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 415.899.0700May 2006

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### 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 th is 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 particip ated 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 sin cere appreciation.May 2006

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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 A ND 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 EM ISSIONS 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 ................................................................. 4-3May 2006

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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 Commerci al 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- 1May 2006

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### APPENDICES

Appendix A: Development of Ca lendar Year 2002 County-Level Fuel Specification Data Appendix B: Geographic Ch aracterization of Ship Emissions for the U.S. West Coast - Final Task 1 Report – Corbett and Wang, 2005

TABLES

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

Table 2-2. Elemental car bon/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 range s used for allocation to counties............................. 5-2 Table 5-2. National fleet average emission f actors (gram per gallon) from EPA (1997) ..... 5-3 Table 5-3. Locomotive activity grow th projection for this work.......................................... 5-5 Table 5-4. Locomotive emission rate projections ................................................................. 5-6

Table 7-1. Commerci al 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 IV L 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 Alaska n port ocean-going vessels in 1996 ................... 7-10

Table 7-13. Emission estimates for Alaska n 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 nonr oad annual emissions by fuel type..................... 9-37

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

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

Table 9-8. 2018 Western states total nonr oad 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............................................................. 9-45May 2006

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### FIGURES

Figure 2-1. Day of week pr ofiles by vehicle class.................................................................. 2-6 Figure 2-2. Weekday hour of da y profiles by vehicle class ................................................... 2-7 Figure 2-3. Day of week pr ofiles by vehicle class.................................................................. 2-7 Figure 3-1. Statewide VMT growth rates, 2002 to 2018........................................................ 3-6 Figure 5-1. County level rail ac tivity 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 ra il fuel consumption by railroad......................................... 5-5 Figure 7-1. Raw offshore emission estimat es and with near port emissions substituted (Blue grids i ndicate no emissions over water)................................... 7-2 Figure 7-2. Offshore emissions by state and grid cells repl aced with near port data............. 7-3 Figure 7-3. Grid assignment of California in-trans it (green) and port/inshore (red) emissions............................................................................... 7-11 Figure 7-4. Distinct zones include d in the WRAP commercial marine emissions inventory. .......................................................................................... 7-12 Figure 7-5. Commercial marine inst alled power trend and analysis ................................... .7-13 Figure 8-1. Unpaved road dust coar se 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. Sour ce: Adelman et al., 2006.............................................. 9-4 Figure 9-2. 2018 WRAP region Base Case annual emissions contributions by source category. Sour ce: Adelman et al., 2006.............................................. 9-5 Figure 9-3. 2002 Western states on-ro ad emissions (% by vehicle class and fuel type). ...................................................................................................... 9-6 Figure 9-4. 2002 Western states aver age annual on-road NOx emissions by vehicle class and fuel type. ............................................................................. 9-6 Figure 9-5. 2002 Western states aver age annual on-road VOC emissions by vehicle class and fuel type. .................................................................................. 9-7 Figure 9-6. 2002 Western states aver age annual on-road PM10 emissions by vehicle class and fuel type. ............................................................................. 9-7 Figure 9-7. 2002 Western states aver age annual on-road SO2 emissions by vehicle class and fuel type. ............................................................................. 9-8 Figure 9-8. 2002 Western states aver age annual on-road CO emissions by vehicle class and fuel type ................................................................................... 9-8 Figure 9-9. Spatial distribution of all RP O 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 averag e annual off-road NOx emissions by source category. ............................................................................................ 9-16 Figure 9-18. 2002 western states averag e annual off-road VOC emissions by source category. ............................................................................................ 9-17 Figure 9-19. 2002 western states averag e annual off-road PM10 emissions by source category. ............................................................................................ 9-17May 2006

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### Figure 9-20. 2002 western states averag e annual off-road SO2 emissions by source category ............................................................................................. 9-18 Figure 9-21. 2002 western states averag e 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 mo bile annual average daily NOx emissions, 2002 – 2018...................................................................................... 9-26 Figure 9-30. Western states total mo bile annual average daily VOC emissions, 2002 – 2018...................................................................................... 9-27 Figure 9-31. Western states total mo bile annual average daily PM10 emissions, 2002 – 2018...................................................................................... 9-28 Figure 9-32. Western states total mo bile annual average daily PM2.5 emissions, 2002 – 2018...................................................................................... 9-29 Figure 9-33. Western states total mo bile annual average daily SO2 emissions, 2002 – 2018...................................................................................... 9-30 Figure 9-34. Western states total mo bile 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 .................................................................... 9-32May 2006

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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 Da ta 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 High way Administration

FTP Federal Test Procedure GCVTC Grand Canyon Visibil ity 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 GasMay 2006

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### LTO Landing and take-off

MARPOL International Convention for th e Prevention of Pollution from Ships MHDDV Medium heavy-duty diesel vehicle MPO Metropolitan Pl anning 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

NH 3 

### Ammonia NIPER National Institute for Petroleum and Energy Research NPIAS National Plan of Integrated Airport System NOx Nitrogen oxides

NO 2 

### Nitrogen dioxide

NREL National Renewa ble Energy Laboratory OC Organic carbon

PM Particulate matter

PM 10 

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

PM 2.5 

### Particulate matter less than or equal to 2.5 micrometers 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 Oper ator Kernel Emissions

SO 2 

### Sulfur dioxide

SO 4 

### Sulfate

SO X 

### 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 PartnershipMay 2006

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### iii PREFACE Regulatory Framework for Tribal Visibility Implementation Plans

The Regional Haze Rule explicitly recognizes the au thority 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 su bject to state jurisdiction.

2. Federally recognized tribes may apply for a nd receive delegation of federal authority to implement CAA programs, including visibility regul ation, or "reasonably se verable" elements of such programs (40 CFR §§49.3, 49.7). The mechanism for this delegation is a Tribal Implementation Plan (TIP). A reasonably severabl e 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 regulator y requirements.

3. The Regional Haze Rule expressly provides that tribal visibility programs are “not dependent on the strategies selected by th e state or states in which th e tribe is located” (64. Fed. Reg. 35756), and that the authority to implement §309 TI Ps extends to all tribes within the GCVTC region (40 CFR §51.309(d)(12).

4. The EPA has indicated that under the TAR trib es 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). EP A 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 modi fication 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 sovere ignty 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 fr om the “stakeholders” (private interest) which participate on Forums and Comm ittees but are not eligible for the Board. Despite this equality of representation on the Bo ard, tribes are very differently situated than states. There are over four hundred federally re cognized tribes in the WRAP region, including Alaska. The sheer number of tribes makes full pa rticipation impossible. Moreover, many tribesMay 2006

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### iv 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 importa nt 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, includ ing Board members Forum and Committee members and co-chairs, make their best effort to ensure th at WRAP products are in the best interest of the tribes, the environment, and the public. One inte rest is to ensure that WRAP policies, as implemented by states and tribes, will not constr ain 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 stak eholder interests in appr oving this report as a

consensus document.May 2006

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### 1-1 1.0 INTRODUCTION

1.1 BACKGROUND

Visibility impairment is a con cern 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 th e U.S. Environmental Protection Agency announced the Regional Haze Rule, which calls for stat e and federal agencies to work together to improve visibility in 156 national parks and wilder ness 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 pr otection plans to reduce the pollution that causes visibility impairment. Five multi-state regional planning organizations (RPOs) are working to develop the technical basis for th ese plans. The largest of these RPOs is the Western Regional Air Partnership (WRAP), which is made up of West ern 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 re commendations in June 1996 for improving visibility in 16 national parks and wi lderness areas on the Colorado Plateau.

The WRAP is implementing regiona l planning processes to improve visibility in all Western Class I areas by providing the technical and policy tools needed by states a nd tribes to implement the federal regional haze rule. The rule requires the development of mobile source emissions

inventories. WRAP contracted with ENVIRON to develop appr oaches for and to estimate

mobile source emissions for the Western states. Th e first set of mobile source emissions use in WRAP air quality modeling were developed se veral years ago (Polla ck et al., 2004).

The purpose of this project was to update th e previously developed WRAP mobile source emission inventories. Specificall y, the goals of this effort were to survey state and local air quality planning agencies to obtain the most up- to-date activity data a nd modeling inputs, to

account for all of the “on the books” mobile sour ce control measures, an d to use the latest modeling tools available. This report describes in detail the methods used to estimate these revised mobile source emissions, and provi des the resulting emissions estimates.

1.2 EMISSIONS INVENTOR IED UNDER MO BILE SOURCES

Mobile sources include on-road and off-road ve hicles 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 includ e 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 mo ved 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 engine s in the following categories:May 2006

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### 1-2 • 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 fi eld 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 c ounty for all counties in 14 states: Alaska, Arizona, California, Colorado, Id aho, Montana, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Ut ah, 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 th rough 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 (PM 10 

### and PM 2.5 

### ), nitrogen oxides (NO

x 

### ), sulfur dioxide (SO 2 

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

### ), elemental and organic ca rbon (EC/OC), and sulfate (SO4).

Sources : For all pollutants, emissions were esti mated separately by vehicle class for onroad sources and by equipment type/engine ty pe 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. Excep t for California, the on-road

mobile sources emission factors were derive d from EPA’s MOBILE6 model, available atMay 2006

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### 1-3 http://www.epa.gov/OMSWWW/m6.htm . Activity for on-road mob ile 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 se veral agencies provided updated. The California Air Resources Board (C ARB) 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 sources 1 

### , all sources listed above excep t aircraft, commercial marine,

and locomotives. The NONROAD model includes es timates of emission factors, activity levels, and growth factors for all traditional off-road s ources. 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 I NVENTORY 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 th e WRAP Mobile Sources Emission Inventory Update project web page at http://wrapair.org/for ums/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.May 2006

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### 2-1 2.0 EMISSIONS MODELS USED AND ADDITIONAL CALCULATIONS FOR AIR QUALITY MODELING

On-road and off-road mobile source emissions ar e estimated as the products of emission factors and activity estimates. Except fo r 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 us ed 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 da ta 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 prof iles 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 fo r estimating on-road mobile source gram per mile emission factors for VOC (exhaust and evaporative), NO X 

### , CO, PM, NH 3 

### , and SO 2 

### . 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 standard s 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 ve hicle 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.May 2006

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### 2-2 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)

LDGT1 Up to 6000 lb GVW Light-duty gasoline trucks 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

Motorcycles 2 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. Of f-road motorcycles, such as dirt bikes, are modeled as a no-r oad
mobile source in EPA’s NONROAD model.



### The particulate matter emission factors in MOBI LE6 are from an earlier EPA particulates

emission factor model called PART5. The tire an d 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 em ission 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 mo ved 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 estima ted 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 estim ated for this project, th e 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 fact ors 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 t ypes by horsepower rating and fuel type, in the following categories:May 2006

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-terrai n vehicles and off-road motorcycles;

• residential and commercial lawn and ga rden equipment, such as leaf and snowblowers;

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

• recreational marine vessels , such as power boats;

• underground mining equipment; and

• oil field equipment.

The NONROAD model does not include commercial marine, locomotiv e, and aircraft emissions. Emissions for these three source categories are estimated using other EPA methods and guidance documents (described in Sections 5-7). Howe ver, support equipment for aircraft, locomotive, and commercial marine operations and fac ilities are included in the NONROAD model.

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

NO X 

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

### ), sulfur oxides (SO X 

### ), and PM. The model

also estimates emissions of non-exhaust HC for si x 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 sp ark-ignition engines below 25 hp;

$ Phase 2 emission standards for new spark- ignition hand-held e ngines 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 fo r new nonroad compression-ignition engines below 50 hp including recrea tional marine engines;

$ Tier 2 and Tier 3 standards for new nonroa d compression-ignition engines of 50 hp and greater not including recreational mari ne engines greater than 50 hp; and

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

and reduced nonroad diesel fuel sulfur levels.

The NONROAD model provides emission estimates at the national, st ate, 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 (frac tion of available power) A = Activity (hrs/yr) EF = Emission Factor (g/hp-hr)May 2006

G:\WRAP MSEI2\Reporting\Final report\Sec2_emissions_models.doc 

### 2-4 The national or state engine population is estimated and multiplied by the average power, activity, and emission factors. Equipment popula tion by county is estimated in the model by geographically allocating national engine populati on through the use of econometric indicators,

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

as follows:

(County Population) i 

### /(National Population) I 

### = (County Indicator) i 

### /(National Indicator ) i



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

Activity is temporally allocated 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 Boar d (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 gene rate 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 deve loped 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 ac tivity 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. Sp ecifically, particulate matter needed to be split into elemental carbon (EC), or ganic carbon (OC), and sulfate (SO 4 

### ); and NO X 

### needed to be split into NO and NO 2



### .

EC and OC were estimated by a pplying EC/OC fractions to the PM 10 

### and PM 2.5 

### emissions estimates. The EC/OC splits used for these calcula tions are summarized in Table 2-2. These are the same EC/OC fractions used in the previous WRAP mobile sources em issions 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 – PM2.5May 2006

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### 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 severa l studies and reviews of par ticulate composition (e.g. EPA, 2001 and Turpin and Lim, 2000) since the time of the wo rk referenced in Table 2-2, there has not been a comparable comprehensive evaluation of particulate composition. Many particulate source/receptor statistical modeling efforts have b een attempted, but all used source profiles that predate those listed in Table 2-2. A comprehe nsive evaluation of source profiles needs to include the effect of the proper age distribution and maintenance hi story 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 defau lt 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 begi n to perturb the ratio of NO a nd 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 tem poral 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 deficien t 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 ther e 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 databa se 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/pol icy/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 individu al vehicle classes to estimate temporal variation in the vehicle mix. Three sets of profiles were developed: day ofMay 2006

G:\WRAP MSEI2\Reporting\Final report\Sec2_emissions_models.doc 

### 2-6 week profiles by vehicle class (F igure 2-1); hour of day profiles for weekdays, by vehicle class (Figure 2-2); and hour of day profiles for week ends, 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.May 2006

G:\WRAP MSEI2\Reporting\Fi nal report\Sec3_On-road.doc 

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

This section describes the emissions model i nputs and calculations pe rformed 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 in to the air quality model is also described. To estimate on-road emissions, defaults were es tablished 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 de scribed below. All state agencies responded to the su rvey requests for base and futu re 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 EM FAC2002 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 Em ission Inventory (NEI 2002), 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 (H PMS) 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 Proj ect web page, and a survey form was sent to state and local air quality pla nners asking them to review th e 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 se asonal VMT allocation data that was included in the default modeling inputs files posted.

Most of the state and local agencies that responded to the surv ey 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 thes e cases, the VMT estimates had been previously provided to EPA under the CERR an d had already been incorporat ed into the NMIM database.May 2006

G:\WRAP MSEI2\Reporting\Fi nal report\Sec3_On-road.doc 

### 3-2 3.2 MOBILE6 INPUTS

EPA’s MOBILE6 model was used to estima te 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 da tabase 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 mo del emission factor ca lculations, 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 provid e 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 Tabl e 3-1 were assigned to the MOBILE6 roadway types. MOBILE6 freeway roadway type was assigne d to rural interstates, urban interstates, and urban other freeways and expressways. The MO BILE6 arterial roadway type was assigned to rural arterials, rural minor arterials, rural majo r collectors, rural minor collectors, rural locals, urban principal arterials, urban minor arterial s, and urban collectors. The MOBILE6 local roadway type was assigned to urban locals.

3.2.2 Meteorological a nd Altitude Inputs

The default temperature and humidity inputs we re 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 Na tional Climatic Data Center (NCDC).May 2006

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### 3-3 Default altitudes were also extracted from th e 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 an d/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 de scribed in Section 2. Local control programs are implemented in CO or ozone nonattainment areas, and include vehicle Inspection and Maintenance (I/M) programs, An ti-Tampering Programs (ATP), oxygenated fuel programs, and local fuel regulations. Some ar eas also have Stage II (at-the-pump) vehicle refueling controls; however, refu eling 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 input s 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 KingMay 2006

G:\WRAP MSEI2\Reporting\Fi nal report\Sec3_On-road.doc 

### 3-4 State County

WA Snohomish WA Spokane

WA Pierce



### Oxygenated fuel programs are in place in some ar eas as a provision for ma intaining air quality standards. In other areas, ther e is oxygen in the fuels although not required as part of State Implementation Plan. Fuel inputs were develope d from the 2002 fuel survey as described below.

3.2.4 External Files

MOBILE6 allows the user to supp ly a number of external files to override model defaults. Of these, the registration distributi on is perhaps the most key for this effort. The registration distribution defines the age mix of each vehicle ty pe – 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 Ariz ona, Colorado, Montana, Nevada, Oregon, Utah, Washington, and Wyoming.

Other external files were pr ovided by a few areas: Clark C ounty provided VMT fractions, VMT by hour, and hourly starts distributions; Utah pr ovided 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 (ox ygen type, volume or weight percent, and market share), and gasoline and diesel sulfur levels.

For most states, fuel specifications were determ ined 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-Grum man (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 th e resulting default fuel properties, are in Appendix A.

Although many state and local agen cies 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 polluta nts (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; fa ll is September through November; and winter is December through February. In several area s, oxygenated fuels programs changed mid-season (i.e., in the midst of the WRAP three-month seasons ). In those cases, average values were usedMay 2006

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### 3-5 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 individu al monthly runs, and th e effects were found to be very small, less than 1%.

3.3 SEASONAL EMISSIONS CALCULATIONS

After creating MOBILE6 input f iles 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 valu e 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 dete rmine 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 we re 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 spr eadsheets posted to the project web page.

3.4 FUTURE YEAR EMISSIONS

Once the 2002 base year emissions survey respon ses 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 wh ere available. The surveys were sent to

the same state and local contacts who replied to the 2002 base y ear survey; all responded with updates for the future year modeling.

3.4.1 VMT Growth Factors

The default VMT levels were gene rated using the same growth fact ors as used in the previous WRAP mobile sources modeling (P ollack et al., 2004). The sp readsheets 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 (exc ept for Alaska, which was not included in the earlier WRAP mobile sources modeling). VM T updates were provided by many states, and Alaska provided new VMT growth factors.

Figure 3-1 shows the overall VMT growth rates by st ate. For several states, growth rates were provided by county, but for this figure the grow th rates are for the aggregate VMT across all

counties in each state.May 2006

G:\WRAP MSEI2\Reporting\Fi nal report\Sec3_On-road.doc 

### 3-6 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

YearVMT 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 th e same input data and fi les as the 2002 base year modeling that had been compiled from the base y ear 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 federa l 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 St ate of Washington passed a bill that gradually phases out I/M by 2020. In its place, Washingt on was adopting the California Low Emission Vehicle (LEV) II program. The Washington Depa rtment 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 paramete rs 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 th e 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 progr am, it is very difficult toMay 2006

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### 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 whic h 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 abou t their proposed rule refers to Washington’s analysis, which in turn relied on NESCAUM’s analysis: “The state of Washington ha s estimated that by 2020, California standards will reduce smog-forming emi ssions and air toxics by approximately 5% to 8% more than would be achieved by the fede ral standards and 11% to 16% beyond federal standards by 2030. Washington al so estimates a 17% reduction of green house gas emissions by 2020 and 27% by 2030 as a result of the California st andards. 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-leve l 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 em issions for California that are similar in content and format to the emissions for the remaining WRAP states:

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

and 2015.

• The weekday emissions were converted to av erage day using the day-of-week temporal profiles described in Section 2.

• The vehicle types in CARB’s EMFAC model ar e 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 em issions. The scaling factors were derived using the CARB 2002 fuel sulfur content and st atewide average gasoli ne 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 po llutants 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 m odeling is performed. The pollutants includedMay 2006

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### 3-8 in the SMOKE files are VOC (exhaust, evaporative, and total), NO X 

### , CO, NH3, SO 2 

### , 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 a nd all other WRAP states , by year and by season.

The SMOKE files for the thirteen non-Californi a states include emissions by county, roadway type, and vehicle type. Table 3-3 shows the source classificati on codes (SCC) for the on-road emissions categorized by vehicle type and HP MS 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. Th e SCCs in the SMOKE files for the California

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

Annual emissions files in EPA’s National Invent ory 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 pollutant s; the additional pollutants need ed 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 stat es, using the SCCs in Tables 3-3 and 3-4.

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

Mobile Sources Emission Inventor y 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 Fr eeways and Expressways 2201020270 LDGT1 Urban Other Principal Arterial

2201020290 LDGT1 Urban Minor Arterial

2201020310 LDGT1 Urban Collector

2201020330 LDGT1 Urban Local

2201040110 LDGT2 Rural Interstate 2201040130 LDGT2 Rural Other Principal ArterialMay 2006

G:\WRAP MSEI2\Reporting\Fi nal 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 Fr eeways and Expressways 2201040270 LDGT2 Urban Other Principal Arterial

2201040290 LDGT2 Urban Minor Arterial

2201040310 LDGT2 Urban Collector

2201040330 LDGT2 Urban Local

2201070110 HDGV Rural Interstate 2201070130 HDGV Rural Other Principal Arterial

2201070150 HDGV Rural Minor Arterial 2201070170 HDGV Rural Major Collector

2201070190 HDGV Rural Minor Collector

2201070210 HDGV Rural Local

2201070230 HDGV Urban Interstate 2201070250 HDGV Urban Other Freeways and Expressways 2201070270 HDGV Urban Other Principal Arterial

2201070290 HDGV Urban Minor Arterial

2201070310 HDGV Urban Collector

2201070330 HDGV Urban Local

2201080110 MC Rural Interstate 2201080130 MC Rural Other Principal Arterial

2201080150 MC Rural Minor Arterial 2201080170 MC Rural Major Collector

2201080190 MC Rural Minor Collector

2201080210 MC Rural Local

2201080230 MC Urban Interstate 2201080250 MC Urban Other Freeways and Expressways 2201080270 MC Urban Other Principal Arterial

2201080290 MC Urban Minor Arterial

2201080310 MC Urban Collector

2201080330 MC Urban Local

2230001110 LDDV Rural Interstate 2230001130 LDDV Rural Other Principal Arterial

2230001150 LDDV Rural Minor Arterial 2230001170 LDDV Rural Major Collector

2230001190 LDDV Rural Minor Collector

2230001210 LDDV Rural Local

2230001230 LDDV Urban Interstate 2230001250 LDDV Urban Other Freeways and Expressways 2230001270 LDDV Urban Other Principal Arterial

2230001290 LDDV Urban Minor Arterial

2230001310 LDDV Urban Collector

2230001330 LDDV Urban Local

2230060110 LDDT Rural Interstate 2230060130 LDDT Rural Other Principal Arterial

2230060150 LDDT Rural Minor Arterial 2230060170 LDDT Rural Major CollectorMay 2006

G:\WRAP MSEI2\Reporting\Fi nal 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 MCMay 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 esti mating 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-terrai n vehicles and off-road motorcycles;

• residential and commercial lawn and ga rden equipment, such as leaf and snowblowers;

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

• recreational marine vessels , such as power boats;

• underground mining equipment; and

• oil field equipment.

Seasonal average daily emissions were estimated for the thirteen non-California western states using EPA’s NONROAD model. To estimate emi ssions 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 mode ling. 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 ot her off-road sources – locomotive, aircraft,

and commercial marine – are covere d in the following three sections.

4.1. NONROAD MODELING

As discussed in Section 2, the NONROAD2004 mode l 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 incl udes defaults for all inputs affecting the emissions. The modeling inputs include fuel composition, ambient temperatures, equipment population and activity data, and equipm ent population growth factors.

For nonroad diesel fuel sulfur levels, 2002 defaults were deri ved 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/Nort hrop-Grumman (formerly the National Institute for Petroleum and Energy Research, NIPER). A more complete disc ussion of Sierra Research’s methods for compiling the data, and a listing of th e resulting default fuel properties, are in Appendix B. Future year nonroad diesel defau lt sulfur levels were se t 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 lo cal air quality agency contacts, to request review of the default m odeling inputs and any update s that the agencies could provide. Apart from temperatures, wh ich 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 MO BILE6 modeling as discu ssed in Section 2) and nonroad diesel fuel sulfur levels (n ote 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 indicato rs, 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 popul ation allocations, e.g., recreational boat registra tion data. Updated allocation files were used for some categories in Arizona and Wyoming, based on pr evious work done by ENVIRON fo r 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 th e US Department of Agriculture’s 2002 Census of Agriculture, available at http://www.nass.usda.gov/Cens us_of_Agriculture/index.asp . This survey provides estimates of the in-use equipment populations used for agricult ure 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 fo r agricultural equipment (by equipm ent type) were extracted from the Census of Agriculture, and were used to re vise 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 EP A from a proprietary database of equipment sales for several years. No state or local agen cy provided alternative gr owth 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 desc ribed below. For reporting, emissions were summed across equipment types by source category and by either gasoli ne or diesel fuel groupings. CNG and LPG-powered equipment, which account for only a small fraction of emissions, were lumped in to 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 add ition, EPA’s NONROAD Reporting Utility THC-to-VOC conversion factors were also applied, as th e 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 aver age day emissions for some sources and average weekday emissions fo r 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 di d 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 contro ls. (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 EP A NONROAD model day-of-week temporal profiles.

• Emissions for year 2013 were linearly in terpolated from CARB’s 2010 and 2015, if 2013 emissions were not provided.

• The CARB equipment type codes were ma tched to EPA NONROAD equipment codes (SCCs) using a CARB cross-reference file.

• For those sources where annual, summer, and wi nter 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 em issions were provided, seasonal emissions were estimated using the seasonal allocati on factors for Califor nia 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 po llutants 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 m odeling is performed. The pollutants included in the SMOKE files are VOC, NO X 

### , CO, NH3, SO 2 

### , 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 a nd all other WRAP states , by year and by season.

The SMOKE files for the thirteen non-Californi a states include emissi ons 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 Invent ory 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 pollutant s; the additional pollutants need ed 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 st ates, using the fully detailed nonroad SCCs.

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

Mobile Sources Emission Inventor y 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 nationa l fuel consumption estimates to counties using emissions data offered by the National Emissions Inventory. For this proj ect a detailed revision to that allocation method was developed for allo cating 2002 national fuel consumption estimates.

Emission factors were also revised to combin e 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 ba sis 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. Th e 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 railroa ds from data reported by the American Short Line & Regional Railroad Association (ASL RRA, 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 St atistics (2002), using data from 1995. The 2002 national activity data we re allocated to each county in the WR AP 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 de monstrated 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 (N EI, 1999 and unchanged for 2002) of allocating locomotive activity. The 1995 activity data were obtained as GIS shapef iles containing track segments and an associated database of rail density per m ile (MGTM/mi) corresponding to those segments. The segment-specific rail density estimates we re 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 an d the midpoint values used in this study. The top end density was reported as an open-ende d range, greater than 100 MGTM/mi, which was

estimated as 120 MGTM/mi. This differs fr om the allocation met hod 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 relativ e importance of those ma in 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 shapef iles 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 produc ts of segment densit ies and county-specific segment lengths to obtain the total county activity as 1995 ton-m iles. The county fraction of 1995 national rail activity was then the sum of activ ity in that county over the sum of activity in all counties. The relative county locomotive activity for the wester n 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 estimat ed 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 obt ained from the EPA (1997). The emission factors for 2002 are summarized in Table 5-2. County le vel 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 SO2 1 

### NH3 2



### 2002 Fleet Average 10.7 27.4 248.8 6.8 16.4 0.116

1 Reported as SO 2 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 pe rcentage 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 elemen tal carbon and 20% as organic carbon.

Sulfate PM (BSFC units) = BSFC * 7.0 * 0.02247 * 0.01 * (SOx fuel 

### - SOx bas 

### ) (2)

where

SOx bas 

### = 0% sulfur for entirely elem ental and organic carbon PM

SOx fuel 

### = % 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 SO 3 

### (with the remainder emitted as SO 2 

### ), which rapidly hydrolyzes in the humid exhaust to hydrated sulfuric acid [H 2 

### SO 4 

### *(7)H 2 

### O] and condenses on other PM. Fr om this assumption arises the molecular weight adjustment of 7.0 (ratio of hydrated sulfuric aci d to elemental sulfur). The figure 0.01 in the equation is to adjust valu es in percent (%) to fractional values.

County level locomotive emissions were estimated for all WRAP counties based on the procedure described above, except for those ar eas for which emissions data were supplied by local or state agencies. Four states - Alas ka, Arizona, Wyoming, and Idaho - and one county - Clark County, NV - supplied more detailed locomo tive 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 railro ad activity (Pollack et al, 2004a; Pollack et al, 2004b). The Alaska Departme nt of Environmental Conser vation (Edwards, 2005) and the Idaho Department of Environmental Quality (R einbold, 2005) supplied their own estimates, as did the Clark County Department of Ai r Quality Management (Li, 2005).

The spatial allocation of annua l locomotive NOx emissions is shown in Figure 5-2. Seasonal emissions were estimated based on an assumpti on of uniform year-round activity. Figure 5-2 shows the effect of the major east-west corri dors from Los Angeles th rough Arizona and New Mexico, Northern California through Nevada, Utah and Wyom ing, and Washington, NorthernMay 2006

G:\WRAP MSEI2\Reporting\Final report\Sec5_loco.doc 

### 5-4 Montana and North Dakota; the north-south corridor through Ca lifornia, Oregon, and Washington; and the coal mining region of eastern Wyoming. Other major and minor routes are also evident though the size of th e county affects the emission to tals estimated, so a major line that runs through a small or narrow county may no t appear significant, and, likewise, a large county may appear over-weighted compared with a neighboring c ounty 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 anal ysis was performed on the historical fuel consumption of the activity of the two predom inant (in the West, Union Pacific and BNSF) railroads’ activity. Figure 5-3 shows the comp any-wide fuel consumption calculated from

historic revenue ton-mile and fu el consumption per revenue ton-m ile. 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 su fficient activity indicator alone because the efficiency (ton-miles per gallon of fuel consumed) of railroads has been improving over time. AAR (2005) provided hi storical efficiency (gallons per ton-mile) for Burlington Northern (predating the merger with the Atchis on Topeka and Santa Fe [ATSF] railroad) and Union Pacific (predating the merger with Southern P acific 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 fuelMay 2006

G:\WRAP MSEI2\Reporting\Final report\Sec5_loco.doc 

### 5-5 consumption for the combined companies was t hus 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 determ ined 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 projec tion factors are listed in Table 5- 3 for the two major railroads and the combined projection. The trends fo r 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

R 2 = 0.8837

y = 0.0897x - 175.96

R 2 = 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

YearFuel 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 em ission 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 estimate s assumed 2500 ppm sulfur in the fuel for all years, and so adjustments were made to the SO 2 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 di rect 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 deri ved by ARB staff, as provided to ENVIRON.

The CARB emissions did not include NH3; NH3 was estimated by developing a scaling factor

based on SO X 

### emissions. Yearly fuel consumpti on estimates were derived based on SO X 

### emissions and the CARB assumed 2500ppm fuel sulfur content. A per-volume NH3 emission factor was applied to the estimated fuel consump tion to estimate NH3 emissions for each year at the county level. Lastly, PM was split among sulfat e, 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, NO X 

### , CO, NH3,

SO 2 

### , PM10, EC10, OC10, SO4(10), PM2.5, EC2.5, OC 2.5, SO4(2.5), coarse PM (PMC), NO, and NO2. Separate files we re prepared for each year.

Annual emissions files in EPA’s National Invent ory 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 pollutant s; the additional pollutants need ed for air quality modeling areMay 2006

G:\WRAP MSEI2\Reporting\Final report\Sec5_loco.doc 

### 5-7 not included. Annual emissions NIF files were pr epared 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 U pdate project web page at http://wrapair.org/forums/ef/UMSI/index.html

.May 2006

G:\WRAP MSEI2\Reporting\Fina l report\Sec6_aircraft.doc 

### 6-1 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 Inve ntory (NEI2002). Activity data for aircraft emissions are takeoff cycles (LTOs), and em ission 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 availa ble 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 consis t of the four aircraft operating modes: taxi and queue, take-off, climb-out, a nd landing. Default values for the amount of time a specific aircraft spends in each mode, or the ti me-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 pi ston-powered, non-commercial aircraft; and

• 

### Military aircraft.

6.1 2002 AIRCRAFT EMISSIONS

6.1.1 NEI2002 Aircraft Emissions Estimate s and Additional Pollutants

For the 2002 aircraft emissions, annual emissi ons files prepared for the NEI2002 formed the

basis of the work. These files were sent to ENVIRON by EPA’s contra ctor, 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 aircra ft/LTO activity data. Additional calculations were performed to estimate the additional polluta nts needed for WRAP modeling. Key elements

of those calculations are describe d by aircraft type below.

Air Carriers – The NEI2002 inventory data for VOC, CO, NOx, and SO 2 

### 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 speciat ion values for NO and NO 2 

### were assumed to be 90% and 10%, respectively, which are the default EPA speciations.

• It was assumed that no NH 3 

### is emitted from air carrier turbine engines, which normally

run lean.

• All of the fuel-bound sulfur was assumed to form SO 2 

### in the engine exhaust.

• Due to the lack of other, more recent sources for aircraft particulat e emission factors, the total suspended particulate (TSP) emissions fr om the air carriers were estimated using a commercial fleet-average emission factor fr om EPA’s 1985 National Acid PrecipitationMay 2006

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### 6-2 Assessment Program (NAPAP). To calculate PM2.5, according to the NEI2002, 97.6% of the particulate matter emitted from Co mmercial Aircraft was assumed to be PM 2.5 

### , as is assumed in the NEI2002.

Air Taxi, General Aviation and Military Aircraft – The NEI2002 inventory data for VOC, CO,
NOx, SO 2 

### , PM10, and PM2.5 for these Aircraft types were used directly. Additional

calculations were made to estimate the emissions of the additional polluta nts in the inventory:

• As for the air carriers, 90% of the NOx emissions were assumed to be NO and 10%

were assumed to be NO 2



### .

• For ammonia, air taxi and military aircraftwere assumed to be dominated by turbinepowered aircraft running lean, thus produci ng a negligible amount of ammonia. For general aviation, ammonia was estimated usi ng 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 f actor for non-catalyst light-duty gasoline engines.

• As for air carriers, all of the fuel -bound sulfur was assumed to form SO 2 

### in the engine

exhaust.

6.1.2 State Updates

The NEI2002-based inventory estimates were updat ed with additional information provided for

six areas:

For Alaska, Sierra Research, unde r contract to the WRAP Emissi ons Forum, developed seasonal

aircraft emissions estimates for all aircraft t ypes 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 th e rest of the aircraft da ta. The most significant

difference was that air carriers a nd air taxis were lumped into one category. These were then coded as the air carriers SCC, and WRAP Alas ka air taxi emissions were set to zero.

For Arizona, the NEI2002-based inventory was u pdated 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 estim ates 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 (P ollack et al., 2004a). This work included detailed EDMS modeling based on activity data obtained from bot h the FAA and local sources.May 2006

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### 6-3 The California Air Resources Bo ard (CARB) provided both base and future year aircraft emissions estimates, discussed below.

6.1.3 Seasonal Emissions Estimates

The NEI2002 aircraft emissions ar e annual estimates, as were most of the updates provided by state and local agencies. To estimate seasonal county-level emission inve ntories, the monthly distribution of activity for airpor ts in the WRAP region was obtai ned 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 annua l air traffic statistics for airports with FAAoperated or FAA-contracted tr affic control towers. The av erage seasonal distribution was calculated by state and aircraft type from th e ATADS dataset. These state-level seasonal distributions were then applied to the annual coun ty-level emissions in eac h state to derive the seasonal county-level emissions for each state.

6.2 FUTURE YEAR AI RCRAFT EMISSIONS

For all states except California, aircraft emissions were projected to the three future years from the 2002 emissions, by county and airc raft type, using FAA LTO forecas ts as the activity data. Emission factors were assumed to be unchanged over time. The International Civil Aviation Organization (ICAO) has promulgated NO

x 

### and CO emission standards for commercial aircraft, exempting general aviation and military engines fr om 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 on line 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 airc raft 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 de velop future year emission inventories.

A small number of counties had no aircraft LTO s 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 AI RCRAFT EMISSIONS

CARB provided annual, winter, a nd summer aircraft emissions es timates 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 Califor nia that are similar in content and format to the emissions for the remaining WRAP states:May 2006

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### 6-4 • The CARB aircraft emissions for commercial ai rcraft 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 calculat ed 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, NO X 

### , CO, NH3, SO 2 

### , 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 Invent ory 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 pollutant s; the additional pollutants need ed for air quality modeling are not included. Annual emissions NIF files were pr epared 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 U pdate project web page at http://wrapair.org/forums/ef/UMSI/index.html

.May 2006

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### 7-1 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 activ ity. In the previous WRAP mobile sources emission inventory work, emissi ons 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 leadin g 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 em ission 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 reti rement of steamship powered vessels.

One issue for modelers was which vertical grid la yer 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 emissi ons should be placed in the se cond 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 re gion of interest, and scaling fact ors by vessel type to estimate all ships. The ship proximity data and methods us ed 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.May 2006

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### 7-2 Emissions estimates using this method were comp ared with the emissions generated using the Puget Sound port activity estimates described belo w. 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 th e traffic to and from Vancouver, Canada. Using the proximity method it would be expected th at 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 fo r Oregon and Washington ports (Puget Sound, Columbia River, Coos Bay, a nd 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 Rive r where the nearest four grid cells now have higher emissions. There are also higher emissions at the en trance 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 invent ory estimates provided by the California Air Resources Board (CARB) includes estimates for sh ips in transit within 100 miles of the coast (CARB, 2005). The transit emi ssions predicted using the proximity method in this work were

zeroed out for the zone where CARB estimates we re 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 replacemen t along the California coast is apparent in the right side of Figure 7-1. The CARB data also included large ve ssel 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.May 2006

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### 7-3 Because the emissions offshore represent entirel y new estimates of emissions in the WRAP modeling domain, a summary of emissions is sh own 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 st ates were defined using the latitudes where the state borders meet the shore, as shown in Figure 7-2. For the n ear port totals in Table 7-2, it

should be noted that the Columbia River vessel traffic (especia lly the transit up and down the river) was primarily allocated to the State of Washington counties.

Table 7-2. 2002 Large ocean-going ship emissi ons 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.May 2006

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### 7-4 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 proj ecting future year estimates.

However, the work scope did not consider ocean -going vessels heading to and from Vancouver,

B.C. and other Canadian traffic th at may pass into or near US waters. The emissions from these

vessels were estimated out 25 miles from the Paci fic Ocean coast and used to compare with the

estimates from the method used to estimate emi ssions 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 emissi on inventory estimates. For the Category 2 engines, the average values show n in Table 7-3 were the average values used to estimate the

emission reductions from the new emission standa rds (Samulski, 1999), and are quite similar to the emission factors for the highest power Catego ry 1 engines. For Category 3 engines, EPA

relied on a review of the base emission factor s 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] NO X 

### [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 en gines with rated speeds of typically 300 to 750 rpm (though most Category 3 medium speed engi nes are found in 400 –550 rpm range) that are typically 4-stroke engines either geared or used w ith 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-Mor se engines) or 4-stroke engi ne designs with rated speeds typically, but not always, above 750 rpm, used either for prop ulsion of smaller vessels or auxiliary power on larger ones. Emission factors for these marine engine types had been derived from previous reviews and emission measurem ent studies [EPA (2000), Environment Canada (1997), Lloyds (1995), ETC (1997), BAH (1991) , Environment Canada (1999), and TRC (1989)].May 2006

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### 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 Ange les (PoLA) emission invent ory 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] NO X 

### [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 publishe d 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 fo r engines built prior to the MARPOL NOx emission reduction requirement. Note the drama tic 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] NO X 

### [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 me dium-speed NOx emission rates from the EPAsponsored work. The PM emission rate for medi um speed engines was well under that for slow speed engines using the same high sulfur heavy (re sidual) 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 par ticulate collection device and in turn the exhaust temperature. Still there is no technical reas on to think that medium speed engines produce significantly lower particulate emi ssions 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).May 2006

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### 7-6 For this study, the IVL emission factors were us ed except for particulate emissions where the slow and medium speed engine particulate emi ssion rate when burning re sidual oil was adjusted to 1.73 and 1.76 g/kW-hr respectively. This is cons istent with what was used in the previous

WRAP commercial marine emi ssion 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 emissi on rates during the hotelling (a t berth) mode were 0.3 g/kW-hr, assuming a lower fuel sulfur level was used fo r this mode, which may not be the situation.

There remains considerable uncertainty about th e particulate emissions rates, especially for engines using high sulfur fuels. The IVL (2004) and ENTEC (2002) estima tes indicate that the authors consider the uncertainty in the PM10 emi ssion rates to be in excess of 50%. This may

stem from the method of collec tion, filter handling, or other factors associated with the hygroscopic nature of the particulate formed from diesel engines burning hi gh sulfur fuels. The particulate emissions rates and sulf ur 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-g oing 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 al locate activity to individual Puge t 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 in cluding the Puget Sound ports totals and Coos Bay. The more general information was used to allocate the more specific activity inform ation to each of the Puget Sound ports and to extrapol ate an estimate of the activit y of the Columbia River ports. This method was identical to previous WRAP emission inventories with the replacement of steamships with motorships of the same gro ss tonnage. The revised emission estimates are shown in Table 7-6 for Puget Sound ports to be us ed 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 in clude 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, whic h significantly increased (by 1.5 to 4 times) theMay 2006

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### 7-7 reduced speed zone and cruise mode emissions for Washington State emi ssions 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 por t were separated into three segments: cruise, reduced speed zone (RSZ), maneuvering and hotell ing/dwelling. For emissions associated with Grays Harbor vessel calls, all em issions were considered to occu r 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, th e maneuvering and hotelling emissions were allocated to the county of the por t of interest. The emissions fo r the reduced speed zone were allocated to the counties along the primary shi pping 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, Sa n Juan, and Whatcom counties. Because shipping lanes often straddle county boundaries, these county designations were made for expedience and could be improve d 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) estimate s from 1996 to 2002 were updated with freight movement information for 2002 compar ed 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 0.88May 2006

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### 7-8 7.2.3 Columbia River Ports

The Columbia River ports were estimated accord ing to the procedure described in EPA (1999b), where a scaling factor was determined with a si milar port, in this case, the Puget Sound totals. Adjustments were made to the actual vessel activ ity 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 sa me with the adjusted

number of vessel calls.

Vessels arriving near the mouth of the Columb ia River are guided by Bar pilots across the Columbia Bar to Astoria (approximately 14 nauti cal miles), where River pilots begin piloting ships to their destination. The River pilots estimate that 12 knot s is a typical average speed for ships once the pilots take command.

There were five major ports in the Columb ia River for which EPA (1999b) identified and

estimated total vessel visits. These total vessel vis its were compared with the total activity for Puget Sound ports (including Grays Harbor) for wh ich a more detailed estimate has already been produced, as shown in the Table 7-8. The por t 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 fo r each Columbia River port were divided by the Puget Sound totals and multiplied by the more de tailed 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 crui se, maneuvering, and hotelling emissions. The resulting emissions adjusted to eliminate steam ships 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 spee d 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 whil e 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 di fferent between the Puget Sound and Columbia

River.May 2006

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### 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 por t are shown in Table 7-10 for 2002. However,

transit emissions occur in the Columbia River do wnstream of each port ra ther than in the port area. The geographic allocation for the transi t (cruise and RSZ) emissions were to the Washington counties (Pacific for cruise and some RSZ, Wahkiakum, Cowlit z, 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 th is 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 asso ciated with the port calls of e ither 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 5May 2006

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### 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 appo rtionment 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 grow th. 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 St ate of California were provided by CARB (CARB, 2005). Emissions were provided for severa l major categories, labeled by ARB as SHIPS IN-TRANSIT, SH IPS MANEUVERING and SHIPS BERTHING. The IN-TRANSIT category was defined as corres ponding to operations on shipping lanes within

100 miles of the California coast. Th e MANUEVERING and BERTHING categories correspond to operations at ports. Emissions were also provided for a category labeled

COMMERCIAL BOATS that accounts for the activ ity of smaller vessel s 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 aMay 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 frac tion 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 la kes or rivers. Figure 7- 3 shows the grid cells to which IN-TRANSIT and the port/inshore em issions 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 a dded to the other WRAP offshore and near port gridded emissions. Any overlap of the two i nventories was eliminat ed. 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

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### 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 emissi ons were derived from a study performed by Corbett and Wang (2005). This pr ojection was based on an investigation of the

historic trend in the la rger vessels’ installed power. The in stalled 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 ye ar activity. The fit of the historic data was equivalent whether an exponentia l fit (equivalent to compound a nnual growth rate (CAGR)) or a linear regression was used. For this work, ther efore, an average of the exponential and linear regression was used to project future year commercial marine activity, per agreement andMay 2006

G:\WRAP MSEI2\Reporting\ Final report\Sec7_CMV.doc 

### 7-13 discussion with CARB staff, and thereby matc hing CARB projections (C ARB, 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 MOVEME NT REGULATORY ACTIVITY

In the WRAP region, there have be en 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 emi ssion reduction plans are summ arized by the West Coast Collaborative ( http://www.westcoastcolla borative.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 th is work was done they have largely not been Linear Correlation y = 2.46E+07x - 4.88E+10

R 2 = 6.96E-01 Exponential Correlation

y = 3.98E-47e 0.0633x

R 2 = 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

YearPower Installed (kW)May 2006

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### 7-14 included in the WRAP emissions inventory proj ections. 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 en gines 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 exte nt 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, th e basic approach for estimating large vessel emissions in the mid-Pacific shipping lanes was developed by Prof essor James Corbett. Since the time of the

WRAP commercial marine emi ssions inventory evaluation in 2005, Professor Corbett, under contract to CARB, has developed a re vision to his approa ch, described at http://www.ocean.udel.edu/cms/jcor bett/sea/NorthAmericanSTEEM/ . This modified approach is

to be used for the evaluation of the Sulfur Emission Control Area (SECA, described at http://www.westcoastcollabor ative.org/wkgrp-marine.htm ) for the United States submittal.May 2006

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### 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 update d silt loading values, updated and revised activity estimates , and the application of trans port fractions (Pollack et al., 2004). The aim of that work was to resolve larg e differences in road dust emissions in adjacent counties, and to use a consistent methodology ac ross 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 prov ided by state and local contacts as part of the base and future year survey work. Any updated road dust contro ls 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 met hodology. Resources required to do a more complete update with the latest methodology woul d be large, and since road dust is not an important contributor to regiona l 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 emi ssions were therefore not estimated for Alaska in this effort. For California, road du st emissions provided by CARB were used.

Road dust emissions in the previous work incl uded 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 fractio ns that were applied depend on the vegetative characteristics of each county, and were calculated as the weig hted average of vegetation-specific transport fractions in each county. For the current work, updated transport fractions were available, but were applied to the road dus t emissions (and other dust sources) in the SMOKE emissions processing rather than in the develo pment 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 a nd local agencies were asked to provide updated information to revise the earlier paved and unpaved road dus t 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 estima tes, and state and local agencies were asked to review and update these fractions if they had appropriate availabl e data. The survey also asked for a review and update of the road dust c ontrols assumptions used in the prior work.May 2006

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### 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 includ e VMT from unpaved roads. This was the case for Pima County (AZ), Idaho, Nevada (all but Cl ark County), Utah, Wyoming. This meant that on-road vehicle emissions estimates, which us e 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 estimati ng 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 road s 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 c ounters at several sites across the state that yielded an average daily traffic on unpaved roads. For this exam ple the state-wide average AADT used on unpaved roads is 360 vehicle per day.

For example, if the average AADT on unpaved ro ads is 360 vehicles per day the "effective" AADT would be calculated for a county w ith a low population density as follows: Effective AADT = 360 x 0.044/6.8 = 2.23,

where 0.044 is the population density for count y "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 mile s 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, th e 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. Th ree regions responded with updates to 2018 information: Maricopa County and Utah prov ided updated 2018 unpaved road VMT, and the Washington DEQ said to assume no grow th in unpaved VMT between 2002 and 2018.May 2006

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### 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 Inventor y, 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 cont rol level of 79 percen t, applied to urban and rural roads in

serious PM nonattainment areas and to urban ro ads in moderate PM nonattainment areas. The penetration factor used varied by road type a nd nonattainment area cla ssification (serious or moderate). For unpaved roads, the control meas ure and level of control assumed by EPA varied by PM nonattainment area classification and by rura l and urban areas. On urban unpaved roads in moderate PM nonattainment areas, the assume d control was paving the unpaved roads. This control was applied with a control efficiency of 96 percent and a pene tration rate of 50 percent.

On rural roads in serious PM nonattainment ar eas, chemical stabili zation was the assumed control; this control was applied with an a ssumed control efficiency of 75 percent and penetration rate of 50 percent. On urban unpa ved roads in serious PM nonattainment areas, paving and chemical stabilization were the assume d control measures, with an assumed overall control efficiency of 90 percent and penetration rate of 75 percen t. Updates to these control assumptions were provided by Maricopa County (AZ) and Yakima County (WA).

8.2 ROAD DUST EMISSI ONS 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 a nd emissions were calculated by county and roadway type by linearly interpolating from th e previous 1996 and 2003 estimates. Transport

fractions were backed out of these estimates.

The 2002 paved and unpaved VMT were then u pdated. 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 u pdated 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 estima te on-road vehicle emissions were then used to derive seasonal paved a nd unpaved road dust emissions.

Figure 8-1 compares coarse PM (PM10 – PM 2.5) 2002 total unpaved road dust emissions between the previous WRAP inventory and this re vision, 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 co ntrol 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 incr eased unpaved road VMT in the current effort.May 2006

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### 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 we re calculated in an analogous manner as the 2002 emissions: A scaling factor of (update d 2018 VMT/previous 2018 VMT) was calculated by county and roadway type, and applied to the pr evious 2018 road dust emissions to derive the updated emissions estimates. This calcula tion was done separately for paved and unpaved roads, for both PM10 and PM2.5. The seasonal allocation factors we re then applied to determine

seasonal emissions. The same controls were assu med 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 include d), 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 Invent ory 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 WYTons/yr Previous version

Current revisionMay 2006

G:\WRAP MSEI2\Reporting\Fina l report\Sec8_RoadDust.doc 

### 8-5 Summary spreadsheets and tables were also prepared, and were posted on the WRAP Mobile Sources Emission Inventory U pdate project web page at http://wrapair.org/forums/ef/UMSI/index.html

.May 2006

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### 9-1 9.0 RESULTS

This section provides the emissions estimates fo r 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 re sults are presented here in a seri es of tables and graphs; more detailed emissions by state, c ounty, and source category are availa ble 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, Fi gures 9-1 and 9-2 show th e contributions by source category for all emissions estimated for th e 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 lesse r 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 sm aller fraction of overall NOx emissions. The

contribution of NOx emissions from on-road vehi cles has dropped significantly, from 34 percent to 16 percent, as older vehicles have been scra pped and replaced by newe r vehicles that meet more stringent standards (for both light- and heavy-duty vehicl es). The off-road equipment emissions contribution is similar in both year s. There are newer standards for off-road equipment in future years, but they start later th an for on-road vehicles, and have a longer phasein period. Shipping emissions in 2018 contribut e 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 differe nt pollutants. The emissions are listed by state and fuel type in Table 9-1. The graphs allow a vi sual comparison of emissions by state, as well as showing the relative contributions by vehicle cl ass and fuel type. For all po llutants except SO2, California has the highest emissions of a ll of the states; California on-road SO2 emissions are lower because of state regulations re quiring low sulfur fuels.

Figure 9-9 shows the spatial dist ribution of on-road NO emissions for on-road vehicles on a July weekday for 2002. This plot was prepared by the WRAP Regional Mode ling Center (RMC) for quality assurance (QA) purposes. The plot show s the emissions for the contiguous 48 states, as prepared by the WRAP and the ot her four Regional Planning Orga nizations (RPOs), as well as Canada and Mexico. What is evident in the pl ot 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\Fi nal report\Sec9_Results.doc 

### 9-2 Tables 9-2 to 9-4 list the annual emissions for th e 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 st ringent 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 emissi ons by about 60 percent, and CO emissions by about 50 percent. On-road PM2.5 emissions ar e also decreasing, though at a lesser rate, about 25 percent from 2002 to 2018. On-road SO2 emi ssions are reduced dram atically from 2002 to 2008, as federal regulations require the introducti on of low-sulfur gasoline and diesel fuels during that period.

9.3 OFF-ROAD EMISSIONS

Figure 9-16 shows the contributi ons 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 recr eational marine are the largest contributors to off-road VOC emissions, and lawn and garden equipment also dominate CO emissions. Agricultural and construction equipment are th e largest contributors to both PM10 and PM2.5

emissions.

Figures 9-17 through 9-21 show the annual aver age TPD emissions by source category and state for the different pollutants. The emissions are list ed by state and fuel type in Table 9-5. As for on-road emissions, California has the highest emi ssions of all of the st ates 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 emi ssions. There are significant reductions in all pollutants except CO as the off-road equipm ent fleet is turned ove r and newer engines are introduced that meet more stringe nt standards. Off-road NOx emissions are reduced by almost 40 percent from 2002 to 2018, VOC emissions by a bout 37 percent, and PM emissions by about 43 percent. CO emissions for off-road equipmen t are increasing slightly , and SO2 emissions are reduced dramatically as the low sulfur nonr oad 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 o ff-road mobile source emissions over time from 2002 to 2018, excluding commercial marine emissions. For all mobile sources combined (except commercial marine), th ere 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 emissi ons, about 37 percent in CO emissions, and nearly 90 percent in SO2 emissions.May 2006

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### 9-3 Offshore commercial marine emissions were not in cluded 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 emissi ons. Table 9-10 shows the west coast 2018 NOx and SO2 emissions for all mobile sources, includ ing commercial marine. In the future years,

NOx and SO2 emissions have been reduced from a ll other mobile sources (except aircraft), but

commercial marine emissions NOx and SO2 emissions are increasing with incr eased 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 PM 10 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 incl uded in these tables; th ey 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 increas e in vehicle miles travelled.

Figure 9-35 shows the QA plot of the spatial dist ribution of road dust coarse PM emissions for on a July weekday for 2002. The plot shows th at 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 2006G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc9-4PMCPM2.5PM10VOCNH3SO22000 – 2004 WRAP RegionAnnual EmissionsContributions by SourceCategoryNOxCOPMCPM2.5PM10VOCNH3SO22000 – 2004 WRAP RegionAnnual EmissionsContributions by SourceCategoryNOxCOArea NH3 ModelBiogenic On Road MobileNonroad Mobile Road DustFugitive Dust Windblown DustPoint Offshore Ar/PtFire ShippingOil & gasArea NH3 ModelBiogenic On Road MobileNonroad Mobile Road DustFugitive Dust Windblown DustPoint Offshore Ar/PtFire ShippingOil & gas31.6%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%1.2%5.1%2.6%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%2.5%2.9%3.8%8.9%20.9%47.2%14.9%3.8%0.3%0.1%Figure 9-1.2000 – 2004 WRAP region annual emissions contributions by source category. Source: Adelman et al., 2006.May 2006G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc9-5PMCPM2.5PM10VOCNH3SO22018 WRAP Region Base CaseAnnual EmissionsContributions by SourceCategoryNOxCOArea NH3 ModelBiogenic On Road MobileNonroad Mobile Road DustFugitive Dust Windblown DustPoint Offshore Ar/PtFire ShippingOil & gas36.2%5.6%3.2%16.9%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%3.5%13.4%3.5%10.1%21.5%43.3%0.9%0.4%3.8%16.5%Figure 9-2.2018 WRAP region Base Case annual emissions contributionsby source category. Source: Adelman et al., 2006.May 2006

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### 9-6 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

VOC NOX CO PM10 PM2.5 SO2 NH3Percent 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 WYTons 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\Fi nal 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 WYTons 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 WYTons 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\Fi nal 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 WYTons 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 WYTons 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\Fi nal 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\Fi nal report\Sec9_Results.doc 

### 9-10 

### 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000Annual 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\Fi nal report\Sec9_Results.doc 

### 9-11 0 500 1000 1500 2000 2500 3000Annual 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\Fi nal report\Sec9_Results.doc 

### 9-12 0 20 40 60 80 100 120 140Annual 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\Fi nal report\Sec9_Results.doc 

### 9-13 0 10 20 30 40 50 60 70 80 90 100Annual 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\Fi nal report\Sec9_Results.doc 

### 9-14 0 10 20 30 40 50 60 70 80 90Annual 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\Fi nal report\Sec9_Results.doc 

### 9-15 0 5000 10000 15000 20000 25000 30000 35000 40000Annual 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\Fi nal report\Sec9_Results.doc 

### 9-16 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

VOC NOx CO PM10 PM2.5 SO2 NH3Percent of Total Emissions Underground Mining Equipment

Recreational Equipment

Railroad Equipment

Pleasure Craft

Oil Field Equipment

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 CATons 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\Fi nal 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 CATons 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 CATons 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\Fi nal 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 CATons 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 CATons 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\Fi nal 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\Fi nal report\Sec9_Results.doc 

### 9-20 0 500 1000 1500 2000 2500 3000Annual 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\Fi nal report\Sec9_Results.doc 

### 9-21 0 200 400 600 800 1000 1200 1400 1600Annual 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\Fi nal report\Sec9_Results.doc 

### 9-22 0 50 100 150 200 250Annual 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\Fi nal report\Sec9_Results.doc 

### 9-23 0 50 100 150 200 250Annual 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\Fi nal report\Sec9_Results.doc 

### 9-24 0 20 40 60 80 100 120 140 160 180 200Annual 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\Fi nal report\Sec9_Results.doc 

### 9-25 0 2000 4000 6000 8000 10000 12000 14000Annual 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\Fi nal report\Sec9_Results.doc 

### 9-26 0 1000 2000 3000 4000 5000 6000 7000 8000Annual 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\Fi nal report\Sec9_Results.doc 

### 9-27 0 500 1000 1500 2000 2500 3000 3500 4000 4500Annual 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\Fi nal report\Sec9_Results.doc 

### 9-28 0 50 100 150 200 250 300 350Annual 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\Fi nal report\Sec9_Results.doc 

### 9-29 0 50 100 150 200 250 300Annual 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 av erage daily PM2.5 emissions, 2002 – 2018(does not include commercial marine).May 2006

G:\WRAP MSEI2\Reporting\Fi nal report\Sec9_Results.doc 

### 9-30 0 50 100 150 200 250 300Annual 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\Fi nal report\Sec9_Results.doc 

### 9-31 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000Annual 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\Fi nal 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 2006G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc9-33Table 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 Diesel GasolineALASKA370 8,296 7,276 8,921 1,764 147,640 302 143 261 94ARIZONA4,179 106,235 89,803 88,171 18,452 1,179,480 3,079 1,529 2,615 990CALIFORNIA 9,764 315,178 248,866 332,215 44,984 3,217,797 5,634 9,960 4,781 5,738COLORADO6,561 94,271 58,228 83,597 39,564 1,360,285 2,358 1,407 2,023 948IDAHO703 26,278 17,811 26,779 3,575 423,498 623 460 534 312MONTANA1,252 42,212 20,362 33,236 5,304 584,575 842 497 720 348NEVADA563 35,704 8,997 32,084 2,640 398,956 336 513 285 319NEW MEXICO3,780 34,978 32,897 34,924 22,446 538,555 1,278 651 1,091 434NORTH DAKOTA572 12,226 11,430 13,299 2,671 208,662 460 225 393 152OREGON3,115 85,635 40,671 70,928 14,236 1,071,042 1,816 1,179 1,573 805SOUTH DAKOTA692 13,037 14,562 14,644 3,209 218,051 581 255 496 171UTAH2,079 46,982 34,783 42,570 13,140 732,333 1,074 756 918 498WASHINGTON5,070 135,115 83,391 118,596 24,923 1,697,021 3,288 1,954 2,814 1,349WYOMING1,233 13,022 26,616 11,938 8,043 227,843 793 178 679 121Total 39,936 969,167 695,691 911,901 204,950 12,005,736 22,465 19,707 19,182 12,278* 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 NH3VMTTotal Total Total Total Total Total Total TotalALASKA 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 38,554 235,886971 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 * PM2.5 *StateVOC NOx COMay 2006G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc9-34Table 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 Gasoline DieselALASKA2575,1664,2325,9431,250105,605203133166ARIZONA3,26875,68959,72064,53315,312828,7032,4911,6632,009CALIFORNIA 8,745210,877206,245215,68740,7822,126,2274,68211,7543,824COLORADO5,17058,54842,16455,90032,755880,3201,8601,3201,523IDAHO50316,71911,09917,8892,629281,114447419364MONTANA88626,64513,01322,5094,376388,377726416598NEVADA47226,8066,30224,0402,327317,321294598239NEW MEXICO2,85722,49522,03424,83317,865369,8901,008636820NORTH DAKOTA3656,6636,1517,8761,747127,184297184242OREGON2,17756,44528,65450,70112,387749,6391,5081,1051,263SOUTH DAKOTA5328,0589,4159,9702,527149,743453242369UTAH1,64132,93325,09433,64111,045522,705885787721WASHINGTON3,85886,75355,91978,21019,3981,134,0602,4691,7652,026WYOMING8607,74816,4688,3955,907145,777568161463Total 31,592641,547506,512620,128170,3068,126,66517,89121,18214,626* 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 NH3VMTTotal Total Total Total Total Total Total TotalALASKA5,42310,175106,8553362491145375,263,761,063ARIZONA78,957124,253844,0154,1543,0626086,17762,360,885,147CALIFORNIA219,622421,9332,167,00916,43510,8951,60624,831COLORADO63,71898,065913,0743,1812,3554695,00349,235,781,423IDAHO17,22328,989283,7438666251501,63015,866,808,392MONTANA27,53135,522392,7531,1428601491,53815,456,431,874NEVADA27,27830,342319,6488926012202,61724,413,396,217NEW MEXICO25,35346,867387,7551,6441,2192342,44924,281,452,671NORTH DAKOTA7,02914,027128,931481356687387,287,475,377OREGON58,62279,355762,0262,6131,9623793,94838,574,459,271SOUTH DAKOTA8,59019,385152,270695520919389,409,925,235UTAH34,57558,735533,7491,6721,2142853,03729,539,802,645WASHINGTON90,611134,1291,153,4584,2343,1545966,18161,535,911,146WYOMING8,60824,863151,683730565696196,751,816,264Total 673,1391,126,6408,296,97139,07427,6385,03960,242349,977,906,724* PM emissions include exhaust, brake wear, and tire wear emissions.StatePM10 * PM2StateVOC NOx COMay 2006G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc9-35Table 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 Diesel GasolineALASKA2013,6002,3344,03759190,38214513511183ARIZONA2,68259,63432,59648,4857,557673,7581,9461,7551,4621,091CALIFORNIA 7,058153,343143,638151,73235,2361,489,9463,80913,1872,9717,967COLORADO4,26744,51825,15541,07917,538787,6741,4481,3721,115850IDAHO39912,1806,24112,4391,271238,713331436253268MONTANA76020,3239,05619,2792,072325,638631439497272NEVADA41620,5013,60117,9301,154280,400239680181407NEW MEXICO2,35416,29212,91517,3188,919316,151779664595411NORTH DAKOTA2674,3503,1665,028773101,383202175154107OREGON1,68642,45719,88139,6435,603606,9311,1831,135947708SOUTH DAKOTA4415,7255,4886,8941,262126,497349249267154UTAH1,35725,51415,00125,8295,628446,411690850528524WASHINGTON2,96463,24332,67856,2489,817908,1881,8341,7621,4251,101WYOMING6795,8679,2216,4362,836121,170421164322102Total 25,530477,548320,970452,378100,2576,513,24214,00723,00510,82914,045* 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 NH3VMTTotal Total Total Total Total Total Total TotalALASKA3,8016,37290,973280193525745,612,631,896ARIZONA62,31681,082681,3153,7012,5536816,89369,196,740,935CALIFORNIA160,400295,3701,525,18316,99610,9381,74326,879COLORADO48,78566,234805,2132,8201,9655155,43753,470,363,618IDAHO12,57918,680239,9847675211631,78317,283,487,713MONTANA21,08328,336327,7091,0707691701,74817,388,826,117NEVADA20,91721,530281,5549195892593,08428,652,313,876NEW MEXICO18,64630,233325,0701,4431,0062572,66226,409,377,126NORTH DAKOTA4,6178,194102,155377261687407,284,197,119OREGON44,14359,523612,5342,3181,6554174,33142,084,271,224SOUTH DAKOTA6,16712,382127,759599421991,00410,101,861,956UTAH26,87040,830452,0401,5401,0533273,43333,127,162,995WASHINGTON66,20888,926918,0053,5952,5266376,65165,788,792,672WYOMING6,54615,657124,007586424756757,300,217,365Total 503,078773,3486,613,49937,01224,8745,46665,897383,700,244,613* PM emissions include exhaust, brake wear, and tire wear emissions.StatePM10 * PM2.5 *StateVOC NOx COMay 2006G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc9-36Table 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 Diesel GasolineALASKA1782,8911,1982,89034685,4961171428486ARIZONA2,59050,28116,66836,8214,843625,3331,7911,9261,2681,192CALIFORNIA 5,768115,40696,453106,60731,8041,038,9183,18214,4552,4078,841COLORADO4,06637,43213,54031,68610,551751,0211,2351,486886918IDAHO3619,9803,2479,073761225,768270465193284MONTANA74417,8015,98416,0511,529309,209502494362305NEVADA40016,6911,94213,102752264,664209743149444NEW MEXICO2,23513,3177,01612,7225,471302,048665713475439NORTH DAKOTA2213,2641,5113,39042289,481152172108105OREGON1,50834,88312,58829,5343,682556,6328401,228612764SOUTH DAKOTA4154,6832,9755,078778118,965299266213164UTAH1,29321,4008,23919,1113,489424,100587936419575WASHINGTON2,51248,14516,49939,4085,563813,2471,3961,8381,0051,140WYOMING6184,6734,7974,9341,673110,847344173246107Total22,910380,848192,659330,40871,6665,715,72911,59025,0388,42515,364* 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 NH3VMTTotal Total Total Total Total Total Total TotalALASKA3,0694,08885,842259170566125,984,083,743ARIZONA52,87153,489630,1763,7172,4607627,60476,683,204,547CALIFORNIA121,175203,0601,070,72317,63711,2481,89729,562COLORADO41,49845,226761,5722,7211,8045685,89458,265,177,164IDAHO10,34012,320226,5297354761771,93018,700,167,033MONTANA18,54522,035310,7389966671951,97619,775,017,157NEVADA17,09115,044265,4169535932863,38431,477,060,492NEW MEXICO15,55319,739307,5201,3799142812,87728,668,617,848NORTH DAKOTA3,4854,90289,902324213687397,265,376,615OREGON36,39142,122560,3142,0681,3764614,72546,054,049,449SOUTH DAKOTA5,0988,053119,7425653771081,07510,879,625,452UTAH22,69327,350427,5891,5239943683,81136,801,812,054WASHINGTON50,65755,908818,8113,2342,1446797,08870,092,205,086WYOMING5,2909,731112,520517352817257,848,618,467Total403,757523,0675,787,39536,62823,7895,98872,001418,495,015,107* PM emissions include exhaust, brake wear, and tire wear emissions.StatePM10 * PM2.5 *StateVOC NOx COMay 2006G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc9-37Table 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 Diesel GasolineALASKA1,574 16,161 7,991 885 11,363 69,530 935 578 918 525 561 50 4 9ARIZONA6,310 50,719 58,602 7,865 25,522 520,067 4,354 1,840 3,945 1,655 4,158 68 30 17CALIFORNIA168,437 25,014 123,578 204,742 1,011,861 135,241 8,824 13,887 7,458 12,763 7,056 484 502 44COLORADO5,471 32,968 55,448 7,134 26,819 366,178 4,184 1,089 4,043 977 2,344 128 31 12IDAHO2,691 20,790 25,254 2,697 14,391 141,046 2,421 625 2,340 574 3,347 64 11 6MONTANA3,842 10,171 49,136 1,559 15,848 84,008 3,203 335 3,100 300 4,151 56 26 3NEVADA2,642 15,506 30,462 2,144 14,503 156,422 1,775 533 1,715 485 1,389 15 17 5NEW MEXICO2,942 10,931 43,394 1,944 11,878 112,408 2,139 390 2,066 347 3,493 35 22 4NORTH DAKOTA5,122 8,400 54,195 1,465 22,661 73,408 5,006 273 4,853 243 6,814 38 30 3OREGON4,543 35,008 45,470 7,112 21,519 310,228 3,894 1,277 3,767 1,156 6,201 137 27 11SOUTH DAKOTA4,318 8,461 37,218 1,969 20,584 72,127 4,409 285 4,273 254 5,722 35 22 3UTAH4,133 22,811 43,498 3,699 19,535 185,898 3,294 763 3,187 693 4,463 51 25 7WASHINGTON6,181 55,235 63,364 10,740 29,738 496,225 5,218 2,025 5,043 1,828 8,394 190 37 18WYOMING4,091 9,661 75,752 923 13,125 60,150 2,931 276 2,839 250 5,843 27 38 3TOTAL 222,297 321,836 713,362 254,879 1,259,348 2,782,936 52,588 24,176 49,548 22,050 63,936 1,378 823 145*Does not include Commercial Marine2002 Western States Total Nonroad Emissions All Fuel Types (tons)VOC NOx CO PM10 PM2.5 SO2 NH3State Total Total Total Total Total Total TotalALASKA17,736 8,876 80,893 1,513 1,443 611 12ARIZONA57,030 66,467 545,589 6,194 5,600 4,226 48CALIFORNIA193,451 328,320 1,147,102 22,710 20,222 7,540 546COLORADO38,438 62,581 392,997 5,274 5,021 2,472 43IDAHO23,480 27,951 155,437 3,047 2,914 3,411 17MONTANA14,013 50,696 99,856 3,538 3,400 4,206 29NEVADA18,147 32,606 170,925 2,309 2,199 1,403 22NEW MEXICO13,873 45,338 124,286 2,529 2,413 3,528 26NORTH DAKOTA13,521 55,659 96,069 5,279 5,096 6,852 33OREGON39,552 52,583 331,747 5,171 4,923 6,338 38SOUTH DAKOTA12,779 39,187 92,711 4,693 4,527 5,756 25UTAH26,944 47,197 205,433 4,057 3,880 4,514 32WASHINGTON61,415 74,103 525,963 7,243 6,871 8,584 55WYOMING13,752 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 MarineSO2 NH3VOC NOX CO PM10 PM2.5May 2006G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc9-38Table 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 Diesel GasolineALASKA1,401 17,658 7,470 851 8,230 83,558 742 659 729 600 392 30 4 10ARIZONA5,927 35,599 50,270 6,436 25,088 572,187 3,905 1,634 3,492 1,465 1,417 34 35 18CALIFORNIA120,357 19,872 106,636 166,355 904,004 138,889 10,241 11,345 8,669 10,445 1,824 317 726 188COLORADO4,812 27,041 49,947 5,008 26,051 407,694 3,596 1,152 3,468 1,036 1,539 25 37 13IDAHO2,270 20,117 23,637 2,016 13,376 158,923 1,881 681 1,815 625 695 13 14 7MONTANA3,455 10,110 42,810 1,194 15,322 95,186 2,736 370 2,646 332 1,017 7 31 4NEVADA2,331 11,974 26,193 1,658 14,054 174,131 1,505 540 1,452 491 529 8 19 5NEW MEXICO2,684 9,175 36,051 1,426 11,533 126,146 1,849 403 1,785 359 841 8 26 4NORTH DAKOTA4,473 7,953 50,456 1,246 20,920 83,358 4,028 290 3,903 259 1,298 6 37 3OREGON3,759 28,850 40,072 5,138 19,646 349,531 2,961 1,320 2,861 1,194 1,194 23 32 12SOUTH DAKOTA3,641 7,895 36,650 1,557 18,771 80,477 3,438 296 3,330 265 1,034 7 28 3UTAH3,485 21,237 37,574 2,652 17,923 208,561 2,584 834 2,495 756 1,118 14 30 8WASHINGTON5,242 44,147 55,782 7,852 27,841 550,263 4,025 2,016 3,882 1,820 1,658 37 44 19WYOMING3,936 9,762 61,651 717 13,596 67,122 2,697 303 2,612 275 1,303 5 44 3TOTAL 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 1,107 296*Does not include Commercial Marine2008 Western States Total Nonroad Emissions All Fuel Types (tons)VOC NOx CO PM10 PM2.5 SO2 NH3State Total Total Total Total Total Total TotalALASKA19,059 8,321 91,788 1,401 1,329 422 14ARIZONA41,527 56,705 597,275 5,539 4,957 1,451 53CALIFORNIA140,229 272,992 1,042,893 21,586 19,114 2,141 914COLORADO31,854 54,955 433,744 4,748 4,503 1,564 50IDAHO22,387 25,653 172,300 2,562 2,440 709 21MONTANA13,565 44,005 110,509 3,107 2,978 1,024 35NEVADA14,305 27,850 188,185 2,045 1,943 537 25NEW MEXICO11,859 37,476 137,679 2,252 2,145 849 30NORTH DAKOTA12,426 51,702 104,278 4,318 4,162 1,304 40OREGON32,610 45,209 369,177 4,282 4,055 1,217 44SOUTH DAKOTA11,537 38,206 99,248 3,734 3,595 1,040 31UTAH24,722 40,226 226,484 3,418 3,252 1,132 37WASHINGTON49,390 63,635 578,104 6,042 5,702 1,695 62WYOMING13,698 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 MarineSO2 NH3VOC NOX CO PM10 PM2.5May 2006G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc9-39Table 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 Diesel GasolineALASKA1,405 15,005 7,176 907 8,454 85,651 681 615 670 559 286 32 4 10ARIZONA5,684 32,218 46,834 5,325 24,087 609,196 3,408 1,641 3,005 1,468 459 37 39 19CALIFORNIA107,045 15,001 100,839 117,095 891,596 124,223 10,597 7,728 8,950 7,101 1,578 204 692 85COLORADO4,029 23,750 44,179 3,835 23,182 432,956 2,703 1,135 2,600 1,019 310 27 41 14IDAHO1,776 17,372 20,360 1,628 11,281 165,832 1,327 636 1,277 584 83 15 15 7MONTANA2,952 8,576 39,325 1,018 13,386 99,541 2,147 342 2,073 306 50 7 33 4NEVADA2,059 10,639 23,794 1,417 13,474 186,992 1,211 527 1,166 479 228 9 21 6NEW MEXICO2,436 7,973 34,110 1,144 10,886 134,972 1,556 387 1,501 344 71 9 28 4NORTH DAKOTA3,535 6,810 43,830 1,086 16,666 86,443 2,937 276 2,845 246 51 7 40 4OREGON3,035 24,985 34,956 3,843 16,823 369,721 2,158 1,286 2,081 1,161 115 25 35 13SOUTH DAKOTA2,708 6,700 30,476 1,232 14,458 82,287 2,374 279 2,298 249 40 7 30 4UTAH2,894 18,058 32,691 1,952 15,694 218,232 1,938 778 1,867 705 122 15 33 8WASHINGTON4,317 38,333 49,008 5,905 24,375 581,200 2,995 1,973 2,882 1,778 202 39 48 20WYOMING3,733 8,482 59,895 691 13,401 68,396 2,441 290 2,363 262 54 6 48 4TOTAL 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 1,108 201*Does not include Commerical Marine.2013 Western States Total Nonroad Emissions All Fuel Types (tons)VOC NOx CO PM10 PM2.5 SO2 NH3State Total Total Total Total Total Total TotalALASKA16,410 8,083 94,105 1,296 1,229 318 15ARIZONA37,901 52,159 633,282 5,049 4,473 495 58CALIFORNIA122,046 217,934 1,015,819 18,326 16,051 1,782 776COLORADO27,779 48,014 456,139 3,838 3,620 337 55IDAHO19,148 21,988 177,113 1,963 1,861 97 23MONTANA11,528 40,344 112,927 2,488 2,379 57 38NEVADA12,698 25,211 200,466 1,738 1,645 237 27NEW MEXICO10,410 35,255 145,858 1,944 1,845 80 33NORTH DAKOTA10,345 44,917 103,108 3,213 3,091 58 43OREGON28,020 38,799 386,543 3,445 3,242 140 48SOUTH DAKOTA9,409 31,708 96,745 2,653 2,547 47 33UTAH20,952 34,643 233,926 2,716 2,572 137 41WASHINGTON42,650 54,912 605,575 4,969 4,660 241 67WYOMING12,215 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.SO2 NH3VOC NOX CO PM10 PM2.5May 2006G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc9-40Table 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 Diesel GasolineALASKA1,431 11,551 6,713 1,051 8,502 84,994 604 537 596 487 314 33 5 11ARIZONA5,593 31,010 42,509 5,349 22,072 653,150 2,831 1,647 2,440 1,471 507 39 43 20CALIFORNIA100,935 10,713 101,379 65,933 887,988 101,551 11,086 3,692 9,340 3,407 1,682 207 749 83COLORADO3,526 21,481 37,048 3,828 19,532 461,760 1,798 1,104 1,721 990 342 29 45 15IDAHO1,472 14,533 16,485 1,659 9,166 172,469 823 572 788 525 86 16 17 8MONTANA2,655 6,986 35,549 1,003 11,573 103,474 1,629 302 1,571 269 53 8 36 4NEVADA1,873 9,943 20,766 1,438 12,265 200,527 839 517 805 470 252 10 24 6NEW MEXICO2,288 7,188 32,055 1,136 9,983 144,249 1,266 371 1,219 329 79 9 31 5NORTH DAKOTA2,975 5,687 36,357 1,075 13,009 89,692 2,008 256 1,945 226 55 7 43 4OREGON2,575 22,411 28,690 3,795 13,220 393,671 1,340 1,255 1,285 1,130 125 27 38 14SOUTH DAKOTA2,145 5,610 23,299 1,175 10,633 84,888 1,461 257 1,413 229 43 7 32 4UTAH2,485 15,039 26,544 1,918 12,430 228,368 1,212 704 1,162 636 136 16 36 9WASHINGTON3,725 34,929 40,747 5,872 19,796 618,715 1,934 1,947 1,851 1,753 220 42 52 21WYOMING3,631 6,924 58,642 758 13,174 68,923 2,217 261 2,146 236 58 6 53 4TOTAL 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 1,204 207*Does not include Commerical Marine.2018 Western States Total Nonroad Emissions All Fuel Types (tons)VOC NOx CO PM10 PM2.5 SO2 NH3State Total Total Total Total Total Total TotalALASKA12,982 7,765 93,496 1,141 1,084 347 16ARIZONA36,603 47,858 675,222 4,478 3,911 546 64CALIFORNIA111,648 167,312 989,539 14,778 12,747 1,889 832COLORADO25,007 40,877 481,292 2,902 2,711 371 60IDAHO16,006 18,144 181,634 1,395 1,313 102 24MONTANA9,641 36,552 115,047 1,931 1,840 61 41NEVADA11,816 22,203 212,792 1,356 1,274 261 30NEW MEXICO9,475 33,191 154,232 1,637 1,548 89 36NORTH DAKOTA8,662 37,432 102,700 2,264 2,171 62 47OREGON24,986 32,485 406,891 2,594 2,416 152 51SOUTH DAKOTA7,755 24,474 95,522 1,718 1,641 50 36UTAH17,524 28,461 240,798 1,916 1,798 153 45WASHINGTON38,654 46,619 638,511 3,882 3,604 263 73WYOMING10,555 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.SO2 NH3VOC NOX CO PM10 PM2.5May 2006G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc9-41Table 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 4California 243,480131,930111,550NA NA 581,081240,00613,91772,818Oregon 43,26541,1132,1527361,415111,59936,49170615,386Washington 58,92244,69214,2313,46710,764201,98749,8701,46522,768Total 345,667217,734127,9334,20412,179894,667326,36716,089110,971Summary of 2002 West Coast SO2 Emissions (tons per year)Total Near-Port - Zone 3 Inshore - Zone 4California 120,24074,18146,059NA NA 4,0347997335,964Oregon 23,86323,1197445322123,4485,232711,035Washington 32,96425,1307,8352,4835,3525,5396,9091421,532Total 177,068122,43054,6373,0155,56413,02212,9419478,532Commercial MarineStateONROADStateONROADTotal Offshore - Zone 1Coastal/In-Shore - Zones 2,3,4Coastal/In-Shore - Zones 2,3,4Offshore - Zone 1LocomotiveNONROAD Aircraft LocomotiveTotalNONROAD AircraftCommercial MarineTable 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 4California 478,468282,351196,117NA NA 203,06090,95228,46047,900Oregon 92,56587,9614,6041,5763,02842,12219,31589612,274Washington 126,09095,64130,4497,41923,03055,90826,6831,77418,163Total 697,123465,953231,170 8,99526,058301,090136,95031,13078,336Summary of 2018 West Coast SO2 Emissions (tons per year)Total Near-Port - Zone 3 Inshore - Zone 4California 272,278178,20094,079NA NA 1,8976011,24345Oregon 57,30455,5171,7871,27751046154908Washington 79,17860,36318,8155,96312,8526797717412Total 408,760294,080114,681 7,24013,3623,0377321,50666ONROAD NONROAD Aircraft LocomotiveONROAD NONROAD Aircraft LocomotiveTotal Offshore - Zone 1Coastal/In-Shore - Zones 2,3,4StateCommercial MarineTotal Offshore - Zone 1Coastal/In-Shore - Zones 2,3,4StateCommercial MarineMay 2006G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc9-42Table 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 9,55728.36 28.29 23.42 24.17COLORADO 9,85829.65 28.31 23.67 26.37IDAHO 5,51117.03 17.88 12.64 13.06MONTANA 3,86012.01 11.59 8.87 9.91NEVADA 3,45910.39 10.65 8.30 8.54NEW MEXICO 6,23218.89 18.29 15.02 15.90NORTH DAKOTA2,8758.83 8.58 6.80 7.30OREGON 10,34331.42 34.97 23.76 24.04SOUTH DAKOTA2,6248.07 7.69 6.17 6.71UTAH 5,10915.55 15.64 12.00 12.69WASHINGTON 14,02641.97 43.77 34.23 33.76WYOMING 1,6925.18 5.13 3.92 4.39Total 75,146227 231 179 1872002 Paved Road Dust Emissions - PM2.5State Annual (tpy) Winter (tpd) Spring (tpd) Summer (tpd) Autumn (tpd)ARIZONA 2,3897.09 7.08 5.86 6.05COLORADO 2,4657.42 7.09 5.93 6.60IDAHO 1,3784.27 4.48 3.17 3.28MONTANA 9653.01 2.91 2.23 2.49NEVADA 8652.60 2.66 2.08 2.14NEW MEXICO 1,5584.73 4.58 3.76 3.98NORTH DAKOTA7192.22 2.15 1.71 1.83OREGON 2,5867.87 8.75 5.95 6.02SOUTH DAKOTA6562.03 1.93 1.55 1.69UTAH 1,2773.89 3.91 3.00 3.18WASHINGTON 3,50710.53 10.98 8.59 8.47WYOMING 4231.30 1.29 0.98 1.10Total 18,78757 58 45 47May 2006G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc9-43Table 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 33,82491.9894.0290.4994.17COLORADO 13,54239.5335.6036.0337.31IDAHO 45,922127.26114.44132.49129.05MONTANA 604,7461,710.811,564.911,619.021,730.42NEVADA 10,47628.3726.9630.2529.23NEW MEXICO 14,82342.6840.4837.4041.88NORTH DAKOTA40,447116.56106.80102.82117.30OREGON 95,013233.50235.25305.92266.21SOUTH DAKOTA57,021168.24155.54137.71163.70UTAH 2,1625.725.516.346.13WASHINGTON 57,718128.97147.05199.00160.10WYOMING 4000.980.991.311.09Total 976,0942,6952,5282,6992,7772002 Unpaved Road Dust Emissions - PM2.5State Annual (tpy) Winter (tpd) Spring (tpd) Summer (tpd) Autumn (tpd)ARIZONA 5,07913.8114.1213.5914.14COLORADO 2,0315.935.345.405.60IDAHO 6,88819.0917.1719.8719.36MONTANA 90,712256.62234.74242.85259.56NEVADA 1,6954.594.364.894.73NEW MEXICO 2,2236.406.075.616.28NORTH DAKOTA6,06717.4816.0215.4217.59OREGON 14,25235.0235.2945.8939.93SOUTH DAKOTA8,55325.2423.3320.6624.55UTAH 3240.860.830.950.92WASHINGTON 8,65819.3522.0629.8524.02WYOMING 600.150.150.200.16Total 146,543405379405417May 2006G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc9-44Table 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 13,77940.8340.7433.8134.90COLORADO 13,22539.7938.0031.7435.37IDAHO 7,27422.4823.6016.6817.24MONTANA 5,78818.0617.4413.2514.82NEVADA 5,20015.5615.9112.5712.90NEW MEXICO 8,36725.3524.5420.1721.35NORTH DAKOTA 2,8428.718.466.747.23OREGON 14,32543.5248.4332.9133.30SOUTH DAKOTA 3,36210.349.867.908.60UTAH 7,78823.7423.8718.2519.32WASHINGTON 18,05954.0456.3644.0843.46WYOMING 2,1806.676.615.055.65Total 102,1903093142432542018 Paved Road Dust Emissions - PM2.5State Annual (tpy) Winter (tpd) Spring (tpd) Summer (tpd) Autumn (tpd)ARIZONA 3,44510.2110.198.468.73COLORADO 3,3069.969.517.958.85IDAHO 1,8195.645.914.184.33MONTANA 1,4474.534.373.333.72NEVADA 1,3003.893.983.143.23NEW MEXICO 2,0926.356.155.055.35NORTH DAKOTA 7112.182.121.691.82OREGON 3,58110.8912.128.248.34SOUTH DAKOTA 8412.612.481.992.16UTAH 1,9475.945.974.574.84WASHINGTON 4,51513.5514.1411.0610.91WYOMING 5451.671.661.271.42Total 25,54877796164May 2006G:\WRAP MSEI2\Reporting\Final report\Sec9_Results.doc9-45Table 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 47,891130.26133.20128.06133.29COLORADO 17,86552.1546.9647.5349.22IDAHO 60,617165.68152.30176.33169.88MONTANA 740,1372,085.071,913.791,991.982,116.51NEVADA 15,79542.7740.6545.6144.07NEW MEXICO 19,91158.0753.9749.8556.40NORTH DAKOTA 38,643111.36102.0498.23112.07OREGON 131,662323.56326.00423.92368.90SOUTH DAKOTA 72,851214.94198.72175.94209.14UTAH 2,0895.525.336.125.92WASHINGTON 57,718128.96147.03198.98160.09WYOMING 5131.271.261.671.41Total 1,205,6923,3203,1213,3443,4272018 Unpaved Road Dust Emissions - PM2.5State Annual (tpy) Winter (tpd) Spring (tpd) Summer (tpd) Autumn (tpd)ARIZONA 7,18919.5519.9919.2220.01COLORADO 2,6807.827.047.137.38IDAHO 9,09324.8522.8426.4525.48MONTANA 111,020312.76287.07298.80317.48NEVADA 2,5937.026.677.497.23NEW MEXICO 2,9878.718.107.488.46NORTH DAKOTA 5,79616.7015.3114.7316.81OREGON 19,74948.5348.9063.5955.34SOUTH DAKOTA 10,92832.2429.8126.3931.37UTAH 3130.830.800.920.89WASHINGTON 8,65819.3422.0629.8524.01WYOMING 770.190.190.250.21Total 181,083499469502515May 2006

G:\WRAP MSEI2\Reporting\Fi nal report\Sec10_refs.doc 

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### 10-4 Garg et al. 2000. “Brake Wear Particulate Matter Emissions,” Environmental Science and Technology , Vol. 34, No. 21.

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### Appendix A

Development of Calendar Year 2002 Count y-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 C ounty-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 pr evious 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.xl s). 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 att ached file were extracted from data published by the Alliance of

Automobile Manufactur ers (the Alliance) 1 

### and data published by TRW/Northrop-Grumman (formerly the National Institute for Pe troleum and Energy Research, NIPER). 2,3 

### The Alliance

1 “Alliance of Automobile Manufacturers North American Fu el Survey - Gasoline & Diesel Fuel,” published by the

Alliance of Automobile Manufacturers. The winter 200 2 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 Pl ains (used for Eastern ND and SD);

• District 9 - North Mounta in States (MT, WY, ID, Ea stern WA, and Eastern OR);

• District 10 - Central Moun tain States (CO and UT);

• District 11 - New Me xico and West Texas; • District 12 - West Southwest (AZ, Southern NV, and Southeastern CA);

• District 13 - Pacific Northwes t (Western WA and Western OR);

• District 14 - Northern Calif ornia and Northern Nevada; and

• District 15 - Southern California.

Each county was “mapped” to one of the areas wh ere fuel specification data were available. Although this was at times a subjective proce ss, 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 coun ties were mapped to the Alliance city if it was felt that the fuels would be similar. For exampl e, the Alliance data for Denver were used to

reflect those counties in Colorado that are s ubject 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 assignmen ts 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 cont ain gasoline data for summer and winter, and they report gasoline specifications separately for regular, mid-grade, and premium. A composite gasolinespecification for each county and season (winter and summer) was estimated by volumeweighting the three grades of gasoline based on annual statew ide 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 re ported in the Alliance and TRW surveys. As a result, the following approach was used to gene rate gasoline specifications for spring (assumed to be April) and fall (a ssumed to be October):

• RVP - The approach used in the National Emissions Inventory (NEI) 5 

### to develop monthly estimates of gasoline RVP from January (win ter) 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 A 6 

### ) is the same as that for July, implying that the July volatility should be assigned to April. For areas th at 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, whic h 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 as sumed 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 domai n. 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 Dies el 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. En vironmental Protection Agency, January 2004.

6 Note that April reflects a transition month in ASTM volatili ty 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 re port 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 c onducted in the winter and th e 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 ar e 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 Di esel fuel. For this analysis, estimates for Diesel fu el sulfur level were obtained th rough discussions with the Alaska Department of Environmental Conservation. Aver age values of those data were used for the northern portion of Alaska. Fo r Southeast Alaska (i.e., Juneau and surrounding boroughs), fuel is generally barged in from Nort hern Washington. Thus, fuel sp ecifications 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 so me 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 sa mples at all from the Western Region.

For California and Maricopa County, AZ, regulati ons 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 th at 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 PropertiesSummer 2002 Gasoline PropertiesStateCountyDataRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCountySource(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)02013 02 013 AK Aleutians East B AAM-FB 14.5 160 0.0 0.0 0.0 12.6 130 0.0 0.0 0.002016 02 016 AK Aleutians West C AAM-FB 14.5 160 0.0 0.0 0.0 12.6 130 0.0 0.0 0.002020 02 020 AK Anchorage Ed ANCH 14.5 146 0.0 9.5 3.3 12.6 133 0.0 0.0 0.002050 02 050 AK Bethel Ed AAM-FB 14.5 160 0.0 0.0 0.0 12.6 130 0.0 0.0 0.002060 02 060 AK Bristol Bay Borough AAM-FB 14.5 160 0.0 0.0 0.0 12.6 130 0.0 0.0 0.002068 02 068 AK Denali Borough AAM-FB 14.5 160 0.0 0.0 0.0 12.6 130 0.0 0.0 0.002070 02 070 AK Dillingham Ed AAM-FB 14.5 160 0.0 0.0 0.0 12.6 130 0.0 0.0 0.002090 02 090 AK Fairbanks Ed AAM-FB 14.5 162 0.0 0.0 0.0 12.6 133 0.0 0.0 0.002100 02 100 AK Haines Ed NIPER-13 13.7 324 0.0 0.0 0.0 7.5 342 0.0 0.0 0.002110 02 110 AK Juneau Ed NIPER-13 13.7 324 0.0 0.0 0.0 7.5 342 0.0 0.0 0.002122 02 122 AK Kenai Penninsula AAM-FB 14.5 162 0.0 0.0 0.0 12.6 133 0.0 0.0 0.002130 02 130 AK Ketchikan Ed NIPER-13 13.7 324 0.0 0.0 0.0 7.5 342 0.0 0.0 0.002150 02 150 AK Kodiak Island Ed AAM-FB 14.5 160 0.0 0.0 0.0 12.6 130 0.0 0.0 0.002164 02 164 AK Lake and Peninsula AAM-FB 14.5 160 0.0 0.0 0.0 12.6 130 0.0 0.0 0.002170 02 170 AK Matanuska-Susitna AAM-FB 14.5 162 0.0 0.0 0.0 12.6 133 0.0 0.0 0.002180 02 180 AK Nome Ed AAM-FB 14.5 160 0.0 0.0 0.0 12.6 130 0.0 0.0 0.002185 02 185 AK North Slope Ed AAM-FB 14.5 160 0.0 0.0 0.0 12.6 130 0.0 0.0 0.002188 02 188 AK Northwest Arctic AAM-FB 14.5 160 0.0 0.0 0.0 12.6 130 0.0 0.0 0.002201 02 201 AK Prince Of Wales NIPER-13 13.7 324 0.0 0.0 0.0 7.5 342 0.0 0.0 0.002220 02 220 AK Sitka Ed NIPER-13 13.7 324 0.0 0.0 0.0 7.5 342 0.0 0.0 0.002232 02 232 AK Skagway-Yakutat NIPER-13 13.7 324 0.0 0.0 0.0 7.5 342 0.0 0.0 0.002240 02 240 AK Southeast Fairbanks AAM-FB 14.5 162 0.0 0.0 0.0 12.6 133 0.0 0.0 0.002261 02 261 AK Valdez-Cordova E AAM-FB 14.5 162 0.0 0.0 0.0 12.6 133 0.0 0.0 0.002270 02 270 AK Wade Hampton Ed AAM-FB 14.5 160 0.0 0.0 0.0 12.6 130 0.0 0.0 0.002280 02 280 AK Wrangell-Petersburg NIPER-13 13.7 324 0.0 0.0 0.0 7.5 342 0.0 0.0 0.002282 02 282 AK Yakutat Borough AAM-FB 14.5 160 0.0 0.0 0.0 12.6 130 0.0 0.0 0.002290 02 290 AK Yukon-Koyukuk Ed AAM-FB 14.5 160 0.0 0.0 0.0 12.6 130 0.0 0.0 0.004001 04 001 AZ Apache NIPER-11 11.5 235 0.2 0.0 0.0 7.7 268 0.0 0.0 0.004003 04 003 AZ Cochise NIPER-11 11.5 235 0.2 0.0 0.0 7.7 268 0.0 0.0 0.004005 04 005 AZ Coconino NIPER-12 11.9 0 10.3 0.0 1.8 7.0 66 8.1 0.0 1.404007 04 007 AZ Gila NIPER-12 11.9 0 10.3 0.0 1.8 7.0 66 8.1 0.0 1.404009 04 009 AZ Graham NIPER-11 11.5 235 0.2 0.0 0.0 7.7 268 0.0 0.0 0.004011 04 011 AZ Greenlee NIPER-11 11.5 235 0.2 0.0 0.0 7.7 268 0.0 0.0 0.004012 04 012 AZ La Paz NIPER-12 11.9 0 10.3 0.0 1.8 7.0 66 8.1 0.0 1.404013 04 013 AZ Maricopa AAM-PX 9.0 10 0.1 9.4 3.3 6.8 94 9.1 0.0 1.604015 04 015 AZ Mohave NIPER-12 11.9 0 10.3 0.0 1.8 7.0 66 8.1 0.0 1.404017 04 017 AZ Navajo NIPER-11 11.5 235 0.2 0.0 0.0 7.7 268 0.0 0.0 0.004019 04 019 AZ Pima NIPER-12 11.9 0 10.3 0.0 1.8 7.0 66 8.1 0.0 1.404021 04 021 AZ Pinal NIPER-12 11.9 0 10.3 0.0 1.8 7.0 66 8.1 0.0 1.404023 04 023 AZ Santa Cruz NIPER-11 11.5 235 0.2 0.0 0.0 7.7 268 0.0 0.0 0.004025 04 025 AZ Yavapai NIPER-12 11.9 0 10.3 0.0 1.8 7.0 66 8.1 0.0 1.404027 04 027 AZ Yuma NIPER-12 11.9 0 10.3 0.0 1.8 7.0 66 8.1 0.0 1.406001 06 001 CA Alameda AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306003 06 003 CA Alpine AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306005 06 005 CA Amador AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.3Winter 2002 Gasoline PropertiesSummer 2002 Gasoline PropertiesStateCountyDataRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCountySource(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)06007 06 007 CA Butte AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306009 06 009 CA Calaveras AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306011 06 011 CA Colusa AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306013 06 013 CA Contra Costa AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306015 06 015 CA Del Norte AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306017 06 017 CA El Dorado AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306019 06 019 CA Fresno AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306021 06 021 CA Glenn AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306023 06 023 CA Humboldt AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306025 06 025 CA Imperial AAM-LA 11.6 20 10.2 0.9 2.1 7.0 10 10.4 0.6 2.106027 06 027 CA Inyo AAM-LA 11.6 20 10.2 0.9 2.1 7.0 10 10.4 0.6 2.106029 06 029 CA Kern AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306031 06 031 CA Kings AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306033 06 033 CA Lake AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306035 06 035 CA Lassen AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306037 06 037 CA Los Angeles AAM-LA 11.6 20 10.2 0.9 2.1 7.0 10 10.4 0.6 2.106039 06 039 CA Madera AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306041 06 041 CA Marin AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306043 06 043 CA Mariposa AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306045 06 045 CA Mendocino AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306047 06 047 CA Merced AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306049 06 049 CA Modoc AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306051 06 051 CA Mono AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306053 06 053 CA Monterey AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306055 06 055 CA Napa AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306057 06 057 CA Nevada AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306059 06 059 CA Orange AAM-LA 11.6 20 10.2 0.9 2.1 7.0 10 10.4 0.6 2.106061 06 061 CA Placer AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306063 06 063 CA Plumas AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306065 06 065 CA Riverside AAM-LA 11.6 20 10.2 0.9 2.1 7.0 10 10.4 0.6 2.106067 06 067 CA Sacramento AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306069 06 069 CA San Benito AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306071 06 071 CA San Bernardino AAM-LA 11.6 20 10.2 0.9 2.1 7.0 10 10.4 0.6 2.106073 06 073 CA San Diego AAM-LA 11.6 20 10.2 0.9 2.1 7.0 10 10.4 0.6 2.106075 06 075 CA San Francisco AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306077 06 077 CA San Joaquin AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306079 06 079 CA San Luis Obispo AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306081 06 081 CA San Mateo AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306083 06 083 CA Santa Barbara AAM-LA 11.6 20 10.2 0.9 2.1 7.0 10 10.4 0.6 2.106085 06 085 CA Santa Clara AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306087 06 087 CA Santa Cruz AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306089 06 089 CA Shasta AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306091 06 091 CA Sierra AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306093 06 093 CA Siskiyou AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306095 06 095 CA Solano AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306097 06 097 CA Sonoma AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.3Winter 2002 Gasoline PropertiesSummer 2002 Gasoline PropertiesStateCountyDataRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCountySource(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)06099 06 099 CA Stanislaus AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306101 06 101 CA Sutter AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306103 06 103 CA Tehama AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306105 06 105 CA Trinity AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306107 06 107 CA Tulare AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306109 06 109 CA Tuolumne AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306111 06 111 CA Ventura AAM-LA 11.6 20 10.2 0.9 2.1 7.0 10 10.4 0.6 2.106113 06 113 CA Yolo AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.306115 06 115 CA Yuba AAM-SF 12.4 10 3.2 1.1 0.9 7.1 10 5.0 1.2 1.308001 08 001 CO Adams AAM-DN 14.2 149 0.0 9.7 3.4 9.0 167 0.0 4.2 1.408003 08 003 CO Alamosa NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008005 08 005 CO Arapahoe AAM-DN 14.2 149 0.0 9.7 3.4 9.0 167 0.0 4.2 1.408007 08 007 CO Archuleta NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008009 08 009 CO Baca NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008011 08 011 CO Bent NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008013 08 013 CO Boulder AAM-DN 14.2 149 0.0 9.7 3.4 9.0 167 0.0 4.2 1.408015 08 015 CO Chaffee NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008017 08 017 CO Cheyenne NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008019 08 019 CO Clear Creek NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008021 08 021 CO Conejos NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008023 08 023 CO Costilla NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008025 08 025 CO Crowley NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008027 08 027 CO Custer NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008029 08 029 CO Delta NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008031 08 031 CO Denver AAM-DN 14.2 149 0.0 9.7 3.4 9.0 167 0.0 4.2 1.408033 08 033 CO Dolores NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008035 08 035 CO Douglas AAM-DN 14.2 149 0.0 9.7 3.4 9.0 167 0.0 4.2 1.408037 08 037 CO Eagle NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008039 08 039 CO Elbert NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008041 08 041 CO El Paso AAM-DN 14.2 149 0.0 9.7 3.4 9.0 167 0.0 4.2 1.408043 08 043 CO Fremont NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008045 08 045 CO Garfield NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008047 08 047 CO Gilpin NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008049 08 049 CO Grand NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008051 08 051 CO Gunnison NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008053 08 053 CO Hinsdale NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008055 08 055 CO Huerfano NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008057 08 057 CO Jackson NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008059 08 059 CO Jefferson AAM-DN 14.2 149 0.0 9.7 3.4 9.0 167 0.0 4.2 1.408061 08 061 CO Kiowa NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008063 08 063 CO Kit Carson NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008065 08 065 CO Lake NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008067 08 067 CO La Plata NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008069 08 069 CO Larimer AAM-DN 14.2 149 0.0 9.7 3.4 9.0 167 0.0 4.2 1.408071 08 071 CO Las Animas NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008073 08 073 CO Lincoln NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.0Winter 2002 Gasoline PropertiesSummer 2002 Gasoline PropertiesStateCountyDataRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCountySource(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)08075 08 075 CO Logan NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008077 08 077 CO Mesa NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008079 08 079 CO Mineral NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008081 08 081 CO Moffat NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008083 08 083 CO Montezuma NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008085 08 085 CO Montrose NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008087 08 087 CO Morgan NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008089 08 089 CO Otero NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008091 08 091 CO Ouray NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008093 08 093 CO Park NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008095 08 095 CO Phillips NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008097 08 097 CO Pitkin NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008099 08 099 CO Prowers NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008101 08 101 CO Pueblo NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008103 08 103 CO Rio Blanco NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008105 08 105 CO Rio Grande NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008107 08 107 CO Routt NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008109 08 109 CO Saguache NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008111 08 111 CO San Juan NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008113 08 113 CO San Miguel NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008115 08 115 CO Sedgwick NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008117 08 117 CO Summit NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008119 08 119 CO Teller NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008121 08 121 CO Washington NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008123 08 123 CO Weld NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.008125 08 125 CO Yuma NIPER-10 12.8 176 0.0 0.0 0.0 7.8 175 0.0 0.0 0.016001 16 001 ID Ada NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016003 16 003 ID Adams NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016005 16 005 ID Bannock NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016007 16 007 ID Bear Lake NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016009 16 009 ID Benewah NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016011 16 011 ID Bingham NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016013 16 013 ID Blaine NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016015 16 015 ID Boise NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016017 16 017 ID Bonner NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016019 16 019 ID Bonneville NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016021 16 021 ID Boundary NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016023 16 023 ID Butte NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016025 16 025 ID Camas NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016027 16 027 ID Canyon NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016029 16 029 ID Caribou NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016031 16 031 ID Cassia NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016033 16 033 ID Clark NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016035 16 035 ID Clearwater NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016037 16 037 ID Custer NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016039 16 039 ID Elmore NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.0Winter 2002 Gasoline PropertiesSummer 2002 Gasoline PropertiesStateCountyDataRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCountySource(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)16041 16 041 ID Franklin NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016043 16 043 ID Fremont NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016045 16 045 ID Gem NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016047 16 047 ID Gooding NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016049 16 049 ID Idaho NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016051 16 051 ID Jefferson NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016053 16 053 ID Jerome NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016055 16 055 ID Kootenai NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016057 16 057 ID Latah NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016059 16 059 ID Lemhi NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016061 16 061 ID Lewis NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016063 16 063 ID Lincoln NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016065 16 065 ID Madison NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016067 16 067 ID Minidoka NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016069 16 069 ID Nez Perce NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016071 16 071 ID Oneida NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016073 16 073 ID Owyhee NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016075 16 075 ID Payette NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016077 16 077 ID Power NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016079 16 079 ID Shoshone NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016081 16 081 ID Teton NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016083 16 083 ID Twin Falls NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016085 16 085 ID Valley NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.016087 16 087 ID Washington NIPER-9 12.7 195 0.0 0.0 0.0 8.3 304 0.0 0.0 0.030001 30 001 MT Beaverhead AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030003 30 003 MT Big Horn AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030005 30 005 MT Blaine AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030007 30 007 MT Broadwater AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030009 30 009 MT Carbon AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030011 30 011 MT Carter AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030013 30 013 MT Cascade AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030015 30 015 MT Chouteau AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030017 30 017 MT Custer AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030019 30 019 MT Daniels AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030021 30 021 MT Dawson AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030023 30 023 MT Deer Lodge AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030025 30 025 MT Fallon AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030027 30 027 MT Fergus AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030029 30 029 MT Flat Head AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030031 30 031 MT Gallatin AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030033 30 033 MT Garfield AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030035 30 035 MT Glacier AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030037 30 037 MT Golden Valley AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030039 30 039 MT Granite AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030041 30 041 MT Hill AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030043 30 043 MT Jefferson AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.0Winter 2002 Gasoline PropertiesSummer 2002 Gasoline PropertiesStateCountyDataRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCountySource(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)30045 30 045 MT Judith Basin AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030047 30 047 MT Lake AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030049 30 049 MT Lewis and Clark AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030051 30 051 MT Liberty AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030053 30 053 MT Lincoln AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030055 30 055 MT Mc Cone AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030057 30 057 MT Madison AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030059 30 059 MT Meagher AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030061 30 061 MT Mineral AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030063 30 063 MT Missoula AAM-BL 13.9 365 0.0 10.0 3.4 8.7 389 0.0 0.0 0.030065 30 065 MT Musselshell AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030067 30 067 MT Park AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030069 30 069 MT Petroleum AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030071 30 071 MT Phillips AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030073 30 073 MT Pondera AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030075 30 075 MT Powder River AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030077 30 077 MT Powell AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030079 30 079 MT Prairie AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030081 30 081 MT Ravalli AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030083 30 083 MT Richland AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030085 30 085 MT Roosevelt AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030087 30 087 MT Rosebud AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030089 30 089 MT Sanders AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030091 30 091 MT Sheridan AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030093 30 093 MT Silver Bow AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030095 30 095 MT Stillwater AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030097 30 097 MT Sweet Grass AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030099 30 099 MT Teton AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030101 30 101 MT Toole AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030103 30 103 MT Treasure AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030105 30 105 MT Valley AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030107 30 107 MT Wheatland AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030109 30 109 MT Wibaux AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030111 30 111 MT Yellowstone AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.030113 30 113 MT Yellowstone Park AAM-BL 13.9 405 0.0 0.0 0.0 8.7 389 0.0 0.0 0.032001 32 001 NV Churchill NIPER-14 11.6 10 2.2 0.0 0.4 7.2 37 2.9 0.0 0.532003 32 003 NV Clark AAM-LV 8.9 20 0.2 9.2 3.2 8.3 27 0.2 0.0 0.032005 32 005 NV Douglas NIPER-14 11.6 10 2.2 0.0 0.4 7.2 37 2.9 0.0 0.532007 32 007 NV Elko NIPER-14 11.6 10 2.2 0.0 0.4 7.2 37 2.9 0.0 0.532009 32 009 NV Esmeralda AAM-LV 8.9 20 0.2 9.2 3.2 8.3 27 0.2 0.0 0.032011 32 011 NV Eureka NIPER-14 11.6 10 2.2 0.0 0.4 7.2 37 2.9 0.0 0.532013 32 013 NV Humboldt NIPER-14 11.6 10 2.2 0.0 0.4 7.2 37 2.9 0.0 0.532015 32 015 NV Lander NIPER-14 11.6 10 2.2 0.0 0.4 7.2 37 2.9 0.0 0.532017 32 017 NV Lincoln AAM-LV 8.9 20 0.2 9.2 3.2 8.3 27 0.2 0.0 0.032019 32 019 NV Lyon NIPER-14 11.6 10 2.2 0.0 0.4 7.2 37 2.9 0.0 0.532021 32 021 NV Mineral AAM-LV 8.9 20 0.2 9.2 3.2 8.3 27 0.2 0.0 0.0Winter 2002 Gasoline PropertiesSummer 2002 Gasoline PropertiesStateCountyDataRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCountySource(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)32023 32 023 NV Nye AAM-LV 8.9 20 0.2 9.2 3.2 8.3 27 0.2 0.0 0.032027 32 027 NV Pershing NIPER-14 11.6 10 2.2 0.0 0.4 7.2 37 2.9 0.0 0.532029 32 029 NV Storey NIPER-14 11.6 10 2.2 0.0 0.4 7.2 37 2.9 0.0 0.532031 32 031 NV Washoe NIPER-14 12.6 9 0.0 7.8 2.7 7.2 37 2.9 0.0 0.532033 32 033 NV White Pine NIPER-14 11.6 10 2.2 0.0 0.4 7.2 37 2.9 0.0 0.532510 32 510 NV Carson City NIPER-14 11.6 10 2.2 0.0 0.4 7.2 37 2.9 0.0 0.535001 35 001 NM Bernalillo AAM-ALB 12.7 170 0.0 8.6 3.0 8.8 240 2.0 2.0 1.035003 35 003 NM Catron NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035005 35 005 NM Chaves NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035006 35 006 NM Cibola NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035007 35 007 NM Colfax NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035009 35 009 NM Curry NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035011 35 011 NM De Baca NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035013 35 013 NM Dona Ana NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035015 35 015 NM Eddy NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035017 35 017 NM Grant NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035019 35 019 NM Guadalupe NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035021 35 021 NM Harding NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035023 35 023 NM Hidalgo NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035025 35 025 NM Lea NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035027 35 027 NM Lincoln NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035028 35 028 NM Los Alamos AAM-ALB 12.7 170 0.0 8.6 3.0 8.8 240 2.0 2.0 1.035029 35 029 NM Luna NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035031 35 031 NM Mc Kinley NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035033 35 033 NM Mora NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035035 35 035 NM Otero NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035037 35 037 NM Quay NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035039 35 039 NM Rio Arriba NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035041 35 041 NM Roosevelt NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035043 35 043 NM Sandoval AAM-ALB 12.7 170 0.0 8.6 3.0 8.8 240 2.0 2.0 1.035045 35 045 NM San Juan NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035047 35 047 NM San Miguel NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035049 35 049 NM Santa Fe AAM-ALB 12.7 170 0.0 8.6 3.0 8.8 240 2.0 2.0 1.035051 35 051 NM Sierra NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035053 35 053 NM Socorro NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035055 35 055 NM Taos NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035057 35 057 NM Torrance AAM-ALB 12.7 170 0.0 8.6 3.0 8.8 240 2.0 2.0 1.035059 35 059 NM Union NIPER-11 11.5 237 0.2 0.0 0.0 7.7 270 0.0 0.0 0.035061 35 061 NM Valencia AAM-ALB 12.7 170 0.0 8.6 3.0 8.8 240 2.0 2.0 1.038001 38 001 ND Adams AAM-BL 14.0 413 0.0 0.0 0.0 8.6 421 0.0 0.0 0.038003 38 003 ND Barnes NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038005 38 005 ND Benson NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038007 38 007 ND Billings AAM-BL 14.0 413 0.0 0.0 0.0 8.6 421 0.0 0.0 0.038009 38 009 ND Bottineau NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038011 38 011 ND Bowman AAM-BL 14.0 413 0.0 0.0 0.0 8.6 421 0.0 0.0 0.038013 38 013 ND Burke AAM-BL 14.0 413 0.0 0.0 0.0 8.6 421 0.0 0.0 0.0Winter 2002 Gasoline PropertiesSummer 2002 Gasoline PropertiesStateCountyDataRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCountySource(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)38015 38 015 ND Burleigh NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038017 38 017 ND Cass NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038019 38 019 ND Cavalier NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038021 38 021 ND Dickey NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038023 38 023 ND Divide AAM-BL 14.0 413 0.0 0.0 0.0 8.6 421 0.0 0.0 0.038025 38 025 ND Dunn AAM-BL 14.0 413 0.0 0.0 0.0 8.6 421 0.0 0.0 0.038027 38 027 ND Eddy NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038029 38 029 ND Emmons NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038031 38 031 ND Foster NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038033 38 033 ND Golden Valley AAM-BL 14.0 413 0.0 0.0 0.0 8.6 421 0.0 0.0 0.038035 38 035 ND Grand Forks NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038037 38 037 ND Grant NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038039 38 039 ND Griggs NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038041 38 041 ND Hettinger AAM-BL 14.0 413 0.0 0.0 0.0 8.6 421 0.0 0.0 0.038043 38 043 ND Kidder NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038045 38 045 ND La Moure NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038047 38 047 ND Logan NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038049 38 049 ND Mc Henry NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038051 38 051 ND Mc Intosh NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038053 38 053 ND Mc Kenzie AAM-BL 14.0 413 0.0 0.0 0.0 8.6 421 0.0 0.0 0.038055 38 055 ND Mc Lean NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038057 38 057 ND Mercer NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038059 38 059 ND Morton NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038061 38 061 ND Mountrail AAM-BL 14.0 413 0.0 0.0 0.0 8.6 421 0.0 0.0 0.038063 38 063 ND Nelson NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038065 38 065 ND Oliver NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038067 38 067 ND Pembina NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038069 38 069 ND Pierce NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038071 38 071 ND Ramsey NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038073 38 073 ND Ransom NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038075 38 075 ND Renville NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038077 38 077 ND Richland NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038079 38 079 ND Rolette NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038081 38 081 ND Sargent NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038083 38 083 ND Sheridan NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038085 38 085 ND Sioux NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038087 38 087 ND Slope AAM-BL 14.0 413 0.0 0.0 0.0 8.6 421 0.0 0.0 0.038089 38 089 ND Stark AAM-BL 14.0 413 0.0 0.0 0.0 8.6 421 0.0 0.0 0.038091 38 091 ND Steele NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038093 38 093 ND Stutsman NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038095 38 095 ND Towner NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038097 38 097 ND Traill NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038099 38 099 ND Walsh NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038101 38 101 ND Ward NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038103 38 103 ND Wells NIPER-7 13.3 212 0.0 0.0 0.0 8.1 278 0.0 0.0 0.038105 38 105 ND Williams AAM-BL 14.0 413 0.0 0.0 0.0 8.6 421 0.0 0.0 0.0Winter 2002 Gasoline PropertiesSummer 2002 Gasoline PropertiesStateCountyDataRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCountySource(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)41001 41 001 OR Baker NIPER-9 12.7 198 0.0 0.0 0.0 8.3 309 0.0 0.0 0.041003 41 003 OR Benton NIPER-13 13.7 310 0.0 0.0 0.0 7.5 326 0.0 0.0 0.041005 41 005 OR Clackamas NIPER-13 14.1 150 0.0 8.8 3.0 7.5 326 0.0 0.0 0.041007 41 007 OR Clatsop NIPER-13 13.7 310 0.0 0.0 0.0 7.5 326 0.0 0.0 0.041009 41 009 OR Columbia NIPER-13 13.7 310 0.0 0.0 0.0 7.5 326 0.0 0.0 0.041011 41 011 OR Coos NIPER-13 13.7 310 0.0 0.0 0.0 7.5 326 0.0 0.0 0.041013 41 013 OR Crook NIPER-9 12.7 198 0.0 0.0 0.0 8.3 309 0.0 0.0 0.041015 41 015 OR Curry NIPER-13 13.7 310 0.0 0.0 0.0 7.5 326 0.0 0.0 0.041017 41 017 OR Deschutes NIPER-9 12.7 198 0.0 0.0 0.0 8.3 309 0.0 0.0 0.041019 41 019 OR Douglas NIPER-13 13.7 310 0.0 0.0 0.0 7.5 326 0.0 0.0 0.041021 41 021 OR Gilliam NIPER-9 12.7 198 0.0 0.0 0.0 8.3 309 0.0 0.0 0.041023 41 023 OR Grant NIPER-9 12.7 198 0.0 0.0 0.0 8.3 309 0.0 0.0 0.041025 41 025 OR Harney NIPER-9 12.7 198 0.0 0.0 0.0 8.3 309 0.0 0.0 0.041027 41 027 OR Hood River NIPER-9 12.7 198 0.0 0.0 0.0 8.3 309 0.0 0.0 0.041029 41 029 OR Jackson NIPER-13 14.1 150 0.0 8.8 3.0 7.5 326 0.0 0.0 0.041031 41 031 OR Jefferson NIPER-9 12.7 198 0.0 0.0 0.0 8.3 309 0.0 0.0 0.041033 41 033 OR Josephine NIPER-13 13.7 310 0.0 0.0 0.0 7.5 326 0.0 0.0 0.041035 41 035 OR Klamath NIPER-13 13.7 310 0.0 0.0 0.0 7.5 326 0.0 0.0 0.041037 41 037 OR Lake NIPER-9 12.7 198 0.0 0.0 0.0 8.3 309 0.0 0.0 0.041039 41 039 OR Lane NIPER-13 13.7 310 0.0 0.0 0.0 7.5 326 0.0 0.0 0.041041 41 041 OR Lincoln NIPER-13 13.7 310 0.0 0.0 0.0 7.5 326 0.0 0.0 0.041043 41 043 OR Linn NIPER-13 13.7 310 0.0 0.0 0.0 7.5 326 0.0 0.0 0.041045 41 045 OR Malheur NIPER-9 12.7 198 0.0 0.0 0.0 8.3 309 0.0 0.0 0.041047 41 047 OR Marion NIPER-13 13.7 310 0.0 0.0 0.0 7.5 326 0.0 0.0 0.041049 41 049 OR Morrow NIPER-9 12.7 198 0.0 0.0 0.0 8.3 309 0.0 0.0 0.041051 41 051 OR Multnomah NIPER-13 14.1 150 0.0 8.8 3.0 7.5 326 0.0 0.0 0.041053 41 053 OR Polk NIPER-13 13.7 310 0.0 0.0 0.0 7.5 326 0.0 0.0 0.041055 41 055 OR Sherman NIPER-9 12.7 198 0.0 0.0 0.0 8.3 309 0.0 0.0 0.041057 41 057 OR Tillamook NIPER-13 13.7 310 0.0 0.0 0.0 7.5 326 0.0 0.0 0.041059 41 059 OR Umatilla NIPER-9 12.7 198 0.0 0.0 0.0 8.3 309 0.0 0.0 0.041061 41 061 OR Union NIPER-9 12.7 198 0.0 0.0 0.0 8.3 309 0.0 0.0 0.041063 41 063 OR Wallowa NIPER-9 12.7 198 0.0 0.0 0.0 8.3 309 0.0 0.0 0.041065 41 065 OR Wasco NIPER-9 12.7 198 0.0 0.0 0.0 8.3 309 0.0 0.0 0.041067 41 067 OR Washington NIPER-13 14.1 150 0.0 8.8 3.0 7.5 326 0.0 0.0 0.041069 41 069 OR Wheeler NIPER-9 12.7 198 0.0 0.0 0.0 8.3 309 0.0 0.0 0.041071 41 071 OR Yamhill NIPER-13 14.1 150 0.0 8.8 3.0 7.5 326 0.0 0.0 0.046003 46 003 SD Aurora NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046005 46 005 SD Beadle NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046007 46 007 SD Bennett NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046009 46 009 SD Bon Homme NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046011 46 011 SD Brookings NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046013 46 013 SD Brown NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046015 46 015 SD Brule NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046017 46 017 SD Buffalo NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046019 46 019 SD Butte NIPER-9 12.8 203 0.0 0.0 0.0 8.3 316 0.0 0.0 0.046021 46 021 SD Campbell NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.0Winter 2002 Gasoline PropertiesSummer 2002 Gasoline PropertiesStateCountyDataRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCountySource(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)46023 46 023 SD Charles Mix NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046025 46 025 SD Clark NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046027 46 027 SD Clay NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046029 46 029 SD Codington NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046031 46 031 SD Corson NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046033 46 033 SD Custer NIPER-9 12.8 203 0.0 0.0 0.0 8.3 316 0.0 0.0 0.046035 46 035 SD Davison NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046037 46 037 SD Day NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046039 46 039 SD Deuel NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046041 46 041 SD Dewey NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046043 46 043 SD Douglas NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046045 46 045 SD Edmunds NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046047 46 047 SD Fall River NIPER-9 12.8 203 0.0 0.0 0.0 8.3 316 0.0 0.0 0.046049 46 049 SD Faulk NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046051 46 051 SD Grant NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046053 46 053 SD Gregory NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046055 46 055 SD Haakon NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046057 46 057 SD Hamlin NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046059 46 059 SD Hand NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046061 46 061 SD Hanson NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046063 46 063 SD Harding NIPER-9 12.8 203 0.0 0.0 0.0 8.3 316 0.0 0.0 0.046065 46 065 SD Hughes NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046067 46 067 SD Hutchinson NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046069 46 069 SD Hyde NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046071 46 071 SD Jackson NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046073 46 073 SD Jerauld NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046075 46 075 SD Jones NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046077 46 077 SD Kingsbury NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046079 46 079 SD Lake NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046081 46 081 SD Lawrence NIPER-9 12.8 203 0.0 0.0 0.0 8.3 316 0.0 0.0 0.046083 46 083 SD Lincoln NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046085 46 085 SD Lyman NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046087 46 087 SD Mc Cook NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046089 46 089 SD Mc Pherson NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046091 46 091 SD Marshall NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046093 46 093 SD Meade NIPER-9 12.8 203 0.0 0.0 0.0 8.3 316 0.0 0.0 0.046095 46 095 SD Mellette NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046097 46 097 SD Miner NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046099 46 099 SD Minnehaha NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046101 46 101 SD Moody NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046103 46 103 SD Pennington NIPER-9 12.8 203 0.0 0.0 0.0 8.3 316 0.0 0.0 0.046105 46 105 SD Perkins NIPER-9 12.8 203 0.0 0.0 0.0 8.3 316 0.0 0.0 0.046107 46 107 SD Potter NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046109 46 109 SD Roberts NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046111 46 111 SD Sanborn NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046113 46 113 SD Shannon NIPER-9 12.8 203 0.0 0.0 0.0 8.3 316 0.0 0.0 0.0Winter 2002 Gasoline PropertiesSummer 2002 Gasoline PropertiesStateCountyDataRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCountySource(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)46115 46 115 SD Spink NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046117 46 117 SD Stanley NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046119 46 119 SD Sully NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046121 46 121 SD Todd NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046123 46 123 SD Tripp NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046125 46 125 SD Turner NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046127 46 127 SD Union NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046129 46 129 SD Walworth NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046135 46 135 SD Yankton NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.046137 46 137 SD Ziebach NIPER-7 13.3 210 0.0 0.0 0.0 8.1 275 0.0 0.0 0.049001 49 001 UT Beaver NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049003 49 003 UT Box Elder NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049005 49 005 UT Cache NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049007 49 007 UT Carbon NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049009 49 009 UT Daggett NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049011 49 011 UT Davis NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049013 49 013 UT Duchesne NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049015 49 015 UT Emery NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049017 49 017 UT Garfield NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049019 49 019 UT Grand NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049021 49 021 UT Iron NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049023 49 023 UT Juab NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049025 49 025 UT Kane NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049027 49 027 UT Millard NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049029 49 029 UT Morgan NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049031 49 031 UT Piute NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049033 49 033 UT Rich NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049035 49 035 UT Salt Lake NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049037 49 037 UT San Juan NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049039 49 039 UT Sanpete NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049041 49 041 UT Sevier NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049043 49 043 UT Summit NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049045 49 045 UT Tooele NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049047 49 047 UT Uintah NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049049 49 049 UT Utah NIPER-10 14.4 195 0.0 7.0 2.4 7.8 173 0.0 0.0 0.049051 49 051 UT Wasatch NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049053 49 053 UT Washington NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049055 49 055 UT Wayne NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.049057 49 057 UT Weber NIPER-10 12.8 173 0.0 0.0 0.0 7.8 173 0.0 0.0 0.053001 53 001 WA Adams NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053003 53 003 WA Asotin NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053005 53 005 WA Benton NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053007 53 007 WA Chelan NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053009 53 009 WA Clallam NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053011 53 011 WA Clark AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953013 53 013 WA Columbia NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.0Winter 2002 Gasoline PropertiesSummer 2002 Gasoline PropertiesStateCountyDataRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCountySource(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)53015 53 015 WA Cowlitz AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953017 53 017 WA Douglas NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053019 53 019 WA Ferry NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053021 53 021 WA Franklin NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053023 53 023 WA Garfield NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053025 53 025 WA Grant NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053027 53 027 WA Grays Harbor AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953029 53 029 WA Island AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953031 53 031 WA Jefferson AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953033 53 033 WA King AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953035 53 035 WA Kitsap AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953037 53 037 WA Kittitas NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053039 53 039 WA Klickitat NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053041 53 041 WA Lewis AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953043 53 043 WA Lincoln NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053045 53 045 WA Mason AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953047 53 047 WA Okanogan NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053049 53 049 WA Pacific AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953051 53 051 WA Pend Oreille NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053053 53 053 WA Pierce AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953055 53 055 WA San Juan AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953057 53 057 WA Skagit AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953059 53 059 WA Skamania AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953061 53 061 WA Snohomish AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953063 53 063 WA Spokane NIPER-9 13.4 258 0.0 9.7 3.3 8.3 304 0.0 0.0 0.053065 53 065 WA Stevens NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053067 53 067 WA Thurston AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953069 53 069 WA Wahkiakum AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953071 53 071 WA Walla Walla NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053073 53 073 WA Whatcom AAM-SE 14.3 270 0.0 2.5 0.9 7.8 329 0.0 2.7 0.953075 53 075 WA Whitman NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.053077 53 077 WA Yakima NIPER-9 12.7 196 0.0 0.0 0.0 8.3 304 0.0 0.0 0.056001 56 001 WY Albany NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056003 56 003 WY Big Horn NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056005 56 005 WY Campbell NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056007 56 007 WY Carbon NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056009 56 009 WY Converse NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056011 56 011 WY Crook NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056013 56 013 WY Fremont NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056015 56 015 WY Goshen NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056017 56 017 WY Hot Springs NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056019 56 019 WY Johnson NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056021 56 021 WY Laramie NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056023 56 023 WY Lincoln NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056025 56 025 WY Natrona NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056027 56 027 WY Niobrara NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.0Winter 2002 Gasoline PropertiesSummer 2002 Gasoline PropertiesStateCountyDataRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCountySource(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)56029 56 029 WY Park NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056031 56 031 WY Platte NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056033 56 033 WY Sheridan NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056035 56 035 WY Sublette NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056037 56 037 WY Sweetwater NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056039 56 039 WY Teton NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056041 56 041 WY Uinta NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056043 56 043 WY Washakie NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.056045 56 045 WY Weston NIPER-9 12.7 192 0.0 0.0 0.0 8.3 297 0.0 0.0 0.0Spring and fall gasoline properties by county.Spring 2002 Gasoline PropertiesFall 2002 Gasoline PropertiesStateCountyRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCounty(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)02013 02 013 AK Aleutians East B 14.5 145 0.0 0.0 0.0 14.5 145 0.0 0.0 0.002016 02 016 AK Aleutians West C 14.5 145 0.0 0.0 0.0 14.5 145 0.0 0.0 0.002020 02 020 AK Anchorage Ed 14.5 140 0.0 0.0 0.0 14.5 140 0.0 0.0 0.002050 02 050 AK Bethel Ed 14.5 145 0.0 0.0 0.0 14.5 145 0.0 0.0 0.002060 02 060 AK Bristol Bay Borough 14.5 145 0.0 0.0 0.0 14.5 145 0.0 0.0 0.002068 02 068 AK Denali Borough 14.5 145 0.0 0.0 0.0 14.5 145 0.0 0.0 0.002070 02 070 AK Dillingham Ed 14.5 145 0.0 0.0 0.0 14.5 145 0.0 0.0 0.002090 02 090 AK Fairbanks Ed 14.5 148 0.0 0.0 0.0 14.5 148 0.0 0.0 0.002100 02 100 AK Haines Ed 13.7 333 0.0 0.0 0.0 13.7 333 0.0 0.0 0.002110 02 110 AK Juneau Ed 13.7 333 0.0 0.0 0.0 13.7 333 0.0 0.0 0.002122 02 122 AK Kenai Penninsula 14.5 148 0.0 0.0 0.0 14.5 148 0.0 0.0 0.002130 02 130 AK Ketchikan Ed 13.7 333 0.0 0.0 0.0 13.7 333 0.0 0.0 0.002150 02 150 AK Kodiak Island Ed 14.5 145 0.0 0.0 0.0 14.5 145 0.0 0.0 0.002164 02 164 AK Lake and Peninsu 14.5 145 0.0 0.0 0.0 14.5 145 0.0 0.0 0.002170 02 170 AK Matanuska-Susitna 14.5 148 0.0 0.0 0.0 14.5 148 0.0 0.0 0.002180 02 180 AK Nome Ed 14.5 145 0.0 0.0 0.0 14.5 145 0.0 0.0 0.002185 02 185 AK North Slope Ed 14.5 145 0.0 0.0 0.0 14.5 145 0.0 0.0 0.002188 02 188 AK Northwest Arctic 14.5 145 0.0 0.0 0.0 14.5 145 0.0 0.0 0.002201 02 201 AK Prince Of Wales 13.7 333 0.0 0.0 0.0 13.7 333 0.0 0.0 0.002220 02 220 AK Sitka Ed 13.7 333 0.0 0.0 0.0 13.7 333 0.0 0.0 0.002232 02 232 AK Skagway-Yakutat 13.7 333 0.0 0.0 0.0 13.7 333 0.0 0.0 0.002240 02 240 AK Southeast Fairbanks 14.5 148 0.0 0.0 0.0 14.5 148 0.0 0.0 0.002261 02 261 AK Valdez-Cordova E 14.5 148 0.0 0.0 0.0 14.5 148 0.0 0.0 0.002270 02 270 AK Wade Hampton Ed 14.5 145 0.0 0.0 0.0 14.5 145 0.0 0.0 0.002280 02 280 AK Wrangell-Petersburg 13.7 333 0.0 0.0 0.0 13.7 333 0.0 0.0 0.002282 02 282 AK Yakutat Borough 14.5 145 0.0 0.0 0.0 14.5 145 0.0 0.0 0.002290 02 290 AK Yukon-Koyukuk Ed 14.5 145 0.0 0.0 0.0 14.5 145 0.0 0.0 0.004001 04 001 AZ Apache 8.7 251 0.0 0.0 0.0 9.3 251 0.0 0.0 0.004003 04 003 AZ Cochise 8.7 251 0.0 0.0 0.0 8.7 251 0.0 0.0 0.004005 04 005 AZ Coconino 8.7 33 8.1 0.0 1.4 8.7 33 8.1 0.0 1.404007 04 007 AZ Gila 8.7 33 8.1 0.0 1.4 8.7 33 8.1 0.0 1.404009 04 009 AZ Graham 8.7 251 0.0 0.0 0.0 8.7 251 0.0 0.0 0.004011 04 011 AZ Greenlee 8.7 251 0.0 0.0 0.0 8.7 251 0.0 0.0 0.004012 04 012 AZ La Paz 8.7 33 8.1 0.0 1.4 8.7 33 8.1 0.0 1.404013 04 013 AZ Maricopa 8.7 52 9.1 0.0 1.6 8.7 52 9.1 0.0 1.604015 04 015 AZ Mohave 8.7 33 8.1 0.0 1.4 8.7 33 8.1 0.0 1.404017 04 017 AZ Navajo 8.7 251 0.0 0.0 0.0 9.3 251 0.0 0.0 0.004019 04 019 AZ Pima 8.7 33 8.1 0.0 1.4 8.7 33 8.1 0.0 1.404021 04 021 AZ Pinal 8.7 33 8.1 0.0 1.4 8.7 33 8.1 0.0 1.404023 04 023 AZ Santa Cruz 8.7 251 0.0 0.0 0.0 8.7 251 0.0 0.0 0.004025 04 025 AZ Yavapai 8.7 33 8.1 0.0 1.4 8.7 33 8.1 0.0 1.404027 04 027 AZ Yuma 8.7 33 8.1 0.0 1.4 8.7 33 8.1 0.0 1.406001 06 001 CA Alameda 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306003 06 003 CA Alpine 8.7 10 5.0 1.2 1.3 9.5 10 5.0 1.2 1.306005 06 005 CA Amador 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.3Spring 2002 Gasoline PropertiesFall 2002 Gasoline PropertiesStateCountyRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCounty(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)06007 06 007 CA Butte 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306009 06 009 CA Calaveras 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306011 06 011 CA Colusa 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306013 06 013 CA Contra Costa 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306015 06 015 CA Del Norte 8.7 10 5.0 1.2 1.3 9.5 10 5.0 1.2 1.306017 06 017 CA El Dorado 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306019 06 019 CA Fresno 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306021 06 021 CA Glenn 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306023 06 023 CA Humboldt 8.7 10 5.0 1.2 1.3 9.5 10 5.0 1.2 1.306025 06 025 CA Imperial 7.0 15 10.4 0.6 2.1 7.0 15 10.4 0.6 2.106027 06 027 CA Inyo 8.7 15 10.4 0.6 2.1 8.7 15 10.4 0.6 2.106029 06 029 CA Kern 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306031 06 031 CA Kings 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306033 06 033 CA Lake 8.7 10 5.0 1.2 1.3 9.5 10 5.0 1.2 1.306035 06 035 CA Lassen 8.7 10 5.0 1.2 1.3 9.5 10 5.0 1.2 1.306037 06 037 CA Los Angeles 7.0 15 10.4 0.6 2.1 7.0 15 10.4 0.6 2.106039 06 039 CA Madera 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306041 06 041 CA Marin 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306043 06 043 CA Mariposa 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306045 06 045 CA Mendocino 8.7 10 5.0 1.2 1.3 9.5 10 5.0 1.2 1.306047 06 047 CA Merced 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306049 06 049 CA Modoc 8.7 10 5.0 1.2 1.3 9.5 10 5.0 1.2 1.306051 06 051 CA Mono 8.7 10 5.0 1.2 1.3 8.7 10 5.0 1.2 1.306053 06 053 CA Monterey 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306055 06 055 CA Napa 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306057 06 057 CA Nevada 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306059 06 059 CA Orange 7.0 15 10.4 0.6 2.1 7.0 15 10.4 0.6 2.106061 06 061 CA Placer 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306063 06 063 CA Plumas 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306065 06 065 CA Riverside 7.0 15 10.4 0.6 2.1 7.0 15 10.4 0.6 2.106067 06 067 CA Sacramento 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306069 06 069 CA San Benito 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306071 06 071 CA San Bernardino 7.0 15 10.4 0.6 2.1 7.0 15 10.4 0.6 2.106073 06 073 CA San Diego 7.0 15 10.4 0.6 2.1 7.0 15 10.4 0.6 2.106075 06 075 CA San Francisco 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306077 06 077 CA San Joaquin 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306079 06 079 CA San Luis Obispo 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306081 06 081 CA San Mateo 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306083 06 083 CA Santa Barbara 8.7 15 10.4 0.6 2.1 7.0 15 10.4 0.6 2.106085 06 085 CA Santa Clara 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306087 06 087 CA Santa Cruz 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306089 06 089 CA Shasta 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306091 06 091 CA Sierra 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306093 06 093 CA Siskiyou 8.7 10 5.0 1.2 1.3 9.5 10 5.0 1.2 1.306095 06 095 CA Solano 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306097 06 097 CA Sonoma 8.7 10 5.0 1.2 1.3 9.5 10 5.0 1.2 1.3Spring 2002 Gasoline PropertiesFall 2002 Gasoline PropertiesStateCountyRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCounty(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)06099 06 099 CA Stanislaus 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306101 06 101 CA Sutter 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306103 06 103 CA Tehama 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306105 06 105 CA Trinity 8.7 10 5.0 1.2 1.3 9.5 10 5.0 1.2 1.306107 06 107 CA Tulare 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306109 06 109 CA Tuolumne 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306111 06 111 CA Ventura 8.7 15 10.4 0.6 2.1 7.0 15 10.4 0.6 2.106113 06 113 CA Yolo 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.306115 06 115 CA Yuba 8.7 10 5.0 1.2 1.3 7.1 10 5.0 1.2 1.308001 08 001 CO Adams 9.0 158 0.0 4.2 1.4 9.9 158 0.0 4.2 1.408003 08 003 CO Alamosa 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008005 08 005 CO Arapahoe 9.0 158 0.0 4.2 1.4 9.9 158 0.0 4.2 1.408007 08 007 CO Archuleta 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008009 08 009 CO Baca 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008011 08 011 CO Bent 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008013 08 013 CO Boulder 9.0 158 0.0 4.2 1.4 9.9 158 0.0 4.2 1.408015 08 015 CO Chaffee 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008017 08 017 CO Cheyenne 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008019 08 019 CO Clear Creek 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008021 08 021 CO Conejos 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008023 08 023 CO Costilla 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008025 08 025 CO Crowley 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008027 08 027 CO Custer 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008029 08 029 CO Delta 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008031 08 031 CO Denver 9.0 158 0.0 4.2 1.4 9.9 158 0.0 4.2 1.408033 08 033 CO Dolores 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008035 08 035 CO Douglas 9.0 158 0.0 4.2 1.4 9.9 158 0.0 4.2 1.408037 08 037 CO Eagle 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008039 08 039 CO Elbert 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008041 08 041 CO El Paso 9.0 158 0.0 4.2 1.4 9.9 158 0.0 4.2 1.408043 08 043 CO Fremont 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008045 08 045 CO Garfield 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008047 08 047 CO Gilpin 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008049 08 049 CO Grand 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008051 08 051 CO Gunnison 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008053 08 053 CO Hinsdale 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008055 08 055 CO Huerfano 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008057 08 057 CO Jackson 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008059 08 059 CO Jefferson 9.0 158 0.0 4.2 1.4 9.9 158 0.0 4.2 1.408061 08 061 CO Kiowa 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008063 08 063 CO Kit Carson 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008065 08 065 CO Lake 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008067 08 067 CO La Plata 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008069 08 069 CO Larimer 9.0 158 0.0 4.2 1.4 9.9 158 0.0 4.2 1.408071 08 071 CO Las Animas 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008073 08 073 CO Lincoln 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.0Spring 2002 Gasoline PropertiesFall 2002 Gasoline PropertiesStateCountyRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCounty(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)08075 08 075 CO Logan 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008077 08 077 CO Mesa 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008079 08 079 CO Mineral 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008081 08 081 CO Moffat 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008083 08 083 CO Montezuma 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008085 08 085 CO Montrose 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008087 08 087 CO Morgan 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008089 08 089 CO Otero 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008091 08 091 CO Ouray 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008093 08 093 CO Park 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008095 08 095 CO Phillips 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008097 08 097 CO Pitkin 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008099 08 099 CO Prowers 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008101 08 101 CO Pueblo 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008103 08 103 CO Rio Blanco 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008105 08 105 CO Rio Grande 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008107 08 107 CO Routt 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008109 08 109 CO Saguache 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008111 08 111 CO San Juan 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008113 08 113 CO San Miguel 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008115 08 115 CO Sedgwick 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008117 08 117 CO Summit 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008119 08 119 CO Teller 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008121 08 121 CO Washington 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008123 08 123 CO Weld 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.008125 08 125 CO Yuma 8.7 175 0.0 0.0 0.0 9.4 175 0.0 0.0 0.016001 16 001 ID Ada 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016003 16 003 ID Adams 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016005 16 005 ID Bannock 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016007 16 007 ID Bear Lake 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016009 16 009 ID Benewah 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.016011 16 011 ID Bingham 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016013 16 013 ID Blaine 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016015 16 015 ID Boise 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016017 16 017 ID Bonner 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.016019 16 019 ID Bonneville 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016021 16 021 ID Boundary 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.016023 16 023 ID Butte 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016025 16 025 ID Camas 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016027 16 027 ID Canyon 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016029 16 029 ID Caribou 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016031 16 031 ID Cassia 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016033 16 033 ID Clark 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016035 16 035 ID Clearwater 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.016037 16 037 ID Custer 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016039 16 039 ID Elmore 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.0Spring 2002 Gasoline PropertiesFall 2002 Gasoline PropertiesStateCountyRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCounty(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)16041 16 041 ID Franklin 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016043 16 043 ID Fremont 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016045 16 045 ID Gem 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016047 16 047 ID Gooding 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016049 16 049 ID Idaho 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.016051 16 051 ID Jefferson 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016053 16 053 ID Jerome 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016055 16 055 ID Kootenai 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.016057 16 057 ID Latah 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.016059 16 059 ID Lemhi 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016061 16 061 ID Lewis 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.016063 16 063 ID Lincoln 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016065 16 065 ID Madison 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016067 16 067 ID Minidoka 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016069 16 069 ID Nez Perce 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.016071 16 071 ID Oneida 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016073 16 073 ID Owyhee 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016075 16 075 ID Payette 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016077 16 077 ID Power 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016079 16 079 ID Shoshone 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.016081 16 081 ID Teton 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016083 16 083 ID Twin Falls 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016085 16 085 ID Valley 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.016087 16 087 ID Washington 8.7 250 0.0 0.0 0.0 9.4 250 0.0 0.0 0.030001 30 001 MT Beaverhead 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030003 30 003 MT Big Horn 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030005 30 005 MT Blaine 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030007 30 007 MT Broadwater 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030009 30 009 MT Carbon 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030011 30 011 MT Carter 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030013 30 013 MT Cascade 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030015 30 015 MT Chouteau 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030017 30 017 MT Custer 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030019 30 019 MT Daniels 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030021 30 021 MT Dawson 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030023 30 023 MT Deer Lodge 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030025 30 025 MT Fallon 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030027 30 027 MT Fergus 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030029 30 029 MT Flat Head 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030031 30 031 MT Gallatin 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030033 30 033 MT Garfield 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030035 30 035 MT Glacier 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030037 30 037 MT Golden Valley 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030039 30 039 MT Granite 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030041 30 041 MT Hill 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030043 30 043 MT Jefferson 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.0Spring 2002 Gasoline PropertiesFall 2002 Gasoline PropertiesStateCountyRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCounty(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)30045 30 045 MT Judith Basin 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030047 30 047 MT Lake 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030049 30 049 MT Lewis and Clark 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030051 30 051 MT Liberty 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030053 30 053 MT Lincoln 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030055 30 055 MT Mc Cone 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030057 30 057 MT Madison 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030059 30 059 MT Meagher 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030061 30 061 MT Mineral 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030063 30 063 MT Missoula 8.7 377 0.0 0.0 0.0 10.9 377 0.0 0.0 0.030065 30 065 MT Musselshell 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030067 30 067 MT Park 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030069 30 069 MT Petroleum 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030071 30 071 MT Phillips 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030073 30 073 MT Pondera 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030075 30 075 MT Powder River 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030077 30 077 MT Powell 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030079 30 079 MT Prairie 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030081 30 081 MT Ravalli 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030083 30 083 MT Richland 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030085 30 085 MT Roosevelt 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030087 30 087 MT Rosebud 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030089 30 089 MT Sanders 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030091 30 091 MT Sheridan 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030093 30 093 MT Silver Bow 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030095 30 095 MT Stillwater 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030097 30 097 MT Sweet Grass 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030099 30 099 MT Teton 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030101 30 101 MT Toole 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030103 30 103 MT Treasure 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030105 30 105 MT Valley 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030107 30 107 MT Wheatland 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030109 30 109 MT Wibaux 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030111 30 111 MT Yellowstone 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.030113 30 113 MT Yellowstone Park 8.7 397 0.0 0.0 0.0 10.9 397 0.0 0.0 0.032001 32 001 NV Churchill 8.7 23 2.9 0.0 0.5 9.3 23 2.9 0.0 0.532003 32 003 NV Clark 8.7 23 0.2 0.0 0.0 8.7 23 0.2 0.0 0.032005 32 005 NV Douglas 8.7 23 2.9 0.0 0.5 9.3 23 2.9 0.0 0.532007 32 007 NV Elko 8.7 23 2.9 0.0 0.5 9.3 23 2.9 0.0 0.532009 32 009 NV Esmeralda 8.7 23 0.2 0.0 0.0 8.7 23 0.2 0.0 0.032011 32 011 NV Eureka 8.7 23 2.9 0.0 0.5 9.3 23 2.9 0.0 0.532013 32 013 NV Humboldt 8.7 23 2.9 0.0 0.5 9.3 23 2.9 0.0 0.532015 32 015 NV Lander 8.7 23 2.9 0.0 0.5 9.3 23 2.9 0.0 0.532017 32 017 NV Lincoln 8.7 23 0.2 0.0 0.0 8.7 23 0.2 0.0 0.032019 32 019 NV Lyon 8.7 23 2.9 0.0 0.5 9.3 23 2.9 0.0 0.532021 32 021 NV Mineral 8.7 23 0.2 0.0 0.0 8.7 23 0.2 0.0 0.0Spring 2002 Gasoline PropertiesFall 2002 Gasoline PropertiesStateCountyRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCounty(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)32023 32 023 NV Nye 8.7 23 0.2 0.0 0.0 8.7 23 0.2 0.0 0.032027 32 027 NV Pershing 8.7 23 2.9 0.0 0.5 9.3 23 2.9 0.0 0.532029 32 029 NV Storey 8.7 23 2.9 0.0 0.5 9.3 23 2.9 0.0 0.532031 32 031 NV Washoe 8.7 23 2.9 0.0 0.5 9.6 23 2.9 0.0 0.532033 32 033 NV White Pine 8.7 23 2.9 0.0 0.5 9.3 23 2.9 0.0 0.532510 32 510 NV Carson City 8.7 23 2.9 0.0 0.5 9.3 23 2.9 0.0 0.535001 35 001 NM Bernalillo 8.8 205 2.0 2.0 1.0 9.7 205 2.0 2.0 1.035003 35 003 NM Catron 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035005 35 005 NM Chaves 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035006 35 006 NM Cibola 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035007 35 007 NM Colfax 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035009 35 009 NM Curry 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035011 35 011 NM De Baca 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035013 35 013 NM Dona Ana 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035015 35 015 NM Eddy 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035017 35 017 NM Grant 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035019 35 019 NM Guadalupe 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035021 35 021 NM Harding 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035023 35 023 NM Hidalgo 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035025 35 025 NM Lea 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035027 35 027 NM Lincoln 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035028 35 028 NM Los Alamos 8.8 205 2.0 2.0 1.0 9.7 205 2.0 2.0 1.035029 35 029 NM Luna 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035031 35 031 NM Mc Kinley 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035033 35 033 NM Mora 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035035 35 035 NM Otero 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035037 35 037 NM Quay 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035039 35 039 NM Rio Arriba 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035041 35 041 NM Roosevelt 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035043 35 043 NM Sandoval 8.8 205 2.0 2.0 1.0 9.7 205 2.0 2.0 1.035045 35 045 NM San Juan 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035047 35 047 NM San Miguel 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035049 35 049 NM Santa Fe 8.8 205 2.0 2.0 1.0 9.7 205 2.0 2.0 1.035051 35 051 NM Sierra 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035053 35 053 NM Socorro 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035055 35 055 NM Taos 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035057 35 057 NM Torrance 8.8 205 2.0 2.0 1.0 9.7 205 2.0 2.0 1.035059 35 059 NM Union 8.7 253 0.0 0.0 0.0 9.3 253 0.0 0.0 0.035061 35 061 NM Valencia 8.8 205 2.0 2.0 1.0 9.7 205 2.0 2.0 1.038001 38 001 ND Adams 8.7 417 0.0 0.0 0.0 10.9 417 0.0 0.0 0.038003 38 003 ND Barnes 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038005 38 005 ND Benson 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038007 38 007 ND Billings 8.7 417 0.0 0.0 0.0 10.9 417 0.0 0.0 0.038009 38 009 ND Bottineau 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038011 38 011 ND Bowman 8.7 417 0.0 0.0 0.0 10.9 417 0.0 0.0 0.038013 38 013 ND Burke 8.7 417 0.0 0.0 0.0 10.9 417 0.0 0.0 0.0Spring 2002 Gasoline PropertiesFall 2002 Gasoline PropertiesStateCountyRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCounty(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)38015 38 015 ND Burleigh 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038017 38 017 ND Cass 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038019 38 019 ND Cavalier 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038021 38 021 ND Dickey 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038023 38 023 ND Divide 8.7 417 0.0 0.0 0.0 10.9 417 0.0 0.0 0.038025 38 025 ND Dunn 8.7 417 0.0 0.0 0.0 10.9 417 0.0 0.0 0.038027 38 027 ND Eddy 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038029 38 029 ND Emmons 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038031 38 031 ND Foster 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038033 38 033 ND Golden Valley 8.7 417 0.0 0.0 0.0 10.9 417 0.0 0.0 0.038035 38 035 ND Grand Forks 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038037 38 037 ND Grant 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038039 38 039 ND Griggs 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038041 38 041 ND Hettinger 8.7 417 0.0 0.0 0.0 10.9 417 0.0 0.0 0.038043 38 043 ND Kidder 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038045 38 045 ND La Moure 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038047 38 047 ND Logan 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038049 38 049 ND Mc Henry 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038051 38 051 ND Mc Intosh 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038053 38 053 ND Mc Kenzie 8.7 417 0.0 0.0 0.0 10.9 417 0.0 0.0 0.038055 38 055 ND Mc Lean 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038057 38 057 ND Mercer 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038059 38 059 ND Morton 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038061 38 061 ND Mountrail 8.7 417 0.0 0.0 0.0 10.9 417 0.0 0.0 0.038063 38 063 ND Nelson 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038065 38 065 ND Oliver 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038067 38 067 ND Pembina 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038069 38 069 ND Pierce 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038071 38 071 ND Ramsey 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038073 38 073 ND Ransom 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038075 38 075 ND Renville 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038077 38 077 ND Richland 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038079 38 079 ND Rolette 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038081 38 081 ND Sargent 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038083 38 083 ND Sheridan 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038085 38 085 ND Sioux 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038087 38 087 ND Slope 8.7 417 0.0 0.0 0.0 10.9 417 0.0 0.0 0.038089 38 089 ND Stark 8.7 417 0.0 0.0 0.0 10.9 417 0.0 0.0 0.038091 38 091 ND Steele 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038093 38 093 ND Stutsman 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038095 38 095 ND Towner 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038097 38 097 ND Traill 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038099 38 099 ND Walsh 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038101 38 101 ND Ward 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038103 38 103 ND Wells 8.7 245 0.0 0.0 0.0 10.6 245 0.0 0.0 0.038105 38 105 ND Williams 8.7 417 0.0 0.0 0.0 10.9 417 0.0 0.0 0.0Spring 2002 Gasoline PropertiesFall 2002 Gasoline PropertiesStateCountyRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCounty(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)41001 41 001 OR Baker 8.7 254 0.0 0.0 0.0 10.4 254 0.0 0.0 0.041003 41 003 OR Benton 8.7 318 0.0 0.0 0.0 10.8 318 0.0 0.0 0.041005 41 005 OR Clackamas 8.7 238 0.0 0.0 0.0 11.0 238 0.0 0.0 0.041007 41 007 OR Clatsop 8.7 318 0.0 0.0 0.0 10.8 318 0.0 0.0 0.041009 41 009 OR Columbia 8.7 318 0.0 0.0 0.0 10.8 318 0.0 0.0 0.041011 41 011 OR Coos 8.7 318 0.0 0.0 0.0 10.8 318 0.0 0.0 0.041013 41 013 OR Crook 8.7 254 0.0 0.0 0.0 10.4 254 0.0 0.0 0.041015 41 015 OR Curry 8.7 318 0.0 0.0 0.0 10.8 318 0.0 0.0 0.041017 41 017 OR Deschutes 8.7 254 0.0 0.0 0.0 10.4 254 0.0 0.0 0.041019 41 019 OR Douglas 8.7 318 0.0 0.0 0.0 10.8 318 0.0 0.0 0.041021 41 021 OR Gilliam 8.7 254 0.0 0.0 0.0 10.4 254 0.0 0.0 0.041023 41 023 OR Grant 8.7 254 0.0 0.0 0.0 10.4 254 0.0 0.0 0.041025 41 025 OR Harney 8.7 254 0.0 0.0 0.0 10.4 254 0.0 0.0 0.041027 41 027 OR Hood River 8.7 254 0.0 0.0 0.0 10.4 254 0.0 0.0 0.041029 41 029 OR Jackson 8.7 238 0.0 0.0 0.0 11.0 238 0.0 0.0 0.041031 41 031 OR Jefferson 8.7 254 0.0 0.0 0.0 10.4 254 0.0 0.0 0.041033 41 033 OR Josephine 8.7 318 0.0 0.0 0.0 10.8 318 0.0 0.0 0.041035 41 035 OR Klamath 8.7 318 0.0 0.0 0.0 10.8 318 0.0 0.0 0.041037 41 037 OR Lake 8.7 254 0.0 0.0 0.0 10.4 254 0.0 0.0 0.041039 41 039 OR Lane 8.7 318 0.0 0.0 0.0 10.8 318 0.0 0.0 0.041041 41 041 OR Lincoln 8.7 318 0.0 0.0 0.0 10.8 318 0.0 0.0 0.041043 41 043 OR Linn 8.7 318 0.0 0.0 0.0 10.8 318 0.0 0.0 0.041045 41 045 OR Malheur 8.7 254 0.0 0.0 0.0 10.4 254 0.0 0.0 0.041047 41 047 OR Marion 8.7 318 0.0 0.0 0.0 10.8 318 0.0 0.0 0.041049 41 049 OR Morrow 8.7 254 0.0 0.0 0.0 10.4 254 0.0 0.0 0.041051 41 051 OR Multnomah 8.7 238 0.0 0.0 0.0 11.0 238 0.0 0.0 0.041053 41 053 OR Polk 8.7 318 0.0 0.0 0.0 10.8 318 0.0 0.0 0.041055 41 055 OR Sherman 8.7 254 0.0 0.0 0.0 10.4 254 0.0 0.0 0.041057 41 057 OR Tillamook 8.7 318 0.0 0.0 0.0 10.8 318 0.0 0.0 0.041059 41 059 OR Umatilla 8.7 254 0.0 0.0 0.0 10.4 254 0.0 0.0 0.041061 41 061 OR Union 8.7 254 0.0 0.0 0.0 10.4 254 0.0 0.0 0.041063 41 063 OR Wallowa 8.7 254 0.0 0.0 0.0 10.4 254 0.0 0.0 0.041065 41 065 OR Wasco 8.7 254 0.0 0.0 0.0 10.4 254 0.0 0.0 0.041067 41 067 OR Washington 8.7 238 0.0 0.0 0.0 11.0 238 0.0 0.0 0.041069 41 069 OR Wheeler 8.7 254 0.0 0.0 0.0 10.4 254 0.0 0.0 0.041071 41 071 OR Yamhill 8.7 238 0.0 0.0 0.0 11.0 238 0.0 0.0 0.046003 46 003 SD Aurora 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046005 46 005 SD Beadle 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046007 46 007 SD Bennett 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046009 46 009 SD Bon Homme 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046011 46 011 SD Brookings 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046013 46 013 SD Brown 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046015 46 015 SD Brule 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046017 46 017 SD Buffalo 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046019 46 019 SD Butte 8.7 259 0.0 0.0 0.0 9.4 259 0.0 0.0 0.046021 46 021 SD Campbell 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.0Spring 2002 Gasoline PropertiesFall 2002 Gasoline PropertiesStateCountyRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCounty(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)46023 46 023 SD Charles Mix 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046025 46 025 SD Clark 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046027 46 027 SD Clay 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046029 46 029 SD Codington 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046031 46 031 SD Corson 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046033 46 033 SD Custer 8.7 259 0.0 0.0 0.0 9.4 259 0.0 0.0 0.046035 46 035 SD Davison 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046037 46 037 SD Day 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046039 46 039 SD Deuel 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046041 46 041 SD Dewey 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046043 46 043 SD Douglas 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046045 46 045 SD Edmunds 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046047 46 047 SD Fall River 8.7 259 0.0 0.0 0.0 9.4 259 0.0 0.0 0.046049 46 049 SD Faulk 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046051 46 051 SD Grant 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046053 46 053 SD Gregory 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046055 46 055 SD Haakon 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046057 46 057 SD Hamlin 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046059 46 059 SD Hand 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046061 46 061 SD Hanson 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046063 46 063 SD Harding 8.7 259 0.0 0.0 0.0 9.4 259 0.0 0.0 0.046065 46 065 SD Hughes 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046067 46 067 SD Hutchinson 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046069 46 069 SD Hyde 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046071 46 071 SD Jackson 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046073 46 073 SD Jerauld 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046075 46 075 SD Jones 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046077 46 077 SD Kingsbury 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046079 46 079 SD Lake 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046081 46 081 SD Lawrence 8.7 259 0.0 0.0 0.0 9.4 259 0.0 0.0 0.046083 46 083 SD Lincoln 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046085 46 085 SD Lyman 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046087 46 087 SD Mc Cook 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046089 46 089 SD Mc Pherson 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046091 46 091 SD Marshall 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046093 46 093 SD Meade 8.7 259 0.0 0.0 0.0 9.4 259 0.0 0.0 0.046095 46 095 SD Mellette 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046097 46 097 SD Miner 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046099 46 099 SD Minnehaha 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046101 46 101 SD Moody 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046103 46 103 SD Pennington 8.7 259 0.0 0.0 0.0 9.4 259 0.0 0.0 0.046105 46 105 SD Perkins 8.7 259 0.0 0.0 0.0 9.4 259 0.0 0.0 0.046107 46 107 SD Potter 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046109 46 109 SD Roberts 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046111 46 111 SD Sanborn 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046113 46 113 SD Shannon 8.7 259 0.0 0.0 0.0 9.4 259 0.0 0.0 0.0Spring 2002 Gasoline PropertiesFall 2002 Gasoline PropertiesStateCountyRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCounty(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)46115 46 115 SD Spink 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046117 46 117 SD Stanley 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046119 46 119 SD Sully 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046121 46 121 SD Todd 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046123 46 123 SD Tripp 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046125 46 125 SD Turner 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046127 46 127 SD Union 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046129 46 129 SD Walworth 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046135 46 135 SD Yankton 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.046137 46 137 SD Ziebach 8.7 243 0.0 0.0 0.0 9.5 243 0.0 0.0 0.049001 49 001 UT Beaver 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049003 49 003 UT Box Elder 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049005 49 005 UT Cache 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049007 49 007 UT Carbon 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049009 49 009 UT Daggett 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049011 49 011 UT Davis 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049013 49 013 UT Duchesne 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049015 49 015 UT Emery 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049017 49 017 UT Garfield 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049019 49 019 UT Grand 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049021 49 021 UT Iron 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049023 49 023 UT Juab 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049025 49 025 UT Kane 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049027 49 027 UT Millard 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049029 49 029 UT Morgan 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049031 49 031 UT Piute 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049033 49 033 UT Rich 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049035 49 035 UT Salt Lake 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049037 49 037 UT San Juan 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049039 49 039 UT Sanpete 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049041 49 041 UT Sevier 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049043 49 043 UT Summit 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049045 49 045 UT Tooele 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049047 49 047 UT Uintah 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049049 49 049 UT Utah 8.7 184 0.0 0.0 0.0 9.6 184 0.0 0.0 0.049051 49 051 UT Wasatch 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049053 49 053 UT Washington 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049055 49 055 UT Wayne 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.049057 49 057 UT Weber 8.7 173 0.0 0.0 0.0 9.4 173 0.0 0.0 0.053001 53 001 WA Adams 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053003 53 003 WA Asotin 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053005 53 005 WA Benton 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053007 53 007 WA Chelan 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053009 53 009 WA Clallam 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053011 53 011 WA Clark 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953013 53 013 WA Columbia 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.0Spring 2002 Gasoline PropertiesFall 2002 Gasoline PropertiesStateCountyRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCounty(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)53015 53 015 WA Cowlitz 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953017 53 017 WA Douglas 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053019 53 019 WA Ferry 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053021 53 021 WA Franklin 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053023 53 023 WA Garfield 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053025 53 025 WA Grant 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053027 53 027 WA Grays Harbor 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953029 53 029 WA Island 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953031 53 031 WA Jefferson 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953033 53 033 WA King 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953035 53 035 WA Kitsap 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953037 53 037 WA Kittitas 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053039 53 039 WA Klickitat 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053041 53 041 WA Lewis 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953043 53 043 WA Lincoln 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053045 53 045 WA Mason 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953047 53 047 WA Okanogan 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053049 53 049 WA Pacific 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953051 53 051 WA Pend Oreille 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053053 53 053 WA Pierce 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953055 53 055 WA San Juan 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953057 53 057 WA Skagit 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953059 53 059 WA Skamania 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953061 53 061 WA Snohomish 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953063 53 063 WA Spokane 8.7 281 0.0 0.0 0.0 10.7 281 0.0 0.0 0.053065 53 065 WA Stevens 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053067 53 067 WA Thurston 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953069 53 069 WA Wahkiakum 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953071 53 071 WA Walla Walla 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053073 53 073 WA Whatcom 8.7 299 0.0 2.7 0.9 11.0 299 0.0 2.7 0.953075 53 075 WA Whitman 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.053077 53 077 WA Yakima 8.7 250 0.0 0.0 0.0 10.4 250 0.0 0.0 0.056001 56 001 WY Albany 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056003 56 003 WY Big Horn 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056005 56 005 WY Campbell 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056007 56 007 WY Carbon 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056009 56 009 WY Converse 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056011 56 011 WY Crook 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056013 56 013 WY Fremont 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056015 56 015 WY Goshen 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056017 56 017 WY Hot Springs 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056019 56 019 WY Johnson 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056021 56 021 WY Laramie 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056023 56 023 WY Lincoln 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056025 56 025 WY Natrona 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056027 56 027 WY Niobrara 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.0Spring 2002 Gasoline PropertiesFall 2002 Gasoline PropertiesStateCountyRVPSulfurMTBEEtOHOxygenRVPSulfurMTBEEtOHOxygenFIPSNo.No.StateCounty(psi)(ppm)(vol%)(vol%)(wt%)(psi)(ppm)(vol%)(vol%)(wt%)56029 56 029 WY Park 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056031 56 031 WY Platte 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056033 56 033 WY Sheridan 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056035 56 035 WY Sublette 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056037 56 037 WY Sweetwater 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056039 56 039 WY Teton 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056041 56 041 WY Uinta 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056043 56 043 WY Washakie 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.056045 56 045 WY Weston 8.7 244 0.0 0.0 0.0 9.4 244 0.0 0.0 0.0On-road and off-road diesel fuel properties by county and season.2002 Diesel Fuel Sulfur Levels (ppm)2002 Diesel Fuel Sulfur Levels (ppm)StateCountyDataOn-HighwayDataOff-HighwayFIPSNo.No.StateCountySourceWinterSummerSpringFallSourceWinterSummerSpringFall02013 02 013 AK Aleutians East B ADEC-RRL 750 750 750 750 ADEC-RRL 750 750 750 75002016 02 016 AK Aleutians West C ADEC-RRL 750 750 750 750 ADEC-RRL 750 750 750 75002020 02 020 AK Anchorage Ed ADEC-URB 750 2500 750 750 ADEC-URB 750 2500 750 75002050 02 050 AK Bethel Ed ADEC-RRL 750 750 750 750 ADEC-RRL 750 750 750 75002060 02 060 AK Bristol Bay Borough ADEC-RRL 750 750 750 750 ADEC-RRL 750 750 750 75002068 02 068 AK Denali Borough ADEC-RRL 750 750 750 750 ADEC-RRL 750 750 750 75002070 02 070 AK Dillingham Ed ADEC-RRL 750 750 750 750 ADEC-RRL 750 750 750 75002090 02 090 AK Fairbanks Ed ADEC-URB 750 2500 750 750 ADEC-URB 750 2500 750 75002100 02 100 AK Haines Ed AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340002110 02 110 AK Juneau Ed AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340002122 02 122 AK Kenai Penninsula ADEC-URB 750 2500 750 750 ADEC-URB 750 2500 750 75002130 02 130 AK Ketchikan Ed AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340002150 02 150 AK Kodiak Island Ed ADEC-RRL 750 750 750 750 ADEC-RRL 750 750 750 75002164 02 164 AK Lake and Peninsu ADEC-RRL 750 750 750 750 ADEC-RRL 750 750 750 75002170 02 170 AK Matanuska-Susitna ADEC-URB 750 2500 750 750 ADEC-URB 750 2500 750 75002180 02 180 AK Nome Ed ADEC-RRL 750 750 750 750 ADEC-RRL 750 750 750 75002185 02 185 AK North Slope Ed ADEC-RRL 750 750 750 750 ADEC-RRL 750 750 750 75002188 02 188 AK Northwest Arctic ADEC-RRL 750 750 750 750 ADEC-RRL 750 750 750 75002201 02 201 AK Prince Of Wales AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340002220 02 220 AK Sitka Ed AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340002232 02 232 AK Skagway-Yakutat AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340002240 02 240 AK Southeast Fairbanks ADEC-URB 750 2500 750 750 ADEC-URB 750 2500 750 75002261 02 261 AK Valdez-Cordova E ADEC-URB 750 2500 750 750 ADEC-URB 750 2500 750 75002270 02 270 AK Wade Hampton Ed ADEC-RRL 750 750 750 750 ADEC-RRL 750 750 750 75002280 02 280 AK Wrangell-Petersburg AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340002282 02 282 AK Yakutat Borough ADEC-RRL 750 750 750 750 ADEC-RRL 750 750 750 75002290 02 290 AK Yukon-Koyukuk Ed ADEC-RRL 750 750 750 750 ADEC-RRL 750 750 750 75004001 04 001 AZ Apache NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240004003 04 003 AZ Cochise NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240004005 04 005 AZ Coconino NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340004007 04 007 AZ Gila NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340004009 04 009 AZ Graham NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240004011 04 011 AZ Greenlee NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240004012 04 012 AZ La Paz NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340004013 04 013 AZ Maricopa NIPER-N 360 360 360 360 NIPER-N 360 360 360 36004015 04 015 AZ Mohave NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340004017 04 017 AZ Navajo NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240004019 04 019 AZ Pima NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340004021 04 021 AZ Pinal NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340004023 04 023 AZ Santa Cruz NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240004025 04 025 AZ Yavapai NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340004027 04 027 AZ Yuma NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340006001 06 001 CA Alameda AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506003 06 003 CA Alpine AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506005 06 005 CA Amador AAM-LA 70 60 65 65 AAM-LA 70 60 65 652002 Diesel Fuel Sulfur Levels (ppm)2002 Diesel Fuel Sulfur Levels (ppm)StateCountyDataOn-HighwayDataOff-HighwayFIPSNo.No.StateCountySourceWinterSummerSpringFallSourceWinterSummerSpringFall06007 06 007 CA Butte AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506009 06 009 CA Calaveras AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506011 06 011 CA Colusa AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506013 06 013 CA Contra Costa AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506015 06 015 CA Del Norte AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506017 06 017 CA El Dorado AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506019 06 019 CA Fresno AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506021 06 021 CA Glenn AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506023 06 023 CA Humboldt AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506025 06 025 CA Imperial AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506027 06 027 CA Inyo AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506029 06 029 CA Kern AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506031 06 031 CA Kings AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506033 06 033 CA Lake AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506035 06 035 CA Lassen AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506037 06 037 CA Los Angeles AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506039 06 039 CA Madera AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506041 06 041 CA Marin AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506043 06 043 CA Mariposa AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506045 06 045 CA Mendocino AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506047 06 047 CA Merced AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506049 06 049 CA Modoc AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506051 06 051 CA Mono AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506053 06 053 CA Monterey AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506055 06 055 CA Napa AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506057 06 057 CA Nevada AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506059 06 059 CA Orange AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506061 06 061 CA Placer AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506063 06 063 CA Plumas AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506065 06 065 CA Riverside AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506067 06 067 CA Sacramento AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506069 06 069 CA San Benito AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506071 06 071 CA San Bernardino AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506073 06 073 CA San Diego AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506075 06 075 CA San Francisco AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506077 06 077 CA San Joaquin AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506079 06 079 CA San Luis Obispo AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506081 06 081 CA San Mateo AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506083 06 083 CA Santa Barbara AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506085 06 085 CA Santa Clara AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506087 06 087 CA Santa Cruz AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506089 06 089 CA Shasta AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506091 06 091 CA Sierra AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506093 06 093 CA Siskiyou AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506095 06 095 CA Solano AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506097 06 097 CA Sonoma AAM-LA 70 60 65 65 AAM-LA 70 60 65 652002 Diesel Fuel Sulfur Levels (ppm)2002 Diesel Fuel Sulfur Levels (ppm)StateCountyDataOn-HighwayDataOff-HighwayFIPSNo.No.StateCountySourceWinterSummerSpringFallSourceWinterSummerSpringFall06099 06 099 CA Stanislaus AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506101 06 101 CA Sutter AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506103 06 103 CA Tehama AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506105 06 105 CA Trinity AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506107 06 107 CA Tulare AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506109 06 109 CA Tuolumne AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506111 06 111 CA Ventura AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506113 06 113 CA Yolo AAM-LA 70 60 65 65 AAM-LA 70 60 65 6506115 06 115 CA Yuba AAM-LA 70 60 65 65 AAM-LA 70 60 65 6508001 08 001 CO Adams AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008003 08 003 CO Alamosa AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008005 08 005 CO Arapahoe AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008007 08 007 CO Archuleta AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008009 08 009 CO Baca AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008011 08 011 CO Bent AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008013 08 013 CO Boulder AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008015 08 015 CO Chaffee AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008017 08 017 CO Cheyenne AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008019 08 019 CO Clear Creek AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008021 08 021 CO Conejos AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008023 08 023 CO Costilla AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008025 08 025 CO Crowley AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008027 08 027 CO Custer AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008029 08 029 CO Delta AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008031 08 031 CO Denver AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008033 08 033 CO Dolores AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008035 08 035 CO Douglas AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008037 08 037 CO Eagle AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008039 08 039 CO Elbert AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008041 08 041 CO El Paso AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008043 08 043 CO Fremont AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008045 08 045 CO Garfield AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008047 08 047 CO Gilpin AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008049 08 049 CO Grand AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008051 08 051 CO Gunnison AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008053 08 053 CO Hinsdale AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008055 08 055 CO Huerfano AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008057 08 057 CO Jackson AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008059 08 059 CO Jefferson AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008061 08 061 CO Kiowa AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008063 08 063 CO Kit Carson AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008065 08 065 CO Lake AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008067 08 067 CO La Plata AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008069 08 069 CO Larimer AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008071 08 071 CO Las Animas AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008073 08 073 CO Lincoln AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 24002002 Diesel Fuel Sulfur Levels (ppm)2002 Diesel Fuel Sulfur Levels (ppm)StateCountyDataOn-HighwayDataOff-HighwayFIPSNo.No.StateCountySourceWinterSummerSpringFallSourceWinterSummerSpringFall08075 08 075 CO Logan AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008077 08 077 CO Mesa AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008079 08 079 CO Mineral AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008081 08 081 CO Moffat AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008083 08 083 CO Montezuma AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008085 08 085 CO Montrose AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008087 08 087 CO Morgan AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008089 08 089 CO Otero AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008091 08 091 CO Ouray AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008093 08 093 CO Park AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008095 08 095 CO Phillips AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008097 08 097 CO Pitkin AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008099 08 099 CO Prowers AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008101 08 101 CO Pueblo AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008103 08 103 CO Rio Blanco AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008105 08 105 CO Rio Grande AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008107 08 107 CO Routt AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008109 08 109 CO Saguache AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008111 08 111 CO San Juan AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008113 08 113 CO San Miguel AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008115 08 115 CO Sedgwick AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008117 08 117 CO Summit AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008119 08 119 CO Teller AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008121 08 121 CO Washington AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008123 08 123 CO Weld AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240008125 08 125 CO Yuma AAM-DN 330 330 330 330 NIPER-RM 2400 2400 2400 240016001 16 001 ID Ada NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016003 16 003 ID Adams NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016005 16 005 ID Bannock NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016007 16 007 ID Bear Lake NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016009 16 009 ID Benewah NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016011 16 011 ID Bingham NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016013 16 013 ID Blaine NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016015 16 015 ID Boise NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016017 16 017 ID Bonner NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016019 16 019 ID Bonneville NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016021 16 021 ID Boundary NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016023 16 023 ID Butte NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016025 16 025 ID Camas NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016027 16 027 ID Canyon NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016029 16 029 ID Caribou NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016031 16 031 ID Cassia NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016033 16 033 ID Clark NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016035 16 035 ID Clearwater NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016037 16 037 ID Custer NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016039 16 039 ID Elmore NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 24002002 Diesel Fuel Sulfur Levels (ppm)2002 Diesel Fuel Sulfur Levels (ppm)StateCountyDataOn-HighwayDataOff-HighwayFIPSNo.No.StateCountySourceWinterSummerSpringFallSourceWinterSummerSpringFall16041 16 041 ID Franklin NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016043 16 043 ID Fremont NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016045 16 045 ID Gem NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016047 16 047 ID Gooding NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016049 16 049 ID Idaho NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016051 16 051 ID Jefferson NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016053 16 053 ID Jerome NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016055 16 055 ID Kootenai NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016057 16 057 ID Latah NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016059 16 059 ID Lemhi NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016061 16 061 ID Lewis NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016063 16 063 ID Lincoln NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016065 16 065 ID Madison NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016067 16 067 ID Minidoka NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016069 16 069 ID Nez Perce NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016071 16 071 ID Oneida NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016073 16 073 ID Owyhee NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016075 16 075 ID Payette NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016077 16 077 ID Power NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016079 16 079 ID Shoshone NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016081 16 081 ID Teton NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016083 16 083 ID Twin Falls NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016085 16 085 ID Valley NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240016087 16 087 ID Washington NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240030001 30 001 MT Beaverhead AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030003 30 003 MT Big Horn AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030005 30 005 MT Blaine AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030007 30 007 MT Broadwater AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030009 30 009 MT Carbon AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030011 30 011 MT Carter AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030013 30 013 MT Cascade AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030015 30 015 MT Chouteau AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030017 30 017 MT Custer AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030019 30 019 MT Daniels AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030021 30 021 MT Dawson AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030023 30 023 MT Deer Lodge AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030025 30 025 MT Fallon AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030027 30 027 MT Fergus AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030029 30 029 MT Flat Head AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030031 30 031 MT Gallatin AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030033 30 033 MT Garfield AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030035 30 035 MT Glacier AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030037 30 037 MT Golden Valley AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030039 30 039 MT Granite AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030041 30 041 MT Hill AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030043 30 043 MT Jefferson AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 24002002 Diesel Fuel Sulfur Levels (ppm)2002 Diesel Fuel Sulfur Levels (ppm)StateCountyDataOn-HighwayDataOff-HighwayFIPSNo.No.StateCountySourceWinterSummerSpringFallSourceWinterSummerSpringFall30045 30 045 MT Judith Basin AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030047 30 047 MT Lake AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030049 30 049 MT Lewis and Clark AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030051 30 051 MT Liberty AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030053 30 053 MT Lincoln AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030055 30 055 MT Mc Cone AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030057 30 057 MT Madison AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030059 30 059 MT Meagher AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030061 30 061 MT Mineral AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030063 30 063 MT Missoula AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030065 30 065 MT Musselshell AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030067 30 067 MT Park AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030069 30 069 MT Petroleum AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030071 30 071 MT Phillips AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030073 30 073 MT Pondera AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030075 30 075 MT Powder River AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030077 30 077 MT Powell AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030079 30 079 MT Prairie AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030081 30 081 MT Ravalli AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030083 30 083 MT Richland AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030085 30 085 MT Roosevelt AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030087 30 087 MT Rosebud AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030089 30 089 MT Sanders AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030091 30 091 MT Sheridan AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030093 30 093 MT Silver Bow AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030095 30 095 MT Stillwater AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030097 30 097 MT Sweet Grass AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030099 30 099 MT Teton AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030101 30 101 MT Toole AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030103 30 103 MT Treasure AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030105 30 105 MT Valley AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030107 30 107 MT Wheatland AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030109 30 109 MT Wibaux AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030111 30 111 MT Yellowstone AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240030113 30 113 MT Yellowstone Park AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240032001 32 001 NV Churchill NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340032003 32 003 NV Clark NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340032005 32 005 NV Douglas NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340032007 32 007 NV Elko NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340032009 32 009 NV Esmeralda NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340032011 32 011 NV Eureka NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340032013 32 013 NV Humboldt NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340032015 32 015 NV Lander NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340032017 32 017 NV Lincoln NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340032019 32 019 NV Lyon NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340032021 32 021 NV Mineral NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 34002002 Diesel Fuel Sulfur Levels (ppm)2002 Diesel Fuel Sulfur Levels (ppm)StateCountyDataOn-HighwayDataOff-HighwayFIPSNo.No.StateCountySourceWinterSummerSpringFallSourceWinterSummerSpringFall32023 32 023 NV Nye NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340032027 32 027 NV Pershing NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340032029 32 029 NV Storey NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340032031 32 031 NV Washoe NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340032033 32 033 NV White Pine NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340032510 32 510 NV Carson City NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340035001 35 001 NM Bernalillo AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035003 35 003 NM Catron AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035005 35 005 NM Chaves AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035006 35 006 NM Cibola AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035007 35 007 NM Colfax AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035009 35 009 NM Curry AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035011 35 011 NM De Baca AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035013 35 013 NM Dona Ana AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035015 35 015 NM Eddy AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035017 35 017 NM Grant AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035019 35 019 NM Guadalupe AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035021 35 021 NM Harding AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035023 35 023 NM Hidalgo AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035025 35 025 NM Lea AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035027 35 027 NM Lincoln AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035028 35 028 NM Los Alamos AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035029 35 029 NM Luna AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035031 35 031 NM Mc Kinley AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035033 35 033 NM Mora AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035035 35 035 NM Otero AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035037 35 037 NM Quay AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035039 35 039 NM Rio Arriba AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035041 35 041 NM Roosevelt AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035043 35 043 NM Sandoval AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035045 35 045 NM San Juan AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035047 35 047 NM San Miguel AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035049 35 049 NM Santa Fe AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035051 35 051 NM Sierra AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035053 35 053 NM Socorro AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035055 35 055 NM Taos AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035057 35 057 NM Torrance AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035059 35 059 NM Union AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240035061 35 061 NM Valencia AAM-ALB 330 350 340 340 NIPER-RM 2400 2400 2400 240038001 38 001 ND Adams AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240038003 38 003 ND Barnes NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038005 38 005 ND Benson NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038007 38 007 ND Billings AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240038009 38 009 ND Bottineau NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038011 38 011 ND Bowman AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240038013 38 013 ND Burke AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 24002002 Diesel Fuel Sulfur Levels (ppm)2002 Diesel Fuel Sulfur Levels (ppm)StateCountyDataOn-HighwayDataOff-HighwayFIPSNo.No.StateCountySourceWinterSummerSpringFallSourceWinterSummerSpringFall38015 38 015 ND Burleigh NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038017 38 017 ND Cass NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038019 38 019 ND Cavalier NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038021 38 021 ND Dickey NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038023 38 023 ND Divide AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240038025 38 025 ND Dunn AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240038027 38 027 ND Eddy NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038029 38 029 ND Emmons NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038031 38 031 ND Foster NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038033 38 033 ND Golden Valley AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240038035 38 035 ND Grand Forks NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038037 38 037 ND Grant NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038039 38 039 ND Griggs NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038041 38 041 ND Hettinger AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240038043 38 043 ND Kidder NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038045 38 045 ND La Moure NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038047 38 047 ND Logan NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038049 38 049 ND Mc Henry NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038051 38 051 ND Mc Intosh NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038053 38 053 ND Mc Kenzie AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240038055 38 055 ND Mc Lean NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038057 38 057 ND Mercer NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038059 38 059 ND Morton NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038061 38 061 ND Mountrail AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240038063 38 063 ND Nelson NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038065 38 065 ND Oliver NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038067 38 067 ND Pembina NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038069 38 069 ND Pierce NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038071 38 071 ND Ramsey NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038073 38 073 ND Ransom NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038075 38 075 ND Renville NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038077 38 077 ND Richland NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038079 38 079 ND Rolette NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038081 38 081 ND Sargent NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038083 38 083 ND Sheridan NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038085 38 085 ND Sioux NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038087 38 087 ND Slope AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240038089 38 089 ND Stark AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 240038091 38 091 ND Steele NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038093 38 093 ND Stutsman NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038095 38 095 ND Towner NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038097 38 097 ND Traill NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038099 38 099 ND Walsh NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038101 38 101 ND Ward NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038103 38 103 ND Wells NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371038105 38 105 ND Williams AAM-BL 270 250 260 260 NIPER-RM 2400 2400 2400 24002002 Diesel Fuel Sulfur Levels (ppm)2002 Diesel Fuel Sulfur Levels (ppm)StateCountyDataOn-HighwayDataOff-HighwayFIPSNo.No.StateCountySourceWinterSummerSpringFallSourceWinterSummerSpringFall41001 41 001 OR Baker NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340041003 41 003 OR Benton AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041005 41 005 OR Clackamas AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041007 41 007 OR Clatsop AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041009 41 009 OR Columbia AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041011 41 011 OR Coos AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041013 41 013 OR Crook NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340041015 41 015 OR Curry AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041017 41 017 OR Deschutes NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340041019 41 019 OR Douglas AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041021 41 021 OR Gilliam NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340041023 41 023 OR Grant NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340041025 41 025 OR Harney NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340041027 41 027 OR Hood River NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340041029 41 029 OR Jackson AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041031 41 031 OR Jefferson NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340041033 41 033 OR Josephine AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041035 41 035 OR Klamath AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041037 41 037 OR Lake NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340041039 41 039 OR Lane AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041041 41 041 OR Lincoln AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041043 41 043 OR Linn AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041045 41 045 OR Malheur NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340041047 41 047 OR Marion AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041049 41 049 OR Morrow NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340041051 41 051 OR Multnomah AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041053 41 053 OR Polk AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041055 41 055 OR Sherman NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340041057 41 057 OR Tillamook AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041059 41 059 OR Umatilla NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340041061 41 061 OR Union NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340041063 41 063 OR Wallowa NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340041065 41 065 OR Wasco NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340041067 41 067 OR Washington AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340041069 41 069 OR Wheeler NIPER-M 370 370 370 370 NIPER-W 3400 3400 3400 340041071 41 071 OR Yamhill AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340046003 46 003 SD Aurora NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046005 46 005 SD Beadle NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046007 46 007 SD Bennett NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046009 46 009 SD Bon Homme NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046011 46 011 SD Brookings NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046013 46 013 SD Brown NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046015 46 015 SD Brule NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046017 46 017 SD Buffalo NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046019 46 019 SD Butte NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240046021 46 021 SD Campbell NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 37102002 Diesel Fuel Sulfur Levels (ppm)2002 Diesel Fuel Sulfur Levels (ppm)StateCountyDataOn-HighwayDataOff-HighwayFIPSNo.No.StateCountySourceWinterSummerSpringFallSourceWinterSummerSpringFall46023 46 023 SD Charles Mix NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046025 46 025 SD Clark NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046027 46 027 SD Clay NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046029 46 029 SD Codington NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046031 46 031 SD Corson NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046033 46 033 SD Custer NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240046035 46 035 SD Davison NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046037 46 037 SD Day NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046039 46 039 SD Deuel NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046041 46 041 SD Dewey NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046043 46 043 SD Douglas NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046045 46 045 SD Edmunds NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046047 46 047 SD Fall River NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240046049 46 049 SD Faulk NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046051 46 051 SD Grant NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046053 46 053 SD Gregory NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046055 46 055 SD Haakon NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046057 46 057 SD Hamlin NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046059 46 059 SD Hand NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046061 46 061 SD Hanson NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046063 46 063 SD Harding NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240046065 46 065 SD Hughes NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046067 46 067 SD Hutchinson NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046069 46 069 SD Hyde NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046071 46 071 SD Jackson NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046073 46 073 SD Jerauld NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046075 46 075 SD Jones NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046077 46 077 SD Kingsbury NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046079 46 079 SD Lake NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046081 46 081 SD Lawrence NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240046083 46 083 SD Lincoln NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046085 46 085 SD Lyman NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046087 46 087 SD Mc Cook NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046089 46 089 SD Mc Pherson NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046091 46 091 SD Marshall NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046093 46 093 SD Meade NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240046095 46 095 SD Mellette NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046097 46 097 SD Miner NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046099 46 099 SD Minnehaha NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046101 46 101 SD Moody NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046103 46 103 SD Pennington NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240046105 46 105 SD Perkins NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240046107 46 107 SD Potter NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046109 46 109 SD Roberts NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046111 46 111 SD Sanborn NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046113 46 113 SD Shannon NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 24002002 Diesel Fuel Sulfur Levels (ppm)2002 Diesel Fuel Sulfur Levels (ppm)StateCountyDataOn-HighwayDataOff-HighwayFIPSNo.No.StateCountySourceWinterSummerSpringFallSourceWinterSummerSpringFall46115 46 115 SD Spink NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046117 46 117 SD Stanley NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046119 46 119 SD Sully NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046121 46 121 SD Todd NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046123 46 123 SD Tripp NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046125 46 125 SD Turner NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046127 46 127 SD Union NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046129 46 129 SD Walworth NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046135 46 135 SD Yankton NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371046137 46 137 SD Ziebach NIPER-C 360 360 360 360 NIPER-C 3710 3710 3710 371049001 49 001 UT Beaver NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340049003 49 003 UT Box Elder NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049005 49 005 UT Cache NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049007 49 007 UT Carbon NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049009 49 009 UT Daggett NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049011 49 011 UT Davis NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049013 49 013 UT Duchesne NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049015 49 015 UT Emery NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049017 49 017 UT Garfield NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340049019 49 019 UT Grand NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049021 49 021 UT Iron NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340049023 49 023 UT Juab NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049025 49 025 UT Kane NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340049027 49 027 UT Millard NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049029 49 029 UT Morgan NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049031 49 031 UT Piute NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340049033 49 033 UT Rich NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049035 49 035 UT Salt Lake NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049037 49 037 UT San Juan NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049039 49 039 UT Sanpete NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049041 49 041 UT Sevier NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049043 49 043 UT Summit NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049045 49 045 UT Tooele NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049047 49 047 UT Uintah NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049049 49 049 UT Utah NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049051 49 051 UT Wasatch NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049053 49 053 UT Washington NIPER-N 360 360 360 360 NIPER-W 3400 3400 3400 340049055 49 055 UT Wayne NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240049057 49 057 UT Weber NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240053001 53 001 WA Adams NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053003 53 003 WA Asotin NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053005 53 005 WA Benton NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053007 53 007 WA Chelan NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053009 53 009 WA Clallam NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053011 53 011 WA Clark AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053013 53 013 WA Columbia NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 34002002 Diesel Fuel Sulfur Levels (ppm)2002 Diesel Fuel Sulfur Levels (ppm)StateCountyDataOn-HighwayDataOff-HighwayFIPSNo.No.StateCountySourceWinterSummerSpringFallSourceWinterSummerSpringFall53015 53 015 WA Cowlitz AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053017 53 017 WA Douglas NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053019 53 019 WA Ferry NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053021 53 021 WA Franklin NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053023 53 023 WA Garfield NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053025 53 025 WA Grant NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053027 53 027 WA Grays Harbor AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053029 53 029 WA Island AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053031 53 031 WA Jefferson AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053033 53 033 WA King AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053035 53 035 WA Kitsap AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053037 53 037 WA Kittitas NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053039 53 039 WA Klickitat NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053041 53 041 WA Lewis AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053043 53 043 WA Lincoln NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053045 53 045 WA Mason AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053047 53 047 WA Okanogan NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053049 53 049 WA Pacific AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053051 53 051 WA Pend Oreille NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053053 53 053 WA Pierce AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053055 53 055 WA San Juan AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053057 53 057 WA Skagit AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053059 53 059 WA Skamania AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053061 53 061 WA Snohomish AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053063 53 063 WA Spokane NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053065 53 065 WA Stevens NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053067 53 067 WA Thurston AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053069 53 069 WA Wahkiakum AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053071 53 071 WA Walla Walla NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053073 53 073 WA Whatcom AAM-SE 360 340 350 350 NIPER-W 3400 3400 3400 340053075 53 075 WA Whitman NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340053077 53 077 WA Yakima NIPER-L 390 390 390 390 NIPER-W 3400 3400 3400 340056001 56 001 WY Albany NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056003 56 003 WY Big Horn NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056005 56 005 WY Campbell NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056007 56 007 WY Carbon NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056009 56 009 WY Converse NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056011 56 011 WY Crook NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056013 56 013 WY Fremont NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056015 56 015 WY Goshen NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056017 56 017 WY Hot Springs NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056019 56 019 WY Johnson NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056021 56 021 WY Laramie NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056023 56 023 WY Lincoln NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056025 56 025 WY Natrona NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056027 56 027 WY Niobrara NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 24002002 Diesel Fuel Sulfur Levels (ppm)2002 Diesel Fuel Sulfur Levels (ppm)StateCountyDataOn-HighwayDataOff-HighwayFIPSNo.No.StateCountySourceWinterSummerSpringFallSourceWinterSummerSpringFall56029 56 029 WY Park NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056031 56 031 WY Platte NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056033 56 033 WY Sheridan NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056035 56 035 WY Sublette NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056037 56 037 WY Sweetwater NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056039 56 039 WY Teton NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056041 56 041 WY Uinta NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056043 56 043 WY Washakie NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 240056045 56 045 WY Weston NIPER-RM 350 350 350 350 NIPER-RM 2400 2400 2400 2400

### Appendix B

Geographic Characterization of Ship Em issions 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 Obse rvations 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 pollutant s emitted from marine vessels account for a significant portion of emissions i nventory and contribute to air quality problems in many coastal and metropolitan regions, and globally (1, 2). Better understanding is n eeded of the spatial distribution of ship activities and emissions to as sess 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 r outes, and ship activities (3) . Geographically characterizing the actual positions of the components of the MTS is critical to better unde rstand 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 th eir spatial distribution, the activity level of the pollution source must be determined, emi ssions 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 atmos pheric scientists, pollution modelers, and policy makers to evaluate potential imp acts of pollution on the environment and human

health.

Attempts to geographically assign internati onal shipping traffic began nearly a decade ago. Corbett et al. geographically resolved gl obal inventory of ship emissions by deriving international shipping traffic intensities fr om voluntarily reporte d, ship-based weather

observations (1, 3, 4) . Olivier et al. assigned global emissions estimates to major shipping lanes (5) . These approaches showed qualitatively goo d agreement with regionally resolved ship

inventories (6, 7) , but resolution differences or inconsis tent assumptions prevented reconciling geographic assignment of ship activ ity among different spatial scales (1) . Recently, Endresen et al. presented a new and perhaps more accura te methodology for globally assigning emissions from international marine bunkers, which is a significant contribut ion. They argued that emissions can be allocated more accurately acro ss ship traffic profiles by using vessel size to weight the alloca tion to each grid (1, 8) . Regionally, ENTEC presen ted the distribution of shipping emissions over the European Monito ring and Evaluation Pr ogramme (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 approach es for geographic charac terization of marine vessel emissions developed by researchers a nd published in pee r-reviewed literature (1, 3, 4)

. Recent developments and possible improve ments have been taken into account (8, 10, 11)

. Efforts have been made in this work to addre ss potential statistical a nd 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 deve lop 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 incl ude 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 coor dinates for the four corn ers of the raster files are: SW corner (-2736, -2088), NW corner (-273 6, 1944), SE corner (-216, -2088), NE corner (- 216, 1944).

The approach employed in this work is illust rated in Figure 1. We adopted global ship

emissions inventories co-d eveloped by one of the researchers ofthis work and published in peerreviewed journal (1) . Adjustments have been made to refl ect 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 co ast ship traffic and emissions. Efforts have been made to improve the representa tiveness of ICOADS of the emi ssions from commercial cargo fleet by selecting only observations made by cargo ships, trimming several ships with exceptionally high reporting biases, and weighti ng observations by ship power, a major factor that determines the magnitude of ship air emissi ons 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 emi ssions. 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 larges t database of commercially-availab le 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 sh ip 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 inve ntory 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 characteriza tion of ship emissions with the approach for this work (the other prerequisites include a good representation of ship traffi c pattern and intensity, and a good ship dataset). There are, however, significant di fferences 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 significan t 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 cons ume international marine fuels, and increased previous ship emissions inventories for polluta nts, 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 Ko ehler 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 assu mptions suggested by Endresen et al. into their activity-based model, such as operating hour s, 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 ag reement 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 revise d inputs suggested by Endr esen 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 th e uncertainty of the results of this model. We

believe that model results under these revised in puts data currently represent the best global

emissions inventories and were a dopted 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 deri ving data from Lloyd’s Register of Ships. The attributes in the dataset include unique ship id entifier, which associates vessel type and other characteristics used to analyze the representati veness of the ICOADS fleet of the world fleet. This information also can be used for the analys is of emissions by ship segments, and ship total main engine power, which is used to wei ght 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 eval uate individual ship obs ervation frequencies in ICOADS. All ships included in our dataset have valid total main engine power data.

Totally 15,048 unique vessel identifiers are incl uded 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 unders tand that this ship data set 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 classi fied into nine groups, including container ship, tanker, bulk carrier, general cargo shi p, 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 lo cation and intensity the fleet ac tivity must be depicted. This can be done using sampled data representing flee twide spatial activity, an d theoretically would

### be most accurate if all ships reported their loca tions regularly. There are two data sets, ICOADS

and Automated Mutual-assistance Vessel Rescue system (AMVER), which have been used by researchers as proxies of globa l ship traffic to geographically resolve the global emissions

inventories (3, 4, 8) . Although there is some overlap be tween the COADS and AMVER datasets

with about 33% of the AMVER vessels are weathe r observation vessels, diffe rent data sets lead to highly different regional pe rturbations of air pollutants (8) . This analysis uses vessel traffic patterns derived from ICOADS to represent spatial distributio n of shipping lanes and vessel traffic on those shipping lanes. In this sec tion, 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 seve ral research institutes. ICOADS data is collected primarily from ships (merchan t, ocean research, fishing, navy, etc) and moored and drifting buoys. The dataset contains the iden tifier 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 Coas t Guard, is a global sh ip reporting system used worldwide by search and rescue authorities to arrange for assistance to persons in distress at sea. Participation is fr ee, voluntary, and open to merchant ship s 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 outsid e the normal criteria, such as cruise ships, research vessels and fish proce ssors. Participating ships are exp ected to regularly report their positions to AMVER to improve the chance that th e ship closest to the position of distress be

identified (16)

. Table 2. Summary of ICOADS

Year Total Obs. Identified ships 1 Obs. by
identified

ships Cargo Ships 2 Obs. by

cargo ships Cargo Ships

after trimming 3 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 Regi ster, we identified 2,564 unique ships report to ICOADS in year 2002, about 2,177 of which ar e 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 dec line 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 “del ayed 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 th e 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. Ultimatel y, combining these overlapping data sets may also improve the quality of data for this purpose.



## Addressing Sampling Bias

Since ship type, size, and pow er can be identified for i ndividual ICOADS observations by matching ICOADS ship identifie rs with ship registry inform ation, and since ship emissions can be distributed accordingly to take into acco unt 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 th e use a combination of three-year (2000-2002) ICOADS samples, the geographic representativene ss of ICOADS of ship traffic and emissions can be improved. Detailed discussions ar e 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 cove ring the areas with stationary

vessels (8) . They also observed that ICOADS perhap s 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 resear ch, 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 emi ssions inventory to represent only these commercial marine vessels. (T his 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 fr equent reporting by a fe w vessels. Figure 2 shows that five (or 0.2%) of the 2,453 cargo ve ssels made 7.20% of the ICOADS observations by cargo vessels from 2000 to 2002. Most of thes e observations were made in the northern Atlantic. We believe these ships are probably resp onsible for much of the over-sampling bias in the Atlantic, which would directly affect the qua lity of West Coast i nventories (potentially underestimating emissions in the West). We trimmed these over-reported cargo vessels to

address this bias. We chose a natural break be tween 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 shipsCumulative 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 be st reflects the international cargo fleet by vessel types, size, and repor ting frequency, followed by Purp le Finder (PF), and ICOADS (8) ; however, they compared observations in AM VER 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, partic ularly liquid and dry bulk cargo ships, perhaps because participation in AMVER has been generally limited to me rchant ships of all flags over 1000 GT, on a voyage of 24 hours or longer (8) . Our analysis indicate s 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 repres ent the world ship traffic as ch aracterized by international fleet registries. Of course, comb ining AMVER and ICOADS data se ts 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 cont ainer ship and refrigerated cargo (i.e., reefer) traffic – especially cont ainer ship traffic – and that ICOADS under-sampled general cargo ship and tanker traffic – particularly genera l cargo ship traffic. By comparison, AMVER oversampled bulk carrier, tanker, and co ntainer ship traffic, especia lly 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 ReeferPercent of total

ICOADS AMVER World Cargo Fleet 2004 World Cargo Fleet 1996, 2000



### Figure 3

. Comparison between world cargo fl eet profile and ICOADS and AMVER observations by ship type. ICOADS as three -year (2000-2002) data with five over-reported vessels trimmed; AMVER as d erived from Endresen et al. (8) ; World cargo fleet 2004 as derived from Lloyd's Ship Register versio n 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 signific antly fewer emissions to the region between 0°-20° north. Although the emissions assi gned to latitudes based on PF mostly fall between ICOADS and AMVER, we ca nnot conclude that PF better represents the spatial distribution of ship traffic sinc e 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, base d 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 Spercent of total

ICOADS 00-02 ICOADS 00-02P COADS 96 PF AMVER AMVER GT



### Figure 4. Comparison of relative distribution of emi ssion 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-rep orted vessels trimmed; ICOADS 00-02P as three-year (2000-2002) number of cargo vessel obs ervations 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 d erived 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 cer tain 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 obser vations and associate them with actual power, tonnage, etc., using ship registry databases en ables 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 comparis on 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 clearl y 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 anal ysis of twenty years of monthly resolved ICOADS data demonstrat es 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 wo rld 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 conditi ons 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 ve ssels of the same type is large and nonrandom. This can be quantified and corrected at a global level th rough simple weighting adjustments.

4. Variability in reporting fre quency 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 (2000-20 02) are all located in the North Atlantic.

5. Items 2 and 4 require more advanced adjustme nt and/or validation with independent data samples or comprehensive traffic analysis.

Geographically resolved emissions invent ories can be quickly produced based on

ICOADS or AMVER. We understand that neith er dataset is SPATIALLY RANDOM, since international trade and shipping is clearly not random. We woul d not favor one version over the other arbitrarily without valida ting using plotted routes base d 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 pr otected and used only in a bona fide maritime emergency as claimed by AMVER (16) . Only one or two researcher s, all of them are non-U.S. researchers, have been given access to AMVE R data for research purposes under special agreement. Given the difficulty of obtaining AMVE R 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 ov er-reporting and non-response ships.



# Choosing Coordinate System and Resolution

Generally there are two types of coordinate system: Geographic C oordinate Systems and Projected Coordinate Systems. C oordinate system can be chosen based on needs, including the extent of the map, the spatial at tributes that should be preserve d most, etc. The ESRI ArcGIS offers many coordinate system s that we can choose from.

Maps produced with different projected c oordinate systems look different. The map projection process will cause distortions in one or more of the following spatial properties: distance, area, shape, and dire ction. Although no projection can pr eserve 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 expens e 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. pol ar region or equatorial region?)

• What shape is the area you’re mapping? Is it square? Is it wi der in the east–west

direction?

• How big is the area you’re mapping? On la rge-scale maps, such as street maps, distortion may be negligible because your ma p 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 ea rth, distortion may have a bigg er 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, whic h 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 e ffects of different means that c ould potentially to some extent correct the statistical and geogr aphical sampling bias of ICOADS and improve the accuracy of the geographic distribution of ship emissions.



## Evaluating the Effects of Using Three-Y ear 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 condi tion is that temporal ch anges 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 statis tical and geographical sampling bias can be potentially reduced and, th erefore, the accuracy of the distribution of emissions can be potentially improved.

According to navigation knowledge and experi ence, ships travel al ong well-established shipping lanes between each pair of ports with certain deviation (20) . The fact that there are

established distances be tween different ports and well-unders tood by cargo logistics planners is good evidence that ships travel along well define d 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 patt ern does not change much annually. With these understandings, we didn’t anticipat e significant annual changes of the geographic distribution of global ship traffic, and accordingly the distribu tion of air emissions from shipping activities won’t change much annually under the assumpti on that air emissions are proportional to the intensity of ship traffic which can be weighted if possible.

Figure 5a shows the distribution of marine ve ssel nitrogen emissions inventory based on ICOADS 2002 data weighted by ship power. Fi gure 5b shows the distribution based on a combination of three-year (2000-2002) data al so weighted by ship power. They look quite similar except that shipping lanes in Figure 5b lo ok smoother and emissions are assigned to more

### grids. Given the facts that the number of obs ervations almost tripled in the three-year combination dataset, and the change of geographic distribution of ship tra ffic 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 inventor y 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 thre e-year data by subtracting the gridded raster data for Figure 5a and Figure 5b. Figure 6a show s 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 repor t 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 th e map. This phenomenon may be caused by

inherent randomness of ship reporting.

### (a) (b) Figure 6. a) Comparison between the distribut ion 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 re solution 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 sma ller than with the finer resolution. This suggests that using three-year data improve s the quality of the spatial repres entation, and that the effect of using three-year data on a region al scale significantly larger than the grid size is not significant.

We confirmed that multi-year ICOADS da ta didn’t change much the geographic

distribution of the emission inve ntories by evaluating the whole west coast is one grid; the difference in total emissions is be only about 3% . With more data in th e 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, w ith the correction of th e randomness of ship reporting on shipping lanes, and on the other han d, 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. W ith this understanding, we decided to use the combination of three year (2000-2002) data as th e proxy of the distributi on of ship traffic.

### (a) (b) Figure 7. a) Comparison between the distribut ion 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 to tal 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 ki lowatts. 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 pe rhaps improved the accur acy of the distribution of ship emissions. In this sect ion, we discuss the effects of us ing ship engine power, which is more directly related to ship emi ssions, to weight the observations.

### (a) (b)

Figure 8. a) Comparison between the distribut ion of ship nitrogen emission with power weighted and non-weighted method; and b) P ercent 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. Fi gure 8a, which was produced by subtracting nonweighted emission distribution from the powe r-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 w ith 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 emissi ons assigned to ocean-shipping lanes while

decreases the emissions to coas tal routes. The powe r-weighted method incr eases the nitrogen emissions assigned to the area s howed in Figure 8a by about 18%.

Figure 8b was produced by dividi ng the raster data for Figur e 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 sh ip type. Observations of cargo vessels in ICOADS are used as a proxy of worl d ship traffic without adjustment.

With the facts that ships vary greatly in size, engine power, etc., and ships, primary container ships and tankers (see Fi gure 9), travel along the U.S. We st 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 ove rview 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 engi nes in the ICOADS sample.

2. Assign an emissions indicator to each obser vation using rated vessel power. When

we convert to a raster fo rmat, we sum these to iden tify which 36km x 36km gridded cells have the greatest contri bution to emissions accordingly.

3. Apply equation 1 to each grid cell to conve rt 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 pa ttern, and we distribute th e 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 be lieve the approach we use is simpler, with

### fewer assumptions, and can produ ce datasets with sufficient accu racy for this task and the purpose of this work.

We produced monthly emission inventories for 2002 for the six pollutants, including
NOx, SOx, CO 2 

### , CH 4 

### , PM 10 

### , and CO. The inventories are firs t bounded by the uncertainty of the global inventories with lower bound, best guess, and upper bound th ree 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 e i 

### as emissions of one pollutant from ce ll i in one period, year 2002 or one month of 2002 in this work; e g 

### as the global ship emissions invent ory for that pollutant in 2002; p g 

### is the sum of the total main engine power of ICOADS observations; p i 

### 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 pa rt of the name denotes the pollutant, where n for nitrogen, s for sulfur, c2 for CO 2 

### , hc for CH 4 

### , co for CO, and pm for PM 10 

### . 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 monthl y raster files has the fourth part, which denotes the month of the emissions. Per the request of the client, the raster file s were converted into AS CII files with ArcGIS

conversion tool. The name of th e 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 th e upper bound of the inventory for annual nitrogen emissions;
and co2carbon_lower_07.txt is the lo wer bound of the carbon emissions as CO 2 

### 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 deve lopments 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-repor ting and non-response ships to improve the accuracy of the products-geographically resolved emissions inventories.

After a review of current avai lable ship emissions inventorie s, we believe the inventories produced by Corbett and Koehler’s model with ad justed inputs are the best for this work. i

g g

i 

# p p e

e × =

### By comparing ICOADS with AMVER dataset, we understand that ne ither actual ship

traffic or either dataset is SPATIALLY RANDOM. We would not favor one version over the other arbitrarily without valida ting using plotted routes base d 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 si milar 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 inve ntories. With more data in the three-year dataset, the nonresponse bias can be reduced, the distribution may be statistically more re liable, and the shipping lanes look smoother with gaps being filled, and emissions are assigned perhaps more accurate.

With ship type, size, and power being iden tified 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 th e delayed mode data is blended into ICOADS. This could be a potential improvement in the future of the accur acy of geographically resolved ships emissions using ICOADS as a proxy.

Future effort (described in optional task s under this scope of work) includes deriving traffic profile from port activities data, like th e USACE Entrance and Clearance data to validate and/or recalibrate emissions prof iles according to shipping activiti es 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.



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### 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 Reefer Refrigerated Cargo Ship Container 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