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2017 National Emissions Inventory
Complete Release
Technical Support Document
April 2020
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April 2020
2017 National Emissions Inventory
Technical Support Document
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Emissions Inventory and Analysis Group
Research Triangle Park, North Carolina
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Contents
List of Tables vii
List of Figures xiii
Acronyms and Chemical Notations xv
1 Introduction 1-1
1.1 What data are included in the 2017 NEI (April 2020 release)? 1-1
1.2 What is included in this documentation? 1-2
1.3 Where can I obtain the 2017 NEI data? 1-2
1.3.1 Emission Inventory System Gateway 1-2
1.3.2 NEI main webpage 1-2
1.3.3 Modeling files 1-3
1.4 Why is the NEI created? 1-3
1.5 How is the NEI created? 1-3
1.6 Who are the target audiences for the 2017 NEI? 1-5
1.7 What are appropriate uses of the 2017 NEI and what are the caveats about the data? 1-6
1.8 Updates in the 2017 NEI, from the February 2020 version 1-7
2 2017 NEI contents overview 2-1
2.1 What are EIS sectors? 2-1
2.2 How is the NEI constructed? 2-3
2.2.1 Toxics Release Inventory data 2-4
2.2.2 Chromium speciation 2-4
2.2.3 HAP augmentation 2-6
2.2.4 PM augmentation 2-7
2.2.5 Other EPA datasets 2-7
2.2.6 Data Tagging 2-7
2.2.7 Inventory Selection 2-8
2.3 What are the sources of data in the 2017 NEI? 2-8
2.4 What are the top sources of some key pollutants? 2-10
2.5 How does this NEI compare to past inventories? 2-12
2.5.1 Differences in approaches 2-12
2.5.2 Differences in emissions between the 2017 and 2014 NEI 2-14
2.6 How well are tribal data and regions represented in the 2017 NEI? 2-15
2.7 What does the 2017 NEI tell us about mercury? 2-17
2.8 References for 2017 inventory contents overview 2-25
3 Point sources 3-1
3.1 Point source approach: 2017 3-1
i
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3.1.1 QA review of S/L/T data 3-1
3.1.2 Sources of EPA data and selection hierarchy 3-2
3.1.3 Particulate matter augmentation 3-5
3.1.4 Chromium speciation 3-5
3.1.5 Use of the 2017 Toxics Release Inventory 3-5
3.1.6 HAP augmentation based on emission factor ratios 3-11
3.1.7 Cross-dataset tagging rules for overlapping pollutants 3-12
3.1.8 Additional quality assurance and findings 3-13
3.2 Airports: aircraft-related emissions 3-13
3.2.1 Sector Description 3-14
3.2.2 Sources aircraft emissions estimates 3-14
3.3 Rail yard-related emissions 3-14
3.4 EGUs 3-15
3.5 Landfills 3-16
3.6 2017EPA_gapfills 3-18
3.7 BOEM 3-18
3.8 PM species 3-18
3.9 References for point sources 3-18
4 Nonpoint sources 4-1
4.1 Nonpoint source approaches 4-1
4.1.1 Sources of data overview and selection hierarchies 4-1
4.1.2 Revised Nonpoint Survey 4-3
4.1.3 New for the 2017 NEI: Wagon Wheel and Input Templates 4-4
4.1.4 New for the 2017 NEI: Cross-dataset tagging 4-6
4.1.5 Nonpoint PM augmentation 4-7
4.1.6 Nonpoint HAP augmentation 4-8
4.1.7 EPA nonpoint data 4-8
4.2 Nonpoint non-combustion-related mercury sources 4-12
4.2.1 Description of sources 4-12
4.2.2 Sources of data 4-13
4.2.3 EPA-developed emissions 4-14
4.2.4 References 4-37
4.3 Agriculture - Crops and Livestock Dust 4-39
4.3.1 Sector description 4-39
4.3.2 Sources of data 4-39
4.3.3 EPA-developed methodology 4-41
4.3.4 References 4-47
4.4 Agriculture - Fertilizer Application 4-49
4.4.1 Sector description 4-49
4.4.2 Sources of data 4-49
4.4.3 EPA-developed emissions 4-50
4.4.4 References 4-57
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4.5 Agriculture - Livestock Waste 4-57
4.5.1 Sector description 4-57
4.5.2 Sources of data 4-57
4.5.3 EPA-developed emissions 4-60
4.5.4 References 4-78
4.6 Biogenics - Vegetation and Soil 4-83
4.6.1 Sector description 4-83
4.6.2 Sources of data 4-84
4.6.3 EPA-developed emissions 4-84
4.6.4 References 4-89
4.7 Nonpoint Gasoline Distribution 4-89
4.7.1 Description of sources 4-89
4.7.2 Sources of data 4-90
4.7.3 EPA-developed emissions 4-92
4.7.4 References 4-123
4.8 Commercial Cooking 4-124
4.8.1 Sector description 4-124
4.8.2 Sources of data 4-124
4.8.3 EPA-developed emissions 4-125
4.8.4 References 4-131
4.9 Dust - Construction Dust 4-131
4.9.1 Sector description 4-131
4.9.2 Sources of data 4-132
4.9.3 EPA-developed emissions for residential construction 4-132
4.9.4 EPA-developed emissions for non-residential construction 4-142
4.9.5 EPA-developed emissions for road construction 4-149
4.10 Dust - Paved Road Dust 4-155
4.10.1 Sector description 4-155
4.10.2 Sources of data 4-155
4.10.3 EPA-developed emissions 4-156
4.10.4 References 4-160
4.11 Dust - Unpaved Road Dust 4-160
4.11.1 Sector description 4-160
4.11.2 Sources of data 4-160
4.11.3 EPA-developed emissions 4-161
4.11.4 References 4-166
4.12 Fires - Agricultural Field Burning 4-167
4.12.1 Sector description 4-167
4.12.2 Sources of data 4-167
4.12.3 EPA-developed emissions for agricultural field burning 4-168
4.12.4 References for agricultural field burning 4-173
4.13 Fuel Combustion - Industrial and Commercial/Institutional Boilers and ICEs 4-173
4.13.1 Sector description 4-173
4.13.2 Sources of data 4-174
4.13.3 EPA-developed emissions 4-178
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4.13.4 References 4-187
4.14 Fuel Combustion - Residential - Natural Gas, Oil, and Other 4-187
4.14.1 Sector description 4-187
4.14.2 Sources of data 4-188
4.14.3 EPA-developed emissions 4-189
4.14.4 References 4-196
4.15 Fuel Combustion - Residential - Wood 4-196
4.15.1 Sector description 4-196
4.15.2 Sources of data 4-197
4.15.3 EPA-developed emissions 4-198
4.15.4 References 4-204
4.16 Industrial Processes - Mining and Quarrying 4-204
4.16.1 Sector description 4-204
4.16.2 Sources of data 4-205
4.16.3 EPA-developed emissions 4-205
4.16.4 References 4-215
4.17 Industrial Processes - Oil and Gas Production 4-215
4.17.1 Sector description 4-215
4.17.2 Sources of data 4-215
4.17.3 EPA emissions calculation approach: EPA Oil and Gas Emissions Estimation Tool 4-221
4.18 Miscellaneous Non-industrial NEC: Cremation - Human and Animal 4-231
4.18.1 Sector description 4-231
4.18.2 Sources of data 4-231
4.18.3 EPA-developed emissions 4-232
4.18.4 References 4-243
4.19 Miscellaneous Non-Industrial NEC: Residential Charcoal Grilling 4-243
4.19.1 Sector description 4-243
4.19.2 Sources of data 4-243
4.19.3 EPA-developed emissions 4-244
4.19.4 References 4-250
4.20 Miscellaneous Non-Industrial NEC: Portable Gas Cans 4-251
4.20.1 Source category description 4-251
4.20.2 Sources of data 4-251
4.20.3 EPA-developed emissions 4-252
4.20.4 References 4-254
4.21 Mobile - Commercial Marine Vessels 4-255
4.21.1 Sector description 4-255
4.21.2 Sources of data 4-256
4.21.3 Quality assurance 4-257
4.22 Mobile - Locomotives (Nonpoint) 4-257
4.22.1 Sector description 4-257
4.22.2 Sources of data 4-257
4.22.3 EPA-developed emissions 4-258
4.22.4 Quality assurance 4-258
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4.22.5
References
4-259
4.23 Solvents - Consumer and Commercial Solvent Use: Agricultural Pesticides 4-259
4.23.1 Source category description 4-259
4.23.2 Sources of data 4-259
4.23.3 EPA-developed emissions 4-260
4.23.4 References 4-275
4.24 Solvents - Consumer and Commercial Solvent Use: Asphalt Paving 4-276
4.24.1 Source category description 4-276
4.24.2 Sources of data 4-277
4.24.3 EPA-developed emissions 4-278
4.24.4 References 4-286
4.25 Solvents: All other Solvents 4-286
4.25.1 Sector description 4-286
4.25.2 Sources of data 4-286
4.25.3 EPA-developed emissions 4-290
4.25.4 References 4-306
4.26 Waste Disposal: Composting 4-306
4.26.1 Source category description 4-307
4.26.2 Sources of data 4-307
4.26.3 EPA-developed emissions 4-308
4.26.4 References 4-314
4.27 Waste Disposal: Open Burning 4-314
4.27.1 Source category description 4-314
4.27.2 Sources of data 4-314
4.27.3 EPA-developed emissions for yard waste 4-316
4.27.4 EPA-developed emissions for land clearing debris 4-321
4.27.5 EPA-developed emissions for residential household waste 4-339
4.28 Waste Disposal: Nonpoint POTWs 4-347
4.28.1 Source category description 4-347
4.28.2 Sources of data 4-347
4.28.3 EPA-developed emissions 4-347
4.28.4 References 4-351
5 Nonroad Equipment - Diesel, Gasoline and Other 5-1
5.1 Sector Description 5-1
5.2 MOVES-Nonroad 5-2
5.3 Default MOVES code and database 5-3
5.4 Additional Data: Nonroad County Databases (CDBs) 5-3
5.5 MOVES runs 5-5
5.6 Use of California Submitted Emissions 5-6
5.7 References for nonroad mobile 5-7
6 Onroad Mobile - All Vehicles and Refueling 6-1
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6.1 Sector description 6-1
6.2 Overview of Input Data Sources for 2017 6-1
6.2.1 New 2017 Vehicle Populations and Fleet Characteristics 6-1
6.2.2 EPA Default Vehicle Speeds and VMT Distributions 6-3
6.3 Sources of data and selection hierarchy 6-4
6.4 California-submitted onroad emissions 6-4
6.5 Agency-submitted MOVES inputs 6-6
6.5.1 Overview of MOVES input submissions 6-6
6.5.2 OA checks on MOVES CDB Tables 6-11
6.6 Tribal Emissions Submittals 6-12
6.7 EPA default MOVES inputs 6-12
6.7.1 Sources of default data by MOVES CDB table 6-12
6.7.2 Default California emission standards 6-14
6.8 Calculation of Emissions 6-14
6.8.1 Preparation of onroad emissions data for the continental U.S 6-14
6.8.2 Representative counties and fuel months 6-16
6.8.3 Temperature and humidity 6-19
6.8.4 VMT, vehicle population, speed, and hoteling activity data 6-20
6.8.5 Public release of the NEI county databases 6-24
6.8.6 Seeded CDBs 6-24
6.8.7 Unseeded CDBs 6-24
6.8.8 Run MOVES to create emission factors 6-24
6.8.9 Run SMOKE to create emissions 6-24
6.8.10 Post-processing to create an annual inventory 6-25
6.8.11 Additional MOVES and SMOKE runs with EPA-generated age distributions 6-26
6.9 Summary of quality assurance methods 6-26
6.10 Supporting data 6-27
6.11 References for onroad mobile 6-33
7 Events - Wild and Prescribed Fires 7-1
7.1 Sector description and overview 7-1
7.2 Sources of data 7-2
7.3 EPA methods summary 7-3
7.3.1 National Fire Information Data 7-3
7.3.2 State/Local/Tribal fire information 7-5
7.3.3 Emissions Estimation Methodology 7-8
7.4 Quality Assurance (QA) of Final Results 7-14
7.4.1 Input Fire Information Data Sets 7-14
7.4.2 Daily Fire Locations from SmartFire2 7-15
7.4.3 Emissions Estimates 7-15
7.4.4 Additional quality assurance on final results, and some post-final corrections 7-15
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7.5 Emissions Summaries 7-16
7.6 References 7-24
List of Tables
Table 1-1: Point source reporting thresholds (potential to emit) for CAPs in the AERR 1-4
Table 1-2: Examples of major current uses of the NEI 1-5
Table 2-1: EIS sectors/source categories with EIS data category emissions reflected 2-1
Table 2-2: Valid chromium pollutant codes 2-4
Table 2-3: EIS sectors and associated 2017 CAP and total HAP emissions (thousands of tons/year) 2-11
Table 2-4: Emission differences (tons) for CAPs, 2017 minus 2014v2 NEI 2-15
Table 2-5: Emission differences (tons) for select HAPs, 2017 minus 2014v2 NEI 2-15
Table 2-6: Tribal participation in the 2017 NEI 2-16
Table 2-7: Facilities on Tribal lands with 2017 NEI emissions from EPA only 2-17
Table 2-8: 2017 NEI Hg emissions (tons) for each dataset type and group 2-18
Table 2-9: Point inventory emissions by reporting agency 2-20
Table 2-10: Trends in NEI mercury emissions - 1990, 2005, 2008 v3, 2011v2 and 2014v2 NEI and 2017 NEI ... 2-23
Table 3-1: Data sets and selection hierarchy used for 2017 NEI August release point source data category 3-3
Table 3-2: Mapping of TRI pollutant codes to EIS pollutant codes 3-6
Table 3-3: Landfill gas emission factors for 29 EIS pollutants 3-17
Table 4-1: Data sources and selection hierarchy used for most nonpoint sources 4-1
Table 4-2: S/L/T Input Templates submitted for the 2017 NEI 4-4
Table 4-3: EPA-estimated emissions sources expected to be exclusively nonpoint 4-9
Table 4-4: Emission sources with potential nonpoint and point contribution 4-11
Table 4-5: SCCs and emissions (lbs) comprising the nonpoint non-combustion Hg sources in the 2017 NEI 4-12
Table 4-6: Agencies reporting emissions to non-combustion mercury source categories 4-13
Table 4-7: Lifetime in hours and years for each bulb type 4-18
Table 4-8: Average number of filled teeth per person and percentage of fillings containing mercury by age group
4-19
Table 4-9: Total mercury in thermometers sold and mercury available from thermometers, annually 4-20
Table 4-10: US Census age groups and filling groups 4-23
Table 4-11: Emissions Factors for mercury from landfills working face 4-26
Table 4-12: Mercury used in CFLs (mg/bulb) as determined by three different studies 4-26
Table 4-13: Mercury used in linear fluorescent bulbs (mg/bulb) as determined by two different studies 4-27
Table 4-14: Mercury emissions factors for CFLs, linear fluorescents and HIDs 4-27
Table 4-15: Mercury emissions factors for dental amalgam 4-28
Table 4-16: Mercury emissions factors for thermostats and thermometers 4-28
Table 4-17: Sample calculations for mercury emissions from landfills in New Hanover County, NC 4-32
Table 4-18: Sample calculations for mercury emissions from switches and relays for Hartford County, CT 4-32
Table 4-19: Sample calculations for mercury emissions from fluorescent lamp breakage for Hartford County, CT
4-33
Table 4-20: Sample calculations for mercury emissions from dental amalgam for Hartford County, CT 4-34
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Table 4-21: Sample calculations for mercury emissions from thermostats and thermometers for Hartford County,
CT 4-36
Table 4-22: EPA-generated Dust from animal hooves and feet SCCs with level 3 and 4 descriptions 4-39
Table 4-23: Agencies reporting emissions to the dust from crops and animal feet/hooves 4-39
Table 4-24: Number of passes or tillings per year 4-42
Table 4-25: Animal units equivalent factors 4-43
Table 4-26: Acres tilled by tillage type, in 2012 4-45
Table 4-27: Sample calculations for PM10-FIL emissions from conservation tilling from corn in Clay County, AL. 4-
46
Table 4-28: SCCs in the Agricultural Fertilizer Application sector 4-49
Table 4-29: Agencies that submitted fertilizer application NH3 emissions in the 2017 NEI 4-50
Table 4-30: Environmental variables needed for an EPIC simulation 4-52
Table 4-31: Contiguous US fertilizer totals and emissions for the 2017 NEI and 2014 NEI 4-57
Table 4-32: Nonpoint SCCs with 2017 NEI emissions in the Livestock Waste sector 4-57
Table 4-33: Point SCCs with 2014 NEI emissions in the Livestock Waste sector - reported only by States 4-60
Table 4-34: Agencies that submitted Ag Livestock Waste emissions in the 2017 NEI 4-60
Table 4-35: National-level animal population data trend from 2014 NEI to draft 2017 NEI 4-61
Table 4-36: VOC speciation fractions used to estimate HAP Emissions for Livestock Waste 4-63
Table 4-37: Description and sources of model inputs and parameters 4-73
Table 4-38: Model Input parameters related to manure characteristics 4-73
Table 4-39: Tuned model parameters for beef, swine, and poultry 4-74
Table 4-40: Tuned Parameter Values by practice and animal type 4-75
Table 4-41: Sample Calculations for NH3, VOC and Toluene emissions from swine in Cochise County, AZ 4-77
Table 4-42: SCCs for biogenic sources 4-83
Table 4-43: Agencies that submitted biogenics emissions 4-84
Table 4-44: Meteorological variables required by BEIS 3.61 4-85
Table 4-45: Nonpoint bulk gasoline terminals, gas stations, and storage and transfer SCCs in the 2017 NEI .... 4-90
Table 4-46: Nonpoint aviation gasoline distribution SCCs in the 2017 NEI 4-91
Table 4-47: Agencies reporting emissions to gasoline distribution source categories 4-92
Table 4-48: Ranges and midpoints for data withheld from state and county business patterns 4-98
Table 4-49: HAP speciation factors for stage I gasoline distribution 4-99
Table 4-50: Tank trucks in transit VOC emission factors 4-100
Table 4-51: VOC Emissions Factors for Aviation Gasoline Distribution-Stage 1 (2501080050) 4-102
Table 4-52: HAP Emissions Factors for Aviation Gasoline Distribution-Stage 1 (2501080050) 4-102
Table 4-53: VOC Emissions Factors for Aviation Gasoline Distribution-Stage 2 (2501080100) 4-103
Table 4-54: HAP Emissions Factors for Aviation Gasoline Distribution-Stage 2 (2501080100) 4-103
Table 4-55: 1998 Post-MACT Control Emissions 4-104
Table 4-56: Refinery, Bulk Terminal, and Natural Gas Plant Stocks of Motor Gasoline, 2017 4-105
Table 4-57: Movement of Finished Motor Gasoline (thousand barrels) by Pipeline in PAD Districts, 2017 4-108
Table 4-58: States by PAD District 4-108
Table 4-59: Assumptions for Bulk Terminals Using Aviation Gasoline 4-113
Table 4-60: Sample calculations for benzene emissions for Apache County, AZ in 2017 from Stage I Gasoline
Distribution 4-116
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Table 4-61: Sample calculations for benzene emissions for Apache County, AZ in 2017 from Stage I Gasoline
Distribution 4-116
Table 4-62: Sample calculations for benzene emissions for Apache County, AZ in 2017 from Stage I Gasoline
Distribution 4-117
Table 4-63: Sample calculations for benzene emissions for Apache County, AZ in 2017 from Stage I Gasoline
Distribution 4-117
Table 4-64: Sample calculations for benzene emissions for Apache County, AZ in 2017 from Stage I Gasoline
Distribution 4-118
Table 4-65: Sample calculations for benzene emissions for Apache County, AZ in 2017 from Stage I Gasoline
Distribution 4-119
Table 4-66: Sample Calculations for Emissions from Aviation Gasoline-Stage 1 in Autauga County, AL 4-121
Table 4-67: Sample Calculations for Emissions from Aviation Gasoline-Stage 1 in Autauga County, AL 4-122
Table 4-68: Source Classification Codes used in the Commercial Cooking sector 4-124
Table 4-69: Agencies that submitted Commercial Cooking emissions 4-124
Table 4-70: Hoovers database restaurant types 4-125
Table 4-71: Percent of restaurants with each type of cooking device 4-126
Table 4-72: Average number of devices by restaurant type* 4-126
Table 4-73: Average amount of meat cooked per year on each cooking device (tons) 4-127
Table 4-74: Sample VOC emissions calculations from commercial cooking on flat griddles in Apache county, AZ 4-
130
Table 4-75: SCCs in the Construction Dust sector 4-131
Table 4-76: S/L/Ts that submitted Construction Dust emissions 4-132
Table 4-77: Housing Start Data for 2017 4-133
Table 4-78: Breakdown of 2 to 4-unit structures 4-133
Table 4-79: Surface soil removed per unit type 4-136
Table 4-80: Emissions factors for residential construction 4-137
Table 4-81: Sample calculations for PM-10 PRI and PM25-PRI emissions from residential construction of 2-unit
structures in Suffolk County, MA 4-139
Table 4-82: Ranges and midpoints for data withheld from State and County Business Patterns 4-144
Table 4-83: 2017 CBP for NAICS 2361 in Arizona 4-144
Table 4-84. Sample calculations for non-residential construction in Grand Traverse County, Michigan 4-147
Table 4-85: Spending per mile and acres disturbed per mile by highway type 4-150
Table 4-86: Sample calculations for urban interstate, urban other arterial, and urban collector road construction
in Newport County, Rl 4-152
Table 4-87: SCCs in the paved road dust sector 4-155
Table 4-88: Agencies that submitted paved road dust emissions 4-155
Table 4-89: Assumed paved roads silt loading by road type (gm2) based on ADTV range 4-156
Table 4-90: Average vehicle weights by FWHA vehicle class 4-157
Table 4-91: MOVES and FWHA vehicle type crosswalk 4-157
Table 4-92: FHWA road types 4-158
Table 4-93: Penetration rate of Paved Road vacuum sweeping 4-158
Table 4-94: SCC in the unpaved road dust sector 4-160
Table 4-95: Agencies that submitted unpaved road dust emissions 4-161
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Table 4-96: Constants for unpaved roads re-entrained dust emission factor equation 4-162
Table 4-97: Surface material silt content values (%) for unpaved roads by state 4-162
Table 4-98: Speeds modeled by roadway type on unpaved roads 4-163
Table 4-99: FHWA road types 4-163
Table 4-100: Nonpoint Agricultural Field Burning SCCs in the 2017 NEI 4-167
Table 4-101: PM2.5 emissions submitted by reporting agency 4-168
Table 4-102: Revised Ag Burning Emission factors (lbs/ton) for VOC 4-171
Table 4-103: Select HAP Emission factors (lb/ton) used in EPA Methods by crop type for entire US 4-171
Table 4-104: Comparison of State vs EPA 2017 PM2.5 emissions (tons) for agencies that submitted 4-172
Table 4-105: Nonpoint ICI SCCs in the 2017 NEI 4-174
Table 4-106: Agencies reporting nonpoint ICI sector emissions 4-175
Table 4-107: Comprehensive State/Local agency submittal status for ICI estimates in the 2017 NEI 4-176
Table 4-108: Assumptions about non-combustion use of fuel by fuel type and state 4-179
Table 4-109: Anthracite and Bituminous Coal Distribution for the Residential and Commercial Sectors 4-181
Table 4-110: Mapping of NAICS codes to ICI sectors 4-182
Table 4-111: Sample calculations for PM25-PRI emissions from nonpoint industrial sector source
bituminous/subbituminous coal combustion in Alamance County, NC 4-185
Table 4-112: Non-wood residential fuel combustion SCCs in the 2017 NEI 4-188
Table 4-113: Agencies reporting non-wood residential fuel combustion emissions 4-188
Table 4-114: EIA State Energy Data System Fuel Codes 4-189
Table 4-115: Anthracite and Bituminous Coal Distribution for the Residential and Commercial Sectors 4-190
Table 4-116: S02 and PM Emissions Factors for Residential Anthracite and Bituminous Coal Combustion .... 4-193
Table 4-117: State-Specific Sulfur Content for Bituminous Coal (SCC 2104002000) 4-193
Table 4-118: Sample calculations for CO emissions from residential heating from distillate fuel oil in Allegheny
County, PA 4-195
Table 4-119 : RWC sector SCCs in the 2017 NEI 4-197
Table 4-120: Agencies reporting RWC emissions 4-197
Table 4-121: Distribution profiles for woodstoves and fireplace inserts by Census Region 4-200
Table 4-122: Distribution profiles for central heaters 4-200
Table 4-123: Sample calculations for PM25-PRI emissions from non-EPA certified woodstoves in Delaware
County, OH 4-203
Table 4-124: Mining and Quarrying sector SCCs in the 2017 NEI 4-205
Table 4-125: Agencies reporting Mining and Quarrying emissions 4-205
Table 4-126: NAICS Codes for Metallic and Non-Metallic Mining 4-207
Table 4-127: Withheld data ranges and midpoints 4-208
Table 4-128: 2016 County Business Pattern for NAICS 2123 in Arizona 4-208
Table 4-129: Emissions factors for Mining and Quarrying (2325000000) 4-212
Table 4-130: Sample calculations for estimating PM25-PRI emissions from mining and quarrying in Barbour
County, Alabama 4-213
Table 4-131: Point and Nonpoint SCCs used for the Oil and Gas Production Sector 4-215
Table 4-132: Data Source for Oil and Gas Production Data in the 2017 NEI 4-220
Table 4-133: EPA Oil and Gas estimates added to Alaska for the 2017 NEI 4-225
Table 4-134: Additional VOC emissions (tons/yr) added to the Alaska Oil and Gas inventory 4-226
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Table 4-135: New SCCs created to assist UT DAQ's pipeline and midstream processes reporting 4-229
Table 4-136: Human and animal cremation SCCs 4-231
Table 4-137: Agencies that submitted human and/or animal cremation emissions 4-232
Table 4-138: Human cremation rate by state 4-233
Table 4-139: Emissions factors for the cremation of human and animal blood and tissues 4-237
Table 4-140: Estimated amount of material in restored teeth 4-238
Table 4-141: Sample calculations for mercury emissions from human cremation for the 85+ age group and
cremation of cats in Clark County, ID 4-240
Table 4-142: Agencies reporting Residential Charcoal Grilling emissions 4-243
Table 4-143: Emissions Factors for Residential Grilling (2810025000) 4-247
Table 4-144: Sample calculations for VOC emissions from residential grilling in Ada County, Idaho 4-249
Table 4-145: PFC SCCs in the 2017 NEI 4-251
Table 4-146: Agencies reporting PFC emissions 4-251
Table 4-147: Toxic to VOC ratios for benzene and naphthalane from PFCs 4-254
Table 4-148: Toxic to VOC ratios for Other HAPs (Vapor Displacement, Permeation, Spillage and Evaporation).. 4-
254
Table 4-149: New Commercial Marine Vessel SCCs and emission types in EPA estimates 4-256
Table 4-150: Retired Commercial Marine Vessel SCCs 4-256
Table 4-151: Locomotive SCCs, descriptions, and EPA estimation status 4-257
Table 4-152: Submitting SLT agencies with number of pollutants reported for each SCC 4-257
Table 4-153: Pesticide application SCCs estimates generated by EPA and S/L/Ts 4-259
Table 4-154: Agencies that submitted pesticide emissions in the 2017 NEI 4-260
Table 4-155: Terms used to screen out consumer products 4-261
Table 4-156: Crosswalk between USGS compound name and CA DPR chemical name 4-263
Table 4-157: HAP Emissions Factors 4-273
Table 4-158: Sample calculations for VOC/HAP emissions from 2,4-D agricultural pesticide application in Autauga
County, AL 4-274
Table 4-159: Asphalt Paving SCCs in the 2017 NEI 4-277
Table 4-160: Agencies that reported emissions for Asphalt application in the 2017 NEI 4-277
Table 4-161: State-level asphalt usage (tons) in 2008 4-278
Table 4-162: Cutback Asphalt MSDS 4-282
Table 4-163: Emulsified Asphalt MSDS 4-282
Table 4-164: Chemical Composition Assumptions for Cutback Asphalt 4-282
Table 4-165: Chemical Composition Assumptions for Emulsified Asphalt 4-282
Table 4-166: Emissions Factors for Cutback Asphalt Usage 4-283
Table 4-167: Emissions Factors for Emulsified Asphalt Usage 4-283
Table 4-168: Sample calculations for VOC emissions from emulsified asphalt use in Barnstable County,
Massachusetts 4-284
Table 4-169: Nonpoint solvent SCCs in the 2017 NEI 4-286
Table 4-170: Agencies that reported emissions for Solvents in the 2017 NEI 4-289
Table 4-171: Source Categories That Use Population Activity Data 4-290
Table 4-172: Source Categories That Use Lane Mile Activity Data 4-291
Table 4-173: Source Categories That Use Employment Activity Data 4-291
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Table 4-174: Ranges and midpoints for data withheld from state and county business patterns 4-293
Table 4-175: 2016 County Business Pattern for NAICS 322220 in Kentucky 4-293
Table 4-176: Solvent Usage (million lbs) in the US 4-294
Table 4-177: 2016-2010 paint ratio 4-296
Table 4-178: Coatings sold (gallons) in 2010 4-296
Table 4-179: VOC Emissions Factors (lb/each) for Solvent Utilization 4-297
Table 4-180: States for which controlled emissions factors are used 4-299
Table 4-181: HAP speciation factors for solvent use 4-300
Table 4-182: Sample calculations for VOC emissions from solvent utilization in Apache County, AZ 4-305
Table 4-183: Composting SCCs in the 2017 NEI 4-307
Table 4-184: Agencies reporting composting emissions in the 2017 NEI 4-307
Table 4-185: Annual Waste (million tons) generated and recovered in the US in 2015 4-308
Table 4-186: State-level food waste composting (tons) 4-309
Table 4-187: Ranges and midpoints for data withheld from state and county business patterns 4-311
Table 4-188: 2016 County Business Pattern for NAICS 562212 in Arizona 4-311
Table 4-189: Emissions Factors for Composting of Greenwaste (2680003000) 4-312
Table 4-190: Sample calculations for VOC emissions from greenwaste composting in Apache County, AZ 4-313
Table 4-191: Open burning SCCs in the 2017 NEI 4-314
Table 4-192: Agencies that reported emissions for Open Burning in the 2017 NEI 4-315
Table 4-193: Annual Waste Generated in the US in 2015 4-316
Table 4-194: Adjustment for Percentage of Forested Acres 4-317
Table 4-195: Emissions Factors for Open Burning of Leaf Species 4-318
Table 4-196: Emissions Factors for Open Burning of Brush Species 4-318
Table 4-197: Sample calculations for CO emissions from open burning in Autauga County, AL 4-319
Table 4-198: Housing Start Data for 2017 4-323
Table 4-199: Breakdown of 2- to 4-unit structures in 2017 4-323
Table 4-200: Surface soil removed per unit type 4-325
Table 4-201: Spending per Mile and Acres Disturbed per Mile by Highway Type 4-327
Table 4-202: Fuel Loading Factors by Vegetation Type 4-328
Table 4-203: Ranges and midpoints for data withheld from State and County Business Patterns 4-330
Table 4-204: 2016 CBP for NAICS 2361 in Arizona 4-331
Table 4-205: Emissions Factors for Open Burning of Land Clearing Debris (SCC 2610000500) 4-332
Table 4-206: Sample calculations for PM25-PRI emissions from open burning of land clearing debris in McLean
County, IL 4-333
Table 4-207: Annual RHW generated (tons/person) in the U.S. in 2015 4-340
Table 4-208: Emission factors for Open Burning of RHW 4-341
Table 4-209: Sample calculations for CO and VOC emissions from open burning in Autauga County, AL 4-345
Table 4-210: Agencies that submitted POTW emissions in the 2017 NEI 4-347
Table 4-211: Emission Factors for Publicly Owned Treatment Works 4-348
Table 4-212: Sample calculations for benzene emissions for nonpoint source POTWs for Autauga County, Al 4-
350
Table 5-1: MOVES-Nonroad equipment and fuel types 5-1
Table 5-2: Pollutants produced by MOVES-Nonroad for 2017 NEI 5-2
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Table 5-3: Selection hierarchy for the Nonroad Mobile data category 5-4
Table 5-4: Submitted MOVES-Nonroad input tables, by agency 5-4
Table 5-5: Contents of the Nonroad Mobile supplemental folder 5-5
Table 5-6: HAPs calculated using MOVES ratios for California Nonroad SCCs 5-6
Table 6-1: Older vehicle adjustments showing the fraction of IHS vehicle populations to retain for 2017 NEl 6-2
Table 6-2: CAP pollutant basis for each HAP for California onroad 6-4
Table 6-3: MOVES2014b CDB tables 6-6
Table 6-4: Number of counties with submitted data, by state and key MOVES CDB table 6-9
Table 6-5: Tribes that Submitted Onroad Mobile Emissions Estimates for the 2017 NEI 6-12
Table 6-6: Source of EPA-developed information for key data tables in MOVES CDBs 6-13
Table 6-7: States adopting California LEV standards and start year 6-14
Table 6-8: Maximum allowable miles-per-year per-vehicle average by source type 6-22
Table 6-9: Agency submittal history for Onroad Mobile inputs and emissions 6-27
Table 6-10: Onroad Mobile data file references for the 2017 NEI 6-30
Table 7-1: SCCs for wildland fires 7-2
Table 7-2: 2017 NEI Wildfire and Prescribed Fires selection hierarchy 7-2
Table 7-3: PM species for all events, computed as fraction of total PM2.5 7-3
Table 7-4: National fire information databases used in EPA's 2017 NEI wildland fire emissions estimates 7-3
Table 7-5: Brief description of fire activity information submitted for 2017 NEI inventory use 7-6
Table 7-6: 2017 National SmartFire2 Reconciliation Weights 7-10
Table 7-7: Emission factor regions used to assign HAP emission factors for the 2017 NEI 7-11
Table 7-8: Prescribed fire HAP emission factors (lb/ton fuel consumed) for the 2017 NEI 7-11
Table 7-9: Wildfire HAP emission factors (lbs/ton fuel consumed) for the 2017 NEI 7-12
Table 7-10: CONUS (lower 48 states) and Alaska and Hawaii fire type information for 2017 NEI WLFs 7-18
Table 7-11: Summary of acres burned and PM2.5 emissions by state, fire type, and combustion phase 7-20
List of Figures
Figure 2-1: Relative contributions for various data sources of Point emissions for CAPs and select HAPs 2-9
Figure 2-2: Relative contributions for various data sources of Nonpoint emissions for CAPs and select HAPs.. 2-10
Figure 2-3: Data sources of Hg emissions (tons) in the 2017 NEI, by data category 2-18
Figure 2-4: Trends in NEI Mercury emissions (tons) 2-25
Figure 4-1: "Bidi" modeling system used to compute 2017 Fertilizer Application emissions 4-51
Figure 4-2: USDA farm production regions used in FT-C simulations 4-52
Figure 4-3: Simplified FEST-C system flow of operations in estimating NH3 emissions 4-54
Figure 4-4: NEI 2014 "bidi" Fertilizer Application NH3 Emissions 4-55
Figure 4-5: 2017 NEI "bidi" Fertilizer Application NH3 Emissions 4-56
Figure 4-6: 2017 -2014 NEI "bidi" Fertilizer Application Emissions in tons NH3 4-56
Figure 4-7: Regional distribution of beef cattle on feed 4-66
Figure 4-8: Regional distribution of dairy housing practices from 2007 NAHMS for Eastern and Western U.S.. 4-67
Figure 4-9: Distribution of storage and application practices across the U.S 4-68
Figure 4-10: Regional distribution of swine manure management practices 4-69
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Figure 4-11: Regional distribution of layer housing types 4-70
Figure 4-12: Emission factors as a function of temperature reported in the prior literature and from the NAEMS
4-72
Figure 4-13: Annual VOC emissions for year 2017 for 12km modeling domain 4-85
Figure 4-14: Alaska 9-km modeling domain 4-87
Figure 4-15: Hawaii 9-km modeling domain 4-88
Figure 4-16: Puerto Rico and Virgin Islands 9-km modeling domain 4-88
Figure 4-17: Total 2017 NEI Agricultural Burning PM2.5 Emissions by state 4-172
Figure 4-18: Data source for Oil and Gas emissions in the 2017 NEI 4-220
Figure 4-19: State-level 2017 NEI, SLT, and EPA NOX emission comparisons 4-230
Figure 4-20: State-level 2017 NEI, SLT, and EPA VOC emission comparisons 4-231
Figure 4-21: Types of Asphalt Paving processes 4-276
Figure 6-1: Counties for which agencies submitted local data for at least 1 CDB table* 6-8
Figure 6-2: Representative county groups for the 2017 NEI 6-18
Figure 7-1: 2017 NEI Wildland Fire Data Sources including S/L/Ts 7-6
Figure 7-2: Processing flow for fire emission estimates in the 2017 NEI inventory 7-9
Figure 7-3: Default fire type assignment by state and month in cases where a satellite detect is only source of
fire information 7-10
Figure 7-4: BlueSky Modeling Framework 7-14
Figure 7-5: Annual comparison of PM2.5 emissions for lower 48 states 7-17
Figure 7-6: Annual comparison of area burned for lower 48 states 7-17
Figure 7-7: Monthly acres burned by fire type for 2017 NEI CONUS Wildland Fires 7-18
Figure 7-8: Monthly PM2.5 by fire type for 2017 NEI CONUS Wildland Fires 7-19
Figure 7-9: Total 2017 NEI area burned by state 7-22
Figure 7-10: Total 2017 NEI PM2.5 emissions by state 7-22
Figure 7-11: 2017NEI county PM2.5 emissions in tons per square mile 7-23
Figure 7-12: 2017NEI county area burned in acres per square mile 7-24
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Acronyms and Chemical Notations
AERR Air Emissions Reporting Rule
APU Auxiliary power unit
BEIS Biogenics Emissions Inventory System
CI Category 1 (commercial marine vessels)
C2 Category 2 (commercial marine vessels)
C3 Category 3 (commercial marine vessels)
CAMD Clean Air Markets Division (of EPA Office of Air and Radiation)
CAP Criteria Air Pollutant
CBM Coal bed methane
CDL Cropland Data Layer
CEC North American Commission for Environmental Cooperation
CEM Continuous Emissions Monitoring
CENRAP Central Regional Air Planning Association
CERR Consolidated Emissions Reporting Rule
CFR Code of Federal Regulations
CH4 Methane
CMU Carnegie Mellon University
CMV Commercial marine vessels
CNG Compressed natural gas
CO Carbon monoxide
C02 Carbon dioxide
CSV Comma Separated Variable
dNBR Differenced normalized burned ratio
E10 10% ethanol gasoline
EDMS Emissions and Dispersion Modeling System
EF emission factor
EGU Electric Generating Utility
EIS Emission Inventory System
EAF Electric arc furnace
EF Emission factor
El Emissions Inventory
EIA Energy Information Administration
EMFAC Emission FACtor (model) - for California
EPA Environmental Protection Agency
ERG Eastern Research Group
ERTAC Eastern Regional Technical Advisory Committee
FAA Federal Aviation Administration
FACTS Forest Service Activity Tracking System
FCCS Fuel Characteristic Classification System
FETS Fire Emissions Tracking System
FWS United States Fish and Wildlife Service
FRS Facility Registry System
GHG Greenhouse gas
GIS Geographic information systems
GPA Geographic phase-in area
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GSE Ground support equipment
HAP Hazardous Air Pollutant
HCI Hydrogen chloride (hydrochloric acid)
Hg Mercury
HMS Hazard Mapping System
ICR Information collection request
l/M Inspection and maintenance
IPM Integrated Planning Model
KMZ Keyhole Markup Language, zipped (used for displaying data in Google Earth
LRTAP Long-range Transboundarv Air Pollution
LTO Landing and takeoff
LPG Liquified Petroleum Gas
MARAMA Mid-Atlantic Regional Air Management Association
MATS Mercury and Air Toxics Standards
MCIP Meteorology-Chemistry Interface Processor
MMT Manure management train
MOBILE6 Mobile Source Emission Factor Model, version 6
MODIS Moderate Resolution Imaging Spectroradiometer
MOVES Motor Vehicle Emissions Simulator
MW Megawatts
MWC Municipal waste combustors
NAA Nonattainment area
NAAQS National Ambient Air Quality Standards
NAICS North American Industry Classification System
NARAP North American Regional Action Plan
NASF National Association of State Foresters
NASS USDA National Agriculture Statistical Service
NATA National Air Toxics Assessment
NCD National County Database
NEEDS National Electric Energy Data System (database)
NEI National Emissions Inventory
NESCAUM Northeast States for Coordinated Air Use Management
NFEI National Fire Emissions Inventory
NG Natural gas
NH3 Ammonia
NMIM National Mobile Inventory Model
NO Nitrous oxide
N02 Nitrogen dioxide
NOAA National Oceanic and Atmospheric Administration
NOx Nitrogen oxides
03 Ozone
OAQPS Office of Air Quality Standards and Planning (of EPA)
OEI Office of Environmental Information (of EPA)
ORIS Office of Regulatory Information Systems
OTAQ Office of Transportation and Air Quality (of EPA)
PADD Petroleum Administration for Defense Districts
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PAH Polycyclic aromatic hydrocarbons
Pb Lead
PCB Polychlorinated biphenyl
PM Particulate matter
PM25-CON Condensable PM2.5
PM25-FIL Filterable PM2.5
PM25-PRI Primary PM2.55 (condensable plus filterable)
PM2.5 Particulate matter 2.5 microns or less in diameter
PM10 Particular matter 10 microns or less in diameter
PM10-FIL Filterable PM10
PM10-PRI Primary PM10
POM Polycyclic organic matter
POTW Publicly Owned Treatment Works
PSC Program system code (in EIS)
RFG Reformulated gasoline
RPD Rate per distance
RPP Rate per profile
RPV Rate per vehicle
RVP Reid Vapor Pressure
Rx Prescribed (fire)
SCC Source classification code
SEDS State Energy Data System
SFvl SMARTFIRE version 1
SFv2 SMARTFIRE version 2
S/L/T State, local, and tribal (agencies)
SMARTFIRE Satellite Mapping Automated Reanalvsis Tool for Fire Incident Reconciliation
SMOKE Sparse Matrix Operator Kernel Emissions
S02 Sulfur dioxide
S04 Sulfate
TAF Terminal Area Forecasts
TEISS Tribal Emissions Inventory Software Solution
TRI Toxics Release Inventory
UNEP United Nations Environment Programme
USDA United States Department of Agriculture
VMT Vehicle miles traveled
VOC Volatile organic compounds
USFS United States Forest Service
WebFIRE Factor Information Retrieval System
WFU Wildland fire use
WLF Wildland fire
WRAP Western Regional Air Partnership
WRF Weather Resea rch and Forecasting Model
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1 Introduction
The Environmental Protection Agency (EPA) has released a complete 2017 National Emissions Inventory (NEl),
hereafter referred to as the "2017 NEI". The EPA has released two prior versions of the 2017 NEI containing
some, but not all, data categories. This full 2017 NEI release supersedes the partial February 2020 and August
2019 NEI releases. The 2017 NEI was the first inventory that EPA released incrementally prior to this full release.
The 2017 NEI February release is available on the web at the 2017 NEI Data page.
1.1 What data are included in the 2017 Nil (April 2020 release)?
The NEI is a national compilation of air emission estimates of criteria air pollutants (CAPs), the precursors of
CAPs, and hazardous air pollutants (HAPs). The hazardous air pollutants that are included in the NEI are based
on Section 112(b) of the Clean Air Act. State, local and tribal (S/L/T) air agencies submit emission estimates to
EPA and the Agency adds information from EPA emissions programs, such as the emission trading program,
Toxics Release Inventory (TRI), and data collected during rule development or compliance testing. The NEI
includes estimates of emissions from stationary sources (large and small industries, commercial, institutional
and consumer), mobile sources, fires and biogenic emissions. EPA uses the NEI in rule development, non-
attainment area designations, and as an input to various reports and assessments. This document discusses all
components of the NEI and where useful, highlights differences between the 2017 NEI and the most-recent
publicly-available full NEI release, Version 2 of the 2014v2 NEI (2014v2 NEI). The NEI program develops datasets,
blends data from these multiple sources, and performs data processing steps that further enhance, quality
assure, and augment the compiled data.
The emissions data in the NEI are compiled at different levels of granularity, depending on the data category. For
point sources (in general, large facilities), emissions are inventoried at a process-level within a facility. The point
data are collected from S/L/T air agencies and the EPA emissions programs including the TRI, the Acid Rain
Program, and Maximum Achievable Control Technology (MACT) standards development. For nonpoint sources
(typically smaller, yet pervasive sources) and mobile sources (both onroad and nonroad), emissions are given as
county totals. For marine vessel and railroad in-transit sources, emissions are given at the sub-county polygon
shape-level. For wildfires and prescribed burning, the data are compiled as day-specific, coordinate-specific
(similar to point) events in the "event" portion of the inventory, and these emission estimates are further
stratified by smoldering and flaming components.
The pollutants included in the NEI are the pollutants associated with the National Ambient Air Quality Standards
(NAAQS), known as CAPs, as well as HAPs associated with EPA's Air Toxics Program. The CAPs have ambient
concentration limits or are precursors for pollutants with such limits from the NAAQS program. These pollutants
include lead (Pb), carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOCs), sulfur
dioxide (S02), particulate matter 10 microns or less (PM10), particulate matter 2.5 microns or less (PM2.5), and
ammonia (NH3), which is technically not a CAP, but an important PM precursor. The HAP pollutants include the
187 remaining HAP pollutants (methyl ethyl ketone was removed) from the original 188 listed in Section 112(b)
of the 1990 Clean Air Act Amendments1. There are many different types of HAPs. For example, some are acid
gases such as hydrochloric acid (HCI); others are heavy metals such as mercury (Hg), nickel and cadmium; and
others are organic compounds such as benzene, formaldehyde, and acetaldehyde. Greenhouse gases (GHGs) are
included in the NEI for fires, mobiles sources, and point sources where reported.
1 The original of HAPs is available on the EPA Technology Transfer Network - Air Toxics Web Site.
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1.2 What is included in this documentation?
This technical support document (TSD) provides a reference for the 2017 NEI (April 2020) release. The primary
purpose of this document is to explain the sources of information included in the inventory. This includes
showing the sources of data and types of sources that are used for each data category, and then providing more
information about the EPA-created components of the data. After the introductory material included in this
section, Section 2 provides an overview of the contents of the inventory and some high-level summaries,
including comparisons to the 2014v2 NEI. Section 2 also provides a summary on the mercury emissions. Section
3 provides an overview of point sources. Section 4 provides information about nonpoint sources, including
descriptions by source category or sector of the EPA emission estimates and tools. Biogenic emissions, which are
released with the nonpoint data category, are also discussed in Section 4. Sections 5 and 6 provide information
for the nonroad mobile and onroad mobile data categories, respectively. Fires (wild and prescribed burning) are
discussed in Section 7.
1.3 Where can I obtain the 2017 NEI data?
The 2017 NEI data are available in several different ways listed below. Data are available to the reporting
agencies and EPA staff via the Emission Inventory System (EIS).
1.3.1 Emission Inventory System Gateway
The EIS Gateway is available to all EPA staff, EIS data submitters (i.e., the S/L/T air agency staff), Regional
Planning Organization staff that support state, local and tribal agencies, and contractors working for the EPA on
emissions related work. The EIS reports functions can be used to obtain raw input datasets and create summary
files from these datasets as well as older versions of the NEI such as 2014, 2011, and 2008. The 2017 NEI dataset
in the EIS is called "2017NEI_Apr2020." Note that if you run facility-, unit- or process-level reports in the EIS, you
will get the 2017 NEI emissions, but the facility inventory, which is dynamic in the EIS, will reflect more current
information. For example, if an Agency ID has been changed since the time we ran the reports for the public
website (April 2020), then that new Agency ID will be in the Facility Inventory or a Facility Configuration report in
the EIS but not in the report on the public website nor the Facility Emissions Summary reports run on the
"2017NEI_Apr2020" dataset in the EIS.
1.3.2 NEI main webpage
Next, data from the EIS are exported for public release on the 2017 NEI Data webpage. The 2017 NEI Data page
includes the most recent publicly-available version of the 2017 NEI. The 2017 NEI webpage includes the 2017
NEI plan and schedules, all publicly-available supporting materials by inventory data category (e.g., point,
nonpoint, nonroad mobile, onroad mobile, events), and this TSD.
Two types of point data summaries are available on the 2017 NEI Data page, facility summaries and process-
level summaries. The source classification codes (SCC) data files section of the webpage provides the process
level summaries for all data categories. These detailed CSV files (provided in zip files) contain emissions at the
process level. Due to their size, they are broken out into EPA regions. Facility-level by pollutant summaries are
also available. These CSV files must be "linked" (as opposed to imported) to open them with Microsoft® Access®.
County and tribe-level summaries for events are also provided.
The 2017 NEI Data page also includes a query tool that allows for summaries by EIS Sector (see Section ) or the
more traditional Tier 1 summary level (for CAPs only) used in the EPA Trends Report. Summaries from the 2017
NEI Data site include national-, state-, and county-level emissions for CAPs, HAPs and GHGs. You can choose
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which states, EIS Sectors, Tiers, and pollutants to include in custom-generated reports to download Comma
Separated Value (CSV) files to import into Microsoft® Excel®, Access®, or other spreadsheet or database tools.
Biogenic emissions and tribal data (but not tribal onroad emissions) are also available from this tool. Tribal
summaries are also posted under the "Additional Summary Data" section of this page.
The 2017 NEI Documentation page includes links to the NEI TSD and supporting materials referenced in this TSD.
This page is a working page, meaning that content is updated as new products are developed.
1.3.3 Modeling files
The modeling files, provided on the Air Emissions Modeling website, are provided in formats that can be read by
the Sparse Matrix Operator Kernel Emissions (SMOKE). These files are also CSV formats that can be read by
other systems, such as databases. The modeling files provide the process-level emissions apportioned to release
points, and the release parameters for the release points. Release parameters include stack height, stack exit
diameter, exit temperature, exit velocity and flow rate. The EPA may make changes to the NEI modeling files
prior to use. The 2017 modeling platform is based on the 2017 NEI and is under development; it is expected to
be posted in the spring of 2020. Any changes between the NEI and modeling platform data will be described in
an accompanying TSD for the 2017 Emissions Modeling Platform, which would also be posted at the above
website.
The SMOKE flat files for the April 2020 version of the 2017 NEI, for all data categories, will be posted on the
2017 NEI Flat Files FTP site.
1.4 Why is the NEI created?
The NEI is created to provide the EPA, federal, state, local and tribal decision makers, and the national and
international public the best and most complete estimates of CAP and HAP emissions. While the EPA is not
directly obligated to create the NEI, the Clean Air Act authorizes the EPA Administrator to implement data
collection efforts needed to properly administer the NAAQS program. Therefore, the Office of Air Quality
Planning and Standards (OAQPS) maintains the NEI program in support of the NAAQS. Furthermore, the Clean
Air Act requires States to submit emissions to the EPA as part of their State Implementation Plans (SIPs) that
describe how they will attain the NAAQS. The NEI is used as a starting point for many SIP inventory development
efforts and for states to obtain emissions from other states needed for their modeled attainment
demonstrations.
While the NAAQS program is the basis on which the EPA collects CAP emissions from the S/L/T air agencies, it
does not require collection of HAP emissions. For this reason, the HAP reporting requirements are voluntary.
Nevertheless, the HAP emissions are an essential part of the NEI program. These emissions estimates allow EPA
to assess progress in meeting HAP reduction goals described in the Clean Air Act amendments of 1990. These
reductions seek to reduce the negative impacts to people of HAP emissions in the environment, and the NEI
allows the EPA to assess how much emissions have been reduced since 1990.
1.5 How is the NEI created?
The Air Emissions Reporting Rule (AERR) is the regulation that requires state and local agencies to submit CAP
emissions, and the Emissions Inventory System is the data system used to collect, QA, and compile those
submittals as well as EPA augmentation data. Most S/L/T air agencies also provide voluntary submissions of HAP
emissions. The 2008 NEI was the first inventory compiled using the AERR, rather than its predecessor, the
Consolidated Emissions Reporting Rule (CERR). The 2017 NEI is the fourth AERR-based inventory, and
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improvements in the 2017 NEI process reflect lessons learned by the S/L/T air agencies and EPA from the prior
NEI efforts. The AERR requires agencies to report all sources of emissions, except fires and biogenic sources.
Reporting of open fire sources, such as wildfires, is encouraged, but not required. Sources are divided into large
groups called "data categories": stationary sources are "point" or "nonpoint" (county totals) and mobile sources
are either onroad (cars and trucks driven on roads) or nonroad (locomotives, aircraft, marine, off-road vehicles
and nonroad equipment such as lawn and garden equipment).
The AERR has emissions thresholds above which States must report stationary emissions as "point" sources,
with the remainder of the stationary emissions reported as "nonpoint" sources.
The AERR changed the way these reporting thresholds work, as compared to the CERR, by changing these
thresholds to "potential to emit" thresholds rather than actual emissions thresholds. In both the CERR and the
AERR, the emissions that are reported are actual emissions, despite that the criteria for which sources to report
is now based on potential emissions. The AERR requires emissions reporting for point sources every year, with
additional requirements every third year in the form of lower point source emissions thresholds, and 2017 is one
of these third-year inventories.
Table 1-1 provides the potential-to-emit reporting thresholds that applied for the 2017 NEI cycle. "Type B" is the
terminology in the rule that represents the lower emissions thresholds required for point sources in the triennial
years. The reporting thresholds are sources with potential to emit of 100 tons/year or more for most criteria
pollutants, with the exceptions of CO (1000 tons/year), and, updated starting with the 2014 inventory, Pb (0.5
tons/year, actual). As shown in the table, special requirements apply to nonattainment area (NAA) sources,
where even lower thresholds apply. The relevant ozone (03), CO, and PM10 nonattainment areas that applied
during the year that the S/L/T agencies submitted their data for the 2017 NEI are available on the
Nonattainment Areas for Criteria Pollutants (Green Book) web site.
Table 1-1: Point source reporting thresholds (potential to emit) for CAPs in the AERR
Pollutant
Triennial reporting thresholds1
Type B Sources
Thresholds within Nonattainment Areas
(1) so2
>100
>100
(2) VOC
>100
03(moderate) > 100
03 (serious) > 50
03 (severe) > 25
03 (extreme) > 10
(3) NOx
>100
>100
(4) CO
>1000
03 (all areas) > 100
CO (all areas) > 100
(5)Lead
>0.5 (actual)
>0.5 (actual)
(6) Primary PMio
>100
PMio(moderate) >100
PMio(serious) >70
(7) Primary PM2.s
>100
>100
(8) NH3
>100
>100
thresholds for point source determination shown in tons per year of potential to emit as
defined in 40 CFR part 70, with the exception of lead.
Based on the AERR requirements, S/L/T air agencies submit emissions or model inputs of point, nonpoint,
onroad mobile, nonroad mobile, and fires emissions sources. With the exception of California, reporting
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agencies were required to submit model inputs for onroad and nonroad mobile sources instead of emissions.
For the 2017 NEl, all these emissions and inputs were required to be submitted to the EPA per the AERR by
December 31, 2018 (with an extension given through January 15, 2019). Once the initial reporting NEI period
closed, the EPA provided feedback on data quality such as suspected outliers and missing data by comparing to
previously established emissions ranges and past inventories. In addition, the EPA augmented the S/L/T data
using various sources of data and augmentation procedures. This documentation provides a detailed account of
EPA's quality assurance and augmentation methods.
1.6 Who are the target audiences for the 2017 NEI?
The comprehensive nature of the NEI allows for many uses and, therefore, its target audiences include EPA staff
and policy makers, the U.S. public, other federal and S/L/T decision makers, and other countries. Table 1-2
below lists the major current uses of the NEI and the plans for use of the 2017 NEI in those efforts. These uses
include those by the EPA in support of the NAAQS, Air Toxics, and other programs as well as uses by other
federal and regional agencies and for international needs. In addition to this list, the NEI is used to respond to
Congressional inquiries, provide data that supports university research, and allow environmental groups to
understand sources of air pollution.
Table 1-2: Examples of major current uses of the NEI
Audience
Purposes
U.S. Public
Learn about sources of air emissions
EPA-NAAQS
Regulatory Impact Analysis - benefits estimates using air quality modeling
NAAQS Implementations, including State Implementation Plans (SIPs)
Monitoring Rules
Final NAAQS designations
NAAQS Policy Assessments
Integrated Science Assessments
Transport Rule air quality modeling (e.g., Clean Air Interstate Rule, Cross-State Air Pollution Rule)
EPA-Air toxics
National Air Toxics Assessment (NATA)
Mercury and Air Toxics Standard - mercury risk assessment and Regulatory Impact Assessment
National Monitoring Programs Annual Report
Toxicity Weighted emission trends for the Government Performance and Reporting Act (GPRA)
Residual Risk and Technology Review - starting point for inventory development
EPA-other
NEI Reports - analysis of emissions inventory data
Report on the Environment
Air Emissions website for providing graphical access to CAP emissions for state maps and Google
Earth views of facility total emissions
Department of Transportation, national transportation sector summaries of CAPs
Black Carbon Report to Congress
Other federal or
regional agencies
Modeling in support of Regional Haze SIPs and other air quality issues
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DRAFT
Audience
Purposes
International
United Nations Environment Programme (UNEP) - global and North American Assessments
The Organization for Economic Co-operation and Development (OECD) - environmental data and
indicators report
UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP) - emission reporting
requirements, air quality modeling, and science assessments
Community Emissions Data System (CEDS) - science network for earth system, climate, and
atmospheric modeling
Commission for Environmental Cooperation (CEC) - North American emissions inventory
improvement and reduction policies
U.S. and Canada Air Quality Reports
Arctic Contaminants Action Program (ACAP) - national environmental and emission reduction
strategy for the Arctic Region
Other outside
parties
Researchers and graduate students
1.7 What are appropriate uses of the 2017 NEI and what are the caveats about the data?
As shown in the preceding section, the NEI provides a readily-available comprehensive inventory of both CAP
and HAP emissions to meet a variety of user needs. Although the accuracy of individual emissions estimates will
vary from facility-to-facility or county-to-county, the NEI largely meets the needs of these users in the aggregate.
Some NEI users may wish to evaluate and revise the emission estimates for specific pollutants from specific
source types for either the entire U.S. or for smaller geographical areas to meet their needs. Regulatory uses of
the NEI by the EPA, such as for interstate transport, always include a public review and comment period. Large-
scale assessment uses, such as the NATA study, also provide review periods and can serve as an effective
screening tool for identifying potential risks.
One of the primary goals of the NEI is to provide the best assessment of current emissions levels using the data,
tools and methods currently available. For significant emissions sectors of key pollutants, the available data,
tools and methods typically evolve over time in response to identified deficiencies and the need to understand
the costs and benefits of proposed emissions reductions. As these method improvements have been made,
there have not been consistent efforts to revise previous NEI year estimates to use the same methods as the
current year. Therefore, care must be taken when reviewing different NEI year publications as a time series with
the goal of determining the trend or difference in emissions from year to year. An example of such a method
change in the 2008 NEI v3 and 2011 NEI is the use of the Motor Vehicle Emissions Simulator (MOVES) model for
the onroad data category. Previous NEI years had used the Mobile Source Emission Factor Model, version 6
(MOBILE6) and earlier versions of the MOBILE model for this data category. The 2011 NEI (2011v2) also used an
older version of MOVES (2014) that has been updated in the current 2017 NEI (MOVES2014b). The current
version of MOVES also calculates nonroad equipment emissions, adding VOCs and toxics, updating the gasoline
fuels used for nonroad equipment to be consistent with those used for onroad vehicles. These changes in
MOVES lead to a small increase in nonroad NOx emissions in some locations, introducing additional uncertainty
when comparing 2017 NEI to past inventories.
Other significant emissions sectors have also had improvements and, therefore, trends are also impacted by
inconsistent methods. Examples include paved and unpaved road PM emissions, ammonia fertilizer and animal
waste emissions, oil and gas production, residential wood combustion, solvents, industrial and
commercial/institutional fuel combustion and commercial marine vessel emissions.
Users should take caution in using the emissions data for filterable and condensable components of particulate
matter (PM10-FIL, PM2.5-FIL and PM-CON), which is not complete and should not be used at any aggregated
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level. These data are provided for users who wish to better understand the components of the primary PM
species, where they are available, in the disaggregated, process-specific emissions reports. Where not reported
by S/L/T agencies, the EPA augments these components (see Section 2.2.4). However, not all sources are
covered by this routine, and in mobile source and fire models, only the primary particulate species are
estimated. Thus, users interested in PM emissions should use the primary species of particulate matter (PM10-
PRI and PM25-PRI), described in this document simply as PM10 and PM2.5.
1.8 Updates in the 2017 NEI, from the February 2020 version
Below is a list of updates in this full 2017 (April 2020) release compared to the February 2020 interim 2017 NEI
release:
• Nonpoint data category, including biogenics, commercial marine vessels, and non-rail yard estimates
• Onroad mobile data category
• The components of particulate matter: organic carbon, elemental carbon, sulfates, nitrates, and crustal
material
• Diesel PM emissions from diesel fueled airport ground support equipment and locomotives
• Minor data additions and corrections provide by state, local, and tribal partners
• Complete TSD, set of data summaries, and query tools on the 2017 NEI Data web site
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2 2017 NEI contents overview
2.1 What are EIS sectors?
First used for the 2008 NEI, EIS Sectors continue to be used for all 2017 NEI data categories. The sectors were
developed to better group emissions for both CAP and HAP summary purposes. The sectors are based simply on
grouping the emissions by the emissions process as indicated by the SCC to an EIS sector. In building this list, we
gave consideration not only to the types of emissions sources our data users most frequently ask for, but also to
the need to have a relatively concise list in which all sectors have a significant amount of emissions of at least
one pollutant. The SCC-EIS Sector cross-walk used for the summaries provided in this document is available for
download from the Source Classification Codes (SCCs) website. No changes were made to the SCC-mapping or
sectors used for the 2017 NEI except where SCCs were retired, or new SCCs were added.
Some of the sectors include the nomenclature "NEC," which stands for "not elsewhere classified." This simply
means that those emissions processes were not appropriate to include in another EIS sector and their emissions
were too small individually to include as its own EIS sector.
Since the 2008 NEI, the inventory has been reported and compiled in EIS using five major data categories: point,
nonpoint, onroad, nonroad and events. The event category is used to compile day-specific data from prescribed
burning and wildfires. While events could be other intermittent releases such as chemical spills and structure
fires, prescribed burning and wildfires have been a focus of the NEI creation effort and are the only emission
sources contained in the event data category.
Table 2-1 shows the EIS sectors or source category component of the EIS sector in the left most column. EIS data
categories -Point, Nonpoint, Onroad, Nonroad, and Events- that have emissions in these sectors/source
categories are also reflected.
As Table 2-1 illustrates, many EIS sectors include emissions from more than one EIS data category because the
EIS sectors are compiled based on the type of emissions sources rather than the data category. Note that the
emissions summary sector "Mobile - Aircraft" is reported partly to the point and partly to the nonpoint data
categories and "Mobile - Commercial Marine Vessels" and "Mobile - Locomotives" are reported to the nonpoint
data category. We include biogenics emissions, "Biogenics - Vegetation and Soil," in the nonpoint data category
in the EIS; however, we document biogenics in its own Section (8). NEI users who aggregate emissions by EIS
data category rather than EIS sector should be aware that these changes will give differences from historical
summaries of "nonpoint" and "nonroad" data unless care is taken to assign those emissions to the historical
grouping.
Table 2-1: EIS sectors/source categories with EIS data category emissions reflected
Component
EIS Sector or EIS Sector: Source Category Name
Point
Nonpoint
Onroad
Nonroad
Event
Agriculture - Crops & Livestock Dust
0
Agriculture - Fertilizer Application
0
Agriculture - Livestock Waste
0
0
Biogenics - Vegetation and Soil
0
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Component
EIS Sector or EIS Sector: Source Category Name
Point
Nonpoint
Onroad
Nonroad
Event
Bulk Gasoline Terminals
0
0
Commercial Cooking
0
Dust - Construction Dust
0
0
Dust - Paved Road Dust
0
Dust - Unpaved Road Dust
0
Fires - Agricultural Field Burning
0
Fires - Prescribed Burning
0
Fires - Wildfires
0
Fuel Comb - Comm/lnstitutional - Biomass
0
0
Fuel Comb - Comm/lnstitutional - Coal
0
0
Fuel Comb - Comm/lnstitutional - Natural Gas
0
0
Fuel Comb - Comm/lnstitutional - Oil
0
0
Fuel Comb - Comm/lnstitutional - Other
0
0
Fuel Comb - Electric Generation - Biomass
0
Fuel Comb - Electric Generation - Coal
0
Fuel Comb - Electric Generation - Natural Gas
0
Fuel Comb - Electric Generation - Oil
0
Fuel Comb - Electric Generation - Other
0
Fuel Comb - Industrial Boilers, ICEs - Biomass
0
0
Fuel Comb - Industrial Boilers, ICEs - Coal
0
0
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
0
0
Fuel Comb - Industrial Boilers, ICEs - Oil
0
0
Fuel Comb - Industrial Boilers, ICEs - Other
0
0
Fuel Comb - Residential - Natural Gas
0
Fuel Comb - Residential - Oil
0
Fuel Comb - Residential - Other
0
Fuel Comb - Residential - Wood
0
Gas Stations
0
0
0
Industrial Processes - Cement Manufacturing
0
Industrial Processes - Chemical Manufacturing
0
0
Industrial Processes - Ferrous Metals
0
Industrial Processes - Mining
0
0
Industrial Processes - NEC
0
0
Industrial Processes - Non-ferrous Metals
0
0
Industrial Processes - Oil & Gas Production
0
0
Industrial Processes - Petroleum Refineries
0
0
Industrial Processes - Pulp & Paper
0
Industrial Processes - Storage and Transfer
0
0
Miscellaneous Non-Industrial NEC: Residential Charcoal Grilling
0
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Component
EIS Sector or EIS Sector: Source Category Name
Point
Nonpoint
Onroad
Nonroad
Event
Miscellaneous Non-Industrial NEC: Portable Gas Cans
0
Miscellaneous Non-Industrial NEC: Nonpoint Hg
0
Miscellaneous Non-Industrial NEC (All other)
0
0
Mobile - Aircraft
0
Mobile - Commercial Marine Vessels
0
Mobile - Locomotives
0
0
Mobile - NonRoad Equipment - Diesel
0
0
Mobile - NonRoad Equipment - Gasoline
0
0
Mobile - NonRoad Equipment - Other
0
0
Mobile - Onroad - Diesel Heavy Duty Vehicles
0
Mobile - Onroad - Diesel Light Duty Vehicles
0
Mobile - Onroad - Gasoline Heavy Duty Vehicles
0
Mobile - Onroad - Gasoline Light Duty Vehicles
0
Solvent - Consumer & Commercial Solvent Use: Agricultural Pesticides
0
Solvent - Consumer & Commercial Solvent Use: Asphalt Paving
0
Solvent - Consumer & Commercial Solvent Use: All Other Solvents
0
Solvent - Degreasing
0
0
Solvent - Dry Cleaning
0
0
Solvent - Graphic Arts
0
0
Solvent - Industrial Surface Coating & Solvent Use
0
0
Solvent - Non-Industrial Surface Coating
0
Waste Disposal: Open Burning
0
Waste Disposal: Nonpoint POTWs
0
Waste Disposal: Human Cremation
0
Waste Disposal: Nonpoint Hg
0
Waste Disposal (all remaining sources)
0
0
2.2 How is the NEI constructed?
Data in the NEI come from a variety of sources. The emissions are predominantly from S/L/T agencies for both
CAP and HAP emissions. In addition, the EPA quality assures and augments the data provided by states to assist
with data completeness, particularly with the HAP emissions since the S/L/T HAP reporting is voluntary.
The NEI is built by data category for point, nonpoint, nonroad mobile, onroad mobile and events. Each data
category contains emissions from various reporters in multiple datasets which are blended to create the final
NEI "selection" for that data category. Each data category selection includes S/L/T data and numerous other
datasets that are discussed in more detail in each of the following sections in this document. In general, S/L/T
data take precedence in the selection hierarchy, which means that it supersedes any other data that may exist
for a specific county/tribe/facility/process/pollutant. In other words, the selection hierarchy is built such that
the preferred source of data, usually S/L/T, is chosen when multiple sources of data are available. There are
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exceptions, to this general rule, which arise based on quality assurance checks and feedback from S/L/Ts that we
will discuss in later sections.
The EPA uses augmentation and additional EPA datasets to create the most complete inventory for
stakeholders, for use in such applications as NATA, air quality modeling, national rule assessments, international
reporting, and other reports and public inquiries. Augmentation to S/L/T data, in addition to EPA datasets, fill in
gaps for sources and/or pollutants often not reported by S/L/T agencies. The basic types of augmentation are
discussed in the following sections.
2.2.1 Toxics Release Inventory data
The EPA used air emissions data from the 2017 Toxics Release Inventory (TRI) to supplement point source HAP
and NH3 emissions provided to EPA by S/L/T agencies. For 2017, all TRI emissions values that could reasonably
be matched to an EIS facility with some certainty and with limited risk of double-counting nonpoint emissions
were loaded into the EIS for viewing and comparison if desired, but only those pollutants that were not reported
anywhere at the EIS facility by the S/L/T agency were included in the 2017 NEI.
The TRI is an EPA database containing data on disposal or other releases including air emissions of over 650 toxic
chemicals from approximately 21,000 facilities. One of TRI's primary purposes is to inform communities about
toxic chemical releases to the environment. Data are submitted annually by U.S. facilities that meet TRI
reporting criteria. Section 3 provides more information on how TRI data was used to supplement the point
inventory.
2.2.2 Chromium speciation
The 2017 reporting cycle included 5 valid pollutant codes for chromium, as shown in Table 2-2.
Table 2-2: Valid chromium pollutant codes
Pollutant Code
Description
Pollutant Category Name
Speciated?
1333820
Chromium Trioxide
Chromium Compounds
yes
16065831
Chromium III
Chromium Compounds
yes
18540299
Chromium (VI)
Chromium Compounds
yes
7440473
Chromium
Chromium Compounds
no
7738945
Chromic Acid (VI)
Chromium Compounds
yes
In the above table, all pollutants but "chromium" are considered speciated, and so for clarity, chromium
(pollutant 7440473) is referred to as "total chromium" in the remainder of this section. Total chromium could
contain a mixture of chromium with different valence states. Since one key inventory use is for risk assessment,
and since the valence states of chromium have very different risks, speciated chromium pollutants are the most
useful pollutants for the NEI. Therefore, the EPA speciates S/L/T-reported and TRI-based total chromium into
hexavalent chromium and non-hexavalent chromium. Hexavalent chromium, or Chromium (VI), is considered
high risk and other valence states are not. Most of the non-hexavalent chromium is trivalent chromium
(Chromium III); therefore, the EPA characterized all non-hexavalent chromium as trivalent chromium. The 2017
NEI does not contain any total chromium, only the speciated pollutants shown in Table 2-2.
This section describes the procedure we used for speciating chromium emissions from total chromium that was
reported by S/L/T agencies.
We used the EIS augmentation feature to speciate S/L/T agency reported total chromium. For point sources, the
EIS uses the following priority order for applying the factors:
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1) By Process ID
2) By Facility ID
3) By County
4) By State
5) By Emissions Type (for NP only)
6) By SCC
7) By Regulatory Code
8) By NAICS
9) A Default value if none of the others apply
If a particular emissions source of total chromium is not covered by the speciation factors specified by any of the
first 8 attributes, a default value of 34 percent hexavalent chromium, 66 percent trivalent chromium is applied.
For the 2017 chromium augmentation, only the "By Facility ID" (2), "By SCC" (6), and "By Default" (9) were used
on S/L/T-reported total chromium values. ForTRI dataset chromium, the "By NAICS" (8) option was primarily
used, although a small number of "By Facility" (2) occurrences were used rather than NAICS. The EIS generates
and stores an EPA dataset containing the resultant hexavalent and trivalent chromium species. For all other data
categories (e.g., nonpoint, onroad and nonroad), chromium speciation is performed at the SCC level.
This procedure generated hexavalent chromium (Chromium (VI)) and trivalent chromium (Chromium III), and it
had no impact on S/L/T agency data that were provided as one of the speciated forms of chromium. The sum of
the EPA-computed species (hexavalent and trivalent chromium) equals the mass of the total chromium (i.e.,
pollutant 7440473) submitted by the S/L/T agencies.
The EPA then used this dataset in the 2017 NEI selection by adding it to the data category-specific selection
hierarchy and by excluding the S/L/T agency unspeciated chromium from the selection through a pollutant
exception to the hierarchy.
Most of the speciation factors used in the 2017 NEI are SCC-based and are the same as were used in 2011 and
2014, based on data that have long been used by the EPA for NATA and other risk projects. However, some
values are updated with every inventory cycle. New data may be developed by OAQPS during rule development
or review of NATA data. The speciation factors are accessed in the EIS through the reference data link
"Augmentation Profile Information." A chromium speciation "profile" is a set of output multiplication factors for
a type of emissions source. The profile data for chromium are stored in the same tables as the HAP
augmentation factors described in Section 2.2.3. The speciation factors are a specific case of HAP augmentation
whereby the "output pollutants" are always hexavalent chromium and trivalent chromium, and the "input
pollutant" is always chromium. There are 3 main tables and a summary table. The summary table excludes the
metadata and comments regarding the derivation of the factors and assignment to SCCs; to learn more of the
derivation of the factor or assignment of "profile" to a source, the main tables (not summary table) should be
consulted.
The three main tables are:
• Augmentation Profile Names and Input Pollutants - general information about the profile and source of
the profile names and factors.
• Augmentation Multiplication Factors - provides the output pollutants and multiplication factors
associated with a given Augmentation Profile and input pollutant.
• Augmentation Assignments - provides the assignment of the profile to the data source (the list of 9
items above).
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The summary table is the Augmentation Multiplication Factors and Assignments, a composite table that
provides a view of all the combinations of output pollutants and assignment information associated with a given
profile.
For non-EIS users, the data from the main tables were downloaded and provided as described in Section 3
(3.1.4-S/L/T chromium speciation, 3.1.6 -TRI chromium speciation and 3.1.6, HAP augmentation).
2.2.3 HAP augmentation
The EPA supplements missing HAPs in S/L/T agency-reported data. HAP emissions are calculated by multiplying
appropriate surrogate CAP emissions by an emissions ratio of HAP to CAP emission factors. For the 2017 NEI, we
augmented HAPs for the point and nonpoint data categories. Generally, for point sources, the CAP-to-HAP ratios
were computed using uncontrolled emission factors from the WebFIRE database (which contains primarily
AP-42 emissions factors). For nonpoint sources, the ratios were computed from the EPA-generated nonpoint
data, which contain both CAPs and HAPs where applicable.
HAP augmentation is performed on each emissions source (i.e., specific facility and process for point sources,
county and process level for nonpoint sources) using the same EIS augmentation feature as described in
chromium speciation. However, unlike chromium speciation, there is no default augmentation factor so that not
every process that has S/L/T CAP data will end up with augmented HAP data.
HAP augmentation input pollutants are S/L/T-submitted VOC, PM10-PRI, PM25-PRI, S02, and PM10-FIL. The
resulting output can be a single output pollutant or a full suite of output pollutants. Not every source that has a
CAP undergoes HAP augmentation (i.e., livestock NH3 and fugitive dust PM25-PRI). The sum of the HAP
augmentation factors does not need to equal 1 (100%); however, we try to ensure, for example, that the sum of
HAP-VOC factors is less than 1 for mass balance. HAP augmentation factors are grouped into profiles that
contain unique output pollutant factors related to a type of source. Assigning these profiles to the individual
sources depends on the source attributes, commonly the SCC.
There are business rules specific to each data category discussed in the point (Section 3.1.6) and nonpoint
(4.1.6) sections of the TSD. The ultimate goal is to prevent double-counting of HAP emissions between S/L/T
data and the EPA HAP augmentation output, and to prevent, where possible, adding HAP emissions to S/L/T-
submitted processes that are not desired. NEI developers use their judgment on how to apply HAP
augmentation to the resulting NEI selection.
Caveats
HAP augmentation does have limitations; HAP and CAP emission factors from WebFIRE do not necessarily use
the same test methods. In some situations, the VOC emission factor is less than the sum of the VOC HAP
emission factors. In those situations, we normalize the HAP ratios so as not to create more VOC HAPs than VOC.
We are also aware that there are many similar SCCs that do not always share the same set of emission
factors/output pollutants. We do not apply ratios based on emission factors from similar SCCs other than for
mercury from combustion SCCs. We would prefer to get HAPs reported from reporting agencies or get the data
from other sources (compliance data from rule), but such data are not always available.
Because much of the AP-42 factors are 20+ years old, many incremental edits to these factors have been made
over time. We have removed some factors based on results of NATA reviews. For example, we discovered
ethylene dichloride was being augmented for SCCs related to gasoline distribution. This pollutant was associated
with leaded gasoline which is no longer used. Therefore, we removed it from our HAP augmentation between
2011 NEI v2 and 2014. We also received specific facility and process augmentation factors resulting from the
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NATA reviews. More discussion of the underlying data used for the 2017 NEI August2019 point version is
discussed in Section 3.1.6.
For point sources, HAPs augmentation data are not used when S/L/T air agency data exists at any process at the
facility for the same pollutant. That means that if a S/L/T reports a particular HAP at some processes but misses
others, then those other processes will not be augmented with that HAP.
2.2.4 PM augmentation
Particulate matter (PM) emissions species in the NEI are: primary PM10 (called PM10-PRI in the EIS and NEI) and
primary PM2.5 (PM25-PRI), filterable PM10 and filterable PM2.5 (PM10-FIL and PM25-FIL) and condensable PM
(PM-CON). The EPA needed to augment the S/L/T agency PM components for the point and nonpoint
inventories to ensure completeness of the PM components in the final NEI and to ensure that S/L/T agency data
did not contain inconsistencies. An example of an inconsistency is if the S/L/T agency submitted a primary PM2.5
value that was greater than a primary PM10 value for the same process. Commonly, the augmentation added
condensable PM or PM filterable (PM10-FIL and/or PM25-FIL) where none was provided, or primary PM2.5
where only primary PM2.5 was provided.
In general, emissions for PM species missing from S/L/T agency inventories were calculated by applying factors
to the PM emissions data supplied by the S/L/T agencies. These conversion factors were first used in the 1999
NEI's "PM Calculator" as described in an NEI conference paper [ref 1], The resulting methodology allows the EPA
to derive missing PM10-FIL or PM25-FIL emissions from incomplete S/L/T agency submissions based on the SCC
and PM controls that describe the emissions process. In cases where condensable emissions are not reported,
conversion factors are applied to S/L/T agency reported PM species or species derived from the PM Calculator
databases. The PM Calculator has undergone several edits since 1999; now called the "PM Augmentation Tool,"
this Microsoft ® Access ® database is no longer made available because it should not be run for any purpose
other than gap-filling the final NEI selection.
The PM Augmentation Tool is used only for point and nonpoint sources, and the output from the tool is heavily-
screened prior to use in the NEI. This screening is done to prevent trivial overwriting of S/L/T data from PM
Augmentation Tool calculations, particularly for primary PM submittals by S/L/Ts. More details on the caveats to
using the PM Augmentation Tool are discussed in Section 3 on point sources and Section 4 on nonpoint sources.
2.2.5 Other EPA datasets
In addition to TRI, chromium speciation, HAP and PM augmentation, the EPA generates other data to produce a
complete inventory. Examples of EPA data for point sources, discussed in Section 3, include EPA landfills, electric
generating units (EGUs), and aircraft.
2.2.6 Data Tagging
S/L/T agency data generally is used first when creating the NEI selection. When S/L/T data are used, then the NEI
would not use other data (primarily EPA data from stand-alone datasets or HAP, PM or TRI augmentation) that
also may exist for the same process/pollutant. Thus, in most cases the S/L/T agency data are used; however, for
several reasons, sometimes we need to exclude, or "tag out" S/L/T agency data. Examples of these "S/L/T tags"
are when S/L/T agency staff alert the EPA to exclude their data (because of a mistake or outdated value), or
when EPA staff find problems with submitted data. Another example is when S/L/T emissions data are
significantly less than TRI and are presumed to be incomplete, which can happen for S/L/T that use automated
gap-filling procedures for facilities that do not voluntarily provide HAP emissions. These automated procedures
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gap-fill only for processes that have emission factors and miss processes/pollutants that may have been
reported to TRI using other means besides published emission factors.
In previous NEI years data tagging had also been used to avoid double-counting emissions by using emissions
from more than one dataset because the two datasets were at different levels of granularity and thus not able
to be integrated to the full process level of detail required by the standard selection hierarchy software. The
primary example of this is the TRI dataset, which provides facility-total emissions rather than individual process-
level emissions. Because the TRI emissions must be stored to a single emission process that is not the same as
that used by the S/L/T agency, the standard hierarchy selection software would use both. Thus, tagging was
used to "block" any TRI values where the S/L/T had reported the same pollutant at any process(es) within the
same facility. For the 2017 NEI, a series of additional rules were added to the selection hierarchy to avoid such
tagging. Point source datasets are now identified as being either Process-level, Unit-level, or Facility-level
granularity, and the selection software now uses those identifications to avoid double-counting, avoiding the
need for those types of tags.
2.2.7 Inventory Selection
Once all S/L/T and EPA data are quality assured in the EIS, and all augmentation and data tagging are complete,
then we use the EIS to create a data category-specific inventory selection. To do this, each EIS dataset is
assigned a priority ranking prior to running the selection with EIS. The EIS then performs the selection at the
most detailed inventory resolution level for each data category. For point sources, this is the process and
pollutant level. For nonpoint sources, it is the process (SCC)/shape ID (i.e., ports) and pollutant level. For onroad
and nonroad sources, it is process/pollutant, and for events it is day/location/process and pollutant. At these
resolutions, the inventory selection process uses data based on highest priority and excludes data where it has
been tagged. The EPA then quality assures this final blended inventory to ensure expected processes/pollutants
are included or excluded. The EIS uses the inventory selection to also create the SMOKE Flat Files, EIS reports
and data that appear on the NEI website.
2.3 What are the sources of data in the 2017 NEI?
This section shows the contributions of S/L/T agency data to total emissions for the point and nonpoint data
categories. Figure 2-1 shows the proportion of CAP, select HAPs, and HAP group emissions from various data
sources in the NEI for point data category sources. Except for PM2.5, most point emissions come from S/L/T-
submitted data. PM augmentation (see Section 2.2.4), which is based off incomplete S/L/T submittals of PM,
accounts for a significant portion of PM point emissions. The data sources shown in the figure are described in
more detail in Section 3.
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DRAFT
Figure 2-1: Relative contributions for various data sources of Point emissions for CAPs and select HAPs
100% — — — — — — — — — — — —
90%
70%
60%
50%
40%
30%
20%
10%
0%
^
rCV
,9V-
V
' ^ J? JF ^
I EPA Carry Forward
EPA Other
EPA Air/Rail/CMV
EPAEGU
EPA HAP & PM Aug
EPATRI
S/L/T
Figure 2-2 shows the proportion of CAP, select FIAPs, and FIAP group emissions from various data sources in the
NEI for nonpoint data category sources. Biogenic sources, all EPA data, are not included in this table. Acid Gases
include the following pollutants: hydrogen cyanide, hydrochloric acid, hydrogen fluoride, and chlorine. FIAP VOC
emissions consist of dozens of VOC FIAP species, that in-aggregate, should be less than VOC in our QA checks.
FIAP metal emissions consist of the following compound groups: Antimony, Arsenic, Beryllium, Cadmium,
Chromium, Cobalt, Lead, Manganese, Mercury, Nickel and Selenium. More than 50% of nonpoint pollutant
totals come from some type of EPA source; however, as discussed in Section 4.1, S/L/T-submitted nonpoint
activity data is absorbed into EPA nonpoint tools and are therefore classified as "EPA" data. The large "EPA
Other" bars for PM10 and PM2.5 are predominantly dust sources from unpaved roads, agricultural dust from
crop cultivation, and construction dust.
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DRAFT
Figure 2-2: Relative contributions for various data sources of Nonpoint emissions for CAPs and select HAPs
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
<9 J*.0
,/* jf
I
\T
^ .rf" ^ 4? £ vo°
^ ttr
-------
Table 2-3: EIS sectors and associated 2017 CAP and total HAP emissions (thousanc
Sector
CO
NH3
NOX
PM2.5
PM10
S02
Black
Carbon
voc
Lead
Total
HAPs1
Agriculture - Crops & Livestock Dust
794
4,034
n
Agriculture - Fertilizer Application
926
Agriculture - Livestock Waste
2,569
0.04
0.11
2.20E-03
228
25
Bulk GasolineTerminals
1.07
3.41E-03
0.43
0.04
0.05
9.05E-03
3.48E-04
131
6.42E-04
6.25
Commercial Cooking
46
4.73E-03
118
126
7.28E-04
4.05
19
8.80
Dust - Construction Dust
0.12
0.10
119
1,145
7.52E-03
9.52E-05
0.03
2.26E-03
0.05
Dust - Paved Road Dust
225
950
2.34
Dust - Unpaved Road Dust
574
5,768
0.56
Fires - Agricultural Field Burning
301
63
13
31
43
4.24
3.35
38
8.41
Fires - Prescribed Fires
8,870
145
165
805
948
78
26
2,042
416
Fires - Wildfires
19,487
319
231
1,655
1,953
135
53
4,578
978
Fuel Comb - Comm/lnstitutional - Biomass
20
0.14
9.09
13
15
0.95
0.47
0.73
3.39E-04
0.38
Fuel Comb - Comm/lnstitutional - Coal
1.21
5.99E-03
3.90
0.45
1.09
13
0.02
0.15
5.04E-04
0.42
Fuel Comb - Comm/lnstitutional - Natural Gas
119
1.46
144
4.42
4.69
1.45
0.29
9.99
1.82E-03
0.94
Fuel Comb - Comm/lnstitutional - Oil
14
0.23
36
2.74
2.91
6.27
0.42
2.64
2.64E-03
0.23
Fuel Comb - Comm/lnstitutional - Other
10
0.05
12
0.58
0.60
1.12
0.04
1.16
3.56E-04
0.19
Fuel Comb - Electric Generation - Biomass
19
0.61
11
1.64
1.91
1.47
0.06
0.91
1.21E-03
1.00
Fuel Comb - Electric Generation - Coal
453
5.77
924
74
97
1,319
2.92
17
7.70E-03
7.77
Fuel Comb - Electric Generation - Natural Gas
78
12
145
24
25
7.30
1.61
9.49
5.76E-04
3.71
Fuel Comb - Electric Generation - Oil
7.73
0.62
55
4.31
5.24
40
1.31
1.58
8.63E-04
0.22
Fuel Comb - Electric Generation - Other
30
0.45
24
2.94
3.08
18
0.16
3.22
5.44E-04
1.90
Fuel Comb - Industrial Boilers, ICEs- Biomass
251
2.29
102
118
137
17
4.39
9.45
5.48E-03
5.31
Fuel Comb - Industrial Boilers, ICEs - Coal
32
0.60
88
12
48
212
0.49
0.71
6.18E-03
5.17
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
293
8.49
558
22
23
16
1.46
63
3.55E-03
27
Fuel Comb - Industrial Boilers, ICEs - Oil
25
0.28
86
6.16
6.95
19
1.32
5.50
8.05E-03
0.52
Fuel Comb - Industrial Boilers, ICEs - Other
78
0.88
47
8.16
9.53
35
0.62
6.92
2.07E-03
1.82
Fuel Comb - Residential - Natural Gas
89
34
205
4.05
4.21
1.37
0.27
12
1.17E-04
0.59
Fuel Comb - Residential - Oil
8.90
1.56
33
3.35
3.84
12
0.39
1.06
2.04E-03
0.08
Fuel Comb - Residential - Other
8.78
0.10
31
0.17
0.19
0.72
0.01
1.19
5.88E-07
0.02
Fuel Comb - Residential - Wood
2,398
17
39
337
339
8.70
19
333
9.66E-07
66
Gas Stations
0.03
1.39E-03
0.02
9.56E-04
9.56E-04
3.59E-04
3.33E-05
443
2.48E-04
52
Industrial Processes - Cement Manuf
102
1.75
105
6.69
11
26
0.20
5.30
2.29E-03
1.87
Industrial Processes - Chemical Manuf
139
24
66
17
22
123
0.40
94
4.35E-03
25
Industrial Processes - Ferrous Metals
328
0.20
59
24
33
22
0.48
11
0.04
1.92
Industrial Processes - Mining
20
0.06
20
61
455
0.95
0.06
1.44
3.58E-03
0.16
Industrial Processes - NEC
163
21
158
78
133
138
1.34
183
0.04
50
Industrial Processes - Non-ferrous Metals
148
0.43
14
10
13
53
0.16
11
0.02
4.51
Industrial Processes - Oil & Gas Production
631
0.27
616
12
13
64
0.58
2,455
1.83E-04
112
Industrial Processes - Petroleum Refineries
57
2.60
68
17
20
60
0.95
52
3.79E-03
9.96
Industrial Processes - Pulp & Paper
90
5.06
74
31
40
24
0.93
124
3.80E-03
50
Industrial Processes - Storage and Transfer
6.28
3.67
2.67
13
35
1.08
0.21
195
5.35E-03
11
Miscellaneous Non-Industrial NEC
108
5.41
3.22
15
18
0.15
0.60
80
7.20E-04
16
Mobile - Aircraft
644
192
9.11
10
26
3.14
87
0.47
24
Mobile - Commercial Marine Vessels
43
0.14
314
7.34
7.72
7.75
5.51
17
9.19E-04
1.41
Mobile - Locomotives
116
0.36
600
17
17
0.71
13
28
1.05E-04
12
Mobile - Non-Road Equipment - Diesel
397
1.16
835
60
62
1.18
46
76
7.52E-05
37
Mobile - Non-Road Equipment - Gasoline
10,100
0.74
193
37
40
1.14
4.47
1,009
4.78E-12
325
Mobile - Non-Road Equipment - Other
250
0.01
46
2.52
2.52
1.00
0.91
8.11
1.63
Mobile - On-Road Diesel Heavy Duty Vehicles
455
7.44
1,399
57
90
3.87
29
100
22
Mobile - On-Road Diesel Light Duty Vehicles
360
1.41
147
5.73
8.33
0.40
3.69
39
7.07
Mobile - On-Road non-Diesel Heavy Duty Vehicles
621
1.37
62
1.37
3.71
0.47
0.21
30
8.28
Mobile - On-Road non-Diesel Light Duty Vehicles
18,078
90
1,887
50
138
21
9.37
1,507
418
Solvent - Consumer & Commercial Solvent Use
0.02
6.36E-03
0.02
0.02
4.66E-03
8.43E-04
1,610
174
s of tons/year)
2-11
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DRAFT
Sector
CO
NH3
NOX
PM2.5
PM10
S02
Black
Carbon
voc
Lead
Total
HAPs1
Solvent - Degreasing
0.03
0.02
0.02
0.07
0.08
6.21E-05
5.01E-04
183
3.32E-04
65
Solvent - Dry Cleaning
4.35E-04
3.75E-04
0.02
0.02
2.10E-06
2.51E-04
3.70
5.50
Solvent - Graphic Arts
0.08
0.07
0.11
0.09
0.09
8.18E-03
5.87E-04
346
2.74E-06
24
Solvent - Industrial Surface Coating & Solvent Use
5.00
0.39
2.41
4.17
4.57
0.35
0.06
493
4.32E-03
56
Solvent - Non-Industrial Surface Coating
0.02
337
48
Waste Disposal
1,303
21
81
203
227
25
21
172
0.01
40
Sub Total (no federal waters)
66,805
4,297
9,907
5,699
17,103
2,550
279
17,218
0.66
3,169
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
49
7.55E-03
44
0.41
0.41
0.03
0.03
1.16
1.18E-06
1.18E-06
Fuel Comb - Industrial Boilers, ICEs - Oil
1.15
2.83E-04
4.91
0.21
0.21
0.41
0.16
0.24
2.52E-06
2.52E-06
Fuel Comb - Industrial Boilers, ICEs - Other
4.02E-04
1.51E-05
4.81E-04
2.39E-05
2.39E-05
3.30E-06
1.62E-06
4.31E-05
2.36E-09
2.36E-09
Industrial Processes - Oil & Gas Production
1.50
5.42E-04
0.80
9.13E-03
9.29E-03
0.02
2.41E-05
37
8.46E-08
8.46E-08
Industrial Processes - Storage and Transfer
0.63
Mobile - Commercial Marine Vessels
55
0.48
531
25
27
175
4.85
26
3.15E-03
2.14
Sub Total (federal waters)
107
0.49
581
26
28
175
5.05
65
3.16E-03
2.14
Sub Total (all but vegetation and soil)
66,912
4,298
10,488
5,725
17,131
2,725
284
17,282
0.67
3,171
Biogenics - Vegetation and Soil
4,083
22
1,367
25,823
3,028
Total
70,995
4,320
11,855
5,725
17,131
2,725
284
43,106
0.67
6,199
1 Total HAP does not include diesel PM, which is not a HAP listed by the Clean Air Act.
2.5 How does this NEI compare to past inventories?
Many similarities exist between the 2017 NEI approaches and past NEI approaches, notably that the data are
largely compiled from data submitted by S/L/T agencies for CAPs, and that the HAP emissions are augmented by
the EPA to differing degrees depending on geographical jurisdiction because they are a voluntary contribution
from the partner agencies. In 2017, S/L/T participation was somewhat more comprehensive than in 2014,
though both were good. The NEI program continues with the 2017 NEI to work towards a complete compilation
of the nation's CAPs and HAPs. The EPA provided feedback to S/L/T agencies during the compilation of the data
on critical issues (such as potential outliers, missing SCCs, missing Hg data and coke oven data) as has been done
in the past, collected responses from S/L/T agencies to these issues, and improved the inventory for the release
based on S/L/T agency feedback. In addition to these similarities, there are some important differences in how
the 2017 NEI has been created and the resulting emissions, which are described in the following two
subsections.
2.5.1 Differences in approaches
With any new inventory cycle, changes to approaches are made to improve the process of creating the inventory
and the methods for estimating emissions. The key changes for the 2017 cycle are highlighted here.
To improve the process, we learned from the prior three triennial inventories (for 2008, 2011, and 2014)
compiled with the EIS. We made changes to pollutant, SCC, and NAICS codes, refined quality assurance checks
and features that were used to assist in quality assurance and streamlined the Nonpoint Survey (introduced for
the 2014 NEI) to assist with S/L/T and EPA data reconciliation for the nonpoint data. The update to the nonpoint
survey helped S/L/Ts and EPA avoid double counting and ensure a complete inventory between the different
sources of data.
In addition to process changes, we improved emissions estimation methods for all data categories. We
summarize the differences in approaches in the following sections.
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2.5.1.1 Point data category
For point sources, the only change was our use of EPA-developed HAP emission estimates for the EGUs covered
by the Mercury and Air Toxics Standards (MATS) review, rather than the SLT reported values. HAP augmentation
improvements are described in Section 3.1.6. More information on point source improvements is available in
Section 3.
2.5.1.2 Nonpoint data category
We made method improvements for many stationary nonpoint sectors (Section 4). The EPA creates and
provides emissions tools to S/L/T agencies for their use, and we use these tools ourselves to fill in emissions
values where not provided by S/L/T/ agencies. New for the 2017 NEI development process, was the introduction
of "Input Templates" -see Section 4.1.3. EPA provided default Input Templates to S/L/T inventory developers for
them to modify and return to EPA. We encouraged S/L/Ts to submit inputs rather than direct emission
submittals for many nonpoint categories. We also streamlined the Nonpoint Survey (Section 4.1.2), first
introduced for the 2014 NEI development cycle, to simplify the options and improve transparency. By default, all
Nonpoint Survey responses were set to "Yes -Supplement my data with EPA Estimates" to ensure complete
coverage in the absence of S/L/T feedback.
PM2.5 emissions from agricultural tilling decreased due to assumption change of reducing the number of tilling
passes for corn and soybeans. Most states saw a significant increase in PM2.5 and VOCfrom commercial
cooking, a result of using new activity data on the number of restaurants, as opposed to continuing to use a
growth rate from 2002 data that was used for recent NEIs. Large decreases in residential fuel combustion for
S02 is a result of a decrease in consumption and more significantly, using a lower default sulfur content for
distillate fuel oil: 500ppm in 2017 vs 3% (30,000ppm) in 2014. Large decreases in PM2.5 and NOX for open
burning, land clearing debris is due to a new assumption that land clearing debris is limited to only rural parts of
counties.
We updated the approach for computing nonpoint Industrial and Commercial/Institutional (ICI) fuel combustion,
limiting it state-level subtraction of point inventory throughput (fuel consumption); we no longer compute
nonpoint ICI via point emissions subtraction or county-level activity data subtraction. To facilitate this, we
provided S/L/Ts with cross-references from point inventory facilities to existing U.S. Energy Information
Administration (EIA) ICI sector assignments and fuel mapping. We relied on S/L/Ts to provide EPA with these
state-level inputs via 4 different Input Template options.
We updated the activity data for residential wood combustion via a national survey of wood burning in 2018,
leading to more robust accounting of outdoor recreational burning and improved characterization of central
heaters from both cordwood and pellet-fired hydronic heaters and furnaces. We also obtained local input data
for several states. For the livestock waste sector, we updated the animal counts methodology based on what's
used in EPA's GHG program, which includes animal sub-types that had been left out in 2014. Using this more
robust approach results in increases in the dairy cattle and the broiler category animal counts, and thus
increases NH3 emissions from this sector where those animals are more prevalent in the US. For the Ag fertilizer
sector, methods were essentially the same as in 2014; however, newer model versions for CMAQ and FEST-C
were used. The previous version of CMAQ used for the 2014 NEI fertilizer emission only from vegetated land.
This has been corrected in CMAQ 5.3 with the STAGE deposition option and results in higher NH3 emission rates
in agricultural areas before crop germination and in areas with sparse vegetation coverage. Additionally, the
updated FEST-C vl.4 module corrected an error in the nitrogen budget form an earlier version of the model used
in the 2014 NEI. Also, in the 2017 NEI, annual state and USDA fertilizer data was used to adjust FEST-C fertilizer
2-13
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rates. The adjusted FEST-C fertilizer rates were correspondingly increased to better match USDA and data
submitted by the states.
For road dust, we changed the method used to determine the vehicle miles traveled (VMT) on paved and
unpaved roads in each county. Both the methods for the 2014v2 and 2017 NEI used the 2008 NMIM run as the
starting point for estimating the state ratio of VMT on paved or unpaved roads. However, in 2014v2, the
estimated VMT on paved/unpaved roads were arbitrarily redistributed within Census regions, to smooth out
sharp differences in emissions across state lines. This redistribution is not done for the 2017 NEI in order to
better preserve the integrity of the original state VMT data submitted to the Federal Highway Administration
(FHWA). Also, an additional step was added to update the 2008 NMIM paved/unpaved ratios used for local and
rural minor collector road types by using state level 2016 FHWA data on paved vs. unpaved road length for these
road types.
We refined emissions calculation approaches for the oil and gas exploration and production sectors to reflect
new processes, such as CBM dewatering engines, updated default assumptions, such as the quantity of VOC
being captured by control devices at storage tanks, and made use of newly available activity data, including the
most recent and appropriate subpart W basin factors available.
For all nonpoint categories, we updated the activity data to use the newest data available, at the time, to
represent the 2017 inventory year; in most cases, this is year-2017 activity data. Most emission changes for all
nonpoint sources not otherwise discussed in this section resulted from these activity data updates -be they from
EPA or new for 2017, provided directly from S/L/Ts.
The Biogenic database incorporated a new version of the Biogenic Emissions Landcover Database (BELD5) and
provides updates for all states, including Alaska, Hawaii, Puerto Rico and the U.S. Virgin Islands.
2.5.1.3 Onroad and nonroad data categories
For mobile sources, onroad methodology used the same model as in 2014 with updated mobile source activity
data such as vehicle miles travelled (VMT). The MOVES model was updated for nonroad estimates. For both, we
relied on model inputs provided by S/L/T agencies and other sources, except for California and Tribes, who
submitted emissions estimates. Sections 5 and 6 provide more detail on these improvements.
2.5.1.4 Events data category
We also made several improvements to approaches for fire sources, as further described in Section 7. For the
agricultural fires sector (in nonpoint category), we updated the VOC emissions factors, as well as the HAPs to
line up with what's in SPECIATE. Specifically, for the 2017 NEI, we reviewed the crop residue burning VOC
speciation profiles in the SPECIATE database, located the original source of this information, and derived new
VOC emission factors and new HAP emission factors from the same measurement study. We also brought in
some new VOC data for sugarcane burning based on new studies. In addition, we omitted the grass/pasture
burning from the agricultural fires sector in and moved it to the Events (as prescribed fires) category for the
2017 NEI. For wildfires and prescribed fires, we improved how VOCs were estimated in areas of the country
where duff-fuels are prevalent -primarily in Florida, Louisiana and Minnesota.
2.5.2 Differences in emissions between the 2017 and 2014 NEI
This section presents a comparison from the 2014v2 NEI to the 2017 NEI. Table 2-4 compares CAP emissions for
the 2017 minus 2014v2 NEI for seven highly aggregated emission sectors. Table 2-5 compares emissions for
select HAPs for the 2017 minus 2014v2 NEI-for the same seven highly aggregated emission sectors. Emissions
2-14
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from the biogenic (natural) sources are excluded, and the wildfire sector is shown separately for CAPs and HAPs.
While Pb is a CAP for the purposes of the NAAQS, due to toxic attributes and inclusion in previous national air
toxics assessments (NATA), it is reviewed here with the HAPs. The HAPs selected for comparison are based on
their national scope of interest as defined by NATA. With a couple notable exceptions, CAP emissions are lower
overall in 2017 than in 2014 (v2). Some specific sector/pollutants increased in 2017 from 2014. The increases in
fuel combustion CO, benzene, and formaldehyde is primarily from the new residential wood combustion survey-
based burn rate and appliance use profiles. The increase in PM10 for industrial processes is mostly from new
(more coal mining) activity data for mining and quarrying. The increase in industrial processes NH3, and
miscellaneous lead are from state and local agency submittals.
As discussed in Section 7, there were comparatively more wildfires in 2017 than 2014, explaining the significant
increases in wildfire emissions for 2017. Year 2014 was a generally quiet year for such fires.
Table 2-4: Emission differences (tons) for CAPs, 2017 minus 2014v2 NEI
Broad Sector
CO
NH3
NOX
PM10
PM2.5
S02
voc
Fuel Combustion
11,551
-18,316
-760,669
-118,878
-90,940
-2,088,948
-15,376
Highway Vehicles
-4,923,456
-8,101
-1,384,295
-64,680
-49,023
-2,897
-540,838
Industrial Processes
-222,226
3,083
-119,942
38,513
-16,448
-63,453
-668,200
Miscellaneous
-885,779
602,443
-28,653
-1,796,208
-251,144
-2,100
-17,928
Nonroad Mobile
-1,711,825
-373
-499,897
-46,466
-43,251
-30,873
-534,460
Total Difference,
excluding wildfires
-7,731,733
578,737
-2,793,456
-1,987,720
-450,807
-2,188,272
-1,776,802
Total % Difference,
excluding wildfires
-14%
17%
-22%
-12%
-10%
-48%
-12%
Wildfires
9,000,048
147,066
111,394
907,232
768,965
63,904
2,111,711
Table 2-5: Emission differences (tons) for select HAPs, 2017 minus 2014v2 NEI
Broad Sector
Acrolein
Benzene
Ethylene
Oxide
Formaldehyde
Hexavalent
Chromium
Lead
Fuel Combustion
-83
517
0
5,804
-6
-51
Highway Vehicles
-705
-16,409
-12,102
0
Industrial Processes
668
-1,747
-24
7,554
4
-21
Miscellaneous
515
-2,608
-17
-4,904
1
0
Nonroad Mobile
424
-9,044
-3,070
-1
10
Total Difference,
excluding wildfires
819
-29,291
-42
-6,719
-2
-63
Total % Difference,
excluding wildfires
2%
-16%
-27%
-2%
-4%
-9%
Wildfires
23,583
24,024
149,000
2.6 How well are tribal data and regions represented in the 2017 NEI?
Thirteen tribes submitted data to the EIS for 2017 as shown in Table 2-6. In this table, a "CAP, HAP" designation
indicates that both criteria and hazardous air pollutants were submitted by the tribe; "GHG" indicates
greenhouse gases were submitted. CAP indicates that only criteria pollutants were submitted. Facilities on tribal
land were augmented using TRI, HAPs and PM in the same manner as facilities under the state and local
jurisdictions, as explained in Section 3.1, therefore, Tribal Nations in Table 2-6 with just a CAP flag will also have
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some HAP emissions in most cases. Eight additional tribal agencies, shown in Table 2-7, which did not submit
any data, are represented in the point data category of the 2017 NEI due to the emissions added by the EPA. The
emissions for these facilities are from the EPA gap fill datasets for airports, EGUs, and TRI data. Furthermore,
many nonpoint datasets included in the NEI are presumed to include tribal activity. Most notably, the oil and gas
nonpoint emissions have been confirmed to include activity on tribal lands because the underlying database
contained data reported by tribes. See Section 4.17 for more information.
Table 2-6: Tribal participation in the 2017 NEI
Tribal Agency
Point
Nonpoint
Onroad
Nonroad
Assiniboine and Sioux Tribes of the Fort Peck Indian
Reservation
CAP, HAP
Coeur d'Alene Tribe
CAP, HAP
CAP, HAP
CAP, HAP
CAP, HAP
Kootenai Tribe of Idaho
CAP, HAP
CAP, HAP
CAP, HAP
Morongo Band of Cahuilla Mission Indians of the Morongo
Reservation, California
CAP
Nez Perce Tribe
CAP, HAP
CAP, HAP
CAP, HAP
CAP, HAP
Northern Cheyenne Tribe
CAP
CAP
CAP
CAP
Sac and Fox Nation of Missouri in Kansas and Nebraska
Reservation
Salt River Pima Maricopa Indian Community (SRPMIC) EPNR
CAP,
HAP,
GHG
CAP
Shoshone-Bannock Tribes of the Fort Hall Reservation of
Idaho
CAP, HAP
CAP, HAP
CAP, HAP
CAP, HAP
Southern Ute Indian Tribe
CAP,
HAP,
GHG
CAP, HAP
United Keetoowah Band of Cherokee Indians in Oklahoma
Ute Mountain Tribe of the Ute Mountain Reservation
CAP, HAP
Yakama Nation Reservation
CAP,
HAP,
GHG
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Table 2-7: Facilities on Tribal lands with 2017 NEI emissions from EPA only
Tribal Agency
EPA data used
Assiniboine and Sioux Tribes of the Fort Peck Indian Reservation, Montana
Airports
Fond du Lac Band of Lake Superior Chippewa
Airports
Gila River Indian Community
TRI
Navajo Nation
EGUs
Omaha Tribe of Nebraska
Airports
Southern Ute Indian Tribe
Airports
Tohono O-Odham Nation Reservation
TRI
Ute Indian Tribe of the Uintah & Ouray Reservation, Utah
EGUs
2.7 What does the 2017 NEI tell us about mercury?
The NEI documentation includes this Hg section because of the importance of this pollutant and because the
sectors used to categorize Hg are different than the sectors presented for the other pollutants. The Hg sectors
primarily focus on regulatory categories and categories of interest to the international community; emissions
are summarized by these categories at the end of this section, in Table 2-10.
Mercury emission estimates in the 2017 NEI sum to 32.8 tons, with 32.2 tons from stationary sources and 0.6
tons from mobile sources (including commercial marine vessels and locomotives). Due to large decreases of
emissions from sources within the regulated categories, particularly the coal-fired electric generating units
(EGU), most of the emissions are from sources other than the categories trended. The "other" includes a large
variety of different source types including fluorescent light breakage, landfills, specialty chemical manufacturing,
mineral products and other fuel combustion besides boilers and process-heaters. Of the regulatory categories
trended, the three with highest emissions in the 2017 NEI are: electric arc furnaces (4.7 tons), coal -fired EGU
with units larger than 25 megawatts (MW) (4.4 tons) and boilers and process heaters (2.6 tons). Unlike previous
NEIs, coal-fired EGUs no longer comprise the largest portion of the mercury emissions in NEI.
Mercury emissions from the coal fired EGU with units larger than 25 MW are from the database developed for
the Residual Risk Assessment for the Coal- and Oil-Fired EGU Source Category in Support of the 2019 Risk and
Technology Review Proposed Rule, which is also referred to as the Mercury and Air Toxics Standards (MATS),
[ref 2], Most of the units' emission estimates were from data reported to the Clean Air Markets Division (CAMD),
but in some cases emission factors from WebFIRE or the Electric Power Research Institute were used, along with
heat input from CAMD. EPA loaded these estimates into EIS as the "2017EPA_MATS" dataset, and they were
used in the NEI selection hierarchy ahead of the S/L/T data.
A summary of all data sources used to create the 2017 Hg inventory are shown in Figure 2-3.
2-17
-------
DRAFT
Figure 2-3: Data sources of Hg emissions (tons) in the 2017 NEI, by data category
25
20
>-
D
15
10
I other EPA
ISLT
EPA mobile
I EPA TRI
I EPA NONPOINT
EPA HAPaug
EPA EGU MATS
point
nonpoint
onroad
nonroad
In the above figure the "EPA mobile" accounts for all EPA datasets containing onroad, nonroad, commercial
marine vessel and locomotive (also referred to as rail) emissions. The "other EPA" accounts for numerous gap
filling datasets in which EPA obtained or estimated mercury emissions (via calling the state, carrying it forward
from previous year inventories or estimating with emission factors). The "EPA HAP aug" dataset uses SLT-
submitted particulate matter emissions and emission factor ratios (Hg-to-PM) to compute Hg at the process
level. The EPA EGU MATS dataset contains data from the MATS rule development described above.
In addition to Figure 2-3, Table 2-8 lists the emissions by data source with the above data sets further broken
out. More information on these datasets is available in Section 3.1.2 for point, Section 4.1.1 for nonpoint,
Section 5 for nonroad mobile, and Section 6 for onroad mobile sources.
Table 2-8: 2017 NEI Hg emissions (tons) for each dataset type and group
Data Category
Data Set
Brief description
Hg
emissions
(tons)
Point
S/L/T
State, local, tribal agency-submitted
14
2017EPA_TRI
Toxics Release Inventory
5.2
EPA EGU MATS
Mercury and Air Toxics Rule
4.1
EPA HAP Aug
Computed based on S/L/T CAPs
0.2
2017EPA_gapfills
Missing data
0.04
2017EPA EGU
Non-MATS electric generating units
0.01
2017EPA SPPD PCWP
Plywood and composite wood products rule
0.009
2017ERTAC Rail
Locomotives using the ERTAC methodology
0.007
2017EPA LF
landfills
0.004
Nonpoint
2017EPA NONPOINT
All nonpoint categories except mobile sources
7.3
S/L/T
State, local, tribal agency-submitted
0.7
EPA HAP Aug
Computed based on S/L/T CAPs
0.4
2-18
-------
Data Category
Data Set
Brief description
Hg
emissions
(tons)
2017EPA_CarryFwd
Laboratory emissions carried forward from
2014
0.3
2017ERTAC Rail
Locomotives using the ERTAC methodology
0.2
2017EPA CMV
Commercial Marine Vessels
0.001
Nonroad
2017EPA MOVES
MOVES model
0.02
2017EPA Ca MOVES
California adjusted by MOVES model for HAPs
0.005
Onroad
2017EPA MOVES
MOVES model
0.3
2017EPA_Ca_MOVES
California adjusted by MOVES model for HAPs
0.01
The datasets are described in more detail starting in Sections 3 and 4, and we highlight some key datasets here.
For point sources, we gap-filled Hg that was not reported by S/L/Ts in the same way as other HAPs - including
use of the TRI (see Section 3.1.5), EPA HAP Augmentation or "HAP Aug" in the figure (see Section 2.2.3), and
other EPA data developed for gap filling. Electric arc furnaces (EAFs) were gap filled using HAP aug and TRI only.
The HAP augmentation used facility specific augmentation factors developed so that the resultant emissions
would be the same as was used in 2014. This approach was used to provide a more automated approach than
to submit the same emissions year after year, that would (via the use of CAPs) account for changes in activity.
The 2014 estimates were developed by applying a 34% reduction to 2011 NEI emissions (process level). The
2011 NEI emissions were based on data developed for the National Emission Standards for Hazardous Air
Pollutants (NESHAP) for Area Sources: Electric Arc Furnace Steelmaking Facilities (subpart YYYYY). The 34% value
was the average reduction from a limited 3 facility test program in 2016 (the range was 11-70%) -based on
personal communication with Donna Lee Jones, EPA lead for the NESHAP. The sum of HAP aug mercury for EAFs
is about 0.07 tons. We used the same approach as in 2014 for using TRI data associated EAFs in that we
excluded S/L/T estimates at non-EAF processes if they were significantly lower than the TRI Hg value. The sum
of TRI Hg for EAFs is about 1 ton. The largest contribution to total EAF emissions is S/L/T data which sum to
about 3.6 tons.
For municipal waste combustors (MWCs), we gap filled a few facilities by requesting the Hg from specific states
that report some pollutants for these facilities but exclude mercury (see section 3.6).
The nonpoint non-combustion-related and cremation categories used the same or very similar approaches as
were developed for the 2014 NEI, though activity data was updated. For laboratory activities, however, the
mercury emissions continue to be carried forward from the 2008 NEI with no activity updates. The
methodologies are described in Section 4.2. EPA estimates for these categories are included in the
"2017EPA_NONPOINT" (along with other EPA nonpoint category estimates) shown in Figure 2-3 and Table 2-8.
Some of these categories have a point contribution, though the specific categories do not exactly line up
between the nonpoint and point data categories. They are summarized below:
• switches and relays - emissions from the shredding and crushing of cars containing Hg components at
auto crushing yards, SCC = 2650000002: Waste Disposal, Treatment, and Recovery; Scrap and Waste
Materials; Scrap and Waste Materials; Shredding (1.26 tons nonpoint; 0.006 tons point)
• landfill "working face" emissions associated with the release of mercury via churning/crushing of new
material added to the landfill, SCC= 2620030001: Waste Disposal, Treatment, and Recovery; Landfills;
2-19
-------
Municipal; Dumping/Crushing/Spreading of New Materials (working face) (0.435 tons nonpoint, total
point landfill Hg is 0.08 tons)
• thermometers and thermostats - the portion that emit mercury prior to disposal at landfills or
incinerators, SCC=2650000000: Waste Disposal, Treatment, and Recovery; Scrap and Waste Materials;
Scrap and Waste Materials; Total: All Processes (0.12 tons nonpoint)
• dental amalgam - emissions at dentist offices and from evaporation in teeth, SCC=2850001000:
Miscellaneous Area Sources; Health Services; Dental Alloy Production; Overall Process (0.46 tons
nonpoint, 0.69 lbs point)
• general laboratory activities, SCC = 2851001000: Miscellaneous Area Sources; Laboratories; Bench Scale
Reagents; Total (0.32 tons nonpoint, 4 lbs point)
• fluorescent lamp breakage, SCC= 2861000000: Miscellaneous Area Sources; Fluorescent Lamp
Breakage; Non-recycling Related Emissions; Total (0.91 tons nonpoint)
• fluorescent lamp recycling, SCC= 2861000010: Miscellaneous Area Sources; Fluorescent Lamp Breakage;
Recycling Related Emissions; Total (less than 1 lb nonpoint, point sum of breakage and recycling = 80
lbs)
• animal cremation, SCC= Miscellaneous Area Sources; Other Combustion; Cremation; Animals (2 lbs
nonpoint, 48 lbs point)
• human cremation - emissions primarily due to mercury in dental amalgam, SCC=2810060100:
Miscellaneous Area Sources; Other Combustion; Cremation; Humans (1.77 tons nonpoint, 0.18 tons
point)
Since mercury is a HAP, it is reported voluntarily by S/L/T agencies. For the point data category of the 2017 NEI,
S/L/T agencies reported emissions in 46 states. Two tribal agencies reported point source Hg. Table 2-9 provides
the tons of emissions from EPA, the SLT, and the resulting percent of emissions for the point data category.
Table 2-9: Point inventory emissions by reporting agency
From
From
Percent
Agency
EPA
Agency
from
State
Type
Agency
(tons)
(tons)
Agency
AK
State
Alaska Department of Environmental Conservation
4.87E-02
0.00E+00
0.00
AL
State
Alabama Department of Environmental Management
2.05E-01
1.15E+00
84.90
AL
Local
Jefferson County (AL) Department of Health
6.97E-02
2.31E-01
76.80
AR
State
Arkansas Department of Environmental Quality
1.12E-01
5.23E-01
82.38
AZ
State
Arizona Department of Environmental Quality
7.45E-02
2.43E-01
76.54
AZ
Local
Maricopa County Air Quality Department
4.93E-02
0.00E+00
0.00
CA
State
California Air Resources Board
3.20E-01
7.15E-01
69.08
CO
State
Colorado Department of Public Health and Environment
1.48E-01
2.23E-01
60.05
Connecticut Department of Energy and Environmental
CT
State
Protection
2.15E-03
9.84E-02
97.86
DC
State
DC-District Department of the Environment
3.10E-03
0.00E+00
0.00
Delaware Department of Natural Resources and
DE
State
Environmental Control
1.25E-03
1.51E-01
99.18
FL
State
Florida Department of Environmental Protection
1.63E-01
4.39E-01
72.90
GA
State
Georgia Department of Natural Resources
1.85E-01
0.00E+00
0.00
GU
Territory
Guam County
1.39E-04
0.00E+00
0.00
2-20
-------
From
From
Percent
Agency
EPA
Agency
from
State
Type
Agency
(tons)
(tons)
Agency
HI
State
Hawaii Department of Health Clean Air Branch
1.20E-02
9.69E-03
44.68
IA
State
Iowa Department of Natural Resources
8.58E-02
2.56E-01
74.90
ID
State
Idaho Department of Environmental Quality
6.55E-01
3.04E-03
0.46
IL
State
Illinois Environmental Protection Agency
1.97E-01
5.68E-01
74.26
IN
State
Indiana Department of Environmental Management
1.16E+00
2.05E-01
15.05
KS
State
Kansas Department of Health and Environment
6.58E-02
7.16E-02
52.08
KY
Local
Louisville Metro Air Pollution Control District
1.80E-02
1.09E-01
85.86
KY
State
Kentucky Division for Air Quality
1.87E-01
1.06E-01
36.13
LA
State
Louisiana Department of Environmental Quality
4.04E-01
1.33E-01
24.71
MA
State
Massachusetts Department of Environmental Protection
1.94E-02
5.00E-04
2.51
MD
State
Maryland Department of the Environment
8.83E-02
0.00E+00
0.00
ME
State
Maine Department of Environmental Protection
1.18E-04
4.81E-02
99.75
Ml
State
Michigan Department of Environmental Quality
1.47E-01
1.53E-01
50.88
MN
State
Minnesota Pollution Control Agency
7.31E-02
5.18E-01
87.63
MO
State
Missouri Department of Natural Resources
2.61E-01
5.23E-01
66.71
MS
State
Mississippi Dept of Environmental Quality
5.64E-02
2.11E-01
78.90
MT
State
Montana Department of Environmental Quality
1.08E-01
2.00E-04
0.18
Forsyth County Office of Environmental Assistance and
NC
Local
Protection
1.57E-05
2.49E-03
99.37
Western North Carolina Regional Air Quality Agency
NC
Local
(Buncombe Co.)
3.13E-03
2.59E-03
45.26
NC
State
North Carolina Department of Environmental Quality
7.34E-02
5.39E-01
88.00
ND
State
North Dakota Department of Health
4.62E-01
0.00E+00
0.00
NE
Local
Lincoln/Lancaster County Health Department
4.43E-03
9.61E-05
2.12
NE
State
Nebraska Environmental Quality
1.12E-01
1.31E-01
53.86
NH
State
New Hampshire Department of Environmental Services
2.82E-03
2.32E-02
89.14
NJ
State
New Jersey Department of Environment Protection
6.80E-03
5.98E-02
89.80
NM
Local
City of Albuquerque
8.57E-03
0.00E+00
0.00
NM
State
New Mexico Environment Department Air Quality Bureau
2.75E-02
2.00E-03
6.79
Clark County Department of Air Quality and Environmental
NV
Local
Management
2.28E-02
0.00E+00
0.00
NV
Local
Washoe County Health District
1.93E-05
0.00E+00
0.00
NV
State
Nevada Division of Environmental Protection
8.65E-01
3.44E-04
0.04
NY
State
New York State Department of Environmental Conservation
2.28E-03
1.98E-01
98.86
OH
State
Ohio Environmental Protection Agency
7.09E-01
1.01E+00
58.65
OK
State
Oklahoma Department of Environmental Quality
9.77E-02
1.54E-01
61.18
OR
State
Oregon Department of Environmental Quality
7.59E-03
7.14E-02
90.40
PA
Local
Allegheny County Health Department
4.79E-03
4.70E-03
49.54
PA
State
Pennsylvania Department of Environmental Protection
2.73E-01
1.09E+00
79.92
PA
State
Philadelphia Air Management Services
8.57E-04
4.00E-04
31.81
PR
Territory
Puerto Rico
4.47E-02
0.00E+00
0.00
Rl
State
Rhode Island Department of Environmental Management
1.13E-04
2.69E-02
99.58
2-21
-------
State
Agency
Type
Agency
From
EPA
(tons)
From
Agency
(tons)
Percent
from
Agency
sc
State
South Carolina Department of Health and Environmental
Control
2.68E-02
7.67E-01
96.63
SD
State
South Dakota Department of Environment and Natural
Resources
2.33E-02
0.00E+00
0.00
TN
State
Tennessee Department of Environmental Conservation
1.56E-01
5.78E-02
27.00
TN
Local
Chattanooga Air Pollution Control Bureau (CHCAPCB)
1.19E-02
0.00E+00
0.00
TN
Local
Knox County Department of Air Quality Management
1.02E-01
1.00E-02
8.94
TN
Local
Memphis and Shelby County Health Department - Pollution
Control
1.41E-01
4.54E-03
3.13
TN
Local
Metro Public Health of Nashville/Davidson County
8.36E-05
0.00E+00
0.00
TX
State
Texas Commission on Environmental Quality
6.28E-01
1.75E+00
73.63
UT
State
Utah Division of Air Quality
6.28E-02
6.31E-01
90.95
VA
State
Virginia Department of Environmental Quality
1.21E-01
3.09E-01
71.91
VT
State
Vermont Department of Environmental Conservation
4.26E-04
1.20E-04
21.97
WA
State
Washington State Department of Ecology
1.28E-01
8.97E-03
6.55
WA
Local
Olympic Region Clean Air Agency
0.00E+00
5.15E-03
100.00
WA
Local
Southwest Clean Air Agency
3.59E-02
2.94E-03
7.56
Wl
State
Wisconsin Department of Natural Resources
1.06E-01
1.02E-01
48.97
WV
State
West Virginia Division of Air Quality
1.88E-01
1.00E-01
34.77
WY
State
Wyoming Department of Environmental Quality
1.85E-01
2.01E-01
52.02
Tr
be
Coeur d'Alene Tribe
1.84E-05
1.71E-03
98.94
Tr
be
Navajo Nation
7.72E-02
0.00E+00
0.00
Tr
be
Nez Perce Tribe
3.83E-05
0.00E+00
0.00
Tr
be
Salt River Pima Maricopa Indian Community (SRPMIC) EPNR
8.60E-07
2.31E-04
99.63
Tr
be
Southern Ute Indian Tribe
2.11E-06
0.00E+00
0.00
Tr
be
Tohono O-Odham Nation Reservation
4.50E-06
0.00E+00
0.00
Tr
be
Ute Indian Tribe of the Uintah & Ouray Reservation, Utah
1.85E-03
0.00E+00
0.00
Tr
be
Yakama Nation Reservation
1.02E-07
0.00E+00
0.00
Eleven states (ID, IL, MD, MN, NC, NY, OR, RI,TX, VA, WV), 4 local agencies (Chattanooga, TN, Knox County, TN,
Nashville/Davidson County, TN and Memphis, TN) and 5 tribal agencies reported Hg to the nonpoint data
category. The tribal agencies are Coeur d'Alene Tribe; Kootenai Tribe of Idaho; Shoshone-Bannock Tribes of the
Fort Hall Reservation of Idaho; Nez Perce Tribe; and Southern Ute Indian Tribe.
Table 2-10 and Figure 2-4 show the 2014 NEI mercury emissions for the key categories of interest in comparison
to other triennial inventory years and the baseline HAP inventory of 1990. The 2005 data are from the MATS
2005 modeling platform. Two comma-separated values included in the zip file, 2017nei supdata mercurv.zip.
provide the category assignments at the facility-process level for point sources, and the county-SCC level for
nonpoint, onroad and nonroad sources. Individual point source processes were matched to categories based on
the process-level or unit-level category assignments used in the previous triennial NEI (2014 NEI) as a starting
point, and then supplemented with manual assignments considering SCC, NAICS, facility category codes,
emission factor information (e.g., fuel combusted) and facility names. For the commercial/Industrial Sold Waste
2-22
-------
Incineration (CISWI) category, a set of facilities were provided by the CISWI project lead [ref 3], Some of these
had been categorized as Portland cement but were re-categorized to CISWI.
Table 2-10: Trends in NEI mercury emissions - 1990, 2005, 2008 v3, 2011v2 and 2014v2 NEI and 2017 NEI
Source Category
1990
(tpy)
Baseline
11/2005
2005
(tpy)
MATS
3/2011
2008
(tpy)
2008v3
2011
(tpy)
2014
(tpy)
2017
(tpy)
Notes
Utility Coal Boilers
(Electricity Generation
Units - EGUs,
combusting coal)
58.8
52.2
29.4
26.8
22.9
4.4
This category includes only units >
25 MW. (smaller units are included
in boiler and process heater
category) Coal units and integrated
gasified coal combustion units,
(excludes Hg estimated for startup
gas/oil)
Hospital/Medical/
Infectious Waste
Incineration
51
0.2
0.1
0.1
0.02
0.003
Municipal Waste
Combustors
57.2
2.3
1.3
1.0
0.6
0.4
Industrial,
Commercial/Institutional
Boilers and Process
Heaters
14.4
6.4
4.2
3.6
3.2
2.5
includes electricity generating units
where less than 25 MW.
Mercury Cell Chlor-Alkali
Plants
10
3.1
1.3
0.5
0.1
0.1
Electric Arc Furnaces
7.5
7.0
4.8
5.4
5.0
4.7
Assumed a 34% reduction from
2011 levels for those units that
were gap filled due to lack of S/L/T
or TRI data.
Commercial/Industrial
Sold Waste Incineration
Not
available
1.1
0.02
0.01
0.01
0.06
Possibly an underestimate due to
missing sources and overlap in
categorization of cement kilns and
hazardous waste incineration in
facilities that can burn multiple
fuels
Hazardous Waste
Incineration
6.6
3.2
1.3
0.7
0.8
1.0
Portland Cement Non-
Hazardous Waste
5.0
7.5
4.2
2.9
3.2
1.7
Gold Mining
4.4
2.5
1.7
0.8
0.6
0.9
includes fugitive emissions at
mines such as TRI emissions at
fugitive release points that were
not reported by S/L/T
Sewage Sludge
Incineration
2
0.3
0.3
0.3
0.3
0.4
2-23
-------
Source Category
1990
(tpy)
Baseline
11/2005
2005
(tpy)
MATS
3/2011
2008
(tpy)
2008v3
2011
(tpy)
2014
(tpy)
2017
(tpy)
Notes
Mobile Sources
Not
available
1.2
1.8
1.3
1.0
0.6
Sum of all of onroad, nonroad,
locomotives and commercial
marine vessels. Decrease mainly
due to rail emissions resulting from
emission factor changes.
Other Categories
29.5
18
10.7
13
14.0
16.0
Nonpoint increased by a ton due to
increases in open burning of
household waste and human
cremation. Point increased by a
ton due to various industries
including ferrous and nonferrous
metals production (primary and
secondary), chemical
manufacturing and mineral
products such as gypsum.
Total (all categories)
246
105
61
56
52
33
2-24
-------
DRAFT
Figure 2-4: Trends in NEI Mercury emissions (tons)
g Other
¦ Electric Arc Furnaces
¦ Portland Cement Manufacturing
Industrial,Commercial,Institutional Boilers
¦ Municipal Waste Combustors
¦ Medical Waste Combustors
¦ Utility Coal Boilers
mssss
ills
2008 2011 2014 2017
The top emitting 2014 Mercury categories are: electric arc furnaces (rank 1); EGUs (rank 2); industrial,
commercial and institutional boilers and process heaters (rank 3); and Portland cement (excluding hazardous
waste kilns) (rank 4).
As shown in Table 2-10, 2017 Hg emissions are 19 tons lower than in the 2014. This difference is primarily due to
lower Hg emissions from EGUs covered by MATS; two other categories with large decreases are industrial,
commercial/institutional boilers and process heaters and Portland cement facilities. For EGUs, the decrease is a
combination of fuel switching to natural gas, the installation of Fig controls to comply with state rules and
voluntary reductions, early compliance with MATS, and the co-benefits of Fig reductions from control devices
installed for the reduction of S02 and PM because of state and federal actions, such as New Source Review
enforcement actions. For industrial and commercial/institutional boilers, there appears to be fewer boilers using
coal. The decrease in the Portland cement category appears to be due to decreases at existing facilities. Some
Portland cement facilities have had large decreases in emissions, particularly in CA, FL, Ml, TN, IN and PA.
2.8 References for 2017 inventory contents overview
1. Strait, R.; MacKenzie, D.; and Fluntley, R., 2003. PM Augmentation Procedures for the 1999 Point and
Area Source NEI. 12th International Emission Inventory Conference - "Emission Inventories - Applying
New Technologies", San Diego, April 29 - May 1, 2003.
2. U.S. Environmental Protection Agency, 2018. Residual Risk Assessment for the Coal- and Oil-Fired EGU
Source Category in Support of the 2019 Risk and Technology Review Proposed Rule. Office of Air Quality
Planning and Standards, Docket No. EPA-FIQ-OAR-2018-0794-0070, December 2018.
2-25
250
200
150
100
50
0
1990
2005
-------
Email from Nabanita Modak, EPA, to Janice Godfrey, EPA (cc: Madeleine Strum, EPA and Eric Goehl, EPA)
with attached spreadsheet "Facility FRS_NEI IDS For CISWI Units030917.xlsx" emailed 9/6/2019.
2-26
-------
3 Point sources
This section provides a description of sources that are in the point data category. Point sources are included in
the inventory as individual facilities, usually at specific latitude/longitude coordinates, rather than as county or
tribal aggregates. These facilities include large energy and industrial sites, such as electric generating utilities
(EGUs), mines and quarries, cement plants, refineries, large gas compressor stations, and facilities that
manufacture pulp and paper, automobiles, machinery, chemicals, fertilizers, pharmaceuticals, glass, food
products, and other products. Additionally, smaller points sources are included voluntarily by S/L/T agencies,
and can include small facilities such as crematoria, dry cleaners, and even gas stations. These smaller sources
may appear in one state but not another due to the voluntary nature of providing smaller sources. There are
also some portable sources in the point source data category, such as hot mix asphalt facilities, which relocate
frequently as a road construction project progress. The point source data category also includes emissions from
the landing and take-off portions of aircraft operations, the ground support equipment at airports, and
locomotive emissions within railyards. Within a point source facility, emissions are estimated and reported for
individual emission units and processes. Those emissions are associated with any number of stack and fugitive
release points that each have parameters needed for atmospheric modeling exercises.
The approach used to build the 2017 National Emissions Inventory (NEI) for all point sources is discussed in
Section 3.1 through Section 3.8. Some changes to aircraft for the 2017 NEI are also discussed in Section 3.2, and
revisions to rail yard estimates for 2017 are included in Section 3.3.
3.1 Point source approach: 2017
The general approach to building the NEI point source inventory is to use state/local/tribal (S/L/T)-submitted
emissions, locations, and release point parameters wherever possible. Missing emissions values are gap-filled
with EPA data where available. Quality assurance reviews of the emission values, locations, and release point
modeling parameters are done by the EPA on the most significant emission sources and where data does not
pass quality assurance checks.
3.1.1 QA review of S/L/T data
State/local/tribal agency submittals for the 2017 NEI point sources were accepted through January 15, 2019. We
then compared facility-level pollutant sums appearing in either the 2017 NEI S/L/T-submitted values or the
2014v2 NEI. The comparison included all facilities and pollutants, including any missing from the 2017 submittals
(i.e., present in 2014 but not 2017) as well as any that were new in the 2017 submittals and all that were
common to both years. The comparison table also showed the 2017 emission values from the 2017 Toxics
Release Inventory (TRI). We added columns that showed the percent differences between the 2017 S/L/T
agency-submitted facility totals and the 2014 NEIv2 and 2017 TRI datasets. To create a more focused review and
comparison table, we limited these results to include only cases where the 2017 S/L/T agency-submitted facility
total was more than 50 percent different from the 2014 facility total and with an absolute mass value of the
difference greater than a pollutant-specific threshold amount2. When a facility-pollutant combination was new
in 2017 or appeared only in the 2014 NEI v2, we included those values only when they exceeded the absolute
2 These thresholds are available on the 2014vl Supplemental Data FTP site as file
"2014_point_pollutant_thresholds_qa_flagl.xlsx"
3-1
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mass values greater than the pollutant-specific thresholds because the percent differences were undefined. We
provided3 the resulting table of 3,860 records to S/L/T agencies for review.
State/local/tribal edits to address any emissions values were accepted in the Emissions Inventory System (EIS)
until July 1, 2019. The S/L/T agencies did not change most of the highlighted values. Where the comparisons
were exceptionally suspect, the EPA contacted the agencies by phone or by email if no edits had been made to
obtain confirmation of the reported values. For a small number of cases, neither confirmation nor edits were
obtained, and the value was tagged to be excluded from selection for the NEI. In some but not all of these
instances, a value from TRI or the CAMD data sets was available as a replacement.
Similar to previous NEI years, we quality assured the latitude-longitude coordinates at both the site level and the
release point level. In previous NEI cycles, we had reviewed, verified, and locked (in EIS) approximately 5,500
site-level coordinates of the most significant emitting facilities. For the 2017 NEI coordinate review, we
compared all other site coordinate pairs to the county boundaries for the FIPS county codes reported for those
facilities. We then identified all facilities that met both of the following criteria: (1) more than 50 tons total
criteria pollutant emissions or more than 20 pounds total hazardous air pollutants (HAPs) for 2017, (2) the
coordinates caused the location of the facility to be more than a half mile outside of its indicated county. For
these facilities, we reviewed the location using Google Earth, edited the location as needed in EIS, and locked
the location in EIS.
In addition, we compared the release point coordinates of all release points with any 2017 emissions to their
site level coordinates, whether protected or not. In cases where we found a difference of more than 0.003
degrees in either latitude plus longitude, we reviewed the release point coordinates in Google Earth and either
confirmed that the release point appeared to be on the facility's footprint or we removed the release point's
coordinates, which will allow the modeling files to inherit the site coordinates. Site coordinates were edited and
locked as needed as part of this release point coordinate review. A new critical QA check was also implemented
in EIS, beginning with the 2018 NEI point source submittals, that does not allow the reporting of facility and
release point coordinates that differ by more than a facility-specific amount for either latitude or longitude. The
tolerance amount was set at 0.003 for most facilities, but that tolerance was increased for facilities where the
above review had confirmed that the individual release point coordinates were valid. Some smaller footprint
facilities that had to be reviewed due to apparent violations also had the tolerance set lower as part of the
above review. As of the release of the 2017 final NEI dated April 2020, approximately 9,900 facilities had verified
and locked site coordinates, and all release points used for 2017 emissions were within the facility-specific
tolerance of their site coordinates.
3.1.2 Sources of EPA data and selection hierarchy
Table 3-1 lists the datasets that we used to compile the 2017 NEI point inventory and the hierarchy used to
choose which data value to use for the NEI when multiple data sets are available for the same emissions source
(see Section 2.2 for more detail on the EIS selection process).
The EPA developed all datasets other than those containing S/L/T agency data and the dataset containing
emissions from offshore oil and gas platforms in federal waters in the Gulf of Mexico. The primary purpose of
the EPA datasets is to add or "gap fill" pollutants or sources not provided by S/L/T agencies, to resolve
inconsistencies in S/L/T agency-reported pollutant submissions for particulate matter (PM) (Section 3.1.3) and to
speciate S/L/T agency reported total chromium into hexavalent and trivalent forms (Section 3.1.4).
3 We emailed the Emission Inventory System data submitters the table and instructions on March 13, 2019.
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The hierarchy or "order" provided in the tables below defines which data are to be used for situations where
multiple datasets provide emissions for the same pollutant and emissions process. The dataset with the lowest
order number on the list is preferentially used over other datasets. The table includes the rationale for why each
dataset was assigned its position in the hierarchy. In addition to the order of the datasets, the selection also
considers whether individual data values have been tagged (see Section 2.2.6). Any data that were tagged by the
EPA in any of the datasets were not used. State/local/tribal agency data were tagged only if they were deemed
to be likely outliers and were not addressed during the S/L/T agency data reviews. As in earlier NEI years, the
2017vl point source selection also excluded dioxins, furans and radionuclides. The EPA has not evaluated the
completeness or accuracy of the S/L/T agency dioxin and furan values nor radionuclides and does not have plans
to supplement these reported emissions with other data sources to compile a complete and accurate estimate
for these pollutants as part of the NEI. The 2017 NEI point source inventory does include greenhouse gas
emissions. Facility total values for four GHGs (C02, CH4, N20, and SF6) were copied from the U.S. Greenhouse
Gas Inventory Report website and matched to EIS facilities.
Table 3-1: Data sets and selection hierarchy used for 2017 NEI August release point source data category
Dataset name
Description and Rationale for the Order of the Selected Datasets
Order
2017EPA_PMSpecies
Speciated PM2.5 data. A result of offline emissions speciation where the
resulting PM25-PRI selection emissions are split into the 5 PM species:
elemental (black) carbon (EC), organic carbon (OC), nitrate (N03), sulfate
(S04), and the remainder of PM25-PRI (PMFINE). Diesel engine PM25-PRI
and PM10 are also copied as DIESEL-PM pollutants.
1
2017EPA_GHG
Facility-level emissions for four specific GHGs from the USEPA's Greenhouse
Gas Reporting Program
2
2017EPA_EGUmats
Emission unit level emissions for 29 HAPs from the Mercury and Air Toxics
(MATS) RTR modeling file for electric generating utilities (EGUs)
3
Responsible Agency Data
Set
S/L/T agency submitted data. These data are selected ahead of lower
hierarchy datasets except where individual values in the S/L/T agency
emissions were suspected outliers that were not addressed during the draft
review and therefore tagged by the EPA.
4
2017EPA_Cr_Aug
Hexavalent and trivalent chromium speciated from S/L/T agency reported
chromium. EIS augmentation function creates the dataset by applying
multiplication factors by SCC, facility, process or North American Industry
Classification System (NAICS) code to S/L/T agency total chromium. See
Section 3.1.4.
5
2017EPA_PM-Aug
PM components added to gap fill missing S/L/T agency data or make
corrections where S/L/T agency have inconsistent emissions across PM
components. Uses ratios of emission factors from the PM Augmentation
Tool for covered source classification codes (SCCs). For SCCs without
emission factors in the tool, checks/corrects discrepancies or missing PM
species using basic relationships such as ensuring that primary PM is
greater than or equal to filterable PM (see Section 3.1.3).
6
2017EPA_EGU
CAP and HAP emission unit level emissions from either the annual sum of
CAMD hourly CEM data for S02 and NOx or from emission factors used in
previous NEI year inventories from AP-42 and other sources multiplied by
2017 CAMD heat input data.
7
2017EPA_TRI
TRI data for the year 2017 (see Section 3.1.5). These data are selected for a
facility only when the S/L/T agency data do not include emissions for a
given pollutant at any process for that facility.
8
3-3
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Dataset name
Description and Rationale for the Order of the Selected Datasets
2017EPA TRIcr
TRI data reported as total chromium for the year 2017 speciated into the
chromium III and chromium VI valence amounts, usually by use of a NAICs-
based speciation profile, but possibly by use of a facility-specific profile.
2017EPA_Airports
CAP and HAP emissions for aircraft operations including commercial,
general aviation, air taxis and military aircraft, auxiliary power units and
ground support equipment computed by the EPA for approximately 20,000
airports. Methods include the use of the Federal Aviation Administration's
(FAA's) Aviation Environmental Design Tool (AEDT) (see Section 3.2).
2017EPA BOEM
2017 Gulfwide Emission Inventory CAP emissions from Offshore oil
platforms located in Federal Waters in the Gulf of Mexico developed by the
U.S. Department of the Interior, Bureau of Ocean and Energy Management
(BOEM), Regulation, and Enforcement in the National Inventory Input
Format and converted to the CERS format by the EPA. The state code for
data from the data set is "DM" (Federal Waters).
2017EPA LF
Landfill emissions developed by EPA using methane data from the EPA's
GHG reporting rule program.
2017EPA SPPD PCWP
Subset of the Plywood and Composite Wood Products Manufacture (PCWP)
Risk and Technology Review (RTR) data used for gap filling HAPs at facilities
and updating facility configurations. Facilities were initially selected if either
formaldehyde or benzene were greater than 0.1 tpy. The PCWP rule
information can be found on the Plywood and Composite Wood Products
2017EPA_HAPAug
HAP data computed from S/L/T agency criteria pollutant data using
HAP/CAP EF ratios based on the EPA Factor Information Retrieval System
(WebFIRE) database as described in Section 3.1.6. These data are selected
below the TRI data because the TRI data are expected to be better.
2017EPA_HAPAug-
PMaug
This dataset was created in the same fashion as the 2017EPA_HAPAug
dataset above and is a supplement to it. This dataset contains HAPs
calculated by applying a ratio to PM10-FIL emissions, for those instances
where the S/L/T dataset did not contain any PM10-FIL emissions, but the
PM augmentation routine was able to calculate a PM10-FIL value from
some PM species that was reported by the S/L/T.
2017ERTAC Rail
2017 estimates compiled by the Eastern Regional Technical Advisory
Committee (ERTAC) for most rail yards in the US. The ERTAC effort was
comprised of a collaborative of state/local agencies, rail companies, and the
Federal Rail Administration. Yard emissions are associated with the
operation of switcher engines at each yard.
2017EPA_gapfills
2014 emissions values for 212 facilities and 12 pollutants not reported in
2017 S/L/T datasets but appear to still be operating and were above CAP
reporting thresholds in 2014. This data set also includes 2017 mercury
emissions for 6 municipal waste combustor facilities that were provided
(outside of EIS) by Maryland and Massachusetts.
2017EPA 2016TRI
2016 TRI ethylene oxide emission estimates for 6 facilities that are still
operating but were not reported by S/L/T or are missing from the 2017 TRI.
3-4
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3.1.3 Particulate matter augmentation
Particulate matter emissions components4 in the NEI are: primary PM10 (called PM10-PRI in the EIS and NEI)
and primary PM2.5 (PM25-PRI), filterable PM10 (PM10-FIL) and filterable PM2.5 (PM25-FIL) and condensable
PM (PM-CON, which is all within the PM2.5 portion on PM, i.e., PM25-PRI = PM25-FIL + PM-CON). The EPA
needed to augment the S/L/T agency PM components to ensure completeness of the PM components in the
final NEI and to ensure that S/L/T agency data did not contain inconsistencies. An example of an inconsistency is
if the S/L/T agency submitted a primary PM2.5 value that was greater than a primary PM10 value for the same
process. Commonly, the augmentation added condensable PM or PM filterable (PM10-FIL and/or PM25-FIL)
where no value was provided, or primary PM2.5 where only primary PM10 was provided. Additional information
on the procedure is provided in the 2008 NEI PM augmentation documentation [ref 1],
In general, emissions for PM species missing from S/L/T agency inventories were calculated by applying factors
to the PM emissions data supplied by the S/L/T agencies. These conversion factors were first used in the 1999
NEI's "PM Calculator" as described in an NEI conference paper [ref 2], The resulting methodology allows the EPA
to derive missing PM10-FIL or PM25-FIL emissions from incomplete S/L/T agency submissions based on the SCC
and PM controls that describe the emissions process. In cases where condensable emissions are not reported,
conversion factors developed are applied to S/L/T agency reported PM species or species derived from the PM
Calculator databases.
3.1.4 Chromium speciation
An overview of chromium speciation, as it impacts both the point and nonpoint data category, is discussed in
Section 2.2.2.
The EIS generates and stores an EPA dataset containing the resultant hexavalent and trivalent chromium
species. The EPA then used this dataset in the 2017 NEI selection by adding it to the selection hierarchy shown in
Table 3-1, excluding the S/L/T agency total chromium from the selection through a pollutant exception to the
hierarchy. This EIS feature does not speciate chromium from any of the EPA datasets because the EPA data
contains only speciated chromium.
For the 2017 NEI, the EPA named this dataset "2017EPA_Cr_Aug." Most of the speciation factors used in the
2017 NEI are SCC-based and are the same as were used for the 2008, 2011 and 2014 NEIs. There are some
facility-specific factors resulting from reviews of previous year (e.g., 2014 and 2011) National Air Toxics
Assessment (NATA) data. Facility-specific factors were also provided for several facilities by the state of Indiana.
The factors "SLT_based_chromium_speciation.zip", based on data that have long been used by the EPA for
NATA and other risk projects, are available on the 2017 Supplemental data FTP site.
3.1.5 Use of the 2017 Toxics Release Inventory
The EPA used air emissions data from the 2017 TRI to supplement point source HAP and ammonia emissions
provided to the EPA by S/L/T agencies. The resulting augmentation dataset is labeled as "2017EPA_TRI" in the
Table 3-1 selection hierarchy shown above. For 2017, all TRI emissions values that could reasonably be matched
to an EIS facility were loaded into the EIS for viewing and comparison if desired, but only those pollutants that
were not reported anywhere at the EIS facility by the S/L/T agency were included in the 2017 NEI. The October
4 We use the term "components" here rather than "species" to avoid confusion with the PM2.5 "species" that are used for
air quality modeling (e.g., organic carbon, elemental carbon, sulfate, nitrate, and other PM).
3-5
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2018 version of these data were used, however, where emissions changes between this version and the April
2019 version of the 2017 TRI data exceeded 2%, the April 2019 version was used.
The basis of the 2017EPA_TRI dataset is the US EPA's 2017 Toxics Release Inventory (TRI) Program. The TRI is an
EPA database containing data on disposal or other releases including air emissions of over 650 toxic chemicals
from approximately 21,000 facilities. One of TRI's primary purposes is to inform communities about toxic
chemical releases to the environment. Data are submitted annually by U.S. facilities that meet TRI reporting
criteria.
The approach used for the 2017 NEI was like that used for the 2014 NEI. The TRI emissions were included in the
EIS (and the NEI) as facility-total stack and facility-total fugitive emissions processes, which matches the
aggregation detail of the TRI database. For the 2017 NEI Point inventory (PT), a change was made in how we
avoid double-counting of TRI and other data sources (primarily the S/L/T data). Rather than tagging each
individual TRI facility-based value for wherever the S/L/T had reported that pollutant at any process(es) within
the same facility, we enhanced the EIS selection software to not use values from a "Facility" level dataset if a
more preferred dataset (the S/L/T datasets) had the pollutant at that facility, (see section 2.2.6). In addition to
using this new "facility-based rule" in the selection software, we also implemented a new "pollutant family rule"
into the selection software, which prevents pollutants defined as belonging to the same overlapping family of
pollutants from being selected for use if a higher preference dataset has already provided a pollutant value for
that family. This procedure had also been accomplished using tagging in previous NEI years.
The following steps describe in more detail the development of the 2017EPA_TRI dataset.
1. Update the TRI_ID to EISJD facility-level crosswalk
For the 2017 NEI, the same crosswalk list of TRI IDs that was used for the 2014 NEI was used as a starting
point. A limited review of the 2017 TRI facilities was conducted to identify new facilities with significant
emissions that had not been previously matched to an EIS facility. A total of approximately 50 additional
TRI facilities were added to the crosswalk for 2017.
2. Map TRI pollutant codes to valid EIS pollutant codes and sum where necessary
Table 3-2 provides the pollutant mapping from TRI pollutants to EIS pollutants. Many of the 650 TRI
pollutants do not have any EIS counterpart, and so are not shown in Table 3-2. In addition, several EIS
pollutants may be reported to TRI as either of two TRI pollutants. For example, both Pb and Pb
compounds may be reported to TRI, and similarly for several other metal and metal compound TRI
pollutants. Table 3-2 shows where such pairs of TRI pollutants both correspond to the same EIS
pollutant. In such cases, we summed the two TRI pollutants together as part of the step of assigning the
TRI emissions to valid EIS pollutant codes. For the 2017 NEI, a total of 197 TRI pollutant codes were
mapped to 185 unique EIS pollutant codes. Similar to the 2011 and 2014 NEIs, we did not use TRI
emissions reported for TRI pollutants: "Certain Glycol Ethers," "Dioxin and Dioxin-like Compounds,"
Dichlorobenzene (mixed isomers)," and "Toluene di-isocyanate (mixed isomers)," because they do not
represent the same scope as the EIS pollutants: "Glycol ethers," "Dioxins/Furans as 2,3,7,8-TCDD TEQs,"
"1,4-Dichlorobenzene," and "2,4-Di-isocyanate," respectively. We maintained TRI stack and fugitive
emissions separately during the summation step and maintained that separation through the storage of
the TRI emissions in the EIS.
Table 3-2: Mapping of TRI pollutant codes to EIS pollutant codes
TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
79345
1,1,2,2-TETRACHLOROETHANE
79345
1,1,2,2-TETRACH LOROETH ANE
3-6
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TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
79005
1,1,2-TRICHLOROETHANE
79005
1,1,2-TRICHLOROETHANE
57147
1,1-DIMETHYL HYDRAZINE
57147
1,1-DIMETHYL HYDRAZINE
120821
1,2,4-TRICHLOROBENZENE
120821
1,2,4-TRICHLOROBENZENE
96128
l,2-DIBROMO-3-CHLOROPROPANE
96128
l,2-DIBROMO-3-CHLOROPROPANE
57147
1,1-DIMETHYL HYDRAZINE
57147
1,1-Dimethyl Hydrazine
106887
1,2-BUTYLENE OXIDE
106887
1,2-EPOXYBUTANE
75558
PROPYLENEIMINE
75558
1,2-PROPYLENIMINE
106990
1,3-BUTADIENE
106990
1,3-BUTADIENE
542756
1,3-DICHLOROPROPYLENE
542756
1,3-DICHLOROPROPENE
1120714
PROPANE SULTONE
1120714
1,3-PROPANESULTONE
106467
1,4-DICHLOROBENZENE
106467
1,4-DICHLOROBENZENE
25321226
DICHLOROBENZENE (MIXED ISOMERS)
NA- pollutant not used
95954
2,4,5-TRICHLOROPHENOL
95954
2,4,5-TRICHLOROPHENOL
88062
2,4,6-TRICHLOROPHENOL
88062
2,4,6-TRICHLOROPHENOL
94757
2,4-DICHLOROPHENOXY ACETIC ACID
94757
2,4-DICHLOROPHENOXY ACETIC ACID
51285
2,4-DINITROPHENOL
51285
2,4-DINITROPHENOL
121142
2,4-DINITROTOLUENE
121142
2,4-DINITROTOLUENE
53963
2-ACETYLAMINOFLUORENE
53963
2-ACETYLAMINOFLUORENE
79469
2-NITROPROPANE
79469
2-NITROPROPANE
91941
3,3'-DICHLOROBENZI DINE
91941
3,3'- DICHLOROBENZIDINE
119904
3,3'-DIMETHOXYBENZIDINE
119904
3,3'- DIMETHOXYBENZIDINE
119937
3,3'-DIMETHYLBENZIDINE
119937
3,3'-DIMETHYLBENZIDINE
101144
4,4'-METHYLENEBIS(2-CHLOROANILINE)
101144
4,4'-METHYLENEBIS(2-CHLORANILINE)
101779
4,4'-METHYLEN EDI ANILINE
101779
4,4'-METHYLENEDIANILINE
534521
4,6-DINITRO-O-CRESOL
534521
4,6-DINITRO-O-CRESOL
92671
4-AMINOBIPHENYL
92671
4-AMINOBIPHENYL
60117
4-DIMETHYLAMINOAZOBENZENE
60117
4-DIMETHYLAMINOAZOBENZENE
100027
4-NITROPHENOL
100027
4-NITROPHENOL
75070
ACETALDEHYDE
75070
ACETALDEHYDE
60355
ACETAMIDE
60355
ACETAMIDE
75058
ACETONITRILE
75058
ACETONITRILE
98862
ACETOPHENONE
98862
ACETOPHENONE
107028
ACROLEIN
107028
ACROLEIN
79061
ACRYLAMIDE
79061
ACRYLAMIDE
79107
ACRYLIC ACID
79107
ACRYLIC ACID
107131
ACRYLONITRILE
107131
ACRYLONITRILE
107051
ALLYL CHLORIDE
107051
ALLYL CHLORIDE
7664417
AMMONIA
NH3
AMMONIA
62533
ANILINE
62533
ANILINE
7440360
ANTIMONY
7440360
ANTIMONY
N010
ANTIMONY COMPOUNDS
7440360
ANTIMONY
7440382
ARSENIC
7440382
ARSENIC
N020
ARSENIC COMPOUNDS
7440382
ARSENIC
1332214
ASBESTOS (FRIABLE)
1332214
ASBESTOS
71432
BENZENE
71432
BENZENE
92875
BENZIDINE
92875
BENZIDINE
98077
BENZOIC TRICHLORIDE
98077
BENZOTRICHLORIDE
100447
BENZYL CHLORIDE
100447
BENZYL CHLORIDE
7440417
BERYLLIUM
7440417
BERYLLIUM
N050
BERYLLIUM COMPOUNDS
7440417
BERYLLIUM
92524
BIPHENYL
92524
BIPHENYL
117817
DI(2-ETHYLHEXYL) PHTHALATE
117817
BIS(2-ETHYLHEXYL)PHTHALATE
542881
BIS(CHLOROMETHYL) ETHER
542881
BIS(CHLOROMETHYL)ETHER
75252
BROMOFORM
75252
BROMOFORM
7440439
CADMIUM
7440439
CADMIUM
N078
CADMIUM COMPOUNDS
7440439
CADMIUM
156627
CALCIUM CYANAMIDE
156627
CALCIUM CYANAMIDE
133062
CAPTAN
133062
CAPTAN
63252
CARBARYL
63252
CARBARYL
75150
CARBON DISULFIDE
75150
CARBON DISULFIDE
56235
CARBON TETRACHLORIDE
56235
CARBON TETRACHLORIDE
463581
CARBONYL SULFIDE
463581
CARBONYL SULFIDE
3-7
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TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
120809
CATECHOL
120809
CATECHOL
57749
CHLORDANE
57749
CHLORDANE
7782505
CHLORINE
7782505
CHLORINE
79118
CHLOROACETIC ACID
79118
CHLOROACETIC ACID
108907
CHLOROBENZENE
108907
CHLOROBENZENE
510156
CHLOROBENZILATE
510156
Chlorobenzilate
67663
CHLOROFORM
67663
CHLOROFORM
107302
CHLOROMETHYL METHYL ETHER
107302
CHLOROMETHYL METHYL ETHER
126998
CHLOROPRENE
126998
CHLOROPRENE
7440473
CHROMIUM
7440473
CHROMIUM
N090
CHROMIUM COMPOUNDS (EXCEPT CHROMITE
ORE MINED IN THE TRANSVAAL REGION)
7440473
CHROMIUM
7440484
COBALT
7440484
COBALT
N096
COBALT COMPOUNDS
7440484
COBALT
1319773
CRESOL (MIXED ISOMERS)
1319773
CRESOL/CRESYLIC ACID (MIXED ISOMERS)
108394
M-CRESOL
108394
M-CRESOL
95487
O-CRESOL
95487
O-CRESOL
106445
P-CRESOL
106445
P-CRESOL
98828
CUMENE
98828
CUMENE
N106
CYANIDE COMPOUNDS
57125
CYANIDE
74908
HYDROGEN CYANIDE
57125
CYANIDE
132649
DIBENZOFURAN
132649
DIBENZOFURAN
84742
Dl BUTYL PHTHALATE
84742
DIBUTYL PHTHALATE
111444
BIS(2-CHLOROETHYL) ETHER
111444
DICHLOROETHYL ETHER
62737
DICHLORVOS
62737
DICHLORVOS
111422
DIETHANOLAMINE
111422
DIETHANOLAMINE
64675
DIETHYL SULFATE
64675
DIETHYL SULFATE
131113
DIMETHYL PHTHALATE
131113
DIMETHYL PHTHALATE
77781
DIMETHYL SULFATE
77781
DIMETHYL SULFATE
79447
DIMETHYLCARBAMYL CHLORIDE
79447
DIMETHYLCARBAMOYL CHLORIDE
N120
DIISOCYANATES
NA- pollutant not used
26471625
TOLUENE DIISOCYANATE (MIXED ISOMERS)
NA- pollutant not used
584849
TOLUENE-2,4-DIISOCYANATE
584849
2,4-TOLUENE DIISOCYANATE
N150
DIOXIN AND DIOXIN-LIKE COMPOUNDS
NA- pollutant not used
106898
EPICHLOROHYDRIN
106898
EPICHLOROHYDRIN
140885
ETHYL ACRYLATE
140885
ETHYL ACRYLATE
51796
URETHANE
51796
ETHYL CARBAMATE
75003
CHLOROETHANE
75003
ETHYL CHLORIDE
100414
ETHYLBENZENE
100414
ETHYL BENZENE
106934
1,2-DIBROMOETHANE
106934
ETHYLENE DIBROMIDE
107062
1,2-DICHLOROETHANE
107062
ETHYLENE DICHLORIDE
107211
ETHYLENE GLYCOL
107211
ETHYLENE GLYCOL
151564
ETHYLENEIMINE
151564
ETHYLENEIMINE
75218
ETHYLENE OXIDE
75218
ETHYLENE OXIDE
96457
ETHYLENE THIOUREA
96457
ETHYLENE THIOUREA
75343
ETHYLIDENE DICHLORIDE
75343
ETHYLIDENE DICHLORIDE
50000
FORMALDEHYDE
50000
FORMALDEHYDE
N230
CERTAIN GLYCOL ETHERS
171
N/A Pollutant not used
76448
HEPTACHLOR
76448
HEPTACHLOR
118741
HEXACHLOROBENZENE
118741
HEXACHLOROBENZENE
87683
HEXACHLORO-l,3-BUTADIENE
87683
HEXACHLOROBUTADIENE
77474
HEXACHLOROCYCLOPENTADIENE
77474
HEXACHLOROCYCLOPENTADIENE
67721
HEXACHLOROETHANE
67721
HEXACHLOROETHANE
110543
N-HEXANE
110543
HEXANE
302012
HYDRAZINE
302012
HYDRAZINE
7647010
HYDROCHLORIC ACID (1995 AND AFTER "ACID
AEROSOLS" ONLY)
7647010
HYDROCHLORIC ACID
7664393
HYDROGEN FLUORIDE
7664393
HYDROGEN FLUORIDE
123319
HYDROQUINONE
123319
HYDROQUINONE
7439921
LEAD
7439921
LEAD
N420
LEAD COMPOUNDS
7439921
LEAD
58899
LINDANE
58899
1,2,3,4,5,6-HEXACHLOROCYCLOHEXANE
3-8
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TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
108316
MALEIC ANHYDRIDE
108316
MALEIC ANHYDRIDE
7439965
MANGANESE
7439965
MANGANESE
N450
MANGANESE COMPOUNDS
7439965
MANGANESE
7439976
MERCURY
7439976
MERCURY
N458
MERCURY COMPOUNDS
7439976
MERCURY
67561
METHANOL
67561
METHANOL
72435
METHOXYCHLOR
72435
METHOXYCHLOR
74839
BROMOMETHANE
74839
METHYL BROMIDE
74873
CHLOROMETHANE
74873
METHYL CHLORIDE
71556
1,1,1-TRICHLOROETHANE
71556
METHYL CHLOROFORM
74884
METHYL IODIDE
74884
METHYL IODIDE
108101
METHYL ISOBUTYL KETONE
108101
METHYL ISOBUTYL KETONE
624839
METHYL ISOCYANATE
624839
METHYL ISOCYANATE
80626
METHYL METHACRYLATE
80626
METHYL METHACRYLATE
1634044
METHYL TERT-BUTYL ETHER
1634044
METHYLTERT-BUTYL ETHER
75092
DICHLOROMETHANE
75092
METHYLENE CHLORIDE
60344
METHYL HYDRAZINE
60344
METHYLHYDRAZINE
121697
N,N-DIMETHYLANILINE
121697
N,N-DIMETHYLANILINE
68122
N,N-DIMETHYLFORM AMIDE
68122
N,N-DIMETHYLFORMAMIDE
91203
NAPHTHALENE
91203
NAPHTHALENE
7440020
NICKEL
7440020
NICKEL
N495
NICKEL COMPOUNDS
7440020
NICKEL
98953
NITROBENZENE
98953
NITROBENZENE
684935
N-NITROSO-N-METHYLUREA
684935
N-NITROSO-N-METHYLUREA
90040
O-ANISIDINE
90040
O-ANISIDINE
95534
O-TOLUIDINE
95534
O-TOLUIDINE
123911
1,4-DIOXANE
123911
P-DIOXANE
56382
PARATHION
56382
PARATHION
82688
QUINTOZENE
82688
PENTACHLORONITROBENZENE
87865
PENTACHLOROPHENOL
87865
PENTACHLOROPHENOL
108952
PHENOL
108952
PHENOL
75445
PHOSGENE
75445
PHOSGENE
7803512
PHOSPHINE
7803512
PHOSPHINE
7723140
PHOSPHORUS (YELLOW OR WHITE)
7723140
PHOSPHORUS
85449
PHTHALIC ANHYDRIDE
85449
PHTHALIC ANHYDRIDE
1336363
POLYCHLORINATED BIPHENYLS
1336363
POLYCHLORINATED BIPHENYLS
120127
ANTHRACENE
120127
ANTHRACENE
191242
BENZO(G,H,l)PERYLENE
191242
BENZO[G,H,l,]PERYLENE
85018
PHENANTHRENE
85018
PHENANTHRENE
N590
POLYCYCLIC AROMATIC COMPOUNDS
130498292
PAH, TOTAL
106503
P-PHENYLENEDIAMINE
106503
P-PHENYLENEDIAMINE
123386
PROPIONALDEHYDE
123386
PROPIONALDEHYDE
114261
PROPOXUR
114261
PROPOXUR
78875
1,2-DICHLOROPROPANE
78875
PROPYLENE DICHLORIDE
75569
PROPYLENE OXIDE
75569
PROPYLENE OXIDE
91225
QUINOLINE
91225
QUINOLINE
106514
QUINONE
106514
QUINONE
7782492
SELENIUM
7782492
SELENIUM
N725
SELENIUM COMPOUNDS
7782492
SELENIUM
100425
STYRENE
100425
STYRENE
96093
STYRENE OXIDE
96093
STYRENE OXIDE
127184
TETRACHLOROETHYLENE
127184
TETRACHLOROETHYLENE
7550450
TITANIUM TETRACHLORIDE
7550450
TITANIUM TETRACHLORIDE
108883
TOLUENE
108883
TOLUENE
95807
2,4-DIAMINOTOLUENE
95807
TOLUENE-2,4-DI AMINE
8001352
TOXAPHENE
8001352
TOXAPHENE
79016
TRICHLOROETHYLENE
79016
TRICHLOROETHYLENE
121448
TRIETHYLAMINE
121448
TRIETHYLAMINE
1582098
TRIFLURALIN
1582098
TRIFLURALIN
108054
VINYL ACETATE
108054
VINYL ACETATE
75014
VINYL CHLORIDE
75014
VINYL CHLORIDE
75354
VINYLIDENE CHLORIDE
75354
VINYLIDENE CHLORIDE
3-9
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TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
108383
M-XYLENE
108383
M-XYLENE
95476
O-XYLENE
95476
O-XYLENE
106423
P-XYLENE
106423
P-XYLENE
1330207
XYLENE (MIXED ISOMERS)
1330207
XYLENES (MIXED ISOMERS)
An electronic database of the TRI/NEI Pollutant Crosswalk showing NEI and TRI pollutant mappings can
be downloaded from the "State/Local/Tribal (S/L/T), National Emission Inventory (NEI), Toxic Release
Inventory (TRI) Mapping" portion of the Product Design Team website. It should be noted that while
HCN is in the NEI and the electronic mapping shows NEI HCN to TRI HCN, we brought in both TRI HCN
and TRI CN emissions as NEI CN. We did this to avoid double counting of S/L/T CN with TRI HCN since
some S/L/T include HCN emissions as CN.
3. Split TRI total chromium emissions into hexavalent and trivalent emissions
The TRI allows facilities to report either "Chromium" or "Chromium compounds/' but not the hexavalent
or trivalent chromium species that are needed for the NEI (see Section 3.1.3). Because the only
characterization available for the TRI facilities or their emissions is the facilities' NAICS codes, we created
a NAICS-based set of fractions to split the TRI-reported total chromium emissions into the hexavalent
and trivalent chromium species. A table of Standard Industrial Classification (SlC)-based chromium split
fractions was available from earlier year NEI usage of TRI databases, which had been compiled by SIC
rather than NAICS. The earlier SIC-based fractions were used wherever they could be re-assigned to a
closely matching NAICS description.
Unfortunately, not all SIC-based fractions could be assigned this way, so we computed NAICS-based split
fractions for any NAICS codes in the 2017 TRI data that did not already have an SIC-to-NAICS assigned
split fraction. These factors were used for the remaining TRI-reported chromium. To calculate the NAICS-
based factors, we summed by NAICS the total amounts of chromium III and chromium VI for the entire
U.S. in the 2014 draft NEI data. These 2017 NEI S/L/T emissions were either reported directly by the
S/L/T agencies as chromium III and chromium VI, or they had been split from S/L/T agency-reported
total chromium by the EPA using the procedures described in Section 3.1.4. Those procedures largely
rely on either SCC-based or Regulatory code-based split factors. The derived NAICS split factors,
therefore, represent a weighted average of the SCC and Regulatory code-based split factors, weighted
according to the mass of each chromium valence in the 2017 NEI for that NAICS.
After all TRI facilities with chromium had been assigned a NAICS-based split factor, the factors were
applied separately to both the TRI stack and fugitive total chromium emissions. This resulted in
speciated chromium emissions for each facility's stack and fugitive emissions that were included in the
EIS as part of the 2017EPA_TRI dataset.
Similar to S/L/T chromium speciation data, the TRI chromium speciation data includes some facility-
specific values resulting from 2011 and/or 2014 NATA reviews or provided by S/L/T for use in the 2017
NEI. The TRI-chromium speciation data "TRI_based_chromium_speciation.zip" is available are available
on the 2017 Supplemental data FTP site.
4. Review high TRI emissions values for and exclude any data suspected to be outliers
A review and comparison of the largest TRI emissions values was conducted for several key high-risk
pollutants. The following pollutants were specifically reviewed, although a few extremely large values
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for some of the other TRI pollutants were also noticed and treated in the same manner: Hg, Pb,
chromium, manganese, nickel, arsenic, 1,3 butadiene, benzene, toluene, ethyl benzene, p-xylene,
methanol, acrolein, carbon tetrachloride, tetrachloroethylene, methylene chloride, acrylonitrile, 1,4-
dichlorobenzene, ethylene oxide, hydrochloric acid, hydrogen fluoride, chlorine, 2,4-toluene
diisocyanate, hexamethylene diisocyanate, and naphthalene. The review included looking at the largest
10 emitting facilities for each of the pollutants in the 2017 TRI dataset itself to identify large differences
between facilities and unexpected industry types. Comparisons were then made to the 2014 TRI and the
2017 draft NEI emissions values from S/L/T agencies for any suspect facilities identified by that review
(as described above in Section 3.1.1).
5. Write the 2017 TRI emissions to EIS Process IDs with stack and fugitive release points
The total facility stack and total facility fugitive emissions values from the above steps were written to a
set of EIS process IDs created to reflect those facility total type emissions. In most cases, the EIS process
IDs for a given facility already existed in EIS as a result of earlier NEI.
6. Revise SCCs on the EIS Processes used for the TRI emissions
The 2002 and 2005 NEIs had assigned all the TRI emissions to a default process code SCC of 39999999,
which caused a large amount of HAP emissions to be summed to a misleading "miscellaneous" sector.
The 2008 NEI approach reduced this problem somewhat because it apportioned all TRI emissions to the
multiple processes and SCCs that were used by the S/L/T agencies to report their emissions, but this
apportioning created other distortions. The 2011 NEI reverted back to loading the TRI emissions as the
single process stack and fugitive values as reported by facilities to the TRI, but we revised the SCCs on
those single processes to something other than the default 39999999 wherever possible. The purpose of
this is to allow the TRI emissions to map to a more appropriate EIS sector. For the 2017 NEI, we retained
the 2011 approach, process IDs, and SCCs.
On occasion, TRI SCCs are updated where the process is known based on the type of facility or SCCs from
processes for which CAPs were reported. However, there has not been a systematic approach to fill in all
SCCs and for large industrial facilities, it would not be possible due to the variety of different process
operations that can occur at such facilities.
3.1.6 HAP augmentation based on emission factor ratios
The 2017EPA_HAP-augmentation dataset was used for gap filling missing HAPs in the S/L/T agency-reported
data. We calculated HAP emissions by multiplying the appropriate surrogate CAP emissions (provided by S/L/T
agencies) by an emissions ratio of HAP to CAP EFs. For point sources, these EF ratios were largely the same as
were used in the 2008 NEI v3, though additional quality assurance resulted in some changes. The ratios were
computed using the EFs from WebFIRE and are based solely on the SCC code. The computation of these point
HAP to CAP ratios is described in detail in the 2008 NEI documentation. Section 3.1.5.
For pollutants other than Hg, we computed ratios for only the SCCs in WebFIRE that met specific criteria: 1) the
CAP and HAP WebFIRE EFs were both based on uncontrolled emissions and, 2) the units of the EF had to be the
same or be able to be converted to the same units. In addition, for Hg, we added ratios for point SCCs that were
not in WebFIRE for both PM10-FIL (the CAP surrogate for Hg) and Hg by using Hg or PM10-FIL factors for similar
SCCs and computing the resulting ratio. That process is described (and supporting data files provided) in the
2008 NEI documentation (Section 3.1.5.2), since these additional Hg augmentation factors were used in the
2008 NEI v3 as well.
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A HAP augmentation feature was built into the EIS for the 2011 cycle, and the HAP EF ratios are available to the
EIS users through the reference data link "Augmentation Profile Information." The same tables provide both the
HAP augmentation factors and chromium speciation factors and were discussed in Section 2.2.2.
Since the initial set of HAP augmentation factors, factors and/or SCC-assignments were added including facility-
specific HAP augmentation factors resulting from NATA reviews. Also new for the 2017 NEI are facility-specific
coke oven to S02 ratios used to compute coke oven emissions for specific facilities with operating coke ovens
that were missing coke oven emissions. We have been also exploring using test-based emission factor ratios in
place of WebFIRE-based ratios where data are sufficient to do so. Users interested in the few test-based factors
that do not have access to EIS can download the full set of HAP augmentation factors from the 2017
Supplemental data FTP site ("HAPaugmentation.zip") and peruse the metadata information (data source and
factor comments) to extract them.
A key facet of our approach is that the resulting HAP augmentation dataset does duplicate HAPs from the S/L/T
agency data or other EPA datasets. The extra step of data tagging of the HAP augmentation dataset was taken to
ensure the NEI would not use the data from the HAP augmentation dataset for facilities where the HAP was
reported by an S/L/T agency at any process at the facility or where the HAP was included in the EPA TRI dataset.
For example, if a facility reported formaldehyde at process A only, and the WebFIRE emission factor database
yields formaldehyde emissions for processes A, B, and C, then we would not use any records from the HAP
augmentation dataset containing formaldehyde from any processes at the facility. If that facility had no
formaldehyde, but the TRI dataset had formaldehyde for any processes at that facility, then the NEI would still
not use formaldehyde from the HAP augmentation dataset for any of the processes (it would use the TRI data).
If the EPA EGU dataset contained formaldehyde for that facility, we would use the HAP augmentation set but
not for any process at the same unit as EPA EGU dataset. If the EPA EGU dataset contained formaldehyde at
process A or any other process within the same unit as process A, then the HAP augmentation dataset would be
used for processes B and C, but not process A.
This approach was taken to be conservative in our attempt to prevent double counted emissions, which is
necessary because we know that some states aggregate their HAP emissions and assign to fewer or different
processes than their CAP emissions. These types of differences are expected since CAPs are required to be
submitted at the process level, but HAPs are entirely voluntary for the NEI's reporting rule. We used the EIS new
pollutant overlapping business rules (Section 3.3.17) to prevent double counting of pollutants belonging to
pollutant groups that may overlap with other pollutants in that group.
One of the changes we made from previous NEI's is that we no longer tag out point source HAP augmentation
values where the HAP augmentation value exceeded the maximum emissions reported by any S/L/T agency for
the same SCC/pollutant combination, or if no S/L/T agency reported any values for the same SCC/pollutant.
3.1.7 Cross-dataset tagging rules for overlapping pollutants
Several HAPs can be reported as individual chemicals or chemicals that reflect a group which can overlap with
individual chemicals, e.g., o-Xylene and Xylenes (mixed isomers). In previous NEI cycles, we tagged out data to
prevent double counting of pollutants across datasets that overlap one another. For the 2017 NEI, a software
solution that occurs during the blending process was developed so that overlapping pollutants would be
excluded from the selection. The business rules were documented as part of the 2017 NEI plan (see Appendix 5).
One change to these "Proposed" rules that we implemented for the 2017 NEI is that we allow individual xylene
isomers to be reported with Xylenes (mixed isomers) within the same dataset. The cross-data business rules
used are the same as documented the plan.
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One issue that came up with these rules regards the hexavalent chromium and trivalent chromium in the
2017EPA_CR_Aug dataset. This dataset, which contains S/L/T speciated chromium (i.e., hexavalent and trivalent
chromium), is separate from the S/L/T datasets but contains data that could be largely characterized as S/L/T
data. While we intended to allow S/L/T to report either unspeciated chromium or hexavalent chromium along
with chromic acid VI chromium trioxide at the same process, the software did not allow the hexavalent
chromium in the 2017EPA_CR_Aug dataset to be used with S/L/T chromic acid VI. This occurred only in 2 states,
NC and KY. For KY, the speciated chromium was less than 0.1 lb and no corrections were made. In NC, there was
about 500 lbs hex chromium that would have been dropped so we corrected it. The correction was for NCto
incorporate the speciated chromium from2017EPA_CR_Aug into their dataset (instead of unspeciated
chromium) so that both pollutants would be used in the 2017 NEI selection. All records where EPA speciated
chromium data were used include an emissions comment to that effect.
3.1.8 Additional quality assurance and findings
Prior to the release of the data, we created national summaries of key pollutants and sectors. The list below
provides findings and associated follow-up steps:
• We created a preliminary summary of mercury from point source emissions, even in the absence of the
other sectors that feed the final mercury summary that will be included in Section 2 of the
documentation once the NEI is complete. Such a summary has been included in past documentation for
other inventories. This summary revealed a possible underestimation of mercury from the Commercial
and Industrial Solid Waste Incineration (CISWI) sector. Since not all sources are reported to NEI as point
sources, the NEI may not include all CISWI sources. In addition, the Hg estimates of these sources are
highly uncertain, could be underestimated, and the EPA is currently working to get improved mercury
and other emissions estimates for these sources.
• We summarized hydrazine emissions and found a significantly larger hydrazine estimate in Arkansas
than had been present in past inventories. This makes Hydrazine emissions overall in the NEI increase
since 2014. We contacted the air office of the Arkansas Department of Environmental Quality, and the
inventory staff there confirmed the accuracy of these emissions.
• We summarized ethylene oxide emissions and found that several facilities did not report ethylene oxide
to both the state air agency and to the TRI program in 2017, but those facilities were still operating in
2017. To gap-fill those missing emissions, we used the 2016 TRI data.
• We summarized hexavalent chromium emissions and found a significant increase in emissions since
2014. We identified some missing emissions for sources in NC and worked with NC to include those
chromium emissions. We did not find any errors in hexavalent chromium in the 2017 data, which shows
an increase in these emissions as compared to the 2014 NEI. This could be due to a more complete
inventory or to an actual increase.
3.2 Airports: aircraft-related emissions
The EPA estimated emissions related to aircraft activity for all known U.S. airports, including seaplane ports and
heliports, in the 50 states, Puerto Rico, and U.S. Virgin Islands. All of the approximately 20,000 individual airports
are geographically located by latitude/longitude and stored in the NEI as point sources. As part of the
development process, S/L/T agencies had the opportunity to provide both activity data as well emissions to the
NEI. When activity data were provided, the EPA used that data to calculate the EPA's emissions estimates.
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3.2.1 Sector Description
The aircraft sector includes all aircraft types used for public, private, and military purposes. This includes four
types of aircraft: (1) commercial, (2) air taxis (AT), (3) general aviation (GA), and (4) military. A critical detail
about the aircraft is whether each aircraft is turbine- or piston-driven, which allows the emissions estimation
model to assign the fuel used, jet fuel or aviation gas, respectively. The fraction of turbine- and piston-driven
aircraft is either collected or assumed for all aircraft types.
Commercial aircraft include those used for transporting passengers, freight, or both. Commercial aircraft tend to
be larger aircraft powered with jet engines. Air taxis carry passengers, freight, or both, but usually are smaller
aircraft and operate on a more limited basis than the commercial aircraft. General aviation includes most other
aircraft used for recreational flying and personal transportation. Finally, military aircraft are associated with
military purposes, and they sometimes have activity at non-military airports.
The national AT and GA fleets include both jet- and piston-powered aircraft. Most of the AT and GA fleets are
made up of larger piston-powered aircraft, though smaller business jets can also be found in these categories.
Military aircraft cover a wide range of aircraft types such as training aircraft, fighter jets, helicopters, and jet-
and piston-powered planes of varying sizes.
The NEI also includes emission estimates for aircraft auxiliary power units (APUs) and aircraft ground support
equipment (GSE) typically found at airports, such as aircraft refueling vehicles, baggage handling vehicles and
equipment, aircraft towing vehicles, and passenger buses. These APUs and GSE are located at the airport
facilities as point sources along with the aircraft exhaust emissions.
3.2.2 Sources aircraft emissions estimates
Aircraft exhaust, GSE, and APU emissions estimates are associated with aircrafts' landing and takeoff (LTO) cycle.
LTO data were available from both S/L/T agencies and FAA databases. For airports where the available LTO
included detailed aircraft-specific make and model information (e.g., Boeing 747-200 series), we used the FAA's
Aviation Environmental Design Tool (AEDT) to estimate emissions. Note that this is the first NEI to use this
model. 2008 and 2011 used the FAA's previous model, Emissions and Dispersion Modeling System (EDMS).
Therefore, comparisons of aircraft emissions output may be a function of model revisions, rather than an actual
trend in emissions. For airports where FAA databases do not include such detail, the EPA used assumptions
regarding the percent of LTOs that were associated with piston-driven (using aviation gas) versus turbine-driven
(using jet fuel) aircraft. Then, the EPA estimated emissions based on the percent of each aircraft type, LTOs, and
EFs The emissions factors used, as well as the complete methodology for estimating aircraft exhaust from LTOs
is in the aircraft documentation available in the document "2017Aircraft_main_19aug2019.pdf" on the 2017
Supplemental data FTP site. Only Texas and California submitted aircraft emissions.
In addition to airport facility point, the EPA also estimated in-flight Pb (from aviation gas) emissions that are
allocated to counties in the nonpoint inventory. Details about EPA's estimates
(2017Aircraft_lnflightLead_19aug2019.pdf), including a summary of state-level in-flight lead estimates
"2017Aircraft_lnflightLeadByState_19aug2019.csv" can be found on the 2017 Supplemental data FTP site.
3.3 Rail yard-related emissions
The 2017 NEI includes estimates compiled by the Eastern Regional Technical Advisory Committee (ERTAC) for
most rail yards in the US. The ERTAC effort was comprised of a collaborative of state/local agencies, rail
companies, and the Federal Rail Administration. Yard emissions are associated with the operation of switcher
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engines at each yard. The project is documented in a report " 2017Rail_main_21aug2019.pdf" on the 2017
Supplemental data FTP site. S/L/Ts submitted point rail yard emissions were given priority over the ERTAC
estimates when present.
3.4 EGUs
The EPA developed a single combined dataset of emission estimates for EGUs to be used to fill gaps for
pollutants and emission units not reported by S/L/T agencies. For the 2017EPA_EGU dataset, the emissions were
estimated at the unit level, because that is the level at which the CAMD heat input activity data and the MATS-
based emissions factors and the CAMD CEM data are available. The 2017EPA_EGU dataset was developed from
three separate estimation sources. The three sources were the 2010 MATS rule development testing program
EFs for 15 HAPs; annual sums of S02 and NOx emissions based on the hourly CEM emissions reported to the
EPA's CAMD database; and heat-input based EFs that were built from AP-42 EFs and fuel heat and sulfur
contents as part of the 2008 NEI development effort. We used the 2014 annual throughputs in BTUs from the
CAMD database with the two EF sets to derive annual emissions for 2017. A small number of the AP-42-based
estimates were discarded because the fuels or control configurations were found to be different than what they
were during the 2008 development effort that provided the heat-input based EFs that were available.
As shown above in Table 3-1, the selection hierarchy was set such that S/L/T-submitted data was used ahead of
the values in the 2017EPA_EGU dataset. In the 2011 NEI, the EPA EGU estimated emissions that were derived
from the MATS testing program were used ahead of the S/L/T values, unless the S/L/T submittal indicated that
the value was from either a CEM or a recent stack test. For the 2017 NEI, we used the S/L/T-reported values
wherever they were reported (unless they were tagged out as an outlier), including where a MATS-based value
existed in the 2017EPA EGU dataset. In addition, we made the MATS emission factors available to S/L/T agencies
far in advance of the data being submitted so that facilities and/or S/L/T agencies could choose to use that
information to compute emissions if it was most applicable.
We assumed that all heat input came from the primary fuel, and the EFs used reflected only that primary fuel.
This introduces a small amount of uncertainty as many EGU units use a small amount of alternative fuels. The
resultant unit-level estimates had to be loaded into EIS at the process-level to meet the EIS requirement that
emissions can only be associated with the most detailed level. To do this for the EGU sectors, we needed to
bridge the unit level (i.e., the boiler or gas turbine unit as a whole) to the process level (i.e., the individual fuels
burned within the units). So, the EPA emissions were assigned to a single process for the primary fuel that was
used by the responsible S/L/T agency for reporting the largest portion of their emissions. The EPA emissions
were then "tagged out" wherever the S/L/T agency had reported the same pollutant at any process within the
same emission unit. This approach prevented double counting of a portion of the S/L/T-reported emissions in
cases where the S/L/T agency may have reported a unit's emissions using two different coal processes and a
small oil process, for example.
The matching of the 2017EPA_EGU dataset to the responsible agency facility, unit and process IDs was done
largely by using the ORIS plant and CAMD boiler IDs as found in the CAMD heat input activity dataset and linking
these to the same two IDs as had been stored in EIS. We also compared the facility names and counties for
agreement between the S/L/T-reported values and those in CAMD, and we revised the matches wherever
discrepancies were noted. As a final confirmation that the correct emissions unit and a reasonable process ID in
EIS had been matched to the EPA data, the magnitudes of the S02 and NOx emissions for all preliminary
matches were compared between the S/L/T agency-reported datasets and the EPA dataset. We identified and
resolved several discrepancies from this emissions comparison.
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Alternative facility and unit IDs needed for matching with other databases
The 2017 NEI data contains two sets of alternate unit identifiers related to the ORIS plant and CAMD boiler IDs
(as found in the CAMD heat input activity dataset) for export to the Sparse Matrix Operator Kernel Emissions
(SMOKE) modeling file. The first set is stored in EIS with a Program System Code (PSC) of "EPACAMD." The
alternate unit IDs are stored as a concatenation of the ORIS Plant ID and CAMD boiler ID with "CAMDUNIT"
between the two IDs. These IDs are exported to the SMOKE file in the fields named ORIS_FACILITY_CODE and
ORIS_BOILER_ID. These two fields are used by the SMOKE processing software to replace the annual NEI
emissions values with the appropriate hourly CEM values at model run time. The second set of alternate unit IDs
are stored in EIS with a PSC of "EPAIPM" and are exported to the SMOKE file as a field named "IPM_YN." The
SMOKE processing software uses this field to determine if the unit is one that will have future year projections
provided by the integrated planning model (IPM). The storage format of these alternate EPAIPM unit IDs, in both
EIS and in the exported SMOKE file, replicates the IDs as found in the National Electric Energy Data
System (NEEDS) database used as input to the IPM model. The NEEDS IDs are a concatenation of the ORIS plant
ID and the CAMD boiler ID, with either a "_B_" or a "_G_" between the two IDs, indicating "Boiler" or
"Generator." The ORIS Plant IDs and CAMD boiler IDs as stored in the CAMD Business System (CAMDBS) dataset
and in the NEEDS database are almost always the same, but there are occasional differences for the same unit.
The EPACAMD alternate unit IDs available in the 2017 NEI are believed to be a complete set of all those that can
safely be used for the purpose of substituting hourly CEM values without double-counting during SMOKE
processing. The EPAIPM alternate unit IDs in the 2017 NEI are not a complete listing of all the NEEDS/IPM units,
although most of the larger emitters do have an EPAIPM alternate unit ID. The NEEDS database includes a much
larger set of smaller, non-CEM units.
3.5 Landfills
The point source emissions in the EPA's Landfill dataset includes CO and 28 HAPs, as shown in Table 3-3. This set
of pollutants was included in the 1999 NEI, and we continue to use the same set of pollutants each year for a
consistent time series. To estimate emissions, we used the methane emissions reported by landfill operators in
compliance with Subpart HH of the Greenhouse Gas Reporting Program (GHGRPi as a "surrogate" activity
indicator. We converted the methane as reported in Mg C02 equivalent to Mg as actual methane emitted by
dividing by 23 (the Global Warming Potential of methane believed to be used in the version of the 2017 GHGRP
facility inventory) to get Mg methane emitted, and then multiplied by 1.1023 to get tons methane emitted5. We
created emission factors for CO and the 28 HAPs on a per ton of methane emitted basis using the default
concentrations (ppmv) in AP-42 Section 2.4 (final section dated Jan 1998), Table 2.4-1. The concentrations for
toluene and benzene were taken from Table 2.4-2 of AP-42, for the case of "no or unknown" co-disposal history.
Per Equation 4 of that AP-42 section, Mp=Qp x MWp x constant (at any given temperature). Writing this
equation twice, for the mass of any pollutant "P" and for methane (CH4), and dividing Mp by McH4 yields:
Mp / MCH4 = (Qp x MWp x k) / QCH4 x MWCH4 x k) = (Qp/QcH4) x (MWp/MWcH4)
in units of pounds pollutant "P" per pound CH4.
5 For more information on C02 equivalent and global warming potential, please refer to EPA's page "Understanding Global
Warming Potentials".
3-16
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A rearrangement of Equation 3 of that AP-42 section provides Qp/ QCH4 = 1-82 x Cp/1000000, where the 1.82 is
based upon a default methane concentration of 55 % (550,000 ppm). Plugging this expression for Qp/ QcH4 into
the first expression yields:
Mp / MCH4 = (1.82 x Cp/1000000) x (MWp/ MWCH4) x 2000, units of pounds p/ton CH4
Mp / McH4 = (1-82 x Cp/1000000) x (MWp/16) x 2000 = Cp x MWp / 4395.6
Table 3-3: Landfill gas emission factors for 29 EIS pollutants
Pollutant
code
Pollutant description
MW
ppmv
MW x
ppmv
lbs/Ton
ch4
CO
Carbon monoxide
28.01
141
3949.41
0.89849
108883
toluene
92.13
39.3
3620.709
0.82371
1330207
Xylenes
106.16
12.1
1284.536
0.29223
75092
Dichloromethane (methylene chloride)
84.94
14.3
1214.642
0.27633
7783064
Hydrogen sulfide
34.08
35.5
1209.84
0.27524
127184
Perchloroethylene (tetrachloroethylene)
165.83
3.73
618.5459
0.14072
110543
Hexane
86.18
6.57
566.2026
0.12881
100414
Ethylbenzene
106.16
4.61
489.3976
0.11134
75014
Vinyl chloride
62.5
7.34
458.75
0.10437
79016
Trichloroethylene (trichloroethene)
131.4
2.82
370.548
0.08430
107131
Acrylonitrile
53.06
6.33
335.8698
0.07641
75343
1,1-Dichloroethane (ethylidene dichloride)
98.97
2.35
232.5795
0.05291
108101
Methyl isobutyl ketone
100.16
1.87
187.2992
0.04261
79345
1,1,2,2-Tetrachloroethane
167.85
1.11
186.3135
0.04239
71432
benzene
78.11
1.91
149.1901
0.03394
75003
Chloroethane (ethyl chloride)
64.52
1.25
80.65
0.01835
71556
1,1,1-Trichloroethane (methyl chloroform)
133.41
0.48
64.0368
0.01457
74873
Chloromethane
50.49
1.21
61.0929
0.01390
75150
Carbon disulfide
76.13
0.58
44.1554
0.01005
107062
1,2-Dichloroethane (ethylene dichloride)
98.96
0.41
40.5736
0.00923
106467
Dichlorobenzene
147
0.21
30.87
0.00702
463581
Carbonyl sulfide
60.07
0.49
29.4343
0.00670
108907
Chlorobenzene
112.56
0.25
28.14
0.00640
78875
1,2-Dichloropropane (propylene dichloride)
112.99
0.18
20.3382
0.00463
75354
1,1-Dichloroethene (vinylidene chloride)
96.94
0.2
19.388
0.00441
67663
Chloroform
119.39
0.03
3.5817
0.00081
56235
Carbon tetrachloride
153.84
0.004
0.61536
0.00014
106934
Ethylene dibromide
187.88
0.001
0.18788
0.00004
7439976
Mercury (total)
200.61
0.000292
0.05857812
0.00001
3-17
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3.6 2017EPA_gapf ills
This EPA dataset is used to fill in miscellaneous emissions which were not reported by S/L/T agencies for 2017,
and for which no EPA dataset has 2017 emissions, but which are believed to exist in 2017. These unreported
facilities and pollutants were identified as part of the QA review steps performed on the S/L/T data (see Section
3.1.1). A total of 95 unique facilities across 4 different States and 88 different pollutants are represented in this
dataset. Most of the additions were for Indiana (73 facilities), which did not submit emissions for all of their
operating facilities for 2017. 2016 NEI emissions were copied into the gapfilIs dataset for those facilities. Nine
facilities in Pinal County, AZ were also added using 2016 NEI emissions. NOx emissions only were added for
eleven coal mines in Wyoming which have significant emissions from trucks and other mobile equipment which
were not included in WYDEQ's point source dataset, and which are not expected to be adequately covered in
EPA's nonroad emission estimates. WYDEQ sent 2017 facility totals for these facilities mobile emissions to be
added to the 2017 NEI PT. Lastly, mercury emissions for two municipal waste combustors in Maryland and four
municipal waste combustors in Massachusetts were added.
3.7 BOEM
The U.S. Department of the Interior, Bureau of Ocean and Energy Management (BOEM) estimates emissions of
CAPs in the Gulf of Mexico from offshore oil platforms in Federal waters, and these data have been previously
incorporated into the NEI. More information on the 2017 Outer Continental Shelf (OCS) offshore data is
available on the BOEMS OCS Emissions Inventory - 2017 site.
3.8 PM species
For the 2017 NEI PT inventory, the five species (EC, OC, S04, N03, and other) of PM2.5-PRI and diesel PM (which
are estimated for diesel mobile engines such as locomotives and diesel-fueled ground support equipment) were
not included. These species will be generated in full NEI release in the Spring of 2020, similar to earlier NEI years
by using the PM speciation ratios as found on the Air Emissions Modeling website.
3.9 References for point sources
1. Dorn, J, 2012. Memorandum: 2011 NEI Version 2 - PM Augmentation approach. Memorandum to Roy
Huntley, US EPA. (PM augmt 2011 NEIv2 feb2012.pdf, accessible in the file "2008nei_reference.zip" on
the 2008v3 NEI FTP site.
2. Strait et al. (2003). Strait, R.; MacKenzie, D.; and Huntley, R., 2003. PM Augmentation Procedures for the
1999 Point and Area Source NEI. 12th International Emission Inventory Conference - "Emission
Inventories - Applying New Technologies", San Diego, April 29 - May 1, 2003.
3-18
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4 Nonpoint sources
This section includes all sources that are in the nonpoint data category, including biogenics. These sources are
reported/generated at the county level, though some sources such as rail lines and shipping lanes and ports are
more-finely resolved to the county/shape identifier (ID) (polygon) level. Stationary sources that are inventoried
at facilities and stacks (coordinates) are discussed in the previous Point Section 3. This section discusses all
sources in the Nonpoint inventory. Some "nonroad" mobile sources such as trains and commercial marine
vessels reside and are discussed here in the nonpoint data category, not in the Nonroad Equipment Section 5.
4,1 Nonpoint source approaches
Nonpoint source data are provided by state, local, and tribal (S/L/T) agencies, and for certain sectors and/or
pollutants, they are supplemented with data from the EPA. This section describes the various sources of data
and the selection priority for each of the datasets to use for building the National Emissions Inventory (NEI)
when multiple data sources are available for the same emissions source. Section 2.2 provides more information
on the data selection process.
4.1.1 Sources of data overview and selection hierarchies
Table 4-1 describes the datasets comprising most of the nonpoint inventory, and the hierarchy for combining
these datasets in construction of the NEI. Commercial marine vessels sector-specific data are provided in the
stand-alone dataset "2017EPA_CMV". While the bulk of these datasets are for stationary sources of emissions,
some of these datasets contain mobile sources so that emissions from ports, shipping lanes and rail yards could
be included as nonpoint sources. The following table includes the rationale for why each dataset was assigned
its position in the hierarchy. We excluded certain pollutants from stationary sources in the 2017 NEI:
greenhouse gases and pollutants in the pollutant groups "dioxins/furans" and "radionuclides"1^. The EPA has not
evaluated the completeness or accuracy of the S/L/T agency dioxin and furan values nor radionuclides and does
not have plans to supplement these reported emissions with other data sources to compile a complete estimate
for dioxin and furans nor radionuclides as part of the NEI.
Table 4-1: Data sources and selection hierarchy used for most nonpoint sources
Dataset name
Description and Rationale for the Order of the Selected Datasets
Order
2017EPA_PMSpecies
Speciated PM2.5 data. A result of offline emissions speciation where the
resulting PM25-PRI selection emissions are split into the 5 PM species:
elemental (black) carbon (EC), organic carbon (OC), nitrate (N03), sulfate
(S04), and the remainder of PM25-PRI (PMFINE). Diesel engine PM25-PRI and
PM10 are also copied as DIESEL-PM pollutants.
1
ft Dioxins/furans include all pollutants with pollutant category name of: Dioxins/Furans as 2,3,7,8-TCDD TEQs, or
Dioxins/Furans as 2,3,7,8-TCDD TEQs - WH02005, both of which were valid pollutant groups for reporting 2014 emissions.
Radionuclides have the pollutant category name of "radionuclides" The specific compounds and codes are in the pollutant
code tables in EIS.
4-1
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Dataset name
Description and Rationale for the Order of the Selected Datasets
2017PMaug_SLT_NP
PM components added to gap fill missing S/L/T agency data or make
corrections where S/L/T agency have inconsistent emissions across PM
components. Uses ratios of emission factors from the PM Augmentation Tool
for covered source classification codes (SCCs). For SCCs without emission
factors in the tool, checks/corrects discrepancies or missing PM species using
basic relationships such as ensuring that primary PM is greater than or equal
to filterable PM (see Section 4.1.5).
Responsible Agency
Data Set
S/L/T agency submitted data. These data are selected ahead of other
datasets. The only other situation where S/L/T agency emissions are not used
is where certain records are tagged in the Emissions Inventory System (EIS)
(at the specific source/pollutant level). This occurs: 1) for hierarchy purposes
to allow EPA nonpoint emissions to be used ahead of S/L/T agency data
where states asked for EPA data to be used in place of their data and 2)
where S/L/T agency data were suspected outliers.
2017EPA_HAPAug
HAP data computed from S/L/T agency criteria pollutant data using ratios of
HAP to CAP emission factors. The emission factors used to create the ratios
are the same emission factors as are used in creating the EPA estimates (i.e.,
in the EPA nonpoint emission tools). This dataset is below the S/L/T agency
data so that the S/L/T agency HAP data are used first. HAP augmentation is
discussed in Section 4.1.6.
2017EPA_HAPAug-
PMaug
This dataset was created in the same fashion as the 2017EPA_HAPAug
dataset above and is a supplement to it. This dataset contains HAPs calculated
by applying a ratio to PM10-FIL emissions, for those instances where the
S/L/T dataset did not contain any PM10-FIL emissions, but the PM
augmentation routine was able to calculate a PM10-FIL value from some PM
species that was reported by the S/L/T.
2017EPA_Cr_Aug
Hexavalent and trivalent chromium speciated from S/L/T agency reported
chromium. EIS augmentation function creates the dataset by applying
multiplication factors by SCC, facility, process or North American Industry
Classification System (NAICS) code to S/L/T agency total chromium. See
Section 2.2.2.
2017ERTAC Rail
Blend of SLT-submitted and collaboratively generated diesel line and diesel
yard locomotive data (referred to as "rail" in this document) emissions
estimates. See Section 4.22.
2017EPA NONPOINT
All nonpoint EPA estimates are included in this dataset except those listed
elsewhere in this table. This dataset includes sources with and without point
source subtraction and outputs from most of the EPA tools, including the
"wagon wheel" with (if provided) SLT-submitted Input Templates (see Section
4.1.7). This dataset also includes biogenic emissions. Examples of sources in
this dataset include agricultural fertilizer application, most livestock waste,
industrial and commercial/institutional fuel combustion, residential wood
combustion, solvent utilization, oil and gas exploration and production, open
burning, agricultural field burning, road and construction dust, and portable
fuel containers.
2017EPA_Airports
2017 aircraft in-flight emissions (Lead only)
2017EPA_CarryFwd
2014v2 NEI data from 2014 EPA nonpoint estimates that were not updated
for 2017: mercury from laboratories; bench scale reagents.
4-2
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The EPA developed all datasets listed above except for the "Responsible Agency Selection/' which contains only
S/L/T agency data. We used various methods and databases to compile the EPA generated datasets, which are
further described in subsequent subsections. The primary purpose of the EPA datasets is to add or "gap fill"
pollutants or sources not provided by S/L/T agencies, to resolve inconsistencies in S/L/T agency-reported
pollutant submissions for PM (Section 2.2.4) and to speciate S/L/T agency reported total chromium into
hexavalent and trivalent forms (Section 2.2.2). The hierarchy or "order" provided in Table 4-1 defines which data
are preferentially used when multiple datasets could provide emissions for the same pollutant and emissions
process. The dataset with the lowest order on the list is preferentially used over other datasets.
In addition to the order of the datasets, the hierarchy was also influenced by the EIS feature of data tagging
(Section 2.2.6). Any data that were tagged by EPA in any of the datasets were not used. S/L/T agency data were
tagged for two reasons: 1) S/L/Ts requested that their data not be used, and 2) EPA found unexpected pollutants
for a source. Due to improvements in the new nonpoint survey (next section), there was very little need to tag
EPA nonpoint data for 2017. If S/L/T agencies report zero emissions, then backfilling with other datasets will not
occur. There are two ways that S/L/T agencies can prevent inappropriately backfilled emissions from being
included in the NEI: 1) S/L/T agencies can submit zeros for any pollutant they do not want filled in (the EPA data
will otherwise fill in for all pollutants that are on the nonpoint expected pollutant list), 2) S/L/T agencies can
complete the nonpoint survey and specify "No..." to prevent any EPA estimates from backfilling where S/L/Ts did
not submit data, or 3) the EPA can add tags to backfill datasets that prevent the tagged pollutants from being
included in the NEI. The first option is most straightforward and takes care of any possible augmentation from
the numerous other datasets in the selection hierarchy. The second option was developed as a quick tool for
S/L/Ts to essentially prevent the need to "tag out" EPA data yet achieve the same goal.
4.1.2 Revised Nonpoint Survey
The nonpoint survey, first developed for the 2014vl NEI, then refined for the 2014v2 NEI, was streamlined
further for the 2017 NEI. The purpose of the nonpoint survey is to increase the accuracy and transparency in
how the nonpoint inventory is built using EPA and S/L/T agency data. The nonpoint inventory includes all
nonpoint source categories that EPA generates estimates for.
Because each agency has their own universe of sources and inventory development approaches, each agency
reports nonpoint estimates a little differently. The nonpoint survey gathers information specifically for each SLT
regarding which source categories are covered by point, nonpoint, or both, and about where point source
reconciliation needs to be done to nonpoint activity.
The new nonpoint survey has an "Accept All Emissions Estimates" button on the home page for S/L/Ts that did
not submit emissions for any nonpoint sector. Note, acceptable S/L/T activity inputs (next section) provided to
EPA were absorbed into EPA tools and therefore became "EPA" estimates. For S/L/Ts that wanted to prevent
some EPA data from backfilling, there were options to edit the default responses for each SCC or accept EPA
estimates for entire sectors. The optional reasons to select "No" (and this was applied for each SCC that EPA
generates estimates) are: 1) I do not have this Source, 2) This source is included in my Point Source
contributions, 3) My agency uses different SCCs, and 4) My inventory is complete; it does not need to be
supplemented. And additional option to select "Yes -Supplement Only for Missing Pollutants at my reported
Counties or Tribe" was provided to allow only missing (expected) pollutants to be added for locations where
emissions were submitted for at least one pollutant. More information on the new nonpoint survey is available
in Section 5.4.6 of the 2017 NEI Plan.
4-3
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4.1.3 New for the 2017 NEI: Wagon Wheel and Input Templates
A central database, called the "Wagon Wheel", was developed to house all inputs and calculate emissions for
most nonpoint source categories. In past inventories, EPA shared different tools to S/L/Ts, many with the same
inputs; this process was very inefficient and prone to human error as many tools shared similar inputs and
different versions of these tools were often used by S/L/Ts vs the "final" versions ultimately regarded as "EPA"
for the NEI. The Wagon Wheel links each activity input tables to the appropriate sector/module such that
refreshing one dataset ensures the next tool iteration captures it for all appropriate sectors. The full list of
nonpoint source categories/tools included in the Wagon Wheel is provided in Section 5.4.2 of the 2017 NEI
plan.
EPA strongly encouraged S/L/Ts to provide only inputs to the Wagon Wheel because, often late in the inventory
development cycle, EPA finds a need for a tool update (e.g., error, or new, improved information, and so if
S/L/Ts submitted emissions (rather than inputs) using an old version of the tool, then their submitted data could
be out of date or incorrect.
EPA provided blank input templates for all Wagon Wheel source categories. These blank templates included all
default activity data, and as these default activity data were updated, the input templates and the wagon wheel
were updated to incorporate it. S/L/Ts then provided their completed input templates back to EPA where their
updated data, after rudimentary quality assurance, were used to supersede the default data in the template
and ultimately the Wagon Wheel. In this process, all S/L/T-submitted input activity data became "EPA" data.
Input activity data also included information on controls and emission factors where provided.
With one key exception, S/L/Ts could opt out of submitting input templates as EPA methods did not need S/L/T
inputs to compute reasonable nonpoint estimates. EPA used S/L/T-submitted point emissions as default for
nonpoint reconciliation for solvents, stage 2 gasoline distribution, and publicly owned treatment works
(POTWs); and, little to no overlap with the point inventory would be expected for most other nonpoint source
categories in the Wagon Wheel. However, for Industrial and Commercial/Institutional (ICI) nonpoint fuel
combustion, we relied on S/L/T-submitted throughput (fuel consumption) data, ideally from their Point
inventories. As discussed in Section 4.13.3.6, EPA provided four different options for submitting throughput for
the ICI tool. Only three state reporting agencies and two territories did not submit ICI emissions, an input
template, or a nonpoint survey indicating there were no nonpoint ICI emissions.
A complete list of all S/L/T-submitted wagon wheel input activity data is provided in Table 4-2. The input
templates that are needed for point inventory reconciliation are shaded.
S/L Agency
Central Database
ICI fuel combustion
POTWs
Solvents
Stage 1 Gas Distribution
c
Ag Tilling ^
Commercial Cooking
0)
tes s
"D
£
CO
_l
jbmii
bjO
c
'u
15
r+
Road Dust o_
CJ
Residential Heating ^
us
Residential Wood Combustion o
7 NEI
M
£
CO
d
o3
bjO
c
'c
2
Compost
Asphalt Paving
Cremation
Animal Population
Construction Dust
Alabama
X
Alaska
X
X
Arizona
X
X
X
X
X
X
X
4-4
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S/L Agency
Central Database
ICI fuel combustion
POTWs
Solvents
Stage 1 Gas Distribution
Ag Tilling
Commercial Cooking
Landfills
Grilling
Road Dust
Residential Heating
Residential Wood Combustion
Mining & Quarrying
Compost
Asphalt Paving
Cremation
Animal Population
Construction Dust
Arkansas
X
X
X
X
Connecticut
X
X
X
X
X
X
Delaware
District of
Columbia
X
X
X
Florida
X
Georgia
X
X
X
X
Hawaii
X
Idaho
X
Illinois
Indiana
X
Iowa
X
X
X
X
X
Kansas
X
X
X
X
X
X
X
X
X
X
X
X
Jefferson Co, KY
X
Kentucky
Louisiana
X
X
X
X
Maine
X
X
X
X
Maryland
X
Massachusetts
X
X
X
Michigan
X
X
X
X
X
X
Mississippi
Missouri
X
X
Montana
Nebraska
X
New Hampshire
Clark Co, NV
X
Nevada
New Jersey
X
X
New Mexico
X
New York
X
X
North Carolina
X
X
X
X
X
X
North Dakota
X
Ohio
X
X
X
X
Oklahoma
X
X
Oregon
X
Pennsylvania
X
Rhode Island
X
X
X
South Carolina
X
X
X
X
South Dakota
Knox Co, TN
X
X
4-5
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S/L Agency
Central Database
ICI fuel combustion
POTWs
Solvents
Stage 1 Gas Distribution
Ag Tilling
Commercial Cooking
Landfills
Grilling
Road Dust
Residential Heating
Residential Wood Combustion
Mining & Quarrying
Compost
Asphalt Paving
Cremation
Animal Population
Construction Dust
Nashville, TN
X
Tennessee
X
X
Texas
Utah
X
Vermont
X
X
X
X
X
X
X
X
X
Virginia
X
Washington
X
X
West Virginia
X
Wisconsin
X
X
X
X
X
X
Wyoming
Puerto Rico
U.S, Virgin Islands
4.1.4 New for the 2017 NEI: Cross-dataset tagging
The 2017 nonpoint inventory was compiled in a much different manner than the 2014 NEI beyond the
implementation of the Wagon Wheel and associated S/L/T-submitted input templates. For 2017, we also
developed and applied the following EIS automated data exclusion rules: Nonpoint Survey Rule, Pollutant
Grouping Rule, and the Option Group/Option Set Rule. When applied, these rules greatly minimized the need to
"tag" out data that would otherwise be needed to prevent double counting of emissions across pollutant
groups, SCCs and from different sources.
4.1.4.1 Nonpoint Survey Rule
For the first time, for the2017 NEI, the nonpoint survey responses were directly applied to the nonpoint
selection in the EIS. All S/L/Ts that completed the nonpoint surveys (green status button on the home screen for
the nonpoint survey), had their responses directly applied in the NEI selection. For each "EPA Tool Estimate
Category", nonpoint survey responses were applied if the "Category Complete?" column was saved and
submitted as "Yes". By default, all nonpoint survey responses were defaulted to "Yes -Supplement my data with
EPA estimates. This simply means that if S/L/T data was not submitted, and EPA data exists (for that
process/pollutant), then EPA data will be in the NEI with a caveat to the 2 rules discussed in the next two
sections. S/L/Ts were strongly encouraged to leave the SCCs as default (yes) if they were submitting nonpoint
inputs, because S/L/T inputs were absorbed into EPA tools and became "EPA" data.
4.1.4.2 Pollutant Grouping Rules
In previous NEI cycles, we tagged out data to prevent double counting of pollutants across datasets that overlap
one another. For the 2017 NEI, a software solution that occurs during the blending process was developed so
that overlapping pollutants would be excluded from the selection. Business rules were developed to select data
with overlapping pollutants across datasets, to allow different datasets included in a selection to be blended
4-6
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together in a way that avoids double counting due to overlapping pollutants. Because there are several HAPs
that belong to pollutant groups or represent a pollutant group themselves, these rules are needed to prevent
both a group and individual pollutant in that group from being used for the same process or facility. The
implementation of these rules is automated in the EIS. These rules are applied at the process level (location and
SCC) for nonpoint sources and prevents lower-hierarchy dataset pollutants/pollutant groups from possible
double-counts. For example, if an S/L/T reports "Xylenes (Mixed Isomers), then any EPA (lower hierarchy) -
generated individual (or mixed) isomers will not make it into the NEI. Rules for the following pollutant groups
were applied: xylenes, cresols, polychlorinated biphenyls (PCBs), glycol ethers, chromium, nickel, and PAHs. A
complete discussion of the cross dataset tagging proposed rules, applied to the nonpoint inventory selection are
available in Appendix 5 of the 2017 NEI Plan. One change to these "Proposed" rules that we implemented for
the 2017 NEI is that we allow individual xylene isomers to be reported with Xylenes (mixed isomers) within the
same dataset.
4.1.4.3 Option Group/Option Set Rule
We applied the EIS Option Group/Option Set (OGOS) feature for the first time in the 2017 nonpoint NEI. In the
Source Classification Code table, we can define SCCs that have a hierarchical nature. That is, there may be a
"general" group, as well as more specific SCCs within the same group. These relationships are defined by the
"Option Group / Option Set" (OGOS) fields in the SCC table. When EPA and SLT datasets are placed in an NEI
selection, there is the potential for double counting of data sources (emissions) across these data sources. For
example, the EPA may report emissions to a "general" SCC while SLTs report data to detailed SCCs. Without
OGOS evaluation, both sets of data would be included in the NEI selection. The current OGOS rules employed in
the Selection assumes that if a SLT submits data, they are summitting data for the entire group and no additional
data sets are to be used to "back-fill" any SCCs within the same option set. The desired function is for the
selection to back-fill any SCCs within the same option set. Refer to "Appendix 6 - Option Group Option Set
Enhancement EIS Requirements.pdf" on the 2017 National Emissions Inventory Documentation website for a
comprehensive discussion on the OGOS business rules being implemented in EIS for the 2017 nonpoint NEI. A
draft list of OGOS assignments for all nonpoint data category SCCs is provided in the "Appendix 4 - 2017
Nonpoint Proposed OptionGroup-OptionSet" workbook on the 2017 National Emissions Inventory
Documentation website.
4.1.5 Nonpoint PM augmentation
Section 2.2.4 provides an overview of PM augmentation in the 2017 NEI and explains that we used a PM
Augmentation tool. The tool creates two output tables for each data category: Additions and Overwrites. We
post-processed these output tables prior to loading the data in the EIS. In this section, we describe the post-
processing issues that are specific to the nonpoint inventory. We post-processed these data to prevent
inadvertently overriding S/L/T agency primary PM10 and PM2.5 data (i.e., EIS pollutants PM10-PRI and PM2.5-
PRI). The PM Augmentation Tool computes the condensable (PM-CON) and filterable PM components (PM10-FIL
and PM25-FIL) and re-computes primary PM10 and PM2.5 when the sum of the components differed by more
than the slim tolerance assumed by the tool. We decided to remove these "overwrites" for primary PM10 and
PM2.5 whenever the summed PM from the components was within 0.01 tons of S/L/T-provided primary PM10
or PM2.5 totals. This tolerance was higher than the one used by the tool, but we wanted the NEI to reflect that
the data source for the primary PM10 and PM2.5 was from the S/L/T agency and not the EPA augmentation
dataset. We used summed components from the tool to overwrite the S/L/T agency data in the NEI selection
when this difference exceeded 0.01 tons and S/L/T agencies reported both primary PM10 and PM2.5; however,
4-7
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this was a very rare occurrence. Nationally, these overwrites resulted in only a 0.78-ton replacement of primary
PM2.5.
4.1.6 Nonpoint HAP augmentation
For nonpoint sources, we derived HAP augmentation ratios from the emission factors used to develop the EPA
nonpoint source estimates. The EPA nonpoint HAP emission estimates are computed in EPA nonpoint database
"tools" (e.g., previously discussed wagon wheel, oil and gas tool), or stand-alone databases such as that used for
agricultural burning and livestock waste. Because we used the same emission factors for these augmentation
ratios, the ratios of HAP to CAPs for augmented S/L/T agency data are the same as the HAP to CAP ratios for the
EPA-only data. For access by non-EIS users, the zip file called "HAPaugmentation.zip", on the 2017 NEI
Supplemental data FTP site, provides the emission ratios that the EPA used for augmenting point and nonpoint
data categories. The nonpoint HAP augmentation factors were updated as compared to what was used for the
2014 NEI, particularly for the oil and gas sector. The EPA staff responsible for the nonpoint sectors use their
discretion for how to augment HAP emissions and work with the S/L/T agencies to reflect as complete and
accurate set of pollutants as possible for the many source types. In general, if a S/L/T agency submitted a partial
list of the HAPs that would be augmented for a given category, then we allowed the missing HAPs to be gap-
filled with the HAP augmentation data. These missing HAPs are determined by comparing the Expected
Pollutant List for Nonpoint SCCs with those that S/L/T agencies submitted. However, this approach has a risk of
potentially violating VOC mass balance, whereby the sum of the VOC HAPs exceeds the VOC total. Thus, special
cases occur when such problems are identified. In the limited cases where this occurred, we applied the
business rules defined in Section 3.3.2 in the 2017 NEI Plan to tag out S/L/T data causing this violation; in this
case, S/L/T-submitted HAP-VOCs were replaced with HAP augmentation (generally based on S/L/T-submitted
VOC) -based HAP-VOC estimates.
4.1.7 EPA nonpoint data
For the 2017 NEI, the EPA developed emission estimates for many nonpoint sectors in collaboration with a
consortium of inventory developers from various state agencies regional planning organizations called the
NOnpoint Method Advisory (NOMAD) Committee. The broad NOMAD committee meets approximately monthly
to discuss the overall progress on the various sectors for which tools and/or estimates are being developed or
refined. More detailed NOMAD subcommittees were established for key nonpoint source categories/sectors
including, but not limited to, oil and gas exploration and production, residential wood combustion, agricultural
NH3 sources including agricultural fertilizer and livestock, solvents, and industrial and commercial/institutional
fuel combustion. These subgroups collaborate on methodologies, emission factors, and SCCs, allowing the EPA
to prepare the "default" emission estimates/methodologies and/or input template formats for S/L/T agencies
using the group's final approaches. The NOMAD committees were formed in preparation for the 2014 NEI and
continued with the 2017 NEI development cycle. The primary focus of the 2017 NEI cycle was on Wagon Wheel
and the associated input template development. This shift towards seeking more S/L/T input activity data,
rather than emission submittals makes for a more transparent quality assurance process as we now have readily
available tracking of the inputs as well as resulting outputs (emissions). We can ensure that the methodology
used to estimate the final emissions for all Wagon Wheel sectors is consistent.
During the 2017 NEI inventory development cycle, S/L/T agencies, using the nonpoint survey (Section 4.1.2),
could accept the NOMAD/EPA estimates to supplement/fulfill their nonpoint emissions reporting requirements.
The EPA encouraged S/L/T agencies that did not use the EPA's estimates or tools to improve upon these
"default" methodologies and submit input data directly. Table 4-3 and Table 4-4 describe the sectors for which
EPA developed emission estimates. They separately list emissions sectors entirely comprised of data in the
4-8
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nonpoint (i.e., not point source) data category (Table 4-3), such as residential heating, from sectors that may
overlap with the point sources (Table 4-4). For sectors that overlap, some emissions will be submitted as point
sources and other emissions in the same state or county are submitted as nonpoint, for example, fuel
combustion at commercial or institutional facilities. The EPA attempted to include all EPA-estimated nonpoint
emissions that overlap if it was determined that the category was missing from the S/L/T agency data. All EPA
methodologies are discussed in the remaining nonpoint sectors that follow; however, some tables (primarily
emission factors) were too large to include in this TSD, and we direct the reader to the appropriate name
provided in zip files posted on the 2017 NEI Supplemental Nonpoint data FTP site, for these cases. The SCCs
associated with the EPA nonpoint data categories can be found on the EPA SCC Search website. The sections
following these tables include information on key pollutants submitted by S/L/T agencies for each nonpoint
source category or EIS sector.
Table 4-3: EPA-estimated emissions sources expected to be exclusively nonpoint
EPA-estimated emissions
TSD
source
EIS Sector(s)
Section
Name of supporting documentation
Agriculture -
Agricultural Fertilizer
Fertilizer
Application
Application
4.4
2017_Fertilizer_Application_Supplemental_Data.zip
Fires -
Agricultural Field
Agricultural Field Burning
Burning
4.12
AgBurning_Emission_Factors_HAPs_2017NEI.xlsx
Solvent -
Consumer &
Agricultural Pesticide
Commercial
Agricultural Pesticides NEMO 2017 FINAL_4-2
Application
Solvent Use
4.23
update.docx
Agriculture -
Crops &
Agricultural Tilling NEMO 2017 FINAL_4-2
Agricultural Tilling
Livestock Dust
4.3
update.docx
Agriculture -
Animal Husbandry
Livestock Waste
4.5
Agricultural Livestock NEMO 2017 FINAL.docx
Solvent -
Consumer &
Commercial
Asphalt Paving
Solvent Use
4.24
Asphalt NEMO 2017 FINAL_4-2 update.docx
Aviation Gasoline
Aviation Gasoline Distribution Stage 1 NEMO 2017
Distribution Stage 1
Gas Stations
4.7
DRAFT_v2_4-2 update.docx
Aviation Gasoline
Aviation Gasoline Distribution Stage 2 NEMO 2017
Distribution Stage 2
Gas Stations
4.7
DRAFT_4-2 update.docx
Commercial
Commercial Cooking
Cooking
4.8
Commercial Cooking NEMO FINAL_4-2 update.docx
Miscellaneous
Non-Industrial
Composting
NEC
4.26
Composting NEMO 2017 FINAL_4-2 update.docx
Dust from
Dust -
Commercial/Institutional
Construction
Construction Dust - Nonresidential NEMO 2017
Construction
Dust
4.9
FINAL_4-2 update.docx
4-9
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EPA-estimated emissions
source
EIS Sector(s)
TSD
Section
Name of supporting documentation
Dust from Livestock
Hooves/Feet
Agriculture -
Crops &
Livestock Dust
4.3
Dust from Hooves NEMO_FINAL_4-2 update.docx
Dust from Residential
Construction
Dust -
Construction
Dust
4.9
Residential Construction Dust NEMO 2017 FINAL_4-
2 update.docx
Dust from Road
Construction
Dust -
Construction
Dust
4.9
Road Construction Dust NEMO 2017 FINAL_4-2
update.docx
Human and Animal
Cremation
Waste Disposal
4.18
Cremation NEMO 2017 FINAL_4-2 update.docx
Locomotives, non-Rail
Yard
Mobile -
Locomotives
4.22
2017Rail_main_21aug2019.pdf (from ../point
directory)
Mining and Quarrying
Industrial
Processes -
Mining &
Quarrying
4.16
Mining & quarrying NEMO 2017 FINAL_4-2
update.docx
Nonpoint Mercury from:
Dental Amalgam
Production, Fluorescent
Lamp Breakage,
Fluorescent Lamp
Recycling, Switches and
Relays, Thermometers
and Thermostats
Miscellaneous
Non-Industrial
NEC
4.2
Other Mercury NEMO 2017 FINAL_4-6 update.docx
Open Burning, Land
Clearing Debris
Waste Disposal
4.27
Open Burning Land Clearing Debris NEMO 2017
DRAFT 4-2.docx
Open Burning, Yard
Waste Debris
Waste Disposal
4.27
Open Burning Yard Waste NEMO 2017 FINAL_4-2
update.docx
Open Burning, Residential
Household Waste
Waste Disposal
4.27
Open Burning RHW NEMO 2017 FINAL_4-2
update.docx
Paved and Unpaved Road
Dust
Dust - Paved
Road Dust, Dust
- Unpaved Road
Dust
4.10,
4.11
Road Dust NEMO FINAL revised 4 9 2020.docx
Portable Fuel Containers
Miscellaneous
Non-Industrial
NEC
4.20
Portable Fuel Container Inventory 2017_vl.docx
Residential Charcoal
Grilling
Miscellaneous
Non-Industrial
NEC
4.19
Residential Barbecue Grilling NEMO FINAL_4-2
update.docx
4-10
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EPA-estimated emissions
TSD
source
EIS Sector(s)
Section
Name of supporting documentation
Fuel Comb -
Residential -
Natural Gas, Fuel
Comb -
Residential - Oil,
Fuel Comb -
Residential Heating, non-
Residential -
Residential Heating NEMO 2017 FINAL_4-2
wood
Other
4.14
update.docx
Fuel Comb -
Residential Wood
Residential -
Combustion
Wood
4.15
Residential Wood Combustion DRAFT.DOCX
Working Face Landfills
Waste Disposal
4.2
Landfills NEMO 2017 FINAL_4-2 update.docx
Table 4-4: Emission sources with potential nonpoint and point contribution
EPA-estimated
emissions source
EIS Sector(s)
TSD
Section
Name of supporting documentation
Industrial and
Commercial/lnstitutiona
1 Fuel Combustion
Fuel Comb - Industrial
Boilers, ICEs - All
Fuels, Fuel Comb -
Commercial/lnstitutio
nal - All Fuels
4.13
ICI NEMO FINAL_4-2 updated.docx
Nonpoint Gasoline
Distribution
Bulk Gasoline
Terminals,
Gas Stations,
Industrial Processes-
Storage and Transfer
4.7
Stage 1 Gasoline Distribution NEMO FINAL_7-18-
2019_4-2 updated.docx
OIL_GAS_TOOL_2017_NEI_PRODUCTION_Vl_2.zip,
OIL_GAS_TOOL_2017_NEI_EXPLORATION_Vl_3.zip
Oil and Gas Production
and Extraction
Industrial Processes -
Oil & Gas Production
4.17
t
2017 NEI Oil and Gas Tool Subpart W
Analysis_3_14_2019.zip,
2017 Oil and Gas Memos.zip,
2017 Nonpoint Oil and Gas Emission Estimation
Tool Revisions_Vl 4_ll_2019.docx,
EPA_2015b_NSPS OOOOa TSD August 2015.pdf,
Oil_and_Gas_Tool_Documentation_vl.2_2017.zip,
Sept. discussion notes withe EPA and ERG.docx
POTWs
Waste Disposal
4.28
POTWs NEMO FINAL_4-2 updated.docx
4-11
-------
EPA-estimated
emissions source
EIS Sector(s)
TSD
Section
Name of supporting documentation
Solvent Utilization
Solvent - Consumer &
Commercial Solvent
Use,
Solvent - Degreasing,
Solvent - Dry Cleaning,
Solvent - Graphic Arts,
Solvent - Industrial
Surface Coating &
Solvent Use,
Solvent - Non-
Industrial Surface
Coating
4.25
Solvent NEMO 2017 FINAL_7-8-2019_4-2
updated.docx
4,2 Nonpoint non-combustion-related mercury sources
This category includes the following mercury emission categories: Landfills (working face), Switches and Relays,
Fluorescent Lamp Breakage, Dental Amalgam, General Laboratory Activities, Thermostats, Thermometers,
Fluorescent Lamp Recycling, and Batteries. Human and animal cremation estimates include CAPs as well as
mercury and are discussed later in Section 4.18.
4.2.1 Description of sources
These sources include a mix of EPA-generated and SLT-submitted emissions for the SCCs listed in Table 4-5. EPA
updated some of the activity data to year 2017 in the 2017 NEI. Additional descriptions of the individual types of
activities are provided in the source-specific sub-sections below.
Table 4-5: SCCs and emissions (lbs) comprising the nonpoint non-combustion Hg sources in the 2017 NEI
Description
see
Sector
SCC Description
2014v2
2017
Landfill working
face
2620030001
Waste Disposal
Landfills; Municipal;
Dumping/Crushing/Spreading
of New Materials (working
face)
763
871
Scrap waste:
Thermostats
and
Thermometers
2650000000
Waste Disposal
Scrap and Waste Materials;
Scrap and Waste Materials;
Total: All Processes
241
234
Shredding:
Switches and
Relays
2650000002
Waste Disposal
Scrap and Waste Materials;
Scrap and Waste Materials;
Shredding
3,372
2,519
Dental Amalgam
Production
2850001000
Miscellaneous
Non-Industrial
NEC
Miscellaneous Area Sources;
Health Services; Dental Alloy
Production; Overall Process
923
916
Fluorescent
Lamp Breakage
2861000000
Miscellaneous
Non-Industrial
NEC
Miscellaneous Area Sources;
Fluorescent Lamp Breakage;
1,676
1,815
4-12
-------
Description
see
Sector
SCC Description
2014v2
2017
Non-recycling Related
Emissions; Total
Fluorescent
Lamp Recycling
2861000010
Miscellaneous
Non-Industrial
NEC
Miscellaneous Area Sources;
Fluorescent Lamp Breakage;
Recycling Related Emissions;
Total
0.6
0.08
General
Laboratory
Activities
2851001000
Miscellaneous
Non-Industrial
NEC
Miscellaneous Area Sources;
Laboratories; Bench Scale
Reagents; Total
635
633
TOTAL
7,611
6,988
None of these categories are distinct regulatory sectors and are therefore put into the "EPA Other" category in
the mercury summary provided in Table 2-8.
4.2.2 Sources of data
The history of these EPA estimates dates to the 2011 NEI. The 2011 NEI nonpoint Hg estimates were developed
in collaboration with an Eastern Regional Technical Advisory (ERTAC) workgroup set up for focus on these
nonpoint emissions sources. For the 2014v2 NEI, the activity data for all source categories except General
Laboratory Activities (2851001000) were updated to year 2014, and these were further updated for select year
2017 activity for the 2017 NEI. These estimates were then merged with S/L/T agency data as part the NEI
selection hierarchy defined in Section 4.1.1. The EPA encouraged S/L/T agencies that did not use EPA's estimates
or tools to improve upon these "default" 2017 methodologies and submit further improved data. The S/L/T data
replaced the EPA estimates in the counties where S/L/T agencies provided data. Table 4-6 lists the agencies and
SCCs that were submitted for these nonpoint mercury sources; the S/L/T emissions from these agencies/SCCs
replace EPA estimates in 2017 NEI.
Table 4-6: Agencies reporting emissions to non-combustion mercury source categories
Agency
Landfill Working Face
Scrap waste: Thermostats
and Thermometers
Shredding: Switches and
Relays
Dental Amalgam
Production
Fluorescent Lamp
Breakage
Fluorescent Lamp
Recycling
General Laboratory
Activities
Coeur d'Alene Tribe
X
X
X
X
X
X
Illinois Environmental Protection Agency
X
X
X
X
Kootenai Tribe of Idaho
X
X
X
X
Maryland Department of the Environment
X
X
X
Memphis and Shelby County Health Department -
Pollution Control
X
X
X
X
X
X
4-13
-------
Agency
Landfill Working Face
Scrap waste: Thermostats
and Thermometers
Shredding: Switches and
Relays
Dental Amalgam
Production
Fluorescent Lamp
Breakage
Fluorescent Lamp
Recycling
General Laboratory
Activities
Metro Public Health of Nashville/Davidson
County
X
X
X
X
X
Minnesota Pollution Control Agency
X
X
X
Nez Perce Tribe
X
X
X
X
X
Shoshone-Bannock Tribes of the Fort Hall
Reservation of Idaho
X
X
X
X
X
X
4.2.3 EPA-developed emissions
Landfills (working face)
The EPA estimated mercury emissions for landfill working face emissions. While the amount of mercury in
products placed in landfills has tended to decrease in recent years, there is still a significant amount of mercury
in place at landfills across the country. There are three main pathways for mercury emissions at landfills: (1)
emissions from landfill gas (LFG) systems, including flare and vented systems; (2) emissions from the working
face of landfills where new waste is placed; and (3) emissions from the closed, covered portions of landfills [ref
1], Emissions from LFG systems are considered point sources and are already included in the NEI as submissions
from S/L/T agencies or from the point source dataset that gap fills these landfill emissions (2017EPA_LF).
Lindberg et al. (2005) [ref 1] found that emissions from the closed, covered portions of landfills are negligible
and are similar to background soil emission rates. Therefore, this methodology focuses on emissions from the
working face of landfills.
The calculations for estimating the emissions from landfills involve first estimating the amount of waste each
landfill receives in a year. The total amount of waste in place for each landfill in a county is available from the US
EPA's Landfill Methane Outreach Program (LMOP) database. The total amount of waste in place for each landfill
is divided by the number of years a landfill is operational to estimate the amount of waste a landfill receives
each year. The amount of waste that a landfill receives each year is multiplied by an average emissions factor to
calculate the total mercury emissions from landfills for each county.
Switches and Relays
Switches and relays make up the largest potential source of mercury from products that intentionally contain
mercury. Mercury is an excellent electrical conductor and is liquid at room temperature, making it useful in a
variety of products, including switches used to indicate motion or tilt, as the mercury will flow when the switch
is in a certain position, completing the circuit.
While mercury switches in cars were phased out as of the 2002 model year, there are still millions of cars on the
road that contain them. The switches and relays in these cars are potential emissions sources when the cars are
recycled at the end of their useful lives, which involves crushing and shredding of the car. The shredded material
is then sent to an arc furnace to recycle the steel. To avoid double counting point source emissions from arc
4-14
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furnaces, this source category only includes an estimate of nonpoint emissions from crushing/shredding
operations.
The calculations for estimating mercury emissions from switches and relays involve first estimating the number
of switches unrecovered by the state by taking the difference between the total estimated number of switches
available and the total switches recovered in each state. The number of unrecovered switches is then
apportioned to each county based on the number of car recycling facilities from the US Census County Business
Patterns data for NAICS 423930. The total amount of switches unrecovered by county is multiplied by the
emissions factor for mercury to estimate mercury emissions from switches and relays.
Fluorescent Lamp Breakage/Recycling
Fluorescent lights are a potentially significant source of mercury emissions. Although each lamp contains only a
small amount of mercury, which has been decreasing in recent years, the increased demand for fluorescent
lamps could lead to increases in mercury emissions. Increased demand for fluorescent lamps, particularly
compact fluorescents, is driven partly by the phase out of many types of incandescent bulbs from the Energy
Independence and Security Act of 2007 (PL 110-140 § 321).
In addition to emissions of mercury from the breakage of fluorescent light bulbs (SCC 2861000000), there is a
small amount of emissions from recycling fluorescent bulbs (SCC 2861000010).
The calculations for estimating the emissions from fluorescent lamp breakage and recycling involve first
estimating the average life, in hours, of various fluorescent lamp types. Data from a Freedonia Group Industry
Study on the U.S. lamp market is used to estimate the total number of lamps that are discarded or recycled. The
number of bulbs recycled is calculated using a recycling rate percentage. This number is then subtracted from all
bulbs discarded or recycled to determine the number of bulbs discarded. The activity data are allocated to the
county-level based on the share of the population present in each county. An emissions factor is calculated using
the amount of mercury available in each fluorescent bulb type. The total amount of fluorescent bulbs recycled
or discarded is multiplied by the emissions factor for mercury to estimate mercury emissions from fluorescent
lamp breakage and recycling.
Dental Amalgam
Dental amalgam is used to fill cavities in teeth, and it is composed of approximately 45% mercury [ref 2];
however, the use of dental amalgam is declining due to the increased popularity of composite fillings for teeth
[ref 3], Nevertheless, there is still a small amount of mercury emissions from dental amalgam in restored teeth.
There are two potential sources of mercury emissions from dental amalgam: emissions from the preparation of
amalgam in dental offices, and emissions directly from restored teeth.
The calculations for estimating the emissions from dental amalgam include estimating emissions from both
dental fillings and dental office preparation. The number of fillings by age group (for dental fillings) and the total
mercury sold in dental amalgam (for dental office preparation) are allocated to the county-level based on the
share of the population present in each county. The dental filling data by age group are multiplied by the
percent of mercury present in dental fillings to determine the amount of mercury from dental fillings. The total
amount of mercury from dental fillings and from dental office preparation are multiplied by emissions factors
for mercury and summed together to estimate the total mercury emissions from dental amalgam.
General Laboratory Activities
4-15
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Documentation for previous versions of the NEI have cited personal communications with USGS staff for
estimates of the amount of mercury used in general laboratory activities. In discussions with Robert Virta of the
USGS (2013), EPA learned that the USGS stopped conducting its survey of the end uses of mercury in the
economy in 2002 [ref 4], However, the Interstate Mercury Education and Reduction Clearinghouse (IMERC)
tracks the use of mercury-added chemical products that are sold as a consistent mixture of chemicals [ref 5],
Since this trend indicates that the use of mercury-added chemical products has remained relatively consistent
since 2002, the estimate of mercury emissions from general laboratory activities in the 2008 NEI is pulled
forward for the 2017 NEI.
Thermostats/Thermometers
Mercury has been used in thermostats to switch on or off a heater or air conditioner based on the temperature
of a room. Most of the historic production of mercury thermostats came from three corporations: Honeywell,
White-Rogers, and General Electric. In 1998 these corporations formed the Thermostat Recycling Corporation
(TRC), a voluntary program that attempts to collect and recycle mercury thermostats as they come out of service
[ref 6],
Mercury thermometers have all but been phased out in the United States, with the USEPA and National Institute
of Standards and Technology (NIST) working to phase out mercury thermometers in industrial and laboratory
settings. NIST issued notice in 2011 that it would no longer calibrate mercury-in-glass thermometers for
traceability purposes. EPA issued a rule in 2012 that provides flexibility to use alternatives to mercury
thermometers when complying with certain regulations pertaining to petroleum refining, power generation, and
PCB waste disposal. Furthermore, thirteen states have laws that limit the manufacture, sale, and/or distribution
of mercury-containing fever thermometers [ref 7], Nevertheless, given the historical prevalence of mercury
thermometers, it is likely that a significant amount of mercury remains in thermometers in homes in the United
States
The calculations for estimating the emissions from thermostats and thermometers involve first estimating the
total number of thermostats disposed and the amount of mercury in thermometers available for release. The
number of thermostats disposed and the amount of mercury in thermometers available for release are allocated
to the county-level based on the share of the population present in each county. The total number of
thermostats disposed and the amount of mercury in thermometers available for release are multiplied by the
emissions factor for mercury and summed together to estimate mercury emissions from thermostats and
thermometers.
4.2.3.1 Activity data
Landfills (working face)
The U.S. EPA's Landfill Methane Outreach Program (LMOP) maintains a database of the landfills in the United
States with information on the total amount of waste in place, as well as the opening and closing years of the
landfill and the county where the landfill is located [ref 8], The average number of tons of waste each landfill
receives is estimated by dividing the total waste in place by the number of years the landfill has been operating.
Only landfills that were open in 2014 are included in the analysis.
To determine the number of years each landfill has been active, the year the landfill opened is subtracted from
2017. Only landfills that are open in 2017 are included in the analysis.
OPt = 2017 - 0;
4-16
(1)
-------
Where:
OPi = Total number of years of operation for each landfill I
Oi = Year landfill I opened
The average number of tons of waste each landfill receives is estimated by dividing the total waste in place by
the number of years the landfill has been operating.
Where:
Wi = Average tons of waste that landfill I receives per year
WPi = Total waste in place in landfill /, in tons
OPi = Total number of years of operation for landfill I
Some counties have multiple landfills, so emissions within the county are summed in these instances.
wc = Y wt (3)
Where:
Wc = Average tons of waste from n landfills in county c
Wi = Average tons of waste that landfill I receives per year
Switches and Relays
The End of Life Vehicle Solutions Corporation (ELVS) provides information on the estimated number of switches
available for recovery in each state and the amount of switches actually recovered in 2017 [ref 9, ref 10]. There
were 1.8 million mercury-containing automobile switches available nationwide in 2017 and 217,634 switches
collected for recycling, for a collection rate of 11.7%. Therefore, there were approximately 1.6 million
unrecycled automotive switches in 2017 in the United States. The state level number of switches unrecovered is
calculated by taking the difference between the total estimated number of switches available and the total
switches recovered in each state.
UnSs = TotSs — RecSs (1)
Where:
UnSs = Total switches unrecovered by state s
TotSs = Total switches available in state s
RecSs = Total switches recovered by state s
Fluorescent Lamp Breakage/Recycling
Data from a Freedonia Group Industry Study on the U.S. lamp market were used to estimate that approximately
1.485 billion mercury containing lamps, including compact fluorescents (CFLs), linear, and high impact discharge
4-17
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(HID) lamps, were discarded or recycled in 2017 [ref 11], Bulb sales for 2002, 2007, 2012 and projections for
2017 were obtained from Freedonia; sales for all other years were calculated by extrapolating data. Average
rated life (hours) of lamp types is used to calculate lifetimes (years), assuming that CFLs are on for 4 hours per
day and all other fluorescents and HIDs are on for 8 hours per day [ref 12, ref 13], Table 4-7 includes the lifetime
data assumed for each bulb type. The lifetime data are used to estimate the year in which bulbs that are
discarded or recycled in 2017 would have been purchased.
Table 4-7: Lifetime in hours and years for each bulb type
Bulb Type
Life (hrs)
Life (yr)
Purchase Year*
Number of bulbs (million)
CFL
9,000
6
2011
722
Linear
25,000
9
2008
583
HID
17,000
6
2011
180
Total
-
-
-
1,485
*lf bulbs are discarded or recycled in 2017
TotB = V PBb (FL1)
t—
-------
Dental Amalgam
According to a NEWMOA's IMERC factsheet (2015) [ref 15], the amount of mercury in dental amalgam was
estimated to be 15.97 tons (31,940 lbs.) in 2013.
The amount of mercury emissions from restored teeth is estimated using data from the National Institutes of
Health's National Institute of Dental and Craniofacial Research, which provides estimates of the average number
of filled teeth per person, from the CDC National Health and Nutrition Examination Survey (NHANES), in nine
different age brackets: 2-5 years, 6-11 years, 12-15 years, 16-19 years, 20-34 years, 35-49 years, 50-64 years, 65-
74 years, and 75 and up [ref 16]. The filling data for the age groups 6-11 years, 12-15 years, and 16-19 years are
averaged together as are the filling data for the age groups 65-74 years and 75 and up to match the U.S. Census
age category, 5-19 and 65 and up. Table 4-8 lists the average number of filled teeth per person by age group.
Table 4-8: Average number of filled teeth per person and percentage of fillings containing mercury by age group
Age Group
Average Number of
Filled Teeth Per Person
Percentage of Fillings
Containing Mercury
0-4
0.47
15.8%
5-19
1.756
31.6%
20-34
4.61
40.8%
35-49
7.78
50%
50-64
9.20
62.5%
65+
8.69
75.0%
According to the American Dental Association (ADA 1998) more than 75% of restorations before the 1970s used
amalgam, which declined to 50% by 1991 [ref 17]. Using these numbers, it is assumed that 40.8% of the filled
teeth for 20-34 age group contain amalgam, 50% of filled teeth in the 35-49 age group, 62.5% of filled teeth in
the 50-64 age group, and 75% of filled teeth for people over 65. The BAAQMD memorandum is used to estimate
that 31.6% of filled teeth in the 1-19 age group contain amalgam. The Food and Drug Administration has
discouraged the use of dental amalgam in children under 6 [ref 18]. While EPA does not have data on the
percent of fillings containing dental amalgam for the 0-4 age group, it is assumed that the percentage of fillings
containing mercury in this age group is approximately half that of the overall under 20 age group.
Thermostats/Thermometers
A 2002 EPA report estimated that 2-3 million thermostats came out of service in 1994 [ref 19]. A 2013 report
from a consortium of environmental groups, which assumed that the estimate from the 2002 EPA report
remained viable, estimated that the TRC collects at most 8% of the retired thermostats each year [ref 20], A
literature search revealed no new data that could be used to estimate the number of thermostats coming out of
service. Therefore, using this estimate, there are approximately 2.3 million thermostats that are not recycled
each year.
DispTs = RemTs x (1 — 0.08) (Tl)
Where:
DispTs = Total thermostats disposed
RemTs = Total thermostats removed from service
4-19
-------
Data from a NEWMOA's IMERC factsheet suggests that there were 546 lbs. of mercury used in thermometers in
2013 [ref 21]. Using past NEWMOA IMERC thermometer data we forecasted the values for mercury in 2014-
2017. See Table 4-9 for the amount of mercury used in thermometers each year from 2013-2017.
Table 4-9: Total mercury in thermometers sold and mercury available from thermometers, annually
Year
Total Mercury in
Thermometers Sold (lbs.)
Mercury Available from
Thermometers each year (lbs.)
2013
546
519
2014
532
1,024
2015
523
1,496
2016
514
1,936
2017
506
2,345
The US EPA assumes that the average lifespan of a glass thermometer is 5 years, and that 5% of glass
thermometers are broken each year [ref 19]. Therefore, using the pounds of mercury available in thermometers
each year (shown in Table 4-9 above) there would be an estimated 2,345 pounds of mercury remaining in
thermometers in 2017 (accounting for the breakage rate each year). The following equation calculates the total
amount of mercury remaining in thermometers for each year during the lifespan of the thermometer. To
calculate the value at the 5 year lifespan mark, the following equation (equation T2) needs to be used to
calculate the value for years 2 through 5, with each year building upon the previous year (i.e., the calculation
needs to be conducted for all years to find the final year 5 data). See Table 4-9 for the final values of mercury
available from thermometers in 2017, and Section 4.2.3.5 for detailed calculations on how to arrive at the final
number.
HgTmn= (HgTmn_1x95%) + HgTmSoldn (T2)
Where:
HgTm„ = Amount of mercury remaining in thermometers in year n, in pounds
HgTm„-i = Amount of mercury remaining in thermometers in the year prior to year n, in pounds
HgTmSold„ = Amount of mercury in thermometers in year 1, in pounds
n = Year
King et al. (2008) [ref 22] estimate that during the period 2000-2006 there were 350 lbs. of mercury from
thermometers collected in recycling programs.
Subtracting the amount of mercury removed due to thermometers being collected in recycling programs from
the total amount of mercury remaining in thermometers in 2017 estimates the total amount of mercury in
thermometer available for release, in tons. Therefore, there were 1,995 lbs. (0.99 tons) of mercury available for
release in 2017.
1 ton (T3)
HgTRl = (HgTms - HgTRrri) x 1 '
2,000 lbs.
Where:
HgTRl = Amount of mercury in thermometers available for release, in tons
HgTm5 = Amount of mercury remaining in thermometers in year 5, the lifespan of a thermometer, in
pounds
4-20
-------
HgTRm = Amount of mercury removed in thermometer collections, in pounds
4,2.3.2 Allocation procedure
Landfills (working face)
The EPA LMOP database provides data at the county level; therefore, no allocation procedure is needed for this
source.
Switches and Relays
The number of unrecovered switches is apportioned to each county based on the number of car recycling
facilities. The number of car recycling facilities is estimated using establishment data for recyclable material
merchant wholesalers (NAICS 423930) from the U.S. Census Bureau's 2016 County Business Patterns (CBP) [ref
23],
The number of car recycling facilities by county from the US Census County Business Patterns data is first
summed to the state level.
¦X
(SR2)
Where:
Fs = Total car recycling facilities in state s
Fc = Total car recycling facilities in county c
The share of state car recycling facilities by county is calculated by taking the total number of car recycling
facilities in a given county by the total number of car recycling facilities in the state.
Z7 Z7 Fc (SR3)
FracFr = —
F
1 S
Where:
FracFc = Total fraction of state car recycling facilities in county c
Fc = Total car recycling facilities in county c
Fs = Total car recycling facilities in state s
The share of unrecovered switches by county is calculated using the state number of unrecovered switches and
the total share of state car recycling facilities by county, calculated above.
UnSc = UnSs X FracFc (SR4)
Where:
UnSc = Total switches unrecovered in county c
UnSs = Total switches unrecovered in state s
FracFc = Total share of state car recycling facilities in county c
4-21
-------
Fluorescent Lamp Breakage/Recycling
The national-level mercury emissions from fluorescent lamp breakage are allocated to each county based on
population.
Where:
FracPr =
(FL4)
us
Frac Pc,
Pc
Pus
Fraction of total US population in county c
Population in county c
Population in the US
The fraction of total US population in a county is multiplied by the national data for fluorescent bulbs recycled
discarded to calculate the number of fluorescent bulbs recycled or discarded at the county-level.
For fluorescent bulbs discarded:
DiscBc = FracPc x DiscB
(FL5)
Where:
DiscBc
Frac Pc
DiscB
Total number of bulbs discarded in county c, in million units
Fraction of total US population in county c
Total number of bulbs discarded in the US, in million units
For fluorescent bulbs recycled:
RecBc = FracPc x RecB
(FL6)
Where:
RecBc = Total number of bulbs recycled in county c, in million units
FracPc = Fraction of total US population in county c
RecB = Total number of bulbs recycled in the US, in million units
Dental Amalgam
The amount of mercury from dental office preparations, based on the amount of mercury in dental amalgam
from NEWMOA's IMERC factsheet [ref 15], are allocated to the county level based on population.
FracPc =
Pc (DAI)
Pus
Where:
FracPc = Fraction of total US population in county c
Pc = Total population in county c
4-22
-------
Pus
Total population for the United States
The county-level population fraction is multiplied by the amount of mercury sold for dental amalgam to
calculate the total mercury from dental office preparations by county.
HgOc = FracPc X HgDA (DA2)
Where:
HgOc = Total mercury from dental office preparations in county c, in pounds
FracPc = Fraction of total US population in county c
HgDA = Total mercury sold for dental amalgam in the US, in pounds
The emissions from filled teeth are allocated to each county by multiplying the county population by the
proportion of the national population in each age group, the average number of filled teeth per person, and the
fraction of fillings containing mercury (Table 4-10; fraction = percentage/100). The age groups listed in Table
4-10, hereafter referred to as filling groups, are different than official US census bureau age groups; therefore
national fractions of each US census bureau age group were calculated, summed, and multiplied by county level
population to estimate the county level population for each filling group. Table 4-10 shows how the US Census
age groups correspond to each filling group.
Table 4-10: US Census age groups and filling groups
US Census
Age Group
Corresponding
Filling Age Group
Under 5
0-4
5-9
5-19
10-14
15-19
20-24
20-34
25-29
30-34
35-39
35-49
40-44
45-49
50-54
50-64
55-59
60-64
65-69
65+
70-74
75-79
80-84
85 and up
First, the share of total population each US Census age group represents to the entire US population is
calculated.
FracPa =
rUS
4-23
(DA3)
-------
Where:
FracPa = Fraction of the total US population in Census Bureau age group a
Pa = Total population in Census Bureau age group a
Pus = Total population for the United States
The fraction of the population for each US Census age group is then summed to match the filling groups.
FracPfg = ^ FracPa (DA4)
'a
Where:
FracPfg = Fraction of the total US population in filling group fg
FracPa, = Fraction of the total US population in census bureau age group a, where age group a falls
within filling group fg
The fraction of population for each filling group is multiplied by the county-level population data to get the total
population for each filling group.
Pfg,c = FracPfg x pc (DA5)
Where:
Pfg,c = Total population in filling group fg in county c
FracPfg = Fraction of the total US population in filling group fg
Pc = Total population in county c
The filling group county-level population is multiplied by the average number of fillings per person in each filling
group to determine the total number of fillings in each filling group in each county.
Ffg,c = Pfg.c X Ffg (DA6)
Where:
Ffg,c = Total fillings in filling group fg in county c
Pfg,c = Total population in filling group fg in county c
Ffg = Average number of fillings per person in filling group fg
The total fillings in each filling group is then multiplied by the fraction of fillings that contain mercury in each
filling group to determine the total number of fillings by filling group in each county.
Where:
Hdpfg,c Ffg,c ^ FtclcHQF^g
(DA7)
HgFfg.c
Ffg,c
FracHgFfg =
Total fillings containing mercury in filling group fg in county c
Total fillings in filling group fg in county c
Fraction of fillings containing mercury in filling group fg
4-24
-------
Thermostats/Thermometers
The national-level mercury emissions from thermostats and thermometers are allocated to the county level
based on population.
FracPc = -p- (T1)
Pus
Where:
FracPc
Pc
Pus
Fraction of total US population in county c
Total population in county c
Total population for the United States
The fraction of the US population in the county is multiplied by the national data for thermostats and
thermometers to calculate the number of thermostats disposed and the amount of mercury in thermometers
available for release at the county-level.
For thermostats:
DispTsc = FracPc x DispTs
(T2)
Where:
DispTsc = Total thermostats disposed of in county c
FracPc = Fraction of total US population in county c
DispTs = Total thermostats disposed of in the US
For thermometers:
HgTmc = FracPc x HgTmRl (T3)
Where:
HgTmc = Amount of mercury in thermometers available for release in county c, in pounds
FracPc = Fraction of total US population in county c
HgTmRl = Amount of mercury in thermometers available for release in the US, in tons
4.2,3.3 Emission factors
Landfills (working face)
The emissions factor for mercury from landfills was developed using an average of mercury emissions factors for
the working face of landfills from two different studies [ref 1, ref 24],
4-25
-------
Lindberg et al. (2005) [ref 1] measured mercury emissions from the working face of four landfills in Florida and
determined an average emissions factor of 2.5 mg/ton of waste, or 5.51 x 10 s Ibs./ton of waste placed in a
landfill annually. Babineau et al. (2016) [ref 24] determined that the average mercury content of municipal solid
waste (MSW) in Minnesota is 0.00175 Ibs./tonS. It is assumed that 0.1% of mercury from MSW in landfills is
volatized to the air, so the emissions factor from Babineau et al. [ref 24] is estimated to be 1.75 x 10 s Ibs./ton of
waste. These emissions factors, presented in Table 4-11, are averaged together to yield an average emissions
factor of 3.63 x 10 s Ibs./ton of waste.
Table 4-11: Emissions Factors for mercury from landfills working face
Pollutant
Pollutant
Emissions
Emissions
Emissions Factor
Code
Factor
Factor Units
Reference
Mercury
7439976
5.51E-06
Ibs./ton
1
Mercury
7439976
1.75E-06
Ibs./ton
24
Mercury
7439976
3.63E-06
Ibs./ton
Average of above
Switches and Relays
The response to comments for the 2007 EPA Significant New Use Rule on Mercury Switches (72 Fed. Reg.
56903), suggests that the weighted average amount of mercury in switches is 1.2 grams (0.0026 lbs.) [ref 25], A
report by Griffith et al. (2001) [ref 26] shows that 60% of mercury in switches is released at the shredding
operation, while 40% is sent to arc furnaces for smelting. Therefore, the emissions factor for switches is 60% of
the emissions factor reported in the 2007 EPA Significant New Use Rule on Mercury Switches response to
comment document, 0.00156 lbs. per switch.
Fluorescent Lamp Breakage/Recycling
The average amount of mercury in a CFL has been studied extensively, with the amount of mercury in each CFL
commonly reported as 1.27-4.0 mg (2.63 mg average, Table 4-12). Linear fluorescent bulbs contain more
mercury than CFLs, with a range of 8.3 to 12 mg per bulb (10.15 average, Table 4-13). Data from the USGS
suggests that there is an average of 17 mg of mercury per HID bulb [ref 27],
lie 4-12: Mercury used in CFLs (mg/bulb) as determined by three different stuc
Study
Average Amount of
Mercury per CFL (mg)
Source
Li and Jin (2011)
1.27
[ref 28]
Arendt and Katers
(2013)
4.00*
[ref 29]
Singhvi et al. (2011)
2.63
[ref 30]
Average
2.63
--
** The average Hg content of MSW in Minnesota listed in the reference document as 0.87 parts per million (ppm). A
conversion factor of 0.002 is used to convert from ppm to Ibs./ton - resulting in an average Hg content of 0.00175 Ibs./ton.
4-26
-------
*Adjusted from 4.5 mg to 4 mg due to increased market penetration of Energy Star
CFLs with a lower Hg content.
Table 4-13: Mercury used in linear fluorescent bulbs (mg/bulb) as determined by two different studies
Study
Average Amount of Mercury
per Linear Fluorescent Bulb
(mg)
Source
Aucott et al. (2004)
12.0
[ref 31]
NEMA (2005)
8.3
[ref 32]
Average
10.2
--
Cain et. al (2007) [ref 33] provides the most comprehensive materials flow analysis of mercury intentionally used
in products. Their analysis estimates that 10% of all mercury used in fluorescent light bulbs is eventually
released to the atmosphere after production and before disposal, with the majority being released during
transport to the disposal facility.
The emissions factor for CFL, linear, and HID bulbs are calculated by multiplying the average amount of mercury
per bulb discussed above by 10%.
EFb.p = Hgb x 0.10
(FL4)
Where:
EFb,P = Emissions factor by bulb b for pollutant p, in mg/bulb
Hgb = Average mercury content per bulb b, in mg
The emissions factors for all three bulb types can be found in Table 4-14.
Table 4-14: Mercury emissions factors for CFLs, linear fluorescents and HIDs
Bulb type
Pollutant
Pollutant Code
Emissions Factor
Emissions Factor
Units
CFL
Mercury
7439976
0.263
mg/bulb
Linear
Mercury
7439976
1.015
mg/bulb
HID
Mercury
7439976
1.7
mg/bulb
A weighted average of all three emissions factors in Table 4-14 is calculated to estimate total emissions from all
fluorescent lamp breakage. The first step estimates the fraction each bulb represents of the total amount of
bulbs discarded and recycled.
FracTotBh =
PBb
TotB
(FL5)
Where:
FracTotBb = Fraction of bulb type b discarded and recycled
PBb = Total number of bulb type fa discarded and recycled, in million bulbs
TotB = Total number of bulbs discarded and recycled in the US, in million bulbs
4-27
-------
A weighted emissions factor for fluorescent lamp breakage is then calculated by multiplying the fraction the
bulb type represents of the total number of bulbs by the bulb type-specific emissions factor.
EFbr,p = EFb p x FracTotB^j x ^2.2 x 10 6 ^
Where:
EFbr,P = Weighted emissions factor for pollutant p for fluorescent bulb breakage, br, in Ibs./bulb
EFb,P = Emissions factor for bulb type b and pollutant p, in mg/bulb (see Table 4-14)
FracTotBb = Fraction of the number of bulb type fa discarded and recycled
For mercury-containing bulb recycling, the US EPA has estimated an emissions factor of 0.00088 mg/bulb (1.9 x
109 Ibs./bulb) [ref 34],
Dental Amalgam
US EPA (1997) estimates that 2% of mercury used in dental offices is emitted to the air [ref 34],
Richardson et al. (2011) [ref 35] estimate emissions from filled teeth of approximately 0.3 ng/day of mercury per
filled tooth, or 2.4 x 10"7 lbs. per year per filled tooth. The emissions factors used for estimating mercury
emissions from dental amalgam are shown in Table 4-15.
Table 4-15: Mercury emissions factors for dental amalgam
Activity
Pollutant
Pollutant
Code
Emissions
Factor
Emissions
Factor Units
Source
Released from dental
offices
Mercury
7439976
0.02
Lbs./Lb.
34
Filled teeth
Mercury
7439976
2.4xl0"7
Lbs./tooth
filled
35
Thermostats/Thermometers
The 2002 EPA report estimates that there are 3 grams of mercury per thermostat [ref 19], Cain et al. (2007) [ref
33] estimate that 1.5% of mercury in "control devices," including thermostats, is emitted to the air before it is
disposed of at a landfill or incinerator. Therefore, the amount of mercury emitted is 0.045 grams per thermostat,
or 9.92x 10"5 lbs. per thermostat [ref 28],
Leopold (2002) [ref 19] estimates that 5% of thermometers are broken each year. EPA assumes that the
remaining 95% of thermometers that are not broken are still in use and therefore do not contribute to
emissions. Cain et al. (2007) [ref 33] estimate that 10% of mercury from thermometers is emitted to the air
before disposal in a landfill Therefore the emissions factor is estimated to be 10 lbs. of mercury emissions per
ton of mercury in thermometers.
The emissions factors used for estimating mercury emissions from thermostats and thermometers are shown in
Table 4-16.
Table 4-16: Mercury emissions factors
:or thermostats and thermometers
Type
Pollutant
Pollutant
Code
Emissions
Factor
Emissions
Factor Units
Source
Thermostats
Mercury
7439976
9.92 x 10"5
Lbs./Thermostat
28, 33
4-28
-------
Type
Pollutant
Pollutant
Code
Emissions
Factor
Emissions
Factor Units
Source
Thermometers
Mercury
7439976
10
Lbs./Ton
19, 33
4.2.3.4 Controls
There are no controls assumed for these sources.
4.2.3.5 Emissions
Landfills (working face)
The total mercury emissions from landfills, in pounds, is estimated by multiplying the average tons of waste that
each landfill receives per year by the average emissions factor in Table 4-11. The emissions are reported at the
county level for the county that the landfill is located in.
Ep,c = WCX EFp (1)
Where:
Ep,c = Annual emissions of pollutant p in county c, in lbs.
Wc = Average tons of waste from all landfills in county c
EFP = Average emissions factor for pollutant p, in Ibs./ton
Switches and Relays
The total county-level mercury emissions from switches and relays, in pounds, is estimated by multiplying the
total switches unrecovered for each county by the emissions factor.
ESlPlC = UnSc X EFs p (SR5)
Where:
Es,p,c = Annual emissions of pollutant p in county c from switches and relays, s, in lbs.
UnSc = Total switches unrecovered by county c
EFs,p = Emissions factor for pollutant p for switches and relays, s, in Ibs./switch
Fluorescent Lamp Breakage/Recycling
The total county-level mercury emissions for fluorescent lamp breakage and recycling, in pounds, is estimated
by multiplying the total fluorescent lamps broken or recycled for each county by the emissions factor.
For fluorescent lamp breakage:
Ebr,p,c = (DiscBc X 1,000 units) X EFbrp (FL4)
Where:
Ebr,P,c = Annual emissions of pollutant p from fluorescent bulb breakage, br, by county c, in lbs.
DiscBc = Total number of bulbs discarded for county c, in million units
4-29
-------
EFbr,p = Weighted emissions factor for pollutant p for fluorescent bulb breakage, br, in Ibs./bulb
For fluorescent lamp recycling:
Er,p,c = (RecBc X 1,000 units) X EFr p (FL5)
Where:
Er,p,c —
RecBc =
EFr,p =
Dental Amalgam
The total county-level mercury emissions for dental amalgam from fillings, in pounds, is estimated by multiplying
the total number of fillings containing mercury for each county by the emissions factor.
Annual emissions of pollutant p from fluorescent lamp recycling, r, by county c, in lbs.
Total number of bulbs recycled for county c, in million bulbs
Weighted emissions factor for pollutant p for fluorescent bulb recycling, r, in Ibs./bulb
Ef,p,c = ^ H3Ffa,c x EFf,P (DA7)
Where:
Ef,P,c = Annual emissions of pollutant p from dental fillings, /, by county c, in lbs.
HgFfg,c = Total fillings containing mercury in filling group fg in county c
EffiP = Emissions factor for pollutant p from dental fillings, /, in Ibs./tooth filled
The total county-level mercury emissions for dental office preparation, in pounds, is estimated by multiplying
the total pounds mercury from dental office preparations for each county by the emissions factor.
E0,p,c = HgOc X EFo p (DAS)
Where:
E0/p,c = Annual emissions of pollutant p from dental office preparations, o, by county c, in lbs.
HgOc = Total mercury from dental office preparations by county c, by pounds
EF0iP = Emissions factor for pollutant p for dental office preparations, o, by Ibs./lb.
The emissions from dental fillings and dental office preparations are summed to get the total mercury emissions
from dental amalgam.
Eda,p,c r "I" Eo,p,c (DA9)
Where:
Eda,P,c = Annual emissions of pollutant p from total dental amalgam, da, by county c, in lbs.
Ef,P,c = Annual emissions of pollutant p from dental fillings, /, by county c, in lbs.
Eop,p,c = Annual emissions of pollutant p from dental office preparations , o, by county c, in lbs.
4-30
-------
Thermostats/Thermometers
The total county-level mercury emissions for thermostats, in pounds, is estimated by multiplying the total
number of thermostats disposed in each county by the emissions factor.
Ets,p,c DispTsc X EFtsp (Tl)
Where:
Ets,p,c = Annual emissions of pollutant p for thermostats in county c, in lbs.
DispTsc = Total thermostats disposed in county c
Efts,p = Emissions factor for pollutant p for thermostats, ts, in Ibs./thermostat
The total county-level mercury emissions for thermometers, in pounds, is estimated by multiplying the total
amount of mercury remaining in thermometers over their lifespan for each county by the emissions factor.
Et,p,c = HgTmc x EFt p (12)
Where:
Et,p,c = Annual emissions of pollutant p for thermometers in county c, in lbs.
HgTrric = Amount of mercury remaining in thermometers over their lifespan in county c, in lbs.
EFt/P = Emissions factor for pollutant p for thermometers, in Ibs./ton
The emissions from thermostats and thermometers are summed to get the total mercury emissions.
Eft,p.C Ets,p,c Et,p,c 0~3)
Where:
Ett,p,c = Annual emissions of pollutant p for thermostats and thermometers in county c, in lbs.
Ets,P,c = Annual emissions of pollutant p for thermostats in county c, in lbs.
Etm,p,c = Annual emissions of pollutant p for thermometers in county c, in lbs.
4.2.3.6 Example calculations
Landfills (working face)
Table 4-17 lists sample calculations to determine the mercury emissions from landfills in New Hanover County,
North Carolina. The landfill used in this calculation is the New Hanover County Secure Landfill in New Hanover
County, NC. New Hanover County, NC only has one landfill, so equation 3 is only including this one value.
4-31
-------
Table 4-17: Sample calculations for mercury em
ssions from landfills in New Hanover County, NC
Eq.
#
Equation
Values for New Hanover County, North
Carolina
Result
1
OP; = 2017 - 0;
2017 - 1979
38 years that
New Hanover
County
Secure
Landfill will
be open
2
II
4,845,027 tons
3 8 years
127,501
average tons
of waste per
year for the
New Hanover
County
Secure
Landfill
3
Wc = ^Wi
N/A; there is only one landfill in Hanover
County, NC
111,191
average tons
of waste per
year for the
New Hanover
County, NC
4
EP,C = ^CX EFp,
, lbs.
127,501 tons x (3.63 x 10~6)
tons
0.46 pounds
of mercury
for New
Hanover
County, NC
Switches and Relays
Table 4-18 lists sample calculations to estimate the mercury emissions from switches and relays in Hartford
County, Connecticut.
Table 4-18: Sample calculations for mercury emissions from switches and relays for Hartford County, CT
Eq.
#
Equation
Values for Hartford County,
Connecticut
Result
1
UnSs = TotSs — RecSs
22,000 switches available
— 618 switches recovered
21,382
unrecovered
switches in
Connecticut
2
II
M
a
All facilities in Connecticut
85 car recycling
facilities in
Connecticut
3
Fc
FracFc = —
F
1 S
18 facilities in Hartford County, CT
0.2118 share of
state car recycling
facilities in Hartford
County, CT
85 facilities in CT
4-32
-------
Eq.
#
Equation
Values for Hartford County,
Connecticut
Result
4
UnSc = UnSs x FracFc
21,382 unrecovered switches
x 0.2118 share of state facilities
4,528 unrecovered
switches in
Hartford County, CT
5
l''s,i>,c UnSc x EFS p
lbs.
4,528 switches x 0.00156 -
switch
7.06 pounds of
mercury from
switches and relays
in Hartford County,
CT
Fluorescent Lamp Breakage/Recycling
Table 4-19_lists sample calculations to estimate the mercury emissions from fluorescent lamp breakage in
Hartford County, Connecticut.
Table 4-19: Sample calculations for mercury emissions from fluorescent lamp breakage for Hartford County, CT
Eq.
#
Equation
Values for Hartford County,
Connecticut
Result
1
TotB = ^ PBb
t—>b
all bulbs recycled or discarded
1,485 million bulbs
discarded and
recycled in the US
in 2014
2
RecB = TotB x RR
1,485 million recycled and discarded
bulbs x 23% recycling rate
341 million bulbs
recycled in the US
in 2014
3
DiscB = TotB — RecB
1,485 million recycled and discarded
bulbs — 341 million recycled bulbs
1,143 million bulbs
discarded in the US
in 2014
4
Pc
FracPc = ——
Pus
895,388 people in Hartford County,
0.272% of total US
population is in
Hartford County, CT
318,857,056 people in the US
5
DiscBc = FracPc x DiscB
0.00272 x 1,143 million bulbs
3.109 million
fluorescent bulbs
discarded in
Hartford County, CT
6
RecBc = FracPc x RecB
0.00272 x 341 million bulbs
0.928 million
fluorescent bulbs
recycled in Hartford
County, CT
7
EFb,P = H9b x °-10
CFL: 2.63 mg Hg X 10%
Linear: 10.2 mg Hg x 10%
HID: 17 mg Hg x 10%
0.263 mg Hg/CFL
bulb
1.02 mg Hg/linear
bulb
1.7 mg Hg/HID bulb
4-33
-------
Eq.
#
Equation
Values for Hartford County,
Connecticut
Result
8
PBb
FracTotBb =
b TotB
722 million CFL bulbs
CFL:
1,485 million bulbs total
583 million Linear bulbs
Linear:
1,485 million bulbs total
180 million HID bulbs
HID:
1,485 million bulbs total
48.6% of total for
CFL
39.2% of total for
Linear
12.1% of total for
HID
9
EFbr,p = {T,bEFb,p x FracTotBb) x
(2.2 x 10-6—)
V mgj
((0-263 Sx 4a6%)+
12.1%)) x (2.2 x 10-6^)
1.61 xlO"6 lbs.
Hg/bulb weighted
emissions factor for
mercury for
fluorescent lamp
breakage
10
Ebr,p,c (DiscBc) X EFbr p
3,109,617 bulbs
x (l.61
_ lbs. Hg\
Xl° bulb )
5.0 lbs. of mercury
from fluorescent
lamp breakage in
Hartford County, CT
11
Er,p,c = (RecBc) x EFr p
928,846 bulbs X (l.94
_Q lbs. Hg\
Xl° bulb )
1.8 x 10"4 lbs. of
mercury from
fluorescent lamp
recycling in
Hartford County, CT
Dental Amalgam
Table 4-20_lists sample calculations to determine the mercury emissions from dental amalgam in Hartford
County, Connecticut. The example will show the process for the 5-19 age group, with the total sum of emissions
in the final step.
Table 4-20: Sample calculations for mercury emissions from dental amalgam for Hartford County, CT
Eq.
#
Equation
Values for Hartford County, Connecticut
Result
1
Pc
FracPc = ——
Pus
895,338 people in Hartford County, CT
329,164,967 people in the US
0.272% of total US
population is in
Hartford County, CT
2
HgOc = FracPc x HgDA
0.272% X 31,940 lbs.
86.88 lbs. total
mercury from
dental office
preparations in
Hartford County, CT
4-34
-------
Eq.
#
Equation
Values for Hartford County, Connecticut
Result
3
Pa
FracPa = —
rus
20,304,238 people, 5 to 9 age grouj
6.23% of total US
population for 5-9
age group
6.38% of total US
population for 10-
14 age group
6.49% of total US
population for 14-
19 age group
325,719,178 people in the US
20,778,454 people, 10 to 14 aqe i
10 to 14:
325,719,178 people in the U
21,131,660 people, 14 to 19 age
15 to 19:
325,719,178 people in the U
4
FracPfg = y FracPa
^ 6.23% + 6.38% + 6.49%
19.1006% of total
US population for
5-19 age group
5
Pfg,c I' l'cicl'ig X Pc
19.1006% X
895,338 people in Hartford County, CT
171,025 people in
the 5-19 age group
in Hartford County,
CT
6
fyg,c = Pfg.c x Ffg
171,025 people 5 —
19 in Hartford County, CT x
1.756 fillings, 5 — 19 age group
300,433 fillings in
the 5-19 age group
in Hartford County,
CT
7
HQpfg,c Ffg,c ^ FtclcHgF^g
300,433 fillings, 5
— 19 age group x 31.6%
94,936 total fillings
containing mercury
in the 5-19 age
group in Hartford
County, CT
8
Ff,p,c ~ ^
94,936fillings with mercury, 5 —
19 age group x (2.4 x 10-7 l—. )
V tooth filled/
0.023 pounds of
mercury emissions
from fillings in the
5-19 age group
(0.722 pounds of
mercury in all age
groups) in Hartford
County, CT
9
Fo,p,c HgOc x EF0 p
lbs.
86.88 Ibs.x 0.02 —
lb.
1.74 pounds of
mercury emissions
from dental office
preparations in
Hartford County, CT
10
Fda,p,c ~ Ff,p,c Fo,p,c
0.722 pounds + 1.74 pounds
2.46 pounds of
mercury from
dental amalgam in
Hartford County, CT
Thermostats/Thermometers
4-35
-------
Table 4-21 lists sample calculations to determine the mercury emissions from thermostats and thermometers in
Hartford County, Connecticut.
Table 4-21: Sample calculations for mercury emissions from thermostats and thermometers for Hartford
County, CT
Eq.
#
Equation
Values for Hartford County, Connecticut
Result
2,300,000
1
DispTs = RemTs x (1 — 8%)
2,500,000 thermostats removed from ser
x 92%
thermostats
disposed of in the
United States in
2017
2
HgTmn = (HgTmnx
95%) +HgTm1
y = 1: 546 lbs X 95%
y = 2: (518.7 Ibs.x 95%) + 532 lbs.
y = 3: (1,024 Ibs.x 95%) + 523 lbs.
y = 4: (1,496 Ibs.x 95%) + 514 lbs.
y = 5: (1,935 Ibs.x 95%) + 506 lbs.
2,345 pounds of
mercury available
for release in
thermometers in
year 2017
3
HgTRl = (HgTm5 -
HVTRm> * 2,000°L
1 ton
2,345 lbs. -350 Ibs.x —
2,000 lbs.
0.99 tons of total
mercury in
thermometers
available for
release
4
Pc
FracPc = ——
Pus
895,388people in Hartford County, CT
329,164,967 people in the US
0.272% of total US
population is in
Hartford County, CT
5
DispTsc = FracPc x DispTs
0.272% x 2,300,000 thermostats
6,256 thermostats
disposed in
Hartford County, CT
6
HgTmc = FracPc x HgTmRl
0.272% x 0.99 tons
0.0027 tons of
mercury from
thermometers
available for
release in Hartford
County, CT
7
Ets,p,c DispTsc x EFts p
6,256 thermostats x (9.92 x
10-5 lbs¦ ^
thermostatJ
0.62 pounds of
mercury emissions
from thermostats
in Hartford County,
CT
8
Et,p,c (1 f] I' iTir x EFf p
lbs.
0.0027 tons x 10
ton
0.027 pounds of
mercury emissions
from thermometers
in Hartford County,
CT
4-36
-------
Eq.
#
Equation
Values for Hartford County, Connecticut
Result
9
Ett,p.c ~ Ets,p,c Et,p,c
0.62 lbs. +0.027 lbs.
0.647 pounds of
mercury emissions
from thermostats
and thermometers
in Hartford County,
CT
4.2.3.7 Changes from the 2014 methodology
There are no methodology changes from the 2014 NEI development. However, activity information has been
updated to year 2017 for state-level data on the number of recyclers, number of switches recovered, and the
amount of mercury recovered, as well as the number of switches available for recovery.
4.2.3.8 Puerto Rico and U.S. Virgin Islands
For landfills, Puerto Rico and the U.S. Virgin Islands use the same methodology as the rest of the U.S. However,
for all other sources, because insufficient data exists to calculate emissions for the counties in Puerto Rico and
the US Virgin Islands, emissions are based on two proxy counties in Florida: 12011, Broward County for Puerto
Rico and 12087, Monroe County for the US Virgin Islands. The total emissions in pounds for these two Florida
counties are divided by their respective populations creating a pound per capita emission factor. For each
Puerto Rico and US Virgin Island county, the pound per capita emission factor is multiplied by the county
population (from the same year as the inventory's activity data) which serves as the activity data. In these cases,
the throughput (activity data) unit and the emissions denominator unit are "EACH".
4.2.4 References
1. Lindberg, S.E., G.R. Southworth, M.A. Bogle, T.J. Biasing, J. Owens, K. Roy, H. Zhang, T. Kuiken, J. Price, D.
Reinhart, and H. Sfeir. 2005. Airborne Emission of Mercury from Municipal Solid Waste. I: New
Measurements from Six Operating Landfills in Florida. Journal of the Air and Waste Management
Association, 55: 859-869, last accessed May 2018.
2. Rathore, M., Singh, A., & Pant, V. A. 2012. The Dental Amalgam Toxicity Fear: A Myth or Actuality.
Toxicology International, 19(2), 81-88, last accessed August 2018.
3. Vandeven, J.A. and S.L. McGinnis. 2005. An Assessment of Mercury in the Form of Amalgam in Dental
Wastewater in the United States. Water, Air, and Soil Pollution, 164:349-366, last accessed May 2018.
4. Virta, R. 2013. US Geological Survey. Personal communication with David Cooley, Abt Associates, August
21, 2013.
5. IMERC. 2015. IMERC Fact Sheet - Formulated Mercury-Added Products, last accessed August 2018.
6. Thermostat Recycling Corporation. 2018. About, last accessed May 2018.
7. US EPA. 2016. Phasing out of Mercury Thermometers Used in Industrial and Laboratory Settings, last
accessed August 2018.
8. U.S. EPA. 2018. Landfill Methane Outreach Program, last accessed May 2018.
9. End of Life Vehicle Solutions Corporation. 2018a. Collection Reporting, last accessed May 2018.
10. End of Life Vehicle Solution Solutions Corporation. 2018b. Estimating Population of Mercury
Convenience Light Switches, last accessed May 2018.
11. Freedonia Group, 2013. Industry Study 3054 Lamps.
12. Buildings.com, 2008. Fluorescent Lamps 101, last accessed May 2018.
13. Bulbs.com. Learning Center. What Does Average Rated Life Mean?, last accessed May 2018
4-37
-------
14. Silveira, Geraldo TR, and Shoou-Yuh Chang, 2010. Fluorescent lamp recycling initiatives in the United
States and a recycling proposal based on extended producer responsibility and product stewardship
concepts. Waste Management & Research, 29(6):656-668, last accessed May 2018.
15. NEWMOA. 2015a. IMERC Fact Sheet Mercury Use in Dental Amalgam, last accessed August 2018.
16. National Institute of Dental and Craniofacial Research. 2013. Dental Caries (Tooth Decay), last accessed
May 2018.
17. American Dental Association (ADA). 1998. Dental Amalgam: Update on Safety Concerns. Journal of the
American Dental Association, 129:494:503, last accessed May 2018.
18. Food and Drug Administration. 2017. About Dental Amalgam Fillings., last accessed August 2018.
19. Leopold, B.R. 2002. Use and Release of Mercury in the United States. U.S. Environmental Protection
Agency. Report EPA/600/R-02/104, last accessed May 2018.
20. Natural Resources Defense Council, Product Stewardship Institute, Clean Water Fund, and Mercury
Policy Project. 2013. Turning Up the Heat II: Exposing the continued failures of the manufacturers'
thermostat recycling program, last accessed August 2018.
21. NEWMOA. 2015b. IMERC Fact Sheet Mercury Use in Measuring Devices, last accessed August 2018
22. King, S. et al. May 2008. Reducing Mercury in the Northeast United States. EM Magazine. Air and Waste
Management Association, last accessed August 2018.
23. US Census Bureau, 2016. County Business Patterns- last accessed May 2018.
24. Babineau, I., Wu, C.Y., Jackson, A., Minnesota Pollution Control Agency. "Emission Factor Development
for Mercury Emitted From Municipal Solid Waste during Processing and Handling." In proceedings of the
109th Annual Meeting of the A&WMA, New Orleans, LA. June 2016.
25. US EPA. 2007. Mercury Switches in Motor Vehicles: Significant New Use Rule, last accessed May 2018.
26. Griffith, C., et al. 2001. Toxics in Vehicles: Mi :port by Ecology Center. Great Lakes United, and
University of Tennessee Center for Clean Products and Clean Technologies, last accessed May 2018.
27. Goonan, T.G. 2006. Mercury Flow Through the Mercury-Containing Lamp Sector of the Economy of the
United States. US Geological Survey Scientific Investigations Report 2006-5264, last accessed May 2018.
28. Li, Y. and L. Jin. 2011. Environmental Release of Mercury from Broken Compact Fluorescent Lamps.
Environmental Engineering Science, 28:687-691, last accessed May 2018.
29. Arendt, J. and J.F. Katers. 2013. Compact fluorescent lighting in Wisconsin: elevated atmospheric
emission and landfill deposition post-EISA implementation. Waste Management and Research, 0:1-12,
last accessed August 2018.
30. Singhvi, R, A. Taneja, V. Kansal, C.J. Gasser, and D.J. Kalnicky. 2011. Determination of Total Metallic
Mercury in Compact Fluorescent Lamps (CFLs). Environmental Forensics, 12:143-148, last accessed May
2018.
31. Aucott, M., M. McLinden, and M. Winka. 2004. Release of Mercury from Broken Fluorescent Bulbs. New
Jersey Department of Environmental Protection. Environmental Assessment and Risk Analysis Element,
Research Project Summary, last accessed May 2018.
32. National Electrical Manufacturers Association (NEMA). 2005. Fluorescent and other Mercury-Containing
Lamps and the Environment, last accessed May 2018.
33. Cain, A., S. Disch, C. Twaroski, J. Reindl, and C.R. Case. 2007. Substance Flow Analysis of Mercury
Intentionally Used in Products in the United States. Journal of Industrial Ecology, 11: 61-75, last accessed
May 2018.
34. US EPA. 1997. Mercury Study Report to Congress. Volume II: An Inventory of Anthropogenic Mercury
Emissions in the United States, last accessed May 2018.
35. Richardson, G.M., R. Wilson, D. Allard, C. Purtill, S. Douma, and J. Graviere. 2011. Mercury exposure and
risks from dental amalgam in the US population, post-2000. Science of the Total Environment, 409:4257-
4268, last accessed May 2018.
4-38
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4,3 Agriculture - Crops arid Livestock Dust
This sector includes fugitive dust estimates from both agricultural tilling and dust kicked up by livestock animal
hooves and feet. These sources are significant contributors of atmospheric dust, both fine and coarse particulate
matter (PM2.5 and PM10, respectively).
4.3.1 Sector description
Agricultural Tilling
Fugitive dust emissions from agricultural tilling (SCC=2801000003) 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-PRI, PM10-FIL, PM25-PRI, and PM25-FIL. Since there is no condensable PM (PM-
CON) emissions for this category, PM10-PRI emissions are equal to PM10-FIL emissions and PM25-PRI emissions
are equal to PM25-FIL. Particulate emissions from agricultural tilling were computed by multiplying a crop-
specific emissions factor by an activity factor, as described below.
Dust kicked up by animals
The SCCs that belong to this sector are provided in Table 4-22. The level 1 and level 2 SCC description for these
SCCs is "Miscellaneous Area Sources; Agricultural Production - Livestock". Hoof emissions were estimated for
beef and dairy cattle, swine, and dust emissions from poultry feet were also examined. Fugitive dust emissions
from hooves/feet were estimated for primary and filterable PM: PM10-PRI, PM10-FIL, PM25-PRI, and PM25-FIL.
Since there are no condensable PM (PM-CON) emissions for this category, PM10-PRI emissions are equal to
PM10-FIL emissions and PM25-PRI emissions are equal to PM25-FIL.
Table 4-22: EPA-generated Dust
rom animal hooves and feet SCCs with level 3 and 4 descriptions
SCC
SCC Level 3
SCC Level 4
2805001000
Beef cattle
Dust Kicked-up by Hooves
2805001010
Dairy Cattle
Dust Kicked-up by Hooves
2805001020
Broilers
Dust Kicked-up by Feet
2805001030
Layers
Dust Kicked-up by Feet
2805001040
Swine
Dust Kicked-up by Hooves
2805001050
Turkeys
Dust Kicked-up by Feet
4.3.2 Sources of data
Several S/L/T agencies submitted data for agricultural tilling and/or other agriculture production -crops-sources
and for dust from hooves/feet. These agencies and SCCs-submitted are listed in Table 4-23.
Table 4-23: Agencies reporting emissions to the dust from crops and animal feet/hooves.
SCC
SCC Level 3 and 4
S/L/Ts reporting emissions
2801000000
Agriculture -
Crops; Total
Maricopa County Air Quality Department
4-39
-------
see
SCC Level 3 and 4
S/L/Ts reporting emissions
2801000003
Agriculture -
Crops; Tilling
California Air Resources Board
Clark County Department of Air Quality and Environmental
Management
Coeur d'Alene Tribe
Delaware Department of Natural Resources and Environmental
Control
Idaho Department of Environmental Quality
Illinois Environmental Protection Agency
Kootenai Tribe of Idaho
Maricopa County Air Quality Department
Maryland Department of the Environment
Memphis and Shelby County Health Department - Pollution Control
Metro Public Health of Nashville/Davidson County
New Hampshire Department of Environmental Services
Nez Perce Tribe
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Utah Division of Air Quality
2801000005
Agriculture -
Crops; Harvesting
Coeur d'Alene Tribe
Delaware Department of Natural Resources and Environmental
Control
Idaho Department of Environmental Quality
Kootenai Tribe of Idaho
Maricopa County Air Quality Department
Nez Perce Tribe
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Washington State Department of Ecology
2801000008
Agriculture -
Crops; Transport
Maricopa County Air Quality Department
2801600000
Country Grain
Elevators; Total
Coeur d'Alene Tribe
Idaho Department of Environmental Quality
Kootenai Tribe of Idaho
Maricopa County Air Quality Department
Nez Perce Tribe
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
2805001000
Livestock; Beef
cattle - finishing
operations on
feedlots (drylots);
Dust Kicked-up by
Hooves
Coeur d'Alene Tribe
Idaho Department of Environmental Quality
Kootenai Tribe of Idaho
Memphis and Shelby County Health Department - Pollution Control
Metro Public Health of Nashville/Davidson County
Nez Perce Tribe
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
2805001010
Livestock; Dairy
Cattle; Dust
Kicked-up by
Hooves
Coeur d'Alene Tribe
Idaho Department of Environmental Quality
Kootenai Tribe of Idaho
Nez Perce Tribe
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
2805001020
Livestock; Broilers;
Dust Kicked-up by
Feet
4-40
-------
see
SCC Level 3 and 4
S/L/Ts reporting emissions
2805001030
Livestock; Layers;
Dust Kicked-up by
Feet
2805001040
Livestock; Swine;
Dust Kicked-up by
Hooves
2805001050
Livestock-
Turkeys; Dust
Kicked-up by Feet
4.3.3 EPA-developed methodology
Ag Tilling overview
The calculations for estimating emissions from agricultural tilling involves distributing state-level tilling data by
tilling type (conservation, no-till, and conventional) to the county level and calculating a ratio of conservation,
no-till, and conventional tilling for each county. That ratio is used to estimate the type of tillage for each crop
type for each tilling type in each county. The type of tillage is used to develop a county-level emissions factor for
each crop type and tilling type, which is used to calculate county-level PM10-FIL, PM10-PRI, PM25-FIL, and
PM25-PRI emissions
Dust kicked up by feet and hooves overview
The calculations for estimating emissions from animal hooves and feet are performed for each animal type using
emission factors for each animal unit (e.g., pigs under 55 pounds, pigs 55 pounds to market, sows, boars),
multiplied by state animal counts and allocated to counties using available data discussed in the following
sections.
4.3.3.1 Emission factor equation
Agricultural Tilling
The county-level emission factors for agricultural tilling (in lbs per acre) are specific to the crop type and tilling
method (e.g., conventional tillage corn, no-till soybean, etc.) and were calculated using the following equation
[ref 1, ref 2]:
EFp,t,x,c c x k x sc x Pf (2)
Where:
EFP/t,x,c = Emissions factor for pollutant p, crop tilling type t, and crop type x in county c, in Ibs./acre
c = Constant 4.8 Ibs./acre-pass
k = Dimensionless particle size multiplier (PM10-FIL and PM10-PRI = 0.21; PM25-FIL and PM25-PRI
= 0.042)
sc = Percent silt content of surface soil (%) in county c, defined as the mass fraction of particles
smaller than 50 pim diameter found in surface soil
pt = Number of passes or tillings in a year by crop tilling type, t
The U.S. Department of Agriculture and the National Cooperative Soil Survey define silt content of surface soil as
the percentage of particles (mass basis) of diameter smaller than 50 micrometers (nm) found in the surface
4-41
-------
soil.§§ The soil sample data used to estimate county-level, average silt content values are from the National
Cooperative Soil Survey Microsoft Access Soil Characterization Database [ref 3], This database contains the most
commonly requested data from the National Cooperative Soil Survey Laboratories including data from the
Kellogg Soil Survey Laboratory and cooperating universities.
EPA applied specific selection criteria to the database to ensure that all samples are comparable and relevant to
this analysis. The selection criteria included selecting only samples taken inside the United States with a
preparation code of S and a horizon top of zero centimeters or a master horizon of A or O. A preparation code of
S signifies that the sample is the air-dried whole soil passing through a 3-inch sieve and a horizon top of zero or
master horizon of A or O ensures that the sample is taken at the surface.
In some cases, the sample metadata did not indicate a county, but included latitude and longitude coordinates.
In these cases, the state and county information are determined based on the latitude and longitude
coordinates and added to the sample entry in the database.
The average silt content for a county is calculated by summing the total silt content of all the samples in the
county and dividing by the number of samples in the county. For counties without samples, the average silt
content is calculated by summing the total silt content of soil samples in neighboring counties and dividing by
the number of samples in the neighboring counties. If neighboring counties also lacked sample data, then the
county is assigned the average silt value of soil samples within the state.
Table 4-24 shows the number of passes or tillings in a year for each crop for conservation use, no-till and
conventional use [ref 4], These values are used as pt in equation 1 to estimate the county-level emissions
factors. Mulch till and ridge till tillage systems are classified as conservation use, while 0 to 15 percent residue
and 15 to 30 percent residue tillage systems are classified as conventional use.
Table 4-24: Number of passes or tillings per year
Crop
Conservation Use
No-Till
Conventional Use
Barley
3
3
5
Beans
3
3
3
Canola
3
3
3
Corn
1
0
2
Cotton
5
5
8
Cover
1
1
1
Fallow
1
1
1
Fall-seeded/Winter Wheat
3
3
5
Forage
3
3
3
Hay
3
3
3
Oats
3
3
5
Peanuts
3
3
3
Peas
3
3
3
Permanent Pasture
0
0
1
Potatoes
3
3
3
Rice
5
5
5
Rye
3
3
5
Sorghum
1
1
6
Soybeans
1
0
2
Spring Wheat
1
1
4
Sugarbeets
3
3
3
§§ Note that this definition is different than the U.S. Environmental Protection Agency's definition that includes all particles
(mass basis) of diameter smaller than 75 micrometers.
4-42
-------
Crop
Conservation Use
No-Till
Conventional Use
Sugarcane
3
3
3
Sunflowers
3
3
3
Tobacco
3
3
3
Dust Kicked up by Hooves
Dust emission factors were obtained from a variety of different literature articles [ref 5 through ref 24] for each
livestock type. From the literature, calculations were done to obtain the emission factor for each pollutant in the
desired form. No references for PM2.5 emission factors were found in the extensive literature search for Beef
Cattle. To complete PM2.5 for this tool, the Dairy Cattle PM10 to PM2.5 ratio of 4.81118266481148 from this tool
was used and is based on ratios ultimately derived from AP-42 [ref 1], The general methodology for computing
emission factors is provided below:
1. Determine if study calculated emission factors (EF) for pollutants
2. If the study did calculate EFs, then convert (if necessary) to ton/year/1000 head
3. If the study did not calculate EF, calculate EF if possible
4. To calculate the EF, the following equation* is used:
EF (ton/year/1000 head) = Emission rate (ton/year) / Animal Units
* Adapted from Equation 2-1 from the NRC's Scientific Basis for Estimating Air Emissions from Animal
Feeding Operations: Interim Report (2002)
5. Make sure the emission rate (typically given) is in the correct units (ton/year)
6. Calculate the animal units using the following equation from the Wisconsin Department of Natural
Resources:
AU = Equivalent Factor * Number of Animals
Where the equivalent factor is obtained from Table 4-25 and the number of animals is obtained from
the study.
Note: In some cases, the weight of the animals is also necessary to obtain the equivalent factor.
7. Convert the AU to number of animals, assuming 1 AU = 500 kg
8. Calculate the emission factor in tons/year/head
Multiply calculated emission factor by 1000 to get the tons/year/1000 head
Table 4-25: Animal units equivalent factors
Animal type
Specification
AU Equivalent Factor
Cattle
Dairy/Beef Calves (under 400lbs)
0.20
Dairy Cattle
Milking & Dry Cows
1.40
Dairy Cattle
Heifers (800-1200 lbs)
1.10
Dairy Cattle
Heifers (400 - 800 lbs)
0.60
Beef Cattle
Steers or Cows (400 lbs to market)
1.00
Beef Cattle
Bulls
1.40
Cattle
Veal Calves
0.50
Swine
Pigs (up to 55 lbs)
0.10
Swine
Pigs (55 lbs to market)
0.40
Swine
Sows
0.40
Swine
Boars
0.50
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Animal type
Specification
AU Equivalent Factor
Chicken
Layers - non-liquid manure system
0.01
Chicken
Broilers/pullets - non-liquid manure system
0.005
Chicken
Bird - liquid manure system
0.033
Turkeys
Turkey
0.018
4,3,3,2 Activity data
Agricultural Tilling
The basis of agricultural tilling emission estimates is the number of acres of crops tilled in each county by crop
type and tillage type. These data were estimated based on data from the USDA 2012 Census of Agriculture [ref
25], The USDA Census of Agriculture reports acres harvested for a given crop at the county level but does not
provide tilling data for each crop type at the county level. To calculate acres harvested per tilling type for each
crop, the breakdown of tilling types (conservation, no-till, and conventional) at the county-level was applied to
the acres harvested for each crop type at the county level.
The USDA Census of Agriculture redacts some county-level data to avoid disclosing data for individual farms.
Missing county-level data for acres harvested by crop type and tilling type were calculated using the difference
between the state and national level reported data and the sum of the county-level data by state.
When county level tilling data are unavailable, the total state level tilling data by tilling type, conservation, no-
till, and conventional are distributed to the county level for each crop. The difference between the county-level
data for acres harvested by crop tilling type and the state-level data for acres harvested by crop tilling are
equally distributed to the counties without data.
Q-S,t 2 ®-c,t (1)
-m,t
Where:
am,t p
L.7T
om,t = County-level land tilled by crop tilling type, t, for counties missing tilling data, m, in acres
as,t = Land tilled by crop tilling type t in state s, in acres
oc,t = Sum of county-level land tilled by crop tilling type, t, in acres
Cm,t = Number of counties missing county-level land tilled data by crop tilling type, t
USDA provides data on the number of acres tilled by tillage type (conservation, no-till, and conventional) in each
county [ref 26], but not by tillage type and crop type in each county. To estimate tillage by crop type in each
county, a ratio is determined based on the number of acres in each county tilled by each tillage type to the total
acres tilled by all tillage types. This calculation uses either the data directly reported by USDA or the data gap-
filled by equation 1.
= ac,t (°r am,t) (2)
2 Q-c,t (PT
Where:
rC/t = Ratio of crop tilling type t to total all crop tilling types in county c
oc,t = Land tilled by crop tilling type t in county c, in acres
am,t = Land tilled by crop tilling type t for counties missing data, m, in acres
4-44
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The ratio is then used to estimate the county-level acres harvested by crop type from the 2012 Census of
Agriculture to the tilling type (conservation, no-till, and conventional) at the county-level.
®t,C,X 1~C,t ^ ^c,x (^)
Where:
at,c,x = Land tilled by crop tilling type t and crop type x in county c, in acres
rC/t = Ratio of crop tilling type t to total all crop tilling types in county c
ac,x = Acres harvested of crop type x in county c, in acres
Tilling data for permanent pasture followed a different methodology. Conventional tilling data are available for
the state of Utah [ref 27], For Utah, a ratio of the conventional tilling acres to the total acres of permanent
pasture is developed (0.0023) and applied to the total acreage data for permanent pasture from the 2012
Census of Agriculture to determine the number of conventional tilled permanent pasture acres by county in
other states. It is assumed that the remainder of the permanent pasture acres is not tilled, so the remaining
distribution of permanent pasture acres is then distributed to no till acres and conservation tilling acres are left
as zero.
A summary of national-level acres tilled in 2012 for each tilling type are presented in Table 4-26 [ref 3],
Table 4-26: Acres (millions) tilled by tillage type, in 2012
Tillage system
National acres tilled
No-Till
658.07
Conservation
162.19
Conventional
273.16
Total
1,093.42
Dust kicked up by hooves and feet
The activity data for this source category is based on livestock counts (average annual number of standing head)
and population information by state and county used to develop U.S. EPA's Greenhouse Gas Inventory [ref 28],
This data set is derived from multiple data sets from the United States Department of Agriculture (USDA),
particularly the National Agricultural Statistics Service (NASS) survey and census [ref 29], The USDA NASS survey
dataset, which represents latest available, 2017 national livestock data, was used to obtain the livestock counts
for as many counties as possible across the United States. For a full description of the GHG livestock population
estimation methodology, refer to the above referenced citation for the EPA's GHG inventory document.
Generally, counties not specifically included in the NASS survey data set (e.g., due to business confidentially
reasons) were gap-filled based on the difference in the reported state total animal counts and the sum of all
county-level reported animal counts from the NASS survey dataset. State-level data on animal counts for all the
non-reported NASS survey counties from the GHG population dataset were distributed to individual counties
based on the proportion of animal counts in those counties from the 2012 NASS census (the 2012 census data is
generally more complete in terms of county coverage).
Pa,c,2017 = Pa,s,2017 X ra,c,2012 (4)
Where:
Pa,c,2017 = Estimated 2017 population of animal type a in county c
Pa,s,2017 = NASS survey reported 2017 state-level population of animal type a in state s
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ra,c,2oi2 = Ratio of animal county- to state-level animal counts from the 2012 NASS census for
animal type a in county c
4,3.3,3 Example calculations
Agricultural Tilling
Particulate matter emissions from agricultural tilling are computed by multiplying crop- and county-specific
emissions factors by crop- and county-specific data on tilling activity. The emissions are then summed across al
tilling types and crop types.
T x l^\
v-" v-" 1 ton » '
Ep'c = Zj Zj EFP't'x'c x Ut'c x 2000 lb
t= 1 X=1
Where:
Ep,c = Annual total agricultural tilling county level emissions of pollutant p in county cfrom all crop
tilling types, in tons
EFP/t,x,c = Emissions factor for pollutant p, crop tilling type t, and crop type x in county c, in Ibs./acre
at,x,c = Land tilled by crop tilling type t, and crop type x in county c, in acres
Table 4-27 provides a sample calculation for PM10-FIL emissions for conservation tilling from corn in Clay
County, Alabama. For total PM10-FIL emissions, the calculations below would need to be repeated for all crop
types for all three tilling types, and then summed in equation 5 for total emissions.
Table 4-27: Sample calculations for PM10-FIL emissions from conservation tilling from corn in Clay County, AL
Eq.#
Equation
Values for Clay County, AL
Result
1
Q-S,t 2 Q-C,t
311,942 acres — 298,042 acres
1,069.23 acres for
conservation tilling
in Clay County, AL
®m,t ^
^m,t
13 missing counties
2
ac,t {or am>t)
2 &C,t (pr CLyyi^
1,069.23 acres
1,489.23 acres
0.718 ratio of
conservation tilling
to all tilling for Clay
County, AL
3
&t,c,x ^ &c,x
0.718 x 89 acres
63.9 acres corn
harvested using
conservation tilling
in Clay County, AL
4
EFp,t,x,c = cxkx S°-6 x pt
4.8 pounds x 0.21 x 28.930 6 x 1 pass
acre—pass
7.59 pounds per
acre for
conservation tilling
from corn in Clay
County, AL
5
T X
1 ton
Ep'c ~ Zj Zj EFP't'x'c x at'c x 2000 lb
t=1 x=l
pounds 1 ton
7.59 x 63.9 acres x —
acre 2000 lb
0.24 tons PM10-FIL
emissions from
conservation tilling
for corn in Clay
County, AL*
Dust kicked up by hooves and feet
4-46
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A general method to calculate the emissions per county for a given pollutant can be calculated by multiplying
the emission factor for the given livestock type by the animal activity in each county. However, some
manipulation is necessary to obtain the desired result.
To calculate the dust emissions due to hooves, the first step is to divide the emission factor (ton per year per
1000 head) by 1000. The resulting emission factor is then multiplied by the number of animals (head) in the
region to get the emission (tons per year).
If the emission factor of PM2.5 emitted by beef cattle is approximately 10 ton per year per 1000 head and the
farm is known to have 100 beef cattle, then the emission of this pollutant by the farm can be calculated using
the following procedure:
1. Convert the emission factor from tons per year per 1000 head to tons per year per head
10 tons per year per 1000 head / 1000 = 10/1000 tons per year per head
= .01 tons per year per head
2. Calculate the emissions (tons/year):
Emissions = Emission Factor*Number of head
Emissions = 0.01 tons per year per head*100 head = 1 ton per year
4.3.3.4 Controls
There are no controls assumed for ag tilling and dust kicked up by hooves and feet.
4.3.3.5 Changes from 2014 methodology
There are no significant changes in methodology from that in the 2014 NEI for agricultural tilling. For dust kicked
up by animals, activity data has been updated to year 2017, and new SCCs for animal types have been created.
4.3.3.6 Puerto Rico and Virgin Islands emissions calculations: Agricultural Tilling
Since insufficient data exists to calculate emissions for the counties in Puerto Rico and the US Virgin Islands,
emissions are based on two proxy counties in Florida: 12011, Broward County for Puerto Rico and 12087,
Monroe County for the US Virgin Islands. The total emissions in tons for these two Florida counties are divided
by their respective populations creating a tons per capita emissions factor. For each Puerto Rico and US Virgin
Island county, the tons per capita emissions factor is multiplied by the county population (from the same year as
the inventory's activity data) which served as the activity data. In these cases, the throughput (activity data) unit
and the emissions denominator unit are "EACH".
4.3.4 References
1. U.S. Environmental Protection Agency. 1985. Compilation of Air Pollutant Emission Factors, 4th Edition,
AP-42, Volume 1: Stationary Point and Area Sources, page 11.2.2-1. Research Triangle Park, North
Carolina.
2. Midwest Research Institute. 1981. The Role of Agricultural Practices in Fugitive Dust Emissions, page
117. Prepared for California Air Resources Board.
3. U.S. Department of Agriculture, National Cooperative Soil Survey. NCSS Microsoft Access Soil
Characterization Database.
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4. Woodard, Kenneth R. 1996. Agricultural Activities Influencing Fine Particulate Matter Emissions,
Midwest Research Institute; corn and soybean tilling passes updated based on data from Kansas and
Iowa.
5. Bonifacio, H.F., Maghirang, R.G., Auvermann, B.W., Razote, E.B., Murphy, J.P. and Harner III, J.P., 2012.
Particulate matter emission rates from beef cattle feedlots in Kansas—Reverse dispersion modeling.
Journal of the Air & Waste Management Association, 62(3), pp.350-361.
6. Burns, R.T., Li, H., Moody, L., Xin, H., Gates, R., Overhults, D. and Earnest, J., 2008. Quantification of
particulate emissions from broiler houses in the southeastern United States. In Livestock Environment
VIII, 31 August-4 September 2008, Iguassu Falls, Brazil (p. 15). American Society of Agricultural and
Biological Engineers.
7. Costa, A. and Guarino, M., 2009a. Particulate matter concentration and emission factor in three
different laying hen housing systems. Journal of Agricultural Engineering, 40(3), pp.15-24.
8. Costa, A. and Guarino, M., 2009b. Definition of yearly emission factor of dust and greenhouse gases
through continuous measurements in swine husbandry. Atmospheric Environment, 43(8), pp.1548-
1556.
9. Demmers, T.G.M., Saponja, A., Thomas, R., Phillips, G.J., McDonald, A.G., Stagg, S., Bowry, A. and
Nemitz, E., 2010. Dust and ammonia emissions from UK poultry houses. In XVII World Congress of the
International Commission of Agricultural and Biosystems Engineering (CIGR) CIGR, Quebec city, Canada.
10. Fabbri, C., L. Valli, M. Guarina, A. Costa, and V. Mazzotta. 2007. Ammonia, methane, nitrous oxide, and
particulate matter emissions from two different buildings for laying hens. Biosystems Eng. 97(4): 441-
455.
11. Hayes, M.D., Xin, H., Li, H., Shepherd, T., Zhao, Y. and Stinn, J.P., 2012. Ammonia, greenhouse gas, and
particulate matter concentrations and emissions of aviary layer houses in the Midwestern USA. In 2012
IX International Livestock Environment Symposium (ILES IX) (p. 3). American Society of Agricultural and
Biological Engineers
12. Hinz, T., Linke, S., Karlowski, J., Myczko, R., Kuczynski, T. and Berk, J., 2007. PM emissions in and from
force-ventilated turkey and dairy cattle houses as factor of health and the environment. Gert-Jan
Monteny, p.305.
13. Joo, H.S., Ndegwa, P.M., Heber, A.J., Ni, J.Q., Bogan, B.W., Ramirez-Dorronsoro, J.C. and Cortus, E.L.,
2013. Particulate matter dynamics in naturally ventilated freestall dairy barns. Atmospheric
Environment, 69, pp.182-190.
14. Lacey, R.E., Redwine, J.S. and Parnell, C.B., 2003. Particulate matter and ammonia emission factors for
tunnel-ventilated broiler production houses in the Southern US. Transactions of the ASAE, 46(4),
p.1203.
15. Li, S., Li, H., Xin, H. and Burns, R.T., 2009. Particulate matter emissions from a high-rise layer house in
Iowa. In 2009 Reno, Nevada, June 21-June 24, 2009 (p. 1). American Society of Agricultural and
Biological Engineers.
16. Li, S., Li, H., Xin, H. and Burns, R.T., 2011a. Particulate matter concentrations and emissions of a high-rise
layer house in Iowa. Transactions of the ASABE, 54(3), pp.1093-1101.
17. Li, H., Xin, H., Burns, R.T., Jacobson, L.D., Noll, S., Hoff, S.J., Harmon, J.D., Koziel, J.A. and Hetchler, B.P.,
2011b. Air emissions from torn and hen turkey houses in the US Midwest. Transactions of the ASABE,
54(1), pp.305-314.
18. Lim, T.T., Heber, A.J., Ni, J.Q., Gallien, J.X. and Xin, H., 2003. Air quality measurements at a laying hen
house: Particulate matter concentrations and emissions. In Air Pollution from Agricultural Operations-Ill
(p. 249). American Society of Agricultural and Biological Engineers.
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19. Marchant, C.C., Moore, K.D., Wojcik, M.D., Martin, R.S., Pfeiffer, R.L., Prueger, J.H. and Hatfield, J.L.,
2011. Estimation of dairy particulate matter emission rates by lidar and inverse modeling. Transactions
of the ASABE, 54(4), pp.1453-1463.
20. Qi, R., H. B. Manbeck, and R. G. Maghirang. 1992. Dust net generation rate in a poultry layer house.
Trans. ASAE 35(5): 1639-1645.
21. Roumeliotis, T.S. and Van Heyst, B.J., 2007. Size fractionated particulate matter emissions from a broiler
house in Southern Ontario, Canada. Science of the Total Environment, 383(1), pp.174-182.
22. Takai, H., Pedersen, S., Johnsen, J.O., Metz, J.H.M., Koerkamp, P.G., Uenk, G.H., Phillips, V.R., Holden,
M.R., Sneath, R.W., Short, J.L. and White, R.P., 1998. Concentrations and emissions of airborne dust in
livestock buildings in Northern Europe. Journal of agricultural engineering research, 70(1), pp.59-77.
23. Winkel, A., Mosquera, J., Koerkamp, P.W.G., Ogink, N.W. and Aarnink, A.J., 2015. Emissions of
particulate matter from animal houses in the Netherlands. Atmospheric Environment, 111, pp.202-212.
24. Zhao, L., Lim, T.T., Sun, H. and Diehl, C.A., 2005. Particulate matter emissions from a Ohio belt-battery
layer barn. In 2005 ASAE Annual Meeting (p. 1). American Society of Agricultural and Biological
Engineers.
25. 2012 Census of Agriculture. United States Department of Agriculture, and through Quickstats NASS 2.0.
accessed September 2015.
26. Email from Christy Meyer, U.S. Department of Agriculture, National Agricultural Statistics Service to
Marissa Hoer, Abt Associates, September 2015.
27. Email from Greg Mortensen, Utah Department of Environmental Quality to Jonathan Dorn, Abt
Associates, 2014_UtahDeptAg_DNR_Tilling_Stats.xlsx, February 2016.
28. U.S. EPA. 2019. Inventory of Greenhouse Gas Emissions and Sinks, 1990-2017. Chapters 5.1, 5.2 and
Appendices 3.10 and 3.11. EPA 430-R-19-001.
29. United States Department of Agriculture National Agricultural Statistics Service Quick Stats.
4,4 Agriculture - Fertilizer Application
4.4.1 Sector description
Fertilizer in this category refers to any nitrogen-based compound, or mixture containing such a compound, that
is applied to land to improve plant fitness. The SCCs that compose this sector in the 2017 NEI are provided in
Table 4-28. The SCC level 1, 2 and 3 description is "Miscellaneous Area Sources; Agriculture Production - Crops;
Fertilizer Application" for both SCCs. EPA-estimated emissions are for SCC 2801700099 and discussed further
below.
Table 4-28: SCCs in the Agricultural Fertilizer Application sector
SCC
SCC Level 4 Description
EPA
S/L/T
2801700000
Total Fertilizers
X
2801700099
Miscellaneous Fertilizers
X
X
4.4.2 Sources of data
The agricultural fertilizer application sector includes data from the S/L/T agencies and the default EPA-generated
agricultural fertilizer emissions. The agencies listed in Table 4-29 submitted emissions for this sector; agencies
not listed used EPA estimates for the entire sector. It should be noted that Delaware was the only state to also
submit N02 emissions (to the same counties as NH3 was reported) for this sector.
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Table 4-29: Agencies that submitted fertilizer application NH3 emissions in the 2017 NEI
Region
Agency
S/L/T
3
Delaware Department of Natural Resources and Environmental Control
State
5
Illinois Environmental Protection Agency
State
9
California Air Resources Board
State
9
Maricopa County Air Quality Department
Local
10
Coeur d'Alene Tribe
Tribe
10
Kootenai Tribe of Idaho
Tribe
10
Nez Perce Tribe
Tribe
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
4.4.3 EPA-developed emissions
4.4.3.1 2017 methodology
Direct flux measurements of ammonia (NH3) over agricultural fields and natural vegetation over the past few
decades have demonstrated that vegetation and soil can either be a source or a sink of atmospheric NH3. The
direction and magnitude of the exchange depends on the concentration gradient between the canopy and the
atmosphere. The bidirectional approach taken here accounts, in the most comprehensive way possible, for
estimated NH3 emissions from this complex process. The NH3 emissions estimated here are for fertilizer that has
been applied to the soil. Emissions from the application processes are estimated in the manure management
portion of livestock emissions. The approach to calculating emissions from this sector in 2017 is consistent with
the methodology used for the 2014 NEI. The bidirectional version of CMAQ (v5.3) [ref 1] and the Fertilizer
Emissions Scenario Tool for CMAQ FEST-C (vl.3) [ref 2] were used to estimate ammonia (NH3) emissions from
agricultural soils. These estimates were then loaded into EIS for use in the 2017 NEI. The approach to estimate
2017 fertilizer emissions consists of these steps:
• Run FEST-C to produce nitrate (N03), Ammonium (NH4+, including Urea), and organic (manure) nitrogen
(N) fertilizer usage estimates
• Use USDA Economic Research Services crop specific fertilizer use data and state submitted data to
adjust the FEST-C fertilizer totals to match the USDA and State submitted.
• CMAQ model with bidirectional ("bidi") NH3 exchange to generate gaseous ammonia NH3 emission
estimates.
• Calculate county-level emission factors as the ratio of bidirectional CMAQ NH3 fertilizer emissions to
FEST-C total N fertilizer application.
• Assign the NH3 emissions to one SCC: "...Miscellaneous Fertilizers" (2801700099).
An iterative calculation will be applied to estimate fertilizer emissions for the 2017 NEI. We first estimate
fertilizer application by crop type for 2017 using FEST-C modeled data. After receipt and addressing of
comments to the extent possible, we then adjusted the 2017 fertilizer application estimates using state
submitted data, currently only Iowa, and USDA Economic Research Service state and crop specific survey data.
The USDA and state submitted annual fertilizer data was used to estimate the ratio of UDSA/state fertilizer use
to FEST-C annual total fertilizer estimates for each state and crop with USDA or state data. This ratio is then
applied to the FEST-C fertilizer application rates for each state and crop with data. A maximum annual
fertilization rate was set in the FEST-C simulation and annual adjusted totals were limited to this rate to prevent
unrealistically higher fertilization rates. The we ran the CMAQ v5.3 model with the Surface Tiled Aerosol and
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DRAFT
Gaseous Exchange (STAGE) deposition option with bidirectional exchange to estimate fertilizer and biogenic NH3
emissions for 2017. We use this approach for three reasons: (1) FEST-C estimates fertilizer applications based on
crop nutrient needs which is typically lower than real world fertilization rates; (2) FEST-C fertilizer timing and
application methods are assumed to be correct; and (3) This CMAQ model option allows us to incorporate state
submitted and USDA reported data into the final fertilization emission estimates.
FEST-C is the software program that processes land use and agricultural activity data to develop inputs for the
CMAQ model when run with bidirectional exchange. FEST-C reads land use data from the Biogenic Emissions
Landuse Dataset (BELD), meteorological variables from the Weather Research and Forecasting model [ref 3], and
nitrogen deposition data from a previous or historical average CMAQ simulation. FEST-C, then uses the USDA's
Environmental Policy Integrated Climate (EPIC) modeling system [ref 4] to simulate the agricultural practices and
soil biogeochemistry and provides information regarding fertilizer timing, composition, application method and
amount. Figure 4-1 below provides a comprehensive flowchart if the complete EPIC/FEST-C/WRF "bidi"
modeling system.
Figure 4-1: "Bidi" modeling system used to compute 2017 Fertilizer Application emissions
The Fertilizer Emission Scenario Tool for CMAQ
(FEST-C)
The following activity parameters were input into the EPIC model:
• Grid cell meteorological variables from WRF
• Initial soil profiles/soil selection
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DRAFT
• Presence of 21 major crops: irrigated and rain fed hay, alfalfa, grass, barley, beans, grain corn,
silage corn, cotton, oats, peanuts, potatoes, rice, rye, grain sorghum, silage sorghum, soybeans,
spring wheat, winter wheat, canola, and other crops (e.g. lettuce, tomatoes, etc.)
• Fertilizer sales to establish the type/composition of nutrients applied
• Management scenarios for the 10 USDA production regions (Figure 4-2) [ref 5], These include
irrigation, tile drainage, intervals between forage harvest, fertilizer application method (injected
versus surface applied), and equipment commonly used in these production regions.
Figure 4-2: USDA farm production regions used in FT-C simulations
We used the WRF meteorological model to provide grid cell meteorological parameters for 2016 using a national
12-km rectangular grid covering the continental U.S. The meteorological parameters in Table 4-30 below were
used as EPIC model inputs.
Table 4-30: Environmental variables needed for an EPIC simulation
EPIC input variable
Variable Source
Daily Total Radiation (MJ m2)
WRF
Daily Maximum 2-m Temperature (C)
WRF
Daily minimum 2-m temperature (C)
WRF
Daily Total Precipitation (mm)
WRF
Daily Average Relative Humidity (unitless)
WRF
Daily Average 10-m Wind Speed (m s'1)
WRF
Daily Total Wet Deposition Oxidized N (g/ha)
CMAQ
Daily Total Wet Deposition Reduced N (g/ha)
CMAQ
Daily Total Dry Deposition Oxidized N (g/ha)
CMAQ
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EPIC input variable
Variable Source
Daily Total Dry Deposition Reduced N (g/ha)
CMAQ
Daily Total Wet Deposition Organic N (g/ha)
CMAQ
Initial soil nutrient and pH conditions in EPIC are based on the 1992 USDA Soil Conservation Service (CSC) Soils-5
survey. The EPIC model then is run for 25 years using current fertilization and agricultural cropping techniques to
estimate soil nutrient content and pH for the 2017 EPIC/WRF/CMAQ simulation.
The presence of crops in each model grid cell was determined through the use of USDA Census of Agriculture
data (2006) and USGS National Land Cover data (2011). These two data sources were used to compute the
fraction of agricultural land in a model grid cell and the mix of crops grown on that land.
Fertilizer sales data and the 6-month period in which they were sold were extracted from the 2014 Association
of American Plant Food Control Officials (AAPFCO). AAPFCO data are used to identify the composition (e.g. urea,
nitrate, organic) of the fertilizer used, and the amount applied is estimated using the modeled crop demand.
These data are useful in making a reasonable assignment of what kind of fertilizer is being applied to which
crops.
Management activity data refers to data used to estimate representative crop management schemes. We used
the USDA Agricultural Resource Management Survey (ARMS) to provide management activity data. These data
cover 10 USDA production regions and provide management schemes for irrigated and rain fed hay, alfalfa,
grass, barley, beans, grain corn, silage corn, cotton, oats, peanuts, potatoes, rice, rye, grain sorghum, silage
sorghum, soybeans, spring wheat, winter wheat, canola, and other crops (e.g. lettuce, tomatoes, etc.).
The variables shown below are provided in the "2017_Fertilizer_Application_Supplemental_Data.zip" file (on the
2017 NEI Supplemental Data FTP site) for purposes of assessing crop data:
• Fertilizer application timing • Area planted
• Plant/harvest dates • Crop yields
• Fertilizer application rates by crop and
4.4.3,2 Emision factors
The emission factors were derived from the 2017 CMAQ FEST-C outputs. Total fertilizer emission factors for each
month and county were computed by taking the ratio of total fertilizer NH3 emissions (short tons) to total
nitrogen fertilizer application (short tons).
12 km by 12 km gridded NH3 emissions were mapped to a county shape file polygon. The cell was assigned to a
county if the grid centroid fell within the county boundary. An example calculation adjustment of FEST-C
fertilizer rates using state or USDA data is provided here:
county
justed,crop
= max
FertFEST-C,crop, ^eTtmaXlcrop
(1)
Where:
Fertadjusted,crop = The FEST-C 12km grid cell adjusted fertilization rate,
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FGrtsubmittedj
FERTf
Hcrop
Fertma
DRAFT
= The USDA or State submitted state mean annual application data for the specified
crop, in kg ha"1,
= The initial FEST-C 12km grid cell fertilization rate for the state being considered,
= The number of grid cells with fertilization use for the specified crop in the state,
= The maximum fertilization rate estimated from EPIC for the crop.
County-level fertilizer emissions (NH3) for 2017 are derived from the diagnostic emission output from a 2017
CMAQ FEST-C model simulation (for details see Bash et al. 2013). With this modeling system, it would be difficult
to perform a sample calculation; this is not something that could be demonstrated in a spreadsheet. These
emissions are computed via the full chemical transport model, as illustrated in Figure 4-3.
Figure 4-3: Simplified FEST-C system flow of operations in estimating NH3 emissions
Modeled
EPIC
Data
Ammonium
pH,and
application method
Modeled
WRF
Data
Soil Moisture
& Temperature
CMAQ
Modeled
Atmospheric
NH,
Deposition
4.4.3.3 Comparison to 2014 methodology
The 2017 fertilizer estimates are based on the CMAQ FEST-C "bidirectional" approach outlined in Figure 4-3 that
couples meteorological inputs, CMAQ and the EPIC modeling system through the FEST-C interface. This
approach used for deriving ammonia emissions for the 2017 NEI is substantially the same as the approach used
for the 2014 NEI fertilizer estimates, section 4.4; however, newer model versions for CMAQ and FEST-C were
used. These estimates used FEST-C vl.4 simulations with CMAQ 5.3 beta using the land use specific deposition
option, Surface Tiled Aerosol and Gaseous Exchange (STAGE), and bidirectional NH3 exchange. The previous
version of CMAQ used for the 2014 NEI fertilizer emission only from vegetated land. This has been corrected in
CMAQ 5.3 with the STAGE deposition option and results in higher NH3 emission rates in agricultural areas before
crop germination and in areas with sparse vegetation coverage. Additionally, FEST-C vl.4 corrected an error in
the nitrogen budget form an earlier version of the model used in the 2014 NEI. This results in approximately 38%
lower fertilization estimates than used in the 2014 NEI, see Table 4-31, and thus lower emission estimates in
much of the US in Figure 4-6. This emission reduction was largely offset when annual state and USDA fertilizer
data was used at adjust FEST-C rates. The adjusted FEST-C fertilizer rates were increased by approximately 20%
with the exceptions of wheat (50% increase) and cotton (60% increase) to better match USDA and data
submitted by the states. Crops without state or USDA fertilizer data were adjusted by the mean adjustment
4-54
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DRAFT
factor from all the crops with state or USDA submitted data, approximately a 20% increase. Large increase in
fertilizer rates for cotton and wheat resulted in a large increase in NH3 emissions from fertilizer due to the
typically alkali soils and warm climate where these crops are grown. Emission maps for the 2014 NEl, these 2017
NEI estimates, and a difference map are provided below in Figure 4-4, Figure 4-5, and Figure 4-6, respectively.
Figure 4-4: NEI 2014 "bidi" Fertilizer Application NH3 Emissions
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DRAFT
Figure 4-5: 2017 NEI "bidi" Fertilizer Application NH3 Emissions
Total CMAQ Fertilizer NH3 emissions short tons
Figure 4-6: 2017 -2014 NEI "bidi" Fertilizer Application Emissions in tons NH3
2017-2014 Fertilizer Emissions (short tons)
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Table 4-31: Contiguous US fertilizer totals and emissions for the 2017 NEI and 2014 NEI
2017 FINAL
2017 DRAFT
2014 V2
2014 VI
EPIC FERTILIZER APPLICATION
13,604,640
11,451,713
18,851,866
20,314,303
(TONS N)
CMAQ EMISSIONS (TONS N)
986,509
592,218
883,526
948,616
MEAN ANNUAL EMISSIONS
7.3% total,
4.8% total,
4.7% total,
4.7% total,
FACTOR*
12.5% of
8.9% of
9.8% of
9.1% of
urea/NH4
urea/NH4
urea/NFU
urea/NFU
FERTILIZER USE** (TONS N)
Not Available
Not Available
13,295,000
12,814,000
* Defined as the annual emissions divided by the annual fertilizer application
** USDA Economic Research Service, Fertilizer Use and Price
Additional Information regarding the 2014 methodology and the development of the 2017 methodology can be
found in the Air Emissions Inventory Training site. 2014 NEI training, search for "Key Ammonia sectors".
4.4.4 References
1. Community Multiscale Air Quality (CMAQ v5.3) model.
2. Fertilizer Emission Scenario Tool for CMAQ (FEST-C) system.
3. Weather Research Forecast (WRF) model.
4. Environmental Policy Integrated Climate (EPIC) model.
5. Cooter, E.J., Bash, J.O., Benson V., Ran, L.-M.; Linking agricultural crop management and air-quality
models for regional to national-scale nitrogen deposition assessments. Biogeosciences, 9, 4023-4035,
2012.
4.5 Agriculture - Livestock Waste
4.5.1 Sector description
The emissions from this category are primarily from domesticated animals intentionally reared for the
production of food, fiber, or other goods or for the use of their labor. The livestock included in the EPA-
estimated emissions include beef cattle, dairy cattle, goats, horses, poultry, sheep, turkeys and swine. A few
S/L/T agencies reported data from a few other categories in this sector such as domestic and wild animal waste,
though these emissions are small compared to the livestock listed above. The domestic and wild animal waste
emissions are not included for every state and not estimated by the EPA. The pollutants that EPA reports using
its methods for this sector are NH3 and VOC (VOC is always just 8% of NH3), and some VOC-HAPs that vary by
animal type as described below.
4.5.2 Sources of data
Table 4-32 shows the nonpoint SCCs covered by the EPA estimates and by the S/L/T agencies that submitted
data. The SCC level 2, 3 and 4 descriptions are also provided. The SCC level 1 description is "Miscellaneous Area
Sources" for all SCCs.
Table 4-32: Nonpoint SCCs with 2017 NEI emissions in the Livestock Waste sector
SCC
Description
EPA
S/L/T
2805001100
Agriculture Production - Livestock; Beef cattle - finishing operations on
feedlots (drylots); Confinement
X
4-57
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see
Description
EPA
S/L/T
2805001200
Agriculture Production - Livestock; Beef cattle - finishing operations on
feedlots (drylots); Manure handling and storage
X
2805001300
Agriculture Production - Livestock; Beef cattle - finishing operations on
feedlots (drylots); Land application of manure
X
2805002000
Agriculture Production - Livestock; Beef cattle production composite; Not
Elsewhere Classified
X
X
2805003100
Agriculture Production - Livestock; Beef cattle - finishing operations on
pasture/range; Confinement
X
2805007100
Agriculture Production - Livestock; Poultry production - layers with dry manure
management systems; Confinement
X
X
2805007300
Agriculture Production - Livestock; Poultry production - layers with dry manure
management systems; Land application of manure
X
2805008100
Agriculture Production - Livestock; Poultry production - layers with wet
manure management systems; Confinement
X
2805008200
Agriculture Production - Livestock; Poultry production - layers with wet
manure management systems; Manure handling and storage
X
2805008300
Agriculture Production - Livestock; Poultry production - layers with wet
manure management systems; Land application of manure
X
2805009100
Agriculture Production - Livestock; Poultry production - broilers; Confinement
X
X
2805009200
Agriculture Production - Livestock; Poultry production - broilers; Manure
handling and storage
X
2805009300
Agriculture Production - Livestock; Poultry production - broilers; Land
application of manure
X
2805010100
Agriculture Production - Livestock; Poultry production - turkeys; Confinement
X
X
2805010200
Agriculture Production - Livestock; Poultry production - turkeys; Manure
handling and storage
X
2805010300
Agriculture Production - Livestock; Poultry production - turkeys; Land
application of manure
X
2805018000
Agriculture Production - Livestock; Dairy cattle composite; Not Elsewhere
Classified
X
X
2805019100
Agriculture Production - Livestock; Dairy cattle - flush dairy; Confinement
X
2805019200
Agriculture Production - Livestock; Dairy cattle - flush dairy; Manure handling
and storage
X
2805019300
Agriculture Production - Livestock; Dairy cattle - flush dairy; Land application of
manure
X
2805020002
Agriculture Production - Livestock; Cattle and Calves Waste Emissions; Beef
Cows
X
2805021100
Agriculture Production - Livestock; Dairy cattle - scrape dairy; Confinement
X
2805021200
Agriculture Production - Livestock; Dairy cattle - scrape dairy; Manure handling
and storage
X
2805021300
Agriculture Production - Livestock; Dairy cattle - scrape dairy; Land application
of manure
X
2805022100
Agriculture Production - Livestock; Dairy cattle - deep pit dairy; Confinement
X
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SCC
Description
EPA
S/L/T
Agriculture Production - Livestock; Dairy cattle - deep pit dairy; Manure
2805022200
handling and storage
X
Agriculture Production - Livestock; Dairy cattle - deep pit dairy; Land
2805022300
application of manure
X
Agriculture Production - Livestock; Dairy cattle - drylot/pasture dairy;
2805023100
Confinement
X
Agriculture Production - Livestock; Dairy cattle - drylot/pasture dairy; Manure
2805023200
handling and storage
X
Agriculture Production - Livestock; Dairy cattle - drylot/pasture dairy; Land
2805023300
application of manure
X
Agriculture Production - Livestock; Swine production composite; Not
2805025000
Elsewhere Classified (see also 28-05-039, -047, -053)
X
X
Agriculture Production - Livestock; Poultry Waste Emissions; Not Elsewhere
2805030000
Classified (see also 28-05-007, -008, -009)
X
2805030007
Agriculture Production - Livestock; Poultry Waste Emissions; Ducks
X
2805030008
Agriculture Production - Livestock; Poultry Waste Emissions; Geese
X
Agriculture Production - Livestock; Horses and Ponies Waste Emissions; Not
2805035000
Elsewhere Classified
X
X
Agriculture Production - Livestock; Swine production - operations with lagoons
2805039100
(unspecified animal age); Confinement
X
Agriculture Production - Livestock; Swine production - operations with lagoons
2805039200
(unspecified animal age); Manure handling and storage
X
Agriculture Production - Livestock; Swine production - operations with lagoons
2805039300
(unspecified animal age); Land application of manure
X
2805040000
Agriculture Production - Livestock; Sheep and Lambs Waste Emissions; Total
X
X
Agriculture Production - Livestock; Goats Waste Emissions; Not Elsewhere
2805045000
Classified
X
X
Agriculture Production - Livestock; Swine production - deep-pit house
2805047100
operations (unspecified animal age); Confinement
X
Agriculture Production - Livestock; Swine production - deep-pit house
2805047300
operations (unspecified animal age); Land application of manure
X
Agriculture Production - Livestock; Swine production - outdoor operations
2805053100
(unspecified animal age); Confinement
X
2806010000
Domestic Animals Waste Emissions; Cats; Total
X
2806015000
Domestic Animals Waste Emissions; Dogs; Total
X
2807025000
Wild Animals Waste Emissions; Elk; Total
X
2807030000
Wild Animals Waste Emissions; Deer; Total
X
Table 4-33 presents the three "Industrial Processes" point SCCs reported by 2 states for NH3 emissions:
California and Delaware. Point source emissions from this sector are negligible, particularly for NH3, compared
to the nonpoint emissions (many orders of magnitude lower). The SCC level 1 and 2 descriptions is "Industrial
Processes; Food and Agriculture" for all SCCs. Generally, these emissions are ignored in the Nonpoint NH3
emissions accounting process. Some other states have reported some PM, PM species, and some HAPs using
point source SCCs, however, most of those emission totals are small (we do not report PM or components for
this sector in our methods), and will be ignored in all subsequent discussions here, and will not be included in
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the totals in other parts of this document for this sector. No point source subtraction is deemed necessary for
this sector.
Table 4-33: Point SCCs with 2014 NEI emissions in the Livestock Waste sector - reported only by States
see
SCC Level Three
SCC Level Four
CA
DE
30202120
Broilers
Enteric, Confinement, Manure Handling,
Storage, Land Application
X
30202001
Beef Cattle Feedlots
Feedlots: General
X
The agencies listed in Table 4-34 submitted emissions for this sector; agencies not listed used EPA estimates for
the entire sector. Some agencies submitted emissions for the entire sector (100%), while others submitted only
a portion of the sector (totals less than 100%). In cases where a full submittal was not made, EPA data was used
to backfill according to the information provided in the nonpoint survey for this sector.
Tab
e 4-34: Agencies that submitted Ag Livestock Waste emissions in the 2017 NEI
Region
Agency
S/L/T
1
Massachusetts Department of Environmental Protection
State
3
Delaware Department of Natural Resources and Environmental Control
State
5
Illinois Environmental Protection Agency
State
8
Utah Division of Air Quality
State
9
California Air Resources Board
State
9
Maricopa Air Quality Department (county in AZ)
State
10
Coeur d'Alene Tribe
Tribe
10
Idaho Department of Environmental Quality
State
10
Kootenai Tribe of Idaho
Tribe
10
Nez Perce Tribe
Tribe
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
4.5.3 EPA-developed emissions
Animal waste from livestock results in emissions of both NH3 (ammonia) and, Volatile Organic Compounds
(VOCs), as introduced in the 2014 NEI for this sector. VOCs emitted by livestock can be defined as any compound
of carbon (excluding carbon monoxide, carbon dioxide, carbonic acid, metallic carbides or carbonates, and
ammonium carbonate) that may participate in atmospheric photochemical reactions and is emitted by livestock.
Livestock are domesticated farm animals raised in an agricultural setting for home use or profit. Following the
model-development work of Carnegie Mellon University (CMU) [ref 102], the following livestock were
evaluated: dairy cattle, beef cattle, swine, and poultry (layers and broilers) as part of the model. These animals
make up over 90% of NH3 emissions from this sector. For the 2017 NEI, EPA also estimated NH3 (and VOC)
emissions for goats, sheep, turkeys, and horses. For these animals, emissions were estimated using a nationwide
emission factor multiplied by the appropriate animal count as described below.
The general approach to calculating NH3 emissions due to livestock is to multiply the emission factor (in kg per
year per animal) by the number of animals in the county. VOC emissions were estimated by multiplying a
national VOC/NH3 emissions ratio (VOC = 8% of NH3 emissions) by the county NH3 emissions.
In the 2017 NEI, the EPA methodology for ammonia emissions that results from the use of the CMU model,
includes all processes from the housing/grazing, storage and application of manure from beef cattle, dairy cattle,
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swine, broiler chicken, and layer chicken production, and these are assigned to the "EPA" SCCs listed in Table
4-32. It is assumed the EFs used also take into account, on average, all the management practices that are used
in waste treatment for each of those animals.
4.5.3.1 Overview of calculations
The general approach to calculating NH3 emissions due to livestock is to multiply the emission factor (in kg per
year per animal) by the number of animals in the county. The state-level NH3 emissions factors are generated
using the CMU Ammonia Model [ref 18, ref 102] for dairy cattle, beef cattle, poultry layers, poultry broilers, and
swine. EFs for the other animals comes from a naitonwide average, which is coupled with the number of animals
in the county. VOC emissions were estimated by multiplying a national VOC/NH3 (0.08) emissions ratio by the
county-level NH3 emissions. HAP emissions were estimated by multiplying the county-level VOC emissions by
HAP/VOC ratios.
4.5.3.2 Activity data
The activity data for this source category is based on livestock counts (average annual number of standing head)
and population information by state and county used to develop U.S. EPA's Greenhouse Gas (GHG) Inventory
[ref 99], This data set is derived from multiple data sets from the United States Department of Agriculture
(USDA), particularly the National Agricultural Statistics Service (NASS) survey and census [ref 100], The USDA
NASS survey dataset, which represents latest available, 2017 national livestock data, is used to obtain the
livestock counts for as many counties as possible across the United States. This is a new and more robust
method that has been introduced into the 2017 NEI for this category for estimating population counts. There are
several improvements in this animal counting procedure, including better accounting of the dairy and beef cattle
counts by relying on the EPA's Office of Atmospheric Programs (OAP) Cattle Enteric Fermentation Model (CEFM)
that is used in developing EPA's official GHG inventory livestock population dataset for cattle; the official EPA
GHG inventory is developed by EPA/OAP. The CEFM uses a cattle transition matrix to simulate the population of
cattle from birth to slaughter, using starting point USDA populations, calving rates, weight gain, and death rates
over the course of the year to produce an annual average standing population. A description of the CEFM is
provided in many of the references cited in this document.
To give an idea of changes from 2014v2NEI to 2017 NEI counts based on these improved procedures a summary
Table 4-35 is shown below for the major animals that the CMU model estimates emissions for. These data do
not include state inputs to population counts, but most of the count data for the entire US is based on EPA
information, so it gives an accurate depiction of the changes in animal population counts going from the 2014
NEI to the 2017 NEI.
Table 4-35: National-level animal population data trend from 2014 NEI to draft 2017 NEI
Livestock
Category
2014 NEIv2
2017 Draft
NEI
% Increase in
2017 Draft NEI
Beef
79,367,367
81,559,685
3%
Dairy
9,035,195
18,893,022
109%
Swine
67,766,007
72,151,500
6%
Poultry - Layers
362,319,588
497,677,000
37%
Poultry - Broilers
1,506,271,264
1,621,052,369
8%
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The change in beef, swine and poultry-broilers shown in the above summary table is due mainly to normal
operational and production growth in those livestock categories; however, note that the 2014 NEIv2 populations
originated primarily from 2012 NASS data, so it has a 5-yr growth term since the last population dataset used.
The significant change in the dairy and poultry-layers categories are due to the inclusion of new sub-categories
within those livestock groups that were not previously included in the 2014 NEI populations. For the dairy cattle
category, heifers and calves are now included in population totals in addition to mature dairy cows. For poultry-
layers, pullets (young hens) are now included in the population total for this category. These additions may
account for some of the discrepancy noticed in the 2014 NEIv2 NH3 estimates where EPA estimates were low
(around half for dairy cattle) compared to state estimates for these two livestock categories
Generally, counties not specifically included in the NASS survey data set (e.g., due to business confidentially
reasons) were gap-filled based on the difference in the reported state total animal counts and the sum of all
county-level reported animal counts. State-level data on animal counts from the GHG inventory were distributed
to counties based on the proportion of animal counts in those counties from the 2012 NASS census.
4.5.3.3 Allocation procedure
The USDA survey reports the livestock counts at the county level for many counties, so no allocation is
necessary. The procedure for gap-filling missing county-level data using state-level data is described in the
previous section.
4.5.3.4 Emission factor development
CMU developed a model to estimate NH3 emissions from livestock [ref 18, ref 102], This model produces daily-
resolved, climate level emissions factors for a particular distribution of management practices for each county
and animal type (for dairy cows, beef cattle, swine, poultry layers, and poultry broilers only), as expressed as
emissions/animal. These county level emissions factors are then combined together to create a state level
emissions factor for each animal type. For the 2014 NEI v2, these state level emissions factors were back
calculated from the CMU model using statewide emissions divided by statewide animal totals. Thus, the CMU
model provides a state specific emission factor for each animal type (NH3 emissions/head). For the non-CMU
model animals that EPA estimates emissions for, we are reliant on use of population counts that come from the
same source as described above combined with one national EF for each animal type (horses, goats, turkeys,
and sheep) [ref 104],
To develop emissions factors for the 2017 NEI for the CMU-based animals, the CMU model was modified to use
hourly meteorological data and two runs were performed using 2014 and 2017 meteorological data. The ratio of
the 2017 to 2014 CMU model values were then applied to the 2014 back calculated state-level emissions factors
to develop emissions factors for the 2017 NEI. As discussed in the 2014 NEI TSD, VOC emissions were estimated
as 8% of NH3 across the board. The 8% was simply derived from where states had reported both NH3 and VOC
in the previous inventory: there were 106 counties which provided emissions for both pollutants, and the
Pa,c,2017 — Pa,s,2017 x ra,c,2012
(1)
Where:
Estimated population of animal type a in county c
NASS survey reported state-level population of animal type a in state s
Ratio of animal county- to state-level animal counts from the 2012 NASS census for
animal type a in county c
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average ratio was 0.08 tons of VOC for every ton of NH3. This ratio is multiplied by all county level NH3 emissions
in NEI 2017 to estimate VOC emissions for each county. This ratio does not vary by state or animal type.
HAP emissions were estimated by multiplying county-specific VOC emissions by speciation factors that are
animal-specific as shown in Table 4-36 below. All of the HAP VOC fractions were obtained from EPA's SPECIATE
database [ref 101]. As per the availability in SPECIATE, there are total of 6 VOC HAPs estimated for beef cattle, 5
VOC HAPs for dairy cattle, 4 VOC HAPs for swine, and 14 (same) VOC HAPs for layers and broilers (poultry).
Table 4-36: VOC speciation fractions used to estimate HAP Emissions for Livestock Waste
see
Animal Type
HAP
Fraction of VOC
SPECIATE
Profile Number
2805002000
Beef Cattle
1,4-Dichlorobenzene
0.0013
2805002000
Beef Cattle
Methyl isobutyl Ketone
0.0008
2805002000
Beef Cattle
Toluene
0.0110
95240
2805002000
Beef Cattle
Chlorobenzene
0.0001
2805002000
Beef Cattle
Phenol
0.0006
2805002000
Beef Cattle
Benzene
0.0001
2805007100
Poultry—Layers
Methyl isobutyl ketone
0.0169
2805007100
Poultry—Layers
Toluene
0.0018
2805007100
Poultry—Layers
Phenol
0.0024
2805007100
Poultry—Layers
N-hexane
0.0111
2805007100
Poultry—Layers
Chloroform
0.0025
2805007100
Poultry—Layers
Cresol/Cresylic Acid
(mixed isomers)
0.0048
2805007100
Poultry—Layers
Acetamide
0.0075
95223
2805007100
Poultry—Layers
Methanol
0.0608
2805007100
Poultry—Layers
Benzene
0.0052
2805007100
Poultry—Layers
Ethyl Chloride
0.0031
2805007100
Poultry—Layers
Acetonitrile
0.0088
2805007100
Poultry—Layers
Dichloromethane
0.0002
2805007100
Poultry—Layers
Carbon Disulfide
0.0034
2805007100
Poultry—Layers
2-Methyl Napthalene
0.0006
2805009100
Poultry-Broilers
Methyl isobutyl ketone
0.0169
2805009100
Poultry-Broilers
Toluene
0.0018
95223
2805009100
Poultry-Broilers
Phenol
0.0024
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see
Animal Type
HAP
Fraction of VOC
SPECIATE
Profile Number
2805009100
Poultry-Broilers
N-hexane
0.0111
2805009100
Poultry-Broilers
Chloroform
0.0025
2805009100
Poultry-Broilers
Cresol/Cresylic Acid
(mixed isomers)
0.0048
2805009100
Poultry-Broilers
Acetamide
0.0075
2805009100
Poultry-Broilers
Methanol
0.0608
2805009100
Poultry-Broilers
Benzene
0.0052
2805009100
Poultry-Broilers
Ethyl Chloride
0.0031
2805009100
Poultry-Broilers
Acetonitrile
0.0088
2805009100
Poultry-Broilers
Dichloromethane
0.0002
2805009100
Poultry-Broilers
Carbon Disulfide
0.0034
2805009100
Poultry-Broilers
2-Methyl Napthalene
0.0006
2805018000
Dairy Cattle
Toluene
0.0018
2805018000
Dairy Cattle
Cresol/Cresylic Acid
(mixed isomers)
0.0276
2805018000
Dairy Cattle
Xylenes (mixed isomers)
0.0046
8897
2805018000
Dairy Cattle
Methanol
0.3542
2805018000
Dairy Cattle
Acetaldehyde
0.0141
2805025000
Swine
Toluene
0.0047
2805025000
Swine
Phenol (Carbolic Acid)
0.0179
95241
2805025000
Swine
Benzene
0.0035
2805025000
Swine
Acetaldehyde
0.0155
For the non-CMU animals (goats, sheep, horses, and turkeys), animal-specific HAP speciation profiles were not
available in the literature, so the following assignments were made:
• Sheep and Goats: Same HAP fractions as Dairy Cattle
• TurkeysSame: HAP fractions as Chicken-Broilers
• Horses: Same HAP fractions as Beef Cattle
Meteorological Data Used in Adjusting FEM Emission Factors
The source code provided for FEM model contained weather data for 2014. It did not use standard identifiers
(WBAN ID) and was limited to a small number of observations with an unknown source. The FEM weather data
used a single monthly value for wind, temperature, and precipitation. FEM interpolated this data to hourly using
different techniques. For temperature, a standard deviation was used to raise and lower the mean temperature
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in the month. For wind speed, the average monthly value was used for all hours. For precipitation, monthly
amounts were divided into days (an hours) based upon a parameter defining the frequency of rain in a month.
The source code was modified to accommodate a true hourly processing of the met data. For the years 2014
and 2017, ISD (Integrated Surface Database) files from NOAA were processed into a yearly-hourly data file.
Individual weather station files were retrieved from NOAA for all stations in the US.
This is an automated process whereby a year and certain inclusion criteria are set (country codes, missing value
limits, etc.) and a direct indexed file is created of all passing stations. In the case of FEM, all stations in the US
were included with a maximum of 4000 missing hours for temperature and wind speed and a maximum of 40
consecutive hours without temperature or wind speed. The system automatically fills in missing values using
linear interpolation between missing hours.
To determine the weather characteristics for the year, the county centroid is matched to the nearest weather
station in the yearly-hourly file. Emissions factors are calculated using every hour of the year for the county
location and the model farm types located within the county.
Animal Practice Documentation
The animal practice documentation summarizes the information provided in A. McQulling's dissertation entitled,
"Ammonia emissions from livestock in the United States: from farm-level models to a new national inventory"
[ref 102], This work was funded by EPA grant number RD834549 [ref 103],
Ammonia emissions from livestock depend on two major factors—the management practices employed by the
producers (i.e. what housing, storage and application methods are used) and the environmental conditions of
location where the farm is situated (i.e. temperatures, wind speeds, precipitation). All of these factors have
significant impacts on the conditions of the manure and waste (e.g. water content, total ammoniacal nitrogen
concentration) and as a result can enhance or reduce the emissions of ammonia from these sources.
The CMU model requires farm-type inputs which describe the type of animal housing, manure storage and
application methods used for a particular location. Each location is expected to have some combination of
practices; for example, in a single county, some of the swine farms may use deep-pit housing, lagoon storage,
and irrigation application while other farms use shallow-pit housing with lagoon storage and injection
application. In order to understand the differences in regional preferences for particular manure management
strategies, information was extracted from the most recent National Animal Health Monitoring Surveys done by
the USDA. The beef cattle NAHMS was completed in 2007 and feedlot beef in 2011; dairy cattle data was from
2002 and 2007; swine data were collected for 2006 and 2012, and the most recent poultry NAHMS was
completed for 2010. The most recent data available had limited spatial resolution (compared to previous work
[ref 1, ref 2]), and so the model is only able to resolve large-scale regional differences in practices. For beef cow-
calf systems, the United States was divided into four regions, but only two regions for beef housed on feedlots.
For swine, the country was divided into three regions—Midwest, East, and South, and for layers, there were four
regions—Northeast, Southeast, Central and West. An additional limitation in the data available for the
characterization of the farm practices was that for some of the questions asked by the study, results were only
reported in terms of percent of operations which used a particular practice. This may give too much weight to
the practices used on smaller farms which have a relatively small contribution to the overall level of ammonia
emissions from a particular livestock type or practice. Thus, some uncertainty is expected as a result of the
limited quantity of data available regarding manure management practices throughout the country.
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DRAFT
As was previously discussed by Pinder et al. [ref 3], one of the factors most limiting to the FEM's skill is the lack
of information about manure managment practices throughout the country. It is unclear whether these
uncertainties result in the overprediction or underprediction of total ammonia emissions from livestock in the
United States.
Beef
Information regarding beef manure management practices was provided through the USDA National Animal
Health Monitoring Study (NAHMS) with a regional distribution of practices. Beef data were provided for beef
housed on feedlots as well as those that are a part of cow-calf systems. Cow-calf systems are those in which
cattle are left on pasture or rangeland and the cows are kept with their calves, often until the calves are 1-2
years old and ready for sale. Feedlots are a much denser style of production in which large numbers of cattle are
housed on concrete or packed earth lots and fed a mixture of corn and grains. Using the information from
NAHMS and the animal numbers in the USDA 2012 agriculture census, the fraction of cattle in each state that
were housed on feedlots as opposed those raised in a pasture-based farm system was discerned.
The distribution of manure management practices for the states included in the National Animal Health
Monitoring System (NAHMS) (as split between feedlots and cow-calf systems) is based on liteature [ref 4 - ref
8], The regional distribution of cattle on feed can be seen in the Figure 4-7. States in the West include: Arizona,
California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming. The
states in the Central region are: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North
Dakota, South Dakota, and Wisconsin. Texas and Oklahoma are in the South Central region. The remaining states
are in the East. There have been relatively few studies that have characterized the emissions from cow-calf or
pasture-based systems in the United States, especially compared to the emissions characterization that has
been done at a variety of Texas and Oklahoma feedlots. The grazing portion of the beef farm emission model is
therefore less constrained and may result in the underprediction of emissions of ammonia from beef not housed
on feedlots.
Figure 4-7: Regional distribution of beef cattle on feed
West Central South Central East
~ Cattle on Feed H Cow-calf System
Based on the information provided by NAHMS and the USDA Agricultural census, two manure managment trains
(MMTs) are considered. The first is an all grazing system where emissions are affected by the rate of manure
infiltration and directly exposed to the elements (temperature, windspeed, precipitation). The alternative is a
feedlot system with solid manure storage and broadcast application.
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DRAFT
Dairy
The distribution of practices used in dairy cattle is unlikely to have changed substantially in the years following
the work of Pinder et al, [ref 1, ref 2], as seen when comparing the two most recent NAHMS results (from 2002
and 2007) to the 1996 NAHMS data used in the cited work. However, the data available for the 2002 and 2007
NAHMS was less regionally specific than was used in the previous work [ref 9 - ref 13], The manure
management practice information received at that time included state-specific data, something not available for
the current study years. Addtionally, storage and application data for 2002 and 2007 was only available by
fraction of surveyed operations rather than by population which may give too much weight to practices
employed primarily at smaller dairy farms. Manure management practices can be described regionally as either
in the West or East; the distribution of practices is shown below in Figure 4-8 and Figure 4-9. Eastern States
include Minnesota, Iowa, Missouri, Arkansas, Louisiana and eastward. Western states are the rest of the
continental US. Regionally separated data was not available from the 2007 NAHMS, and results are presented in
terms of percent of farming operations rather than percent of animal population, which may lead to over
representation of minor practices.
Figure 4-8: Regional distribution of dairy housing practices from 2007 NAHMS for Eastern and Western U.S.
Eastern Housing
Western Housing
CD Housed
~ Seasonally Grazed
~ Grazed
Eastern Housing Types
Western Housing Types
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DRAFT
Figure 4-9: Distribution of storage and application practices across the U.S.
Dairy Storage Types by % of operations
Application Method by % of Operations
~ Tank
~ Basin
~ Solid
~ Lagoon
~ Broadcast
~ Irrigation
CD Surface (Trailing Hose)
~ Subsurface (Injection)
Swine
There is significant regional variability in the housing types and manure management practices (in terms of
storage and application) for swine production in the United States. Some of the management choices made are
the result of meteorological limitations (i.e. deep-pit versus shallow-pit housing) while others are chosen for
economic reasons (less expensive to use irrigation application rather than injection).
Using the information provided by NAHMS, regional distributions of management practices can be described
[ref 14 - ref 17]. The United States can be broken into three regions based on this data: the South, the Midwest,
and the East. Each of these groups of states has a unique distribution of housing, storage, and application
practices, seen in Figure 4-10. The Midwest includes: Idaho, Iowa, Minnesota, Montana, North Dakota,
Nebraska, South Dakota, Wisconsin and Wyoming. The Eastern states include Connecticut, Delaware, Illinois,
Indiana, Maine, Maryland, Massachusetts, Michigan, New Hampshire, New Jersey, New York, Ohio,
Pennsylvania, Rhode Island, and Vermont. The remainder of the states are included in the Southern region.
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DRAFT
Figure 4-10: Regional distribution of swine manure management practices
Southern Housing
Midwestern Housing
Eastern Housing
Midwestern Storage
Eastern Storage
Eastern Application
~ Irrigatic
~ Injection
~ Flush
~ Deep
Southern Storage
~ Basin
~ Lagoon
Southern Application
Midwestern Application
Poultry
Broilers
The major differences in broiler chicken production occur not in terms of farm type, but in the frequency with
which barns are entirely cleaned out of their litter material; literature suggests that barns that are cleaned out
more frequently have lower emissions than those in which litter material is built up and reused [ref 19 - ref 22].
Additional factors that may alter the emissions from these facilities include what the bedding or litter material is
made up of as well as how long each barn stays empty between flocks. There is not sufficient data to include
either bedding material or the time between flocks within the emissions inventory. In fact, much of the
variability that might be caused by these factors on a single farm will likely be averaged out as a result of short
lifecycle of these birds, which take less than two months to reach market size. Additionally, pasture-raised or
organic practices are not included as they make up a very small fraction of total bird population and the
emissions from these farms has not been characterized in the literature. The limited data available regarding
manure storage and application from broiler housing may result in the underestimation of ammonia emissions
from this animal type.
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DRAFT
Layers
There are two major housing types used in the production of layer chickens in the United States. These are
high-rise layer houses and manure-belt layer houses. The primary difference between these two housing types is
the frequency with which manure is removed; in high-rise barns, manure is removed 1-2 times each year, while
manure is removed on a daily or weekly basis from manure-belt barns, which results in lower housing emissions
and ammonia concentrations but leaves greater quantities in the manure that is headed toward storage and
application or processing. High-rise housing operations are more prevalent than manure-belt houses throughout
the United States (Figure 4-11), but manure-belt are somewhat more common in the western and central
portions of the United States. The majority of ammonia emissions from poultry are expected to be from housing
(particularly for high-rise facilities). The West includes: Arizona, California, Colorado, Idaho, Montana, Nevada,
New Mexico, Oklahoma, Oregon, Texas, Utah, Washington, and Wyoming. The Central states are: Arkansas,
Illinois, Indiana, Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and
Wisconsin. Southeastern states are: Alabama, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina,
South Carolina, Tennessee, Virginia, West Virginia. The remaining states are considered to be in the Northeast.
There are some limitations on the abiility of the FEM for both the storage and application of poultry manure as
there have been few studies to characterize these emissions.
Figure 4-11: Regional distribution of layer housing types
West Central Southeast Northeast
Additionally, the most recent NAHMS information does not capture the more recent trend towards cage-free
housing or pasture-raised layer chickens [ref 23 - ref 25], Cage-free housing is a relatively minor housing
practice currently (<10% of all layer chickens are raised on cage free farms, but state-specific data is unavailable
so this may vary significantly by state, and this may not represent a similar fraction of total eggs produced), but
is poised to grow as a result of concerns about animal health and welfare and the demand for cage-free eggs
increases. According to the most recently completed NAHMS, cage-free production occurs at approximately 3%
of large layer operations (more than 100,000 layers), and approximately one-quarter of smaller farms. The data
provided by NAHMS does not specify the fractions of total layer populations raised at particular farm sizes, but
large farms have become increasingly common and it is expected that most eggs are produced from larger farms
[ref 25], Cage-free and organic products are more likely to come from smaller farms whose emissions have not
been well-characterized in the literature. Cage-free production is more common in Europe than the United
States, so emissions studies from Europe could be used to better characterize cage-free housing emissions [ref
26-ref 28],
Model Parameters
The FEM is a tuned model that applies adjustments to approximate observed data. However, the model
evaluation does not reflect the ability of the FEM to predict completely independent measurements but the
4-70
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ability of a relatively simple process-based model, with a single set of mass transfer parameters for each manure
management practice, to describe the full range of observed variability.
The National Air Emissions Monitoring Study (NAEMS) data and literature data are displayed in Figure 4-12.
Results in Figure 4-12 are displayed by animal type and management stage as follows: a) free-stall dairy housing
emissions, b) dairy lagoon storage emissions, c) deep-pit and flush-type swine housing emissions, d) swine
lagoon and basin storage emissions, e) litter-based broiler housing emissions, and f) manure-belt (MB) and high-
rise (HR) layer housing emissions. (1 AU = animal unit = 500 kg live animal weight). The range of temperatures
studied is most extended for layer hens. With the additional NAEMS data, an apparent inverse relationship
between temperature and ammonia emissions is observed, something that was not clear in the prior literature.
It has been suggested that this inverse relationship (higher emissions factors for lower temperatures) is related
to the drying out of manure in hot barns with high ventilation rates [ref 30], At lower temperatures, barn
ventilation is reduced (to conserve heat) and manure dries slowly, and, therefore more manure urea can be
broken down into ammonia, which is then available for volatilization. Additionally, for some practices,
particularly for swine storage, emissions factors from NAEMS were uniformly higher than those previously
reported in the literature, for both high and low temperatures. As a result of these differences, the FEM's tuned
parameters were adjusted so that model emission factors fell between NAEMS and literature data, weighting
the literature studies equally with the NAEMS observations so as not to over-tune to only the literature or
NAEMS data. There is significant value in both previously published studies as well as in the values reported by
NAEMS, so the re-tuning done is to ensure that this work takes advantage of all available data.
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DRAFT
Figure 4-12: Emission factors as a function of temperature reported in the prior literature and from the NAEMS
ro200-
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400-
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Literature (MB)
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NAEMS (HR) -
+ ' +
+ +
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X X
X
10 20 30
Temperature (°C)
40
-10
++
4 + ~ ++* rfK
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4X»* .«
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~ A
0 10 20 30
Temperature (°C)
40
Manure characteristics
Manure characteristics are important input parameters to the model because they govern the amount of
nitrogen available for emission, whether or not the nitrogen present is likely to be volatilized, and how well the
waste can infiltrate into the soil during manure application. These parameters have been selected based on
information extracted from published literature as well as reports from the National Air Emissions Monitoring
study. Table 4-37 describes the types of parameters and inputs critical to the model and Table 4-38 presents
information about manure volume, nitrogen concentration and pH levels in the waste from each type of animal
included in the model.
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Table 4-37: Description and sources of model inputs and parameters
Data Type
Description
Source of input or parameter
Input or Tuned
Parameter?
Meteorology
Temperature (°C)
Wind speed (m/s)
Precipitation
From National Climate Data Center, based
on farm location
Input value (monthly
average for seasonal
emissions, daily values
for daily model run)
Manure
Management
Practice
Type of housing,
storage, or
application
Unique to each farm type; farm types have
a unique set of inputs
Input value
Resistance
Parameters
Surface mass
transfer resistance
from manure to
atmosphere
Tuned based on literature and NAEMS
observations to agree with previous work;
constant for a particular management
practice (for a particular animal type)
Tuned Parameters
Table 4-38: Model Input parameters related to manure characteristics
Parameter
Name
Animal Type
Range of Values
Value
Used in
Model
Units
Source
Manure
Volume
Beef
12-17
15
1 animal1 day 1
2, 31
Dairy
1 animal1 day 1
2
Swine
4-10
6
1 animal1 day 1
32
Poultry-Layer
0.088
0.088
1 animal1 day 1
33, 34
Poultry-Broiler
4.9
4.9
1 finished animal1
33
Manure Urea
Concentration
Beef
47-70
kg N animal1 year1
33
Dairy
kg N animal1 year1
2
Swine
11-35
kg N animal1 year1
34, 35
Poultry-Layer
0.5-0.6
0.55
kg N animal1 year1
33
Poultry-Broiler
0.05-0.06
0.055
kg N finished animal1
33
Housing pH
Beef
7.7
7.7
36
Dairy
7.5-8.3
7.7
2
Swine
6.5-7.5
7
37
Poultry-Layer
7.1-7.6 (MB); 8.4-8.7
7.3
38, 39
Poultry-Broiler
8
8
40
Storage pH
Dairy
7.0-8.0
7.5
1
Swine
7.5-8
7.7
35
Application pH
Beef
7.5
7.5
41
Dairy
7.0-7.7
7.3
2
Swine
7.8-8.2
8
42
Poultry-Layer
7.2
7.2
43
Poultry-Broiler
8.8
8.8
44
Storage pH
Beef
7.7
7.7
2
Dairy
7.5-8.3
7.7
2
There are a limited number of studies which describe the manure nitrogen and manure pH for each animal type.
As a result, there is considerable uncertainty in these input values which can result in significant uncertainty in
predicted emissions from the model.
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Tunable parameters
The FEM is a balance between an empirical approach and first-principles process-based model. A nitrogen mass
balance and a process description of ammonia losses are used, but the FEM model parameters are tuned to
reproduce measured emissions factors. Model complexity is limited to the most important emissions processes
and to inputs that are typically available. The strategy pursued for developing process-based models is guided by
the need to build emissions inventories, and the requirements and data limitations associated with this
application. Previous measurement campaigns also often sampled emissions from a single part of the
production process. This means that information about the emissions process from the start to end of
production might be lacking, making nitrogen mass balance in the system difficult. The lack of whole-farm
measurements is one gap in much of the literature available and a benefit of the estimates of ammonia
emissions produced by the FEM.
There are 2-3 tunable parameters associated with each sub-model in the farm emissions model. These tunable
parameters allow adjustment of model-predicted emissions and to correct for the unknowns and uncertainties
of the input parameters and to ensure that the model-predicted values are consistent with those that have been
reported in the literature and in the National Air Emissions monitoring study; they are constant for a particular
farm type—tuning is not done for a particular farm—and as a result, there can be significant disagreement
between model predictions and the measured emissions for a single farm. The goal of the FEM is not necessarily
to capture the emissions of single farms perfectly, but rather to capture the effects of various parameters on
emissions on a farm typical of a certain set of practices.
In the FEM, as previously described [ref 29, ref 45, ref 46], ammonia emissions are estimated as a function of the
nitrogen present in the waste and the mass transfer resistance. This resistance is made up of the following
three parts: the aerodynamic (ra), quasi-laminar (r/,), and surface resistances (rs) [ref 47], Aerodynamic and
quasi-laminar resistances are used to describe the resistance to transport in the gaseous layer above the animal
wastes [ref 45, ref 48, ref 49], These parameters are based on widely used theoretical formulas and are not
tuned. The third part of the resistance is the surface resistance from diffusion closest to the gas-liquid (manure)
interface. Here, the surface resistance is a function of tuned parameters as well as temperature which ensures
the modeled ammonia emission factors are consistent with observations; Table 4-39 lists which tunable
parameters are used for each animal and each sub-model.
These values are specific to a particular practice for a particular animal type. This means that a free stall dairy
with lagoon storage and injection application would employ the same tuned parameters whether it was located
in New York or California. Conversely, two farms in the same location but utilizing different manure
management practices would have different tuned parameters in their sub-models. The values that have been
used for each of these parameters can be found in Table 4-40.
Table 4-39: Tuned model parameters for beef, swine, and poultry
Sub-model
Animal Type
Description
Tuning/Evaluation
Sources
Housing
Cattle: Beef & Dairy
Swine
Poultry: Broiler & Layer
Resistance parameters Hi, H2
50-67, 68-72, 73-78,
79-84
Storage
Dairy Cattle
Swine
Resistance parameters Si, S2
85-90
Application
Cattle: Beef & Dairy
Swine
Poultry: Broiler & Layer
Resistance parameters Ai, A2, A3
91, 92, 93-95, 96-97
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Sub-model
Animal Type
Description
Tuning/Evaluation
Sources
Grazing
Cattle: Dairy & Beef
Resistance parameters Gi, G2
98
Table 4-40: Tuned Parameter Values by practice and animal type
Sub-model
Animal Type
Description
Parameter Values
Housing
Beef cattle
Beef Feedlot
H,=0.1 (s*m ^"C1), H2=-0.01 (s2rrf2)
Swine
Swine—shallow pit
H,=0.08(s*m_1), H2=-0.004(s*m"1*°C"1)
Swine—deep pit
H,=0.1(s*m_1), H2=-0.008(s*m"1*°C1)
Poultry-Layer
Layer—Manure belt
H,=0.3(s*m_1), H2=-0.015(s*m"1*°C"1)
Layer—High Rise
H,=0.22(s*m_1), H2=-0.02(s*m"1*°C"1)
Poultry-Broiler
Broiler
H,=0.15(s*m_1), H2=-0.035(s*m"1*°C"1)
Storage
Swine
Swine lagoon
Si=0.20(s*m1)J S2=4.00(s*m1*°C1)
Swine basin
Si=0.11(s*m_1), S2=2.24(s*m"1*°C"1)
Application
Beef cattle
Beef—broadcast
A,=0.0004, (sttV^Az =0.88, A3=-1.4
Swine
Swine—irrigation
A,=0.001(s*m_1), A2 =-10, A3=20
Swine—injection
A,=0.01(s*m1), A2 =-15, A3=40
Grazing
Beef Cattle
Beef Pasture
G,= 0.12(s*m1), G2=5.4
4.5.3.5 Controls
There are no controls assumed for this category.
4.5.3.6 Emissions calculation procedure
Back Calculating the 2014 NH3 Emissions Factors from the CMU Model
Because we could not get the model to reproduce results properly using 2014 inputs, EPA had to use a scaling
approach to estimate emissions from the CMU FEM model for the 2017 NEI. This is described in this section.
The emissions estimates in NEI 2014 vl came from the CMU model. These emissions were then divided by the
model's animal population figures to estimate the statewide NH3 emission factor.
EFS,a,2014 ~ Es,a,2014 ~ ^s,a,2014 (2)
Where:
EFs, a,2014 — 2014 NH3 emissions factor from the CMU model for animal type a and state s (kg/head)
Es,a,2oi4 = 2014 NH3 emissions from the CMU model for animal type a and state s (kg)
AS/a2oi4 = 2014 animal count for animal type a and state s (head)
Calculating the 2017 NH3 Emissions Factors
The 2017 NH3 emissions factors are estimated by multiplying the NH3 emissions factors from the 2014 NEI CMU
model run with the ratio of the 2017 to 2014 CMU model runs performed with the updated hourly metrological
data.
EFS,a,2017 = EFs a 2014 x EFcMU,s,a,2017 ~=~ FFCMUs ai20i4 (3)
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Where:
EFs, a,2017— 2017 NH3 emissions factor for animal type a and state s (kg/head)
EFs, a,2014 — 2014 NH3 emissions factor from the 2014 NEI CMU model run for animal type o and state s
(kg/head)
Ecmujs,a,2017= 2017 NH3 emissions factor from the 2017 CMU model run for animal type a and state s
(kg/head)
EcMu,s,a,2ou= 2014 NH3 emissions factor from the updated 2014 CMU model run for animal type a and
state s (kg/head)
Calculating 2017NH3 Emissions due to Livestock
Emissions are calculated by multiplying the state specific NH3 emission factor (in NH3/head) by the number of
animals in a given county in that state.
EC,a,2017 = EFS,a,2017 x Ac,a,2017 x 2.2/2000 (4)
Where:
Ec,a,2oi7= 2017 NH3 emissions for animal type a and county c (ton)
EFs, a,2017— 2017 NH3 emissions factor for animal type a and state s in which the county is located
(kg/head)
Ac,a,2017= 2017 animal count for animal type a and state s (head)
2.2/2000 = conversion factor from kg to tons
Calculating 2017 VOC Emissions due to Livestock
VOC emissions are calculated using the ratio of VOC to NH3 emissions from livestock. That ratio is 0.08 kg of VOC
for every kg of NH3.
Evoc,c,a,2017 = VOC/NH3 X Eca2017 (5)
Where:
Evoc,c,a,2017= 2017 VOC emissions for animal type a and county c (ton)
VOC/NHs = 0.08
Ec,a,2oi7= 2017 NH3 emissions for animal type a and county c (ton)
Calculating 2017 HAP Emissions due to Livestock
HAP emissions are calculated using the ratio of HAP to VOC emissions from livestock. These ratios are derived
from the SPECIATE database as discussed above in Section 4.5.3.4.
EhAP,c,a,2017 — yQQ X Evoc,c,a,2017 x 2000
Where:
Ehap,c,a,2017 = 2017 HAP emissions for animal type a and county c (lb)
HAP/VOC = speciation factor derived from the SPECIATE database and listed in Table 2
Evoc,c,a,2017= 2017 VOC emissions for animal type a and county c (ton)
2000 = Conversion factor from tons to pounds
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Alaska and Hawaii
The CMU model does not cover Alaska or Hawaii (only the lower 48 states); however, the animal counts
database does have values for Alaska and Hawaii. To estimate NH3 (and other pollutant) emissions for Alaska
and Hawaii, the state-level emissions factors from Idaho were used as a surrogate for Alaska and state-level
emissions factors from Florida were used as a surrogate for Hawaii.
4.5.3.7 Point source subtraction
Point source subtraction was not performed for this category.
4.5.3.8 Example calculations
Table 4-41 lists sample calculations to determine NH3, VOC and Toluene emissions from swine production in
Cochise County, Arizona.
Table 4-41: Sample Calculations for NH3, VOC and Toluene emissions from swine in Cochise County, AZ
Eq. #
Equation
Values for Cochise County, AZ
Result
2
EFSia, 2014
— Es,a, 2014
~ As,a,2014
= 9,370 kg NH3 h- 925 swine
10.13 kg NH3
per head of
swine in
Cochise
County in
2014
3
EFs, a, 2017
— EFsa, 2014
X EFCMU,S,a,2017
~=~ EFCMU,S,a,2014
= 10.13 kg NH3 per swine x 1.019159
10.32 kg NH3
per head of
swine in
Cochise
County in
2017
4
EC,a, 2017
= EFS,a, 2017
X Ac,a,2017
X 2.2/2000
= 10.32 kg NH3 x 30,693 swine x 2.2 /2000
348.6 tons of
NH3 emissions
from swine in
Cochise
County in
2017
5
EvOC,c, a, 2017
= voc/nh3
X EC,a,2017
= 0.08 X 348.6 tons of NH3
27.89 tons of
VOC
emissions
from swine in
Cochise
County in
2017
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Eq. #
Equation
Values for Cochise County, AZ
Result
6
EHAP,c,a,2017
HAP
VOC
x EV0C,c,a,2017
X 2000
= 0.0047 X 27.89 tons VOC X 2000
262.1 lb of
toluene from
swine in
Cochise
County in
2017
4.5.3.9 Changes from the 2014 methodology
The methodology for estimating county-level animal counts is based on the U.S. EPA's Greenhouse Gas
Inventory. This data set is derived from multiple data sets from the United States Department of Agriculture
(USDA), particularly the National Agricultural Statistics Service (NASS) survey and census. In addition, the NH3
emissions factors were updated to 2017 by growing 2014 emissions factors based on the ratio of 2017 to 2014
emission rates from CMU model runs with updated 2014 and 2017 hourly meteorological data from NOAA. The
basic CMU model structure stays the same as that used in the 2014 NEI process via assistance from CMU.
4.5.3.10 Puerto Rico and U.S. Virgin Islands
Due to the lack of animal counts in Puerto Rico and the U.S. Virgin Islands, emissions are not estimated for these
territories.
4.5.4 References
1. R. W. Pinder, N. J. Pekney, C. I. Davidson, and P. J. Adams, "A process-based model of ammonia
emissions from dairy cows: improved temporal and spatial resolution," Atmos. Environ., vol. 38, no. 9,
pp. 1357-1365, Mar. 2004.
2. R. W. Pinder, R. Strader, C. I. Davidson, and P. J. Adams, "A temporally and spatially resolved ammonia
emission inventory for dairy cows in the United States," Atmos. Environ., vol. 38, no. 23, pp. 3747-
3756, Jul. 2004.
3. R. W. Pinder, P. J. Adams, S. N. Pandis, and A. B. Gilliland, "Temporally resolved ammonia emission
inventories: Current estimates, evaluation tools, and measurement needs," J. Geophys. Res.
Atmospheres, vol. Ill, no. D16, p. D16310, Aug. 2006.
4. USDA-APHIS, "Feedlot 2011 - Part I: Management Practices on US Feedlots with a Capacity of 1000 or
More Head," 2013.
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4.6 Biogenics - Vegetation and Soil
Biogenic emissions are emissions that come from natural sources. They need to be accounted for in
photochemical grid models, as most types are widespread and ubiquitous contributors to background air
chemistry. In the NEI, only the emissions from vegetation and soils are included. Other relevant sources not
included in the NEI are volcanic emissions (geogenic), lightning oxides of nitrogen (NOx), and sea salt. Biogenic
emissions from vegetation and soils are computed using a model that utilizes spatial information on vegetation,
land use and environmental conditions of temperature and solar radiation. The model inputs are typically
horizontally allocated (gridded) data, and the outputs are gridded biogenic emissions, which can then be
speciated and utilized as input to photochemical grid models.
4.6.1 Sector description
In the 2017 NEI, biogenic emissions are included in the nonpoint data category, in the EIS sector "Biogenics -
Vegetation and Soil." Table 4-42 lists the two source classification codes (SCCs) used in the 2017 NEI that
comprise this sector. The level 1 and 2 SCC description for both SCCs is "Natural Sources; Biogenic" and the full
Tier 3 description for both SCCs is "Natural Resources; Biogenic; Vegetation". These two SCCs have distinct
pollutants: SCC 2701220000 has only NOX emissions, and SCC 2701200000 has emissions for carbon monoxide
(CO), volatile organic compounds (VOC) and three VOC hazardous air pollutants (HAPs): formaldehyde,
acetaldehyde and methanol.
Table 4-42: SCCs for biogenic sources
SCC
SCC Level 3
SCC Level 4
2701200000
Vegetation
Total
2701220000
Vegetation/Agriculture
Total
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4.6.2 Sources of data
The biogenics sector includes data from the S/L/T agency submitted data and the default EPA generated
emissions. The agencies listed in Table 4-43 submitted emissions for this sector; agencies not listed used EPA
estimates for the entire sector.
Table 4-43: Agencies that submitted biogenics emissions
Region
Agency
S/L/T
9
California Air Resources Board
State
9
Maricopa County Air Quality Department
Local
10
Coeur d'Alene Tribe
Tribe
10
Kootenai Tribe of Idaho
Tribe
10
Nez Perce Tribe
Tribe
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
4.6.3 EPA-developed emissions
4.6.3.1 Continental U.S.
The biogenic emissions for the 2017 National Emissions Inventory (NEI) were computed based on 2017
meteorology data from the Weather Research and Forecasting (WRF) model version 3.8 (WRFv3.8) and using the
Biogenic Emission Inventory System, version 3.61 (BEIS3.61) model within the Sparse Matrix Operator Kernel
Emissions (SMOKE) modeling system version 4.7. The BEIS3.61 model creates gridded, hourly, model-species
emissions from vegetation and soils at 12-kilometer horizontal resolution. The 12-kilometer gridded hourly data
are summed to monthly and annual level (see Figure 4-13) and are mapped from 12-kilometer grid cells to
counties using a standard mapping file.
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> 1800
1600
1400
1200
1000
800
600
400
< 200
BEIS produces biogenic emissions for a modeling domain which includes the contiguous 48 states in the U.S.,
parts of Mexico, and Canada. The NEI uses the biogenic emissions from counties from the contiguous 48 states
and Washington, DC. The model-species are those associated with the Carbon Bond mechanism version 6 (CB6).
The NEI pollutants produced are: CO, VOC, NOx, methanol, formaldehyde and acetaldehyde. VOC is the sum of
all biogenic species except CO and nitrogen oxide (NO). Mapping of BEIS species to NEI pollutants is as follows:
• NO maps to NOx
• FORM maps to formaldehyde
• ALD2 maps to acetaldehyde
• MEOH maps to methanol
• VOC is the sum of all biogenic species except CO and NO
BEIS3.61 includes a two-layer canopy model. Layer structure varies with light intensity and solar zenith angle [ref
2]. Both layers of the canopy model include estimates of sunlit and shaded leaf area based on solar zenith angle
and light intensity, direct and diffuse solar radiation, and leaf temperature [ref 1, ref 2], The canopy model
requires additional meteorological inputs as compared to previous versions of BEIS, and these meteorology
inputs must be in a data file format that is output from the Meteorology-Chemistry Interface Processor (MCIP).
MCIP is also used to convert WRF outputs to inputs for the Community Multi-scale Air Quality (CMAQ) model.
The meteorology input data fields used by BEIS are shown in Table 4-44.
Table 4-44: Meteorological variables required by BEIS 3.61
Variable
Description
LAI
leaf-area index
PRSFC
surface pressure
02
mixing ratio at 2 m
DRAFT
Figure 4-13: Annual VOC emissions for year 2017 for 12km modeling domain
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Variable
Description
RC
convective precipitation per
meteorological time step
RGRND
solar rad reaching surface
RN
non-convective precipitation per
meteorological time step
RSTOMI
inverse of bulk stomatal resistance
SLYTP
soil texture type by USDA category
SOIM1
volumetric soil moisture in top cm
SOIT1
soil temperature in top cm
TEMPG
skin temperature at ground
USTAR
cell averaged friction velocity
RADYNI
inverse of aerodynamic resistance
TEMP2
temperature at 2 m
Important updates were made to two other input datasets used in the BEIS3.61 modeling system for the
2017NEI. The Biogenic Emissions Landcover Database version 5 (BELD5) was used as the input gridded land use
information in generating 2017NEI estimates. BELD version 4.1 (BELD4.1) was used to generate 2014NEI
estimates. The other input dataset change involved updating the dry leaf biomass (grams/m2) values for various
vegetation types. The BELD5 includes the following datasets:
• Newer version of the Forest Inventory and Analysis, FIA version 8.0
• Agricultural land use from the 2017 US Department of Agriculture (USDA) crop data layer
• Global Moderate Resolution Imaging Spectroradiometer (MODIS) 20 category data with enhanced lakes
and Fraction of Photosynthetically Active Radiation (FPAR) for vegetation coverage from National Center
for Atmospheric Research (NCAR)
o Note BELD4.1 used 2011 USGS National Land Cover Data (NLCD) limited to the USA and MODIS
20 category land use for the rest of the world.
• Canadian BELD land use. Updates to Version 4 of the Biogenic Emissions Landuse Database (BELD4) for
Canada and Impacts on Biogenic VOC Emissions.
The FIA database reports on status and trends in forest area and location; in the species, size, and health of
trees; in total tree growth, mortality, and removals by harvest; in wood production and utilization rates by
various products; and in forest land ownership. The FIA database version 8.0 includes recent updates of these
data through the year 2017 (from 2001). Earlier versions of BELD used an older version of the FIA database that
had included data only through the year 2014. Canopy coverage is based on the MODIS 20 category data. The
FIA includes approximately 250,000 representative plots of species fraction data that are within approximately
75 km of one another in areas identified as forest by the MODIS canopy coverage. For all land areas in the
United States, 500-meter grid spacing land cover data from the MODIS is used.
The processing of the BELD5 data follows the spatial allocation methods [ref 2] like BELD 4. However, MODIS
land use categories and FPAR are used in the place of NLCD land use and forest coverage. MODIS land use has
the additional broadleaf evergreen and deciduous needleleaf land use types and only one developed land use
type.
BELD4.1 used lookup tables for species leaf biomass. In BELD5, allometric relationships from the FIA v8.0
database were utilized to estimate foliage biomass per species. This resulted in better agreement with measured
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DRAFT
foliage biomass. BVOC emissions are understood to originate from foliage thus these biomass changes directly
impacted the BEIS emission factors.
4.6.3.2 Alaska, Hawaii, Puerto Rico and Virgin Islands
The 2017NEI also include biogenic emissions estimates for counties in the states of Alaska and Hawaii, and for
the territories of Puerto Rico and Virgin Islands. The BEIS3.61 modeling system and WRFv3.8 meteorology data
for year 2017 were used to produce gridded biogenic emissions for 3 separate modeling domains at 9-km
horizontal resolution. The modeling domain for Alaska is shown in Figure 4-14. The land use data used for
generating input data for BEIS3.61 included the MODIS 20 category dataset and the FIA version 8.0 used for
estimating biomass input information.
Figure 4-14: Alaska 9-km modeling domain
The modeling domains for Hawaii, Puerto Rico and Virgin Islands are shown in Figure 4-15 and Figure 4-16,
respectively. Both Puerto Rico and Virgin Islands territories are in the same 9-km modeling domain. The MODIS
20 category land use dataset was the only dataset used for land use/vegetation input into BEIS3.61.
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DRAFT
Figure 4-15: Hawaii 9-km modeling domain
O
Figure 4-16: Puerto Rico and Virgin Islands 9-km modeling domain
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The 9-kilometer gridded hourly data from these modeling domains are summed to monthly and annual level and
are mapped from 9-kilometer grid cells to counties using a standard mapping file in a similar manner as was
done for the contiguous 48 states. The mapping of BEIS species to NEI pollutants for these states and territories
was also done in the same manner as the contiguous 48 states.
4.6.4 References
1. Pouliot, G. and J. Bash, 2015. Updates to Version 3.61 of the Biogenic Emission Inventory System (BEIS).
Presented at Air and Waste Management Association conference, Raleigh, NC, 2015.
2. Bash, J.O., Baker, K.R., Beaver, M.R., Park, J.-H., Goldstein, A.H., 2016. Evaluation of improved land use
and canopy representation in BEIS with biogenic VOC measurements in California.
4.7 Nonpoint Gasoline Distribution
This section includes discussion of all nonpoint sources in three EIS sectors: Bulk Gasoline Terminals, Gas
Stations, and Industrial Processes - Storage and Transfer. Many of the sources in these sectors include sources
reported to the point inventory as well; therefore, the EPA nonpoint survey is useful to avoid double-counting
S/L/T-reported point emissions with EPA-estimated nonpoint emissions.
4.7.1 Description of sources
This section is broken into two categories: those sources related to stage 1 gasoline distribution and those
related to aviation gasoline.
4.7,1.1 Stage 1 gasoline distribution
Stage 1 gasoline distribution is covered by the 2014 NEI in both the point and nonpoint data categories. In
general terms, Stage 1 gasoline distribution is the emissions associated with gasoline handling excluding
emissions from refueling activities. Stage I gasoline distribution includes the following gasoline emission points:
1) bulk terminals; 2) pipeline facilities; 3) bulk plants; 4) tank trucks; and 5) unloading at service stations.
Emissions from Stage I gasoline distribution occur as gasoline vapors are released into the atmosphere. These
Stage I processes are subject to EPA's maximum available control technology (MACT) standards for gasoline
distribution [ref 1],
Emissions from gasoline distribution at bulk terminals and bulk plants take place when gasoline is loaded into a
storage tank or tank truck, from working losses (for fixed roof tanks), and from working losses and roof seals (for
floating roof tanks). Working losses consist of both breathing and emptying losses. Breathing losses are the
expulsion of vapor from a tank vapor space that has expanded or contracted because of daily changes in
temperature and barometric pressure; these emissions occur in the absence of any liquid level change in the
tank. Emptying losses occur when the air that is drawn into the tank during liquid removal saturates with
hydrocarbon vapor and expands, thus exceeding the fixed capacity of the vapor space and overflowing through
the pressure vacuum valve [ref 2],
Emissions from tank trucks in transit occur when gasoline vapor evaporates from (1) loaded tank trucks during
transportation of gasoline from bulk terminals/plants to service stations, and (2) empty tank trucks returning
from service stations to bulk terminals/plants [ref 3], Pipeline emissions result from the valves and pumps found
at pipeline pumping stations and from the valves, pumps, and storage tanks at pipeline breakout stations. Stage
I gasoline distribution emissions also occur when gasoline vapors are displaced from storage tanks during
unloading of gasoline from tank trucks at service stations (Gasoline Service Station Unloading) and from gasoline
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vapors evaporating from service station storage tanks and from the lines going to the pumps (Underground
Storage Tank Breathing and Emptying).
4.7.1.2 Aviation gasoline distribution, stage 1 and 2
Aviation gasoline (also called "AvGas") is the only aviation fuel that contains lead as a knock-out component for
small reciprocating, piston-engine crafts in civil aviation [ref 4], Commercial and military aviation rarely use this
fuel. AvGas is shipped to airports and is filled into bulk terminals, and then into tanker trucks. These processes
fall under the definition of stage 1, displacement vapors during the transfer of gasoline from tank trucks to
storage tanks, and vice versa. Stage 2 involves the transfer of fuel from the tanker trucks into general aviation
aircraft.
4.7.2 Sources of data
Sources in the EIS sectors for Bulk Gasoline Terminals, Gas Stations, and Industrial Processes - Storage and
Transfer do not focus solely on gasoline; however, for the purposes of developing the NEI, these SCCs are the
only ones that EPA estimates in these sectors. EPA does not develop calculation tools that estimate emissions
from transfer of naphtha, distillate oil, inorganic chemicals, kerosene, residual oil, or crude oil. Therefore, sector
level emissions for these three EIS sectors will include sources not related to gasoline distribution, some from
the point inventory.
Table 4-45 shows all non-Aviation Gasoline SCCs in the nonpoint data category for EIS sectors Bulk Gasoline
Terminals, Gas Stations, and Industrial Processes - Storage and Transfer. For Stage 1 Gasoline Distribution, the
nonpoint SCCs covered by the EPA estimates are also noted. Table 4-46 shows, for Aviation Gasoline, the
nonpoint SCCs covered by the EPA estimates and by the S/L/T agencies that submitted data. The SCC level 2, 3
and 4 SCC descriptions are also provided. The SCC level 1 description is "Storage and Transport" for all SCCs in
both tables.
Table 4-45: Nonpoint bulk gasoline terminals, gas stations, and storage and transfer SCCs in the 2017 NEI
SCC
Description
Sector
EPA
S/L/T
2501050120
Petroleum and Petroleum Product Storage; Bulk
Terminals: All Evaporative Losses; Gasoline
Bulk Gasoline
Terminals
X
X
2501055120
Petroleum and Petroleum Product Storage; Bulk
Plants: All Evaporative Losses; Gasoline
Bulk Gasoline
Terminals
X
X
2501060051
Petroleum and Petroleum Product Storage;
Gasoline Service Stations; Stage 1: Submerged
Filling
Gas Stations
X
X
2501060052
Petroleum and Petroleum Product Storage;
Gasoline Service Stations; Stage 1: Splash Filling
Gas Stations
X
X
2501060053
Petroleum and Petroleum Product Storage;
Gasoline Service Stations; Stage 1: Balanced
Submerged Filling
Gas Stations
X
X
2501060201
Petroleum and Petroleum Product Storage;
Gasoline Service Stations; Underground Tank:
Breathing and Emptying
Gas Stations
X
X
2501070053
Petroleum and Petroleum Product Storage;
Diesel Service Stations; Stage 1: Balanced
Submerged Filling
Gas Stations
X
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see
Description
Sector
EPA
S/L/T
2501995120
Petroleum and Petroleum Product Storage; All
Storage Types: Working Loss; Gasoline
Industrial Processes -
Storage and Transfer
X
2505010000
Petroleum and Petroleum Product Transport;
Rail Tank Car; Total: All Products
Industrial Processes -
Storage and Transfer
X
2505020000
Petroleum and Petroleum Product Transport;
Marine Vessel; Total: All Products
Industrial Processes -
Storage and Transfer
X
2505020030
Petroleum and Petroleum Product Transport;
Marine Vessel; Crude Oil
Industrial Processes -
Storage and Transfer
X
2505020060
Petroleum and Petroleum Product Transport;
Marine Vessel; Residual Oil
Industrial Processes -
Storage and Transfer
X
2505020090
Petroleum and Petroleum Product Transport;
Marine Vessel; Distillate Oil
Industrial Processes -
Storage and Transfer
X
2505020120
Petroleum and Petroleum Product Transport;
Marine Vessel; Gasoline
Industrial Processes -
Storage and Transfer
X
2505020150
Petroleum and Petroleum Product Transport;
Marine Vessel; Jet Naphtha
Industrial Processes -
Storage and Transfer
X
2505020180
Petroleum and Petroleum Product Transport;
Marine Vessel; Kerosene
Industrial Processes -
Storage and Transfer
X
2505030120
Petroleum and Petroleum Product Transport-
Truck; Gasoline
Industrial Processes -
Storage and Transfer
X
X
2505040120
Petroleum and Petroleum Product Transport-
Pipeline; Gasoline
Industrial Processes -
Storage and Transfer
X
X
2510000000
Organic Chemical Storage; All Storage Types:
Breathing Loss; Total: All Products
Industrial Processes -
Storage and Transfer
X
2520010000
Inorganic Chemical Storage;
Commercial/Industrial: Breathing Loss; Total: All
Products
Industrial Processes -
Storage and Transfer
X
Table 4-46: Nonpoint aviation gasoline distribution SCCs in the 2017 NEI
see
Description
Sector
EPA
S/L/T
2501080050
Petroleum and Petroleum Product Storage; Airports: Aviation
Gasoline; Stage 1: Total
Gas
Stations
X
X
2501080100
Petroleum and Petroleum Product Storage; Airports: Aviation
Gasoline; Stage 2: Total
Gas
Stations
X
X
2501080201
Petroleum and Petroleum Product Storage; Airports: Aviation
Gasoline; Underground Tank: Breathing and Emptying
Gas
Stations
X
The agencies listed in Table 4-47 submitted emissions for these sectors. Agencies not listed used EPA estimates
for the entire sector.
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Table 4-47: Agencies reporting emissions to gasoline distribution source categories
Agency
Bulk Gasoline
Terminals
Gas Stations
Industrial Processes -
Storage and Transfer
Alaska Department of Environmental Conservation
X
California Air Resources Board
X
X
X
Coeur d'Alene Tribe
X
X
X
Connecticut Department of Energy and Environmental Protection
X
Delaware Department of Natural Resources and Environmental Control
X
X
Idaho Department of Environmental Quality
X
X
Illinois Environmental Protection Agency
X
X
X
Knox County Department of Air Quality Management
X
Kootenai Tribe of Idaho
X
X
Maricopa County Air Quality Department
X
X
X
Maryland Department of the Environment
X
X
Massachusetts Department of Environmental Protection
X
X
Memphis and Shelby County Health Department - Pollution Control
X
X
X
Metro Public Health of Nashville/Davidson County
X
X
X
New Hampshire Department of Environmental Services
X
X
New Jersey Department of Environment Protection
X
X
X
New York State Department of Environmental Conservation
X
X
X
Nez Perce Tribe
X
X
X
Salt River Pima Maricopa Indian Community (SRPMIC) EPNR
X
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
X
X
X
Southern Ute Indian Tribe
X
Texas Commission on Environmental Quality
X
Utah Division of Air Quality
X
X
X
Virginia Department of Environmental Quality
X
X
Washoe County Health District
X
X
4.7.3 EPA-developed emissions
Bulk Terminals
The calculations for estimating VOC and HAP emissions from bulk terminals involve first multiplying the 1998
national VOC emissions developed in support of the Gasoline Distribution MACT standard by the ratio of the
national volume of wholesale gasoline supplied between 1998 and 2017. Emissions from HAPs are calculated by
multiplying VOC emissions by a national average speciation profile. National VOC and HAP emissions are
allocated to states using data on refinery, bulk terminal, and natural gas plant stocks of motor gasoline in each
state. State-level VOC and HAP emissions are then allocated to each county based on employment at petroleum
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bulk stations and terminals from the US Census County Business Patterns data for NAICS 42471 (Petroleum Bulk
Stations and Terminals).
Pipelines
The calculations for estimating VOC and HAP emissions from pipelines involve first multiplying the 1998 national
VOC emissions developed in support of the Gasoline Distribution MACT standard by the 2017 to 1998 ratio of
national volume of wholesale gasoline supplied. Emissions from HAPs are calculated by multiplying VOC
emissions by a national average speciation profile. National VOC and HAP emissions are allocated to Petroleum
Administration for Defense (PAD) District using data on the movement of finished motor gasoline in PAD District.
PAD District-level VOC and HAP emissions are then allocated to each county based on employment at petroleum
bulk stations and terminals from the US Census County Business Patterns data for NAICS 42471 (Petroleum Bulk
Stations and Terminals).
Bulk Plants
The calculations for estimating VOC and HAP emissions from bulk plants involve first calculating bulk plant
gasoline throughput in the US based on data from the U.S. Energy Information Administration (EIA). National
bulk plant gasoline throughput is then allocated to each county based on the number of petroleum bulk stations
and terminals from the US Census County Business Patterns data for NAICS 42471. The number of petroleum
bulk stations and terminals by county is multiplied by the emissions factor for VOC to estimate VOC emissions
from bulk plants. County-level benzene speciation profiles are multiplied by VOC emissions to estimate benzene
emissions from bulk plants. National average speciation profiles for all other HAPs are multiplied by VOC
emissions to estimate HAP emissions from bulk plants.
Tank Trucks in Transit
The calculations for estimating VOC and HAP emissions from tank trucks in transit involve first calculating
county-level total gasoline consumption by summing onroad gasoline consumption and nonroad gasoline
consumption in each county. County-level gasoline consumption is multiplied by the emissions factor for VOC to
estimate VOC emissions from tank trucks in transit. County-level benzene speciation profiles are multiplied by
VOC emissions to estimate benzene emissions from tank trucks in transit. National average speciation profiles
for all other HAPs are multiplied by VOC emissions to estimate HAP emissions from tank trucks in transit.
Underground Storage Tank (UST) Breathing and Storing
The calculations for estimating VOC and HAP emissions from UST breathing and storing involve first calculating
county-level gasoline consumption by summing onroad gasoline consumption and nonroad gasoline
consumption in each county. County-level gasoline consumption is multiplied by the emissions factor for VOC to
estimate VOC emissions from UST breathing and storing. County-level benzene speciation profiles are multiplied
by VOC emissions to estimate benzene emissions from UST breathing and storing. National average speciation
profiles for all other HAPs are multiplied by VOC emissions to estimate HAP emissions from UST breathing and
storing.
Gasoline Service Station Unloading
The calculations for estimating VOC and HAP emissions from gasoline service station unloading involve first
calculating county-level total gasoline consumption by summing monthly onroad gasoline consumption and
nonroad gasoline consumption in each county by fuel subtype. Monthly county-level gasoline consumption is
then allocated to submerged, splash, and balanced filling technologies based on assumptions about the
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percentage of each filling technology used in each county. True vapor pressure is calculated for each county,
month, and fuel subtype. Uncontrolled loading loss of liquid is calculated using true vapor pressure,
temperature, molecular weight, and a saturation factor for the filling technology. Uncontrolled loading loss of
liquid loaded is multiplied by monthly county-level gasoline consumption by fuel type to estimate VOC emissions
from loading loss. Controlled VOC emissions are calculated by multiplying VOC emissions from loading loss by a
control efficiency value. Controlled VOC emissions are subtracted from VOC emissions from loading loss to
estimate monthly county-level VOC emissions by fuel subtype. Total county-level VOC emissions are calculated
by summing monthly county-level VOC emissions by fuel subtype. County-level benzene speciation profiles are
multiplied by VOC emissions to estimate benzene emissions from gasoline service station unloading. National
average speciation profiles for all other HAPs are multiplied by VOC emissions to estimate HAP emissions from
gasoline service station unloading.
Aviation Gasoline Stage 1
The calculations for estimating emissions from stage 1 aviation gasoline distribution involve first estimating the
amount of aviation gasoline consumed in each county, based on state-level aviation gasoline consumption data
from the Energy Information Administration (EIA). State-level aviation gasoline consumption is distributed to the
counties based on the proportion of Landing-Take Offs (LTOs). The total amount of gasoline consumed is used to
estimate non-fugitive and fugitive VOC emissions, as well as hazardous air pollutant (HAP) emissions.
Aviation Gasoline Stage 2
The calculations for estimating emissions from stage 2 aviation gasoline distribution involve first estimating the
amount of aviation gasoline consumed in each county based on state-level aviation gasoline consumption data
from the Energy Information Administration (EIA). State-level aviation gasoline consumption is distributed to the
counties based on the proportion of Landing-Take Offs (LTOs). The total amount of gasoline consumed is used to
estimate VOC and hazardous air pollutant (HAP) emissions.
4.7.3.1 Activity data
Bulk Terminals and Pipelines
Emissions from bulk terminals and pipelines are calculated by growing the 1998 emissions estimates developed
in support of the Gasoline MACT standard. Therefore, there is no activity data for this source category.
The activity data for estimating emissions from bulk plants are national volume of bulk plant gasoline
throughput. The ElA's Petroleum Navigator reports the volume of finished motor gasoline supplied in the U.S
[ref 5], The volume of finished motor gasoline is assumed to be the same as total gasoline consumption, and the
volume of bulk plant gasoline throughput is assumed to be 9 percent of total gasoline consumption [ref 6],
Bulk Plants
GTus,bp — Vus x 0-09
(1)
Where:
GTus, bp —
Vus
Bulk plant gasoline throughput in the U.S., in thousand barrels
Volume of finished motor gasoline in the U.S., in thousand barrels
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Tank Trucks in Transit
The activity data for tank trucks in transit is county-level total gasoline consumption. County-level nonroad
gasoline consumption is estimated by allocating MOVES derived state/SCC-level nonroad gasoline consumption
to the county-level based on nonroad county/SCC-level C02 emissions [ref 7], County-level onroad consumption
was estimated by subtracting the NMIM-derived national nonroad consumption from the ElA's estimate of
finished motor gasoline supplied and then allocating to counties using NMIM-derived onroad county-level C02
emissions [ref 7], County-level onroad consumption and county-level nonroad consumption are estimated by
summing county-level monthly consumption estimates.
Where:
GCor,c
GCoR,m
Where:
GCm,c
GCm,m
GC0r,c —
GC,
OR,m
(2)
Onroad gasoline consumption in county c, in gallons
Onroad gasoline consumption in county cfor month m, in gallons
GCNRc —
GC,
NR,m
(3)
Nonroad gasoline consumption in county c, in gallons
Nonroad gasoline consumption in county cfor month m, in gallons
County-level tank truck gasoline throughput is estimated by summing county-level onroad and nonroad
estimates, and multiplying the sum by 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) [ref 6],
= (GC0r,c + GCnr.c) x 1-09 (4)
Where:
GCc,t = Total gasoline consumption in county c, in gallons
GCor.c = Onroad gasoline consumption in county c, in gallons
GCNr,c = Nonroad gasoline consumption in county c, in gallons
Underground Storage Tank (UST) Breathing and Storing
The activity data for underground storage tank breathing and storing is county-level gasoline consumption,
calculated as described above in the tank trucks in transit section.
Gasoline Service Station Unloading
The activity data for gasoline service station unloading is county-level total gasoline consumption for each
month and fuel subtype from MOVES [ref 7],
County-level gasoline consumption is estimated by summing onroad gasoline consumption and nonroad
gasoline consumption and multiplying the sum by 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) [ref 6],
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GCc.t.m.f ~ (GCc,oR,m,f ~l~ GCClNR,m,f) * 1.09
(5)
Where:
GCc,t,m,f = Total gasoline consumption in county c for month m for fuel subtype/, in gallons
GCc, oR,m,f — Onroad gasoline consumption in county c for month m for fuel subtype /, in gallons
GCc,NR,m,f = Nonroad gasoline consumption in county cfor month m for fuel subtype/, in gallons
The county-level gasoline consumption is allocated to submerged, splash, and balanced filling technologies.
Percentages of each filling technology are derived from the EIIP study [ref 8], State, local, and tribal (SLT)
agencies may submit input templates to update theses default assumptions about the percentage of delivered
fuel by filling technology.
GCc,ft,m,f ~ GCc tjn.f * Percftc (6)
Where:
GCcjtmj = Total gasoline consumption in county c for filling technology ft for month m for fuel
subtype/, in gallons
GCc,t,m,f = Total gasoline consumption in county c for month m for fuel subtype/, in gallons
Percft,c = Percentage of filling technology ft in county c
Aviation Gasoline Stage 1 and 2
The activity data for this source category is the amount of aviation gasoline consumed, which is estimated using
data from the ElA's State Energy Data System (SEDS) [ref 9], The SEDS MSN Code AVTCP is used to identify the
total consumption of aviation gasoline in units of thousand barrels. Data are then converted to units of gallons.
AGS = AGBS X 42 9alloUS7barrei (7)
Where:
AGs = Annual consumption of AvGas for state s, in gallons
AGBS = Annual consumption of AvGas for state s, in barrels
4.7.3,2 Allocation procedure
Bulk Terminals
Emissions from bulk terminals are calculated by growing the 1998 emissions estimates developed in support of
the Gasoline MACT standard. The national-level emissions are allocated to the states based on the fraction of
refinery, bulk terminal, and natural gas plant stocks in each state. The state-level emissions are distributed to
the counties based on employment in NAICS 42471.
Pipelines
Emissions from pipelines are calculated by growing the 1998 emissions estimates developed in support of the
Gasoline MACT standard. The national-level emissions are allocated to the PAD Districts based on data on the
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movement of finished motor gasoline by pipeline between PAD Districts from the EIA. The emissions in each
PAD District are distributed to the counties based on employment in NAICS 42471.
Bulk Plants
The national volume of bulk plant gasoline throughput is allocated to counties using County Business Patterns
employment data for NAICS code 42471 (Petroleum Bulk Stations and Terminals) [ref 10]. The number of
petroleum bulk stations and terminals is first summed to the national level.
(8)
Empus = ^ Empc
Where:
Empus = Number of petroleum bulk stations and terminals in the U.S.
Empc = Number of petroleum bulk stations and terminals in county c
The fraction of petroleum bulk stations and terminals by county is calculated by dividing the total number of
petroleum bulk stations and terminals in each county by the total number of petroleum bulk stations and
terminals in the U.S.
(9)
„ „ Empc
EmpFracc =
Empus
Where:
EmpFracc = Total fraction of petroleum bulk stations and terminals in county c
Empc = Number of petroleum bulk stations and terminals in county c
Empus = Number of petroleum bulk stations and terminals in the U.S.
The county-level volume of bulk plant gasoline throughput is calculated by multiplying the fraction of petroleum
bulk stations and terminals in each county by the national volume of bulk plant gasoline throughput.
GTC
.bp ~ GTUSibp x EmpFracc (10)
Where:
GTc,bP = Bulk plant gasoline throughput in county c, in thousand barrels
GTus,bP = Bulk plant gasoline throughput in the U.S., in thousand barrels, from equation 1
EmpFracc = Total fraction of petroleum bulk stations and terminals in county c
Due to concerns with releasing confidential business information, the CBP does not release exact numbers for a
given NAICS code if the data can be traced to an individual business. Instead, a series of range codes is used.
Many counties and some smaller states have only one petroleum bulk station and terminal facility, leading to
withheld data in the county and/or state business pattern data. To estimate employment in counties and states
with withheld data, the following procedure is used for NAICS code 42471.
To gap-fill withheld state-level employment data:
a. State-level data for states with known employment in NAICS 42471 are summed to the national level.
b. The total sum of state-level known employment from step a is subtracted from the national total
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reported employment for NAICS 42471 in the national-level CBP to determine the employment total for
the withheld states.
c. Each of the withheld states is assigned the midpoint of the range code reported for that state. Table
4-48 lists the range codes and midpoints.
d. The midpoints for the states with withheld data are summed to the national level.
e. An adjustment factor is created by dividing the number of withheld employees (calculated in step b of
this section) by the sum of the midpoints (step d).
f. For the states with withheld employment data, the midpoint of the range for that state (step c) is
multiplied by the adjustment factor (step e) to calculate the adjusted state-level employment for
landfills.
These same steps are then followed to fill in withheld data in the county-level business patterns.
g. County-level data for counties with known employment are summed by state.
h. County-level known employment is subtracted from the state total reported in state-level CBP (or, if the
state-level data are withheld, from the state total estimated using the procedure discussed above).
i. Each of the withheld counties is assigned the midpoint of the range code (Table 4-48).
j. The midpoints for the counties with withheld data are summed to the state level.
k. An adjustment factor is created by dividing the number of withheld employees (step h) by the sum of
the midpoints (step j).
I. For counties with withheld employment data, the midpoints (step i) are multiplied by the adjustment
factor (step k) to calculate the adjusted county-level employment for landfills.
Table 4-48: Ranges and midpoints for data withheld from state and county business patterns
Employment
Code
Ranges
Midpoint
A
0-19
10
B
20-99
60
C
100-249
175
E
250-499
375
F
500-999
750
G
1,000-2,499
1,750
H
2,500-4,999
3,750
1
5,000-9,999
7,500
J
10,000-
24,999
17,500
K
25,000-
49,999
37,500
L
50,000-
99,999
75,000
M
100,000+
Tank Trucks in Transit. Underground Storage Tank (UST) Breathing and Storing, and Gasoline Service Station
Unloading
The activity data for these sources is available at the county-level; therefore, county allocation is not needed.
Aviation Gasoline Distribution Stage 1 and 2
State-level gasoline consumption (from equation 7) is allocated to the county-level using the ratio of county-to-
state-level LTOs. State and county LTO data for 2017 were compiled by the U.S. EPA's Office of Air Quality,
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Planning and Standards (OAQPS) [ref 11],
Where:
RLTOr =
LTOc
LTO<
(11)
RLTOc --
- The
LTOc --
= The
LTOs --
= The
LTO data for turbine-powered airplanes were excluded because turbine-powered planes do not use aviation
gasoline. Additionally, LTOs at airports that do not have aviation gasoline refueling, according to data from FAA
Form 5010, were also excluded [ref 12].
The state-level gasoline consumption values from equation 7 are multiplied by the proportion of LTOs in each
county to estimate the county-level amount of aviation gasoline consumed.
AGC = AGS X RLTOc (12)
Where:
AGC = Annual consumption of AvGas in county c, in gallons
RLTOc = The ratio of landing-take offs (LTOs) in county c
4.7,3.3 Emission factors
Bulk Terminals
Emissions from bulk terminals are calculated by growing the 1998 emissions estimates developed in support of
the Gasoline MACT standard. Therefore, there are no activity-based emissions factors for bulk terminals.
HAP emissions are estimated using speciation profiles shown in Table 4-49. Note that the values shown in Table
4-49 are percentages and should be divided by 100 before being multiplied by the VOC emissions.
Table 4-49: HAP speciation factors for stage I gasoline distribution.
HAP
Pollutant
Code
Percentage of
VOC Emissions
Reference
Benzene
71432
0.27
13
2,2,4-
Trimethylpentane
540841
0.75
13
Cumene
98828
0.012
13
Ethyl Benzene
100414
0.053
13
n-Hexane
110543
1.8
13
Naphthalene
91203
0.00027
13
Toluene
108883
1.4
13
Xylenes
1330207
0.56
13
Pipelines
Emissions from pipelines are calculated by growing the 1998 emissions estimates developed in support of the
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Gasoline MACT standard. Therefore, there are no activity-based emissions factors for pipelines. HAP emissions
are estimated using speciation profiles shown in Table 4-49.
Bulk Plants
The VOC emissions factor for bulk plants is 8.62 pounds of VOC per 1,000 gallons of gasoline [ref 13], HAP
emissions are calculated using speciation profiles from Table 4-49, with the exception of benzene. Speciation
profiles for benzene emissions from bulk plants are based on county-specific refueling emissions data from
MOVES [ref 14].
Tank Trucks in Transit
The VOC emissions factor for tank trucks in transit is 0.06 pounds of VOC per 1,000 gallons of gasoline. As shown
in Table 4-50, the VOC emission factor is the sum of the individual emission factors reported in the Gasoline
Distribution EIIP guidance document for gasoline-filled trucks (traveling to service station/bulk plant for delivery)
and vapor-filled trucks (traveling to bulk terminal/plant for reloading) [ref 3],
Table 4-50: Tank trucks in transit VOC emission factors
Transit Type
VOC Emission Factor
Reference
Vapor-Filled Trucks
0.055 lb/1,000 gallons
7
Gasoline Filled Trucks
0.005 lb/1,000 gallons
7
Total
0.06 lb/1,000 gallons
HAP emissions are calculated using speciation profiles from Table 4-49, except for benzene. Speciation profiles
for benzene emissions from bulk plants are based on county-specific refueling emissions data from MOVES.
Underground Storage Tank (UST) Breathing and Storing
The VOC emissions factor for underground storage tank breathing and storing is 1 pound per 1,000 gallons. The
VOC emissions factor for underground storage tank breathing and storing is recommended by the Gasoline
Distribution EIIP guidance document [ref 3],
HAP emissions are calculated using speciation profiles from Table 4-49, except for benzene. Speciation profiles
for benzene emissions from bulk plants are based on county-specific refueling emissions data from MOVES.
Gasoline Service Station Unloading
To calculate the VOC emissions factor for gasoline service station unloading, first calculate the true vapor
pressure for each county and month using the following equation and data from MOVES [ref 7]:
Geographic-specific information on the temperature of gasoline and the method of loading were obtained from
a Stage I and II gasoline emission inventory study prepared for the EIIP.
The true vapor pressure is calculated using the following equation:
f = I
c,m,f |
0.7553 -
+
413
Tc.m + 459.6
S0'5 log10(RVPcmj) —
1.854-
1042
2416
Tc.rn + 459.6
2.013
logio (RVPc>m,f)
8742
Tc.rn + 459.6
:0.5
(13)
Tc.m + 459.6
+ 15.64
4-100
-------
Where:
Pc,m,f = Stock true vapor pressure for county c in month m for fuel subtype/, in pounds per square
inch absolute
TC/m = Stock temperature for county c in month m, in degrees Fahrenheit
RVPc,m,f = Reid vapor pressure for county c in month m for fuel subtype/, in pounds per square inch
5 = Slope of the ASTM distillation curve at 10 percent evaporated, in degrees Fahrenheit per
percent (assumed that S=3.0 for gasoline per Figure 7.1-14a of AP-42) [ref 13]
The following equation is used to calculate the VOC emissions factor for gasoline service station unloading:
LCimif = 12.46 x Sft x Pcm f x M/T (14)
Where:
Lc,m,f = Uncontrolled loading loss of liquid loaded, in pounds per thousand gallons
Sft = Saturation factor for filling technology ft
Pc,m,f = True vapor pressure of liquid loaded, in pounds per square inch absolute
M = Molecular weight of vapors, in pounds per pound per mole
T = Temperature of liquid loaded (Rankine) [ref 8]
HAP emissions are calculated using speciation profiles from Table 4-49, except for benzene. Speciation profiles
for benzene emissions from bulk plants are based on county-specific refueling emissions data from MOVES.
Aviation Gasoline Distribution Stage 1
Emission factors for stage 1 aviation gasoline distribution are reported in Table 4-51 and Table 4-52. The
emissions factors for fugitive and non-fugitive VOC are taken from the TRC report Estimation of Alkylated Lead
Emissions, Final Report [ref 4], The emissions factors for the HAPs are taken from multiple sources: the TRC
report; the EPA report Locating and Estimating Air Emissions from Source of Ethylene Dichloride [ref 14]; a
memorandum to EPA/OAQPS [ref 15], and a personal email between EPA/OAQPS employees [ref 16], The tables
list the emission factors as reported in the original references, and the emission factors that have been
converted (if necessary) for use in the NEI emissions calculations.
4-101
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Table 4-51: VOC Emissions Factors for Aviation Gasoline Distribution-Stage 1 (2501080050)
Pollutant
Emission Source
Emission
Factor
(original)
Emission
Factor Units
(original)
Emission
Factor
(converted)
Emission
Factor Units
(converted)
Factor
Reference
VOC
Aviation Gas Unloading/
Tank Filling - tank fill
1,081
mg/l
gasoline*
9.02E-3
LB/GAL
AvGas
Table 7 in
Reference
4
Aviation Gas Unloading/
Tank Filling - Storage tank
working
432
3.61E-3
Aviation Gas Tank Truck
Filling - Composite
1,235
1.03E-2
Aviation Gas Storage Tank
- Breathing losses
203
1.69E-3
Aviation Gas - Fugitive
from valves
0.26
kg/valve/day
5.73E-1
LB/valve/day
Aviation Gas - Fugitive
from pumps
2.7
kg/seal/day
5.95E0
LB/seal/day
Converted from mg/l to LB/GAL using conversion factors of 3.785 liters per gallon and 453,592 mg per pound.
Table 4-52: HAP Emissions Factors for Aviation Gasoline Distribution-Stage 1 (2501080050)
Pollutant
Pollutant Code
Emission
Factor
(original)
Emission
Factor
Units
(original)
Emission
Factor
(converted)
Emission
Factor
Units
(converted)
Factor
Reference
Ethylene Dichloride
107062
0.26
mg/l
gasoline*
2.17E-6
LB/GAL
AvGas
14
Lead**
7439921
6.27E-6
4
2,2,4-
Trimethylpentane
540841
8.00E-3
15
Benzene
71432
9.00E-3
Cumene
98828
1.00E-4
LB/ LB VOC
16
Ethylbenzene
100414
1.00E-3
Hexane
110543
1.60E-2
Naphthalene
91203
5.00E-4
15
Toluene
108883
1.30E-2
Xylene
1330207
5.00E-3
* Converted from mg/l to LB/GAL using conversion factors of 3.785 liters per gallon and 453,592 mg per pound.
** The 2011 NEI included tetraethyl lead (TEL) with an emission factor of 9.78E-6 Ibs./lb. VOC. In 2017, EPA only
accounts for the emissions of elemental lead. The TEL emission factor was modified by multiplying by the ratio
of the atomic mass of lead to the atomic mass of TEL, or 64.06%.
Aviation Gasoline Distribution Stage 2
Emission factors for stage 2 of aviation gasoline distribution are reported in Table 4-53 and Table 4-54. The
emissions factors for VOC are taken from the TRC report Estimation of Alkylated Lead Emissions, Final Report
[ref 4]. The emissions factors for the HAPs are taken from multiple sources: the TRC report; the EPA report
4-102
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Locating and Estimating Air Emissions from Source of Ethylene Dichloride [ref 14]; a memorandum to
EPA/OAQPS [ref 15]; and a personal email between OAQPS employees [ref 16], The tables list the emission
factors as reported in the original references, and the emission factors that have been converted (if necessary)
for use in the NEI emissions calculations.
Table 4-53: VOC Emissions Factors for Aviation Gasoline Distribution-Stage 2 (2501080100)
Pollutant
Emission Source
Emission
Factor
(original)
Emission
Factor
Units
(original)
Emission
Factor
Emission
Factor
Units
Factor
Reference
VOC
Fuel Transfer from TankerTrucks
to General Aviation Aircraft
1,420*
mg/l
gasoline**
8.27E-4
LB/GAL
AvGas
4
* This emission factor represents the sum of the emission factor for uncontrolled displacement losses (1,340
mg/l) and spillage (80 mg/l).
** Converted from mg/l to LB/GAL using conversion factors of 3.785 liters per gallon and 453,592 mg per pound.
Table 4-54: HAP Emissions Factors for Aviation Gasoline Distribution-Stage 2 (2501080100)
Pollutant
Pollutant
Code
Emission
Source
Emission
Factor
(original)
Emission
Factor Units
(original)
Emission
Factor
(converted)
Emission
Factor
Units
(converted)
Factor
Reference
Ethylene
Dichloride
107062
All
processes
0.226*
mg/l
gasoline**
1.88 E-6
LB/GAL
AvGas
14
Lead***
7439921
All
processes
8.50 E-8
4
2,2,4-
Trimethylpentane
540841
All
processes
8.00E-3
LB/ LB
VOC
15
Benzene
71432
All
processes
9.00E-3
Cumene
98828
All
processes
1.00E-4
16
Ethylbenzene
100414
All
processes
1.00E-3
15
Hexane
110543
All
processes
1.60E-2
Naphthalene
91203
All
processes
5.00E-4
Toluene
108883
All
processes
1.30E-2
Xylene
1330207
All
processes
5.00E-3
* This emission factor represents the sum of the emission factor for uncontrolled displacement losses (0.21
mg/l) and spillage (0.016 mg/l).
** Converted from mg/l to LB/GAL using conversion factors of 3.785 liters per gallon and 453,592 mg per pound.
4-103
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*** The 2011 NEI included tetraethyl lead (TEL) with an emission factor of 9.78E-6 LB/GAL AvGas. In 2017, EPA
only accounts for the emissions of elemental lead. The TEL emission factor was modified by multiplying by the
ratio of the atomic mass of lead to the atomic mass of TEL, or 64.06%.
4.7.3.4 Controls
There are county-level control efficiencies for service station unloading, including assumptions about the
percentage of gasoline unloaded under different filling technologies: splash, submerged, or balanced. There are
no controls assumed for all other sources.
4.7,3.5 Emissions
Bulk Terminals
Emissions of VOCs for bulk terminals and pipelines are calculated by multiplying 1998 national emissions
estimates developed in support of the Gasoline Distribution MACT standard (Table 4-55) by the 2017 to 1998
ratio of the national volume of wholesale gasoline supplied [ref 17, ref 18]. Emissions are converted from
megagrams (Mg) to tons.
G2017 (15)
Evoc,us,bt = EMACT,us,bt x 7 x 1-1023 ton per Mg
"1998
Where:
Evoqusm = Annual national-level emissions of VOC from bulk terminals, in tons
Emact,usm = 1998 national VOC emission estimates developed for Gasoline Distribution MACT standard
from bulk terminals, in Mg
G2017 = National volume of wholesale gasoline supplied in 2017, in thousand barrels per day
G1998 = National volume of wholesale gasoline supplied in 1998, in thousand barrels per day
Table 4-55: 1998 Post-MACT Control Emissions
Emission Point
1998 Post-MACT Control
Emissions (Mg)
Reference
Pipelines
79,830
5
Bulk Terminals
137,555
5
National VOC emissions are allocated to states using the fraction of refinery, bulk terminal, and natural gas plant
stocks of motor gasoline in each state (see Table 4-56) [ref 19].
GasFracs = — (16)
MUS
Where:
GasFracs = Fraction of motor gasoline in state s
Ms = Amount of motor gasoline in state s
Mus = Amount of motor gasoline in the U.S.
4-104
-------
Table 4-56: Refinery, Bulk Terminal, and Natural Gas Plant Stocks of Motor Gasoline, 2017
State
Motor Gasoline
(Thousand Barrels)
State
Motor Gasoline
(Thousand Barrels)
Alabama
205
Montana
357
Alaska
793
Nebraska
92
Arizona
87
Nevada
146
Arkansas
175
New Hampshire
*
California
286
New Jersey
376
Colorado
190
New Mexico
108
Connecticut
*
New York
17
Delaware
*
North Carolina
200
District of Columbia
*
North Dakota
48
Florida
732
Ohio
970
Georgia
268
Oklahoma
348
Hawaii
1
Oregon
68
Idaho
276
Pennsylvania
25
Illinois
410
Rhode Island
*
Indiana
352
South Carolina
228
Iowa
183
South Dakota
77
Kansas
325
Tennessee
195
Kentucky
378
Texas
3,855
Louisiana
1,662
Utah
127
Maine
*
Vermont
30
Maryland
*
Virginia
150
Massachusetts
7
Washington
383
Michigan
266
West Virginia
36
Minnesota
363
Wisconsin
133
Mississippi
1,213
Wyoming
455
Missouri
202
Total
16,798
* No Data Reported
The fraction of stocks of motor gasoline in each state is then used to distribute the VOC and HAP emissions.
Evoc,bt,s — GasFracs x Evocusbt (17)
Where:
Evoc,bt,s = Annual VOC emissions in state s from bulk terminals, in tons
GasFracs = Fraction of motor gasoline in state s
Evoc,us,bt = Annual national-level VOC emissions from bulk terminals, in tons
State-level VOC emissions are allocated to counties using the fraction of petroleum bulk stations and terminals
facilities employees in each county from the US Census County Business patterns data for NAICS code 42471 [ref
10],
„ „ Empc
EmpFracr =
Emps
4-105
-------
Where:
EmpFraCc = Fraction of petroleum bulk stations and terminals facilities employees in county c
Empc = Number of petroleum bulk stations and terminals facilities employees in county c
Emps = Number of petroleum bulk stations and terminals facilities employees in state s
Due to concerns with releasing confidential business information, the CBP does not release exact numbers for a
given NAICS code if the data can be traced to an individual business. Instead, a series of range codes is used.
Many counties and some smaller states have only one petroleum bulk station and terminal facility, leading to
withheld data in the county and/or state business pattern data. To estimate employment in counties and states
with withheld data, the procedure discussed in Section 4.7.3.2 is used for NAICS code 42471.
The fraction of petroleum bulk stations and terminals facilities employees in each county is then used to
distribute the VOC emissions.
Evoc.bt.c = EmpFracc x EV0Cibt,s (19)
Where:
Evoc,bt,c = Annual VOC emissions from bulk terminals in county c, in tons
EmpFraCc = Fraction of petroleum bulk stations and terminals facilities employees in county c
Evoc,bt,s = Annual VOC emissions from bulk terminals in state s, in tons
Emissions of HAPs are calculated by multiplying emissions of VOCs by a national average speciation profile
(Table 4-49) [ref 20],
Ep,c,bt ~ Eyoc,c,bt * Sp (20)
Where:
EP,bt = Annual emissions of pollutant p in county c from bulk terminal, in tons
Evocm = Annual VOC emissions in county c from bulk terminals, in tons
Sp = Speciation profile of pollutant p, as a fraction of VOC emissions
Pipelines
Emissions of VOCs for pipelines are calculated by multiplying 1998 national estimates developed in support of
the Gasoline Distribution MACT standard (Table 4-55) by the 2017 to 1998 ratio of the national volume of
wholesale gasoline supplied [ref 17, ref 18], Emissions are converted to tons.
Evoc,us,pi — Emact,us.,pI x r
U
2017
x 1.1023 tonper Mg
(21)
1998
Where:
Evoc,us,pi = Annual national-level emissions of VOC from pipelines, in tons
Emact,us,pi = 1998 national VOC emission estimates developed for Gasoline Distribution MACT standard
from pipelines, in Mg
G2017 = National volume of wholesale gasoline supplied in 2017, in thousand barrels per day
G1998 = National volume of wholesale gasoline supplied in 1998, in thousand barrels per day
4-106
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National VOC and HAP emissions are allocated to PAD Districts using the fraction of the total amount of finished
motor gasoline that originated in each PAD District in 2017. There are five PAD Districts across the United States.
PAD District 1 comprises seventeen states plus the District of Columbia along the Atlantic Coast; PAD District 2
comprises fifteen states in the Midwest; PAD District 3 comprises six states in South Central U.S.; PAD District 4
comprises five states in the Rocky Mountains; and PAD District 5 comprises seven states along the West Coast.
These data, which are displayed below in Table 4-57, are reported in Table 37 of Volume 1 of Petroleum Supply
Annual 2017 [ref 21]. States in each PAD District are shown in Table 4-58.
MUS
Where:
Mpo (22)
PADDFracpD = v 1
PADDFracpo = Fraction of motor gasoline in PAD District PD
Mpd = Amount of finished motor gasoline in PAD District PD, in thousand barrels
Mus = Amount of finished motor gasoline in the U.S., in thousand barrels
Evoc,PD,pi ~ PADDFracPD X EVoc,us,pi (23)
Where:
Evoqpd,pi = Annual VOC emissions from pipelines in PAD District PD, in tons
PADDFracpo = Fraction of motor gasoline in PAD District PD
Evoc,us,Pi = Annual national-level VOC emissions of from pipelines, in tons
Pipeline emissions in each PAD District are allocated to counties based on County Business Patterns employment
data. Because employment data for NAICS code 48691 (Pipeline Transportation of Refined Petroleum Products)
are often withheld due to confidentiality reasons, the number of employees in NAICS code 42471 (Petroleum
Bulk Stations and Terminals) are used for this allocation. To better account for the location of refined petroleum
pipelines, however, no activity is allocated to States which had employees in this NAICS code but did not have
employees in NAICS code 48691 (i.e., District of Columbia, Idaho, Maine, New Hampshire, Vermont, and West
Virginia). To allocate pipeline emissions in each PAD District to counties, first the county level employment data
for NAICS code 42471 is summed to the PAD District.
EmpPD = I Empc
(24)
Where:
EmpPD = Number of petroleum bulk stations and terminals facilities employees in PAD District PD
Empc = Number of petroleum bulk stations and terminals facilities employees in county c
The fraction of petroleum bulk stations and terminals employees in each county is used to allocate the emissions
from the PAD District to counties.
Empc (25)
EmpFracc =
EmpPD
4-107
-------
Where:
EmpFraCc = Fraction of petroleum bulk stations and terminals facilities employees in county c
Empc = Number of petroleum bulk stations and terminals facilities employees in county c
EmpPD = Number of petroleum bulk stations and terminals facilities employees in PAD District PD
Due to concerns with releasing confidential business information, the CBP does not release exact numbers for a
given NAICS code if the data can be traced to an individual business. Instead, a series of range codes is used.
Many counties and some smaller states have only one petroleum bulk station and terminal facility, leading to
withheld data in the county and/or state business pattern data. To estimate employment in counties and states
with withheld data, the procedure discussed in Section 4.7.3.2 is used for NAICS code 42471.
The fraction of petroleum bulk stations and terminals facilities employees in each county is then used to
distribute the VOC emissions.
Evoc,c,pi = EmpFracc X EV0CPD vi (26)
Where:
Evoc,c,pi = Annual VOC emissions from pipelines in county c, in tons
EmpFraCc = Fraction of petroleum bulk stations and terminals facilities employees in county c
Evoc,pd,pi= Annual VOC emissions from pipelines in PAD District PD, in tons
Emissions of HAPs are calculated by multiplying emissions of VOCs by a national average speciation profile [ref
13]. Table 4-49 includes these speciation profiles. Total VOC emission estimates are used so emissions represent
total emissions.
Ep,c,pi Evoc,c,pi ^ Sp (27)
Where:
Ep,c,pi = Annual emissions of pollutant p from pipelines in county c, in tons
Evoc,c,pi = Annual VOC emissions from pipelines in county c, in tons
Sp = Speciation profile of pollutant p, as a fraction of VOC emissions
Table 4-57: Movement of Finished Motor Gasoline (thousand barrels) by Pipeline in PAD Districts, 2017
PADD
Gasoline Moved (thousand barrels)
PADD Fraction
1
40,770
0.34
2
20,438
0.17
3
44,536
0.37
4
10,034
0.08
5
3,856
0.03
Table 4-58: States by PAD District
PAD District 1
PAD District 2
PAD District 3
PAD District 4
PAD District 5
Connecticut
Illinois
Alabama
Colorado
Alaska
Delaware
Indiana
Arkansas
Idaho
Arizona
Florida
Iowa
Louisiana
Montana
California
Georgia
Kansas
Mississippi
Utah
Hawaii
4-108
-------
PAD District 1
PAD District 2
PAD District 3
PAD District 4
PAD District 5
Maine
Kentucky
New Mexico
Wyoming
Nevada
Maryland
Michigan
Texas
Oregon
Massachusetts
Minnesota
Washington
New Hampshire
Missouri
New Jersey
Nebraska
New York
North Dakota
North Carolina
Ohio
Pennsylvania
Oklahoma
Rhode Island
South Dakota
South Carolina
Tennessee
Vermont
Wisconsin
Virginia
West Virginia
Bulk Plants
VOC emissions from bulk plants are estimated by multiplying the VOC emission factor by county-level volume of
bulk plant gasoline throughput.
EVoc,c,bp = EFvoc bp/1000 gallons x GTc bp x 42 gallons per barrel (28)
Where:
Evoc,c,bP = Annual emissions of VOC from bulk plants in county c, in pounds
EFvoc,bp = Emissions factor for VOC from bulk plants, in pounds per 1,000 gallons
GTc,bP = Gasoline throughput for bulk plants in county c, in thousand barrels
Benzene emissions are estimated by multiplying VOC emissions by county-level speciation profiles from MOVES
[ref 7],
EBZ,c,bp ~ EvoC,c,bp * Sgzc (29)
Where:
EBz,c,bP = Annual emissions of benzene from bulk plants in county c, in pounds
Evoc,c,bP = Annual emissions of VOC from bulk plants in county c, in pounds
Sbz,c = Speciation profile for benzene for bulk plants in county c, as a fraction of VOC
All other HAPs emissions are estimated by multiplying VOC emissions by the national average speciation profiles
displayed in Table 4-49.
Ep,c,bp Ev0C,c,bp ^ ^p,c (3^)
4-109
-------
Where:
Ep,c,bp = Annual emissions of pollutant p from bulk plants in county c, in pounds
Evoc,c,bP = Annual emissions of VOC from bulk plants in county c, in pounds
Sp,c = Speciation profile for pollutant p for bulk plants in county c, as a fraction of VOC
Tank Trucks in Transit
VOC emissions from tank trucks in transit are calculated by multiplying county-level total gasoline consumption
by the VOC emission factor for tank trucks in transit.
Evoc,c,tt — EFVoc,tt x '
GCc,t (31)
1000 gallons
Where:
Evoc,c,tt = Annual emissions of VOC from tank trucks in transit in county c, in pounds
EFvoqu = Emissions factor for VOC from tank trucks in transit, in pounds per 1,000 gallons
GCc,t = Gasoline consumption for tank trucks in transit in county c, gallons
Benzene emissions are estimated by multiplying VOC emissions by county-level speciation profiles from MOVES.
^BZ,c,tt = EvOC,c,tt x $BZ,c (32)
Where:
£sz,c,tt = Annual emissions of benzene from tank trucks in transit in county c, in pounds
Evoc,c,tt = Annual emissions of VOC from tank trucks in transit in county c, in pounds
Sbz,c = Speciation profile for benzene for tank trucks in transit in county c, as a fraction of VOC
All other HAPs emissions are estimated by multiplying VOC emissions by the national average speciation profiles
in Table 4-49.
Ep,c,tt ~ Evoc,c,tt x SpiC (33)
Where:
EP,c,tt = Annual emissions of pollutant p from tank trucks in transit in county c, in pounds
Evoc,c,tt = Annual emissions of VOC from tank trucks in transit in county c, in pounds
Sp,c = Speciation profile for pollutant p for tank trucks in transit in county c, as a fraction of VOC
Underground Storage Tank (UST) Breathing and Storing
VOC emissions from UST breathing and storing are calculated by multiplying county-level total gasoline
consumption by the VOC emission factor for UST breathing and storing.
F — FF GCc* O4)
bvOC,c,ust £fV0C,USt X 100() gaRons
4-110
-------
Where:
Evoc,c,ust = Annual emissions of VOCfrom UST breathing and storing in county c, in pounds
EFvoc,ust = Emissions factor for VOC from UST breathing and storing, in pounds per 1,000 gallons
GCc,t = Gasoline consumption for UST breathing and storing in county c, in gallons
Benzene emissions are estimated by multiplying VOC emissions by county-level speciation profiles from MOVES.
EBZ,c,ust ~ EvoC,c,ust * SBZ,c (35)
Where:
Ebz,c,usi = Annual emissions of benzene from UST breathing and storing in county c, in pounds
Evoc,c,ust = Annual emissions of VOCfrom UST breathing and storing in county c, in pounds
Sbz,c = Speciation profile for benzene for UST breathing and storing in county c, as a fraction of VOC
All other HAPs emissions are estimated by multiplying VOC emissions by the national average speciation profiles
displayed in Table 4-49.
Ep,c,ust Ev0C,c,ust ^ ^p,c (36)
Where:
EP,c,ust = Annual emissions of pollutant p from UST breathing and storing in county c, in pounds
Evoc,c,ust = Annual emissions of VOCfrom UST breathing and storing in county c, in pounds
Sp,c = Speciation profile for pollutant p for UST breathing and storing in county c, as a fraction of
VOC
Gasoline Service Station Unloading
County-level uncontrolled loading loss of liquid loaded VOC emissions are calculated by multiplying the loading
loss calculated in equation 9 by the total gasoline consumption in each county for each filling technology.
GCc,ft,m,f . (37)
EVOC,c,m,f,ft,ll - 1000 gallons X Lc,m,f
Where:
E vo c, c, mjjt, ii = VOC emissions from loading loss in county c for month m for filling technology ft and fuel
subtype/, in pounds
GCc,ft,m,f = Total gasoline consumption in county cfor month m for filling technology ft and fuel subtype
f in gallons
Lc,m,f = Uncontrolled loading loss of liquid loaded for county c for month m and fuel subtype/, in
pounds per thousand gallons
County-level controlled VOC emissions are calculated by multiplying loading loss VOC emissions by a county-
level control efficiency [ref 8], Emissions are divided by 100 to convert the control efficiency from a percentage.
4-111
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Evoc,c,m.f,ft,ct ~ EvoC,c.,m,f,ft,ll * CEc/100 (38)
Where:
Evoc,c,m,fjt,ct = Controlled VOC emissions in county cfor month m for filling technology ft and fuel subtype
/ in pounds
Evoc,c,m,fjt,u = VOC emissions from loading loss in county c month m for filling technology ft and fuel
subtype/, in pounds
CEc = Control efficiency value for county c, as a percentage
County-level monthly VOC emissions by fuel subtype and filling technology are calculated by subtracting
controlled VOC emissions from VOC emissions from loading loss.
EvOC.c.mJJt ~ EvoC.c.mj,ft,ll ~ ^VOC,c,m.f,ft,ct (39)
Where:
Evoc,c,m,f,ft = VOC emissions in from gasoline service station unloading county cfor month m for filling
technology ft and fuel subtype/, in pounds
Evoc,c,m,fjt,ct = Controlled VOC emissions in county cfor month m for filling technology ft and fuel subtype
/ in pounds
Evoc,c,m,fjt,u = VOC emissions from loading loss in county c month m for filling technology ft and fuel
subtype/ in pounds
County-level total VOC emissions by filling technology are calculated by summing VOC emissions for each month
and fuel subtype.
F -X F (40)
£VOC,c,ft ~ / _ ^VOC,c,m,f,ft
Where:
Evoc,c,ft = Annual VOC emissions in from filling type ft for gasoline service station unloading for
county c, in pounds
Evoc,c,m,f,ft = VOC emissions in from gasoline service station unloading county cfor month m for filling
technology ft and fuel subtype/ in pounds
Benzene emissions are estimated by multiplying VOC emissions by county-level speciation profiles from MOVES.
EbZ,c,ssu ~ EvoC,c,ssu * SbZ,c. (41)
Where:
Ebz,c,ssu = Annual emissions of benzene from gasoline service station unloading in county c, in pounds
Evoc,c,ssu = Annual emissions of VOC from gasoline service station unloading in county c, in pounds
Sbz,c = Speciation profile for benzene for gasoline service station unloading in county c, as a fraction
of VOC
All other HAPs emissions are estimated by multiplying VOC emissions by the national average speciation profiles
displayed in Table 4-49.
4-112
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Ep,c,ust ~ EvoC,c,ust * Sp C
(42)
Where:
EP,c,ssu = Annual emissions of pollutant p from gasoline service station unloading in county c, in pounds
Evoc,c,ssu = Annual emissions of VOC from gasoline service station unloading in county c, in pounds
Sp,c = Speciation profile for pollutant p for gasoline service station unloading in county c, as a
fraction of VOC
Aviation Gasoline Distribution Stage 1
The annual aviation gasoline consumed in each county is used with the emissions factors in Table 4-51 and Table
4-52 to estimate emissions. Emissions of non-fugitive VOC from multiple sources, including tank truck filling and
storage tank breathing, are estimated by multiplying gasoline consumed by the emissions factor in Table 4-51.
For VOC, emissions are multiplied by a conversion factor to convert from tons to pounds.
NFETiC = AGC X EFV0Cir -H 2000 lbs/ton (43)
Where:
NFEr,c = Annual non-fugitive VOC emissions for source r in county c, in tons per year
EFVoc,r = VOC emission factor for source r, units vary based on pollutant.
Fugitive VOC emissions from valves and pumps are estimated by multiplying gasoline consumed by the
emissions factor in Table 4-51. Assumptions concerning bulk terminals used in these calculations can be found in
Table 4-59.
Table 4-59: Assumptions for Bulk Terminals Using Aviation Gasoline
Parameter
Data
Reference
Number of Bulk Plant Equivalents (U.S.)
2,442 plants
Number of valves per bulk plant
50 valves/plant
4, Table 2-
8
Number of pumps per bulk plant
2 pumps/plant
Number of seals per bulk plant
4 seals/pump
Number of days per year used
300 days
Where:
VFEC = BPE xFx EFV0C>r X D X LT°c/LTOus - 2000 lbs/ton (44)
PFEC = BPE xPxSx EFV0Cr xDx LT°c/LTOus - 2000 lbs/ton (45)
PFEC = Annual fugitive VOC emissions from valves in county c, in tons
VFEC = Annual fugitive VOC emissions from pumps in county c, in tons
BPE = Number of bulk plant equivalents in the U.S.
V = Number of valves per plant in the U.S.
P = Number of pumps per plant in the U.S.
4-113
-------
5 = Number of seals per plant in the U.S.
D = Number of days used per year
LTOc = The number of LTOs in county c
LTOus = The number of LTOs in the United States
Total Annual VOC emissions in each county are estimated by summing the fugitive emissions (from equations 35
and 36) and all sources of non-fugitive emissions (from equation 34).
v-1 (46)
Evoc.c = 2, NFEc + PFEC + VFEC
r
Where:
Evoqc = Annual VOC emissions in county c, in tons
Emissions of all HAPs, except ethylene dichloride, are estimated by applying speciation factors found in Table
4-52 to the annual VOC emissions. For HAPs, no conversion factor is needed, and the emissions are reported in
tons.
Eh,c — Evoc.c x$Eh (47)
Where:
Eh,c = Annual emissions of HAP h in county c, in tons per year
SFh = Speciation factor for HAP h, in tons of HAP emissions per ton of VOC emissions
Ethylene dichloride emissions are calculated by multiplying the gasoline consumed in each county (from
equation 33) by the emission factor from Table 4-49. For ethylene dichloride, emissions are multiplied by a
conversion factor to convert from to pounds tons.
Ee c = AGC X EFe X 0.0005 tons/lb (48)
Where:
Ee,c = Annual emissions of ethylene dichloride in county c, in tons
EFe = Emission factor for ethylene dichloride, in lbs. of ethylene dichloride per gallon of AvGas
Aviation Gasoline Distribution Stage 2
The annual aviation gasoline consumed in each county is used with the emissions factors in Table 4-53 and Table
4-54 to estimate emissions. Emissions of VOC are estimated by multiplying gasoline consumed by the emissions
factor in Table 4-53. For VOC, emissions are multiplied by a conversion factor to convert from tons to pounds.
Evoc.c = AGc X EFV0C X 0.0005 tons/lb (49)
Where:
Evoqc = Annual VOC emissions in county c, in tons
AGC = Annual consumption of AvGas in county c, in gallons
EFvoc = VOC emission factor, in tons of VOC per gallon of AvGas
4-114
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Emissions of all HAPs, except ethylene dichloride and lead, are estimated by applying speciation factors found in
Table 4-54 to the annual VOC emissions. For HAPs, no conversion factor is needed, and the emissions are
reported in tons.
Eh,c — Evoc.c x (50)
Where:
Eh,c = Annual emissions of HAP h in county c, in tons per year
Evoqc = Annual VOC emissions in county c, in tons
SFh = Speciation factor for HAP h, in tons of HAP emissions per ton of VOC emissions
Ethylene dichloride and lead emissions are calculated by multiplying the gasoline consumed (from equation 12)
by the emission factor from Table 4-54. For lead and ethylene dichloride, emissions are multiplied by a
conversion factor to convert from pounds to tons.
EPiC = AGC X EFp X 0.0005 tons/lb (51)
Where:
Ep,c = Annual emissions of pollutant p in county c, in tons
EFP = Emission factor for pollutant p, in lbs. of pollutant per gallon of AvGas
4.7.3.6 Point Source Subtraction
There are no point source-specific SCCsfor stage 1 and stage 2 aviation gasoline distribution; therefore, point
source subtraction is not performed for these sources. However, some stage I gasoline emissions are reported in
the point source inventory. To avoid double counting of emissions, point source emissions are subtracted from
the total emissions from each source category to estimate the nonpoint emissions from each source category.
Point source emissions are mapped to nonpoint source SCCs using the crosswalk shown in Table 14 of the
document "Stage I Gasoline Distribution NEMO FINAL_7-18-2019_4-2 updated.docx" on the 2017 NEI
Supplemental data FTP site. The point source emissions table is also provided in an Excel input template. Point
source emissions are submitted by SLT agencies.
NPEP:C,SCC ^P,C,SCC PEp c,SCC (52)
Where:
NPEP/C,scc = Annual nonpoint source emissions of pollutant p from each SCC in county c
EP,c,scc = Annual total emissions of pollutant p from each SCC in county c
PEp,c,scc = Annual total point source emissions of pollutant p from each SCC in county c
4.7.3.7 Example calculations
The tables below show sample calculations for estimating VOC and benzene emissions for stage I gasoline
distribution. Each SCC relies on a speciation factor to estimate the benzene emissions from the VOC emissions.
Note that bulk terminals and pipelines have a different benzene speciation factor than the other SCCs. The
speciation factor for bulk terminals and pipelines in 0.0027. All other SCCs use a county-specific benzene
speciation factor. See section 4.7.3.3 for more information.
4-115
-------
Bulk Terminals
Table 4-60: Sample calculations for benzene emissions for Apache County, AZ in 2017 from Stage I Gasoline
Distribution
Eq. #
Equation
Values for Apache County, AZ
Result
15
EvOC,US,bt
r w ^2017
— ^MACT,US,bt X r
"1998
x 1.1023 tonper Mg
137555 Mg
9327thousand barrels per day
8253 thousand barrels per day
x 1.1023 tonper Mg
171359 tons VOC
emissions in the US
16
Ms
GasFracq =
MUS
205 thousand barrels
16798 thousand barrels
.0052
17
Evoc,bt,s GasFracs
x Evoc,us,bt
.0052 x 171359 tons
891.1 tons VOC
emissions in Arizona
18
r Z7 EmPc
EmpFracr =
Emps
6.54 employees
732 employees
.0089
19
Evoc.bt.c = EmpFracc
X EvOC,bt,s
. 0089 x 891.1 tons
7.93 tons VOC
emissions in Apache
County, AZ
20
Ep,c,bt ~ Eyoc,c,bt x Sp
7.93 tons x 0.0027 speciation factor
.0214 tons benzene
emissions in Apache
County, AZ
Pipelines
Table 4-61: Sample calculations for benzene emissions for Apache County, AZ in 2017 from Stage I Gasoline
Distribution
Eq. #
Equation
Values for Apache County, AZ
Result
21
Evoc,us,pi
r w ^2017
— ACT,US.,pi X r
"1998
x 1.1023 tonper Mg
137555 Mg
9327 thousand barrels per day
8253 thousand barrels per day
x 1.1023 tonper Mg
171359 tons VOC
emissions in the US
22
Mpo
PADDFracpn = ——
MUS
3,856 thousand barrels in PAD District 5
119,634 gasoline in US
0.32
23
EvOC,PD,pl
= PADDFracpD
x Evoc,us,pi
0.32 x 171359 tons
5,523 tons VOC
emissions in PAD
District 5
24
EmpPD = ^ Empc
]>Pe
10641 employees in
PAD District 5
25
„ ^ Empc
EmpFracr =
EmpPD
6.54 employees
10641 employees
.00061
4-116
-------
Eq. #
Equation
Values for Apache County, AZ
Result
26
Evoc,c,pi
= EmpFracc x EV0CiPDiPi
. 00061 x 5,523 tons
3.37 tons VOC
emissions in Apache
County, AZ
27
Ep,c,pl EvOC,c,pl ^ ^p
3.37 x 0.0027 speciation factor
0.9 tons benzene
emissions in Apache
County, AZ
Bulk Plants
Table 4-62: Sample calculations for benzene emissions for Apache County, AZ in 2017 from Stage I Gasoline
Distribution
Eq. #
Equation
Values for Apache County, AZ
Result
1
GTUSibp = Vus x 0.09
3404186 thousand barrels x 0.09
306377 thousand
barrels
8
Empus = ^ Empc
73908 employees in
the US
9
r n Empc
EmpFracr =
Empus
6.54 employees
73908 employees
.000089
10
GTC = GTUS x EmpFracc
306377 thousand barrels x .000089
27.11 thousand barrels
in Apache County
28
EvOC,c,bp
EFVOC,bp w
1000 gallon
x 42 gallons per Mbbl
8.62 pounds per 1,000 gallons
h- 1000 gallons
x 27.11 thousand barrels
x 42 gallons per Mbbl
9.8 pounds VOC
emissions in Apache
Count, AZ
29
EBZ,c,bp ~ EyoC,c,bp * $BZ,c
9.8 pounds
x 0.0061 speciation factor
.06 pounds benzene
emissions in Apache
County, AZ
Tank Trucks in Transit
Table 4-63: Sample calculations for benzene emissions for Apache County, AZ in 2017 from Stage I Gasoline
Distribution
Eq. #
Equation
Values for Apache County, AZ
Result
2
GCqr,c ~ GCoR m
GCoR,m
44,007,116.5 gallons of
onroad gasoline
consumed in Apache
County, AZ
4-117
-------
Eq. #
Equation
Values for Apache County, AZ
Result
3
GCnr,c = ^ ' GCNR m
^ ' GCjyRm
913,078.6 gallons of
nonroad gasoline
consumed in Apache
County, AZ
4
GCct = (fiC0Rc
+ GCNRc)
X 1.09
(44,007,116.5 gallons
+ 913,078.6 gallons)
x 1.09
48,963,012.6 gallons of
gasoline consumed in
Apache County, AZ
31
Evoc,c,tt
= (EFvoc.tt x ^Q,t)
/1000 gallons
(. 06 pounds per 1000 gallons
x 48,963,012.6 gallons)
/1000 gallons
2937.7 pounds VOC
emissions in Apache
County, AZ
32
EBz,c,tt = Evoc,c,tt X $BZ,c
2937.7 pounds
x 0.0061 speelation f actor
17.9 pounds benzene
emissions Apache
County, AZ
Underground Storage Tank (UST) Breathing and Storing
Table 4-64: Sample calculations for benzene emissions for Apache County, AZ in 2017 from Stage I Gasoline
Distribution
Eq. #
Equation
Values for Apache County, AZ
Result
2
GCoR,c ~ GCoR m
GCoR,m
44,007,116.5 gallons of
onroad gasoline
consumed in Apache
County, AZ
3
GCnR,c ~ GCftiR m
^ ' GCftiRm
913,078.6 gallons of
nonroad gasoline
consumed in Apache
County, AZ
4
GCct = (fiC0Rc
+ GCNR,c)
x 1.09
(4,4007,116.5 gallons
+ 913,078.6 gallons)
x 1.09
48,963,012.6 gallons of
gasoline consumed in
Apache County, AZ
34
Evoc,c,ust
— (EFV0C,ust x GCct)
/1000 gallons
(1 pound per 1000 gallons
x 48,963,012.62 gallons)
/1000 gallons
48,963 pounds VOC
emissions in Apache
County, AZ
35
EBZ,c,ust ~ EvoC,c,ust X $BZ,c
48,963 pounds
x 0.0061 speelation f actor
298.7 pounds benzene
emissions in Apache
County, AZ
Gasoline Service Station Unloading
These sample calculations use splash filling as an example, and the equations use fuel subtype 10 and January as
4-118
-------
an example. These calculations would need to be repeated using every month and both fuel subtypes to
calculate values for each filling technology (splash, submerged, and balance).
Table 4-65: Sample calculations for benzene emissions for Apache County, AZ in 2017 from Stage I Gasoline
Distribution
Eq
. #
Equation
Values for Apache County, AZ
Result
GCc,t,m,f (fi^c,OR,m,f + GCc,NR,m,f) * 1-09
(1,650,266.8 gallons
+ 11,985.2 gallons')
x 1.09
18,111,854.
7 gallons
GCc,ft,m,f ~ GCc,t,m,f * PerCftc
18,111,854.7 gallons
x 0 % splash filling
0 gallons
splash
filling in
Apache
County, AZ
c,m,f
¦{
0.7553
413
13
Tc.m + 459.6
1042
Tc.m + 459.6
S0-5\og10(RVPc>mJ)-
so.5
0.7553
413
1.8
+
2416
Trm + 459.6
+
60 + 459.6
1042
60 + 459.6
2416
60 + 459.6
3 log10(10.61) —
3°.5
1.8!
5.54
pounds per
square inch
absolute
- 2.013
+ 15
logio (RVPcjnJ) -
8742
,64j
Tc.m + 459.6
- 2.013
+ 15.64
log10(10.61)
8742
60 + 459.6
14
— 12.46 X Sft X Pcrn,f X M/T
12.46 x 1.45 saturation factor
x 5.54 pounds per square inch absolutt
65.5 pounds per pound per mole
x ¦
520 Rankine
12.61
pounds per
1000
gallons
4-119
-------
Eq
. #
Equation
Values for Apache County, AZ
Result
0 pounds
VOC
35
G ^c,f t,vn,f
EVOC,c,m,f- 1000 gallons X Lc,m,f
0 gallons splash filling
1000 gallons
x 12.61 pounds per 1000 gallons
emissions
from
uncontrolle
d loading
loss in
Apache
County, AZ
in January
for fueling
subtype 10
for splash
filling
0 pounds
controlled
VOC
36
EvOC,c,m.f,ft,ct ~ EvoC,c,m,f,ft,ll * CEc/100
0 pounds x 0 control efficiency/100
emissions
in Apache
County, AZ
in January
for fueling
subtype 10
for splash
filling
0 pounds
total VOC
37
EvOC.c.mJJt ~ ^VOC.c.m.f.ft.ll
~ EvOC,c,m.f,ft,ct
0 pounds — 0 pounds
emissions
in Apache
County, AZ
in January
for fueling
subtype 10
for splash
filling
0 pounds
total VOC
38
EvOC,c,ft = ^ EvOC,C,m,f,ft
^ ' EvoC,c,m,f,ft
emissions
in Apache
County, AZ
for splash
filling
4-120
-------
Eq
. #
Equation
Values for Apache County, AZ
Result
0 pounds
benzene
emissions
39
EBZ,c,ft = EV0C,C,ft X $BZ,c
0 pounds x 0.0061 speciation factor
in Apache
County, AZ
for splash
filling
Aviation Gasoline Distribution Stage 1
Table 4-66 lists sample calculations to determine the VOC emissions from stage 1 aviation gasoline distribution
in Autauga County, Alabama.
Table 4-66: Sample Calculations for Emissions from Aviation Gasoline-Stage 1 in Autauga County, AL
Eq.#
Equation
Values for Autauga, AL
Result
7
AGS
= AGBS
x 42 ®al^0nS/barrel
57,000 barrels x 42 9all°US/barrei
2,394,000 gallons of
AvGas consumed in AL
11
LTOr
RLTOc =
c LTOs
3,064 LTOs in Autauga
689,947 LTOs inAL
0.00444 fraction of LTOs
in Autauga County, AL
12
AGc — AGS x RLTOc
2,394,000 gal AvGas in Al x 4.44
x 10 ~3 fraction
10,633 gallons of AvGas
consumed in Autauga
County, AL
43
NFEr c = AGC X EFV0C,r ~=~
2000 lbs/ton
10,633 gal AvGas in Autauga x 9.02
x 10~3lbs. VOC per gal AvGas
+ 2000 U>s/t(m
0.048 tons VOC emissions
from tank filling in
Autauga County, AL
10,633 gal AvGas in Autauga x 3.61
x 10~3lbs. VOC per gal AvGas
+ 2000lbs/ton
0.0192 tons VOC
emissions from storage
tank working in Autauga
County, AL
10,633 gal AvGas in Autauga x 1.03
x 10~2lbs. VOC per gal AvGas
+ 2000 U>s/t(m
0.0548 tons VOC
emissions from composite
in Autauga County, AL
10,633 gal AvGas in Autauga x 1.69
x 10~3lbs. VOC per gal AvGas
+ 2000lbs/ton
0.00901 tons VOC
emissions from breathing
losses in Autauga County,
AL
44
VFEC = BPE X V X
EFvoc.r x D X
LTOc/
/ltous ¦
2000lbs/ton
2,442 plants in US x
i-n valves/ v
bU /plantx
0.573 lbs. per valve per day x
300 days x 3'°64/28,353,661 H"
2000 lbs/ton
1.13 tons fugitive VOC
emissions from valves in
Autauga County, AL
4-121
-------
Eq.#
Equation
Values for Autauga, AL
Result
45
PFEC = BPE x P xS x
EFvoc.r x D X
LTOc/
' ITOus ¦
2000 lbs/ton
2,442 plants in US x
„pumps, m seals/ v
z / plant /pump*
5.95 lbs. per seal per day x
300 days x 3-064/23 353 55! -
2000 ,bslton
1.89 tons fugitive VOC
emissions from pumps in
Autauga County, AL
46
Evoc.c = ^ NFEC + PFEC
* + VFEC
0.131 tons + 1.13 tons + 1.89 tons
3.15 total annual tons
VOC emissions from
AvGas distribution in
Autauga County, AL
Aviation Gasoline Distribution Stage 2
Table 4-67 lists sample calculations to determine the VOC, lead, and ethylene dichloride emissions from stage 2
aviation gasoline distribution in Autauga County, Alabama.
Table 4-67: Sample Calculations for Emissions from Aviation Gasoline-Stage 1 in Autauga County, AL
Eq.#
Equation
Values for Apache County, AZ
Result
7
AGS
= AGBS
gallons/
' barrel
57,000 barrels x 42 9all°US/barrei
2,394,000 gallons of
AvGas consumed in AL
11
LTOc
RLTOc = ~rz~
c LTOs
3,064 LTOs in Autauga
689,947 LTOs inAL
0.00444 fraction of
LTOs in Autauga
County, AL
12
AGc — AGS x RLTOc
2,394,000 gal AvGas in Al x 4.44
x 10 ~3 fraction
10,633 gallons of
AvGas consumed in
Autauga County, AL
49
Evoc.c = AGcx EFVoc x
0.0005 tons/lb
10,633 gal of AvGas in Autauga x
0.0136 lbs. VOC per gal x 0.0005 tons/lb
0.0723 tons VOC
emissions from AvGas
distribution in Autauga
County, AL
51
F
= AGC X EFp
x 0.0005 tons/lb
10,633 gal of AvGas in Autauga x 1.88 x
10-6 lbs. of ethylene dichloride per gal x
0.0005 tons/lb
1.0E-5 tons ethylene
dichloride emissions
from AvGas
distribution in Autauga
County, AL
F
P,c
= AGC X EFp
x 0.0005 tons/lb
10,633 gal of AvGas in Autauga x 8.50 x
10-8 lbs. of lead per gal x
0.0005 tons/lb
4.52E-7 tons of lead
emissions from AvGas
distribution in Autauga
County, AL
4,7.3.8 Changes for the 2014 methodology
For every source except Aviation Gasoline Distribution Stage 2, there are no significant changes from the
methodology used to calculate the 2014 v2 NEI emissions. For Aviation Gasoline Distribution Stage 2, the only
change to the methodology used to estimate the 2014 v2 NEI emissions is that the VOC emission factor for fuel
4-122
-------
transfer from tanker trucks to aircraft was decreased from 1.36E-2 lbs. VOC/gallon AvGas to 8.27E-4 lbs.
VOC/gallon AvGas after reviewing the emission factor reference more carefully.
4.7.3.9 Puerto Rico and U.S. Virgin Islands
Since insufficient data exists to calculate emissions for the counties in Puerto Rico and the US Virgin Islands,
emissions are based on two proxy counties in Florida: 12011, Broward County for Puerto Rico and 12087,
Monroe County for the US Virgin Islands. The total emissions in pounds for these two Florida counties are
divided by their respective populations creating a pound per capita emission factor. For each Puerto Rico and US
Virgin Island county, the pound per capita emission factor is multiplied by the county population (from the same
year as the inventory's activity data) which serves as the activity data. In these cases, the throughput (activity
data) unit and the emissions denominator unit are "EACH".
4.7.4 References
1. U.S. Environmental Protection Agency, "National Emission Standards for Source Categories: Gasoline
Distribution (Stage I), 40 CFR Part 63." Office of Air Quality Planning and Standards, February 28, 1997.
Pages 9087-9093.
2. U.S. Environmental Protection Agency, "Gasoline Distribution Industry (Stage I) - Background
Information for Proposed Standards," EPA-453/R94-002a, Office of Air Quality Planning and Standards,
January 1994.
3. Eastern Research Group, Inc., "Volume III: Chapter 11, Gasoline Marketing (Stage I and Stage II), Revised
Final," prepared for the Emission Inventory Improvement Program, January 2001.
4. TRC Environmental Corporation. 1993. Estimation of Alkylated Lead Emissions, Final Report. Prepared
for the U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards. RTP, NC.
5. U.S. Department of Energy, Energy Information Administration, Petroleum Navigator - Product
Supplied.
6. Cavalier, Julia, MACTEC, Inc., personal communication, "RE: Percentage of Gasoline Transported Twice
By Truck," with Stephen Shedd, U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards, Emission Standards Division, July 6, 2004.
7. 2008 NMIM runs performed by E.H. Pechan and Associates, Inc. for Laurel Driver, U.S. Environmental
Protection Agency, Office of Transportation Air Quality. The NMIM model version was 20071009 with
Mobile version M6203CHC\M6203ChcOxFixNMIM.exe.
8. Pacific Environmental Services, Inc., "Draft Summary of the Analysis of the Emissions Reported in the
1999 NEI for Stage I and Stage II Operations at Gasoline Service Stations," prepared for the U.S.
Environmental Protection Agency and the Emission Inventory Improvement Program, September 2002.
9. Energy Information Administration. 2019. State Energy Data System (SEDS): 1960-/ >mpletei.
Consumption in Physical Units. U.S. Department of Energy. Washington, D.C.
10. U.S. Census Bureau, 2016 County Business Patterns, released April 2018.
11. [LTObyCtyandSCC.mdb], electronic file from Laurel Driver, U.S. Environmental Protection Agency,
OAQPS, to U.S. Environmental Protection Agency, OAQPS. Aircraft operations data for 2017 compiled
from FAA's Terminal Area Forecasts (TAF) and 5010 Forms.
12. Federal Aviation Administration (FAA). 2017. Form 5010. Airport Data and Contact Information.
13. U.S. Environmental Protection Agency, "Compilation of Air Pollutant Emission Factors, AP 42, Fifth
Edition, Volume I: Stationary Point and Area Sources, Chapter 7: Liquid Storage Tanks," Office of Air
Quality Planning and Standards, Emission Inventory Group, September 1997.
14. U.S. Environmental Protection Agency. 1984. Locating and Estimating Air Emissions from Sources of
Ethylene Dichloride. Table 16, EPA-450/4-84-007d. RTP, NC.
15. Memorandum from Greg LaFlam and Tracy Johnson (PES) to Stephen Shedd (EPA/OAQPS). Speciated
Hazardous Air Pollutants - Baseline Emissions and Emissions Reductions Under the Gasoline Distribution
4-123
-------
NESHAP. August 9, 1996.
16. Personal Communication via e-mail from Stephen Shedd (EPA/OAQPS) to Laurel Driver (EPA/OAQPS). E-
mail dated May 29, 2002.
17. U.S. Department of Energy, Energy Information Administration, "U.S. Daily Average Supply and
Distribution of Crude Oil and Petroleum Products," Table 2 in Petroleum Supply Annual 2017, Volume 1.
released August 31, 2018
18. U.S. Department of Energy, Energy Information Administration, "U.S. Daily Average Supply and
Distribution of Crude Oil and Petroleum Products," Table 2 in Petroleum Supply Annual 1998, Volume 1.
released June 1999
19. U.S. Department of Energy, Energy Information Administration, "Refinery, Bulk Terminal, and Natural
Gas Plant Stocks of Selected Petroleum Products by PAD District and State, 2017" Table 35 in Petroleum
Supply Annual 2( ume 1. released August 31, 2018
20. Hester, Charles, MACTEC, Inc. Memorandum from Charles Hester, MACTEC, Inc., to Stephen Shedd, U.S.
Environmental Protection Agency, Office of Air Quality Planning and Standards, Emission Standards
Division, "Review of Data on HAP Content in Gasoline," May 18, 2006.
21. U.S. Department of Energy, Energy Information Administration, "Movements of Crude Oil and
Petroleum Products by Pipeline Between PAD Districts, 2017," Table 37 in Petroleum Supply Annual
2017, Volume 1, released August 31, 2018
4.8 Commercial Cooking
4.8.1 Sector description
Commercial cooking refers to the cooking of meat, including steak, hamburger, poultry, pork, and seafood, and
french fries on five different cooking devices: chain-driven (conveyorized) charbroilers, underfired charbroilers,
deep-fat fryers, flat griddles and clamshell griddles. Table 4-68 lists the SCCs in the commercial cooking sector;
EPA estimates emissions for all SCCs in this sector. The SCC level 1 and 2 descriptions are "Industrial Processes;
Food and Kindred Products: SIC 20" for all SCCs.
Table 4-68: Source Classification Codes used in the Commercial Cooking sector
SCC
SCC Description, level 3
SCC Descriptions, level 4
2302002100
Commercial Cooking - Charbroiling
Conveyorized Charbroiling
2302002200
Commercial Cooking - Charbroiling
Under-fired Charbroiling
2302003000
Commercial Cooking - Frying
Deep Fat Frying
2302003100
Commercial Cooking - Frying
Flat Griddle Frying
2302003200
Commercial Cooking - Frying
Clamshell Griddle Frying
4.8.2 Sources of data
The commercial cooking sector includes data from the S/L/T agency submitted data and the default EPA
generated emissions. The agencies listed in Table 4-88 submitted emissions for this sector; agencies not listed
used EPA estimates for the entire sector.
Table 4-69: Agencies that submitted Commercial Cooking emissions
Region
Agency
S/L/T
3
Maryland Department of the Environment
State
4
Memphis and Shelby County Health Department - Pollution Control
Local
4
Metro Public Health of Nashville/Davidson County
Local
4-124
-------
Region
Agency
S/L/T
5
Illinois Environmental Protection Agency
State
6
Texas Commission on Environmental Quality
State
8
Utah Division of Air Quality
State
9
California Air Resources Board
State
9
Maricopa County Air Quality Department
Local
9
Washoe County Health District
Local
10
Coeur d'Alene Tribe
Tribe
10
Idaho Department of Environmental Quality
State
10
Kootenai Tribe of Idaho
Tribe
10
Nez Perce Tribe
Tribe
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
4.8.3 EPA-developed emissions
The calculations for estimating the emissions from commercial cooking involve first estimating the amount of
meat and french fries cooked on various cooking devices in each county. These data are estimated using the
number of restaurants, by specific restaurant type, from the Dun & Bradstreet (D&B) Hoovers Database [ref 1]
and assumptions concerning the percent of those restaurants with specific cooking devices, the number of
devices per restaurant, and the amount of meat cooked per device from a California Air Resources Board (CARB)
sponsored survey [ref 2], The amount of french fries cooked by the foodservice industry is from a report
prepared for Potatoes USA [ref 3], The total amount of meat or french fries cooked on each device is multiplied
by emissions factors for CAPS including, VOC, CO, PM10 and PM25, and various HAPs to estimate emissions of
these pollutants from commercial cooking.
4.8.3.1 Activity data
The activity data for this source category is the amount of meat and potatoes cooked on each type of cooking
device in each county. These amounts are estimated based on the number of restaurants in a county that use
commercial cooking equipment, the percent of restaurants with each type of cooking device, the average
number of cooking devices per restaurant, and the average amount of meat or potatoes cooked on each device.
Data concerning the number of restaurants in each county are from the Dun & Bradstreet (D&B) Hoovers
Database [ref 1], Hoovers data are proprietary and were purchased by EPA for use in the NEl; EPA provides users
with aggregated data on county level restaurants by type. The relevant restaurants pulled from the Hoovers
Database and their primary SIC codes are listed in Table 4-70.
Table 4-70: Hoovers database restaurant types
Restaurant Type
Primary SIC Code
Ethnic Food
5812-01
Fast Food
5812-03
Family
5812-05
Seafood
5812-07
Steak & BBQ
5812-08
4-125
-------
The number of restaurants by type in each county, pulled from the Hoovers database, is then multiplied by the
percentage of restaurants by type with commercial cooking equipment in order to calculate the number of
restaurants with the specific cooking devices in each county; these percentages are shown in Table 4-71. The
data on cooking devices and meat cooked are from a survey on charbroiling activity in the state of California [ref
2].
Table 4-71: Percent of restaurants with each type of cooking device
Restaurant
Conveyorized
Underfired Char-
Deep-Fat
Flat
Clamshell
Type
Char-broilers
broilers
Fryers
Griddles
Griddles
Ethnic
3.5
47.5
81.9
62.7
4.0
Fast Food
18.6
30.8
96.8
51.9
14.7
Family
10.1
60.9
91.4
82.9
1.4
Seafood
0.0
52.6
100.0
36.8
10.5
Steak & BBQ
6.9
55.2
82.8
89.7
0.0
Source: Reference 2, Table 4
Rt,c,d Rt,c ^ FTCLCt d
(1)
Where:
Rt,c,e = Number of type t restaurants in county c with cooking device d
Rt,c = Number of type t restaurants in county c
Fracte = Fraction of type t restaurants with cooking device d
The number of restaurants in each county with cooking devices are then multiplied by the average number of
cooking devices by restaurant type shown Table 4-72, from the same California Survey dataset, to calculate the
total number of cooking devices.
Table 4-72: Average number of devices by restaurant type*
Restaurant
Conveyorized
Underfired
Deep-Fat
Flat
Clamshell
Type
Char-broilers
Char-broilers
Fryers
Griddles
Griddles
Ethnic
1.62
1.54
1.63
1.88
1.80
Fast Food
1.07
1.58
3.10
1.43
2.09
Family
1.71
1.29
2.34
2.03
-
Seafood
-
1.10
2.47
1.11
1.50
Steak & BBQ
-
1.63
2.42
1.35
-
"Only includes restaurants with at
east one piece of the equipment. Source: Reference 2, Table 5.
Df,c,d Rf,c,d * Et:d (2)
Where:
Dt,c,d = Total number of cooking device d in county c from type t restaurants
Rt,c,d = Number of type t restaurants in county c with cooking device d
Et,d = Average number of cooking device d at type t restaurants
The number of cooking devices in each restaurant type from equation 2 are summed across restaurant types to
estimate the total number of cooking devices in each county.
4-126
-------
D,
c,d
"I
D
(3)
t,c,d
Where:
Dc,d
Dt,c,d
Total number of cooking devices dfrom all restaurants in county c
Total number of cooking device d in restaurant type t in county c
The total number of cooking devices in each county is used to determine the amount of meat cooked in that
county. The average amount of meat cooked on each cooking device is listed in Table 4-73.
Table 4-73: Average amount of meat cooked per year on each cooking c
Meat Type
Conveyorized
Char-broilers
Underfired
Char-broilers
Deep-Fat
Fryers
Flat
Griddles
Clamshell
Griddles
Steak
6.1
4.7
4.7
4.3
2.4
Hamburger
20.7
7.0
7.1
9.4
34.2
Poultry
10.7
8.4
14.9
5.2
5.7
Pork
1.5
3.8
1.5
2.9
3.1
Seafood
3.1
3.7
4.1
2.4
16.4
Other
-
1.1
7.1
1.5
-
evice (tons)
Source: Reference 2, Table 13
Mi,d,c ~ DCid * mi,d
(4)
Where:
Mi,d,c = Total amount of meat type i cooked on device d in county c, in tons
Dc,d = Total number of cooking device dfrom all restaurants in county c
= Average amount of meat type i cooked on device d, in tons
The amount of french fries cooked in each county is calculated based on the amount of frozen potatoes used in
the foodservice industry. According to a report prepared for Potatoes USA, 5,977 million pounds of frozen
potatoes were used in the food service industry in 2017 [ref 1], Frozen potatoes used in limited service
restaurants account for approximately 74% of the total, and those used in full-service restaurants account for
the remaining 26%. The process used to distribute the national amount of french fries cooked to the county-
level is discussed in the next section.
4.8.3.2 Allocation procedure
In 2017, 5,977 million pounds of frozen potatoes were used in limited and full-service restaurants in the U.S [ref
3], In order to allocate this value to the county-level, fractions of the number of limited and full-service
restaurants in each county are used. To create these fractions, it is assumed that limited service restaurants are
D&B classified fast food restaurants and full services restaurants are represented by all other D&B restaurant
codes. County-level fast food and other restaurants are summed, and then divided by the national number of
fast food or other restaurants in order to develop the county-level fractions.
RFracumc
Rlim,c
Rlim,US
(5)
4-127
-------
Where:
RFracnmtc
RFracfuiu
Rlim,c
Rfuiu
Rlim, US
Rfull, US
RFrac,
R
¦full,c
full,c
(6)
R
'full,US
Fraction of limited service restaurants in county c
Fraction of full service restaurants in county c
The number of limited service restaurants in county c
The number of full service restaurants in county c
The number of limited service restaurants in the U.S.
The number of full service restaurants in the U.S.
The fraction of limited and full-service restaurants in each county is then used to distribute the amount of frozen
potatoes cooked. Approximately 4,414 million pounds of frozen potatoes were used in limited service
restaurants in the US in 2017 and 1,563 million pounds were used in full-service restaurants [ref 3],
Flim,c ^FrClCnm,c X fum^us
t- 2000 lbs per ton
(7)
Ffull.c — RFracfull,c x /full,us
h- 2000 lbs per ton
(8)
Where:
Fnm,c = Amount of french fries cooked in limited service restaurants in county c, in tons
Ffuii,c = Amount of french fries cooked in full service restaurants in county c, in ton
RFracum,c = Fraction of limited service restaurants in county c
RFracfuii,c = Fraction of full service restaurants in county c
film,us = Amount of french fries cooked in limited service restaurants in the U.S., in lbs.
ffull,us = Amount of french fries cooked in full service restaurants in the U.S., in lbs.
The amount of french fries cooked in limited and full-service restaurants are then summed to the county level.
Fall,c ~ Flim,C Ffull,c (9)
Where:
Fan,c = Amount of french fries cooked in county c, in tons
Fnm,c = Amount of french fries cooked in limited service restaurants in county c, in tons
Ffuiu = Amount of french fries cooked in full service restaurants in county c, in tons
4.8.3.3 Emission factors
Emissions factors for CAPs from commercial cooking are reported in Table 6 in the Commercial Cooking NEMO
FINAL document on the 2017 NE1 Supplemental data FTP site. CAP emissions factors are taken from the article
Emissions from Charbroiling and Grilling of Chicken and Beef [ref 4], and a South Coast Air Quality Management
District Report (SCAQMD) [ref 5], According to the most recent PM Augmentation tool, Primary PM is equal to
Filterable PM and there are assumed to be no condensible PM emissions from commercial cooking. Emissions
factors for HAPs from commercial cooking are reported in Table 7 in the Commercial Cooking NEMO FINAL
document. HAP emissions factors are also from Emissions from Charbroiling and Grilling of Chicken and Beef [ref
4], and an EPA report on emissions from street vendor cooking devices [ref 6],
4-128
-------
4,8.3,4 Controls
There are no controls assumed for this category.
4,8.3.5 Emissions
To calculate emissions of CAPs, the total amount of meat and potatoes cooked on each cooking device in each
county is multiplied by the appropriate emissions factor (listed in Table 6 in the Commercial Cooking NEMO
FINAL document). The amount of french fries cooked is converted from pounds to tons, and all emissions are
converted to tons.
EvAA,c = Mi,d,c x EFp,i,d + 2000 lbs Per ton (10)
Ep,f,d,c Fall,c ^ EFp,f,d ~ 2000 lbs p6T tOTi (H)
Where:
EP,Uc = Annual emissions of pollutant p from cooking meat type i on device d in county c, in tons
EP,f,d,c = Annual emissions of pollutant p from cooking french fries, /, on device d in county c, in tons
M,-d,c = Total amount of meat type i cooked on device d in county c, in tons
Faii,c = Total amount of french fries cooked in county c, in tons
EFP/iid = Emissions factor for pollutant p, in lbs. of pollutant per ton of meat type i cooked on device d
EFp,ftd = Emissions factor for pollutant p, in lbs. of pollutant per ton of french fries cooked on device d
Emissions of HAPs are also calculated by multiplying an emissions factor (Table 7 in the Commercial Cooking
NEMO FINAL document) by the amount of meat cooked on each cooking device. Note that cooking of french
fries does not result in HAP emissions. For HAPs, no conversion factor is needed, and emissions are reported in
pounds.
Ep,i,d,c = ^i,d,c * EFp^d (12)
Where:
Ep,uc = Annual emissions of pollutant p from cooking meat type i on device d in county c, in pounds
Mird,c = Total amount of meat type i cooked on device d in county c, in tons
EFp,ird = Emissions factor for pollutant p, in lbs. of pollutant per ton of meat type i cooked on device d
The emissions are summed for all types of meat and french fries to estimate the total emissions from each
cooking device type in each county.
F -V F +F (13)
^p,d,c ^p,i,d,c £p,f,d,c
Where:
EP,d,c = Total annual emissions of pollutant p from cooking device d in county c
Fp/i,d,c = Annual emissions of pollutant p from cooking meat type i on device d in county c
EP/f,d,c = Annual emissions of pollutant p from cooking french fries, /, on device d in county c
4.8.3,6 Example calculations
Table 4-74 lists sample calculations to determine the VOC emissions from commercial cooking on flat griddles in
Apache County, Arizona. The first two equations use fast food restaurants as an example, and equations 4 and
4-129
-------
10 use hamburgers as an example. However, these calculations would need to be repeated to calculate values
for all restaurant and meat types.
Table 4-74: Sample VOC emissions calculations from commercial cooking on flat gridd
es in Apache county, AZ
Eq.
#
Equation
Values for Apache County, AZ
Result
3.114 fast food
1
Rf,c,d ^t,c ^ FrCLCf d
6 fast food rest.
x 51.9% with flat griddles
restaurants in Apache
County, AZ with flat
griddles
2
Df,c,d ~ Rf,c,d * ^t,d
3.114 fast food rest, with flat griddles
x 1.43 flat griddles per rest.
4.45 flat griddles in
fast food restaurants
in Apache County, AZ
3
Dc,d ~ ^ ' Dt,c,d
Flat griddles in Apache County, AZ
9.5 flat griddles in all
restaurants in Apache
County, AZ
4
Mi,d,c ~ DCid * mi,d
9.5 flat griddles in Apache
x 9.4 tons of hamburger cooked on flat
griddles
89.3 tons of
hamburger cooked on
flat griddles in Apache
County, AZ
5
Rlim,c
RFraclimc =
Klim,US
N/A
Equation is for deep-
fat fryers; example is
for flat griddles
6
Rfull,c
RFraCfUllc =
Kfull,US
N/A
Equation is for deep-
fat fryers; example is
for flat griddles
7
Flim,c
— RFracum c x fnmius
h- 2000 lbs per ton
N/A
Equation is for deep-
fat fryers; example is
for flat griddles
8
Ffull,c
= RFraCfUu c x ffUuiUS
h- 2000 lbs per ton
N/A
Equation is for deep-
fat fryers; example is
for flat griddles
9
Fall,c ~ Fiimc + Ffuiic
N/A
Equation is for deep-
fat fryers; example is
for flat griddles
0.00625 tons VOC
10
Ep,i,d,c
~ Mi:d,c * EFp i d
h- 2000 lbs per ton
89.3 tons of hamburger cooked
x 0.14 lbs. VOC per ton hamburger
h- 2000 lbs. per ton
emissions from
cooking hamburgers
on flat griddles in
Apache County, AZ
11
Ep,f,d,c
~ Fall,c * EFpj c[
h- 2000 lbs per ton
N/A
Equation is for deep-
fat fryers; example is
for flat griddles
4-130
-------
Eq.
#
Equation
Values for Apache County, AZ
Result
12
Ep,i,d,c ~ Mi d c X EFp i d
NA
Equation is for HAPs;
example is for VOC
13
Ep,d,c ~ Ep,i,d,c
Ep,f,d,c
VOC emissions in Apache County
0.04 tons VOC
emissions from flat
griddles in Apache
County, AZ
4,8,3,7 Changes for the 2014 NEI methodology
The methodology used to calculate commercial cooking emissions for the 2014 v2 NEI used data on the number
of restaurants in each county, according to US NAICS codes, to grow emissions data from the 2002 NEI
commercial cooking category. This was completed as EPA did not have access to the more specific D&B data on
restaurants in each county. For the 2017 NEI, EPA has access to the D&B data and is therefore using the 2002
NEI methodology (which is also used by the state of California).
4.8.4 References
1. Dun and Bradford Hoovers database, 2018.
2. Public Research Institute, 2001. Charbroiling Activity Estimation. Prepared for the California Air
Resources Board and California EPA.
3. Technomic, 2017. Volumetric Assessment of the Foodservice Potato Market. Prepared for Potatoes USA.
4. McDonald, J., B. Zielinska, E. Fujita, J. Sagebiel, J. Chow, and J. Watson, 2003. "Emissions from
Charbroiling and Grilling of Chicken and Beef." Journal of Air & Waste Management Association. 53:185-
194.
5. Norbeck, Joseph, 1997. Further Development of Emission Test Methods and Development of Emission
Factors for Various Commercial Cooking Operations. Prepared for the South Coast Air Quality
Management District.
6. US EPA, 1999. Emissions from Street Vendor Cooking Devices (Charcoal Grilling). Prepared by ARCADIS
Geraghty & Miller.
4.9 Dust - Construction Dust
4.9.1 Sector description
Construction dust refers to residential and non-residential construction activity, which are functions of acreage
disturbed for construction. This sector will be divided below when describing the calculation of EPA's emissions.
Table 4-75 lists the nonpoint SCCs associated with this sector in the 2017 NEI. The SCC level 1 and 2 descriptions
is "Industrial Processes; Construction: SIC 15 -17" for all SCCs.
Table 4-75: SCCs in the Construction Dust sector
SCC
SCC Level Three
SCC Level Four
2311010000
Residential
Total
2311020000
Industrial/Commercial/Institutional
Total
2311030000
Road Construction
Total
4-131
-------
4.9.2 Sources of data
The construction dust sector includes data from the S/L/T agency submitted data and the default EPA generated
construction dust emissions. The agencies listed in Table 4-76 submitted Residential (Res), Industrial and
Commercial/Institutional (ICI), and/or Road construction emissions for this sector; agencies not listed used EPA
estimates for the entire sector. Some agencies submitted emissions for the entire sector, while others submitted
only a portion of the sector.
Table 4-76: S/L/Ts that submitted Construction Dust emissions
Region
Agency
Res
ICI
Road
1
New Hampshire Department of Environmental Services
~
2
New Jersey Department of Environment Protection
~
~
~
3
Delaware Department of Natural Resources and Environmental Control
~
~
~
3
Maryland Department of the Environment
~
~
~
4
Memphis and Shelby County Health Department - Pollution Control
~
~
~
4
Metro Public Health of Nashville/Davidson County
~
~
~
5
Illinois Environmental Protection Agency
~
~
~
8
Utah Division of Air Quality
~
~
~
9
California Air Resources Board
~
~
9
Clark County Department of Air Quality and Environmental Management
~
~
~
9
Maricopa County Air Quality Department
~
~
~
9
Washoe County Health District
~
~
~
10
Coeur d'Alene Tribe
~
~
~
10
Idaho Department of Environmental Quality
~
~
~
10
Kootenai Tribe of Idaho
~
~
~
10
Nez Perce Tribe
~
~
~
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
~
~
~
10
Washington State Department of Ecology
~
~
~
4.9.3 EPA-developed emissions for residential construction
Emissions from residential construction activity are a function of the acreage disturbed and volume of soil
excavated for residential construction. Residential construction activity is developed from data obtained from
the U.S. Department of Commerce (DOC)'s Bureau of the Census.
4.9.3.1 Activity data
There are two activity calculations performed for residential construction: acres of soil disturbed, and volume of
soil removed for basements.
Determine the Number of Housing Starts in Each County
The US Census Bureau has 2017 data for New Privately Owned Housing Units Started by Purpose and Design [ref
1] which provides data on housing starts based on the groupings of 1 unit, 2-4 units, and 5 or more units.
Regional-level results are also provided for quarterly totals and 1-unit structures in Table 4-77 [ref 1], In order to
4-132
-------
breakdown the 2 to 4-unit category, data from a consultation with the Census Bureau in 2002 are used;
approximately 1/3 of the housing starts are for 2-unit structures, and 2/3 are for 3- and 4-unit structures.
The 2017 US Census Bureau New Privately Owned Housing Units Started by Purpose and Design [ref 1] data for
2-4 units are distributed to two categories, 2 and 3-4 units, based on a ratio for 2 and 3-4 units calculated from
the 2000 US Census Bureau National Housing Starts data [ref 2], for each quarter in 2017. Note that 2000 is the
last full year when Census housing starts data are available separately for 2-unit and 3-4-unit homes. Table 4-78
shows a breakdown of the 2 unit and 3-4-unit structures based on the following calculation.
Sq,n = (y^)x Sq,2~4 ^
Where:
$Q,n =
Un
Ut
Sq,2-4 =
Table 4-77: Housing Start Data for 2017
Housing starts, by quarter, Q, and number of units, n (2 units or 3-4 units), in thousand units
Number of housing starts by number of units, n, from the 2000 National Housing Starts data,
in thousand housing starts
Total number of housing starts for both 2 units and 3-4 units from the 2000 National Housing
Starts data, in thousand housing starts
Number of 2-4 units by quarter, Q, from the 2017 New Privately Owned Housing Units Started
by Purpose and Design data, in thousand units
Quarter
Total
Structure
Region
Regional Starts of Structures
with 1 unit
1 unit
2 to
4
units
5 units
or
more
NE
MW
S
W
NE
MW
S
W
Ql-14
206.0
134.0
2.0
70.0
23.0
21.0
113.0
49.0
9.0
14.0
79.0
32.0
Q2-14
275.0
183.0
3.0
89.0
28.0
53.0
130.0
62.0
15.0
34.0
91.0
42.0
Q3-14
282.0
178.0
4.0
100.0
32.0
49.0
134.0
65.0
14.0
32.0
92.0
39.0
Q4-14
241.0
154.0
4.0
84.0
26.0
39.0
118.0
58.0
13.0
25.0
83.0
32.0
Table 4-78
: Breakdown of 2 to 4-unit structures
Quarter
2 to 4 units
2 units
3-4 units
Ql-14
2.0
0.74
1.26
Q2-14
3.0
1.11
1.89
Q3-14
4.0
1.47
2.53
Q4-14
4.0
1.47
2.53
Ratios of the number of 2, 3-4, and 5 or more-unit structures are then used to estimate the number of
structures of each type in each region. The ratios are calculated by dividing the housing starts by quarter for
each unit type by the total housing starts for buildings with 2 or more units.
So,n , ,
rQ,n = -f- (2)
JQ.t
Where:
= Ratio of structures with number of units, n, to total number of units by quarter, Q
4-133
-------
Sq,„ = Housing starts, by quarter, Q, and number of units, n, from distributed calculation in Step 1 for
the 2-unit or 3-4 unit categories or directly from the 2017 New Privately Owned Housing
Units Started by Purpose and Design data for the 5 units or more category, in thousand
housing starts
SQ/t = Housing starts, by quarter, Q, for total number of buildings with 2 or more units, t (excludes 1-
unit category), in thousand housing starts
The ratio is then used to distribute the 2017 New Privately Owned Housing Units Started by Purpose and Design
regional data for all unit types to the 2, 3-4, or 5 or more-unit categories within each Census region - Northeast,
Midwest, South, and West.
Q,n,rgn ~ rQ,n * iJ^^t,rgn ~ :rgn) (3)
Where:
AQ,n,rgn = Number of housing units started in quarter 0, by number of units, n, and region of the country,
rgn, in thousand units
rQ/„ = Ratio of structures with number of units, n, to total number of units by quarter, Q
RSt,rgn = Total regional starts from the 2017 New Privately Owned Housing Units Started by Purpose and
Design data, in thousand housing starts
RSijgn = Regional starts of structures with 1 unit from the 2017 New Privately Owned Housing Units
Started by Purpose and Design data, in thousand housing starts
Data from the Census report New Privately Owned Housing Units Authorized Unadjusted Units [ref 3] is used to
calculate a conversion factor to determine the ratio of structures to units in the 5 or more-unit category. The
conversion factor is calculated by dividing the total number of units in structures with 5 or more units by region
[ref 2] by the total number of buildings with 5 or more units by region [ref 3],
_ U5,rgn
^^5,rgn ~ D (4)
B,
5,rgn
Where:
CF5,rgn = Ratio of 5 units or more to the number of buildings with 5 units or more by region, rgn
U5,rgn = Total number of 5 or more units by region, rgn
Bsjgn = Total number of buildings with 5 or more units by region, rgn
Structures started by category are then calculated at a regional level by summing the number of housing unit
starts across all four quarters and dividing by the number of units in each building type. For the 3-4-unit type,
the number of units per building is 3.5. The value is multiplied by 1,000 because the Census data are in units of
thousand building starts.
For buildings with 1, 2, or 3-4 units:
QjOI^Q.n.rgn) * 1,000
n —tQ\ Q.n.rgnJ ' (5}
°n,rgn = ~
Where:
B„,rg„ = Number of building starts by the unit number category, n, and by region, rgn
AQ,n,rgn = Number of housing units started in quarter 0, by number of units, n, and region of the country,
4-134
-------
n
rgn, in thousand units
= Number of units per building
For buildings with 5 or more units:
B,
n,rgn
(2qi^Q.n.rgn) x 1-000
CFS
(6)
Where:
Bn,rgn = Number of building starts by the unit number category, n, and by region, rgn
AQ,n,rgn = Number of housing units started in quarter Q, by number of units, n, and region of the country,
rgn, in thousand units
CFs = Ratio of 5 units or more to the number of buildings with 5 units or more
Annual county-level building permit data were purchased from the US Census Bureau for 2017 [ref 4], The 2017
County Level Residential Building Permit dataset has 2017 data to allocate regional housing starts to the county
level. This results in county-level housing starts by number of units.
The number of building permits for each unit number category by region is calculated by summing the county-
level Census data to the Census region level.
BPn,rgn = Number of building permits by the unit number category, n, and by region, rgn
BP„/C = Number of building permits by the unit number category, n, and by county, c
The ratio of the number of building permits by county to the total number of building permits by region in which
the county is located, for each unit number category, is then calculated.
Rbp,c = Ratio of building permits, BP, to total regional building permits in county c
BP„/C = Number of building permits by the unit number category, n, and by county, c
BPn,rgn = Number of building permits by the unit number category, n, and by region, rgn
The final number of building starts for each unit type category is then calculated at the county-level by
multiplying the number of structures started at the regional level and the building permit ratio.
(7)
Where:
(8)
Where:
(9)
Where:
Bn,c = Number of building starts by the unit number category, n, and by county, c
B„,rgn = Number of building starts by the unit number category, n, and by region, rgn
Rbp,c = Ratio of building permits, BP, to total regional building permits in county c
4-135
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Determine Amount of Soil Removed for Basements
To calculate basement soil removal, the 2017 Characteristics of New Single-Family Houses Completed,
Foundation table [ref 5] is used to estimate the percentage of 1-unit structures that have a basement at the
regional level. The data indicate whether the structure has a full/partial basement, slab or other type, or crawl
space. However, only structures with full/partial basements are used in this calculation.
_ BMfp,rgn
t>Mrgn — Dn4 (10)
BMt:rgn
Where:
BMrgn = Fraction of basements for buildings in the region
BMfpjgn = Number of full or partial basements, fp, by region, rgn
BMt,rgn = Total number of houses regardless of basement type (full/partial, slab/other, crawl space by
region, rgn
To estimate the number of building starts with and without basements in each county, the county level estimate
of the number of 1-unit starts (from equation 9) is multiplied by the percent of 1-unit houses in the region that
have a basement.
Bc,bm Bn.c X BMrgn (11)
Bc,nBM = Bn.c x (1 — BMrgn) (Ha)
Where:
Bc,bm = Number of building starts by county, c, with a basement, BM
Bc,nBM = Number of building starts by county, c, without a basement, BM
B„/C = Number of building starts by the unit number category, n, and by county, c
BMrgn = Fraction of basements for buildings in the region
Basement volume is calculated by assuming a house with a 2000 square foot footprint 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.
Determine Amount of Soil Disturbed by Unit Type
The number of acres of soil disturbed by the construction of residential buildings is calculated for apartment
buildings, buildings with 2 units, and buildings with 1 unit. Table 4-79 below shows the assumptions used for the
surface area disturbed for each unit type. Buildings with unit types of 3-4 and 5 or more are grouped together as
apartments in this step.
Table 4-79: Surface soil removed per unit type
Structure Type
Acres disturbed
1-Unit
1/4 acre per structure
2-Unit
1/3 acre per structure
Apartment
1/2 acre per structure
For apartment buildings (sum of 3-4 and 5 or more units) and buildings with 2 units:
4-136
-------
^n,c Bn.c X CLri
(12)
Where:
Sn,c = Surface soil disturbed by building construction by county, c, and unit type category, n, in acres
B„/C = Number of building starts by the unit type category, n, and by county, c
a„ = Acres of surface soil disturbed by each unit type category, n. See Table 4-79 for values for each
type.
For buildings with 1 unit, with or without a basement:
^n,c Bc,BM ^ ®7i (1^)
Where:
Sn,c = Surface soil disturbed by building construction by county, c, and unit type category, n, in acres
Bc,bm = Number of buildings by county, c, with or without a basement, BM
a„ = Acres of surface soil disturbed by each unit type category, n. See Table 4-79 for values for each
type.
4.9.3.2 Allocation procedure
Annual county building permit data were purchased from the US Census Bureau for 2017 [ref 4], The 2017
County Level Residential Building Permit dataset is used to allocate regional housing starts to the county level.
4.9.3.3 Emission factors
Initial PM10 emissions from construction of single family, 2-unit, and apartments structures are calculated using
the emissions factors given in Table 4-80 [ref 5], These emissions factors describe average "unit operations,"
such as "loading and unloading of earth and aggregate materials, land clearing and general vehicle traffic" [ref
6], They therefore take into account the entire duration of construction, and not simply the duration of active
excavation. The duration of construction activity for houses is assumed to be 6 months and the duration of
construction for apartments is assumed to be 12 months.
Table 4-80: Emissions factors for residential construction
Type of Structure
Emissions Factor
Duration of
Construction
Apartments
0.11 tons PMlO/acre-month
12 months
2-Unit Structures
0.032 tons PMlO/acre-month
6 months
1-unit Structures with
Basements
0.011 tons PMlO/acre-month
6 months
0.059 tons PM10/1000 cubic
yards
1-Unit Structures w/o
Basements
0.032 tons PMlO/acre-month
6 months
To account for the soil moisture level, the PM10 emissions are weighted using the 30-year average precipitation-
evaporation (PE) values from Thornthwaite's PE Index. Average precipitation evaporation values for each state
are estimated based on PE values for specific climatic divisions within a state. The average PE value for the test
sites from which the PM10 emissions factor was developed is 24 [ref 6], Equation 14 is used to adjust the
county-level emissions factor based on this PE value.
4-137
-------
To account for the silt content, the PM10-PRI 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 [ref 7], The U.S. Department of Agriculture and the
National Cooperative Soil Survey define silt content of surface soil as the percentage of particles (mass basis) of
diameter smaller than 50 micrometers (nm) found in the surface soil [ref 8], Note that this definition is different
than the U.S. Environmental Protection Agency's definition [ref 9] that includes all particles (mass basis) of
diameter smaller than 75 micrometers. This database contains the most commonly requested data from the
National Cooperative Soil Survey Laboratories including data from the Kellogg Soil Survey Laboratory and
cooperating universities. The average silt content for the test sites from which the PM10 emissions factor was
developed is 9% [ref 6], Equation 7 is used to adjust the county-level emissions factor based on this silt content
value.
24 s
^pmio=-^tX— (14)
Where:
AFpmio = PM10-PRI adjustment factor
PE = precipitation-evaporation value for each State
s = % dry silt content, by county, in soil for area being inventoried
This adjustment factor is used to adjust the PM10-PRI emissions factor for each unit type category - apartment,
2-unit, 1-unitwith basement, and 1-unit without basement.
EFp,n,c AFpMio X Dn X EForig (15)
Where:
EFPt„tc = Adjusted county-level, c, PM10-PRI emissions factor, p, for each unit type category, n, in
tons/acre
AFpmio = PM10-PRI adjustment factor
D„ = Duration of construction by unit type category, n, in months. See Table 4-80 for duration
values.
EForig = Original unadjusted PM10 emissions factor, in tons/acre. See Table 4-80 for original emissions
factors
4.9.3.4 Controls
There are no controls assumed for this category.
4.9.3.5 Emissions
The PM10-PRI emissions are calculated by taking the sum of the surface soil disturbed by county and unit type
category and multiplying it by the corresponding adjusted PM10-PRI emissions factor. Once PM10-PRI
adjustments have been made, PM25-PRI emissions are estimated by applying a particle size multiplier of 0.10 to
PM10-PRI emissions [ref 8], Primary PM emissions are equal to filterable emissions since there are no
condensible emissions from residential construction.
The PM10-PRI emissions are calculated at the county-level by multiplying the surface soil disturbed from
construction for each unit type by the corresponding emissions factor for that unit type, and then summed
across unit types.
4-138
-------
N
IPMlO.c ~
~ Sn,c * EFpM10,n,c
=1
Where:
Epmio,c = Total PM10-PRI emissions in county c, in tons
S„/C = Surface soil disturbed by building construction by county, c, and unit type category, n
EFPt„tc = Adjusted county-level, c, PMio emissions factor, p, for each unit type category, n, in tons/acre
The PM25-PRI emissions are calculated based on the assumption that they are 10% of the PM10-PRI emissions.
EpM2.5,c = EpMlO,c x 0.1 (2)
Where:
Epm2.5,c = Total county-level, c, PM25-PRI emissions
Epmio,c = Total county-level, c, PM10-PRI emissions
0.1 = Particle size multiplier
4.9.3.6 Sample calculations
Table 4-81 shows sample calculations for PM10-PRI and PM25-PRI emissions from residential construction for a
2-unit structure in Suffolk County, Massachusetts. The first 3 equations use the first quarter (Ql) of 2017 for 2-
unit structures as an example. However, these calculations would need to be repeated to calculate values for all
4 quarters for all 3-unit sizes. Note that structures with 5 or more units and structures with 1 unit with or
without a basement have additional steps not shown in the sample calculations here.
Table 4-81: Sample calculations for PM-10 PRI and PM25-PRI emissions from residential construction of 2-unit
structures in Suffolk County, MA.
Eq.
#
Equation
Values for Suffolk County, MA
Result
1
SQ,n = (jj^J X SQ,2-4
/14 two unit housing starts in 2002\
V 38 total housing starts in 2002 J
X
2 two to four unit housing starts in Q1 2017
0.74 thousand
housing starts
for 2-unit
structures in Ql
2017, nationally
2
^Q,n
0.74 two unit housing starts
0.01 ratio of
buildings with 2
units to all 2 or
more-unit
housing starts
for Ql 2017,
nationally
7y,n ^
72 two or more unit housing starts
3
AQ,n,rgn ~ rQ,n * (•
-RSi)
0.01
x (23 total Ql housing starts in Northeast
— 9 one unit housing starts in Northeast)
0.14 thousand
housing starts
for 2-unit
structures for
Ql 2017 in the
Northeast
4-139
-------
Eq.
#
Equation
Values for Suffolk County, MA
Result
4
cf5 = Us'rgn
Bs,r
N/A
Equation is for 5
or more-unit
buildings;
example is for 2-
unit buildings
5
Bn,rgn
(2qi^Q.n.rgn) * 1,000
n
0.772 two unit structures x 1,000
2 units per building
386 2-unit
structures
constructed in
the Northeast
6
Bn,rgn
(2qi^Q.n.rgn) * 1,000
CFS
N/A
Equation is for 5
or more-unit
buildings;
example is for 2-
unit buildings
7
BP = > BP
urn,rgn / urn,c
Northeast two unit building permits
1,545 2-unit
structure
building permits
in the Northeast
8
p BPn,c
49 Suffolk county building permits
0.03172 ratio of
county-level
building permits
to regional-level
building permits
in Suffolk
County, MA
^UH,C gp
urn,rgn
1,545 Northeast building permits
9
Bn,c ~ Bn,rgn * ^BP,c
386 X 0.03172
12.25 total 2-
unit structure
building starts
for Suffolk
County, MA
10
BMr,n = B™'Aw9n
® B M+
ui lt,rgn
N/A
Equation is for
1-unit buildings;
example is for 2-
unit buildings
11
Bc,BM ~ Bn.c * BMrgn
N/A
Equation is for
1-unit buildings;
example is for 2-
unit buildings
12
Sn.c ®n.c ^
12.25 two unit structures
x 0.33 acres per structure
4.08 acres
surface soil
disturbed by 2-
unit structures
in Suffolk
County, MA
4-140
-------
Eq.
#
Equation
Values for Suffolk County, MA
Result
13
^n,c Bc,BM ^ an
N/A
Equation is for
1-unit buildings;
example is for 2-
unit buildings
14
24 s
AFpMW = PEXWo
24
119.7 PE value for Massachusetts
27.07% silt content
x
9%
0.603 PM10-PRI
adjustment
factor for 2-unit
structures in
Suffolk County,
MA
15
EFp,n,c ~ AFpMio * Dm,n
x FF ¦
ul orig
0.603 x 6 months x 0.032 tons per acre
0.1158
tons/acre PM10-
PRI emissions
factor for 2-unit
structures in
Suffolk County,
MA
16
EpMio,c ~ Sn,c x EFp n c
4.08 acres x 0.1158 tons per acre
0.47 tons PM10-
PRI emissions
for 2-unit
structures in
Suffolk County,
MA
17
EpM2.S,c = EpMlO,c X 0-1
0.47 tons x 0.1
0.047 tons
PM25-PRI
emissions for 2-
unit structures
in Suffolk
County, MA
4,9,3,7 Updates in 2017 methodology
Except for activity data updates, there are no significant changes from the methodology used in the 2014 NEI.
4,9,3.8 Puerto Rico and Virgin Islands
Since insufficient data exist to calculate emissions for the counties in Puerto Rico and the US Virgin Islands,
emissions are based on two proxy counties in Florida: 12011, Broward County for Puerto Rico and 12087,
Monroe County for the US Virgin Islands. The total emissions in tons for these two Florida counties are divided
by their respective populations creating a tons per capita emissions factor. For each Puerto Rico and US Virgin
Island county, the tons per capita emissions factor is multiplied by the county population (from the same year as
the inventory's activity data) which served as the activity data. In these cases, the throughput (activity data) unit
and the emissions denominator unit are "EACH".
4-141
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4,9,3.9 References for residential construction
1. U.S. Census Bureau, New Privately Owned Housing Units Started by Purpose and Design in 2017,
accessed March 2019.
2. U.S. Census Bureau, 2001. Housing Starts, Table 1. New Privately-Owned Housing Units Started.
3. U.S. Census Bureau, New Privately-Owned Housing Units Authorized - Unadjusted Units for Regions,
Divisions, and States, Annual 2017, Table 2au.
4. U.S. Census Bureau, Annual Housing Units Authorized by Building Permits CO2017A, purchased March
2019.
5. U.S. Census Bureau, 2017 Characteristics of New Housing. Characteristics of New Single-Family Houses
Completed, Annual 2017, Foundation Table.
6. Midwest Research Institute. 1996. Improvement of Specific Emission Factors (BACM Project No. 1).
Prepared for South Coast Air Quality Management District.
7. U.S. Department of Agriculture, National Cooperative Soil Survey, NCSS Microsoft Access Soil
Characterization Database.
8. Cowherd, C. J. Donaldson, R. Hegarty, and D. Ono. 2006. Proposed Revisions to Fine Fraction Ratios Used
for AP-42 Fugitive Dust Emission Factors. 15th International Emission Inventory Conference, New
Orleans, LA.
9. Midwest Research Institute. 1999. Estimating Particulate Matter Emissions from Construction
Operations. Prepared for Emission Factor and Inventory Group, Office of Air Quality Planning and
Standards US EPA.
4.9.4 EPA-developed emissions for non-residential construction
The calculations for estimating the emissions from non-residential construction involve first estimating the acres
disturbed from non-residential construction in each county. The value of national-level non-residential
construction spending is available from the U.S. Census Bureau and is converted to acreage disturbed using a
conversion factor from a report by the Midwest Research Institute (MRI). The national-level acres disturbed are
distributed to counties based on the proportion of non-residential construction employment in each county.
Emissions factors for PM10 and PM25 are calculated based on precipitation-evaporation values and dry silt
content in each county. The total amount of acres disturbed is multiplied by these emissions factors to estimate
emissions of PM from non-residential construction.
4.9.4.1 Activity data
The activity data for this source category is the acreage disturbed from non-residential construction, which is
estimated using data from the U.S. Census Bureau's Annual Value of Construction Put in Place in the U.S [ref 1],
and a conversion factor from MRI's Estimating Particulate Matter Emissions from Construction Operations, Final
Report [ref 2], The national-level non-residential construction spending data are allocated to the county-level
based on the proportion of non-residential construction employees (NAICS 2362) in each county. Employment
data are taken from the U.S. Census Bureau's 2017 County Business Patterns (CBP), and gaps in employment
data are filled using a process described in detail in the next section.
(1)
CSc = EmpFrc x CSus
(2)
4-142
-------
Where:
EmpFrc = The fraction of non-residential construction employees in county c
Empc = The number of non-residential construction employees in county c
Empus = The number of non-residential construction employees in the US
CSc = Non-residential construction spending in county c
CSus = Non-residential construction spending in the US
Non-residential construction spending is converted to acres disturbed using a conversion factor from MRI's
report. For the average acres disturbed per million dollars of non-residential construction, MRI reported a
conversion factor of 2 acres/$l million (in 1992 constant dollars). The 1992 conversion factor is adjusted to 2017
using the Price Deflator (Fisher) Index of New Single-Family Houses under Construction [ref 3], In 2017 the
conversion factor was 1.009 acres per million dollars spent on non-residential construction activities.
2 acres PDi qq7
Apd2017 = .... x —(3)
$1 TYlilliOTl ^^2017
Where:
Apd2oi7 = Acres disturbed per million dollars in 2017
PD1992 = Price Deflator (Fisher) Index value in 1992
PD2017 = Price Deflator (Fisher) Index value in 2017
County-level non-residential construction spending (from equation 2) is then multiplied by this conversion factor
to estimate county-level acreage disturbed from non-residential construction activities.
Ac = CSc X Apd2on (4)
Where:
Ac = Acres disturbed from non-residential construction in county c
CSc = Non-residential construction spending in county c
Apd2oi7 = Acres disturbed per million dollars in 2017
4.9.4.2 Allocation procedure
Employment data are obtained from the U.S. Census Bureau's 2017 County Business Patterns (CBP) [ref 4], Due
to concerns with releasing confidential business information, the CBP does not release exact numbers for a
given NAICS code if the data can be traced to an individual business. Instead, a series of range codes is used. To
estimate employment in counties and states with withheld data, the following procedure is used for NAICS code
2362 (non-residential construction).
To gap-fill withheld state-level employment data:
1. State-level data for states with known employment are summed to the national level.
2. State-level known employment is subtracted from the national total reported in the national-level CBP.
3. Each of the withheld states is assigned the midpoint of the range code. Table 4-82 lists the range codes
and midpoints.
4. The midpoints for the states with withheld data are summed to the national level.
5. An adjustment factor is created by dividing the number of withheld employees (calculated in step 2 of
this section) by the sum of the midpoints (step 4)
4-143
-------
6. For the states with withheld employment data, the midpoint of the range for that state (step 3) is
multiplied by the adjustment factor (step 5) to calculate the adjusted state-level employment for non-
residential construction.
These same steps are then followed to fill in withheld data in the county-level business patterns.
1. County-level data for counties with known employment are summed by state.
2. County-level known employment is subtracted from the state total reported in state-level CBP (or, if the
state-level data are withheld, from the state total estimated using the procedure discussed above).
3. Each of the withheld counties is assigned the midpoint of the range code (Table 4-82).
4. The midpoints for the counties with withheld data are summed to the state level.
5. An adjustment factor is created by dividing the number of withheld employees (step 2) by the sum of
the midpoints (step 4).
6. For counties with withheld employment data, the midpoints (step 3) are multiplied by the adjustment
factor (step 5) to calculate the adjusted county-level employment for non-residential construction.
Note that step 5 adjusts all counties within each state with withheld employment data by the same state-based
proportion. It is unlikely that actual employment corresponds exactly with this smoothed adjustment method,
but this method is the best option given the availability of the data.
Table 4-82: Ranges anc
midpoints for c
ata withheld from State and Co
Range Letter
Ranges
Midpoint
A
0-19
10
B
20-99
60
C
100-249
175
E
250-499
375
F
500-999
750
G
1,000-2,499
1,750
H
2,500-4,999
3,750
1
5,000-9,999
7,500
J
10,000-24,999
17,500
K
25,000-49,999
37,500
L
50,000-99,999
75,000
M
100,000+
For example, take the 2017 CBP data for NAICS 2362 (non-residential construction) in Arizona provided in Table
4-83.
Table 4-83: 2017 CBP for NAICS 2361 in Arizona
FIPS state
FIPS county
NAICS
empflag
emp
04
001
2362
A
withheld
04
003
2362
B
withheld
04
005
2362
177
04
007
2362
11
04
009
2362
A
withheld
04
011
2362
H
withheld
04
012
2362
A
withheld
04
013
2362
7,945
04
015
2362
47
4-144
-------
FIPS state
FIPS county
NAICS
empflag
emp
04
017
2362
79
04
019
2362
2,220
04
021
2362
112
04
023
2362
A
withheld
04
025
2362
171
04
027
2362
359
1. The total of employees not including withheld counties is 11,121.
2. The state-level CBP reports 13,952 employees for NAICS 2362. The difference is 2,831.
3. County 001 is given a midpoint of 10 (since range code A is 0-19) and County Oil is given a midpoint of
3,750.
4. State total for these all withheld counties is 3,850.
5. 2,831/3,850 = 0.74.
6. The adjusted employment for county 001 is 10 x 0.74 = 7.35. County 011 has an adjusted employment
of 3,750x0.74 = 2,757.47.
The county-level employment data are used to allocate the national-level non-residential construction spending
data to the county-level (see equations 1 and 2).
4.9.4.3 Emission factors
Due to regional variances in soil moisture and silt content, emissions factors for PM10 and PM25 are calculated
for each county. The initial PM10 emissions factor from non-residential construction is 0.19 tons/acre-month
[ref 5], The duration of construction activity for non-residential construction is assumed to be 11 months.
To account for the soil moisture level, the PM10 emissions are weighted using the 30-year average precipitation-
evaporation (PE) values from Thornthwaite's PE Index. Average precipitation evaporation values for each state
are estimated based on PE values for specific climatic divisions within a state [ref 5], The average PE value for the
test sites from which the PM10 emissions factor was developed is 24. Equation 5 adjusts the county-level
emissions factor based on this PE value.
To account for the silt content, the PM10 emissions are weighted using average silt content for each county.
EPA uses the National Cooperative Soil Survey Microsoft Access Soil Characterization Database to develop
county-level, average silt content values for surface soil [ref 6], The U.S. Department of Agriculture and the
National Cooperative Soil Survey define silt content of surface soil as the percentage of particles (mass basis) of
diameter smaller than 50 micrometers (nm) found in the surface soil. Note that this definition is different than
the U.S. Environmental Protection Agency's definition that includes all particles (mass basis) of diameter smaller
than 75 micrometers. This database contains the most commonly requested data from the National Cooperative
Soil Survey Laboratories including data from the Kellogg Soil Survey Laboratory and cooperating universities. The
average silt content for the test sites from which the PM10 emissions factor was developed is 9%. Equation 5
adjusts the county-level emissions factor based on this silt content value.
24 S
EFpmio.c = efpMW x x (5)
Where:
EFpmio,c = PMioemission factor corrected for soil moisture and silt content in state s and county c, in
tons/acre-month
4-145
-------
efpMio = Initial PMio emissions factor for non-residential construction, 0.19 tons/acre-month
PES = Precipitation-evaporation value for state s
Sc = Percent dry silt content in soil for county c
Once PMio adjustments have been made, PM2.5 emissions are set to 10% of PMi0.[ref 7]
EFpM2S,c = 0.10 X EFpmio,c (6)
Where:
EFpmio,c = PMio emission factor corrected for soil moisture and silt content in state s and county c, in
tons/acre-month
EFPm25,c = PM2.5 emission factor corrected for soil moisture and silt content in county c, in tons/acre-month
Primary PM emissions are equal to filterable emissions as there are no condensible emissions from dust from
non-residential construction.
4.9.4.4 Controls
There are no controls assumed for this category.
4.9.4.5 Emissions
The total annual PM emissions from non-residential construction in each county are calculated by multiplying
the acres disturbed by the emissions factors calculated in equations 5 and 6 and by the duration of construction
activity.
Ep,c = Ac X EFp c X M (7)
Where:
Ep,c = Annual emissions of pollutant p in county c
Ac = Acres disturbed from non-residential construction in county c
EFpmio,c = PMio emission factor corrected for soil moisture and silt content in state s and county c, in
tons/acre-month
EFPm25,c = PM2.5 emission factor corrected for soil moisture and silt content in county c, in tons/acre-
month
M = Duration of construction activity in months, assumed to be 11 months
4.9.4.6 Sample calculations
Table 4-84 lists sample calculations to determine the dust emissions from non-residential construction in Grand
Traverse County, Michigan.
4-146
-------
Table 4-84. Sample calculations for non-residential construction in Grand Traverse County, Michigan
Eq.
#
Equation
Values for Grand Traverse County, Ml
Result
1
Empc
120 nonres construction employees
0.000206
fraction of
non-
residential
construction
employees in
Grand
Traverse
County, Ml
LiTipi lc
Empus
582,574 nonres construction employees
2
CSc = EmpFracc x
C$us
0.000206 fraction of employees in Grand Traverse x
$ 347,666 million in nonres construction spending in the US
$71.61 million
in non-
residential
construction
spending in
Grand
Traverse
County, Ml
3
. , 2 acres
Apdv = -——— x
" y $l million
pd1992
PDy
2 acres disturbed 57 in 1992
$1 million X 113 in 2017
1.009 acres
disturbed per
million dollars
spent on non-
residential
construction
spending
4
Ac = CSc x Apdy
acres disturbed
$ 71.61 million x 1.009
million $
72.25 acres
disturbed
from non-
residential
construction
in Grand
Traverse
County, Ml
5
EFpmw,c = efpM\ o x
24 sc
PES 9%
24 21.95%
0.19 tons per acre month x x
F 103.6 9%
0.1073 tons
PM10 per
acre-month of
non-
residential
construction
in Grand
Traverse
County, Ml
4-147
-------
Eq.
#
Equation
Values for Grand Traverse County, Ml
Result
0.0107 tons
PM25 per acre
month on
6
EFpM25,c = 0-10 x
EFpmio.c
0.10 x 0.1073 tons per acre month
non-
residential
construction
in Grand
Traverse
County, Ml
85.3 tons
PM10
emissions
tons
72.25 acres x 0.1073 x 11 months
acre — month
from non-
residential
construction
in Grand
7
Ep,c Ac X EFp c
XM
Traverse
County, Ml
tons
72.25 acres x 0.0107 x 11 months
acre — month
8.5 tons PM25
emissions
from non-
residential
construction
in Grand
Traverse
County, Ml
4,9,4,7 Updates in 2017 methodology
Except for activity data updates, there are no significant changes from the methodology used in the 2014 NEI.
4.9.4.8 Puerto Rico and Virgin Islands
Since insufficient data exists to calculate emissions for the counties in Puerto Rico and the US Virgin Islands,
emissions are based on two proxy counties in Florida: 12011, Broward County for Puerto Rico and 12087,
Monroe County for the US Virgin Islands. The total emissions in tons for these two Florida counties are divided
by their respective populations creating a tons per capita emission factor. For each Puerto Rico and US Virgin
Island counties, the tons per capita emission factor is multiplied by the county population (from the same year
as the inventory's activity data) which serve as the activity data. In these cases, the throughput (activity data)
unit and the emissions denominator unit are "EACH".
4.9.4.9 References for non-residential construction
1. U.S. Census Bureau, 2017. Value of Construction Put in Place.
2. Midwest Research Institute. 1999. Estimating Particulate Matter Emissions from Construction
Operations, Final Report, prepared for the Emission Factor and Inventory Group, Office of Air Quality
Planning and Standards, U.S. Environmental Protection Agency. Table 5-2.
3. U.S. Census Bureau. 2017. Price Deflator (Fisher) Index of New Single-Family Houses Under Construction
4-148
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4. U.S. Census Bureau, County Business Patterns. 2017. Complete County File [14.4mb zip]
5. Midwest Research Institute. 1996. Improvement of Specific Emission Factors (BACM Project No. 1).
Prepared for South Coast Air Quality Management District.
6. U.S. Department of Agriculture, National Cooperative Soil Survey, NCSS Microsoft Access Soil
Characterization Database.
7. Midwest Research Institute. 2006. Background Document for Revisions to Find Fraction Ratios Used for
AP-42 Fugitive Dust Emissions Factors. Prepared for Wester Governors 'Association.
4.9.5 EPA-developed emissions for road construction
The calculations for estimating the emissions from road construction involve first estimating the acres disturbed
from new road constructed in each county. The amount of state-level road construction spending by road type is
available from the Federal Highway Administration (FHWA) and is converted to acreage disturbed using
conversion factors from the Florida Department of Transportation (FLDOT). The state-level acreage disturbed by
road type is summed together and distributed to the counties based on the proportion of building starts in each
county. Emissions factors for PM10 and PM25 are calculated based on precipitation-evaporation values and dry
silt content in each county. The total amount of acres disturbed is multiplied by these emissions factors to
estimate emissions of PM from road construction.
4.9.5.1 Activity data
The activity data for this source category is the acreage disturbed from new road construction, which is
estimated using data from FHWA's Highway Statistics, State Highway Agency Capital Outlay 2014, Table SF-12A
[ref 1] and FLDOT's Generic Cost per Mile Models [ref 2], From the FHWA table, the following construction types
are used: New Construction, Relocation, Added Capacity, Major Widening, and Minor Widening. Each of the
following road types have spending broken out for each construction type:
1. Interstate, urban
2. Interstate, rural
3. Other arterial, urban
4. Other arterial, rural
5. Collectors, urban
6. Collectors, rural
Construction spending for each road type is summed across all construction types to determine the total annual
highway spending for each road type.
ct
(1)
Where:
5s, r
HSs,r
ct
Annual highway spending for road type r in state s, in dollars
Construction type
Annual spending per construction type in state s for road type r, in dollars
State expenditure data are converted to miles of new road and acres disturbed per mile of new road by applying
conversions based on data obtained from FLDOT. The conversions are shown in Table 4-85, and the acres
4-149
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disturbed per mile conversions are calculated by multiplying the FLDOT's total affected roadway width (including
all lanes, shoulders, and areas affected beyond the road width) in feet by the number of feet in a mile and
converting the resulting land area from ft2to acres [ref 2], Total affected roadway with is the sum of the
numbers of lanes (assumed at 12 feet each), number of shoulders, and area affected beyond the road width (25
feet). There are 5,280 feet in a mile, and 43,560 ft2 in an acre.
HSsr
RCmsr=—— 2
m's'r TDM
RCnsr = RCmsr x ADM (3)
Where:
RCm,s,r = Miles of FHWA road type r constructed in state s
RCa/S,r = Acres of land disturbed for construction of FHWA road type r in state s
HSs,r = Annual highway spending for road type r in state s
TDM = Conversion of dollars spent to road miles constructed, in thousand dollars per mile
ADM = Conversion of road miles constructed to acres disturbed, in acres per mile
Table 4-85: Spending per mile and acres c
isturbed per mile by hig
nway type
Road Type
Thousand
Total Affected
Acres Disturbed
Dollars per mile
Roadway Width (ft)*
per mile
Urban Areas, Interstate
6,895
94
11.4
Rural Areas, Interstate
3,810
89
10.8
Urban Areas, Other Arterials
4,112
63
7.6
Rural Areas, Other Arterials
2,076
55
6.6
Urban Areas, Collectors
4,112
63
7.6
Rural Areas, Collectors
2,076
55
6.6
The acres of land disturbed by road type can then be summed across all road types in a state to calculate the
total state-level acreage disturbed due to new road construction.
-L
As = > RCa,s (4)
Where:
As = Acres of land disturbed for all road construction in state s
RCas = Acres of land disturbed for construction of FHWA road type r in state s
The process used to distribute the state-level amount of acreage disturbed to the counties is discussed in the
next section.
4.9.5,2 Allocation procedure
Building permits data, used as a surrogate for road construction activity, from the U.S. Census Bureau are used
to allocate the state-level acres disturbed by road construction to the county-level [ref 3], Specifically, the ratio
of the county-to state-level number of building starts is calculated and multiplied by the state-level acreage
disturbed (from equation 4) to estimate the county-level acreage disturbed by road construction.
4-150
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Buildc
(5)
Ac = As x BFracc
(6)
Where:
BFraCc = The fraction of building starts in countyc
Buildc = The number of building starts in county c
Builds = The number of building starts in state s
Ac = Acres of land disturbed for road construction in county c
As = Acres of land disturbed for all road construction in state s
4,9.5.3 Emission factors
Due to regional variances in soil moisture and silt content, uncontrolled emissions factors for PM10 and PM25
are adjusted for each county. The initial uncontrolled PM10 emissions factor from construction of roads is 0.42
tons/acre-month [ref 4], 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.
To account for the soil moisture level, the uncontrolled PM10 emissions are weighted using the 30-year average
precipitation-evaporation (PE) values from Thornthwaite's PE Index. Average precipitation evaporation values
for each state are estimated based on PE values for specific climatic divisions within a state [ref 4], The average
PE value for the test sites from which the PM10 emissions factor was developed is 24. Equation 7 adjusts the
county-level uncontrolled emissions factor based on this PE value.
To account for the silt content, the uncontrolled PM10 emissions are weighted using average silt content for
each county. EPA uses the National Cooperative Soil Survey Microsoft Access Soil Characterization Database to
develop county-level, average silt content values for surface soil [ref 5], The U.S. Department of Agriculture and
the National Cooperative Soil Survey define silt content of surface soil as the percentage of particles (mass basis)
of diameter smaller than 50 micrometers (pim) found in the surface soil. Note that this definition is different
than the U.S. Environmental Protection Agency's definition that includes all particles (mass basis) of diameter
smaller than 75 micrometers. This database contains the most commonly requested data from the National
Cooperative Soil Survey Laboratories including data from the Kellogg Soil Survey Laboratory and cooperating
universities. The average silt content for the test sites from which the PM10 emissions factor was developed is
9%. Equation 7 adjusts the county-level uncontrolled emissions factor based on this silt content value.
Where:
UEFpmio,c = Uncontrolled PMioemission factor corrected for soil moisture and silt content in state s and
county c, in tons/acre-month
EFpmio = Initial PMio emissions for road construction, 0.42 tons/acre-month
PEs = Precipitation-evaporation value for state s
Sc = Percent dry silt content in soil for county c
Once uncontrolled PMio adjustments have been made, uncontrolled PM2.5 emissions are set to 10% of PMio.
(7)
UEFPM2S,c
= 0.10 X UEF,
PM10,c
(8)
4-151
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Where:
UfFP/v)io,c = Uncontrolled PMioemission factor corrected for soil moisture and silt content in state s and
county c, in tons/acre-month
UEFPm25,c = Uncontrolled PM2.5 emission factor corrected for soil moisture and silt content in county c, in
tons/acre-month
Primary PM emissions are equal to filterable emissions as there are no condensible dust emissions from road
construction.
4.9.5.4 Controls
Dust emissions from road construction are generally controlled by watering the construction site. The Midwest
Research Institute recommends using a control efficiency of 50% for PM10 and PM25 emissions from road
construction [ref 6],
EFP c = 0.50 X UEFpc
(9)
Where:
EFP,c
UEFn
Controlled emissions factor of pollutant p in county c
Uncontrolled emissions factor of pollutant p in county c
4.9,5.5 Emissions
The total annual dust emissions from road construction in each county are multiplied by the emissions factors
calculated in equation 9. The duration of construction activity for road construction is assumed to be 12 months.
Ep C — Ac X EFp c X M
(10)
Where:
Ac
EFpmio,c
EFpM25,c
M
Annual emissions of pollutant p in county c
Acres of land disturbed for road construction in county c
Controlled PM10emission factor corrected for soil moisture and silt content in state s and
county c, in tons/acre-month
Controlled PM2.5 emission factor corrected for soil moisture and silt content in county c, in
tons/acre-month
Duration of construction activity in months
4.9.5.6 Sample calculations
Table 4-86 Lists sample calculations to determine the dust emissions from road construction in Newport County,
Rhode Island.
Table 4-86: Sample calculations for urban interstate, urban other arterial, and urban collector road construction
in Newport County, Rl
Eq.
#
Equation
Values for Newport County, Rl
Result
1
Hss,r=y ss r
t—ict
$1,000 + $9,155,000
$9,156,000 spent on
urban interstate
construction in Rl
4-152
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Eq.
#
Equation
Values for Newport County, Rl
Result
$1,276,000 + $2,471,000
$3,747,000 spent on
urban other arterial
construction in Rl
$2,583,000
$2,583,000 spent on
urban collector
construction in Rl
2
HSs r
nr
m's,r TDM
$9,156,000
6,895,000 $ per mile
1.328 miles of urban
interstate
constructed in Rl
$3,747,000
4,112,000 $ per mile
0.911 miles of urban
other arterial
constructed in Rl
$2,683,000
4,112,000 $ per mile
0.628 miles of urban
collector
constructed in Rl
3
RCa s r = RCms r x ADM
1.328 miles x 11.4 acres per mile =
15.1 acres disturbed
from urban
interstate
construction in Rl
0.911 miles x 7.6 acres per mile
6.9 acres disturbed
from urban other
arterial construction
in Rl
0.628 miles x 7.6 acres per mile
4.8 acres disturbed
from urban
collector
construction in Rl
4
As = ^ ' RCa,s
15.1 acres + 6.9 acres + 4.8 acres
26.78 acres
disturbed from
urban road
construction in Rl
5
Buildr
185 building starts in Newport County
0.194 fraction of
building starts in
Newport County, Rl
Builds
952 building starts in RI
6
Ac = As x BFracc
26.78 acres x 0.194
5.20 acres disturbed
from urban road
construction in
Newport County, Rl
7
24
UEFpMW c — EFPM10 X
PES
9%
0.42 tons/acre
24
— month x
132
41,45%
x
9%
0.3517 tons per
acre-month
uncontrolled PM10
emissions from road
construction in
Newport County, Rl
4-153
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Eq.
#
Equation
Values for Newport County, Rl
Result
8
UEFPM2s,c = 0.10
x UEFPMi 0,c
0.10 x 0.3517 tons/acre — month
0.0352 tons per
acre-month PM25
emissions from road
construction in
Newport County, Rl
9
EFPc — 0.50 X UEFpc
0.50 x 0.3514 tons per acre — month
0.1758 tons per
care-month
controlled PM10
emissions from new
road construction in
Newport County, Rl
0.50 x 0.0352 tons per acre — month
0.0176 tons per
care-month
controlled PM25
emissions from new
road construction in
Newport County, Rl
10
EPiC Ac X EFp c X M
5.2 acres x 0.1758 tons/acre
— month x 12
10.98 tons PM10
from urban road
construction in
Newport County, Rl
5.2 acres x 0.0176 tons/acre
— month x 12
1.98 tons PM25
from urban road
construction in
Newport County, Rl
4.9.5.7 Updates in 2017 methodology
The only methodology change from that used to calculate the 2014 NEI emissions is the addition of a 50%
control due to watering of construction sites.
4.9.5.8 Puerto Rico and Virgin Islands
Since insufficient data exists to calculate emissions for the counties in Puerto Rico and the US Virgin Islands,
emissions are based on two proxy counties in Florida: 12011, Broward County for Puerto Rico and 12087,
Monroe County for the US Virgin Islands. The total emissions in tons for these two Florida counties are divided
by their respective populations creating a tons per capita emission factor. For each Puerto Rico and US Virgin
Island county, the tons per capita emission factor is multiplied by the county population (from the same year as
the inventory's activity data) which served as the activity data. In these cases, the throughput (activity data) unit
and the emissions denominator unit are "EACH".
4.9.5.9 References for road construction
1. Federal Highway Administration. Table SF-12A, State Highway Agency Capital Outlay -2014.
2. Florida Department of Transportation. Generic Cost per Mile Models for 2018
3. U.S. Census Bureau. 2015. Annual Housing Units Authorized by Building Permits CO2017A, purchased
March 2019.
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4. Midwest Research Institute. 1996. Improvement of Specific Emission Factors (BACM Project No. 1).
Prepared for South Coast Air Quality Management District.
5. U.S. Department of Agriculture, National Cooperative Soil Survey, NCSS Microsoft Access Soil
Characterization Database.
6. Midwest Research Institute. 1999. Estimating Particulate Matter Emissions from Construction
Operations, Final Report, prepared for the Emission Factor and Inventory Group, Office of Air Quality
Planning and Standards, U.S. Environmental Protection Agency. Table 5-2.
4.10 Dust - Paved Road Dust
4.10.1 Sector description
The paved road dust sector reflects emissions of particulate matter from vehicles driving over paved roads. The
SCCs that belong in this sector are provided in Table 4-87. EPA estimates emissions for total fugitives only.
Fugitive dust emissions from paved road traffic were estimated for PM10-PRI, PM10-FIL, PM25-PRI, and PM25-
FIL. Since there are no PM-CON emissions for this category, PM10-PRI emissions are equal to PM10-FIL
emissions and PM25-PRI emissions are equal to PM25-FIL emissions.
Ta
lie 4-87: SCCs in the paved road dust sector
see
see Level 1
SCC Level 2
SCC Level 3
SCC Level 4
2294000000
Mobile Sources
Paved Roads
All Paved Roads
Total: Fugitives
2294000002
Mobile Sources
Paved Roads
All Paved Roads
Total: Sanding/Salting - Fugitives
4.10.2 Sources of data
The paved road dust sector includes data from the S/L/T agency submitted data and the default EPA generated
emissions. The agencies listed in Table 4-88 submitted emissions for this sector; agencies not listed used EPA
estimates for the entire sector.
Table 4-88: Agencies that submitted paved road dust emissions
Region
Agency
S/L/T
1
New Hampshire Department of Environmental Services
State
3
Delaware Department of Natural Resources and Environmental Control
State
3
Maryland Department of the Environment
State
4
Metro Public Health of Nashville/Davidson County
Local
4
Memphis and Shelby County Health Department - Pollution Control
Local
8
Northern Cheyenne Tribe
Tribe
9
California Air Resources Board
State
9
Maricopa County Air Quality Department
Local
9
Washoe County Health District
Local
10
Coeur d'Alene Tribe
Tribe
10
Idaho Department of Environmental Quality
State
10
Kootenai Tribe of Idaho
Tribe
10
Nez Perce Tribe
Tribe
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
10
Washington State Department of Ecology
State
4-155
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4.10.3 EPA-developed emissions
Uncontrolled paved road emissions were calculated at the county level by roadway type for the year 2017. This
was done by multiplying the county/roadway class paved road vehicle miles traveled (VMT) by the appropriate
paved road emission factor. Next, control factors were applied to the paved road emissions in PMio
nonattainment and maintenance status counties. Emissions by roadway class were then totaled to the county
level for reporting in the NEI. The following provides further details on the emission factor equation,
determination of paved road VMT, and controls.
4.10.3.1 Emission factors
Re-entrained road dust emissions for paved roads were estimated using paved road VMT and the emission
factor equation from AP-42 [ref 1]:
E = [kx(sL)a91x(W)102]
Where:
E = paved road dust emission factor (g/VMT)
k = particle size multiplier (g/VMT)
sL = road surface silt loading (g/ m2) (dimensionless in eq.)
W = average weight (tons) of all vehicles traveling the road (dimensionless in eq.)
The particle size multipliers for both PM10-PRI/-FIL and PM25-PRI/-FIL for paved roads came from AP-42. Paved
road silt loadings were assigned to each of the fourteen functional roadway classes (seven urban and seven
rural) based on the average annual daily traffic volume (ADTV) of each functional system by county [ref 2], The
silt loading values per average daily traffic volume come from the ubiquitous baseline values from Section 13.2.1
of AP-42 and are provided in Table 4-89.
Table 4-89: Assumed paved roads silt loading by roac
type (gm2)
Dased on ADTV range
FHWA road type
0-499
500-4,999
5,000-9,999
10,000+
Rural Interstate
0.015
0.015
0.015
0.015
Rural Other Freeways and Expressways
0.015
0.015
0.015
0.015
Rural Other Principal Arterial
0.6
0.2
0.06
0.03
Rural Minor Arterial
0.6
0.2
0.06
0.03
Rural Major Collector
0.6
0.2
0.06
0.03
Rural Minor Collector
0.6
0.2
0.06
0.03
Rural Local
0.6
0.2
0.06
0.03
Urban Interstate
0.015
0.015
0.015
0.015
Urban Other Freeways and Expressways
0.015
0.015
0.015
0.015
Urban Other Principal Arterial
0.6
0.2
0.06
0.03
Urban Minor Arterial
0.6
0.2
0.06
0.03
Urban Major Collector
0.6
0.2
0.06
0.03
Urban Minor Collector
0.6
0.2
0.06
0.03
Urban Local
0.6
0.2
0.06
0.03
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Average daily traffic volume (ADTV) was calculated by dividing an estimate of VMT by functional road length and
then by 365. State FHWA road length by functional road type data was broken down to the county level by
multiplying by the ratio of county VMT to state VMT for each FHWA road type.
To better estimate paved road fugitive dust emissions, the average vehicle weight was estimated by road type
for each county in the U.S. based on the 2017 VMT by vehicle type. The VMT for each vehicle type (per MOVES
road type and county) was divided by the sum of the VMT of all vehicle types for the given road type in each
county. This ratio was multiplied by the vehicle type mass (see Table 4-90) and summed to road type for each
county to calculate a VMT-weighted average vehicle weight for each county/road type combination in the
database. The VMT-weighted average vehicle weight by MOVES vehicle type was converted to FWHA vehicle
type using the crosswalk in Table 4-91 to be used in the emission factor equation above.
Ta
lie 4-90: Average vehicle weights by FWHA vehicle class
Source Mass
MOVES Vehicle Type
(tons)
Motorcycle
0.285
Passenger Car
1.479
Passenger Truck
1.867
Light Commercial Truck
2.0598
Intercity Bus
19.594
Transit Bus
16.556
School Bus
9.070
Refuse Truck
23.114
Single Unit Short-haul Truck
8.539
Single Unit Long-haul Truck
6.984
Motor Home
7.526
Combination Short-haul Truck
22.975
Combination Long-haul Truck
24.601
Table 4-91: MOVES and FWHA vehicle type crosswalk
MOVES Road Type Description
FWHA Road Type
Rural Restricted Access
Rural Interstate
Rural Unrestricted Access
Rural Principal Arterial
Rural Unrestricted Access
Rural Minor Arterial
Rural Unrestricted Access
Rural Collector
Rural Unrestricted Access
Rural Local
Urban Restricted Access
Urban Interstate
Urban Unrestricted Access
Urban Principal Arterial
Urban Unrestricted Access
Urban Minor Arterial
Urban Unrestricted Access
Urban Collector
Urban Unrestricted Access
Urban Local
*Note: Other Freeways and Expressways were not included in the crosswalk, and so were assumed to be restricted access
like Interstates.
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4,10,3,2 Activity data
Generally, VMT on US roads can be obtained from the Federal Highway Administration (FHWA). Total VMT in
each county in 2017 is provided by FHWA to EPA for use in EPA's MOtor Vehicle Emission Simulator (MOVES)
model to calculate emissions for the mobile sector. The road dust methodology uses these same county-level
VMT data from FHWA. FHWA categorizes roads into 14 different types based on road function and access; these
road types can be found in Table 4-92.
Table 4-92: FHWA road types
FHWA Road Type
Rural Interstate
Rural Other Freeways and Expressways
Rural Other Principal Arterial
Rural Minor Arterial
Rural Major Collector
Rural Minor Collector
Rural Local
Urban Interstate
Urban Other Freeways and Expressways
Urban Other Principal Arterial
Urban Major Collector
Urban Minor Collector
Urban Local
Urban Minor Arterial
To estimate the portion of the total VMT occurring on paved roads, first the VMT on unpaved roads were
estimated using a procedure to estimate proportion of unpaved vs. paved VMT (see the full description for VMT
development in the "Activity Data" subsection under the Unpaved Road Dust section below). The estimated
VMT on unpaved roads was then subtracted from the total VMT from MOVES to estimate the VMT on paved
roads for each road type category where applicable.
4.10.3.3 Allocation
County level emissions were calculated by multiplying the county unpaved VMT (by road type) by the emission
factors calculated according to Section 4.10.3.1 above and aggregating based on county and urban/rural
classification.
4.10.3.4 Controls
Paved road dust controls were applied by county to urban and rural roads in serious PMio nonattainment areas
and to urban roads in moderate PMio nonattainment areas. The assumed control measure is vacuum sweeping
of paved roads twice per month. A control efficiency of 79% was assumed for this control measure [ref 3], The
assumed rule penetration varies by roadway class and PMio nonattainment area classification (serious or
moderate). The rule penetration rates are shown in Table 4-93. Rule effectiveness was assumed to be 100% for
all counties where this control was applied.
Table 4-93: Penetration rate of Paved Roac
vacuum sweeping
PMio Nonattainment Status
Roadway Class
Vacuum Sweeping Penetration Rate
Moderate
Urban Freeway & Expressway
0.67
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PMio Nonattainment Status
Roadway Class
Vacuum Sweeping Penetration Rate
Moderate
Urban Minor Arterial
0.67
Moderate
Urban Collector
0.64
Moderate
Urban Local
0.88
Serious
Rural Minor Arterial
0.71
Serious
Rural Major Collector
0.83
Serious
Rural Minor Collector
0.59
Serious
Rural Local
0.35
Serious
Urban Freeway & Expressway
0.67
Serious
Urban Minor Arterial
0.67
Serious
Urban Collector
0.64
Serious
Urban Local
0.88
Note that the controls were applied at the county/roadway class level, and the controls differ by roadway class.
No controls were applied to interstate or principal arterial roadways because these road surfaces typically do
not have vacuum sweeping. In the excel spreadsheet, the total emissions for all roadway classes were summed
to the county level. Therefore, the emissions at the county level can represent several different control
efficiency and rule penetration levels and may include both controlled and uncontrolled emissions in the
composite value.
4.10.3.5 Meteorological adjustment
After controls were applied, emissions were summed to the county level and converted to tons prior to applying
the meteorological adjustment. The meteorological adjustment accounts for the reduction in fugitive dust
emissions via the impact of precipitation and other meteorological factors over each hour of the year and then
averaged to an annual meteorological adjustment factor for each grid cell in each county, aggregated to a single
county-level factor. The county-level meteorological adjustment factors were developed by EPA based on the
ratio of the unadjusted to meteorology-adjusted 2017 county-level emissions from the SMOKE Flat Files. The
county-level meteorological adjustment is a scalar between 0 and 1 that is multiplied by the estimated
emissions, where lower-values/greater-reductions are typically found in areas with more frequent precipitation.
4.10.3.6 Changes from the 2014 NEI methodology
The largest change from the methodology used to calculate the 2014v2 NEI emissions from road dust is the
method used to determine the VMT on paved and unpaved roads in each county. Both the methods for the
2014v2 and 2017 NEI used the 2008 National Mobile Inventory Model (NMIM) run as the starting point for
estimating the ratio of VMT on paved vs. unpaved roads. However, in 2014v2, the estimated VMT on unpaved
roads were redistributed within Census regions as an additional step, to smooth out sharp differences in
emissions across state lines. This redistribution is not done for the 2017 NEI in order to better preserve the
integrity of the original SLT VMT data submitted to FHWA. An additional step was, however, added to update
the 2008 NMIM paved/unpaved ratios used for local and rural minor collector road types by using state level
2017 FHWA data on paved vs. unpaved road length for these road types.
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4.10.3.7 Puerto Rico and Virgin Islands
Since insufficient data exists to calculate emissions for the counties in Puerto Rico and the US Virgin Islands,
emissions are based on two proxy counties in Florida: Broward (state-county FIPS=12011) for Puerto Rico and
Monroe (state-county FIPS=12087) for the US Virgin Islands. The total emissions in tons for these two Florida
counties are divided by their respective populations creating a tons per capita emission factor. For each Puerto
Rico and US Virgin Island county, the tons per capita emission factor is multiplied by the county population (from
the same year as the inventory's activity data) which served as the activity data. In these cases, the throughput
(activity data) unit and the emissions denominator unit are "EACH".
4.10.3.8 Known issue with 2017 estimates
Just prior to release, we discovered a minor error with VMT being double counted for one FHWA road type,
"Urban Minor Arterial". This impacts only paved road estimates, leading to an approximate 2% overestimate of
EPA-generated paved road estimates; the largest state-level error was in Texas, approximately 7%. This error is
fixed in the 2017EPA_NONPOINT dataset, but not the NEI. If another 2017 NEI selection is processed at a later
date, the corrected estimates will be picked up.
4.10.4 References
1. United States Environmental Protection Agency, Office of Air Quality Planning and Standards.
"Compilation of Air Pollutant Emission Factors, AP-42, Fifth Edition, Volume I: Stationary Point and Area
Sources, Section 13.2.1, Paved Roads." Research Triangle Park, NC. January 2011.
2. U.S. Department of Transportation, Federal Highway Administration. Highway Statistics 2016. Table HM-
51. Office of Highway Policy Information. Washington, DC. September 2018.
3. E.H. Pechan & Associates, Inc. "Phase II Regional Particulate Strategies; Task 4: Particulate Control
Technology Characterization," draft report prepared for U.S. Environmental Protection Agency, Office of
Policy, Planning and Evaluation. Washington, DC. June 1995.
4,11 Dust-Unpaved Road Dust
4.11.1 Sector description
The unpaved road dust sector reflects emissions of particulate matter from vehicles driving over unpaved roads.
The SCCs that belong in this sector are provided in Table 4-94.Fugitive dust emissions from unpaved road traffic
were estimated for PM10-PRI, PM10-FIL, PM25-PRI, and PM25-FIL. Since there are no PM-CON emissions for this
category, PM10-PRI emissions are equal to PM10-FIL emissions and PM25-PRI emissions are equal to PM25-FIL
emissions.
Table 4-94: SCC in the unpavec
road dust sector
see
SCC Level 1
SCC Level 2
SCC Level 3
SCC Level 4
2296000000
Mobile Sources
Unpaved Roads
All Unpaved Roads
Total: Fugitives
4.11.2 Sources of data
The paved road dust sector includes data from the S/L/T agency submitted data and the default EPA generated
emissions. The agencies listed in Table 4-95 submitted emissions for this sector; agencies not listed used EPA
estimates for the entire sector.
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Table 4-95: Agencies that submitted unpaved road dust emissions
Region
Agency
S/L/T
1
New Hampshire Department of Environmental Services
State
3
Maryland Department of the Environment
State
4
Metro Public Health of Nashville/Davidson County
Local
4
Memphis and Shelby County Health Department - Pollution Control
Local
8
Northern Cheyenne Tribe
Tribe
9
California Air Resources Board
State
9
Maricopa County Air Quality Department
Local
9
Washoe County Health District
Local
10
Alaska Department of Environmental Conservation
State
10
Coeur d'Alene Tribe
Tribe
10
Kootenai Tribe of Idaho
Tribe
10
Nez Perce Tribe
Tribe
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
10
Washington State Department of Ecology
State
4.11.3 EPA-developed emissions
Uncontrolled unpaved road emissions were calculated at the county level by roadway type for the year 2017.
This was done by multiplying the county/roadway class unpaved road vehicle miles traveled (VMT) by the
appropriate unpaved road emission factor. Next, control factors were applied to the unpaved road emissions in
PM io nonattainment and maintenance area counties. Emissions by roadway class were then totaled to the
county level and adjusted for meteorological conditions. The following provides further details on the emission
factor equation, determination of unpaved road VMT, and controls.
4.11.3.1 Emission factors
Re-entrained road dust emissions for unpaved roads were estimated using paved road VMT and the emission
factor equation from AP-42 [ref 1]:
E = [k x (s/12)1x (SPD/30)0-5] / (M/O.5)02- C
Where k and C are empirical constants given in Table 4-96, with:
E = unpaved road dust emission factor (Ib/VMT)
k = particle size multiplier (Ib/VMT)
s = surface material silt content (%)
SPD = mean vehicle speed (mph)
M = surface material moisture content (%)
C = emission factor for 1980's vehicle fleet exhaust, brake wear, and tire wear (Ib/VMT)
Values used for the particle size multiplier and the 1980's vehicle fleet exhaust, brake wear, and tire wear are
provided in Table 4-96, and come from AP-42 defaults.
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Table 4-96: Constants for unpaved roads re-entrained dust emission factor equation
Constant
PM25-PRI/PM25-FIL
PMio-PRI/PMio-FIL
k (Ib/VMT)
0.18
1.8
C
0.00036
0.00047
Average State-level unpaved road silt content values, developed as part of the 1985 NAPAP Inventory, were
obtained from the Illinois State Water Survey [ref 2], Silt contents of over 200 unpaved roads from over 30
States were obtained. Average silt contents of unpaved roads were calculated for each sate that had three or
more samples for that State. For States that did not have three or more samples, the average for all samples
from all States was used as a default value. The silt content values are reported by State in Table 4-97.
Table 4-97: Surface material silt content values [%) for unpaved roads by state
States
Surface material
silt content (%)
OR
7.2
WY
7.1
MT
6.6
MO
6.5
TX
5.6
NC
5.1
NY
4.7
OK
4.4
NM
4.3
NE, Wl
4.2
AL, AR, AZ, CT, DE, DC, FL, GA, ID, KS, KY, LA, ME, MD, MA, MS, NH, NJ, ND, Rl, SC, UT, VT,
WA, WV
3.9
AK, HI
3.8
PA
3.3
VA
3.2
OH, SD
3.1
AZ
3.0
MN
2.7
CA, IL, IN, Ml
2.6
IA
2.5
TN
2.0
NV
1.7
CO
1.5
Table 4-98 lists the speeds modeled on the unpaved roads by roadway class. These speeds were determined
based on the average speeds modeled for onroad emission calculations and weighted to determine a single
average speed for each of the roadway classes [ref 3], The roadway class "Urban collector" with an average
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speed of 20 mph was split into two sub-categories, "Urban major collector" and "Urban minor collector", to
correspond to the roadway types found in the 2017 VMT data.
Table 4-98: Speeds modeled by roadway type on unpaved roads
Unpaved Roadway Type
Speed (mph)
Rural Minor Arterial
39
Rural Major Collector
34
Rural Minor Collector
30
Rural Local
30
Urban Other Principal Arterial
20
Urban Minor Arterial
20
Urban Major Collector
20
Urban Minor Collector
20
Urban Local
20
A report by Cowherd et al. [ref 4] estimates a range of 0.3% to 1.1% for surface material moisture content (M)
from different road samples across regions of the country. EPA used expert judgment to assign surface material
moisture content values from this range to counties based on 2017 regional patterns of soil moisture and
precipitation.
4.11.3.2 Activity data
Generally, VMT on US roads can be obtained from the Federal Highway Administration (FHWA). FHWA
categorizes roads into 14 different types based on road function and access; these road types can be found in
Table 4-99.
Table 4-99: FHWA road types
FHWA Road Type
Rural Interstate
Rural Other Freeways and Expressways
Rural Other Principal Arterial
Rural Minor Arterial
Rural Major Collector
Rural Minor Collector
Rural Local
Urban Interstate
Urban Other Freeways and Expressways
Urban Other Principal Arterial
Urban Major Collector
Urban Minor Collector
Urban Local
Urban Minor Arterial
Total VMT in each county in 2017 is provided by FHWA to EPA for use in EPA's MOtor Vehicle Emission Simulator
(MOVES) model to calculate emissions for the mobile sector. The road dust methodology uses these county-
level VMT data from FHWA.
The county-level VMT from FHWA includes total VMT, but it does not provide data how much of that VMT is on
paved or unpaved roads. FHWA provides state-level data on the amount of VMT on paved and unpaved roads in
2017 for most road types, except for three: Rural Local, Urban Local, and Rural Minor Collector [ref 5], To
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determine how much of the total VMT is on paved or unpaved roads, the total VMT in each county is multiplied
by the ratio of state-level VMT on paved or unpaved roads to total state-level VMT on each road type.
VMT„/usr (1)
VMTv/ucr = VMTtcr X '
P/u,c,r t,c,r VMTtsr
Where:
VMTp/u,c,r = Paved or unpaved vehicle miles traveled in county c on FHWA road type r
VMTt,c,r = Total vehicle miles traveled in county c on FHWA road type r, from equation 1
VMTp/u,s,r = Paved or unpaved vehicle miles traveled in state s on FHWA road type r
Because paved and unpaved VMT data were unavailable from FHWA for 2017 for the Rural Local, Urban Local,
and Rural Minor Collector road types, ratios for those road types were developed using state-level results from a
2008 model run from the National Mobile Inventory Model (NMIM), a precursor to MOVES. To account for the
fact that some states have paved many of their unpaved roads since 2008, an adjustment factor was developed
based on the change in unpaved road length. While FHWA does not provide 2017 data on paved or unpaved
VMT for those three road types, it does provide 2016 data on paved and unpaved road length for these road
types [ref 6], The adjustment factor is based on the change in the ratio of paved or unpaved road length 2016 to
the ratio in 2008.
AF„
LcYIC] thp /Uv,r,2016 ( 1 )
Lengtht sr 2016
p/u,r,s Lengthp/u^2008
Lengtht s r 2oos
Where:
AFp/u,s,r = Adjustment factor for paved or unpaved vehicle miles traveled in state s on FHWA road
type r
Lengthp/u,s,r,2016= Paved or unpaved road length in state s for FHWA road type r in 2016
Lengtht,s,r,2016 = Total road length in state s for FHWA road type r in 2016
Lengthp/u,s,r,2008= Paved or unpaved road length in state s for FHWA road type r in 2008
Lengthp/u,s,r,2008= Total road length in state s for FHWA road type r in 2008
This adjustment factor is multiplied by the paved or unpaved VMT ratio from NMIM for Rural Local, Urban Local,
and Rural Minor Collector roads.
VMTp /u,s,r (21b)
VMTp/u,c,r ~ VMTt}C:r * YMT * AFp/u s,r
t,s,r
Where:
VMTp/u,c,r = Paved or unpaved vehicle miles traveled in county c on FHWA road type r
VMTt,c,r = Total vehicle miles traveled in county c on FHWA road type r, from equation 1
VMTp/u,s,r = Paved or unpaved vehicle miles traveled in state s on FHWA road type r (from NMIM)
VMTt,s,r = Total vehicle miles traveled in state s on FHWA road type r
AFp/u,s,r = Adjustment factor for paved or unpaved vehicle miles traveled in state s on FHWA road
(from equation 2a)
As an example, if a state paved many of its unpaved roads between 2008 and 2016, then the adjustment factor
for unpaved roads would be less than 1, reducing the estimated ratio of unpaved VMT to total VMT (and,
therefore, increasing the ratio of paved VMT to total VMT).
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In addition, it is assumed that there is no VMT on unpaved roads for urban road types or in counties with a
population density greater than 3,000 people per square mile. For these cases, all VMT is assumed to be on
paved roads.
4.11.3.3 Allocation
The total VMT used to estimate emissions from road dust is available at the county level. The amount of paved
and unpaved VMT in each county is estimated using state-level ratios, as described in the previous "Activity
data" section. County level emissions were calculated by multiplying the county unpaved VMT (by road type) by
the emission factors calculated in 4.11.3.1.
4.11.3.4 Controls
The controls assumed for unpaved roads varied by PMio nonattainment area classification and by urban and
rural areas. On urban unpaved roads in moderate PMio nonattainment areas, paving of the unpaved road was
assumed and a control efficiency of 96 percent and a rule penetration of 50 percent were applied. Controls were
not applied to rural unpaved roads in moderate nonattainment areas. Chemical stabilization, with a control
efficiency of 75 percent and a rule penetration of 50 percent, was assumed for rural areas in serious PMio
nonattainment areas. A combination of paving and chemical stabilization, with a control efficiency of 90 percent
and a rule penetration of 75 percent, was assumed for urban unpaved roads in serious PMio nonattainment
areas. In counties currently at maintenance status, controls were assumed based on the severity (moderate or
serious) of their prior nonattainment status. Some counties had multiple partial areas with differing levels of
nonattainment. In these cases, controls were assumed to be applied based on the most serious level of
nonattainment found within a given county.
Note that the controls were applied at the county level, and the controls differ by urban vs. rural roadway class.
In the final emissions table, the emissions for all roadway classes were summed to the county level. Therefore,
the emissions at the county level can represent several different control effectiveness and rule penetration
levels. However, the control efficiency and rule penetration values were reported in the Controlled Emissions
worksheet at the county level for urban and rural roadways separately.
4.11.3.5 Meteorological adjustment
After controls were applied, emissions were summed to the county level and converted to tons prior to applying
the meteorological adjustment. The meteorological adjustment accounts for the reduction on fugitive dust
emissions via the impact of precipitation and other meteorological factors over each hour of the year and then
averaged to an annual meteorological adjustment factor for each grid cell in each county, aggregated to a single
county-level factor. For example, wet roads will result in significantly lower dust emissions. The county-level
meteorological adjustment factors were developed by EPA based on the ratio of the unadjusted to meteorology-
adjusted 2017 county-level emissions from the SMOKE Flat Files. The county-level meteorological adjustment is
a scalar between 0 and lthat is multiplied by the estimated emissions, where lower-values/greater-reductions
are typically found in areas with more frequent precipitation.
4.11.3.6 Changes from the 2014 NEI methodology
The methodology described above contains several adjustments from the methodology used to compose the
2014v2 version. The largest change from the methodology used to calculate the 2014v2 NEI emissions from road
dust is the method used to determine the VMT on paved and unpaved roads in each county. Both the methods
for the 2014v2 and 2017 NEI used the 2008 National Mobile Inventory Model (NMIM) run as the starting point
for estimating the ratio of VMT on paved or unpaved roads. However, in 2014v2, the estimated VMT on
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unpaved roads were redistributed within Census regions, to smooth out sharp differences in emissions across
state lines. This redistribution is not done for the 2017 NEI in order to preserve the integrity of the original SLT
VMT data submitted to FHWA. An additional step was added to update the 2008 NMIM paved/unpaved ratios
used for local and rural minor collector road types by using state level 2016 FHWA data on paved vs. unpaved
road length for these road types.
In the 2014v2 NEI, emissions from unpaved roads were also redistributed within states based on proportion of
rural population. The goal of this redistribution was to move emissions from unpaved roads out of cities into
rural areas where unpaved roads are more likely to occur; however, upon review, this redistribution was
considered too arbitrary in nature, sacrificing the spatial integrity of the FHWA source data, and was not done
for the 2017 NEI. As an alternative to address anomalies of unexplained unpaved VMT occurring on urban roads,
for the 2017 NEI, it is assumed that urban road types do not have emissions from unpaved roads. This
assumption was not made for the 2014v2 NEI.
A change was made to the value used for surface material moisture content, "M", in the AP-42 emission factor
equation. Previously, a single national default value of 0.5% was used for all counties. For 2017 NEI, values of
0.3% or 1.1% were used to assign surface material moisture content values to counties based on regional
patterns of soil moisture and precipitation. Previously, a value of 0.5 percent for M was chosen as the national
default as sufficient resources were not available at the time the emissions were calculated to determine more
representative values.
4.11.3.7 Puerto Rico and Virgin Islands
Since insufficient data exists to calculate emissions for the counties in Puerto Rico and the US Virgin Islands,
emissions are based on two proxy counties in Florida: Broward (state-county FIPS=12011) for Puerto Rico and
Monroe (state-county FIPS=12087) for the US Virgin Islands. The total emissions in tons for these two Florida
counties are divided by their respective populations creating a tons per capita emission factor. For each Puerto
Rico and US Virgin Island county, the tons per capita emission factor is multiplied by the county population (from
the same year as the inventory's activity data) which served as the activity data. In these cases, the throughput
(activity data) unit and the emissions denominator unit are "EACH".
4.11.4 References
1. United States Environmental Protection Agency, Office of Air Quality Planning and Standards.
Compilation of Air Pollutant Emission Factors, AP-42, Fifth Edition, Volume I: Stationary Point and Area
Sources. Section 13.2.2, Unpaved Roads. Research Triangle Park, NC. November 2006.
2. W. Barnard, G. Stensland, and D. Gatz, Illinois State Water Survey, "Evaluation of Potential Improvements in
the Estimation of Unpaved Road Fugitive Emission Inventories," paper 87-58.1, presented at the 80th Annual
Meeting of the APCA. New York, New York. June 21-26,1987
3. United States Environmental Protection Agency, 2011 National Emissions Inventory, version 2 Technical
Support Document. Research Triangle Park, NC. August 2015.
4. Cowherd, C., M.A. Grelinger, C. Kies, and T.G. Pace. 2002. Improved Activity Levels for National Emission
Inventories of Fugitive Dust from Paved and Unpaved Roads. Presentation at 11th International Emission
Inventory Conference. Atlanta, Georgia, April 15-18, 2002.
5. Data provided to Abt Associates by Robert Rozycki, FHWA.
6. Federal Highway Administration, "Highway Statistics, 2016." Table HM-51.
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4.12 Fires-Agricultural Field Burning
4.12.1 Sector description
Agricultural burning refers to fires that occur over lands used for cultivating crops and agriculture. Another term
for this sector is crop residue burning. In past NEIs for this sector, it was exclusively limited to emissions resulting
in the burning of crops. However, in the 2014 NEl, we included grass/pasture burning SCCs into this sector.
However, for technical reasons, we have moved the grass/pasture burning to the Events data category for the
2017 NEI, thereby causing this sector to once again only house emissions resulting from burning of crops.
4.12.2 Sources of data
Table 4-100 shows, the agricultural field burning SCCs covered by the EPA estimates and by the State/Local and
Tribal agencies that submitted data. The leading SCC description is "Miscellaneous Area Sources; Agriculture
Production - Crops - as nonpoint; Agricultural Field Burning - whole field set on fire;" for all SCCs in the table.
The basic SCC structure introduced in the 2014 NEI is retained here, with the exception of moving the
grassland/pastures/rangeland emissions to the Events data category. For example, the SCCs that were added in
2014 to better describe the specific crops being burned, including fields in which two or more crops are burned,
are retained here.
Note that many general crops are included in the SCC 2801500000, and it also is the SCC to report into for "crops
unknown." Note that EPA reported emissions into all of the "double crops" SCCs, as in 2014.
Table 4-100: Nonpoint Agricultural Field Burning SCCs in the 2017 NEI
SCC
Description
EPA
S/L/T
2801500000
Unspecified crop type and Burn Method
X
X
2801500112
Field Crop is Alfalfa: Backfire Burning
X
2801500130
Field Crop is Barley: Burning Techniques Not Significant
X
2801500141
Field Crop is Bean (red): Headfire Burning
X
X
2801500142
Field Crop is Bean (red): Backfire Burning
X
2801500150
Field Crop is Corn: Burning Techniques Not Important
X
X
2801500151
Double Crop Winter Wheat and Corn
X
2801500152
Double Crop Corn and Soybeans
X
2801500160
Field Crop is Cotton: Burning Techniques Not Important
X
2801500170
Field Crop is Grasses: Burning Techniques Not Important
X
2801500171
Fallow
X
X
2801500182
Field Crop is Hay (wild): Backfire Burning
X
2801500192
Field Crop is Oats: Backfire Burning
X
2801500202
Field Crop is Pea: Backfire Burning
X
2801500220
Field Crop is Rice: Burning Techniques Not Significant
X
2801500250
Field Crop is Sugar Cane: Burning Techniques Not Significant
X
2801500262
Field Crop is Wheat: Backfire Burning
X
X
2801500263
Double Crop Winter Wheat and Cotton
X
2801500264
Double Crop Winter Wheat and Soybeans
X
X
2801500300
Orchard Crop Unspecified
X
2801500320
Orchard Crop is Apple
X
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see
Description
EPA
S/L/T
2801500330
Orchard Crop is Apricot
X
2801500350
Orchard Crop is Cherry
X
2801500390
Orchard Crop is Nectarine
X
2801500410
Orchard Crop is Peach
X
2801500420
Orchard Crop is Pear
X
2801500430
Orchard Crop is Prune
X
2801500500
Vine Crop Unspecified
X
2801500600
Forest Residues Unspecified
X
As an example of what agencies submitted, the agencies listed in Table 4-101 submitted PM2.5 emissions for
this sector; agencies not listed used EPA estimates for the entire sector. Some agencies submitted emissions for
the entire sector while others submitted only a portion of the sector. When an agency submits less than 100%,
their Nonpoint Survey responses, along with other general business rules for building the NEI, are used to
backfill with EPA estimates as appropriate.
Table 4-101: PM2.5 emissions submitted by reporting agency
Region
Agency
S/L/T
2
New Jersey Department of Environment Protection
State
4
Georgia Department of Natural Resources
State
5
Illinois Environmental Protection Agency
State
9
California Air Resources Board
State
10
Coeur d'Alene Tribe
Tribe
10
Idaho Department of Environmental Quality
State
10
Kootenai Tribe of Idaho
Tribe
10
Nez Perce Tribe
Tribe
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
10
Washington State Department of Ecology
State
4.12.3 EPA-developed emissions for agricultural field burning
By way of history for this sector, in the 2008 NEI, crop residue emission estimates were developed using satellite
detects occurring over land types classified as "agricultural" and uncertain field sizes or were sporadically
reported by a handful of states. In the 2011 NEI, the method described in McCarty et al. 2009 [ref 1] and
McCarty 2011 [ref 2] was employed to estimate the emissions from this sector with the exception that states
could submit their own estimates. However, this produced significant state to state variability between states
that submitted their own data and states that did not. In addition, we received comments that many false
detects (EPA emission estimates were too high) occurred using this method (due to dark fields resulting from
irrigation) Therefore, a consistent methodology across multiple years for the CONUS has not yet been developed
for this sector. To address these issues, in the 2014 NEI, a simple and efficient method was been developed to
estimate emissions from crop residue that can easily be applied across multiple years over the CONUS at
minimal cost. The method was developed by EPA Office of Research and Development and the reader is directed
to a paper in press for details on the methods described below [ref 3], This is the basic method used for the
2017 NEI, with the changes/improvements made as noted below.
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The approach developed for use in the 2014 NEI, and used again for the 2017 NEI, already improves on previous
estimates [ref 1, ref 2] as follows:
• Multiple satellite detections are used to locate fires using an operational product
• Field Size estimates are based on field work studies in multiple states (rather than a one size fits all
approach)
• This method allows for intra-annual as well as annual changes in crop land use
• This method incorporates comments on this sector from past NEI efforts to improve the method and
remove some of the false detects that occurred in the 2011 NEI
• Additional processing of the HMS data was done to remove 2 types of duplicates
• This method uses USDA NASS Cropland Data Layer (CDL) (USDA, 2015a) [ref 4] information to separate
grass/pasture lands, which include Pasture/Grass, Grassland Herbaceous, and Pasture/Hay lands from all
other agricultural burning and to identify the crop type
• Removal of agricultural fires from the Hazard Mapping System (HMS) dataset before the application of
the SMARTFIRE2 system for wildfires and prescribed fires to eliminate double counting in the NEI and
the use of state information to further identify fires as crop residue burning rather than another type of
fire
• To further identify fires as crop residue burning rather than some type of wildfire. Our 2014 NEI
approach complements the method used to estimate emissions from wildfires and prescribed fires
because we use crop level land use information to identify crop residue fires and grassland ('rangeland')
fires. The remaining fire detections are used in SMARTFIRE to estimate emissions in forested areas
where fuel loadings are available from the National Forest Service.
4.12.3.1 Improvements/Changes in the 2017 NEI
For the 2017 NEI, we have made a few revisions to the method used to estimate this sector compared to the
2014 NEI and will summarize them here. As discussed previously, all details on 2014 methods (and thus, the
starting point for our 2017 methodology) can be found in our published article "Development of the crop
residue and rangeland burning in the 2014 National Emissions Inventory using information from multiple
sources" [ref 3] as well as in Section 7 of our 2014 NEI Technical Support Document.
• In all prior NEIs for this sector, the VOC and HAP emission factors were inconsistent. The HAP emission
factors were copied from the HAP emission factors for wildfires while the VOC emission factors were
scaled from the CO emission factors. Therefore, the VOC emission factors had no consistency with the
HAP emission factors. For the 2017 NEI, we reviewed the crop residue burning VOC speciation profiles in
the SPECIATE database, located the original source of this information, and derived new VOC emission
factors and new HAP emission factors from the same measurement study. The measurement study was
focused on wheat straw and rice straw burning. We averaged these two emission factors for the
remaining crop types, excluding sugarcane. For sugarcane, we located a new reference for sugarcane
HAP emission factors and incorporated these into the dataset. Since the total mass was not reported in
this paper, we used another reference for the total VOC for sugarcane. Sugarcane is unique because it is
the only crop type that is burned pre-harvest and has different VOC emission factors compared to other
crop types. In this database, we have revised all the VOC and HAP emission factors so that the HAP
estimates are consistent with the VOC speciation derived from the SPECIATE profiles. The new VOC EFs
are shown in the Tables that follow in this section. All non-VOC EFs used in the 2017 NEI remain the
same as that used in the 2014 NEI as shown in the TSD for the 2014 NEI. For the new VOC and HAP EFs
used in the 2017 NEI, readers are referred to SPECIATE5.0 data and its documentation.
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• A second new feature of the 2017 database is that we have calculated, on a per fire basis, the heat
release of each fire. This information is needed for a plume rise calculation within a chemical transport
modeling system. In prior NEIs, we put all the emissions into layer 1. While this is not critical for the NEI
(this sector in in the nonpoint category, where heat values are not required), for emissions processing
for air quality modeling, this is a key improvement, and is thus noted here.
• A third feature of the database is that we have filtered out the satellite detections for 2017 to exclude
areas covered by snow during the winter months. Certain crop types (corn and soybeans) have been
excluded from these midwestern states: Iowa, Kansas, Indiana, Illinois, Michigan, Missouri, Minnesota,
Wisconsin, Ohio. This update is partially based on comments we received from some of these states in
the 2014 NEI development cycle.
• Finally, to avoid double counting with the wildfire inventory, all grassland detections of fires outside of
the Flint Hills in Kansas and Oklahoma have been incorporated into the wildfire and prescribed fire
inventory process and are not part of this database. These fires are included as appropriate in our
wildland fire inventory, which is part of the "EVENTS" data category (see Section 7). While EPA did not
report grassland fires to this sector in 2017, a few tribes did. Their emissions were miniscule compared
to other totals, and while described in the above tables, they were too small to consider including in
emission summaries.
4.12.3.2 Activity Data
As with the 2014 process, the HMS satellite product is the main system used for the 2017 NEI. The HMS satellite
product is an operational satellite product showing hot spots and smoke plumes indicative of fire locations. It is
a blended product using algorithms for the Geostationary Operational Environmental Satellite (GOES) Imager,
the Polar Operational Environmental Satellite (POES) Advanced Very High Resolution Radiometer (AVHRR),
Moderate Resolution Imaging Spectroradiometer (MODIS) and more recently the Visible Infrared Imaging
Radiometer Suite (VIIRS). These satellite detections are provided at 0.001 degrees latitude or longitude, but they
are derived from active fire satellite products ranging in spatial accuracy from 375 m to 4km. To identify the crop
type and to distinguish agricultural fires from all other fires in the HMS product, the USDA Cropland Data Layer
(CDL) (USDA, 2015a) [ref 4] was employed. This dataset is produced annually by the USDA National Agricultural
Statistics Service and provides high resolution (30 meter) detailed crop information to accurately identify crop
types for agricultural fires. Based on field reconnaissance of McCarty (2013) [ref 5], a "typical" field size was
assumed for each burn location, which varied by region of the country. The assumed field sizes can be found on
the file "draft_2014_ag_grasspasture_emissions_nei_may62015.xlsx" on the 2014vl Supplemental Data FTP
site.
4.12.3.3 Emission Factors
Emission Factors for CO, NOx, S02, PM2.5 and PM10 were based on Table 1 from McCarty (2011) [ref 3], The
emission factors in McCarty (2011) were based on mean values from all available literature at the time. Emission
Factors for NH3 were derived from the 2002 NEI crop residue emission estimates using the ratio of NH3/NOx and
the NOx emission factor in Table 1 from McCarty (2011). These emission factors are shown in the 2014 NEI TSD.
As discussed above the VOC EFs were improved for the 2017 NEI, as shown below in Table 4-107.
A subset of the HAP emission factors is shown in Table 4-103. These are based on updated VOC work mentioned
above. The full set of HAP emission factors, available on the 2017 NEI Supplemental data FTP site, also includes
the following HAPs: isopropylbenzene, n-hexane, o-xylene, propionaldehyde, styrene, toluene, 2,2,4-
trimethylpentane, and m, p-xylenes.
4-170
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Table 4-102: Revised Ag Burning Emission factors (lbs/ton) for VOC
Crop Type
Emission Factor
Corn
18.47
Wheat
18.69
Soybean
18.57
Cotton
18.47
Fallow
18.47
Rice
18.26
Sugarcane
3.68
All Other crops/Default
18.47
Double Crop Wheat/Soybeans
18.58
Double Crop Corn/Soybeans
18.58
Double Crop Wheat/Cotton
18.58
Table 4-103: Se
ect HAP Emission factors (lb/ton) used in EPA Methods by crop type for entire US
Crop Type
see
Acetaldehyde
Benzene
1,3-
butadiene
Ethylbenzene
Formaldehyde
Unspecified/General/
Default
2801500000
1.521677
0.227658
0.161739
0.026645
1.025634
Red Bean
2801500141
1.521677
0.227658
0.161739
0.026645
1.025634
Red Bean
2801500142
1.521677
0.227658
0.161739
0.026645
1.025634
Corn
2801500150
1.521677
0.227658
0.161739
0.026645
1.025634
Wheat and Corn
2801500151
1.311003
0.224041
0.144669
0.020768
1.19077
Corn and Soybeans
2801500152
1.521677
0.227658
0.161739
0.026645
1.025634
Cotton
2801500160
1.521677
0.227658
0.161739
0.026645
1.025634
Fallow
2801500171
1.521677
0.227658
0.161739
0.026645
1.025634
Rice
2801500220
1.943024
0.234892
0.195879
0.038401
0.695364
Sugarcane
2801500250
0.0896
0.033
0
0.00162
0.3
Wheat
2801500262
1.10033
0.220424
0.127599
0.01489
1.355905
Wheat and Cotton
2801500263
1.311003
0.224041
0.144669
0.020768
1.19077
Wheat and Soybeans
2801500264
1.311003
0.224041
0.144669
0.020768
1.19077
4,12,3.4 Emission Estimates for 2017
Figure 4-17 summarizes 2017 NEI PM2.5 emission estimates by state, sorted from largest to smallest, based on
the 2017 NEI. Florida, Washington, California, Georgia, and North Dakota are the top emitters. Some of these
emissions come from S/L/T submissions, and some from EPA estimates. Tribal emissions are not shown here. A
total of about 30,000 tons of PM2.5 are estimated to be emitted by this sector. Note that in the 2014 NEI, this
sector total is significantly higher due to the additional inclusion of grassland/pasture burning. Shown in Table
4-104 are comparisons of PM2.5 emissions for those states that submitted PM2.5 vs EPA estimates. Only a few
states submitted. Of those states that submitted to EIS, only 3 states (GA, ID, IL) and tribes included HAPS in
their ag burning emission submittals. Only Idaho indicated to supplement their data with EPA estimates via the
Nonpoint Survey. A total of about 33,000 tons of PM2.5 are estimated to be emitted for this sector using EPA
methods alone, compared to about 30,000 when these SLT emissions are also factored into the final NEI.
4-171
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DRAFT
Figure 4-17: Total 2017 NEI Agricultural Burning PM2.5 Emissions by state
4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
< < <
> (-) O
g 2 £
< i: Q I- - s
¦J O - 5 2 O
o
5 2
= S <¦ = >;
2 >
Table 4-104: Comparison of State vs EPA 2017 PM2.5 emissions (tons) for agencies that submitted
State/Tribe
S/L/T-submitted
EPA-generated
California
2,348
6,600
Georgia
2,077
1,577
Idaho
1,310
557
Illinois
23
44
New Jersey
221
0
Washington
2,703
1,148
Tribes Total
859
0
4.12,3.5 Quality assurance of final estimates
Some of the QA was implemented as part of the new methodology (discussed in Section 4.12.3.1 above) was
applied for this sector. Additional review of the quality of EPA's data included addressing of S/L/T comments as
we received them during the 2017 NEI process. In addition, the following checks were done on EPA data:
• Comparison to past NEI estimates, and explaining differences noted
4-172
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• Check of diurnal profile using day specific data generated by EPA methods with existing profiles used for
air quality modeling
• Using past comments received from S/L/Ts for this sector to ground truth estimates
• Ensuring HAPs and VOC speciation line up as expected
The QA of S/L/T-submitted data included checking with EPA estimates, working with S/L/Ts to understand why
differences exist, and making sure pollutant coverage is complete.
We do not expect to make any major changes or improvements (e.g., methodology, pollutants expected) to this
sector for the 2020 NEI. We will respond to specific comments we do receive for this sector.
4.12.4 References for agricultural field burning
1. McCarty, J.L., S. Korontzi, C. O. Justice, and T. Loboda. 2009. The spatial and temporal distribution of
crop residue burning in the contiguous United States. Science of the Total Environment 407 (21), 5701-
5712.
2. McCarty, J. L. 2011. Remote Sensing-Based Estimates of Annual and Seasonal Emissions from Crop
Residue Burning in the Contiguous United States. Journal of the Air & Waste Management Association 61
(1), 22-34.
3. Pouliot, G., Rao, V., McCarty, J. L., and A. Soja. 2017. Development of the crop residue and rangeland
burning in the 2014 National Emissions Inventory using information from multiple sources. Journal of the
Air & Waste Management Association Vol. 67, Issue 5.
4. United States Department of Agriculture. 2015a. USDA National Agricultural Statistics Service Cropland
Data Layer for 2015.
5. Personal communication with Dr J. McCarty, 2013, Michigan Technological Institute.
4.13 Fuel Combustion - Industrial and Commercial/Institutional Boilers and ICEs
Industrial, Commercial, and Institutional (ICI) fuel combustion sources are a significant portion of the total
emissions inventory for many areas and include emissions from boilers, engines, and other combustion sources
from the industrial, commercial, and institutional sectors that are not reported as point sources. This source
category includes emissions from combustion of coal, distillate fuel oil, residual fuel oil, kerosene, liquefied
petroleum gas (LPG), natural gas, and wood. Unless all ICI combustion emission sources are provided in an S/L/T
point inventory submittal, it is necessary for inventory preparers to estimate ICI combustion nonpoint source
emissions.
4.13.1 Sector description
The EIS sectors documented in this section include these nonpoint emissions from ICI fuel combustion:
Fuel Comb - Industrial Boilers, ICEs - Biomass
Fuel Comb - Industrial Boilers, ICEs - Coal
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
Fuel Comb - Industrial Boilers, ICEs - Oil
Fuel Comb - Industrial Boilers, ICEs - Other
Fuel Comb - Comm/lnstitutional - Biomass
Fuel Comb - Comm/lnstitutional - Coal
Fuel Comb - Comm/lnstitutional - Natural Gas
Fuel Comb - Comm/lnstitutional - Oil
4-173
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• Fuel Comb - Comm/lnstitutional - Other
We document all these sectors in this section because EPA generates all the nonpoint emissions from these EIS
sectors via the "ICI Tool" module. S/L/Ts were encouraged to submit Point inventory activity data -via many
options reflecting sector and fuel type- in order to compute the "remaining" nonpoint emissions component to
these sectors.
4.13.2 Sources of data
Table 4-105 shows, for ICI fuel combustion, the nonpoint SCCs covered by the EPA ICI Tool as well emissions
directly submitted by State/Local and Tribal agencies. The SCC level 2, 3 and 4 descriptions are also provided
except for the last SCC (2801520000), where the full SCC description is provided. The SCC level 1 description is
"Stationary Source Fuel Combustion" for all SCCs except the last one. The leading sector description is "Fuel
Comb" for all SCCs.
Table 4-105: Nonpoint ICI SCCs in the 2017 NEI
SCC
Description
Sector
EPA
SLT
2102001000
Industrial; Anthracite Coal; Total: All Boiler
Types
Fuel Comb - Industrial Boilers, ICEs
- Coal
X
X
2102002000
Industrial; Bituminous/Subbituminous
Coal; Total: All Boiler Types
Fuel Comb - Industrial Boilers, ICEs
- Coal
X
X
2102004000
Industrial; Distillate Oil; Total: Boilers and
IC Engines
Fuel Comb - Industrial Boilers, ICEs
-Oil
X
2102004001
Industrial; Distillate Oil; All Boiler Types
Fuel Comb - Industrial Boilers, ICEs
-Oil
X
X
2102004002
Industrial; Distillate Oil; All IC Engine Types
Fuel Comb - Industrial Boilers, ICEs
-Oil
X
X
2102005000
Industrial; Residual Oil; Total: All Boiler
Types
Fuel Comb - Industrial Boilers, ICEs
-Oil
X
X
2102006000
Industrial; Natural Gas; Total: Boilers and
IC Engines
Fuel Comb - Industrial Boilers, ICEs
- Natural Gas
X
X
2102007000
Industrial; Liquified Petroleum Gas (LPG);
Total: All Boiler Types
Fuel Comb - Industrial Boilers, ICEs
- Other
X
X
2102008000
Industrial; Wood; Total: All Boiler Types
Fuel Comb - Industrial Boilers, ICEs
- Biomass
X
X
2102010000
Industrial; Process Gas; Total: All Boiler
Types
Fuel Comb - Industrial Boilers, ICEs
- Other
X
2102011000
Industrial; Kerosene; Total: All Boiler Types
Fuel Comb - Industrial Boilers, ICEs
-Oil
X
X
2103001000
Commercial/Institutional; Anthracite Coal;
Total: All Boiler Types
Fuel Comb - Comm/lnstitutional -
Coal
X
X
2103002000
Commercial/Institutional;
Bituminous/Subbituminous Coal; Total: All
Boiler Types
Fuel Comb - Comm/lnstitutional -
Coal
X
X
2103004000
Commercial/Institutional; Distillate Oil;
Total: Boilers and IC Engines
Fuel Comb - Comm/lnstitutional -
Oil
X
4-174
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see
Description
Sector
EPA
SLT
2103004001
Commercial/Institutional; Distillate Oil;
Boilers
Fuel Comb - Comm/lnstitutional -
Oil
X
X
2103004002
Commercial/Institutional; Distillate Oil; IC
Engines
Fuel Comb - Comm/lnstitutional -
Oil
X
X
2103005000
Commercial/Institutional; Residual Oil;
Total: All Boiler Types
Fuel Comb - Comm/lnstitutional -
Oil
X
X
2103006000
Commercial/Institutional; Natural Gas;
Total: Boilers and IC Engines
Fuel Comb - Comm/lnstitutional -
Natural Gas
X
X
2103007000
Commercial/Institutional; Liquified
Petroleum Gas (LPG); Total: All Combustor
Types
Fuel Comb - Comm/lnstitutional -
Other
X
X
2103008000
Commercial/Institutional; Wood; Total: All
Boiler Types
Fuel Comb - Comm/lnstitutional -
Biomass
X
X
2103011000
Commercial/Institutional; Kerosene; Total:
All Combustor Types
Fuel Comb - Comm/lnstitutional -
Oil
X
X
2801520000
Miscellaneous Area Sources; Agriculture
Production - Crops; Orchard Heaters;
Total, all fuels
Fuel Comb - Industrial Boilers, ICEs
- Other
X
The agencies listed in Table 4-106 submitted emissions for these sectors. Agencies not listed used EPA estimates
for all ICI sectors; most of the agencies not listed provided input activity data used to subtract point throughput
data for subtraction.
Table 4-106: Agencies reporting nonpoint ICI sector emissions
4-3
4-5
4jj
.25
.25
.25
£
— V)
£
£ u
£
£
Agency
3 E
T3 O
= £
Industr
Coal
£ ns
|3
I 2
3
"O —
= o
Industr
Other
1 1
§ .2
U GO
Commy
Coal
£ 3
o w
u z
E
£ _
u O
Commy
Other
Alaska Department of Environmental
X
X
X
X
X
Conservation
California Air Resources Board
X
X
X
X
X
X
X
Chattanooga Air Pollution Control
X
X
X
X
X
X
X
X
X
Bureau (CHCAPCB)
Coeur d'Alene Tribe
X
X
X
X
X
X
X
X
X
Delaware Department of Natural
X
X
X
X
X
X
Resources and Environmental Control
Idaho Department of Environmental
X
X
X
X
X
X
X
X
X
X
Quality
Illinois Environmental Protection
X
X
X
X
X
X
X
X
X
X
Agency
Kootenai Tribe of Idaho
X
X
X
X
X
X
X
X
X
Maricopa County Air Quality
X
X
X
X
X
X
X
Department
4-175
-------
4-3
4-5
4jj
.25
.25
.25
c
— V)
£
£ u
£
£
Agency
3 E
T3 O
= £
Industr
Coal
£ ns
|3
I 2
3
"O —
= o
Industr
Other
1 1
§ .2
U GO
Commy
Coal
£ 3
o w
u z
E
£ _
u O
Commy
Other
Memphis and Shelby County Health
X
X
X
X
X
X
Department - Pollution Control
Metro Public Health of
X
X
X
X
X
X
X
X
X
Nashville/Davidson County
Minnesota Pollution Control Agency
X
X
X
X
X
X
X
X
X
X
New Hampshire Department of
X
X
X
X
X
X
X
Environmental Services
New Jersey Department of
X
X
X
X
X
X
Environment Protection
Nez Perce Tribe
X
X
X
X
X
X
X
X
X
Northern Cheyenne Tribe
X
X
X
X
Oregon Department of Environmental
X
X
X
X
X
X
X
X
X
Quality
Shoshone-Bannock Tribes of the Fort
X
X
X
X
X
X
X
X
X
Hall Reservation of Idaho
Southern Ute Indian Tribe
X
Texas Commission on Environmental
X
X
X
X
X
X
X
X
Quality
Utah Division of Air Quality
X
X
X
X
X
X
X
X
Washoe County Health District
X
X
X
X
X
X
New for the 2017 NEI and discussed in Section 5.4.3 of the 2.017 NEI Plan, was a request for States and Locals to
submit total fuel consumption data if they were not submitting their own emission estimates. As discussed later
in section 4.13.3.6, we developed several options for reporting agencies to submit this fuel consumption input
data. Most states submitted emissions, input activity data, or both depending on the specific SCC. A couple of
states completed the Nonpoint Survey (see Section 4.1.2) and indicated that all their ICI emissions were included
in their Point inventory submittal (Colorado and Kentucky); and nonpoint ICI emissions are therefore zero for
these states. Conversely, a couple states (Nevada outside of Clark and Washoe counties, Mississippi, Montana,
and South Dakota) did not submit emissions or inputs and therefore ICI nonpoint estimates are entirely based
on state-total fuel consumption data, which is likely an overestimate as it would double-count any point
inventory ICI emissions.
Table 4-107: Comprehensive State/Local agency submittal status for ICI estimates in the 2017 NEI
State/Local Agency
Data Submitted?
Nonpoint Survey Response (if no data
provided)
Alabama
Input data
Alaska
Input data &
Emissions
Arizona
Input data
Arizona - Phoenix/Maricopa County
Emissions
Arkansas
Input data
4-176
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State/Local Agency
Data Submitted?
Nonpoint Survey Response (if no data
provided)
California
Emissions
Colorado
Survey
No - This source is included in my Point
Source contributions
Connecticut
Input data
District of Columbia
Input data
Delaware
Emissions
Florida
Input data
Georgia
Input data
Hawaii
Input data
Idaho
Emissions
Illinois
Emissions
Indiana
Input data
Iowa
Input data
Kansas
Input data
Kentucky
No - This source is included in my Point
Source contributions
Kentucky -Louisville/Jefferson County
Input data
Louisiana
Input data
Maine
Input data
Maryland
Input data
Massachusetts
Input data
Michigan
Input data
Minnesota
Emissions
Mississippi
Yes - Supplement My Data with EPA
Estimates
Missouri
Input data
Montana
Nebraska
Input data
Nevada
Supplement Only At Reported Location -
SCCs
Nevada -Clark County
Input data
Nevada - Washoe County
Emissions
New Hampshire
Emissions
New Jersey
Emissions
New Mexico
Input data
New York
Input data
North Carolina
Input data
North Dakota
Input data
Ohio
Input data
Oklahoma
Input data
Oregon
Input data &
Emissions
4-177
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State/Local Agency
Data Submitted?
Nonpoint Survey Response (if no data
provided)
Pennsylvania
Input data
Rhode Island
Input data
South Carolina
Input data
South Dakota
Yes - Supplement My Data with EPA
Estimates
Tennessee
Input data
Tennessee - Chattanooga
Emissions
Tennessee - Knoxville/Knox County
Input data
Tennessee - Memphis/Shelby County
Emissions
Tennessee - Nashville/Davidson County
Input data &
Emissions
Texas
Emissions
Utah
Emissions
Vermont
Input data
Virginia
Input data
Washington
Input data
West Virginia
Input data
Wisconsin
Input data
Wyoming
Input data
Puerto Rico
Yes - Supplement My Data with EPA
Estimates
U.S. Virgin Islands
4.13.3 EPA-developed emissions
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) [ref 1], 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. Fuel consumption from nonpoint sources
in each state is determined by subtracting fuel consumption from point sources from total fuel consumption.
Estimated nonpoint source fuel consumption in each state is distributed to the county level based on the
proportion of employment in the industrial and commercial sectors.
4.13.3.1 Activity data
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 [ref 1] for all fuel types except distillate. Distillate fuel consumption is taken from ElA's
Form 821 data, which reports distillate sales by state and sector for 2016 [ref 2], State, local, and tribal (SLT)
agencies are expected to submit state-level fuel consumption data from point sources in these sectors. The
state-level point source fuel consumption is subtracted from the total fuel consumption to estimate the fuel
consumption from nonpoint sources. The point source subtraction method is described in more detail in section
4.13.3.6.
Total fuel consumption is adjusted to account for the fraction of fuel consumed by nonroad mobile sources,
4-178
-------
whose emissions are included in the nonroad inventory. This fraction is based on results from the National
Mobile Inventory Model (NMIM), a precursor to EPA's Motor Vehicle Emission Simulator (MOVES). This
adjustment is particularly important for distillate fuel oil consumption. The ICI tool uses distillate consumption
data from Form 821 rather than SEDS because Form 821 reports more detailed data by sector, and the ICI tool
uses different stationary source fuel consumption assumptions by sector, including the industrial, commercial,
farm, off-highway, and oil company sectors. Note that fuel consumption in the farm, off-highway, and oil
company sectors are mapped to the industrial sector in the ICI tool. Assumptions about the fraction of fuel
consumed by stationary sources are shown in an appendix.
The total fuel consumption is also adjusted to account for fuel used as an input to industrial processes where it
is not combusted. These assumptions are based on the EIA Manufacturing Energy Consumption Survey (MECS)
[ref 3], which reports both total fuel consumption and non-combustion use of fuel by Census region.
Assumptions about non-combustion use of fuel are shown in Table 4-108. In some cases, EIA withholds the
regional-level data on non-combustion use of fuel because it is less than 0.5 million barrels. In these cases, a
value of 0.25 million barrels is used as the amount of regional-level non-combustion use of fuels.
Note that the stationary source adjustment is performed for fuel consumption from both the industrial and
commercial/institutional sectors, while the non-combustion use of fuel adjustment is performed only for fuel
consumption in the industrial sector.
AFf,s,x TFf s x X SS^s x X (1 ncf,s,industrial)
(1)
Where:
AFf/S,x = Consumption of fuel/by stationary sources in state s in sector x
TFf/S,x = Total consumption of fuel/in state s in sector x, from EIA SEDS
SSfAX = Fraction of fuel /consumed by stationary sources in state s in sector x
ncfAX = Fraction of fuel/used as an industrial input and is not combusted in state s in the industrial
sector, from Table 4-108
Table 4-108: Assumptions about non-combustion use o
State
Coal
Distillate
LPG
Natural Gas
Residual Oil
Kerosene
AK
0.0%
8.3%
6.3%
1.6%
0.0%
0.0%
AL
29.4%
11.1%
98.9%
13.3%
81.8%
0.0%
AR
29.4%
11.1%
98.9%
13.3%
81.8%
0.0%
AZ
0.0%
8.3%
6.3%
1.6%
0.0%
0.0%
CA
0.0%
8.3%
6.3%
1.6%
0.0%
0.0%
CO
0.0%
8.3%
6.3%
1.6%
0.0%
0.0%
CT
75.0%
8.3%
91.3%
1.0%
0.0%
0.0%
DC
29.4%
11.1%
98.9%
13.3%
81.8%
0.0%
DE
29.4%
11.1%
98.9%
13.3%
81.8%
0.0%
FL
29.4%
11.1%
98.9%
13.3%
81.8%
0.0%
GA
29.4%
11.1%
98.9%
13.3%
81.8%
0.0%
HI
0.0%
8.3%
6.3%
1.6%
0.0%
0.0%
IA
44.0%
6.3%
80.0%
4.3%
100.0%
0.0%
ID
0.0%
8.3%
6.3%
1.6%
0.0%
0.0%
IL
44.0%
6.3%
80.0%
4.3%
100.0%
0.0%
fuel by fuel type and state
4-179
-------
State
Coal
Distillate
LPG
Natural Gas
Residual Oil
Kerosene
IN
44.0%
6.3%
80.0%
4.3%
100.0%
0.0%
KS
44.0%
6.3%
80.0%
4.3%
100.0%
0.0%
KY
29.4%
11.1%
98.9%
13.3%
81.8%
0.0%
LA
29.4%
11.1%
98.9%
13.3%
81.8%
0.0%
MA
75.0%
8.3%
91.3%
1.0%
0.0%
0.0%
MD
29.4%
11.1%
98.9%
13.3%
81.8%
0.0%
ME
75.0%
8.3%
91.3%
1.0%
0.0%
0.0%
Ml
44.0%
6.3%
80.0%
4.3%
100.0%
0.0%
MN
44.0%
6.3%
80.0%
4.3%
100.0%
0.0%
MO
44.0%
6.3%
80.0%
4.3%
100.0%
0.0%
MS
29.4%
11.1%
98.9%
13.3%
81.8%
0.0%
MT
0.0%
8.3%
6.3%
1.6%
0.0%
0.0%
NC
29.4%
11.1%
98.9%
13.3%
81.8%
0.0%
ND
44.0%
6.3%
80.0%
4.3%
100.0%
0.0%
NE
44.0%
6.3%
80.0%
4.3%
100.0%
0.0%
NH
75.0%
8.3%
91.3%
1.0%
0.0%
0.0%
NJ
75.0%
8.3%
91.3%
1.0%
0.0%
0.0%
NM
0.0%
8.3%
6.3%
1.6%
0.0%
0.0%
NV
0.0%
8.3%
6.3%
1.6%
0.0%
0.0%
NY
75.0%
8.3%
91.3%
1.0%
0.0%
0.0%
OH
44.0%
6.3%
80.0%
4.3%
100.0%
0.0%
OK
29.4%
11.1%
98.9%
13.3%
81.8%
0.0%
OR
0.0%
8.3%
6.3%
1.6%
0.0%
0.0%
PA
75.0%
8.3%
91.3%
1.0%
0.0%
0.0%
Rl
75.0%
8.3%
91.3%
1.0%
0.0%
0.0%
SC
29.4%
11.1%
98.9%
13.3%
81.8%
0.0%
SD
44.0%
6.3%
80.0%
4.3%
100.0%
0.0%
TN
29.4%
11.1%
98.9%
13.3%
81.8%
0.0%
TX
29.4%
11.1%
98.9%
13.3%
81.8%
0.0%
UT
0.0%
8.3%
6.3%
1.6%
0.0%
0.0%
VA
29.4%
11.1%
98.9%
13.3%
81.8%
0.0%
VT
75.0%
8.3%
91.3%
1.0%
0.0%
0.0%
WA
0.0%
8.3%
6.3%
1.6%
0.0%
0.0%
Wl
44.0%
6.3%
80.0%
4.3%
100.0%
0.0%
WV
29.4%
11.1%
98.9%
13.3%
81.8%
0.0%
WY
0.0%
8.3%
6.3%
1.6%
0.0%
0.0%
The SEDS data do not distinguish between anthracite and bituminous/subbituminous coal consumption
estimates. The EIA table "Domestic Distribution of U.S. Coal by Destination State, Consumer, Origin and Method
of Transportation" [ref 4] provides state-level coal distribution data for 2006 that is used to estimate the fraction
of coal consumption that is anthracite and bituminous/subbituminous. Table 4-109 presents these anthracite
and bituminous coal ratios for each state.
4-180
-------
Table 4-109: Anthracite and Bituminous Coal Distribution for the Resic
State
Ratio of
Ratio of
State
Ratio of
Ratio of
Bituminous
Anthracite
Bituminous
Anthracite
Alabama
1.000
0.000
Montana
1.000
0.000
Alaska
1.000
0.000
Nebraska
1.000
0.000
Arizona
0.814
0.186
Nevada
1.000
0.000
Arkansas
0.814
0.186
New Hampshire
0.000
1.000
California
1.000
0.000
New Jersey
0.000
1.000
Colorado
0.996
0.004
New Mexico
1.000
0.000
Connecticut
0.000
1.000
New York
0.600
0.400
Delaware
0.814
0.186
North Carolina
1.000
0.000
Dist. Columbia
1.000
0.000
North Dakota
1.000
0.000
Florida
0.814
0.186
Ohio
0.873
0.127
Georgia
1.000
0.000
Oklahoma
0.917
0.083
Hawaii
1.000
0.000
Oregon
1.000
0.000
Idaho
0.979
0.021
Pennsylvania
0.194
0.806
Illinois
0.998
0.002
Rhode Island
0.000
1.000
Indiana
0.947
0.053
South Carolina
0.997
0.003
Iowa
0.999
0.001
South Dakota
1.000
0.000
Kansas
1.000
0.000
Tennessee
0.994
0.006
Kentucky
0.998
0.002
Texas
0.814
0.186
Louisiana
1.000
0.000
Utah
1.000
0.000
Maine
0.000
1.000
Vermont
0.000
1.000
Maryland
0.929
0.071
Virginia
0.963
0.037
Massachusetts
0.500
0.500
Washington
1.000
0.000
Michigan
0.667
0.333
West Virginia
0.905
0.095
Minnesota
0.997
0.003
Wisconsin
0.991
0.009
Mississippi
1.000
0.000
Wyoming
1.000
0.000
Missouri
1.000
0.000
ential and Commercial Sectors
The SEDS data on industrial and commercial coal consumption are split into consumption of anthracite and
bituminous/subbituminous coal based on the ratios in Table 4-109.
AFant/bit,s,x ~ AFCOal,s,x * ^ant/bit,s
(2)
Where:
AFant/bits,
AFcoal,s,x
Rant/bit,s
Adjusted anthracite or bituminous coal consumption in state s in sector x
Total adjusted coal consumption in state s in sector x, from equation 1
Ratio of anthracite or bituminous coal to total coal in state s, from Table 4-109
The EIA Form 821 data report total distillate consumption, but the NEI requires data separately on consumption
by boilers and engines, because there are substantially different emissions factors for distillate boilers and
engines. The ICI tool uses assumptions based on the EIA MECS [ref 3] and the EIA Commercial Building Energy
Consumption Survey (CBECS) [ref 5], These data sources suggest that in the industrial sector, 60 percent of
distillate consumption is by boilers and 40 percent by engines, and in the commercial sector, 95 percent is by
boilers and 5 percent is by engines.
4-181
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AFboiler/engine,.s,x ~ AF,
distillate,s,x * ^boiler/engine,s,x
(3)
Where:
AFb oiler/engine,s
AFdistillates,x
Rb oiler/engine,s,x
Adjusted distillate consumption in boilers or engines state s in sector x
Total adjusted distillate consumption in state s in sector x, from equation 1
Ratio of distillate consumption by boilers or engines in state s in sector x
Following the adjustments to the total fuel consumption, the total fuel consumption data is also adjusted to
subtract fuel consumption from point sources, which is accounted for in the point source inventory. Point source
fuel consumption data by fuel type and sector is submitted by SLT agencies. This point source subtraction
procedure is described in more detail in section 4.13.3.6. The point source subtraction step is performed at the
state level, and it is done before the allocation procedure discussed in section 4.13.3.2 and before the emissions
calculations discussed in section 4.13.3.5.
4.13.3.2 Allocation procedure
SEDS data are reported at the state level. Following the adjustments to the state level fuel consumption
discussed in section 4.13.3.1 and the point source subtraction discussed below in section 4.13.3.6, 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 Patterns [ref 6], 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.
NPFf,c,x = NPFf SX X
empc,x
emPs,x
(4)
Where:
NPFf,c,x
NPFf,s,x
empCiX
emps,x
Adjusted nonpoint consumption of fuel/in county c in sector x
Adjusted nonpoint consumption of fuel/in state s in sector x, from equation 6
Employment in county c in sector x
Employment in state s in sector x
Employment in each sector is determined based on the crosswalk between North American Industrial
Classification System (NAICS) codes in the Point inventory and sectors, as shown in Table 4-110, where
"Commercial" is interchangeable with the EIS "Commercial/Institutional" sector definition.
Table 4-110: Mapping of NAICS codes to ICI sectors
NAICS
Sector
11
Industrial
21
Industrial
2212
Commercial
2213
Commercial
23
Industrial
31
Industrial
32
Industrial
4-182
-------
NAICS
Sector
33
Industrial
42
Commercial
44
Commercial
45
Commercial
48 (except 4862)
Commercial
49
Commercial
51
Commercial
52
Commercial
53
Commercial
54
Commercial
55
Commercial
56
Commercial
61
Commercial
62
Commercial
71
Commercial
72
Commercial
81
Commercial
92
Commercial
4.13.3.3 Emission factors
The emissions factors for ICI sectors are from AP-42 [ref 7] and a spreadsheet developed in 2010 by EPA and the
Eastern Regional Technical Advisory Committee [ref 8], The emissions factors for ammonia are taken from one
of two reports from EPA on ammonia emissions in the ICI sectors [ref 9, ref 10]. The emissions factors for
hazardous air pollutants from wood combustion in the ICI sectors are taken from EPA's SPECIATE database [ref
11]. These emission factors are provided in Table 5 of the Appendix to the "ICI NEMO FINAL_4-2 updated.docx"
document on the 2017 NEl Supplemental data FTP site.
4.13.3.4 Controls
There are no controls assumed for this category. However, the ICI tool includes options for SLT agencies to
submit pollutant-, SCC-, and county-specific control factors if needed. These control factors are a number
between 0 and 1 that is multiplied by the emissions for that pollutant, SCC, and county. These factors allow SLT
agencies to "fine tune" emissions estimates based on their understanding of how specific national and local
rules combined with their penetration/effectiveness could lead to "composite-rule" emission factors for specific
counties and pollutants. The relative difference between these "composite-rule" and default ICI tool emission
factors can then be used to compute SCC-, county-, and pollutant-specific "controls."
Alternatively, SLT agencies can adjust the emissions factors; however, this would affect the calculation of
emissions for all counties in the state.
4.13.3.5 Emissions
Emissions in each ICI sector are estimated by multiplying the county-level nonpoint source fuel consumption by
the emission factors from Table 5 of the Appendix to the "ICI NEMO FINAL_4-2 updated.docx" document on the
2017 NEI Supplemental data FTP site.
4-183
-------
Ep,f,c,x /V I'1 X EFp j X
(5)
Where:
Annual emissions of pollutant p from fuel type/in county c in sectorx
Nonpoint source consumption of fuel type/in county c in sectorx
Emissions factor for pollutant p, fuel type/, and sectorx
4.13.3.6 Point Source subtraction
The adjusted fuel consumption discussed in section 4.13.3.1 is an estimate of the state-level total fuel
combusted for all sources, including point and nonpoint sources. To estimate the fuel consumption from only
nonpoint sources, the fuel consumption from point sources is subtracted from the total adjusted fuel
consumption. The fuel consumption from point sources is provided to EPA by SLT agencies.
The starting point for computing state-level point fuel consumption (PFfS/X) begins by matching NEI (ElS/state)
facility identifier codes with EIA facilities in EIA-923 data [ref 12] to identify facilities that are in the industrial,
commercial, or electric utility sectors. NEI facilities that match EIA-923 facilities with EIA sector assignments of 4
(Commercial NAICS Non-Co-gen) or 5 (Commercial NAICS Cogen) are assigned as "Commercial/Institutional"
whose point source throughput activity data (consumption) are subject to Point subtraction from EIA SEDS.
Similarly, NEI facilities that match EIA-923 facilities with EIA sector assignments of 6 (Industrial NAICS Non-Co-
gen) or 7 (Industrial NAICS Cogen) are assigned as "Commercial/Institutional" whose point source throughput
activity data (consumption) are subject to Point subtraction from EIA SEDS. NEI facilities that match EIA-923
facilities with EIA sector assignments of 1, 2 or 3 (Electric Utility, NAICS-22 Non-Cogen, and NAICS-22 Cogen,
respectively) are assigned as "EGU" and thus not subject to Point "ICI" subtraction. An existing EIA 923 to NEI
(ElS/state) facility ID cross-reference to EIA ICI sectors is available for each state
"Proposed_facility_to_ICI_sector_assignments_2016NEI_14decl8_.csv" on the 2017 NEI Supplemental
data FTP site.
The remaining facilities that are not matched to EIA-923 facilities are then assigned to "Industrial",
"Commercial/Institutional" or "N/A" based on facility NAICS codes provided in Table 4-110.
Once all point facilities have been mapped to the appropriate sector via either the EIA-923 or the NAICS
assignments, the point inventory fuel consumption data are then aggregated by fuels using one of four different
options to identify the fuel:
• Option A: By NAICS and SCC. In this option, SLT agencies submit state-level point source data
aggregated by NAICS code and SCC. NAICS codes are used to map the point source fuel consumption to
the appropriate ICI sector according to the mapping in Table 4-110. SCCs are used to identify the type of
fuel consumed, according to the mapping in Table 7 of the Appendix to the "ICI NEMO FINAL_4-2
updated.docx" document on the 2017 NEI Supplemental data FTP site.
• Option B: By NAICS and Fuel Type. If the SLT agency knows the type of fuel consumed at each facility,
the agency can submit fuel consumption by fuel type and NAICS. As with option A, the NAICS code will
be used to map the fuel consumption to the appropriate sector.
• Option C: Point Source Fuel Consumption By Sector and Fuel Type. If the SLT agency has an alternative
approach for determining the state-level fuel consumption by point sources in the industrial and
commercial/institutional sectors by fuel type, the agency can submit this data directly.
• Option D: Nonpoint Source Fuel Consumption By Sector and Fuel Type. If the SLT agency has an
alternative approach for determining the state-level fuel consumption by nonpoint sources in the
4-184
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industrial and commercial/institutional sectors by fuel type, the agency can submit this data directly. If
the SLT agency chooses this option, point source subtraction is not needed, and the nonpoint source
fuel consumption will be used directly to estimate emissions without further adjustment.
NPFf,s,x ~ AFf siX PFf S,x (6)
Where:
NPFf/S,x = Adjusted nonpoint consumption of fuel/in state s in sector x
AFfiS,x = Total consumption of fuel/in state s in sector x, adjusted as discussed in section
4.13.3.1
PFfrS,x = Consumption of fuel/by points sources in states in sector x
Following point source subtraction at the state level, the estimated state-level nonpoint source fuel
consumption is distributed to the states based on employment in the industrial and commercial sectors. This
allocation procedure is discussed in section 4.13.3.2.
4.13.3.7 Example calculations
Table 4-111 lists sample calculations to determine PM25-PRI emissions from nonpoint source
bituminous/subbituminous coal combustion in the industrial sector in Alamance County, North Carolina. Note
that the equations in the table are listed in the order of the calculations, not in the order in which they are
presented in this NEMO. Note also that the point source fuel consumption used in in equation 6 is just shown as
an example and is not actual point source fuel consumption data submitted by an SLT agency.
Table 4-111: Sample calculations for PM25-PRI emissions from nonpoint industrial sector source
bituminous/subbituminous coal combustion in Alamance County, NC
Eq. #
Equation
Values for Alamance County, NC
Result
1
= T Ff,s,x
X SSfiSiX x (1
— ncf,s,industrial)
454 thousand tons coal consumption in the industrial sector ir
1 [fraction of coal used by stationary sources] x (1 —
0.2632 [fraction of coal in NC used as input to industrial proc
334.5
thousand tons
adjusted
industrial coal
consumption
in NC
2
AFant/bit,s,x
~ AFcoai s x
* Pant/bit,s
334.5 thousand tons coal
x 1 [fraction of bit/subbit coal consumption]
334.5
thousand tons
industrial
bituminous/
subbituminou
s coal
consumption
in NC
3
AFb0ner / engineSiX
~ AFdistiiidtg^ x
* ^boiler/engine,s,
N/A
Not needed
for coal
consumption
4-185
-------
Eq. #
Equation
Values for Alamance County, NC
Result
34.5 thousand
tons industrial
6
NPFfiSiX
~ AFf,s,x
- PFf,s,x
334.5 thousand tons bit/subbit coal
— 300 tons point source bit
/subbit coal consumption
nonpoint
source
bituminous/
subbituminou
s coal
consumption
0.71 thousand
tons industrial
nonpoint
4
NPFf,c,x
= NPFf>s>x
empcx
emPs,x
34.5 thousand tons
17,733 industrial employees in Alamance
861,292 industrial employees in NC
source
bituminous/
subbituminou
s coal
consumption
in Alamance
County, NC
1,732 lbs.
(0.866 tons)
PM25-PRI
emissions
from
5
F t-
up,f,c,x
= NPFfiCiX
x EFp j x
0.71 thousand tons x 2.44 lbs PM25 — PRI/ton
industrial
nonpoint
source
bituminous/
subbituminou
s coal
consumption
in Alamance
County
4.13.3.8 Changes from the 2014 methodology
The current method uses a different approach to point source subtraction compared to the 2014 method. The
2014 method used point source SCCs to identify both the sector and fuel type for fuel consumption by point
sources. In the current method, the EIA-923 data is first used to assign point inventory facilities as Industrial,
Commercial/Institutional, or neither; then, facilities' NAICS codes are used to determine which of the remaining
facilities are either Industrial or Commercial/Institutional. The current method also now allows four different
options for submitting state-level fuel consumption data.
In addition, in the current method, point source subtraction is conducted at the state level, rather than the
county level. In the 2014 method, total fuel consumption was distributed to the county level before point source
subtraction, and then point source subtraction was conducted using county-level point source data.
4-186
-------
Finally, the 2014 method allowed point source subtraction using either point source emissions or point source
fuel consumption. In the current method, point source subtraction is conducted using only point source fuel
consumption; point source subtraction using emissions is no longer allowed.
4.13,3.9 Puerto Rico and U.S. Virgin Islands
Since insufficient data exists to calculate emissions from the ICI sectors for the counties in Puerto Rico and the
US Virgin Islands, emissions are based on two proxy counties in Florida: 12011, Broward County for Puerto Rico
and 12087, Monroe County for the US Virgin Islands. The total emissions in tons for these two Florida counties
are divided by their respective populations creating a tons per capita emissions factor. For each Puerto Rico and
US Virgin Island county, the tons per capita emissions factor is multiplied by the county population (from the
same year as the inventory's activity data) which served as the activity data. In these cases, the throughput
(activity data) unit and the emissions denominator unit are "EACH".
4.13.4 References
1. Energy Information Administration, 2018. State Energy Data System, 2017 data.
2. Energy Information Administration. 2018. Form 821: Sales of Distillate Fuel Oil by End Use. 2016 data.
3. Energy Information Administration. 2018. Manufacturing Energy Consumption Survey, 2014 data.
4. Energy Information Administration. 2008. "Domestic Distribution of U.S. Coal by Destination State,
Consumer, Origin and Method of Transportation"
5. Energy Information Administration. 2015. Commercial Building Energy Consumption Survey. 2012 data.
6. U.S. Census Bureau. 2018. 2016 County Business Patterns.
7. U.S. Environmental Protection Agency. 1996. Compilation of Air Pollutant Emission Factors, 5th Edition.
AP-42, Volume I: Stationary Point and Area Sources. Research Triangle Park, North Carolina.
8. EPA and Eastern Regional Technical Advisory Committee. 2010. Excel file:
state_comparison_ERTAC_SS_version7_5_Mar 16 2010.xls
9. Battye, W. Battye, C. Overcash, and S. Fudge. 1994. Development and Selection of Ammonia Emission
Factors: Final Report. Durham, NC: EC/R Incorporated. Prepared for USEPA Office of Research and
Development.
10. E.H. Pechan and Associates, Inc. 2003. Estimating Ammonia Emissions from Anthropogenic Sources,
Draft Report. Durham, NC. Prepared for USEPA Emission Factor and Inventory Group.
11. EPA. 2016. SPECIATE v4.5. Fireplace wood combustion - pine wood.
12. Energy Information Administration. 2018. Form 923 Electricity Sector Data, 2017 data.
4.14 Fuel Combustion - Residential - Natural Gas, Oil, and Other
Residential heating includes the combustion of fuel, including coal, distillate oil, kerosene, natural gas, and
liquefied propane gas (LPG) to heat homes. Common uses of energy associated with this category include space
heating, water heating, and cooking. This category does not include the combustion of wood, which is estimated
separately in Section 4.15.
4.14.1 Sector description
The EIS sectors documented in this section include these emissions from residential fuel combustion:
• Fuel Comb - Residential - Natural Gas. Includes fuel natural gas only. Residential natural gas combustion
is natural gas that is burned to heat residential housing as well as in grills, hot water heaters, and dryers.
4-187
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• Fuel Comb - Residential - Oil. Includes the fuels: distillate oil, kerosene, and residual oil. Residual oil is
not an EPA-estimated category, and no agencies submitted data for it in 2017. Residential distillate oil
combustion is oil that is burned in residential housing. Residential kerosene combustion is kerosene that
is burned in residential housing. Common uses of energy associated with this sector include space
heating, water heating, cooking, and running a wide variety of other equipment.
• Fuel Comb - Residential - Other: Includes the fuels: coal, liquid petroleum gas (LPG), and "biomass; all
except wood". Note that "biomass; all except wood" is not an EPA-estimated category and no agency
submitted data for it in 2017. Residential coal combustion is coal that is burned in residential housing.
Residential LPG combustion is liquefied propane gas that is burned in residential housing. Common uses
of energy associated with this sector include space heating, water heating, and cooking.
4.14.2 Sources of data
Table 4-112 shows, for non-wood Residential heating, the nonpoint SCCs covered by the EPA estimates and by
the State/Local and Tribal agencies that submitted data. The SCC level 3 and 4 SCC descriptions are also
provided. The SCC level 1 and 2 descriptions is "Stationary Source Fuel Combustion; Residential" for all SCCs.
According to the State Energy Data System (SEDS) 2017 Consumption tables published by the Energy
Information Administration (EIA) [ref 1], there was no residential coal combustion in 2017. However, the old
methodology is retained here and provided in an EPA workbook, and as seen in Table 4-112, with zero
emissions, in case a state would like to use their own coal consumption data
Table 4-112: Non-wood residential fuel combustion SCCs in the 2017 NEI
SCC
Description
Sector
EPA
SLT
2104002000
Bituminous/Subbituminous Coal; Total:
All Combustor Types
Fuel Comb - Residential - Other
0
X
2104004000
Distillate Oil; Total: All Combustor Types
Fuel Comb - Residential - Oil
X
X
2104006000
Natural Gas; Total: All Combustor Types
Fuel Comb - Residential - Natural Gas
X
X
2104007000
Liquified Petroleum Gas (LPG); Total: All
Combustor Types
Fuel Comb - Residential - Other
X
X
2104011000
Kerosene; Total: All Heater Types
Fuel Comb - Residential - Oil
X
X
The agencies listed in Table 4-113 submitted emissions for these sectors. Agencies not listed uses EPA estimates
for the entire sector.
Table 4-113: Agencies reporting non-wood residential fuel combustion emissions
Agency
Oil
Other
Natural
Gas
Alaska Department of Environmental Conservation
X
X
California Air Resources Board
X
X
X
Coeur d'Alene Tribe
X
X
X
Delaware Department of Natural Resources and Environmental Control
X
X
X
Idaho Department of Environmental Quality
X
X
X
Illinois Environmental Protection Agency
X
X
X
Kootenai Tribe of Idaho
X
X
X
Maricopa County Air Quality Department
X
X
X
Maryland Department of the Environment
X
X
X
4-188
-------
Massachusetts Department of Environmental Protection
X
X
X
Memphis and Shelby County Health Department - Pollution Control
X
X
X
Metro Public Health of Nashville/Davidson County
X
X
X
New Hampshire Department of Environmental Services
X
X
X
New Jersey Department of Environment Protection
X
X
X
New York State Department of Environmental Conservation
X
X
X
Nez Perce Tribe
X
X
X
Northern Cheyenne Tribe
X
X
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
X
X
X
Southern Ute Indian Tribe
X
X
Texas Commission on Environmental Quality
X
X
X
Utah Division of Air Quality
X
X
Washoe County Health District
X
X
X
4.14.3 EPA-developed emissions
The general approach to calculating emissions for these SCCs is to take state-level fuel consumption from the
EIA State Energy Data System (SEDS) [ref 1] and allocate it to the county level based on data from the Census
Bureau on the number of homes in each county that use each fuel type [ref 2], County-level fuel consumption is
multiplied by emissions factors to calculate emissions.
Note that SEDS no longer includes data on residential coal consumption, as it is assumed to be near zero, and
therefore emissions will be nonexistent for residential coal consumption. However, the methodology for
estimating emissions from coal has been retained if states have additional data on residential coal consumption
that they would like to use.
The calculations for estimating emissions from residential heating involve distributing state-level energy
consumption data from SEDS to each county based on the proportion of houses in that county that use each fuel
type as a primary fuel source. Additional calculations are necessary to distribute coal consumption to anthracite
or bituminous coal consumption and to distribute fuel oil consumption to distillate fuel oil and kerosene
consumption. County-level consumption of each fuel is multiplied by an emissions factor to estimate emissions
of criteria air pollutants (CAPs) and hazardous air pollutants (HAPs).
4.14,3.1 Activity data
The amount of fuel consumed by residential sector in the United States from SEDS [ref 1] is used to estimate
emissions for this source category. The relevant fuel codes from SEDS are shown in Table 4-114.
Tab
e 4-114: EIA State Energy Data System Fuel Codes
Fuel
SEDS Fuel Code
Coal
CLRCP
Distillate fuel oil
DFRCP
Kerosene
KSRCP
Natural Gas
NGRCP
LPG
LGRCP
The SEDS data do not distinguish between anthracite and bituminous/subbituminous coal consumption
estimates. The EIA table "Domestic Distribution of U.S. Coal by Destination State, Consumer, Origin and Method
4-189
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of Transportation" [ref 3] provides state-level residential coal distribution data for 2006 that is used to estimate
the fraction of coal consumption that is anthracite and bituminous/subbituminous. The amount of anthracite
distributed to each state and the total coal delivered to each state is used to estimate the proportion of
anthracite and bituminous coal consumption. Table 4-115 presents the anthracite and bituminous coal ratios for
each state.
Ta
lie 4-115: Anthracite and Bituminous Coal Distribution for the Residential and Commercial Sectors
State
Ratio of
Ratio of
State
Ratio of
Ratio of
Bituminous
Anthracite
Bituminous
Anthracite
Alabama
1.000
0.000
Montana
1.000
0.000
Alaska
1.000
0.000
Nebraska
1.000
0.000
Arizona
0.814
0.186
Nevada
1.000
0.000
Arkansas
0.814
0.186
New Hampshire
0.000
1.000
California
1.000
0.000
New Jersey
0.000
1.000
Colorado
0.996
0.004
New Mexico
1.000
0.000
Connecticut
0.000
1.000
New York
0.600
0.400
Delaware
0.814
0.186
North Carolina
1.000
0.000
Dist. Columbia
1.000
0.000
North Dakota
1.000
0.000
Florida
0.814
0.186
Ohio
0.873
0.127
Georgia
1.000
0.000
Oklahoma
0.917
0.083
Hawaii
1.000
0.000
Oregon
1.000
0.000
Idaho
0.979
0.021
Pennsylvania
0.194
0.806
Illinois
0.998
0.002
Rhode Island
0.000
1.000
Indiana
0.947
0.053
South Carolina
0.997
0.003
Iowa
0.999
0.001
South Dakota
1.000
0.000
Kansas
1.000
0.000
Tennessee
0.994
0.006
Kentucky
0.998
0.002
Texas
0.814
0.186
Louisiana
1.000
0.000
Utah
1.000
0.000
Maine
0.000
1.000
Vermont
0.000
1.000
Maryland
0.929
0.071
Virginia
0.963
0.037
Massachusetts
0.500
0.500
Washington
1.000
0.000
Michigan
0.667
0.333
West Virginia
0.905
0.095
Minnesota
0.997
0.003
Wisconsin
0.991
0.009
Mississippi
1.000
0.000
Wyoming
1.000
0.000
Missouri
1.000
0.000
The SEDS data on residential coal consumption are split into consumption of anthracite and
bituminous/subbituminous coal based on the ratios in Table 4-115.
Where:
FCant/bit,s FCcoal,s * ^ant/bit (1)
FCant/bit,s = anthracite or bituminous coal consumption in state s, in tons
FCcoai,s = total fuel consumption of coal in state s from SEDS, in tons
Rant/bit = ratio of anthracite or bituminous coal to total coal, as found in Table 4-115
4-190
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4,14.3,2 Allocation procedure
State-level fuel consumption is allocated to each county using the US Census Bureau's 5-year estimate Census
Detailed Housing Information [ref 2], which includes the number of housing units using a specific type of fuel for
their primary fuel source. State fuel consumption is allocated to each county using the ratio of the number of
houses using each fuel in each county to the total number of houses using each fuel in the state.
For most fuels, the fuel type in SEDS matches well to the fuel type used in the Census data. However, the Census
data report only for total fuel oil, which does not distinguish between distillate fuel oil and kerosene. Therefore,
the ratio of distillate fuel oil versus kerosene in the heating fuel oil mix, which is used to determine the fraction
of homes in each county that use distillate and those that use kerosene, is calculated.
p _ FCdfo/ker,s (2)
Kdfo/ker,s ~ TpK
1 udfo,s ' 1 uker,s
Where:
Rdfo/ker,s = ratio of residential distillate fuel oil or kerosene to total distillate fuel oil and kerosene in state s
Adf0/ker,s = fuel consumption of distillate fuel oil or kerosene in state s from SEDS, in thousand barrels
Then, the ratio of distillate fuel oil or kerosene to total fuel oil is used to determine how many housing units in
each county use distillate fuel oil or kerosene.
HUdfo/ker,c HUf0,c * ^dfo/ker,s (3)
Where:
HUdf0/ker,c = housing units in county c using distillate fuel oil or kerosene as the primary heating fuel
HUf0,c = housing units in county c using any fuel oil as primary heating fuel
To distribute the state-level energy consumption data for all fuel types, the ratio of county-level housing units
using each fuel type as primary heating fuel to state-level housing units using that fuel type is calculated. This
ratio is used to distribute state-level fuel consumption to the county level. The county-level values for housing
units using distillate oil and kerosene as primary fuel are calculated in equations 2 and 3 above.
_ HUf,c (4)
f'C ~ HUfiS
Where:
R/c = ratio of homes in county c to homes in state s that use fuel/as primary heating fuel
HUf/C = housing units in county c using fuel type/as primary heating fuel
HUf/S = housing units in state s using fuel type/as primary heating fuel
The state-level fuel consumption of each fuel type from SEDS is multiplied by the county-level ratio of homes
using each fuel type. State-level fuel consumption of anthracite and bituminous/subbituminous coal is
calculated in equation 1 in Section 4.14.3.1.
4-191
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FCf,c. — FCf,s x Rf,c
(5)
Where:
FQC = fuel consumption of fuel type /in county c, in tons, thousand barrels, or thousand cubic feet
FQS = fuel consumption of fuel type/in state s, in tons, thousand barrels, or thousand cubic feet,
from SEDS
R/c = ratio of homes in county c to homes in state s that use fuel/as primary heating fuel
Fuel consumption of distillate fuel oil is converted from barrels to gallons using a conversion factor of 42 gallons
per barrel.
4.14.3.3 Emission factors
All emissions factors for CAPs, except ammonia, are from AP-42 [ref 4], The ammonia emissions factor is from
EPA's Estimating Ammonia Emissions from Anthropogenic Sources, Draft Final Report [ref 5], In some cases, HAP
emissions factors are from a memorandum to EPA called "Baseline Emission Inventory of HAP Emissions from
MACT Sources - Interim Final Report" [ref 6],
For many residential heating fuels, the emissions factors for S02 and PM species are adjusted using sulfur or ash
content data for the fuel at the county level. Note that for coal emissions, this step need only be done if a state
supplies data on residential coal consumption, because SEDS currently assumes zero residential coal
consumption.
fFX;P = emissions factor of pollutant p for fuel type/in state s
SACX = sulfur or ash content for fuel type/in state s
EFunadjj = unadjusted emissions factor for fuel type/, from EPA AP-42
A summary of the emissions factors for all fuel types for residential heating: anthracite coal,
bituminous/subbituminous coal, distillate fuel oil, kerosene, LPG, and natural gas is provided in Table 5 of the
"Residential Heating NEMO 2017 FINAL_4-2 update.docx" document on the 2017 NEl Supplemental data FTP
site.
For coal combustion, the S02 emission factors are based on the sulfur content of the coal burned, and some of
the PM emission factors for anthracite coal require information on the ash content of the coal. State-specific
coal sulfur contents for bituminous coal are obtained from the ElA's Coal Data Browser and applied at the
county level [ref 7], Bituminous sulfur content data can be found in the Coal Consumption and Quality Data Set,
filtered to only account for commercial and institutional sources. For anthracite coal, an ash content value of
13.38% and a sulfur content of 0.89% are applied to all counties except those in New Mexico (ash content
16.61%, sulfur content 0.77%), Washington (ash content 12%, sulfur content 0.9%), and Virginia (ash content
13.38%, sulfur content 0.43%). Table 4-116 shows the coal S02 and PM emissions factors. Table 4-117 presents
the bituminous coal sulfur content values used for each state.
EFf,s,p = SACf s X EFl
unad j,f
(6)
Where:
4-192
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Table 4-116: S02 and PM Emissions Factors for Residentia Anthracite and Bituminous Coal Combustion
Pollutant
Emissions Factor
(lb/ton)
Data Source,
AP-42 Table No.
Anthracite Emissions Factors (SCC 2104001000)
PM-CON
0.08 * % Ash
1.2-3 (stoker)
PM10-FIL
10
1.2-3 (hand-fired)
PM25-FIL
4.6
Fig. 1.2-1 (ratio of
PM25/PM 10=1.25/2.70=0.46)
0.46*10=4.6
PM10-PRI
10 + 0.08 * % Ash
1.2-3
PM25-PRI
4.6+ 0.08*% Ash
1.2-3 and Fig 1.2-1
Sulfur Dioxide
39 * % Sulfur
1.2-1 (residential space heater)
Bituminous Emissions Factors (SCC 2104002000)
PM-CON
1.04+
1.1-5 (stoker)
PM10-FIL
6.2
1.1-4 (hand-fed)
PM25-FIL
3.8
1.1-11 (underfeed stoker)
PM10-PRI
7.24
Sum of FIL and CON
PM25-PRI
4.84
Sum of FIL and CON
Sulfur Dioxide
31 * % Sulfur
1.1-3 (hand-fed)
Emissions factor provic
conversion factor of 26
ed in AP-42 is 0.04 Ib/MMBtu. This is multiplied by the
VIMBtu/ton provided in AP-42 for bituminous coal.
Table 4-117: State-Specific Sulfur Content for Bituminous Coa
State
Percent Sulfur
Content
State
Percent Sulfur
Content
Alabama
0.00
Montana
0.46
Alaska
0.15
Nebraska
0.00
Arizona
0.00
Nevada
0.00
Arkansas
0.00
New Hampshire
0.00
California
0.00
New Jersey
0.00
Colorado
0.31
New Mexico
0.00
Connecticut
0.00
New York
0.00
Delaware
0.00
North Carolina
1.63
District of Columbia
0.51
North Dakota
0.64
Florida
0.00
Ohio
0.88
Georgia
0.00
Oklahoma
0.00
Hawaii
0.00
Oregon
0.00
Idaho
0.00
Pennsylvania
0.83
Illinois
3.21
Rhode Island
0.00
Indiana
2.95
South Carolina
0.00
Iowa
2.60
South Dakota
0.00
Kansas
0.00
Tennessee
0.00
Kentucky
0.71
Texas
0.00
Louisiana
0.00
Utah
0.00
Maine
0.00
Vermont
0.00
Maryland
0.00
Virginia
1.08
Massachusetts
0.00
Washington
0.00
Michigan
0.00
West Virginia
0.00
Minnesota
0.22
Wisconsin
0.78
Mississippi
0.00
Wyoming
0.44
(SCC 2104002000)
4-193
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State
Percent Sulfur
Content
State
Percent Sulfur
Content
Missouri
3.03
The emissions factors for CO, VOC, and some HAPs for anthracite coal are the emissions factors provided in AP-
42 for bituminous coal. See Table 5 of the "Residential Heating NEMO 2017 FINAL_4-2 update.docx" document
on the 2017 NEl Supplemental data FTP site for the reference for each emissions factor. Emission rates for these
pollutants are dependent upon combustion efficiency, with the mass of emissions per unit of heat input
generally increasing with decreasing unit size. No anthracite emission rates are provided for residential heaters
for these pollutants. Therefore, it was felt that it the AP-42 emission rates from bituminous coal that are derived
for smaller hand-fed units, are more appropriate to use than applying anthracite emissions factors derived for
much larger boilers.
Note that while AP-42 provides emissions factors for emissions of some metals from coal combustion, these
factors are based on tests at controlled and/or pulverized coal boilers. These test conditions are not expected to
be a good representation of emission rates for metals from residential heaters, so these pollutants are not
included.
For all counties in the United States, the distillate oil consumed by residential combustion is assumed to be No. 2
fuel oil with a heating value of 140,000 Btu per gallon. The S02 emissions factor for distillate oil assumes a sulfur
content of 500 parts per million (ppm) and is calculated at the county level [ref 8],
Emissions factors for kerosene are based on the emissions factors for distillate oil, which are multiplied by a
factor of 135/140 to convert them for this use. This factor is based on the ratio of the heat content of kerosene
(135,000 Btu/gallon) to the heat content of distillate oil (140,000 Btu/gallon) [ref 4], Criteria pollutant and HAP
emissions factors are from the same sources discussed above for distillate fuel oil. The distillate sulfur content
(500 ppm) is used for kerosene as well [ref 8],
Pollutant emissions factors for residential LPG are based on the residential natural gas emissions factors. The
natural gas emissions factors [ref 9] are converted to LPG emissions factors by multiplying by 96,750 Btu/gallon.
4.14.3.4 Controls
There are no controls assumed for this category.
4.14.3.5 Emissions
The criteria pollutant and HAP emissions from residential heating are calculated by multiplying the distributed
county-level residential fuel consumption by the corresponding emissions factor for each pollutant. The adjusted
emissions factors for S02 and PM for anthracite and bituminous/subbituminous coal are calculated above in
equation 6 in Section 4.14.3.3.
1 ton (7)
Efcv = FCf c x EFf v x
!,c,P f,c f,p 2000 lb
Where:
Ef,c,p = annual emissions of pollutant p from combustion of fuel type/in county c, in tons
FCf/C = fuel consumption of fuel type/in county c, in tons, thousand barrels, or thousand cubic feet,
from equation 5
4-194
-------
EFf/P = emissions factor pollutant p and fuel type/, in pounds of emissions per unit (tons, thousand
barrels, or thousand cubic feet) of fuel consumption, from Table 5 of the "Residential Heating
NEMO 2017 FINAL_4-2 update.docx" document on the 2017 NEI Supplemental data FTP site.
4.14.3.6 Example calculations
Table 4-118 provides sample calculations for CO emissions from residential heating from distillate fuel oil in
Allegheny County, PA.
Table 4-118: Sample calculations for CO emissions from residential heating from distillate fuel oil in Allegheny
County, PA
Eq.#
Equation
Values for Allegheny County, PA
Result
1
FCanth/bit,s
~ FCCoal,s * Ranth/bit
N/A
This example
is for
distillate.
Equation 1 is
for coal.
2
Rdfo/ker,s
FCdfo/ker,s
FCdfo,s FCker.s
15,062 thousand barrels
0.9844 ratio
of DFO to
total fuel oil
(15,062 thousand barrels + 238 thousand barrels)
3
HUdfo/ker,c
— HUf0C X Rdfo/ker,s
8,081 houses x 0.9844
7,955.30
houses using
DFO in
Allegheny
County, PA
4
R -HUf'c
f'C HUfiS
7,955.30 houses
916,301.2 houses
0.0086
county
housing
allocation
ratio for
Allegheny
County, PA
5
FCf,c
= FCf,s x Rf,c
x 42 gal. per barrel
15,062 thous. barrels x 0.0086 x 42 gal. per barrel
5,492.25
thousand
gallons DFO
consumed in
Allegheny
County, PA
6
EFanth/bit,s,p
— SACj's X EFunadjj
N/A
This example
is for
distillate.
Equation 6 is
for coal.
7
Ef,c,p
= FCf c X EFf p
1 ton
X 2000 lb
1 ton
5,492.25 thous. gal.x 5 lbs. per thous. gal x
13.7 tons CO
from DFO in
Allegheny
County, PA
4-195
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4.14.3.7 Changes from the 2014 methodology
The 2017 methodology used a lower sulfur content value of 500 ppm for distillate fuel oil and kerosene
compared to the value of 3% used in the 2014 methodology.
4.14.3.8 Puerto Rico and U.S. Virgin Islands
Since insufficient data exist to calculate emissions for the counties in Puerto Rico and the US Virgin Islands,
emissions are based on two proxy counties in Florida: 12011, Broward County for Puerto Rico and 12087,
Monroe County for the US Virgin Islands. The total emissions in tons for these two Florida counties are divided
by their respective populations creating a tons per capita emissions factor. For each Puerto Rico and US Virgin
Island county, the tons per capita emissions factor is multiplied by the county population (from the same year as
the inventory's activity data) which served as the activity data. In these cases, the throughput (activity data) unit
and the emissions denominator unit are "EACH".
4.14.4 References
1. U.S. Department of Energy. Energy Information Administration (EIA). 2019. State Energy Data System
(SEDS): 1960-2017 Consumption.
2. U.S. Census Bureau. 2018. American Community Survey. B25040 House Heating Fuel. 2017 ACS 5-Year
Estimates.
3. U.S. Department of Energy, Energy Information Administration. 2018. "Domestic Distribution of U.S.
Coal by Destination State, Consumer, Origin and Method of Transportation".
4. U.S. Environmental Protection Agency. 1996. Compilation of Air Pollutant Emission Factors, 5th Edition,
AP-42. Volume 1: Stationary Point and Area Sources. Research Triangle Park, North Carolina.
5. U.S. Environmental Protection Agency. 2004. Emission Inventory Improvement Program. Estimating
Ammonia Emissions from Anthropogenic Sources, Draft Final Report. Prepared by E.H. Pechan and
Associates, Inc. Research Triangle Park, NC.
6. Porter, Fred, U.S. Environmental Protection Agency, Emission Standards Division. Note to Anne Pope,
U.S. Environmental Protection Agency/Emissions Monitoring and Analysis Division. Comments on
Industrial Boiler information in the "Baseline Emission Inventory of HAP Emissions from MACT Sources -
Interim Final Report," September 18, 1998. November 13, 1998.
7. U.S. Department of Energy, Energy Information Administration. 2017. Coal Data Browser.
8. U.S. Environmental Protection Agency. 2016. Technical Support Document (TSD) Preparation of
Emissions Inventories for the Version 6.3, 2011 Emissions Modeling Platform. Prepared by U.S.
Environmental Protection Agency Office of Air and Radiation Office of Air Quality Planning and
Standards Air Quality Assessment Division.
9. Huntley, Roy. 2012. Spreadsheet: "natgas_procgas_lpg_pm_efs_not_ap42_032012_revisions.xls"
4.15 Fuel Combustion - Residential - Wood
4.15.1 Sector description
Residential wood combustion (RWC) appliances, such as fireplaces, fireplace inserts, woodstoves, central
heaters (indoor furnaces and hydronic heaters), and other outdoor wood-burning devices, are significant
sources of air pollution in the United States—especially during winter months. RWC emits large amounts of fine
particulate matter (PM25-PRI), volatile organic compounds (VOCs), and hazardous air pollutants (HAPs) that are
known to contribute to poor human health, air quality, and visibility. We further differentiate freestanding
woodstoves and inserts into three categories: conventional (not EPA-certified), EPA certified catalytic, and EPA-
certified non-catalytic. Generally, the conventional units were produced before 1988. Units constructed after
4-196
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1988 had to meet EPA emission standards. In addition, characterize central heaters by fuel type (cordwood vs
pellet-fired) and location (indoor vs outdoor for hydronic heaters). For shorthand, we refer to the Residential
Wood Combustion sector as "RWC" in the remaining documentation.
4.15.2 Sources of data
Table 4-119 shows, for RWC, the SCCs covered by the EPA estimates and by the State/Local and Tribal agencies
that submitted data. The SCC level 3 and 4 SCC descriptions are also provided. The SCC level 1 and 2 descriptions
is "Stationary Source Fuel Combustion; Residential" for all SCCs.
Table 4-119 : RWC sector SCCs in the 2017 NEI
SCC
Description
EPA
S/L/T
2104008100
Wood
Fireplace: general
X
X
2104008210
Wood
Woodstove: fireplace inserts; non-EPA certified
X
X
2104008220
Wood
Woodstove: fireplace inserts; EPA certified; non-catalytic
X
X
2104008230
Wood
Woodstove: fireplace inserts; EPA certified; catalytic
X
X
2104008300
Wood
Woodstove: freestanding, general
X
2104008310
Wood
Woodstove: freestanding, non-EPA certified
X
X
2104008320
Wood
Woodstove: freestanding, EPA certified, non-catalytic
X
X
2104008330
Wood
Woodstove: freestanding, EPA certified, catalytic
X
X
2104008400
Wood
Woodstove: pellet-fired, general (freestanding or FP insert)
X
X
2104008510
Wood
Furnace: Indoor, cordwood-fired, non-EPA certified
X
X
2104008530
Wood
Furnace: Indoor, pellet-fired, general
X
X
2104008610
Wood
Hydronic heater: outdoor
X
X
2104008620
Wood
Hydronic heater: indoor
X
X
2104008630
Wood
Hydronic heater: pellet-fired
X
X
2104008700
Wood
Outdoor wood burning device, NEC (fire-pits, chimeneas, etc)
X
X
2104009000
Firelog; Total: All Combustor Types
X
X
The agencies listed in Table 4-120 submitted emissions for RWC. Agencies not listed uses EPA estimates for the
entire sector.
Table 4-120: Agencies reporting RWC emissions
Region
Agency
S/L/T
4
Metro Public Health of Nashville/Davidson County
Local
5
Illinois Environmental Protection Agency
State
5
Minnesota Pollution Control Agency
State
6
Texas Commission on Environmental Quality
State
8
Southern Ute Indian Tribe
Tribe
9
California Air Resources Board
State
9
Maricopa County Air Quality Department
Local
9
Washoe County Health District
Local
10
Alaska Department of Environmental Conservation
State
10
Coeur d'Alene Tribe
Tribe
10
Idaho Department of Environmental Quality
State
10
Kootenai Tribe of Idaho
Tribe
4-197
-------
Region
Agency
S/L/T
10
Nez Perce Tribe
Tribe
10
Northern Cheyenne Tribe
Tribe
10
Oregon Department of Environmental Quality
State
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
4.15.3 EPA-developed emissions
To improve estimates in this sector, the EPA, along with the Commission on Environmental Cooperation (CEC),
the Northeast States for Coordinated Air Use Management (NESCAUM), and Abt Associates, conducted a
national survey of wood-burning activity in 2018. The results of this survey were used to estimate county-level
burning activity, as discussed in more detail below.
The activity data for this category is the amount of wood burned in each county, which is based on data from
the CEC survey on the fraction of homes in each county that use each wood-burning appliance and the average
amount of wood burned in each appliance [ref 1], These assumptions are used with the number of occupied
homes in each county to estimate the total amount of wood burned in each county, in cords for cordwood
appliances and tons for pellet appliances. Cords of wood are converted to tons using county-level density factors
from the U.S. Forest Service [ref 2], Emissions are calculated by multiplying the tons of wood burned by
emissions factors.
4.15.3.1 Activity data
The activity data for RWC relies on assumptions developed from the CEC survey. The survey received 2,984
responses, and it asked questions about whether and how often the respondent used the different wood
burning appliances and how much wood they burned annually. It also asked demographic questions about the
respondents. EPA used statistical regression approaches to develop appliance fractions and burn rates for each
county, based on predictor variables from the survey responses. These predictor variables include:
• The number of heating degree days in 2017 associated with the climate zone where the respondent
lives, from NOAA [ref 3],
• The population density in 2017 of the county the respondent lives in, from the Census Bureau [ref 4],
• Whether the zip code where the respondent lives is considered urban or rural, according to data from
the Census Bureau [ref 5],
• The percentage of forest cover in the county whether the respondent lives, according to the Biogenic
Emissions Landuse Database (BELD, v4.1) [ref 6],
• The fraction of homes that use natural gas as a primary heat source in 2017 in the county where the
respondent lives, according to data from the American Community Survey [ref 7],
• The type of home the respondent lives in (single family detached, single family attached, multifamily,
mobile), based on responses in the CEC survey.
The regression analysis compared all respondents who said they used a given appliance, such as a woodstove, to
develop an equation based on each of these predictor variables. For example, survey respondents who lived in
areas with more heating degree days (i.e. colder climates) or areas where few homes used natural gas as a
primary heat sources (i.e. they might not have much natural gas service) tended to be more likely to say that
they used a given wood-burning appliance.
The regression equation estimates the probability that a home in each county, with a given set of predictor
variables, will use each wood-burning appliance. Therefore, when values of the predictor variables from each
4-198
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county are plugged into the equation, the result is a county-specific appliance fraction, which represents the
fraction of homes in that county that use each wood-burning appliance. For example, urban counties with a low
number of heating degree days, high population density, low forest cover, and many homes using natural gas
tend to have a low appliance fraction for most appliances. County-specific appliance fractions are calculated
separately for six appliance types: fireplaces, fireplace inserts, woodstoves, pellet stoves, central heaters (e.g.
wood boilers or furnaces), and outdoor recreational equipment (such as fire pits). The process for splitting these
appliance types into each of the 15 SCCs is discussed below.
Burn rates, which represent the average amount of wood burned in each appliance, are also calculated using
regression analysis and the same predictor variables listed above. When county-level values of the predictor
variables are plugged into the burn rate regression equation, the result is county-specific burn rates for each
appliance type. The burn rates include the same appliance types as the appliance fractions.
The appliance fractions and burn rates are multiplied by the number of occupied homes in each county from the
American Community Survey [ref 7] to estimate the amount of wood burned in each county, in cords or tons,
depending on whether the appliance burns cordwood or pellets. For devices that burn cordwood, the estimated
number of cords burned in each county is multiplied by a county-level wood density factor from the U.S. Forest
Service [ref 2],
Wc,a = HCX AFca x BRc a x Dc (1)
Where:
Wc,a = Amount of wood burned in appliance type a in county c, in tons per year
Hc = Number of occupied homes in county c
AFc,a = Appliance fraction for appliance type a in county c, determined from the CEC survey
BRc,a = Burn rate for appliance type a in county c, determined from the CEC survey, in cords or tons
burned per appliance
Dc = Wood density factor for county c, in tons per cord of wood (used only for cordwood appliance
types)
As discussed above, the appliance fractions and burn rates are used to estimate wood-burning activity at the
appliance level in each county. This activity for certain appliance types must be distributed from the appliance
level to the specific SCC level. For example, wood burned in "woodstoves" must be apportioned to three SCCs:
non-EPA certified stoves, EPA certified non-catalytic stoves, and EPA certified catalytic stoves. For woodstoves
and fireplace inserts, EPA used distribution profiles based on a combination of data from the 2015 EIA
Residential Energy Consumption Survey (RECS) and the state of Minnesota's 2014/2015 residential wood survey.
Data from RECS is used to determine whether woodstoves or fireplace inserts are EPA certified. Although RECS
does not specifically ask whether the woodstove is EPA certified, it does ask the age of the appliance. It is
assumed that any appliance in the oldest age bin in RECS (20 years or older) is uncertified.*** All appliances less
than 20 years old are assumed to be EPA certified. The split between EPA certified non-catalytic and catalytic
stoves is based on data provided by Minnesota from their 2014/2015 residential wood survey, which suggests
*" A 20-year-old appliance in the 2015 RECS would have been manufactured in 1995, which is after the 1988 NSPS for wood
stoves. However, this is the oldest age bin in RECS. EPA lacks data on the fraction of appliances in this age bin that were
manufacturer before or after 1988. Therefore EPA assumed that all appliances in this age bin were uncertified.
4-199
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that certified stoves are 60 percent non-catalytic and 40 percent catalytic. The distribution profiles for
woodstoves and fireplace inserts are shown in Table 4-121.
The CEC survey data were seen to be more reliable for developing distribution profiles for central heaters,
including wood boilers and furnaces. Survey respondents listed whether they owned a furnace or a boiler,
whether it was located inside or outside the home, and whether it burned cordwood or pellets. These responses
were used to develop distribution profiles for the central heaters. The distribution profiles for central heaters
are shown in Table 4-122.
The default distribution profiles are estimated at the Census Region level for woodstoves and fireplace inserts
and nationally for central heaters, but the RWC tool allows the profiles to be adjusted for each county. Not all
appliance types need to be distributed. Appliance populations of fireplaces, pellet stoves, and outdoor
recreational equipment are estimated directly from the regression equations and are not multiplied distribution
fractions.
The amount of wood-burning activity in each SCC in each county is determined by multiplying the county-level
wood-burning activity by appliance type by the distribution profile for each SCC.
Wc,scc — Wc,a X DPSCC (2)
Where:
Wc, see — Amount of wood burned in each SCC in county c, in tons per year
Wc,a = Amount of wood burned in appliance type a in county c, in cords or tons per year, from
equation 1
DPscc = Distribution profile for each SCC from Table 4-121 or Table 4-122, depending on the appliance
type
Table 4-121: Distribution profiles for woodstoves and fireplace inserts by Census Region
Woodstove or Fireplace
Census
Region
Insert Type
NE
MW
S
W
Uncertified
0.16
0.12
0.31
0.31
Certified Catalytic
0.34
0.35
0.28
0.28
Certified Non-catalytic
0.50
0.53
0.41
0.41
Table 4-122: Distribution profiles for central heaters
Type of Central Heater
SCC
Distribution Profile
Indoor pellet boiler
2104008630
0.01
Indoor pellet furnace
2104008530
0.03
Indoor cordwood boiler
2104008620
0.23
Indoor cordwood furnace
2104008510
0.37
Outdoor cordwood boiler
2104008610
0.36
After an initial review of the wood-burning activity predicted by the appliance fractions and burn rates develop
from the CEC survey data, EPA decided to make two adjustments to the estimates. The first adjustment corrects
the total wood-burning activity in each state. The amount of residential wood-burning activity initially predicted
by the appliance fractions and burn rates was significantly higher than the state-level totals reported by ElA's
State Energy Data System (SEDS) [ref 8] for most states. As a result, EPA developed an adjustment factor to
normalize the state-level residential wood-burning activity predicted by the tool to the amount predicted by
4-200
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SEDS. The SEDS adjustment factor is developed by summing the predicted amount of wood-burning activity (in
cords) to the state level in each state and dividing it by the state-level amount of residential wood consumption
reported by SEDS. SEDS reports wood consumption in Btu, rather than cords; therefore, the wood-burning
activity predicted by the RWC tool is converted from cords to Btu using a conversion factor of 20 million Btu per
cord, from the SEDS documentation. In addition, SEDS only includes wood consumption for residential heating;
therefore, predicted wood consumption from outdoor recreational wood-burning (2104008700) and wax
firelogs (2104009000) are not summed to calculate the SEDS adjustment.
rAT, T,WcScc (3)
SAFs = w
vvs,SEDS
Where:
SAFs = SEDS adjustment factor for state s
Wc, see — Amount of wood burned in each SCC in county c, in tons per year
WSi seds - Amount of wood consumption in state s reported by SEDS
The second adjustment EPA made to the predicted wood consumption relates to central heaters and outdoor
recreational equipment. After an initial review of predicted wood-burning activity, EPA felt that the estimated
amount of wood burned in these appliances in dense urban areas was unreasonably high. Therefore, EPA
developed a second adjustment factor based on the housing density (homes/mi2) in each county, based on the
equation for a sigmoid curve. The housing density adjustment factor is calibrated such that it approaches 0
when county-level housing density approaches 1,000 homes/mi2. The housing density adjustment factor is
multiplied by the predicted wood-burning activity only for central heating appliances (wood boilers and
furnaces) and outdoor recreational wood-burning appliances.
1 (4)
haf = I- 1
c I _|_ g-0.01 (HDC-500)
Where:
FIAFs = Housing density adjustment factor for county c
HDC = Housing density in county c, in homes/mi2
The SEDS and housing density adjustment factors are multiplied by the county-level predicted wood-burning
activity to develop the adjusted wood-burning activity in each county.
AWCiSCC = WcSCC X SAFs X HAFC (5)
Where:
AWC, see — Adjusted amount of wood burned in each SCC in county c, in tons per year
SAFs = SEDS adjustment factor for state s
HAFs = Housing density adjustment factor for county c
Note that the appliance fractions and burn rates provided in the input templates already take into account the
housing density and SEDS adjustments. Therefore, the input templates for RWC do not ask SLT agencies to
submit values for the housing density or SEDS adjustments. Rather, SLT agencies need only to submit revisions
to the appliance fractions and burn rates themselves. Equations 4 and 5 are included here only to provide more
information about how the appliance fractions and burn rates were adjusted.
4-201
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4,15,3,2 Allocation procedure
Appliance fractions and burn rates are calculated at the county-level. There is no need to allocate data to the
county level for this category.
4.15.3.3 Emission factors
Emissions factors for RWC come primarily from AP-42 [ref 9] and Houck and Eagle (2006) [ref 10], but also from
Houck et al. (2001) [ref 11], Many of the HAP emissions factors are from Hays et al. (2003) [ref 12]. Emissions
factors for wax firelogs are from Li and Rosenthal (2006) [ref 13]. Additional emission factors are taken from
Houck et al. (2001) [ref 11] and Aurell et al. (2012) [ref 14]. Emission factors for all SCCs are provided in Table 3
of the appendix in the "Residential Wood Combustion_DRAFT.DOCX" document on the 2017 NEI Supplemental
data FTP site.
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) [ref 15], 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.
While the NSPS was expected to decrease emissions for hydronic heaters and furnaces in 2015, EPA lacks data
on the fraction of these appliances in use that were manufactured after the 2015 NSPS went into effect.
Therefore, EPA made no changes to the emissions factors for hydronic heaters or furnaces.
4.15.3.4 Controls
There are no controls assumed for this category. However, SLT agencies may submit state- or county-level
control factors that will adjust the emissions by SCC.
4.15.3.5 Emissions
Emissions from RWC are calculated by multiplying the adjusted amount of wood burned in each SCC in each
county by SCC- and pollutant-specific emissions factors.
EClscc,p — AWc,sec x EFScc,p (6)
Where:
Ec,scqp = Emissions of pollutant p from each SCC in county c
AWC, see — Adjusted amount of wood burned in each SCC in county c, in tons per year
EFscc,p = Emissions factor for pollutant p for each SCC, from Table 3 of the appendix in the
"Residential Wood Combustion_DRAFT.DOCX" document on the 2017 NEI Supplemental data
.
4.15.3.6 Example calculations
Table 4-123 lists sample calculations for the estimation of emissions of PM25-PRI from non-EPA certified wood
stoves in Delaware County, OH.
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Note that the appliance fractions and burn rates provided in the input templates already take into account the
housing density and SEDS adjustments. Therefore, the input templates for RWC do not ask SLT agencies to
submit values for the housing density or SEDS adjustments. Rather, SLT agencies need only to submit revisions
to the appliance fractions and burn rates themselves. Equations 4 and 5 are included here only to provide more
information about how the appliance fractions and burn rates were adjusted.
Table 4-123: Sample calculations for PM25-PRI emissions from non-EPA certified woodstoves in Delaware
County, OH
Eq. #
Equation
Values for Delaware County, OH
Result
1
WCta = HCX AFca x BRca
x Dc
67,701 homes x 0.0751 x 1.9304 x
1.3341 tons/cord
13,094 tons of wood
burned in
woodstoves
2
WC,SCC = Wc,a X DPSCC
13,094x0.12
1,571 tons of wood
burned in non-EPA
certified woodstoves
3
„ . „ Ws,SEDS
SAFs = Zwc,scc
14,714 BBtu
28,369 BBtu
0.52 SEDS
adjustment factor
4
HAFC
1
= +1
I -)- g-0.01 (HDc-500)
1
+ 1
I -)- e-0.01 (153 -500)
0.97 housing
adjustment factor
5
A WCiscc = WCiscc x SAFs
x HAFC
1,571x0.52x0.97
792 adjusted tons of
wood burned in non-
EPA certified
woodstoves
6
Ec,scc,p = AWc,scc x EFscc.p
792 x 30.6 lb/ton
24,235 lbs. (12.12
tons) PM25-PRI from
non-EPA certified
woodstoves in
Delaware County,
OH
4.15.3.7 Changes from the 2014 methodology
The largest changes from the 2014 methodology are the source of the data used to develop the appliance
fractions and burn rates. In 2014, the appliance fractions and burn rates were calculated based on survey data
from the 2009 EIA RECS, while in 2017 the appliance fractions and burn rates are calculated based on the CEC
survey data. In addition, while EPA lacked data in 2014 to estimate county-level appliance fractions and burn
rates for outdoor recreational wood-burning equipment and wax firelogs, EPA was able to estimate appliance
fractions and burn rates for these categories for 2017 using data from the CEC survey. The general approach for
using regression analysis to develop county level appliance fractions and burn rates is unchanged from 2014.
Another change involves the estimation of emissions for three additional SCCs: indoor pellet boilers, indoor
pellet furnaces, and indoor hydronic heaters.
4.15.3.8 Puerto Rico and U.S. Virgin Islands
Insufficient data exists to calculate emissions for the counties in Puerto Rico and the US Virgin Islands, so
emissions are based on two proxy counties in Florida: 12011, Broward County for Puerto Rico and 12087,
Monroe County for the US Virgin Islands. The total emissions in tons for these two Florida counties are divided
4-203
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by their respective populations creating a tons per capita emission factor. For each Puerto Rico and US Virgin
Island County, the tons per capita emission factor is multiplied by the county population (from the same year as
the inventory's activity data) which served as the activity data. In these cases, the throughput (activity data) unit
and the emissions denominator unit are "EACH".
4.15.4 References
1. CEC. 2019. Residential Wood Use Survey to Improve Black Carbon Emissions Inventory Data for Small-
Scale Biomass Combustion. Montreal, Canada: Commission for Environmental Cooperation.
2. U.S. Department of Agriculture (USDA). 2009. "Timber Products Output Survey," U.S. Forest Service,
retrieved via query.
3. NOAA. 2019. Degree Days Statistics. Washington, DC: National Weather Service, Climate Prediction
Center.
4. U.S. Census Bureau. 2019. County Population Totals and Components of Change: 2010-2018.
Washington, DC.
5. U.S. Census Bureau. 2018. Urban and Rural. Washington, DC.
6. U.S. Environmental Protection Agency. 2018. Biogenic Emissions Sources.
7. U.S. Census Bureau. 2018. American Community Survey. Washington, DC.
8. Energy Information Administration. 2019. State Energy Data System.
9. U.S. Environmental Protection Agency. 1996. AP-42, Fifth Edition, Chapter 1 External Combustion
Sources, Sections 1.9 Residential Fireplaces and 1.10 Residential Wood Stove.
10. Houck, J.E. and B.N. Eagle. 2006. Task 6 Technical Memorandum 4 (Final Report): Control Analysis and
Documentation for Residential Wood Combustion in the MANE-VU Region. Prepared for MARAMA.
11. Houck, J.E., J. Crouch, and R.H. Huntley. 2001. Review of Wood Heater and Fireplace Emission Factors.
Technical presentation at the International Emission Inventory Conference. Denver, CO.
12. Hays, M.D., et al. 2003. Polycyclic aromatic hydrocarbon size distributions in aerosols from appliances of
residential wood combustion as determined by direct thermal desorption—GC/MS. Journal of Aerosol
Science, 34:1061-1084.
13. Li, V.S. and S.R. Rosenthal. 2006. Content and Emission Characteristics of Artificial Wax Firelogs. Poster
presentation at 15th International Emission Inventory Conference. New Orleans, Louisiana. May 15-18,
2006.
14. Aurell, J., B.K. Gullett, D. Tabor, et al. 2012. Semivolatile and Volatile Organic Compound Emissions from
Wood-Fired Hydronic Heaters. Environmental Science and Technology, 46: 7898-7904.
15. U.S. Environmental Protection Agency. 2015. Regulatory Impact Analysis (RIA) for Residential Wood
Heaters NPSP Revision. Final Report. Research Triangle Park, NC.
4.16 Industrial Processes - Mining and Quarrying
4.16.1 Sector description
Mining and quarrying activities produce particulate matter (PM) emissions due to the variety of processes used
to extract the ore and associated overburden, including drilling and blasting, loading and unloading, and
overburden replacement. Fugitive dust emissions for mining and quarrying operations are the sum of emissions
from the mining of metallic and nonmetallic ores and coal. Each of these mining operations has specific
emissions factors accounting for the different means by which the resources are extracted.
4-204
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4.16.2 Sources of data
Table 4-124 shows, for mining and quarrying, the SCCs covered by the EPA estimates and by the State/Local and
Tribal agencies that submitted data. The SCC level 3 and 4 SCC descriptions are also provided. The SCC level 1
and leading level 2 descriptions is "Industrial Processes; Mining and Quarrying:" for all SCCs.
Table 4-124: Mining and Quarrying sector SCCs in the 2017 NEI
SCC
Description
EPA
S/L/T
2325000000
SIC 14; All Processes; Total
X
X
2325020000
SIC 14; Crushed and Broken Stone; Total
X
2325030000
SIC 14; Sand and Gravel; Total
X
2325060000
SIC 10; Lead Ore Mining and Milling; Total
X
The agencies listed in Table 4-125 submitted emissions for mining and quarrying. Agencies not listed use EPA
estimates for the entire sector.
Table 4-125: Agencies reporting Mining and Quarrying emissions
Region
Agency
S/L/T
1
Rhode Island Department of Environmental Management
State
2
New Jersey Department of Environment Protection
State
3
Maryland Department of the Environment
State
4
Knox County Department of Air Quality Management
Local
4
Memphis and Shelby County Health Department - Pollution Control
Local
4
Metro Public Health of Nashville/Davidson County
Local
5
Illinois Environmental Protection Agency
State
6
Texas Commission on Environmental Quality
State
7
Missouri Department of Natural Resources
State
8
Utah Division of Air Quality
State
9
California Air Resources Board
State
9
Clark County Department of Air Quality and Environmental Management
Local
9
Maricopa County Air Quality Department
Local
9
Washoe County Health District
Local
10
Alaska Department of Environmental Conservation
State
10
Coeur d'Alene Tribe
Tribe
10
Idaho Department of Environmental Quality
State
10
Nez Perce Tribe
Tribe
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
4.16.3 EPA-developed emissions
Four specific activities are included in the emissions estimate for mining and quarrying operations: overburden
removal, drilling and blasting, loading and unloading, and overburden replacement. Not included are the
transfer and conveyance operations, crushing and screening operations, and storage since the dust emissions
from these activities are assumed to be well controlled. Fugitive dust emissions for mining and quarrying
operations are the sum of emissions from the mining of metallic and nonmetallic ores and coal. Emissions for
each activity are calculated by multiplying the emissions factors by the activity data.
4-205
-------
4,16,3.1 Activity data
Activity data for this source category include state-level metallic and non-metallic (a.k.a. mineral) crude ore
handled at surface mines from the U.S. Geologic Survey (USGS) [ref 1] and mine-specific coal production data for
surface mines from the Energy Information Administration (EIA) [ref 2], Emissions are not estimated for
underground mining given that emissions factors are calculated exclusively for surface activity.
In some cases, the amount of mining waste is withheld for some states to avoid disclosing company proprietary
data. To estimate state-level withheld waste data the fraction of crude ore production in the state is multiplied
by the amount of waste data withheld at the national level. The national-level amount of waste withheld is
calculated by subtracting all known state-level waste values (i.e. those that are not withheld) from the national-
level waste value. Note that this calculation only needs to be completed for states where state-level mining
waste data are withheld.
Os
Ws=7Tx W"s (!)
uus
Where:
Ws = Amount of metallic and non-metallic mining waste for state s, in metric tons
Wus = Amount of metallic and non-metallic mining waste withheld at the national level, in metric
tons
Os = Amount of crude ore produced in state s, in metric tons
Ous = Amount of crude ore produced at the national level, in metric tons
The data on state-level mining production and waste is split into production and waste for metallic and non-
metallic ores using the fraction of national-level metallic and non-metallic ore production. Values are also
converted from metric tons to short tons. Throughout the remainder of this document references to "ton(s)"
refer to short tons, while metric tons will be explicitly labeled.
MPU = (Ws + 0S) X ^ X 1.1023 tm/metric tm (2)
Where:
MPt,s = Amount of mining material type t (i.e. either metallic or non-metallic ore) produced in state s,
in tons
Ws = Amount of total metallic and non-metallic mining waste for state s, in metric tons
Os = Amount of crude ore produced in state s, in metric tons
MPt,us = Amount of mining material type t produced at the national-level, in metric tons
MPus = Total metallic and non-metallic ore production at the national level, in metric tons
4,16.3.2 Allocation procedure
The state-level data on metallic and non-metallic mining materials (from equation 2) is distributed to the county
level based on the proportion of employees in the metallic and non-metallic ore sectors (see Table 4-126 for a
list of NAICS codes), from the U.S. Census Bureau County Business Patterns [ref 3], Separate fractions are
determined for metallic ore mining employees and non-metallic ore mining employees in each county.
Emptr
EmpFracu = (3)
4-206
-------
Where:
EmpFract,c = The fraction of mining employees for material type t in county c
Emptc =The number of mining employees for material type t in county c
Empts =The number of mining employees for material type t in state s
Table 4-126: NAICS Codes for Metallic and Non-Metallic Mining
NAICS Code
Description
2122
Metal Ore Mining
212210
Iron Ore Mining
21222
Gold Ore and Silver Ore Mining
212221
Gold Ore Mining
212222
Silver Ore Mining
21223
Copper, Nickel, Lead, and Zinc Mining
212231
Lead Ore and Zinc Ore Mining
212234
Copper Ore and Nickel Ore Mining
21229
Other Metal Ore Mining
212291
Uranium-Radium-Vanadium Ore Mining
212299
All Other Metal Ore Mining
2123
Nonmetallic Mineral Mining and Quarrying
21231
Stone Mining and Quarrying
212311
Dimension Stone Mining and Quarrying
212312
Crushed and Broken Limestone Mining and Quarrying
212313
Crushed and Broken Granite Mining and Quarrying
212319
Other Crushed and Broken Stone Mining and Quarrying
21232
Sand, Gravel, Clay, and Ceramic and Refractory Minerals Mining and
Quarrying
212321
Construction Sand and Gravel Mining
212322
Industrial Sand Mining
212324
Kaolin and Ball Clay Mining
212325
Clay and Ceramic and Refractory Minerals Mining
21239
Other Nonmetallic Mineral Mining and Quarrying
212391
Potash, Soda, and Borate Mineral Mining
212392
Phosphate Rock Mining
212393
Other Chemical and Fertilizer Mineral Mining
212399
All Other Nonmetallic Mineral Mining
Due to concerns with releasing confidential business information, the CBP does not release exact numbers for a
given NAICS code if the data can be traced to an individual business. Instead, a series of range codes is used. To
estimate employment in counties and states with withheld data, the following procedure is used for NAICS code
being computed.
To gap-fill withheld state-level employment data:
m. State-level data for states with known employment in each NAICS are summed to the national level,
n. The total sum of state-level known employment from step a is subtracted from the national total
reported employment for each NAICS in the national-level CBP to determine the employment total for
the withheld states.
o. Each of the withheld states is assigned the midpoint of the range code reported for that state. Table
4-207
-------
4-127 lists the range codes and midpoints,
p. The midpoints for the states with withheld data are summed to the national level,
q. An adjustment factor is created by dividing the number of withheld employees (calculated in step b of
this section) by the sum of the midpoints (step d).
r. For the states with withheld employment data, the midpoint of the range for that state (step c) is
multiplied by the adjustment factor (step e) to calculate the adjusted state-level employment for
landfills.
These same steps are then followed to fill in withheld data in the county-level business patterns,
s. County-level data for counties with known employment are summed by state.
t. County-level known employment is subtracted from the state total reported in state-level CBP (or, if the
state-level data are withheld, from the state total estimated using the procedure discussed above),
u. Each of the withheld counties is assigned the midpoint of the range code (Table 4-127).
v. The midpoints for the counties with withheld data are summed to the state level,
w. An adjustment factor is created by dividing the number of withheld employees (step h) by the sum of
the midpoints (step j).
x. For counties with withheld employment data, the midpoints (step i) are multiplied by the adjustment
factor (step k) to calculate the adjusted county-level employment for landfills.
Table 4-127: Withheld data ranges and midpoints
Employment
Code
Employment
Range
Midpoint
A
0-19
10
B
20-99
60
C
100-249
175
E
250-499
375
F
500-999
750
G
1,000-2,499
1,750
H
2,500-4,999
3,750
1
5,000-9,999
7,500
J
10,000-24,999
17,500
K
25,000-49,999
37,500
L
50,000-99,999
75,000
M
100,000+
For example, take the 2016 CBP data for NAICS 2123 (Nonmetallic Mineral Mining and Quarrying) in Arizona
provided in Table 4-128.
Table 4-128: 2016 County Business Pattern for NAICS 2123 in Arizona
State
FIPS
County
FIPS
County
Name
NAICS
Employment
Code
Employment
04
001
Apache
2123
B
withheld
04
003
Cochise
2123
16
04
005
Coconino
2123
A
withheld
04
007
Gila
2123
10
04
009
Graham
2123
B
withheld
04
012
La Paz
2123
A
withheld
04
013
Maricopa
2123
563
04
015
Mohave
2123
69
4-208
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State
FIPS
County
FIPS
County
Name
NAICS
Employment
Code
Employment
04
017
Navajo
2123
65
04
019
Pima
2123
121
04
021
Pinal
2123
201
04
023
Santa Cruz
2123
A
withheld
04
025
Yavapai
2123
133
04
027
Yuma
2123
51
Note: Counties in Arizona that do not have employment in mining and quarrying
are excluded from this table.
7. The total number of employees reported at the county level is 1,229.
8. The state-level CBP reports 1,363 employees for NAICS 2123. This means that there are 134 employees
withheld at the county level.
9. The counties with withheld data are assigned midpoints according to the employment codes in Table
4-127. For example, County 001 is given a midpoint of 60 employees (since employment code B is 20-
99).
10. The sum of the midpoints for all withheld counties is 150 employees.
11. The adjustment factor is 134/150 = 0.8933.
12. The adjusted employment for county 001 is 60 x 0.8933 = 54 employees.
Once county- and state-level metal and non-metal employment are known for each county, the ratio of county
to state employees (from equation 3) is multiplied by the state-level metal and non-metal production (from
equation 2) to calculate county-level production.
MPt c = MPt s x EmpFract c (4)
Where:
MPt,c = Amount of mining material type t produced in county c, in tons
MPt,s = Amount of mining material type t (i.e. either metallic or non-metallic ore) produced in
state s, in tons
EmpFract,c = The fraction of mining employees for material type t in county c
4.16.3.3 Emission factors
Emissions factors are calculated separately for metallic ore mining, non-metallic ore mining, and coal mining.
This section describes those calculations and the relevant data sources.
Metallic Ore Mining
The emissions factor for metallic ore mining includes emissions from overburden removal, drilling and blasting,
and loading and unloading activities, and are taken from emissions factors for copper ore mining from EPA's
National Air Pollutant Emission Trends Procedures Document for 1900-1996 [ref 4]. The emissions factors are
applied to all three activities with PM10/TSP ratios of 0.35 for overburden removal [ref 5], 0.81 for drilling and
blasting [ref 6], and 0.43 for loading and unloading operations [ref 6],
EFpMio.m = EF0 1(8 x EFb) + EFt + EFd (5)
4-209
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Where:
EFpMio,m = PM10-PRI metallic ore mining emissions factor, in Ibs./ton
EF0 = PM10-PRI open pit overburden removal emissions factor for copper ore, in Ibs./ton
B = Fraction of total ore production that is obtained by blasting at metallic ore mines
EFb = PM10-PRI drilling/blasting emissions factor for copper ore, in Ibs./ton
EFi = PM10-PRI loading emissions factor for copper ore, in Ibs./ton
EFd = PM10-PRI truck dumping emissions factor for copper ore, in Ibs./ton
Using values from the National Air Pollutant Emission Trends Procedures Document for 1900-1996, Table 3.1-3,
the PM10-PRI emissions factor is calculated as:
0.0548 lbs/ton = 0.0003 + (0.57625 X 0.0008) + 0.022 + 0.032 (5a)
The PM25-PRI emissions factor is assumed to be 12.5% of the PM10-PRI emissions factor.
EFpM25,m — EFpMW.m x 0-125 (6)
0.0069 = 0.0548 x 0.125 (6a)
Where:
EFpM25,m = PM25-PRI metallic ore mining emissions factor, in Ibs./ton
EFpMio,m = PM10-PRI metallic ore mining emissions factor, in Ibs./ton
Non-Metallic Ore Mining
The emissions factor for non-metallic ore mining includes overburden removal, drilling and blasting, and loading
and unloading activities. The emissions factor is based on western surface coal mining operations from AP-42
[ref 7] and a PM10/TSP ratio.
EF,
PM10,nm
= EFV + (D x EFr) + EFa + (0.5 X (EFe + EFt))
(7)
Where:
EFpmio, nm
EFV
D
EFr
EFa
EFe
EFt
PM10-PRI non-metallic ore mining emissions factor, in Ibs./ton
PM10-PRI open pit overburden removal emissions factor at western surface coal mining
operations, in Ibs./ton
fraction of total ore production that is obtained by blasting at non-metallic ore mines
PM10-PRI drilling/blasting emissions factor at western surface coal mining operations, in
Ibs./ton
PM10-PRI loading emissions factor at western surface coal mining operations, in Ibs./ton
PM10-PRI truck unloading: end dump-coal emissions factor at western surface coal mining
operations, in Ibs./ton
PM10-PRI truck unloading: bottom dump-coal emissions factor at western surface coal
mining operations, in Ibs./ton
Applying the TSP emissions factors developed for western surface coal mining operations from AP-42 [ref 7] and
a PM10/TSP ratio of 0.4 [ref 8] yields the following non-metallic ore mining emissions factor:
4-210
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0.293 Ibs./ton = 0.225 + (0.61542 X 0.00005) + 0.05 + 0.5 (0.0035 + 0.033)
(7a)
The PM25-PRI emissions factor is assumed to be 12.5% of the PM10-PRI emissions factor.
EFpM25,nm ^*10,nm * 0.125
0.037 lbs/ton = 0.293 x 0.125
(8)
(8a)
Where:
EFpM25,nm = PM25-PRI non-metallic ore mining emissions factor, in Ibs./ton
EFpMio,nm = PM10-PRI non-metallic ore mining emissions factor, in Ibs./ton
Coal Mining
The emissions factor for coal mining includes overburden removal, drilling and blasting, loading and unloading
and overburden replacement activities. The amount of overburden material handled is assumed to equal ten
times the quantity of coal mined, and coal unloading is assumed to split evenly between end-dump and bottom-
dump operations. The emissions factor is based on the PMio emissions factors developed for western surface
coal mining operations from AP-42 [ref 7],
EFPMW,co = (10 X (EFt0 + EFor + EFdtj) + EFV + EFr + EFa + (0.5 X (EFe + EFt))
(9)
Where:
EFpmio, CO ~
EFt0
EF0r
EFdt
EFV
EFr
EFa
EFe
EFt
PM10-PRI coal mining emissions factor, in Ibs./ton
PM10-PRI emissions factor for truck loading overburden at western surface coal mining
operations, in Ibs./ton of overburden
PM10-PRI emissions factor for overburden replacement at western surface coal mining
operations, in Ibs./ton of overburden
PM10-PRI emissions factors for truck unloading: bottom dump-overburden at western surface
coal mining operations, in Ibs./ton of overburden
PM10-PRI open pit overburden removal emissions factor at western surface coal mining
operations, in Ibs./ton
PM10-PRI drilling/blasting emissions factor at western surface coal mining operations, in
Ibs./ton
PM10-PRI loading emissions factor at western surface coal mining operations, in Ibs./ton
PM10-PRI truck unloading: end dump-coal emissions factor at western surface coal mining
operations, in Ibs./ton
PM10-PRI truck unloading: bottom dump-coal emissions factor at western surface coal mining
operations, in Ibs./ton
Applying the PM10-PRI emissions factors developed for western surface coal mining operations [ref 7] yields the
following coal mining emissions factor:
0.513 lbs/ton = (10 X (0.015 + 0.001 + 0.006)) + 0.225 + 0.00005 + 0.05
+ (0.5 X (0.0035 + 0.033)) ( 3)
4-211
-------
The PM25-PRI emissions factor is assumed to be 12.5% of the PM10-PRI emissions factor.
EFpM25,co — EFiq co X 0.125 (10)
Where:
EFPm25,co= PM25-PRI coal mining emissions factor, in Ibs./ton
EFpMio,co= PM10-PRI coal mining emissions factor, in Ibs./ton
PM-FILand PM2.5-PRI Emissions Factors
PM-FIL emissions factors are assumed to be the same as PM-PRI emissions factors. In reality, there is a small
amount of PM-CON emissions included in the PM-PRI emissions, but insufficient data exists to estimate the PM-
CON portion. In 2006, the EPA adopted new PM2.5/PM10 ratios for several fugitive dust categories and
concluded that the PM2.5/PM10 ratios for fugitive dust categories should be in the range of 0.1 to 0.15 [ref 9],
Consequently, a ratio of 0.125 was applied to the PM10 emissions factors to estimate PM2.5 emissions factors
for mining and quarrying. A summary of emissions factors is presented in Table 4-129.
Table 4-129: Emissions factors for Mining and Quarrying (2325000000)
Mining Type
Pollutant
Emissions
Factor
Emissions
Factor Units
Emissions Factor
Reference
Metallic
PM10-PRI
0.0548
Ibs./ton
4
Metallic
PM10-FIL
0.0548
Ibs./ton
4
Metallic
PM25-PRI
0.0069
Ibs./ton
PMlOx 0.125
Metallic
PM25-FIL
0.0069
Ibs./ton
PMlOx 0.125
Non-Metallic
PM10-PRI
0.293
Ibs./ton
7,8
Non-Metallic
PM10-FIL
0.293
Ibs./ton
7,8
Non-Metallic
PM25-PRI
0.037
Ibs./ton
PMlOx 0.125
Non-Metallic
PM25-FIL
0.037
Ibs./ton
PMlOx 0.125
Coal
PM10-PRI
0.513
Ibs./ton
7
Coal
PM10-FIL
0.513
Ibs./ton
7
Coal
PM25-PRI
0.064
Ibs./ton
PMlOx 0.125
Coal
PM25-FIL
0.064
Ibs./ton
PMlOx 0.125
4.16.3.4 Controls
There are no controls assumed for this category.
4.16.3.5 Emissions
Emissions from mining and quarrying are calculated by multiplying the amount of mining material produced
(from equation 4 for metallic and non-metallic mining, and from the EIA [ref 2] for coal) by an emissions factor
(from Table 4-129).
Ep,t,c = EFtiP X MPt c (11)
Where:
Et,P,c = Annual emissions of pollutant p from mining material type t in county c, in lbs.
4-212
-------
EFt/P = Emissions factor for pollutant p from mining material type t, in Ibs./ton of material produced
MPt,c = Amount of mining material type t produced in county c, in tons
The final step of the process is to sum the mining emissions estimates for each pollutant in each county.
Emissions estimates are then converted from pounds to tons.
AEp,c = £ £p,t,c X 0.0005 ton/lb (12)
Where:
AEP/C = Annual emissions of pollutant p in county c, in tons
Et,P,c = Annual emissions of pollutant p from mining material type t in county c, in lbs.
4.16.3.6 Example calculations
The steps below provide sample calculations to determine the PM25-PRI emissions from mining and quarrying
operations in Barbour County, Alabama. Constant emissions factor calculations that are used in all counties are
not repeated here.
Table 4-130 provides a summary of these calculations. Note that equations 5-10 produce constant emissions
factors that are used in all counties. Those calculations are not repeated here.
Table 4-130: Sample calculations for estimating PM25-PRI emissions from mining and quarrying in Barbour
County, Alabama
Eq.#
Equation
Values for Barbour County, AL
Result
1
ws=7T-*wus
uus
N/A
Waste data is
not withheld for
Alabama.
2
MPt,s
f ¦ MPt.us
- « + Os) x Mf,us
v 1 1 n?3 short ton/
'metricton
(3,720 + 42,900)
X (2,660,000 -h 5,060,000)
v 1 1n?3ton/
/metricton
27,015
thousand tons
metallic ore in
Alabama
(3,720 + 42,900)
X (2,400,000 -h 5,060,000)
v 1 1n?3t°n/
/metricton
24,375
thousand tons
non-metallic ore
in Alabama
3
„ „ EmPt,c
EmpFract c —
Empt>s
67 metallic mining employess in Barbour
Metallic
employee
fraction of 1 for
Barbour County,
AL
67 metallic mining employees in Alabamc
8 nonmetallic mining employess in Barbo
Nonmetallic
employee
fraction of 4.5 x
10"3for Barbour
County, AL
1,778 nonmetallic mining employess in A
4-213
-------
Eq.#
Equation
Values for Barbour County, AL
Result
4
MPtc = MPt s x EmpFract c
27,015 tons x 1
27,015
thousand tons
metallic ore in
Barbour County,
AL
24,375 tons x 4.5 x 10-3
112 thousand
tons non-
metallic ore in
Barbour County,
AL
11
Ep,t,c = EFt p x MPt c
0.0068 lbs-/ton X 27,015,167 tons
184,922.19 lbs.
PM25-PRI
emissions from
metallic ore in
Barbour County,
AL
0.037 lbs-/ton X 112,039 tons
4,107.38 lbs.
PM25-PRI
emissions from
non-metallic ore
in Barbour
County, AL
0.064 ^s/ton x 0 tons
0 lbs. PM25-PRI
from coal
mining in
Barbour County,
AL
12
AEp,c
= YjEp,cx 0.000Sshort ton/lb
184,922.19 lbs. +4,107.38 lbs. +0 lbs.
x 0.0005 ton/lb
95 tons PM25-
PRI from mining
and quarrying in
Barbour County,
AL
4.16.3.7 Changes from the 2014 methodology
There are no significant changes for this methodology from the methodology used for the 2014 NEI.
4.16.3.8 Puerto Rico and U.S. Virgin Islands
Since insufficient data exists to calculate emissions for the counties in Puerto Rico and the US Virgin Islands,
emissions are based on two proxy counties in Florida: 12011, Broward County for Puerto Rico and 12087,
Monroe County for the US Virgin Islands. The total emissions in tons for these two Florida counties are divided
by their respective populations creating a tons per capita emissions factor. For each Puerto Rico and US Virgin
Island county, the tons per capita emissions factor is multiplied by the county population (from the same year as
the inventory's activity data) which served as the activity data. In these cases, the throughput (activity data) unit
and the emissions denominator unit are "EACH".
4-214
-------
4.16.4
References
1. U.S. Geologic Survey. Minerals Yearbook 2012.
2. U.S. Department of Energy, Energy Information Administration. "Detailed data from the EIA-7A and the
U.S. Mine Safety and Health Administration", data pulled for year 2017.
3. U.S. Census Bureau. 2016 County Business Patterns.
4. U.S. Environmental Protection Agency. 1998. National Air Pollutant Emission Trends Procedure
Document for 1900-1996, EPA-454/R-98-008.
5. U.S. Environmental Protection Agency, AP-42, Fifth Edition, Volume 1, Chapter 13: Miscellaneous
Sources, Section 13.2.4: Aggregate Handling and Storage Piles.
6. U.S. Environmental Protection Agency. 1986. Generalized Particle Size Distributions for Use in Preparing
Size-Specific Particulate Emissions Inventories, EPA-450/4-86-013.
7. U.S. Environmental Protection Agency, AP-42, Fifth Edition, Volume 1, Chapter 11: Mineral Products
Industry, Section 11.9: Western Surface Coal Mining.
8. United States Environmental Protection Agency, AIRS Facility Subsystem Source Classification Codes and
Emission Factor Listing for Criteria Air Pollutants, EPA-450/4-90-003, March 1990.
9. Midwest Research Institute. 2006. Background Document for Revisions to Fine Fraction Ratios Used for
AP-42 Fugitive Dust Emission Factors. MRI Project No. 110397.
4.17 Industrial Processes - Oil and Gas Production
4.17.1 Sector description
This sector includes processes associated with the exploration and drilling at oil, gas, and coal bed methane
(CBM) wells and the equipment used at the well sites to extract the product from the well and deliver it to a
central collection point or processing facility.
4.17.2 Sources of data
Table 4-131 lists the processes below with their corresponding SCCs; the SCCs used by EPA to estimate nonpoint
emissions are marked in second column. SCCS with asterisks (*) denote new SCCs and created for the 2017
inventory. The set of asterisked SCCs that EPA does not use (denoted by * with no Y) were created for the 2017
inventory based on a request by the state of Utah. Note also that the SCCs in this list are only the SCCs that
either the EPA used or the submitting State agencies used in the 2017 NEI. All of the SCCs that the EPA oil and
gas tool uses are nonpoint SCCs.
Table 4-131: Point and Nonpoint SCCs used for the Oil and Gas Production Sector
Data
Category
EPA uses
SCC
SCC Description (Abbreviated)
Nonpoint
2310000000
Total: All Processes (doesn't distinguish oil or gas)
Nonpoint
Y
2310000220
Drill Rigs
Nonpoint
2310000230
Workover Rigs
Nonpoint
(no, was used
in 2014)ftt
2310000330
Artificial Lift
ftt This SCC was replaced with the code 2310011600, new for the tool in 2017.
4-215
-------
Data
Category
EPA uses
see
SCC Description (Abbreviated)
Nonpoint
(no, was used
in 2014)***
2310000550
Produced Water
Nonpoint
Y*
2310000551
Industrial Processes; Oil and Gas Exploration and Production; All
Processes; Produced Water from CBM Wells
Nonpoint
Y*
2310000552
Industrial Processes; Oil and Gas Exploration and Production; All
Processes; Produced Water from Gas Wells
Nonpoint
Y*
2310000553
Industrial Processes; Oil and Gas Exploration and Production; All
Processes; Produced Water from Oil Wells
Nonpoint
Y
2310000660
Hydraulic Fracturing Engines
Nonpoint
2310001000
On-Shore, Total: All Processes
Nonpoint
2310002000
through
2310002421
Off-Shore Oil & Gas Production:
Total: All Processes, Flares: Continuous Pilot Light, Flares: Flaring
Operations, Pneumatic Pumps: Gas And Oil Wells, Pressure/Level
Controllers, Cold Vents
Nonpoint
2310010000
Industrial Processes; Oil and Gas Exploration and Production; Crude
Petroleum; Total: All Processes
Nonpoint
Y
2310010100
Crude Petroleum; Oil Well Heaters
Nonpoint
Y
2310010200
Crude Petroleum; Oil Well Tanks - Flashing &
Standing/Working/Breathing
Nonpoint
Y
2310010300
Crude Petroleum; Oil Well Pneumatic Devices
Nonpoint
2310010700
Crude Petroleum; Oil Well Fugitives
Nonpoint
2310010800
Crude Petroleum; Oil Well Truck Loading
Nonpoint
(no, was used
in 2014)§§§
2310011000
On-shore oil production; Total: All Processes
Nonpoint
Y *
2310011001
On-Shore Oil Production; Associated Gas Venting
Nonpoint
2310011020
On-shore oil production
Storage Tanks: Crude Oil
Nonpoint
2310011100
On-shore oil production
Heater Treater
Nonpoint
Y
2310011201
On-shore oil production
Tank Truck/Railcar Loading: Crude Oil
Nonpoint
2310011450
On-shore oil production
Wellhead
Nonpoint
2310011500
On-shore oil production
Fugitives: All Processes
Nonpoint
Y
2310011501
On-shore oil production
Fugitives: Connectors
Nonpoint
Y
2310011502
On-shore oil production
Fugitives: Flanges
Nonpoint
Y
2310011503
On-shore oil production
Fugitives: Open Ended Lines
Nonpoint
2310011504
On-shore oil production
Fugitives: Pumps
Nonpoint
Y
2310011505
On-shore oil production
Fugitives: Valves
Nonpoint
2310011506
On-shore oil production
Fugitives: Other
Nonpoint
Y *
2310011600
On-shore oil production
Artificial Lift Engines
*** The single SCC previously used to categorize emissions from produced water has been disaggregated into 3 new SCCs,
one each for CBM, gas, and oil wells: 2310000551, 2310000552, 2310000553.
§§§This SCC was replaced with the more accurately descriptive 2310011001, associated gas venting.
4-216
-------
Data
Category
EPA uses
see
SCC Description (Abbreviated)
Nonpoint
2310012000
through
2310012526
Off-Shore Oil Production;
Total: All Processes, Storage Tanks: Crude Oil, Fugitives, Connectors:
Oil Streams, Fugitives, Flanges: Oil, Fugitives, Valves: Oil, Fugitives,
Other: Oil, Fugitives, Connectors: Oil/Water Streams, Fugitives,
Flanges: Oil/Water, Fugitives, Other: Oil/Water
Nonpoint
2310020000
Natural Gas; Total: All Processes
Nonpoint
Y, PA only
2310020600
Natural Gas; Compressor Engines
Nonpoint
2310020700
2310020800
Natural Gas; Gas Well Fugitives, Gas Well Truck Loading
Nonpoint
Y
2310021010
On-Shore Gas Production; Storage Tanks: Condensate
Nonpoint
2310021011
On-Shore Gas Production; Condensate Tank Flaring
Nonpoint
Y
2310021030
On-Shore Gas Production; Tank Truck/Railcar Loading: Condensate
Nonpoint
Y
2310021100
On-Shore Gas Production; Gas Well Heaters
Nonpoint
2310021101
Natural Gas Fired 2Cycle Lean Burn Compressor Engines < 50 HP
Nonpoint
Y
2310021102
Natural Gas Fired 2Cycle Lean Burn Compressor Engines 50 To 499
HP
Nonpoint
2310021103
Natural Gas Fired 2Cycle Lean Burn Compressor Engines 500+ HP
Nonpoint
2310021109
On-Shore Gas Production; Total: All Natural Gas Fired 2Cycle Lean
Burn Compressor Engines
Nonpoint
2310021201
Natural Gas Fired 4Cycle Lean Burn Compressor Engines <50 HP
Nonpoint
Y
2310021202
Natural Gas Fired 4Cycle Lean Burn Compressor Engines 50 To 499
HP
Nonpoint
2310021203
Natural Gas Fired 4Cycle Lean Burn Compressor Engines 500+ HP
Nonpoint
2310021209
Total: All Natural Gas Fired 4Cycle Lean Burn Compressor Engines
Nonpoint
Y
2310021251
On-Shore Gas Production; Lateral Compressors 4 Cycle Lean Burn
Nonpoint
Y
2310021300
On-Shore Gas Production; Gas Well Pneumatic Devices
Nonpoint
2310021301
Natural Gas Fired 4Cycle Rich Burn Compressor Engines <50 HP
Nonpoint
Y
2310021302
Natural Gas Fired 4Cycle Rich Burn Compressor Engines 50 To 499
HP
Nonpoint
2310021303
Natural Gas Fired 4Cycle Rich Burn Compressor Engines 500+ HP
Nonpoint
2310021309
Total: All Natural Gas Fired 4Cycle Rich Burn Compressor Engines
Nonpoint
Y PA only
2310021310
On-Shore Gas Production; Gas Well Pneumatic Pumps
Nonpoint
Y
2310021351
On-Shore Gas Production; Lateral Compressors 4 Cycle Rich Burn
Nonpoint
Y
2310021400
On-Shore Gas Production; Gas Well Dehydrators
Nonpoint
2310021401
Nat Gas Fired 4Cycle Rich Burn Compressor Engines <50 HP w/NSCR
Nonpoint
2310021402
Nat Gas Fired 4Cycle Rich Burn Compressor Engines 50 To 499 HP
w/NSCR
Nonpoint
2310021403
Nat Gas Fired 4Cycle Rich Burn Compressor Engines 500+ HP
w/NSCR
Nonpoint
2310021411
On-Shore Gas Production; Gas Well Dehydrators - Flaring
Nonpoint
2310021450
On-Shore Gas Production; Wellhead
Nonpoint
Y PA only
2310021500
On-Shore Gas Production; Gas Well Completion - Flaring
Nonpoint
Y
2310021501
On-Shore Gas Production; Fugitives: Connectors
4-217
-------
Data
Category
EPA uses
see
SCC Description (Abbreviated)
Nonpoint
Y
2310021502
On-Shore Gas Production
Fugitives: Flanges
Nonpoint
Y
2310021503
On-Shore Gas Production
Fugitives: Open Ended Lines
Nonpoint
2310021504
On-Shore Gas Production
Fugitives: Pumps
Nonpoint
Y
2310021505
On-Shore Gas Production
Fugitives: Valves
Nonpoint
Y
2310021506
On-Shore Gas Production
Fugitives: Other
Nonpoint
Y PA only
2310021509
On-Shore Gas Production
Fugitives: All Processes
Nonpoint
2310021600
On-Shore Gas Production
Gas Well Venting
Nonpoint
2310021601
On-Shore Gas Production
Gas Well Venting - Initial Completions
Nonpoint
2310021602
On-Shore Gas Production
Gas Well Venting - Recompletions
Nonpoint
Y
2310021603
On-Shore Gas Production
Gas Well Venting - Blowdowns
Nonpoint
2310021604
On-Shore Gas Production
Gas Well Venting - Compressor Startups
Nonpoint
2310021605
On-Shore Gas Production
Shutdowns
Gas Well Venting - Compressor
Nonpoint
2310021700
On-Shore Gas Production; Miscellaneous Engines
Nonpoint
2310021801
Industrial Processes; Oil and Gas Exploration and Production; On-
Shore Gas Production; Pipeline Blowdowns and Pigging
Nonpoint
*++++
2310021802
Industrial Processes; Oil and Gas Exploration and Production; On-
Shore Gas Production; Pipeline Leaks
Nonpoint
*****
2310021803
Industrial Processes; Oil and Gas Exploration and Production; On-
Shore Gas Production; Midstream gas venting for maintenance,
startup, shutdown, or malfunction
Nonpoint
2310022000
through
2310022506
Off-Shore Gas Production;
Total: All Processes, Storage Tanks: Condensate, Turbines: Natural
Gas
Boilers/Heaters: Natural Gas, Diesel Engines, Amine Unit
Dehydrator, Fugitives, Connectors: Gas Streams, Fugitives, Flanges:
Gas Streams, Fugitives, Valves: Gas, Fugitives, Other: Gas
Nonpoint
2310023000
Coal Bed Methane NG/Dewatering Pump Engines
Nonpoint
2310023010
On-Shore CBM Production/Storage Tanks: Condensate
Nonpoint
2310023030
On-Shore CBM Production/Tank Truck Railcar Loading: Condensate
Nonpoint
2310023100
On-Shore CBM Production/CBM Well Heaters
Nonpoint
Y
2310023102
On-Shore CBM Production/CBM Fired 2 Cycle Lean Burn
Compressor Engines 50 to 499 HP
Nonpoint
Y
2310023202
On-Shore CBM Production/CBM Fired 4 Cycle Lean Burn
Compressor Engines 50 to 499 HP
Nonpoint
2310023251
On-Shore CBM Production/Lateral Compressors 4 Cycle Lean Burn
Nonpoint
Y
2310023300
On-Shore CBM Production Pneumatic Devices
5-6 Created by request of UT
4-218
-------
Data
Category
EPA uses
see
SCC Description (Abbreviated)
Nonpoint
Y
2310023302
On-Shore CBM Production/CBM Fired 4 Cycle Rich Burn Compressor
Engines 50 to 499 HP
Nonpoint
Y
2310023310
Coal Bed Methane NG/Pneumatic Pumps
Nonpoint
2310023351
On-Shore CBM Production/Lateral Compressors 4 Cycle Rich Burn
Nonpoint
2310023400
Coal Bed Methane NG/Dehydrators
Nonpoint
2310023509
Coal Bed Methane Fugitives
Nonpoint
Y
2310023511
On-Shore CBM Production/Fugitives: Connectors
Nonpoint
Y
2310023512
On-Shore CBM Production/Fugitives: Flanges
Nonpoint
Y
2310023513
On-Shore CBM Production/Fugitives: Open Ended Lines
Nonpoint
Y
2310023515
On-Shore CBM Production/Fugitives: Valves
Nonpoint
Y
2310023516
On-Shore CBM Production/Fugitives: Other
Nonpoint
Y
2310023600
On-Shore CBM Exploration: CBM Well Completion: All Processes
Nonpoint
2310023603
On-Shore CBM Production/CBM Well Venting - Blowdowns
Nonpoint
2310023606
On-Shore CBM Exploration/Mud Degassing
Nonpoint
2310030220
2310030401
Natural Gas Liquids; Gas Well Tanks - Flashing
&Standing/Working/Breathing; Gas Well Water Tank Losses; Gas
Plant Truck Loading
Nonpoint
Y
2310111100
On-shore Oil Exploration; Mud Degassing
Nonpoint
Y
2310111401
On-shore Oil Exploration; Oil Well Pneumatic Pumps
Nonpoint
Y
2310111700
On-shore Oil Exploration; Oil Well Completion: All Processes
Nonpoint
2310111701
On-Shore Oil Exploration; Oil Well Completion: Flaring
Nonpoint
2310112401
On-shore Oil Exploration; Oil Well Pneumatic Pumps
Nonpoint
Y
2310121100
Off-shore Oil Exploration; Mud Degassing
Nonpoint
Y
2310121401
Off-shore Oil Exploration; Gas Well Pneumatic Pumps
Nonpoint
Y
2310121700
Off-shore Oil Exploration; Gas Well Completion: All Processes
Nonpoint
2310122100
Off-shore Gas Exploration; Mud Degassing
Nonpoint
*PA only
2310300220
All Processes - Conventional Drill Rigs
Nonpoint
*PA only
2310321010
Oil and Gas Production - Conventional Storage Tanks - Condensate
Nonpoint
*PA only
2310321100
Oil and Gas Production - Conventional Gas Well Heaters
Nonpoint
*PA only
2310321400
Oil and Gas Production - Conventional Gas Well Dehydrators
Nonpoint
*PA only
2310321603
Oil and Gas Production - Conventional Gas Well Venting -
Blowdowns
Nonpoint
*PA only
2310400220
All Processes - Unconventional Drill Rigs
Nonpoint
*PA only
2310421010
Oil and Gas Production - Unconventional Storage Tanks -
Condensate
Nonpoint
*PA only
2310421100
Oil and Gas Production - Unconventional Gas Well Heaters
Nonpoint
*PA only
2310421400
Oil and Gas Production - Unconventional Gas Well Dehydrators
Nonpoint
*PA only
2310421603
Oil and Gas Production - Unconventional Gas Well Venting -
Blowdowns
4-219
-------
DRAFT
Data
Category
EPA uses
see
SCC Description (Abbreviated)
Point
31000101
through
31000506,
Various descriptions;
Excludes 31000104 through 31000108 and 31000140 through
31000145, which are in the sector "Industrial Processes - Storage
and Transfer"
Point
31088801
through
31088811
Fugitive Emissions; Specify in Comments Field
Point
31700101
Natural Gas Transmission and Storage Facilities; Pneumatic
Controllers Low Bleed
For the nonpoint data category, S/L/Ts have four options for providing data to the NEI for the Oil and Gas
Production Sector. They may: 1) accept the tool with the defaults populated in the tool by EPA, 2) choose to
provide EPA with input data to incorporate in the tool, 3) run the tool themselves (presumably updating the
inputs and subtracting point sources), or 4) use their own tools and methodology to provide estimates. If a
submitting agency failed to let EPA know their preference via completing the nonpoint survey, then EPA data
was input by default. Figure 4-18 shows these state-level data sources for the oil and gas sector.
Figure 4-18: Data source for Oil and Gas emissions in the 2017 NEI
Data Source Map 2017 NEI
:: *ta Source
Table 4-132 summarizes the data that was submitted by states in the oil and gas production sector for both
point and nonpoint.
Table 4-132: Data Source for Oil and Gas Production Data in the 2017 NEI
State
Nonpoint
Point
AL
EPA estimates only
Submitted to Point Inventory
AK
EPA Tool with revised inputs and SLT
Submitted to Point Inventory
4-220
-------
State
Nonpoint
Point
AZ
EPA Tool with revised inputs
Subm
tted to Point Inventory
AR
EPA estimates only
Subm
tted to Point Inventory
CA
EPA Tool and SLT
Subm
tted to Point Inventory
CO
SLT only
Subm
tted to Point Inventory
CT
Subm
tted to Point Inventory
FL
EPA estimates only
Subm
tted to Point Inventory
GA
Subm
tted to Point Inventory
ID
EPA estimates only
Subm
tted to Point Inventory
IL
EPA Tool with revised inputs
Subm
tted to Point Inventory
IA
Subm
tted to Point Inventory
IN
EPA estimates only
Subm
tted to Point Inventory
KS
EPA Tool with revised inputs
Subm
tted to Point Inventory
KY
EPA estimates only
Subm
tted to Point Inventory
LA
EPA estimates only
Subm
tted to Point Inventory
MA
Subm
tted to Point Inventory
MD
No estimates no activity this NEI
Subm
tted to Point Inventory
ME
Subm
tted to Point Inventory
Ml
EPA estimates only
Subm
tted to Point Inventory
MN
Subm
tted to Point Inventory
MS
EPA estimates only
Subm
tted to Point Inventory
MO
EPA estimates only
Subm
tted to Point Inventory
MT
EPA estimates only
Subm
tted to Point Inventory
NC
Subm
tted to Point Inventory
NE
EPA estimates only
Subm
tted to Point Inventory
NJ
Subm
tted to Point Inventory
NV
EPA estimates only
Subm
tted to Point Inventory
NM
EPA estimates only
Subm
tted to Point Inventory
NY
EPA estimates only
Subm
tted to Point Inventory
ND
EPA estimates only
Subm
tted to Point Inventory
OH
EPA Tool with revised inputs
Subm
tted to Point Inventory
OK
EPA Tool and SLT
Subm
tted to Point Inventory
OR
EPA estimates only
PA
EPA Tool with revised inputs
Submitted to Point Inventory
SC
Submitted to Point Inventory
SD
EPA estimates only
TN
EPA estimates only
Submitted to Point Inventory
TX
SLT only
Submitted to Point Inventory
UT
EPA Tool and SLT
Submitted to Point Inventory
VA
EPA estimates only
Submitted to Point Inventory
WV
SLT only (used Tool)
Submitted to Point Inventory
Wl
Submitted to Point Inventory
WY
SLT only
Submitted to Point Inventory
4.17.3 EPA emissions calculation approach: EPA Oil and Gas Emissions Estimation Tool
The EPA furthered the development of the existing oil and gas emissions estimation tool that was originally
developed for the 2011 NEI, which is a MS Access database that uses a bottom-up approach to build a national
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inventory. More information on the tool can be found in the documentation provided by ERG, entitled "2017
Nonpoint Oil and Gas Emission Estimation Tool, version 1.2" in the file
"Oil and Gas Tool Documentation vl.2 2017.zip". There are two modules, as was put in place in the 2014
tool: Exploration and Production. Changes that have been incorporated in the 2017 Oil and Gas Production and
Exploration tools since 2014 are addressed in the changes memos by ERG. The memos "2017 Oil and Gas
Memos.zip" are from February 14 (the filename is 1_14 but the memo is from February), April 11, July 22, and
October 23, 2019. In addition, a memo outlining the additional data from the GHG Reporting Program (subpart
W) is entitled 2017 NEI Oil and Gas Tool Subpart W Analysis 3 14 2019.zip.
In general, the tool calculates emissions for each piece of equipment on a well pad (like condensate tanks or
dehydrators, for example) in a county or basin, based on average equipment counts taken from either surveys,
literature searches, or the GHG reporting program, also accounting for control devices and gas composition in
each county. County-level details are important, since well pads can vary significantly from region to region,
basin to basin, and county to county. A well site in Denver, CO in the Denver-Julesburg Basin might look very
different from one in the Marcellus Shale in PA, due to changes in technology over time (when the well was first
drilled), geologic formations of the oil and gas reservoirs themselves (which also change over time—the ratio of
oil to gas changes as pressure in the reservoir is released), and regulations in place guiding the equipment used
on site.
The math used in the oil and gas tool is more complex than most other categories, as it uses equations like the
ideal gas law and mass balances, in conjunction with more traditional emission rate equations (activity * EF =
emissions); thus, the work is best completed in database format. Overall, there are hundreds of inputs to the oil
and gas tool, and these are broken down into three basic categories: activity data, basin factors, and emission
factors. These inputs to the tool are filled in by EPA, and published with the tool, along with their references.
Region specific inputs are preferable and are used when available. Extrapolated inputs from nearby counties in
the same basin are then used to fill in gaps in data. National defaults are filled in where no other data is
available, and attempts are made to align inputs as much as possible with the GHG reporting program and
emissions inventory.
4.17.3.1 Activity data
As with the 2014 Tool, the primary source of activity data is the commercially available database developed by
Drillinglnfo called HPDI, or also called the Dl Desktop database. HPDI supplies activity such as number of wells,
oil, gas, condensate, and water production, feet drilled, spud counts, and other data. There are cases where this
data is not complete, and in those cases, EPA supplemented with data from RIGDATA, from various state oil and
gas commissions, and directly from Tool users. The following SLTs provided updated activity inputs for the 2017
Tool:
• Arizona (Exploration data)
• Ohio (Production)
• Kansas (Production)
• Oklahoma (Production)
• Pennsylvania (Production)
• Texas (Production)
• Illinois (Production)
• West Virginia (Production/Exploration).
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In addition, State/County FIPS codes were updated for several counties in South Dakota and Alaska, county-level
average temperatures for 2017 were updated nationally, and county-level ozone attainment status as of
6/30/18 were updated.
Basin Factors
Basin factors include factors that are secondary to activity, and include assumptions about equipment counts on
a per well basis, (e.g., the number of pneumatic controllers per well, or the average HP of an engine at a well
site) as well as gas speciation profiles (fraction of benzene, toluene, xylene or ethylbenzene in natural gas at a
particular point in the well pad, e.g., post separator).
For 2017 inputs, GHGRP data gathered under subpart W was analyzed to develop updated basin factors for
several source categories including storage tanks, dehydrators, fugitive equipment leaks, heaters, pneumatic
devices, and wellhead compressor engines. See "Summary of Analysis of 2017 GHGRP Subpart W Data for Use in
the 2017 NEI Nonpoint Oil and Gas Emission Estimation Tool" memo dated March 14, 2019.
Regarding tank control and capture, EPA updated default factors for condensate and oil storage tanks. These
defaults have been applied nationally for all counties based on recent National Oil and Gas Committee
discussions. From a cursory look, we believe there are several contributors to tank leaks and emissions evading
being captured and routed to control devices:
• Inadequate design sizing of vapor collection systems or PRVs, seals etc,
• Inadequate staging down of pressure, resulting in flashing (stepping down the process in stages helps
reduce the flash gas)
• Worn seals and gaskets on thief hatches and PRVs
• Ambient temperatures affect detection of leaks by IR camera (seems like the lowest frequency of leaks
detected, 0.5% leaking, was mid-winter; this appears to be a function of ground temperature and the
inability of the camera to distinguish between the vapor plume and the ground)
• Age of tanks/well pads/equipment
• VOC content/API Gravity
• High volume of liquid production
• Frequency of monitoring/compliance/enforcement—realization that rule efficiency and capture
efficiency are tied
At this point in time, EPA does not have an exact figure to apply for this value but has started the process to try
to quantify this amount and find it necessary to decrease the total amount of control because of the above
known factors. In order to have a combined capture and control efficiency of 80%, control efficiency defaults
were updated to 95% and capture efficiency defaults were updated to 84%. This was applied to oil tanks,
condensate tanks, and condensate CBM tanks.
We updated select basin factors in Ohio County, WV based on ERG ORD study. Data was updated for
condensate tanks, dehydrators, fugitives, gas-actuated pumps, heaters, pneumatic devices, produced water, and
wellhead compressors.
The "DEHYD_FRACTION_FLARES" for Alaska was updated from "0" to "1" based on communication with the
Alaska Department of Environmental Conservation (ADEC) on September 18, 2019.
We updated gas composition data for Pennsylvania was provided by the Pennsylvania Department of
Environmental Protection (PADEP), and was included for gas-actuated pumps, fugitives, and pneumatic devices.
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We updated gas composition data for Liquids Unloading for the 5 counties in the Uinta Basin, Utah was also
included, using EPA SPECIATE4.5, 2016, Profile 95418.
4.17.3.2 Emission factors
Emission factors are also a part of the formula for estimating emissions, and in the Oil and Gas tool the
nomenclature is set such that we only call the standard national factors, like from AP-42 combustion equations,
"emission factors." Updates for AP-42 factors in the tool included 1) references for Artificial Lifts, Lateral
Compressors, and Wellhead Compressors have been revised to provide specific details identifying AP-42 Section
and table number, and 2) added AP-42 emission factors for PM10-FIL, PM25-FIL, and PM-CON for Artificial Lifts,
CBM Dewatering Pumps, Dehydrators, Heaters, Lateral Compressors, and Wellhead Compressors.
Well completion emission factors have been changing over the last three inventory cycles. For the 2011 Tool,
EPA started out using the CenSARA default factor (736 MCF/completion) for both conventional and
unconventional oil well completions as the national default. Based on EPA guidance from the reg development
folks, we dropped this factor as the national default for both conventional and unconventional oil well
completions, so there were no national default factors for oil well completions (see the 11/21/2014 OAP
Changes Memo). Note that we still used the CenSARA factors for the CenSARA states.
For the 2014 Tool, we started out where we left off with the 2011 Tool. However, the NSPS OOOOa revisions
were being proposed during development/revisions of the 2014 Tool, and data on unconventional oil well
completions became available. In version 2 of 2014, the emission factor for unconventional well completions
mentioned in the report came from Table 4-2 of the TSD for the NSPS OOOOa revisions. The final version of the
2014 Tool (v2) used this value (999 MCF/completion) as the national default EF for unconventional oil well
completions.
For the 2017 Tool, we updated the default unconventional oil well completion EF to synchronize with the GHG
Emissions Inventory based on data from the "Draft Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-
2017" (new value of 1602 MCF/completion) and began using the CenSARA EF for conventional oil well
completions (736 MCF/completion) as the national default. This was documented in the 4/11/2019 Changes
memo. These emission factors are used where no county-specific data was available.
Emissions factors for non-road engines used in drilling and hydraulic fracturing have been updated using the
MOVES model to represent the 2017 calendar year.
4.17.3.3 Other tool changes
Coalbed Methane Dewatering Pumps have been added as a new source category. An SCC has been added for
Coalbed Methane Dewatering Pump Engines (2310023000). There are currently no default input data (number
of hours, HP, fraction electric, or load factors) for this category, so no default emission estimates are
generated from the Tool.
Vapor Recovery Units (VRU) have been added as a control device for crude oil and condensate storage tanks.
Previously, only combustion devices (flares, enclosed combustors) were considered. VRU prevalence on a
county-level is expected to be available from data reported under GHGRP Subpart W.
We also provided the capability to address controls for produced water tanks in the 2017 tool.
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4,17.3.4 Point source subtraction
Some states count upstream oil and gas production processes as point sources, and therefore have a need to
subtract these from the nonpoint part of the inventory. The tool allows for point source subtraction on either an
activity or emissions basis, and a few states have taken advantage of this feature. Because of the complicated
process of data merging and selection, this process is less than perfect, in that if a source has CAP emissions to
subtract but not HAPs (as they aren't included in their point inventory), the emissions for a single source may be
divided across he point and nonpoint parts of the inventory. Thus, when an inventory looks at VOC emissions
and compares these to a sum of HAP VOCs, there may appear to be inconsistencies.
4.17,3,5 State-specific correspondence
Alaska
After the draft selection was run, EPA reached out to ADEC to determine why there are orders of magnitudes of
difference between tool estimates and ADEC's submission. Through Skype calls, EPA was educated on the
inherent differences in how oil and gas operations are conducted in Alaska versus in the CONUS; this discussion
is captured in discussion notes "Sept. discussion notes with EPA and ERG" on the NEl Supplemental FTP site. In
addition, ADEC staff determined that many emissions may still be missing from their permits and thus their
inventories. It was mutually decided that Alaska would accept EPA estimates for the county/SCCs shown in Table
4-133.
Table 4-133: EPA Oil and Gas estimates added to Alaska for the 2017 NEI
FIPS
SOURCE CATEGORY
see
SCC SHORTENED
02122
FUGITIVES
2310011501
On-Shore Oil Production /Fugitives: Connectors
02122
FUGITIVES
2310011502
On-Shore Oil Production /Fugitives: Flanges
02122
FUGITIVES
2310011503
On-Shore Oil Production /Fugitives: Open Ended Lines
02122
FUGITIVES
2310011505
On-Shore Oil Production /Fugitives: Valves
02122
FUGITIVES
2310021501
On-Shore Gas Production /Fugitives: Connectors
02122
FUGITIVES
2310021502
On-Shore Gas Production /Fugitives: Flanges
02122
FUGITIVES
2310021503
On-Shore Gas Production /Fugitives: Open Ended Lines
02122
FUGITIVES
2310021505
On-Shore Gas Production /Fugitives: Valves
02122
FUGITIVES
2310021506
On-Shore Gas Production /Fugitives: Other
02122
LIQUIDS UNLOADING
2310021603
On-Shore Gas Production / Gas Well Venting - Blowdowns
02122
PNEUMATIC DEVICES
2310010300
Oil Production Pneumatic Devices
02122
PNEUMATIC DEVICES
2310021300
On-Shore Gas Production Pneumatic Devices
02122
WELL COMPLETIONS
2310111700
On-Shore Oil Exploration: Oil Well Completion: All Processes
02122
WELL COMPLETIONS
2310121700
On-Shore Gas Exploration: Gas Well Completion: All
Processes
02185
FUGITIVES
2310011501
On-Shore Oil Production /Fugitives: Connectors
02185
FUGITIVES
2310011502
On-Shore Oil Production /Fugitives: Flanges
02185
FUGITIVES
2310011503
On-Shore Oil Production /Fugitives: Open Ended Lines
02185
FUGITIVES
2310011505
On-Shore Oil Production /Fugitives: Valves
02185
FUGITIVES
2310021501
On-Shore Gas Production /Fugitives: Connectors
02185
FUGITIVES
2310021502
On-Shore Gas Production /Fugitives: Flanges
02185
FUGITIVES
2310021503
On-Shore Gas Production /Fugitives: Open Ended Lines
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FIPS
SOURCE CATEGORY
see
SCC SHORTENED
02185
FUGITIVES
2310021505
On-Shore Gas Production /Fugitives: Valves
02185
FUGITIVES
2310021506
On-Shore Gas Production /Fugitives: Other
This amounted to the additions for Alaska's inventory shown in Table 4-134.
Table 4-134: Additional VOC emissions (tons/yr) added to the Alaska Oil and Gas inventory
Source Category
Kenai Peninsula
North Slope
WELL COMPLETIONS
46.9
-
LIQUIDS UNLOADING
17.7
-
FUGITIVES
184.4
127.4
PNEUMATIC DEVICES
182.6
-
Alaska agreed to research blowdowns and drill rigs for the 2020 NEl, but it was determined that these emissions
were likely much less than what was estimated in the EPA oil and gas tool, so they were not included in the 2017
NEI for lack of a good estimate.
Alaska does not have storage tanks at well pads in the same way that they do in the continental US. For this
reason, EPA and Alaska agreed to zero out these emissions. The tanks that do exist are mobile equipment and
therefore are not permitted. From the emissions submitted in fee assessments, the emissions are very small,
and not applicable to every well pad and every activity (well servicing, exploration of development drilling).
ADEC acknowledged that produced fluids will sometimes be stored during drilling activities and then disposed
down the well annulus either at the same location or an approved Class II injection well. There may be a short
period of time that these fluids are on site and could result in some emissions.
EPA agreed to zero this out in the tool for the 2017 NEI. ADEC will review these tanks for the 2020 NEI and
reconvene with EPA to determine if there is any value in adding this to the EPA tool.
For well completions and workovers, EPA acknowledged that based on some comparisons to subpart W
methane emissions, North Slope emissions seem to be too high. EPA agreed to zero those out on the North
Slope, and ADEC agreed to take the tool estimate for the other counties (similar order of magnitude for Cook
Inlet, according to subpart W.)
ADEC researched the number of dehydrators, and, in reviewing the number provided and emissions reported, it
seems like the proposed reduction of 98% to accommodate still vent control makes sense since most of these
had emission controls. ADEC believes that the dehydrators are included in the reported NEI submittal are
accurate and will agree to the 98% reduction for still vent controls.
Regarding associated gas emissions, ADEC said that it is a general state policy that venting/flaring is a waste of
state resources and is a loss of money to operators. The Subpart W reporting shows this to be very small and
does not compare to the tool volumes. EPA agreed to zero this out.
For Mud degassing, ADEC noted that most mudding operations are in a module that is within the drill rig. ADEC
provided a slide with some of the mud facilities on the slide pack. Because they are in a structure,
OSHA/AKOSH/Fire regulations are strict for safety purposes. ADEC will research this more for 2020. EPA agreed
to zero this out in the tool for 2017.
Regarding pneumatic pumps and devices in the North Slope and Cook Inlet, pneumatic pumps are listed as an
insignificant emission source, so it is not included in the permit or in any reports. ADEC agreed that the tool
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volumes for the devices and pumps were acceptable for 2017 since they were close to the Subpart W reporting.
ADEC will research this for the 2020 NEI for EPA tool updates.
For liquids unloading, when compared to subpart W methane emissions, it is very small in the North Slope and
EPA offered to zero this out. For Cook Inlet, the tool is very close to the Subpart W reporting, so ADEC agreed to
keeping the tool estimates for the 2017 NEI.
California
The California Air Resource Board (CARB) submits their own data, for the most part, for the NEI. EPA reached out
to the CARB to determine why there are orders of magnitude differences in the oil and gas sector. CARB replied
that they stand by their estimates. EPA proposed that the SCCs that CARB included in their submission did not
include these sources (based on a look at their Emissions Inventory Codes (EIC) to SCC mapping that CARB
provided):
• 2310011001 Oil - Associated Gas Venting
• 2310011600 Artificial Lift Engines
• 2310010100 Oil Well Heaters
• 2310010200 Oil Well Tanks
• 2310000553 Produced water Oil Wells
CARB therefore changed their Nonpoint Survey to include EPA estimates for these 5 SCCs. California was the
only state to resubmit data between November 2019 and April 2020 selection. This increased VOC by about
16,500 tons, which brings it more in line, but still lower than expectations.
Colorado
EPA contacted the Colorado Department of Public Health and Environment (CDPHE) regarding potentially
missing carbon monoxide. CDPHE responded that they summed carbon monoxide for all area engines and
submitted them to miscellaneous engines. CDPHE approved of EPA backfilling carbon monoxide for other
combustion sources based on NOx. Most oil and gas development in La Platta County is on tribal lands; the
portion on state land is minimal.
Oklahoma
The Oklahoma Department of Environmental Quality (OK DEQ) uses a mix of both EPA estimates (for the
exploration module) and their own emissions using the oil and gas tool (production module only). OK DEQ
allows EPA to do HAP augmentation for the SCCs that they submit. One difference between OK DEQ's SCC
emissions dataset and EPA's SCCs are that OK DEQ aggregates their equipment-specific fugitive emissions into
Fugitive All Process SCCs for oil, gas and CBM wells.
Pennsylvania
The PADEP relies on EPA to run the oil and gas tool but utilizes alternative SCCs for several source categories in
order to differentiate their emissions for conventional and unconventional oil and gas operations. PA DEP
provides unconventional well API numbers which EPA then subtracts from the tool to determine the
conventional portion. The process is:
1) Run the tool with basin factors that the Mid-Atlantic Regional Air Management Association (MARAMA)
provided for the 2014 NEI oil and gas sector for
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• Artificial lifts
• Associated gas
• Condensate tanks
• Crude tanks
• Dehydrators
• Fugitives
• Gas-actuated pumps (oil and gas wells)
For associated gas, condensate tanks, crude oil tanks, and dehydrators, if the Tool sources were the 2017
GHGRP factors recently documented, these were not replaced. EPA also incorporated gas composition profiles
provided by the PA DEP.
2) Remove the activity data related to the emissions data provided by PA using API numbers for unconventional
wells.
3) Run the tool for adjusted emissions.
4) Use the "conventional only" SCCs to replace the more "general" Tool SCCs for 5 source categories:
a. Drilling
b. Gas Well Condensate Tanks
c. Gas Well Heaters
d. Gas Well Dehydrators
e. Gas Well Liquids Unloading
For perspective, here is the activity data:
1) HPDI
a. # wells = 80,343
b. Gas Production = 5,476,303,221 MCF
c. Liquids Production = 6,563,695 BBL
2) PA Unconventional Wells (matched by API #)
a. # wells = 5,765
b. Gas Production = 3,655,597,522 MCF
c. Liquids Production = 1,441,312 BBL
Thus,
% Wells that match between PA unconventional and HPDI = 7% (5,765 wells/80,343 wells)
% of Gas Production of PA unconventional wells and HPDI = 67% (3,655,597,522 MCF/5,476,303,221 MCF)
% of Liquids Production of PA unconventional wells and HPDI = 22% (1,441,312 BBL/6,563,695 BBL)
While the Pennsylvania unconventional wells account for 67% of the gas production and 22% of the liquids
production, it translates to approximately 2% of the VOC and benzene emissions. This makes sense, because the
majority of the emission calculations in the tool are based on well counts, not production. Thus, running the tool
for the remainder of the conventional wells (93%) will still produce the majority of emissions.
Utah
The Utah Division of Air Quality (UT DAQ) collects its own oil and gas inventory from the oil/gas extraction
industry. They also use the EPA oil and gas tool to supplement source categories that are not collected through
their inventory.
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It should be noted that the UT DAQ inventory engine emissions are not broken down in the same categories
(SCCs) used in the oil and gas tool. Thus, instead of using the tool's SCCs, the engine data is submitted to generic
oil/gas production "miscellaneous engines" SCCs (CBM is also submitted to the misc gas SCC):
2310011600 - Industrial Processes - Oil & Gas Production, On-Shore Oil Production, Miscellaneous Engines
2310021700 - Industrial Processes - Oil & Gas Production, On-Shore Gas Production, Miscellaneous Engines
UT DAQ has been making a rigorous effort to find missing VOCs in their inventory. One method for doing this is
to take subpart W data, determine the methane to VOC ratio, and estimate emissions. UT DAQ did this for
several categories, some of which didn't exist as SCCs in the Emissions Inventory System.
• For liquids unloading, UT DAQ will submit to the same liquids unloading SCC we use in the tool.
• For associated gas venting and flaring: we compared the numbers of UT DAQ's estimate and the tool;
the tool's is higher, so Greg won't submit this SCC, and the tool will backfill.
• For "blowdowns and pigging" there are legitimate reasons to create a new SCC, because EPA Region 8 is
considering restricting these types of operations during inversion events, to prevent high ozone in
wintertime, for example.
• For "midstream pipeline leaks" like when there's an upset at a compressor station, or the station is
starting up, shutting down or malfunctioning or inoperable, EPA also sees a need to create a new SCC in
the 231xxxx area of the SCC table. One option that we don't want to take is to put in with regular
fugitives because they're different and we want to be able to discern between the two.
New SCCs, shown in Table 4-135, were created for pipeline blowdowns and pigging, pipeline leaks, midstream
gas venting for maintenance, startup shutdown and maintenance.
Table 4-135: New SCCs created to assist UT DAQ's pipeline and midstream processes reporting
SCC
SCC Description
2310021801
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production;
Pipeline Blowdowns and Pigging
2310021802
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production;
Pipeline Leaks
2310021803
Industrial Processes; Oil and Gas Exploration and Production; On-Shore Gas Production;
Midstream gas venting for maintenance, startup, shutdown, or malfunction
Wyoming
The Wyoming Department of Environmental Quality (WY DEQ) has and submits its own oil and gas inventory.
However, when QA'ing the inventory, Wyoming showed a large difference in VOC from gas well completions in
Sublette County. In the 2014v2 NEI, there were 42,000 tons submitted. Conversations with WY DEQ showed that
the 2014 VOC emissions for that county were not correct; submissions from Wyoming were in pounds, not tons,
and a correction was attempted back in 2017 but was not successful. WY DEQ agreed that for the 2017 NEI, they
would accept the EPA estimate for Sublette County for well completions. We tagged out the Nonpoint Survey
response for SCC 2310021500 which essentially treats the record like "Supplement with EPA data."
In addition, WY DEQ used different SCCs for pneumatic pumps, oil tanks, and well completions. WY DEQ
provided this information:
• 2310021500=Comp. Workover Vent & Flare (oil well) = 420.5 tons
• 2310111700=Comp. Workover Vent & Flare (gas well) = 14,327.9 tons
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• 2310000660=Completion Engine (oil, gas well) = 64.2 tons
• 2310111401=Pneumatic Pump (oil well) = 71.7 ton
• 2310021310=Pneumatic Pump (gas well) = 1,375.8 tons
• 2310011020=Tanks & Pressurized Vessels (oil well) = 24,779.4 ton
• 2310021010=Tanks & Pressurized Vessels (gas well) = 11,616.6 tons
4.17.3.6 Quality Assurance
The following figures were produced to perform QA on the draft selection for NOX (Figure 4-19) and VOC (Figure
4-20) estimates; in both figures, "Emissions 2017 Feb-Select" (blue bars) represent the emissions that went into
the 2017 NEI. Note that this is a log scale, and that states that submitted are the only ones that show up in
yellow. The states that show the most differences are Alaska and California, so EPA reached out to those states.
Note that the final NEI selection (in blue) shows a compromise between EPA's estimates and the SLT's original
submissions. Details on these compromises and correspondence with EPA is documented in the previous
section.
Figure 4-19: State-level 2017 NEI, SLT, and EPA NOX emission comparisons
State NOx Comparison ?.(>1 ? MT'i SIT and EPA
tons/year
100000
10000
1000
100
10
i* n ¥ < 5
O 2 3
^ O "1
88 Emissions 2017 Feb-Select
tr
<
< < -J
u>
-------
1000000
100000
10000
1000
100
10
DRAFT
Figure 4-20: State-level 2017 NEl, SLT, and EPA VOC emission comparisons
State VOC Comparison 2017 NEI, SLT and EPA
Tons/Year
x Q 5 * ^igiqRsiZpi^Fpb-^elepV ^Eqjis^ioris 2glZrSLI ^^mjssign^qi^
34o
O > rsj dc
~ Z < O
4.18 Miscellaneous Non-industrial NEC: Cremation - Human and Animal
4.18.1 Sector description
The cremation of human remains results in emissions of particulate matter, S02, NOx, VOC, CO, and HAPs. It is a
significant source of mercury emissions, due to mercury in dental fillings, as well as mercury in blood and
tissues. In 2017, human cremation resulted in the emissions of approximately 1.8 tons of mercury.
The cremation of animals also results in emissions of CAPs and HAPs, though it emits less mercury than human
cremation. In 2017, animal cremation resulted in the emissions of approximately 2 lbs. of mercury.
SCCs for human and animal cremation are provided in Table 4-136.
Table 4-136: H uman and animal cremation SCCs
see
see Level 1
SCC Level 2
SCC Level 3
SCC Level 4
2810060100
Miscellaneous Area Sources
Other Combustion
Cremation
Humans
2810060200
Miscellaneous Area Sources
Other Combustion
Cremation
Animals
4.18.2 Sources of data
The paved road dust sector includes data from the S/L/T agency submitted data and the default EPA generated
emissions. The agencies listed in Table 4-137 submitted emissions for this sector; agencies not listed used EPA
estimates for the entire sector. Virginia only submitted emissions for human cremation. Maricopa county,
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Maryland, and Washoe counties did not include mercury estimates for either human or animal cremation.
Rhode Island did not submit mercury estimates for animal cremation (but did for human cremation).
Table 4-137: Agencies that submitted human and/or animal cremation emissions
Region
Agency
S/L/T
1
Rhode Island Department of Environmental Management
State
3
Maryland Department of the Environment
State
3
Virginia Department of Environmental Quality
State
4
Knox County Department of Air Quality Management
Local
4
Memphis and Shelby County Health Department - Pollution Control
Local
5
Illinois Environmental Protection Agency
State
9
Maricopa County Air Quality Department
Local
9
Washoe County Health District
Local
10
Coeur d'Alene Tribe
Tribe
10
Idaho Department of Environmental Quality
State
10
Nez Perce Tribe
Tribe
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
4.18.3 EPA-developed emissions
The calculations for estimating emissions from human cremation involve estimating the number of deaths in
each age group in each county, using data from the Centers for Disease Control and Prevention. The number of
deaths is multiplied by the average weight by age group and the state-level cremation rate from the National
Funeral Directors Association to estimate the total amount of cremations in each county in terms of mass. This
number is multiplied by an emissions factor to estimate the emissions of CAPs and HAPs. Emissions of mercury
include emissions from mercury in fillings in teeth and in blood and tissues. The emissions from mercury in
fillings are estimated based on data on the number of filled teeth per person in each age group and assumptions
about the proportion of fillings that contain mercury and the amount of mercury in each filling.
The calculations for estimating emissions from animal cremation involve determining the number of cremated
animals nationally and distributing this number to each county based on population. The number of cremated
animals is multiplied by average weights for cats and dogs to determine the amount of cremations in each
county in terms of mass. This number is multiplied by an emissions factor to estimate the emissions of CAPs and
HAPS.
4.18.3.1 Activity data
Human Cremation
The activity data for human cremation is based on the number of deaths in each county in 13 age groups, from
the Centers for Disease Control and Prevention WONDER database [ref 1], Data for some counties are withheld
in the WONDER database. These gaps are filled using the data on the total number of deaths by age group in
each state (which includes the number of deaths that are withheld at the county level). First, the sum of the
reported county-level number of deaths in each age group and state is subtracted from the reported state-level
number of deaths in each age group to determine the total number of deaths withheld at the county level in
each state and age group.
4-232
-------
Deaths_withheldsa = Deaths_statesa — ^ Deaths_countysa
Where:
Deaths_withhelds,a = Total number of withheld deaths in state s in age group a
Deaths_states,a = Total number of deaths reported at the state level in state s in age group a
Deaths_countys,a = Total number of deaths reported at the county level in state s in age group a
The total number of withheld deaths are distributed to the counties based on the proportion of population in
those counties to the total state population.
Pop_rati0c=^
Where:
Pop_ratioc = The population ratio used to distribute withheld deaths in state sto county c
Popc = The total population of county c
Pops = The total population of state s
The number of withheld deaths in each state is multiplied by the county population ratio to distribute the
withheld deaths to the counties. Note that this step is only performed for counties where county-level data on
number of deaths is withheld; this step is not performed where county-level data on deaths is reported.
Deathsca = Deaths_withheldsa x Pop_ratioc (H3)
Where:
Deathsc,a = The number of deaths in county c in age group a
Deaths_withhelds,a = Total number of withheld deaths in state s in age group a, from equation HI
Pop_ratioc = The population ratio used to distribute withheld deaths in state s to county c,
from equation H2
The total number of deaths in each county (either reported directly in the CDC WONDER database or estimated
using equation H3) is multiplied by a state-level cremation rate, reported by the National Funeral Directors
Association (NFDA) [ref 2], shown in Table 4-138. It is assumed that the state-level cremation rate applies to all
counties within the state.
Cremationsca = Deathsca x Cremations ate s (H4)
Where:
Cremationsc,a = The number of human cremations in county c in age group a
Deathsc,a = The number of deaths in county c in age group a
Cremation_rates = The rate of human cremations in state s, from Table 4-138 [ref 2]
Table 4-138: Human cremation rate by state
4-233
-------
State
Cremation Rate
Alabama
23.1%
Alaska
66.3%
Arizona
66.1%
Arkansas
32.7%
California
63.4%
Colorado
68.6%
Connecticut
50.3%
Delaware
46.2%
District of Columbia
40.0%
Florida
62.4%
Georgia
37.1%
Hawaii
72.7%
Idaho
56.8%
Illinois
42.8%
Indiana
36.6%
Iowa
42.2%
Kansas
44.6%
Kentucky
24.5%
Louisiana
26.3%
Maine
70.0%
Maryland
40.6%
Massachusetts
43.4%
Michigan
54.9%
Minnesota
57.2%
Mississippi
18.2%
Missouri
39.7%
Montana
72.8%
Nebraska
43.8%
Nevada
76.9%
New Hampshire
70.3%
New Jersey
40.6%
New Mexico
58.9%
New York
39.6%
North Carolina
39.8%
North Dakota
35.3%
Ohio
42.3%
Oklahoma
39.0%
Oregon
74.1%
Pennsylvania
43.1%
Rhode Island
46.6%
South Carolina
37.4%
South Dakota
35.4%
Tennessee
28.1%
Texas
39.3%
Utah
31.2%
Vermont
67.3%
Virginia
36.1%
Washington
75.5%
West Virginia
27.3%
Wisconsin
52.5%
Wyoming
66.7%
4-234
-------
The CDC provides estimates of the average weight of individuals in each age group [ref 3], This number is
multiplied by the number of cremations in each county in each age group and then summed across all age
groups to estimate the total amount of cremations in tons in each county.
Cremations_tonsc =
Z1 ton
CremationSf „ x W„ x —
c,a a 2<00() lhs
(H5)
a=1
Where:
Cremations_tonsc= The weight of humans cremated in county c, in tons
CremationSc = The number of human cremations in county c, from equation H4
= The average weight of individuals from age group a
Animal Cremation
The Pet Loss Professionals Alliance (PLPA) conducted a survey that estimated that there were 1,840,965 pet
cremations in 2012, and that 99 percent of deceased pets are cremated [ref 4], In addition, the Humane Society
of the United States estimates that there are 2,700,000 adoptable dogs and cats euthanized in animal shelters
each year [ref 5], It is assumed that all of these shelter animals are cremated. Therefore, there are a total of
approximately 4,540,965 animal creations each year. Note that this estimate does not double count the number
of animal cremations, because the PLPA study counts the number of cremations of pets—i.e. animals that are
owned by people—whereas the Humane Society estimates are for animals in shelters that were not adopted.
The population of cats and dogs is approximately 52.5 percent cats and 48.5 percent dogs [ref 5], Using this
percentage and the total number of pets and shelter animals cremated annually, a total number of cats and a
total number of dogs cremated annually can be calculated.
Cremationsc/d us = Ratioc/d x (Cremations_petsus + Cremations_shelterus) (Al)
Cremations_pets us = Total number of pets cremated annually in the United States
Cremations_shelter us = Total number of shelter animals cremated annually in the United States
The average weight of a domestic cat is approximately 4.5 kg (9.9 pounds) [ref 6], The average weight of a dog is
difficult to determine due to large differences in breeds, but an average across breeds is 48.5 pounds [ref 7].-
Note that this is a straight average of the average adult weight for male and female dogs across breeds. It is not
a weighted average that takes into account the popularity of different breeds in the United States. To calculate
the weight, in tons, of both cats and dogs cremated annually, the average weight values are multiplied by the
total number of cats and total number of dogs cremated annually.
Where:
CremationSc/d
RatiOc/d
= Total cats, c, or dogs, d, cremated annually in the United States
= Ratio of cats, c, or dogs, d, in the pet population
Cremations_tonsc/d = Cremationsc/d x Weightc/d x
1 ton
(A2)
2,000 pounds
Where:
Cremations_tonsc/d,us = Total weight, in tons, of cats, c, or dogs, d, cremated annually in the United
States
4-235
-------
Cremationsc/d,us
Weightc/d
= Total cats, c, or dogs, d, cremated annually in the United States
= Average weight per animal, in pounds, of cats, c, or dogs, d
Once the weight of cats and weight of dogs cremated annually has been calculated, these values can be summed
to derive a total weight of animals cremated annually. The total weight of cremated animals in 2014 was
approximately 53,441 tons.
Cremations Jons animai = Cremations _tonsc + Cremations _tonsd (A3)
Where:
Cremations_tonsa„imai,us = Total weight of animals cremated annually in the United States, in tons
Cremations_tonsc,us = Total weight of cats, c, cremated annually in the United States, in tons
Cremations_tonsd,us = Total weight of dogs, d, cremated annually in the United States, in tons
4.18.3.2 Allocation procedure
Human Cremation
The number of deaths is reported by the CDC at the county level. Therefore, these data do not need to be
allocated. For counties with withheld data on the number of deaths, the total number of withheld deaths is
distributed to counties based on the proportion of population in those counties, as described in equations Hl-
H3.
Animal Cremation
The estimated national-level total weight of animals cremated are allocated to the county level based on the
ratio of population in each county to the total national population.
Pope
Cremations Jons animalc = Cremations Jons animalus x - (Al)
r°Pus
Where:
Cremations_ tonsa„imai,c
Cremations_ tonsanimai,us
Popc
Popus
4.18.3.3 Emission factors
Human and Animal Cremation - Blood and Tissues
The emissions factors for human and animal cremation for CAPs are from AP-42 [ref 8], and a report by EPA on
emissions tests of a crematory [ref 9], and are in units of pounds of emissions per ton cremated. The emissions
factors for most HAPs are a report from the California Air Resources Board [ref 10], as well as from the EPA
emissions test of a crematory. The mercury emissions factor is from a review of multiple studies [ref 11], These
emission factors do not include emissions from dental fillings. As shown in Table 4-139, EPA uses the same
emissions factors for emissions from cremation of blood and tissues for both humans and animals.
= Total weight of animals cremated in county c, in tons
= Total weight of animals cremated annually in the United States, in tons, from
equation A3
= The total population of county c
= The total population of the United States
4-236
-------
Table 4-139: Emissions factors for the cremation of human and animal blood and tissues
Pollutant
Pollutant Code
Emission
Factor
(lbs/ton)
Source
Carbon Monoxide
CO
2.947
8
Lead
7439921
0.009
9
Nitrogen Oxides
NOX
3.560
8
PM10 Primary
PM10-PRI
3.036
8 (65% of total PM)
PM2.5 Primary
PM25-PRI
2.022
8 (43.3% of total
PM)
Sulfur Dioxide
S02
2.173
8
Volatile Organic
Compounds
VOC
0.299
8
Acenaphthene
83329
1.303E-06
10
Acenaphthylene
208968
8.971E-07
10
Acetaldehyde
75070
9.269E-04
10
Anthracene
120127
2.389E-06
10
Arsenic
7440382
5.097E-04
10
Benzo(a)anthracene
56553
1.166E-07
10
Benzo(a)pyrene
192972
4.720E-07
10
Benzo(b)fluoranthene
205992
1.737E-07
10
Benzo(g,h,i)perylene
191242
5.874E-07
10
Benzo(k)fluoranthene
207089
1.486E-07
10
Beryllium
7440417
1.760E-05
10
Cadmium
7440439
2.940E-03
9
Chromium (VI)
18540299
1.829E-04
10
Chrysene
218019
2.880E-07
10
Cobalt
7440484
8.869E-05
10
Dibenz(a,h)anthracene
53703
1.349E-07
10
Fluoranthene
206440
1.337E-06
10
Fluorene
86737
3.760E-06
10
Formaldehyde
50000
2.469E-04
10
Hydrogen Chloride
7647010
3.595E+00
9
Hydrogen Fluoride
7664393
8.651E-03
10
lndeno(l,2,3-cd)pyrene
193395
1.440E-07
10
Mercury
7439976
1.324E-04
10
Naphthalene
91203
7.520E-04
10
Nickel
7440020
4.149E-04
10
Phenanthrene
85018
1.531E-05
10
Pyrene
129000
1.474E-06
10
Selenium
7782492
4.971E-04
10
4-237
-------
Human Cremation - Dental Mercury
In addition to mercury emitted from the cremation of blood and tissues, mercury is also emitted due to the
cremation of dental fillings. The Bay Area Air Quality Management District (BAAQMD) issued a report in 2012
estimating the average amount of mercury in teeth per person for ten age groups, based on data from CDC's
National Health and Nutrition Examination Survey [ref 12]. Table 4-140 shows the estimated amount of material
in restored teeth by age group from the BAAQMD study [ref 12], which is matched to the age groups used by the
CDC Wonder database, which is the source of data on deaths by age group.
The BAAQMD memorandum is used to estimate that 31.6 percent of filled teeth in the 5-24 age groups contain
amalgam. According to the American Dental Association (ADA 1998) more than 75 percent of restorations
before the 1970s used dental amalgam, which declined to 50 percent by 1991. Using these numbers, it is
assumed that 50 percent of the filled teeth for 25-44 age groups contain amalgam, 62.5 percent of filled teeth in
the 45-64 age group, and 75 percent of filled teeth for people over 65. The Food and Drug Administration has
discouraged the use of dental amalgam in children under 6 [ref 13], While EPA does not have data on the
percent of fillings containing dental amalgam for the 1-4 age group, it is assumed that this age group has
approximately half the dental amalgam of the other age groups under 20 years old. It is also assumed that
children under the age of 1 have no dental mercury. The analysis also assumes that 45 percent of all amalgam-
containing fillings are mercury, based on information from the Food and Drug Administration [ref 13].
Table 4-140: Estimated amount of material in restorec
teeth
Age Groups in CDC
WONDER Database
Age Groups in BAAQMD
Memorandum
Avg. Material in
Restored Teeth (g)
% of Fillings
Containing Mercury
< 1 year
0-4 years+
0.000
0.0%
1-4 years
0.160
15.8%
5-9 years
5-14 years
0.720
31.6%
10-14 years
15-19 years
15-24 years
1.070
31.6%
20-24 years
25-34 years
25-34 years
2.230
50.0%
35-44 years
35-44 years
3.290
50.0%
45-54 years
45-54 years
4.310
62.5%
55-64 years
55-64 years
4.320
62.5%
65-74 years
65-74 years
3.780
75.0%
75-84 years
75-84 years
3.650
75.0%
85+ years
85+ years
2.960
75.0%
The emissions factor for mercury in teeth is calculated by multiplying the average amount of material in restored
teeth per person by the percentage of fillings containing mercury in each age group and the proportion of
mercury in dental amalgam (approximately 45 percent).
lb
EF_teethHs a = Material x Conta,nHga x HgProportion x 0.0022 - (H6)
Where:
EF_teethHg,a = Emission factor for mercury emissions from teeth due to cremation for age group a, in
lbs. per cremation
4-238
-------
Materialc = The average amount of material in restored teeth for age group a, in grams, from Table
4-140
ContainHga = The proportion of people in age group a with fillings that contain mercury, from Table
4-140
HgProportion = The proportion of dental amalgam that is mercury (approximately 45 percent)
4.18.3.4 Controls
There are no controls assumed for this source category.
4.18.3.5 Emissions
Human Cremation
To estimate the emissions of CAPs from human cremation, the total number of human cremations in each
county, in tons, is multiplied by the emissions factor for each pollutant, from Table 4-139.
Emissionsp c = Cremation_tonsc x EFp (H7)
Where:
Emissionsp c = Emissions of pollutant p from human cremation in county c, in pounds
Cremations_tonsc= The number of human cremations in county c, in tons
EFP = Emissions factor for pollutant p from human cremation, in lbs. per ton
The emissions from mercury in teeth are estimated based on the number of cremations rather than the weight.
To estimate the emissions of mercury from teeth during human cremation, the number of cremations in each
age group is multiplied by the emissions factor for each age group and then summed across age groups.
Emissions _teethHg
Si
,C ~ 1 Cremationsca x EF_teethHg a (H8)
a=1
Where:
Emissions_teethHg,c = Emissions of mercury in teeth from human cremation in county c, in pounds
Cremationsc,a = The number of human cremations in county c in age group a
EF_teethHg,a = Emissions factor for mercury emissions from teeth due to cremation for age group a,
in lbs. per cremation
The emissions from mercury from blood and tissues are estimated by multiplying the total number of
cremations in each county, in tons, by the emissions factor for mercury from blood and tissues.
Emissions _tissueHgc = Cremations_tonsc x EF_tissueHg (H9)
Where:
Emissions_tissueHg,c= Emissions of mercury in tissues from human cremation in county c, in pounds
Cremations_tonsc = The number of human cremations in county c, in tons
EF_tissueHg,a = Emissions factor for mercury emissions from blood and tissues due to cremation for
in lbs. per ton
4-239
-------
The total emissions of mercury from cremation in each county is calculated by adding the emissions of mercury
from teeth and the emissions of mercury from tissues.
EmissionsHgc = Emissions_teethHgc + Emissions_tissueHgc (H10)
Where:
EmissionsHg,c = Emissions of mercury from human cremation in county c, in pounds
Emissions_teethHg,c = Emissions of mercury in teeth from human cremation in county c, in pounds
Emissions_tissueHg,c= Emissions of mercury in tissues from human cremation in county c, in pounds
Animal Cremation
Emissionsp c = Cremation_tonsc x EFp (A5)
Where:
Emissionsp c = Emissions of pollutant p from animal cremation in county c, in pounds
Cremations_tonsc= The number of animal cremations in county c, in tons
EFP = Emissions factor for pollutant p from animal cremation, in lbs. per ton
4.18,3.6 Sample calculations
Table 4-141 lists the sample calculations for estimating mercury emissions from human cremation in the 85+ age
group and animal cremation of cats in Clark County, ID. To estimate the total emissions in Clark County, these
steps would be repeated to estimate emissions from all age groups and from cremation of dogs.
Table 4-141: Sample calculations for mercury emissions from human cremation for the 85+ age group and
cremation of cats in Clark County, ID
Eq.
#
Equation
Values for Clark County, ID
Result
HI
Deaths_withheldsa
= Deaths_states a
— ^ Deaths_countysa
4,013 state level deaths
— 3,997 total county level deaths
16
withheld
deaths in
Idaho
H2
P0Vr
873 people in Clark County
0.442
population
ratio
Vratioc pop^
1,975 total population of counties with withheld deatl
H3
Deathsca
— DeathsWithheidsa
x Poprat(0c
16 withheld deaths x 0.442 population ratio
7 deaths
in Clark
County, ID
H4
Cremationsca
= Deathsca
x Cremationrates
7 deaths x 56.8% cremation rate
4
cremation
s in Clark
County, ID
4-240
-------
Eq.
#
Equation
Values for Clark County, ID
Result
H5
Cremations _tonsc
A
= ^ Cremationsc a x Wa
a=1
1 ton
X 2,000 lbs
4 cremations x 158.25 lbs per person in
85 + age group h- 2000 lbs per ton
0.3165
tons
cremation
s in Clark
County, ID
H6
EF _teeth,Hg a
= Materiala
x ContainHga
x HgProportion
lb
x 0.0022 —
9
2.96 g mercury x 75 % with mercury x
45% of fillings are mercury x 0.0022
0.0022 lbs.
mercury
per
cremation
H7
Emissionspc
= CremationtOTlSc x EFp
N/A
Complete
d in
equation
H9 for
mercury
H8
EYfiissionstggtfr^g c
A
= ^ Cremationsc a
a=1
x EFteethHg a
4 cremations x 0.0022 lbs per cremation
0.0088 lbs.
mercury
from teeth
in 85+ age
group in
Clark
County, ID
H9
1'. ITliSSiOHS{issue ij{j c
= CremationstOTlSc
^ EFtissueHg
0.3165 tons cremations x 0.0015 lbs per ton
0.00047
lbs.
mercury
from
tissues in
85+ age
group in
Clark
County, ID
HI
0
EmissionsHgc
Emissions
+ Emissions tissue Hgc
0.0088 lbs from teeth + 0,00047 lbs. from tissues
0.0093 lbs.
mercury
from
cremation
of 85+ age
group in
Clark
County ID
A1
Cremationsc/dus
= Ratioc/d
x (Cremations_petsus
+ Cremations _shelterus)
52.5% of cats in pet population
x (1,840,965 pet cremations
+ 2,700,000 shelter animal cremations)
2,384,006
cremated
cats in the
U.S.
4-241
-------
Eq.
#
Equation
Values for Clark County, ID
Result
A2
Cremations tons c
d
= Cremationsc x Weightc
d d
1 ton
X
2,000 pounds
2,384,006 cremated cats x 9.9 lbs per cat
h- 2000 lbs per ton
11,800
tons of
cremated
cats in the
U.S.
A3
Cremationstons animal
= CremationstonSc
+ Cremations tonSd
N/A
Cremation
s of dogs
are not
estimated
in this
sample
calculation
A4
Cremations Jons animaiiC
C r emations_tonsanimaiUS
Popr
x —
Popus
873 people in Clark
11,800 cremated cats x -
329,164,967 people in US
0.03 tons
cats
cremated
in Clark
County, ID
A5
Emissionsp c
= CremationtOTlSc x EFp
0.03 x 0.0015 lbs per ton
0.000045
lbs.
mercury
emissions
from
cremation
of cats in
Clark
County, ID
4.18.3.7 Updates in 2017 methodology
There is one slight change from the 2014 methodology for the estimation of emissions from human cremation.
In the 2014 methodology, the emissions factor for mercury emissions from cremation of blood and tissues was
in units of per cremation. In the 2017 methodology, EPA uses the same emissions factor, but converted it to a
per-ton emissions factor. The per-ton emissions factor is multiplied by the number of tons cremated in each
county.
The most significant difference from the 2014 methodology for the estimation of emissions from animal
cremation is that EPA now estimates emissions of pollutants other than mercury. In the 2017 methodology, EPA
uses the emissions factors for cremation of human blood and tissues to estimate emissions from animals.
4.18.3.8 Puerto Rico and U.S. Virgin Islands
Since insufficient data exists to calculate emissions from human cremation for the counties in Puerto Rico and
the US Virgin Islands, emissions are based on two proxy counties in Florida: 12011, Broward County for Puerto
Rico and 12087, Monroe County for the US Virgin Islands. The total emissions in tons for these two Florida
counties are divided by their respective populations creating a tons per capita emissions factor. For each Puerto
Rico and US Virgin Island county, the tons per capita emissions factor is multiplied by the county population
(from the same year as the inventory's activity data) which served as the activity data. In these cases, the
throughput (activity data) unit and the emissions denominator unit are "EACH".
4-242
-------
Emissions from animal cremation are based on county population; therefore, the emissions from animal
cremation in Puerto Rico and the Virgin Islands are calculated using the method described for the rest of the
counties.
4.18.4 References
1. CDC. 2017 WONDER Database. Table 2, last accessed March 2019.
2. National Funeral Directors Association (NFDA). 2017. The NFDA Cremation and Burial Report: Research,
Statistics and Projections, last accessed March 2019.
3. CDC 2016. Anthropometric Reference Data for Children and Adults: United States, 2011-2014. Vital
Health Statistics, Series 3, Number 29, last accessed August 2018.
4. Pet Loss Professionals Alliance (PLPA). 2013. Pet Loss Professionals Alliance Releases Finding of Inaugural
Professional Survey, last accessed August 2018.
5. Humane Society of the United States. 2014. Pets by the Numbers, last accessed August 2018.
6. Mattern, M.Y. and D.A. McLennan. 2000. Phylogeny and Speciation of Felids. Cladistics, 16: 232-253.
7. Modern Puppies. Breed Weight Chart, last accessed August 2018.
8. U.S. Environmental Protection Agency. 1993. AP-42: Compilation of Air Emissions Factors, Fifth Edition,
Volume I, Chapter 2.3 - Medical Waste Incineration, Tables 2.3-2 and 2.3-15.
9. U.S. Environmental Protection Agency. 1999. Emission Test Evaluation of a Crematory at Woodlawn
Cemetery in the Bronx, NY, Vol. I-III, EPA-454/R-99-049.
10. California Air Resources Board. 1999. Development of Toxic Emissions Factors from Source Test Data
Collected Under the Air Toxics Hot Spots Program, Part II, Volume I. Prepared by GE Energy and
Environmental Research Corporation.
11. Reindl, J. 2012. Summary of References on Mercury Emissions from Crematoria, last accessed August
2018.
12. Lundquist, J.H. 2012. Mercury Emissions from the Cremation of Human Remains. Bay Area Air Quality
Management District.
13. Food and Drug Administration. 2017. About Dental Amalgam Fillings, last accessed August 2018.
4.19 Miscellaneous Non-Industrial NEC: Residential Charcoal Grilling
4.19.1 Sector description
Residential barbecue grilling emissions include emissions from the burning of charcoal (including the use of
lighter fluid) and emissions from all types of meat cooked on charcoal, gas, and electric grills. Combustion
emissions from gas barbecue grills are not included. This source category (SCC=2810025000) is one of many
components in the Miscellaneous Non-Industrial sector. The SCC description is "Miscellaneous Area Sources;
Other Combustion; Charcoal Grilling - Residential (see 23-02-002-xxx for Commercial); Total".
4.19.2 Sources of data
This source category includes a mix of S/L/T data, where provided, and EPA-generated emissions. The agencies
listed in Table 4-142 submitted emissions for residential charcoal grilling. Agencies not listed uses EPA estimates.
Table 4-142: Agencies reporting Residential Charcoal Grilling emissions
Region
Agency
S/L/T
1
Massachusetts Department of Environmental Protection
State
4
Memphis and Shelby County Health Department - Pollution Control
Local
4-243
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Region
Agency
S/L/T
4
Metro Public Health of Nashville/Davidson County
Local
5
Illinois Environmental Protection Agency
State
6
Texas Commission on Environmental Quality
State
9
Maricopa County Air Quality Department
Local
10
Coeur d'Alene Tribe
Tribe
10
Idaho Department of Environmental Quality
State
10
Kootenai Tribe of Idaho
Tribe
10
Nez Perce Tribe
Tribe
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
4.19.3 EPA-developed emissions
Emissions from this source category include criteria pollutants, (CO, NOx, PM10-PRI, PM25-PRI and VOC) and
HAP emissions from residential barbecue grilling. Sources of emissions include burning charcoal and using lighter
fluid in charcoal grills, and cooking meat on charcoal, gas, and electric grills. To perform the relevant calculations
data are needed on activities and emissions factors for those activities. Activity data includes information about
total charcoal sold, total meat cooked, and total amount of lighter fluid used.
4.19,3.1 Activity data
There are three types of activity data for this source category: (1) amount of meat cooked on charcoal grills; (2)
amount of meat cooked on gas and electric grills; and (3) number of grilling events using lighter fluid. Each of
these types of activity data is discussed in the subsections below.
Meat cooked on charcoal grills
This source category includes emissions from the amount of charcoal burned and the amount of meat cooked.
The total amount of charcoal sold in the United States is based on data from the Heath, Patio, and Barbecue
Association (HPBA) [ref 1], which is distributed to each county based on the proportion of 1-4 unit homes in
each county, from the U.S. Census Bureau [ref 2], This distribution procedure is discussed in more detail in
Section 4.19.3.2. We assume that all charcoal sold is burned.
The amount of meat cooked is determined based on assumptions about the amount of meat cooked per pound
of charcoal sold. This calculation assumes 17.64 charcoal briquettes per pound of charcoal sold [ref 3] and 0.033
pounds of meat cooked per briquette -[ref 4], These numbers are multiplied together to calculate a value of
0.588 pounds of meat cooked per pound of charcoal sold.
0.588 lb. meat cooked per lb. charcoal sold (1)
= 17.64 briquettes per lb. charcoal x 0.033 lb. meat cooked per briquette
Meat cooked on gas and electric grills
The amount of meat cooked on gas grills is calculated based on assumptions about the ratio of gas grilling to
charcoal grilling, including that charcoal grills represent 41% of grills and gas/electric grills represent 59% [ref 4],
and that charcoal grills are used 27 times per year and gas/electric grills are used 45 times per year [ref 5], This
calculation results in an estimated ratio of 2.398, meaning that for every pound of meat cooked on a charcoal
grill an additional 2.398 pounds of meat are cooked on a gas or electric grill.
4-244
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2.398 gas or electric grilling ratio
45 times per year(gas or electric) x 59% gas or electric grills
(2)
27 times per year (char ocal) x 41% charcoal grills
The values from equations 1 and 2 are used with national data on the amount of charcoal sold from the HPBA
[ref 1] to estimate the total amount of meat cooked on charcoal, gas, and electric grills. This national charcoal
sales data is distributed to the counties based on the number of homes in each county, as described in the
following section.
Grilling events using lighter fluid
This calculation is based on the percentage of homes that have a grill (80%) [ref 6], the percentage of grills that
are charcoal grills (41%) [ref 5], the percentage of charcoal grills that use lighter fluid (37%) [ref 7], and the
number of times per year that charcoal grills are used (27) [ref 6], This results in a value of approximately 3.28
grilling events per household per year where lighter fluid is used.
This number is multiplied by the number of occupied homes in each county to determine the total number of
grilling events in each county where lighter fluid is used. Seen Section 4.19.3.2 on allocation procedure for
information on calculating the number of occupied 1-4-unit households.
Where:
nLF.c = Number of grilling events in county c where lighter fluid is used
Hc,o = Total occupied households of 1-4 units in county c
3.28 = Number of grilling events with lighter fluid per home, from equation 3
4.19,3,2 Allocation procedure
National data on the amount of charcoal sold is distributed to the counties based on the proportion of occupied
1-4-unit homes in each county. It is assumed that households in larger apartment buildings would not have the
space to have or use an outdoor grill. The data on the number of occupied 1-4 unit homes in each county is from
the U.S. Census Bureau American Community Survey [ref 2], Occupied households between 1 and 4 units are
estimated using the sum of total 1-4-unit households and the fraction of total occupied households in the US.
3.28 grilling events with lighter fluid
= 80% homes with a grill x 41% grills that are charcoal
x 37% charcoal grills that use lighter fluid
x 27 times per year charcoal grills are used
(3)
^lf,c Hc,o ^ 3.28
(4)
units=1
(5)
(6)
Where:
Total occupied households of 1-4 units in county c
4-245
-------
Hc,t = Total households in county c
Hus,o = Total occupied households in the United States
Hus,t = Total households in the United States
HRC = Ratio of occupied households of 1-4 units in county c to total households of 1-4 units in United
States
The national-level data on charcoal sales is distributed to the counties using the ratio from equation 6.
Charcoalc = HRC x Charcoalus x 2000 lbs. per ton (7)
Where:
Charcoalc = Amount of charcoal sold in county c, in pounds
HRc = Ratio of households of 1-4 units in county cto total households of 1-4 units in United States
Charcoalus = Amount of charcoal sold in the United States, in tons
The amount of charcoal sold in each county (from equation 7) is multiplied by the amount of meat cooked per
pound of charcoal (from equation 1) to estimate the amount of meat cooked on charcoal grills in each county.
MeatcharcoaiiC = Charcoalc x 0.588 (8)
Where:
Meatcharcoai,c = Amount of meat cooked on charcoal grills in county c, in pounds
Charcoalc = Amount of charcoal sold in county c, in pounds
0.588 = Pounds of meat cooked per pound of charcoal, from equation 1
The amount of meat cooked on charcoal grills is used with the ratio from equation 2 to estimate the amount of
meat cooked on gas or electric grills.
Meatgas^eieC)C M e at char coai,c x 2.398
Where:
Meatgas/eiec,c = Amount of meat cooked on gas or electric grills in county c, in pounds
Meatcharcoai,c = Amount of meat cooked on charcoal grills in county c, in pounds
2.398 = Ratio of meat cooked on gas or electric grills to charcoal grills, from equation 2
The amount of meat cooked on charcoal and on gas or electric grills is added together to determine the total
amount of meat cooked on grills in each county.
Meatt c Meatgas/elec,c Meatcharcoal,c (10)
Where:
Meattc = Total amount of meat cooked on grills in county c, in pounds
Meatgas/eiec,c = Amount of meat cooked on gas or electric grills in county c, in pounds
Meatcharcoai,c = Amount of meat cooked on charcoal grills in county c, in pounds
4-246
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4.19.3.3 Emission factors
The emissions factors are shown in Table 4-143, including the actual emissions factor used in the calculations,
and the original emissions factor from the reference, if it is different from the actual factor. The emissions
factors for CO, NOX, PM10-PRI, PM25-PRI, and VOC are from EPA's report, Emissions from Street Vendor Cooking
Devices (Charcoal Grilling) [ref 8], There is also a separate emissions factor for VOC from lighter fluid, from the
South Coast Air Quality Management District, Rule 1174 [ref 9], The HAP emission factors are speciation factors
from the EPA SPECIATE database [ref 10], which are speciation factors for charbroiling meat.
Table 4-143: Emissions Factors for Residential Grilling (2810025000)
Pollutant
Code
Emissions
Emissions
Emissions
Factor (actual)
Emissions
Emissions
Pollutant
Factor
(original)
Factor Units
(original)
Factor Units
(actual)
Factor
Reference
CO
CO
162.97a
325.93
NOX
NOX
3.37a
6.74
PM10-PRI
PM10-PRI
9.10a
g/kg meat
18.19
Ibs./ton meat
6, Table E-2
PM25-PRI
PM25-PRI
n/a
14.56b
0.94a
1.88
VOC
VOC
0.02
Ibs./grilling
event
6, section
(c)(1)
1,3-Butadiene
106990
1.04E-02
2,2,4-
Trimethylpentane
540841
1.12E-03
Acetaldehyde
75070
1.09E-01
Anthracene
120127
1.09E-05
Benzene
71432
8.26E-03
Ethyl Benzene
100414
1.09E-03
Fluoranthene
206440
3.98E-05
Formaldehyde
50000
1.38E-01
Ibs./lb. VOC
6
Hexane
110543
4.38E-03
m-Xylene
108383
5.97E-04
Naphthalene
91203
8.94E-04
o-Xylene
95476
1.09E-03
Phenanthrene
85018
1.20E-04
Propionaldehyde
123386
5.01E-02
p-Xylene
106423
5.97E-04
Pyrene
129000
5.67E-05
Toluene
108883
3.98E-03
a. Based on average of test numbers MCI, MC2, MC3, MC6, MC7, and MC8 from the table showing
emissions factors for emissions per kg meat cooked. See Table E-2 in Reference 9.
b. PM25-PRI emission factor is based on assumption that PM25-PRI = PM10-PRI x 0.8.
4.19.3.4 Controls
There are no controls assumed for this category.
4.19.3.5 Emissions
The emissions of PM10-PRI, PM25-PRI, and VOC for residential barbecue grilling are calculated by multiplying
the amount of meat grilled in each county (from equation 10) by the emissions factors from Table 4-143.
4-247
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Meatt r
Evc = ———— 1 xEFvmeat (11)
p' 2000 lbs. per ton p'
Where:
Ep,c = Emissions of pollutant p from grilling meat in county c, in pounds
Meattc = Total amount of meat cooked on grills in county c, in pounds
EFP/meat = Emissions factor for pollutant p from grilling meat
It is assumed that CO and NOX emissions are from charcoal combustion, and there are no significant emissions
of these pollutants from gas or electric grills. Therefore, to estimate CO and NOX emissions, the emissions
factors for these pollutants are multiplied by the amount of meat cooked on charcoal (from equation 8), rather
than the total amount of meat cooked.
j? _ Meattfiarcoalc t?t? 111 \
bc°/N°X'C - 2000 Ibs.perton X ttc°/N0X (lla)
Where:
Eco/nox,c = Emissions of pollutant CO or NOX from grilling meat in county c, in pounds
Meatcharcoai,c = Total amount of meat cooked on charcoal grills in county c, in pounds
EFCo/nox= Emissions factor for CO or NOX from grilling meat
For VOC, there is a separate calculation to account for emissions from lighter fluid use, in which the number of
grilling events per year where lighter fluid is used (from equation 4) is multiplied by an emissions factor of 0.02
lbs. VOC/grilling event (Table 4-143).
EvOC,LF,c = nLF,c X EFvOC.LF (12)
Where:
Evoc,lf,c = Emissions of VOC from lighter fluid use in county c, in pounds
nLF.c = Number of grilling events in county c where lighter fluid is used
EFvoqlf = Emissions factor for VOC from lighter fluid use
These VOC emissions are added to the VOC emissions from grilling meat to determine the total VOC emissions
from residential grilling.
Evoc.c = EvOC,LF,c + EvOC,meat,c (13)
Where:
Evoqc = Total emissions of VOC from residential grilling in county c, in pounds
Evoc,lf,c = Emissions of VOC from lighter fluid use in county c, in pounds
Evoc,meat,c = Emissions of VOC from grilling meat in county c, in pounds
Emissions of HAPs are calculated by multiplying the total VOC emissions by the speciation factors in Table 4-143.
Eh,c ~ Evoc,c * EFh (14)
4-248
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Where:
Eh,c = Emissions of HAP h in county c, in pounds
Evoqc = Total emissions of VOC from residential grilling in county c, in pounds
EFh = Emissions factor for HAP h
4.19,3.6 Example calculations
Sample calculations for estimating VOC emissions from residential grilling in Ada County, ID, are shown in Table
4-144. Note that equations 1, 2, and 3 result in constant values for each county, so these calculations are not
repeated here. See Section 4.19.3.1 for more information about these equations.
Table 4-144: Sample calculations for VOC emissions from residential grilling in Ada County, Idaho
Eq.#
Equation
Values for Ada County, ID
Result
5
units=4
Hc,o= Yj Hc*
units=l
x H"s-°
Hus,t
138,929 1
— 4 unit homes in Ada County
x (154,408 occupied homes in Ada County)
/(162,766 Total homes in Ada County)
131,795 occupied
homes in Ada
County, ID
4
^LF,c HC)o X 3.28
131,795 occupied homes in Ada County
x 3.28 grilling events per home
432,287 grilling
events in Ada
County, ID
6
Hco
lift _ c'u
131,795 occupied homes in Ada County
0.00148 ratio of
homes in Ada
County, ID
C
89,010,502 homes in U. S.
7
Charcoalc = HRC x
Charcoalus x
2000 lbs. per ton
0.00148 x 890,910 tons charcoal x
2000 lbs. per ton
2,638,284.3
pounds charcoal
in Ada County, ID
8
MecLtcharcoaic
= Charcoalc x 0.588
2,638,284.3 lbs. charcoal x 0.588
1,551,311 lbs.
meat grilled on
charcoal grills in
Ada County, ID
9
Meatgas/eiecc
— Meatcharcoalc
X 2.398
1,551,311 lbs. meat x 2.398
3,720,044 lbs.
meat grilled on
gas or electric
grills in Ada
County, ID
10
Meattc
— Meatgas/eiecc
+ M££Jtcharcoa(,c
1,551,311 lbs. meat + 3,720,044 lbs. meat
5,271,355 lbs.
meat grilled in
Ada County, ID
11
Meattr
T? — w
P,c 2000 Ibs.per ton
EFp,meat
5,271,355 lbs. meat
x 1.88 lbs. per ton
2000 lbs. per ton
4,955 lbs. VOC
from grilling meat
in Ada County, ID
12
Evoc,lf,c
= nLF,c X EFV0C,LF
432,287 grilling events
x 0.02 lb. per grilling event
8,645 lbs. VOC
from lighter fluid
in Ada County, ID
4-249
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Eq.#
Equation
Values for Ada County, ID
Result
13
Evoc,c
= EvOC,LF,c + EvOC,meat,c
4,955 lbs. VOC + 8,645 lbs. VOC
13,601 lbs. VOC
from residential
grilling in Ada
County, ID
4.19.3.7 Changes from the 2014 methodology
There is one change from the methodology used to estimate the 2014 v2 NEI. In 2014, emissions of CO and NOX
were estimated by multiplying an emission factor by the amount of charcoal burned. The EPA reference reports
emission factors both in terms of meat and charcoal grilled and in terms of just meat grilled [ref 8], In order to
maintain consistency with the emissions of other criteria pollutants, the 2017 methodology will use the emission
factors for meat grilled. As a result, the CO and NOX emissions are estimated by multiplying the amount of meat
grilled (rather than the amount of charcoal burned) by the emission factor. EPA maintains the assumption that
CO and NOX are generated only by charcoal grills.
4.19.3.8 Puerto Rico and U.S. Virgin Islands
Emissions from Puerto Rico are calculated using the same method described above. Insufficient data exists to
calculate emissions for the counties in the US Virgin Islands, so emissions are based on a proxy county in Florida:
12087, Monroe County. The total emissions in lbs. for this Florida County is divided by its population creating a
Ibs.-per-capita emission factor. For each US Virgin Island County, the lbs. per capita emission factor is multiplied
by the county population (from the same year as the inventory's activity data) which serves as the activity data.
In these cases, the throughput (activity data) unit and the emissions factor denominator unit are "EACH".
4.19.4 References
1. Hearth, Patio and Barbecue Association (HPBA), Statistics/Barbecue Statistics/Charcoal Shipments for
2013, accessed April 2015.
2. U.S. Census Bureau. Community Facts, Housing, Selected Housing Characteristics, American Community
Survey 5-Year Estimates, 2017.
3. Kingsford email on the weight of their charcoal briquettes 4/11/2015.
4. Hearth, Patio and Barbecue Association (HPBA), Statistics, BBQ Grill Shipments, accessed April 2015.
5. Hearth, Patio & Barbecue Association (HPBA), 2011 State of the Barbecue Industry Report, accessed
April 2015.
6. Hearth, Patio & Barbecue Association (HPBA), 2014 State of the Barbecue Industry Report.
7. Hearth, Patio and Barbecue Association (HPBA) 3/23/2015 email from Jessica Boothe on how many
people with charcoal grills use lighter fluid.
8. U.S. Environmental Protection Agency. 1999. Emissions from Street Vendor Cooking Devices (Charcoal
Grilling). EPA/600/SR-99/048.
9. South Coast Air Quality Management District. October 5, 1990. "Rule 1174. "Control of Volatile Organic
Compound Emissions from the Ignition of Barbecue Charcoal" accessed May 2015.
10. U.S. Environmental Protection Agency. 2014. SPECIATE Database, version 4.4. Speciation profile 4553,
meat charbroiling. Speciation profile was adjusted to be based on VOC, rather than total organic gases
(TOG), by removing methane from the profile.
4-250
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4,20 Miscellaneous Non-Industrial NEC: Portable Gas Cans
4.20.1 Source category description
There are several sources of emissions associated with portable fuel containers (PFC) used for storage of
gasoline. These sources include vapor displacement and spillage while refueling the gas can at the pump,
spillage during transport, permeation and evaporation from the gas can during transport and storage, and vapor
displacement and spillage while refueling equipment. Vapor displacement and spillage while refueling nonroad
equipment from PFCs are included in the nonroad inventory. This section describes how other types of PFC
emissions are accounted for in the NEI. This source category is one of many components in the Miscellaneous
Non-industrial sector.
Table 4-145 shows the SCCs covered by this source category. The SCC level 3 and 4 descriptions are also
provided. The leading SCC description is "Storage and Transport; Petroleum Product Storage" for all SCCs.
Table 4-145: PFC SCCs in the 2017 NEI
SCC
Description
2501011011
Residential Portable Gas Cans; Permeation
2501011012
Residential Portable Gas Cans; Evaporation (includes Diurnal losses)
2501011013
Residential Portable Gas Cans; Spillage During Transport
2501011014
Residential Portable Gas Cans; Refilling at the Pump - Vapor Displacement
2501011015
Residential Portable Gas Cans; Refilling at the Pump - Spillage
2501012011
Commercial Portable Gas Cans; Permeation
2501012012
Commercial Portable Gas Cans; Evaporation (includes Diurnal losses)
2501012013
Commercial Portable Gas Cans; Spillage During Transport
2501012014
Commercial Portable Gas Cans; Refilling at the Pump - Vapor Displacement
2501012015
Commercial Portable Gas Cans; Refilling at the Pump - Spillage
4.20.2 Sources of data
The agencies listed in Table 4-146 submitted PFC emissions; agencies not listed used EPA estimates for all PFC
sources.
Table 4-146: Agencies reporting PFC emissions
Region
Agency
S/L/T
2
New Jersey Department of Environment Protection
State
2
New York State Department of Environmental Conservation
State
3
Delaware Department of Natural Resources and Environmental Control
State
3
Maryland Department of the Environment
State
5
Illinois Environmental Protection Agency
State
9
Maricopa County Air Quality Department
Local
10
Coeur d'Alene Tribe
Tribe
10
Idaho Department of Environmental Quality
State
10
Kootenai Tribe of Idaho
Tribe
10
Nez Perce Tribe
Tribe
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
4-251
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4.20.3 EPA-developed emissions
For the 2017 NEI, where states did not submit their own data, we relied on an inventory developed for the Tier 3
motor vehicle and fuel standards rule [ref 1], This inventory assumed all fuel dispensed from PFCs was E10, with
an average RVP of 8.7 psi. Use of ethanol in gasoline fuels can increase evaporative emissions from PFCs,
relative to E0, for several reasons. First, if E10 fuels have higher volatility than corresponding E0 fuels, that can
increase evaporation and vapor displacement. Second, ethanol in gasoline increases permeation of fuel through
gas can materials. Finally, the lower energy content of ethanol fuels leads to more frequent refueling, and, thus,
greater emissions from spillage and displacement while filling the gas can at the pump.
The use of ethanol also changes the mix of hydrocarbons in the evaporated fuel. In particular, it can change the
fraction of several hazardous air pollutants as well as ethanol.
As part of the 2007 regulation controlling emissions of hazardous pollutants from mobile sources (MSAT2 rule),
EPA promulgated requirements to control VOC emissions from gas cans. The methodology we used to develop
emission inventories for gas cans was developed for that regulation and is described in the regulatory impact
analysis for the rule and in an accompanying technical support document [ref 2, ref 3], However, while that
regulation included estimates for spillage emissions occur when refueling equipment, most of these emissions is
already included in the nonroad equipment inventory. Thus, we did not include these emissions in the PFC
inventory for the NEI. Vapor displacement for nonroad equipment container refueling was also subtracted from
vapor displacement in the PFC inventory to avoid double counting these emissions.
4.20.3.1 VOC allocation
For the NEI, emissions had to be separated into commercial and residential fuel container emissions. Total state
level PFC emissions were allocated to the categories by using national level residential and commercial emission
splits from the MSAT2 rule for each of the categories using the following equations:
F = Fx
residential,XXXXJY
F = Fx
commercialXXXXJY
(2)
f Re.v
v Re.v + Com _
r Com
v-P7
(i)
Re.v + Com
where E was the emissions of the category being split, XXXX was year, YY was state, and Res and Com were the
national residential and commercial PFC emissions.
Permeation and evaporation were also separated as follows:
EAAA,XXXX,YY,perm = ^AAA,XXXX,YY,perm&evap X O-3387 (3)
EAAA,XXXX,YY,evap ^AAA,XXXX,YY,perm&evap X 0.3387) (4)
The fraction 0.3387 represents the fraction of combined permeation and evaporative emissions attributable to
permeation, based on data from the California Air Resources Board.
4-252
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4,20,3,2 VOC emissions
Permeation and evaporation
These emissions are represented by the following SCCs:
• 2501011011 - Residential Portable Fuel Containers: Permeation
• 2501011012 - Residential Portable Fuel Containers: Evaporation
• 2501012011 - Commercial Portable Fuel Containers: Permeation
• 2501012012 - Commercial Portable Fuel Containers: Evaporation
Emissions from these SCCs are impacted by 2007 MSAT rule standards limiting evaporation and permeation
emissions from these containers to 0.3 grams of hydrocarbons per day [ref 4], Inventory estimates developed for
calendar year 2018 in EPA's Tier 3 vehicle rule modeling platform [ref 5] reflect the impact of these standards, as
well as impacts of RVP and oxygenate use. These Tier 3 inventories were interpolated from earlier 2015 and
2020 MSAT2 rule inventories and assumed 100% E10. They were judged to be reasonable approximations of the
2014 inventory, although increases in activity between 2014 and 2018 means emissions will be overestimated in
the 2014 NEI.
Vapor Displacement
Vapor displacement emissions occur while refueling containers at the pump. These emissions are represented
by the following SCCs:
• 2501011014 - Residential Portable Fuel Containers: Refilling at the Pump: Vapor Displacement
• 2501012014 - Commercial Portable Fuel Containers: Refilling at the Pump: Vapor Displacement
These emissions are not impacted by MSAT2 rule standards but are impacted by RVP and oxygenate use.
Inventory estimates developed for calendar year 2018 in EPA's Tier 3 vehicle rule modeling platform were
judged to be reasonable approximations of the 2014 inventory, although increases in activity between 2014 and
2018 means emissions will be overestimated in the 2014 NEI.
Spillage
Spillage occurs during transport and refilling at the pump. These emissions are represented by the following
SCCs:
• 2501011013 - Residential Portable Fuel Containers: Spillage During Transport
• 2501011015 - Residential Portable Fuel Containers: Refilling at the Pump: Spillage
• 2501012013 - Commercial Portable Fuel Containers: Spillage During Transport
• 2501012015 - Commercial Portable Fuel Containers: Refilling at the Pump: Spillage
These emissions are not impacted by MSAT2 standards or RVP. However, composition of the emissions is
impacted by oxygenate. VOC emissions for these SCCs are carried forward from 2011.
4,20.3.3 Hazardous air pollutants
Hazardous air pollutants found in liquid gasoline will be present as a component of VOC emissions. These MSATs
include benzene, ethyl benzene, toluene, hexane, xylenes, 2,2,4-trimethylpentane, and naphthalene. For vapor
displacement emissions of benzene and naphthalene, toxic to VOC ratios were obtained from headspace vapor
4-253
-------
profiles from EPAct test fuels [ref 6], For permeation emissions of these pollutants, vehicle permeation
speciation data from Coordinating Research Council (CRC) technical reports E-77-2b and E-77-2c were used [ref
7, ref 8], We relied on three-day diurnal profiles from the CRC data. For evaporative emissions resulting from
changes in ambient temperatures, speciation data from the Auto/Oil program were used for EO and E10 [ref 9],
Table 4-147 lists the toxic to VOC ratios for each type of PFC emission.
Table 4-147: Toxic to VOC ratios for benzene and naphthalene from PFCs
Pollutant
Process
Speciation Surrogate
E10
Benzene
Vapor Displacement
Vehicle Headspace
0.0087
Permeation
Vehicle Permeation
0.0227
Evaporation
Vehicle Evap
0.0340
Naphthalene
Vapor Displacement
Vehicle Headspace
0.0000
Permeation
Vehicle Permeation
0.0004
Evaporation
Vehicle Evap
0.0004
Emissions of other air toxics for permeation, evaporation, and vapor displacement were all estimated from the
EPAct headspace vapor displacement profile for E10 (SPECIATE profile 8870). Toxic to VOC ratios are provided in
Table 4-148.
Table 4-148: Toxic to VOC ratios for Other HAPs (Vapor Displacement, Permeation, Spillage and Evaporation)
Pollutant
Toxic to VOC Ratio
Ethylbenzene
0.0068
Hexane
0.0616
Toluene
0.0521
Xylenes (o,m,p)
0.0300
2,2,4-T rimethylpentane
0.0540
4.20.4 References
1. U. S. EPA. 2014. "Development of Air Quality Reference Case Upstream and Portable Fuel Container
Inventories for the Tier 3 Final Rule." Memorandum from Rich Cook, Margaret Zawacki and Zoltan Jung
to Docket, February 25, 2014, Docket No. EPA-HQ-OAR-2011-0135.
2. U. S. EPA. 2007. Final Regulatory Impact Analysis: Control of Hazardous Air Pollutants from Mobile
Sources; EPA420-R-07-002; Office of Transportation and Air Quality, Ann Arbor, Ml.
3. Landman, L. C. (2007) Estimating Emissions Associated with Portable Fuel Containers (PFCs). U. S. EPA,
Assessment and Standards Division, National Vehicle and Fuel Emissions Laboratory, Ann Arbor, Ml,
Report No. EPA420-R-07-001.
4. Federal Register. 2007. Control of Hazardous Air Pollutants from Mobile Sources. 72 (37): 8428-8570.
5. U.S. EPA. 2014. Emissions Modeling Technical Support Document: Tier 3 Motor Vehicle and Emission
and Fuel Standards. Office of Air Quality Planning and Standards, Research Triangle Park, NC, Report No.
EPA-454/R-13-003, February 2014.
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6. U. S. EPA. 2011. Hydrocarbon Composition of Gasoline Vapor Emissions from Enclosed Fuel Tanks. Office
of Research and Development and Office of Transportation and Air Quality. Report No. EPA-420-R-11-
018. EPA Docket EPA-HQ-OAR-2011-0135.
7. U. S. EPA. 2010. Evaporative Emissions from In-Use Vehicles: Test Fleet Expansion (> t '' b|.
Prepared by Harold Haskew and Associates for Assessment and Standards Division, Office of
Transportation and Air Quality, October, 2010.
8. Coordinating Research Council. 2010. Study to Determine Evaporative Emission Breakdown, Including
Permeation Effects and Diurnal Emissions, Using E20 Fuels on Aging Enhanced Evaporative Emissions
Certified Vehicles. Report No. E-77-2c.
9. Auto/Oil Air Quality Improvement Research Program. 1996. Phase I and II Test Data. Prepared by
Systems Applications International, Inc.
4.21 Mobile-Commercial Marine Vessels
The 2017 NEI includes emissions from commercial marine vessel (CMV) activity in the 50 states, Puerto Rico, and
US Virgin Isles, out to 200 nautical miles from the US coastline.
4.21.1 Sector description
The CMV sector includes boats (excluding pleasure craft covered by the MOVES/NONROAD model) and ships
used either directly or indirectly in the conduct of commerce or military activity. Most vessels in this category
are powered by diesel engines that are either fueled with distillate or residual fuel oil blends. In previous NEIs,
we assumed that Category 3 (C3) vessels primarily used residual blends while Category 1 and 2 (CI and C2)
vessels typically used distillate fuels. For the 2017 NEI, SCCs and fuel details, including emission factors, have
been updated.
The C3 inventory includes vessels which use C3 engines for propulsion. C3 engines are defined as having
displacement above 30 liters per cylinder. The resulting inventory includes emissions from both propulsion and
auxiliary engines used on these vessels, as well as those on gas and steam turbine vessels. Geographically, the
inventories include port and interport emissions that occur within the area that extends 200 nautical miles (nm)
from the official U.S. shoreline, which is roughly equivalent to the border of the U.S. Exclusive Economic Zone.
Only some of these emissions are allocated to states based on official state boundaries that typically extend 3
miles offshore.
The CI and C2 vessels tend to be smaller ships that operate closer to shore, and along inland and intercoastal
waterways. Naval vessels are not included in this inventory, though Coast Guard vessels are included as part of
the CI and C2 vessels.
The CMV source category does not include recreational marine vessels, which are generally less than 100 feet in
length, most being less than 30 feet, and powered by either inboard or outboard. These emissions are included
in those calculated by the MOVES model; they reside in the nonroad data category and EIS "Mobile - Non-Road
Equipment" sectors of the 2017 NEI.
The 2017 NEI CMV estimates no longer employ the emissions type (M=maneuvering, H=hotelling, C=cruise,
Z=reduced speed zone) used in previous NEIs. Also, for 2017, new SCCs were created for CMV as noted in Table
4-149 below, to replace SCCs, shown in Table 4-150, used in previous NEI versions. Emission factors vary by SCC.
In addition, the 2017 NEI does not utilize shape files in the same manner as earlier NEIs. Although the detailed
2017 files used for air quality modeling have 1-hour emissions in detailed spatial grids, the NEI estimate are
annual and in over-water-only shape file codes for port estimates and in county FIPs codes for underway
4-255
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emissions. The port shapes do not cross counties and can be readily summed to individual port or to county.
Shape files are posted on the 2017 NEI page under the link Commercial Marine Vessel GIS Shape Files (port and
underway, current and retired).
Table 4-149: New Commercial Marine Vessel SCCs and emission types in EPA estimates
see
SCC Level One
SCC Level Two
SCC
Level
Three
SCC Level Four
2280002101
Mobile
Sources
Marine Vessels,
Commercial
Diesel
C1C2 Port emissions: Main Engine
2280002102
Mobile
Sources
Marine Vessels,
Commercial
Diesel
C1C2 Port emissions: Auxiliary Engine
2280002201
Mobile
Sources
Marine Vessels,
Commercial
Diesel
C1C2 Underway emissions: Main
Engine
2280002202
Mobile
Sources
Marine Vessels,
Commercial
Diesel
C1C2 Underway emissions: Auxiliary
Engine
2280002103
Mobile
Sources
Marine Vessels,
Commercial
Diesel
C3 Port emissions: Main Engine
2280002104
Mobile
Sources
Marine Vessels,
Commercial
Diesel
C3 Port emissions: Auxiliary Engine
2280002203
Mobile
Sources
Marine Vessels,
Commercial
Diesel
C3 Underway emissions: Main Engine
2280002204
Mobile
Sources
Marine Vessels,
Commercial
Diesel
C3 Underway emissions: Auxiliary
Engine
2280003103
Mobile
Sources
Marine Vessels,
Commercial
Residual
C3 Port emissions: Main Engine
2280003104
Mobile
Sources
Marine Vessels,
Commercial
Residual
C3 Port emissions: Auxiliary Engine
2280003203
Mobile
Sources
Marine Vessels,
Commercial
Residual
C3 Underway emissions: Main Engine
2280003204
Mobile
Sources
Marine Vessels,
Commercial
Residual
C3 Underway emissions: Auxiliary
Engine
Table 4-150: Retired Commercial Marine Vessel SCCs
SCC
SCC Level One
SCC Level Two
SCC Level Three
SCC Level Four
2280002100
Mobile Sources
Marine Vessels, Commercial
Diesel
Port emissions
2280002200
Mobile Sources
Marine Vessels, Commercial
Diesel
Underway emissions
2280003100
Mobile Sources
Marine Vessels, Commercial
Residual
Port emissions
2280003200
Mobile Sources
Marine Vessels, Commercial
Residual
Underway emissions
4.21.2 Sources of data
EPA's CMV estimates use satellite-based automatic identification system (AIS) activity data from the US Coast
Guard. The details of these calculation are available in the document "Methodology Documentation for EPA's
Commercial Marine Emissions Estimates" on the 2017 NEI Data home page.
Five states submitted CMV emissions to EIS (California, Delaware, New Jersey, and Washington). Texas supplied
estimates outside the EIS in retired SCCs. However, after review, all agreed to use of EPA's AIS estimates as
superior to their submittals.
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4.21.3 Quality assurance
The QA procedures on the EPA-developed CMV estimates are detailed in the CMV-specific documentation.
Although SLT submittals were reviewed and compared to EPA's, no SLT estimates were used in the 2017 NEI.
4.22 Mobile - Locomotives (Nonpoint)
This section documents (rail) emissions in the nonpoint data category. Refer to Section 3.3 for information on
rail yard emissions in the point data category.
4.22.1 Sector description
The locomotive sector includes railroad locomotives powered by diesel-electric engines. A diesel-electric
locomotive uses 2-stroke or 4-stroke diesel engines and an alternator or a generator to produce the electricity
required to power its traction motors. The locomotive source category is further divided up into categories:
Class I line haul, Class ll/lll line haul, Passenger, Commuter, and Yard. Table 4-151 below indicates locomotive
SCCs and whether they are included in EPA estimated emissions. If not in EPA estimates, then all emissions from
that SCC that appear in the inventory are from S/L/T agencies.
Table 4-151: Locomotive SCCs, descriptions, and EPA estimation status
SCC
Description
EPA Estimated?
Data Category
2285002006
Mobile Sources Railroad Equipment Diesel Line
Haul Locomotives: Class 1 Operations
Yes - at county-level
Nonpoint
2285002007
Mobile Sources Railroad Equipment Diesel Line
Haul Locomotives: Class II / III Operations
Yes - at county-level
Nonpoint
2285002008
Mobile Sources Railroad Equipment Diesel Line
Haul Locomotives: Passenger Trains (Amtrak)
Yes - at county-level
Nonpoint
2285002009
Mobile Sources Railroad Equipment Diesel Line
Haul Locomotives: Commuter Lines
Yes - at county-level
Nonpoint
2285002010
Railroad Equipment Diesel Yard Locomotives
No
Nonpoint
28500201
Internal Combustion Engines Railroad Equipment
Diesel Yard
Yes - as point sources
Point
4.22.2 Sources of data
The locomotives sector includes data from SLT agency-provided emissions data, and an EPA dataset of
locomotive emissions. EPA-estimated emissions from select locomotive SCCs as indicated in Table 4-151 above.
The agencies listed in Table 4-152 also submitted emissions to locomotive SCCs.
Table 4-152: Submitting SLT agencies with number of pollutants reported for each SCC
SLT dataset
Agency
io
r--
00
-------
SLT dataset
Agency
2285002006
2285002007
2285002008
2285002009
2285002010
28500201
2017NCDAQ
North Carolina
10
2017TR180
Shoshone-Bannock Tribes of the Fort Hall
Reservation of Idaho
43
2017TR181
Coeur d'Alene Tribe
43
43
2017TR182
Nez Perce Tribe
43
2017TR183
Kootenai Tribe of Idaho
43
2017TXCEQ
Texas
43
43
43
43
2017UTDAQ
Utah
6
6
6
6
2017VADEQ
Virginia
6
6
2017WADOE
Washington
6
6
6
6
2017WashoeCty
Washoe County, NV
6
5
2017ERTAC_Rail
EPA
52
52
52
52
52
4.22.3 EPA-developed emissions
EPA's 2017 rail emissions were developed by LADCO and the State of Illinois, with support from various other
states in a collaborative team called Easter Regional Technical Advisory Committee (ERTAC). ERTAC used
confidential line-haul activity data, in millions of gross ton (MGT) route miles per link, from the Federal Railroad
Administration (FRA) for 2016. Adjusted rail fuel consumption index values were used to allocate each Class 1
railroad's fuel use to links based on MGT. The Association of American Railroads (AAR) provided ERTAC Rail with
locomotive fleet mix information for 2017 for emission factor application. Since the rail link-based activity was
confidential, ERTAC provided county-level emissions summaries to EPA.
Rail yard emissions were calculated based on supply fuel use and/or yard switcher counts provided by rail
companies. For Class II and III rail lines, location data is available online as part of Bureau of Transportation
Statistics' National Transportation Atlas Database (NTAD). Detailed documentation methodology for this work is
available in the Specification Sheet: Rail 2017 National Emissions Inventory on the 2017 Supplemental data FTP
site.
The ERTAC effort developed emissions for criteria pollutants and greenhouse gases.
4.22.3.1 Hazardous Air Pollutant Emissions Estimates
HAP emissions were estimated by applying speciation profiles to the VOC or PM estimates. These "HAP
fractions" have been updated for 2017 [ref 1] and are thereby different than those used for 2014 NEI. These
profiles are posted in the workbook "2017Rail_HAP_AugmentationProfileAssignmentFactors_20200128.xlsx" on
the 2017 Supplemental data FTP site.
HAP estimates were calculated at the yard and link level, after the criteria emissions had been allocated. Where
submitting agencies did not supply HAPs, those estimates were also derived via this VOC/PM speciation method.
4.22.4 Quality assurance
EPA and agency-submitted values were compared to find instances where point and nonpoint rail yard SCCs may
duplicate. This occurs when agencies submitted nonpoint in the same counties where EPA had point yards. In
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this case, where rail yard point locations existed within the county, SLT county-level emissions were reassigned
to yards to avoid double counting point and county emissions estimates.
4.22.5 References
1. Reichle, L.J., R. Cook, C.A. Yanca, D.B. Sontag, 2015. Development of organic gas exhaust speciation
profiles for nonroad spark-ignition and compression-ignition engines and equipment. Journal of the Air
& Waste Management Association, Vol 65, 2015, Issue 10.
4,23 Solvents - Consumer and Commercial Solvent Use: Agricultural Pesticides
There are three sections in this documentation that discuss nonpoint sources of Consumer and Commercial
Solvent Use. This section discusses agricultural pesticides; the following section discusses asphalt paving, and
the third section discusses all other Solvent sources, including the remaining sources in the Consumer and
Commercial Solvent Use sector. The reason these sources are broken up within this EIS sector is because the EPA
methodologies for estimating the emissions are different.
4.23.1 Source category description
While Agricultural Pesticide Application is part of Consumer and Commercial Solvents sector, the nature of its
methodology is significantly different from most of the other sources in this sector. Pesticides are substances
used to control nuisance species and can be classified by targeted pest group: weeds (herbicides), insects
(insecticides), fungi (fungicides), and rodents (rodenticides). They can be further described by their chemical
characteristics: synthetics, non-synthetics (petroleum products), and inorganics. Different pesticides are made
through various combinations of the pest-killing material, also called the active ingredient, and various solvents
(which serve as carriers for the active ingredient). Both types of ingredients contain volatile organic compounds
(VOC) that may be emitted to the air during application or after application as a result of evaporation [ref 1],
Approximately 68 to 75 percent of pesticides used in the United States are applied to agricultural lands, both
cropland and pasture. Agricultural pesticides continue to be a cost-effective means of controlling weeds, insects,
and other threats to the quality and yield of food production. Since application rates for a particular pesticide
may vary from region to region, the regional application rates should be considered when estimating potential
VOC emissions.
4.23.2 Sources of data
As seen in Table 4-153, this source category includes data from S/L/Ts and EPA for Agricultural application
(SCC=2461850000). New Jersey and Marland also reported emissions for Surface Application (2461800001) and
Soil Incorporation (2461800002). The leading SCC description is "Solvent Utilization; Miscellaneous Non-
Industrial: Commercial" for all SCCs.
Table 4-153: Pesticide application SCCs estimates generated by EPA and S/L/Ts
SCC
Description
EPA
S/L/T
2461800001
Pesticide Application: All Processes; Surface Application
MD, NJ
2461800002
Pesticide Application: All Processes; Soil Incorporation
MD
2461850000
Pesticide Application: Agricultural; All Processes
X
X
The agencies listed in Table 4-154 submitted pesticide emissions; agencies not listed used EPA estimates.
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Table 4-154: Agencies that submitted pesticide emissions in the 2017 NEI
Region
Agency
S/L/T
1
New Hampshire Department of Environmental Services
State
2
New Jersey Department of Environment Protection
State
3
Maryland Department of the Environment
State
4
Memphis and Shelby County Health Department - Pollution Control
Local
4
Metro Public Health of Nashville/Davidson County
Local
5
Illinois Environmental Protection Agency
State
9
California Air Resources Board
State
9
Maricopa County Air Quality Department
Local
10
Coeur d'Alene Tribe
Tribe
10
Kootenai Tribe of Idaho
Tribe
10
Nez Perce Tribe
Tribe
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
4.23.3 EPA-developed emissions
The USGS provides county-level estimates of pesticide application, in its preliminary county-level pesticide use
estimates [ref 2], These data provide information about the total application of each active ingredient in a
pesticide product (e.g. 2,4-D, atrazine, captan). There are often many different pesticide products with the same
active ingredient. For example, the California Department of Pesticide Regulation's (CA DPR) database [ref 3]
lists 49 registered pesticide products with atrazine as the active ingredient, each with slightly different
formulations, including different proportions of active ingredient and solvents. The CA DPR database includes
information on the mass fraction of active ingredient in each pesticide product. EPA uses this information to
calculate an average VOC emissions factor for each active ingredient listed in the CA DPR database. This VOC
emissions factor is multiplied by the amount of active ingredient applied in each county, from the USGS report,
to estimate VOC emissions in each county. For active ingredients not in the CA DPR database, a weighted
emissions factor is calculated by weighting the emissions factors from the CA DPR database with total pounds of
active ingredient reported in the USGS report. HAP emissions are calculated by multiplying the total pounds of
active ingredients applied in each county by an emissions factor.
4.23,3.1 Activity data
The activity for pesticide application is the pounds of active ingredient applied per pesticide at the county level
for the years 2016 and 2017, from the USGS preliminary county-level pesticide use estimates [ref 2] which gives
county-level pesticide data in terms of kg of active ingredient applied. The data estimate preliminary annual
county-level pesticide use for 387 herbicides, insecticides, and fungicides applied to agricultural crops grown in
the conterminous United States during 2016 and 2017. For all states except California, pesticide-use data are
compiled from proprietary surveys of farm operations located within U.S. Department of Agriculture Crop
Reporting Districts (CRDs). Surveyed pesticide-use data are used in conjunction with county annual harvested-
crop acres reported by the U.S. Department of Agriculture 2012 Census of Agriculture and the 2013 County
Agricultural Production Survey to calculate use rates per harvested-crop acre, or an "estimated pesticide use"
(EPest) rate, for each crop by year. County-use estimates are then calculated by USGS by multiplying EPest rates
by harvested-crop acres for each pesticide crop combination. Use estimates for California in the USGS data are
obtained from annual CA DPR Pesticide Use Reports.
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The USGS report calculates both EPest-low and EPest-high rates. The EPest-high rates are used here to estimate
VOC emissions. Both methods incorporated surveyed and extrapolated rates to estimate pesticide use for
counties, but EPest-low and EPest-high estimations differ in how situations are treated when a CRD was
surveyed and pesticide use was not reported for a pesticide-by-crop combination. If use of a pesticide on a crop
is not reported in a surveyed CRD, EPest-low reports zero use in the CRD for that pesticide-by-crop combination.
EPest-high, however, treats the unreported use for that pesticide-by-crop combination in the CRD as
unsurveyed, and pesticide-by-crop use rates from neighboring CRDs and, in some cases, CRDs within the same
Farm Resources Region are used to calculate the pesticide-by-crop EPest-high rate for that CRD.
Due to data limitations, the USGS report does not contain active ingredient usages for Alaska and Hawaii.
However, the Census of Agriculture [ref 5] contains acres treated with pesticide by county for Alaska and Hawaii
and these values are used to estimate emissions.
4.23.3.2 Allocation procedure
The activity data are reported at the county level and do not need to be allocated.
4.23.3.3 Emission factors
The VOC emissions factors are derived for each active ingredient based on the pesticide profiles database
maintained by the CA DPR [ref 2], This database contains the chemical formulation for pesticide products
registered in the State of California and provides key inputs for the development of VOC emissions factors,
including the mass fraction of the active ingredient and the emission potential (EP) of registered pesticide
products. The EP value represents the VOC content of the pesticide product and it is determined empirically
through thermogravimetric analysis. Since the CA DPR database lists both agricultural and non-agricultural
pesticide products, it is necessary to screen out entries that were likely formulated as a consumer product.
Pesticide products that contained terms suggesting non-agricultural applications are excluded. Terms used to
screen out likely consumer products are listed in Table 4-155.
Table 4-155: Terms used to screen out consumer products
ALGAE
DEODORIZING
GERM
MRSA
STAIN
ANT
DETERGENT
HAMSTER
ORNAMENTAL
SWIM
BATHROOM
DISHWASHER
HOME
POND
TICK
BEDBUG
DISINFECT
HORNET
POTTY
TURF
BEE
DOG
HORSE
PRESCRIPTION
WASP
CAT
DRAIN
HOUSE
RAT
WIPES
CATTLE
EQUINE
INDOOR
ROACH
YARD
CLEANER
FLEA
KLEEN
RODENTICIDE
DECK
FLY
LANDSCAPE
ROOF
DEGREASER
FOGGER
LAWN
SANI
DEODORIZER
GERBIL
MOUSE
SPA
Each record in the CA DPR database is for a specific pesticide product, and provides the product name, primary
active ingredient, mass percent of active ingredient, emission potential (EP), registration number, and method
used to estimate the EP. The pesticide-specific EP of reactive organic gases (i.e., the mass percentage of product
that contributes to VOC emissions) and the mass percent of active ingredient are used to calculate pesticide-
specific VOC emissions factors.
4-261
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The CA DPR emission potential database [ref 2] provides the pesticide-specific emissions potential of reactive
organic gases (i.e., the mass percentage of each pesticide product that contributes to VOC emissions) and the
mass percent of active ingredient. To determine the total amount of pesticide product applied (i.e. both the
active ingredient and the solvent) the amount of active ingredient applied (from USGS data) [ref 3] is divided by
the mass percent of active ingredient, which is divided by 100 to convert from percent to fraction.
TP
Al
pest,US
pest,US fjj p
pest,US
Too
(1)
Where:
TPPest,us = Total pesticide applied for each active ingredient in the United States, in lbs.
Al pest,us = Total active ingredient applied of each pesticide type in the United States, in lbs.
MPPest,us = Average mass percent of active ingredient in each pesticide type in the United States, in percent
Next, the total national-level VOC emissions from each pesticide type are estimated by multiplying the total
pesticide applied by the pesticide-specific emissions potential of reactive organic gases (i.e., the mass
percentage of each pesticide that contributes to VOC emissions), from the CA DPR database [ref 2],
EvOC,US,pest ~ TPpest.US x'
EP'
rog,pest
"Too
(2)
Where:
E VOC, US, pest
TPpest,US
EP rog,pest
mass
Total national-level VOC emissions for each pesticide type, in lbs.
Total pesticide applied of each pesticide type in the United States, in lbs.
Emissions potential of reactive organic gases for each pesticide, expressed as % of pesticide
The VOC emissions factor for each pesticide type is calculated by dividing the total national-level VOC emissions
for each pesticide type by the total active ingredient applied for each pesticide type.
_ EvOC,US,pest (3)
zrpest ~ ~7~j
Alpest,US
Where:
EFpest = Pesticide-specific emissions factor, in pounds VOC / pound active ingredient
Evoc,us,pest = Total national-level VOC emissions for each pesticide type, in lbs.
Al pest,us = Total active ingredient applied of each pesticide type in the United States, in lbs.
For active ingredients not in the CA DPR database, a weighted average emissions factor (EFavg) is calculated. This
weighted average is estimated by weighting the emissions factors from the CA DPR database using the total
pounds of active ingredient reported in the USGS report "Preliminary Estimates of Annual Agricultural Pesticide
Use for Counties of the Conterminous United States, 2013" [ref 3], A crosswalk between compound name in the
USGS database and the chemical name in the CA DPR database is provided in Table 4-156. Note that any
pesticide compound from the USGS database that is not in the CA DPR data are marked with the word
4-262
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"AVERAGE," to denote that the weighted average VOC emissions factors of 0.4 pounds of VOC per pound of
active ingredient is used to estimate VOC emissions for the application of that pesticide. The pesticide-specific
VOC emissions factors for all pesticides from the CA DPR database are shown in Table 5 in the document
"Agricultural Pesticides NEMO 2017 FINAL_4-2 update.doc" on the 201? NEI Supplemental FTP site.
Table 4-156: Crosswalk between USGS compound name and CA DPR chemical name
USGS Compound Name
CA DPR Compound Name
2,4-D
2,4-D
2,4-DB
2,4-DB ACID
6-BENZYLADENINE
AVERAGE
ABAMECTIN
ABAMECTIN
ACEPHATE
ACEPHATE
ACEQUINOCYL
ACEQUINOCYL
ACETAMIPRID
ACETAMIPRID
ACETOCHLOR
AVERAGE
ACIBENZOLAR
ACIBENZOLAR-S-METHYL
ACIFLUORFEN
ACIFLUORFEN, SODIUM SALT
ALACHLOR
ALACHLOR
ALDICARB
ALDICARB
ALUMINUM PHOSPHIDE
ALUMINUM PHOSPHIDE
AMECTOCTRADIN
AMETOCTRADIN
AMETRYN
AMETRYNE
AMINOPYRALID
AMINOPYRALID, TRIISOPROPANOLAMINE SALT
ASULAM
ASULAM, SODIUM SALT
ATRAZINE
ATRAZINE
AVI GLYCINE
AVERAGE
AZADIRACHTIN
AZADIRACHTIN
AZINPHOS-METHYL
AZINPHOS-METHYL
AZOXYSTROBIN
AZOXYSTROBIN
BACILLUS AMYLOLIQUIFACIEN
BACILLUS AMYLOLIQUEFACIENS STRAIN D747
BACILLUS CEREUS
BACILLUS CEREUS, STRAIN BP01
BACILLUS FIRMUS
BACILLUS FIRMUS (STRAIN 1-1582)
BACILLUS PUMILIS
BACILLUS PUMILUS GHA 180
BACILLUS SUBTILIS
BACILLUS SUBTILIS GB03
BACILLUS THURINGIENSIS
BACILLUS THURINGIENSIS (BERLINER)
BENFLURALIN
AVERAGE
BENOMYL
BENOMYL
BENSULFURON
BENSULFURON METHYL
BENSULIDE
BENSULIDE
BENTAZONE
BENTAZON, SODIUM SALT
BIFENAZATE
BIFENAZATE
BIFENTHRIN
BIFENTHRIN
BISPYRIBAC
BISPYRIBAC-SODIUM
BOSCALID
BOSCALID
4-263
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USGS Compound Name
CA DPR Compound Name
BROMACIL
BROMACIL
BROMOXYNIL
BROMOXYNIL BUTYRATE
BUPROFEZIN
BUPROFEZIN
BUTRALIN
AVERAGE
CALCIUM POLYSULFIDE
AVERAGE
CAPTAN
CAPTAN
CARBARYL
CARBARYL
CARBOPHENOTHION
CARBOPHENOTHION
CARBOXIN
CARBOXIN
CARFENTRAZONE-ETHYL
CARFENTRAZONE-ETHYL
CHINOMETHIONAT
AVERAGE
CHLORANTRANILIPROLE
CHLORANTRANILIPROLE
CHLORETHOXYFOS
AVERAGE
CHLORFENAPYR
CHLORFENAPYR
CHLORIMURON
AVERAGE
CHLORMEQUAT
CHLORMEQUAT CHLORIDE
CHLORONEB
CHLORONEB
CHLOROPICRIN
CHLOROPICRIN
CHLOROTHALONIL
CHLOROTHALONIL
CHLORPROPHAM
CHLORPROPHAM
CHLORPYRIFOS
CHLORPYRIFOS
CHLORSULFURON
CHLORSULFURON
CLETHODIM
CLETHODIM
CLODINAFOP
AVERAGE
CLOFENTEZINE
CLOFENTEZINE
CLOMAZONE
CLOMAZONE
CLOPYRALID
CLOPYRALID
CLORANSULAM-METHYL
AVERAGE
CLOTHIANIDIN
CLOTHIANIDIN
CONIOTHYRIUM MINITANS
CONIOTHYRIUM MINITANS STRAIN CON/M/91-08
COPPER
COPPER
COPPER HYDROXIDE
COPPER HYDROXIDE
COPPER OCTANOATE
COPPER OCTANOATE
COPPER OXYCHLORIDE
COPPER OXYCHLORIDE
COPPER OXYCHLORIDE S
COPPER OXYCHLORIDE SULFATE
COPPER SULFTRIBASIC
COPPER SULFATE (BASIC)
COPPER SULFATE
COPPER SULFATE (PENTAHYDRATE)
CPPU
AVERAGE
CRYOLITE
CRYOLITE
CUPROUS OXIDE
COPPER OXIDE (OUS)
CYAN AM IDE
AVERAGE
CYAZOFAMID
CYAZOFAMID
4-264
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USGS Compound Name
CA DPR Compound Name
CYCLANILIDE
CYCLANILIDE
CYCLOATE
CYCLOATE
CYDIA POMONELLA
AVERAGE
CYFLUFENAMID
CYFLUFENAMID
CYFLUTHRIN
CYFLUTHRIN
CYHALOFOP
CYHALOFOP-BUTYL
CYHALOTHRIN-GAMMA
AVERAGE
CYHALOTHRIN-LAMBDA
AVERAGE
CYMOXANIL
CYMOXANIL
CYPERMETHRIN
CYPERMETHRIN
CYPROCONAZOLE
AVERAGE
CYPRODINIL
CYPRODINIL
CYROMAZINE
CYROMAZINE
CYTOKININ
CYTOKININ
DAMINOZIDE
DAMINOZIDE
DAZOMET
DAZOMET
DCPA
AVERAGE
DECAN-1-OL
AVERAGE
DELTAMETHRIN
DELTAMETHRIN
DESMEDIPHAM
DESMEDIPHAM
DIAZINON
DIAZINON
DICAMBA
DICAMBA
DICHLOBENIL
DICHLOBENIL
DICHLOROPROPENE
AVERAGE
DICHLORPROP
DICHLORPROP, BUTOXYETHANOL ESTER
DICLOFOP
DICLOFOP-METHYL
DICLORAN
DICLORAN
DICLOSULAM
AVERAGE
DICOFOL
DICOFOL
DICROTOPHOS
DICROTOPHOS
DIENOCHLOR
DIENOCHLOR
DIETHATYL
DIETHATYL-ETHYL
DIFENOCONAZOLE
DIFENOCONAZOLE
DIFLUBENZURON
DIFLUBENZURON
DIFLUFENZOPYR
DIFLUBENZURON
DIMETHENAMID
DIMETHENAMID-P
DIMETHENAMID-P
DIMETHENAMID-P
DIMETHIPIN
DIMETHIPIN
DIMETHOATE
DIMETHOATE
DIMETHOMORPH
DIMETHOMORPH
DIMETHYL DISULFIDE
AVERAGE
DINOSEB
DINOSEB
4-265
-------
USGS Compound Name
CA DPR Compound Name
DINOTEFURAN
DINOTEFURAN
DIQUAT
DIQUAT DIBROMIDE
DISULFOTON
DISULFOTON
DITHIOPYR
DITHIOPYR
DIURON
DIURON
DODINE
DODINE
EMAMECTIN
EMAMECTIN BENZOATE
ENDOSULFAN
ENDOSULFAN
ENDOTHAL
EN DOTH ALL, DISODIUM SALT
EPTC
EPTC
ESFENVALERATE
ESFENVALERATE
ETHALFLURALIN
ETHALFLURALIN
ETHEPHON
ETHEPHON
ETHION
ETHION
ETHOFUMESATE
ETHOFUMESATE
ETHOPROPHOS
ETHOPROP
ETOXAZOLE
ETOXAZOLE
ETRIDIAZOLE
AVERAGE
FAMOXADONE
AVERAGE
FATTY ALCOHOLS
AVERAGE
FENAMIDONE
FENAMIDONE
FENAMIPHOS
FENAMIPHOS
FENARIMOL
FENARIMOL
FENBUCONAZOLE
FENBUCONAZOLE
FENBUTATIN OXIDE
FENBUTATIN-OXIDE
FENHEXAMID
FENHEXAMID
FENOXAPROP
FENOXAPROP-ETHYL
FENOXYCARB
FENOXYCARB
FENPROPATHRIN
FENPROPATHRIN
FENPYROXIMATE
FENPYROXIMATE
FENTIN
FENTIN HYDROXIDE
FERBAM
FERBAM
FIPRONIL
FIPRONIL
FLAZASULFURON
FLAZASULFURON
FLONICAMID
FLONICAMID
FLORASULAM
FLORASULAM
FLUAZIFOP
FLUAZIFOP-BUTYL
FLUAZINAM
FLUAZINAM
FLUBENDIAMIDE
FLUBENDIAMIDE
FLUCARBAZONE
AVERAGE
FLUDIOXONIL
FLUDIOXONIL
FLUFENACET
AVERAGE
4-266
-------
USGS Compound Name
CA DPR Compound Name
FLUMETRALIN
FLUOMETURON
FLUMETSULAM
AVERAGE
FLUMICLORAC
F LU M1CLO RAC-P E NTYL
FLUMIOXAZIN
FLUMIOXAZIN
FLUOMETURON
FLUOMETURON
FLUOPICOLIDE
FLUOPICOLIDE
FLUOPYRAM
FLUOPYRAM
FLUOXASTROBIN
FLUOXASTROBIN
FLURIDONE
FLURIDONE
FLUROXYPYR
FLUROXYPYR
FLUTHIACET-M ETHYL
AVERAGE
FLUTOLANIL
FLUTOLANIL
FLUTRIAFOL
FLUTRIAFOL
FLUVALINATE-TAU
AVERAGE
FLUXAPYROXAD
FLUXAPYROXAD
FOMESAFEN
AVERAGE
FORAMSULFURON
FORAMSULFURON
FORMETANATE
FORMETANATE HYDROCHLORIDE
FOSETYL
FOSETYL-AL
GALLEX
META-CRESOL
GAMMA AMINOBUTYRIC ACID
AVERAGE
GIBBERELLIC ACID
GIBBERELLINS
GLUFOSINATE
GLUFOSI NATE-AMMONIUM
GLYPHOSATE
GLYPHOSATE
HALOSULFURON
HALOSULFURON-METHYL
HARPIN PROTEIN
HARPIN PROTEIN
HEXAZINONE
HEXAZINONE
HEXYTHIAZOX
HEXYTHIAZOX
HYDRAMETHYLNON
HYDRAMETHYLNON
HYDRATED LIME
CALCIUM HYDROXIDE
HYDROGEN PEROXIDE
HYDROGEN PEROXIDE
HYMEXAZOL
AVERAGE
IBA
IBA
IMAZALIL
IMAZALIL
IMAZAMETHABENZ
IMAZAMETHABENZ
IMAZAMOX
IMAZAMOX
IMAZAPIC
IMAZAPIC
IMAZAPYR
IMAZAPYR
IMAZAQUIN
AVERAGE
IMAZETHAPYR
IMAZETHAPYR
IMAZOSULFURON
IMAZOSULFURON
IMIDACLOPRID
IMIDACLOPRID
4-267
-------
USGS Compound Name
CA DPR Compound Name
INDAZIFLAM
INDAZIFLAM
INDOXACARB
INDOXACARB
IODOSULFURON
AVERAGE
IPCONAZOLE
IPCONAZOLE
IPRODIONE
IPRODIONE
ISOXABEN
ISOXABEN
ISOXAFLUTOLE
AVERAGE
KAOLIN CLAY
KAOLIN
KINOPRENE
KINOPRENE
KRESOXIM-M ETHYL
KRESOXIM-METHYL
LACTOFEN
AVERAGE
L-GLUTAMIC ACID
GLUTAMIC ACID
LINURON
LINURON
MALATHION
MALATHION
MALEIC HYDRAZIDE
MALEIC HYDRAZIDE
MANCOZEB
MANCOZEB
MANDIPROPAMID
MANDIPROPAMID
MANEB
MANEB
MCPA
MCPA
MCPB
MCPB, SODIUM SALT
MECOPROP
MECOPROP-P
MEFENOXAM
MEFENOXAM
MEPIQUAT
MEPIQUAT CHLORIDE
MESOSULFURON
MESOSULFURON-METHYL
MESOTRIONE
MESOTRIONE
METALAXYL
METALAXYL
METALDEHYDE
METALDEHYDE
METAM
METAM-SODIUM
METAM POTASSIUM
METAM-SODIUM
METCONAZOLE
METCONAZOLE
METHAMIDOPHOS
METHAMIDOPHOS
METHIDATHION
METHIDATHION
METHIOCARB
METHIOCARB
METHOMYL
METHOMYL
METHOXYFENOZIDE
METHOXYFENOZIDE
METHYL BROMIDE
METHYL BROMIDE
METHYL BROMIDE
METHYL BROMIDE
METHYL IODIDE
METHYL IODIDE
METHYL PARATHION
METHYL PARATHION
METIRAM
METIRAM
METOLACHLOR
METOLACHLOR
METOLACHLOR-S
METOLACHLOR
4-268
-------
USGS Compound Name
CA DPR Compound Name
METRAFENONE
METRAFENONE
METRIBUZIN
METRIBUZIN
METSULFURON
METSULFURON-METHYL
MEVINPHOS
MEVINPHOS
MSMA
MSMA
MYCLOBUTANIL
MYCLOBUTANIL
MYROTHECIUM VERRUCARIA
MYROTHECIUM VERRUCARIA, DRIED FERMENTATION SOLIDS
NALED
NALED
NAPHTHYLACETAMIDE
AVERAGE
NAPHTHYLACETIC ACID
AVERAGE
NAPROPAMIDE
NAPROPAMIDE
NAPTALAM
NAPTALAM, SODIUM SALT
NEEM OIL
AVERAGE
NICOSULFURON
NICOSULFURON
NORFLURAZON
NORFLURAZON
NOSEMA LOCUSTAE CANN
NOSEMA LOCUSTAE SPORES
NOVALURON
NOVALURON
ORTHOSULFAMURON
ORTHOSULFAMURON
ORYZALIN
ORYZALIN
OXADIAZON
OXADIAZON
OXAMYL
OXAMYL
OXYDEMETON-M ETHYL
OXYDEMETON-METHYL
OXYFLUORFEN
OXYFLUORFEN
OXYTETRACYCLINE
OXYTETRACYCLINE HYDROCHLORIDE
PACLOBUTRAZOL
PACLOBUTRAZOL
PARAQUAT
PARAQUAT DICHLORIDE
PARATHION
PARATHION
PELARGONIC ACID
AVERAGE
PENDIMETHALIN
PENDIMETHALIN
PENOXSULAM
PENOXSULAM
PENTHIOPYRAD
PENTHIOPYRAD
PERMETHRIN
PERMETHRIN
PETROLEUM DISTILLATE
PETROLEUM DISTILLATES
PETROLEUM OIL
PETROLEUM NAPHTHENIC OILS
PHENMEDIPHAM
PHENMEDIPHAM
PHORATE
PHORATE
PHOSMET
PHOSMET
PHOSPHORIC ACID
PHOSPHORIC ACID
PICLORAM
PICLORAM
PINOXADEN
PINOXADEN
PIPERONYL BUTOXIDE
PIPERONYL BUTOXIDE
POLYHEDROSIS VIRUS
POLYHEDRAL OCCLUSION BODIES (OB'S) OF THE NUCLEAR
4-269
-------
USGS Compound Name
CA DPR Compound Name
POLYOXORIM
AVERAGE
POTASSIUM BICARBONATE
POTASSIUM BICARBONATE
POTASSIUM OLEATE
AVERAGE
PRIMISULFURON
AVERAGE
PRODIAMINE
PRODIAMINE
PROFENOFOS
PROFENOFOS
PROHEXADIONE
PROHEXADIONE CALCIUM
PROMETRYN
PROMETRYN
PROPAMOCARB HCL
PROPAMOCARB HYDROCHLORIDE
PROPANIL
PROPANIL
PROPARGITE
PROPARGITE
PROPAZINE
PROPAZINE
PROPICONAZOLE
PROPICONAZOLE
PROPOXYCARBAZONE
AVERAGE
PROPYZAMIDE
PROPYZAMIDE
PROSULFURON
AVERAGE
PROTHIOCONAZOLE
PROTHIOCONAZOLE
PSEUDOMONAS FLUORESCENS
PSEUDOMONAS FLUORESCENS, STRAIN A506
PYMETROZINE
PYMETROZINE
PYRACLOSTROBIN
PYRACLOSTROBIN
PYRAFLUFEN ETHYL
PYRAFLUFEN-ETHYL
PYRASULFOTOLE
AVERAGE
PYRETHRINS
PYRETHRINS
PYRIDABEN
PYRIDABEN
PYRIMETHANIL
PYRIMETHANIL
PYRIPROXYFEN
PYRIPROXYFEN
PYRITHIOBAC-SODIUM
PYRITHIOBAC-SODIUM
PYROXASULFONE
AVERAGE
PYROXSULAM
PYROXSULAM
QUINCLORAC
QUINCLORAC
QUINOXYFEN
QUINOXYFEN
QUINTOZENE
AVERAGE
QUIZALOFOP
QUIZALOFOP-ETHYL
RIMSULFURON
RIMSULFURON
ROTENONE
ROTENONE
SABADILLA
SABADILLA ALKALOIDS
SAFLUFENACIL
SAFLUFENACIL
SETHOXYDIM
SETHOXYDIM
SILICATES
SILICA AEROGEL
SIMAZINE
SIMAZINE
SODIUM CHLORATE
SODIUM CHLORATE
SODIUM CHLORATE
SODIUM CHLORATE
4-270
-------
USGS Compound Name
CA DPR Compound Name
SPINETORAM
SPINETORAM
SPINOSYN
SPINOSAD
SPIRODICLOFEN
SPIRODICLOFEN
SPIROMESIFEN
SPIROMESIFEN
SPIROTETRAMAT
SPIROTETRAMAT
STREPTOMYCIN
STREPTOMYCIN
SULFCARBAMIDE
AVERAGE
SULFENTRAZONE
SULFENTRAZONE
SULFOMETURON
SULFOMETU RON-METHYL
SULFOSATE
AVERAGE
SULFOSULFURON
SULFOSULFURON
SULFOXAFLOR
SULFOXAFLOR
SULFUR
SULFUR
SULFURIC ACID
SULFURIC ACID
TCMTB
TCMTB
TEBUCONAZOLE
TEBUCONAZOLE
TEBUFENOZIDE
TEBUFENOZIDE
TEBUPIRIMPHOS
AVERAGE
TEBUTHIURON
TEBUTHIURON
TEFLUTHRIN
AVERAGE
TEMBOTRIONE
TEMBOTRIONE
TERBACIL
TERBACIL
TERBUFOS
AVERAGE
TETRABOROHYDRATE
AVERAGE
TETRACONAZOLE
TETRACONAZOLE
TETRATHIOCARBONATE
AVERAGE
THIABENDAZOLE
THIABENDAZOLE
THIACLOPRID
THIACLOPRID
THIAMETHOXAM
THIAMETHOXAM
THIAZOPYR
THIAZOPYR
THIDIAZURON
THIDIAZURON
THIENCARBAZONE-METHYL
AVERAGE
THIFENSULFURON
THIFENSULFURON-M ETHYL
THIOBENCARB
THIOBENCARB
THIODICARB
THIODICARB
THIOPHANATE-METHYL
THIOPHANATE-METHYL
THIRAM
THIRAM
TOPRAMEZONE
AVERAGE
TRALKOXYDIM
TRALKOXYDIM
TRIADIMEFON
TRIADIMEFON
TRIADIMENOL
TRIADIMENOL
TRI-ALLATE
TRIALLATE
4-271
-------
USGS Compound Name
CA DPR Compound Name
TRIASULFURON
AVERAGE
TRIBENURON METHYL
TRIBENURON-METHYL
TRIBUFOS
AVERAGE
TRICLOPYR
TRICLOPYR, BUTOXYETHYL ESTER
TRIFLOXYSTROBIN
TRIFLOXYSTROBIN
TRIFLOXYSULFURON
TRIFLOXYSULFURON-SODIUM
TRIFLUMIZOLE
TRIFLUMIZOLE
TRIFLURALIN
TRIFLURALIN
TRIFLUSULFURON
AVERAGE
TRINEXAPAC
TRINEXAPAC-ETHYL
TRITICONAZOLE
TRITICONAZOLE
UNICONAZOLE
UNICONIZOLE-P
VINCLOZOLIN
VINCLOZOLIN
ZETA-CYPERMETHRIN
AVERAGE
ZINC
ZINC CHLORIDE
ZINEB
ZINEB
ZIRAM
ZIRAM
ZOXAMIDE
AVERAGE
The emissions factor is calculated for these active ingredients based on calculating a weighted average emissions
factor for all active ingredients. The weights are determined by dividing the active ingredient applied for each
pesticide type by the total active ingredients applied for all pesticides types. These weights are multiplied by the
emissions factor for each pesticide (calculated in equation 3), and then summed to determine the weighted
average emissions factor.
PEST
ravg ~ / t^pest aj a c,rpest
p£?=lLPest=1 Pest>US
Al-npqt TIQ
FT? - \ --pest,US w
/ w—.nT?cr . _ ^ Ed r i
Where:
EFavg = Weighted average emissions factor, in pounds VOC / pound active ingredient
AIPest,us = Total active ingredient applied of each pesticide type in the United States, in lbs.
EFpest = Pesticide-specific emissions factor, in pounds VOC / pound active ingredient
The HAP emissions factors are from El IP and are based on vapor pressure of the active ingredient [ref 1],
Compounds with a vapor pressure between 1 x 10"4and 1 x 10 s mm Hg at 20°Cto 25°C have an emissions factor
of 700 Ibs./ton (or 0.35 lbs./lb.). Compounds with a vapor pressure greater than 1 x 10"4 mm Hg at 20°C to 25°C
have an emissions factor of 1,160 Ibs./ton (or 0.58 lbs./lb.). The subset of HAPs is extracted from the list of
active ingredients and is shown in Table 4-157 along with the HAP emissions factors. If the calculated emissions
factor for any HAP is greater than the VOC emissions factor for that active ingredient, calculated in equation 3,
then the HAP emissions factor is set equal to the VOC emissions factor.
4-272
-------
Table 4-157: HAP Emissions Factors
Compound
Pollutant
Code
Vapor
Pressure
(mm Hg at
20°C to 25 °C)
Emissions
Factor
(lbs. per lb.
active
ingredient)
Source
2,4-D
94757
8 x 10"6
0.35
Reference 1, Tables 9.4-2 and 9.4-4
CAPTAN
133062
8 x 10"8
0.1441
Set equal to VOC emissions factor
calculated from the CA DPR. See
Table 4-156.
CARBARYL
63252
1.2 x 10"6
0.3208
Set equal to VOC emissions factor
calculated from the CA DPR. See
Table 4-156.
METHYL BROMIDE
74839
1,420
0.58
Vapor pressure: Reference 2
Emissions factor: Reference 1, Table
9.4-4
METHYL IODIDE
74884
400
0.58
Vapor pressure: Reference 4
Emissions factor: Reference 1, Table
9.4-4
PARATHION
56382
5 x 10"6
0.35
Reference 1, Tables 9.4-2 and 9.4-4
TRIFLURALIN
1582098
1.1 x 10"4
0.58
Reference 1, Tables 9.4-2 and 9.4-4
For Alaska and Hawaii, data from the conterminous United States is used to develop average emissions factors
by pollutant in terms of emissions per acre treated with pesticides. This is calculated by summing the total
emissions by pollutant for the conterminous United States and dividing by the total acres treated in the
conterminous United States.
4.23.3.4 Controls
There are no controls assumed for this category.
4.23.3.5 Emissions
VOC and HAP emissions are calculated by multiplying the amount of active ingredient applied in each county,
from the USGS database, by the appropriate emissions factor. The emissions factor for VOC is calculated using
equations 1-4. The emissions factors for the HAPs are listed in Table 4-157.
The VOC emissions are calculated by multiplying the active ingredients applied in each county per year by the
corresponding emissions factor.
Z1 ton (5)
Alpest,c * EFpest X 2000 ijj
Where:
Evoqc
Alpest,c
EFp est
4-273
= Annual emissions of VOC from pesticide active ingredient applications in county c, in tons
= Active ingredient of each pesticide type applied in county c, in pounds
= Pesticide-specific emissions factor, in pounds VOC / pound active ingredient
-------
Note that if the active ingredient (Alpest) is included in the CA DPR database, then the pesticide-specific emissions
factor is used (£Fpest); for all other active ingredients, the weighted average emissions factor is used (EFavg).
The HAP emissions are calculated by multiplying the active ingredients applied in each county per year by the
corresponding emissions factor. The HAPs listed in Table 4-157 correspond to the active ingredients in the USGS
database. For example, emissions of the HAP captan only occur from applications of the active ingredient
captan. Emissions are then summed across pesticide types to estimate the total county-level emissions for each
HAP.
PEST
(6)
ripest,c ^ EFp pest
pest=1
Where:
EP,c
EFp ,pest
Alpest,c
Emissions of pollutant p from pesticide applications in county c, in lbs.
Emissions factor for pollutant p, in pounds emissions / pound active ingredient
Active ingredient of each pesticide type applied in county c, in pounds
Note that the HAP emissions factors are from the El IP [ref 1], If the HAP emissions factor for a certain pesticide
type exceeds the VOC emissions factor calculated for that pesticide type as calculated in equations 1 and 2, then
the HAP emissions factor is set equal to the VOC emissions factor.
For Alaska and Hawaii, emissions are estimated by multiplying the acres treated with pesticides by pollutant-
specific emissions per acre emissions factors.
4.23.3,6 Example calculations
Table 4-158 lists sample calculations to determine the VOC and 2,4-D emissions from 2,4-Dichlorophenoxy
Acetic Acid (2,4-D). The sample calculations show the emission calculations for the pesticide 2,4-D only. To
estimate the total county-level emissions, the process would need to be repeated for each pesticide.
Table 4-158: Sample calculations for VOC/HAP emissions from 2,4-D agricultural pesticide application in Autauga
County, AL
Eq.
#
Equation
Values for Autauga County, AL
Result
1
Tp _ ripest,US
41,912,210 lbs 2,4 — D active ingredient
865,954,752 lbs
total 2,4-D
pesticide
applied in the
United States
* * pest,US Mp
'v**pest,US
100
4.84 mass percent
100
2
FP
t, _ rr, „ zrrog,pest
EVOC,US,pest ~ 1 rpest,US x ^qq
865,954,752 lbs total 2,4
4.0
— D pesticide x
F 100
34,638,190 lbs
VOC emissions
from 2,4-D in
the United
States
3
EvOC,US,pest
34,638,190 lbs VOC
0.826 lbs. VOC/
lb. 2,4-D active
ingredient
^lpest AJ
Alpest,US
41,912,210 lbs 2,4 — D active ingredient
4-274
-------
Eq.
#
Equation
Values for Autauga County, AL
Result
4
PEST
_ V MVest
C ravg / yPEST a j
Pest
x EFpest
N/A
This calculation
is not needed,
as 2,4-D is
included in the
CA DPR
database.
5
Evoc.c ~ ^ ' AIpest,c * EFpest
1 ton
X 2000 lb
8020 lbs. 2,4 — D active ingredient
1 ton
X 0.826 X ———rr
2000 lb
3.31 tons VOC
emissions from
2,4-D in Autauga
County, AL
6
PEST
Ep,c AIpest,c ^ EFp pest
pest=1
8020 lbs. 2,4 — D active ingredient
x 0.35
2,807 pounds
2,4-D emissions
from 2,4-D in
Autauga County,
AL
4.23.3.7 Changes from the 2014 methodology
As discussed in Section 4.23.3.1, EPA developed an emissions estimation methodology for Alaska and Hawaii
counties that was not used for the 2014 NEI.
4.23.3.8 Puerto Rico and U.S. Virgin Islands
Since insufficient data exist to calculate emissions for the counties in Puerto Rico and the US Virgin Islands,
emissions are based on two proxy counties in Florida: 12011, Broward County for Puerto Rico and 12087,
Monroe County for the US Virgin Islands. The total emissions in tons for these two Florida counties are divided
by their respective populations creating a tons per capita emissions factor. For each Puerto Rico and US Virgin
Island county, the tons per capita emissions factor is multiplied by the county population (from the same year as
the inventory's activity data) which served as the activity data. In these cases, the throughput (activity data) unit
and the emissions denominator unit are "EACH".
4.23.4 References
1. U.S. Environmental Protection Agency. 2001. Emissions Inventory Improvement Program, Vol. 3, Ch. 9,
Pesticides - Agricultural and Nonagricultural, Section 5.1, p. 9.5-4.
2. United States Geological Survey. 2017. Archived preliminary county-level pesticide use estimates.
3. Personal communication from Pam Wofford, California Department of Pesticide Regulation to Jonathan
Dorn, Abt Associates, "CDPR_Emission_Potential_Database_10_2015.xlsx", January 2016
4. U.S. Environmental Protection Agency. 2000. Health Effects Notebook for Hazardous Air Pollutants.
5. U.S. Department of Agriculture, 2012, 2012 Census of Agriculture. National Agricultural Statistics
Service.
4-275
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DRAFT
4.24 Solvents - Consumer and Commercial Solvent Use: Asphalt Paving
4.24.1 Source category description
Asphalt paving is the process of applying asphalt concrete to seal or repair the surface of roads, parking lots,
driveways, walkways, or airport runways. Asphalts for paving are mainly used in two ways. They are either mixed
with aggregates at plants and hauled to the paving site and then compacted on the road, or they are sprayed in
relatively thin layers with or without aggregates. Plant mixed asphalt products are called asphalt concrete mix.
As seen in Figure 4-21, these can be produced and laid down hot, using asphalt cements, or cold, using
emulsions or cutbacks. These mixes usually contain about 5% asphalt and 95% aggregates by weight. Aggregates
give the mix most of its ability to carry or resist loads while the asphalt coats and binds the aggregate structure.
Hot laid mixes, also called hot mix asphalt (HMA), are produced by mixing heated aggregates and asphalt
cements in special mixing plants. These very strong, stiff mixes are usually used for surface and subsurface layers
in highways, airports, parking lots, and other areas which carry heavy or high-volume traffic. HMA uses an
asphaltic binding agent which includes asphalt cement as well as any material added to modify the original
asphalt cement properties. Cold asphalt mixes are produced by mixing damp, cold aggregates with emulsions or
cutbacks at mixing plants — either stationary plants or portable ones brought to the site. Although not as strong
and stiff as hot mix, cold mixes may be more economical and flexible, and less polluting. They are used for areas
with intermediate and low traffic, for open graded mixes, and for patching. Sprayed asphalt applications include
asphalt-aggregate applications, usually called surface treatments or seal coats, and asphalt-only applications
such as tack coat, prime coat, fog seal, and dust prevention [ref 1],
Figure 4-21: Types of Asphalt Paving processes
A new, third type of mix, warm-mix asphalt (WMA), has become increasingly popular. In this type of mixture,
various methods are used to significantly reduce mix production temperature by 30 to over 100°F. These
methods include (1) using chemical additives to lower the high-temperature viscosity of the asphalt binder; (2)
techniques involving the addition of water to the binder, causing it to foam; and (3) two-stage processes
4-276
-------
involving the addition of hard and soft binders at different points during mix production. WMA has several
benefits, including lower cost (since significantly less fuel is needed to heat the mix), lower emissions and so
improved environmental impact, and potentially improved performance because of decreased age hardening
[ref 2],
Note that these source categories do not include emissions from the use of hot mix asphalt (HMA) or warm mix
asphalt (WMA). Estimates of emissions of volatile organic compounds (VOC), and hazardous air pollutants
(HAPs) from asphalt paving are based on the amount of cutback and emulsified asphalt used.
4.24.2 Sources of data
As seen in Table 4-159, this source category includes data from the S/L/T agency submitted data and the default
EPA generated emissions. EPA estimates emissions for both cutback and emulsified asphalt paving. New Jersey
and Maryland also reported emissions for "Asphalt Application: All Processes; Total: All Solvent Types"
(2461020000). The leading SCC description is "Solvent Utilization; Miscellaneous Non-industrial: Commercial" for
all SCCs.
Table 4-159: Asphalt Paving SCCs in the 2017 NEI
SCC
Description
EPA
S/L/T
2461020000
Asphalt Application: All Processes; Total: All Solvent Types
X
2461021000
Cutback Asphalt; Total: All Solvent Types
X
X
2461022000
Emulsified Asphalt; Total: All Solvent Types
X
X
The agencies listed in Table 4-160 reported emissions for at least one of the above SCCs. Maryland, New Jersey
and Washoe county reported emissions for the general "Asphalt Application SCC" (2461020000) as these
emissions were not covered by cutback and emulsified estimates.
Table 4-160: Agencies that reported emissions for Asphalt application in the 2017 NEI
Region
Agency
S/L/T
1
New Hampshire Department of Environmental Services
State
2
New Jersey Department of Environment Protection
State
3
Delaware Department of Natural Resources and Environmental Control
State
3
Maryland Department of the Environment
State
3
Virginia Department of Environmental Quality
State
4
Memphis and Shelby County Health Department - Pollution Control
Local
4
Metro Public Health of Nashville/Davidson County
Local
5
Illinois Environmental Protection Agency
State
6
Texas Commission on Environmental Quality
State
8
Utah Division of Air Quality
State
9
California Air Resources Board
State
9
Maricopa County Air Quality Department
Local
9
Washoe County Health District
Local
10
Coeur d'Alene Tribe
Tribe
10
Idaho Department of Environmental Quality
State
10
Kootenai Tribe of Idaho
Tribe
10
Nez Perce Tribe
Tribe
4-277
-------
Region
Agency
S/L/T
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
4.24.3 EPA-developed emissions
The calculations for estimating the emissions from asphalt use involve first estimating the amount of cutback
and emulsified asphalt used in each county. The amount of state-level cutback and emulsified asphalt used in
2008 is available from an Asphalt Institute report. Asphalt use is adjusted to 2017 using a ratio of the vehicle
miles traveled (VMT) in the US in 2017 to US VMT in 2008. The amount of state-level asphalt used is then
distributed to the counties based on the county-level utilization of paved roads. The total amount of asphalt
used is multiplied by emissions factors for VOC and HAPS to estimate emissions of these pollutants from asphalt
usage.
4.24,3.1 Activity data
The activity data for this source category is the amount of cutback and emulsified asphalt used, which is from a
2008 survey from the Asphalt Institute [ref 3], The 2008 data are used for the 2008, 2011, and 2014 NEI, as
research suggests that more recent data are not readily available. The 2008 asphalt data are adjusted to account
for changing use of roads, parking lots, driveways, walkways, or airport runways, using ratio of US VMT in 2017 to
US VMT in 2008. State-level VMT data are obtained from the Federal Highway Administration (FHWA) report:
State-level annual vehicle miles traveled (VMT) by FHWA road class, 2017 [ref 4],
VMTUSy (1)
VMTFrac = y K '
V MTus2ooq
AUs t = VMTFrac X UAUs t (2)
Where:
VMTFrac = The fraction of US VMT in 2008 to US VMT in 2013
VMTus, 2008 = Total VMT in the US in 2008
VMTus,y = Total VMT in the US in 2013
AUs,t = The amount of asphalt type t used in state s, in tons of asphalt per year, from equation 2
UAUs,t = The amount of unadjusted asphalt type t used in state s, in tons of asphalt per year, from
Table 4-161
Table 4-161 shows the total state-level amount of cutback and emulsified asphalt used in the U.S in 2008. The
process used to distribute the state-level amount of asphalt used to the counties is discussed in section 4.24.3.2.
Table 4-161: State-level asphalt usage
tons) in 2008
State
Cutback
Emulsified
Alabama
1,728
18,988
Alaska
0
1,108
Arizona
7,917
62,416
Arkansas
1,442
9,201
California
30,657
151,767
Colorado
331
837
Connecticut
0
0
4-278
-------
State
Cutback
Emulsified
Delaware
0
0
District of Columbia
0
150
Florida
809
19,459
Georgia
1,136
7,848
Hawaii
0
0
Idaho
2,880
41,805
Illinois
18,889
146,873
Indiana
290
17,427
Iowa
4,874
13,570
Kansas
3,641
0
Kentucky
456
16,137
Louisiana
175
6,418
Maine
0
0
Maryland
0
2,080
Massachusetts
0
805
Michigan
52
31,250
Minnesota
1,604
67,082
Mississippi
259
45,035
Missouri
7,385
36,933
Montana
1,614
17,880
Nebraska
2,997
35,376
Nevada
948
15,971
New Hampshire
0
0
New Jersey
0
0
New Mexico
320
58,048
New York
0
32,954
North Carolina
0
143
North Dakota
7,323
22,701
Ohio
3,214
22,777
Oklahoma
8,724
9,157
Oregon
865
34,918
Pennsylvania
26,844
69,671
Rhode Island
0
0
South Carolina
0
0
South Dakota
19,034
44,691
Tennessee
894
34,561
Texas
14,618
154,613
Utah
549
7,039
Vermont
0
0
Virginia
670
41,249
Washington
5,774
24,263
West Virginia
0
3,581
Wisconsin
8,188
18,925
Wyoming
227
5,292
4-279
-------
4,24.3,2 Allocation procedure
Asphalt usage data are not available at the county-level, therefore state -level data are allocated to the county
based on road utilization numbers calculated from FHWA data.
State-level VMT data are obtained from the FHWA report: State-level annual vehicle miles traveled (VMT) by
FHWA road class, 2017 [ref 4], EPA used the state-level data and 2011 MOVES data to allocate VMT to the
county-level.
VMT,
s,r
VMTcr = MOVEScr X „„„„„
c'r c'r MOVES,
s,r
(3)
Where:
VMTc,r = The amount of VMT on road type r in county c from EPA, in millions of miles
MOVESc,r = The amount of VMT on road type r in county c from the 2011 MOVES run
VMTs,r = The amount of VMT on road type r in state s from FHWA, in millions of miles
MOVESs,r = The amount of VMT on road type r in state s from the 2011 MOVES run
The county-level VMT is used to calculate the fraction of VMT in each county.
VMT,
VMTFrcr = -
c'r VMT,
c,r
s,r
(4)
Where:
VMTFrc,r = The fraction of VMT on road type r in county c
VMTc,r = The amount of VMT on road type r in county c from EPA, in millions of miles
VMTs,r = The amount of VMT on road type r in state s from FHWA, in millions of miles
State-level lane-miles [ref 5] and paved road miles [ref 6] from FHWA are used to calculate an estimate of state
lane-miles that are paved by road type.
PMsr
PLM,r = —
s'r PUM,
x LM,
(5)
s,r
s,r
Where:
PLMs,r
= The
amount
of
PMs,r
= The
amount
of
PUMsj
= The
amount
of
LMs,r
= The
amount
of
State-level VMT from FHWA and paved lane-miles (from equation 3) are used to calculate a state-level
utilization measure for paved roads by road type.
_ VMT,
Us'r = ~PLM,
s,r
(6)
s,r
4-280
-------
Where:
Us,r = Utilization of paved road type r in state s
VMTs,r = The amount of VMT on road type r in state s from FHWA
PLMs,r = The amount of paved lane-miles of road type r in state s
County-level utilization of paved roads by road type is calculated based on the fraction of county-level VMT
(from equation 2).
UCiT = VMTFrcr x Usr (7)
Where:
Uc,r = Utilization of paved road type r in state s
VMTFrc,r = The fraction of VMT on road type r in county
Us,r = Utilization of paved road type r in state s
County-level utilization values are summed across all road types and then summed to the state level.
(8)
= YarUc'r
s = ^ Uc (9)
Where:
Us = The total utilization of paved roads in state s
Uc = The total utilization of paved roads in county c
Uc,r = Utilization of paved road type r in state s
The fraction of county-level utilization is calculated based on the ratio of total utilization at the county level to
state level.
UFrc = — (10)
U
us
Where:
UFrc = The fraction of paved road utilization in county c
Us = The total utilization of paved roads in state s
Uc = The total utilization of paved roads in county c
County-level asphalt usage is the calculated by multiplying the fraction of county-level paved road utilization by
the amount of cutback and emulsified asphalt used from Table 4-161.
AUCX = UFrc x AUSX (11)
4-281
-------
Where:
AUc t = The amount of asphalt type t used in county c, in tons of asphalt per year
UFrc = The fraction of paved road utilization in county c
AUs,t = The amount of asphalt type t used in state s, in tons of asphalt per year, from Table 4-161
4.24.3.3 Emission factors
The emissions factors for VOC and HAPs are developed based on information from material safety and data
sheets (MSDS) for cutback and emulsified asphalt provided in Table 4-162 and Table 4-163, respectively.
Table 4-162: Cutback Asp
halt MSDS
Product Supplier
MSDS/SDS ID
Valero
2013V04
Asphalt Emulsion Industries
CUT-SDS-1
Martin Asphalt Company
Jan 2007
Mohawk Asphalt Emulsions
UN1999
Asphalt & Fuel Supply
211
Valero
211
Valero
210
Table 4-163: Emulsified Asphalt MSDS
Product Supplier
MSDS/SDS ID
Marathon
0137MAR019
Marathon
0138MAR019
Asphalt Emulsion Industries
EMU-SDS-1
U.S. Oil & Refining Co.
951
Emissions factors for HAPs are calculated using the assumptions found in Table 4-164 and Table 4-165 from the
average of MSDS values for cutback and emulsified asphalt, respectively.
Table 4-164: Chemical Composition Assumptions
Pollutant
Average % by
Weight
% Weight
Volatilized
Naphtha
40
95
Naphthalene & PAH
0.58
95
Toluene
0.59
95
Xylene
0.99
95
Benzene
0.19
95
Ethylbenzene
0.49
95
Hydrogen Sulfide
0.09
95
or Cutback Asphalt
Table 4-165: Chemical Composition Assumptions for Emulsified Asphalt
Pollutant
Average %
by Weight
% Weight
Volatilized
Naphtha
10
95
Naphthalene & PAH
0.29
95
Hydrogen Sulfide
0.09
95
The total amount of cutback asphalt used nationally is 190,613 tons and the amount of emulsified asphalt used
4-282
-------
is 1,374,693 tons.
Where:
Zus,P,t = AUus,t x 2000^ x %Wp t x %Vp (12)
ton
EFP,t = (13)
/iUus,t
Zus,P,t = The amount of pollutant p emitted from use of asphalt type t in the United States, in lbs. of
pollutant per year
EFPrt = Emissions factor for pollutant p from asphalt type t, in lbs. of pollutant per ton of asphalt
AUus,t = Total usage of asphalt type t, in tons of asphalt per year
%WP/t = Average percent by weight of pollutant p from asphalt type t
%VP = Average percent weight of pollutant p volatilized
Emission factors for VOC are calculated by summing the amount of pollutant emitted each year for all HAPs,
except hydrogen sulfide.
EFi
voc,t ~
ZpZt
AUus,t
(14)
Where:
EFV0C,t = VOC emissions factor for asphalt type t, in lbs. of VOC per ton of asphalt
Zp,t = The amount of pollutant emitted from use of asphalt type t, where p is equal to al
pollutants except hydrogen sulfide, in lbs. of pollutant per year
AUus,t = Total usage of asphalt type t, in tons of asphalt per year
The resulting emissions factors for asphalt paving are reported in Table 4-166 and Table 4-167.
Table 4-166: Emissions Factors for Cutback Aspha
t Usage
Pollutant
Pollutant Code
Emissions Factor
Emissions Factor Units
Volatile Organic
Compounds
VOC
813.96
Ibs./ton asphalt
Benzene
71432
3.6
Ibs./ton asphalt
Ethylbenzene
100414
9.3
Ibs./ton asphalt
Naphthalene
91203
11.0
Ibs./ton asphalt
Toluene
108883
11.2
Ibs./ton asphalt
Xylenes (mixed isomers)
1330207
18.8
Ibs./ton asphalt
Hydrogen Sulfide
7783064
1.7
Ibs./ton asphalt
Source: Based on MSDS values from Table 4-164
Table 4-167: Emissions Factors for Emulsified Asphalt Usage
Pollutant
Pollutant Code
Emissions Factor
Emissions Factor Units
Volatile Organic
Compounds
VOC
195.5
Ibs./ton asphalt
Naphthalene
91203
5.5
Ibs./ton asphalt
4-283
-------
Pollutant
Pollutant Code
Emissions Factor
Emissions Factor Units
Hydrogen Sulfide
7783064
1.7
Ibs./ton asphalt
Source: Based on MSDS values from Table 4-165
4.24.3.4 Controls
There are no controls assumed for this category.
4.24.3.5 Emissions
The total asphalt usage in each county is multiplied by the emissions factors in Table 4-166 and Table 4-167 to
estimate emissions.
Ep,c,t = EFPit X AUc t (15)
Where:
EP,c,t = Annual emissions of pollutant p in county c from use of asphalt type t, in lbs. of pollutant
EFPrt = Emissions factor for pollutant p from asphalt type t, in lbs. of pollutant per ton of asphalt
AUc,t = The amount of asphalt type t used in county c, in tons of asphalt per year
4.24.3.6 Example calculations
Table 4-168 lists sample calculations to determine the VOC emissions from emulsified asphalt used in Barnstable
County, Massachusetts. The equations 2 through 7 use asphalt use on rural interstates as an example; however,
these calculations would need to be repeated for all 14 FHWA road types.
Table 4-168: Sample calculations for VOC emissions from emulsified asphalt use in Barnstable County,
Massachusetts
Eq.#
Equation
Values for Barnstable County, MA
Result
1
VMTFr
VMTus>y
VMTUS,2oog
3,025,659 Million Miles
2,973,509 Million Miles
1.02 VMT fraction between
2008 and 2017
2
AUs,t
= VMTFrac
x UAUs t
1.02 x 805 tons of emulsified asphalt in MA
819 tons of adjusted
emulsified asphalt used in
MA
3
VMTCir =
MOVEScr X
VMTs,r
MOVESsr
Barnstable County VMT on rural interstates from EPA
153,721,475.26 vehicle
miles traveled on rural
interstates in Barnstable
County, MA
4
VMTFrcr =
VMTcr
VMTsr
153.72 million vehicle mi. in Barnstable County
778.15 million vehicle mi. in MA
0.198 fraction of rural
interstate VMT in
Barnstable County, MA
5
PLMsr =
PMs r '
—— X LMS r
PUMsr s'r
63.65 paved mi.
x 275.25 lane mi.
63.65 total mi.
275.25 rural interstate
paved lane miles in MA
6
usr =—^
s'r PLMsr
778.15 million vehicle mi. in MA
275.25 paved lane mi. in MA
2.83 utilization factor of
paved rural interstates in
MA
4-284
-------
Eq.#
Equation
Values for Barnstable County, MA
Result
7
Uc,r ~
VMTFrcr x
Us,r
0.198 VMT fraction x 2.83 utilization factor
0.558 utilization factor of
paved rural interstates in
Barnstable County, MA
8
Uc=
Utilization of all paved roads in Barnstable
(This is based on repeating calculations for equations
1-5 for all 14 FHWA road types.)
2.18 Barnstable County
utilization of paved roads
in MA
9
Us = £ E/c
Utilization of paved roads in all counties in MA
46.20 utilization of paved
roads in MA
10
Uc
UFrc =tj-
V S
2.18 utilization in Barnstable County
46.20 utilization in MA
0.05 fraction of utilization
of paved roads in
Barnstable County, MA
11
AUCX =
UFrc x AUs t
0.05 fraction utilized in Barnstable County x
819 tons emulsified asphalt in MA
37.91 tons of emulsified
asphalt used in Barnstable
County, MA
12
Pp,t =
AUus.t x
lbs
2000—x
ton
%WPit X %Vp
1,350,999 tons emulsified asphalt per year x
2000—x 0.10 x 0.95
ton
256,689,810 lbs. naphtha
emitted per year from
emulsified asphalt
1,350,999 tons emulsified asphalt per year x
2000—X 0.0029 X 0.95
ton
7,444,004 lbs. naphthalene
emitted per year from
emulsified asphalt
13
rr
' ~ AUmt
N/A
Emissions factors for HAPs
are not used to calculate
the emissions factor for
VOC
14
EFvoc.t
IpZt
AUus,t
256,689,810 lbs. naptha + 7,444,004 lbs. naphthaler
195.51 lbs. VOC emitted
per ton of emulsified
asphalt used
1,350,999 tons of emulsified asphalt
15
Ep,c,t ~
EFp,t x AUc,t
195.51 lbs. VOC per top emulsified asphalt x
37.91 tons emulsified asphalt
7,411.78 tons VOC emitted
from emulsified asphalt
use in Barnstable County,
MA
4.24.3.7 Changes from the 2014 methodology
State-level asphalt use is adjusted in the 2017 methodology using a ratio of VMT in the inventory year to VMT in
2008, the year of the original asphalt data.
4.24.3.8 Puerto Rico and U.S. Virgin Islands
Insufficient data exists to calculate emissions for the counties in Puerto Rico and the US Virgin Islands, so
emissions are based on two proxy counties in Florida: 12011, Broward County for Puerto Rico and 12087,
Monroe County for the US Virgin Islands. The total emissions in tons for these two Florida counties are divided
by their respective populations creating a tons per capita emission factor. For each Puerto Rico and US Virgin
Island County, the tons per capita emission factor is multiplied by the county population (from the same year as
4-285
-------
the inventory's activity data) which served as the activity data. In these cases, the throughput (activity data) unit
and the emissions denominator unit are "EACH".
4.24.4 References
1. Wisconsin Transportation Bulletin, No. 1, Understanding and Using Asphalt. 1996.
2. National Cooperative Highway Research Program (NCHRP) Report 673. A Manual for Design of Hot Mix
Asphalt with Commentary, 2011.
3. Asphalt Institute, 2008. 2008 Asphalt Usage Survey for the United States and Canada.
4. FHWA, 2017. Functional System Travel-2017, Annual Vehicle Miles (Table VM-2).
5. FHWA, 2017. Functional System Lane-Length-2017, Lane-Miles (Table HM-60).
6. FHWA, 2017. Functional System Length-2017, Miles by Type of Surface - Rural (Table HM-51).
4.25 Solvents: All other Solvents
This section includes discussion on all nonpoint solvent sources except for agricultural pesticide application (see
Section 4.23) and asphalt paving (see Section 4.24). The reason these sources are discussed separately is
because the EPA methodologies for estimating the emissions are different.
4.25.1 Sector description
Solvent utilization includes a variety of industrial, commercial and residential applications of solvents that are
not captured in the point source inventory. Estimates of emissions of volatile organic compounds (VOC) and
hazardous air pollutants (HAPs) from solvent utilization are based on national-level estimates of solvent usage
from the Freedonia Group [ref 1],
4.25.2 Sources of data
EPA's solvent category includes architectural surface coatings, industrial surface coatings, degreasing, graphic
arts, dry cleaning, consumer and commercial (includes personal care products and household products),
automotive aftermarket, adhesives and sealants, and FIFRA related products (pesticides excluding those for
agricultural use).
Table 4-169 shows for solvents, the nonpoint SCCs covered by the EPA estimates and where SLTs submitted
data. The SCC level 2, 3 and 4 SCC descriptions are also provided. The SCC level 1 description is "Solvent
Utilization" for all SCCs. Note that the SCCs in this list are only the SCCs that either the EPA used or the
submitting State agencies used in the 2017 NEI, and not a comprehensive list of all "active" Solvent SCCs. Also
note the solvent SCCs (see table footnote) that were discussed in previous sections.
Table 4-169: Nonpoint solvent SCCs in the 2017 NEI
SCC
Description
EPA
S/L/T
Sector
2401001000
Surface Coating; Architectural Coatings;
Total: All Solvent Types
X
X
Solvent - Non-Industrial
Surface Coating
2401005000
Surface Coating; Auto Refinishing: SIC 7532;
Total: All Solvent Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401005700
Surface Coating; Auto Refinishing: SIC 7532;
Top Coats
X
Solvent - Industrial Surface
Coating & Solvent Use
2401008000
Surface Coating; Traffic Markings; Total: All
Solvent Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
4-286
-------
see
Description
EPA
S/L/T
Sector
2401010000
Surface Coating; Textile Products: SIC 22;
Total: All Solvent Types
X
Solvent - Industrial Surface
Coating & Solvent Use
2401015000
Surface Coating; Factory Finished Wood: SIC
2426 thru 242; Total: All Solvent Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401020000
Surface Coating; Wood Furniture: SIC 25;
Total: All Solvent Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401025000
Surface Coating; Metal Furniture: SIC 25;
Total: All Solvent Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401030000
Surface Coating; Paper: SIC 26; Total: All
Solvent Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401035000
Surface Coating; Plastic Products: SIC 308;
Total: All Solvent Types
X
Solvent - Industrial Surface
Coating & Solvent Use
2401040000
Surface Coating; Metal Cans: SIC 341; Total:
All Solvent Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401045000
Surface Coating; Metal Coils: SIC 3498; Total:
All Solvent Types
X
Solvent - Industrial Surface
Coating & Solvent Use
2401050000
Surface Coating; Miscellaneous Finished
Metals: SIC 34 - (341 + 3498); Total: All
Solvent Types
X
Solvent - Industrial Surface
Coating & Solvent Use
2401055000
Surface Coating; Machinery and Equipment:
SIC 35; Total: All Solvent Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401060000
Surface Coating; Large Appliances: SIC 363;
Total: All Solvent Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401065000
Surface Coating; Electronic and Other
Electrical: SIC 36 - 363; Total: All Solvent
Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401070000
Surface Coating; Motor Vehicles: SIC 371;
Total: All Solvent Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401075000
Surface Coating; Aircraft: SIC 372; Total: All
Solvent Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401080000
Surface Coating; Marine: SIC 373; Total: All
Solvent Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401085000
Surface Coating; Railroad: SIC 374; Total: All
Solvent Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401090000
Surface Coating; Miscellaneous
Manufacturing; Total: All Solvent Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401100000
Surface Coating; Industrial Maintenance
Coatings; Total: All Solvent Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401200000
Surface Coating; Other Special Purpose
Coatings; Total: All Solvent Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2415000000
Degreasing; All Processes/All Industries;
Total: All Solvent Types
X
X
Solvent - Degreasing
4-287
-------
see
Description
EPA
S/L/T
Sector
2420000000
Dry Cleaning; All Processes; Total: All Solvent
Types
X
X
Solvent - Dry Cleaning
2420000055
Dry Cleaning; All Processes;
Perchloroethylene
X
Solvent - Dry Cleaning
2420000999
Dry Cleaning; All Processes; Solvents: NEC
X
Solvent - Dry Cleaning
2425000000
Graphic Arts; All Processes; Total: All Solvent
Types
X
X
Solvent - Graphic Arts
2425010000
Graphic Arts; Lithography; Total: All Solvent
Types
X
Solvent - Graphic Arts
2425020000
Graphic Arts; Letterpress; Total: All Solvent
Types
X
Solvent - Graphic Arts
2425030000
Graphic Arts; Rotogravure; Total: All Solvent
Types
X
Solvent - Graphic Arts
2425040000
Graphic Arts; Flexography; Total: All Solvent
Types
X
Solvent - Graphic Arts
2440000000
Miscellaneous Industrial; All Processes; Total:
All Solvent Types
X
Solvent - Industrial Surface
Coating & Solvent Use
2440020000
Miscellaneous Industrial; Adhesive
(Industrial) Application; Total: All Solvent
Types
X
Solvent - Industrial Surface
Coating & Solvent Use
2460000000
Miscellaneous Non-industrial: Consumer and
Commercial; All Processes; Total: All Solvent
Types
X
Solvent - Consumer &
Commercial Solvent Use
2460100000
Miscellaneous Non-industrial: Consumer and
Commercial; All Personal Care Products;
Total: All Solvent Types
X
X
Solvent - Consumer &
Commercial Solvent Use
2460200000
Miscellaneous Non-industrial: Consumer and
Commercial; All Household Products; Total:
All Solvent Types
X
X
Solvent - Consumer &
Commercial Solvent Use
2460400000
Miscellaneous Non-industrial: Consumer and
Commercial; All Automotive Aftermarket
Products; Total: All Solvent Types
X
X
Solvent - Consumer &
Commercial Solvent Use
2460500000
Miscellaneous Non-industrial: Consumer and
Commercial; All Coatings and Related
Products; Total: All Solvent Types
X
X
Solvent - Consumer &
Commercial Solvent Use
2460600000
Miscellaneous Non-industrial: Consumer and
Commercial; All Adhesives and Sealants;
Total: All Solvent Types
X
X
Solvent - Consumer &
Commercial Solvent Use
2460800000
Miscellaneous Non-industrial: Consumer and
Commercial; All FIFRA Related Products;
Total: All Solvent Types
X
X
Solvent - Consumer &
Commercial Solvent Use
4-288
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see
Description
EPA
S/L/T
Sector
2460900000
Miscellaneous Non-industrial: Consumer and
Commercial; Miscellaneous Products (Not
Otherwise Covered); Total: All Solvent Types
X
X
Solvent - Consumer &
Commercial Solvent Use
2461023000
Miscellaneous Non-industrial: Commercial;
Asphalt Roofing; Total: All Solvent Types
X
Solvent - Consumer &
Commercial Solvent Use
2461100000
Miscellaneous Non-industrial: Commercial;
Solvent Reclamation: All Processes; Total: All
Solvent Types
X
Solvent - Consumer &
Commercial Solvent Use
The agencies listed in Table 4-170 submitted at least VOC emissions for the EIS sectors discussed in this section:
Consumer & Commercial Use, Degreasing, Dry Cleaning, Graphic Arts, Industrial Surface Coating & Solvent Use,
and Non-Industrial Surface Coating. Agencies not listed used EPA estimates for the entire sector.
Table 4-170: Agencies that reported emissions for Solvents in the 2017 NEI
Region
Agency
S/L/T
Consumer/
Commercial
Degreasing
Dry Cleaning
Graphic Arts
Industrial Surface
Coating
Non-Industrial
Surface Coating
1
Massachusetts Department of Environmental
Protection
State
X
X
X
X
X
1
New Hampshire Department of Environmental
Services
State
X
X
X
X
1
Rhode Island Department of Environmental
Management
State
X
X
X
X
X
2
New Jersey Department of Environment Protection
State
X
X
X
X
X
X
2
New York State Department of Environmental
Conservation
State
X
3
Delaware Department of Natural Resources and
Environmental Control
State
X
X
X
X
X
X
3
Maryland Department of the Environment
State
X
X
X
X
X
X
3
Virginia Department of Environmental Quality
State
X
X
X
X
X
X
4
Georgia Department of Natural Resources
State
X
4
Memphis and Shelby County Health Department -
Pollution Control
Local
X
X
X
X
X
X
4
Metro Public Health of Nashville/Davidson County
Local
X
X
X
X
X
X
5
Illinois Environmental Protection Agency
State
X
X
X
X
X
X
5
Minnesota Pollution Control Agency
State
X
X
X
X
X
X
6
Texas Commission on Environmental Quality
State
X
X
X
X
X
X
8
Utah Division of Air Quality
State
X
X
X
X
X
X
9
California Air Resources Board
State
X
X
X
X
X
X
9
Maricopa County Air Quality Department
Local
X
X
X
X
X
X
9
Washoe County Health District
Local
X
X
X
X
X
X
4-289
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10
Coeur d'Alene Tribe
Tribe
X
X
X
X
X
X
10
Idaho Department of Environmental Quality
State
X
X
X
X
X
X
10
Kootenai Tribe of Idaho
Tribe
X
X
X
X
10
Nez Perce Tribe
Tribe
X
X
X
X
X
X
10
Northern Cheyenne Tribe
Tribe
X
X
10
Oregon Department of Environmental Quality
State
X
X
X
X
X
X
10
Shoshone-Bannock Tribes of the Fort Hall
Reservation of Idaho
Tribe
X
X
X
X
X
X
4.25.3 EPA-developed emissions
The emissions from solvent use are calculated based on national-level data on solvent use from the Freedonia
Group [ref 1], This data is used to develop emissions factors per capita, per employee, or per lane mile of
highway, depending on the SCC. The emissions factors are used to estimate VOC emissions in each county. HAP
emissions are estimated using the VOC emissions and HAP speciation factors. Because the data from Freedonia
is for total solvent use, point source emissions must be subtracted to estimate the nonpoint source emissions.
4.25.3.1 Activity data
The activity data for solvent utilization varies by SCC; it is based on population data from the U.S. Census Bureau,
lane miles data from the Federal Highway Administration, or employment data from the U.S. Census Bureau.
Population
The activity data for the categories listed in Table 4-171 are based on county-level population data. Population
data are from the U.S. Census Bureau's population estimates for 2017 [ref 2],
Tab
e 4-171: Source Categories That Use Population Activity Data
SCC
Description
2401001000
Architectural Coatings
2401100000
Industrial Maintenance Coatings
2401200000
Other Special Purpose Coatings
2460100000
All Personal Care Products
2460200000
All Household Products
2460400000
All Automotive Aftermarket Products
2460600000
All Adhesives and Sealants
2460800000
All FIFRA Related Products
2460500000
All Coatings and Related Products
2460900000
Misc. Products
Lane Miles
County-level lane mile data are used as activity data for one source category (Table 4-172). The Federal Highway
Administration (FHWA) provides state-level lane mile data yearly as part of the Highway Statistics Report [ref 3],
State-level data is allocated to the county level using population data. The process used to distribute the state-
level lane miles data to the counties is discussed in section 4.25.3.2.
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Table 4-172: Source Categories That Use Lane Mile Activity Data
see
Description
2401008000
Traffic Markings
Employment Data
The source categories listed in Table 4-173 use county-level employment data as activity data. Employment data
are provided by the U.S. Census Bureau's 2016 County Business Patterns (CBP) [ref 4],
Table 4-173: Source Categories That Use Employment Activity Data
see
Description
NAICS
2401005000
Auto Refinishing
81112, 4411, 4412
2401015000
Factory Finished Wood
321
2401020000
Wood Furniture
337110, 337121, 337122, 337127*, 337211, 337212,
337215*
2401025000
Metal Furniture
337124, 337127*, 337214, 337215*
2401030000
Paper
322220
2401040000
Metal Cans
33243
2401055000
Machinery and Equipment
3331, 3332, 3333, 33341
2401060000
Large Appliances
3352
2401065000
Electronics and Other Electrical
331318, 331420, 331491, 335921, 335929, 335311
2401070000
Motor Vehicles
3361, 3362, 3363
2401075000
Aircraft
3364
2401085000
Railroad
3365
2401080000
Marine
3366, 488390
2401090000
Misc. Manufacturing
339, 3369
2415000000
Degreasing: All Processes/All
Industrial
331, 332, 333, 334, 335, 336, 337, 339, 441, 483, 484, 485,
488, 8111, 8112
2425000000
Graphic Arts
32311, 322211, 322212, 322219, 322220, 322230, 322291,
322299
2420000000
Dry Cleaning
812320
*Employment c
ata is split equally between Wood Furniture and Metal Furniture source categories
Employment data for select NAICS codes and counties must be allocated based on state-level data. The process
used to distribute the state-level amount employment data to the counties is discussed in section 4.25.3.2.
4.25.3.2 Allocation procedure
Lane Miles
Lane miles data is published yearly by FHWA at the state-level. Population data is used to allocate the state-level
data to the county-level. In order to allocate the state-level data, a fraction of county to state-level population is
created.
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(1)
Pc
PFracc = —
Pst
Where:
PFracc
Pc
Population fraction for county c
Population of county c
Population of state st where county c is located
This fraction is then applied to the state-level lane miles data to estimate county-level lane miles.
(2)
LMC = PFracc x LMst
Where:
LMC = Lane-miles in county c
PFracc = Population fraction for county c
LMst = Lane miles in state st where county c is located
Employment Data
Employment data are from the U.S. Census Bureau's 2016 CBP. Due to concerns with releasing confidential
business information, the CBP does not release exact numbers for a given North American Industrial
Classification Standard (NAICS) code if the data can be traced to an individual business. Instead, a series of range
codes is used. Many counties and some smaller states have only one business per NAICS code, leading to
withheld data in the county and/or state business pattern data. To estimate employment in counties and states
with withheld data, the following procedure is used for NAICS code 322220.
To gap-fill withheld state-level employment data:
a. State-level data for states with known employment in NAICS 322220 are summed to the national level.
b. The total sum of state-level known employment from step a is subtracted from the national total
reported employment for NAICS 322220 in the national-level CBP to determine the employment total
for the withheld states.
c. Each of the withheld states is assigned the midpoint of the range code reported for that state. Table
4-174 lists the range codes and midpoints.
d. The midpoints for the states with withheld data are summed to the national level.
e. An adjustment factor is created by dividing the number of withheld employees (calculated in step b of
this section) by the sum of the midpoints (step d).
f. For the states with withheld employment data, the midpoint of the range for that state (step c) is
multiplied by the adjustment factor (step e) to calculate the adjusted state-level employment for
landfills.
These same steps are then followed to fill in withheld data in the county-level business patterns.
g. County-level data for counties with known employment are summed by state.
h. County-level known employment is subtracted from the state total reported in state-level CBP (or, if the
state-level data are withheld, from the state total estimated using the procedure discussed above).
i. Each of the withheld counties is assigned the midpoint of the range code (Table 4-174).
j. The midpoints for the counties with withheld data are summed to the state level.
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k. An adjustment factor is created by dividing the number of withheld employees (step h) by the sum of
the midpoints (step j).
I. For counties with withheld employment data, the midpoints (step i) are multiplied by the adjustment
factor (step k) to calculate the adjusted county-level employment for landfills.
Table 4-174: Ranges and midpoints for data withheld from state and county business patterns
Employment
Code
Ranges
Midpoint
A
0-19
10
B
20-99
60
C
100-249
175
E
250-499
375
F
500-999
750
G
1,000-2,499
1,750
H
2,500-4,999
3,750
1
5,000-9,999
7,500
J
10,000-
24,999
17,500
K
25,000-
49,999
37,500
L
50,000-
99,999
75,000
M
100,000+
r example, take the 2016 CBP data for NAICS 322220 (paper bag and coated and treated paper manufacturing)
Kentucky provided in Table 4-175.
Table 4-175: 2016 County Business Pattern for NAICS 322220 in Kentucky
State
FIPS
County
FIPS
County
Name
NAICS
Employment
Code
Employment
21
015
Boone
322220
F
withheld
21
041
Carroll
322220
B
withheld
21
097
Harrison
322220
F
withheld
21
111
Jefferson
322220
391
21
117
Kenton
322220
A
withheld
21
211
Shelby
322220
338
21
213
Simpson
322220
F
withheld
21
219
Todd
322220
B
withheld
Note: Counties in Kentucky that do not have employment in paper bag and
coated and treated paper manufacturing are excluded from this table.
1. The total number of known county-level employees in Kentucky is 729.
2. The state-level CBP reports 2,517 employees for NAICS 322220 in Kentucky. This means there are 1,788
employees total for the 6 counties for which data are withheld.
3. The counties with withheld data are assigned midpoints according to their employment code in Table
4-174. For example, Carroll County is given a midpoint of 60 employees (since range code B is 20-99) and
Kenton County is given a midpoint of 10 employees.
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4. The state total of the midpoints for all withheld counties is 2,380 employees.
5. The adjustment factor is 1,788/2,380 = 0.7513.
6. The adjusted employment for Carroll County is 60 x 0.7513 = 45. Kenton County has an adjusted
employment of 10 x 0.7513 = 8 employees.
4.25.3.3 Emission factors
Emissions factors for most solvent utilization categories are based on national-level estimates of solvent usage
from the Freedonia Group [ref 1], The Freedonia data includes historical usage of solvents in 2015 and projected
solvent usage for 2020. Assuming a linear change in solvent demand, EPA estimated solvent usage for 2017
(Table 4-176).
Table 4-176: Solvent Usage (million lbs) in the US
Description
2015
2017
2020
Paints & Coatings Solvent Demand: Architectural
735
777
840
Paints & Coatings Solvent Demand: Other
1,318
1,321
1,325
Printing Ink Solvent Demand
1,132
1,134
1,138
Cleaning Products Solvent Demand: Household
653
657
662
Cleaning Products Solvent Demand: Industrial & Institutional
385
390
398
Cosmetics & Toiletries Solvent Demand
628
645
670
Adhesives & Sealants Solvent Demand
572
600
643
Transportation Solvent Demand: Motor Vehicles
61
62
64
Dry Cleaning
20
18
16
Table 13, in the document "Solvent NEMO 2017 FINAL_7-8-2019_4-2 updated.docx" on the 2017 NEI
Supplemental FTP site, shows a crosswalk between the source categories and the data used to calculate their
emissions factors. Some categories, such as personal care products, use only the Freedonia Group data. For
these categories, the emissions factor is calculated by dividing the total amount of solvent used by the
categories' activity data.
Fs X 1,000,000 (3)
£F*=—a;—
Where:
EFS = Emissions factor for source category s
Fs = The Freedonia Group data for source category s, in million pounds per year
As = National-level activity data for source category s, either population, lane miles, or employment
Freedonia data does not include usage estimates for all surface coating categories, therefore, additional data is
used to allocate the non-architectural solvent data to the SCC level. A previous version of this methodology used
data from the U.S. Census Bureau's report on Paint and Allied Products to determine solvent use from surface
coating, but this report was not produced after 2010 [ref 5], EPA grew the 2010 data from the most recent
version of this report to estimate solvent use for surface coating in 2017. The estimated 2017 value is used to
calculate the fraction of non-architectural coating use from each source category for surface coating. This
4-294
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fraction is then applied to total non-architectural solvent demand from the Freedonia Group to calculate 2017
solvent use for surface coating categories.
To grow the 2010 Paint and Allied Products data to 2017, EPA uses the U.S. Census Bureau's Annual Survey of
Manufactures data on the value of paint shipments in 2010 and 2016 [ref 6], the most recent data at the time of
the publication of this methodology. At the time Using the relevant product codes (see Table 4-177), the value
of paint shipments are summed for each category for 2010 and 2016. There are not corresponding product
codes for all surface coating SCCs; in these cases, the general paint and coating manufacturing data are used.
The 2016 value of shipments for each category are converted to 2010 USD by multiplying by 0.9075, a
conversation factor from the U.S. Bureau of Labor Statistics [ref 7],
TSs,y= ^ (4)
NAICS
TSs,2oi6c = ^s,20i6 x 0.9075 (5)
Where:
TSs,y = Total value of shipments for source category s in year in year y, in thousand dollars
VSy = Value of shipments in year y, in thousand dollars
NAICS = NAICS codes corresponding to source category s
TSs, 2016c - Total value of shipments for source category s in 2016, converted to 2010 USD
A ratio of the 2010 value of shipments, from the Survey of Manufactures, to 2010 volume of paint, from the
Paint and Allied Products report, was then used with the converted 2016 value of shipments to estimate the
2016 volume of paint.
VPS,2016 — res,2oi6C x
^s,2010 (6)
TS.
s, 2010
Where:
VPs, .2016 — Volume of paint for source category s in 2016, in thousand gallons
TSS/ 2016c - Total value of shipments for source category s in 2016, converted to 2010 USD
VPs, .2010 — Volume of paint for source category s in 2010, in thousand gallons
TSS/ .2010 — Total value of shipments for source category s in 2010, in thousand dollars
The estimated volume of paint in 2016 is then used to create a 2016 to 2010 paint ratio (Table 4-177). The paint
ratio represents the fraction change in surface coating solvent use in each source category between 2010 and
2016. For example, a paint ratio greater than 1 means there was an increase in solvent use in that source
category between 2010 and 2016.
PRS =
VPS, 2016 (7)
VP,
s, 2010
Where:
PRS = 2016-2010 Paint Ratio
VPs, .2016 — Volume of paint for source category s in 2016, in thousand gallons
VPs, .2010 — Volume of paint for source category s in 2010, in thousand gallons
4-295
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Table 4-177: 2016-2010 paint ratio
Product Codes
Description
Paint Ratio
325510
Paint and coating manufacturing
1.246
321
Wood Products
1.327
337
Furniture
1.196
32222/322220
Paper bag and coated and treated paper manufacturing
0.981
332431 & 332439
Metal Can and Container Manufacturing
0.835
3352
Household Appliances
1.121
3361
Motor Vehicle Manufacturing
1.513
3364
Aircraft Manufacturing
1.262
336510
Railroad rolling stock manufacturing
1.461
3366
Boat Manufacturing
1.088
339
Misc. Manufacturing
0.929
3331, 3332, 3333,33341
Machinery Manufacturing
0.901
335921, 335929, 335311
Electronics Manufacturing
0.944
The paint ratios are multiplied by the volume of paint sold in 2010 from the Paint and Allied Products report for
each SCC to estimate the volume of paint sold in 2017.
^s,2017 = PRs x VPS
,2010 (8)
(8)Where:
VPs, .2017 - Volume of paint for source category s in 2017, in thousand gallons
PRS = 2016-2010 Paint Ratio for source category s, from Table 4-177
VPs, .2010 — Volume of paint for source category s in 2010, in thousand gallons
The total amount of non-architectural coatings from the Paint and Allied Products data [ref 5] is also calculated
in order to estimate the fraction of non-architectural coatings for each SCC. The report includes data on the total
amount of coatings sold in 2010, as well as the amount of architectural and powder coatings sold; these values
are subtracted from the total to estimate the volume of non-architectural coatings (Table 4-178). These values
are adjusted to 2017 using the paint and coating manufacturing paint ratio.
NAC2017 — (TC2oio ~ AC2010 — PQoio) * PRpaint and coatings (9)
Where:
NAC2017
TC2010
AC2010
PC2010
PRpaint and coatings
Volume of non-architectural coatings sold in 2017, in gallons
Total volume of coatings sold in 2010, in gallons
Volume of architectural coatings sold in 2010, in gallons
Volume of powder coatings sold in 2010, in gallons
Paint ratio for paint and coating manufacturing, from Table 4-177
Table 4-178: Coatings sold (gallons) in 2010
Type of Coating
Amount Sold
Volume of Total Coatings Sold
1,301,333,355
Volume of Architectural Coatings
651,626,800
Volume of Powder Coatings
75,774,600
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Type of Coating
Amount Sold
Volume of Non-architectural Coatings
573,931,955
The volume of paint sold in 2017 for each SCC (from equation 8) is then divided by the total volume of non-
architectural coatings to estimate the fraction of non-architectural paint from each SCC. This fraction is then
multiplied by the volume of solvent demand from "paints and coatings: other" for 2017 from Freedonia.
VPS 2017 x 1000 (10)
NAFrac? =
s NAC-
2017
>^As,20i7 — NAFracs x Ot/i20i7 (H)
Where:
NAFracs= Fraction of non-architectural coatings from source category s
VPs, .2017 - Volume of paint for source category s in 2017, in thousand gallons
NAC2017 = Volume of non-architectural coatings sold in 2017, in gallons
SDs,2oi7 = Solvent demand for source category s in 2017, in million pounds
Oth2oi7 = Other paint and coatings solvent demand in 2017 from Freedonia, in million pounds
After solvent use is estimated for each surface coating category, equation 3 is used to calculate the emissions
factor for each SCC.
There are three exceptions to this method for surface coating solvents: aircraft coatings, railroad coatings, and
other special purpose coatings. Data for solvent use for other special purpose coatings is not available in the
2010 version of the Paint and Allied Products Report. Therefore, data for special purpose coatings from the 2006
version of the report was pulled forward and adjusted to 2017 using the same method as reported above.
The Paint and Allied Products report also aggregates aircraft and railroad coatings in the "other transportation
equipment finishes" category. The 2010 volume of paint is grown to 2017 and used to determine solvent
demand by the same method as described above. Solvent demand for the other transportation category was
then divided in half and assigned equally to the aircraft and railroad SCCs.
Emissions factors for the three Consumer and Commercial categories—including FIFRA related products,
coatings and related products, and misc. products—are not estimated by using Freedonia data, but rather come
from EPA's Air Emissions Inventory Improvement Program (EIIP) [ref 8],
The architectural coatings, industrial maintenance coatings, and consumer solvents source categories have
controlled emissions factors that are used for states that have enacted regulations to control the VOC emissions
from these types of solvents. These controlled emissions factors are discussed in section 4.25.3.4.
VOC emissions factors for all SCCs in this category are listed in Table 4-179.
Table 4-179: VOC Emissions Factors
lb/each) for Solvent Utilization
SCC
Description
Emissions
Factor
Activity
Data
Source
2401001000
Architectural Coatings
2.36
Pop.
Freedonia Group, U.S. Census Bureau
2401001000
Architectural Coatings
(controlled)
1.88
Pop.
ERTAC, U.S. Census Bureau
2401005000
Auto Refinishing
75.58
Emp.
Freedonia Group, U.S. Census Bureau
4-297
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see
Description
Emissions
Factor
Activity
Data
Source
2401008000
Traffic Markings
9.80
Lane
Miles
Freedonia Group, U.S. Census Bureau
2401015000
Factory Finished Wood
44.71
Emp.
Freedonia Group, U.S. Census Bureau
2401020000
Wood Furniture
282.87
Emp.
Freedonia Group, U.S. Census Bureau
2401025000
Metal Furniture
769.02
Emp.
Freedonia Group, U.S. Census Bureau
2401030000
Paper
398.22
Emp.
Freedonia Group, U.S. Census Bureau
2401040000
Metal Cans
2,239.43
Emp.
Freedonia Group, U.S. Census Bureau
2401055000
Machinery and Equipment
34.28
Emp.
Freedonia Group, U.S. Census Bureau
2401060000
Large Appliances
168.96
Emp.
Freedonia Group, U.S. Census Bureau
2401065000
Electronic and Other Electrical
15.58
Emp.
Freedonia Group, U.S. Census Bureau
2401070000
Motor Vehicles
160.31
Emp.
Freedonia Group, U.S. Census Bureau
2401075000
Aircraft
15.40
Emp.
Freedonia Group, U.S. Census Bureau
2401085000
Railroad
212.90
Emp.
Freedonia Group, U.S. Census Bureau
2401080000
Marine
176.75
Emp.
Freedonia Group, U.S. Census Bureau
2401090000
Misc. Manufacturing
69.99
Emp.
Freedonia Group, U.S. Census Bureau
2401100000
Industrial Maintenance
Coatings
0.36
Pop.
Freedonia Group, U.S. Census Bureau
2401100000
Industrial Maintenance
Coatings (controlled)
0.15
Pop.
ERTAC, U.S. Census Bureau
2401200000
Other Special Purpose
Coatings
0.01
Pop.
Freedonia Group, U.S. Census Bureau
2415000000
Degreasing: All Processes/All
Industries
32.36
Emp.
Freedonia Group, U.S. Census Bureau
2425000000
Graphic Arts
1,583.65
Emp.
Freedonia Group, U.S. Census Bureau
2460100000
All Personal Care Products
1.96
Pop.
Freedonia Group, U.S. Census Bureau
2460100000
All Personal Care Products
(controlled)
1.15
Pop.
Ozone Transport Commission, U.S.
Census Bureau
2460200000
All Household Products
1.99
Pop.
Freedonia Group, U.S. Census Bureau
2460200000
All Household Products
(controlled)
1.17
Pop.
Ozone Transport Commission, U.S.
Census Bureau
2460400000
All Automotive Aftermarket
Products
0.19
Pop.
Freedonia Group, U.S. Census Bureau
2460400000
All Automotive Aftermarket
Products (controlled)
0.11
Pop.
Ozone Transport Commission, U.S.
Census Bureau
2460600000
All Adhesives and Sealants
1.82
Pop.
Freedonia Group, U.S. Census Bureau
2460600000
All Adhesives and Sealants
(controlled)
1.07
Pop.
Ozone Transport Commission, U.S.
Census Bureau
2460800000
All FIFRA Related Products
1.78
Pop.
EIIP, 111:5, Table 5.4-1
4-298
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see
Description
Emissions
Factor
Activity
Data
Source
2460800000
All FIFRA Related Products
(controlled)
1.05
Pop.
Ozone Transport Commission, U.S.
Census Bureau
2460500000
All Coatings and Related
Products
0.95
Pop.
EIIP, 111:5, Table 5.4-1
2460500000
All Coatings and Related
Products (controlled)
0.56
Pop.
Ozone Transport Commission, U.S.
Census Bureau
2460900000
Misc. Products
0.07
Pop.
EIIP, 111:5, Table 5.4-1
2460900000
Misc. Products (controlled)
0.04
Pop.
Ozone Transport Commission, U.S.
Census Bureau
2420000000
Dry Cleaning
20.40
Emp.
Freedonia Group, U.S. Census Bureau*
2420000000
Dry Cleaning
118.35
Emp
Freedonia Group, U.S. Census Bureau*
* Dry cleaning emissions factor assumes that 85 percent of dry cleaning solvents are perchloroethvlene.
which are not considered VOCs.
4.25.3.4 Controls
Some states have regulations that limit the VOC content of solvent-containing products that are sold. In this
methodology, these controls are taken into account where appropriate by using the controlled emissions factors
shown in Table 4-179. In particular, the emissions factors for architectural coatings and industrial maintenance
coatings are reduced for the states listed in Table 4-180, based on calculations done for the 2011 National
Emissions Inventory by the Eastern Regional Technical Advisory Committee (ERTAC).
In addition, EPA developed controlled emissions factors for the consumer solvent categories, including personal
care products, household products, automotive aftermarket products, adhesives and sealants, FIFRA regulated
products, coatings, and miscellaneous consumer products. The controlled emissions factors were taken from the
Ozone Transport Commission, based on emissions factors for states that had implemented model rules for
consumer solvents [ref 9], Note that the Ozone Transport Commission includes a single emissions factor for all
consumer solvents (5.15 Ibs./person), while EPA uses individual emissions factors for each of the seven
consumer solvent categories. To estimate controlled emissions factors for the individual solvent categories, the
uncontrolled emissions factors were scaled so that the sum of the factors equaled 5.15 Ibs./person.
Table 4-180: States for which controlled emissions factors are used
State
Architectural
Coatings
Industrial
Maintenance Coatings
Consumer
Solvents
AZ
u
u
CA
u
u
u
CT
u
u
u
DE
u
u
u
DC
u
u
u
ME
u
u
u
MD
u
u
u
MA
u
u
u
NH
u
u
u
NJ
u
u
u
NY
u
u
u
4-299
-------
State
Architectural
Coatings
Industrial
Maintenance Coatings
Consumer
Solvents
PA
u
u
u
Rl
u
u
u
TX
u
u
VT
u
u
VA
u
u
u
The solvent tool also allows users to adjust emissions factors to account for controls and to implement a county-
level control factor.
4.25.3.5 Emissions
Total VOC emissions from solvent utilization are calculated by multiplying the activity data for the source
category by the calculated emissions factor for that category.
Evocc,s = Ac,s x EFVOc,s (12)
Where:
Evoc,c,s = Annual VOC emissions in county c for source category s, in tons per year
Ac,s = Activity data for county c associated with source category s
EFvoqs = Calculated VOC emissions factor for source category s
HAP emissions are estimated using the VOC emissions and HAP speciation factors shown in Table 4-181. This
step is completed after the point source subtraction step discussed in Section 4.25.3.6.
Ep,c,s Evoc,c,s ^ SFp s (13)
Where:
Ep:c,s
EvOC,c,s
SFP,s
Table 4-181: HAP speciation factors for solvent use
= Annual emissions of HAP p county c for source category s, in tons per year
= Annual VOC emissions in county c for source category s, in tons per year
= Speciation factor for HAP p for source category s
see
Pollutant
Code
Pollutant Description
Speciation
Factor
2401001000
123911
1,4-Dioxane (1,4-Diethyleneoxide)
0.00002
2401001000
584849
2,4-Toluene diisocyanate
0.00002
2401001000
101688
4,4'-Methylenediphenyl diisocyanate (MDI)
0.00014
2401001000
75070
Acetaldehyde
0.0001
2401001000
117817
Bis(2-ethylhexyl)phthalate (DEHP)
0.00003
2401001000
98828
Cumene
0.00038
2401001000
84742
Dibutyl phthalate
0.00002
2401001000
131113
Dimethyl phthalate
0.00001
4-300
-------
see
Pollutant
Code
Pollutant Description
Speciation
Factor
2401001000
100414
Ethylbenzene
0.00248
2401001000
107211
Ethylene glycol
0.05049
2401001000
50000
Formaldehyde
0.00002
2401001000
171
Glycol Ethers
0.02065
2401001000
110543
Hexane
0.00015
2401001000
67561
Methanol
0.012184699
2401001000
80626
Methyl methacrylate
0.00012
2401001000
108101
Methyl isobutyl ketone(Hexone)
0.000980163
2401001000
91203
Naphthalene
0.00046
2401001000
100425
Styrene
0.00102
2401001000
108883
Toluene
0.0397
2401001000
121448
Triethylamine
0.00006
2401001000
108054
Vinyl acetate
0.00012
2401001000
1330207
Xylenes (mixed isomers)
0.0034
2401005000
107211
Ethylene glycol
0.0016
2401005000
171
Glycol Ethers
0.00953
2401005000
108101
Methyl isobutyl ketone (Hexone)
0.0103
2401005000
108883
Toluene
0.018
2401005000
1330207
Xylenes (mixed isomers)
0.0034
2401015000
171
Glycol Ethers
0.01382
2401015000
108101
Methyl isobutyl ketone(Hexone)
0.0103
2401015000
108883
Toluene
0.0397
2401015000
1330207
Xylenes (mixed isomers)
0.0034
2401100000
171
Glycol Ethers
0.01382
2401100000
108101
Methyl isobutyl ketone(Hexone)
0.0103
2401100000
108883
Toluene
0.0397
2401100000
1330207
Xylenes (mixed isomers)
0.0034
2401200000
171
Glycol Ethers
0.01382
2401200000
108101
Methyl isobutyl ketone(Hexone)
0.0103
2401200000
108883
Toluene
0.0397
2401200000
1330207
Xylenes (mixed isomers)
0.0034
2401090000
171
Glycol Ethers
0.01382
2401090000
108101
Methyl isobutyl ketone(Hexone)
0.0103
2401090000
108883
Toluene
0.0397
2401090000
1330207
Xylenes (mixed isomers)
0.0034
2401080000
171
Glycol Ethers
0.01382
2401080000
108101
Methyl isobutyl ketone(Hexone)
0.0103
2401080000
108883
Toluene
0.0397
2401080000
1330207
Xylenes (mixed isomers)
0.0034
2401085000
171
Glycol Ethers
0.01382
2401085000
108101
Methyl isobutyl ketone(Hexone)
0.0103
4-301
-------
see
Pollutant
Code
Pollutant Description
Speciation
Factor
2401085000
108883
Toluene
0.0397
2401085000
1330207
Xylenes (mixed isomers)
0.0034
2401075000
171
Glycol Ethers
0.01382
2401075000
108101
Methyl isobutyl ketone(Hexone)
0.0397
2401075000
108883
Toluene
0.0397
2401075000
1330207
Xylenes (mixed isomers)
0.0034
2401070000
171
Glycol Ethers
0.01382
2401070000
108101
Methyl isobutyl ketone(Hexone)
0.0103
2401070000
108883
Toluene
0.0397
2401070000
1330207
Xylenes (mixed isomers)
0.0034
2401065000
171
Glycol Ethers
0.01382
2401065000
108101
Methyl isobutyl ketone(Hexone)
0.0103
2401065000
108883
Toluene
0.0397
2401065000
1330207
Xylenes (mixed isomers)
0.0034
2401060000
171
Glycol Ethers
0.01382
2401060000
108101
Methyl isobutyl ketone(Hexone)
0.0103
2401060000
108883
Toluene
0.0397
2401060000
1330207
Xylenes (mixed isomers)
0.0034
2401055000
171
Glycol Ethers
0.01382
2401055000
108101
Methyl isobutyl ketone(Hexone)
0.0103
2401055000
108883
Toluene
0.0397
2401055000
1330207
Xylenes (mixed isomers)
0.0034
2401040000
171
Glycol Ethers
0.01382
2401040000
108101
Methyl isobutyl ketone(Hexone)
0.0103
2401040000
108883
Toluene
0.0397
2401040000
1330207
Xylenes (mixed isomers)
0.0034
2401030000
171
Glycol Ethers
0.01382
2401030000
108101
Methyl isobutyl ketone(Hexone)
0.0103
2401030000
108883
Toluene
0.0397
2401030000
1330207
Xylenes (mixed isomers)
0.0034
2401025000
171
Glycol Ethers
0.01382
2401025000
108101
Methyl isobutyl ketone(Hexone)
0.0103
2401025000
108883
Toluene
0.0397
2401025000
1330207
Xylenes (mixed isomers)
0.0034
2401008000
108883
Toluene
0.0397
2401008000
1330207
Xylenes (mixed isomers)
0.0034
2415000000
108883
Toluene
0.078204196
2460900000
67561
Methyl Alcohol
0.0933
2460900000
108883
Toluene
0.00268
2460800000
67561
Methyl Alcohol
0.0933
2460800000
108883
Toluene
0.003221139
4-302
-------
see
Pollutant
Code
Pollutant Description
Speciation
Factor
2460600000
67561
Methyl Alcohol
0.0933
2460600000
108883
Toluene
0.003221139
2460500000
67561
Methyl Alcohol
0.0933
2460500000
108883
Toluene
0.00268
2460400000
107211
Ethylene Glycol
0.1595
2460400000
67561
Methyl Alcohol
0.0933
2460400000
108883
Toluene
0.00268
2460200000
67561
Methyl Alcohol
0.0933
2460200000
108883
Toluene
0.003221139
2460100000
67561
Methyl Alcohol
0.0933
2460100000
108883
Toluene
0.003529334
2460000000
67561
Methyl Alcohol
0.0933
2460000000
108883
Toluene
0.00268
2425000000
67561
Methyl Alcohol
0.02634987
2425000000
108101
Methyl Isobutyl Ketone
0.0004259
2425000000
108883
Toluene
0.0397
2425000000
1330207
Xylene
0.0034
2401015000
107211
Ethylene glycol
0.0045
2401100000
107211
Ethylene glycol
0.0045
2401200000
107211
Ethylene glycol
0.0045
2401090000
107211
Ethylene glycol
0.0045
2401080000
107211
Ethylene glycol
0.0045
2401085000
107211
Ethylene glycol
0.0045
2401075000
107211
Ethylene glycol
0.0045
2401070000
107211
Ethylene glycol
0.0045
2401065000
107211
Ethylene glycol
0.0045
2401060000
107211
Ethylene glycol
0.0045
2401055000
107211
Ethylene glycol
0.0045
2401040000
107211
Ethylene glycol
0.0045
2401030000
107211
Ethylene glycol
0.0045
2401025000
107211
Ethylene glycol
0.0045
2415000000
110543
N-hexane
0.000057282
2415000000
111773
Methyl carbitol (2-(2-methoxyethoxy)ethanol) (degme)
0.019346982
2415000000
112345
2-(2-butoxyethoxy)ethanol (butyl carbitol)
0.03330946
2415000000
127184
Perchloroethylene (Tetrachloroethylene)
0.010597163
2415000000
1330207
Xylenes (Mixed Isomers)
0.087841886
2415000000
67561
Methyl alcohol (methanol)
0.050236279
2415000000
71432
Benzene
0.001432049
2415000000
71556
1,1,1-trichloroethane
0.053014454
2415000000
75092
Dichloromethane (methylene chloride)
0.00614349
2415000000
79016
Trichloroethylene
0.030201913
4-303
-------
see
Pollutant
Code
Pollutant Description
Speciation
Factor
2415000000
86748
Carbazole
0.001074037
2415000000
91203
Naphthalene
4.29615E-05
2415000000
98828
Isopropylbenzene (orcumene; 2-Phenylpropane)
4.29615E-05
4.25,3.6 Point source subtraction
Point source subtraction is necessary for this category to ensure that solvent emissions are not double counted
with the point source inventory. In order to accomplish this, nonpoint source solvent SCCs must be linked to
corresponding point SCCs, using point source emissions data supplied by state, local, or tribal (SLT) agencies and
a point-nonpoint source crosswalk, shown in Table 14 in the appendix of document "Solvent NEMO 2017
FINAL_7-8-2019_4-2 updated.docx" on the 2017 NEI Supplemental FTP site.
Point source subtraction should be completed at the county level using uncontrolled point source emissions.16
Np;,c — TEs c X PSs c (14)
Where:
NPs,c = Nonpoint source solvent emissions in county c for source category s, in tons per year
TEs,c = Total solvent emissions s in county c for source category s, in tons per year
PSs,c = Point source solvent emissions in county c for source category s, in tons per year
If county-level data is not available, state-level emissions can be allocated to the county level using population
or employment data.
Note that if point source subtraction results in a negative number because the point source emissions from
solvents are larger than the estimated total emissions from solvents, the Solvent Tool will zero out emissions for
that source category in that county.
After point source subtraction, the HAP emissions are speciated from the estimated nonpoint source VOC
emissions, as discussed in section 4.25.3.5.
4.25,3.7 Example calculations
Table 4-182 lists sample calculations to determine the VOC emissions from traffic coating solvent utilization in
Apache County, Arizona.
16 There is one point source category for Adhesives and Sealants (40200710) that maps to the nonpoint Adhesives and
Sealants category (2460600000). The Solvents methodology assumes that emissions from the nonpoint Adhesives and
Sealants category are controlled in some states, as discussed in section 4.25.3.4. However, these controls are specific to
consumer solvents, rather than the types of solvents likely used by point sources. Therefore, EPA still recommends
subtracting uncontrolled point source emissions for this source category.
4-304
-------
Table 4-182: Sample calculations for VOC emissions from solvent utilization in Apache County, AZ
Eq.#
Equation
Values for Apache County, AZ
Result
1
Pc
PFracc = —
C Pst
71,606 people in Apache County
7,016,270 people in Arizona
0.0102 share of
the population of
Arizona in Apache
County
2
LMC
= PFracc x LMst
0.0102 x 144,959 lane miles in Arizona
1,479 lane miles in
Apache County
3
Fs x 1,000,000
EFs = ~ :
N/A
Equation 3 is not
used at this point
in the method for
traffic coatings
4
TSS,y = ^ VSy
NAICS
Product code 325510 is used for traffic coatings
2010 value of
shipments is
19,994,229
thousand USD.
2016 value of
shipments is
27,445,132
thousand USD
5
TSs, 2016c
= TSSi2016 x 0.9075
27,445,132 thous. USD X 0.9075
Value of 2016
paint shipments in
2010 USD is
24,906,457
thousand USD
6
^s,2016
= TSSi 2016c
^s,2010
^s,2010
19,994,229 thous. USD
24,906,457 thous. USD ,
1,301,333 thous. gal. paint
1,621,048
thousand gallons
of paint sold in
2016
7
pp ^^s,2016
S VP
v rs,2010
1,621,048 thous. gal. of paint in 2016
1,301,333 thous. gal. of paint in 2010
1.246 ratio of
2016 to 2010
paint
8
^s,2017
= PRS X 1^PS,2010
1.246 x 37,335 thous. gal. traffic
coatings sold in 2010
46,508 thousand
gallons of traffic
coatings sold in
2017
9
^•^^2017
= (TC2oio _ ^^2010
— PQoio)
X PRpaint and coatings
(1,301,333,355 gal. —651,626,800 gal.
-75,774,600 gal.) X 1.246
714,936,882
gallons of non-
architectural
coatings sold in
2017
4-305
-------
Eq.#
Equation
Values for Apache County, AZ
Result
10
NAFracs
VPs,2017 x 1000
^•^^2017
46,508 thous. gal. traffic coatings x 1000
714,936,882 gal. non — arch, coatings
6.5% of non-
architectural
coatings sold in
2017 are traffic
coatings
11
SDs, 2017
= NAFracs
x Oth2QX7
6.5% x 1,320.80 mil. lbs. other solvent
demand in 2017
85.92 million
pounds of traffic
coating solvent
demand in 2017
3
Fs x 1,000,000
FF —
85.92mil. lbs. traffic coatings sold in 2017 x 1,000,000
9.80 pounds of
VOC emitted per
lane mile
S As
8,765,578 lane miles in 2017
12
Evocc.s
= ACiS x EFVoc,s
1,479 lane miles x 9.80 lbs. VOC per lane mile
14,498 lbs. of VOC
emitted from
traffic coatings in
Apache County,
AZ
4.25.3.8 Changes from the 2014 methodology
There are no significant changes from the methodology used to calculate the 2014 v2 NEI emissions.
4.25.3.9 Puerto Rico and U.S. Virgin Islands
Emissions from Puerto Rico are calculated using the same method described above. For the U.S. Virgin Islands,
emissions are calculated using 2010 population data [ref 10], because 2016 Census Data does not exist for the
U.S. Virgin Islands.
4.25.4 References
1. The Freedonia Group. 2016. Industry Study #3429, Solvents.
2. U.S. Census Bureau. 2017 Total Population. American Community Survey.
3. Federal Highway Administration. Highway Statistics 2017, section 4.4.1.4.
4. U.S. Census Bureau. 2016 County Business Patterns.
5. U.S. Census Bureau. 2010. MA325F: Paints and Allied Products.
6. U.S. Census Bureau. 2016. Annual Survey of Manufactures.
7. U.S. Bureau of Labor Statistics. CPI Inflation Calculator.
8. U.S. Environmental Protection Agency. 1996. Air Emissions Inventory Improvement Program (EIIP),
Volume III: Chapter 5 Consumer and Commercial Solvent Use.
9. Ozone Transport Commission. 2016. Technical Support Document for the 2011 Ozone Transport
Commission/Mid-Atlantic Northeastern Visibility Union Modeling Platform. Appendix A.
10. U.S. Census Bureau, Decennial Censuses, 2010 Census: Summary File 1.
4.26 Waste Disposal: Composting
There are four sections in this documentation that discuss nonpoint inventory Waste Disposal. This section
discusses Composting, the next section (4.27) discusses Open Burning, and the third section (4.28) discusses
Publicly-Owned Treatment Works (POTWs), and the fourth section was a broad discussion of nonpoint non-
4-306
-------
combustion sources of mercury (see Section 4.2), which included several Waste Disposal sector sources. The
reason these sources are broken up within this EIS sector is because the EPA methodologies for estimating the
emissions are different.
4.26.1 Source category description
Greenwaste composting includes the diversion of yard waste, food waste, and other biogenic waste from
landfills to composting facilities. Estimates of emissions of volatile organic compounds (VOC), ammonia (NH3),
and three hazardous air pollutants (HAPs), acetaldehyde; methanol; and naphthalene, from greenwaste
composting are based on the amount of food and yard waste composted. Composting of biogenic waste is
currently not included in emissions estimates for this category as activity data on this waste type is not available.
Note that this source category does not include the composting of biosolids from wastewater treatment plants
or manure management facilities. There are separate SCCs for biosolids (2680001000) and for a mixture of
greenwaste and biosolids (2680002000). EPA is not currently estimating emissions for these SCCs. If S/L/Ts
report any emissions for the mixture SCC, emissions from the greenwaste portion of that mixture may be
duplicative of some or all of the EPA emissions estimates described here. Note also that this source category
estimates emissions from composting facilities but does not estimate emissions from backyard composting.
4.26.2 Sources of data
Table 4-183 shows, for composting, the SCCs covered by the EPA estimates and by the State/Local and Tribal
agencies that submitted data. The SCC level 3 and 4 SCC descriptions are also provided. The SCC level 1 and
leading level 2 descriptions is "Waste Disposal, Treatment, and Recovery; Composting:" for all SCCs.
Table 4-183: Composting SCCs in the 2017 NEI
SCC
Description
EPA
S/L/T
2680001000
100% Biosolids (e.g., sewage sludge, manure, mixtures of these matls); All
Processes
X
2680002000
Mixed Waste (e.g., a 50:50 mixture of biosolids and green wastes); All Processes
X
2680003000
100% Green Waste (e.g., residential or municipal yard wastes); All Processes
X
X
The agencies listed in Table 4-184 submitted emissions for composting in the 2017 NEI. Agencies not listed used
EPA estimates unless they responded "No..." in the nonpoint survey.
Table 4-184: Agencies reporting composting emissions in the 2017 NEI
Region
Agency
100%
Green
Waste
100%
Biosolids
Mixed
Waste
1
Massachusetts Department of Environmental Protection
X
4
Louisville Metro Air Pollution Control District
X
4
Memphis and Shelby County Health Department - Pollution Control
X
4
Metro Public Health of Nashville/Davidson County
X
4
North Carolina Department of Environmental Quality
X
7
Iowa Department of Natural Resources
X
8
Utah Division of Air Quality
X
9
California Air Resources Board
X
9
Maricopa County Air Quality Department
X
4-307
-------
10 Nez Perce Tribe
X
4.26.3 EPA-developed emissions
The calculations for estimating the emissions from greenwaste composting involve first estimating the amount
of food and yard waste composted in each county. The amount of state-level food waste composted is available
from the EPA report Food Waste Management in the United States, 2014 [ref 1], The amount of state-level yard
waste composted is estimated by calculating the per-capita amount of yard waste composted using national
data from the EPA report Advancing Sustainable Materials Management: 2015 Fact Sheet [ref 2] and multiplying
that by the state population. The state-level yard and food waste are summed together and distributed to the
counties based on the proportion of employment at solid waste landfills. The total amount of greenwaste
composted is multiplied by emissions factors for VOC and NH3 to estimate emissions of these pollutants from
greenwaste composting.
4.26.3.1 Activity Data
The activity data for this source category is the amount of food and yard waste composted, which is estimated
using data from two EPA reports: the national-level amount of yard waste composted comes from Advancing
Sustainable Materials Management: 2015 Fact Sheet and the state-level amount of food waste composted
comes from Food Waste Management in the United States, 2014 [ref 1, ref 2], Table 4-185 shows the total
national-level amount of yard waste generated and recovered for composting.
Table 4-185: Annual Waste (million tons) generatec
Material
Waste Generated
Waste Recovered
Yard trimmings
34.72
21.29
and recovered in the US in 2015
The values from Table 4-185 are used with the U.S. population in 2017 of 329 million people [ref 3] to determine
per-capita values of food and yard waste recovered for composting.
nr. _ Wyard.US (1)
*^yard,US ~ D
rUS
Where:
PCyard,us = Per-capita yard waste recovered for composting in the US, in tons per person per year
Wyard,us = Total annual yard waste recovered in the US, in tons/year
Pus = US population
This calculation results in per-capita values of approximately 0.066 tons per person per year of yard waste
recovered for composting. Please note that EPA data on composting does not include backyard composting.
The per-capita yard waste values from equation 1 are multiplied by the population of each state to estimate the
state-level amount of yard waste recovered for composting.
Wyard,s ~ PCyard,US Ps (2)
Where:
Wyard,s = Annual yard waste recovered for composting in state s, in tons
4-308
-------
PCyard,us= Per-capita yard waste recovered for composting in the US, in tons per person per year
Ps = Population of state s
EPA reports the amount of food waste composted at the state level in the report Food Waste Management in
the United States, 2015 [Table 3 in ref 1], These values are shown in Table 4-186. EPA collected these data from
state environmental websites and contacts with state agencies. The data year for each state is listed and
represents the latest data available. The data were not altered from the original reference for use in this
methodology.
Table 4-186: State-level
ood waste composting
tons)
State
Food
Composted
Data
Year
State
Food
Composted
Data
Year
California
715,119
2012
Nevada
35,869
2014
Colorado
29,130
2013
New Hampshire
110
2012
Connecticut
4,644
2013
New Jersey
28,634
2012
Delaware
17,626
2013
New York
44,405
2013
Florida
158,711
2014
North Carolina
38,014
2014
Georgia
8,021
2014
Ohio
81,450
2014
Hawaii
39,287
2014
Oregon
50,143
2013
Indiana
13,525
2013
Pennsylvania
56,851
2013
Iowa
4,334
2010
Rhode Island
150
2014
Kansas
1,127
2010
South Carolina
4,277
2014
Maine
1,658
2010
Tennessee
1,500
2013
Maryland
69,643
2014
Texas
188
2012
Massachusetts
2,753
2014
Vermont
14,738
2013
Michigan
8,700
2013
Virginia
2,454
2014
Minnesota
46,751
2013
Washington
65,221
2013
Mississippi
242
2013
Wisconsin
8,677
2013
Missouri
16,000
2014
Total
1,569,952
The state-level amount of total greenwaste composted is the sum of the state-level food and yard waste
composted.
Wgw,s ~ Wyard,s Wfood,s (3)
Where:
WGw,s = Annual total greenwaste recovered for composting in state s, in tons
Wyard,s = Annual yard waste recovered for composting in state s, in tons
Wfood,s = Annual food waste recovered for composting in state s, in tons, from Table 4-186
The process used to distribute the state-level amount of greenwaste composted to the counties is discussed in
next section.
4-309
-------
4,26.3,2 Allocation Procedure
Comprehensive data on the county locations of composting facilities is not available. As a result, the analysis
assumes that greenwaste composting facilities are co-located with solid waste landfills.
State-level food greenwaste composting activity (from equation 3) is allocated to the county-level using
employment at solid waste landfills (NAICS code 562212). Specifically, state-level estimates of greenwaste
collected for composting are multiplied by the ratio of county- to state- level number of employees at landfills.
Employment data are from the U.S. Census Bureau's 2016 County Business Patterns (CBP) [ref 4], Due to
concerns with releasing confidential business information, the CBP does not release exact numbers for a given
NAICS code if the data can be traced to an individual business. Instead, a series of range codes is used. Many
counties and some smaller states have only one solid waste landfill, leading to withheld data in the county
and/or state business pattern data. To estimate employment in counties and states with withheld data, the
following procedure is used for NAICS code 562212.
To gap-fill withheld state-level employment data:
a. State-level data for states with known employment in NAICS 562212 are summed to the national level.
b. The total sum of state-level known employment from step a is subtracted from the national total
reported employment for NAICS 562212 in the national-level CBP to determine the employment total
for the withheld states.
c. Each of the withheld states is assigned the midpoint of the range code reported for that state. Table
4-187 lists the range codes and midpoints.
d. The midpoints for the states with withheld data are summed to the national level.
e. An adjustment factor is created by dividing the number of withheld employees (calculated in step b of
this section) by the sum of the midpoints (step d).
f. For the states with withheld employment data, the midpoint of the range for that state (step c) is
multiplied by the adjustment factor (step e) to calculate the adjusted state-level employment for
landfills.
These same steps are then followed to fill in withheld data in the county-level business patterns.
g. County-level data for counties with known employment are summed by state.
h. County-level known employment is subtracted from the state total reported in state-level CBP (or, if the
state-level data are withheld, from the state total estimated using the procedure discussed above).
i. Each of the withheld counties is assigned the midpoint of the range code (Table 4-187).
j. The midpoints for the counties with withheld data are summed to the state level.
k. An adjustment factor is created by dividing the number of withheld employees (step h) by the sum of
the midpoints (step j).
Empc
(4)
Where:
EmpFraCi
Empc
Emps
The fraction of landfill employees in county c
The number of landfill employees in county c
The number of landfill employees in state s
4-310
-------
I. For counties with withheld employment data, the midpoints (step i) are multiplied by the adjustment
factor (step k) to calculate the adjusted county-level employment for landfills.
Table 4-187: Ranges and midpoints for data withheld from state and county business patterns
Employment Code
Ranges
Midpoint
A
0-19
10
B
20-99
60
C
100-249
175
E
250-499
375
F
500-999
750
G
1,000-2,499
1,750
H
2,500-4,999
3,750
1
5,000-9,999
7,500
J
10,000-24,999
17,500
K
25,000-49,999
37,500
L
50,000-99,999
75,000
M
100,000+
example, take the 2016 CBP data for NAICS 562212 (Landfills) in Arizona provided in Table 4-188.
Table 4-188: 2016 County Business Pattern for NAICS 562212 in Arizona
State
FIPS
County
FIPS
County
Name
NAICS
Employment
Code
Employment
04
001
Apache
562212
B
withheld
04
007
Gila
562212
A
withheld
04
012
La Paz
562212
A
withheld
04
013
Maricopa
562212
296
04
015
Mohave
562212
B
withheld
04
017
Navajo
562212
B
withheld
04
021
Pinal
562212
40
04
023
Santa Cruz
562212
withheld
04
025
Yavapai
562212
A
withheld
04
027
Yuma
562212
B
withheld
Note: Counties in Arizona that do not have employment in solid waste landfills
are excluded from this table.
13. The total number of known county-level employees in Arizona is 336.
14. The state-level CBP reports 522 employees for NAICS 562212 in Arizona. This means there are 186
employees total for the 8 counties for which data are withheld.
15. The counties with withheld data are assigned midpoints according to their employment code in Table
4-187. For example, Apache County is given a midpoint of 60 employees (since range code B is 20-99)
and Gila County is given a midpoint of 10 employees.
16. The state total of the midpoints for all withheld counties is 270 employees.
17. The adjustment factor is 186/272 = 0.6889.
4-311
-------
18. The adjusted employment for Apache County is 60 x 0.6889 = 41. Gila County has an adjusted
employment of 10 x 0.6889 = 7 employees.
Once county- and state-level employment have been estimated, the ratio of county to state employees (from
equation 4) is calculated and multiplied by the state-level greenwaste recovered for composting (from equation
3) to calculate the amount of waste composted in each county.
Wgw.c = EmpFracc x WGWs (5)
Where:
WGw,c = Annual total greenwaste composted in county c, in tons
WGw,s = Annual total greenwaste recovered for composting in state s, in tons
EmpFracc = The fraction of landfill employees in county c
4.26.3.3 Emissions Factors
Emissions factors for greenwaste composting are reported in Table 4-189. The emissions factors for VOC and
ammonia (NH3) are taken from the California Air Resources Board Emissions Inventory Methodology for
Composting Facilities [ref 5] and are unaltered from the original reference. The emissions factors for the HAPs
(acetaldehyde, methanol, and naphthalene) are taken from Kumar et al [ref 6],
Table 4-189: Emissions Factors for Composting of Greenwaste (2680003000)
Pollutant
Pollutant
Emissions
Emissions
Emissions Factor
Code
Factor
Factor Units
Reference
VOC
VOC
4.67
Ibs./ton
5
Ammonia
NH3
0.66
compost
Acetaldehyde
75070
0.0014
Methanol
67561
0.1279
Ibs./lbs. VOC
6
Naphthalene
91203
0.005
4.26.3.4 Controls
There are no controls assumed for this category.
4.26.3.5 Emissions
The total annual greenwaste composted in each county is multiplied by the emissions factors in The ammonia
emissions factor was obtained from an EPA report [ref 4] and the VOC emissions factor was based on a TriTAC
study [ref 5], Emissions factors for HAPs were derived using 1996 area source emissions estimates that were
provided by Bob Lucas [ref 6] and the 1996 nationwide flow rate [ref 7], These HAP emissions factors were then
multiplied by the 2008 to 2002 VOC emissions factor ratio (0.85/9.9) to obtain the final HAP emissions factors
applied in the 2017 inventory.
to estimate emissions. For VOC and NH3, the emissions are multiplied by a conversion factor to convert from
pounds to tons.
1 ton (6)
EP,C = W,GW,C x EFp x 2000 Ws
4-312
-------
Where:
Ep,c = Annual emissions of pollutant p in county c, in tons for VOC and NH3 and lbs. for HAPs
WGw,c = Annual total greenwaste recovered for composting in state s, in tons
EFP = Emissions factor for pollutant p, in tons of pollutant per ton of greenwaste composted
Emissions of HAPs are estimated by applying speciation factors found in Table 4-189 to annual VOC emissions.
For HAPS, no conversion factor is needed, and the emissions are reported in tons.
Eh,c = E-voc.c x SFh (7)
Where:
Eh,c = Annual emissions of HAP h in county c, in tons per year
Evoqc = Annual VOC emissions in county c, in tons
SFh = Speciation factor for HAP h, in tons of HAP emissions per ton of VOC emissions
4.26,3.6 Sample Calculations
Table 4-190 lists sample calculations to determine the VOC emissions from composting of greenwaste in Apache
County, Arizona.
Table 4-190: Sample calculations for VOC emissions from greenwaste composting in Apache County, AZ
Eq. #
Equation
Values for Apache County, AZ
Result
1
n n Wyard,US
L^yard,US ~ D
rUS
21.08 million tons yard waste
329 million people
0.064 tons yard
waste per person
per year
2
Wyard,s ~ PCyard,US * Ps
0.064 tons yard waste per person
x 7,016,270 people in AZ
449,041 tons yard
waste composted in
AZ
3
^'gw,s ~ WTyard,s ^food,s
449,041 tons yard waste
+ 0 tons food waste
443,520 tons green-
waste composted in
AZ
4
r. EmPc
41 employees in Apache County
0.079 fraction of
solid waste
employees in
Apache County, AZ
Lj / / LU1 1 LtL n
Emps
522 employees inAZ
5
Wgw,c = EmpFracc x WGWiS
0.079 fraction
x 443,520 tons greenwaste composted
35,038 tons
greenwaste
composted in
Apache County, AZ
6
Ep,c ^GW,c ^ EFp
1 ton
X 2000 lbs.
35,038tons greenwaste
x 4.67 lbs. VOC per ton greenwaste
1 ton
X 2000 lbs.
82 tons VOC
emissions from
greenwaste
composting in
Apache County, AZ
4.26.3.7 Changes from 2014 Methodology
There are no significant changes from the methodology used to calculate the 2014 v2 NEI emissions.
4-313
-------
4,26.3.8 Puerto Rico and U.S. Virgin Islands Emissions Calculations
Emissions from Puerto Rico are calculated using the same method described above. For the U.S. Virgin Islands,
emissions are calculated using 2010 population data [ref 7], since 2014 Census Data does not exist for the U.S.
Virgin Islands.
4.26.4 References
1. U.S. EPA. 2016. Food Waste Management in the United States, 2014. Office of Resource Conservation
and Recovery.
2. U.S. EPA. 2018. Advancing Sustainable Materials Management: Facts and Figures Report. 2015 Facts and
Figures Sheet, Tablel. Generation, Recovery, and Discards of Products in MSW, 2015.
3. U.S. Census Bureau. 2017 Total Population. American Community Survey.
4. U.S. Census Bureau. 2016 County Business Patterns.
5. California Air Resources Board. 2015. ARB Emissions Inventory Methodology for Composting Facilities.
Table A-4. Emission factor data taken from Draft Staff Report on Proposed Amended Rule 1133.1
(chipping and grinding activities) and Proposed Rule 1133.3 (emissions reductions from greenwaste
composting operations),Table 111-3.
6. Kumar, A., C.P. Alaimo, R. Horowitz, F.M. Mitloehner, M.J. Kleeman, and P.G. Green. 2011. Volatile
organic compound emissions from green waste composting: Characterization and ozone formation.
Atmospheric Environment, 45:1841-1848.
7. U.S. Census Bureau, Decennial Censuses, 2010 Census: Summary File 1.
4.27 Waste Disposal: Open Burning
4.27.1 Source category description
This source category includes several types of intentional burning for waste disposal purposes, except for
agricultural purposes. This source category includes open burning of municipal solid waste, land clearing debris,
and different types of yard waste.
Open burning of yard waste is the purposeful burning of leaf and brush species in outdoor areas, and emission
estimates for leaf and brush waste burning are a function of the amount of waste burned per year. Open
burning of land clearing debris is the purposeful burning of debris, such as trees, shrubs, and brush, from the
clearing of land for the construction of new buildings and highways. Emission estimates from open burning of
land clearing debris are a function of the amount of material or fuel subject to burning per year. Open burning of
residential household waste (RHW) is the purposeful burning of RHW in outdoor areas. Emission estimates for
RHW burning are a function of the amount of waste burned per year.
4.27.2 Sources of data
Table 4-191 shows, for open burning, the nonpoint SCCs in the 2017 NEI, whether generated by EPA, or provided
by SLTs. The SCC level 3 and 4 descriptions are also provided and the SCC level 1 and 2 descriptions are "Waste
Disposal, Treatment, and Recovery; Open Burning" for all SCCs.
Table 4-191: Open burning SCCs in the 2017 NEI
SCC
Description
EPA
S/L/T
2610000100
All Categories; Yard Waste - Leaf Species Unspecified
X
X
4-314
-------
see
Description
EPA
S/L/T
2610000300
All Categories; Yard Waste - Weed Species Unspecified
(incl Grass)
X
2610000400
All Categories; Yard Waste - Brush Species Unspecified
X
X
2610000500
All Categories; Land Clearing Debris (use 28-10-005-000
for Logging Debris Burning)
X
X
2610030000
Residential; Household Waste (use 26-10-000-xxx for
Yard Wastes)
X
X
The agencies listed in Table 4-192 submitted emissions for the three types of open burning discussed in this
section: residential household waste, yard waste (leaf, weed and brush), and land clearing debris. Some agencies
submitted emissions with zero emissions for some sources. Agencies not listed used EPA estimates for these
sources.
Table 4-192: Agencies that reported emissions for Open Burning in t
he 2017 NEI
Agency
Household
Waste
Yard Waste
Land Clearing
Debris
California Air Resources Board
X
X
Coeur d'Alene Tribe
X
X
X
Delaware Department of Natural Resources and Environmental Control
X
X
X
Georgia Department of Natural Resources
X
Idaho Department of Environmental Quality
X
X
Illinois Environmental Protection Agency
X
X
X
Kootenai Tribe of Idaho
X
X
X
Maricopa County Air Quality Department
0
X
Maryland Department of the Environment
X
X
X
Memphis and Shelby County Health Department - Pollution Control
X
Metro Public Health of Nashville/Davidson County
X
X
X
Minnesota Pollution Control Agency
X
New Jersey Department of Environment Protection
X
X
0
New York State Department of Environmental Conservation
X
Nez Perce Tribe
X
X
X
North Carolina Department of Environmental Quality
X
X
X
Northern Cheyenne Tribe
X
X
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
X
X
X
Texas Commission on Environmental Quality
X
X
Utah Division of Air Quality
X
X
0
Washington State Department of Ecology
X
X
Washoe County Health District
X
X
4-315
-------
4.27.3 EPA-developed emissions for yard waste
The calculations for estimating the emissions from the burning of yard waste involve first estimating the amount
of leaf and brush waste generated in each county. The amount of waste generated in the U.S. is available from
the EPA report Advancing Sustainable Materials Management: 2015 Fact Sheet [ref 1], The amount of county-
level yard waste burned is estimated by calculating the per capita amount of leaf and brush waste generated
using the national data from the EPA report, and multiplying that by the number of people likely to burn waste
in each county. The number of people likely to burn waste is based on the rural population in each county from
the 2010 census. The total amount of yard waste burned is multiplied by emissions factors for criteria air
pollutants (CAPs) and hazardous air pollutants (HAPs) to estimate emissions of these pollutants from yard waste
burning.
4.27.3.1 Activity data
The activity data for this source category is the amount of leaf and brush waste generated, which is estimated
using data the EPA report Advancing Sustainable Materials Management: 2015 Fact Sheet [ref 1], The report
presents the total mass of waste generated from the residential and commercial sectors in the United States by
type of waste for the calendar year 2015.
Table 4-193 shows the national-level yard waste generated and the corresponding per capita values. The per
capita value of yard waste subject to burning was developed based on EPA's total amount of waste generated
{Table 1 in ref 1], According to the 2010 version of the same EPA report, residential waste generation accounts
for 55-65% of the total waste from the residential and commercial sectors [ref 2]; for the per capita calculation,
the median value of 60% of total waste generated is assumed. This number is multiplied by the amount of yard
waste generated and divided by the U.S. population in 2015 (319 million people) [ref 3] to determine the per
capita amount of yard waste generated in the United States.
PC =
r uyw
YW X 0.60
(1)
Ky,us
Where:
PCyw = Per capita value of yard waste in the US, in tons per person
YW = Annual yard waste generated, in million tons
Py,us = Population of the US for year of inventory, in million people
The per capita value of yard waste is estimated to be 0.065 tons per person in 2015.
Table 4-193: Annual Waste Generated in the US in 2015
Material
Weight Generated
(million tons)
Tons per person
Yard
34.50
0.065
As open burning is generally not practiced in urbanized areas, only the rural population in each county is
assumed to practice open burning. The rural and urban populations are taken from 2010 U.S. Census data [ref
4], It is assumed that 24% of the rural population burns yard waste [ref 5],
4-316
-------
PBurnc = RPopc x 0.24
(2)
Where:
PBurric = Population likely to burn in county c
RPopc = Rural population in county c in 2010
The number of people likely to burn waste in each county (from equation 2) is then used with the value of per
capita yard waste generated (from equation 1) and two assumptions to determine the amount of leaf and brush
waste burned. The first assumption concerns the composition of yard waste; of the total amount of yard waste
generated, yard waste composition is assumed to be 25 percent leaves, 25 percent brush, and 50 percent grass
by weight [ref 6], However, open burning of grass clippings is not typically practiced by homeowners, and as
such only estimates for leaf burning and brush burning are developed.
The second assumption adjusts for variations in vegetation; the percentage of forested acres (including rural
forest and urban forest) is determined using Version 2 of the Biogenic Emission Landuse Database (BELD2)
within the Biogenic Emissions Inventory System (BEIS). Based on this percentage, county-level yard waste values
are adjusted according to the values in Table 4-194. To better account for the native vegetation that likely
occurs in residential yards of farming states, agricultural land acreage is subtracted before calculating the
percentage of forested acres. All municipios in Puerto Rico and counties in the U.S. Virgin Islands, Hawaii, and
Alaska were assumed to have greater than 50 percent forested acres.
LWC = PBurnc x PCyw x YWFrt x AFfa c ^
(4)
BWC = PBurnc x PCyw x YWFrt x AFfa c
Where:
LWC
BWC
PBurric
PCyW
YWFrt
AFfa,c
Table 4-194: Adjustment for Percentage of Forested Acres
= Annual leaf waste burned in county c, in tons
= Annual brush waste burned in county c, in tons
= Population likely to burn in county c, from equation 2
= Per capita value of yard waste in the US, in tons per person, from equation 1
= Fraction of total yard waste for waste type t (leaf or brush)
= Adjustment factor based on percent of forested acres in county c, from Table 4-194
Percent Forested Acres per
County
Adjustment for Yard Waste
Generated
< 10%
0% generated
> 10% & < 50%
50% generated
>50%
100% generated
4.27,3,2 Allocation procedure
National values for the amount of waste generated are distributed to the counties based on rural population, as
described in Section 4.27.3.1.
4-317
-------
4.27.3.3 Emission factors
Emissions factors for open burning of yard are reported in Table 4-195 and Table 4-196. The emissions factors
for CAPs are from AP-42 [ref 7], the emissions inventory improvement program [ref 8], and an ERTAC workgroup
[ref 10]. For burning of leaves, emissions factors for PM25 are calculated by multiplying the PM10 emissions
factor by a ratio of 0.7709. Emissions factors for HAPs are from an EPA Control Technology Center report [ref 9],
Table 4-195: Emissions Factors for Open Burning of Leaf Species
Pollutant
Pollutant
Code
Emission
Factor
(original)
Emission
Factor Units
(original)
Emission
Factor
(converted)
Emission
Factor Units
(converted)
Reference &
Table No.
CO
CO
112
Ibs./ton
—
—
Reference 7, Table
2.5-6
Nitrogen Oxides
NOX
6.2
Ibs./ton
-
-
Reference 10
PM10-FIL
PM10-FIL
38
Ibs./ton
—
—
Reference 7, Table
2.5-6
PM10-PRI
PM10-PRI
38
Ibs./ton
—
—
Reference 7, Table
2.5-6
PM25-FIL
PM25-FIL
29.3
Ibs./ton
-
-
0.7709 * PM10
PM25-PRI
PM25-PRI
29.3
Ibs./ton
-
-
0.7709 * PM10
Sulfur Dioxide
S02
0.76
Ibs./ton
-
-
Reference 10
VOC
VOC
28
Ibs./ton
—
—
Reference 7, Table
2.5-6
Cumene
98828
0.01325
Ibs./ton
Reference 9
Ethyl Benzene
100414
0.048
Ibs./ton
Reference 9
Phenol
108952
0.115
Ibs./ton
Reference 9
Styrene
100425
0.1015
Ibs./ton
Reference 9
Table 4-196: Emissions Factors for Open Burning of Brush Species
Pollutant
Pollutant
Code
Emission
Factor
(original)
Emission
Factor Units
(original)
Emission
Factor
(converted)
Emission
Factor Units
(converted)
Reference &
Table No.
CO
CO
140
Ibs./ton
—
—
Reference 7,
Table 2.5-5
Nitrogen Oxides
NOX
5
Ibs./ton
-
-
Reference 10
PM10-PRI
PM10-PRI
17
Ibs./ton
-
-
Reference 8
PM10-FIL
PM10-FIL
17
Ibs./ton
-
-
Reference 8
PM25-PRI
PM25-PRI
13.1
Ibs./ton
-
-
0.7709 * PM10
PM25-FIL
PM25-FIL
13.1
Ibs./ton
-
-
0.7709 * PM10
Sulfur Dioxide
SO 2
1.66
Ibs./ton
-
-
Reference 10
VOC
VOC
19
Ibs./ton
—
—
Reference 7,
Table 2.5-5
Cumene
98828
0.01325
Ibs./ton
Reference 9
Ethyl Benzene
100414
0.048
Ibs./ton
Reference 9
Phenol
108952
0.115
Ibs./ton
Reference 9
Styrene
100425
0.1015
Ibs./ton
Reference 9
4-318
-------
4.27.3.4 Controls
Controls for residential yard waste burning are generally in the form of a ban on open burning of waste in a
given municipality or county. However, literature suggests that burn bans are not 100% effective. It is therefore
assumed that approximately 25% of the residents that may burn yard waste would burn do so even if a ban is in
place. For counties that have burn bans, the assumption is applied by multiplying 0.25 by the annual waste
burned. Currently no counties are assumed to have burn bans in place.
If county c has a burn ban
Then LWC = LWC x 0.25
If county c has a burn ban
Then BWC = BWC x 0.25
(5)
(6)
Where:
LWC = Annual leaf waste burned in county c, in tons
BWC = Annual brush waste burned in county c, in tons
4.27.3.5 Emissions
The annual amount of leaf and brush waste burned in each county is multiplied by the emissions factors listed in
Table 4-195 and Table 4-196 to estimate emissions. Emissions for leaves and residential brush are calculated
separately, since emission factors vary by yard waste type.
EPiC = LWC X EFp
EP,c = BWc X EFp
(7)
(8)
Where:
E,
P,C
LWC
BWC
EFd
Annual emissions of pollutant p in county c
Annual leaf waste burned in county c, in tons
Annual brush waste burned in county c, in tons
Emission factor for pollutant p, in lbs. of pollution per ton of waste burned
4.27.3,6 Example calculations
Table 4-197 lists sample calculations to determine the CO emissions from open burning of yard waste in Autauga
County, Alabama.
Table 4-197: Sample calculations for CO emissions from open burning in Autauga County, AL
Eq. #
Equation
Values for Autauga County, AL
Result
1
YW X 0.60
PC
34.5 million tons x 0.60
0.065 tons yard
waste per person per
year
1 - d
ry,us
318.85 million people
2
PBurnc = RPopc x 0.24
22,921 people x 0.24
5,501 people likely
to burn in Autauga
County, AL
4-319
-------
Eq. #
Equation
Values for Autauga County, AL
Result
3
LWC = PBurnc x PCyw x YWFrt
* AFfa,c
5,501 people x 0.065 tons x 0.25
x 1
89.39 tons of leaf
waste burned in
Autauga County, AL
4
BWC = PBurnc x PCyw x YWFrt
* AFfa,c
5,501 people x 0.065 tons x 0.25
x 1
89.39 tons of brush
waste burned in
Autauga County, AL
5
If county c has a burn ban
Then LWC = LWC x 0.25
N/A
Autauga County, AL
does not have a burn
ban
6
If county c has a burn ban
ThenBWc = BWC x 0.25
N/A
Autauga County, AL
does not have a burn
ban
7
EPiC = LWC x EFp
89.39 tons of leaf waste
x 112 lbs. per ton
5.01 tons CO
emissions from
burning of leaf waste
in Autauga County,
AL
8
EPiC = BWC X EFp
89.39 tons of brush waste x
140 lbs. per ton
6.26 tons CO
emissions from
burning of brush
waste in Autauga
County, AL
4.27.3.7 Changes from the 2014 methodology
The 2017 emissions inventory methodology for yard waste burning includes a change to the method for
determining population likely to burn. The 2014 v2 NEI methodology determined the population likely to burn
by identifying the rural and "like rural" population in each county in 2010 and using the fraction of 2010 rural
and like rural population to total population in order to determine the rural population in 2014. The 2017
methodology only uses the 2010 rural population to determine the population likely to burn.
4.27.3.8 Puerto Rico and U.S. Virgin Islands
Emissions from Puerto Rico are calculated using the same method described above. For the U.S. Virgin Islands,
emissions are calculated using 2010 population data, since 2017 Census Data does not exist for the U.S. Virgin
Islands.
4.27.3.9 References
1. U.S. Environmental Protection Agency. 2018. Advancing Sustainable Materials: 2015 Fact Sheet. Table 1.
Generation, Recovery and Discards of Materials in MSW, 2015 (in millions of tons and percent of
generation of each material).
2. U.S. Environmental Protection Agency. 2011. Municipal Solid Waste Generation, Recycling, and Disposal
in the United States: Facts and Figures for 2010—Fact Sheet, p. 4.
3. U.S. Census Bureau. Total Population, American Community Survey.
4. U.S. Census Bureau, Decennial Censuses, 2010 Census: Summary File 1.
5. Environment Canada. 2001. "Household Garbage Disposal and Burning." Prepared by Environics
Research Group.
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6. Two Rivers Regional Council of Public Officials and Patrick Engineering, Inc. 1994. "Emission
Characteristics of Burn Barrels/' prepared for the U.S. Environmental Protection Agency, Region V.
7. U.S. Environmental Protection Agency. 1992. AP-42, Fifth Edition, Volume 1, Chapter 2: Solid Waste
Disposal. Section 2.5: Open Burning.
8. Eastern Research Group, Inc. Emissions Inventory Improvement Program. Volume III: Chapter 16, Open
Burning. 2001.
9. U.S. Environmental Protection Agency, Control Technology Center. 1997. Evaluation of Emissions from
the Open Burning of Household Waste in Barrels. EPA-600/R-97-134a.
10. Huntley, Roy, 2009. Spreadsheet "state comparison ERTAC SS version? 4 Nov 23 2009.xls".
4.27.4 EPA-developed emissions for land clearing debris
The emissions from open burning from land clearing debris are estimated based on the number of acres
disturbed from non-residential, residential, and road construction. The number of acres disturbed is multiplied
by a fuel loading factor to determine the amount of land clearing debris burned in each county. This number is
multiplied by emissions factors to determine emissions of CAPs and HAPs.
4.27.4.1 Activity data
The amount of material burned is estimated using the county-level total number of acres disturbed by
residential, non-residential, and road construction. County-level weighted loading factors are applied to the
total number of construction acres to convert acres to tons of available fuel.
Acres Disturbed from Non-Residential Construction
The activity data for this non-residential construction is the acreage disturbed from non-residential construction,
which is estimated using data from the U.S. Census Bureau's Annual Value of Construction Put in Place in the U.S
[ref 1], and a conversion factor from MRI's Estimating Particulate Matter Emissions from Construction
Operations, Final Report [ref 2], The national-level non-residential construction spending data are allocated to
the county-level based on the proportion of non-residential construction employees in each county.
Employment data are taken from the U.S. Census Bureau's 2016 County Business Patterns (CBP), and gaps in
employment data are filled using a process described in detail in section 4.27.4.2.
Where:
EmpFrc
Empc
Empus
CSc
CSus
EmpFrc =
Empc
Empus
CSc = EmpFrc x CSus
The fraction of non-residential construction employees in county c
The number of non-residential construction employees in county c
The number of non-residential construction employees in the US
Non-residential construction spending in county c
Non-residential construction spending in the US
(1)
(2)
Non-residential construction spending is converted to acres disturbed using a conversion factor from MRI's
report. For the average acres disturbed per million dollars of non-residential construction, MRI reported a
conversion factor of 2 acres/$l million (in 1992 constant dollars). The 1992 conversion factor is adjusted to 2017
using the Price Deflator (Fisher) Index of New Single-Family Houses under Construction [ref 3], In 2017 the
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conversion factor was 1.009 acres per million dollars spent on non-residential construction activities.
2 acres PD1992 (3)
^Pd2017= $1 million* ~PD^~7
Where:
Apd2oi7 = Acres disturbed per million dollars in 2017
PD1992 = Price Deflator (Fisher) Index value in 1992
PD2017 = Price Deflator (Fisher) Index value in 2017
County-level non-residential construction spending (from equation 2) is then multiplied by this conversion factor
to estimate county-level acreage disturbed from non-residential construction activities.
ANRC = CSc X Apd,2oi7 ^ ^
Where:
ANRC = Acres disturbed from non-residential construction in county c
CSc = Non-residential construction spending in county c, in million dollars
Apd2oi7 = Acres disturbed per million dollars in 2017
Acres Disturbed from Residential Construction
The US Census Bureau has 2017 data for Housing Starts - New Privately Owned Housing Units Started [ref 4, ref
5], which provides regional level housing starts based on the groupings of 1 unit, 2-4 units, 5 or more units.
Regional-level results are also provided in Table 4-198 for quarterly totals and 1-unit structures [ref 5], The 2- to
4-unit category is broken down using a ratio calculated from the 2000 US Census Bureau National Housing Starts
data for 2 and 3-4 units [ref 6], for each quarter in 2017. Note that 2000 is the last full year when Census housing
starts data were available separately for 2-unit and 3-4-unit homes. Table 4-199 shows a breakdown of the 2
units and 3-4-unit structures based on the following calculation.
(5)
) x Sq,2-4
SQ,n ~ (jj~) X SQ,2-4
Where:
Sq,„ = Housing starts, by quarter, Q, and number of units, n (2 units or 3-4 units), in thousand units
U„ = Number of housing starts by number of units, n, from the 2000 National Housing Starts data,
in thousand housing starts
Ut = Total number of housing starts for both 2 units and 3-4 units from the 2000 National Housing
Starts data, in thousand housing starts
Sq,2-4 = Number of 2-4 units by quarter, Q, from the 2017 New Privately Owned Housing Units Started
by Purpose and Design data, in thousand units
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Table 4-198: Housing Start Data for 2017
Quarter
Total
Structure
Region
Starts of Structures with 1 unit
1 unit
2 to 4
units
5 units
or more
NE
MW
S
W
NE
MW
S
W
Ql-14
267.0
181.0
2.0
84.0
24.0
29.0
150.0
64.0
12.0
21.0
107.0
41.0
Q2-14
327.0
238.0
3.0
86.0
31.0
56.0
152.0
89.0
16.0
41.0
122.0
58.0
Q3-14
319.0
230.0
3.0
86.0
31.0
50.0
155.0
84.0
20.0
35.0
119.0
56.0
Q4-14
290.0
200.0
3.0
87.0
28.0
45.0
141.0
76.0
15.0
33.0
104.0
48.0
Table 4-199: Breakdown of 2- to 4-unit structures in 2017
Quarter
Structure
2 to 4
units
2 units
3-4 units
Ql-14
2.0
0.74
1.26
Q2-14
3.0
1.11
1.89
Q3-14
3.0
1.11
1.89
Q4-14
3.0
1.11
1.89
Ratios of the number of 2, 3 and 4, and 5-unit structures are then used to estimate the number of structures of
each type in each region. The ratios are calculated by dividing the housing starts by quarter for each unit type by
the total housing starts for buildings with more than 2 units.
_ SQ,n (6)
rQ,n ~
JQ.t
Where:
rQ/n = Ratio of structures with number of units, n, to total number of units by quarter, Q
SQ,„ = Housing starts, by quarter, Q, and number of units, n, from distributed calculation in Step 1 for
the 2-unit or 3-4-unit categories or directly from the 2017 New Privately Owned Housing
Units Started by Purpose and Design data for the 5 units or more category, in thousand
housing starts
SQ/t = Housing starts, by quarter, Q, for total number of units greater than 2 units, t (excludes 1-unit
category), in thousand housing starts
The ratio is then used to distribute the 2017 New Privately Owned Housing Units Started by Purpose and Design
[ref 5] regional data for all unit types to the 2, 3-4, or 5 or more unit categories within each Census region -
Northeast, Midwest, South, and West.
Q,n,rgn ~ rQ,n * iJ^^t,rgn ~ ^gn) (7)
Where:
AQ,n,rgn = Number of housing units started in quarter 0, by number of units, n, and region of the country,
rgn, in thousand units
rQ/„ = Ratio of structures with number of units, n, to total number of units by quarter, Q
RSt,rgn = Total regional starts from the 2017 New Privately Owned Housing Units Started by Purpose and
Design data, in thousand housing starts
RSijgn = Regional starts of structures with 1 unit from the 2017 New Privately Owned Housing Units
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Started by Purpose and Design data, in thousand housing starts
Data from the Census report New Privately Owned Housing Units Authorized Unadjusted Units [ref 7] is used to
calculate a conversion factor to determine the ratio of structures to units in the 5 or more unit category. The
conversion factor is calculated by dividing the total number of units in structures with 5 or more units by region
[ref 6] by the total number of buildings with 5 or more units by region [ref 7],
nT? _ ^5,rgn ^
5 — "o
"5 ,rgn
Where:
CF5,rgn = Ratio of 5 units or more to the number of buildings with 5 units or more by region, rgn
U5,rgn = Total number of 5 or more units by region, rgn
Bsjgn = Total number of buildings with 5 or more units by region, rgn
Structures started by category are then calculated at a regional level by summing the number of housing unit
starts across all four quarters and dividing the by number of units in each building type. For the 3-4-unit type,
the number of units per building is 3.5. The value is multiplied by 1,000 because the Census data are in units of
thousand building starts.
For buildings with 1, 2, or 3-4 units:
(Iqi AQ,nrgn)x 1,000
"n.rgn
Where:
B„,rgn = Number of building starts by the unit number category, n, and by region, rgn
AQ,n,rgn = Number of housing units started in quarter Q, by number of units, n, and region of the country,
rgn, in thousand units
n = Number of units per building
For buildings with 5 or more units:
(10)
_ (2qi^Q.n.rgn) x 1-000
Hn,rgn ~ ^
Where:
B„,rgn = Number of building starts by the unit number category, n, and by region, rgn
AQ,n,rgn = Number of housing units started in quarter Q, by number of units, n, and region of the country,
rgn, in thousand units
CFs = Ratio of 5 units or more to the number of buildings with 5 units or more
Annual county building permit data were purchased from the US Census Bureau for 2017 [ref 8], The 2017
County Level Residential Building Permit dataset has 2017 data to allocate regional housing starts to the county
level. This results in county level housing starts by number of units.
The number of building permits for each unit number category by region is calculated by summing the county-
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level Census data to the region level.
(11)
BPn,rgn ~ BPn.c
Where:
BPnjgn = Number of building permits by the unit number category, n, and by region, rgn
BP„/C = Number of building permits by the unit number category, n, and by county, c
The ratio of the number of building permits by county to the total number of building permits by region in which
the county is located, for each unit number category, is then calculated.
BP„ r (12)
p _ n'c
kbp,c ~
BP
urn,rgn
Where:
Rbp,c = Ratio building permits, BP, to total regional building permits in county c
BP„/C = Number of building permits by the unit number category, n, and by county, c
BPn,rgn = Number of building permits by the unit number category, n, and by region, rgn
The final number of building starts for each unit type category is then calculated at the county-level by
multiplying the number of structures started at the regional level and the building permit ratio.
(13)
Bn,c ~ Bn rgn * Rbp,c
Where:
Bn,c = Number of building starts by the unit number category, n, and by county, c
B„,rg„ = Number of building starts by the unit number category, n, and by region, rgn
Rbp,c = Ratio building permits, BP, to total regional building permits in county, c
The number of acres of surface area disturbed by the construction of residential buildings is calculated for
apartment buildings, buildings with 2 units, and buildings with 1 unit. Table 4-200 shows the assumptions used
for the surface area disturbed for each unit type. Buildings with unit types of 3-4 and 5 or more are grouped
together as apartments in this step.
Table 4-200: Surface soil removed per unit type
Structure
Acres disturbed
1-Unit
1/4 acre/structure
2-Unit
1/3 acre/structure
Apartment
1/2 acre/structure
The acres of soil disturbed by the construction of residential buildings are calculated for apartment buildings,
buildings with 2 units, and buildings with 1 unit.
(14)
ARn,c Bn.c ^
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Where:
ARn,c = Surface soil disturbed by building construction by county, c, and unit type category, n, in acres
B„/C = Number of building starts by the unit number category, n, and by county, c
a„ = Acres of surface soil disturbed by each unity type category, n. See Table 4-200.
Acres Disturbed by Road Construction
The activity data for this source category is the acreage disturbed from new road construction, which is
estimated using data from FHWA's Highway Statistics, State Highway Agency Capital Outlay 2014, Table SF-12A
[ref 9] and FLDOT's Generic Cost per Mile Models [ref 10]. From the FHWA table, the following columns are used
New Construction, Relocation, Added Capacity, Major Widening, and Minor Widening. These columns are also
differentiated according to the following six classifications:
1. Interstate, urban
2. Interstate, rural
3. Other arterial, urban
4. Other arterial, rural
5. Collectors, urban
6. Collectors, rural
Construction spending for each road type is summed across all construction types to determine the total annual
highway spending for each road type.
Hss,r=y ssr (i5)
'ct
Where:
HSs,r = Annual highway spending for road type r in state s, in dollars
ct = Construction type
Ss,r = Annual spending per construction type for road type r in state s, in dollars
State expenditure data are converted to miles of new road and acres disturbed per mile of new road based on
conversions obtained from FLDOT [ref 10]. These conversions are shown in Table 4-201 and the acres disturbed
per mile conversions are calculated by multiplying the total affected roadway width (including all lanes,
shoulders, and areas affected beyond the road width) by one mile and converting the resulting land area to
acres.
Where:
„ _ _ HSs r (16)
m,s,r ~ T[)M
RCa>s>r = RCms r X ADM (17)
RCm,s,r = Miles of FHWA road type r constructed in state s
RCa/S,r = Acres of land disturbed for construction of FHWA road type r in state s
HSs,r = Annual highway spending for road type r in state s
TDM = Conversion of dollars spent to road miles constructed, in thousand dollars per mile
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ADM = Conversion of road miles constructed to acres disturbed, in acres per mile
Table 4-201: Spending per Mile and Acres Disturbed per Mile by Highway Type
Road Type
Thousand
Dollars per mile
Total Affected
Roadway Width
(ft)*
Acres
Disturbed per
mile
Urban Areas, Interstate
6,895
94
11.4
Rural Areas, Interstate
3,810
89
10.8
Urban Areas, Other
Arterials
4,112
63
7.6
Rural Areas, Other
Arterials
2,076
55
6.6
Urban Areas, Collectors
4,112
63
7.6
Rural Areas, Collectors
2,076
55
6.6
*Total Affected Roadway Width = (lane width (12 ft) * number of lanes) + (shoulder
width * number of shoulders) + area affected beyond road width (25 ft)
The acres of land disturbed by road type can then be summed across all road types in a state to calculate the
total state-level acreage disturbed due to new road construction.
-I
arcs — y RCa,s
(18)
Where:
ARCS = Acres of land disturbed for all road construction in state s
RCa,s = Acres of land disturbed for construction of FHWA road type r in state s
Similar to residential construction, county-level building permits data from the U.S. Census Bureau are used to
allocate the state-level acres disturbed by road construction to the county [ref 11], Specifically, the ratio of the
county-to state-level number of building starts is calculated and multiplied by the state-level acreage disturbed
(from equation 18) to estimate the county-level acreage disturbed by road construction.
Buildr
BFracc = (19)
Builds
ARCc = ARCS X BFracc (20)
Where:
BFracc = The fraction of building starts in county c
Buildc = The number of building starts in county c
Builds = The number of building starts in state s
ARCc = Acres of land disturbed for road construction in county c
ARCS = Acres of land disturbed for all road construction in state s
Converting Acres Disturbed to Tons of Land Clearing Debris Burned
The total acres disturbed by all construction types is calculated by summing the acres disturbed from residential,
non-residential, and road construction.
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Where:
TADC = ANRC + ARnc) + ARC,
(21)
C
TADC = Total acres disturbed in from nonresidential, residential, and road construction in county c
ANRC = Acres disturbed from non-residential construction in county c
AR„/C = Acres of surface soil disturbed from residential construction in county c and unit type category
n (summed to one value for residential construction for the county)
ARCc = Acres of land disturbed for road construction in county c
Version 2 of the Biogenic Emissions Land cover Database (BELD2) within EPA's Biogenic Emission Inventory
System (BEIS) is used to identify the acres of hardwoods, softwoods, and grasses in each county.
Because BELD2 does not contain data on Alaska and Hawaii, the acres of hardwoods, softwoods, and grasses in
each county is estimated by using the state-level land cover statistics from the USGS National Land Cover
Database on the percent land cover under each vegetation type [ref 12], These percentages are multiplied by
the county area (acres), from the U.S. Census Bureau [ref 13],
(22)
AaK/HI.cJ = LAAk/HI,c X LCAK/HI,%,f
Where:
AAk/hi,cj = Total acres of each fuel type, /, for each county, c, in Alaska or Hawaii
LAak/hi,c, = County acres from the U.S. Census Bureau of each fuel type,/, for each county, c, in Alaska
or Hawaii
LCAk/hi,%j = Land cover percentages for each fuel type (hardwood, softwood, grass) in Alaska or Hawaii
Table 4-202 presents the average fuel loading factors by vegetation type. The average loading factors for slash
hardwood and slash softwood are adjusted by a factor of 1.5 to account for the mass of tree that is below the
soil surface that would be subject to burning once the land is cleared [ref 14], Weighted average county-level
loading factors are calculated by multiplying the average loading factors by the percent contribution of each
type of vegetation class to the total land area for each county.
Acf (23)
WFLFcJ = X LFf
"c, total
Where:
WFLFCrf = Weighted average fuel loading factor by for fuel type/in county c
Acj = Acres of land cover in county c, by fuel type/(from BELD2 for continental U.S.; from equation
22 for Alaska and Hawaii)
Ac,total = Total acres of land cover of all fuel types in county c
LFf = Fuel loading factor by fuel type,/, in tons/acre, from Table 4-202
Table 4-202: Fuel Loading Factors by Vegetation Type
Vegetation Type
Unadjusted Average
Fuel Loading Factor
(Ton/acre)
Adjusted Average
Fuel Loading Factor
(Ton/acre)
Hardwood
66
99
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Vegetation Type
Unadjusted Average
Fuel Loading Factor
(Ton/acre)
Adjusted Average
Fuel Loading Factor
(Ton/acre)
Softwood
38
57
Grass
4.5
Not Applicable
The weighted average county-level loading factors for each fuel type are then summed across fuel types to
calculate a single weighted average loading factor for each county.
¦I,
WFLFC = WFLFC j
(24)
Where:
WFLFc = Weighted average fuel loading factors for county c
WFLFc,f = Weighted average fuel loading factor by for fuel type/in county c
The county-level total acres disturbed are then multiplied by the weighted average loading factor to derive tons
of land clearing debris.
(25)
LCDc = TADc X WFLFC
Where:
LCDc = Land clearing debris in county c, in tons
TADC = Total acres disturbed in county c
WFLFc = Weighted average fuel loading factors for county c
The total land clearing debris burned per county is calculated by multiplying acres of land clearing debris by
county by a control factor, based on the percent of urban land from the 2010 U.S. Census data [ref 13], See
Section 4.27.4.4 for more information on the control factor.
(26)
BLCDc = LCDc X CFc
Where:
= Land clearing debris burned in county c, in tons
= Land clearing debris in county c, in tons
= Control factor. The control factor is 1 for counties with less than 80% urban population and 0
for Colorado or in counties with an urban population of 0.8% or more based on the 2010 U.S.
Census data [ref 13] as no burning occurs in these counties. See Section 4.27.4.4 for more
information on the control factor.
4.27.4.2 Allocation procedure
BLCDc
LCDc
CFc
Acres disturbed by Non-residential Construction - County business patterns allocation
Employment data are obtained from the U.S. Census Bureau's 2016 County Business Patterns (CBP) [ref 15]. Due
to concerns with releasing confidential business information, the CBP does not release exact numbers for a
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given NAICS code if the data can be traced to an individual business. Instead, a series of employment flags is
used. To estimate employment in counties and states with withheld data, the following procedure is used for
NAICS code 2362 (non-residential construction).
To gap-fill withheld state-level employment data:
1. State-level data for states with known employment are summed to the national level.
2. State-level known employment is subtracted from the national total reported in the national-level CBP.
3. Each of the withheld states is assigned the midpoint of the employment flag. Table 4-203 lists the
employment flags and midpoints.
4. The midpoints for the states with withheld data are summed to the national level.
5. An adjustment factor is created by dividing the number of withheld employees (calculated in step 2 of
this section) by the sum of the midpoints (step 4)
6. For the states with withheld employment data, the midpoint of the range for that state (step 3) is
multiplied by the adjustment factor (step 5) to calculate the adjusted state-level employment for non-
residential construction.
These same steps are then followed to fill in withheld data in the county-level business patterns.
1. County-level data for counties with known employment are summed by state.
2. County-level known employment is subtracted from the state total reported in state-level CBP (or, if the
state-level data are withheld, from the state total estimated using the procedure discussed above).
3. Each of the withheld counties is assigned the midpoint of the employment flag (Table 4-203).
4. The midpoints for the counties with withheld data are summed to the state level.
5. An adjustment factor is created by dividing the number of withheld employees (step 2) by the sum of
the midpoints (step 4).
6. For counties with withheld employment data, the midpoints (step 3) are multiplied by the adjustment
factor (step 5) to calculate the adjusted county-level employment for non-residential construction.
Note that step 5 adjusts all counties within each state with withheld employment data by the same state-based
proportion. It is unlikely that actual employment corresponds exactly with this smoothed adjustment method,
but this method is the best option given the availability of the data.
Table 4-203: Ranges and midpoints for data withheld from State and County Business Patterns
Employment Flag
Employment Range
Midpoint
A
0-19
10
B
20-99
60
C
100-249
175
E
250-499
375
F
500-999
750
G
1,000-2,499
1,750
H
2,500-4,999
3,750
1
5,000-9,999
7,500
J
10,000-24,999
17,500
K
25,000-49,999
37,500
L
50,000-99,999
75,000
M
100,000+
For example, take the 2016 CBP data for NAICS 2362 (nonresidential construction) in Arizona provided in Table
4-204.
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Table 4-204: 2016 CBP for NAICS 2361 in Arizona
State
FIPS
County
FIPS
NAICS
Employment
Flag
Employment
04
001
2362
B
withheld
04
003
2362
125
04
005
2362
166
04
007
2362
24
04
011
2362
B
withheld
04
012
2362
A
withheld
04
013
2362
8,580
04
015
2362
64
04
017
2362
53
04
019
2362
2,085
04
021
2362
115
04
023
2362
16
04
025
2362
260
04
027
2362
233
1. The total of employees not including withheld counties is 11,831.
2. The state-level CBP reports 11,721 employees for NAICS 2362. The difference is 110.
3. Withheld counties are given the midpoint of the employment range. County 001 is given a midpoint of
60 (since employment flag A is 20 - 99) and County 012 is given a midpoint of 10 (since employment flag
H is 0-19).
4. State total for these all withheld counties is 130.
5. 110/130 = 0.846.
6. The adjusted employment for county 001 is 60 x 0.846 = 51.36 employees. County 012 has an adjusted
employment of 10 x 0.846 = 8.46 employees.
The county-level employment data are used to allocate the national-level non-residential construction spending
data to the county-level (see equation 1).
Acres disturbed by Residential Construction - Building permits allocation
Annual county building permit data were purchased from the U.S. Census Bureau for 2017 [ref 8] and used to
allocate regional housing starts to the county level. This results in county level housing starts by number of units.
See equations 11-13 in section 4.27.4.1.
Acres Disturbed by Road Construction - Building permits allocation
State-level estimates of acres disturbed by road construction is distributed to the counties based on county-level
data on residential building starts from the U.S. Census Bureau [ref 11], See equations 19 and 20 in section
4.27.4.1.
4.27.4.3 Emission factors
Emissions factors are reported in Table 4-205 below. Emissions factors for CAPs and HAPs are from the AP-42
and U.S. EPA Emissions Inventory Improvement Program [ref 16, ref 17]. The PM25 to PM10 emissions factor
ratio for brush burning (0.7709) is multiplied by the PM10 emissions factors for land clearing debris burning to
develop PM25 emissions factors. Emissions factors for HAPs are from an EPA Control Technology Center report
[ref 18].
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Table 4-205: Emissions Factors for Open Burning of Land Clearing Debris (SCC 2610000500)
Pollutant
Pollutant Code
Emissions Factor
(lb/ton)
Emission Factor Reference
voc
VOC
11.3
Reference 16, Table 2.5-5, a
NOX
NOX
4.0
Reference 16, Table 2.5-5, b
CO
CO
164.8
Reference 17, Table 16.4-2, c
PM10-FIL
PM10-FIL
20.4
Reference 17, Table 16.4-2, c
PM25-FIL
PM25-FIL
18.6
Reference 17, Table 16.4-2, c
PM10-PRI
PM10-PRI
20.4
Reference 17, Table 16.4-2, c
PM25-PRI
PM25-PRI
18.6
Reference 17, Table 16.4-2, c
Cumene
98828
0.012
Reference 16, Table 16.4-3, d
Ethyl Benzene
100414
0.048
Reference 16, Table 16.4-3, d
Phenol
108952
0.115
Reference 16, Table 16.4-3, d
Styrene
100425
0.102
Reference 16, Table 16.4-3, d
a. Average of factors for forest residues.
b. Emissions factor is from footnote to Table 2.5-5
c. Average of factors from Table 16.4-2 except for last two rows (test burn with blower)
d. Average of factors from Table 16.4-3 except for last two columns (test burn with blower)
4.27.4.4 Controls
Controls for land clearing debris burning are generally in the form of a ban on open burning of waste in a given
municipality or county. Counties that are more than 80% urban by land area determined by the 2010 U.S.
Census data [ref 13], are assumed not to practice any open burning of land clearing debris. Therefore, CAP and
HAP emissions from open burning of land clearing debris are zero in these counties.
Additionally, it is assumed that even in counties that are less than 80% urban by land area, open burning will
only be practiced in areas that are rural. Therefore, the total land clearing debris burned per county (from
equation 26) will be scaled based on the fraction of rural land area in each county from the 2010 Census.
RLandr
BLCDr c = BLCDc X — —
TLanclr
(27)
Where:
BLCDr,c = Land clearing debris burned in rural areas by county, c, in tons
BLCDc = Land clearing debris burned by county, c, in tons
RLandc = Amount of rural land by land area in county c
TLandc = Total amount of land in county c
Further controls on burning (i.e., burn bans in rural areas) are represented by multiplying the land clearing
debris burned in rural counties by a burn ban's effectiveness; effectiveness is a value between 0 and 1.
BLCDr c = BLCDr c X BEC
(28)
4-332
-------
Where:
BLCDr,c = Land clearing debris burned in rural areas by county, c, in tons
BEC = Burn ban effectiveness in county c
In this methodology, burn ban effectiveness is represented by a single value between 0 and 1 that is multiplied
by the amount of land clearing debris burned in the rural areas of each county. In practice, the burn ban
effectiveness is a function of both a rule's penetration and effectiveness. Rule penetration refers to the extent
to which a regulation covers emissions for a specified controlled area, and effectiveness concerns the ability of
the regulatory program to achieve emissions reductions compared to full compliance. By default, the burn ban
effectiveness for each county is 1 (i.e. the methodology assumes no burn bans in each county), although this
may be updated by state, local, or tribal agencies.
4.27.4.5 Emissions
County-level criteria pollutant and HAP emissions are calculated by multiplying the mass of land clearing debris
burned in rural areas per year (from equation 28) by an emissions factor from Table 4-205.
1 ton (29)
Ecv = BLCDrc X EFV X
c'p r'c v 2000 lb
Where:
Ec,p = Emissions by county, c, and pollutant, p, in tons
BLCDr,c = Land clearing debris burned in rural areas by county, c, in tons
EFP = Emissions factor by pollutant, p, in pounds/ton
4.27.4.6 Example calculations
Table 4-206 shows sample calculations for PM25-PRI emissions from open burning of land clearing debris in
McLean County, Illinois. Equations 5 through 7 use the first quarter (Ql) of 2017 for 2-unit structures as an
example. However, these calculations would need to be repeated to calculate values for all 4 quarters for all 3
unit sizes. Note that structures with 5 or more units and structures with 1 unit with or without a basement have
additional steps not shown in the sample calculations here. Equations 15 through 20 use urban roads as an
example for acres of land disturbed from road construction. For full calculations of acres of land disturbed from
road construction the calculations for rural roads would also need to be incorporated.
Table 4-206: Sample calculations for PM25-PRI emissions from open burning of land clearing debris in McLean
County, IL
Eq. #
Equation
Values for McLean County, IL
Result
0.000241
fraction on non-
1
EmpFrc
Empc
140 nonres construction employees
residential
construction
employees in
McLean County,
IL
Empus
581,963 nonres construction employees
4-333
-------
Eq. #
Equation
Values for McLean County, IL
Result
2
CSc = EmpFrc x CSU5
0.000241fraction of employees
x $ 347,666 million in nonres
construction spending in the US
$83.79 million in
non-residential
construction
spending in
McLean County,
IL
3
2 acres PD^qq7
'[rid — w
2 acres disturbed 57 in 1992
1.009 acres
disturbed per
million dollars
spent on non-
residential
construction
spending,
nationally
2017 $1 million PD2 017
$1 million s 113 m 2017
4
ANRC = CSc x j4pdy
acres disturbed
$83.79 million x 1.009
million $
84.4 acres
disturbed from
non-residential
construction in
McLean County,
IL
5
5Q,n = (jj^J X 5Q,2-4
/14 two unit housing starts in 2002\
V 38 total housing starts in 2002 J
X
2 two to four unit housing starts in Q1 2(
0.74 thousand
housing starts
for 2-unit
structures in Q1
2017, nationally
6
So,n
0.74 two unit housing starts
0.01 ratio of
buildings with 2
units to all units
greater than 2
for Q1 2017,
nationally
7U,n c
JQ.t
72 two or more unit housing starts
7
AQ,n,rgn ~ rQ,n x (•~
0.01
x (21 total Q1 housing starts in Midwest
— 14 one unit housing starts in Midwest)
0.07 thousand
housing starts
for 2-unit
structures for
Q1 2017 in the
Midwest
8
„ „ ^5 ,rgn
Lrc, — _
B5,r
N/A
Equation is for 5
or more unit
buildings;
example is for 2-
unit buildings
9
(X^Q.n.rgn) x 1-000
t>n,rgn ~
0.775 two unit structures x 1,000
2 units per building
388 2-unit
structures
constructed in
the Midwest
4-334
-------
Eq. #
Equation
Values for McLean County, IL
Result
10
_ Qj^Q.n.rgn) * 1,000
"n.rgn ~
N/A
Equation is for 5
or more unit
buildings;
example is for 2-
unit buildings
11
BPn,rgn ~ BPn.c
Midwest two unit building permits
1,571 2-unit
structure
building permits
in the Midwest
12
BPn,c
1 McLean County building permits
0.000637 ratio
of county-level
building permits
to regional-level
building permits
in McLean
County, IL
^BF,C
L,rn,rgn
1,571 Midwest building permits
13
Bn,c ~ Bn,rgn * ^BP,c
388 two unit building starts in the Midwe
x 0.000637
0.25 total 2-unit
structure
building starts
for McLean
County, IL
14
ARn,c ®n.c ^
0.25 two unit structures
x 0.33 acres per structure
0.08 acres
surface soil
disturbed by 2-
unit structures
in McLean
County, IL
15
H Ss,r = "V 5Sr
'ct
$20,399,000 + $33,029,000
+ $93,892,000
$147,320,000
spent on urban
interstate
construction in
IL
$58,519,000 + $2,626,000
+ $35,1367,000
+ $206,057,000
+ $17,193,000
$319,532,000
spent on urban
other arterial
construction in
IL
$16,093,000 + $338,000 + $355,000
$16,786,000
spend on urban
collector
construction in
IL
16
H Ss r
r>r a''
m's'r TDM
$147,320,000
6,895,000 $ per mile
21.4 miles of
urban interstate
constructed in IL
4-335
-------
Eq. #
Equation
Values for McLean County, IL
Result
$319,532,000
4,112,000 $ per mile
77.7 miles of
urban other
arterial
constructed in IL
$16,786,000
4,112,000 $ per mile
4.1 miles of
urban collector
constructed in IL
17
R(-a,s,r = RCm,s,r ^ ADM
21.4 miles x 11.4 acres per mile
242.9 acres
disturbed from
urban interstate
construction in
IL
77.7 miles x 7.6 acres per mile
589.6 acres
disturbed from
urban other
arterial
construction in
IL
4.1 miles x 7.6 acres per mile
31 acres
disturbed from
urban collector
construction in
IL
18
ARCS = ^ RCa,s
242.9 acres + 589.6 acres + 31 acres
863.5 acres
disturbed from
urban road
construction in
IL
19
Buildr
246 building starts in McLean County
0.012 fraction of
building starts in
McLean County,
IL
L) 1 1 ttC/j
Builds
20,578 building starts in IL
20
ARCc = ARCS x BFracc
863.5 acres x 0.012
10.4 acres
disturbed from
urban road
construction in
McLean County,
IL
4-336
-------
Eq. #
Equation
Values for McLean County, IL
Result
21
TADC = ANRC + Snc)
+ ARCc
84.4 acres + 62.02* acres
+ 13.95** acres
* note that the value for residential
construction is for all unit types, not just 2-
unit buildings as shown in example above
** note the value for road construction is for
all road types, not just urban roads as shown
in the example above
160.4 total acres
disturbed in
McLean County,
IL
22
AaK/HI.cJ = LAAk/HI,c
X LCAK/HI,%,f
N/A
Equation is for
Alaska or Hawaii
23
WFLFcj = X LFf
^total
17,516 acres
— x 99 tons per acre
758,793 acres
2.3 tons/ acre
weighted factor
for hardwood
fuel in McLean
County, IL
0 acres
— x 57 tons per acre
758,793 acres
0.0 tons/ acre
weighted factor
for softwood
fuel in McLean
County, IL
741,276 acres
— x 4.5 tons per acre
758,793 acres
4.4 tons/ acre
weighted factor
for grass fuel in
McLean County,
IL
24
WFLFC = ^ WFLFcj
tons tons tons
2.3 +0.0 + 4.4
acre acre acre
6.7 tons/acre
weighted factor
for all fuels in
McLean County,
IL
25
LCDc = TADC X VKFLFC
tOTLS
160.4 acres x 6.7
acre
1,071 tons of
land clearing
debris in
McLean County,
IL
26
BLCDc = LCDc X CFc
1,071 tons x 1 control factor
1,071 tons of
land clearing
debris burned in
McLean County,
IL
4-337
-------
Eq. #
Equation
Values for McLean County, IL
Result
27
RLandr
BLCDr c — BLCDc X
TLandc
1,071 tons
2,923,414,473 m2 rural land
X 3,064,933,852 m2 total land
1,022 tons of
land clearing
debris burned in
rural areas in
McLean County,
IL
28
1 ton
Ecv = BLCDrc X EFV X ———rr
c'p r'c p 2000 lb
lb 1 ton
1,022 tons x 13.1053 x —
ton 2000 lb
6.7 tons PM25-
PRI emissions in
McLean County,
IL
4.27.4.7 Changes from the 2014 methodology
There main change to this methodology from the methodology used to calculate the 2014 v2 NEI is that the
estimated amount of land clearing debris in each county is multiplied by the fraction of rural land area in each
county. This step was not done in the methodology used for the 2014 v2 NEI.
4.27.4.8 Puerto Rico and U.S. Virgin Islands
Since insufficient data exist to calculate emissions for the counties in Puerto Rico and the US Virgin Islands,
emissions are based on two proxy counties in Florida: 12011, Broward County for Puerto Rico and 12087,
Monroe County for the US Virgin Islands. The total emissions in tons for these two Florida counties are divided
by their respective populations creating a tons per capita emissions factor. For each Puerto Rico and US Virgin
Island county, the tons per capita emissions factor is multiplied by the county population (from the same year as
the inventory's activity data) which served as the activity data. In these cases, the throughput (activity data) unit
and the emissions denominator unit are "EACH".
4.27.4.9 References
1. U.S. Census Bureau. 2017. Value of Construction Put in Place.
2. Midwest Research Institute. 1999. Estimating Particulate Matter Emissions from Construction
Operations, Final Report (prepared for the Emission Factor and Inventory Group, Office of Air Quality
Planning and Standards, U.S. Environmental Protection Agency).
3. U.S. Census Bureau. Price Deflator (Fisher) Index of New Single-Family Houses Under Construction.
4. U.S. Census Bureau. 2018. New Privately Owned Housing Units Started. Annual Data.
5. U.S. Census Bureau. 2018. New Privately Owned Housing Units by Purpose and Design.
6. U.S. Census Bureau. 2001. Housing Starts. Table 1.
7. U.S. Census Bureau. New Privately Owned Housing Units Authorized - Unadjusted Units for Regions.
Divisions, and States. Annual 2017, Table 2au.
8. U.S. Census Bureau, Annual Housing Units Authorized by Building Permits CQ2017A.
9. Federal Highway Administration. 2014 Highway Spending.
10. Florida Department of Transportation. Cost Per Mile Models.
11. 2017 Building Permits data from US Census BPS01.
12. U.S. Geological Survey (USGS). 2015. National Land Cover Database.
13. U.S. Census Bureau, Decennial Censuses. 2010. Census: Summary File 1.
14. D.V. Sandberg, D.E. Ward, R.D. Ottmar, C.C. Hardy, T.E. Reinhardt, and J.N. Hall. 1989. Mitigation of
Prescribed Fire Atmospheric Pollution through Increased Utilization of Hardwoods, Piled Residues, and
4-338
-------
Long-Needled Conifers. Final Report. USDA Forest Service, Pacific Northwest Research Station, Fire and
Air Resource Management.
15. U.S Census Bureau, County Business Patterns. 2016. Complete County File f!5.6mb zipl.
16. U.S. Environmental Protection Agency. 1992. AP-42, Fifth Edition, Volume 1, Chapter 2: Solid Waste
Disposal.
17. U.S. Environmental Protection Agency. 2001. Emission Inventory Improvement Program, Volume III,
Chapter 16, Open Burning.
18. U.S. Environmental Protection Agency. 1997. Evaluation of Emissions from the Open Burning of
Household Waste in Barrels, EPA-600/R-97-134a.
4.27.5 EPA-developed emissions for residential household waste
The calculations for estimating the emissions from the burning of residential household waste (RHW) involve
first estimating the amount of combustible waste generated in each county. The amount of waste generated in
the U.S. is available from the EPA report, Advancing Sustainable Materials Management: 2015 Fact Sheet [ref 1],
The amount of county-level RHW burned is estimated by calculating the per capita amount of RHW generated
using the national data from EPA and multiplying that by the number of people likely to burn waste in each
county. The number of people likely to burn waste is based on the rural population in each county from the
2010 census. To estimate emissions from RHW burning, pollutant emissions factors are multiplied by the
amount of combustible waste burned. Emissions factors for PM, VOC, and HAPs are from the literature, whereas
emissions factors for CO, NOX, and S02 are adjusted based on the ratio of total waste to combustible waste.
4.27.5.1 Activity data
The activity data for this source category is the amount of RHW burned in each county, which is estimated using
data the EPA report Advancing Sustainable Materials Management: 2015 Fact Sheet [ref 1], The report presents
the total mass of waste generated from the residential and commercial sectors in the United States by type of
waste for the calendar year 2015.
Table 4-207 shows the total national-level waste generated by type and the corresponding per capita values. Per
capita values of RHW subject to burning were developed based on EPA's total amount of waste generated in
2015. According to the 2010 version of the same EPA report, residential waste generation accounts for 55-65%
of the total waste from the residential and commercial sectors [ref 2]; for the per capita calculation, the median
value of 60% of total waste generated is assumed. This number is multiplied by the sums of the total and
combustible waste, respectively. Each number is then divided by the U.S. population in 2017 (329 million
people) [ref 3] to determine separate per capita values for total and combustible waste. Note that yard waste is
not included in either per capita value as emissions from the burning of yard waste are calculated in separate
SCCs.
(1)
ZTW X 0.60
(2)
Where:
PCc
PC,
¦cwaste
¦twaste
Per capita value of combustible waste in the U.S., in tons per person
Per capita value of total waste in the U.S., in tons per person
4-339
-------
Com = Types of combustible waste (not including yard waste)
T = All types of waste (not including yard waste)
W = Annual weight of waste, in million tons
Py,us = Population of the U.S. for year of inventory, in million people
The per capita value of combustible household waste is estimated to be 0.354 tons generated per person in
2015, and the per capita value of total waste is 0.420 tons generated per person.
Table 4-207: Annual RHW generated (tons/person) in t
he U.S. in 2015
Material
Weight Generated
(million tons)
Total per
person
Combustible
per person
Paper
68.61
0.129
0.129
Glass
11.48
0.022
0
Metals
Steel
17.69
0.033
0. 0
Aluminum
3.53
0.007
0.0
Other nonferrous
2.04
0.004
0.0
Total Metals
23.26
0.044
0.0
Plastics
32.25
0.061
0.061
Rubber/leather
8.21
0.015
0.015
Textiles
16.22
0.031
0.031
Wood
16.12
0.030
0.030
Other
4.44
0.008
0.008
Total Materials
180.59
0.340
0.274
Other wastes
Food
38.40
0.072
0.072
Yard
34.50
0.0
0.0
Miscellaneous
inorganic
3.97
0.007
0.007
Total Other
76.87
0.080
0.080
TOTAL RHW
257.46
0.420
0.354
Source: Reference 1, Table 1
As open burning of RHW is generally not practiced in urban areas, only the rural population in each county is
assumed to practice open burning. The rural and urban populations are taken from 2010 U.S. Census data for
each county [ref 4], It is assumed that 24% of the rural population burns RHW [ref 5],
PBurnc = RPopc x 0.24
Where:
RPop c = Rural population in county c in 2010
PBurric = Population likely to burn RHW in county c
The number of people likely to burn waste in each county (from equation 3) is then used with the values of per
capita household waste subject to burning (from equations 1 and 2) to determine the amount of household
RHW burned.
CWstc PBurnc x PCcwasie
4-340
(4)
-------
Where:
CWstc = Annual combustible RHW burned in county c, in tons
PBurric = Population likely to burn in county c
PCcwaste = Per capita value of combustible waste in the U.S., in tons per person
4.27.5.2 Allocation procedure
National values for the amount of waste generated are distributed to the counties based on rural population, as
described in Section 4.27.5.1.
4.27.5.3 Emission factors
Emissions factors for open burning of RHW are reported in Table 4-208. The emissions factors for CO, NOX, PM,
SO2, and VOC and some HAPs are from AP-42 [ref 6] and the EPA report Evaluation of Emissions from the Open
Burning of Household Waste in Barrels [ref 7], Emissions factors for HAPs are from an EPA Office of Research and
Development report [ref 8] and a Minnesota Pollution Control Agency Report [ref 9], For HAP emissions factors
from the EPA Control Technology Center report [ref 7], the emissions factors are based on an average of
emissions factors for non-recyclers. This assumes that a person burning RHW in their yard is more likely to be a
non-recycler than an avid recycler.
Table 4-208: Em ission
actors for Open Burning of RHW
Pollutant
Pollutant
Code
Emission
Factor
(original)
Emission
Factor
Units
(original)
Emission
Factor
(converted)
Emission
Factor
Units
(converted)
Reference & Table
No.
Carbon Monoxide
CO
85
Ibs./ton
100.61
Ibs./ton
Reference 6, Table
2.5-1; original
factor based on
total waste;
converted factor
based on
combustible waste
Nitrogen Oxides
NOX
6
Ibs./ton
7.10
Ibs./ton
Reference 6, Table
2.5-1; original
factor based on
total waste;
converted factor
based on
combustible waste
PM10-FIL
PM10-
FIL
18.76
g/kg
38
Ibs./ton
Reference 7
(average of non-
recyclers)
PM10-PRI
PM10-
PRI
18.76
g/kg
38
Ibs./ton
Reference 7
(average of non-
recyclers)
PM25-FIL
PM25-
FIL
17.44
g/kg
34.8
Ibs./ton
Reference 7
(average of non-
recyclers)
4-341
-------
Pollutant
Pollutant
Code
Emission
Factor
(original)
Emission
Factor
Units
(original)
Emission
Factor
(converted)
Emission
Factor
Units
(converted)
Reference & Table
No.
PM25-PRI
PM25-
PRI
17.44
g/kg
34.8
Ibs./ton
Reference 7
(average of non-
recyclers)
Sulfur Oxides
SO 2
1
Ibs./ton
1.184
Ibs./ton
Reference 6, Table
2.5-1; original
factor based on
total waste;
converted factor
based on
combustible waste
VOC
VOC
-
mg/kg
7.409
Ibs./ton
Reference 6, Table
3-6 (sum of HAP
VOC emissions
factors)
1,2,4-trichlorobenzene
120821
0.1
mg/kg
2.00E-04
Ibs./ton
Reference 8, Table
3-6
1,4-dichlorobenzene
106467
0.03
mg/kg
6.00E-05
Ibs./ton
Reference 8, Table
3-6
2,4,6-Trichlorophenol
88062
0.19
mg/kg
3.80E-04
Ibs./ton
Reference 8, Table
3-6
2-Methylnapthalene
91576
8.53
mg/kg
1.70E-02
Ibs./ton
Reference 8, Table
3-6
Acenaphthene
83329
0.64
mg/kg
1.28E-03
Ibs./ton
Reference 8, Table
3-6
Acenaphthylene
208968
7.34
mg/kg
1.47E-02
Ibs./ton
Reference 8, Table
3-6
Acetaldehyde
75070
428.4
mg/kg
8.55E-01
Ibs./ton
Reference 8, Table
3-6
Acetophenone
98862
4.69
mg/kg
9.36E-03
Ibs./ton
Reference 8, Table
3-6
Acrolein
107028
26.65
mg/kg
5.32E-02
Ibs./ton
Reference 8, Table
3-6
Anthracene
120127
1.3
mg/kg
2.59E-03
Ibs./ton
Reference 8, Table
3-6
Benz[a]anthracene
56553
1.51
mg/kg
3.01E-03
Ibs./ton
Reference 8, Table
3-6
Benzene
71432
979.75
mg/kg
1.96E+00
Ibs./ton
Reference 8, Table
3-6
Benzo[a]pyrene
50328
1.4
mg/kg
2.79E-03
Ibs./ton
Reference 8, Table
3-6
1,3-Butadiene
106990
141.25
mg/kg
2.82E-01
Ibs./ton
Reference 8, Table
3-6
Benzo[b]fluoranthene
205992
1.86
mg/kg
3.71E-03
Ibs./ton
Reference 8, Table
3-6
4-342
-------
Pollutant
Pollutant
Code
Emission
Factor
(original)
Emission
Factor
Units
(original)
Emission
Factor
(converted)
Emission
Factor
Units
(converted)
Reference & Table
No.
Benzo[g,h,i,]Perylene
191242
1.3
mg/kg
2.59E-03
Ibs./ton
Reference 8, Table
3-6
Benzo[k]fluoranthene
207089
0.67
mg/kg
1.34E-03
Ibs./ton
Reference 8, Table
3-6
Bis (2-Ethylhexyl)
Phthalate
117817
23.79
mg/kg
4.75E-02
Ibs./ton
Reference 8, Table
3-6
Chloromethane
74873
163.25
mg/kg
3.26E-01
Ibs./ton
Reference 8, Table
3-6
Chrysene
218019
1.8
mg/kg
3.59E-03
Ibs./ton
Reference 8, Table
3-6
Cresol/Cresylic Acid
(Mixed Isomers)
1319773
68.77
Mg/kg
1.37E-01
Ibs./ton
Reference 8, Table
3-6
Dibenzo[a,h]anthracene
53703
0.27
mg/kg
5.40E-04
Ibs./ton
Reference 8, Table
3-6
Dibutyl Phthalate
84742
3.45
mg/kg
6.89E-03
Ibs./ton
Reference 8, Table
3-6
Ethyl Benzene
100414
181.75
mg/kg
3.63E-01
Ibs./ton
Reference 8, Table
3-6
Fluoranthene
206440
2.77
mg/kg
5.53E-03
Ibs./ton
Reference 8, Table
3-6
Fluorene
86737
2.99
mg/kg
5.97E-03
Ibs./ton
Reference 8, Table
3-6
Formaldehyde
50000
443.65
mg/kg
8.85E-01
Ibs./ton
Reference 8, Table
3-6
Dibenzofuran
132649
3.64
mg/kg
7.26E-03
Ibs./ton
Reference 8, Table
3-6
Hexachlorobenzene
118741
0.04
mg/kg
8.00E-05
Ibs./ton
Reference 8, Table
3-6
lndeno[l,2,3-c,d]pyrene
193395
1.27
mg/kg
2.53E-03
Ibs./ton
Reference 8, Table
3-6
Isophorone
78591
9.25
mg/kg
1.85E-02
Ibs./ton
Reference 8, Table
3-6
Methylene Chloride
75092
17
mg/kg
3.39E-02
Ibs./ton
Reference 8, Table
3-6
Mercury
7439976
8.74E-04
Ibs./ton
-
Reference 9
Naphthalene
91203
11.36
mg/kg
2.27E-02
Ibs./ton
Reference 8, Table
3-6
Pentachloronitrobenzene
82688
0.01
mg/kg
2.00E-05
Ibs./ton
Reference 8, Table
3-6
Phenanthrene
85018
5.33
mg/kg
1.06E-02
Ibs./ton
Reference 8, Table
3-6
Phenol
108952
112.66
mg/kg
2.25E-01
Ibs./ton
Reference 8, Table
3-6
4-343
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Pollutant
Pollutant
Code
Emission
Factor
(original)
Emission
Factor
Units
(original)
Emission
Factor
(converted)
Emission
Factor
Units
(converted)
Reference & Table
No.
Polychlorinated
Biphenyls (PCBs)
1336363
0.126
mg/kg
2.51E-04
Ibs./ton
Reference 8, Table
3-6
Propionaldehyde
123386
112.6
mg/kg
2.25E-01
Ibs./ton
Reference 8, Table
3-6
Pyrene
129000
3.18
mg/kg
6.35E-03
Ibs./ton
Reference 8, Table
3-6
Styrene
100425
527.5
mg/kg
1.05E+00
Ibs./ton
Reference 8, Table
3-6
Toluene
108883
372
mg/kg
7.42E-01
Ibs./ton
Reference 8, Table
3-6
Xylenes (Mixed Isomers)
1330207
38
mg/kg
7.58E-02
Ibs./ton
Reference 8, Table
3-6
a. Emissions factor for 1,4-Dichlorobenzene is reported as <1 mg/kg. The factor used for this methodology
assumes that the actual value is 0.333 mg/kg.
b. Emissions factor for Pentachlorophenol is reported as <0.0025 and <0.0018 g/kg. The factor used for this
methodology assumes that the actual value is 5.3E-05 g/kg.
The emissions factors for PM, VOC, and HAPs were developed based on the amount of combustible waste
burned. Emissions factors for CO, NOX, and S02 were developed based on the amount of total waste burned;
therefore, these factors need to be adjusted to be used with the values of combustible waste burned. This is
accomplished by multiplying the emissions factors by a ratio of the total per capita waste to combustible per
capita waste in 2015.
_ P^twaste (5)
EFp.com = EFpj X "ET
*^cwaste
Where:
EFP = Emission factor for pollutant p, in lbs. of pollution per ton of waste burned
Com = Types of combustible waste (not including yard waste)
T = All types of waste (not including yard waste)
PCcwaste = Per capita value of combustible waste in the US, in tons per person
PCtwaste = Per capita value of total waste in the US, in tons per person
4.27.5.4 Controls
Controls for residential household waste burning are generally in the form of a ban on open burning of waste in
a given municipality or county. However, literature suggests that burn bans are not 100% effective. It is
therefore assumed that approximately 25% of the residents that may burn trash in the yard would burn waste
even if a ban is in place. For counties that have burn bans, the assumption is applied by multiplying 0.25 by the
annual waste burned. Currently no counties are assumed to have burn bans in place.
If county c has a burn ban (6)
Then CWstc = CWstc x 0.25
4-344
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Where:
CWstc = Annual combustible RHW burned in county c, in tons
4.27.5.5 Emissions
The annual amount of combustible RHW burned in each county is multiplied by the emissions factors listed in
Table 4-208 to estimate emissions.
(7)
Ep,c CWstc X EFp Com
Where:
Ep,c = Annual emissions of pollutant p in county c
EFPicom = Emission factor for pollutant p, in lbs. of pollution per ton of combustible waste burned
CWstc = Annual combustible RHW burned in county c, in tons
4.27.5.6 Example calculations
Table 4-209 lists sample calculations to determine the CO and VOC emissions from open burning in Autauga
County, Alabama.
Table 4-209: Sample calculations for CO and VOC emissions from open burning in Autauga County, AL
Eq. #
Equation
Values for Autauga County, AL
Result
1
Icom^X 0.60
188.22 million tons of waste x 0.60
0.354 tons
combustible
waste per person
per year
' ^cwaste ~ D
ry,us
318.85 million people
2
ZNCWx 0.60
nn _ ",l'
r ^twaste ~ D
rUS
222.96 million tons of waste x 0.60
318.85 million people
0.420 tons total
waste per person
per year
3
PBurnc = RPopc x 0.24
22,921 people x 0.24
5,501 people
likely to burn in
Autauga County,
AL
4
CWstc = PBurnc
v PC
^ r ucwaste
5,501 people x
0.354 tons combustible waste per person
1,947.4 tons of
combustible
waste burned in
Autauga County,
AL
5
PC
^.rK*twaste
EFp,Com ~ EFp T X
* ^cwaste
0.42 tons per person
85 lbs. per ton x —
0.354 tons per person
100.8 lbs. of CO
per ton of
combustible
waste burned
6
If county c has a burn ban
Then CWstc = CWstc
x 0.25
N/A
Autauga County,
AL does not have
a burn ban
4-345
-------
Eq. #
Equation
Values for Autauga County, AL
Result
7
Ep c CWstc X EFp Com
1,947.4 tons x 100.8 lbs. per ton
98.14 tons CO
emissions from
burning of RHW in
Autauga County,
AL
1,947.4 tons x 8.46 lbs. per ton
8.23 tons VOC
emissions from
burning of RHW in
Autauga County,
AL
4.27.5.7 Changes from the 2014 methodology
The 2017 emissions inventory methodology for RHW burning includes changes to the method for determining
population likely to burn, and changes to the emissions factors for CO, NOX, and S02. The 2014 v2 NEI
methodology determined the population likely to burn by identifying the rural and "like rural" population in
each county in 2010 and using the fraction of 2010 rural and like rural population to total population in order to
determine the rural population in 2014. The 2017 methodology only uses the 2010 rural population to
determine the population likely to burn.
Additionally, the 2014 v2 NEI methodology used emissions factors for CO, NOX, and S02 that corresponded to
the amount of combustible plus non-combustible waste burned. The 2017 methodology uses a ratio of
combustible to total waste burned in order to adjust the CO, NOX, and S02 emissions factors to be used with
the amount of combustible waste burned.
4.27.5.8 Puerto Rico and U.S. Virgin Islands
Emissions from Puerto Rico are calculated using the same method described above. For the U.S. Virgin Islands,
emissions are calculated using 2010 population data, since 2017 Census Data does not exist for the U.S. Virgin
Islands.
4.27.5.9 References
1. U.S. Environmental Protection Agency. 2018. Advancing Sustainable Materials: 2015 Fact Sheet. Table 1.
Generation, Recovery and Discards of Materials in MSW, 2015 (in millions of tons and percent of
generation of each material).
2. U.S. Environmental Protection Agency. 2011. Municipal Solid Waste Generation. Recycling, and Disposal
in the United States: Facts and Figures for 2010—Fact Sheet, p. 4.
3. U.S. Census Bureau. Total Population. American Community Survey.
4. U.S. Census Bureau, Decennial Censuses, 2010 Census: Summary File 1.
5. Environment Canada. 2001. "Household Garbage Disposal and Burning." Prepared by Environics
Research Group.
6. U.S. Environmental Protection Agency. 1995. Compilation of Air Pollutant Emission Factors, AP-42, Fifth
Edition, Volume I: Stationary Point and Area Sources, Section 2.5 Open Burning. Research Triangle Park,
NC.
7. U.S. Environmental Protection Agency. 1997. Evaluation of Emissions from the Open Burning of
Household Waste in Barrels" EPA-600/R-97-134a.
8. U.S. Environmental Protection Agency. 2002. Emissions of organic air toxics from open burning: a
comprehensive review. EPA-600/R-02-076.
9. Babineau, I., Wu, C.Y., Jackson, A., Minnesota Pollution Control Agency. 2016. "Emission Factor
4-346
-------
Development for Mercury Emitted From Municipal Solid Waste during Processing and Handling." In
proceedings of the 109th Annual Meeting of the A&WMA, New Orleans, LA.
4.28 Waste Disposal: Nonpoint POTWs
4.28.1 Source category description
This source category, Publicly Owned Treatment Works (POTW), includes treatment works owned by a state,
municipality, city, town, special sewer district, or other publicly owned and financed entity as opposed to a
privately (industrial) owned treatment facility. The definition includes intercepting sewers, outfall sewers,
sewage collection systems, pumping, power, and other equipment. The wastewater treated by these POTWs is
generated by industrial, commercial, and domestic sources [ref 1], The SCC that EPA uses for estimated
nonpoint emissions is 2630020000; the SCC description is "Waste Disposal, Treatment, and Recovery;
Wastewater Treatment; Public Owned; Total Processed".
4.28.2 Sources of data
The agencies listed in Table 4-210 submitted emissions for POTWs; agencies not listed used EPA estimates.
Table 4-210: Agencies that submitted POTW emissions in the 2017 NEI
Region
Agency
S/L/T
2
New York State Department of Environmental Conservation
State
3
Maryland Department of the Environment
State
4
Knox County Department of Air Quality Management
Local
4
Memphis and Shelby County Health Department - Pollution Control
Local
4
Metro Public Health of Nashville/Davidson County
Local
5
Illinois Environmental Protection Agency
State
6
Texas Commission on Environmental Quality
State
8
Utah Division of Air Quality
State
9
California Air Resources Board
State
9
Maricopa County Air Quality Department
Local
9
Washoe County Health District
Local
10
Coeur d'Alene Tribe
Tribe
10
Idaho Department of Environmental Quality
State
10
Kootenai Tribe of Idaho
Tribe
10
Nez Perce Tribe
Tribe
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
10
Washington State Department of Ecology
State
4.28.3 EPA-developed emissions
The calculations for estimating the emissions from POTWs involve multiplying the wastewater flow rate by
emissions factors for VOCs, NH3, and 53 HAPs.
4.28.3.1 Activity data
The activity data for this source category is the wastewater flow rate. The EPA Clean Watersheds Needs Survey
provides flow rate by facility and estimates the national POTW flow rate in 2012 for all facilities as 32,822 million
4-347
-------
gallons per day (MMGD) [ref 2], The nationwide flow rate includes Puerto Rico and the US Virgin Islands. To
estimate flow rates in 2017, facility-level daily flow rates in 2012 are multiplied by the ratio of 2017 to 2012
population in the county where the facility resides [ref 3], County-level annual 2017 wastewater flow rates are
calculated by summing the daily flow rates for all POTWs within the county and multiplying by 365 days in a
year.
(1)
n
FRc,2017 = Y FRfi2012 X 365 x
rc, 2012
Where:
FRC,2017 = The annual wastewater flow rate of county c in 2017
FRf, 2012 — The daily wastewater flow rate at facility/in 2012
Pc,2oi7 = Total population of county c in 2017
Pc,2ou = Total population of county c in 2012
4.28.3.2 Allocation procedure
For a given county, county-level wastewater flow rates are calculated by summing the flow rates for all POTWs
within the county.
4.28.3.3 Emission factors
Emissions factors for POTWs are reported in Table 4-211. The ammonia emissions factor was obtained from an
EPA report [ref 4] and the VOC emissions factor was based on a TriTAC study [ref 5], Emissions factors for HAPs
were derived using 1996 area source emissions estimates that were provided by Bob Lucas [ref 6] and the 1996
nationwide flow rate [ref 7], These HAP emissions factors were then multiplied by the 2008 to 2002 VOC
emissions factor ratio (0.85/9.9) to obtain the final HAP emissions factors applied in the 2017 inventory.
Table 4-211: Emission Factors for Publicly Owned Treatment Works
Emissions
Pollutant
Emissions Factor
Factor
Pollutant
Code
(Ibs./MMGAL)
Reference(s)
1,1,2,2-Tetrachloroethane
79345
1.75E-06
6,7
1,1,2-Trichloroethane
79005
1.17E-06
6,7
1,2,4-Trichlorobenzene
120821
8.67E-05
6,7
1,3-Butadiene
106990
2.51E-05
6,7
1,4-Dichlorobenzene
106467
2.16E-04
6,7
l-Chloro-2,3-Epoxypropane
106898
4.52E-06
6,7
2,4-Dinitrotoluene
121142
4.81E-05
6,7
2-Nitropropane
79469
2.92E-07
6,7
Acetaldehyde
75070
3.10E-04
6,7
Acetonitrile
75058
3.45E-04
6,7
Acrolein
107028
3.84E-04
6,7
Acrylonitrile
107131
3.86E-04
6,7
Allyl Chloride
107051
1.94E-05
6,7
Ammonia
NH3
1.69E-01
4
4-348
-------
Emissions
Pollutant
Emissions Factor
Factor
Pollutant
Code
(Ibs./MMGAL)
Reference(s)
Benzene
71432
6.73E-03
6, 7
Benzyl Chloride
100447
8.17E-06
6, 7
Biphenyl
92524
7.52E-05
6, 7
Carbon Disulfide
75150
4.32E-03
6, 7
Carbon Tetrachloride
56235
1.12E-03
6, 7
Chlorobenzene
108907
4.83E-04
6, 7
Chloroform
67663
6.44E-03
6, 7
Chloroprene
126998
2.38E-05
6, 7
Cresols/Cresylic Acid (Isomers and Mixture)
1319773
1.61E-06
6, 7
Dimethyl Sulfate
77781
1.31E-06
6, 7
Ethyl Acrylate
140885
1.75E-06
6, 7
Ethyl Benzene
100414
7.66E-03
6, 7
Ethylene Oxide
75218
2.22E-04
6, 7
Formaldehyde
50000
1.97E-05
6, 7
Glycol Ethers
171
1.15E-02
6, 7
Hexachlorobutadiene
87683
7.29E-07
6, 7
Hexachlorocyclopentadiene
77474
5.83E-07
6, 7
Methanol
67561
1.14E-02
6, 7
Methyl Chloroform
71556
5.63E-04
6, 7
Methyl Isobutyl Ketone
108101
2.69E-03
6, 7
Methyl Methacrylate
80626
3.11E-04
6, 7
Methyl Tert-Butyl Ether
1634044
6.37E-05
6, 7
Methylene Chloride
75092
9.10E-03
6, 7
N,N-Dimethylaniline
121697
3.22E-04
6, 7
Naphthalene
91203
1.31E-03
6, 7
Nitrobenzene
98953
6.56E-06
6, 7
O-Toluidine
95534
1.75E-06
6, 7
P-Dioxane
123911
1.79E-05
6, 7
Propionaldehyde
123386
3.50E-06
6, 7
Propylene Dichloride
78875
1.15E-05
6, 7
Propylene Oxide
75569
7.32E-04
6, 7
Styrene
100425
2.73E-03
6, 7
Tetrachloroethylene
127184
4.27E-03
6, 7
Toluene
108883
1.23E-02
6, 7
Trichloroethylene
79016
3.06E-04
6, 7
Vinyl Acetate
108054
7.66E-05
6, 7
Vinyl Chloride
75014
6.71E-06
6, 7
Vinylidene Chloride
75354
4.23E-04
6, 7
Volatile Organic Compounds
VOC
8.50E-01
5
Xylenes (Mixture of 0, M, And P Isomers)
1330207
5.98E-02
6,7
4.28,3,4 Controls
There are no controls assumed for this category.
4-349
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4.28.3.5 Emissions
Emissions are estimated by multiplying an emissions factor by the county flow rate. A conversion factor was
used to convert pounds to tons.
(2)
1 ton
Eyi r 1(1'1 7 — FRr 1(1'1 7 X EFy, x
v,c,L017 c,L017 p 2000 lbs.
Where:
EP,c,2oi7 = Nonpoint emissions in 2017 of pollutant p in county c, in tons
FRc,2017 = Flow rate in 2017 in county c, in MMGY
EFP = Emissions factor for pollutant p, in lbs. per MMGAL
4.28.3.6 Point source subtraction
The county-level flow rates include all facilities reported as POTWs in the EPA Clean Watersheds Needs Survey.
In some cases, SLT agencies might include facilities under their point source inventory reporting. In these cases,
SLT agencies have two options for submitting state-level point source data to EPA for point source subtraction:
• Option A: County-level flow rates associated with POTWs reported as point sources; or
• Option B: County-level emissions of VOC and NH3 for POTWs reported as point sources.
4.28.3.7 Example calculations
Table 4-212 lists sample calculations to determine the benzene emissions for nonpoint source POTWs for
Autauga County, Alabama.
Table 4-212: Sample calculations for benzene emissions for nonpoint source POTWs for Autauga County, AL
Eq. #
Equation
Values for Autauga County, AL
Result
1
n
^c,2017 = ^ FRf,2012 X 365
/=1
P c, 2017
X 1
Pc, 2012
2.866 MMGD x 365 days x
55,504 people
54,927 people
1,057.07 MMGY
2
Ep,c,2017 = FPc,2017 x EFp
1 ton
x 2000 lbs.
1,057.07 MMGY X 0.00673 Ib/MMG X
1 ton
2000 lbs.
0.003557 tons
benzene per year
4.28.3.8 Changes from the 2014 methodology
County-level flow rates in 2017 were determined by summing facility-level data to the county-level rather than
allocating the national flow rate to counties based on the ratio of county to US population.
4.28.3.9 Puerto Rico and U.S. Virgin Islands
Emissions from Puerto Rico are calculated using the same method described above. For the U.S. Virgin Islands,
emissions are not multiplied by the ratio of 2017 to 2012 population since 2017 Census Data does not exist for
the U.S. Virgin Islands.
4-350
-------
4.28.4 References
1. U.S. EPA, 64FR57572, National Emission Standards for Publicly Owned Treatment Works. Final Rule, 40
CFR Part 63, 26 October 1999.
2. U.S. Environmental Protection Agency, Clean Watersheds Needs Survey 2012 Data and Reports, Detail
Report.
3. U.S. Census Bureau. Total Population. American Community Survey.
4. Stephen M. Roe, Melissa D. Spivey, Holly C. Lindquist, Kirstin B. Thesing, and Randy P. Strait, E.H. Pechan
& Associates, Inc., Estimating Ammonia Emissions from Anthropogenic Nonagricultural Sources - Draft
Final Report, prepared for U.S. Environmental Protection Agency, Emission Inventory Improvement
Program, April 2004.
5. Prakasam Tata, Jay Witherspoon, Cecil Lue-Hing (eds.), VOC Emissions from Wastewater Treatment
Plants: Characterization, Control, and Compliance, Lewis Publishers, 2003, p. 261.
6. Memorandum from Bob Lucas, U.S Environmental Protection Agency to Greg Nizich, U.S. Environmental
Protection Agency, "Review of Baseline Emissions Inventory," 16 October 1998.
7. U.S. Environmental Protection Agency, Facilities Database (Needs Survey) - Frequently Asked Questions.
accessed 30 April 2019.
4-351
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5 Nonroad Equipment - Diesel, Gasoline and Other
Although "nonroad" is used to refer to all mobile sources that are not on-highway, this section addresses
nonroad equipment other than locomotives, aircraft, or commercial marine vessels. Locomotive emissions from
railyards and aircraft and associated ground support equipment are described in Section 3. The nonpoint portion
of locomotives and commercial marine vessel emissions will be provided with the nonpoint section when it is
later-available with the full 2017 NEI release.
5,1 Sector Description
This section deals specifically with emissions processes calculated by the nonroad component of EPA's MOVES
model (herein referred to as MOVES-Nonroad) [ref 1] and the family of off-road models used by California [ref
2], They include nonroad engines and equipment, such as lawn and garden equipment, construction equipment,
engines used in recreational activities, portable industrial, commercial, and agricultural engines. Nonroad
equipment emissions are included in every state, the District of Columbia, Puerto Rico, and the U.S. Virgin
Islands.
Nonroad mobile source emissions are generated by a diverse collection of equipment from lawn mowers to
locomotive support. MOVES-Nonroad estimates emissions from nonroad mobile sources using a variety of fuel
types, as shown in Table 5-1.
Table 5-1: MOVES-Nonroad equipment and fuel types
Equipment Types
Fuel Types
Recreational
Construction
Industrial
Lawn and Garden
Agriculture
Commercial
Logging
Airport Ground Support Equipment (GSE; excludes aircraft)*
Underground Mining
Compressed Natural Gas (CNG)
Diesel
Gasoline
Liquified Petroleum Gas (LPG)
Oilfield**
Pleasure Craft (recreational marine; excludes commercial marine
vessels)
Railroad (excludes locomotives)
*Although MOVES-Nonroad estimates emissions from GSE, the results are not used in the NEI. NEI GSE
estimates are instead calculated via the Federal Aviation Administration's Aviation Environmental
Design Tool (AEDT).
**Although MOVES-Nonroad estimates emissions from Oilfield equipment, the results are not used in
the NEI, because they are duplicative of results from EPA's Oil and Gas Tool used in nonpoint source
calculations.
5-1
-------
5.2 MOVES-Nonroad
MOVES2014b, the latest public release of EPA's Motor Vehicle Emissions Simulator (MOVES) Model, estimates
daily emissions for total hydrocarbons (THC), nitrogen oxides (NOx), carbon monoxide (CO), carbon dioxide
(C02), particulate matter 10 microns and less (PM10), and sulfur dioxide (S02), as well as calculating fuel
consumption. MOVES2014b (version 20180726 [ref 1] uses ratios from some of these emissions to calculate
emissions for particular matter 2.5 microns and less (PM2.5), methane, ammonia (NH3), four more aggregate
hydrocarbon groups (NMHC, NMOG, TOG, and VOC), 14 hazardous air pollutants (HAPs), 17 dioxin/furan
congeners, 32 polycyclic aromatic hydrocarbons, and six metals. For a complete list of these pollutants, see
Table 5-2. All the input and activity data required to run MOVES-Nonroad are contained within the MOVES
default database, which is distributed with the model. State- and county-specific data can be used by creating a
supplemental database known as a county database (CDB) and specifying it in the MOVES run specification
(runspec). State, local and tribal (S/L/T) agencies can update the data within the CDBs to produce emissions
estimates that accurately reflect local conditions and equipment usage. MOVES first uses the data in the CDBs
and fills in any missing data from the MOVES default database.
Table 5-2: Pollutants produced by MOVES-Nonroad for 2017 NEI
Pollutant ID
Pollutant Name
Pollutant ID
Pollutant Name
1
Total Gaseous Hydrocarbons
83
Phenanthrene particle
2
Carbon Monoxide (CO)
84
Pyrene particle
3
Oxides of Nitrogen (NOx)
86
Total Organic Gases
5
Methane (CH4)
87
Volatile Organic Compounds
20
Benzene
88
NonHAPTOG
21
Ethanol
90
Atmospheric C02
22
MTBE
99
Brake Specific Fuel Consumption (BSFC)
23
Naphthalene particle
100
Primary Exhaust PM10 - Total
24
1,3-Butadiene
110
Primary Exhaust PM2.5 - Total
25
Formaldehyde
130
1,2,3,7,8,9-Hexachlorodibenzo-p-Dioxin
26
Acetaldehyde
131
Octachlorodibenzo-p-dioxin
27
Acrolein
132
1,2,3,4,6,7,8-Heptachlorodibenzo-p-Dioxin
30
Ammonia (NH3)
133
Octachlorodibenzofuran
31
Sulfur Dioxide (S02)
134
1,2,3,4,7,8-Hexachlorodibenzo-p-Dioxin
40
2,2,4-Trimethylpentane
135
1,2,3,7,8-Pentachlorodibenzo-p-Dioxin
41
Ethyl Benzene
136
2,3,7,8-Tetrachlorodibenzofuran
42
Hexane
137
1,2,3,4,7,8,9-Heptachlorodibenzofuran
43
Propionaldehyde
138
2,3,4,7,8-Pentachlorodibenzofuran
44
Styrene
139
1,2,3,7,8-Pentachlorodibenzofuran
45
Toluene
140
1,2,3,6,7,8-Hexachlorodibenzofuran
46
Xylene
141
1,2,3,6,7,8-Hexachlorodibenzo-p-Dioxin
60
Mercury Elemental Gaseous
142
2,3,7,8-Tetrachlorodibenzo-p-Dioxin
61
Mercury Divalent Gaseous
143
2,3,4,6,7,8-Hexachlorodibenzofuran
62
Mercury Particulate
144
1,2,3,4,6,7,8-Heptachlorodibenzofuran
63
Arsenic Compounds
145
1,2,3,4,7,8-Hexachlorodibenzofuran
65
Chromium 6+
146
1,2,3,7,8,9-Hexachlorodibenzofuran
66
Manganese Compounds
168
Dibenzo(a,h)anthracene gas
67
Nickel Compounds
169
Fluoranthene gas
5-2
-------
Pollutant ID
Pollutant Name
Pollutant ID
Pollutant Name
68
Dibenzo(a,h)anthracene particle
170
Acenaphthene gas
69
Fluoranthene particle
171
Acenaphthylene gas
70
Acenaphthene particle
172
Anthracene gas
71
Acenaphthylene particle
173
Benz(a)anthracene gas
72
Anthracene particle
174
Benzo(a)pyrene gas
73
Benz(a)anthracene particle
175
Benzo(b)fluoranthene gas
74
Benzo(a)pyrene particle
176
Benzo(g,h,i)perylene gas
75
Benzo(b)fluoranthene particle
177
Benzo(k)fluoranthene gas
76
Benzo(g,h,i)perylene particle
178
Chrysene gas
77
Benzo(k)fluoranthene particle
181
Fluorene gas
78
Chrysene particle
182
lndeno(l,2,3,c,d)pyrene gas
79
Non-Methane Hydrocarbons
183
Phenanthrene gas
80
Non-Methane Organic Gases
184
Pyrene gas
81
Fluorene particle
185
Naphthalene gas
82
lndeno(l,2,3,c,d)pyrene particle
5.3 Default MOVES code and database
The nonroad runs were executed using MOVES2014b, the most current publicly-released version of MOVES
available at the time. The code version for this release is moves20180726. The default database is
movesdb20181022, the same one released publicly with MOVES2014b.
Additionally, national updates that were made to the MOVES2014b default database for the 2016vl Platform
were used in the MOVES-Nonroad run for the 2017 NEI. This includes updated surrogate data for allocating
national populations of Agricultural and Construction equipment to the state and county levels, as described in
the 2016vl Platform Nonroad Mobile Emissions Specification Sheet [ref 4],
5.4 Additional Data: Nonroad County Databases (CDBs)
MOVES uses county databases (CDBs) to provide detailed local information for developing nonroad emissions.
The EPA encouraged S/L/T agencies to submit MOVES-Nonroad CDBs to the Emission Inventory System (EIS) for
the 2017 NEI. Data not provided in CDBs is automatically supplied from the MOVES default database. As is also
true for MOVES onroad runs, even if an agency submitted fuel or meteorological data, the EPA's values for these
data parameters were used. Fuels values were developed specifically for the 2017 NEI, based on the extensive
refinery gate batch dataset collected as a part of EPA's fuel compliance programs. The meteorological data were
provided by OAQPS and were derived from a Weather Research and Forecasting Model (WRF) version 3.8 [ref 5]
run.
Table 5-3 shows the selection hierarchy for the nonroad data category. The modified MOVES default database
for MOVES2014b containing refinements to construction and agricultural sectors [ref 4],
(movesdb20181022_nrupdates) and state-submitted inputs in CDBs were used to run MOVES-Nonroad to
produce emissions for all states other than California. California-submitted emissions were used.
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Table 5-3: Selection hierarchy for the Nonroad Mobile data category
Priority
Dataset
Notes
1
Responsible Agency
Data Set
Several tribes submitted nonroad emissions: Northern Cheyenne
Tribe, Kootenai Tribe of Idaho, Coeur d'Alene Tribe, Nez Perce Tribe,
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho.
California submitted emissions calculated with their own model,
OFFROAD2007.*
2
2017EPA_Ca_MOVES
Includes California CAPs and HAPs speciated from California VOC and
PM based on MOVES ratios
3
2017EPA_MOVES
EPA defaults and S/L/T-supplied input data from 2017 NEI process
* Metro Public Health of Nashville/Davidson County also successfully submitted nonroad emissions but agreed
that EPA MOVES data should be used instead.
EPA asked S/L/T agencies to provide model inputs (CDBs) for 2017. Table 5-4 shows the S/L/T agencies that
submitted nonroad model inputs for the 2017 NEI via the EIS Gateway. Table 5-4 also shows data carried over
from prior NEI submittals for the LADCO states for day and month allocations. Two agencies submitted CDBs
through the EIS are not listed in the table (Delaware state and Davidson County, Tennessee), because they
provided only a ZoneMonthHour table that EPA did not use in the 2017 NEI.
Table 5-4: Submitted MOVES-Nonroad input tables,
State
Code
State or
County(ies) in
the Agency
nrbaseyearequippopulation
(source populations)
nrdayallocation
(allocation to day type)
nrfuelsupply(
(allocation of fuels)
nrgrowthindex
(population growth)
nrhourallocation
(allocation to diurnal pattern)
nrmonthallocation
(seasonal allocation)
nrsourceusetype
(yearly activity)
nrstatesurrogate
(allocation to counties)
countyyear
(stage II information)
nrequipmenttype
(surrogate selection)
nrsurrogate
(surrogate identification)
nrscc (SCCs)
4
Arizona -
Maricopa Co.
A
X
A
A
A
A
A
9
Connecticut
A
13
Georgia
A
A
16
Idaho
C
17
Illinois
D
18
Indiana
C
D
19
Iowa
C
D
26
Michigan
C
D
27
Minnesota
A
C
D
29
Missouri
D
36
New York
A
A
X
A
A
A
A
A
39
Ohio
C
D
48
Texas
A
A
X
A
A
A
A
A
A
49
Utah
B
A
A
A
E
53
Washington
A
A
A
55
Wisconsin
D
ay agency.
A Submitted data.
5-4
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B Submitted data with modification: deleted records that were not snowmobile source types 1002-1010.
c 2014NEIv2 data used for 2017 NEI.
D Spreadsheet "Iadco_nei2017_nrmonthallocation.xlsx" (see discussion below)
E Submitted data with modification: deleted records that were not the snowmobile surrogate ID 14.
x Submitted data not used in 2017 NEI. The GA NRFuelSupply table is only used to divide counties into groups.
The 557 submitted CDBs used for the MOVES-Nonroad run are included in the full set of 3,225 CDBs collected
together in 2017_NonroadCDBs.zip in the 2017 NEI Supplemental nonroad mobile data FTP site. Outside of the
557 CDBs with the data inputs outlined above in Table 5.4, EPA also created a new CDB for each of the other US
counties with only the fuel tables to receive the information EPA developed from the refinery gate batch
dataset. The rest were run using the MOVES default database, which does not require CDBs. A list of all 3,225
U.S. counties and their corresponding CDBs, if any, is available in 2017_nonroad_counties_FinalList.xlsx. These
supplemental nonroad mobile data contents are listed in Table 5-5 and are all available on the 2017 NEI
Supplemental nonroad mobile data FTP site.
Table 5-5: Contents of the Nonroad Mobile supplemental folder
File or Folder
Description
1
2017_NonroadCDBs.zip
Submitted nonroad CDBs used to run MOVES2014b and
EPA CDBs containing only 2017 EPA fuels.
2
2017 nonroad counties FinalList.xlsx
List of all counties and their CDBs.
3
2017_zonemonthhour.zip
Zonemonthhour table (meteorology data).
4
2017_NonroadRunspecs.zip
Runspecs for all counties.
7
2017_postprocess_nraq_nrvoc.zip
Post-processing scripts for MOVES runs.
8
2017NR_CaEIS_SCC_Crosswalk.xlsx
File mapping California emission inventory codes (EICs) to
EPA SCCs.
5.5 MOVES runs
In the 2017 NEI Supplemental nonroad mobile data FTP site, the Excel® file
2017_nonroad_counties_nei2014vl_FinalList.xlsx lists all 3,225 counties and their corresponding CDBs. The
CDBs that were used are in 2017_NonroadCDBs.zip in the online NRSupplemental Data folder. If no CDB was
listed for a county, that county was run with the MOVES default database for MOVES2014b
(movesdb20180517). The supplemental nonroad mobile data is listed in Table 5-5.
MOVES was run for each county in a single, separate run specification file (runspec). All the runspecs are in the
2017_NonroadRunspecs.zip file in the online NRSupplemental Data folder. The MOVES-Nonroad runs were
checked for completeness and absence of error messages in the run logs. The output was post-processed to
consolidate each county into a single database and to produce SMOKE-ready input. The scripts that performed
these processes are in 2017_postprocess_nraq_nrvoc.zip in the 2.017 NEI Supplemental nonroad mobile data
FTP site. The MOVES runs created monthly, day type (weekday and weekend) total inventories for every U.S.
county, and post-processing scaled the day totals to monthly and annual values.
The following additional steps were taken on the monthly MOVES nonroad outputs to prepare data for loading
into EIS:
1. The gas and particle components of PAHs (e.g., Chrysene, Fluorene) were combined.
2. The individual mercury species were combined into total mercury (i.e., pollutant 7439976).
3. Emissions were aggregated from the more detailed SCCs modeled in MOVES to the SCCs modeled in
SMOKE.
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4. Modes for exhaust and evaporative were removed from pollutant names and separated out into the
emis_type data field in flat file 10 files that were then loaded into EIS.
5. Pollutants produced by MOVES but not accepted in the NEI were removed (e.g., ethanol, NONHAPTOG,
and total hydrocarbons).
6. Five speciated PM2.5 species were added based on speciation profiles (i.e., elemental carbon, organic
carbon, nitrate, sulfate and other PM2.5).
7. DIESEL-PM10 and DIESEL-PM25 were added by copying the PM10 and PM2.5 pollutants (respectively) as
DIESEL-PM pollutants for all diesel SCCs.
8. Airport ground support equipment emissions were removed.
9. Oil and gas field equipment emissions were removed.
10. Emissions from Wade Hampton Census Area, Alaska (FIPS code 02270) were reassigned to Kusilvak
Census Area (FIPS code 02158) to reflect a name and FIPS code change for 2017.
11. Incorporated California-submitted nonroad emissions.
Following the completion of the MOVES runs, railway maintenance emissions were removed from specific
counties / census areas in Alaska because Alaska DEC specified that this type of activity not happen in those
areas. Specifically, emissions from SCCs 2285002015, 2285004015, 2285006015 were removed from the
following counties / census areas: 02013, 02016, 02050, 02060, 02070, 02100, 02105, 02110, 02130, 02150,
02158, 02164, 02180, 02185, 02188, 02195, 02198, 02220, 02240, 02261, 02275, and 02282. Alaska DEC also
specified some counties / census areas in which logging and agricultural emissions do not happen, but the
emissions for the specified SCCs were already zero in the specified areas.
5.6 Use of California Submitted Emissions
California submitted criteria and HAP nonroad emissions for EPA's use in the NEI. California estimates emissions
with a California-specific model and converts them from their EIC codes to SCC codes via a crosswalk
(2017NR_CaEIS_SCC_Crosswalk.xlsx). The California criteria emissions were used directly. However, the HAP
values were incongruent with the criteria estimates.
MOVES was run for California to establish county/SCC-level ratios of VOC/VOC-HAP and PM/HAP-metal. The
ratios were applied to California-provided VOC and PM to estimate HAPs. VOC-HAP and HAP-Metals are
indicated in Table 5-6.
Table 5-6: HAPs calculated using MOVES ratios for California Nonroad SCCs
Pollutant
Pollutant Code
HAP Type
1,2,3,4,6,7,8-Heptachlorodibenzofuran
67562394
HAP-VOC
1,2,3,4,6,7,8-Heptachlorodibenzo-p-Dioxin
35822469
HAP-VOC
1,2,3,4,7,8,9-Heptachlorodibenzofuran
55673897
HAP-VOC
1,2,3,4,7,8-Hexachlorodibenzofuran
70648269
HAP-VOC
1,2,3,4,7,8-Hexachlorodibenzo-p-Dioxin
39227286
HAP-VOC
1,2,3,6,7,8-Hexachlorodibenzofuran
57117449
HAP-VOC
1,2,3,6,7,8-Hexachlorodibenzo-p-Dioxin
57653857
HAP-VOC
1,2,3,7,8,9-Hexachlorodibenzofuran
72918219
HAP-VOC
1,2,3,7,8,9-Hexachlorodibenzo-p-Dioxin
19408743
HAP-VOC
1,2,3,7,8-Pentachlorodibenzofuran
57117416
HAP-VOC
1,2,3,7,8-Pentachlorodibenzo-p-Dioxin
40321764
HAP-VOC
1,3-Butadiene
106990
HAP-VOC
2,2,4-Trimethylpentane
540841
HAP-VOC
5-6
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Pollutant
Pollutant Code
HAP Type
2,3,4,6,7,8-Hexachlorodibenzofuran
60851345
HAP-VOC
2,3,4,7,8-Pentachlorodibenzofuran
57117314
HAP-VOC
2,3,7,8-Tetrachlorodibenzofuran
51207319
HAP-VOC
2,3,7,8-Tetrachlorodibenzo-p-Dioxin
1746016
HAP-VOC
Acenaphthene
83329
HAP-VOC
Acenaphthylene
208968
HAP-VOC
Acetaldehyde
75070
HAP-VOC
Acrolein
107028
HAP-VOC
Anthracene
120127
HAP-VOC
Arsenic
7440382
HAP-Metal
Benz[a] Anthracene
56553
HAP-VOC
Benzene
71432
HAP-VOC
Benzo[a]Pyrene
50328
HAP-VOC
Benzo[b]Fluoranthene
205992
HAP-VOC
Benzo[g,h,i,]Perylene
191242
HAP-VOC
Benzo[k]Fluoranthene
207089
HAP-VOC
Chromium (VI)
18540299
HAP-Metal
Chrysene
218019
HAP-VOC
Dibenzo[a,h] Anthracene
53703
HAP-VOC
Ethyl Benzene
100414
HAP-VOC
Fluoranthene
206440
HAP-VOC
Fluorene
86737
HAP-VOC
Formaldehyde
50000
HAP-VOC
Hexane
110543
HAP-VOC
lndeno[l,2,3-c,d]Pyrene
193395
HAP-VOC
Manganese
7439965
HAP-Metal
Mercury
7439976
HAP-Metal
Naphthalene
91203
HAP-VOC
Nickel
7440020
HAP-Metal
Octachlorodibenzofuran
39001020
HAP-VOC
Octachlorodibenzo-p-Dioxin
3268879
HAP-VOC
Phenanthrene
85018
HAP-VOC
Propionaldehyde
123386
HAP-VOC
Pyrene
129000
HAP-VOC
Styrene
100425
HAP-VOC
Toluene
108883
HAP-VOC
Xylenes (Mixed Isomers)
1330207
HAP-VOC
In addition, C02 data were added to the California data based on EPA estimates, because C02 emissions were
not provided in the submission. We also speciated CARB total PM2.5 and PM10 using the same approach as for
other states and copied the PM2.5 and PM10 to DIESEL-PM "pollutants" for all diesel SCCs.
5,7 References for nonroad mobile
1. MOVES-Nonroad, its documentation and technical reports can be found here: Nonroad Technical
Reports.
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2. CARB's group of models for off-road equipment may be linked to from this site: Mobile Source Emissions
Inventory.
3. MOVES2Q14b, its default database, documentation and technical reports.
4. National Emissions Inventory Collaborative (2019). Specification Sheet - 2016vl Platform Nonroad
Mobile Emissions. Retrieved from the Specification Sheet: Mobile Nonroad.
5. Detailed information on The Weather Research & Forecasting Model (WRF) may be found here:
Weather Research and Forecasting Model and here: Skamarock, W.C., et al., National Center for
Atmospheric Research, Mesoscale and Microscale Meteorology Division, Boulder CO, June 2008,
NCAR/TN-475+STR, A Description of the Advanced Research WRF Version 3.
6. Crosswalk of CA EIC to SCC: 2017NR CaEIS SCC Crosswalk.xlsx
5-8
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6 Onroad Mobile - All Vehicles and Refueling
6.1 Sector description
Onroad mobile sources include emissions from motorized vehicles that are normally operated on public
roadways. This includes passenger cars, motorcycles, minivans, sport-utility vehicles, light-duty trucks, heavy-
duty trucks, and buses. The sector includes emissions generated from parking areas as well as emissions while
the vehicles are moving. The sector also includes "hoteling" emissions, which refers to the time spent idling in a
diesel long-haul combination truck during federally mandated rest periods of long-haul trips.
The 2017 NEI is comprised of emission estimates calculated based on the MOVES model run with S/L/T-
submitted activity data when provided, except for California and tribes, for which the NEI includes submitted
emissions. In cases where S/L/T submitted data is not provided, EPA-developed default activity based on data
from the Federal Highway Administration.
6.2 Overview of Input Data Sources for 2017
EPA received new MOVES county database (CDB) submittals (1,693 databases) from S/L/T agencies and new
2017 vehicle registration data MOVES tables from the Coordinating Research Council (CRC) A-115 project [ref 1],
which EPA adapted and applied in some areas of the country. The new CDBs and registration data required a re-
analysis to determine counties with similar fleet characteristics for representative county groups. Like
2014NEIv2, age distributions for representative county CDBs reflect a population-weighted average of the
member county age distributions. Also unchanged from 2014 v2, EPA relied on vehicle speed and vehicle-miles
traveled (VMT) distributions from the CRC A-100 study [ref 2] for some areas of the country. The CDBs and
representative county groups are discussed in Sections 6.5 and 6.8.2.1, respectively.
6.2.1 New 2017 Vehicle Populations and Fleet Characteristics
In areas where there is no acceptable S/L/T data available, the 2017 NEI onroad inventory is based on 2017
vehicle populations, source type age distributions, and fuel type fractions from the CRC A-115 study. The CRC
procured a July 1, 2017 draw date vehicle registration database from IHS Markit (IHS). Motorcycles are an
exception to the July 1 draw date, because they were only available for January. The dataset contained a county-
level summary of all registered vehicles in the US, which IHS retrieves from each state's Department of Motor
Vehicles (DMV) and compiles. IHS then decodes vehicle identification numbers (VINs) to assign each vehicle a
MOVES source type code. The database IHS provided to CRC did not include VINs or identify individual vehicles,
but rather was a summary of the population in each county by parameters including make, model, model year,
gross vehicle weight (GVW) class, and other fields. A finding reported by CRC A-115 was that the 2014 IHS
dataset reflected higher light-duty vehicle populations than corresponding state agency analyses of the same
DMV data, and the differences tend to increase with increasing age (older vehicles). Through the CRC A-115
study, adjustment factors were developed for older vehicles and the discrepancy in the vehicle counts was dealt
with by releasing MOVES input datasets based on both the raw and adjusted information. The adjustment
factors were based on differences in LDV population by model year for one state in the year 2014, applied to all
areas of the US for 2017. EPA repeated the comparison of IHS and state agency data but with updated 2017
information and a wider geographic area of 16 S/L/T agencies as described below.
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Although 32 S/L/T agencies participated in the data submittal process, only half provided both LDV populations
(MOVES 'SourceTypeYear' table) and age distributions (MOVES 'SourceTypeAgeDistribution' table) based on
registration data from a time period relevant for comparison with the 2017 IHS data. These 16 agencies
developed their MOVES inputs based on a 2017 or 2018 draw date of registration data. Some of the other
agencies provided only one type of data (e.g., population but no age distribution) or data with outdated (e.g.,
year 2013) or unknown draw dates. For the 16 areas that could be compared, EPA first re-apportioned the
relative populations of passenger cars (source type 21) and light-duty trucks (source types 31 and 32) at the
county level to match IHS to account for state inconsistencies in VIN decoding. EPA then allocated each county's
LDV source type population to vehicle model years for comparison with IHS and found that the IHS populations
for 2017 were higher than the state data by 6.5 percent for cars and 5.9 percent for light-trucks. Similar to the
2014 comparison for one state by CRC, EPA found that the discrepancies in the 2017 data between IHS and
states are larger for older vehicles. Table 6-1 shows the adjustments EPA made to the 2017 IHS data prior to use
in the NEI.
Table 6-1: Older vehicle adjustments showing the fraction of IHS vehicle populations to retain for 2017 NEI
Model Year
Cars
Light
pre-1989
0.675
0.769
1989
0.730
0.801
1990
0.732
0.839
1991
0.740
0.868
1992
0.742
0.867
1993
0.763
0.867
1994
0.787
0.842
1995
0.776
0.865
1996
0.790
0.881
1997
0.808
0.871
1998
0.819
0.870
1999
0.840
0.874
2000
0.838
0.896
2001
0.839
0.925
2002
0.864
0.921
2003
0.887
0.942
2004
0.926
0.953
2005
0.941
0.966
2006
1
0.987
2007-2017
1
1
EPA also removed the county-specific fractions of antique license plate vehicles present in the registration
summary from IHS. Nationally, the prevalence of antique plates is only 0.8 percent, but it is as high as 6 percent
in some states (e.g., Mississippi). All states without any CDB submittals received the EPA age distribution data,
and some states with submittals were overridden, as decided on a case-by-case basis. Section 6.3 lists the
submitted data that was accepted vs. replaced with the EPA age distribution data for the 2017 NEI.
EPA calculated the adjustment factors representing the fraction of population remaining, with two exceptions.
The model year range from 2006/2007 to 2017 received no adjustment and the model year 1987 received a
capped adjustment that equals the adjustment for model year 1988. The Table 6-1 adjustments were applied to
the 2017 IHS-based age distributions from CRC project A-115 prior to use in the NEI.
In addition to removing the older and antique plate vehicles from the IHS data, EPA also removed outlier age
distributions that showed excessively "new" fleets, usually for light commercial trucks, in about 25 counties. The
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most extreme example of this was a light commercial truck age distribution where over 50 percent of the
population in the entire county is 0 or 1 year old. This situation where the registration data reflects a county-
wide young fleet is possible, for example, if the headquarters of a leasing or rental company owns a lot of
vehicles relative to the county-wide vehicle population. We dealt with these cases by preferentially excluding
them from the representative county calculation of age distribution. For counties that were the only county in
the group, we made a substitution with an age distribution for the same source type from another county in the
same metropolitan statistical area (MSA). This clean-up step avoids creating artificial low spots of LDV emissions
in these outlier counties.
In areas where submitted vehicle population data were accepted for NEI, EPA still reapportioned the relative
populations of cars vs. light-duty trucks (while retaining the magnitude of the light-duty vehicles from the
submittals) using the county-specific percentages from the IHS data. In this way, the categorization of cars
versus light trucks is consistent from state to state. The county total light-duty vehicle populations were
preserved through this process.
6.2.2 EPA Default Vehicle Speeds and VMT Distributions
Previously, the Coordinating Research Council sponsored the A-100 project to develop improved, local inputs of
vehicle speeds and VMT distributions for use in MOVES and SMOKE based on vehicle telematics data [ref 2],
NEIs prior to 2014v2 used nationwide averages for MOVES inputs 'AvgSpeedDistribution/ 'HourVMTFraction/
and 'DayVMTFraction' in many counties without submitted information. Similar to 2014 v2, for 2017 NEI,
several states reviewed the CRC A-100 data products specific to their counties and requested that EPA use the
CRC data instead of the submittal. EPA reviewed all submitted data on speed distributions, and hour/day VMT
fractions and in some cases where the submitted data did not show appropriate distinctions between road,
weekday/weekend, and vehicle types EPA overrode submittals with the county-specific information available
from CRC A-100. The 2017 NEI also incorporates SMOKE input files based on the CRC A-100 hourly speed
distributions and diurnal and weekly VMT temporal profiles.
Additional diurnal and weekly VMT temporal profiles were developed based on the DayVMTFraction and
HourVMTFraction tables from the MOVES CDBs. For states and counties where DayVMTFraction and
HourVMTFraction tables were submitted by local agencies, temporal profiles based on those tables were used in
place of the CRC A-100-based profiles, with some exceptions as outlined below.
For weekly temporal profiles, since the DayVMTFraction table only specifies a total weekday and total weekend
allocation instead of allocations for each individual day of the week, new weekly profiles were developed based
on a combination of DayVMTFractions and the CRC A-100-based profiles. Total weekday and total weekend
allocation are based on DayVMTFraction, while individual day allocation (e.g., Monday as a fraction of the total
weekday allocation, Sunday as a fraction of the total weekend allocation) are based on CRC A-100.
DayVMTFraction tables were used in all states and counties with locally submitted data, except for school buses
and refuse trucks. The vast majority of school bus and refuse truck activity occurs on weekdays, and locally
submitted DayVMTFraction tables did not account for that; therefore, a high weekday / low weekend profile
was used nationwide for school buses and refuse trucks. For diurnal temporal profiles, all locally submitted
HourVMTFraction tables were used as-is, except for invalid data (e.g., profiles with 100% of activity
concentrated in hour 1). HourVMTFraction values and profiles are distinct for weekdays and weekends.
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6.3 Sources of data and selection hierarchy
The EPA calculated the onroad emissions for 2017 for all states using the most recent version of MOVES,
MOVES2014b (code version: 20180726, database version: movesdb20181022). The sources of MOVES input
data vary by area, representing a mix of local data, past NEI data, EPA defaults, and some MOVES defaults. More
state and local agencies than ever before have submitted local input data for MOVES. The S/L/T agencies that
submitted data for 2017 are listed below in Section 6.10. The EPA used programs within the Sparse Matrix
Operator Kernel Emissions (SMOKE) modeling system that integrate with MOVES to generate the emission
inventories in all 50 states for each hour of the year. These emissions are summed over all hours and across road
types to develop the emissions for the NEI. For the state of California, the EPA used onroad emissions provided
by California based on the EMFAC model.
As in past NEIs, the data selection hierarchy for 2017 favored local input data over EPA-developed information.
For areas that did not submit a MOVES CDB for this NEI, the EPA used a 2017 CDB containing EPA-developed
information including 2017 VMT, population, and hoteling activity with new activity specific to 2017, as
described in Section 6.8.4.
6.4 California-submitted onroad emissions
California is the only state agency for which an onroad emissions submittal was used in the 2017 NEI. California
uses their own emission model, EMFAC 2017, which uses EICs instead of SCCs. For the 2014NEIv2, the EPA and
California worked together to develop a code mapping to better match EMFAC's EICs to EPA MOVES' detailed
set of SCCs that distinguish between off-network and on-network and brake and tire wear emissions. This level
of detail is needed for modeling but not specifically for the NEI, because the NEI uses simplified/more
aggregated SCCs than used in modeling. The mapping file was updated for the 2017 NEI by the California Air
Resource Board (CARB) and applied to the EMFAC outputs prior to providing the data to EPA.
California provided their CAP emissions, excluding NH3, by county using EPA SCCs after applying the mapping.
For the 2017 NEI, we needed to add NH3, HAPs, C02, N20, and methane. HAPs and methane were added using
MOVES-based scaling factors - for example, the ratio of emissions for a HAP compared to either VOC or PM25
(excluding brake and tire PM) from MOVES, for each county and SCC in California. The basis pollutant is VOC for
all VOC HAPs (e.g., benzene, hexane), and is PM25 for all metals and for dioxins/furans. PAHs have both a gas
component and particulate component, and so the basis pollutant for each PAH was chosen to be either VOC,
PM2.s, or a mix (PM2 5 for diesel, and VOC for other fuel types, including gasoline) based on the relative
magnitude of the gas and particulate components of each HAP from MOVES. The pollutant basis for each HAP is
listed in Table 6-2. A table of factors (2017NEI California onroad HAP augmentation factors.csv) used to
augment the California emissions is referenced in the supporting data Table 6-10.
Table 6-2: CAP pollutant basis for each HAP for California onroad
Pollutant code
Description
Basis for gasoline
Basis for diesel
100414
Ethylbenzene
VOC
VOC
100425
Styrene
VOC
VOC
106990
Butadiene, 1,3-
VOC
VOC
107028
Acrolein
VOC
VOC
108883
Toluene
VOC
VOC
110543
Hexane
VOC
VOC
120127
Anthracene
VOC
VOC
123386
Propionaldehyde
VOC
VOC
129000
Pyrene
VOC
PM2.5
6-4
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Pollutant code
Description
Basis for gasoline
Basis for diesel
1330207
Xylenes (mixed isomers)
voc
voc
1746016
Dioxins/Furans
PM2.5
PM2.5
18540299
Chromium VI
PM2.s
PM2.s
191242
Benzo[g,h,i,]Perylene
PM2.s
PM2.s
193395
lndeno[l,2,3-c,d]Pyrene
PM2.s
PM2.s
19408743
Dioxins/Furans
PM2.s
PM2.s
205992
Benzo[b]Fluoranthene
voc
PM2.s
206440
Fluoranthene
voc
PM2.s
207089
Benzo[k]Fluoranthene
voc
PM2.s
208968
Acenaphthylene
voc
voc
218019
Chrysene
voc
PM2.5
3268879
Dioxins/Furans
PM2.5
PM2.s
35822469
Dioxins/Furans
PM2.s
PM2.s
39001020
Dioxins/Furans
PM2.s
PM2.s
39227286
Dioxins/Furans
PM2.s
PM2.s
40321764
Dioxins/Furans
PM2.s
PM2.s
50000
Formaldehyde
voc
voc
50328
Benzo[a]Pyrene
PM2.5
PM2.5
51207319
Dioxins/Furans
PM2.s
PM2.s
53703
Dibenzo[a,h] Anthracene
PM2.s
PM2.s
540841
Trimethylpentane, 2,2,4-
voc
voc
55673897
Dioxins/Furans
PM2.5
PM2.5
56553
Benz[a] Anthracene
voc
PM2.s
57117314
Dioxins/Furans
PM2.5
PM2.s
57117416
Dioxins/Furans
PM2.s
PM2.s
57117449
Dioxins/Furans
PM2.s
PM2.s
57653857
Dioxins/Furans
PM2.s
PM2.s
60851345
Dioxins/Furans
PM2.s
PM2.s
67562394
Dioxins/Furans
PM2.s
PM2.s
70648269
Dioxins/Furans
PM2.s
PM2.s
71432
Benzene
voc
voc
72918219
Dioxins/Furans
PM2.5
PM2.5
7439965
Manganese
PM2.s
PM2.s
7439976
Mercury, Unspeciated
PM2.s
PM2.s
7440020
Nickel
PM2.s
PM2.s
7440382
Arsenic
PM2.s
PM2.s
75070
Acetaldehyde
voc
voc
83329
Acenaphthene
voc
voc
85018
Phenanthrene
voc
voc
86737
Fluorene
voc
voc
91203
Naphthalene
voc
voc
NH3, C02, and N20 were added using a different method. For these three pollutants, the state-wide emissions
total matches MOVES, but distributed to counties and SCCs using California-provided data from another
pollutant (for NH3, this was CO; for C02 and N20, this was S02). This way, the overall magnitude of emissions is
based on MOVES, but the distribution of those emissions between counties and vehicles is based on California
6-5
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data. The factors used for these pollutants are computed by taking MOVES state total emissions divided by the
CARB state total for CO or S02. The pollutant emissions are computed as follows:
C02 = S02 * 115363.66
N20 = S02 * 3.06
NH3 = CO * 0.019
CARB estimates onroad refueling emissions outside of the EMFAC model; they provided these to the EPA, and
we assigned them to the onroad refueling SCC 2201000062 (Mobile Sources; Highway Vehicles - Gasoline;
Refueling; Total Spillage and Displacement). The two EIC codes mapped to this SCC are: EIC 33037811000000
(Petroleum Marketing / Vehicle Refueling - Vapor Displacement Losses / Gasoline (Unspecified)) and EIC
33038011000000 (Petroleum Marketing / Vehicle Refueling - Spillage / Gasoline (Unspecified)). The refueling
dataset provided by CARB included HAPs, but for consistency with the non-refueling emissions, refueling HAPs
were instead recomputed using the same methodology as the non-refueling emissions.
6.5 Agency-submitted MOVES inputs
Many state and local agencies provided county-level MOVES inputs in the form of CDBs. This established format
requirement enables the EPA to more efficiently scan for errors and manage input datasets. The EPA screened
all submitted data using several quality assurance scripts that analyze the individual tables in each CDB to look
for missing or unrealistic data values. The EPA also reviewed submitted age distributions, speed distributions,
and hourly VMT distributions in consideration of whether to accept these data vs. county-specific EPA defaults.
6.5.1 Overview of MOVES input submissions
State and local agencies prepare complete sets of MOVES input data in the form of one CDB per county. One
way agencies can ensure a correctly formatted CDB is to use the MOVES graphical user interface (GUI) county
data manger (CDM) importer. With a proper template created for a single county, a larger set of counties (e.g.,
statewide) can be updated systematically with county-specific information if the preparer has well-organized
county data and familiarity with MySQL queries. However, there is no requirement of MySQL experience to
prepare the NEI submittal because the user can instead rely on the CDM to help build the individual CDBs one at
a time. Table 6-3 lists the tables in each CDB and describes its content or purpose. Note that several of the tables
are optional, which means that they may be left blank without consequence to a MOVES run's completeness of
results. If an optional CDB table is populated, the data override MOVES internal calculations and produce a
different result that may better represent local conditions.
Table 6-3: MOVES2014b CDB tables
Table Name
Description of Content
auditlog
Information about the creation of the database
avft
Fuel type fractions
avgspeeddistribution
Average speed distributions
county
Description of the county
countyyear
Description of the Stage 2 refueling control program
dayvmtfraction
Fractions to distribute VMT between day types
fuelformulation
Fuel properties
fuelsupply
Fuel differences by month of year
6-6
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Table Name
Description of Content
fuelusagefraction
Fraction of the time that E85 vs. gasoline is used in flex-fuel engine
vehicles
hotellingactivitydistribution
Optional table - fraction of hoteling hours in which the power source is
the main engine, diesel APU, electric APU, or engine-off
hotellinghours
Optional table - total hoteling hours
hourvmtfraction
Fractions to distribute VMT across hours in a day
hpmsvtypeday
VMT input by HPMS vehicle group, month, and day type (1 of 4 options)
hpmsvtypeyear
VMT input by HPMS vehicle group, as annual total (2 of 4 options)
imcoverage
Description of the inspection and maintenance program
importstartsopmodedistribution
Optional table - engine soak distributions
monthvmtfraction
Fractions to distribute VMT across 12 months of the year
roadtype
Optional table - fraction of highway driving time spent on ramps
roadtypedistribution
Fractions to distribute VMT across the road types
sourcetypeagedistribution
Distribution of vehicle population by age
sourcetypedayvmt
VMT input by source use type, month, and day type (3 of 4 options)
sourcetypeyear
Vehicle populations
sourcetypeyearvmt
VMT input by source use type, as annual total (4 of 4 options)
starts
Optional table - starts activity, replacing the MOVES-generated starts
table
startshourfraction
Optional table - fractions to distribute starts across hours in a day
startsmonthadjust
Optional table - fractions to vary the vehicle starts by month of year
startsperday
Optional table - total number of starts in a day
startssourcetypefraction
Optional table -fractions to distribute starts among MOVES source types
state
Description of the state
year
Year of the database
zone
Allocations of starts, extended idle and vehicle hours parked to the county
zonemonthhour
Temperature and relative humidity values
zoneroadtype
Allocation of source hours operating to the county
emissionratebyage
Implementation of California standards [not normally part of a CDB but
included for NEI because state-specific data is applicable]
S/L/T agencies submitted a total of 1,693 CDBs for the 2017 NEI. Previously, agencies submitted 1,816 CDBs for
the 2014 NEI and 1,426 CDBs for the 2011 NEI. Agencies submitting data through the EPA Emissions Inventory
System (EIS), provided completed CDBs (i.e., each required table populated), along with documentation and a
submission checklist indicating which of the CDB tables contained local data. Table 6-4 summarizes these
submission checklists, showing the number of counties within each submittal for which the information was
local data, as opposed to a default. Empty slots in the table indicate that the state or county did not provide
local data for that particular CDB table. The grand totals of counties across all states show that VMT and
population (found in the 'HPMSVtypeYear' and 'sourceTypeYear' tables, respectively) were the most commonly
provided local data types.
6-7
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DRAFT
Figure 6-1 shows the geographic coverage of CDB submissions where the state or local agency submitted data
that was used for at least one table (dark blue). The light blue areas are counties for which the NEI uses EPA
default 2017 CDBs.
Figure 6-1: Counties for which agencies submitted local data for at least 1 CDB table*
* Submitting areas are shown in dark blue
6-8
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Table 6-4: Number of counties with submitted data, by state and key MOVES CDB table
State/County
avft
avgspeeddistribution
countyyear
Dayvmtfraction
fuelformulation
fuelsupply
fuelusagefraction
hotellingactivitydistribution
hotellinghours
hourvmtfraction
hpmsvtypeyear
imcoverage
monthvmtfraction
onroadretrofit
roadtype
roadtypedistribution
sourcetypeagedistribution
sourcetypeyear
sou rcetypeyea rvmt
starts
startsperday
Alaska
32
32
32
32
32
32
32
Arizona
12
Arizona (Maricopa)
1
1
1
1
1
1
1
1
i
1
1
1
1
Arizona (Pima)
1
1
1
1
1
i
1
1
1
1
Connecticut
8
8
8
8
8
8
8
8
8
8
8
Delaware
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
District of Columbia
1
1
1
1
1
1
1
1
1
1
1
1
Florida
67
67
67
67
67
67
67
67
67
Georgia
24
13
1
47
24
159
13
159
24
159
159
159
20
Idaho
44
44
44
44
44
44
44
44
44
44
44
44
44
Illinois
102
10
102
10
10
10
102
102
11
102
102
102
102
Kentucky (Jefferson)
1
1
1
1
1
Maine
16
16
16
16
16
16
16
1
16
16
16
16
Maryland
24
24
24
24
24
24
24
24
24
24
24
24
24
24
Massachusetts
14
14
14
14
14
14
Michigan
7
7
7
7
7
7
Minnesota
87
87
87
87
87
87
87
87
87
Missouri
48
5
115
11
115
115
5
115
Nevada (Clark)
1
1
1
1
1
1
1
1
1
Nevada (Washoe)
1
1
1
1
1
1
1
1
New Hampshire
10
10
10
10
New Jersey
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
New York
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
6-9
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State/County
avft
avgspeeddistribution
countyyear
Dayvmtfraction
fuelformulation
fuelsupply
fuelusagefraction
hotellingactivitydistribution
hotellinghours
hourvmtfraction
hpmsvtypeyear
imcoverage
monthvmtfraction
onroadretrofit
roadtype
roadtypedistribution
sourcetypeagedistribution
sourcetypeyear
sourcetypeyearvmt
starts
startsperday
North Carolina
18
3
3
18
100
48
100
100
100
Ohio
88
16
88
8
8
88
88
88
7
88
88
88
88
Pennsylvania
67
67
67
67
67
67
67
67
67
67
67
67
Rhode Island
5
5
5
5
5
5
5
5
5
South Carolina
46
46
46
46
Tennessee
1
1
1
1
1
1
1
1
Tennessee (Knox)
1
1
1
1
1
1
1
1
Texas
25
254
25
254
25
25
25
25
254
254
25
254
25
254
254
254
25
Utah
29
29
29
29
29
29
5
29
29
29
29
Vermont
14
14
14
14
14
14
14
14
Virginia
30
17
40
13
13
40
133
10
40
133
133
133
Washington
1
39
1
39
39
5
39
39
39
39
West Virginia
55
55
55
55
55
55
Wisconsin
8
9
72
72
7
72
72
72
72
Total
65
100
57
101
76
90
77
34
34
105
168
70
119
10
33
146
152
157
87
25
20
6-10
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6.5.2 QA checks on MOVES CDB Tables
The EPA reviewed lists of CDB data errors flagged by quality assurance scripts and reviewed graphs of submitted
age distributions, speed distributions, and hour VMT fractions. The quality assurance scripts report the potential
errors by compiling a list into a summary database table. The list of potential errors includes the CDB name,
table name, a numeric error code, and in some cases the suspect data value or sum of values. EPA reviewed all
potential errors, identified which ones needed to be addressed, and then coordinated with the responsible
state/local agency to clarify whether the data were correct or needed revision.
The quality assurance scripts are designed to identify not only the types of errors that would cause MOVES to
crash (e.g., missing or badly formatted tables) but also those that would give erroneous results. EPA reviewed
the graphs of submitted age distributions, speeds, and VMT hourly fractions to consider their use vs. alternative
county-level data available from CRC studies. Examples of suspected unreasonable values include (a) a mix of
vehicle type population or VMT that shows more heavy-duty vehicles or VMT than shown for light-duty, (b) age
distributions that are skewed to older vehicles rather than newer, or (c) atypical VMT temporal patterns such as
significantly higher VMT in winter than summer, which we would not normally expect, or higher VMT overnight
than during daytime. The quality assurance scripts used for the CDBs are available with the
QA scripts or 2017.zip archive as listed in the supporting data in Table 6-10.
Many of the 1693 submitted CDBs required at least one update due to missing or incorrect data, incorrect table
formatting, or excess data (more than required), which was removed prior to use. The missing or incorrect data
included the following problems:
• Age distribution represented a different data year than 2017 (i.e., LDV recession "dip" shifted by several
years)
• Incorrect table keys on CDB tables f SourceTypeAgeDistribution', 'RoadType')
• Incorrect column order on CDB tables ('IMCoverage', 'RoadType')
• Missing weekend (day type 2) activity in CDB tables: f AvgSpeedDistribution/ 'HourVMTFraction')
• Weekday activity (day type 5) repeated as weekend activity (day type 2)
• Empty tables for 'year' and/or VoadTypeDistribution' tables
• Inconsistent splits of cars and light-duty trucks across states
• Ramp fractions unrealistically high (e.g., 60% up to 100%)
• IMCoverage table covered gasoline but not flex-fuel vehicles
• IMCoverage table contained wrong countylD
• RoadType table incorrect structure
• Expected VMT tables required for MOVES2014b (SourceTypeDayVMT, SourceTypeYearVMT, and
HPMSVtypeDay) were missing
• Values sum to 0 for some source types in the 'RoadTypeDistribution* table
• Old data (year 2014) re-submitted for 'HotellingHours'
• Old MOVES default data (year 2014) submitted for 'HotellingActivityDistribution'
• Erroneous, missing, or gap-filled values in 'hourVMTFraction'
o 100% of VMT allocated to hour 1 for road types thought to not exist in the county,
o Missing data for weekdays (day type 5)
o Flat hourly profiles for some source types
o Statewide average data applied to all counties in a state
o MOVES default data submitted
o Multiple-hour blocks used in the profile instead of hourly variation
6-11
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• Erroneous or gap-filled values in 'avgSpeedDistribution'
o 75 mph on nearly all road types and hours
o Zero time in bin 1 (speeds 0 to 2.5 mph), even on unrestricted roads (surface streets with
intersections).
o No variation in speeds by hour of day or weekday/weekend
o No variation in speeds by road type
o Speeds notably higher (instead of lower or similar) during weekday peak periods
The EPA resolved each of the above data problems by coordinating with state/local agencies individually and/or
presenting intentions during monthly meetings with the multi-jurisdictional organization (MJO) MOVES
workgroup. In some cases, the agency preferred to submit a corrected CDB, which the EPA contractor (ERG)
reviewed again to verify the intended correction. In other cases, the agency provided the EPA with instructions
for a "spot correction" to a table or simply accepted the EPA's proposed update. ERG also corrected formatting
problems with the database tables. In some cases, tables had missing data fields and/or table keys; the missing
fields did not house important content, but their presence is required for MOVES to run. EPA's final decisions on
the data source (submittal vs. EPA-developed information) for age distribution, speed distribution, and hourly
VMT fractions can be found in the documentation spreadsheet "2017vl Documentation of CDB Input
Data 20200327.xlsx" posted with the 2017 NEI supplemental data files.
6.6 Tribal Emissions Submittals
Tribal onroad emissions were submitted and used in the 2017 NEI. The submitting tribal agencies are listed in
Table 6-5.
Table 6-5: Tribes that Submitted Onroad Mobile Emissions Estimates for the 2017 NEI
Coeur d'Alene Tribe
Kootenai Tribe of Idaho
Nez Perce Tribe
Northern Cheyenne Tribe
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
6.7 EPA default MOVES inputs
6.7.1 Sources of default data by MOVES CDB table
The EPA used CDBs constructed with EPA-generated data for counties where agencies did not submit input data.
The EPA developed new 2017 estimates of VMT, vehicle population, and hoteling at the county- and SCC-level
for use in the subsequent SMOKE-MOVES processing step and inserted these data into the CDBs where states
did not provide data. The SMOKE files contain this information at the resolution of SCC, which includes the
source type, fuel type, and road type. When inserted into the CDB table for source type VMT
(sourceTypeYearVMT), we sum over the fuel and road type. Similarly, for population, we sum over the SCC fuel
type to aggregate population to the source type level for the CDB table containing population (sourceTypeYear).
In contrast, the hoteling activity detail is much more disaggregated in the two MOVES tables (hotellingHours and
hotellingActivityDistribution) compared to the SMOKE FF10 hoteling file. The script that inserts these data into
the set of "all CDBs" (ReverseFFlO Script 20200317.plx) is listed in Table 6-10. States and counties with CDBs
that included EPA-generated activity and projected CDBs are those indicated by light blue shading in Figure 6-1.
Table 6-6 below lists the sources of default information by MOVES CDB table. The spreadsheet "2017vl
6-12
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Documentation of CDB Input Data 20200327.xlsx" provides specific information about where state-supplied
data were used versus default data. Additional detail on processing steps in the IHS data to create 'AVFT and
'SourceTypeAgeDistribution* is provided below Table 6-6.
Table 6-6: Source o
EPA-developed information for key data tables in MOVES CDBs
CDB Table
Default content for 2017 NEI
Avft
2017 IHS data
Avgspeeddistribution
CRC A-100 study
Dayvmtfraction
CRC A-100 study
Fuelformulation
Based on EPA estimates for each county from 2017 refinery gate batch data
Fuelsupply
Based on EPA estimates for each county from 2017 refinery gate batch data
Fuelusagefraction
MOVES2014b default E85 usage
hotellingactivitydistribution
MOVES2014b default APU vs. Main Engine fractions
Hotellinghours
2017 EPA estimates of hoteling based on 2017 VMT
Hourvmtfraction
CRC A-100 study
Hpmsvtypeday
Empty by default
Hpmsvtypeyear
Empty by default
Imcoverage
MOVES2014b
importstartsopmodedistribution
Empty by default
Monthvmtfraction
MOVES2014b
Roadtype
MOVES2014b default ramp fraction of 0.08
Roadtypedistribution
EPA estimates based on FHWA
sourcetypeagedistribution
2017 IHS data adapted from CRC A-115
Sourcetypedayvmt
Empty by default
Sourcetypeyear
2017 IHS data adapted from CRC A-115
Sourcetypeyearvmt
2017 VMT based on FHWA data
Starts
Empty by default
Startshourfraction
Empty by default
Startsmonthadjust
Empty by default
Startsperday
Empty by default
startssourcetypefraction
Empty by default
Zonemonthhour
2017 meteorology data averaged by county
The 'emissionratebyage' tables for some LEV states were populated using
Emissionratebyage
appropriate data described in the guidance for states adopting California
emission standards. These were provided to MOVES as separate databases
from the CDB.
Preparation of VWFT' and 'SourceTypeAgeDistribution' CDB Tables
As mentioned above in Section 6.2.1, national vehicle population data from IHS for 2017 were used to derive
updated age distributions adjusted to remove older vehicles (MOVES 'sourceTypeAgeDistribution' table) and
fuel type splits by source type and model year (MOVES 'AVFT table) in the CDBs. These data were computed at
the county level for the set of "all CDBs" and were a weighted average over county groups for the set of
representative CDBs used in the MOVES runs for NEI. In both cases, EPA preferred to use local data so where
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they were found to be acceptable. Local data were used preferentially and supplemented with the EPA-
developed information where needed. In the EPA-developed data, the source registration data does not reliably
distinguish between short-haul and long-haul activity, and so source types 52 and 53 (single unit trucks) have the
same age distributions, as do source types 61 and 62 (combination unit trucks). In addition, all age distributions
for long-haul trucks (source types 53 and 62) are a national average, because these vehicles are expected to
travel long distances from the county where they are registered. The CRC A-115 report details all assumptions
and gap filling necessary to ensure MOVES compatibility.
6.7.2 Default California emission standards
The EPA populated an alternative MOVES database table 'EmissionRateByAge' in the CDBs for some counties in
the states that have adopted emission standards from California's Low Emission Vehicle (LEV) program. Table
6-7 shows states that adopted the California standards and the year the program began in each state. We
developed these tables to be consistent with the EPA guidance for LEV modeling provided on the EPA web site
[ref 3], The LEV database is included with MOVES Input DBs.zip that is available with the supporting data
described in Table 6-10.
Table 6-7: States adopting California LEV standards and start year
FIPS State ID
State Name
LEV Program Start Year
06
California
1994
09
Connecticut
2008
10
Delaware
2014
23
Maine
2001
24
Maryland
2011
25
Massachusetts
1995
34
New Jersey
2009
36
New York
1996
41
Oregon
2009
42
Pennsylvania
2008
44
Rhode Island
2008
50
Vermont
2000
53
Washington
2009
6.8 Calculation of Emissions
6.8.1 Preparation of onroad emissions data for the continental U.S.
The 2017 NEI includes onroad emissions for every county. The same approach was used for counties inside the
continental U.S. and in the outlying states and territories: the first step is to run MOVES at the county level to
produce lookup tables of emission rates for representative counties, using scripts designed to integrate MOVES
with the SMOKE modeling system (i.e., SMOKE-MOVES). The SMOKE-MOVES approach adapted for NEI
leverages gridded hourly temperature and relative humidity information available from meteorological
modeling used for air quality modeling. This set of programs was developed by the EPA and is also used by
states and regional planning organizations to compute onroad mobile source emissions for regional air quality
modeling. SMOKE-MOVES requires emission rate "lookup" tables generated by MOVES that differentiate
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emissions by process (running, start, vapor venting, etc.), vehicle type, road type, temperature, speed, hour of
day, etc.
To generate the MOVES emission rates for counties in each state across the U.S., the EPA used an automated
process to run MOVES to produce emission factors by temperature and speed for a set of "representative
counties," to which every other county could be mapped, as detailed below. Using the calculated MOVES
emission rates, SMOKE selected appropriate emissions rates for each county, hourly temperature, SCC, and
speed bin and multiplied the emission rate by activity (VMT, vehicle population, or hoteling hours) to produce
emissions. These calculations were done for every county, grid cell, and hour in the continental U.S. and
aggregated by county and SCC for use in the 2017 NEI. The MOVES "RunSpec" files (that provide MOVES input
data for each representative county) are provided in the supplementary materials (see 2017_runspecs_zmh.zip
in Table 6-10). MOVES was run with two special input databases: an LEV table (see Section 6.7.2) and a database
to keep MOVES from making adjustments to NOx based on humidity levels (see Section 6.8.3 for more details).
The databases are included in MOVES Input DBs.zip as described in Table 6-10.
SMOKE-MOVES tools are incorporated into recent versions of SMOKE and can be used with different versions of
the MOVES model. For the 2017 NEI, the EPA used the latest publicly released version: MOVES2014b (version
20180726) [ref 4], Creating the NEI onroad mobile source emissions with SMOKE-MOVES requires numerous
steps, as described in the sections below:
• Determine which counties will be used to represent other counties in the MOVES runs (see Section
6.8.2.1).
• Determine which months will be used to represent other month's fuel characteristics (see Section
6.8.2.2).
• Create representative CDB inputs needed for the MOVES runs (see Section 6.8.6).
• Create inputs needed both by MOVES and by SMOKE, including a list of temperatures and activity data
(see Section 6.8.4).
• Run MOVES to create emission factor tables (see Section 6.8.8).
• Run SMOKE to apply the emission factors to activity data to calculate emissions (see Section 6.8.9).
• Aggregate the results at the county-SCC level for the NEI, summaries, and quality assurance (see Section
6.8.10).
• Added five speciated PM2.5 species based on speciation profiles (i.e., elemental carbon, organic carbon,
nitrate, sulfate and other PM2.5)- See Section 6.8.10.
• Added DIESEL-PM10 and DIESEL-PM25 by copying the PM10 and PM2.5 pollutants (respectively; exhaust
emissions only) as DIESEL-PM pollutants for all diesel SCCs. See Section 6.8.10.
Some things to note about the 2017 NEI that are different from the 2014NEIv2 are:
• SMOKE now adjusts NOx emission factors to account for humidity impacts on the pollutant using the
hourly, gridded met data. To support this change, MOVES was run with relative humidity adjustments to
NOx turned off (see nonoxadj_moves2014b.zip from MOVES lnputDbs.zip in Table 6-10).
• SMOKE now reads in the distribution of vehicle speeds by 16 speed bins by 24 hours for weekday and
weekend day types.
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Some notes about the treatment of specific pollutants are as follows:
• Manganese/7439965 includes the brake and tire contribution.
• Gasoline with 85 percent ethanol (E85) was tracked as a separate fuel.
• Brake and tire PM were tracked separately from exhaust processes, although all non-refueling processes
were combined into broader SCCs prior to loading into EIS.
6.8.2 Representative counties and fuel months
6.8.2.1 Representative counties
Although the EPA develops a CDB for each county in the nation, we only run MOVES for a subset of these to
control the computation time and cost. The representative county approach is also supported by the concept
that the majority of the important emissions-determining differences among counties can be accounted for by
assigning counties to groups with similar properties such as fleet age, a shared l/M program, and shared fuel
controls (e.g., low RVP for summer gasoline). The county used to provide emission rates covering other counties
is called the "representative county." The MCXREF file listed in Table 6-10 provides the mapping of each county
to its representative county. Usually the same MCXREF file is used for all MOVES processes.
In the SMOKE-MOVES framework, temperature- and speed-specific data from the representative county
emission factor lookup tables are multiplied with the activity data for all counties within the corresponding
county group. The activity data specific to individual counties in the inventory includes VMT, vehicle population,
hoteling hours, and hourly speed distributions.
The EPA analyzed the 2017 submitted CDBs, the new 2017 age distributions derived from CRC A-115, and some
MOVES data for non-submitting areas, in order to group similar counties and select representative counties for
2017. In line with previous modeling platforms, the MOVES input data considered for county grouping included
state, altitude, fuel region, presence of an inspection and maintenance (l/M) program, light-duty vehicle average
age, and ramp fraction.
1. State. Only counties within the same state were allowed to be in the same representative county group.
2. Altitude. The altitude of each county came from the MOVES database 'county' table. Values are either
'L' for low altitude (most counties) or 'H' for high. For purposes of representative county selection, counties
in the states of Colorado, Nevada, New Mexico, and Utah are considered high altitude, while all other
counties are considered low altitude.
3. Fuel Region. "Fuel region" refers to a region of counties sharing similar gasoline fuel properties. For
example, those within a state's reformulated gasoline (RFG) area. The data source was the 'regionCounty'
table from the 'moves2l els or otaq 20191210' database listed in Table 6-10.
4. IM Bin. The IM bin is a value of either "0" (no IM) or "1" (has IM) to indicate whether the county is part
of an inspection & maintenance program area in in 2017. We added a third value "2" (yes IM in 2017, but
no IM in future years), to further separate from the "1" category for North Carolina counties which will no
longer require l/M after 2018. The extra category of "2" allows the emissions benefit to be modeled in
future years with the same county groups. The data source for presence of an l/M program was primarily
the 2017 submittals for the NEI. If a county did not positively identify an l/M program in a submittal or did
not have a submittal, the yes/no determination comes from the MOVES database 'IMCoverage' table for
year 2017.
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5. Mean Light-Duty Age. The age distribution of light-duty vehicles (passenger cars, passenger trucks, and
light commercial trucks) were condensed into a single population-weighted average age by county,
reflecting the number of years old in 2017. The mean age was then binned into the six categories listed
below. Only counties that share the same bin were allowed to be in the same representative county group.
The source of the data was submitted age distributions that EPA accepted for use in NEl, supplemented
elsewhere by the adapted 2017 IHS data from CRC A-115.
Bin Description (Mean age in number of years old in 2017)
1 0.0 < Mean Age < 7.0
2 7.0 < Mean Age < 9.0
3 9.0 < Mean Age < 11.0
4 11.0 < Mean Age < 13.0
5 13.0 < Mean Age < 15.0
6 15.0 < Mean Age
6. Ramp bin. MOVES2014b uses a parameter with a value between 0 and 1 called "ramp fraction" to
divide the time driving on restricted access roads into highway ramps and non-ramp. MOVES assigns ramp
driving a more aggressive drive schedule, and therefore they have higher emission rates than cruise on
highways. The data source for ramp fraction was the 2017 submittals, and the MOVES default value of 0.08
ramp fraction elsewhere. Each county's ramp fraction was binned into the five categories below.
Bin Description
1 0 < ramp fraction < 0.05
2 0.05 < ramp fraction < 0.09
3 0.09 < ramp fraction < 0.13
4 0.13 < ramp fraction < 0.17
5 0.17 < ramp fraction
State requests. Several agencies provided comments to EPA on the selection of representative counties for
their states: Maryland, New York, New Jersey, and Wisconsin.
After grouping similar counties, the county with the highest VMT in each group was selected as the
representative county. Figure 6-2 displays a map of the representative counties by state and their corresponding
county groups. The MCXREF file listed in Table 6-10 provides the mapping of each specific county to its
representative county and a map showing the visualization of the county groups
(2017NEI representative county groups.png) are provided. A spreadsheet that includes the data used in the
development of the representative counties is included with the supporting data described in Table 6-10
(2 :presentative Counties Analysis 20191220.xlsx).
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DRAFT
Figure 6-2: Representative county groups for the 2017 NEI
Reference County Groups 2017 NEI
6.8.2.2 Fuel Months
A "fuel month" indicates when a particular set of fuel properties should be used in a MOVES simulation. Similar
to the representative county, the fuel month reduces the computational time of MOVES by using a single month
to represent a set of months during which a specific fuel has been used in a representative county. Because
there are winter fuels and summer fuels, the EPA used January to represent October through April and July to
represent May through September. For example, if the grams/mile exhaust emission rates in January are
identical to February's rates for a given representative county, and temperature (as well as other factors), then
we use a single fuel month to represent January and February. In other words, only one of the months needs to
be modeled through MOVES to obtain the necessary emission factors. The hour-specific VMT, temperature and
other factors for February are still used to calculate emissions in February, but the emission factors themselves
do not need to be created, since one month can sufficiently represent the other month. The fuel months used
for each representative county are provided in the MFMREF file in the supplementary materials (see Table 6-10
for access information).
6.8.2.3 Fuels
For the 2017 NEI, fuel property information included in locally-supplied CDBs was replaced with a fuel supply
developed by EPA (moves201x 2017fuels or otaq 20191210). The EPA fuel supply was derived from refinery
production compliance data, market fuel survey data, and known federal and local regulatory requirements. For
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a national inventory such as the NEl, this approach provides a more consistent and comprehensive result with
respect to fuel use and fuel impacts on emission rates. More details on development of the MOVES fuel supply is
available in this MOVES technical support document: Fuel Supply Defaults: Regional Fuels and the Fuel Wizard in
MOVES2Q14b [ref 51.
The 2017 NEI fuel supply was created by starting with the 2016 NEI fuels and applying adjustments derived from
updated gasoline production data. This was done by comparing 2017 to 2016 gasoline properties as reported on
the mpliance Division website. These adjustments covered six fuel properties and were made
separately for summer and winter season and conventional and reformulated gasoline.
For 2017 the nationwide fuel supply assumed 100% market share E10 ethanol blends in gasoline. All diesel was
assumed to be 15 ppm sulfur, and onroad diesel was 100% market share B5 biodiesel blends nationwide.
6.8.3 Temperature and humidity
Ambient temperature can have a large impact on emissions. Low temperatures are associated with high start
emissions for many pollutants. High temperatures and high relative humidity are associated with greater
running emissions due to the increase in the heat index and resulting higher engine load for air conditioning.
High temperatures also are associated with higher evaporative emissions.
The 12-km gridded meteorological input data for the entire year of 2017 covering the continental U.S. were
derived from simulations of version 3.8 of the Weather Research and Forecasting Model (WRF), Advanced
Research WRF core [ref 6], The WRF Model is a mesoscale numerical weather prediction system developed for
both operational forecasting and atmospheric research applications. The Meteorology-Chemistry Interface
Processor (MCIP) [ref 7] was used as the software for maintaining dynamic consistency between the
meteorological model, the emissions model, and air quality chemistry model.
The EPA applied the SMOKE program Met4moves [ref 8] to the gridded, hourly meteorological data (output
from MCIP) to generate a list of the maximum temperature ranges, average relative humidity, and temperature
profiles that are needed for MOVES to create the emission-factor lookup tables. "Temperature profiles" are
arrays of 24 temperatures that describe how temperatures change over a day, and they are used by MOVES to
estimate vapor venting emissions. The hourly gridded meteorological data (output from MCIP) was also used
directly by SMOKE (see Section 6.8.9).
The temperature lists were organized based on the representative counties and fuel months as described in
Section 6.8.2. Temperatures were analyzed for all of the counties that are mapped to the representative
counties, i.e., for the county groups, and for all the months that were mapped to the fuel months. The EPA used
Met4moves to determine the minimum and maximum temperatures in a county group for the January fuel
month and for the July fuel month, and the minimum and maximum temperatures for each hour of the day.
Met4moves also generated temperature profiles using the minimum and maximum temperatures and 10 °F
intervals. In addition to the meteorological data, the representative counties and the fuel months, Met4moves
uses spatial surrogates to determine which grid cells from the meteorological data have roads and uses the WRF
temperature and relative humidity data from those areas. For example, if a county had a mountainous area with
no roads, the grid cells with no roads would be excluded from the meteorological processing. The spatial
surrogates used for the 2017 NEI were based on activity data such as link-based VMT for the year 2016, with the
goal of better characterizing the spatial variability of the onroad mobile source emissions. The use of these new
spatial surrogates required updates to the cross reference of surrogate assignments by vehicle type and process.
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For the 2017 NEI, MOVES was run with the database nonoxadj_moves2014b (part of MOVES Input DBs.zip in
Table 6-10) to prevent the model from making adjustments to NOx based on humidity levels. Instead, gridded
hourly humidity values are used in SMOKE-MOVES to compute NOx adjustments to the unadjusted emissions
output from MOVES.
Met4moves computes the range of temperatures needed by each representative county for each fuel month
(i.e., 5 month summer season or 7 month winter season). When the emission factors are applied by SMOKE, the
appropriate temperature bin and fuel month are used to compute the emissions. The EPA used a 5 °F
temperature bin size for RatePerDistance (RPD), RatePerVehicle (RPV), and RatePerHour (RPH).
Met4moves can be run in daily or monthly mode for producing SMOKE input. In monthly mode, the
temperature range is determined by looking at the range of temperatures over the whole month for that
specific grid cell. Therefore, there is one temperature range per grid cell per month. While in daily mode, the
temperature range is determined by evaluating the range of temperatures in that grid cell for each day. The
output for the daily mode is one temperature range per grid cell per day and is a more detailed approach for
modeling the vapor venting RatePerProfile (RPP) based emissions. The EPA ran Met4moves in daily mode for the
2017 NEI. The temperature data output from Met4moves (2017NEI RepCounty Temperatures.zip) are provided
with the supporting data in Table 6-10.
The resulting temperatures for the representative counties are provided in the supplementary materials (see
Table 6-10 for access information). The gridded, hourly temperature data used are publicly available only upon
request and with provision of a disk media to copy these very large datasets.
6.8.4 VMT, vehicle population, speed, and hoteling activity data
The activity data used to compute onroad mobile source emissions for the 2017 NEI uses EPA-computed data
where state/local agencies did not provide their own data. These "default" (but county-specific) data were
derived from Federal Highway Administration Data (FHWA) information including the published Highway
Statistics 2017 [ref 9], along with county-level VMT data allocated to vehicle type, fuel type, and road type.
Some additional data sources were also used. The development of the default data is described in detail in
2017Default_Onroad_Activity_Data_Documentation.pdf, which is provided with the supporting data in Table
6-10.
As discussed above, SMOKE combines the MOVES emission factors for each representative county with county-
specific VMT, population, and hoteling data to compute the emissions for each individual county. These activity
data are provided to SMOKE in a flat file format, and the source of the data varies according to area of the
country and depending on whether the state/local agency submitted data for the 2017 NEI. The final activity
data is a combination of submitted data and EPA-developed data. The data are provided with the supporting
data in Table 6-10 (2017NEI onroad activity final.zip).
For the counties for which an agency submitted a CDB (the dark blue areas shown previously in Figure 6-1), the
EPA ran scripts to extract the agency-submitted data from the CDBs and reformatted it into the flat file text file
format that can be input to SMOKE (i.e., FF10). For the non-submitting areas of the U.S. (light blue areas in
Figure 6-1), the EPA VMT, population, and hoteling were used. The 2017 default speeds are from the CRC A-100
study. The CDBs use a distribution of speeds specific to hour, vehicle and road type, and weekday/weekend day
types. SMOKE uses these same data, but the 16 speed bin distributions are averaged by hour, SCC, county, and
weekday/weekend days. The speed data used for the 2017 NEI (2017NEI speed spdist.zip) are included with
the supporting data in Table 6-10.
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The FF10 creation scripts that read submitted CDBs are described separately by activity type below, followed by
discussion on how the EPA created the default 2017 activity data for VMT, population, speed, and hoteling for
non-submitting areas.
6.8.4.1 VMTFFlOfile creation
The FFlO-generation scripts read VMT flexibly from either the MOVES CDB table 'sourceTypeYearVMT,' which
contains annual VMT organized by MOVES source type, or 'HPMSVtypeYear,' which contains annual VMT by
groups of MOVES source types. The scripts disaggregate the VMT into fuel type, model year, and road type using
a combination of other CDB tables as well as some MOVES default tables. First, the annual VMT is divided into
model year using the CDB table with age distribution and the MOVES default database table containing relative
annual mileage accumulation by age ('SourceTypeAge'). The scripts use these tables to create travel fractions for
each source type and model year that sums to one (1) by source type.
Next, the VMT is further divided into fuel type categories of gasoline, diesel, CNG, E85, and electric vehicles -
preferentially by using submitted MOVES CDB tables 'AVFT' to determine the split of engine-fuel types by model
year and 'FuelUsageFraction' to determine the percent of flex-fuel engines that actually use E85. Flex-fuel
engines refer to those capable of operating on either E85 or conventional gasoline, the percentage of which
could be a function of local availability of the alternative fuel. Because the AVFT and FuelUsageFraction tables
are optional tables in a MOVES CDB, they were not always populated in a submitted database. In cases where
data were not provided, the FFlO-generation scripts automatically default to MOVES national distributions of
fuel types and/or E85 availability, using the 'SampleVehiclePopulation' and 'FuelUsageFraction' tables of the
model default database to fill the missing data. It is worth noting that several states do not have any VMT (or
vehicle population) associated with flex-fuel vehicles because they submitted data indicating either no flex-fuel
vehicle population or zero E85 fuel supply in the CDB tables.
Finally, the FFlO-generation scripts read the CDB table 'RoadTypeDistribution' to further split VMT (by fuel type)
into the four MOVES road types (urban and rural, restricted and unrestricted access). The scripts aggregate VMT
across model years to the SCC level (i.e., MOVES source type, fuel type, and road type) and reports annual and
monthly VMT (using the 'MonthVMTFraction' CDB table) for each SCC in each county into a consolidated list.
6.8.4.2 Population FFlOfile creation
The FFlO-generation script that creates the SMOKE vehicle population (i.e., VPOP) data operates similarly to the
VMT script just described, except that the calculations do not use travel fractions to disaggregate population by
model year. First, the script reads the CDB 'SourceTypeYear' table, which contains 2017 population by MOVES
source type and divides it into model years based on the submitted CDB 'SourceTypeAgeDistribution' table. For
each vehicle model year, the scripts apportion vehicle populations to fuel types using the submitted CDB tables
'AVFT' and 'FuelUsageFraction,' or, if no data were provided, uses the national default corresponding data
tables described in Section 6.8.4.1.
The FF10 scripts then aggregate population from the model year level back up to the SCC level (MOVES source
type and fuel type, and the road type 1). The CreateFFlO script and the Reverse FF10 script that pull activity data
in and out of CDBs are included with the or_scripts_2017.zip file that is included with the supporting data
described in Table 6-10.
After the vehicle population and VMT data were finalized, the population and VMT were compared by county
and source type to look for inconsistencies between the two datasets. Specifically, counties and source types
with an unreasonably high miles per year per-vehicle average (VMT divided by VPOP) were identified and
addressed. For counties and source types with a VMT/VPOP ratio above the threshold in Table 6-8, the vehicle
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population was increased so that the new VMT/VPOP ratio would equal the maximum allowable ratio. The
thresholds used were based on the 90th to 95th percentile of VMT/VPOP ratio for each source type. The vehicle
populations were adjusted to produce reasonable VMT/VPOP ratios because MOVES can output unrealistic
emission factors when the VMT/VPOP ratios are extremely high.
Table 6-8: Maximum allowable miles-per-year per-ve
licle average by source type
MOVES source type
Source type description
Maximum VMT/VPOP ratio
(miles per year)
11
Motorcycle
7,500
21
Passenger Car
31,000
31
Passenger Truck
31,000
32
Light Commercial Truck
31,000
41
Intercity Bus
130,000
42
Transit Bus
90,000
43
School Bus
30,000
51
Refuse Truck
60,000
52
Single Unit Short-haul Truck
45,000
53
Single Unit Long-haul Truck
60,000
54
Motor Home
7,000
61
Combination Short-haul Truck
150,000
62
Combination Long-haul Truck
150,000
6.8.4.3 Speed FFlOfile creation
SMOKE uses speed data for all counties to lookup the appropriate VMT-based emission factors by speed bin and
SCC. The FF10 "SPEED" input for SMOKE is one of two speed-related inputs; the other, described below, contains
hourly speed distributions by SCC and county, separately for weekdays and weekends. The FF10 speed file for
SMOKE contains a single daily average speed by SCC and county as an annual average and for each of the 12
months.
The FFlO-generation scripts read the CDB table 'avgSpeedDistribution,' which contains the fraction of VMT by
16 speed bins for each source type, day type (weekday/weekend), and hour. The scripts calculate a weighted
average to arrive at the average day values.
6.8.4.4 Speed Distribution
The SPDPRO file was used to develop previous NEI datasets, but for 2017 NEI, the SPDIST was used instead of
the SPDPRO. The SPDIST file is generated by reformatting the MOVES 'avgSpeedDistribution' CDB table into a
form that can be accepted by SMOKE. The speed distribution (SPDIST) input for SMOKE is optional. Out of the
three possible ways to model vehicle speeds in SMOKE, SPDIST provides the highest resolution to best match
vehicle activity with the lookup tables of emission factors, which for the running processes are listed by MOVES
16 speed bins. The SPDIST file lists the fraction of time in each hour spent in each of the 16 speed bins, for
weekday and weekend day types, by county, source type, and road type. MOVES provides distinct emission
factors for each of the 16 speed bins, and the SPDIST tells SMOKE-MOVES how to weight each of the speed bins
when computing the total emissions. For example, if the SPDIST specifies 55% of time is spent in speed bin 8 and
45% of time is spent in speed bin 9 for a particular county, hour/day, and SCC, the emission factors for those two
speed bins are weighted according to those ratios. The SMOKE-MOVES calculations also take unit conversions
into account, as the SPDIST fractions are per unit time, while RPD emission factors are per unit distance.
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6,8,4,5 Hoteling FFlOfile creation.
Hoteling activity refers to the time spent idling in a diesel long-haul combination truck during federally-
mandated rest periods of long-haul trips. Drivers may spend these rest periods with the main engine on, a
smaller auxiliary power unit (APU) engine on, plugged into an electric source if available, or simply leave the
engine off. MOVES and the NEI track the emissions from hoteling using the main engine idling versus those from
APUs separately. SMOKE reads each type of hoteling hours by SCC and matches them to the appropriate MOVES
emission factor from the 'RatePerHour' lookup table.
Submitting agencies have the option to directly provide MOVES with the number of hoteling hours (via the
'hotellingHours' table) and the percent of trucks by model year that use APUs (the 'hotellingActivityDistribution'
table). These CDB tables are optional. When they are present, the FFlO-generation scripts read them and
translate them into the FF10 formats for SMOKE. If they are empty, the FFlO-generation scripts calculate the
hoteling consistently with the methodology used internally to MOVES when these tables are empty. Thus, the
scripts multiply the VMT for diesel-fueled long-haul combination truck VMT on restricted access roads (urban
and rural together) and with the national average rate of hoteling. For the 2017 NEI, the national average rate of
hoteling was estimated by EPA to be 0.007248 hours per mile, which is a reduction from the 0.027337 hours per
mile average used for 2014 NEI. The scripts use the submitted fractions of APU usage where available and rely
on MOVES defaults otherwise.
For the 2017 NEI, EPA calculated all hoteling hours from the final VMT by SCC and county. These hoteling hours
were inserted into the final set of "all CDBs" released with the modeling platform (see Section 6.10). The
representative CDBs were not updated, nor do they need these data to generate hoteling emission factors. For
the 2017 NEI, an adjustment to hoteling was made to address concerns raised by stakeholders about hoteling
hours being artificially concentrated in areas with large amounts of combination truck VMT, but which were not
necessarily areas that trucks stopped to take long rest breaks. This is particularly an issue in heavily traveled
urban areas. The hoteling hours per county were compared to the number of truck stop spaces identified in the
Shapefile on which the surrogate that spatially allocates hoteling emissions to grid cells is based. This Shapefile
was created collaboratively with states during the development of the 2011 NEI and updated during subsequent
NEI efforts. In the analysis, for each county, the maximum number of hoteling hours per year that could be
supported by the number of specified parking spaces was computed using the formula:
max hours / year = number of spaces * 24 hours/day * 365 days/year
This assumes that all spaces are filled at all hours of the day. The maximum number of hours was subtracted
from the number of hours assigned to that county to determine if the county was over-allocated with hoteling
hours as compared to the known spots. For counties with at least 2 million over-allocated hours, a manual
review of truck stop spaces was conducted using Google Earth. In cases where evidence of additional spaces was
found, the number of spaces was adjusted and a factor was computed so that when that factor was multiplied
by hours, the max hours per year matched those available with the adjusted number of spaces (i.e., hoteling
hours were no longer over-allocated to the county). For the remaining over-allocated counties, no analysis was
performed and a factor to adjust the hoteling hours down to match the max hours per year for each county was
computed and applied, although it was assumed that any county can support a minimum of 105,120 hoteling
hours (i.e., 12 spaces' worth). No adjustments to hoteling hours were made in counties for which hoteling hours
were substantially under-allocated as compared to the number of available spots. Ideally, hoteling hours would
be properly allocated to counties by someone familiar with traffic patterns in the local area. The spreadsheet
used for this analysis (2017NEI hoteling by county versus truck stop parking 20200117.xlsx) is listed in Table
6-10.
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6.8.5 Public release of the NEI county databases
Two sets of 2017 CDBs are available for download: (1) seeded CDBs, which have been altered to produce
emission rates for all sources, roads and processes, and (2) unseeded CDBs intended to be used with MOVES
Inventory Calculation. The unseeded CDBs are available for all U.S. counties, but the seeded CDBs are only
available for the representative counties. See Table 6-10 for access details.
6.8.6 Seeded CDBs
The seeded county databases can be used with MOVES to generate emission factor lookup tables for SMOKE-
MOVES. In order to create representative county CDBs for MOVES runs for SMOKE-MOVES modeling, the EPA
performed a "seeding" step, whereby values of zero (0) were updated to a small value of le-15. This seeding
ensures that the lookup tables will be fully populated regardless of whether the representative county itself
included activity for all of the categories covered. Seeding is necessary because counties mapping to the
representative county may require an emission factor that would otherwise be missing. Note that the seeded
CDBs each contain activity data for all of the counties represented by the CDB, not for a single county. The
scripts used to develop the seeded CDBs are included in the or scripts 2017.zip file described in Table 6-10.
6.8.7 Unseeded CDBs
In contrast to the seeded CDBs, the unseeded CDBs do not have any seeding performed on them and include
activity data only for the individual county. This set of CDBs is true to the local conditions and could be used for
MOVES inventory mode runs. The unseeded CDBs merge the databases that were agency-submitted with the
default CDBs for 2017 with updates based on CRC A-115 and CRC A-100 study data. The unseeded CDB tables
'SourceTypeYearVMT/ 'SourceTypeYear/ 'HotellingHours/ and 'HotellingActivityDistribution' are consistent
with the SMOKE-ready files of 2017 VMT, population, and hoteling. Activity data can be taken in and out of the
unseeded individual county CDBs using the CreateFFlO and ReverseFFlO scripts included in the
or script: £ file described in Table 6-10.
6.8.8 Run MOVES to create emission factors
The EPA ran MOVES for each representative county using January fuels and July fuels for the range of
temperatures spanned by the represented county group and set of months associated with each fuel set
(January and July). A runspec generator script created a series of runspecs (MOVES jobs) based on the outputs
from Met4moves temperature information for all months of the year. Specifically, the script used a 5-degree
temperature bin with the minimum and maximum temperature ranges from Met4moves and used the idealized
diurnal profiles from Met4moves to generate a series of MOVES runs that captured the full range of
temperatures for the county group for the months assigned to each fuel. The MOVES runs resulted in four
emission factors tables for each representative county and fuel month: rate per distance (RPD), rate per vehicle
(RPV), rate per hour (RPH), and rate per profile (RPP). After the MOVES runs were completed, the post-
processor script Moves2smk converted the MySQL tables into EF files that can be read by SMOKE. For more
details on Moves2smk, see the SMOKE documentation [ref 10]. The post-processor scripts are available in
2017nei or postprocessing jars.zip as described in Table 6-10.
6.8.9 Run SMOKE to create emissions
To prepare the NEI emissions, the EPA first generated emissions at an hourly resolution using more detailed
SCCs than are found in the NEI (i.e., by road type and aggregate processes). The SMOKE-MOVES program
Movesmrg performs this function by combining activity data, meteorological data, and emission factors to
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produce gridded, hourly emissions. The EPA ran Movesmrg for each of the four sets of emission factor tables
(RPD, RPV, RPH, and RPP). During the Movesmrg run, the program used the hourly, gridded temperature (for
RPD, RPV, and RPH) or daily, gridded temperature profile (for RPP) to select the proper emissions rates and
compute emissions. These calculations were done for all counties and SCCs in the SMOKE inputs, covering the
continental U.S., as well as separate runs covering outlying areas (e.g. Alaska and Hawaii).
The emissions processes in RPD model the on-roadway driving emissions. This includes the following emission
processes: vehicle exhaust, evaporation, evaporative permeation, refueling, brake wear, and tire wear. For RPD,
the activity data is monthly VMT, monthly speed (i.e., SMOKE variable of SPEED), and hourly speed distributions
(i.e., SPDIST in SMOKE). The SMOKE program Temporal takes temporal profiles specific to vehicle type and road
type and distributes the monthly VMT to day of the week and hour. Movesmrg reads the speed distribution data
for that county and SCC and the temperature from the gridded hourly (MCIP) data and uses these values to look-
up the appropriate emission factors (EFs) from the representative county's EF table. It then multiplies this EF by
temporalized and gridded VMT for that SCC to calculate the emissions for that grid cell and hour. This is
repeated for each pollutant and SCC in that grid cell. The default diurnal and weekly VMT temporal profiles are
based on the CRC A-100 study, which was completed in time for the 2014NEIv2.
The emission processes in RPV model the parked emissions. This includes the following emission processes:
vehicle exhaust, evaporative, evaporative permeation, and refueling. For RPV, the activity data is vehicle
population (VPOP). Movesmrg reads the temperature from the gridded hourly data and uses the temperature
plus SCC and the hour of the day to look up the appropriate EF from the representative county's EF table. It then
multiplies this EF by the gridded VPOP for that SCC to calculate the emissions for that grid cell and hour. This
repeats for each pollutant and SCC in that grid cell.
The emissions processes in RPH model the parked emissions for combination long-haul trucks (source type 62)
that are hoteling. This includes the following modes: extended idle and APUs. For RPH, the activity data is
monthly hoteling hours. The SMOKE program Temporal takes a temporal profile and distributes the monthly
hoteling hours to day of the week and hour. Movesmrg reads the temperature from the gridded hourly (MCIP)
data and uses these values to look-up the appropriate emission factors from the representative county's EF
table. It then multiplies this EF by temporalized and gridded HOTELING hours for that SCC to calculate the
emissions for that grid cell and hour. This is repeated for each pollutant and SCC in that grid cell.
The emission processes in RPP model the parked emissions for vehicles that are key-off. This includes the mode
vehicle evaporative (fuel vapor venting). For RPP, the activity data is VPOP. Movesmrg reads the gridded diurnal
temperature range (Met4moves' output for SMOKE). It uses this temperature range to determine a similar
idealized diurnal profile from the EF table using the temperature min and max, SCC, and hour of the day. It then
multiplies this EF by the gridded VPOP for that SCC to calculate the emissions for that grid cell and hour. This
repeats for each pollutant and SCC in that grid cell.
The result of the Movesmrg processing is hourly data as well as daily reports for each of the four processing
streams (RPD, RPV, RPH, and RPP). The results include emissions for every county in the continental U.S.
6.8.10 Post-processing to create an annual inventory
For the purposes of the NEI, the EPA needed emissions data by county, SCC and pollutant. The EPA ran SMOKE-
MOVES at a more detailed level including road type and emission processes (e.g., extended idle) and summed
over road types and processes to create the more aggregate NEI SCCs. The EPA developed and used a set of
scripts to combine the emissions from the four sets of reports and from all days to create the annual inventory.
6-25
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The post processing scripts are named aq cb6 saprc !Aug2019, nata 20200204, and nei 20170718. They are
available in the platform documentation (see Section 6.10).
Five speciated PM2.5 pollutants were added to the NEI data for summary purposes. Note that air quality
modeling uses a finer breakdown of these pollutants. The added pollutants are based on speciation profiles (i.e.,
elemental carbon, organic carbon, nitrate, sulfate and other PIVh.s)- DIESEL-PM10 and DIESEL-PM25 were also
added by copying the PM10 and PM2.5 pollutants (respectively) as DIESEL-PM pollutants for all diesel SCCs. See
Section 2.2.5 for more details.
6.8.11 Additional MOVES and SMOKE runs with EPA-generated age distributions
Comparisons of emissions data output from SMOKE-MOVES between 2016 and 2017 showed some unexpected
increases in emissions from 2016 to 2017. Various MOVES inputs were reviewed for their potential to contribute
to these increases. The initial MOVES and SMOKE runs incorporated state and local agency-submitted age
distribution tables. A second set of MOVES and SMOKE runs was then performed based on EPA-generated age
distribution tables for all submitting state and local agencies. The outputs from the runs with agency-submitted
and EPA-generated age distributions were then compared with each other and with prior year datasets. Based
on the emissions differences, it was found that some agency-submitted age distributions were a substantial
contributor to increases in emissions from 2016 to 2017.
The agency-submitted and EPA-generated age distributions were plotted and reviewed for unusual features.
These plots were used to help guide the final decisions in terms of whether agency-submitted age distributions
would be used for each state and source type. Submitting agencies were notified of these decisions through
memoranda developed for each agency. The EPA contacted a few agencies that had unusual issues with their
age distributions. The final decisions on the use of agency-submitted vs EPA-generated age distributions are
summarized in the spreadsheet 2017vl Documentation of CDB Input Data 20200327.xlsx. After the age
distribution decisions were made, it was not necessary to perform a third set of MOVES and SMOKE runs.
Instead, results from the first two runs were merged, using emissions from the first run for areas where agency-
submitted age distributions were accepted, and emissions from the second run where EPA-generated age
distributions were used.
6.9 Summary of quality assurance methods
The EPA performed a series of checks and comparisons against both the inputs and the resulting emissions to
quality assure the onroad inventory. These checks are in addition to the ones described on the underlying CDBs.
The following is a list of the more significant checks that were performed:
• The 2017 NEI emissions were compared to the 2016vl platform and 2014v2 emissions to make sure that
all SCCs, counties, and pollutants were covered and as a general quality assurance of the emissions. As a
result of this comparison, age distributions were changed from submittal to EPA default for the county
for a handful of states and source types. This is documented in the spreadsheet 2017vl Documentation
of CDB Input Data 20200327.xlsx available for download with the platform.
• Comparisons of 2017 with 2016 and 2014NEIv2 emissions were done using spreadsheets that compared
emissions from the three years using various groupings, including but not limited to county-level, the
first 6 digits of the SCC (fuel + MOVES source type), and grouping by light-duty and heavy-duty.
6-26
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• Maps of county-level CAP and select HAP emissions were prepared for each MOVES source type and
rate (e.g., RPD), including maps of the difference between 2017 emissions versus 2016vl and 2014NEIv2
emissions.
The maps and spreadsheets helped to identify areas with suspect activity data or emission factors, and the EPA
followed up on any suspect areas to investigate further and resolve problems if any were found. Folders
containing a number of QA maps, plots, and summaries are referenced as part of the supporting data in Table
6-10.
6.10 Supporting data
Onroad 2017 emissions were computed by EPA estimates based primarily on input data submitted by state and
local agencies and secondarily using EPA-developed input data, except for the state of California. Table 6-9
provides the submittal history of these county databases. The onroad scripts and data files used in the
calculations are listed in Table 6-10. The files and datasets listed in Table 6-10 are all available on the 2017 NEI
Supplemental Data FTP site.
Table 6-9: Agency submittal history for Onroad Mobile inputs and emissions
Agency Organization
Onroad CDB Submission
Date (MM/DD/YYYY)
Onroad
Emissions
Submission Date
(MM/DD/YYYY)
Notes
Alaska Department of
Environmental
Conservation
02/05/2019
Arizona Department of
Environmental Quality
02/07/2019
Clark County Department of
Air Quality
01/14/2019
Connecticut Bureau of Air
Management
02/05/2019
Department of Energy and
Environment (Washington
D.C.)
01/09/2019
Delaware Department of
Natural Resources
01/15/2019
Florida Department of
Environmental Protection
02/04/2019
Georgia Department of
Natural Resources
09/13/2018
Idaho Department of
Environmental Quality
02/05/2019
Illinois EPA
09/27/2018
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Agency Organization
Onroad CDB Submission
Date (MM/DD/YYYY)
Onroad
Emissions
Submission Date
(MM/DD/YYYY)
Notes
Knox County (Tennessee)
Department of Air Quality
Management
01/02/2019
Louisville (Kentucky) Metro
Air Pollution Control District
02/21/2019
Maine Department of
Environmental Protection
02/06/2019
Maricopa County (Arizona)
Air Quality Department
09/28/2018
Maryland Department of
the Environment
02/05/2019
Massachusetts Department
of Environmental
Protection
02/05/2019
Metro Public Health of
Nashville/Davidson County
02/08/2019
Michigan Department of
Environmental Quality
01/15/2019
Minnesota Pollution Control
Agency
03/05/2019
Missouri Department of
Natural Resources
01/15/2019
04/09/2019
New Hampshire
Department of
Environmental Services
12/03/2018
New Jersey Department of
Environment Protection
02/13/2019
New York Department of
Environmental
Conservation
02/19/2019
04/09/2019
North Carolina DEQ,
Division of Air Quality
02/13/2019
Ohio EPA
02/05/2019
Pennsylvania Department
of Environmental
Protection
02/08/2019
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Agency Organization
Onroad CDB Submission
Date (MM/DD/YYYY)
Onroad
Emissions
Submission Date
(MM/DD/YYYY)
Notes
Pima Association of
Governments (Tuscon,
Arizona)
01/31/2019
Rhode Island Department of
Environmental
Management
EPA constructed the
Rhode Island CDBs from
spreadsheets provided
by RIDEM.
South Carolina Department
of Health and
Environmental Control
02/05/2019
Texas Commission on
Environmental Quality
02/07/2019
TCEQ later provided a
correction to the CDB for
Travis County.
Utah Division of Air Quality
12/20/2018
Vermont Department of
Environmental
Conservation
02/05/2019
Virginia Department of
Environmental Quality
02/06/2019
Washington State
Department of Ecology
02/06/2019
Washoe County (Nevada)
Health District, Air Quality
Management Division
03/19/2019
West Virginia Division of Air
Quality
01/14/2019
Wisconsin Department of
Natural Resources
02/08/2019
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Table 6-10: Onroad Mobile c
ata file references for the 2017 NEI
File Name
Description
1
2017NEI_default_onroad_activity_
approach.pdf
Describes method used for EPA default VMT,
VPOP, data used in counties for which data were
not submitted by S/L/T agencies.
2
Folder CDBs_for_all_counties contains
2017_CDBs_stateXX.zip where XX is the two-
digit state FIPS code
"Unseeded" CDBs for all counties in the U.S.
archived separately by state. These may not
produce fully populated emission rates tables
across all categories without "seeding". Activity
data and age distributions are specific to each
county and not aggregated.
3
Folder CDBs_for_rep_counties contains
2017_RepCDBs_Seeded_26march2020.zip
"Seeded" CDBs for representative counties in the
continental U.S. used to develop 2017 NEI. These
should produce fully populated rates tables
because values of zero in the MOVES input tables
have been updated to small numbers (le-15).
Age distributions and AVFT are vehicle-
population-weighted across all represented
counties. VMT and population are summed
across all represented counties.
4
Folder CDBs_for_rep_counties contains
2017_RepCounty_Runspecs.zip
The MOVES2014b run specifications (runspecs)
for the representative counties for running
MOVES in emissions rate mode (used for SMOKE-
MOVES). Note that CDB names should be
updated to the YYYYMMDD version date
20200326.
The archive "2017_runspecs_zmh.zip" contains
60,879 individual archive files with a .jar
extension, which can be unzipped using most
standard unzipping software. Each jar contains
one MOVES runspec file and the corresponding
meteorology input database required for a single
MOVES run for NEI. EPA divided the 60,879 runs
across 706 computers using Amazon Web
Services. Each computer ran between 86 and 87
MOVES runs in series, on average. The number of
MOVES runs corresponds to 353 representative
counties, 2 fuel months, and approximately 86-87
met conditions per county-month combination.
6-30
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File Name
Description
5
2017NEI_onroad_activity_final.zip
All three data types are in FF10 format for SMOKE
and are a combination of EPA estimates, agency
submittals, and corrections:
1.Vehicle population by county and SCC covering
every county in the U.S.,
2.VMT annual and monthly by county and SCC
covering every county in the U.S., and
3.Hoteling hours annual and monthly by county
covering every county in the U.S. including
hours of extended idle and hours of auxiliary
power units for combination long-haul trucks
only.
6
2017NEI_RepCounty_Temperatures.zip
The temperature and relative humidity bins for
running MOVES to create the full range of
emissions factors necessary to run SMOKE-
MOVES and the ZMH files used to run MOVES.
Generated by running the SMOKE Met4moves
program.
7
MFMREF_2017nei_27mar2020_vl.csv
Fuels cross reference (MFMREF) is a table that
maps representative fuel months to calendar
months for each representative county. The
MFMREF file is an input to SMOKE.
8
MCXREF_2017nei_18mar2020_vl.csv
2017NEI_representative_county_groups.png
County cross reference file (MCXREF) is a table
that shows every US county along with the
representative county used as its surrogate. The
MCXREF is an input to SMOKE. A map showing
the county groups is also available.
9
2017NEI_speed_spdist.zip
These data are in FF10 format for SMOKE and are
a combination of EPA estimates, agency
submittals, and corrections:
1. Average speed in miles per hour, annual and
monthly values, by county and SCC covering
every county in the U.S. and
2. Weekend and weekday hourly speed
distributions (SPDIST) in miles per hour, by
county and SCC covering every county in the
U.S.
10
The archive QA_scripts_or_2017.zip includes
the QA script:
CDB_QA_Checks_MOVES2014b_v2_upd.sql
ERG_20191023.sql
Scripts designed to catch errors that would cause
MOVES to fail during a run and to identify
unreasonable data values.
6-31
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File Name
Description
11
The archive or_scripts_2017.zip includes the
FF10 generation scripts:
l_HPMS_VMT_POP_db_20190422.sql
2_CreateFF10_fromMOVES2014CDB_v6_20
200107.sql
FF10 generation scripts read CDB tables and
produce SMOKE-formatted activity input files for
use in SMOKE-MOVES. The SMOKE files include
VMT, vehicle population, hoteling hours, speed,
and SPDPRO.
12
The archive or_scripts_2017.zip contains the
script
ReverseFF10_Script_20200317.plx
The reverse FF10 script populates CDBs from
SMOKE-formatted activity files VMT, vehicle
population, and hoteling hours to fill the MOVES
CDB tables SourceTypeYearVMT,
SourceTypeYear, HotellingHours, and
HotellingActivityDistribution.
13
Folders with QA / review products:
age_distribution_plots
draft_ emissions_ review
emissions_and_activity_maps
summaries
Plots, maps, and summaries for quality assurance
and data visualization are available in several
folders to assist interested parties in better
understanding the data.
14
2017vl Documentation of CDB Input
Data_20200327.xlsx
Spreadsheet that shows how state-submitted and
default data were merged together to prepare
2017 NEI.
15
2017_Representative_Counties_Analysis_20
191220.xlsx
Spreadsheet of representative county
characteristics.
16
2017NEI_hoteling_by_county_versus_
truck_stop_parking_20200117.xlsx
Spreadsheet documenting computation of
adjustment factors applied to hoteling hours
where there were more hours assigned than the
available truck stop parking spaces could support.
17
The archive
2017nei_or_postprocessing_jars.zip includes
the scripts
postprocess_aq_cb6_saprc_lAug2019.jar
postprocess_nata_20200204.jar
postprocess_nei_20160718.jar
MOVES lookup table post-processing scripts that
can create emission factor tables for various
chemical mechanisms and purposes (e.g., the
NEI).
18
The archive or_scripts_2017.zip includes the
script and fuels data table:
UpdateFuels_20191226.plx
moves201x_2017fuels_or_otaq_20191210
Perl script that inserts 2017 fuels provided by
OTAQ into each CDB. The 2017 fuels are listed in
the MySQL database
'moves201x_2017fuels_or_otaq_20191210'
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File Name
Description
19
The archive or_scripts_2017.zip includes the
script and meteorological data tables:
UpdateMet_20200319.plx
met_2017nei_local_no_temp_adj
met_2017nei_local_no_temp_adj_outsideco
nus
Perl script that inserts met data into set of "all
CDBs" intended for inventory mode. The
representative CDBs do not use this data. The
2017 met data is listed in the MySQL database
,met_2017nei_local_no_temp_adj7
20
The archive or_scripts_2017.zip includes the
representative county seeding scripts:
SeedingScript_ERG.sql
'seed'
seedCDBs.py
These items can be used to seed a set of
representative CDBs so that they produce
complete lookup tables. SeedingScript_ERG.sql is
a MySQL script that turns 0 values into small
values of le-15. The MySQL database 'seed' is
required by the script. The python script
seedCDBs.py is a wrapper to run the MySQL script
"SeedingScript_ERG.sql" on a batch of CDBs. This
script also updates the version of the CDB name
to the current date (YYYYMMDD format). The
CDB naming convention is
'c01015y2017_YYYYMMDD' for county 1015
calendar year 2017.
21
2017NEI_California_onroad_HAP_augmenta
tion_f actors, csv
Factors used to augment the California Air
Resources Board submitted criteria pollutant data
with HAPs.
22
The archive MOVES_lnput_DBs.zip includes
databases LEV.zip and
nonoxadj_moves2014b.zip
Databases used when running MOVES include
LEV.zip that represents where California LEV rules
apply and nonoxadj_moves2014b.zip which
causes MOVES not to make humidity-based
adjustments to NOx emissions, so that they can
instead by applied using hourly, grid-cell based
humidity values.
6,11 References for onroad mobile
1. Coordinating Research Council. 2019. Developing Improved Vehicle Population Inputs for the 2017
National Emissions Inventory. Report No. A-115.
2. Coordinating Research Council. 2017. Improvement of Default Inputs for MOVES and SMOKE-MOVES:
Final Report. Report No. A-100.
3. U.S. EPA, Tools to Develop or Convert MOVES Inputs. LEV and early NLEV modeling information for
MOVES2014-20141022.
4. U.S. EPA. MOVES2Q14b: Latest Version of MOtor Vehicle Emission Simulator (MOVES).
5. U.S. EPA. MOVES Onroad Technical Reports.
6. The Weather Research & Forecasting Model. Skamarock, W.C., et al., National Center for Atmospheric
Research, Mesoscale and Microscale Meteorology Division, Boulder CO, June 2008, NCAR/TN-475+STR,
A Description of the Advanced Research WRF Version 3.8.
7. Meteorology-Chemistry Interface Processor (MCIP) version 4.3.
8. User's Guide for SMOKE, including MOVES integration tools.
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9. Federal Highway Administration. Highway Statistics 2017.
10. Scripts that interface between SMOKE and MOVES, MOVES Utility Scripts and SMOKE-MOVES.
6-34
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7 Events - Wild and Prescribed Fires
7.1 Sector description and overview
Wildfires and prescribed burns (Wildland Fires in sum, WLFs) that occur during the inventory year are included
in the NEI as "event" sources. Emissions from these fires, as well as agricultural fires, make up the National Fire
Emissions Inventory (NFEI). For the 2017 NFEI, the EPA calculated emissions from agricultural fires separately
from WLF emissions as described elsewhere in this TSD. This portion of the document describes the calculation
of WLF emissions portion of the 2017 NEI.
Estimated emissions from wildfires and prescribed burns in the 2017 NEI (termed in the remainder of this
section as the "2017 NEI"—as this section only pertains to WLFs) are calculated from burned area data. Input
data sets are collected from State/Local/Tribal (S/L/T) agencies and from national agencies and organizations.
S/L/T agencies that provide input data were also asked to complete the NEI Wildland Fire Inventory Database
Questionnaire, which consists of a self-assessment of data completeness. Raw burned area data compiled from
S/L/T agencies and national data sources are cleaned and combined to produce a comprehensive burned area
data set. Emissions are then calculated using fire emission models that rely on burned area as well as fuel and
weather information. The resulting emissions are compiled by date and location as day-specific emission
estimates.
For purposes of emission inventory preparation, wildland fire (WLF) is defined as "any non-structure fire that
occurs in the wildland (an area in which human activity and development are essentially non-existent, except for
roads, railroads, power lines, and similar transportation facilities). Wildland fire activity is categorized by the
conditions under which the fire occurs. These conditions influence important aspects of fire behavior, including
smoke emissions. In the 2017 NEI, data processing is conducted differently depending on the fire type, as
defined below:
Wildfire (WF): "any fire started by an unplanned ignition caused by lightning; volcanoes; other acts of nature;
unauthorized activity; or accidental, human-caused actions, or a prescribed fire that has developed into a
wildfire."
Prescribed (Rx) fire: "any fire intentionally ignited by management actions in accordance with applicable laws,
policies, and regulations to meet specific land or resource management objectives." Prescribed fire is one type
of fuels treatment. Fuels treatments are vegetation management activities intended to modify or reduce
hazardous fuels. Fuels treatments include prescribed fires, wildland fire use, and mechanical treatment.
Agricultural burning is a type of prescribed fire, specifically used on land used or intended to be used for raising
crops or grazing. This is dealt with in a different section of this document.
Pile burning is a type of prescribed fire in which fuels are gathered into piles before burning. In this type of
burning, individual piles are ignited separately. Pile burn emissions are not currently included in the NEI due to
lack of usable data and default methods. EPA continues to work to develop methods for estimating emissions of
this source type.
Table 7-1 lists the Source Classification Codes (SCCs) that define the different types of WLFs in the 2017 NEI,
both for EPA data and for S/L/T agency data. The leading SCC description for these SCCs is "Miscellaneous Area
Sources; Other Combustion - as Event". Since the 2014 NEI, the EPA has compiled WLF emissions by smoldering
and flaming phases. The SCCs shown in Table 7-1 are used to denote this differentiation. There are five valid
7-1
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mates into each of these SCCs. One difference to
SCCs for events in EIS for the 2017 NEI, and EPA reports esti
note for the 2017 NEI is that we have included a specific SCC (2801500170) that houses only the grassland fires
of "Flint Hills/' which occur over much of KS and a small part of OK. The other SCCs are carried over from the
2014 NEI. The SCCs that were available for pile burns in the 2014 NEI have been omitted here, since EPA does
not yet have a default method for estimating those emissions. In addition, other grassland fires (other than
"Flint Hills" fires) are processed via the SF2/BS process described below and inventoried along with other
wildfires. Please note that in the 2014 NEI, these grassland fires were all inventoried as part of agricultural fires
(in the nonpoint data category), and here we are switching to housing them in the events data category. This
decision was made based on some analysis done during the 2016 Modeling Platform Collaborative inventory
process [ref 1],
Table 7-1: SCCs for wildland fires
SCC
Description
2801500170
Grassland fires; prescribed
2810001001
Forest Wildfires; Smoldering; Residual smoldering only (includes grassland wildfires)
2810001002
Forest Wildfires; Flaming (includes grassland wildfires)
2811015001
Prescribed Forest Burning; Smoldering; Residual smoldering only
2811015002
Prescribed Forest Burning; Flaming
7.2 Sources of data
The WLF EIS sectors include data only from three components: S/L/T agency-provided emissions data for
Georgia and Washington (day-specific data in events format), the EPA dataset created from SMARTFire version 2
(SF2/BS), which used available state inputs, and a PM2.5 speciation file that contains the five components of
PM2.5 for each fire. This merged information is the basis of the WLF 2017 NEI. The hierarchy of data used to
compile the 2017 NEI was very straightforward: the PM2.5 speciation dataset comes first, followed by Georgia's
and Washington's submitted emissions data, followed by EPA's dataset, as shown in Table 7-2.
Table 7-2: 2017 NEI Wildfire and Prescribed Fires selection hierarchy
Priority
Dataset Name
Dataset Content
Is Dataset in EIS?
1
PM2.5 Speciation
PM2.5 species for all data
Yes
1
State/Local/Tribal Data
Submitted data as discussed above
Yes
2
2017EPA_EVENT
Emissions from SFv2
Yes
The NEI includes only Georgia and Washington-provided data for that S/L/T; in other words, there were no
additions with any EPA-based data based on the questionnaire GA and WA submitted that indicated their
submissions were complete for each of these states. Both Georgia and Washington were supplied HAP to VOC
ratios by EPA, which they used to estimate HAPs based on their VOC emissions to calculate HAP emissions, so
that these emissions calculations were used consistent with what was used for the remainder of the U.S. via the
EPA methods. In 2017, while tribes submitted some WLF emissions data, they were not explicitly used in the
BS/SF2 processing. Instead, EPA used the nationwide NEI WLF emission estimates and developed tribal land
emission estimates using appropriate shapefiles and GIS. These estimates over tribal lands are available as part
of the public release of 2017 Events data.
The S/L/Ts were not permitted to submit PM2.5 speciated emissions, which are required in the NEI. These PM
species pollutants include EC, OC, S04, N03, and "other" (PMFINE). These were estimated for all events data
(WA, GA, and all other states) by EPA using the fractions shown in Table 7-3.
7-2
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Table 7-3: PM species for all events, computed as fraction of total PM2.5
Species
Fraction
PEC
0.0323
POC
0.4688
PN03
0.0003
PS04
0.0013
PMFINE
0.4973
7.3 EPA methods summary
Preparation of the EPA WLF emissions begins with raw input data and ends with daily estimates of emissions
from flaming combustion and smoldering combustion phases. Flaming combustion is combustion that occurs
with a flame. Flaming combustion is more complete combustion and is more prevalent with fuels that have a
high surface-to-volume ratio, a low bulk density, and low moisture content. Smoldering combustion is
combustion that occurs without a flame. Smoldering combustion is less complete and produces some pollutants,
such as PM2.5, VOCs, and CO at higher rates than flaming combustion. Smoldering combustion is more prevalent
with fuels that have low surface-to-volume ratios, high bulk density, and high moisture content. Models
sometimes differentiate between smoldering emissions that are lofted with a smoke plume and those that
remain near the ground (residual emissions). In the 2017 NEI, all flaming emissions are made up of any
component that has a flaming component to it while the smoldering emissions are the residual smoldering
component that is generated by the CONSUME model, as described further below. The emissions estimates
were estimated and compiled separately for flaming and smoldering combustion phases of fire to facilitate air
quality modeling and fine-scale research in areas such as health impacts of smoke emissions, where the known
impacts of varying PM and VOC composition by combustion phase likely play a role.
In the 2017 NEI process, EPA developed draft 2017 emission estimates based just on default information. S/L/Ts
had an opportunity to review these estimates and: 1) accept them as final, 2) submit activity data and a
questionnaire (as detailed below), or 3) provide comments. In developing final 2017 WLF estimates, EPA took
into consideration all 3 of these items. If an S/L/T accepted the draft estimates, those estimates were not
changed in the process to develop final estimates.
7.3.1 National Fire Information Data
Numerous fire information databases are available from U.S. national government agencies. Some of the
databases are available via the internet while others must be obtained directly from agency staff. Table 7-4
provides the national fire information databases that were used for the EPA's 2017 NEI methods for wildland fire
emissions estimates, including the website where the 2017 data were downloaded.
Table 7-4: National fire information databases used in EPA's 2017 NEI wildland fire emissions estimates
Dataset Name
Fire Types
Format
Agency
Coverage
Source
Hazard Mapping System
(HMS)
WF/ RX
CSV
NOAA
North
America
Hazard Mapping System Fire
and Smoke Product
7-3
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Dataset Name
Fire Types
Format
Agency
Coverage
Source
Geospatial Multi-Agency
Coordination (GeoMAC)
WF
SHP
USGS
Entire US
Geosciences and
Environmental Change
Science Center
Incident Command System
Form 209: Incident Status
Summary (ICS-209)
WF/ RX
CSV
Multi
Entire US
FAMWEB Data Warehouse
ICS-209
National Association of State
Foresters (NASF)
WF
CSV
Multi
Participating
US states
FAMWEB Home
Forest Service Activity
Tracking System (FACTS)
RX
SHP
USFS
Entire US
Hazardous Fuel Treatment
Reduction: Polygon
US Fish and Wildland Service
(USFWS) fire database
WF/ RX
CSV
USFWS
Entire US
Direct communication with
USFWS
The Hazard Mapping System (HMS) was developed in 2001 by the National Oceanic and Atmospheric
Administration's (NOAA) National Environmental Satellite and Data Information Service (NESDIS) as a tool to
identify fires over North America in an operational environment. The system utilizes geostationary and polar
orbiting environmental satellites. Automated fire detection algorithms are employed for each of the sensors.
When possible, HMS data analysts apply quality control procedures for the automated fire detections by
eliminating those that are deemed to be false and adding hotspots that the algorithms have not detected via a
thorough examination of the satellite imagery.
The HMS product used for the 2017 NEI inventory consisted of daily comma-delimited files containing fire detect
information including latitude-longitude, satellite used, time detected, and other information. Landcover was
spatially associated with each HMS detects using the Cropland Data Layer (CDL). HMS detects over croplands
were removed from the input files so that only wildland fires are included. Unlike in prior wildland fire NEIs all
grassland fire HMS satellite detects were included in the EPA's 2017 NEI wildland fire emissions estimates. These
grassland fires were processed through SmartFire2 and BlueSky with the other wildland fires.
GeoMAC (Geospatial Multi-Agency Coordination) is an online wildfire mapping application designed for fire
managers to access maps of current U.S. fire locations and perimeters. The wildfire perimeter data is based
upon input from incident intelligence sources from multiple agencies, GPS data, and infrared (IR) imagery from
fixed wing and satellite platforms. Fires in the year-specific GeoMAC shapefile with dates outside of 2017 were
removed. Some polygons have geometries which cause errors in SmartFire2 processing. These problematic
polygons were simplified using standard GIS methods.
The Incident Status Summary, also known as the "ICS-209" is used for reporting specific information on
significant fire incidents. The ICS-209 report is a critical interagency incident reporting tool giving daily
'snapshots' of the wildland fire management situation and individual incident information which include fire
behavior, size, location, cost, and other information. Data from two tables in the ICS-209 database were merged
and used for the EPA's 2017 NEI wildland fire emissions estimates: the
SIT209_HISTORY_INCIDENT_209_REPORTS table contained daily 209 data records for large fires, and the
SIT209_HISTORY_INCIDENTS table contained summary data for additional smaller fires. Some entries in the ICS-
209 database contained location and date errors. In situations where the errors were obvious in nature, such as
swapped latitude and longitudes or a typo in the year of the data, then appropriate corrections were made.
7-4
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Fires with unclear location and date issues or those fires without an associated burned area were removed.
Significant location errors for some large fires were noted and corrected in the 2017 ICS-209 database.
The National Association of State Foresters (NASF) is a non-profit organization composed of the directors of
forestry agencies in the states, U.S. territories, and District of Columbia to manage and protect state and private
forests, which encompass nearly two-thirds of the nation's forests. The NASF compiles fire incident reports from
agencies in the organization and makes them publicly available. The NASF fire information includes dates of fire
activity, acres burned, and fire location information. Similar to entries in the ICS-209 database, entries with
obvious and resolvable date and location errors were corrected. Fires with unclear location and date issues or
those fires without an associated burned area were removed.
The US Forest Service (USFS) compiles a variety of fire information every year. Year 2017 data from the USFS
Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) were acquired and used for 2017 NEI
emissions inventory development. This database includes information about activities related to fire/fuels,
silviculture, and invasive species. The FACTS database consists of shapefiles for prescribed burns that provide
acres burned and start and ending time information. As detailed earlier, all fires labeled as pile burns were
removed because the EPA does not currently develop emissions for pile burning.
The US Fish and Wildland Service (USFWS) also compiles wildfire and prescribed burn activity on their federal
lands every year. Year 2017 data were acquired from USFWS through direct communication with USFWS staff
and were used for 2017 NEI emissions inventory development. The USFWS fire information provided fire type,
acres burned, latitude-longitude, and start and ending times. As with the FACTS dataset, fires labeled as pile
burns were removed because the EPA does not currently develop emissions for pile burning.
7.3.2 State/Local/Tribal fire information
As in previous NEI years and building off the 2016 modeling platform collaborative efforts, S/L/Ts were asked to
submit fire occurrence/activity data for the 2017 NEI. A template form containing the desired format for data
submittals was provided to S/L/T air agencies. A map of all states that returned the template form is shown in
Figure 7-1. States that did not return the template form are shown in gray and had emissions based only on
national default data. In total, 20 states returned the template form for the EPA's 2017 NEI wildland fire
emissions estimates processing. The states that returned the forms directly to the EPA are Alaska, Alabama,
Arizona, Delaware, Georgia, Florida, Hawaii, Iowa, Kansas, Massachusetts, New Jersey, Nevada, North Carolina,
South Carolina, Utah, and Washington. Four other states -Idaho, Montana, Oregon, and Wyoming- had forms
returned by the Western Regional Air Partnership (WRAP) as part of the Fire Emissions Tracking System (FETS).
In addition to supplying activity data, S/L/Ts that supplied such data were also requested to complete a
questionnaire to help EPA determine how complete their activity data submissions were.
7-5
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DRAFT
Figure 7-1: 2017 NEI Wildland Fire Data Sources including S/L/Ts
When fire activity or emissions were provided by S/L/Ts the data were evaluated by EPA and further feedback
on the data submitted by the state was requested at times. Table 7-5 provides a summary of the type of data
submitted by each S/L/T agency and includes spatial, temporal, acres burned, and other information provided by
the agencies.
Table 7-5: Brief description of fire activity information submitted for 2017 NEI inventory use.
S/L/T name
Fire Types
Description
Alaska
WF/RX
Latitude-longitude, FCCS fuel beds, and acres burned for wildfire and
prescribed burns
Alabama
WF/RX
Start and end dates, latitude-longitude, and acres burned for wildfire and
prescribed burns
Arizona
RX
Day-specific, latitude-longitude, and acres burned for prescribed burns
Delaware
RX
Day-specific, latitude-longitude, and fuel loading for prescribed burns.
Opted to use national default datasets
Florida
WF/RX
Start and end dates, latitude-longitude, and acres burned for wildfire and
prescribed burns
Georgia
WF/RX
Emissions data submitted included all fires types via EIS. The wildfire and
prescribed burn data were provided as daily, point emissions sources.
Iowa
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire and
prescribed burns. Did not contain end dates.
Idaho
RX
Day-specific, latitude-longitude, acres burned, and fuel loading for
prescribed burns. Data included pile burn activity, which was removed prior
to emissions estimation.
Kansas
RX
Day-specific, county-centroid, and acres burned for Flint Hills prescribed
grassland burning
7-6
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S/L/T name
Fire Types
Description
Massachusetts
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire and
prescribed burns. Did not contain end dates.
Montana
RX
Day-specific, latitude-longitude, acres burned, and fuel loading for
prescribed burns. Data included pile burn activity, which was removed prior
to emissions estimation.
New Jersey
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire and
prescribed burns. Did not contain end dates. Opted to use national default
datasets.
North Carolina
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire and
prescribed burns. Camp Lejeune activity carried forward from 2014
estimates.
Nevada
WF
Day-specific, latitude-longitude, and acres burned for wildfires.
Oregon
RX
Day-specific, latitude-longitude, acres burned, and fuel loading for
prescribed burns. Data included pile burn activity, which was removed prior
to emissions estimation.
South Carolina
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire and
prescribed burns
Utah
WF/RX
Day-specific, latitude-longitude, and acres burned for wildfire and
prescribed burns
Washington
WF/RX
Emissions data submitted included all fires types via EIS. The wildfire and
prescribed burn data were provided as daily, point emissions sources.
Wyoming
RX
Day-specific, latitude-longitude, acres burned, and fuel loading for
prescribed burns. Data included pile burn activity, which was removed prior
to emissions estimation.
In order to develop a format that could be ingested into SMARTFire or directly into Bluesky certain
preprocessing steps were taken with the S/L/T submitted datasets. The names of columns and formats were
changed to match what the processors required. Additionally, all datasets were reviewed for invalid locations or
those that were spatially identified as occurring outside the submitting state. Obvious location errors, such as
those where the latitude and longitude were swapped or a sign was missing, were fixed. The Alabama and Iowa
submittals contained many valid locations that were outside of the respective state by a large distance. Without
additional information identifying an activity location within the respective state, these records were dropped.
Overall the records dropped accounted for a very small portion of the total activity.
The temporal approach for the S/L/T varied based on the information provided in the submitted data and
direction from the individual agencies. Iowa, Kansas, and Massachusetts submitted activity without end dates.
Each of these states provided direction to assume that all fires lasted for a single day. Alabama, Florida, North
Carolina, South Carolina, and Utah all provided end dates along with start dates, however it was necessary to
apportion the activity to each day in the range to develop daily emissions. In the case of Alabama, North
Carolina, and South Carolina multi-day fires were assumed to have an equal proportion of the total event
activity on each day of the event. Alaska, Florida, and Utah utilized a different approach where an attempt was
made to reconcile the daily events in SmartFire2 against the HMS activity. Where a multi-day event could be
matched to HMS detections the number of HMS detections on each day within the event were used to
apportion the total event activity. When a spatial and temporal match could not be made between the
submitted data a flat approach was used for the multi-day event as described for Alabama, North Carolina, and
South Carolina.
7-7
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The following states required additional preprocessing steps:
• Alaska. Start and end dates were not included in the submission. Dates were filled from the national
default data using the submitted location information and fire name. After some discussion, Alaska
approved of the use of EPA WLF estimates for their entire domain.
• Kansas. The activity for the Flint Hills region was spatially reapportioned from the county-level to 2011
NLCD grass land area at centroids of 4 km grid cells. Weighting of activity was done using the area of
overlap between the grass land grid cells and the respective county.
• North Carolina. The 247-day long Pocosin fire was dropped from the submitted data with direction from
the state.
7.3.3 Emissions Estimation Methodology
The national and S/L/T data mentioned earlier were used to estimate daily wildfire and prescribed burn
emissions from flaming combustion and smoldering combustion phases for the 2017 NEI inventory. Flaming
combustion is more complete combustion than smoldering and is more prevalent with fuels that have a high
surface-to-volume ratio, a low bulk density, and low moisture content. Smoldering combustion occurs without a
flame, is a less complete burn, and produces some pollutants, such as PM2.5, VOCs, and CO, at higher rates than
flaming combustion. Smoldering combustion is more prevalent with fuels that have low surface-to-volume
ratios, high bulk density, and high moisture content. Models sometimes differentiate between smoldering
emissions that are lofted with a smoke plume and those that remain near the ground (residual emissions), but
for the purposes of the 2017 NEI inventory the residual smoldering emissions were allocated to the smoldering
SCCs ending in "1", while the lofted smoldering emissions were assigned to the flaming emissions SCCs ending in
Figure 7-2 is a schematic of the data processing stream for the 2017 NEI inventory for wildfire and prescribe
burn sources. The EPA's 2017 NEI wildland fire emissions estimates were estimated using Satellite Mapping
Automated Reanalysis Tool for Fire Incident Reconciliation version 2 (SMARTFIRE2) and BlueSky Framework.
SMARTFIRE2 is an algorithm and database system that operate within a geographic information system (GIS).
SMARTFIRE2 combines multiple sources of fire information and reconciles them into a unified GIS database. It
reconciles fire data from space-borne sensors and ground-based reports, thus drawing on the strengths of both
data types while avoiding double-counting of fire events. At its core, SMARTFIRE2 is an association engine that
links reports covering the same fire in any number of multiple databases. In this process, all input information is
preserved, and no attempt is made to reconcile conflicting or potentially contradictory information (for
example, the existence of a fire in one database but not another). Further details of the SMARTFIRE2 process as
applied to NEI development can be found in the literature [ref 2],
For the 2017 NEI inventory, the national and S/L/T fire information was input into SMARTFIRE2 and then merged
and reconciled together based on user-defined weights for each fire information dataset. The relative weights
used for the national data stream are shown in Table 7-6. A dataset type with a higher ranking gets preference
for that attribute in the reconciled activity. The output from SMARTFIRE2 was daily acres burned by fire type,
and latitude-longitude coordinates for each fire. The fire type assignments were made using the fire information
datasets. If the only information for a fire was a satellite detect for fire activity, then Figure 7-3 was used to
make fire type assignment by state and by month.
7-8
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DRAFT
Figure 7-2: Processing flow for fire emission estimates in the 2017 NEI inventory
Input Data Sets
(state/local/tribal and national data sets)
Fuel Moisture and
Fuel Loading Data
Smoke Modeling (BlueSky Framework)
Daily smoke emissions
for each fire
Emissions Post-Processin:
g
o
Final Wildland Fire Emissions Inventory
Data Preparation
Data Aggregation and Reconciliation
(SmartFire2)
Daily fire locations
with fire size and type
7-9
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DRAFT
Figure 7-3: Default fire type assignment by state and month in cases where a satellite detect is only source of
fire information
None
Table 7-6: 2017 National SmartFire2 Reconciliation Weights
Rank
Location
Weight
Size Weight
Shape
Weight
Growth
Weight
Name
Weight
Fire Type
Weight
1
SLT
Supplemental
Data
SLT
Supplemental
Data
GeoMAC
SLT
Supplemental
Data
GeoMAC
SLT
Supplemental
Data
2
GeoMAC
GeoMAC
FACTS
HMS
ICS-209
ICS-209
3
HMS
FACTS
HMS
GeoMAC
NASF
GeoMAC
4
FACTS
ICS-209
SLT
Supplemental
Data
ICS-209
FETS
NASF
5
ICS-209
FETS
FETS
NASF
USFWS
FETS
6
FETS
NASF
ICS-209
USFWS
FACTS
FACTS
7
NASF
USFWS
NASF
FETS
HMS
USFWS
8
USFWS
HMS
USFWS
FACTS
SLT
Supplemental
Data
HMS
Supplemental S/L/T activity from Arizona, Idaho, Montana, Nevada, Oregon, and Wyoming were incorporated
with the national defaults into the national data reconciliation stream. States that submitted complete activity
datasets were not processed through SmartFire2 with the default national activity. An exception is for those
states that used HMS fire detections for daily apportionment of activity data. Alaska, Florida, and Utah all had
their submitted data reconciled against the HMS fire detections. All resulting activity that was identified only
through HMS was removed from the final activity dataset so that only state-submitted event values were used
for emissions estimates. State-submitted activity from Iowa, Kansas, Massachusetts, North Carolina, and South
7-10
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Carolina were not processed through SmartFire2. Instead each activity dataset was converted into daily activity
files in a format that can be read directly by the BlueSky Framework.
The BlueSky Modeling Framework version 3.5 (revision #38169) was used to calculate fuel loading and
consumption, and emissions using various models depending on the available inputs as well as the desired
results. The contiguous United States and Alaska, where Fuel Characteristic Classification System (FCCS) fuel
loading data are available, were processed using the modeling chain described in Figure 7-4. The Fire Emissions
Production Simulator (FEPS) in the BlueSky Framework generated all the CAP emission factors for wildland fires
used in the 2017 NEI inventory [ref 3], The HAP emission factors used in this work came from Urbanski, 2014 [ref
4], These emission factors were regionalized and handled differently by wild and prescribed fire. Table 7-7 below
outlines the regionalization scheme used while Table 7-8 and Table 7-9 show the HAP EFs employed in this work
separately for wild and prescribed fires. Note the differences, in bold in Table 7-7, for wildfires and prescribed
burning region assignments for Alaska and Wisconsin.
Table 7-7: Emission factor regions used to assign HAP emission factors for the 2017 NEI
Region
Wildfires
Prescribed burning
Region 1
AZ, CA, IA, IL, IN, KS, MO, NM, NV, OH, OK, TX
AZ, CA, IA, IL, IN, KS, MO, NM, NV, OH, OK, TX
Region 2
AK, AL, AR, CT, DC, DE, FL, GA, HI, KY, LA, MA,
MD, ME, Ml, MN, MS, NC, NH, NJ, NY, PA, PR,
Rl, SC, TN, VA, VI, VT, Wl, WV
AL, AR, CT, DC, DE, FL, GA, HI, KY, LA, MA, MD,
ME, Ml, MN, MS, NC, NH, NJ, NY, PA, PR, Rl, SC,
TN, VA, VI, VT, WV
Region 3
CO, ID, MT, ND, NE, OR, SD, UT, WA, WY
AK, CO, ID, MT, ND, NE, OR, SD, UT, WA, Wl,
WY
Table 7-8: Prescribed fire HAP emission factors (lb/ton fuel consumed) for the 2017 NEI
HAP
Flaming
Smoldering
Region 1
Region 2
Region 3
Region 1
Region 2
Region 3
1,3-Butadiene (HAP 106990)
0.272326792
0.516619944
0.362434922
0.272326792
0.516619944
0.362434922
Acetaldehyde (HAP 75070)
1.678013616
1.283540248
2.240688827
1.678013616
1.283540248
2.240688827
Acetonitrile (HAP 75058)
0.322386864
0.064076892
0.43051662
0.322386864
0.064076892
0.43051662
Acrolein (HAP 107028)
0.512615138
0.646776131
0.684821786
0.512615138
0.646776131
0.684821786
Acrylic Acid (HAP 79107)
0.070084101
0.058069684
0.094112936
0.070084101
0.058069684
0.094112936
Anthracene (HAP 120127)
0.005
0.005
0.005
0.005
0.005
0.005
Benz(a)anthracene (HAP 56553)
0.0062
0.0062
0.0062
0.0062
0.0062
0.0062
Benzene (HAP 71432)
0.450540649
0.566680016
0.600720865
0.450540649
0.566680016
0.600720865
Benzo(a)fluoranthene (HAP
203338)
0.0026
0.0026
0.0026
0.0026
0.0026
0.0026
Benzo(a)pyrene (HAP 50328)
0.00148
0.00148
0.00148
0.00148
0.00148
0.00148
Benzo(c)phenanthrene (HAP
195197)
0.0039
0.0039
0.0039
0.0039
0.0039
0.0039
Benzo(e)pyrene (HAP 192972)
0.00266
0.00266
0.00266
0.00266
0.00266
0.00266
Benzo(ghi)perylene (HAP 191242)
0.00508
0.00508
0.00508
0.00508
0.00508
0.00508
Benzo(k)fluoranthene (HAP
207089)
0.0026
0.0026
0.0026
0.0026
0.0026
0.0026
7-11
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HAP
Flaming
Smoldering
Region 1
Region 2
Region 3
Region 1
Region 2
Region 3
Benzofluoranthenes (HAP
56832736)
0.00514
0.00514
0.00514
0.00514
0.00514
0.00514
Carbonyl Sulfide (HAP 463581)
0.000534
0.000534
0.000534
0.000534
0.000534
0.000534
Chrysene (HAP 218019)
0.0062
0.0062
0.0062
0.0062
0.0062
0.0062
Fluoranthene (HAP 206440)
0.00673
0.00673
0.00673
0.00673
0.00673
0.00673
Formaldehyde (HAP 50000)
2.515018022
3.366039247
4.475370445
2.515018022
3.366039247
4.475370445
lndeno(l,2,3-cd)pyrene (HAP
193395)
0.00341
0.00341
0.00341
0.00341
0.00341
0.00341
m,p-Xylenes (HAP 1330207)
0.216259511
0.160192231
0.288346015
0.216259511
0.160192231
0.288346015
Methanol (HAP 67561)
2.306768122
1.974369243
5.036043252
2.306768122
1.974369243
5.036043252
Methyl Chloride (HAP 74873)
0.128325
0.128325
0.128325
0.128325
0.128325
0.128325
Methylanthracene (HAP
26914181)
0.00823
0.00823
0.00823
0.00823
0.00823
0.00823
Methylbenzopyrenes (HAP
65357699)
0.00296
0.00296
0.00296
0.00296
0.00296
0.00296
Methylchrysene (HAP 41637905)
0.0079
0.0079
0.0079
0.0079
0.0079
0.0079
Methylpyrene, fluoranthene (HAP
2381217)
0.00905
0.00905
0.00905
0.00905
0.00905
0.00905
n-Hexane(HAP 110543)
0.048057669
0.024028835
0.064076892
0.048057669
0.024028835
0.064076892
Naphthalene (HAP 91203)
0.486583901
0.398478174
0.650780937
0.486583901
0.398478174
0.650780937
o-Xylene (HAP 95476)
0.07609131
0.050060072
0.100120144
0.07609131
0.050060072
0.100120144
Perylene (HAP 198550)
0.000856
0.000856
0.000856
0.000856
0.000856
0.000856
Phenanthrene (HAP 85018)
0.005
0.005
0.005
0.005
0.005
0.005
Pyrene(HAP 129000)
0.00929
0.00929
0.00929
0.00929
0.00929
0.00929
Styrene (HAP 100425)
0.10412495
0.080096115
0.138165799
0.10412495
0.080096115
0.138165799
Toluene (HAP 108883)
0.344413296
0.398478174
0.45855026
0.344413296
0.398478174
0.45855026
Table 7-9: Wildfire HAP emission factors (lbs/ton fuel consumed) for the 2017 NEI
HAP
Flaming
Smoldering
Region 1
Region 2
Region 3
Region 1
Region 2
Region 3
1,3-Butadiene (HAP 106990)
0.272326792
0.140168202
0.362434922
0.272326792
0.140168202
0.362434922
Acetaldehyde (HAP 75070)
1.678013616
1.908289948
2.240688827
1.678013616
1.908289948
2.240688827
Acetonitrile (HAP 75058)
0.322386864
0.600720865
0.43051662
0.322386864
0.600720865
0.43051662
Acrolein (HAP 107028)
0.512615138
0.582699239
0.684821786
0.512615138
0.582699239
0.684821786
Acrylic Acid (HAP 79107)
0.070084101
0.080096115
0.094112936
0.070084101
0.080096115
0.094112936
Anthracene (HAP 120127)
0.005
0.005
0.005
0.005
0.005
0.005
benz(a)anthracene (HAP 56553)
0.0062
0.0062
0.0062
0.0062
0.0062
0.0062
Benzene(HAP 71432)
0.450540649
1.101321586
0.600720865
0.450540649
1.101321586
0.600720865
Benzo(a)fluoranthene (HAP
203338)
0.0026
0.0026
0.0026
0.0026
0.0026
0.0026
Benzo(a)pyrene (HAP 50328)
0.00148
0.00148
0.00148
0.00148
0.00148
0.00148
7-12
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HAP
Flaming
Smoldering
Region 1
Region 2
Region 3
Region 1
Region 2
Region 3
Benzo(c)phenanthrene (HAP
195197)
0.0039
0.0039
0.0039
0.0039
0.0039
0.0039
Benzo(e)pyrene (HAP 192972)
0.00266
0.00266
0.00266
0.00266
0.00266
0.00266
Benzo(ghi)perylene (HAP
191242)
0.00508
0.00508
0.00508
0.00508
0.00508
0.00508
Benzo(k)fluoranthene (HAP
207089)
0.0026
0.0026
0.0026
0.0026
0.0026
0.0026
Benzofluoranthenes (HAP
56832736)
0.00514
0.00514
0.00514
0.00514
0.00514
0.00514
Carbonyl Sulfide (HAP 463581)
0.000534
0.000534
0.000534
0.000534
0.000534
0.000534
Chrysene (HAP 218019)
0.0062
0.0062
0.0062
0.0062
0.0062
0.0062
Fluoranthene (HAP 206440)
0.00673
0.00673
0.00673
0.00673
0.00673
0.00673
Formaldehyde (HAP 50000)
2.515018022
3.954745695
4.475370445
2.515018022
3.954745695
4.475370445
lndeno(l,2,3-cd)pyrene (HAP
193395)
0.00341
0.00341
0.00341
0.00341
0.00341
0.00341
m,p-Xylenes (HAP 1330207)
0.216259511
0.120144173
0.288346015
0.216259511
0.120144173
0.288346015
Methanol (HAP 67561)
2.306768122
2.613135763
5.036043252
2.306768122
2.613135763
5.036043252
Methyl Chloride (HAP 74873)
0.128325
0.128325
0.128325
0.128325
0.128325
0.128325
Methylanthracene (HAP
26914181)
0.00823
0.00823
0.00823
0.00823
0.00823
0.00823
Methylbenzopyrenes (HAP
65357699)
0.00296
0.00296
0.00296
0.00296
0.00296
0.00296
Methylchrysene (HAP 41637905)
0.0079
0.0079
0.0079
0.0079
0.0079
0.0079
Methylpyrene,-fluoranthene
(HAP 2381217)
0.00905
0.00905
0.00905
0.00905
0.00905
0.00905
n-Hexane(HAP 110543)
0.048057669
0.054064878
0.064076892
0.048057669
0.054064878
0.064076892
Naphthalene (HAP 91203)
0.486583901
0.554665599
0.650780937
0.486583901
0.554665599
0.650780937
o-Xylene (HAP 95476)
0.07609131
0.054064878
0.100120144
0.07609131
0.054064878
0.100120144
Perylene (HAP 198550)
0.000856
0.000856
0.000856
0.000856
0.000856
0.000856
Phenanthrene (HAP 85018)
0.005
0.005
0.005
0.005
0.005
0.005
Pyrene (HAP 129000)
0.00929
0.00929
0.00929
0.00929
0.00929
0.00929
Styrene (HAP 100425)
0.10412495
0.11814177
0.138165799
0.10412495
0.11814177
0.138165799
Toluene (HAP 108883)
0.344413296
0.480576692
0.45855026
0.344413296
0.480576692
0.45855026
7-13
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DRAFT
Figure 7-4: BlueSky Modeling Framework
For the 2017 NEI inventory, the FCCSv2 spatial vegetation cover was upgraded to the LANDFIRE vl.4 fuel
vegetation cover. The FCCSv3 fuel bed characteristics were implemented along with LANDFIREvl.4 to provide
better fuel classification for the BlueSky Framework. The LANDFIREvl.4 raster data were aggregated from the
native resolution and projection to 200-meter resolution using a nearest-neighbor methodology. Aggregation
and reprojection was required to allow these data to work in the BlueSky Framework.
Outputs from each BlueSky Framework processing stream were aggregated into an annual file. Fires identified as
being over water by FCCS were removed because they produce no fuel consumption in the CONSUME model
and thus no emissions. Emissions for some prescribed burns were proportionally adjusted to account for an
overestimate of duff consumption in CONSUME. Those states in the eastern United States had duff consumption
capped at 5 tons per acre, while those in the west had duff consumption capped at 20 tons per acre.
7.4 Quality Assurance (QA) of Final Results
Different types of QA were generally applied with the different parts of the process described above. The
summary below briefly describes the QA checks used in these processes.
7.4.1 Input Fire Information Data Sets
• Reviewed input data sets to identify data gaps.
• Identified fire incidents that appeared to be double-counted in individual data sets and removed
duplicate records.
• Examined fires with long durations or conflicts between date fields such as start date and report date to
identify fires that may have erroneous dates and made necessary corrections.
• Reviewed fire locations to ensure that they fell within the United States. Obvious errors in data entry
such as the reversal of latitude and longitude were corrected where possible.
• Reviewed large and small fires in each data set for validity.
• Modified distant fires (in different states) with the same names to ensure that the events were not
associated.
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7.4.2 Daily Fire Locations from SmartFire2
Quality assurance actions applied to daily fire locations from SmartFire2 included:
• Checked the location, fire type, duration, underlying fire activity input data, final shape, and final size for
large fire events (i.e., area burned >20,000 acres) to ensure that the results were reasonable.
• Checked large fire events by state and by name, removed duplicate events, and renamed fires as
needed.
• Reviewed large fire events with multiple data sources to ensure that SmartFire2 reconciliation rankings
were correct and produced sensible results.
• Identified and removed fire event duplicates incorrectly created by the SmartFire2 reconciliation
process.
• Checked fire events with large differences between the calculated fire area and the geometric fire area.
Since the shape and area are calculated separately in SmartFire2, a large discrepancy can indicate errors
in reconciliation. For the 2014 NWLFEI, no errors of this sort were identified.
7.4.3 Emissions Estimates
Quality assurance actions applied to resulting emissions estimates included:
• Checked the location of all final fires and emission estimates. Fires falling outside of the United States
were removed. Some fires near the border were retained if fuel information was available in that
location.
• Identified fire records that were incorrectly associated and adjusted fire event size and emissions
proportionally.
• Produced and reviewed summary tables and plots of the 2017 fire inventory data.
• Compared acres burned by state to National Interagency Fire Center (NIFC) data to ensure the summary
values were within reasonable range, knowing that NIFC acres burned tend to be underestimated.
7.4.4 Additional quality assurance on final results, and some post-final corrections
WLF emissions developed using the methods described above were compared to EPA's 2016 estimates, and all
the way back to 2005, since the models used are similar. The spatial (and temporal) patterns seen in the data
correspond to what was expected in 2017. In general, 2017 was a "worse" fire year than many previous years
(including 2016 and 2014) as more acres were burned, so the emissions are expected to be higher in 2017
compared to 2014 and 2016. The trends graphic shown in the next section below (see Figure 7-5 and Figure 7-6)
indicates how the 2017 PM2.5 estimates compare to other years (using similar methods). These trends
represent only the lower 48 states.
After completing the 2017 WLF in February 2020 and posting summary files on the El Data Website on
February 27, 2020, EPA was alerted by the state of MN to a potential error in the way our 2017 methods
estimate HAP emissions in areas of the country where there is a prevalence of prescribed fires burning duff-
based fuels. In such cases, our methods caused HAPs to be incorrectly estimated due to the fact that the HAP
emissions process was done outside of the BSF process; this caused HAP and CAP trends to differ in direction for
these types of fuels.
Further review of 2017 Event emissions identified a discrepancy between CAPs and HAPs for approximately 0.5%
of fires nationally. Three states were primarily affected: FL, LA, and MN. The issue stemmed from how the post-
BSF RX duff adjustments were done. HAPs are calculated off of consumption post-BSF, while the CAPs are
7-15
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calculated in the BSF pathway. The CONSUME module in the BSF over-estimates the duff consumed for
prescribed fires over fuel beds with high duff fuel loading in regions such as Minnesota. Prescribed fire emissions
are adjusted based on region to limit the duff consumption produced by CONSUME to a more reasonable value.
The processing stream properly adjusts these high duff loading fires for CAP pollutants, however for the NEI
process, the HAP pollutants are calculated separately. As a result, the HAP pollutants were not adjusted in the
same way as the CAP pollutants resulting in overall higher than expected HAP values.
To adjust the emissions appropriately, EPA took the duff adjustment emissions logic that sets a region-based cap
on duff consumption and applied it to the consumption values, then recalculated the HAPs for the fires where
there is a reduction in consumption due to a reduction in duff consumption. We verified the CAP numbers
calculated with this adjusted consumption against what was previously generated and calculated the new HAP
numbers. As noted earlier, three states had notable HAP reductions: FL, LA, and MN. Other states had much
smaller reductions.
These revisions were processed through the Emissions Inventory System (EIS) and summary files were posted on
the 2017 NEI Data website on April 8, 2020.
Another area that has been identified as a potential disconnect is between the NEI and SPECIATE, and also
involves the HAPs that are in the inventory for 2017 NEI for WLFs. The HAP emission estimates in the 2017 NEI
come directly from the way processing was done in the BSF for HAPs by combustion phase. The WLF VOC
profiles that are in SPECIATE5.0 [ref 5] come from the work of Urbanski [ref 4], In the case of SPECIATE5.0, these
profiles were created directly from the data in the Urbanski paper. The factors were regionalized based on the
fuels tested based on the EFs for the 187 species tested and computed weight percent of each that can be
multiplied by VOC to get HAP emission estimates.
Urbanski emissions factors are provided as regionalized mass factors by fire type regardless of combustion
phase. The NEI HAPs are calculated by applying the Urbanski emissions factors directly to the total biomass
consumed by fire as calculated in CONSUME. NEI CAPs are calculated in FEPS v2 using combustion phase-specific
emissions factors. In FEPS the mass factor for VOC is approximately 3 times higher for the smoldering and
residual phases than for the flaming phase. The differences in how the HAP and CAP factors are provided and
applied result in different rates of change in the individual NEI pollutants within the emissions classes as total
consumption changes. These rates differ depending on the relative proportion of each combustion phase in
each individual fire. Speciation profiles from SPECIATE are applied to inventory VOC, which is calculated from
the total of the flaming and smoldering phase VOC emissions or the total residual phase VOC depending on SCC.
The HAP values calculated with the speciation profiles therefore implicitly contain the differences in factors by
phase for the CAP values, whereas the NEI HAP values reflect the application of factors without consideration of
phase
EPA feels that the NEI HAP values are more reasonable as they better reflect what was presented in Urbanski,
which contains EFs for a complete fire regardless of phase type (a weighted average). We suggest using the NEI
HAPs to represent WLFs in all assessments with 2017 moving forward.
7.5 Emissions Summaries
This section shows several graphics and tables that describe emissions of wild and prescribed fires in the 2017
NEI based on the methods discussed above.
In Figure 7-5 and Figure 7-6, the trend in PM2.5 emissions and acres burned is shown from 2006 to 2017. Over
this 12-year time frame similar SF2/BS frameworks were used to estimate these emissions. However, it should
7-16
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DRAFT
be noted that the estimates are much more robust for NEI years (2008, 2011, 2014 and 2017) since S/L/T
involvement and data acquisition from S/L/Ts is much higher. In addition, year 2016 was generated with limited
national fire information databases. It can be noted from both these graphics that the year to year variability is
more controlled by wildfire activity. In recent years, however, the amount of prescribed fire activity has been on
the rise as seen in Figure 7-5 and Figure 7-6. At this point, it is unclear whether this is due to true increases in
prescribed fire activity across the US, or whether its increasing due to better and more complete reporting.
Figure 7-5: Annual comparison of PM2.5 emissions for lower 48 states
3,000
—. 2,500
on '
c
o
¦*->
S 2,000
g 1,500
1,000
500
I
I
I WF
RX
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Year
Figure 7-6: Annual comparison of area burned for lower 48 states
Table 7-10 shows acres burned, PM2.5, NOx and VOC emissions by the states of AK, HI, and all the lower 48
states combined. Alaska has a significant amount of the total acres burned in the US in 2017, and (as evident
7-17
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DRAFT
from Figure 7-5 and Figure 7-6) 2017 was a generally bad fire year compared to the other 11 years shown in the
trend lines above.
Table 7-10: CONUS (lower 48 states) and Alaska and Hawaii fire type information for 2017 NEI WLFs
Fire Type
Millions of Acres
PM2.5 (Tons)
NOx (Tons)
VOC (Tons)
CONUS Wildfires
8.67
1,283,871
192,966
3,518,534
CONUS Prescribed
14.54
803,347
164,209
2,037,071
Alaska All
0.67
372,386
37,882
1,061,964
Hawaii All
0.01
936
190
2,478
Total
23.90
2,460,540
395,247
6,620,048
Figure 7-7 and Figure 7-8 show acres burned and PM2.5 emissions for all fires by month in 2017. The total
emissions that result from month-to-month result from a combination of different fuels that burn in different
fires. It is seen that wild fires are more prevalent in the hotter months, and prescribed fires occur more often in
the colder months of 2017.
Figure 7-7: Monthly acres burned by fire type for 2017 NEI CONUS Wildland Fires
5
4.5
.
-------
500
450
'J/T
c
O
400
¦*->
O
O
350
O
tH
300
c
0
250
\n
w
200
E
LU
u-)
150
rvl
100
Q-
50
0
DRAFT
Figure 7-8: Monthly PM2.5 by fire type for 2017 NEI CONUS Wildland Fires
I
I
I
I WF
RX
123456789 10 11 12
Month
Next, Table 7-11 shows a summary of acres burned and PM2.5 by state, fire type and combustion phase. In
terms of total WLF acres burned, several states are shown to have more than one million acres burned in 2017,
with KS and TX being the highest acres burned states. However, due to the nature of fuels burned and the type
of fire that occurs in the various States, CA and AK are highest for estimated PM2.5 emissions.
7-19
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al
,372
,996
,222
,662
,426
,807
76
4_
,058
,449
366
,576
737
253
,468
,413
,160
,192
386
246
128
,598
,831
922
,523
,518
115
,938
Table 7-11: Summary of acres burned and PM2.5 emissions by state, fire type, and combustion phase
Area (Acres)
Prescribed
Wildfire
Total
PM2.5 Emissions (tons)
Prescribed
Flaming
Smoldering
Total
Wildfire
Flaming
Smoldering
784,520
19,865
804,385
31,835
5,221
37,055
2,075
297
26,915
647,524
674,439
1,343
47
1,390
229,027
141,969
207,248
430,594
637,841
7,033
5,903
12,937
23,566
8,656
602,658
33,908
636,566
45,972
9,959
55,931
7,352
1,311
156,371
1,377,051
1,533,422
8,687
4,594
13,280
224,304
108,122
92,125
174,825
266,951
4,343
2,134
6,477
9,711
4,096
710
264
974
64
10
74
64
12
1,920
22
1,942
85
25
110
0
1,431,895
200,509
1,632,404
50,024
8,413
58,436
12,537
1,521
1,075,287
53,551
1,128,838
38,131
4,782
42,913
5,765
2,683
5,000
5,865
10,865
357
213
571
347
18
111,534
695,123
806,657
7,753
3,491
11,244
93,352
40,224
147,286
1,980
149,266
13,616
3,924
17,539
559
178
47,916
1,251
49,167
3,258
1,158
4,416
207
46
17,856
9,532
27,387
1,177
352
1,530
1,200
268
2,784,939
421,000
3,205,939
89,153
4,159
93,312
17,529
884
118,110
23,779
141,889
8,762
1,918
10,680
6,870
1,291
643,794
16,875
660,670
44,070
10,274
54,344
1,920
272
2,349
1,003
3,352
222
70
291
271
115
11,953
1,961
13,914
564
181
745
186
60
80
368
449
83
45
34,644
4,827
39,471
1,970
722
2,692
1,203
395
157,607
9,578
167,185
8,968
5,640
14,608
1,518
1,312
513,094
20,878
533,972
19,508
3,225
22,732
826
97
801,412
17,989
819,402
77,657
13,261
90,918
4,624
899
133,191
163,474
12,836
1,056,885
875
1,151,120
1,190,076
164,348
1,163,955
7,942
7,395
233
4,769
1,667
80
12,712
9,062
314
133,093
91_
17,563
58,425
24^
1,375
7-20
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State
Area (Acres)
PM2.5 Emissions (tons)
Prescribed
Wildfire
Total
Prescribed
Wildfire
Flaming
Smoldering
Total
Flaming
Smoldering
Total
New Hampshire
2,282
140
2,422
221
71
292
41
14
56
New Jersey
19,893
6,159
26,052
1,175
313
1,487
487
115
603
New Mexico
91,479
172,643
264,122
2,070
1,043
3,113
10,950
5,775
16,725
New York
7,491
1,525
9,016
574
142
716
322
77
399
North Carolina
182,685
37,267
219,953
7,006
1,708
8,715
3,551
542
4,094
North Dakota
157,283
13,461
170,744
5,435
2,808
8,243
516
111
627
Ohio
20,800
1,481
22,280
1,539
603
2,142
304
50
354
Oklahoma
1,079,262
501,268
1,580,530
44,799
6,142
50,942
36,015
3,831
39,846
Oregon
203,293
615,390
818,683
16,835
9,465
26,300
151,873
61,866
213,739
Pennsylvania
25,551
1,567
27,118
2,295
613
2,908
322
60
381
Rhode Island
303
31
334
26
3
29
6
1
7
South Carolina
417,008
13,808
430,816
16,299
4,258
20,556
1,290
162
1,451
South Dakota
82,349
77,052
159,401
4,059
1,136
5,196
12,861
3,782
16,643
Tennessee
183,020
1,500
184,520
12,901
2,214
15,114
322
44
365
Texas
1,562,103
711,212
2,273,315
47,970
7,185
55,154
19,796
6,690
26,486
Utah
11,193
240,773
251,966
526
227
753
18,477
9,469
27,946
Vermont
1,473
46
1,519
89
32
120
10
2
12
Virginia
140,941
8,006
148,947
7,719
1,776
9,495
1,960
314
2,274
Washington
128,978
425,330
554,308
2,420
2,420
83,296
41,508
124,804
West Virginia
44,206
6,187
50,393
4,246
1,218
5,465
1,813
348
2,161
Wisconsin
67,153
738
67,891
3,890
1,013
4,903
145
51
196
Wyoming
60,190
104,883
165,072
3,619
1,301
4,920
4,373
1,252
5,625
Grand Total
14,575,658
9,319,469
23,895,127
665,841
139,466
805,307
1,144,579
510,655
1,655,233
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DRAFT
Figure 7-9 and Figure 7-10 show 2017 total area (acres) burned and PM2.5 emissions by state, respectively. It
summarizes the data in Table 7-11 in map format. The Southeast states are seen to be dominated by prescribed
fires and the western states by wildfires. This is a typical pattern we see from NEI-to-NEI. In addition, for acres
burned, KS is seen to dominate and for PM2.5 emissions, CA (in the lower 48) is seen to be dominant.
Figure 7-9: Total 2017 NEI area burned by state
Figure 7-10: Total 2017 NEI PM2.5 emissions by state
Fire Type
¦ Prescribed tons
M Wildfire tons
^ 50000 tons
7-22
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DRAFT
PM2.5 emissions per square mile are shown in Figure 7-lland acres burned per square mile are shown in Figure
7-12. The patterns seen correspond to the other graphics and tables shown in this section and are fairly typical
of a given NEI for WLFs.
Figure 7-11: 2017NEI county PM2.5 emissions in tons per square mile
tons per square mile
0.000 - 0.050
0.050 - 0.500
I 0.500 - 2.000
¦ 2.000- 5.000
¦ 5.000 - 10.000
¦ 10.000-60.000
7-23
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DRAFT
Figure 7-12: 2017NEI county area burned in acres per square mile
2.00 - 5.00
5.00 - 20.00
20.00 - 50.00
50.00 - 375,00
7.6 References
1. US EPA, 2019. 2016 Emissions Inventory development for Modeling Platform work, 2014-2016 Version 7 Air
Emissions Modeling Platforms
2. Larkin, N.K., S. M. Raffuse, S. Huang, N. Pavlovic, and V. Rao, The Comprehensive Fire Information Reconciled
Emissions (CFIRE) Inventory: Wildland Fire Emissions Developed for the 2011 and 2014 U.S. National
Emissions Inventory, submitted to JAWMA, Dec 2019.
3. Larkin, N.K., S.M. O'Neill, R. Solomon, C. Krull, S. Raffuse, M. Rorig, J. Peterson, and S.A. Ferguson. 2009. The
BlueSky smoke modeling framework. International Journal of Wildland Fire, 18, 906-920
4. Urbanski S.P. (2014) Wildland fire emissions, carbon, and ciimatei emissions factors. Forest Ecology and
Management, 317, 51-60.
5. EPA's SPEC I ATE 5.0. June 2020.
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