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2014 National Emissions Inventory, Version 2
Technical Support Document
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EPA-454/B-19-034
July 2018
2014 National Emissions Inventory, Version 2 Technical Support Document
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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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 2014 NEI, Version 2? 1-1
1.2 What is included in this documentation? 1-1
1.3 Where can I obtain the 2014 NEI data? 1-2
1.3.1 Emission Inventory System Gateway 1-2
1.3.2 NEI main webpage 1-2
1.3.3 Air Emissions and "Where you live" 1-3
1.3.4 Modeling files 1-3
1.4 Why is the NEI created? 1-3
1.5 How is the NEI created? 1-4
1.5.1 NEI 2014 v2 point source updates 1-6
1.5.2 NEI 2014 v2 nonpoint source updates 1-7
1.5.3 NEI 2014 v2 mobile source updates 1-7
1.5.4 NEI 2014 v2 fires updates 1-7
1.6 Who are the target audiences for the 2014 NEI? 1-8
1.7 What are appropriate uses of the 2014 NEI and what are the caveats about the data? 1-8
1.8 Known issues in the 2014v2 NEI 1-9
2 2014 NEI contents overview 2-1
2.1 What are EIS sectors and what list was used for this document? 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 2-5
2.2.4 PM augmentation 2-7
2.2.5 Other EPA datasets 2-7
2.2.6 Data Tagging 2-8
2.2.7 Inventory Selection 2-8
2.3 What are the sources of data in the 2014 NEI? 2-8
2.4 What are the top sources of some key pollutants? 2-17
2.5 How does this NEI compare to past inventories? 2-19
2.5.1 Differences in approaches 2-19
2.5.2 Differences in emissions between 2014 and 2011 NEI 2-20
2.6 How well are tribal data and regions represented in the 2014 NEI? 2-22
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2.7 What does the 2014 NEI tell us about mercury? 2-23
2.8 References for 2014 inventory contents overview 2-30
3 Point sources 3-1
3.1 Point source approach: 2014vl 3-1
3.1.1 QA review of S/L/T data 3-1
3.1.2 Sources of EPA data and selection hierarchy 3-3
3.1.3 Particulate matter augmentation 3-5
3.1.4 Chromium speciation 3-5
3.1.5 Use of the 2014 Toxics Release Inventory 3-6
3.1.6 HAP augmentation based on emission factor ratios 3-13
3.2 Airports: aircraft-related emissions: updated in 2014v2 3-14
3.2.1 Sector Description 3-15
3.2.2 Sources aircraft emissions estimates 3-15
3.3 Rail yard-related emissions: updated in 2014v2 3-16
3.3.1 Sector Description 3-16
3.3.2 Sources rail yard emissions estimates 3-16
3.4 EGUs 3-16
3.5 Landfills 3-18
3.6 Other/carryforward 3-19
3.7 BOEM 3-20
3.8 PM species 3-20
3.9 Point source approach for the 2014v2 NEI 3-20
3.10 References for point sources 3-22
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 The Nonpoint Survey 4-4
4.1.3 Nonpoint PM augmentation 4-5
4.1.4 Nonpoint HAP augmentation 4-6
4.1.5 EPA nonpoint data 4-6
4.2 Nonpoint non-combustion-related mercury sources 4-10
4.2.1 Source Description 4-10
4.2.2 EPA-developed mercury emissions from landfills (working face) 4-14
4.2.3 EPA-Developed Emissions from Thermostats 4-15
4.2.4 EPA-Developed Emissions from Thermometers 4-15
4.2.5 EPA-Developed Emissions from Switches and Relays 4-16
4.2.6 EPA-Developed Emissions for Human Cremation 4-17
4.2.7 EPA-Developed Emissions for Animal Cremation 4-19
4.2.8 EPA-Developed Emissions for Dental Amalgam Production 4-20
4.2.9 EPA-Developed Emissions for Fluorescent Lamp Breakage (not recycled) 4-22
4.2.10 EPA-Developed Emissions for Fluorescent Lamp Breakage (recycling) 4-24
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4.2.11 EPA-Developed Emissions for General Laboratory Activities 4-24
4.2.12 Agency-reported emissions 4-25
4.2.13 References for nonpoint mercury sources 4-25
4.3 Agriculture - Crops & Livestock Dust 4-27
4.3.1 Sector description 4-27
4.3.2 Sources of data 4-27
4.3.3 EPA-developed emissions for agriculture, crops and livestock dust 4-28
4.3.4 Summary of quality assurance methods 4-35
4.3.5 References for agricultural crops & livestock dust 4-36
4.4 Agriculture - Fertilizer Application 4-37
4.4.1 Sector description 4-37
4.4.2 Sources of data 4-38
4.4.3 EPA-developed emissions for fertilizer application: revised for 2014v2 4-38
4.4.4 References for agriculture fertilizer application 4-45
4.5 Agriculture - Livestock Waste 4-45
4.5.1 Sector description 4-45
4.5.2 Sources of data 4-45
4.5.3 EPA-developed livestock waste emissions data: new for 2014v2 4-48
4.5.4 References for agriculture livestock waste 4-56
4.6 Nonpoint Gasoline Distribution 4-57
4.6.1 Description of sources 4-57
4.6.2 Sources of data 4-58
4.6.3 EPA-developed emissions for Stage 1 Gasoline Distribution 4-62
4.6.4 EPA-developed emissions for Aviation Gasoline 4-63
4.6.5 State Submittals for Aviation Gasoline 4-63
4.6.6 Updates for 2014v2 4-64
4.6.7 References for nonpoint gasoline distribution 4-64
4.7 Commercial Cooking 4-64
4.7.1 Sector description 4-64
4.7.2 Sources of data 4-65
4.7.3 EPA-developed emissions for commercial cooking 4-65
4.7.4 References for commercial cooking 4-69
4.8 Dust - Construction Dust 4-70
4.8.1 Sector description 4-70
4.8.2 Sources of data 4-70
4.8.3 EPA-developed emissions for residential construction 4-71
4.8.4 EPA-developed emissions for non-residential construction 4-74
4.8.5 EPA-developed emissions for road construction 4-76
4.9 Dust - Paved Road Dust 4-79
4.9.1 Sector description 4-79
4.9.2 Sources of data 4-80
4.9.3 EPA-developed emissions for paved road dust 4-80
4.9.4 References for paved road dust 4-85
4.10 Dust - Unpaved Road Dust 4-85
4.10.1 Sector description 4-85
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4.10.2 Sources of data 4-85
4.10.3 EPA-developed emissions for unpaved road dust 4-86
4.10.4 References for unpaved road dust 4-90
4.11 Fires -Agricultural Field Burning 4-91
4.11.1 Sector Description 4-91
4.11.2 Sources of data: revised for 2014v2 4-91
4.11.3 EPA-developed emissions for agricultural field burning 4-93
4.11.4 References for agricultural field burning 4-98
4.12 Fuel Combustion -Industrial and Commercial/Institutional Boilers and ICEs 4-99
4.12.1 Sector description 4-99
4.12.2 Sources of data 4-99
4.12.3 EPA-developed emissions for ICI fuel combustion 4-109
4.12.4 References for ICI fuel combustion 4-118
4.13 Fuel Combustion - Residential - Natural Gas, Oil and Other 4-119
4.13.1 Sector description 4-119
4.13.2 Sources of data 4-119
4.13.3 EPA-developed emissions for residential heating - natural gas, oil and other fuels 4-122
4.13.4 References for fuel combustion -residential - natural gas, oil and other 4-135
4.14 Fuel Combustion - Residential - Wood 4-136
4.14.1 Sector Description 4-136
4.14.2 Sources of data 4-136
4.14.3 EPA-developed emissions for residential wood combustion: minor revisions for 2014v2 NEI. 4-137
4.14.4 Issues for 2017 NEI consideration 4-146
4.14.5 References for residential wood combustion 4-147
4.15 Industrial Processes - Mining and Quarrying 4-147
4.15.1 Sector description 4-147
4.15.2 Source of data 4-148
4.15.3 EPA-developed emissions for mining and quarrying 4-148
4.15.4 References for mining and quarrying 4-154
4.16 Industrial Processes - Oil & Gas Production 4-154
4.16.1 Sector description 4-154
4.16.2 Source of data 4-154
4.16.3 EPA-developed emissions for oil and gas production 4-159
4.16.4 Notes on observations in 2014 NEI estimates 4-162
4.17 Miscellaneous Non-Industrial NEC: Residential Charcoal Grilling 4-166
4.17.1 Source category description 4-166
4.17.2 Source of data 4-166
4.17.3 EPA-developed emissions for residential charcoal grilling 4-166
4.17.4 References for residential charcoal grilling 4-169
4.18 Miscellaneous Non-Industrial NEC: Portable Gas Cans 4-170
4.18.1 Source category description 4-170
4.18.2 Source of data 4-170
4.18.3 EPA-developed emissions for portable gas cans: no change for 2014v2 NEI 4-171
4.18.4 References for PFCs 4-175
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4.19 Mobile - Commercial Marine Vessels 4-175
4.19.1 Sector description 4-175
4.19.2 Sources of data 4-176
4.19.3 EPA-developed emissions for commercial marine vessels: revised for 2014v2 NEI 4-178
4.19.4 Known Issue: County FIPS error in Alaska 4-205
4.19.5 Summary of quality assurance between EPA and S/L/T submittals 4-205
4.19.6 References for commercial marine vessels 4-205
4.20 Mobile - Locomotives (Nonpoint) 4-207
4.20.1 Sector description 4-207
4.20.2 Sources of data 4-207
4.20.3 EPA-developed emissions for nonpoint locomotives: new for 2014v2 NEI 4-208
4.20.4 Summary of quality assurance 4-209
4.21 Solvent - Consumer & Commercial Solvent Use: Agricultural Pesticides 4-209
4.21.1 Source category description 4-209
4.21.2 Sources of data 4-209
4.21.3 EPA-developed emissions for agricultural pesticide application 4-210
4.21.4 References for agricultural pesticides 4-231
4.22 Solvent - Consumer & Commercial Use: Asphalt Paving - Cutback and Emulsified 4-231
4.22.1 Sector description 4-231
4.22.2 Sources of data 4-232
4.22.3 EPA-developed emissions for asphalt paving: unchanged for the 2014v2 NEI 4-234
4.22.4 References for asphalt paving 4-241
4.23 Solvents: All other Solvents 4-242
4.23.1 Sector description 4-242
4.23.2 Sources of data 4-242
4.23.3 EPA-developed emissions from the Solvent Tool, new for 2014v2 4-247
4.23.4 References for solvents: all other solvents 4-251
4.24 Waste Disposal: Open Burning 4-251
4.24.1 Source category description 4-252
4.24.2 Sources of data 4-252
4.24.3 EPA-developed emissions for open burning: updated for 2014v2 NEI 4-256
4.24.4 References for open burning 4-261
4.25 Waste Disposal: Nonpoint POTWs 4-262
4.25.1 Source category description 4-262
4.25.2 Sources of data 4-263
4.25.3 EPA-developed emissions for nonpoint POTWs: no changes for 2014v2 NEI 4-263
4.26 Waste Disposal: Human Cremation 4-264
4.26.1 Source category description 4-264
4.26.2 Sources of data 4-264
4.26.3 EPA-developed emissions for human cremation: new for 2014v2 NEI 4-265
4.26.4 References for human cremation 4-265
5 Nonroad Equipment - Diesel, Gasoline and Other 5-1
5.1 Sector Description 5-1
5.2 MOVES-NONROAD 5-1
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5.3 Changes for the 2014v2 NEI 5-3
5.4 Default MOVES code and database 5-3
5.5 Additional Data: NONROAD County Databases (CDBs) 5-4
5.6 Conversion of NMIM NCDs to MOVES CDBs 5-6
5.7 MOVES runs 5-7
5.8 NMIM Runs 5-8
5.9 Quality Assurance: Comparison with NMIM 5-8
5.10 Use of California Submitted Emissions 5-11
5.11 References for nonroad mobile 5-11
6 Onroad Mobile - All Vehicles and Refueling 6-1
6.1 Sector description 6-1
6.2 Important Changes for 2014v2 NEI 6-1
6.2.1 New 2014 Vehicle Populations and Fleet Characteristics 6-1
6.2.2 New Vehicle Speeds and VMT Distributions 6-2
6.3 Sources of data and selection hierarchy 6-2
6.4 California-submitted onroad emissions 6-2
6.5 Agency-submitted MOVES inputs 6-3
6.5.1 Overview of MOVES input submissions 6-3
6.5.2 OA checks on MOVES CDB Tables 6-7
6.6 Tribal Emissions Submittals 6-8
6.7 EPA default MOVES inputs 6-9
6.7.1 Sources of default data by MOVES CDB table 6-9
6.7.2 Default California emission standards 6-11
6.8 Calculation of EPA Emissions 6-12
6.8.1 EPA-developed onroad emissions data for the continental U.S 6-12
6.8.2 Representative counties and fuel months 6-13
6.8.3 Temperature and humidity 6-16
6.8.4 VMT, vehicle population, speed, and hoteling activity data 6-18
6.8.5 Public release of the NEI county databases 6-20
6.8.6 Seeded CDBs 6-21
6.8.7 Unseeded CDBs 6-21
6.8.8 Run MOVES to create emission factors 6-21
6.8.9 Run SMOKE to create emissions 6-21
6.8.10 Onroad mobile emissions data for Alaska, Hawaii, Puerto Rico, and the Virgin Islands 6-22
6.8.11 Post-processing to create annual inventory 6-23
6.9 Summary of quality assurance methods 6-23
6.10 Supporting data 6-24
6.11 References for onroad mobile 6-29
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7 Wildland Fires (Wild and Prescribed Fires) in the 2014 NEI 7-1
7.1 Sector description and overview 7-1
7.2 Sources of data 7-2
7.3 EPA methods summary 7-2
7.3.1 Activity data 7-4
7.3.2 State, Local, and Tribal fire activity 7-4
7.4 Data preparation and processing 7-6
7.4.1 S/L/T data preparation 7-6
7.4.2 National data preparation 7-8
7.4.3 Event reconciliation and emissions calculations 7-8
7.4.4 BlueSky Framework emissions modeling 7-8
7.4.5 Dataset post-processing 7-12
7.5 Development of the NEI 7-13
7.6 Quality assurance 7-15
7.6.1 Input Fire Information Data Sets 7-15
7.6.2 Daily Fire Locations from SmartFire2 7-16
7.6.3 Emissions Estimates 7-16
7.6.4 Additional quality assurance on final results 7-16
7.7 Summary of results 7-18
7.8 Improvements in the 2014 NEI vl compared to the 2011 NEI 7-20
7.8.1 Fire activity data 7-20
7.8.2 SmartFire2 processing 7-21
7.8.3 Emission factors 7-21
7.9 Future areas of improvement 7-21
7.9.1 More accurate fuel loading 7-21
7.9.2 Pile burn emissions 7-21
7.9.3 SmartFire2 improvements 7-22
7.9.4 VOC emission factors 7-22
7.9.5 Centralized fire information database 7-22
7.10 References for wildland fires 7-22
8 Biogenics - Vegetation and Soil 8-1
8.1 Sector description 8-1
8.2 Sources of data overview and selection hierarchy 8-3
8.3 Spatial coverage and data sources for the sector 8-3
8.4 References for biogenics 8-3
LI es
Table 1-1: Point source reporting thresholds (potential to emit) for CAPs in the AERR for the year 2014 NEI.... 1-6
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Table 1-2: Examples of major current uses of the NEI 1-8
Table 2-1: EIS sectors/source categories with EIS data category emissions reflected, and where provided,
document sections 2-2
Table 2-2: Valid chromium pollutant codes 2-4
Table 2-3: Point inventory percentage submitted by reporting agency to total emissions mass 2-11
Table 2-4: Nonpoint inventory percentange submitted by reporting agency to total emissions mass 2-14
Table 2-5: EIS sectors and associated 2014v2 CAP emissions and total HAP (1000 short tons/year) 2-17
Table 2-6: Emission differences (tons) for CAPs, 2014v2 minus 2011v2 NEIs 2-21
Table 2-7: Emission differences (tons) for CAPs, 2014v2 minus 2014vl NEIs 2-21
Table 2-8: Emission differences (tons) for select HAPs, 2014v2 minus 2011v2 NEIs 2-22
Table 2-9: Emission differences (tons) for select HAPs, 2014v2 minus 2014vl NEIs 2-22
Table 2-10: Tribal participation in the 2014 NEI 2-23
Table 2-11: Facilities on Tribal lands with 2014 NEI emissions from EPA only 2-23
Table 2-12: 2014v2 NEI Hg emissions (tons) for each dataset type and group 2-24
Table 2-13: Point inventory percentage submitted by reporting agency to State total Hg emissions mass 2-27
Table 2-14: Trends in NEI mercury emissions - 1990, 2005, 2008 v3, 2011v2 and 2014v2 NEI 2-28
Table 3-1: Data sets and selection hierarchy used for 2014vl NEI point source data category 3-3
Table 3-2: Mapping of TRI pollutant codes to EIS pollutant codes 3-7
Table 3-3: Pollutant groups 3-12
Table 3-4: Agencies that submitted aircraft-related emissions for 2014vl, except as noted 3-15
Table 3-5: Landfill gas emission factors for 29 EIS pollutants 3-18
Table 3-6: Data sets and selection hierarchy used for the 2014v2 point source data category 3-22
Table 4-1: Data sources and selection hierarchy used for most nonpoint sources 4-1
Table 4-2: Data sources and selection hierarchy used for the Agricultural Field Burning sector 4-2
Table 4-3: Data sources and selection hierarchy used for the Commercial Marine Vessels sector 4-3
Table 4-4: Data sources and selection hierarchy used for the Locomotives sector 4-3
Table 4-5: EPA-estimated emissions sources expected to be exclusively nonpoint 4-7
Table 4-6: Emissions sources with potential nonpoint and point contribution 4-9
Table 4-7: SCCs and emissions (lbs) comprising the nonpoint non-combustion Hg sources in the 2014 NEI 4-10
Table 4-8: S/L/T-reported mercury nonpoint non-combustion emissions (lbs) 4-11
Table 4-9: Comparison of age groups in the CDC WONDER database (activity data) and the BAAQMD
memorandum 4-18
Table 4-10: Average number of filled teeth per person and percentage of fillings containing mercury by age
group 4-21
Table 4-11: Mercury used in CFLs (mg/bulb) as determined by three different studies 4-23
Table 4-12: Mercury used in linear fluorescent bulbs (mg/bulb) as determined by two different studies 4-23
Table 4-13: SCCs used in the 2014 NEI for the Agriculture - Crops & Livestock Dust sector 4-27
Table 4-14: Percentage of total PM Agricultural Tilling emissions submitted by reporting agency 4-27
Table 4-15: Animal Units Equivalent Factors 4-30
Table 4-16: Number of passes or tillings per year in 2014v2 NEI 4-31
Table 4-17: Acres tilled by tillage type, in 2012 4-32
Table 4-18: Number of passes or tillings per year in 2014vl NEI, replaced in 2014v2 with new values 4-32
Table 4-19: Source categories for agricultural Fertilizer Application 4-38
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Table 4-20: Percentage of total fertilizer application NH3 emissions submitted by reporting agency 4-38
Table 4-21: Environmental variables needed for an EPIC simulation 4-41
Table 4-22: Nonpoint SCCs with 2014 NEI emissions in the Livestock Waste sector 4-45
Table 4-23: Point SCCs with 2014 NEI emissions in the Livestock Waste sector - reported only by States 4-47
Table 4-24: Percentage of total Livestock NH3 emissions submitted by reporting agency 4-48
Table 4-25: EPA-estimated livestock emission SCCs 4-49
Table 4-26: EPA-estimated sources carried forward from 2011 4-49
Table 4-27: Summary of Use of 2014 Survey or 2012 Census Animal Populations 4-49
Table 4-28: Reference links for each management practice 4-52
Table 4-29: VOC speciation fractions used to estimate HAP Emissions for the Livestock Sector 4-55
Table 4-30: Nonpoint Bulk Gasoline Terminals, Gas Stations, and Storage and Transfer SCCs with 2014 NEI
emissions 4-58
Table 4-31: Nonpoint Aviation Gasoline Distribution SCCs with 2014 NEI emissions 4-60
Table 4-32: Percentage of Gasoline Distribution VOC emissions submitted by reporting agency 4-60
Table 4-33: S/L/Ts and SCCs where EPA Gasoline Stage 1 Distribution estimates were tagged out 4-63
Table 4-34: Source Classification Codes used in the Commercial Cooking sector 4-64
Table 4-35: Percentage of Commercial Cooking PM2.5 and VOC emissions submitted by reporting agency 4-65
Table 4-36: Ratio of filterable particulate matter to primary particulate matter for PM2.5 and PM10 by SCC 4-67
Table 4-37: Fraction of restaurants with source category equipment and average number of units per restaurant
4-67
Table 4-38: Average amount of food cooked per year (tons/year) on each type of Commercial Cooking
equipment 4-68
Table 4-39: State agencies that requested EPA tag out Commercial Cooking sources 4-69
Table 4-40: SCCs in the 2014 NEI Construction Dust sector 4-70
Table 4-41: Percentage of Construction Dust PM2.5 emissions submitted by reporting agency 4-70
Table 4-42: Surface soil removed per unit type 4-71
Table 4-43: Emission factors for Residential Construction 4-72
Table 4-44: Spending per mile and acres disturbed per mile by highway type 4-77
Table 4-45: SCCs in the 2014 NEI Paved Road Dust sector 4-79
Table 4-46: Percentage of Paved Road Dust PM2.5 emissions submitted by reporting agency 4-80
Table 4-47: Average vehicle weights by FWHA vehicle class 4-81
Table 4-48: MOVES and FWHA vehicle type crosswalk 4-81
Table 4-49: Penetration rate of Paved Road vacuum sweeping 4-82
Table 4-50: Counties where meteorological adjustment factors were not applied 4-83
Table 4-51: SCC in the 2014 NEI Unpaved Road Dust sector 4-85
Table 4-52: Percentage of Unpaved Road Dust PM2.5 emissions submitted by reporting agency 4-85
Table 4-53: Constants for unpaved roads re-entrained dust emission factor equation 4-87
Table 4-54: Speeds modeled by roadway type on unpaved roads 4-87
Table 4-55: Unpaved Ratios by Census Region and Road Type 4-87
Table 4-56: Nonpoint SCCs with 2014 NEI emissions in the Agricultural Field Burning sector 4-91
Table 4-57: Percentage of agricultural fire/grass-pasture burning PM2.5 emissions submitted by reporting agency
4-92
Table 4-58: Emission factors (lbs/ton), fuel loading (tons/acre) and combustion completeness (%) for CAPs... 4-94
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Table 4-59: Acres burned and PM2.5 emissions by state using EPA methods 4-96
Table 4-60: ICI fuel combustion SCCs with 2014 NEI emissions 4-99
Table 4-61: Percentage of ICI fuel combustion NOx emissions submitted by reporting agency 4-101
Table 4-62: Percentage of ICI fuel combustion S02 emissions submitted by reporting agency 4-103
Table 4-63: Percentage of ICI fuel combustion PM2.5 emissions submitted by reporting agency 4-106
Table 4-64: Stationary source adjustments for industrial sector distillate fuel consumption 4-111
Table 4-65: Stationary source adjustments for commercial sector distillate fuel consumption 4-111
Table 4-66: Default assumptions for distillate boiler/engine splits 4-112
Table 4-67: Industrial sector percent of total energy consumption from non-fuel use estimates 4-112
Table 4-68: NAICS Code 31-33 (Manufacturing) employment data for Maine 4-114
Table 4-69: CAP emission factors for ICI source categories 4-116
Table 4-70: Non-wood residential heating SCCs with 2014 NEI emissions 4-119
Table 4-71: Percentage of non-wood residential heating NOx, PM2.5 and VOC emissions submitted by reporting
agency 4-120
Table 4-72: 2006 percent bituminous coal distribution for the residential and commercial sectors 4-123
Table 4-73: Residential natural gas combustion emission factors 4-124
Table 4-74: Residential distillate oil combustion emission factors 4-125
Table 4-75: Residential kerosene combustion emission factors 4-126
Table 4-76: S02 and PM emission factors for residential anthracite and bituminous coal combustion 4-127
Table 4-77: State-specific sulfur content for bituminous coal (SCC 2104002000) 4-128
Table 4-78: Residential anthracite coal combustion emission factors 4-129
Table 4-79: Residential bituminous coal combustion emission factors 4-129
Table 4-80: Residential LPG combustion emission factors 4-132
Table 4-81: RWC sector SCCs in the 2014 NEI 4-136
Table 4-82: Reporting agency PM2.5 and VOC percent contribution to total NEI emissions for RWC sector.... 4-136
Table 4-83: Certification profiles for woodstoves 4-142
Table 4-84: Certification profiles for fireplaces 4-142
Table 4-85: PM 10 woodstove standards and emission factors (lb/ton) 4-145
Table 4-86: 2014vl and 2014v2 NEI emission factors (lb/ton) for PM10 and CO 4-145
Table 4-87: SCCs for Industrial Processes- Mining and Quarrying 4-147
Table 4-88: Percentage of Mining and Quarrying PM2.5 and PM10 emissions submitted by reporting agency . 4-148
Table 4-89: Summary of Mining and Quarrying emission factors 4-150
Table 4-90: NAICS codes for metallic and non-metallic mining 4-151
Table 4-91: 2006 County Business Pattern data for NAICS 31-33 in Maine 4-152
Table 4-92: Nonpoint SCCs with 2014 NEI emissions in the Oil and Gas Production sector 4-155
Table 4-93: Point SCCs in the Oil and Gas Production sector 4-158
Table 4-94: Percentage of total Oil and Gas Production NOx and VOC nonpoint emissions submitted by reporting
agency 4-159
Table 4-95: State involvement with Oil and Gas Production submittals 4-161
Table 4-96: EPA oil and gas fugitive SCCs tagged out in Oklahoma in the 2014 NEI 4-163
Table 4-97: Additional non-EPA-estimated oil and gas fugitive SCCs Oklahoma submitted in the 2014 NEI.... 4-163
Table 4-98: Percentage of Residential Charcoal Grilling PM2.5 emissions submitted by reporting agency 4-166
Table 4-99: Residential Charcoal Grilling emissions factors (lb/ton) 4-168
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Table 4-100: SCCs with 2014 NEI emissions for PFCs 4-170
Table 4-101: Percentage of PFC VOC emissions submitted by reporting agency 4-171
Table 4-102: Toxic to VOC ratios for PFCs 4-173
Table 4-103: Toxic to VOC ratios for other HAPs vapor displacement, permeation and evaporation 4-174
Table 4-104: CMV SCCs and emission types in EPA estimates 4-176
Table 4-105: Percentage of CMV PM2.5, NOx and VOC emissions submitted by reporting agency 4-176
Table 4-106: Agencies that provided CMV submittals for the2014vl and 2014v2 NEI 4-177
Table 4-107: Vessel-specific routing data 4-179
Table 4-108: IMO-vessel speed data 4-180
Table 4-109: Vessel power attributes by vessel type 4-182
Table 4-110: Alaska ports and vessel calls 4-184
Table 4-111: Estimated maneuvering time by vessel type 4-185
Table 4-112: Category 1 and 2 average maximum speed by vessel type 4-187
Table 4-113: Power rating by dredging type 4-188
Table 4-114: Summary of national kilowatt-hours by dredging vessel type 4-189
Table 4-115: Research vessel characteristics matching by reference 4-189
Table 4-116: Summary of Coast Guard underway activity 4-190
Table 4-117: General location of Coast Guard underway activities 4-190
Table 4-118: State fish landing data for Great Lakes and Pacific States 4-191
Table 4-119: IMO underway cruising vessel speed and engine load factors for bulk carriers, containerships, and
tankers 4-194
Table 4-120: Auxiliary operating loads 4-194
Table 4-121: Category 3 emission factors (g/kW-hours) 4-195
Table 4-122: Calculated low load multiplicative adjustment factors 4-196
Table 4-123: Tier emission factors for vessels equipped with Category 2 propulsion engines (g/kW-hours)... 4-196
Table 4-124: Vessel tier population by type for vessels equipped with CI or C2 propulsion engines 4-197
Table 4-125: Vessel tier population by type for vessels equipped with CI or C2 propulsion engines 4-197
Table 4-126: 2014 EPA-estimated vessel activity (kW-hrs) and emissions (tons) by propulsion engine and mode 4-
198
Table 4-127: 2014 EPA CMV emissions by vessel type 4-198
Table 4-128: 2014 vessel activity (kW-hrs) and EPA emissions (tons) by propulsion engine and SCC 4-199
Table 4-129: Alaska commercial fishing catcher vessel count 4-203
Table 4-130: County FIPs Corrections for Alaska CMV Shape Emissions 4-205
Table 4-131: Locomotives SCCs, descriptions and EPA estimation status 4-207
Table 4-132: Source Category Codes with emissions submitted by reporting agency 4-208
Table 4-133: Agricultural Pesticide Application SCCs estimated by EPA and S/L/Ts 4-209
Table 4-134: Percentage of Agricultural Pesticide Application VOC emissions submitted by reporting agency4-210
Table 4-135: Terms used to screen out consumer products 4-210
Table 4-136: Crosswalk between USGS compound name and CA DPR chemical name 4-212
Table 4-137: VOC emission factors for EPA-estimated Agricultural Pesticide Application 4-221
Table 4-138: HAP emission factors for EPA-estimated Agricultural Pesticide Application 4-229
Table 4-139: Asphalt Paving SCCs estimated by EPA and S/L/Ts 4-232
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Table 4-140: Percentage of cutback and emulsified Asphalt Paving VOC emissions submitted by reporting agency
4-233
Table 4-141: Sources of activity data and related parameters, where G=given and C=computed 4-237
Table 4-142: Sources of emission factors and related parameters, where G=given and C=computed 4-239
Table 4-143: Cutback asphalt computed average chemical composition information 4-240
Table 4-144: Updated emission factors and expected pollutants by SCC vs. pre-existing factors 4-240
Table 4-145: Nonpoint Solvent SCCs with 2014 NEI emissions 4-242
Table 4-146: EIS sector-specific percentage of Solvent VOC emissions submitted by reporting agency 4-246
Table 4-147: S/L/Ts that requested EPA not backfill nonpoint Solvent estimates with EPA estimates 4-249
Table 4-148: Open Burning SCCs with 2014 NEI emissions 4-252
Table 4-149: Percentage of Open Burning NOx, PM2.5 and VOC emissions submitted by reporting agency 4-252
Table 4-150: Surface Acres Disturbed per Unit Type 4-256
Table 4-151: Spending per Mile and Acres Disturbed per Mile by Highway Type 4-258
Table 4-152: Fuel Loading Factors (tons/acres) by Vegetation Type 4-258
Table 4-153: Adjustment for Percentage of Forested Acres 4-261
Table 4-154: Percentage of nonpoint POTW VOC and PM2.5 emissions submitted by reporting agency 4-263
Table 4-155: Percentage of nonpoint human cremation NOx emissions submitted by reporting agency 4-264
Table 5-1: MOVES-NONROAD equipment and fuel types 5-1
Table 5-2: Pollutants produced by MOVES-NONROAD for 2014 NEI 5-2
Table 5-3: Selection hierarchy for the Nonroad Mobile data category 5-4
Table 5-4: Nonroad Mobile S/L/T submissions for the 2014 NEI** 5-5
Table 5-5: States for which one or more CDBs were created from NCD20160513_nei2014vl and for which
NONROAD files were included 5-5
Table 5-6: Contents of the Nonroad Mobile supplemental folder 5-6
Table 5-7: Conversion of NONROAD data files to MOVES tables 5-7
Table 5-8: MySQL scripts to convert intermediate to MOVES tables 5-7
Table 5-9: States with absolute percent difference (MOVES-NMIM) > 0.01% for S02 exhaust* 5-8
Table 5-10: Comparison of NMIM to MOVES-NONROAD* 5-10
Table 6-1: MOVES2014a CDB tables 6-3
Table 6-2: Number of counties with submitted data, by state and key MOVES CDB table 6-5
Table 6-3: Tribes that Submitted Onroad Mobile Emissions Estimates for the 2014NEI 6-9
Table 6-4: Source of defaults for key data tables in MOVES CDBs 6-9
Table 6-5: States adopting California LEV standards and start year 6-12
Table 6-6: Agency submittal history for Onroad Mobile inputs and emissions 6-24
Table 6-7: Onroad Mobile data file references for the 2014 NEI 6-27
Table 7-1: SCCs for wildland fires 7-2
Table 7-2: 2014 NEI Wildfire and Prescribed Fires selection hierarchy 7-2
Table 7-3: Model chain for the Hawaii and Puerto Rico portion of the 2014 national wildland fire emissions
inventory development 7-9
Table 7-4: Emission factor regions used to assign HAP emission factors for the 2014vl NWLFEI 7-9
Table 7-5: Prescribed fire HAP emission factors (lb/ton fuel consumed) for the 2014 NEI 7-10
Table 7-6: Wild fire HAP emission factors (lbs/ton fuel consumed) for the 2014 NEI 7-11
Table 7-7: PM2.5 speciation factors used to calculate PM2.5 components for wildfires and prescribed fires 7-13
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Table 7-8: Summary of NEI acres burned and PM2.5 emissions by state, fire type, and combustion phase 7-18
Table 8-1: SCCs for Biogenics - Vegetation and Soil 8-1
Table 8-2: Meteorological variables required by BEIS 3.61 8-2
List of Figures
Figure 2-1: Relative contributions for various data sources of Nonpoint emissions for CAPs and select HAPs.... 2-9
Figure 2-2: Relative contributions for various data sources of Point emissions for CAPs and select HAPs 2-10
Figure 2-3: Data sources of Hg emissions (tons) in the 2014v2 NEI, by data category 2-24
Figure 2-4: Trends in NEI Mercury emissions (tons) 2-30
Figure 4-1: Bidirectional flux modeling system used to compute 2014 Fertilizer Application emissions 4-40
Figure 4-2: USDAfarm production regions used in FEST-C simulations 4-41
Figure 4-3: Simplified FEST-C system flow of operations in estimating NH3 emissions 4-43
Figure 4-4: 2011v2 NEI Fertilizer Application emissions 4-44
Figure 4-5: 2014v2 NEI "bidi" Fertilizer Application emissions 4-44
Figure 4-6: Process to produce location and practice specific daily emission factors 4-52
Figure 4-7: Composite emission factors for a specific day, location, and animal type 4-53
Figure 4-8: Spatial distribution of PM2.5 emissions by county, EPA method 4-98
Figure 4-9: U.S. Census Regions and Census Divisions 4-139
Figure 4-10: AIA climate zones from the 1978-2005 RECS 4-141
Figure 4-11: Example route for ship movement from Port A to Port B via a RSZ 4-180
Figure 4-12: Emission calculations for underway operations 4-181
Figure 4-13: State and federal waters of the United States 4-185
Figure 4-14: Horsepower for Alaskan fishing vessels 4-192
Figure 4-15: New underway shapes for Puerto Rico and the U.S. Virgin Islands 4-200
Figure 4-16: Spatial allocation of 2014 support vessel activity 4-202
Figure 4-17: Spatial allocation of 2014 ferry activity 4-202
Figure 4-18: Spatial allocation of 2014 Coast Guard activity 4-203
Figure 4-19: Spatial allocation of 2014 commercial fishing activity 4-204
Figure 4-20: Types of Asphalt Paving processes 4-232
Figure 4-21: ElA-based U.S. asphalt road oil consumption estimates 4-235
Figure 4-22: ElA-based state-level road oil consumption trends 4-236
Figure 6-1: Counties for which agencies submitted local data for at least 1 CDB table are shown in dark blue... 6-7
Figure 6-2: Representative county groups for the 2014 NEI 6-15
Figure 7-1: Processing flow for wildland fire emission estimates in the NEI 7-3
Figure 7-2: The coverage of state-submitted fire activity data sets 7-5
Figure 7-3: Model chain for the contiguous United States and Alaska portion of the 2014 national wildland fire
emissions inventory development 7-9
Figure 7-4: PM2.5 WLF emissions trends from 2007-2014 using SF2 (for the lower 48 states) 7-17
Figure 7-5: 2014 NEI wildland fire PM2.5 emission density 7-20
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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
CHIEF Clearinghouse for Inventories and Emissions Factors
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
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GHG Greenhouse gas
GIS Geographic information systems
GPA Geographic phase-in area
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 MeteoroIog v-Chemistrv 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)
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ORIS Office of Regulatory Information Systems
OTAQ Office of Transportation and Air Quality (of EPA)
PADD Petroleum Administration for Defense Districts
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 PM2.5 (condensable plus filterable)
PM2.5 Particulate matter 2.5 microns or less in diameter
PM 10 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 Research and Forecasting Model
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oduction
1,1. What data are included in the 2014 NEI, Version 2?
The 2014 National Emissions Inventory (NEI), version 2, hereafter referred to as the "2014 NEI" or "2014v2"
when version number is important to note, is a national compilation of criteria air pollutant (CAP) and hazardous
air pollutant (HAP) emissions. These data are collected from state, local, and tribal (S/L/T) air agencies and the
Environmental Protection Agency (EPA) emissions programs including the Toxics Release Inventory (TRI), the
Acid Rain Program, and Maximum Achievable Control Technology (MACT) standards development. The 2014v2
is synonymous with "2014 NEI" and replaces version 1 of the 2014 NEI released in December, 2016. This
document discusses all components of the NEI, and highlights differences in version 2 over those in version 1
where necessary. 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. 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 (PMio), 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 and mobile sources only.
1.2 What is included in this documentation?
This technical support document (TSD) provides a central reference for the 2014 NEI. 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
explains the source categories and/or sectors that we use for summarizing the 2014 NEI and for organizing this
document, and it provides an overview of the contents of the inventory and a summary of 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. Sections 5 and 6
1 The original of HAPs is available on the EPA Technology Transfer Network - Air Toxics Web Site.
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provide documentation for the nonroad mobile and onroad mobile data categories, respectively. Fires (wild and
prescribed burning) are described in Section 7, and biogenic emissions are described in Section 8.
The 2014 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 the 2014 NEI and older versions of the NEI such as 2011 and 2008. The 2014
NEI in the EIS is called "2014 NEI FINAL V2." Note that if you run facility-, unit- or process-level reports in the EIS,
you will get the 2014 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 (January 2017), 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 "2014 NEI FINAL V2" in the EIS. Use the link provided above for more information about how
to obtain an account and to access the gateway itself.
1.3.2 NEI main webpage
Next, data from the EIS are exported for public release on the NEI main webpage. There are two pages related
to the 2014 NEI on the NEI main page website: "2014 NEI Data" and "2014 NEI Documentation." The 2014 NEI
Data page includes the most recent publicly-available version of the 2014 NEI; this is 2014v2 as of February
2018. The 2014 NEI Documentation page includes the 2014 NEI plan and schedules, all publicly-available
supporting materials by inventory data category (e.g., point, nonpoint, onroad mobile, nonroad mobile, events),
this TSD, as well as the 2014vl NEI TSD.
The 2014 NEI Data page includes a query tool that allows for summaries by EIS Sector (see Section 2.4) or the
more traditional Tier 1 summary level (CAPs only) used in the EPA Trends Report. Summaries from the 2014 NEI
Data site include national-, state-, and county-level emissions for CAPs, HAPs and GHGs. You can choose which
states, EIS Sectors, Tiers, and pollutants to include in custom-generated reports to download Comma Separated
Value (CSV) files to import into Microsoftฎ Excelฎ, Accessฎ, 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 source classification codes (SCC) data files section of the webpage provides detailed data files for point,
nonpoint, onroad and nonroad data categories via a pull-down menu. These detailed CSV files (provided in zip
files) contain emissions at the process level. Due to their size, all but the nonpoint data are broken out into EPA
regions. Facility-level by pollutant and events by pollutant summaries are also available. These CSV files must be
"linked" (as opposed to imported) to open them with Microsoftฎ Accessฎ.
The 2014 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.
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1.3.3 Air Emissions and "Where you live"
NOTE: Please review table legends which provide the NEI year and version when using the data from these sites.
The Air Emissions website provides emissions of CAPs except for NH3 using point-and-click maps and bar charts
to provide access to summary and detailed emissions data. The maps, charts, and underlying data (in CSV
format) can be saved from the website and used in documents or spreadsheets.
In addition, the "Where you live" feature of the Air Emissions website allows users to select states and EIS
sectors (see Section 2.1) to create KMZ files used by Google Earth. You must have Google Earth installed on your
computer to open the files. You can customize the maps to select the facility types of interest (e.g., airport, steel
mill, petroleum refinery, pulp and paper plant), and all other facility types will go into an "Other" category on
the maps. The resulting maps allow you to click on the icons for each facility to get a chart of emissions
associated with each facility for all criteria pollutants.
1.3.4 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 2014 modeling platform is based on the 2014 NEI and is under development; it is expected to
be posted in the spring of 2018. Any changes between the NEI and modeling platform data will be described in
an accompanying TSD for the 2014 Emissions Modeling Platform, which would also be posted at the above
website.
SMOKE flat files by emissions modeling "sector" are available for download on the 2014v2 NEI-based Emissions
Modeling FTP siteftp://ftp.epa.gov/Emislnventory/2014/flat files/. These flat files are the emissions based on
the 2014v2 NEI and can be input into SMOKE for processing for air quality modeling. However, for onroad and
nonroad mobile sources, we use more finely resolved data for air quality modeling. The data files for nonroad
mobile emissions use monthly emissions values. For onroad mobile sources, the emissions are computed hourly
based on gridded meteorological data and emission factors. Therefore, these aggregated annual onroad and
nonroad modeling files should not be used directly for modeling. Refer to the README file for more details on
how to access these SMOKE flat files.
For point and nonpoint sources, the modeling files have the sources split into smaller source groupings
(modeling sectors) for emissions modeling because emissions processing methods vary between these source
groupings.
1.4 i -
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
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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.
15 i ฆฆฆ s-- ' %
The NEI is created based on both regulatory and technical components. The Air Emissions Reporting Rule (AERR)
is the regulation that requires states to submit CAP emissions, and provides the framework for voluntary
submission 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 2014 NEI is the third AERR-based inventory,
and improvements in the 2014 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 every year, with additional
requirements every third year in the form of lower point source emissions thresholds, and 2014 is one of these
third-year inventories.
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Table 1-1 provides the potential-to-emit reporting thresholds that applied for the 2014 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 in the 2014 AERR, 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 PMio nonattainment areas that applied during the year
that the S/L/T agencies submitted their data for the 2014 NEI are available on the Nonattainment Areas for
Criteria Pollutants (Green Book) web site. While not applicable to the 2014 NEI, the AERR thresholds have been
further revised to reflect 70 tons/year for PMio, PM2.5, and PM precursors for sources within PM10 and PM2.5
nonattainment areas.
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Table 1-1: Point source reporting thresholds (potential to emit) for CAPs in the AERR for the year 2014 NEI
2014 NEI thresholds: potential to emit (tons/yr)
Pollutant
Everywhere
(Type B
sources)
NAA sources1
1
so2
>100
>100
2
VOC
>100
03 (moderate) > 100
3
VOC
03 (serious) > 50
4
VOC
03(severe)> 25
5
VOC
03 (extreme) > 10
6
NOx
>100
>100
7
CO
>1000
03 (all areas) > 100
8
CO
CO (all areas) > 100
9
Pb
> 0.5 (actual)
> 0.5 (actual)
10
PM10
>100
PM10 (moderate) > 100
11
PM10
PM10 (serious) > 70
12
PM2.5
>100
>100
13
nh3
>100
>100
1 NAA = Nonattainment Area. Special point source reporting thresholds apply for certain
pollutants by type of nonattainment area. The pollutants by nonattainment area are:
Ozone: VOC, NOx, CO; CO: CO; PMi0: PMi0
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
agencies were required to submit model inputs for onroad and nonroad mobile sources instead of emissions.
For the 2014vl NEI, all these emissions and inputs were required to be submitted to the EPA per the AERR by
December 31, 2015 (with an extension given through January 15, 2016). 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.
1,5.1 NEI 2014 w2 point source updates
The 2014vl NEI point source file was produced on July 16, 2016. The 2014v2 was produced November 15, 2017.
The process for producing the point source emissions was different from that of the 2014vl NEI (and previous
year inventories) in that we used the 2014vl as the starting point, and incorporated targeted changes to that
dataset rather than re-generating the entire point inventory from the S/L/T and EPA datasets. To do this, the
2014vl NEI was converted to a dataset, and changes were incorporated into new EPA change datasets (for more
detail see the 2014v2 NEI selection hierarchy presented in Section 3.9. In addition, we tagged out 2014vl NEI
data that was found to be incorrect per the S/L/T comments. Facility configuration data such as geographic
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coordinates and release parameters were updated directly to EIS by S/L/T or by EPA. More information on the
2014v2 updates are provided in Section 3.9.
1.5.2 NEi 2014 v2 nonpoirrt: source updates
There are numerous changes in the nonpoint data category for 2014v2; highlights include, but are not restricted
to the following:
Updated emission factors for agricultural fertilizer application from 2011 to 2014 model outputs
New EPA estimate for livestock dust that did not exist in 2014vl
Added VOCs for livestock waste and some animal population updates for several states
Where available, we updated activity data for many EPA nonpoint tools and EPA estimates
Re-introduction of precipitation adjustment to unpaved and paved roads greatly reduces PM emissions
in 2014v2 for these sources
Recomputed HAPs for agricultural field burning for most states to satisfy QA checks
Revised boiler/engine split for distillate industrial and commercial/institutional fuel combustion
New port shapes redrawn such that emissions are placed only over water and not port land area; also
new submittals for the Great Lakes states and Delaware
New EPA estimates for locomotives, county-level replaces link-based estimates
New activity data for oil and gas production and exploration, updated basin-specific activity data and
emission factors, and some states resubmitted data
Mercury tools updated from year 2011 to year 2014 activity data; general laboratory activities, missing
in 2014vl, are carried forward from the 2011 NEI
Each subsection in the Nonpoint Section (4) discusses in detail how the EPA data changed between 2014vl and
2014v2. S/L/Ts also resubmitted data based on their own review.
1.5.3 NEi 2014 v2 mobile source updates
Three states provided updates to their nonroad inputs: Delaware, Georgia and North Carolina. There were more
substantial updates for the onroad data category:
New 2014 vehicle populations and fleet characteristics,
New default vehicle speed distributions and relative hourly and day-type VMT distributions and the local
level,
New county database submittals and minor changes to the representative county groups based on new
2014 age distribution data,
Age distributions for representative county databases now reflect population-weighted average of the
member county age distributions,
1.5.4 NEI 2014 v2 fires updates
Wild land and prescribed fire emissions were altered in two states for the 2014v2 NEI: Georgia and Washington.
For Georgia, their 2014vl VOC HAPs violated our QA check of being less than the VOC estimates. For 2014v2,
EPA provided Georgia with appropriate HAP emission factors that were then used for 2014v2. For Washington,
they provided their own estimates and documentation for 2014v2 to replace EPA estimates used in 2014vl.
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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 2014 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
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
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
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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
(MOBILES) 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 2014 NEI (MOVES2014a). The new version
of MOVES (used in both 2014vl and 2014v2) 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 2014 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
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). Flowever, 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 PMio and PM2.5-
1,8 Kn ฆ \ v .s ;. v V. NEI
Not every identified issue in the 2014vl NEI was resolved for the 2014v2 NEI. Below is a list of issues in the
2014v2 NEI that we intend to resolve in the 2017 NEI:
Reconcile EPA tool emission factors and EIS FIAP augmentation profiles, ensure VOC FIAP vs VOC QA
check is possible
Improved emission factors for key source categories, to be determined
General mistakes in execution:
o We "over-tagged" EPA nonpoint estimates for several states and source categories. These tags
were intended to apply to only 1 pollutant but were erronesouly applied to all pollutants.
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However, these missing EPA estimates are very small for CAPs and most HAPs except for the
following states and sectors:
ฆ Idaho: Cumulative 6,110 tons of CO, 45 tons of NH3, 80 tons of NOX, 855 tons of PM2.5
and PM10, 13 tons of S02 and 1,250 tons of VOC from residential wood combustion
sources freestanding and insert non-certified and certified-catalytic wood stoves (SCCs
2104008210, 2104008230, 2104008310, and 2104008330).
ฆ Wyoming: 13 tons of NOX from gas well dehydrators (SCC 2310021400) and 7 tons of
NOX, 39 tons of CO, and 84 tons of S02 from "Oil Well Tanks - Flashing &
Standing/Working/Breathing" (SCC 2310010200).
o We did not remove a double-count in New Jersey ICI distillate fuel combustion (approximately
1,000 tons of NOX)
o Missing HAPs for an agricultural burning SCC
o Minnesota alerted EPA that several nonpoint sources had minor issues. EPA estiamtes for
residential wood combustion emissions for certified catalytic freestanding and insert wood
stoves were erroneously gap-filled where MN-submitted data did not exist; this resulted in
approximately 131 tons of PM2.5 emissions from EPA that should not have been included.
Similar undesired EPA gap-filling of solvent degreasing (1,319 tons of VOC) and mercury from
human cremation (13 pounds of mercury) were identified.
Improved point subtraction when computing nonpoint fuel industrial and commercial/institutional
combustion
Improved characterization of unpaved roads
Improved coverage of survey data for residential wood combustion
New emissions source for agricultural silage (VOC)
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2 i contents overview
2.1 What are EiS sectors and what list was used for this document?
First used for the 2008 NEl, EIS Sectors continue to be used for the 2014 NEI. 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 based on the SCC to the 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 in the
comma-separated values (CSV) file "source classification codes (9).csv" that can be imported into a Microsoftฎ
Excel ฎ spreadsheet. No changes were made to the SCC-mapping or sectors used for the 2014 NEI except where
SCCs were retired or new SCCs were added. Users of the NEI are free to obtain the SCC-level data. SCCs and their
associated sectors are available from the SCC Search Page.
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 compiled using five major categories that are also data categories in
the EIS: 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. This table also identifies in the rightmost column the section number of this
document that provides more information about that EIS sector or source category if the EPA was involved in
creating emissions for that component of the NEI. Many Industrial Processes-related EIS sectors do not have
detailed sector-specific documentation because the emissions are comprised almost exclusively from S/L/T point
and/or nonpoint submittals. As discussed in the next section, the EPA had little, if any, input to these sectors
other than augmenting HAPs or tagging out unexpected data.
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 EIS
sector "Mobile - Aircraft" is part of the point and nonpoint data categories and "Mobile - Commercial Marine
Vessels" and "Mobile - Locomotives" is part of 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.
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Table 2-1: EIS sectors/source categories with EIS data category emissions reflected, and where provided,
document sections
Component
EIS Sector or EIS Sector: Source Category Name
Point
Nonpoint
Onroad
Nonroad
Event
Document
Section(s)
Agriculture - Crops & Livestock Dust
0
4.3
Agriculture - Fertilizer Application
0
4.4
Agriculture - Livestock Waste
0
0
4.5
Biogenics - Vegetation and Soil
0
8
Bulk Gasoline Terminals
0
0
4.6
Commercial Cooking
0
4.7
Dust - Construction Dust
0
0
4.8
Dust - Paved Road Dust
0
4.9
Dust - Unpaved Road Dust
0
4.10
Fires - Agricultural Field Burning
0
4.11
Fires - Prescribed Burning
0
7
Fires - Wildfires
0
7
Fuel Comb - Comm/lnstitutional - Biomass
0
0
4.12
Fuel Comb - Comm/lnstitutional - Coal
0
0
4.12
Fuel Comb - Comm/lnstitutional - Natural Gas
0
0
4.12
Fuel Comb - Comm/lnstitutional - Oil
0
0
4.12
Fuel Comb - Comm/lnstitutional - Other
0
0
4.12
Fuel Comb - Electric Generation - Biomass
0
3.4
Fuel Comb - Electric Generation - Coal
0
3.4
Fuel Comb - Electric Generation - Natural Gas
0
3.4
Fuel Comb - Electric Generation - Oil
0
3.4
Fuel Comb - Electric Generation - Other
0
3.4
Fuel Comb - Industrial Boilers, ICEs - Biomass
0
0
4.12
Fuel Comb - Industrial Boilers, ICEs - Coal
0
0
4.12
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
0
0
4.12
Fuel Comb - Industrial Boilers, ICEs - Oil
0
0
4.12
Fuel Comb - Industrial Boilers, ICEs - Other
0
0
4.12
Fuel Comb - Residential - Natural Gas
0
4.13
Fuel Comb - Residential - Oil
0
4.13
Fuel Comb - Residential - Other
0
4.13
Fuel Comb - Residential - Wood
0
4.14
Gas Stations
0
0
4.6
Industrial Processes - Cement Manufacturing
0
Industrial Processes - Chemical Manufacturing
0
0
Industrial Processes - Ferrous Metals
0
Industrial Processes - Mining
0
0
4.15
Industrial Processes - NEC
0
0
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Component
EIS Sector or EIS Sector: Source Category Name
Point
Nonpoint
Onroad
Nonroad
Event
Document
Section(s)
Industrial Processes - Non-ferrous Metals
0
0
Industrial Processes - Oil & Gas Production
0
0
4.16
Industrial Processes - Petroleum Refineries
0
0
Industrial Processes - Pulp & Paper
0
Industrial Processes - Storage and Transfer
0
0
4.6
Miscellaneous Non-Industrial NEC: Residential Charcoal Grilling
0
4.17
Miscellaneous Non-Industrial NEC: Portable Gas Cans
0
4.18
Miscellaneous Non-Industrial NEC: Nonpoint Hg
0
4.2
Miscellaneous Non-Industrial NEC (All other)
0
0
Mobile - Aircraft
0
3.2
Mobile - Commercial Marine Vessels
0
4.19
Mobile - Locomotives
0
0
3.3 &
4.20
Mobile - NonRoad Equipment - Diesel
0
0
5
Mobile - NonRoad Equipment - Gasoline
0
0
5
Mobile - NonRoad Equipment - Other
0
0
5
Mobile - Onroad - Diesel Heavy Duty Vehicles
0
6
Mobile - Onroad - Diesel Light Duty Vehicles
0
6
Mobile - Onroad - Gasoline Heavy Duty Vehicles
0
6
Mobile - Onroad - Gasoline Light Duty Vehicles
0
6
Solvent - Consumer & Commercial Solvent Use: Agricultural
Pesticides
0
4.21
Solvent - Consumer & Commercial Solvent Use: Asphalt Paving
0
4.22
Solvent - Consumer & Commercial Solvent Use: All Other Solvents
0
4.23
Solvent - Degreasing
0
0
4.23
Solvent - Dry Cleaning
0
0
4.23
Solvent - Graphic Arts
0
0
4.23
Solvent - Industrial Surface Coating & Solvent Use
0
0
4.23
Solvent - Non-Industrial Surface Coating
0
4.23
Waste Disposal: Open Burning
0
4.24
Waste Disposal: Nonpoint POTWs
0
4.25
Waste Disposal: Human Cremation
0
4.26
Waste Disposal: Nonpoint Hg
0
4.2
Waste Disposal (all remaining sources)
0
0
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.
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The NEI is built by data category for point, nonpoint, nonroad mobile, onroad mobile and events. Each data
category has a self-contained inventory where multiple datasets are blended to create the final NEI "selection."
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/pollutant/process. 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 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. These exceptions are implemented by NEI developers using "tags" within EIS.
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 2014 Toxics Release Inventory (TRI) to supplement point source HAP
and NH3 emissions provided to EPA by S/L/T agencies. For 2014, 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 considered for inclusion
in the 2014 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 2014 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 EPAspeciates 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
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(Chromium III); therefore, the EPA characterized all non-hexavalent chromium as trivalent chromium. The 2014
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:
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
For the 2014 chromium augmentation, only the "By Facility ID" (2), "By SCC" (6), and "By Default" (9) were used.
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 2014 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. It was not necessary to speciate chromium from any of the EPA datasets, because
the EPA data contains only speciated chromium.
Most of the speciation factors used in the 2014 NEI are SCC-based and are the same as were used in 2011, based
on data that have long been used by the EPA for NATA and other risk projects. However, some of the values
were updated based on data used or developed by OAQPS during rule development and for the 2011 NATA
review. The speciation factors are accessed in the EIS through the reference data link "Augmentation Priority
Order." The "Priority Data" table provides the factors used for point sources, and the "Priority Data Area"
provides the factors used for data in the nonpoint/onroad/nonroad categories. For access by non-EIS users, the
factors are included in the zip file ChromiumAugFactors.zip. If a particular emission source of total chromium is
not covered by the speciation factors specified by any of these attributes, a default value of 34 percent
hexavalent chromium, 66 percent trivalent chromium is applied.
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 2014 NEI, we
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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, 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) and nonpoint (Section
4). 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 the 2011 NATA review. 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, which we incorporated into for the augmentation for 2014 NEI.
HAP augmentation can sometimes create HAP emissions that exceed the largest S/L/T-reported value nationally
for a given pollutant and SCC. These high values are screened out via tags (see Section 2.2.6) and are not in the
2014 NEI. These tagged values are available for S/L/T air agency review. While they could be valid, they could
also indicate a CAP emissions overestimate or incorrect SCC assignment for a source.
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
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others, then those other processes will not be augmented with that HAP. A more thorough review of that
situation was done for mercury for 2014v2, which led to some additional augmented Hg being used.
2.2.4 Plvl augmentation
Particulate matter (PM) emissions species in the NEI are: primary PMio (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 PM10 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 ฎ Access ฎ database is available on the NEI PM Augmentation site.
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 data sets
In addition to TRI, chromium speciation, HAP and PM augmentation, the EPA generates other data to produce a
complete inventory. A new EPA dataset in the 2014 NEI "2014EPA_PMspecies", provides speciated PM2.5 and
"DIESEL" PM emissions for the point, nonpoint, onroad mobile, and nonroad mobile data categories. This
dataset is a result of offline emissions speciation where the NEI PM25-PRI emissions are split into the five PM2.5
species: elemental (also referred to as "black") carbon (EC), organic carbon (OC), nitrate (N03), sulfate (S04),
and the remainder of PM25-PRI (PMFINE). Also adds a copy of PM2.5-PRI and PM10-PRI from diesel engines,
relabeled as DIESEL-PM25 and DIESEL-PM10, respectively, are added pollutants in this dataset.
Examples of EPA data for point sources, discussed in Section 3, include EPA landfills, electric generating units
(EGUs), airports, railyards, and offshore oil and gas platforms.
For nonpoint sources, discussed in Section 4, other EPA data are the defaults that are provided in the EPA
nonpoint tools that S/L/Ts agency staff can generate emission estimates. Examples of these nonpoint tools
include residential wood combustion, industrial and commercial/institutional fuel combustion, solvent
utilization, fugitive dust, oil and gas exploration and production and agricultural pesticide application. The EPA
also generates emission estimates as stand-alone datasets that do not have editable inputs; examples of these
datasets include biogenics, agricultural livestock and fertilizer application.
2-7
-------
We develop and document EPA-generated nonroad mobile-type sources that are in the nonpoint inventory
separate from the nonroad equipment sources. These nonpoint, but nonroad mobile-type, sources include rail
emissions except railyards and commercial marine vessel ports and in-transit (underway) sources.
We only incorporate data from these other EPA datasets for sources and pollutants that are not provided by
S/L/T data. We perform analysis to prevent double-counting of S/L/T agency and EPA data, including using the
information included in a nonpoint survey that S/L/T air agencies provided. The information provided by the
survey indicates whether nonpoint source categories are covered in partly or wholly in point submittals,
represented by another reported process (SCC) type, or are not present in their state or local jurisdiction.
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. An example of the latter scenario is when a S/L/T agency
reported only one HAP where several others would be expected, or a S/L/T agency has resubmitted older
inventory data. The EPA sector leads contact S/L/T data submitters in cases where the EPA tags out S/L/T data
and gives the S/L/T agencies an opportunity to correct problems themselves.
In addition to S/L/T tags, a more common tag is to block EPA-generated data from being used, which would
otherwise backfill in "gaps" in S/L/T agency data. For example, S/L/T agencies may inventory all Stage 1 gasoline
distribution in their point inventory submittal and have none remaining for the nonpoint inventory; EPA
nonpoint Stage 1 gasoline distribution estimates therefore need to be tagged out to prevent EPA nonpoint data
from backfilling a complete (point) S/L/T inventory. The EPA tags are far more common and automated for the
nonpoint data category where a new nonpoint survey was created for the 2014 NEI. The nonpoint survey is
described in more detail in Section 4.
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 (which includes facility and unit). For nonpoint sources, it is the process (SCC)/shape ID (i.e., rail
lines, ports and shipping lanes) 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.
This section shows the contributions of S/L/T agency data to total emissions for each major data category. Figure
2-1 shows the proportion of CAP, select HAPs, and HAP 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. HAP VOC
2-8
-------
emissions consist of dozens of VOC HAP species, that in-aggregate, should be less than VOC in our QA checks.
HAP 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, except for S02 and VOC which are slightly more-covered by S/L/T
submittals. The large "EPA Other" bar for PMio is predominantly dust sources from unpaved roads, agricultural
dust from crop cultivation, and construction dust.
Figure 2-1: 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%
I EPA Air/Rail/CMV
EPA Carry Forward
I EPA HAP & PM Aug
I EPA Other
IS/L/T
ฐฐ ^ ^ J* & ^ฐฐ v*6
J?
^ s7* xr
Figure 2-2 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 PM, most point emissions come from S/L/T-submitted data. PM
augmentation (see Section 2.2.3) accounts for a significant portion of PM point emissions. The data sources
shown in the figure are described in more detail in Section 3.
2-9
-------
Figure 2-2: Relative contributions for various data sources of Point emissions for CAPs and select HAPs
I EPA Air/Rail/CMV
EPA Carry Forward
I EPAEGU
EPA HAP & PM Aug
I EPA Other
NATA Revisions
IS/L/T
ITRI
We did not compute relative contributions of emissions from nonroad and onroad data categories because of
the nature in how emissions are created for these sources -via a mix of S/L/T and EPA activity data and
processed through the MOVES2014 model. California, which uses its own onroad and nonroad mobile models,
was the only state that provided emissions rather than inputs for EPA models (this is in accordance with the
AERR). All other states were required to provide inputs to the EPA models. Onroad and nonroad mobile data
categories use the MOVES emissions model, and the EPA primarily collected model inputs from S/L agencies for
these categories and ran the models using these inputs to generate the emissions. The S/L agencies that
provided inputs are presented in the nonroad and onroad portions of the document, Section 5 and Section 6,
respectively.
The tables below provide more detail about which S/L/T agencies submitted data to the NEI for the point and
nonpoint data categories. In Sections 3 through 6, we explain more about what data were used by the EPA to
create the NEI for each sector. Usually, the EPA uses the data provided by the S/L/T agencies as described above
in Section 2.2.6. Table 2-3 presents the percentages of total agency-wide point source emissions mass provided
by that air agency. A value of 100 percent reflects a pollutant where all emissions were submitted by the S/L/T
agency and no other data or augmentation was used. Conversely, missing entries reflect that the reporting
agency provided no emissions for that pollutant; a value of zero indicates very small, but not-zero, emissions
submitted by the reporting agency.
Table 2-4 provides a similar table, but for the entire nonpoint data category, excluding biogenic emissions. We
did not create similar tables for nonroad and onroad mobile data categories because input data, not emissions
are collected from S/L/T reporting agencies (except for California, where all emissions come from the state).
Sections 5 and 6 describe which reporting agencies submitted MOVES inputs for these sectors. Similar tables are
provided at a more refined level in Section 4 for various nonpoint data category sector groups such as
Residential Wood Combustion, Oil and Gas Production, Industrial and Commercial/Institutional Fuel Combustion
and Gasoline Distribution.
2-10
-------
Table 2-3: Point inventory percentage submitted by reporting agency to total emissions mass
Agency
CO
NH3
NOX
PM10
PM2.5
S02
voc
Lead
HAP
VOC
HAP
Metals
Acid
Gases
Alabama Department of
Environmental Management
87
90
95
100
93
48
90
64
98
Alaska Department of
Environmental Conservation
52
99
94
89
25
92
62
78
74
Arizona Department of
Environmental Quality
67
84
90
77
59
97
56
63
37
75
58
Arkansas Department of
Environmental Quality
84
80
98
98
8
100
98
40
91
81
99
Assiniboine and Sioux Tribes of
the Fort Peck Indian Reservation
0
97
4
1
56
96
11
1
California Air Resources Board
52
97
72
86
85
84
91
11
50
29
51
Chattanooga Air Pollution Control
Bureau (CHCAPCB)
73
93
94
98
42
73
96
51
94
27
100
City of Albuquerque
58
1
74
54
35
79
75
1
54
1
29
Clark County Department of Air
Quality and Environmental
Management
85
85
73
94
76
91
52
11
90
18
Coeur d'Alene Tribe
100
100
81
56
100
100
8
0
Colorado Department of Public
Health and Environment
80
95
98
95
99
97
20
86
58
95
Confederated Tribes of the Colville
Reservation, Washington
100
100
66
84
100
100
Connecticut Department of Energy
and Environmental Protection
47
94
93
92
91
97
85
6
43
43
99
DC-District Department of the
Environment
98
97
97
97
100
97
86
39
Delaware Department of Natural
Resources and Environmental
Control
86
61
85
70
57
85
74
10
73
84
99
Florida Department of
Environmental Protection
73
64
88
0
99
86
22
81
44
99
Fond du Lac Band of Lake Superior
Chippewa
Georgia Department of Natural
Resources
79
92
91
54
49
99
95
27
5
Gila River Indian Community
Hawaii Department of Health
Clean Air Branch
50
100
87
91
90
98
80
31
28
11
93
Idaho Department of
Environmental Quality
76
99
92
29
33
99
86
6
17
9
2
Illinois Environmental Protection
Agency
99
99
97
100
92
100
99
98
98
94
100
2-11
-------
Agency
CO
NH3
NOX
PM10
PM2.5
S02
voc
Lead
HAP
VOC
HAP
Metals
Acid
Gases
Indiana Department of
Environmental Management
97
75
96
100
84
81
63
68
97
Iowa Department of Natural
Resources
91
93
97
99
97
100
99
65
96
66
100
Kansas Department of Health and
Environment
87
96
96
100
94
21
89
45
100
Kentucky Division for Air Quality
96
99
100
99
67
76
57
21
Knox County Department of Air
Quality Management
89
100
0
100
99
89
79
53
32
Louisiana Department of
Environmental Quality
93
94
98
92
98
49
89
61
66
Louisville Metro Air Pollution
Control District
66
91
93
99
99
100
97
55
83
93
100
Maine Department of
Environmental Protection
86
100
97
0
99
95
33
89
74
71
Maricopa County Air Quality
Department
Maryland Department of the
Environment
48
43
85
0
0
99
63
35
45
43
100
Massachusetts Department of
Environmental Protection
39
96
82
76
82
4
3
2
14
Memphis and Shelby County
Health Department - Pollution
Control
51
20
56
19
3
98
79
37
71
39
100
Metro Public Health of
Nashville/Davidson County
26
61
90
63
92
83
59
7
100
Michigan Department of
Environmental Quality
88
65
97
23
17
100
97
50
77
71
98
Minnesota Pollution Control
Agency
76
92
96
11
0
99
96
56
91
90
100
Mississippi Dept of Environmental
Quality
82
72
92
2
2
100
93
34
90
37
100
Missouri Department of Natural
Resources
93
96
97
32
24
100
96
58
87
54
98
Montana Department of
Environmental Quality
73
9
95
100
94
47
0
44
0
Morongo Band of Cahuilla Mission
Indians of the Morongo
Reservation, California
100
100
100
7
100
100
100
Navajo Nation
Nebraska Environmental Quality
84
95
95
34
15
100
92
30
75
36
10
Nevada Division of Environmental
Protection
92
98
99
100
95
31
14
2-12
-------
Agency
CO
NH3
NOX
PM10
PM2.5
S02
voc
Lead
HAP
VOC
HAP
Metals
Acid
Gases
New Hampshire Department of
Environmental Services
67
95
93
99
70
31
50
87
2
New Jersey Department of
Environment Protection
48
100
78
94
93
92
91
36
60
49
34
New Mexico Environment
Department Air Quality Bureau
90
55
98
97
91
99
94
11
69
12
93
New York State Department of
Environmental Conservation
58
84
82
94
87
98
82
25
73
78
97
Nez Perce Tribe
100
100
100
100
100
100
100
100
99
100
North Carolina Department of
Environment and Natural
Resources
74
90
91
94
83
99
93
33
91
78
99
North Dakota Department of
Health
83
73
98
0
0
100
93
38
86
45
100
Northern Cheyenne Tribe
Ohio Environmental Protection
Agency
94
94
98
100
97
44
29
76
95
Oklahoma Department of
Environmental Quality
90
80
95
94
80
98
94
62
78
70
94
Omaha Tribe of Nebraska
Oregon Department of
Environmental Quality
78
86
97
59
98
94
20
8
0
Pennsylvania Department of
Environmental Protection
84
89
98
100
96
69
87
55
100
Puerto Rico
58
97
98
96
97
57
61
11
Rhode Island Department of
Environmental Management
63
90
74
85
37
74
79
5
74
21
82
Shoshone-Bannock Tribes of the
Fort Hall Reservation of Idaho
100
100
100
100
100
100
South Carolina Department of
Health and Environmental Control
94
98
95
98
90
97
97
45
95
71
100
South Dakota Department of
Environment and Natural
Resources
65
98
66
64
100
96
Southern Ute Indian Tribe
91
99
95
92
97
91
Tennessee Department of
Environmental Conservation
90
37
98
86
60
100
98
33
91
70
99
Texas Commission on
Environmental Quality
100
54
100
100
91
100
100
96
90
75
99
Tohono O-Odham Nation
Reservation
Utah Division of Air Quality
83
96
97
99
97
100
91
0
7
0
97
2-13
-------
Agency
CO
NH3
NOX
PM10
PM2.5
S02
voc
Lead
HAP
VOC
HAP
Metals
Acid
Gases
Ute Indian Tribe of the Uintah &
Ouray Reservation, Utah
Vermont Department of
Environmental Conservation
56
76
87
85
91
82
0
42
0
8
Virgin Islands
Virginia Department of
Environmental Quality
71
79
91
96
78
88
87
56
57
40
99
Washington State Department of
Ecology
84
77
88
93
90
97
91
15
28
42
23
Washoe County Health District
1
91
4
18
12
3
79
West Virginia Division of Air
Quality
92
76
99
100
96
67
86
84
100
Wisconsin Department of Natural
Resources
80
75
89
97
14
98
97
24
88
74
95
Wyoming Department of
Environmental Quality
97
100
99
99
88
100
99
21
91
55
99
Yakama Nation Reservation
100
100
100
52
100
100
Table 2-4: Nonpoint inventory percentange submitted by reporting agency to total emissions mass
Agency
CO
NH3
NOX
PM10
PM2.5
S02
VOC
Lead
HAP
VOC
HAP
Metals
Acid
Gases
Alabama Department of
Environmental Management
Alaska Department of
Environmental Conservation
4
9
0
0
4
1
Arizona Department of
Environmental Quality
27
2
16
1
7
37
63
6
14
2
Arkansas Department of
Environmental Quality
20
1
19
6
8
1
8
0
2
Assiniboine and SiouxTribes of the
Fort Peck Indian Reservation
100
100
100
42
60
100
100
100
100
98
California Air Resources Board
32
51
89
76
55
70
47
46
51
65
57
Chattanooga Air Pollution Control
Bureau (CHCAPCB)
6
6
25
0
0
5
75
13
4
3
City of Albuquerque
31
27
82
1
3
87
2
13
0
3
Clark County Department of Air
Quality and Environmental
Management
19
5
43
73
78
99
0
Coeur d'Alene Tribe
5
71
15
83
48
19
41
100
14
98
100
Colorado Department of Public
Health and Environment
49
66
1
3
66
Connecticut Department of Energy
and Environmental Protection
9
3
35
4
8
9
71
24
19
34
2-14
-------
Agency
CO
NH3
NOX
PM10
PM2.5
S02
voc
Lead
HAP
VOC
HAP
Metals
Acid
Gases
DC-District Department of the
Environment
33
2
53
1
3
11
90
29
6
3
Delaware Department of Natural
Resources and Environmental
Control
89
98
95
91
94
94
96
66
43
7
Florida Department of
Environmental Protection
7
1
15
4
17
29
63
24
60
1
Georgia Department of Natural
Resources
69
2
23
5
25
42
38
Hawaii Department of Health
Clean Air Branch
Idaho Department of
Environmental Quality
21
75
34
49
57
65
80
95
47
98
100
Illinois Environmental Protection
Agency
89
99
69
67
79
98
94
71
56
80
100
Indiana Department of
Environmental Management
3
0
20
0
1
12
10
44
10
38
Iowa Department of Natural
Resources
10
0
20
49
52
18
41
65
5
36
Kansas Department of Health and
Environment
1
0
3
0
0
70
19
25
2
7
Kentucky Division for Air Quality
Knox County Department of Air
Quality Management
18
4
38
6
12
41
81
15
5
3
Kootenai Tribe of Idaho
7
83
20
85
52
11
52
100
18
94
100
Louisiana Department of
Environmental Quality
10
0
4
3
13
32
26
12
4
1
Louisville Metro Air Pollution
Control District
15
5
40
13
32
50
48
7
4
2
Maine Department of
Environmental Protection
4
27
32
2
4
18
60
30
5
5
29
Maricopa County Air Quality
Department
4
15
83
53
2
22
Maryland Department of the
Environment
59
8
75
93
81
81
88
77
25
33
10
Massachusetts Department of
Environmental Protection
12
59
62
70
39
91
45
Memphis and Shelby County
Health Department - Pollution
Control
21
4
70
3
8
31
1
71
0
2
Metro Public Health of
Nashville/Davidson County
13
51
39
9
39
43
34
62
0
Michigan Department of
Environmental Quality
77
13
91
10
38
91
92
86
30
77
53
2-15
-------
Agency
CO
NH3
NOX
PM10
PM2.5
S02
voc
Lead
HAP
VOC
HAP
Metals
Acid
Gases
Minnesota Pollution Control
Agency
90
2
41
15
49
75
77
75
54
81
39
Mississippi Dept of Environmental
Quality
Missouri Department of Natural
Resources
4
0
34
0
1
11
20
75
0
45
Montana Department of
Environmental Quality
Morongo Band of Cahuilla Mission
Indians of the Morongo
Reservation, California
100
100
100
100
100
100
100
45
32
100
Nebraska Environmental Quality
Nevada Division of Environmental
Protection
New Hampshire Department of
Environmental Services
6
3
88
46
28
95
33
New Jersey Department of
Environment Protection
29
80
85
80
58
93
91
New Mexico Environment
Department Air Quality Bureau
New York State Department of
Environmental Conservation
14
2
67
26
32
82
85
94
30
92
6
Nez Perce Tribe
8
91
22
92
71
32
52
100
19
99
100
North Carolina Department of
Environment and Natural
Resources
36
0
32
7
26
22
2
4
2
8
92
North Dakota Department of
Health
Northern Cheyenne Tribe
100
100
100
99
100
100
99
72
Ohio Environmental Protection
Agency
9
0
37
1
4
36
75
52
12
33
78
Oklahoma Department of
Environmental Quality
50
0
76
1
6
68
87
33
2
43
0
Oregon Department of
Environmental Quality
44
2
30
2
11
60
69
16
22
4
Pennsylvania Department of
Environmental Protection
12
1
53
5
13
11
60
3
8
1
Puerto Rico
0
4
0
0
1
0
0
0
Rhode Island Department of
Environmental Management
3
3
14
1
2
10
24
11
6
31
Sac and Fox Nation of Missouri in
Kansas and Nebraska Reservation
100
100
100
14
25
100
100
100
22
99
100
2-16
-------
Agency
CO
NH3
NOX
PM10
PM2.5
S02
voc
Lead
HAP
VOC
HAP
Metals
Acid
Gases
Shoshone-Bannock Tribes of the
Fort Hall Reservation of Idaho
100
100
100
97
90
100
100
100
100
99
100
South Carolina Department of
Health and Environmental Control
9
1
23
5
18
13
63
4
4
0
South Dakota Department of
Environment and Natural
Resources
Tennessee Department of
Environmental Conservation
11
1
18
7
15
6
0
86
0
31
Texas Commission on
Environmental Quality
61
1
99
2
12
89
94
15
2
17
United Keetoowah Band of
Cherokee Indians in Oklahoma
100
Utah Division of Air Quality
55
26
76
17
22
19
82
Vermont Department of
Environmental Conservation
88
11
56
35
72
95
50
26
59
8
Virgin Islands
Virginia Department of
Environmental Quality
14
5
36
4
14
67
68
73
51
30
3
Washington State Department of
Ecology
81
4
83
82
83
93
19
12
27
1
99
Washoe County Health District
96
17
99
94
72
100
78
94
3
85
100
West Virginia Division of Air
Quality
46
0
70
2
8
79
91
9
83
34
0
Wisconsin Department of Natural
Resources
10
0
37
2
8
23
53
28
4
21
Wyoming Department of
Environmental Quality
40
43
1
3
95
81
69
Table 2-5 provides a summary of CAP and total HAP emissions for all EIS sectors, including the biogenic
emissions from vegetation and soil. Emissions in federal waters and from vegetation and soils have been split
out and totals both with and without these emissions are included. Emissions in federal waters include offshore
drilling platforms and commercial marine vessel emissions outside the typical 3-10 nautical mile boundary
defining state waters. All emissions values are bounded by the caveats and methods described by this
documentation.
Table 2-5: EIS sectors and associated 2014v2 CAP emissions and total HAP (1000 short tons/year)
Sector
CO
nh3
NOx
PMz.S
PM10
SO2
VOC
Black
Carbon
Lead
Total
HAPs1
Agriculture - Crops & Livestock Dust
986
5,001
11
Agriculture - Fertilizer Application
787
Agriculture - Livestock Waste
2,075
4.16
23
180
0.21
2.63E-04
15
Bulk GasolineTerminals
0.93
4.12E-04
0.45
0.03
0.04
8.01E-03
125
3.58E-04
2.01E-04
6.13
Commercial Cooking
33
89
96
16
2.98
4.79E-05
6.79
2-17
-------
Sector
CO
nh3
NOx
PMz.S
PM10
SO2
voc
Black
Carbon
Lead
Total
HAPs1
Dust - Construction Dust
0.07
0.08
125
1,209
0.02
0.04
5.37E-05
1.08E-03
0.07
Dust - Paved Road Dust
179
783
1.86
Dust - Unpaved Road Dust
660
6,642
0.64
Fires - Agricultural Field Burning
583
93
20
65
87
6.43
40
7.04
2.23E-04
26
Fires - Prescribed Fires
8,681
138
152
781
920
72
1,980
79
384
Fires - Wildfires
10,487
172
119
886
1,046
71
2,466
84
451
Fuel Comb - Comm/lnstitutional - Biomass
19
0.19
8.55
12
14
0.93
0.69
0.43
2.85E-04
0.37
Fuel Comb - Comm/lnstitutional - Coal
3.56
0.01
9.27
0.81
1.90
35
0.29
0.03
1.59E-03
1.39
Fuel Comb - Comm/lnstitutional - Natural Gas
133
1.47
165
5.12
5.42
1.44
11
0.34
1.94E-03
1.07
Fuel Comb - Comm/lnstitutional - Oil
12
0.49
48
4.45
4.78
20
2.80
0.60
1.12E-03
0.18
Fuel Comb - Comm/lnstitutional - Other
11
0.06
12
0.63
0.66
1.21
1.16
0.04
3.52E-04
0.21
Fuel Comb - Electric Generation - Biomass
22
0.74
12
1.73
2.04
1.80
1.04
0.06
1.42E-03
1.60
Fuel Comb - Electric Generation - Coal
579
8.90
1,516
147
195
3,155
22
6.01
0.04
64
Fuel Comb - Electric Generation - Natural Gas
90
13
146
24
25
8.74
9.28
1.65
9.16E-04
3.33
Fuel Comb - Electric Generation - Oil
9.22
0.78
72
6.79
8.13
63
1.70
1.45
1.49E-03
0.38
Fuel Comb - Electric Generation - Other
31
2.20
25
2.88
3.25
16
3.68
0.16
9.42E-04
1.79
Fuel Comb - Industrial Boilers, ICEs - Biomass
303
3.01
115
149
177
20
9.62
5.51
7.1E-03
6.31
Fuel Comb - Industrial Boilers, ICEs - Coal
34
0.73
119
13
41
335
0.88
0.54
0.01
12
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
317
8.29
601
23
24
16
61
1.52
3.03E-03
21
Fuel Comb - Industrial Boilers, ICEs - Oil
25
0.34
83
6.28
7.29
27
5.29
1.38
0.02
0.53
Fuel Comb - Industrial Boilers, ICEs - Other
110
0.87
57
13
14
51
8.81
0.87
2.72E-03
2.48
Fuel Comb - Residential - Natural Gas
98
48
228
3.84
4.10
1.50
14
0.26
1.27E-04
0.86
Fuel Comb - Residential - Oil
9.91
1.88
36
4.03
4.63
57
1.26
0.47
2.59E-03
0.09
Fuel Comb - Residential - Other
13
0.14
35
0.24
0.29
1.85
1.45
0.02
4.78E-06
0.06
Fuel Comb - Residential - Wood
2,108
15
31
315
316
7.71
340
18
8.32E-05
58
Gas Stations
0.04
1.87E-04
0.01
9.07E-04
9.08E-04
4.6E-04
438
4.E-05
2.05E-04
58
Industrial Processes - Cement Manufacturing
99
1.08
118
7.50
13
41
13
0.21
3.11E-03
3.27
Industrial Processes - Chemical Manufacturing
151
23
72
16
21
133
85
0.38
2.99E-03
28
Industrial Processes - Ferrous Metals
350
0.22
60
29
36
26
14
0.53
0.05
2.11
Industrial Processes - Mining
11
0.10
5.50
53
383
1.14
1.34
0.07
4.91E-03
0.84
Industrial Processes - NEC
183
16
171
81
142
137
190
1.42
0.05
47
Industrial Processes - Non-ferrous Metals
268
0.62
16
13
17
67
14
0.20
0.03
6.56
Industrial Processes - Oil & Gas Production
688
0.35
709
20
20
81
3,104
0.11
8.28E-04
109
Industrial Processes - Petroleum Refineries
48
2.45
69
17
19
58
53
1.01
2.91E-03
9.86
Industrial Processes - Pulp & Paper
100
5.30
74
32
41
29
126
0.92
4.01E-03
53
Industrial Processes - Storage and Transfer
6.87
5.39
5.74
17
45
2.97
201
0.24
3.E-03
12
Miscellaneous Non-Industrial NEC
243
5.02
7.05
15
18
0.19
85
0.62
5.81E-04
18
Mobile - Aircraft
412
147
9.30
11
17
47
7.17
0.46
13
Mobile - Commercial Marine Vessels
66
0.16
420
11
12
48
11
5.36
1.1E-03
1.23
Mobile - Locomotives
124
0.38
712
20
22
0.84
37
16
1.82E-03
3.06
Mobile - Non-Road Equipment - Diesel
577
1.39
1,099
83
86
2.06
114
64
52
Mobile - Non-Road Equipment - Gasoline
11,668
0.85
235
50
55
1.16
1,537
6.09
485
Mobile - Non-Road Equipment - Other
415
0.01
67
2.20
2.20
0.45
14
0.40
2.40
Mobile - On-Road Diesel Heavy Duty Vehicles
652
6.67
2,115
92
127
3.44
162
52
2.05E-04
33
Mobile - On-Road Diesel Light Duty Vehicles
520
1.10
173
7.16
9.99
0.37
52
4.73
4.89E-05
9.20
Mobile - On-Road non-Diesel Heavy Duty Vehicles
784
1.03
81
1.64
4.14
0.53
36
0.27
2.2E-05
10
Mobile - On-Road non-Diesel Light Duty Vehicles
22,482
100
2,510
62
163
24
1,966
12
1.53E-03
545
Solvent - Consumer & Commercial Solvent Use
0.01
0.01
1,621
5.33E-04
213
Solvent - Degreasing
5.35E-03
0.04
0.01
0.08
0.08
2.95E-05
172
5.44E-04
3.84E-04
72
Solvent - Dry Cleaning
1.57E-03
7.44E-04
9.38E-03
9.42E-03
4.21E-05
6.18
1.19E-04
0.84
Solvent - Graphic Arts
0.11
0.08
0.13
0.13
0.14
0.02
388
l.E-03
2.61E-05
29
Solvent - Industrial Surface Coating & Solvent Use
5.68
0.45
2.82
3.67
4.12
0.17
539
0.06
2.52E-03
73
Solvent - Non-Industrial Surface Coating
0.02
326
44
Waste Disposal
1,974
29
110
231
278
32
227
24
0.01
45
2-18
-------
Sector
CO
NH3
NOx
PMz.S
PM10
SO2
voc
Black
Carbon
Lead
Total
HAPs1
Sub Total (no federal waters)
65,537
3,571
12,589
5,381
18,183
4,674
16,883
424
0.73
3,043
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
48
6.71E-03
42
0.39
0.39
0.03
0.99
0.03
1.05E-06
Fuel Comb - Industrial Boilers, ICEs - Oil
1.17
5.62E-06
5.03
0.25
0.25
0.44
0.27
0.19
7.38E-07
Fuel Comb - Industrial Boilers, ICEs - Other
l.E-03
3.36E-05
1.25E-03
6.39E-05
6.39E-05
1.64E-05
4.21E-04
4.92E-06
5.25E-09
Industrial Processes - Oil & Gas Production
1.22
8.07E-03
1.68
0.03
0.03
0.04
46
5.7E-05
1.28E-06
Industrial Processes - Storage and Transfer
0.88
Mobile - Commercial Marine Vessels
111
0.28
825
24
26
127
27
7.59
1.91E-03
1.21
Sub Total (federal waters)
161
0.29
874
25
26
128
76
7.81
1.92E-03
1.21
Sub Total (all but vegetation and soil)
65,698
3,572
13,463
5,406
18,210
4,802
16,958
431
0.73
3,044
Biogenics - Vegetation and Soil2
6,654
22
903
38,672
5,294
Total
72,353
3,594
14,366
5,406
18,210
4,802
55,630
431
0.73
8,338
1 Total HAP does not include diesel PM, which is not a HAP listed by the Clean Air Act.
2 Biogenic vegetation and soil emissions excludes emissions from Alaska, Hawaii, and territories.
2.5 How does this NEI compare to past inventories?
Many similarities exist between the 2014 NEI approaches and past NEI (including 2014vl) 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 2014, S/L/T participation was somewhat more
comprehensive than in 2011, though both were good. The NEI program continues with the 2014 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 2014 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 2014 cycle are highlighted here.
To improve the process, we learned from the prior two triennial inventories (for 2008 and 2011) compiled with
the EIS. We made changes to pollutant and SCC codes, refined quality assurance checks and features that were
used to assist in quality assurance, and created a Nonpoint Survey to assist with S/L/T and EPA data
reconciliation for the nonpoint data. 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. For point
sources, the primary changes were our use of HAP emission rates for EGUs, HAP augmentation improvements,
and the use of an expected pollutant QA check. For EGUs, we chose to defer to S/L/T-provided HAP data rather
than override their submissions using emission factors developed from the Mercury and Air Toxics Standards
(MATS) test program as we had done in 2008 and 2011. Instead, we provided these the HAP emission factors to
S/L/T agencies so their inventory staff could use them. HAP augmentation improvements are described in
Section 3.1.6 and the expected pollutant QA is described in Section 3.1.1. More information on point source
improvements is available in Section 3.
2-19
-------
We also made method improvements for many stationary nonpoint sectors (see also in 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. We updated methods for residential wood combustion
to improve the geographic allocation of appliances, burn rates and controls. We updated the agricultural
livestock ammonia method to reflect a new method devised by researchers to incorporate more process-based
methods and new observational data. We updated the approach for agricultural tilling to use USDA Census of
Agriculture data on harvested acres and tillage type rather than a national top-down approach. We refined
emissions calculation approaches for the oil and gas exploration and production sectors to reflect new processes
and made use of newly available data. For all nonpoint categories except for nonpoint mercury sectors, we
updated the activity data to use the newest data available, at the time, to represent the 2014 inventory year.
One method change was made for road dust that was not an improvement in 2014vl, but was fixed in the
2014v2 NEI. In 2014vl, we did not use a "precipitation" adjustment for road dust that was included in the 2011
NEI. We removed this adjustment because air quality modelers use gridded meteorology, soil moisture, snow
cover and other parameters to remove (zero out) dust emissions on an hourly basis, and we did not want to
have this effect applied twice in air quality modeling -and using two likely-different methods. The 2011
precipitation adjustment is essentially smoothed over the entire year and used different (not gridded,
temporally-resolved) data. However, the resulting 2014vl emissions did not reflect the actual emissions
associated from the road dust processes, and caused a significant increase in PM emission trends from prior
NEIs. Therefore, as discussed in greater detail in Section 4.9.3.5 and Section 4.10.3.5, we re-applied a
meteorological adjustment, based on 2014vl emissions modeling, to paved and unpaved road dust PM
estimates for the 2014v2 NEI.
For mobile sources, we updated mobile source activity data such as vehicle miles travelled (VMT) to reflect
2014, we used updated mobile source models, and we used new mobile model inputs provided by S/L/T
agencies and other sources. Sections 5 and 6 provide more detail on these improvements.
We also made several improvements to approaches for fire sources, as further described in Section 7. For
agricultural fires, we used an improved satellite-based approach and added a distinction between grass and
pasture burning processes. For wildfires and prescribed fires, we used 2014-specific satellite data and collected
2014-specific ground based observational data from many state forestry agencies. For these fires, we also
estimated the flaming and smoldering components of emissions separately and retained this delineation in the
final inventory. Finally, we revised several HAP emission factors based on the peer reviewed literature.
2,5.2 Differences in emissions between 2014 and 2011 NEI
This section presents a comparison from the 2011v2 NEI to the 2014v2 NEI. Table 2-6 and Table 2-7 compare
emissions for the CAPs for the 2014v2 minus 2011v2 NEI, and for 2014v2 minus 2014vl NEIs, respectively, for
seven highly aggregated emission sectors. Table 2-8 and Table 2-9 compare emissions for select HAPs for the
2014v2 minus 2011v2 NEI, and for 2014v2 minus 2014vl NEIs, respectively, for the same seven highly
aggregated emission sectors. Emissions 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 2014 (v2) than in 2011 (v2). Some specific
sector/pollutants increased in 2014 from 2011. The increases in industrial processes VOC is off-set by more
substantial cumulative decreases in fuel combustion and mobile sources. A small increase in fuel combustion
2-20
-------
NH3 is more than offset by large reductions from agriculture (miscellaneous) sources. Mobile source sector
emissions are lower in 2014 than 2011, continuing a trend found between 2008 and 2011. Wildfire CAP
emissions are lower in 2014 than in 2011, which is consistent with the general observation that 2014 was a
generally quiet year for such fires. CAP emission increases in 2014 occur for the following sectors:
Fuel Combustion - natural gas from residential and industrial boilers and internal combustion engines (NH3)
Industrial Processes - oil and gas production (VOC).
Table 2-6: Emission differences (tons) for CAPs, 2014v2 minus 2011v2 NEIs
Broad Sector
CO
nh3
NOx
PMio
PMz.5
S02
VOC
Fuel Combustion
-530,653
3,989
-454,859
-110,138
-92,874
-1,623,060
-111,203
Industrial Processes
-176,239
-13,238
-9,576
-127,551
-40,273
-91,126
334,910
Miscellaneous
-697,269
-610,108
-6,942
-1,918,556
-277,869
-6,519
-247,245
Highway Vehicles
-2,918,889
-15,012
-991,212
-66,557
-34,435
-1,062
-426,178
Nonroad Mobile
-1,687,099
-484
-401,334
-36,213
-34,603
-58,228
-396,832
Total Difference,
excluding wildfires
-6,010,149
-634,852
-1,863,923
-2,259,015
-480,054
-1,779,994
-846,549
Total % Difference,
excluding wildfires
-10%
-16%
-13%
-12%
-10%
-28%
-6%
Wildfires
-2,214,402
-31,661
-65,655
-280,235
-238,930
-24,388
-425,060
Table 2-7: Emission differences (tons) for CAPs, 2014v2 minus 2014vl NEIs
Broad Sector
CO
nh3
NOx
PMio
PM2.5
S02
VOC
Fuel Combustion
-66,060
-1,305
-41,593
-61,035
-36,336
-135,515
-13,938
Industrial Processes
-157,427
83
-104,621
-96,786
-10,827
9,101
-71,681
Miscellaneous
-207,110
-303,021
-3,871
-6,169,504
-806,974
-4,566
299,211
Highway Vehicles
2,601,462
4,367
213,927
-5,562
3,800
-227
163,429
Nonroad Mobile
-44,726
-25
-117,556
-5,367
-5,044
-6,802
-8,677
Total Difference,
excluding wildfires
2,126,138
-299,900
-53,715
-6,338,255
-855,380
-138,008
368,344
Total % Difference,
excluding wildfires
4%
-8%
0%
-27%
-16%
-3%
3%
Wildfires
160,312
2,622
1,569
15,770
13,356
1,015
37,649
There are various changes in CAP emissions between 2014vl and 2014v2. The most significant increases are in
onroad mobile and wildfires. The increase in industrial processes S02 is from an increase in S/L/T-submitted
chemical manufacturing emissions. Roughly half the increase in miscellaneous VOC is from the introduction of
VOC for livestock waste and the rest from solvent utilization. The biggest change between 2014vl and 2014v2
was the reintroduction of the precipitation reduction to unpaved and paved road dust for PM.
For the select HAPs reviewed, Table 2-8 indicates a mixture of overall increases and decreases between 2011
and 2014, with the largest increases in some VOC HAPs for industrial, miscellaneous and nonroad sources. Some
of the largest decreases are for highway vehicle VOC HAPs and fuel combustion. VOC HAPs increase for nonroad
mobile sources mostly result from using a new model (MOVES2014 rather than NONROAD) and newer emission
factors for nonroad equipment in 2014 and resulting different emissions factors in MOVES2014. Unlike CAPs,
updated HAP emission factors from wildfires result in some HAP emissions that are higher in 2014 than in 2011,
with the most substantial increase for formaldehyde. HAP emission increases in sectors, include the following:
Fuel Combustion - biomass, coal and oil combustion (Pb).
Industrial Processes -oil and gas production (VOC HAPs)
2-21
-------
Miscellaneous - agricultural field burning and prescribed fires (acrolein), construction and road dust (Pb)
Nonroad Mobile - aircraft and gasoline, diesel and other equipment (acrolein, formaldehyde)
There were smaller changes in HAPs between 2014vl and 2014v2. As seen in Table 2-9, the largest increases in
2014v2 are from highway vehicles and new HAP estimates for wildfires and prescribed burning sources. Sizable
decreases in miscellaneous sources are from agricultural field burning and solvents and decreases in industrial
processes are from oil and gas sources.
Table 2-8: Emission differences (tons) for select HAPs, 2014v2 minus 2011v2 NEIs
Broad Sector
Acrolein
Benzene
Ethylene
Oxide
Formaldehyde
Hexavalent
Chromium
Lead
Fuel Combustion
-245
-3,616
-8
-3,647
-14
13
Industrial Processes
350
3,881
-57
8,712
-17
-72
Miscellaneous
3,665
-33,759
-79
-2,632
0
3
Highway Vehicles
-467
-10,271
-5,812
0
Nonroad Mobile
2,205
-844
16,170
-1
-31
Total Difference,
excluding wildfires
5,508
-44,609
-145
12,791
-32
-87
Total % Difference,
excluding wildfires
19%
-20%
-49%
5%
-46%
-11%
Wildfires
737
-29,726
3,550
Table 2-9: Emission differences (tons) for select HAPs, 2014v2 minus 2014vl NEIs
Broad Sector
Acrolein
Benzene
Ethylene
Oxide
Formaldehyde
Hexavalent
Chromium
Lead
Fuel Combustion
-15
-954
0
-1,151
0
2
Industrial Processes
-78
-1,174
-38
-363
-5
0
Miscellaneous
-1,761
-3,433
-2
-3,349
-16
0
Highway Vehicles
151
5,394
1,851
0
0
Nonroad Mobile
-47
-88
-639
0
0
Total Difference,
excluding wildfires
-1,749
-254
-40
-3,651
-22
3
Total % Difference,
excluding wildfires
-5%
0%
-21%
-1%
-36%
0%
Wildfires
542
447
3,518
Twelve tribes submitted data to the EIS for 2014 as shown in Table 2-10. In this table, a "CAP, HAP" designation
indicates that both criteria and hazardous air pollutants were submitted by the tribe. 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 2-10 with just a CAP flag will also have some HAP emissions in most cases.
Seven additional tribal agencies, shown in Table 2-11, which did not submit any data, are represented in the
point data category of the 2014 NEI due to the emissions added by the EPA. The emissions for these facilities are
from the EPA gap fill datasets for airports, EGUs, TRI data, and data carried forward from the 2011 NEI that were
not provided in the 2014 submittal. 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
2-22
-------
on tribal lands because the underlying database contained data reported by tribes. See Section 4.16 for more
information.
Table 2-10: Tribal participation in the 2014 NEI
Tribal Agency
Point
Nonpoint
Onroad
Nonroad
Assiniboine and Sioux Tribes of the Fort Peck Indian
Reservation
CAP, HAP
CAP, HAP
Coeur d'Alene Tribe
CAP, HAP
CAP, HAP
CAP, HAP
CAP, HAP
Confederated Tribes of the Colville Reservation,
Washington
CAP
Kootenai Tribe of Idaho
CAP, HAP
CAP, HAP
CAP, HAP
Morongo Band of Cahuilla Mission Indians of the Morongo
Reservation, California
CAP, HAP
CAP, HAP
CAP
Nez Perce Tribe
CAP, HAP
CAP, HAP
CAP, HAP
CAP, HAP
Northern Cheyenne Tribe
CAP, HAP
CAP
CAP
Sac and Fox Nation of Missouri in Kansas and Nebraska
Reservation
CAP, HAP
Shoshone-Bannock Tribes of the Fort Hall Reservation of
Idaho
CAP, HAP
CAP, HAP
CAP, HAP
CAP, HAP
Southern Ute Indian Tribe
CAP, HAP
United Keetoowah Band of Cherokee Indians in Oklahoma
CAP
Yakama Nation Reservation
CAP
Table 2-11: Facilities on Tribal lands with 2014 NEI emissions from EPA only
Tribal Agency
EPA data used
Fond du Lac Band of Lake Superior Chippewa
Airports
Gila River Indian Community
TRI
Navajo Nation
Prior Year NEI Carry-forward, EGUs
Northern Cheyenne Tribe
Airports
Omaha Tribe of Nebraska
Airports
Tohono O-Odham Nation Reservation
TRI
Ute Indian Tribe of the Uintah & Ouray Reservation,
Utah
Airports, EGUs
0 'J -.J... ' .<ฆ
This 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 2-14.
Mercury emission estimates in the 2014v2 NEI sum to 52 tons, with 51 tons from stationary sources (not
including commercial marine vessels and locomotives) and 1 ton from mobile sources (including commercial
marine vessels and locomotives). Of the stationary source emissions, the inventory shows that 22.9 tons come
from coal, petroleum coke or oil-fired EGUs with units larger than 25 megawatts (MW), with coal-fired units
making up the vast majority (i.e., petroleum coke and oil-fired boilers account for less than 0.1 ton) of that total.
As with previous NEIs, coal-fired EGUs comprise the largest portion of the mercury emissions in the 2014v2 NEI.
2-23
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The data sources used to create the 2014 Hg inventory are shown in Figure 2-3.
Figure 2-3: Data sources of Hg emissions (tons) in the 2014v2 NEI, by data category
50
45
40
35
CUD
30
X
LO
25
c
p
1
20
15
10
5
0
ฆ EPA HAP Aug
ฆ S/L/T
ฆ 2014EPA_Nonpoint_V2
1 EPA mobile
ฆ 2014EPA_TRI
2014EPA_NATA
1 2014EPA_EGU
Point
Nonpoint
Onroad
Nonroad
In the above figure the "EPA mobile" accounts for all EPA datasets containing onroad, nonroad, CMV and
locomotive emissions. The 2014EPA_NATA dataset contains EPA revisions to Hg emissions including additional
gap filling of emissions not reported by S/L/T and updated railyard emissions.
In addition to Figure 2-3, Table 2-12 lists the emissions by data source with EPA mobile further broken out. More
information on the 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 2-12: 2014v2 NEI Hg emissions (tons) for each dataset type and group
Data Category
Data Source
Hg emissions
Point
S/L/T
33.5
2014EPA TRI
5.4
2014EPA EGU
3.6
EPA NATA
1.0
EPA HAP Aug
0.1
2014EPA LF
0.01
Nonpoint
EPA_Nonpoint_V2
5.5
S/L/T
1.2
EPA Rail
0.5
EPA HAP Aug
0.5
EPA CMV
0.01
2-24
-------
Data Category
Data Source
Hg emissions
Onroad
EPA onroad
0.3
S/L/T
0.04
Nonroad
S/L/T
0.04
EPA nonroad
0.02
The datasets are described in more detail starting in Sections 3 and 4, and we highlight some key datasets here.
For EGUs, we gap-filled where S/L/Ts did not provide emissions using unit specific and "bin"-average emission
factors collected from a test program conducted primarily in 2010 to support the MATS rule [ref 2], and used
2014-specific activity from the Clean Air Markets Division Data. The MATS-based Hg data are labeled "EPA EGU"
in the figure; all mercury emissions from the EPA EGU dataset use MATS-based data.
We gap-filled Hg 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 (see Section 2.2.5). For 2014v2, however, we conducted additional gap filling for mercury. We used
TRI data associated with electric arc furnaces (EAFs) that we had excluded in 2014vl due to our business rule of
not using TRI data at a facility where there were S/L/T-submitted estimates. We determined that for some EAFs,
the S/L/T-submitted estimates were not associated with EAFs (they were associated with fuel combustion). In
addition, we gap filled EAFs that were not reported by S/L/Ts and for which there was no TRI estimate 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.
For municipal waste combustors (MWCs), we compared the 2014vl estimates with 2015 emissions data on
waste-to-energy facilities collected for the "Inventory of U.S. sources of mercury emissions to the atmosphere"
[ref 3], We worked with several states to review their estimates, which led to some changes from their 2014vl
data. We also gap filled MWCs that were missing from the NEI. One MWC unit tested in 2014 was not changed
despite it being significantly higher than the 2015 data. It was determined [ref 4] that the 2014 test was
influenced by an abnormally high (and not representative) Hg inlet concentration (about 10-100 times higher
than average) during the stack test. Because these test data were used for the annual emission factor for the
unit, this one facility was estimated to emit approximately 320 lbs out of a total of 1244 lbs (30% of the national
total).
For 2014v2, EPA updated the estimates for the nonpoint non-combustion-related and cremation categories;
laboratory activities which was carried forward from the 2011 NEI "as-is." The methodologies are described in
Section 4. EPA estimates for these categories are included in the "2014EPA_NONPOINT_V2" (along with other
EPA nonpoint category estimates) shown in Figure 2-3 and Table 2-12 and include:
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 (1.7 tons)
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;
Municipal; Dumping/Crushing/Spreading of New Materials (working face) (0.4 tons)
2-25
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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 (0.1 tons)
dental amalgam - emissions at dentist offices and from evaporation in teeth, SCC=2850001000:
Miscellaneous Area Sources; Health Services; Dental Alloy Production; Overall Process (0.5 tons)
general laboratory activities, SCC = 2851001000: Miscellaneous Area Sources; Laboratories; Bench Scale
Reagents; Total (0.3 tons)
fluorescent lamp breakage, SCC= 2861000000: Miscellaneous Area Sources; Fluorescent Lamp
Breakage; Non-recycling Related Emissions; Total (0.8 tons)
fluorescent lamp recycling, SCC= 2861000010: Miscellaneous Area Sources; Fluorescent Lamp Breakage;
Recycling Related Emissions; Total (less than 1 lb)
animal cremation, SCC= Miscellaneous Area Sources; Other Combustion; Cremation; Animals (0.07 tons
nonpoint plus 0.01 tons point)
human cremation - emissions primarily due to mercury in dental amalgam, SCC=2810060100:
Miscellaneous Area Sources; Other Combustion; Cremation; Humans (1.4 tons nonpoint plus 0.1 tons
point)
While most of the data for these categories use the EPA estimates, some S/L/Ts also provide estimates for some
of these nonpoint sources. The values in parentheses are the total nonpoint portion except for animal and
human cremation which include the component from point sources.
Other nonpoint estimates changed between 2014vl and 2014v2. Corrections were made from the 2014vl
augmentation of Hg from diesel engines and turbines. An Hg-to-PM2.5-PRI ratio was computed that was
consistent with the ICI Combustion Tool (see Section 4.12), resulting in a large decrease in Hg emissions. We
updated the approach for residential wood combustion resulting in an increase in Hg emissions.
Since mercury is a HAP, it is reported voluntarily by S/L/T agencies. For the 2014 NEI, S/L/T agencies reported
emissions in 42 states for 2014vl, and an additional 3 states provided emissions for 2014v2 that hadn't provided
emissions for vl. No tribal agencies reported point source Hg. Table 2-13 identifies the states for which state or
local agencies provided data; 16 states (CA, DE, IN, LA, MA, MD, MN, NC, NJ, NY, OH, OR, Rl VA, VT, Wl, WY)
submitted additional emissions or changes to their emissions for 2014v2. In addition, for the 2014v2, KY
requested that EPA use EPA EGU Hg estimates ahead of KY state-submitted estimates (no changes were made to
the local Louisville agency estimates). Twenty-one states (AZ, CA, CT, DE, ID, IL, LA, MD, ME, Ml, MN, NC, NY, OH,
OK, OR, Rl, VA, VT, WA, WV), 2 local agencies (Washoe County and Memphis) and 10 tribal agencies reported Hg
to the nonpoint data category. Seven tribal agencies reported Hg to the nonpoint data category: Assiniboine and
Sioux Tribes of the Fort Peck Indian Reservation, Montana; Coeur d'Alene Tribe of the Coeur d'Alene
Reservation, Idaho; Morongo Band of Cahuilla Mission Indians of the Morongo Reservation, California; Kootenai
Tribe of Idaho; Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho; Nez PerceTribe of Idaho; and Sac
& Fox Nation of Missouri in Kansas and Nebraska.
In contrast to the 2011 NEI, most of the point Hg in 2014 is from S/L/Ts and not the EPA EGU dataset. This is
because we changed the selection hierarchy to use the S/L/T data ahead of the MATS EFs from the EPA's EGU
dataset. Instead, the EPA provided the MATS EFs to S/L/Ts, so that they could use them if they chose.
2-26
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Table 2-13: Point inventory percentage submitted by reporting agency to State total Hg emissions mass
State
Agency
Agency
Type
Percent of
State Total
AL
Alabama Department of Environmental Management
State
50
AL
Jefferson County (AL) Department of Health
Local
21
AR
Arkansas Department of Environmental Quality
State
81
AZ
Arizona Department of Environmental Quality
State
90
CA
California Air Resources Board
State
32
CO
Colorado Department of Public Health and Environment
State
39
CT
Connecticut Department of Energy and Environmental Protection
State
99
DE
Delaware Department of Natural Resources and Environmental Control
State
99
FL
Florida Department of Environmental Protection
State
70
HI
Hawaii Department of Health Clean Air Branch
State
38
IA
Iowa Department of Natural Resources
State
97
ID
Idaho Department of Environmental Quality
State
0.4
IL
Illinois Environmental Protection Agency
State
93
IN
Indiana Department of Environmental Management
State
95
KS
Kansas Department of Health and Environment
State
100
KY
Kentucky Division for Air Quality
State
28
KY
Louisville Metro Air Pollution Control District
Local
13
LA
Louisiana Department of Environmental Quality
State
23
MA
Massachusetts Department of Environmental Protection*
State
37
MD
Maryland Department of the Environment*
State
16
ME
Maine Department of Environmental Protection
State
100
Ml
Michigan Department of Environmental Quality
State
97
MN
Minnesota Pollution Control Agency
State
100
MO
Missouri Department of Natural Resources
State
98
MS
Mississippi Dept of Environmental Quality
State
85
MT
Montana Department of Environmental Quality
State
3
NC
Forsyth County Office of Environmental Assistance and Protection
Local
0.5
NC
North Carolina Department of Environmental Quality
State
82
NC
Western North Carolina Regional Air Quality Agency (Buncombe Co.)
Local
2
ND
North Dakota Department of Health
State
78
NE
Lincoln/Lancaster County Health Department
Local
2
NE
Nebraska Environmental Quality
State
2
NH
New Hampshire Department of Environmental Services
State
97
NJ
New Jersey Department of Environment Protection
State
90
NV
Nevada Division of Environmental Protection
State
41
NY
New York State Department of Environmental Conservation
State
100
OH
Ohio Environmental Protection Agency
State
82
OK
Oklahoma Department of Environmental Quality
State
95
OR
Oregon Department of Environmental Quality*
State
0.05
PA
Allegheny County Health Department
Local
3
PA
Pennsylvania Department of Environmental Protection
State
88
2-27
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State
Agency
Agency
Type
Percent of
State Total
PA
Philadelphia Air Management Services
Local
1
Rl
Rhode Island Department of Environmental Management
State
100
SC
South Carolina Department of Health and Environmental Control
State
100
TN
Knox County Department of Air Quality Management
Local
13
TN
Memphis and Shelby County Health Department - Pollution Control
Local
10
TN
Tennessee Department of Environmental Conservation
State
40
TX
Texas Commission on Environmental Quality
State
99
VA
Virginia Department of Environmental Quality
State
45
VT
Vermont Department of Environmental Conservation
State
54
WA
Olympic Region Clean Air Agency
Local
2
WA
Southwest Clean Air Agency
Local
27
WA
Washington State Department of Ecology
State
11
Wl
Wisconsin Department of Natural Resources
State
95
WV
West Virginia Division of Air Quality
State
99
WY
Wyoming Department of Environmental Quality
State
67
*Emissions were provided for v2 during the NATA review. The dataset is 2014EPA_NATASLT.
Table 2-14 and Figure 2-4 show the 2014 NEI mercury emissions for the key categories of interest in comparison
to 1990. Also shown are the previous 2 triennial NEI years along with the most recent 2005 emissions, which
were used in support of the MATS rule. Two Microsoft ฎ Excel ฎ databases included in the zip file,
2014nei_supdata_mercury.zip, provides the category assignments at the facility-process level for point sources,
the county-SCC level for nonpoint sources, and the county level for 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 2011v2 NEI. In some cases, manual assignments had to be made where data were not
reported by the S/L/Ts and were gap-filled using the TRI. SCC and facility category codes were also used.
Table 2-14: Trends in NEI mercury emissions - 1990, 2005, 2008 v3, 2011v2 and 2014v2 NEI
Source Category
1990 (tpy)
Baseline for
HAPs,
11/14/2005
2005(tpy)
MATS
proposal
3/15/2011
2008
(tpy)
2008v3
2011
(tpy)
2011v2
2014
(tpy)
2014v2
Notes
Utility Coal Boilers
(Electricity Generation
Units - EGUs,
combusting coal)
58.8
52.2
29.4
26.8
22.9
This category includes only units > 25
MW. (smaller units are included in
boiler and process heater category)
Includes coal units (and excludes Hg
estimated for startup gas/oil) and 3
integrated gasified coal combustion
units.
Hospital/Medical/
Infectious Waste
Incineration
51
0.2
0.1
0.1
0.02
Known issues: missing 2 facilities (UT
and ND); these would bring the total
to 0.03 tons.
2-28
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Source Category
1990 (tpy)
Baseline for
HAPs,
11/14/2005
2005(tpy)
MATS
proposal
3/15/2011
2008
(tpy)
2008v3
2011
(tpy)
2011v2
2014
(tpy)
2014v2
Notes
Municipal Waste
Combustors
57.2
2.3
1.3
1.0
0.6
One unit had an abnormally high
(and not representative) Hg inlet
concentration (about 10-100 times
higher than average) during the stack
test. If 2015 emissions for that facility
were used the total emissions would
be 0.5.
Industrial,
Commercial/Institutional
Boilers and Process
Heaters
14.4
6.4
4.2
3.6
3.2
includes electricity generating units
where less than 25 MW.
Mercury Cell Chlor-Alkali
Plants
10
3.1
1.3
0.5
0.1
Electric Arc Furnaces
7.5
7.0
4.8
5.4
5.0
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
Hazardous Waste
Incineration
6.6
3.2
1.3
0.7
0.8
Portland Cement Non-
Hazardous Waste
5.0
7.5
4.2
2.9
3.2
Gold Mining
4.4
2.5
1.7
0.8
0.6
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
Mobile Sources
Not
available
1.2
1.8
1.3
1.0
Sum of all of onroad, nonroad,
locomotives and commercial marine
vessels
Other Categories
29.5
18
10.7
13
14.0
Total (all categories)
246
105
61
56
52
2-29
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Figure 2-4: Trends in NEI Mercury emissions (tons)
300
250
200
ss Other
ฆ Electric Arc Furnaces (EAF)
ฆ Portland Cement Manufacturing
150 - ฆ Industrial,Commercial,
Institutional Boilers
100
50
I I ฆ ฆ ฆ
1990 2005 2008 2011 2014
Municipal Waste Combustors
I Medical Waste Incinerators
I Utility Coal Boilers
The top emitting 2014 Mercury categories are: EGUs (rank 1); electric arc furnaces (rank 2); Portland cement
(excluding hazardous waste kilns) and industrial, commercial and institutional boilers and process heaters (rank
3).
As shown in Table 2-14, 2014 Hg emissions are 4 tons lower than in the 2011. Almost four tons of this difference
is due to lower Hg emissions from EGUs covered by MATS; three other categories with large decreases are
industrial, commercial/institutional boilers and process heaters, municipal waste combustors and chlor-alkali
plants. The gold mining decrease is somewhat offset by the inclusion of fugitive emissions at gold mines which
may have not been fully accounted for in previous inventories. For EGUs, the decrease is a combination of fuel
switching to natural gas, the installation of Hg controls to comply with state rules and voluntary reductions, early
compliance with MATS, and the co-benefits of Hg 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. The lower
Hg is consistent with a 28 percent decrease in S02from point sources. For industrial and
commercial/institutional boilers, there appears to be fewer boilers using coal. In the Hg chlor alkali industries,
facilities have been switching technologies to eliminate Hg emissions from chlorine production. Many switched
prior to 2008, and in 2014, there were two facilities still using the Hg chlor alkali process.
2.8 References for 2014 inventory contents overview
1. 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.
2. U.S. Environmental Protection Agency, 2011. Memorandum: Emissions Overview: Hazardous Air
Pollutants in Support of the Final Mercury and Air Toxics Standard. Office of Air Quality Planning and
Standards, EPA-454/R-11-014, November 2011.
2-30
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3. Bolate, Yenaxika, May 2017. Inventory of U.S. sources of mercury emissions to the atmosphere. Master's
Thesis, Department of Earth and Environmental Engineering, Fu Foundation School of Engineering,
Columbia University. Advisors: Profs. N.J. Themelis and A.C. Bourtsalas.
4. A.C. (Thanos) Bourtsalas Research Scientist, Earth Engineering Center, Columbia University. Email
Athanasios Bourtsalas to Madeleine Strum. 12/21/2017.
2-31
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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 progresses. 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. Stationary sources that
are inventoried at county-resolution are discussed in the Nonpoint Section 4.
The approach used to build the 2014vl National Emissions Inventory (NEI) for all point sources is discussed in
Section 3.1 through Section 3.8. Some changes to aircraft for the 2014v2 NEI are also discussed in Section 3.2,
and revisions to rail yard estimates for 2014v2 are included in Section 3.3. A comprehensive discussion of the
changes to the 2014v2 point inventory are presented in Section 3.9.
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 OA review of S/L/T data
State/local/tribal agency submittals for the 2014 NEI vl point sources were accepted through January 15, 2016.
We then compared facility-level pollutant sums appearing in either the 2014 NEI S/L/T-submitted values or the
2011v2 NEI. The comparison included all facilities and pollutants, including any missing from the 2014 submittals
(i.e., present in 2011 but not 2014) as well as any that were new in the 2014 submittals and all that were
common to both years. We included additional columns to the comparison table to show the 2014 emission
values from the 2014 Toxics Release Inventory (TRI) and the 2014 Clean Air Markets Division (CAMD) sulfur
dioxide (S02) and nitrogen oxide (NOx) continuous emissions monitoring (CEM) data. We added columns that
showed the percent differences between the 2014 S/L/T agency-submitted facility totals and each of these three
comparison datasets. To create a more focused review and comparison table, we limited these results to include
only cases where the 2014 S/L/T agency-submitted facility total was more than 50 percent different from the
2011 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 2014 or appeared only in the 2011 NEI v2, we
2 These thresholds are available on the 2014vl Supplemental Data FTP site as file
"2014_point_pollutant_thresholds_qa_flagl.xlsx"
3-1
-------
included those values only when they exceeded the absolute mass values greater than the pollutant-specific
thresholds because the percent differences were undefined. We provided3 the resulting table of 4,428 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, 2016. 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 2,500
site-level coordinates of the most significant emitting facilities. For the 2014 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 the following criteria: (1) more than 50 tons total criteria
pollutant emissions or more than 20 pounds total hazardous air pollutants (HAPs) for 2014, (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 2014 emissions to their
site level coordinates, whether protected or not. In cases that we found a difference of more than 0.005 degrees
(approximately 0.25 miles) in total latitude plus longitude, we reviewed the release point coordinates in Google
Earth and edited as needed in EIS, and the site-level coordinates were then locked in EIS. This check was able to
find two cases: (1) where the independently-reported release point coordinates may indicate either a suspect
site-level coordinate, even if plotting within the correct county, or (2) an inaccurate release point coordinate.
We also made a third quality assurance check to ensure that the coordinates for any release point that had
emissions greater than 10 pounds for any key high-risk HAP that was within 0.005 degrees of a verified site
coordinate. This check resulted in additional site coordinate reviews and protections. Finally, the site
coordinates as found in the EPA's Facility Registry System were compared to those in EIS. Any facilities where
these coordinates differed by more than 0.01 degrees and with greater than 50 tons criteria emissions or 500
pounds HAP emissions were reviewed, edited, and protected as needed.
We also attempted to find important cases of emissions being incorrectly reported as emitting at ground level
through a fugitive release rather than through a stack. To do this, we reviewed emission processes with 2014
emissions data to identify instances where S/L/T agencies reported an apparent combustion sources over 50
tons of NOx as emitting through a fugitive release point. The largest such emission processes were individually
reviewed to see if there was an existing stack release point with valid parameters in EIS that looked like it may
have been the intended release point. Where such a possible match was found, the emissions process in the EIS
facility inventory was adjusted to use that stack release point. Where no such stack release point existed within
the facility, a new stack release point with a default height of 100 feet, diameter of 1 foot, velocity of 50 feet per
second and a temperature of 300 degrees was created and used for the emission process. A total of 57 such new
stacks were created under this step.
3 We emailed the Emission Inventory System data submitters the table and instructions on February 27, 2016.
3-2
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3.1.2 Sources of EPA data and selection hierarchy
Table 3-1 lists the datasets that we used to compile the 2014 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).
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 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. The 2014vl point source selection
also excluded greenhouse gases, dioxins and 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 estimate for
dioxin and furans nor radionuclides as part of the NEI. The EPA's official inventory of greenhouse gases (GHGs) is
compiled separately from the NEI criteria and hazardous air pollutant inventory and is available on the U.S.
Greenhouse Gas Inventory Report website.
Table 3-1: Data sets and selection hierarchy used for 2014vl NEI point source data category
Dataset name
Description and Rationale for the Order of the Selected Datasets
Order
2014EPA_PM-Aug
PM species 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). This dataset is ahead of the S/L/T agency
data in order to correct the S/L/T agency values that had inconsistencies
across PM components.
1
Responsible Agency
Selection
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.
2
2014EPA_EGU
HAP and CAP emissions from 3 sources:
1. Emissions factors (EFs) for lead (Pb), mercury (Hg), other HAP metals,
acid gas HAP and PM emissions from the Mercury and Air Toxics (MATS)
rule testing program for electric generating utilities(EGUs) along with
2014 CAMD heat input data
2. Annual sum of CAMD hourly CEM data for S02 and NOx
3. EFs used in previous year inventories from AP-42 and other sources
along with 2014 CAMD heat input data.
3
3-3
-------
Dataset name
Description and Rationale for the Order of the Selected Datasets
Order
2014EPA_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.
4
2014EPA_Oth_CarryFwd
2011 emissions values for 212 facilities and 12 pollutants not reported in
2014 S/L/T datasets but appear to still be operating and were above CAP
reporting thresholds in 2011. Includes Coke Oven Emissions adds for 5
facilities.
5
2014EPA_TRI
TRI data for the year 2014 (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.
6
2014EPA_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) Emissions and Dispersion Modeling System (EDMS) (see Section
3.2).
7
2014EPA_Rail
CAP and HAP emissions for diesel rail yard locomotives. CAP emissions
computed using yard-specific EFs, yard-specific fleet information, and using
national fuel values that have been allocated to rail yards using an
approximation of line haul activity within the yard. HAP emissions
computed using HAP-to-CAP emission ratios (see Section 3.3).
8
2011EPA_LF
Landfill emissions developed by EPA using methane data from the EPA's
GHG reporting rule program. The dataset contains only those landfills for
which no pollutants were reported to EIS by the S/L/T agency in the 2014
reporting year.
9
2014EPA_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 and 2014EPA_Oth_CarryFwd because the TRI data are
expected to be better. These data are selected for a facility only when not
included in the S/L/T agency data.
10
2014EPA_HAP-
Aug_PMaug
This dataset was created in the same fashion as the 2014EPA_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.
11
2014EPA_BOEM
2011 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 this data set is "DM" (Federal Waters). For the 2014vl NEI, we
used the 2011 BOEM data because the 2014 BOEM data was not available
in time for 2014vl.
12
3-4
-------
Dataset name
Description and Rationale for the Order of the Selected Datasets
Order
2014EPA_PMspecies
Adds speciated PM2.5 data to resulting selection. This is 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). Also adds a copy of PM2.5-PRI and PM10-PRI from diesel
engines, relabeled as DIESEL-PM pollutants.
13
2014_EPA_MOVES
This dataset was listed in the point source hierarchy in error. It does not
contain any point source emissions values.
14
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. The PM Calculator, has undergone several edits since 1999; now called the "PM
Augmentation Tool," this Microsoft ฎ Access ฎ database is available on the PM Augmentation web site.
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 2014 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 2014 NEI, the EPA named this dataset "2014EPA_Cr_Aug." Most of the speciation factors used in the
2014 NEI are SCC-based and are the same as were used for the 2008 and 2011 NEIs. The factors are based on
data that have long been used by the EPA for the National Air Toxics Assessment and other risk projects and are
available on the 2014vl Supplemental data FTP site.
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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3.1.5 Use of the 2014 Toxics Release Inventory
The EPA used air emissions data from the 2014 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 "2014EPA_TRI" in the
Table 3-1 selection hierarchy shown above. For 2014, 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 considered for inclusion in the 2014
NEI.
The basis of the 2014EPA_TRI dataset is the US EPA's 2011 Toxics Release Inventory (IRQ 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 TRI database used for this project was named TRI_2014_US.csv and was downloaded on February
10, 2016, from the TRI Basic Data Files: Calendar Years 198? - 2016 web site.
The approach used for the 2014 NEI was the same as that used for the 2011 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. Double-counting of TRI and other data sources was
prevented by tagging (and not using) any TRI pollutant emissions for a facility where the S/L/T agency or a higher
priority (as per Table 3-1) EPA dataset also had a pollutant emissions value for any unit and process within that
facility.
The following steps describe in more detail the development of the 2014EPA_TRI dataset.
1. Update the TRI_ID to EISJD facility-level crosswalk
For the 2014 NEI, the same crosswalk list of TRI IDs that was used for the 2011 NEI was used as a starting
point. A review of the 2014 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 150
additional TRI facilities were added to the crosswalk for 2014.
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 2014 NEI, a total of 184 TRI pollutant codes were
mapped to 172 unique EIS pollutant codes. Similar to the 2011 NEI, 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.
3-6
-------
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-TETRACHLOROETHANE
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'-DIMETH0XYBENZIDINE
119904
3,3'-Dimethoxybenzidine
119937
3,3'-DIMETHYLBENZIDINE
119937
3,3'-DIMETHYLBENZIDINE
101144
4,4'-METHYLENEBIS(2-CHL0R0ANILINE)
101144
4,4'-METHYLENEBIS(2-CHLORANILINE)
101779
4,4'-METHYLEN EDI ANILINE
101779
4,4'-METHYLENEDIANILINE
534521
4,6-DINITR0-0-CRES0L
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
3-7
-------
TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
56235
CARBON TETRACHLORIDE
56235
CARBON TETRACHLORIDE
463581
CARBONYL SULFIDE
463581
CARBONYL SULFIDE
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
DIBUTYLPHTHALATE
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
ETHYLBENZENE
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
H EXACH LOROCYCLOPENTADIENE
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
3-8
-------
TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
N420
LEAD COMPOUNDS
7439921
LEAD
58899
LINDANE
58899
1,2,3,4,5,6-HEXACHLOROCYCLOHEXANE
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
METHYL TERT-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
PROPION ALDEHYDE
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
3-9
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TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
75014
VINYL CHLORIDE
75014
VINYL CHLORIDE
75354
VINYLIDENE CHLORIDE
75354
VINYLIDENE CHLORIDE
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)
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 2014 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 2014 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 2014 draft 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 2014EPA_TRI dataset.
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
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 2014 TRI dataset itself to identify large differences
between facilities and unexpected industry types. Comparisons were then made to the 2011 TRI and the
2014 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).
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5. Write the 2014 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 the 2002 and 2005 NEI inventories which were
used to populate the original EIS data system. Those NEI years contained the TRI stack and fugitive totals
as single processes. Where such legacy NEI process IDs did not exist in the EIS, they were created.
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 2014 NEI, we retained
the 2011 approach, process IDs, and SCCs.
To assign a SCC, we first determined for each facility and release type (stack or fugitive) which EIS Sector
had the largest amount of S/L/T agency-reported emissions in the 2011 draft NEI. Within the largest EIS
sector for the facility and release type, we then determined which single SCC had the largest emissions.
The emissions values used were sums of emissions across all pollutants except carbon monoxide (CO),
carbon dioxide (C02), and NOx, with all units converted to tons. Excluding CO and C02 was done because
their high mass would overwhelm the contribution of the other criteria pollutants, and NOx was
excluded because the HAPs that we are trying to assign to an appropriate summation sector are more
closely associated with S02 or PM emissions. The usage of the default 39999999 SCC has not been
completely eliminated as a result of this approach, because there remain a number of S/L/T agency-
reported criteria emissions for some facilities in EIS for which that is the most viable SCC choice. In the
rare cases that the S/L/T agency used 39999999 for the majority of their emissions, this SCC assignment
approach did not work.
7. Tag TRI pollutant emissions in EIS to avoid double counting with other datasets
Because the 2014 NEI does not attempt to place the TRI emissions at the same processes used by the
S/L/T agency datasets or other EPA datasets that are higher in the EIS selection hierarchy, it is necessary
to tag any TRI emissions values stored in the EIS wherever the same pollutant is already reported by a
S/L/T agency or one of the more preferred EPA datasets for a given EIS facility. In addition to a direct
comparison of individually matching pollutants between these datasets, it is also necessary to compare
to any of the related EIS pollutant codes that are in the same pollutant group.
Table 3-3 shows the EIS pollutant groups that had to be accounted for in this comparison. For example,
if the S/L/T agency data or the 2014EPA_EGU dataset included "Xylenes (Mixed Isomers)" for a facility,
any of the related individual xylene isomers would be tagged in the 2014EPA_TRI dataset in the EIS as
well as any "Xylenes (Mixed Isomers)." Tagging an emissions value in the EIS in any dataset makes that
emissions value not available for selection to the NEI.
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Table 3-3: Pollutant groups
Group Name
Pollutant Code
Pollutant
7440473
Chromium
1333820
Chromium Trioxide
Chromium
7738945
Chromic Acid (VI)
18540299
Chromium (VI)
16065831
Chromium III
1330207
Xylenes (Mixed Isomers)
Xylenes (Mixed
95476
o-Xylene
Isomers)
106423
p-Xylene
108383
m-Xylene
Cresol/Cresylic
Acid (Mixed
Isomers)
1319773
Cresol/Cresylic Acid (Mixed Isomers)
95487
o-Cresol
108394
m-Cresol
106445
p-Cresol
1336363
Polychlorinated Biphenyls (PCBs)
2050682
4,4'-Dichlorobiphenyl (PCB-15)
2051243
Decachlorobiphenyl (PCB-209)
2051607
2-Chlorobiphenyl (PCB-1)
Polychlorinated
Biphenyls
25429292
Pentachlorobiphenyl
26601649
Hexachlorobiphenyl
26914330
Tetrachlorobiphenyl
28655712
Heptachlorobiphenyl
53742077
Nonachlorobiphenyl
55722264
Octachlorobiphenyl
7012375
2,4,4'-Trichlorobiphenyl (PCB-28)
130498292
PAH, total
120127
Anthracene
129000
Pyrene
189559
Dibenzo[a,i]Pyrene
189640
Dibenzo[a,h]Pyrene
191242
Benzo[g,h,l,]Perylene
191300
Dibenzo[a,l]Pyrene
192654
Dibenzo[a,e] Pyrene
192972
Benzo[e]Pyrene
Polycyclic
Organic Matter
(POM)
193395
lndeno[l, 2,3-c,d] Pyrene
194592
7H-Dibenzo[c,g]carbazole
195197
Benzolphenanthrene
198550
Perylene
203123
Benzo(g,h,i)Fluoranthene
203338
Benzo(a)Fluoranthene
205823
Benzo[j]fluoranthene
205992
Benzo[b]Fluoranthene
206440
Fluoranthene
207089
Benzo[k]Fluoranthene
208968
Acenaphthylene
218019
Chrysene
224420
Dibenzo[a,j]Acridine
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Group Name
Pollutant Code
Pollutant
226368
Dibenz[a,h]acridine
2381217
1-Methylpyrene
2422799
12-Methylbenz(a)Anthracene
250
PAH/POM - Unspecified
26914181
Methylanthracene
3697243
5-Methylchrysene
41637905
Methylchrysene
42397648
1,6-Dinitropyrene
42397659
1,8-Dinitropyrene
50328
Benzo[a]Pyrene
53703
Dibenzo[a,h] Anthracene
5522430
1-Nitropyrene
56495
3-Methylcholanthrene
56553
Benz[a] Anthracene
56832736
Benzofluoranthenes
57835924
4-Nitropyrene
57976
7,12-Dimethylbenz[a] Anthracene
602879
5-Nitroacenaphthene
607578
2-Nitrofluorene
65357699
Methylbenzopyrene
7496028
6-Nitrochrysene
779022
9-Methyl Anthracene
8007452
Coal Tar
832699
1-Methylphenanthrene
83329
Acenaphthene
85018
Phenanthrene
86737
Fluorene
86748
Carbazole
90120
1-Methylnaphthalene
91576
2-Methylnaphthalene
91587
2-Chloronaphthalene
Cyanide &
57125
Cyanide
Compounds
74908
Hydrogen Cyanide
7440020
Nickel
Nickel &
12035722
Nickel Subsulfide
Compounds
1313991
Nickel Oxide
604
Nickel Refinery Dust
3.1.6 HAP augmentation based on emission factor ratios
The 2014EPA_HAP-augmentation dataset was used for gap filling missing HAPs in the S/L/T agency-reported
data. These missing HAPs are determined by comparing the "Expected Pollutant List for Point SCCs" with those
that S/L/T agencies submitted. 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.
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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.
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 Priority Order." The same tables ("Priority Data" and
"Priority Data Area") provide both the HAP augmentation factors and chromium speciation factors. The "Priority
Data" table provides chromium speciation and HAP augmentation factors for point sources; the "Priority Data
Area" table provides them for nonpoint sources. These tables provide the SCC, CAP surrogate, HAP and
multiplication factor (HAP to CAP ratio). For access by non-EIS users, the zip file called "2014HAPAugFactors.zip"
provides the emission ratios used for point and nonpoint data categories.
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
tagging to tag records from the 2014EPA_HAP-augmentation dataset to prevent double counting. Because some
HAPs are in pollutant groups, if any one HAP in that group was reported by the state anywhere at the facility,
then we tagged all HAPs in that group. We used the same groups as provided in Table 3-3.
We also tagged all 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. This occurred a total of 9607 times.
3.2 Airports: aircraft-related emissions: updated in 2014v2
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
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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.
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
EDMS to estimate emissions. For airports where FAA databases do not include such detail, the EPA used
assumptions regarding the percent of these 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. Then, the EPA estimates emissions based on the percent of each aircraft type, LTOs,
and EFs. Emissions factors for 'generic' aircraft, those without the make/model detail are available in the
"nei20145_genericef_table.pdf" file on the 2014v2 Supplemental Data FTP site. State agencies listed in Table 3-4
provided at least some component of aircraft-related emissions to the NEI.
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 can be found in the
"neiair2014_fin.pdf" file, also on the 2014v2 Supplemental Data FTP site.
Table 3-4: Agencies that submitted aircraft-related emissions for 2014vl, except as noted
Agency
Summary
Notes
Delaware Department of Natural Resources
and Environmental Control
Dover Air Force base
submitted for 2014v2
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Agency
Summary
Notes
Georgia Department of Natural Resources
Unpaved airstrip
(nonpoint) in 2 counties.
Hartsfield airport
submitted for 2014v2.
Illinois Environmental Protection Agency
737 airports' emissions
Tennessee Department of Environmental
Conservation
Military aircraft emissions
at one facility
Texas Commission on Environmental Quality
2005 airports' emissions
EPA o- and m-xylene tagged to
avoid double count with TX's
'mixed xylene' records
Utah Division of Air Quality
Military aircraft emissions
at one facility
See Section 4.20 for details on the emission estimation for rail line segment emissions which are stored in the
nonpoint sector. The 2014v2 NEI includes non-zero emissions estimates for 955 rail yards. These emissions are
associated with the operation of switcher engines at each yard.
33.1 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.
3,3,2 Sources rail yard emissions estimates
Rail yard estimates were compiled by the Eastern Regional Technical Advisory Committee's (ERTAC) rail group.
The group coordinated with the Federal Rail Administration to rail yard switcher activity data and apply the
equipment-specific emission factors appropriate. Their report on this work is available in the
"Railv2_3ERTAC_Rail_2014_lnventory_Documentation_20170220.pdf" file on the 2014v2 Supplemental Data
FTP site.
Rail yard point emissions are limited to one SCC (28500201). For 2014, the following agencies submitted rail
yards: Illinois, Maryland, Minnesota, New Jersey and Texas. These submitted data were compared to EPA
estimates. Where necessary, the EPA values were tagged to prohibit double counting. Nonpoint rail yard
submittals were allowed and were also checked for double counting with point.
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 2014EPA_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 2014EPA_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's database; and heat-input based EFs that were built from AP-42 EFs and fuel heat and sulfur
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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 2014. 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 2014EPA_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 2014 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 2014EPA 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 2014EPA_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 made revisions to 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.
Alternative facility and unit IDs needed for matching with other databases
The 2014 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
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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 2014 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 2014 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.
The point source emissions in the EPA's Landfill dataset includes CO and 28 HAPs, as shown in Table 3-5. 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 2014 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)> units of pounds
p/pound CH4
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 3-5: 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
5 For more information on CO2 equivalent and global warming potential, please refer to EPA's page "Understanding Global
Warming Potentials".
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Pollutant
code
Pollutant description
MW
ppmv
MW x
ppmv
lbs/Ton
ch4
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
3,6-
This EPA dataset is used to fill in miscellaneous emissions which were not reported by S/L/T agencies for 2014,
and for which no EPA dataset has 2014 emissions, but which are believed to exist in 2014. 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 212 unique facilities and 12 different pollutants are represented in this dataset. The only HAP
pollutant included in this dataset is coke oven emissions, added for five facilities (three in Ohio, one each in
Virginia and Michigan), where the States reported other emissions for the facility but not the coke oven
emissions pollutant. The 2011 NEI coke oven emissions for these five facilities were carried forward to this 2014
dataset as is, without change. All other pollutants added were criteria pollutants, and only where 2011
emissions values indicated that emissions had been greater than the required pollutant reporting thresholds.
Many of these additions were for Maricopa County, Arizona (15 facilities) and the Navajo Nation (12 facilities),
neither of which submitted any point emissions for 2014, and for Indiana (171 facilities), which submitted a large
amount of facilities including both criteria and many HAP pollutants but which did not get some criteria
pollutants included in 2014 for some facilities due to a processing error. In addition, eight facilities in California
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and one facility in Wisconsin were also included in this dataset. All emissions values for 2014 were set equal to
the 2011 NEI v2 emissions values.
3.7 1
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. The 2014 offshore data were not available in time for inclusion in the 2014 vl NEI,
thus, we carried forward the 2011 BOEM emissions. The only step taken with the data from BOEM for 2011 was
convert the data to the CERS format needed to load to EIS, which included using the code "DM" for Federal
waters in place of a state postal code. More information on these data is available at the BOEM 2011 Gulfwide
Emission Inventory website.
The "2014EPA_PMspecies" dataset was created by the EPA by calculating speciated PM2.5 emissions from all
contains a speciation of PM2.5-PRI into five component species (EC, OC, S04, N03, and other). These calculations
were made using the EPA's 2011 version 6.3 emissions modeling platform available from the Emissions
Modeling Clearinghouse website. In addition, this dataset contains a copy of PM2.5-PRI and PM10-PRI pollutants
from locomotive diesel engines processes at railyards and aircraft ground support equipment using diesel fuel.
These copied data records are simply relabeled as PM-diesel pollutants so that the diesel PM "pollutant" can
more easily be identified in the inventory. No stationary sources running with diesel fuel are labeled as PM-
diesel "pollutants".
For the 2014v2 point sources, two methods of taking S/L/T edits were used. The first method involved having
the S/L/Ts send Excel spreadsheet "change sheets" showing the existing 2014vl data for selected facilities
(1,561 total) based on initial risk projections to identify potential outliers as a part of the National Air Toxics
Assessment (NATA) emissions review. Two sets of changes sheets containing 2014vl data were provided: 1)
process level emissions, and 2) release point geographic coordinates and parameters. U.S. EPA then reviewed
and incorporated all accepted changes into one of two U.S. EPA emissions edit datasets (2014EPA_NATASLT or
2014EPA_NATA) or into the EIS facility inventory. U.S. EPA had originally intended to only use this method as it
was deemed easier to review and track changes, which were intended be limited to significant errors that would
potentially impact NATA results. Due to request by S/L/Ts, U.S. EPA included the second method for S/L/Ts to
submit the NATA review edits to either their agency emissions datasets or to the facility inventory in EIS directly.
The U.S. EPA then pulled any significant emission changes from the S/L/T emissions datasets and wrote those
into one of the two U.S. EPA emissions edit datasets. Any edits submitted by S/L/Ts directly to the EIS facility
inventory were also available and used for production of the 2014v2 NEI point source file via this second
method.
In addition to making edits to their own data (via either of the two methods) S/L/T, EPA Regional Offices and
EPA TRI program staff reviewed and provided changes to the 2014vl EPA augmented data (e.g., data from the
TRI program or the HAP augmentation datasets) via the change sheet method.
Emissions changes from the two methods are in one of two U.S. EPA emissions data sets: 2014EPA_NATASLT
and 2014EPA_NATA. Different datasets were used to distinguish changes to EPA data from changes to S/L/T
data. There are approximately 60 facilities with NATA-related changes contained in the 2014EPA_NATA dataset
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and 110 facilities with NATA-related changes in the 2014EPA_NATASLT dataset. Other NATA-related changes
include the tagging out (removal) of emissions from processes or facilities that were determined via the S/L/T
review to have not been operating or were double counted.
For the second method (S/L/T direct submittal to EIS), U.S. EPA originally planned that any facility that showed a
difference of at least 50 tons (annual) in total criteria pollutants, either an increase or a decrease, compared to
the 2014vl facility criteria pollutant total would be considered significant enough to incorporate those edits into
the 2014v2 NEI. It was desirable to limit the volume of submitted edits to only those that were significant due to
the time and resources needed to build a completely new 2014v2 point inventory that would also negate the
benefits of all QA review and confidence developed in the 2014vl file.
A numeric comparison of facility-pollutant sums as they appeared in the S/L/T 2014 emissions datasets as they
appeared on June 16, 2017 (after the close of the S/L/T 2014v2 submittal window) to the corresponding sums in
the 2014vl NEI was done. The absolute values of each pollutant-specific difference (for criteria pollutants) were
added together to get a facility total change value from the 2014vl NEI. This step avoided having any criteria
pollutants that appeared in the 2014vl file only due to EPA Augmentation steps (PM Augmentation or TRI
ammonia sources) from impacting the results. A set of 368 facilities that were either new or edited by more than
50 tons was identified. For these 368 facilities, all pollutants (including both criteria and hazardous), at all
processes, were submitted to emissions dataset "2014EPA_NATASLT". This was one of two emissions datasets
(the other being "2014EPA_NATA") that were used to override or add to the base "2014 NEI Final VI" file used
for the 2014v2 selection. Along with the S/L/T submitted emissions values, all calculation parameters, operating
details, and reporting period details that were present in the edited S/L/T 2014 datasets were also written to
"2014EPA_NATASLT". In addition to these "primary" reported pollutants, it was necessary to also develop
updated estimates for any PM Augmentation, HAP Augmentation, Chrome speciation, and 7 PM species values
that had been derived from those primary pollutants. Those were all developed "off-line" from EIS for the small
subset of 2014vl records being impacted, using the same ratios that EIS has stored and uses for those
augmentations. The derived edited values were also written to the "2014EPA_NATASLT" emissions dataset.
Where the S/L/T edited 2014 datasets included additional HAPs not seen in the S/L/Ts vl submittals, and those
HAPs had been accounted for in 2014vl via HAP Augmentation or TRI emissions records, the vl emissions
records were tagged out from the "2014 NEI Final VI" file as well.
The comparison at the facility-pollutant level back to 2014vl totals also revealed some pollutants that existed
for a facility in 2014vl, but which were completely absent from the S/L/T 2014 edited emissions datasets as they
appeared in June 2017. Where these pollutants had appeared in 2014vl due to S/L/T emissions records (as
opposed to EPA augmentation or TRI records) which were now absent, it was necessary to tag out the old
emissions values from the "2014 NEI Final VI" file so that they would not be picked up from there and included
again as part of the 2014v2 selection. Where these deleted pollutants were VOC or PM10-FIL values that had
been used to derive HAP Augmentation values, the augmented values were similarly tagged out from "2014 NEI
Final VI".
Apart from the planned method 2 approach to identify and amend facilities with significant (greater than 50
ton/year) criteria pollutant changes, a file was created of facilities that were entirely new in the S/L/T 2014v2
edits, regardless of emission amounts. These records, along with any needed U.S. EPA augmentation or
speciation records, were also written to the "2014 NEI Final VI" file. The PM2.5 species (i.e., EC, OC, etc.) from
these datasets were not used, however, because we re-speciated PM after combining all other datasets to
ensure consistency with the 2014v2 PM2.5 emissions.
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In addition to the above edits received from S/L/Ts, U.S. EPA also received a set of point emissions data for 2014
from the US Bureau of Ocean Management (BOEM) for off-shore oil and gas platforms in federal waters in the
Gulf of Mexico. These data had not been available in time for the 2014vl NEI, so the 2011 data for those
platforms had been included instead as a surrogate for 2014. The actual 2014 BOEM emissions data had been
loaded into EIS by BOEM in time for the 2014v2 selection, and so that singular responsible agency dataset was
included as part of the selection hierarchy for 2014v2. All facilities, processes, and pollutants that were
contained in the "2014 NEI Final VI" file from the earlier 2011 surrogate data but were not also in the actual
2014 BOEM dataset were tagged out of the "2014 NEI Final VI" file being used as part of the selection.
Finally, U.S. EPA conducted a review of the vl mercury emissions and made changes primarily to municipal
waste combustors and electric arc furnaces. For MWCs, some S/L/T data were found to be under or
overestimated and were corrected and missing data were gap-filled. For electric arc furnaces, missing data were
gap-filled. Data revisions provided by S/L/T were put into the 2014EPA_NATASLT dataset; EPA gap-filled
emissions were included in 2014EPA_NATA. More details on mercury emissions are provided in Section 2.7.
The 2014v2 EPA datasets were combined with the 2014vl NEI in the hierarchy provide in Table 3-6. See Table
3-1 for the 2014vl NEI hierarchy. A process level summary on the 2014v2 NEI will provide the data source from
Table 3-1 for any data from the 2014v2 NEI dataset. For the 7 PM species, the process level summaries will not
include the "2014EPA_PMspeciesV2" dataset name, but rather the dataset from which the PM2.5 was derived.
Table 3-6: Data sets and selection hierarchy used for the 2014v2 point source data category
Dataset name
Description and Rationale for the Order of the Selected Datasets
Order
2014EPA_NATA
Changes to EPA data (i.e., TRI and HAP augmentation data from 2014vl)
resulting from the 2014NATA review and the 2014 updated rail yard
emissions, covering over 800 rail yards.
1
2014EPA_NATASLT
Changes to S/L/T data resulting from the 2014 NATA emissions review and
changes to S/L/T data that met the criteria for use in the NEI.
2
2014EPA_BOEM
2014 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, Regulation, and Enforcement.
3
The state code for data from this data set is "DM" (Federal Waters). For the
2014v2 NEI, we replaced the 2011 BOEM data with this dataset.
2014_NEI Final VI
This dataset contains the data from the selection done for the 2014vl NEI,
except for any data tagged out due to the NATA review, and to replace the
2011 BOEM data and 2011 rail yards with 2014 data
4
Overrides to the above: In addition to the 2014vl overrides, we used the 2014EPA_PMSpeciesV2 dataset to
override each of the 7 PM Species: elemental (black) carbon (EC), organic carbon (OC), nitrate (N03), sulfate
(S04), the remainder of PM25-PRI (PMFINE), diesel fine particulate (DIESEL-PM25) and diesel coarse
particulate (DIESEL-PM10) present in any of the above datasets. The 2014EPA_PMSpeciesV2 dataset was
created by speciating the PM2.5 from a draft 2014v2 comprised of the above 4 datasets.
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 reference documents of the 2008
NEI documentation.
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.
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4 Nonpoint sources
This section includes all sources that are in the nonpoint data category. 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 except Biogenics which is discussed in Section 8. Some "nonroad" mobile sources such as trains and
commercial marine vessels reside in the nonpoint data category and are discussed here and not in the Nonroad
Equipment Section 5.
4,1 . ^ ; >.]ฆ !source
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. Agricultural field burning, commercial marine vessels and locomotives
utilize sector-specific databases provided in Table 4-2, Table 4-3 and Table 4-4, respectively. 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 four
tables includes the rationale for why each dataset was assigned its position in the hierarchy. We excluded
certain pollutants from stationary sources in the 2014 NEI: greenhouse gases and pollutants in the pollutant
groups "dioxins/furans" and "radionuclides"6. 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
2014EPA_PMspecies
_V2
Adds speciated PM2.5 data to resulting selection. This is 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).
Also adds a copy of PM2.5-PRI and PM10-PRI from diesel engines, relabeled
as DIESEL-PM pollutants. See Section 2.2.5.
1
6 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
-------
Dataset name
Description and Rationale for the Order of the Selected Datasets
Order
2014PMaug_v2NP
Adds nonpoint inventory PM species to fill in missing S/L/T agency data or
make corrections where S/L/T agency data have inconsistent emissions
across PM species. Uses the PM Augmentation Tool for processes covered
by that database. For SCCs without emission factors in the tool,
checks/corrects discrepancies or missing PM species using basic
relationships such as ensuring that PMXX FIL is less than or equal PMXX PRI
(See Section 2.2.4).
2
Responsible Agency
Selection
S/L/T agency submitted data; multiple datasets - one for each reporting
agency. 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.
3
2014EPA_Cr_Aug_v2
Hexavalent and trivalent chromium speciated from S/L/T agency reported
chromium. The EIS augmentation function creates the dataset by applying
multiplication factors by source classification code (SCC) to S/L/T agency
"total" chromium. See Section 2.2.2.
4
2014EPA_HAPAug
_V2
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 2.2.3.
5
2014EPA_NON POINT
_V2
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. This dataset also
includes biogenic emissions. Examples of sources in this dataset include:
fertilizer, most livestock, industrial and commercial/institutional fuel
combustion, residential wood combustion, solvent utilization, oil and gas
exploration and production, open burning, agricultural burning, road and
construction dust, and portable fuel containers.
6
2014_EPA_NP_
from2011
2011 v2 NEI data from 2011 EPA nonpoint estimates that were not updated
for 2014: livestock waste from ducks, geese, horses, goats and sheep.
7
2014EPA_Airports
2014 aircraft in-flight emissions (Lead only)
8
Table 4-2: Data sources and selection hierarchy used for the Agricultural Field Burning sector
Dataset name
Description and Rationale for the Order of the Selected Datasets
Order
2014EPA_PMspecies
_V2
Adds speciated PM2.5 data to resulting selection. This is a result of offline
emissions speciation where the resulting PM25-PRI selection emissions are
1
4-2
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Dataset name
Description and Rationale for the Order of the Selected Datasets
Order
split into the 5 PM species: elemental (black) carbon (EC), organic carbon
(OC), nitrate (N03), sulfate (S04), and the remainder of PM25-PRI (PMFINE).
Also adds a copy of PM2.5-PRI and PM10-PRI from diesel engines, relabeled
as DIESEL-PM pollutants. See Section 2.2.5.
2014v2_AgFires
2
Table 4-3: Data sources and selection hierarchy used for the Commercial Marine Vessels sector
Dataset name
Description and Rationale for the Order of the Selected Datasets
Order
2014EPA_PMspecies
_V2
Adds speciated PM2.5 data to resulting selection. This is 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).
Also adds a copy of PM2.5-PRI and PM10-PRI from diesel engines, relabeled
as DIESEL-PM pollutants. See Section 2.2.5.
1
2014LADCO_CMV
Adds speciated PM2.5 data to resulting selection. This is 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).
Also adds a copy of PM2.5-PRI and PM10-PRI from diesel engines, relabeled
as DIESEL-PM pollutants. See Section 2.2.5.
2
2014SLTv2_CMV
S/L/T agency submitted CMV data for 2014v2. See Section 4.19.
3
2014Augv2_CMV
HAP data computed from S/L/T agency criteria pollutant CMV 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). HAP augmentation is
discussed in Section 2.2.3.
4
2014EPAv2_CMV
EPA commercial marine vessel (CMV) emissions estimates. See Section 4.19.
5
Table 4-4: Data sources and selection hierarchy used for the Locomotives sector
Dataset name
Description and Rationale for the Order of the Selected Datasets
Order
2014EPA_PMspecies
_V2
Adds speciated PM2.5 data to resulting selection. This is 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).
Also adds a copy of PM2.5-PRI and PM10-PRI from diesel engines, relabeled
as DIESEL-PM pollutants. See Section 2.2.5.
1
2014SLTv2_Rail
S/L/T agency submitted locomotives data for 2014v2. See Section 4.20.
2
2014AUGv2_Rail
HAP data computed from S/L/T agency criteria pollutant locomotives 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). HAP augmentation
is discussed in Section 2.2.3.
3
4-3
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Dataset name
Description and Rationale for the Order of the Selected Datasets
Order
2014EPAv2_Rail
EPA locomotive (referred to as "rail" in this document) emissions estimates.
See Section 4.20.
4
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 through Table 4-4 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. Many EPA nonpoint
data were tagged, primarily because of S/L/T feedback in the Nonpoint Survey (see Section 4.1.2).
Special caveat on backfilling with non-S/L/T data
The hierarchal backfilling that occurs in the selection process can create unexpected artifacts to the resulting
inventory selection. For example, if S/L/T agencies do not submit emissions for a pollutant, and emissions for
that pollutant exist in other datasets, then non-S/L/T data will show up in the NEI selection for these pollutants.
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), or 2) the EPA can add tags to backfill datasets that
prevent the tagged pollutants from being included in the NEI. The first option is more straightforward and takes
care of any possible augmentation from the numerous other datasets in the selection hierarchy.
4.1,2 The Nonpoint Survey
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 many source categories that can
overlap with sources that can also be reported as a point source; and because the potential for overlap varies by
source category and reporting agency, it is important that we have information about how each agency treats
inventory development for all nonpoint source types. For example, some agencies voluntarily report gas stations
as point sources, which are sources that overlap with the nonpoint refueling emissions used by most states.
Thus, in building the EPA nonpoint inventory, the EPA needs to know whether all gas stations are reported as
point sources or only some of them (such as for certain counties), so that we know to what degree we should
include nonpoint refueling emissions in the NEI for that state or local area.
The nonpoint survey is available only to reporting agencies and is organized by emissions sector, where the first
yes/no question is whether the sector exists in an agency's jurisdiction. If the answer is "no", then the user
moves on to the next sector. If the answer is "yes", then the survey provides numerous additional questions
4-4
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using drop-down lists for agencies to choose responses. These questions include whether the data are reported
solely in the point or nonpoint inventories and whether the EPA or alternative nonpoint SCCs are used by the
S/L/T agency. The survey also allows the S/L/T agency to specify their preference for the NEI to include EPA
emissions rather than S/L/T emissions; this goes against the hierarchies in Table 4-1, Table 4-2, Table 4-3, and
Table 4-4; therefore, a response to use EPA emissions rather than S/L/T emissions help to automate the
generation of S/L/T nonpoint "tags". When the entire survey is complete, EPA generates a couple sets of data
tags:
1) EPA tags: where S/L/T agencies indicate that the sources do not exist in their area, or where all data
are reported in the point submittal. Any EPA data for these sources will be tagged out.
2) S/L/T tags: where S/L/T agencies indicate that they would prefer that the EPA data are used instead
of their nonpoint submittal. Without the tags, the EPA data will not be used where S/L/T agency
data exists because the EPA data are lower in the selection hierarchy (see Table 3-1).
To explain the nonpoint survey for the 2014 NEI cycle, the EPA provided a webinar to S/L/T agencies on the
nonpoint survey in July of 2015. This webinar is available on the available on the Air Emissions Inventory Training
website.
Nonpoint Survey for version 2 of the 2014 NEI
It is important to note that the nonpoint survey was sent to the S/L/Ts prior to the beginning of the 2014 NEI
cycle, and used for the development of version 1 of the 2014 NEI. We did not send out a new survey prior to the
development of version 2 of the 2014 NEI; therefore, unless S/L/Ts informed us otherwise, all survey responses
were carried forward from 2014vl to this 2014v2 NEI.
4,1,3 Nonpoint PM augmentation
Section 2.2.4 provides an overview of PM augmentation in the 2014 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 PMio 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, this was a
rare occurrence. Nationally, these overwrites resulted in only a 264-ton increase in primary PM2.5 and was found
primarily for fuel combustion sources where primary PM10 greatly exceeded primary PM2.5 and computed
condensable and filterable components indicated that the submitted primary PM2.5 was too low. In some cases,
S/L/T agencies reported all 5 PM components, but the sum of (for example) PM-CON and PM25-FIL was different
4-5
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from S/L/T-reported PM25-PRI. We recommended that the S/L/T agencies review PM25-PRI overwrite values
during the NEI review period prior to NEI release.
4.1.4 Nonpoint HAP augmentation
For nonpoint sources, we derived HAP augmentation ratios were derived from the emission factors used to
develop the EPA nonpoint source estimates. The EPA nonpoint HAP emission estimates are computed in EPA
nonpoint spreadsheet and database "tools". 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 "2014HAPAugFactors.zip" provides the emission ratios that the
EPA used for augmenting point and nonpoint data categories. The nonpoint HAP augmentation factors were
greatly improved as compared to what was used for the 2011 NEI, particularly for the oil and gas sector. For
2014, instead of national average factors, we added county-specific factors to the HAP augmentation, consistent
with what is in the Oil and Gas Tool. We made this improvement in response to comments from the National Oil
and Gas Committee that gas composition is highly variable and is dependent on geographic formations at a finer
spatial granularity than the oil and gas basin.
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. For
example, for agricultural burning we removed the S/L/T agency HAPs and used only the HAP augmentation
(computed from the S/L/T-submitted CAPs.
We also tagged records from the HAP Augmentation dataset where they duplicated records in certain other EPA
datasets, but for which the EIS selection hierarchy would not do everything we wanted. Thus, we tagged HAP
augmentation values where the HAP Augmentation pollutant belonged to the same pollutant group as a
different pollutant reported by the S/L/T agency. For example, if the HAP Augmentation dataset had o-xylene,
and the S/L/T agency reported total xylenes, then we tagged the o-xylene in the HAP Augmentation dataset. The
resultant tagging was done for the xylenes, Polycyclic Aromatic Hydrocarbons (PAHs) and cresols groups listed in
Table 3-3 and discussed in Section 3.1.5 in the context of a similar issue that comes up using the Toxics Release
Inventory (TRI) for point source augmentation.
4.1.5 EPA nonpoint data
For the 2014 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 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
4-6
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including agricultural pesticides, fertilizer and livestock, various dust sources, solvents, industrial and
commercial/institutional fuel combustion, mercury, and gasoline distribution. These subgroups collaborate on
methodologies, emission factors, and SCCs, allowing the EPA to prepare the "default" emission estimates for
S/L/T agencies using the group's final approaches. The NOMAD committees were formed in preparation for the
2014 NEI; however, time and resource constraints limited the scope of some of the work that could be
accomplished. For example, the mercury NOMAD team identified several source categories where methodology
and/or activity data need revision, and this collaboration will propagate into a future NEI, but for the 2014 NEI,
2011 NEI estimates are carried forward.
During the 2014 NEI inventory development cycle, S/L/T agencies, using the nonpoint survey (Section 4.1.2),
could accept the NOMAD/EPA estimates to 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 further improved data.
Table 4-5 and
Table 4-6 describe the sectors for which EPA developed emission estimates. They separately list emissions
sectors entirely comprised of data in the nonpoint (i.e., not point source) data category (Table 4-5), such as
residential heating, from sectors that may overlap with the point sources (
Table 4-6). 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 provided in zip files posted on the 2014v2 Supplemental Nonpoint data FTP site.
which is the directory containing most supporting data files listed in Table 4-5 and
Table 4-6. Agricultural field burning and nonpoint mercury estimates are provided in other directories listed in
Table 4-5. Emission sources that use data from the 2014vl NEI are identified in the column "Carried Forward?"
in these tables. The SCCs associated with the EPA nonpoint data categories are in an Excelฎ file on the 2014vl
NEI Supplemental data FTP site. 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 4-5: EPA-estimated emissions sources expected to be exclusively nonpoint
("Carried Forward" indicates whether EPA data were carried forward from the 2011v2 NEI.)
EPA-estimated emissions source
description
Carried
FnriA/arH?
EIS Sector(s) Name
Name of supporting data file or other
reference
Agricultural Tilling
Agriculture - Crops &
Livestock Dust
Ag Tilling v4.2.zip
Dust from livestock
Agriculture - Crops &
Livestock Dust
2014V2_Dust_from_Hooves_Emission_lnvent
ory_Tool_25Septl7.xlsx
Fertilizer Application
Agriculture - Fertilizer
Application
Emissions_and_fertilizer_2011_2014_v2DRAF
Trltedit.xlsx
4-7
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EPA-estimated emissions source
description
Carried
FnriA/arH?
EIS Sector(s) Name
Name of supporting data file or other
reference
Animal Husbandry
Agriculture - Livestock
Waste
I_aglivestock_2014neiv2_octfinal2017.zip
Commercial Cooking
Commercial Cooking
Commercial Cooking_vl.5_2017-05-26.zip
Composting
Waste Disposal
Compost 4.1.zip
Dust from Residential,
Commercial/Institutional and Road
Construction
Dust - Construction
Dust
Construction Dust_2016-ll-ll.zip
Paved and Unpaved Roads
Dust - Paved Road
Dust
Dust - Unpaved Road
Dust
Paved Roads for 2014v2.zip
Unpaved Roads for 2014v2.zip
Crop and range/pasture-land
burning
X
Fires - Agricultural
Field Burning
crop_residue_burning_in_2014.pdf
Residential Heating: bituminous
and anthracite coal, distillate oil,
kerosene, natural gas, LPG
Fuel Comb
Residentm - Other
Residential Heating_vl.3_2016-ll-14.zip
Residential Heating; Fireplaces,
woodstoves, fireplace inserts, pellet
stoves, indoor furnaces, outdoor
hydronic heaters, and firelogs
Fuel Comb
Resident m - Wood
RWC_Tool_v3.2.zip
Aviation Gasoline Stage 1+ Stage 2
Gas Stations
Aviation Gasoline v4.1_2016-ll-ll.zip
Mining and Quarrying
Industrial Processes-
Mining
Mining&Quarrying_v2.3_2016-ll-ll.zip
Portable Gas Cans: Residential and
Commercial
X
Miscellaneous Non-
Industrial NEC
2014_Portable_Fuel_Containers_25nov2015.
zip
Agricultural Pesticide Application
Solvent - Consumer &
Commercial Solvent
Use
Agricultural Pesticides_v2.1_2016-ll-ll.zip
Cutback Asphalt Paving -Cutback
and Emulsified
X
Solvent - Consumer &
Commercial Solvent
Use
Asphalt Paving v2.zip
Open Burning - Brush, Residential
Household Waste, Land Clearing
Debris
Waste Disposal
2014 Open Burning NEI v2.zip
Human Cremation -non-mercury
Miscellaneous Non-
Industrial NEC
2014v2_Human_cremation_EPA.zip
Mercury from:
Dental Amalgam Production,
Fluorescent Lamp Breakage
Miscellaneous Non-
Industrial NEC
Waste Disposal
2014 NEI v2 Mercury Nonpoint.zip
4-8
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EPA-estimated emissions source
description
Carried
FnriA/arH?
EIS Sector(s) Name
Name of supporting data file or other
reference
(Landfill emissions), Fluorescent
Lamp Recycling, Human and Animal
Cremation, Switches and Relays,
Working Face Landfill,
Thermometers and Thermostats
Table 4-6: Emissions sources with potential nonpoint and point contribution
("Carried Forward" indicates whether EPA data were carried forward from the 2011v2 NEI.)
EPA-estimated emissions source
description
Carried
EIS Sector(s) Name
Link to supporting data file
Gasoline Distribution - Stage 1:
Bulk Plants, Bulk Terminals,
Pipelines, Service Station
Unloading, Underground
Storage Tanks, Trucks in Transit;
Bulk Gasoline Terminals
Gas Stations
Industrial Processes-
Storage and Transfer
Stage 1 Gasoline Distribution for NEI
v2.zip
Stage 1 PS Subtraction vl.2.zip
Industrial,
Commercial/Institutional Fuel
Combustion
Fuel Comb - Industrial
Boilers, ICEs All Fuels
Fuel Comb - Commercial/
Institutional - All Fuels
ICI vl.6.zip
Oil and Gas Production
Industrial Processes - Oil &
Gas Production
OIL_GAS_TOOL_2014_NEI_PRODUCTION
_V2_2.zip
Oil and Gas Exploration
Industrial Processes - Oil &
Gas Production
OIL_GAS_TOOL_2014_NEI_EXPLO RATIO
N_V2_3.zip
Publicly Owned Treatment
Works
X
Waste Disposal
2014_POTW_nonpoint_emissions_23ma
rch2016.zip
4-9
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T3 1
I
EPA-estimated emissions source
~ !
re 1
description
(J (
L
EIS Sector(s) Name
Link to supporting data file
Solvent - Consumer &
Commercial Solvent Use
(except Ag Pesticides and
Asphalt Paving)
Solvent - Degreasing
Solvent - Graphic Arts
Solvent Utilization
Solvent - Dry Cleaning
Solvent_Tool_vl.7.zip
Solvent - Graphic Arts
Solvent - Industrial
Surface Coating & Solvent
Use
Solvent - Non-Industrial
Surface Coating
4,2 Nonpoint non-combustion-related mercury sources
4,2.1 Source Description
This source category includes numerous nonpoint mercury sources from a variety of waste disposal and other
activities. Table 4-7 provides the emissions sources and SCCs for nonpoint mercury. For the 2014 vl NEl, the EPA
carried forward estimates of mercury for several nonpoint emissions sources that had been newly developed for
2011. The general laboratory activities emissions (600 pounds of Hg), carried forward from 2008 for the 2011 v2
NEI were erroneously dropped in the 2014vl but were picked up in the 2014v2 NEI selection. EPA updated the
activity data to year 2014 for all other sources of non-combustion nonpoint inventory mercury in the 2014v2
NEI. Additional descriptions of the individual types of activities are provided in the source-specific sub-sections
below.
Table 4-7: SCCs and emissions (lbs) comprising the nonpoint non-combustion Hg sources in the 2014 NEI
Description
see
Sector
SCC Description
2014vl
2014v2
Landfill working
face
2620030001
Waste Disposal
Landfills; Municipal;
Dumping/Crushing/Spreading
of New Materials (working
face)
828
763
Scrap waste:
Thermostats and
Thermometers
2650000000
Waste Disposal
Scrap and Waste Materials;
Scrap and Waste Materials;
Total: All Processes
243
241
Shredding:
Switches and
Relays
2650000002
Waste Disposal
Scrap and Waste Materials;
Scrap and Waste Materials;
Shredding
4,293
3372
4-10
-------
Description
see
Sector
SCC Description
2014vl
2014v2
Human
Cremation
2810060100
Miscellaneous
Non-Industrial
NEC
Miscellaneous Area Sources;
Other Combustion; Cremation;
Humans
2,292
2,864
Animal
Cremation
2810060200
Miscellaneous
Non-Industrial
NEC
Miscellaneous Area Sources;
Other Combustion; Cremation;
Animals
80.2
134
Dental Amalgam
Production
2850001000
Miscellaneous
Non-Industrial
NEC
Miscellaneous Area Sources;
Health Services; Dental Alloy
Production; Overall Process
804
923
Fluorescent
Lamp Breakage
2861000000
Miscellaneous
Non-Industrial
NEC
Miscellaneous Area Sources;
Fluorescent Lamp Breakage;
Non-recycling Related
Emissions; Total
803
1,676
Fluorescent
Lamp Recycling
2861000010
Miscellaneous
Non-Industrial
NEC
Miscellaneous Area Sources;
Fluorescent Lamp Breakage;
Recycling Related Emissions;
Total
0.2
0.6
General
Laboratory
Activities
2851001000
Miscellaneous
Non-Industrial
NEC
Miscellaneous Area Sources;
Laboratories; Bench Scale
Reagents; Total
N/A
635
TOTAL
9,343
10,608
None of these categories are distinct regulatory sectors and are therefore put into the "EPA Other" category in
the mercury summary provided in Table 2-12. Detailed documentation on the methods is provided in a
memorandum "2014_Mercury_documentation_ 109-12-2016.pdf" provided in the supplemental
documentation.
The 2011 nonpoint Hg estimates used in 2014vl were developed in collaboration with an Eastern Regional
Technical Advisory (ERTAC) workgroup set up for focus on these nonpoint emissions sources. For 2014v2 NEI,
the activity data for all source categories except General Laboratory Activities (2851001000) were updated to
year 2014 and 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"
2011 methodologies (with 2014 activity data) 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 4-8 lists the agencies, SCCs and
emissions that were submitted for these nonpoint mercury sources; the S/L/T emissions from these agencies
replace EPA estimates in 2014 NEI.
Table 4-8: S/L/T-reported mercury nonpoint non-combustion emissions (lbs)
Region
Agency
S/L/T
SCC
Description
Sector
S/L/T
Emissions
1
Maine Department
of Environmental
Protection
State
2810060100
Human Cremation
Miscellaneous
Non-Industrial
NEC
9
4-11
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S/L/T
Region
Agency
S/L/T
see
Description
Sector
Emissions
Vermont Department
Miscellaneous
of Environmental
Non-Industrial
1
Conservation
State
2810060100
Human Cremation
NEC
14
New York State
Department of
Environmental
Landfill: Working
2
Conservation
State
2620030001
Face
Waste Disposal
25
New York State
Department of
Scrap Waste:
Environmental
Thermostats and
2
Conservation
State
2650000000
Thermometers
Waste Disposal
14
New York State
Department of
Environmental
Shredding: Switches
2
Conservation
State
2650000002
and Relays
Waste Disposal
248
New York State
Department of
Miscellaneous
Environmental
Non-Industrial
2
Conservation
State
2810060100
Human Cremation
NEC
204
New York State
Department of
Miscellaneous
Environmental
Non-Industrial
2
Conservation
State
2810060200
Animal Cremation
NEC
5
New York State
Department of
Miscellaneous
Environmental
Dental Amalgam
Non-Industrial
2
Conservation
State
2850001000
Production
NEC
33
New York State
Department of
Miscellaneous
Environmental
Fluorescent Lamp
Non-Industrial
2
Conservation
State
2861000000
Breakage
NEC
50
Maryland
Miscellaneous
Department of the
Fluorescent Lamp
Non-Industrial
3
Environment
State
2861000000
Breakage
NEC
36
Virginia Department
Miscellaneous
of Environmental
Non-Industrial
3
Quality
State
2810060100
Human Cremation
NEC
23
Illinois
Miscellaneous
Environmental
Non-Industrial
5
Protection Agency
State
2810060100
Human Cremation
NEC
0
Illinois
Miscellaneous
Environmental
Dental Amalgam
Non-Industrial
5
Protection Agency
State
2850001000
Production
NEC
61
Illinois
Miscellaneous
Environmental
General Laboratory
Non-Industrial
5
Protection Agency
State
2851001000
Activities
NEC
31
4-12
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S/L/T
Region
Agency
S/L/T
see
Description
Sector
Emissions
Illinois
Miscellaneous
Environmental
Fluorescent Lamp
Non-Industrial
5
Protection Agency
State
2861000000
Breakage
NEC
41
Illinois
Miscellaneous
Environmental
Fluorescent Lamp
Non-Industrial
5
Protection Agency
State
2861000010
Recycling
NEC
0
Miscellaneous
Minnesota Pollution
Dental Amalgam
Non-Industrial
5
Control Agency
State
2850001000
Production
NEC
15
Miscellaneous
Minnesota Pollution
General Laboratory
Non-Industrial
5
Control Agency
State
2851001000
Activities
NEC
9
Miscellaneous
Minnesota Pollution
Fluorescent Lamp
Non-Industrial
5
Control Agency
State
2861000000
Breakage
NEC
14
Miscellaneous
Ohio Environmental
Non-Industrial
5
Protection Agency
State
2810060100
Human Cremation
NEC
41
Miscellaneous
Washoe County
Non-Industrial
9
Health District
Local
2810060100
Human Cremation
NEC
72
Miscellaneous
Washoe County
Non-Industrial
9
Health District
Local
2810060200
Human Cremation
NEC
53
Miscellaneous
Non-Industrial
10
Coeur d'Alene Tribe
Tribe
2810060100
Human Cremation
NEC
0
Miscellaneous
Non-Industrial
10
Coeur d'Alene Tribe
Tribe
2810060200
Human Cremation
NEC
0
Idaho Department of
Miscellaneous
Environmental
Non-Industrial
10
Quality
State
2810060100
Human Cremation
NEC
8
Idaho Department of
Miscellaneous
Environmental
Non-Industrial
10
Quality
State
2810060200
Human Cremation
NEC
0
Miscellaneous
Kootenai Tribe of
Non-Industrial
10
Idaho
Tribe
2810060100
Human Cremation
NEC
0
Miscellaneous
Kootenai Tribe of
Non-Industrial
10
Idaho
Tribe
2810060200
Human Cremation
NEC
0
Miscellaneous
Non-Industrial
10
Nez Perce Tribe
Tribe
2810060100
Human Cremation
NEC
0
4-13
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S/L/T
Region
Agency
S/L/T
see
Description
Sector
Emissions
Miscellaneous
Non-Industrial
10
Nez Perce Tribe
Tribe
2810060200
Human Cremation
NEC
0
Shoshone-Bannock
Tribes of the Fort
Miscellaneous
Hall Reservation of
Non-Industrial
10
Idaho
Tribe
2810060100
Human Cremation
NEC
0
Shoshone-Bannock
Tribes of the Fort
Miscellaneous
Hall Reservation of
Non-Industrial
10
Idaho
Tribe
2810060200
Human Cremation
NEC
0
Total
1,007
4,2,2 EPA-developed mercury emissions from 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 (2014EPA_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.
4.2.2.1 Activity Data
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 2], 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.
4.2.2.2 A/iocation Approach
The EPA LMOP database provides data at the county level.
4.2.2.3 Emission Factor
Lindberg et al. (2005) [ref 1], measured mercury emissions from the working face of four landfills in Florida and
determined emission factors per ton of waste placed in a landfill annually, ranging from 1-6 mg per ton of waste.
The average of these emission factors is 2.5 mg/ton of waste, or 5.51 x 10 s lbs/ton of waste.
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4.2.2.4 Example Calculation
The New Hanover County Secure Landfill in New Hanover, NC is estimated to receive approximately 117,368
tons of waste annually.
117,368 tons of waste x 5.51 x 10 s lbs Hg/ton of waste = 0.65 lbs Hg emissions
4.2.3 EPA-Developed Emissions from Thermostats
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.
4.23,1 Activity Data
The 2002 EPA report estimated that 2-3 million thermostats came out of service in 1994 [ref 3], A 2013 report
from a consortium of environmental groups assumes that the estimate from the 2002 report remains viable, and
it estimates that the TRC collects at most 8% of the retired thermostats each year [ref 4], Therefore, using this
estimate, there are approximately 2.3 million thermostats that are not recycled each year.
4.2.3.2 Allocation Approach
The national-level mercury emissions are apportioned to each county based on 2014 population from the U.S.
Census Bureau, except for 2010 population data used for the Virgin Islands.
4.23.3 Emission Factor
The 2002 EPA report estimates that there are 3 grams of mercury per thermostat [ref 3], Cain et al. (2007) [ref 5]
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 9.9 x 10 s lbs. per thermostat.
4.2.3.4 Example Calculation
2.3 million improperly disposed thermostats x 9.9 x 10~5 lbs per thermostat = 228 lbs mercury emissions
Shelby County, TN has 938,803 people, or 0.29% of the national population. The mercury emissions from
thermostats in Shelby County, TN are estimated by the following:
228 lbs national mercury emissions x 0.29% = 0.672 lbs mercury emissions
4.2.4 EPA-Developed Emissions from Thermometers
Mercury thermometers have all but been phased out in the United States, with the U.S. EPA and National
Institute of Standards and Technology (NIST) working to phase out mercury thermometers in industrial and
laboratory settings. NIST issued a notice in 2011 that it would no longer calibrate mercury-in-glass
thermometers for tracking purposes. The 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
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generation, and polychlorinated biphenyl (PCB) waste disposal [ref 6], Furthermore, thirteen states have laws
that limit the manufacture, sale, and/or distribution of mercury-containing fever thermometers [ref 6],
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.
4,2,4,1 Activity Data
Data from the Northeast Waste Management Officials' Association (NEWMOA) Interstate Mercury Education
and Reduction Clearinghouse (IMERC) database suggests that there were 713 lbs of mercury used in
thermometers in 2007 [ref 6], We assume that this value is held constant each year through 2011.
The U.S. EPA assumes that the average lifespan of a glass thermometer is 5 years, and that 5% of glass
thermometers are broken each year [ref 3],7 Therefore, if 546 lbs. of mercury are used in thermometers each
year there would be an estimated 2,470 lbs of mercury remaining in thermometers in 2014 (accounting for the
breakage rate each year).
NEWMOA [ref 6] estimates that during the period 2000-2006 there were 350 lbs of mercury from thermometers
collected in recycling programs.
Therefore, there were 2,120 lbs (1.06 tons) of mercury available for release in 2014.
4,2 A.2 Allocs fion Approach
The national-level mercury emissions from thermometers are allocated to the county level based on 2011
population.
Emission Factor
Cain et al. (2007) [ref 5] estimates that 10% of mercury from thermometers is emitted to the air before disposal
in a landfill, and Leopold (2002) [ref 3] estimates that 5% of thermometers are broken each year. Therefore, the
emission factor is estimated to be 10 lbs of mercury emissions per ton of mercury in thermometers.
4,2.4,4 Example Calculation
1.06 tons of mercury in broken thermometers x 10 lbs emissions per ton = 10.6 lbs of emissions
Boise County, ID has 76,824 people, or 0.0021% of the national population. The mercury emissions from broken
thermometers for Boise County are estimated by the following:
14.4 lbs national emissions x 0.0021% = 0.00022 lbs emissions
4,2,5 EPA-Developed Emissions from 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
7 The US EPA does not explain what happens to the remaining 75% of unbroken thermometers after the estimated 5-year
lifespan, but it does suggest that recycling, such as through Fisher Scientific's thermometer trade-in program, may account
for some of the remaining thermometers.
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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, which are potential emissions sources when the cars are crushed and shredded during
recycling at the end of their useful lives. The shredded material is then sent to an arc furnace to recycle the
steel. To avoid double counting point source emissions from arc furnaces, this source category only includes an
estimate of nonpoint emissions from crushing and shredding operations.
A.7.5.1 Activity Data
A 2011 report from the North Carolina Department of Environment and Natural Resources [ref 8] provides
information on the estimated number of switches available for recovery in each state and the number of
switches recovered in 2014. There were 2.6 million mercury-containing automobile switches available
nationwide in 2014 and 513,877 switches collected for recycling, for a collection rate of 19.67%. These
nationwide estimates are supported by similar data from the Quicksilver Caucus [ref 9], Therefore, there were
approximately 2.1 million unrecycled automotive switches in 2014.
2,5,2 Allocation Approach
The number of unrecovered switches is apportioned to each county based on the number of car recycling
facilities (NAICS 423930) from the 20144 U.S. Census Bureau County Business Patterns.
''4-, , 3, .5 tn ussson F ctciot
The response to comments for the 2007 EPA Significant New Use Rule on Mercury Switches (72 FR 56903),
suggests that the weighted average amount of mercury in switches is 1.2 grams (0.0026 lbs). A 2001 report by
Griffith et al. [ref 10] shows that 60% of mercury in switches is released at the shredding operation, while 40% is
sent to arc furnaces for smelting. Therefore, the emission factor for switches is 0.00156 lbs. per switch.
4.2.5.4 Example Calculation
Alabama had 53,811 unrecovered vehicle switches in 2014. Baldwin County, AL has 4 car recycling facilities,
which represents 2% of the facilities in the state. Therefore, that county is apportioned switches as follows:
53,811 switches in AL x 2% = 1,092.6 switches in Baldwin County, AL
Emissions are estimated as follows:
1,092.6 switches x 0.00156 lbs/switch = 1.70 lbs Hg emissions
4,2,6 EPA-Developed Emissions for Human Cremation
The cremation of individuals with mercury fillings and mercury in blood and tissues can result in mercury
emissions. Cremation is becoming increasingly popular, with 40.6% of individuals being cremated in 2010, up
from 33% in 2006, according to the Cremation Association of North America (CANA) [ref 11], Note, human
cremation for other pollutants was computed separately, and is discussed in Section 4.26.
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4.2.6.1 Activity Data
The Centers for Disease Control and Prevention WONDER database contains information on the number of
deaths in each county in each year for 13 different age groups through 2014 [ref 12]. Table 4-9 provides the data
that we pulled from the WONDER database, which withheld data from some counties. Emission factor data is
derived from the Bay Area Air Quality Management District (BAAQMD) [ref 13], The county gaps were filled
using the state totals (which included the number of deaths that were withheld at the county level). The
difference between the state-level data and the sum of the reported county-level deaths was apportioned to the
counties not included in the WONDER database based on their 2014 population.
The CANA data [ref 11] provides statistics on cremation rates by state as of 2010. It is assumed that the state-
level cremation rate applies to all counties in the state.
Table 4-9: Comparison of age groups in the CDC WONDER database (activity data) and the BAAQMD
memorandum
Age Groups in CDC
WONDER Database
Age Groups in
BAAQMD
Memorandum
Avg. Material in
Restored Teeth (g)
% of Fillings
Containing
Mercury
% of Mercury in
Dental Amalgam
< 1 year
0-4 years*
0.000
0.0%
45.0%
1-4 years
0.160
31.6%
45.0%
5-9 years
5-14 years
0.720
31.6%
45.0%
10-14 years
0.720
31.6%
45.0%
15-19 years
15-24 years
1.070
31.6%
45.0%
20-24 years
1.070
50.0%
45.0%
25-34 years
25-34 years
2.230
50.0%
45.0%
35-44 years
35-44 years
3.290
62.5%
45.0%
45-54 years
45-54 years
4.310
62.5%
45.0%
55-64 years
55-64 years
4.320
75.0%
45.0%
65-74 years
65-74 years
3.780
75.0%
45.0%
75-84 years
75-84 years
3.650
75.0%
45.0%
85+ years
85+ years
2.960
75.0%
45.0%
* It is assumed that children under the age of 1 have no dental mercury.
4,2.6.2
The CDC WONDER database contains data at the county level. The CANA statistics on the cremation rate are at
the state level, but it is assumed that this rate applies to all counties in the state.
4.2.6.3 Emission Factor
The Bay Area Air Quality Management District (BAAQMD) issued a memorandum calculating the average
amount of dental mercury in each human in ten different age groups based on data from the CDC's National
Health and Nutrition Examination Survey (NHANES) [ref 13]. The age groups from the BAAQMD memorandum
match well with the age groups from the CDC WONDER database (Table 4-9).
The emission factors were developed using the NHANES data to determine the number of individuals in each
age group with 1, 2, 3, or 4 or more restored teeth. These numbers were used along with a year-2004 published
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report that estimated the average mass of material in tooth restorations used in 1, 2, 3, or 4 or more teeth to
determine a weighted average mass of material in tooth restorations per individual in each age group [ref 14].
The approach then accounts for the fact that not all fillings are made with mercury. According to the American
Dental Association [ref 15] more than 75% of restorations before the 1970s used dental amalgam, which
declined to 50% by 1991. Using these numbers, it is assumed that 50% of the filled teeth for 20-34 age group
contain amalgam, 62.5% of filled teeth in the 35-49% age group, and 75% of filled teeth for people over 50. The
BAAQMD memorandum was used to estimate that 31.6% of filled teeth in the 1-19 age group contain amalgam.
The analysis also assumes that 45% of all amalgam-containing fillings are mercury.
The BAAQMD memorandum states that their assumptions are conservative, and could result in an
overestimation of mercury emissions given that the analysis assumes that none of the mercury initially placed in
the teeth is lost over time, even though data shows some loss of mercury from dental restorations, though the
rate of loss is dependent on many factors, including area, age, and composition of the amalgam.
In addition to the amount of mercury in teeth, Reindl [ref 16] estimates mercury emissions from blood and
tissues (but not dental amalgam) from humans at 0.000132 Ibs./cremation, assuming an average weight at
cremation of 176 lbs.
Example Calculations
Estimating mercury in teeth:
There were 112 deaths in the 75-84 age group in Autauga County, AL in 2014. The emission factor for that age
group is 1.2319 grams of mercury, or 0.0027 lbs., per cremated human. Alabama has a cremation rate of 23.1%.
To calculate the mercury emissions from this age group, these numbers are multiplied together:
112 deaths in the 75-84 year age group x 23.1% cremation rate x 0.0027 lbs. Hg/cremation
= 0.069 lbs. Hg emissions for the 75-84 year age group in Autauga County, AL
Estimating mercury in blood and tissues:
112 deaths in the 75-84 year age group x 23.1% cremation rate x 0.000132 lbs. Hg/cremation
= 0.00342 lbs. Hg emissions for the 75-84 year age group in Autauga County, AL
Total mercury emissions:
0.069 + 0.00342 = 0.0733 lbs. Hg emissions
This is repeated for each age group in Table 4-9 in each county.
4,2.7 EPA-Developed Emissions for Animal Cremation
Animal tissues contain mercury, similar to humans. A 2012 survey from the Pet Loss Professionals Alliance [ref
17] found that 99% of deceased pets are cremated, with the remaining 1% receiving burial. Therefore, mercury
from animal tissues through cremation can be a source of nonpoint mercury emissions.
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4.2.7.1 ActMtyD3t3
The PLPA survey estimates that there were 1,840,965 pet cremations in 2012. In addition, the Humane Society
of the United States [ref 18) estimates that there are 2,700,000 dogs and cats euthanized in animal shelters each
year. It is assumed that 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 petsi.e. animals that are owned by
peoplewhereas the Humane Society estimates are for animals in shelters that were not adopted.
The population of cats and dogs is approximately 52.5% cats and 48.5% dogs [ref 18]. The average weight of a
domestic cat is approximately 12.5 lbs [ref 19]. The average weight of a dog is difficult to determine due to large
differences in breeds, but one estimate suggests it is 35 lbs. [ref 20], Therefore, the total weight of cremated
animals is approximately 53,441 tons.
<3,2.7,2 Allocation Approach
The national-level mercury emissions from animal cremation are allocated to the county level based on 2014
human population.
4.2,7.3 Emission Factor
Emission factors for mercury emissions from animal cremations are not available from the literature. Reindl [ref
16) estimates mercury emissions from blood and tissues (but not dental amalgam) from humans at 0.0015
lbs/ton. This emission factor appears to be the most appropriate available emission factor for animals, given that
it does not include dental amalgam. This approach assumes that pets have the same exposure, adsorption rates,
and accumulation of Hg as humans, on average.
4,/, / ,.4 txamplG Calculation
Total mercury emissions from animal cremations:
53,441 tons cremated animals x 0.0015 lbs/ton = 80.2 lbs mercury emissions
Walla Walla County, Washington has 59,844 people, or 0.019% of the national population. The mercury
emissions from animal cremations in Walla Walla are estimated by the following:
80.2 lbs national mercury emissions x 0.019% = 0.015 lbs mercury emissions
4.2.8 EPA-Developed Emissions for Dental Amalgam Production
Dental amalgam is used to fill cavities in teeth, and it is composed of approximately 45% mercury [ref 13]. The
use of mercury in dental amalgam is declining, however, due to the increased popularity of composite fillings for
teeth [ref 21]. 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 a small amount of emissions directly from restored teeth.
4.2.8.1 Activity Data
The amount of amalgam prepared in dental offices was estimated using NEWMOA's IMERC database [ref 22],
which estimates that 15.97 tons (31,940 lbs) of mercury in dental amalgam were used in 2013.
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The amount of mercury emissions from restored teeth was estimated using data from the National Institutes of
Health's National Institute of Dental and Craniofacial Research [ref 23], which provides estimates of the average
number of filled teeth per person in three different age brackets: 20-34 years, 35-49 years, and 50-64 years. The
number of filled teeth for other age groups was estimated using the CDC National Health and Nutrition
Examination Survey (NHANES). Table 4-10 lists the average number of filled teeth per person by age group.
Table 4-10: 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
0-5
0.44
31.6
5-19
1.23
31.6
20-34
4.61
50.0
35-49
7.78
62.5
50-64
9.20
75.0
65+
6.47
75.0
According to the American Dental Association [ref 15] more than 75% of restorations before the 1970s used
amalgam, which declined to 50% by 1991. Using these numbers, it is assumed that 50% of the filled teeth for 20-
34 age group contain amalgam, 62.5% of filled teeth in the 35-49% age group, and 75% of filled teeth for people
over 50. The BAAQMD memorandum was used to estimate that 31.6% of filled teeth in the 1-19 age group
contain amalgam.
4.2.8.2 Affocafion Approach
The emissions from dental office preparations were allocated to the county level based on 2014 population.
The emissions from filled teeth were allocated to each county by multiplying the county population by the
proportion of the national population in each age group (from 2014 U.S. Census Bureau data, except 2010
vintage for Virgin Islands), the average number of filled teeth per person, and the percentage of fillings
containing mercury (Table 4-9). The emissions were then added across age groups.
4.2.8.3 Emission Factor
U.S. EPA [ref 24] estimates that 2% of mercury used in dental offices is emitted to the air.
Richardson et al. [ref 25] estimate emissions from filled teeth of approximately 0.3 ng/day of mercury emissions
per filled tooth, or 2.4 x 10"7 lbs. per year per filled tooth.
4.2.8.4 Example Calculation
Emissions from dental office preparations:
31,940 lbs Hg x 2% = 638.8 lbs emissions
Orleans Parish, LA has 384,320 people, representing 0.121% of the national population. The mercury emissions
from dental office preparations in Orleans Parish are estimated by the following:
638.8 lbs national emissions x 0.121% = 0.77 lbs Hg mercury emissions from dental offices
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Emissions from restored teeth:
Nationally, 14.5% of the population is in the 65+ age group. This age group has an average of 6.47 fillings per
person, and 75% of their fillings contain mercury. The emissions from restored teeth in Orleans Parish, LA are
estimated by the following:
384,320 people x 14.5% in 65+ age bracket x 6.47 fillings per person x 75% of fillings with mercury x 2.4 x 10"7
lbs per year per filled tooth
= 0.065 lbs mercury in the 65+ age bracket in Orleans Parish
This is repeated for each age group in Table 4-10 for each county.
4,2.9 EPA-Developed Emissions for Fluorescent Lamp Breakage (not recycled)
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, particularly compact fluorescents, driven partly by the phase out of many types of incandescent bulbs
from the Energy Independence and Security Act of 2007 (PL 110-140 ง 321), could lead to increases in mercury
emissions.
4.2.9.1 Activity Data
Data from a Freedonia Group Industry Study on the U.S. lamp market was used to estimate that 1.4 billion
mercury containing lamps, including CFLs and high impact discharge (HID) lamps, were discarded or recycled in
2014. 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 (hrs) of lamp types were used to calculate
lifetimes (yrs), assuming that CFLs are on for 4 hours per day and all other fluorescents and HIDs are on for 8
hours per day (Buildings.com, 2008) [ref 26],
According to a 2010 study by Silveira and Chang [ref 27], the recycling rate for mercury containing lamps in the
U.S. is 23%. Taking into account recycling, this suggests that there were approximately 1.1 billion mercury-
containing lamps discarded at landfills in 2014.
Allocation Approach
The national-level mercury emissions from fluorescent lamp breakage are allocated to each county based on
2014 population.
4.2.9.3 Emission Factor
Cain et. al [ref 28] 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 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 4-11). Linear fluorescent bulbs contain more
mercury than CFLs, with a range of 8.3 to 12 mg per bulb (10.15 average, Table 4-12). Data from the USGS
suggests that there is an average of 17 mg of mercury per HID bulb [ref 29],
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Table 4-11: Mercury used in CFLs (mg/bulb) as determined by three different studies
Study
Average Amount of
Mercury per CFL (mg)
Li and Jin [ref 30]
1.27
Katers et al. [ref 31]
4.00
Singhvi et al. [ref 32]
2.63
Average
2.63
Table 4-12: Mercury used in linear fluorescent bulbs (mg/bulb) as determined by two different studies
Study
Average Amount of Mercury per
Linear Fluorescent Bulb (mg)
Aucott et al. [ref 33]
12.0
NEMA [ref 34]
8.3
Average
10.2
Therefore, the emission factor for CFLs would be:
2.63 mg per CFL x 10% = 0.263 mg of emissions per CFL
The emission factor for linear bulbs would be:
10.15 mg per linear bulb xl0% = 1.015 mg per linear bulb
The emission factor for HID bulbs would be:
17 mg per HID bulb x 10% = 1.7 mg per HID bulb
4.2.9.4 Example Calculation
Emissions from CFLs:
519 million discarded bulbs x 0.263 mg per CFL
= 136.4 million mg mercury emissions from CFLs
Emissions from linear bulbs:
462 million discarded bulbs x 1.015 mg per bulb
= 472.3 million mg mercury emissions from linear bulbs
Emissions from HID bulbs:
112 million discarded bulbs x 1.7 mg per bulb
= 190.3 million mg mercury emissions from HID bulbs
Total mercury emission from breakage of mercury-containing bulbs:
136.4 million mg + 472.3 million mg + 190.3 million mg = 799 million mg
= 799 kg
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= 1,758 lbs mercury emissions
Weston County, WY was estimated to have 7,201 people in 2014, or 0.0023% of the national population. The
emissions for Weston County are estimated as follows:
1,758 lbs national Hg emissions x 0.0023% of national population = 0.04 lb. Hg emissions
4.2.10 EPA-Developed Emissions for Fluorescent Lamp Breakage (recycling)
In addition to emissions of mercury from the breakage of fluorescent light bulbs (SCC 2861000000), there are a
small amount of emissions from recycling fluorescent bulbs.
4.2.10.1 AdMtyData
The activity data were previously described in Section 4.2.9.1. Considering recycling rates, this suggests that
there were approximately 327 million mercury-containing lamps recycled in 2014.
4.2.10.2 AffocationApprmch
The national-level mercury emissions from the recycling of mercury-containing lamps are allocated to each
county based on 2014 population.
4,2,103 Emission Factor
The U.S. EPA [ref 24] has estimated an emission factor from mercury-containing bulb recycling of 0.00088
mg/lamp (1.9 x 10"9 Ib./lamp).
4,2,10.4 Example Calculation
Emissions from recycling of mercury-containing bulbs:
327 million bulbs recycled x 1.9 x 10"9 lb/lamp = 0.6 lbs mercury emissions
Cumberland County, ME has a population of 281,797 people, or 0.09% of the national population. The emissions
from the recycling of mercury-containing bulbs in Cumberland County, ME were estimated by the following:
0.6 lbs mercury emissions x 0.09% = 0.00057 lbs mercury emissions
4.2.11 EPA-Developed Emissions for General Laboratory Activities
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 [ref 35], it was determined that because the USGS stopped conducting its survey of the end uses of
mercury in the economy in 2002 it would be impossible to state with any confidence an estimate of the amount
of mercury used in general laboratory activities in 2014. The estimate from the 2008 NEI was pulled forward for
the 2011 NEI. Further literature searches again revealed no data that could be used to estimate mercury
emissions for this source category; therefore, the estimate from the 2008 NEI was pulled forward for the 2014
NEI.
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This category accounts for approximately 600 pounds of mercury of EPA-estimated mercury; however, as seen
in Table 4-8, Minnesota and Illinois reported 40 cumulative pounds of mercury for this source and the 2008-
based EPA estimates for the remaining states fill out the rest of the emissions in the 2014 v2 NEI.
4.2.12 Agency-reported emissions
Agency-reported emissions for all non-combustion nonpoint mercury sources were summarized in Table 4-8 in
Section 4.2.1. Eight states, 1 local and 3 tribal agencies reported one or more of these nonpoint mercury sources
for 2014 NEI.
4.2.13 References for nonpoint mercury sources
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.
2. US EPA, 2014. Landfill Methane Outreach Program. Last accessed May 2014.
3. Leopold, B.R. 2002. Use and Release of Mercury in the United States. U.S. Environmental Protection
Agency. Report EPA/600/R-02/104.
4. 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.
5. 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.
6. US EPA. 2014b. Phase-Out of Mercury Thermometers Used in Industrial and Laboratory Settings,
available on the Mercury in Your Environment website.
7. Northeast Waste Management Officials' Association (NEWMOA). 2008. Trends in Mercury Use in
Products: Summary of the Interstate Mercury Education and Reduction Clearinghouse (IMERC) Mercury-
added Products Database.
8. NC Department of Environment and Natural Resources. 2011. Mercury Switch Removal Program 2011
Annual Report.
9. Quicksilver Caucus. 2012. Third Compendium of States' Mercury Activities. The Environmental Council of
the States.
10. Griffith, C., et al. 2001. Toxics in Vehicles: Mercury. A Report by Ecology Center, Great Lakes United, and
University of Tennessee Center for Clean Products and Clean Technologies. Last accessed May 2014.
11. Cremation Association of North America (CANA). 2011. Annual Statistics Report 2011. Last accessed May
2014.
12. CDC. 2014. WONDER Database. Last accessed July 2016.
13. Lundquist, J.H. 2012. Mercury Emissions from the Cremation of Human Remains. Bay Area Air Quality
Management District.
14. Adegbembo, A.O., P.A. Watson, and S. Rokni. 2004. Estimating the Weight of Dental Amalgam
Restorations. Journal of the Canadian Dental Association, 70:30-30e.
15. American Dental Association (ADA). 1998. Dental Amalgam: Update on Safety Concerns. Journal of the
American Dental Association, 129:494:503.
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16. Reindl, J. 2012. Summary of References on Mercury Emissions from Crematoria. Last accessed May
2014.
17. Pet Loss Professionals Alliance (PLPA). 2013. Pet Loss Professionals Alliance Releases Finding of Inaugural
Professional Survey. Last accessed May 2014.
18. Human Society of the United States. 2014. Pets by the Numbers. Last accessed May 2014.
19. National Geographic. 2014. Domestic Cat. Last accessed May 2014.
20. Animal Ark. 2012. Humane Society Admits to Illegally Dumping Animal Remains.
21. 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.
22. Northeast Waste Management Officials' Association (NEWMOA). 2015. IMERC Fact Sheet Use in Dental
Amalgam .Last accessed July 2016.
23. National Institute of Dental and Craniofacial Research. 2013. Dental Caries (Tooth Decay) in Adults (Age
20 to 64). Last accessed May 2014.
24. US EPA. 1997. Mercury Study Report to Congress. Volume II: An Inventory of Anthropogenic Mercury
Emissions in the United States. Last accessed May 2014.
25. 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.
26. Buildings.com, 2008. Fluorescent Lamps 101. Last accessed July 2016.
27. 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
28. 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.
29. 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.
30. Li, Y. and L. Jin. 2011. Environmental Release of Mercury from Broken Compact Fluorescent Lamps.
Environmental Engineering Science, 28:687-691.
31. Katers, J.F., R. Winter, A. Snippen. 2009. The Influence of Increased Use of Compact Fluorescent Lighting
on Environmental Mercury Emissions. Proceedings of the International Conference on Waste
Technology; 2009, pll51. Conference Proceeding Article.
32. 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.
33. 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 2014.
34. National Electrical Manufacturers Association (NEMA). 2005. Fluorescent and other Mercury-Containing
Lamps and the Environment. Last accessed May 2014.
35. Virta, R. 2013. US Geological Survey. Personal communication with David Cooley, Abt Associates, August
21, 2013.
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4,3,1 Sector description
Cropland dust and dust from animal hooves are significant sources of atmospheric dust, both fine and coarse
particulate matter (PM2.5 and PM10, respectively). The SCCs that are in this sector for the 2014 NEI are
provided in Table 4-13. The SCC level 1 description is "Miscellaneous Area Sources" for all SCCs. The EPA
estimates emissions for fugitive dust emissions from agricultural tilling (SCC 2801000003) and new for 2014v2,
dust kicked up by hooves (SCC 2805001000), highlighted in the table; the methodology is described in Section
4.3.3.
Table 4-13: SCCs used in the 2014 NEI for the Agriculture - Crops & Livestock Dust sector
SCC
SCC Level 2
SCC Level 3
SCC Level 4
2801000000
Agriculture Production - Crops
Agriculture - Crops
Total
2801000003
Agriculture Production - Crops
Agriculture - Crops
Tilling
2801000005
Agriculture Production - Crops
Agriculture - Crops
Harvesting
2801000007
Agriculture Production - Crops
Agriculture - Crops
Loading
2801000008
Agriculture Production - Crops
Agriculture - Crops
Transport
2801600000
Agriculture Production - Crops
Country Grain Elevators
Total
2805001000
Agriculture Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Dust Kicked-up by
Hooves
4.3,2 Sources of data
The agricultural crops and livestock dust sector includes data from S/L/T agency submitted data and the default
EPA generated emissions. The agencies listed in Table 4-14 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%).
Table 4-14: Percentage of total PM Agricultural Tilling emissions submitted by reporting agency
Region
Agency
S/L/T
O
rH
a.
PMz.5
1
New Hampshire Department of Environmental Services
State
81
81
2
New Jersey Department of Environment Protection
State
44
46
3
Maryland Department of the Environment
State
87
90
4
Georgia Department of Natural Resources
State
0
0
4
Metro Public Health of Nashville/Davidson County
Local
60
5
Illinois Environmental Protection Agency
State
96
97
7
Iowa Department of Natural Resources
State
72
78
7
Sac and Fox Nation of Missouri in Kansas and Nebraska Reservation
Tribe
100
100
8
Assiniboine and Sioux Tribes of the Fort Peck Indian Reservation
Tribe
100
100
8
Utah Division of Air Quality
State
4
3
9
California Air Resources Board
State
34
28
10
Coeur d'Alene Tribe
Tribe
100
100
10
Idaho Department of Environmental Quality
State
83
82
10
Kootenai Tribe of Idaho
Tribe
100
100
4-27
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Region
Agency
S/L/T
O
rH
O.
PM2.5
10
Nez Perce Tribe
Tribe
100
100
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
100
100
10
Washington State Department of Ecology
State
90
89
4,3,3 EPA-clevelopeel emissions for agriculture, crops and livestock dust
4.33.1 Source Category 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 Hooves
While hoof emissions are primarily considered to be emissions made by cattle, swine and sheep, poultry
emissions of dust were also examined. Fugitive dust emissions from hooves 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. There did not exist separate
animal-specific SCCs for dust kicked up by hooves (or feet); therefore, all animals were aggregated to the one
available SCC (for "Beef cattle", SCC 2805001000) for 2014v2. We decided to wait until the 2017 NEI cycle to
separate out the dust kicked up by hooves/feet emissions by animal type. For 2014v2 purposes this SCC
represents the total for all livestock. In 2017 we hope to utilize a new approach to help with consistency in SCC
descriptions and will separate by animal type at that time.
433.2 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 and were calculated using the following equation [ref 1, ref 2]:
EF 4.8 X k X S X Pcrop,tilling type
where:
k = dimensionless particle size multiplier (PMio = 0.21; PM2.5 = 0.042),
s = silt content of surface soil (%), and
p = number of passes or tillings in a year for a given crop and tilling method.
The U.S. Department of Agriculture (USDA) 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
4-28
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the surface soil.8 The soil sample data used to estimate county-level, average silt content values are from the
National Cooperative Soil Survey Microsoftฎ Accessฎ 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.
The 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 were reverse geocoded from the coordinates and added to the
sample entry in the database.
After gap-filling the missing state and county information, the average silt content for a county was 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 was 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 was assigned the average silt value of soil
samples within the state.
Dust Kicked up by Hooves
Dust emission factors were obtained from a variety of different literature articles [ref 4 through ref 23] 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 in the PM Augmentation tool. 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
8 Note that this is different than the U.S. Environmental Protection Agency's definition that includes all particles (mass
basis) of diameter smaller than 75 micrometers.
4-29
-------
Where the equivalent factor is obtained from Table 4-15 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
9. Multiply calculated emission factor by 1000 to get the tons/year/1000 head
Table 4-15: 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
Chicken
Layers - non-liquid manure system
0.01
Chicken
Broilers/pullets - non-liquid manure system
0.005
Chicken
Bird - liquid manure system
0.033
Ducks
Liquid manure system
0.2
Ducks
Non-liquid manure system
0.01
Turkeys
Turkey
0.018
Sheep
Sheep
0.1
Horses
Horses
2
4.33.3 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
24], 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 county-level tilling type data for 2012 was
provided by the USDA upon request [ref 25],
Several counties had data for acres harvested by crop type from the USDA Census of Agriculture, but did not
have acres for each tilling type. For these counties, we used the state percentages of conservation, no-till, and
conventional tilling as a surrogate for county data.
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.
4-30
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Tilling data for permanent pasture followed a different methodology. Conventional tilling data were available for
the state of Utah [ref 26], A ratio of the conventional tilling acres to the total acres of permanent pasture for
Utah was 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 was distributed to no till acres and conservation tilling acres were left as
zero.
Table 4-16 shows the number of passes or tillings in a year for each crop for conservation use, no-till and
conventional use [ref 27], 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 4-16: Number of passes or tillings per year in 2014v2 NEI
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
0
0
0
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
Sugarcane
3
3
3
Sunflowers
3
3
3
Tobacco
3
3
3
A summary of national-level acres tilled in 2012 for each tilling type are presented in Table 4-17.
4-31
-------
Table 4-17: Acres tilled by tillage type, in 2012
Tillage system
National (millions of) acres tilled in 2012
No-Till
658.07
Conservation
162.19
Conventional
273.16
Total
1,093.42
Agricultural Tilling: New in 2014v2
The 2012 Census of Agriculture does not include information about cover crops, so emissions from tilling for
cover crops were not estimated for the 2014 NEI. Review from a couple of agencies led to changes in
methodology for this sector; no-till passes were increased for all counties, which resulted in a reduction in EPA-
estimated PM emissions.
In 2014vl, the number of passes or tillings per year for corn, cover and soybeans were greater, as shown in
Table 4-18.
Table 4-18: Number of passes or tillings per year in 2014vl NEI, replaced in 2014v2 with new values
Crop
Conservation Use
No-Till
Conventional Use
Corn
2
2
6
Cover
1
1
1
Soybeans
1
1
6
Dust Kicked up by Hooves
The United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASSi Quick Stats
program was utilized to obtain the activity data. The 2014 USDA Survey was used to obtain the livestock count
for as many counties as possible across the United States. Because the survey did not cover the entire country,
the USDA 2012 Census was used to fill in much of the remaining entities. However, the 2012 Census and the
2014 Survey were not spatially complete when combined, so it was necessary to calculate the missing county
data using the following methods:
For Swine and Poultry: For missing counties, the total value for the counties present is added up and then
subtracted from the statewide reported value. This will result in the missing number of animals from the state.
From there, the number of counties reporting (D - Did not report) are counted and the total missing animals is
divided by the number of counties that did not report. This resulting number is then allocated to each county
that reported a (D) value. The counties skipped in the survey are given a value of 0.
Example:
County 1: 20
County 2: 45
County 3:(D)
County 4: 5
County 5:(D)
State total: 100
1. Calculate sum of all counties: 20 + 45 + 5 = 70
4-32
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2. Calculate number of cattle missing from counties: 100 - 70 = 30
3. Since 2 states did not report values: 30/2 =15
4. Allocate 15 animals to County 3 and 15 animals to County 5
Therefore, the county animal totals are as follows:
County 1: 20
County 2: 45
County 3:15
County 4: 5
County 5:15
For Cattle: Following the work of Carnegie Mellon University (CMU), the total beef cattle is equal to the total
cattle (including calves) minus the dairy cattle. To get the correct number of total cattle, a method similar to
what is described above is used. For the counties missing data, the total value for the counties present is added
up and then subtracted from the statewide reported value. This number is then divided by the total number of
states that did not report the total number of cattle. The dairy cattle missing in each county are calculated using
the formula:
# Dairy Cattle = # Dairy Cattle missing in county*(Total Cattle (incl. calves) in county/sum of Total Cattle in all
counties missing data)
Then, finally, the beef cattle can be calculated using the formula:
# Beef Cattle = Total # Cattle - # Dairy Cattle
Example:
County
Total Cattle (including calves)
Beef Cattle
Dairy Cattle
1
30
20
2
100
30
3
20
(D)
4
(D)
(D)
5
(D)
10
Total State Cattle: 250
Total Dairy Cattle: 100
1. Get total cattle: 30+100+20 = 150
Total missing cattle: 100, therefore 50 cattle go to each county that did not report
County
Total Cattle (including calves)
Beef Cattle
Dairy Cattle
1
30
20
2
100
30
3
20
(D)
4
50
(D)
5
50
10
2. Get total dairy cattle:
Missing number of dairy cattle: 100 - 20 - 30 - 10 = 40
Total number of cattle in counties missing dairy: 20 + 50 = 70
4-33
-------
# dairy/county = 40* (total number of cattle in missing county/70)
Therefore, the number of dairy cattle in:
County 3 = 40*(20/70) = ~11
County 4 = 40*(50/70) = ~29
County
Total Cattle (including calves)
Beef Cattle
Dairy Cattle
1
30
20
2
100
30
3
20
11
4
50
29
5
50
10
3. Calculate total beef by subtracting dairy cattle from total cattle.*
County
Total Cattle (including calves)
Beef Cattle
Dairy Cattle
1
30
10
20
2
100
70
30
3
20
9
11
4
50
21
29
5
50
40
10
Sum
250
150
100
*lt is important to note that the total beef cattle obtained from the US Census is the actual total for beef cattle in each
county. However, the procedures listed above were followed for the census data when data wasn't given.
Example calculation
Agricultural Tilling
The following equation was used to determine the emissions from agricultural tilling for 2012 [ref 1, ref 2], The
county-level activity data are the acres of land tilled for a given crop and tilling type. The equation is adjusted to
estimate PMio and PM2.5 emissions using the following parameters: a particle size multiplier, the silt content of
the surface soil, the number of tillings per year for a given crop and tilling type, and the acres of land tilled for a
given crop and tilling type.
E - Ic x k x s X Pcrop,tilling type * CI crop,tilling type
where: E = PM10-FIL or PM25-FIL emissions
c = constant 4.8 Ibs/acre-pass
k = dimensionless particle size multiplier (PMio=0.21;
s = percent silt content of surface soil, defined as the
diameter found in surface soil
p = number of passes or tillings in a year
o = acres of land tilled (activity data)
Dust Kicked up by Hooves
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.
PM2.5=0.042)
mass fraction of particles smaller than 50 pim
4-34
-------
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
=13.35 Controls
No controls were accounted for in the emission estimations.
4333 Changes from 2011 Methodology: Agricultural Tilling
The 2008 emission estimates were based on data from the Conservation Technology Information Center's
National Crop Residue Management Survey [ref 28], This survey was discontinued in 2008; therefore, in 2014
the agricultural tilling emissions were created by applying growth factors to the 2008 agricultural tilling dataset.
These growth factors were derived from state- level USDA statistics on various crop types.
The 2014 agricultural tilling emissions were estimated using data on harvested acres and tillage type obtained
from the USDA's 2012 Census of Agriculture. This included data on fallow and permanent pasture that were
previously estimated using a top-down allocation approach based on farm numbers.
4 333 Puerto Rico and US IVirgin Islands Emissions Calculations: Agricultural Tilling'
Since insufficient data exists to calculate emissions for the counties in Puerto Rico and the U.S. Virgin Islands,
emissions are based on two proxy counties in Florida: Broward County (FIPS state county code = 12011) for
Puerto Rico and Monroe County (FIPS = 12087) for the U.S. 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 U.S. 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".
4,3.4 Summary of quality assurance methods
Metals for this sector were submitted by only one agency. The emissions were estimated using ratios of metals
to PM2.5. While these ratios were very small numbers; the resulting calculations gave very large amounts of
metals. For example, the state-submitted emissions of Hg from agricultural tilling (for the one agency) was
nearly 10 percent of the national mercury inventory. Because these data were not available for other states and
4-35
-------
because the resulting high emissions seemed extremely suspect, we did not include the state-submitted metals
in the NEI.
4,3,5 References for agricultural crops & livestock dust
1. The Role of Agricultural Practices in Fugitive Dust Emissions, T.A. Cuscino, Jr., et al., California Air
Resources Board, Sacramento, CA, June 1981.
2. Memorandum from Chatten Cowherd of Midwest Research Institute, to Bill Kuykendal of the U.S.
Environmental Protection Agency, Emission Factor and Inventory Group, and W.R. Barnard of E.H.
Pechan & Associates, Inc., September 1996.
3. U.S. Department of Agriculture, National Cooperative Soil Survey (NCSSi Soil Characterization Database.
accessed September 2015.
4. 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 KansasReverse dispersion modeling.
Journal of the Air & Waste Management Association, 62(3), pp.350-361.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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.
10. 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
11. 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.
12. 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.
13. 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.
14. 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.
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15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. 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.
22. 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.
23. 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.
24. 2012 Census of Agriculture. United States Department of Agriculture, and through Quickstats NASS 2.0.
accessed September 2015.
25. Email from Christy Meyer, U.S. Department of Agriculture, National Agricultural Statistics Service to
Marissa Hoer, Abt Associates, September 2015.
26. Email from Greg Mortensen, Utah Department of Environmental Quality to Jonathan Dorn, Abt
Associates, 2014_UtahDeptAg_DNR_Tilling_Stats.xlsx, February 2016.
27. Agricultural Activities Influencing Fine Particulate Matter Emissions, Woodard, Kenneth R., Midwest
Research Institute, March 1996.
28. National Crop Residue Management Survey. Conservation Technology Information Center, 2008,
accessed September 2015.
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 2014 NEI are provided in Table
4-19. The SCC level 1 description is "Miscellaneous Area Sources" for all SCCs. EPA-estimated emissions are for
SCC 2801700099 and discussed in Section 4.4.3.
4-37
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Table 4-19: Source categories for agricultural Fertilizer Application
see
see Level 2
SCC Level 3
SCC Level 4
2801700001
Agr
culture Production - Crops
Fertilizer Application
Anhydrous Ammonia
2801700002
Agr
culture Production - Crops
Fertilizer Application
Aqueous Ammonia
2801700003
Agr
culture Production - Crops
Fertilizer Application
Nitrogen Solutions
2801700004
Agr
culture Production - Crops
Fertilizer Application
Urea
2801700005
Agr
culture Production - Crops
Fertilizer Application
Ammonium Nitrate
2801700006
Agr
culture Production - Crops
Fertilizer Application
Ammonium Sulfate
2801700007
Agr
culture Production - Crops
Fertilizer Application
Ammonium Thiosulfate
2801700010
Agr
culture Production - Crops
Fertilizer Application
N-P-K (multi-grade nutrient fertilizers)
2801700011
Agriculture Production - Crops
Fertilizer Application
Calcium Ammonium Nitrate
2801700012
Agriculture Production - Crops
Fertilizer Application
Potassium Nitrate
2801700013
Agriculture Production - Crops
Fertilizer Application
Diammonium Phosphate
2801700014
Agriculture Production - Crops
Fertilizer Application
Monoammonium Phosphate
2801700015
Agriculture Production - Crops
Fertilizer Application
Liquid Ammonium Polyphosphate
2801700099
Agriculture Production - Crops
Fertilizer Application
Miscellaneous Fertilizers
4.4,2 Sources of cists
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 4-20 submitted emissions for this sector; agencies
not listed used EPA estimates for the entire sector. Some agencies submitted emissions for the entire sector
(totals of 100%), while others submitted only a portion of the sector (totals less than 100%).
Table 4-20: Percentage of total fertilizer application NH3 emissions submitted by reporting agency
Region
Agency
S/L/T
Ammonia
4
Georgia Department of Natural Resources
State
0
5
Illinois Environmental Protection Agency
State
100
7
Sac and Fox Nation of Missouri in Kansas and Nebraska Reservation
Tribe
100
9
California Air Resources Board
State
57
10
Idaho Department of Environmental Quality
State
100
10
Coeur d'Alene Tribe
Tribe
100
10
Kootenai Tribe of Idaho
Tribe
100
10
Nez Perce Tribe
Tribe
100
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
100
4,4,3 EPA-clevelopeel emissions for fertilizer application: revised for 2Q14v2
The approach to calculating emissions from this sector in 2014 is a completely new methodology. For 2014, the
bidirectional version of CMAQ (v5.0.2) [ref 1] and the Fertilizer Emissions Scenario Tool for CMAQ FEST-C (vl.2)
[ref 2] were used to estimate ammonia (NH3) emissions from agricultural soils. These estimates were then
loaded into EIS for use in the 2014v2 NEI. The approach to estimate 2014v2 fertilizer emissions consists of these
steps:
4-38
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Run FEST-C and CMAQ model with bidirectional ("bidi") NH3 exchange to produce year 2014 nitrate
(N03) Ammonium (NH4+, including Urea), and organic (manure) nitrogen (N) fertilizer usage
estimates, and gaseous ammonia NH3 emission estimates respectively.
Calculate county-level emission factors for 2014 as the ratio of bidirectional CMAQ NH3 fertilizer
emissions to FEST-C total N fertilizer application.
Assign the 2014 NH3 emissions to one SCC: "...Miscellaneous Fertilizers" (2801700099).
FEST-C reads land use data from the Biogenic Emissions Landuse Dataset (BELD) version 4, meteorological
variables from the Weather Research and Forecasting (WRF v3.7.1) model [ref 3], and nitrogen deposition data
from a previous or historical average CMAQ simulation. The Environmental Policy Integrated Climate (EPIC)
modeling system [ref 4] provides information regarding fertilizer timing, composition, application method and
amount.
The FEST-C and CMAQ simulations were used to directly estimate emission rates based on 2014 inputs. This is a
refinement from the earlier 2014vl estimates that relied on emission factors calculated from a 2011 model
simulation applied to 2014 FEST-C county level fertilizer application estimates. Additionally, for 2014v2, these
revised FEST-C estimates of fertilizer application were reduced for pasture and hay due to estimates of fertilizer
use and hay yield being higher than USDA estimates. This resulted in a reduction of NH3 emissions, primarily in
the Southeastern U.S.
FEST-C model outputs are discussed in detail in the "NH3_Fert_Fact_Sheet_v2.docx" included in the zip file
"2014_Fertilizer_Application_vl.0_22apr2016.zip" on the 2014vl NEl Supplemental data FTP site. Figure 4-1
provides a comprehensive flowchart if the complete EPIC/FEST-C/WRF "bidi" modeling system.
4-39
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Figure 4-1: Bidirectional flux modeling system used to compute 2014 Fertilizer Application emissions
The Fertilizer Emission Scenario Tool for CMAQ
(FEST-C)
Crops
Meteorology
| Deposit
isition
Fertilizer N
The system works for:
Any domains covering the CONUS,
southern Canada and northern Mexico.
Four WRF projections (longitude/latitude,
Lambert conformal conic, Universal polar
stereographic, and Mercator).
Non-Fertilizer
NEI Emission
Inventories
WRF
Spatial Allocator
Tools
BELD4
(NLCD/MODIS,
Trees, Crops)
CMAQ
Bi-directional
NH3 Flux
modeling
Agri. Ecosystem
Assessment
(yield, soil erosion, water
quantity/quality)
Environmental
Policy Integrated
Climate (EPIC)
Java-based
Fertilizer Tool
Interface
4.4.3.1 Activity Data
The following activity parameters were input into the EPIC model
Grid cell meteorological variables from WRF (see Table 4-21)
Initial soil profiles/soil selection
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]
4-40
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Figure 4-2: USDA farm production regions used in FEST-C simulations
Pacific
rthe.
Lake
Northern
Plains
Mo un tail
Southern 1 Dejta
Plains \ L-,
Southeas
We used the WRF meteorological model to provide grid cell meteorological parameters for 2014 using a national
12-km rectangular grid covering the continental U.S. The meteorological parameters in Table 4-21 were used as
EPIC model inputs.
Table 4-21: 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
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 2014 EPIC/WRF/CMAQ simulation.
The presence of crops in each model grid cell was determined using USDA Census of Agriculture data (2012) 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.
4-41
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Fertilizer sales data and the 6-month period in which they were sold were extracted from the 2006 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.).
4.4.3.2 Emission Factors; revised fbr2014v2
The emission factors were derived from the 2014 FEST-C outputs (rather than 2011 FEST-C outputs used in
2014vl). 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 into a county shape file polygon if the grid level centroid
falls within the bounds of the county-level polygon. With additional time and resources, spatial allocator
technique could be refined to allow for more accurate county-level estimates.
County-level fertilizer emissions (NH3) for 2014 are estimated directly from a 2014 CMAQ model simulation.
4.4., i, ,:> Example GBiculs r/o/i
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.
4-42
-------
Figure 4-3: Simplified FEST-C system flow of operations in estimating NH3 emissions
Modeled
Modeled
WRF
Data
Ammonium
pH, and
application method
Soil Moisture
& Temperature
CMAQ
Modeled
Atmospheric
NH,
Fertilizer
Applied?
NH4^N03
Emissions
Nitrification
Deposition
4.4.3.4 Comparison to 2011 Methodology
The 2014 NEI fertilizer estimates are based on a new "bidi" approach that couples meteorological inputs, CMAQ
and the EPIC modeling system. The 2011v2 NEI fertilizer estimates are based on the Carnegie Mellon (CMU)
Ammonia Model v.3.6. In short, the methodologies are completely different. Documentation of the
methodology for the 2011 EPA dataset used in 2014vl as well as the county-level data and maps used for
2014vl are in the zip file "2014_Fertilizer_Application_vl.0_22apr2016.zip" on the 2014vl NEI Supplemental
data FTP site.
Emission maps for the 2011v2 NEI and the 2014v2 NEI estimates are provided below in Figure 4-4 and Figure
4-5, respectively. In addition, the "Emissions_and_fertilizer_2011_2014_v2DRAFTrltedit.xlsx" Excel workbook
provided on the 2014v2 Supplemental Data FTP site, includes the comparison of these 2014 county-level
emissions (column N) to 2011 (not 2011 NEI) estimates (column H) using the "bid" approach.
4-43
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Figure 4-4: 2011v2 NEI Fertilizer Application emissions
2011 EIS Fertilizer NH3 Emissions
NH3 emissions (g/m2)
^ 0.0-0.1
0.1 -0.2
~ 0.2-0.4
ฆ 0.4-0.8
Figure 4-5: 2014v2 NEI "bidi" Fertilizer Application emissions
2014 V2 ORD Fertilizer NH3 Emissions
NH3 Emissions (g/m2)
I 0.0-0.1
4-44
-------
4,4,4 References for agriculture fertilizer application
1. Community Multiscale Air Quality (CMAQ v5.1) model, available on the CMAS web site.
2. Fertilizer Emission Scenario Tool for CMAQ (FEST-C) system, available on the CMAS FEST-C site.
3. The Weather Research Forecast (WRF) model.
4. Environmental Policy Integrated Climate (EPIC) model, available for download on the EPIC & APEX
Models site.
5. Cooter, E.J., Bash, J.O., Benson V., Ran, L.-M.; Linking agricultural management and air-quality models
for regional to national-scale nitrogen deposition assessments. Biogeosciences, 9, 4023-4035, 2012.
45 Agricuiture - 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, ducks, geese, goats, horses, poultry, sheep, 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.
4.5.2 Sources of data
Table 4-22 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 4-22: Nonpoint SCCs with 2014 NEI emissions in the Livestock Waste sector
SCC
Description
EPA
State
Tribe
2805001100
Agriculture Production - Livestock; Beef cattle - finishing operations on
feedlots (drylots); Confinement
X
X
2805001200
Agriculture Production - Livestock; Beef cattle - finishing operations on
feedlots (drylots); Manure handling and storage
X
X
2805001300
Agriculture Production - Livestock; Beef cattle - finishing operations on
feedlots (drylots); Land application of manure
X
X
2805002000
Agriculture Production - Livestock; Beef cattle production composite; Not
Elsewhere Classified
X
X
X
2805003100
Agriculture Production - Livestock; Beef cattle - finishing operations on
pasture/range; Confinement
X
X
2805007100
Agriculture Production - Livestock; Poultry production - layers with dry
manure management systems; Confinement
X
X
X
2805007300
Agriculture Production - Livestock; Poultry production - layers with dry
manure management systems; Land application of manure
X
X
2805008100
Agriculture Production - Livestock; Poultry production - layers with wet
manure management systems; Confinement
X
X
4-45
-------
see
Description
EPA
State
Tribe
2805008200
Agriculture Production - Livestock; Poultry production - layers with wet
manure management systems; Manure handling and storage
X
X
2805008300
Agriculture Production - Livestock; Poultry production - layers with wet
manure management systems; Land application of manure
X
X
2805009100
Agriculture Production - Livestock; Poultry production - broilers;
Confinement
X
X
X
2805009200
Agriculture Production - Livestock; Poultry production - broilers; Manure
handling and storage
X
X
2805009300
Agriculture Production - Livestock; Poultry production - broilers; Land
application of manure
X
X
2805010100
Agriculture Production - Livestock; Poultry production - turkeys;
Confinement
X
X
2805010200
Agriculture Production - Livestock; Poultry production - turkeys; Manure
handling and storage
X
X
2805010300
Agriculture Production - Livestock; Poultry production - turkeys; Land
application of manure
X
X
2805018000
Agriculture Production - Livestock; Dairy cattle composite; Not Elsewhere
Classified
X
X
X
2805019100
Agriculture Production - Livestock; Dairy cattle - flush dairy; Confinement
X
X
2805019200
Agriculture Production - Livestock; Dairy cattle - flush dairy; Manure
handling and storage
X
X
2805019300
Agriculture Production - Livestock; Dairy cattle - flush dairy; Land
application of manure
X
X
2805020002
Agriculture Production - Livestock; Cattle and Calves Waste Emissions;
Beef Cows
X
X
2805021100
Agriculture Production - Livestock; Dairy cattle - scrape dairy;
Confinement
X
X
2805021200
Agriculture Production - Livestock; Dairy cattle - scrape dairy; Manure
handling and storage
X
X
2805021300
Agriculture Production - Livestock; Dairy cattle - scrape dairy; Land
application of manure
X
X
2805022100
Agriculture Production - Livestock; Dairy cattle - deep pit dairy;
Confinement
X
X
2805022200
Agriculture Production - Livestock; Dairy cattle - deep pit dairy; Manure
handling and storage
X
X
2805022300
Agriculture Production - Livestock; Dairy cattle - deep pit dairy; Land
application of manure
X
X
2805023100
Agriculture Production - Livestock; Dairy cattle - drylot/pasture dairy;
Confinement
X
X
2805023200
Agriculture Production - Livestock; Dairy cattle - drylot/pasture dairy;
Manure handling and storage
X
X
4-46
-------
SCC
Description
EPA
State
Tribe
2805023300
Agriculture Production - Livestock; Dairy cattle - drylot/pasture dairy;
Land application of manure
X
X
2805025000
Agriculture Production - Livestock; Swine production composite; Not
Elsewhere Classified (see also 28-05-039, -047, -053)
X
X
X
2805030000
Agriculture Production - Livestock; Poultry Waste Emissions; Not
Elsewhere Classified (see also 28-05-007, -008, -009)
X
X
2805030007
Agriculture Production - Livestock; Poultry Waste Emissions; Ducks
X
X
X
2805030008
Agriculture Production - Livestock; Poultry Waste Emissions; Geese
X
X
X
2805035000
Agriculture Production - Livestock; Horses and Ponies Waste Emissions;
Not Elsewhere Classified
X
X
X
2805039100
Agriculture Production - Livestock; Swine production - operations with
lagoons (unspecified animal age); Confinement
X
X
2805039200
Agriculture Production - Livestock; Swine production - operations with
lagoons (unspecified animal age); Manure handling and storage
X
X
2805039300
Agriculture Production - Livestock; Swine production - operations with
lagoons (unspecified animal age); Land application of manure
X
X
2805040000
Agriculture Production - Livestock; Sheep and Lambs Waste Emissions;
Total
X
X
X
2805045000
Agriculture Production - Livestock; Goats Waste Emissions; Not
Elsewhere Classified
X
X
X
2805047100
Agriculture Production - Livestock; Swine production - deep-pit house
operations (unspecified animal age); Confinement
X
X
2805047300
Agriculture Production - Livestock; Swine production - deep-pit house
operations (unspecified animal age); Land application of manure
X
X
2805053100
Agriculture Production - Livestock; Swine production - outdoor
operations (unspecified animal age); Confinement
X
X
2806010000
Domestic Animals Waste Emissions; Cats; Total
X
2806015000
Domestic Animals Waste Emissions; Dogs; Total
X
2807020001
Wild Animals Waste Emissions; Bears; Black Bears
X
2807020002
Wild Animals Waste Emissions; Bears; Grizzly Bears
X
2807025000
Wild Animals Waste Emissions; Elk; Total
X
2807030000
Wild Animals Waste Emissions; Deer; Total
X
2807040000
Wild Animals Waste Emissions; Birds; Total
X
Table 4-23 presents the three "Industrial Processes" point SCCs reported by 2 states: California and Wisconsin.
Point source emissions from this sector are negligible, particularly for NH3, compared to the nonpoint emissions
(3 orders of magnitude lower). The SCC level 1 and 2 descriptions is "Industrial Processes; Food and Agriculture"
for all SCCs.
Table 4-23: Point SCCs with 2014 NEI emissions in the Livestock Waste sector - reported only by States
SCC
SCC Level Three
SCC Level Four
CA
Wl
30202001
Beef Cattle Feedlots
Feedlots: General
X
X
4-47
-------
SCC
SCC Level Three
SCC Level Four
CA
Wl
30202020
Dairy Cattle
Enteric, Confinement, Manure Handling,
Storage, Land Application
X
30202101
Eggs and Poultry Production
Manure Handling: Dry
X
The agencies listed in Table 4-24 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%).
Table 4-24: Percentage of total Livestock NH3 emissions submitted by reporting agency
Region
Agency
S/L/T
Ammonia
1
Maine Department of Environmental Protection
State
32
2
New Jersey Department of Environment Protection
State
80
3
Delaware Department of Natural Resources and Environmental Control
State
98
4
Georgia Department of Natural Resources
State
3
5
Illinois Environmental Protection Agency
State
98
6
United Keetoowah Band of Cherokee Indians in Oklahoma
Tribe
100
7
Sac and Fox Nation of Missouri in Kansas and Nebraska Reservation
Tribe
100
8
Utah Division of Air Quality
State
21
9
California Air Resources Board
State
46
10
Coeur d'Alene Tribe
Tribe
100
10
Idaho Department of Environmental Quality
State
100
10
Kootenai Tribe of Idaho
Tribe
100
10
Nez Perce Tribe
Tribe
100
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
100
4.5.3 EPA-developed livestock waste emissions data: new for 2Q14v2
Animal waste from livestock results in emissions of both NH3 (ammonia) and, new for 2014v2, Volatile Organic
Compounds (VOCs). 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 work of Carnegie Mellon University
(CMU), the following livestock were evaluated: dairy cattle, beef cattle, swine, and poultry (layers and broilers).
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 by the county NH3 emissions.
In the 2014 NEI, the EPA methodology for ammonia emissions includes all processes from the housing/grazing,
storage and application of manure from beef cattle, dairy cattle, swine, broiler chicken, and layer chicken
production, and these are assigned to the SCCs listed in Table 4-25. The SCC level 1 and 2 descriptions is
"Miscellaneous Area Sources; Agriculture Production - Livestock" for all SCCs.
4-48
-------
Table 4-25: EPA-estimated livestock emission SCCs
SCC
SCC Level 3 Description
SCC Level 4 Description
2805002000
Beef cattle production composite
Not Elsewhere Classified
2805007100
Poultry production - layers with dry manure
management systems; Confinement
Confinement
2805009100
Poultry production - broilers; Confinement
Confinement
2805018000
Dairy cattle composite
Not Elsewhere Classified
2805025000
Swine production composite
Not Elsewhere Classified
Cows, swine and chickens account for 95% of national NH3 emissions from livestock waste in 2014. However,
there are also emissions from other animals such as horses, turkeys, goats, etc. Due to resource constraints at
EPA, 2014 emissions were not updated for several animal types and are assumed to be the same as 2011
emissions, except in cases where S/L/T agencies provided updated 2014 emissions for these sources. These EPA-
estimated emissions, carried forward from the 2011 NEI, are listed in Table 4-26. The SCC level 1 and 2
descriptions is "Miscellaneous Area Sources; Agriculture Production - Livestock" for all SCCs.
Table 4-26: EPA-estimated sources carried forward from 2011
SCC
SCC Level 3 Description
SCC Level 4 Description
2805030007
Poultry Waste Emissions
Ducks
2805030008
Poultry Waste Emissions
Geese
2805035000
Horses and Ponies Waste Emissions
Not Elsewhere Classified
2805040000
Sheep and Lambs Waste Emissions
Total
2805045000
Goats Waste Emissions
Not Elsewhere Classified
4.. $, j /I Cl7!//DB t'd
The United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) Quick Stats
program [ref 18] was utilized to obtain the activity data. The 2014 USDA Survey was used to obtain the livestock
count for as many counties as possible across the United States. Because the survey did not cover the entire
country, the USDA 2012 Census was used to fill in much of the remaining entities. However, the 2012 Census
and the 2014 Survey were not spatially complete when combined, so it was necessary to calculate the missing
county data using the methods described below. Table 4-27 outlines the use of the 2012 Census and 2014
Survey in the creation of the livestock populations.
Table 4-27: Summary of Use of 2014 Survey or 2012 Census Animal Populations
Animal Type
Source
Broilers
There is no 2014 data in the Survey on Broiler Inventory at either the county or state level.
Therefore, the inventory reflects the 2012 state level totals. 2014v2 NEI county level
populations were adjusted to ensure that the county totals match the 2012 state level totals.
Layers
For Layers, the 2014v2 NEI animal populations are based on 2012 state level inventories,
with a few exceptions. These inventories have been updated to reflect the 2014 state level
inventories where 2014 data was available. There were 30 states with 2014 state level layer
population data, and a growth factor was applied to 2012 county level populations to reflect
the change in population between 2012 and 2014 state level totals.
4-49
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Animal Type
Source
Hogs
For hogs, there were four states in the 2014vl NEI dataset that had 2014 county level data
(MT, NC, ND, OK). No update is needed for those four. The other 46 states were updated to
reflect the 2014 state level total. The county populations were multiplied by the growth
factor between the NASS 2012 and 2014 state level data. This allows all 50 states to have the
sum of their county inventories match the 2014 NASS State level data.
Dairy Cattle
No update was provided to the 2014vl NEI dataset, except for a few states with error
corrections. The sum of all county level data for each state matches the NASS state inventory
totals.
Beef Cattle
No update was provided to the 2014vl NEI dataset. The sum of all county level data for each
state matches the NASS state inventory totals.
For Swine and Poultry: For missing counties, the total value for the counties present is added up and then
subtracted from the statewide reported value. This will result in the missing number of animals from the state.
From there, the number of counties reporting (D - Did not report) are counted and the total missing animals is
divided by the number of counties that did not report. This resulting number is then allocated to each county
that reported a (D) value. The counties skipped in the survey are given a value of 0.
Example:
County 1: 20
County 2: 45
County 3:(D)
County 4: 5
County 5:(D)
State total: 100
5. Calculate sum of all counties: 20 + 45 + 5 = 70
6. Calculate number of cattle missing from counties: 100 - 70 = 30
7. Since 2 states did not report values: 30/2 =15
8. Allocate 15 animals to County 3 and 15 animals to County 5
Therefore, the county animal totals are as follows:
County 1: 20
County 2: 45
County 3: 15
County 4: 5
County 5: 15
For Cattle: Following the work of CMU, the total beef cattle is equal to the total cattle (including calves) minus
the dairy cattle. To get the correct number of total cattle, a method similar to what is described above is used.
For the counties missing data, the total value for the counties present is added up and then subtracted from the
statewide reported value. This number is then divided by the total number of states that did not report the total
number of cattle. The dairy cattle missing in each county are calculated using the formula:
# Dairy Cattle = # Dairy Cattle missing in county*(Total Cattle (incl. calves) in county/sum of Total Cattle in all
counties missing data)
Then, finally, the beef cattle can be calculated using the formula:
4-50
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# Beef Cattle = Total # Cattle - # Dairy Cattle
Example:
County
Total Cattle (including calves)
Beef Cattle
Dairy Cattle
1
30
20
2
100
30
3
20
(D)
4
(D)
(D)
5
(D)
10
Total State Cattle: 250
Total Dairy Cattle: 100
4. Get total cattle: 30+100+20 = 150
Total missing cattle: 100, therefore 50 cattle go to each county that did not report
County
Total Cattle (including calves)
Beef Cattle
Dairy Cattle
1
30
20
2
100
30
3
20
(D)
4
50
(D)
5
50
10
5. Get total dairy cattle:
Missing number of dairy cattle: 100 - 20 - 30 - 10 = 40
Total number of cattle in counties missing dairy: 20 + 50 = 70
# dairy/county = 40* (total number of cattle in missing county/70)
Therefore, the number of dairy cattle in:
County 3 = 40*(20/70) = ~11
County 4 = 40*(50/70) = ~29
County
Total Cattle (including calves)
Beef Cattle
Dairy Cattle
1
30
20
2
100
30
3
20
11
4
50
29
5
50
10
6. Calculate total beef by subtracting dairy cattle from total cattle.*
County
Total Cattle (including calves)
Beef Cattle
Dairy Cattle
1
30
10
20
2
100
70
30
3
20
9
11
4
50
21
29
5
50
40
10
Sum
250
150
100
*lt is important to note that the total beef cattle obtained from the US Census is the actual total for beef cattle in each
county. However, the procedures listed above were followed for the census data when data wasn't given.
4-51
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a.53.2 Emission Factors
CMU developed a new model to estimate daily ammonia emission factors for cows, swine and chickens. The
model estimates emissions from a typical farm, using a particular set of practices, for a particular set of
meteorological conditions [refs 1-1], The model estimates the mass balance of nitrogen through the farm
system, accounting for nitrogen lost to the atmosphere and infiltrated into the soil.
CMU developed a model to estimate NH3 emissions from livestock [ref 1], The model estimates emissions from a
typical farm, using a particular set of practices, for a particular set of meteorological conditions [ref 2, ref 3], The
model estimates the mass balance of nitrogen through the farm system, accounting for nitrogen lost to the
atmosphere and infiltrated into the soil.
This model produces daily-resolved, climate level emissions factors for a particular distribution of management
practices for each county and animal type, 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. These state
level emissions factors were back calculated from the CMU model using statewide emissions divided by
statewide animal totals, and those are the emissions factors used in this analysis. Thus, the CMU model provides
a state specific NH3 emissions/head emission factor for each animal type.
VOC emission factors come from the ratio of NH3 to VOC emissions in counties which provided an estimate of
both pollutants in the 2014 vl NEI. There were 106 counties which provided emissions for both pollutants, and
the 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 2014v2 to estimate VOC emissions for each county. This ratio does not vary by state or animal
type.
The model inputs and outputs are shown in Figure 4-6.
Figure 4-6: Process to produce location and practice specific daily emission factors
Farm Emission Model
FBM
385 daily EFs for
a particular
location and set
of practices
Meteorology:
Dally Avg T
Daily Avg windspeed
Daily Total precipitation
Animal Type:
Housing Practice
Storage Practice
Application Practice
The calculation procedure to translate the output for a particular farm/farm configuration is shown in Figure 4-7.
The US distribution of management practices is based on reports from the NAHMS (National Animal Health
Monitoring Study) [ref 4 - ref 16] and are provided by management practice in Table 4-28.
Table 4-28: Reference links for each management practice
Management Practice
Reference(s)
Swine
5, 15, 16
Dairy
6,7
Beef
10
Poultry
4, 9, 14
Layers
12, 13
4-52
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Management Practice
Reference(s)
Feedlots
8, 11
Figure 4-7: Composite emission factors for a specific day, location, and animal type
Daily EFUk.,
Daily EF2jJ
k,a
Daily EFnjiki8
Composite
Daily EFj k a
County-level emissions for an animal type for a particular day were calculated as shown in Equation 1.
Emissions] k a (d county) = EFj,k,a x Populationka (1)
The total emissions in any given day were then be calculated by adding up all the emissions in each county for all
animal types. This is shown in Equation 2.
r. ( kg \ all animal types ( kg \ . .
Emissions; k 1 = y,__ 1 Emissions; kn (2)
\cL-countyJ ^a-1 J,a,a \d.countyJ * '
Total annual emissions for each location were calculated by summing the daily emissions over the entire year;
this is described in Equation 3.
Emissionsk (^) = Emissionsjjk (3)
The calculation that was completed for total annual emissions (for all animal types and all locations) is shown in
Equation 4.
Emissionstotal (y) = Counties Emissionsk (d_c^nty) (4)
45.3.3 Example Calculation
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.
Back Calculating the Emissions Factors from the CMU Model
The emissions estimates in the 2014vl NEI came from the CMU model. These emissions were then divided by
the model's animal population figures to estimate the statewide NH3 emission factor. In Cochise County, AZ,
there were 925 head of swine [ref 17, ref 18]. Those accounted for 9370 kg of NH3.
State NH3 Emissions Factor = Emissions / Number of Animals
= 9370/925
= 10.13 kg NHa/head
4-53
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Note that this EF is the same for all counties in Arizona. Pima County had 5744 kg of NH3and 567 head of swine,
or 10.13 kg NH3/head.
NHi Emission due to Livestock
Emissions are calculated by multiplying the state specific NH3 emission factor (in NH3/head) by the number of
animals in each county. For example, in Calhoun County, AL, there were 7,400 head of beef cattle in 2014. The
Alabama emission factor for beef cattle from the CMU model was 3.68 kg of NH3/head/year.
Calculate the emissions:
Beef Cattle NH3 Emissions = Emission Factor * Number of Animals
= 3.68 * 7,400
= 27,224 kg NH3
VOC Emission 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. Therefore, the VOC emissions from beef cattle in Calhoun County, AL would be calculated as
follows:
Beef Cattle VOC Emissions = VOC/NH3 ratio * NH3 Emissions
= 0.08 * 27,224 kg NH3
= 2,186 kg VOC
4.53.4 improvements in the 2014v2 NEi
The animal populations used in the 2014vl NEI had several consistent problems which have been corrected. In
many cases, the total animal population of all counties is significantly different from the NASS state population
total for either 2012 or 2014. For example, the 2014vl NEI had a total swine population of 109,000, which does
not match the state total in the NASS for either 2012 or 2014. This has been corrected so that the total swine
inventory in Arizona counties equals the 2014 NASS state total of 139,000. This type of error occurs in other
animal datasets as well. For broilers, there were no 2014 state level NASS animal populations, so the data should
reflect the 2012 state level census data. The 2014vl NEI showed a broiler population of 13,402 in Rhode Island,
while the 2012 dataset shows a population of 18,396. Matching the 2014v2 NEI dataset with the most recently
available state level totals (either 2012 or 2014) ensures an improved animal population dataset than that seen
in the 2014vl NEI.
Estimation of Hazardous Air Pollutants (HAPs) for Livestock
HAPs for this sector were estimated by multiplying county-specific VOC emissions by speciation factors that are
animal-specific as shown in Table 4-29. All the HAP VOC fractions were obtained from EPA's SPECIATE database
[ref 19]. 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).
4-54
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Table 4-29: VOC speciation fractions used to estimate HAP Emissions for the Livestock Sector
see
Animal Type
HAP
Fraction of VOC
SPECIATE Profile Number
280500200
Beef Cattle
1,4-Dichlorobenzene
0.0013
280500200
Beef Cattle
Methyl isobutyl Ketone
0.0008
280500200
Beef Cattle
Toluene
0.011
95240
280500200
Beef Cattle
Chlorobenzene
0.0001
280500200
Beef Cattle
Phenol
0.0006
280500200
Beef Cattle
Benzene
0.0001
2805007100
PoultryLayers
Methyl isobutyl ketone
0.0169
2805007100
PoultryLayers
Toluene
0.0018
2805007100
PoultryLayers
Phenol
0.0024
2805007100
PoultryLayers
N-hexane
0.0111
2805007100
PoultryLayers
Chloroform
0.0025
2805007100
PoultryLayers
Cresol/Cresylic Acid
(mixed isomers)
0.0048
95223
2805007100
PoultryLayers
Acetamide
0.0075
2805007100
PoultryLayers
Methanol
0.0608
2805007100
PoultryLayers
Benzene
0.0052
2805007100
PoultryLayers
Ethyl Chloride
0.0031
2805007100
PoultryLayers
Acetonitrile
0.0088
2805007100
PoultryLayers
Dichloromethane
0.0002
2805007100
PoultryLayers
Carbon Disulfide
0.0034
2805007100
PoultryLayers
2-Methyl Naphthalene
0.0006
2805009100
Poultry-Broilers
Methyl isobutyl ketone
0.0169
2805009100
Poultry-Broilers
Toluene
0.0018
2805009100
Poultry-Broilers
Phenol
0.0024
2805009100
Poultry-Broilers
N-hexane
0.0111
2805009100
Poultry-Broilers
Chloroform
0.0025
2805009100
Poultry-Broilers
Cresol/Cresylic Acid
(mixed isomers)
0.0048
95223
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 Naphthalene
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
4-55
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Other pollutants reported for this sector
It should be noted that EPA only estimated NH3, VOC, and VOC-HAPs (as listed above) for this sector. Other
pollutants reported (such as PM) come entirely from SLT-reported estimates. HAPs were estimated according to
the VOC emissions generated by EPA using the fractions shown in Table 4-29, when there was no SLT-reported
VOC value.
-.5.3.5 Comparison to2011 methodology
The NEI 2011v2 EPA methodology was mostly based on the CMU Ammonia Model v. 3.6 which attributed
monthly emissions as a function of temperature to calculate ammonia emissions with county-level animal
populations and emission factors. The EPA did modify some of the emission factors from the original model for
the 2011 NEI. Additional documentation for the 2011 inventory can be found in the 2011 National Emissions
Inventory, version 2 Technical Support Document.
In contrast, the 2014 emissions inventory for dairy and beef cattle, hogs and poultry are based on the daily
emission factors for a regionally specific distribution of manure management practices. 2014 emissions for all
other animals are unchanged from 2011 methodology.
4.5.4 References for agriculture livestock waste
1. McQuilling, A. M. & Adams, P. J. Semi-empirical process-based models for ammonia emissions from
beef, swine, and poultry operations in the United States. Atmos. Environ. 120, 127-136 (2015).
2. Pinder, R., Strader, R., Davidson, C. & Adams, P. A temporally and spatially resolved ammonia emission
inventory for dairy cows in the United States. Atmos. Environ. 38.23, 3747-3756 (2004).
3. Pinder, R., Pekney, N., Davidson, C. & Adams, P. A process-based model of ammonia emissions from
dairy cows: improved temporal and spatial resolution. Atmos. Environ. 38.9, 1357-1365 (2004).
4. USDA-APHIS. 2011. Poultry 2010: Structure of the US Poultry Industry, 2010.
5. USDA-APHIS. 2008. Swine 2006 Part III: Reference of Swine Health, Productivity, and General
Management in the United States, 2006.
6. USDA-APHIS, 2002. Dairy 2002- Part 1: Reference of Dairy Health and Management in the United
States, 2002.
7. USDA-APHIS, 2007. Dairy 2007- Part III: Reference of Dairy Cattle Health and Management Practices in
the United States, 2007.
8. USDA-APHIS. 2013. Feedlot 2011 Part I: Management Practices on US Feedlots w ' pacity of 1000
or More Head.
9. USDA-APHIS. 2005. Poultry '04 Part III: Reference of Management Practices in Live-Poultry Markets in
the United States, 2004.
10. USDA-APHIS, 2009. Beef 2007-08 - Part III: Changes in the US Beef Cow-calf Industry. 1993-2008.
11. USDA-APHIS, 2013. Feedlot 2011 Part II: Management Practices on US Feedlots with a capacity of
Fewer than 1000 Head.
12. USDA-APHIS, 2014. Layers 2013-Part 1: Reference of Health and Management Practices on Table-Egg
Farms in the United States 2013.
13. USDA-APHIS, 2000. Part II: Reference of 1999 Table Egg Layer Management in the US.
14. USDA-APHIS, 2005. Poultry '04 Part II: Reference of Health and Management of Gamefowl Breeder
Flocks in the United States, 2004.
4-56
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15. USDA-APHIS, 2008. Swine 2006 Part IV: Changes in the US Pork Industry, 1990-2006.
16. USDA-APHIS, 2008. Swine 2.006 Part II: Reference of Swine Health and Health Management Practices
in the United States. 2006.
17. USDA-NASS, 2012. 2012 Census of Agriculture.
18. USDA-NASS, 2014. National Agricultural Statistics Service: Quick Stats.
19. U.S. Environmental Protection Agency. 2016. SPECIATE Database v4.5.
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.
4.6.1 Description of sources
This section is broken into two categories: those sources related to Stage 1 gasoline distribution, and those
related to aviation gasoline.
4,6,1,1 . 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 1 gasoline distribution includes the following gasoline-specific emission
sources: 1) bulk terminals; 2) pipeline facilities; 3) bulk plants; 4) tank trucks; and 5) service stations (which can
be further subdivided into Filling and Breathing & Emptying). Emissions from Stage 1 gasoline distribution occur
as gasoline vapors are released into the atmosphere. These stage 1 processes are subject to the EPA's maximum
available control technology (MACT) standards for gasoline distribution.
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.
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. 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 1
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
vapors evaporating from service station storage tanks and from the lines going to the pumps (Underground
Storage Tank Breathing and Emptying).
4-57
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4.6.1.2 Aviation Gasoline, Stage 1 and2
Aviation gasoline is another piece of the Gasoline Distribution grouping in the NEI, and fall under the sector "gas
stations." It is the only aviation fuel that contains lead as a knock-out component for small reciprocating, piston-
engine crafts in civil aviation. Commercial and military aviation rarely use this fuel. Aviation Gasoline 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.
These processes are subject to EPA's maximum available control technology (MACT) standards for gasoline
distribution. Stage 2, on the other hand, involves the transfer of fuel from the tanker trucks into general aviation
aircraft.
4,6,2 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 4-30 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 4-31 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 4-30: Nonpoint Bulk Gasoline Terminals, Gas Stations, and Storage and Transfer SCCs with 2014 NEI
emissions
SCC
Description
Sector
EPA
State
Local
Tribe
2501000150
Petroleum and Petroleum Product
Storage; All Storage Types: Breathing
Loss; Jet Naphtha
Industrial Processes -
Storage and Transfer
X
2501050120
Petroleum and Petroleum Product
Storage; Bulk Terminals: All Evaporative
Losses; Gasoline
Bulk Gasoline
Terminals
X
X
X
2501055120
Petroleum and Petroleum Product
Storage; Bulk Plants: All Evaporative
Losses; Gasoline
Bulk Gasoline
Terminals
X
X
X
2501060050
Petroleum and Petroleum Product
Storage; Gasoline Service Stations; Stage
1: Total
Gas Stations
X
2501060051
Petroleum and Petroleum Product
Storage; Gasoline Service Stations; Stage
1: Submerged Filling
Gas Stations
X
X
X
2501060052
Petroleum and Petroleum Product
Storage; Gasoline Service Stations; Stage
1: Splash Filling
Gas Stations
X
X
X
4-58
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see
Description
Sector
EPA
State
Local
Tribe
2501060053
Petroleum and Petroleum Product
Storage; Gasoline Service Stations; Stage
1: Balanced Submerged Filling
Gas Stations
X
X
X
X
2501060201
Petroleum and Petroleum Product
Storage; Gasoline Service Stations;
Underground Tank: Breathing and
Emptying
Gas Stations
X
X
X
X
2501070053
Petroleum and Petroleum Product
Storage; Diesel Service Stations; Stage 1:
Balanced Submerged Filling
Gas Stations
X
X
2501070201
Petroleum and Petroleum Product
Storage; Diesel Service Stations;
Underground Tank: Breathing and
Emptying
Gas Stations
X
2501995120
Petroleum and Petroleum Product
Storage; All Storage Types: Working Loss;
Gasoline
Industrial Processes -
Storage and Transfer
X
2501995180
Petroleum and Petroleum Product
Storage; All Storage Types: Working Loss;
Kerosene
Industrial Processes -
Storage and Transfer
X
2505000120
Petroleum and Petroleum Product
Transport; All Transport Types; 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
2505020900
Petroleum and Petroleum Product
Transport; Marine Vessel; Tank Cleaning
Industrial Processes -
Storage and Transfer
X
2505030120
Petroleum and Petroleum Product
Transport; Truck; Gasoline
Industrial Processes -
Storage and Transfer
X
X
X
X
4-59
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see
Description
Sector
EPA
State
Local
Tribe
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
2525000000
Inorganic Chemical Transport; All
Transport Types; Total: All Products
Industrial Processes -
Storage and Transfer
X
Table 4-31: Nonpoint Aviation Gasoline Distribution SCCs with 2014 NEI emissions
see
Description
Sector
EPA
State
Local
Tribe
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 4-32 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%).
Table 4-32: Percentage of Gasoline Distribution VOC emissions submitted by reporting agency
Region
Agency
Sector
VOC
1
Maine Department of Environmental Protection
Gas Stations
27
1
Massachusetts Department of Environmental Protection
Bulk Gasoline Terminals
100
1
Massachusetts Department of Environmental Protection
Gas Stations
85
1
Massachusetts Department of Environmental Protection
Industrial Processes - Storage
and Transfer
15
1
New Hampshire Department of Environmental Services
Gas Stations
56
1
New Hampshire Department of Environmental Services
Industrial Processes - Storage
and Transfer
100
2
New Jersey Department of Environment Protection
Gas Stations
100
2
New Jersey Department of Environment Protection
Industrial Processes - Storage
and Transfer
100
2
New York State Department of Environmental Conservation
Bulk Gasoline Terminals
100
2
New York State Department of Environmental Conservation
Gas Stations
100
2
New York State Department of Environmental Conservation
Industrial Processes - Storage
and Transfer
100
3
Delaware Department of Natural Resources and
Environmental Control
Gas Stations
100
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Region
Agency
Sector
voc
3
Delaware Department of Natural Resources and
Environmental Control
Industrial Processes - Storage
and Transfer
100
3
Maryland Department of the Environment
Gas Stations
100
3
Maryland Department of the Environment
Industrial Processes - Storage
and Transfer
100
3
Virginia Department of Environmental Quality
Gas Stations
95
3
Virginia Department of Environmental Quality
Industrial Processes - Storage
and Transfer
51
3
Georgia Department of Natural Resources
Bulk Gasoline Terminals
100
3
Georgia Department of Natural Resources
Gas Stations
96
3
Georgia Department of Natural Resources
Industrial Processes - Storage
and Transfer
3
4
Knox County Department of Air Quality Management
Bulk Gasoline Terminals
100
4
Knox County Department of Air Quality Management
Gas Stations
100
4
Knox County Department of Air Quality Management
Industrial Processes - Storage
and Transfer
2
4
Metro Public Health of Nashville/Davidson County
Gas Stations
14
4
Metro Public Health of Nashville/Davidson County
Industrial Processes - Storage
and Transfer
49
5
Illinois Environmental Protection Agency
Gas Stations
100
5
Illinois Environmental Protection Agency
Industrial Processes - Storage
and Transfer
31
5
Michigan Department of Environmental Quality
Bulk Gasoline Terminals
100
5
Michigan Department of Environmental Quality
Gas Stations
100
5
Michigan Department of Environmental Quality
Industrial Processes - Storage
and Transfer
11
5
Ohio Environmental Protection Agency
Bulk Gasoline Terminals
0
6
Texas Commission on Environmental Quality
Bulk Gasoline Terminals
100
7
Iowa Department of Natural Resources
Gas Stations
71
8
Utah Division of Air Quality
Bulk Gasoline Terminals
19
8
Utah Division of Air Quality
Gas Stations
69
8
Utah Division of Air Quality
Industrial Processes - Storage
and Transfer
13
9
California Air Resources Board
Bulk Gasoline Terminals
25
9
California Air Resources Board
Gas Stations
100
9
California Air Resources Board
Industrial Processes - Storage
and Transfer
91
9
Clark County Department of Air Quality and Environmental
Management
Bulk Gasoline Terminals
49
9
Morongo Band of Cahuilla Mission Indians of the Morongo
Reservation, California
Gas Stations
100
9
Washoe County Health District
Gas Stations
100
9
Washoe County Health District
Industrial Processes - Storage
and Transfer
100
10
Alaska Department of Environmental Conservation
Bulk Gasoline Terminals
51
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Region
Agency
Sector
VOC
10
Coeur d'Alene Tribe
Gas Stations
100
10
Coeur d'Alene Tribe
Industrial Processes - Storage
and Transfer
100
10
Idaho Department of Environmental Quality
Gas Stations
66
10
Idaho Department of Environmental Quality
Industrial Processes - Storage
and Transfer
100
10
Kootenai Tribe of Idaho
Gas Stations
100
10
Kootenai Tribe of Idaho
Industrial Processes - Storage
and Transfer
100
10
Nez Perce Tribe
Gas Stations
100
10
Nez Perce Tribe
Industrial Processes - Storage
and Transfer
100
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of
Idaho
Gas Stations
100
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of
Idaho
Industrial Processes - Storage
and Transfer
100
10
Washington State Department of Ecology
Gas Stations
71
4,6,3 EPA-developed emissions for Stage i Gasoline Distribution
The detailed calculation approach used by the EPA to estimate emission from stage I gasoline distribution can be
found on the 2014v2 Supplemental Data FTP site in the file "Stage I Gasoline Distribution for NEI v2.zip." In
short, the EPA broke stage 1 gasoline emissions into six basic parts: 1) bulk terminals; 2) pipeline facilities; 3)
bulk plants; 4) tank trucks; and 5) service stations (which can be further subdivided into Filling and Breathing &
Emptying).
For bulk terminals and pipeline facilities, there are no activity-based VOC emission factors, so estimates from
1998 developed in support of the Gasoline Distribution MACT standard [ref 1] are scaled up to 2014, based on a
ratio of the national volume of wholesale gasoline supplied. This information comes from the Petroleum Supply
Annual, provided by the Energy Information Administration [ref 2],
For bulk plants, the activity information comes from the national volume of gasoline passing through bulk plants
in 2014, which is assumed to be nine percent of total gasoline consumption. The gasoline consumption data was
obtained from the Energy Information Administration's Petroleum Navigator website.
The activity data for tank trucks in transit also comes from the ElA's Petroleum Navigator website, and the
gasoline throughput for tank trucks was computed by multiplying the county-level gasoline consumption
estimates by a factor of 1.09, to account for gasoline that is transported more than once in each area (for
example, transported from bulk terminal to bulk plant and then from bulk plant to service station [ref 3],
Underground storage tank breathing and emptying, as well as filling operations, depend on more complicated
information that takes into account vapor pressures, average temperatures, and molecular weights, and relies
on the MOtor Vehicle Emission Simulator (MOVES) for some of the inputs for these equations [ref 4],
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4.63.1 Point Source Subtraction
Point source subtraction removes the activity and emissions associated with point source contributions to the
total activity. For example, emissions from transfer stations are included in the S/L/T agency submissions for
those transfer stations with large enough emissions to trigger point source reporting (see Section 1.5). The EPA
performed the point source subtraction of S/L/T agency point inventory emissions and uploaded the results to
the 2014EPA_NONPOINT_V2 dataset. The crosswalk for point to nonpoint sources that EPA used is included in
the Access database in the zipped file noted in Section 4.6.3 above.
4.6,3,2 ฃB4 Tagged Data
The results of the nonpoint survey showed that many states submit several SCCs for gasoline distribution in the
point sector of their inventories. All the EPA nonpoint data were therefore tagged for these S/L/T-SCC
combinations, shown in Table 4-33, to avoid double counting emissions.
Table 4-33: S/L/Ts and SCCs where EPA Gasoline Stage 1 Distribution estimates were tagged out
Tag Reason
see
S/L/T agencies
All in Point
2501050120 (bulk gas terminals)
Chattanooga, CO, IL, KY, ME, Maricopa County,
MS, NE, OR, Washoe County, WY
2501055120 (bulk plants)
Chattanooga, CO, IL, KY, ME, Maricopa County,
MD, MS, NE, NH, OR, Rl, Washoe County, WY
2501060051, 52, 53, and 201 (gas
service stations stage 1)
CO
2505030120 (truck)
CA, NE
2505040120 (pipeline)
NE
Do not have this
type of source
2501050120 (bulk gas terminals)
NJ
2501055120 (bulk plants)
AK, NJ
2501060052 (splash filling)
Chattanooga, Knox County, OFI, UT, VA
2501060053 (balanced submerged)
Chattanooga, OFI
2505030120 (truck)
Washoe County
2505040120 (pipeline)
CO, DE, MD, Rl, Washoe County
Use different SCCs
2501055120 (bulk plants)
CA
4.6.4 EPA-developed emissions for Aviation Gasoline
The detailed calculation approach used by EPA to estimate emission from stage I gasoline distribution can be
found on the 2014v2 Supplemental Data FTP site in the file "Aviation Gasoline v4.1_2016-ll-ll.zip". The
amount of aviation gasoline consumed by each state in 2014 was obtained from the Energy Information
Administration (EIA) State Energy Data System (SEDS) [ref 5], This information was used to calculate county-level
emissions estimates for one criteria pollutant and ten FIAPs. More information on the assumptions (e.g., number
of bulk plant processes) and details on emission factors can be found in the zip file documentation.
4.6.5 State Submittals for Aviation (Baseline
Only a handful of states submitted to these SCCs for Aviation Gasoline. These states were Delaware, Illinois,
Maryland, Maine, Michigan, New Jersey and Utah. A few states indicated in the Nonpoint Survey that the EPA
should supplement their submissions with EPA data, with the reasoning that they do not have this type of
source. These S/L/Ts were New York, Chattanooga, Tennessee and Knox County, Tennessee. In addition,
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California and Colorado indicated that all their emissions for aviation gasoline are covered in the point source
category of their submissions, so no EPA estimates were included in the NEI for these states.
4.6.6 Updates for 2014v2
The 2014v2 updates are limited to the following:
Updated County Business Patterns and State-level employment data to 2014 US Census Bureau data,
used in Aviation Gasoline and Gas Distribution estimates.
Updated the "FillingTechnology" table for gasoline distribution to account for International Fire Code
(IFC) adoptions by states and counties. For counties that have adopted the IFC, it is assumed that there
is no (0%) splash filling. Counties that had splash filling were moved to submerged.
4.6.7 References for nonpoint gasoline distribution
1. U.S. Environmental Protection Agency, "Gasoline Distribution Industry (Stage l)-Background Information
for Promulgated Standards," EPA-453/R94-002b, Office of Air Quality Planning and Standards,
November 1994.
2. 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 2014, Volume 1,
released September 2015.
3. 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.
4. U.S. Environmental Protection Agency, The MOVES Team, "Gallons of gasoline consumed in each county
by market share of RVP (fuel formulation) by month for calendar year 2011," CountyGallons2011.zip,
created February 2016.
5. Energy Information Administration. State Energy Data System (SEDS): 1960-2014 (complete).
Consumption in Physical Units. U.S. Department of Energy. Washington, D.C. December 2016.
4,7 Commercial Cooking
4,7,1 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 4-34 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 4-34: 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
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4,7.2 Sources of data
The agencies listed in Table 4-35 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%).
Table 4-35: Percentage of Commercial Cooking PM2.5 and VOC emissions submitted by reporting agency
Region
Agency
PM2.5
VOC
2
New Jersey Department of Environment Protection
100
100
2
New York State Department of Environmental Conservation
100
100
3
Delaware Department of Natural Resources and Environmental Control
100
100
3
Maryland Department of the Environment
100
100
4
Knox County Department of Air Quality Management
100
100
5
Illinois Environmental Protection Agency
100
100
6
Texas Commission on Environmental Quality
100
100
9
California Air Resources Board
5
54
9
Washoe County Health District
100
100
10
Coeur d'Alene Tribe
100
100
10
Idaho Department of Environmental Quality
100
100
10
Kootenai Tribe of Idaho
100
100
10
Nez Perce Tribe
100
100
10
Shoshone-Bannock Tribes of the Fort Flail Reservation of Idaho
100
100
4,7,3 EPA-developed emissions for commercial cooking
The approach for estimating emissions from commercial cooking in 2014 consists of three general steps, as
follows:
Determine county-level activity, i.e., the number of restaurants in each county in 2014;
Determine the fraction of restaurants with commercial cooking equipment, the average number of units
of each type of equipment per restaurant, and the average amount of food cooked on each type of
equipment; and
Apply emission factors to each type of food for each type of commercial cooking equipment.
More information on the estimation methods can be found in the documentation for commercial cooking,
entitled "Commercial Cooking_vl.5_2017-05-26.zip" on the 2014v2 Supplemental Data FTP site.
Activity Data: updated for 2014v2
Data on the number of restaurants in each county are available from the U.S. Census Bureau County Business
Patterns database [ref 1], which reports the number of restaurants (categorized by NAICS code) in each county.
In general, our approach for the 2014 NEI was to grow the detailed activity data from the 2002 NEI, and so we
will provide more information about the 2002 NEI approach here.
The 2002 NEI is the most recent inventory for which we estimated emissions from commercial cooking using
restaurant-level data rather than population data. The 2002 approach used the Dun and Bradstreet industry
database, which contains more specific information on the type of restaurant in each county. The approach for
the 2002 NEI identifies five specific categories of restaurants that are likely to have the equipment that matches
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the source categories for commercial cooking emissions, including: ethnic food restaurants, fast food
restaurants, family restaurants, seafood restaurants, and steak & barbecue restaurants. Because Dun and
Bradstreet data for 2014 were not readily available, the number of restaurants in each county was estimated
using a two-step process. First the number of restaurants in 2002 was estimated using the following equation:
REST = Eijmn,2002
l'2002 FRACj X UNlTSj X FOODjm X EFjmn K '
where:
REST, 2002 the total number of restaurants in county / in 2002
Eijmn,2002 = the emissions of pollutant n from food m cooked on source category j in county i in 2002,
as reported in the National Emissions Inventory
FRACj = the fraction of restaurants in those categories that have equipment in source j
UNlTSj = the average number of units of source category j in each restaurant
FOODjm = the average amount of food m cooked on source category j
EFjmn = the emission factor for pollutant n from food m cooked on source category j
The values of FRACj, UNITS,, and FOOD,, came from Potepan [ref 2], The emission factors are from an E.H. Pechan
and Associates memorandum [ref 3],
Next, a growth factor based on the change in the number of restaurants in each county between 2002 and 2014
was generated using data from the U.S. Census Bureau County Business Patterns database for NAICS code
722511 (Full-Service Restaurants) and NAICS code 722513 (Limited-Service Restaurants). For example, if the
number of restaurants in a county increased from 100 to 125 between 2002 and 2014, the growth factor would
be 1.25; in some cases, the number of restaurants decreased, and the growth factor was less than 1. This growth
factor was multiplied by the number of restaurants in each county in 2002, as shown in equation 2, to estimate
the number of restaurants in 2014:
REST( 2014 = RESTi,2002 x GFi (2)
where GF, is the growth factor for county /'.
4.7.3.2 Emission Factors
Emission factors for each type of food on each type of commercial cooking equipment [EFjmn] came from a
technical memorandum developed by E.H. Pechan and Associates [ref 2], This information remains the most
complete catalog of emission factors for commercial cooking; a recent review of the literature on emissions
from cooking revealed no new studies with a similar breadth of pollutants analyzed [ref 4], The PM emission
factors from E.H. Pechan and Associates only contain primary PM. The emission factors for filterable PM were
derived by applying ratios to primary PM (Table 4-36). The condensable particulate matter condensable PM
emission factors were derived by subtracting PM10-FIL from PMio-PRI.
HAP emissions from deep-fat frying, flat griddle frying, and clamshell griddle frying are estimated using
speciation factors from EPA's SPECIATE database [ref 5], These speciation factors are provided in the
documentation for Commercial Cooking, entitled "Commercial Cooking_vl.5_2017-05-26.zip" on the 2014v2
Supplemental Data FTP site.
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Table 4-36: Ratio of filterable particulate matter to primary particulate matter for PM2.5 and PM10 by SCC
Cooking Device
SCC
PM25-FIL/ PM25-PRI
PM10-FIL/ PM10-PRI
Conveyorized Charbroiling
2302002100
0.00321
0.00331
Underfired Charbroiling
2302002200
0.00287
0.00297
Flat Griddle Frying
2302003100
0.00201
0.00264
Clamshell Griddle Frying
2302003200
0.00241
0.00283
4, /, 5 * >5 Emissions
After estimating the number of restaurants in 2014 using Equation 2, the amount of emissions in 2014 was
determined by rearranging Equation 1, as shown in Equation 3:
Eijmn,2014 = RESTLi2014 X FRACj X UNITSj X FOODjm X EFjmn (3)
where EiJmn,2014 is the emissions of pollutant n from food m cooked on commercial equipment j in county i in
2014.
The fraction of restaurants with commercial cooking equipment (FRACj), the average units of equipment per
restaurant (UNITSj), and the average amount of each type of food cooked on each type of equipment (FOODj),
were obtained from Potepan (2001) [ref 2], Potepan reports the fraction of restaurants with commercial cooking
equipment subcategorized by restaurant types: ethnic food restaurants, fast food restaurants, family
restaurants, seafood restaurants, and steak & barbecue restaurants). To use these data, we calculated a
weighted average of these fractions to determine an overall fraction of the number of all restaurants across all
five subcategories that utilize commercial cooking equipment. Furthermore, because Potepan reports that 31%
of all restaurants fall into one of those five subcategories, the weighted averages were multiplied by 0.31 to
determine the fraction of all restaurants in each county with commercial cooking equipment. These numbers
are reported in Table 4-37. The percentage of restaurants with under-fired charbroilers (12.5%) is similar to a
more recent survey in North Carolina [ref 6], which found that 13% of surveyed restaurants employed
charbroilers. The North Carolina survey did not include the other types of commercial cooking equipment
reported here.
Table 4-37: Fraction of restaurants with source category equipment and average number of units per restaurant
Source Category
SCC
Percent of Restaurants
with Equipment (FRACj)
Average Number of Units
Per Restaurant (UNITSj)
Conveyorized Charbroiling
2302002100
3.6%
1.3
Under-fired Charbroiling
2302002200
12.5%
1.5
Deep Fat Frying
2302003000
28.0%
2.5
Flat Griddle Frying
2302003100
18.4%
1.6
Clamshell Griddle Frying
2302003200
2.8%
1.7
Potepan also estimated the average annual amount of food cooked on each type of commercial cooking
equipment (FOODj). These numbers are reported in Table 4-38 below. The amount of french fried potatoes
cooked in deep-fat fryers was estimated by dividing the total weight of frozen potatoes utilized in domestic food
service (6.9 million tons, [ref 7]) by the estimated number of deep-fryers in the United States (303,918 deep-
fryers).
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Table 4-38: Average amount of food cooked per year (tons/year) on each type of
Commercial Cooking equipment
Food
Conveyorized
Charbroiling
Under-fired
Charbroiling
Deep Fat
Frying
Flat Griddle
Frying
Clamshell
Griddle Frying
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
-
Potatoes
-
-
21.3
-
-
4.73.4 Example Calculations
Determining the Number of Restaurants in Each County in 2002
RESTi 2002 ~
Eijmn,2002
FRACj X UNITSj X F00Djm X EFjmn
203 restaurants =
8.76
PM2 5,Underfired-Charbroilers
0.125 x 1.54 x 7.02 x 0.032
Emissions of PIVh.sfrom underfired charbroilers in county i in 2002 were 8.76 tons. To determine the number of
restaurants that generated these emissions in 2002, the emissions are divided by the fraction of restaurants that
use underfired charbroilers (0.125), the average number of underfired charbroilers used at each restaurant
(1.54), the average amount of hamburger cooked on each underfired charbroiler (7.02 tons/year), and the
emission factor for PIVh.sfrom hamburger cooked on underfired charbroilers (0.032 tons PM2.5 per ton of
hamburger). The result shows that there were 203 restaurants in county i in 2002. This process is repeated for
each SCC (Table 4-34) and each type of food (Table 4-38) in each county.
Determining the Number of Restaurants in Each County in 2014
Using the estimated number of restaurants in 2002, the number of restaurants in 2014 was determined by
employing a growth factor based on the change in the number of restaurants between 2002 and 2014 as
determined by the U.S. Census Bureau County Business Statistics Database [ref 1],
REST;
i, 2014
= REST,
i,2002
x GFj
235 restaurants = 203 restaurants x 1.16
There were 203 restaurants estimated to be in county i in 2002. Data from the U.S. Census Bureau show that
there was a 16% increase in the number of restaurants in county i between 2002 and 2014. The growth factor
(1.16) was multiplied by 203 to estimate that there were 235 restaurants in county i in 2014. Note that the
actual number of restaurants in 2014 as determined from the U.S. Census Bureau County Business Statistics
database is not equal to RESTi/2ou as determined by the equation above because the emissions from the 2002
NEI were calculated using activity data from the Dun and Bradstreet database, rather than the U.S. Census
Bureau County Business Statistics database.
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Determining the Emissions in 2014
The emissions in 2014 were determined using the following equation:
Eijmn,2014 = RESTi,2014 x FRACj X UNITSj X FOODjm X EFjmn
10.16 tons PM25 = 235 x 0.125 x 1.54 x 7.02 x 0.032
There were 235 restaurants in county i in 2014. This was multiplied by the fraction of restaurants that use
underfired charbroilers (0.125), the average number of underfired charbroilers used at each restaurant (1.54),
the average amount of hamburger cooked on each underfired charbroiler (7.02 tons/year), and the emission
factor for PIVh.sfrom hamburger cooked on underfired charbroilers (0.032 tons PM2.5 per ton of hamburger). The
result shows that the emissions of PM2.5 in county i were 10.16 tons in 2014.
4.7,3.5 Changes from 2011 Methodology
The growth factors were updated using data on the number of restaurants in 2002 and 2014 from the U.S.
Census Bureau County Business Statistics Database.
4.7.3.6 Puerto Rico and US Virgin Islands Emissions Calculations
Insufficient data exists to calculate emissions for the counties in Puerto Rico and the US Virgin Islands; therefore,
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 U.S. 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 U.S. 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".
4.7.3.7 ฃB4 tags and corrections made for v2
Some states indicated on their nonpoint survey that they did not have one or more of the sources EPA estimates
in this sector, so we did not use EPA estimates for these SCCs in the NEI. These states (or territories) and SCCs
are given in Table 4-39.
Table 4-39: State agencies that requested EPA tag out Commercial Cooking sources
State
see
Description
Alaska
2302002100
Commercial Cooking - Charbroiling; Conveyorized Charbroiling
Alaska
2302002200
Commercial Cooking - Charbroiling; Under-fired Charbroiling
Nebraska
2302003200
Commercial Cooking - Frying; Clamshell Griddle Frying
Puerto Rico
2302002100
Commercial Cooking - Charbroiling; Conveyorized Charbroiling
Puerto Rico
2302003200
Commercial Cooking - Frying; Clamshell Griddle Frying
4,7.4 References for commercial cooking
1. United States Census Bureau, 2014 County Business Patterns, accessed August 2016
2. Potepan, M. 2001. Charbroiling Activity Estimation. Public Research Institute, report for the California
Air Resources Board and the California Environmental Protection Agency, accessed October 2015
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3. E.H. Pechan and Associates. 2003. Methods for Developing a National Inventory for Commercial Cooking
Processes: Technical Memorandum, accessed October 2015
4. Abdullahi, K.L, J.M. Delgado-Saborit, and R.M. Harrison. 2013. Emissions and indoor concentrations of
particulate matter and its specific chemical components from cooking: a review. Atmospheric
Environment, 71: 260-294.
5. U.S. Environmental Protection Agency. 2016. SPECIATE Database v4.5.
6. North Carolina Division of Air Quality. 2013. Supplement Section 110(a)(1) Maintenance Plan - February
2013, Appendix B, Section 4.4.4.. accessed October 2015
7. United States Potato Board. 2011. Potato Sales and Utilization Estimates 2001-2010. accessed October
2015
4.8 Dust-Construction Dust
4,8,1 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 4-40 lists the nonpoint SCCs associated with this sector in the 2014 NEI. EPA estimates emissions for the
indicated SCCs in the table. The SCC level 1 and 2 descriptions is "Industrial Processes; Construction: SIC 15 -17"
for all SCCs.
Table 4-40: SCCs in the 2014 NEI Construction Dust sector
EPA estimates?
SCC
SCC Level Three
SCC Level Four
2311000000
Construction: SIC 15-17
All Processes: Total
Y
2311010000
Residential
Total
2311010000
Residential
Vehicle Traffic
Y
2311020000
Industrial/Commercial/Institutional
Total
Y
2311030000
Road Construction
Total
4,8,2 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 4-41 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%).
Table 4-41: Percentage of Construction Dust PM2.5 emissions submitted by reporting agency
Region
Agency
PMz.5
1
New Hampshire Department of Environmental Services
4
2
New Jersey Department of Environment Protection
100
3
Delaware Department of Natural Resources and Environmental Control
100
3
Maryland Department of the Environment
100
5
Illinois Environmental Protection Agency
100
8
Utah Division of Air Quality
75
9
California Air Resources Board
100
9
Clark County Department of Air Quality and Environmental Management
100
9
Maricopa County Air Quality Department
100
9
Washoe County Health District
100
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Region
Agency
PMz.5
10
Coeur d'Alene Tribe
100
10
Idaho Department of Environmental Quality
100
10
Kootenai Tribe of Idaho
100
10
Nez Perce Tribe
100
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
100
4,8,3 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.
4.8,3.1 AcfmtyData
There are two activity calculations performed for this SCC, acres of surface soil disturbed and volume of soil
removed for basements.
Surface soil disturbed
The US Census Bureau has 2014 data for New Privately Owned Housing Units Started by Purpose and Design [ref
1] which provides regional level housing starts based on the groupings of 1 unit, 2-4 units, 5 or more units. A
consultation with the Census Bureau in 2002 gave a breakdown of approximately 1/3 of the housing starts being
for 2 unit structures, and 2/3 being for 3 and 4 unit structures. The 2-4 unit category was then divided into 2-
units, and 3-4 units based on this ratio.
New Privately Owned Housing Units Authorized Unadjusted Units [ref 2] gives a conversion factor to determine
the ratio of structures to units in the 5 or more unit category. For example, if a county has one 40-unit
apartment building, the ratio would be 40/1. If there are 5 different 8 unit buildings in the same project, the
ratio would be 40/5. Structures started by category are then calculated at a regional level.
Annual county building permit data were purchased from the US Census Bureau for 2014 [ref 3], The 2014
County Level Residential Building Permit dataset has 2014 data to allocate regional housing starts to the county
level. This results in county-level housing starts by number of units. Table 4-42 provides surface areas that were
assumed disturbed for each unit type:
Table 4-42: Surface soil removed per unit type
Unit type
Surface acres disturbed
1-Unit
1/4 acre/structure
2-Unit
1/3 acre/structure
Apartment
1/2 acre/structure
The 3-4 unit category was considered to be an apartment. Multiplication of housing starts to soil removed
results in number of acres disturbed for each unit category.
Basement soil removal
To calculate basement soil removal, the 2014 Characteristics of New Single-Family Houses Completed,
Foundation table [ref 4] is used to estimate the percentage of 1 unit structures that have a basement (on the
regional level). The county-level estimate of number of 1 unit starts is multiplied by the percent of 1 unit houses
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in the region that have a basement to get the number of basements in a county. Basement volume is calculated
by assuming a 2000 square foot house has a basement dug to a depth of 8 feet (making 16,000 ft3 per
basement). An additional 10% is added for peripheral dirt bringing the total to 17,600 ft3 (651.85 yd3) per
basement.
4.8.3.2 Emission Factors
Initial PMio emissions from construction of single family, two-family, and apartments structures are calculated
using the emission factors given in Table 4-43 [ref 5], 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 4-43: Emission factors for Residential Construction
Type of Structure
Emission Factor
Duration of
Construction
Apartments
0.11 tons PMio/acre-month
12 months
2-Unit Structures
0.032 tons PMio/acre-month
6 months
1-unit Structures with
Basements
0.011 tons PMio/acre-month
6 months
0.059 tons PMio/1000 cubic
yards
1-Unit Structures w/o
Basements
0.032 tons PMio/acre-month
6 months
Regional variances in construction emissions are corrected using soil moisture level and silt content. These
correction parameters are applied to initial PMio emissions from residential construction to develop the final
emissions inventory.
To account for the soil moisture level, the PMio emissions are weighted using the 30-year average precipitation-
evaporation (PE) values from Thornthwaite's PE Index. Average precipitation evaporation values for each State
were estimated based on PE values for specific climatic divisions within a State.
To account for the silt content, the PMio 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 6], 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 equation for PMio emissions corrected for soil moisture and silt content is:
24 S
CorrectedEPMl0 = Initial PMl0 x x
where:
Corrected EPMio = PMio emissions corrected for soil moisture and silt content,
PE = precipitation-evaporation value for each State,
S =% dry silt content in soil for area being inventoried.
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Once PMio adjustments have been made, PM25-FIL emissions are estimated by applying a particle size multiplier
of 0.10 to PM10-FIL emissions [ref 7], Primary PM emissions are equal to filterable emissions since there are no
condensable emissions from residential construction.
Example Calculation
PMio Emissions
construction
where:
HSunit X SMunit
= Regional Housing Starts x (county building permits/Regional building permits)
= Area or volume of soil moved for the given unit type
= Construction time (in months) for given unit type
= Unadjusted emission factor for PMio for the given unit type
= PM Adjustment factor
Construction
EFunit
AdjpM
As an example, in Beaufort County, North Carolina, 2010 acres disturbed and PMio emissions from 1-unit
housing starts without a basement are calculated as follows:
Aunit = 345,000x (142/342,534) x 0.921(Fraction without basement) * 0.25 acres/unit
= 131.72 units * 0.25 acres/unit = 32.9 acres
AdjpM = (24/110.1) * (39.58/9) = 0.958
PM io Emissions - (32.8 acres x 6 months x 0.032 tons PMio/acre-month) x 0.958 - 6.06 tons
4.83.4 Updates to 2011 Methodology
The housing starts and soil removed were updated using the latest data from the U.S. Census Bureau. The
county-level silt values were updated and are now based on soil sampling data contained in the National
Cooperative Soil Survey Microsoft Access Soil Characterization Database. There were no updates in
methodology between 2014vl and 2014v2 for this sector.
'i.8.3.S Puerto Rico and US Virgin Islands Emissions Calculations
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".
4.8.3.6 References for residential construction
1. U.S. Census Bureau. New Privately Owned Housing Units Started by Purpose and Design in 2014.
accessed September 2015.
2. U.S. Census Bureau, New Privately Owned Housing Units Authorized - Unadjusted Units for Regions.
Divisions, and States. Annual 2014, Table 2au. Accessed September 2015.
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3. U.S. Census Bureau, Annual Housing Units Authorized by Building Permits CO2014A, purchased
September 2015.
4. U.S. Census Bureau, Type of Foundation in New One-Family Houses Completed, from Characteristics of
New Single-Family Houses Completed, accessed September 2015.
5. Midwest Research Institute. Improvement of Specific Emission Factors (BACM Project No. 1). Prepared
for South Coast Air Quality Management District. March 29, 1996.
6. U.S. Department of Agriculture. National Cooperative Soil Survey (NCSSi Soil Characterization Database.
accessed September 2015.
7. Cowherd. C., J. Donaldson and R. Hegarty, Midwest Research Institute; D. Ono, Great Basin UAPCD
Proposed Revisions to Fine Fraction Ratios Used for AP-42 Fugitive Dust Emission Factors, accessed
September 2015.
4,8,4 EPA-developed emissions for non-residential construction
Emissions from industrial/commercial/institutional (non-residential) construction activity are a function of the
acreage disturbed for non-residential construction.
4,8.4,1 Activity Data
The activity data are the number of acres disturbed for non-residential construction and are estimated by
multiplying the value of non-residential construction put in place by the number of acres disturbed per million
dollars. Annual Value of Construction Put in Place in the U.S [ref 1] contains the 2014 national value of non-
residential construction. The national value of non-residential construction put in place (in millions of dollars)
was allocated to counties using county-level non-residential construction employment data (NAICS Code 2362)
obtained from County Business Patterns (CBP) [ref 2], Because some counties' employment data were withheld
due to privacy concerns, the following procedure was adopted to estimate the number of county-level withheld
employees:
1. State totals for the known county-level employees were subtracted from the total number of employees
reported in the CBP state level file [ref 3], This results in the total number of withheld employees in the
state.
2. The midpoint of the range code was used as an initial estimate (so for instance in the 1-19 employees
range, an estimate of 10 employees would be used) and a state total of the withheld employees was
computed.
3. A ratio of estimated employees (Step 2) to withheld employees (Step 1) was then used to adjust the
county-level estimates up or down so that the state total of adjusted estimates matches the state total
of withheld employees (Step 1).
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) [ref 4], EPA adjusted the 1992 conversion factor to 2014
using the Price Deflator (Fisher) Index of New Single-Family Houses Under Construction [ref 5], By taking the
ratio of the 2014 and 1992 Annual Index values and applying it to the 1992 factor, a value of 1.01 acres/$l
million (= 2/(113/57)) was estimated.
4.8.4,2 Emission Factors
Initial PMio emissions from construction of non-residential buildings are calculated using an emission factor of
0.19 tons/acre-month [ref 6], The duration of construction activity for non-residential construction is assumed
to be 11 months. Since there are no condensable emissions, primary PM emissions are equal to filterable
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emissions. Once PMio-xx emissions are developed, PM25-xx emissions are estimated by applying a particle size
multiplier of 0.10 to PMio-xx emissions [ref 7],
Regional variances in construction emissions are corrected using soil moisture level and silt content. These
correction parameters are applied to initial PMio emissions from non-residential construction to develop the
final emissions inventory.
To account for the soil moisture level, the PMio emissions are weighted using the 30-year average precipitation-
evaporation (PE) values from Thornthwaite's PE Index. Average precipitation evaporation values for each State
were estimated based on PE values for specific climatic divisions within a State [ref 4],
To account for the silt content, the PMio 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 8], 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 equation for PMio emissions corrected for soil moisture and silt content is:
24 S
CorrectedEPMl0 = Initial EPM10 x x
where:
Corrected EPMio = PMio emissions corrected for soil moisture and silt content,
PE = precipitation-evaporation value for each State,
S =% dry silt content in soil for area being inventoried.
Once PMio adjustments have been made, PM2.5 emissions are set to 10% of PMio.
4.8,4,3 Example Calculation
EmiSSiOnsPMio = Nspending x (Empcounty/ EmpNational) x Apd X EFAdj x M
where:
Nspending = National spending on nonresidential construction (million dollars)
Empcounty = County-level employment in nonresidential construction
EmpNational = National level employment in nonresidential construction
Apd = Acres per million dollars (national data)
EFAdj = Adjusted PMio emission factor (ton/acre-month)
M = duration of construction activity (months)
As an example, in Grand Traverse County, Michigan, 2014 acres disturbed and PMio emissions from non-
residential construction are calculated as follows:
EmissionsPMio = 347,666 x $106 x (103/560,616) x 1.01 acres/$106 x EFAdj x M
= 70 acres x 0.1073 ton/acre-month x 11 months
= 83 tons PMio
where EFAdj is calculated as follows:
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EFAdj = 0.19 ton/acre-month * (24/103.6 * 21.95/9)
= 0.1073 ton/acre-month
4.8.4.4 Changes from 2011 andZOMvl Methodology
The Annual Value of Construction Put in Place, employment data and the acres/$ million conversion factors
were updated using the latest (year 2014) data from the U.S. Census Bureau (from 2013 data in 2014vl). The
county-level silt values were updated and are now based on soil sampling data contained in the National
Cooperative Soil Survey Microsoft Access Soil Characterization Database.
4.8.4.5 Puerto Rico and US Virgin Islands Emissions Calculations
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".
4.8.4.6 References for non-residential construction dust
1. U.S. Census Bureau, Value of Construction Put in Place at a Glance, accessed September 2015.
2. U.S Census Bureau, County Business Patterns: 2014. "Complete County File [14.4mb zip]," accessed
August 2016.
3. U.S. Census Bureau, County Business Patterns: 2014. "Complete State File [lO.Omb zip]," accessed
August 2016.
4. 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).
5. U.S. Census Bureau, Price Deflator (Fisher) Index of New Single-Family Houses Under Construction.
accessed September 2015.
6. Midwest Research Institute. Improvement of Specific Emission Factors (BACM Project No. 1). Prepared
for South Coast Air Quality Management District. March 29, 1996.
7. Midwest Research Institute. Background Document for Revisions to Fine Fraction Rations Used for AP-42
Fugitive Dust Emission Factors, Proposed Fine Fraction Ratios, Table 1 (preparedfor Western Governors'
Association).
8. U.S. Department of Agriculture, National Cooperative Soil Survey (NCSS) Soil Characterization Database,
accessed September 2015.
4.8.5 EPA-developed emissions for road construction
Emissions from road construction activity are a function of the acreage disturbed for road construction. Road
construction activity is developed from data obtained from the Federal Highway Administration (FHWA).
4.8.5.1 Activity Data
The Federal Highway Administration's Highway Statistics, State Highway Agency Capital Outlay 2014, Table SF-
12A [ref 1], outlines spending by state in several different categories. For this SCC, the following columns are
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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
The State expenditure data are then converted to new miles of road constructed using $/mile conversions
obtained from the Florida Department of Transportation (FLDOT) in 2014 [ref 2], A conversion of $6.8
million/mile is applied to the urban interstate expenditures and a conversion of $3.8 million/mile is applied to
the rural interstate expenditures. For expenditures on other urban arterial and collectors, a conversion factor of
$4.1 million/mile is applied, which corresponds to all other projects. For expenditures on other rural arterial and
collectors, a conversion factor of $2.1 million/mile is applied, which corresponds to all other projects.
The new miles of road constructed are used to estimate the acreage disturbed due to road construction. The
total area disturbed in each state is calculated by converting the new miles of road constructed to acres using an
acres disturbed/mile conversion factor for each road type as given in Table 4-44.
Table 4-44: 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 disturbed per mile data shown in Table 4-44 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. Building permits [ref 3] are used to allocate the state-level acres disturbed by road
construction to the county. A ratio of the number of building starts in each county to the total number of
building starts in each state is applied to the state-level acres disturbed to estimate the total number of acres
disturbed by road construction in each county.
4.8.5.2 Emission Factors
Initial PMio emissions from construction of roads are calculated using an emission factor of 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. The duration of
construction activity for road construction is assumed to be 12 months.
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Regional variances in construction emissions are corrected using soil moisture level and silt content. These
correction parameters are applied to initial PMio emissions from road construction to develop the final
emissions inventory.
To account for the soil moisture level, the PMio emissions are weighted using the 30-year average precipitation-
evaporation (PE) values from Thornthwaite's PE Index. Average precipitation evaporation values for each State
were estimated based on PE values for specific climatic divisions within a State [ref 4],
To account for the silt content, the PMio 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 5], 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 equation for PMio emissions corrected for soil moisture and silt content is:
24 S
CorrectedEPMl0 = InitialsPMl0 x x
where:
Corrected EPMio = PMio emissions corrected for soil moisture and silt content,
PE = precipitation-evaporation value for each State,
S =% dry silt content in soil for area being inventoried.
Once PMio adjustments have been made, PM2.5 emissions are set to 10% of PMio. Primary PM emissions are
equal to filterable emissions since there are no condensable emissions from road construction.
4.8,5.3 Example Calculation
EmissionsPMio = ฃ(HDrt x MCrt x ACrt) x (HSCounty/ HSstate) x EFAdj x M
where:
HDrt = Highway Spending for a specific road type
MCrt = Mileage conversion for a specific road type
ACrt = Acreage conversion for a specific road type
HScounty = Housing Starts in a given county
HSstate = Housing Starts in a given State
EFAdj = Adjusted PMio Emission Factor
M = duration of construction activity
As an example, in 2014, in Newport County, Rhode Island, acres disturbed and PMio emissions from urban
interstate, urban other arterial, and urban collector road construction are calculated as follows:
EmissionsPMio = I(HDrt x MCrt x ACrt) x (HSCounty / HSstate) x EFAdj x M
= ($14,255/$6,895/mi x 11.4 acres/mi) * (185/952) + ($l,304/$4,112/mi x 7.6 acres/mi) * (185/952) +
($7,144/$4,112/mi x 7.6 acres/mi) * (185/952) x EFAdj x M
= 7.59 acres x 0.35 ton/acre-month x 12 months
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= 32.06 tons PMio
where EFAdj is calculated as follows:
EFAdj = 0.42 ton/acre-month * (24/132 * 41.45/9)
= 0.35 ton/acre-month
Updates to 2011 and2014vl Methodology
The FHWA data on roadway spending were updated to 2014 (from 2008 for 2014vl). The data source for $/mile,
total affected roadway width, and acres disturbed per mile for new road construction for interstate, other
arterials, and collector roads was changed from the North Carolina DOT 2000 data, used in the 2011
methodology, to the 2014 Florida DOT data.
ii.8.55 Puerto Rico and US Virgin Islands Emissions Calculations
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".
4.8,5.6 References for road construction
1. Federal Highway Administration, 2014 Highway Spending, accessed July 2016.
2. Florida DOT Cost Per Mile Models for 2014, accessed September 2015.
3. Annual Housing Units Authorized by Building Permits CO2014A, purchased from US Department of
Census, September 2015.
4. Midwest Research Institute. Improvement of Specific Emission Factors (BACM Project No. 1). Prepared
for South Coast Air Quality Management District. March 29, 1996.
5. U.S. Department of Agriculture, National Cooperative Soil Survey (NCSS) Soil Characterization Database.
accessed September 2015.
4.9 Dust - Dust
4.9.1 Sector description
The SCCs that belong to this sector are provided in Table 4-45. EPA estimates emissions for particulate matter
for the first SCC in this table. 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.
Table 4-45: SCCs in the 2014 NEI Paved Road Dust sector
SCC
SCC 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
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4,9,2 So u rces of d ata
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 4-46 submitted emissions for this sector; agencies not listed used EPA
estimates for the entire sector.
Table 4-46: Percentage of Paved Road Dust PM2.5 emissions submitted by reporting agency
Region
Agency
S/L/T
PM2.5
1
Massachusetts Department of Environmental Protection
State
100
1
New Hampshire Department of Environmental Services
State
100
2
New Jersey Department of Environment Protection
State
100
2
New York State Department of Environmental Conservation
State
100
3
Delaware Department of Natural Resources and Environmental Control
State
100
3
Maryland Department of the Environment
State
100
8
Northern Cheyenne Tribe
Tribe
100
8
Utah Division of Air Quality
State
100
9
California Air Resources Board
State
100
9
Clark County Department of Air Quality and Environmental Management
Local
100
9
Maricopa County Air Quality Department
Local
100
9
Morongo Band of Cahuilla Mission Indians of the Morongo Reservation, California
Tribe
100
9
Washoe County Health District
Local
100
10
Coeur d'Alene Tribe
Tribe
100
10
Idaho Department of Environmental Quality
State
100
10
Kootenai Tribe of Idaho
Tribe
100
10
Nez Perce Tribe
Tribe
100
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
100
10
Washington State Department of Ecology
State
100
4,93 EPA-developed emissions for paved road dust
Uncontrolled paved road emissions were calculated at the county level by roadway type and year. 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 PM10 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.
4.9,3.1 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)
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sL = road surface silt loading (g/ m2) (dimensionless in eq.)
W = average weight (tons) of all vehicles traveling the road (dimensionless in eq.)
The uncontrolled PM10-PRI/-FIL and PM25-PRI/-FIL emission factors are provided in the tab "Emission Factors"
of the calculation workbook by county and roadway class. They are provided without utilizing any precipitation
correction.
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 traffic volume 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.
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 2011 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 4-47) 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 4-48 to be used in the emission factor equation above.
Ta
lie 4-47: 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 4-48: 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
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MOVES Road Type Description
FWHA Road Type
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.
4333 Activity Data
Total annual VMT estimates by county and roadway class were derived from a 2011 EPA Motor Vehicle Emission
Simulator (MOVES) modelling run. To estimate the portion of the total VMT occurring on paved roads, first the
VMT on unpaved roads were estimated using 2013 state-level FHWA data on length of unpaved roads by road
type [ref 2] and 1996 ratios from FHWA (the last year these data were available) on average daily traffic volume
per mile of unpaved road by road type [ref 3], The estimated VMT on unpaved roads was subtracted from the
total VMT from MOVES to estimate the VMT on paved roads.
4.9.3.3 Allocation
Total VMT from the MOVES modelling run is available at the county level. VMT on unpaved roads was estimated
at the state level and allocated to the county level based on proportion of rural population. The allocated
unpaved VMT was subtracted from the total VMT from MOVES to estimate the paved VMT.
4.9.3.4 Cbnim/s
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 4], The
assumed rule penetration varies by roadway class and PMio nonattainment area classification (serious or
moderate). The rule penetration rates are shown in Table 4-49. Rule effectiveness was assumed to be 100% for
all counties where this control was applied.
Table 4-49: Penetration rate of Paved Road vacuum sweeping
PMio Nonattainment Status
Roadway Class
Vacuum Sweeping Penetration Rate
Moderate
Urban Freeway & Expressway
0.67
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
4-82
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PMio Nonattainment Status
Roadway Class
Vacuum Sweeping Penetration Rate
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.
iiS.35 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 after it rains 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 2014vl NEI 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.
EPA inadvertently used the same meteorological adjustment factors for paved roads as unpaved roads. This is
insignificant (less than 1% difference) for 99% of the counties because the gridded meteorology tends to vary
little in each county, and it is only in (spatially) larger counties where unpaved and paved roads are allocated to
many different grid cells where the potential for differences in county-averaged unpaved vs paved road
meteorological adjustments can occur. The 33 counties in Table 4-50 are missing the adjustment factors for
unpaved roads. All these counties are very urban and do not have any unpaved roads (e.g. DC, NYC counties,
etc.). Because these counties were missing the adjustment for unpaved roads, we therefore did not apply a
meteorological adjustment factor for the paved roads either.
Table 4-50: Counties where meteorological adjustment factors were not applied
FIPS
State
County Name
08031
CO
Denver
10001
DE
Kent
10003
DE
New Castle
10005
DE
Sussex
11001
DC
District of Columbia
18097
IN
Marion
34017
NJ
Hudson
34039
NJ
Union
4-83
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36061
NY
New York
36081
NY
Queens
36085
NY
Richmond
42045
PA
Delaware
42101
PA
Philadelphia
51013
VA
Arlington
51510
VA
Alexandria city
51540
VA
Charlottesville city
51570
VA
Colonial Heights city
51580
VA
Covington city
51600
VA
Fairfax city
51610
VA
Falls Church city
51660
VA
Harrisonburg city
51670
VA
Hopewell city
51678
VA
Lexington city
51683
VA
Manassas city
51685
VA
Manassas Park city
51690
VA
Martinsville city
51710
VA
Norfolk city
51740
VA
Portsmouth city
51760
VA
Richmond city
51775
VA
Salem city
51830
VA
Williamsburg city
51840
VA
Winchester city
4.9,3,6 Changes from the2011 and2014vl Methodology
The methodology described above contains several adjustments from the methodology used to compose the
2011 version. This is due in part to differences in data sources used to compile the inventory. In 2014vl, the
factors used to adjust for precipitation were removed from the 2011 emission factor equation, and precipitation
was not accounted for in the final inventory. However, as discussed in the previous section, the meteorological
adjustment was re-introduced in the 2014v2 NEI.
The VMT data used in 2014 was based on EPA's MOVES model, whereas 2011 VMT data was based on its
precursor NMIM model. For this reason, the vehicle types (and as such vehicle weights) changed from 2011 to
2014, though a VMT-weighted average vehicle weight was calculated by county and road type in both years.
Furthermore, the VMT data used in 2011 was at the state-level, while the 2014 version had been further broken
down into counties. For this reason, subsequent worksheets (including ADTV and silt loading) which were
calculated at the state level in 2011 could be immediately calculated at the county level without further
manipulation in 2014. The paved roadway types in the 2014 VMT dataset included two additional types not
4-84
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found in the 2011 version. The category "Rural: Other Freeways and Expressways" was newly added, and
"Urban: Collector" was further broken down into major and minor collector roads.
4,9,3,7 Puerto Rico and US Virgin islands Emissions Calculations
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".
4,9,4 References for paved road dust
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 2013. Office of
Highway Policy Information. Washington, DC. September 2015.
3. Federal Highway Administration, Highway Statistics 1996. Table HM-67.
4. 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.
4,10,1 Sector description
There is only one SCC for this sector, provided in Table 4-51, in the 2014 NEI. EPA estimates emissions for
particulate matter for this SCC. 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 4-51: SCC in the 2014 NEI Unpaved Road Dust sector
SCC
SCC Level 1
SCC Level 2
SCC Level 3
SCC Level 4
2296000000
Mobile Sources
Unpaved Roads
All Unpaved Roads
Total: Fugitives
4,10,2 Sources of data
The unpaved road dust sector includes data from the S/L/T agency submitted data and the default EPA
generated emissions. The agencies listed in Table 4-52 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%).
Table 4-52: Percentage of Unpaved Road Dust PM2.5 emissions submitted by reporting agency
Region
Agency
S/L/T
PM2.5
1
Massachusetts Department of Environmental Protection
State
100
2
New Jersey Department of Environment Protection
State
100
4-85
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Region
Agency
S/L/T
PMz.5
3
Maryland Department of the Environment
State
100
8
Northern Cheyenne Tribe
Tribe
100
9
California Air Resources Board
State
100
9
Morongo Band of Cahuilla Mission Indians of the Morongo Reservation, California
Tribe
100
9
Washoe County Health District
Local
100
10
Washington State Department of Ecology
State
100
4,10,3 EPA-developed emissions 'for unpaved road dust
Uncontrolled unpaved road emissions were calculated at the county level by roadway type for the year 2014.
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 10 nonattainment and maintenance area counties. Emissions by roadway class were then totaled to the
county level and adjusted for meteorological conditions. Emissions were then aggregated to the state level and
distributed to counties based on US Census rural and "like rural" population [ref 1], The following provides
further details on the emission factor equation, determination of unpaved road VMT, and controls.
4,10.3.1 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 2]:
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 4-53, 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)
The uncontrolled emission factors without precipitation corrections are in the worksheet "Emission Factor
Calculations" by county and roadway class.
Values used for the particle size multiplier and the 1980's vehicle fleet exhaust, brake wear, and tire wear are
provided in Table 4-53, and come from AP-42 defaults.
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 3], 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 by State, and identifies if the values were
based on a sample average or default value.
4-86
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Table 4-53: 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
Table 4-54 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 4] The roadway class "Urban collector" with an average
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 2014 VMT data.
Table 4-54: 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
The 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 locally-specific values for this variable.
4,103.2 Activity Data
Total annual VMT estimates by county and roadway class were derived from a 2008 NMIM run providing state-
level estimates of VMT by road type and by road surface type.
Total annual VMT estimates by county and roadway class were derived from a 2014 MOVES run providing
county-level estimates of total (paved and unpaved) VMT by road type. Unpaved VMT was calculated by
multiplying total VMT in each county by a census region-level ratio of unpaved VMT to total VMT.
Unpaved VMT from Version 1/jy census region and road type
TotCil VMT from M0VES}jy census region and road type
Table 4-55 lists the census region-level ratios. These ratios were calculated based on the sum of the unpaved
VMT in each census region in the EPA dataset calculated for the 2011 NEI divided by the sum of the total VMT in
each census region. The origin of the unpaved/total split from the 2011 NEI was from data from FHWA from
1996 (the last year these data were available) [ref 5],
Table 4-55: Unpaved Ratios by Census Region and Road Type
Region
FHWA Road Type
Unpaved Ratio
Midwest Region
Rural Interstate
0.00E+00
Midwest Region
Rural Local
2.70E-01
Midwest Region
Rural Major Collector
7.18E-03
Midwest Region
Rural Minor Arterial
0.00E+00
Midwest Region
Rural Minor Collector
5.82E-02
4-87
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Region
FHWA Road Type
Unpaved Ratio
Midwest Region
Rural Other Freeways and Expressways
0.00E+00
Midwest Region
Rural Other Principal Arterial
0.00E+00
Midwest Region
Urban Interstate
0.00E+00
Midwest Region
Urban Local
8.99E-02
Midwest Region
Urban Major Collector
3.88E-03
Midwest Region
Urban Minor Arterial
4.72E-04
Midwest Region
Urban Minor Collector
1.73E-01
Midwest Region
Urban Other Freeways and Expressways
0.00E+00
Midwest Region
Urban Other Principal Arterial
0.00E+00
Northeast Region
Rural Interstate
0.00E+00
Northeast Region
Rural Local
4.08E-02
Northeast Region
Rural Major Collector
1.29E-04
Northeast Region
Rural Minor Arterial
0.00E+00
Northeast Region
Rural Minor Collector
1.09E-03
Northeast Region
Rural Other Freeways and Expressways
0.00E+00
Northeast Region
Rural Other Principal Arterial
0.00E+00
Northeast Region
Urban Interstate
0.00E+00
Northeast Region
Urban Local
3.03E-03
Northeast Region
Urban Major Collector
3.71E-06
Northeast Region
Urban Minor Arterial
0.00E+00
Northeast Region
Urban Minor Collector
1.74E-04
Northeast Region
Urban Other Freeways and Expressways
0.00E+00
Northeast Region
Urban Other Principal Arterial
0.00E+00
South Region
Rural Interstate
0.00E+00
South Region
Rural Local
1.72E-01
South Region
Rural Major Collector
1.61E-03
South Region
Rural Minor Arterial
0.00E+00
South Region
Rural Minor Collector
1.63E-02
South Region
Rural Other Freeways and Expressways
0.00E+00
South Region
Rural Other Principal Arterial
0.00E+00
South Region
Urban Interstate
0.00E+00
South Region
Urban Local
3.17E-02
South Region
Urban Major Collector
9.23E-04
South Region
Urban Minor Arterial
3.12E-04
South Region
Urban Minor Collector
1.49E-02
South Region
Urban Other Freeways and Expressways
0.00E+00
South Region
Urban Other Principal Arterial
0.00E+00
West Region
Rural Interstate
0.00E+00
West Region
Rural Local
3.03E-01
West Region
Rural Major Collector
7.03E-03
West Region
Rural Minor Arterial
0.00E+00
West Region
Rural Minor Collector
1.23E-01
West Region
Rural Other Freeways and Expressways
0.00E+00
West Region
Rural Other Principal Arterial
0.00E+00
West Region
Urban Interstate
0.00E+00
West Region
Urban Local
6.13E-02
West Region
Urban Major Collector
3.26E-04
4-88
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Region
FHWA Road Type
Unpaved Ratio
West Region
Urban Minor Arterial
1.20E-04
West Region
Urban Minor Collector
3.24E-03
West Region
Urban Other Freeways and Expressways
0.00E+00
West Region
Urban Other Principal Arterial
0.00E+00
4,10.3.3 Allocation
County level emissions were calculated by multiplying the county unpaved VMT (by road type) by the emission
factors calculated in Section 4.10.3.1 and aggregating based on county and urban/rural classification.
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.
4.10.3.5 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 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 after it rains 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 2014vl NEI 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.
EPA inadvertently used the same meteorological adjustment factors for paved roads as unpaved roads. This is
insignificant (less than 1% difference) for 99% of the counties because the gridded meteorology tends to vary
little in each county, and it is only in (spatially) larger counties where unpaved and paved roads are allocated to
many different grid cells where the potential for differences in county-averaged unpaved vs paved road
meteorological adjustments can occur. The 33 counties in Table 4-50 (see Section 4.9.3.5) are missing the
adjustment factors for unpaved roads. All these counties are very urban and do not have any unpaved roads
4-89
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(e.g. DC, NYC counties, etc.). Because these counties were missing the adjustment for unpaved roads, we
therefore did not apply a meteorological adjustment factor for the paved roads either.
4.103,6 Emissions Redistribution Procedure
Unpaved roads are generally not located in urban centers, such as New York City or Chicago, so emissions were
redistributed away from these areas to reflect this. Emissions were summed to the state-level and redistributed
back to the county level based on the proportion of county to state rural and "like-rural" population, according
to the 2010 Census. "Like-rural" population is defined as the population of urbanized areas and urban clusters
with population densities' equal to or less than the maximum rural population density value for all counties in
the US.
4.10.3.7 Changes from 2011 and 2014vl Methodology
The methodology described above contains several adjustments from the methodology used to compose the
2011 version. This is due in part to differences in data sources used to compile the inventory. In 2014vl, the
factors used to adjust for precipitation were removed from the 2011 emission factor equation, and precipitation
was not accounted for in the final inventory. However, as discussed in Section 4.10.3.5, the meteorological
adjustment was re-introduced in the 2014v2 NEI. Also, in 2014v2, VMT was obtained from a MOVES run instead
an NMIM run, and separated in paved and unpaved values based on census-region level ratios. Emissions were
also redistributed based on rural and "like-rural" county population.
4.10.3.8 Puerto H/co and US Virgin islands Emissions Calculations
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".
4.10,4 References for unpaved road dust
1. U.S. Census Bureau. 2010 Census Urban and Rural Classification and Urban Area Criteria.
2. United States Environmental Protection Agency, Office of Air Quality Planning and Standards.
Compilation of Air Pollutant Emission Factors, AP-42, Fifth Edition, Volume 1: Stationary Point and Area
Sources. Section 13.2.2, Unpaved Roads. Research Triangle Park, NC. January 2011.
3. 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
4. United States Environmental Protection Agency, 2011 National Emissions Inventory, version 2. Technical
Support Document. Research Triangle Park, NC. August 2015.
5. Federal Highway Administration, Highway Statistics 1996, Table HM-67.
4-90
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4,11 iiTlIflg
4.11.1 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 NEI, we have included grass/pasture burning SCCs into this sector.
Thus, this sector includes both crop residue burning as well as grass/pasture burning.
4.11.2 Sources of data: revised for 2014v2
Table 4-56 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.
New SCCs were added to this sector compared to the 2011 NEI to house the emissions that occur on
grassland/pastures/rangeland. In addition, SCCs were added to better describe the specific crops being burned,
including fields in which two or more crops are burned.
Note that many general crops are included in the SCC 2801500000, and it also is the SCC to report into for "crops
unknown." The new SCC (2801500170) was added for grass/pasture burning for this sector for the 2014 NEI. All
of the SCCs for "double crops" are also new to the 2014 NEI, and EPA reported emission into these SCCs as part
of the methods described below.
Table 4-56: Nonpoint SCCs with 2014 NEI emissions in the Agricultural Fie
d Burning sector
SCC
Description
EPA
State
Tribe
2801500000
Unspecified crop type and Burn Method
X
X
2801500100
Field Crops Unspecified
X
X
2801500111
Field Crop is Alfalfa: Headfire Burning
X
2801500120
Field Crop is Asparagus: Burning Techniques Not Significant
X
2801500141
Field Crop is Bean (red): Headfire Burning
X
X
X
2801500150
Field Crop is Corn: Burning Techniques Not Important
X
X
2801500151
Double Crop Winter Wheat and Corn
X
X
2801500152
Double Crop Corn and Soybeans
X
X
2801500160
Field Crop is Cotton: Burning Techniques Not Important
X
X
2801500170
Field Crop is Grasses: Burning Techniques Not Important
X
X
X
2801500171
Fallow
X
X
2801500181
Field Crop is Hay (wild): Headfire Burning
X
X
2801500201
Field Crop is Pea: Headfire Burning
X
2801500220
Field Crop is Rice: Burning Techniques Not Significant
X
X
2801500250
Field Crop is Sugar Cane: Burning Techniques Not Significant
X
X
2801500261
Field Crop is Wheat: Headfire Burning
X
X
2801500262
Field Crop is Wheat: Backfire Burning
X
X
2801500263
Double Crop Winter Wheat and Cotton
X
X
2801500264
Double Crop Winter Wheat and Soybeans
X
X
2801500300
Orchard Crop Unspecified
X
2801500320
Orchard Crop is Apple
X
X
4-91
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see
Description
EPA
State
Tribe
2801500330
Orchard Crop is Apricot
X
X
2801500350
Orchard Crop is Cherry
X
X
2801500360
Orchard Crop is Citrus (orange, lemon)
X
2801500390
Orchard Crop is Nectarine
X
X
2801500400
Orchard Crop is Olive
X
2801500410
Orchard Crop is Peach
X
X
2801500420
Orchard Crop is Pear
X
X
2801500430
Orchard Crop is Prune
X
X
2801500500
Vine Crop Unspecified
X
X
2801500600
Forest Residues Unspecified
X
The agencies listed in Table 4-57 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 (100%), while others
submitted only a portion of the sector (totals less than 100%). Only Idaho submitted revised estimates between
2014vl and 2014v2.
Table 4-57: Percentage of agricultural fire/grass-pasture burning PM2.5 emissions submitted by reporting agency
Region
Agency
S/L/T
PM2.5
2
New Jersey Department of Environment Protection
State
98
4
Florida Department of Environmental Protection
State
100
4
Georgia Department of Natural Resources
State
100
4
South Carolina Department of Health and Environmental Control
State
100
5
Illinois Environmental Protection Agency
State
100
5
Indiana Department of Environmental Management
State
94
7
Iowa Department of Natural Resources
State
100
9
Arizona Department of Environmental Quality
State
24
9
California Air Resources Board
State
100
9
Hawaii Department of Health Clean Air Branch
State
100
10
Coeur d'Alene Tribe
Tribe
100
10
Idaho Department of Environmental Quality
State
66
10
Kootenai Tribe of Idaho
Tribe
100
10
Nez Perce Tribe
Tribe
100
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
100
10
Washington State Department of Ecology
State
98
When we created the 2014v2 NEl, the S/L/T data had hierarchy over the EPA data (developed as described in the
next section) for all CAP submissions. As such, S/L/T CAP emissions were carried forth from the 2014vl inventory
and no backfilling with EPA data was done. Additionally, in going from 2014vl to 2014v2, only the state of Idaho
revised their CAP emissions, and that data was used in 2014v2. Any "zero" submissions were left as zero in the
2014vl NEI for those counties and pollutants. For HAPs, due to many failed QA checks using a mix of EPA and
SLT-submitted VOC-HAP data in 2014vl, EPA used its HAP augmentation factors (as available in EIS) on a state by
state basis, applying those HAP VOC fractions to VOC emissions submitted by the state at a county level to
develop the 2014v2 VOC-HAP inventory for this sector. If there was no VOC submitted by the SLT, then the
corresponding VOC estimated using EPA methods was used. For the States of Florida and Louisiana, robust
4-92
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state-specific HAP augmentation factors were not available; thus, national average VOC-HAP augmentation
factors were used to estimate the VOC HAPs. Thus, no VOC-HAPs submitted by any SLTs were used in the
2014v2 inventory for this sector (all SLT-submitted HAPs in 2014vl were removed). Any PM-based HAPs
submitted by the SLTs were retained as submitted, no further augmentation was done on those HAPs. The
actual EPA-data based ratios provided along with all the other HAP augmentation ratios can be accessed in EIS.
4.11.3 EPA-developed emissions for agricultural field burning
In the 2008 NEl, 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. With this in mind,
for the 2014 NEI, a simple and efficient method has 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],
The approach developed for use in the 2014 NEI 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 (4)
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 kind of wildfire. Our 2014 NEI
approach described in this paper 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 (aka 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. i i.3. t Activity Dst.3
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
4-93
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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. According the USDA, the pasture and grass-related land cover categories
have traditionally had very low classification accuracy in the CDL (USDA, 2015b) [ref 5], Moderate spatial and
spectral resolution satellite imagery is not ideal for separating grassy land use types, such as urban open space
versus pasture for grazing versus CRP grass. To further complicate the matter, the pasture and grass-related
categories were not always classified consistently from state to state or year to year (USDA, 2015b). In an effort
to eliminate user confusion and category inconsistencies the 1997-2013 CDLs were recoded and re-released in
January 2014 to better represent pasture and grass-related categories (USDA, 2015b). A new category named
Grass/Pasture (code 176) collapses the following historical CDL categories: Pasture/Grass (code 62), Grassland
Herbaceous (code 171), and Pasture/Hay (code 181). This new code (176) has been used to create a single
grass/pasture emission source category separate from all other crop types. Based on field reconnaissance of
McCarty (2013) [ref 6], 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.
4,113.2 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). Factor ratios for VOC/CO and the CO emission factors
from Table 1 in McCarty (2011) were used to estimate VOC Emission Factors.
Table 4-58 summarizes CAP emission factors, fuel loading, and combustion completeness used in this analysis.
For the Hazardous Air Pollutants (HAPs), state-specific HAP augmentation factors were used as they exist in EIS;
these factors are constant across all SCCS, and were developed from a previous version of the VOC/HAP
inventory for this sector. These HAP augmentation factors are provided in the file
"agburning_HAPaug2014NEIv2_table.xlsx" on the 2014v2 Supplemental Data Fill3 site.
Table 4-58: Emission factors (lbs/ton), fuel loading (tons/acre) and combustion completeness (%) for CAPs
Crop Type
Fuel
Loading
Combustion
%
CO
NOx
S02
PM2.5
O
rH
O.
VOC
nh3
corn
4.20a
75a
106.10a
4.60a
2.38a
9.94a
21.36a
6.60c
19.32b
wheat
1.90a
85a
110.28a
4.75a
0.88a
8.07a
14.10a
7.60c
33.73b
soybean
2.50a
75a
127.70a
6.33a
3.13a
12.38a
17.73a
11.97c
44.94b
cotton
2.18a
65a
146.12a
6.89a
3.13a
12.38a
17.73a
11.97c
48.92b
fallow
2.18a
75a
127.79a
5.60a
2.34a
12.31a
17.00a
11.97c
16.24b
rice
3.00a
75a
105.27a
6.23a
2.77a
4.72a
6.61a
5.00c
26.17b
sugarcane
4.75a
65a
116.95a
6.06a
3.32a
8.69a
9.83a
9.00c
43.03b
lentils
2.94a
75a
127.79a
5.60a
2.34a
12.31a
17.00a
11.97c
39.76b
Other crops
1.90a
85a
182.lla
4.31a
0.80a
23.23a
31.64a
10.70c
12.52b
Dbl. Crop
3.05d
80d
108.19d
4.68d
1.63 d
9.00d
17.73 d
7.10d
26.53 d
Dbl. Crop
3.19d
75d
116.95d
5.10d
2.36d
11.13d
19.18d
8.45d
21.41d
4-94
-------
Crop Type
Fuel
Loading
Combustion
%
CO
NOx
S02
PM2.5
O
rH
a.
VOC
nh3
Dbl. Crop
2.18d
75d
127.79d
5.60d
2.34d
12.31d
17.00d
11.97d
39.74d
Dbl. Crop
2.04d
80d
119.04d
5.17d
1.61d
10.19d
15.55d
6.35d
36.74d
Dbl. Crop
2.04d
80d
119.04d
5.17d
1.61d
10.19d
15.55d
6.35d
36.74d
Dbl. Crop
3.05d
80d
108.19d
4.68d
1.63 d
9.00d
17.73 d
10.80d
19.63 d
Dbl. Crop
2.04d
75d
128.20d
5.82d
2.01d
10.22d
15.91d
11.97d
41.33 d
Dbl. Crop
2.34d
7d
136.91d
6.61d
3.13d
12.38d
17.73 d
11.97d
46.94d
Dbl. Crop
2.34d
75d
127.75d
5.96d
2.74d
12.35d
17.36d
11.97d
42.35d
Dbl. Crop
3.35d
75d
116.90d
5.46d
2.76d
11.16d
19.55d
11.97d
22.94d
Dbl. Crop
2.2d
80d
118.99d
5.54d
2.01d
10.22d
15.91d
9.79d
39.33 d
Dbl. Crop
2.04d
80d
119.04d
5.17d
1.61d
10.19d
15.55d
9.79d
36.74d
Pasture_Gra
1.9a
85a
182.lla
4.31a
0.80a
23.23a
31.64a
10.70c
12.52b
a: McCarty (2011) [ref 2], Fuel Loading and Combustion completeness from Data and Methods Section Table 1 converted to
lbs/ton for factors
b 2002 NEI NH3/NOx ratio
c VOC AP42 factors ratio to CO factors from McCarty 2011.
d average of two field crops
4.11.33 Computing EPA estimates
The general procedure for generating final 2014 NEI vl EPA estimates is outlined here. The reader is referred to
Pouliot et al., 2016 [ref 3] for further details. The HMS satellite detections were processed through 5 layers of
filtering to find crop residue and rangeland burning.
The first layer of filtering removed all detections outside the lower 48 states.
The second layer of filtering removed the detections that were identified as wildland and prescribed
fires because they occurred in a non-agricultural region. This identification was made by intersecting the
USDA Crop Data Layers (CDL) with the remaining HMS detects to determine a crop type. Given that the
satellite detections are at best known to 100 meters and the CDL information is known to 30-meter
resolution, the process of intersecting these two datasets results in some uncertainty with respect to
spatial accuracy of the fire locations.
The third layer of filtering involved the use of snow cover estimates. Using the daily maximum snow
cover data from a Weather Research and Forecasting Model (WRF) model simulation for 2014, HMS
satellite detections from GOES, MODIS, and AVHRR that were coincident with snow cover were deemed
not to be crop residue burning but some other type of fire.
The fourth layer of filtering was based on comments (from the draft 2014 NEI estimates posted in June
2015) from specific states regarding specific crops.
o Corn and soybean detections for these eight Midwestern states (Iowa, Indiana, Illinois,
Michigan, Missouri, Minnesota, Wisconsin, and Ohio) were deemed to be a different type of fire
other than crop residue burning. The reasoning is based on a communication from Iowa State
University Extension and Outreach: "Burning corn and soybean fields is just NOT a practice that
is used in Iowa or many other Midwest States as a way of preparing the fields for planting a
subsequent crop. Yes, there are rare occasions where corn residue is burnt off a field but it
would not even bel% of the crop acres. An example would be if the residue washed and piled
up in an area it may be burnt to allow tillage, planting and other practices to occur. Another rare
4-95
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occasion is when accidental field fires occur during harvesting of the corn crop. But again, this
would be less than 1% of the crop acres."
o Communication from the state of Indiana was similar to that of Iowa with respect to corn and
soybeans.
o The other six Midwestern states (Illinois, Michigan, Missouri, Minnesota, Wisconsin, and Ohio)
were included because of their proximity to the Indiana and Iowa so that the method would
consistent at a regional scale. These fires that are not being identified as crop residue burning or
rangeland burning are being classified as accidental rather than intentional burning,
o Also as part of the 4th layer of filtering, if localized state information identified a fire as being
accidental but in the vicinity of agricultural land, we deemed these fires not to be crop residue
burning but in the wildfire category. This was the case for the state of Delaware.
The fifth level of filtering was the process of removing duplicates. The remaining HMS satellite
detections were checked for two types of duplicates. If a GOES satellite detection was within 2 km and
within an hour of another detection, the detection was deemed to be a duplicate and removed.
Identical latitude and longitude detections to 3 decimal places on the same day across all satellites were
also deemed to be duplicates and they were removed. For the first type of duplicate, approximately 1%
of the total detections
Then, using the CA emission factors in Table 4-58, and the assumed state-specific field size, daily emissions were
estimated for each fire detection. Emissions for the grass/pasture category were mapped to a single source
classification code (SCC 2801500170) for use in the NEI. Emissions for all the remaining CDL categories were
mapped to a set of source classification codes. Theses codes and the mapping is available 2014 NEI
Documentation web site. HAPs were estimated using state-specific HAP augmentation factors (fractions that are
multiplied by VOC emissions to get HAPs) found in EIS for this sector.
Emission Estimates for 2014
Table 4-59 summarizes state level estimates of crop residue burning by acres burned and PM2.sfor 2014 using
the EPA methods described above. The top two states for crop residue burning (PM2.5 and acres) were California
and Kansas. The top two states for grass/pasture burns were Kansas and Oklahoma. For Grasslands, we would
expect these two states to have the largest acres burned because of the annual prescribed burning of the Flint
Hills Grasslands and the large geographical extent of these regions. The grass/pasture burns are also known as
rangeland burning, based on the definition of the grass/pasture land use in the Cropland Data Layer. Figure 4-8
provides a spatial map of the annual emissions by county for 2014 using this method for crop residue and
rangeland burning. We note that crop residue and rangeland burning is not widespread but occurs in a few
specific regions of the country.
Table 4-59: Acres burned and PM2.5 emissions by state using EPA methods
State
2014 Crop
Acres
2014 Crop PMz.s
(tons/yr)
2014 Grass/Pasture
Acres
2014 Grass/Pasture
PM2.5 (tons/yr)
Alabama
21,000
307
32,240
605
Arizona
8,240
118
2,800
53
Arkansas
137,160
1,371
28,400
533
California
202,560
2,854
51,240
961
Colorado
4,240
63
3,840
72
Florida
147,540
2,142
79,440
1,490
Georgia
100,240
1,351
39,360
738
4-96
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State
2014 Crop
Acres
2014 Crop PMz.s
(tons/yr)
2014 Grass/Pasture
Acres
2014 Grass/Pasture
PM2.5 (tons/yr)
Idaho
50,880
650
35,400
664
Illinois
1,680
18
7,980
150
Indiana
660
7
3,480
65
Iowa
3,660
69
14,940
280
Kansas
180,720
2,207
461,600
8,655
Kentucky
8,000
110
7,760
146
Louisiana
87,920
1,052
20,000
375
Maryland
800
10
160
3
Massachusetts
80
2
40
1
Michigan
640
11
480
9
Minnesota
17,280
220
4,200
79
Mississippi
45,600
537
21,200
398
Missouri
31,980
327
71,880
1,348
Montana
32,760
428
32,640
612
Nebraska
29,820
419
25,200
473
Nevada
360
5
520
10
New Jersey
160
3
120
2
New Mexico
1,120
17
7,120
134
New York
600
10
320
6
North Carolina
32,000
406
8,200
154
North Dakota
117,480
1,402
29,700
557
Ohio
400
5
1,320
25
Oklahoma
49,440
506
299,600
5,618
Oregon
29,400
433
54,240
1,017
Pennsylvania
360
6
440
8
South Carolina
16,080
197
12,480
234
South Dakota
18,660
270
8,160
153
Tennessee
8,400
102
10,440
196
Texas
74,480
961
184,000
3,450
Utah
1,520
23
880
17
Vermont
40
1
0
0
Virginia
3,760
56
4,280
80
Washington
70,920
883
43,200
810
West Virginia
200
3
520
10
Wisconsin
720
13
2,640
50
Wyoming
2,720
48
2,240
42
TOTAL
1,542,280
19,623
1,614,700
30,276
4-97
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Figure 4-8: Spatial distribution of PM2.5 emissions by county, EPA method
105
90
75
rtj
a>
60 5?
1/1
c
o
45
30
15
4.11.3.4 Quality assurance of final estimates
Some of the QA was done as part of the new methods used for this sector, and described above. Further review
of the quality of EPA's data included addressing of S/L/T comments as outlined in earlier sections of this section.
In addition, the following checks were done on EPA data:
Comparison to past NEI estimates, and explaining differences noted
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
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.
It is not expected that we will make any major changes/improvements to this sector (methods, pollutants
reported, etc.) in going from vl to v2. We will address those comments we do receive to the best of our ability
and with resources that we have.
4.11.4 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.
4-98
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4. United States Department of Agriculture. 2015a. USDA National Agricultural Statistics Service Cropland
Data Layer for 2015.
5. United States Department of Agriculture. 2015b. USDA National Agricultural Statistics Service Cropland
Data Layer Frequently Asked Questions, accessed April 1, 2015
6. Personal communication with Dr J. McCarty, 2013, Michigan Technological Institute.
4.12 Fuel Combustion -Industrial and Commercial/Institutional Boilers and tCEs
Emissions from Industrial, Commercial, and Institutional (ICI)fuel combustion are a significant portion of the
total emissions inventory for many areas. 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. Because there are specific challenges associated with estimating ICI nonpoint source emissions, the
EPA developed a Microsoftฎ Access-based ICI Combustion Tool to assist S/L/Ts in estimating nonpoint emissions
from ICI fuel combustion for the 2014 National Emission Inventory. We discuss the ICI tool in Section 4.12.3.
4.12.1 Sector description
The EIS sectors to be documented here include nonpoint emissions from ICI fuel combustion:
Fuel Combustion - Commercial/Institutional Boilers, ICEs - Biomass
Fuel Combustion - Commercial/Institutional Boilers, ICEs - Coal
Fuel Combustion - Commercial/Institutional Boilers, ICEs - Natural Gas
Fuel Combustion - Commercial/Institutional Boilers, ICEs - Oil
Fuel Combustion - Commercial/Institutional Boilers, ICEs - Other
Fuel Combustion - Industrial Boilers, ICEs - Biomass
Fuel Combustion - Industrial Boilers, ICEs - Coal
Fuel Combustion - Industrial Boilers, ICEs- Natural Gas
Fuel Combustion - Industrial Boilers, ICEs - Oil
Fuel Combustion - Industrial Boilers, ICEs - Other
We document all these sectors in this section because EPA generates all the nonpoint emissions from these EIS
sectors via an ICI Tool. S/L/Ts were encouraged to use this tool to generate and submit all their nonpoint ICI
emissions.
4.12.2 Sources of data
Table 4-60 shows, for ICI fuel combustion, the nonpoint SCCs covered by the EPA estimates and by the
State/Local and Tribal agencies that submitted data. The SCC level 2, 3 and 4 SCC 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 (2801520000). The leading sector
description is "Fuel Comb"(ustion) for all SCCs.
Table 4-60: ICI fuel combustion SCCs with 2014 NEI emissions
Sector type
SCC
Description
EPA
State
Local
Tribe
Comm/lnstitutional -
Biomass
2103008000
Commercial/Institutional; Wood; Total:
All Boiler Types
X
X
X
X
Comm/lnstitutional -
Coal
2103001000
Commercial/Institutional; Anthracite
Coal; Total: All Boiler Types
X
X
X
X
4-99
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Sector type
see
Description
EPA
State
Local
Tribe
Comm/lnstitutional -
Coal
2103002000
Commercial/Institutional;
Bituminous/Subbituminous Coal; Total:
All Boiler Types
X
X
X
Comm/lnstitutional -
Natural Gas
2103006000
Commercial/Institutional; Natural Gas;
Total: Boilers and IC Engines
X
X
X
Comm/lnstitutional -
Oil
2103004000
Commercial/Institutional; Distillate Oil;
Total: Boilers and IC Engines
X
X
Comm/lnstitutional -
Oil
2103004001
Commercial/Institutional; Distillate Oil;
Boilers
X
X
X
Comm/lnstitutional -
Oil
2103004002
Commercial/Institutional; Distillate Oil; IC
Engines
X
X
X
Comm/lnstitutional -
Oil
2103005000
Commercial/Institutional; Residual Oil;
Total: All Boiler Types
X
X
X
Comm/lnstitutional -
Oil
2103011000
Commercial/Institutional; Kerosene;
Total: All Combustor Types
X
X
X
Comm/lnstitutional -
Other
2103007000
Commercial/Institutional; Liquified
Petroleum Gas (LPG); Total: All
Combustor Types
X
X
X
Industrial Boilers,
ICEs - Biomass
2102008000
Industrial; Wood; Total: All Boiler Types
X
X
X
X
Industrial Boilers,
ICEs - Coal
2102001000
Industrial; Anthracite Coal; Total: All
Boiler Types
X
X
X
Industrial Boilers,
ICEs - Coal
2102002000
Industrial; Bituminous/Subbituminous
Coal; Total: All Boiler Types
X
X
X
Industrial Boilers,
ICEs - Natural Gas
2102006000
Industrial; Natural Gas; Total: Boilers and
IC Engines
X
X
X
Industrial Boilers,
ICEs-Oil
2102004000
Industrial; Distillate Oil; Total: Boilers and
IC Engines
X
Industrial Boilers,
ICEs-Oil
2102004001
Industrial; Distillate Oil; All Boiler Types
X
X
X
X
Industrial Boilers,
ICEs-Oil
2102004002
Industrial; Distillate Oil; All IC Engine
Types
X
X
X
X
Industrial Boilers,
ICEs-Oil
2102005000
Industrial; Residual Oil; Total: All Boiler
Types
X
X
X
Industrial Boilers,
ICEs-Oil
2102011000
Industrial; Kerosene; Total: All Boiler
Types
X
X
X
X
Industrial Boilers,
ICEs - Other
2102007000
Industrial; Liquified Petroleum Gas (LPG);
Total: All Boiler Types
X
X
X
X
Industrial Boilers,
ICEs - Other
2102012000
Industrial; Waste oil; Total
X
Industrial Boilers,
ICEs - Other
2801520000
Miscellaneous Area Sources; Agriculture
Production - Crops; Orchard Heaters;
Total, all fuels
X
4-100
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The agencies listed in Table 4-61 submitted nonpoint inventory NOx emissions for these sectors; agencies not
listed used EPA estimates for all ICI sectors. Some agencies submitted emissions for the entire sector (100%),
while others submitted only a portion of the sector (totals less than 100%). Table 4-62 provides the same agency
submittal information for S02 and Table 4-63 provides the same information for (primary) PM2.5 agency
submittals.
Table 4-61: Percentage of ICI fuel combustion NOx emissions submittec
Region
Agency
Comm/lnst Biomass
Comm/lnst Coal
Comm/lnst Nat Gas
Comm/lnst Oil
Comm/lnst Other
Ind Biomass
Ind Coal
Ind Nat Gas
Ind Oil
Ind Other
1
Connecticut Department of Energy
and Environmental Protection
100
100
100
100
100
100
100
100
1
Maine Department of Environmental
Protection
100
100
100
100
100
100
1
Massachusetts Department of
Environmental Protection
100
100
100
100
100
100
100
100
1
New Hampshire Department of
Environmental Services
100
100
100
100
100
100
100
1
Rhode Island Department of
Environmental Management
100
100
100
100
100
100
100
100
1
Vermont Department of
Environmental Conservation
100
100
100
100
100
100
100
2
New Jersey Department of
Environment Protection
100
100
100
100
100
2
New York State Department of
Environmental Conservation
100
100
100
100
100
100
100
100
2
Puerto Rico
100
100
100
100
3
DC-District Department of the
Environment
100
100
100
100
100
100
3
Delaware Department of Natural
Resources and Environmental Control
100
100
100
100
100
3
Maryland Department of the
Environment
100
100
100
100
3
Pennsylvania Department of
Environmental Protection
100
100
100
100
100
100
100
100
100
100
3
Virginia Department of
Environmental Quality
100
100
100
100
100
100
100
100
3
West Virginia Division of Air Quality
100
100
100
100
100
100
4
Chattanooga Air Pollution Control
Bureau (CHCAPCB)
100
100
100
100
100
100
4
Florida Department of Environmental
Protection
100
100
100
100
100
100
by reporting agency
4-101
-------
Region
Agency
Comm/lnst Biomass
Comm/lnst Coal
Comm/lnst Nat Gas
Comm/lnst Oil
Comm/lnst Other
Ind Biomass
Ind Coal
Ind Nat Gas
Ind Oil
Ind Other
4
Georgia Department of Natural
Resources
100
100
100
100
12
4
Knox County Department of Air
Quality Management
100
100
100
100
100
100
4
Louisville Metro Air Pollution Control
District
100
100
100
100
100
100
100
4
Memphis and Shelby County Health
Department - Pollution Control
100
100
100
100
100
100
100
4
Metro Public Health of
Nashville/Davidson County
100
4
North Carolina Department of
Environment and Natural Resources
100
100
100
100
100
100
100
100
4
South Carolina Department of Health
and Environmental Control
100
100
100
100
100
100
100
4
Tennessee Department of
Environmental Conservation
100
100
100
100
100
100
100
100
5
Illinois Environmental Protection
Agency
100
100
100
100
100
5
Indiana Department of Environmental
Management
100
100
100
100
100
100
100
5
Michigan Department of
Environmental Quality
100
100
48
100
100
100
100
31
100
5
Minnesota Pollution Control Agency
100
100
100
100
100
100
100
100
100
5
Ohio Environmental Protection
Agency
100
100
100
100
100
100
100
100
100
5
Wisconsin Department of Natural
Resources
100
100
100
100
100
100
100
100
6
Arkansas Department of
Environmental Quality
100
100
100
100
100
100
100
6
City of Albuquerque
100
100
100
100
100
100
100
100
100
6
Louisiana Department of
Environmental Quality
100
100
100
100
100
100
100
6
Oklahoma Department of
Environmental Quality
100
100
100
100
100
100
100
6
Texas Commission on Environmental
Quality
100
100
100
100
100
100
7
Iowa Department of Natural
Resources
100
100
100
100
100
100
100
7
Kansas Department of Health and
Environment
100
100
100
100
100
100
100
4-102
-------
Region
Agency
Comm/lnst Biomass
Comm/lnst Coal
Comm/lnst Nat Gas
Comm/lnst Oil
Comm/lnst Other
Ind Biomass
Ind Coal
Ind Nat Gas
Ind Oil
Ind Other
7
Missouri Department of Natural
Resources
100
100
100
100
100
100
100
8
Assiniboine and Sioux Tribes of the
Fort Peck Indian Reservation
100
8
Northern Cheyenne Tribe
100
100
100
100
8
Utah Division of Air Quality
100
100
100
100
100
100
100
100
9
Arizona Department of
Environmental Quality
100
100
100
100
100
100
100
100
100
9
California Air Resources Board
96
100
59
81
100
77
9
Clark County Department of Air
Quality and Environmental
Management
100
100
100
100
100
100
9
Morongo Band of Cahuilla Mission
Indians of the Morongo Reservation,
California
100
9
Washoe County Health District
100
100
100
100
100
100
10
Alaska Department of Environmental
Conservation
7
100
100
94
10
Coeur d'Alene Tribe
100
100
100
100
100
100
100
100
100
10
Idaho Department of Environmental
Quality
100
100
100
100
100
100
100
100
100
10
Kootenai Tribe of Idaho
100
100
100
100
100
100
100
100
10
Nez Perce Tribe
100
100
100
100
100
100
100
100
100
10
Oregon Department of Environmental
Quality
100
100
100
100
100
100
100
100
10
Shoshone-Bannock Tribes of the Fort
Hall Reservation of Idaho
100
100
100
100
100
100
100
100
100
10
Washington State Department of
Ecology
100
100
100
100
100
100
100
100
100
Table 4-62: Percentage of ICI fuel combustion S02 emissions submitted by reporting agency
Region
Agency
Comm/lnst Biomass
Comm/lnst Coal
Comm/lnst Nat Gas
Comm/lnst Oil
Comm/lnst Other
Ind Biomass
Ind Coal
Ind Nat Gas
Ind Oil
Ind Other
1
Connecticut Department of Energy and
Environmental Protection
100
100
100
100
100
100
100
100
4-103
-------
Region
Agency
Comm/lnst Biomass
Comm/lnst Coal
Comm/lnst Nat Gas
Comm/lnst Oil
Comm/lnst Other
Ind Biomass
Ind Coal
Ind Nat Gas
Ind Oil
Ind Other
1
Maine Department of Environmental
Protection
100
100
100
100
100
100
1
Massachusetts Department of
Environmental Protection
100
100
100
100
100
100
100
100
1
New Hampshire Department of
Environmental Services
100
100
100
100
100
100
100
1
Rhode Island Department of
Environmental Management
100
100
100
100
100
100
100
100
1
Vermont Department of
Environmental Conservation
100
100
100
100
100
100
100
2
New Jersey Department of
Environment Protection
100
100
100
100
100
2
New York State Department of
Environmental Conservation
100
100
100
100
100
100
100
100
2
Puerto Rico
100
100
3
DC-District Department of the
Environment
100
100
100
100
100
100
3
Delaware Department of Natural
Resources and Environmental Control
100
100
100
100
100
3
Maryland Department of the
Environment
100
100
100
100
3
Pennsylvania Department of
Environmental Protection
100
100
100
100
100
100
100
100
100
100
3
Virginia Department of Environmental
Quality
100
100
100
100
100
100
100
100
3
West Virginia Division of Air Quality
100
100
100
100
100
100
4
Chattanooga Air Pollution Control
Bureau (CHCAPCB)
100
100
100
100
100
100
4
Florida Department of Environmental
Protection
100
100
100
100
100
100
4
Georgia Department of Natural
Resources
100
100
100
88
4
Knox County Department of Air Quality
Management
100
100
100
100
100
100
4
Louisville Metro Air Pollution Control
District
100
100
100
100
100
100
100
4
Memphis and Shelby County Health
Department - Pollution Control
100
100
100
100
100
100
100
4-104
-------
Region
Agency
Comm/lnst Biomass
Comm/lnst Coal
Comm/lnst Nat Gas
Comm/lnst Oil
Comm/lnst Other
Ind Biomass
Ind Coal
Ind Nat Gas
Ind Oil
Ind Other
4
Metro Public Health of
Nashville/Davidson County
100
4
North Carolina Department of
Environment and Natural Resources
100
100
100
100
100
100
100
100
4
South Carolina Department of Health
and Environmental Control
100
100
100
100
100
100
100
4
Tennessee Department of
Environmental Conservation
100
100
100
100
100
5
Illinois Environmental Protection
Agency
100
100
100
100
100
5
Indiana Department of Environmental
Management
100
100
100
100
100
100
100
5
Michigan Department of
Environmental Quality
100
100
39
100
100
100
100
69
100
5
Minnesota Pollution Control Agency
100
100
100
100
100
100
100
100
100
5
Ohio Environmental Protection Agency
100
100
100
100
100
100
100
100
100
5
Wisconsin Department of Natural
Resources
100
100
100
100
100
100
100
100
6
Arkansas Department of
Environmental Quality
100
100
100
100
100
100
6
City of Albuquerque
100
100
100
100
100
100
100
100
6
Louisiana Department of
Environmental Quality
100
100
100
100
100
100
100
6
Oklahoma Department of
Environmental Quality
100
100
100
100
100
100
100
6
Texas Commission on Environmental
Quality
100
100
100
100
100
100
7
Iowa Department of Natural Resources
100
100
100
100
100
100
100
100
7
Kansas Department of Health and
Environment
100
100
100
100
100
100
100
7
Missouri Department of Natural
Resources
100
100
100
100
100
100
100
8
Assiniboine and Sioux Tribes of the
Fort Peck Indian Reservation
100
8
Northern Cheyenne Tribe
100
100
100
100
8
Utah Division of Air Quality
100
100
60
100
100
100
100
100
9
Arizona Department of Environmental
Quality
100
100
100
100
100
100
100
100
100
9
California Air Resources Board
100
100
100
100
100
100
4-105
-------
Region
Agency
Comm/lnst Biomass
Comm/lnst Coal
Comm/lnst Nat Gas
Comm/lnst Oil
Comm/lnst Other
Ind Biomass
Ind Coal
Ind Nat Gas
Ind Oil
Ind Other
9
Clark County Department of Air Quality
and Environmental Management
100
100
100
100
9
Morongo Band of Cahuilla Mission
Indians of the Morongo Reservation,
California
100
9
Washoe County Health District
100
100
100
100
100
100
10
Alaska Department of Environmental
Conservation
100
92
75
10
Coeur d'Alene Tribe
100
100
100
100
100
100
100
100
100
10
Idaho Department of Environmental
Quality
100
100
100
100
100
100
100
100
100
10
Kootenai Tribe of Idaho
100
100
100
100
100
100
100
100
10
Nez Perce Tribe
100
100
100
100
100
100
100
100
100
10
Oregon Department of Environmental
Quality
100
100
100
100
100
100
100
100
10
Shoshone-Bannock Tribes of the Fort
Hall Reservation of Idaho
100
100
100
100
100
100
100
100
100
10
Washington State Department of
Ecology
100
100
100
100
100
100
100
100
100
Table 4-63: Percentage of ICI fuel combustion PM2.5 emissions submittec
Region
Agency
Comm/lnst Biomass
Comm/lnst Coal
Comm/lnst Nat Gas
Comm/lnst Oil
Comm/lnst Other
Ind Biomass
Ind Coal
Ind Nat Gas
Ind Oil
Ind Other
1
Connecticut Department of Energy and
Environmental Protection
100
100
100
100
100
100
100
100
1
Maine Department of Environmental
Protection
100
100
100
100
100
100
1
Massachusetts Department of
Environmental Protection
100
100
1
100
100
100
0
100
1
New Hampshire Department of
Environmental Services
100
100
100
97
100
100
100
1
Rhode Island Department of
Environmental Management
100
100
100
100
100
100
100
by reporting agency
4-106
-------
Region
Agency
Comm/lnst Biomass
Comm/lnst Coal
Comm/lnst Nat Gas
Comm/lnst Oil
Comm/lnst Other
Ind Biomass
Ind Coal
Ind Nat Gas
Ind Oil
Ind Other
1
Vermont Department of
Environmental Conservation
100
100
100
100
100
100
100
2
New Jersey Department of
Environment Protection
100
100
100
100
100
2
New York State Department of
Environmental Conservation
100
100
100
100
100
100
100
100
2
Puerto Rico
2
66
3
DC-District Department of the
Environment
100
100
100
100
100
100
3
Delaware Department of Natural
Resources and Environmental Control
100
67
100
100
100
3
Maryland Department of the
Environment
100
100
100
3
Pennsylvania Department of
Environmental Protection
100
100
100
100
100
100
100
100
100
100
3
Virginia Department of Environmental
Quality
100
100
100
100
100
100
100
100
3
West Virginia Division of Air Quality
100
100
100
100
100
100
4
Chattanooga Air Pollution Control
Bureau (CHCAPCB)
100
100
100
100
4
Florida Department of Environmental
Protection
100
100
100
100
100
100
4
Georgia Department of Natural
Resources
100
100
100
2
4
Knox County Department of Air Quality
Management
100
100
100
100
100
100
4
Louisville Metro Air Pollution Control
District
100
100
100
100
100
100
100
4
Memphis and Shelby County Health
Department - Pollution Control
100
100
2
100
100
100
100
4
North Carolina Department of
Environment and Natural Resources
100
100
100
100
100
100
100
100
4
South Carolina Department of Health
and Environmental Control
100
100
100
100
100
100
100
4
Tennessee Department of
Environmental Conservation
100
100
100
100
91
99
98
100
4-107
-------
Region
Agency
Comm/lnst Biomass
Comm/lnst Coal
Comm/lnst Nat Gas
Comm/lnst Oil
Comm/lnst Other
Ind Biomass
Ind Coal
Ind Nat Gas
Ind Oil
Ind Other
5
Illinois Environmental Protection
Agency
100
100
100
100
100
5
Indiana Department of Environmental
Management
100
100
100
100
100
100
100
5
Michigan Department of
Environmental Quality
100
100
5
Minnesota Pollution Control Agency
100
100
100
100
100
100
100
100
100
5
Ohio Environmental Protection Agency
100
100
100
100
100
100
100
100
100
5
Wisconsin Department of Natural
Resources
100
100
100
100
100
100
100
100
6
City of Albuquerque
100
100
100
100
100
96
100
6
Louisiana Department of
Environmental Quality
100
100
100
100
100
100
100
6
Oklahoma Department of
Environmental Quality
100
100
100
100
100
100
100
6
Texas Commission on Environmental
Quality
100
99
100
100
63
100
7
Iowa Department of Natural Resources
100
100
100
100
100
100
7
Kansas Department of Health and
Environment
100
100
100
100
100
100
100
7
Missouri Department of Natural
Resources
100
100
100
100
100
100
100
8
Northern Cheyenne Tribe
100
8
Utah Division of Air Quality
100
100
100
100
100
100
100
100
9
Arizona Department of Environmental
Quality
100
100
100
100
100
100
100
100
100
9
California Air Resources Board
100
100
98
100
100
100
9
Clark County Department of Air Quality
and Environmental Management
100
100
100
100
9
Morongo Band of Cahuilla Mission
Indians of the Morongo Reservation,
California
100
9
Washoe County Health District
100
4-108
-------
Region
Agency
Comm/lnst Biomass
Comm/lnst Coal
Comm/lnst Nat Gas
Comm/lnst Oil
Comm/lnst Other
Ind Biomass
Ind Coal
Ind Nat Gas
Ind Oil
Ind Other
10
Coeur d'Alene Tribe
100
100
100
100
100
100
100
100
100
10
Idaho Department of Environmental
Quality
100
100
100
100
100
100
100
100
100
10
Kootenai Tribe of Idaho
100
100
100
100
100
100
100
100
10
Nez Perce Tribe
100
100
100
100
100
100
100
100
100
10
Oregon Department of Environmental
Quality
100
100
100
100
100
100
100
100
10
Shoshone-Bannock Tribes of the Fort
Hall Reservation of Idaho
100
100
100
100
100
100
100
100
100
10
Washington State Department of
Ecology
100
100
100
100
100
100
100
100
100
4.12.3 EPA-developed emissions for 1C1 fuel combustion
The primary data source behind the ICI Combustion Tool is total state-level ICI energy consumption data
released annually as part of the Energy Information Administration's State Energy Data System (SEDS) [ref 1],
The ICI Combustion Tool processes the SEDS data and adjusts the data to account for the fraction of fuel
consumed by nonroad mobile sources whose emissions are included in the nonroad inventory and by non-fuel
combustion uses of energy, such as product feedstocks. Through a user-friendly interface, users can update the
underlying assumptions in the adjustment methodology. The ICI Combustion Tool also includes a nonpoint
source to point source crosswalk and allows the user to perform point source activity subtractions to avoid
double counting of emissions between their point and nonpoint inventories. The ICI Combustion Tool generates
outputs in EPA's Emissions Inventory System (EIS) format, ready for submission to the EIS. Complete ICI
Combustion Tool documentation and a User's Guide are available in the file "ICI vl.6.zip" on the 2014v2
Supplemental Data FTP site.
ICI combustion nonpoint source emissions are calculated using Equation 1.
Es,f=As,f* Fs,f (1)
where:
E = computed emissions,
A = emissions activity,
F = emissions factor,
s = sector (Industrial or Commercial/Institutional),
f = fuel type (coal, natural gas, distillate oil, residual oil, liquefied petroleum gas, kerosene and
wood).
The key emissions activity data inputs in the emissions estimation methodology are:
4-109
-------
1. Total Industrial and total Commercial/Institutional energy consumption by fuel type and state for a given
year;
2. Industrial energy consumed for non-fuel purposes by fuel type and state in that year;
3. ICI distillate oil and liquefied petroleum gas (LPG) consumption by state from nonroad mobile sources
for the year of interest;
4. ICI energy consumption by sector, state, and fuel type for point sources for the given year; and
5. County-level employment by ICI sector and state for the year of interest.
The ICI Tool also relies on emission factors relating emission rates to the volume of fuel burned by sector/fuel
type, and the sulfur content of coal consumed in each sector by state for the given year.
ICI combustion emissions are directly related to the sector, type, and volume of fuel burned. The EIA is
responsible for developing official federal government estimates of energy consumption. The EIA estimates
annual energy consumption at the state-level as part of the State Energy Data System (SEDS) [ref 1], The SEDS
reports energy consumption estimates by state, sector, fuel type, and year. The SEDS provides data for each of
five consuming sectors, including Industrial and Commercial (note that the SEDS' definition of "Commercial"
includes Institutional sector use). The EIA also publishes additional detailed estimates of state-level fuel oil and
kerosene consumption estimates in their Fuel Oil and Kerosene Sales publication [ref 2], This publication
provides state-level annual end use sales of No.l, No. 2, and No. 4 distillate fuel oil for commercial, industrial, oil
company, farm, off-highway construction, and other uses - these data are used to differentiate stationary from
mobile source distillate fuel consumption.
Activity data adjustments
Fuel-specific adjustments
Coal - For coal combustion, it is necessary to compile data representing a subset of total sector coal
consumption. Data representing non-coke plant consumption are compiled from EIA because coal consumed by
coke plants is accounted for in the point source inventory. The SEDS data do not provide coal consumption
estimates by type of coal (i.e., anthracite versus bituminous/subbituminous). Therefore, state-level ICI coal
distribution data for 2013 from the ElA's Annual Coal Distribution Report 2013 are used to allocate coal
consumption between the two types of coal [ref 3], The 2013 ratio of anthracite coal consumption to total coal
consumption is used for this allocation procedure.
Distillate Oil and LPG - The SEDS ICI distillate oil and LPG consumption data include consumption estimates for
equipment that are typically included in the nonroad sector inventory. In particular, SEDS considers the
following nonroad source category activities to be part of the industrial sector: farming, logging, mining, and
construction.
In order to avoid double-counting of distillate oil consumption between the nonpoint and nonroad sector
emission inventories, the more detailed distillate oil consumption estimates reported in ElA's Fuel Oil and
Kerosene Sales are combined with assumptions used in the regulatory impact analysis (RIA) for EPA's nonroad
diesel emissions rulemaking [ref 3, ref 4],
For distillate fuel, Table 4-64 presents the assumptions that are applied to the state-level Commercial sector
distillate oil consumption data published in Fuel Oil and Kerosene Sales to estimate Commercial sector stationary
source consumption.
4-110
-------
Table 4-64: Stationary source adjustments for industrial sector distillate fuel consumption
EIA Energy Sector
Distillate Fuel Type
% of Total Consumption
from Stationary Sources
No. 1 Distillate Fuel Oil
60
No. 2 Distillate Fuel Oil
100
Industrial
No. 2 Distillate/Low and
15a
High Sulfur Diesel
No. 4 Distillate Fuel Oil
100
Farm
Diesel
0
Other Distillate Fuel Oil
100
Off-Highway (Construction and Other)
Distillate Fuel Oil
5
Oil Company
Distillate Fuel Oil
50
aThis value differs from the 0% assumption adopted in EPA's nonroad diesel emissions rulemaking because
it is known that some diesel fuel is used by stationary sources (a 15 percent value was selected for use as
an approximate mid-point of a potential range of 8% to 24% stationary source use computed from a
review of data from the ElA's Manufacturing Energy Consumption Survey and Fuel Oil and Kerosene Sales).
Table 4-65 presents the assumptions that are applied to the state-level Commercial sector distillate oil
consumption data published in Fuel Oil and Kerosene Sales to estimate Commercial sector stationary source
consumption.
Table 4-65: Stationary source adjustments for commercial sector distillate fuel consumption
EIA Energy Sector
Distillate Fuel Type
% of Total Consumption
from Stationary Sources
Commercial
No. 1 Distillate Fuel Oil
80
No. 2 Distillate Fuel Oil
100
No. 2 Distillate/Ultra-Low,
Low, and High Sulfur Diesel
0a
No. 4 Distillate Fuel Oil
100
a A very small portion of total commercial/institutional diesel is consumed by point
sources (SCC 203001xx).
To avoid double-counting of LPG consumption, the ICI Tool uses data from the EPA National Mobile Inventory
Model (NMIM) for 2006 to calculate the national volume of nonroad LPG consumption from agriculture, logging,
mining, and construction source categories. This estimate is then divided into the SEDS total LPG consumption
estimate to yield the proportion of total ICI LPG consumption attributable to the nonroad sector in that year
(8.72% for industrial sources and 17.72% for commercial/institutional sources). It is assumed that these
proportions are appropriate for future inventory years. This estimate of the nonroad portion of LPG
consumption is subtracted from each state's ICI LPG consumption estimate reported in SEDS.
Distillate oil is reported by EIA as the total consumption of distillate. Therefore, as shown in Table 4-66,
assumptions must be made to determine the amount of distillate consumed by boilers and internal combustion
engines; these values are an update in the 2014v2 NEI. The default assumptions were calculated using data from
the EIA, but S/L/T agencies are encouraged to update the default assumptions with better state-level data, if
available.
4-111
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The default boiler/engine split assumptions for industrial distillate consumption were calculated using data from
ElA's 2010 Manufacturing Energy Consumption Survey (MECS), Table 5.5 [ref 6], which provides data on distillate
consumption by end use for the industrial sector. The boiler/engine split was calculated at the national level,
because data was withheld for too many end uses at the regional level. The following end uses from MECS are
assumed to be associated with engines: electricity generation (which assumes the electricity is generated using
internal combustion engine generators) and machine drive (which includes use by motors, pumps, etc.). All
other end uses are assumed to be associated with boilers. The total national-level distillate consumption for
engine-based and boiler-based end uses is 6 million barrels and 9 million barrels, respectively. Therefore, we
assume that the boiler/engine split for industrial distillate is 60% boilers and 40% engines.
The default boiler/engine split assumptions for commercial distillate consumption were calculated using data
from ElA's Commercial Building Energy Consumption Survey (CBECS), Table E9 [ref 7], which provides data on
distillate consumption by end use for the commercial sector. It is assumed that space heating and water heating
are associated with boilers (211 trillion Btu) and "other" is associated with engines (10 trillion Btu). The result is
a default boiler/engine split for commercial distillate of 95% boilers and 5% engines. Note that this approach
may overestimate the number of engines in the commercial sector, since the "other" end use category could
also include boilers. Nevertheless, the data show that the vast majority of distillate consumption in the
commercial sector is for space and water heating.
Table 4-66: Default assumptions for distillate boiler/engine splits
Industrial
Commercial/Institutional
Boiler
60%
95%
Engine
40%
5%
Non-fuel specific adjustments
Some industrial sector energy is consumed for non-fuel purposes, such as natural gas that is used as a feedstock
in chemical manufacturing plants and to make nitrogenous fertilizer, and LPG that is used to create intermediate
products that are ultimately made into plastics. To estimate the volume of fuel that is associated with industrial
combustion, it is necessary to subtract the volume of fuel consumption for non-energy uses from the volume of
total fuel consumption.
The identification of feedstock usage was initially based upon the non-fuel use assumptions incorporated into
the ElA's GHG emissions inventory for 2005 [ref 5], The following fuels are assumed to be used entirely for non-
fuel purposes: asphalt and road oil, feedstocks (naphtha <401 ฐF), feedstocks (other oils >401 ฐF), lubricants,
miscellaneous petroleum products, pentanes plus, special naphthas, and waxes. In addition, it is also assumed
that kerosene and motor gasoline are used entirely as fuel without any non-fuel purposes. The remaining fuels
(i.e., coal [non-coke], distillate oil, LPG, natural gas, and residual oil) are used both for fuel and non-fuel
purposes. The regional non-fuel fractions for distillate oil, LPG, natural gas, non-coke coal and residual oil are
derived from non-fuel (feedstock) and total energy use statistics contained in ElA's 2010 Manufacturing Energy
Consumption Survey (MECS) [ref 6] and are presented in Table 4-67. Note, non-fuel use of distillate fuel oil was
not reported at the regional level; therefore, the default nonfuel use fractions are based on national nonfuel use
of distillate fuel oil. In addition, non-fuel use was reported in EIA data as "less than 0.5" for non-coke coal, LPG
and residual oil in West and residual coal in the northeast; in these cases, a value of 0.25 was used to estimate
the default nonfuel use fractions.
Table 4-67: Industrial sector percent of total energy consumption from non-fuel use estimates
Fuel
Northeast
Midwest
South
West
4-112
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Non-Coke Coal
63
38
26
4
Natural Gas
1
5
14
2
LPG
33
88
99
6
Distillate Oil
4
4
4
4
Residual Oil
5
50
68
20
Point source energy adjustments
To ensure that fuel consumption is not double-counted in the point source inventory, it is also necessary to
subtract point source inventory fuel use from the fuel consumption estimates developed from the above steps.
Equation 2 illustrates the approach to performing point source subtractions.
Ns,f=Ts,f-Ps,f (2)
where:
N = nonpoint fuel consumption,
T = total fuel consumption,
P = point source fuel consumption,
s = sector (Industrial or Commercial/Institutional),
f = fuel type (coal, natural gas, distillate oil, residual oil, liquefied petroleum gas, kerosene and
wood).
The first step in the point source subtraction procedure is to identify how each ICI combustion nonpoint source
classification code (SCC) links to associated ICI combustion point SCCs. The ICI Combustion Tool includes two
such crosswalks: one between each Industrial fuel combustion nonpoint SCC and related point SCCs, and an
analogous crosswalk developed for Commercial/Institutional fuel combustion SCCs. One issue to note is that
natural gas consumed as pipeline fuel is not included by the SEDS within the Industrial sector. Therefore, it is
necessary to exclude pipeline natural gas consumption in performing natural gas combustion subtraction. This
consumption may be included within industrial sector natural gas internal combustion engine records (SCC
202002xx).
An issue that must be considered is the geographic resolution at which point source subtractions should be
performed. While locations of point sources are accurately known at (and below) the county-level, total ICI
combustion activity is much less clear. Because of the level of uncertainty associated with the county
distribution of total ICI fuel consumption, S/L/Ts may wish to perform the ICI combustion point source
subtractions at the state-level, and then allocate the resulting nonpoint source fuel consumption to counties. On
the contrary, if S/L/Ts have more accurate county-level fuel consumption values then point source subtraction
can be performed at the county-level. The ICI Tool is designed to prioritize county-level data over state-level
data, so where county-level data exists, the ICI Tool will perform county-level subtractions before using state-
level data.
If an agency does not have county- or state-level point source activity data, emissions data can be used in the
place of activity data in the point source subtraction procedure. The procedure follows the same steps, except
that the emissions are calculated first, and then the point source activity data are subtracted from the total
emissions.
4-113
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4.12,3.2 County allocation of state activity
Because the EIA only reports energy consumption down to the state-level, it is necessary to develop a procedure
to allocate ElA's fuel consumption estimates (after adjustments noted in sections above) to counties. For the
NEI, the procedure relies on the use of allocation factors developed from the county-level number of employees
in the Industrial sector and the county number of employees in the Commercial/Institutional sector. Because EIA
fuel consumption data originate from fuel sector-specific surveys of energy suppliers,9 we reviewed these survey
forms/instructions for further details on what individual economic sectors EIA considers comprising the
Industrial and Commercial sector. Based on this review, we compiled employment data for manufacturing sector
North American Industrial Classification System (NAICS) codes (i.e., NAICS 31-33) for use in allocating Industrial
fuel combustion. The only source of NAICS-code based EIA definitions of the Commercial energy sector is a
"rough crosswalk" between Commercial building types and NAICS codes developed for ElA's Commercial
Building Energy Consumption Survey (CBECS) [ref 7], Except for NAICS code 814 (Private Households), this
crosswalk links all NAICS codes between 42 and 92 with Commercial building energy consumption.
The ICI Combustion Tool compiles employment data for these NAICS codes from two Bureau of the Census
publications -County Business Patterns (for private sectors), and Census of Governments (for public
administration sectors) [ref 8, ref 9], For NAICS code 92, county-level employment is estimated from local
government employment data in the Census of Governments.10 Employment estimates from each source are
then combined to estimate total Commercial/ Institutional sector employment by county. The state-level fuel
combustion by fuel type estimates in each sector are then allocated to each county using the ratio of the
number of Industrial or Commercial/Institutional employees in each county in each state.
Due to concerns with releasing confidential business information, County Business Patterns (CBP) withholds
values for a given county/NAICS code if it would be possible to identify data for individual facilities. In such
cases, the Census reports a letter code, representing a particular employment size range. We used the following
procedure to estimate data for withheld counties/NAICS codes.
1. County-level employment for counties with reported values are totaled by state for the applicable NAICS
code.
2. The value from step 1 is subtracted from the state employment value for the NAICS code.
3. Each of the withheld counties is assigned an initial employment estimate reflecting the midpoint of the
CBP range code (e.g., code A, which reflects 1-19 employees, is assigned an estimate of 10 employees).
4. The initial employment estimates from step 3 are then summed to the state level.
5. The value from step 2 is divided by the value from step 4 to yield an adjustment factor to apply to the
initial employment estimates to yield employment values that will sum to the state employment total
for the applicable NAICS code.
6. The final county-level employment values are estimated by multiplying the initial employment estimates
from step 3 by the step 5 adjustment factors.
Table 4-68 illustrates the employment estimation procedure with an example of CBP data reported for Maine.
Table 4-68: NAICS Code 31-33 (Manufacturing) employment data for Maine
FIPSSTATE
FIPSCTY
NAICS
EMPFLAG
EMP
23
1
31
6,774
9 For natural gas, for example - EIA-176 "Annual Report of Natural and Supplemental Gas Supply and Disposition."
10 County-level federal and state government employment data are not available from the Bureau of the Census.
4-114
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FIPSSTATE
FIPSCTY
NAICS
EMPFLAG
EMP
23
3
31
3,124
23
5
31
10,333
23
7
31
1,786
23
9
31
1,954
23
11
31
2,535
23
13
31
1,418
23
15
31
F
0
23
17
31
2,888
23
19
31
4,522
23
21
31
948
23
23
31
1
0
23
25
31
4,322
23
27
31
1,434
23
29
31
1,014
23
31
31
9,749
The total of employees not including counties 015 and 023 is 52,801.
County Business Patterns reports 59,322 state employees in NAICS 31the difference is 6,521.
County 015 is given a midpoint of 1,750 (since range code F is 1,000-2,499) and County 023 is given a
midpoint of 17,500.
State total for these two counties is 19,250.
6,521/19,250 = 0.33875.
The final employment estimate for county 015 is 1,750 x 0.33875 = 593. The county 023 final employment
estimate is computed as 17,500 x 0.33875 = 5,928.
4.12.33 Emission factors
Table 4-69 lists the CAP emission factors used in the ICI Combustion Tool. The CAP and HAP emission factors for
each nonpoint source fuel combustion category included in the ICI Combustion Tool are primarily EPA emission
factors. Most of the emission factors are from the EPA/ERTAC2 database and EPA's AP-42 report, Compilation of
Air Pollutant Emission Factors [ref 10, ref 11], The ammonia emission factors for wood combustion are from an
Emission Inventory Improvement Program (EIIP) guidance document [ref 12].
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. For the industrial
and commercial/institutional sectors, state-specific coal sulfur contents for bituminous coal are obtained from
the ElA's quarterly coal report [ref 13]. For anthracite coal, an ash content value of 13.38% and a sulfur content
of 0.89% are applied to all states.
4-115
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Table 4-69: CAP emission
actors for ICI source categories
see
Description
Emission
Factor
Units1
voc
NOx
CO
S02
PM25-
FIL
PM10-
FIL
PM-
CON
nh3
2102001000
Industrial
Anthracite Coal
lb/ton
0.3
9
0.6
39 *
Sฐ/o
0.48 *
A%
1.1 *
A%
0.08*A%
0.03
2102002000
Industrial
Bitum/Subbitum
Coal
lb/ton
0.05
11
5
38 *
Sฐ/o
1.4
12
1.04
0.03
2102004000
Industrial
Distillate Oil
lb/1000
gal
0.2
20
5
142 *
Sฐ/o
0.25
1
1.3
0.8
2102005000
Industrial
Residual Oil
lb/1000
gal
0.28
55
5
157 *
Sฐ/o
4.67 *
(1.12
* S% +
0.37)
7.17 *
(1.12 *
Sฐ/o +
0.37)
1.5
0.8
2102006000
Industrial
Natural Gas
Ib/MMcf
5.5
100
84
0.6
0.11
0.2
0.322
3.2
2102007000
Industrial LPG 3
lb/1000
gal
0.52
14.2
8
0.06
0.01
0.02
0.03
0.34
2102008000
Industrial Wood
5
Ib/MMBtu
0.02
0.22
0.6
0.025
0.43
0.5
0.017
0.008
2102011000
Industrial
Kerosene
lb/1000
gal
0.19
19.3
4.8
142 *
Sฐ/o7
0.24
0.96
1.25
0.77
2103001000
Comm/lnst
Anthracite Coal
lb/ton
0.3
9
0.6
39 *
Sฐ/o
0.48 *
A%
1.1 *
A%
0.08 *
A%
0.03
2103002000
Comm/lnst
Bitum/Subbitum
Coal
lb/ton
0.05
11
5
38 *
Sฐ/o
1.4
12
1.04
0.03
2103004000
Comm/lnst
Distillate Oil
lb/1000
gal
0.34
20
5
142 *
Sฐ/o
0.83
1.08
1.3
0.8
2103005000
Comm/lnst
Residual Oil
lb/1000
gal
1.13
55
5
157 *
Sฐ/o
1.92 *
(1.12
* S% +
0.37)
5.17 *
(1.12 *
Sฐ/o +
0.37)
1.5
0.8
2103006000
Comm/lnst
Natural Gas
Ib/MMcf
5.5
100
84
0.6
0.11
0.2
0.32
0.49
2103007000
Comm/lnst LPG
lb/1000
gal
0.52
14.2
8
0.06
0.01
0.02
0.03
0.05
4-116
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SCC
Description
Emission
Factor
Units1
voc
NOx
CO
S02
PM25-
FIL
PM10-
FIL
PM-
CON
nh3
2103008000
Comm/lnst
Wood 5
Ib/MMBtu
0.02
0.22
0.6
0.025
0.43
0.5
0.017
0.006
2103011000
Comm/lnst
Kerosene
lb/1000
gal
0.33
19.3
4.8
142 *
Sฐ/o
0.8
1.04
1.3
0.8
Source: Unless otherwise noted, ERTAC emission factors used to support the 2011 NEI [ref 10].
Notes: 1 lb = pound; ton = short ton; gal = gallon; MMcf = million cubic feet; MMBtu = million British thermal units; bbl =
barrels; S% = percent sulfur content; A% = percent
ash content
2 The EPA ERTAC emission factor workbook [ref 10] for this emission factors (EF) contains an error. The change log
in the ERTAC workbook conflicts with the actual changes made to the emission factors spreadsheet. The PM-
CON EF should be 0.32 Ib/MMcf for 2102006000 instead of the 0.49 Ib/MMcf value reported in the ERTAC
workbook.
3 Emission factors from Commercial/Institutional LPG.
4 The EPA ERTAC emission factor workbook [ref 10] for this emission factors (EF) contains an error. The change log
in the ERTAC workbook conflicts with the actual changes made to the emission factors spreadsheet. The NH3 EF
should be 0.3 lb/1000 gal for 2102007000 instead of the 0.05 lb/1000 gal value reported in the ERTAC workbook.
5 Emission factors from AP-42, Section 1.6, Wood Residue Combustion in Boilers [ref 4],
6 Emission factor from Pechan, 2004 [ref 12] (converted from lb/ton using 0.08 ton/MMBtu for Industrial sector
and 0.0625 ton/MMBtu for Commercial sector).
7 The EPA ERTAC emission factor workbook [ref 10] for this emission factors (EF) contains an error. The ERTAC
workbook uses the equation 157*S%. The correct EF equation is 142*S%.
In the ICI Tool, users may edit the assumptions about the sulfur and ash content of fuels, using the form "Sulfur
and Ash Content of Fuels" from the "Edit Assumptions" form. Assumptions about sulfur content can be adjusted
at the state level for bituminous/subbituminous coal, anthracite coal, residual oil, and distillate oil. Sulfur
content assumptions can also be adjusted at the county level for distillate oil. Assumptions about ash content
can be adjusted at the state level for anthracite coal.
ICI Tool changes in the 2014v2 NEi
In addition to updating the default distillate oil boiler/engine split (see Table 4-66) for the 2014v2 NEI, users may
now also add user defined control efficiencies for each SCC in each county, using the "Control Efficiencies for
Nonpoint SCCs" table, which can be accessed from the "Edit Assumptions" form. Control efficiencies entered
into this table are used to adjust the final reported emissions using the following equation:
Controlled Nonpoint Emissionssc
= Uncontrolled Nonpoint Emissionssc x (1 Control Efficiencysc)
Where Controlled Nonpoint Emissions are the final reported nonpoint emissions, Uncontrolled Nonpoint
Emissions are the emissions estimated after point source subtraction but before the application of the control
efficiency, Control Efficiency is the user-supplied control efficiency, s is SCC, and c is county.
Note that the control efficiency must be a number between 0 and 1. The default control efficiencies in the tool
are 0 for all counties and SCCs.
4-117
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4,
.3.5 Known issues in the 2014v2 NEI
EPA accidentally left state-submitted double-counts for both Industrial (SCC 2102004000) and
Commercial/Institutional (SCC 2103004000) Distillate Oil - Total Boilers and IC Engines in New Jersey. This yields
approximately 1,000 tons of both NOX and S02 that are already accounted for in the engine-specific and boiler-
specific ICI distillate oil SCCs. EPA plans to incorporate a selection procedure in the 2017 NEI that will prevent
the mixing of these specific and more general SCCs/double-counts. In addition, with very few large ICI sources
not including some type of control device, we plan to significantly restrict, or remove, the ability to compute
nonpoint ICI emissions by simple point inventory emission subtraction; but rather, require point inventory
throughput (activity data) subtraction.
4,12,4 References for ICI fuel combustion
1. EIA, 2015a: Energy Information Administration, State Energy Data System, Consumption Estimates.
2013, U.S. Department of Energy, Washington DC, released July 24, 2015.
2. EIA, 2013a: Energy Information Administration, Sales of Distillate Fuel Oil by End Use. 2013, U.S.
Department of Energy, Washington DC.
3. EIA, 2015b: Energy Information Administration, Annual Coal Distribution Report: Archive. Domestic
Distribution of U.S. Coal by Destination State, Consumer, Destination and Method of Transportation,
U.S. Department of Energy, Washington DC, 2013 data file, release date April 16, 2015.
4. EPA, 2003: Draft Regulatory Impact Analysis: Control of Emissions from Nonroad Diesel Engines. EPA
420-R-03-008. U.S. Environmental Protection Agency, Office of Transportation and Air Quality, April.
5. EIA, 2007: Energy Information Administration, Documentation for Emissions of Greenhouse Gases in the
United States 2005, U.S. Department of Energy, Washington, DC, October 2007; DOE/EIA-0638 (2005).
6. EIA, 2013b. Energy Information Administration, 2010 MECS Survey Data. U.S. Department of Energy,
Energy Information Administration, release date 2013.
7. EIA, 2013c: Energy Information Administration, "Appendix Table A-51. ElA's Commercial Sector: Building
Activities and NAICS Industries," Commercial Building Energy Consumption Survey. U.S. Department of
Energy, Washington DC, accessed July 2013.
8. Bureau of the Census, 2015a: County Business Patterns 2013, U.S. Department of Commerce,
Washington DC, accessed August 2015.
9. Bureau of the Census, 2015b: Annual Survey of Public Employment & Payroll (ASPEPi. March 2012, 2012
Census of Governments, U.S. Department of Commerce, Washington DC, accessed August 2015.
10. Huntley, R., 2009. U.S. Environmental Protection Agency, Eastern Regional Technical Advisory
Committee (ERTACi. Excel file: state_comparison_ERTAC_SS_version7.2_23nov2009.xls
11. EPA, 2010. U.S. Environmental Protection Agency, AP-42, Compilation of Air Pollutant Emission Factors.
Volume 1. Stationary Point and Area Sources, accessed June 2013.
12. Pechan, 2004: E.H. Pechan & Associates, Inc. Estimating Ammonia Emissions from Anthropogenic
Nonagricultural Sources - Draft Final Report, prepared for the Emission Inventory Improvement
Program, April 2004.
13. EIA, 2012. Quarterly Coal Report. January - March 2012. U.S. Department of Energy, Energy Information
Administration.
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4.13.1 Sector description
The EIS sectors to be documented here are:
"Fuel Comb - Residential - Natural Gas" which includes the 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.
"Fuel Comb - Residential - Oil" which includes the fuels: (1) distillate oil, (2) kerosene and (3) residual oil.
Residual oil is not an EPA-estimated category, and no agencies submitted data for it in 2014. 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" which includes the fuels: (1) coal, (2) liquid petroleum gas (LPG) and
(3) "Biomass; all except Wood". Note that "Biomass; all except Wood" is not an EPA-estimated category,
and no S/L/T agency submitted data for it for the 2014 NEI. Residential Coal Combustion is coal that is
burned to heat 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.
4.13.2 Sources of data
Table 4-70 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) 2013 Consumption tables published by the Energy
Information Administration (EIA) [ref 1], there was no residential coal combustion in 2013. However, the old
methodology is retained here and provided in an EPA workbook, and as seen in Table 4-70, with zero emissions,
in case a state would like to use their own coal consumption data.
Table 4-70: Non-wood residential heating SCCs with 2014 NEI emissions
Sector Fuel
SCC
Description
EPA
State
Local
Tribe
Natural Gas
2104006000
Natural Gas; Total: All Combustor Types
X
X
X
X
Oil
2104004000
Distillate Oil; Total: All Combustor Types
X
X
X
X
Oil
2104011000
Kerosene; Total: All Heater Types
X
X
X
X
Other
2104001000
Anthracite Coal; Total: All Combustor Types
0
0
0
Other
2104002000
Bituminous/Subbituminous Coal; Total: All
Combustor Types
0
X
X
Other
2104007000
Liquified Petroleum Gas (LPG); Total: All
Combustor Types
X
X
X
X
The agencies listed in Table 4-71 submitted emissions for these sectors; 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%).
4-119
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Table 4-71: Percentage of non-wood residential heating NOx, PM2.5 and VOC emissions submitted by reporting
agency
Region
Agency
S/L/T
Sector Fuel
NOx
PM2.5
VOC
1
Massachusetts Department of Environmental
Protection
State
Natural Gas
100
100
100
1
Massachusetts Department of Environmental
Protection
State
Oil
100
100
100
1
Massachusetts Department of Environmental
Protection
State
Other
100
100
100
1
New Hampshire Department of Environmental
Services
State
Natural Gas
100
99
100
1
New Hampshire Department of Environmental
Services
State
Oil
100
100
100
1
New Hampshire Department of Environmental
Services
State
Other
100
100
100
1
Vermont Department of Environmental
Conservation
State
Natural Gas
100
100
2
New Jersey Department of Environment
Protection
State
Natural Gas
100
100
100
2
New Jersey Department of Environment
Protection
State
Oil
100
100
100
2
New Jersey Department of Environment
Protection
State
Other
100
100
100
2
New York State Department of Environmental
Conservation
State
Natural Gas
100
100
100
2
New York State Department of Environmental
Conservation
State
Oil
100
100
100
2
New York State Department of Environmental
Conservation
State
Other
100
100
100
3
Delaware Department of Natural Resources and
Environmental Control
State
Natural Gas
100
100
100
3
Delaware Department of Natural Resources and
Environmental Control
State
Oil
100
100
100
3
Delaware Department of Natural Resources and
Environmental Control
State
Other
100
100
100
3
Maryland Department of the Environment
State
Natural Gas
100
100
3
Maryland Department of the Environment
State
Oil
100
100
100
3
Maryland Department of the Environment
State
Other
100
28
100
3
Virginia Department of Environmental Quality
State
Natural Gas
100
100
100
3
Virginia Department of Environmental Quality
State
Oil
100
100
100
3
Virginia Department of Environmental Quality
State
Other
100
100
100
4
Metro Public Health of Nashville/Davidson County
State
Natural Gas
100
100
4
Metro Public Health of Nashville/Davidson County
State
Oil
90
89
4
Metro Public Health of Nashville/Davidson County
State
Other
100
100
5
Illinois Environmental Protection Agency
State
Natural Gas
100
100
100
5
Illinois Environmental Protection Agency
State
Oil
100
100
100
5
Illinois Environmental Protection Agency
State
Other
100
100
100
4-120
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Region
Agency
S/L/T
Sector Fuel
NOx
PM2.5
voc
5
Michigan Department of Environmental Quality
State
Natural Gas
100
100
5
Michigan Department of Environmental Quality
State
Oil
100
100
5
Michigan Department of Environmental Quality
State
Other
100
100
6
Texas Commission on Environmental Quality
State
Natural Gas
100
100
100
6
Texas Commission on Environmental Quality
State
Oil
100
100
6
Texas Commission on Environmental Quality
State
Other
100
100
100
7
Sac and Fox Nation of Missouri in Kansas and
Nebraska Reservation
Tribe
Other
100
100
8
Assiniboine and SiouxTribes of the Fort Peck
Indian Reservation
Tribe
Natural Gas
100
100
8
Assiniboine and SiouxTribes of the Fort Peck
Indian Reservation
Tribe
Other
100
100
8
Northern Cheyenne Tribe
Tribe
Natural Gas
100
100
8
Northern Cheyenne Tribe
Tribe
Oil
100
100
8
Northern Cheyenne Tribe
Tribe
Other
100
100
8
Utah Division of Air Quality
State
Natural Gas
100
100
100
8
Utah Division of Air Quality
State
Other
100
100
100
9
Arizona Department of Environmental Quality
State
Natural Gas
100
100
100
9
Arizona Department of Environmental Quality
State
Oil
100
100
100
9
Arizona Department of Environmental Quality
State
Other
100
100
100
9
California Air Resources Board
State
Natural Gas
100
100
100
9
California Air Resources Board
State
Oil
89
90
96
9
California Air Resources Board
State
Other
100
100
100
9
Morongo Band of Cahuilla Mission Indians of the
Morongo Reservation, California
Tribe
Natural Gas
100
100
9
Washoe County Health District
Local
Natural Gas
100
100
9
Washoe County Health District
Local
Oil
100
100
9
Washoe County Health District
Local
Other
100
100
10
Alaska Department of Environmental Conservation
State
Natural Gas
9
6
10
Coeur d'Alene Tribe
Tribe
Natural Gas
100
100
100
10
Coeur d'Alene Tribe
Tribe
Oil
100
100
100
10
Coeur d'Alene Tribe
Tribe
Other
100
100
100
10
Idaho Department of Environmental Quality
State
Natural Gas
100
100
100
10
Idaho Department of Environmental Quality
State
Oil
100
100
100
10
Idaho Department of Environmental Quality
State
Other
100
100
100
10
Kootenai Tribe of Idaho
Tribe
Natural Gas
100
100
100
10
Kootenai Tribe of Idaho
Tribe
Oil
100
100
100
10
Kootenai Tribe of Idaho
Tribe
Other
100
100
100
10
Nez Perce Tribe
Tribe
Natural Gas
100
100
100
10
Nez Perce Tribe
Tribe
Oil
100
100
100
10
Nez Perce Tribe
Tribe
Other
100
100
100
10
Shoshone-Bannock Tribes of the Fort Hall
Reservation of Idaho
Tribe
Natural Gas
100
100
100
4-121
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Region
Agency
S/L/T
Sector Fuel
NOx
PM2.5
voc
10
Shoshone-Bannock Tribes of the Fort Hall
Reservation of Idaho
Tribe
Oil
100
100
100
10
Shoshone-Bannock Tribes of the Fort Hall
Reservation of Idaho
Tribe
Other
100
100
100
4,13,3 EPA-developed emissions for residential heating - natural gas, oil and other fuels
The general approach to calculating emissions for all fuel types is to take state-level fuel-specific (natural gas,
distillate oil, kerosene, coal, and LPG) consumption from the EIA and allocate it to the county level using the
methods described below. County-level fuel consumption is multiplied by the emission factors to calculate
emissions.
4.133,1 Activity data: new for 2014v2
Natural Gas. Distillate Oil. Kerosene, and LPG
The state-level volume of each of these fuel types consumed by residential combustion in the United States was
used to estimate emissions. Fuel type consumption by energy use sector was obtained from the State Energy
Data System (SEDS) 2014 Consumption tables published by the EIA [ref 1], Year 2013 consumption data were
used in 2014vl because these data were the latest data available when the 2014vl inventory was prepared.
Natural gas consumption is represented in the SEDS table by the Data Series Name (MSN) NGRCP. Distillate
consumption is represented in the SEDS table by the Data Series Name (MSN) DFRCP. Kerosene consumption is
represented in the SEDS table by the Date Series Name (MSN) KSRCP. LPG consumption is represented in the
SEDS table by the Data Series Name (MSN) LGRCP.
State-level fuel type consumption was allocated to each county using the US Census Bureau's 2014 5-year
estimate Census Detailed Housing Information [ref 2]; for 2014vl, a 2013 5-year estimate was used. These data
include the number of housing units using a specific type of fuel for residential heating. State fuel type
consumption was allocated to each county using the ratio of the number of houses burning natural gas, distillate
oil, kerosene, or LPG in each county to the total number of houses burning natural gas, distillate oil, kerosene, or
LPG in the state.
Coal
The mass of coal consumed by residential combustion in the U.S. was used to estimate emissions. Coal
consumption by energy use sector is presented in State Energy Data System (SEDS) 2014 Consumption tables
published by the Energy Information Administration (EIA) [ref 1], Year 2013 consumption data were used in
2014vl because these data were the latest data available when the 2014vl inventory was prepared. Coal
consumption is represented in the SEDS table by the Data Series Name (MSN) CLRCP.
EIA data do not distinguish between anthracite and bituminous coal consumption estimates. The EIA table
"Domestic Distribution of U.S. Coal by Destination State, Consumer, Origin and Method of Transportation,"
provides state-level residential coal distribution data for 2006 that was used to estimate anthracite and
bituminous coal consumption. The amount of anthracite distributed to each state and the total coal delivered to
each state were used to estimate the proportion of anthracite and bituminous coal consumption [ref 3], The
2006 ratio of anthracite (and bituminous) coal consumption to total coal consumption was used to distribute the
ElA's total residential sector coal consumption data by coal type. Table 4-72 presents the 2006-based percent of
total bituminous coal for each state. The percent anthracite coal is computed as the remaining percent (if any).
4-122
-------
-72: 2006 percent bituminous coal distri
Dution for the resic
ential and commercial
State
Percent Bituminous
State
Percent Bituminous
Alabama
100
Montana
100
Alaska
100
Nebraska
100
Arizona
81.4
Nevada
100
Arkansas
81.4
New Hampshire
0
California
100
New Jersey
0
Colorado
99.6
New Mexico
100
Connecticut
0
New York
60
Delaware
81.4
North Carolina
100
Dist. Columbia
100
North Dakota
100
Florida
81.4
Ohio
87.3
Georgia
100
Oklahoma
91.7
Hawaii
100
Oregon
100
Idaho
97.9
Pennsylvania
19.4
Illinois
99.8
Rhode Island
0
Indiana
94.7
South Carolina
99.7
Iowa
99.9
South Dakota
100
Kansas
100
Tennessee
99.4
Kentucky
99.8
Texas
81.4
Louisiana
100
Utah
100
Maine
0
Vermont
0
Maryland
92.9
Virginia
96.3
Massachusetts
50
Washington
100
Michigan
66.7
West Virginia
90.5
Minnesota
99.7
Wisconsin
99.1
Mississippi
100
Wyoming
100
Missouri
100
State-level coal consumption was allocated to each county using the US Census Bureau's 2014 5-year estimate
Census Detailed Housing Information [ref 2]; for 2014vl, a 2013 5-year estimate was used. These data include
the number of housing units using a specific type of fuel for residential heating. State coal consumption was
allocated to each county using the ratio of the number of houses burning coal in each county to the total
number of houses burning coal in the state.
Control factors
No control measures are assumed for any non-wood residential heating sources.
4,13.33 Emission factors
Natural Gas
Criteria pollutant emission factors for natural gas are from AP-42 [ref 4], The ammonia emission factor is from
EPA's Estimating Ammonia Emissions from Anthropogenic Sources, Draft Final Report [ref 5], HAP emission
factors are from AP-42 and "Documentation for the 1999 Base Year Nonpoint Area Source National Emission
4-123
-------
Inventory for Hazardous Air Pollutants." [ref 6] According to AP-42 (maximum value provided) [ref 4], natural gas
has a heat content of 1,050 million BTU per million cubic feet. This value was required to convert those emission
factors originally given in units "pounds per million Btu" to units "pounds per million cubic feet." The grains of
sulfur per million cubic feet are assumed to be 2000 [ref 7], Some emission factors were revised based on
recommendations by an ERTAC advisory panel composed of state and EPA personnel.
County-level criteria pollutant and HAP emissions were calculated by multiplying the total natural gas consumed
in each county per year by an emission factor. Table 4-73 provides a summary of the pollutants, pollutant codes,
and emission factors for residential combustion of natural gas.
Table 4-73: Residential natural gas combustion emission factors
Pollutant
Code
Pollutant Code Description
Emission Factor
(LB/E6FT3)
129000
PYRENE
0.00000525
206440
FLUORANTHENE
0.00000315
50000
FORMALDEHYDE
0.07875
71432
BENZENE
0.002205
75070
ACETALDEHYDE
0.00001365
85018
PHENANTHRENE
0.00001785
86737
FLUORENE
0.00000294
91203
NAPHTHALENE
0.0006405
CO
CARBON MONOXIDE
40
NH3
AMMONIA
20
NOX
NITROGEN OXIDES
94
PM10-PRI
PRIMARY PMio (INCLUDES FILTERABLES + CONDENSIBLES)
0.52
PM25-PRI
PRIMARY PM2.5 (INCLUDES FILTERABLES + CONDENSIBLES)
0.43
PM10-FIL
PRIMARY PM10, FILTERABLE PORTION ONLY
0. 2
PM25-FIL
PRIMARY PM2.5, FILTERABLE PORTION ONLY
0.11
PM-CON
PRIMARY PM CONDENSIBLE PORTION ONLY
0.32
SO 2
SULFUR DIOXIDE
0.6
VOC
VOLATILE ORGANIC COMPOUNDS
5.5
Distillate Oil
Criteria pollutant emission factors for distillate oil are from AP-42 [ref 4], 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 and a sulfur content of 0.30% [ref 7], Dioxin/furan and HAP emission factors are from
"Documentation of Emissions Estimation methods for Year 2000 and 2001 Mobile Source and Nonpoint Source
Dioxin Inventories" [ref 8] and "Documentation for the 1999 Base Year Nonpoint Area Source National Emission
Inventory for Hazardous Air Pollutants," [ref 6] respectively. Sulfur content was 0.30% and was obtained from
data compiled in preparing the 1999 residential coal combustion emissions estimates [ref 7], The ammonia
emission factor is from EPA's Estimating Ammonia Emissions from Anthropogenic Sources, Draft Report [ref 5],
Table 4-74 provides a summary of the pollutants, pollutant codes, and emission factors for residential
combustion of distillate oil.
4-124
-------
Table 4-74: Residential distillate oil combustion emission factors
Pollutant
Code
Pollutant Code Description
Emissions Factor
(LB/E3GAL)
Reference
120127
ANTHRACENE
1.22E-06
6
129000
PYRENE
4.21E-06
6
1746016
2,3,7,8-TETRACHLORODIBENZO-P-DIOXIN
4.66E-10
8
191242
BENZO[G,H,l,]PERYLENE
2.25E-06
6
193395
INDENO[l,2,3-C,D] PYRENE
2.11E-06
6
206440
FLUORANTHENE
4.92E-06
6
208968
ACENAPHTHYLENE
2.53E-07
6
218019
CHRYSENE
2.39E-06
6
3268879
OCTACHLORODIBENZO-P-DIOXIN
5.49E-10
8
39001020
OCTACHLORODIBENZOFURAN
2.50E-10
8
50000
FORMALDEHYDE
3.37E-02
6
51207319
2,3,7,8-TETRACHLORODIBENZOFURAN
4.41E-10
8
53703
DIBENZO[A,H]ANTHRACENE
1.69E-06
6
56553
BENZ[A] ANTHRACENE
4.07E-06
6
71432
BENZENE
2.11E-04
6
7439921
LEAD
1.26E-03
6
7439965
MANGANESE
8.43E-04
6
7439976
MERCURY
4.21E-04
6
7440020
NICKEL
4.21E-04
6
7440382
ARSENIC
5.62E-04
6
7440417
BERYLLIUM
4.21E-04
6
7440439
CADMIUM
4.21E-04
6
16065831
Chromium III
0.000345556
18540299
Chromium (VI)
7.58538E-05
75070
ACETALDEHYDE
4.92E-03
6
7782492
SELENIUM
2.11E-03
6
83329
ACENAPHTHENE
2.11E-05
6
85018
PHENANTHRENE
1.05E-05
6
86737
FLUORENE
4.50E-06
6
91203
NAPHTHALENE
1.14E-03
6
CO
CARBON MONOXIDE
5.00E+00
8
NH3
AMMONIA
1.00E+00
5
NOX
NITROGEN OXIDES
1.80E+01
4
PM10-FIL
PRIMARY PMio, FILTERABLE PORTION ONLY
1.08E+00
4
PM10-PRI
PRIMARY PMio (INCLUDES FILTERABLES + CONDENSIBLES)
2.38E+00
4
PM25-FIL
PRIMARY PM2.5, FILTERABLE PORTION ONLY
8.30E-01
4
PM25-PRI
PRIMARY PM2.5 (INCLUDES FILTERABLES + CONDENSIBLES)
2.13E+00
4
PM-CON
PRIMARY PM CONDENSIBLE PORTION ONLY (< 1 MICRON)
1.30E+00
4
SO 2
SULFUR DIOXIDE
4.26E+01
4
VOC
VOLATILE ORGANIC COMPOUNDS
7.00E-01
4
4-125
-------
Kerosene
Emission factors for distillate oil were used for kerosene, but the distillate oil emission factors were 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
emission factors are from AP-42. [ref 4], Dioxin/furan and HAP emission factors are from "Documentation of
Emissions Estimation methods for Year 2000 and 2001 Mobile Source and Nonpoint Source Dioxin Inventories"
[ref 8] and "Documentation for the 1999 Base Year Nonpoint Area Source National Emission Inventory for
Hazardous Air Pollutants," [ref 6] respectively. Distillate sulfur content (0.30%) was used for kerosene as well
[ref 7], Table 4-75 provides a summary of the pollutants, pollutant codes, and emission factors for residential
combustion of kerosene.
Table 4-75: Residential kerosene combustion emission factors
Pollutant
Code
Pollutant Code Description
Emissions Factor
(LB/E3BBL)
120127
ANTHRACENE
4.95E-05
129000
PYRENE
0.00017067
1746016
2,3,7,8-TETRACHLORODIBENZO-P-DIOXIN
1.89E-08
191242
BENZO[G,H,l,]PERYLENE
9.10E-05
193395
1NDE NO [1,2,3-C,D] PYRENE
8.53E-05
206440
FLUORANTHENE
0.00019912
208968
ACENAPHTHYLENE
1.02E-05
218019
CHRYSENE
9.67E-05
3268879
OCTACHLORODIBENZO-P-DIOXIN
2.22E-08
39001020
OCTACHLORODIBENZOFURAN
1.01E-08
50000
FORMALDEHYDE
1.3653684
51207319
2,3,7,8-TETRACHLORODIBENZOFURAN
1.79E-08
53703
DIBENZO[A,H]ANTHRACENE
6.83E-05
56553
BENZ[A]ANTHRACENE
0.00016498
71432
BENZENE
0.00853355
7439921
LEAD
0.05120132
7439965
MANGANESE
0.03413421
7439976
MERCURY
0.01706711
7440020
NICKEL
0.01706711
7440382
ARSENIC
0.02275614
7440417
BERYLLIUM
0.01706711
7440439
CADMIUM
0.01706711
16065831
Chromium III
0.013995026
18540299
Chromium (VI)
0.003072079
75070
ACETALDEHYDE
0.19911623
7782492
SELENIUM
0.08533553
83329
ACENAPHTHENE
0.00085336
85018
PHENANTHRENE
0.00042668
86737
FLUORENE
0.00018205
91203
NAPHTHALENE
0.04608118
4-126
-------
Pollutant
Code
Pollutant Code Description
Emissions Factor
(LB/E3BBL)
NH3
AMMONIA
40.5
CO
CARBON MONOXIDE
202.5
NOX
NITROGEN OXIDES
729
PM10-PRI
PRIMARY PMio (INCLUDES FILTERABLES + CONDENSIBLES)
96.39
PM25-PRI
PRIMARY PM2.5 (INCLUDES FILTERABLES + CONDENSIBLES)
86.265
PM10-FIL
PRIMARY PM10, FILTERABLE PORTION ONLY
43.74
PM25-FIL
PRIMARY PM2.5, FILTERABLE PORTION ONLY
33.615
PM-CON
PRIMARY PM CONDENSIBLE PORTION ONLY (ALL LESS THAN 1 MICRON)
52.65
SO 2
SULFUR DIOXIDE
1,725.30
VOC
VOLATILE ORGANIC COMPOUNDS
28.35
Coal
All emission factors except ammonia are from AP-42 [ref 4], The ammonia emission factor is from EPA's
Estimating Ammonia Emissions from Anthropogenic Sources, Draft Final Report [ref 5],
Table 4-76 shows the S02 and PM emission factors. The S02 emission factors require information on the sulfur
content of the coal burned, while some of the PM emission factors for anthracite coal require information on
the ash content of the coal. State-specific sulfur and ash contents of anthracite and bituminous coal were
obtained from data compiled in preparing the 1999 residential coal combustion emissions estimates [ref 7], This
study mostly relied on data obtained from US Geological Survey COALQUAL database. States not included in the
database but that reported coal usage were assigned values based on their proximity to coal seams or using an
average value for Pennsylvania (see report for details of the analysis). Note that the PM condensable emission
factor provided in AP-42 is 0.04 Ib/MMBtu. This was multiplied by the conversion factor of 26 MMBtu/ton
provided in AP-42 for bituminous coal. Table 4-77 presents the bituminous coal sulfur content values used for
each state. For anthracite coal, an ash content value of 13.38% and a sulfur content of 0.89% were applied to all
states except 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 4-76: S02 and PM emission factors for residential anthracite and bituminous coal combustion
Pollutant
Emission Factor
(lb/ton)
Data Source,
AP-42 [ref 4] Table No.
Anthracite Emission 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 PM2.5/PMio=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
SO 2
39 * % Sulfur
1.2-1 (residential space heater)
Bituminous Emission Factors (SCC 2104002000)
PM-CON
1.04
1.1-5 (stoker)
PM10-FIL
6.2
1.1-4 (hand-fed)
4-127
-------
Pollutant
Emission Factor
(lb/ton)
Data Source,
AP-42 [ref 4] Table No.
PM25-FIL
3.8
1.1-11 (underfeed stoker)
PM10-PRI
7.24
1.1-5 and 1.1-4
PM25-PRI
4.84
1.1-5 and 1.1-11
SO 2
31 * % Sulfur
1.1-3 (hand-fed)
NOTE: PMio, PM2.5, and condensable PM emission factors for bituminous coal as well as
filterable emission factors for PM10 and PIVh.sfor anthracite coal do not require ash content.
Table 4-77: State-specific sulfur content for bituminous coal (SCC 2104002000)
State
Percent Sulfur
Content
State
Percent Sulfur
Content
Alabama
2.08
Montana
0.6
Alaska
0.31
Nebraska
2.43
Arizona
0.47
Nevada
2.3
Arkansas
1.2
New Hampshire
2.42
California
0.47
New Jersey
2.42
Colorado
0.61
New Mexico
0.75
Connecticut
2.42
New York
2.42
Delaware
1.67
North Carolina
1.62
District of Columbia
1.67
North Dakota
0.97
Florida
1.28
Ohio
3.45
Georgia
1.28
Oklahoma
3.08
Hawaii
1
Oregon
0.5
Idaho
0.31
Pennsylvania
2.42
Illinois
3.48
Rhode Island
2.42
Indiana
2.49
South Carolina
1.28
Iowa
4.64
South Dakota
0.97
Kansas
5.83
Tennessee
1.62
Kentucky
1.93
Texas
1.14
Louisiana
0.86
Utah
0.8
Maine
2.42
Vermont
2.42
Maryland
1.67
Virginia
1.19
Massachusetts
2.42
Washington
0.5
Michigan
1.2
West Virginia
1.25
Minnesota
0.97
Wisconsin
1
Mississippi
1.24
Wyoming
0.87
Missouri
3.39
Table 4-78 presents a summary of the emission factors for residential anthracite coal combustion (SCC
2104001000) for all pollutants. Table 4-79 presents a summary of the emission factors for residential bituminous
coal combustion (SCC 2104002000) for all pollutants. Note that the emission factor provided in AP-42 is 0.04
Ib/MMBtu. This was multiplied by the conversion factor of 26 MMBtu/ton provided in AP-42 for bituminous
coal.
4-128
-------
Table 4-78: Residential anthracite coal combustion emission factors
Pollutant
Code
Pollutant Code Description
Emissions
Factor
(LB/TON)
Data Source, AP-
42 [ref 4] Table
No.
83329
ACENAPHTHENE
0.000022
1.2-5
208968
ACENAPHTHYLENE
0.000086
1.2-5
120127
ANTHRACENE
0.000025
1.2-5
56553
BENZO[A]ANTHRACENE (Benz[a]Anthracene)
0.000071
1.2-5
50328
BENZO[A]PYRENE
0.0000053
1.2-5
192972
BENZO[E]PYRENE
0.0000062
1.2-5
191242
BENZO[G,H,l,]PERYLENE
0.0000055
1.2-5
207089
BENZO[K]FLUORANTHRENE (Benzo[k]Fluoranthene)
0.000025
1.2-5
218019
CHRYSENE
0.000083
1.2-5
206440
FLUORANTHRENE (Fluoranthene)
0.00017
1.2-5
86737
FLUORENE
0.000025
1.2-5
7647010
HYDROGEN CHLORIDE
1.2
1.1-15
7664393
HYDROGEN FLUORIDE
0.15
1.1-15
91203
NAPHTHALENE
0.00022
1.2-5
7439976
MERCURY
0.00013
1.2-7
198550
PERYLENE
0.0000012
1.2-5
85018
PHENANTHRENE
0.00024
1.2-5
129000
PYRENE
0.00012
1.2-5
CH4
METHANE
8
1.2-6
CO
CARBON MONOXIDE
275
1.1-3
NH3
AMMONIA
2
[ref 5]
NOX
NITROGEN OXIDES
3
1.2-1
PM10-FIL
PRIMARY PMio, FILTERABLE PORTION
10
1.2-3
PM10-FIL
PRIMARY PM2.5, FILTERABLE PORTION
4.6
1.2-3 & Fig 1.2-1
VOC
VOLATILE ORGANIC COMPOUNDS
10
1.1-19
Ta
)le 4-79: Residential bituminous coal combustion emission factors
Pollutant
Code
Pollutant Code Description
Emissions
Factor
(LB/TON)
Data Source, AP-
42 [ref 4] Table
No.
532274
2-CHLOROACETOPHENONE
0.000007
1.1-14
121142
2,4-DINITROTOLUENE
0.00000028
1.1-14
3697243
5-METHLY CHRYSENE
2.2E-08
1.1-13
83329
ACENAPHTHENE
0.00000051
1.1-13
208968
ACENAPHTHYLENE
0.00000025
1.1-13
75070
ACETALDEHYDE
0.00057
1.1-14
98862
ACETOPHENONE
0.000015
1.1-14
107028
ACROLEIN
0.00029
1.1-14
120127
ANTHRACENE
0.00000021
1.1-13
4-129
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Pollutant
Code
Pollutant Code Description
Emissions
Factor
(LB/TON)
Data Source, AP-
42 [ref 4] Table
No.
56553
BENZ[A]ANTHRACENE
0.00000008
1.1-13
71432
BENZENE
0.0013
1.1-14
50328
BENZO[A]PYRENE
3.8E-08
1.1-13
191242
BENZO[G,H,l,]PERYLENE
2.7E-08
1.1-13
100447
BENZYL CHLORIDE
0.0007
1.1-14
92524
BIPHENYL
0.0000017
1.1-13
117817
BIS(2-ETHYLHEXYL)PHTHALATE
0.000073
1.1-14
75252
BROMOFORM
0.000039
1.1-14
75150
CARBON DISULFIDE
0.00013
1.1-14
108907
CHLOROBENZENE
0.000022
1.1-14
67663
CHLOROFORM
0.000059
1.1-14
218019
CHRYSENE
0.0000001
1.1-13
98828
CUMENE
0.0000053
1.1-14
57125
CYANIDE
0.0025
1.1-14
77781
DIMETHYL SULFATE
0.000048
1.1-14
100414
ETHYL BENZENE
0.000094
1.1-14
75003
ETHYL CHLORIDE
0.000042
1.1-14
106934
ETHYLENE DIBROMIDE
0.0000012
1.1-14
107062
ETHYLENE DICHLORIDE
0.00004
1.1-14
206440
FLUORANTHENE
0.00000071
1.1-13
86737
FLUORENE
0.00000091
1.1-13
50000
FORMALDEHYDE
0.00024
1.1-14
110543
HEXANE
0.000067
1.1-14
7647010
HYDROGEN CHLORIDE
1.2
1.1-15
7664393
HYDROGEN FLUORIDE
0.15
1.1-15
193395
INDENO[l,2,3-C,D]PYRENE
6.1E-08
1.1-13
78591
ISOPHORONE
0.00058
1.1-14
7439976
MERCURY
0.000083
1.1-18
CH4
METHANE
5
1.1-19
74839
METHYL BROMIDE
0.00016
1.1-14
74873
METHYL CHLORIDE
0.00053
1.1-14
80626
METHYL METHACRYLATE
0.00002
1.1-14
1634044
METHYL TERT BUTYL ETHER
0.000035
1.1-14
75092
METHYLENE CHLORIDE
0.00029
1.1-14
91203
NAPHTHALENE
0.000013
1.1-13
N20
NITROUS OXIDE
0.04
1.1-19
85018
PHENANTHRENE
0.0000027
1.1-13
108952
PHENOL
0.000016
1.1-14
123386
PROPIONALDEHYDE
0.00038
1.1-14
4-130
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Pollutant
Code
Pollutant Code Description
Emissions
Factor
(LB/TON)
Data Source, AP-
42 [ref 4] Table
No.
129000
PYRENE
0.00000033
1.1-13
100425
STYRENE
0.000025
1.1-14
127184
TETRACHLOROETHYLENE
0.000043
1.1-14
108883
TOLUENE
0.00024
1.1-14
108054
VINYL ACETATE
0.0000076
1.1-14
1330207
XYLENES
0.000037
1.1-14
CO
CARBON MONOXIDE
275
1.1-3
NH3
AMMONIA
2
[ref 5]
NOX
NITROGEN OXIDES
9.1
1.1-3
PM10-FIL
PRIMARY PMio, FILTERABLE PORTION
6.2
1.1-4
PM25-FIL
PRIMARY PM2.5, FILTERABLE PORTION
3.8
1.1-11
PM-CON
PRIMARY PM CONDENSIBLE PORTION
1.04
1.1-5
PM10-PRI
PRIMARY PM10 (FILT + COND)
7.24
1.1-4, 1.1-5
PM25-PRI
PRIMARY PM2.5 (FILT + COND)
4.84
1.1-5, 1.1-11
VOC
VOLATILE ORGANIC COMPOUNDS
10
1.1-19
For CO and VOC, the emission factors listed for anthracite coal are the emission factors provided in AP-42 for
bituminous coal. 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
were provided for residential heaters for these pollutants. Therefore, it was felt that it the AP-42 emission rates
from bituminous coal that were derived for smaller hand-fed units, were more appropriate to use than applying
anthracite emission factors derived for much larger boilers.
Note that while AP-42 provides emission factors for some metals, these were based on tests at controlled
and/or pulverized coal boilers. These are not expected to be a good representation of emission rates for metals
from residential heaters, so these pollutants are not included.
The criteria pollutant and HAP emissions were calculated by multiplying the total coal consumed in each county
per year by the corresponding emission factor.
LPG
Pollutant emission factors for residential LPG are based on the residential natural gas emission factors [ref 4, ref
6, ref 7], For all counties in the United States, the natural gas consumed by residential combustion is assumed to
have a heating value of 1,020 Btu per cubic foot and a sulfur content of 2,000 grains per million cubic feet [ref
4], Those natural gas emission factors originally presented in the units "pounds per million cubic feet" were
converted to energy-based units using the 1,020 Btu/cubic foot conversion factor. Once all the natural gas
emission factors were converted to energy-based units, the natural gas emission factors were converted to LPG
emission factors by multiplying by 96,750 Btu/gallon. Some emission factors were revised based on
recommendations by an ERTAC advisory panel composed of state and EPA personnel. Table 4-80 provides a
summary of the pollutants, pollutant codes, and emission factors for residential combustion of LPG.
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Table 4-80: Residential LPG combustion emission factors
Pollutant
Code
Pollutant Code Description
Emissions
Factor
(LB/E3BBL)
129000
Pyrene
2.09E-05
206440
Fluoranthene
1.26E-05
50000
Formaldehyde
3.14E-01
71432
Benzene
8.78E-03
75070
Acetaldehyde
5.44E-05
85018
Phenanthrene
7.11E-05
86737
Fluorene
1.17E-05
91203
Naphthalene
2.55E-03
CO
CO
1.60E+02
NH3
Ammonia
1.95E+00
NOX
NOx
5.63E+02
PM10-PRI
PRIMARY PMio (INCLUDES FILTERABLES + CONDENSIBLES)
2.07E+00
PM25-PRI
PRIMARY PM2.5 (INCLUDES FILTERABLES + CONDENSIBLES)
1.71E+00
PM10-FIL
PRIMARY PM10, FILTERABLE PORTION ONLY
7.97E-01
PM25-FIL
PRIMARY PM2.5, FILTERABLE PORTION ONLY
4.38E-01
PM-CON
PRIMARY PM CONDENSIBLE PORTION ONLY (<1 MICRON)
1.28E+00
SO 2
S02
2.39E+00
VOC
VOC
2.19E+01
-.xampie Calculations
)s, Distillate, Kerosene, and LPG Equations
Emissions are calculated for each county using emission factors and activity as:
where:
Ex,p
FCX
EFx,f
And FCX
where:
FCX x EFX
: annual emissions for fuel type x and pollutant p,
: annual fuel consumption for fuel type x,
: emission factor for fuel type x and pollutant p,
' Astate X (Hcounty/ Hstate)
Astate = state activity data from EIA
Hcounty = number of houses in the county using the fuel type as the primary heating fuel. For distillate
and kerosene, this is the sum of both fuels.
Hstate = number of houses in the state using the fuel type as the primary heating fuel. For distillate and
kerosene, this is the sum of both fuels.
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Natural Gas Example
Using Allegheny County, PA as an example:
The State of Pennsylvania had a reported use of 254,816 million cubic feet of natural gas in the residential sector
in 2014. Allegheny County, PA had 444,844 houses out of the state total of 2,529,063 that use natural gas as the
primary heating fuel. This equates to a share of 17.59% of the natural gas used for residential heating in the
state. From Table 4-73, the CO emission factor is 40 lb/million ft3.
Eco = 254,816 million ft3 x (444,844 houses / 2,529,063 houses) x 40 lb CO/ million ft3
= 1,792,812 lb CO or 896.41 tons CO
Distillate Oil Example
Using Allegheny County, PA as an example:
The State of Pennsylvania had a reported use of 15,798 thousand barrels of distillate oil and 358 barrels of
kerosene in the residential sector in 2014. Allegheny County, PA had 8,081 houses that use distillate fuel oil or
kerosene as the primary heating fuel. Using the state ratio of distillate to kerosene, Allegheny County can be
assumed to have 7,902 houses using distillate as the primary heating fuel, out of 910,155 houses in the state.
This equates to a share of 0.89% of the distillate oil used for residential heating in the state. From Table 4-74,
the emission factor for CO is 5 lb/thousand gallons. Because the emission factor is in lbs/thousand gallons, a
conversion factor of 42 gallons per barrel is applied.
AAiegheny = 15,798 thousand barrels x 7,902 houses / 910,155 houses) x 42 gal / barrel
= 5,760.62 thousand gallons
EmisAiegheny,co = 5,760.2 thousand gallons x 5 lb CO/ thousand gallons
= 28,803 lbs CO or 14.4 tons CO
Kerosene Example
Using Allegheny County, PA as an example:
The State of Pennsylvania had a reported use of 15,798 thousand barrels of distillate oil and 358 thousand
barrels of kerosene in the residential sector in 2014. Allegheny County, PA had 8,081 houses that use distillate
fuel oil or kerosene as the primary heating fuel. Using the state ratio of distillate to kerosene, Allegheny County
can be assumed to have 179.07 houses using kerosene as the primary heating fuel, out of 20,625 houses in the
state. This equates to a share of 0.87% of the kerosene used for residential heating in the state. From Table
4-75, the CO Emission factor is 202.5 lb/thousand barrels. Because the emission factor is in lbs/thousand gallons,
a conversion factor of 42 gallons per barrel is applied.
AAiegheny = 358 thousand barrels x (179.07 houses / 20,625 houses)
= 3.1 thousand gallons
EmisAiegheny, co = 3.1 thousand gallons x 202.5 lb CO/ thousand gallons
= 629.4 lbs CO or 0.31 tons CO
LPG Example
Using Allegheny County, PA as an example:
The State of Pennsylvania had a reported use of 4,909 thousand barrels of LPG in the residential sector in 2014.
Allegheny County, PA had 4,460 houses out of the state total of 189,112 that use LPG as the primary heating
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fuel. This equates to a share of 2.36% of the LPG used for residential heating in the state. From Table 4-80, the
CO emission factor is 159.6 lb/thousand barrels.
Eco = 4,909 thousand barrels x (4,460 houses / 189,112 houses) x 159.6 lb/thousand barrels
= 18,480 lb CO or 9.24 tons CO
Coal Equations
Annual emissions are calculated for each county using emission factors and activity as:
Ex,p FCx x (1 - CEX;P) x EFX;P
where:
EX;P = annual emissions for fuel type x and pollutant p (lb/year),
FCX = annual county-level fuel consumption for fuel type x,
CEX;P = control efficiency for fuel type x and pollutant p, and
EFX;P = emission factor for fuel type x and pollutant p.
County-level fuel consumption is calculated using:
FCX Astate X RatiOAnth, Bit X RatiOcounty houses
where:
Astate = total tons of coal reported by the EIA,
RatiOAnth, Bit = ratio reported in Table 4-72, and
RatiOcounty houses = county allocation ratio based on number of houses burning coal.
Coal Example
Using Allegheny County, PA as an example:
(numbers are from 2011 inventory, SEDS data showed no coal consumption in any state in 2014)
The State of Pennsylvania had a reported use of 20,121 tons of coal in the residential sector in 2010. Statewide
anthracite coal use is calculated using the ratio of anthracite to bituminous in Table 4-72 for PA: 80.6%.
Allegheny County, PA had 183 houses out of the state total of 67,986 that use coal as the primary heating fuel.
This equates to a share of 0.27% of the coal used for residential heating in the state. Thus, the anthracite fuel
consumption for Allegheny County is:
FCAiiegheny,anth = 20,121 x 0.806 x 0.0027 = 44 tons anthracite coal
The PM2.5-PRI emission factor for residential heating with anthracite coal is 4.6 + 0.08 Ibs/tonx state-specific %
ash content (see Table 4-77). The ash content is 13.38%, (see Section 4.13.3.3) so the emission factor is 5.67
lbs/ton.
EmisAii egheny, a nth, PM 2.5-pri - 44 tons anthracite coal x 5.67 lbs PM2.5-PRI per ton coal
= 249 lbs PM2.5-PRI
4,1335 Changes from 2011 and2014vl Methodology
All fuels
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Activity data were updated to 2013 SEDS for 2014vl and 2014 SEDS for 2014v2, and allocated to counties using
the US Census Bureau's 2013 (for 2014vl) and 2014 (for 2014v2) 5-year estimate Census Detailed Housing
Information.
Distillate and Kerosene
In addition to the updated activity data, for distillate and kerosene, the more significant difference between
2011 and 2014 was the allocation of distillate oil consumption. The US Census Bureau Detailed Housing
Information category for homes using distillate oil also includes kerosene as a fuel source. To tease apart the
number of houses using each of these fuels, the number was multiplied by the ratio of state distillate or
kerosene consumption to the total state consumption of distillate oil and kerosene. These steps were not taken
in 2011.
4.13,3.6 Puerto Eko and US Virgin Islands Emissions Calculations
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 County (FIPS state county code = 12011) for
Puerto Rico and Monroe 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".
4,13,4 References for fuel combustion -residential natural gas, oil and other
1. U.S. Department of Energy, Energy Information Administration (EIA). State Energy Data System (SEDS):
1960-2014 Consumption. Washington, DC 2015, accessed July 2016.
2. U.S. Census Bureau. B25040 House Heating Fuel, 2009-2013 American Community Survey 5-Year
Estimates, accessed July 2014.
3. EIA, 2008. U.S. Department of Energy, Energy Information Administration, Domestic Distribution of U.S.
Coal by Destination State, Consumer, Origin and Method of Transportation. 2006, accessed September
2015.
4. U.S. Environmental Protection Agency. Compilation of Air Pollutant Emission Factors, 5th Edition. AP-42,
Volume I: Stationary Point and Area Sources. Research Triangle Park, North Carolina. 1996.
5. Pechan, 2004: E.H. Pechan & Associates, Inc. Estimating Ammonia Emissions from Anthropogenic
Nonagricultural Sources - Draft Final Report, prepared for the Emission Inventory Improvement
Program, April 2004.
6. U.S. Environmental Protection Agency, Emission Factors and Inventory Group. "Documentation for the
1999 Base Year Nonpoint Area Source National Emission Inventory for Hazardous Air Pollutants."
Prepared by Eastern Research Group, Inc. Morrisville, NC. September 2002.
7. U.S. Environmental Protection Agency. Emission Factor and Inventory Group. Final Summary of the
Development and Results of a Methodology for Calculating Area Source Emissions from Residential Fuel
Combustion. Prepared by Pacific Environmental Services, Inc. Research Triangle Park, NC. September
2002, accessed September 2015.
8. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards. "Documentation of
Emissions Estimation methods for Year 2000 and 2001 Mobile Source and Nonpoint Source Dioxin
Inventories." Prepared by E.H. Pechan & Associates, Inc., Durham, NC. May 2003.
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fuel cotTtuUHtvH ~ rvfc&iuemtdl ปvuUU
4,14.1 Sector Description
This source category includes residential wood burning devices such as fireplaces, fireplaces with inserts
(inserts), free standing woodstoves, pellet stoves, outdoor hydronic heaters (also known as outdoor wood
boilers), indoor furnaces, and outdoor burning in firepits and chimeneas. We further differentiate free standing
woodstoves and inserts into three categories: conventional (not EPA certified); EPA certified, catalytic; and EPA
certified, noncatalytic. Generally, the conventional units were constructed prior to 1988. Units constructed after
1988 had to meet EPA emission standards and they are either catalytic or non-catalytic. For shorthand, we refer
to the Residential Wood Combustion sector as "RWC" in the remaining documentation.
Table 4-81 shows the SCCs used in the 2014 NEI from in this sector. EPA estimates emissions for all SCCs in this
sector. The SCC level 1 and 2 descriptions is "Stationary Source Fuel Combustion; Residential" for all SCCs.
Table 4-81: RWC sector SCCs in the 2014 NEI
SCC
SCC Level Three*
SCC Level Four
2104008100
Wood
Fireplace: general
2104008210
Wood
Woodstove: fireplace inserts; non-EPA certified
2104008220
Wood
Woodstove: fireplace inserts; EPA certified; non-catalytic
2104008230
Wood
Woodstove: fireplace inserts; EPA certified; catalytic
2104008310
Wood
Woodstove: freestanding, non-EPA certified
2104008320
Wood
Woodstove: freestanding, EPA certified, non-catalytic
2104008330
Wood
Woodstove: freestanding, EPA certified, catalytic
2104008400
Wood
Woodstove: pellet-fired, general (freestanding or FP insert)
2104008510
Wood
Furnace: Indoor, cordwood-fired, non-EPA certified
2104008610
Wood
Hydronic heater: outdoor ("outdoor wood boilers")
2104008700
Wood
Outdoor wood burning device, NEC (fire-pits, chimeneas, etc)
2104009000
Firelog
Total: All Combustor Types
4,14,2 Sources of data
The RWC sector includes emissions from both S/L/T agencies and from the EPA. As is the case with most
nonpoint sources, RWC data submitted by S/L/Ts is used over EPA data when provided. The EPA worked with
S/L/Ts to modify the RWC Tool for the 2014 NEI. While many reporting agencies were involved in discussions on
the development of the EPA's RWC Tool used for the 2014 NEI, many opted to run the tool with their own
customized inputs and assumptions, or decided to submit their own estimates developed outside the RWC Tool.
The agencies listed in Table 4-82 submitted at least PM2.5 and/or VOC 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%).
Table 4-82: Reporting agency PM2.5 and VOC percent contribution to total NEI emissions for RWC sector
Region
Agency
S/L/T
PM2.5
VOC
1
Vermont Department of Environmental Conservation
State
100
100
3
Delaware Department of Natural Resources and Environmental Control
State
100
100
4
Metro Public Health of Nashville/Davidson County
Local
84
5
Illinois Environmental Protection Agency
State
100
100
4-136
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Region
Agency
S/L/T
PMz.5
voc
5
Michigan Department of Environmental Quality
State
100
100
5
Minnesota Pollution Control Agency
State
99
100
6
Louisiana Department of Environmental Quality
State
96
99
6
Texas Commission on Environmental Quality
State
100
100
7
Sac and Fox Nation of Missouri in Kansas and Nebraska Reservation
Tribe
100
100
8
Northern Cheyenne Tribe
Tribe
100
100
9
Arizona Department of Environmental Quality
State
100
100
9
California Air Resources Board
State
100
100
9
Morongo Band of Cahuilla Mission Indians of the Morongo Reservation, California
Tribe
100
100
9
Washoe County Health District
Local
91
97
10
Coeur d'Alene Tribe
Tribe
100
100
10
Kootenai Tribe of Idaho
Tribe
100
100
10
Nez Perce Tribe
Tribe
100
100
10
Oregon Department of Environmental Quality
State
94
95
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
100
100
10
Washington State Department of Ecology
State
96
97
4,14.3 EPA-developed emissions for residential wood combustion: minor revisions for 20i4v2 NEf
The EPA collaborated with State, Local and Regional Planning Organization representatives to create a new
methodology for the RWC Tool for 2014vl NEI. Some minor updates were included after v3.0 for Version 3.2 of
the RWC Tool used for the 2014v2 NEI. The changes to the EPA methodology between 2014vl NEI (v3.0 of the
RWC tool) and the 2014v2 NEI (v3.2) are highlighted in following sections where they apply.
The RWC Tool is designed to allow users the ability to apply county-specific inputs on various types of activity
data including appliance fractions, burn rates, certification profiles and burn ban assumptions. We also allowed
for state-to-county allocations of outdoor wood boilers and indoor furnaces to be computed by inverse
population density rather than the default rural population; however, after comparing county allocations
between the two methods, very few stakeholders saw the inverse population density option as a better option.
Emissions in the RWC Tool are computed using the equation here:
Emissions = Homes x ApplianceFrac x BurnRate x WoodDensity x AdjustFactor x EF
where,
Emissions = annual emissions (ton/year) for a specific appliance (SCC), county and pollutant
Homes = number of occupied homes in each county,
ApplianceFrac = fraction of homes in each county that use the appliance,
BurnRate = average amount of wood burned per appliance (cords/appliance),
WoodDensity = density of firewood (tons/cord),
AdjustFactor = county and SCC-specific adjustment factor to account for burn bans,
EF = emission factor (tons of pollutant emitted/ton of fuel used)
There is a specific approach for different appliance types (SCCs) for each of the terms in the above equation. The
activity data for RWC is the total amount of wood burned. It is estimated by multiplying the number of occupied
homes in each county by the appliance fraction to estimate the number of appliances operated annually in the
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county. This number is multiplied by the burn rate to estimate the total amount of wood burned in each
appliance in each county.
4.143.1 Occupied Homes in each County
Because appliance fractions are estimated in terms of the fraction of occupied units by appliance type, it is
important that county population also be based on the number of occupied units. The number of occupied
housing units is derived from the U.S. Census Bureau 2014 American Community Survey [ref 1], which reports on
the number of homes by the type of house:
Single-family detached homes,
Single-family attached homes,
Multi-family homes with 2-4 units,
Multi-family homes with more than 5 units, and
Mobile homes, boats, recreational vehicles, vans, etc.
Each of these home types is further divided into urban and rural homes; for example, the number of urban
single-family detached homes, the number of rural single-family detached homes, and so on. Using the
proportion of total urban and rural homes in each county from the 2010 U.S. Census [ref 2], the RWC Tool
therefore computes up to 10 different classes occupied housing units per county.
4.143.2 Appliance fractions: updated for 2014v2 NEi
Appliance fractions are the fraction of occupied homes in each county that uses each type of wood burning
appliance. These appliance fractions are mapped to the 10 different types of occupied homes in each county.
The appliance fractions are calculated using two main data sources: The Energy Information Administration (EIA)
year-2009 "RECS" Residential Energy Combustion Survey [ref 3] and the 2013 American Housing Survey (AHS)
[ref 4], It is important to note that the most recent RECS data is for year 2009. As of May 2017, year 2013 RECS
data, likely more-aligned with year 2014 wood usage, is not yet available. Year 2014 AHS data was not made
available until after the development of this RWC Tool in the spring of 2017. Both the RECS and AHS includes
survey data that asks respondents whether they use a given wood burning appliance.
The RECS data includes a nationally representative sample of wood burning characteristics for each type of
housing unit. The 2009 RECS is based on 12,083 households used to represent the 113.6 million occupied
homes. The RECS provides information on the average wood consumption used as primary and secondary
heating by each of the 4 U.S. Census Regions -see Figure 4-9. The AHS data includes information on wood usage
for each U.S. Census Division by type of wood burning device: Stoves, Fireplaces with inserts, and fireplaces
without inserts. The AHS data also delineates between various population density characteristics within each
Census Division: central city of metro area, outside central city but within metro area, and outside the metro
area.
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Figure 4-9: U.S. Census Regions and Census Divisions
WEST
Pacifie
Mountain
MIDWEST
NORTHEAST
Middle
Atlantic
New
Englant
ฆEastH
North Central
West
North Central
H *ฆ
West East
South Central [ South Central
SOUTH
South
Atlantic
Fireplaces, Woodstoves. and Indoor Furnaces
The methodology for estimating the appliance fraction from fireplaces, fireplace inserts, freestanding
woodstoves, pellet stoves, and indoor furnaces uses the ElA's RECS microdata, which consists of 27,187
individual survey responses between 1997 and 2009. RECS asks a wide variety of questions related to home
energy use, including several that are important for RWC emissions estimation:
The appliance used for the main heat source in the home,
The fuel used for the main heat source in the home,
Whether the home uses a woodstove for a secondary heat source,
Whether the home uses a fireplace for a secondary heat source.
The amount of wood burned (cords) annually by the home.
The RECS data also includes demographic data about the respondent, including their census division location,
the number of heating degree days in their area, the type of house they live in, and whether their home is in an
urban or rural setting.
The appliance fractions were estimated using a regression technique called logistic regression that estimates the
likelihood of a binary (i.e. yes or no) outcome. In this case the outcome is whether or not the home uses the
wood burning appliance. The result of the logistic regression analysis is an equation that uses the demographic
variables to predict the proportion of homes in each county that uses each appliance:
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1
^ ^ g-(p0+p1-HDD+p2'HomeType+p3-UrbanRural+p4-ApplType+p5-BurnType)
where:
p = the probability that a home in a given county uses a given wood burning appliance
HDD = the number of heating degree days in each county from NOAA [ref 5]
HomeType = the type of home (5 types: single-family detached, single-family attached, multifamily with
2-4 units, multifamily with 5+ units, and mobile homes),
UrbanRural = whether the home is in an urban or rural setting,
ApplType = appliance type (fireplaces, woodstoves, and furnaces), and
BurnTypes = whether the appliance is used for primary/main heat or other heating (only main heating
was used for furnaces)
The logistic regression analysis estimates the coefficients (/?,) used in the equation. When those coefficients are
used with the predictor variables listed above, the equation estimates the probability that a home uses a wood
burning appliance.
An example of the distribution of heating degree days is shown in Figure 4-10. We include heating degree days
in the logistic regression equation to refine the spatial allocation within the large Census Regions. For example,
we would not expect primary heating from woodstoves to be similar between West Virginia and Florida -both
states are in the South Census Region. Alternatively, for most regions, there did not appear to be enough survey
responses to allocate appliances to more fine-scale Census Division.
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Figure 4-10: AIA climate zones from the 1978-2005 RECS
Climate Zones
| Zone 1 is less than 2,000 ODD and greater than 7,000 HDD
Zona 2 is less than 2,000 CDD and 5,500-7 000 HDD
[ | Zone 3 Is less than 2,000 CDD and 4,000-5,499 HDD
I Zone 4 is less than 2,000 CDD and less than 4.000 HDD
I Zone 5 is 2,000 CDD of more and less itian 4,000 HDD
The result of the logistic regression analysis is 40 unique appliance fractions for each county. These appliance
fractions are multiplied by the number of homes in each county in each category. For example, the appliance
fraction for main heating by woodstoves in urban mobile homes is multiplied by the number of urban mobile
homes in each county to determine the total number of woodstoves that were used for main heating in urban
mobile homes. This process is repeated for all home types, appliance types, and burn types
New for the 2014v2 NEI (RWC Tool V3.2), for fireplaces, the appliance fractions are also adjusted to account for
the fraction of fireplaces that burn natural gas or propane rather than wood. Data from RECS suggests that
approximately 49 percent of fireplaces in urban homes and 47 percent of fireplaces in rural homes burn wood.
The default assumption of the RWC tool is that all woodstoves are 100 percent wood burning.
Certification Profiles
Because the data from ElA's RECS does not specify whether the respondent uses a woodstove or fireplace insert
that is certified, the general data on the number of woodstoves and fireplaces must be split into specific SCCs
based on assumptions. In the RWC tool, we developed "certification profiles" that are grouped by Appliance
Type (woodstove or fireplace) and Census Region.
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The certification profile assumptions can be adjusted in the tool, but the profile ratios when grouped by
appliance type and region should sum to 1. For example, the sum of the profile ratios for woodstoves in the
Midwest Census Region should equal 1.
Table 4-83 shows the certification profiles for woodstoves, which are used to split the general data on
woodstove populations into four SCCs: freestanding non-EPA certified stoves, freestanding EPA certified non-
catalytic stoves, freestanding EPA certified catalytic stoves, and pellet stoves. RECS data is used to estimate
these certification profiles. Although RECS does not specifically ask whether the woodstove is EPA certified, the
2009 edition does ask the age of the appliance. It is assumed that any appliance older than 20 years old is
uncertified, since the appliance would have been built prior to the first New Source Performance Standard
(NSPS) for woodstoves, finalized in 1988. All appliances less than 20 years old are assumed to be EPA certified.
The certification profile for pellet stoves is based on the proportion of respondents to RECS that use a
woodstove but their main fuel source is wood pellets, rather than cordwood. Reporting agencies have the ability
to modify these profiles by appliance type to the county-level, but for EPA estimates, a national default is used.
Once the RECS data is used to determine the proportion of stoves that are certified vs. noncertified, data
provided by Minnesota from their 2014/2015 residential wood survey is used to determine the proportion of
certified stoves that are noncatalytic vs. catalytic. There was not enough information in the RECs data to refine
the certification profiles by geographic region; therefore, these profiles are the same nationally for all types of
woodstoves.
Table 4-83: Certification profiles for woodstoves
see
Description
Northeast
Midwest
South
West
2104008310
Woodstove: freestanding, non-EPA certified
0.286
0.286
0.286
0.286
2104008320
Woodstove: freestanding, EPA certified, non-
catalytic
0.355
0.355
0.355
0.355
2104008330
Woodstove: freestanding, EPA certified, catalytic
0.237
0.237
0.237
0.237
2104008400
Woodstove: pellet-fired, general
0.122
0.122
0.122
0.122
Total
1
1
1
1
Table 4-84 shows the certification profiles for fireplaces, which are used to split the general data on fireplace
populations into four SCCs: general fireplaces, non-EPA certified fireplace inserts, EPA certified non-catalytic
inserts, and EPA certified catalytic inserts. The AHS asks respondents whether their fireplace has an insert, and
reports these data at the census region level. The split between certified and non-certified, and catalytic and
non-catalytic inserts are based on data provided by Minnesota from their 2014/2015 residential wood survey.
Table 4-84: Certification profiles
:or fireplaces
see
Description
Northeast
Midwest
South
West
2104008110
Fireplace: general
0.487
0.438
0.575
0.523
2104008210
Woodstove: fireplace inserts, non-EPA certified
0.278
0.305
0.23
0.258
2104008220
Woodstove: fireplace inserts, EPA certified,
non-catalytic
0.182
0.199
0.151
0.169
2104008230
Woodstove: fireplace inserts, EPA certified,
catalytic
0.053
0.058
0.044
0.050
Total
1
1
1
1
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Outdoor Hydremic Heaters (OHHs)
For OHHs (outdoor wood boilers), a different approach is used to determine the number of appliances in use.
There are not enough survey responses to RECS by respondents that use OHHs to allow for the type of
regression analysis used for the other appliance types. Therefore, the appliance fractions for OHHs are
calculated using data from the American Housing Survey. In 2011 (the only year in which this question was
included in the AHS), the AHS asked whether the respondent used an OHH. Like the RECS data, the AHS include
demographic data about the respondent, including their census region and division location, and climate zone,
which is defined by number of heating degree days.
The total number of estimated OHHs are divided into each unique combination of census region and climate
zone. This total OHHs population is then distributed to each county within the unique census region and climate
zone based on proportion of rural population. For example, there are estimated to be approximately 15,000
OHHs in the coldest climate zone of the Northeast census region, which includes 100 counties. These 15,000
OHHs are distributed to the counties with the highest proportion of rural population.
There are two exceptions to this methodology. The first is that for the West census region, the OHH population
is apportioned based on unique combinations of census division (rather than census region) and climate zone. In
the west, OHH sales and usage are under significantly more scrutiny in the Pacific census division compared to
the mountain census division; it therefore does not make sense to treat appliance profiles the same in the entire
region. The second is that there were some states, specifically, Michigan, Ohio, and Wisconsin that (initially)
preferred to distribute the OHHs based on inverse population density rather than rural population. In this way,
most of the OHHs are distributed to the least dense (people/mi2) counties. The RWC tool offers the capability in
the "Edit Assumptions" window to redistribute the emissions from OHHs and furnaces based on inverse
population density rather than rural population. On further inspection of the OHH emissions resulting from this
method, one of these Midwest states opted to resubmit RWC emissions. In short, we advise to use caution if
considering using the inverse population method.
The appliance fractions for OHHs are estimated by dividing the number of OHHs distributed to each county by
the number of occupied houses in each county in 2011. This number is then multiplied by the number of
occupied houses in 2014 to estimate the county-level OHH population in 2014.
Wax Firelogs and Other Outdoor Wood Burning Devices
Data were unavailable to update the activity data for wax firelogs and outdoor wood burning devices (e.g.
firepits or chimeneas). The activity data for these source categories is pulled forward from the 2011 NEI
methodology, which is based mostly on AHS data, though for firelogs, includes a 30% downward adjustment to
account for natural gas usage (Houck, 2003).
Burn ra tes: additional user option for2014v2 NEi
Burn rates are the amount of wood burned annually for each appliance, reflected in cords for all appliance types
except for firelogs, which are expressed as tons. The burn rates for fireplaces, woodstoves and indoor furnaces
are estimated from the same 2009 RECS data used to create the appliance fractions.
Similar to the methodology for estimating the appliance fractions, the burn rates are estimated using regression
analysis based on each unique combination of home type, urban or rural setting, appliance type, and burn type.
The results of the regression analysis show that the number of heating degree days is not a significant predictor
variable for most of the United States, and therefore it is not included in the analysis for all census regions,
except for the South Atlantic division within the South region. The South Atlantic division -spanning disparate
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climates from West Virginia to Florida- therefore includes heating degree days for allocation. The rest of the
South region -east south central and west south central- uses a "rest-of South region" allocation that does not
include heating degree days in its allocation.
The burn rates match the level of specificity of the appliance fractions. For example, there are unique burn rates
and appliance fractions for each county for rural mobile homes that use fireplaces as a secondary heat source,
as well as all other combinations of home type, appliance type, and burn type.
The AHS data used to estimate the appliance fractions for OHHs does not include data on the amount of wood
burned. Therefore, the burn rates for OHHs are pulled forward from the 2011 methodology, which is based
largely on expert judgment. Burn rates were zeroed out for all counties with greater than 1,500 housing units
per square mile. Additional burn rate information from state or local surveys was carried over from the 2011
methodology for California, Oregon, Washington, Minnesota and Vermont. Otherwise, the general approach
uses expert judgment to estimate burn rates for OHHs and scales them based on climate zone.
Similarly, the burn rates for wax firelogs and outdoor wood burning devices are pulled forward from the 2011
NEI methodology, which is also based mostly on expert judgment.
New to the RWC Tool v3.2 (2014v2 NEI), users were allowed to provide county and appliance-specific burn rates
to override the RECS-based (EPA) defaults in the tool
4.14.3.4 iA/ooddensity
The density of oven dried wood is used to compute average density of wood by county because emission factors
developed by EPA are based on oven dried wood mass units. Dried wood density data are obtained from the
U.S. Forest Service (USDA, 2007) [ref 6] for various wood species. The Forest Service developed a database
(called the Timber Products Output) that contains survey results of sawmill operators that includes the volume
of wood by species for several different categories of use - one of the uses being fuel wood.
Using the oven dried density by species multiplied by the per-species volumes gives a per species weight which
is summed to calculate the total weight for the county. This is then divided by the total volume of wood in the
county to get the average density by county. If a county specific density is not available, regional averages are
used instead.
The calculated density by county from the Forest Service data is then converted to tons/cords. Officially a cord is
defined as a stack of wood 4 feet wide, 8 feet long, and 4 feet tall or 128 cubic feet. However, we instead
assume a value of 80 cubic feet per cord to account for air spaces in the stack.
For wax firelogs, density is assumed to not vary from county to county, and a density of 4.005 tons per cord is
used. This is based on the volume of a typical 5 pound firelog. For wax firelogs, a cord is assumed to be 128 ft3
because air spaces assumptions are not applicable.
4.14.3.5 Emission factors: updated for 2014v2 NEI
The emission factors in the RWC Tool are expressed as tons of pollutant produced for every ton of wood burned.
The emission factors were last reviewed for the 2011 NEI by the Eastern Regional Technical Advisory Committee
(ERTAC). The complete list of emission factors and their references are available in the RWC Tool and RWC Tool
V3.0 PDF documentation available on the 2014vl Supplemental Data FTP site.
Many of the emission factors used to determine national emission estimates for RWC are from EPA's AP-42
document (Tables 1.9-1, 1.10-3, and 1.10-4). Some of the stove and insert factors were adjusted based on new
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data developed in the reference Review of Wood Heater and Fireplace Emission Factors (Houck et al. 2001) [ref
7]. The emission factors generated by Houck, et. al. for 7-PAH and 16-PAH are lower than the associated AP-42
emission factors. Therefore, the AP-42 PAH emission factors were adjusted downward by 62% for conventional
woodstoves, 51% for catalytic woodstoves, and 40% for non-catalytic woodstoves.
Version 3.2 of the RWC Tool, used for the 2014v2 NEI, changes were made to all emission factors for EPA-
certified non-catalytic and catalytic wood stoves and fireplace inserts to account for an increase in appliances
that meet emissions standards from EPA and Washington state.
As seen in Table 4-85, the particulate matter (PMio) emission factors used for the 2014vl NEI, the RWC Tool
v3.0, are based on an average of the Phase I and Phase II emission factors from the 1988 New Source
Performance Standards (NSPS) included in AP-42. While EPA did not update the federal NSPS until 2015, the
Regulatory Impact Analysis (RIA) for the 2015 NSPS [ref 8] notes that the state of Washington introduced more
stringent emissions standards for woodstoves in 1995. These standards result in approximately 40 percent less
emissions than the Phase II EPA NSPS.
Table 4-85: PM io woodstove standards and emission factors (lb/ton)
Standard
Source
Years
Catalytic
Non-catalytic
1988 NSPS Phase 1
AP-42
1988-1990
19.6
20.0
1988 NSPS Phase II
AP-42
1990-1995
16.2
14.6
Washington Standards
2015 NSPS
1995-2015
9.72
8.76
When EPA calculated the baseline residential wood combustion emissions for the 2015 NSPS RIA, they assumed
that shipments of woodstoves after 1995 would meet the more stringent Washington state standards. Because
the EPA-certified non-catalytic and catalytic SCCs include many stoves of various ages that meet different
standards, we crafted a methodology to estimate the number of woodstoves that fall under each of the
standards. This enabled the creation of a weighted-average emission factor for certified woodstoves.
ElA's RECS contains data on energy use in homes, including the age of heating devices (including woodstoves)
used in homes in the United States. RECS data are available for the years 1997, 2001, 2005, and 2009. We then
used the RECS data to determine the proportion of stoves in each data year that fall under each standard, and
then, projected the data to determine the proportion of stoves in 2014 that would meet each standard. As seen
in Table 4-86, we then used this proportion to determine a weighted average emission factor for PMi0and CO
for use in the new RWC Tool (v3.2) for the 2014v2 NEI.
Table 4-86: 2014vl and 2014v2 NEI emission factors (lb/ton) for PMio and CO
2014vl NEI Factors
2014v2 NEI Factors
o
rH
a.
CO
PMio
CO
Catalytic
20.4
104.4
15.2
92.3
Non-catalytic
19.6
140.8
14.5
122.6
For the different wood stove emissions standards, AP-42 only provides different emission factors for PMio and
CO. For all other pollutants, including HAPs, we can adjust the emission factors based on the percent decrease in
the PMio emission factor, which is 25% for catalytic and 26% for non-catalytic stoves.
The emissions factor for mercury was taken from AP-42, Chapter 1.6 Wood Residue Combustion in Boilers. The
original emission factor of 3.50E-06 lbs. Hg/MMBtu was converted to a factor of 4.26E-05 lbs. Hg/ton of wood
using a heating value of 15.3 MMBtu/cord from the U.S. Forest Service [ref 6] and an average density from the
RWC Tool of 1.26 tons per cord.
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4.143.6 Other inputs: Appliance and Bum Ban Assumptions
The RWC tool also allows users to make county and SCC-specific adjustments to account for appliance or burn
bans. Users can update the inputs with additional SCCs and counties where the emissions should be adjusted.
The calculated throughput and emissions for that SCC and county will be multiplied by the user-specified
"Adjustment Factor". If, for example, a county has banned OHHs, then add the county FIPS code and the correct
SCC (2104008610 for OHHs), and set the adjustment factor to 0. This will zero out the throughput and emissions
for OHHs in that county.
Similarly, if a county has instituted a burn ban that is expected to reduce burning by 50%, the adjustment factor
could be set to 0.5. This would reduce the calculated throughput and emissions for the listed SCC by 50%. To-
date, EPA includes only OHH and indoor furnace zero outs for southern New York, provided by the NY State
Department of Environmental Conservation.
4,14,4 issues for 2017 NEI consideration
There are many known issues in the RWC Tool used for the 2014v2 NEI. Resources will determine how much can
be included in the next version of the RWC Tool. Some known issues are lack of survey data in most areas.
Having local appliance profiles and burn rate information is a high priority.
Firelogs and Other outdoor equipment
These "recreational RWC" estimates are carried forward from the 2011v2 NEI. We have not been able to find
more updated information on these sources. Discussions with reporting agencies indicate that these emissions,
particularly for other outdoor equipment like fire pits and chimeneas, vary greatly by geography from north to
south.
Outdoor Hydronic Heaters
Burn rates information for OHHs is generally lacking in RECS and AHS data and in most available surveys. This is
an ongoing area of need.
Emission Factors
Emission factors needs longer-term additional work for all appliance types. There are questions about
unexpected factors when comparing non-catalytic to catalytic stoves, VOC HAPs to VOC factors, and how single
burn-rate devices -not subject to the 1998 NSPS- are accounted for in the appliance profiles. Many emission
factors rely on AP-42 factors, ERTAC studies, or worse, an inconsistent blend between multiple sources for the
same appliance type.
Land Use Data
We would like to pursue a longer-term effort to analyze the impact of land cover to better-apportion emissions
intra-Census Division or Region and climate zone; intuitively, in the absence of robust survey local data, we
would expect less wood burning in areas with less available wood.
Lack of local survey data for appliance profiles and burn rates
There is very little local survey data included in the appliance profiles and burn rate calculations. A fledgling RWC
Survey, targeting over 75,000 households over 15 states in different geographic regions, will be conducted in the
spring and summer of 2018. Analysis on the survey results later in 2018 should hopefully improve the local
activity data in these states and hopefully other nearby states with similar RWC consumption characteristics.
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Inverse Population Allocation Option
The inverse population density approach redistributes the number of estimated OHHs and indoor furnaces
within a state so that areas with the lowest population density get the highest number of appliances. There are
currently only three states that use this approach: Michigan, Ohio, and Wisconsin. However, feedback from
these states suggests that this approach results in too many emissions in some very rural counties. In the next
version of the tool, we will attempt to limit the redistribution of appliances so that no county is estimated to
have more than 10 percent of its homes with an OHH or indoor furnace.
4,14.5 References for residential wood combustion
1 U.S. Census Bureau. 2016a. American Community Survey, accessed April 2016.
2 U.S. Census Bureau. 2010 Census data.
3 Energy Information Administration (EIA). 2016. Residential Energy Consumption Survey (RECS), accessed
April 2016.
4 U.S. Census Bureau. 2016b. American Housing Survey, accessed April 2016.
5 National Oceanic and Atmospheric Administration (NOAA). 2016. Degree Day Statistics, accessed April
2016.
6 U.S. Department of Agriculture (USDA). 2007. Timber Products Output Survey. Forestry Service,
retrieved via query November 2007.
7 Houck, J., Crouch, J., Huntley, R., Review of Wood Heater and Fireplace Emission Factors. 10th
International Emission Inventory Conference - "One Atmosphere, One Inventory, Many Challenges",
Denver, CO, May 1 -3, 2001.
8 U.S. EPA. 2015. Regulatory Impact Analysis for Residential Wood Heaters NSPS Revision. EPA-452/R-15-
001.
4.15 Industrial Processes - Mining and Quarrying
;settui ueioipuus'i
Mining and quarrying activities produce particulate 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 emission factors
accounting for the different means by which the resources are extracted.
The 2014 NEI has emissions for the two SCCs shown in Table 4-87 for this sector. The leading SCC description is
"Industrial Processes; Mining and Quarrying: for all SCCs in the table. The EPA-estimated emissions cover only
the "All Processes" SCC 2325000000. Emissions for "Lead Ore-Mining and Milling" SCC were submitted by
Missouri.
Table 4-87: SCCs for Industrial Processes- Mining and Quarrying
SCC
Description
2325000000
All Processes; Total
2325060000
Lead Ore Mining and Milling; Total
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4,15,2 Source of data
The mining and quarrying 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. Some agencies submitted emissions for the entire sector (100%), while
others submitted only a portion of the sector (totals less than 100%).
Table 4-88
Percentage of Mining and Quarrying PM2.5 and PM10 emissions submitted by reporting agency
Region
Agency
PM10
PM2.5
2
New Jersey Department of Environment Protection
100
100
3
Maryland Department of the Environment
99
99
4
Knox County Department of Air Quality Management
100
100
7
Missouri Department of Natural Resources
60
75
8
Assiniboine and Sioux Tribes of the Fort Peck Indian Reservation
100
100
8
Utah Division of Air Quality
100
100
9
Clark County Department of Air Quality and Environmental Management
100
100
9
Washoe County Health District
100
100
10
Alaska Department of Environmental Conservation
7
10
Coeur d'Alene Tribe
100
100
10
Idaho Department of Environmental Quality
100
100
10
Nez Perce Tribe
100
100
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
100
100
4.1S.3 tPA-deveiopeo Emissions for mining snd QU3trying
The below sections explain how the PM10 and PM2.5 emissions for the EPA data (SCC 2325000000; Industrial
Processes; Mining and Quarrying: SIC 14; All Processes; Total) were developed.
4.13,3,1 Emission Factors
Metallic Ore Mining
The emissions factor for metallic ore mining includes overburden removal, drilling and blasting, and loading and
unloading activities. The total suspended particulate (TSP) emission factors developed for copper ore mining are
applied to all three activities with PM10/TSP ratios of 0.35 for overburden removal, 0.81 for drilling and blasting,
and 0.43 for loading and unloading operations [ref 1], The emissions factor equation for metallic ore mining is:
EFmo = EF0 + (B x EFb) + EFi + EFd
where,
EFmo = metallic ore mining emissions factor (lbs/ton)
EF0 = PM 10 open pit overburden removal emission factor for copper ore (lbs/ton)
B = fraction of total ore production that is obtained by blasting at metallic ore mines
EFb = PM 10 drilling/blasting emission factor for copper ore (lbs/ton)
EF, = PM 10 loading emission factor for copper ore (lbs/ton)
EFd = PM 10 truck dumping emission factor for copper ore (lbs/ton)
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Applying the copper ore mining TSP emission factors [ref 2] and PMio/TSP ratios yields the following metallic ore
mining emissions factor:
EFmo = 0.0003 + (0.57625 x 0.0008) + 0.022 + 0.032 = 0.0548 lbs/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.
EFnmo = EFv + (D x EFr) + EFa + 0.5 (EFe + EFt;
where,
EFnmo = non-metallic ore mining emissions factor (lbs/ton)
EFV = PM io open pit overburden removal emission factor at western surface coal mining
operations (lbs/ton)
D = fraction of total ore production that is obtained by blasting at non-metallic ore mines
EFr = PM io drilling/blasting emission factor at western surface coal mining operations (lbs/ton)
EFa = PM io loading emission factor at western surface coal mining operations (lbs/ton)
EFe = PM io truck unloading; end dump-coal emission factor at western surface coal mining
operations (lbs/ton)
EFt = PM io truck unloading; bottom dump-coal emission factor at western surface coal
mining operations (lbs/ton)
Applying the TSP emission factors developed for western surface coal mining operations from AP-42 [ref 3] and
a PMio/TSP ratio of 0.4 [ref 4] yields the following non-metallic ore mining emissions factor:
EFnmo = 0.225 + (0.61542 x 0.00005) + 0.05 + 0.5 (0.0035 + 0.033) = 0.293 lbs/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 equation for coal mining is:
where,
EFC = (10 x (EFto + EFor + EFdt)) + EFV + EFr +EFa + (0.5 x (EFe + EFt))
EFC = coal mining emissions factor (lbs/ton)
EFto =PMio emission factor for truck loading overburden at western surface coal mining
operations (lbs/ton of overburden)
EFor = PMio emission factor for overburden replacement at western surface coal mining
operations (lbs/ton of overburden)
EFdt = PMio emission factors for truck unloading: bottom dump-overburden at western surface
coal mining operations (lbs/ton of overburden)
EFV = PM io open pit overburden removal emission factor at western surface coal mining
operations (lbs/ton)
EFr = PM io drilling/blasting emission factor at western surface coal mining operations (lbs/ton)
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EFa = PM 10 loading emission factor at western surface coal mining operations (lbs/ton)
EFe = PM 10 truck unloading: end dump-coal emission factor at western surface coal mining
operations (lbs/ton)
EFt = PM 10 truck unloading; bottom dump-coal emission factor at western surface coal mining
operations (lbs/ton)
Applying the PMio emission factors developed for western surface coal mining operations [ref 3] yields the
following coal mining emissions factor:
EFC = (10 x (0.015 + 0.001 + 0.006)) + 0.225 + 0.00005 + 0.05 + (0.5 x (0.0035 + 0.033)) = 0.513
lbs/ton
PM-FIL emission factors are assumed to be the same as PM-PRI emission factors; however there is a small
amount of PM-CON emissions included in the PM-PRI emissions but insufficient data exists to tease out 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 5], Consequently,
a ratio of 0.125 was applied to the PM10 emission factors to estimate PM2.5 emission factors for mining and
quarrying. A summary of these emission factors is presented in Table 4-89.
Table 4-89: Summary of Mining and Quarrying emission factors
Mining Type
Pollutant
Code
Factor Numeric
Value
Factor Unit
Numerator
Factor Unit
Denominator
Coal
PM10-PRI
0.513
LB
TON
Coal
PM10-FIL
0.513
LB
TON
Coal
PM25-PRI
0.064
LB
TON
Coal
PM25-FIL
0.064
LB
TON
Metallic
PM10-PRI
0.0548
LB
TON
Metallic
PM10-FIL
0.0548
LB
TON
Metallic
PM25-PRI
0.0068
LB
TON
Metallic
PM25-FIL
0.0068
LB
TON
Non-Metallic
PM10-PRI
0.293
LB
TON
Non-Metallic
PM10-FIL
0.293
LB
TON
Non-Metallic
PM25-PRI
0.037
LB
TON
Non-Metallic
PM25-FIL
0.037
LB
TON
Activity
Emissions were estimated by obtaining state-level metallic and non-metallic crude ore handled at surface mines
from the U.S. Geologic Survey (USGS) [ref 6] and mine specific coal production data for surface mines from the
EIA [ref 7], Emissions were not estimated for underground mining given that emission factors are calculated
exclusively for surface activity. Since some of the USGS metallic and non-metallic minerals waste data associated
with ore production are withheld to avoid disclosing company proprietary data, an allocation procedure was
developed to estimate the withheld data. For states with withheld waste data, the state fraction of national ore
production was multiplied by the national undisclosed waste value to estimate the state withheld data. In
addition, the USGS only reports metallic and non-metallic minerals production data separately at the national-
level (e.g., the production data are combined at the state-level). To estimate metallic versus non-metallic ore
production and associated waste at the state-level, the state-level total production and waste data were
multiplied by the national metallic or non-metallic percentage of total production.
4-150
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4.15.3.3 Allocation: updated in 2014v2 NE!
State-level metallic and non-metallic crude ore and associated waste handled was allocated to the county-level
using employment. Specifically, state-level activity data were multiplied by the ratio of county-to state-level
number of employees in the metallic and non-metallic mining industries. See Table 4-90 for a list of these NAICS
codes.
Table 4-90: 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
Employment data were obtained from the U.S. Census Bureau's 2014 County Business Patterns (CBP) [ref 8] -
updated from 2012 CBP in the 2014vl NEI. 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 with withheld data, the following
procedure is used for each NAICS code being computed.
4-151
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1. County-level data for counties with known employment are totaled by state.
2. #1 subtracted from the state total reported in state-level CBP.
3. Each of the withheld counties is assigned the midpoint of the range code (e.g., A:l-19 employees would
be assigned 10).
4. These midpoints are then summed to the state level.
5. #2 is divided by #4 as an adjustment factor to the midpoints.
6. #5 is multiplied by #3 to get the adjusted county-level employment.
Note that step 5 adjusts all counties 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.
For example, take the 2006 CBP data for NAICS 31-33 (Manufacturing) in Maine provided in Table 4-91.
Table 4-91: 2006 County Business Pattern data for NAICS 31-33 in Maine
State
FIPS
County
FIPS
NAICS
Employment
Flag
Number of
Employees
23
001
31
6,774
23
003
31
3,124
23
005
31
10,333
23
007
31
1,786
23
009
31
1,954
23
011
31
2,535
23
013
31
1,418
23
015
31
F
0
23
017
31
2,888
23
019
31
4,522
23
021
31
948
23
023
31
1
0
23
025
31
4,322
23
027
31
1,434
23
029
31
1,014
23
031
31
9,749
1. The total of employees not including counties 015 and 023 is 52801.
2. The state-level CBP reports 59,322 employees for NAICS 31. The difference is 6,521.
3. County 015 is given a midpoint of 1,750 (since range code F is 1000-2499) and County 023 is given a
midpoint of 17,500.
4. State total for these two counties is 19,250.
5. 6,521/19,250 = 0.33875.
6. The adjusted employment for county 015 is 1,750*0.33875 = 593. County 023 has an adjusted
employment of 17,500*0.33875 = 5,928.
In the event that data at the state level are withheld, a similar procedure is first performed going from the U.S.
level to the state level. For example, known state-level employees are subtracted from the U.S. total yielding the
total withheld employees. Next the estimated midpoints of the withheld states are added together and
4-152
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compared (by developing a ratio) to the U.S. total withheld employees. The midpoints are then adjusted by the
ratio to give an improved estimate of the state total.
4153,4 Controls
No controls were accounted for in the emissions estimation.
Emissions Equa tion and Sample Calm la tion
Fugitive dust emissions for mining and quarrying operations are the sum of emissions from the mining of
metallic and nonmetallic ores and coal:
E Em "t" En + Ec
where,
E = PM 10 emissions from mining and quarrying operations
Em = PM 10 emissions from metallic ore mining operations
En = PM io emissions from non-metallic ore mining
Ec = PM io emissions from coal mining operations
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. Emissions for each activity are calculated using the
following equation:
where,
EFx A
E = PM io emissions from operation (e.g., metallic ore, non-metallic ore, or coal mining; lbs)
EF = emissions factor associated with operation (lbs/ton)
A = ore handled in mining operation (tons)
As an example, in 2012 Barbour County, Alabama handled 13,507,583 tons of metallic ore and associated waste,
113,501 tons of non-metallic ore and associated waste, and 0 tons of coal. Mining and quarrying PMio-PRI
emissions for Barbour County are:
EPM 10-PRI, Barbour County [(13,507,583x0.0548) + (113,501x0.293) + (0x0.513)]/2000 = 386 tons
The division by 2000 is to convert from pounds to tons.
4.15,3,6 Changes from 2011 and2014vl Methodology
For the 2014 NEI, the activity data are updated to year 2012 for the 2014vl NEI and 2014 for the 2014v2 NEI
using the most recent USGS and EIA data on metallic and non-metallic crude ore handled and coal production.
The allocation procedure uses 2014 (2012 for 2014vl NEI) employment data from the U.S. Census Bureau. In
addition, the allocation procedure in 2014 allocates state-level metallic and non-metallic activity to the county-
level using the respective county fraction of metallic and non-metallic state employees that work in the county.
In 2011, the allocation procedure combined the metallic and non-metallic employees to generate a single county
4-153
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allocation factor. The 2014 allocation methodology is an improvement because it more precisely assigns the
mining emissions to counties where the mining is occurring.
4.15,3.7 Puerto Rico and US Virgin Islands Emissions Calculations
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".
4.15.4 References for mining and quarrying
1. United States Environmental Protection Agency. Generalized Particle Size Distributions for Use in
Preparing Size-Specific Particulate Emissions Inventories, EPA-450/4-86-013, July 1986.
2. United States Environmental Protection Agency, National Air Pollutant Emission Trends Procedure
Document for 1900-1996, EPA-454/R-98-008, May 1998.
3. United States Environmental Protection Agency, AP-42, Fifth Edition, Volume 1, Chapter 11: Mineral
Products Industry, Section 11.9: Western Surface Coal Mining, accessed July 2015.
4. 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.
5. Midwest Research Institute, Background Document for Revisions to Fine Fraction Ratios Used for AP-42
Fugitive Dust Emission Factors. MRI Project No. 110397, November 2006, accessed July 2015.
6. United States Geologic Survey, Minerals Yearbook 2012. accessed July 2015.
7. Energy Information Administration, Detailed data from the EIA-7A and the U.S. Mine Safety and Health
Administration, data pulled for year 2014, accessed August 2016.
8. U.S. Census Bureau, 2014 County Business Patterns, accessed August 2016
4,16 Industrial Processes Oil & Gas Production
4.16.1 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.
4.16.2 Source of data
Table 4-92 shows 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 descriptions are also provided. The leading SCC description is
"Industrial Processes; Oil and Gas Exploration and Production;" for all SCCs.
New SCCs, created for the 2014vl inventory are noted in the table, and additional new SCCs created at State's
request for 2014v2, are also indicated with a "v2" in the "New?" column. Several of these new SCCs are not used
by EPA but were created for states that wanted to preserve the difference between conventional and
unconventional formations for their own reporting needs. 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 2014 NEI. All of the SCCs that the EPA Oil
4-154
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and Gas Tool uses are nonpoint SCCs. There are several point inventory SCCs in the oil and gas production sector
as well. Emissions or activity from these SCCs, listed in Table 4-93, are subtracted from nonpoint estimates using
in the EPA's Oil and Gas Tool, discussed in the next section.
Table 4-92: Nonpoint SCCs with 2014 NEI emissions in the Oil and Gas Production sector
see
New?
Description
EPA
State
Tribe
2310000000
All Processes; Total: All Processes
X
2310000220
All Processes; Drill Rigs
X
X
2310000230
All Processes; Workover Rigs
X
2310000330
All Processes; Artificial Lift
X
X
2310000550
All Processes; Produced Water
X
X
2310000660
All Processes; Hydraulic Fracturing Engines
X
X
2310001000
All Processes; On-shore; Total: All Processes
X
X
2310002000
Off-Shore Oil and Gas Production; Total: All Processes
X
2310002301
Off-Shore Oil and Gas Production; Flares: Continuous Pilot Light
X
2310002305
Off-Shore Oil and Gas Production; Flares: Flaring Operations
X
2310002401
Off-Shore Oil and Gas Production; Pneumatic Pumps: Gas and Oil Wells
X
2310002411
Off-Shore Oil and Gas Production; Pressure/Level Controllers
X
2310002421
Off-Shore Oil and Gas Production; Cold Vents
X
2310010000
Crude Petroleum; Total: All Processes
X
2310010100
Crude Petroleum; Oil Well Heaters
X
X
2310010200
Crude Petroleum; Oil Well Tanks - Flashing &
Standing/Working/Breathing
X
X
2310010300
Crude Petroleum; Oil Well Pneumatic Devices
X
X
2310010700
Crude Petroleum; Oil Well Fugitives
X
2310010800
Crude Petroleum; Oil Well Truck Loading
X
2310011000
On-Shore Oil Production
Total: All Processes
X
X
2310011020
On-Shore Oil Production
Storage Tanks: Crude Oil
X
2310011100
On-Shore Oil Production
Heater Treater
X
2310011201
On-Shore Oil Production
Tank Truck/Railcar Loading: Crude Oil
X
X
2310011450
On-Shore Oil Production
Wellhead
X
2310011500
On-Shore Oil Production
Fugitives: All Processes
X
2310011501
On-Shore Oil Production
Fugitives: Connectors
X
X
2310011502
On-Shore Oil Production
Fugitives: Flanges
X
X
2310011503
On-Shore Oil Production
Fugitives: Open Ended Lines
X
X
2310011504
On-Shore Oil Production
Fugitives: Pumps
X
2310011505
On-Shore Oil Production
Fugitives: Valves
X
X
2310011506
On-Shore Oil Production
Fugitives: Other
X
2310011600
v2
On-Shore Oil Production
Artificial Lift Engines
X
2310012000
Off-Shore Oil Production
Total: All Processes
X
2310012020
Off-Shore Oil Production
Storage Tanks: Crude Oil
X
2310012511
Off-Shore Oil Production
Fugitives, Connectors: Oil Streams
X
2310012512
Off-Shore Oil Production
Fugitives, Flanges: Oil
X
2310012515
Off-Shore Oil Production
Fugitives, Valves: Oil
X
4-155
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see
New?
Description
EPA
State
Tribe
2310012516
Off-Shore Oil Production; Fugitives, Other: Oil
X
2310012521
Off-Shore Oil Production; Fugitives, Connectors: Oil/Water Streams
X
2310012522
Off-Shore Oil Production; Fugitives, Flanges: Oil/Water
X
2310012525
Off-Shore Oil Production; Fugitives, Valves: Oil/Water
X
2310012526
Off-Shore Oil Production; Fugitives, Other: Oil/Water
X
2310020000
Natural Gas; Total: All Processes
X
2310020600
Natural Gas; Compressor Engines
X
2310020700
Natural Gas; Gas Well Fugitives
X
2310020800
Natural Gas; Gas Well Truck Loading
X
2310021010
On-Shore Gas Production; Storage Tanks: Condensate
X
X
2310021011
On-Shore Gas Production; Condensate Tank Flaring
X
2310021030
On-Shore Gas Production; Tank Truck/Railcar Loading: Condensate
X
X
2310021100
On-Shore Gas Production; Gas Well Heaters
X
X
2310021101
On-Shore Gas Production; Natural Gas Fired 2Cycle Lean Burn
Compressor Engines < 50 HP
X
2310021102
On-Shore Gas Production; Natural Gas Fired 2Cycle Lean Burn
Compressor Engines 50 To 499 HP
X
X
2310021103
On-Shore Gas Production; Natural Gas Fired 2Cycle Lean Burn
Compressor Engines 500+ HP
X
2310021201
On-Shore Gas Production; Natural Gas Fired 4Cycle Lean Burn
Compressor Engines <50 HP
X
2310021202
On-Shore Gas Production; Natural Gas Fired 4Cycle Lean Burn
Compressor Engines 50 To 499 HP
X
X
2310021203
On-Shore Gas Production; Natural Gas Fired 4Cycle Lean Burn
Compressor Engines 500+ HP
X
2310021251
On-Shore Gas Production; Lateral Compressors 4 Cycle Lean Burn
X
X
2310021300
On-Shore Gas Production; Gas Well Pneumatic Devices
X
X
2310021301
On-Shore Gas Production; Natural Gas Fired 4Cycle Rich Burn
Compressor Engines <50 HP
X
2310021302
On-Shore Gas Production; Natural Gas Fired 4Cycle Rich Burn
Compressor Engines 50 To 499 HP
X
X
2310021303
On-Shore Gas Production; Natural Gas Fired 4Cycle Rich Burn
Compressor Engines 500+ HP
X
2310021310
On-Shore Gas Production; Gas Well Pneumatic Pumps
X
2310021351
On-Shore Gas Production; Lateral Compressors 4 Cycle Rich Burn
X
X
2310021400
On-Shore Gas Production; Gas Well Dehydrators
X
X
2310021401
On-Shore Gas Production; Nat Gas Fired 4Cycle Rich Burn Compressor
Engines <50 HP w/NSCR
X
2310021402
On-Shore Gas Production; Nat Gas Fired 4Cycle Rich Burn Compressor
Engines 50 To 499 HP w/NSCR
X
2310021403
On-Shore Gas Production; Nat Gas Fired 4Cycle Rich Burn Compressor
Engines 500+ HP w/NSCR
X
4-156
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see
New?
Description
EPA
State
Tribe
2310021411
On-Shore Gas Production
Gas Well Dehydrators - Flaring
X
2310021450
On-Shore Gas Production
Wellhead
X
2310021500
On-Shore Gas Production
Gas Well Completion - Flaring
X
2310021501
On-Shore Gas Production
Fugitives: Connectors
X
X
2310021502
On-Shore Gas Production
Fugitives: Flanges
X
X
2310021503
On-Shore Gas Production
Fugitives: Open Ended Lines
X
X
2310021504
On-Shore Gas Production
Fugitives: Pumps
X
2310021505
On-Shore Gas Production
Fugitives: Valves
X
X
2310021506
On-Shore Gas Production
Fugitives: Other
X
X
2310021509
On-Shore Gas Production
Fugitives: All Processes
X
2310021600
On-Shore Gas Production
Gas Well Venting
X
2310021601
On-Shore Gas Production
Gas Well Venting - Initial Completions
X
2310021602
On-Shore Gas Production
Gas Well Venting - Recompletions
X
2310021603
On-Shore Gas Production
Gas Well Venting - Blowdowns
X
X
2310021604
On-Shore Gas Production
Gas Well Venting - Compressor Startups
X
2310021605
On-Shore Gas Production
Gas Well Venting - Compressor Shutdowns
X
2310021700
On-Shore Gas Production
Miscellaneous Engines
X
2310022000
Off-Shore Gas Production
Total: All Processes
X
2310022010
Off-Shore Gas Production
Storage Tanks: Condensate
X
2310022051
Off-Shore Gas Production
Turbines: Natural Gas
X
2310022090
Off-Shore Gas Production
Boilers/Heaters: Natural Gas
X
2310022105
Off-Shore Gas Production
Diesel Engines
X
2310022410
Off-Shore Gas Production
Amine Unit
X
2310022420
Off-Shore Gas Production
Dehydrator
X
2310022501
Off-Shore Gas Production
Fugitives, Connectors: Gas Streams
X
2310022502
Off-Shore Gas Production
Fugitives, Flanges: Gas Streams
X
2310022505
Off-Shore Gas Production
Fugitives, Valves: Gas
X
2310022506
Off-Shore Gas Production
Fugitives, Other: Gas
X
2310023010
Y
Coal Bed Methane Natural Gas; Storage Tanks: Condensate
X
X
2310023030
Y
Coal Bed Methane Natural Gas; Tank Truck/Railcar Loading: Condensate
X
X
2310023100
Y
Coal Bed Methane Natural Gas; CBM Well Heaters
X
X
2310023102
Y
Coal Bed Methane Natural Gas; CBM Fired 2Cycle Lean Burn Compressor
Engines 50 To 499 HP
X
X
2310023202
Y
Coal Bed Methane Natural Gas; CBM Fired 4Cycle Lean Burn Compressor
Engines 50 To 499 HP
X
X
2310023251
Y
Coal Bed Methane Natural Gas; Lateral Compressors 4 Cycle Lean Burn
X
X
2310023300
Y
Coal Bed Methane Natural Gas; Pneumatic Devices
X
X
2310023302
Y
Coal Bed Methane Natural Gas; CBM Fired 4Cycle Rich Burn Compressor
Engines 50 To 499 HP
X
X
2310023310
Y
Coal Bed Methane Natural Gas; Pneumatic Pumps
X
X
2310023351
Y
Coal Bed Methane Natural Gas; Lateral Compressors 4 Cycle Rich Burn
X
X
4-157
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see
New?
Description
EPA
State
Tribe
2310023400
Y
Coal Bed Methane Natural Gas
Dehydrators
X
X
2310023509
Y
Coal Bed Methane Natural Gas
Fugitives
X
2310023511
Y
Coal Bed Methane Natural Gas
Fugitives: Connectors
X
X
2310023512
Y
Coal Bed Methane Natural Gas
Fugitives: Flanges
X
X
2310023513
Y
Coal Bed Methane Natural Gas
Fugitives: Open Ended Lines
X
X
2310023515
Y
Coal Bed Methane Natural Gas
Fugitives: Valves
X
X
2310023516
Y
Coal Bed Methane Natural Gas
Fugitives: Other
X
X
2310023600
Y
Coal Bed Methane Natural Gas
CBM Well Completion: All Processes
X
X
2310023603
Y
Coal Bed Methane Natural Gas
CBM Well Venting - Blowdowns
X
X
2310023606
Y
Coal Bed Methane Natural Gas
Mud Degassing
X
X
2310030300
v2
Natural Gas Liquids: Gas Well Water Tank Losses
X
2310030401
Natural Gas Liquids; Gas Plant Truck Loading
X
2310111100
On-Shore Oil Exploration; Mud Degassing
X
X
2310111401
On-Shore Oil Exploration; Oil Well Pneumatic Pumps
X
X
2310111700
On-Shore Oil Exploration; Oil Well Completion: All Processes
X
X
2310112401
Off-Shore Oil Exploration; Oil Well Pneumatic Pumps
X
2310121100
On-Shore Gas Exploration; Mud Degassing
X
X
2310121401
On-Shore Gas Exploration; Gas Well Pneumatic Pumps
X
X
2310121700
On-Shore Gas Exploration; Gas Well Completion: All Processes
X
X
2310122100
Off-Shore Gas Exploration; Mud Degassing
X
2310321010
Y
On-Shore Gas Production - Conventional; Storage Tanks: Condensate
X
2310321100
Y
On-Shore Gas Production - Conventional; Gas Well Heaters
X
2310321400
Y
On-Shore Gas Production - Conventional; Gas Well Dehydrators
X
2310321603
Y
On-Shore Gas Production - Conventional; Gas Well Venting - Blowdowns
X
2310400220
Y
All Processes - Unconventional; Drill Rigs
X
2310421010
Y
On-Shore Gas Production - Unconventional; Storage Tanks: Condensate
X
2310421100
Y
On-Shore Gas Production - Unconventional; Gas Well Heaters
X
2310421400
Y
On-Shore Gas Production - Unconventional; Gas Well Dehydrators
X
2310421603
Y
On-Shore Gas Production - Unconventional; Gas Well Venting -
Blowdowns
X
Table 4-93: Point SCCs in the Oil and Gas Production sector
SCC(s)
Abbreviated description
31000101 through 31000506
Various descriptions;
Excludes 31000104 through 31000108 and 31000140 through 31000145,
which are in the sector "Industrial Processes - Storage and Transfer"
31088801 through 31088811
Fugitive Emissions; Specify in Comments Field
31700101
Natural Gas Transmission and Storage Facilities; Pneumatic Controllers Low
Bleed
The agencies listed in Table 4-94 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%).
4-158
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Table 4-94: Percentage of total Oil and Gas Production NOx and VOC nonpoint emissions submitted by reporting
agency
Region
Agency
NOx
VOC
2
New York State Department of Environmental Conservation
99
100
3
Pennsylvania Department of Environmental Protection
79
52
3
West Virginia Division of Air Quality
100
100
5
Illinois Environmental Protection Agency
100
100
5
Michigan Department of Environmental Quality
100
100
5
Ohio Environmental Protection Agency
100
100
6
Oklahoma Department of Environmental Quality
100
100
6
Texas Commission on Environmental Quality
100
100
8
Assiniboine and SiouxTribes of the Fort Peck Indian Reservation
100
100
8
Colorado Department of Public Health and Environment
100
100
8
Utah Division of Air Quality
97
85
8
Wyoming Department of Environmental Quality
95
77
9
California Air Resources Board
98
85
10
Alaska Department of Environmental Conservation
7
0
4,163 EPA-deveioped emissions for oil and gas production
The EPA improved the existing Oil and Gas Tool that was developed for the 2011 NEl, which is a MS Access
database that uses a bottom up approach to build a national inventory. New for 2014 are two modules (rather
than one) for the Oil and Gas Tool: Exploration and Production. This was a necessary change due to the increase
in input data; when EPA expanded the specificity of the tool (county-level inputs rather than basin level inputs,
some division between conventional and unconventional processes), we reached the limitations of MS Access,
so dividing the database into two parts was a necessity. More information on the tool can be found in the
documentation provided by ERG for each module on the 2014 NEI Supplemental data FTP site. For the
Production module, this documentation is entitled "OilGas_Toollnstruction_Production_v2_2_20170601.pdf,"
found in zip file "OIL_GAS_TOOL_2014_NEI_PRODUCTION_V2_2.zip". For the Exploration module, this
documentation is entitled "OilGas_Toollnstruction_Exploration_v2_3_20170821.pdf," found in zip file
"OIL_GAS_TOOL_2014_NEI_EXPLORATION_V2_3.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 changes over timethe ratio of
oil to gas changes as pressure in the reservoir is released), and regulations in place guiding the equipment
needed 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 (PV=nRT) and mass balances, in conjunction with more traditional emission rate
equations (activity x EF = emissions) to calculate 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.
4-159
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Activity data is taken primarily from a commercially available database developed by Drillinglnfo called HPDI
(number of wells, oil, gas, condensate and water production, feed drilled, spud counts, and other data). There
are cases where this data isn't complete, and in those cases, the state oil and gas commission databases are
mined for data. In addition, after verification by the states, sometimes this data is modified to correct the data.
Some examples of these are for OH and TX. In the case of Ohio, the state representative noted that the number
of conventional versus unconventional well counts was out of proportion, and there were far fewer
unconventional wells than HPDI listed. For Texas, the state representative compared the well counts to those of
his internal state system, and realized that HPDI data led to double-counting of wells (due to leases). Therefore,
these numbers were corrected within the tool, based on corrections by the state.
Basin factors include factors that are secondary to "activity," and include assumptions about equipment counts
on a per well basis (e.g. number of pneumatic controllers per well, or 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).
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, e.g. from AP-42 combustion equations,
"emission factors."
These inputs (activity, basin & emission factors) 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 as much as possible with the Greenhouse Gas
Reporting Program (GHGRP) and the Greenhouse Gas Emissions Inventory (GHGEI).
4,163.1 Point Source Subtraction
Further complication ensues when some states count some wells as point sources, and therefore have a need to
subtract these from the nonpoint part of the inventory. The Oil and Gas Tool allows emissions from point
sources to be subtracted on an activity or emissions basis. This piece of the puzzle is less perfect, in that if a
source has CAP emissions to subtract but not HAPs, the emissions for a single source may be divided across the
point and nonpoint parts of the inventory. Thus, when an inventory developer looks at VOC emissions and
compares these to a sum of HAP-VOCs, there may appear to be inconsistencies.
Sources of Data Overview and Selection Hierarchy
S/L/Ts have four options for providing data to the NEI for the Oil and Gas sector:
1. Accept the outputs from the EPA Oil and Gas Tools with the EPA-populated defaults,
2. Choose to provide EPA the input data to incorporate in the tools,
3. Run the tools themselves (presumably updating the inputs), or
4. Use their own tools and methodology to provide estimates.
If a reporting agency fails to submit nonpoint data or state a preference via the nonpoint survey, then EPA data
was input by default. Table 4-95 summarizes the data, or nonpoint survey option preference, that was
submitted by states in the oil and gas sector.
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Table 4-95: State involvement with Oil and Gas Production submittals
State
Nonpoint Approach
Point Submittal?
AK
EPA tool for some SCCs (survey) & State submission, state
submitted revisions for 2014v2
Yes
AL
no survey, will use EPA tool
Yes
AR
EPA tool
Yes
AZ
EPA tool
Yes
CA
State submitted nonpoint emissions, state submitted revisions
for 2014v2
Yes
CO
State submitted nonpoint emissions
Yes
CT
No oil and gas
Yes
FL
EPA tool
Yes
GA
No oil and gas
Yes
IA
No oil and gas
Yes
ID
EPA tool
IL
State submitted nonpoint emissions, state submitted revisions
for 2014v2
Yes
IN
EPA tool
Yes
KS
EPA tool with State inputs
Yes
KY
no survey, will use EPA tool
Yes
LA
EPA tool
Yes
MD
no survey, will use EPA tool
Yes
ME
No oil and gas
Yes
Ml
State submitted nonpoint emissions, state submitted revisions
for 2014v2
Yes
MN
No oil and gas
Yes
MO
EPA tool
Yes
MS
no survey, will use EPA tool
Yes
MT
no survey, will use EPA tool
Yes
NC
No oil and gas
Yes
ND
EPA tool
Yes
NE
no survey, will use EPA tool
Yes
NJ
No oil and gas
Yes
NM
EPA tool with State inputs
Yes
NV
EPA tool
Yes
NY
State submitted nonpoint emissions, state submitted revisions
for 2014v2
Yes
OH
EPA & State
Yes
OK
State CAP submissions, relied on HAP aug for HAPs (point source
data lacked HAP emissions, so could not be subtracted)
Yes
OR
EPA tool
PA
EPA (exploration segment) & State (inadvertently forgot entire
exploration segmente.g., drill rigs, fracking engines, heaters in
version 1), state submitted revisions for 2014v2
Yes
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State
Nonpoint Approach
Point Submittal?
sc
No oil and gas
Yes
TN
EPA tool
TX
State submitted nonpoint emissions, state submitted revisions
for 2014v2
Yes
UT
EPA & State, state submitted revisions for 2014v2
Yes
VA
EPA tool
Yes
Wl
No oil and gas
Yes
WV
State submitted nonpoint emissions, state submitted revisions
for 2014v2
Yes
WY
EPA & State, state submitted revisions for 2014v2
Yes
4,16.4 Notes on observations in 2014 NE! estimates
This section discusses significant changes in the 2014vl NEI compared to the 2011 NEI. Section 4.16.4.1 lists
some known issues in the 2014vl NEI and Section 4.16.4.2 walks through changes that made it into the 2014v2
NEI.
Alaska: Alaska's VOC emissions went down since 2011. This is because the tool in 2011 assumed storage tanks
exist. This was corrected by conversations with industry and AK state representatives, who had a chance to
review the tool for 2014, and clarified for EPA that storage tanks do not exist in AK due to the very cold
temperatures (everything is sent to pipeline.)
California: On reviewing the data, EPA noticed that CA data when compared to EPA data was very low. A state
inventory developer explained that they used the 2011 tool and revised the inputs largely based on an industry
survey. This survey, in comparison to default inputs in the EPA Oil and Gas Tool, revealed:
lower number of dehydrators/well,
lower activity for artificial lifts (most artificial lifts are electric),
fewer tanks flared (most use VRUs),
30% lower operating hours for compressor engines,
50% lower fugitives (no open-ended lines),
more wells per compressor.
Colorado: Colorado's emissions were lower than they were in 2011, and in fact were closer to the tool emissions
than they were in 2011. The nonpoint inventory developer clarified that in the Ozone 9-county nonattainment
area, the point source inventory omitted well pad sources from his NEI point source submittal to avoid double
counting area (nonpoint) source data. Area source oil and gas production also decreased in the nonattainment
area between 2011 to 2014 due to decline in production from old wells and much greater control of emissions
from new wells.
Idaho: Idaho is a new state in 2014. There are some new wells that were listed by HPDI.
North Dakota: Emissions for VOC have risen significantly, likely due to increased production in the Bakken Shale
area.
Oklahoma: Oklahoma used different SCCs for fugitives. Tagging of EPA SCCs noted in Table 4-96 was necessary
to avoid double-counting with the Oklahoma-submitted fugitive emissions shown in Table 4-97 that are not in
the EPA oil and gas tool. Oklahoma emissions for the SCCs in Table 4-96 have since been removed from the oil
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and gas tool. Table 4-97 includes emissions not in the original EPA oil and gas tool and contain all fugitive
emissions and malfunctioning pneumatic emissions for Oklahoma.
Table 4-96: EPA oil and gas fugitive SCCs tagged out in Oklahoma in the 2014 NEI
see
Description
2310011501
On-Shore Oil Production /Fugitives: Connectors
2310011502
On-Shore Oil Production /Fugitives: Flanges
2310011503
On-Shore Oil Production /Fugitives: Open Ended Lines
2310011505
On-Shore Oil Production /Fugitives: Valves
2310021501
On-Shore Gas Production /Fugitives: Connectors
2310021502
On-Shore Gas Production /Fugitives: Flanges
2310021503
On-Shore Gas Production /Fugitives: Open Ended Lines
2310021505
On-Shore Gas Production /Fugitives: Valves
2310021506
On-Shore Gas Production /Fugitives: Other
2310023511
On-Shore CBM Production /Fugitives: Connectors
2310023512
On-Shore CBM Production /Fugitives: Flanges
2310023513
On-Shore CBM Production /Fugitives: Open Ended Lines
2310023515
On-Shore CBM Production /Fugitives: Valves
2310023516
On-Shore CBM Production /Fugitives: Other
Table 4-97: Additional non-EPA-estimated oil and gas fugitive SCCs Oklahoma su
amitted in the 2014 NEI
see
Description
2310011500
Fugitives: All Processes (Oil wells)
2310021509
Fugitives: All Processes (Gas wells)
2310023509
Fugitives (CBM wells)
Pennsylvania: Pennsylvania's emissions were very low. See "Known Issues" notes in the next Section (4.16.4.1).
Texas: A state inventory developer noted some discrepancies between what TCEQ ultimately submitted to the
2014 NEI and what the EPA Tools would have generated. Many activity data and parameters in the tool were
updated by TCEQ, including:
well counts and production data,
fraction of gas wells with compressor engines,
pneumatic device counts,
hydraulic pump engine equipment profiles,
mud degassing VOC content,
piping fugitive VOC content,
number of dehydrators per well
For well counts and production data, TCEQ explained how reporting at the lease level to the Texas Railroad
Commission leads to double counting in the HDPI data. TCEQ explained that leases can contain multiple wells
and both of those wells would report production data at the lease level, so then both wells would be listed with
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the same production (i.e., double counting). For the variable "fraction of gas wells with compressor engines/'
TCEQ made revisions to the tool to account for the presumption that in general, most wells do not need
compression in the first year, and thereafter, in most areas, about a third of wells need compression.
Furthermore, in order to be consistent with OAP use of HPDI data, the Oil and Gas Tool developers shifted some
gas wells to oil wells based on the GHGRP GOR definition - about 10% of gas wells were shifted to oil wells
(which impacts compressor engine emissions), and about 95% of condensate was shifted to oil (which impacts
storage tank and loading loss emissions).
TCEQ's improved inputs to the Oil and Gas Tool were incorporated into the Oil and Gas Tool for 2014 vl.
Wyoming: Wyoming's emissions, in comparison to EPA's estimates for WY, were much lower, in general, for
VOC. This can likely be attributed to tighter regulations on emissions. However, some HAPs such as xylenes and
benzene were orders of magnitude higher; this should be revisited by EPA in 2014v2.
Known Issues in the 2014vt NEi
Dehydrator Emissions: In August 2016, EPA found an issue with the dehydrator emissions algorithm (brought to
our attention by the Texas Commission on Environmental Quality. As part of the emissions algorithm for
dehydrators, the Tool develops estimates for still vents, reboilers, and flaring. It was discovered that the flaring
portion of the emissions algorithm was programmed incorrectly. This error affects only states that used the Tool
for Dehydrators (one SCC) and if the "fraction to flares" variable is populated. Where this is the case (which EPA
believes is only a few states), the VOC and HAP emissions for the flaring portion are 1000 times higher than they
should be. However, for the Tool overall, the VOC changes from the dehydrator issue overestimated VOC by
~8.6%. However, almost all of that (7.8%) was for Texas. The states affected by the dehydrator issue in the Tool
include TX, UT, WY, SD, ND, and NM, but TX, UT, and WY provide their own nonpoint oil and gas inventories to
the NEI. The % change in VOC for the states using the tool are 2.8% (NM), 1.2% (ND), and 6.1% (SD). Also, the
error/fix also affect NOx (3.7% total Tool), and CO (14.3% total Tool). As with VOC, most of the NOx and CO
change comes from Texas.
Pennsylvania: We found an issue with PA late in the process (September, 2016). For PA, data submittals were
provided by the state (PADEP) for unconventional sources, and by MARAMA on behalf of PA for conventional
sources. After reviewing the data submittals, there was a potential issue of category incompleteness for the
sectorit appears the entire Exploration module was not submitted. Several large sources (drill rigs, fracking
engines, heaters, for example) were not included.
Thus, EPA has decided to allow EPA data to backfill where SCCs were not submitted. For 2014vl, EPA untagged
all of EPA data and so there may be some double counting (overlapping SCCsfugitives and enginesPA uses
one SCC for fugitives while EPA uses 5, and PA uses one SCC for engines while EPA uses 3 or more). PA did not
complete their nonpoint survey for oil and gas with the specificity needed to reconcile this easily. EPA planned
to work with PA DEP to interpret their data submittals prior to 2014v2.
Utah: EPA noticed a very high VOC (leading to high HAPs in the augmentation) number for Uintah County. EPA
contacted UT's inventory developer, Greg Mortensen, and he replied that the figure is based off the projection
from the 2006 WRAP inventory. Utah has not used the Oil and Gas Tool. The 2006 base year for dehydrators
(15,327 tons) is grown by the gas production growth factor (2006 vs 2014 production) which is approximately
1.52. This results in about 23,000 tons of VOC for 2014. However, they are in the midst of incorporating some
new data they have collected in Uintah County based on a survey they've conducted on operators in the area.
According to Greg, this figure will be reduced to around 3,686 tons in Uintah County when they substitute the
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numbers from the producer inventory we recently collected. Utah expected to make this correction in the
2014v2 NEI.
Updates in the 2014v2 NEI
Activity Updates
Activity was updated for 2014v2, using most current available HPDI data. Also, based on comments from the
Environ/Ramboll study of Oil and Gas in the NEI, activity associated with C02 wells were removed. Some double
counting in a few counties was eliminated. WV DPE provided its own numbers for production and exploration,
and this data was added to the tool. Overall, this resulted in only a few changes in oil of note from 2014vl: AR
down 27%, VA up 26%, and WV down 63%. Natural gas production changes of note from 2014vl: VA down 37%,
WV up 20%, AL down 5% and AK up 5%.
Basin Factors and Emission Factors
Updated gas composition data were obtained from EPA's SPECIATE database and BOEM (Arctic Air Quality
Modeling Study) and input into the tool for Associated Gas, Condensate Tanks, Crude Oil Tanks, Dehydrators,
Fugitives, Gas Actuated Pumps, Liquids Unloading, Loading Operations, Pneumatic Devices, and Well
Completions for certain counties in 10 states.
Flare VOC and Formaldehyde emission factors were updated based on AP-42 updates (Section 13.5, 12/2016)
and SPECIATE updates (Profile #FLR99) to 0.66 (Ib/MMBtu) and 0.08302 (Ib/MMBtu), respectively.
Updated basin-level "WELLHEAD_FRACTION_GASWELLS_NEED_COMPRESSION" values were derived from data
submitted to EPA under Subpart W of the GHGRP for 2015. Counties previously using EPA default values (based
on the 2012 CenSARA study) were updated, and existing state or RPO-supplied data were retained. The default
factor was lowered from 0.208 (compressors/well) to 0.078 (compressors/well), and was used where no
updated basin- level data was available from the 2015 GHGRP data.
Based on guidance received from Madeleine Strum that the current AP-42 carbon tetrachloride factors used in
the tool are based only on "Non-Detect" values, emission factors for carbon tetrachloride were removed from
the tool for compressor engines and artificial lift engines. Emission factor updates were made to certain basin
factor data in the Permian and San Juan Basin counties in NM, based on data provided by NM/WRAP. Updates
were also made to wellhead compressor engine sizes and loads, fraction of wells needing compression, and
crude and condensate tank flare fractions in TX based on data provided by TCEQ.
Tool Updates
There were a few other updates that corrected algorithms. For example, the tool was updated to apply the same
VOC control percentages to HAPS from lateral compressor engines as is currently done for well pad compressor
engines.
Corrections to Tagging
Another error in the 2014vl NEI was corrected. EPA inadvertently allowed several EPA data SCCs of Oil and Gas
Production into the final 2014vl NEI selection. This was since corrected, and now there's no additional EPA data
in the 2014v2 NEI, resulting in lower emissions overall for Colorado.
State Resubmissions
Several states resubmitted data during the window opening between the 2014vl and 2014v2 NEI. This included
WY, UT, OK, WV, and CO.
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Utah, for the most part, asked for no changes between versions on tagging. They submitted zeroes for anything
they didn't want EPA data on, but still needed some EPA data, like mud degassing. UT also does not submit HAPs
and relies on EPA for HAP augmentation. Utah resubmitted produced water pondsthe emission factor for the
ponds was too high in 2014vl. They replaced the EF and used a hybrid approach (not based on throughput) and
this only affected VOC in 2 counties and 2 SCCs. VOC decreased in the SLT submission significantlyby about
half for this SCC.
WY emissions changed significantly. Due to budget constraints, they weren't able to submit a complete
inventory (they estimated it covered about 80%) in time for 2014vl, but were able to submit corrections in time
for 2014v2.
4.17.1 Source category description
Residential barbecue grilling emissions include emissions from the burning of charcoal and all types of outdoor
meat grilling. Combustion emissions from gas barbecues are not included. Emissions estimates are for charcoal
and all types of meat cooked on charcoal, gas, and electric grills. 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".
4.17.2 Source of data
The 2014 NEI was the first time that EPA has provided estimates for this source category; these emissions were
not covered on a national basis for previous inventory years. Members of the NOMAD Committee (ID and TX)
were instrumental in developing this methodology. An inventory developer in Idaho developed the method,
based on one used in Idaho for many years. An inventory developer from TCEQthen created a tool in MS Access,
and provided instructions, which makes the method easy to use for all reporting agencies.
This source category includes data from the S/L/T agency submitted data and the default EPA generated
emissions. The agencies listed in Table 4-98 submitted 100% of their PM2.5 emissions for this sector; agencies not
listed used EPA estimates for the entire sector.
Table 4-98: Percentage of Residential Charcoal Grilling PM2.5 emissions submitted
Dy reporting agency
Region
Agency
S/L/T
SCC
PM2.5
6
Texas Commission on Environmental Quality
State
2810025000
100
9
Washoe County Health District
Local
2810025000
100
10
Coeur d'Alene Tribe
Tribe
2810025000
100
10
Idaho Department of Environmental Quality
State
2810025000
100
10
Kootenai Tribe of Idaho
Tribe
2810025000
100
10
Nez Perce Tribe
Tribe
2810025000
100
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
2810025000
100
4.17.3 EPA-developed emissions for residential charcoal grilling
Activity data
The activity data needed to estimate emissions from residential charcoal grilling is the number of 2013
households from 1-4 units, the amount of charcoal used in 2013, and the amount of meat cooked during
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outdoor grilling on charcoal, gas, and electric grills. None of the activity data was updated for the 2014v2 NEI.
The household data was obtained from the US Census Bureau 2013 5-year estimates [ref 1, ref 2], The fraction
of occupied households to total households was used on the total households of 1-4 units to calculate the
occupied 1-4 unit households. The amount of charcoal sold in Idaho was calculated (from the Hearth, Patio and
Barbeque Association BBQ Statistics total charcoal sold in 2013 [ref 3]) using national occupied 1-4 unit
households. The fraction of each state's occupied 1-4 unit households compared to the national occupied 1-4
unit households was used on the total charcoal sold in the United States to get the state portion of charcoal
sold. Each county was then apportioned tons of charcoal based on their fraction of the total number of 1-4 unit
households in each state. It was assumed that those in larger apartment units would not have the space to have
or use an outdoor grill.
The activity data for the weight of meat cooked was calculated using some generally accepted information
about charcoal grilling. It is generally assumed that about 30 charcoal briquettes are needed to cook a pound of
meat [ref 4], Information from Kingsford on the average weight of their charcoal briquettes indicated that there
are about 17.64262 briquettes/lb of charcoal [ref 5], Using this figure, the number of briquettes was calculated
for each county and divided by 30 to get the total weight of meat cooked with charcoal per county.
The gas and electric grill meat totals were estimated using some HPBA statistics. Their 2011 State of the
Barbecue Industry Report [ref 6] estimated that households with charcoal grills cook about 27 times per year.
Those with gas grills cook about 45 times per year. The later reports don't have this information, so the
assumption is that it has remained about the same. The HPBA 5-year average sales figures indicate that about
41% of the grills sold were charcoal grills [ref 7], and the other 59% are gas/electric grills [ref 8], Since the
number of grilling events for charcoal grills is 27 compared to 45 grilling events for gas/electric grills, and only
41% of grilling households have charcoal grills, estimating the amount of meat cooked by the other methods is
more complicated.
There were about 2,774 tons of meats cooked in Idaho from charcoal grilling. So, we have gas/electric meat
cooked (the unknown) / charcoal meat cooked = (gas/electric grilling events * the percent of gas/electric grills) /
(charcoal grilling events * the percent of charcoal grills) * (total charcoal meat cooked in Idaho) + total charcoal
meat cooked in Idaho = total meat cooked in Idaho from all grilling. The whole formula would be: total meat
grilled / 2,775 = (45*59%) / (27*41%) * 2775 + 2775 = 9,431 tons of meat cooked from all barbecue methods in
Idaho. Or take the amount of meat from charcoal grilling and multiply by 3.3984, which will give about the same
result (total meat estimated / charcoal meat grilled).
Emissions from charcoal lighting fluid can also be estimated for each county. The HPBA estimates that about
37% of those who use charcoal also use lighter fluid to start their grills [ref 10], They also estimate that about
80% of households have a grill of some type [ref 7], The number of charcoal lighter fluid households is estimated
by taking 80% of the households and multiplying by the 41% using charcoal grills. Then take 37% of those to
estimate the number of households using the lighter fluid. Each of these would then have about 27 barbecue
events per year. Lighter fluid is estimated to emit about 0.02 lbs of VOC per barbecue event ref 11], The
resulting formula is:
1-4 unit occupied households * 80% with grills * 41% with charcoal grills * 37% using lighter fluid * 0.02 lbs of
VOC.
4,1 ?. 3.2 Emission foctors: updated for 2014v2 NEI
CAP emission factors for charcoal grilling were obtained from "Emissions from Street Vendor Cooking Devices"
[ref 9], an EPA report developed by the U.S.-Mexico Border Information Center on Air Pollution. This same
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report indicates that most of the PM and VOC emissions come from the cooking of meat. The CO and NOx
emissions come from the burning of the charcoal. So, all VOC and HAPs from VOC, and the PM10/PM2.5 emissions
use the total tons of meat cooked to estimate emissions. The CO and NOx emissions were estimated using the
total tons of charcoal used for cooking. Idaho used averages from Table E-2 of that report which summarizes the
g/kg emissions per weight of both charcoal and meat. Tables 3-1 through 3-4 of the EPA report were used for
estimating HAPs emissions. These were averaged and used where they match up with pollutants in the EPA NEI
pollutant list. The test results from charcoal-only and the one test with a cover were not used in the averages.
New for 2014v2, the HAP emission factors were revised to correct the issue where the sum of HAP VOC
emissions exceeded the VOC emissions; the new HAP VOC emission factors for 2014v2 are now based on
"commercial cooking underfired charbroiling" (SCC 2302002200). The g/kg emission factors were converted to
lb/ton (factor of 2). The resulting emission factors are listed in Table 4-99.
lie 4-99: Resic
ential Charcoal Grilling emissions factors (Ib/tc
Code
Pollutant
Emissions Factor
CO
CO
3.314E+02
NOX
NOx
7.111E+00
PM25-PRI
PM2.5 Primary
1.474E+01
PM10-PRI
PM10 Primary
1.842E+01
VOC
VOC
1.703E+00
106990
1,3-Butadiene
1.779E-02
540841
2,2,4-Trimethylpentane
1.915E-03
91576
2-Methylnaphthalene
8.112E-03
100027
4-Nitrophenol
1.628E-02
208968
Acenaphthylene
2.552E-03
75070
Acetaldehyde
1.850E-01
98862
Acetophenone
4.377E-03
120127
Anthracene
1.860E-05
71432
Benzene
1.407E-02
132649
Dibenzofuran
4.159E-03
16672392
Diethyl Phthalate
1.427E-02
100414
Ethyl Benzene
1.864E-03
206440
Fluoranthene
6.780E-05
86737
Fluorene
1.547E-03
50000
Formaldehyde
2.342E-01
110543
Hexane
7.456E-03
108383
M-Xylene
1.017E-03
91203
Naphthalene
1.523E-03
95476
O-Xylene
1.864E-03
85018
Phenanthrene
2.050E-04
108952
Phenol
5.007E-02
123386
Propionaldehyde
8.541E-02
106423
P-Xylene
1.017E-03
129000
Pyrene
9.660E-05
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Code
Pollutant
Emissions Factor
100425
Styrene
3.232E-01
108883
Toluene
6.778E-03
Lighter fluid VOC emissions were estimated [ref 10] to be 0.02 lbs per barbecue event as noted above. These
were added to the VOC emissions estimated from the grilling of meat since there is no separate SCC to list these
emissions.
Emission calculations are based on the activity data of tons of meat or charcoal used per county multiplied by
the g/kg of meat or charcoal emission factors converted to lb/ton.
4.17.33 ControlFactors
No control measures are assumed for this category.
4,173,4 Example Calculation
Emissions are calculated for each county using emission factors and activity as:
E y ; Ax X E F y :
where:
EX;P = annual emissions for category x and pollutant p;
Ax = calculated pounds of meat or charcoal associated with category x;
EFx,p = emission factor for category x and pollutant p (pound/ton of meat or charcoal).
Example
The 2013 1-4 unit occupied households for Ada County was 129,646. Using the fraction of the Ada County
population compared to Idaho, the total tons of charcoal used in Ada County was 977.2 tons or 1,954,334.3
pounds. Using 30 briquettes needed to cook a pound of meat and figuring that there are 17.64262 charcoal
briquettes in a pound of charcoal, the amount of charcoal grilled meat cooked in Ada County was 574.7 tons.
(1,954,334.3 lbs of charcoal x 17.64262 briquettes/lbs of charcoal / 30 briquettes/lb of meat cooked / 2000 to
convert to tons). Then using the formula noted above, the total meat cooked from all grilling in Ada County was
1,952.9 tons. The calculation would be: 574.7 * 3.3984, or 574.7 * (45*59%) / (27*41%) * 574.7) + 574.7 =
1,952.9.)
The emission factor for PM10-PRI is 18.42 lb/ton of meat grilled
Epmio-pri = 1,952.9 tons meat grilled x 18.42 pounds PMlO-PRI/ton of meat grilled / 2000
= 17.99 tons PM10-PRI
4,17,4 References for residential charcoal grilling
1. U.S. Census Bureau. Community Facts, Housing, Selected Housing Characteristics, American Community
Survey 5-Year Estimates, accessed April 2015.
2. U.S. Census Bureau. Guided Search, Selected Housing Characteristics, American Community Survey 5-
Year Estimates (DP04) Counties.
3. Hearth, Patio and Barbecue Association (HPBA), Statistics/Barbecue Statistics/Charcoal Shipments for
2013. accessed April 2015.
4. Hearth, Patio and Barbecue Association (HPBA) 3/23/2015 email from Jessica Boothe on how many
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briquettes to use to cook a pound of meat or chicken.
5. Kingsford email on the weight of their charcoal briquettes 4/11/2015.
6. Hearth, Patio & Barbecue Association (HPBA), 2011 State of the Hearth Industry Report, accessed April
2015.
7. Hearth, Patio & Barbecue Association (HPBA), 2014 State of the Barbecue Industry Report, accessed
April 2015.
8. Hearth, Patio and Barbecue Association (HPBA), Statistics. BBQ Grill Shipments, accessed April 2015.
9. U.S. Environmental Protection Agency, 1999. Emissions from Street Vendor Cooking Devices (Charcoal
Grilling}. EPA/600/SR-99/048, June 1999, accessed October, 2012.
10. Hearth, Patio and Barbecue Association (HPBA) 3/23/2015 email from Jessica Boothe on how many
people with charcoal grills use lighter fluid.
11. 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.
4.18 Miscellaneous Non-Industrial NEC: Portable Gas Cans
4.18.1 Source category description
There are several sources of emissions associated with portable gas cans, hereafter referred to as PFCs (portable
fuel containers). These sources, used for gasoline, 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.
4.13.2 Source of data
Table 4-100 shows the SCCs covered by the EPA estimates and by the State/Local and Tribal agencies that
submitted data. The SCC level 3 and 4 descriptions are also provided. The leading SCC description is "Storage and
Transport; Petroleum and Petroleum Product Storage" for all SCCs.
Table 4-100: SCCs with 2014 NEI emissions for PFCs
SCC
Description
EPA
State
Tribe
2501011011
Residential Portable Gas Cans; Permeation
X
X
X
2501011012
Residential Portable Gas Cans; Evaporation (includes Diurnal losses)
X
X
X
2501011013
Residential Portable Gas Cans; Spillage During Transport
X
X
X
2501011014
Residential Portable Gas Cans; Refilling at the Pump - Vapor
Displacement
X
X
2501011015
Residential Portable Gas Cans; Refilling at the Pump - Spillage
X
X
2501012011
Commercial Portable Gas Cans; Permeation
X
X
X
2501012012
Commercial Portable Gas Cans; Evaporation (includes Diurnal losses)
X
X
X
2501012013
Commercial Portable Gas Cans; Spillage During Transport
X
X
X
2501012014
Commercial Portable Gas Cans; Refilling at the Pump - Vapor
Displacement
X
X
2501012015
Commercial Portable Gas Cans; Refilling at the Pump - Spillage
X
X
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This source category includes data from the S/L/T agency submitted data and the default EPA generated
emissions. The agencies listed in Table 4-101 submitted at least VOC emissions; agencies not listed used EPA
estimates for all PFC sources. Some agencies submitted emissions for the entire sector (100%), while others
submitted only a portion of the sector (totals less than 100%).
Ta
lie 4-101: Percentage of PFC VOC emissions submitted by reporting agency
Region
Agency
S/L/T
VOC
2
New Jersey Department of Environment Protection
State
100
2
New York State Department of Environmental Conservation
State
87
3
Delaware Department of Natural Resources and Environmental
Control
State
100
3
Maryland Department of the Environment
State
93
5
Illinois Environmental Protection Agency
State
100
10
Coeur d'Alene Tribe
Tribe
100
10
Nez Perce Tribe
Tribe
100
10
Kootenai Tribe of Idaho
Tribe
100
10
Idaho Department of Environmental Quality
State
100
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
100
4,18.3 EPA-developed emissions for portable gas cans; no change for 2014v2 NE!
PFC emissions are impacted by a 2007 regulation controlling emissions of hazardous pollutants from mobile
sources (MSAT2 rule). In this rule EPA promulgated requirements to control VOC emissions from gas cans. The
methodology used to develop emission inventories for gas cans was initially described in the regulatory impact
analysis for the rule and in an accompanying technical support document [ref 1, ref 2], The inventory
development approach used for the NEI is still based on the analyses done for this rule.
Below, data and methods are described for development of portable fuel container (PFC) inventories in the 2014
National Emissions Inventory (NEI).
VOC Allocation
PFC inventories in the MSAT2 rule were developed for different emissions scenarios in several calendar years
(1990, 2005, 2010, 2015, 2020, and 2030) at the State level for 6 categories of emissions: 1) vapor displacement
while refilling containers at the pump, 2) spillage while refilling at the pump, 3) spillage during transport, 4)
vapor displacement while refueling equipment, 5) spillage while refueling equipment, and 6) permeation and
evaporation.
For the NEI, emissions had to separate 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
residentiaLXXXX ,YY
r Re.v
F = Fx
commerciaLXXXX JY
\Res + Com J
r Com ^
VR GS + CoM;
(1)
(2)
where,
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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:
^AAA,XXXX,YY,perm ~ ^AAA,XXXX,YY,perm&evap X 0-3387 (3)
EAAA,XXXX,YY,evap ~ ^AAA,XXXX ,YY ,perm&evap ^ _ 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.
Once the state VOC emissions were allocated to the residential and commercial components of the categories,
they were assigned SCC codes. Finally, state emissions were allocated to the counties using the ratio of county
to State fuel consumption:
EXXXX,YYYYY, AAA,SCC ^XXXX ,YY, AAA,SCC
C ConsumptionYYYYY ^
Consumption
yy y
(5)
where,
Exxxx,yyyyy,aaa,see where the emissions for year XXXX, county with FIPS code YYYYY, emission scenario AAA, and SCC
shown in Table 4-100, EXxxx,yy,aaa,scc were the state level emissions for year XXXX, state YY, emission scenario
AAA, and SCC in Table 4-100, ConsumptionYYYY was the county fuel consumption and ConsumptionYY was the
state fuel consumption.
Below are descriptions of how 2014 PFC inventories for various types of pollutants were developed for the 2014
NEI, for different groups of SCCs.
i ฅL/L*S
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 3], Inventory estimates developed
for calendar year 2018 in EPA's Tier 3 vehicle rule modeling platform [ref 4] 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.
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Vapor Displacement
Vapor displacement emissions occur while refueling containers at the pump. These emissions are represented
by the following SCCs:
25010111014 - Residential Portable Fuel Containers: Refilling at the Pump: Vapor Displacement
25010112014 - 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, the composition of the emissions is
impacted by oxygenate. VOC emissions for these SCCs are carried forward from 2011.
4,183.2 Air Toxics
Permeation. Evaporation and Vapor Displacement
MSATs found in liquid gasoline will be present as a component of VOC emissions. These MSATs include benzene,
ethanol, and naphthalene. For vapor displacement, toxic to VOC ratios were obtained from headspace vapor
profiles from EPAct test fuels [ref 5], For permeation emissions, vehicle permeation speciation data from
Coordinating Research Council (CRC) technical reports E-77-2b and E-77-2c were used [ref 6, ref 7], 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 E0 and E10 [ref 8], Table 4-102 lists the
toxic to VOC ratios for each type of PFC emission.
Table 4-102: Toxic to VOC ratios for PFCs
Pollutant
Process
Speciation Surrogate
E0
E10
Benzene
Vapor Displacement
Vehicle Headspace
0.0077
0.0087
Benzene
Permeation
Vehicle Permeation
0.0250
0.0227
Benzene
Evaporation
Vehicle Evap
0.0336
0.0340
Naphthalene
Vapor Displacement
Vehicle Headspace
0.0000
0.0000
Naphthalene
Permeation
Vehicle Permeation
0.0004
0.0004
Naphthalene
Evaporation
Vehicle Evap
0.0004
0.0004
Ethanol
Vapor Displacement
Vehicle Headspace
0
0.0645
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Pollutant
Process
Speciation Surrogate
E0
E10
Ethanol
Permeation
Vehicle Permeation
0
0.2020
Ethanol
Evaporation
Vehicle Evap
0
0.1190
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 4-103.
Table 4-103: Toxic to VOC ratios for other HAPs vapor displacement, permeation 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-Trimethylpentane
0.0540
Spillage
Since spillage emissions were carried forward from the 2011 NEI, the HAP estimation approach for these
emissions reflects the methods used for that inventory. The methods used in the 2011 NEI are described below.
To calculate the benzene emissions for each PFC SCC in each county the following formulas was used:
x0.36 (6)
Benzene XXXXYYYYYSCC VOCxxxx YYYYY scc x
^ Benzene rejUd^XXXXjYYY ^
^ VOCr^ue^XXXXjYYY j
where,
XXXX was the year, YYYYY was the FIPS code of the county, and SCC was an SCC code shown in Table 4-100.
In the equations the factor 0.36 represents an adjustment based on the nationwide percentage of benzene in
gasoline vapor from gasoline distribution with an RVP of 10 psi at 60ฐF [ref 9], This factor is based on the ratio of
the percentage of benzene in gasoline vapor from gasoline distribution of 0.27%, divided by the percentage of
benzene in vehicle refueling emissions of 0.74% benzene in vehicle refueling emissions [ref 1],
For all other HAPs, the PFC emissions were created by multiplying the PFC VOC emissions by the county-level
ratio of HAP LDGV evaporative emissions by the VOC LDGV evaporative emissions for the county or:
HAP - VOC y
11^L1 XXXX,YYYYY,SCC y XXXX,YYYYY,SCC A
(hap ^
11^L1 LDGV,XXXX,YYYYY
VOC
y LDGV,XXXX,YYYYY
(V
, where the subscripts are as denoted previously. Using the LDGV evaporative emissions means only HAPs in the
onroad inventory with LDGV evaporative emissions would have PFC emissions. Naphthalene was also multiplied
by a factor of 0.0054, based on data from the same study used to adjust benzene, where the where the
percentage of naphthalene in VOC from gasoline distribution vapor emissions was 0.00027, in contrast to about
0.05% naphthalene in vehicle refueling emissions from highway vehicles.
One modification was made to spillage estimates from the 2011 NEI. The 2011 inventory did not account for
impacts of the fuel benzene standard implemented in 2011 because of the 2007 MSAT [ref 1], This rule
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established a 0.62% volume standard for benzene, whereas the national average benzene content standard
prior to the rule was about 1.0%. Thus, PFC benzene emissions for these SCCs were scaled by a ratio of 0.62/1 to
account for impacts of this rule.
4.13,4 References for PFCs
1. 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.
2. 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.
3. Federal Register. 2007. Control of Hazardous Air Pollutants from Mobile Sources. 72 (37): 8428-8570.
4. 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.
5. 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.
6. U. S. EPA. 2010. Evaporative Emissions from In-Use Vehicles: Test Fleet Expansion {i RC E-77-2b).
Prepared by Harold Haskew and Associates for Assessment and Standards Division, Office of
Transportation and Air Quality, October, 2010.
7. 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.
8. Auto/Oil Air Quality Improvement Research Program. 1996. Phase I and II Test Data. Prepared by
Systems Applications International, Inc.
9. Hester, Charles. 2006. Review of Data on HAP Content in Gasoline. Memorandum from MACTECto
Steve Shedd, U. S. EPA, March 23, 2006. This document is available in Docket EPA-HQ-OAR-2003-0053.
4,19 Mobile - Commercial Marine Vessels
The 2014v2 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.
4,19,1 Sector description
The CMV sector includes boats and ships used either directly or indirectly in the conduct of commerce or
military activity. The majority of vessels in this category are powered by diesel engines that are either fueled
with distillate or residual fuel oil blends. For the purpose of this inventory, we assume that Category 3 (C3)
vessels primarily use residual blends while Category 1 and 2 (CI and C2) vessels typically used distillate fuels.
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.
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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 2014 NEI.
Each of the commercial marine SCCs requires an appropriate emissions type (M=maneuvering, H=hotelling,
C=cruise, Z=reduced speed zone) because emission factors vary by emission type. Each SCC and emissions type
combination was allocated to a shape file identifier in the nonpoint inventory. The allowed combinations are
shown in Table 4-104. The default values are those assumed when the actual emission type may be unknown;
for example, emissions that occur in shipping lanes are assumed to be 'cruising' and cannot be 'hotelling', which
only occurs at ports. Port "Ports_Mar2017.zip" and underway "ShippingLanes_Apr25017.zip" GIS shape files
used in 2014v2 are available on the2014v2 Supplemental Rail and CMV Data FTP site.
Table 4-104: CMV SCCs and emission types in EPA estimates
SCC
Description
Allowed
Default
2280002100
Marine Vessels, Commercial Diesel Port
M
M
2280002200
Marine Vessels, Commercial Diesel Underway
C
C
2280003100
Marine Vessels, Commercial Residual Port
H
H
2280003100
Marine Vessels, Commercial Residual Port
M
H
2280003200
Marine Vessels, Commercial Residual Underway
C
C
2280003200
Marine Vessels, Commercial Residual Underway
Z
C
4,19,2 Sources of data
This source category includes data from the S/L/T agency submitted data and the default EPA generated
emissions. The state agencies listed in Table 4-105 submitted at least PM2.5, NOx and VOC emissions; agencies
not listed used EPA estimates for all CMV sources. Some agencies submitted emissions for the entire sector
(100%), while others submitted only a portion of the sector (totals less than 100%). For this sector, there are
sub-county-level estimates from EPA that were backfilled for some shape IDs where the state data did not exist.
California and Texas also submitted FIAP emissions, but the other states only submitted 6 CAPs: CO, NOx, PM25,
PM10, S02, and VOC.
Table 4-105: Percentage of CMV PM2.5, NOx and VOC emissions submitted by reporting agency
Region
Agency
PM2.5
NOx
VOC
1
New Flampshire Department of Environmental Services
98
92
97
2
New Jersey Department of Environment Protection
65
57
88
3
Delaware Department of Natural Resources and
Environmental Control
96
91
89
5
Illinois Environmental Protection Agency
100
100
100
5
Indiana Department of Environmental Management
100
100
100
5
Michigan Department of Environmental Quality
100
100
100
5
Minnesota Pollution Control Agency
100
100
100
5
Ohio Environmental Protection Agency
100
100
100
5
Wisconsin Department of Natural Resources
100
100
100
4-176
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Region
Agency
PMz.5
NOx
voc
6
Louisiana Department of Environmental Quality
0
0
0
6
Texas Commission on Environmental Quality
100
100
100
7
Iowa Department of Natural Resources
100
100
100
7
Missouri Department of Natural Resources
100
100
100
9
California Air Resources Board
100
100
100
10
Washington State Department of Ecology
97
94
94
4.19.2.1 Significant Revisions for 2014v2 NEl
Significant changes between versions are:
1. All of the port shapes were redrawn such that emissions would be placed over water and not on port
land area. See EPA method documentation for details.
2. New submittals were added for Lake Michigan Air Directors Consortium (LADCO) states and Delaware.
EPA's CMV estimates were using activity data from Entrance and Clearance Waterbourne Commerce (both from
Army Corps of Engineers) and from a 2007 EPA census of Category 1 and 2 vessel activities. The activity data
were adjusted for typical engine loads for the modes of operation and multiplied by emission factors by engine
category. The details of these calculation, also available in "CMVv2_2EPAMethodsReference_20180209.pdf" on
the on the2014v2 Supplemental Rail and CMV Data FTP site, are provided below. For 2014v2, the Lake Michigan
Air Directors Consortium (LADCO) submitted emissions estimate for several states (see Table 4-106). The
documentation on those estimates is not discussed here but is available in a stand-alone document
"CMVv2_3LADCOMethodsReference_Sept 2015.pdf" on the2014v2 Supplemental Rail and CMV Data FTP site as
well.
Where SLT emissions data were submitted, they replaced EPA-default emissions in the 2014 selections. For the
2014v2 NEl, these submitted estimates were re-apportioned according to area where the shape files were
redrawn.
Table 4-106: Agencies that provided CMV submittals for the2014vl and 2014v2 NEl
Agency
Number of
Pollutants
Submitted for
2014vl
Submitted for
2014v2
California
58
Y
Delaware
Y
Illinois
6
Y
Y - LADCO
replacement
Indiana
6
Y-LADCO
Iowa
6
Y-LADCO
New
Flampshire
6
Y
Minnesota
6
Y-LADCO
Michigan
6
Y-LADCO
Missouri
6
Y - LADCO
New Jersey
6
Y
Ohio
6
Y - LADCO
Texas
48
Y
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Agency
Number of
Pollutants
Submitted for
2014vl
Submitted for
2014v2
Washington
6
Y
Wisconsin
6
Y-LADCO
EPA
49
LADCO provided a file of estimates that EPA submitted on their behalf. The states identified above agreed to the
LADCO submittal. The following pollutants were included: CO, C02, VOC, NOX, PM10-PRI, PM25-PRI. EPA added
S02 based on a ratio of N0x/S02 of 3.09 for C3 vessels, and EPA estimates were retained for CI and C2 vessels.
HAPs were added based on the toxic fractions used in the EPA estimates.
4,19,3 EPA-deveioped emissions for commercial marine vessels: revised for 2014v2 NEI
This section summarizes the approach used to estimate emissions including compilation of 1) activity data
(kilowatt hours or kW), 2) engine operating load factors, and 3) emission factors HAP speciation profiles.
Regarding vessel activities, the following data sources were used to develop vessel characteristics and quantify
traffic patterns:
Entrance and Clearance (E&C) - This data set captures vessels involved in international trade,
documenting where a vessel came from and its next port of call [ref 1], These vessel-specific ship
movements were linked to their individual engine characteristics [ref 2] to calculate kilowatt hours.
Most of the vessels in this data set are equipped with Category 3 propulsion engines, although some
vessels were identified that are equipped with Category 1 and 2 propulsion engines.
Waterborne Commerce (WC) - The U.S. Army Corps of Engineers provided a data set of domestic vessel
movements for tugs and barges, bulk carriers, tankers, and other vessels [ref 3], These data are provided
as domestic trips along a defined route and mapped to the NEI ports and shipping lane segments.
Typical vessel speeds by vessel type were used in conjunction with the distance associated with each
trip to estimate the hours of operation which were applied to the vessels' propulsion power to get
kilowatt hours.
Category 1 and 2 Study - For this inventory, the EPA's 2007 Category 1 and 2 vessels census was
updated with more recent data, specifically for ferries, survey vessels, ships involved with offshore oil
and gas activities, dredging, and U.S. Coast Guard operations. For these smaller vessels, less detailed
information was available about their characteristics or traffic patterns, therefore, the kilowatt hours
were estimated based on typical operations and applied to typical vessel power ratings.
Note all activity data were adjusted for typical engine loads for the modes of operation included in this study
(i.e., cruising, reduced speed zone (RSZ), maneuvering, and hoteling). The adjusted kilowatt hours were applied
to EPA emission factors by engine category as follows:
/ g \ D (NM)
Emissions= EF ( ) x rTT-r- xLF *Vp (kW)
VkWh/ VsM
hr
Where:
EF
D
Vs
LF
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EPA Emission factor, in grams per kilowatt-hour (kWh)
Distance along segment or RSZ (NM)
0.94 x maximum vessel speed = cruising speed or RSZ speed limit (NM/hr)
Load Factor (fraction less than 1)
-------
Vp = Vessel Power (kW)
D/Vs is used to estimate operating hours for E&C data and WC data. For C1/C2 study, typical operating hours are
used instead. Also, if vessel speed is unknown, typical speed by vessel type was used (nautical miles/hr or knots).
More detailed equations are available in Appendix A of the EPA document "Commercial Marine Vessels - 2014
NEI Commercial Marine Vessels Final" [ref 4],
Activity data for entrance and clearance
Entrance and Clearance
Vessel-specific routing data were available from the U.S. Army Corps of Engineers' 2012 E&C data [ref 1] for
approximately 11,000 U.S. and foreign flagged vessels involved in international trade that complies with U.S.
Customs and Clearance reporting requirements, as summarized in Table 4-107.
Table 4-107: Vessel-specific routing data
Standard Type
Total Vessel Count
Domestic Flagged
Foreign Flagged
Barge
350
244
106
Bulk Carrier
3,294
11
3,283
Bulk Carrier, Laker
89
35
54
Buoy Tender
4
0
4
Container
1,319
51
1,268
Crude Oil Tanker
754
8
746
Dredger
2
1
1
Drilling
51
7
44
Fishing
248
142
106
FPSO
2
0
2
General Cargo
1,086
24
1,062
Icebreaker
2
0
2
Jackup
4
3
1
LNG Tanker
45
0
45
LPG Tanker
156
0
156
Misc.
47
17
30
Passenger
173
7
166
Pipelaying
14
0
14
Reefer
185
0
185
Research
61
31
30
RORO
92
7
85
Supply
255
197
58
Support
75
34
41
Tanker
1,428
14
1,414
Tug
679
533
146
Vehicle Carrier
465
20
445
Well Stimulation
3
1
2
Total
10,883
1,387
9,496
These vessels were linked to their individual routes based on the originating port and the destination port. For
the 2014 NEI, the E&C data were mapped to 7,176 routes comprising 410 unique ports, 174 of which are
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domestic U.S. ports. The waterway network was also edited to include 1,005 segments associated with RSZs
based on the EPA's Regulatory Impact Assessment [ref 5] for Category 3 vessels summarized Appendix B. Where
the RSZ speed was unknown, a typical value of 10 knots was used.
To calculate hours of operation, the length of each route was divided by the vessel speed. Where a vessel travels
through a RSZ, the vessel speed was reduced, thus increasing the hours of operation along that segment. Figure
4-11 provides an example of a vessel traveling from port Qto port R, moving through a 10 NM RSZ segment
followed by a 40 NM normal cruising segment.
Figure 4-11: Example route for ship movement from Port A to Port B via a RSZ
RSZ
PT | 10 (NM) 1I 40 (NM)
Q
4 Knots RSZ Cruising (15 Knots)
Hours to transit each segment were estimated for each vessel based on the distance traveled and the vessel
cruising speed, which was assumed to be 94 percent of the vessel's maximum speed as obtained from
Information Handling Services' [ref 2] Register of Ships. These cruising speeds were additionally reduced based
on the latest International Maritime Organization (IMO) Greenhouse Gas emission inventory [ref 6] that
quantifies actual vessel speeds and engine operating loads for select vessel types, accounting for recent
practices to reduce fuel consumption known as slow steaming. The IMO data are presented in Table 4-108.
Table 4-108: IMO-vessel speed data
Ship Type
Size
Category
Size
Units
Ratio of average
at-sea speed to
design speed
Percent of
total
population
Weight
amount
Weighted
Cruising
Speed Factor
0-9999
0.84
0.9%
0.007403
10000-34999
0.82
25.1%
0.20571
Bulk
Carrier
35000-59999
0.82
36.0%
0.295272
60000-99999
dwt
0.83
31.7%
0.263082
0.822751023
100000-
199999
0.81
6.2%
0.050227
200000+
0.84
0.1%
0.001058
0-999
0.77
4.9%
0.038087
1000-1999
0.73
11.8%
0.086059
2000-2999
0.7
12.5%
0.087716
Container
3000-4999
TEU
0.68
32.8%
0.223116
0.681508656
5000-7999
0.65
28.6%
0.185944
8000-11999
0.65
9.0%
0.058409
12000-14500
0.66
0.3%
0.002176
14500+
0.6
0.0%
0
Oil Tanker
0-4999
dwt
0.8
0.1%
0.001094
0.782982216
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Ship Type
Size
Category
Size
Units
Ratio of average
at-sea speed to
design speed
Percent of
total
population
Weight
amount
Weighted
Cruising
Speed Factor
5000-9999
0.75
0.3%
0.002052
10000-19999
0.76
0.0%
0
20000-59999
0.8
3.6%
0.028454
60000-79999
0.81
15.6%
0.12632
80000-11999
0.78
43.4%
0.338249
120000-
199999
0.77
32.6%
0.250698
200000+
0.8
4.5%
0.036115
dwt = dead weight tonnage; TEU = twenty foot equivalent units
For RSZs, a vessel's speed was assumed to be the zone's speed unless the vessel's cruising speed was lower. For
example, a vessel with a cruising speed of 12 knots traveling through a waterway segment with a reduced speed
of 14 knots was assumed to be operating at 12 knots.
The hours of operation were applied to the vessel's power, which was adjusted for typical engine operating
loads to get kilowatt hours. In turn, the kilowatt hours were applied to the appropriate EPA emission factor
based on the vessel engine's category to estimate criteria pollutant emissions. The flow of emissions calculations
for underway vessels is illustrated in Figure 4-12.
Figure 4-12: Emission calculations for underway operations
Underway (Cruisrg/Reduced Speed)
EFs
Hours of Operation
Load Factors
by Mode
A;? >i ty
Kile watt Hours
Vessel
Sh'pp ng Lane
R5Z
Vessel characteristics data were compiled from IHS Register of Ships [ref 2] and linked to vessels included in the
2012 E&C data. The vessel characteristics included the following data:
Vessel identification codes
Vessel name
Country of registry
Call sign
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Vessel type
Gross/net tonnage
Vessel power
Auxiliary engine power
Piston stroke length/cylinder diameter (to calculate vessel category)
Maximum vessel speed.
Approximately 89 percent of the E&C vessels could be matched to their characteristics by cross referencing
multiple attributes such as IMO identification code, country of registry, gross tonnage, net tonnage, vessel type,
and vessel name. For the remaining vessels that could not be matched, vessel attributes were developed for
each vessel type based on the matched vessel in the IHS data. If the vessel type was unknown, aggregate
attributes derived from all matched vessels in the IHS data set were developed and used. Note that the auxiliary
engine data in the IHS data set was poorly populated; therefore, vessel type surrogates were developed based
on vessels that reported auxiliary engine power. The vessel power data used in this study are presented in Table
4-109.
Table 4-109: Vessel power attributes by vessel type
Standard Type
Count
Avg Main
hrs
Avg Aux
kW
Avg Max
Speed
Default
Vessel
Category
Bulk Carrier
3,177
8,990
1,935
14.3
3
Bulk Carrier, Laker
80
7,069
2,216
13.7
3
Buoy Tender
4
4,266
12.6
2
Container
1,218
39,284
7,851
23.2
3
Crude Oil Tanker
731
15,070
2,888
15.1
3
Drilling
7
15,806
12,840
11.7
2
Fishing
123
1,262
272
2.3
1
FPSO
2
18,123
11.5
3
General Cargo
1,020
6,130
1,619
14.6
3
Icebreaker
2
21,844
12.0
2
Jackup
4
1,643
270
3.5
1
LNG Tanker
44
29,607
8,129
19.2
3
LPG Tanker
151
8,557
3,021
15.8
3
Misc.
35
2,805
631
10.0
1
Passenger
168
45,760
4,477
20.4
3
Pipelaying
14
11,355
5,037
12.6
2
Reefer
182
8,930
3,328
18.9
3
Research
55
5,395
1,905
11.2
2
RORO
72
9,479
4,006
16.7
3
Supply
255
3,201
662
10.1
1
Support
73
6,590
2,305
9.7
2
Tanker
1,423
8,474
2,730
14.5
3
Tug
396
3,440
348
7.7
2
Vehicle Carrier
441
13,829
3,729
19.8
3
Well Stimulation
3
7,697
340
8.2
3
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Individual vessel movements were compiled as origination and destination pairs for each U.S. port included in
the E&C data. The E&C data includes only vessels that enter or leave U.S. waters at some point in the trip. Over
49 percent of the records were for vessels that visit a single U.S. port during a single trip. Similarly, over 49
percent of the records were for vessels that visited multiple U.S. ports in one trip and less than one percent of
the records was for between domestic U.S. ports only.
Because the E&C data report the departure of a vessel from a U.S. port and the arrival of the same vessel in the
destination port associated with the trip, it was necessary to adjust the vessel movement data to avoid double
counting of trips. To avoid the double counting only the entrance or clearance of the trip and not both are
counted. Evaluating the duplicate trips was also an important quality check on the E&C dataideally there
should be a duplicate departure and arrival record for every trip, thus validating the completeness of the data.
For example, for a vessel traveling from Long Beach to San Diego would typically have four E&C records:
Arrival at Long Beach
Departure from Long Beach (to San Diego)
Arrival at San Diego (from Long Beach)
Departure from San Diego.
Of the 23,008 unique ship movements for domestic origination and destination pairs, 85 percent of the vessel
movements had corresponding arrivals and departures; 3,481 (15 percent) had an odd number of records,
indicating that a vessel movement may be missing.
In many cases, the missing vessel movements were associated with an arrival in one port and a departure from
an adjacent port, suggesting that the missing vessel movement was between the two adjacent ports. For
example, the data may show only three records:
Arrival at Long Beach
Departure from Los Angeles (to San Diego)
Arrival at San Diego (from Los Angeles)
Departure from San Diego.
This dataset would thus suggest a missing Los Angeles to Long Beach trip.
To account for this type of error, adjacent ports were aggregated, reducing the unique vessel routes or
movements to 19,883. Of the final 19,883 routes, only 4 percent of the vessel movements (attributed to 815
routes) had a missing arrival or departure. Many of the remaining missing ship movements were associated with
the U.S. protectorates in the Caribbean Sea, where the arrival and departure information occasionally appeared
to be switched.
The issue of duplicate trips was not a concern for foreign vessel movements because the E&C documents arrivals
and departures for only U.S. ports, which means that a departure from a U.S. port to a foreign port or an arrival
from a foreign port to a U.S. port would always be a unique trip.
Adjustments were also made for Alaskan trips. The E&C data reported activity for 52 Alaskan ports, however,
the vast majority of those are small ports and have very little traffic. To capture the majority of emissions, only
the top 13 Alaska ports, which accounted for 94 percent of the Alaska traffic, were included. Table 4-110 lists
the Alaska ports and associated vessel calls.
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Table 4-110: Alaska ports and vessel calls
Ports
Total of
Count
Domestic
Foreign
Fraction of
Alaska Total
Juneau, AK
1,892
1,812
80
0.27
Ketchikan, AK
1,699
1,136
563
0.20
Skagway, AK
1,390
1,330
60
0.20
Anchorage, AK
563
526
37
0.08
Kivalina, AK
481
481
0.03
Sitka, AK
326
302
24
0.05
lliuliuk Harbor, AK
212
76
136
0.02
Dutch Harbor, AK
196
84
112
0.02
Whittier, AK
182
65
117
0.02
Seward, AK
149
109
40
0.02
Icy Strait, AK
132
110
22
0.02
Wrangell, AK
88
15
73
0.01
Haines, AK
82
81
1
0.01
Once the E&C origination and destination port pairs were defined, trips were routed over a custom waterway
network based on the U.S. Army Corps of Engineers' navigable waterway network using a Geographic
Information System (GIS) and network analysis. The routes were then intersected with EPA's NEI shapefiles of
ports and shipping lanes. Shipping lanes associated with RSZs were coded to allow for adjustment in vessel
speed, time spent transiting the RSZ, and engine operating load.
Because U.S. territorial waters extend out 200 nautical miles from the coast (Figure 4-1311, international vessel
routes were mapped only to the U.S. federal waters/international waters boundary. The distance traveled was
calculated based on the route the vessel was assigned. Each waterway segment was coded to differentiate
normal cruising versus RSZ operations.
11 These are the official US territorial waters from NOAA, which are generally 200nm but do vary in some places due to
foreign entities, etc. Spreading/condensing of emissions depends more on how the emissions were developed than the
shapes we use here and is a frequent topic of conversation with modelers.
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Figure 4-13: State and federal waters of the United States
Blue/Light Blue = state and federal water boundaries
4.19.3,2 Activity data for entrance & clearance time spent maneuvering/dockside
E&C data do not include details about time spent in each ship movement mode. Typical maneuvering times by
vessel type were used to estimate time spent in this mode. Maneuvering durations for different vessel types
were obtained from Entec's European emission inventory [ref 7] and are presented in Table 4-111. Note half of
the maneuvering time presented in Table 4-111 was assumed to be approaching the terminal and half departing
from the terminal.
Table 4-111: Estimated maneuvering time by vessel type
Vessel Type
Maneuvering Time
(hours)
Bulk Carrier
1
Bulk Carrier, Laker
1
Buoy Tender
1.7
Container
1
Crude Oil Tanker
1.5
General Cargo
1
LNG Tanker
1
LPG Tanker
1
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Vessel Type
Maneuvering Time
(hours)
Misc.
1
Passenger
0.8
Reefer
1
RORO
1
Tanker
1
Tug
1.7
Vehicle Carrier
1
To quantify the duration a vessel spends dockside, the E&C data were organized chronologically for individual
vessels to determine when a vessel arrives at the dock and when it leaves. Some of the dockside durations
seemed unreasonably high, indicating that either an arrival or departure was missing or out of sequence. These
anomalies were identified and removed from the analysis. The data were then averaged by vessel type to
develop port specific dockside duration times. It should be noted that the E&C data recorded the day the vessel
arrived and the day the vessel departed. The daily periods were multiplied by 24 hours to get hourly values. If a
vessel arrived and departed in the same day it was assumed that the dockside duration was 12 hours.
The EPA provided hourly containership dockside data for 15 ports [ref 8], For the 2014 NEI, these containership
data replaced containership E&C data for the following ports:
Ports of Los Angeles and Long Beach
Ports of New York and New Jersey
Port of Seattle
Port of Houston
Port of Baltimore
Port of Savannah
Port of Norfolk
Port of Charleston
Port of New Orleans
Port of Mobile
Port of Miami
Port of Philadelphia
Port of Tampa
Port of San Juan
Port of Portland
Additionally, dockside duration data were identified for ports that developed their own inventories. These data
were assumed to be the highest quality and replaced E&C and EPA containership data. 2014 Detailed port data
were obtained from the following ports:
Port of Los Angeles
Ports of New York and New Jersey
Port of San Francisco
Port of San Diego
4.19.33 Activity dsta for vmierbome commerce
As with the E&C data, the Army Corps of Engineers Waterborne Commerce Data (WCD) provides vessel trips for
individual vessels operating over a specified route. The WCD also includes vessel power ratings and distance of
each route. The distance data were evaluated using typical vessel speeds to calculate hours of operation to
transit a specified route. Note, hours of operation were adjusted for slower speeds transiting RSZs. The cruising
speeds for each vessel type were compiled from a variety of sources. The primary data source was the IHS data;
vessels equipped with Category 1 and 2 propulsion engines were identified and grouped by vessel type and
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averages of the vessel's maximum speed were developed for each grouping. These values are shown in Table
4-112. The cruising speed was assumed to be 94% of the average maximum speed.
Table 4-112: Category 1 and 2 average maximum speed by vessel type
Vessel Type
Vessel
Count
Average Maximum
Speed (knots)
Bulk Carrier
376.00
10.09
Bulk Carrier, Laker
27.00
13.74
Buoy Tender
197.00
6.90
Container
111.00
8.48
Crude Oil Tanker
44.00
6.97
Drilling
39.00
11.74
Fishing
13,652.00
5.67
Floating Production and Storage Offloading
10.00
4.90
General Cargo
7,179.00
8.09
Icebreaker
27.00
10.52
Jackup
173.00
4.25
LNG Tanker
3.00
9.33
LPG Tanker
183
10.83
Miscellaneous
2,014
6.83
Passenger
3,017
15.67
Pipelaying
280
6.39
Reefer
183
9.62
Research
951
9.79
RORO
1,997
11.28
Supply
3,409
12.98
Support
1,036
10.42
Tanker
2,880
8.28
Tug
15,660
8.54
Vehicle Carrier
20
14.42
Well Stimulation
30
8.63
Because the WCD contain confidential business information not available to the general public, the activity data
were aggregated to develop national total activities and reapportioned to appropriate NEI underway shapes.
This approach provided reasonable national estimates while protecting the confidential business aspects of the
WCD. The spatial allocation was developed in GIS using an approach similar to that used for the E&C data. The
WCD were evaluated to identify consolidated routes using both the port and location names for the origins and
destinations. For example, routes to and from "St. Thomas, VI" were combined with routes to and from "St.
Thomas Harbor Virgin Islands." We also removed routes where the origin and destination were the same,
because these records were considered to be inter-terminal maneuvering and are likely to be included in the
maneuvering assumptions. This consolidation process reduced the number of unique routes from 40,775 to
27,991. The remaining routes were mapped in GIS using a shortest-distance based network analysis, and the
routes were again intersected with NEI shapes to identify which routes passed through each shape. This
intersection process identified portions of some routes that passed outside of US waters, for example, from
Miami to Puerto Rico. For each route, the total length within US waters was divided by the total length of the
route to obtain the percentage of the route activity that occurs in US waters. The activity data were adjusted
accordingly to remove kilowatt hours that occurred in international waters.
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Next, for each shipping lane segment shape, the number of vessel trips that passed through were totaled.
Ta - R1+R2
Where:
Ta = Total number of trips on segment a
Ri = Number of trips on route 1
R2 = Number of trips on route 2
The length of the waterway through each shape was calculated and multiplied by the number of trips that occur
along the shape. This value was divided by the national total for trips multiplied by the length to determine the
percentage of the national total activity to allocate to each shape.
P = (T * L)/(NT * NL)
Where:
P = Percentage of national activity
T = Total trips for the NEI underway shape
L = Waterway segment length within underway shape
NT = National trip total
LN = National waterway network length total
Updating the Category 1 and 2 Vessel Census activity data
Since E&C includes only larger internationally-travelling vessels, additional data sources were needed to fill data
gaps, particularly for smaller CI and C2 vessel population involved in domestic traffic.
Dredging
As part of the effort to update the EPA's CI and C2 vessel data, dredging data were compiled as a new vessel
category. To estimate dredging activities for different types of dredging vessels, operating days were obtained
from the U.S. Army Corps of Engineers database of dredging contracts for the entire country [ref 9], This
database included contracts from 2012 to 2014. For contracts active since 2012, only the portion of the
contracts that were active during 2014 were used in this inventory. The 2014 dredging activities are presented in
Appendix C [ref 4] by job name, dredging equipment, and actual operating days.
Operating hours were calculated from the number of days active in 2014, assuming a utilization rate
documented in the Category 1/2 Vessel Census of 90% time spent dredging, excluding equipment positioning,
maintenance, and refueling times. The U.S. Army Corps of Engineers data did not include horsepower or kW
ratings for the engines on the dredging vessels but did include a dredging vessel type. A literature search of the
dredging vessel types provided a kW rating for a typical vessel in each category, as summarized in Table 4-113.
Table 4-113: Power rating by dredging type
Type
Contract Code
kW
Source
Bucket or mechanical
B
1,600
Anderson, 2008 [ref 10]
Hopper
H
7,272
TCEQ, 2012, [ref4]
Non-conventional (Specialty) Type
N
2,093
Van Oord 2015 [ref 11]
Pipeline (Cutterhead)
P
7,161
TCEQ, 2012 [ref 4]
Pipeline and Hopper Combination
Y
4080
Robinson et al. 2011 [ref 12]
Undefined
U
5028
Average of compiled dredging data
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The typical kW ratings in Table 4-113 were matched by dredge type to each contracted vessel noted in Appendix
C [ref 4], The matched power rating was multiplied by the utilization rate and dredging duration to estimate kW-
hours which are summarized in Table 4-114.
Table 4-114: Summary of national kilowatt-hours by dredging vessel type
Type
Total kW-hr
Bucket or mechanical
63,659,520
Hopper
302,526,835
Non-conventional (specialty) type
15,280,574
Pipeline (cutterhead)
654,286,248
Undefined
5,973,264
Dredging activities were spatially apportioned to ship channels based on the job name. The job names indicated
general location, such as a bay area or a waterway portion; however, they did not provide sufficient information
to precisely locate the dredging activities or even geographic extent of the project. Best effort was given to
identify the waterway segments in EPA's GIS shape files that most closely match the limited location
information. It should be noted that these activities have been increasing over the past several years to
accommodate larger vessels that will be able to transit the new Panama Canal.
Research Vessels
A list of current US research vessels was obtained from the University of Delaware's International Research Ship
Information and Schedule database [ref 13], In the 2007 vessel census study [ref 14], only 31 research vessels
were included. Using the University of Delaware's research vessels website for this inventory, 251 vessels were
identified. This gave a more accurate representation of CI research vessels, which were undercounted in the
original CI and C2 census. Twenty-three of these vessels had detailed trip schedules for 2014, and activity in
days was determined for these vessels. The list did not have vessel identification numbers or codes, so an online
search was implemented to find vessel identification codes for the remaining vessels. Where identification codes
could be found, the vessels were linked to research vessels in the IHS database, providing details on the engine
power ratings and engine category. However, not all vessels were matched and another online search was
implemented to obtain engine power ratings for the unmatched vessels. During this process, 35 vessels were
removed from this analysis because information was found that indicated that the vessel was not in service in
2014 or not powered by a diesel combustion engine (e.g. electric powered remotely operated vehicle (ROV)).
Detailed results are presented in Appendix D [ref 4], Summary of research vessel matching activities are
provided in Table 4-115.
Table 4-115: Research vessel characteristics mate
Research Vessels Matching
Original
251
IHS match
77
Online search
109
Annual schedule
23
Removed
35
ling by reference
For research vessels without engine power ratings, the matched vessel data were averaged to provide a default
of 732 kW which was used to gap fill missing research vessel power data.
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For the 2014 inventory, the duration of each research mission was used when available. For the vessels with no
activity data, an average value (220 days converted to 5,280 hours) was obtained from the previous Category 1
and 2 Census report. This default duration data was used to when vessel schedule data were not available. The
vessel power data were applied to the duration data to calculate kW-hrs for the research vessels.
Coast Guard
A roster of U.S. Coast Guard vessels was provided by the US Coast Guard's (USCG) External Coordination Division
[ref 15]. Among the data given were vessel name, horsepower, and annual underway hours for 246 USCG
cutters (Appendix E, ref 4) and over 1,600 smaller boats. Fifty-eight percent of the smaller vessels were gas
powered and excluded from this analysis. Also boats which were flagged as retired were also excluded from this
analysis. This reduced the Coast Guard Boat list to 652 vessels.
All vessel power ratings were converted from horsepower to kW using the conversion factor 1 HP = 0.7457 kW.
The vessel power ratings were multiplied by underway hours also provided by the U.S. Coast Guard to estimate
kW-hours per vessel. As Table 4-116 indicates, approximately 95 percent of activity is related to cutter
operations and 5 percent is associated with the smaller boats. The Coast Guard data also included general
information about where the vessels operated; for the 2014 NEI inventory, each vessel's kW-hours were
associated with the area of operation and summarized in Table 4-117.
Table 4-116: Summary of Coast Guard underway activity
Vessel Type
Number of Vessels
Total kW-hours
Cutter
267
2,125,794,310
Boats
652
117,895,003
Total
919
2,243,689,313
Table 4-117: General location of Coast Guard underway activities
Area
Total kW-hours
Arkansas River
1,025,173
Atlantic
643,954,356
Elizabeth River
92,689,163
Great Lakes
53,675,432
Gulf
129,482,530
Illinois River
343,721
Lower Atchafalaya River
625,932
Mississippi River
3,349,678
Ohio River
1,276,438
Pacific
1,311,967,588
Puget Sound
3,793,450
Tennessee River
1,115,487
Willamette River
354,849
Lake Champlain
35,515
Total
2,243,689,312
As the vessel fleet roster quantified at sea hours of operation, an inquiry was sent to the Coast Guard to ask
specifically about in-port activities for the cutters. The Coast Guard staff indicated that cutters generally use
shore power whenever it is available. There are some instances where maintenance, testing, or training could
necessitate the need to run on ship's power. Because of these exceptions, it is estimated that the time on ship's
power is no more than 10 hours per 30 days of in-port time. This means that while in-port, a Coast Guard cutter
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is estimated to be on shore power "99% of the time" [ref 16]. As this response indicates, in-port ship activity is
relatively small, so it was not included in this version of the NEI.
Note, currently the NEI does not include emission estimates from U.S. Naval exercises in U.S. waters. It is
anticipated that data may be available in 2016 that will allow inclusion of these vessels.
Commercial Fishing
To obtain the most accurate survey of commercial fishing vessels operating in the United States, regional offices
of the National Oceanic and Atmosphere Administration (NOAA) were contacted. Of the offices contacted, only
Northeast, Southeast (including the Gulf of Mexico), West Coast, and Alaska provided data. Data for the Great
Lakes, Puerto Rico, and the U.S. Virgin Islands were not obtained. Upon further research, it was found that
fishing vessels in Puerto Rico and the Virgin Islands are almost all powered by small single engines, diesels too
small to be considered CI vessels or gasoline powered vessels not included in this inventory effort.
Due to confidentiality concerns, the responding NOAA regions were not able to provide specific vessel
information. The Northeast [ref 17] and Southeast [ref 18] region provided the data on annual number of trips,
vessel count, and days absent by port or county, which were used to estimate and spatially allocate annual
hours of operation.
Data obtained from the West Coast regional office [ref 19] were not used in this inventory because the data
provided only quantified the number of vessels operating and amount of fish caught by port. Data to quantify
hours of operation were not provided. To gap fill the West Coast and the Great Lakes hours of operation, the
NOAA website's commercial fishery landings by state [ref 20] were used to calculate a percent change between
2006 and 2013 commercial fish landings in pounds. It should be noted that data for 2014 was not available at
the time, so 2013 data were used. Fishing vessel activity values in terms of kW-hours developed in the original
Category 1 and 2 Census Study [ref 14] for the West Coast and Great Lakes were extrapolated using the percent
change summarized in Table 4-118.
Table 4-118: State fish landing data for Great Lakes and Pacific States
Year
(lbs)
Great Lakes
Pacific
Ml
MN
OH
Wl
Total
CA
HI
OR
WA
Total
2006
9,350,764
308,409
4,241,973
4,449,476
18,350,622
341,660,769
26,020,904
282,846,344
241,606,439
892,134,456
2013
9,487,700
457,374
4,812,541
3,850,262
18,607,877
363,798,075
32,447,284
339,589,404
273,796,328
1,009,631,091
Percent
Change
1.5
48.3
13.5
-13.5
1.4
6.5
24.7
20.1
13.3
13.2
It is expected that the Alaska fishing vessel activity data would be significant as it represents about half of the
U.S. fish landings. But the NOAA data [ref 21] obtained from the Alaska region was problematic as it
documented the fleet size to be 2,267 vessels, noting the average duration at-sea per trip was 3 days, but could
not provide an estimate of the number of trips these vessels made. Data from the Alaska Commercial Fisheries
Entry Commission (CFEC) website which tracked Alaskan fishing vessels for the year 2014 [ref 22] was used to
evaluate the state's fishing fleet. The database included build date, horsepower rating, and duration at sea for
10,058 individual vessels. As seen in Figure 4-14, assessing the horsepower of the vessels included in the
database revealed that many of the vessels had very small or had no kW ratings. It was uncertain whether these
smaller vessels were powered by recreational gasoline marine engines.
4-191
-------
Figure 4-14: Horsepower for Alaskan fishing vessels
Alaska Fishing Vessels
4500
4000
3500
> 3000
ฃ 2500
% 2000
ฃ 1500
1000
500
0
4^208
3681
L185
1519
.18^845243620
556110 1240401232000122140010000100100
^ ^ ^ ^ ^ ^ ^ ^ A^
-------
Ferries
The U.S. Department of Transportation's Bureau of Transportation Statistics maintains a database of ferry
vessels and activity [ref 26], This database includes ferry vessels characteristics by operator, trip segment, and
terminal information. Individual vessels were linked to operators to develop operator fleet profiles which could
be matched to trip segments. The operator fleet profiles included average vessel power and speed. The trip
segments did not include travel distance or time information, so GIS tools were used to determine the distance
between originating and destination terminals for each segment. During the process, duplicate trip segments
were consolidated. Segment travel time was calculated using the segment distances and typical vessel speeds.
Each segment had a season start date, as well as a count of trips. Total kW-hrs for each segment that an
operator used were calculated using the following equation.
WTV = number of trips made in a week for vessel V
kWv = kW rating of main engines for vessel V
Offshore oil and gas support vessels:
For the purpose of this inventory, 2011 estimates for the offshore oil and gas support vessels operating in the
Gulf of Mexico were obtained from the Bureau of Ocean Energy Management [ref 25], These vessels include:
Seismic survey vessels
Crew boats
Supply boats
Drilling rigs
Anchor handling tugs
Offshore tugs
Pipelaying vessels
The 2011 estimates were adjusted to 2014 based on changes in the Gulf of Mexico's annual crude oil
production.
4,19.3,4 Engine operating toads
Because the activity data used to develop the 2014 NEI did not include engine operating load data or actual
vessel speeds, typical operating loads were compiled for each vessel type based on published reports. Initially
engine operating load assumptions were taken from the EPA's Current Methodologies in Preparing Port
Emission Inventories [ref 27], This guidance document provided a typical cruising load factor of 0.83. Engine load
data from the most recent IMO GHG study [ref 6] were also evaluated. The data in the IMO study included an
assessment of bulk carriers, containerships, and tanker speed and engine loads, which accounted for the
practice of slow steaming. The IMO data were weighed based on the fleet composition of the E&C data linked up
to the IHS vessel characteristics, as provided in Table 4-119.
kW-hrs = (Ds / Sv) x (SL x [WTV / 7]) x kWv
Where:
Ds
Sv
SL
distance of segment S in nautical miles between the start and end ports
typical speed of vessel V in knots
length of the ferry season in days
4-193
-------
Table 4-119: IMO underway cruising vessel speed and engine load factors for bulk carriers,
containerships, and tankers
Average at-sea
Engine Load
Weighted
Ship
Size
Main Engine Load
Percent of
Weight
Load
Type
Size Category
Units
Factor (% MCR)
Total Pop.
Fraction
Factor
0-9999
70
0.9
0.0062
10000-34999
59
25.1
0.1480
Bulk
35000-59999
dwt
58
36.0
0.2089
0.5893
Carrier
60000-99999
60
31.7
0.1902
100000-199999
57
6.2
0.0353
200000+
62
0.1
0.0008
0-999
52
4.9
0.0257
1000-1999
45
11.8
0.0531
2000-2999
39
12.5
0.0489
Container
3000-4999
TEU
36
32.8
0.1181
0.3672
5000-7999
32
28.6
0.0915
8000-11999
32
9.0
0.0288
12000-14500
34
0.3
0.0011
14500+
28
0.0
0.0000
0-4999
67
0.1
0.0009
5000-9999
49
0.3
0.0013
10000-19999
49
0.0
0.0000
Oil
20000-59999
dwt
55
3.6
0.0196
0.5158
Tanker
60000-79999
57
15.6
0.0889
80000-11999
51
43.4
0.2212
120000-199999
49
32.6
0.1595
200000+
54
4.5
0.0244
dwt = dead weight tonnage; TEU = twenty foot equivalent units
Load factors for RSZ were developed based on vessel speed which was either the maximum speed of the RSZ or
the cruising speed of the vessel, which ever value was the smaller. The vessel speed was used in conjunction
with the vessel's maximum speed and the propeller rule to estimate the propulsion engine operating load while
in the RSZ.
LF = (AS/MS)3
Where:
LF = Load Factor (percent)
AS = Actual Speed (knots)
MS = Maximum Speed (knots)
Propulsion engine load factor for maneuvering was assumed to be 0.2, based on Entec's European emission
inventory [ref 7], It is recommended that future versions of this inventory consider reviewing AIS in port data to
more accurately quantify maneuvering loads. It was also assumed that the auxiliary engines would be operating
during maneuvering based on EPA port guidance [ref 27] as summarized in Table 4-120.
Table 4-120: Auxiliary operating loads
Vessel Types
Maneuver
Hotel
Bulk Carrier
0.45
0.1
4-194
-------
Vessel Types
Maneuver
Hotel
Bulk Carrier, Laker
0.45
0.1
Buoy Tender
0.45
0.22
Container
0.48
0.19
Crude Oil Tanker
0.33
0.26
Drilling
0.45
0.22
Fishing
0.45
0.22
FPSO
0.45
0.22
General Cargo
0.45
0.22
Icebreaker
0.45
0.22
Jackup
0.45
0.22
LNG Tanker
0.33
0.26
LPG Tanker
0.33
0.26
Misc.
0.45
0.22
Passenger
0.8
0.64
Pipelaying
0.45
0.22
Reefer
0.67
0.32
Research
0.45
0.22
RORO
0.45
0.26
Supply
0.45
0.22
Support
0.45
0.22
Tanker
0.33
0.26
Tug
0.45
0.22
Vehicle Carrier
0.45
0.22
Well Stimulation
0.45
0.22
While the vessel is dockside, it was assumed that propulsion engines would not be operating and the auxiliary
engines were operating at the loads noted in Table 4-120. For vessels equipped with C 1 and C2 propulsion
engines it was assumed that neither the propulsion nor the auxiliary engines would be operating while dockside
to conserve fuel. This version of the NEI also did not include activity or emissions associated with boilers used to
generate steam or to run cargo handling equipment and pumps.
4,1935 Emission factors and HAP speciation profiles
Vessels equipped with Category 3 propulsion engines
As the dominant propulsion engine configuration for large Category 3 vessels is the slow speed diesel (SSD)
engine, the following SSD emission factors were used for Category 3 propulsion engines. Medium speed diesel
(MSD) emission factors were used for auxiliary engines associated with these larger vessels. For the 2014
inventory, it was assumed that Emission Control Area (ECA) compliant fuels were used while transiting U.S.
waters. Emission factors for vessels equipped with Category 3 propulsion engines [ref 28] are presented in Table
4-121.
Table 4-121: Category 3 emission factors (g/kW-hours)
Type
Engine
Fuel
NOx
VOCa
HC
CO
S02
C02
O
rH
Q.
PMz.5 b
SSD
Main
1% Sulfur
14.7
0.6318
0.6
1.4
3.62
588.86
0.45
0.42
MSD
Aux
1% Sulfur
12.1
0.4212
0.4
1.1
3.91
636.6
0.47
0.43
From: U.S. EPA/OTAQ, Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive Engines
and Marine Compression Ignition Engines Less than 30 Liters Per Cylinder, March 2008 [ref 28],
4-195
-------
a Hydrocarbon (HC) was converted to VOC using a conversion factor of 1.053 as provided in [ref 28]
b PM2.5 was assumed to be 97 percent of PM 10 using [ref 28]
Note that this approach assumes that all large vessels will implement fuel switching before 2014 to comply with
the 1% fuel sulfur standard, and use of controls such as scrubbing of high sulfur fuels, which is also an option to
meet regulations, will be minimal.
If an engine load factor is less than 20 percent of the engine operating load, the emission factors were adjusted
to account for operations outside the engines typical optimal load. For this 2014 inventory, these low load
periods tend to occur during vessel movements in the RSZ. The low load adjustment factors used in this
inventory were obtained from the EPA port guidance [ref 27] and are provided in Table 4-122.
Table 4-122: Calculated low
oad multiplicative adjustment
"actors
Load
NOx
HC
CO
PM
S02
C02
1%
11.47
59.28
19.32
19.17
5.99
5.82
2%
4.63
21.18
9.68
7.29
3.36
3.28
3%
2.92
11.68
6.46
4.33
2.49
2.44
4%
2.21
7.71
4.86
3.09
2.05
2.01
5%
1.83
5.61
3.89
2.44
1.79
1.76
6%
1.60
4.35
3.25
2.04
1.61
1.59
7%
1.45
3.52
2.79
1.79
1.49
1.47
8%
1.35
2.95
2.45
1.61
1.39
1.38
9%
1.27
2.52
2.18
1.48
1.32
1.31
10%
1.22
2.20
1.96
1.38
1.26
1.25
11%
1.17
1.96
1.79
1.30
1.21
1.21
12%
1.14
1.76
1.64
1.24
1.18
1.17
13%
1.11
1.60
1.52
1.19
1.14
1.14
14%
1.08
1.47
1.41
1.15
1.11
1.11
15%
1.06
1.36
1.32
1.11
1.09
1.08
16%
1.05
1.26
1.24
1.08
1.07
1.06
17%
1.03
1.18
1.17
1.06
1.05
1.04
18%
1.02
1.11
1.11
1.04
1.03
1.03
19%
1.01
1.05
1.05
1.02
1.01
1.01
20%
1.00
1.00
1.00
1.00
1.00
1.00
Vessels equipped with Category 1 / Category 2 propulsion engine
Activity data for smaller vessels equipped with CI and C2 engines are aggregated together, therefore Category 2
emission factors (Table 4-123) were used for these vessels as these factors tended to provide more conservative
emission estimates.
Table 4-123: Tier emission factors for vessels equipped with Category 2 propulsion engines (g/kW-hours)
Tier
O
rH
a.
NOx
HC
CO
VOC*
PM25 b
S02
C02
0
0.32
13.36
0.134
2.48
0.141102
0.3104
0.006
648.16
1
0.32
10.55
0.134
2.48
0.141102
0.3104
0.006
648.16
2
0.32
8.33
0.134
2.00
0.141102
0.3104
0.006
648.16
3
0.11
5.97
0.07
2.00
0.073710
0.1067
0.006
648.16
From: U.S. EPA/OTAQ, Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive
Engines and Marine Compression Ignition Engines Less than 30 Liters per Cylinder, March 2008 [ref 28],
4-196
-------
a HC was converted to VOC using a conversion factor of 1.053 as provided in the above reference.
b PM2.5 was assumed to be 97 percent of PM10 using the above reference.
The Tier emission factors noted in Table 4-124 were weighted relative to the vessel type based on the year the
vessel was manufactured. Table 4-125 shows the vessel age distribution by Tier.
Table 4-124: Vessel tier population by type for vessels equipped with CI or C2 propulsion engines
Trip
Count
Vessel
Count
Vessel Type
Total*
Tier Level
Percent Tier
0
1
2
3
0
1
2
3
5,330
51
Bulk Carrier
51
46
5
90.2
0
9.8
0
932
23
Bulk Carrier, Laker
23
23
100
0
0
0
5
3
Buoy Tender
3
3
100
0
0
0
200
2
Container
2
2
100
0
0
0
2,421
25
Containership
25
22
3
88
12
0
0
140,767
426
Crewboat / Supply
/ Utility Vessel
425
298
37
87
3
70.1
8.7
20.5
0.7
7
5
Drilling
5
2
3
40
0
60
0
19,026
13
Excursion /
Sightseeing Vessel
13
12
1
92.3
0
7.7
0
276
45
Fishing
45
43
2
95.6
4.4
0
0
29,660
153
General Cargo
152
93
11
48
61.2
7.2
31.6
0
8
2
Icebreaker
2
2
100
0
0
0
10
3
Jackup
3
2
1
66.7
0
33.3
0
8
2
LPG Tanker
2
2
0
0
100
0
247,369
35
Misc.
33
28
2
3
84.8
6.1
9.1
0
749
26
Passenger
26
24
1
1
92.3
3.8
3.8
0
4,666
18
Passenger Carrier
18
15
3
83.3
16.7
0
0
61
10
Pipelaying
10
10
100
0
0
0
344,540
1,626
Pushboat
1,625
1,348
43
214
20
83
2.6
13.2
1.2
63
12
Reefer
12
12
100
0
0
0
346
42
Research
42
35
1
6
83.3
2.4
14.3
0
1,771
19
RORO
19
17
1
1
89.5
5.3
5.3
0
230
3
RO-RO Vessel
3
3
100
0
0
0
4,778
243
Supply
243
126
31
86
51.9
12.8
35.4
0
808
66
Support
66
28
7
31
42.4
10.6
47
0
Table 4-125: Vessel tier population by type for vessels equipped with CI or C2 propulsion engines
Trip
Count
Vessel
Count
Vessel Type
Total*
Tier Level
Percent Tier
0
1
2
3
0
1
2
3
5553
102
Tanker
101
47
11
43
46.5
10.9
42.6
0
3962
336
Tug
336
286
13
35
2
85.1
3.9
10.4
0.6
14251
867
Tugboat
867
630
48
172
17
72.7
5.5
19.8
2
2
1
Well Stimulation
1
1
100
0
0
0
95606
4159
Total / Average Percent Tier
4,153
3,158
214
739
42
76
5.2
17.8
1
4-197
-------
Note this approach does not account for early introduction of controls by vessel operators, compliance with
more stringent local standards, or participation in voluntary emission reduction programs such as California's
Carl Moyer Program or the Texas Emission Reduction Plan (TERP).
Hazardous air pollutant emissions were estimated by applying speciation profiles (Appendix F, ref 4) to the VOC
estimates for organic HAPs and PM estimates for metal HAPs using the following equation:
E = AxSF
Where:
E = Annual emissions for HAP (tons)
A = Annual emissions for speciation base (tons)
SF = Speciation factor (unit less fraction)
Emission Summaries
Based on the approach documented above, Table 4-126 summarizes activity and emissions by vessel propulsion
engine category and mode. Table 4-127 also summaries emissions by vessel type.
Table 4-126: 2014 EPA-estimatec
vessel activity (kW-hrs) and emissions (tons
by propulsion engine anc
mode
Category
Source
see
Mode
Total Activity
(kW-hr)
NOx
O
rH
a.
pm25
S02
VOC
Cat 1/2
E&C
2280002100
Maneuvering
742,228,543
1,179
44
40
333
39
Cat 1/2
E&C
2280002200
Cruising
945,222,365
9,648
255
247
5
113
Cat 1/2
Misc-
C1/C2
2280002100
Maneuvering
4,086,763,051
11,316
285
276
5
126
Cat 1/2
Misc-
C1/C2
2280002200
Cruising
13,348,660,561
336,909
10,409
10,097
2,258
5,785
Cat 1/2
WBD
2280002100
Maneuvering
2,090,680,129
5,754
147
143
3
65
Cat 1/2
WBD
2280002200
Cruising
19,795,947,087
196,657
5,049
4,898
94
2,228
Cat3
E&C
2280003100
Dock
27,735,673,393
39,098
1,540
1,409
12,665
1,503
Cat3
E&C
2280003100
Maneuvering
7,217,499,394
6,568
216
200
1,758
267
Cat3
E&C
2280003200
Cruising
64,474,040,733
586,555
17,956
16,759
144,444
25,210
Cat3
E&C
2280003200
Reduced
Speed Zone
7,055,981,077
22,034
713
666
5,492
1,319
Total
147,492,696,332
1,215,718
36,614
34,735
167,058
36,654
Note: Misc C1/C2 includes: Coast Guard, dredging, ferries, fishing, offshore oil & gas support, and research.
Table 4-127: 2014 EPA CMV emissions by vessel type
Vessel Type
Total Activity (kW-hr)
NOx
O
rH
a.
pm25
S02
VOC
Bulk Carrier
16,502,188,704
108,528
3,278
3,070
23,396
4,264
Bulk Carrier, Laker
591,085,436
4,349
129
121
865
161
Buoy Tender
2,647,731
32
1
1
0
0
Coast Guard
2,150,964,635
26,292
630
611
12
278
Containership
53,193,329,151
220,943
6,808
6,359
50,912
9,048
Dredging
1,041,726,442
12,273
294
285
5
130
Excursion / Sightseeing Vessel
4,319,972
50
1
1
0
1
4-198
-------
Vessel Type
Total Activity (kW-hr)
NOx
O
rH
Q.
pm25
S02
VOC
Ferries
5,641,357,376
32,678
825
800
16
365
Fishing
6,585,566,278
76,606
1,852
1,797
34
817
General Cargo
4,462,901,347
36,436
1,126
1,052
8,522
1,472
Miscellaneous
1,101,196,066
4,247
108
105
53
53
Offshore Oil & Gas*
669,380,168
182,540
6,653
6,454
2,188
4,128
Passenger
11,886,827,285
123,561
3,835
3,576
30,586
5,254
Reefer
1,082,375,467
9,645
303
282
2,425
406
Research
2,015,808,882
22,507
573
556
11
253
RO-RO
2,369,916,464
20,995
574
547
1,998
469
Tanker, Crude Oil
7,192,697,038
42,670
1,329
1,238
10,710
1,819
Tanker, LNG/LPG
1,461,972,434
13,291
412
384
3,314
567
Tanker, Miscellaneous
14,088,889,926
121,580
3,725
3,508
22,470
4,221
Tug
11,197,514,271
119,306
3,005
2,913
250
1,343
Vehicle Carrier
4,250,031,261
37,187
1,154
1,076
9,291
1,608
Total
147,492,696,332
1,215,718
36,614
34,735
167,058
36,654
* Note: Some Offshore Oil & Gas emissions were derived from the BOEM Emission Inventory which did not include activity
data.
4.19.3.6 Allocation of port and underway emissions
Ports and underway activity and emissions are summarized in Table 4-128. Note that in this version of the
marine vessel component of the NEI, auxiliary emissions for underway operations were considered less
significant than other modes and were not included in this version of the NEI marine vessel inventory, such that
actual underway emissions may be slightly higher than the values presented in Table 4-128.
Table 4-128: 2014 vessel activity (kW-hrs) and EPA emissions (tons) by propulsion engine and SCC
SCC Description
SCC
Total Activity
(kW-hr)
NOx
PMio
pm25
S02
VOC
Diesel Port
2280002100
6,919,671,722
18,250
476
459
341
230
Diesel Underway
2280002200
34,089,830,013
543,214
15,713
15,242
2,357
8,125
Residual Port
2280003100
34,953,172,787
45,666
1,756
1,609
14,423
1,770
Residual
Underway
2280003200
71,530,021,810
608,589
18,669
17,425
149,936
26,529
Total
147,492,696,332
1,215,718
36,614
34,735
167,058
36,654
EPA has continued to develop and improve port shapes using a variety of resources. First, GIS data or maps
provided directly from the ports were used to delineate port boundaries. Next, maps or port descriptions from
local port authorities and port districts were used in combination with existing GIS data to identify port
boundaries. Finally, satellite imagery from tools such as Google Earth and street layers from StreetMap USA
were used to delineate port areas. Originally, primary emphasis was placed on mapping the 117 ports with C3
vessel activity using available shapefiles of the port area. As the availability of CI and C2 activity improved,
additional port shapes were required to represent their emissions. The NEI port shapefiles were revised to
include 114 additional ports from the 2014 inventory. Further revisions over the years have increased the count
of the current 5,649 port shapes for the 2014vl inventory. 2014v2 revisions reduced the number of port shapes
dramatically, to 915.
4-199
-------
In all cases, port shapes were split by county boundary, such that no shape crosses county lines, to facilitate
totaling of emissions to the state or county level. Each port shape was identified by the port name and state and
county FIRS in addition to a unique Shape ID. In most cases, port shapes were created on land bordering
waterways and coastal areas. However, the additional port shapes created in this effort were generated as small
circles with a radius of 0.25 miles that cover both land and water. Additionally, activity data such as Automatic
Identification System (AIS) indicated that vessels frequently have maneuvering/hoteling activities further
offshore than previously anticipated. As such, the underway shapes were duplicated, given new IDs, and added
to the port shapefile to provide a place to put these activities if state or local agencies wish to include them.
Underway shapes remain unchanged with the exception of new shapes added to represent state and federal
waters around Puerto Rico and the U.S. Virgin Islands as shown in Figure 4-15.
Britist^Virgin Islands
Figure 4-15: New underway shapes for Puerto Rico and the U.S. Virgin Islands
International Waters
US Federal Waters
State waters equivalent,
divided by county/municipio
Spatial allocation of the activity data varied by data source. Port activity was allocated to the origin and
destination port shapes. E&C data and the WCD were routed along a waterway network, then the routes were
intersected with EPA's shapefiles shipping lanes for NEI. For the E&C data, underway activity for each vessel trip
was divided among the NEI shapes based on the portion of the route that passed through each shape. The
length of the waterway segment passing through each shape was divided by the total trip length to calculate the
percentage of the trip's activity to assign to each shape.
V = (L/T)* A
Where:
V = Activity for shape V
L = Length of waterway segment within shape V
T = Total trip length
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A = Total trip activity
For WCD, hoteling and maneuvering activity was allocated to the nearest water-based port shapes for each
origin and destination. For underway activity, the length of the waterway through each shape was calculated
and multiplied by the number of trips in that shape. This value was divided by the national total for trips
multiplied by length to determine the percentage of the national total activity to allocate to each shape.
P = (T * L)/(NT*NL)
Where:
P = Percentage of national activity
T = Total trips for the NEI underway shape
L = Waterway segment length within underway shape
NT = National trip total
LN = National waterway network length total
Offshore oil and gas support vessel data derived from AIS data used by BOEM was limited to federal waters and
was assigned to the associated shape, though the more refined activity can be seen in Figure 4-16. Research
vessel activity was allocated to shapes based on the spatial allocation from the Category 1 and Category 2
Census [ref 14]. Dredging activities were spatially apportioned to ship channels based on the job name. The job
names indicated general location, such as a bay area or a waterway portion; however, they did not provide
sufficient information to precisely locate the dredging activities or even extent of the project. Best effort was
given to identify the waterway segments in GIS that most closely match the limited location information. Ferry
activity was split to 65% port and 35% underway, and all terminals were mapped using the coordinates available
in the National Census of Ferry Operators [ref 26], Activity was then allocated to the port or underway shape
nearest each ferry terminal. The underway spatial allocation can be seen in Figure 4-17. U.S. Coast Guard activity
was provided by region, NEI shapes in each region were identified, and underway activity was allocated to
individual shapes as a fraction of the total region's area as shown in Figure 4-18.
4-201
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Alabama
Mississippi
Florida
Louisiana
SiBfe
.ฑ3-. . i ซฃ'
Support Vessel density in AIS data
Figure 4-16: Spatial allocation of 2014 support vessel activity
Figure 4-17: Spatial allocation of 2014 ferry activity
2014 Ferry Activity
4-202
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Figure 4-18: Spatial allocation of 2014 Coast Guard activity
2014 Coast Guard Activity
Fishing vessel activity was spatially allocated using different methods based on available regional data. Alaska
fishing activity was spatially apportioned based on NOAA data that listed the number of catcher vessels by
region for the Aleutian Islands, Western Alaska, Central Gulf of Alaska, and Eastern Gulf of Alaska as shown in
Table 4-129, The NEI shapes were assigned to these regions in GIS, and then emissions were spatially allocated
by region based on shape area.
Table 4-129: Alaska commercial fishing catcher vessel count
Area
Catcher Vessels
Percent
Aleutian Islands
494
23
Western Alaska
64
3
Central Gulf of Alaska
728
34
Eastern Gulf of Alaska
854
40
The Northeast NOAA data provided fishing activity by city or by state [ref 17]. Cities were mapped, and activity
values were assigned to the nearest port and underway shape ID, In some cases, the city name was unknown, so
the activity was divided between other known ports within that state proportionate to their activity values. For
the southeast and the west coast, total activity was provided by state. Statewide activity was divided as 95%
underway and 5% in-port and then allocated to shapes based on the previous fishing allocation in the Category 1
and Category 2 Census [ref 14]. The final fishing allocation can be seen in Figure 4-19.
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Figure 4-19: Spatial allocation of 2014 commercial fishing activity
2014 Fishing Activity
4.19.3.7 Summary of quality assurance methods for EPA -developed emissions
While developing the EPA 2014 marine vessel inventory, data quality checks were implemented at
critical points; this included comparison with earlier data sets used to develop the CI and C2 inventory,
published emission factors, and previous NEI emission estimates for all engine categories.
All calculations were checked by experience staff members of the team.
During data transfers into the project database, quality assurance checks were implemented and data
summary tables generated to ensure that no corrupted data were transferred and the record count was
consistent with the transfer.
All assumptions were documented and discussed with team members to ensure that the assumptions
were reasonable and consistent with other known data points.
Microsoft Access data queries were documented and reviewed by experience staff who were not
directly involved in developing the current databases.
GIS imagery were reviewed to identify any spatial anomalies in the data.
Where anomalies were found during these checks, additional research was implemented to determine
whether the identified issue was correct or whether there was an error in developing the estimate.
EPA compared shape-, state-, and county-level sums in (1) EPA default data, (2) state/local/tribal (S/L/T) agency
submittals, and (3) the resultant 2011 NEI selection by:
Pollutants, SCCs, and SCC-emission types
Emissions summed to agency and SCC level.
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4,19,4 Known Issue: County FIPS error in Alaska
The new port shapes developed for the 2014v2 NEI erroneously included three Alaska county FIP codes which
are no longer valid due to county FIP changes made prior to 2014. The error was not corrected. No emissions
were lost, but data users should be cautioned that county sums in Alaska will not be accurate. Table 4-130
below summarizes the correct FIPs for each CMV shape and the magnitude of CMV NOx emissions sums that
should have been reallocated to the corrected county. This error will be remedied in the 2017 NEI.
Table 4-130: County FIPs Corrections for Alaska CMV Shape Emissions
Retired
FIPs
Revised
FIPs
Shape
ID
CMV Nox
2014v2
02201
02198
20598
0.96
02201
02198
20602
0.91
02201
02198
20603
0.91
02201
02275
20604
0.92
02201
02198
20605
0.91
02201
02198
20619
2.27
02232
02105
20190
191.34
02232
02105
20191
110.11
02232
02105
20192
80.59
02232
02230
20336
238.76
02232
02105
20601
0.91
02232
02105
20837
50.48
02280
02198
20171
878.68
02280
02195
20539
2.50
02280
02275
20599
1.85
4.19.5 Summary of quality assurance between EPA and S/L/T submittals
Submitted EPA estimates were compared to EPA's. These checks were performed:
Shape files used. Because CMV estimates must be allocated to port and underway GIS polygons (shape
files), it was important to check for potentially erroneous double counting where EPA and states used
different shapes. Where necessary, EPA estimates were tagged, for example in Texas where the state
provided all emissions to be included in the NEI. In other areas, like Washington, only certain ports had
been studied and provided and thus EPA estimates in other areas were used.
Reasonableness comparisons of pollutant totals. This check led to replacing California's provided HAPs
with EPA-augmented ones.
Individual pollutants compared to pollutant groups to avoid including both.
Where HAPs were not submitted, HAP-Aug was applied to estimate HAPs from submitted criteria
pollutants.
Chromium compounds were split into hex- and tri-valent chromium.
Missing criteria estimates. This check found that California did not provide NH3 for all processes. In these
cases, EPA NH3 records are used in the NEI if they exist for the same processes.
4.19.6 References for commercial marine vessels
1. U.S. Army Corps of Engineers, 2015a. 2012 Entrance and Clearance Data, downloaded 2015.
2. Information Handling Service (IHS), 2014. Register of Ships Provided 2014.
3. U.S. Army Corps of Engineers, 2015b. 2013 Waterborne Commerce Data, Provided 2015.
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4. U.S. Environmental Protection Agency, 2015. Commercial Marine Vessels - 2014 NE1 Commercial Marine
Vessels Final.
5. U.S. Environmental Protection Agency (US EPA), 2003. Final Regulatory Support Document: Control of
Emissions from New Marine Compression-Ignition Engines at or above 30 Liters per Cylinder, EPA420-R-
03-004, January 2003.
6. International Maritime Organization (IMO), 2014. Reduction of GHG Emissions from Ships, Third IMO
GHG Study 2014 - Final Report.
7. Entec UK Limited (Entec), 2002. Quantification of emissions from ships associated with ship movements
between ports in the European Community, European Commission Final Report, July 2002
8. U.S. Environmental Protection Agency (US EPA), 2015. Containership dockside data - Provided to Richard
Billings via email.
9. U.S. Army Corps of Engineers, 2014. Navigation Data Center, U.S. Waterway Data, Dredging Information
System Dredging Contracts.
10. Anderson, M; Michigan Technology University (MTU), 2008. Comparison of common dredging
equipment air
emissions.http://digitalcommons.mtu.edu/cgi/viewcontent.cgi?article=1214&context=etds
11. Van Oord, 2015. Water Injection Dredging, downloaded June 2015.
12. Robinson, S.P.; P. D. Theobald; G. Hayman; L. S. Wang; P. A. Lepper; V. Humphrey; S. Mumford,
Measurement of Underwater Noise Arising from Marine Aggregate Dredging Operations. MALSF (MEPF
Ref no. 09/P108), Published February 2011.
13. University of Delaware/Oceanic Information Center, 2015. International Research Ship Information and
Schedule, downloaded 2015.
14. U.S. Environmental Protection Agency (US EPA), 2007. Project report: Category 2 Vessel Census. Activity,
and Spatial Allocation Assessment and Category 1 and Category 2 In-port/At-Sea Splits, February 16,
2007.
15. U.S. Coast Guard/External Coordination Division (CG-823), 2015a. Vessel Fleet Roster (email
correspondence with Robert Mason).
16. U.S. Coast Guard/External Coordination Division (CG-823), 2015b. Information on cold ironing practices
with the U.S. Coast Guard (email correspondence with LTJG Luka Serdar, Informal Inquiries Manager).
17. National Oceanic and Atmospheric Administration, 2015b. Email correspondence with Kelley Mcgrath,
NOAA Northeast Region, kellev.mcgrath@noaa.gov, April 30, 2015.
18. National Oceanic and Atmospheric Administration, 2015d. Email correspondence with David Gloeckner,
NOAA Southeast Region, david.gloeckner@noaa.gov, June 23, 2015.
19. National Oceanic and Atmospheric Administration, 2015c. Email correspondence with Craig D'Angelo,
NOAA West Coast, craig.dangelo@noaa.gov, June 17, 2015.
20. National Oceanic and Atmospheric Administration (NOAA)/National Marine Fisheries Service, 2015a
Fisheries of the United States 2013: Current Fishery Statistics No. 2013, downloaded in 2015.
21. National Oceanic and Atmospheric Administration, 2015e. Email correspondence with Mary Furuness,
NOAA Alaska, mary.furuness@noaa.gov, July 2, 2015.
22. State of Alaska, Commercial Fisheries Entry Commission, 2015. CFEC Public Search Application Yearly
Downloads.
23. North Pacific Fishery Management Council, April 2012. Fishing Fleet Profiles.
24. Alaska Department of Fish and Game/Division of Commercial Fisheries, 2014. Commercial Fishing
Season in Alaska.
25. Bureau of Ocean Energy Management (BOEM), 2013. 2011 Gulfwide Emission Inventory.
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26. U.S. Department of Transportation/Bureau of Transportation Statistics, 2014. National Commercial Ferry
Operators Database.
27. U.S. Environmental Protection Agency (US EPA), 2009. Current Methodologies in Preparing Mobile
Source Port-Related Emission Inventories: Final Report
28. U.S. EPA/OTAQ, Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive
Engines and Marine Compression Ignition Engines Less than 30 Liters Per Cylinder, March 2008.
4,20 Mobile - npoint)
This section documents locomotives (rail) emissions in the nonpoint data category. For information on rail yard
emissions in the point data category, refer to Section 3.3.
4,20,1 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 4-131 below indicates locomotive
SCCs and whether EPA estimated emissions. If EPA did not estimate the emissions, then all emissions from that
SCC that appear in the inventory are from S/L/T agencies.
Table 4-131: Locomotives 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 - in shape
files
Nonpoint
2285002007
Mobile Sources; Railroad Equipment; Diesel; Line
Haul Locomotives: Class II / III Operations
Yes-in shape
files
Nonpoint
2285002008
Mobile Sources; Railroad Equipment; Diesel; Line
Haul Locomotives: Passenger Trains (Amtrak)
No
Nonpoint
2285002009
Mobile Sources; Railroad Equipment; Diesel; Line
Haul Locomotives: Commuter Lines
No
Nonpoint
2285002010
Mobile Sources; Railroad Equipment; Diesel; Yard
Locomotives
No
Nonpoint
28500201
Internal Combustion Engines; Railroad Equipment;
Diesel; Yard Locomotives
Yes - as point
sources
Point
4,20,2 Sources of data
The nonpoint component of this source category includes data from the S/L/T agency submitted data and the
default EPA generated emissions. The state agencies listed Table 4-132 in submitted at least PM2.5, NOx and VOC
emissions for the indicated SCCs; agencies not listed used EPA estimates for all nonpoint rail.
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Table 4-132: Source Category Codes with emissions submitted by reporting agency
Region
Agency
S/L/T
2285002006
2285002007
2285002008
2285002009
2285002010
1
Massachusetts Department of Environmental Protection
State
X
3
Maryland Department of the Environment
State
X
X
X
X
3
Virginia Department of Environmental Quality
State
X
X
4
North Carolina Department of Environment and Natural
Resources
State
X
6
Texas Commission on Environmental Quality
State
X
X
7
Sac and Fox Nation of Missouri in Kansas and Nebraska
Reservation
Tribe
X
8
Assiniboine and SiouxTribes of the Fort Peck Indian
Reservation
Tribe
X
9
California Air Resources Board
State
X
X
X
X
X
9
Morongo Band of Cahuilla Mission Indians of the Morongo
Reservation, California
Tribe
X
9
Washoe County Flealth District
Local
X
10
Alaska Department of Environmental Conservation
State
X
10
Washington State Department of Ecology
State
X
X
X
4,20,3 EPA-developed emissions for nonpoint locomotives: new for 2014v2 NE1
All EPA estimates used in the 2014vl NEI were replaced for the 2014v2 NEI. Shapes (links) used in 2014vl were
abandoned and 2014v2 estimates are at the county-level.
EPA used emissions estimates developed by the Eastern Regional Technical Advisory Committee's (ERTAC) rail
group. The group coordinated with the Federal Rail Administration to collect link-based activity data and apply
the equipment-specific emission factors appropriate. Their report on this work is available in the document
"Railv2_3ERTAC_Rail_2014_lnventory_Documentation_20170220.pdf" on the2014v2 Supplemental Rail and
CMV Data FTP site.
Hazardous Air Pollutant Emissions Estimates
FIAP emissions were estimated by applying speciation profiles to the VOC or PM estimates. Because California
uses low sulfur diesel fuel and emission factors specific for California railroad fuels were available, calculations
of California's emissions were done separately from the other reporting agencies. FIAP estimates were
calculated at the yard and link level, after the criteria emissions had been allocated. Where submitting agencies
did not supply FIAPs, those estimates were also derived via this VOC/PM speciation method. EPA's FIAP
speciation factors are available in the spreadsheet "Railv2_4FlapSpeciation_20170220.xlsx" on the2014v2
Supplemental Rail and CMV Data FTP site.
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4,20,4 Summary of quality assurance
EPA and S/L/T 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 this case, EPA point yard records were tagged.
Different variations of the same pollutant were used by agencies and EPA. For instance, individual
xylenes versus mixed xylene compounds. When agencies submitted total chromium, the value was
apportioned to hex- and trivalent chromium.
Suspiciously high or low emissions. As advised by California, all CA HAPs were tagged and EPA values
used instead.
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.
4.21.1 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 (Al), and various
solvents (which serve as carriers for the Al). Both types of ingredients contain volatile organic compounds (VOC)
that may be emitted to the air during application or after application because of evaporation [ref 1],
4.21.2 Sources of data
As seen in Table 4-133, this source category includes data from the S/L/T agency submitted data and the default
EPA generated emissions. EPA estimates emissions for only Agricultural application (SCC=2461850000). New
Jersey and Maryland also reported emissions for Surface Application (2461800001) and Maryland also reported
estimates for Soil Incorporation (2461800002). The leading SCC description is "Solvent Utilization; Miscellaneous
Non-industrial: Commercial" for all SCCs.
Table 4-133: Agricultural Pesticide Application SCCs estimated by EPA and S/L/Ts
SCC
Description
EPA
State
Local
Tribe
2461800001
Pesticide Application: All Processes; Surface Application
X
2461800002
Pesticide Application: All Processes; Soil Incorporation
X
2461850000
Pesticide Application: Agricultural; All Processes
X
X
X
The agencies listed in Table 4-134 submitted 100% of their VOC emissions for agricultural pesticide application;
agencies not listed used EPA estimates for the entire sector.
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Ta
lie 4-134: Percentage of Agricultural Pesticide Application VOC emissions submitted by reporting agency
Region
Agency
S/L/T
VOC
1
New Hampshire Department of Environmental Services
State
100
2
New Jersey Department of Environment Protection
State
100
3
Delaware Department of Natural Resources and Environmental
Control
State
100
5
Illinois Environmental Protection Agency
State
100
7
Sac and Fox Nation of Missouri in Kansas and Nebraska Reservation
Tribe
100
9
California Air Resources Board
State
100
10
Coeur d'Alene Tribe
Tribe
100
10
Idaho Department of Environmental Quality
State
100
10
Kootenai Tribe of Idaho
Tribe
100
10
Nez Perce Tribe
Tribe
100
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
100
4,213 EPA-deveioped emissions for agricultural pesticide application
This is the first time that EPA has provided estimates for this source category; therefore, these emissions are
new for the 2014 NEI, and were not covered on a national basis for previous inventory years. Members of the
NOMAD Committee (Idaho and Texas) were instrumental in developing this methodology. An inventory
developer in Idaho developed the method, based on one used in Idaho for many years. An inventory developer
from TCEQ (TX) then created a tool in MS Access, and also provided instructions, which makes the method easy
to use for all reporting agencies.
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.
4.21.3.1 Emission factors
The VOC emission factor is derived for each active ingredient based on the pesticide profiles database
maintained by the California Department of Pesticide Regulation [ref 2], The California Department of Pesticide
Regulation's (CA DPR) database contains the chemical formulation for pesticides registered in the State of
California and provides key inputs for the development of VOC emission factors. These key inputs include mass
fraction of each 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 (TGA). Because the CA DPR database lists both agricultural and non-agricultural pesticide products, it
was necessary to screen out entries that were likely formulated as a consumer product. Pesticide products that
contained terms suggesting non-agricultural applications were excluded. Terms used to screen out likely
consumer products are listed in Table 4-135.
Table 4-135: 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
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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 DPR database is for a specific pesticide product, and provides product name, primary active
ingredient, the 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 were used to calculate pesticide-
specific VOC emission factors.
EFpesticide = l/(Al%/100) X (EProg/100)
where:
EFpesticide = pesticide-specific emissions factor (lb VOC / lb Al)
Al% = average mass percent of active ingredient in pesticide
EProg = emissions potential of reactive organic gases (expressed as % of pesticide mass)
For active ingredients not in the DPR database, a weighted average emission factor (EFavg) was calculated. This
weighted average was estimated by weighting the emission factors from the DPR database using the total
pounds of active ingredient reported in the USGS report "Estimated Annual Agricultural Pesticide Use for
Counties of the Conterminous United States, 2008-2012" [ref 3], A crosswalk between compound name in the
USGS database and the chemical name in the CA DPR database is provided in Table 4-136.
EFavg ^pesticides(EFpesticide X Al/T)
where:
EFavg = average emissions factor (lb VOC / lb Al)
EF pesticide = pesticide-specific emissions factor (lb VOC / lb Al)
Al = active ingredient applied (lb)
T = total mass of all active ingredients applied (lb)
This resulted in an EFavg value of 0.4 pounds of VOC per pound of active ingredient. The VOC emission factors by
active ingredient are shown in Table 4-137.
For the estimation of HAP emissions, a variation of the EIIP's preferred method (9-4.1) based on vapor pressure
of the active ingredient was implemented. The subset of HAPs was extracted from the list of active ingredients
and is shown in Table 4-138 along with the HAP emission factors. Note that these HAPs are also VOCs and are
therefore included in the pesticide-specific VOC emission factors calculated above.
The HAP emissions are based on the quantity of active ingredient applied and are estimated as follows:
Ehap = Al x EFhap
where:
Ehap = HAP emissions from pesticide active ingredient applications in pounds;
4-211
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EFhap = emission factor in pounds of emission per pound of active ingredient from El IP Table 9.4-4
based on vapor pressure of HAP. If the EIIP method resulted in HAP emissions exceeding VOC
emissions, then the emissions factor was set to the pesticide-specific VOC emissions factor
calculated above for total VOC emissions.
Table 4-136: Crosswalk between USGS compound name and CA DPR chemical name
USGS compound name
CA DPR chemical 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-M ETHYL
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
AVIGLYCINE
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 SUBTILISGB03
BACILLUS THURINGIENSIS
BACILLUS THURINGIENSIS (BERLINER)
BENFLURALIN
AVERAGE
BENOMYL
BENOMYL
BENSULFURON
BENSULFURON METHYL
BENSULIDE
BENSULIDE
BENTAZONE
BENTAZON, SODIUM SALT
BIFENAZATE
BIFENAZATE
BIFENTHRIN
BIFENTHRIN
BISPYRIBAC
BISPYRIB AC-SODIUM
BOSCALID
BOSCALID
4-212
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USGS compound name
CA DPR chemical 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
CHLOROPICRIN
CHLOROPICRIN
CHLOROPICRIN
CHLOROPICRIN
CHLOROPICRIN
CHLOROPICRIN
CHLOROPICRIN
CHLOROPICRIN
CHLOROTHALONIL
CHLOROTHALONIL
CHLORPROPHAM
CHLORPROPHAM
CHLORPYRIFOS
CHLORPYRIFOS
CHLORSULFURON
CHLORSULFURON
CLETHODIM
CLETHODIM
CLODINAFOP
AVERAGE
CLOFENTEZINE
CLOFENTEZINE
CLOMAZONE
CLOMAZONE
CLOPYRALID
CLOPYRALID
CLORANSULAM-M ETHYL
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 SULF TRIBASIC
COPPER SULFATE (BASIC)
COPPER SULFATE
COPPER SULFATE (PENTAHYDRATE)
CPPU
AVERAGE
4-213
-------
USGS compound name
CA DPR chemical name
CRYOLITE
CRYOLITE
CUPROUS OXIDE
COPPER OXIDE (OUS)
CYANAMIDE
AVERAGE
CYAZOFAMID
CYAZOFAMID
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-M ETHYL
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
4-214
-------
USGS compound name
CA DPR chemical name
DIMETHOATE
DIMETHOATE
DIMETHOMORPH
DIMETHOMORPH
DIMETHYL DISULFIDE
AVERAGE
DINOSEB
DINOSEB
DINOTEFURAN
DINOTEFURAN
DIQUAT
DIQUAT DIBROMIDE
DISULFOTON
DISULFOTON
DITHIOPYR
DITHIOPYR
DIURON
DIURON
DODINE
DODINE
EMAMECTIN
EMAMECTIN BENZOATE
ENDOSULFAN
ENDOSULFAN
ENDOTHAL
ENDOTHALL, 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
4-215
-------
USGS compound name
CA DPR chemical name
FLUBENDIAMIDE
FLUBENDIAMIDE
FLUCARBAZONE
AVERAGE
FLUDIOXONIL
FLUDIOXONIL
FLUFENACET
AVERAGE
FLUMETRALIN
FLUOMETURON
FLUMETSULAM
AVERAGE
FLUMICLORAC
FLUMICLORAC-PENTYL
FLUMIOXAZIN
FLUMIOXAZIN
FLUOMETURON
FLUOMETURON
FLUOPICOLIDE
FLUOPICOLIDE
FLUOPYRAM
FLUOPYRAM
FLUOXASTROBIN
FLUOXASTROBIN
FLURIDONE
FLURIDONE
FLUROXYPYR
FLUROXYPYR
FLUTHIACET-METHYL
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
4-216
-------
USGS compound name
CA DPR chemical name
IMAZAQUIN
AVERAGE
IMAZETHAPYR
IMAZETHAPYR
IMAZOSULFURON
IMAZOSULFURON
IMIDACLOPRID
IMIDACLOPRID
INDAZIFLAM
INDAZIFLAM
INDOXACARB
INDOXACARB
IODOSULFURON
AVERAGE
IPCONAZOLE
IPCONAZOLE
IPRODIONE
IPRODIONE
ISOXABEN
ISOXABEN
ISOXAFLUTOLE
AVERAGE
KAOLIN CLAY
KAOLIN
KINOPRENE
KINOPRENE
KRESOXIM-METHYL
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-M ETHYL
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
4-217
-------
USGS compound name
CA DPR chemical name
METHYL PARATHION
METHYL PARATHION
METIRAM
METIRAM
METOLACHLOR
METOLACHLOR
METOLACHLOR-S
METOLACHLOR
METRAFENONE
METRAFENONE
METRIBUZIN
METRIBUZIN
METSULFURON
METSULFURON-M ETHYL
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-METHYL
OXYDEMETON-METHYL
OXYFLUORFEN
OXYFLUORFEN
OXYTETRACYCLINE
OXYTETRACYCLINE HYDROCHLORIDE
PACLOBUTRAZOL
PACLOBUTRAZOL
PARAQUAT
PARAQUAT DICHLORIDE
PARATH ION
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
4-218
-------
USGS compound name
CA DPR chemical name
PICLORAM
PICLORAM
PINOXADEN
PINOXADEN
PIPERONYL BUTOXIDE
PIPERONYL BUTOXIDE
POLYHEDROSIS VIRUS
POLYHEDRAL OCCLUSION BODIES (OB'S) OF THE NUCLEAR
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
ROTE NONE
ROTENONE
SABADILLA
SABADILLA ALKALOIDS
SAFLUFENACIL
SAFLUFENACIL
4-219
-------
USGS compound name
CA DPR chemical name
SETHOXYDIM
SETHOXYDIM
SILICATES
SILICA AEROGEL
SIMAZINE
SIMAZINE
SODIUM CHLORATE
SODIUM CHLORATE
SODIUM CHLORATE
SODIUM CHLORATE
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-M ETHYL
AVERAGE
THIFENSULFURON
THIFENSULFURON-METHYL
THIOBENCARB
THIOBENCARB
THIODICARB
THIODICARB
THIOPHANATE-M ETHYL
THIOPHANATE-METHYL
THIRAM
THIRAM
4-220
-------
USGS compound name
CA DPR chemical name
TOPRAMEZONE
AVERAGE
TRALKOXYDIM
TRALKOXYDIM
TRIADIMEFON
TRIADIMEFON
TRIADIMENOL
TRIADIMENOL
TRI-ALLATE
TRIALLATE
TRIASULFURON
AVERAGE
TRIBENURON METHYL
TRIBENURON-M ETHYL
TRIBUFOS
AVERAGE
TRICLOPYR
TRICLOPYR, BUTOXYETHYL ESTER
TRIFLOXYSTROBIN
TRIFLOXYSTROBIN
TRIFLOXYSULFURON
TRIFLOXYSULFU RON-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
Table 4-137: VOC emission factors for EPA-estimated Agricultural Pesticide Application
PESTICIDE
Average VOC per LB Al (lb)
2,4-D
0.827
2,4-DB ACID
0.067
ABAMECTIN
15.236
ACEPHATE
0.275
ACEQUINOCYL
0.135
ACETAMIPRID
0.207
ACIBENZOLAR-S-M ETHYL
0.063
ACIFLUORFEN, SODIUM SALT
1.887
ALACHLOR
0.513
ALDICARB
0.064
ALUMINUM PHOSPHIDE
0.055
AMETOCTRADIN
0.041
AMETRYNE
0.024
AMINOPYRALID, TRIISOPROPANOLAMINE SALT
0.16
ASULAM, SODIUM SALT
0.202
ATRAZINE
0.148
4-221
-------
PESTICIDE
Average VOC per LB Al (lb)
AZADIRACHTIN
10.092
AZINPHOS-METHYL
0.464
AZOXYSTROBIN
0.344
BACILLUS AMYLOLIQUEFACIENS STRAIN D747
0.076
BACILLUS CEREUS, STRAIN BP01
0.106
BACILLUS FIRMUS (STRAIN 1-1582)
0.052
BACILLUS PUMILUS GHA 180
2,050.00
BACILLUS SUBTILISGB03
190.333
BACILLUS THURINGIENSIS (BERLINER)
0.487
BENOMYL
0.074
BENSULFURON METHYL
0.031
BENSULIDE
0.553
BENTAZON, SODIUM SALT
0.053
BIFENAZATE
0.084
BIFENTHRIN
1.566
BISPYRIBAC-SODIUM
0.038
BOSCALID
0.229
BROMACIL
0.85
BUPROFEZIN
0.164
CALCIUM HYDROXIDE
0.003
CAPTAN
0.144
CARBARYL
0.321
CARBOPHENOTHION
0.446
CARBOXIN
0.437
CARFENTRAZONE-ETHYL
0.653
CHLORANTRANILIPROLE
0.364
CHLORFENAPYR
0.137
CHLORMEQUAT CHLORIDE
0.586
CHLORONEB
0.074
CHLOROPICRIN
1.272
CHLOROTHALONIL
0.113
CHLORPROPHAM
0.325
CHLORPYRIFOS
1.538
CHLORSULFURON
0.028
CLETHODIM
1.84
CLOFENTEZINE
0.147
CLOMAZONE
0.149
CLOPYRALID
0.05
CLOTH IANIDIN
0.153
CONIOTHYRIUM MINITANS STRAIN CON/M/91-08
0.698
COPPER
0.218
COPPER HYDROXIDE
0.06
4-222
-------
PESTICIDE
Average VOC per LB Al (lb)
COPPER OCTANOATE
2.198
COPPER OXIDE (OUS)
0.029
COPPER OXYCHLORIDE
0.023
COPPER OXYCHLORIDE SULFATE
0.026
COPPER SULFATE (BASIC)
0.048
COPPER SULFATE (PENTAHYDRATE)
0.062
CRYOLITE
0.025
CYAZOFAMID
0.166
CYCLANILIDE
2.468
CYCLOATE
0.507
CYFLUFENAMID
0.175
CYFLUTHRIN
1.736
CYHALOFOP-BUTYL
0.452
CYMOXANIL
0.044
CYPERMETHRIN
1.521
CYPRODINIL
0.049
CYROMAZINE
0.228
CYTOKININ
0.254
DAMINOZIDE
0.045
DAZOMET
1
DELTAMETHRIN
3.949
DESMEDIPHAM
3.668
DIAZINON
0.76
DICAMBA
0.084
DICHLOBENIL
0.434
DICLOFOP-M ETHYL
1.042
DICLORAN
0.087
DICOFOL
0.424
DICROTOPHOS
0.258
DIENOCHLOR
0.182
DIFENOCONAZOLE
1.12
DIFLUBENZURON
0.159
DIMETHENAMID-P
0.135
DIMETHIPIN
0.367
DIMETHOATE
0.83
DIMETHOMORPH
0.038
DINOSEB
0.455
DINOTEFURAN
0.191
DIQUAT DIBROMIDE
1.456
DISULFOTON
1.186
DITHIOPYR
0.955
DIURON
0.072
4-223
-------
PESTICIDE
Average VOC per LB Al (lb)
DODINE
0.049
EMAMECTIN BENZOATE
3.055
ENDOSULFAN
0.492
EPTC
0.517
ESFENVALERATE
8.919
ETHALFLURALIN
1.554
ETHEPHON
0.302
ETHION
0.397
ETHOFUMESATE
0.691
ETHOPROP
0.416
ETOXAZOLE
0.059
FENAMIDONE
0.101
FENAMIPHOS
1.043
FENARIMOL
1.404
FENBUCONAZOLE
0.049
FENBUTATIN-OXIDE
0.058
FENHEXAMID
0.037
FENOXAPROP-ETHYL
3.132
FENOXYCARB
0.655
FENPROPATHRIN
1.469
FEN PYROXI MATE
8.721
FENTIN HYDROXIDE
0.039
FERBAM
0.045
FIPRONIL
6.463
FLAZASULFURON
0.148
FLONICAMID
0.06
FLORASULAM
0.052
FLUAZIFOP-BUTYL
1.464
FLUAZINAM
0.406
FLUBENDIAMIDE
0.102
FLUDIOXONIL
0.308
FLUMICLORAC-PENTYL
0.565
FLUMIOXAZIN
0.075
FLUOMETURON
0.046
FLUOPICOLIDE
0.136
FLUOPYRAM
0.291
FLUOXASTROBIN
0.172
FLURIDONE
0.629
FLUROXYPYR
0.279
FLUTOLANIL
0.031
FLUTRIAFOL
0.331
FLUXAPYROXAD
0.02
4-224
-------
PESTICIDE
Average VOC per LB Al (lb)
FORAMSULFURON
0.252
FORMETANATE HYDROCHLORIDE
0.011
FOSETYL-AL
0.049
GIBBERELLINS
2.819
GLUFOSI NATE-AMMONIUM
0.442
GLUTAMIC ACID
0.063
GLYPHOSATE
0.159
HALOSULFURON-METHYL
0.032
HARPIN PROTEIN
1.233
HEXAZINONE
0.142
H EXYTH1AZOX
0.423
HYDRAMETHYLNON
0.614
HYDROGEN PEROXIDE
0.356
IBA
0.559
IMAZALIL
0.794
IMAZAMETHABENZ
0.504
IMAZAMOX
0.016
IMAZAPIC
0.016
IMAZAPYR
0.025
IMAZETHAPYR
0.019
IMAZOSULFURON
0.049
IMIDACLOPRID
0.305
INDAZIFLAM
0.416
INDOXACARB
0.453
IPCONAZOLE
0.122
IPRODIONE
0.203
ISOXABEN
0.103
KAOLIN
0.015
KINOPRENE
0.466
KRESOXIM-M ETHYL
0.034
LINURON
0.077
MALATHION
0.409
MALEIC HYDRAZIDE
0.015
MANCOZEB
0.047
MANDIPROPAMID
0.209
MANEB
0.071
MCPA
0.47
MCPB, SODIUM SALT
1.206
MECOPROP-P
0.622
MEFENOXAM
0.587
MEPIQUAT CHLORIDE
0.661
MESOSULFURON-M ETHYL
0.822
4-225
-------
PESTICIDE
Average VOC per LB Al (lb)
MESOTRIONE
0.236
META-CRESOL
73.605
METALAXYL
0.506
METALDEHYDE
0.691
METAM-SODIUM
0.566
METCONAZOLE
0.369
METHAMIDOPHOS
0.71
METHIDATHION
1.068
METHIOCARB
0.22
METHOMYL
0.115
METHOXYFENOZIDE
0.223
METHYL BROMIDE
1.159
METHYL IODIDE
1.212
METHYL PARATHION
0.502
METIRAM
0.11
METOLACHLOR
0.198
METRAFENONE
0.074
METRIBUZIN
0.087
METSULFURON-M ETHYL
0.037
MEVINPHOS
0.534
MSMA
0.315
MYCLOBUTANIL
0.451
MYROTHECIUM VERRUCARIA, DRIED FERMENTATION SOLIDS
0.127
NALED
0.494
NAPROPAMIDE
0.385
NAPTALAM, SODIUM SALT
0.588
NICOSULFURON
0.037
NORFLURAZON
0.031
NOSEMA LOCUSTAE SPORES
7.085
NOVALURON
2.273
ORTHOSULFAMURON
0.097
ORYZALIN
0.212
OXADIAZON
0.182
OXAMYL
0.721
OXYDEMETON-M ETHYL
0.928
OXYFLUORFEN
1.012
OXYTETRACYCLINE HYDROCHLORIDE
0.199
PACLOBUTRAZOL
0.983
PARAQUAT DICHLORIDE
0.311
PARATHION
0.357
PENDIMETHALIN
0.559
PENOXSULAM
0.208
4-226
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PESTICIDE
Average VOC per LB Al (lb)
PENTHIOPYRAD
0.054
PERMETHRIN
3.345
PETROLEUM DISTILLATES
1.142
PETROLEUM NAPHTHENIC OILS
0.884
PHENMEDIPHAM
3.129
PHORATE
0.448
PHOSMET
1.162
PHOSPHORIC ACID
0.434
PICLORAM
0.398
PINOXADEN
10.388
PIPERONYL BUTOXIDE
4.504
POLYHEDRAL OCCLUSION BODIES (OB'S) OF THE NUCLEAR
8.922
POTASSIUM BICARBONATE
0.027
PRODIAMINE
0.126
PROFENOFOS
0.367
PROMETRYN
0.184
PROPAMOCARB HYDROCHLORIDE
0.18
PROPANIL
0.099
PROPARGITE
0.196
PROPAZINE
0.2
PROPICONAZOLE
1.052
PROPYZAMIDE
0.055
PROTHIOCONAZOLE
0.139
PSEUDOMONAS FLUORESCENS, STRAIN A506
0.022
PYMETROZINE
0.02
PYRACLOSTROBIN
0.549
PYRAFLUFEN-ETHYL
5.343
PYRETHRINS
6.737
PYRIDABEN
0.019
PYRIMETHANIL
0.188
PYRIPROXYFEN
1.387
PYRITHIOB AC-SODIUM
0.193
PYROXSULAM
0.135
QUINCLORAC
0.121
QUINOXYFEN
0.06
QUIZALOFOP-ETHYL
4.121
RIMSULFURON
0.07
ROTENONE
0.808
SAB AD ILLA ALKALOIDS
2.018
SAFLUFENACIL
0.015
SETHOXYDIM
3.751
SILICA AEROGEL
0.381
4-227
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PESTICIDE
Average VOC per LB Al (lb)
SIMAZINE
0.089
SODIUM CHLORATE
0.025
SPINETORAM
0.138
SPINOSAD
0.483
SPIRODICLOFEN
0.229
SPIROMESIFEN
0.119
SPIROTETRAMAT
0.101
STREPTOMYCIN
0.133
SULFENTRAZONE
0.128
SULFOMETU RON-METHYL
0.076
SULFOSULFURON
0.027
SULFOXAFLOR
0.06
SULFUR
0.013
SULFURIC ACID
0.088
TCMTB
0.995
TEBUCONAZOLE
0.178
TEBUFENOZIDE
0.163
TEBUTHIURON
0.075
TEMBOTRIONE
0.096
TERBACIL
0.023
TETRACONAZOLE
0.492
THIABENDAZOLE
0.117
THIACLOPRID
0.119
THIAMETHOXAM
0.178
THIAZOPYR
1.756
THIDIAZURON
0.396
THIFENSULFURON-METHYL
0.049
THIOBENCARB
0.158
THIODICARB
0.133
THIOPHANATE-METHYL
0.118
THIRAM
0.219
TRALKOXYDIM
0.141
TRIADIMEFON
0.162
TRIADIMENOL
0.243
TRIALLATE
0.573
TRIBENURON-M ETHYL
0.03
TRICLOPYR, BUTOXYETHYL ESTER
0.433
TRIFLOXYSTROBIN
0.083
TRIFLOXYSULFU RON-SODIUM
0.014
TRIFLUMIZOLE
0.067
TRIFLURALIN
0.737
TRINEXAPAC-ETHYL
2.386
4-228
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PESTICIDE
Average VOC per LB Al (lb)
TRITICONAZOLE
0.24
UNICONIZOLE-P
125.636
VINCLOZOLIN
0.055
ZINC CHLORIDE
0.329
ZINEB
0.082
ZIRAM
0.031
Table 4-138: HAP emission factors for EPA-estimated Agricultural Pesticide Application
Compound
Pollutant
Code
Vapor
Pressure
(mm Hg at
20ฐC to 25ฐC)
Emission
Factor
(lb per
lb Al)
Source
2,4-D
94757
0.000008
0.35
EIIP, Volume 3, Chapter 9, Table 9.4-4 [ref 1]
CAPTAN
133062
0.00000008
0.1441
Set equal to VOC emissions factor calculated
from the CA DPR [ref 2]
CARBARYL
63252
0.0000012
0.3208
Set equal to VOC emissions factor calculated
from the CA DPR [ref 2]
METHYL BROMIDE
74839
1,420
0.58
EIIP, Volume 3, Chapter 9, Table 9.4-4 [ref 1]
METHYL IODIDE
74884
400
0.58
EIIP, Volume 3, Chapter 9, Table 9.4-4 [ref 1]
PARATHION
56382
0.0000378
0.35
EIIP, Volume 3, Chapter 9, Table 9.4-4 [ref 1]
TRIFLURALIN
1582098
0.00011
0.58
EIIP, Volume 3, Chapter 9, Table 9.4-4 [ref 1]
ivi'ty dsta; updated for 2014v2 NEi
The activity for pesticide application is the pounds of active ingredient applied per pesticide for the year 2013
(versus year 2012 in the 2014vl NEI). These data are available from the USGS report "Preliminary Estimates of
Annual Agricultural Pesticide Use for Counties of the Conterminous United States, 2013" [ref 3], which gives
county-level pesticide data in terms of kg of active ingredient applied. The report estimates preliminary annual
county-level pesticide use for 387 (vs 423 herbicides in the 2012 report used in 2014vl), insecticides, and
fungicides applied to agricultural crops grown in the conterminous United States during 2013. 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 were used in
conjunction with county annual harvested-crop acres reported by the U.S. Department of Agriculture 2007 and
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
were then calculated by multiplying EPest rates by harvested-crop acres for each pesticide crop combination.
Use estimates for California were obtained from annual Department of Pesticide Regulation-Pesticide Use
Reports.
The USGS report calculates both EPest-low and EPest-high rates. The EPest-high rates were 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 differed in how they treated situations when a CRD was
surveyed and pesticide use was not reported for a particular pesticide-by-crop combination. If use of a pesticide
on a crop was 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
4-229
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as un-surveyed, 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 the CRD.
Due to data limitations in the USGS report, active ingredient usages for Alaska and Hawaii were pulled forward
from 2011.
4.2133 Controls
No controls were accounted for in the emissions estimation.
4.21.3.4 Example Calculation
Emissions were estimated by summing the product of the active ingredient applied and the emissions factor for
each pesticide at the county-level:
Total VOC EmiSSiOnScounty = ^pesticide (Al x EF)
Taking Autauga County, Alabama as an example:
2,874.9 kg of active ingredient of 2,4-D was applied
2,874.9 kg x 2.20462 lb/kg = 6,338.1 lb active ingredient.
EF2,4-d = 0.8273 (lb VOC/lb Al)
Emissions are calculated by multiplying activity data by the emissions factor:
EmissionsAutauga,2,4-d = 6,338.1 lb Al x 0.8273 lb VOC/lb Al = 5,244 lb VOC
This process was then repeated for all pesticide compounds and summed to the county level, resulting in
approximately 39,585 lb, or 19.8 tons, of VOC emitted due to agricultural pesticide application in Autauga
County.
4.233.5 Changes from 2011 and2014vl Methodology
In the 2011 inventory, data estimating harvested acres per crop in each county was multiplied by the percent of
acres treated to yield the number of acres treated for each combination of crop and pesticide compound in a
given county. This acreage was multiplied by an application rate of active ingredient applied per treated acre
(calculated using Crop Life Foundation Database application rates and 2007 USDA Census of Agriculture harvest
acres). The result was the pounds of active ingredient applied for each compound and crop type at the county
level. The mass of active ingredient was then multiplied by an average emissions factor derived from the CA DPR
pesticide database.
Since the Crop Life Foundation Database was discontinued in 2008, the 2014 inventory uses county-level active
ingredient applied for all crop types from the USGS report for year 2012 in the 2014vl NEI and for year 2013 in
the 2014v2 NEI. The amount of active ingredient (kg) applied was available at the county level by pesticide
compound, but not by crop. The mass of active ingredient was then multiplied by pesticide-specific emission
factors derived from the CA DPR 2015 pesticide database (rather than an average emissions factor). In addition,
the 2014 methodology includes HAP emissions estimates for all counties, except those in Alaska, Hawaii, Puerto
Rico and the U.S. Virgin Islands (due to data limitations).
4313.6 Puerto Rico and US Virgin Islands Emissions Calculations
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
4-230
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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".
4,21,4 References for agricultural pesticides
1. United States Environmental Protection Agency, "Pesticides - Agricultural and Nonagricultural", Vol. 3,
Ch. 9, Section 5.1, p. 9.5-4, Emissions Inventory Improvement Program, June 2001.
2. California Department of Pesticide Regulation, "CDPR_Emission_Potential_Database_10_2015.xlsx",
provided by Pam Wofford, Environmental Program Manager, CA DPR to Jonathan Dorn, Associate, Abt
Associates (January 2016).
3. United States Geological Survey, Preliminary Estimates of Annual Agricultural Pesticide Use for Counties
of the Conterminous United States. 2013. accessed July 2016.
4.22 Solvent - Consumer & Commercial Use: Asphalt Paving - Cutback and Emulsified
4.2,2,1 Sector description
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 4-20, 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],
4-231
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Figure 4-20: Types of Asphalt Paving processes
OR
Asphalt Concrete Mix
CMA
cold mix asphalt
HMA
hot mix asphalt
asphalt binder + aggregate
- asphalt cement
- performance additives
emulsions
- asphalt cement
- water
- emulsifying agent
cutback +
- asphalt cement
- petroleum solvents
OR
aggregate
aggregate
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 100ฐF. 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
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],
4.22.2 Sources of data
As seen in Table 4-139, 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 4-139: Asphalt Paving SCCs estimated by EPA and S/L/Ts
SCC
Description
EPA
State
Local
Tribe
2461020000
Asphalt Application: All Processes; Total: All Solvent Types
X
2461021000
Cutback Asphalt; Total: All Solvent Types
X
X
X
X
2461022000
Emulsified Asphalt; Total: All Solvent Types
X
X
X
X
The agencies listed in Table 4-140 submitted VOC emissions for cutback and/or emulsified asphalt paving;
agencies not listed used EPA estimates for the entire sector.
4-232
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Table 4-140: Percentage of cutback and emulsified Asphalt Paving VOC emissions submitted by reporting agency
Region
Agency
S/L/T
see
Description
VOC
2
New Jersey Department of Environment
Protection
State
2461021000
Cutback Asphalt; Total:
All Solvent Types
100
3
Delaware Department of Natural
Resources and Environmental Control
State
2461021000
Cutback Asphalt; Total:
All Solvent Types
100
3
Maryland Department of the
Environment
State
2461021000
Cutback Asphalt; Total:
All Solvent Types
100
3
Virginia Department of Environmental
Quality
State
2461021000
Cutback Asphalt; Total:
All Solvent Types
100
5
Illinois Environmental Protection
Agency
State
2461021000
Cutback Asphalt; Total:
All Solvent Types
100
5
Michigan Department of Environmental
Quality
State
2461021000
Cutback Asphalt; Total:
All Solvent Types
100
5
Minnesota Pollution Control Agency
State
2461021000
Cutback Asphalt; Total:
All Solvent Types
100
6
Texas Commission on Environmental
Quality
State
2461021000
Cutback Asphalt; Total:
All Solvent Types
100
8
Assiniboine and Sioux Tribes of the Fort
Peck Indian Reservation
Tribe
2461021000
Cutback Asphalt; Total:
All Solvent Types
100
8
Utah Division of Air Quality
State
2461021000
Cutback Asphalt; Total:
All Solvent Types
100
9
California Air Resources Board
State
2461021000
Cutback Asphalt; Total:
All Solvent Types
64
9
Maricopa County Air Quality
Department
Local
2461021000
Cutback Asphalt; Total:
All Solvent Types
100
9
Washoe County Health District
Local
2461021000
Cutback Asphalt; Total:
All Solvent Types
100
10
Coeur d'Alene Tribe
Tribe
2461021000
Cutback Asphalt; Total:
All Solvent Types
100
10
Idaho Department of Environmental
Quality
State
2461021000
Cutback Asphalt; Total:
All Solvent Types
100
10
Kootenai Tribe of Idaho
Tribe
2461021000
Cutback Asphalt; Total:
All Solvent Types
100
10
Nez Perce Tribe
Tribe
2461021000
Cutback Asphalt; Total:
All Solvent Types
100
10
Shoshone-Bannock Tribes of the Fort
Hall Reservation of Idaho
Tribe
2461021000
Cutback Asphalt; Total:
All Solvent Types
100
1
New Hampshire Department of
Environmental Services
State
2461022000
Emulsified Asphalt;
Total: All Solvent Types
100
2
New Jersey Department of Environment
Protection
State
2461022000
Emulsified Asphalt;
Total: All Solvent Types
100
3
Delaware Department of Natural
Resources and Environmental Control
State
2461022000
Emulsified Asphalt;
Total: All Solvent Types
100
3
Maryland Department of the
Environment
State
2461022000
Emulsified Asphalt;
Total: All Solvent Types
100
3
Virginia Department of Environmental
Quality
State
2461022000
Emulsified Asphalt;
Total: All Solvent Types
100
4-233
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Region
Agency
S/L/T
see
Description
voc
5
Illinois Environmental Protection
Agency
State
2461022000
Emulsified Asphalt;
Total: All Solvent Types
100
5
Michigan Department of Environmental
Quality
State
2461022000
Emulsified Asphalt;
Total: All Solvent Types
100
5
Minnesota Pollution Control Agency
State
2461022000
Emulsified Asphalt;
Total: All Solvent Types
100
6
Texas Commission on Environmental
Quality
State
2461022000
Emulsified Asphalt;
Total: All Solvent Types
100
8
Utah Division of Air Quality
State
2461022000
Emulsified Asphalt;
Total: All Solvent Types
100
9
California Air Resources Board
State
2461022000
Emulsified Asphalt;
Total: All Solvent Types
94
9
Maricopa County Air Quality
Department
Local
2461022000
Emulsified Asphalt;
Total: All Solvent Types
100
9
Washoe County Health District
Local
2461022000
Emulsified Asphalt;
Total: All Solvent Types
100
10
Coeur d'Alene Tribe
Tribe
2461022000
Emulsified Asphalt;
Total: All Solvent Types
100
10
Idaho Department of Environmental
Quality
State
2461022000
Emulsified Asphalt;
Total: All Solvent Types
100
10
Kootenai Tribe of Idaho
Tribe
2461022000
Emulsified Asphalt;
Total: All Solvent Types
100
10
Nez Perce Tribe
Tribe
2461022000
Emulsified Asphalt;
Total: All Solvent Types
100
10
Shoshone-Bannock Tribes of the Fort
Hall Reservation of Idaho
Tribe
2461022000
Emulsified Asphalt;
Total: All Solvent Types
100
4,22,3 EPA-developed emissions for asphalt paving: unchanged for the 2014v2 NEl
Additional information about asphalt paving practices and terminology is provided in the nonpoint asphalt
paving method development document "2014_NPt_Asphalt_18nov2015_edit03302016.zip" on the 2014vl
Supplemental Data FTP site.
EPA estimated emissions from paving processes that use cold mix asphalt - cutback and emulsified, but not from
the use of hot mix asphalt or WMA. For the 2014 NEI vl, the EPA could not find readily available information on
the composition of FIMA asphalt binder or from WMA products. Emission estimates from FIMA/WMA paving are
not provided at this time.
Act/Wtydata
The EPA's pre-existing emissions estimation method for paving using cutback or emulsified asphalt cement
applies 2008 usage data by the Asphalt Institute. The 2008 usage data for cutback and emulsified asphalt is also
applied for the 2014 NEI vl. General on-line data searches did not yield more recent and available information
on cutback and emulsified asphalt usage though data may be available for purchase from Freedonia. Several
information sources indicate that the Asphalt Institute which performed periodic surveys through 2008, stopped
surveys efforts of that type after 2008. The EPA contacted the Asphalt Institute to see if more recent activity
data is available and was provided the copyright protected 2014 survey report. While that data is not presented
here, review indicated little difference between the national-level 2008 and the 2014 use amounts for cutback
4-234
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asphalt and a larger increase in the national 2014 emulsified usage compared to the 2008 use value, i.e., a 20
percent change from 2008. The Asphalt Institute 2008 survey indicated many states had zero usage for cutback
asphalt- specifically AK, CT, DE, DC, HI, ME, MD, MA, NH, NJ, NY, NC, Rl, SC, VT, and WV. Some of those states
also were noted with zero usage for emulsified asphalt. Based on comparison of the 2008 activity with the
MANE-VU 2007 inventory [ref 3] and the 2011v2 NEI, it appears that the proposed estimates for the 2014 NEI
asphalt emissions may under-estimate (zero out emissions) for the MARAMA states when many of those states
have emissions in the 2007 MANE-VU inventory and in the 2011 NEI v2. The use of 2008 activity data as a
surrogate for the 2014 NEI likely under-estimates some states' use of cutback and emulsified asphalts, and
perhaps more so for emulsified. The survey report acknowledged that manufacturers or resellers in some states
may have not reported or under-reported due to confidentiality concerns.
The rate of growth pattern for asphalt use between 2008 and 2014 was also reviewed by looking at several on-
line sources such as Freedonia brochures [ref 4] and, as seen in Figure 4-21, the U.S. Energy Information
Administration (EIA) State Energy Data System (SEDS) [ref 5], Freedonia suggests that demand for asphalt in the
United States will rebound from the sharp declines in the 2007-2012 period, driven by stronger economic
growth and increased construction activity, though demand in 2017 is expected to remain below the 2007 level.
The US and Canada are significant consumers of asphalt for roofing products; demand for those products will
rise with increased building construction expenditures. The study says demand for asphalt in both paving and
roofing applications will be driven by the recovering US economy and increasing construction activity in the
country. Review of the EIA SEDs data to determine the trend in asphalt product sales and consumption since
2008, specifically the petroleum end-use industrial sector of asphalt and road oil - indicates that state-level
consumption (see Figure 4-22) of asphalt and road oil between the years of 2008-2013 experienced a general
decline or approximately flat growth.
Figure 4-21: ElA-based U.S. asphalt road oil consumption estimates
160
140
120
100
80
60
40
20
U.S. Asphalt and Road Oil Consumption 2008- 2013
million barrels
EIA State Energy Data
152,497,000 barrels
2013
118,045,000 barrels
Diff 2013-2008
-34,452,000 barrels
2008
2009
2010
2011
2012
2013
4-235
-------
Figure 4-22: ElA-based state-level road oil consumption trends
State Asphalt and Road Oil Consumption 2008 - 2013
million barrels
El A State Energy Data
14
12
10
8
6
4
2
0
>>-1
ZZQ
X
H
^ -J
< <
a: n < o h O Lu
< < O o O Q Q
_i
<
_i
O Q LLl I 12
z z 2 z z
^ cc < e o Q
O o Q- w co
H>5
Q LU
Z
I-
2
--2008 -#-2009 -#-2010 -#-2011 --2012 -*-2013
Source: El A State Energy Data 1960-2012,2013
Data Files / Consumption/All Consumption Estimates in Physical Units/ All States
http://www.eia.gov/state/seds/seds-data-complete.cfm?sid=US#CompleteDataFile
The FHWA (Federal Highway Administration) is also a potential source of activity data via their contract with the
National Paving Association to survey states about their use of asphalt and reclaimed materials. The FWHA and
National Paving Association survey of 2013 [ref 6] state-level asphalt usage cites an increased use of warm-mix
asphalt and recycled content. There is no discussion however of the binder composition or the amount of solvent
that may be attributed to the HMA (hot-mix) or WMA. The objective of the survey was to quantify the use of
recycled materials and WMA produced by the asphalt pavement industry in each state. The results include an
estimate of 351 million tons of HMA/WMA plant mix asphalt produced in 2013, of which WMA is 106 million
tons. While the 2008 data usage indicated some states with zero use of cutback and emulsified asphalt for
paving, there are no states with an estimated zero HMA/WMA asphalt production for 2013.
Additional discussion and review of the activity data is provided in the nonpoint asphalt paving method
development document "2014_NPt_Asphalt_18nov2015_edit03302016.zip" on the 2014vl Supplemental Data
FTP site. That discussion includes a comparison of the 2008 usage for cutback and emulsified asphalt that EPA
last obtained from the Asphalt Institute with the state summary of HMA/WMA asphalt production for 2013.
Many state and or local jurisdictions restrict or ban the use of highly evaporative asphalt mixtures such as
cutback asphalt during months of potentially poor air quality, i.e., typically in the warmer, sunny months. Paving
using cutback asphalt may be scheduled and resume in other parts of the year when evaporation of the VOC
content will not influence ozone formation as much. For the purposes of the NEI annual county-level estimate, it
may be assumed that the county allocation of asphalt usage will eventually be used at some point during the
year, rather than assuming emissions are 'zeroed-out' - unless bans are in place. If agencies are developing an
inventory for SIP purposes, a monthly inventory could be calculated to account for monthly variations in process
activity, unless restricted use or bans. EPA's processing of the annual emission inventory for regional air quality
modeling may not take that into account unless county, SCC-specific spatial and temporal factors can be
developed and applied, which is typically outside of the scope of limited resources unless the SCC emissions are
4-236
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particularly significant relative to other emission sources. Table 4-141 summarizes the activity data applied and
the sources.
Table 4-141: Sources of activity data and related parameters, where G=given and C=computed
Parameter
Source Reference Use Note
G
Quantity of asphalt used by
state, by asphalt type -
cutback, emulsified
Annual 2008 national tons
2008 Asphalt Usage Survey, purchased from Asphalt Institute
The state-level 2008 activity was used for the 2008 and the 2011 NEI.
This asphalt use is assumed to be for asphalt cement, rather than for
asphalt concrete which is composed of both aggregate (~95% by weight)
and asphalt cement (~5%by weight).
G
State VMT2013 FHWA Roads
State-level annual vehicle miles traveled (VMT) by FHWA road class, 2013.
FHWA Report VM-2. 2013 [ref 71.
C
County VMTfhWA Roads for 2014 NEI
Estimate of county-level annual VMT by FHWA road class, for 2014 NEI.
This approximation of county-level annual VMT for 2014 is based on the
equation:
COUnty VMTfhWA Road Type for 2014 NEI =
2011NEIv2 Co U nty V MT MOVES_N El RoadTypeX (2013 StateVMTFHWA Road Type /2013
State MOVES_NEI Road Type)
See ElAG's NEI documentation file:
C
County VMT fraction
of State VMT
Estimate of county fraction of the state VMT by FHWA road class, for 2014
NEI.
This approximation is based on the equation:
(2014 County VMTFHWARoad / 2013 State VMTFHWARoad)
= (County VMT/ State VMT)fhwa Road for 2014 nei
G
State Lane-Miles2oi3fhwaRoads
State lane-miles bv FHWA road class, 2013. FHWA Report HM-6Q, 2013.
G
State Paved
Road MileS2013 FHWA Roads
State paved road miles bv FHWA road class, 2013. FHWA Report HM-51,
2013.
C
State Paved
Lane-MileS2013 FHWA Roads
Estimate of state lane-miles that are paved by FHWA road class, for 2013
based on the equation:
[state paved road miles2oi3 fhwa Road / (state paved + unpaved road miles)2oi3
FHWA Road] X state Iane-miles20i3 fhwa Road = state paved lane-miles2oi3 fhwa Road
C
State Utilization
Paved2013 FHWA Roads
Estimate of state-level utilization measure for paved road surface by
FHWA road class, for 2013 based on the equation:
(stateVMT2oi3 fhwa Road /state paved lane-miles2oi3 fhwa Road) = state
utilization paved roads20i3 fhwa Road
C
County Utilization
Paved2013 FHWA Roads
Estimate of the county-level utilization measure for paved road surface by
FHWA road class is calculated by applying the county/state VMT fraction
to the state paved road utilization measure.
(county VMT/ state VMT)Fhwa Road for 2014 nei x (state utilization paved
madS2013 FHWA Road)
= county utilization paved roads2oi3 fhwa road
C
County Utilization Sum2oi3
County-to-State Utiliz
Sum 2013
Sum the county utilization by FHWA roads to county total and sum the
county totals to state total.
C
County Utilization Fraction
of State Utilization
Estimate of county fraction of the state utilization measure for paved road
surface is based on the equation:
(county utilization paved2oi3/ CountyToStateSum utilization paved2oi3)
4-237
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Parameter
Source Reference Use Note
c
County Asphalt Usage for
2014NEI
County-level cutback asphalt usage estimated by allocating state-level
usage data to county based on the estimate of county utilization paved
roads2oi3 using the equation:
(state-level asphalt usage x (county utilization paved2oi3/
CountyToStateSum utilization paved2oi3)
= county asphalt usage for2oi4NEi
Distribution of Activity Data to the County
While the 2008 asphalt usage from the pre-existing method was applied again for the 2014 NEI vl, the
procedure for distributing the state asphalt use to county-level usage was updated with the intent to simplify
the method by using ready available FHWA data reports to develop a utilization measure for paved roads. The
utilization measure focuses on the quantity of travel on paved roads. The pre-existing EPA distribution
procedure applies 10+ year old FHWA data no longer published concerning traffic volume with conversion to
VMT (vehicle miles travelled) using assumed speeds. The intent of the update was to develop a state-to-county
activity distribution factor that is computationally more stream-lined, requires less operating assumptions, and
uses current and routinely available FHWA highway statistics reports rather than carry forward and build a
factor upon old data (1996) as a surrogate for information no longer published (HM-67 Miles by Surface Type
and Average Daily Traffic Volume Group, last published in 1997). The update also intends to allocate paving to
areas with the highest travel. This isn't a perfect methodology as all roads get paved at some point in time, even
low-usage rural roads on their own maintenance schedule, but it may be a reasonable approximation.
The update considers the following performance measures and definitions that may be applied by state DOTs
and MPOs [ref 8],
Dimension Performance Measure Definition
Quantity of Travel Vehicle miles traveled Average Annual Daily Traffic * Length
Utilization Vehicles per lane-mile Average Annual Daily Traffic * Length/lane miles
The operating assumption is that the county-level paved road utilization is similar to the calculated state-level
paved road utilization measure, and may be related based on the county VMT fraction of state VMT. The general
steps using the activity parameters in the above Table are as follows.
Step 1. Develop state road utilization measure by road surface.
Utilization measure = VMT/ lane-miles.
By FHWA road type, the amount of lane-miles that are paved may be expressed as: (state paved road
miles/ state paved + unpaved road miles) x state lane-miles = state paved lane-miles.
State utilization measure for paved road surface = (state VMT / (state paved lane-miles)
Step 2. Compute county-to-state fraction for quantity of travel, i.e., vehicle miles traveled.
By FHWA road type, the county-to-state fraction, vehicle miles traveled = County VMT/ State VMT.
Estimate of annual county VMT based on MOVES mobile source model is provided by EPA.
Step 3. Compute county-level utilization measure for paved roads.
By FHWA road type, apply the county/state VMT fraction (Step 2) to the state road utilization measure
by paved road type (Step 1) to obtain the county-level road utilization measure for paved roads.
County utilization paved roads = (County VMT/state VMT) x (State utilization measure for paved road
surface)
4-238
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Step 4. Sum the county utilization by FHWA roads to county total and sum the county totals to state
total.
Step 5. Estimate the county fraction of the state utilization measure for paved road surface as: County
utilization paved roads / county-to-state sum utilization paved
The county fraction of state utilization measure computed in step 5 is multiplied by the state asphalt usage to
distribute the state-level asphalt use to county usage.
4,22.3.2 Emission Factors
The annual mass emission rate factors for cutback and emulsified asphalt are updated using the 2008 asphalt
consumption data and MSDS (Material Safety Data Sheets) information to reflect the composition of cutback
and emulsified paving mixtures used today. Table 4-142 summarizes the sources of emission factors and related
parameters.
Table 4-142: Sources of emission factors and related parameters, where G=given and C=computed
Parameter
Source Reference Use Notes
c
Emission Factor VOC,
HAPs
Emission factors are updated for 2014 NEI. Basis includes: 2008 annual
asphalt cement use data from Asphalt Institute; average chemical
composition information from available online MSDS - specific diluent, %
weight fraction; and assumed %weight emitted.
See factors in Table 4-143 and equations in method discussion section.
G
Asphalt cement
consumption
Annual 2008 national
tons
The 2008 activity usage by state (2008 Asphalt Usage Survey, from Asphalt
Institute) is summed to national. Cutback usage = 187,328 tons; Emulsified
usage = 1,350,999 tons.
G
Diluent(s) and Average
pet of each diluent in
asphalt cement
Determination that likely multiple diluents are present in asphalt cement
(binder) and an average weight percent of diluent in asphalt cement is
assumed based on MSDS information.
Specific diluent and properties are referenced in method discussion section.
G
Density of asphalt
The density of asphalt is assumed similar to that of water, 8.34 lbs/gal which
seems reasonable based on relative density information in MSDS.
G
Density of diluent (s)
Density measures for each diluent are referenced in method discussion
section. While density measures were gathered/recorded, they are not used
for weight % calculations.
G
Pet by wgt of volatile
(diluent) emitted in
product
95% of total solvent is assumed emitted; with 5% of total solvent assumed
retained in the product.
C
Emissions
Emissions = County-Level Asphalt Usage * Emission Factors
Emission factors (lbs pollutant emitted/ ton asphalt, cutback or emulsified) were calculated using parameters in
the above table:
Ibs/yr cutback (or emulsified) cement x avg % weight diluent = Ibs/yr diluent
Ibs/yr diluent x avg weight % volatile emitted = Ibs/yr diluent emitted
annual mass emission rate: (lbs poll emitted/yr) / (tons asphalt used/yr) = lb/ton
4-239
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Material Safety Data Sheets (MSDS) for cutback and emulsified asphalt were searched on-line and reviewed as a
general way to assess the physical parameters used in the pre-existing emission factor calculation - regarding
material composition, percent concentrations, and density measures. The MSDS typically cover a range of
graded asphalts and note that petroleum asphalt is mixed with varying proportions of solvent, fuel oils,
kerosene, and/or petroleum residues and the composition varies depending on source of crude and
specifications of final product. Information from several MSDS are summarized below. Based on the MSDS
information, the following values, seen in Table 4-143, were developed and applied as average composite
surrogates. The information for cutback is based primarily on rapid cure though ethylbenzene is cited for
presence in medium and slow cure mixtures. In the MSDS, the units of the concentration percent is seldom
confirmed as whether percent by volume or percent by weight. When it was specified on the emulsified and
cutback sheets reviewed, it was percent by weight. References for several ASTM (American Society for Testing
and Materials) standard methods for sampling and testing the composition of bituminous paving materials were
reviewed to form the assumption that the concentration percentages are mass percentages.
Additional information, including the use of specific MSDS, is provided in the nonpoint asphalt paving method
development document "2014_NPt_Asphalt_18nov2015_edit03302016.zip" on the 2014vl Supplemental Data
.
Table 4-143: Cutback asphalt computed average chemical composition information
Chemical Composition, i.e., VOCs, HAPs
Avg % by Weight
Density
Note
Asphalt
60-90
8.34 lb/gal
Relative Density ~ 0.9-.99,
water=l
Naptha, i.e., VM&P, Stoddards solv
40
6.3 lb/gal
15C/60F (CDC/NIOSH)
Naphthalene
0.49
(0.58 w PAH)
9.5 lb/gal
20C/68F (CDC/NIOSH), SG 1.16
Toluene
0.59
7.2 lb/gal
20C/68F (CDC/NIOSH)
Xylene
0.99
7.2 lb/gal
20C/68F (CDC/NIOSH)
Benzene
0.19
7.3 lb/gal
20C/68F (CDC/NIOSH)
Ethylbenzene
0.49
7.2 lb/gal
20C/68F (CDC/NIOSH)
Polycyclic Aromatic Hydrocarbons
0.09
Add to weight % as
naphthalene
Hydrogen Sulfide
0.09
8.3 lb/ gal
SG 1.19 (gas)
The units of the updated emission factors, seen in Table 4-144 are different than for the pre-existing factors. A
conservative conversion of the existing lbs/ barrel value to terms of lbs/ton is done using the conversion factor:
5.5 barrels of road oil / ton [ref 4],
Table 4-144: Updated emission factors and expected pollutants by SCC vs. pre-existing factors
SCC
Description
Pollutant
Pollutant
Code
Update
lb/ton
Pre-existing
lb/barrel
2461021000
Cutback Asphalt,
Total: All Solvent
Types
voc
VOC
813.96
88.0
Benzene
71432
3.6
Ethylbenzene
100414
9.3
2.02
Naphthalene
91203
11.0
Toluene
108883
11.2
5.63
Xylenes (Mixed Isomers)
1330207
18.8
10.74
Hydrogen Sulfide
7783064
1.7
2461022000
Emulsified All
VOC
VOC
195.5
9.2
4-240
-------
see
Description
Pollutant
Pollutant
Code
Update
lb/ton
Pre-existing
lb/barrel
Asphalt, Total:
Solvent Types
Naphthalene
91203
5.5
Hydrogen Sulfide
7783064
1.7
Example: 88 lbs VOC/ barrel x 5.5 barrels/ton = 484 lb VOC/ ton
The updated emission factors include (three) additional HAPs (hazardous air pollutants) based on review of
some current available MSDS composition information. The pre-existing HAP factors were based on a percent
weight of VOC from the EPA 1996 NTI (National Toxics Inventory).
The nonpoint asphalt paving method development document
"2014_NPt_Asphalt_18nov2015_edit03302016.zip" on the 2014vl Supplemental Data FTP site includes a
discussion of the basis for the pre-existing emission factors and the specific calculations for the updated factors.
4,223,3 Some Possible Steps for Further Improvement in the 2017 NEI
The method updates for the 2014vl NEI involved contacting the FHWA, the Asphalt Institute, and the NAPA.
FHWA staff responded that they do not collect nor track information on cutback and emulsified asphalt usage on
the National Highway System and that emulsions are generally used in maintenance activities and not new
construction or re-construction. Staff from the Asphalt Institute responded to provide their copyright protected
2014 survey report with request that it not be further distributed. As of this writing, response was not received
from the NAPA.
FHWA may be able to obtain information from their paving industry partners, i.e., NAPA to help quantify the
composition of WMA and HMA. For HMA and WMA, knowing the use amounts that may include solvents with
evaporative potential and also whether there are amounts of cutback and emulsified not covered by their
annual survey purposes, could improve both activity and composition information to update the emission factor
calculations. NAPA also conducts FHWA co-sponsored research of which on-line brochure indicates that NAPA
drafted a report [ref 8] comparing criteria air pollutant emissions of warm-mix technologies and hot-mix
technologies - available upon request from NAPA and that the report was not released to the public because
additional stack emissions testing is needed to determine the extent of criteria air pollutant reduction with the
use of warm-mix technologies. Current asphalt use (activity) data may also be available for purchase from
Freedonia.
More in-depth on-line literature searches, e.g., Science Direct, could also be conducted to see if research results
exist that describe measured volatile composition of asphalt mixtures used today. That could be another way to
further assess emission characteristics of the VOC and individual chemical species.
The nonpoint asphalt paving method development document
"2014_NPt_Asphalt_18nov2015_edit03302016.zip" on the 2014vl Supplemental Data FTP site includes a list of
some possible contacts for more information.
4.22,4 References for asphalt paving
1. Wisconsin Transportation Bulletin No. 1, Understanding and Using Asphalt
2. National Cooperative Highway Research Program (NCHRP) Report 673. A Manual for Design of Hot Mix
Asphalt with Commentary. 2011
3. MARAMA. 2011. 2007/2017/2020 Modeling Emissions Inventory Version 2 Preliminary Trends Analysis.
4. Freedonia Brochure - Asphalt Paving.
4-241
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5. EIA SEDS, Prices and Expenditures, Petroleum Overview, accessed 2015.
6. Annual Asphalt Pavement Industry Survey on Recycled Materials and Warm-Mix Asphalt Usage: 2009-
2013. Information Series 138. National Asphalt Paving Association.
7. FHWA Traffic Analysis Toolbox Volume VI: Definition, Interpretation, and Calculation of Traffic Analysis
Tools Measures of Effectiveness (MOEsi, Tables 6 and 7.
8. National Asphalt Pavement Association Research Project Summary Brochure 2015.
4,23 ฆ Solvents
This section includes discussion on all nonpoint solvent sources except for agricultural pesticide application (see
Section 4.21) and asphalt paving (see Section 4.22). The reason these sources are discussed separately is
because the EPA methodologies for estimating the emissions are different.
4.23.1 Sector description
Solvent usage is covered in the NEI for 2014 by many SCCs and is comprised of industrial, commercial, and
residential applications. EPA's solvents 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).
4.23.2 Sources of data
Table 4-145 shows, for Solvents, the nonpoint SCCs covered by the EPA estimates and by the State/Local and
Tribal agencies that 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 2014 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 4-145: Nonpoint Solvent SCCs with 2014 NEI emissions
SCC
Description
EPA
State
Local
Tribe
Sector
2401001000
Surface Coating; Architectural
Coatings; Total: All Solvent Types
X
X
X
X
Solvent - Non-Industrial
Surface Coating
2401001050
Surface Coating; Architectural
Coatings; All Other Architectural
Categories
X
Solvent - Non-Industrial
Surface Coating
2401005000
Surface Coating; Auto
Refinishing: SIC 7532; Total: All
Solvent Types
X
X
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401005700
Surface Coating; Auto
Refinishing: SIC 7532; Top Coats
X
Solvent - Industrial Surface
Coating & Solvent Use
2401005800
Surface Coating; Auto
Refinishing: SIC 7532; Clean-up
Solvents
X
Solvent - Industrial Surface
Coating & Solvent Use
2401008000
Surface Coating; Traffic Markings;
Total: All Solvent Types
X
X
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401010000
Surface Coating; Textile Products:
SIC 22; Total: All Solvent Types
X
Solvent - Industrial Surface
Coating & Solvent Use
4-242
-------
see
Description
EPA
State
Local
Tribe
Sector
2401015000
Surface Coating; Factory Finished
Wood: SIC 2426 thru 242; Total:
All Solvent Types
X
X
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401020000
Surface Coating; Wood Furniture:
SIC 25; Total: All Solvent Types
X
X
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401025000
Surface Coating; Metal Furniture:
SIC 25; Total: All Solvent Types
X
X
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401030000
Surface Coating; Paper: SIC 26;
Total: All Solvent Types
X
X
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401035000
Surface Coating; Plastic Products:
SIC 308; Total: All Solvent Types
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401040000
Surface Coating; Metal Cans: SIC
341; Total: All Solvent Types
X
X
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401045000
Surface Coating; Metal Coils: SIC
3498; Total: All Solvent Types
X
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
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401060000
Surface Coating; Large
Appliances: SIC 363; Total: All
Solvent Types
X
X
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401065000
Surface Coating; Electronic and
Other Electrical: SIC 36 - 363;
Total: All Solvent Types
X
X
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401070000
Surface Coating; Motor Vehicles:
SIC 371; Total: All Solvent Types
X
X
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401075000
Surface Coating; Aircraft: SIC 372;
Total: All Solvent Types
X
X
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401080000
Surface Coating; Marine: SIC 373;
Total: All Solvent Types
X
X
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401085000
Surface Coating; Railroad: SIC
374; Total: All Solvent Types
X
X
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401090000
Surface Coating; Miscellaneous
Manufacturing; Total: All Solvent
Types
X
X
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401100000
Surface Coating; Industrial
Maintenance Coatings; Total: All
Solvent Types
X
X
X
X
Solvent - Industrial Surface
Coating & Solvent Use
2401200000
Surface Coating; Other Special
Purpose Coatings; Total: All
Solvent Types
X
X
X
X
Solvent - Industrial Surface
Coating & Solvent Use
4-243
-------
see
Description
EPA
State
Local
Tribe
Sector
2415000000
Degreasing; All Processes/All
Industries; Total: All Solvent
Types
X
X
X
X
Solvent - Degreasing
2420000000
Dry Cleaning; All Processes; Total:
All Solvent Types
X
X
X
X
Solvent - Dry Cleaning
2425000000
Graphic Arts; All Processes; Total:
All Solvent Types
X
X
X
X
Solvent - Graphic Arts
2440000000
Miscellaneous Industrial; All
Processes; Total: All Solvent
Types
X
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
X
X
Solvent - Consumer &
Commercial Solvent Use
2460140000
Miscellaneous Non-industrial:
Consumer and Commercial:
Personal Care Products: Powders:
Total: All Solvent Types
X
Solvent - Consumer &
Commercial Solvent Use
2460200000
Miscellaneous Non-industrial:
Consumer and Commercial; All
Household Products; Total: All
Solvent Types
X
X
X
X
Solvent - Consumer &
Commercial Solvent Use
2460400000
Miscellaneous Non-industrial:
Consumer and Commercial; All
Automotive Aftermarket
Products; Total: All Solvent Types
X
X
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
X
X
Solvent - Consumer &
Commercial Solvent Use
2460600000
Miscellaneous Non-industrial:
Consumer and Commercial; All
Adhesives and Sealants; Total: All
Solvent Types
X
X
X
X
Solvent - Consumer &
Commercial Solvent Use
2460800000
Miscellaneous Non-industrial:
Consumer and Commercial; All
FIFRA Related Products; Total: All
Solvent Types
X
X
X
X
Solvent - Consumer &
Commercial Solvent Use
4-244
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see
Description
EPA
State
Local
Tribe
Sector
2460900000
Miscellaneous Non-industrial:
Consumer and Commercial;
Miscellaneous Products (Not
Otherwise Covered); Total: All
Solvent Types
X
X
X
X
Solvent - Consumer &
Commercial Solvent Use
2461000000
Miscellaneous Non-industrial:
Commercial; All Processes; Total:
All Solvent Types
X
Solvent - Consumer &
Commercial Solvent Use
2461020000*
Miscellaneous Non-industrial:
Commercial; Asphalt Application:
All Processes; Total: All Solvent
Types
X
Solvent - Consumer &
Commercial Solvent Use
2461021000*
Miscellaneous Non-industrial:
Commercial; Cutback Asphalt;
Total: All Solvent Types
X
X
X
X
Solvent - Consumer &
Commercial Solvent Use
2461022000*
Miscellaneous Non-industrial:
Commercial; Emulsified Asphalt;
Total: All Solvent Types
X
X
X
X
Solvent - Consumer &
Commercial Solvent Use
2461023000
Miscellaneous Non-industrial:
Commercial; Asphalt Roofing;
Total: All Solvent Types
X
Solvent - Consumer &
Commercial Solvent Use
2461024000
Miscellaneous Non-industrial:
Commercial; Asphalt Pipe
Coating; Total: All Solvent Types
X
Solvent - Consumer &
Commercial Solvent Use
2461160000
Miscellaneous Non-industrial:
Commercial; Tank/Drum
Cleaning: All Processes; Total: All
Solvent Types
X
Solvent - Consumer &
Commercial Solvent Use
2461800001*
Miscellaneous Non-industrial:
Commercial; Pesticide
Application: All Processes;
Surface Application
X
Solvent - Consumer &
Commercial Solvent Use
2461800002*
Miscellaneous Non-industrial:
Commercial; Pesticide
Application: All Processes; Soil
Incorporation
X
Solvent - Consumer &
Commercial Solvent Use
2461850000*
Miscellaneous Non-industrial:
Commercial; Pesticide
Application: Agricultural; All
Processes
X
X
X
Solvent - Consumer &
Commercial Solvent Use
2461900000
Miscellaneous Non-industrial:
Commercial: Miscellaneous
Products: NEC: Total: All Solvent
Types
X
Solvent - Consumer &
Commercial Solvent Use
* These sources are discussed in Section 4.21 (Agricultural Pesticides) and Section 4.22 (Asphalt Paving)
The agencies listed in Table 4-146 submitted at least VOC emissions for all the EIS Solvent sectors discussed in
this section: Consumer & Commercial Use, Degreasing, Dry Cleaning, Graphic Arts, Industrial Surface Coating &
4-245
-------
Solvent Use, and Non-Industrial Surface Coating. 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%).
Ta
lie 4-146: EIS sector-specific percentage of Solvent VOC emissions submitted by reporting agency
Region
Agency
S/L/T
Consumer/
Commercial
Degreasing
Dry Cleaning
Graphic Arts
Ind. Sfc. Coat +
Solv. Use
Non-lnd. Sfc.
Coating
1
Connecticut Department of Energy and Environmental
Protection
State
100
100
100
100
100
100
1
Maine Department of Environmental Protection
State
98
100
100
100
100
100
1
Massachusetts Department of Environmental
Protection
State
100
100
100
100
100
1
New Hampshire Department of Environmental Services
State
18
100
100
77
100
1
Rhode Island Department of Environmental
Management
State
100
98
35
2
New Jersey Department of Environment Protection
State
100
100
100
100
100
100
2
New York State Department of Environmental
Conservation
State
95
100
100
100
100
100
3
DC-District Department of the Environment
Local
99
100
100
100
100
100
3
Delaware Department of Natural Resources and
Environmental Control
State
100
100
100
100
100
100
3
Maryland Department of the Environment
State
94
99
100
100
98
100
3
Pennsylvania Department of Environmental Protection
State
74
100
100
100
100
3
Virginia Department of Environmental Quality
State
96
100
100
90
100
3
West Virginia Division of Air Quality
State
94
100
100
100
100
100
4
Chattanooga Air Pollution Control Bureau (CHCAPCB)
Local
86
100
100
100
100
100
4
Florida Department of Environmental Protection
State
77
100
100
100
100
100
4
Georgia Department of Natural Resources
State
100
100
75
4
Knox County Department of Air Quality Management
State
85
100
100
100
100
100
4
Louisville Metro Air Pollution Control District
Local
88
100
100
48
100
4
Metro Public Health of Nashville/Davidson County
Local
50
100
18
100
4
South Carolina Department of Health and
Environmental Control
State
91
100
100
100
100
100
5
Illinois Environmental Protection Agency
State
100
100
100
100
100
100
5
Indiana Department of Environmental Management
State
100
58
5
Michigan Department of Environmental Quality
State
94
100
100
100
100
100
5
Minnesota Pollution Control Agency
State
83
100
100
99
100
100
5
Ohio Environmental Protection Agency
State
88
100
100
100
100
5
Wisconsin Department of Natural Resources
State
76
100
100
100
4-246
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Region
Agency
S/L/T
Consumer/
Commercial
Degreasing
Dry Cleaning
Graphic Arts
Ind. Sfc. Coat +
Solv. Use
Non-lnd. Sfc.
Coating
6
Louisiana Department of Environmental Quality
State
88
100
100
100
100
100
6
Oklahoma Department of Environmental Quality
State
73
100
100
100
100
100
6
Texas Commission on Environmental Quality
State
95
100
100
100
100
100
7
Iowa Department of Natural Resources
State
55
100
100
100
100
100
7
Kansas Department of Health and Environment
State
63
100
100
100
100
100
7
Missouri Department of Natural Resources
State
100
100
35
7
Sac and Fox Nation of Missouri in Kansas and Nebraska
Reservation
Tribe
100
8
Assiniboine and Sioux Tribes of the Fort Peck Indian
Reservation
Tribe
100
8
Northern Cheyenne Tribe
Tribe
100
100
8
Utah Division of Air Quality
State
80
100
100
100
100
100
9
Arizona Department of Environmental Quality
State
72
100
100
100
100
100
9
California Air Resources Board
State
94
100
4
16
64
6
9
Clark County Department of Air Quality and
Environmental Management
Local
0
9
Maricopa County Air Quality Department
Local
9
100
100
6
100
9
Washoe County Health District
Local
55
100
100
100
61
100
10
Coeur d'Alene Tribe
Tribe
100
100
100
100
100
100
10
Idaho Department of Environmental Quality
State
100
100
100
100
100
100
10
Kootenai Tribe of Idaho
Tribe
100
100
100
100
10
Nez Perce Tribe
Tribe
100
100
100
100
100
100
10
Oregon Department of Environmental Quality
State
67
100
100
100
100
100
10
Shoshone-Bannock Tribes of the Fort Hall Reservation
of Idaho
Tribe
100
100
100
100
100
100
* The EIS sector Consumer & Commercial EIS includes agricultural pesticide application and asphalt paving, sources
discussed in previous sections.
4,23.3 EPA-developed emissions from the Solvent Tool; new for 2014v2
New for 2014 is a MS Access tool which calculates emissions for almost all the solvent categories estimated by
EPA. More information on the solvents tool can be found in the documentation entitled, "Solvent Tool
Documentation vl_7," found in "Solvent_Tool_vl.7.zip"on the 2014v2 Supplemental Nonpoint data FTP site.
There are three SCCs that are highlighted in Table 4-145 that EPA estimates and are not covered by the MS
Access Tool, which include Agricultural Pesticide Application and Cutback and Emulsified Asphalt Paving.
The benefits of consolidating the solvent categories into MS Access are twofold. Activity data can be a common
thread amongst many of these SCCs, eliminating the need to upload data repeatedly to many different MS Excel
4-247
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workbooks. Also, the tool can export final emissions data to staging table format, making uploading final
emissions data to EIS easier and less of a burden to EIS data submitters.
In general, the solvent tool uses activity factors that are based either on employment or population, with a
notable exception of Lane Miles for Traffic Marking applications. Most point source data do not rely on these
same activity inputs, which makes conducting point source subtraction on an activity basis difficult. Therefore,
the tool was developed to accept point source data for subtraction in two ways: either activity or an emissions
Point/Nonpoint SCC Crosswalk.
In addition, much work was done to improve the point/nonpoint crosswalk, so that point source subtraction
could be done within the tool. The crosswalk was updated with the addition of approximately 65 SCCs.
States were given the option to accept EPA estimates. However, this premise relies heavily on the assumption
that there are no point sources to subtract. Because EPA lacks the resources to complete point source
subtraction on behalf of the states, it is possible that this may have led to double-counting of emissions.
4.233,1 Notes about the Solvent Toot for 2014vl
Retired SCCs Unretired for NJ
New Jersey noted late in the submission period that EPA had retired several SCC codes that were meaningful to
their inventory. NJ asked that EPA un-retire these codes, with the rationale that the Ozone Transport
Commission Stationary and Area Source Committee targets high VOC area source categories for regulation,
based on California regulations. Therefore, EPA made the decision to un-retire these codes in a silent fashion.
The categories include: Consumer Products, Autobody Refinishing, Pesticide Application, Graphic Arts, and
Asphalt Paving. EPA then needed to go back and review the nonpoint survey to make sure that any double-
counting didn't occur at this point.
Two Versions/Graphic Arts
It should also be noted that two Versions of the Solvent Tool were released for states to use in the 2014vl NEI.
In the history of the ERTAC committee, two different methodologies have been used for the estimation of
Graphic Arts emissions. One is based on employment, using a lb VOC/employee unit, and the other is based on
population, using a lb VOC/capita unit. States differed on their preference, so it was decided by the NOMAD
Committee to release two versions of the tool, identical in nature except for the graphic arts emission factor and
activity. While EPA gave states the allowance to choose which methodology to use, EPA made the final decision
to use the employment methodology for EPA estimates.
This did cause issues for Graphic Arts for the 2014vl NEI. Publishing two tools created disparities; population-
based often resulted in emissions a factor of ten or greater than the employment basis. Several states revised
their emissions accordingly.
Incorrect HAPs for Tool
Another disparity that had to be addressed in 2014vl was that the HAPs that were published in the Solvent Tool
on SharePoint in time for S/L/Ts to utilize in 2014vl were ones that were EPA had derived from some EPA/SPPD
data in the 2011v2 NEI. These HAPs emission factors had never been reviewed by S/L/Ts, as they were only input
into the 2011v2 NEI (due to timing of the development of the HAPs). In retrospect, these HAPs were very
different from previous inventories (completely different pollutant sets) and were not extrapolated in a
technically-defensible manner. Therefore, because the published tool used faulty HAP emission factors, EPA had
to tag out S/L/T-submitted solvent HAPs. These HAPs were then created from S/L/T-submitted VOC emissions
4-248
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via the HAP augmentation file, which used speciation factors from VOC to create VOC HAPs. New HAPs were
developed to have more correct HAPs included in the 2014vl NEI.
The VOC HAP factors are weight fractions of chemical species comprising total reactive VOCs. The speciation
factor, or weight fraction, for each HAP is multiplied by the nonpoint VOC emissions (i.e. after point source
subtraction). The speciation factors have historically been based on data from the Freedonia Group [ref 1] which
provides information on the amount of solvent demand by solvent type (e.g. toluene, xylene, etc.). The
speciation factors are developed by dividing the demand for each solvent type by the total solvent usage.
Previous editions of the Freedonia data broke this information down by type of solvent and industry; however,
the most recent version of the Freedonia data breaks it down by either type of solvent or industry, but not both.
For this reason, if a newly calculated speciation factor using 2013 Freedonia data is significantly larger (i.e. by an
order of magnitude) than the factor used in the 2011 NEI, then the factor is not changed and the 2011 factor is
carried forward.
The tool was revised for the 2014v2 NEI; however, no changed to HAPs are noted because we used the correct
factors in 2014vl by using HAP Augmentation factors in EIS, rather than the Solvent tool to compute HAPs in
2014vl.
State Tagged Data
A few states (NH, TX, and VA) requested that we tag out their data after reviewing it in the draft. These were for:
NH surface coating (electronic and other electrical, factory finished wood, and machinery and equipment), TX
surface coating (special purpose coatings), and VA traffic markings and ag pesticides. As requested by inventory
developers in these state air agencies, EPA estimates were used in lieu of the state submitted data.
EPA Tagged Data
Several S/L/Ts, listed in Table 4-147, answered on the nonpoint survey that they did not have specific solvent
categories in their area of responsibility, or that these sources were completely covered in their point inventory
submittal; therefore, EPA tagged out any emissions from the 2014 EPA Nonpoint Dataset to ensure that EPA
emissions did not backfill where S/L/Ts did not submit nonpoint estimates.
Table 4-147: S/L/Ts that requested EPA not backfill nonpoint Solvent estimates with EPA estimates
S/L/T
Solvent category(s)
Reason to not include in NP
Inventory
AK
Ag Pesticides, Surface Coating (auto, factory wood,
industrial maintenance, motor vehicles, special purpose,
wood furniture, architectural coatings)
Do not have this type of source
CA
Consumer & Commercial (adhesives/sealants, personal care
products)
Use different SCCs
Chattanooga
County
Dry cleaning, Consumer & Commercial (adhesives/sealants,
automotive aftermarket, coatings, FIFRA, household,
personal care, miscellaneous); Surface Coating (architectural
coatings, auto refinishing, electronic, factory wood,
industrial maintenance, marine, metal cans, metal furniture,
other special purpose, paper, traffic markings, wood
furniture)
No to Use EPA estimates
CO
Degreasing, Dry Cleaning, Graphic Arts, all Surface Coatings
(except architectural coatings)
All covered in point source inv.
4-249
-------
S/L/T
Solvent category(s)
Reason to not include in NP
Inventory
CT
Dry Cleaning, Consumer & Commercial (adhesives/sealants,
automotive aftermarket, coatings, FIFRA, household,
personal care, miscellaneous), Surface Coating (architectural
coatings, auto refinishing, factory wood, industrial
maintenance, appliances, metal cans, metal furniture, other
special purpose, railroad, traffic markings)
No to Use EPA estimates
NH
Graphic Arts
All covered in point inventory
DC
Degreasing, Dry Cleaning, Consumer & Commercial
(automotive aftermarket, coatings, FIFRA, household
personal care, misc. products, adhesives/sealants), Surface
Coatings (architectural coatings, auto refinishing, industrial
maintenance, misc. manuf., special purpose wood furniture,
marine)
No to Use EPA estimates
DE
Surface Coating (motor vehicles, special purpose)
Do not have this type of source
IL
Dry Cleaning
No to use EPA estimates
IA
Consumer & Commercial (adhesive/sealant, automotive
aftermarket, coatings, FIFRA, household, personal care,
miscellaneous), Surface Coating (arch. Coatings)
No to use EPA estimates
KY
Degreasing, Dry Cleaning
All covered in point inventory
KY
Surface Coating (industrial maintenance, machinery, metal
cans, special purpose)
Do not have this type of source
KY
Surface Coating (aircraft, electronic, appliances, marine,
metal furniture, miscellaneous manufacturing, motor
vehicles, paper, railroad)
No to use EPA estimates
Knox County
Consumer & Commercial (adhesives/sealants, auto
aftermarket, coatings, FIFRA, household, personal care,
misc. products, marine)
No to use EPA estimates
MS
Surface Coating (aircraft, auto refinishing, electronic, factory
wood, industrial maintenance, appliances, machinery,
marine, metal cans, metal furniture, miscellaneous
manufacturing, motor vehicles, other special purpose,
paper, traffic markings, wood furniture)
All covered in point inventory
NV
Surface Coating (marine)
Do not have this type of source
NH
Surface Coating (large appliances)
Do not have this type of source
NJ
Surface Coating (wood furniture)
Do not have this type of source
NJ
Consumer & Commercial (adhesives/sealants, auto
aftermarket, coatings, FIFRA, household, personal care,
misc. products), Surface Coating (auto refinishing)
No to use EPA estimates
OH
Surface Coating (architectural coatings)
No to use EPA estimates
OK
Consumer & Commercial (adhesives/sealants, auto
aftermarket, coatings, FIFRA, household, personal care,
misc. products), Surface Coatings (architectural coatings,
auto refinishing, factory wood, industrial maintenance,
metal cans, metal furniture, special purpose coatings, paper,
traffic markings, wood furniture)
No to use EPA estimates
PR
Ag Pesticide, Surface Coating (metal cans, metal furniture,
paper, railroad, architectural coatings)
Do not have this type of source
4-250
-------
S/L/T
Solvent category(s)
Reason to not include in NP
Inventory
Rl
Dry Cleaning
All covered in point inventory
Rl
Surface Coating (motor vehicles)
Do not have this type of source
SC
Surface Coating (auto refinishing, industrial maintenance,
traffic markings)
No to use EPA estimates
Washoe
County
Surface Coating (factory finished wood, industrial
maintenance coatings, metal furniture, special purpose,
railroad)
No to use EPA estimates
Wl
Consumer & Commercial (adhesives/sealants, auto
aftermarket, coatings, FIFRAZ, household, personal care,
miscellaneous products), Surface Coating (architectural
coatings)
No to use EPA estimates
WY
Surface Coating (metal can)
Do not have this type of source
4,23.3.2 Known Issues in the 2014vl NE/ and2017 NE! considerations
The Solvent Tool developers realized that when they updated the HAP speciation factors, they used the
incorrect codes for two of the HAP pollutants from traffic markings. They accidentally used the code for methyl
isobutyl ketone when they should have used toluene, and further, they used the code for toluene when they
should have used xylenes. This was corrected in the version of the tool, used and posted for, 2014v2 NEI.
Another issue noted by Virginia concerns traffic marking and was corrected for in the 2014v2 NEI.
Suggested Improvements for the Solvents Tool for the 2017 NEI (from the NOMAD Committee)
HAP point inventory subtraction, even if the S/L/T doesn't provide HAPs
Standardize the sort of counties/SCCs between tools
Look into whether additional columns added to the excel sheets will foul up the import feature (as
Missouri noted)
Add a warning screen that point source subtraction should be on an "uncontrolled" basis
Provide a column in the Emission Factor which give the source of the factors
Provide a column in the Emission Factor table to show the relationship between VOC and HAP
Population of an emissions comment field, summarizing all mapped-point source SCCs
Reporting period comment field to update if updating population
4.23.4 References for solvents: all other solvents
1. Freedonia Group, The. 2013 Solvents to 2018. Study 2357
There are three sections in this documentation that discuss nonpoint inventory Waste Disposal. This section
discusses Open Burning; the next section discusses Publicly-Owned Treatment Works (POTWs), and the third
section was a broad discussion of nonpoint non-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.
4-251
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4.24.1 Source category description
This sector 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.
4.24.2 Sources of data
Table 4-148 shows, for open burning, the nonpoint SCCs in the 2014 NEI as well as SCCs that the EPA estimates.
The SCC level 3 and 4 SCC descriptions are also provided. The SCC level 1 and 2 descriptions are "Waste Disposal,
Treatment, and Recovery; Open Burning" for all SCCs.
Table 4-148: Open Burning SCCs with 2014 NEI emissions
EPA
Estimate?
SCC
Description
Y
2610000100
All Categories; Yard Waste - Leaf Species Unspecified
2610000300
All Categories; Yard Waste - Weed Species Unspecified (including
Grass)
Y
2610000400
All Categories; Yard Waste - Brush Species Unspecified
Y
2610000500
All Categories; Land Clearing Debris (use 28-10-005-000 for Logging
Debris Burning)
Y
2610030000
Residential; Household Waste (use 26-10-000-xxx for Yard Wastes)
The agencies listed in Table 4-149 submitted VOC emissions for open burning; agencies not listed used EPA
estimates for these sources.
Table 4-149: Percentage of Open Burning NOx, PM2.5 and VOC emissions submitted by reporting agency
Region
Agency
S/L/T
SCC
Description
NOx
PM2.5
VOC
Vermont Department of
Residential; Household
1
Environmental Conservation
State
2610030000
Waste
100
100
New Jersey Department of
All Categories; Yard Waste
2
Environment Protection
State
2610000100
- Leaf Species Unspecified
100
100
100
All Categories; Yard Waste
New Jersey Department of
- Brush Species
2
Environment Protection
State
2610000400
Unspecified
100
100
100
New Jersey Department of
Residential; Household
2
Environment Protection
State
2610030000
Waste
100
100
100
Delaware Department of Natural
Resources and Environmental
All Categories; Yard Waste
3
Control
State
2610000100
- Leaf Species Unspecified
100
100
100
Delaware Department of Natural
All Categories; Yard Waste
Resources and Environmental
- Brush Species
3
Control
State
2610000400
Unspecified
100
100
100
Delaware Department of Natural
Resources and Environmental
All Categories; Land
3
Control
State
2610000500
Clearing Debris
100
100
100
Delaware Department of Natural
Resources and Environmental
Residential; Household
3
Control
State
2610030000
Waste
100
100
100
4-252
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Region
Agency
S/L/T
see
Description
NOx
PMz.5
voc
3
Maryland Department of the
Environment
State
2610000100
All Categories; Yard Waste
- Leaf Species Unspecified
100
100
100
3
Maryland Department of the
Environment
State
2610000400
All Categories; Yard Waste
- Brush Species
Unspecified
100
100
100
3
Maryland Department of the
Environment
State
2610000500
All Categories; Land
Clearing Debris
100
100
100
3
Maryland Department of the
Environment
State
2610030000
Residential; Household
Waste
100
100
100
4
Georgia Department of Natural
Resources
State
2610000500
All Categories; Land
Clearing Debris
100
100
100
4
North Carolina Department of
Environment and Natural
Resources
State
2610000100
All Categories; Yard Waste
- Leaf Species Unspecified
100
100
100
4
North Carolina Department of
Environment and Natural
Resources
State
2610000400
All Categories; Yard Waste
- Brush Species
Unspecified
100
100
100
4
North Carolina Department of
Environment and Natural
Resources
State
2610000500
All Categories; Land
Clearing Debris
100
100
100
4
North Carolina Department of
Environment and Natural
Resources
State
2610030000
Residential; Household
Waste
100
100
100
5
Illinois Environmental Protection
Agency
State
2610000100
All Categories; Yard Waste
- Leaf Species Unspecified
100
100
100
5
Illinois Environmental Protection
Agency
State
2610000400
All Categories; Yard Waste
- Brush Species
Unspecified
100
100
100
5
Illinois Environmental Protection
Agency
State
2610000500
All Categories; Land
Clearing Debris
100
100
100
5
Illinois Environmental Protection
Agency
State
2610030000
Residential; Household
Waste
100
100
100
5
Minnesota Pollution Control
Agency
State
2610030000
Residential; Household
Waste
65
91
6
Texas Commission on
Environmental Quality
State
2610000100
All Categories; Yard Waste
- Leaf Species Unspecified
100
100
100
6
Texas Commission on
Environmental Quality
State
2610000400
All Categories; Yard Waste
- Brush Species
Unspecified
100
100
100
6
Texas Commission on
Environmental Quality
State
2610030000
Residential; Household
Waste
100
100
100
7
Sac and Fox Nation of Missouri in
Kansas and Nebraska Reservation
Tribe
2610000300
All Categories; Yard Waste
- Weed Species
Unspecified (incl Grass)
100
7
Sac and Fox Nation of Missouri in
Kansas and Nebraska Reservation
Tribe
2610030000
Residential; Household
Waste
100
100
100
4-253
-------
Region
Agency
S/L/T
see
Description
NOx
PMz.5
voc
8
Northern Cheyenne Tribe
Tribe
2610000100
All Categories; Yard Waste
- Leaf Species Unspecified
100
100
8
Northern Cheyenne Tribe
Tribe
2610000300
All Categories; Yard Waste
- Weed Species
Unspecified (incl Grass)
100
100
8
Northern Cheyenne Tribe
Tribe
2610000400
All Categories; Yard Waste
- Brush Species
Unspecified
100
100
8
Northern Cheyenne Tribe
Tribe
2610030000
Residential; Household
Waste
100
100
100
8
Utah Division of Air Quality
State
2610000100
All Categories; Yard Waste
- Leaf Species Unspecified
100
100
100
8
Utah Division of Air Quality
State
2610000400
All Categories; Yard Waste
- Brush Species
Unspecified
100
100
100
8
Utah Division of Air Quality
State
2610030000
Residential; Household
Waste
100
100
100
9
California Air Resources Board
State
2610000300
All Categories; Yard Waste
- Weed Species
Unspecified (incl Grass)
100
100
100
9
Maricopa County Air Quality
Department
Local
2610000500
All Categories; Land
Clearing Debris
100
100
100
9
Morongo Band of Cahuilla
Mission Indians of the Morongo
Reservation, California
Tribe
2610030000
Residential; Household
Waste
100
100
100
9
Washoe County Health District
Local
2610030000
Residential; Household
Waste
100
100
100
10
Coeur d'Alene Tribe
Tribe
2610000100
All Categories; Yard Waste
- Leaf Species Unspecified
100
100
100
10
Coeur d'Alene Tribe
Tribe
2610000300
All Categories; Yard Waste
- Weed Species
Unspecified (incl Grass)
100
100
100
10
Coeur d'Alene Tribe
Tribe
2610000400
All Categories; Yard Waste
- Brush Species
Unspecified
100
100
100
10
Coeur d'Alene Tribe
Tribe
2610030000
Residential; Household
Waste
100
100
100
10
Idaho Department of
Environmental Quality
State
2610000100
All Categories; Yard Waste
- Leaf Species Unspecified
100
100
100
10
Idaho Department of
Environmental Quality
State
2610000300
All Categories; Yard Waste
- Weed Species
Unspecified (incl Grass)
100
100
100
4-254
-------
Region
Agency
S/L/T
see
Description
NOx
PMz.5
voc
All Categories; Yard Waste
Idaho Department of
- Brush Species
10
Environmental Quality
State
2610000400
Unspecified
100
100
100
Idaho Department of
Residential; Household
10
Environmental Quality
State
2610030000
Waste
100
100
100
All Categories; Yard Waste
10
Kootenai Tribe of Idaho
Tribe
2610000100
- Leaf Species Unspecified
100
100
100
All Categories; Yard Waste
- Weed Species
10
Kootenai Tribe of Idaho
Tribe
2610000300
Unspecified (incl Grass)
100
100
100
All Categories; Yard Waste
- Brush Species
10
Kootenai Tribe of Idaho
Tribe
2610000400
Unspecified
100
100
100
Residential; Household
10
Kootenai Tribe of Idaho
Tribe
2610030000
Waste
100
100
100
All Categories; Yard Waste
10
Nez Perce Tribe
Tribe
2610000100
- Leaf Species Unspecified
100
100
100
All Categories; Yard Waste
- Weed Species
10
Nez Perce Tribe
Tribe
2610000300
Unspecified (incl Grass)
100
100
100
All Categories; Yard Waste
- Brush Species
10
Nez Perce Tribe
Tribe
2610000400
Unspecified
100
100
100
Residential; Household
10
Nez Perce Tribe
Tribe
2610030000
Waste
100
100
100
Shoshone-Bannock Tribes of the
All Categories; Yard Waste
10
Fort Hall Reservation of Idaho
Tribe
2610000100
- Leaf Species Unspecified
100
100
100
All Categories; Yard Waste
Shoshone-Bannock Tribes of the
- Weed Species
10
Fort Hall Reservation of Idaho
Tribe
2610000300
Unspecified (incl Grass)
100
100
100
All Categories; Yard Waste
Shoshone-Bannock Tribes of the
- Brush Species
10
Fort Hall Reservation of Idaho
Tribe
2610000400
Unspecified
100
100
100
Shoshone-Bannock Tribes of the
Residential; Household
10
Fort Hall Reservation of Idaho
Tribe
2610030000
Waste
100
100
100
Washington State Department of
All Categories; Yard Waste
10
Ecology
State
2610000100
- Leaf Species Unspecified
100
100
100
All Categories; Yard Waste
Washington State Department of
- Weed Species
10
Ecology
State
2610000300
Unspecified (incl Grass)
100
100
100
All Categories; Yard Waste
Washington State Department of
- Brush Species
10
Ecology
State
2610000400
Unspecified
100
100
100
Washington State Department of
Residential; Household
10
Ecology
State
2610030000
Waste
100
100
100
4-255
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4,24,3 EPA-developed emissions 'for open burning! updated for 2014v2 NEI
4,24.3,1 Land Charing Debris
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. Criteria air pollutant (CAP) and
hazardous air pollutant (HAP) emission estimates from open burning of land clearing debris are a function of the
amount of material or fuel subject to burning per year.
The amount of material burned was estimated using the county-level total number of acres disturbed by
residential, non-residential, and road construction. County-level weighted loading factors were applied to the
total number of construction acres to convert acres to tons of available fuel.
Acres Disturbed from Residential Construction
The US Census Bureau has 2014 data for Housing Starts - New Privately Owned Housing Units Started [ref 1,
ref2], which provides regional level housing starts based on the groupings of 1 unit, 2-4 units, 5 or more units. A
consultation with the Census Bureau in 2002 gave a breakdown of approximately 1/3 of the housing starts being
for 2-unit structures, and 2/3 being for 3 and 4-unit structures. The 2-4-unit category was divided into 2-units,
and 3-4 units based on this ratio. To determine the number of structures for each grouping, the 1-unit category
was divided by 1, the 2-unit category was divided by 2, and the 3-4-unit category was divided by 3.5. The 5 or
more unit category may be made up of more than one structure. New Privately Owned Housing Units Authorized
Unadjusted Units [ref 3] gives a conversion factor to determine the ratio of structures to units in the 5 or more
unit category. For example, if a county has one 40-unit apartment building, the ratio would be 40/1. If there are
5 different 8-unit buildings in the same project, the ratio would be 40/5. Structures started by category are then
calculated at a regional level. The table Annual Housing Units Authorized by Building Permit [ref 4] has 2014 data
at the county level to allocate regional housing starts to the county level. This results in county level housing
starts by number of units. Table 4-150 shows the surface areas assumed disturbed for each unit type.
Table 4-150: Surface Acres Disturbed per Unit Type
Unit Type
Surface Acres Disturbed
1-Unit
1/4 acre/structure
2-Unit
1/3 acre/structure
Apartment
1/2 acre/structure
The 3-4 unit and 5 or more unit categories were considered to be apartments. Multiplication of housing starts to
surface acres disturbed results in total number of acres disturbed for each unit category.
Acres Disturbed from Non-Residential Construction
Annual Value of Construction Put in Place in the U.S [ref 5] has the 2014 National Value of Non-residential
construction. The national value of non-residential construction put in place (in millions of dollars) was allocated
to counties using county-level non-residential construction (NAICS Code 2362) employment data obtained from
County Business Patterns (CBP). [ref 6], Because some county employment data are withheld due to privacy
concerns, the following procedure was adopted:
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1. State totals for the known county level employees were subtracted from the number of employees
reported in the state level version of CBP. This results in the total number of withheld employees in the
state.
2. A starting estimate of the midpoint of the range code was used (so for instance in the 1-19 employees
range, an estimate of 10 employees would be used) and a state total of the withheld counties was
computed.
3. A ratio of estimated employees (Step 2) to withheld employees (Step 1) was then used to adjust the
county level estimates up or down so the state total of adjusted guesses should match state total of
withheld employees (Step 1).
In 1999 a figure of 2 acres/$106 was developed. The Bureau of Labor Statistics Producer Price Index [ref 7] lists
costs of the construction industry from 1999-2014.
2014 acres per $106 = 1999 acres per $106 x (1999 PPI / 2014 PPI)
= 2 acres/$106 (132.9/ 232.1)
= 1.145 acres per $106
Acres Disturbed by Road Construction
The Federal Highway Administration provides data on spending by state in several different categories of road
construction and maintenance in Highway Statistics, Section IV- Highway Finance, Table SF-12A, State Highway
Agency Capital Outlay [ref 8] for year 2014. For this SCC, the following sets of data (or columns) are used: New
Construction, Relocation, Added Capacity, Major Widening, and Minor Widening. Each of these data sets is also
differentiated according to the following six roadway classifications:
1. Interstate, urban
2. Interstate, rural
3. Other arterial, urban
4. Other arterial, rural
5. Collectors, urban
6. Collectors, rural
The State expenditure data are then converted to new miles of road constructed using $/mile conversions
obtained from the Florida Department of Transportation (FLDOT) in 2014 [ref 9], A conversion of $6.8
million/mile is applied to the urban interstate expenditures and a conversion of $3.8 million/mile is applied to
the rural interstate expenditures. For expenditures on other urban arterial and collectors, a conversion factor of
$4.1 million/mile is applied, which corresponds to all other projects. For expenditures on other rural arterial and
collectors, a conversion factor of $2.1 million/mile is applied, which corresponds to all other projects.
The new miles of road constructed are used to estimate the acreage disturbed due to road construction. The
total area disturbed in each state is calculated by converting the new miles of road constructed to acres using an
acres disturbed/mile conversion factor for each road type as given in Table 4-151.
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Table 4-151: Spending per Mile and Acres Disturbed per Mile by Hig
iway 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)
County-level building permits data are used to allocate the state-level acres disturbed by road construction to
the county [ref 10]. A ratio of the number of building starts in each county to the total number of building starts
in each state was applied to the state-level acres disturbed to estimate the total number of acres disturbed by
road construction in each county.
Converting Acres Disturbed to Tons of Land Clearing Debris Burned
Version 2 of the Biogenic Emissions Landuse Database (BELD2) within EPA's Biogenic Emission Inventory System
(BEIS) [ref 11] was used to identify the acres of hardwoods, softwoods, and grasses in each county. Table 4-152
presents the average fuel loading factors by vegetation type. The average loading factors for slash hardwood
and slash softwood were 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 12]. Weighted average county-level loading factors
were 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.
Table 4-152: Fuel Loading Factors (tons/acres) by Vegetation Type
Vegetation Type
Unadjusted Average Fuel Loading Factor
Adjusted Average Fuel Loading Factor
Hardwood
66
99
Softwood
38
57
Grass
4.5
Not Applicable
The total acres disturbed by all construction types was calculated by summing the acres disturbed from
residential, non-residential, and road construction. The county-level total acres disturbed were then multiplied
by the weighted average loading factor to derive tons of land clearing debris.
Because BELD2 does not contain data on Alaska and Hawaii, the acres of hardwoods, softwoods, and grasses in
each county was 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 13], These percentages were multiplied by
the county area (acres), from the U.S. Census Bureau [ref 14].
Controls for land clearing debris burning are generally in the form of a ban on open burning of waste in each
municipality or county. Counties that were more than 80% urban, by land area, determined by the 2010 U.S.
Census data [ref 14], were assumed not to practice any open burning. Therefore, criteria pollutant and HAP
emissions from open burning of land clearing debris are zero in these counties. In addition, the State of Colorado
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implemented a state-wide ban on open burning. Emissions from open burning of land clearing debris in all
Colorado counties were assumed to be zero.
Activity data and emissions for Clark County, NV, were zeroed out based on data from the Clark County
Department of Air Quality that indicates that there is very little vegetation to be cleared in that county and that
there is an effective burn ban in place.
Emission factors for CAPs were developed by EPA in consultation with ERTAC, and are based primarily on the AP-
42 report [ref 15, ref 16]. The PM2.5 to PM10 emission factor ratio for brush burning (0.7709) was multiplied by
the PM10 emission factors for land clearing debris burning to develop PM2.5 emission factors. Emission factors
for HAPs are from an EPA Control Technology Center report [ref 17].
There were several significant changes from the 2011 inventory. This included the utilization of a newer
information source to determine the spending per mile and acres disturbed per mile for each roadway type. The
previous inventory calculations were based on information from the NC DOT from 2000, while this inventory
instead uses data obtained from the FL DOT in 2014.
Additionally, the 80% urban no-burn threshold was based on the ratio of urban to rural population in the 2011
NEI methodology. These ratios were replaced with ratios based on urban and rural land area. In both cases, the
data are from the 2010 census.
For the 2014v2 NEI, we updated the following activity data over what was used, or missing, in the 2014vl NEI:
Added S02 emissions using an emissions factor from burning brush in yard waste
Updated Federal Highway Administration spending data from year 2013 to year 2014
Updated County and State Business Patterns data from year 2013 to year 2014
Updated Puerto Rico and Virgin Islands Populations to year 2014
Removed emissions for locality as dictated by new data presented to EPA
4,243,2 Residential Household Waste
Open burning of residential municipal solid waste (MSW) is the purposeful burning of MSW in outdoor areas.
Criteria air pollutant (CAP) and hazardous air pollutant (HAP) emission estimates for MSW burning are a function
of the amount of waste burned per year.
The amount of household MSW burned was estimated using data from EPA's report Advancing Sustainable
Materials Management: 2013 Fact Sheet [ref 18,ref 19]. 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 2013.
According to the 2010 version of the EPA report, residential waste generation accounts for 55-65 percent of the
total waste from the residential and commercial sectors [ref 20], For the calculation of per capita household
waste subject to burning, the median value of 60 percent was assumed. This information was used to calculate a
daily estimate of combustible per capita household waste of 1.91 Ibs/person/day, and a daily estimate of
combustible plus non-combustible per capita household waste of 2.62 Ibs/person/day. Burning of yard waste is
included in SCC 2610000100 and SCC 2610000400; therefore, it is not part of residential MSW. Approximately 24
percent of the rural population that may open burn does so [ref 21].
Since open burning is generally not practiced in urban areas, only the rural and like rural population in each
county was assumed to practice open burning. Like rural population is defined as the population of urbanized
areas and urban clusters with population densities' equal to or less than the maximum rural population density
value for all counties. The ratio of rural and like rural to total population was obtained from 2010 U.S. Census
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data [ref 14]. This ratio was then multiplied by the 2014 U.S. Census Bureau estimate [ref 22] of the population
in each county to obtain the county-level rural population for 2014. The county-level rural population was then
multiplied by the per capita household waste subject to burning to determine the amount of rural household
MSW generated in each county in 2014.
Controls for residential MSW burning are generally in the form of a ban on open burning of waste in each
municipality or county. However, literature suggests that burn bans are not 100% effective. It was 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 [ref 21]. For counties that have burn bans, the assumption was applied by multiplying 0.25 by the
number of persons estimated to practice open burning. For example, the State of Colorado implemented a
state-wide ban on open burning, and this method was employed for all counties in Colorado.
Emission factors for CAPs were developed by the U.S. Environmental Protection Agency (EPA) in consultation
with the Eastern Regional Technical Advisory Committee and based primarily on the AP-42 report [ref 15, ref 16,
ref 23], Emission factors for HAPs are from an EPA Control Technology Center report and an EPA Office of
Research and Development report [ref 23, ref 17]. Emissions from dioxin congeners are also available, but these
are excluded from the NEI due to their uncertainty.
For the 2014v2 NEI, we updated the following assumptions over what was used in the 2014vl NEI:
The computation of "like rural" population in each county
We now assume that counties with burn bans will still have 25% of people likely to still burn despite the
bans
4.24.33 Yard Waste-Leaf and Brush Debris
Open burning of yard waste is the purposeful burning of leaf and brush species in outdoor areas. Criteria air
pollutant (CAP) and hazardous air pollutant (HAP) emission estimates for leaf and brush waste burning are a
function of the amount of waste burned per year.
The amount of household MSW burned was estimated using data from EPA's Advancing Sustainable Materials
Management: 2013 Fact Sheet [ref 18, ref 19]. 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 2013. According
to the 2010 version of the EPA report, residential waste generation accounts for 55-65 percent of the total
waste from the residential and commercial sectors [ref 20], For the calculation of per capita yard waste subject
to burning, the median value of 60 percent was assumed. This information was used to calculate a daily estimate
of the per capita yard waste of 0.36 Ibs/person/day. Of the total amount of yard waste generated, the yard
waste composition was assumed to be 25 percent leaves, 25 percent brush, and 50 percent grass by weight [ref
24],
Open burning of grass clippings is not typically practiced by homeowners, and therefore, only estimates for leaf
burning and brush burning were developed. Approximately 25 to 32 percent of all waste that is subject to open
burning is actually burned [ref 24], A median value of 28 percent is assumed to be burned in all counties in the
United States.
The per capita estimate was then multiplied by the 2014 population in each county that is expected to burn
waste. Since open burning is generally not practiced in urban areas, only the rural population and "like rural"
population in each county was assumed to practice open burning. Like rural population is defined as the
population of urbanized areas and urban clusters with population densities equal to or less than the maximum
rural population density value for all counties. The ratio of rural and like rural to total population was obtained
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from 2010 U.S. Census data [ref 14]. This ratio was then multiplied by the 2014 U.S. Census Bureau estimate [ref
22] of the population in each county to obtain the county-level rural population for 2014.
The percentage of forested acres from Version 2 of BELD2 within BEIS was used to adjust for variations in
vegetation [ref 11], The percentage of forested acres per county (including rural forest and urban forest) was
then determined. To better account for the native vegetation that would likely be occurring in the residential
yards of farming States, agricultural land acreage was subtracted before calculating the percentage of forested
acres. Table 4-153 presents the ranges that were used to adjust the amount of yard waste that is assumed to be
generated per county. 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.
Table 4-153: Adjustment for Percentage of Forested Acres
Percent Forested Acres per County
Adjustment for Yard Waste Generated
< 10%
0% generated
>= 10% to < 50%
50% generated
>=50%
100% generated
Controls for residential MSW 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 was 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 was applied by multiplying .25 by the number
of persons estimated to practice open burning. For example, the State of Colorado implemented a state-wide
ban on open burning, and this method was employed for all counties in Colorado.
Counties that were more than 80% urban, by land area, determined by the 2010 U.S. Census data, were
assumed not to practice any open burning. Therefore, criteria pollutant and HAP emissions from residential yard
waste burning are zero in these counties. In addition, the State of Colorado implemented a state-wide ban on
open burning. Emissions from open burning of residential yard waste in all Colorado counties were assumed to
be zero.
Emission factors for CAPs were developed by the EPA in consultation with the Eastern Regional Technical
Advisory Committee [ref 15]. For leaf burning, emission factors for PM2.5 were calculated by multiplying the
PM 10 leaf burning emission factors by the PM2.5 to PM10 emission factor ratio for brush burning (0.7709).
Emission factors for HAPs are from an EPA Control Technology Center report. Emissions from dioxin congeners
are also available, but these are excluded from the NEI due to their uncertainty.
For the 2014v2 NEI, we updated the following assumptions over what was used in the 2014vl NEI:
The computation of "like rural" population in each county
We now assume that counties with burn bans will still have 25% of people likely to still burn despite the
bans
4,24,4 References for open burning
1. U.S. Census Bureau. New Privately Owned Housing Units Started, Annual Data.
2. U.S. Census Bureau. New Privately Owned Housing Units Started in the United States by Purpose and
Design.
3. U.S. Census Bureau. Table 2au. New Privately Owned Housing Units Authorized Unadjusted Units for
Regions. Divisions, and States. Annual 2014.
4. Annual Housing Units Authorized by Building Permits CO2014A, purchased from US Department of
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Census
5. U.S. Census Bureau. Construction Spending: Historical Value Put in Place.
6. U.S. Census Bureau, 2014 County Business Patterns, accessed August 2016.
7. Bureau of Labor Statistics, Producer Price Index. Table BMNR.
8. Federal Highway Administration, 2014 Highway Spending, accessed August 2016.
9. Florida DOT Cost Per Mile Models for 2014.
10. 2014 Building Permits Survey data from US Census "BPS01".
11. Pierce, T., C. Geron, L. Bender, R. Dennis, G. Tonnesen, A. Guenther, 1998. Influence of increased
isoprene emissions on regional ozone modeling. Journal of Geophysical Research, v. 103, no. D19,
25611-25629.
12. Ward, D.E., C.C. Hardy, D.V. Sandberg, and T.E. Reinhardt. "Mitigation of Prescribed Fire Atmospheric
Pollution through Increased Utilization of Hardwoods, Piled Residues, and Long-Needled Conifers." Final
Report. USDA Forest Service, Pacific Northwest Research Station, Fire and Air Resource Management.
1989.
13. U.S. Geological Survey (USGS). 2015. National Land Cover Database 2011 (NLCD 2011).
14. U.S. Census Bureau, Decennial Censuses. 2010 Census: Summary File 1.
15. Huntley, Roy, U.S. Environmental Protection Agency, Eastern Regional Technical Advisory Committee
(ERTAC), Excel file: state_comparison_ERTAC_SS_version7.2_23nov2009.xls.
16. U.S. 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 2.5
Open Burning. Research Triangle Park, NC. October 1992.
17. U.S. Environmental Protection Agency, Evaluation of Emissions from the Open Burning of Household
Waste in Barrels: Volume 1. Technical Report. EPA-600/R-97-134a, Control Technology Center.
November 1997.
18. U.S. Environmental Protection Agency, Advancing Sustainable Materials: 2013 Fact Sheet. Table 1.
"Generation, Recovery and Discards of Materials in MSW, 2013(in millions of tons and percent of
generation of each material)," February 2014, accessed July 2016.
19. U.S. Environmental Protection Agency, Advancing Sustainable Materials: 2013 Fact Sheet. Table 2.
"Generation, Recovery and Discards of Materials in MSW, 2013(in millions of tons and percent of
generation of each product)," February 2014, accessed July 2016.
20. U.S. Environmental Protection Agency, Municipal Solid Waste Generation. Recycling, and Disposal in the
United States: Facts and Figures for 2010Fact Sheet, p. 4, December 2011, accessed April 2012.
21. Environment Canada. "Household Garbage Disposal and Burning." Prepared by Environics Research
Group. March 2001.
22. U.S. Census Bureau. Annual Estimates of the Resident Population: April 1. 2010 to July 1. 2014. 2014
Populations Estimates, accessed December 2015.
23. U.S. Environmental Protection Agency, Office of Research and Development. "Emissions of organic air
toxics from open burning: a comprehensive review." EPA-600/R-02-076. October 2002.
24. Two Rivers Regional Council of Public Officials and Patrick Engineering, Inc. "Emission Characteristics of Burn
Barrels," prepared for the U.S. Environmental Protection Agency, Region V. June 1994.
4,25,1 Source category description
This sector, 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. The SCC that EPA uses for estimated nonpoint
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emissions is 2630020000; the SCC description is "Waste Disposal, Treatment, and Recovery; Wastewater
Treatment; Public Owned; Total Processed".
4,25,2 Sources of data
The agencies listed in Table 4-154 submitted VOC emissions for POTWs; agencies not listed used EPA estimates.
Table 4-154: Percentage of nonpoint POTW VOC and PM2.5 emissions submittec
by reporting agency
Region
Agency
S/L/T
VOC
PM2.5
1
Maine Department of Environmental Protection
State
100
1
Vermont Department of Environmental Conservation
State
100
2
New York State Department of Environmental Conservation
State
100
3
Maryland Department of the Environment
State
100
4
Knox County Department of Air Quality Management
Local
100
4
Metro Public Health of Nashville/Davidson County
Local
100
5
Illinois Environmental Protection Agency
State
100
5
Michigan Department of Environmental Quality
State
100
5
Ohio Environmental Protection Agency
State
100
6
Texas Commission on Environmental Quality
State
100
8
Utah Division of Air Quality
State
100
9
Clark County Department of Air Quality and Environmental Management
Local
100
9
Washoe County Health District
Local
100
10
Coeur d'Alene Tribe
Tribe
100
10
Idaho Department of Environmental Quality
State
100
10
Kootenai Tribe of Idaho
Tribe
100
10
Nez Perce Tribe
Tribe
100
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
100
10
Washington State Department of Ecology
State
100
4,25,3 EPA-deveioped emissions for nonpoint POTWs: no changes for 2014v2 NEi
The general approach to calculating 2014 emissions for POTWs is to multiply the 2012 flow rate by the emission
factors for VOCs, ammonia, and 53 HAPs. The emissions are allocated to the county level using methods
described below. More details including references to the documentation can be found in the document
"2014_POTW_nonpoint_emissions_23mar2016.zip" on the 2014vl Supplemental Data FTP site.
4.253.1 Activity dsia
The EPA Clean Watersheds Needs Survey reports the existing flow rate in 2012 for POTWs as 28,296 million
gallons per day (MMGD). The nationwide flow rate includes Puerto Rico and the U.S. Virgin Islands. Flow rates
were allocated to each county by the county proportion of the U.S. population.
It should be noted that the derivation of the nationwide flow rate for the 2014 nonpoint POTW emissions
inventory differs from the derivation of the nationwide flow rate used to estimate year 2011 nonpoint POTW
emissions. The methodology for the 2011 nonpoint POTW emissions inventory used a projected 2010
nationwide flow rate of 39,780 MMGD that was available from an EPA report. The projection was based on
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Needs Surveys from 1984 to 1996. The 2012 nationwide flow rate used for the 2014 inventory is not a
projection, but a value directly reported by the 2012 Needs Survey.
4,253,2 Emission Factors
The ammonia emission factor was obtained from a report to EPA, while the VOC emission factor was based on a
TriTAC study. Emission factors for the 52 HAPs were derived using 1996 area source emissions estimates that
were provided by ESD and the 1996 nationwide flow rate. These HAP emission factors were then multiplied by
the 2008 to 2002 VOC emission factor ratio (0.85/9.9) to obtain the final HAP emission factors applied in the
2014 inventory.
Emissions calcula tion
Emissions per county for a given pollutant were computed by multiplying the pollutant emission factor
(lb/million gallon) by the county flow rate (million gallons). This process was repeated for all counties in the U.S.,
Puerto Rico, and the U.S. Virgin Islands, and the result was pollutant specific nonpoint POTW county-level
emissions.
The next step was to determine whether there are POTW point source emissions and to subtract those point
source emissions from the total nonpoint emissions. The EIS was queried for POTW point sources, and the
resulting output contained facility-level HAP and CAP emissions in fifteen states. The fifteen states were: CA, CO,
FL, IA, IL, MA, MD, Ml, MN, NC, NJ, NY, PA, TN, and TX. The facility-level point source emissions were summed to
county and pollutant, and then were subtracted from the nonpoint POTW emissions by county and pollutant.
For counties where the point source emissions were larger than the corresponding nonpoint emissions, the
nonpoint emissions were set to zero.
m Crerr
4.26.1 Source category description
This sector includes non-mercury emissions from human cremation; the mercury component of human
cremation utilizes a slightly different methodology described in Section 4.2.6.. The SCC for human cremation is
2810060100; the SCC description is "Miscellaneous Area Sources: Other Combustion: Cremation: Humans".
4.26.2 Sources of data
The agencies listed in Table 4-155 submitted at least NOX nonpoint emissions for human cremation; agencies
not listed used EPA estimates. Values under 100 indicate that EPA estimates were used for some counties.
Table 4-155: Percentage of nonpoint human cremation NOx emissions submitted by reporting agency
Region
Agency
S/L/T
NOx
1
Maine Department of Environmental Protection
State
100
2
New York State Department of Environmental Conservation
State
100
3
Maryland Department of the Environment
State
69
3
Virginia Department of Environmental Quality
State
100
4
Knox County Department of Air Quality Management
Local
100
5
Ohio Environmental Protection Agency
State
100
7
Missouri Department of Natural Resources
State
25
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Region
Agency
S/L/T
NOx
9
Maricopa County Air Quality Department
Local
100
9
Washoe County Health District
Local
100
10
Coeur d' Alene Tribe
Tribe
100
10
Idaho Department of Environmental Quality
State
100
10
Nez Perce Tribe
Tribe
100
10
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribe
100
4.26.3 EPA-developed emissions 'for human cremation: new 'for 2014v2 NE!
EPA estimates were accidentally not included in the 2014vl NEI; however, nationally, EPA only estimated 1,249
tons of NOX in 2014. For the 2014v2 NEI, we started with the 2011v2 NEI methodology and updated the
following to create year 2014 estimates:
population data to year 2014 using data from the U.S. Census [ref 1]
number of state-level deaths to year 2014 [ref 2]
percentage of bodies cremated in the U.S. updated to year 2014 [ref 3]
emissions factor for chromium III and chromium VI from the EPA SPECIATE database [ref 4] and update
to Cadmium emission factor
The 2014 EPA changes to the 2011 activity data are summarized in the spreadsheet "2014 modifications" in the
workbook "human_cremation_281006011_emissions_modified_for_2014v2.xlsx" in the file
"2014v2_Human_cremation_EPA.zip" on the 2014v2 Supplemental Data FTP site. More details on the activity
data, emission factors and calculations are included in the workbook.
4.26.4 References 'for human cremation
1. U.S. Census Bureau, Population Division. Annual Estimates of the Resident Population: April 1, 2010 to
July 1, 2016, Year 2014 data, accessed March 2017.
2. Kochanek KD, Sherry, MA, Xu J, Murphy SL, Tejada-Vera, B, "Number of deaths, death rates, and age-
adjusted death rates for major causes of death: United States, each state, Puerto Rico, Virgin Islands,
Guam, American Samoa, and Northern Marianas, 2014" Table 19. Deaths, death rates, and age-adjusted
death rates: United States, and each state and territory, final 2014, National Vital Statistics Reports, vol
65 no 4. p.21, Hyattsville, MD: National Center for Health Statistics, June 30, 2016.
3. Cremation Association of North America, Industry Statistical Information. Annual Statistics Report,
accessed March 2017.
4. U.S. Environmental Protection Agency. 2016. SPECIATE Database v4.5.
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5 Nonroad Equipmen ;sel, Gasoline and Other
Although "nonroad" is used to refer to all transportation 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. Section 4 includes
descriptions of the nonpoint portion of locomotives and the commercial marine vessel emissions.
; . ฆ \X \
This section deals specifically with emissions processes calculated by the EPA's NONROAD2008 model [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 Virgin Islands.
Nonroad mobile source emissions are generated by a diverse collection of equipment from lawn mowers to
locomotive support. 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 NONROAD2008 estimates GSE, the results are not used in the NEI. NEI GSE estimates are
instead calculated via the Federal Aviation Administration's Emission and Dispersion Modeling System
(EDMS).
**Although NONROAD2008 estimates oil field 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.
NONROAD2008, the latest public release of EPA's NONROAD Model, estimates daily emissions for total
hydrocarbons (THC), nitrogen oxides (NOx), carbon monoxide (CO), carbon dioxide (C02), particulate matter 10
microns and less (PMio), and sulfur dioxide (S02), as well as calculating fuel consumption. MOVES2014a (version
20151201) [ref 3] uses ratios from some of these emissions to calculate emissions for particulate matter 2.5
5-1
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microns and less (PM25), methane, ammonia (NH3), 4 more aggregate hydrocarbon groups (NMHC, NMOG, TOG,
and VOC), 14 hazardous air pollutants (HAPs), 17 dioxin/furan congeners, 32 polycyclic aromatic hydrocarbons,
and 6 metals. For a complete list of these pollutants, see Table 5-2. All of the input and activity data required to
run MOVES-NONROAD are contained within the Motor Vehicle Emissions Simulator (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.
MOVES-NONROAD is the new way of running NONROAD2008. Nonroad emissions for previous NEIs have been
produced by running NONROAD2008 for all U.S. counties using the National Mobile Inventory Model (NMIM)
[ref 4], Now superseded by MOVES, NMIM was the EPA's consolidated mobile emissions estimation system that
allowed the EPA to produce nonroad mobile emissions in a consistent and automated way for the entire
country. NMIM was basically a user interface for NONROAD2008. It took data from the NMIM County Database
(NCD) and used it to write input files for NONROAD2008 (called "opt" files), executed NONROAD2008, picked up
the output, and put it into a MySQL database. It also generated additional pollutant estimates as ratios to those
produced by NONROAD. As part of the EPA's continuing efforts to upgrade the NONROAD model, it was moved
from NMIM into MOVES2014. Although MOVES is primarily a user interface for NONROAD, just as NMIM was,
data are now stored in standard MySQL tables, the same as for the onroad sources, which are much easier to
access and update than the original NONROAD ASCII files. The transfer to MOVES was tested by verifying that
the NONROAD model and MOVES2014 produced identical results for the species produced by stand-alone
NONROAD (THC, CO, C02, NOx, S02, PM10, and fuel consumption). MOVES-NONROAD also includes improved
estimation of HAPs, which are creating by post-processing NONROAD2008 output. MOVES2014-NONROAD
produced THC, NOx, PM10, PM25, CO, S02, NH3, C02, and fuel consumption. MOVES2014a added the ability to
calculate all of the species mentioned above and listed in Table 5-2. At the same time, it based these calculations
on much newer and better data than had been used in NMIM [refs 5,6],
Table 5-2: Pollutants produced by MOVES-NONROAD for 2014 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
5-2
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Pollutant ID
Pollutant Name
Pollutant ID
Pollutant Name
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
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
Three states provided 2014v2 updates to their nonroad inputs: Delaware Department of Natural Resources and
Environmental Control, Georgia Department of Natural Resources and North Carolina Department of Air Quality
(NCDAQ). See Section 5.5 below for additional details.
5.4 Default MOVES code and database
The nonroad runs were executed using MOVES2014a, the most current publicly-released version of MOVES
available at the time. The code version for this release is moves20151201. A modification was made to one Java
class (ApplicationRunner) to allow MOVES to run NONROAD2008 on a Linux distributed processing system. This
change had no effect on the modeling output and will be included in all future versions of MOVES. The code with
the change is referred to as moves20151201a. The default database is movesdb20151201, the same one
released publically with MOVES2014a. When NONROAD2008 was incorporated into MOVES, the default data
built into NONROAD2008 was converted to MySQL tables and included in movesdb20151201.
5-3
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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 2014 NEI. To facilitate the transition from NMIM to MOVES for 2014vl, the EPA also accepted NONROAD
inputs in the old format of the NCD. The NCD inputs were converted to CDBs in MOVES format. 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. The fuels were those in the MOVES default database for MOVES2014a, movesdb20151201 (see also
Section 6.8.2.3). The meteorological data were provided by OAQPS and were derived from a Weather Research
and Forecasting Model (WRF) [ref 7] run.
Table 5-3 shows the selection hierarchy for the nonroad data category. The MOVES default database for
MOVES2014a (movesdb20151201) 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.
Table 5-3: Selection hierarchy for the Nonroad Mobile data category
Priority
Dataset
Notes
1
S/L/T-supplied emissions
Several tribes submitted NONROAD emissions. California
used their own model, OFFROAD.
(Texas ran NONROAD2008 using their data. These data are
present in EIS, but were not selected for the 2014NEI. Texas
also supplied NCD inputs which were converted and used in
MOVESNONROAD)
2
S/L/T-supplied input data
from 2014 NEI process
3
S/L/T-supplied input data
from previous NEIs
4
Movesdb20151201
All data from Movesdb20151201
The EPA asked S/L/T agencies to provide model inputs (CDBs or NCDs) instead of emissions for 2014. However,
some agencies also submitted nonroad emissions. Table 5-4 shows the S/L/T agencies that submitted nonroad
emissions and/or activity data for the 2014 NEI via the EIS Gateway. The NCDs all went into the database
NCD20160513_nei2014vl, which was used to run NMIM to compare with the MOVES-NONROAD runs. Most of
the state- and county-specific data in this NCD was converted to CDBs for the MOVES run. The
NCD20160513_nei2014vl database also contained data which had been submitted by S/L/Ts previously,
primarily for the 2011 NEI. This S/L/T data were also converted to CDBs for the MOVES-NONROAD runs. Table
5-4 shows all the states for which either CDBs were submitted or created from the NCD20160513_nei2014vl
database. The latter includes those submitted for 2014 and those submitted in earlier NEI processes.
If a CDB was supplied as part of the 2014 NEI process, earlier data from NCD20160513_nei2014vl that was
converted to CDBs was not used. States for which one or more CDBs were created from
NCD20160513_nei2014vl and for which NONROAD files were included are listed in Table 5-5. Only Texas
submitted valid NCD data for 2014. Florida submitted a nonroad NCD, but it contained only onroad data. Several
allocation files were submitted for Pima County (Arizona) that assigned all of the state's activity to that county,
so it was not used. The user-supplied allocation files incorrectly have set the state total surrogates the same as
Pima. Since equipment activity and population was not supplied with the Pima submission, the result is that the
whole state population is assigned to Pima County. Our solution to this problem was to use the MOVES results
5-4
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for Arizona without rerunning. Although there is probably some good information in the Pima submission,
timing prohibited its use. Their submission is for 2014, whereas the default data that was included was for 2002,
so changing state totals to match 2002 would not be correct and therefore it was not used.
Table 5-4: Nonroad Mobile S/L/T submissions for the 2014 NEI**
Agency Organization
State
2014 Nonroad Emissions
California Air Resources Board
CA
Coeur d'Alene Tribe
ID
Kootenai Tribe of Idaho
ID
Metro Public Health of Nashville/Davidson County
TN
Nez Perce Tribe
ID
Northern Cheyenne Tribe
MT
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
ID
Texas Commission on Environmental Quality
TX
2014 Nonroad CDB
Illinois Environmental Protection Agency
IL
New York State Department of Environmental Conservation
NY
North Carolina Department of Environment and Natural Resources
NC
Washington State Department of Ecology
WA
Washoe County Health District
NV
2014 Nonroad NCD+
Texas Commission on Environmental Quality
TX
* Florida submitted a Nonroad NCD, but it contained only onroad data. Several allocation
files were submitted for Pima County that assigned all of the state's activity to that
county, so it was not used.
**California and tribal emissions submittals are included in the 2014v2 NEI. All other
state/counties used MOVES estimates from EPA model runs, with submitted input.
Table 5-5: States for which one or more CDBs were created from NCD20160513_nei2014vl and for which
NONROAD files were included
Name
FIPS
Pop
Act
Alo*
Grw
Sea
Colorado
08
1
Connecticut
09
X
Delaware
10
X
17
Georgia
13
10
Illinois
17
X
X
2
X
X
Indiana
18
X
X
2
X
X
Iowa
19
X
2
X
Maryland
24
X
Michigan
26
X
X
2
X
X
Minnesota
27
X
3
X
X
Nevada
32
10
New Hampshire
33
X
5-5
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Name
FIPS
Pop
Act
Alo*
Grw
Sea
New Jersey
34
X
New York
36
1
North Carolina
37
X
Ohio
39
X
X
2
X
X
Rhode Island
44
X
Texas
48
X
X
19
X
X
Washington
53
2
Wisconsin
55
X
X
2
X
X
* "Alo" is allocation of equipment population from state to county, based on one of 19 possible surrogates. The number
in the "Alo" column is the number of files, one for each surrogate. "Act" is activity in hours per year. "Pop" is equipment
population. "Grw" is growth of population from a number of base years. MOVES will use the correct surrogate and
closest base year. "Sea" (seasonality) is temporal allocation of activity to different seasons. In MOVES, this allocation is
bv month and state. "FIPS" is the 2-digit Federal Information Processing Standard state code.
The 320 submitted CDBs used for the MOVES-NONROAD run are collected together in NonroadCDBs.zip in the
NRSupplementalData folder. CDBs were used only for states/counties that submitted CDBs or NCDs, including
submissions prior to 2014. The rest were run using the MOVES default database, which does not require CDBs. A
list of all 3,224 U.S. counties and their corresponding CDBs, if any, is available in
nonroad_counties_nei2014vl_FinalList.xlsx. The contents of the NRSupplementalData folder are listed in Table
5-6 and are available on the 2014vl Supplemental nonroad mobile data FTP site.
Table 5-6: Contents of the Nonroad Mobile supplemental folder
File or Folder
Description
2014vl_NonroadCDBs.zip
Submitted CDBs used to run MOVES-NONROAD.
NonroadCDBs_2014v2_DE_GA_NC_20170824.zip
Submitted CDBs used to run MOVES-NONROAD
updated for 2014v2
2014vl nonroad counties nei2014vl FinalList.xlsx
List of all counties and their CDBs.
2014vl_zonemonthhour2014.zip
Zonemonthhour table (meteorology data).
2014vl_NonroadRunspecs.zip
2014v2_Nonroad_Runspecs_DE_GA_NC.zip
Runspecs for all counties.
2014vl_NmimToMovesConversion.zip
Folder containing two subfolders corresponding to
the two steps of the NMIM to MOVES conversion.
2014vl_NCD20160513_nei2014vl_nrextfiles.zip
The NONROAD files from the external files folder of
NCD20160513 nei2014vl.
2014vl_postprocess_nrnei_20160523.jar
Post-processing scripts for MOVES runs.
2014vl_EICtoEPA_SCCmapping.xlsx
File mapping California emission inventory codes
(ElCs)to EPA SCCs.
5.6 Conversion of NMIM NCDs to MOVES CDBs
Conversion from NMIM NCDs to MOVES CDBs was done in two steps. First, the data packets in the NCD ASCII
files were converted into intermediate MySQL tables with the same column headings. Second, the resulting
MySQL tables were converted into MOVES tables and stored in the correct CDB.
The state- and county-specific custom data files that NONROAD2008 uses are text files that are stored in a folder
called ExternalFiles within the NCD. It is these text files that the S/L/T agencies submit. The files are activity
(hours per year by SCC and horsepower category), allocation files (allocation of equipment population from
5-6
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state to county level), growth, population, and seasonality (how equipment usage varies with season). These
data files may be found in the NCD20160513_nei2014vl_nrextfiles folder in the online NRSupplementalData
folder. All the NRSupplemental data and scripts are listed in Table 5-6. The NR external files contain one or more
"packets" of data. Table 5-7 shows the data files and the packets they contain. These packets were converted by
a Python program (ProcessNRTxtFiles.py) into Intermediate MySQL tables, as shown in Table 5-8.
Table 5-7: Conversion of NONROAD data files to MOVES tables
NR
NONROAD
Intermediate MySQL
data file
data file packet
tables
MOVES tables
Pop
Population
Population*
nrbaseyearequippopulation
Act
Activity
Activity*
nrsourceusetype
Alo
Indicators
Allocation*
nrstatesurrogate
Grw
Indicators
Growthindicators
Growth
Growth*
Nrgrowthindex
Scrappage
Growthscrappage
Alternate scrappage
Growthaltscrappage
Sea
Regions
Region
Monthly
Monthlyadjfactors*
nrmonthallocation
Daily
Dailyadjfactors*
nrdayallocation
These are the intermediate MySQL tables that were converted into MOVES tables by the scripts listed in Table 5-8.
Table 5-8: MySQL scripts to convert intermediate to MOVES tables
Script
Comment
GenerateMovesNr_activity.sql
If pop is provided
GenerateMovesNR_activity_nopop.sql
If pop is not provided
GenerateMovesNr_allocation.sql
GenerateMovesNr_dailyadjfactors.sql
GenerateMovesNr_growth.sql
Converts only the "Growth" packet
GenerateMovesNr_monthlyadjfactors.sql
GenerateMovesNr_population.sql
The intention was to convert all intermediate tables to MOVES tables, but time and resource limitations
restricted us to the most important tables. Only Texas submitted NCDs for 2014.
5.7 MOVES runs
In the online NRSupplementalData folder, the Excelฎ file nonroad_counties_nei2014vl_FinalList.xlsx lists all
3,224 counties and their corresponding CDBs. If no CDB was listed for a county, that county was run with the
MOVES default database for MOVES2014a (movesdb20151201). The NRSupplemental Data is listed in Table 5-6.
There were 16 unique state CDBs and 304 unique county CDBs from five states. We constructed the MOVES
runspecs so that if a state CDB existed, it was included first, followed by a county CDB. There was only one
county with both state and county CDBs. There were 16+304 = 320 CDBs used in the full MOVES-NONROAD run.
The CDBs that were used are in nei2014vl_CDBs in the online NRSupplementatalData folder
MOVES was run for each county, using two runspecs: one for diesel equipment, which included horsepower
output, and one for all other fuels without horsepower output. All the runspecs are in the NonroadRunspecs
folder in the online NRSupplementatalData 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 output. The scripts that performed these processes are in
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postprocess_nrnei_20160523.jar in the online NRSupplementatalData folder. The MOVES runs created monthly
inventories for every U.S. county and post-processing was also done on these monthly outputs.
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. Modes for exhaust and evaporative were removed from pollutant names and separated out into the
emis_type data field in flat file 2010 files that were then loaded into EIS.
4. Pollutants produced by MOVES but not accepted in the NEI were removed (e.g., ethanol, NONHAPTOG,
and total hydrocarbons).
5. Five speciated PM2.5 species were added based on speciation profiles (i.e., elemental carbon, organic
carbon, nitrate, sulfate and other PM2.5). See Section 2.2.5.
6. DIESEL-PM10 and DIESEL-PM25 were added by copying the PM10 and PM2.5 pollutants (respectively) as
DIESEL-PM pollutants for all diesel SCCs. See Section 2.2.5.
7. Airport ground support equipment emissions were removed.
8. Bedford City, Virginia emissions were combined with Bedford County, Virginia emissions.
9. Incorporated California-submitted nonroad emissions.
For comparison purposes, NMIM was run using the NCD20160513_nei2014vl database. We checked to ensure
that no error messages were created during the runs for each geographical area. Furthermore, NMIM generates
the same number of output records for each RunlD-FIPSCountylD-FIPSStatelD-Year-Month combination.
Therefore, we confirmed that each output table included the correct number of records for this combination of
fields. As with the MOVES runs, the NMIM runs were post-processed to produce monthly inventories for every
U.S. county in SMOKE-ready format.
For the 2014vl NEI, we compared the MOVES-NONROAD results to the NMIM results. S02 was valuable as a
comparison species because nearly zero differences in results were expected if activity inputs were the same.
Thirty-nine states showed S02 differences less than 0.01 percent. Table 5-9 shows the fourteen states that had
S02 differences greater than 0.01 percent.
Table 5-9: States with absolute percent difference (MOVES-NMIM) > 0.01% for SO2 exhaust*
State FIPS
MOVES-NMIM
2014
Code
State
% diff
CDB
NCD
36
New York
-29.743%
X
4
Arizona
-29.684%
53
Washington
-24.787%
X
37
North Carolina
-10.399%
X
17
Illinois
-9.956%
X
39
Ohio
7.696%
grw
2
Alaska
6.248%
27
Minnesota
5.819%
grw
5-8
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State FIPS
MOVES - NMIM
2014
Code
State
% diff
CDB
NCD
55
Wisconsin
5.145%
grw
26
Michigan
1.637%
grw
24
Maryland
1.376%
pop
48
Texas
-0.040%
grw
18
Indiana
-0.039%
grw
33
New Hampshire
-0.019%
pop
* Sorted in order of decreasing absolute difference
We investigated the reasons behind the larger observed S02 differences. The large differences for states that
submitted CDBs (-10 percent to -30 percent, in Illinois, New York, North Carolina, and Washington) are
attributed to those submittals. Submitted CDBs were expected to contain different data than
NCD20160513_nei2014vl. Some states with differences of 2 percent to 8 percent (Michigan, Minnesota, Ohio,
and Wisconsin) are attributed to NCD growth files that were only partially converted to CDBs. There are four
data packets in the NONROAD growth file. Due to resource limitations, a conversion script was written for only
one of them (see Section 5.6). The region packet in the seasonality file did not require conversion because in
MOVES, every state has its own seasonality, as defined in the nrmonthallocation table. The growth packets that
were not converted for 2014NEIvl were converted for the 2014NEIv2.
A NCD for Pima County, Arizona, was submitted, which was used to produce the NMIM results. However, this
NCD included allocation files with Pima County allocation surrogates set equal to the state total. The result was
that all of the state's emissions were assigned to Pima county, while reasonable allocations were assigned to
other counties. Because of this error, the MOVES run was performed without using data from the submittal. As a
result, the differences between the MOVES-NONROAD and NMIM-based runs were nearly 30 percent.
In Alaska, between 2007 and 2008, three counties were eliminated and five new ones formed. The eliminated
county FIPS codes were 02201, 02232, and 02280. The newly formed county FIPS codes were 02105, 02195,
02230, 02195, and 02198. The NMIM counties were correct, but produced zero emissions for the five new
counties. Therefore, MOVES was 6 percent higher. The 24 Alaska counties for which NMIM produced S02
emissions agreed exactly with MOVES.
Comparing MOVES and NMIM for states with good agreement in SO2 (Table 5-10) demonstrates differences due
to effects other than activity. Differences in VOC and HAPs were expected since they are both post-processed
from THC, and MOVES uses newer emission factor data than NMIM [ref 8], The HAPs generally increased
dramatically, which is reflected in the overall increase shown in the table (the sum of 52 species). NOx increased
slightly and CO decreased slightly due to a change in the conversion factor of ethanol volume percent to oxygen
weight percent from 0.3448 in NMIM [ref 9] to 0.3653 in MOVES. The direction and small size of these changes
was expected. Overall, the changes in criteria air pollutants (CAPs) are small, and provide confidence that the
transfer of NONROAD2008 from NMIM to MOVES was successful. We have examined the large changes in HAPs
individually and confirmed that these changes agree with our updates.
In addition to the comparison of NMIM and MOVES, county plots of NOx, S02, and VOC for of 2014 MOVES were
compared and reviewed, along with comparison plots and spreadsheets of 2014 NMIM versus 2011NEIv2.
County plots of MOVES nonroad activity hours and population along with plots of NOx emissions per unit activity
by nonroad category (agriculture, industrial, lawn and garden, etc.) were also developed and reviewed.
5-9
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Table 5-10: Comparison of NMIM to MOVES-NONROAD*
Pollutant Code
Pollutant Name
Percent Difference
CO
CO
-1.28%
CO 2
CO 2
0.98%
NH3
NH3
0.00%
NOX
NOx
0.34%
PM10-PRI
PM10-PRI
0.00%
PM25-PRI
PM25-PRI
0.00%
SO 2
S02
0.00%
VOC
VOC
-1.68%
200
Mercury Elemental Gaseous
23.64%
201
Mercury Divalent Gaseous
14.58%
202
Mercury Particulate
2.02%
50000
Formaldehyde
103.17%
50328
Benzo(a)pyrene
1122.47%
53703
Dibenzo(a,h)anthracene
1383.69%
56553
Benz(a)anthracene
612.21%
71432
Benzene
26.70%
75070
Acetaldehyde
63.19%
83329
Acenaphthene
675.35%
85018
Phenanthrene
702.97%
86737
Fluorene
494.41%
91203
Naphthalene
300.49%
100414
Ethyl Benzene
61.64%
100425
Styrene
182.84%
106990
1,3-Butadiene
61.39%
107028
Acrolein
306.56%
108883
Toluene
32.78%
110543
Hexane
31.90%
120127
Anthracene
419.28%
123386
Propionaldehyde
49.94%
129000
Pyrene
269.93%
191242
Benzo(g,h,i)perylene
841.48%
193395
lndeno(l,2,3,c,d)pyrene
1065.88%
205992
Benzo(b)fluoranthene
928.25%
206440
Fluoranthene
273.50%
207089
Benzo(k)fluoranthene
989.73%
208968
Acenaphthylene
574.35%
218019
Chrysene
777.29%
540841
2,2,4-Trimethylpentane
149.54%
1330207
Xylene
5.59%
1746016
2,3,7,8-Tetrachlorodibenzo-p-Dioxin
-96.58%
3268879
Octachlorodibenzo-p-dioxin
-100.00%
7439965
Manganese Compounds
-0.13%
5-10
-------
Pollutant Code
Pollutant Name
Percent Difference
7440020
Nickel Compounds
-4.50%
7440382
Arsenic Compounds
-84.51%
18540299
Chromium 6+
-97.18%
19408743
1,2,3,7,8,9-Hexachlorodibenzo-p-Dioxin
-99.93%
35822469
1,2,3,4,6,7,8-Heptachlorodibenzo-p-Dioxin
-99.99%
39001020
Octachlorodibenzofuran
-100.00%
39227286
1,2,3,4,7,8-Hexachlorodibenzo-p-Dioxin
-99.88%
40321764
1,2,3,7,8-Pentachlorodibenzo-p-Dioxin
-98.45%
51207319
2,3,7,8-Tetrachlorodibenzofuran
-99.01%
55673897
1,2,3,4,7,8,9-Heptachlorodibenzofuran
-99.98%
57117314
2,3,4,7,8-Pentachlorodibenzofuran
-98.72%
57117416
1,2,3,7,8-Pentachlorodibenzofuran
-99.76%
57117449
1,2,3,6,7,8-Hexachlorodibenzofuran
-99.67%
57653857
1,2,3,6,7,8-Hexachlorodibenzo-p-Dioxin
-99.31%
60851345
2,3,4,6,7,8-Hexachlorodibenzofuran
-99.81%
67562394
1,2,3,4,6,7,8-Heptachlorodibenzofuran
-99.94%
70648269
1,2,3,4,7,8-Hexachlorodibenzofuran
-99.83%
72918219
1,2,3,7,8,9-Hexachlorodibenzofuran
-99.77%
* Differences from the 39 states for which SO2 was within 0.01%. Positive values mean MOVES is larger.
5.10
California submitted nonroad emissions for EPA's use in the NEl, and we used these emissions directly. Prior to
preparing the emissions for submission, the California Air Resources Board (CARB) updated the mapping of their
EICs to EPA's detailed SCCs used for emissions modeling that include the off network, on-network, and brake
and tire wear categories. CARB provided their HAP and CAP emissions by county using these more detailed SCCs.
The updated version of the mapping is posted with the supplemental data in the Excel file
2014vl_EICtoEPA_SCCmapping.xlsx. 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
PM 10 using the same approach as for other states (see Section 5.7) and copied the PM2.5 and PM10 to DIESEL-PM
"pollutants" for all diesel SCCs.
5.11 References for nonroad mobile
1. U.S. Environmental Protection Agency, NONROAD2008a Model, NONROAD Model (Nonroad Engines,
Equipment, and Vehicles. Office of Transportation and Air Quality, April 2009.
2. California Air Resources Board, Mobile Source Emissions Inventory - Off-Road Gasoline Motor Vehicles.
3. U.S. Environmental Protection Agency, MOVES2Q14a: Latest Version of Motor Vehicle Emission Simulator
(MOVES).
4. U.S. Environmental Protection Agency, National Mobile Inventory Model (NMIM).
5. U.S. Environmental Protection Agency, Speciation Profiles and Toxic Emission Factors for Non-road Engines,
EPA-420-R-15-019, November 2015.
6. Lawrence Reichle, Rich Cook, Catherine Yanca, and Darrell Sonntag. Development of Organic Gas Exhaust
Speciation Profiles for Nonroad Spark Ignition and Compression Ignition Engines and Equipment. 2015.
Journal of the Air and Waste Management Association, 65: 1185-1193.
5-11
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7. National Center for Atmospheric Research, Mesoscale and Microscale Meteorology Division, Weather
Research and Forecasting Model. Boulder CO, June 2008, NCAR/TN-475+STR, A Description of the
Advanced Research WRF Version 3.
8. U.S. Environmental Protection Agency, Speciation of Total Organic Gas and Particulate Matter Emissions
from On-road Vehicles in MOVES2Q14. EPA-420-R-15-022, November 2015.
9. U.S. Environmental Protection Agency, EPA's National Inventory Model (NMIIV ' isolidated Emissions
Modeling System for MOBILES and NONROAD, EPA420-R-05-024, December 2005.
5-12
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Dnroad 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 2014 NEI vl 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.
The EPA made several substantial improvements in default data for the 2014v2 NEI that include new 2014
vehicle populations and fleet characteristics, as well as new default vehicle speed distributions and relative
hourly and day type VMT distributions at the local level from the CRC A-100 study [ref 1], In addition, other
changes in 2014v2 included new CDB submittals (526 databases) and minor changes to the representative
county groups based on the new 2014 age distribution data. Also new for the 2014v2, age distributions for
representative county CDBs now reflect a population-weighted average of the member county age distributions.
The major changes in default data are described in detail below, and the CDBs and representative county groups
are discussed in Sections 6.5 6.8.2.1, respectively.
6,2,1 New 2014 Vehicle Populations arid Fleet Characteristics
The 2014v2 NEI uses updated 2014 vehicle populations, source type age distributions, and fuel type fractions
created from data purchased from IHS Markit (IHS). Under contract with EPA, ERG purchased the mid-year 2014
vehicle registration database from IHS, which contains a county-level summary of all registered vehicles in the
US. IHS retrieves its information from each state DMV, compiles it in-house, decodes the vehicle identification
numbers (VINs), and assigns each record a MOVES source type code. The database IHS provided did not include
VINs or identify individual vehicles, but rather provided a summary count of the population in each county by
parameters including make, model, model year, gross vehicle weight (GVW) class, and other fields. In total,
there were over 44 million records in the IHS database that identified 277 million vehicles registered in the US as
of July 1, 2014. ERG analyzed and made minor changes to the database, then wrote a program to calculate
county-level age distributions and fuel type fractions, to populate the MOVES CDB tables
'SourceTypeAgeDistribution' and 'AVFT (i.e., Alternative Vehicle and Fuel Technologies), respectively [ref 2],
EPA used the IHS vehicle population data to create EPA default vehicle population data to be used for areas of
the country for which source type populations were not provided in 2014 CDB submittals. In areas for which
vehicle population data was provided, 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
information from the IHS data. In this way, car and light trucks are treated more consistently from state to state
than in previous NEIs.
6-1
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6.2.2 New Vehicle Speeds and VMT Distributions
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. The CRC A-100
study concluded several interesting findings, including higher speeds for heavy trucks than light and medium
vehicles in peak hours clear differences in speed profiles and VMT patterns across vehicle category and city. A
sensitivity case study conducted as part of the CRC work showed an emissions impact of up to 9%, 5% and 14%
in VOC, NOX, and PM2.5 respectively, for an annual average day with MOVES Inventory Mode. The emissions
sensitivity showed much larger changes at the hourly level. Previous NEIs have used nationwide averages for
these inputs in many counties, and v2 uses the MOVES-formatted tables 'AvgSpeedDistribution/
'HourVMTFraction/ and 'DayVMTFraction' in all CDBs except for New York, because they specifically requested
that their submittal data be used instead of data from vehicle telematics. Several states reviewed the CRC A-100
data products specific to their counties and requested that EPA use the new data over their local data. In
addition to updating CDBs, the 2014v2 NEI also incorporates SMOKE input files based on the CRC A-100 hourly
speeds and diurnal and weekly VMT temporal profiles.
, c-
The EPA calculated the onroad emissions for 2014 for all states using the most recently released version of
MOVES, MOVES2Q14a (code version: 20151201, database version: movesdb20161117). The sources of MOVES
input data vary by area, representing a mix of local data, past NEI data, and some MOVES defaults. More state
and local agencies than ever before submitted local input data for MOVES. The S/L/T agencies that submitted
data for 2014 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 the
lower 48 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 areas outside the continental U.S. (AK, HI, Virgin Islands, and Puerto Rico),
the EPA ran MOVES in Inventory Mode (rather than with SMOKE-MOVES) to directly estimate emissions12. 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 2014 favored local input data over default information. For areas
that did not submit a MOVES CDB for this NEI, the EPA projected the corresponding CDB from the most recent
version (2011 v2) from year 2011 to 2014. In all projected CDBs, the EPA updated the older 2011 vehicle miles
travelled (VMT), population, and hoteling activity with new activity specific to 2014, described in Section 6.8.4.
California is the only state agency for which an onroad emissions submittal was used in the 2014vl NEI and
these emissions are unchanged in the 2014v2 NEI. California uses their own emission model, EMFAC, which uses
EICs instead of SCCs. 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 detail is needed for modeling but not for the NEI, because the NEI uses
simplified/more aggregated SCCs than used in modeling. This code mapping is provided in
"2014vl_EICtoEPA_SCCmapping.xlxs." California provided their CAP and HAP emissions by county using EPA
SCCs after applying the mapping. The California-submitted emissions data provided CAPs (including NH3), HAPs
and methane, but did not include C02. Therefore, the 2014 NEI includes MOVES-based C02 estimates for
California. There was one vehicle/fuel type combination included in the CARB data, gas intercity buses (first 6
12 More information on the Inventory Mode for MOVES2014a is available in the MOVES2Q14a User Guide.
6-2
-------
digits of the SCC = 220141), that did not match to an SCC generated using MOVES, so we mapped it to gasoline
single unit short-haul trucks (220152).
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)).
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.
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 6-1 lists each table in a 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 6-1: MOVES2014a 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
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)
6-3
-------
Table Name
Description of Content
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 1,815 CDBs for the 2014vl NEI and they submitted one new CDB and
updated 525 of the 2014vl submittals, for a total of 1,816 CDBs for use in 2014v2. Previously for the 2011 NEI,
the number of submitted CDBs totaled 1,363 and 1,426 in vl and v2, respectively. Agencies submitting data
through the EIS, provided completed CDBs (i.e., each table populated), along with documentation and a
submission checklist indicating which of the CDB tables contained local data. Table 6-2 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 ('HPMSVtypeYear' and 'SourceTypeYear' tables, respectively) were the most commonly provided
local data types.
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 CDBs were
developed by EPA based on the 2011 v2 NEI.
6-4
-------
Table 6-2: Number of counties with submitted data, by state anc
State/County
avft
avgspeeddistribution
countyyear
dayvmtfraction
emissionratebyage
fuelformulation
fuelsupply
fuelusagefraction
hotellingactivitydistribution
hotellinghours
hourvmtfraction
hpmsvtypeyear
imcoverage
monthvmtfraction
onroadretrofit
roadtype
roadtypedistribution
sourcetypeagedistribution
sourcetypedayvmt
sourcetypeyear
starts
startsperday
Alaska
29
29
i
29
29
29
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
8
8
8
8
8
8
Delaware
3
3
3
3
3
3
3
3
3
3
3
3
District of Columbia
1
1
1
1
1
1
1
1
1
Georgia
24
13
159
24
159
13
159
159
159
159
20
Idaho
44
44
44
44
44
44
2
44
44
44
44
Illinois
102
102
102
102
102
102
102
102
11
102
102
102
102
Kentucky (Jefferson)
1
1
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
24
Massachusetts
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
Michigan
7
83
83
7
7
7
83
83
83
83
83
83
83
Minnesota
87
87
87
87
87
87
87
87
Missouri
115
115
5
115
115
Nevada (Clark)
1
1
1
1
1
1
1
Nevada (Washoe)
1
1
1
1
1
1
1
1
1
1
New Hampshire
10
10
10
10
10
New Jersey
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
New Mexico
1
1
1
New York
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
key MOVES CDB table
6-5
-------
CT)
CT)
Total
Wisconsin
West Virginia
Washington
Virginia
Vermont
Utah
Texas
Tennessee
Tennessee (Memphis)
Tennessee (Knox)
Tennessee
(Chattanooga)
South Carolina
Rhode Island
Pennsylvania
Oregon
Ohio
North Carolina
State/County
o
ฆ1^
NJ
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00
00
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ID
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00
00
NJ
O
avgspeeddistribution
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00
NJ
Ui
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CT>
00
00
countyyear
1064
OJ
ID
NJ
Ui
ฆ1^
Ui
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00
00
dayvmtfraction
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CT>
emissionratebyage
un
CT>
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fuelformulation
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00
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H*
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ฆ1^
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ฆ1^
CT>
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fuelsupply
W
H*
CT>
00
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fuelusagefraction
W
CT>
W
NJ
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ฆ1^
hotellingactivitydistribution
W
VO
VO
NJ
Ui
ฆ1^
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CT>
hotellinghours
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Un
OJ
<*D
NJ
Ui
ฆ1^
<*D
Ui
CT>
00
00
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O
hourvmtfraction
1816
NJ
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ID
OJ
ฆ1^
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Ui
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00
O
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hpmsvtypeyear
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00
W
U1
O
ฆ1^
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ID
NJ
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ฆ1^
Ui
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CT>
ฆ1^
00
imcoverage
1267
Ui
Ui
OJ
H*
OJ
ฆ1^
ฆ1^
NJ
Ui
ฆ1^
Ui
CT>
00
00
monthvmtfraction
W
vo
onroadretrofit
NJ
00
W
NJ
ID
NJ
Ui
ฆ1^
roadtype
1618
NJ
Ui
Ui
OJ
ID
OJ
ฆ1^
NJ
ID
NJ
Ui
ฆ1^
ID
ฆ1^
CT>
Ui
CT>
00
00
O
O
roadtypedistribution
1754
NJ
Ui
Ui
OJ
ID
OJ
ฆ1^
ฆ1^
NJ
ID
NJ
Ui
ฆ1^
Ui
CT>
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CT>
00
00
1-^
o
o
sourcetypeagedistribution
sourcetypedayvmt
1813
NJ
Ui
Ui
OJ
ID
OJ
ฆ1^
ฆ1^
NJ
ID
NJ
Ui
ฆ1^
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ฆ1^
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00
O
O
sourcetypeyear
N>
NJ
NJ
Ui
ฆ1^
H*
starts
W
00
H*
startsperday
-------
Figure 6-1: Counties for which agencies submitted local data for at least 1 CDB table are shown in dark blue
Washoe County, NV
Jefferson County, KY
Bernalillo County, NM
Pima County, AZ
Clark County, NV
Maricopa County
6.5.2 OA checks on MOVES CDB Tables
The EPA used two separate quality assurance scripts to scan submitted CDBs and flag potential data errors. The
scripts report the potential errors by compiling a list into a summary quality assurance 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 that caused the script to flag the particular table. EPA reviewed all of the 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 first quality assurance script is one that the EPA updates for each version of the NEI for which states are
asked to submit CDBs through the EIS. This script was designed to catch errors that would cause MOVES to fail
during a run. The second script was designed to catch unreasonable data values that wouldn't necessarily cause
MOVES to fail, but could cause it to produce unreasonable model outputs. Examples of suspected unreasonable
values include (a) a mix of vehicle type population or VMT that shows more heavy-duty (HD) vehicles or VMT
than shown for light-duty (LD), (b) age distributions that are skewed to older vehicles rather than newer, or (c)
atypical VMT temporal patterns such as higher VMT in winter than summer or higher VMT overnight than during
daytime.
6-7
-------
Nearly 90 percent of the submitted 1,815 CDBs in vl 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:
Missing age distributions for some HD source types (most commonly buses)
Age distribution for some source types not summing to 1 (e.g., 0.93 or 3.5)
Negative values in the Hoteling Activity Distribution table
Missing weekend (day type 2) activity across one or more CDB tables: VMT (via the
'SourceTypeDayVMT table), average speed distributions, hourly VMT fractions, and/or starts per day
Completely empty or missing source types in the Hour, Day, or Month VMT fractions
Old inspection and maintenance (l/M) programs included as active, but known to have previously ended
Incorrect year (e.g., 2013, but should be 2014) in the population table
Fleet mix too large for HD vehicles (e.g., combination truck population 100 times larger than that of
passenger cars)
All freeways in a state have zero ramps
Nearly 50 percent of the new submitted 526 CDBs for v2 required a correction in order for MOVES to be able to
use the database. The following problems were addressed:
Wrong year listed in one or more tables
Duplicate entries in the HPMSVtypeYear table
IMCoverage table covered gasoline but not flex-fuel vehicles
RoadType table structure not compatible with MOVES2014a
Expected VMT tables required for MOVES2014a (SourceTypeDayVMT, SourceTypeYearVMT, and
HPMSVtypeDay) were missing
The EPA resolved each of the above data problems by coordinating with state/local agencies individually. In
some cases, the agency preferred to submit a corrected CDB, which the EPA contractor 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 MOVES2014a to run. One state's table formatting
problems were so widespread that we rebuilt the states' databases using a template MOVES CDB and filled
them with the content from the submittal. We also removed the following unnecessary, excess data content
from several tables in several states' submissions:
2011 entries for vehicle population, age distribution, and year tables (presumably carried over from
2011 NEI, presented in addition to 2014 data).
Invalid input road types in the VoadType' CDB table including road types 6, 7, 8, 9 (associated with
separating ramps from freeways) and 100 (associated with the MOVES nonroad model) generated by
the County Data Manager template.
Tribal onroad emissions were submitted and used in the 2014vl NEI and these emissions are unchanged in the
2014v2 NEI. The submitting tribal agencies are listed in Table 6-3.
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Table 6-3: Tribes that Submitted Onroad Mobile Emissions Estimates for the 2014NEI
Coeur d'Alene Tribe
Kootenai Tribe of Idaho
Morongo Band of Cahuilla Mission Indians of the Morongo Reservation, California
Nez Perce Tribe
Northern Cheyenne Tribe
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
6 J EPA default MOVES inputs
6,7,1 Sources of default data by MOVES CDB fable
The EPA used 2014vl CDBs for counties where agencies did not submit them for 2014v2. The EPA developed
new 2014 estimates of VMT, vehicle population, and hoteling at the county- and SCC-level for use in the
subsequent SMOKE-MOVES processing step. In the CDBs, we used these v2 activity estimates for 2014 to
overwrite any default data. States and counties with CDBs that included 2014 EPA-generated activity and
projected CDBs are those indicated by light blue shading in Figure 6-1. Table 6-4 below lists the sources of
default information by MOVES CDB table. The spreadsheet
2014NEIV2_Plans_for_CDB_lnput_Data_07072017b.xls 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 in Table 6-4.
Table 6-4: Source of defaults for key data tables in MOVES CDBs
CDB Table
Default content for 2014v2 NEI
avft
2014 IHS data
avgspeeddistribution
CRC A-100 study
dayvmtfraction
CRC A-100 study
fuelformulation
Based on EPA estimates for each county from 2014 refinery data
fuelsupply
Based on EPA estimates for each county from 2014 refinery data
fuelusagefraction
MOVES2014a default E85 usage
hotellingactivitydistribution
MOVES2014a default APU vs. Main Engine fractions
hotellinghours
2014 EPA estimates of hoteling based on 2014 VMT
hourvmtfraction
CRC A-100 study
hpmsvtypeday
Empty by default
hpmsvtypeyear
Empty by default
imcoverage
2014 NEI vl
importstartsopmodedistribution
Empty by default
monthvmtfraction
2014 NEI vl
roadtype
2014 NEI vl
roadtypedistribution
EPA estimates based on FHWA
sourcetypeagedistribution
2014 IHS data
sourcetypedayvmt
Empty by default
sourcetypeyear
2014 IHS data, with EPA modification
sourcetypeyearvmt
2014 EPA estimates of VMT based on FHWA data and 2014 IHS data
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CDB Table
Default content for 2014v2 NEI
starts
Empty by default
startshourfraction
Empty by default
startsmonthadjust
Empty by default
startsperday
Empty by default
startssourcetypefraction
Empty by default
zonemonthhour
2014 meteorology data averaged by county
emissionratebyage
The 'emissionratebyage' tables for some counties were populated using
appropriate data described in the guidance for states adopting California
emission standards
Preparation of 'AVFT' and 'SourceTypeAgeDistribution' CDB Tables
As mentioned above in Section 6.2.1, national vehicle population data from IHS were used to derive updated
'SourceTypeAgeDistribution' and the alternative vehicle fuel type 'AVFT' tables in the CDBs. The IHS data
provided county-specific vehicle counts by source type, fuel type, and model year. From these data, two sets of
'SourceTypeAgeDistribution' and 'AVFT' tables were generated: one set with unique distributions calculated
independently for each county, and another set with distributions population-weighted over the 2014v2
representative county groups. The grouped tables were used in the representative CDBs seeded for running
MOVES in emission factor mode. The individual county tables were used in the full set of CDBs that are
unseeded and appropriate for running MOVES in inventory mode. Both sets of age distribution tables are
provided in .csv form with the 2014v2 NEI onroad supporting data (see Table 6-7). More discussion on database
seeding can be found in Sections 6.8.6 and 6.8.7.
The IHS data did not contain vehicle counts for every possible source type and county combination, so some gap
filling was necessary. Data for sourceTypelD 41 (Intercity Bus) were not reliably distinguishable from
sourceTypelD 42 (Transit Bus) in the IHS data, so we used a county-specific bus age distribution to represent
these two bus types for each county. Similarly, source types 52 and 53 (single unit trucks) could not be
distinguished, nor could source types 61 and 62. We also calculated national averages for the long-haul source
types 53 (Single Unit Long-haul Truck) and 62 (Combination Unit Long-haul Truck) because these vehicles tend to
operate regionally or nationally rather than in their county of registration. Missing countylD/sourceTypelD
combinations were filled using national averages for the sourceTypelD. In summary, the following averaging for
age distribution was performed:
Source type 53 (single unit long-haul) and 62 (combination long-haul) age distributions use the IHS
national average
All other source types (11,21,31,32,41,42,43,51,52,54,61) are population-weighted averaged over rep
county group
Source type 41 and 42 have the same age distribution for any given area (because IHS could not reliably
distinguish between Intercity vs. Transit Buses)
Some county groups had missing age distribution for a source type due to no registered vehicles. This
happened only for Refuse Trucks (51) and non-school buses (41/42). Where there were no registered
vehicles in a county group, the IHS national average age distribution for the source type was used.
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The MOVES 'AVFT' table defines the fraction of vehicles of a specific fuel type (e.g., gasoline, diesel ethanol-85,
electric) for a given source type and model year; the fuelEngFraction sums to one for each unique
sourceTypelD/modelYearlD combination. The 'AVFT'table fuel type fractions for each county were calculated in
a similar manner to the 'SourceTypeAgeDistribution' table: the population for each unique sourceTypelD,
modelYearlD, fuelTypelD, and engTechID combination was divided by the total population for that source type
and model year. While light-duty electric vehicles are included in the AVFT table, heavy-duty electric vehicles
were not because those combinations are not allowable in MOVES. In addition, any heavy duty E85 and CNG
fractions were re-mapped to gasoline vehicles.
For MOVES compatibility, the 'AVFT distributions for certain source type IDs (Intercity Bus and Combination
Unit Long-haul Truck) were set to 100% diesel even though other fuel types were present in the IHS data.
EPA's preference was to use the IHS-derived age distributions everywhere unless state agencies opted out. Four
states preferred to use their submitted data for the 'SourceTypeAgeDistribution' and/or 'AVFT tables submitted
for the NEI. Georgia, New Jersey, New York and Ohio CDBs retained the submitted 'SourceTypeAgeDistribution'
tables and New York retained its submitted 'AVFT tables. The only change to these four states' data was to
population-weight the distributions over v2 county groups.
After the 'AVFT tables were created as described above, a final gap filling step was performed to ensure that
each existing sourceTypelD and modelYearlD combination with data had listed all allowable fuelTypelDs for
MOVES (populated with zeros, rather than missing from the table), which prevents the model from
supplementing a CDB 'AVFT' distribution that already summed to 1 with model default values. Both the grouped
and county-specific age distribution tables are provided in .csv form with the 2014NEIv2 onroad supporting data
(see Table 6-7).
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
6-5 shows which states 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],
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Table 6-5: 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
6,8,1 EPA-developed onroad emissions data for the continental U.S.
For the 2014 NEI, the EPA estimated emissions for every county. For the continental U.S., the EPA used county-
specific inputs and programs that integrate inputs and outputs for the MOVES model with the SMOKE modeling
system (i.e., SMOKE-MOVES) to take advantage of the gridded hourly temperature information available from
meteorology modeling used for air quality modeling. This set of programs was developed by the EPA and also is
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
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 2014 NEI. The MOVES "RunSpec" files (that provide MOVES input
data for each representative county) are provided in the supplementary materials (see Table 6-7 for access
information).
The EPA used a different approach for states and territories outside the lower 48 states. For Alaska, the EPA ran
MOVES in Inventory Mode, during which MOVES computes the emissions instead of emission rates, for every
county and month, using county-specific inputs and meteorological data. For Hawaii, Puerto Rico and the Virgin
Islands, MOVES was run in Inventory Mode for the months of January and August, with the months of May
through September using the August emissions and the other months using January emissions. More
information is provided Section 6.8.10.
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SMOKE-MOVES tools are incorporated into recent versions of SMOKE and can be used with different versions of
the MOVES model. For the 2014 NEI vl, the EPA used the latest publicly-released version: MOVES2014a (version
20151201) [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 MOVES inputs needed only for the MOVES runs (see Section 6.6). For example, MOVES requires
county-specific information on age distributions and inspection-maintenance programs for each of the
representative counties.
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
6.8.11)
Some things to note about the 2014v2 NEI that are different from the 2011v2 NEI and 2014vl NEI are:
Manganese/7439965 now includes the brake and tire contribution, whereas in 2011v2 NEI, manganese
did not include brake and tire contributions.
Gasoline with 85 percent ethanol (E85) was tracked as a separate fuel in the 2014vl NEI, while in the
2011v2 NEI, it was combined with regular gasoline.
Five speciated PM2.5 species were added based on speciation profiles (i.e., elemental carbon, organic
carbon, nitrate, sulfate and other PM2.5). See Section 2.2.5.
DIESEL-PM10 and DIESEL-PM25 were added by copying the PM10 and PM2.5 pollutants (respectively) as
DIESEL-PM pollutants for all diesel SCCs. See Section 2.2.5.
Brake and tire PM was tracked separately from exhaust processes, although all non-refueling processes
were combined into broader SCCs prior to loading into EIS.
For Colorado, refueling emissions were removed from all counties for which Colorado reported refueling
in the point source data category.
6,8,2 Representative counties and fuel months
Representative counties
Although the EPA develops a CDB for each county in the nation, only a subset of these were run with MOVES
based on an assumption that most 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 to other
counties is called the "representative county." This approach of running MOVES for representative counties
helps reduce computation time by reducing the number of MOVES runs to generate a nationwide inventory. The
MCXREF file listed in Table 6-6 provides the mapping of each county to its representative county. Usually the
same MCXREF file is used for all MOVES processes. However, the emission factors for hoteling Ramsey County,
Minnesota were discovered to be zero late in the process of creating the 2014v2 NEI. To address this issue,
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Anoka and Ramsey County, MN were mapped to use hoteling emission factors from Hennepin County, MN. This
additional MCXREF file is listed in Table 6-6 and was only used for hoteling emissions processes.
In the SMOKE-MOVES framework, temperature- and speed-specific data from the emission factor lookup tables
generated for the representative counties are multiplied with the county-level 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 speeds.
The EPA used the 2014 age distributions derived from IHS data to re-evaluate the 2014vl representative county
groups and as a result, added 12 new representative counties for 2014v2. In general, we desired to keep the
county groups as similar as possible between 2014vl and 2014v2. However, we also wanted to ensure that the
introduction of new vehicle age data would be reflected in the representative county emission factors
appropriately. In some cases, we split 2014vl county groups when the average age of light-duty vehicles in
particular counties was significantly newer or older than vehicle age the rest of the counties in the group. We
performed the analysis by first calculating the average age of light duty (LD) vehicles in each county, where LD
included the three source types passenger car, passenger truck, and light commercial truck. The average age
was then assigned a bin number 1 through 6 according to the following six ranges of 0-7 years old, 7-9, 9-11, 11-
13, 13-15, and more than 15 years old. Next, we examined the spread of age bins within the existing vl county
groups. For counties whose age bin became a non-neighboring bin (at least 2 bins away) from the vl age bin of
the group, we moved the county out. For example, if a representative county group was age bin 3 in vl, and the
new data resulted in LD average ages of bin 3-6, then bin 3 and 4 were left in the vl group, and 5 and 6 formed a
new county group. Figure 6-2 displays a map of the representative counties by state and their corresponding
county groups. The MCXREF file listed in Table 6-7 provides the mapping of each specific county to its
representative county.
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Figure 6-2; Representative county groups for the 2014 NEI
Reference counties are outlined in black.
Number of counties assigned to each
reference county are labelled.
Reference County Groups 2014 V2
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 6-7
for access information).
6.8.2.3 Fuels
Although state/local-submitted CDBs may have included information about fuel properties, this fuel information
was replaced for the MOVES runs for the 2014v2 NEI using fuel properties developed for a set of fuel regions
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that was generated by the EPA in July 2017 for the 2014v2 NEI (moves201x_2014fuels). The EPA developed
these data using a combination of purchased fuel survey data, proprietary fuel refinery information and known
federal and local regulatory constraints. Our past analyses of state/local-submitted fuel information has led us to
conclude that our replacement of the data is more accurate and the best way to treat all parts of the country
consistently with respect to fuel use and the fuel impacts on emission rates. The updated fuel information used
for the 2014v2 NEI for calendar year 2014 will be reflected in future versions of the MOVES model.
The steps used to determine the fuel properties in each fuel region are as follows:
1. Fuel properties from proprietary refinery certification data were compiled on a regional basis (based on
typical pipeline delivery areas).
2. Properties within a region for finished fuel batches (e.g., no conventional blendstock for oxygenate
blending (CBOB), reformulated blendstock for oxygenate blending (RBOB) or oxygen backout (OBO) fuel
batches) produced in 2010, excluding reformulated gasoline (RFG), were averaged to generate non-
ethanol conventional gasoline fuel properties within that region, for a given month.
3. RFG fuel properties were based on RFG fuel compliance survey data, and oxygenate levels were
assumed to be 10 percent ethanol (E10, no MTBE).
4. Refinery modeling results generated for the Renewable Fuel Standard were used to adjust the regional
conventional gasoline fuel properties to account for ethanol blending up to E10, for a given month.
5. Additional adjustments to fuel properties were performed on individual counties within a region, based
on refinery modeling, for known local regulatory constraints such as low-RVP or oxygenate level
mandates.
6. Appropriate E10 and conventional gasoline fuel market shares were calculated on a regional basis for
the level of ethanol produced in 2014, after ethanol required for RFG compliance was taken into
account.
7. Gasoline fuel properties and ethanol market shares were applied to each county regionally and
accounting for known local regulatory constraints.
8. Diesel properties were assumed to be 15 parts per million nationally with no significant biodiesel
penetration.
The regional fuel supply database used for the 2014v2 is an external MOVES database called
moves201x_2014fuels available for download with the modeling platform (see Section 6.10). A detailed
description of the development of the default national fuel supply is provided in the documentation for the
MOVES model and on the MOVES Technical Reports webpage [ref 5],
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 2014 covering the continental U.S. were
derived from simulations of version 3.4 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
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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 version 4.3) 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 ฐF
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. We updated the
spatial surrogates used for the 2014 NEI from those used in the 2011 NEI with 2014 activity such as link-based
VMT 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.
To account for changes in relative humidity, there is a pairing of relative humidity to temperature bins.
Met4moves calculated an average relative humidity for the county group for all grid cells that make up that
temperature bin. In other words, for all grid cells and hours within a single temperature bin and county group, it
extracts and averages the corresponding relative humidity. Met4moves repeats this calculation for each
temperature bin and county group, and finally repeats the whole process for each fuel month. When the
emission factors are applied by SMOKE, the appropriate temperature bin and fuel month specific relative
humidity was used for all runs of the county group. The EPA used a 5 ฐF 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 RatePerProfil (RPP) based emissions. The EPA ran Met4moves in daily mode for the
2014 NEI.
The resulting temperatures for the representative counties are provided in the supplementary materials (see
Table 6-7 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.
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6.8.4 ฅIฅ!T, vehicle population, speed, and hoteling activity data
The activity data used to compute onroad mobile source emissions for the 2014 NEI uses EPA defaults 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 2014 [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
2014v2_2014_Default_Onroad_Activity_Data_Documentation.pdf, which is provided with the supporting data
in Table 6-7.
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 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 2014 NEI.
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 reformat 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 2014v2 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 into an hourly speed, by
SCC, county, and weekday/weekend days.
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 2014 activity data for VMT, population, and hoteling for non-
submitting areas.
6.8.4.1 VMTFF10 We creation
As for the 2014v2, the FFlO-generation scripts read VMT from the MOVES CDB table 'sourceTypeYearVMT/
which contains 2014 annual VMT organized by MOVES source type. The scripts disaggregate the source type
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 f 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 werenot 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. States without E85 in the 2014v2 NEI include
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Connecticut, New Jersey, and New York. In the 2011 NEI, all counties had some E85 vehicles because the FF10
script read only MOVES national data, rather than CDB fuel split and E85 availability information.
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 FF10 file 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 2014 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 was 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). As with the VMT by SCC, there is no E85 vehicle population in
Connecticut, New Jersey, or New York due to agency-submitted data describing the local E85 supply as zero.
Speed FF10 fiie creation
SMOKE uses speed data for all counties to lookup the appropriate VMT-based emission factors by speed bin and
SCC. The FF10 "SPD" input for SMOKE is one of two speed-related inputs; the other, described below, contains
hourly speeds 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 for the annual average and 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 Profile creation
The speed profile (SPDPRO) input for SMOKE is optional and supersedes the FF10SPD input. The FF10 SPEED file
contains average speed data by county and SCC with no time variation, while the SPDPRO contains average
speed data by county, SCC, hour, and weekday/weekend. The FF10 SPEED file is read by the SMOKE program
Smkinven, and the SPDPRO is read by the Movesmrg program. The values in the FF10 SPEED file are only used by
SMOKE-MOVES if a SPDPRO entry is not available. However, regardless of whether or not you have a SPDPRO,
SMOKE-MOVES requires that you have an FF10 SPEED file. SMOKE uses speed data for all counties in order to
lookup the appropriate VMT-based emission factors by speed bin and SCC. The scripts read the same MOVES
CDB tables as used when creating the FF10 SPEED file, though instead of aggregating to a daily average, the
scripts preserve the hourly detail. The scripts compile SPDPRO data listing one average speed per hour of day by
SCC and county for weekday/weekend day types
6.8.4.5 Hotel'ing FF10 file crea tion.
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
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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.
Because the 2014 NEI is the first to use the 2014a version of MOVES, it is the first NEI to have the option for
agencies 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, which in year 2014 is estimated by EPA to be 0.027337 hours per mile. The
scripts use the MOVES default fractions of APU usage, which in MOVES2014a is zero percent APU usage through
model year 2009, and 30 percent APU usage in model years 2010 and later. The remaining hoteling hours are
assumed to occur with the main engine on.
For the 2014v2 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. 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 to compare the hoteling hours with available spaces is listed in Table 6-7, along with a separate
spreadsheet that estimates the reductions to hoteling emissions for key pollutants.
6,8,5 Public release of the NEI county databases
Two sets of 2014v2 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. Both types of CDBs are available for
all U.S. counties, except that the seeded CDBs intended to be used with MOVES Inventory Calculation. The
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unseeded CDBs are available for all U.S. counties, but that the seeded CDBs are only available for the
representative counties. See Table 6-7 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 them 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 had 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.
6.8.7 Unseeded CDBs
In contrast to the seeded CDBs, the unseeded CDBs do not have any seeding performed on them. This set of
CDBs is true to the local conditions. The unseeded CDBs merge the databases that were agency-submitted with
the 1,409 default CDBs that were carried over from the 2014vl with updates based on HIS and CRC study data.
The unseeded CDB tables 'SourceTypeYearVMT/ 'SourceTypeYear/ 'HotellingHours/ and
'HotellingActivityDistribution' are consistent with the SMOKE-ready files of 2014 VMT, population, and hoteling.
The CDBs created by EPA (i.e., ones for which there was no submittal by S/L/T agencies) include the 2014 default
VMT in the 'SourceTypeYearVMT' tables rather than the 'HPMSVtypeYear' tables (used in the past EPA defaults),
which are now empty. The 2014 default hoteling information is included in the CDB tables 'HotellingHours' and
'HotellingActivityDistribution.' As in the past NEI, the 2014 EPA-default vehicle populations are included in the
'SourceTypeYear' tables in the non-submitted CDBs.
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, see the SMOKE documentation [ref 8],
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 Movesmrg SMOKE-MOVES
program performs this function by combining activity data, meteorological data, and emission factors to
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
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compute emissions. These calculations were done for all counties and SCCs in the SMOKE inputs, covering the
continental U.S.
The emissions process RPD is for modeling the driving emissions. This includes the following modes (i.e.,
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 profiles for
weekday versus weekend (i.e., SPDPRO 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 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 temporal profiles were updated for
the 2014v2 NEI based on the CRC-A-100 study.
The emission processes in RPV model the parked emissions. This includes the following modes: 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 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 the four processing streams
(RPD, RPV, RPH, and RPP). The results include emissions for every county in the continental U.S.
6,8,10 On road mobile emissions data for Alaska, Hawaii, Puerto Rico, and the Virgin Islands
Since the meteorological data used by the EPA for running SMOKE-MOVES covers only the continental U.S., the
EPA used the MOVES Inventory Mode to create emissions for Alaska, Hawaii, Puerto Rico and the Virgin Islands.
These runs used the average monthly hourly temperatures and humidity values derived from the National
Climatic Data Center temperature and humidity data for calendar year 2014. The emissions generated by the
Inventory Mode MOVES runs characterized all pollutants, including a full set of metals and dioxins.
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These emission inventory estimates were not derived using the same SMOKE-MOVES process used for the other
counties. Instead, each county was run independently using the Inventory Mode of the MOVES2014a model.
This approach directly calculates the inventory in each county using the inputs provided in each of the county
databases. For Hawaii, Puerto Rico, and the Virgin Islands, MOVES was run for only January and July due to the
relatively modest temperature variation over the year for these islands. All other months were mapped to those
months to create an annual estimate of the emissions. Due to the greater meteorological variation in Alaska,
MOVES was run for every month of the year.
The MOVES inputs used for these emissions are:
The MOVES CDM databases,
The run specifications used to run MOVES, and
The MySQL database containing the tables that describe the temperatures and relative humidity values
used for these states and territories.
These inputs are provided in the supplementary materials (see Table 6-7 for access information).
6.8.11 Post-processing to create 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.
The onroad emissions for Alaska, Hawaii, Puerto Rico and the Virgin Islands, which the EPA generated via
MOVES in Inventory Mode were appended to the onroad inventory generated from SMOKE-MOVES to create
the final emissions. These estimates are the same in the 2014vl and 2014v2 NEI. This complete inventory was
loaded into the EIS dataset "2014_EPA_MOVES "as the EPA estimates for the onroad sector.
Five speciated PM2.5 species were added based on speciation profiles (i.e., elemental carbon, organic carbon,
nitrate, sulfate and other PM2.5)- 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.
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 2014v2 NEI emissions were compared to the 2014vl and 2011v2 NEI emissions to make sure that all
SCCs, counties, and pollutants were covered and as a general quality assurance of the emissions.
Comparisons of 2014 and 2011 emissions were done using spreadsheets that compared emissions from
the two years using (a) groupings at the first 6 digits of the SCC (fuel + MOVES source type) and (b)
grouping by light-duty and heavy-duty.
Maps of county-level NOx, PM2.5 and VOC were prepared for each fuel + MOVES source type
combination, total light-duty, total heavy-duty, that included maps of the difference between 2014v2
emissions versus 2014vl NEI and 2011v2 NEI.
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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.
Onroad 2014 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 6-6
provides the submittal history of these county databases. The onroad scripts and data files used in the
calculations are listed in Table 6-7. The files and datasets listed in Table 6-7 are all available the 2014vl
Supplemental onroad data FTP site.
Table 6-6: Agency submittal history
:or 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
VI:01/14/2016
Chattanooga Air Pollution
Control Bureau
V2:05/10/2017
City of Albuquerque (New
Mexico) Environmental Health
Department
VI:01/14/2016
Clark County Department of
Air Quality
VI: 01/22/2016
Coeur d'Alene Tribe*
VI: 01/07/2016
Connecticut Bureau of Air
Management
VI: 01/14/2016
Department of Energy and
Environment (Washington
D.C.)
VI: 12/17/2015
Delaware Department of
Natural Resources
VI: 01/15/2016
Georgia Department of
Natural Resources
VI: 12/21/2015
and 05/17/2016
V2:05/10/2017
Idaho Department of
Environmental Quality
VI:12/17/2015
Illinois EPA
VI:12/01/2015
Knox County (Tennessee)
Department of Air Quality
Management
VI: 12/29/2015
Kootenai Tribe of Idaho*
VI: 01/07/2016
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Agency Organization
Onroad CDB
Submission Date
(MM/DD/YYYY)
Onroad
Emissions
Submission Date
(MM/DD/YYYY)
Notes
Louisville (Kentucky) Metro Air
Pollution Control District
VI:06/03/2015
Maine Department of
Environmental Protection
VI: 01/26/2016
V2:05/05/2017
Maricopa County (Arizona) Air
Quality Department
VI: 12/07/2015
Maryland Department of the
Environment
VI: 01/07/2016
Massachusetts Department of
Environmental Protection
VI: 11/23/2015
Memphis and Shelby County
Health Department - Pollution
Control
V2:05/16/2017
Metro Public Health of
Nashville/Davidson County
VI: 01/15/2016
Agency sent VPOP and VMT
via email on 6/7/2016.
Michigan Department of
Environmental Quality
VI:01/13/2016
Minnesota Pollution Control
Agency
VI:12/17/2015
and 04/08/2016
Missouri Department of
Natural Resources
VI:03/07/2016
and 06/08/2016
Morongo Band of Cahuilla
Mission Indians of the
Morongo Reservation,
California*
VI: 12/14/2015
New Hampshire Department
of Environmental Services
VI: 12/18/2015
and04/15/2016
New Jersey Department of
Environment Protection
VI: 01/14/2016
New York Department of
Environmental Conservation
VI:03/14/2016
Nez Perce Tribe*
VI:01/07/2016
North Carolina DEQ, Division
of Air Quality
VI: 01/14/2016
Northern Cheyenne Tribe
VI: 12/01/2015
Ohio EPA
VI: 01/12/2016
and 03/18/2016
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Agency Organization
Onroad CDB
Submission Date
(MM/DD/YYYY)
Onroad
Emissions
Submission Date
(MM/DD/YYYY)
Notes
Oregon Department of
Environmental Quality
VI: 01/13/2016
Pennsylvania Department of
Environmental Protection
VI: n03/04/2016
Pima Association of
Governments (Tuscon,
Arizona)
VI:01/27/2016
EPA imported the submittal
into MySQL tables and
renamed the database (to
match the NEI naming
convention) and removed
the empty non-CDB tables.
Rhode Island Department of
Environmental Management
VI: 02/11/2016
Shoshone-Bannock Tribes of
the Fort Hall Reservation of
Idaho*
VI: 01/07/2016
South Carolina Department of
Health and Environmental
Control
VI: 12/01/2015
Tennessee Department of
Environment and
Conservation
VI: 12/15/2015
V2:05/17/2017
Texas Commission on
Environmental Quality
VI:01/28/2016
VI: 01/07/2016
Texas emissions are
available in EIS, but Texas'
inputs are reflected in
EPAMOVES results and in
the NEI.
Utah Division of Air Quality
VI:12/01/2016
and 04/01/2016
Vermont Department of
Environmental Conservation
VI: 01/15/2016
V2:05/19/2017
Virginia Department of
Environmental Quality
VI:12/21/2015
V2:05/16/2017
Washington State Department
of Ecology
VI: 12/01/2015
V2: 04/12/2017
Washoe County (Nevada)
Health District, Air Quality
Management Division
VI: 01/11/2016
and 05/13/2016
VI: 05/13/2016
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Agency Organization
Onroad CDB
Submission Date
(MM/DD/YYYY)
Onroad
Emissions
Submission Date
(MM/DD/YYYY)
Notes
West Virginia Division of Air
Quality
VI: 12/16/2015
Wisconsin Department of
Natural Resources
VI:01/15/2016
V2:05/16/2017
* Tribal emissions data submitted to EIS were inadvertently not included in the 2014vl NEI but will be in version 2. Tribal
territory emissions are not calculated by EPA, because they are not in the county databases.
Table 6-7: Onroad Mobile data file references for the 2014 NEI
File Name
Description
2014NEIv2_default_onroad_activity_
approach_022118.pdf
Describes method used for EPA default VMT, VPOP, and
hoteling hours data used in counties for which data were not
submitted byS/L/T agencies.
Folder CDBs_for_all_counties contains
2014v2CDBs_stXX.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.
2014NEIv2_repCounty_CDBs_seeded_
26sepl7.zip
"Seeded" CDBs for representative counties in the continental
U.S. used to develop 2014NEIv2. These should produce fully
populated rates tables because values of zero in the MOVES
input tables have been updated to small numbers (le-15). It
only includes the approximately 300 rep. counties and does
not include AK, HI, VI, or PR. Age distributions are vehicle-
population-weighted across all represented counties.
2014v2_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.
2014v2_RepCounty_Runspecs.zip
The MOVES2014a run specifications (runspecs) for the
representative counties for running MOVES in emissions rate
mode (used for SMOKE-MOVES).
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File Name
Description
2014NEIv2_RepCounty_Temperatures
.zip
2014v2_RepCounty_Temperatures_
MOVES_zmh.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.
MFMREF_2014v2_10jul2017_v0.txt
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.
2014vl_AKHIPRVI_Runspecs.zip
The MOVES2014 run specifications (runspecs) for all counties
in Alaska, Hawaii, Puerto Rico and the Virgin Islands. These
are for running MOVES in Inventory Mode.
MCXREF_2014v2_10jul2017_v0.txt
MCXREF_2014v2_10jan2018_nf_v2_
for MN.txt
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 special
version is used to compute hoteling emissions in Minnesota
to correct an issue with hoteling emission factors.
2014NEIv2_speed_spdpro.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 profiles (SPDPRO) in
miles per hour, by county and SCC covering every county in
the U.S.
2014v l_CDB_QA_Checks_
MOVES2014a_vl
2014v l_QA_Checks_v8_
2December2015.sql
Scripts designed to catch errors that would cause MOVES to
fail during a run and to identify unreasonable data values.
generateFF10_from_CDBs.zip
populateCDBs_from_FF10.zip
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. Populate CDBs from FF10 scripts read
SMOKE-formatted activity files: VMT, vehicle population, and
hoteling hours, and update the MOVES CDB tables
SourceTypeYearVMT, SourceTypeYear, HotellingHours, and
HotellingActivityDistribution.
2014v2_EICtoEPA_SCCmapping.xlxs
Maps California EMFAC codes to MOVES SCCs
2014NEIV2_Plans_for_CDB_lnput_Dat
a 07072017b.xlsx
Spreadsheet that shows how state-submitted and default
data were merged together to prepare 2014NEIv2.
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File Name
Description
2014NEI_v2_Representative_Counties
_List_20170620_for_documentation.xl
sx
Spreadsheet of representative county characteristics.
2014v 2_h ote 1 i ng_by_co u nty_ve rs u s_
truck_stop_parking_102817.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.
2014v2_onroad_RPH_reduced_hotelli
ng_comparison.xlsx
Spreadsheet that estimates the change in emissions due to
the reduction in hoteling hours.
2014v2_avft_grouped_csvs.zip
2014v2_agedist_grouped_csvs.zip
Grouped AVFT and age distribution .csv files used to compute
emission factors for representative counties and to create the
CDBs in 2014NEIv2_repCounty_CDBs_seeded_26sepl7.zip
2014v2_avft_individual_csvs.zip
2014v2_agedist_individual_csvs.zip
County-specific AVFT and age distribution .csv files
appropriate for inventory modeling of specific counties and
used to create the CDBs in the folder CDBs_for_all_counties
1. Coordinating Research Council. 2017. Improvement of Default Inputs for MOVES and SMOKE-MOVES.
Report No. A-100.
2. Memorandum. ERG. 2017. Analysis of IHS Registration Data and Preparation of WA 5-08 Task 1
Deliverables.
3. U.S. Environmental Protection Agency, LEV and early NLEV modeling information for MOVES2Q14-
20141022
4. U.S. Environmental Protection Agency, MOVES2Q14a: Latest Version of Motor Vehicle Emission
Simulator (MOVES).
5. U.S. Environmental Protection Agency, MOVES Technical Reports
6. National Center for Atmospheric Research, Mesoscale and Microscale Meteorology Division, Weather
Research and Forecasting Model. Boulder CO, June 2008, NCAR/TN-475+STR, A Description of the
Advanced Research WRF Version 3.
7. Meteorology-Chemistry Interface Processor (MCIP) version 4.3.
8. User's Guide for SMOKE, including MOVES integration tools.
9. Federal Highway Administration. Highway Statistics 2014.
10. MOVES Utility Scripts, and Scripts that interface between SMOKE and MOVES
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7 Wildland Fires (Wild and Prescribed Fires) in the 2014INE1
7.1 Sector el 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 2014 NFEI, the EPA calculated emissions from agricultural fires separately
from WLF emissions as described separately in Section 4.11. This portion of the document describes the
calculation of WLF emissions portion of the 2014 NEI. The reader is referred to a draft report [ref 1] for more
information, details, and website information for the EPA estimates described in this section.
Estimated emissions from wildfires and prescribed burns in the 2014 NEI (termed in the remainder of this
section as the "2014 NEI"as this section only pertains to WLFs) are calculated from burned area data. Input
data sets are collected from 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.
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 2014 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 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 2011 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". In the 2014 NEI, the EPA has compiled WLF emissions by smoldering and
flaming phases. The SCCs shown in are used to denote this differentiation. There are six valid SCCs for events in
7-1
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EIS. The four rows with "EPA Generated?" equals "Yes" are the SCCs into which EPA and S/L/Ts generally
compile their data in the 2014 NEI. EPA only generates estimates for these four SCCs.
Table 7-1: SCCs for wildland fires
see
Description
EPA Generated?
2810001000
Forest Wildfires; Total (Smoldering + Flaming) for Wildfires
2810001001
Forest Wildfires; Smoldering
Yes
2810001002
Forest Wildfires; Flaming
Yes
2811015000
Managed Burning, Slash (Logging Debris); Pile Burning
2811015001
Prescribed Forest Burning; Smoldering
Yes
2811015002
Prescribed Forest Burning; Flaming
Yes
7,2 S<
The WLF EIS sectors include data only from two components: S/L/T agency-provided emissions data for Georgia
and Washington (day-specific data in Events format), and the EPA dataset created from SmartFire version 2
(SF2), which used available state inputs. This merged information is the basis of the WLF 2014 NEI. The hierarchy
of data used to compile the 2014 NEI was very straightforward: Georgia's and Washington's data comes first,
followed by EPA's dataset, as shown in Table 7-2. 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. Georgia was supplied HAP to
VOC ratios 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 2014, no tribes submitted WLF emissions data, and the EPA did not assign any fires based on the tribal land
boundaries. These fires were assigned to the states within which the tribal lands fall. One tribe did submit
activity data, which was used in the processing of those data into emissions for that State.
Table 7-2: 2014 NEI Wildfire and Prescribed Fires selection hierarchy
Priority
Dataset Name
Dataset Content
Is Dataset in EIS?
1
State/Local/Tribal Data
Submitted data as discussed above
Yes
2
2014EPA_EVENT
Emissions from SFv2
Yes
7.3 EPA
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), but for purposes of the 2014 NEI vl those emissions are combined
under smoldering emissions of fire. The emissions estimates were estimated and compiled separately for
flaming and smoldering combustion phases of fire to facilitate climate modeling and fine-scale research in areas
such as health impacts of smoke emissions.
7-2
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Figure 7-1 shows the sequence of processing steps. First, input data sets are obtained from S/L/T agencies and
national sources. The data sets are cleaned to eliminate errors and to standardize formatting for the data. Data
sets submitted by various S/L/T agencies are appended together for subsequent processing. Appropriate
cleaned data sets from S/L/T agencies and national sources are selected on the basis of data availability, data
completeness, and geographic area; they are then reconciled into a single, comprehensive daily fire location
data set using SmartFire2. These daily fire locations, along with fuel moisture and fuel loading data, are used by
the BlueSkv Framework [ref 2] to estimate fuel consumption and smoke emissions. Emissions are then
computed for use in the 2014 NEI.
While Figure 7-1 shows a single processing stream, the 2014 NEI for wildland fires was prepared using six
separate streams that covered different geographic areas [ref 1], Each of the streams was processed in a similar
manner, with some modification of the smoke modeling approach for fires in Hawaii and Puerto Rico (these
modifications are discussed later in this section). Finally, the outputs from all of the streams were compiled into
the NEI.
Figure 7-1: Processing flow for wildland fire emission estimates in the NEI
Input Data Sets
(state/local/tribal and national data sets)
<* G
Data Preparation
Fuel Moisture and
Fuel Loading Data
Smoke Modeling (BlueSky Framework)
Daily smoke emissions
for each fire
Emissions Post-Processing
Final Wildland Fire Emissions Inventory
Data Aggregation and Reconciliation
(SmartFire2)
Daily fire locations
with fire size and type
7-3
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7.3.1 Activity data
In addition to S/L/T submitted data and national default data sets, auxiliary data for fuel loading and fuel
moisture were obtained [ref 1] to support emission calculations.
7.3.2 State, Local, and Tribal fire activity
In spring 2015, S/L/T agencies were invited by EPA and USFS to submit all fire occurrence data in any format for
use in developing the 2014 NEI. In winter 2015, the submitting agencies were asked to self-assess the
completeness of their data by completing the NEI Wildland Fire Inventory Database Questionnaire [Appendix A
in ref 1] Overall, the EPA used a total of 54 data sets from 22 individual states and one Indian Nation. Twenty of
the 22 states and the Indian Nation responded to the questionnaire. At a minimum, input data were required to
include information about the date, location, fire type, and size of individual fires. Of the 54 data sets, eight
were excluded from the NEI because they were determined to lack the minimum descriptive information
necessary. Fourteen additional data sets were not used because they were duplicated by regional data from the
Fire Emissions Tracking System (FETS). FETS wildland fire information was obtained from the Western Regional
Air Partnership (WRAP) through EPA. The FETS data set included fire activity for eight states: Arizona, Colorado,
Idaho, Montana, Oregon, Utah, Washington, and Wyoming.
As a result of the data collected and assessed, fire activity data from 22 states and one Indian Nation (32
individual data sets and FETS data) were included in the 2014 NEI. Figure 7-2 shows the states that submitted
fire activity data and questionnaire responses, and identifies states where data were incorporated into the 2014
NEI vl. In the figure, states shown in green (as well as the Kaw Nation in Oklahoma and counties in California,
Nevada, and Arizona) submitted usable data; blue colored states provided usable data via FETS; yellow colored
states submitted unusable data; gray colored states did not provide data; and states shown with lines responded
to the database questionnaire.
7-4
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Figure 7-2: The coverage of state-submitted fire activity data sets
Status
Covered by National Data
Data Submitted; Not Used
Data Submitted; Used
FETS Data Used
SLTs With Questionnaire Returned
7.3.2.1 National fire activity data sources
In addition to the data provided by S/L/T agencies, fire data sets with national coverage from the following
sources were also used to develop the 2014 WLF NEI:
Hazard Mapping System (HMS) data published by the National Oceanic and Atmospheric
Administration (NOAA)were acquired and agricultural fires were removed. See Section 4.11 on
agricultural fires for more a description as to what was done and why.
Incident Status Summary (ICS-209) Reports in application (.exe) format were acquired via the National
Fire and Aviation Management Web Applications website. Upon execution, the application file created a
Microsoft Access database containing the fire activity data. Data from two tables in the database were
merged and used: 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.
U.S. Fish and Wildlife Service (USFWS) fire information data were provided by the USFWS.
National Association of State Foresters (NASF) fire information data were downloaded from the
National Fire and Aviation Management Web Applications website. Only wildfire data were included.
Forest Service Activity Tracking System (FACTS) fire information data were supplied by the USFS. Only
fuel treatment data were included.
Geospatial Multi-Agency Coordination (GeoMAC) fire perimeter data were downloaded via the USGS
GeoMAC wildland fire support website.
7-5
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U.S. Department of the Interior (DOI) prescribed fire data were extracted from the National Fire Plan
Operations and Reporting System (NFPORS) and supplied by the USFS. This is a new data source that
was not used in previous efforts. See [ref 1] for more details.
7,3,2,2 Ancillary activity data sources
The fire emission modeling framework used in processing the NEI requires information about burned fuels to
estimate emissions. Two key parameters for computing burned fuel, fuel moisture observations and fuel loading
were obtained for use in subsequent processing:
Fuel moisture: Fire weather observation files (fdr_obs.dat) were downloaded for each analysis day from
the USFS archive on 2/19/2016 and used as inputs to the Fuel_Moisture_WIMS module in the BlueSky
Framework [ref 3],
Fuel loading: The Fuel Characteristic Classification System (FCCS) 1-km fuels shapefile and lookup table
for the contiguous United States were provided by the USFS AirFire Team. The Alaskan FCCS 1-km fuels
shapefile and lookup table were acquired from the USFS Fire and Environmental Research Applications
Team's website. Fuels information for Hawaii and Puerto Rico were not required as estimated fuel
loadings available in the Fire Inventory from the National Center for Atmospheric Research (FINN)
module [ref 4] were used.
7.4 and processing
The raw input data were reviewed to determine whether the necessary information was included in each data
set. At a minimum, input data were required to include information about the date, location, fire type, and size
of individual fires. At a minimum, valid input data were required. Data sets that included at least the minimum
required information were examined for data quality and, in cases where the minimum data quality criteria
were not met, the invalid data points were modified or removed [see ref 1 for more details on these algorithms].
Agricultural and pile burns were removed from data sets during data preparation or after emission estimation
because agricultural burns were processed separately by EPA, and usable pile burn data and a general method
for estimating pile burn emissions for the purpose of the NEI were lacking.
7.4.1 S/L/T data preparation
Each S/L/T data set and any accompanying metadata were reviewed to determine its coverage and included
information. Eight data sets were excluded from subsequent processing because the data sets lacked the
required minimum information (see Appendix B in ref 1). Data sets containing a valid end date value for fires
were also noted, and fire durations were calculated when available. All S/L/T data sets were cleaned to:
include only fires falling within the relevant geographic boundary,
include only fires with valid start dates falling within 2014 (unless end date is in 2014, in which
case fires that started in 2013 were retained),
include only fires with a valid area greater than zero (0) acres,
remove agricultural fires,
remove pile burns,
modify invalid end dates by changing invalid end dates to be the same as the start date (end
dates were considered to be invalid if they fell before the start date, if they fell more than three
weeks after the start date for prescribed fires, or if they fell more than one week after the start
data and had an area less than 10 acres),
standardize column names,
7-6
-------
add a unique ID field and populate the field with unique IDs,
transform point locations provided in projected coordinate systems to geographic coordinates,
combine all data sets for each state into a single state data set.
Besides these cleaning steps, data sets were visually reviewed and, where warranted, further adjusted.
Adjustments included changing the sign of longitude values for Alabama data to ensure that fires fell in the
western hemisphere, and manually cleaning various issues with location information for the Iowa data set.
Additional minor adjustments to individual fire records were made to correct assumed typos in key fields,
including latitude, longitude, and date. An example of such an adjustment would be changing the start date of a
fire from 04/05/2015 to 04/05/2014 where the end date was provided as 04/06/2014. Manual review of the
data sets was assisted by the creation of an automated report for each data set showing the number of valid fire
records that was located within the relevant geographic boundary and occurred during 2014, the geographic
distribution of fires and fire types, the distribution of fire start date, the distribution of fire end date and
duration where applicable, and the distribution of fire size.
The FETS regional data set was adjusted using the steps outlined above. However, additional preparation was
required for the Oregon fire data sets. First, the Oregon wildfire data set was found to have a large number of
fires outside the state. The locations of these fires were corrected. Second, the locations of prescribed fires
statewide were reported in township/range/section format rather than as geographic coordinates. To identify
an approximate location for these fires, we used the Bureau of Land Management Geo Communicator Township
Geocoder Web Service to assign an approximate geographic location for these fires based on the description of
the fire location that was supplied in township/range/section format.
Six states and one local agency submitted data independently but were also covered by FETS regional data. Each
submitted state or local data set was compared to the available FETS data. The state and local data duplicated
the FETS data exactly in all cases. For these jurisdictions, we used FETS data in place of state- or local-submitted
data.
S/L/T data sets were assessed for completeness based on the information included on the Database
Questionnaire. Data submitters reported the data inclusion level (e.g., always or sometimes) and estimated
percent completeness of data sets in categories based on fire types, primary agencies or actors, and land
ownerships. The responses, along with any additional input from data submitters, were used to determine
which national data sets would best supplement the S/L/T data, if any.
Data sets representing 14 states and one Indian Nation were reported as incomplete across multiple categories,
and subsequent processing included all available national data sets as supplemental data. These S/L/T data sets
were merged into a "supplement with all" data set for subsequent processing. Also included in the "supplement
with all" category were three states that did not respond to the data questionnaire but submitted data that met
the minimum requirements for necessary fire information.
The following five states included either no national data sets or only a subset of available national data sets as
supplementary data, according to state feedback
South Carolina. The South Carolina data sets were reported as 100% complete for all categories and as a
result, the data sets were not supplemented with any national data sets.
Alaska. Similarly, Alaska reported 100% completeness for its data set. However, because each raw data
record represented a single wildfire over its entire spatial and temporal extent, we supplemented the
data for Alaska with the HMS data set to provide improved fire growth and location information. Any
resulting fires that were solely based on HMS data were removed in subsequent processing.
7-7
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Georgia. The Georgia questionnaire reported that fires associated with a federal primary agency were
not included, so only federal data (USFWS, FACTS, NFPORS, and federally reported GeoMAC) were used
to supplement the state's data. However, the EPA-estimated emissions through this approach were
ultimately not used in the NEI because Georgia elected to submit their own emissions.
Florida. On the basis of Florida's questionnaire response, its data set was supplemented with federally
reported wildfires only in the USFWS and GeoMAC data sets.
North Carolina. At the state's request, the North Carolina data set was supplemented with only the
FACTS data and USFWS data for Pee Dee and Great Dismal Swamp National Wildlife Refuges.
7.4.2 National data preparation
National data sets were prepared in a process similar to the state data set processing: data sets were checked to
ensure the minimum necessary information was included, data sets were cleaned, and data set formats were
standardized. Some data set-specific cleaning was also performed. Typical cleaning steps included correcting or
removing fire locations outside the United States, correcting poorly formatted dates, and correcting end dates
that fell either before the start date or an implausible length of time after the start date.
7.4.3 Event reconciliation and emissions calculations
Once S/L/T and national fire activity data were reviewed and cleaned, they were imported into the SF2 data
platform for association and reconciliation to remove duplicate fires and assimilate into daily fire locations with
fire size and type information. In addition, to develop the 2014 EPA estimates, comments received from all of
the states that submitted comments on the 2014 draft emission estimates were addressed to the extent
possible. The final step was that the SF2 output was then processed through the BlueSky Framework to estimate
fuel loading, fuel consumption, and ultimately smoke emissions for each daily fire location. These smoke
estimates were post-processed and compiled into the final wildland fire emissions inventory. Please consult the
STI documentation [ref 1] for more details on these steps and how the hierarchy and reconciliation was
implemented.
7.4.4 BlueSky Framework emissions modeling
Daily fire emissions were calculated from daily fire location files using the BlueSky Framework. The framework
supports the calculation of emissions using various models depending on the available inputs as well as the
desired results. Data for the NEI was calculated by using two different model chains based on the location of the
fire. The contiguous United States and Alaska, where FCCSfuel loading data are available, were processed using
the modeling chain described in Figure 7-3. Hawaii and Puerto Rico, which do not have FCCS fuel loading
information available, were processed using a different modeling chain (Table 7-3, Figure 7-3). See Appendix C in
ref 1 for a full description of the Bluesky Framework modeling process.
7-8
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Figure 7-3: Model chain for the contiguous United States and Alaska portion of the 2014 national wildland fire
emissions inventory development
Emission
Factors
(FEPS v2)
SmartFire2
BlueSky Framework v3.5.1
Dates
Type
Size
Fuels
(FCCS v2)
Location
Emission
Moisture
(WIMSvl)
Consumption
(Consume v4.1)
Table 7-3: Model chain for the Hawaii and Puerto Rico portion of the 2014 national wildland fire emissions
inventory development
Data Type
Model Used
Version Information
Fire activity data
SmartFire2
Version 2.0, Build 42022
Fuel loading
FINN vl
As implemented in BlueSky
Framework 3.5.1, revision
47693
Fuel consumption
FINN vl
Emissions
FINN vl
The Fire Emissions Production Simulator (FEPS) in the Bluesky Framework generates all the CAP emission factors
for WLFs used in the NEI. However, for the 2014 NEI, the FEPS module has been updated to calculate emissions
of HAPs and to calculate the smoldering and flaming components of emissions. In addition, the module was
modified to compute emissions using regionalized HAP emission factors developed for this effort, which reflect
differences in fire emissions in different parts of the country. The reader is referred to the FEPS module of the
Bluesky model for CAP emission factors (see FEPS link listed above). The HAP emission factors used in this work
came from Urbanski, 2015 [ref 5], These emission factors were regionalized and handled differently by wild and
prescribed fire. Table 7-4 outlines the regionalization scheme used while Table 7-5 and Table 7-6 show the HAP
EFs employed in this work separately for wild and prescribed fires. Note the differences, in bold in Table 7-4, for
wildfires and prescribed burning region assignments for Alaska and Wisconsin.
Table 7-4: Emission factor regions used to assign HAP emission factors for the 2014vl NWLFEI
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
7-9
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Region
Wildfires
Prescribed burning
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 7-5: Prescribed fire HAP emission factors (lb/ton fuel consumed) for the 2014 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
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
7-10
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HAP
Flaming
Smoldering
Region 1
Region 2
Region 3
Region 1
Region 2
Region 3
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 7-6: Wild fire HAP emission factors (lbs/ton fuel consumed) for the 2014 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
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
7-11
-------
HAP
Flaming
Smoldering
Region 1
Region 2
Region 3
Region 1
Region 2
Region 3
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
The FINN module (not BlueSky) was used for Hawaii and Puerto Rico, since FCCS data were not available for
these regions, and FINN is capable of calculating emissions globally. FINN uses satellite-derived land cover data,
estimated fuel loadings, and emission factors to model smoke emissions.
However, the FINN module does not compute emissions for VOCs or HAPs. Estimates of emissions of these
species for Hawaii and Puerto Rico were based on the C02 outputs from FINN. The average ratios of VOCs and
HAPs to C02 for wildland fires in grassland/herbaceous land cover, which is most similar to the vegetation type
that burned in Hawaii and Puerto Rico, were calculated for the contiguous United States and applied to the C02
emissions of Hawaii and Puerto Rico fires to estimate VOC and HAP emissions.
7.4,5 Dataset post-processing
Daily fire emission estimates from BlueSky Framework were post-processed to address known issues and
prepare data for final use [ref 6], Post-processing included adjustment of the calculated duff consumption for
certain fires, removal of agriculture and pile burns, speciation of PM2.5 emissions, and final formatting.
The FEPS emission estimates for the contiguous United States and Alaska were corrected to address a known
issue with emission estimates for prescribed fires in areas with large duff depths [ref 6], To address
overestimation of duff consumption in these fires, a scaling factor was calculated and applied to each fire to
reduce phase-specific consumption and emissions. This adjustment was applied as follows:
1. New duff consumption of each prescribed burn was recalculated by setting a "cap" value for the duff
consumption. For burns in western states (all states west of Texas, plus the Dakotas), the duff
consumption cap was set to 20 tons per acre. For eastern states, the duff consumption cap was set to 5
tons per acre. These caps were developed in consultation with USFS and U.S. DOI experts. For each fire,
the exceedance in duff consumption was calculated by subtracting capped duff consumption from the
original duff consumption.
2. The new total consumption of each prescribed burn was calculated by removing the exceedance in duff
consumption from the original total consumption.
3. The scaling factor for each prescribed burn was calculated as the ratio of the new total consumption
over the original total consumption.
4. Finally, the burn-specific scaling factor was applied to phase-specific consumption (flaming, smoldering,
and residual) and daily emissions of all pollutants to compute new fuel consumption and emissions.
7-12
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Emissions from agricultural and pile burns are not accounted for in the 2014 NEI. Any fires that were identified
as agricultural or pile burns in the modeling output were removed from the WLF NEI.
The 2014 NEI includes speciated components of PM2.5 for the first time. PM2.5 components were calculated as a
fraction of total PM2 5 by multiplying emissions by the speciation factors provided by EPA based on EPA's
modeling platforms and SPECIATE 4.0. Table 7-7 provides the speciation factors used for the 2014 NEI.
Table 7-7: PM2.5 speciation factors used to calculate PM2.5 components for wildfires and prescribed fires
Pollutant
Wildfires
Prescribed burning
EC
0.09490
0.10930
OC
0.46180
0.50190
S04
0.01260
0.00330
N03
0.00132
0.01070
Other
0.42938
0.37480
Some updates to the outputs were made at the request of data providers, based on comments on the draft WLF
EPA inventory. Four wildfires in the state of Delaware, representing all calculated wildfire activity for the state,
were removed because it was known that no wildfires had occurred in 2014. The names of some fires in
Michigan were also updated.
75
As stated previously, only Georgia and Washington submitted emissions for this data category. For all the other
states, the emissions developed as outlined above by EPA methods were the basis for the inventory. In
Washington's case, their data was accepted as submitted and no additions were made with EPA data.
Appropriate HAP EFs were provided as shown in Table 7-5 and Table 7-6 that enabled them to compute the
same HAPs that EPA estimates. In Georgia's case, because their initial HAP submission violated some QA checks
on total HAPs having to be less than bulk VOC, we provided HAP:VOC fractions according to EPA estimates for
their State. Georgia used these ratios and their VOC estimates to compute HAP emissions. Otherwise, as with
Washington, Georgia's data was accepted as submitted, and no additions were made with EPA data. No HAP
augmentation was necessary for either state. Both states submitted PM2.5 species according to the fractions
shown in Table 7-7.
Georgia's methods were very similar to EPA's methods. Georgia provided the following documentation on their
methods:
Georgia Environmental Protection Division (EPD) has developed 2014 Georgia wildland fire
emission inventory using the same fuel consumption and emission factors as was used to develop
2011 Georgia wildland fire emission inventory, which has been included as part of NEI 2011. Such
fuel consumption and emission factors are developed as part of the Southeastern Modeling,
Analysis, and Planning (SEMAP) fire emission inventory project and were considered as the best
knowledge from fire and forest managers in the Southeast. Burned area [estimates] are based on
2014 burning records obtained from Georgia Forestry Commission and three military bases, as
well as burning records of wildland fires on federal lands. No satellite fire detection data were
used in Georgia EPD estimates. To fulfill the requirement of separating emission by flaming and
smoldering combustion phases for NEI 2014, Georgia EPD ran CONSUME to generate separate
emissions by flaming, smoldering and residual smoldering and calculated emission fractions by
7-13
-------
combustion phases assuming that flaming and smoldering in CONSUME corresponds to flaming,
and residual smoldering in CONSUME corresponds to smoldering. This assumption is made
because the emissions during flaming and smoldering often coexist.
Washington provided the following reasons for having to estimate their own emissions after reviewing
EPA's draft estimates in vl of the WLF inventory:
Version 1 of the 2014 Fire NEI for Washington State included many sources of information: NASF, FWS,
FACTS, NFPORS, ICS, GeoMAC, HMS, and FETS. The data based on HMS assumes size and fire type, so all
fire locations in the NEI vl (Rx, WF, and AG) based solely on HMS were spatiotemporally cross-checked
with state databases of agricultural and prescribed pile burning. Spatiotemporally cross-checking fire
databases (using GIS and satellite imagery) showed that many fire types were incorrect. There were 197
fire locations classified as agricultural burns (because they were marked as pasture/grassland in the CDL)
that we re-classified as wildfire (e.g. parts of the Carlton Complex WF and Mills Canyon WF). There were
15 fire locations classified as agricultural burns (because they were marked as pasture/grassland in the
CDL) that we re-classified as prescribed burns (e.g. in the Umatilla National Forest). The remaining
agricultural burns in the NEI vl were corrected for size and crop-type as able, combined with our state
agricultural burn permit databases, and then submitted to EPA for NEI v2 (nonpoint). Note that many
agricultural burns in Washington State are pile burns, but that the nonpoint submission rules assume all
agricultural burns are "whole field set on fire". So, agricultural pile burns had to be submitted with
fictional "acres burned" activity data. The Rx pile burns detected by HMS that were misclassified as
broadcast burns in NEI vl were corrected and combined with the other Rx pile burns in our state
databases (same as pile burn data in FETS). All Rx pile burns were submitted as nonpoint data to EPA for
NEI v2.
After the fire types were corrected and pile burns were accounted for, there were some updates to fuel
loading for WF and Rx broadcast burns. Fuel loading in the FCCS map used by BlueSky is inaccurate for
several fuel types, so they were updated with more realistic fuel loading and BlueSky was rerun for the
affected fires.
FCCS #0 ("urban" aka unknown fuel) had 1 inch of duff added
FCCS #235 (Idaho Fescue - Bluebunch Wheatgrass Grassland) had 1 inch of duff added
o FCCS #41 (Idaho Fescue - Bluebunch Wheatgrass Grassland) and #315 (Interior alpine forb
grassland) were replaced with FCCS #235 with 1 inch of duff added
FCCS #56 (Sagebrush Shrubland) had 1 inch of duff added
o FCCS #60 (Sagebrush Shrubland - Sparse), #308 (Low sagebrush shrubland), and #311 (Salt-
desert shrubland) were replaced with FCCS #56 with 1 inch of duff added
FCCS #57 (Wheatgrass - Cheatgrass grassland) had 1 inch of duff added
All fire locations with FCCS #900 (water) were changed to the nearest non-water fuel type.
All "events" data submitted by Washington State used the same emission factors and splitting of
flaming/smoldering emissions that were used by EPA.
While Alaska accepted our methods and emission estimates, they had these specific comments for
documentation:
1. ADEC uses specific fuel load factor for 80% by area or 20 biggest fires and used load factor is very likely
different from fuel load factor EPA uses. The fuel load factors (canopy EPA) are provided to ADEC by
7-14
-------
AICC in LANDFIRE files and site specific. For example, 3 biggest fires in 2014 had the following fuel
factors tons/acre:
100 Mile
32.919961
Funny River
49.816033
OK RX
21.84224
2. ADEC assumes 100% of fuel load consumed.
3. ADEC uses adjusted to Alaska vegetation types, should not lead to a big discrepancy as at least 80% area
factors are site specific see 1 above.
4. EPA uses fuel moisture in % from nearest WIMS and ADEC uses the following moisture gradation vwet,
moist, mod, dry, vdry depending on month and location (FEPS Moisture regime curve).
5. ADEC uses simplified approach in smoldering emission calculations and we are interested in total
emissions and EPA is interested in hourly emissions (likely for modeling purposes) and in total.
Similarly, NC accepted our emission estimates, but wanted these comments included in the documentation for
the 2014v2 NEI:
SmartFire Data Reconciliation Process: Our understanding is that a prescribed fire could be merged with
a wildfire when they overlap in space and time (e.g., within 1 km apart on the same day) even when the
fires come from the same data sources (i.e., State2014_NC). For these cases, the fire with the largest
acreage is selected and classified as a wildfire. Going forward, it will be most helpful if the methodology
could be changed to keep the fires separate so that wildfire acreage is not overestimated and prescribed
fire acreage underestimated in the inventory. As I mentioned on the phone, it is important to be able to
keep track of the type of each fire since they are treated differently under the regional haze rule (and
exceptional events rule as well).
Data Source Codes: If the methodology cannot be changed as noted previously, it will be helpful to
provide a data source code to identify when a prescribed fire is merged with a wildfire when they
overlap in space and time. This would be very helpful for understanding when the state submitted data
are modified by the system.
7.6 Q iranee
Quality assurance steps were implemented at each step of processing of the 2014 NEI to ensure the integrity of
the product. In general, quality control involved review of data sets to ensure that data did not contain errors
and reflected the most accurate available information. Quality control was performed on input fire information
data sets, SF2 daily fire location output, and BlueSky Framework emissions estimates.
7.6.1 Input Fire Information Data Sets
Input data set quality control is described in the data preparation section above. In general, the following steps
were followed.
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.
7-15
-------
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.
7.6.2 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.
7.6.3 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.
Removed any fires in Alaska that had only FIMS as a source.
Produced and reviewed summary tables and plots of the 2014 fire inventory data.
Compared acres burned by state to National Interagency Fire Center data as well as the 2015 National
Prescribed Fire Use Survey Report (of 2014 data) to ensure the summary values were within reasonable
range.
7.6.4 Additional quality assurance on final results
WLF emissions developed using the methods described above were compared to EPA's 2011 estimates, since
the models used are similar. The spatial (and temporal) patterns seen in the data correspond to what was
expected in 2014, and how the domains changed from 2011 -In general, 2014 was a "better" fire year than 2011
as fewer acres were burned (about 30% less), so the emissions are expected to be lower in 2014 compared to
2011. The trends graphic in Figure 7-4 shows how the 2014 PM2.5 estimates compare to other years (using
similar methods). These trends represent only the lower 48 states.
7-16
-------
Figure 7-4: PM2.5 WLF emissions trends from 2007-2014 using SF2 (for the lower 48 states)
2500 1
J 2000
1500
ill nil
2007 2008 2009 2010 2011 2014
Year
In comparing the 2014 estimates to previous years, the following points of QA that were made should also be
noted:
2011 emissions are much lower than 2014. However, it is within the range of the previous 5 inventories.
The average wildland fire PM2.5 emissions for 2007-2010 and 2011 is 1.66 million tons, while 2014 total
emission is 1.47 million tons (excluding Alaska, Hawaii, and Puerto Rico).
The major difference between 2014 and previous years is in wildfires because prescribed burn emissions
stay relatively consistent over the years, averaging 792 thousand tons for previous years vs. 770
thousand tons for 2014 (excluding Alaska, Hawaii, and Puerto Rico). Wildfire activity is driven by the
state of the climate, which varies greatly from year to year and from region to region, as well as by other
factors such as fuel accumulation, human activity, lightning storm, etc. Many of the checks made on
these parameters match what would be expected to happen to WLF emissions in 2014 in that domain.
Examples of this type of QA include: 2014 was one of the wettest years for AK, which explains the
decrease in wildfire activity in Alaska. The opposite was seen in California where it had suffered a few
consecutive years of drought and experienced greater wildfire activity in 2014 than in 2011. Yet another
example is 2011 was the driest year on record for Texas so it made sense that Texas had higher
emissions in 2011 than in 2014.
Georgia and Washington were the only states to submit emissions data. A comparison of the data between the
Georgia-submitted emissions and SF2-generated emissions for Georgia showed a very good match for wildfires,
but a marginal match for prescribed fires. Due to that concern and some concerns that Georgia had on the
spatial extent of emissions estimate on a county basis for Georgia in SF2 and on VOC emissions being too high
with EPA methods, they submitted their own emissions in 2014. Similarly, in comparing EPA-generated emission
estimates with WA's estimates, they decided they needed to submit emissions for the reasons outlined earlier
as part of the comments they sent to EPA. In moving forward, another vital part of QA is to better understand
state-submitted comments even though they accepted our emission estimates for the 2014 NEI.
7-17
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7.7 Summary g1
In the 2014 NEI estimates, wildland fires burned about 15.2 million acres in the United States and emitted
almost 1.7 million tons of PM2.5. Of this area, about 4.2 million acres (24%) were burned by wildfires and 10.9
million acres (76%) by prescribed fires. Wildfire PM2.5 emissions account for 53% and prescribed burns account
for 47% of the total emissions in this emissions inventory. Table 7-8 summarizes acres burned and PM2.5
emissions by state, fire type, and combustion phase. Additional details can be found in the STI documentation
referenced below. Note that the GA and WA numbers listed below are from the S/L/T submission they made to
this data category.
Table 7-8: Summary of NEI acres burned and PM2.5 emissions by state, fire type, and combustion phase
State
Area (Acres
PM2.5 (Tons)
Total
Wildfire
Prescribed
Fire
Total
PM2.5
Emissions
Wildfire
Prescribed Fire
Subtotal
Flaming
Smolde
ring
Sub
total
Flaming
Smolde
ring
Alabama
1,140,870
74,433
1,066,437
69,117
9,001
2,882
6,119
60,116
20,528
39,588
Alaska
294,644
290,177
4,467
173,411
172,420
141,490
30,929
991
717
274
Arizona
367,897
249,873
118,023
26,939
20,557
10,525
10,032
6,381
4,279
2,102
Arkansas
449,046
21,713
427,333
48,493
4,112
2,400
1,712
44,380
26,567
17,814
California
788,143
635,494
152,649
295,438
271,220
203,701
67,519
24,218
16,483
7,735
Colorado
88,950
33,803
55,147
6,312
805
359
446
5,507
3,686
1,821
Connecticut
606
118
488
68
15
6
9
53
14
39
Delaware
3,013
0
3,013
160
0
0
0
160
57
104
Florida
1,802,824
110,910
1,691,914
97,306
6,377
1,949
4,428
90,929
29,297
61,631
Georgia (S/L/T)
1,380,782
23,176
1,357,606
56,281
1,142
1,032
110
55,141
48,319
6,821
Hawaii
56,920
0
56,920
11,150
0
0
0
11,150
0
11,150
Idaho
374,339
229,963
144,375
54,357
35,133
23,186
11,948
19,224
13,524
5,700
Illinois
139,138
2,816
136,322
9,901
303
153
150
9,598
4,505
5,092
Indiana
55,577
1,190
54,387
5,306
141
69
72
5,165
2,949
2,216
Iowa
212,266
12,761
199,506
12,396
987
432
555
11,409
4,521
6,888
Kansas
490,050
124,687
365,363
24,405
6,843
2,254
4,589
17,562
5,244
12,318
Kentucky
113,246
48,999
64,247
30,106
22,464
13,888
8,576
7,642
3,978
3,664
Louisiana
711,525
44,039
667,486
86,691
26,711
24,764
1,947
59,980
43,931
16,049
Maine
3,038
216
2,822
477
53
39
14
424
305
119
Maryland
19,076
3,168
15,909
2,836
1,487
1,334
153
1,349
986
363
Massachusetts
2,858
1,284
1,575
284
133
47
86
152
89
63
Michigan
33,478
3,287
30,191
2,710
331
147
184
2,379
1,342
1,036
Minnesota
297,587
4,934
292,653
22,630
850
473
376
21,780
12,150
9,630
Mississippi
562,702
41,745
520,956
26,913
3,284
1,123
2,161
23,629
8,921
14,708
Missouri
501,719
31,394
470,324
63,143
7,057
4,748
2,309
56,086
36,992
19,094
Montana
226,966
35,729
191,237
27,392
6,008
4,951
1,057
21,384
15,494
5,890
Nebraska
160,720
23,796
136,924
7,530
1,135
476
658
6,395
2,599
3,796
Nevada
100,586
85,116
15,470
9,466
8,672
5,180
3,492
794
562
232
New Hamp.
447
79
369
56
16
8
8
40
17
22
New Jersey
32,359
8,953
23,406
7,327
3,966
3,286
680
3,361
2,728
633
7-18
-------
State
Area (Acres
PM2.5 (Tons)
Total
Wildfire
Prescribed
Fire
Total
PM2.5
Emissions
Wildfire
Prescribed Fire
Subtotal
Flaming
Smolde
ring
Sub
total
Flaming
Smolde
ring
New Mexico
142,832
56,547
86,285
9,005
5,676
3,531
2,145
3,329
2,035
1,295
New York
9,788
2,945
6,843
1,207
464
255
209
743
443
299
N. Carolina
153,600
25,053
128,547
13,881
3,008
1,898
1,110
10,872
6,750
4,123
N. Dakota
135,184
1,383
133,802
9,870
87
35
52
9,783
5,085
4,699
Ohio
27,726
4,003
23,723
3,511
1,378
802
575
2,133
1,164
969
Oklahoma
541,760
163,871
377,888
41,022
14,244
5,607
8,637
26,778
13,047
13,731
Oregon
1,311,203
1,005,701
305,501
135,085
94,823
63,336
31,487
40,262
30,512
9,750
Pennsylvania
21,382
5,384
15,998
3,338
1,499
888
611
1,839
1,169
669
Puerto Rico
21,593
193
21,400
576
2
0
2
574
0
574
Rhode Island
246
24
222
16
5
3
3
11
3
7
S. Carolina
401,805
14,722
387,083
22,180
1,664
540
1,124
20,516
8,519
11,997
S. Dakota
96,903
15,262
81,642
15,265
2,049
1,325
724
13,216
9,026
4,190
Tennessee
127,020
22,836
104,184
16,576
5,592
2,492
3,100
10,984
4,492
6,492
Texas
804,389
159,399
644,990
50,670
22,768
17,540
5,228
27,902
11,637
16,265
Utah
118,434
48,240
70,194
6,486
2,591
1,295
1,296
3,896
2,238
1,658
Vermont
1,345
163
1,181
112
27
11
16
85
52
33
Virginia
117,354
16,774
100,580
16,682
5,395
2,957
2,439
11,287
6,248
5,038
Washington
(S/L/T)
637,056
513,889
123,157
119,126
104,950
39,225
65,724
14,176
4,403
9,772
West Virginia
47,657
15,397
32,259
12,676
7,103
4,372
2,731
5,573
3,721
1,851
Wisconsin
69,246
2,868
66,378
4,314
196
72
124
4,118
2,005
2,113
Wyoming
62,704
15,763
46,941
6,863
1,502
1,072
430
5,361
3,999
1,361
Grand Total
15,177,838
4,239,624
10,938,214
1,658,014
875,230
622,039
253,191
782,784
402,698
380,086
In the 2014 NEl, the table above and Figure 7-5 (Puerto Rico data is not shown) shows that the bulk of emissions
originate from two regions: The West and the Southeast. This spatial distribution of emissions is consistent with
previous national fire inventories. Spring and winter emissions are mostly from the southeastern states, where
prescribed burning is a common land management practice in spring, and, to a lesser extent, at the end of the
year. Summer/fall emissions occur primarily in the West, particularly in California, Oregon, Washington, and
Idaho.
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Figure 7-5: 2014 NEI wildland fire PM2 s emission density
2014 Wildland Fire PM2s Emission Density
tons per square mile
7.8 Improvements in the 2014 NEI vl compared to the 2011 NEI
The methods used to develop the 2014 WLF NEI included several changes and improvements over methods
used in the previous NEI cycle (2011).
7.8.1 Fire activity data
The 2014 NEI incorporates a total of 30 S/L/T and national fire activity data sets (23 S/L/T and 7 national data
sets), similar to the breadth of the data used for the 2011 NEI (31 total, 24 S/L/T and 7 national data sets).
However, in the 2014 effort, S/L/T data submitters were asked to respond to a data questionnaire by providing
data completeness information for their data. We could use this self-assessed information from 21 S/L/T
agencies to better understand their data and make an informed decision about how their fire activity data
should be supplemented with national data sets (Table 7-2). Instead of applying the national fire activity data
sets universally to all S/L/T entities, as was done for the 2011 NEI, data supplement policies were directly guided
by S/L/T input to ensure the final fire activity data best represented S/L/T knowledge.
In addition, the FACTS dataset for 2014 was obtained in polygon format, an improvement over the point data
used in the 2011 NEI. Polygons provide more accurate fire location, shape, and size information. Also, NFPORS
fire activity data for the DOI was added to the national data sets that helped improve the fire emissions
estimates.
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7.8.2 SrnartFire2 processing
During SF2 processing of fire activity data, two software issues were identified and workarounds to address
these issues were made. First, some daily fire records were lost when daily exports were created (saving one
export file per day). In previous years, daily export was the preferable export method due to system
performance concerns. However, upgraded computing resources for SF2 allowed for exporting all of 2014's data
at once, eliminating the inadvertent loss of some daily fire records.
Second, it was found that some input fires were incorrectly associated with two separate fire events, resulting in
double counting of acres burned. This issue was caused by reconciling fire events twice in an effort to prevent
double counting caused by another reason, namely, fires that intersect within spatial and temporal uncertainties
are not associated and reconciled. The issue was resolved by developing a standalone R script to sift through SF2
inputs and outputs to identify the duplicated fire events. The duplicates were removed from subsequent
processing. Refer to the STI documentation [ref 1] for further details.
7.8.3 Emission factors
As previously mentioned, updated HAP emission factors were provided by EPA based on a peer reviewed
publication [ref 5], The new emission factors were region- and fire-type-specific and were based on the latest
research carried out by the Missoula Fire Sciences Laboratory at the USFS. A complete list of these emission
factors was provided earlier and is available in the literature.
7.9 Future areas of improvement
7.9.1 More accurate fuel loading
A limitation of the BlueSky Framework v3.5.1 is that it only accepts fire location point input. For a given fire
location, the fuel bed assignment is based upon the point location. When a fire is small, the fuel bed at a single
point may be representative of the primary fuels burned. However, for large fires, basing the fuel loading within
the fire perimeter on a single point could result in significant over- or under-estimation of fuels consumed,
possibly biasing the emission estimate. We recommend exploring options to provide more accurate fuel loading
information for large fires. Potentially, this could be achieved by modifying SF2 and BlueSky Framework so that a
given fire could be represented by multiple points or a polygon instead of one single point.
7.9.2 Pile burrs emissions
During the data collection process, we received pile burn data sets from 13 S/L/T data submitters. In addition,
pile burn data were included in the data we acquired from two national sources, NFPORS and FACTS. To
reasonably estimate emissions from pile burns, two pieces of pile information are required: count and fuel
loading of the piles (fuel loading may also be estimated from pile volume and composition). There was only one
state whose pile burn data provided the minimum amount of information. In cases where the minimum
required information is not provided, estimating pile burn emissions requires the use of default values for either
pile count or pile fuel loading. However, due to time and budgetary constraints, it was not feasible to request
missing information from data submitters or develop default values collectively with both the research
community and S/L/T agencies for the 2014 NEI vl.
Most of the pile burn data sets for 2014 included hundreds or thousands of records, suggesting that the
emissions from pile burning practices are not trivial. For future El development, we recommend that methods
for estimating pile burn emissions be considered. Inclusion of pile burns in future Els would provide a more
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complete estimation of emissions from wildland fires. To do this with more confidence requires default
information to be available on pile burns in the Bluesky framework.
7.9.3 Sma?tFire2 improvements
Two issues were identified with SF2 during the development of the 2014 NEI. First, daily fire records may be lost
when daily exports are created. Second, input fires can be incorrectly associated into two separate fire events,
resulting in double counting of acres burned. Although corrective steps were adopted to mitigate the impacts
the issues had on the data, these bugs should be addressed before future SF2 development.
7.9.4 VOC emission factors
At least two states, Georgia and Alaska, have noted that the emission factor for VOC used for the NEI is too high
as default from Bluesky. It is recommended that a literature review of VOC emission factors be conducted and
that the most up-to-date value(s) be utilized for future emission inventory development.
7.9.5 Centralized fire information database
Beginning with the 2011 version, the NEI has incorporated S/L/T fire activity data sets. The collection, review,
cleaning, and standardization of a few dozen data sets require a significant amount of time and labor. This
process could be streamlined if there were a centralized fire activity database where S/L/T agencies could store
all their fire activity data. All the data would be stored in one place and in one universal format. Such a
centralized database would not only save both time and money for future emission inventory development, but
also potentially serve other purposes such as prescribed burn planning, permitting, and tracking. Loading and
quality assuring these data in EIS could be investigated for future NEIs.
7.10
1. Sonoma Technology, Inc. (ShihMing Huang, Nathan Pavlovic, and Yuan Du), Technical Documentation for
Wildfire and Prescribed Fire Portion of the 2014 National Emissions Inventory, Draft Report prepared for
U.S. EPA (STI-916054-6590-DR), October 2016.
2. Larkin N.K., O'Neill S.M., Solomon R., Raffuse S., Strand T.M., Sullivan D.C., Krull C., Rorig M., Peterson J., and
Ferguson S.A. (2009) The BlueSkv smoke modeling framework. Int. J. Wildland Fire, 18(8), 906-920, (STI-
3784), December.
3. Du Y., Raffuse S.M., and Reid S.B. (2013) Technical guidance for using SmartFire2 / BlueSky Framework to
develop national wildland fire emissions inventories. User's guide prepared for the U.S. Environmental
Protection Agency, Research Triangle Park, NC by Sonoma Technology, Inc., Petaluma, CA, STI-910414-5593,
April 26.
4. Wiedinmyer C., Akagi S.K., Yokelson R.J., Emmons L.K., Al-Saadi J.A., Orlando J.J., and Soja A.J. (2011) The
Fire INventorv from NCAR (FINN}: a high resolution global model to estimate the emissions from open
burning. Geosci. Model Dev., 4, 625-641.
5. Urbanski S.P. (2014) Wildland fire emissions, carbon, and climate: emissions factors. Forest Ecology and
Management, 317, 51-60.
6. Du Y., Huang S., Raffuse S.M., and Reid S. (2013) Preparation of version 2 of the wildland fire emissions
inventory for 2011. Technical memorandum prepared for the U.S. Environmental Protection Agency,
Research Triangle Park, NC, by Sonoma Technology, Inc., Petaluma, CA, STI-910414-5641, April 26.
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8 Biogenics - Vegetation am
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 NEl, only the emissions from vegetation and soils are included, but other relevant sources
include volcanic emissions, 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.
8.1 Sector description
In the 2014 NEI, biogenic emissions are included in the nonpoint data category, in the EIS sector "Biogenics -
Vegetation and Soil." Table 8-1 lists the two source classification codes (SCCs) used in the 2014 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 8-1: SCCs for Biogenics - Vegetation and Soil
SCC
SCC Level 3
SCC Level 4
2701200000
Vegetation
Total
2701220000
Vegetation/Agriculture
Total
The biogenic emissions for the 2014 National Emissions Inventory (NEI) were computed based on 2014
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. The BEIS3.61 model creates gridded, hourly, model-species emissions from
vegetation and soils. The 12-kilometer gridded hourly data are summed to monthly and annual level, and are
mapped from 12-kilometer grid cells to counties using a standard mapping file. 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 2005 chemical mechanism (CB05). The NEI
pollutants produced are: CO, VOC, NOx, methanol, formaldehyde and acetaldehyde. VOC is the sum of all
biogenic species except CO, nitrogen oxide (NO), and sesquiterpene (SESQ). Mapping of BEIS pollutants 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, NO, SESQ
BEIS3.61 has some important updates from BEIS 3.14. These include the incorporation of Version 4.1 of the
Biogenic Emissions Land Use Database (BELD4) for the 2011v6.3 platform, and the incorporation of a canopy
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model to estimate leaf-level temperatures [ref 1], BEIS3.61 includes a two-layer canopy model. Layer structure
varies with light intensity and solar zenith angle. 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 2],
The new algorithm 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 8-2.
Table 8-2: Meteorological variables required by BEIS 3.61
Variable
Description
LAI
leaf-area index
PRSFC
surface pressure
Q2
mixing ratio at 2 m
RC
convective precipitation per met TSTEP
RGRND
solar rad reaching surface
RN
non-convective precipitation per met TSTEP
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
BELD version 4.1 is based on an updated version of the U.S. Department of Agriculture (USDA) and U.S. Forest
Service (USFS) Forest Inventory and Analysis (FIA) database. FIA 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 5.1 includes recent updates of these data through the year 2014 (from 2001). Earlier versions of BELD
used an older version of the FIA database that had included data only through the year 2012. Canopy coverage is
based on the Landsat satellite National Land Cover Database (NLCD) product from 2011. 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 NLCD canopy coverage. The 2011 NLCD provides land cover
information with a native data grid spacing of 30 meters. For land areas outside the conterminous United States,
500-meter grid spacing land cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) is
used.
Other improvements to the BELDv4.1 data included the following:
Used 30-meter NASA's Shuttle Radar Topography Mission (SRTM) elevation data which will more
accurately define the elevation ranges of the vegetation species.
Used the 2011 30-meter USDA Cropland Data Layer (CDL) data to improve the BELD4 agricultural
categories.
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After 2014vl of the NEl, additional quality assurance of the BELD4.1 resulted in minor corrections to the
land use data in three states including Washington, Texas and Florida. These minor corrections were
implemented in the 2014v2 NEI and represent about less than 1% reduction in biogenic emissions in
these three states.
8.2 sources of dsta overview and sstfictton nisrsrchy
The only source of data for this sector is the EPA-estimated emissions from BEIS3.61. States are neither required
nor encouraged to report biogenic emissions, and no state has done this. The name of the EPA dataset in the EIS
is: 2014EPA_biogenics.
8.3 :ฆ - c .v.'. for the sector
The spatial coverage of the biogenics emissions is governed by the 2011 Version 6 Air Emissions Modeling
Platforms modeling domain which covers all counties in the lower 48 states.
FI4.
References for biogenics
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.
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/B-19-034
Environmental Protection Air Quality Assessment Division July 2018
Agency Research Triangle Park, NC
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