2017 washington comprehensive emissions inventory

2017 Washington Comprehensive Emissions Inventory Technical Support Document

Prepared by

Farren Herron-Thorpe Tom Malamakal Sally Otterson

Air Quality Program Washington State Department of Ecology Olympia, Washington

Updated May 22, 2020Publication #20-02-012

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Publication and Contact Information This document is available on the Department of Ecology’s website at: https://fortress.wa.gov/ecy/publications/summarypages/2002012.html

For more information contact:

Air Quality Program P.O. Box 47600 Olympia, WA 98504-7600 Phone: 360-407-6800

Washington State Department of Ecology—www.ecology.wa.gov

• Headquarters, Olympia 360-407-6000

• Northwest Regional Office, Bellevue 425-649-7000

• Southwest Regional Office, Olympia 360-407-6300

• Central Regional Office, Union Gap 509-575-2490

• Eastern Regional Office, Spokane 509-329-3400

ADA Accessibility The Department of Ecology is committed to providing people with disabilities access to information and services by meeting or exceeding the requirements of the Americans with Disabilities Act (ADA), Section 504 and 508 of the Rehabilitation Act, and Washington State Policy #188.

To request an ADA accommodation, contact Ecology by phone at 360-407-6800 or email at [email protected]. For Washington Relay Service or TTY call 711 or 877-833-6341. Visit Ecology’s website for more information.

https://fortress.wa.gov/ecy/publications/summarypages/2002012.html

https://www.ecology.wa.gov/

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Correction Log

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List of Acronyms

ADT Average Daily Traffic

ADVMT Average Daily Vehicle Miles Traveled

AOP Air Operating Permit

AQPPS Air Quality Program Permitting System

CAA Clean Air Act

CAP Criteria Air Pollutant

CDL Cropland Data Layer

CRO Central Regional Office

DNR Department of Natural Resources

EF Emission Factor

EI Emissions Inventory

EPA Environmental Protection Agency

ERO Eastern Regional Office

HAP Hazardous Air Pollutant

HMS Hazard Mapping System

HPMS Highway Performance Monitoring System

ICI Industrial, Commercial, and Institutional

MOVES Motor Vehicle Emission Simulator

NAAQS National Ambient Air Quality Standards

NEC Not Elsewhere Classified

NEI National Emissions Inventory

OFM Office of Financial Management

OHU Occupied Housing Units

ORVR Onboard Refueling Vapor Recovery

PE Precipitation Effectiveness

PM Particulate Matter

POTW Publicly Owned Treatment Works

SIP State Implementation Plan

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SMARTFIRE Satellite Mapping Automatic Reanalysis Tool for Fire Incident Reconciliation

TPY Tons Per Year

TRI Toxics Release Inventory

ULSD Ultra-Low Sulfur Diesel

VMT Vehicle Miles Traveled

VOC Volatile Organic Compounds

VRS Vapor Recovery System

WRAP Western Regional Air Partnership

WSDOT Washington State Department of Transportation

WWTP Wastewater Treatment Plant

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Table of Contents 1 Introduction ........................................................................................................................ 1

1.1 Purpose and Background ............................................................................................ 1 1.2 Pollutants and Emissions Sources ............................................................................... 1 1.3 Spatial and Temporal Resolution ................................................................................. 2

2 Statistics Used Throughout the Inventory ........................................................................... 2 2.1 County Demographics ................................................................................................. 2 2.2 Meteorological Parameters .......................................................................................... 4

3 Base Year 2017 Emissions Estimates ................................................................................ 8 3.1 Point Sources .............................................................................................................. 8 3.2 Onroad Mobile Sources ............................................................................................... 9 3.3 Paved Road Dust .......................................................................................................14 3.4 Unpaved Road Dust ...................................................................................................18 3.5 Construction Dust .......................................................................................................22 3.6 Nonroad Mobile Vehicles and Equipment (NEC) ........................................................25 3.7 Locomotives ...............................................................................................................27 3.8 Ships ..........................................................................................................................29 3.9 Marine Cargo Handling Equipment .............................................................................30 3.10 Silvicultural Burning (Prescribed Burning) ...................................................................31 3.11 Wildfires......................................................................................................................33 3.12 Agricultural Burning ....................................................................................................36 3.13 Municipal Solid Waste Burning ...................................................................................40 3.14 Publicly Owned Treatment Works (POTW) .................................................................42 3.15 Gasoline Service Stations ...........................................................................................45 3.16 Residential Wood Combustion ....................................................................................48 3.17 Industrial and Commercial/Institutional (ICI) Fuel Use ................................................55 3.18 Agricultural Harvesting Operations .............................................................................57 3.19 Agricultural Tilling .......................................................................................................60 3.20 Structure & Vehicle Fires ............................................................................................64 3.21 Natural/Biogenic .........................................................................................................66 3.22 Emissions Calculated by EPA .....................................................................................68

4 Emissions Summaries .......................................................................................................72

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List of Tables Table 1-1. Emissions Category Pollutant Checklist ..................................................................................... 2 Table 2-1. Population and Occupied Housing Units .................................................................................... 3 Table 2-2. Meteorological Station County Assignments .............................................................................. 4 Table 2-3. Average Monthly Minimum Temperature (°Fahrenheit) ............................................................. 5 Table 2-4. Average Monthly Maximum Temperature (°Fahrenheit) ............................................................ 5 Table 2-5. Precipitation Days Greater Than 0.01 Inches ............................................................................. 6 Table 2-6. Monthly Precipitation (Inches) .................................................................................................... 6 Table 2-7. Monthly Heating Degree Days .................................................................................................... 7 Table 3-1. Average Daily VMT for Rural Roads......................................................................................... 10 Table 3-2. Average Daily VMT for Urban Roads ....................................................................................... 11 Table 3-3. Vehicle Population, 2017 .......................................................................................................... 12 Table 3-4. MOVES Model Parameters ...................................................................................................... 13 Table 3-5. WSDOT VMT Monthly Adjustment Factors .............................................................................. 14 Table 3-6. Silt Loading on Roads (grams per square meter) ...................................................................... 15 Table 3-7. Average Vehicle Weight for Rural and Urban Roads (Tons) .................................................... 15 Table 3-8. Average Daily VMT by ADT Class ............................................................................................ 16 Table 3-9. Paved Road Dust Emission Factors for “Dry Day” (grams per mile) ........................................ 17 Table 3-10. ADVMT on Unpaved Roads – Total of County and City Jurisdictions .................................... 19 Table 3-11. Unpaved Roads PM10 Emission Rates (pounds per mile) ...................................................... 21 Table 3-12. Unpaved Roads PM2.5 Emission Rates (pounds per mile) ..................................................... 21 Table 3-13. Precipitation Effectiveness Index Values ................................................................................ 24 Table 3-14. Sectors Included in Ecology’s Nonroad Mobile Category ...................................................... 25 Table 3-15. Boats Registered .................................................................................................................... 26 Table 3-16. Locomotive Emission Factors (grams per gallon of fuel consumed) ...................................... 27 Table 3-17. Locomotive Fuel Consumption (gallons) ................................................................................ 28 Table 3-18. Silvicultural Burning Emission Factors (pounds per ton consumed) ...................................... 31 Table 3-19. Silvicultural Burning Activity .................................................................................................... 32 Table 3-20. Wildfire Area Burned by County (acres) ................................................................................. 34 Table 3-21. Largest Wildfires in Washington ............................................................................................. 35 Table 3-22. Average Fuel Loading for Agricultural Field Burns ................................................................. 37 Table 3-23. Agricultural Residue Burned (tons) ......................................................................................... 38 Table 3-24. Agricultural Burn Emission Factors (pounds per ton of material consumed) ......................... 39 Table 3-25. Emission Rates in Pounds Per Ton of Combustible Material Burned .................................... 40 Table 3-26. Public Wastewater Treatment Plant Throughput (millions of gallons) .................................... 43 Table 3-27. POTW HAP Emission Factors (pounds per million gallons) ................................................... 44 Table 3-28. Gasoline Tank and Truck VOC Emission Factors (pounds per thousand gallons) ................ 46 Table 3-29. Gasoline Station VOC Speciation for Toxics .......................................................................... 46 Table 3-30. Fuel Energy Conversion Factors ............................................................................................ 46 Table 3-31. Vehicle Refueling Emission Factors in grams per gallon ....................................................... 47 Table 3-32: Surveys and Survey Groups .................................................................................................... 48 Table 3-33. County to Survey Assignments ............................................................................................... 49 Table 3-34. Amount of Wood Burned Per Device ...................................................................................... 51 Table 3-35. Tons of Wood Burned for Home Heating ............................................................................... 52 Table 3-36. Pollutant Emission Factors in Pounds Per Ton Burned ........................................................... 54 Table 3-37. State Industrial and Commercial/Institutional Fuel Use .......................................................... 55 Table 3-38. Area of Crops Harvested (Acres)............................................................................................ 58 Table 3-39. Agricultural Harvesting Emission Rates (lb/acre) ................................................................... 59 Table 3-40. Area of Farmland (Acres) ....................................................................................................... 61 Table 3-41. Farming Practices and Silt Content ........................................................................................ 62 Table 3-42. Tilling Passes for Field Crops ................................................................................................. 63 Table 3-43. Structure and Vehicle Fire Emission Factors (pounds per Ton consumed) ........................... 64 Table 3-44. Structure and Vehicle Fire Counts .......................................................................................... 65 Table 3-45. MEGAN Functional Plant Types ............................................................................................. 66

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Table 3-46. MEGAN v2.1 Biogenic Emission Factors by Class Number (µg/m2/hr) .................................. 67 Table 3-47. Accepted CAP Emissions Sources Calculated by EPA .......................................................... 68 Table 4-1. Statewide Emissions in Tons per Year ..................................................................................... 72 Table 4-2. Annual Statewide Emissions Source Percentages by Pollutant ............................................... 73 Table 4-3. County PM10 Emissions Estimates in Tons per Year ............................................................... 74 Table 4-4. County PM2.5 Emissions Estimates in Tons per Year ............................................................... 75 Table 4-5. County SO2 Emissions Estimates in Tons per Year ................................................................. 76 Table 4-6. County NOx Emissions Estimates in Tons per Year ................................................................ 77 Table 4-7. County VOC Emissions Estimates in Tons per Year................................................................ 78 Table 4-8. County CO Emissions Estimates in Tons per Year .................................................................. 79 Table 4-9. County NH3 Emissions Estimates in Tons per Year ................................................................. 80

2017 Washington State Comprehensive Emissions Inventory Page 1

1 Introduction 1.1 Purpose and Background

This document describes the methods and data sources employed to estimate emissions for the 2017 Washington State Comprehensive Emissions Inventory. The emissions inventory (EI) is based on indicators of activity (e.g. population, registered vehicles, land use area) and scientifically developed emission rates that are specific to a source category. The EI is not based on ambient air quality monitor observations used by EPA to enforce the National Ambient Air Quality Standards (NAAQS), nor does it account for air pollution that originated outside the state boundaries (e.g. wildfire smoke from other states/countries).

The comprehensive EI includes emissions from point sources (e.g. large facilities that report their emissions annually), area sources (e.g. residential, agricultural, and commercial activities), mobile sources (e.g. cars, trucks, trains, boats), biogenic sources (e.g. vegetation and soil microbes), and event-driven sources (e.g. wildfires and prescribed burning) of air pollution. The Department of Ecology uses the EI as part of the overall air quality management program. This includes supporting air quality modeling, State Implementation Plan (SIP) attainment/maintenance planning, other air quality planning and rule efforts, public information, point source fee generation, and to meet federal air quality reporting requirements. The Department of Ecology compiles a comprehensive EI for every third year, following the same schedule as the Environmental Protection Agency (EPA) National Emissions Inventory (NEI) process. The next comprehensive EI will be for base year 2020.

Discretion should be used when comparing results between different EI years. What appear to be emissions changes may instead reflect: (1) changes in emissions estimation models, methodologies, and /or emission rates, (2) sources and/or pollutants included in the inventory, and (3) correction of errors in prior inventories.

1.2 Pollutants and Emissions Sources

The EI includes estimates of relevant criteria air pollutants (CAPs) for all sources inventoried. The Clean Air Act (CAA) defines CAPs as carbon monoxide (CO), lead (Pb), Ozone (O3), nitrogen oxides (NOX), particulate matter (PM), and sulfur dioxide (SO2). Lead emissions are calculated by EPA for aircraft that use leaded fuel, but lead is not included in this EI. Ozone is a secondary pollutant formed in the atmosphere and is not directly emitted. NOx and volatile organic compounds (VOCs) are ozone precursors and are included in this EI. PM is estimated for two size bins (PM10 and PM2.5), as specified in the NAAQS. Ammonia (NH3) is technically not a CAP but it is included in this EI because it is an important PM precursor and a Toxic Air Pollutant (TAP). Hazardous air pollutants (HAPs) are also estimated, but only for sources where information was readily available. Categories that include VOC emissions may also include HAPs.

See Table 1-1 for information about the main pollutants included for each emissions category.


1.3 Spatial and Temporal Resolution

Emissions were estimated for each county. Total annual emissions were estimated. If seasonal or other shorter timeframe estimates are required for a project, temporal profiles or other information may be used to adjust the inventories to the needed temporal scale. Table 1-1. Emissions Category Pollutant Checklist

Emissions Category Abbr. CO NH3 NOX PM10 PM25 SO2 VOC Large point sources, usually with Title V AOP POINT X X X X X X X Industrial/Commercial/Institutional fuel use F_ICI X X X X X X X Residential non-wood fuel use (home heating) F_RES X X X X X X X Residential wood combustion (home heating) RWC X X X X X X X Residential open burning: yard waste, trash, land clearing OB_RES X X X X X X X Agricultural open burning OB_AG X X X X X X X Prescribed (Silvicultural) open burning OB_RX X X X X X X X Wildfires WF X X X X X X X Aircraft: military, commercial, general aviation AIR X X X X X X Recreational boats BOAT X X X X X X X Railroad (locomotives) RR X X X X X X X Ships (commercial marine vessels) SHIP X X X X X X X Nonroad mobile equipment and vehicles (NEC) NRM X X X X X X X Onroad mobile sources ORM X X X X X X X Industrial, commercial, and consumer solvent use SOLV X Gasoline storage and transport PETROL X Paved and unpaved road dust ROADS X X Construction Dust CONST X X Dust from agricultural tilling and harvesting TILL_HARV X X Livestock LIVE X X X X Fertilizer application FERT X Food and kindred products FOOD X X X X Natural emissions from soil and vegetation NAT X X X Miscellaneous MISC X X X X X X X

2 Statistics Used Throughout the Inventory 2.1 County Demographics

Emissions estimation methods for many source categories rely on surrogate parameters as indicators of activity. Population and occupied housing units (OHU) are two of the most common. Estimates for 2017 shown in the table below were obtained from the State Office of Financial Management (OFM).1

In past inventories, the demographics were split into incorporated and unincorporated areas. In the 2017 inventory, the demographics are split into rural and urban. The rural-urban split is used in estimating emissions from some sources.


Table 2-2. Population and Occupied Housing Units

County Urban Population

Rural Population

Total Population

Urban OHU

Rural OHU

Total OHU

Adams 12,151 7,719 19,870 3,326 2,744 6,069 Asotin 20,702 1,588 22,290 8,747 661 9,408

Benton 167,440 26,060 193,500 62,750 9,040 71,790 Chelan 55,003 21,827 76,830 21,120 8,199 29,320 Clallam 47,554 26,686 74,240 21,339 11,028 32,367

Clark 405,497 65,503 471,000 150,961 21,646 172,607 Columbia 2,700 1,400 4,100 1,151 640 1,791

Cowlitz 74,911 30,990 105,900 29,805 11,599 41,404 Douglas 30,198 11,222 41,420 10,825 3,837 14,662

Ferry 0 7,740 7,740 0 3,278 3,278 Franklin 77,280 13,050 90,330 22,857 3,488 26,345 Garfield 0 2,200 2,200 0 973 973

Grant 57,934 37,696 95,630 19,069 12,871 31,940 Grays Harbor 43,770 29,200 72,970 17,613 11,275 28,888

Island 43,716 39,074 82,790 16,847 16,947 33,793 Jefferson 13,164 18,196 31,360 6,324 8,219 14,543

King 2,085,773 67,927 2,153,700 846,399 24,637 871,036 Kitsap 219,967 44,333 264,300 84,125 16,524 100,649 Kittitas 25,926 18,804 44,730 10,471 7,352 17,823

Klickitat 8,578 13,082 21,660 3,535 5,401 8,936 Lewis 30,221 47,219 77,440 11,847 18,604 30,451

Lincoln 0 10,700 10,700 0 4,556 4,556 Mason 22,689 40,501 63,190 7,770 16,802 24,572

Okanogan 8,270 33,840 42,110 3,370 13,669 17,039 Pacific 7,522 13,728 21,250 3,415 6,239 9,655

Pend Oreille 2,237 11,133 13,370 899 4,724 5,622 Pierce 801,545 57,855 859,400 299,558 20,909 320,467

San Juan 0 16,510 16,510 0 7,936 7,936 Skagit 87,361 36,739 124,100 33,424 13,956 47,380

Skamania 0 11,690 11,690 0 4,754 4,754 Snohomish 703,737 85,663 789,400 260,243 30,393 290,636

Spokane 429,665 70,135 499,800 172,274 26,323 198,597 Stevens 9,104 35,406 44,510 3,503 14,053 17,556

Thurston 217,656 59,244 276,900 86,798 22,425 109,223 Wahkiakum 0 4,030 4,030 0 1,795 1,795 Walla Walla 50,974 10,426 61,400 18,830 3,835 22,665

Whatcom 159,791 56,509 216,300 65,000 20,456 85,457 Whitman 35,922 12,718 48,640 13,516 5,329 18,845

Yakima 193,618 59,382 253,000 64,513 19,259 83,772 State Total 6,152,577 1,157,723 7,310,300 2,382,225 436,376 2,818,601


2.2 Meteorological Parameters Similar to demographics, meteorological parameters are used to estimate emissions for several source categories. Temperature, heating degree days, and rainfall are described here. Daily minimum and maximum temperatures and precipitation for 2017 were obtained from several airports with meteorological stations.2 Monthly averages were calculated from the daily data. Counties were associated with meteorological stations based upon the predominant areas of traffic and population. County assignments to each meteorological station are shown below. Table 2-3. Meteorological Station County Assignments

Met Station Airport Code County Assignments WALLA WALLA CITY COUNTY AP KALW Columbia, Walla Walla

BELLINGHAM INTL AP KBLI Whatcom WILLIAM R FAIRCHILD KCLM Clallam, Jefferson

THE DALLES MUNICIPAL ARPT KDLS Klickitat WENATCHEE/PANGBORN KEAT Chelan, Douglas

ELLENSBURG/BOWERS KELN Kittitas FRIDAY HARBOR KFHR San Juan

SPOKANE INTERNATIONAL AP KGEG Spokane HOQUIAM AP KHQM Grays Harbor, Pacific

KELSO-LONGVEIW AWOS KKLS Cowlitz, Lewis, Wahkiakum LEWISTON NEZ PERCE CNTY AP KLWS Asotin, Garfield

MOSES LAKE/GRANT CO KMWH Adams, Grant, Lincoln OLYMPIA AIRPORT KOLM Thurston

OMAK KOMK Ferry, Okanogan, Pend Oreille, Stevens SNOHOMISH CO KPAE Island, Skagit, Snohomish

PORTLAND INTERNATIONAL AP KPDX Clark, Skamania PASCO/TRI-CITIES KPSC Benton, Franklin

PULLMAN/MOSCOW RGNL KPUW Whitman BREMERTON NATIONAL KPWT Kitsap

SEATTLE-TACOMA INTL AP KSEA King, Pierce SHELTON/SANDERSON KSHN Mason YAKIMA AIR TERMINAL KYKM Yakima


Table 2-4. Average Monthly Minimum Temperature (°Fahrenheit)

Airport Code Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

KALW 16 27 39 41 48 56 63 63 55 42 37 27 KBLI 27 30 39 42 46 51 53 54 50 40 39 30

KCLM 27 30 35 38 43 46 49 51 48 39 35 31 KDLS 20 27 37 39 47 54 60 60 54 40 37 30 KEAT 15 19 31 38 46 54 61 62 52 37 31 23 KELN 13 17 30 36 42 49 56 55 46 32 31 23 KFHR 30 33 39 41 44 47 47 49 47 40 39 32 KGEG 13 21 32 36 44 51 59 59 50 36 31 21 KHQM 32 35 39 42 46 51 51 53 52 42 42 35 KKLS 26 34 40 41 46 51 51 56 51 41 40 31

KLWS 20 28 35 40 45 53 61 60 52 40 34 25 KMWH 14 18 32 38 43 50 56 55 47 34 30 23 KOLM 26 31 36 37 43 47 48 50 47 36 38 29 KOMK 16 19 30 35 43 49 57 57 46 32 31 21 KPAE 31 34 39 42 47 51 53 56 53 44 40 33 KPDX 26 34 40 41 48 54 56 59 55 43 41 33 KPSC 15 23 36 40 43 52 55 55 48 37 33 26

KPUW 15 25 34 36 42 47 52 51 47 37 34 23 KPWT 29 33 38 41 43 47 47 50 46 37 37 30 KSEA 31 35 39 43 48 53 55 58 55 44 41 34 KSHN 28 32 37 39 43 47 50 51 47 36 37 30 KYKM 15 22 33 36 44 51 56 56 48 34 30 24

Table 2-5. Average Monthly Maximum Temperature (°Fahrenheit)


KALW 31 44 58 64 77 84 97 94 81 66 55 39 KBLI 44 47 51 59 67 71 74 77 74 61 52 45

KCLM 44 44 49 54 62 66 70 74 71 60 50 45 KDLS 31 44 57 63 81 84 94 96 84 67 55 43 KEAT 26 35 52 60 76 82 93 92 79 62 46 33 KELN 29 38 52 60 76 82 93 95 81 64 49 37 KFHR 45 47 52 59 67 72 75 78 73 60 51 45 KGEG 28 38 49 57 72 81 90 90 76 58 46 33 KHQM 46 49 51 56 62 67 69 73 71 63 52 49 KKLS 42 50 54 60 72 75 82 85 80 65 53 46

KLWS 33 47 55 62 77 84 97 96 82 64 52 38 KMWH 28 37 56 63 78 85 94 95 82 64 49 35 KOLM 44 49 52 59 71 75 82 85 78 63 52 46 KOMK 28 34 51 61 76 83 95 95 81 63 45 31 KPAE 43 46 51 57 66 70 75 78 74 60 51 46 KPDX 40 49 55 60 74 78 85 89 81 66 55 47 KPSC 30 43 59 67 81 87 97 96 83 67 55 39



KPUW 30 42 50 57 69 77 89 90 77 60 48 35 KPWT 42 46 49 56 69 73 81 84 76 62 50 46 KSEA 45 48 52 59 70 75 81 84 76 63 53 47 KSHN 46 49 52 59 71 75 82 86 79 64 52 47 KYKM 32 42 60 64 80 86 97 96 83 67 54 41

Table 2-6. Precipitation Days Greater Than 0.01 Inches

Airport Code Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year

KALW 12 16 22 17 7 9 0 2 3 12 18 14 132 KBLI 11 21 30 16 11 11 2 2 8 16 22 15 165

KCLM 10 20 23 18 13 5 0 1 7 11 22 17 147 KDLS 10 15 19 13 7 5 0 2 5 5 18 6 105 KEAT 10 11 16 15 11 3 0 2 1 5 14 8 96 KELN 4 14 14 14 9 4 0 1 3 5 15 6 89 KFHR 7 12 16 8 10 2 0 0 5 12 14 11 97 KGEG 11 17 22 17 10 6 0 0 5 8 20 15 131 KHQM 13 21 31 25 13 14 2 2 13 15 28 16 193 KKLS 13 22 25 25 12 12 2 2 8 17 25 18 181

KLWS 12 15 20 15 10 7 1 1 5 10 16 15 127 KMWH 12 14 16 11 9 3 0 1 3 6 15 9 99 KOLM 16 21 29 23 12 9 1 1 8 12 25 17 174 KOMK 8 13 15 13 10 5 0 0 3 2 12 9 90 KPAE 5 20 26 22 15 11 0 0 8 15 20 16 158 KPDX 14 22 24 24 12 10 0 2 7 15 22 17 169 KPSC 9 11 14 9 5 5 0 2 3 4 16 7 85

KPUW 13 16 23 20 11 9 1 2 5 11 19 10 140 KPWT 12 17 29 10 14 9 0 4 8 9 25 14 151 KSEA 14 21 27 23 15 7 0 1 7 13 23 17 168 KSHN 14 22 29 24 14 10 0 2 8 11 25 16 175 KYKM 13 13 16 12 6 4 0 9 4 3 16 8 104

Table 2-7. Monthly Precipitation (Inches)


KALW 1.28 2.61 3.76 2.1 1.28 1.39 0 0.02 0.8 1.65 3.24 2.79 20.92 KBLI 1.87 4.07 5.96 2.84 1.11 1.22 0.08 0.09 1.49 5.63 5.72 6.69 36.77

KCLM 2.38 6.09 6.13 2.09 1.5 0.2 0 0.14 0.35 4.24 10.87 6.25 40.24 KDLS 1.09 2.09 2.36 1.65 0.26 0.44 0 0.05 0.76 1.6 2.52 1.13 13.95 KEAT 1.06 1.85 1.25 2.25 0.61 0.2 0 0.06 0.02 0.88 1.37 0.9 10.45 KELN 0.29 1.55 1.29 2.41 1.17 0.24 0 0.09 0.06 1.16 1.54 0.23 10.03 KFHR 0.52 1.18 0.69 0.68 0.74 0.1 0 0 0.3 2.5 1.39 1.75 9.85 KGEG 1.93 4.46 4.11 1.63 1.31 0.71 0 0 1.21 1.49 3.01 2.95 22.81



KHQM 5.58 7.64 16.02 8.35 3.84 2.07 0.1 0.26 2.88 7.5 16.19 8.13 78.56 KKLS 4.02 8.25 8.21 6.15 2.18 2.04 0.08 0.19 2.5 6.36 7.22 5.56 52.76

KLWS 0.76 2.1 3.53 1.9 1.6 0.63 0.01 0.02 0.56 1.19 1.79 2.63 16.72 KMWH 1.13 1.63 1.29 0.94 0.44 0.29 0 0.02 0.12 0.89 1.86 0.52 9.13 KOLM 4.1 9.15 11.4 5.56 2.98 1.42 0.05 0.11 1.18 6.65 12.03 7.28 61.91 KOMK 2.04 1.67 2.3 2.66 1.53 0.28 0 0 0.11 1.11 1.61 0.8 14.11 KPAE 0.51 5.19 5.76 3.44 3.69 0.54 0 0 0.83 3.42 7.45 4.01 34.84 KPDX 4.21 10.36 7.28 4.51 1.92 1.08 0 0.06 2.35 4.6 6.37 3.16 45.90 KPSC 0.89 1.68 1.43 1.23 0.48 0.5 0 0.05 0.16 0.67 1.23 0.92 9.24

KPUW 1.43 3.3 5.3 1.92 2 1.01 0.02 0.07 0.68 2.61 3.02 3.36 24.72 KPWT 6.15 10.38 14 3.4 2.63 2.09 0 0.06 1.58 6.18 4.82 6.14 57.43 KSEA 4.38 8.68 7.34 4.13 2.28 1.63 0 0.02 0.59 4.83 8.37 5.83 48.08 KSHN 6.37 10.14 17 7.98 2.59 2.48 0 0.27 1.6 7.55 17.78 8.88 82.64 KYKM 2.07 2.64 1.1 1.42 0.55 0.19 0 0.86 0.37 1.19 1.39 0.36 12.14

Table 2-8. Monthly Heating Degree Days


KALW 1,292 820 515 366 151 17 0 0 75 351 572 997 5,157 KBLI 912 740 611 430 263 131 51 28 127 440 591 847 5,171

KCLM 923 784 712 569 383 267 172 112 189 480 676 844 6,111 KDLS 1,223 825 553 420 121 31 0 0 48 364 571 888 5,045 KEAT 1,369 1,068 723 487 187 36 0 0 96 489 795 1,147 6,397 KELN 1,373 1,052 749 512 222 65 0 4 128 510 657 1,047 6,320 KFHR 850 694 601 449 294 176 126 77 161 459 604 824 5,313 KGEG 1,374 1,000 755 558 252 58 0 2 156 552 801 1,181 6,690 KHQM 809 650 619 477 346 202 153 94 137 378 542 725 5,132 KKLS 952 647 550 431 219 106 16 7 87 377 544 811 4,748

KLWS 1,188 777 610 419 180 36 0 0 94 397 649 1,048 5,399 KMWH 1,362 1,053 651 431 183 50 0 0 121 491 773 1,114 6,228 KOLM 918 697 647 514 264 142 29 22 123 476 604 852 5,288 KOMK 1,344 1,067 761 511 197 54 0 0 134 549 812 1,204 6,633 KPAE 864 696 623 466 273 144 39 21 95 407 587 793 5,008 KPDX 982 662 543 426 169 60 0 2 65 323 509 786 4,526 KPSC 1,321 894 545 356 150 21 0 0 87 394 617 1,015 5,402

KPUW 1,320 881 714 554 316 134 3 16 175 523 723 1,127 6,484 KPWT 910 714 656 313 275 171 49 25 137 484 648 838 5,220 KSEA 839 656 591 425 209 88 1 0 65 353 537 766 4,530 KSHN 871 678 637 483 254 132 23 8 105 455 618 829 5,092 KYKM 1,296 923 580 453 165 45 0 0 101 437 693 1,010 5,704


3 Base Year 2017 Emissions Estimates To estimate emissions, four sub-tasks were completed for each source category. The four tasks were: 1) estimate the activity level for 2017, 2) if necessary, adjust/allocate the activity level (or emissions) spatially to the county level, 3) determine emission rates per the activity, and 4) estimate emissions in tons per year (TPY). The tasks are described below for each source category. Emissions estimates are listed in Section 4.

3.1 Point Sources Industrial, commercial, or institutional stationary sources which emit criteria and/or hazardous air pollutants are called point sources. Major point sources are those with the potential to emit 100 tons per year or more of any one criteria pollutant or a combination of criteria pollutants, and/or point sources with the potential to emit 10 tons per year or more of any single Hazardous Air Pollutant, or 25 tons per year or more of a combination of Hazardous Air Pollutants (Section 112, CAA). Facilities with a major source potential-to-emit are included in Title V Air Operating Permit (AOP) programs unless a facility voluntarily adopts federally enforceable permit limits that reduce their potential-to-emit below the criteria and HAPs thresholds. Facilities that adopt these limits are called Synthetic Minor sources. Local air agencies, Ecology regional offices, and Ecology’s Industrial Section and Nuclear Waste Program (regulating authorities) regulate facilities in their jurisdictions according to state and local regulations and air operating permit programs. Emissions inventories are collected by Ecology annually from the facilities either directly or via the local air agency and EPA. All Title V sources (major) are included in the point source inventory. Synthetic Minor sources are included at the discretion of the regulating authority. EPA augments the point source data using a variety of sources including speciation profiles, the Toxics Release Inventory (TRI), airport reports, railyard estimates, landfill models, and other sources. Some of the augmented data was not accepted into Ecology's final inventory due to the possibility of double-counting emissions from sources. For instance, rail yards and airports were separated out of the EPA point source inventory. The final point source inventory is a combination of state, local, and federal estimates that represents a mutually exclusive list of point sources not represented in the other categories. The emissions for each individual point source in this category are available on the Air Emissions Inventory webpage.

3.1.1 Activity Level Individual facility throughputs and production rates determine the activity level for each facility.

3.1.2 Spatial Allocation Point sources are allocated to counties based on their address or geographic coordinates.

3.1.3 Emission Rates and Estimates Emissions estimates for each facility are calculated using a variety of emissions estimation methods: continuous emissions monitors, stack test data, mass balance, best professional judgment, manufacturer’s specifications, speciation profiles, EPA emission factors (e.g., AP-42), and/or other state, manufacturer, or research group emission factors. Methods are selected considering permit conditions, data availability, and resource constraints.

https://ecology.wa.gov/Air-Climate/Air-quality/Air-quality-targets/Air-emissions-inventory


3.2 Onroad Mobile Sources Onroad mobile source emissions are those generated by operating vehicles on public roadways. Emissions from fuel combustion and evaporation, and brake and tire wear were estimated. Emissions generated while refueling due to spillage and vapor displacement can be found in section 3.15.2.2. EPA's Motor Vehicle Emission Simulator (MOVES) model version 2014b with database version 20181022 was used to calculate emissions. MOVES combines basic vehicle activity information with information about vehicle and fuel characteristics, emissions control programs, meteorological information, and other parameters to estimate emissions. The basic activity data are vehicle miles traveled (VMT) and vehicle population. VMT, vehicle population, and a brief description of the MOVES input parameters are described below. The MOVES Technical Guidance for SIP inventories and the MOVES User’s Guide were used in developing many of the inputs to MOVES.3, 4 Detailed information about the MOVES input parameters are in a separate document. Emissions were generated for the four seasons by choosing one month from the representative season to run – January for winter, April for spring, July for summer, and October for fall. There is a discrepancy for long haul and short haul truck emissions between the 2017 Comprehensive EI and the 2017 NEI. This discrepancy is due to the updated truck age distribution that EPA used in their MOVES modeling, derived from the CRC A-115 project.5 The 2017 Comprehensive EI values may be updated when EPA releases a new version of MOVES that includes the updated truck age distributions.

3.2.1 Vehicle Miles Traveled (VMT) VMT are used in MOVES to calculate emissions while the vehicle is in motion or during short periods of idling. The source of county VMT data for 2017 used in this inventory was the Washington State Department of Transportation (WSDOT) under the national Department of Transportation’s Highway Performance Monitoring System (HPMS).6 HPMS is a system of traffic counts collected over several urban and rural sampling areas. WSDOT makes estimates of county VMT by roadway (functional) classification using the HPMS data (see Table 3-1 for rural roads and Table 3-2 for urban roads).

3.2.2 Vehicle Population Vehicle population is used to calculate emissions while a vehicle is stationary. The emissions come from engine starts, extended idling, and some fuel evaporation processes. Vehicles are classified by age and type. There are thirteen vehicle types in MOVES within six broader categories: cars, motorcycles, light-duty trucks, heavy-duty single unit trucks, heavy-duty combination unit trucks, and buses. Three sources were used to calculate vehicle population (see Table 3-3). The first was the Washington State Department of Licensing (DOL). DOL registers non-governmental vehicles annually.7 Government vehicles have a one-time registration. Because DOL does not register public transit and school buses each year, alternate sources of information were obtained. Transit andIntercity bus data came from the MOVES default database. School bus information for 2017 was obtained from the Washington State Office of the Superintendent of Public Instruction (OSPI).8


Table 3-9. Average Daily VMT for Rural Roads (All Values in Thousands)

County Interstate Other

Freeway / Expressway

Other Principal Arterial

Minor Arterial

Major Collector

Minor Collector Local

Adams 661 504 0 0 165 47 99 Asotin 0 22 0 17 27 10 22

Benton 731 0 0 257 135 91 49 Chelan 0 257 372 197 215 58 63 Clallam 0 54 335 1 393 97 56

Clark 710 5 41 176 398 108 49 Columbia 0 0 79 0 61 10 33

Cowlitz 1,502 0 45 170 177 56 70 Douglas 0 149 183 52 80 35 112

Ferry 0 0 31 46 135 19 109 Franklin 0 548 0 0 226 61 61 Garfield 0 2 78 0 36 21 24

Grant 675 364 99 383 383 168 154 Grays Harbor 0 211 261 235 227 55 74

Island 0 19 385 144 115 110 37 Jefferson 0 164 314 128 98 97 63

King 1,309 338 362 540 531 325 61 Kitsap 0 171 260 70 282 60 27 Kittitas 2,481 0 179 0 178 85 110

Klickitat 0 35 291 77 194 29 92 Lewis 632 107 259 204 285 59 101

Lincoln 309 84 103 81 149 20 129 Mason 0 223 345 46 335 70 48

Okanogan 0 245 142 365 210 140 266 Pacific 0 17 194 169 174 27 62

Pend Oreille 0 43 67 118 49 30 70 Pierce 343 0 27 634 206 47 75

San Juan 0 0 0 0 89 45 19 Skagit 964 120 126 290 501 101 65

Skamania 0 23 195 47 67 24 46 Snohomish 735 115 304 556 344 162 71

Spokane 364 375 253 237 657 196 142 Stevens 0 149 291 106 285 75 213

Thurston 591 253 40 192 488 58 71 Wahkiakum 0 0 72 0 25 4 21 Walla Walla 0 141 238 119 149 31 45

Whatcom 481 20 98 193 723 140 73 Whitman 0 232 285 1 242 39 124

Yakima 796 29 199 173 681 166 273 State Total 13,284 5,019 6,553 6,024 9,715 2,976 3,279


Table 3-10. Average Daily VMT for Urban Roads (All Values in Thousands)

County Interstate Other

Freeway / Expressway

Other Principal Arterial

Minor Arterial

Major Collector

Minor Collector Local

Adams 0 23 0 30 10 1 19 Asotin 0 6 44 95 30 0 21

Benton 600 662 594 680 352 26 343 Chelan 0 149 167 209 141 3 96 Clallam 0 67 292 93 143 21 93

Clark 1,996 1,292 1,358 1,059 517 17 767 Columbia 0 0 0 0 0 0 0

Cowlitz 643 96 304 314 125 2 169 Douglas 0 161 233 99 51 1 62

Ferry 0 0 0 0 0 0 0 Franklin 434 207 174 145 73 1 140 Garfield 0 0 0 0 0 0 0

Grant 97 143 134 201 59 8 104 Grays Harbor 0 239 344 89 142 23 125

Island 0 0 159 172 55 10 61 Jefferson 0 0 64 0 61 0 36

King 14,017 5,503 9,443 7,201 3,181 94 4,522 Kitsap 0 1,751 732 824 341 29 472 Kittitas 114 4 69 53 56 1 28

Klickitat 0 0 0 0 0 0 0 Lewis 925 0 125 184 55 9 67

Lincoln 0 0 0 0 0 0 0 Mason 0 46 134 11 72 1 34

Okanogan 0 23 0 56 5 10 24 Pacific 0 0 0 0 0 0 0

Pend Oreille 0 0 0 0 0 0 0 Pierce 3,468 3,044 4,338 3,247 1,126 10 2,066

San Juan 0 0 0 0 0 0 0 Skagit 485 188 362 413 182 6 200

Skamania 0 0 0 0 0 0 0 Snohomish 5,027 1,540 2,656 2,079 1,143 25 1,692

Spokane 2,111 307 3,052 1,448 468 0 918 Stevens 0 0 0 0 0 0 0

Thurston 1,985 401 806 1,051 339 5 565 Wahkiakum 0 0 0 0 0 0 0 Walla Walla 0 184 136 158 56 1 59

Whatcom 989 10 418 653 397 18 320 Whitman 0 0 115 50 35 0 29

Yakima 592 314 751 742 305 27 327 State Total 33,483 16,360 27,004 21,356 9,520 349 13,359


Table 3-11. Vehicle Population, 2017

County Motorcycles Cars Light Trucks Buses

Single Unit

Trucks

Combination Unit

Trucks Total

Adams 483 11,319 9,433 98 549 61 21,943 Asotin 1,077 12,605 10,505 39 616 68 24,910

Benton 8,128 104,372 86,984 330 5,350 595 205,759 Chelan 4,894 44,686 37,242 142 2,124 236 89,324 Clallam 3,306 42,643 35,540 126 2,052 228 83,895

Clark 15,629 242,033 201,713 840 13,022 1,447 474,684 Columbia 258 3,210 2,675 17 114 13 6,287

Cowlitz 4,329 61,870 51,564 276 2,928 326 121,293 Douglas 2,072 23,102 19,254 96 1,146 128 45,798

Ferry 358 5,153 4,295 37 214 24 10,081 Franklin 2,290 46,543 38,790 225 2,498 277 90,623 Garfield 87 1,421 1,185 11 60 7 2,771

Grant 3,517 53,164 44,308 293 2,644 294 104,220 Grays Harbor 2,776 40,089 33,410 171 2,017 224 78,687

Island 5,222 50,784 42,324 102 2,289 254 100,975 Jefferson 1,748 20,538 17,116 57 867 96 40,422

King 54,185 984,713 820,670 2,826 59,541 6,620 1,928,555 Kitsap 13,325 139,509 116,268 392 7,307 812 277,613 Kittitas 2,991 25,607 21,341 118 1,236 138 51,431

Klickitat 1,243 14,347 11,957 90 598 66 28,301 Lewis 3,693 50,979 42,487 220 2,141 238 99,758

Lincoln 529 7,308 6,091 95 296 32 14,351 Mason 3,343 39,236 32,699 142 1,747 194 77,361

Okanogan 1,926 25,307 21,092 111 1,164 130 49,730 Pacific 923 14,272 11,895 78 588 66 27,822

Pend Oreille 756 9,817 8,182 43 370 41 19,209 Pierce 31,654 412,956 344,163 1,445 23,759 2,642 816,619

San Juan 1,097 11,848 9,875 18 457 51 23,346 Skagit 6,092 73,770 61,481 272 3,431 381 145,427

Skamania 670 8,429 7,025 24 323 36 16,507 Snohomish 31,445 392,226 326,886 1,215 21,824 2,427 776,023

Spokane 18,715 245,308 204,442 875 13,818 1,536 484,694 Stevens 2,281 30,277 25,234 149 1,230 137 59,308

Thurston 11,720 152,915 127,441 507 7,655 851 301,089 Wahkiakum 186 2,838 2,365 13 112 13 5,527 Walla Walla 2,532 30,016 25,016 116 1,697 189 59,566

Whatcom 8,675 115,994 96,670 319 5,980 665 228,303 Whitman 1,208 19,133 15,946 134 1,345 149 37,915

Yakima 6,690 133,841 111,545 477 6,994 778 260,325 State Total 262,053 3,704,178 3,087,109 12,539 202,103 22,470 7,290,452


3.2.3 MOVES Input Parameters MOVES includes a default database that summarizes emissions relevant information for all counties in the United States. Default data may be replaced by local data to improve the estimates. Ecology developed local data for many of the parameters in MOVES. Input parameters were developed that were characteristic of local conditions for each county and month. Some of the parameters presented here required local data. For others, EPA guidance recommended that local data be used. The parameters are shown in the table below. Note that the vehicle Inspection and Maintenance (I/M) program was only operated in Clark, King, Pierce, Snohomish, and Spokane Counties. Table 3-12. MOVES Model Parameters

Parameter Data Source References

Vehicle population DOL, OSPI, FTA 7, 7, 9 VMT WSDOT with EPA default tailoring 6, 9 Temporal allocation to month, day of week, and hour WSDOT with EPA default tailoring 10

Vehicle Inspection and Maintenance (I/M) Program (aka Vehicle Check Program) Dept. of Ecology I/M program records 11, 12, 13

California Emissions Standards CA standards are incorporated into the MOVES model as an option 14

Fuel parameters State regulations and EPA default data 15 Hourly temperatures County defaults Road type distribution WSDOT with EPA default tailoring 6, 9 Vehicle age distribution Local (DOL, OSPI, FTA) 7, 8, 8 Speeds Default Ramp fraction Default

Vehicle refueling Dept. of Ecology and Default 16, 17, 18, 19


3.3 Paved Road Dust Dust emissions are generated as vehicles pass along paved roadways and disturb the layer of loose material on or near the road surface. This material contains particulate matter from soil, brake and tire wear, exhaust, and other substances. However, the paved road dust calculation excludes emissions from exhaust and brake and tire wear, which are estimated as part of the onroad mobile sources emissions (see Section 0).

3.3.1 Activity Level and Spatial Allocation The measure of activity is VMT (see Table 3-1). VMT was available by county, so no spatial allocation was necessary. The monthly adjustment factors for VMT used in calculating emissions are shown in Table 3-5. The HPMS Average Daily VMT (ADVMT) estimate includes travel over both paved and unpaved roads; however, they are not distinguished from one another. A slight overestimation of ADVMT does occur when using HPMS ADVMT as the paved road activity level. The overestimation is not significant, though, since unpaved ADVMT is only estimated to be 0.5% of the entire ADVMT (see Section 0). Table 3-13. WSDOT VMT Monthly Adjustment Factors

Road Type Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Rural interstate 0.77 0.85 0.93 1.00 1.03 1.12 1.22 1.24 1.10 1.03 0.89 0.83 Other rural arterial 0.77 0.86 0.90 0.95 1.04 1.12 1.25 1.26 1.11 1.03 0.84 0.79

Other rural 0.72 0.77 0.82 0.87 1.05 1.23 1.42 1.36 1.20 1.01 0.84 0.71 Urban interstate 0.92 0.97 0.99 1.01 1.06 0.99 0.98 0.98 0.99 0.95 0.91 0.91

Other urban arterial 0.92 0.95 0.98 1.01 1.06 1.07 1.06 1.07 1.03 1.00 0.93 0.93

3.3.2 Emission Rates The PM10 and PM2.5 emission rates in grams per mile were calculated using equation 2 in EPA's AP42.20, 21 Equation 2 estimates an emission rate for annual average conditions by incorporating a precipitation correction factor. This equation was used to calculate monthly emission rates, as shown below.

E = [k (sL)0.91 (W)1.02 ] x [1-(P/4N)]

where E is the emission factor in g/VMT k = g/VMT particle size multiplier (1 for PM10, 0.25 for PM2.5) sL = silt loading in g/m2 W = mean vehicle weight (tons) P = number of days with at least 0.01 inches of precipitation in the given month N = number of days in the given month

AP42 provides recommended values of average and worst-case silt loading on roads for several average daily traffic (ADT) classes. For this inventory, the average silt loading values were used (see Table 3-6). ADT was calculated by dividing the ADVMT values in Table 3-1 by the number of centerlane miles for each county and HPMS road classification (see Table 3-8).


Table 3-14. Silt Loading on Roads (grams per square meter) Road Access Type ADT Range Silt Loading Limited (includes interstates and other freeways) > 10,000 0.015

Unlimited (includes all roads except interstates and other freeways) > 10,000 0.03 All 5,000 - 10,000 0.06 All 500 - 5,000 0.2 All 0 - 500 0.6

Mean vehicle weight for the HPMS road classes was calculated using information from the 2014 onroad inventory (see Table 3-7). MOVES-default in-use vehicle weights for each MOVES vehicle type were weighted by WSDOT's VMT estimates to calculate average vehicle weight on each HPMS road class. The distribution of VMT by vehicle type over each road class was calculated using WSDOT's travel fractions for major vehicle types22 and MOVES output of VMT by more specific vehicle classes. Days per month of precipitation greater than 0.01 inches in 2017 were obtained (see Section 2.2, Table 2-5). County assignments were made as shown in Table 2-2. Dry day PM10 emission rates calculated with the AP42 Equation 2 ranged from 0.066 to 2.473 g/mi while PM2.5 emission rates ranged from 0.016 to 0.618 g/mi. Table 3-15. Average Vehicle Weight for Rural and Urban Roads (Tons)

Road Type Avg Weight Rural - Interstate 5.065

Rural - Other Freeway/Expressway 3.838 Rural - Other Principal Arterial 3.842

Rural - Minor Arterial 3.833 Rural - Major collector 3.465 Rural - Minor Collector 3.465

Rural - Local 3.474 Urban - Interstate 3.459

Urban - Other Freeway/Expressway 2.935 Urban - Other Principal Arterial 2.937

Urban - Minor Arterial 2.936 Urban - Major collector 3.022 Urban - Minor Collector 3.025

Urban - Local 3.024

3.3.3 Emissions Estimates Monthly emissions were calculated using the equations below.

Monthly Emissions = V x M x D x E x (lb/454 g) x (1 T/2000 lb) where V = ADVMT

M = monthly VMT adjustment factor (Table 3-5) D = number of days in month, and E = emission factor in g/VMT, calculated as described in Section 3.3.2 (see Table 3-9)


Table 3-16. Average Daily VMT by ADT Class

County >10,000 limited

> 10,000 unlimited

5,000 - 10,000 all

500 - 5,000 all

0 - 500 all

Adams 660,870 0 22,537 544,437 330,641 Asotin 0 43,814 0 153,357 97,061

Benton 1,993,906 594,154 680,470 1,112,572 140,143 Chelan 406,816 166,891 581,332 709,901 63,209 Clallam 120,299 292,328 0 1,083,751 149,884

Clark 3,997,703 1,399,883 1,240,677 1,807,232 48,979 Columbia 0 0 0 78,708 103,898

Cowlitz 2,240,957 303,778 313,503 744,216 70,100 Douglas 160,537 232,617 149,456 446,125 228,267

Ferry 0 0 0 212,108 128,032 Franklin 1,188,395 174,490 144,604 440,048 121,451 Garfield 0 0 0 80,053 81,582

Grant 915,353 133,929 363,966 1,133,229 425,582 Grays Harbor 449,754 343,855 88,817 942,046 199,047

Island 0 159,251 719,489 289,695 97,946 Jefferson 164,464 64,064 128,061 568,762 99,309

King 21,166,726 16,644,336 1,433,973 8,122,301 61,100 Kitsap 1,921,076 991,789 823,953 1,254,525 27,314 Kittitas 2,598,390 68,758 0 467,260 222,750

Klickitat 0 0 34,554 561,115 121,210 Lewis 1,556,737 125,355 183,601 919,076 226,616

Lincoln 308,634 0 0 268,006 297,204 Mason 269,000 134,479 345,396 534,042 82,024

Okanogan 0 0 79,578 1,118,011 290,506 Pacific 0 0 17,085 537,795 89,008

Pend Oreille 0 0 110,092 118,403 148,060 Pierce 6,854,410 4,337,815 3,273,557 4,088,368 75,495

San Juan 0 0 0 133,989 18,652 Skagit 1,757,520 488,141 413,218 1,281,213 64,748

Skamania 0 0 217,546 114,053 70,145 Snohomish 7,416,475 2,960,388 2,634,671 3,366,437 70,922

Spokane 2,781,481 3,304,905 2,059,557 2,237,570 142,683 Stevens 0 0 440,382 390,325 287,569

Thurston 3,230,036 846,101 1,242,659 1,454,042 70,564 Wahkiakum 0 0 0 72,059 49,911 Walla Walla 184,290 0 515,805 483,069 135,777

Whatcom 1,480,015 417,996 771,007 1,790,487 72,628 Whitman 0 115,108 0 844,027 190,932

Yakima 1,731,131 751,028 741,831 1,710,800 438,498 State Total 65,554,973 35,095,254 19,771,380 42,213,214 5,639,444


Table 3-17. Paved Road Dust Emission Factors for “Dry Day” (grams per mile)

Access Type Road Type Low ADT High ADT PM10 EF

PM2.5 EF

Limited Rural Interstate 10000 999999 0.115 0.029

All Rural Other Frwy/Exprwy 500 5000 0.911 0.228

All Rural Other Frwy/Exprwy 5000 10000 0.305 0.076

Limited Rural Other Frwy/Exprwy 10000 999999 0.086 0.022

All Rural Other Principal Arterial 500 5000 0.912 0.228

All Rural Other Principal Arterial 5000 10000 0.305 0.076

Unlimited Rural Other Principal Arterial 10000 999999 0.162 0.041

All Rural Minor Arterial 0 500 2.473 0.618



All Rural Major collector 0 500 2.232 0.558



All Rural Minor Collector 0 500 2.231 0.558

All Rural Minor Collector 500 5000 0.821 0.205

All Rural Local 0 500 2.237 0.559

Limited Urban Interstate 10000 999999 0.078 0.019

All Urban Rural Other Frwy/Exprwy 500 5000 0.693 0.173

All Urban Rural Other Frwy/Exprwy 5000 10000 0.232 0.058

Limited Urban Other Frwy/Exprwy 10000 999999 0.066 0.016

All Urban Other Principal Arterial 5000 10000 0.232 0.058

Unlimited Urban Other Principal Arterial 10000 999999 0.123 0.031

All Urban Minor Arterial 500 5000 0.694 0.173

All Urban Minor Arterial 5000 10000 0.232 0.058

Unlimited Urban Minor Arterial 10000 999999 0.123 0.031

All Urban Major collector 500 5000 0.714 0.179

All Urban Minor Collector 0 500 1.943 0.486

All Urban Minor Collector 500 5000 0.715 0.179

All Urban Local 0 500 1.942 0.486

All Urban Local 500 5000 0.715 0.179


3.4 Unpaved Road Dust Similar to paved roads, dust emissions are generated as vehicles pass along unpaved roadways and disturb the layer of loose material on or near the road surface. This material contains particulate matter from soil, brake and tire wear, exhaust, and other substances. The unpaved road dust calculation excludes emissions from exhaust and brake and tire wear, which are estimated as part of the onroad mobile sources emissions (see Section 0).

3.4.1 Activity Level Similar to onroad mobile sources and paved road dust, ADVMT is used to estimate unpaved road activity and calculate dust emissions. Travel over unpaved roads is included in HPMS ADVMT estimates, but are not separated from the paved road travel. Two agencies provided information on unpaved roads. The County Road Administration Board (CRAB) provided roadway mileage and ADVMT estimates on unpaved road by county.23 WSDOT provided estimates of city jurisdiction unpaved roadway mileage for each county.24 The CRAB data was used to develop an average daily traffic (ADT) per centerlane-mile factor (52 ADT/mi). This factor was multiplied by the WSDOT city centerlane-mileage data in order to estimate ADVMT. Unpaved roads on federal, state, and tribal lands are missing from this inventory. ADVMT on unpaved roads was included in the HPMS data, so it is essentially being double counted within paved road estimates. The overestimation is not significant, though, since unpaved ADVMT is only estimated to be 0.5% of the entire ADVMT.

3.4.2 Spatial Allocation Spatial adjustments were not necessary since the ADVMT was available by county. Monthly temporal adjustments for the VMT were made using the WSDOT temporal adjustment factors for urban roads shown in Table 3-5.


Table 3-18. ADVMT on Unpaved Roads – Total of County and City Jurisdictions County ADVMT

Adams 35,827 Asotin 6,599

Benton 12,410 Chelan 6,523 Clallam 806

Clark 1,049 Columbia 15,577

Cowlitz 281 Douglas 73,826

Ferry 19,040 Franklin 35,518 Garfield 18,463

Grant 62,617 Grays Harbor 3,733

Island 169 Jefferson 4,670

King 55,866 Kitsap 3,619 Kittitas 4,254

Klickitat 16,153 Lewis 2,173

Lincoln 49,632 Mason 2,969

Okanogan 30,731 Pacific 3,938

Pend Oreille 21,194 Pierce 3,270

San Juan 1,454 Skagit 2,821

Skamania 1,400 Snohomish 1,750

Spokane 108,113 Stevens 44,644

Thurston 4,813 Wahkiakum 743 Walla Walla 13,043

Whatcom 5,048 Whitman 51,997

Yakima 58,457 State Total 785,189

3.4.3 Emission Rates Unpaved road dust emissions were estimated according to Equation 2 in AP42.25 The equation includes an adjustment for rainfall which acts as a control efficiency term by assuming that


emissions occur only on days where the rainfall is below 0.01 inches. The equation was modified to calculate monthly emission rates. The original and modified AP42 equations are shown below.

E = [k (s/12)a (S/30)d / (Mdry/0.5)c – C] x [(365-p)/365] (original AP42 Equation 2) E = {[k (s/12)a (S/30)d / (Mdry/0.5)c] – C} x [(n-p)/n] (modified AP42 Equation 2)

where E is the emission factor in lb/VMT k = particle size multiplier (1.8 for PM10, 0.18 for PM2.5) s = silt content (%) a = PM10 constant (1) c = PM10 constant (0.2) d = PM10 constant (0.5) S = speed in mph C = material from exhaust, brake and tire wear in lb/mi (AP42 default: 0.00047 for PM10, 0.00036 for PM2.5) n = number of days in the given month p = number of days with at least 0.01 inches of precipitation in the given month Mdry = surface material moisture content (%)

Monthly days of precipitation greater than 0.01 inches were taken from Table 2-5. Vehicle speed was not available. The VMT-weighted average speed on local roads for Spokane in 2002 was used as an estimate (30 mph).26

The surface material silt content (3.2%) and moisture content (1%) were obtained from the Western Regional Air Partnership (WRAP).27, 28 Silt values used by EPA in the 1999 NEI were derived from sampling data taken from a database of approximately 200 samples from 30 states.29 Washington was not sampled. EPA used the national average of 3.9% for all states not sampled when they prepared the 1999 NEI. Rather than using the national average for states that were not sampled, the WRAP calculated an average of 3.2% for western states using the silt content database.27 The WRAP value was used in this inventory.

Calculated monthly emission rates are shown in Table 3-11 and Table 3-12. County assignments were the same as described in Section 2.2.

3.4.4 Emissions Estimates Monthly emissions were calculated using the equation below. Annual emissions were calculated by summing the monthly emissions.

Monthly Emissions (Tons) = V x M x D x E x (1 T/2000 lb)

where V = ADVMT M = monthly VMT adjustment factor (Table 3-5) D = number of days in month, and E = emission factor in lb/VMT, calculated as described in Section 3.4.3


Table 3-19. Unpaved Roads PM10 Emission Rates (pounds per mile) Station Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec KALW 0.256 0.179 0.121 0.181 0.323 0.292 0.417 0.390 0.376 0.256 0.167 0.229 KBLI 0.269 0.104 0.013 0.195 0.269 0.264 0.390 0.390 0.306 0.202 0.111 0.215 KCLM 0.283 0.119 0.108 0.167 0.242 0.348 0.417 0.404 0.320 0.269 0.111 0.189 KDLS 0.283 0.194 0.162 0.237 0.323 0.348 0.417 0.390 0.348 0.350 0.167 0.337 KEAT 0.283 0.253 0.202 0.209 0.269 0.376 0.417 0.390 0.403 0.350 0.223 0.310 KELN 0.364 0.209 0.229 0.223 0.296 0.362 0.417 0.404 0.376 0.350 0.209 0.337 KFHR 0.323 0.239 0.202 0.306 0.283 0.390 0.417 0.417 0.348 0.256 0.223 0.269 KGEG 0.269 0.164 0.121 0.181 0.283 0.334 0.417 0.417 0.348 0.310 0.139 0.215 KHQM 0.242 0.104 0.000 0.070 0.242 0.223 0.390 0.390 0.237 0.215 0.028 0.202 KKLS 0.242 0.089 0.081 0.070 0.256 0.250 0.390 0.390 0.306 0.189 0.070 0.175 KLWS 0.256 0.194 0.148 0.209 0.283 0.320 0.404 0.404 0.348 0.283 0.195 0.215 KMWH 0.256 0.209 0.202 0.264 0.296 0.376 0.417 0.404 0.376 0.337 0.209 0.296 KOLM 0.202 0.104 0.027 0.097 0.256 0.292 0.404 0.404 0.306 0.256 0.070 0.189 KOMK 0.310 0.224 0.215 0.237 0.283 0.348 0.417 0.417 0.376 0.390 0.250 0.296 KPAE 0.350 0.119 0.067 0.111 0.215 0.264 0.417 0.417 0.306 0.215 0.139 0.202 KPDX 0.229 0.089 0.094 0.083 0.256 0.278 0.417 0.390 0.320 0.215 0.111 0.189 KPSC 0.296 0.253 0.229 0.292 0.350 0.348 0.417 0.390 0.376 0.364 0.195 0.323 KPUW 0.242 0.179 0.108 0.139 0.269 0.292 0.404 0.390 0.348 0.269 0.153 0.283 KPWT 0.256 0.164 0.027 0.278 0.229 0.292 0.417 0.364 0.306 0.296 0.070 0.229 KSEA 0.229 0.104 0.054 0.097 0.215 0.320 0.417 0.404 0.320 0.242 0.097 0.189 KSHN 0.229 0.089 0.027 0.083 0.229 0.278 0.417 0.390 0.306 0.269 0.070 0.202 KYKM 0.242 0.224 0.202 0.250 0.337 0.362 0.417 0.296 0.362 0.377 0.195 0.310

Table 3-20. Unpaved Roads PM2.5 Emission Rates (pounds per mile)

Station Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec KALW 0.025 0.018 0.012 0.018 0.032 0.029 0.041 0.039 0.037 0.025 0.017 0.023 KBLI 0.027 0.010 0.001 0.019 0.027 0.026 0.039 0.039 0.030 0.020 0.011 0.021 KCLM 0.028 0.012 0.011 0.017 0.024 0.035 0.041 0.040 0.032 0.027 0.011 0.019 KDLS 0.028 0.019 0.016 0.023 0.032 0.035 0.041 0.039 0.035 0.035 0.017 0.033 KEAT 0.028 0.025 0.020 0.021 0.027 0.037 0.041 0.039 0.040 0.035 0.022 0.031 KELN 0.036 0.021 0.023 0.022 0.029 0.036 0.041 0.040 0.037 0.035 0.021 0.033 KFHR 0.032 0.024 0.020 0.030 0.028 0.039 0.041 0.041 0.035 0.025 0.022 0.027 KGEG 0.027 0.016 0.012 0.018 0.028 0.033 0.041 0.041 0.035 0.031 0.014 0.021 KHQM 0.024 0.010 0.000 0.007 0.024 0.022 0.039 0.039 0.023 0.021 0.003 0.020 KKLS 0.024 0.009 0.008 0.007 0.025 0.025 0.039 0.039 0.030 0.019 0.007 0.017 KLWS 0.025 0.019 0.015 0.021 0.028 0.032 0.040 0.040 0.035 0.028 0.019 0.021 KMWH 0.025 0.021 0.020 0.026 0.029 0.037 0.041 0.040 0.037 0.033 0.021 0.029 KOLM 0.020 0.010 0.003 0.010 0.025 0.029 0.040 0.040 0.030 0.025 0.007 0.019 KOMK 0.031 0.022 0.021 0.023 0.028 0.035 0.041 0.041 0.037 0.039 0.025 0.029 KPAE 0.035 0.012 0.007 0.011 0.021 0.026 0.041 0.041 0.030 0.021 0.014 0.020 KPDX 0.023 0.009 0.009 0.008 0.025 0.028 0.041 0.039 0.032 0.021 0.011 0.019 KPSC 0.029 0.025 0.023 0.029 0.035 0.035 0.041 0.039 0.037 0.036 0.019 0.032 KPUW 0.024 0.018 0.011 0.014 0.027 0.029 0.040 0.039 0.035 0.027 0.015 0.028 KPWT 0.025 0.016 0.003 0.028 0.023 0.029 0.041 0.036 0.030 0.029 0.007 0.023 KSEA 0.023 0.010 0.005 0.010 0.021 0.032 0.041 0.040 0.032 0.024 0.010 0.019 KSHN 0.023 0.009 0.003 0.008 0.023 0.028 0.041 0.039 0.030 0.027 0.007 0.020 KYKM 0.024 0.022 0.020 0.025 0.033 0.036 0.041 0.029 0.036 0.037 0.019 0.031


3.5 Construction Dust Construction dust was estimated by the EPA Nonpoint Methods Advisory Committee (NOMAD) for road construction, residential construction, and nonresidential construction. Construction dust emissions are based on the total amount of soil disturbed. Additionally, each of the three categories had emissions corrected using silt content and soil moisture parameters. Silt content factors were derived for each county, but EPA used the same soil moisture parameter for the entire state. Ecology calculated soil moisture parameters at the county level using meteorological data from 22 airport weather stations across the state. Construction dust emissions estimates were then recalculated by Ecology, using the county-specific soil moisture.

3.5.1 Activity Level

Road construction activity was estimated by EPA using the the Federal Highway Administration’s Highway Statistics for New Construction, Relocation, Added Capacity, Major Widening, and Minor Widening. These categories were also differentiated according to the road type. The State expenditure data were then converted to new miles of road constructed using $/mile conversions obtained from the Florida Department of Transportation (FLDOT) in 2014. A conversion of $6.9 million/mile is applied to the urban interstate expenditures and a conversion of $3.8 million/mile is applied to the rural interstate expenditures. For expenditures on other urban arterial and collectors, a conversion factor of $4.1 million/mile is applied. For expenditures on other rural arterial and collectors, a conversion factor of $2.1 million/mile is applied. The new miles of road constructed are used to estimate the acreage disturbed due to road construction. The total area disturbed in each state is calculated by converting the new miles of road constructed to acres using an acres disturbed/mile conversion factor for each road type which ranges from 6.6 to 11.4 acres disturbed per mile of constructed road.

Residential construction activity was estimated by EPA using acres of surface soil disturbed and volume of soil removed for basements. Surface soil disturbed was estimated using the 2017 New Privately Owned Housing Units Started by Purpose and Design 30 dataset from the US Census Bureau. The amount of soil removed were assumed to be ¼ acre, 1/3 acre, and ½ acre for 1-unit, 2-unit, and apartment structures, respectively. To calculate basement soil removal, the 2017 table of Characteristics of New Single-Family Houses Completed, Foundation 31 was used by EPA to estimate the percentage of 1 unit structures that have a basement (on the regional level). The county level estimate of number of 1-unit starts was multiplied by the percent of 1 unit houses in the region that have a basement to get the number of basements in a county. Basement volume was calculated by assuming a 2000 square foot house has a basement dug to a depth of 8 feet (making 16,000 ft3 per basement). An additional 10% is added for peripheral dirt bringing the total to 17,600 ft3 (651.85 yd3) per basement.

Non-residential construction activity was estimated by EPA using acres disturbed and were estimated by multiplying the value of non-residential construction put in place (obtained from the US Census Bureau) by the number of acres disturbed per million dollars. EPA used a factor of 1.009 acres/$1 million to convert from dollars to acres disturbed.

3.5.2 Spatial Allocation

Building permits were used by EPA to allocate the state-level acres disturbed by road construction to the county level. A ratio of the number of building starts in each county to the total number of building starts in each state was applied to the state-level acres disturbed to estimate the total number of acres disturbed by road construction in each county.


Residential construction activity data was already at the county level, so no spatial adjustment was needed.

The national value of non-residential construction put in place was allocated to counties using county-level non-residential construction employment data obtained from the US Census Bureau.

3.5.3 Emission Rates

Initial PM10 emissions from construction of roads were calculated by EPA using an emission factor of 0.42 tons/acre-month. This emission factor represents the large amount of dirt moved during the construction of roadways, reflecting the high level of cut and fill activity that occurs at road construction sites. The duration of construction activity for road construction is assumed to be 12 months.

Initial PM10 emissions from construction of residential structures were calculated using emission factors that ranged from 0.032 tons PM10/acre-month (1-unit structures without basements) to 0.11 tons PM10/acre-month (apartments). The duration of construction activity for houses was assumed to be 6 months and the duration of construction for apartments was assumed to be 12 months.

Initial PM10 emissions from construction of non-residential buildings were calculated by EPA using an emission factor of 0.19 tons/acre-month. The duration of construction activity for non-residential construction is assumed to be 11 months.

Regional variances in construction emissions were corrected using soil moisture level and silt content. To account for the silt content, the PM10 emissions are weighted using average silt content for each county. EPA used the National Cooperative Soil Survey Microsoft Access Soil Characterization Database to develop county-level, average silt content values for surface soil. To account for the soil moisture level, the PM10 emissions were initially weighted using a 30-year average precipitation-effectiveness (PE) value from Thornthwaite’s PE Index, derived from precipitation and evaporation estimates. EPA used a PE-Index of 127.9 for the entire state of Washington, which is suitable for western Washington, but overestimates soil moisture for eastern Washington. County-specific PE Index values were calculated by Ecology and used to make the final correction for construction dust emissions. The table below shows the PE Index values used by Ecology.

3.5.4 Emissions Estimates The equation for PM10 emissions corrected for soil moisture and silt content is:

where: Corrected EPM10 = PM10 emissions corrected for soil moisture and silt content, PE = precipitation-evaporation value for each State, S = % dry silt content in soil for area being inventoried.

Once PM10 adjustments were made, PM2.5 emissions were set to 10% of PM10.

Corrected E InitialPE

SPM PM E10 10= × ×

249%


Table 3-21. Precipitation Effectiveness Index Values County PE-Index

Adams 17.6 Asotin 28.2

Benton 14.5 Chelan 18.9 Clallam 87.5

Clark 92.5 Columbia 41.5

Cowlitz 99.7 Douglas 18.9

Ferry 41.7 Franklin 14.5 Garfield 28.2

Grant 17.6 Grays Harbor 182.9

Island 96.5 Jefferson 87.5

King 113.2 Kitsap 159.5 Kittitas 20.2

Klickitat 37.6 Lewis 99.7

Lincoln 17.6 Mason 216.4

Okanogan 41.7 Pacific 182.9

Pend Oreille 41.7 Pierce 113.2

San Juan 22.3 Skagit 96.5

Skamania 92.5 Snohomish 96.5

Spokane 48.4 Stevens 41.7

Thurston 142.5 Wahkiakum 99.7 Walla Walla 41.5

Whatcom 87.7 Whitman 60.3

Yakima 18.0


3.6 Nonroad Mobile Vehicles and Equipment (NEC) Nonroad mobile emissions estimates account for exhaust from nonroad mobile vehicles and equipment that combust fuel. However, nonroad mobile emissions for commercial ships, seaport support equipment, locomotives, and aircraft are estimated separately and thus not included here. Each nonroad sector includes multiple equipment types and horsepower classes. The sectors included in Ecology’s nonroad mobile category and corresponding examples are listed in the table below. Note that recreational marine vessels are discussed here but their emissions are listed separately in the BOAT category. Table 3-22. Sectors Included in Ecology’s Nonroad Mobile Category

Sector Examples Agricultural Equipment Tractors, Combines, Tillers, etc.

Airport Support Equipment Ground Support, Servicing Aircraft Recreational Marine Vessels Pleasure Craft, Personal Boats

Construction and Mining Equipment Crushers, Pavers, Backhoes, etc. Commercial Equipment Pumps, Compressors, Welders, etc.

Industrial Equipment Forklifts, Scrubbers, Tractors, etc. Lawn and Garden Equipment Chainsaws, Blowers, Lawn Mowers, etc.

Logging Equipment Chainsaws, Shredders, etc. Recreational Equipment Snowmobiles, ATVs, Golf Carts, etc.

Railroad Maintenance Equipment Railway Maintenance Equipment EPA's Motor Vehicle Emission Simulator (MOVES) model version 2014b with database movesdb20180517 was used to estimate nonroad emissions. MOVES combines basic nonroad activity information with information about equipment, vehicle and fuel characteristics, emissions control programs, meteorological information, and other parameters to estimate emissions. MOVES2014b was released in 2018 and included a major update to nonroad information. Updates included improvements to nonroad engine population growth rates, nonroad Tier 4 engine emission rates, and sulfur levels of nonroad diesel fuels.

3.6.1 MOVES Input Parameters MOVES includes a default database that summarizes emissions relevant information for all counties in the United States. Default data may be replaced by local data to improve the estimates. Default data was used for all parameters except the recreational marine vessel spatial surrogate. The default surrogate for recreational marine vessels is water surface area. Water surface area can overestimate recreational boat usage in counties with large areas of open water (e.g., Clallam). A new sptial allocation based on county boat registrations was substituted for the default. Boat registrations for 2017 were provided by the Washington Department of Licensing32, shown in Table 3-15.

3.6.2 Emission Rates Emissions were generated by the model for the four seasons by choosing one month from the representative season to run – January for winter, April for spring, July for summer, and October for fall.


Table 3-23. Boats Registered County Inboard Outboard

Adams 15 436 Asotin 23 812

Benton 402 6,696 Chelan 339 3,869 Clallam 655 2,748

Clark 809 11,119 Columbia 5 142

Cowlitz 310 4,235 Douglas 105 1,985

Ferry 29 353 Franklin 111 2,469 Garfield 4 94

Grant 172 4,288 Grays Harbor 198 2,219

Island 1,127 4,196 Jefferson 853 1,647

King 10,090 34,574 Kitsap 2,621 7,412 Kittitas 121 1,473

Klickitat 53 593 Lewis 221 2,290

Lincoln 102 980 Mason 539 3,768

Okanogan 85 1,464 Pacific 166 800

Pend Oreille 30 998 Pierce 3,433 19,238

San Juan 1,168 1,385 Skagit 1,844 5,165

Skamania 35 398 Snohomish 4,112 19,291

Spokane 661 14,215 Stevens 198 3,124

Thurston 1,328 7,141 Wahkiakum 63 346 Walla Walla 71 1,447

Whatcom 2,200 5,515 Whitman 37 920

Yakima 245 4,628 State Total 34,580 184,473


3.7 Locomotives

Emissions from Class I line-haul and switch yard locomotives were estimated using EPA guidance and information from the railroads. U.S. Class I railroads are line-haul freight railroads with operating revenue in excess of $250 million or more after applying the revenue deflator formula which adjusts for inflation using the Railroad Freight Price Index relative to 1991 ($463.9 million in 2017).33,34,35,36,37 The Class I railroads that operate in Washington are Burlington Northern Santa Fe Railway (BNSF) and Union Pacific Railroad (UP). Amtrak passenger train emissions were also included in this inventory. Note that EPA estimated Class 2 and 3 locomotive emissions as part of the 2017 NEI, which was accepted by Ecology. A special Northwest International Air Quality Environmental Science and Technology Consortium (NW-AIRQUEST; formerly Northwest Regional Technical Center) project conducted by the Oregon Department of Environmental Quality (ODEQ) found that emissions from Class II and III railroad locomotives were a small percentage of total locomotive emissions.38, 39

Amtrak, BNSF, and UP all provided 2017 activity information.

3.7.1 Activity Level Activity level is measured in gallons of diesel consumed by locomotives. All of the railroads provided 2017 fuel use for line haul and switch yard locomotives by county, shown in the table on the next page.40

3.7.2 Emission Rates All railroad companies provided activity data by county for 2017. All locomotive fuel used in 2017 was Ultra-Low Sulfur Diesel (ULSD) which contained 15 ppm sulfur content. Emission factors (shown in table below) were taken from EPA documentation,41 which also defines VOC emission rates as 1.053 x Hydrocarbons and PM2.5 emission rates as 0.97 x PM10. Table 3-24. Locomotive Emission Factors (grams per gallon of fuel consumed)

Pollutant Class I Freight Line

Switch Yards

Passenger Line

Carbon Monoxide 26.6 38.1 26.6 Nitrogen Oxides 114 206 112

PM10 2.9 4.5 2.8 Sulfur Dioxide 0.094 0.094 0.094 Hydrocarbons 4.6 11.8 4.6

3.7.3 Emissions Estimates For emissions calculated with speciation profiles, the equations used were: TPY = (PM10 emissions in TPY) x (pollutant PM10 fraction) TPY = (VOC emissions in TPY) x (pollutant VOC fraction)


Table 3-25. Locomotive Fuel Consumption (gallons)

County Class I Freight Line

Switch Yards

Passenger Line

Adams 9,567,328 0 369,380 Asotin 0 0 0

Benton 8,522,959 0 104,390 Chelan 2,446,658 0 102,142 Clallam 0 0 0

Clark 6,005,864 246,972 176,744 Columbia 1,507 0 0

Cowlitz 5,044,008 44,904 322,629 Douglas 243,470 0 26,499

Ferry 0 0 0 Franklin 5,583,527 449,040 68,576 Garfield 0 0 0

Grant 1,750,328 0 92,827 Grays Harbor 8,719 0 0

Island 0 0 0 Jefferson 0 0 0

King 5,975,562 913,367 311,615 Kitsap 0 0 0 Kittitas 408,821 0 0

Klickitat 12,265,173 0 147,913 Lewis 4,067,071 22,452 227,927

Lincoln 5,307,896 0 131,050 Mason 0 0 0

Okanogan 0 0 0 Pacific 0 0 0

Pend Oreille 0 0 0 Pierce 4,828,595 284,944 332,260

San Juan 0 0 0 Skagit 2,011,829 44,904 87,688

Skamania 4,488,650 0 65,204 Snohomish 3,698,796 471,492 247,966

Spokane 9,968,030 449,040 147,270 Stevens 125,736 134,712 0

Thurston 2,251,077 0 203,048 Wahkiakum 0 0 0 Walla Walla 2,348,286 22,452 0

Whatcom 1,427,871 179,616 103,105 Whitman 216,381 0 0

Yakima 1,636,408 44,904 0 State Total 100,200,551 3,308,799 3,268,231


3.8 Ships

Two separate annual emissions inventories were used to estimate emissions from ships: one for emissions occurring on the Columbia and Snake River systems, and one for Puget Sound, and the Strait of Juan de Fuca.

Columbia and Snake Rivers

EPA's 2017 NEI estimates were used for the Columbia and Snake Rivers. See Section 3.22.

Puget Sound, Strait of Juan de Fuca

Starcrest Consulting Group, LLC prepared a 2016 inventory for Puget Sound and Strait of Juan de Fuca under contract to the Puget Sound Maritime Air Forum.42 The inventory was a bottom-up, activity-based emissions inventory which provides detailed information on the five major source categories associated with the marine activities: ocean-going vessels, harbor vessels, cargo handling equipment, onroad heavy-duty vehicles, and rail operations. This was the third inventory prepared by Starcrest for the Pugest Sound area. The others represented the years 2005 and 2011.

The 2016 Starcrest emissions inventory was used for ocean-going vessels, harbor vessels (minus pleasure craft), and cargo handling equipment (see Section 0) in the 2017 comprehensive inventory.

The area covered by the maritime inventory is part of the Emission Control Area (ECA). In 2015, fuel used in the ECA was reduced to 0.1% fuel-sulfur level (or equivalent). Emissions of sulfur dioxide and particulate emissions decreased significantly from prior years due to the lower fuel sulfur content.

3.8.1 Activity Level and Emission Rates and Emissions Estimates

Activity level and emission rates of ships are described in the Starcrest report. The Starcrest report addressed PM10, PM2.5, NOX, SO2, CO, and VOC. It also addressed diesel particulate matter, black carbon, and greenhouse gases. The Starcrest report did not provide toxics estimates.

Emissions in tons per year were taken directly from the source data; however, EPA reporting limitations did not allow accurate spatial allocation of some of the emissions. All harbor craft emissions were allocated to major ports. Ecology reallocated the emissions more accurately. Ferry emissions were assigned to ferry routes using individual ferry emissions from Starcrest's Scenario Tool and 2016 ferry route assignments from Washington State Ferries. Fishing and excursion boats were assigned based on water surface area. Harbor tugs and assist/escort boats were left within their original port area assignments. Ocean tugs and all other harbor craft were assigned based on ocean-going vessel ship lane emissions.

SCC codes used to categorize emissions were updated by EPA after the work on the inventory was well underway. Ecology re-categorized the emissions to the new codes, but there wasn't enough information to be completely accurate. However, C3 ship emissions were taken directly from the 2017 NEI which allocated C3 emissions to counties based on transponder data.


3.9 Marine Cargo Handling Equipment The Puget Sound and Strait of Juan de Fuca 2016 maritime inventory prepared by Starcrest and described in Section 3.8 included port and near-port emissions from cargo handling equipment. The ports included in the marine cargo handling equipment inventory were Anacortes, Everett, Olympia, Northwest Seaport Alliance North and South Harbors, Port Angeles, Seattle, and Tacoma. The Argo, Fife, and BNSF SIG rail yards were also included. Similar to ships, the 2016 inventory was used as an estimate of 2017 emissions.

3.9.1 Activity Level and Emission Rates Activity level and emission rates are described in the Starcrest inventory documentation.42

3.9.2 Spatial Allocation Port and rail yard emissions were assigned to their respective counties.

3.9.3 Emissions Estimates Emissions in tons per year were taken directly from the source data. There is potential for double-counting in this category since it overlaps with equipment in the NONROAD model. However, in the NONROAD model the county allocations are based on surrogates which are not counted as port activity (Section 3.5). Therefore, emissions from the seaports were not subtracted from the NONROAD model output totals.


3.10 Silvicultural Burning (Prescribed Burning) Silvicultural burning, also discussed as prescribed burning, includes rangeland burns, logging debris burns and forest health burns. It does not include land clearing nor agricultural burns. Silvicultural burning is done by the Department of Natural Resources (DNR), Department of Fish & Wildlife (WDFW), U.S. Forest Service (USFS), Bureau of Indian Affairs (BIA), Bureau Of Land Management (BLM), US Fish and Wildlife Service (USFWS), and private industry.

3.10.1 Activity Level and Emission Rates

Prescribed burns are conducted either as pile burns, understory burns, or broadcast burns. Activity for pile burns is reported as tons of material burned while understory and broadcast burns are reported as “acres burned” and have a corresponding fuel loading (tons/acre) from which tons of material burned is calculated. Emissions for silvicultural burns were calculated using historical emission factors that were applied to the tonnage burned.

The amount and type of material burned was obtained from the DNR completed burns database and Ecology’s Air Quality Program Permitting System (AQPPS). All burn permits obtained from DNR were considered as prescribed burns. Most burn permits from AQPPS are for agricultural burns (see Section 3.10) but reported burns of rangeland and forest vegetation were included here. Burn permits were not available for prescribed burns done by the BIA. Furthermore, the DNR completed burns database was incomplete and contained errors, for which some gap-filling was done based on DNR planned burn database and satellite hot-spot detects. Prescribed burn location data was also obtained from EPA, based on satellite detected hot-spots (from SMARTFIRE2) which included rough estimates of fire size. The prescribed burn data from EPA was checked for errors (e.g. incorrect fire type) and then spatiotemporally cross-checked against the DNR and Ecology data to avoid double-counting. All burns added from the EPA dataset were considered pile burns unless fuels were grass/shrub or there were nearby DNR permits that specified a broadcast burn; hot-spots categorized as piles were assumed to be 100 tons (the maximum burn size allowed without a DNR permit) while hot-spots categorized as broadcast burns were assumed to be 46 acres (from SMARTFIRE2).

Table 3-26. Silvicultural Burning Emission Factors (pounds per ton consumed)

Pollutant EF PM10 15.5 PM2.5 13.5 CO 76 NOX 4 VOC 19 SO2 0.1

3.10.2 Spatial Adjustments No spatial adjustments were necessary. All burns were identified either by their coordinates or by county and Public Land Survey System (PLSS) legal description (i.e. section-township-range).

3.10.3 Emissions Estimates Prescribed burn emissions were calculated assuming 100% combustion completeness: TPY = (tons burned) x (pollutant lb/T) x (1 T/2000 lb)


Table 3-27. Silvicultural Burning Activity

County Pile Burns (Tons)

Broadcast & Understory

Burns (Acres) Adams 25 510 Asotin 128 1,704

Benton 0 200 Chelan 6,763 2,384 Clallam 29,206 25

Clark 2,868 0 Columbia 133 0

Cowlitz 4,482 0 Douglas 23 295

Ferry 8,764 1,383 Franklin 144 163 Garfield 711 307

Grant 107 4,940 Grays Harbor 31,538 0

Island 780 0 Jefferson 18,505 0

King 175 0 Kitsap 130 0 Kittitas 3,210 821

Klickitat 26,413 299 Lewis 15,305 0

Lincoln 481 80 Mason 5,283 0

Okanogan 26,067 5,709 Pacific 7,644 0

Pend Oreille 8,980 342 Pierce 1,655 3

San Juan 0 0 Skagit 10,315 0

Skamania 3,781 0 Snohomish 3,485 0

Spokane 10,441 340 Stevens 24,653 252

Thurston 971 100 Wahkiakum 1,979 0 Walla Walla 1,928 348

Whatcom 5,771 0 Whitman 129 0

Yakima 7,973 2,935 State Total 270,946 23,139


3.11 Wildfires Wildfire emissions were initially estimated for the 2017 EPA NEI by an EPA contractor. The Satellite Mapping Automatic Reanalysis Tool for Fire Incident Reconciliation (SMARTFIRE) v2 was used to estimate fire location and size, the Wildland Fire Assessment System (WFAS) National Fuel Moisture Database (NFDM) to estimate fuel moisture, and the BlueSky modeling framework to estimate the resultant emissions. The EPA effort used SMARTFIRE v2 to combine fire locations reported by the following sources: Incident Command Summary (ICS-209) reports, the National Oceanic and Atmospheric Administration (NOAA) Hazard Mapping System (HMS), fire perimeters from the Geospatial Multi-Agency Coordination (GeoMAC) group, the National Association of State Foresters (NASF) fire database, the US Forest Service ACtivity Tracking System (FACTS), and the US Fish and Wildlife Service (FWS) prescribed burn database. All fire locations detected by HMS that were within the boundaries of the USDA Cropland Data Layer (CDL) were classified as agricultural burns, except for pasture land. All remaining fire locations detected by HMS were classified as wildfire if they occurred between June 1 and August 31 and there was no other information available.

3.11.1 Activity Level Ecology reviewed the EPA’s wildfire emissions estimates and found many errors with fire type and size. These errors occurred presumably due to complications with SMARTFIRE, which was not developed to handle many sources of input. The EPA wildfire emissions data was corrected after crosschecking against InciWeb reports, the WildCAD online system, DNR post-burn reports, and GeoMAC perimeters. Overall, the fixes to wildfire data resulted in ~100 fewer fire locations and ~100,000 more acres burned than the original EPA data. Most of the decrease in wildfire locations was due to incorrect fire type while most of the increase in wildfire acres was due to missing data for the Jolly Mountain, 400, and Diamond Creek fires. The county level estimate of wildfire area burned is shown in Table 3-20. Washington State’s largest wildfires in 2017 are shown in Table 3-21.

3.11.2 Spatial Adjustments No spatial adjustments were necessary. All fire locations were identified by their coordinates.

3.11.3 Emissions Estimates The BlueSky modeling framework incorporates a variety of modeling components to determine emissions. This effort simulated emissions in BlueSky using the following model pathway: the Fuel Characteristic Classification System (FCCS) to determine fuel loading, the Consume model to estimate the percentage of fuels consumed, and the Fire Emission Production Simulator (FEPS) to estimate total emissions by pollutant type.


Table 3-28. Wildfire Area Burned by County (acres) County Area Burned

Adams 7,301 Asotin 4

Benton 31,679 Chelan 24,232 Clallam 4

Clark 13 Columbia 2,199

Cowlitz 9 Douglas 19,680

Ferry 5,406 Franklin 7,976 Garfield 3,183

Grant 49,796 Grays Harbor 103

Island 2 Jefferson 1

King 1,405 Kitsap 1 Kittitas 65,983

Klickitat 566 Lewis 36

Lincoln 3,094 Mason 9

Okanogan 102,948 Pacific 3

Pend Oreille 11,952 Pierce 11,912

San Juan 4 Skagit 292

Skamania 1,164 Snohomish 123

Spokane 378 Stevens 773

Thurston 635 Wahkiakum 0 Walla Walla 1,022

Whatcom 77 Whitman 896



Table 3-29. Largest Wildfires in Washington

Fire Name Area Burned (Acres) Counties

400 24,650 Kittitas, Yakima American 3,858 Yakima

Bridge Creek 3,803 Ferry Canyon Creek 3,819 Okanogan

Deife Road 1,660 Lincoln Diamond Creek 98,514 Okanogan

East Saddle 17,484 Adams, Franklin, Grant Eltopia 5,000 Franklin

Glade 3 11,052 Yakima Hayes Road 2,493 Douglas Jack Creek 4,659 Chelan

Jolly Mountain 38,735 Kittitas Monitor 1,097 Chelan

Monument Hill 6,262 Grant Noisy Creek 4,235 Pend O'Reille Norse Peak 51,728 Pierce, Yakima

North Fork Hughes 7,389 Pend O'Reille O'brien Road 3,000 Adams

Rattlesnake Hills 3,389 Yakima Redford Canyon 1,182 Ferry

Sawmill Creek 1,154 King Silver Dollar 33,781 Benton, Grant Snake River 3,000 Garfield

Spartan 8,728 Chelan Straight Hollow 8,414 Douglas

Sutherland Canyon 29,117 Douglas, Grant Uno Peak 8,851 Chelan

Whetstone Rd 1,350 Columbia Whitehall 3,440 Douglas, Grant


3.12 Agricultural Burning Agricultural burning in Washington is defined as "the burning of vegetative debris from an agricultural operation necessary for disease or pest control, necessary for crop propagation and/or crop rotation, or where identified as a best management practice by the agricultural burning practices and research task force established in RCW [Revised Code of Washington] 70.94.650 or other authoritative source on agricultural practices." 43 All agricultural burning in Washington requires a permit by law, but compliance is not 100%. In addition to the agricultural burn permits obtained, satellite-detected hot-spot data for agricultural land was obtained from EPA. The burn data for agricultural land from EPA was checked for errors (e.g. incorrect fire type, size, or crop) and then spatiotemporally cross-checked against the permit data for redundancy. Both datasets were then aggregated together. It is noted here that in Chelan, Douglas, Kittitas, Klickitat, and Okanogan Counties some orchards are removed but not replanted and thus do not qualify for agricultural burning permits. Emissions from these tear-outs are not consistently included in the inventory due to lack of permits and dependence on satellite detection.

3.12.1 Activity Level The activity level for agricultural burning is the amount of residue consumed. The general equation and sources of each parameter are described below.

tons consumed = acres burned x fuel loading x combustion completeness factor or

tons consumed = tons burned x combustion completeness factor

Acres or Tons Burned

Department of Ecology Central and Eastern Regional Offices (CRO, ERO)

The Department of Ecology maintains the Air Quality Program Permit System (AQPPS) database. The permits include crop type, general location (legal description), and permit issue date. Pile burn permits include the amount of material burned in tons while field burn permits include acreage and fuel loading estimates.44 Permits issued in 2017 were selected for this inventory.

Local Air Authority Permits

The agricultural burn permit database does not contain information for counties outside of Ecology air jurisdiction. Permit information was obtained for Benton, Island, Skagit, Whatcom, and Yakima counties. No permit information was obtained for counties under SWCAA, SRCAA, ORCAA, or PSCAA jurisdiction; but some information was obtained via satellite.

Satellite-Detected Hot-Spots Provided by EPA

EPA provided agricultural burn emissions based on satellite hot-spot detects that occurred at locations within the National Agricultural Statistics Service Cropland Data Layer (NASS CDL). For the state of Washington, each hot-spot was assumed to be a 120 acre burn. Due to known issues with this method, EPA made an update so that grassland locations in the CDL were not assumed to be agricultural burns. Several other burns were removed by Ecology due to double-counting with AQPPS data or incorrect fire type (e.g. wildfires on agricultural land). In addition, some burns were transferred from the EPA wild/prescribed fire data to the agricultural burning database due to


incomplete coverage in the NASS CDL. The 2017 Washingon State Department of Agriculture Land Use GIS data was used to determine crop type for reclassified EPA data.

Fuel Loading

The overall average loading factor for wheat field burns was 4.2 tons/acre. This is comparable to the 4 tons/acre average found in a special study performed by Air Sciences Incorporated.45 Air Sciences Incorporated performed a field study funded by Ecology, the Washington Association of Wheat Growers, and the US EPA-Region 10 to measure emissions and develop emission rates for wheat stubble field burning. The loading factor was 0.7 tons/acre higher than the wheat field loading factor of 3.2 tons/acre in AP42 (adjusted for fuel consumption).

The agricultural burn permit database included a loading factor for each field burn. For burns detected by satellite, the EPA’s default fuel loading was used. Overall average fuel loading and combustion completeness for field burns is shown in the table below for the following crop groups: barley; beans; corn; fallow land; hay, grass, pasture and Conservation Reserve Program (CRP) land; lentils; potatoes, wheat (including triticale), and all other field crops.

Table 3-30. Average Fuel Loading for Agricultural Field Burns

Crop Group Fuel Loading (Tons/Acre)

Combustion Completeness

Barley 2.4 85%

Beans, Dry 1.7 85%

Corn 3.4 75%

Fallow 2.2 75%

Hay, Grass, Pasture, CRP 1.9 85%

Lentils 2.9 75%

Other 1.9 85%

Potatoes 1.9 85%

Wheat 4.2 85% All field and pile burns were summed together to generate the total tons of agricultural residue burned in 2017, shown in the table below. Note that Yakima permit data did not include crop type, so they were grouped in the “Other” category.

3.12.2 Emission Rates Emission rates were taken from EPA's AP42 (VOC, CO, NOX), a San Joaquin Valley study (NOX)46, the Air Sciences Incorporated report (PM10, PM2.5, CH4)45, and the 2014 NEI Agricultural Burning Emission Factors spreadsheet (NH3)47. Emission rates for NH3 are based on the NOx/NH3 emissions ratio in the 2002 NEI and the NOx emissions rate from McCarty et al. (2011)48.

3.12.3 Emissions Estimates Emissions estimates were calculated with the equation below. TPY = (tons consumed) x (pollutant lb/T) x (1 T/2000 lb)


Table 3-31. Agricultural Residue Burned (tons)

County Wheat Corn Grass, Hay,

Pasture, CRP, and Fallow

Tree Plantations, Orchards, & Vineyards

Other or Unknown

Adams 9,195 642 572 746 303 Asotin 0 0 1,672 0 95

Benton 456 3,528 3,420 18,337 1,824 Chelan 0 0 230 8,231 0

Clark 228 0 228 228 0 Columbia 93,544 53 10,422 0 2,660

Douglas 0 0 1,596 6,533 2 Ferry 0 0 228 0 0

Franklin 18,493 260 1,596 8,076 1,783 Garfield 50,093 0 2,625 0 935

Grant 3,194 4,833 2,322 19,463 2,514 Grays Harbor 0 504 684 0 0

Island 0 0 228 22 0 Kittitas 0 0 0 119 17

Klickitat 0 0 1,140 2,151 0 Lewis 0 0 3,648 0 0

Lincoln 7,272 0 1,179 0 10 Mason 0 0 0 228 0

Okanogan 0 0 684 4,193 7 Pacific 0 0 228 0 0

Pend Oreille 0 0 1,378 0 0 San Juan 0 0 75 0 0

Skagit 0 579 588 32 1,373 Snohomish 0 0 456 0 228

Spokane 1,596 0 1,368 0 1,265 Stevens 0 0 916 0 210

Thurston 0 0 684 0 456 Wahkiakum 0 0 456 0 0 Walla Walla 101,319 0 13,163 1,009 12,485

Whatcom 0 504 243 12 858 Whitman 66,590 0 8,754 0 903

Yakima 0 5,040 7,980 10,488 16,772 State Total 351,981 15,942 68,763 79,866 44,700


Table 3-32. Agricultural Burn Emission Factors (pounds per ton of material consumed)

Crop Burn Season CO NOX PM10 PM2.5 SO2 VOC NH3

Barley spring 67 5.1 6.8 6.8 0.1 5.3 12.52

Barley fall 117 5.1 12.3 12.3 0.1 12 12.52

Wheat and triticale spring 67 4.3 6.8 6.8 0.9 5.3 33.73

Wheat and triticale fall 117 4.3 12.3 12.3 0.9 12 33.73

Corn all 108 3.3 13.8 13.2 0.4 12 19.32

CRP, grass, pasture all 101 4.5 15.7 15 0.6 15 12.52

Hay - Alfalfa all 106 4.5 44.3 42.2 0.6 28 12.52

Hay - Other all 139 4.5 31.5 30 0.6 17 12.52

Apple all 42 5.2 3.9 3.7 0.1 3 12.52

Apricot all 49 5.2 5.9 5.6 0.1 6 12.52

Cherry all 44 5.2 7.9 7.4 0.1 8 12.52

Mixed fruit trees all 42 5.2 3.9 3.7 0.1 3 12.52

Peach all 42 5.2 5.9 5.6 0.1 4 12.52

Pear all 57 5.2 8.8 8.3 0.1 7 12.52

Berries all 117 5.2 20.7 19.7 0.1 18 12.52

Beans, legumes, lentils all 186 5.2 42.3 40.3 0.1 36 39.76

Peas all 147 5.2 30.5 29.1 0.1 29 12.52

Potatoes all 117 4.5 20.7 19.7 0.6 18 12.52

Vineyards all 117 5.2 20.7 19.7 0.1 18 12.52

Other Crops all 117 5.2 20.7 19.7 0.1 18 12.52

Limbs & Brush all 140 4.3 16.3 14.5 0.1 19 12.52

Willow all 140 5.2 16.3 14.5 0.1 19 12.52


3.13 Municipal Solid Waste Burning Municipal Solid Waste Burning is outdoor burning of household waste. The EPA methodology was used to calculate this category.

3.13.1 Activity Level The measure of activity for residential trash burning is the amount of material burned. The EPA method assumes that 0.42 tons of waste are generated per person per year and that 0.354 tons of that waste is combustible or “burnable”. This assumes that a person burning RHW in their yard is more likely to be a non-recycler than an avid recycler. Open burning of trash is generally not practiced in urban areas, so only the rural population in each county is assumed to practice open burning. The EPA default used assumes that 24% of the rural population burns trash. Trash burning is illegal in Washington State but compliance is not %100. The EPA default assumes that 25% of households will burn trash even when it is banned. The amount of residential trash burned was estimated using the following equation: Rural Population x 0.354 (tons/person/year) x 28% x 25%.

3.13.2 Emission Rates Emissions factors for open burning of municipal solid waste are listed in the table below. The emissions factors for CO, NOX, PM, SO2, and VOC and some HAPs are from AP-4249 and the EPA report Evaluation of Emissions from the Open Burning of Household Waste in Barrels50. Emissions factors for HAPs are from an EPA Office of Research and Development report51 and a Minnesota Pollution Control Agency Report52. HAP emissions factors are based on an average of emissions factors for non-recyclers. Emission factors were applied to the combustible waste burned.

3.13.3 Emissions Estimates Emissions estimates were calculated with the equations below. TPY = (tons burned) x (pollutant lb/T) x (1 T/2000 lb) Table 3-33. Emission Rates in Pounds Per Ton of Combustible Material Burned

Pollutant Name Pollutant Code

Emission Rate (lbs/ton)

Carbon Monoxide CO 100.61 Nitrogen Oxides NOX 7.1 PM10 PM10-PRI 38 PM2.5 PM25-PRI 34.8 Sulfur Oxides SO2 1.184 VOC VOC 7.409 1,2,4-trichlorobenzene 120821 2.00E-04 1,4-dichlorobenzene 106467 6.00E-05 2,4,6-Trichlorophenol 88062 3.80E-04 2-Methylnapthalene 91576 1.70E-02 Acenaphthene 83329 1.28E-03 Acenaphthylene 208968 1.47E-02


Pollutant Name Pollutant Code

Emission Rate (lbs/ton)

Acetaldehyde 75070 8.55E-01 Acetophenone 98862 9.36E-03 Acrolein 107028 5.32E-02 Anthracene 120127 2.59E-03 Benz[a]anthracene 56553 3.01E-03 Benzene 71432 1.96E+00 Benzo[a]pyrene 50328 2.79E-03 Benzo[g,h,i,]Perylene 191242 2.59E-03 Benzo[k]fluoranthene 207089 1.34E-03 Bis (2-Ethylhexyl) Phthalate 117817 4.75E-02 1,3-Butadiene 106990 2.82E-01 Benzo[b]fluoranthene 205992 3.71E-03 Chloromethane 74873 3.26E-01 Chrysene 218019 3.59E-03 Cresol/Cresylic Acid (Mixed Isomers) 1319773 1.37E-01 Dibenzo[a,h]anthracene 53703 5.40E-04 Dibutyl Phthalate 84742 6.89E-03 Ethyl Benzene 100414 3.63E-01 Fluoranthene 206440 5.53E-03 Fluorene 86737 5.97E-03 Formaldehyde 50000 8.85E-01 Dibenzofuran 132649 7.26E-03 Hexachlorobenzene 118741 8.00E-05 Indeno[1,2,3-c,d]pyrene 193395 2.53E-03 Isophorone 78591 1.85E-02 Methylene Chloride 75092 3.39E-02 Naphthalene 91203 2.27E-02 Pentachloronitrobenzene 82688 2.00E-05 Phenanthrene 85018 1.06E-02 Phenol 108952 2.25E-01 Polychlorinated Biphenyls (PCBs) 1336363 2.51E-04 Propionaldehyde 123386 2.25E-01 Pyrene 129000 6.35E-03 Styrene 100425 1.05E+00 Toluene 108883 7.42E-01 Xylenes (Mixed Isomers) 1330207 7.58E-02


3.14 Publicly Owned Treatment Works (POTW) Emissions from publicly owned wastewater treatment plants (WWTPs) are included in the POTW category. POTWs are distinct and separate from industrial wastewater treatment.

3.14.1 Activity Level Activity for POTWs is measured in gallons of wastewater treated, which was obtained from the Permit and Reporting Information System (PARIS)53 Discharge Monitoring Reports (DMRs) 54. Reports for 2017 were selected from Municipal National Pollutant Discharge Elimination System Individual Permits (NPDES IP), Municipal to ground State Waste Discharge Permits (SWDP), and Reclaimed Water Individual Permits. The amount of wastewater treated at POTWs is shown in Table 3-26.

3.14.2 Emission Rates An ammonia emission factor of 0.169 pounds per million gallons was obtained from a report to EPA,55 while the VOC emission factor of 0.85 pounds per million gallons was based on a study from a California based Technical Advisory Committee (Tri-TAC).56 Emission factors for the 52 HAPs were derived using 1996 area source emissions estimates that were provided by EPA’s Environmental Sciences Division (ESD)57 and the 1996 nationwide flow rate.58 These HAP emission factors were then multiplied by the 2008 to 2002 VOC emission factor ratio (0.85/9.9) to obtain the final HAP emission factors applied in the 2017 inventory. The HAP emission factors are listed in Table 3-27.

3.14.3 Spatial Allocation WWTPs were allocated to counties based on their geographical coordinates.

3.14.4 Emissions Estimates Annual emissions for each county were calculated according to the following equation:

TPY = (million gallons treated) x (pollutant lb/million gallons) x (1 T/2000 lb)


Table 3-34. Public Wastewater Treatment Plant Throughput (millions of gallons) County Wastewater Treated

Adams 640 Asotin 349

Benton 4,938 Chelan 1,624 Clallam 1,372

Clark 12,735 Columbia 92

Cowlitz 4,028 Douglas 639

Ferry 84 Franklin 2,130 Garfield 52

Grant 1,948 Grays Harbor 2,646

Island 599 Jefferson 396

King 72,842 Kitsap 4,579 Kittitas 1,775

Klickitat 443 Lewis 1,844

Lincoln 207 Mason 1,072

Okanogan 528 Pacific 548

Pend Oreille 147 Pierce 19,573

San Juan 141 Skagit 3,068

Skamania 95 Snohomish 23,403

Spokane 16,949 Stevens 663

Thurston 5,711 Wahkiakum 42 Walla Walla 2,388

Whatcom 6,338 Whitman 1,678



Table 3-35. POTW HAP Emission Factors (pounds per million gallons)

Pollutant Code Description Emission Factor 100414 ETHYL BENZENE 0.00766 100425 STYRENE 0.00273 100447 BENZYL CHLORIDE 0.00000817 106467 1,4-DICHLOROBENZENE 0.000216 106898 1-CHLORO-2,3-EPOXYPROPANE 0.00000452 106990 1,3-BUTADIENE 0.0000251 107028 ACROLEIN 0.000384 107051 ALLYL CHLORIDE 0.0000194 107131 ACRYLONITRILE 0.000386 108054 VINYL ACETATE 0.0000766 108101 METHYL ISOBUTYL KETONE 0.00269 108883 TOLUENE 0.0123 108907 CHLOROBENZENE 0.000483 120821 1,2,4-TRICHLOROBENZENE 0.0000867 121142 2,4-DINITROTOLUENE 0.0000481 121697 N,N-DIMETHYLANILINE 0.000322 123386 PROPIONALDEHYDE 0.0000035 123911 P-DIOXANE 0.0000179 126998 CHLOROPRENE 0.0000238 127184 TETRACHLOROETHYLENE 0.00427

1330207 XYLENES 0.0598 140885 ETHYL ACRYLATE 0.00000175

1634044 METHYL TERT-BUTYL ETHER 0.0000637 171 GLYCOL ETHERS 0.0115

1319773 CRESOLS/CRESYLIC ACIDS 0.00000161 50000 FORMALDEHYDE 0.0000197 56235 CARBON TETRACHLORIDE 0.00112 67561 METHANOL 0.0114 67663 CHLOROFORM 0.00644 71432 BENZENE 0.00673 71556 METHYL CHLOROFORM 0.000563 75014 VINYL CHLORIDE 0.00000671 75058 ACETONITRILE 0.000345 75070 ACETALDEHYDE 0.00031 75092 METHYLENE CHLORIDE 0.0091 75150 CARBON DISULFIDE 0.00432 75218 ETHYLENE OXIDE 0.000222 75354 VINYLIDENE CHLORIDE 0.000423 75569 PROPYLENE OXIDE 0.000732 77474 HEXACHLOROCYCLOPENTADIENE 0.000000583 77781 DIMETHYL SULFATE 0.00000131 78875 PROPYLENE DICHLORIDE 0.0000115 79005 1,1,2-TRICHLOROETHANE 0.00000117 79016 TRICHLOROETHYLENE 0.000306 79345 1,1,2,2-TETRACHLOROETHANE 0.00000175 79469 2-NITROPROPANE 0.000000292 80626 METHYL METHACRYLATE 0.000311 87683 HEXACHLOROBUTADIENE 0.000000729 91203 NAPHTHALENE 0.00131 92524 BIPHENYL 0.0000752 95534 O-TOLUIDINE 0.00000175 98953 NITROBENZENE 0.00000656


3.15 Gasoline Service Stations

Emissions from gasoline service stations result from evaporation of gasoline vapors during underground tank filling, underground tank breathing and emptying, tank truck transit, and vehicle refueling.

Underground tank filling in Washington is controlled by use of vapor balance systems known as Stage I control. Controls for vehicle refueling include onboard canisters known as onboard refueling vapor recovery (ORVR), and at-the-pump special nozzles known as vapor recovery systems (VRS), both of which capture vapors from the vehicle gas tank during refueling. At-the-pump control is also known as known as Stage II.

3.15.1 Activity Level, Temporal and Spatial Allocation

The measure of activity is gallons of gasoline distributed in the county. EPA used the MOVES model to calculate monthly county-level gasoline consumption by summing onroad and nonroad gasoline consumption.

County-level gasoline consumption is then allocated to submerged balanced filling based on assumptions about the percentage of filling technology used in each county. True vapor pressure is calculated for each county, month, and fuel subtype. Percentages of the filling technology are derived from the EIIP study.59

Following EPA methods, gasoline throughput for tank trucks was computed by multiplying the county-level gasoline consumption estimates by a factor of 1.09 to account for gasoline that is transported more than once in a given area (i.e., transported from bulk terminal to bulk plant and then from bulk plant to service station).


Underground Tank Filling, Breathing and Emptying, Transit Losses

Emission rates for underground tank filling, breathing and emptying, and transit losses were taken from documentation released by the EPA for gasoline distribution for the 2017 NEI.60

County-level gasoline consumption is multiplied by the emissions factor for VOC to estimate VOC emissions from UST filling, breathing/emptying, and transit losses. County-level benzene speciation profiles are multiplied by VOC emissions to estimate benzene emissions from UST breathing and emptying. National average speciation profiles for all other HAPs are multiplied by VOC emissions to estimate HAP emissions from UST breathing and emptying.

Per the local air authorities, all underground tank filling was assumed to be controlled using a vapor balance system to recover displaced gasoline vapors as the tank is filled (balanced submerged filling).

In the NEI, EPA provided emission factors for filling, breathing/emptying, and transit losses. Emission factors in lbs/1000 gallons were calculated from the NEI information. EPA also provided toxics speciation profiles. The factors are shown in the table below. VOC factors are expressed as lbs/1000 gallons. Toxics are expressed as a percentage of the VOC.


Table 3-36. Gasoline Tank and Truck VOC Emission Factors (pounds per thousand gallons)

Source Emissions Factor Underground Tank Filling varies by county 0.814 - 0.831 Tank Breathing and Emptying 1.0 Truck Transit 0.06

Table 3-37. Gasoline Station VOC Speciation for Toxics

Name CAS Percent of VOC 2,2,4-Trimethylpentane 540841 0.75% Cumene 98828 0.012% Ethyl Benzene 100414 0.053% n-Hexane 110543 1.8% Naphthalene 91203 0.00027% Toluene 108883 1.4% Xylenes 1330207 0.56% Benzene 71432 Varies by county, 1.12%-1.20%

Vehicle Refueling

Vehicle refueling emissions come from vapors displaced from the automobile tank by dispensed gasoline and from spillage. Refueling emissions may be controlled by VRS and ORVR. VRS is required in Clark, Cowlitz, King, Kitsap, Pierce, and Snohomish Counties, and for very large stations near residences in other counties.61 ORVR is a federal emissions control program for light duty gasoline vehicles. It was phased in over several years beginning in 1998.

Emission factors were calculated with the MOVES model using King County as a representative county. MOVES energy (MMBTUs) and emissions (grams) outputs were combined with conversion factors for fuel energy (MMBTUs/gallon)62 to calculate emission factors in g/gallon. Refueling factors were calculated with and without VRS. Gasoline was assumed to contain 10% ethanol (E10). Similar to the onroad category, values for January, April, July, and October were averaged for an annual estimate.

Table 3-38. Fuel Energy Conversion Factors

Fuel MMBTUs/gallon Gasoline (E0) 0.125 Ethanol (E100) 0.084 Gasoline (E10) 0.121


Table 3-39. Vehicle Refueling Emission Factors in grams per gallon

3.15.3 Emissions Estimates Annual emissions for each county were calculated according to the following equation:

TPY = (gallons) x (pollutant lb/gallons) x (1 T/2000 lb)

Pollutant Without VRS With VRS VOC 1.117 0.400 2,2,4-Trimethylpentane 0.038 0.014 Benzene 0.004 0.001 Ethanol 0.134 0.048 Ethyl Benzene 0.019 0.007 Hexane 0.028 0.010 MTBE 0.000 0.000 Toluene 0.160 0.057 Xylene 0.072 0.026


3.16 Residential Wood Combustion Residential wood combustion (RWC) consists of home heating and recreational use of wood-burning equipment. Emissions from woodstoves, fireplaces, fireplace inserts, pellet stoves, indoor furnaces, and outdoor hydronic heaters are included. Other outdoor wood burning devices, such as fire pits, are part of the residential outdoor burning category for the 2017 EI.


The measure of activity for residential wood combustion is the amount of wood burned. EPA estimated RWC emissions based on the 2018 Comission on Environmental Cooperation (CEC) nationwide survey. EPA supplemented the CEC survey with information from the 2015 Energy Information Administration (EIA) Residential Energy Consumption Survey (RECS) and the state of Minnesota’s 2014/2015 residential wood survey.

Ecology replaced some EPA assumptions with data from other surveys conducted by WSU, the National Research Center, and Kittitas county. Specifically, Ecology replaced appliance fractions of woodstoves and fireplace inserts for predominantly urban counties (King, Pierce, Snohomish, Spokane, Clark, Yakima, Thurston, Kitsap, Benton, and Whatcom) and replaced burn rates of woodstoves and fireplace inserts for all counties. Ecology also replaced the estimate of occupied housing units with our own 2017 demographic data.

The residential wood combustion activity data replaced by Ecology was taken from the three residential wood combustion surveys previously used for our state. The first Washington survey was conducted by Washington State University in 2001 (WSU2001).63 It divided the state into regions and compiled results for each region. The WSU survey had 749 completed surveys for WA. The second Washington survey was conducted by the National Research Center in 2007 (NRC2007).64, 65 It covered seven geographic areas in the central Puget Sound region and consisted of 1,015 completed surveys. The third Washington survey was conducted by the Kittitas County Health Department in 2014 and 2015. It covered Kittitas County and consisted of 1174 responses. The surveys solicited complete information to conduct an inventory. The Washington survey areas are listed below.

Table 3-40: Surveys and Survey Groups

Survey Survey Group Geographic Area Kit2014-15 Ellensburg and other areas Kittitas County occupied housing unit weighted average NRC2007 PS_King King County total NRC2007 PS_Kitsap Kitsap County total NRC2007 PS_PieN Pierce County Non-Urban Growth Area NRC2007 PS_PieU Pierce County Urban Growth Area NRC2007 PS_SnoD Snohomish County - Darrington NRC2007 PS_SnoM Snohomish County - Marysville, Lake Stevens, North Everett NRC2007 PS_SnoO Snohomish County - All other areas

WSU2001 Eastern WA_Range Adams, Asotin, Benton, Franklin, Garfield, Grant, Lincoln, Whitman Counties

WSU2001 Eastern WA Forested Chelan, Columbia, Douglas, Ferry, Kittitas, Klickitat, Okanogan, Pend Oreille, Spokane, Stevens, Walla Walla, Yakima


The Washington surveys were used for each county group as shown below. Table 3-41. County to Survey Assignments

County(ies) Survey Group Kittitas Kittitas King PS_King Kitsap PS_Kitsap

Pierce PS_Pierce = Household weighted average of PS_PieN and PS_PieU

Snohomish PS_Snohomish = Household weighted average of PS_SnoD, PS_SnoM, and PS_SnoO

Adams, Asotin, Benton, Franklin, Garfield, Grant, Lincoln, Whitman EWA_Range

Chelan, Columbia, Douglas, Ferry, Klickitat, Okanogan, Pend Oreille, Spokane, Stevens, Walla Walla, Yakima

EWA Forested

Clallam, Clark, Cowlitz, Grays Harbor, Island, Jefferson, Lewis, Mason, Pacific, San Juan, Skagit, Skamania, Thurston, Wahkiakum, Whatcom

PS_KitPieSno = Household weighted average of all the PS survey areas except King Co.*

The EPA and Washington surveys were used to determine the percentage of households using wood burning devices and the annual amount of wood burned per device for fireplaces, woodstoves, inserts, and pellet stoves. For woodstoves and fireplace inserts, EPA used distribution profiles based on a combination of data from RECS and Minnesota surveys such that 31% of devices in western states are uncertified, 41% are certified noncatalytic, and 28% are certified catalytic. For central heaters, EPA used distribution profiles based on the CEC survey such that 1% are indoor pellet boilers, 3% are indoor pellet furnaces, 23% are indoor cordwood boilers, 37% are indoor cordwood furnaces, and 36% are outdoor cordwood boilers. The percentage of occupied households using wood burning devices are shown in the table below. To calculate emissions using emission factors in lb/ton burned, the weight of the wood burned was estimated. Pellets are sold in 40-lb sacks, and fire logs were estimated at 8 lb per log. The weight of a cord of wood varies with moisture content and species type. EPA calculated the weight of wood burned using wood density factors (e.g. for pine) from the US Forest Service, which varied from 2034 to 2056 pounds per cord for counties in Washington state.66


Table 3-41. Device Usage (Percent of Occupied Housing Units)

County Fireplaces Woodstoves Fireplace Inserts

Central Heaters

Pellet Stoves Wax Logs

Adams 3.8% 11.1% 5.3% 1.4% 3% 0% Asotin 7.4% 7.8% 4.3% 1% 2.5% 0%

Benton 3.9% 5% 3% 1% 2.4% 0% Chelan 6% 16.7% 7.6% 3.5% 3% 0% Clallam 7.7% 15.5% 9.2% 2.3% 3.2% 0%

Clark 10.3% 12.3% 8.1% 1.5% 2.5% 0% Columbia 4.7% 15.9% 8.0% 2.3% 3.5% 0%

Cowlitz 9.5% 14.4% 9% 2.3% 2.9% 0% Douglas 3.3% 13.6% 6.4% 2% 2.8% 0%

Ferry 6.5% 25.9% 9.6% 6.8% 4.5% 0% Franklin 4.8% 9.1% 5.2% 1% 2.6% 0% Garfield 3.6% 20.1% 7.5% 3.6% 4.6% 0%

Grant 2.9% 11.9% 6.1% 1.4% 2.6% 0% Grays Harbor 9.5% 15% 8.8% 2.5% 3.2% 0%

Island 5.3% 12.6% 6.4% 1.7% 3.6% 0% Jefferson 9.3% 18.8% 10.4% 3.3% 3.9% 0%

King 11.1% 5.9% 5.9% 1% 1.8% 1.9% Kitsap 8.5% 15.3% 12.9% 1.2% 2.6% 0% Kittitas 6.4% 14.8% 5.9% 3.4% 3.1% 0%

Klickitat 5.6% 14.2% 6.8% 2.1% 3.7% 0% Lewis 9.5% 17.3% 9.5% 3.2% 3.5% 0%

Lincoln 4.2% 18.5% 6.1% 3.6% 4.7% 0% Mason 9.1% 19.1% 10% 3.5% 4% 0%

Okanogan 5.2% 20.4% 8.4% 4.1% 4.1% 0% Pacific 6.3% 17.8% 9.1% 2.9% 3.7% 0%

Pend Oreille 8.8% 25% 10.5% 6.7% 4.2% 0% Pierce 10.7% 11.2% 6.7% 1.3% 2.3% 1.2%

San Juan 4.3% 18.6% 7.6% 2.8% 5.1% 0% Skagit 12.5% 10.9% 6.2% 1.9% 3.1% 0%

Skamania 10.1% 25.1% 10% 7.6% 4.3% 0% Snohomish 12.2% 12.4% 8% 1.4% 2.4% 0%

Spokane 7.6% 11.5% 5.2% 1.7% 2.5% 0% Stevens 8.4% 20.8% 8.1% 5.3% 4.1% 0%

Thurston 10.7% 12.3% 8.1% 1.3% 2.7% 0% Wahkiakum 8.2% 23% 10.5% 4.6% 4.8% 0% Walla Walla 5.4% 8.4% 4.5% 1% 2.7% 0%

Whatcom 10.2% 12.3% 8.1% 2.5% 2.7% 0% Whitman 3.6% 7.4% 3.3% 1.1% 2.1% 0%

Yakima 6.7% 11.5% 5.2% 1.3% 2.8% 0%


Table 3-42. Amount of Wood Burned Per Device

County Fireplaces (Cords)

Woodstoves (Cords)

Fireplace Inserts (Cords)

Central Heaters (Cords)

Pellet Stoves (Tons)

Wax Logs (Tons)

Adams 0.42 1.40 1.30 2.61 0.40 0.78 Asotin 0.36 1.40 1.30 2.57 0.34 0.78

Benton 0.36 1.40 1.30 2.57 0.36 0.78 Chelan 0.34 2.70 1.70 3.02 1.27 0.78 Clallam 0.35 2.03 1.98 2.82 0.90 0.78

Clark 0.27 2.03 1.98 2.43 0.87 0.78 Columbia 0.39 2.70 1.70 2.77 0.64 0.78

Cowlitz 0.32 2.03 1.98 2.82 1.05 0.78 Douglas 0.38 2.70 1.70 2.78 0.58 0.78

Ferry 0.41 2.70 1.70 3.11 1.76 0.78 Franklin 0.38 1.40 1.30 2.59 0.34 0.78 Garfield 0.47 1.40 1.30 2.78 0.73 0.78

Grant 0.41 1.40 1.30 2.69 0.47 0.78 Grays Harbor 0.35 2.03 1.98 2.78 0.94 0.78

Island 0.37 2.03 1.98 2.51 0.53 0.78 Jefferson 0.37 2.03 1.98 2.86 1.11 0.78

King 0.23 1.31 1.35 1.92 0.91 0.78 Kitsap 0.29 1.94 1.57 2.39 0.65 0.78 Kittitas 0.36 2.00 2.10 2.94 1.05 0.78

Klickitat 0.41 2.70 1.70 2.64 0.56 0.78 Lewis 0.36 2.03 1.98 2.87 1.18 0.78

Lincoln 0.47 1.40 1.30 2.79 0.66 0.78 Mason 0.38 2.03 1.98 2.84 1.12 0.78

Okanogan 0.41 2.70 1.70 2.90 1.06 0.78 Pacific 0.39 2.03 1.98 2.83 0.93 0.78

Pend Oreille 0.37 2.70 1.70 3.15 2.04 0.78 Pierce 0.28 1.42 1.70 2.58 0.77 0.78

San Juan 0.47 2.03 1.98 2.61 0.56 0.78 Skagit 0.35 2.03 1.98 2.65 0.65 0.78

Skamania 0.38 2.03 1.98 3.14 2.30 0.78 Snohomish 0.29 2.86 2.29 2.61 0.74 0.78

Spokane 0.32 2.70 1.70 2.75 0.67 0.78 Stevens 0.38 2.70 1.70 3.04 1.45 0.78

Thurston 0.31 2.03 1.98 2.52 0.64 0.78 Wahkiakum 0.42 2.03 1.98 2.86 1.18 0.78 Walla Walla 0.38 2.70 1.70 2.52 0.32 0.78

Whatcom 0.32 2.03 1.98 2.89 1.00 0.78 Whitman 0.37 1.40 1.30 2.58 0.42 0.78

Yakima 0.36 2.70 1.70 2.60 0.49 0.78


Table 3-43. Tons of Wood Burned for Home Heating

County Fireplaces Woodstoves Fireplace Inserts

Central Heaters

Pellet Stoves

Wax Logs TOTAL

Adams 98 973 429 235 73 0 1,809 Asotin 259 1,056 540 249 79 0 2,184

Benton 1,047 5,164 2,877 1,816 612 0 11,516 Chelan 599 13,467 3,872 3,138 1,102 0 22,179 Clallam 903 10,468 6,028 2,197 919 0 20,516

Clark 4,985 44,190 28,376 6,429 3,771 0 87,751 Columbia 33 790 249 118 40 0 1,231

Cowlitz 1,320 12,390 7,608 2,742 1,252 0 25,312 Douglas 192 5,529 1,647 824 244 0 8,435

Ferry 88 2,333 546 709 261 0 3,938 Franklin 489 3,447 1,826 704 234 0 6,700 Garfield 17 282 97 101 33 0 529

Grant 392 5,472 2,595 1,263 391 0 10,112 Grays Harbor 978 8,998 5,165 2,040 880 0 18,061

Island 693 8,836 4,408 1,494 642 0 16,072 Jefferson 508 5,640 3,038 1,413 622 0 11,221

King 22,164 68,289 70,813 17,062 14,541 12,925 205,794 Kitsap 2,568 30,663 20,959 3,010 1,709 0 58,909 Kittitas 420 5,383 2,262 1,801 590 0 10,456

Klickitat 210 3,475 1,055 510 185 0 5,436 Lewis 1,071 10,981 5,871 2,873 1,250 0 22,047

Lincoln 93 1,212 374 476 141 0 2,296 Mason 863 9,787 5,002 2,525 1,094 0 19,271

Okanogan 368 9,554 2,479 2,052 735 0 15,188 Pacific 241 3,582 1,776 827 329 0 6,754

Pend Oreille 187 3,867 1,021 1,214 486 0 6,776 Pierce 9,890 52,280 37,594 11,117 5,715 3,073 119,669

San Juan 163 3,076 1,231 604 224 0 5,298 Skagit 2,112 10,774 5,960 2,433 970 0 22,248

Skamania 188 2,483 964 1,162 469 0 5,266 Snohomish 10,499 104,970 54,087 10,572 5,111 0 185,240

Spokane 4,874 63,366 18,041 9,795 3,389 0 99,465 Stevens 583 10,123 2,480 2,934 1,055 0 17,175

Thurston 3,750 27,963 17,956 3,727 1,853 0 55,249 Wahkiakum 63 859 383 242 102 0 1,649 Walla Walla 482 5,259 1,801 584 192 0 8,318

Whatcom 2,841 21,653 13,904 6,265 2,294 0 46,956 Whitman 260 2,018 841 533 162 0 3,815

Yakima 2,111 26,737 7,612 3,015 1,128 0 40,602 State Total 78,599 607,390 343,767 110,806 54,882 15,998 1,211,442


3.16.2 Emission Rates Emissions factors for RWC (see Table 3-36) come primarily from AP-42 67 and Houck and Eagle (2006) 68, but also from Houck et al. (2001) 69. Many of the HAP emissions factors are from Hays et al. (2003) 70. Emissions factors for wax firelogs are from Li and Rosenthal (2006) 71. Additional emission factors are taken from Aurell et al. (2012) 72. For certified woodstoves and fireplace inserts, EPA is using the emissions factors from the Regulatory Impact Analysis (RIA) for the 2015 New Source Performance Standards (NSPS) 73, which is based on the woodstove emissions standards from the state of Washington in 1995. The RIA notes that the emissions factors for woodstove, fireplace inserts, and pellet stoves will not decrease from that level until the Step 2 standards become effective in 2020. Therefore, EPA used the Washington state emissions factors to estimate 2017 emissions for these categories.

3.16.3 Spatial Allocation Spatial allocation was not necessary since occupied housing units were available by county.

3.16.4 Emissions Estimates Annual emissions for each county and wood burning device were calculated according to the following equation: TPY = (OHU) x (usage fraction) x (T burned/device-yr) x (pollutant lb/T) x (T/2000 lb) where OHU= number of occupied housing units in the county


Table 3-44. Pollutant Emission Factors in Pounds Per Ton Burned

Pollutant FP

IN & WS Certified Catalytic

IN & WS Certified NonCat

IN & WS Un-

certified PS Firelog

Ammonia 1.8 0.67 0.67 1.7 0.3 Carbon Monoxide 149 92.3 122.6 230.8 15.9 125.1 Nitrogen Oxides 2.6 1.49 1.69 2.8 3.8 7.7 Primary PM10 23.6 9.72 8.76 30.6 3.06 29.3 Primary PM2.5 23.6 9.72 8.76 30.6 3.06 28.4 Sulfur Dioxide 0.4 0.30 0.30 0.4 0.32 VOCs 18.9 11.2 8.88 53 2.20 39.6 1,3-Butadiene 0.157 0.15 0.13 0.39 9.50E-04 Acenaphthene 2.30E-03 2.99E-03 6.21E-03 1.68E-03 Acenaphthylene 2.60E-02 9.54E-03 0.13 7.48E-03 Acetaldehyde 1.07 0.40 0.47 0.62 9.40E-02 Acrolein 0.12 2.34E-02 2.99E-02 9.10E-02 1.01E-02 Anthracene 3.06E-03 2.69E-03 8.69E-03 2.32E-03 Benzene 0.69 1.09 0.71 1.94 2.89E-02 1.07 Benzo[a]anthracene 4.30E-04 4.27E-04 5.77E-04 1.20E-03 Benzo[a]fluoranthene 2.39E-04 2.37E-04 3.21E-04 Benzo[a]Pyrene 1.00E-03 7.30E-04 7.25E-04 9.79E-04 6.70E-03 1.20E-03 Benzo[b]fluoranthene 4.41E-04 4.38E-04 5.92E-04 1.12E-03 Benzo[e]Pyrene 4.39E-04 4.36E-04 5.89E-04 Benzo[g,h,i,]Perylene 1.50E-04 1.49E-04 2.01E-04 6.80E-04 Benzo[k]Fluoranthene 3.79E-04 3.77E-04 5.09E-04 6.00E-04 Cadmium 1.48E-05 2.20E-05 Chrysene 3.51E-04 3.49E-04 4.72E-04 7.52E-05 1.88E-03 Cresols /Cresylic Acids 0.36 0.40 0.34 0.16 1.55E-02 Dibenzo[a,h]Anthracene 2.92E-05 2.90E-05 3.92E-05 6.00E-04 Fluoranthene 1.85E-04 1.84E-04 2.49E-04 5.48E-05 4.28E-03 Fluorene 5.35E-03 4.19E-03 1.49E-02 5.48E-03 Formaldehyde 1.79 0.73 1.64 1.45 0.32 Indeno[1,2,3-c,d]Pyrene 3.04E-04 3.02E-04 4.08E-04 6.80E-04 Manganese 1.04E-04 1.70E-04 Mercury 4.26E-05 4.26E-05 4.26E-05 4.26E-05 4.26E-05 Methylchrysene 4.35E-05 4.32E-05 5.84E-05 Naphthalene 0.27 7.11E-02 4.31E-02 0.18 0.42 9.76E-02 Nickel 1.48E-05 1.40E-05 o-Xylene 0.14 0.20 Perylene 1.16E-04 1.15E-04 1.55E-04 Phenanthrene 1.83E-02 3.53E-02 4.84E-02 3.32E-05 1.72E-02 Phenol 0.47 0.30 0.36 0.30 2.50E-02 Pyrene 1.62E-04 1.61E-04 2.17E-04 4.84E-05 4.24E-03 Toluene 0.39 0.73


3.17 Industrial and Commercial/Institutional (ICI) Fuel Use Industrial and Commercial/Institutional (ICI) fuel combustion sources include emissions from boilers, engines, and other combustion sources from the industrial, commercial, and institutional sectors that are not reported as large point sources. This category includes emissions from combustion of coal, distillate fuel oil, residual fuel oil, kerosene, liquefied petroleum gas (LPG), natural gas, and wood. The calculations for estimating emissions from the ICI sectors include estimating the total fuel consumption by sector in each state, using data from the Energy Information Administration (EIA) State Energy Data System (SEDS). Total fuel consumption is adjusted to account for fuel consumed by mobile sources in each sector and fuel used as an input to industrial processes but is not combusted. Washington submitted state-level fuel consumption data from point sources in these sectors to EPA, using information from the WEIRS database and as reported by local clean air agencies. The state-level point source fuel consumption was subtracted from the total fuel consumption to estimate the fuel consumption from nonpoint sources, and EPA calculated the subsequent emissions.

3.17.1 Activity Level The activity data for this source category is total fuel consumption in the industrial and commercial/institutional sectors. The default data for this category are obtained from the total 2017 state-level fuel consumption in each sector from EIA SEDS 74 for all fuel types except distillate. Distillate fuel consumption is taken from EIA’s Form 821 data 75, which reports distillate sales by state and sector for 2016. Table 3-45. State Industrial and Commercial/Institutional Fuel Use

Sector Fuel Units SEDS Point Nonpoint Commercial Coal Tons 0 0 0 Commercial Distillate Thousand Gallons 354 350 4.8 Commercial Kerosene Thousand Gallons 168 35 133 Commercial LPG Thousand Gallons 36,288 250 29,608 Commercial Natural Gas Million Cubic Feet 60,096 4,409 55,687 Commercial Residual Thousand Gallons 0 0 0 Commercial Wood Million BTU 2,741,000 0 2,741,000 Industrial Distillate Thousand Gallons 8,923 1,106 7,074 Industrial Kerosene Thousand Gallons 0 2 0 Industrial LPG Thousand Gallons 62,454 50,885 2,560 Industrial Natural Gas Million Cubic Feet 80,656 105,108 0 Industrial Residual Thousand Gallons 0 1,790,117 0 Industrial Wood Million BTU 79,782,000 17,326,789 5,776,000

An adjustment was made to the nonpoint estimate of industrial wood combustion, since it was unreasonably high. After consultation with a senior Ecology engineer, and consistent with the previous EI, it was decided to estimate nonpoint industrial wood combustion at 25% of the total point source value.


3.17.2 Spatial Allocation The estimated state-level nonpoint source activity data in each state is distributed to the county level based on employment in the industrial or commercial sector from the Census Bureau’s County Business Patterns76. The adjusted nonpoint fuel consumption in each state is distributed to the county based on the proportion of employment in each county in each sector to the total employment at the state level in each sector.

3.17.3 Emission Rates and Emissions Emissions were calculated as: E = A x EF where A is the amount of fuel used (reference table of fuel use), and EF is the emission rate in lbs/unit. The emission factors are provided in the Appendix to the “ICI NEMO FINAL_4-2 updated.docx” document on the 2017 NEI Supplemental data FTP site.

ftp://newftp.epa.gov/Air/nei/2017/doc/supporting_data/nonpoint/


3.18 Agricultural Harvesting Operations The Western Regional Air Partnership (WRAP) published a handbook for calculating dust emissions which includes harvesting operations. Harvesting emissions are generated by three different operations: crop handling by the harvest machine, loading of the harvested crop into trailers or trucks, and transport by trailers or trucks in the field. Emissions from these operations are in the form of solid particulates composed mainly of raw plant material and soil dust that is entrained into the air.77 The WRAP Handbook recommended the methodology and emission rates used by the California Air Resources Board (CARB).

3.18.1 Activity Level and Spatial Adjustments

Acres harvested by crop type are the measure of activity. Every 5 years an extensive national census is made. The USDA National Agricultural Statistics Service (NASS) published the USDA/WSDA crop data and Census data on-line for query and download 78. Statistics from the 2017 census were obtained for several major crop and fruit types. “Acres Harvested” statistics were used for most crops, except for fruit statistics that are reported as “Acres Bearing”. The data included individual county acreage estimates for several crop types, but individual county acreage was considered confidential in some instances. State totals were used to evenly apportion remaining crop acreage to individual counties when county estimates were withheld.

Harvested acreage by county is shown in Table 3-38, with similar crop types grouped as follows: Beans & Peas: Dry Beans, Lima Beans, Snap Beans, Lentils, Dry Peas, Green Peas Fruit: Apples, Apricots, Berries, Cherries, Grapes, Nectarines, Peaches, Pears, Plums Grains: Barley, Canola, Corn, Wheat Hay & Haylage: Alfalfa Hay, Hay, Alfalfa Haylage, Haylage Potatoes: Potatoes Other Crops: Hops, Oats, Onions


Table 3-46. Area of Crops Harvested (Acres)

County Beans & Peas Fruit Grains Hay &

Haylage Potatoes Other Crops

Adams 11,460 4,435 280,403 9,141 21,780 1,279 Asotin 2 305 26,751 5,229 0 119

Benton 12,207 41,294 143,454 11,204 42,046 25,289 Chelan 505 20,293 305 2,220 2 1 Clallam 2,066 276 5,648 4,647 9 126

Clark 1,882 1,302 796 16,190 10 122 Columbia 15,205 169 71,399 3,361 384 119

Cowlitz 471 1,540 5,588 4,206 384 119 Douglas 1,723 11,495 165,733 5,331 1 119

Ferry 506 55 4,640 9,995 384 1 Franklin 8,974 16,584 96,359 84,585 29,983 3,757 Garfield 207 95 101,972 1,960 0 119

Grant 41,327 66,532 219,903 138,569 47,052 7,230 Grays Harbor 2,323 553 4,395 10,173 6 1

Island 8 528 5,083 5,532 3 4 Jefferson 1 116 1 3,103 2 2

King 1,974 469 1,764 8,519 20 42 Kitsap 11 102 4 8,110 11 6 Kittitas 2,563 1,001 2,925 41,195 384 44

Klickitat 2,460 2,700 57,092 36,587 4 10 Lewis 852 543 6,718 38,359 2 107

Lincoln 3,366 1,138 364,578 26,610 384 287 Mason 4 1,002 89 8,409 2 1

Okanogan 218 19,408 12,267 30,485 6 217 Pacific 142 1,285 0 6,698 3 1

Pend Oreille 0 60 0 16,155 0 0 Pierce 435 535 5,021 5,751 5 9

San Juan 1,854 251 4,909 3,994 5 2 Skagit 2,525 2,446 16,813 17,668 9,896 10

Skamania 141 321 0 974 384 0 Snohomish 40 833 6,021 11,686 979 74

Spokane 41,351 503 155,112 62,233 384 1,165 Stevens 1,946 249 5,429 39,985 384 395

Thurston 11 1,246 311 15,038 13 15 Wahkiakum 1 1,419 0 4,078 1 1 Walla Walla 24,538 14,498 205,165 17,261 10,223 1,647

Whatcom 1,877 11,953 13,005 36,486 1,635 130 Whitman 181,807 1,034 573,641 20,502 1 120

Yakima 74 90,393 75,075 45,948 1,027 29,161 State Total 367,055 318,964 2,638,369 818,177 167,801 71,849


3.18.2 Emission Rates Emission rates were adapted from the WRAP Handbook. The handbook assumed that PM2.5 was 15% of the PM10. No control measures were assumed. Table 3-47. Agricultural Harvesting Emission Rates (lb/acre)

Crop PM10 PM2.5 APPLES 0.08 0.012

APRICOTS 0.08 0.012 BARLEY 5.80 0.870

BEANS, OTHER 1.68 0.252 BEANS, SNAP 0.17 0.026

BERRIES 0.08 0.012 CANOLA 5.80 0.870

CHERRIES 0.08 0.012 CORN FOR GRAIN 1.68 0.252

CORN FOR SILAGE 0.17 0.026 CORN, SWEET 0.08 0.012

GRAPES 0.17 0.026 HAY, ALFALFA 0.17 0.026

HAY, OTHER 1.68 0.252 HAYLAGE (SILAGE) 0.17 0.026

HOPS 5.80 0.870 LENTILS 1.68 0.252

NECTARINES 0.08 0.012 OATS 5.80 0.870

ONIONS 1.68 0.252 ONIONS, GREEN 0.08 0.012

PEACHES 0.08 0.012 PEARS 0.08 0.012

PEAS 0.17 0.026 PLUMS 0.08 0.012

POTATOES 1.68 0.252 WHEAT 5.80 0.870

3.18.3 Emissions Estimates

Annual county emissions estimates of agricultural harvesting were calculated by multiplying the emission rates by the number of harvested acres by crop type.

TPY = (acres) x (lb pollutant/acre) x (1 T/2000 lb)


3.19 Agricultural Tilling Fugitive dust emissions from agricultural tilling include the airborne soil particulate emissions produced during the preparation of agricultural lands for planting. Fugitive dust emissions from agricultural tilling were estimated for PM10 and PM2.5.

3.19.1 Activity Level Acres tilled by crop type are the measure of activity. The total number of acres of farmland for all crop types by county was obtained from the WSDA 2017 Agricultural Land Use GIS Data.78 All farmland data was aggregated to the following crop types: barley, beans, canola, corn, fallow, alfalfa, hay/grass/silage (non-alfalfa), oat, other, pasture, peas, potato, rye, sorghum, soybean, sunflower, tobacco, beets, and wheat. All hay and seed categories were aggregated to their respective crop types where possible (e.g. alfala hay and alfafa seed are treated as “alfalfa”). Only wheat fallow and tilled fallow were included for “fallow” crop type. The WSDA GIS Data did not separate winter and spring wheat, so the corresponding ratio from the 2014 EI was applied to the total 2017 wheat acreage. Acres farmed by county for crop type groups are shown in Table 3-40.

3.19.2 Emissions Rates

The emission rates for agricultural tilling are based on the number of tilling passes made per year and the silt content of the soil. The number of tilling passes by crop type and farming practice (e.g. conservation or conventional land use) were taken from the Midwest Research Institute 79 and revised based on information from the WSU College of Agriculture 80. All pastureland is assumed to be no-till. The fraction of acres farmed that are conventional, no-till, or in conservation were obtained from the 2014 EI.

The following equation was used to determine the particle emission rates from agricultural tilling for 2017 81 82.

Emissions Rate (lbs/acre) = c × k × s0.6 × p

where: c = constant of 4.8 lbs/acre-pass k = dimensionless particle size multiplier (PM10=0.21; PM2.5=0.042) s = percent silt content of surface soil, defined as the mass fraction of particles

smaller than 50 μm diameter found in surface soil p = number of tilling passes in a year

Silt content by county was based on the National Cooperative Soil Survey data 83.


Table 3-48. Area of Farmland (Acres)

County Beans & Peas Grains Hay &

Haylage Potatoes Fallow Other Crops

Adams 17,501 327,681 24,102 24934 237,506 11,348 Asotin 166 24,296 5,051 25,991 143

Benton 8,196 138,227 10,516 31934 80,168 27,183 Chelan 235 695 286 85 Clallam 819 3,004 35

Clark 2,662 10,949 663 Columbia 27,495 104,289 3,085 25,793 365

Cowlitz 646 591 3,418 Douglas 2,026 165,971 3,239 94 178,603 390

Ferry 945 6,180 293 Franklin 12,005 121,079 77,341 39715 68,924 13,904 Garfield 1,111 92,098 1,108 56,531 1,508

Grant 39,821 241,634 154,580 45269 103,099 16,161 Grays Harbor 396 1,567 8,154

Island 7 938 5,758 104 Jefferson 17 1,920

King 74 1,311 5,772 55 Kitsap 8 75 Kittitas 199 3,664 49,005 229 48 135

Klickitat 1,040 60,159 33,172 532 24,314 3,005 Lewis 473 2,573 28,881 10

Lincoln 10,767 393,716 28,872 4089 256,725 3,132 Mason 16 585

Okanogan 40 14,712 21,733 3 11,150 250 Pacific 2,118

Pend Oreille 430 11,501 119 Pierce 136 473 3,935 129

San Juan 298 4,075 Skagit 224 17,873 14,897 9998 1,480

Skamania 1 639 Snohomish 10 9,055 12,400 439 465

Spokane 54,019 171,712 57,744 481 23,166 10,407 Stevens 254 11,345 41,517 349 3,426 273

Thurston 167 362 6,622 104 Wahkiakum 1,787 27 Walla Walla 26,936 211,644 28,372 14062 124,013 1,496

Whatcom 17,797 28,416 2797 4 Whitman 152,465 549,010 16,335 162,376 940

Yakima 364 74,110 39,695 338 6,900 33,279 State Total 356,538 2,763,316 757,247 175,265 1,389,312 127,199


Table 3-49. Farming Practices and Silt Content County No-Till Conservation Conventional Silt Content

Adams 2% 50% 48% 62% Asotin 75% 10% 14% 63%

Benton 30% 27% 43% 43% Chelan 11% 54% 35% 36% Clallam 6% 4% 90% 41%

Clark 8% 3% 90% 52% Columbia 33% 42% 25% 74%

Cowlitz 0% 37% 63% 38% Douglas 2% 28% 69% 48%

Ferry 19% 10% 72% 52% Franklin 5% 34% 61% 40% Garfield 36% 35% 29% 67%

Grant 4% 24% 72% 48% Grays Harbor 2% 1% 96% 49%

Island 15% 10% 74% 32% Jefferson 57% 13% 30% 22%

King 5% 2% 93% 36% Kitsap 26% 22% 52% 29% Kittitas 7% 13% 80% 41%

Klickitat 35% 9% 56% 41% Lewis 4% 13% 83% 49%

Lincoln 18% 36% 47% 64% Mason 12% 29% 59% 37%

Okanogan 29% 20% 51% 44% Pacific 3% 10% 87% 34%

Pend Oreille 15% 11% 75% 45% Pierce 39% 9% 51% 24%

San Juan 21% 4% 75% 40% Skagit 8% 4% 88% 31%

Skamania 0% 8% 92% 33% Snohomish 4% 1% 95% 36%

Spokane 36% 39% 25% 52% Stevens 7% 10% 83% 47%

Thurston 19% 2% 79% 42% Wahkiakum 97% 0% 3% 58% Walla Walla 37% 37% 26% 69%

Whatcom 3% 3% 94% 51% Whitman 20% 52% 29% 66%

Yakima 19% 21% 61% 33%


Table 3-50. Tilling Passes for Field Crops

Crop Conservation

Use Conventional

Use Alfalfa 3 3 Barley 3 5 Beans 3 3 Canola 3 3 Corn 2 6 Cover 1 1 Fallow 1 4 Fall-seeded Wheat 2 4 Hay 1 1 Oats 3 5 Other 1 1 Peas 3 3 Potatoes 3 3 Rye 3 5 Sorghum 1 6 Soybeans 1 6 Spring Wheat 1 2 Sugarbeets 3 3 Sunflowers 3 3 Tobacco 3 3


Annual county emissions estimates of agricultural tilling were calculated using the equation below.

TPY = (acres tilled) x (number of passes) x (lb pollutant/acre) x (1 T/2000 lb)


3.20 Structure & Vehicle Fires

Emissions from accidental structure and vehicle fires were estimated. Structure fires resulting from unintentional actions, arson, or natural events were included. Vehicle fires included any commercial or private vehicles authorized for use on public roads. Fire emissions were estimated using methods in the EIIP.84,85


National structure and vehicle fire records are maintained by the Department of Homeland Security US Fire Administration. Records are kept on the National Fire Incident Reporting System (NFIRS).86 Structure and vehicle fire data was taken from the NFIRS database by county for 2017. As of 2012, reporting to NFIRS is optional and one county (Ferry) did not report their 2017 data, so the most recently reported year (2011) was used.


The EIIP provided loading and emission factors.8484,85 The loading factor was 1.15 tons consumed per structure fire and 0.25 tons consumed per vehicle fire. Emission factors are shown below. PM10 and PM2.5 were estimated from total PM factors using California size fractions for unplanned structure fires (profile 137).87

Table 3-51. Structure and Vehicle Fire Emission Factors (pounds per Ton consumed)

Pollutant Pollutant Code Structure Fire EF

Vehicle Fire EF

CO CO 60 125 NOX NOX 1.4 4 PM PM 10.8 100 PM10 PM10 10.6 98 PM2.5 PM2.5 9.9 91 VOC VOC 11 32 Acrolein 107028 4.41 N/A Formaldehyde 50000 1.02 N/A Hydrochloric acid 7647010 15.11 N/A Hydrogen cyanide 74908 35.49 N/A


Annual county emissions estimates were calculated by multiplying the emission rates by the number of fires and loading per fire.

TPY = (#fires) x (Loading Factor) x (emission rate in lb/T) x (1 T/2000 lb)


Table 3-52. Structure and Vehicle Fire Counts

County Structure Fires

Vehicle Fires

Adams 1 0 Asotin 27 4

Benton 188 111 Chelan 124 49 Clallam 72 13

Clark 296 176 Columbia 6 5

Cowlitz 132 91 Douglas 42 19

Ferry 2 1 Franklin 40 38 Garfield 7 2

Grant 95 95 Grays Harbor 165 40

Island 78 20 Jefferson 80 20

King 1,606 783 Kitsap 220 82 Kittitas 80 68

Klickitat 7 5 Lewis 179 58

Lincoln 1 0 Mason 119 41

Okanogan 40 15 Pacific 43 11

Pend Oreille 26 8 Pierce 532 241

San Juan 29 9 Skagit 113 57

Skamania 6 0 Snohomish 403 188

Spokane 531 185 Stevens 59 18

Thurston 268 123 Wahkiakum 6 3 Walla Walla 29 17

Whatcom 103 47 Whitman 49 11

Yakima 384 170 State Total 6,188 2,824


3.21 Natural/Biogenic Emissions from natural/biogenic activity of trees, shrubs, and soil were estimated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) v2.188. Monthly MEGAN results were obtained from the AIRPACT-5 framework at Washington State University for 2017. It should be noted that MEGAN does not include detailed information about agricultural vegetation in Washington.

3.21.1 Activity Level MEGAN is a process-based model that uses landcover (e.g. plant functional type and leaf area index) and meteorology to estimate emissions from soil and vegetation. Meteorological information was obtained from the UW WRF 4-km forecasts. Plant activity is determined within the model by using a leaf age model, a canopy environment model, soil moisture algorithms, CO2 algorithms, and light and temperature algorithms. The different plant functional types are shown in the table below. Table 3-53. MEGAN Functional Plant Types

Class # Plant Type 1 Needleleaf Evergreen Temperate Tree 2 Needleleaf Evergreen Boreal Tree 3 Needleleaf Deciduous Boreal Tree 4 Broadleaf Evergreen Tropical Tree 5 Broadleaf Evergreen Temperate Tree 6 Broadleaf Deciduous Tropical Tree 7 Broadleaf Deciduous Temperate Tree 8 Broadleaf Deciduous Boreal Tree 9 Broadleaf Evergreen Temperate Shrub

10 Broadleaf Deciduous Temperate Shrub 11 Broadleaf Deciduous Boreal Shrub 12 Arctic C3 Grass 13 Cool C3 Grass 14 Warm C4 Grass 15 Crop

3.21.2 Emission Rates The MEGAN model estimates emissions of individual VOCs, NO, and CO. The VOCs reported include isoprene, methanol, ethanol, acetaldehyde, acetone, alpha-pinene, beta-pinene, t-beta-ocimene, limonene, ethene, and propene, terpenoids, monoterpenes and sesquiterpenes. The emission factors for each pollutant by plant class # are shown in the table below. AIRPACT groups many of these VOCs together and they were added together to get a total VOC estimate.


Table 3-54. MEGAN v2.1 Biogenic Emission Factors by Class Number (µg/m2/hr) Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Isoprene 600 3000 1 7000 10000 7000 10000 11000 2000 4000 4000 1600 800 200 1

Myrcene 70 70 60 80 30 80 30 30 30 50 30 0.3 0.3 0.3 0.3

Sabinene 70 70 40 80 50 80 50 50 50 70 50 0.7 0.7 0.7 0.7

Limonene 100 100 130 80 80 80 80 80 60 100 60 0.7 0.7 0.7 0.7

3-Carene 160 160 80 40 30 40 30 30 30 100 30 0.3 0.3 0.3 0.3

t-β-Ocimene 70 70 60 150 120 150 120 120 90 150 90 2 2 2 2

β-Pinene 300 300 200 120 130 120 130 130 100 150 100 1.5 1.5 1.5 1.5

α-Pinene 500 500 510 600 400 600 400 400 200 300 200 2 2 2 2

Other Monoterpenes 180 180 170 150 150 150 150 150 110 200 110 5 5 5 5

α-Farnesene 40 40 40 60 40 60 40 40 40 40 40 3 3 3 4

β-Caryophyllene 80 80 80 60 40 60 40 40 50 50 50 1 1 1 4

Other Sesquiterpenes 120 120 120 120 100 120 100 100 100 100 100 2 2 2 2

232-MBO 700 60 0.01 0.01 0.01 0.01 0.01 2 0.01 0.01 0.01 0.01 0.01 0.01 0.01

Methanol 900 900 900 500 900 500 900 900 900 900 900 500 500 500 900

Acetone 240 240 240 240 240 240 240 240 240 240 240 80 80 80 80

CO 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600

Bidirectional VOC 500 500 500 500 500 500 500 500 500 500 500 80 80 80 80

Stress VOC 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300

Other VOC 140 140 140 140 140 140 140 140 140 140 140 140 140 140 140

3.21.3 Emissions Estimates Annual county emissions estimates were calculated by adding the monthly MEGAN emissions estimates for 2017. Emissions estimates of VOCs, NO, and CO came directly from the process described above. Emissions estimates of acetaldehyde, formaldehyde, and methanol were obtained by using the VOC split factors applied by EPA for the NEI, which ranged from 2-3%, 3-5%, and 6-15% respectively.


3.22 Emissions Calculated by EPA The Dept. of Ecology acquired CAP emission estimates from EPA for several source types, as calculated in the 2017 NEI. The Dept. of Ecology acquired HAP emission estimates from EPA for all non-point sources as available. Some of the source categories acquired from EPA were calculated using inputs from Ecology and are discussed in the corresponding sections of this document. The technical support document for EPA 2017 National Emission Inventory provides information about each source type calculated by EPA.89

3.22.1 Acquired Source Types for CAPs Select EPA 2017 NEI CAP emissions were combined with the rest of the Comprehensive EI. Table 3-47 lists all source types acquired with the corresponding SCC and category abbreviation. Category abbreviations are defined in Table 1-1. Table 3-55. Accepted CAP Emissions Sources Calculated by EPA

Category Source Type SCC AIR Aircraft /Air Taxi /Piston 2275060011

AIR Aircraft /General Aviation /Turbine 2275050012

AIR Aircraft /General Aviation /Piston 2275050011

AIR Aircraft /Commercial Aircraft /Total: All Types 2275020000

AIR Aircraft /Military Aircraft /Total 2275001000

AIR Aircraft /Air Taxi /Turbine 2275060012

AIR Aircraft /Aircraft Auxiliary Power Units /Total 2275070000

CONST Mining &Quarrying /All Processes /Total 2325000000

F_ICI Stationary Fuel Comb /Commercial/Institutional /Natural Gas /Total: Boilers and IC Engines 2103006000

F_ICI Stationary Fuel Comb /Industrial /Natural Gas /Total: Boilers and IC Engines 2102006000

F_ICI Stationary Fuel Comb /Industrial /Residual Oil /Total: All Boiler Types 2102005000

F_ICI Fuel Combustion; Industrial; Distillate Oil Engine 2102004002

F_ICI Stationary Fuel Comb /Industrial /Bituminous/Subbituminous Coal /Total: All Boiler Types 2102002000

F_ICI Stationary Fuel Comb /Industrial /Kerosene /Total: All Boiler Types 2102011000

F_ICI Stationary Fuel Comb /Commercial/Institutional /Kerosene /Total: All Combustor Types 2103011000

F_ICI Stationary Fuel Comb /Industrial /Liquified Petroleum Gas /Total: All Boiler Types 2102007000

F_ICI Stationary Fuel Comb /Commercial/Institutional /Liquified Petroleum Gas /Total: All Combustor Types 2103007000

F_ICI Fuel Combustion; Industrial; Distillate Oil Boiler 2102004001

F_ICI Stationary Fuel Comb /Commercial/Institutional /Residual Oil /Total: All Boiler Types 2103005000

F_ICI Fuel Combustion; Comm/Inst; Dist. Oil Engine 2103004002

F_ICI Fuel Combustion; Comm/Inst; Distillate Oil Boiler 2103004001

F_ICI Stationary Fuel Comb /Commercial/Institutional /Bituminous/Subbituminous Coal /Total: All Boiler Types 2103002000

F_ICI Stationary Fuel Comb /Commercial/Institutional /Anthracite Coal /Total: All Boiler Types 2103001000

F_ICI Stationary Fuel Comb /Commercial/Institutional /Wood /Total: All Boiler Types 2103008000

F_ICI Stationary Fuel Comb /Industrial /Anthracite Coal /Total: All Boiler Types 2102001000

F_ICI Stationary Fuel Comb /Industrial /Wood /Total: All Boiler Types 2102008000

F_RES Stationary Fuel Comb /Residential /Kerosene /Total: All Heater Types 2104011000

F_RES Stationary Fuel Comb /Residential /Natural Gas /Total: All Combustor Types 2104006000

F_RES Stationary Fuel Comb /Residential /Liquified Petroleum Gas /Total: All Combustor Types 2104007000


Category Source Type SCC F_RES Stationary Fuel Comb /Residential /Distillate Oil /Total: All Combustor Types 2104004000

FERT Agric - Crops /Fertilizer Application /Miscellaneous Fertilizers 2801700099

FOOD Food & Kindred Products /Commercial Cooking - Charbroiling /Conveyorized Charbroiling 2302002100

FOOD Food & Kindred Products /Commercial Cooking - Charbroiling /Under-fired Charbroiling 2302002200

FOOD Food & Kindred Products /Commercial Cooking - Frying /Clamshell Griddle Frying 2302003200

FOOD Food & Kindred Products /Commercial Cooking - Frying /Flat Griddle Frying 2302003100

FOOD Food & Kindred Products /Commercial Cooking - Frying /Deep Fat Fying 2302003000

LIVE Agric - Livestock /Beef cattle production composite /Not Elsewhere Classified 2805002000

LIVE Agric - Livestock /Dairy cattle composite /Not Elsewhere Classified 2805018000

LIVE Agric - Livestock /Layers - Dust Kicked-up by Feet 2805001030

LIVE Agric - Livestock /Turkeys - Dust Kicked-up by Feet 2805001050

LIVE Agric - Livestock /Broilers - Dust Kicked-up by Feet 2805001020

LIVE Agric - Livestock /Dairy Cattle - Dust Kicked-up by Hooves 2805001010

LIVE Agric - Livestock /Beef cattle - finishing operations on feedlots (drylots) /Dust Kicked-up by Hooves 2805001000

LIVE Agric - Livestock /Swine - Dust Kicked-up by Hooves 2805001040

LIVE Agric - Livestock /Goats Waste Emissions /Not Elsewhere Classified 2805045000

LIVE Agric - Livestock /Sheep & Lambs Waste Emissions /Total 2805040000

LIVE Agric - Livestock /Horses & Ponies Waste Emissions /Not Elsewhere Classified 2805035000

LIVE Agric - Livestock /Swine production composite /Not Elsewhere Classified 2805025000

LIVE Agric - Livestock /Poultry production - broilers /Confinement 2805009100

LIVE Agric - Livestock /Poultry production - layers with dry manure management systems /Confinement 2805007100

LIVE Agric - Livestock /Poultry production - turkeys /Confinement 2805010100

MISC Composting /100% Green Waste (e.g., residential or municipal yard wastes) /All Processes 2680003000

MISC Charcoal Grilling - Residential /Total 2810025000

MISC Landfills: Muni Dump/Crush/Spread New Material 2620030001

MISC Cremation /Humans 2810060100

MISC Cremation /Animals 2810060200

MISC Fluorescent Lamp Breakage /Non-recycling Related Emissions /Total 2861000000

MISC Laboratories /Bench Scale Reagents /Total 2851001000

MISC Fluorescent Lamp Breakage /Recycling Related Emissions /Total 2861000010

MISC Health Services /Dental Alloy Production /Overall Process 2850001000

MISC Scrap & Waste Materials /Scrap & Waste Materials /Shredding 2650000002

MISC Scrap & Waste Materials /Scrap & Waste Materials /Total: All Processes 2650000000

OB_AG Agric - Crops /Field Burning - /Crop is Pea: Backfire Burning 2801500202

OB_AG Agric - Crops /Field Burning - /Vine Crop Unspecified 2801500500

OB_AG Agric - Crops /Field Burning - /Orchard Crop is Pear 2801500420

OB_AG Agric - Crops /Field Burning - /Orchard Crop is Peach 2801500410

OB_AG Agric - Crops /Field Burning - /Orchard Crop is Cherry 2801500350

OB_AG Agric - Crops /Field Burning - /Orchard Crop is Apricot 2801500330

OB_AG Agric - Crops /Field Burning - /Orchard Crop is Apple 2801500320

OB_AG Agric - Crops /Field Burning - /Unspecified 2801500000

OB_AG Agric - Crops /Field Burning - /Crop is Wheat: Backfire Burning 2801500262


Category Source Type SCC OB_AG Agric - Crops /Field Burning - /Crop is Alfalfa: Backfire Burning 2801500112

OB_AG Agric - Crops /Field Burning - /Crop is Oats: Backfire Burning 2801500192

OB_AG Agric - Crops /Field Burning - /Crop is Hay (wild): Backfire Burning 2801500182

OB_AG Agric - Crops /Field Burning - /Crop is Fallow: Burning Techniques Not Important 2801500171

OB_AG Agric - Crops /Field Burning - /Crop is Grasses: Burning Techniques Not Important 2801500170

OB_AG Agric - Crops /Field Burning - /Crop is Corn: Burning Techniques Not Important 2801500150

OB_AG Agric - Crops /Field Burning - /Crop is Bean (red): Backfire Burning 2801500142

OB_AG Agric - Crops /Field Burning - /Crop is Barley: Burning Techniques Not Significant 2801500130

OB_AG Agric - Crops /Field Burning - /Orchard Crop Unspecified 2801500300

OB_RES Stationary Fuel Comb /Residential /Wood /Outdoor wood burning device, NEC (fire-pits, chimeas, etc) 2104008700

OB_RES Open Burning /All Categories /Yard Waste - Leaf Species Unspecified 2610000100

OB_RES Open Burning /All Categories /Yard Waste - Brush Species Unspecified 2610000400

OB_RES Open Burning /Residential /Household Waste 2610030000

OB_RES Open Burning /All Categories /Land Clearing Debris 2610000500

PETROL Petrol & Petrol Product Storage /Airports : Aviation Gasoline /Stage 1: Total 2501080050

PETROL Petrol & Petrol Product Storage /Airports : Aviation Gasoline /Stage 2: Total 2501080100

PETROL Petrol & Petrol Product Transport /Pipeline /Gasoline 2505040120

PETROL Gasoline Service Stations /Stage 1: Balanced Submerged Filling 2501060053

PETROL Gasoline Service Stations /Underground Tank: Breathing and Emptying 2501060201

PETROL Residential Portable Gas Cans /Refilling at the Pump - Spillage 2501011015

PETROL Petrol & Petrol Product Storage /Bulk Plants: All Evaporative Losses /Gasoline 2501055120

PETROL Petrol & Petrol Product Storage /Bulk Terminals: All Evaporative Losses /Gasoline 2501050120

PETROL Commercial Portable Gas Cans /Refilling at the Pump - Spillage 2501012015

PETROL Commercial Portable Gas Cans /Refilling at the Pump - Vapor Displacement 2501012014

PETROL Commercial Portable Gas Cans /Spillage During Transport 2501012013

PETROL Commercial Portable Gas Cans /Evaporation (includes Diurnal losses) 2501012012

PETROL Commercial Portable Gas Cans /Permeation 2501012011

PETROL Residential Portable Gas Cans /Spillage During Transport 2501011013

PETROL Residential Portable Gas Cans /Permeation 2501011011

PETROL Residential Portable Gas Cans /Evaporation (includes Diurnal losses) 2501011012

PETROL Residential Portable Gas Cans /Refilling at the Pump - Vapor Displacement 2501011014

PETROL Petrol & Petrol Product Transport /Truck /Gasoline 2505030120

RR Railroad Equipment /Diesel /Yard Locomotives 2285002010

RR Railroad Equipment /Diesel /Line Haul Locomotives: Passenger Trains (Amtrak) 2285002008

RR Railroad Equipment /Diesel /Line Haul Locomotives: Class I Operations 2285002006

RR Railroad Equipment /Diesel /Line Haul Locomotives: Class II / III Operations 2285002007

RWC Stationary Fuel Comb /Residential /Wood /Woodstove: freestanding, EPA certified, catalytic 2104008330

RWC Stationary Fuel Comb /Residential /Wood /Hydronic heater: outdoor 2104008610

RWC Stationary Fuel Comb /Residential /Wood /Hydronic heater: indoor 2104008620

RWC Stationary Fuel Comb /Residential /Wood /Furnace: Indoor, pellet-fired, general 2104008530

RWC Stationary Fuel Comb /Residential /Wood /Hydronic heater: pellet-fired 2104008630

RWC Stationary Fuel Comb /Residential /Wood /Woodstove: pellet-fired, general (freestanding or FP insert) 2104008400


Category Source Type SCC RWC Stationary Fuel Comb /Residential /Wood /Woodstove: freestanding, EPA certified, non-catalytic 2104008320

RWC Stationary Fuel Comb /Residential /Wood /Woodstove: freestanding, non-EPA certified 2104008310

RWC Stationary Fuel Comb /Residential /Wood /Woodstove: fireplace inserts; EPA certified; catalytic 2104008230

RWC Stationary Fuel Comb /Residential /Wood /Woodstove: fireplace inserts; EPA certified; non-catalytic 2104008220

RWC Stationary Fuel Comb /Residential /Wood /Woodstove: fireplace inserts; non-EPA certified 2104008210

RWC Stationary Fuel Comb /Residential /Wood /Fireplace: general 2104008100

RWC Stationary Fuel Comb /Residential /Firelog /Total: All Combustor Types 2104009000

SHIP Marine Vessels, Commercial / Diesel / C3 Underway emissions: Main Engine 2280002203

SHIP Marine Vessels, Commercial / Diesel / C3 Port emissions: Auxiliary Engine 2280002104

SOLV Misc Non-industrial: Commercial /Pesticide Application: Agricultural /All Processes 2461850000

SOLV Misc Non-industrial: Commercial /Cutback Asphalt /Total: All Solvent Types 2461021000

SOLV Misc Non-industrial: Commercial /Emulsified Asphalt /Total: All Solvent Types 2461022000

SOLV Misc Non-indus: Consumer & Comm /All Household Products /Total: All Solvent Types 2460200000

SOLV Misc Non-indus: Consumer & Comm /Misc Products (Not Otherwise Covered) /Total: All Solvent Types 2460900000

SOLV Misc Non-indus: Consumer & Comm /All FIFRA Related Products /Total: All Solvent Types 2460800000

SOLV Misc Non-indus: Consumer & Comm /All Adhesives & Sealants /Total: All Solvent Types 2460600000

SOLV Misc Non-indus: Consumer & Comm /All Coatings & Related Products /Total: All Solvent Types 2460500000

SOLV Misc Non-indus: Consumer & Comm /All Auto Aftermarket Products /Total: All Solvent Types 2460400000

SOLV Misc Non-indus: Consumer & Comm /All Personal Care Products /Total: All Solvent Types 2460100000

SOLV Degreasing /All Processes/All Industries /Total: All Solvent Types 2415000000

SOLV Dry Cleaning /All Processes /Total: All Solvent Types 2420000000

SOLV Graphic Arts /All Processes /Total: All Solvent Types 2425000000

SOLV Surface Coating /Aircraft /Total: All Solvent Types 2401075000

SOLV Surface Coating /Other Special Purpose Coatings /Total: All Solvent Types 2401200000

SOLV Surface Coating /Industrial Maintenance Coatings /Total: All Solvent Types 2401100000

SOLV Surface Coating /Misc Manufacturing /Total: All Solvent Types 2401090000

SOLV Surface Coating /Marine /Total: All Solvent Types 2401080000

SOLV Surface Coating /Architectural Coatings /Total: All Solvent Types 2401001000

SOLV Surface Coating /Motor Vehicles /Total: All Solvent Types 2401070000

SOLV Surface Coating /Electronic & Other Electrical /Total: All Solvent Types 2401065000

SOLV Surface Coating /Large Appliances /Total: All Solvent Types 2401060000

SOLV Surface Coating /Machinery & Equipment /Total: All Solvent Types 2401055000

SOLV Surface Coating /Metal Cans /Total: All Solvent Types 2401040000

SOLV Surface Coating /Paper /Total: All Solvent Types 2401030000

SOLV Surface Coating /Metal Furniture /Total: All Solvent Types 2401025000

SOLV Surface Coating /Wood Furniture /Total: All Solvent Types 2401020000

SOLV Surface Coating /Factory Finished Wood /Total: All Solvent Types 2401015000

SOLV Surface Coating /Traffic Markings /Total: All Solvent Types 2401008000

SOLV Surface Coating /Auto Refinishing /Total: All Solvent Types 2401005000

SOLV Surface Coating /Railroad /Total: All Solvent Types 2401085000


4 Emissions Summaries Annual emissions summaries are presented below for criteria pollutants and ammonia. Toxic emissions are not shown here but can be generated from the emissions inventory database upon request. More detailed category breakouts can also be generated. Abbreviations used are listed in Table 1-1. Table 4-56. Statewide Emissions in Tons per Year

Category CO NH3 NOx PM10 PM2.5 SO2 VOC AIR 17,426 4,739 316 273 655 1,877 BOAT 31,011 4 2,835 118 110 2 7,162 CONST 39,607 4,061

F_ICI 5,361 46 5,230 2,765 2,058 902 293 F_RES 2,037 925 5,047 57 49 121 280 FERT 14,135

FOOD 1,383 3,590 3,335 504 LIVE 20,436 10,923 2,222 1,635 MISC 2,139 197 52 434 356 4 1,511 NAT 55,938 3,110 261,427 NRM 219,340 36 20,447 2,211 2,106 32 17,170 OB_AG 21,218 6,184 1,070 2,796 2,729 153 2,392 OB_RES 9,526 172 252 1,811 1,737 89 1,136 OB_RX 13,650 2,249 718 2,784 2,425 18 3,413 ORM 526,071 2,221 100,722 5,628 3,094 258 57,308 PETROL 11,554 POINT 46,708 395 25,299 4,079 3,337 9,576 8,629 ROADS 63,383 9,594

RR 3,303 9 14,792 377 366 12 646 RWC 81,392 568 1,254 9,470 9,463 205 12,881 SHIP 3,473 3 17,079 392 361 307 1,116 SOLV 85,131 TILL_HARV 147,240 29,058

WF 1,488,728 24,343 15,635 147,269 124,804 9,757 349,931 Total 2,528,706 71,922 218,282 445,250 201,537 22,091 825,997


Table 4-57. Annual Statewide Emissions Source Percentages by Pollutant Category CO NH3 NOx PM10 PM2.5 SO2 VOC

AIR 0.69% 2.17% 0.07% 0.14% 2.96% 0.23% BOAT 1.23% 0.01% 1.30% 0.03% 0.05% 0.01% 0.87% CONST 8.90% 2.01%

F_ICI 0.21% 0.06% 2.40% 0.62% 1.02% 4.08% 0.04% F_RES 0.08% 1.29% 2.31% 0.01% 0.02% 0.55% 0.03% FERT 0.00% 19.65% 0.00% 0.00% 0.00% 0.00% 0.00% FOOD 0.05% 0.81% 1.65% 0.06% LIVE 28.41% 2.45% 1.10% 0.20% MISC 0.08% 0.27% 0.02% 0.10% 0.18% 0.02% 0.18% NAT 2.21% 1.42% 31.65% NRM 8.67% 0.05% 9.37% 0.50% 1.04% 0.14% 2.08% OB_AG 0.84% 8.60% 0.49% 0.63% 1.35% 0.69% 0.29% OB_RES 0.38% 0.24% 0.12% 0.41% 0.86% 0.40% 0.14% OB_RX 0.54% 3.13% 0.33% 0.63% 1.20% 0.08% 0.41% ORM 20.80% 3.09% 46.14% 1.26% 1.54% 1.17% 6.94% PETROL 1.40% POINT 1.85% 0.55% 11.59% 0.92% 1.66% 43.35% 1.04% ROADS 14.24% 4.76%

RR 0.13% 0.01% 6.78% 0.08% 0.18% 0.05% 0.08% RWC 3.22% 0.79% 0.57% 2.13% 4.70% 0.93% 1.56% SHIP 0.14% 0.00% 7.82% 0.09% 0.18% 1.39% 0.14% SOLV 10.31% TILL_HARV 33.07% 14.42%

WF 58.87% 33.85% 7.16% 33.08% 61.93% 44.17% 42.36% Total 100% 100% 100% 100% 100% 100% 100%

Washington State 2017 Comprehensive Emissions Inventory Page 74

Table 4-58. County PM10 Emissions Estimates in Tons per Year County OB_AG AIR FOOD SHIP TILL_HARV CONST ROADS F_ICI POINT LIVE RR MISC NRM BOAT ORM F_RES OB_RES RWC OB_RX WF

Adams 59 3 6 21,426 36 2,503 7 402 32 1 49 0 50 0 6 17 7 200 Asotin 13 0 7 535 132 455 5 150 0 2 7 0 12 0 5 21 89 0

Benton 148 3 89 6,207 5,265 1,331 57 51 323 28 12 63 4 154 0 40 77 3 638 Chelan 23 1 42 21 580 721 33 3 48 9 5 22 2 68 0 29 207 176 6,918 Clallam 6 34 54 93 112 491 17 2 66 0 5 17 2 60 0 33 185 230 4

Clark 3 5 180 5 278 2,160 848 156 188 213 22 28 94 6 288 2 114 638 22 11 Columbia 428 0 1 3,852 17 881 1 135 0 0 38 0 5 0 2 11 1 68

Cowlitz 3 46 9 78 518 399 50 381 143 18 8 20 2 130 0 39 229 35 3 Douglas 24 3 15 11,682 418 4,544 7 10 155 1 2 31 1 42 0 13 75 6 525

Ferry 3 0 2 167 550 1,280 3 1 64 1 0 15 0 11 0 6 38 100 1,278 Franklin 149 6 28 9,014 1,168 2,384 30 792 21 4 49 1 63 0 18 61 4 65 Garfield 177 0 0 3,229 1 1,024 0 78 0 0 13 0 5 0 2 5 10 25

Grant 155 23 36 19,801 704 4,302 31 27 1,981 7 5 83 2 101 0 34 94 84 1,282 Grays Harbor 10 3 32 3 198 59 596 20 361 73 1 5 17 1 69 0 34 164 244 27

Island 3 2 30 14 104 113 245 12 5 140 0 6 16 3 46 1 38 143 6 2 Jefferson 4 13 11 7 49 494 8 193 23 0 3 15 1 34 0 19 103 143 1

King 123 1,350 81 117 9,992 5,934 1,021 125 214 25 118 536 25 1,500 25 332 1,572 1 1,602 Kitsap 9 109 52 4 403 694 48 27 12 0 17 54 6 168 3 68 441 1 1 Kittitas 0 4 29 741 653 694 10 233 1 4 22 1 121 0 20 101 55 37,976

Klickitat 20 2 6 2,401 66 1,177 6 119 286 40 1 21 0 23 0 11 48 212 102 Lewis 36 7 36 470 206 613 28 566 416 15 6 20 1 98 0 44 204 119 10

Lincoln 44 1 5 19,609 370 3,144 1 181 19 1 63 1 29 0 7 23 4 30 Mason 0 9 21 0 9 108 389 13 15 89 1 4 11 2 46 0 38 178 41 4

Okanogan 17 6 13 982 137 2,403 9 354 2 3 15 1 49 0 29 140 388 29,696 Pacific 2 0 10 0 20 22 383 5 13 50 0 2 6 1 20 0 14 62 59 0

Pend Oreille 12 0 3 136 112 1,406 2 7 59 1 1 12 1 13 0 9 65 84 11,169 Pierce 11 370 34 46 1,475 1,938 238 320 110 20 49 224 12 632 5 164 880 13 15,428

San Juan 1 12 9 48 58 102 140 6 20 0 2 8 1 6 0 13 48 1 Skagit 19 7 64 34 1,082 438 670 53 368 232 7 8 35 4 138 1 46 206 80 139

Skamania 0 2 6 11 174 2 46 15 1 11 0 13 0 9 53 29 879 Snohomish 7 24 372 27 558 1,694 1,596 431 15 268 15 43 168 13 542 5 165 1,388 27 22

Spokane 31 17 238 5,789 6,224 6,531 169 153 203 36 32 114 8 386 5 116 706 99 26 Stevens 11 1 13 1,488 153 3,005 10 155 234 3 3 19 2 40 0 29 165 202 116

Thurston 11 5 126 0 80 699 885 55 0 266 9 18 54 5 226 1 86 381 9 278 Wahkiakum 4 0 0 2 3 19 92 1 24 0 0 1 0 4 0 4 15 15 Walla Walla 774 2 25 9,268 348 948 25 137 231 10 4 49 1 44 1 16 73 22 11

Whatcom 13 6 110 14 1,465 1,511 912 97 797 658 6 12 53 4 158 2 71 318 45 47 Whitman 305 5 13 22,887 95 2,899 15 21 179 5 2 98 1 36 1 13 36 1 12

Yakima 291 5 107 3,326 2,888 4,257 82 19 1,771 7 16 64 3 197 1 74 298 118 38,674 State Total 2,796 316 3,590 388 147,240 39,607 63,383 2,765 4,079 10,923 377 434 2,211 118 5,628 57 1,811 9,470 2,784 147,269


Table 4-59. County PM2.5 Emissions Estimates in Tons per Year County OB_AG AIR FOOD SHIP TILL_HARV CONST ROADS F_ICI POINT LIVE RR MISC NRM BOAT ORM F_RES OB_RES RWC OB_RX WF

Adams 59 2 6 4,244 4 322 5 81 31 1 48 0 36 0 6 17 6 169 Asotin 13 0 6 103 13 64 4 31 0 1 7 0 7 0 5 21 77 0

Benton 141 2 83 1,221 526 222 43 37 67 27 10 61 3 86 0 39 77 3 540 Chelan 21 1 39 4 58 125 24 3 10 9 4 21 2 40 0 28 207 153 5,863 Clallam 4 31 50 18 12 117 13 1 14 0 4 16 2 33 0 31 185 200 4

Clark 3 4 168 5 55 219 205 115 164 43 22 23 89 6 160 2 110 638 19 10 Columbia 424 0 1 760 2 104 1 28 0 0 35 0 3 0 2 11 1 58

Cowlitz 2 43 8 15 58 98 36 356 29 17 6 19 2 84 0 37 229 30 2 Douglas 23 3 14 2,312 42 506 5 4 32 1 2 30 1 24 0 12 75 5 445

Ferry 3 0 2 32 68 154 2 1 13 1 0 14 0 7 0 6 38 87 1,083 Franklin 147 5 26 1,790 119 280 22 162 20 4 47 1 38 0 17 61 3 55 Garfield 176 0 0 631 0 116 0 16 0 0 13 0 3 0 2 5 9 21

Grant 148 19 33 3,933 73 545 22 18 409 7 4 80 2 64 0 32 94 73 1,086 Grays Harbor 10 2 30 2 39 6 128 14 350 15 1 5 16 1 38 0 31 164 213 23

Island 3 2 28 13 20 12 60 9 4 29 0 5 15 3 26 1 36 143 5 1 Jefferson 3 12 10 1 6 91 6 127 5 0 2 15 1 21 0 18 103 125 1

King 113 1,254 75 23 1,023 1,130 770 111 44 24 97 507 23 754 22 328 1,566 1 1,358 Kitsap 7 101 48 1 41 149 36 3 2 0 14 52 5 89 3 65 441 1 1 Kittitas 0 3 27 147 68 136 8 48 1 3 21 1 88 0 19 101 48 32,183

Klickitat 19 1 5 471 7 161 5 47 60 38 1 20 0 14 0 10 48 185 86 Lewis 34 6 33 92 23 141 21 307 64 14 5 19 1 63 0 41 204 103 9

Lincoln 44 1 4 3,869 37 366 1 37 18 1 61 1 20 0 7 23 4 26 Mason 0 8 19 0 2 13 79 10 9 19 1 4 11 2 28 0 35 178 36 3

Okanogan 16 5 12 194 16 333 7 74 2 2 15 1 31 0 27 140 338 25,166 Pacific 2 0 9 0 4 3 74 4 9 10 0 1 6 0 12 0 13 62 52 0

Pend Oreille 11 0 2 27 13 167 2 5 12 1 1 11 0 9 0 8 65 73 9,465 Pierce 9 344 31 8 155 464 179 266 21 20 40 213 11 319 5 161 879 11 13,074

San Juan 1 10 8 44 11 11 23 4 4 0 1 8 1 4 0 12 48 1 Skagit 18 5 60 31 214 46 149 39 354 46 7 7 33 4 84 1 44 206 70 118

Skamania 0 2 1 1 35 2 10 14 1 11 0 8 0 9 53 26 745 Snohomish 7 19 346 24 111 177 388 312 54 14 35 159 12 290 4 159 1,388 24 19

Spokane 30 15 221 1,133 625 824 127 121 42 35 26 109 7 207 4 112 706 86 22 Stevens 11 1 12 296 18 362 7 112 48 3 2 18 2 26 0 26 165 176 98

Thurston 10 4 117 0 16 76 192 42 0 51 9 15 52 4 127 1 82 381 8 235 Wahkiakum 3 0 0 2 0 2 19 1 5 0 0 1 0 2 0 3 15 13 Walla Walla 759 2 23 1,824 35 140 18 133 48 10 3 48 1 25 0 16 73 19 9

Whatcom 13 5 102 13 292 152 196 71 779 136 6 10 51 4 89 1 68 318 39 40 Whitman 302 4 12 4,487 10 352 11 11 36 5 2 95 0 21 0 12 36 1 10

Yakima 277 4 100 657 290 580 61 3 366 6 13 61 2 115 1 70 298 103 32,774 State Total 2,729 273 3,335 357 29,058 4,061 9,594 2,058 3,337 2,222 366 356 2,106 110 3,094 49 1,737 9,463 2,425 124,804


Table 4-60. County SO2 Emissions Estimates in Tons per Year County OB_AG AIR SHIP F_ICI POINT RR MISC NRM BOAT ORM F_RES OB_RES RWC OB_RX WF

Adams 4 0 2 1 0 1 0 2 0 0 0 0 21 Asotin 0 0 2 0 0 0 0 0 0 0 0 1 0

Benton 2 0 18 9 1 0 1 0 7 1 4 2 0 62 Chelan 0 0 11 0 0 0 0 3 0 1 5 1 452 Clallam 1 60 5 2 0 0 0 0 2 0 3 4 1 0

Clark 0 1 9 53 43 1 0 1 0 28 4 7 14 0 1 Columbia 36 0 0 0 0 0 0 0 0 0 0 0 7

Cowlitz 1 18 18 616 1 0 0 0 6 0 3 6 0 0 Douglas 0 2 2 3 0 0 0 0 2 0 0 2 0 52

Ferry 0 0 1 0 0 0 0 0 0 1 1 1 97 Franklin 8 10 11 1 0 1 0 3 0 1 1 0 7 Garfield 20 0 0 0 0 0 0 0 0 0 0 0 3

Grant 3 15 11 3 0 0 1 0 4 0 1 2 1 130 Grays Harbor 0 0 5 7 248 0 0 0 0 3 1 3 4 2 2

Island 0 0 14 4 0 0 0 0 2 2 4 3 0 0 Jefferson 1 13 3 46 0 0 0 0 1 1 2 3 1 0

King 523 42 310 227 1 1 7 1 66 57 6 30 0 101 Kitsap 2 18 14 0 0 1 0 7 8 1 9 0 0 Kittitas 0 1 3 0 0 0 0 6 1 0 3 0 2,401

Klickitat 0 1 2 11 1 0 0 0 1 0 0 1 1 7 Lewis 0 2 10 1,799 0 0 0 0 4 1 4 5 1 1

Lincoln 3 0 0 1 0 1 0 1 0 0 1 0 3 Mason 0 12 0 5 0 0 0 0 0 2 0 4 4 0 0

Okanogan 0 1 3 0 0 0 0 2 0 1 4 3 2,191 Pacific 0 0 0 2 2 0 0 0 0 1 0 1 2 0 0

Pend Oreille 0 0 1 25 0 0 0 0 1 0 0 2 1 682 Pierce 2 21 75 212 1 1 3 0 26 11 3 18 0 971

San Juan 0 6 55 2 0 0 0 0 0 1 1 1 0 Skagit 0 1 6 18 514 0 0 0 0 6 2 4 5 1 9

Skamania 0 1 0 0 0 0 1 0 1 1 0 57 Snohomish 0 10 6 160 11 0 0 2 0 23 8 3 29 0 1

Spokane 1 46 52 26 1 0 2 0 15 10 2 15 1 2 Stevens 0 0 4 9 0 0 0 0 2 1 1 4 1 9

Thurston 0 2 0 16 0 0 0 1 0 10 2 7 8 0 18 Wahkiakum 0 0 4 0 0 0 0 0 0 0 0 0 0 Walla Walla 42 1 9 890 0 0 1 0 2 1 1 2 0 1

Whatcom 0 9 27 34 4,877 0 0 1 0 7 3 7 7 0 3 Whitman 28 3 5 3 0 0 2 0 2 1 2 1 0 1

Yakima 3 3 28 0 0 0 1 0 8 3 6 6 1 2,463 State Total 153 655 300 902 9,576 12 4 32 2 258 121 89 205 18 9,757


Table 4-61. County NOx Emissions Estimates in Tons per Year County OB_AG AIR SHIP F_ICI POINT RR MISC NAT NRM BOAT ORM F_RES OB_RES RWC OB_RX WF

Adams 21 2 10 1,267 0 102 633 3 1,398 7 1 2 2 50 Asotin 3 0 9 0 0 45 81 6 228 24 1 2 23 0

Benton 56 1 121 254 1,110 1 111 674 60 2,911 52 6 10 1 141 Chelan 19 1 59 354 1 109 187 40 1,342 5 5 24 45 711 Clallam 7 3,042 34 35 0 1 42 118 50 1,168 12 5 22 59 0

Clark 1 6 236 273 559 877 3 42 827 108 5,283 269 16 85 6 1 Columbia 197 0 1 7 0 45 246 1 112 2 0 1 0 16

Cowlitz 6 439 73 3,130 708 1 50 176 41 3,111 13 6 27 9 0 Douglas 18 14 13 7 34 0 82 375 17 786 2 2 9 1 118

Ferry 0 0 3 2 35 0 126 55 4 241 2 1 4 26 181 Franklin 59 57 46 823 0 85 589 20 1,368 33 3 7 1 16 Garfield 99 0 1 0 0 43 164 1 98 1 0 1 3 6

Grant 65 150 48 39 275 1 169 1,023 35 2,385 6 6 11 22 303 Grays Harbor 2 2 142 33 615 40 1 64 124 24 1,340 22 5 20 63 4

Island 0 1 741 24 12 0 1 6 125 81 903 75 6 17 2 0 Jefferson 3 627 14 475 0 0 42 116 52 746 18 3 12 37 0

King 3,739 2,856 2,171 2,121 1,000 14 75 4,354 708 21,846 1,872 39 244 0 150 Kitsap 12 1,801 108 16 2 15 417 174 2,955 174 9 58 0 0 Kittitas 0 3 21 51 0 97 188 15 3,329 32 3 12 14 3,609

Klickitat 7 5 9 205 1,560 0 118 195 6 519 9 2 6 55 12 Lewis 7 14 45 7,289 581 1 100 172 25 2,252 20 7 24 31 1

Lincoln 16 1 3 727 0 116 805 11 749 7 1 3 1 7 Mason 0 123 7 22 0 28 1 40 84 50 1,032 16 6 21 11 0

Okanogan 11 8 17 56 0 221 156 13 1,135 5 5 16 100 3,974 Pacific 0 0 1 9 26 0 0 28 39 13 447 4 2 7 15 0

Pend Oreille 3 0 4 20 41 0 72 55 8 314 1 1 8 22 971 Pierce 12 1,220 481 1,400 802 6 64 1,934 287 9,763 525 20 124 3 1,451

San Juan 0 43 2,740 10 0 0 4 54 66 171 11 2 6 0 Skagit 5 5 1,140 89 3,563 275 1 51 310 123 3,015 130 7 24 21 16

Skamania 0 4 572 0 59 39 4 298 4 2 6 8 89 Snohomish 1 76 830 611 150 602 5 65 1,341 321 9,187 609 21 181 7 2

Spokane 8 317 352 635 1,425 4 121 1,150 120 6,349 469 15 93 26 4 Stevens 2 1 16 503 100 0 180 173 29 960 24 5 19 52 16

Thurston 2 15 9 128 4 358 2 40 471 109 4,137 189 12 50 2 29 Wahkiakum 1 0 171 1 0 0 10 8 5 86 1 1 2 4 Walla Walla 240 8 42 832 392 0 79 583 12 902 43 2 9 6 3

Whatcom 3 60 811 161 3,378 236 2 53 474 142 3,074 217 11 43 12 5 Whitman 140 24 23 38 187 0 114 1,257 7 718 29 2 4 0 3

Yakima 82 24 143 8 255 2 223 675 40 4,066 115 11 39 30 3,743 State Total 1,070 4,739 16,815 5,230 25,299 14,792 52 3,110 20,447 2,835 100,722 5,047 252 1,254 718 15,635


Table 4-62. County VOC Emissions Estimates in Tons per Year County OB_AG AIR FOOD SHIP F_ICI POINT LIVE RR RR MISC NAT PETROL SOLV NRM BOAT ORM F_RES OB_RES RWC OB_RX WF

Adams 56 3 1 1 53 54 53 0 2,333 101 2,099 91 16 365 0 3 22 8 415 Asotin 12 0 1 1 5 0 0 1 1,394 18 184 46 30 209 1 3 26 109 1

Benton 123 3 13 7 49 37 47 46 104 3,078 340 4,024 304 259 1,658 3 25 104 4 1,365 Chelan 21 2 6 3 2 15 14 100 11,374 121 1,667 282 146 847 0 21 272 216 16,486 Clallam 9 5 216 2 1 5 0 0 2 6,990 136 588 222 107 825 1 18 245 282 10

Clark 3 7 26 23 15 120 27 38 36 15 4,435 386 3,847 695 406 3,474 15 72 893 27 27 Columbia 370 0 0 0 4 0 0 0 2,878 11 83 826 5 61 0 1 15 1 144

Cowlitz 5 6 32 4 641 11 30 29 5 7,044 131 1,123 228 153 1,334 1 21 301 43 6 Douglas 20 13 2 1 3 4 1 1 1 2,486 66 742 94 74 454 0 8 102 7 1,122

Ferry 2 0 0 0 0 3 2 0 0 13,580 19 60 455 13 132 0 3 49 123 2,944 Franklin 138 42 4 3 157 37 36 100 2,221 438 3,152 130 94 782 2 11 80 5 136 Garfield 145 0 0 0 4 0 0 0 1,546 8 50 38 3 38 0 1 7 12 53

Grant 129 81 5 3 30 226 12 10 3 4,908 494 5,372 280 161 1,126 0 21 123 103 2,705 Grays Harbor 7 3 5 12 2 167 12 2 0 3 10,788 114 568 257 80 786 1 17 216 300 62

Island 2 3 4 56 1 34 6 0 0 2 710 78 572 138 168 741 3 18 191 7 4 Jefferson 5 2 44 1 45 2 0 0 1 7,229 90 242 193 74 418 1 10 135 176 3

King 1,240 187 168 122 917 36 46 46 659 10,258 2,592 20,142 4,525 1,395 11,776 106 250 2,095 2 3,847 Kitsap 15 15 107 6 100 2 1 0 105 2,272 317 1,892 440 308 2,118 9 44 625 1 3 Kittitas 0 5 4 1 10 2 2 2 7,808 184 485 246 55 850 2 13 130 67 91,097

Klickitat 14 4 1 1 207 14 66 66 98 9,319 62 356 257 22 302 0 6 65 260 241 Lewis 25 13 5 3 283 139 25 23 3 13,901 187 768 206 84 1,032 1 21 265 145 24

Lincoln 40 2 1 0 7 31 29 0 3,394 68 339 146 37 230 0 3 29 5 64 Mason 0 61 3 0 1 52 3 1 0 2 5,899 95 446 142 142 707 1 18 232 50 9

Okanogan 12 10 2 1 12 3 0 1 19,910 219 1,479 121 55 608 0 16 186 475 68,889 Pacific 2 0 1 0 0 62 9 0 0 1 4,930 33 146 121 31 268 0 6 82 73 1

Pend Oreille 9 0 0 0 147 4 2 0 0 10,043 21 94 305 36 213 0 5 84 103 26,975 Pierce 16 53 96 27 756 31 36 31 25 8,189 1,262 8,187 1,567 732 6,001 30 118 1,203 16 37,040

San Juan 1 30 1 146 1 1 0 0 1 541 72 117 96 72 200 0 6 64 3 Skagit 17 9 9 87 5 1,424 65 12 12 4 7,077 547 1,924 353 218 1,468 7 25 265 98 328

Skamania 0 0 0 2 24 24 0 8,981 20 97 343 15 174 0 4 66 36 2,098 Snohomish 5 48 52 37 34 554 58 28 28 24 9,487 722 7,210 1,445 744 5,540 34 112 1,950 33 52

Spokane 27 146 34 20 312 14 63 60 117 6,709 747 4,538 773 518 4,059 27 77 994 121 58 Stevens 9 2 2 1 252 14 5 3 1 15,613 67 350 203 116 672 1 14 212 247 269

Thurston 8 11 18 3 7 187 61 15 13 9 4,907 323 2,388 370 277 2,350 10 50 527 11 660 Wahkiakum 3 0 0 7 0 1 0 0 0 1,682 6 30 6 13 53 0 2 20 19 Walla Walla 671 10 3 2 180 7 17 13 2 3,020 260 2,030 208 55 570 2 9 99 27 22

Whatcom 11 40 15 71 9 1,685 157 11 11 7 6,935 354 2,081 444 237 1,936 12 38 442 55 111 Whitman 267 18 2 1 31 14 9 1 2 2,678 183 747 163 34 359 2 6 47 1 25

Yakima 242 20 15 8 391 416 11 9 106 14,879 662 4,910 411 175 2,576 6 40 420 144 92,632 State Total 2,392 1,877 504 1,105 293 8,629 1,635 646 598 1,511 261,427 11,554 85,131 17,170 7,162 57,308 280 1,136 12,881 3,413 349,931


Table 4-63. County CO Emissions Estimates in Tons per Year County OB_AG AIR FOOD SHIP F_ICI POINT RR MISC NAT NRM BOAT ORM F_RES OB_RES RWC OB_RX WF

Adams 534 133 3 11 294 4 1,376 889 55 4,511 3 26 137 32 1,729 Asotin 78 1 3 9 0 8 492 489 100 1,495 10 31 165 434 2

Benton 946 122 35 123 146 256 57 1,307 4,049 889 14,881 22 227 677 15 5,711 Chelan 162 68 16 61 79 25 1,927 2,348 543 7,219 2 160 1,676 863 70,167 Clallam 243 13 584 34 35 0 26 1,209 2,198 500 6,617 4 165 1,532 1,128 42

Clark 30 225 70 34 281 511 197 139 757 10,191 1,513 31,539 112 617 5,759 109 116 Columbia 3,794 1 0 2 1 2 748 2,020 18 558 0 10 92 5 599

Cowlitz 120 18 55 76 3,095 162 35 1,310 2,286 577 12,526 5 191 1,884 170 27 Douglas 187 139 6 14 3 8 12 1,014 956 259 4,180 1 69 636 28 4,690

Ferry 13 14 1 3 1 4 2 2,346 1,071 49 1,228 1 27 299 492 12,466 Franklin 1,234 344 11 48 186 21 1,129 1,633 317 7,170 14 95 504 19 565 Garfield 1,645 1 0 0 0 1 560 259 12 422 0 8 40 50 222

Grant 1,012 827 14 50 66 59 23 2,207 2,677 545 10,468 2 168 766 411 11,286 Grays Harbor 57 127 12 19 34 1,130 5 27 1,694 2,133 312 6,899 8 160 1,348 1,198 262

Island 14 95 12 176 25 10 0 30 135 2,105 800 5,407 25 185 1,198 30 16 Jefferson 172 5 133 14 722 0 14 1,318 1,232 428 3,621 5 90 838 703 12

King 8,950 516 746 2,212 1,615 223 582 1,879 78,358 6,821 117,536 768 1,975 12,845 7 16,394 Kitsap 437 42 275 110 2 83 413 6,674 1,590 17,979 66 347 4,016 5 11 Kittitas 3 176 11 21 12 17 1,500 1,565 204 9,748 12 102 797 270 388,114

Klickitat 112 67 2 10 160 364 7 1,722 1,104 83 2,578 4 47 407 1,042 1,023 Lewis 181 300 14 46 1,695 131 27 2,615 1,972 327 9,778 7 197 1,648 582 101

Lincoln 379 65 2 3 165 3 1,612 1,235 142 2,645 3 29 177 22 266 Mason 9 207 8 1 22 124 3 22 1,069 1,466 586 5,579 6 171 1,441 201 39

Okanogan 116 263 5 17 7 14 3,376 1,095 193 5,581 1 124 1,146 1,902 291,999 Pacific 10 6 4 0 9 4 0 8 767 863 139 2,208 1 62 506 290 4

Pend Oreille 64 10 1 4 2 5 5 1,738 898 124 1,699 1 36 511 410 115,045 Pierce 491 144 309 492 2,029 174 246 1,469 24,143 3,185 55,653 215 934 7,610 64 157,827

San Juan 4 398 3 393 10 0 8 101 1,138 486 1,234 3 57 398 15 Skagit 118 297 25 302 92 1,379 64 41 1,361 3,574 1,113 13,418 51 232 1,666 392 1,395

Skamania 2 1 4 134 3 1,797 945 56 1,457 1 39 399 144 8,931 Snohomish 35 1,025 143 234 638 114 136 216 1,717 23,321 3,381 50,683 247 887 12,566 132 223

Spokane 213 1,053 93 359 305 322 159 1,711 12,263 1,830 36,102 195 607 6,400 485 246 Stevens 51 59 5 16 710 16 15 3,115 1,204 417 5,309 9 113 1,303 989 1,139

Thurston 56 198 49 6 130 1 78 89 841 5,598 1,197 21,460 75 449 3,411 44 2,808 Wahkiakum 20 0 0 16 1 0 2 268 96 58 430 0 15 124 75 Walla Walla 5,719 97 10 43 713 81 18 1,112 1,847 188 4,873 17 86 622 106 92

Whatcom 78 291 42 161 166 32,073 53 63 1,266 6,290 1,252 16,347 84 362 2,855 219 471 Whitman 2,714 206 5 24 59 26 12 1,825 1,717 117 3,459 12 63 290 5 106

Yakima 1,631 195 42 147 6 55 76 3,134 5,438 605 21,572 46 365 2,702 577 394,567 State Total 21,218 17,426 1,383 3,444 5,361 46,708 3,303 2,139 55,938 219,340 31,011 526,071 2,037 9,526 81,392 13,650 1,488,728


Table 4-64. County NH3 Emissions Estimates in Tons per Year County OB_AG SHIP FERT F_ICI POINT LIVE RR MISC NRM BOAT ORM F_RES OB_RES RWC OB_RX WF

Adams 145 1,037 0 661 1 0 1 0 24 1 0 1 5 29 Asotin 9 95 0 58 0 0 0 4 5 1 1 72 0

Benton 156 627 1 39 467 1 14 1 0 60 10 6 5 3 95 Chelan 45 162 1 28 0 14 0 0 28 1 5 11 142 1,147 Clallam 1 118 0 1 61 0 0 0 25 1 5 10 186 1

Clark 6 0 119 2 17 332 0 1 2 0 111 54 12 41 18 2 Columbia 1,405 528 0 47 0 0 1 0 2 0 0 1 1 10

Cowlitz 0 115 1 74 136 0 0 0 0 57 2 5 13 28 0 Douglas 42 557 0 7 55 0 0 0 0 18 0 2 4 5 78

Ferry 1 111 0 32 0 0 0 0 5 0 1 2 81 205 Franklin 327 724 0 1,968 0 14 1 0 29 7 2 3 3 9 Garfield 737 287 0 52 0 0 0 2 0 0 0 8 4

Grant 222 1,562 0 2 2,828 0 0 1 0 45 1 5 5 68 188 Grays Harbor 7 0 151 0 25 155 0 0 0 0 30 3 5 9 197 4

Island 1 0 6 0 0 75 0 0 0 17 6 5 8 5 0 Jefferson 0 116 0 38 21 0 0 0 15 0 2 6 116 0

King 0 163 18 6 450 1 89 9 1 551 363 23 91 1 268 Kitsap 0 44 1 31 0 14 1 0 68 26 3 28 1 0 Kittitas 1 209 0 123 0 0 0 0 51 4 3 5 44 6,337

Klickitat 17 266 0 5 177 1 14 0 0 11 1 0 3 172 17 Lewis 18 237 0 44 1,740 0 0 0 0 44 3 5 11 96 2

Lincoln 110 869 0 83 0 0 1 0 14 1 0 1 4 4 Mason 1 0 75 0 36 0 0 0 0 20 2 5 10 33 1

Okanogan 26 312 0 152 0 0 0 0 23 0 2 8 313 4,792 Pacific 1 0 61 0 108 0 0 0 9 0 2 3 48 0

Pend Oreille 7 74 0 2 46 0 0 0 0 6 0 0 3 68 1,877 Pierce 0 114 4 54 384 0 2 3 0 239 101 10 54 10 2,577

San Juan 0 0 32 0 17 0 0 0 2 0 1 3 0 Skagit 15 0 254 1 14 810 0 0 1 0 60 21 6 12 65 23

Skamania 127 0 19 0 0 0 0 6 1 1 3 24 146 Snohomish 3 0 222 6 2 719 0 2 3 0 212 112 9 89 22 4

Spokane 40 588 3 10 180 1 15 2 0 141 94 6 45 80 4 Stevens 6 198 0 179 0 0 0 0 18 4 1 9 163 19

Thurston 6 0 93 1 767 0 0 1 0 91 33 11 25 7 46 Wahkiakum 2 0 26 0 17 0 0 0 2 0 0 1 12 Walla Walla 1,593 665 0 22 87 0 0 1 0 19 8 2 4 17 2

Whatcom 10 0 301 1 32 1,962 0 1 1 0 64 35 10 20 36 8 Whitman 1,006 2,104 0 175 0 0 2 0 16 5 2 2 1 2

Yakima 220 790 1 1 5,194 0 14 1 0 81 20 9 19 95 6,444 State Total 6,184 3 14,135 46 395 20,436 9 197 36 4 2,221 925 172 568 2,249 24,343


1 Estimate of 2017 urban and rural population and occupied housing units by county. Data from the Small Area Estimate Program (SAEP). Washington State Office of Financial Management. Sept. 11, 2018. https://www.ofm.wa.gov/washington-data-research/population-demographics/population-estimates/small-area-estimates-program.

2 Global Surface Summary of Day data files for 2017. National Climatic Data Center. ftp://ftp.ncdc.noaa.gov/pub/data/gsod/. Stations information at ftp://ftp.ncdc.noaa.gov/pub/data/noaa/

3 MOVES2014 Technical Guidance: Using MOVES to Prepare Emission Inventories for State Implementation Plans and Transportation Conformity. Transportation and Climate Division, Office of Transportation and Air Quality, U.S. Environmental Protection Agency. EPA-420-B-15-007. January 2015.

4 Motor Vehicle Emission Simulator (MOVES), User Guide for MOVES2014. Assessment and Standards Division, Office of Transportation and Air Quality, U.S. Environmental Protection Agency. EPA-420-B-14-055. July 2014.

5 Email from EPA NEI team to Onroad Data Submitter: Tom Malamakal. File: TruckAge distributions_Washington.docx

6 Washington State Department of Transportation Highway Performance Monitoring System (HPMS) 2017 DVMT by County & FC. Washington State Department of Transportation. Spreadsheet Mi-DVMT2017COrpt.xlsx.

7 Department of Licensing electronic data. Active registrations as of December 2017. 8 School bus database from the Office of Superintendent of Public Instruction. Website:

https://eds.ospi.k12.wa.us/BusDepreciation/default.aspx?pageName=busSearch. Feb. 6, 2018

9 2017 Travel Activity by Vehicle Type and Functional Class. Washington State Department of Transportation. Spreadsheet Travel Activity by Veh Type_2017.xlsx. July 25, 2018.

10 Email from Guorong Liu, Washington State Department of Transportation to Sally Otterson, Washington State Department of Ecology. Transmitting spreadsheets with monthly, day-of-week, and hourly adjustment factors. Seasonal Factor_08.xls, Day of Week Factor_08.xls, Hourly Factor_08.xls. Nov. 24, 2009.

11 WAC 173-422 (http://apps.leg.wa.gov/wac/default.aspx?cite=173-422) and WAC 173-422A (http://apps.leg.wa.gov/wac/default.aspx?cite=173-422A). Last accessed 2/27/2014.

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13 The total waived of all tests, total tested first test and total failed first test by model year, test type and test station (Gasoline Only) Years 2007 & 2008. Washington State Department of Ecology. Feb. 2010.

14 Instructions for Using LEV and NLEV Inputs for MOVES 2014. Assessment and Standards Division, Office of Transportation and Air Quality, U.S. Environmental Protection Agency. EPA-420-B-14-060a. October 2014.

15 40 CFR 80.27. http://www.gpo.gov/fdsys/pkg/CFR-2010-title40-vol16/pdf/CFR-2010-title40-vol16-sec80-27.pdf. Accessed 2/27/2014.

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