIM database for the NEI2002 (EPA/Pechan, 2005). These data were 

determined from 2002 observations from the National Climatic Data Center (NCDC). 
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Default altitudes were also extracted from the NMIM database for the NEI2002, as determined 

by EPA. In the NMIM database, all counties in the States of Colorado, Nevada, New Mexico, 

and Utah (except Washington County) are modeled as high altitude areas; all counties in all other 

States are treated as low altitude. 

For the WRAP modeling, updated meteorological and/or altitude inputs were provided for all 

counties in Arizona, Idaho, Utah, and Wyoming. 

3.2.3 Control Programs 

Vehicle emission factors are affected by federal and local control programs. MOBILE6 includes 

the effects of all “on-the-books” federal control programs, as described in Section 2. Local 

control programs are implemented in CO or ozone nonattainment areas, and include vehicle 

Inspection and Maintenance (I/M) programs, Anti-Tampering Programs (ATP), oxygenated fuel 

programs, and local fuel regulations. Some areas also have Stage II (at-the-pump) vehicle 

refueling controls; however, refueling emissions are not included in this analysis (they are 

included in the WRAP area source emissions). Table 3-2 lists the counties modeled with I/M 

programs. The latest control program inputs were provided for all of these counties. 

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

State County 

AK Anchorage 

AK Fairbanks 

AZ Maricopa 

AZ Pima 

CO Adams 

CO Arapahoe 

CO Boulder 

CO Broomfield 

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 
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State County 

WA Snohomish 

WA Spokane 

WA Pierce 

Oxygenated fuel programs are in place in some areas as a provision for maintaining air quality 

standards. In other areas, there is oxygen in the fuels although not required as part of State 

Implementation Plan. Fuel inputs were developed from the 2002 fuel survey as described below. 

3.2.4 External Files

MOBILE6 allows the user to supply a number of external files to override model defaults. Of 

these, the registration distribution is perhaps the most key for this effort. The registration 

distribution defines the age mix of each vehicle type – the fraction of registered vehicles from 

each of 25 model years for each vehicle type. For areas that did not provide their own 

registration distributions, the MOBILE6 national defaults were used. Local registration 

distributions were provided for counties in Arizona, Colorado, Montana, Nevada, Oregon, Utah, 

Washington, and Wyoming. 

Other external files were provided by a few areas: Clark County provided VMT fractions, VMT 

by hour, and hourly starts distributions; Utah provided speed bin distributions; and Idaho DEQ 

provided VMT fractions for Ada and Canyon Counties. 

3.2.5 On-Road Fuel Properties 

Seasonal fuel properties were specified for all counties for MOBILE6 runs. The fuel properties 

include RVP, oxygenated fuel specifications (oxygen type, volume or weight percent, and 

market share), and gasoline and diesel sulfur levels. 

For most states, fuel specifications were determined by Sierra Research from an analysis of 2002 

city-specific fuel survey data from the Alliance of Automobile Manufacturers and regional 

survey data published by TRW/Northrop-Grumman (formerly the National Institute for 

Petroleum and Energy Research, NIPER). A more complete discussion of Sierra Research’s 

methods for compiling the data, and a listing of the resulting default fuel properties, are in 

Appendix A. 

Although many state and local agencies had provided EPA MOBILE6 input files for use in 

NEI2002, EPA did not incorporate the fuel inputs from these files unless all fuel parameters 

required for estimating both criteria air pollutants (CAPs) and hazardous air pollutants (HAPs) 

were provided, and of the WRAP states only CO and WA had done so. Almost all areas 

provided updated fuel inputs for use in the modeling. 

The WRAP modeling was done on a seasonal basis, with three-month seasons: spring is March 

through May; summer is June through August; fall is September through November; and winter 

is December through February. In several areas, oxygenated fuels programs changed mid-season 

(i.e., in the midst of the WRAP three-month seasons). In those cases, average values were used 
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for each fuel parameter for the three months in the season. Sensitivity analyses were performed 

to evaluate the effect of averaging fuel oxygenate and/or RVP content to represent a seasonal run 

relative to averaging the emissions from individual monthly runs, and the effects were found to 

be very small, less than 1%. 

3.3 SEASONAL EMISSIONS CALCULATIONS 

After creating MOBILE6 input files using the inputs discussed above, MOBILE6 was run for 

each county in each season, by roadway type. The MOBILE6 seasonal emission factors were 

then multiplied by the seasonal VMT. These seasonal emission calculations were performed at 

the SCC level; i.e., for each seasonal VMT value representing a given county, vehicle type, and 

roadway type, the corresponding emission factor was multiplied by the VMT value. The speed 

and MOBILE6 roadway type were used to determine the appropriate roadway type of the 

emission factors. For summary files, emissions were then aggregated by light- and heavy-duty 

vehicles and by diesel and gasoline fuel types for each county and season. These summary files 

of the seasonal emissions by county were posted to the project web page 

(http://wrapair.org/forums/ef/UMSI/index.html). Annual emissions were also calculated and are 

available by county and by state in the spreadsheets posted to the project web page. 

3.4 FUTURE YEAR EMISSIONS 

Once the 2002 base year emissions survey responses were all received and processed, another 

survey was sent to state and local agencies to request VMT and modeling inputs for estimating 

on-road emissions for the three future years – 2008, 2013, and 2018. Default VMT and 

modeling inputs for all three future years were provided with the survey, and recipients were 

asked to provide updated VMT and modeling inputs where available. The surveys were sent to 

the same state and local contacts who replied to the 2002 base year survey; all responded with 

updates for the future year modeling. 

3.4.1 VMT Growth Factors 

The default VMT levels were generated using the same growth factors as used in the previous 

WRAP mobile sources modeling (Pollack et al., 2004). The spreadsheets provided with the 

survey included VMT estimates for each year and also the growth factors used to grow the VMT 

from 2002 to each of the three future years (except for Alaska, which was not included in the 

earlier WRAP mobile sources modeling). VMT updates were provided by many states, and 

Alaska provided new VMT growth factors. 

Figure 3-1 shows the overall VMT growth rates by state. For several states, growth rates were 

provided by county, but for this figure the growth rates are for the aggregate VMT across all 

counties in each state. 
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0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

2002 2004 2006 2008 2010 2012 2014 2016 2018

Year

VMT Growth Factor (relative to 2002)

Alaska

Arizona

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming

Figure 3-1. Statewide VMT growth rates, 2002 to 2018. 

3.4.2 MOBILE6 Inputs

Default MOBILE6 inputs for all three future years were provided in spreadsheets and sent to the 

state and local contacts. These defaults were the same input data and files as the 2002 base year 

modeling that had been compiled from the base year survey as described above, except for fuel 

specifications. Default fuel sulfur levels were set to the levels mandated by federal regulation: 

30 ppm for gasoline sulfur (required by the federal Tier 2 regulations), and 15 ppm for on-road 

diesel sulfur (required by the federal HDDV 2007 regulations). 

Before the future year modeling was done, the State of Washington passed a bill that gradually 

phases out I/M by 2020. In its place, Washington was adopting the California Low Emission 

Vehicle (LEV) II program. The Washington Department of Ecology survey contact said that 

because it was difficult to account for the CA LEV II program in their MOBILE6 modeling, they 

kept the I/M program parameters in place (Otterson, 2005). 

Oregon has also adopted temporary rules for an Oregon LEV program, and the Oregon DEQ is 

scheduled to propose permanent adoption of the Oregon LEV standards in the summer of 2006. 

The program requires that new motor vehicles sold in Oregon meet California's vehicle emission 

standards beginning with model year 2009. Because this rule was not a permanent rulemaking, 

its effects are not included in the MOBILE6 modeling. 

The emissions benefits of adoption of the CA LEV program are controversial. With the many 

differences between the federal Tier 2 and California LEV program, it is very difficult to 
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G:\WRAP MSEI2\Reporting\Final report\Sec3_On-road.doc 3-7

accurately estimate the differences, and MOBILE6 is not set up to model adoption of the CA 

LEV program. There have been studies in which the effects were estimated for some areas 

(NESCAUM/CSI, 2003; ERG/CSI, 2004), and other states considering adoption rely on these 

studies. Oregon’s Fact Sheet about their proposed rule refers to Washington’s analysis, which in 

turn relied on NESCAUM’s analysis: “The state of Washington has estimated that by 2020, 

California standards will reduce smog-forming emissions and air toxics by approximately 5% to 

8% more than would be achieved by the federal standards and 11% to 16% beyond federal 

standards by 2030. Washington also estimates a 17% reduction of green house gas emissions by 

2020 and 27% by 2030 as a result of the California standards. DEQ expects that similar benefits 

would be achieved in Oregon.” 

3.5 PROCESSING OF CALIFORNIA EMISSIONS 

CARB provided on-road emissions that were generated using their EMFAC2002 model, with 

updated activity data that is not yet available in the publicly accessible version of the model. 

CARB provided average (ton per day, TPD) weekday county-level annual, summer, and winter 

emissions for on-road vehicles for calendar years 2002, 2008, 2010, 2015, and 2020. These 

emissions were provided by vehicle type but not by roadway type, as EMFAC does not have 

emission factors by roadway type. 

A number of processing steps were required to generate on-road emissions for California that are 

similar in content and format to the emissions for the remaining WRAP states: 

• Calendar year 2013 emissions were interpolated from CARB’s emissions for years 2010 

and 2015. 

• The weekday emissions were converted to average day using the day-of-week temporal 

profiles described in Section 2. 

• The vehicle types in CARB’s EMFAC model are different from those in MOBILE. The 

vehicle types were mapped to the eight MOBILE5 vehicle types. CARB provided a 

cross-reference file for this purpose. 

• Per discussion with CARB, spring and fall emissions were estimated as 

(4 * annual TPD – summer TPD – winter TPD)/2 

• The CARB files did not include ammonia emissions. Ammonia emissions were 

estimated using scaling factor based on SO2 emissions. The scaling factors were derived 

using the CARB 2002 fuel sulfur content and statewide average gasoline and diesel fuel 

economy (obtained from a statewide EMFAC2002 run). 

When these steps were run, the estimation of additional pollutants and generation of SMOKE 

and NIF files proceeded as for the rest of the WRAP states, as described below. 

3.6 GENERATION OF SMOKE AND NIF FILES 

Emissions files were generated in the format needed for SMOKE emissions processing. For 

these files, and for use in air quality modeling, the additional pollutants described in Section 2 

were calculated. Seasonal emissions SMOKE files were generated only for years 2002 and 

2018, the years for which the WRAP air quality modeling is performed. The pollutants included 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec3_On-road.doc 3-8

in the SMOKE files are VOC (exhaust, evaporative, and total), NOX, CO, NH3, SO2, PM10, 

EC10, OC10, SO4(10), PM2.5, EC2.5, OC2.5, SO4(2.5), coarse PM (PMC), NO, and NO2. 

Separate files were prepared for California and all other WRAP states, by year and by season. 

The SMOKE files for the thirteen non-California states include emissions by county, roadway 

type, and vehicle type. Table 3-3 shows the source classification codes (SCC) for the on-road 

emissions categorized by vehicle type and HPMS roadway classification. As CARB’s EMFAC 

model does not differentiate by roadway type, the SMOKE files generated for California are 

categorized by county and vehicle type only. The SCCs in the SMOKE files for the California 

on-road emissions are shown in Table 3-4. 

Annual emissions files in EPA’s National Inventory Format (NIF) were also prepared and 

submitted to the WRAP Emissions Data Management System (EDMS), the on-line repository of 

all WRAP emissions data (http://www.wrapedms.org/default_login.asp). These NIF files 

contain a smaller set of pollutants; the additional pollutants needed for air quality modeling are 

not included. Annual emissions NIF files were prepared for each of the four years modeled, 

separately for California and non-California states, using the SCCs in Tables 3-3 and 3-4. 

Summary spreadsheets, tables, and graphs were also prepared, and were posted on the WRAP 

Mobile Sources Emission Inventory Update project web page at 

http://wrapair.org/forums/ef/UMSI/index.html. 

Table 3-3. SCC codes for on-road emissions in SMOKE files (non-California states). 

SCC Vehicle Class Facility Type 

2201001110 LDGV Rural Interstate 

2201001130 LDGV Rural Other Principal Arterial 

2201001150 LDGV Rural Minor Arterial 

2201001170 LDGV Rural Major Collector 

2201001190 LDGV Rural Minor Collector 

2201001210 LDGV Rural Local 

2201001230 LDGV Urban Interstate 

2201001250 LDGV Urban Other Freeways and Expressways 

2201001270 LDGV Urban Other Principal Arterial 

2201001290 LDGV Urban Minor Arterial 

2201001310 LDGV Urban Collector 

2201001330 LDGV Urban Local 

2201020110 LDGT1 Rural Interstate 

2201020130 LDGT1 Rural Other Principal Arterial 

2201020150 LDGT1 Rural Minor Arterial 

2201020170 LDGT1 Rural Major Collector 

2201020190 LDGT1 Rural Minor Collector 

2201020210 LDGT1 Rural Local 

2201020230 LDGT1 Urban Interstate 

2201020250 LDGT1 Urban Other Freeways and Expressways 

2201020270 LDGT1 Urban Other Principal Arterial 

2201020290 LDGT1 Urban Minor Arterial 

2201020310 LDGT1 Urban Collector 

2201020330 LDGT1 Urban Local 

2201040110 LDGT2 Rural Interstate 

2201040130 LDGT2 Rural Other Principal Arterial 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec3_On-road.doc 3-9

SCC Vehicle Class Facility Type 

2201040150 LDGT2 Rural Minor Arterial 

2201040170 LDGT2 Rural Major Collector 

2201040190 LDGT2 Rural Minor Collector 

2201040210 LDGT2 Rural Local 

2201040230 LDGT2 Urban Interstate 

2201040250 LDGT2 Urban Other Freeways and Expressways 

2201040270 LDGT2 Urban Other Principal Arterial 

2201040290 LDGT2 Urban Minor Arterial 

2201040310 LDGT2 Urban Collector 

2201040330 LDGT2 Urban Local 

2201070110 HDGV Rural Interstate 

2201070130 HDGV Rural Other Principal Arterial 

2201070150 HDGV Rural Minor Arterial 

2201070170 HDGV Rural Major Collector 

2201070190 HDGV Rural Minor Collector 

2201070210 HDGV Rural Local 

2201070230 HDGV Urban Interstate 

2201070250 HDGV Urban Other Freeways and Expressways 

2201070270 HDGV Urban Other Principal Arterial 

2201070290 HDGV Urban Minor Arterial 

2201070310 HDGV Urban Collector 

2201070330 HDGV Urban Local 

2201080110 MC Rural Interstate 

2201080130 MC Rural Other Principal Arterial 

2201080150 MC Rural Minor Arterial 

2201080170 MC Rural Major Collector 

2201080190 MC Rural Minor Collector 

2201080210 MC Rural Local 

2201080230 MC Urban Interstate 

2201080250 MC Urban Other Freeways and Expressways 

2201080270 MC Urban Other Principal Arterial 

2201080290 MC Urban Minor Arterial 

2201080310 MC Urban Collector 

2201080330 MC Urban Local 

2230001110 LDDV Rural Interstate 

2230001130 LDDV Rural Other Principal Arterial 

2230001150 LDDV Rural Minor Arterial 

2230001170 LDDV Rural Major Collector 

2230001190 LDDV Rural Minor Collector 

2230001210 LDDV Rural Local 

2230001230 LDDV Urban Interstate 

2230001250 LDDV Urban Other Freeways and Expressways 

2230001270 LDDV Urban Other Principal Arterial 

2230001290 LDDV Urban Minor Arterial 

2230001310 LDDV Urban Collector 

2230001330 LDDV Urban Local 

2230060110 LDDT Rural Interstate 

2230060130 LDDT Rural Other Principal Arterial 

2230060150 LDDT Rural Minor Arterial 

2230060170 LDDT Rural Major Collector 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec3_On-road.doc 3-10

SCC Vehicle Class Facility Type 

2230060190 LDDT Rural Minor Collector 

2230060210 LDDT Rural Local 

2230060230 LDDT Urban Interstate 

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

SCC Vehicle Class 

2230070000 HDDV 

2230060000 LDDV 

2230000000 LDDV 

2201070000 HDGV 

2201020000 LDGT1 

2201040000 LDGT2 

2201000000 LDGV 

2201080000 MC 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec4_NONROAD.doc 4-1

4.0 OFF-ROAD EQUIPMENT EMISSIONS METHODOLOGY 

This section describes the methods for estimating 2002 base year and 2008/2013/2018 future 

year emissions for so-called traditional off-road equipment. Equipment types included here are 

in the following categories: 

• airport ground support, such as terminal tractors; 

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

• construction equipment, such as graders and back hoes; 

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

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

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

snowblowers; 

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

• recreational marine vessels, such as power boats; 

• underground mining equipment; and 

• oil field equipment. 

Seasonal average daily emissions were estimated for the thirteen non-California western states 

using EPA’s NONROAD model. To estimate emissions from these sources, defaults were 

established for all NONROAD model input parameters. These default inputs were sent to state 

and local air quality planning agencies in the WRAP states (except for California), and they were 

requested to provide the most up-to-date modeling. For California, the Air Resources Board 

(CARB) provided all off-road emissions estimates from their own modeling system, which has 

similar equipment types. 

The methodology for estimating emissions for the other off-road sources – locomotive, aircraft, 

and commercial marine – are covered in the following three sections. 

4.1. NONROAD MODELING

As discussed in Section 2, the NONROAD2004 model was used to estimate off-road emissions 

for the non-California WRAP states. The model was run for each county for each season in the 

base and future years. 

The NONROAD model estimates emissions, and includes defaults for all inputs affecting the 

emissions. The modeling inputs include fuel composition, ambient temperatures, equipment 

population and activity data, and equipment population growth factors. 

For nonroad diesel fuel sulfur levels, 2002 defaults were derived by Sierra Research from 

analysis of 2002 city-specific fuel survey data from the Alliance of Automobile Manufacturers 

and regional survey data published by TRW/Northrop-Grumman (formerly the National Institute 

for Petroleum and Energy Research, NIPER). A more complete discussion of Sierra Research’s 

methods for compiling the data, and a listing of the resulting default fuel properties, are in 

Appendix B. Future year nonroad diesel default sulfur levels were set to those required by the 

federal Tier 4 nonroad diesel rule – 500 ppm in 2008, and 15 ppm in 2013 and 2018. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec4_NONROAD.doc 4-2

A survey letter and accompanying spreadsheets were sent to state and local air quality agency 

contacts, to request review of the default modeling inputs and any updates that the agencies 

could provide. Apart from temperatures, which many agencies provided, few updated modeling 

inputs were provided. The Alaska DEC and a few other agencies provided updated gasoline 

sulfur levels (the same as used in the MOBILE6 modeling as discussed in Section 2) and 

nonroad diesel fuel sulfur levels (note that Alaska has some exemptions from the federal rule). 

Key inputs for determining NONROAD equipment emissions and the equipment population and 

activity data, and allocation factors. As described in Section 2, NONROAD equipment 

population by county is estimated in the model by geographically allocating national engine 

population through the use of econometric indicators, such as construction valuation. EPA 

encourages state and local agencies to develop local data from surveys, but such work is 

expensive and difficult to carry out, and only a few agencies in the country have done so. 

However, some information is available at state agencies for population allocations, e.g., 

recreational boat registration data. Updated allocation files were used for some categories in 

Arizona and Wyoming, based on previous work done by ENVIRON for AZ DEQ (Pollack et al., 

2004) and WY DEQ (Pollack et al., 2005). In addition, updated allocation files were provided 

for some categories for Colorado and Washington. 

Updated agricultural equipment populations were developed based on the US Department of 

Agriculture’s 2002 Census of Agriculture, available at 

http://www.nass.usda.gov/Census_of_Agriculture/index.asp. This survey provides estimates of 

the in-use equipment populations used for agriculture in each county of the US, and was used to 

develop equipment population files to replace the defaults in EPA’s NONROAD2004 model. 

State total populations for agricultural equipment (by equipment type) were extracted from the 

Census of Agriculture, and were used to revise the NONROAD2004 model state populations. 

For estimating future year emissions, the NONROAD model incorporates the effects of all “on 

the books” regulations, as described in Section 2. The model contains growth factors for all 

equipment types, which have been derived by EPA from a proprietary database of equipment 

sales for several years. No state or local agency provided alternative growth factors, so the 

model defaults were used in all cases. 

The NONROAD model estimates county-level emissions of VOC, NOx, CO, PM10, PM2.5, and 

SO2; other pollutants were estimated as described below. For reporting, 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. 

The NONROAD model outputs weekday emissions. Weekday-to-average-day adjustment 

factors were applied to all emissions, based on temporal adjustment factors developed by EPA 

that are internal to the model. In addition, EPA’s NONROAD Reporting Utility THC-to-VOC 

conversion factors were also applied, as the NONROAD core model outputs total hydrocarbons 

only. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec4_NONROAD.doc 4-3

4.2 PROCESSING OF CALIFORNIA EMISSIONS 

CARB provided emissions estimates for traditional off-road equipment types that were generated 

using internal models and spreadsheets. CARB provided county-level average day emissions for 

some sources and average weekday emissions for others; and annual totals for some sources, 

summer/winter seasonal for others. For most source categories, emissions were provided for 

years 2002, 2008, 2010, 2015, and 2018. 

A number of processing steps were required to generate off-road emissions for California that are 

similar in content and format to the emissions for the remaining WRAP states: 

• CARB’s emissions estimates did not include the effects of EPA’s Tier 4 nonroad diesel 

equipment standards. Adjustment factors by equipment type were developed from a 

NONROAD run with and without Tier 4 controls. (This is not an option when running 

the NONROAD model. Rather, modified NONROAD tech.dat file was developed for 

this purpose.) 

• For those sources where weekday emissions were provided, the weekday emissions were 

converted to average day using the EPA NONROAD model day-of-week temporal 

profiles. 

• Emissions for year 2013 were linearly interpolated from CARB’s 2010 and 2015, if 2013 

emissions were not provided. 

• The CARB equipment type codes were matched to EPA NONROAD equipment codes 

(SCCs) using a CARB cross-reference file. 

• For those sources where annual, summer, and winter emissions were provided, spring and 

fall emissions were estimated as 

(4 * annual TPD – summer TPD – winter TPD)/2 

• For those sources for which only annual emissions were provided, seasonal emissions 

were estimated using the seasonal allocation factors for California in the NONROAD 

model. 

• The CARB files did not include ammonia emissions. Ammonia emissions were 

estimated using scaling factor based on SO2 emissions, derived using gasoline and 

nonroad diesel fuel sulfur values provided by CARB. 

When these steps were run, the estimation of additional pollutants and generation of SMOKE 

and NIF files proceeded as for the rest of the WRAP states, as described below. 

4.3 GENERATION OF SMOKE AND NIF FILES 

Emissions files were generated in the format needed for SMOKE emissions processing. For 

these files, and for use in air quality modeling, the additional pollutants described in Section 2 

were calculated. Seasonal emissions SMOKE files were generated only for years 2002 and 

2018, the years for which the WRAP air quality modeling is performed. The pollutants included 

in the SMOKE files are VOC, NOX, CO, NH3, SO2, PM10, EC10, OC10, SO4(10), PM2.5, 

EC2.5, OC2.5, SO4(2.5), coarse PM (PMC), NO, and NO2. 

Separate files were prepared for California and all other WRAP states, by year and by season. 

The SMOKE files for the thirteen non-California states include emissions by county and nonroad 

equipment SCC, which indicates equipment type and fuel type. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec4_NONROAD.doc 4-4

Annual emissions files in EPA’s National Inventory Format (NIF) were also prepared and 

submitted to the WRAP Emissions Data Management System (EDMS), the on-line repository of 

all WRAP emissions data (http://www.wrapedms.org/default_login.asp). These NIF files 

contain a smaller set of pollutants; the additional pollutants needed for air quality modeling are 

not included. Annual emissions NIF files were prepared for each of the four years modeled, 

separately for California and non-California states, using the fully detailed nonroad SCCs. 

Summary spreadsheets, tables, and graphs were also prepared, and were posted on the WRAP 

Mobile Sources Emission Inventory Update project web page at 

http://wrapair.org/forums/ef/UMSI/index.html. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec5_loco.doc 5-1 

5.0 LOCOMOTIVE EMISSIONS METHODOLOGY 

County level locomotive emissions estimates were estimated as the product of locomotive fuel 

consumption and average locomotive emission factors. Previous WRAP locomotive emissions 

estimates (Pollack et al., 2004) allocated national fuel consumption estimates to counties using 

emissions data offered by the National Emissions Inventory. For this project a detailed revision 

to that allocation method was developed for allocating 2002 national fuel consumption estimates. 

Emission factors were also revised to combine line-haul and switching engines because only 

national total fuel consumption was available. Additional emission factors for ammonia and fuel 

sulfur provided by EPA were also incorporated and form the basis from which sulfur dioxide 

was estimated. 

5.1 2002 LOCOMOTIVE EMISSIONS 

This work spatially allocated 2002 national locomotive activity, in the form of fuel consumption, 

using historic data of freight movements. The 2002 Class I railroad activity data were derived 

from national fuel consumption data reported by the Association of American Railroads (AAR, 

2003), and the activity data for Class II/III railroads from data reported by the American Short 

Line & Regional Railroad Association (ASLRRA, 1999 and Benson, 2004). To allocate this 

national fuel consumption to the county level, ENVIRON used the most recent county level rail 

activity estimates available. These activity estimates were ton-miles of freight movement 

estimated by the Bureau of Transportation Statistics (2002), using data from 1995. The 2002 

national activity data were allocated to each county in the WRAP states using the fraction of the 

1995 national rail activity that occurred in each county and then multiplying that fraction by the 

2002 national rail activity, as demonstrated in equation (1). 

CA02 = NA02 * (CA95/NA95) (1) 

where 

CA02 = 2002 county locomotive fuel consumption 

NA02 = 2002 national locomotive fuel consumption 

CA95 = 1995 county million gross ton miles (MGTM) 

NA95 = 1995 national total MGTM 

The spatial allocation of the national emissions in this work followed the methods of the EPA 

National Emission Inventory (NEI, 1999 and unchanged for 2002) of allocating locomotive 

activity. The 1995 activity data were obtained as GIS shapefiles containing track segments and 

an associated database of rail density per mile (MGTM/mi) corresponding to those segments. 

The segment-specific rail density estimates were provided as ranges. For each segment, the 

midpoint of the density range was assumed to represent the average track loading on that 

segment. Table 5-1 shows a list of the ranges and the midpoint values used in this study. The 

top end density was reported as an open-ended range, greater than 100 MGTM/mi, which was 

estimated as 120 MGTM/mi. This differs from the allocation method used in the NEI 2002, 

which represented the top end traffic density as 100 MGTM/mi. The use of 120 MGTM/mi is 

expected to more accurately reflect the relative importance of those main line track segments 

than using the minimum value of 100 MGTM/mi. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec5_loco.doc 5-2 

Table 5-1. Track segment density ranges used for allocation to counties (MGTM/mi). 

Density ID Segment Density Range Assumed Segment Density 

0 unknown, abandoned, or dummy 0 

1 0.1 to 4.9 2.5 

2 5.0 to 9.9 7.45 

3 10.0 to 19.9 14.95 

4 20.0 to 39.9 29.95 

5 40.0 to 59.9 49.95 

6 60.0 to 99.9 79.95 

7 100.0 and greater 120 

To obtain county level rail density from track segment density, a shapefile was first created that 

contained all US counties. Next, the two shapefiles were projected to the same map projection 

so that the counties were overlaid by the BTS track segments. Then, track segments were 

intersected by the county borders so that county-specific track segments were created. For each 

county it was then possible to sum the products of segment densities and county-specific 

segment lengths to obtain the total county activity as 1995 ton-miles. The county fraction of 

1995 national rail activity was then the sum of activity in that county over the sum of activity in 

all counties. The relative county locomotive activity for the western States is shown in Figure 

5-1. 

Figure 5-1. County level rail activity in the WRAP states. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec5_loco.doc 5-3 

Year 2002 county rail fuel consumption was estimated using the 1995 county fraction of national 

rail activity as demonstrated in equation (1). National locomotive fleet average emissions factors 

with units of grams per gallon of fuel were obtained from the EPA (1997). The emission factors 

for 2002 are summarized in Table 5-2. County level emissions of hydrocarbons (HC), NOx and 

particulate matter (PM10) were calculated by multiplying 2002 county level fuel consumption by 

these emission factors. 

Table 5-2. National fleet average emission factors (gram per gallon) from EPA (1997). 

Engine Type HC CO NOx PM SO21 NH32

2002 Fleet Average 10.7 27.4 248.8 6.8 16.4 0.116 

1

 Reported as SO2 and derived from an average sulfur level of 2600 ppm. (EPA, 2004b) 2

 EPA (2004a) 

One issue was to determine the fraction of the total PM emissions that is sulfate. Equation (2) 

was derived from test data from an EPA study that measured the PM weight change that resulted 

from a change in the fuel sulfur level. The percentage of sulfate PM was estimated to be 19.4%. 

The remaining PM was split between EC and OC using the historic National Emission Trends 

report estimate of 80% as elemental carbon and 20% as organic carbon. 

Sulfate PM (BSFC units) = BSFC * 7.0 * 0.02247 * 0.01 * (SOxfuel - SOxbas) (2) 

 where 

SOxbas = 0% sulfur for entirely elemental and organic carbon PM 

SOxfuel = % sulfur in fuel used (0.26%) 

 Sulfate PM = 0.0004 (g/gram fuel) or 1.32 (g/gallon) or 19.4% of the PM rate in 

Table 5-2. 

Equation (2) was derived by estimating that the fuel sulfur partially (2.247%) converts to SO3

(with the remainder emitted as SO2), which rapidly hydrolyzes in the humid exhaust to hydrated 

sulfuric acid [H2SO4*(7)H2O] and condenses on other PM. From this assumption arises the 

molecular weight adjustment of 7.0 (ratio of hydrated sulfuric acid to elemental sulfur). The 

figure 0.01 in the equation is to adjust values in percent (%) to fractional values. 

County level locomotive emissions were estimated for all WRAP counties based on the 

procedure described above, except for those areas for which emissions data were supplied by 

local or state agencies. Four states - Alaska, Arizona, Wyoming, and Idaho - and one county - 

Clark County, NV - supplied more detailed locomotive emissions estimates from local surveys 

and other information derived from specific activity in those states. In the case of Arizona and 

Wyoming, ENVIRON performed surveys of all railroad activity (Pollack et al, 2004a; Pollack et 

al, 2004b). The Alaska Department of Environmental Conservation (Edwards, 2005) and the 

Idaho Department of Environmental Quality (Reinbold, 2005) supplied their own estimates, as 

did the Clark County Department of Air Quality Management (Li, 2005). 

The spatial allocation of annual locomotive NOx emissions is shown in Figure 5-2. Seasonal 

emissions were estimated based on an assumption of uniform year-round activity. Figure 5-2 

shows the effect of the major east-west corridors from Los Angeles through Arizona and New 

Mexico, Northern California through Nevada, Utah and Wyoming, and Washington, Northern 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec5_loco.doc 5-4 

Montana and North Dakota; the north-south corridor through California, Oregon, and 

Washington; and the coal mining region of eastern Wyoming. Other major and minor routes are 

also evident though the size of the county affects the emission totals estimated, so a major line 

that runs through a small or narrow county may not appear significant, and, likewise, a large 

county may appear over-weighted compared with a neighboring county with less through 

mileage. 

Figure 5-2. County level rail NOx emissions (tons per year) in the WRAP states.

5.2 PROJECTED FUTURE YEAR LOCOMOTIVE EMISSIONS 

To estimate future year activity, a trend analysis was performed on the historical fuel 

consumption of the activity of the two predominant (in the West, Union Pacific and BNSF) 

railroads’ activity. Figure 5-3 shows the company-wide fuel consumption calculated from 

historic revenue ton-mile and fuel consumption per revenue ton-mile. National freight transfers 

and the regression of fuel efficiency were used to determine the fuel consumption trend over as 

long a period as possible. Freight transfers (ton-mile) are not a sufficient activity indicator alone 

because the efficiency (ton-miles per gallon of fuel consumed) of railroads has been improving 

over time. AAR (2005) provided historical efficiency (gallons per ton-mile) for Burlington 

Northern (predating the merger with the Atchison Topeka and Santa Fe [ATSF] railroad) and 

Union Pacific (predating the merger with Southern Pacific and others). The historic trend in fuel 

efficiency for each company (Union Pacific and Burlington Northern) was combined with the 

revenue ton-mile for Union Pacific and Southern Pacific, and BN and ATSF. A trend in fuel 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec5_loco.doc 5-5 

consumption for the combined companies was thus estimated from 1990 through 2002 as shown 

in Figure 5-3 despite the merger activity that occurred during this period. The future year 

projected activity was then determined from a linear regression of the fuel consumption for the 

combined company operations of the predominant railroads in their current configuration as 

Union Pacific and BNSF. 

Figure 5-3. Trends in historical rail fuel consumption by railroad. 

The resulting future year projection factors are listed in Table 5-3 for the two major railroads and 

the combined projection. The trends for the two railroads are very similar. 

Table 5-3. Locomotive activity growth projection for this work. 

Comparison Years Union Pacific BNSF Combined

2008 / 2002 1.13 1.15 1.14 

2013 / 2002 1.24 1.27 1.26 

2018 / 2002 1.35 1.40 1.37 

y = 0.0894x - 174.92

R2

 = 0.8837

y = 0.0897x - 175.96

R2

 = 0.9481

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

1988 1990 1992 1994 1996 1998 2000 2002 2004

Year

Fuel Consumption Estimated (Billion Gallons)

Union Pacific Relative Efficiency

Burlington Northern Relative Efficiency

Linear (Union Pacific Relative Efficiency)

Linear (Burlington Northern Relative Efficiency)
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec5_loco.doc 5-6 

In addition to projected railroad activity, the emission rates were projected using EPA future year 

emission rates (1997, Regulatory Support Document), as shown in Table 5-4. 

Table 5-4. Locomotive emission rate projections. 

Comparison Years HC CO NOx PM SO2* NH3 

2008/2002 0.892 1.000 0.693 0.882 0.192 1 

2013/2002 0.819 1.000 0.627 0.802 0.006 1 

2018/2002 0.763 1.000 0.580 0.740 0.006 1 

* Fuel sulfur averaged 2600 ppm in 2002, assumed to average 500 ppm in 2008 and 15 ppm in 2013 and 2018. 

(EPA, Clean Air Nonroad Diesel Rule Fact Sheet, May, 2004) PM emission rates were not adjusted for fuel sulfur 

level though a reduction should be realized with low sulfur fuel.

The overall emissions from locomotives for future years were then determined by combining the 

activity growth in Table 5-3 and the emission rate projections in Table 5-4. 

5.3 CALIFORNIA LOCOMOTIVE EMISSIONS 

CARB provided locomotive emissions for the base and three future years from their internal 

emissions data bases. CARB’s emission estimates assumed 2500 ppm sulfur in the fuel for all 

years, and so adjustments were made to the SO2 and PM emissions to reflect the lower mandated 

levels in future years. Federal requirements are for sulfur levels to be 500 ppm in 2008 and 15 

ppm in 2013 and 2018. However, ARB expects fuel sulfur levels to be 129 in 2008. SO2 

emissions were adjusted using a direct scalar of the fuel sulfur levels assumed in the emissions 

estimated by ARB and the regulated levels. The PM emissions were adjusted to reflect the lower 

sulfur levels using a PM adjustment derived by ARB staff, as provided to ENVIRON. 

The CARB emissions did not include NH3; NH3 was estimated by developing a scaling factor 

based on SOX emissions. Yearly fuel consumption estimates were derived based on SOX

emissions and the CARB assumed 2500ppm fuel sulfur content. A per-volume NH3 emission 

factor was applied to the estimated fuel consumption to estimate NH3 emissions for each year at 

the county level. Lastly, PM was split among sulfate, EC, and OC using the same methods as for 

the other states described above. 

5.4 GENERATION OF SMOKE AND NIF FILES 

Emissions files were generated in the format needed for SMOKE emissions processing. Annual 

average day county-level locomotive emissions SMOKE files were generated, for all WRAP 

states combined, only for years 2002 and 2018, the years for which the WRAP air quality 

modeling is performed. The pollutants included in the SMOKE files are VOC, NOX, CO, NH3, 

SO2, PM10, EC10, OC10, SO4(10), PM2.5, EC2.5, OC2.5, SO4(2.5), coarse PM (PMC), NO, 

and NO2. Separate files were prepared for each year. 

Annual emissions files in EPA’s National Inventory Format (NIF) were also prepared and 

submitted to the WRAP Emissions Data Management System (EDMS), the on-line repository of 

all WRAP emissions data (http://www.wrapedms.org/default_login.asp). These NIF files 

contain a smaller set of pollutants; the additional pollutants needed for air quality modeling are 
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G:\WRAP MSEI2\Reporting\Final report\Sec5_loco.doc 5-7 

not included. Annual emissions NIF files were prepared for each of the four years modeled, for 

all states combined. 

Summary spreadsheets and tables were also prepared, and were posted on the WRAP Mobile 

Sources Emission Inventory Update project web page at 

http://wrapair.org/forums/ef/UMSI/index.html. 
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6.0 AIRCRAFT EMISSIONS METHODOLOGY 

County-level aircraft emissions for 2002 for the WRAP states were obtained from work 

performed for EPA’s 2002 National Emissions Inventory (NEI2002). Activity data for aircraft 

emissions are takeoff cycles (LTOs), and emission factors are primarily from the Federal 

Aviation Administration (FAA) Emissions and Dispersion Modeling System (EDMS). The 

2002 emissions were projected to future years using forecast LTOs available from the FAA. 

More detailed estimates were provided for some states. 

The FAA EDMS model combines specified aircraft and activity levels with default emissions 

factors in order to estimate annual inventories for a specific airport. Aircraft activity levels in 

EDMS are expressed in terms of LTOs, which consist of the four aircraft operating modes: taxi 

and queue, take-off, climb-out, and landing. Default values for the amount of time a specific 

aircraft spends in each mode, or the time-in-modes (TIMs), are coded into EDMS. 

Aircraft emissions are estimated for four aircraft categories: 

• Air carriers, which are larger turbine-powered commercial aircraft with at least 60 seats or 

18,000 lbs payload capacity; 

• Air taxis, which are commercial turbine or piston-powered aircraft with less than 60 seats 

or 18,000 lbs payload capacity; 

• General aviation aircraft, which are small piston-powered, non-commercial aircraft; and 

• Military aircraft. 

6.1 2002 AIRCRAFT EMISSIONS 

6.1.1 NEI2002 Aircraft Emissions Estimates and Additional Pollutants 

For the 2002 aircraft emissions, annual emissions files prepared for the NEI2002 formed the 

basis of the work. These files were sent to ENVIRON by EPA’s contractor, Eastern Research 

Group (Billings, 2005). For this work, ERG ran the EDMS model for about 1100 towered 

airports across the U.S. using detailed 2002 aircraft/LTO activity data. Additional calculations 

were performed to estimate the additional pollutants needed for WRAP modeling. Key elements 

of those calculations are described by aircraft type below. 

Air Carriers – The NEI2002 inventory data for VOC, CO, NOx, and SO2 for Air Carriers were 

used directly. Additional calculations were made to estimate the emissions of the additional 

pollutants in the WRAP inventory: 

• The NOx inventory speciation values for NO and NO2 were assumed to be 90% and 

10%, respectively, which are the default EPA speciations. 

• It was assumed that no NH3 is emitted from air carrier turbine engines, which normally 

run lean. 

• All of the fuel-bound sulfur was assumed to form SO2 in the engine exhaust. 

• Due to the lack of other, more recent sources for aircraft particulate emission factors, the 

total suspended particulate (TSP) emissions from the air carriers were estimated using a 

commercial fleet-average emission factor from EPA’s 1985 National Acid Precipitation 
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Assessment Program (NAPAP). To calculate PM2.5, according to the NEI2002, 97.6% 

of the particulate matter emitted from Commercial Aircraft was assumed to be PM2.5, as 

is assumed in the NEI2002. 

Air Taxi, General Aviation and Military Aircraft – The NEI2002 inventory data for VOC, CO, 

NOx, SO2, PM10, and PM2.5 for these Aircraft types were used directly. Additional 

calculations were made to estimate the emissions of the additional pollutants in the inventory: 

• As for the air carriers, 90% of the NOx emissions were assumed to be NO and 10% 

were assumed to be NO2. 

• For ammonia, air taxi and military aircraft were assumed to be dominated by turbinepowered aircraft running lean, thus producing a negligible amount of ammonia. For 

general aviation, ammonia was estimated using a fleet-average fuel consumption rate 

from the EDMS data for piston engines, operational mode-specific fuel flow rates 

weighted by the typical time spent in each mode, average hours of operation estimated 

from FAA data, and a g/gallon emission factor for non-catalyst light-duty gasoline 

engines. 

• As for air carriers, all of the fuel-bound sulfur was assumed to form SO2 in the engine 

exhaust. 

6.1.2 State Updates 

The NEI2002-based inventory estimates were updated with additional information provided for 

six areas: 

For Alaska, Sierra Research, under contract to the WRAP Emissions Forum, developed seasonal 

aircraft emissions estimates for all aircraft types for Alaska in 2002. These data were used 

instead of the NEI2002 data described above. A number of minor modifications needed to be 

made to the data to make them consistent with the rest of the aircraft data. The most significant 

difference was that air carriers and air taxis were lumped into one category. These were then 

coded as the air carriers SCC, and WRAP Alaska air taxi emissions were set to zero. 

For Arizona, the NEI2002-based inventory was updated with emissions estimates from the 

Arizona 2002 inventory work previously done by ENVIRON (Pollack et al., 2004). This work 

included detailed EDMS modeling based on activity data obtained from both the FAA and local 

sources. Further updates were made for specific airports with emissions data provided by Pima 

and Maricopa Counties. 

The Idaho DEQ provided 2002 aircraft emissions for all counties for general aviation and 

military aircraft. 

Clark County (Nevada) provided 2002 emissions estimates for three airports in the county, based 

on a recent airport emissions study (Ricondo, 2004). 

For Wyoming, the NEI2002-based inventory was updated emissions estimates from Wyoming 

2002 inventory work previously done by ENVIRON (Pollack et al., 2004a). This work included 

detailed EDMS modeling based on activity data obtained from both the FAA and local sources. 
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The California Air Resources Board (CARB) provided both base and future year aircraft 

emissions estimates, discussed below. 

6.1.3 Seasonal Emissions Estimates 

The NEI2002 aircraft emissions are annual estimates, as were most of the updates provided by 

state and local agencies. To estimate seasonal county-level emission inventories, the monthly 

distribution of activity for airports in the WRAP region was obtained from the FAA’s Air Traffic 

Activity Data System (ATADS) (http://www.apo.data.faa.gov/main/atads.asp). The ATADS is 

the official source for historical monthly or annual air traffic statistics for airports with FAAoperated or FAA-contracted traffic control towers. The average seasonal distribution was 

calculated by state and aircraft type from the ATADS dataset. These state-level seasonal 

distributions were then applied to the annual county-level emissions in each state to derive the 

seasonal county-level emissions for each state. 

6.2 FUTURE YEAR AIRCRAFT EMISSIONS 

For all states except California, aircraft emissions were projected to the three future years from 

the 2002 emissions, by county and aircraft type, using FAA LTO forecasts as the activity data. 

Emission factors were assumed to be unchanged over time. The International Civil Aviation 

Organization (ICAO) has promulgated NOx and CO emission standards for commercial aircraft, 

exempting general aviation and military engines from the rule (ICAO, 1998), and the majority of 

engines are already meeting this standard. EPA officially promulgated the ICAO standards for 

air carriers in a final rule in November 0f 2005. 

The historic and projected LTO data by airport are available online from the Federal Aviation 

Administration (FAA) Terminal Area Forecast (TAF) database 

(http://www.apo.data.faa.gov/main/taf.asp) for all aircraft categories for which emissions were 

estimated. Projected LTO data for years 2008, 2013 and 2018, and historic data for 2002 were 

used to develop future year growth factors for all aircraft types. Growth factors were calculated 

as the ratio of the sum of LTOs by county and aircraft type in each future year to the sum of 

LTOs by county and aircraft type in 2002. These future year growth factors were then applied to 

2002 emission estimates by county and aircraft to develop future year emission inventories. 

A small number of counties had no aircraft LTOs in 2002 and a significant number of LTOs in 

future years. For these counties, emissions were calculated using projected future year LTOs and 

Emission Factors by aircraft type. 

6.3 CALIFORNIA AIRCRAFT EMISSIONS 

CARB provided annual, winter, and summer aircraft emissions estimates by county and aircraft 

type for the 2002 base year and the three future years. A number of processing steps were 

required to generate off-road emissions for California that are similar in content and format to 

the emissions for the remaining WRAP states: 
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G:\WRAP MSEI2\Reporting\Final report\Sec6_aircraft.doc 6-4 

• The CARB aircraft emissions for commercial aircraft and air taxis were combined. The 

SCC for commercial aircraft was assigned to the combined emissions, and zero emissions 

were assigned to the SCC for air taxis. 

• Spring and call emissions were calculated at the county and SCC level as 

Spring or fall emissions = (4 * annual emissions – winter emissions – summer 

emissions) / 2 

• Ammonia emissions were calculated using NH3/SOX scaling factors at the county and 

SCC level. 

• The additional pollutants needed for WRAP modeling were calculated using speciation 

factors and appropriate formulas. 

6.4 GENERATION OF SMOKE AND NIF FILES 

Emissions files were generated in the format needed for SMOKE emissions processing. 

Seasonal county-level aircraft emissions SMOKE files were generated, for all WRAP states 

combined, only for years 2002 and 2018, the years for which the WRAP air quality modeling is 

performed. The pollutants included in the SMOKE files are VOC, NOX, CO, NH3, SO2, PM10, 

EC10, OC10, SO4(10), PM2.5, EC2.5, OC2.5, SO4(2.5), coarse PM (PMC), NO, and NO2. 

Separate files were prepared for each year. 

Annual emissions files in EPA’s National Inventory Format (NIF) were also prepared and 

submitted to the WRAP Emissions Data Management System (EDMS), the on-line repository of 

all WRAP emissions data (http://www.wrapedms.org/default_login.asp). These NIF files 

contain a smaller set of pollutants; the additional pollutants needed for air quality modeling are 

not included. Annual emissions NIF files were prepared for each of the four years modeled, for 

all states combined. 

Summary spreadsheets and tables were also prepared, and were posted on the WRAP Mobile 

Sources Emission Inventory Update project web page at 

http://wrapair.org/forums/ef/UMSI/index.html. 
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7.0 COMMERCIAL MARINE EMISSIONS METHODOLOGY 

Commercial marine emissions comprise a wide variety of vessel types and uses. Table 7-1 

describes the different types of commercial marine vessel activity. In the previous WRAP 

mobile sources emission inventory work, emissions were estimated for most types of vessels 

(Pollack et al., 2004). Military emissions were not estimated because the activity data are not 

publicly available, and offshore emissions were not considered at that time. 

Table 7-1. Commercial vessel types. 

Source Definition Purpose Geographic Area 

Ocean Traffic Deep draft Ocean-going large vessels Near port 

River Traffic Tow or Push Boats Barge Freight Ocean Traffic 

Tugs Vessel assist and support 

functions Near port 

Ferries River or lake ferrying Regular routes 

Other Commercial 

Vessels 

Smaller support or excursion 

boats Near dock 

Dredges Dredging projects Varies 

Commercial Fishing Market fishing Ocean 

Military Coast Guard and Navy Ocean & Port 

In this work, emissions were estimated for deep draft vessels within shore and near port using 

port call data, and offshore emissions generated from ship location data. The most important 

revision for commercial marine emissions leading to regional haze (PM, SOx, and NOx) was the 

estimation of emissions for the offshore activity, primarily of ocean-going vessels. This activity 

was not previously estimated for the WRAP emission inventory, and has been a subject of 

concern as vessel traffic passes out from and along and upwind of the western coast of the US. 

The other revision conducted here was to update in-shore deep-draft vessel emissions to reflect 

changing fleet mix, especially the retirement of steamship powered vessels. 

One issue for modelers was which vertical grid layer to introduce the deep draft emissions. The 

stack height of 34 to 58 meters (Starcrest, 2004) and plume rise for ocean-going (deep draft) 

vessels indicated that the emissions should be placed in the second vertical layer (above 36 and 

below 73 meters). The plume rise was estimated at 2 meters using standard plume rise models 

with the vessel speed of 17 to 25 knots as the wind speed, exhaust exit rate of 35 to 40 meters per 

second with an average stack diameter of about 1.3 meters (Anderson, 2000). 

7.1 OFF-SHORE EMISSIONS ESTIMATES 

The method used to estimate the offshore marine emissions uses location identification data from 

a sample of vessels within the region of interest, and scaling factors by vessel type to estimate all 

ships. The ship proximity data and methods used to develop the ship population and emissions 

offshore are described in Appendix B. In short, this method uses positioning data generated by a 

subset of the world’s ships, assumes the sample is a random sample, and scales that sample to the 

entire world fleet. 
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Emissions estimates using this method were compared with the emissions generated using the 

Puget Sound port activity estimates described below. The grid cell emission totals at the 

entrance/exit of the Strait of Juan de Fuca using the scaled proximity method were approximately 

half of what were predicted using just the US port traffic, ignoring the traffic to and from 

Vancouver, Canada. Using the proximity method it would be expected that the emissions would 

be underestimated as ships near land, because positioning systems would be turned off or, if 

manually operated, would not be actively engaged during this period of time. This would reduce 

the number of ship indicators in areas near land and underestimate the ship traffic. Therefore, 

emissions in the first whole grid cell and any partial grid cells near the coast were zeroed out and 

replaced with emission estimates derived from the in-port activity for Oregon and Washington 

ports (Puget Sound, Columbia River, Coos Bay, and Grays Harbor) with remaining near coast 

estimates unchanged, as shown in Figure 7-1. The most apparent difference can be observed in 

Figure 7-1 for the grid cells near the mouth of the Columbia River where the nearest four grid 

cells now have higher emissions. There are also higher emissions at the entrance to the Strait of 

Juan de Fuca, but that result is less clear in Figure 7-1. 

Figure 7-1. Raw offshore emission estimates and with near port emissions substituted 

(Blue grids indicate no emissions over water). 

The commercial marine vessel emission inventory estimates provided by the California Air 

Resources Board (CARB) includes estimates for ships in transit within 100 miles of the coast 

(CARB, 2005). The transit emissions predicted using the proximity method in this work were 

zeroed out for the zone where CARB estimates were applicable. They were replaced by the 

CARB transit emissions estimates that were spatially assigned to the coastal shipping lanes 

defined by CARB. The result of this replacement along the California coast is apparent in the 

right side of Figure 7-1. The CARB data also included large vessel activity in ports. Therefore, 

in addition to using the CARB transit emissions for the California coastal zone, the CARB in 

port emissions were used for the California ports. 
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Because the emissions offshore represent entirely new estimates of emissions in the WRAP 

modeling domain, a summary of emissions is shown in Table 7-2 by state compared with the 

emissions near the ports and for California within the coastal zone. For purposes of preparing 

state emissions totals for offshore activity, the states were defined using the latitudes where the 

state borders meet the shore, as shown in Figure 7-2. For the near port totals in Table 7-2, it 

should be noted that the Columbia River vessel traffic (especially the transit up and down the 

river) was primarily allocated to the State of Washington counties. 

Table 7-2. 2002 Large ocean-going ship emissions by location (tons/year). 

State VOC CO NOx PM10 SO2 

Washington (offshore) 1,451 2,941 44,692 3,247 25,130 

Washington (near port) 103 209 3,467 335 2,483 

Washington (within shore) 277 1,206 10,764 763 5,352 

Oregon (offshore) 1,331 2,706 41,113 2,986 23,119 

Oregon (near port) 22 44 736 72 532 

Oregon (within shore) 23 271 1,415 42 212 

California (offshore) 4,269 8,681 131,930 9,587 74,181 

California (coastal zone) 5,387 14,345 111,550 6,042 46,059 

Total 12,863 30,403 345,667 23,074 177,068 

Figure 7-2. Offshore emissions by state and grid cells replaced with near port data. 
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7.2 IN-SHORE PORT REVISIONS TO EMISSIONS (INCLUDING LARGER 

ALASKAN FREIGHT PORTS) 

Ocean-going vessel emissions near ports were revised from the previous WRAP estimates 

(Pollack et al., 2004) to account for fleet turnover and more recent emission rate estimates. The 

fleet turnover aspect of the work considered the entire replacement of steamships with 

motorships, especially for projecting future year estimates. 

However, the work scope did not consider ocean-going vessels heading to and from Vancouver, 

B.C. and other Canadian traffic that may pass into or near US waters. The emissions from these 

vessels were estimated out 25 miles from the Pacific Ocean coast and used to compare with the 

estimates from the method used to estimate emissions off the coast and in the open ocean. 

7.2.1 Emission Factors Revision 

Table 7-3 shows the emission factors estimated by the U.S. EPA for Category 2 and 3 engines 

(EPA, 1999a, 2003) used in previous WRAP emission inventory estimates. For the Category 2 

engines, the average values shown in Table 7-3 were the average values used to estimate the 

emission reductions from the new emission standards (Samulski, 1999), and are quite similar to 

the emission factors for the highest power Category 1 engines. For Category 3 engines, EPA 

relied on a review of the base emission factors by ENVIRON (2002), based on the available data 

to date when the study was conducted. 

Table 7-3. US EPA (1999a, 2003) baseline emission factors for marine engines.

Engine Category 

HC 

[g/kW-hr] 

CO 

[g/kW-hr] 

NOX

[g/kW-hr] 

PM 

[g/kW-hr] 

Category 2 

(5-30 l/cylinder) 0.134 2.48 13.36 0.32 low sulfur 

Category 3 

Medium Speed 

(> 300 rpm, > 30 

l/cylinder) 

0.5* 0.7 16.6 Fuel sulfur 

dependence 

Category 3 

Slow speed 0.5* 1.1 23.6 Fuel sulfur 

dependence 

* Converted from kg/tonne units in Lloyds (1995) using 210 (g/kW-hr) for “medium speed” engines. 

The term “medium speed” refers to Category 3 engines with rated speeds of typically 300 to 750 

rpm (though most Category 3 medium speed engines are found in 400 –550 rpm range) that are 

typically 4-stroke engines either geared or used with diesel-electric drives that turn the propeller 

or power the generators for electrical power for the ship. Category 2 engines have been either 2-

stroke (GM-EMD or Fairbanks-Morse engines) or 4-stroke engine designs with rated speeds 

typically, but not always, above 750 rpm, used either for propulsion of smaller vessels or 

auxiliary power on larger ones. Emission factors for these marine engine types had been derived 

from previous reviews and emission measurement studies [EPA (2000), Environment Canada 

(1997), Lloyds (1995), ETC (1997), BAH (1991), Environment Canada (1999), and TRC 

(1989)]. 
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G:\WRAP MSEI2\Reporting\Final report\Sec7_CMV.doc 7-5 

Since the previous WRAP work, additional studies related to marine engine emissions have been 

published, including Cooper (2001 and 2003) and ENTEC (2002). Emission estimates from 

these studies were cited in the Port of Los Angeles (PoLA) emission inventory report (Starcrest, 

2004). Table 7-4 summarizes emission factors from these studies. 

Table 7-4. Emission factors for marine engines in the Port of Los Angeles emission inventory 

report. 

Engine Category 

HC 

[g/kW-hr] 

CO 

[g/kW-hr] 

NOX

[g/kW-hr] 

PM 

[g/kW-hr] 

Main Engine 

(Medium Speed – Residual Oil) 0.5 1.1 14.0 0.72 

Main Engine 

(Slow Speed – Residual Oil) 0.6 1.4 18.1 1.92 

Auxiliary Engine 

(Medium Speed – Residual Oil) 0.4 1.1 14.7 0.30 

Auxiliary Engine 

(Medium Speed – Gas Oil) 0.4 1.1 13.9 0.30 

The author of the 2002 ENTEC study later published a report to supplement the emission data 

compiled in the ENTEC study for marine engines (IVL, 2004). The emission data used in the 

IVL 2004 study are summarized in Table 7-5 for engines built prior to the MARPOL NOx 

emission reduction requirement. Note the dramatic difference in the slow speed particulate 

emission rate estimates of 1.92 or 1.3 g/kW-hr. 

Table 7-5. Emission factors found in the IVL 2004 report for average 1999 conditions. 

Engine Category 

BSFC 

[g/kW-hr] 

HC 

[g/kW-hr] 

CO 

[g/kW-hr] 

NOX

[g/kW-hr] 

PM 

[g/kW-hr] 

Medium Speed – Residual 

Oil (2.4% sulfur) 215 0.2 1.1 14.0 0.5 

Medium Speed – Gas Oil 

(0.4% sulfur) 205 0.2 1.1 13.2 0.2 

Slow Speed – Residual Oil 

(2.4% sulfur) 195 0.3 0.5 18.1 1.3 

Slow Speed – Gas Oil 

(0.4% sulfur) 185 0.3 0.5 17.0 0.2 

A distinction between Category 2 and 3 medium-speed engines was not made in the earlier 

ENTEC report, and the average ENTEC medium-speed engine NOx emission rate estimate is 

found directly between the Category 2 and 3 medium-speed NOx emission rates from the EPAsponsored work. The PM emission rate for medium speed engines was well under that for slow 

speed engines using the same high sulfur heavy (residual) oil, without an explanation. The PM 

measured from engines using high sulfur fuels will be largely comprised of sulfate aerosols, so it 

will likely be sensitive to temperature of particulate collection device and in turn the exhaust 

temperature. Still there is no technical reason to think that medium speed engines produce 

significantly lower particulate emissions than slow speed engines when burning the same fuel. 

In fact, the particulate emission factors in the ENTEC report are identical for medium and slow 

speed engine during maneuvering (a lower power and less efficient mode with higher specific 

emissions rates) at the same level of 2.4 (g/kW-hr). 
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For this study, the IVL emission factors were used except for particulate emissions where the 

slow and medium speed engine particulate emission rate when burning residual oil was adjusted 

to 1.73 and 1.76 g/kW-hr respectively. This is consistent with what was used in the previous 

WRAP commercial marine emission inventory and reflects the assumption that the average 

sulfur level in the heavy fuels was 3% compared to the average of 2.4% sulfur for residual fuel in 

the IVL report. Particulate emission rates during the hotelling (at berth) mode were 0.3 g/kW-hr, 

assuming a lower fuel sulfur level was used for this mode, which may not be the situation. 

There remains considerable uncertainty about the particulate emissions rates, especially for 

engines using high sulfur fuels. The IVL (2004) and ENTEC (2002) estimates indicate that the 

authors consider the uncertainty in the PM10 emission rates to be in excess of 50%. This may 

stem from the method of collection, filter handling, or other factors associated with the 

hygroscopic nature of the particulate formed from diesel engines burning high sulfur fuels. The 

particulate emissions rates and sulfur relationship used for this work were not intended to be the 

final word on the subject, but provide a reasonable range of estimates consistent with the best 

understanding at this time. 

7.2.2 Revised Estimates for Ocean-Going Vessels 

EPA (1999b) reviewed estimates of the ocean-going vessel activity for Coos Bay and Puget 

Sound ports. In this document, a method is also described to extrapolate ocean-going vessel 

activity for other ports and to allocate activity to individual Puget Sound ports. Two data sources 

existed for the EPA (1999b) report, one of which gathered general information about the total 

number of trips by vessel type for the top 95 US ports, and the other gathered more specific 

information for several ports including the Puget Sound ports totals and Coos Bay. The more 

general information was used to allocate the more specific activity information to each of the 

Puget Sound ports and to extrapolate an estimate of the activity of the Columbia River ports. 

This method was identical to previous WRAP emission inventories with the replacement of 

steamships with motorships of the same gross tonnage. The revised emission estimates are 

shown in Table 7-6 for Puget Sound ports to be used to cross reference to the Columbia River 

ports. Emission rates for motorships are higher for NOx but lower for PM and SOx than for 

steamships. 

Table 7-6. Emission estimates for Puget Sound (excluding Grays Harbor) ocean-going vessels 

in 2002. 

Estimate 

HC 

(tons/year)

CO 

(tons/year)

NOx 

(tons/year)

PM 

(tons/year) 

SO2 

(tons/year)

Cruise (25 miles to 

entrance of the Strait) 52 106 1,759 170 1,400

Reduced Speed Zone 135 275 4,554 440 3,255

Maneuvering 23 67 203 24 164

Hotelling 28 523 2,814 68 302

Total 238 971 9,329 701 5,121

The emissions estimated in Table 7-6 do not include Canadian vessel traffic, and so may 

underestimate the emissions within and just outside the Strait of Juan de Fuca. A Canadian study 

(Levelton, 2002) added in Canadian traffic, which significantly increased (by 1.5 to 4 times) the 
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reduced speed zone and cruise mode emissions for Washington State emissions exclusively in 

the transit modes through the Strait of Juan de Fuca. The scope of this project did not include the 

Canadian traffic emissions, but it should be understood that this emission source affects 

Washington marine emissions. 

For geographic allocation, the emissions for each port were separated into three segments: cruise, 

reduced speed zone (RSZ), maneuvering and hotelling/dwelling. For emissions associated with 

Grays Harbor vessel calls, all emissions were considered to occur in Grays Harbor County. For 

all Puget Sound vessel calls, the cruise condition emissions were assumed to occur in Clallum 

County. For all Puget Sound vessel calls, the maneuvering and hotelling emissions were 

allocated to the county of the port of interest. The emissions for the reduced speed zone were 

allocated to the counties along the primary shipping channel according to estimates of the 

fraction of time spent in each county. For example, port calls to Olympia included RSZ 

emissions in Clallum, Jefferson, Kitsap, Pierce, and Thurston counties; and Port calls to 

Bellingham included transit through Clallum, San Juan, and Whatcom counties. Because 

shipping lanes often straddle county boundaries, these county designations were made for 

expedience and could be improved by plotting emissions along the actual shipping lanes rather 

than the county in general. 

The basic data for the vessel calls and emission estimates were for 1996, the same as the 

previous emission inventory, but the scaling (growth) estimates from 1996 to 2002 were updated 

with freight movement information for 2002 compared to 1996. These scaling factors are shown 

in Table 7-7 for each port. 

Table 7-7. Freight tonnage from 1996 to 2002 by port.

Port 1996 2002 2002/1996

Seattle Harbor, WA 23,547,000 19,591,009 0.83

Tacoma Harbor, WA 21,491,000 20,587,109 0.96

Anacortes Harbor, WA 13,844,000 15,362,650 1.11

Everett Harbor, WA 4,007,000 3,009,175 0.75

Port Angeles Harbor, WA 2,780,000 1,673,985 0.60

Grays Harbor, WA 1,990,000 1,485,991 0.75

Bellingham Harbor, WA 1,419,000 250,000 0.18

Olympia Harbor, WA 1,893,000 1,440,439 0.76

Puget Sound Totals 68,981,000 61,914,367 0.90

Port of Astoria, OR 324,000 95,000 0.29

Port of Kalama, WA 8,223,000 6,386,161 0.78

Port of Longview, WA 5,163,000 4,705,771 0.91

Port of Portland, OR 29,734,000 26,635,044 0.90

Port of Vancouver, WA 7,704,000 6,610,345 0.86

Columbia River Totals 51,148,000 44,432,321 0.87

Coos Bay 3,322,000 1,706,821 0.51

Valdez, AK 77,116,000 50,513,074 0.66

Ketchikan, AK 1,341,000 753,000 0.56

Nikiski, AK 6,608,630 7,235,098 1.09

Anchorage, AK 3,401,000 2,983,137 0.88
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7.2.3 Columbia River Ports 

The Columbia River ports were estimated according to the procedure described in EPA (1999b), 

where a scaling factor was determined with a similar port, in this case, the Puget Sound totals. 

Adjustments were made to the actual vessel activity such as reduced speed zone load and time in 

mode based on discussions with the River Pilots for the Columbia River ports. Other factors, 

such as cruise, maneuvering, and hotelling time and load, were kept the same with the adjusted 

number of vessel calls. 

Vessels arriving near the mouth of the Columbia River are guided by Bar pilots 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 the pilots take command. 

There were five major ports in the Columbia River for which EPA (1999b) identified and 

estimated total vessel visits. These total vessel visits were compared with the total activity for 

Puget Sound ports (including Grays Harbor) for which a more detailed estimate has already been 

produced, as shown in the Table 7-8. The port call information provided here does not 

necessarily match the actual deep draft vessel calls because often smaller ships are included in 

the Army Corps estimates than would be included in a port specific data of deep draft vessels. 

The individual vessel visits by type of vessel for each Columbia River port were divided by the 

Puget Sound totals and multiplied by the more detailed estimate of the Puget Sound ports totals 

to produce an estimate of vessel activity for each of the Columbia River ports. 

 

Table 7-8. Port activity totals as presented by EPA (1999b) for 1995. 

Activity Data 

Puget 

Sound 

Ports 

Port of 

Portland, 

OR 

Port of 

Kalama, 

WA 

Port of 

Vancouver, 

WA 

Port of 

Longview, 

WA 

Port of 

Astoria, 

OR 

RSZ Mileage 

(Nautical) -- 93 69 94 59 14

Bulk Carrier 1378 694 378 446 543 397

Container Ship 2667 540 0 7 0 0

General Cargo 428 69 1 111 44 5

Other 82 0 0 0 0 1

Passenger 101 792 0 20 5 9

Reefer 108 10 0 0 13 0

Roll on/Roll off 795 126 0 7 15 4

Tanker 1000 299 20 28 9 0

Vehicle Carrier 1069 247 0 13 0 0

By using the ratio of total visits, the emissions for the Columbia River ports were directly 

calculated from the Puget Sound totals for cruise, maneuvering, and hotelling emissions. The 

resulting emissions adjusted to eliminate steamships for 1996 activity are shown in Table 7-9; 

Table 7-10 shows the emissions projected to 2002. Cruise conditions are assumed here to begin 

14 miles out from Astoria where the reduce speed zone ends. Reduced speed zone emissions 

used the ratio of total visits and the ratios of load and time in mode for each Columbia River 

port. For instance, the reduced speed zone in the Puget Sound ranges from the entrance of the 

Strait of Juan de Fuca to near each port while the reduced speed zone for the Columbia River 

ports ranges from 14 miles out and in the Columbia River. Both the vessel speed (which affects 

the engine load) and the time in mode are different between the Puget Sound and Columbia 

River. 
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G:\WRAP MSEI2\Reporting\Final report\Sec7_CMV.doc 7-9 

Table 7-9. Emission estimates for Columbia River ocean-going vessels in 1996. 

Estimate 

HC 

(tons/year) 

CO 

(tons/year) 

NOx 

(tons/year) 

PM 

(tons/year) 

SO2 

(tons/year) 

Port of Astoria, OR 6 69 395 14 76

Port of Kalama, WA 11 78 555 30 198

Port of Longview, WA 15 114 789 41 264

Port of Portland, OR 62 330 2662 174 1196

Port of Vancouver, WA 20 118 914 56 377

Table 7-10. Emission estimates for Columbia River ocean-going vessels in 2002. 

Estimate 

HC 

(tons/year) 

CO 

(tons/year) 

NOx 

(tons/year) 

PM 

(tons/year) 

SO2 

(tons/year) 

Port of Astoria, OR 3 22 146 7 44

Port of Kalama, WA 11 65 512 31 212

Port of Longview, WA 18 113 862 51 344

Port of Portland, OR 72 328 2935 209 1470

Port of Vancouver, WA 21 109 920 61 423

Overall emissions for vessels visiting each port are shown in Table 7-10 for 2002. However, 

transit emissions occur in the Columbia River downstream of each port rather than in the port 

area. The geographic allocation for the transit (cruise and RSZ) emissions were to the 

Washington counties (Pacific for cruise and some RSZ, Wahkiakum, Cowlitz, or Clark) below 

each port according to the fraction of time spent in each county. Maneuvering and hotelling 

emissions were allocated to the county of the port, whether Washington or Oregon. 

The port of Coos Bay was determined differently in that no steamships called at this port in 

1996. Therefore, no adjustment to the vessel fleet was made for this work other than using the 

revised NOx emission factors described here. 

7.2.4 Alaskan Ports 

Some Alaskan ports were covered by the EPA (1999b) report on deep draft vessel activity. The 

port calls at these ports were associated with the port calls of either the Puget Sound or Coos Bay 

whichever was most like the ports under consideration. The vessel characteristics at the Alaskan 

ports were assumed to be identical to the ports where such detailed data was available. The 

activity estimates shown in Table 7-11 using the same methodology to estimate the Columbia 

River port activity. 

Table 7-11. Port activity totals as presented by EPA (1999b) compared with Puget Sound Ports 

or Coos Bay. 

Activity Data 

Puget 

Sound 

Ports 

Valdez, 

AK

Ketchikan, 

AK 

Coos 

Bay, OR 

Port of 

Nikiska, 

AK 

Port of 

Anchorage, 

AK 

RSZ Mileage 

(Nautical) Various 27 14 14 84 144

Bulk Carrier 1378 0 77 185 2 13

Container Ship 2667 0 274 -- -- --

General Cargo 428 0 290 78 4 5
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec7_CMV.doc 7-10 

Activity Data 

Puget 

Sound 

Ports 

Valdez, 

AK

Ketchikan, 

AK 

Coos 

Bay, OR 

Port of 

Nikiska, 

AK 

Port of 

Anchorage, 

AK 

Other 82 0 36 470 208 498

Passenger 101 29 976 -- -- --

Reefer 108 0 0 -- -- --

Roll on/Roll off 795 0 0 -- -- --

Tanker 1000 1270 25 -- -- --

Vehicle Carrier 1069 0 0 -- -- --

The emission estimates based on the relative apportionment of activity and emissions for these 

ports are shown in Table 7-12 for 1996 and projected to 2002 in Table 7-13. Because ship calls 

for smaller ports vary widely from one year to the next depending upon freight demands, it may 

not be possible to project long-term activity growth. Also, at the time this work was conducted, 

data were not available for other ports, such as cruise or other vessels in Juneau, in the format 

necessary to estimate emissions. 

Table 7-12. Emission estimates for Alaskan port ocean-going vessels in 1996. 

Estimate 

HC 

(tons/year)

CO 

(tons/year)

NOx 

(tons/year)

PM 

(tons/year) 

SO2 

(tons/year)

Valdez, AK 43 224 1,581 101 688

Ketchikan, AK 23 146 1,080 64 432

Port of Nikiska, AK 0.3 1.3 12 0.9 6.4

Port of Anchorage, AK 1.3 4.7 52 4.2 30

Table 7-13. Emission estimates for Alaskan port ocean-going vessels in 2002. 

Estimate 

HC 

(tons/year)

CO 

(tons/year)

NOx 

(tons/year)

PM 

(tons/year) 

SO2 

(tons/year)

Valdez, AK 28 147 1035 66 451

Ketchikan, AK 13 82 606 36 243

Port of Nikiska, AK 0.3 1.4 13 1.0 7.1

Port of Anchorage, AK 1.2 4.1 46 3.7 27

7.2.5 California Coastal Transit and Ports 

Emissions for commercial marine vessels operating near and within the State of California were 

provided by CARB (CARB, 2005). Emissions were provided for several major categories, 

labeled by ARB as SHIPS IN-TRANSIT, SHIPS MANEUVERING and SHIPS BERTHING. 

The IN-TRANSIT category was defined as corresponding to operations on shipping lanes within 

100 miles of the California coast. The MANUEVERING and BERTHING categories 

correspond to operations at ports. Emissions were also provided for a category labeled 

COMMERCIAL BOATS that accounts for the activity of smaller vessels near ports and on 

interior waterways. To incorporate the data provided by CARB into the WRAP commercial 

marine inventory, the CARB county level emissions estimates were spatially allocated to the 36 

kilometer grid. 

One of two methods was used to spatially allocate the California marine vessel emissions, 

depending on the emission category. For the IN-TRANSIT emissions, CARB provided a 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec7_CMV.doc 7-11 

shapefile that defined the 100 mile coastal zone and a shapefile that defined the shipping lanes 

within that zone. By overlaying the 100 mile zone for each county with the shipping lanes and 

the WRAP grid, it was possible to assign a fraction of the county total emissions to each grid 

cell. For the remaining emissions categories, the emissions were assigned to the grid cells that 

encompassed the major port in the county. The exception to such port assignments were inland 

counties where emissions were assigned to major lakes or rivers. Figure 7-3 shows the grid cells 

to which IN-TRANSIT and the port/inshore emissions were assigned by these procedures. 

 

Figure 7-3. Grid assignment of California in-transit (green) and port/inshore (red) emissions. 

The grid cell assigned emissions were then added to the other WRAP offshore and near port 

gridded emissions. Any overlap of the two inventories was eliminated. This yielded a 

comprehensive emission inventory for commercial marine operations on the west coast that 

encompasses all the zones shown in Figure 7-4. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec7_CMV.doc 7-12 

Figure 7-4. Distinct zones included in the WRAP commercial marine emissions inventory. 

7.3 FUTURE YEAR COMMERCIAL MARINE PROJECTIONS 

Projection factors for future year commercial marine emissions were derived from a study 

performed by Corbett and Wang (2005). This projection was based on an investigation of the 

historic trend in the larger vessels’ installed power. The installed power combines the propulsion 

power of individual vessels and number of calls of each vessel to the WRAP coastal ports. The 

historic trend shown in Figure 7-3 does not provide a sufficient number of years to determine the 

form of the equation to use to project future year activity. The fit of the historic data was 

equivalent whether an exponential fit (equivalent to compound annual growth rate (CAGR)) or a 

linear regression was used. For this work, therefore, an average of the exponential and linear 

regression was used to project future year commercial marine activity, per agreement and 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec7_CMV.doc 7-13 

discussion with CARB staff, and thereby matching CARB projections (CARB, 2005). These 

average projection factors are shown in Table 7-14; they were applied to all three west coast 

states, to all in-shore and offshore emissions. No emission rate decrease was projected because 

international standards are not expected to affect emissions. 

Table 7-14. Projection factors for ocean-going vessels. 

Future Year Relative to 2002

2008 1.39

2013 1.79

2018 2.30

Figure 7-5. Commercial marine installed power trend and analysis. 

7.4 RECENT GOODS MOVEMENT REGULATORY ACTIVITY 

In the WRAP region, there have been several initiatives to better evaluate and regulate emission 

sources related to goods movement including marine vessels, locomotive, cargo handling 

equipment, other off-road equipment, and trucks. 

The regulatory emissions control strategies have largely focused on California through the Air 

Resources Board’s Goods Movement Plan (http://www.arb.ca.gov/planning/gmerp/gmerp.htm). 

Nonregulatory voluntary emission reduction plans are summarized by the West Coast 

Collaborative (http://www.westcoastcollaborative.org/index.htm). These regulatory and 

nonregulatory approaches will likely significantly affect future year emission projections but 

because they were not “on the books” at the time this work was done they have largely not been 

Linear Correlation

y = 2.46E+07x - 4.88E+10

R2

 = 6.96E-01

Exponential Correlation

y = 3.98E-47e0.0633x

R2 = 0.6949

0.0E+00

2.0E+08

4.0E+08

6.0E+08

8.0E+08

1.0E+09

1.2E+09

1.4E+09

1995 2000 2005 2010 2015 2020

Year

Power Installed (kW)
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec7_CMV.doc 7-14 

included in the WRAP emissions inventory projections. The California regulations primarily 

affect the particulate and sulfur emissions from some of the larger off-road emissions sources; 

these include lower fuel sulfur for auxiliary engines of large vessels, shoreside power for large 

vessels at berth, fleet turnover/retrofit for cargo handling equipment, locomotive fleet turnover, 

truck initiatives, and other measures. To the extent that the rules were final, ARB likely included 

the effects of these in their projected emissions, but many rules were not approved by the Board 

until after such inventories were provide to WRAP, and many rules are still under proposal. 

In addition, as described above, the basic approach for estimating large vessel emissions in the 

mid-Pacific shipping lanes was developed by Professor James Corbett. Since the time of the 

WRAP commercial marine emissions inventory evaluation in 2005, Professor Corbett, under 

contract to CARB, has developed a revision to his approach, described at 

http://www.ocean.udel.edu/cms/jcorbett/sea/NorthAmericanSTEEM/. This modified approach is 

to be used for the evaluation of the Sulfur Emission Control Area (SECA, described at 

http://www.westcoastcollaborative.org/wkgrp-marine.htm) for the United States submittal. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec8_RoadDust.doc 8-1 

8.0 ROAD DUST EMISSIONS METHODOLOGY 

In the previous WRAP mobile source emissions inventory work, fugitive road dust emissions for 

unpaved roads were revised from the traditional EPA estimates with updated silt loading values, 

updated and revised activity estimates, and the application of transport fractions (Pollack et al., 

2004). The aim of that work was to resolve large differences in road dust emissions in adjacent 

counties, and to use a consistent methodology across the WRAP region. The revised road dust 

emissions were estimated for 1996, the base year for the original WRAP modeling work, and 

2018. 

For this project, paved and unpaved road dust emissions were updated using the updated VMT 

for the base and future years provided by state and local contacts as part of the base and future 

year survey work. Any updated road dust controls provided were also incorporated into the 

estimates. It is important to note that since the previous WRAP road dust emissions estimates 

were prepared, EPA’s guidance on estimating paved and unpaved road dust emissions was 

updated; see http://www.epa.gov/ttn/chief/ap42/ch13/index.html. For this project, the Emissions 

Forum opted to update the road dust emissions only to reflect updated VMT and controls, and 

not to reflect the updated EPA guidance methodology. Resources required to do a more 

complete update with the latest methodology would be large, and since road dust is not an 

important contributor to regional haze the decision was made to do the simpler updates only. 

Road dust emissions estimates in the earlier WRAP work did not include Alaska, as Alaska was 

not a WRAP member at the time. Road dust emissions were therefore not estimated for Alaska 

in this effort. For California, road dust emissions provided by CARB were used. 

 

Road dust emissions in the previous work included application of a factor to account for 

deposition and other removal mechanisms that tend to lower the amount of dust that is 

transported on a regional basis (i.e., across the 36 km grid cells in the WRAP modeling domain). 

The county-specific transport fractions that were applied depend on the vegetative characteristics 

of each county, and were calculated as the weighted average of vegetation-specific transport 

fractions in each county. For the current work, updated transport fractions were available, but 

were applied to the road dust emissions (and other dust sources) in the SMOKE emissions 

processing rather than in the development of the county-level emissions. 

8.1 ROAD DUST SURVEY

As part of the survey form for future year VMT and modeling inputs for on-road and off-road 

emissions calculations, state and local agencies were asked to provide updated information to 

revise the earlier paved and unpaved road dust emissions. Along with the survey form, 

ENVIRON provided spreadsheets that showed the fractions of total VMT on paved and unpaved 

roads from the previous road dust emissions estimates, and state and local agencies were asked to 

review and update these fractions if they had appropriate available data. The survey also asked 

for a review and update of the road dust controls assumptions used in the prior work. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec8_RoadDust.doc 8-2 

8.1.1 Updates to Paved and Unpaved Road VMT 

An issue that arose is that in several regions the VMT that had been provided for the 2002 base 

year was for paved roads only, and did not include VMT from unpaved roads. This was the case 

for Pima County (AZ), Idaho, Nevada (all but Clark County), Utah, Wyoming. This meant that 

on-road vehicle emissions estimates, which use VMT as the activity data, were slightly 

underestimated in those areas. However, unpaved road VMT is a very small fraction of total 

VMT, and the Emissions Forum decided to not revise the existing on-road vehicle emissions 

estimates. 

Unpaved VMT estimates were provided for all counties in Colorado, Montana, Nevada, Utah, 

and Washington, and for Maricopa County (AZ). The revisions for Montana were significant. 

Montana’s approach for estimating unpaved road VMT was follows: 

“Because traffic on many of the unpaved roads is not systematically monitored, or in 

MDOT lingo called "Off_System", the following approach was used to determine the 

effective average annual daily traffic (AADT) on unpaved roads in each of the 56 counties 

in Montana. With the effective AADT for each county and the miles of unpaved roads that 

was Off-System in each county, the VMT for the unpaved off-system roads was calculated 

in each county. The AADT of these off system unpaved roads was determined based on 

state-wide data from traffic counters at several sites across the state that yielded an average 

daily traffic on unpaved roads. For this example the state-wide average AADT used on 

unpaved roads is 360 vehicle per day. 

For example, if the average AADT on unpaved roads is 360 vehicles per day the "effective" 

AADT would be calculated for a county with a low population density as follows: 

 Effective AADT = 360 x 0.044/6.8 = 2.23, 

where 0.044 is the population density for county "A" and 6.8 is the average population 

density for the 56 counties in Montana. So for the low population density county A, the 

VMT would simply become the effective AADT multiplied by the total length of unpaved 

off-system roads in county A. Or VMT = 2.23 X 100 miles = 223 VMT for county A with 

100 miles of unpaved road. For county "B" which has a high population density of 17.0 

with 50 miles of unpaved off-system road. The effective AADT would be 360 x 17.0/6.8 = 

900 and the VMT would be 900 X 50 miles or 45,000 VMT.” (Carlin, 2005.) 

The result of this approach is that Montana’s unpaved road VMT is several times higher than in 

the previous inventory. 

For future year road dust emissions estimates, the default VMT growth factors were assumed to 

be the same for unpaved VMT as for paved VMT. In the survey, state and local agencies were 

asked to provide updates if available. Three regions responded with updates to 2018 

information: Maricopa County and Utah provided updated 2018 unpaved road VMT, and the 

Washington DEQ said to assume no growth in unpaved VMT between 2002 and 2018. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec8_RoadDust.doc 8-3 

8.1.2 Updates to Road Dust Controls 

The default assumptions for dust controls updates were those set by EPA in their calculation of 

road dust for the 1996 National Emissions Inventory, which was the basis of the previous WRAP 

road dust emissions estimates. The control assumed for paved roads was vacuum sweeping 

twice per month to achieve a control level of 79 percent, applied to urban and rural roads in 

serious PM nonattainment areas and to urban roads in moderate PM nonattainment areas. The 

penetration factor used varied by road type and nonattainment area classification (serious or 

moderate). For unpaved roads, the control measure and level of control assumed by EPA varied 

by PM nonattainment area classification and by rural and urban areas. On urban unpaved roads 

in moderate PM nonattainment areas, the assumed control was paving the unpaved roads. This 

control was applied with a control efficiency of 96 percent and a penetration rate of 50 percent. 

On rural roads in serious PM nonattainment areas, chemical stabilization was the assumed 

control; this control was applied with an assumed control efficiency of 75 percent and 

penetration rate of 50 percent. On urban unpaved roads in serious PM nonattainment areas, 

paving and chemical stabilization were the assumed control measures, with an assumed overall 

control efficiency of 90 percent and penetration rate of 75 percent. Updates to these control 

assumptions were provided by Maricopa County (AZ) and Yakima County (WA). 

8.2 ROAD DUST EMISSIONS CALCULATIONS 

The 2002 road dust emissions estimates were derived by modifying the previous WRAP road 

dust emissions estimates for updates to paved and unpaved road VMT and dust controls. The 

previous inventory covered the years 1996, 2003, 2008, 2013, and 2018. For this effort, the 

default 2002 paved and unpaved road dust VMT and emissions were calculated by county and 

roadway type by linearly interpolating from the previous 1996 and 2003 estimates. Transport 

fractions were backed out of these estimates. 

The 2002 paved and unpaved VMT were then updated. A scaling factor of (updated 2002 

VMT/default 2002 VMT) was then calculated by county and roadway type, and applied to the 

default 2002 road dust emissions to derive the updated emissions estimates. This calculation 

was done separately for paved and unpaved roads, for both PM10 and PM2.5. The same 

seasonal allocation factors for VMT used to estimate on-road vehicle emissions were then used 

to derive seasonal paved and unpaved road dust emissions. 

Figure 8-1 compares coarse PM (PM10 – PM2.5) 2002 total unpaved road dust emissions 

between the previous WRAP inventory and this revision, with transport fractions applied. The 

differences in the emissions from the previous to the current effort seen in this plot are due to 

changes in unpaved road VMT, changes in control measures, and changes in the transport 

fractions. Overall the unpaved road dust emissions are lower, but the most striking change is for 

Montana, which has reported significantly increased unpaved road VMT in the current effort. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec8_RoadDust.doc 8-4 

Figure 8-1. Unpaved road dust coarse PM emissions, with transport fractions applied, from 

previous and current WRAP inventories. 

Paved and unpaved road dust emissions for 2018 were calculated in an analogous manner as the 

2002 emissions: A scaling factor of (updated 2018 VMT/previous 2018 VMT) was calculated 

by county and roadway type, and applied to the previous 2018 road dust emissions to derive the 

updated emissions estimates. This calculation was done separately for paved and unpaved 

roads, for both PM10 and PM2.5. The seasonal allocation factors were then applied to determine 

seasonal emissions. The same controls were assumed for 2018 as used in 2002; no state or local 

agency indicated any changes should be made. 

8.3 GENERATION OF SMOKE AND NIF FILES 

Emissions files were generated in the format needed for SMOKE emissions processing. 

Seasonal county-level road dust emissions SMOKE files were generated, for all WRAP states 

combined (Alaska and California not included), only for years 2002 and 2018, the years for 

which the WRAP air quality modeling is performed. Separate files were prepared for each year. 

Annual emissions files in EPA’s National Inventory Format (NIF) were also prepared and 

submitted to the WRAP Emissions Data Management System (EDMS), the on-line repository of 

all WRAP emissions data (http://www.wrapedms.org/default_login.asp). Annual emissions NIF 

files were prepared for each of the four years modeled, for all states combined (not Alaska or 

California). 

0

40,000

80,000

120,000

160,000

200,000

AZ CA CO ID MT NV NM ND OR SD UT WA WY

Tons/yr

Previous version

Current revision
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec8_RoadDust.doc 8-5 

Summary spreadsheets and tables were also prepared, and were posted on the WRAP Mobile 

Sources Emission Inventory Update project web page at 

http://wrapair.org/forums/ef/UMSI/index.html. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-1 

9.0 RESULTS 

This section provides the emissions estimates for on-road and off-road mobile source emissions, 

and for road dust emissions, developed using the methodologies described in the previous 

sections of this report. The results are presented here in a series of tables and graphs; more 

detailed emissions by state, county, and source category are available in spreadsheets posted on 

the project web page at http://wrapair.org/forums/ef/UMSI/index.html. 

9.1 OVERVIEW OF WRAP EMISSION INVENTORIES

To put the results of this work into context, Figures 9-1 and 9-2 show the contributions by source 

category for all emissions estimated for the WRAP region for 2002 and 2018, respectively. 

Figure 9-1 shows that on-road and off-road mobile source emissions comprise more than half of 

all CO and NOx emissions in 2002, and much lesser fractions for all other pollutants. Shipping 

emissions, though, are a significant contributor to overall SO2 emissions – about 20% in 2002. 

In 2018, mobile source emissions are a much smaller fraction of overall NOx emissions. The 

contribution of NOx emissions from on-road vehicles has dropped significantly, from 34 percent 

to 16 percent, as older vehicles have been scrapped and replaced by newer vehicles that meet 

more stringent standards (for both light- and heavy-duty vehicles). The off-road equipment 

emissions contribution is similar in both years. There are newer standards for off-road 

equipment in future years, but they start later than for on-road vehicles, and have a longer phasein period. Shipping emissions in 2018 contribute to 14 percent of the total NOx and about 22 

percent of the total SO2 emissions. 

9.2 ON-ROAD EMISSIONS 

Figure 9-3 shows the contributions by vehicle class and fuel type to 2002 on-road emissions for 

the pollutants estimated. Light-duty vehicles dominate VOC, CO, and NH3 emissions; while 

heavy-duty vehicles dominate PM emissions. NOx emissions are about evenly split between 

light- and heavy-duty vehicles. 

Figures 9-4 through 9-8 show the annual average ton per day (TPD) emissions by vehicle class, 

fuel type, and state, for the different pollutants. The emissions are listed by state and fuel type in 

Table 9-1. The graphs allow a visual comparison of emissions by state, as well as showing the 

relative contributions by vehicle class and fuel type. For all pollutants except SO2, California 

has the highest emissions of all of the states; California on-road SO2 emissions are lower 

because of state regulations requiring low sulfur fuels. 

Figure 9-9 shows the spatial distribution of on-road NO emissions for on-road vehicles on a July 

weekday for 2002. This plot was prepared by the WRAP Regional Modeling Center (RMC) for 

quality assurance (QA) purposes. The plot shows the emissions for the contiguous 48 states, as 

prepared by the WRAP and the other four Regional Planning Organizations (RPOs), as well as 

Canada and Mexico. What is evident in the plot are the higher on-road NOx emissions in the 

major population centers and also major freeways in rural areas. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-2 

Tables 9-2 to 9-4 list the annual emissions for the three future years, by state and fuel type. 

Figures 9-10 through 9-15 show the changes in on-road mobile source emissions over time from 

2002 to 2018. There are significant reductions in all pollutants as the fleet is turned over and 

newer vehicles are introduced that meet more stringent standards, most importantly the Tier 2 

light-duty vehicle standards beginning with the 2005 model year and the 2007 heavy-duty 

standards. Despite an increase of about 40 percent in VMT, on-road NOx emissions are reduced 

by almost 70 percent from 2002 to 2018, VOC emissions by about 60 percent, and CO emissions 

by about 50 percent. On-road PM2.5 emissions are also decreasing, though at a lesser rate, about 

25 percent from 2002 to 2018. On-road SO2 emissions are reduced dramatically from 2002 to 

2008, as federal regulations require the introduction of low-sulfur gasoline and diesel fuels 

during that period. 

9.3 OFF-ROAD EMISSIONS 

Figure 9-16 shows the contributions to 2002 off-road emissions by source category, excluding 

commercial marine emissions. NOx emissions are dominated by locomotives and construction 

equipment. Lawn and garden equipment and recreational marine are the largest contributors to 

off-road VOC emissions, and lawn and garden equipment also dominate CO emissions. 

Agricultural and construction equipment are the largest contributors to both PM10 and PM2.5 

emissions. 

Figures 9-17 through 9-21 show the annual average TPD emissions by source category and state 

for the different pollutants. The emissions are listed by state and fuel type in Table 9-5. As for 

on-road emissions, California has the highest emissions of all of the states for all pollutants 

except SO2; California off-road SO2 emissions are lower because of state regulations requiring 

low sulfur nonroad diesel fuel in 2002. Figure 9-22 shows the QA plot of the spatial distribution 

of NO emissions for off-road equipment on a July weekday for 2002. 

Tables 9-6 to 9-8 list the annual off-road emissions for the three future years, by state and fuel 

type. Figures 9-23 through 9-28 show the changes in off-road mobile source emissions over time 

from 2002 to 2018, excluding commercial marine emissions. There are significant reductions in 

all pollutants except CO as the off-road equipment fleet is turned over and newer engines are 

introduced that meet more stringent standards. Off-road NOx emissions are reduced by almost 

40 percent from 2002 to 2018, VOC emissions by about 37 percent, and PM emissions by about 

43 percent. CO emissions for off-road equipment are increasing slightly, and SO2 emissions are 

reduced dramatically as the low sulfur nonroad diesel fuel regulations are phased in. 

9.4 TOTAL MOBILE SOURCE EMISSIONS 

Figures 9-29 through 9-34 show the changes in total on-road and off-road mobile source 

emissions over time from 2002 to 2018, excluding commercial marine emissions. For all mobile 

sources combined (except commercial marine), there are significant reductions in all pollutants 

over time – about 55 percent for NOx emissions, about 50 percent for VOC emissions, about 50 

percent for PM10 and 40 percent for PM2.5 emissions, about 37 percent in CO emissions, and 

nearly 90 percent in SO2 emissions. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-3 

Offshore commercial marine emissions were not included in the previous WRAP mobile sources 

emission inventory, and are a significant source of NOx and SO2 emissions. Table 9-9 shows 

the west coast 2002 NOx and SO2 emissions for all mobile sources, including commercial 

marine. The table shows that total commercial marine emissions, offshore plus inshore, are 

about 25 percent of the sum of all other on-road and off-road NOx emissions, and about five 

times the sum of all other mobile SO2 emissions. Table 9-10 shows the west coast 2018 NOx 

and SO2 emissions for all mobile sources, including commercial marine. In the future years, 

NOx and SO2 emissions have been reduced from all other mobile sources (except aircraft), but 

commercial marine emissions NOx and SO2 emissions are increasing with increased ship traffic. 

In 2018, total commercial marine emissions are about 25 percent higher than all other mobile 

NOx emissions, and about five times the sum of all other mobile SO2 emissions. 

9.5 ROAD DUST EMISSIONS 

Tables 9-11 and 9-12 show the estimated PM10 and PM2.5 emissions by state for paved and 

unpaved roads, respectively. Tables 9-13 and 9-14 show the road dust emissions by state in 

2018. (California road dust emissions are not included in these tables; they are reported in the 

area sources and were not revised in this effort.) Road dust emissions increase significantly from 

2002 to 2018, in proportion to the increase in vehicle miles travelled. 

Figure 9-35 shows the QA plot of the spatial distribution of road dust coarse PM emissions for 

on a July weekday for 2002. The plot shows that the clear methodological differences in 

estimating road dust emissions in Montana vs. the rest of the WRAP states, and also differences 

among the calculations by RPO. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 

VOC PM10 PM2.5 PMCSO2 NH3

2000 – 2004 WRAP Re

Annual Emissions 

Contributions by Sour

Category

CO NOx

VOC PM10 PM2.5 PMCSO2 NH3

2000 – 2004 WRAP Re

Annual Emissions 

Contributions by Sour

Category

CO NOx

Area NH3 MoBiogenic On RoaNonroad Mobile Road DFugitive Dust WindbloPoint Offshore Fire ShippinOil & gasArea NH3 MoBiogenic On RoaNonroad Mobile Road DFugitive Dust WindbloPoint Offshore Fire ShippinOil & gas

31.6%

4.5%

1.9%

11.5%

41.3%

9.0% 0.1% 4.5% 5.6%

17.6%

33.7%

6.1%

2.8%

19.6%

10.2%

4.3%

7.3%60.7%

4.2%

2.3%

0.3%

20.9%

85.6%

0.1%

5.1%2.6% 1.2%

5.4%

81.3%

2.0%

1.2% 6.3%

1.8%

4.5%

1.7%

1.0% 14.9%

11.0%

11.5%

16.7%

38.7%

1.4%

1.4%

3.1%

1.1%

35.4%

14.5%

9.1%

23.3%

3.7%

5.3%

3.4% 2.9%2.5%

3.

47.2%

Figure 9-1. 2000 – 2004 WRAP region annual emissions contributions by source category. Source: Adelman e
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 

VOC PM10 PM2.5 PMCSO2 NH3

2018 WRAP Region B

Annual Emission

Contributions by So

Category

CO NOx

Area NH3 Biogenic On RNonroad Mobile Road Fugitive Dust WindPoint OffshFire ShippOil & gas

36.2%

5.6%

16.9% 3.2%

27.5%

10.3%

0.1% 6.2%

9.2%

9.7%

16.2%

8.4%

8.6%

27.7%

14.0%

4.5%

9.5%

63.3%

0.4%

0.5%

21.8%

83.1%

0.7%

1.5%

5.0%

2.6%

7.1%

80.2%

2.0%

1.4%

8.1%

1.4%

1.8%

4.1%

1.0% 13.4%

11.7%

12.4% 16.8%

34.6%

1.2%

1.1%

3.3%

5.7%

30.1%

14.0%

5.4%

8.6%

19.8%

3.1%

2.2%

13.4% 3.5%

43.3%

Figure 9-2. 2018 WRAP region Base Case annual emissions contributions by source category. Source: Adelm
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-6 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

 VOC NOX CO PM10 PM2.5 SO2 NH3 

Percent of Total Emissions

Light Duty Gasoline

Light Duty Diesel

Heavy Duty Gasoline

Heavy Duty Diesel

Figure 9-3. 2002 Western states average annual on-road emissions (% by vehicle class and 

fuel type). 

0.0

200.0

400.0

600.0

800.0

1000.0

1200.0

1400.0

1600.0

1800.0

AK AZ CA CO ID MT NV NM ND OR SD UT WA WY

Tons Per Day

Light Duty Gasoline

Light Duty Diesel

Heavy Duty Gasoline

Heavy Duty Diesel

Figure 9-4. 2002 Western states average annual on-road NOx emissions by vehicle class and 

fuel type. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-7 

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

800.0

900.0

1000.0

AK AZ CA CO ID MT NV NM ND OR SD UT WA WY

Tons Per Day

Light Duty Gasoline

Light Duty Diesel

Heavy Duty Gasoline

Heavy Duty Diesel

Figure 9-5. 2002 Western states average annual on-road VOC emissions by vehicle class and 

fuel type. 

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

AK AZ CA CO ID MT NV NM ND OR SD UT WA WY

Tons Per Day

Light Duty Gasoline

Light Duty Diesel

Heavy Duty Gasoline

Heavy Duty Diesel

Figure 9-6. 2002 Western states average annual on-road PM10 emissions by vehicle class and 

fuel type. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-8 

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

AK AZ CA CO ID MT NV NM ND OR SD UT WA WY

Tons Per Day

Light Duty Gasoline

Light Duty Diesel

Heavy Duty Gasoline

Heavy Duty Diesel

Figure 9-7. 2002 Western states average annual on-road SO2 emissions by vehicle class and 

fuel type. 

0.0

1000.0

2000.0

3000.0

4000.0

5000.0

6000.0

7000.0

8000.0

9000.0

10000.0

AK AZ CA CO ID MT NV NM ND OR SD UT WA WY

Tons Per Day

Light Duty Gasoline

Light Duty Diesel

Heavy Duty Gasoline

Heavy Duty Diesel

Figure 9-8. 2002 Western states average annual on-road CO emissions by vehicle class and 

fuel type. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-9 

Figure 9-9. Spatial distribution of all RPO on-road NO emissions, 2002 July weekday. 

Source: WRAP Regional Modeling Center 

(http://pah.cert.ucr.edu/aqm/308/QA_plan02a36.plots/) 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-10 

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Annual Average Day (Tons Per Day)

California 1592 1156 809 556

Alaska 44 28 17 11

Arizona 488 340 222 147

Colorado 389 269 181 124

Idaho 122 79 51 34

Montana 147 97 78 60

Nevada 113 83 59 41

New Mexico 186 128 83 54

North Dakota 68 38 22 13

Oregon 306 217 163 115

South Dakota 80 53 34 22

Utah 212 161 112 75

Washington 553 367 244 153

Wyoming 106 68 43 27

2002 2008 2013 2018

Figure 9-10. Western states on-road annual average daily NOx emissions, 2002 – 2018. 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-11 

0

500

1000

1500

2000

2500

3000

Annual Average Day (Tons Per Day)

California 890 602 439 332

Alaska 24 15 10 8

Arizona 303 216 171 145

Colorado 276 175 134 114

Idaho 74 47 34 28

Montana 119 75 58 51

Nevada 99 75 57 47

New Mexico 106 69 51 43

North Dakota 35 19 13 10

Oregon 243 161 121 100

South Dakota 38 24 17 14

Utah 134 95 74 62

Washington 384 248 181 139

Wyoming 39 24 18 14

2002 2008 2013 2018

Figure 9-11. Western states on-road annual average daily VOC emissions, 2002 – 2018.
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-12 

0

20

40

60

80

100

120

140

Annual Average Day (Tons Per Day)

California 42.7 45.0 46.6 48.3

Alaska 1.2 0.9 0.8 0.7

Arizona 12.6 11.4 10.1 10.2

Colorado 10.3 8.7 7.7 7.5

Idaho 3.0 2.4 2.1 2.0

Montana 3.7 3.1 2.9 2.7

Nevada 2.3 2.4 2.5 2.6

New Mexico 5.3 4.5 4.0 3.8

North Dakota 1.9 1.3 1.0 0.9

Oregon 8.2 7.2 6.3 5.7

South Dakota 2.3 1.9 1.6 1.5

Utah 5.0 4.6 4.2 4.2

Washington 14.4 11.6 9.9 8.9

Wyoming 2.7 2.0 1.6 1.4

2002 2008 2013 2018

Figure 9-12. Western states on-road annual average daily PM10 emissions, 2002 – 2018.
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-13 

0

10

20

30

40

50

60

70

80

90

100

Annual Average Day (Tons Per Day)

California 28.8 29.8 30.0 30.8

Alaska 1.0 0.7 0.5 0.5

Arizona 9.9 8.4 7.0 6.7

Colorado 8.1 6.5 5.4 4.9

Idaho 2.3 1.7 1.4 1.3

Montana 2.9 2.4 2.1 1.8

Nevada 1.7 1.6 1.6 1.6

New Mexico 4.2 3.3 2.8 2.5

North Dakota 1.5 1.0 0.7 0.6

Oregon 6.5 5.4 4.5 3.8

South Dakota 1.8 1.4 1.2 1.0

Utah 3.9 3.3 2.9 2.7

Washington 11.4 8.6 6.9 5.9

Wyoming 2.2 1.5 1.2 1.0

2002 2008 2013 2018

Figure 9-13. Western states on-road annual average daily PM2.5 emissions, 2002 – 2018.
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-14 

0

10

20

30

40

50

60

70

80

90

Annual Average Day (Tons Per Day)

California 11.1 4.4 4.8 5.2

Alaska 1.8 0.3 0.1 0.2

Arizona 7.4 1.7 1.9 2.1

Colorado 11.4 1.3 1.4 1.6

Idaho 4.4 0.4 0.4 0.5

Montana 4.9 0.4 0.5 0.5

Nevada 1.2 0.6 0.7 0.8

New Mexico 5.3 0.6 0.7 0.8

North Dakota 2.1 0.2 0.2 0.2

Oregon 9.4 1.0 1.1 1.3

South Dakota 2.4 0.3 0.3 0.3

Utah 4.9 0.8 0.9 1.0

Washington 15.2 1.6 1.7 1.9

Wyoming 2.6 0.2 0.2 0.2

2002 2008 2013 2018

Figure 9-14. Western states on-road annual average daily SO2 emissions, 2002 – 2018.
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-15 

0

5000

10000

15000

20000

25000

30000

35000

40000

Annual Average Day (Tons Per Day)

California 8939 5937 4179 2933

Alaska 409 293 249 235

Arizona 3282 2312 1867 1727

Colorado 3835 2502 2206 2086

Idaho 1170 777 657 621

Montana 1616 1076 898 851

Nevada 1100 876 771 727

New Mexico 1537 1062 891 843

North Dakota 579 353 280 246

Oregon 2973 2088 1678 1535

South Dakota 606 417 350 328

Utah 2042 1462 1238 1171

Washington 4718 3160 2515 2243

Wyoming 646 416 340 308

2002 2008 2013 2018

Figure 9-15. Western states on-road annual average daily CO emissions, 2002 – 2018.
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-16 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

VOC NOx CO PM10 PM2.5 SO2 NH3

Percent of Total Emissions

Underground Mining Equipment

Recreational Equipment

Railroad Equipment

Pleasure Craft

Oil Field Equipment

Logging Equipment

Locomotive

Lawn and Garden Equipment

Industrial Equipment

Construction and Mining Equipment

Commercial Equipment

Airport Ground Support Equipment

Aircraft

Agricultural Equipment

Figure 9-16. Western states 2002 off-road emissions (% by source category, does not include 

commercial marine).

0.00

100.00

200.00

300.00

400.00

500.00

600.00

700.00

800.00

900.00

1000.00

AK AZ CO ID MT NV NM ND OR SD UT WA WY CA

Tons per Day

Underground Mining Equipment

Recreational Equipment

Railroad Equipment

Pleasure Craft

Oil Field Equipment

Logging Equipment

Locomotive

Lawn and Garden Equipment

Industrial Equipment

Construction and Mining Equipment

Commercial Equipment

Airport Ground Support Equipment

Aircraft

Agricultural Equipment

Figure 9-17. 2002 Western states average annual off-road NOx emissions by source category 

(does not include commercial marine).
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-17 

0.00

100.00

200.00

300.00

400.00

500.00

600.00

AK AZ CO ID MT NV NM ND OR SD UT WA WY CA

Tons per Day

Underground Mining Equipment

Recreational Equipment

Railroad Equipment

Pleasure Craft

Oil Field Equipment

Logging Equipment

Locomotive

Lawn and Garden Equipment

Industrial Equipment

C

Figure 9-18. 2002 Western states average annual off-road VOC emissions by source category 

(does not include commercial marine).

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

AK AZ CO ID MT NV NM ND OR SD UT WA WY CA

Tons per Day

Underground Mining Equipment

Recreational Equipment

Railroad Equipment

Pleasure Craft

Oil Field Equipment

Logging Equipment

Locomotive

Lawn and Garden Equipment

Industrial Equipment

Construction and Mining Equipment

Commercial Equipment

Airport Ground Support Equipment

Aircraft

Agricultural Equipment

Figure 9-19. 2002 Western states average annual off-road PM10 emissions by source 

category (does not include commercial marine).
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-18 

0.00

5.00

10.00

15.00

20.00

25.00

AK AZ CO ID MT NV NM ND OR SD UT WA WY CA

Tons per Day

Underground Mining Equipment

Recreational Equipment

Railroad Equipment

Pleasure Craft

Oil Field Equipment

Logging Equipment

Locomotive

Lawn and Garden Equipment

Industrial Equipment

Construction and Mining Equipment

Commercial Equipment

Airport Ground Support Equipment

Aircraft

Agricultural Equipment

Figure 9-20. 2002 Western states average annual off-road SO2 emissions by source category 

(does not include commercial marine).

0.00

500.00

1000.00

1500.00

2000.00

2500.00

3000.00

3500.00

AK AZ CO ID MT NV NM ND OR SD UT WA WY CA

Tons per Day

Underground Mining Equipment

Recreational Equipment

Railroad Equipment

Pleasure Craft

Oil Field Equipment

Logging Equipment

Locomotive

Lawn and Garden Equipment

Industrial Equipment

Construction and Mining Equipment

Commercial Equipment

Airport Ground Support Equipment

Aircraft

Agricultural Equipment

Figure 9-21. 2002 Western states average annual off-road CO emissions by source category 

(does not include commercial marine).
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-19 

Figure 9-22. Spatial distribution of all RPO off-road NO emissions, 2002 July weekday. 

Source: WRAP Regional Modeling Center 

(http://pah.cert.ucr.edu/aqm/308/QA_plan02a36.plots/) 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-20 

0

500

1000

1500

2000

2500

3000

Annual Average Day (Tons Per Day)

California 900 748 597 458

Alaska 24 23 22 21

Arizona 182 155 143 131

Colorado 171 151 132 112

Idaho 77 70 60 50

Montana 139 121 111 100

Nevada 89 76 69 61

New Mexico 124 103 97 91

North Dakota 152 142 123 103

Oregon 144 124 106 89

South Dakota 107 105 87 67

Utah 129 110 95 78

Washington 203 174 150 128

Wyoming 210 171 166 163

2002 2008 2013 2018

Figure 9-23. Western states off-road annual average daily NOx emissions, 2002 – 2018 (does 

not include commercial marine). 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-21 

0

200

400

600

800

1000

1200

1400

1600

Annual Average Day (Tons Per Day)

California 530 384 334 306

Alaska 49 52 45 36

Arizona 156 114 104 100

Colorado 105 87 76 69

Idaho 64 61 52 44

Montana 38 37 32 26

Nevada 50 39 35 32

New Mexico 38 32 29 26

North Dakota 37 34 28 24

Oregon 108 89 77 68

South Dakota 35 32 26 21

Utah 74 68 57 48

Washington 168 135 117 106

Wyoming 38 38 33 29

2002 2008 2013 2018

Figure 9-24. Western states off-road annual average daily VOC emissions, 2002 – 2018 (does 

not include commercial marine).
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-22 

0

50

100

150

200

250

Annual Average Day (Tons Per Day)

California 62.2 59.1 50.2 40.5

Alaska 4.1 3.8 3.6 3.1

Arizona 17.0 27.8 13.8 12.3

Colorado 14.4 13.0 10.5 8.0

Idaho 8.3 7.0 5.4 3.8

Montana 9.7 8.5 6.8 5.3

Nevada 6.3 5.6 4.8 3.7

New Mexico 6.9 6.2 5.3 4.5

North Dakota 14.5 11.8 8.8 6.2

Oregon 14.2 11.7 9.4 7.1

South Dakota 12.9 10.2 7.3 4.7

Utah 11.1 9.4 7.4 5.2

Washington 19.8 16.6 13.6 10.6

Wyoming 8.8 8.2 7.5 6.8

2002 2008 2013 2018

Figure 9-25. Western states off-road annual average daily PM10 emissions, 2002 – 2018 

(does not include commercial marine).
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-23 

0

50

100

150

200

250

Annual Average Day (Tons Per Day)

California 55.4 52.4 44.0 34.9

Alaska 4.0 3.6 3.4 3.0

Arizona 15.3 22.2 12.3 10.7

Colorado 13.8 12.3 9.9 7.4

Idaho 8.0 6.7 5.1 3.6

Montana 9.3 8.2 6.5 5.0

Nevada 6.0 5.3 4.5 3.5

New Mexico 6.6 5.9 5.1 4.2

North Dakota 14.0 11.4 8.5 5.9

Oregon 13.5 11.1 8.9 6.6

South Dakota 12.4 9.9 7.0 4.5

Utah 10.6 8.9 7.0 4.9

Washington 18.8 15.6 12.8 9.9

Wyoming 8.5 7.9 7.2 6.5

2002 2008 2013 2018

Figure 9-26. Western states off-road annual average daily PM2.5 emissions, 2002 – 2018 

(does not include commercial marine).
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-24 

0

20

40

60

80

100

120

140

160

180

200

Annual Average Day (Tons Per Day)

California 20.7 5.9 4.9 5.2

Alaska 1.7 1.2 0.9 0.9

Arizona 11.6 4.0 1.4 1.5

Colorado 6.8 4.3 0.9 1.0

Idaho 9.3 1.9 0.3 0.3

Montana 11.5 2.8 0.2 0.2

Nevada 3.8 1.5 0.7 0.7

New Mexico 9.7 2.3 0.2 0.2

North Dakota 18.8 3.6 0.2 0.2

Oregon 17.4 3.3 0.4 0.4

South Dakota 15.8 2.9 0.1 0.1

Utah 12.4 3.1 0.4 0.4

Washington 23.5 4.6 0.7 0.7

Wyoming 16.1 3.6 0.2 0.2

2002 2008 2013 2018

Figure 9-27. Western states off-road annual average daily SO2 emissions, 2002 – 2018 (does 

not include commercial marine).
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-25 

0

2000

4000

6000

8000

10000

12000

14000

Annual Average Day (Tons Per Day)

California 3143 2857 2783 2711

Alaska 222 251 258 256

Arizona 1495 1638 1735 1850

Colorado 1077 1188 1250 1319

Idaho 426 472 485 498

Montana 274 303 309 315

Nevada 468 516 549 583

New Mexico 341 377 400 423

North Dakota 263 286 282 281

Oregon 909 1011 1059 1115

South Dakota 254 272 265 262

Utah 563 621 641 660

Washington 1441 1584 1659 1749

Wyoming 201 221 224 225

2002 2008 2013 2018

Figure 9-28. Western states off-road annual average daily CO emissions, 2002 – 2018 (does 

not include commercial marine). 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-26 

0

1000

2000

3000

4000

5000

6000

7000

8000

Annual Average Day (Tons Per Day)

California 2492 1904 1406 1015

Alaska 69 51 40 32

Arizona 670 496 365 278

Colorado 560 419 313 236

Idaho 199 150 111 83

Montana 286 218 188 161

Nevada 202 159 128 102

New Mexico 310 231 179 145

North Dakota 220 180 146 116

Oregon 450 341 269 204

South Dakota 187 158 121 89

Utah 341 271 207 153

Washington 756 542 394 281

Wyoming 316 239 209 189

2002 2008 2013 2018

Figure 9-29. Western states total mobile annual average daily NOx emissions, 2002 – 

2018(does not include commercial marine). 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-27 

0

500

1000

1500

2000

2500

3000

3500

4000

4500

Annual Average Day (Tons Per Day)

California 1420 986 774 638

Alaska 72 67 55 44

Arizona 459 330 275 245

Colorado 382 262 210 182

Idaho 138 109 87 72

Montana 157 113 89 77

Nevada 149 114 92 79

New Mexico 144 102 80 69

North Dakota 72 53 41 33

Oregon 352 250 198 168

South Dakota 73 55 43 35

Utah 208 162 131 110

Washington 552 384 298 245

Wyoming 77 61 51 43

2002 2008 2013 2018

Figure 9-30. Western states total mobile annual average daily VOC emissions, 2002 – 

2018(does not include commercial marine). 

 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-28 

0

50

100

150

200

250

300

350

Annual Average Day (Tons Per Day)

California 104.9 104.2 96.8 88.8

Alaska 5.4 4.8 4.3 3.8

Arizona 29.6 39.2 24.0 22.5

Colorado 24.8 21.7 18.2 15.4

Idaho 11.3 9.4 7.5 5.8

Montana 13.4 11.6 9.7 8.0

Nevada 8.7 8.0 7.3 6.3

New Mexico 12.2 10.7 9.3 8.3

North Dakota 16.3 13.1 9.8 7.1

Oregon 22.4 18.9 15.8 12.8

South Dakota 15.1 12.1 8.9 6.3

Utah 16.1 13.9 11.7 9.4

Washington 34.2 28.2 23.5 19.5

Wyoming 11.4 10.2 9.1 8.2

2002 2008 2013 2018

Figure 9-31. Western states total mobile annual average daily PM10 emissions, 2002 – 

2018(does not include commercial marine). 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-29 

0

50

100

150

200

250

300

Annual Average Day (Tons Per Day)

California 84.2 82.2 73.9 65.7

Alaska 4.9 4.3 3.9 3.4

Arizona 25.2 30.6 19.2 17.5

Colorado 21.9 18.8 15.3 12.4

Idaho 10.3 8.4 6.5 4.9

Montana 12.2 10.5 8.6 6.9

Nevada 7.7 7.0 6.1 5.1

New Mexico 10.8 9.2 7.8 6.7

North Dakota 15.5 12.4 9.2 6.5

Oregon 20.0 16.5 13.4 10.4

South Dakota 14.2 11.3 8.1 5.5

Utah 14.5 12.2 9.9 7.6

Washington 30.2 24.3 19.7 15.7

Wyoming 10.7 9.5 8.4 7.5

2002 2008 2013 2018

Figure 9-32. Western states total mobile annual average daily PM2.5 emissions, 2002 – 

2018(does not include commercial marine). 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-30 

0

50

100

150

200

250

300

Annual Average Day (Tons Per Day)

California 31.7 10.3 9.7 10.4

Alaska 3.5 1.5 1.0 1.1

Arizona 19.0 5.6 3.2 3.6

Colorado 18.1 5.6 2.3 2.6

Idaho 13.7 2.4 0.7 0.8

Montana 16.4 3.2 0.6 0.7

Nevada 5.1 2.1 1.4 1.5

New Mexico 15.0 3.0 0.9 1.0

North Dakota 20.9 3.8 0.3 0.4

Oregon 26.8 4.4 1.5 1.7

South Dakota 18.2 3.1 0.4 0.4

Utah 17.2 3.9 1.3 1.4

Washington 38.7 6.3 2.4 2.6

Wyoming 18.7 3.8 0.4 0.4

2002 2008 2013 2018

Figure 9-33. Western states total mobile annual average daily SO2 emissions, 2002 – 

2018(does not include commercial marine). 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-31 

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

Annual Average Day (Tons Per Day)

California 12082 8794 6962 5645

Alaska 631 544 507 491

Arizona 4777 3950 3602 3576

Colorado 4912 3690 3456 3405

Idaho 1596 1249 1143 1118

Montana 1890 1379 1207 1167

Nevada 1569 1391 1321 1310

New Mexico 1878 1440 1290 1265

North Dakota 842 639 562 528

Oregon 3882 3099 2737 2650

South Dakota 860 689 615 590

Utah 2605 2083 1879 1831

Washington 6159 4744 4174 3993

Wyoming 847 637 564 533

2002 2008 2013 2018

Figure 9-34. Western states total mobile annual average daily CO emissions, 2002 – 

2018(does not include commercial marine). 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc 9-32 

Figure 9-35. Spatial distribution of all RPO road dust coarse PM emissions, 2002 July 

weekday. Source: WRAP Regional Modeling Center 

(http://pah.cert.ucr.edu/aqm/308/QA_plan02a36.plots/)
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc Table 9-1. 2002 Western states total on-road emissions by fuel type. 

2002 Western States Total On-road Emissions by Fuel Type (tons)

Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline 

ALASKA 370 8,296 7,276 8,921 1,764 147,640 302 143 ARIZONA 4,179 106,235 89,803 88,171 18,452 1,179,480 3,079 1,529 CALIFORNIA 9,764 315,178 248,866 332,215 44,984 3,217,797 5,634 9,960 COLORADO 6,561 94,271 58,228 83,597 39,564 1,360,285 2,358 1,407 IDAHO 703 26,278 17,811 26,779 3,575 423,498 623 460 MONTANA 1,252 42,212 20,362 33,236 5,304 584,575 842 497 NEVADA 563 35,704 8,997 32,084 2,640 398,956 336 513 NEW MEXICO 3,780 34,978 32,897 34,924 22,446 538,555 1,278 651 NORTH DAKOTA 572 12,226 11,430 13,299 2,671 208,662 460 225 OREGON 3,115 85,635 40,671 70,928 14,236 1,071,042 1,816 1,179 SOUTH DAKOTA 692 13,037 14,562 14,644 3,209 218,051 581 255 UTAH 2,079 46,982 34,783 42,570 13,140 732,333 1,074 756 WASHINGTON 5,070 135,115 83,391 118,596 24,923 1,697,021 3,288 1,954 WYOMING 1,233 13,022 26,616 11,938 8,043 227,843 793 178 Total 39,936 969,167 695,691 911,901 204,950 12,005,736 22,465 19,707 * PM emissions include exhaust, brake wear, and tire wear emissions.

2002 Western States Total On-road Emissions All Fuel Types (tons)

VOC NOx CO PM10 * PM2.5 * SO2 NH3 VMT

Total Total Total Total Total Total Total Total

ALASKA 8,666 16,197 149,404 445 355 655 492 4,896,832,612ARIZONA 110,415 177,974 1,197,931 4,609 3,605 2,715 5,034 52,505,231,448CALIFORNIA 324,942 581,081 3,262,781 15,594 10,519 4,034 22,118COLORADO 100,832 141,825 1,399,848 3,765 2,971 4,149 4,317 43,538,870,500IDAHO 26,981 44,590 427,072 1,084 845 1,590 1,430 14,166,793,207MONTANA 43,464 53,598 589,879 1,339 1,068 1,770 1,294 13,572,665,487NEVADA 36,267 41,081 401,596 849 604 455 2,030 19,371,235,904NEW MEXICO 38,759 67,820 561,001 1,928 1,525 1,951 2,132 21,332,076,809NORTH DAKOTA 12,798 24,728 211,333 685 544 771 732 7,335,902,003OREGON 88,750 111,599 1,085,278 2,996 2,378 3,448 3,263 33,246,265,505SOUTH DAKOTA 13,729 29,206 221,260 836 666 873 842 8,498,126,602UTAH 49,061 77,352 745,472 1,830 1,416 1,777 2,453 24,421,066,630WASHINGTON 140,185 201,987 1,721,944 5,242 4,162 5,539 5,213 54,461,603,359WYOMING 14,255 38,554 235,886 971 800 960 538 6,093,734,942Total 1,009,103 1,607,593 12,210,686 42,172 31,460 30,688 51,889 303,440,405,008* PM emissions include exhaust, brake wear, and tire wear emissions.

PM10 * 

State VOC NOx CO
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc Table 9-2. 2008 Western states total on-road emissions by fuel type. 

2008 Western States Total On-road Emissions by Fuel Type (tons)

Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel G

ALASKA 5,166 257 4,232 5,943 1,250 105,605 203 

 

ARIZONA 75,689 3,268 59,720 64,533 15,312 828,703 2,491 

 

CALIFORNIA 8,745 210,877 206,245 215,687 40,782 2,126,227 4,682 

 

COLORADO 58,548 5,170 42,164 55,900 32,755 880,320 1,860 

 

IDAHO 16,719 503 11,099 17,889 2,629 281,114 447 

 

MONTANA 26,645 886 13,013 22,509 4,376 388,377 726 

 

NEVADA 26,806 472 6,302 24,040 2,327 317,321 294 

 

NEW MEXICO 22,495 2,857 22,034 24,833 17,865 369,890 1,008 

 

NORTH DAKOTA 6,663 365 6,151 7,876 1,747 127,184 297 

 

OREGON 56,445 2,177 28,654 50,701 12,387 749,639 1,508 

 

SOUTH DAKOTA 8,058 532 9,415 9,970 2,527 149,743 453 

 

UTAH 32,933 1,641 25,094 33,641 11,045 522,705 885 

 

WASHINGTON 86,753 3,858 55,919 78,210 19,398 1,134,060 2,469 

 

WYOMING 7,748 860 16,468 8,395 5,907 145,777 568 

 

Total 31,592 641,547 506,512 620,128 170,306 8,126,665 17,891 

 

* PM emissions include exhaust, brake wear, and tire wear emissions.

2008 Western States Total On-road Emissions All Fuel Types (tons)

VOC NOx CO PM10 * PM2.5 * SO2 NH3 

Total Total Total Total Total Total Total 

ALASKA 10,175 5,423 106,855 336 249 114 537 5

ARIZONA 124,253 78,957 844,015 4,154 3,062 608 6,177 62

CALIFORNIA 421,933 219,622 2,167,009 16,435 10,895 1,606 24,831

COLORADO 98,065 63,718 913,074 3,181 2,355 469 5,003 49

IDAHO 28,989 17,223 283,743 866 625 150 1,630 15

MONTANA 35,522 27,531 392,753 1,142 860 149 1,538 15

NEVADA 30,342 27,278 319,648 892 601 220 2,617 24NEW MEXICO 46,867 25,353 387,755 1,644 1,219 234 2,449 24

NORTH DAKOTA 14,027 7,029 128,931 481 356 68 738 7

OREGON 79,355 58,622 762,026 2,613 1,962 379 3,948 38

SOUTH DAKOTA 19,385 8,590 152,270 695 520 91 938 9

UTAH 58,735 34,575 533,749 1,672 1,214 285 3,037 29

WASHINGTON 134,129 90,611 1,153,458 4,234 3,154 596 6,181 61

WYOMING 24,863 8,608 151,683 730 565 69 619 6

Total 673,139 1,126,640 8,296,971 39,074 27,638 5,039 60,242 349

* PM emissions include exhaust, brake wear, and tire wear emissions.

State

PM10 * 

State VOC NOx CO
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc Table 9-3. 2013 Western states total on-road emissions by fuel type.

2013 Western States Total On-road Emissions by Fuel Type (tons)

Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline 

ALASKA 3,600 201 2,334 4,037 591 90,382 145 135 

ARIZONA 59,634 2,682 32,596 48,485 7,557 673,758 1,946 1,755 

CALIFORNIA 7,058 153,343 143,638 151,732 35,236 1,489,946 3,809 13,187 

COLORADO 44,518 4,267 25,155 41,079 17,538 787,674 1,448 1,372 

IDAHO 12,180 399 6,241 12,439 1,271 238,713 331 436 

MONTANA 20,323 760 9,056 19,279 2,072 325,638 631 439 

NEVADA 20,501 416 3,601 17,930 1,154 280,400 239 680 

NEW MEXICO 16,292 2,354 12,915 17,318 8,919 316,151 779 664 

NORTH DAKOTA 4,350 267 3,166 5,028 773 101,383 202 175 

OREGON 42,457 1,686 19,881 39,643 5,603 606,931 1,183 1,135 

SOUTH DAKOTA 5,725 441 5,488 6,894 1,262 126,497 349 249 

UTAH 25,514 1,357 15,001 25,829 5,628 446,411 690 850 

WASHINGTON 63,243 2,964 32,678 56,248 9,817 908,188 1,834 1,762 

WYOMING 5,867 679 9,221 6,436 2,836 121,170 421 164 

Total 25,530 477,548 320,970 452,378 100,257 6,513,242 14,007 23,005 

* PM emissions include exhaust, brake wear, and tire wear emissions.

2013 Western States Total On-road Emissions All Fuel Types (tons)

VOC NOx CO PM10 * PM2.5 * SO2 NH3 VMT

Total Total Total Total Total Total Total Total

ALASKA 6,372 3,801 90,973 280 193 52 574 5,612,631,896

ARIZONA 81,082 62,316 681,315 3,701 2,553 681 6,893 69,196,740,935

CALIFORNIA 295,370 160,400 1,525,183 16,996 10,938 1,743 26,879

COLORADO 66,234 48,785 805,213 2,820 1,965 515 5,437 53,470,363,618

IDAHO 18,680 12,579 239,984 767 521 163 1,783 17,283,487,713

MONTANA 28,336 21,083 327,709 1,070 769 170 1,748 17,388,826,117

NEVADA 21,530 20,917 281,554 919 589 259 3,084 28,652,313,876

NEW MEXICO 30,233 18,646 325,070 1,443 1,006 257 2,662 26,409,377,126

NORTH DAKOTA 8,194 4,617 102,155 377 261 68 740 7,284,197,119

OREGON 59,523 44,143 612,534 2,318 1,655 417 4,331 42,084,271,224

SOUTH DAKOTA 12,382 6,167 127,759 599 421 99 1,004 10,101,861,956

UTAH 40,830 26,870 452,040 1,540 1,053 327 3,433 33,127,162,995

WASHINGTON 88,926 66,208 918,005 3,595 2,526 637 6,651 65,788,792,672

WYOMING 15,657 6,546 124,007 586 424 75 675 7,300,217,365

Total 503,078 773,348 6,613,499 37,012 24,874 5,466 65,897 383,700,244,613

* PM emissions include exhaust, brake wear, and tire wear emissions.

State

PM10 * 

State VOC NOx CO
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc Table 9-4. 2018 Western states total on-road emissions by fuel type.

2018 Western States Total On-road Emissions by Fuel Type (tons)

Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline 

ALASKA 2,891 178 1,198 2,890 346 85,496 117 142 

ARIZONA 50,281 2,590 16,668 36,821 4,843 625,333 1,791 1,926 

CALIFORNIA 5,768 115,406 96,453 106,607 31,804 1,038,918 3,182 14,455 

COLORADO 37,432 4,066 13,540 31,686 10,551 751,021 1,235 1,486 

IDAHO 9,980 361 3,247 9,073 761 225,768 270 465 

MONTANA 17,801 744 5,984 16,051 1,529 309,209 502 494 

NEVADA 16,691 400 1,942 13,102 752 264,664 209 743 

NEW MEXICO 13,317 2,235 7,016 12,722 5,471 302,048 665 713 

NORTH DAKOTA 3,264 221 1,511 3,390 422 89,481 152 172 

OREGON 34,883 1,508 12,588 29,534 3,682 556,632 840 1,228 

SOUTH DAKOTA 4,683 415 2,975 5,078 778 118,965 299 266 

UTAH 21,400 1,293 8,239 19,111 3,489 424,100 587 936 

WASHINGTON 48,145 2,512 16,499 39,408 5,563 813,247 1,396 1,838 

WYOMING 4,673 618 4,797 4,934 1,673 110,847 344 173 

Total 380,848 22,910 192,659 330,408 71,666 5,715,729 11,590 25,038 

* PM emissions include exhaust, brake wear, and tire wear emissions.

2018 Western States Total On-road Emissions All Fuel Types (tons)

VOC NOx CO PM10 * PM2.5 * SO2 NH3 VMT

Total Total Total Total Total Total Total Total

ALASKA 4,088 3,069 85,842 259 170 56 612 5,984,083,743

ARIZONA 53,489 52,871 630,176 3,717 2,460 762 7,604 76,683,204,547

CALIFORNIA 203,060 121,175 1,070,723 17,637 11,248 1,897 29,562

COLORADO 45,226 41,498 761,572 2,721 1,804 568 5,894 58,265,177,164

IDAHO 12,320 10,340 226,529 735 476 177 1,930 18,700,167,033

MONTANA 22,035 18,545 310,738 996 667 195 1,976 19,775,017,157

NEVADA 15,044 17,091 265,416 953 593 286 3,384 31,477,060,492

NEW MEXICO 19,739 15,553 307,520 1,379 914 281 2,877 28,668,617,848

NORTH DAKOTA 4,902 3,485 89,902 324 213 68 739 7,265,376,615

OREGON 42,122 36,391 560,314 2,068 1,376 461 4,725 46,054,049,449

SOUTH DAKOTA 8,053 5,098 119,742 565 377 108 1,075 10,879,625,452

UTAH 27,350 22,693 427,589 1,523 994 368 3,811 36,801,812,054

WASHINGTON 55,908 50,657 818,811 3,234 2,144 679 7,088 70,092,205,086

WYOMING 9,731 5,290 112,520 517 352 81 725 7,848,618,467

Total 523,067 403,757 5,787,395 36,628 23,789 5,988 72,001 418,495,015,107

* PM emissions include exhaust, brake wear, and tire wear emissions.

State

PM10 * 

State VOC NOx CO
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc Table 9-5. 2002 Western states total nonroad annual emissions by fuel type.

2002 Western States Total Nonroad Annual Emissions by Fuel Type (tons)

State Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline ALASKA 1,574 16,161 7,991 885 11,363 69,530 935 578 918 525 561 50 

ARIZONA 6,310 50,719 58,602 7,865 25,522 520,067 4,354 1,840 3,945 1,655 4,158 68 CALIFORNIA 168,437 25,014 123,578 204,742 1,011,861 135,241 8,824 13,887 7,458 12,763 7,056 484 

COLORADO 5,471 32,968 55,448 7,134 26,819 366,178 4,184 1,089 4,043 977 2,344 128 IDAHO 2,691 20,790 25,254 2,697 14,391 141,046 2,421 625 2,340 574 3,347 64 MONTANA 3,842 10,171 49,136 1,559 15,848 84,008 3,203 335 3,100 300 4,151 56 

NEVADA 2,642 15,506 30,462 2,144 14,503 156,422 1,775 533 1,715 485 1,389 15 NEW MEXICO 2,942 10,931 43,394 1,944 11,878 112,408 2,139 390 2,066 347 3,493 35 NORTH DAKOTA 5,122 8,400 54,195 1,465 22,661 73,408 5,006 273 4,853 243 6,814 38 OREGON 4,543 35,008 45,470 7,112 21,519 310,228 3,894 1,277 3,767 1,156 6,201 137 SOUTH DAKOTA 4,318 8,461 37,218 1,969 20,584 72,127 4,409 285 4,273 254 5,722 35 UTAH 4,133 22,811 43,498 3,699 19,535 185,898 3,294 763 3,187 693 4,463 5WASHINGTON 6,181 55,235 63,364 10,740 29,738 496,225 5,218 2,025 5,043 1,828 8,394 190 WYOMING 4,091 9,661 75,752 923 13,125 60,150 2,931 276 2,839 250 5,843 27 TOTAL 222,297 321,836 713,362 254,879 1,259,348 2,782,936 52,588 24,176 49,548 22,050 63,936 1,378 *Does not include Commercial Marine

2002 Western States Total Nonroad Emissions All Fuel Types (tons)

VOC NOx CO PM10 PM2.5 SO2 NH3

State Total Total Total Total Total Total TotalALASKA 17,736 8,876 80,893 1,513 1,443 611 12

ARIZONA 57,030 66,467 545,589 6,194 5,600 4,226 48CALIFORNIA 193,451 328,320 1,147,102 22,710 20,222 7,540 546

COLORADO 38,438 62,581 392,997 5,274 5,021 2,472 43IDAHO 23,480 27,951 155,437 3,047 2,914 3,411 17MONTANA 14,013 50,696 99,856 3,538 3,400 4,206 29

NEVADA 18,147 32,606 170,925 2,309 2,199 1,403 22NEW MEXICO 13,873 45,338 124,286 2,529 2,413 3,528 26NORTH DAKOTA 13,521 55,659 96,069 5,279 5,096 6,852 33OREGON 39,552 52,583 331,747 5,171 4,923 6,338 38SOUTH DAKOTA 12,779 39,187 92,711 4,693 4,527 5,756 25UTAH 26,944 47,197 205,433 4,057 3,880 4,514 32WASHINGTON 61,415 74,103 525,963 7,243 6,871 8,584 55WYOMING 13,752 76,675 73,275 3,207 3,089 5,871 41TOTAL 544,132 968,241 4,042,284 76,763 71,598 65,314 967*Does not include Commercial Marine

VOC NOX CO PM10 PM2.5 SO2 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc Table 9-6. 2008 Western states total nonroad annual emissions by fuel type.

2008 Western States Total Nonroad Annual Emissions by Fuel Type (tons)

State Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline ALASKA 1,401 17,658 7,470 851 8,230 83,558 742 659 729 600 392 30 

ARIZONA 5,927 35,599 50,270 6,436 25,088 572,187 3,905 1,634 3,492 1,465 1,417 34 CALIFORNIA 120,357 19,872 106,636 166,355 904,004 138,889 10,241 11,345 8,669 10,445 1,824 317 

COLORADO 4,812 27,041 49,947 5,008 26,051 407,694 3,596 1,152 3,468 1,036 1,539 25 

IDAHO 2,270 20,117 23,637 2,016 13,376 158,923 1,881 681 1,815 625 695 13 MONTANA 3,455 10,110 42,810 1,194 15,322 95,186 2,736 370 2,646 332 1,017 7 

NEVADA 2,331 11,974 26,193 1,658 14,054 174,131 1,505 540 1,452 491 529 8 NEW MEXICO 2,684 9,175 36,051 1,426 11,533 126,146 1,849 403 1,785 359 841 8 NORTH DAKOTA 4,473 7,953 50,456 1,246 20,920 83,358 4,028 290 3,903 259 1,298 6 OREGON 3,759 28,850 40,072 5,138 19,646 349,531 2,961 1,320 2,861 1,194 1,194 23 SOUTH DAKOTA 3,641 7,895 36,650 1,557 18,771 80,477 3,438 296 3,330 265 1,034 7 UTAH 3,485 21,237 37,574 2,652 17,923 208,561 2,584 834 2,495 756 1,118 14 WASHINGTON 5,242 44,147 55,782 7,852 27,841 550,263 4,025 2,016 3,882 1,820 1,658 37 WYOMING 3,936 9,762 61,651 717 13,596 67,122 2,697 303 2,612 275 1,303 5 TOTAL 167,774 271,392 625,197 204,104 1,136,356 3,096,025 46,189 21,846 43,140 19,922 15,860 533 *Does not include Commercial Marine

2008 Western States Total Nonroad Emissions All Fuel Types (tons)

VOC NOx CO PM10 PM2.5 SO2 NH3

State Total Total Total Total Total Total TotalALASKA 19,059 8,321 91,788 1,401 1,329 422 14

ARIZONA 41,527 56,705 597,275 5,539 4,957 1,451 53CALIFORNIA 140,229 272,992 1,042,893 21,586 19,114 2,141 914

COLORADO 31,854 54,955 433,744 4,748 4,503 1,564 50IDAHO 22,387 25,653 172,300 2,562 2,440 709 21MONTANA 13,565 44,005 110,509 3,107 2,978 1,024 35

NEVADA 14,305 27,850 188,185 2,045 1,943 537 25NEW MEXICO 11,859 37,476 137,679 2,252 2,145 849 30NORTH DAKOTA 12,426 51,702 104,278 4,318 4,162 1,304 40OREGON 32,610 45,209 369,177 4,282 4,055 1,217 44SOUTH DAKOTA 11,537 38,206 99,248 3,734 3,595 1,040 31UTAH 24,722 40,226 226,484 3,418 3,252 1,132 37WASHINGTON 49,390 63,635 578,104 6,042 5,702 1,695 62WYOMING 13,698 62,367 80,718 3,000 2,887 1,308 47TOTAL 439,166 829,301 4,232,382 68,035 63,062 16,394 1,404*Does not include Commercial Marine

VOC NOX CO PM10 PM2.5 SO2 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc Table 9-7. 2013 Western states total nonroad annual emissions by fuel type. 

2013 Western States Total Nonroad Annual Emissions by Fuel Type (tons)

State Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline ALASKA 1,405 15,005 7,176 907 8,454 85,651 681 615 670 559 286 32 

ARIZONA 5,684 32,218 46,834 5,325 24,087 609,196 3,408 1,641 3,005 1,468 459 37 CALIFORNIA 107,045 15,001 100,839 117,095 891,596 124,223 10,597 7,728 8,950 7,101 1,578 204 

COLORADO 4,029 23,750 44,179 3,835 23,182 432,956 2,703 1,135 2,600 1,019 310 27 IDAHO 1,776 17,372 20,360 1,628 11,281 165,832 1,327 636 1,277 584 83 15 MONTANA 2,952 8,576 39,325 1,018 13,386 99,541 2,147 342 2,073 306 50 7 

NEVADA 2,059 10,639 23,794 1,417 13,474 186,992 1,211 527 1,166 479 228 9 NEW MEXICO 2,436 7,973 34,110 1,144 10,886 134,972 1,556 387 1,501 344 71 9 NORTH DAKOTA 3,535 6,810 43,830 1,086 16,666 86,443 2,937 276 2,845 246 51 7 OREGON 3,035 24,985 34,956 3,843 16,823 369,721 2,158 1,286 2,081 1,161 115 25 SOUTH DAKOTA 2,708 6,700 30,476 1,232 14,458 82,287 2,374 279 2,298 249 40 7 UTAH 2,894 18,058 32,691 1,952 15,694 218,232 1,938 778 1,867 705 122 15 WASHINGTON 4,317 38,333 49,008 5,905 24,375 581,200 2,995 1,973 2,882 1,778 202 39 WYOMING 3,733 8,482 59,895 691 13,401 68,396 2,441 290 2,363 262 54 6 TOTAL 147,608 233,904 567,474 147,080 1,097,763 3,245,643 38,474 17,895 35,579 16,262 3,648 437 *Does not include Commerical Marine.

2013 Western States Total Nonroad Emissions All Fuel Types (tons)

VOC NOx CO PM10 PM2.5 SO2 NH3

State Total Total Total Total Total Total TotalALASKA 16,410 8,083 94,105 1,296 1,229 318 15

ARIZONA 37,901 52,159 633,282 5,049 4,473 495 58CALIFORNIA 122,046 217,934 1,015,819 18,326 16,051 1,782 776

COLORADO 27,779 48,014 456,139 3,838 3,620 337 55IDAHO 19,148 21,988 177,113 1,963 1,861 97 23MONTANA 11,528 40,344 112,927 2,488 2,379 57 38

NEVADA 12,698 25,211 200,466 1,738 1,645 237 27NEW MEXICO 10,410 35,255 145,858 1,944 1,845 80 33NORTH DAKOTA 10,345 44,917 103,108 3,213 3,091 58 43OREGON 28,020 38,799 386,543 3,445 3,242 140 48SOUTH DAKOTA 9,409 31,708 96,745 2,653 2,547 47 33

UTAH 20,952 34,643 233,926 2,716 2,572 137 41WASHINGTON 42,650 54,912 605,575 4,969 4,660 241 67WYOMING 12,215 60,586 81,797 2,730 2,626 60 52TOTAL 381,512 714,554 4,343,405 56,369 51,840 4,085 1,309*Does not include Commerical Marine.

VOC NOX CO PM10 PM2.5 SO2 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc Table 9-8. 2018 Western states total nonroad annual emissions by fuel type. 

2018 Western States Total Nonroad Annual Emissions by Fuel Type (tons)

State Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline Diesel Gasoline ALASKA 1,431 11,551 6,713 1,051 8,502 84,994 604 537 596 487 314 33 

ARIZONA 5,593 31,010 42,509 5,349 22,072 653,150 2,831 1,647 2,440 1,471 507 39 CALIFORNIA 100,935 10,713 101,379 65,933 887,988 101,551 11,086 3,692 9,340 3,407 1,682 207 

COLORADO 3,526 21,481 37,048 3,828 19,532 461,760 1,798 1,104 1,721 990 342 29 IDAHO 1,472 14,533 16,485 1,659 9,166 172,469 823 572 788 525 86 16 MONTANA 2,655 6,986 35,549 1,003 11,573 103,474 1,629 302 1,571 269 53 8 

NEVADA 1,873 9,943 20,766 1,438 12,265 200,527 839 517 805 470 252 10 NEW MEXICO 2,288 7,188 32,055 1,136 9,983 144,249 1,266 371 1,219 329 79 9 NORTH DAKOTA 2,975 5,687 36,357 1,075 13,009 89,692 2,008 256 1,945 226 55 7 OREGON 2,575 22,411 28,690 3,795 13,220 393,671 1,340 1,255 1,285 1,130 125 27 SOUTH DAKOTA 2,145 5,610 23,299 1,175 10,633 84,888 1,461 257 1,413 229 43 7 UTAH 2,485 15,039 26,544 1,918 12,430 228,368 1,212 704 1,162 636 136 16 WASHINGTON 3,725 34,929 40,747 5,872 19,796 618,715 1,934 1,947 1,851 1,753 220 42 WYOMING 3,631 6,924 58,642 758 13,174 68,923 2,217 261 2,146 236 58 6 TOTAL 137,308 204,005 506,781 95,992 1,063,345 3,406,430 31,050 13,424 28,282 12,158 3,953 457 *Does not include Commerical Marine.

2018 Western States Total Nonroad Emissions All Fuel Types (tons)

VOC NOx CO PM10 PM2.5 SO2 NH3

State Total Total Total Total Total Total TotalALASKA 12,982 7,765 93,496 1,141 1,084 347 16

ARIZONA 36,603 47,858 675,222 4,478 3,911 546 64CALIFORNIA 111,648 167,312 989,539 14,778 12,747 1,889 832

COLORADO 25,007 40,877 481,292 2,902 2,711 371 60IDAHO 16,006 18,144 181,634 1,395 1,313 102 24MONTANA 9,641 36,552 115,047 1,931 1,840 61 41

NEVADA 11,816 22,203 212,792 1,356 1,274 261 30

NEW MEXICO 9,475 33,191 154,232 1,637 1,548 89 36NORTH DAKOTA 8,662 37,432 102,700 2,264 2,171 62 47

OREGON 24,986 32,485 406,891 2,594 2,416 152 51SOUTH DAKOTA 7,755 24,474 95,522 1,718 1,641 50 36UTAH 17,524 28,461 240,798 1,916 1,798 153 45WASHINGTON 38,654 46,619 638,511 3,882 3,604 263 73WYOMING 10,555 59,400 82,097 2,479 2,383 65 57TOTAL 341,313 602,773 4,469,775 44,474 40,440 4,411 1,410*Does not include Commerical Marine.

VOC NOX CO PM10 PM2.5 SO2 
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc Table 9-9. West coast 2002 NOx and SO2 emissions. 

Summary of 2002 West Coast NOx Emissions (tons per year)

Total Near-Port - Zone 3 Inshore - Zone 4

California 243,480 131,930 111,550 NA NA 581,081 240,006 

Oregon 43,265 41,113 2,152 736 1,415 111,599 36,491 

Washington 58,922 44,692 14,231 3,467 10,764 201,987 49,870 

Total 345,667 217,734 127,933 4,204 12,179 894,667 326,367 

Summary of 2002 West Coast SO2 Emissions (tons per year)

Total Near-Port - Zone 3 Inshore - Zone 4

California 120,240 74,181 46,059 NA NA 4,034 799 

Oregon 23,863 23,119 744 532 212 3,448 5,232 

Washington 32,964 25,130 7,835 2,483 5,352 5,539 6,909 

Total 177,068 122,430 54,637 3,015 5,564 13,022 12,941 

Commercial Marine

State ONROAD

State Total Offshore - Zone 1 ONROAD

Coastal/In-Shore - Zones 2,3,4Coastal/In-Shore - Zones 2,3,4

Offshore - Zone 1

Total NONROAD 

NONROAD 

Commercial Marine

Table 9-10. West coast 2002 NOx and SO2 emissions. 

Summary of 2018 West Coast NOx Emissions (tons per year)

Total Near-Port - Zone 3 Inshore - Zone 4

California 478,468 282,351 196,117 NA NA 203,060 90,952 

 

Oregon 92,565 87,961 4,604 1,576 3,028 42,122 19,315 

 

Washington 126,090 95,641 30,449 7,419 23,030 55,908 26,683 

 

Total 697,123 465,953 231,170 8,995 26,058 301,090 136,950 

 

Summary of 2018 West Coast SO2 Emissions (tons per year)

Total Near-Port - Zone 3 Inshore - Zone 4

California 272,278 178,200 94,079 NA NA 1,897 601 

 

Oregon 57,304 55,517 1,787 1,277 510 461 54 

 

Washington 79,178 60,363 18,815 5,963 12,852 679 77 

 

Total 408,760 294,080 114,681 7,240 13,362 3,037 732 

 

ONROAD NONROAD AirONROAD NONROAD Air

Total Offshore - Zone 1

Coastal/In-Shore - Zones 2,3,4

State

Commercial Marine

Total Offshore - Zone 1

Coastal/In-Shore - Zones 2,3,4

State

Commercial Marine
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc Table 9-11. 2002 Paved road dust PM emissions. 

2002 Paved Road Dust Emissions - PM10State Annual (tpy) Winter (tpd) Spring (tpd) Summer (tpd) Autumn (tpd)ARIZONA 9,557 28.36 28.29 23.42 24.17COLORADO 9,858 29.65 28.31 23.67 26.37IDAHO 5,511 17.03 17.88 12.64 13.06MONTANA 3,860 12.01 11.59 8.87 9.91NEVADA 3,459 10.39 10.65 8.30 8.54NEW MEXICO 6,232 18.89 18.29 15.02 15.90NORTH DAKOTA 8.83 8.58 6.80 7.30 2,875

OREGON 10,343 31.42 34.97 23.76 24.04SOUTH DAKOTA 8.07 7.69 6.17 6.71 2,624UTAH 5,109 15.55 15.64 12.00 12.69WASHINGTON 14,026 41.97 43.77 34.23 33.76WYOMING 1,692 5.18 5.13 3.92 4.39Total 75,146 227 231 179 1872002 Paved Road Dust Emissions - PM2.5State Annual (tpy) Winter (tpd) Spring (tpd) Summer (tpd) Autumn (tpd)ARIZONA 2,389 7.09 7.08 5.86 6.05COLORADO 2,465 7.42 7.09 5.93 6.60IDAHO 1,378 4.27 4.48 3.17 3.28MONTANA 965 3.01 2.91 2.23 2.49NEVADA 865 2.60 2.66 2.08 2.14NEW MEXICO 1,558 4.73 4.58 3.76 3.98NORTH DAKOTA 2.22 2.15 1.71 1.83 719

OREGON 2,586 7.87 8.75 5.95 6.02SOUTH DAKOTA 2.03 1.93 1.55 1.69 656UTAH 1,277 3.89 3.91 3.00 3.18WASHINGTON 3,507 10.53 10.98 8.59 8.47WYOMING 423 1.30 1.29 0.98 1.10Total 18,787 57 58 45 47
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc Table 9-12. 2002 Unpaved road dust PM emissions. 

2002 Unpaved Road Dust Emissions - PM10State Annual (tpy) Winter (tpd) Spring (tpd) Summer (tpd) Autumn (tpd)ARIZONA 33,824 91.98 94.02 90.49 94.17

COLORADO 13,542 39.53 35.60 36.03 37.31

IDAHO 45,922 127.26 114.44 132.49 129.05

MONTANA 604,746 1,710.81 1,564.91 1,619.02 1,730.42

NEVADA 10,476 28.37 26.96 30.25 29.23

NEW MEXICO 14,823 42.68 40.48 37.40 41.88

NORTH DAKOTA 116.56 40,447 106.80 102.82 117.30

OREGON 95,013 233.50 235.25 305.92 266.21

SOUTH DAKOTA 168.24 57,021 155.54 137.71 163.70

UTAH 2,162 5.72 5.51 6.34 6.13

WASHINGTON 57,718 128.97 147.05 199.00 160.10

WYOMING 400 0.98 0.99 1.31 1.09

Total 976,094 2,695 2,528 2,699 2,777

2002 Unpaved Road Dust Emissions - PM2.5State Annual (tpy) Winter (tpd) Spring (tpd) Summer (tpd) Autumn (tpd)ARIZONA 5,079 13.81 14.12 13.59 14.14

COLORADO 2,031 5.93 5.34 5.40 5.60

IDAHO 6,888 19.09 17.17 19.87 19.36

MONTANA 90,712 256.62 234.74 242.85 259.56

NEVADA 1,695 4.59 4.36 4.89 4.73

NEW MEXICO 2,223 6.40 6.07 5.61 6.28

NORTH DAKOTA 17.48 6,067 16.02 15.42 17.59

OREGON 14,252 35.02 35.29 45.89 39.93

SOUTH DAKOTA 25.24 8,553 23.33 20.66 24.55

UTAH 324 0.86 0.83 0.95 0.92

WASHINGTON 8,658 19.35 22.06 29.85 24.02

WYOMING 60 0.15 0.15 0.20 0.16

Total 146,543 405 379 405 417
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc Table 9-13. 2018 Paved road dust PM emissions. 

2018 Paved Road Dust Emissions - PM10State Annual (tpy) Winter (tpd) Spring (tpd) Summer (tpd) Autumn (tpd)ARIZONA 13,779 40.83 40.74 33.81 34.90

COLORADO 13,225 39.79 38.00 31.74 35.37

IDAHO 7,274 22.48 23.60 16.68 17.24

MONTANA 5,788 18.06 17.44 13.25 14.82

NEVADA 5,200 15.56 15.91 12.57 12.90

NEW MEXICO 8,367 25.35 24.54 20.17 21.35

NORTH DAKOTA 2,842 8.71 8.46 6.74 7.23

OREGON 14,325 43.52 48.43 32.91 33.30

SOUTH DAKOTA 3,362 10.34 9.86 7.90 8.60

UTAH 7,788 23.74 23.87 18.25 19.32

WASHINGTON 18,059 54.04 56.36 44.08 43.46

WYOMING 2,180 6.67 6.61 5.05 5.65

Total 102,190 309 314 243 254

2018 Paved Road Dust Emissions - PM2.5State Annual (tpy) Winter (tpd) Spring (tpd) Summer (tpd) Autumn (tpd)ARIZONA 3,445 10.21 10.19 8.46 8.73

COLORADO 3,306 9.96 9.51 7.95 8.85

IDAHO 1,819 5.64 5.91 4.18 4.33

MONTANA 1,447 4.53 4.37 3.33 3.72

NEVADA 1,300 3.89 3.98 3.14 3.23

NEW MEXICO 2,092 6.35 6.15 5.05 5.35

NORTH DAKOTA 711 2.18 2.12 1.69 1.82

OREGON 3,581 10.89 12.12 8.24 8.34

SOUTH DAKOTA 841 2.61 2.48 1.99 2.16

UTAH 1,947 5.94 5.97 4.57 4.84

WASHINGTON 4,515 13.55 14.14 11.06 10.91

WYOMING 545 1.67 1.66 1.27 1.42

Total 25,548 77 79 61 64
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc Table 9-14. 2018 Unpaved road dust PM emissions. 

2018 Unpaved Road Dust Emissions - PM10State Annual (tpy) Winter (tpd) Spring (tpd) Summer (tpd) Autumn (tpd)ARIZONA 47,891 130.26 133.20 128.06 133.29

COLORADO 17,865 52.15 46.96 47.53 49.22

IDAHO 60,617 165.68 152.30 176.33 169.88

MONTANA 740,137 2,085.07 1,913.79 1,991.98 2,116.51

NEVADA 15,795 42.77 40.65 45.61 44.07

NEW MEXICO 19,911 58.07 53.97 49.85 56.40

NORTH DAKOTA 38,643 111.36 102.04 98.23 112.07

OREGON 131,662 323.56 326.00 423.92 368.90

SOUTH DAKOTA 72,851 214.94 198.72 175.94 209.14

UTAH 2,089 5.52 5.33 6.12 5.92

WASHINGTON 57,718 128.96 147.03 198.98 160.09

WYOMING 513 1.27 1.26 1.67 1.41

Total 1,205,692 3,320 3,121 3,344 3,427

2018 Unpaved Road Dust Emissions - PM2.5State Annual (tpy) Winter (tpd) Spring (tpd) Summer (tpd) Autumn (tpd)ARIZONA 7,189 19.55 19.99 19.22 20.01

COLORADO 2,680 7.82 7.04 7.13 7.38

IDAHO 9,093 24.85 22.84 26.45 25.48

MONTANA 111,020 312.76 287.07 298.80 317.48

NEVADA 2,593 7.02 6.67 7.49 7.23

NEW MEXICO 2,987 8.71 8.10 7.48 8.46

NORTH DAKOTA 5,796 16.70 15.31 14.73 16.81

OREGON 19,749 48.53 48.90 63.59 55.34

SOUTH DAKOTA 10,928 32.24 29.81 26.39 31.37

UTAH 313 0.83 0.80 0.92 0.89

WASHINGTON 8,658 19.34 22.06 29.85 24.01

WYOMING 77 0.19 0.19 0.25 0.21

Total 181,083 499 469 502 515
May 2006 

G:\WRAP MSEI2\Reporting\Final report\Sec10_refs.doc 10-1 

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Appendix A 

Development of Calendar Year 2002 County-Level Fuel Specification Data 
December 14, 2004 

Memo To: Alison Pollack 

 ENVIRON International Corporation 

 From: Philip Heirigs and Joe Roeschen, Sierra Research 

 Subject: Development of Calendar Year 2002 County-Level Fuel Specification Data 

for the WRAP Modeling Domain 

At your request, Sierra has developed calendar year 2002 gasoline and Diesel fuel specification 

data for each county in the 14-state WRAP modeling domain (i.e., AK, AZ, CA, CO, ID, MT, 

NV, NM, ND, OR, SD, UT, WA, WY). This effort built upon a previous analysis prepared by 

Sierra for the 1996 base year. The data and methodologies used for the 2002 analysis are 

summarized below, and the results are contained in the attached Excel spreadsheet 

(WRAP_Fuel_2002_toENVIRON.xls). If you have any questions or need anything else, please 

call one of us or Jim Lyons at 916-444-6666. 

Gasoline Specifications

For gasoline, the following parameters were recorded: 

$ RVP (in psi); 

$ Sulfur (in ppm); 

$ Volume percent of MTBE and ethanol; and 

$ Weight percent of oxygen. 

Because of differences in fuel properties (and control programs, e.g., RVP and oxygenates) 

across seasons, data were developed for winter, spring, summer, and fall. The fuel specification 

data used to construct the attached file were extracted from data published by the Alliance of 

Automobile Manufacturers (the Alliance)1

 and data published by TRW/Northrop-Grumman 

(formerly the National Institute for Petroleum and Energy Research, NIPER).2,3 The Alliance 

 

1

 “Alliance of Automobile Manufacturers North American Fuel Survey - Gasoline & Diesel Fuel,” published by the 

Alliance of Automobile Manufacturers. The winter 2002 and summer 2002 data were used for this analysis. 2 Dickson, C.L. “Motor Gasolines, Winter 2001-2002,” TRW Petroleum Technologies, July 2002. 

3 Dickson, C.L. “Motor Gasolines, Summer 2002,” Northrop Grumman, March 2003. 
publications contain fuel survey results for the following cities within the WRAP modeling 

domain: 

 • Albuquerque, NM (gasoline and Diesel); 

 • Billings, MT (gasoline and Diesel); 

 • Denver, CO (gasoline and Diesel); 

 • Fairbanks, AK (gasoline only) 

 • Las Vegas, NV (gasoline only); 

 • Los Angeles, CA (gasoline and Diesel); 

 • Phoenix, AZ (gasoline only); 

 • San Francisco, CA (gasoline only); and 

 • Seattle, WA (gasoline and Diesel). 

The TRW gasoline surveys encompass broader geographical regions. The following TRW 

gasoline “Districts” are included within the WRAP modeling domain: 

 • District 7 - Central and Upper Plains (used for Eastern ND and SD); 

 • District 9 - North Mountain States (MT, WY, ID, Eastern WA, and Eastern OR); 

 • District 10 - Central Mountain States (CO and UT); 

 • District 11 - New Mexico and West Texas; 

 • District 12 - West Southwest (AZ, Southern NV, and Southeastern CA); 

 • District 13 - Pacific Northwest (Western WA and Western OR); 

 • District 14 - Northern California and Northern Nevada; and 

 • District 15 - Southern California. 

Each county was “mapped” to one of the areas where fuel specification data were available. 

Although this was at times a subjective process, the following guidelines were generally 

followed. Alliance data were used for counties that contained the cities for which the fuel survey 

data were collected. In addition, surrounding counties were mapped to the Alliance city if it was 

felt that the fuels would be similar. For example, the Alliance data for Denver were used to 

reflect those counties in Colorado that are subject to a wintertime oxygenates program (i.e., 

Denver, Adams, Boulder, etc.), while the TRW data (District 10) were used to reflect gasoline in 

the rest of the state. The county-level assignments were also based on a review of a petroleum 

products pipeline map (published by the National Petroleum Council) as well as the District 

boundary maps contained in the TRW reports. 

The sources summarized above contain gasoline data for summer and winter, and they report 

gasoline specifications separately for regular, mid-grade, and premium. A composite gasoline 

specification for each county and season (winter and summer) was estimated by volumeweighting the three grades of gasoline based on annual statewide sales data published by the 

Energy Information Administration.4

 

 

4

 “Petroleum Marketing Annual 2002,” Energy Information Administration, DOE/EIA-0487(2002), August 2003. 
Fuel specifications for spring and fall are not reported in the Alliance and TRW surveys. As a 

result, the following approach was used to generate gasoline specifications for spring (assumed 

to be April) and fall (assumed to be October): 

 • RVP - The approach used in the National Emissions Inventory (NEI)5

 to develop monthly 

estimates of gasoline RVP from January (winter) and July (summer) data was generally 

followed in this study. That approach uses an interpolation routine in which the winter 

and summer RVP data are used in conjunction with the ASTM volatility class of an area 

for the month being analyzed. However, for all states except Alaska, the ASTM class for 

April (i.e., ASTM class A6) is the same as that for July, implying that the July volatility 

should be assigned to April. For areas that have summertime RVP controls that go 

beyond the 9.0 psi RVP limit imposed in ASTM class A areas, this results in 

unreasonably low RVP levels being assigned to the spring fuel specifications. For those 

areas, we assumed an RVP of 8.7 psi, which reflects the ASTM specification with a 0.3 

psi compliance cushion. For some counties in California, the summertime RVP levels 

were assigned to April, as April 1 is the compliance date for low-volatility fuel in a 

number of California counties. In addition, summertime RVP was assigned to the fall for 

a number of California counties, as the RVP controls extend through October 31 for some 

areas of California. 

 • Oxygenate - Because a number of areas in the Western U.S. have wintertime oxygenate 

requirements that do not extend into the spring or fall, the oxygenate levels observed in 

the summer were assigned to the spring and fall. 

 • Sulfur - Sulfur levels for spring and fall were assumed to be an arithmetic average of the 

summer and winter sulfur levels. 

Diesel Fuel Specifications

For this project, Diesel fuel sulfur level was needed (by season) for on-highway and off-highway 

fuel for each county in the WRAP modeling domain. The data and methods used to generate 

these estimates are summarized below. 

On-Highway Diesel Sulfur - Estimates for on-highway Diesel sulfur level were based on the 

Alliance fuel survey data outlined above for Albuquerque, Billings, Denver, Los Angeles, and 

Seattle. In addition, TRW publishes data on Diesel fuel specifications for the following regions:7

 • Rocky Mountain Region (MT, ID, WY, UT, CO, NM, Eastern AZ, Western ND, and 

Western SD); 

 

5 “Documentation for the Onroad National Emissions Inventory (NEI) for Base Years 1970-2002,” Prepared by 

E.H. Pechan & Associates for the U.S. Environmental Protection Agency, January 2004. 6

 Note that April reflects a transition month in ASTM volatility class for most states, with two classes being assigned 

(e.g., A and C for Southern California). Based on EPA guidance contained in the MOBILE4 User’s Guide, the 

lower volatility level is to be used for cases in which more than one class is listed; thus, class A was used for April 

in most of the areas in the WRAP modeling domain. 

7

 Dickson, C.L. “Diesel Fuel Oils, 2002,” TRW Petroleum Technologies, December 2002. 
 • Western Region (WA, OR, NV, CA, Western AZ); and 

 • Central Region (used for Eastern ND and SD). 

In addition to reporting data by Region, the TRW report also contains data for Districts within 

each Region. For cases in which it was possible to segregate data for specific Districts, those 

data were used in the analysis. 

Similar to the methodology used to generate gasoline specifications by county, each county in 

the modeling domain was mapped to one of the areas where fuel survey data were available. 

While the Alliance surveys are conducted in the winter and the summer, the TRW data do not 

distinguish between winter and summer. Thus, counties that were mapped to the TRW data set 

have the same sulfur level assigned year-round, whereas areas mapped to Alliance data have 

different summer and winter sulfur levels (although they generally are very close to one another). 

Spring and fall estimates from the Alliance data were based on an arithmetic average of the 

winter and summer results. 

Note that neither the Alliance nor the TRW reports contain data on Alaska Diesel fuel. For this 

analysis, estimates for Diesel fuel sulfur level were obtained through discussions with the Alaska 

Department of Environmental Conservation. Average values of those data were used for the 

northern portion of Alaska. For Southeast Alaska (i.e., Juneau and surrounding boroughs), fuel 

is generally barged in from Northern Washington. Thus, fuel specifications from Seattle were 

mapped to the boroughs in the southeast area of the state. 

Off-Highway Diesel Sulfur - The data available on off-highway Diesel fuel specifications are 

extremely limited. For this analysis, we used TRW data for the following regions and calendar 

years: 

 • Rocky Mountain Region - 2002; 

 • Western Region - 1996; and 

 • Central Region - 2001. 

Data from prior to 2002 had to be used in some cases for this study because the 2002 Diesel 

survey only contained a single off-road Diesel fuel sample from the Central Region (which was 

taken from the WI/IL/IN District) and no samples at all from the Western Region. 

For California and Maricopa County, AZ, regulations are in place that require the use of onhighway fuels in off-road equipment. Thus, the sulfur levels in these areas were based on the onroad data. Finally, for Alaska it was assumed that off-road Diesel fuel outside of Southeast 

Alaska had the same sulfur content as on-road Diesel fuel. 
Winter and summer gasoline properties by county. 

Winter 2002 Gasoline Properties Sum

State County Data RVP Sulfur MTBE EtOH Oxygen RVP Su

FIPS No. No. State County Source (psi) (ppm) (vol%) (vol%) (wt%) (psi) (p02013 02 013 AK Aleutians East B AAM-FB 14.5 160 0.0 0.0 0.0 12.6 02016 02 016 AK Aleutians West C AAM-FB 14.5 160 0.0 0.0 0.0 12.6 02020 02 020 AK Anchorage Ed ANCH 14.5 146 0.0 9.5 3.3 12.6 02050 02 050 AK Bethel Ed AAM-FB 14.5 160 0.0 0.0 0.0 12.6 02060 02 060 AK Bristol Bay Borough AAM-FB 14.5 160 0.0 0.0 0.0 12.6 02068 02 068 AK Denali Borough AAM-FB 14.5 160 0.0 0.0 0.0 12.6 02070 02 070 AK Dillingham Ed AAM-FB 14.5 160 0.0 0.0 0.0 12.6 02090 02 090 AK Fairbanks Ed AAM-FB 14.5 162 0.0 0.0 0.0 12.6 02100 02 100 AK Haines Ed NIPER-13 13.7 324 0.0 0.0 0.0 7.5 02110 02 110 AK Juneau Ed NIPER-13 13.7 324 0.0 0.0 0.0 7.5 02122 02 122 AK Kenai Penninsula AAM-FB 14.5 162 0.0 0.0 0.0 12.6 02130 02 130 AK Ketchikan Ed NIPER-13 13.7 324 0.0 0.0 0.0 7.5 02150 02 150 AK Kodiak Island Ed AAM-FB 14.5 160 0.0 0.0 0.0 12.6 02164 02 164 AK Lake and Peninsula AAM-FB 14.5 160 0.0 0.0 0.0 12.6 02170 02 170 AK Matanuska-Susitna AAM-FB 14.5 162 0.0 0.0 0.0 12.6 02180 02 180 AK Nome Ed AAM-FB 14.5 160 0.0 0.0 0.0 12.6 02185 02 185 AK North Slope Ed AAM-FB 14.5 160 0.0 0.0 0.0 12.6 02188 02 188 AK Northwest Arctic AAM-FB 14.5 160 0.0 0.0 0.0 12.6 02201 02 201 AK Prince Of Wales NIPER-13 13.7 324 0.0 0.0 0.0 7.5 02220 02 220 AK Sitka Ed NIPER-13 13.7 324 0.0 0.0 0.0 7.5 02232 02 232 AK Skagway-Yakutat NIPER-13 13.7 324 0.0 0.0 0.0 7.5 02240 02 240 AK Southeast Fairbanks AAM-FB 14.5 162 0.0 0.0 0.0 12.6 02261 02 261 AK Valdez-Cordova E AAM-FB 14.5 162 0.0 0.0 0.0 12.6 02270 02 270 AK Wade Hampton Ed AAM-FB 14.5 160 0.0 0.0 0.0 12.6 02280 02 280 AK Wrangell-Petersburg NIPER-13 13.7 324 0.0 0.0 0.0 7.5 02282 02 282 AK Yakutat Borough AAM-FB 14.5 160 0.0 0.0 0.0 12.6 02290 02 290 AK Yukon-Koyukuk Ed AAM-FB 14.5 160 0.0 0.0 0.0 12.6 04001 04 001 AZ Apache NIPER-11 11.5 235 0.2 0.0 0.0 7.7 04003 04 003 AZ Cochise NIPER-11 11.5 235 0.2 0.0 0.0 7.7 04005 04 005 AZ Coconino NIPER-12 11.9 0 10.3 0.0 1.8 7.0 04007 04 007 AZ Gila NIPER-12 11.9 0 10.3 0.0 1.8 7.0 04009 04 009 AZ Graham NIPER-11 11.5 235 0.2 0.0 0.0 7.7 04011 04 011 AZ Greenlee NIPER-11 11.5 235 0.2 0.0 0.0 7.7 04012 04 012 AZ La Paz NIPER-12 11.9 0 10.3 0.0 1.8 7.0 04013 04 013 AZ Maricopa AAM-PX 9.0 10 0.1 9.4 3.3 6.8 04015 04 015 AZ Mohave NIPER-12 11.9 0 10.3 0.0 1.8 7.0 04017 04 017 AZ Navajo NIPER-11 11.5 235 0.2 0.0 0.0 7.7 04019 04 019 AZ Pima NIPER-12 11.9 0 10.3 0.0 1.8 7.0 04021 04 021 AZ Pinal NIPER-12 11.9 0 10.3 0.0 1.8 7.0 04023 04 023 AZ Santa Cruz NIPER-11 11.5 235 0.2 0.0 0.0 7.7 04025 04 025 AZ Yavapai NIPER-12 11.9 0 10.3 0.0 1.8 7.0 04027 04 027 AZ Yuma NIPER-12 11.9 0 10.3 0.0 1.8 7.0 06001 06 001 CA Alameda AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06003 06 003 CA Alpine AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06005 06 005 CA Amador AAM-SF 12.4 10 3.2 1.1 0.9 7.1 
Winter 2002 Gasoline Properties Sum

State County Data RVP Sulfur MTBE EtOH Oxygen RVP Su

FIPS No. No. State County Source (psi) (ppm) (vol%) (vol%) (wt%) (psi) (p06007 06 007 CA Butte AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06009 06 009 CA Calaveras AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06011 06 011 CA Colusa AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06013 06 013 CA Contra Costa AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06015 06 015 CA Del Norte AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06017 06 017 CA El Dorado AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06019 06 019 CA Fresno AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06021 06 021 CA Glenn AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06023 06 023 CA Humboldt AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06025 06 025 CA Imperial AAM-LA 11.6 20 10.2 0.9 2.1 7.0 06027 06 027 CA Inyo AAM-LA 11.6 20 10.2 0.9 2.1 7.0 06029 06 029 CA Kern AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06031 06 031 CA Kings AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06033 06 033 CA Lake AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06035 06 035 CA Lassen AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06037 06 037 CA Los Angeles AAM-LA 11.6 20 10.2 0.9 2.1 7.0 06039 06 039 CA Madera AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06041 06 041 CA Marin AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06043 06 043 CA Mariposa AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06045 06 045 CA Mendocino AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06047 06 047 CA Merced AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06049 06 049 CA Modoc AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06051 06 051 CA Mono AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06053 06 053 CA Monterey AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06055 06 055 CA Napa AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06057 06 057 CA Nevada AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06059 06 059 CA Orange AAM-LA 11.6 20 10.2 0.9 2.1 7.0 06061 06 061 CA Placer AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06063 06 063 CA Plumas AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06065 06 065 CA Riverside AAM-LA 11.6 20 10.2 0.9 2.1 7.0 06067 06 067 CA Sacramento AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06069 06 069 CA San Benito AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06071 06 071 CA San Bernardino AAM-LA 11.6 20 10.2 0.9 2.1 7.0 06073 06 073 CA San Diego AAM-LA 11.6 20 10.2 0.9 2.1 7.0 06075 06 075 CA San Francisco AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06077 06 077 CA San Joaquin AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06079 06 079 CA San Luis Obispo AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06081 06 081 CA San Mateo AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06083 06 083 CA Santa Barbara AAM-LA 11.6 20 10.2 0.9 2.1 7.0 06085 06 085 CA Santa Clara AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06087 06 087 CA Santa Cruz AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06089 06 089 CA Shasta AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06091 06 091 CA Sierra AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06093 06 093 CA Siskiyou AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06095 06 095 CA Solano AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06097 06 097 CA Sonoma AAM-SF 12.4 10 3.2 1.1 0.9 7.1 
Winter 2002 Gasoline Properties Sum

State County Data RVP Sulfur MTBE EtOH Oxygen RVP Su

FIPS No. No. State County Source (psi) (ppm) (vol%) (vol%) (wt%) (psi) (p06099 06 099 CA Stanislaus AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06101 06 101 CA Sutter AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06103 06 103 CA Tehama AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06105 06 105 CA Trinity AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06107 06 107 CA Tulare AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06109 06 109 CA Tuolumne AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06111 06 111 CA Ventura AAM-LA 11.6 20 10.2 0.9 2.1 7.0 06113 06 113 CA Yolo AAM-SF 12.4 10 3.2 1.1 0.9 7.1 06115 06 115 CA Yuba AAM-SF 12.4 10 3.2 1.1 0.9 7.1 08001 08 001 CO Adams AAM-DN 14.2 149 0.0 9.7 3.4 9.0 08003 08 003 CO Alamosa NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08005 08 005 CO Arapahoe AAM-DN 14.2 149 0.0 9.7 3.4 9.0 08007 08 007 CO Archuleta NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08009 08 009 CO Baca NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08011 08 011 CO Bent NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08013 08 013 CO Boulder AAM-DN 14.2 149 0.0 9.7 3.4 9.0 08015 08 015 CO Chaffee NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08017 08 017 CO Cheyenne NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08019 08 019 CO Clear Creek NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08021 08 021 CO Conejos NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08023 08 023 CO Costilla NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08025 08 025 CO Crowley NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08027 08 027 CO Custer NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08029 08 029 CO Delta NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08031 08 031 CO Denver AAM-DN 14.2 149 0.0 9.7 3.4 9.0 08033 08 033 CO Dolores NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08035 08 035 CO Douglas AAM-DN 14.2 149 0.0 9.7 3.4 9.0 08037 08 037 CO Eagle NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08039 08 039 CO Elbert NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08041 08 041 CO El Paso AAM-DN 14.2 149 0.0 9.7 3.4 9.0 08043 08 043 CO Fremont NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08045 08 045 CO Garfield NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08047 08 047 CO Gilpin NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08049 08 049 CO Grand NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08051 08 051 CO Gunnison NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08053 08 053 CO Hinsdale NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08055 08 055 CO Huerfano NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08057 08 057 CO Jackson NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08059 08 059 CO Jefferson AAM-DN 14.2 149 0.0 9.7 3.4 9.0 08061 08 061 CO Kiowa NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08063 08 063 CO Kit Carson NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08065 08 065 CO Lake NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08067 08 067 CO La Plata NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08069 08 069 CO Larimer AAM-DN 14.2 149 0.0 9.7 3.4 9.0 08071 08 071 CO Las Animas NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08073 08 073 CO Lincoln NIPER-10 12.8 176 0.0 0.0 0.0 7.8 
Winter 2002 Gasoline Properties Sum

State County Data RVP Sulfur MTBE EtOH Oxygen RVP Su

FIPS No. No. State County Source (psi) (ppm) (vol%) (vol%) (wt%) (psi) (p08075 08 075 CO Logan NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08077 08 077 CO Mesa NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08079 08 079 CO Mineral NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08081 08 081 CO Moffat NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08083 08 083 CO Montezuma NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08085 08 085 CO Montrose NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08087 08 087 CO Morgan NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08089 08 089 CO Otero NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08091 08 091 CO Ouray NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08093 08 093 CO Park NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08095 08 095 CO Phillips NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08097 08 097 CO Pitkin NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08099 08 099 CO Prowers NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08101 08 101 CO Pueblo NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08103 08 103 CO Rio Blanco NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08105 08 105 CO Rio Grande NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08107 08 107 CO Routt NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08109 08 109 CO Saguache NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08111 08 111 CO San Juan NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08113 08 113 CO San Miguel NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08115 08 115 CO Sedgwick NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08117 08 117 CO Summit NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08119 08 119 CO Teller NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08121 08 121 CO Washington NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08123 08 123 CO Weld NIPER-10 12.8 176 0.0 0.0 0.0 7.8 08125 08 125 CO Yuma NIPER-10 12.8 176 0.0 0.0 0.0 7.8 16001 16 001 ID Ada NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16003 16 003 ID Adams NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16005 16 005 ID Bannock NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16007 16 007 ID Bear Lake NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16009 16 009 ID Benewah NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16011 16 011 ID Bingham NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16013 16 013 ID Blaine NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16015 16 015 ID Boise NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16017 16 017 ID Bonner NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16019 16 019 ID Bonneville NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16021 16 021 ID Boundary NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16023 16 023 ID Butte NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16025 16 025 ID Camas NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16027 16 027 ID Canyon NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16029 16 029 ID Caribou NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16031 16 031 ID Cassia NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16033 16 033 ID Clark NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16035 16 035 ID Clearwater NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16037 16 037 ID Custer NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16039 16 039 ID Elmore NIPER-9 12.7 195 0.0 0.0 0.0 8.3 
Winter 2002 Gasoline Properties Sum

State County Data RVP Sulfur MTBE EtOH Oxygen RVP Su

FIPS No. No. State County Source (psi) (ppm) (vol%) (vol%) (wt%) (psi) (p16041 16 041 ID Franklin NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16043 16 043 ID Fremont NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16045 16 045 ID Gem NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16047 16 047 ID Gooding NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16049 16 049 ID Idaho NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16051 16 051 ID Jefferson NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16053 16 053 ID Jerome NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16055 16 055 ID Kootenai NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16057 16 057 ID Latah NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16059 16 059 ID Lemhi NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16061 16 061 ID Lewis NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16063 16 063 ID Lincoln NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16065 16 065 ID Madison NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16067 16 067 ID Minidoka NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16069 16 069 ID Nez Perce NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16071 16 071 ID Oneida NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16073 16 073 ID Owyhee NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16075 16 075 ID Payette NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16077 16 077 ID Power NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16079 16 079 ID Shoshone NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16081 16 081 ID Teton NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16083 16 083 ID Twin Falls NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16085 16 085 ID Valley NIPER-9 12.7 195 0.0 0.0 0.0 8.3 16087 16 087 ID Washington NIPER-9 12.7 195 0.0 0.0 0.0 8.3 30001 30 001 MT Beaverhead AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30003 30 003 MT Big Horn AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30005 30 005 MT Blaine AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30007 30 007 MT Broadwater AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30009 30 009 MT Carbon AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30011 30 011 MT Carter AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30013 30 013 MT Cascade AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30015 30 015 MT Chouteau AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30017 30 017 MT Custer AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30019 30 019 MT Daniels AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30021 30 021 MT Dawson AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30023 30 023 MT Deer Lodge AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30025 30 025 MT Fallon AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30027 30 027 MT Fergus AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30029 30 029 MT Flat Head AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30031 30 031 MT Gallatin AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30033 30 033 MT Garfield AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30035 30 035 MT Glacier AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30037 30 037 MT Golden Valley AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30039 30 039 MT Granite AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30041 30 041 MT Hill AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30043 30 043 MT Jefferson AAM-BL 13.9 405 0.0 0.0 0.0 8.7 
Winter 2002 Gasoline Properties Sum

State County Data RVP Sulfur MTBE EtOH Oxygen RVP Su

FIPS No. No. State County Source (psi) (ppm) (vol%) (vol%) (wt%) (psi) (p30045 30 045 MT Judith Basin AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30047 30 047 MT Lake AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30049 30 049 MT Lewis and Clark AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30051 30 051 MT Liberty AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30053 30 053 MT Lincoln AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30055 30 055 MT Mc Cone AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30057 30 057 MT Madison AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30059 30 059 MT Meagher AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30061 30 061 MT Mineral AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30063 30 063 MT Missoula AAM-BL 13.9 365 0.0 10.0 3.4 8.7 30065 30 065 MT Musselshell AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30067 30 067 MT Park AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30069 30 069 MT Petroleum AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30071 30 071 MT Phillips AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30073 30 073 MT Pondera AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30075 30 075 MT Powder River AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30077 30 077 MT Powell AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30079 30 079 MT Prairie AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30081 30 081 MT Ravalli AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30083 30 083 MT Richland AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30085 30 085 MT Roosevelt AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30087 30 087 MT Rosebud AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30089 30 089 MT Sanders AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30091 30 091 MT Sheridan AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30093 30 093 MT Silver Bow AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30095 30 095 MT Stillwater AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30097 30 097 MT Sweet Grass AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30099 30 099 MT Teton AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30101 30 101 MT Toole AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30103 30 103 MT Treasure AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30105 30 105 MT Valley AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30107 30 107 MT Wheatland AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30109 30 109 MT Wibaux AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30111 30 111 MT Yellowstone AAM-BL 13.9 405 0.0 0.0 0.0 8.7 30113 30 113 MT Yellowstone Park AAM-BL 13.9 405 0.0 0.0 0.0 8.7 32001 32 001 NV Churchill NIPER-14 11.6 10 2.2 0.0 0.4 7.2 32003 32 003 NV Clark AAM-LV 8.9 20 0.2 9.2 3.2 8.3 32005 32 005 NV Douglas NIPER-14 11.6 10 2.2 0.0 0.4 7.2 32007 32 007 NV Elko NIPER-14 11.6 10 2.2 0.0 0.4 7.2 32009 32 009 NV Esmeralda AAM-LV 8.9 20 0.2 9.2 3.2 8.3 32011 32 011 NV Eureka NIPER-14 11.6 10 2.2 0.0 0.4 7.2 32013 32 013 NV Humboldt NIPER-14 11.6 10 2.2 0.0 0.4 7.2 32015 32 015 NV Lander NIPER-14 11.6 10 2.2 0.0 0.4 7.2 32017 32 017 NV Lincoln AAM-LV 8.9 20 0.2 9.2 3.2 8.3 32019 32 019 NV Lyon NIPER-14 11.6 10 2.2 0.0 0.4 7.2 32021 32 021 NV Mineral AAM-LV 8.9 20 0.2 9.2 3.2 8.3 
Winter 2002 Gasoline Properties Sum

State County Data RVP Sulfur MTBE EtOH Oxygen RVP Su

FIPS No. No. State County Source (psi) (ppm) (vol%) (vol%) (wt%) (psi) (p32023 32 023 NV Nye AAM-LV 8.9 20 0.2 9.2 3.2 8.3 32027 32 027 NV Pershing NIPER-14 11.6 10 2.2 0.0 0.4 7.2 32029 32 029 NV Storey NIPER-14 11.6 10 2.2 0.0 0.4 7.2 32031 32 031 NV Washoe NIPER-14 12.6 9 0.0 7.8 2.7 7.2 32033 32 033 NV White Pine NIPER-14 11.6 10 2.2 0.0 0.4 7.2 32510 32 510 NV Carson City NIPER-14 11.6 10 2.2 0.0 0.4 7.2 35001 35 001 NM Bernalillo AAM-ALB 12.7 170 0.0 8.6 3.0 8.8 35003 35 003 NM Catron NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35005 35 005 NM Chaves NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35006 35 006 NM Cibola NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35007 35 007 NM Colfax NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35009 35 009 NM Curry NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35011 35 011 NM De Baca NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35013 35 013 NM Dona Ana NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35015 35 015 NM Eddy NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35017 35 017 NM Grant NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35019 35 019 NM Guadalupe NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35021 35 021 NM Harding NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35023 35 023 NM Hidalgo NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35025 35 025 NM Lea NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35027 35 027 NM Lincoln NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35028 35 028 NM Los Alamos AAM-ALB 12.7 170 0.0 8.6 3.0 8.8 35029 35 029 NM Luna NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35031 35 031 NM Mc Kinley NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35033 35 033 NM Mora NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35035 35 035 NM Otero NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35037 35 037 NM Quay NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35039 35 039 NM Rio Arriba NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35041 35 041 NM Roosevelt NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35043 35 043 NM Sandoval AAM-ALB 12.7 170 0.0 8.6 3.0 8.8 35045 35 045 NM San Juan NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35047 35 047 NM San Miguel NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35049 35 049 NM Santa Fe AAM-ALB 12.7 170 0.0 8.6 3.0 8.8 35051 35 051 NM Sierra NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35053 35 053 NM Socorro NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35055 35 055 NM Taos NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35057 35 057 NM Torrance AAM-ALB 12.7 170 0.0 8.6 3.0 8.8 35059 35 059 NM Union NIPER-11 11.5 237 0.2 0.0 0.0 7.7 35061 35 061 NM Valencia AAM-ALB 12.7 170 0.0 8.6 3.0 8.8 38001 38 001 ND Adams AAM-BL 14.0 413 0.0 0.0 0.0 8.6 38003 38 003 ND Barnes NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38005 38 005 ND Benson NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38007 38 007 ND Billings AAM-BL 14.0 413 0.0 0.0 0.0 8.6 38009 38 009 ND Bottineau NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38011 38 011 ND Bowman AAM-BL 14.0 413 0.0 0.0 0.0 8.6 38013 38 013 ND Burke AAM-BL 14.0 413 0.0 0.0 0.0 8.6 
Winter 2002 Gasoline Properties Sum

State County Data RVP Sulfur MTBE EtOH Oxygen RVP Su

FIPS No. No. State County Source (psi) (ppm) (vol%) (vol%) (wt%) (psi) (p38015 38 015 ND Burleigh NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38017 38 017 ND Cass NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38019 38 019 ND Cavalier NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38021 38 021 ND Dickey NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38023 38 023 ND Divide AAM-BL 14.0 413 0.0 0.0 0.0 8.6 38025 38 025 ND Dunn AAM-BL 14.0 413 0.0 0.0 0.0 8.6 38027 38 027 ND Eddy NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38029 38 029 ND Emmons NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38031 38 031 ND Foster NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38033 38 033 ND Golden Valley AAM-BL 14.0 413 0.0 0.0 0.0 8.6 38035 38 035 ND Grand Forks NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38037 38 037 ND Grant NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38039 38 039 ND Griggs NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38041 38 041 ND Hettinger AAM-BL 14.0 413 0.0 0.0 0.0 8.6 38043 38 043 ND Kidder NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38045 38 045 ND La Moure NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38047 38 047 ND Logan NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38049 38 049 ND Mc Henry NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38051 38 051 ND Mc Intosh NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38053 38 053 ND Mc Kenzie AAM-BL 14.0 413 0.0 0.0 0.0 8.6 38055 38 055 ND Mc Lean NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38057 38 057 ND Mercer NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38059 38 059 ND Morton NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38061 38 061 ND Mountrail AAM-BL 14.0 413 0.0 0.0 0.0 8.6 38063 38 063 ND Nelson NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38065 38 065 ND Oliver NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38067 38 067 ND Pembina NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38069 38 069 ND Pierce NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38071 38 071 ND Ramsey NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38073 38 073 ND Ransom NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38075 38 075 ND Renville NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38077 38 077 ND Richland NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38079 38 079 ND Rolette NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38081 38 081 ND Sargent NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38083 38 083 ND Sheridan NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38085 38 085 ND Sioux NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38087 38 087 ND Slope AAM-BL 14.0 413 0.0 0.0 0.0 8.6 38089 38 089 ND Stark AAM-BL 14.0 413 0.0 0.0 0.0 8.6 38091 38 091 ND Steele NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38093 38 093 ND Stutsman NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38095 38 095 ND Towner NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38097 38 097 ND Traill NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38099 38 099 ND Walsh NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38101 38 101 ND Ward NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38103 38 103 ND Wells NIPER-7 13.3 212 0.0 0.0 0.0 8.1 38105 38 105 ND Williams AAM-BL 14.0 413 0.0 0.0 0.0 8.6 
Winter 2002 Gasoline Properties Sum

State County Data RVP Sulfur MTBE EtOH Oxygen RVP Su

FIPS No. No. State County Source (psi) (ppm) (vol%) (vol%) (wt%) (psi) (p41001 41 001 OR Baker NIPER-9 12.7 198 0.0 0.0 0.0 8.3 41003 41 003 OR Benton NIPER-13 13.7 310 0.0 0.0 0.0 7.5 41005 41 005 OR Clackamas NIPER-13 14.1 150 0.0 8.8 3.0 7.5 41007 41 007 OR Clatsop NIPER-13 13.7 310 0.0 0.0 0.0 7.5 41009 41 009 OR Columbia NIPER-13 13.7 310 0.0 0.0 0.0 7.5 41011 41 011 OR Coos NIPER-13 13.7 310 0.0 0.0 0.0 7.5 41013 41 013 OR Crook NIPER-9 12.7 198 0.0 0.0 0.0 8.3 41015 41 015 OR Curry NIPER-13 13.7 310 0.0 0.0 0.0 7.5 41017 41 017 OR Deschutes NIPER-9 12.7 198 0.0 0.0 0.0 8.3 41019 41 019 OR Douglas NIPER-13 13.7 310 0.0 0.0 0.0 7.5 41021 41 021 OR Gilliam NIPER-9 12.7 198 0.0 0.0 0.0 8.3 41023 41 023 OR Grant NIPER-9 12.7 198 0.0 0.0 0.0 8.3 41025 41 025 OR Harney NIPER-9 12.7 198 0.0 0.0 0.0 8.3 41027 41 027 OR Hood River NIPER-9 12.7 198 0.0 0.0 0.0 8.3 41029 41 029 OR Jackson NIPER-13 14.1 150 0.0 8.8 3.0 7.5 41031 41 031 OR Jefferson NIPER-9 12.7 198 0.0 0.0 0.0 8.3 41033 41 033 OR Josephine NIPER-13 13.7 310 0.0 0.0 0.0 7.5 41035 41 035 OR Klamath NIPER-13 13.7 310 0.0 0.0 0.0 7.5 41037 41 037 OR Lake NIPER-9 12.7 198 0.0 0.0 0.0 8.3 41039 41 039 OR Lane NIPER-13 13.7 310 0.0 0.0 0.0 7.5 41041 41 041 OR Lincoln NIPER-13 13.7 310 0.0 0.0 0.0 7.5 41043 41 043 OR Linn NIPER-13 13.7 310 0.0 0.0 0.0 7.5 41045 41 045 OR Malheur NIPER-9 12.7 198 0.0 0.0 0.0 8.3 41047 41 047 OR Marion NIPER-13 13.7 310 0.0 0.0 0.0 7.5 41049 41 049 OR Morrow NIPER-9 12.7 198 0.0 0.0 0.0 8.3 41051 41 051 OR Multnomah NIPER-13 14.1 150 0.0 8.8 3.0 7.5 41053 41 053 OR Polk NIPER-13 13.7 310 0.0 0.0 0.0 7.5 41055 41 055 OR Sherman NIPER-9 12.7 198 0.0 0.0 0.0 8.3 41057 41 057 OR Tillamook NIPER-13 13.7 310 0.0 0.0 0.0 7.5 41059 41 059 OR Umatilla NIPER-9 12.7 198 0.0 0.0 0.0 8.3 41061 41 061 OR Union NIPER-9 12.7 198 0.0 0.0 0.0 8.3 41063 41 063 OR Wallowa NIPER-9 12.7 198 0.0 0.0 0.0 8.3 41065 41 065 OR Wasco NIPER-9 12.7 198 0.0 0.0 0.0 8.3 41067 41 067 OR Washington NIPER-13 14.1 150 0.0 8.8 3.0 7.5 41069 41 069 OR Wheeler NIPER-9 12.7 198 0.0 0.0 0.0 8.3 41071 41 071 OR Yamhill NIPER-13 14.1 150 0.0 8.8 3.0 7.5 46003 46 003 SD Aurora NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46005 46 005 SD Beadle NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46007 46 007 SD Bennett NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46009 46 009 SD Bon Homme NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46011 46 011 SD Brookings NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46013 46 013 SD Brown NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46015 46 015 SD Brule NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46017 46 017 SD Buffalo NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46019 46 019 SD Butte NIPER-9 12.8 203 0.0 0.0 0.0 8.3 46021 46 021 SD Campbell NIPER-7 13.3 210 0.0 0.0 0.0 8.1 
Winter 2002 Gasoline Properties Sum

State County Data RVP Sulfur MTBE EtOH Oxygen RVP Su

FIPS No. No. State County Source (psi) (ppm) (vol%) (vol%) (wt%) (psi) (p46023 46 023 SD Charles Mix NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46025 46 025 SD Clark NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46027 46 027 SD Clay NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46029 46 029 SD Codington NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46031 46 031 SD Corson NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46033 46 033 SD Custer NIPER-9 12.8 203 0.0 0.0 0.0 8.3 46035 46 035 SD Davison NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46037 46 037 SD Day NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46039 46 039 SD Deuel NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46041 46 041 SD Dewey NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46043 46 043 SD Douglas NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46045 46 045 SD Edmunds NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46047 46 047 SD Fall River NIPER-9 12.8 203 0.0 0.0 0.0 8.3 46049 46 049 SD Faulk NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46051 46 051 SD Grant NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46053 46 053 SD Gregory NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46055 46 055 SD Haakon NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46057 46 057 SD Hamlin NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46059 46 059 SD Hand NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46061 46 061 SD Hanson NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46063 46 063 SD Harding NIPER-9 12.8 203 0.0 0.0 0.0 8.3 46065 46 065 SD Hughes NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46067 46 067 SD Hutchinson NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46069 46 069 SD Hyde NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46071 46 071 SD Jackson NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46073 46 073 SD Jerauld NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46075 46 075 SD Jones NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46077 46 077 SD Kingsbury NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46079 46 079 SD Lake NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46081 46 081 SD Lawrence NIPER-9 12.8 203 0.0 0.0 0.0 8.3 46083 46 083 SD Lincoln NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46085 46 085 SD Lyman NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46087 46 087 SD Mc Cook NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46089 46 089 SD Mc Pherson NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46091 46 091 SD Marshall NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46093 46 093 SD Meade NIPER-9 12.8 203 0.0 0.0 0.0 8.3 46095 46 095 SD Mellette NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46097 46 097 SD Miner NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46099 46 099 SD Minnehaha NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46101 46 101 SD Moody NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46103 46 103 SD Pennington NIPER-9 12.8 203 0.0 0.0 0.0 8.3 46105 46 105 SD Perkins NIPER-9 12.8 203 0.0 0.0 0.0 8.3 46107 46 107 SD Potter NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46109 46 109 SD Roberts NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46111 46 111 SD Sanborn NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46113 46 113 SD Shannon NIPER-9 12.8 203 0.0 0.0 0.0 8.3 
Winter 2002 Gasoline Properties Sum

State County Data RVP Sulfur MTBE EtOH Oxygen RVP Su

FIPS No. No. State County Source (psi) (ppm) (vol%) (vol%) (wt%) (psi) (p46115 46 115 SD Spink NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46117 46 117 SD Stanley NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46119 46 119 SD Sully NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46121 46 121 SD Todd NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46123 46 123 SD Tripp NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46125 46 125 SD Turner NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46127 46 127 SD Union NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46129 46 129 SD Walworth NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46135 46 135 SD Yankton NIPER-7 13.3 210 0.0 0.0 0.0 8.1 46137 46 137 SD Ziebach NIPER-7 13.3 210 0.0 0.0 0.0 8.1 49001 49 001 UT Beaver NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49003 49 003 UT Box Elder NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49005 49 005 UT Cache NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49007 49 007 UT Carbon NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49009 49 009 UT Daggett NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49011 49 011 UT Davis NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49013 49 013 UT Duchesne NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49015 49 015 UT Emery NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49017 49 017 UT Garfield NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49019 49 019 UT Grand NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49021 49 021 UT Iron NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49023 49 023 UT Juab NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49025 49 025 UT Kane NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49027 49 027 UT Millard NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49029 49 029 UT Morgan NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49031 49 031 UT Piute NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49033 49 033 UT Rich NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49035 49 035 UT Salt Lake NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49037 49 037 UT San Juan NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49039 49 039 UT Sanpete NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49041 49 041 UT Sevier NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49043 49 043 UT Summit NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49045 49 045 UT Tooele NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49047 49 047 UT Uintah NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49049 49 049 UT Utah NIPER-10 14.4 195 0.0 7.0 2.4 7.8 49051 49 051 UT Wasatch NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49053 49 053 UT Washington NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49055 49 055 UT Wayne NIPER-10 12.8 173 0.0 0.0 0.0 7.8 49057 49 057 UT Weber NIPER-10 12.8 173 0.0 0.0 0.0 7.8 53001 53 001 WA Adams NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53003 53 003 WA Asotin NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53005 53 005 WA Benton NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53007 53 007 WA Chelan NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53009 53 009 WA Clallam NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53011 53 011 WA Clark AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53013 53 013 WA Columbia NIPER-9 12.7 196 0.0 0.0 0.0 8.3 
Winter 2002 Gasoline Properties Sum

State County Data RVP Sulfur MTBE EtOH Oxygen RVP Su

FIPS No. No. State County Source (psi) (ppm) (vol%) (vol%) (wt%) (psi) (p53015 53 015 WA Cowlitz AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53017 53 017 WA Douglas NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53019 53 019 WA Ferry NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53021 53 021 WA Franklin NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53023 53 023 WA Garfield NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53025 53 025 WA Grant NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53027 53 027 WA Grays Harbor AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53029 53 029 WA Island AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53031 53 031 WA Jefferson AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53033 53 033 WA King AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53035 53 035 WA Kitsap AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53037 53 037 WA Kittitas NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53039 53 039 WA Klickitat NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53041 53 041 WA Lewis AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53043 53 043 WA Lincoln NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53045 53 045 WA Mason AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53047 53 047 WA Okanogan NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53049 53 049 WA Pacific AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53051 53 051 WA Pend Oreille NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53053 53 053 WA Pierce AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53055 53 055 WA San Juan AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53057 53 057 WA Skagit AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53059 53 059 WA Skamania AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53061 53 061 WA Snohomish AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53063 53 063 WA Spokane NIPER-9 13.4 258 0.0 9.7 3.3 8.3 53065 53 065 WA Stevens NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53067 53 067 WA Thurston AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53069 53 069 WA Wahkiakum AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53071 53 071 WA Walla Walla NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53073 53 073 WA Whatcom AAM-SE 14.3 270 0.0 2.5 0.9 7.8 53075 53 075 WA Whitman NIPER-9 12.7 196 0.0 0.0 0.0 8.3 53077 53 077 WA Yakima NIPER-9 12.7 196 0.0 0.0 0.0 8.3 56001 56 001 WY Albany NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56003 56 003 WY Big Horn NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56005 56 005 WY Campbell NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56007 56 007 WY Carbon NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56009 56 009 WY Converse NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56011 56 011 WY Crook NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56013 56 013 WY Fremont NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56015 56 015 WY Goshen NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56017 56 017 WY Hot Springs NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56019 56 019 WY Johnson NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56021 56 021 WY Laramie NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56023 56 023 WY Lincoln NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56025 56 025 WY Natrona NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56027 56 027 WY Niobrara NIPER-9 12.7 192 0.0 0.0 0.0 8.3 
Winter 2002 Gasoline Properties Sum

State County Data RVP Sulfur MTBE EtOH Oxygen RVP Su

FIPS No. No. State County Source (psi) (ppm) (vol%) (vol%) (wt%) (psi) (p56029 56 029 WY Park NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56031 56 031 WY Platte NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56033 56 033 WY Sheridan NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56035 56 035 WY Sublette NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56037 56 037 WY Sweetwater NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56039 56 039 WY Teton NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56041 56 041 WY Uinta NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56043 56 043 WY Washakie NIPER-9 12.7 192 0.0 0.0 0.0 8.3 56045 56 045 WY Weston NIPER-9 12.7 192 0.0 0.0 0.0 8.3 
Spring and fall gasoline properties by county.

Spring 2002 Gasoline Properties Fall 2002 Gasoline P

State County RVP Sulfur MTBE EtOH Oxygen RVP Sulfu

FIPS No. No. State County (psi) (ppm) (vol%) (vol%) (wt%) (psi) (ppm02013 02 013 AK Aleutians East B 14.5 145 0.0 0.0 0.0 14.5 02016 02 016 AK Aleutians West C 14.5 145 0.0 0.0 0.0 14.5 02020 02 020 AK Anchorage Ed 14.5 140 0.0 0.0 0.0 14.5 02050 02 050 AK Bethel Ed 14.5 145 0.0 0.0 0.0 14.5 02060 02 060 AK Bristol Bay Borough 14.5 145 0.0 0.0 0.0 14.5 02068 02 068 AK Denali Borough 14.5 145 0.0 0.0 0.0 14.5 02070 02 070 AK Dillingham Ed 14.5 145 0.0 0.0 0.0 14.5 02090 02 090 AK Fairbanks Ed 14.5 148 0.0 0.0 0.0 14.5 02100 02 100 AK Haines Ed 13.7 333 0.0 0.0 0.0 13.7 302110 02 110 AK Juneau Ed 13.7 333 0.0 0.0 0.0 13.7 302122 02 122 AK Kenai Penninsula 14.5 148 0.0 0.0 0.0 14.5 02130 02 130 AK Ketchikan Ed 13.7 333 0.0 0.0 0.0 13.7 302150 02 150 AK Kodiak Island Ed 14.5 145 0.0 0.0 0.0 14.5 02164 02 164 AK Lake and Peninsu 14.5 145 0.0 0.0 0.0 14.5 02170 02 170 AK Matanuska-Susitna 14.5 148 0.0 0.0 0.0 14.5 02180 02 180 AK Nome Ed 14.5 145 0.0 0.0 0.0 14.5 02185 02 185 AK North Slope Ed 14.5 145 0.0 0.0 0.0 14.5 02188 02 188 AK Northwest Arctic 14.5 145 0.0 0.0 0.0 14.5 02201 02 201 AK Prince Of Wales 13.7 333 0.0 0.0 0.0 13.7 302220 02 220 AK Sitka Ed 13.7 333 0.0 0.0 0.0 13.7 302232 02 232 AK Skagway-Yakutat 13.7 333 0.0 0.0 0.0 13.7 302240 02 240 AK Southeast Fairbanks 14.5 148 0.0 0.0 0.0 14.5 02261 02 261 AK Valdez-Cordova E 14.5 148 0.0 0.0 0.0 14.5 02270 02 270 AK Wade Hampton Ed 14.5 145 0.0 0.0 0.0 14.5 02280 02 280 AK Wrangell-Petersburg 13.7 333 0.0 0.0 0.0 13.7 302282 02 282 AK Yakutat Borough 14.5 145 0.0 0.0 0.0 14.5 02290 02 290 AK Yukon-Koyukuk Ed 14.5 145 0.0 0.0 0.0 14.5 04001 04 001 AZ Apache 8.7 251 0.0 0.0 0.0 9.3 204003 04 003 AZ Cochise 8.7 251 0.0 0.0 0.0 8.7 204005 04 005 AZ Coconino 8.7 33 8.1 0.0 1.4 8.7 04007 04 007 AZ Gila 8.7 33 8.1 0.0 1.4 8.7 04009 04 009 AZ Graham 8.7 251 0.0 0.0 0.0 8.7 204011 04 011 AZ Greenlee 8.7 251 0.0 0.0 0.0 8.7 204012 04 012 AZ La Paz 8.7 33 8.1 0.0 1.4 8.7 04013 04 013 AZ Maricopa 8.7 52 9.1 0.0 1.6 8.7 04015 04 015 AZ Mohave 8.7 33 8.1 0.0 1.4 8.7 04017 04 017 AZ Navajo 8.7 251 0.0 0.0 0.0 9.3 204019 04 019 AZ Pima 8.7 33 8.1 0.0 1.4 8.7 04021 04 021 AZ Pinal 8.7 33 8.1 0.0 1.4 8.7 04023 04 023 AZ Santa Cruz 8.7 251 0.0 0.0 0.0 8.7 204025 04 025 AZ Yavapai 8.7 33 8.1 0.0 1.4 8.7 04027 04 027 AZ Yuma 8.7 33 8.1 0.0 1.4 8.7 06001 06 001 CA Alameda 8.7 10 5.0 1.2 1.3 7.1 06003 06 003 CA Alpine 8.7 10 5.0 1.2 1.3 9.5 06005 06 005 CA Amador 8.7 10 5.0 1.2 1.3 7.1 
Spring 2002 Gasoline Properties Fall 2002 Gasoline P

State County RVP Sulfur MTBE EtOH Oxygen RVP Sulfu

FIPS No. No. State County (psi) (ppm) (vol%) (vol%) (wt%) (psi) (ppm06007 06 007 CA Butte 8.7 10 5.0 1.2 1.3 7.1 06009 06 009 CA Calaveras 8.7 10 5.0 1.2 1.3 7.1 06011 06 011 CA Colusa 8.7 10 5.0 1.2 1.3 7.1 06013 06 013 CA Contra Costa 8.7 10 5.0 1.2 1.3 7.1 06015 06 015 CA Del Norte 8.7 10 5.0 1.2 1.3 9.5 06017 06 017 CA El Dorado 8.7 10 5.0 1.2 1.3 7.1 06019 06 019 CA Fresno 8.7 10 5.0 1.2 1.3 7.1 06021 06 021 CA Glenn 8.7 10 5.0 1.2 1.3 7.1 06023 06 023 CA Humboldt 8.7 10 5.0 1.2 1.3 9.5 06025 06 025 CA Imperial 7.0 15 10.4 0.6 2.1 7.0 06027 06 027 CA Inyo 8.7 15 10.4 0.6 2.1 8.7 06029 06 029 CA Kern 8.7 10 5.0 1.2 1.3 7.1 06031 06 031 CA Kings 8.7 10 5.0 1.2 1.3 7.1 06033 06 033 CA Lake 8.7 10 5.0 1.2 1.3 9.5 06035 06 035 CA Lassen 8.7 10 5.0 1.2 1.3 9.5 06037 06 037 CA Los Angeles 7.0 15 10.4 0.6 2.1 7.0 06039 06 039 CA Madera 8.7 10 5.0 1.2 1.3 7.1 06041 06 041 CA Marin 8.7 10 5.0 1.2 1.3 7.1 06043 06 043 CA Mariposa 8.7 10 5.0 1.2 1.3 7.1 06045 06 045 CA Mendocino 8.7 10 5.0 1.2 1.3 9.5 06047 06 047 CA Merced 8.7 10 5.0 1.2 1.3 7.1 06049 06 049 CA Modoc 8.7 10 5.0 1.2 1.3 9.5 06051 06 051 CA Mono 8.7 10 5.0 1.2 1.3 8.7 06053 06 053 CA Monterey 8.7 10 5.0 1.2 1.3 7.1 06055 06 055 CA Napa 8.7 10 5.0 1.2 1.3 7.1 06057 06 057 CA Nevada 8.7 10 5.0 1.2 1.3 7.1 06059 06 059 CA Orange 7.0 15 10.4 0.6 2.1 7.0 06061 06 061 CA Placer 8.7 10 5.0 1.2 1.3 7.1 06063 06 063 CA Plumas 8.7 10 5.0 1.2 1.3 7.1 06065 06 065 CA Riverside 7.0 15 10.4 0.6 2.1 7.0 06067 06 067 CA Sacramento 8.7 10 5.0 1.2 1.3 7.1 06069 06 069 CA San Benito 8.7 10 5.0 1.2 1.3 7.1 06071 06 071 CA San Bernardino 7.0 15 10.4 0.6 2.1 7.0 06073 06 073 CA San Diego 7.0 15 10.4 0.6 2.1 7.0 06075 06 075 CA San Francisco 8.7 10 5.0 1.2 1.3 7.1 06077 06 077 CA San Joaquin 8.7 10 5.0 1.2 1.3 7.1 06079 06 079 CA San Luis Obispo 8.7 10 5.0 1.2 1.3 7.1 06081 06 081 CA San Mateo 8.7 10 5.0 1.2 1.3 7.1 06083 06 083 CA Santa Barbara 8.7 15 10.4 0.6 2.1 7.0 06085 06 085 CA Santa Clara 8.7 10 5.0 1.2 1.3 7.1 06087 06 087 CA Santa Cruz 8.7 10 5.0 1.2 1.3 7.1 06089 06 089 CA Shasta 8.7 10 5.0 1.2 1.3 7.1 06091 06 091 CA Sierra 8.7 10 5.0 1.2 1.3 7.1 06093 06 093 CA Siskiyou 8.7 10 5.0 1.2 1.3 9.5 06095 06 095 CA Solano 8.7 10 5.0 1.2 1.3 7.1 06097 06 097 CA Sonoma 8.7 10 5.0 1.2 1.3 9.5 
Spring 2002 Gasoline Properties Fall 2002 Gasoline P

State County RVP Sulfur MTBE EtOH Oxygen RVP Sulfu

FIPS No. No. State County (psi) (ppm) (vol%) (vol%) (wt%) (psi) (ppm06099 06 099 CA Stanislaus 8.7 10 5.0 1.2 1.3 7.1 06101 06 101 CA Sutter 8.7 10 5.0 1.2 1.3 7.1 06103 06 103 CA Tehama 8.7 10 5.0 1.2 1.3 7.1 06105 06 105 CA Trinity 8.7 10 5.0 1.2 1.3 9.5 06107 06 107 CA Tulare 8.7 10 5.0 1.2 1.3 7.1 06109 06 109 CA Tuolumne 8.7 10 5.0 1.2 1.3 7.1 06111 06 111 CA Ventura 8.7 15 10.4 0.6 2.1 7.0 06113 06 113 CA Yolo 8.7 10 5.0 1.2 1.3 7.1 06115 06 115 CA Yuba 8.7 10 5.0 1.2 1.3 7.1 08001 08 001 CO Adams 9.0 158 0.0 4.2 1.4 9.9 08003 08 003 CO Alamosa 8.7 175 0.0 0.0 0.0 9.4 08005 08 005 CO Arapahoe 9.0 158 0.0 4.2 1.4 9.9 08007 08 007 CO Archuleta 8.7 175 0.0 0.0 0.0 9.4 08009 08 009 CO Baca 8.7 175 0.0 0.0 0.0 9.4 08011 08 011 CO Bent 8.7 175 0.0 0.0 0.0 9.4 08013 08 013 CO Boulder 9.0 158 0.0 4.2 1.4 9.9 08015 08 015 CO Chaffee 8.7 175 0.0 0.0 0.0 9.4 08017 08 017 CO Cheyenne 8.7 175 0.0 0.0 0.0 9.4 08019 08 019 CO Clear Creek 8.7 175 0.0 0.0 0.0 9.4 08021 08 021 CO Conejos 8.7 175 0.0 0.0 0.0 9.4 08023 08 023 CO Costilla 8.7 175 0.0 0.0 0.0 9.4 08025 08 025 CO Crowley 8.7 175 0.0 0.0 0.0 9.4 08027 08 027 CO Custer 8.7 175 0.0 0.0 0.0 9.4 08029 08 029 CO Delta 8.7 175 0.0 0.0 0.0 9.4 08031 08 031 CO Denver 9.0 158 0.0 4.2 1.4 9.9 08033 08 033 CO Dolores 8.7 175 0.0 0.0 0.0 9.4 08035 08 035 CO Douglas 9.0 158 0.0 4.2 1.4 9.9 08037 08 037 CO Eagle 8.7 175 0.0 0.0 0.0 9.4 08039 08 039 CO Elbert 8.7 175 0.0 0.0 0.0 9.4 08041 08 041 CO El Paso 9.0 158 0.0 4.2 1.4 9.9 08043 08 043 CO Fremont 8.7 175 0.0 0.0 0.0 9.4 08045 08 045 CO Garfield 8.7 175 0.0 0.0 0.0 9.4 08047 08 047 CO Gilpin 8.7 175 0.0 0.0 0.0 9.4 08049 08 049 CO Grand 8.7 175 0.0 0.0 0.0 9.4 08051 08 051 CO Gunnison 8.7 175 0.0 0.0 0.0 9.4 08053 08 053 CO Hinsdale 8.7 175 0.0 0.0 0.0 9.4 08055 08 055 CO Huerfano 8.7 175 0.0 0.0 0.0 9.4 08057 08 057 CO Jackson 8.7 175 0.0 0.0 0.0 9.4 08059 08 059 CO Jefferson 9.0 158 0.0 4.2 1.4 9.9 08061 08 061 CO Kiowa 8.7 175 0.0 0.0 0.0 9.4 08063 08 063 CO Kit Carson 8.7 175 0.0 0.0 0.0 9.4 08065 08 065 CO Lake 8.7 175 0.0 0.0 0.0 9.4 08067 08 067 CO La Plata 8.7 175 0.0 0.0 0.0 9.4 08069 08 069 CO Larimer 9.0 158 0.0 4.2 1.4 9.9 08071 08 071 CO Las Animas 8.7 175 0.0 0.0 0.0 9.4 08073 08 073 CO Lincoln 8.7 175 0.0 0.0 0.0 9.4 
Spring 2002 Gasoline Properties Fall 2002 Gasoline P

State County RVP Sulfur MTBE EtOH Oxygen RVP Sulfu

FIPS No. No. State County (psi) (ppm) (vol%) (vol%) (wt%) (psi) (ppm08075 08 075 CO Logan 8.7 175 0.0 0.0 0.0 9.4 08077 08 077 CO Mesa 8.7 175 0.0 0.0 0.0 9.4 08079 08 079 CO Mineral 8.7 175 0.0 0.0 0.0 9.4 08081 08 081 CO Moffat 8.7 175 0.0 0.0 0.0 9.4 08083 08 083 CO Montezuma 8.7 175 0.0 0.0 0.0 9.4 08085 08 085 CO Montrose 8.7 175 0.0 0.0 0.0 9.4 08087 08 087 CO Morgan 8.7 175 0.0 0.0 0.0 9.4 08089 08 089 CO Otero 8.7 175 0.0 0.0 0.0 9.4 08091 08 091 CO Ouray 8.7 175 0.0 0.0 0.0 9.4 08093 08 093 CO Park 8.7 175 0.0 0.0 0.0 9.4 08095 08 095 CO Phillips 8.7 175 0.0 0.0 0.0 9.4 08097 08 097 CO Pitkin 8.7 175 0.0 0.0 0.0 9.4 08099 08 099 CO Prowers 8.7 175 0.0 0.0 0.0 9.4 08101 08 101 CO Pueblo 8.7 175 0.0 0.0 0.0 9.4 08103 08 103 CO Rio Blanco 8.7 175 0.0 0.0 0.0 9.4 08105 08 105 CO Rio Grande 8.7 175 0.0 0.0 0.0 9.4 08107 08 107 CO Routt 8.7 175 0.0 0.0 0.0 9.4 08109 08 109 CO Saguache 8.7 175 0.0 0.0 0.0 9.4 08111 08 111 CO San Juan 8.7 175 0.0 0.0 0.0 9.4 08113 08 113 CO San Miguel 8.7 175 0.0 0.0 0.0 9.4 08115 08 115 CO Sedgwick 8.7 175 0.0 0.0 0.0 9.4 08117 08 117 CO Summit 8.7 175 0.0 0.0 0.0 9.4 08119 08 119 CO Teller 8.7 175 0.0 0.0 0.0 9.4 08121 08 121 CO Washington 8.7 175 0.0 0.0 0.0 9.4 08123 08 123 CO Weld 8.7 175 0.0 0.0 0.0 9.4 08125 08 125 CO Yuma 8.7 175 0.0 0.0 0.0 9.4 16001 16 001 ID Ada 8.7 250 0.0 0.0 0.0 9.4 216003 16 003 ID Adams 8.7 250 0.0 0.0 0.0 9.4 216005 16 005 ID Bannock 8.7 250 0.0 0.0 0.0 9.4 216007 16 007 ID Bear Lake 8.7 250 0.0 0.0 0.0 9.4 216009 16 009 ID Benewah 8.7 250 0.0 0.0 0.0 10.4 216011 16 011 ID Bingham 8.7 250 0.0 0.0 0.0 9.4 216013 16 013 ID Blaine 8.7 250 0.0 0.0 0.0 9.4 216015 16 015 ID Boise 8.7 250 0.0 0.0 0.0 9.4 216017 16 017 ID Bonner 8.7 250 0.0 0.0 0.0 10.4 216019 16 019 ID Bonneville 8.7 250 0.0 0.0 0.0 9.4 216021 16 021 ID Boundary 8.7 250 0.0 0.0 0.0 10.4 216023 16 023 ID Butte 8.7 250 0.0 0.0 0.0 9.4 216025 16 025 ID Camas 8.7 250 0.0 0.0 0.0 9.4 216027 16 027 ID Canyon 8.7 250 0.0 0.0 0.0 9.4 216029 16 029 ID Caribou 8.7 250 0.0 0.0 0.0 9.4 216031 16 031 ID Cassia 8.7 250 0.0 0.0 0.0 9.4 216033 16 033 ID Clark 8.7 250 0.0 0.0 0.0 9.4 216035 16 035 ID Clearwater 8.7 250 0.0 0.0 0.0 10.4 216037 16 037 ID Custer 8.7 250 0.0 0.0 0.0 9.4 216039 16 039 ID Elmore 8.7 250 0.0 0.0 0.0 9.4 2
Spring 2002 Gasoline Properties Fall 2002 Gasoline P

State County RVP Sulfur MTBE EtOH Oxygen RVP Sulfu

FIPS No. No. State County (psi) (ppm) (vol%) (vol%) (wt%) (psi) (ppm16041 16 041 ID Franklin 8.7 250 0.0 0.0 0.0 9.4 216043 16 043 ID Fremont 8.7 250 0.0 0.0 0.0 9.4 216045 16 045 ID Gem 8.7 250 0.0 0.0 0.0 9.4 216047 16 047 ID Gooding 8.7 250 0.0 0.0 0.0 9.4 216049 16 049 ID Idaho 8.7 250 0.0 0.0 0.0 10.4 216051 16 051 ID Jefferson 8.7 250 0.0 0.0 0.0 9.4 216053 16 053 ID Jerome 8.7 250 0.0 0.0 0.0 9.4 216055 16 055 ID Kootenai 8.7 250 0.0 0.0 0.0 10.4 216057 16 057 ID Latah 8.7 250 0.0 0.0 0.0 10.4 216059 16 059 ID Lemhi 8.7 250 0.0 0.0 0.0 9.4 216061 16 061 ID Lewis 8.7 250 0.0 0.0 0.0 10.4 216063 16 063 ID Lincoln 8.7 250 0.0 0.0 0.0 9.4 216065 16 065 ID Madison 8.7 250 0.0 0.0 0.0 9.4 216067 16 067 ID Minidoka 8.7 250 0.0 0.0 0.0 9.4 216069 16 069 ID Nez Perce 8.7 250 0.0 0.0 0.0 10.4 216071 16 071 ID Oneida 8.7 250 0.0 0.0 0.0 9.4 216073 16 073 ID Owyhee 8.7 250 0.0 0.0 0.0 9.4 216075 16 075 ID Payette 8.7 250 0.0 0.0 0.0 9.4 216077 16 077 ID Power 8.7 250 0.0 0.0 0.0 9.4 216079 16 079 ID Shoshone 8.7 250 0.0 0.0 0.0 10.4 216081 16 081 ID Teton 8.7 250 0.0 0.0 0.0 9.4 216083 16 083 ID Twin Falls 8.7 250 0.0 0.0 0.0 9.4 216085 16 085 ID Valley 8.7 250 0.0 0.0 0.0 9.4 216087 16 087 ID Washington 8.7 250 0.0 0.0 0.0 9.4 230001 30 001 MT Beaverhead 8.7 397 0.0 0.0 0.0 10.9 30003 30 003 MT Big Horn 8.7 397 0.0 0.0 0.0 10.9 30005 30 005 MT Blaine 8.7 397 0.0 0.0 0.0 10.9 330007 30 007 MT Broadwater 8.7 397 0.0 0.0 0.0 10.9 330009 30 009 MT Carbon 8.7 397 0.0 0.0 0.0 10.9 330011 30 011 MT Carter 8.7 397 0.0 0.0 0.0 10.9 330013 30 013 MT Cascade 8.7 397 0.0 0.0 0.0 10.9 330015 30 015 MT Chouteau 8.7 397 0.0 0.0 0.0 10.9 330017 30 017 MT Custer 8.7 397 0.0 0.0 0.0 10.9 330019 30 019 MT Daniels 8.7 397 0.0 0.0 0.0 10.9 330021 30 021 MT Dawson 8.7 397 0.0 0.0 0.0 10.9 330023 30 023 MT Deer Lodge 8.7 397 0.0 0.0 0.0 10.9 330025 30 025 MT Fallon 8.7 397 0.0 0.0 0.0 10.9 330027 30 027 MT Fergus 8.7 397 0.0 0.0 0.0 10.9 330029 30 029 MT Flat Head 8.7 397 0.0 0.0 0.0 10.9 330031 30 031 MT Gallatin 8.7 397 0.0 0.0 0.0 10.9 330033 30 033 MT Garfield 8.7 397 0.0 0.0 0.0 10.9 330035 30 035 MT Glacier 8.7 397 0.0 0.0 0.0 10.9 330037 30 037 MT Golden Valley 8.7 397 0.0 0.0 0.0 10.9 330039 30 039 MT Granite 8.7 397 0.0 0.0 0.0 10.9 330041 30 041 MT Hill 8.7 397 0.0 0.0 0.0 10.9 330043 30 043 MT Jefferson 8.7 397 0.0 0.0 0.0 10.9 3
Spring 2002 Gasoline Properties Fall 2002 Gasoline P

State County RVP Sulfur MTBE EtOH Oxygen RVP Sulfu

FIPS No. No. State County (psi) (ppm) (vol%) (vol%) (wt%) (psi) (ppm30045 30 045 MT Judith Basin 8.7 397 0.0 0.0 0.0 10.9 330047 30 047 MT Lake 8.7 397 0.0 0.0 0.0 10.9 330049 30 049 MT Lewis and Clark 8.7 397 0.0 0.0 0.0 10.9 330051 30 051 MT Liberty 8.7 397 0.0 0.0 0.0 10.9 330053 30 053 MT Lincoln 8.7 397 0.0 0.0 0.0 10.9 330055 30 055 MT Mc Cone 8.7 397 0.0 0.0 0.0 10.9 330057 30 057 MT Madison 8.7 397 0.0 0.0 0.0 10.9 330059 30 059 MT Meagher 8.7 397 0.0 0.0 0.0 10.9 330061 30 061 MT Mineral 8.7 397 0.0 0.0 0.0 10.9 330063 30 063 MT Missoula 8.7 377 0.0 0.0 0.0 10.9 330065 30 065 MT Musselshell 8.7 397 0.0 0.0 0.0 10.9 330067 30 067 MT Park 8.7 397 0.0 0.0 0.0 10.9 330069 30 069 MT Petroleum 8.7 397 0.0 0.0 0.0 10.9 330071 30 071 MT Phillips 8.7 397 0.0 0.0 0.0 10.9 330073 30 073 MT Pondera 8.7 397 0.0 0.0 0.0 10.9 330075 30 075 MT Powder River 8.7 397 0.0 0.0 0.0 10.9 330077 30 077 MT Powell 8.7 397 0.0 0.0 0.0 10.9 330079 30 079 MT Prairie 8.7 397 0.0 0.0 0.0 10.9 330081 30 081 MT Ravalli 8.7 397 0.0 0.0 0.0 10.9 330083 30 083 MT Richland 8.7 397 0.0 0.0 0.0 10.9 330085 30 085 MT Roosevelt 8.7 397 0.0 0.0 0.0 10.9 330087 30 087 MT Rosebud 8.7 397 0.0 0.0 0.0 10.9 330089 30 089 MT Sanders 8.7 397 0.0 0.0 0.0 10.9 30091 30 091 MT Sheridan 8.7 397 0.0 0.0 0.0 10.9 30093 30 093 MT Silver Bow 8.7 397 0.0 0.0 0.0 10.9 330095 30 095 MT Stillwater 8.7 397 0.0 0.0 0.0 10.9 330097 30 097 MT Sweet Grass 8.7 397 0.0 0.0 0.0 10.9 330099 30 099 MT Teton 8.7 397 0.0 0.0 0.0 10.9 330101 30 101 MT Toole 8.7 397 0.0 0.0 0.0 10.9 330103 30 103 MT Treasure 8.7 397 0.0 0.0 0.0 10.9 330105 30 105 MT Valley 8.7 397 0.0 0.0 0.0 10.9 330107 30 107 MT Wheatland 8.7 397 0.0 0.0 0.0 10.9 330109 30 109 MT Wibaux 8.7 397 0.0 0.0 0.0 10.9 30111 30 111 MT Yellowstone 8.7 397 0.0 0.0 0.0 10.9 330113 30 113 MT Yellowstone Park 8.7 397 0.0 0.0 0.0 10.9 332001 32 001 NV Churchill 8.7 23 2.9 0.0 0.5 9.3 32003 32 003 NV Clark 8.7 23 0.2 0.0 0.0 8.7 32005 32 005 NV Douglas 8.7 23 2.9 0.0 0.5 9.3 32007 32 007 NV Elko 8.7 23 2.9 0.0 0.5 9.3 32009 32 009 NV Esmeralda 8.7 23 0.2 0.0 0.0 8.7 32011 32 011 NV Eureka 8.7 23 2.9 0.0 0.5 9.3 32013 32 013 NV Humboldt 8.7 23 2.9 0.0 0.5 9.3 32015 32 015 NV Lander 8.7 23 2.9 0.0 0.5 9.3 32017 32 017 NV Lincoln 8.7 23 0.2 0.0 0.0 8.7 32019 32 019 NV Lyon 8.7 23 2.9 0.0 0.5 9.3 32021 32 021 NV Mineral 8.7 23 0.2 0.0 0.0 8.7 
Spring 2002 Gasoline Properties Fall 2002 Gasoline P

State County RVP Sulfur MTBE EtOH Oxygen RVP Sulfu

FIPS No. No. State County (psi) (ppm) (vol%) (vol%) (wt%) (psi) (ppm32023 32 023 NV Nye 8.7 23 0.2 0.0 0.0 8.7 32027 32 027 NV Pershing 8.7 23 2.9 0.0 0.5 9.3 32029 32 029 NV Storey 8.7 23 2.9 0.0 0.5 9.3 32031 32 031 NV Washoe 8.7 23 2.9 0.0 0.5 9.6 32033 32 033 NV White Pine 8.7 23 2.9 0.0 0.5 9.3 32510 32 510 NV Carson City 8.7 23 2.9 0.0 0.5 9.3 35001 35 001 NM Bernalillo 8.8 205 2.0 2.0 1.0 9.7 235003 35 003 NM Catron 8.7 253 0.0 0.0 0.0 9.3 235005 35 005 NM Chaves 8.7 253 0.0 0.0 0.0 9.3 235006 35 006 NM Cibola 8.7 253 0.0 0.0 0.0 9.3 235007 35 007 NM Colfax 8.7 253 0.0 0.0 0.0 9.3 235009 35 009 NM Curry 8.7 253 0.0 0.0 0.0 9.3 235011 35 011 NM De Baca 8.7 253 0.0 0.0 0.0 9.3 235013 35 013 NM Dona Ana 8.7 253 0.0 0.0 0.0 9.3 235015 35 015 NM Eddy 8.7 253 0.0 0.0 0.0 9.3 235017 35 017 NM Grant 8.7 253 0.0 0.0 0.0 9.3 235019 35 019 NM Guadalupe 8.7 253 0.0 0.0 0.0 9.3 235021 35 021 NM Harding 8.7 253 0.0 0.0 0.0 9.3 235023 35 023 NM Hidalgo 8.7 253 0.0 0.0 0.0 9.3 235025 35 025 NM Lea 8.7 253 0.0 0.0 0.0 9.3 235027 35 027 NM Lincoln 8.7 253 0.0 0.0 0.0 9.3 235028 35 028 NM Los Alamos 8.8 205 2.0 2.0 1.0 9.7 235029 35 029 NM Luna 8.7 253 0.0 0.0 0.0 9.3 235031 35 031 NM Mc Kinley 8.7 253 0.0 0.0 0.0 9.3 235033 35 033 NM Mora 8.7 253 0.0 0.0 0.0 9.3 235035 35 035 NM Otero 8.7 253 0.0 0.0 0.0 9.3 235037 35 037 NM Quay 8.7 253 0.0 0.0 0.0 9.3 235039 35 039 NM Rio Arriba 8.7 253 0.0 0.0 0.0 9.3 235041 35 041 NM Roosevelt 8.7 253 0.0 0.0 0.0 9.3 235043 35 043 NM Sandoval 8.8 205 2.0 2.0 1.0 9.7 235045 35 045 NM San Juan 8.7 253 0.0 0.0 0.0 9.3 235047 35 047 NM San Miguel 8.7 253 0.0 0.0 0.0 9.3 235049 35 049 NM Santa Fe 8.8 205 2.0 2.0 1.0 9.7 235051 35 051 NM Sierra 8.7 253 0.0 0.0 0.0 9.3 235053 35 053 NM Socorro 8.7 253 0.0 0.0 0.0 9.3 235055 35 055 NM Taos 8.7 253 0.0 0.0 0.0 9.3 235057 35 057 NM Torrance 8.8 205 2.0 2.0 1.0 9.7 235059 35 059 NM Union 8.7 253 0.0 0.0 0.0 9.3 235061 35 061 NM Valencia 8.8 205 2.0 2.0 1.0 9.7 238001 38 001 ND Adams 8.7 417 0.0 0.0 0.0 10.9 438003 38 003 ND Barnes 8.7 245 0.0 0.0 0.0 10.6 238005 38 005 ND Benson 8.7 245 0.0 0.0 0.0 10.6 238007 38 007 ND Billings 8.7 417 0.0 0.0 0.0 10.9 438009 38 009 ND Bottineau 8.7 245 0.0 0.0 0.0 10.6 238011 38 011 ND Bowman 8.7 417 0.0 0.0 0.0 10.9 438013 38 013 ND Burke 8.7 417 0.0 0.0 0.0 10.9 4
Spring 2002 Gasoline Properties Fall 2002 Gasoline P

State County RVP Sulfur MTBE EtOH Oxygen RVP Sulfu

FIPS No. No. State County (psi) (ppm) (vol%) (vol%) (wt%) (psi) (ppm38015 38 015 ND Burleigh 8.7 245 0.0 0.0 0.0 10.6 238017 38 017 ND Cass 8.7 245 0.0 0.0 0.0 10.6 238019 38 019 ND Cavalier 8.7 245 0.0 0.0 0.0 10.6 238021 38 021 ND Dickey 8.7 245 0.0 0.0 0.0 10.6 238023 38 023 ND Divide 8.7 417 0.0 0.0 0.0 10.9 438025 38 025 ND Dunn 8.7 417 0.0 0.0 0.0 10.9 438027 38 027 ND Eddy 8.7 245 0.0 0.0 0.0 10.6 238029 38 029 ND Emmons 8.7 245 0.0 0.0 0.0 10.6 238031 38 031 ND Foster 8.7 245 0.0 0.0 0.0 10.6 238033 38 033 ND Golden Valley 8.7 417 0.0 0.0 0.0 10.9 438035 38 035 ND Grand Forks 8.7 245 0.0 0.0 0.0 10.6 238037 38 037 ND Grant 8.7 245 0.0 0.0 0.0 10.6 238039 38 039 ND Griggs 8.7 245 0.0 0.0 0.0 10.6 238041 38 041 ND Hettinger 8.7 417 0.0 0.0 0.0 10.9 438043 38 043 ND Kidder 8.7 245 0.0 0.0 0.0 10.6 238045 38 045 ND La Moure 8.7 245 0.0 0.0 0.0 10.6 238047 38 047 ND Logan 8.7 245 0.0 0.0 0.0 10.6 238049 38 049 ND Mc Henry 8.7 245 0.0 0.0 0.0 10.6 238051 38 051 ND Mc Intosh 8.7 245 0.0 0.0 0.0 10.6 238053 38 053 ND Mc Kenzie 8.7 417 0.0 0.0 0.0 10.9 438055 38 055 ND Mc Lean 8.7 245 0.0 0.0 0.0 10.6 238057 38 057 ND Mercer 8.7 245 0.0 0.0 0.0 10.6 238059 38 059 ND Morton 8.7 245 0.0 0.0 0.0 10.6 238061 38 061 ND Mountrail 8.7 417 0.0 0.0 0.0 10.9 438063 38 063 ND Nelson 8.7 245 0.0 0.0 0.0 10.6 238065 38 065 ND Oliver 8.7 245 0.0 0.0 0.0 10.6 238067 38 067 ND Pembina 8.7 245 0.0 0.0 0.0 10.6 238069 38 069 ND Pierce 8.7 245 0.0 0.0 0.0 10.6 238071 38 071 ND Ramsey 8.7 245 0.0 0.0 0.0 10.6 238073 38 073 ND Ransom 8.7 245 0.0 0.0 0.0 10.6 238075 38 075 ND Renville 8.7 245 0.0 0.0 0.0 10.6 238077 38 077 ND Richland 8.7 245 0.0 0.0 0.0 10.6 238079 38 079 ND Rolette 8.7 245 0.0 0.0 0.0 10.6 238081 38 081 ND Sargent 8.7 245 0.0 0.0 0.0 10.6 238083 38 083 ND Sheridan 8.7 245 0.0 0.0 0.0 10.6 238085 38 085 ND Sioux 8.7 245 0.0 0.0 0.0 10.6 238087 38 087 ND Slope 8.7 417 0.0 0.0 0.0 10.9 438089 38 089 ND Stark 8.7 417 0.0 0.0 0.0 10.9 438091 38 091 ND Steele 8.7 245 0.0 0.0 0.0 10.6 238093 38 093 ND Stutsman 8.7 245 0.0 0.0 0.0 10.6 238095 38 095 ND Towner 8.7 245 0.0 0.0 0.0 10.6 238097 38 097 ND Traill 8.7 245 0.0 0.0 0.0 10.6 238099 38 099 ND Walsh 8.7 245 0.0 0.0 0.0 10.6 238101 38 101 ND Ward 8.7 245 0.0 0.0 0.0 10.6 238103 38 103 ND Wells 8.7 245 0.0 0.0 0.0 10.6 238105 38 105 ND Williams 8.7 417 0.0 0.0 0.0 10.9 4
Spring 2002 Gasoline Properties Fall 2002 Gasoline P

State County RVP Sulfur MTBE EtOH Oxygen RVP Sulfu

FIPS No. No. State County (psi) (ppm) (vol%) (vol%) (wt%) (psi) (ppm41001 41 001 OR Baker 8.7 254 0.0 0.0 0.0 10.4 241003 41 003 OR Benton 8.7 318 0.0 0.0 0.0 10.8 341005 41 005 OR Clackamas 8.7 238 0.0 0.0 0.0 11.0 241007 41 007 OR Clatsop 8.7 318 0.0 0.0 0.0 10.8 41009 41 009 OR Columbia 8.7 318 0.0 0.0 0.0 10.8 341011 41 011 OR Coos 8.7 318 0.0 0.0 0.0 10.8 341013 41 013 OR Crook 8.7 254 0.0 0.0 0.0 10.4 241015 41 015 OR Curry 8.7 318 0.0 0.0 0.0 10.8 341017 41 017 OR Deschutes 8.7 254 0.0 0.0 0.0 10.4 241019 41 019 OR Douglas 8.7 318 0.0 0.0 0.0 10.8 341021 41 021 OR Gilliam 8.7 254 0.0 0.0 0.0 10.4 241023 41 023 OR Grant 8.7 254 0.0 0.0 0.0 10.4 241025 41 025 OR Harney 8.7 254 0.0 0.0 0.0 10.4 241027 41 027 OR Hood River 8.7 254 0.0 0.0 0.0 10.4 241029 41 029 OR Jackson 8.7 238 0.0 0.0 0.0 11.0 241031 41 031 OR Jefferson 8.7 254 0.0 0.0 0.0 10.4 241033 41 033 OR Josephine 8.7 318 0.0 0.0 0.0 10.8 341035 41 035 OR Klamath 8.7 318 0.0 0.0 0.0 10.8 341037 41 037 OR Lake 8.7 254 0.0 0.0 0.0 10.4 241039 41 039 OR Lane 8.7 318 0.0 0.0 0.0 10.8 341041 41 041 OR Lincoln 8.7 318 0.0 0.0 0.0 10.8 341043 41 043 OR Linn 8.7 318 0.0 0.0 0.0 10.8 341045 41 045 OR Malheur 8.7 254 0.0 0.0 0.0 10.4 241047 41 047 OR Marion 8.7 318 0.0 0.0 0.0 10.8 341049 41 049 OR Morrow 8.7 254 0.0 0.0 0.0 10.4 241051 41 051 OR Multnomah 8.7 238 0.0 0.0 0.0 11.0 241053 41 053 OR Polk 8.7 318 0.0 0.0 0.0 10.8 341055 41 055 OR Sherman 8.7 254 0.0 0.0 0.0 10.4 241057 41 057 OR Tillamook 8.7 318 0.0 0.0 0.0 10.8 341059 41 059 OR Umatilla 8.7 254 0.0 0.0 0.0 10.4 241061 41 061 OR Union 8.7 254 0.0 0.0 0.0 10.4 241063 41 063 OR Wallowa 8.7 254 0.0 0.0 0.0 10.4 241065 41 065 OR Wasco 8.7 254 0.0 0.0 0.0 10.4 241067 41 067 OR Washington 8.7 238 0.0 0.0 0.0 11.0 241069 41 069 OR Wheeler 8.7 254 0.0 0.0 0.0 10.4 241071 41 071 OR Yamhill 8.7 238 0.0 0.0 0.0 11.0 246003 46 003 SD Aurora 8.7 243 0.0 0.0 0.0 9.5 246005 46 005 SD Beadle 8.7 243 0.0 0.0 0.0 9.5 246007 46 007 SD Bennett 8.7 243 0.0 0.0 0.0 9.5 246009 46 009 SD Bon Homme 8.7 243 0.0 0.0 0.0 9.5 246011 46 011 SD Brookings 8.7 243 0.0 0.0 0.0 9.5 246013 46 013 SD Brown 8.7 243 0.0 0.0 0.0 9.5 246015 46 015 SD Brule 8.7 243 0.0 0.0 0.0 9.5 246017 46 017 SD Buffalo 8.7 243 0.0 0.0 0.0 9.5 246019 46 019 SD Butte 8.7 259 0.0 0.0 0.0 9.4 246021 46 021 SD Campbell 8.7 243 0.0 0.0 0.0 9.5 2
Spring 2002 Gasoline Properties Fall 2002 Gasoline P

State County RVP Sulfur MTBE EtOH Oxygen RVP Sulfu

FIPS No. No. State County (psi) (ppm) (vol%) (vol%) (wt%) (psi) (ppm46023 46 023 SD Charles Mix 8.7 243 0.0 0.0 0.0 9.5 246025 46 025 SD Clark 8.7 243 0.0 0.0 0.0 9.5 246027 46 027 SD Clay 8.7 243 0.0 0.0 0.0 9.5 246029 46 029 SD Codington 8.7 243 0.0 0.0 0.0 9.5 246031 46 031 SD Corson 8.7 243 0.0 0.0 0.0 9.5 246033 46 033 SD Custer 8.7 259 0.0 0.0 0.0 9.4 246035 46 035 SD Davison 8.7 243 0.0 0.0 0.0 9.5 246037 46 037 SD Day 8.7 243 0.0 0.0 0.0 9.5 246039 46 039 SD Deuel 8.7 243 0.0 0.0 0.0 9.5 246041 46 041 SD Dewey 8.7 243 0.0 0.0 0.0 9.5 246043 46 043 SD Douglas 8.7 243 0.0 0.0 0.0 9.5 246045 46 045 SD Edmunds 8.7 243 0.0 0.0 0.0 9.5 246047 46 047 SD Fall River 8.7 259 0.0 0.0 0.0 9.4 246049 46 049 SD Faulk 8.7 243 0.0 0.0 0.0 9.5 246051 46 051 SD Grant 8.7 243 0.0 0.0 0.0 9.5 246053 46 053 SD Gregory 8.7 243 0.0 0.0 0.0 9.5 246055 46 055 SD Haakon 8.7 243 0.0 0.0 0.0 9.5 246057 46 057 SD Hamlin 8.7 243 0.0 0.0 0.0 9.5 246059 46 059 SD Hand 8.7 243 0.0 0.0 0.0 9.5 246061 46 061 SD Hanson 8.7 243 0.0 0.0 0.0 9.5 246063 46 063 SD Harding 8.7 259 0.0 0.0 0.0 9.4 246065 46 065 SD Hughes 8.7 243 0.0 0.0 0.0 9.5 246067 46 067 SD Hutchinson 8.7 243 0.0 0.0 0.0 9.5 246069 46 069 SD Hyde 8.7 243 0.0 0.0 0.0 9.5 246071 46 071 SD Jackson 8.7 243 0.0 0.0 0.0 9.5 246073 46 073 SD Jerauld 8.7 243 0.0 0.0 0.0 9.5 246075 46 075 SD Jones 8.7 243 0.0 0.0 0.0 9.5 246077 46 077 SD Kingsbury 8.7 243 0.0 0.0 0.0 9.5 246079 46 079 SD Lake 8.7 243 0.0 0.0 0.0 9.5 246081 46 081 SD Lawrence 8.7 259 0.0 0.0 0.0 9.4 246083 46 083 SD Lincoln 8.7 243 0.0 0.0 0.0 9.5 246085 46 085 SD Lyman 8.7 243 0.0 0.0 0.0 9.5 246087 46 087 SD Mc Cook 8.7 243 0.0 0.0 0.0 9.5 246089 46 089 SD Mc Pherson 8.7 243 0.0 0.0 0.0 9.5 246091 46 091 SD Marshall 8.7 243 0.0 0.0 0.0 9.5 246093 46 093 SD Meade 8.7 259 0.0 0.0 0.0 9.4 246095 46 095 SD Mellette 8.7 243 0.0 0.0 0.0 9.5 246097 46 097 SD Miner 8.7 243 0.0 0.0 0.0 9.5 246099 46 099 SD Minnehaha 8.7 243 0.0 0.0 0.0 9.5 246101 46 101 SD Moody 8.7 243 0.0 0.0 0.0 9.5 246103 46 103 SD Pennington 8.7 259 0.0 0.0 0.0 9.4 246105 46 105 SD Perkins 8.7 259 0.0 0.0 0.0 9.4 246107 46 107 SD Potter 8.7 243 0.0 0.0 0.0 9.5 246109 46 109 SD Roberts 8.7 243 0.0 0.0 0.0 9.5 246111 46 111 SD Sanborn 8.7 243 0.0 0.0 0.0 9.5 246113 46 113 SD Shannon 8.7 259 0.0 0.0 0.0 9.4 2
Spring 2002 Gasoline Properties Fall 2002 Gasoline P

State County RVP Sulfur MTBE EtOH Oxygen RVP Sulfu

FIPS No. No. State County (psi) (ppm) (vol%) (vol%) (wt%) (psi) (ppm46115 46 115 SD Spink 8.7 243 0.0 0.0 0.0 9.5 246117 46 117 SD Stanley 8.7 243 0.0 0.0 0.0 9.5 246119 46 119 SD Sully 8.7 243 0.0 0.0 0.0 9.5 246121 46 121 SD Todd 8.7 243 0.0 0.0 0.0 9.5 246123 46 123 SD Tripp 8.7 243 0.0 0.0 0.0 9.5 246125 46 125 SD Turner 8.7 243 0.0 0.0 0.0 9.5 246127 46 127 SD Union 8.7 243 0.0 0.0 0.0 9.5 246129 46 129 SD Walworth 8.7 243 0.0 0.0 0.0 9.5 246135 46 135 SD Yankton 8.7 243 0.0 0.0 0.0 9.5 246137 46 137 SD Ziebach 8.7 243 0.0 0.0 0.0 9.5 249001 49 001 UT Beaver 8.7 173 0.0 0.0 0.0 9.4 49003 49 003 UT Box Elder 8.7 173 0.0 0.0 0.0 9.4 49005 49 005 UT Cache 8.7 173 0.0 0.0 0.0 9.4 49007 49 007 UT Carbon 8.7 173 0.0 0.0 0.0 9.4 49009 49 009 UT Daggett 8.7 173 0.0 0.0 0.0 9.4 49011 49 011 UT Davis 8.7 173 0.0 0.0 0.0 9.4 49013 49 013 UT Duchesne 8.7 173 0.0 0.0 0.0 9.4 49015 49 015 UT Emery 8.7 173 0.0 0.0 0.0 9.4 49017 49 017 UT Garfield 8.7 173 0.0 0.0 0.0 9.4 49019 49 019 UT Grand 8.7 173 0.0 0.0 0.0 9.4 49021 49 021 UT Iron 8.7 173 0.0 0.0 0.0 9.4 49023 49 023 UT Juab 8.7 173 0.0 0.0 0.0 9.4 49025 49 025 UT Kane 8.7 173 0.0 0.0 0.0 9.4 49027 49 027 UT Millard 8.7 173 0.0 0.0 0.0 9.4 49029 49 029 UT Morgan 8.7 173 0.0 0.0 0.0 9.4 49031 49 031 UT Piute 8.7 173 0.0 0.0 0.0 9.4 49033 49 033 UT Rich 8.7 173 0.0 0.0 0.0 9.4 49035 49 035 UT Salt Lake 8.7 173 0.0 0.0 0.0 9.4 49037 49 037 UT San Juan 8.7 173 0.0 0.0 0.0 9.4 49039 49 039 UT Sanpete 8.7 173 0.0 0.0 0.0 9.4 49041 49 041 UT Sevier 8.7 173 0.0 0.0 0.0 9.4 49043 49 043 UT Summit 8.7 173 0.0 0.0 0.0 9.4 49045 49 045 UT Tooele 8.7 173 0.0 0.0 0.0 9.4 49047 49 047 UT Uintah 8.7 173 0.0 0.0 0.0 9.4 49049 49 049 UT Utah 8.7 184 0.0 0.0 0.0 9.6 49051 49 051 UT Wasatch 8.7 173 0.0 0.0 0.0 9.4 49053 49 053 UT Washington 8.7 173 0.0 0.0 0.0 9.4 49055 49 055 UT Wayne 8.7 173 0.0 0.0 0.0 9.4 49057 49 057 UT Weber 8.7 173 0.0 0.0 0.0 9.4 53001 53 001 WA Adams 8.7 250 0.0 0.0 0.0 10.4 253003 53 003 WA Asotin 8.7 250 0.0 0.0 0.0 10.4 253005 53 005 WA Benton 8.7 250 0.0 0.0 0.0 10.4 253007 53 007 WA Chelan 8.7 250 0.0 0.0 0.0 10.4 253009 53 009 WA Clallam 8.7 250 0.0 0.0 0.0 10.4 253011 53 011 WA Clark 8.7 299 0.0 2.7 0.9 11.0 253013 53 013 WA Columbia 8.7 250 0.0 0.0 0.0 10.4 2
Spring 2002 Gasoline Properties Fall 2002 Gasoline P

State County RVP Sulfur MTBE EtOH Oxygen RVP Sulfu

FIPS No. No. State County (psi) (ppm) (vol%) (vol%) (wt%) (psi) (ppm53015 53 015 WA Cowlitz 8.7 299 0.0 2.7 0.9 11.0 253017 53 017 WA Douglas 8.7 250 0.0 0.0 0.0 10.4 253019 53 019 WA Ferry 8.7 250 0.0 0.0 0.0 10.4 253021 53 021 WA Franklin 8.7 250 0.0 0.0 0.0 10.4 253023 53 023 WA Garfield 8.7 250 0.0 0.0 0.0 10.4 253025 53 025 WA Grant 8.7 250 0.0 0.0 0.0 10.4 253027 53 027 WA Grays Harbor 8.7 299 0.0 2.7 0.9 11.0 253029 53 029 WA Island 8.7 299 0.0 2.7 0.9 11.0 253031 53 031 WA Jefferson 8.7 299 0.0 2.7 0.9 11.0 253033 53 033 WA King 8.7 299 0.0 2.7 0.9 11.0 253035 53 035 WA Kitsap 8.7 299 0.0 2.7 0.9 11.0 253037 53 037 WA Kittitas 8.7 250 0.0 0.0 0.0 10.4 253039 53 039 WA Klickitat 8.7 250 0.0 0.0 0.0 10.4 253041 53 041 WA Lewis 8.7 299 0.0 2.7 0.9 11.0 253043 53 043 WA Lincoln 8.7 250 0.0 0.0 0.0 10.4 253045 53 045 WA Mason 8.7 299 0.0 2.7 0.9 11.0 253047 53 047 WA Okanogan 8.7 250 0.0 0.0 0.0 10.4 253049 53 049 WA Pacific 8.7 299 0.0 2.7 0.9 11.0 253051 53 051 WA Pend Oreille 8.7 250 0.0 0.0 0.0 10.4 253053 53 053 WA Pierce 8.7 299 0.0 2.7 0.9 11.0 253055 53 055 WA San Juan 8.7 299 0.0 2.7 0.9 11.0 253057 53 057 WA Skagit 8.7 299 0.0 2.7 0.9 11.0 253059 53 059 WA Skamania 8.7 299 0.0 2.7 0.9 11.0 253061 53 061 WA Snohomish 8.7 299 0.0 2.7 0.9 11.0 253063 53 063 WA Spokane 8.7 281 0.0 0.0 0.0 10.7 253065 53 065 WA Stevens 8.7 250 0.0 0.0 0.0 10.4 253067 53 067 WA Thurston 8.7 299 0.0 2.7 0.9 11.0 253069 53 069 WA Wahkiakum 8.7 299 0.0 2.7 0.9 11.0 253071 53 071 WA Walla Walla 8.7 250 0.0 0.0 0.0 10.4 253073 53 073 WA Whatcom 8.7 299 0.0 2.7 0.9 11.0 253075 53 075 WA Whitman 8.7 250 0.0 0.0 0.0 10.4 253077 53 077 WA Yakima 8.7 250 0.0 0.0 0.0 10.4 256001 56 001 WY Albany 8.7 244 0.0 0.0 0.0 9.4 256003 56 003 WY Big Horn 8.7 244 0.0 0.0 0.0 9.4 256005 56 005 WY Campbell 8.7 244 0.0 0.0 0.0 9.4 256007 56 007 WY Carbon 8.7 244 0.0 0.0 0.0 9.4 256009 56 009 WY Converse 8.7 244 0.0 0.0 0.0 9.4 256011 56 011 WY Crook 8.7 244 0.0 0.0 0.0 9.4 256013 56 013 WY Fremont 8.7 244 0.0 0.0 0.0 9.4 256015 56 015 WY Goshen 8.7 244 0.0 0.0 0.0 9.4 256017 56 017 WY Hot Springs 8.7 244 0.0 0.0 0.0 9.4 256019 56 019 WY Johnson 8.7 244 0.0 0.0 0.0 9.4 256021 56 021 WY Laramie 8.7 244 0.0 0.0 0.0 9.4 256023 56 023 WY Lincoln 8.7 244 0.0 0.0 0.0 9.4 256025 56 025 WY Natrona 8.7 244 0.0 0.0 0.0 9.4 256027 56 027 WY Niobrara 8.7 244 0.0 0.0 0.0 9.4 2
Spring 2002 Gasoline Properties Fall 2002 Gasoline P

State County RVP Sulfur MTBE EtOH Oxygen RVP Sulfu

FIPS No. No. State County (psi) (ppm) (vol%) (vol%) (wt%) (psi) (ppm56029 56 029 WY Park 8.7 244 0.0 0.0 0.0 9.4 256031 56 031 WY Platte 8.7 244 0.0 0.0 0.0 9.4 256033 56 033 WY Sheridan 8.7 244 0.0 0.0 0.0 9.4 256035 56 035 WY Sublette 8.7 244 0.0 0.0 0.0 9.4 256037 56 037 WY Sweetwater 8.7 244 0.0 0.0 0.0 9.4 256039 56 039 WY Teton 8.7 244 0.0 0.0 0.0 9.4 256041 56 041 WY Uinta 8.7 244 0.0 0.0 0.0 9.4 256043 56 043 WY Washakie 8.7 244 0.0 0.0 0.0 9.4 256045 56 045 WY Weston 8.7 244 0.0 0.0 0.0 9.4 2
On-road and off-road diesel fuel properties by county and season. 

2002 Diesel Fuel Sulfur Levels (ppm) 2002 Diese

State County Data On-Highway Data 

FIPS No. No. State County Source Winter Summer Spring Fall Source Winter 02013 02 013 AK Aleutians East B ADEC-RRL 750 750 750 750 ADEC-RRL 750 02016 02 016 AK Aleutians West C ADEC-RRL 750 750 750 750 ADEC-RRL 750 02020 02 020 AK Anchorage Ed ADEC-URB 750 2500 750 750 ADEC-URB 750 02050 02 050 AK Bethel Ed ADEC-RRL 750 750 750 750 ADEC-RRL 750 02060 02 060 AK Bristol Bay Borough ADEC-RRL 750 750 750 750 ADEC-RRL 750 02068 02 068 AK Denali Borough ADEC-RRL 750 750 750 750 ADEC-RRL 750 02070 02 070 AK Dillingham Ed ADEC-RRL 750 750 750 750 ADEC-RRL 750 02090 02 090 AK Fairbanks Ed ADEC-URB 750 2500 750 750 ADEC-URB 750 02100 02 100 AK Haines Ed AAM-SE 360 340 350 350 NIPER-W 3400 02110 02 110 AK Juneau Ed AAM-SE 360 340 350 350 NIPER-W 3400 02122 02 122 AK Kenai Penninsula ADEC-URB 750 2500 750 750 ADEC-URB 750 02130 02 130 AK Ketchikan Ed AAM-SE 360 340 350 350 NIPER-W 3400 02150 02 150 AK Kodiak Island Ed ADEC-RRL 750 750 750 750 ADEC-RRL 750 02164 02 164 AK Lake and Peninsu ADEC-RRL 750 750 750 750 ADEC-RRL 750 02170 02 170 AK Matanuska-Susitna ADEC-URB 750 2500 750 750 ADEC-URB 750 02180 02 180 AK Nome Ed ADEC-RRL 750 750 750 750 ADEC-RRL 750 02185 02 185 AK North Slope Ed ADEC-RRL 750 750 750 750 ADEC-RRL 750 02188 02 188 AK Northwest Arctic ADEC-RRL 750 750 750 750 ADEC-RRL 750 02201 02 201 AK Prince Of Wales AAM-SE 360 340 350 350 NIPER-W 3400 02220 02 220 AK Sitka Ed AAM-SE 360 340 350 350 NIPER-W 3400 02232 02 232 AK Skagway-Yakutat AAM-SE 360 340 350 350 NIPER-W 3400 02240 02 240 AK Southeast Fairbanks ADEC-URB 750 2500 750 750 ADEC-URB 750 02261 02 261 AK Valdez-Cordova E ADEC-URB 750 2500 750 750 ADEC-URB 750 02270 02 270 AK Wade Hampton Ed ADEC-RRL 750 750 750 750 ADEC-RRL 750 02280 02 280 AK Wrangell-Petersburg AAM-SE 360 340 350 350 NIPER-W 3400 02282 02 282 AK Yakutat Borough ADEC-RRL 750 750 750 750 ADEC-RRL 750 02290 02 290 AK Yukon-Koyukuk Ed ADEC-RRL 750 750 750 750 ADEC-RRL 750 04001 04 001 AZ Apache NIPER-RM 350 350 350 350 NIPER-RM 2400 04003 04 003 AZ Cochise NIPER-RM 350 350 350 350 NIPER-RM 2400 04005 04 005 AZ Coconino NIPER-N 360 360 360 360 NIPER-W 3400 04007 04 007 AZ Gila NIPER-N 360 360 360 360 NIPER-W 3400 04009 04 009 AZ Graham NIPER-RM 350 350 350 350 NIPER-RM 2400 04011 04 011 AZ Greenlee NIPER-RM 350 350 350 350 NIPER-RM 2400 04012 04 012 AZ La Paz NIPER-N 360 360 360 360 NIPER-W 3400 04013 04 013 AZ Maricopa NIPER-N 360 360 360 360 NIPER-N 360 04015 04 015 AZ Mohave NIPER-N 360 360 360 360 NIPER-W 3400 04017 04 017 AZ Navajo NIPER-RM 350 350 350 350 NIPER-RM 2400 04019 04 019 AZ Pima NIPER-N 360 360 360 360 NIPER-W 3400 04021 04 021 AZ Pinal NIPER-N 360 360 360 360 NIPER-W 3400 04023 04 023 AZ Santa Cruz NIPER-RM 350 350 350 350 NIPER-RM 2400 04025 04 025 AZ Yavapai NIPER-N 360 360 360 360 NIPER-W 3400 04027 04 027 AZ Yuma NIPER-N 360 360 360 360 NIPER-W 3400 06001 06 001 CA Alameda AAM-LA 70 60 65 65 AAM-LA 70 06003 06 003 CA Alpine AAM-LA 70 60 65 65 AAM-LA 70 06005 06 005 CA Amador AAM-LA 70 60 65 65 AAM-LA 70 
2002 Diesel Fuel Sulfur Levels (ppm) 2002 Diese

State County Data On-Highway Data 

FIPS No. No. State County Source Winter Summer Spring Fall Source Winter 06007 06 007 CA Butte AAM-LA 70 60 65 65 AAM-LA 70 06009 06 009 CA Calaveras AAM-LA 70 60 65 65 AAM-LA 70 06011 06 011 CA Colusa AAM-LA 70 60 65 65 AAM-LA 70 06013 06 013 CA Contra Costa AAM-LA 70 60 65 65 AAM-LA 70 06015 06 015 CA Del Norte AAM-LA 70 60 65 65 AAM-LA 70 06017 06 017 CA El Dorado AAM-LA 70 60 65 65 AAM-LA 70 06019 06 019 CA Fresno AAM-LA 70 60 65 65 AAM-LA 70 06021 06 021 CA Glenn AAM-LA 70 60 65 65 AAM-LA 70 06023 06 023 CA Humboldt AAM-LA 70 60 65 65 AAM-LA 70 06025 06 025 CA Imperial AAM-LA 70 60 65 65 AAM-LA 70 06027 06 027 CA Inyo AAM-LA 70 60 65 65 AAM-LA 70 06029 06 029 CA Kern AAM-LA 70 60 65 65 AAM-LA 70 06031 06 031 CA Kings AAM-LA 70 60 65 65 AAM-LA 70 06033 06 033 CA Lake AAM-LA 70 60 65 65 AAM-LA 70 06035 06 035 CA Lassen AAM-LA 70 60 65 65 AAM-LA 70 06037 06 037 CA Los Angeles AAM-LA 70 60 65 65 AAM-LA 70 06039 06 039 CA Madera AAM-LA 70 60 65 65 AAM-LA 70 06041 06 041 CA Marin AAM-LA 70 60 65 65 AAM-LA 70 06043 06 043 CA Mariposa AAM-LA 70 60 65 65 AAM-LA 70 06045 06 045 CA Mendocino AAM-LA 70 60 65 65 AAM-LA 70 06047 06 047 CA Merced AAM-LA 70 60 65 65 AAM-LA 70 06049 06 049 CA Modoc AAM-LA 70 60 65 65 AAM-LA 70 06051 06 051 CA Mono AAM-LA 70 60 65 65 AAM-LA 70 06053 06 053 CA Monterey AAM-LA 70 60 65 65 AAM-LA 70 06055 06 055 CA Napa AAM-LA 70 60 65 65 AAM-LA 70 06057 06 057 CA Nevada AAM-LA 70 60 65 65 AAM-LA 70 06059 06 059 CA Orange AAM-LA 70 60 65 65 AAM-LA 70 06061 06 061 CA Placer AAM-LA 70 60 65 65 AAM-LA 70 06063 06 063 CA Plumas AAM-LA 70 60 65 65 AAM-LA 70 06065 06 065 CA Riverside AAM-LA 70 60 65 65 AAM-LA 70 06067 06 067 CA Sacramento AAM-LA 70 60 65 65 AAM-LA 70 06069 06 069 CA San Benito AAM-LA 70 60 65 65 AAM-LA 70 06071 06 071 CA San Bernardino AAM-LA 70 60 65 65 AAM-LA 70 06073 06 073 CA San Diego AAM-LA 70 60 65 65 AAM-LA 70 06075 06 075 CA San Francisco AAM-LA 70 60 65 65 AAM-LA 70 06077 06 077 CA San Joaquin AAM-LA 70 60 65 65 AAM-LA 70 06079 06 079 CA San Luis Obispo AAM-LA 70 60 65 65 AAM-LA 70 06081 06 081 CA San Mateo AAM-LA 70 60 65 65 AAM-LA 70 06083 06 083 CA Santa Barbara AAM-LA 70 60 65 65 AAM-LA 70 06085 06 085 CA Santa Clara AAM-LA 70 60 65 65 AAM-LA 70 06087 06 087 CA Santa Cruz AAM-LA 70 60 65 65 AAM-LA 70 06089 06 089 CA Shasta AAM-LA 70 60 65 65 AAM-LA 70 06091 06 091 CA Sierra AAM-LA 70 60 65 65 AAM-LA 70 06093 06 093 CA Siskiyou AAM-LA 70 60 65 65 AAM-LA 70 06095 06 095 CA Solano AAM-LA 70 60 65 65 AAM-LA 70 06097 06 097 CA Sonoma AAM-LA 70 60 65 65 AAM-LA 70 
2002 Diesel Fuel Sulfur Levels (ppm) 2002 Diese

State County Data On-Highway Data 

FIPS No. No. State County Source Winter Summer Spring Fall Source Winter 06099 06 099 CA Stanislaus AAM-LA 70 60 65 65 AAM-LA 70 06101 06 101 CA Sutter AAM-LA 70 60 65 65 AAM-LA 70 06103 06 103 CA Tehama AAM-LA 70 60 65 65 AAM-LA 70 06105 06 105 CA Trinity AAM-LA 70 60 65 65 AAM-LA 70 06107 06 107 CA Tulare AAM-LA 70 60 65 65 AAM-LA 70 06109 06 109 CA Tuolumne AAM-LA 70 60 65 65 AAM-LA 70 06111 06 111 CA Ventura AAM-LA 70 60 65 65 AAM-LA 70 06113 06 113 CA Yolo AAM-LA 70 60 65 65 AAM-LA 70 06115 06 115 CA Yuba AAM-LA 70 60 65 65 AAM-LA 70 08001 08 001 CO Adams AAM-DN 330 330 330 330 NIPER-RM 2400 08003 08 003 CO Alamosa AAM-DN 330 330 330 330 NIPER-RM 2400 08005 08 005 CO Arapahoe AAM-DN 330 330 330 330 NIPER-RM 2400 08007 08 007 CO Archuleta AAM-DN 330 330 330 330 NIPER-RM 2400 08009 08 009 CO Baca AAM-DN 330 330 330 330 NIPER-RM 2400 08011 08 011 CO Bent AAM-DN 330 330 330 330 NIPER-RM 2400 08013 08 013 CO Boulder AAM-DN 330 330 330 330 NIPER-RM 2400 08015 08 015 CO Chaffee AAM-DN 330 330 330 330 NIPER-RM 2400 08017 08 017 CO Cheyenne AAM-DN 330 330 330 330 NIPER-RM 2400 08019 08 019 CO Clear Creek AAM-DN 330 330 330 330 NIPER-RM 2400 08021 08 021 CO Conejos AAM-DN 330 330 330 330 NIPER-RM 2400 08023 08 023 CO Costilla AAM-DN 330 330 330 330 NIPER-RM 2400 08025 08 025 CO Crowley AAM-DN 330 330 330 330 NIPER-RM 2400 08027 08 027 CO Custer AAM-DN 330 330 330 330 NIPER-RM 2400 08029 08 029 CO Delta AAM-DN 330 330 330 330 NIPER-RM 2400 08031 08 031 CO Denver AAM-DN 330 330 330 330 NIPER-RM 2400 08033 08 033 CO Dolores AAM-DN 330 330 330 330 NIPER-RM 2400 08035 08 035 CO Douglas AAM-DN 330 330 330 330 NIPER-RM 2400 08037 08 037 CO Eagle AAM-DN 330 330 330 330 NIPER-RM 2400 08039 08 039 CO Elbert AAM-DN 330 330 330 330 NIPER-RM 2400 08041 08 041 CO El Paso AAM-DN 330 330 330 330 NIPER-RM 2400 08043 08 043 CO Fremont AAM-DN 330 330 330 330 NIPER-RM 2400 08045 08 045 CO Garfield AAM-DN 330 330 330 330 NIPER-RM 2400 08047 08 047 CO Gilpin AAM-DN 330 330 330 330 NIPER-RM 2400 08049 08 049 CO Grand AAM-DN 330 330 330 330 NIPER-RM 2400 08051 08 051 CO Gunnison AAM-DN 330 330 330 330 NIPER-RM 2400 08053 08 053 CO Hinsdale AAM-DN 330 330 330 330 NIPER-RM 2400 08055 08 055 CO Huerfano AAM-DN 330 330 330 330 NIPER-RM 2400 08057 08 057 CO Jackson AAM-DN 330 330 330 330 NIPER-RM 2400 08059 08 059 CO Jefferson AAM-DN 330 330 330 330 NIPER-RM 2400 08061 08 061 CO Kiowa AAM-DN 330 330 330 330 NIPER-RM 2400 08063 08 063 CO Kit Carson AAM-DN 330 330 330 330 NIPER-RM 2400 08065 08 065 CO Lake AAM-DN 330 330 330 330 NIPER-RM 2400 08067 08 067 CO La Plata AAM-DN 330 330 330 330 NIPER-RM 2400 08069 08 069 CO Larimer AAM-DN 330 330 330 330 NIPER-RM 2400 08071 08 071 CO Las Animas AAM-DN 330 330 330 330 NIPER-RM 2400 08073 08 073 CO Lincoln AAM-DN 330 330 330 330 NIPER-RM 2400 
2002 Diesel Fuel Sulfur Levels (ppm) 2002 Diese

State County Data On-Highway Data 

FIPS No. No. State County Source Winter Summer Spring Fall Source Winter 08075 08 075 CO Logan AAM-DN 330 330 330 330 NIPER-RM 2400 08077 08 077 CO Mesa AAM-DN 330 330 330 330 NIPER-RM 2400 08079 08 079 CO Mineral AAM-DN 330 330 330 330 NIPER-RM 2400 08081 08 081 CO Moffat AAM-DN 330 330 330 330 NIPER-RM 2400 08083 08 083 CO Montezuma AAM-DN 330 330 330 330 NIPER-RM 2400 08085 08 085 CO Montrose AAM-DN 330 330 330 330 NIPER-RM 2400 08087 08 087 CO Morgan AAM-DN 330 330 330 330 NIPER-RM 2400 08089 08 089 CO Otero AAM-DN 330 330 330 330 NIPER-RM 2400 08091 08 091 CO Ouray AAM-DN 330 330 330 330 NIPER-RM 2400 08093 08 093 CO Park AAM-DN 330 330 330 330 NIPER-RM 2400 08095 08 095 CO Phillips AAM-DN 330 330 330 330 NIPER-RM 2400 08097 08 097 CO Pitkin AAM-DN 330 330 330 330 NIPER-RM 2400 08099 08 099 CO Prowers AAM-DN 330 330 330 330 NIPER-RM 2400 08101 08 101 CO Pueblo AAM-DN 330 330 330 330 NIPER-RM 2400 08103 08 103 CO Rio Blanco AAM-DN 330 330 330 330 NIPER-RM 2400 08105 08 105 CO Rio Grande AAM-DN 330 330 330 330 NIPER-RM 2400 08107 08 107 CO Routt AAM-DN 330 330 330 330 NIPER-RM 2400 08109 08 109 CO Saguache AAM-DN 330 330 330 330 NIPER-RM 2400 08111 08 111 CO San Juan AAM-DN 330 330 330 330 NIPER-RM 2400 08113 08 113 CO San Miguel AAM-DN 330 330 330 330 NIPER-RM 2400 08115 08 115 CO Sedgwick AAM-DN 330 330 330 330 NIPER-RM 2400 08117 08 117 CO Summit AAM-DN 330 330 330 330 NIPER-RM 2400 08119 08 119 CO Teller AAM-DN 330 330 330 330 NIPER-RM 2400 08121 08 121 CO Washington AAM-DN 330 330 330 330 NIPER-RM 2400 08123 08 123 CO Weld AAM-DN 330 330 330 330 NIPER-RM 2400 08125 08 125 CO Yuma AAM-DN 330 330 330 330 NIPER-RM 2400 16001 16 001 ID Ada NIPER-RM 350 350 350 350 NIPER-RM 2400 16003 16 003 ID Adams NIPER-RM 350 350 350 350 NIPER-RM 2400 16005 16 005 ID Bannock NIPER-RM 350 350 350 350 NIPER-RM 2400 16007 16 007 ID Bear Lake NIPER-RM 350 350 350 350 NIPER-RM 2400 16009 16 009 ID Benewah NIPER-RM 350 350 350 350 NIPER-RM 2400 16011 16 011 ID Bingham NIPER-RM 350 350 350 350 NIPER-RM 2400 16013 16 013 ID Blaine NIPER-RM 350 350 350 350 NIPER-RM 2400 16015 16 015 ID Boise NIPER-RM 350 350 350 350 NIPER-RM 2400 16017 16 017 ID Bonner NIPER-RM 350 350 350 350 NIPER-RM 2400 16019 16 019 ID Bonneville NIPER-RM 350 350 350 350 NIPER-RM 2400 16021 16 021 ID Boundary NIPER-RM 350 350 350 350 NIPER-RM 2400 16023 16 023 ID Butte NIPER-RM 350 350 350 350 NIPER-RM 2400 16025 16 025 ID Camas NIPER-RM 350 350 350 350 NIPER-RM 2400 16027 16 027 ID Canyon NIPER-RM 350 350 350 350 NIPER-RM 2400 16029 16 029 ID Caribou NIPER-RM 350 350 350 350 NIPER-RM 2400 16031 16 031 ID Cassia NIPER-RM 350 350 350 350 NIPER-RM 2400 16033 16 033 ID Clark NIPER-RM 350 350 350 350 NIPER-RM 2400 16035 16 035 ID Clearwater NIPER-RM 350 350 350 350 NIPER-RM 2400 16037 16 037 ID Custer NIPER-RM 350 350 350 350 NIPER-RM 2400 16039 16 039 ID Elmore NIPER-RM 350 350 350 350 NIPER-RM 2400 
2002 Diesel Fuel Sulfur Levels (ppm) 2002 Diese

State County Data On-Highway Data 

FIPS No. No. State County Source Winter Summer Spring Fall Source Winter 16041 16 041 ID Franklin NIPER-RM 350 350 350 350 NIPER-RM 2400 16043 16 043 ID Fremont NIPER-RM 350 350 350 350 NIPER-RM 2400 16045 16 045 ID Gem NIPER-RM 350 350 350 350 NIPER-RM 2400 16047 16 047 ID Gooding NIPER-RM 350 350 350 350 NIPER-RM 2400 16049 16 049 ID Idaho NIPER-RM 350 350 350 350 NIPER-RM 2400 16051 16 051 ID Jefferson NIPER-RM 350 350 350 350 NIPER-RM 2400 16053 16 053 ID Jerome NIPER-RM 350 350 350 350 NIPER-RM 2400 16055 16 055 ID Kootenai NIPER-RM 350 350 350 350 NIPER-RM 2400 16057 16 057 ID Latah NIPER-RM 350 350 350 350 NIPER-RM 2400 16059 16 059 ID Lemhi NIPER-RM 350 350 350 350 NIPER-RM 2400 16061 16 061 ID Lewis NIPER-RM 350 350 350 350 NIPER-RM 2400 16063 16 063 ID Lincoln NIPER-RM 350 350 350 350 NIPER-RM 2400 16065 16 065 ID Madison NIPER-RM 350 350 350 350 NIPER-RM 2400 16067 16 067 ID Minidoka NIPER-RM 350 350 350 350 NIPER-RM 2400 16069 16 069 ID Nez Perce NIPER-RM 350 350 350 350 NIPER-RM 2400 16071 16 071 ID Oneida NIPER-RM 350 350 350 350 NIPER-RM 2400 16073 16 073 ID Owyhee NIPER-RM 350 350 350 350 NIPER-RM 2400 16075 16 075 ID Payette NIPER-RM 350 350 350 350 NIPER-RM 2400 16077 16 077 ID Power NIPER-RM 350 350 350 350 NIPER-RM 2400 16079 16 079 ID Shoshone NIPER-RM 350 350 350 350 NIPER-RM 2400 16081 16 081 ID Teton NIPER-RM 350 350 350 350 NIPER-RM 2400 16083 16 083 ID Twin Falls NIPER-RM 350 350 350 350 NIPER-RM 2400 16085 16 085 ID Valley NIPER-RM 350 350 350 350 NIPER-RM 2400 16087 16 087 ID Washington NIPER-RM 350 350 350 350 NIPER-RM 2400 30001 30 001 MT Beaverhead AAM-BL 270 250 260 260 NIPER-RM 2400 30003 30 003 MT Big Horn AAM-BL 270 250 260 260 NIPER-RM 2400 30005 30 005 MT Blaine AAM-BL 270 250 260 260 NIPER-RM 2400 30007 30 007 MT Broadwater AAM-BL 270 250 260 260 NIPER-RM 2400 30009 30 009 MT Carbon AAM-BL 270 250 260 260 NIPER-RM 2400 30011 30 011 MT Carter AAM-BL 270 250 260 260 NIPER-RM 2400 30013 30 013 MT Cascade AAM-BL 270 250 260 260 NIPER-RM 2400 30015 30 015 MT Chouteau AAM-BL 270 250 260 260 NIPER-RM 2400 30017 30 017 MT Custer AAM-BL 270 250 260 260 NIPER-RM 2400 30019 30 019 MT Daniels AAM-BL 270 250 260 260 NIPER-RM 2400 30021 30 021 MT Dawson AAM-BL 270 250 260 260 NIPER-RM 2400 30023 30 023 MT Deer Lodge AAM-BL 270 250 260 260 NIPER-RM 2400 30025 30 025 MT Fallon AAM-BL 270 250 260 260 NIPER-RM 2400 30027 30 027 MT Fergus AAM-BL 270 250 260 260 NIPER-RM 2400 30029 30 029 MT Flat Head AAM-BL 270 250 260 260 NIPER-RM 2400 30031 30 031 MT Gallatin AAM-BL 270 250 260 260 NIPER-RM 2400 30033 30 033 MT Garfield AAM-BL 270 250 260 260 NIPER-RM 2400 30035 30 035 MT Glacier AAM-BL 270 250 260 260 NIPER-RM 2400 30037 30 037 MT Golden Valley AAM-BL 270 250 260 260 NIPER-RM 2400 30039 30 039 MT Granite AAM-BL 270 250 260 260 NIPER-RM 2400 30041 30 041 MT Hill AAM-BL 270 250 260 260 NIPER-RM 2400 30043 30 043 MT Jefferson AAM-BL 270 250 260 260 NIPER-RM 2400 
2002 Diesel Fuel Sulfur Levels (ppm) 2002 Diese

State County Data On-Highway Data 

FIPS No. No. State County Source Winter Summer Spring Fall Source Winter 30045 30 045 MT Judith Basin AAM-BL 270 250 260 260 NIPER-RM 2400 30047 30 047 MT Lake AAM-BL 270 250 260 260 NIPER-RM 2400 30049 30 049 MT Lewis and Clark AAM-BL 270 250 260 260 NIPER-RM 2400 30051 30 051 MT Liberty AAM-BL 270 250 260 260 NIPER-RM 2400 30053 30 053 MT Lincoln AAM-BL 270 250 260 260 NIPER-RM 2400 30055 30 055 MT Mc Cone AAM-BL 270 250 260 260 NIPER-RM 2400 30057 30 057 MT Madison AAM-BL 270 250 260 260 NIPER-RM 2400 30059 30 059 MT Meagher AAM-BL 270 250 260 260 NIPER-RM 2400 30061 30 061 MT Mineral AAM-BL 270 250 260 260 NIPER-RM 2400 30063 30 063 MT Missoula AAM-BL 270 250 260 260 NIPER-RM 2400 30065 30 065 MT Musselshell AAM-BL 270 250 260 260 NIPER-RM 2400 30067 30 067 MT Park AAM-BL 270 250 260 260 NIPER-RM 2400 30069 30 069 MT Petroleum AAM-BL 270 250 260 260 NIPER-RM 2400 30071 30 071 MT Phillips AAM-BL 270 250 260 260 NIPER-RM 2400 30073 30 073 MT Pondera AAM-BL 270 250 260 260 NIPER-RM 2400 30075 30 075 MT Powder River AAM-BL 270 250 260 260 NIPER-RM 2400 30077 30 077 MT Powell AAM-BL 270 250 260 260 NIPER-RM 2400 30079 30 079 MT Prairie AAM-BL 270 250 260 260 NIPER-RM 2400 30081 30 081 MT Ravalli AAM-BL 270 250 260 260 NIPER-RM 2400 30083 30 083 MT Richland AAM-BL 270 250 260 260 NIPER-RM 2400 30085 30 085 MT Roosevelt AAM-BL 270 250 260 260 NIPER-RM 2400 30087 30 087 MT Rosebud AAM-BL 270 250 260 260 NIPER-RM 2400 30089 30 089 MT Sanders AAM-BL 270 250 260 260 NIPER-RM 2400 30091 30 091 MT Sheridan AAM-BL 270 250 260 260 NIPER-RM 2400 30093 30 093 MT Silver Bow AAM-BL 270 250 260 260 NIPER-RM 2400 30095 30 095 MT Stillwater AAM-BL 270 250 260 260 NIPER-RM 2400 30097 30 097 MT Sweet Grass AAM-BL 270 250 260 260 NIPER-RM 2400 30099 30 099 MT Teton AAM-BL 270 250 260 260 NIPER-RM 2400 30101 30 101 MT Toole AAM-BL 270 250 260 260 NIPER-RM 2400 30103 30 103 MT Treasure AAM-BL 270 250 260 260 NIPER-RM 2400 30105 30 105 MT Valley AAM-BL 270 250 260 260 NIPER-RM 2400 30107 30 107 MT Wheatland AAM-BL 270 250 260 260 NIPER-RM 2400 30109 30 109 MT Wibaux AAM-BL 270 250 260 260 NIPER-RM 2400 30111 30 111 MT Yellowstone AAM-BL 270 250 260 260 NIPER-RM 2400 30113 30 113 MT Yellowstone Park AAM-BL 270 250 260 260 NIPER-RM 2400 32001 32 001 NV Churchill NIPER-M 370 370 370 370 NIPER-W 3400 32003 32 003 NV Clark NIPER-N 360 360 360 360 NIPER-W 3400 32005 32 005 NV Douglas NIPER-M 370 370 370 370 NIPER-W 3400 32007 32 007 NV Elko NIPER-M 370 370 370 370 NIPER-W 3400 32009 32 009 NV Esmeralda NIPER-N 360 360 360 360 NIPER-W 3400 32011 32 011 NV Eureka NIPER-M 370 370 370 370 NIPER-W 3400 32013 32 013 NV Humboldt NIPER-M 370 370 370 370 NIPER-W 3400 32015 32 015 NV Lander NIPER-M 370 370 370 370 NIPER-W 3400 32017 32 017 NV Lincoln NIPER-N 360 360 360 360 NIPER-W 3400 32019 32 019 NV Lyon NIPER-M 370 370 370 370 NIPER-W 3400 32021 32 021 NV Mineral NIPER-N 360 360 360 360 NIPER-W 3400 
2002 Diesel Fuel Sulfur Levels (ppm) 2002 Diese

State County Data On-Highway Data 

FIPS No. No. State County Source Winter Summer Spring Fall Source Winter 32023 32 023 NV Nye NIPER-N 360 360 360 360 NIPER-W 3400 32027 32 027 NV Pershing NIPER-M 370 370 370 370 NIPER-W 3400 32029 32 029 NV Storey NIPER-M 370 370 370 370 NIPER-W 3400 32031 32 031 NV Washoe NIPER-M 370 370 370 370 NIPER-W 3400 32033 32 033 NV White Pine NIPER-M 370 370 370 370 NIPER-W 3400 32510 32 510 NV Carson City NIPER-M 370 370 370 370 NIPER-W 3400 35001 35 001 NM Bernalillo AAM-ALB 330 350 340 340 NIPER-RM 2400 35003 35 003 NM Catron AAM-ALB 330 350 340 340 NIPER-RM 2400 35005 35 005 NM Chaves AAM-ALB 330 350 340 340 NIPER-RM 2400 35006 35 006 NM Cibola AAM-ALB 330 350 340 340 NIPER-RM 2400 35007 35 007 NM Colfax AAM-ALB 330 350 340 340 NIPER-RM 2400 35009 35 009 NM Curry AAM-ALB 330 350 340 340 NIPER-RM 2400 35011 35 011 NM De Baca AAM-ALB 330 350 340 340 NIPER-RM 2400 35013 35 013 NM Dona Ana AAM-ALB 330 350 340 340 NIPER-RM 2400 35015 35 015 NM Eddy AAM-ALB 330 350 340 340 NIPER-RM 2400 35017 35 017 NM Grant AAM-ALB 330 350 340 340 NIPER-RM 2400 35019 35 019 NM Guadalupe AAM-ALB 330 350 340 340 NIPER-RM 2400 35021 35 021 NM Harding AAM-ALB 330 350 340 340 NIPER-RM 2400 35023 35 023 NM Hidalgo AAM-ALB 330 350 340 340 NIPER-RM 2400 35025 35 025 NM Lea AAM-ALB 330 350 340 340 NIPER-RM 2400 35027 35 027 NM Lincoln AAM-ALB 330 350 340 340 NIPER-RM 2400 35028 35 028 NM Los Alamos AAM-ALB 330 350 340 340 NIPER-RM 2400 35029 35 029 NM Luna AAM-ALB 330 350 340 340 NIPER-RM 2400 35031 35 031 NM Mc Kinley AAM-ALB 330 350 340 340 NIPER-RM 2400 35033 35 033 NM Mora AAM-ALB 330 350 340 340 NIPER-RM 2400 35035 35 035 NM Otero AAM-ALB 330 350 340 340 NIPER-RM 2400 35037 35 037 NM Quay AAM-ALB 330 350 340 340 NIPER-RM 2400 35039 35 039 NM Rio Arriba AAM-ALB 330 350 340 340 NIPER-RM 2400 35041 35 041 NM Roosevelt AAM-ALB 330 350 340 340 NIPER-RM 2400 35043 35 043 NM Sandoval AAM-ALB 330 350 340 340 NIPER-RM 2400 35045 35 045 NM San Juan AAM-ALB 330 350 340 340 NIPER-RM 2400 35047 35 047 NM San Miguel AAM-ALB 330 350 340 340 NIPER-RM 2400 35049 35 049 NM Santa Fe AAM-ALB 330 350 340 340 NIPER-RM 2400 35051 35 051 NM Sierra AAM-ALB 330 350 340 340 NIPER-RM 2400 35053 35 053 NM Socorro AAM-ALB 330 350 340 340 NIPER-RM 2400 35055 35 055 NM Taos AAM-ALB 330 350 340 340 NIPER-RM 2400 35057 35 057 NM Torrance AAM-ALB 330 350 340 340 NIPER-RM 2400 35059 35 059 NM Union AAM-ALB 330 350 340 340 NIPER-RM 2400 35061 35 061 NM Valencia AAM-ALB 330 350 340 340 NIPER-RM 2400 38001 38 001 ND Adams AAM-BL 270 250 260 260 NIPER-RM 2400 38003 38 003 ND Barnes NIPER-C 360 360 360 360 NIPER-C 3710 38005 38 005 ND Benson NIPER-C 360 360 360 360 NIPER-C 3710 38007 38 007 ND Billings AAM-BL 270 250 260 260 NIPER-RM 2400 38009 38 009 ND Bottineau NIPER-C 360 360 360 360 NIPER-C 3710 38011 38 011 ND Bowman AAM-BL 270 250 260 260 NIPER-RM 2400 38013 38 013 ND Burke AAM-BL 270 250 260 260 NIPER-RM 2400 
2002 Diesel Fuel Sulfur Levels (ppm) 2002 Diese

State County Data On-Highway Data 

FIPS No. No. State County Source Winter Summer Spring Fall Source Winter 38015 38 015 ND Burleigh NIPER-C 360 360 360 360 NIPER-C 3710 38017 38 017 ND Cass NIPER-C 360 360 360 360 NIPER-C 3710 38019 38 019 ND Cavalier NIPER-C 360 360 360 360 NIPER-C 3710 38021 38 021 ND Dickey NIPER-C 360 360 360 360 NIPER-C 3710 38023 38 023 ND Divide AAM-BL 270 250 260 260 NIPER-RM 2400 38025 38 025 ND Dunn AAM-BL 270 250 260 260 NIPER-RM 2400 38027 38 027 ND Eddy NIPER-C 360 360 360 360 NIPER-C 3710 38029 38 029 ND Emmons NIPER-C 360 360 360 360 NIPER-C 3710 38031 38 031 ND Foster NIPER-C 360 360 360 360 NIPER-C 3710 38033 38 033 ND Golden Valley AAM-BL 270 250 260 260 NIPER-RM 2400 38035 38 035 ND Grand Forks NIPER-C 360 360 360 360 NIPER-C 3710 38037 38 037 ND Grant NIPER-C 360 360 360 360 NIPER-C 3710 38039 38 039 ND Griggs NIPER-C 360 360 360 360 NIPER-C 3710 38041 38 041 ND Hettinger AAM-BL 270 250 260 260 NIPER-RM 2400 38043 38 043 ND Kidder NIPER-C 360 360 360 360 NIPER-C 3710 38045 38 045 ND La Moure NIPER-C 360 360 360 360 NIPER-C 3710 38047 38 047 ND Logan NIPER-C 360 360 360 360 NIPER-C 3710 38049 38 049 ND Mc Henry NIPER-C 360 360 360 360 NIPER-C 3710 38051 38 051 ND Mc Intosh NIPER-C 360 360 360 360 NIPER-C 3710 38053 38 053 ND Mc Kenzie AAM-BL 270 250 260 260 NIPER-RM 2400 38055 38 055 ND Mc Lean NIPER-C 360 360 360 360 NIPER-C 3710 38057 38 057 ND Mercer NIPER-C 360 360 360 360 NIPER-C 3710 38059 38 059 ND Morton NIPER-C 360 360 360 360 NIPER-C 3710 38061 38 061 ND Mountrail AAM-BL 270 250 260 260 NIPER-RM 2400 38063 38 063 ND Nelson NIPER-C 360 360 360 360 NIPER-C 3710 38065 38 065 ND Oliver NIPER-C 360 360 360 360 NIPER-C 3710 38067 38 067 ND Pembina NIPER-C 360 360 360 360 NIPER-C 3710 38069 38 069 ND Pierce NIPER-C 360 360 360 360 NIPER-C 3710 38071 38 071 ND Ramsey NIPER-C 360 360 360 360 NIPER-C 3710 38073 38 073 ND Ransom NIPER-C 360 360 360 360 NIPER-C 3710 38075 38 075 ND Renville NIPER-C 360 360 360 360 NIPER-C 3710 38077 38 077 ND Richland NIPER-C 360 360 360 360 NIPER-C 3710 38079 38 079 ND Rolette NIPER-C 360 360 360 360 NIPER-C 3710 38081 38 081 ND Sargent NIPER-C 360 360 360 360 NIPER-C 3710 38083 38 083 ND Sheridan NIPER-C 360 360 360 360 NIPER-C 3710 38085 38 085 ND Sioux NIPER-C 360 360 360 360 NIPER-C 3710 38087 38 087 ND Slope AAM-BL 270 250 260 260 NIPER-RM 2400 38089 38 089 ND Stark AAM-BL 270 250 260 260 NIPER-RM 2400 38091 38 091 ND Steele NIPER-C 360 360 360 360 NIPER-C 3710 38093 38 093 ND Stutsman NIPER-C 360 360 360 360 NIPER-C 3710 38095 38 095 ND Towner NIPER-C 360 360 360 360 NIPER-C 3710 38097 38 097 ND Traill NIPER-C 360 360 360 360 NIPER-C 3710 38099 38 099 ND Walsh NIPER-C 360 360 360 360 NIPER-C 3710 38101 38 101 ND Ward NIPER-C 360 360 360 360 NIPER-C 3710 38103 38 103 ND Wells NIPER-C 360 360 360 360 NIPER-C 3710 38105 38 105 ND Williams AAM-BL 270 250 260 260 NIPER-RM 2400 
2002 Diesel Fuel Sulfur Levels (ppm) 2002 Diese

State County Data On-Highway Data 

FIPS No. No. State County Source Winter Summer Spring Fall Source Winter 41001 41 001 OR Baker NIPER-M 370 370 370 370 NIPER-W 3400 41003 41 003 OR Benton AAM-SE 360 340 350 350 NIPER-W 3400 41005 41 005 OR Clackamas AAM-SE 360 340 350 350 NIPER-W 3400 41007 41 007 OR Clatsop AAM-SE 360 340 350 350 NIPER-W 3400 41009 41 009 OR Columbia AAM-SE 360 340 350 350 NIPER-W 3400 41011 41 011 OR Coos AAM-SE 360 340 350 350 NIPER-W 3400 41013 41 013 OR Crook NIPER-M 370 370 370 370 NIPER-W 3400 41015 41 015 OR Curry AAM-SE 360 340 350 350 NIPER-W 3400 41017 41 017 OR Deschutes NIPER-M 370 370 370 370 NIPER-W 3400 41019 41 019 OR Douglas AAM-SE 360 340 350 350 NIPER-W 3400 41021 41 021 OR Gilliam NIPER-M 370 370 370 370 NIPER-W 3400 41023 41 023 OR Grant NIPER-M 370 370 370 370 NIPER-W 3400 41025 41 025 OR Harney NIPER-M 370 370 370 370 NIPER-W 3400 41027 41 027 OR Hood River NIPER-M 370 370 370 370 NIPER-W 3400 41029 41 029 OR Jackson AAM-SE 360 340 350 350 NIPER-W 3400 41031 41 031 OR Jefferson NIPER-M 370 370 370 370 NIPER-W 3400 41033 41 033 OR Josephine AAM-SE 360 340 350 350 NIPER-W 3400 41035 41 035 OR Klamath AAM-SE 360 340 350 350 NIPER-W 3400 41037 41 037 OR Lake NIPER-M 370 370 370 370 NIPER-W 3400 41039 41 039 OR Lane AAM-SE 360 340 350 350 NIPER-W 3400 41041 41 041 OR Lincoln AAM-SE 360 340 350 350 NIPER-W 3400 41043 41 043 OR Linn AAM-SE 360 340 350 350 NIPER-W 3400 41045 41 045 OR Malheur NIPER-M 370 370 370 370 NIPER-W 3400 41047 41 047 OR Marion AAM-SE 360 340 350 350 NIPER-W 3400 41049 41 049 OR Morrow NIPER-M 370 370 370 370 NIPER-W 3400 41051 41 051 OR Multnomah AAM-SE 360 340 350 350 NIPER-W 3400 41053 41 053 OR Polk AAM-SE 360 340 350 350 NIPER-W 3400 41055 41 055 OR Sherman NIPER-M 370 370 370 370 NIPER-W 3400 41057 41 057 OR Tillamook AAM-SE 360 340 350 350 NIPER-W 3400 41059 41 059 OR Umatilla NIPER-M 370 370 370 370 NIPER-W 3400 41061 41 061 OR Union NIPER-M 370 370 370 370 NIPER-W 3400 41063 41 063 OR Wallowa NIPER-M 370 370 370 370 NIPER-W 3400 41065 41 065 OR Wasco NIPER-M 370 370 370 370 NIPER-W 3400 41067 41 067 OR Washington AAM-SE 360 340 350 350 NIPER-W 3400 41069 41 069 OR Wheeler NIPER-M 370 370 370 370 NIPER-W 3400 41071 41 071 OR Yamhill AAM-SE 360 340 350 350 NIPER-W 3400 46003 46 003 SD Aurora NIPER-C 360 360 360 360 NIPER-C 3710 46005 46 005 SD Beadle NIPER-C 360 360 360 360 NIPER-C 3710 46007 46 007 SD Bennett NIPER-C 360 360 360 360 NIPER-C 3710 46009 46 009 SD Bon Homme NIPER-C 360 360 360 360 NIPER-C 3710 46011 46 011 SD Brookings NIPER-C 360 360 360 360 NIPER-C 3710 46013 46 013 SD Brown NIPER-C 360 360 360 360 NIPER-C 3710 46015 46 015 SD Brule NIPER-C 360 360 360 360 NIPER-C 3710 46017 46 017 SD Buffalo NIPER-C 360 360 360 360 NIPER-C 3710 46019 46 019 SD Butte NIPER-RM 350 350 350 350 NIPER-RM 2400 46021 46 021 SD Campbell NIPER-C 360 360 360 360 NIPER-C 3710 
2002 Diesel Fuel Sulfur Levels (ppm) 2002 Diese

State County Data On-Highway Data 

FIPS No. No. State County Source Winter Summer Spring Fall Source Winter 46023 46 023 SD Charles Mix NIPER-C 360 360 360 360 NIPER-C 3710 46025 46 025 SD Clark NIPER-C 360 360 360 360 NIPER-C 3710 46027 46 027 SD Clay NIPER-C 360 360 360 360 NIPER-C 3710 46029 46 029 SD Codington NIPER-C 360 360 360 360 NIPER-C 3710 46031 46 031 SD Corson NIPER-C 360 360 360 360 NIPER-C 3710 46033 46 033 SD Custer NIPER-RM 350 350 350 350 NIPER-RM 2400 46035 46 035 SD Davison NIPER-C 360 360 360 360 NIPER-C 3710 46037 46 037 SD Day NIPER-C 360 360 360 360 NIPER-C 3710 46039 46 039 SD Deuel NIPER-C 360 360 360 360 NIPER-C 3710 46041 46 041 SD Dewey NIPER-C 360 360 360 360 NIPER-C 3710 46043 46 043 SD Douglas NIPER-C 360 360 360 360 NIPER-C 3710 46045 46 045 SD Edmunds NIPER-C 360 360 360 360 NIPER-C 3710 46047 46 047 SD Fall River NIPER-RM 350 350 350 350 NIPER-RM 2400 46049 46 049 SD Faulk NIPER-C 360 360 360 360 NIPER-C 3710 46051 46 051 SD Grant NIPER-C 360 360 360 360 NIPER-C 3710 46053 46 053 SD Gregory NIPER-C 360 360 360 360 NIPER-C 3710 46055 46 055 SD Haakon NIPER-C 360 360 360 360 NIPER-C 3710 46057 46 057 SD Hamlin NIPER-C 360 360 360 360 NIPER-C 3710 46059 46 059 SD Hand NIPER-C 360 360 360 360 NIPER-C 3710 46061 46 061 SD Hanson NIPER-C 360 360 360 360 NIPER-C 3710 46063 46 063 SD Harding NIPER-RM 350 350 350 350 NIPER-RM 2400 46065 46 065 SD Hughes NIPER-C 360 360 360 360 NIPER-C 3710 46067 46 067 SD Hutchinson NIPER-C 360 360 360 360 NIPER-C 3710 46069 46 069 SD Hyde NIPER-C 360 360 360 360 NIPER-C 3710 46071 46 071 SD Jackson NIPER-C 360 360 360 360 NIPER-C 3710 46073 46 073 SD Jerauld NIPER-C 360 360 360 360 NIPER-C 3710 46075 46 075 SD Jones NIPER-C 360 360 360 360 NIPER-C 3710 46077 46 077 SD Kingsbury NIPER-C 360 360 360 360 NIPER-C 3710 46079 46 079 SD Lake NIPER-C 360 360 360 360 NIPER-C 3710 46081 46 081 SD Lawrence NIPER-RM 350 350 350 350 NIPER-RM 2400 46083 46 083 SD Lincoln NIPER-C 360 360 360 360 NIPER-C 3710 46085 46 085 SD Lyman NIPER-C 360 360 360 360 NIPER-C 3710 46087 46 087 SD Mc Cook NIPER-C 360 360 360 360 NIPER-C 3710 46089 46 089 SD Mc Pherson NIPER-C 360 360 360 360 NIPER-C 3710 46091 46 091 SD Marshall NIPER-C 360 360 360 360 NIPER-C 3710 46093 46 093 SD Meade NIPER-RM 350 350 350 350 NIPER-RM 2400 46095 46 095 SD Mellette NIPER-C 360 360 360 360 NIPER-C 3710 46097 46 097 SD Miner NIPER-C 360 360 360 360 NIPER-C 3710 46099 46 099 SD Minnehaha NIPER-C 360 360 360 360 NIPER-C 3710 46101 46 101 SD Moody NIPER-C 360 360 360 360 NIPER-C 3710 46103 46 103 SD Pennington NIPER-RM 350 350 350 350 NIPER-RM 2400 46105 46 105 SD Perkins NIPER-RM 350 350 350 350 NIPER-RM 2400 46107 46 107 SD Potter NIPER-C 360 360 360 360 NIPER-C 3710 46109 46 109 SD Roberts NIPER-C 360 360 360 360 NIPER-C 3710 46111 46 111 SD Sanborn NIPER-C 360 360 360 360 NIPER-C 3710 46113 46 113 SD Shannon NIPER-RM 350 350 350 350 NIPER-RM 2400 
2002 Diesel Fuel Sulfur Levels (ppm) 2002 Diese

State County Data On-Highway Data 

FIPS No. No. State County Source Winter Summer Spring Fall Source Winter 46115 46 115 SD Spink NIPER-C 360 360 360 360 NIPER-C 3710 46117 46 117 SD Stanley NIPER-C 360 360 360 360 NIPER-C 3710 46119 46 119 SD Sully NIPER-C 360 360 360 360 NIPER-C 3710 46121 46 121 SD Todd NIPER-C 360 360 360 360 NIPER-C 3710 46123 46 123 SD Tripp NIPER-C 360 360 360 360 NIPER-C 3710 46125 46 125 SD Turner NIPER-C 360 360 360 360 NIPER-C 3710 46127 46 127 SD Union NIPER-C 360 360 360 360 NIPER-C 3710 46129 46 129 SD Walworth NIPER-C 360 360 360 360 NIPER-C 3710 46135 46 135 SD Yankton NIPER-C 360 360 360 360 NIPER-C 3710 46137 46 137 SD Ziebach NIPER-C 360 360 360 360 NIPER-C 3710 49001 49 001 UT Beaver NIPER-N 360 360 360 360 NIPER-W 3400 49003 49 003 UT Box Elder NIPER-RM 350 350 350 350 NIPER-RM 2400 49005 49 005 UT Cache NIPER-RM 350 350 350 350 NIPER-RM 2400 49007 49 007 UT Carbon NIPER-RM 350 350 350 350 NIPER-RM 2400 49009 49 009 UT Daggett NIPER-RM 350 350 350 350 NIPER-RM 2400 49011 49 011 UT Davis NIPER-RM 350 350 350 350 NIPER-RM 2400 49013 49 013 UT Duchesne NIPER-RM 350 350 350 350 NIPER-RM 2400 49015 49 015 UT Emery NIPER-RM 350 350 350 350 NIPER-RM 2400 49017 49 017 UT Garfield NIPER-N 360 360 360 360 NIPER-W 3400 49019 49 019 UT Grand NIPER-RM 350 350 350 350 NIPER-RM 2400 49021 49 021 UT Iron NIPER-N 360 360 360 360 NIPER-W 3400 49023 49 023 UT Juab NIPER-RM 350 350 350 350 NIPER-RM 2400 49025 49 025 UT Kane NIPER-N 360 360 360 360 NIPER-W 3400 49027 49 027 UT Millard NIPER-RM 350 350 350 350 NIPER-RM 2400 49029 49 029 UT Morgan NIPER-RM 350 350 350 350 NIPER-RM 2400 49031 49 031 UT Piute NIPER-N 360 360 360 360 NIPER-W 3400 49033 49 033 UT Rich NIPER-RM 350 350 350 350 NIPER-RM 2400 49035 49 035 UT Salt Lake NIPER-RM 350 350 350 350 NIPER-RM 2400 49037 49 037 UT San Juan NIPER-RM 350 350 350 350 NIPER-RM 2400 49039 49 039 UT Sanpete NIPER-RM 350 350 350 350 NIPER-RM 2400 49041 49 041 UT Sevier NIPER-RM 350 350 350 350 NIPER-RM 2400 49043 49 043 UT Summit NIPER-RM 350 350 350 350 NIPER-RM 2400 49045 49 045 UT Tooele NIPER-RM 350 350 350 350 NIPER-RM 2400 49047 49 047 UT Uintah NIPER-RM 350 350 350 350 NIPER-RM 2400 49049 49 049 UT Utah NIPER-RM 350 350 350 350 NIPER-RM 2400 49051 49 051 UT Wasatch NIPER-RM 350 350 350 350 NIPER-RM 2400 49053 49 053 UT Washington NIPER-N 360 360 360 360 NIPER-W 3400 49055 49 055 UT Wayne NIPER-RM 350 350 350 350 NIPER-RM 2400 49057 49 057 UT Weber NIPER-RM 350 350 350 350 NIPER-RM 2400 53001 53 001 WA Adams NIPER-L 390 390 390 390 NIPER-W 3400 53003 53 003 WA Asotin NIPER-L 390 390 390 390 NIPER-W 3400 53005 53 005 WA Benton NIPER-L 390 390 390 390 NIPER-W 3400 53007 53 007 WA Chelan NIPER-L 390 390 390 390 NIPER-W 3400 53009 53 009 WA Clallam NIPER-L 390 390 390 390 NIPER-W 3400 53011 53 011 WA Clark AAM-SE 360 340 350 350 NIPER-W 3400 53013 53 013 WA Columbia NIPER-L 390 390 390 390 NIPER-W 3400 
2002 Diesel Fuel Sulfur Levels (ppm) 2002 Diese

State County Data On-Highway Data 

FIPS No. No. State County Source Winter Summer Spring Fall Source Winter 53015 53 015 WA Cowlitz AAM-SE 360 340 350 350 NIPER-W 3400 53017 53 017 WA Douglas NIPER-L 390 390 390 390 NIPER-W 3400 53019 53 019 WA Ferry NIPER-L 390 390 390 390 NIPER-W 3400 53021 53 021 WA Franklin NIPER-L 390 390 390 390 NIPER-W 3400 53023 53 023 WA Garfield NIPER-L 390 390 390 390 NIPER-W 3400 53025 53 025 WA Grant NIPER-L 390 390 390 390 NIPER-W 3400 53027 53 027 WA Grays Harbor AAM-SE 360 340 350 350 NIPER-W 3400 53029 53 029 WA Island AAM-SE 360 340 350 350 NIPER-W 3400 53031 53 031 WA Jefferson AAM-SE 360 340 350 350 NIPER-W 3400 53033 53 033 WA King AAM-SE 360 340 350 350 NIPER-W 3400 53035 53 035 WA Kitsap AAM-SE 360 340 350 350 NIPER-W 3400 53037 53 037 WA Kittitas NIPER-L 390 390 390 390 NIPER-W 3400 53039 53 039 WA Klickitat NIPER-L 390 390 390 390 NIPER-W 3400 53041 53 041 WA Lewis AAM-SE 360 340 350 350 NIPER-W 3400 53043 53 043 WA Lincoln NIPER-L 390 390 390 390 NIPER-W 3400 53045 53 045 WA Mason AAM-SE 360 340 350 350 NIPER-W 3400 53047 53 047 WA Okanogan NIPER-L 390 390 390 390 NIPER-W 3400 53049 53 049 WA Pacific AAM-SE 360 340 350 350 NIPER-W 3400 53051 53 051 WA Pend Oreille NIPER-L 390 390 390 390 NIPER-W 3400 53053 53 053 WA Pierce AAM-SE 360 340 350 350 NIPER-W 3400 53055 53 055 WA San Juan AAM-SE 360 340 350 350 NIPER-W 3400 53057 53 057 WA Skagit AAM-SE 360 340 350 350 NIPER-W 3400 53059 53 059 WA Skamania AAM-SE 360 340 350 350 NIPER-W 3400 53061 53 061 WA Snohomish AAM-SE 360 340 350 350 NIPER-W 3400 53063 53 063 WA Spokane NIPER-L 390 390 390 390 NIPER-W 3400 53065 53 065 WA Stevens NIPER-L 390 390 390 390 NIPER-W 3400 53067 53 067 WA Thurston AAM-SE 360 340 350 350 NIPER-W 3400 53069 53 069 WA Wahkiakum AAM-SE 360 340 350 350 NIPER-W 3400 53071 53 071 WA Walla Walla NIPER-L 390 390 390 390 NIPER-W 3400 53073 53 073 WA Whatcom AAM-SE 360 340 350 350 NIPER-W 3400 53075 53 075 WA Whitman NIPER-L 390 390 390 390 NIPER-W 3400 53077 53 077 WA Yakima NIPER-L 390 390 390 390 NIPER-W 3400 56001 56 001 WY Albany NIPER-RM 350 350 350 350 NIPER-RM 2400 56003 56 003 WY Big Horn NIPER-RM 350 350 350 350 NIPER-RM 2400 56005 56 005 WY Campbell NIPER-RM 350 350 350 350 NIPER-RM 2400 56007 56 007 WY Carbon NIPER-RM 350 350 350 350 NIPER-RM 2400 56009 56 009 WY Converse NIPER-RM 350 350 350 350 NIPER-RM 2400 56011 56 011 WY Crook NIPER-RM 350 350 350 350 NIPER-RM 2400 56013 56 013 WY Fremont NIPER-RM 350 350 350 350 NIPER-RM 2400 56015 56 015 WY Goshen NIPER-RM 350 350 350 350 NIPER-RM 2400 56017 56 017 WY Hot Springs NIPER-RM 350 350 350 350 NIPER-RM 2400 56019 56 019 WY Johnson NIPER-RM 350 350 350 350 NIPER-RM 2400 56021 56 021 WY Laramie NIPER-RM 350 350 350 350 NIPER-RM 2400 56023 56 023 WY Lincoln NIPER-RM 350 350 350 350 NIPER-RM 2400 56025 56 025 WY Natrona NIPER-RM 350 350 350 350 NIPER-RM 2400 56027 56 027 WY Niobrara NIPER-RM 350 350 350 350 NIPER-RM 2400 
2002 Diesel Fuel Sulfur Levels (ppm) 2002 Diese

State County Data On-Highway Data 

FIPS No. No. State County Source Winter Summer Spring Fall Source Winter 56029 56 029 WY Park NIPER-RM 350 350 350 350 NIPER-RM 2400 56031 56 031 WY Platte NIPER-RM 350 350 350 350 NIPER-RM 2400 56033 56 033 WY Sheridan NIPER-RM 350 350 350 350 NIPER-RM 2400 56035 56 035 WY Sublette NIPER-RM 350 350 350 350 NIPER-RM 2400 56037 56 037 WY Sweetwater NIPER-RM 350 350 350 350 NIPER-RM 2400 56039 56 039 WY Teton NIPER-RM 350 350 350 350 NIPER-RM 2400 56041 56 041 WY Uinta NIPER-RM 350 350 350 350 NIPER-RM 2400 56043 56 043 WY Washakie NIPER-RM 350 350 350 350 NIPER-RM 2400 56045 56 045 WY Weston NIPER-RM 350 350 350 350 NIPER-RM 2400 
Appendix B 

Geographic Characterization of Ship Emissions for the U.S. West Coast – 

Final Task 1 Report – Corbett and Wang, 2005. 
Geographic Characterization of Ship Emissions for 

the U.S. West Coast 

Final Task 1 Report 

Submitted by 

James J. Corbett, P.E., Ph.D. 

Chengfeng Wang 

University of Delaware 

Prepared for 

ENVIRON International Corporation 

101 Rowland Way, Suite 220, Novato, CA 94945-5010 

on behalf of the Western Regional Air Partnership 

14 April 2005 
Table of contents 

Table of contents................................................................................................................. 3 

Background......................................................................................................................... 4 

Overview of this work ........................................................................................................ 5 

Choosing Global Ship Emissions Inventory....................................................................... 6 

Creating Ship Characteristics Dataset................................................................................. 7 

Choosing Marine Vessel Traffic and Emissions Proxy ...................................................... 7 

Description of ICOADS and AMVER ........................................................................... 8 

Number of Vessels Reporting......................................................................................... 8 

Addressing Sampling Bias.............................................................................................. 9 

Summary of the comparison ......................................................................................... 12 

Choosing Coordinate System and Resolution................................................................... 13 

Choosing Appropriate Means to Correct Sampling Bias.................................................. 14 

Evaluating the Effects of Using Three-Year with Single Year ICOADS Data ............ 14 

Evaluating the Effects of Weighting Observations with Ship Engine Power............... 17 

Producing Raster Files ...................................................................................................... 19 

Conclusions, Future Work and Recommendations........................................................... 20 

References......................................................................................................................... 21 

Appendix A....................................................................................................................... 23 
Geographic Characterization of Ship Emissions for 

the U.S. West Coast 

Background 

Research has demonstrated that air pollutants emitted from marine vessels account for a 

significant portion of emissions inventory and contribute to air quality problems in many coastal 

and metropolitan regions, and globally (1, 2). Better understanding is needed of the spatial 

distribution of ship activities and emissions to assess accurately the impact on air quality at local, 

regional, and global scales. Without an accurate representation, scientists cannot effectively 

inform policy decisions regarding human health and environment (1). Policy makers rely upon 

geographical representation of transportation emissions that include ship traffic on multiple 

scales. 

The uneven distribution of emissions from commercial shipping is a result of uneven 

spatial distribution of economic activities, the components of the Marine Transportation System 

(MTS), including ports, sea routes, and ship activities (3). Geographically characterizing the 

actual positions of the components of the MTS is critical to better understand and evaluate MTS 

impacts on safety, security, the environment, and human health. Due to the mobility of ships 

engaged in global trade, poorly integrated models and limited data, however, this task has been a 

great challenge. 

To develop emissions inventories and assign their spatial distribution, the activity level of 

the pollution source must be determined, emissions resulting from this activity must be 

computed, and the results must be assigned to a location (1). While ship inventories for air 

emissions from ships have become more precise, greater improvement is needed in representing 

the geographic allocation of inventory information for atmospheric scientists, pollution modelers, 

and policy makers to evaluate potential impacts of pollution on the environment and human 

health. 

Attempts to geographically assign international shipping traffic began nearly a decade 

ago. Corbett et al. geographically resolved global inventory of ship emissions by deriving 

international shipping traffic intensities from voluntarily reported, ship-based weather 

observations (1, 3, 4). Olivier et al. assigned global emissions estimates to major shipping lanes 

(5). These approaches showed qualitatively good agreement with regionally resolved ship 

inventories (6, 7), but resolution differences or inconsistent assumptions prevented reconciling 

geographic assignment of ship activity among different spatial scales (1). Recently, Endresen et 

al. presented a new and perhaps more accurate methodology for globally assigning emissions 

from international marine bunkers, which is a significant contribution. They argued that 

emissions can be allocated more accurately across ship traffic profiles by using vessel size to 

weight the allocation to each grid (1, 8). Regionally, ENTEC presented the distribution of 

shipping emissions over the European Monitoring and Evaluation Programme (EMEP) domain 

on an individual 50 km x 50 km grid square resolution for the year 2000 (9). 

This work is an application of the approaches for geographic characterization of marine 

vessel emissions developed by researchers and published in peer-reviewed literature (1, 3, 4). 

Recent developments and possible improvements have been taken into account (8, 10, 11). 

Efforts have been made in this work to address potential statistical and geographic sampling bias 
caused by over-reporting and non-response ships. This will be discussed in detailed in the 

following sections. 

Overview of this work 

The objective of this work is to quickly develop simple emissions inventories for six air 

pollutants (NOx, SOx, CO2, HC, PM, and CO) from marine vessels for year 2002 for the U.S. 

West Coast. The final products of this work include this report and raster files (data sets) with 

36km x 36km resolution for the pollutants for each of the 12 months. The value of the grid of the 

raster files is the amount of pollutants emitted from marine vessels in one period in that grid. The 

raster files cover the U.S. West Coast and the coordinates for the four corners of the raster files 

are: SW corner (-2736, -2088), NW corner (-2736, 1944), SE corner (-216, -2088), NE corner (-

216, 1944). 

The approach employed in this work is illustrated in Figure 1. We adopted global ship 

emissions inventories co-developed by one of the researchers of this work and published in peerreviewed journal (1). Adjustments have been made to reflect the comments from our colleagues 

(10, 11). 

The International Comprehensive Ocean-Atmosphere Data Set (ICOADS) (12) was used 

as the proxy of the distribution of U.S. west coast ship traffic and emissions. Efforts have been 

made to improve the representativeness of ICOADS of the emissions from commercial cargo 

fleet by selecting only observations made by cargo ships, trimming several ships with 

exceptionally high reporting biases, and weighting observations by ship power, a major factor 

that determines the magnitude of ship air emissions and is a more direct indicator of emissions 

than gross tonnage used previously (8). Three-year ICOADS samples were used to improve the 

geographic representation of ship traffic and emissions. Detailed discussions are included in the 

following sections. 

Ship data was identified in the Lloyd’s Register CD-ROM, which maintains over 100,000 

vessels and is perhaps the largest database of commercially-available maritime data in the world 

(13). These ship data were then joined with matching vessel information in ICOADS and the 

observations made by ships identified in the ship dataset were weighted by ship power. We 

assume emissions are proportional to ship power. Average emissions per power-weighted 

observation were obtained by dividing global ship emissions inventory by the sum of ship power 

of all individual observations. The sum of power of all individual observations in each raster grid 

in one period was obtained by spatial analysis with ArcGIS 9.0, a product of Environmental 

Systems Research Institute (ESRI) (14). Emissions in each grid were obtained by multiplying 

emissions per unit of ship power with ship power in each grid in one period. 
Figure 1. Approach employed in this work. 

Choosing Global Ship Emissions Inventory 

A reliable and up-to-date ship emission inventory is one of the prerequisites to improve the 

accuracy of geographic characterization of ship emissions with the approach for this work (the 

other prerequisites include a good representation of ship traffic pattern and intensity, and a good 

ship dataset). There are, however, significant differences among the various global ship emission 

inventories (10), inventories estimated by one approach may be 50% higher than inventories 

estimated by another approach (1, 8, 10). 

Corbett and Koehler addressed the significant uncertainty in the global ship emission 

inventories by employing a bottom-up estimate of fuel consumption and vessel activity for 

internationally registered fleets (1). They identified model bias in previous work, which assumed 

internationally registered ships primarily consume international marine fuels, and increased 

previous ship emissions inventories for pollutants, among which, global NOx emissions are more 

than doubled with the updated emissions inventory (1). Results from Corbett and Koehler’s 

analysis are provided in table 1. 

Endresen et al. argued that Corbett and Koehler overestimated fuel consumption and air 

emissions from international shipping and offered differing assumptions and clarifying 

information that reduced the estimate of ship emissions by Corbett and Koehler (10, 11). 

Endresen et al. claimed that reported marine fuel statistics were essentially accurate and 

complete, and they did not argue fundamentally against the accuracy of Corbett and Koehler’s 

activity-based methodology (1, 10, 11). . 

Corbett and Koehler incorporated revised assumptions suggested by Endresen et al. into 

their activity-based model, such as operating hours, load profile, and specific fuel consumption, 

and indeed reduced the estimates from their previous work but within uncertainty bounds (11). 

Their revised model produces rather good agreement with the upper-bound annual fuel use 

claimed by Endresen et al. for the entire registered fleet (11). However, according to Corbett’s 

and Koehler’s activity-based model, even the revised inputs suggested by Endresen et al resulted 

in larger inventories than previously estimated using fuel-based methods. 

Global Ship 

Emissions 

Inventories 

Emissions in 

Each Grid 

Ship Power in Each 

Grid in a Period 

Emissions per Unit 

of Ship Power 

Lloyd’s Ship 

Dataset 

ICOADS 

Marine Vessel 

Observations 

Ship Traffic 

Weighted by Power 
Generally speaking, we believe Corbett’s and Koehler’s activity-based methodology is 

the best model currently available for estimating global emissions inventories from international 

shipping and Endresen et al. have helped narrow the uncertainty of the results of this model. We 

believe that model results under these revised inputs data currently represent the best global 

emissions inventories and were adopted in this work. The revised results are also provided in 

Table 1. 

Table 1. Updated and Revised Global Cargo Fleet Emissions Inventory 

 Global Ship Emissions Inventory (Tg) 

 

Lower-Lower 

Bound 

Lower 

Bound 

Base 

Case 

Upper 

Bound 

Fuel 130 175 203 222 

NOx (N) 4.17 4.68 5 6.79 

SOx (S) 4.21 4.52 4.72 6.54 

CO2 (C) 164 171 176 249 

HC (Total HC) 0.459 0.53 0.574 0.778 

PM (PM10) 0.755 1.02 1.19 1.97 

CO 0.911 1.01 1.08 1.5 

Creating Ship Characteristics Dataset 

We created a ship characteristics dataset by deriving data from Lloyd’s Register of Ships. The 

attributes in the dataset include unique ship identifier, which associates vessel type and other 

characteristics used to analyze the representativeness of the ICOADS fleet of the world fleet. 

This information also can be used for the analysis of emissions by ship segments, and ship total 

main engine power, which is used to weight individual ICOADS observations under the 

assumption that ship emissions are proportional to ship power. Ship identifiers are used as the 

key index of the dataset, and are used to evaluate individual ship observation frequencies in 

ICOADS. All ships included in our dataset have valid total main engine power data. 

Totally 15,048 unique vessel identifiers are included in our dataset. Due to some data 

quality issues, around 150 identifiers may not be unique (about 1% of percent of the 15,048 

unique identifiers); these records have more than two vessels with different ship types, which we 

believe is hardly possible unless the ship has been converted from one type to another. For the 

analysis of this work, we deleted duplicate identifiers. We believe this will not have a significant 

effect on the results of our analysis. We also understand that this ship dataset is only a subset of 

the world commercial fleet, and far from complete due to the missing of ship power data for 

many ships in the Lloyd’s ship dataset. 

Based on criteria from Lloyd’s Register (13), the ships were classified into nine groups, 

including container ship, tanker, bulk carrier, general cargo ship, passenger ship, reefer, RO-RO, 

fishing vessel, and miscellaneous types of ship, to simplify the analysis. For details of ship 

grouping, see appendix A. 

Choosing Marine Vessel Traffic and Emissions Proxy 

Representing spatial (and temporal) activity of commercial shipping is fundamentally similar to 

modeling any mobile source: the location and intensity the fleet activity must be depicted. This 

can be done using sampled data representing fleetwide spatial activity, and theoretically would 
be most accurate if all ships reported their locations regularly. There are two data sets, ICOADS 

and Automated Mutual-assistance Vessel Rescue system (AMVER), which have been used by 

researchers as proxies of global ship traffic to geographically resolve the global emissions 

inventories (3, 4, 8). Although there is some overlap between the COADS and AMVER datasets 

with about 33% of the AMVER vessels are weather observation vessels, different data sets lead 

to highly different regional perturbations of air pollutants (8). This analysis uses vessel traffic 

patterns derived from ICOADS to represent spatial distribution of shipping lanes and vessel 

traffic on those shipping lanes. In this section, we compare the strengths and weaknesses of 

ICOADS and AMVER datasets. 

Description of ICOADS and AMVER 

ICOADS is the world's largest dataset for global marine surface observations, and is 

developed and maintained as a cooperative effort between several research institutes. ICOADS 

data is collected primarily from ships (merchant, ocean research, fishing, navy, etc) and moored 

and drifting buoys. The dataset contains the identifier of the ship, and the time and position of 

the ship when making report besides those meteorological and oceanographic variables (15). 

Table 2 summarizes the ICOADS data used for this work. 

AMVER, sponsored by the United States Coast Guard, is a global ship reporting system 

used worldwide by search and rescue authorities to arrange for assistance to persons in distress at 

sea. Participation is free, voluntary, and open to merchant ships of all flags, but had been limited 

to ships over 1000 gross tons, on a voyage of 24 hours or longer. Recently, however, enrollment 

has been expanded to accommodate vessels outside the normal criteria, such as cruise ships, 

research vessels and fish processors. Participating ships are expected to regularly report their 

positions to AMVER to improve the chance that the ship closest to the position of distress be 

identified (16). 

Table 2. Summary of ICOADS 

Year Total Obs. 

Identified 

ships1

Obs. by 

identified 

ships 

Cargo 

Ships2

Obs. by 

cargo 

ships 

Cargo 

Ships 

after 

trimming3

Obs. by 

cargo 

ships after 

trimming 

2002 966,194 2,564 651,698 2,177 509,843 2,172 474,929 

2001 1,029,132 2,127 631,211 1,803 497,657 1,798 460,329 

2000 1,096,795 1,972 572,037 1,655 449,948 1,650 417,299 

Sum 3,092,121 N/A 1,854,946 N/A 1,457,448 N/A 1,352,557 

1. Using 15,048 unique ship identifiers with valid ship power from Lloyds Registry. 

2. We identified properties for 2,453 unique cargo ships from 2000 to 2002. 

3. Five over-reporting ships are trimmed 

Number of Vessels Reporting

The main source of marine surface observations for ICOADS is the Voluntary Observing Ships 

(VOS) fleet (8), and ICOADS contains as many VOS data as they have been able to gather and 

blend together (17). The number of ships on the VOS Fleet List has continued to decline from a 

peak of 7,700 in 1984/85, and is currently estimated at only about 4000 ships worldwide (18). 

Using the 2004 version of Lloyd’s Ship Register, we identified 2,564 unique ships report 

to ICOADS in year 2002, about 2,177 of which are cargo vessels (excluding fishing, passenger, 
and miscellaneous vessels). Similarly, we identified 1,803 and 1,655 unique cargo vessels from 

2001 and 2000 ICOADS respectively. Since Lloyd’s Ship Register normally does not keep 

records for ships no longer in service (e.g., scrapped), we expected the decline of the number of 

ships identified. Nearly 2,500 unique vessels were identified from the three-year (2000-2002) 

ICOADS. We anticipate that more ships can be identified if we use the same year ICOADS and 

Lloyd’s data and, importantly, when the “delayed mode” data is blended into ICOADS (17). This 

could be a potential future improvement in ICOADS data as a proxy of the accuracy of 

geographically resolved ships emissions. 

In any case, the number of cargo ships that can be identified from ICOADS is much 

higher than that claimed for AMVER by Endresen et al (8). In contrast, although there are about 

12,000 vessels from more than 100 nations in the AMVER database, with approximately 2,700-

3,000 vessels reporting mainly daily (8), we do not know how many more ships report to 

AMVER in a given year than ICOADS. Ultimately, combining these overlapping data sets may 

also improve the quality of data for this purpose. 

Addressing Sampling Bias 

Since ship type, size, and power can be identified for individual ICOADS observations 

by matching ICOADS ship identifiers with ship registry information, and since ship emissions 

can be distributed accordingly to take into account large variation in emission between small and 

large vessels, there is no intrinsic advantage to AMVER over ICOADS as claimed by Endresen 

et al (8). We also show in this work that, with the use a combination of three-year (2000-2002) 

ICOADS samples, the geographic representativeness of ICOADS of ship traffic and emissions 

can be improved. Detailed discussions are included in the following sections. 

Endresen et al. observed that ICOADS data seem to include some stationary platforms, 

and this may cause significant error for regional studies covering the areas with stationary 

vessels (8). They also observed that ICOADS perhaps results in too high emissions in the 

northern Atlantic and in particular between 40° and 60° N, likely caused by overrepresentation of 

non-cargo vessel records (mainly research, support, and fishing vessels) (8). We addressed the 

first issue by only using data reported by ships; we addressed the second issue by selecting only 

ICOADS observations made by cargo vessels and adjusting our emissions inventory to represent 

only these commercial marine vessels. (This effort, then, excludes CMV emissions by 

oceangoing passenger vessels, which may be most relevant for major cruise-ship routes and 

ports.) 

Other potential biases include extremely frequent reporting by a few vessels. Figure 2 

shows that five (or 0.2%) of the 2,453 cargo vessels made 7.20% of the ICOADS observations 

by cargo vessels from 2000 to 2002. Most of these observations were made in the northern 

Atlantic. We believe these ships are probably responsible for much of the over-sampling bias in 

the Atlantic, which would directly affect the quality of West Coast inventories (potentially 

underestimating emissions in the West). We trimmed these over-reported cargo vessels to 

address this bias. We chose a natural break between the sixth and the fifth most frequently 

reported vessels, where the latter made almost three times more observations than the former. 
Natural break point, 

after which 0.2% of total 

ships made 7.2% of 

total observations 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 8% 16% 25% 33% 41% 49% 57% 65% 73% 82% 90% 98%

Cumulative percent of total number of ships

Cumulative percent of total number of observations

Figure 2. Cumulative percent of number of ICOADS observations by cumulative percent of 

number of ships from 2000 to 2002. 

Endresen et al. claim that the AMVER fleet best reflects the international cargo fleet by 

vessel types, size, and reporting frequency, followed by Purple Finder (PF), and ICOADS (8); 

however, they compared observations in AMVER and PF mainly made by cargo vessels with 

observations made by all types of vessels in ICOADS. Endresen et al. also admit that AMVER 

seems to over-represent large cargo vessels, particularly liquid and dry bulk cargo ships, perhaps 

because participation in AMVER has been generally limited to merchant ships of all flags over 

1000 GT, on a voyage of 24 hours or longer (8). Our analysis indicates that ICOADS covers a 

wider range of ships in terms of ship type, size, and engine power (132 ~74,640 kw), ICOADS 

observations perhaps better represent the world ship traffic as characterized by international fleet 

registries. Of course, combining AMVER and ICOADS data sets for the same year could 

provide a better representative sample of spatially resolved ship activity than either data set 

alone. 

Figure 3 shows that ICOADS over-sampled container ship and refrigerated cargo (i.e., 

reefer) traffic – especially container ship traffic – and that ICOADS under-sampled general cargo 

ship and tanker traffic – particularly general cargo ship traffic. By comparison, AMVER oversampled bulk carrier, tanker, and container ship traffic, especially bulk carrier traffic, and 

significantly under-sampled general cargo ship, RO-RO, and reefer traffic. The extent of 

oversampling depicted in Figure 3 assumes that all the ships in the world cargo fleet spend the 

same time at sea and make report at a same interval; unfortunately, neither of the two 

assumptions is true. Further analysis would be required to conclude with confidence that one 

dataset is a better representation of the world cargo traffic over the other one. 
0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Container Ship General Cargo Bulk Carrier Tanker RO-RO Reefer

Percent of total

ICOADS AMVER World Cargo Fleet 2004 World Cargo Fleet 1996, 2000

Figure 3. Comparison between world cargo fleet profile and ICOADS and AMVER 

observations by ship type. ICOADS as three-year (2000-2002) data with five over-reported 

vessels trimmed; AMVER as derived from Endresen et al. (8); World cargo fleet 2004 as 

derived from Lloyd's Ship Register version 2004; World cargo fleet 1996, 2000 as derived 

from Endresen et al. (8). 

Figure 4 shows that unadjusted ICOADS data will assign significantly more emissions 

than AMVER data to the region between 40°-60° north and assign significantly fewer emissions 

to the region between 0°-20° north. Although the emissions assigned to latitudes based on PF 

mostly fall between ICOADS and AMVER, we cannot conclude that PF better represents the 

spatial distribution of ship traffic since there are fewer ships in PF database (8). Most 

importantly, since neither AMVER nor ICOADS sampling is geographically random and both 

data samples also can be spatially biased, based on this comparison only, we cannot prove that 

one dataset is better than another for West Coast regional inventory purposes. We are 

developing independent methods to validate and adjust these data using more complete port call 

statistics. 
0%

5%

10%

15%

20%

25%

30%

35%

40%

60-90 N 40-60 N 20-40 N 0-20 N 0-20 S 20-40 S 40-90 S

percent of total

ICOADS 00-02 ICOADS 00-02P COADS 96 PF AMVER AMVER GT

Figure 4. Comparison of relative distribution of emission indicator at different latitudes 

between ICOADS, AMVER, and PF. ICOADS 00-02 as three-year (2000-2002) number of 

cargo vessel observations with five over-reported vessels trimmed; ICOADS 00-02P as 

three-year (2000-2002) number of cargo vessel observations weighted by power with five 

over-reported vessels trimmed; COADS 96 as derived from Endresen et al. (8) for the year 

1996 including non-cargo vessels; PF as Purple Finder data derived from Endresen et al. 

(8) for the year 2000; AMVER as derived from Endresen et al. (8) for the year 2000; 

AMVER GT as derived from Endresen et al. (8) for the year 2000 and weighted by GT. 

Summary of the comparison 

With the above discussion, we identify certain strengths of the unadjusted ICOADS 

sample. Some are common for both ICOADS and AMVER. These include: 

1. The ability to identify specific vessels for individual ICOADS observations and associate 

them with actual power, tonnage, etc., using ship registry databases enables direct spatial 

consideration of the important factors necessary to evaluate environmental impacts, such 

as ship emissions. 

2. The spatial representation of shipping lanes is excellent, especially for the major trade 

routes. This has been verified by comparison of major trade routes of commodities with 

ICOADS routes according to vessel types associated with these commodities. 

3. Seasonal variation in shipping lanes is consistent over time, and predictable. Weather 

routing behavior is clearly identified in the ICOADS samples, and this seasonal 

variability (analyzed through monthly resolved data). 

4. There is very little inter-annual variability in shipping lanes. An analysis of twenty years 

of monthly resolved ICOADS data demonstrates that from year to year, and season to 
season, the same shipping lanes are used by different sets of vessels reporting to 

ICOADS. 

5. The number of observations and sample of world fleet vessels by type is large enough to 

support statistical analysis and correction for known bias. 

The above discussion also demonstrates that both ICOADS and AMVER datasets are 

biased. The data as obtained has the following known limitations: 

1. Voluntary reporting conditions create a sample bias that is globally non-random in terms 

of the vessel types relative to the world fleet. This can be quantified and corrected at a 

global level through simple weighting adjustments. 

2. Variability in reporting by vessel type may demonstrate different non-random behavior 

locally than implied by the global sample bias, above. 

3. Variability of the reporting frequency among vessels of the same type is large and nonrandom. This can be quantified and corrected at a global level through simple weighting 

adjustments. 

4. Variability in reporting frequency by geographic region is apparent and non-random. 

For example almost all the observations made by the five vessels with the highest 

reporting frequencies in the three years (2000-2002) are all located in the North Atlantic. 

5. Items 2 and 4 require more advanced adjustment and/or validation with independent data 

samples or comprehensive traffic analysis. 

Geographically resolved emissions inventories can be quickly produced based on 

ICOADS or AMVER. We understand that neither dataset is SPATIALLY RANDOM, since 

international trade and shipping is clearly not random. We would not favor one version over the 

other arbitrarily without validating using plotted routes based on port-arrival or find other 

independent samples. The non-random spatial distribution of ICOADS or AMVER samples 

limits the extent we can improve the accuracy of this type of approach. 

However, ICOADS is open to public for research purposes and can be obtained from 

ICOADS website, whereas AMVER is strictly protected and used only in a bona fide maritime 

emergency as claimed by AMVER (16). Only one or two researchers, all of them are non-U.S. 

researchers, have been given access to AMVER data for research purposes under special 

agreement. Given the difficulty of obtaining AMVER data, and importantly, the fact that both 

dataset have strengths and weaknesses in representing global ship traffic, and the fact that neither 

can be proved better than the other, ICOADS is chosen for this work. We make efforts to correct 

potential sampling bias caused by over-reporting and non-response ships. 

Choosing Coordinate System and Resolution 

Generally there are two types of coordinate system: Geographic Coordinate Systems and 

Projected Coordinate Systems. Coordinate system can be chosen based on needs, including the 

extent of the map, the spatial attributes that should be preserved most, etc. The ESRI ArcGIS 

offers many coordinate systems that we can choose from. 

Maps produced with different projected coordinate systems look different. The map 

projection process will cause distortions in one or more of the following spatial properties: 

distance, area, shape, and direction. Although no projection can preserve all these properties, 

each one, each projection is distinguished by its suitability for representing a particular portion 

and amount of the earth’s surface and by its ability to preserve distance, area, shape, or direction. 
Some map projections minimize distortion in one property at the expense of another, while 

others strive to balance the overall distortion (19). 

Here are a few things to consider when selecting a projection (19): 

• Which spatial properties do you want to preserve? 

• Where is the area you’re mapping? (e.g. polar region or equatorial region?) 

• What shape is the area you’re mapping? Is it square? Is it wider in the east–west 

direction? 

• How big is the area you’re mapping? On large-scale maps, such as street maps, 

distortion may be negligible because your map covers only a small part of the earth’s 

surface. On small-scale maps, where a small distance on the map represents a 

considerable distance on the earth, distortion may have a bigger impact, especially if 

you use your map to compare or measure shape, area, or distance. 

Per the request from the client, the USA Contiguous Lambert Conformal Conic 

Projection coordinate system, which is one of the best for middle latitudes, was chosen for this 

work, and 36km X 36km resolution was used. 

Choosing Appropriate Means to Correct Sampling Bias 

In this section, we discuss the effects of different means that could potentially to some extent 

correct the statistical and geographical sampling bias of ICOADS and improve the accuracy of 

the geographic distribution of ship emissions. 

Evaluating the Effects of Using Three-Year with Single Year ICOADS 

Data 

We understand that if two conditions hold, it will be better to use a combination of three-year 

samples rather than a single year data. One condition is that temporal changes of ship traffic 

pattern and intensity are not important or annual changes are not apparent. The other condition is 

that, with more observations in the three-year combination, the statistical and geographical 

sampling bias can be potentially reduced and, therefore, the accuracy of the distribution of 

emissions can be potentially improved. 

According to navigation knowledge and experience, ships travel along well-established 

shipping lanes between each pair of ports with certain deviation (20). The fact that there are 

established distances between different ports and well-understood by cargo logistics planners is 

good evidence that ships travel along well defined paths. We acknowledge different types and 

different sizes of vessels might take different paths between the same origin and destination. We 

also understand that the international trade pattern does not change much annually. With these 

understandings, we didn’t anticipate significant annual changes of the geographic distribution of 

global ship traffic, and accordingly the distribution of air emissions from shipping activities 

won’t change much annually under the assumption that air emissions are proportional to the 

intensity of ship traffic which can be weighted if possible. 

Figure 5a shows the distribution of marine vessel nitrogen emissions inventory based on 

ICOADS 2002 data weighted by ship power. Figure 5b shows the distribution based on a 

combination of three-year (2000-2002) data also weighted by ship power. They look quite 

similar except that shipping lanes in Figure 5b look smoother and emissions are assigned to more 
grids. Given the facts that the number of observations almost tripled in the three-year 

combination dataset, and the change of geographic distribution of ship traffic and emissions in a 

short term is not significant, we understand that it is likely the three-year ICOADS data better 

represent the ship traffic and emissions. 

 (a) (b) 

Figure 5. a) Nitrogen emissions inventory for 2002 distributed using one year of ICOADS 

data (2002); b) Nitrogen emissions inventory for 2002 distributed using three years of 

ICOADS data (2000-2002). 

With ArcGIS spatial analyst tools, the two results can be compared quantitatively. We 

obtained the difference of emissions in using the one-year and three-year data by subtracting the 

gridded raster data for Figure 5a and Figure 5b. Figure 6a shows the result of this comparison. 

For most grids, the difference is 25 tons more or less with in a 36km x 36km grid. For a small 

percent of grids, the difference is between 25 to 100 tons more or less. For an extremely small 

number of grids, the difference may be greater than 100 tons and up to around 550 tons. Most of 

the grids with significant difference are located in major shipping lanes. This is largely caused by 

the fact that ships make report randomly along shipping lanes. 

We further obtained the percent of change by dividing the raster data for Figure 6a with 

the raster data for Figure 5a. Figure 6b shows the result. We noticed that the positive and 

negative numbers occur almost randomly on the map. This phenomenon may be caused by 

inherent randomness of ship reporting. 
 (a) (b) 

Figure 6. a) Comparison between the distribution of ship nitrogen emission with 2000-2002 

three-year data and 2002 single year data; b) Percent of change between the distribution of 

ship nitrogen emission with 2000-2002 three-year data and 2002 single year data. 

 

The effect of using three-year data is resolution dependent. Figure 7a and Figure 7b 

compare the 36km x 36km resolution with a resolution four times coarser (144 X 144 km). 

Although the change of absolute number of tons of emissions in one grid is greater with coarser 

resolution, the percentage of change is much smaller than with the finer resolution. This suggests 

that using three-year data improves the quality of the spatial representation, and that the effect of 

using three-year data on a regional scale significantly larger than the grid size is not significant. 

We confirmed that multi-year ICOADS data didn’t change much the geographic 

distribution of the emission inventories by evaluating the whole west coast is one grid; the 

difference in total emissions is be only about 3%. With more data in the three-year dataset, 

however, non-response bias may be reduced further, the distribution will be statistically more 

reliable, and the shipping lanes look smoother. Moreover, emissions are, on the one hand, 

assigned more to the major shipping lines, with the correction of the randomness of ship 

reporting on shipping lanes, and on the other hand, spread to more areas. We believe the threeyear ICOADS combination is more representative of the general ship traffic pattern, and can 

likely represent emissions more accurately. With this understanding, we decided to use the 

combination of three year (2000-2002) data as the proxy of the distribution of ship traffic. 
 (a) (b) 

Figure 7. a) Comparison between the distribution of ship nitrogen emission with 2000-2002 

three-year data and 2002 single year data; b) Percent of change between the distribution of 

ship nitrogen emission with 2000-2002 three-year data and 2002 single year data. 

Evaluating the Effects of Weighting Observations with Ship Engine 

Power 

Ships vary greatly in size and engine power. With some ships are only hundreds deadweight 

tonnage (DWT), ships with more than 100,000 (DWT) are common, and some tankers are even 

up to half million tons DWT. According, the total engine power for one large ship may be 

thousands times of the engine power of a small ship. In the ICOADS fleet, ship power ranges 

from a hundred kilowatts to more than 70,000 kilowatts. Previous studies assumed ICOADS 

ships were identical and emissions inventories were distributed proportional to the number of 

observations in one location (3). Endresen et al. used ship gross tonnage, which is related to ship 

engine power, to weight the observations, and perhaps improved the accuracy of the distribution 

of ship emissions. In this section, we discuss the effects of using ship engine power, which is 

more directly related to ship emissions, to weight the observations. 
 (a) (b)

Figure 8. a) Comparison between the distribution of ship nitrogen emission with power 

weighted and non-weighted method; and b) Percent of change between the distribution of 

ship nitrogen emission with power weighted and non-weighted method. 

Figure 8a and Figure 8b show the effects of using ship power to weight the ICOADS 

observations to distribute the emissions. Figure 8a, which was produced by subtracting nonweighted emission distribution from the power-weighted distribution, shows the change of 

amount of emissions in each grid. Most changes occur on major shipping lanes, especially the 

great circle route to East Asia for container ships with large engine power. The changes range 

from about negative 300 metric tons to about positive 300 metric tons nitrogen. Generally, the 

power-weighted method increases the emissions assigned to ocean-shipping lanes while 

decreases the emissions to coastal routes. The power-weighted method increases the nitrogen 

emissions assigned to the area showed in Figure 8a by about 18%. 

Figure 8b was produced by dividing the raster data for Figure 8a by the raster data 

produced with non-weighted method. The percent of change ranges from about negative 100% to 

about positive 150%. Most grids have 50% of change of emissions more or less. 
Figure 9. World cargo ship traffic pattern by ship type. Observations of cargo vessels in 

ICOADS are used as a proxy of world ship traffic without adjustment. 

With the facts that ships vary greatly in size, engine power, etc., and ships, primary 

container ships and tankers (see Figure 9), travel along the U.S. West Coast are relatively larger 

than other regions, we believe the power-weighted method is more accurate than the method that 

assumes all ships are identical. We choose the power-weighted method to improve the accuracy 

of this work. 

Producing Raster Files 

As described briefly in the overview discussion at the beginning of the work, we follow the 

following general steps to spatially assign regional emissions. We allocate the global inventory 

work previously published (from Table 1) to locations where the most installed power is 

operating – the major shipping lanes with the most powerful vessels. This is discussed in more 

detail below. 

1. Identify the vessels and engines in the ICOADS sample. 

2. Assign an emissions indicator to each observation using rated vessel power. When 

we convert to a raster format, we sum these to identify which 36km x 36km gridded 

cells have the greatest contribution to emissions accordingly. 

3. Apply equation 1 to each grid cell to convert the power-based emissions indicator to 

an estimate of total emissions in that gridded cell. 

Based on the above discussion, we use the combination of three-year (2000-2002) 

ICOADS data as the proxy of the ship traffic pattern, and we distribute the emission inventory to 

each grid proportional to the number of observations in that grid weighted by the power of the 

ship that made the individual observations. We believe the approach we use is simpler, with 
fewer assumptions, and can produce datasets with sufficient accuracy for this task and the 

purpose of this work. 

We produced monthly emission inventories for 2002 for the six pollutants, including 

NOx, SOx, CO2, CH4, PM10, and CO. The inventories are first bounded by the uncertainty of the 

global inventories with lower bound, best guess, and upper bound three scenarios. Considering 

the discussion addressing the sampling bias, the emissions assigned to a small number of grids 

(36km X 36km) may be extremely uncertain. The uncertainties caused by sampling are generally 

small for most of the grids in the studied area, especially with a coarser resolution. Importantly, 

the uncertainties for neighboring grids often can offset each other with negative and positive 

values. 

The emissions of one pollutant in one cell, 36km X 36km in this work is calculated as 

equation (1): 

(1) 

 Where ei as emissions of one pollutant from cell i in one period, year 2002 or one month 

of 2002 in this work; eg as the global ship emissions inventory for that pollutant in 2002; pg is the 

sum of the total main engine power of ICOADS observations; pi is the sum of the total main 

engine power of ICOADS observations in cell i. 

 Totally 234 raster files were produced. The final products were imported into a 

geodatabase. The name of the raster file has three or four parts. The raster files with a name of 

three parts are annual ship emissions. The first part of the name denotes the pollutant, where n 

for nitrogen, s for sulfur, c2 for CO2, hc for CH4, co for CO, and pm for PM10. The second part 

of the name denotes the scenario, where l for lower bound, b for best guess, and u for upper 

bound. The third part is the last two digits of the year. The name of monthly raster files has the 

fourth part, which denotes the month of the emissions. 

 Per the request of the client, the raster files were converted into ASCII files with ArcGIS 

conversion tool. The name of the ASCII files has three parts: [pollutant]_[case]_[period].txt. For 

example: ch4_base_annual.txt is the base case inventory for annual CH4 emissions; 

noxnitrogen_upper_annual.txt is the upper bound of the inventory for annual nitrogen emissions; 

and co2carbon_lower_07.txt is the lower bound of the carbon emissions as CO2 in July 2002. 

Conclusions, Future Work and Recommendations 

In this work, we employed an approach published in peer-reviewed literature with 

adjustments by taking into account of recent developments to produce ship emissions inventory 

for the U.S. West Coast. Efforts have been made in this work to address potential statistical and 

geographic sampling bias caused by over-reporting and non-response ships to improve the 

accuracy of the products-geographically resolved emissions inventories. 

After a review of current available ship emissions inventories, we believe the inventories 

produced by Corbett and Koehler’s model with adjusted inputs are the best for this work. 

i

g

g

i p

p

e

e = ×
By comparing ICOADS with AMVER dataset, we understand that neither actual ship 

traffic or either dataset is SPATIALLY RANDOM. We would not favor one version over the 

other arbitrarily without validating using plotted routes based on port-arrival or find other 

independent samples. Given the availability of ICOADS data, its apparent larger size, and the 

fact that both datasets have similar strengths and weaknesses in representing global ship traffic, 

ICOADS data are appropriate for this analysis. 

We observed that using multi-year ICOADS data didn’t change much the geographic 

distribution of the emission inventories. With more data in the three-year dataset, the nonresponse bias can be reduced, the distribution may be statistically more reliable, and the shipping 

lanes look smoother with gaps being filled, and emissions are assigned perhaps more accurate. 

With ship type, size, and power being identified for individual ICOADS observations 

based on ship identifiers by joining ICOADS with a ship dataset, we were able to weight 

individual observations with ship power. We believe the power-weighted method is more 

accurate than previous methods that assumed all ships are identical. 

We anticipate that more ships can be identified if we use the same year ICOADS and 

Lloyd’s data and, importantly, if the delayed mode data is blended into ICOADS. This could be a 

potential improvement in the future of the accuracy of geographically resolved ships emissions 

using ICOADS as a proxy. 

Future effort (described in optional tasks under this scope of work) includes deriving 

traffic profile from port activities data, like the USACE Entrance and Clearance data to validate 

and/or recalibrate emissions profiles according to shipping activities records. This may modify 

vessel-type inventories to be more accurate, although it may not explicitly capture vessels 

transiting the West Coast but not calling on U.S. ports. 

References 

1. Corbett, J.J. and H.W. Koehler, Updated Emissions from Ocean Shipping. Journal of 

Geophysical Research, 2003. 108(D20): p. 4650. 

2. EPA and ARCADIS Geraghty & Miller Inc., Commercial Marine Activity for Deep Sea 

Ports in the United States. 1999, Assessment and Modeling Division, Office of Mobile 

Sources, Environmental Protection Agency. 

http://www.epa.gov/otaq/models/nonrdmdl/c-marine/r99020.pdf

3. Corbett, J.J., P.S. Fischbeck, and S.N. Pandis, Global Nitrogen and Sulfur Emissions 

Inventories for Oceangoing Ships. Journal of Geophysical Research, 1999. 104(D3): p. 

3457-3470. 

4. Corbett, J.J. and P.S. Fischbeck, Emissions from Ships. Science, 1997. 278(5339): p. 823-

824. 

5. Olivier, J.G.J., et al., Applications of EDGAR. 2002, National Institute for Public Health 

and the Environment (RIVM), Netherlands Organisation for Applied Scientific Research 

(TNO). http://www.rivm.nl/bibliotheek/rapporten/410200051.pdf

6. Streets, D.G., G.R. Carmichael, and R.L. Arndt, Sulfur Dioxide Emissions and Sulfur 

Deposition from International Shipping in Asian Waters. Atmospheric Environment, 

1997. 31(10): p. 1573-1582. 
7. Streets, D.G., S.K. Guttikunda, and G.R. Carmichael, The Growing Contribution of 

Sulfur Emissions from Ships in Asian Waters 1988-1995. Atmospheric Environment, 

2000. 24(26): p. 4425-4439. 

8. Endresen, Ø., et al., Emission from International Sea Transportation and Environmental 

Impact. Journal of Geophysical Research, 2003. 108(D17): p. 4560. 

9. European Commission and Entec UK Limited, Quantification of Emissions from Ships 

Associated with Ship Movements between Ports in the European Community. 2002, 

European Commission. 

http://europa.eu.int/comm/environment/air/background.htm#transport

10. Endresen, Ø., et al., Substantiation of a lower estimate for the bunker inventory: 

Comment on ‘‘Updated emissions from ocean shipping’’ by James J. Corbett and Horst 

W. Koehler. Journal of Geophysical Research, 2004. 109(D23302). 

11. Corbett, J.J. and H.W. Koehler1, Considering alternative input parameters in an activitybased ship fuel consumption and emissions model: Reply to comment by Øyvind 

Endresen et al. on ‘‘Updated emissions from ocean shipping’’. Journal of Geophysical 

Research, 2004. 109(D23303). 

12. Woodruff, S.D., et al., COADS Release 2 Data and Metadata Enhancements for 

Improvements of Marine Surface Flux Fields. Phys. Chem. Earth, 1998. 23(5-6): p. 517-

526. 

13. Lloyd's Register - Fairplay, Lloyd’s Register of Ships on CD ROM. 2004. Accessed 2005 

14. Environmental Systems Research Institute, Desktop GIS. 2004. 

http://www.esri.com/software/arcgis/about/desktop.html#desktop. Accessed 2004 

15. ICOADS, The International Comprehensive Ocean-Atmosphere Data Set (ICOADS). 

2004. http://dss.ucar.edu/pub/coads/. Accessed July 21, 2004 

16. AMVER, Participating in AMVER. 2005. http://www.amver.com/index.htm. Accessed 

March 27, 2005 

17. Woodruff, S.D., Personal Communication. 2005. 

18. Voluntary Observing Ship Program, The WMO Voluntary Observing Ships (VOS) 

Scheme. 2005. http://vos.noaa.gov/vos_scheme.shtml. Accessed March 27, 2005 

19. ESRI, Understanding Map Projections. 2004. 

20. Kim, H., C. Li, and K. Jeong, E-mail discussion about the dynamics of shipping lanes, C. 

Wang, Editor. 2004. 
Appendix A 

Ship Group Ship Type Ship Group Ship Type 

Aggregates Carrier Deck Cargo Ship 

Alumina Carrier General Cargo 

Bulk / Oil Carrier Palletised Cargo Ship 

Bulk Carrier Barge Carrier 

Cement Carrier Heavy Load Carrier 

Limestone Carrier Livestock Carrier 

Mud Carrier Log-Tipping Ship 

Ore / Oil Carrier Nuclear Fuel Carrier 

Ore Carrier Pearl Shells Carrier 

Powder Carrier Pulp Carrier 

Refined Sugar Carrier Stone Carrier 

Self-Discharging Bulk Carrier 

General Cargo 

Passenger / General Cargo Ship 

Urea Carrier Passenger (Cruise) Ship 

Bulk Carrier 

Wood Chips Carrier Passenger Passenger 

Container Ship Container Ship Reefer Refrigerated Cargo Ship 

Passenger / Container Ship Container Ro-Ro Cargo Ship 

Fish Carrier Landing Craft 

Fish Factory Ship Passenger / Ro-Ro Cargo 

Fishing Support Vessel Passenger Landing Craft 

Fishing Vessel Ro-Ro Cargo 

Live-Fish Carrier 

RO-RO 

Vehicles Carrier 

Seal-Catcher Anchor Hoy 

Trawler Buoy / Lighthouse Vessel 

Fishing 

Whale-Catcher Cable-Layer 

Beer Tanker Crane Ship 

Bitumen Tanker Crewboat 

Chemical / Oil Products Tanker Dredger 

Chemical Tanker Fire-Fighting Vessel 

Coal / Oil Mixture Tanker Hopper Dredger 

Crude Oil Tanker Hospital Vessel 

Edible Oil Tanker Icebreaker 

Fish Oil Tanker Kelp Dredger 

Fruit Juice Tanker Launch (Unspecified) 

Latex Tanker Mooring Vessel 

LNG Tanker Motor Hopper 

LPG Tanker Patrol Vessel 

Molasses Tanker Pilot Vessel 

Oil Products Tanker Pollution Control Vessel 

Oil-Sludge Tanker Pusher Tug 

Vegetable Oil Tanker Research Vessel 

Water Tanker Salvage Ship 

Wine Tanker Search & Rescue Vessel 

Oil Tanker Supply Vessel 

Tanker 

Other Liquids Tanker Tank-Cleaning Vessel 

Pipe-Layer Tender (Unspecified) 

Production Testing Vessel Training Ship 

Standby-Safety Vessel Trans-Shipment Vessel 

Well-Stimulation Vessel Tug 

Other Activities Utility Vessel 

Towing / Pushing Waste Disposal Vessel 

Sail Training Ship Work / Repair Vessel 

Yacht Naval / Naval Auxiliary 

Air Cushion Vehicle Other Non-Merchant Ships 

Drilling Ship Barge 

Offshore Processing Ship Pontoon 

Offshore Supply Ship 

Miscellaneous 

Offshore Tug / Supply Ship 

Miscellaneous 

Offshore Support Vessel