* _ \
iWJ
PRO^
2011 National Emissions Inventory, Version 2
Technical Support Document
-------
-------
EPA-454/B-19-029
August 2015
2011 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
-------
Contents
List of Tables vii
List of Figures xii
Acronyms and Chemical Notations xiv
1 Introduction 1
1.1 What data are included in the 2011 NEI, Version 2? 1
1.2 What is included in this documentation? 1
1.3 Where can I obtain the 2011 v2 NEI data? 2
1.3.1 EPA continues to review and streamline the approach for accessing the NEI data. The 2011 NEI
data are available in several different ways. Emission Inventory System Gateway 2
1.3.2 2011 NEI main webpage 2
1.3.3 Air Emissions and "Where you live" 2
1.3.4 Modeling files 3
1.4 Why is the NEI created? 3
1.5 How is the NEI created? 3
1.5.1 NEI 2011 v2 point source updates 5
1.5.2 NEI 2011 v2 nonpoint source updates 9
1.5.3 NEI 2011 v2 mobile source updates 9
1.5.4 NEI 2011 v2 fires updates 10
1.6 Who are the target audiences for the 2011 NEI? 10
1.7 What are appropriate uses of the NEI 2011 v2 and what are the caveats about the data? 11
2 2011 inventory contents overview 14
2.1 What are EIS sectors and what list was used for this document? 14
2.2 What do the data show about the sources of data in the 2011 NEI? 16
2.3 What are the top sources of some key pollutants? 22
2.4 How does this NEI compare to past inventories? 24
2.4.1 Differences in approaches 24
2.4.2 Differences in emissions between 2011 and 2008 NEI 26
2.5 How well are tribal data and regions represented in the 2011 NEI? 30
2.6 What does this NEI tell us about mercury? 31
3 Stationary sources 37
3.1 Stationary source approaches 37
3.1.1 Sources of data overview and selection hierarchies 37
3.1.2 Particulate matter augmentation 41
3.1.3 Chromium augmentation 42
3.1.4 Use of the 2011 Toxics Release Inventory 43
3.1.5 HAP augmentation based on emission factor ratios 52
i
-------
3.1.6 Priority Facility List 54
3.1.7 EPA nonpoint data 54
3.1.8 References for Stationary sources 61
3.2 Agriculture - Crops & Livestock Dust 61
3.2.1 Sector description 61
3.2.2 Sources of data overview and selection hierarchy 62
3.2.3 Spatial coverage and data sources for the sector 63
3.2.4 EPA-developed agricultural crops and livestock dust emissions data 63
3.2.5 Summary of quality assurance methods 67
3.2.6 References for Agriculture - Crop & Livestock Dust 67
3.3 Agriculture - Fertilizer Application 67
3.3.1 Sector description 67
3.3.2 Sources of data overview and selection hierarchy 68
3.3.3 Spatial coverage and data sources for the sector 71
3.3.4 EPA-developed agricultural fertilizer application emissions data 71
3.3.5 Summary of quality assurance methods 77
3.3.6 References for Agriculture - Fertilizer Application 77
3.4 Agriculture - Livestock Waste 77
3.4.1 Sector description 77
3.4.2 Sources of data overview and selection hierarchy 77
3.4.3 Spatial coverage and data sources for the sector 82
3.4.4 EPA-developed livestock waste emissions data 82
3.4.5 Summary of quality assurance methods 86
3.4.6 References for Agriculture - Livestock Waste 86
3.5 Bulk Gasoline Terminals and Gas Stations 87
3.5.1 Sector description 87
3.5.2 Source of data overview and selection hierarchy 87
3.5.3 Spatial coverage and data sources for the sector 90
3.5.4 EPA-developed emission estimates 90
3.5.5 References for Bulk Gasoline Terminals and Gas Stations 103
3.6 Commercial Cooking 105
3.6.1 Sector description 105
3.6.2 Sources of data overview and selection hierarchy 105
3.6.3 Spatial coverage and data sources for the sector 107
3.6.4 EPA-developed commercial cooking emissions data 107
3.6.5 Summary of quality assurance methods 110
3.6.6 References for Commercial Cooking 110
3.7 Dust - Construction Dust Ill
3.7.1 Sector description Ill
3.7.2 Sources of data overview and selection hierarchy Ill
3.7.3 Spatial coverage and data sources for the sector 113
3.7.4 Construction - Non-Residential - EPA estimates 113
3.7.5 Construction - Residential -EPA estimates 116
3.7.6 Construction - Road- EPA estimates 118
3.8 Dust - Paved Road Dust 121
ii
-------
3.8.1 Sector description 121
3.8.2 Sources of data overview and selection hierarchy 121
3.8.3 Spatial coverage and data sources for the sector 122
3.8.4 EPA methodology for paved road dust 123
3.8.5 Summary of quality assurance methods 127
3.8.6 References for Dust - Paved Road Dust 127
3.9 Dust - Unpaved Road Dust 127
3.9.1 Sector description 127
3.9.2 Sources of data overview and selection hierarchy 128
3.9.3 Spatial coverage and data sources for the sector 129
3.9.4 EPA methodology for unpaved road dust 129
3.9.5 Summary of quality assurance methods 132
3.9.6 References for Dust - Unpaved Road Dust 132
3.10 Fuel Combustion - Electric Generation 133
3.10.1 Sector description 133
3.10.2 Sources of data overview and selection hierarchy 134
3.10.3 Spatial coverage and data sources for the sector 137
3.10.4 PM Augmentation for EGUs 138
3.10.5 EPA-developed EGU emissions data 139
3.10.6 Alternative facility and unit IDs needed for matching with other databases 140
3.10.7 Summary of quality assurance methods 140
3.11 Fuel Combustion - Industrial Boilers, ICEs 140
3.11.1 Sector description 141
3.11.2 Sources of data overview and selection hierarchy 141
3.11.3 Spatial coverage and data sources for the sector 146
3.11.4 EPA-developed fuel combustion -Industrial Boilers, ICEs emissions data 147
3.11.5 Summary of quality assurance methods 148
3.11.6 References for Fuel Combustion - Industrial Boilers, ICEs 149
3.12 Fuel Combustion - Commercial/Institutional 150
3.12.1 Sector description 150
3.12.2 Sources of data overview and selection hierarchy 150
3.12.3 Spatial coverage and data sources for the sector 153
3.12.4 EPA-developed commercial/institutional fuel combustion data 154
3.12.5 Summary of quality assurance methods 156
3.12.6 References for Fuel Combustion - Commercial/Institutional 156
3.13 Fuel Combustion - Residential - Natural Gas, Oil, and Other 157
3.13.1 Source category description 157
3.13.2 Sources of data overview and selection hierarchy 157
3.13.3 Spatial coverage and data sources for the sector 159
3.13.4 EPA Residential Heating estimates for oil, natural gas and other fuels 159
3.13.5 Summary of quality assurance methods 160
3.14 Fuel Combustion - Residential - Wood 160
3.14.1 Sector description 160
3.14.2 Sources of data overview and selection hierarchy 161
3.14.3 Spatial coverage and data sources for the sector 162
3.14.4 EPA-developed residential wood combustion estimates 162
iii
-------
3.14.5 Summary of quality assurance methods 170
3.14.6 References for Fuel Combustion - Residential - Wood 171
3.15 Industrial Processes - Cement Manufacturing 171
3.15.1 Sector description 171
3.15.2 Sources of data overview and selection hierarchy 171
3.15.3 Spatial coverage and data sources for the sector 172
3.16 Industrial Processes - Chemical Manufacturing 172
3.16.1 Sector description 172
3.16.2 Sources of data overview and selection hierarchy 172
3.16.3 Spatial coverage and data sources for the sector 173
3.17 Industrial Processes - Ferrous Metals 173
3.17.1 Sector description 173
3.17.2 Sources of data overview and selection hierarchy 173
3.17.3 Spatial coverage and data sources for the sector 173
3.18 Industrial Processes - Mining 174
3.18.1 Sector description 174
3.18.2 Sources of data overview and selection hierarchy 175
3.18.3 Spatial coverage and data sources for the sector 178
3.18.4 EPA-developed emissions 178
3.18.5 References for Industrial Processes - Mining 183
3.19 Industrial Processes - Non-ferrous Metals 184
3.19.1 Sector description 184
3.19.2 Sources of data overview and selection hierarchy 184
3.19.3 Spatial coverage and data sources for the sector 184
3.20 Industrial Processes - Oil & Gas Production 184
3.20.1 Sector description 184
3.20.2 Sources of data overview and selection hierarchy 187
3.20.3 Spatial coverage and data sources for the sector 189
3.20.4 EPA emissions calculation approach 190
3.20.5 Summary of data quality assurance methods 190
3.21 Industrial Processes - Petroleum Refineries 192
3.21.1 Sector description 192
3.21.2 Sources of data overview and selection hierarchy 192
3.21.3 Spatial coverage and data sources for the sector 193
3.22 Industrial Processes - Pulp & Paper 193
3.22.1 Sector description 193
3.22.2 Sources of data overview and selection hierarchy 193
3.22.3 Spatial coverage and data sources for the sector 193
3.23 Industrial Processes - Storage and Transfer 194
3.23.1 Sector description 194
3.23.2 Sources of data overview and selection hierarchy 194
3.23.3 Spatial coverage and data sources for the sector 194
3.24 Industrial Processes - NEC (Other) 194
3.24.1 Sector description 194
iv
-------
3.24.2 Sources of data overview and selection hierarchy 195
3.24.3 Spatial coverage and data sources for the sector 195
3.25 Miscellaneous Non-industrial NEC (Other) 195
3.25.1 Sector description 195
3.25.2 Sources of data overview and selection hierarchy 195
3.25.3 Spatial coverage and data sources for the sector 203
3.26 Solvent - Consumer & Commercial Solvent Use 203
3.26.1 Sector description 203
3.26.2 Sources of data overview and selection hierarchy 205
3.26.3 Spatial coverage and data sources for the sector 207
3.26.4 Development of EPA Emissions for Consumer and Commercial Solvents 207
3.26.5 Summary of data quality assurance methods 214
3.26.6 References for Solvent -Consumer & Commerical Solvent Use 215
3.27 Solvent - Non-Industrial Surface Coating 215
3.27.1 Sector description 215
3.27.2 Sources of data overview and selection hierarchy 216
3.27.3 Spatial coverage and data sources for the sector 217
3.27.4 EPA-developed emissions 218
3.27.5 Summary of quality assurance methods 219
3.27.6 References for Solvent - Non-Industrial Surface Coating 219
3.28 Solvent - Degreasing 219
3.28.1 Sector description 219
3.28.2 Sources of data overview and selection hierarchy 220
3.28.3 Spatial coverage and data sources for the sector 223
3.28.4 EPA-developed emissions 223
3.28.5 References for Solvent - Degreasing 224
3.29 Solvent - Dry Cleaning 224
3.29.1 Sector description 224
3.29.2 Sources of data overview and selection hierarchy 225
3.29.3 Spatial coverage and data sources for the sector 227
3.29.4 EPA-developed emissions 227
3.29.5 References for Solvent - Dry Cleaning 228
3.30 Solvent - Graphic Arts 228
3.30.1 Sector description 228
3.30.2 Sources of data overview and selection hierarchy 229
3.30.3 Spatial coverage and data sources for the sector 232
3.30.4 EPA-developed emissions 232
3.30.5 References for Solvent - Graphic Arts 233
3.31 Solvent - Industrial Surface Coating 233
3.31.1 Sector description 233
3.31.2 Sources of data overview and selection hierarchy 235
3.31.3 Spatial coverage and data sources for the sector 239
3.31.4 EPA-developed emissions 239
3.31.5 Summary of data quality assurance methods 244
3.31.6 References for Solvent - Industrial Surface Coating 244
v
-------
3,32 Waste Disposal 245
3.32.1 Sector description 245
3.32.2 Spatial coverage and data sources for the sector 248
3.32.3 Selection hierarchy 248
3.32.4 EPA-developed emissions of Open Burning of Leaf and Brush Species 252
3.32.5 EPA-developed emissions of Open Burning of Municipal Solid Waste (MSW) 254
3.32.6 EPA-developed emissions of Open Burning of Land Clearing Debris 257
3.32.7 EPA-developed emissions of Publicly Owned Treatment Works (POTW) 261
3.32.8 EPA-developed emissions of Landfills 263
3.32.9 References for Waste Disposal 267
4 Mobile sources 269
4.1 Mobile sources overview 269
4.2 Aircraft 269
4.2.1 Revisions for the NEI 2011 v2 269
4.2.2 Sector description 269
4.2.3 Sources of data overview and selection hierarchy 270
4.2.4 Spatial coverage and data sources for the sector 271
4.2.5 EPA-developed aircraft emissions estimates 271
4.2.6 Summary of quality assurance methods 273
4.2.7 References for Aircraft 274
4.3 Commercial Marine Vessels 274
4.3.1 Revisions for the NEI 2011 v2 274
4.3.2 Sector description 275
4.3.3 Sources of data overview and selection hierarchy 276
4.3.4 Spatial coverage and data sources for the sector 276
4.3.5 EPA-developed commercial marine vessel emissions data 277
4.3.6 Summary of quality assurance methods 278
4.3.7 References for Commercial Marine Vessels 279
4.4 Locomotives 279
4.4.1 Revisions for the NEI 2011 v2 279
4.4.2 Sector description 279
4.4.3 Sources of data overview and selection hierarchy 280
4.4.4 Spatial coverage and data sources for the sector 280
4.4.5 EPA-developed locomotive emissions data 280
4.4.6 Summary of quality assurance methods 281
4.4.7 References for Locomotives 282
4.5 Nonroad Equipment - Diesel, Gasoline and other 282
4.5.1 Revisions for the NEI 2011 v2 283
4.5.2 Sector description 283
4.5.3 Sources of data overview and selection hierarchy 283
4.5.4 Spatial coverage and data sources for the sector 285
4.5.5 EPA-developed NMIM-based nonroad emissions data 285
4.5.6 References for Nonroad Equipment 290
4.5.7 Sector description 290
4.5.8 Sources of data overview, selection hierarchy, and changes to default data in NEI 2011 v2 290
4.5.9 Calculation of EPA Emissions 299
vi
-------
4.5.10 On-road mobile emissions data for Alaska, Hawaii, Puerto Rico and the Virgin Islands 311
4.5.11 Summary of quality assurance methods 311
4.5.12 Supporting data 312
4.5.13 References for On-road Mobile 317
5 Fires 318
5.1 Wildfires and Prescribed Burning 318
5.1.1 Sector description 318
5.1.2 Sources of data overview and selection hierarchy 319
5.1.3 Spatial coverage and data sources for the sector 320
5.1.4 EPA-developed fire emissions estimates 320
5.1.5 Summary of quality assurance methods 327
5.1.6 References for Wildfires and Prescribed Burning 330
5.2 Fires - Agricultural field Burning 331
5.2.1 Sector description 331
5.2.2 Sources of data overview and selection hierarchy 332
5.2.3 Spatial coverage and data sources for the sector 333
5.2.4 EPA-developed agricultural emissions data 336
5.2.5 Summary of quality assurance methods 340
5.2.6 References for Agricultural Field Burning 342
6 Biogenics - Vegetation and Soil 343
6.1 Sector description 343
6.2 Sources of data overview and selection hierarchy 344
6.3 Spatial coverage and data sources for the sector 344
7 Supporting data and summaries 347
List of Tables
Table 1-1: Point source reporting thresholds (potential to emit) for CAPs in the AERR 4
Table 1-2: Examples of major current uses of the NEI 10
Table 2-1: EIS sectors and associated emissions categories and document sections 14
Table 2-2: EIS sectors and associated CAP emissions and total HAP (1000 short tons/year) 23
Table 2-3: Emission differences (tons) for CAPs, 2011 minus 2008 27
Table 2-4: Emission differences (tons) for select HAPs, 2011 minus 2008 27
Table 2-5: Tribal participation in the 2011 v2 NEI 30
Table 2-6: Facilities on Tribal lands with 2011 NEI emissions from EPA only 31
Table 2-7: 2011 v2 Hg emissions for each dataset type and group 33
Table 2-8: Trends in NEI mercury emissions - 1990, 2005, 2008 v3 and 2011 v2 34
Table 3-1: Data sources and selection hierarchy used for point sources 38
Table 3-2: Data sources and selection hierarchy used for nonpoint sources 40
Table 3-3: Valid chromium pollutant codes 42
Table 3-4: Mapping of TRI pollutant codes to EIS pollutant codes 47
vii
-------
Table 3-5: Pollutant groups 50
Table 3-6: HAP-augmentation dataset nickel species which should not have been used in the NEI 53
Table 3-7: Lead from HAP-augmentation from coal combustion that was not used 53
Table 3-8: New nonpoint Hg sources of emissions in the 2011 v2 NEI 55
Table 3-9: EPA-estimated emissions sources expected to be exclusively nonpoint 55
Table 3-10: Emissions sources with potential nonpoint and point contribution 57
Table 3-11: Algorithm for using survey data to determine source categories that should be augmented with EPA
nonpoint data for Industrial Combustion and Commercial/Institutional Combustion for Oil, Coal, and Other fuels
59
Table 3-12: Algorithm for using survey data to determine source categories that should be augmented with EPA
nonpoint data for Commercial/Institutional Combustion for Natural Gas and Biomass, and Gas Stations 59
Table 3-13: SCCs used in past inventories that were not included in the EPA's 2011 nonpoint estimates 61
Table 3-14: SCCs used in the 2011 NEI for the Agriculture - Crops & Livestock Dust sector 62
Table 3-15: Agencies that submitted Agricultural Crops and Livestock Dust data 62
Table 3-16: 2011 NEI agricultural crops and livestock dust data selection hierarchy 63
Table 3-17: Silt content for soil types in USDA surface soil map 64
Table 3-18: Number of passes or tillings per year 64
Table 3-19: Crosswalk between Crop Residue Management category and USDA data 65
Table 3-20: Acres planted by tillage type, Fallow and pasture in 2008 and 2011 66
Table 3-21: Agencies tagged values for Agriculture - Crop and Livestock Dust 67
Table 3-22: Source categories for Agricultural Fertilizer Application 68
Table 3-23: Agencies that submitted Agricultural Fertilizer Application data 70
Table 3-24: 2011 NEI Agricultural Fertilizer Application data selection hierarchy 70
Table 3-25: Fertilizers assigned to fertilizer groups 73
Table 3-26: Fertilizer Nitrogen content 74
Table 3-27: Fertilizer NH3 emission factors 75
Table 3-28: Agencies tagged values for Agriculture - Fertilizer 77
Table 3-29: Nonpoint SCCs with 2011 NEI emissions in the Livestock Waste sector 78
Table 3-30: Point SCCs with 2011 NEI emissions in the Livestock Waste sector - reported only by States 80
Table 3-31: Agencies that submitted Livestock Waste data 80
Table 3-32: 2011 NEI Agricultural Livestock Waste data selection hierarchy 81
Table 3-33: Emission factors for NH3 emissions used for EPA's Agricultural Livestock Waste data 84
Table 3-34: Agencies tagged values for Agriculture Livestock Waste 86
Table 3-35: 2011 NEI selection hierarchy for datasets used in Bulk Terminals sector 87
Table 3-36: Agencies that submitted data for the sector Bulk Gasoline Terminals and Gasoline Stations 88
Table 3-37: Nonpoint Stage I Gasoline Distribution SCCs 90
Table 3-38: Estimation of national 2008 VOC emissions for Pipelines and Bulk Terminals 91
Table 3-39: HAP speciation profiles and 2008 Bulk Terminal and Pipeline emissions 91
Table 3-40: Movement of finished motor gasoline by pipeline between PAD Districts, 2008 92
Table 3-41: Refinery, Bulk Terminal, and Natural Gas Plant Stocks of Motor Gasoline, 2008 92
Table 3-42: Pipeline Point Source SCCs 93
Table 3-43: Bulk Terminal Point Source SCCs 93
Table 3-44: Bulk Plant HAP Speciation Profiles and Total Emission Estimates 95
viii
-------
Table 3-45: Bulk Plant Point Source SCCs 96
Table 3-46: Tank Trucks in Transit VOC Emission Factors 98
Table 3-47: Tank Trucks in Transit HAP Speciation Profiles and Total Emission Estimates 98
Table 3-48: Tank Trucks in Transit Point Source SCCs 99
Table 3-49: Underground Storage Tank (UST) Breathing and Emptying Emissions 99
Table 3-50: UST Breathing and Emptying Point Source SCCs 100
Table 3-51: Temperature Data Used in Estimating True Vapor Pressure (2F) 101
Table 3-52: Stage I Service Station Unloading HAP Speciation Profiles and Total Emission Estimates 102
Table 3-53: Service Station Unloading: Submerged Fill Point Source SCCs 102
Table 3-54: Service Station Unloading: Splash Fill Point Source SCCs 102
Table 3-55: Service Station Unloading: Balanced Submerged Fill Point Source SCCs 102
Table 3-56: SCCs used in the Commercial Cooking sector 105
Table 3-57: Agencies that submitted Commercial Cooking data 105
Table 3-58: 2011 NEI Commercial Cooking data selection hierarchy 106
Table 3-59: Ratio of filterable PM to primary PM for PM2 s and PMW by SCC 108
Table 3-60: Fraction of restaurants with source category equipment and average number of units per restaurant.
109
Table 3-61: Agencies tagged values for Commercial Cooking 110
Table 3-62: SCCs in the 2011 NEI in the Dust - Construction Dust sector Ill
Table 3-63: Agencies that submitted Construction Dust data 112
Table 3-64: 2011 NEI Construction Dust data selection hierarchy 113
Table 3-65: SCC for Non-Residential Construction 114
Table 3-66: SCC for Residential Construction 116
Table 3-67: Surface soil removed per unit type 116
Table 3-68: Emission factors for Residential Construction 117
Table 3-69: SCC for Road Construction 118
Table 3-70: Spending per mile and acres disturbed per mile by highway type 119
Table 3-71: SCCs used for Paved Road Dust - 2011 NEI 121
Table 3-72: Agencies that submitted Paved Road Dust data 121
Table 3-73: 2011 NEI Paved Road Dust data selection hierarchy 122
Table 3-74: 2011 Silt loadings by state and roadway class used in paved road emission factor calculations (g/m2)
123
Table 3-75: Average vehicle weights by MOBILE6 vehicle class 125
Table 3-76: Penetration rates of paved road vacuum sweeping 126
Table 3-77: SCCs used for Unpaved Road Dust-2011 NEI 127
Table 3-78: Agencies that submitted Unpaved Road Dust emissions data 128
Table 3-79: 2011 NEI Unpaved Road Dust data selection hierarchy 128
Table 3-80: Constants for Unpaved Roads re-entrained dust emission factor Equation [ref 1] 130
Table 3-81: Speeds modeled by roadway type on Unpaved Roads 130
Table 3-82: Assumed values for average daily traffic volume (ADTV) by volume group 131
Table 3-83: Agencies that submitted 2011 EGU data by EGU fuel groups 134
Table 3-84: 2011 NEI EGU data selection hierarchy by EGU fuel groups 136
Table 3-85: Agency-submitted, PM Augmentation, and total PMio and PM2 s emissions for EGU sectors 138
ix
-------
Table 3-86: Agencies that submitted data for the Fuel Combustion - Industrial Boilers, ICEs sectors 142
Table 3-87: 2011 NEI selection hierarchy for datasets used by Fuel Comb - Industrial Boilers, ICEs sectors 144
Table 3-88: Algorithm to determine whether to augment state data with EPA data for Industrial Boilers 144
Table 3-89: Agencies tagged values for Industrial Fuel Combustion in 2011 NEI vl 149
Table 3-90: Agencies that submitted Commercial/Institutional Fuel Combustion data 151
Table 3-91: 2011 NEI Commercial/Institutional Fuel Combustion data selection hierarchy 152
Table 3-92: Assumptions used to estimate Commercial/Institutional stationary source distillate fuel consumption
155
Table 3-93: Agencies tagged values for Commercial/Institutional Fuel Combustion in vl of the 2011 NEI 156
Table 3-94: SCCs in the Residential Fuel Combustion sectors (except Wood) in the 2011 NEI 157
Table 3-95: Agencies that submitted data for Fuel Combustion - Residential Heating - Natural Gas, Oil and Other
158
Table 3-96: SCCs in the Residential Wood Combustion sector in the 2011 NEI 161
Table 3-97: 2011 NEI selection hierarchy for datasets used by the residential wood heating sector 161
Table 3-98: Agencies that submitted data for the sector Fuel Combustion - Residential Heating - Wood 161
Table 3-99: Datasets Included in the Fuel Comb - Residential - Wood sector 162
Table 3-100: MSA's using updated AHS data for residential wood combustion 163
Table 3-101: Emission factors for selected hazardous air pollutants in the RWC tool. The emission factors
updated or added for woodstoves (freestanding and inserts) but were left unchanged for all other SCCs 167
Table 3-102: Updates to burn rates and appliance fractions in counties with more than 4,000 homes per square
mile (except New York County) 167
Table 3-103: Densely populated counties subject to updated appliance fractions and burn rates 168
Table 3-104: Outdoor wood boilers sold from 80% of manufacturers between August 2009 and July 2012 169
Table 3-105: SCCs for Industrial Processes- Mining 174
Table 3-106: Agencies that submitted data for the Industrial Processes - Mining sector 175
Table 3-107: Summary of emission factors 180
Table 3-108: NAICS codes for Metallic and Non-Metallic Mining 180
Table 3-109: 2006 County Business Pattern for NAICS 31-33 in Maine 182
Table 3-110: SCCs used for the Oil and Gas Production sector 185
Table 3-111: Agencies that submitted data for the Industrial Processes - Oil and Gas Production sector 187
Table 3-112: 2011 NEI Industrial Processes - Oil & Gas Production data selection hierarchy 189
Table 3-113: List of comments and resolution for building the 2011 NEI for the Oil and Gas Production sector 191
Table 3-114: Agencies and the SCCs submitted for the Miscellaneous Non-Industrial - NEC sector 196
Table 3-115: SCCs used by S/L/T agencies for Solvent - Consumer & Commercial Solvent Use sector 204
Table 3-116: Agencies that submitted data for Consumer & Commercial Solvents 206
Table 3-117: Data selection hierarchy for the Solvent -Commercial and Consumer Solvent Use sector 207
Table 3-118: Nonpoint SCC estimates developed by EPA for Consumer & Commercial Solvents sector 207
Table 3-119: Consumer and Commercial Solvent Use emission factors 208
Table 3-120: Criteria and HAP emission factors for Asphalt Paving 211
Table 3-121: Estimation of national-level total harvested acres of bentgrass seed 213
Table 3-122: Estimation of county-level harvested acres of bentgrass seed 214
Table 3-123: Non-Industrial Architectural Coatings SCCs in the 2011 NEI 216
Table 3-124: 2011 NEI Architectural Coatings sector data selection hierarchy 216
x
-------
Table 3-125: Agencies that submitted data for the Architectural Coatings sector 216
Table 3-126: Emission Factors for Architectural Coatings used in the 2011 NEI 218
Table 3-127: States with Architectural Coatings rules 219
Table 3-128: SCCs for Solvent Cleaning and Degreasing 219
Table 3-129: Data selection hierarchy for the Solvent -Degreasing sector 221
Table 3-130: Agencies that submitted data for Solvent -Degreasing sector 221
Table 3-131: SCCs for Solvent Utilization - Dry Cleaners 224
Table 3-132: Data selection hierarchy for the Solvent -Dry Cleaning sector 225
Table 3-133: Agencies that submitted data for Solvent -Dry Cleaning sector 225
Table 3-134: Graphic Arts SCCs used in the 2011 NEI 228
Table 3-135: Data selection hierarchy for the Solvent -Graphic Arts sector 230
Table 3-136: Agencies that submitted data for Solvent -Graphic Arts sector 230
Table 3-137: Industrial Solvent Use SCCs in the 2011 NEI 234
Table 3-138: Data selection hierarchy for the Solvent -Industrial Surface Coating sector 236
Table 3-139: EPA and S/L/T agency-submitted point and nonpoint data for Industrial Surface Coating sector.. 236
Table 3-140: EPA emission factors for Industrial Surface Coating used in 2011 NEI 240
Table 3-141: Waste Disposal sector SCCs with locations of section discussion where available 245
Table 3-142: Agencies that submitted Waste Disposal data 248
Table 3-143: 2011 NEI Waste Disposal data selection hierarchy 252
Table 3-144: Open Burning, Leaf and Brush Species SCCs estimated by EPA in the 2011 NEI 253
Table 3-145: Adjustment for percentage of forested acres 253
Table 3-146: Emission factors for Open Burning of Residential MSW (2610030000) 255
Table 3-147: Surface acres disturbed per unit type 258
Table 3-148: Spending per mile and acres disturbed per mile by highway type 259
Table 3-149: Fuel loading factors by vegetation type 260
Table 3-150: Emission factors for Open Burning of Land Clearing Debris (SCC 2610000500) 260
Table 3-151. Hg-only EPA-generated SCCs for Landfills 263
Table 4-1: Source classification codes for the aircraft sector in the 2011 NEI 270
Table 4-2: Agencies that submitted 2011 Aircraft emissions or emissions at facilities identified as "Airports".. 271
Table 4-3: 2011 NEI Aircraft data selection hierarchy 271
Table 4-4: Agencies that submitted Aircraft activity data for EPA's emissions calculation Error! Bookmark not
defined.
Table 4-5: Commercial Marine Vessel SCCs and emission types in EPA estimates 275
Table 4-6: Additional Commercial Marine Vessel SCC used by Washington 276
Table 4-7: Agencies that submitted Commercial Marine Vessels emissions data 276
Table 4-8: 2011 NEIv2 commercial marine vehicle selection hierarchy 276
Table 4-9: Locomotive SCCs, descriptions, and EPA estimation status 279
Table 4-10: Agencies that submitted Locomotives emissions to the 2011 NEI 280
Table 4-11: Comparison of NOx emissions (tons) among EPA, S/L/T agency, and 2011vlNEI selection for Rail . 281
Table 4-12: NMIM Nonroad Equipment and fuel types 283
Table 4-13: Selection hierarchy for the Nonroad mobile Equipment data category 284
Table 4-14: S/L/T agency-submitted data for Nonroad mobile Equipment 284
Table 4-15: NCD tables updated based on State and Local NCD submissions 286
xi
-------
Table 4-16: State-assisted NCD table updates 287
Table 4-17: SCC and emissions type with missing VOC in CA submittal 289
Table 4-18: MOVES CDB tables 292
Table 4-19: Number of counties with submitted data, by state and MOVES CDB input table 293
Table 4-20: Source of defaults for data tables in MOVES CDBs 296
Table 4-21: States adopting California LEV standards, start years 297
Table 4-22: HPMS truck categories and their MOVES source types 299
Table 4-23: Binning scheme for submitted ramp fraction data 302
Table 4-24: Binning scheme for CRC A-88 age distribution data 302
Table 4-25: Agency submittal history for onroad inputs and emissions 312
Table 4-26: Onroad data file references for 2011 v2 NEI 316
Table 5-1: Source classification codes for wildland fires 319
Table 5-2: Agency that submitted wildfire and prescribed burning emissions data 319
Table 5-3: 2011 NEI wildfire and prescribed fires selection hierarchy 320
Table 5-4: Pollutants estimated by EPA* for wildland fires and HAP emission factors 321
Table 5-5: SF2 and State-submitted acres burned for FL WLFs 324
Table 5-6: PM2 s Emission differences (tons) for WLFs between 2011 vl and 2011 v2 329
Table 5-7: SCCs in the NEI for Agricultural Burning 331
Table 5-8: Agencies that submitted agricultural fire emissions to the 2011 NEI 332
Table 5-9: Data source and selection hierarchy used for agricultural fire emissions 333
Table 5-10: Emission estimates for Agricultural Burning (short tons/year) using EPA methods 334
Table 5-11: Agricultural Burning PM2.s emission differences between NEI 2011 vl and 2011 v2 338
Table 6-1: SCCs for Biogenics - Vegetation and Soil 343
Table 6-2: Meteorological variables used by BEIS and air quality modeling 344
Table 6-3: State summary of Biogenics - Vegetation and Soil emissions (short tons/year) 345
List of Figures
Figure 2-1: Data sources for point and nonpoint emissions for criteria pollutants 17
Figure 2-2: Data sources for onroad and nonroad mobile emissions for criteria pollutants 18
Figure 2-3: Data sources of emissions for acid gases and HAP VOCs, by data category 18
Figure 2-4: Data sources of emissions for Pb and HAP metals, by data category 19
Figure 2-5: Point inventory - submission types - includes local agencies 20
Figure 2-6: Nonpoint inventory - submission types - includes local agencies 21
Figure 2-7: On-road inventory - states/locals (dark blue) that submitted activity data 21
Figure 2-8: Nonroad equipment inventory - submission types - does not include local agencies 22
Figure 2-9: Comparison of CAP emissions, 2011 minus 2008, excluding wildfires and biogenics 28
Figure 2-10: Comparison of wildfire CAP emissions, 2011 minus 2008 28
Figure 2-11: Comparison of HAP emissions, 2011 minus 2008, excluding wildfires and biogenics 29
Figure 2-12: Comparison of wildfire HAP emissions, 2011 minus 2008 29
Figure 2-13: Data sources of Hg emissions (tons) in the 2011 v2, by data category 32
Figure 2-14: States with state- or local-provided Hg emissions in the point data category of the 2011 v2 34
xii
-------
Figure 4-1: Dark blue indicates States/Counties that submitted at least 1 CDB input table 295
Figure 4-2: Representative county groups for NEI 2011 v2 301
Figure 5-1: The coverage of state-submitted fire activity data sets 322
Figure 5-2: Proportion of acres burned by type of fire 326
Figure 5-3: 2011 PM2 5 wildfire and prescribed burning emissions using EPA methods 327
Figure 5-4: Difference map of 2011 NEI v2 PM2 5 emissions, with and without large fires 328
Figure 5-5: 2011 PM2 5 wild land fire emissions using EPA methods 329
Figure 5-6: 2011 NEI state-total PM2 5 emissions from agricultural fires 335
Figure 5-7: States that submitted agricultural burning emissions to the NEI 336
Figure 5-8: EPA's Geospatial method for producing Cropland Burning emissions for 2011 NEI 337
Figure 5-9: PM2.5 Emissions from Agricultural Burning, 2011 EPA data 340
Figure 5-10: Comparison of percentage of PM25 emissions assigned to agricultural, prescribed and wild fires. 341
xiii
-------
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
xiv
-------
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 Meteorology-Chemistry Interface Processor
MMT Manure management train
MOBILE6 Mobile Source Emission Factor Model, version 6
MODIS Moderate Resolution Imaging Spectroradiometer
MOVES Motor Vehicle Emissions Simulator
MW Megawatts
MWC Municipal waste combustors
NAA Nonattainment area
NAAQS National Ambient Air Quality Standards
NAICS North American Industry Classification System
NARAP North American Regional Action Plan
NASF National Association of State Foresters
NASS USDA National Agriculture Statistical Service
NATA National Air Toxics Assessment
NCD National County Database
NEEDS National Electric Energy Data System (database)
NEI National Emissions Inventory
NESCAUM Northeast States for Coordinated Air Use Management
NFEI National Fire Emissions Inventory
NG Natural gas
NH3 Ammonia
NMIM National Mobile Inventory Model
NO Nitrous oxide
N02 Nitrogen dioxide
NOAA National Oceanic and Atmospheric Administration
NOx Nitrogen oxides
03 Ozone
OAQPS Office of Air Quality Standards and Planning (of EPA)
OEI Office of Environmental Information (of EPA)
xv
-------
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 s
PM25-FIL Filterable PM25
PM25-PRI Primary PM2 s (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 Reanalysis Tool for Fire Incident Reconciliation
SMOKE Sparse Matrix Operator Kernel Emissions
S02 Sulfur dioxide
SO4 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
xvi
-------
xvii
-------
1 Introduction
1.1 What data are included in the 2011 NEI, Version 2?
The 2011 National Emissions Inventory (NEI), version 2, hereafter referred to as the "2011 v2" (not synonymous
with "2011 NEI" which is a general reference to the 2011 NEI that denotes methods that do not differ between
2011 v2 and version 1 of the 2011 NEI "2011 vl"), is a national compilation of emissions sources collected from
state, local, and tribal air agencies as well as emissions information from the Environmental Protection Agency
(EPA) emissions programs including the Toxics Release Inventory (TRI), emissions trading programs such as the
Acid Rain Program, and data collected as part of EPA regulatory development for reducing emissions of air
toxics. The NEI program develops datasets, blends data from these multiple sources, and performs quality
assurance steps that further enhance and augment the compiled data. The emissions data in the NEI are
compiled for detailed emissions processes within a facility for large "point" sources or as a county total for
smaller "nonpoint" sources and spatially dispersed sources such as on-road and nonroad mobile sources. For
wildfires and prescribed burning, the data are compiled as day-specific events in the "event" portion of the
inventory.
The pollutants included in the NEI are the pollutants associated with the National Ambient Air Quality Standards
(NAAQS), known as criteria air pollutants (CAPs), as well as hazardous air pollutants (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 (VOC), sulfur dioxide (S02), particulate matter 10 microns or less (PMio), particulate
matter 2.5 microns or less (PM2.s) and ammonia (NH3), technically not a CAP, but an important PM precursor.
The HAP pollutants include the 187 remaining HAP pollutants (hydrogen sulfide was removed) from the original
188 listed in Section 112(b) of the 1990 Clean Air Act Amendments1. Key HAP emissions sources include mercury
(Hg), hydrochloric acid (HCI) and other acid gases, heavy metals such as nickel and cadmium, and hazardous
organic compounds such as benzene, formaldehyde, and acetaldehyde.
1.2 What is included in this documentation?
This document provides a central reference for the 2011 v2 NEI. The primary purpose of this document is to
explain the sources of information included in the inventory. This includes showing which sources of data are
used for each sector, and then providing more information about the EPA-created components of the data. For
each emissions sector, we provide a synopsis of the types of sources that are included in that sector.
After the introductory material included in this section, Section 2 explains the sectors that we use for
summarizing the 2011 v2 and 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 stationary sources in the
point and nonpoint data categories, as well as sector-by-sector documentation of the stationary sources.
Sections 4, 5 and 6 provide the sector-by-sector documentation for the mobile, fire and biogenics emissions
respectively. Section 7 provides instructions for accessing supporting materials. A separate document contains
the appendix.
1 The current list of HAPs
1
-------
1.3 Where can I obtain the 2011 v2 NEI data?
1.3.1 EPA continues to review and streamline the approach for accessing the NEI data. The 2011 NEI data are
available in several different ways. Emission Inventory System Gateway
Clearinghouse for Inventories and Emissions Fact IEF)
The Emission Inventory System (EIS) Gateway is available to all EPA staff, EIS data partners responsible for
submitting data to EPA (i.e., the state, local, and tribal air agency staff), Regional Planning Organization staff that
support state, local and tribal agencies, and contractors working for EPA on emissions related work. The
Gateway can be used to obtain raw input datasets and create summary files from these datasets as well as the
2011 NEI general public releases. Use the link provided above for more information about how to obtain an
account and to access the gateway itself. The 2011 v2 NEI in the EIS is called "2011 NEI V2". Note that if you run
facility, unit or process level reports in the EIS, you will get the 2011 v2 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 (March 2015), 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"2011 NEI V2" in the EIS.
1.3.2 2011 NEI main webpage
2011 National Emissions Inventory (NEI) Data
The 2011 NEI webpage is available from the Clearinghouse for Inventories and Emissions factors (CHIEF)
website. It includes a query tool that allows for summaries by EIS Sector (see Section 2.1) or the more traditional
Tier 1 summary level used in the EPA Trends Report. Summaries from this site include national, state-, and
county-level of CAP and HAP emissions. 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 Microsoft®
Excel ® or other spreadsheet tools. Biogenic emissions and tribal data (but not tribal onroad, nonroad or
prescribed burning/wildfire emissions) are also available from this tool. Onroad and nonroad tribal summaries
are posted under the "Additional Summary Data" section of this page.
The SCC data files section of the webpage provide 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 nonpoint are broken out into EPA regions. These CSV files must be
"linked" (as opposed to imported) in order to open them with Microsoft® ACCESS®.
The 2011 NEI webpage also contains Google® fusion tables and maps with facility-level emissions for CAPs and
specific HAPs.
1.3.3 Air Emissions and "Where you live"
Air Emissions Sources
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 CAP pollutants except for ammonia 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.
2
-------
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 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, and exit velocity.
EPA makes changes to the NEI prior to use in modeling, so both the 2011 NEI data as well as the latest available
modeling files can be found at this website. The 2011 modeling platform was based on the 2011 v2 NEI. Any
changes between the NEI and modeling platform data are described in the technical support document for the
2011 Emissions Modeling Platform, which is posted at the above website.
1.4 Why is the NEI created?
The NEI is created to provide EPA, federal and state decision makers, the public, and other countries the best
and most complete estimates of CAP and HAP emissions. While EPA is not directly obligated to create the NEI
under the Clean Air Act, the 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 EPA as part of their State Implementation Plans (SIPs) that describe how they will attain the
NAAQS. The NEI is used as a starting point for many SIP inventory development efforts and for states to obtain
emissions from other states needed for their modeled attainment demonstrations.
While the NAAQS program is the basis on which EPA collects CAP emissions from the state, local, and tribal
(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 EPA to assess how much emissions have been reduced since 1990.
1.5 How is the NEI created?
The NEI is created based on both regulatory and technical components. The Air Emissions Reporting Rule (AERR)
is the rule that requires states to submit emissions of 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 2011 NEI is the second AERR-based
inventory, and improvements in the 2011 NEI process reflect lessons learned by the states and EPA from the
2008 NEI process. The AERR requires agencies to report all sources of emissions, except fires and biogenic
sources. Open fire sources such as wildfires are 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 on-road (cars and trucks driven on roads) or non-road (locomotives, aircraft, marine, off-road vehicles
and nonroad equipment such as lawn and garden equipment).
3
-------
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 to make these thresholds
"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 criterion 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 2011 is one of these third-year
inventories.
Table 1-1 provides the potential-to-emit reporting thresholds that applied for the 2011 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 100 tons/year or more for most criteria
pollutants with the exceptions of CO (1000 tons/year) and Pb (5 tons/year). As shown in the table, special
requirements apply to nonattainment area (NAA) sources, where even lower thresholds apply. The relevant
ozone (03), CO, and PM10 nonattainment areas that applied during the year that the S/L/T agencies submitted
their data for the 2011 NEI
Table 1-1: Point source reporting thresholds (potential to emit) for CAPs in the AERR
Pollutant
2011 NEI thresholds: potential to emit (tons/yr)
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
>5
>5
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: PMio
Based on the AERR requirements, S/L/T agencies submit emissions or model inputs of point, nonpoint, on-road
mobile, nonroad mobile, and fires emissions sources. For on-road and nonroad mobile, states were encouraged
to submit model inputs instead of emissions. For the 2011 NEI, all these emissions and inputs were due to EPA
per the AERR by December 31, 2012 (with an extension given through January 8, 2013). Once the initial
reporting NEI period closed, 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, 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.
4
-------
1.5.1 NEI 2011 v2 point source updates
The NEI 2011 vl point source file was produced on July 23, 2013. The 2011 v2 was produced on November 23,
2014. The overall process and procedures for producing the point source emissions and modeling parameters
for 2011 v2 are very similar to those used for 2011 vl, and the resulting overall emissions magnitudes are very
similar for the two versions, although individual emission sources may differ. The processes and procedures
used to produce 2011 vl were described in the original version of this document and remain largely unedited in
this second version of this documentation. For point sources, 2011 v2 is essentially the 2011 vl inventory with
individual edits and updates from various sources and commenters who reviewed or updated the previous 2011
vl point source inventory. Edits and comments on 2011 vl were received from the following sources:
A. S/L/T air agencies
B. Public comments on the emissions modeling platform built from 2011 vl
C. NATA 2011 reviewers
D. EPA/OAQPS initiated reviews and updates
The various comments resulted in changes to emissions values, release point locations, and release point
modeling parameters. These edits are not believed to impact large-scale regional modeling or emissions trends
in any significant way; and significant impacts on individual facilities are limited in number. In addition, a few
ancillary pieces of data were also updated for v2 by EPA/OAQPS. These include a set of revisions to the Emission
Unit types and the identifiers used to match NEI units to the IPM future year electric generating units and the
base year Continuous Emissions Monitor values reported by facilities to EPA's Clean Air Markets Division. More
details on the v2 edits made for each of the four main reviewer mechanisms are provided below.
A. S/L/T air agencies
The 2011 vl NEI point sources file was based in large part on the emissions data submitted by 82 State, local,
and Tribal air agencies to the EIS data system. All emissions data and facility inventory data (facility names,
locations, release point characteristics, etc) are submitted directly from these 82 air agencies to the EIS data
system, either in bulk xml files sent to EPA's Central Data Exchange or via individual on-line edits made in the EIS
Gateway. After the 2011 vl was released, the same S/L/T agencies had the opportunity to submit updates and
additions to their 2011 data for use in 2011 v2. For the 2011 v2 updates, this process was handled a little
differently than the 2011 vl and 2008 submittal processes. In order to avoid wholesale and possibly unintended
overwriting of 2011 vl data that had been through a draft quality-assurance review and had been available for
further use and review as part of the final 2011 vl, S/L/T agencies were asked to either edit values on-line using
the EIS Gateway or to submit by bulk xml only the changes that they wished to make to 2011 vl data. In
addition, rather than having the EIS Production window open at any time for S/L/T agency edits or xml
submittals, the Production window was opened only upon request and only after a clean and EPA-reviewed
submittal had been made by the S/L/T agencies to the EIS QA Environment. 25 agencies submitted some point
emissions updates and 20 submitted some facility inventory updates by xml batch files during the vl to v2
update cycle. An unknown but probably smaller number of agencies also made smaller volume edits to both
facility inventory and emissions data by individual on-line edits via the EIS Gateway. Most of the edits occurred
during the January to mid-April 2014 review and update period.
The two most significant sets of edits from S/L/T agencies came from Minnesota and North Carolina. Minnesota
re-submitted their entire HAP emissions inventory after the January thru mid-April 2014 review and update
period, just before the 2011 v2 selection was run. As a result, a limited amount of QA review was done on these
values. North Carolina coordinated with EPA/OAQPS to submit a file which included emissions for a large set of
smaller facilities which had not been included in their 2011 vl data. For these facilities NC submitted their
5
-------
emissions estimates for 2008, 2009, or 2010, because they did not have 2011 emissions for these facilities, but
preferred that EPA use the earlier year State emissions values rather than the TRI 2011 values that would
otherwise be used for gap-filling. These facilities are below the NEI triennial year reporting thresholds, and they
report only every fifth year to North Carolina.
B. Public comments on the emissions modeling platform built from vl
A set of emissions modeling platform files based on the 2011 vl was made available for public review and
comment in early 2014. Twenty-seven comment letters were received as a result that resulted in edits being
made to either the EIS facility inventory or the vl emissions values. Many of these comments were from
companies or facilities that operated electric generating units, although a few were from the State air agencies
who also had access to the EIS data system and its submittal and edit processes. The most significant comment
was to add PM-Condensible emissions values (and therefore to increase PM2.5-Primary and PMIO-Primary
emissions values) at eight coal-fired electric power plants located in Pennsylvania. Other comments were to
some of the HAP emissions values for 3 power plants located in New Jersey, to add or revise the unit IDs used by
the IPM model for electric generating units, to revise generating unit design capacities, and edits to release
point parameters. A detailed Response to Comments document on these and other modeling platform
comments is available.
One comment was received from a regional modeling center suggesting that stack parameters from their 2007-
based modeling platform should be used in the EPA 2011 platform. The 2007-based files were accessed and
compared and evaluated against the 2011 facility inventory coordinates and release point parameters, for the
instances where this could be done based on common State identifiers between the two. Where significant
differences in release point coordinates or parameters were identified and where the EIS facility inventory data
(reported by the same State air agencies as the 2007 platform, but at a later date) were also found to be highly
suspect, edits were made to the EIS facility inventory. As part of this review it was noted that one State had
significantly modified the EIS facility inventory for their sources by re-routing many combustion emission
processes to fugitive emission release points, despite the fact that stack release points were already available in
EIS and had been used previously for these same emission processes. A subset of these anomalies that could be
individually reviewed were therefore reset such that the largest combustion processes were routed to the
earlier-used stack release points.
The vl modeling platform had included 17 ethanol production facilities with EPA estimated emissions in support
of a rule-making effort that were not in the 2011 vl. After States had provided their updates to the 2011 for v2,
it was found that 3 of these 17 facilities had been added by States. The remaining 14 facilities were added to the
2011 v2 facility inventory, although with sometimes different coordinates than were used in the vl modeling
platform following a review. However, the EPA-estimated 2011 emissions for these 14 facilities were not added
to EIS until after the 2011 v2 was created.
C. NATA 2011 reviewers
The 2011 vl was used to run preliminary risks assessment modeling in late 2013 as part of the 2011 National Air
Toxics Assessment. The risk results from these preliminary runs were distributed in November 2013 to State,
local, and Tribal air agencies for review and comment, including comments on the emissions values, locations,
and release point modeling parameters. The reviewers of the risk results included additional S/L/T agency
personnel beyond those responsible for compiling and submitting the S/L/T agency data to the NEI for use in vl
and v2. While some reviewers likely had their comments addressed as part of the S/L/T agency v2 review and
update cycle as described in section A above without EPA involvement, a number of reviewers provided written
6
-------
comments to EPA thru the NATA process. All such comments were addressed by EPA and incorporated into the
2011 v2, either by EPA editing the EIS facility inventory or EPA emissions values, or in some cases by having the
S/L/T agency inventory personnel edit the emissions values in their emissions datasets as stored in EIS.
In addition to the available risk results derived from the 2011 vl data, the November 2013 call for comments
also included a list of approximately 500 facility-pollutant combinations that had not been included in vl, but
that EPA was proposing to add to the v2 NEI for final NATA risk modeling. These facility-pollutant combinations
were those that did not appear in the 2011 S/L/T agency emissions submittals to the NEI, but which had
emissions estimates available from facility submittals to the 2011 Toxics Release Inventory via the use of an
emissions range check box. TRI allows facilities with low but difficult to quantify emissions to check one of
several pre-set range boxes to indicate their emissions level range rather than attempting to provide a discreet
emissions value. The lowest such range choices available are 0 to 10 pounds and 10 to 500 pounds. The TRI
emissions summaries use 5 pounds and 250 pounds to represent these range choices in summary tables. In April
and May 2014, EPA attempted to find discrete values for as many of these TRI range values as possible, including
by contacting S/L/T agencies directly and by reviewing other TRI year reports for these facilities. Many of the
discrete values so obtained tended to fall at the very low end of the selected range, or even below the range in
the case of several "10-500" choices. Where no discrete values could be determined, the mid-point of the
ranges were added to the 2011 v2.
D. EPA/OAQPS initiated reviews and updates
Several other updates and edits of various pieces of the 2011 NEI inventory were done between vl and v2,
either as a result of the changed values entered as parts of sections A, B, and C above, or to take advantage of
newer improved datasets.
1. Off-shore oil and gas platform emissions for 2011 were added. 2011 vl included the 2008 emissions for
off-shore Federal waters platforms in the Gulf of Mexico as a gap fill estimate, because the 2011
emissions inventory prepared by the Bureau of Ocean Energy Management was not available in time for
vl. The BOEM's data for 2011 was added to the EIS and included as part of the 2011 v2.
2. TRI emissions were updated for the 2011 v2 to use TRI data as published on the TRI website as of late
April 2014. This dataset included many updates that facilities submitted to TRI as a result of the
preliminary NATA risk reviews that S/L/T agencies performed, as well as other needed changes that
facilities became aware of by other means.
3. As a result of edits, additions, and deletions made to S/L/T agency emissions values, the EPA datasets for
PM-Augmentation and HAP Augmentation had to be reviewed and adjusted. Due to the size of the vl
datasets involved, as well as the relatively limited number and magnitude of edits made to the S/L/T
agency PM and VOC values, for v2 EPA looked at only instances where the responsible agency PM or
VOC emissions had been changed by more than 5 tons. For these instances the PM-Augmentation and
HAP Augmentation values derived by EPA were re-calculated and used to replace the values in the EPA
datasets for PM Augmentation and HAP Augmentation.
4. Also, as a result of edits, additions, and deletions made to S/L/T agency emissions values as well as the
use of an updated TRI emissions dataset, the tags on the individual HAP Augmentation and TRI dataset
emissions values were updated to ensure that emission values from these datasets would not add
double-counted emissions.
7
-------
5. The emissions values and unit identifiers used for the EPA EGU emissions dataset were re-reviewed
against the unit identifiers and emissions used by S/L/T agencies as seen after all S/L/T agency emissions
edits had been accepted. A small number of instances were found where S/L/T agency emissions had
changed unit identifiers between versions. The EPA EGU datasets were revised accordingly to ensure
that double-counting of S/L/T and EPA emissions values would not occur.
6. A revised table of factors for splitting total chromium emissions values into chromium VI and chromium
III values by SCC was received and applied to the 2011 data in May 2014 for use in v2. This work was
done outside of the EIS data system and did not use the EIS function for chromium speciation, because
the EIS factor table has not been updated. The impacts due to the revised factors were negligible, but
one large chromium emitting process in Ohio was noticed as a consequence of re-running these splits.
The chromium values for this one process were confirmed to be erroneous and were tagged out so as
not to be used in v2.
7. An internal EPA review of facilities appearing on the preliminary NATA list of highest risk sources in
November 2013 was done to identify anomalies. Part of this review focused on landfills where EPA was
the source of the emissions values, because the location data for many of these landfills was potentially
using a county centroid value. Locational data and some stack parameter edits were made to a small
number of these preliminary high-risk facilities as a result of this review.
8. Similar to checks done on 2011 vl and earlier year inventories, the facility site coordinates of all v2
emitting facilities were compared against county boundary files. Any facilities with site coordinates
more than 0.5 miles outside of the county boundaries and with either criteria pollutant totals greater
than 5 tons or hazardous pollutant totals greater than 20 pounds (in either the S/L/T reports or in the
draft v2 selection incorporating all emissions datasets) and not verified by earlier reviews were checked
via Google Earth and revised and locked as needed. 17 facilities were revised as a result. Individual
release point coordinates that were not consistent with the newly verified site coordinates were set to
equal the revised site coordinates. California, Alaska, and airport facilities were excluded from these
tighter tolerances of this review due to the number of smaller and difficult to locate facilities.
9. Facility site coordinates for 30 facilities in California that all had the same incorrect latitude-longitude
pair were revised to use the coordinates found in the Federal Registry System for those facilities.
Individual release point coordinates that were not consistent with the newly verified site coordinates
were set to equal the revised site coordinates. Additional California facilities using the same pair of
default coordinates still remain in the EIS and in the 2011 v2, because the emissions for these facilities
were small and because no alternative set of coordinates was available via FRS.
10. A set of approximately 7000 release point latitude-longitude coordinates that had been edited in
previous NEIs because they were too distant from the verified site coordinates for their corresponding
facilities, and which had been revised by S/L/T agencies, were reset to the values that agree with the
verified site coordinates.
11. Approximately 1200 IPM unique IDs from the NEEDs v5.13 draft file was added to the EIS emission units.
July 2014. Approximately 200 of the IPM ids previously existing in the EIS were revised so that they
match exactly to those seen in NEEDs. These revisions will facilitate future checks and updating to
8
-------
revisions to the NEEDs file, although the previous non-matching IPM ID in the EIS were still being
separated out to the PTIPM modeling file as intended. Approximately 300 CAMD CEM IDs were also
added to EIS units. These units allow the hourly CEM emissions values to be used in modeling
applications. The 300 additions were for very small annual emitters however, as earlier work had
focused on having all CEM IDs for the larger SO2 and NOx sources matched.
12. For all EIS facilities that were matched to a TRI facility ID and which had an EIS zip code of "00000", the
EIS zip codes were revised to equal the TRI zip codes.
13. Emission unit types which had been revised by S/L/T agencies back to "unclassified" were reset to the
various types which had been previously set.
14. The NAICs codes for 105 facilities were revised from 33991 (Jewelry and Silverware Manufacturing) to
the NAICs of the TRI facility that they were matched to (usually 332812, Metal Coating and Engraving). It
appears that the conversion done from the old SIC codes to the NAICs codes done in earlier NEI years
not specific enough. Of the 105 facilities, 91 did not have any state facility ID, and were likely TRI-only
facilities. An additional 252 facilities remain in the EIS with the jewelry NAICs but could not be matched
to a TRI facility with an alternative NAICs. However, 211 of these remaining facilities do have State
Facility IDs.
1.5.2 NEI 2011 v2 nonpoint source updates
There were many changes in the nonpoint data category between 2011 vl and 2011 v2 of the NEI; highlights are
given here. As oil and gas was a large focus for the 2011 NEI, EPA continued to make improvements to the EPA
Nonpoint Oil and Gas Emissions Estimati for 2011 v2. Some of the more significant efforts included 1)
better aligning the inputs and emission factors between the EPA's Office of Atmospheric Program (OAP) work on
the Greenhouse Gas (GHG) Emissions Inventory (El) / GHG Reporting Program and the NEI on condensate tanks,
liquids unloading, pneumatic devices and well completions, 2) additional information from the Western Regional
Air Partnership (WRAP) based on new survey data and studies, 3) improved resolution of data (to county level
rather than basin), and 4) new SCCs, including the distinction between Coal Bed Methane (CBM) wells from
other natural gas (NG) wells. Furthermore, some states, including CO, WV, OK, TX, and WY made improvements
to their oil and gas submissions in this time period, and these emissions were included in 2011 v2.
Many states resubmitted data based on EPA or their own review, including CA, CT, DC, DE, IA, ME, Ml, NC, NE,
NY, OK, UT, VA, WA. Some tribes also submitted their data for the first time for the 2011 NEI, and this data was
included in 2011 v2. MN resubmitted many solvents and residential wood combustion emissions, due to errors
found between versions. ID data was tagged for Ag livestock because it was the only state that submitted
pollutants other than ammonia. EPA also made adjustments to publicly owned treatment works (POTW)
emissions, because it was noted in the review of 2011 vl that several point sources with POTW SCCs were not
POTWs based on their facility name. Thus, the tagging that EPA had performed for 2011 vl was not necessary,
and many of these were thus untagged for 2011 v2.
1.5.3 NEI 2011 v2 mobile source updates
The most significant change for mobile sources in this version (2011 v2) is the use of EPA's most current onroad
model MOVES2014. In addition to new modeled emissions results, the SCCs used in the NEI/EIS were changed.
MOVES2014 uses new and additional SCCs. However, for the NEI, SCCs were aggregated at the vehicle and fuel
level and no longer include road class or emissions type.
9
-------
Commercial marine inventories were revised for diesel-powered Class I and II vessels with a new geographic
allocation (from top-down national emissions estimates) to better distribute emissions along river ways and
ports and thereby improve model results. Class III, residual-fueled vessel emissions were revised to correct an
error in the implementation date and resultant controls of Emission Control Areas.
The remaining mobile sectors (nonroad, rail, and aircraft) had minor changes in specific geographic areas, but no
universal corrections or modifications.
1.5.4 NEI 2011 v2 fires updates
In going from 2011 vl to 2011 v2 of the NEI, wild land and prescribed fire emissions were altered for two states:
North Carolina and Delaware. NC submitted their own emissions in going from vl to v2, and EPA accepted those
emissions. This resulted in an over 95% reduction in NC wildfire emissions for v2 compared to vl. Nationally, this
caused emissions to be about 30% lower in 2011 v2 vs 2011 vl. The state of DE also asked for a misclassified
wildfire to be moved to the prescribed fire SCC as well as to omit several anomalous 100-acre fires in Sussex
County, which DE said did not occur. Making these changes resulted in total wildfire emissions being much lower
for DE in v2 (about 96%), but the 2011 vl wildland fires (WLF) emission totals for DE were very low so no effects
were seen on nationwide totals.
For agricultural fires, in going from 2011 vl to 2011 v2 of the NEI, the following changes were made. EPA
decreased emissions for all LADCO and neighboring states (Wl, IL, Ml, IA, MO, and OH) based on comments
received from LADCO that questioned the quality of a satellite's ability to detect very small agricultural fires in
the mid-western region of the US and to avoid false detects. When the states involved confirmed this
information, EPA reduced all emissions by a factor of 0.000189 for these states, resulting in near-zero emissions.
Based on comments from MN, we applied an 87% reduction in emissions rate that they supplied after their
analysis of these data. Overall, this technique resulted in a reduction of between 95-99% of emissions for Wl,
Ml, OH, MO, and IL. Cumulatively, these changes reduced emissions about 34% nationwide.
1.6 Who are the target audiences for the 2011 NEI?
The comprehensive nature of the NEI allows for many uses and therefore its target audiences include EPA staff
and policy makers, the U.S. public, other federal and state decision makers, and other countries. Table 1-2 below
lists the major current uses of the NEI and the plans for use of the 2011 NEI in those efforts. These uses include
those by EPA in support of the NAAQS, Air Toxics, and other programs as well as uses by other federal and
regional agencies and international support. 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
Last NEI
data used
U.S. Public
Learn about sources of air emissions
2011v2
EPA-NAAQS
Regulatory Impact Analysis - benefits estimates using air quality
modeling
Modified 2005 v2, for PM
NAAQS Proposal,
Modified 2008 v2, for PM
NAAQS Final
2011 vl for Ozone NAAQS
Proposal
PM and SO2 NAAQS Implementation
2011vl
10
-------
Audience
Purposes
Last NEI
data used
SO2 NAAQS Monitoring Implementation - Population Weighted
Emissions Index
2008 v3 with some 2009
data
Pb Monitoring Rule
2005 v2
Pb NAAQS final designations
2008 v3
Pb NAAQS Policy Assessment
Modified 2008 v3
Transport Rule air quality modeling (e.g., Clean Air Interstate Rule,
Cross-State Air Pollution Rule)
2011 v2
State Implementation Plans - source of emissions data for regions
outside of the state jurisdiction
2011 v2
EPA-Air toxics
National Air Toxics Assessment (NATA)
2011 v2
Mercury and Air Toxics Standard - mercury risk assessment and
Regulatory Impact Assessment
Modified 2005 v2
Residual Risk and Technology Review - starting point for inventory
development
2011 vl
EPA - other
Inspector General - review of oil and gas industry
2008 vl.5
NEI Report - analysis of emissions inventory data
2011 vl
Report on the Environment
2011 vl
Air Emissions website for providing graphical access to CAP emissions
for state maps and Google Earth views of facility total emissions
2011 v2
Department of Transportation, national transportation sector
summaries of CAPs
2008 vl.5
Black Carbon Report to Congress
Modified 2005 v2
Other federal or
regional agencies
Western Regional Air Partnership - modeling in support of Regional
Haze SIPs and other air quality issues
Modified 2008 v2
(including different oil &
gas, fire and biogenic
emissions)
International
United Nations Economic Commission for Europe's Convention on
Long-range Transboundary Air Pollution (LRTAP)
2011 v2
United Nations Environment Programme (UNEP) -global mercury
program
2008 v2
North American Commission for Environmental Cooperation (CEC) -
North American Regional Action Plan (NARAP) on Mercury
Modified 2005 v2
Other outside
parties
Researchers and graduate students
2011 v2
1.7 What are appropriate uses of the NE! 2011 v2 and what are the caveats about the data?
As shown in the preceding section, the NEI provides a readily-available comprehensive inventory of both CAP
and HAP emissions to meet a variety of user needs. Although the accuracy of individual emissions estimates will
vary from facility-to-facility or county-to-county, the NEI largely meets the needs of these users in the aggregate.
Some NEI users may wish to evaluate and revise the emission estimates for specific pollutants from specific
source types for either the entire US or for smaller geographical areas as their particular needs may dictate.
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. The NATA
provides an effective screening tool for identifying potential risks, the results of which should be reviewed in
more detail, including an assessment of the key emissions and other modeling inputs.
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
11
-------
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) model2 for
the on-road data category. Previous NEI years had used the Mobile Source Emission Factor Model, version 6
(MOBILE6)3 and earlier versions of the MOBILE model for this data category. The previous version of the 2011
NEI (2011vl) used an older version of MOVES (2010b) that has been substantially updated in the current 2011
v2 (MOVES2014). The change of model has been demonstrated to make significant changes in some pollutants.
Other significant emissions sectors which have seen improvements and therefore inconsistent trend data
through the years include paved and unpaved road PM emissions, animal waste ammonia emissions, oil and gas
production, and residential wood combustion emissions. In addition, the 2011 NEI uses updated emissions
factors (EFs) for several metal HAPs and acid gases from coal-fired utility boilers as well as EFs for PM based on
site specific measurements for some units. These EFs were not incorporated in previous year inventories
(however, all 2011 updated EFs except for PM2 s and HCN were used in the 2008 NEI) so trends may for these
pollutants are influenced by method changes as well as actual reductions or increases in emissions.
Outstanding Issues
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, EPA augments these components (see Section 3.1.2). However, not all sources are covered by
this routine, and in mobile source 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 PMi0 and PM2 s.
There is likely to be some double-counting of cyanide and hydrogen cyanide emissions, where we think emission
factors or stack test results are available for both pollutant codes, but it's likely that cyanide emission factors or
tests would include any hydrogen cyanide and possibly other cyanide compounds. There are 31 emission
processes in the point source category of 2011 v2 which have both cyanide and hydrogen cyanide emissions.
The total of both CN and HCN for these 31 processes is 502,000 lbs, although 399,000 lbs is for hydrogen cyanide
at one refinery process. The estimated double-counting would therefore be no more than 50,000 lbs, and the
bulk of the double-counting is for four EGUs in Mississippi, where hydrogen cyanide emissions based upon a
recalled MATs emission factor were not tagged out.
Additional issues were identified as the result of the 2011 NATA comment period. Because this comment period
is still ongoing, we will not list each individual issue but give a brief overview of the types of issues identified.
• There were several corrections provided for data augmented using the TRI. Comments mostly
addressed chromium and other metals, and, in most cases, the emissions were found to be
overestimated. Updated data were provided due to miscalculations by the reporting facility, or the use
of a mid-point value which overestimated the actual emissions. In addition, for chromium, comments
were received on the speciation into hexavalent and trivalent forms. In most cases, the speciation was
2 See MOVES and Other Mobile Source Emissions Models
3 See Transportation, Air Pollution, and Climate Change
12
-------
changed to a higher percent (in some cases to 100%) of trivalent chromium based on product
formulation or testing. Many SLT agencies revised their emissions due to corrections to emission factors,
errors or because they had received updated data from their facilities for 2011. In most cases the
revisions were emissions decreases, but in some cases, emissions increased. In a few cases emissions
were zeroed out (e.g., ethylene oxide from certain hospital sterilizers) because data that the state had
carried forward from previous years was found to be no longer valid.
• Revised emissions based on facility and process-specific information were provided by SLT agencies to
replace some HAPs augmented data SCC-specific emission factor ratios.
• Some HAPs were found to be inappropriately augmented via the emission factor ratio approach
o Nickel from SCC 20300201 - emission factor units for PM and nickel were based on different
throughput units (input versus output) hence nickel should not be augmented for this SCC
o Ethylene dichloride from the following SCCs since this pollutant is associated with leaded
gasoline which is no longer used other than in aviation fuel.:
'40600136','40600144','40600301','40600302','40600306','40600402'
• Some HAPs augmented for oil and gas used default emission factor ratios applied to state-supplied VOC
emission estimates; Uinta basin specific speciation data showed significantly lower HAP fractions than
the default ratios used for the NEI.
13
-------
2 2011 inventory contents overview
2.1 What are ESS sectors and what list was used for this document?
First used for the 2008 NEI, EIS Sectors continue to be used for the 2011 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 source classification code (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 can be
found in the Microsoft® Excel ® spreadsheet "see eissector xwalk 2011neivl.xlsx". No changes were made to
the SCC-mapping or sectors used for the 2008 NEI except where SCCs were retired, or new SCCs were added.
Users of the NEI are free to obtain the SCC-level data and modify the EIS Sector cross-walk to make custom
groupings of their own or to request assistance from EPA to do so.
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, which are also data categories
in the EIS: point, nonpoint, on-road, nonroad and event. 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 in the left most column and identifies the EIS data category associated with that
sector. It also identifies in the rightmost column the section number of this document that provides more
information about that EIS sector. As the column 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. NEI
users who sum emissions by EIS data category rather than EIS sector should be aware that these changes will
give differences from historical summaries of "nonpoint" and "nonroad" data unless care is taken to assign
those emissions to the historical grouping.
Table 2-1: EIS sectors and associated emissions categories and document sections
Sector name
Point
Nonpoint
On-road
Nonroad
Event
Document
Section
Agriculture - Crops & Livestock Dust
0
3.2
Agriculture - Fertilizer Application
0
3.3
Agriculture - Livestock Waste
0
0
3.4
Biogenics - Vegetation and Soil
0
6
Bulk Gasoline Terminals
0
0
3.5
Commercial Cooking
0
3.6
14
-------
Sector name
Point
Nonpoint
On-road
Nonroad
Event
Document
Section
Dust - Construction Dust
0
0
3.7
Dust - Paved Road Dust
0
3.8
Dust - Unpaved Road Dust
0
3.9
Fires - Agricultural Field Burning
0
5.2
Fires - Prescribed Burning
0
5.1
Fires - Wildfires
0
5.1
Fuel Comb - Comm/lnstitutional - Biomass
0
0
3.12
Fuel Comb - Comm/lnstitutional - Coal
0
0
3.12
Fuel Comb - Comm/lnstitutional - Natural Gas
0
0
3.12
Fuel Comb - Comm/lnstitutional - Oil
0
0
3.12
Fuel Comb - Comm/lnstitutional - Other
0
0
3.12
Fuel Comb - Electric Generation - Biomass
0
3.10
Fuel Comb - Electric Generation - Coal
0
3.10
Fuel Comb - Electric Generation - Natural Gas
0
3.10
Fuel Comb - Electric Generation - Oil
0
3.10
Fuel Comb - Electric Generation - Other
0
3.10
Fuel Comb - Industrial Boilers, ICEs - Biomass
0
0
3.11
Fuel Comb - Industrial Boilers, ICEs - Coal
0
0
3.11
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
0
0
3.11
Fuel Comb - Industrial Boilers, ICEs - Oil
0
0
3.11
Fuel Comb - Industrial Boilers, ICEs - Other
0
0
3.11
Fuel Comb - Residential - Natural Gas
0
3.13
Fuel Comb - Residential - Oil
0
3.13
Fuel Comb - Residential - Other
0
3.13
Fuel Comb - Residential - Wood
0
3.14
Gas Stations
0
0
3.5
Industrial Processes - Cement Manufacturing
0
3.15
Industrial Processes - Chemical Manufacturing
0
0
3.16
Industrial Processes - Ferrous Metals
0
3.17
Industrial Processes - Mining
0
0
3.18
Industrial Processes - NEC
0
0
3.24
Industrial Processes - Non-ferrous Metals
0
0
3.19
Industrial Processes - Oil & Gas Production
0
0
3.20
Industrial Processes - Petroleum Refineries
0
0
3.21
Industrial Processes - Pulp & Paper
0
3.22
Industrial Processes - Storage and Transfer
0
0
3.23
Miscellaneous Non-Industrial NEC
0
0
3.25
Mobile - Aircraft
0
0
4.2
Mobile - Commercial Marine Vessels
0
4.3
15
-------
Sector name
Point
Nonpoint
On-road
Nonroad
Event
Document
Section
Mobile - Locomotives
0
0
4.4
Mobile - Non-Road Equ
pment - Diesel
0
0
4.5
Mobile - Non-Road Equ
pment - Gasoline
0
0
4.5
Mobile - Non-Road Equ
pment - Other
0
0
4.5
Mobile - On-road - Diesel Heavy Duty Vehicles
0
4.6
Mobile - On-road - Diesel Light Duty Vehicles
0
4.6
Mobile - On-road - Gasoline Heavy Duty Vehicles
0
4.6
Mobile - On-road - Gasoline Light Duty Vehicles
0
4.6
Solvent - Consumer & Commercial Solvent Use
0
3.26
Solvent - Degreasing
0
0
3.28
Solvent - Dry Cleaning
0
0
3.29
Solvent - Graphic Arts
0
0
3.30
Solvent - Industrial Surface Coating & Solvent Use
0
0
3.31
Solvent - Non-Industrial Surface Coating
0
3.27
Waste Disposal
0
0
3.32
2,2 What do the data show about the sources of data in the 2011 NEI?
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, 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. Additional
details on EPA's augmentation datasets are available in the remainder of this document.
Figure 2-1 shows the proportion of criteria pollutant emissions from various data sources in the NEI for point
and nonpoint sources. For the nonpoint data in the figure (left 7 bars), most of the emissions come from EPA
sources of data, with S/L/T agency data the majority for VOC and S02. The large "EPA Nonpoint" bar for PMW is
predominantly dust sources from unpaved roads (7.7 million tons), agricultural dust from crop cultivation (3.5
million tons), and construction dust (1.1 million tons). For point data in the figure (right 7 bars), most of the
emissions come from S/L/T agency data, with EPA data making up a large proportion only for the PM2 5 with the
EPA PM Augmentation dataset ("EPA PM Aug" in the figure, see Section 3.1.2. The data sources shown in the
figure are described in more detail in Section 3.
16
-------
Figure 2-1: Data sources for point and nonpoint emissions for criteria pollutants
18
16
14
12
> 10
C
o
4->
to _
C 8
o
I TRI
I EPA EGU
I EPA PM Aug
EPA other
IS/L/T
I EPA Nonpoint
I EPA Air/Rail/CMV
I. I 1.1
o
m
X
o
LO
r\j
u
o
m
X
o
LD
rvl
u
u
X
o
i
fN
O
O
u
X
o
i
oi
O
O
Q_
Q_
on
>
Q.
Q.
00
>
NP
NP
NP
NP
NP
NP
NP
PT
PT
PT
PT
PT
PT
PT
1 Nonpoint emission shown here exclude biogenic sources, which are all EPA data
The data sources for the emissions from nonroad and on-road data categories are shown in Figure 2-2. These
show that emissions are comprised primarily using data from EPA. That is because each of these data categories
has its own emissions model and 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 sections covering nonroad, on-road and fires emission sectors (4.5, 4.6 and 5.1). Note that the
scale for NOx and CO in Figure 2-2 is on the right vertical axis in the chart.
17
-------
Figure 2-2: Data sources for onroad and nonroad mobile emissions for criteria pollutants
4.0
3.5
3.0
2.5
° 2.0
1.5
1.0
0.5
0.0
- I
ll
ro
o
in
O
(_>
-Z.
Q_
o_
IS)
>
2
2
Q.
2
Q-
1/1
>
z
OR
OR
OR
OR
OR
NR
NR
NR
NR
NR
OR
OR
NR
x
O
40
35
30
25
20
15
10
5
0
i EPA Nonroad
I EPA Onroad
I EPA other
IS/L/T
C
o
c
o
In Figure 2-3, the nonpoint acid gases are very small, with 4,400 tons from both S/L/T agencies and the EPA
nonpoint dataset. For point sources, the bulk of the acid gases emissions (primarily HCI) comes from two EPA
EGU datasets (73,000 tons) in addition to 45,000 tons from S/L/T agencies, while most of the HAP VOC emissions
come from the S/L/T/ agency data (165,000 tons) and just 30,000 tons from TRI.
Figure 2-3: Data sources of emissions for acid gases and HAP VOCs, by data category
1,000
900
>. 800
tn
B 700
tn
~o
ro 600
tn
O
H 500
400
300
200
100
0
I
i
Acid
HAP-VOC
Acid
HAP-VOC
Acid
HAP-VOC
Acid
HAP-VOC
Gases
Gases
Gases
Gases
NP
NP
PT
PT
OR
OR
NR
NR
I EPA Nonroad
I EPA Onroad
I TRI
I EPAEGU
EPA Air/Rail/CMV
S/L/T
EPA other
EPA Nonpoint
18
-------
Figure 2-4 shows emissions sources for Pb and HAP metal emissions. For nonpoint sources, almost all the
emissions are from the EPA nonroad dataset, which includes emissions from airports, locomotives, and
commercial marine vessels. For point sources, about half of the Pb comes from S/L/T agency data (250 tons),
while the EPA nonroad dataset airport emissions make up a substantial part of the rest (230 tons). For metals,
the point sources data has a significant portion from S/L/T agencies (1,300 tons), with the rest from the EPA EGU
dataset (800 tons), TRI (300 tons), and other EPA datasets (400 tons).
Figure 2-4: Data sources of emissions for Pb and HAP metals, by data category
3.5
£ 3.0
T3
£ 2.5
tn
O
2.0
1.5
1.0
0.5
0.0
I
I
~o
ro
(D
CU
4->
-------
Figure 2-5: Point inventory - submission types - includes local agencies
2011 Point Submissions
| No Submission
~ CAP/HAP |
¦ CAP
Figure 2-6 shows the states and/or local agencies that submitted nonpoint emissions. Forty-two states
submitted CAPs and thirty-four also submitted HAPs. Only eight states did not submit any nonpoint emissions,
and at least some of these notified EPA that EPA's estimates were acceptable for the source types that EPA
estimated. Puerto Rico and Virgin islands did not submit any nonpoint emissions. The state of Nevada did not
submit nonpoint CAPs or HAPs, but the state is colored light blue because of local agency submittals in that
state.
For on-road mobile sources, emissions in all states except California are based on the EPA's run of the
MOVES2014 model. California emissions are estimated by the EMFAC (short for Emission FACtor) model5 and
California has provided CAP and HAP emissions which are used in the 2011 NEI. Figure 2-7 shows the states and
local agencies that submitted at least one table of onroad model inputs. Section 4.6 has more detail and
identifies the local agencies that submitted inputs.
5 See "EMFAC Overview" link available at on CARB Mobile Emissions Inventory website
20
-------
Figure 2-6: Nonpoint inventory - submission types - includes local agencies
2011 Nonpoint Submissions
| None
|CAPHAP
¦ CAP
Figure 2-7: On-road inventory - states/locals (dark blue) that submitted activity data
Clark County, NV
Maricopa County, AZ
21
-------
As seen in Figure 2-8, Texas and California are the only states for which state-submitted emissions are used in
the NEI for the nonroad data category (i.e., nonroad equipment). Again, California has provided EPA CAP and
HAP emissions based on a different model than the other states - the OFFROAD model6. Texas provided CAP
and HAP emissions using the NONROAD model with finer granularity than the National Mobile Inventory Model
(NMIM) that EPA used. Twelve states submitted NONROAD model inputs that EPA used to generate emissions,
and the remaining states accepted EPA estimates. More detail on the states and local agencies that submitted
inputs is provided in Section 4.5.
Figure 2-8: Nonroad equipment inventory - submission types - does not include local agencies
2011 Nonroad Submissions
| Accepted EPA Estimates
| Emissions
| Inputs Ji*
In addition to the maps above, each sector-specific section below has maps that show the distribution of state
and EPA data for CAPs and HAPs. Finally, Appendix A provides a table that shows for each EIS sector whether the
data comes from S/L/T agencies or a selection of EPA created datasets including TRI.
2.3 What are the top sources of some key pollutants?
This section simply provides a summary of criteria pollutants and total HAP emissions for all the 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. These emissions values are subject to change and are bounded by
the caveats and methods described by this documentation.
6 The OFFROAD model and documentation are available at the CARB Mobile Source Emissions Inventory website.
22
-------
Table 2-2: EIS sectors and associated CAP emissions and total HAP (1000 short tons/year)
1000 short tons /' yea!1
Sector
CO
NH,
NO..
W.-..
PMio
SO,
VOC
Lead
Tola!
HAPV
Agriculture - Crops & Livestock Dust
897
4,506
Agriculture - Fertilizer Application
1,183
Agriculture - Livestock Waste
0.13
2,344
0.13
0.19
0.34
8.32E-03
0.19
0.04
Bulk Gasoline Terminals
0.75
0.02
0.33
0.02
0.02
4.11E-03
157
8.33E-04
7.94
Commercial Cooking
31
5.38E-04
85
89
8.28E-05
13
5.37
Dust - Construction Dust
0.08
2.93E-03
0.08
163
1,510
0.02
0.04
0.05
Dust - Paved Road Dust
270
1,131
Dust - Unpaved Road Dust
833
8,339
F
res - Agricultural Field Burning
966
3.47
43
96
143
16
76
4.5E-04
55
F
res - Prescribed Fires
10,092
162
168
903
1,063
83
2,320
255
F
res - Wildfires
12,831
205
187
1,137
1,340
97
2,922
296
Fuel Comb - Comm/lnst
tut
onal - Biomass
19
0.14
8.39
11
13
1.08
0.64
3.27E-04
0.26
Fuel Comb - Comm/lnst
tut
onal - Coal
6.57
0.06
17
1.34
3.29
59
0.22
2.46E-03
1.75
Fuel Comb - Comm/lnst
tut
onal - Natural Gas
113
1.54
154
6.21
7.09
1.64
11
2.48E-03
1.48
Fuel Comb - Comm/lnst
tut
onal - Oil
15
0.74
60
5.72
7.88
56
1.99
8.42E-04
0.12
Fuel Comb - Comm/lnst
tut
onal - Other
9.09
0.03
7.95
0.63
0.66
1.24
0.95
2.81E-04
0.13
Fuel Comb - Electr
c Generat
on - Biomass
21
0.97
11
1.88
2.17
2.35
0.75
8.9E-04
1.66
Fuel Comb - Electr
c Generat
on - Coal
616
9.04
1,791
170
242
4,521
25
0.03
91
Fuel Comb - Electr
c Generat
on - Natural Gas
101
11
172
25
25
5.71
9.85
7.86E-04
3.52
Fuel Comb - Electr
c Generat
on - Oil
13
1.09
89
5.92
8.04
76
2.13
1.44E-03
0.52
Fuel Comb - Electr
c Generat
on - Other
34
2.94
26
2.51
2.86
20
3.25
1.59E-03
1.15
Fuel Comb - Industr
al Bo
lers, ICEs - Biomass
281
2.78
102
128
154
24
9.51
8.33E-03
5.72
Fuel Comb - Industr
al Bo
lers, ICEs - Coal
40
0.61
148
14
33
405
1.24
0.01
15
Fuel Comb - Industr
al Bo
lers, ICEs - Natural Gas
350
6.40
690
26
27
16
68
3.71E-03
22
Fuel Comb - Industr
al Bo
lers, ICEs - Oil
29
0.56
100
8.51
11
91
3.13
3.32E-03
0.58
Fuel Comb - Industr
al Bo
lers, ICEs - Other
122
1.09
56
24
26
53
7.87
3.91E-03
2.04
Fuel Comb- Residential - Natural Gas
94
41
219
4.79
6.10
1.45
13
1.1E-04
0.98
Fuel Comb- Residential - Oil
11
2.08
41
4.59
5.74
90
1.42
2.99E-03
0.10
Fuel Comb - Residential - Other
58
0.46
40
0.98
1.47
8.93
2.98
8.15E-06
0.26
Fuel Comb - Residential - Wood
2,525
20
35
382
383
8.97
444
68
Gas Stations
0.04
2.13E-04
0.03
1.79E-03
1.9E-03
1.51E-03
712
3.73E-04
86
Industrial Processes - Cement Manuf
77
0.91
119
6.54
12
60
4.37
3.79E-03
2.36
Industrial Processes - Chemical Manuf
185
24
75
20
25
133
96
4.64E-03
29
Industrial Processes - Ferrous Metals
417
0.22
56
29
35
29
17
0.05
2.32
Industrial Processes - Mining
33
0.09
33
74
486
2.04
1.63
6.21E-03
0.77
Industrial Processes - NEC
208
28
180
89
150
139
195
0.06
45
Industrial Processes - Non-ferrous Metals
330
0.53
15
16
20
103
15
0.08
9.44
Industrial Processes - Oil & Gas Production
654
0.11
673
17
19
74
2,730
1.2E-04
101
Industrial Processes - Petroleum Refineries
50
2.57
76
21
24
86
55
2.95E-03
6.20
Industrial Processes - Pulp & Paper
106
5.78
71
33
42
32
117
3.74E-03
51
Industrial Processes - Storage and Transfer
19
5.99
15
19
51
8.97
236
6.92E-03
14
Miscellaneous Non-Industrial NEC
11
2.74
2.73
2.12
2.26
0.24
201
7.1E-04
23
Mobile - Aircraft
423
111
7.33
8.63
14
30
0.49
8.04
Mobile - Commercial Marine Vessels
76
0.25
448
20
22
100
14
1.65E-03
1.64
Mobile - Locomotives
132
0.37
865
26
28
8.53
46
2.23E-03
5.00
Mobile - Non-Road Equ
pment - Diesel
624
0.99
1,098
86
89
2.42
111
1.05E-05
25
Mobile - Non-Road Equ
pment - Gasoline
9,764
0.66
198
42
46
0.89
1,496
334
Mobile - Non-Road Equ
pment - Other
546
0.61
87
1.68
1.68
0.62
20
0.09
Mobile - On-Road Diesel Heavy Duty Vehicles
899
6.71
2,951
140
184
3.67
248
46
Mobile - On-Road Diesel Light Duty Vehicles
451
0.93
149
7.74
11
0.32
51
8.61
Mobile - On-Road non-Diesel Heavy Duty Vehicles
1,040
1.11
111
1.87
4.11
0.58
50
14
Mobile - On-Road non-Diesel Light Duty Vehicles
29,472
138
3,588
81
237
31
2,741
767
Solvent - Consumer & Commercial Solvent Use
0.03
0.01
0.01
0.02
7.7E-03
1,677
314
23
-------
.1000 snore tor:1; /' war
Sector
CO
NH,
NO,
PM,.,
FM„.
SO,
VOC
U't'.d
Total
HAPs1
Solvent - Degreasing
0.41
0.03
0.01
0.05
0.06
0.03
148
7.48E-05
24
Solvent - Dry Cleaning
1.88E-04
4.15E-05
5.73E-04
5.73E-04
8.81
9.47
Solvent - Graphic Arts
0.14
0.08
0.15
0.17
0.19
0.01
72
2.21E-05
7.42
Solvent - Industrial Surface Coating & Solvent Use
3.48
0.63
2.38
3.82
4.29
0.43
571
3.22E-03
196
Solvent - Non-Industrial Surface Coating
0.02
334
142
Waste Disposal
1113
34
83
165
192
17
125
0.01
29
3. .13 7
Sub Total (no federal water?)
75.014
4.257
3 5,175
6.117
¦20,772
6.4?-5
18.238
0.8 1
Fuel Comb- Industrial Boilers, ICEs - Natural Gas
65
54
0.33
0.33
0.03
1.40
Fuel Comb - Industrial Boilers, ICEs - Oil
4.06
28
0.47
0.48
3.13
0.46
Fuel Comb - Industrial Boilers, ICEs - Other
1.03E-03
1.24E-03
2.89E-05
2.89E-05
1.02E-05
1.75E-04
Industrial Processes - Oil & Gas Production
1.65
1.92
0.03
0.03
0.03
52
Industrial Processes - Storage and Transfer
0.93
Mobile - Commercial Marine Vessels
117
0.46
930
57
62
369
29
2.96E-03
1.96
Ss:bTotal (federal wster?)
388
O.dS
1.0 j 4
58
61
372
84
2.96E-03
1.96
1 ota! (all but vegetstiun siid ?oN)
75,202
4,257
IS. 189
G..1.75
20,855
6,857
18,103
0.82
1.1*9
Biogenics - Vegetation and SoiP
6,842
1,021
40,728
5,969
Total
82.044
6.257
17.210
6. .17 5
20.835
6.857
59.025
0.82
5,108
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
2.4 How does this NEI compare to past inventories?
Many similarities between the 2011 NEI approaches and past NEI approaches exists, notably that the data are
largely compiled from data submitted by S/L/T agencies for CAPs, and that the HAP emissions have greater
augmentation by EPA because they are a voluntary contribution from the partner agencies. 2011 S/L/T
participation was somewhat more comprehensive than in 2008, though both were good. The NEI program
continues with the 2011 NEI to work towards a complete compilation of the nation's CAPs and HAPs. EPA
provided feedback to states during the compilation of the data on critical issues (such as potential outliers,
missing SCCs, missing mercury [Hg] data and coke oven data) as has been done in the past, and EPA improved
the inventory for the release. In addition to these similarities, there are some important differences in how the
2011 NEI has been created and the resulting emissions, which are described in the following two subsections.
2.4.1 Differences in approaches
With any new inventory cycle, changes to approaches are made to improve the data and process. The key
changes for the 2011 cycle are highlighted here.
The 2011 NEI is the second triennial inventory compiled with the EIS. We made a number of changes to improve
issues we came across in the 2008 NEI including preventing double counting, improving data quality, and
completeness. We made changes to pollutant and SCC codes, added QA checks and added features that were
used to assist in the QA and added flexibility to the data selection process. We retired benzene soluble organics
and methylene chlorine soluble organics and brought back the general "coke oven emissions" to replace these.
We also added a few automated QA checks to the hundreds of existing automated EIS checks. One check
applicable to HAPs was added to prevent double counting of a specific pollutant with the pollutant representing
the aggregated group. For example, submitters may not report both "o-Xylene" and" Xylenes (Mixed Isomers)"
at the same process. This check applied to the following groups: xylenes, cresols, chromium compounds,
polycylic organic matter, glycol ethers and polychlorinated biphenyls. We also required PMio to be greater than
or equal to PM2 s, and we required PMio to be reported if PM2 s was reported for the same process. If either of
these criteria were not met (HAP group, or PMio vs PM2 s magnitude) then none of the pollutants submitted for
24
-------
the process were allowed into the EIS for that process. Another new check was to allow only certain pollutant-
emission type combinations to be reported for on-road and nonroad data categories.
We also implemented a data tagging process in the EIS. This allowed EPA to tag suspect data and communicate
it using the EIS during the QA process to the data submitters, and to enable us to better control the hierarchy of
the data selected for the NEI. Tagged data were not selected for the NEI. Much of the suspect data we tagged
were corrected (and untagged) prior to the 2011 NEI. We also tagged to prevent pollutant/SCC combinations
that were reported by states from being used due to inconsistency. For example, we tagged metal HAPs from
dust-related sources that were submitted by only 1 or 2 states and not estimated by the EPA methods for these
categories. We also tagged data to fine tune the hierarchy of data to use in the 2011 NEI, which is shown for
point and nonpoint data categories in Table 3-1 and Table 3-2 in Section 3 of this document. Within any of the
datasets in those tables, tagged data (from either EPA or S/L/T datasets) were not used.
Chromium speciation and HAP augmentation were added to the EIS. These features allowed us to develop the
chromium speciation and HAP augmentation datasets in a more automated way and for S/L/T to view the
underlying data (tables in the EIS) used to create the augmented values. In addition, we augmented HAPs in the
nonpoint inventory using S/L/T-reported CAPS; we expected this to result in the HAP data to be more consistent
with the S/L/T CAP data.
We also developed new communications/processes to foster more complete inventory submittals from S/L/T
agencies and more complete gap filling of EPA nonpoint data. We used the EIS feature that provides
completeness reports (expected facilities) and informed S/L/T of their completeness status based on the number
of expected facilities for which emissions were submitted and based on the submittal of certain nonpoint
categories. Also geared toward fostering completeness and communications, we surveyed S/L/T regarding their
nonpoint submittals and/or acceptance of EPA nonpoint data. This additional information helped us determine
how to combine the EPA and S/L/T nonpoint data more correctly, preventing double counting and missing data.
To improve on completeness, we added EPA data to industrial, commercial and institutional combustion
categories where S/L/T data were found to be missing. Previously, we did not add EPA data for these categories.
We changed methods for several sectors. We updated methods for residential wood combustion, fires
(agricultural, wild and prescribed), and on-road emissions. We also estimated emissions for industrial,
commercial and institutional biomass burning and used these emissions where not provided by S/L/T. For
prescribed and wild fires and on-road emissions, we collected inputs to models EPA used to estimate emissions.
Using the EIS, S/L agencies submitted on-road inputs in the form of MOVES county database files. Prescribed
and wildfire inputs were collected outside of the EIS. For nonroad mobile sources, we encouraged S/L agencies
to provide inputs to NMIM via the EIS, and we used S/L agency submitted emissions for only California and
Texas.
For EGUs, we used the emission factors developed from the Mercury and Air Toxics Standards (MATS) test
program for PM2.5-FIL and PM-CON, for tested units only. These PM test data were not used for the 2008 NEI
(test data and average emission factors for HAPs were used in both 2008 and 2011). We computed PMW
through PM Augmentation of the MATS PM2.s data and used the resultant EFs along with 2011 heat input to
estimate PMi0 emissions for the tested units. The EPA data were used ahead of the S/L/T PM2 s and PMio except
where the S/L/T PM data were indicated by the S/L/T agency to have been from measurement data.
The point source augmentation approach for using TRI changed in the 2011 NEI. In the 2008 NEI, we summed
the TRI "stack" and "fugitive" emission estimates and apportioned the total based on the corresponding CAP
emissions (PM was used for metal HAPs; VOC for VOC HAPs). In 2011, we kept the TRI breakout of stack and
25
-------
fugitive for the NEI and assigned to generic placeholder stack and fugitive processes in the EIS. We assigned an
SCC code based on the SCC codes used for CAPS (see Section 3.1.4 for further details). The primary difference in
this approach is that in 2008 NEI, the TRI-based HAP emissions were apportioned and present at processes with
CAPs (with the exception of high-risk facilities and mercury-emitting facilities7), whereas in the 2011 NEI, the
TRI-based HAP emissions are grouped at a one or two processes with TRI HAP emissions only. In addition, we
added ammonia, a CAP, using the TRI in 2011, but not for 2008. In both years, if a S/L/T agency reported a
pollutant matching TRI at any process at the facility, then the TRI data for that pollutant was not used in the NEI.
2.4.2 Differences in emissions between 2011 and 2008 NEI
This section presents a comparison from the 2008 v3 to the 2011 v2. Figure 2-9 through Figure 2-12 compare
emissions for the CAPs and for select HAPs using seven highly aggregated emission sectors. Emissions from the
biogenic (natural) sources are excluded, and the wildfire sector is shown separately for CAPs and HAPs in Figure
2-10 and in Figure 2-12. While lead is a CAP for the purposes of the NAAQS, due to toxic attributes and inclusion
in the previous national air toxics assessment (NATA 2005), it is reviewed here with the HAPs. The HAPs selected
for comparison are based on their national scope of interest as defined by NAT A 2005.
In Figure 2-9 through Figure 2-12, the y-axis shows the emissions difference as estimated by subtracting the
2008 emissions from the 2011 emissions. Values greater than zero indicate that 2011 emissions are larger than
2008 values. Note in Figure 2-9 that the emission units for CO, S02, NOx and VOC are in units of millions of tons
(xlO6), while PM2.5 and PMW are in units of hundred thousands of tons (xlO5) and NH3 is in units of tens of
thousands of tons (xlO4). Similarly, y-axis scales vary in Figure 2-11 from thousands of tons (xlO3) for HAPs like
formaldehyde, to actual tons for arsenic. Table 2-3 and Table 2-4 show the emission changes for CAPs and HAPs
respectively, for each pollutant/sector combination; these tables contain the underlying numbers used in Figure
2-9 through Figure 2-12.
CAP emissions are overall lower in 2011 than in 2008, though some specific sector/pollutants increased in 2011
from 2008. Except for wildfires, the increases in NOx, PM2.5, VOC and CO are off-set by more substantial
decreases to result in an overall emissions decrease. Mobile source sector emissions are lower in 2011 than
2008. Wildfire CAP emissions are higher in 2011 than in 2008, with the most substantial increase for CO. CAP
emission increases in 2011 occur for the following sectors:
• Miscellaneous - agricultural field burning (PM2.s, S02, CO, NOx, VOC); waste disposal (CO); prescribed fires
(CO, VOC)
• Fuel Combustion - biomass (CO, VOC)
• Industrial Processes - oil and gas production (VOC, CO, NOx).
For the select HAPs reviewed, Table 2-4 and Figure 2-11 indicate that emissions are higher overall for sectors
except for slight decreases for the metals (chromium, arsenic, and lead) and a more substantial decrease for
ethylbenzene. With the exception of the metals shown and ethylbenzene, sector decreases for the other HAPs
are off-set by more substantial increases to result in an overall emissions increase. While mobile source sector
emissions for these HAPs are lower in 2011 than 2008, those decreases are off-set by increases in other sectors.
Wildfire HAP emissions are higher in 2011 than in 2008, with the most substantial increase for formaldehyde.
HAP emission increases in sectors, include the following:
7 For the 2008 NEI, we added TRI pollutants that were determined to be risk drivers at high risk facilities based on the 2005
NATA, and we added TRI Hg for several key Hg categories regardless of whether CAPs were reported.
26
-------
Miscellaneous - agricultural field burning (formaldehyde, acetaldehyde, 1,3-butadiene); prescribed fires
(formaldehyde, acetaldehyde, 1,3-butadiene, acrolein); gas stations (ethyl benzene)
Industrial Processes -industrial surface coating and solvent use (ethyl benzene)
Fuel Combustion - biomass and natural gas (formaldehyde, acrolein).
Table 2-3: Emission differences (tons) for CAPs, 2011 minus 2008
Sector
CO
NH3
NOx
PMio
PM2.5
SO2
voc
Miscellaneous
1,879,866
-99,646
29,757
-670,863
115,923
26,118
94,222
Fuel Combustion
214,977
487
-1,191,884
-4,383
10,213
-3,594,384
76,412
Industrial Processes
238,316
-19,056
179,548
-331,910
-85,591
-213,929
972,700
Nonroad Mobile
-2,946,001
-317
-559,336
-48,203
-36,844
-182,345
-393,257
Highway Vehicle
-5,801,073
-13,990
-1,071,088
38,926
-55,075
-9,958
-409,578
Total Difference,
excluding wildfires
-6,413,915
-132,521
-2,613,003
-1,016,433
-51,373
-3,974,497
340,498
Total % Difference,
excluding wildfires
-9%
-3%
-15%
-5%
-1%
-37%
2%
Fires - Wildfires
501,308
5,140
88,432
148,057
126,571
25,844
44,637
Tab
e 2-4: Emission c
ifferences (tons) for select HAPs, 2011 minus 2008
Sector
1,3-Butadiene
1,4-Dichlorobeniene
Acetaldehyde
Acrolein
Arsenic
Chromium
Compounds
Ethyl Benzene
Formaldehyde
Lead
Tetrachloroethylene
Miscellaneous
5,972
653
13,308
40
0
-46
4,462
48,266
-2
6,458
Fuel Combustion
-147
0
195
149
-20
-72
25
2,569
-31
-13
Industrial Processes
200
-2
618
877
0
7
1,915
7,622
-36
-31
Nonroad Mobile
-2,392
-2,981
-46
-3
0
-8,511
-7,150
-67
Highway Vehicle
-1,503
1,335
228
0
-15
-8,877
-2,958
Total Difference,
excluding wildfires
2,130
651
12,474
1,247
-23
-125
-10,986
48,348
-136
6,414
Total % Difference,
excluding wildfires
6%
56%
15%
4%
-16%
-21%
-12%
22%
-14%
109%
Fires - Wildfires
5,380
5,423
5,633
34,208
27
-------
Figure 2-9: Comparison of CAP emissions, 2011 minus 2008, excluding wildfires and biogenics
4
2
0
-2
-4
-6
-8
-10
-12
-14
-16
I -
xlO6
xlO5
xlO4
CO S02 NOX VOC PM10 PM2.5 NH3
¦ Miscellaneous Fuel Combustion ¦ Industrial Processes
Nonroad Mobile ¦ Highway Vehicle
Figure 2-10: Comparison of wildfire CAP emissions, 2011 minus 2008
= 300
PM10
PM2.5
Additional information about sources within each sector that drive the decrease or increase observed by
pollutant / sector combination, including where some differences are also due to method changes - are
28
-------
described in this technical support document, or are included in the EPA's "2011 NEI Report"; however, the 2011
NEI report was developed for the vl of the 2011 NEI and updating this report to the current 2011 v2 is not
planned.
Figure 2-11: Comparison of HAP emissions, 2011 minus 2008, excluding wildfires and biogenics
70
60
50
40
30
20
10
0
-10
-20
-30
. I .
fxl03
¦J*' ^ <
-------
2.5 How well are tribal data and regions represented in the 2011 NEI?
Sixteen tribes submitted data to the EIS for 2011 as shown in Table 2-5. 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 2-5 with just a CAP flag will also have some HAP emissions in most cases.
Six additional tribes, shown in Table 2-6, which did not submit any data, are represented in the point data
category of the 2011 NEI due to the emissions added by EPA. The emissions for these facilities are from the EPA
gap fill datasets for airports, electric generating units and the TRI data. Furthermore, many nonpoint datasets
included are presumed to include tribal activity. Most notably, the oil & gas nonpoint emissions have been
confirmed to include activity on tribal lands because the underlying database contained data reported by tribes.
See Section 3.21 for more information.
Table 2-5: Tribal participation in the 2011 v2 NEI
Tribe
Point
Nonpoint
Onroad*
Nonroad*
Bishop Paiute Tribe
CAP, HAP
Coeur d'Alene Tribe
CAP
CAP, HAP
CAP, HAP
CAP, HAP
Confederated Tribes of the Colville Reservation,
Washington
CAP
Eastern Band of Cherokee Indians
CAP, HAP
CAP, HAP
CAP, HAP
Kickapoo Tribe of Indians of the Kickapoo Reservation in
Kansas
CAP
CAP
Kootenai Tribe of Idaho
CAP, HAP
CAP, HAP
CAP, HAP
Navajo Nation
CAP
Nez Perce Tribe
CAP, HAP
CAP, HAP
CAP, HAP
CAP, HAP
Northern Cheyenne Tribe
CAP
Prairie Band of Potawatomi Indians
CAP, HAP
Sac and Fox Nation of Missouri in Kansas and Nebraska
Reservation
CAP, HAP
CAP
Santee Sioux Nation, Nebraska
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
Tohono O'Odham Nation Reservation
CAP, HAP
Washoe Tribe of California and Nevada
CAP, HAP
*onroad and nonroad tribal emissions are not part of the 2011 NEI sector/tier data. They are available from the Onroad and
Nonroad Mobile Tribal Lands Emissions Summaries posted with the 2011 NEI Data or from summaries of the Tribal datasets
in the EIS.
30
-------
Table 2-6: Facilities on Tribal lands with 2011 NEI emissions from EPA only
Tribe
EPA data used
Assiniboine and Sioux Tribes of the Fort Peck Indian
Reservation, Montana
Airport Emissions
Confederated Tribes and Bands of the Yakama
Nation, Washington
TRI data
Fond du Lac Band of the Minnesota Chippewa Tribe
Airport Emissions
Omaha Tribe of Nebraska
Airport Emissions
Tohono O'Odham Nation of Arizona
TRI data
Ute Mountain Tribe of the Ute Mountain
Reservation, Colorado, New Mexico & Utah
Airport Emissions, TRI data and EGU
Emissions
2.6 What does this NEI tell us about mercury?
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 2-8.
Hg emission estimates in the 2011 v2 sum to 56.4 tons, with 55.1 tons from stationary sources (not including
commercial marine vessels and locomotives) and 1.3 tons from mobile sources (including commercial marine
vessels and locomotives). Of the stationary source emissions, the inventory shows that 26.9 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 (26.8 tons) of that total.
For the 2011 v2, EPA revised and added new estimates from several nonpoint categories. Categories that had
not been previously estimated are:
• 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 (2.1 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)
• 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)
Categories with method changes are: human cremation (1.4 tons in 2011 which is the sum of the updated EPA
nonpoint with S/L/T agency reported nonpoint and point); animal cremation (less than 0.1 tons which is the sum
of the updated EPA nonpoint with S/L/T agency reported nonpoint and point); fluorescent lamp breakage (less
than 1 lb.; sum of EPA and S/L/T agency nonpoint); fluorescent lamp recycling (0.4 tons; sum of EPA and S/L/T
agency nonpoint); and dental amalgam (0.4 tons sum of EPA and S/L/T agency nonpoint).
None of these categories are distinct regulatory sectors and are therefore put into the "Other" category in Table
2-8. Previous-year emissions were not revised to include these new emissions or method changes. Detailed
documentation on the methods is provided in a memorandum "Nonpoint Sources of Mercury - documentation
6-26-2014.docx" provided in the supplemental documentation.
31
-------
The data sources used to create the 2011 v2 Hg inventory are shown in Figure 2-13. The datasets are described
in more detail starting in Section 3.1.1, and we highlight some key datasets here.
For EGUs, we used unit specific and "bin"-average emission factors collected from a test program conducted
primarily in 2010 to support the MATS rule8, and used 2011-specific activity from the Clean Air Markets Division
Data and the Department of Energy. The MATS-based Hg data are labeled "EPA EGU" in the figure; all the
mercury emissions from the EPA EGU dataset use MATS-based data. Also, for EGUs, 33% of the Hg data are from
S/L/T agency data instead of the MATS-based data. These data were used for units where S/L/T agency reported
the calculation method to be based either on continuous emissions monitors (CEMs) or test data. In addition,
S/L/T agency data were used for 65% of the other stationary source emissions and is represented by "S/L/T" in
the figure. We used several other datasets developed by EPA including TRI (see Section 3.1.4), EPA HAP
Augmentation or "HAP Aug" in the figure (see Section 3.1.5), and other EPA data developed for gap filling (see
Section 3.1.1).
Figure 2-13: Data sources of Hg emissions (tons) in the 2011 v2, by data category
50
40
45
25
30
35
20
15
10
5
0
¦ EPA EGU
¦ EPA HAPaug
¦ EPA Nonpoint
EPA NV Goldmines
¦ EPA Air/Rail/CMV
¦ EPA Mobile
I TRI
¦ Other EPA
¦ S/L/T
Nonpoint
Point
Nonroad
Onroad
8 See "Memorandum: Emissions Overview: Hazardous Air Pollutants in Support of the Final Mercury and Air Toxics
Standard" EPA-454/R-11-014. 12/1/2011. or at Docket number EPA-HQ-OAR-2009-0234
32
-------
In addition to Figure 2-13, Table 2-7 breaks out the emissions data sources further into the amounts of Hg from
each individual dataset used in the selection. More information on these datasets is available in Sections 3.1.1
for stationary sources, and Section 4 for mobile sources.
Since mercury is a HAP, it is reported voluntarily by S/L/T agencies. For the 2011 v2, 42 states reported point
source Hg emissions; Figure 2-14 identifies the states that included state or local data. No tribal agencies
reported point source Hg. Six tribal agencies reported Hg to the nonpoint data category: Coeur d'Alene Tribe of
the Coeur d'Alene Reservation, Idaho; Eastern Band of Cherokee Indians; Kootenai Tribe of Idaho; Shoshone-
Bannock Tribes of the Fort Hall Reservation of Idaho; Nez Perce Tribe of Idaho, and Sac & Fox Nation of Missouri
in Kansas and Nebraska.
Table 2-7 shows that a large portion of mercury in the point data category is from the 2011EPA_EGU dataset.
This is due to the selection hierarchy. EPA chose to use HAP emissions computed using from EFs developed from
Mercury and Air Toxics Standards (MATS) test program used ahead of S/L/T agency data except where the S/L/T
agency data were from a source test or a continuous emissions monitor (CEMS). EPA used the emissions
calculation method code (a required field) to determine where S/L/T agency data were from a source test or
CEMS.
Table 2-7: 2011 v2 Hg emissions for each dataset type and group
Data
Category
Dataset short name
Mercury Emissions
(tons/yr)
Grouped Data Source
for Chart
2011EPA_NP_Mercury
4.40
EPA other
S/L/T
1.54
S/L/T
2011EPA_NP_NoOvrlp
0.71
EPA Nonpoint
Nonpoint
2011EPA Rail
0.58
EPA Air/Rail/CMV
2011EPA_HAP-Aug
0.41
EPA other
2011EPA_NP_Ovrlp
0.06
EPA Nonpoint
2011EPA CMV
0.04
EPA Air/Rail/CMV
2011EPA CMVLADCO
0.00
EPA Air/Rail/CMV
S/L/T
25.5
S/L/T
2011 EPA EGUs
16.5
EPA EGU
2011EPA TRI
4.07
TRI
2011 NVGLD
0.80
EPA NV Goldmines
Point
2011EPA_CarryForward
0.72
EPA other
2011EPA Other
0.35
EPA other
2011EPA_HAP-Aug
0.30
EPA other
2011EPA Rail
0.05
EPA Air/Rail/CMV
2011 EPA Landfills
0.005
EPA other
Nonroad
S/L/T
0.03
S/L/T
2011EPAMOBILE
0.01
EPA Nonroad
Onroad
2011EPAMOVES2014
0.40
EPA Onroad
S/L/T
0.08
S/L/T
33
-------
Figure 2-14: States with state- or local-provided Hg emissions in the point data category of the 2011 v2
2011 Mercury Submissions
| No Point Mercury
| Point Mercury
Table 2-8 shows the 2011 v2 mercury emissions for the key categories of interest in comparison to 1990. Also
shown are the most recent 2005 emissions, which were used in support of the MATS rule. The Microsoft ® 2013
ACCESS ® database included in the zip file 2011nei supdata mercurv.ziii provides the category assignments at
the facility-process level for point sources, and the county-SCC level for nonpoint, onroad and nonroad data
categories.
Table 2-8: Trends in NEi mercury emissions - 1990, 2005, 2008 v3 and 2011 v2
Source Category
1990 (tpy)
2005(tpy)
2008
2011
Categorization Approach
Baseline for
MATS
(tpy)
(tpy)
HAPs,
proposal
2008 v3
2011 v2
11/14/2005
3/15/2011
Utility Coal Boilers
Regulatory code, NESHAP: MATS rule and unit
(Electricity Generation
58.8
52.2
29.4
26.8
specific info on boiler config (from MATS rule) to
Units - EGUs,
assign fuel, SCC for units not in MATS database
combusting coal)
Hospital/Medical/
Regulatory code: Hospital, Medical, Infectious
Infectious Waste
51
0.2
0.1
0.1
Waste Incineration (HMIWI)
Incineration
Municipal Waste
Regulatory codes: Section 129 rules for Small
Combustors
57.2
2.3
1.3
1.0
Municipal Waste Combustors (MWC) and Large
MWC
Industrial,
SCC list- chose only processes with these SCCs that
Commercial
14.4
6.4
4.2
3.6
were not already tagged with ruie or via manual
Institutional Boilers
approach
and Process Heaters
Mercury Cell Chlor-
10
3.1
1.3
0.5
Regulatory code: NESHAP, Mercury Cell Chlor-Alkali
Alkali Plants
Plants.
34
-------
Source Category
1990 (tpy)
2005(tpy)
2008
2011
Categorization Approach
Baseline for
MATS
(tpy)
(tpy)
HAPs,
proposal
2008 v3
2011 v2
11/14/2005
3/15/2011
Electric Arc Furnaces
Regulatory code: Area Source rule for "Stainless &
7.5
7.0
4.8
5.4
Non-Stainless-Steel Manufacturing: Electric Arc
Furnaces" plus 2 major sources that have EAFs
Commercial/Industrial
Source Classification Code (50200101) and
Sold Waste
Not available
1.1
0.02
0.01
Manually assigned based on how it was categorized
Incineration
in previous inventories
Hazardous Waste
Combination of regulatory code, NESHAP:
Incineration
6.6
3.2
1.3
0.7
Hazardous Waste Incineration, and manual
examination based on examination of unit/process
description and how it was categorized in 2008.
Portland Cement Non-
5.0
7.5
4.2
2.9
Regulatory code: NESHAP, Portland Cement
Hazardous Waste
Manufacturing
Gold Mining
4.4
2.5
1.7
0.8
Regulatory code: NESHAP, Gold Mine Ore
Processing and Production
Sewage Sludge
2
0.3
0.3
0.3
Source Classification Code: 50100506, 50100515,
Incineration
50100516, 50382501, 50100701, 50100793
Mobile Sources
Sum of all onroad, nonroad, locomotives and
Not available
1.2
1.8
1.3
commercial marine vessels (locomotives and
marine used SCC code)
Other Categories
29.5
18
10.7
13
Total (all categories)
246
105
61
56
The top emitting 2011 Mercury categories are: EGUs (rank 1), electric arc furnaces (rank 2), industrial,
commercial and institutional boilers and process heaters (rank 3) and Portland cement excluding hazardous
waste kilns (rank 4).
As shown in Table 2-8, 2011 mercury emissions are 5 tons lower than in the 2008. Almost three tons of this
difference is due to lower mercury emissions from EGUs covered by MATS; three other categories with large
decreases are Portland Cement Manufacturing, Gold Mining and Chlor-Alkali plants. The lower emissions in 2011
are due to a combination of voluntary agreements, state rules, consent decrees, activity levels (e.g., lower
cement production in 2011) and reductions that occurred from facilities prior to MACT compliance dates. For
EGUs, the decrease is due primarily to the installation of Hg controls to comply with state rules and voluntary
reductions, and the co-benefits of Hg reductions from control devices installed for the reduction of S02 and PM
as a result of state and federal actions, such as New Source Review enforcement actions. There has also been an
increased use of natural gas resulting in lower coal usage. The lower Hg is consistent with a 33% decrease in S02.
The cement decrease is due primarily to reductions at existing cement plants, including a voluntary agreement
to install controls by the highest emitting cement plant in 2008, and several plant closures that occurred
between 2008 and 2011. For gold mines, reductions occurred initially due to a voluntary program developed by
EPA Region 9 and Nevada, and then further reductions were achieved through a Nevada state regulatory
program. In the mercury chlor-alkali industry, facilities have been switching technologies to eliminate Hg
emissions from chlorine production. Many switched prior to 2008 and several switched after. In 2011, there
were four facilities using the Hg chlor-alkali process: Olin Corporation in Tennessee and Georgia and PPG in
Louisiana and West Virginia.
For electric arc furnaces (EAFs), emissions increased from 2008 by about a half a ton. The largest increase for
this category occurs in Alabama which relied heavily on EPA estimates for 2008 and solely on estimates from the
35
-------
state and local agency (Jefferson County Health Department) in 2011. Increases occur at existing facilities in this
state. Ohio also shows large increases in emissions, again from existing facilities. However, the data from Ohio
(for both 2008 and 2011) is predominantly from the TRI. For situations where neither the state nor TRI provided
Hg, EPA estimated Hg using 2011 activity data provided by the state with emission factors from a test program
conducted in support of rule development for the EAF industry. These were included in the "2011EPA_Other"
dataset in the EIS. The EFs are provided in the file electric arc furnace testabased efs.zip; they are the same
EFs as were used for gap filling for the 2008 NEI.
For other categories, the difference in emissions from 2008 to 2011 is similarly due to a combination of
methodological differences in the approaches used to develop the two inventories, in addition to changes in
activity between, and reductions implemented by states ahead of Federal regulations and other factors. For the
non-EGU categories, the 2011 NEI primarily uses data submitted by S/L/T agencies. Where S/L/T agency data are
missing EPA supplemented the information using the TRI for the year 2011 and, as discussed in Section 3.1,
other datasets developed by EPA, particularly those for "working face" landfill emissions as well as switches and
relays.
The municipal waste combustor and boiler MACT data gathered by EPA for rule development and used for the
2008 NEI were used in 2011 without adjustment for situations in which S/L/T agency or TRI data were not
available. These data were put into the EIS dataset "2011EPA_CarryForward".
36
-------
3 Stationary sources
This section begins with an overview of the stationary sources comprising most of the point and nonpoint data
categories in Section 3.1. All subsequent sub-sections detail specific stationary EIS sectors, from agricultural,
industrial, commercial, residential fuel combustion and solvents to dust, industrial processes, miscellaneous
sources, and waste disposal.
Note that while some "nonroad" sources such as aircraft, commercial marine vessels and trains reside in the NEI
point and nonpoint data categories, discussion of these sources is provided in the mobile source Section (4) of
this document.
3.1 Stationary source approaches
Stationary source emissions data are inventoried as point sources or nonpoint sources. These data are provided
by S/L/T agencies, and for certain sectors and/or pollutants, they are supplemented with data from EPA. This
section describes the various sources of data and the priority for each of the datasets for choosing the data
value to use for the NEI when multiple data sources are available for the same emissions source.
3.1.1 Sources of data overview and selection hierarchies
Table 3-1 and Table 3-2 describe the datasets comprising the point and nonpoint inventories, respectively, and
the hierarchy for combining these datasets in construction of the NEI. While the bulk of these datasets are for
stationary sources of emissions, some of these datasets contain mobile sources so that emissions from airports
and rail yards could be included as point sources.
EPA developed all datasets other than those containing S/L/T agency data and the dataset containing emissions
from offshore platforms in Federal waters -2011 Bureau of Ocean Energy Management (BOEM) data. We used
various methods and databases to compile the EPA generated datasets, which the tables and subsequent
subsections fully describe. 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 3.1.2) and to speciate S/L/T agency reported total chromium into hexavalent and trivalent forms
(Section 3.1.3).
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. In addition to the order of the datasets, the hierarchy
was also influenced by the new EIS feature of data tagging. 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) if they were deemed to be likely
outliers and were not addressed during the S/L/T agency data reviews, 2) to set the hierarchy to use the
Mercury and Air Toxics Standard (MATS) data ahead of the S/L/T agency data where the S/L/T agency data were
not from either source test or continuous emission monitoring sources. The MATS data covered acid gases
(except HCN which was deemed unreliable and tagged from the EPA dataset), metal HAPs (including lead), and
PM. MATS PM data were used only for units in which both PM2.5-FIL and PM-CON were tested during the MATS
test program. The tables include the rationale for why each dataset was assigned its position in the hierarchy.
37
-------
We excluded pollutants from stationary sources in the 2011 NEI as shown in the last row of both tables: we
excluded greenhouse gases and pollutants in the pollutant groups "dioxins/furans" and "radionuclides"9.
Tab
e 3-1: Data sources and selection hierarchy used for point sources
Dataset name
(Short name* provided
if different)
Description and Rationale for the Order of the Selected Datasets
Order
2011EPA_PM-
Augmentation
(2011EPA_PM-AUG)
PM species added to gap fill missing S/L/T agency data or make corrections
where S/L/T agency have inconsistent PM species' emissions. Uses speciation
factors from the PM Calculator for covered SCCs. For others, checks/corrects
discrepancies or missing PM species using basic relationships such as ensuring
that primary PM is greater than or equal filterable PM (See Section 3.1.2).
This dataset is ahead of the S/L/T agency data because in addition to filling in
missing data, it also corrects S/L/T agency values based on feedback from the
agencies.
1
2011 Responsible
Agency Selection
S/L/T agency submitted data; multiple datasets - one for each reporting
agency. These data are selected ahead of other datasets except the
2011EPA_PM-Augmentation (above). The only other situation where S/L/T
agency emissions are not used is where tagged in the EIS (at the specific
source/pollutant level). This occurs: 1) for hierarchy purposes to allow the
Mercury and Air Toxics Standard (MATS) to be used ahead of S/L/T agency
data except where S/L/T agency data were from source test or continuous
emission monitors and 2) where S/L/T agency data were suspected outliers
that were not addressed.
2
2011EPA_EGU
HAP and CAP emissions from 3 sources:
1. MATS EFs and 2011 throughput—for lead, mercury, other HAP metals,
acid gas HAP and PM emissions from the MATS rule information
collection request, including unit-specific test data and emissions data
derived from EFs from a 2010 testing program and 2011 throughput. PM
used only where PM25-FIL and PM-CON were tested. Throughput
primarily from CAMD but also used EIA and data provided by Puerto Rico
for EGUs
2. CAMD CEMs data for S02 and NOx
3. EFs used in previous year inventories from AP-42 and other sources along
with CAMD heat input data.
3
2011EPA_
chrom_split
Hexavalent and trivalent chromium speciated from S/L/T agency reported
chromium. New 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 chromium. See Section
3.1.3.
4
EPA NV Gold Mines
(2011_NVGLD)
2011 Mercurv emissions from the Nevada Mercury Control Program - Annual
Emissions Reporting -
early copy of the data emailed by Adele Malone, Nevada Division of
Environmental Protection, 11/05/2012
5
9 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 2011 emissions.
Radionuclides have the pollutant category name of "radionuclides" The specific compounds and codes are in the pollutant
code tables in EIS.
38
-------
Dataset name
(Short name* provided
if different)
Description and Rationale for the Order of the Selected Datasets
Order
2011EPA_pther
Variety of EPA gap fill data including: coke oven emissions using state -
provided information for facilities in Kentucky, Michigan and Pennsylvania;
electric arc furnace mercury emissions using activity reported to the EIS by
states and EFs from the ICR test program or S/L/T agency provided
information, emissions for several New Mexico facilities that were provided
by NM after the submission deadline (EPA used the CAP data only), mercury
emissions for Iowa sources that were below Iowa thresholds and were
reported by Iowa as zero, mercury emissions for a boiler in Missouri using
state-provided data.
6
2011EPA_TRI
Toxics Release Inventory data for the year 2011 (see Section 3.1.4). These
data are selected for a facility only when alternative emissions are not
included in the S/L/T agency data.
7
2011EPA_Airports
Emissions of CAP and HAP for aircraft operations including commercial,
general aviation, air taxis and military aircraft, auxiliary power units and
ground support equipment computed by EPA for approximately 20,000
airports. Methods include the use of the Federal Aviation Administration's
Emissions and Dispersion Modeling System. See Section 4.2. EPA airport data
are selected for a county only if S/L/T agency data are not contained in the
first dataset, with the exception of possible airport-related PM data.
8
2011EPA_Rail
Emissions of CAP and HAP for diesel rail yard locomotives at 753 rail yards.
CAP emissions computed using yard-specific emission factors using yard-
specific fleet information and on national fuel values 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 4.4. EPA Rail data
are selected for a county only if S/L/T agency data are not. This dataset also
contains county-level emissions used in the nonpoint selection (Table 3-2).
9
2011EPA_LF
(2011 EPA Landfills)
Landfill emissions developed by EPA using methane data from the EPA's
Greenhouse Gas reporting rule program. Dataset contains landfills only for
which no pollutants were reported by S/L/T agency in the 2011 reporting
year.
10
2011EPA_
Carry Forward-
Previous Year Data
(2011EPA_
Carry Forward)
Variety of estimates used to gap fill important sources/pollutants: 1) coke
oven missing from S/L/T agency data and not in the EPA Other dataset. 2)
Mercury from MWCs and boilers (in 2008 it was in the dataset called "2008
EPA Rule Data from OAQPS/SPPD" 3) Numerous HAPs from an MWC in
California.
11
2011EPA_HAP-
Augmentation
(2011EPA_HAP-Aug)
HAP data computed from S/L/T agency criteria pollutant data using HAP/CAP
emission factor ratios based on the EPA Factor Information Retrieval System
(WebFIRE) database as described in Section 3.1.5. These data are selected
below the TRI data and 2011EPA_CarryForward-PreviousYearData 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.
12
2011EPA_BOEM
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 in the National
Inventory Input Format and converted to the CERS format by EPA. The state
code for data from this data set is "DM" (Federal Waters).
13
39
-------
Dataset name
(Short name* provided
if different)
Description and Rationale for the Order of the Selected Datasets
Order
Exceptions to the hierarchy
1. Excluded dioxin/furan individual pollutants and groups, greenhouse gas pollutants, and radionuclides.
USEPA 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 in
order to compile a complete estimate for dioxin and furans nor radionuclides as part of the NEI.
A The dataset short name is the name that the EIS will list in its process-level reports
Table 3-2: Data sources and selection hierarchy used for nonpoint sources
Dataset name
(Short Name*
provided if different)
Description and Rationale for the Order of the Selected Datasets
Order
2011EPA_PM-
Augmentation
(2011EPA_PM-AUG)
Adds 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 calculator for processes covered by that database. For other
processes, 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 3.1.2).
1
2011EPA_
AgBurningSF2
Agricultural fire emission estimates developed by EPA. See Section 5.2.
2
2011 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 tagged in the
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
2011EPA_chrom_
split
Hexavalent and trivalent chromium speciated from S/L/T agency reported
chromium. New EIS augmentation function creates the dataset by applying
multiplication factors by SCC, facility, process or NAICS code to S/L/T agency
chromium. See Section 3.1.3.
4
2011EPA_HAP-
Augmentation
(2011EPA_HAP-Aug)
HAP data computed from S/L/T agency criteria pollutant data using
HAP/CAP emission factor ratios based on ratios of HAP to CAP emission
factors used in the EPA estimates. This dataset is below the S/L/T agency
data so that the S/L/T agency HAP data are used first.
5
2011EPA_CMVLADCO
Submitted by the Lake Michigan Air Directors Consortium (LADCO) for
state's that approved. See Section 4.3
6
2011EPA CMV
EPA commercial marine vessel emissions estimates. See Section 4.3.
7
2011EPA_Rail
EPA locomotive (referred to as "rail" in this document) emissions estimates.
See Section 4.4.
8
2011EPA_NP_
NoOverlap_w_Pt
(2011EPA_NP_
NoOvrlp)
Contains data for categories primarily for which there was no or limited
possibility of point source contribution (or overlap). Examples include:
residential fuel combustion, consumer solvent utilization, open burning,
agricultural burning, dust, petroleum product transport. The data does
include some where there may be some overlap, such as some solvent
utilization categories. Also includes Hg data used in the 2002 NEI for the
following categories: fluorescent light breakage, fluorescent light recycling,
9
40
-------
Dataset name
(Short Name*
provided if different)
Description and Rationale for the Order of the Selected Datasets
Order
laboratory activities, and dental amalgam. These 2002 NEI data were not
estimated for 2008 or 2011 but are categories that were largely unavailable
from the S/L/T agency data (though some states did report cremation and
where this occurred it was excluded from this dataset).
2011EPA_NP_
Overlap_w_Pt
(2011EPA_NP_Ovrlp)
Contains data for categories for which there was the possibility of point
source contribution (or overlap). These categories include industrial,
commercial and institutional emissions that are often accounted for in the
point source inventory and oil and gas emissions. EPA added these
emissions to the NEI only after analyses to determine if the S/L/T agency
had accounted for them in the point data category. EPA did not adjust
nonpoint data with the point data. See Section 3.1.7.
10
2011EPA_biogenics
Natural emissions from vegetation and soil, computed using 2011
meteorology and the BEIS3.14 model. See Section 6. The order does not
matter because it does not overlap with any other data used in this
selection.
11
2011EPA_NP_Mercury
Mercury only data for select source categories within the waste disposal
(see Section 3.32) and Miscellaneous Non-Industrial NEC (see Section 3.26)
sectors.
12
Exceptions to the hierarchy
1. Excluded dioxin/furan individual pollutants and groups, greenhouse gas pollutants, 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
in order to compile a complete estimate for dioxin and furans nor radionuclides as part of the NEI.
3.1.2 Particulate matter augmentation
Particulate matter (PM) emissions species in the NEI are: primary PMW (called PM10-PRI in the EIS and NEI) and
primary PM25 (PM25-PRI), filterable PM (PMlO-FILand PM25-FIL) and condensable PM (PM-CON). 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 none
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 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 is a Microsoft 8 Access 8 database, available under the "Emission
Inventory Tools" heading.
41
-------
3.1.3 Chromium augmentation
The 2011 reporting cycle has 5 valid pollutant codes for chromium, as shown in Table 3-3.
Table 3-3: 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 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 and why we have performed this augmentation. Hexavalent chromium (Chromium (VI)) is considered
high risk and other valence states are not. Most of the non-hexavalent chromium is trivalent chromium
((Chromium III)); therefore, EPA speciated total chromium into hexavalent and trivalent chromium. The 2011 NEI
does not contain any total chromium; only the speciated pollutants shown in Table 3-3.
This section describes the procedure we used for speciating chromium emissions from total chromium that was
reported by S/L/T agencies. This procedure generated trivalent chromium (Chromium III) and hexavalent
chromium (Chromium (VI)), 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.
We used the new EIS augmentation feature to speciate S/L/T agency reported chromium. The EIS uses the
following priority order for applying the factors: 1) by specific process using the EIS process id, 2) by specific
facility using the EIS facility id, 3) by regulatory code, 4) by NAICS code, and 5) by SCC. The EIS generates and
stores an EPA dataset containing the resultant hexavalent and trivalent chromium species. EPA then used this
dataset in the 2011 NEI selection by adding it to the selection hierarchies shown in Table 3-1 and Table 3-2 and
excludes the S/L/T agency unspeciated 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 2011 NEI, EPA named this dataset "2011EPA_chrom_split". Most of the speciation factors used in the
2011 NEI are SCC-based and are the same as were used in 2008, based on data that have long been used by 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. 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 2011nei supdata chromspeciation.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% hexavalent chromium, 66% trivalent chromium is applied.
42
-------
3.1.4 Use of the 2011 Toxics Release Inventory
EPA used air emissions data from the 2011 Toxic Release Inventory (TRI) to supplement point source HAP and
NH3 emissions provided to EPA by S/L/T agencies. The resulting augmentation dataset is labeled as
"2011EPA_TRI" in the Table 3-1 selection hierarchy shown above. For 2011, 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 2011 NEI.
The basis of the 2011EPA_TRI dataset is the US EPA's 2011 Toxic Release Inventory. 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 2011 US.csv and was downloaded on December 1, 2012.
The approach used for the 2011 NEI differed from that used for the 2008 NEI in that the TRI emissions were not
apportioned to the same EIS processes that S/L/T agencies used to report their PM and VOC emissions. Instead,
the TRI emissions were included in the EIS (and the NEI) as facility-total stack and facility-total fugitive emissions
processes, which reflected 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.
This new approach has several benefits. It does not rely on the need for any PM or VOC surrogate emissions to
have been reported by the S/L/T agency in order to apportion the TRI values among multiple processes. It also
allows most of the TRI emissions to be viewable, comparable, and downloadable from the EIS with the same
detail as was reported to TRI by the facility. In addition to allowing the use of more of the TRI data, especially for
smaller emitting facilities that may not have PM or VOC emissions reported by S/L/T agencies, this approach
allows the TRI data to be loaded into the EIS earlier in the reporting cycle, and there are no process allocations
that need to be re-done when S/L/T agency emissions updates are made.
A key potential disadvantage to this approach was having to choose a useful SCC for the emissions process,
which in the past NEI cycles prior to 2008 led to a "miscellaneous" SCC for all TRI data. The 2008 approach of
apportioning the emissions based on S/L/T agency data allowed for TRI emissions to be associated with more
appropriate SCCs (though limitations applied there as well). To minimize this disadvantage, we implemented an
approach to assign more appropriate SCCs that allow the emissions to at least be lumped into the proper EIS
Sector.
The following steps describe in more detail the development of the 2011EPA_TRI dataset.
1. Develop a TRIJD to EISJD facility-level crosswalk
The TRI emissions database contains the data element TRI Facility ID (TRIJD) which is used to uniquely
identify a facility site. The NEI uses the field "EIS Facility Identifier" (EISJD) to uniquely identify facilities.
The USEPA's Office of Environmental Information (OEI) maintains the Facility Registry System (FRS) data
system as a way to crosswalk such unique identifiers between various EPA programs and data systems.
This FRS linkage had been used as a starting point to develop the needed TRIJD to EISJD crosswalk for
the 2008 NEI. The 2008 effort supplemented the FRS linkage by performing various OA reviews and
comparisons.
43
-------
For 2011, the facility crosswalk used for the 2008 NEI was combined with all TRI IDs that had been
migrated from the 2002 and 2005 NEIs into the EIS as legacy data. This combined file was reviewed to
resolve all occurrences of multiple TRIJDs being matched to a single EISJD and multiple EISJDs being
matched to a single TRIJD. The resolved set of EISJDs was then attached to the complete set of 20,927
TRHDs in the 2011 TRI dataset. A comparison of the TRI to EIS facility information (latitude, longitude,
street address, facility name, city, county, and state) was made and all significant differences were
resolved. This resulted in many previous matches being removed and in the correction of some latitudes
and longitudes in the EIS. Many TRI latitudes and longitudes were also found to be in error compared to
the indicated addresses. TRI facilities with no corresponding EISJD and with over 10,000 pounds total
TRI air emissions of all pollutants, or over 200 pounds of lead, chromium, manganese, mercury, or
cadmium had a search performed for an EIS facility. Several dozen additional matches were found in this
last step.
The complete list of the TRIJD to EISJD facility crosswalk, along with facility name and location
information and emissions levels from both TRI and the EIS, was distributed to all S/L/T agencies for
review and comment, with about a dozen corrections and additions being made to the list as a result.
The final set of crosswalk IDs is stored in the EIS10. For any EIS facility with a valid TRIJD crosswalk, the
TRIJD appears as an Alternate Facility ID for that EIS Facility and that Alternative Facility ID is locked and
"active" (the End date field is null). Note that there are additional legacy TRI IDs still in the EIS as
Alternative Facility IDs which have not been locked, or which may have the End Date field filled. Such TRI
Alternative Facility IDs were not used for writing 2011 TRI emissions values into the EIS. A total of 11,637
TRHDs are currently in the ElS-stored crosswalk as valid and current as of November 25, 2013. Not all of
these TRI facilities reported 2011 emissions. A total of 14,900 TRI facilities reported non-zero air
emissions for 2011.
2. Map TRI pollutant codes to valid EIS pollutant codes and sum where necessary
Table 3-4 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 3-4. In addition, several EIS
pollutants may be reported to TRI as either of two TRI pollutants. For example, both lead and lead
compounds may be reported to TRI, and similarly for several other metal and metal compound TRI
pollutants. Table 3-4 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 2011 NEI, a total of 184 TRI pollutant codes were
mapped to 172 unique EIS pollutant codes. For 2011 we did use TRI ammonia emissions and 11
additional HAP pollutants beyond what had been included from TRI in the 2008 NEI. The TRI pollutants
added for the 2011 NEI are indicated by the right-most column in Table 3-4. Similar to the 2008 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.
10 A file of the crosswalked IDs can be obtained from EIS by running a Facility Configuration Report, for Alternate Facility IDs,
specifying a Program System Code of "EPATRI". From the resulting EIS report, remove all records which have a non-null End
Date, and, also remove all records for which the Alternative Identifier Protected field indicates "no".
44
-------
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 2011 TRI data that did not already have a 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
US in the 2011 draft NEI data. These 2011 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 USEPA using the procedures described in section 3.1.3. 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 2011 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 2011EPA_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 done 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: mercury, lead,
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 2011 TRI dataset itself to identify large differences
between facilities and unexpected industry types. Comparisons were then made to the 2008 TRI and the
2011 draft NEI emissions values from S/L/T agencies for any suspect facilities identified by that review.
Lastly, as part of the S/L/T agency review of the TRI-to-E IS facility matching described in step 1 above,
we also provided to the S/L/T agencies for review and comment the emissions comparisons and
differences of the 2011 TRI, 2008 TRI, and their 2011 submittals for all facilities. The result was a small
set of 2011 TRI emissions values which were too large to be considered reliable enough to be added to
the 2011 NEI. These values were excluded from the 2011EPA_TRI dataset.
In addition to the high outlier values, two other classes of TRI emissions values were included in the
2011EPA_TRI dataset but were originally tagged to be unavailable for selection in the March 2013 draft
NEI. The two classes were TRI emissions values that were less than 10 pounds, and TRI emissions values
45
-------
that appeared to be the result of the facility checking a "range box", indicating that emissions were
somewhere between 0 and 500 pounds or between 0 and 10 pounds, for example. The TRI dataset
reports the "range box" reports as the mid-point of the range, i.e. "0-500" pounds would be recorded as
250 pounds in the dataset. It is thus possible that sources emitting 15 or 20 pounds of some pollutant
may appear as a 250-pound source. Tagging the values of less than 10 pounds kept many 0-10 "range
box" reports as well as many discretely reported small values (e.g. "2.9 pounds") out of the March 2013
draft NEI. For the final 2011 vl NEI selection, the EIS tags on these two classes of TRI emissions values
were removed, allowing those TRI values to be used in the 2011 vl wherever the S/L/T agency had not
reported that pollutant for that facility. The 2011 v2 also retained these range box values as part of the
NEI, although many of them were removed from the 2011 NATA modeling per State comments.
5. Write the 2011 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 reverts back to loading the TRI emissions as the
single process stack and fugitive values as reported by facilities to the TRI, but we have 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.
To assign an 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 CO, C02, and
NOx, with all units converted to tons11. 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 approach did not work.
7. Tag TRI pollutant emissions in EIS to avoid double counting with other datasets
Because the 2011 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
11 In fact, a "SMOKE" modeling file was used as the easiest way to get the file in the right format for this step.
46
-------
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 3-5 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 2011EPA_EGll dataset included "Xylenes (Mixed Isomers)" for a facility,
any of the related individual xylene isomers would be tagged in the 2011EPA_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.
Table 3-4: Mapping of TRI pollutant codes to EIS pollutant codes
TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
New in 2011
79345
1,1,2,2-TETRACH LOROETH ANE
79345
1,1,2,2-TETRACH LOROETH ANE
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
Yes
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-PROPANE SULTONE
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'-DICHLOROBENZIDINE
91941
3,3'-Dichlorobenzidine
Yes
119904
3,3'-DIMETHOXYBENZIDINE
119904
3,3'-Dimethoxybenzidine
Yes
119937
3,3'-DIMETHYLBENZIDINE
119937
3,3'-DIMETHYLBENZIDINE
101144
4,4'-METHYLENEBIS(2-CHLOROANILINE)
101144
4,4'-METHYLENEBIS(2-CHLORANILINE)
101779
4,4'-METHYLEN EDI ANILINE
101779
4,4'-METHYLENEDIANILINE
534521
4,6-DINITRO-O-CRESOL
534521
4,6-DINITRO-O-CRESOL
92671
4-AMINOBIPHENYL
92671
4-AMINOBIPHENYL
60117
4-DIMETHYLAMINOAZOBENZENE
60117
4-DIMETHYLAMINOAZOBENZENE
100027
4-NITROPHENOL
100027
4-NITROPHENOL
75070
ACETALDEHYDE
75070
ACETALDEHYDE
60355
ACETAMIDE
60355
ACETAMIDE
75058
ACETONITRILE
75058
ACETONITRILE
98862
ACETOPHENONE
98862
ACETOPHENONE
107028
ACROLEIN
107028
ACROLEIN
79061
ACRYLAMIDE
79061
ACRYLAMIDE
79107
ACRYLIC ACID
79107
ACRYLIC ACID
107131
ACRYLONITRILE
107131
ACRYLONITRILE
107051
ALLYL CHLORIDE
107051
ALLYL CHLORIDE
7664417
AMMONIA
NH3
Ammonia
Yes
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
47
-------
TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
New in 2011
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
Yes
75252
BROMOFORM
75252
BROMOFORM
7440439
CADMIUM
7440439
CADMIUM
N078
CADMIUM COMPOUNDS
7440439
CADMIUM
156627
CALCIUM CYANAMIDE
156627
CALCIUM CYANAMIDE
133062
CAPTAN
133062
CAPTAN
63252
CARBARYL
63252
CARBARYL
75150
CARBON DISULFIDE
75150
CARBON DISULFIDE
56235
CARBON TETRACHLORIDE
56235
CARBON TETRACHLORIDE
463581
CARBONYL SULFIDE
463581
CARBONYL SULFIDE
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
Yes
67663
CHLOROFORM
67663
CHLOROFORM
107302
CHLOROMETHYL METHYL ETHER
107302
CHLOROMETHYL METHYL ETHER
126998
CHLOROPRENE
126998
CHLOROPRENE
7440473
CHROMIUM
7440473
CHROMIUM
N090
CHROMIUM COMPOUNDS(EXCEPTCHROMITE
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
Yes
132649
DIBENZOFURAN
132649
DIBENZOFURAN
84742
DIBUTYL PHTHALATE
84742
DIBUTYL PHTHALATE
111444
BIS(2-CHLOROETHYL) ETHER
111444
DICHLOROETHYL ETHER
62737
DICHLORVOS
62737
DICHLORVOS
111422
DIETHANOLAMINE
111422
DIETHANOLAMINE
64675
DIETHYL SULFATE
64675
DIETHYL SULFATE
131113
DIMETHYL PHTHALATE
131113
DIMETHYL PHTHALATE
77781
DIMETHYL SULFATE
77781
DIMETHYL SULFATE
79447
DIMETHYLCARBAMYL CHLORIDE
79447
DIMETHYLCARBAMOYL CHLORIDE
N120
DIISOCYANATES
NA- pollutant not used
26471625
TOLUENE DIISOCYANATE (MIXED ISOMERS)
NA- pollutant not used
584849
TOLUENE-2,4-DIISOCYANATE
584849
2,4-Toluene Diisocyanate
Yes
N150
DIOXIN AND DIOXIN-LIKE COMPOUNDS
NA- pollutant not used
106898
EPICHLOROHYDRIN
106898
EPICHLOROHYDRIN
140885
ETHYL ACRYLATE
140885
ETHYL ACRYLATE
51796
URETHANE
51796
ETHYL CARBAMATE
75003
CHLOROETHANE
75003
ETHYL CHLORIDE
100414
ETHYLBENZENE
100414
ETHYL BENZENE
106934
1,2-DIBROMOETHANE
106934
ETHYLENE DIBROMIDE
107062
1,2-DICHLOROETHANE
107062
ETHYLENE DICHLORIDE
107211
ETHYLENE GLYCOL
107211
ETHYLENE GLYCOL
151564
ETHYLENEIMINE
151564
Ethyleneimine
Yes
75218
ETHYLENE OXIDE
75218
ETHYLENE OXIDE
96457
ETHYLENE THIOUREA
96457
ETHYLENE THIOUREA
48
-------
TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
New in 2011
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
H EXACH LOROBUTADIENE
77474
H EXACH LOROCYCLOPENTADIENE
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
N420
LEAD COMPOUNDS
7439921
LEAD
58899
LINDANE
58899
1,2,3,4,5,6-H EXACH LOROCYCLOHEXAN E
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-DIMETHYLFORMAMIDE
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
Yes
90040
O-ANISIDINE
90040
O-ANISIDINE
95534
O-TOLUIDINE
95534
O-TOLUIDINE
123911
1,4-DIOXANE
123911
P-DIOXANE
56382
PARATHION
56382
Parathion
Yes
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
Yes
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-PHENYLENE DIAMINE
106503
P-PHENYLENE DIAMINE
123386
PROPIONALDEHYDE
123386
PROPIONALDEHYDE
114261
PROPOXUR
114261
PROPOXUR
78875
1,2-DICHLOROPROPANE
78875
PROPYLENE DICHLORIDE
75569
PROPYLENE OXIDE
75569
PROPYLENE OXIDE
49
-------
TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
New in 2011
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-DIAMINE
8001352
TOXAPHENE
8001352
TOXAPHENE
79016
TRICHLOROETHYLENE
79016
TRICHLOROETHYLENE
121448
TRIETHYLAMINE
121448
TRIETHYLAMINE
1582098
TRIFLURALIN
1582098
TRIFLURALIN
108054
VINYL ACETATE
108054
VINYL ACETATE
75014
VINYL CHLORIDE
75014
VINYL CHLORIDE
75354
VINYLIDENE CHLORIDE
75354
VINYLIDENE CHLORIDE
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)
Table 3-5: Pollutant groups
Group Name
Pollutant Code
Pollutant
Chromium
7440473
Chromium
1333820
Chromium Trioxide
7738945
Chromic Acid (VI)
18540299
Chromium (VI)
16065831
Chromium III
Xylenes (Mixed
Isomer';}
i330207
Xylenes (Mixed isomers)
95476
o-Xylene
106423
p-Xylene
.108383
in-Xylene
Cresol/Cresylic
Acid (Mixed
Isomers)
1319773
Cresol/Cresylic Acid (Mixed Isomers)
95487
o-Cresol
108394
m-Cresol
106445
p-Cresol
Poiychioi iiiatecl
Biphenyls
3.336363
Polyclilorinated Biphenyls (PCBs)
2050682
4,4' Dlchlo! obipheny! (PCB-.15)
205.1243
Decachiombipheny! (PCB-209)
2051607
25429292
2 CMorobiphenyi (PCB-.1)
P e i it achi o ro b i o Is e 11 v 1
2660J 649
HexriGMorobbhenv!
269.14330
Tei::v;dMo!"ob:pheny!
28655712
H e pis r: 1 ¦ i o rob! pi i e. \y 1
53742077
55722264
Nondclilorobiplienyl
OoljicMoioblphenyi
7012375
2,4,4'-7nchloroblpheiiyl (PCB-28)
Polycyclic
Organic Matter
(POM)
130498292
PAH, total
120127
Anthracene
129000
Pyrene
50
-------
Group Name
Pollutant Code
Pollutant
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
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
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
51
-------
Group Name
Pollutant Code
Pollutant
90120
1-Methylnaphthalene
91576
2-Methylnaphthalene
91587
2-Chloronaphthalene
Cyanide 8:
Con:pounds
57125
Cyanide
74908
Hyd: ogen Cyanide
Nickel &
Compounds
7440020
Nickel
12035722
Nickel Subsulfide
1313991
Nickel Oxide
604
Nickel Refinery Dust
3.1.5 HAP augmentation based on emission factor ratios
The 2011EPA_HAP-augmentation dataset was used for gap filling (supplementing) missing HAPs in the S/L/T
agency-reported data. We calculated HAP emissions by multiplying the appropriate surrogate CAP emissions
(provided by S/L/T agencies) by an emissions ratio of HAP to CAP emission factors. This was also done for the
2008 NEI, but only for the point data category. For the 2011 NEI, we augmented HAP via the use of HAP to CAP
ratios for both point (other than airport-related SCCs) and nonpoint data categories. For point sources, these
emission factor (EF) ratios were largely the same as were used in the 2008 NEI v3, though additional quality
assurance resulted in some changes. The ratios were computed using the EFs from WebFIRE) and are based
solely on the SCC code. The computation of these point HAP to CAP ratios is described in detail in the 2008 NEI
documentation. Section 3.1.5.
In summary, 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. 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.
For nonpoint sources, augmentation ratios were derived from the EFs used to develop the EPA nonpoint source
estimates. This allowed the ratios of augmented HAP to S/L/T agency-submitted CAP to be the same as the HAP
to CAP ratios, and the HAP emissions to be consistent with the S/L/T agency-reported CAP data.
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 "2011nei supdata hapaug.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.
52
-------
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 2011EPA_HAP-augmentation dataset that prevented the possibility of 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
3-5, except we neglected to include the nickel pollutants in our tagging. This caused the inadvertent addition of
nickel emissions from HAP augmentation as listed in Table 3-6.
Table 3-6: HAP-augmentation dataset nickel species which shoulc
not have been usee
in the NEI
State
EIS
Facility
ID
EIS Process
ID
Nickel species in
HAP Augmentation
Dataset
Emissions
(lbs)
Data Set
Potential
Double Count
With:
Minnesota
7146811
27576114
Nickel Oxide
16.5
2011EPA_HAP-Aug
State
Illinois
7337911
43356414
Nickel Oxide
1.3
2011EPA_HAP-Aug
State
Ohio
13429911
100593714
Nickel Oxide
0.034
2011EPA_HAP-Aug
State
Louisiana
7355411
105681714
Nickel
2.3
2011EPA_HAP-Aug
State
Louisiana
7355411
105679214
Nickel
4.1
2011EPA_HAP-Aug
State
Louisiana
7355411
105683114
Nickel
6.3
2011EPA_HAP-Aug
State
Iowa
12807811
94016214
Nickel Oxide
0.5
2011EPA_HAP-Aug
TRI
Iowa
12807811
94016314
Nickel Oxide
0.
2011EPA_HAP-Aug
TRI
We also tagged all point source HAP augmentation values that met one or more of the following criteria: a) the
HAP augmentation value exceeded the maximum emissions reported by any S/L/T agency for the same
SCC/poMutant combination, or if no S/L/T agency reported any values for the same SCC/poMutant, b) SCCs for
coke ovens (potential double count with the "Coke oven emissions" pollutant) and c) waste oil (due to
insufficient information about the waste which would likely impact the ratio), d) if greater than 0.05 tons lead
would have been added from coal combustion. This last criterion impacted 3 sources, as shown in Table 3-7. We
tagged these due to the uncertainty in the WebFIRE emission factor. The value 0.05 tons lead was selected
because it was at the top end of the HAP augmentation values for coal combustion.
Table 3-7: Lead from HAP-augmentation from coa
EIS
Facility ID
EIS
Unit ID
EIS
Process ID
see
State
County
St/Co
FIPS
Facility Name
Unused
Lead (tons)
4944011
30874213
67784214
10200203
Wl
Brown
55009
Georgia-Pacific
Consumer Products LP
0.1800
6478511
87095313
117793514
10200222
WY
Sweet
56037
Green River Trona Plant
0.1500
combustion that was not used.
53
-------
water
6478511
87095513
117793714
10200222
WY
Sweet
water
56037
Green River Trona Plant
0.0600
For nonpoint we did not tag the HAP augmentation dataset where the HAP was reported by the S/L/T agency,
nor where it was present in the EPA nonpoint dataset. This is because the NEI selection hierarchy in the EIS
ensured that the S/L/T agency data would be selected first, HAP-augmentation next, and EPA data third.
However, we did need to tag HAP augmentation values where the pollutant was different from what was
reported by the S/L/T agency but belonged to the same pollutant group. 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, PAHs and cresols groups in Table 3-5.
Similarly, to point, quality assurance of the nonpoint HAP augmentation resulted in tagging of specific lead and
mercury values.
One issue with nonpoint HAP augmentation we found after the release of 2011 vl was an error in the
augmentation of drycleaning tetrachloroethylene. We used a tetrachloroethylene to VOC ratio, but these
pollutants are not related (tetrachloroethylene is not a VOC HAP and the use of tetrachloroethylene at a dry
cleaner is not dependent on the VOC use. These emissions were tagged out for v2, and HAP augmentation of
these SCCs will not occur next (NEI 2014) inventory cycle due to SCC retirements.
3.1.6 Priority Facility List
For the 2011 NEI, EPA developed a Priority Facility List and posted it for reference in order to provide S/L/T
agencies an indication of important facilities on which to focus. EPA constructed the priority facility list based on
select HAPs and CAPS and facilities that contributed to the top 80% nationally of those pollutants in the 2008
NEI v2. However, EPA's QA reviews for emissions outlier values, incorrect locational coordinates, S/L/T agency
reporting completeness and preliminary risk modeling was not restricted or focused on solely the priority facility
list for 2011.
3.1.7 EPA nonpoint data
For the 2011 NEI, the EPA developed emission estimates for many nonpoint sectors in collaboration with a
consortium of state and regional planning organizations called the Eastern Regional Technical Advisory
Committee (ERTAC). This task is referred to by ERTAC as the "Area Source Comparability" project on the ERTAC
website, and a subgroup was developed to work on this project. The purpose of the subgroup and project was to
agree on methodologies, emission factors, and SCCs for a number of important nonpoint sectors, allowing EPA
to prepare the emissions estimates for all states using the group's final approaches. During the 2011 NEI
inventory development cycle, S/L/T agencies could accept the ERTAC/EPA estimates to fulfill their nonpoint
emissions reporting requirements. EPA encouraged S/L/T agencies that did not use EPA's estimates or tools to
improve upon these "default" methodologies and submit further improved data. The ERTAC process is described
in an NEI conference paper [ref 3],
One dataset was created for 2011 v2 that represented mercury emissions from nonpoint categories that span
different sectors. This dataset is called 2011EPA_NP_Mercury and comes at the end of the hierarchy in the
selection. It represents emissions from various mercury sources, described in Table 3-8. Methodologies for these
specific source categories are included in the Sector sections for Waste Disposal (3.32) and Miscellaneous Non-
Industrial NEC (3.25).
54
-------
Table 3-8: New nonpoint Hg sources of emissions in the 2011 v2 NEI
Sector
Source Category Description
see
Emissions (lbs.)
Waste Disposal
Switches and Relays
2650000002
4,292.8
Miscellaneous Non-Industrial NEC
Human Cremation
2810060100
2,291.5
Waste Disposal
Landfills
2620030001
828.0
Miscellaneous Non-Industrial NEC
Fluorescent Lamp Breakage
2861000000
802.7
Miscellaneous Non-Industrial NEC
Dental Amalgam
2850001000
803.8
Miscellaneous Non-Industrial NEC
General Laboratory Activities*
2851001000
600.0
Waste Disposal
Thermostats
2650000000
228.2
Miscellaneous Non-Industrial NEC
Animal Cremation
2810060200
80.2
Waste Disposal
Thermometers
2650000000
14.4
Miscellaneous Non-Industrial NEC
Fluorescent Lamp Recycling
2861000010
0.2
TOTAL
9,941.8
* A new estimate for General Laboratory Activities was not developed, but was pulled forward from the 2008 NEI
Table 3-9 and Table 3-10 describe the sectors for which EPA developed emission estimates. They separately list
emissions sectors entirely comprised of data in the nonpoint (and not point source) data category (Table 3-9),
such as residential heating, from sectors that may overlap with the point sources (Table 3-10). 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. Unlike in 2008, EPA
attempted to include all the EPA-estimated nonpoint emissions that overlap if it was determined that the
category was missing from the S/L/T agency data.
All methodologies are provided in zip files, which is the directory containing all supporting data files listed in
Table 3-9 and Table 3-10. Emission emissions sources using data from former EPA inventories are identified in
the column "Carried Forward" in these tables. The SCCs associated with the EPA nonpoint data categories are in
the excel file list of sources 2011vl nonpoint 20131127.xlsx. The file "2011nei np matrix submittals.xlsx"
has a list of submitting S/L/T agencies and for what nonpoint sectors they submitted data.
Table 3-9: EPA-estimated emissions sources expected to be exclusively nonpoint
EPA-estimated emissions source
description
Carried
Forward?
EIS Sector Name
Name of supporting data file or other
reference
Residential Heating; bituminous and
anthracite coal
Fuel Comb - Residential
-Other
residential consumption coal.zip
Residential Heating; distillate oil
Fuel Comb - Residential
-Oil
residential consumption oil revised 06272012.z
M
Residential Heating; Kerosene
Fuel Comb - Residential
-Oil
residential consumption kerosene.zip
Residential Heating; natural gas
Fuel Comb - Residential
- Natural Gas
residential consumption ng revised 06222012.z
ie
Residential Heating; liquefied
petroleum gas
Fuel Comb - Residential
- Other
residential consumption Ipg.zip
Residential Heating; Fireplaces,
woodstoves, fireplace inserts, pellet
Fuel Comb - Residential
-Wood
rwc estimation tool 2011vl 120612.zip
55
-------
EPA-estimated emissions source
description
Carried
Forward?
EIS Sector Name
Name of supporting data file or other
reference
stoves, indoor furnaces, outdoor
hydronic heaters, and firelogs.
Paved Roads
Dust - Paved Road Dust
roads paved 2011.zip
Unpaved Roads
Dust - Unpaved Road
Dust
roads unpaved 2011.zip
Dust from Residential Construction
Dust-Construction
Dust
construction residential 2011.zip
Dust from Commercial Institutional
Dust-Construction
Dust
construction nonresidential 2011.zip
Dust from Road Construction
Dust-Construction
Dust
construction road 2011.zip
Commercial Cooking
Commercial Cooking
commercial cooking 2302002nnn 2011.zip
Mining and Quarrying
Industrial Processes -
Mining
mining and quarrving.zip
Architectural Coatings
Solvent - Non-Industrial
Surface Coating
surface coatings arch coatings whaps 2011.zip
Traffic Markings
Solvent - Industrial
Surface Coating &
Solvent Use
traffic markings whaps 2011.zip
Railroad surface coating
Solvent - Industrial
Surface Coating &
Solvent Use
surface coating railroad whaps 2011.zip
Consumer & Commercial - All personal
care products
Solvent - Consumer &
Commercial Solvent Use
cons comm personal care products whaps 20
ll.zip
Consumer & Commercial - All
household products
Solvent - Consumer &
Commercial Solvent Use
cons comm misc products whaps 2011.zip
cons comm cleaning products whaps 2011.zip
cons comm auto aftermarket whaps 2011.zip
Consumer & Commercial - All coatings
and related products
Solvent - Consumer &
Commercial Solvent Use
cons comm coatings and related products wh
aps 2011.zip
Consumer & Commercial - All
adhesives and sealants
Solvent - Consumer &
Commercial Solvent Use
cons comm adhesives sealants whaps 2011.zip
Consumer & Commercial - All FIFRA
related products
Solvent - Consumer &
Commercial Solvent Use
cons comm fifra whaps 2011.zip
Cutback Asphalt Paving
X
Solvent - Consumer &
Commercial Solvent Use
asphalt paving cutback 2011.zip
Emulsified Asphalt Paving
X
Solvent - Consumer &
Commercial Solvent Use
asphalt paving emulsified 2011.zip
Consumer Pesticide Application
Solvent - Consumer &
Commercial Solvent Use
cons comm fifra whaps 2011.zip
Commercial Pesticide Application
X
Solvent - Consumer &
Commercial Solvent Use
agricultural pesticides 2011 eis format.zip
Residential Portable Gas Cans
Miscellaneous Non-
Industrial NEC
portable fuel containers 2011.zip
Commercial Portable Gas Cans
Miscellaneous Non-
Industrial NEC
portable fuel containers 2011.zip
Aviation Gasoline Stage 1
X
Gas Stations
av gasoline distribution stagel.zip
Aviation Gasoline Stage 2
X
Gas Stations
av gasoline distribution stage2.zip
Open Burning - Leaves
Waste Disposal
open burning vard waste 2011.zip
Open Burning - Brush
Waste Disposal
open burning vard waste 2011.zip
56
-------
EPA-estimated emissions source
description
Carried
Forward?
EIS Sector Name
Name of supporting data file or other
reference
Open Burning - Residential Household
Waste
Waste Disposal
open burning msw 2011.zip
Open Burning - Land Clearing Debris
Waste Disposal
open burning land clearing debris 2011.zip
Publicly Owned Treatment Works
Waste Disposal
ootw 2011 rev.zip
Agricultural Tilling
Agriculture - Crops &
Livestock Dust
agricultural tilling 2801000003 2011.zip
Fertilizer Application
Agriculture - Fertilizer
Application
ag fertilizer application 2011.zip
Animal Husbandry
X
Agriculture - Livestock
Waste
animal livestock emissions 2011.zip
Dental Preparation and Use
Miscellaneous Non-
Industrial NEC
2011 NEI FTP Directory
General Laboratory Activities
Miscellaneous Non-
Industrial NEC
Documentation for the Final 2002 Nonpoint
Sector (Feb 06 version) National Emission
Inventory for Criteria and HAPs, page A-106
Lamp Breakage (Landfill emissions)
Miscellaneous Non-
Industrial NEC
2011 NEI FTP Directory
Lamp (Fluorescent) Recycling
Miscellaneous Non-
Industrial NEC
2011 NEI FTP Directory
"Carried Forward" indicates whether EPA data were carried forward from the 2008 or other previous year inventory.
Table 3-10: Emissions sources with potential nonpoint and point contribution
EPA-estimated emissions
source description
Carried
Forward?
EIS Sector Name
Link to supporting data file
Industrial, Commercial/Institutional
Fuel Combustion
Fuel Comb - Industrial
Boilers, ICEs-All Fuels
Fuel Comb - Comm/
Institutional - All Fuels
ici fuel combustion bv state/
Oil and Gas Production
Industrial Processes - Oil &
Gas Production
Oil and gas tool v2 20140331.zip
Industrial Surface Coating - Auto
Refinishing
Solvent - Industrial Surface
Coating & Solvent Use
surface coating automobile refinishing 20
llwhaps.zip
Industrial Surface Coating - Factory
Finished Wood
Solvent - Industrial Surface
Coating & Solvent Use
surface coating factorv finished wood 20
llwhaps.zip
Industrial Surface Coating - Wood
Furniture
Solvent - Industrial Surface
Coating & Solvent Use
surface coating wood furniture 2011whap
s rev 4.zip
Industrial Surface Coating - Metal
Furniture
Solvent - Industrial Surface
Coating & Solvent Use
surface coating metal furn 2011whaps.zip
Industrial Surface Coating - Paper
Foil and Film
Solvent - Industrial Surface
Coating & Solvent Use
surface coating paper film foil 2011 wha
ps.zip
Industrial Surface Coating - Metal
Can Coating
Solvent - Industrial Surface
Coating & Solvent Use
surface coatings metal can whaps 2011.zi
£
Industrial Surface Coating -
Machinery and Equipment
Solvent - Industrial Surface
Coating & Solvent Use
surface coating machinery and equip wha
ps2011.zip
Industrial Surface Coating - Large
Appliances
Solvent - Industrial Surface
Coating & Solvent Use
surface coating appliances 2011whaps.zip
57
-------
EPA-estimated emissions
source description
Carried
Forward?
EIS Sector Name
Link to supporting data file
Industrial Surface Coating -
Electronic and other Electric
Coatings
Solvent - Industrial Surface
Coating & Solvent Use
surface coating electronic and other elect
ical coatings whaps 2011.zip
Industrial Surface Coating - Motor
Vehicles
Solvent - Industrial Surface
Coating & Solvent Use
surface coating motor%20vehicles whaps
2011.zip
Industrial Surface Coating - Aircraft
Solvent - Industrial Surface
Coating & Solvent Use
surface coating aircraft mfg 2011whaps.z
ifi
Industrial Surface Coating - Marine
Solvent - Industrial Surface
Coating & Solvent Use
surface coating marine mfgwhaDs2011.ziD
Industrial Surface Coating -
Railroad
Solvent - Industrial Surface
Coating & Solvent Use
surface coating railroad whaos 2011.zip
Industrial Surface Coating -
Miscellaneous Manufacturing
Solvent - Industrial Surface
Coating & Solvent Use
surface coating misc mfg 2011whaps.zip
Industrial Maintenance Coatings
Solvent - Industrial Surface
Coating & Solvent Use
surface coating ind maint coating 2011w
haps.zip
Other Special Purpose Coatings
Solvent - Industrial Surface
Coating & Solvent Use
surface coating other special purpose wh
aps 2011.zip
Degreasing
Solvent - Degreasing
degreasing whaps 2011 eisformat.zip
Graphic Arts
Solvent - Graphic Arts
graphic arts w haps 2011.zip
Dry Cleaning
Solvent - Dry Cleaning
drv cleaning emissions 2011 rev.zip
Gasoline Distribution - Stage 1 Bulk
Plants
X
Bulk Gasoline Terminals
gasoline distribution stage 1 bulk plants
2011.zip
Gasoline Distribution - Stage 1 Bulk
Terminals
X
Bulk Gasoline Terminals
gasoline distribution stage%201%20bulk te
rminals 2011.zip
Gasoline Distribution - Stage 1
Pipelines
Industrial Processes-
Storage and Transfer
gasoline distribution stage 1 pipelines 20
ll.zip
Gasoline Distribution - Stage 1
Service Station Unloading
Gas Stations
gas distribution service station unloading
eis format.zip
Gasoline Distribution - Stage 1
Underground Storage Tanks
Gas Stations
gasoline distribution stage 1 ust 2011.zip
Gasoline Distribution - Stage 1
Trucks In Transit
X
Industrial Processes-
Storage and Transfer
gasoline distribution stage 1 tank trucks
2011.zip
Gasoline Distribution - Stage 2
Refueling at Pump
Gas Stations
gasoline distribution stage 2.zip
Human Cremation
Miscellaneous Non-Industrial
NEC
2011 NEI FTP Directory
"Carried Forward" indicates whether EPA data were carried forward from the 2008 or other previous year inventory.
To determine whether EPA nonpoint data should be added for the categories with possible point/nonpoint
overlap, EPA used information provided by S/L/T agencies regarding their submitted nonpoint data. Specifically,
EPA used a survey of state and local agencies to get details about whether they had performed point/nonpoint
reconciliation, whether they did nonpoint estimates for each SCC, what SCCs they used, whether the state had
any nonpoint sources in a sector, and whether a state preferred to use EPA estimates. This information was
used, in conjunction with a few assumptions, to determine whether EPA should augment the data submitted by
the S/L/T agency with EPA-generated data. Using the Industrial Fuel Combustion sector as an example, because
the EPA-generated data were based on activity data that would cover all industrial combustion sources (both
point and nonpoint), it was necessary to use this methodology so that double counting of emissions would not
58
-------
occur. This comparison was done on a state level basis, except where county agencies are responsible for their
own submissions. The algorithm for determining whether to augment data in the 2011 NEI is given in Table 3-11
and Table 3-12.
Table 3-11: Algorithm for using survey data to determine source categories that should be augmented with EPA
nonpoint data for Industrial Combustion and Commercial/Institutional Combustion for Oil, Coal, and Other fuels
Survey Data
State
Submitted
to Point?
State
Submitted to
Nonpoint?
EPA Action
Rationale
State
indicates that
category is
fully covered
by their point
inventory for
an SCC
Yes
Yes or No
Do not augment
nonpoint data. Tag
EPA data so that it
does not get put into
NEI.
The nonpoint inventory is based on
Energy Information Administration (EIA)
numbers, which takes all fuel combustion
into account. The EIA makes no
distinction between point and nonpoint.
Augmenting would double count point
emissions.
No
No
Augment with EPA
estimates for
nonpoint category.
The EIA data tracks fuel usage by state.
There will be a gap in the data if this
category is not covered by the state at all.
No
Yes
Do not augment
Assume that they filled out the survey
incorrectly, and that they meant that the
category is fully covered by nonpoint.
State
indicates that
category is
fully covered
by their
nonpoint
inventory for
an SCC
No
Yes
Do not augment
Augmenting would double count
nonpoint emissions.
No
No
Augment
The EIA data tracks fuel usage by state.
There will be a gap in the data if this
category is not covered by the state at all.
Yes
Yes or No
Do not augment
Assume that they filled out the survey
incorrectly.
State
indicates that
they do
point/
nonpoint
reconciliation
Yes
No
Augment
We believe that they intended to submit
nonpoint. Though there will be some
double counting, we believe that their
submitted emissions for point would be
lower than if they claimed that their
category was covered fully in point.
Yes or No
Yes
Do not augment
No augmentation is necessary, since
either both point and nonpoint were
submitted, or nonpoint would be double
counted.
No
No
Augment
The EIA data tracks fuel usage by state.
There will be a gap in the data if this
category is not covered by the state at all.
Table 3-12: Algorithm for using survey data to determine source categories that should be augmented with EPA
nonpoint data for Commercial/Institutional Combustion for Natural Gas and Biomass, and Gas Stations
59
-------
Survey Data
State
Submitted
to Point?
State
Submitted to
Nonpoint?
EPA Action
Rationale
State
indicates that
category is
fully covered
by their point
inventory for
an SCC
Yes
No
Sum up their
submissions for point,
and if this number is
not very large (the
sum of the point
submissions is <20%
of the EPA estimate
for nonpoint),
augment their data.
We believe that the state filled out the
survey incorrectly. There must be small
commercial/institutional sources or gas
stations that were not covered by the
point source inventory.
No
No
Augment
The EIA data tracks fuel usage by state.
There will be a gap in the data if this
category is not covered by the state at all.
Yes or No
Yes
Do not augment
Assume that either they filled out the
survey incorrectly, or they submitted for
both point and nonpoint, and we do not
need to augment.
State
indicates that
category is
fully covered
by their
nonpoint
inventory for
err
a n jLL
No
Yes
Do not augment
Augmenting would double count
nonpoint emissions.
No
No
Augment
The EIA data tracks fuel usage by state.
There will be a gap in the data if this
category is not covered by the state at all.
Yes
Yes
Do not augment
Assume that they filled out the survey
incorrectly, but since they have an
inventory that covers both point and
nonpoint, we assume it is complete.
Yes
No
Augment
While there would be some double
counting of point emissions, it would be,
and we believe that there would still be
nonpoint emissions for this category.
State claims
that they do
point/
nonpoint
reconciliation
Yes
No
Augment
Assume that they intended to submit
nonpoint. Though there will be some
double counting, we believe that their
submitted emissions for point would be
lower than if they claimed that their
category was covered fully in point.
Yes or No
Yes
Do not augment
No augmentation is necessary, since
either both point and nonpoint were
submitted, or nonpoint would be double
counted.
No
No
Augment
The EIA data tracks fuel usage by state.
There will be a gap in the data if this
category is not covered by the state at all.
Finally, there are some emissions sources for which EPA did not compute 2011 emissions nor use old inventories
to fill in where states did not provide estimates. These sources are listed in Table 3-13 below. If a state within
60
-------
the NEI data does not include emissions for these emissions sources, then either that state does not have such
sources, or the state did not send EPA these emissions.
Table 3-13: SCCs used in past inventories that were not included in the EPA's 2011 nonpoint estimates
SCC
Description
EIS Sector Name
2309100010
Chromium Electroplating, Hard
Industrial Processes - NEC
2309100030
Chromium Electroplating, Decorative
Industrial Processes - NEC
2309100050
Chromic Acid Anodizing
Industrial Processes - NEC
2461160000
Drum and Barrel Reclamation
Miscellaneous Non-Industrial NEC
2801000000
Cotton Ginning
Agriculture - Crops & Livestock Dust
2805001000
Beef Cattle Feedlots Dust (PM emissions)
Agricultural - Livestock Waste
2830000000
Open Burning - Scrap Tires
Waste Disposal
2850000010
Hospital Sterilization
Miscellaneous Non-Industrial NEC
2862000000
Swimming Pools
Miscellaneous Non-Industrial NEC
2401045000
Surface Coating: Sheet, Strip and Coil
Coatings
Solvent - Industrial Surface Coating &
Solvent Use
2810030000
Structure Fires
Miscellaneous Non-Industrial NEC
2801000007
Grain Elevators: Terminal
Agriculture - Crops & Livestock Dust
3.1.8 References for Stationary sources
1. Dorn, J, 2012. Memorandum: 2011 NEI Version 2 - PM Augmentation approach. Memorandum to Roy
Huntley, US EPA. (PM augmt 2011 NEIv2 feb2012.pdf, accessible in the 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.
3. Dorn, J., Divita, F., Huntley, R., Janssen, M., 2010. Implementing a Collaborative Process to Improve the
Consistency, Transparency, and Accessibility of the Nonpoint Source Emission Estimates in the 2011
National Emissions Inventory, 19th International Emission Inventory Conference - "Emissions Inventories
- Informing Emerging Issues", San Antonio, TX, September 27 - 30, 2010.)
3.2 Agriculture - Crops & Livestock Dust
3.2.1 Sector description
The SCCs that belong to this sector are provided in Table 3-14. EPA estimates emissions for fugitive dust
emissions from agricultural tilling (SCC 2801000003), highlighted in the table; the methodology is described in
Section 3.2.4.
61
-------
Table 3-14: SCCs used in the 2011 NEI for the Agriculture - Crops & Livestock Dust sector
see
see Level 2
SCC Level 3
SCC Level 4
2801000000
Agriculture Production - Crops
Agriculture - Crops
Total
2801000002
Agriculture Production - Crops
Agriculture - Crops
Planting
2801000003
Agriculture Production - Oops
Agriculture - Crops
Tilling
2801000005
Agriculture Production - Crops
Agriculture - Crops
Harvesting
2801000008
Agriculture Production - Crops
Agriculture - Crops
Transport
2801600000
Agriculture Production - Crops
Country Grain Elevators
Total
2805001000
Agriculture Production -
Livestock
Beef cattle - finishing
operations on feed lots
(drylots)
Dust Kicked-up by Hooves (use 28-
05-020, -001, -002, or -003 for
Waste)
*SCC Level 1 for all is "Miscellaneous Area Sources"
3.2.2 Sources of data overview and selection hierarchy
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 3-15 submitted emissions for this sector. Table 3-16
shows the selection hierarchy for datasets included in the agricultural crops and livestock dust sector.
Table 3-15: Agencies that submitted Agricultural Crops and Livestock Dust c
1
fM
1
|
r-1
8
8
8
8
8
s
8
§
§
§
§
§
§
§
Agency
Type
fM
fM
fM
fM
fM
fM
fM
EPA- PM augmentation
EPA
X
X
X
X
0
X
X
EPA - estimated (section 3.2.4)
EPA
X
California Air Resources Board
S
X
Coeur d'Alene Tribe
T
X
X
X
Connecticut Department of Environmental Protection
S
X
Delaware Department of Natural Resources and Environmental Control
S
X
X
Georgia Department of Natural Resources
s
X
X
Hawaii Department of Health Clean Air Branch
s
X
Idaho Department of Environmental Quality
s
X
X
X
Illinois Environmental Protection Agency
s
X
Kansas Department of Health and Environment
s
X
X
Kootenai Tribe of Idaho
T
X
X
X
Louisiana Department of Environmental Quality
s
X
Maricopa County Air Quality Department
L
X
X
X
X
Maryland Department of the Environment
S
X
Metro Public Health of Nashville/Davidson County
L
X
New Hampshire Department of Environmental Services
S
X
New Jersey Department of Environment Protection
S
X
Nez Perce Tribe
T
X
X
X
Sac and Fox Nation of Missouri in Kansas and Nebraska Reservation
T
X
X
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
T
X
X
X
Utah Division of Air Quality
S
X
X
Virginia Department of Environmental Quality
S
X
West Virginia Division of Air Quality
s
X
ata
62
-------
Table 3-16: 2011 NEI agricultural crops and livestock dust data selection hierarchy
Priority
Dataset Name
Dataset Content
1
Responsible Agency Data Set
State and Local Agency submitted emissions
2
2011EPA_PM-Augmentation
Augments PM emissions
3
2011EPA_NP_NoOverlap_w_Pt
EPA-generated data
3.2.3 Spatial coverage and data sources for the sector
Agriculture - Crops & Livestock Dust
rs
P - Point
N - Nonpoint
PN - P&N
All CAPs 1 I EPA IB! SLT EPASSLT
3.2.4 EPA-developed agricultural crops and livestock dust emissions data
EPA estimates emissions for fugitive dust emissions from agricultural tilling (SCC 2801000003); this includes the
airborne soil particulate emissions produced during the preparation of agricultural lands for planting. EPA's
fugitive dust emissions from agricultural tilling were estimated for PMlO-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-FII
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.
63
-------
The county-level emissions factors for agricultural tilling (in lbs. per acre) are specific to the crop and tilling type
and were calculated using the following equation [ref 1, ref 2]:
EF = 4.8 x k x s0-6 x pcrop,
tilling type
where:
k = dimensionless particle size multiplier (PMio = 0.21; PM2 s = 0.042),
s = silt content of surface soil (%),
p = number of passes or tillings in a year for a given crop and tillage type.
The silt content of surface soil is defined as the percentage of particles (mass basis) of diameter smaller than 75
micrometers (|im) found in the soil to a depth of 10 centimeters (cm). Silt contents were assigned by comparing
the United States Department of Agriculture (USDA) surface soil survey map to a USDA county map and
assigning a soil type to each county. Table 3-17 shows silt content assumed for each soil type.
Tab
e 3-17: Silt content for soil types in USDA surface soil map
Soil Type
Silt Content (%)
Silt Loam
52
Sandy Loam
33
Sand
12
Loamy Sand
12
Clay
29
Clay Loam
29
Organic Material
10-82
Loam
40
Table 3-18 shows the number of passes or tillings in a year for each crop for conservation use and conventional
use [ref 3], No till, 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 3-18: Number of passes or tillings per year
Crop
Conservation Use
Conventional Use
Barley
3
5
Beans and Peas
3
3
Canola
3
3
Corn
2
6
Cotton
5
8
Cover
1
1
Fallow
1
1
Fall-seeded Wheat
3
5
Forage
3
3
Hay
3
3
Oats
3
5
Peanuts
3
3
Permanent Pasture
1
1
Potatoes
3
3
Rice
5
5
64
-------
Crop
Conservation Use
Conventional Use
Rye
3
5
Sorghum
1
6
Soybeans
1
6
Spring Wheat
1
4
Sugar beets
3
3
Sugarcane
3
3
Sunflowers
3
3
Tobacco
3
3
Activity Data
Since the CTIC has not prepared an updated National Crop Residue Management (CRM) Survey for 2011, activity
data for this category were updated from the 2008 inventory using growth factors derived from state-level
USDA statistics on various crop types [ref 5], These growth factors were then matched by state and crop type
and applied to the 2008 activity data at the county level. See Table 3-19 for how USDA and CRM categories were
matched.
Table 3-19: Crosswalk between Crop Residue Management category and USDA data
CRM Category
USDA Data Items
Barley
BARLEY - ACRES HARVESTED
Beans and Peas
SUM OF BEANS AND PEAS HARVESTED
Canola
CANOLA - ACRES HARVESTED
Corn
CORN, GRAIN - ACRES HARVESTED
Cotton
COTTON - ACRES HARVESTED
Cover
TOTAL ACRES HARVESTED
Fallow
TOTAL ACRES HARVESTED
Forage
FORAGE, ALFALFA, HAY - ACRES HARVESTED
Hay
FORAGE (EXCL ALFALFA), HAY-ACRES HARVESTED
Oats
OATS - ACRES HARVESTED
Peanuts
PEANUTS - ACRES HARVESTED
Permanent Pasture
TOTAL ACRES HARVESTED
Potatoes
POTATOES - ACRES HARVESTED
Rice
RICE - ACRES HARVESTED
Rye
RYE - ACRES HARVESTED
Sorghum
SORGHUM, GRAIN - ACRES HARVESTED
Soybeans
SOYBEANS - ACRES HARVESTED
Sugar beets
SUGAR BEETS - ACRES HARVESTED
Sugarcane
SUGARCANE, SUGAR & SEED - ACRES HARVESTED
Sunflower
SUNFLOWER - ACRES HARVESTED
Tobacco
TOBACCO - ACRES HARVESTED
Wheat
WHEAT - ACRES HARVESTED
Winter Wheat
WHEAT, WINTER - ACRES HARVESTED
In addition, for those categories where a specific state/crop combination match was not made, the number of
acres tilled were grown using a growth factor based on the total number of farm acres in those states.
65
-------
The basis of agricultural tilling emission estimates was the number of acres of crops tilled in each county by crop
type and tillage type. These data were obtained from the 2008 National Crop Residue Management Survey,
developed by the Conservation Technology Information Center (CTIC) [ref 5], Data summaries are available on
the CTIC web site. The five types of tilling for which emission estimates were calculated are:
Conservation Till Conventional Till
No till/strip till 0 to 15 percent residue till (Intensive till)
Mulch till 15 to 30 percent residue till (Reduced till)
Ridge till
Note that the 2008 activity data for highly erodible land (HEL) overlap the other crop-type-specific data.
Therefore, the HEL and Treated HEL data are not included in the calculation of emissions estimates. A summary
of national-level acres planted in 2008 for each tilling type, and total conservation and conventional acres
planted in 2011, are presented in Table 3-20. Due to data nondisclosure agreements with CTIC, the EPA cannot
release the county-level tillage data by crop type.
Table 3-20: Acres planted by tillage type, Fallow and pasture in 2008 and 2011
Tillage System
Actual National Number
Actual National Number
of Acres Planted in 2008
of Acres Planted in 2011
(million acres)
(million acres)
Conservation
No-Till/Strip Till
74.86
n/a
Ridge-Till
2.32
n/a
Mulch-Till
49.43
n/a
Total Conservation Acres
126.61
124.02
Conventional
Reduced-Till (15-30% cover)
63.31
n/a
Intensive-Till (<15% cover)
105.13
n/a
Total Conventional Acres
168.44
159.13
Total Conservation + Conventional
295.05
283.15
The following equation was used to determine the emissions from agricultural tilling [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 PMi0 and PM2 s 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 = £ C * k * S°-6X pcr0Pitillingtype X 3crop,tilling type
where: E = PM10-FIL or PM25-FIL emissions
c = constant 4.8 Ibs/acre-pass
k = dimensionless particle size multiplier (PMi0=0.21; PM2 5=0.042)
s = percent silt content of surface soil, defined as the mass fraction of particles smaller than 75
(imdiameter found in soil to a depth of 10 cm
p = number of passes or tillings in a year
a = acres of land tilled (activity data)
Controls
66
-------
No controls were accounted for in the EPA emission estimations.
3.2.5 Summary of quality assurance methods
A comparison was performed between emissions from 2011 and 2008. There were no large discrepancies in
emissions from this sector between the two years. However, there were 12 HAPs submitted by California, which
we do not consider to be expected pollutants from this process. These values were tagged. In addition, Louisiana
requested that their submitted values be tagged and not used, because they believed that EPA's estimates were
more up to date (they submitted data identical to 2008 submissions). Table 3-21 summarizes the number of
tagged process-level emissions values from each agency affected by this QA. The EPA tagged the EPA data to
avoid double counting in UT, since UT submitted agricultural dust using other SCCs.
Table 3-21: Agencies tagged values for Agriculture - Crop and Livestock Dust
Agency
Number of
Values Tagged
Tag Reason
California Air Resources Board
672
Unexpected pollutants from this process
Louisiana Department of
Environmental Quality
256
Louisiana asked us to replace their data
(identical to 2008) with EPA estimates.
3.2.6 References for Agriculture - Crop & 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. Agricultural Activities Influencing Fine Particulate Matter Emissions, Woodard, Kenneth R., Midwest
Research Institute, March 1996.
4. National Crop Residue Management Survey, Conservation Technology Information Center, 2008.
5. USDA Quickstats 2.0, Accessed April 2012.
3.3 Agriculture - Fertilizer Application
3.3.1 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 belong to this sector are provided in Table 3-22. EPA-
estimated emissions are highlighted and discussed in Section 3.3.4.
67
-------
Table 3-22: Source categories for Agricultural Fertilizer Application
see
Descriptor 2
Descriptor 4
Descriptor 5
Descriptor 10
Miscellaneous
Agriculture
Fertilizer
2301700001
Area Sources
Production - Crops
Application
A n hydro us A m m o nia
Miscellaneous
Agriculture
Fertilizer
2801700002
Ares Sou; res
Production - Crops
Application
Aciueous Ammonia
Miscellaneous
Agriculture
Fertiliser
2S0J.700003
Area Source:
Production - Crops
Application
Nitrogen Solutions
Miscellaneous
Agriculture
Fertilizer
2801700004
Area Sources
Production - Crops
Application
Urea
Miscellaneous
Agriculture
Fertilizer
2801700005
Area Sources
Production - Crops
Application
Ammonium Nitrate
Miscellaneous
Agriculture
Fertilizer
2801700006
Area Sources
Production - Crops
Application
Ammonium Sulfate
Miscellaneous
Agiiculture
Fertilizer
280.1.700007
Area Sourres
Production - Crops-
Application
Ammonium Thiosulfate
Miscellaneous
Agriculture
Fertilizer
2801700008
Area Sources
Production - Crops
Application
Other Straight Nitrogen
Miscellaneous
Agriculture
Fertilizer
Ammonium Phosphates (see
2801700009
Area Sources
Production - Crops
Application
also subsets (-13, -14, -15)
Miscellaneous
Agriculture
Fertilizer
N- P-K (multi-grade nutrieni
2801700010
Area Sources
Production - Crops
Ac-plication
*e> !-fili7t-r'*
1 - ! ¦>!
Miscellaneous
Agriculture
Fertilizer
28017000.1.1
Area Sources
PioUuction - Crops
Application
Calcium Ammonium Nitrate
Miscellaneous
Agriculture
Fertilizer
280.170001;:
Area Sources
Production - Crops
Application
Potassium Nitrate
Miscellaneous
Agriculture
Fertilizer
2S0J.700013
Area Sources
Production ¦ Crops
Application
Di a i n m o n 1 u m P h osp h a te
Miscellaneous
Agriculture
Fertilizer
2801700014.
Area Sources
Production - Crops
Amplication
Monoammonlum Phosphate
Miscellaneous
Agriculture
Fertilizer
Liquid Ammonium
2801700015
Area Sources
Production - Crops
Application
Polyphosphate
Miscellaneous
Agriculture
Fertilizer
280170005':)
Area Sources
Production - Crops
Application
Miscellaneous Feral:zers
3.3.2 Sources of data overview and selection hierarchy
The agricultural fertilizer application sector includes data from the S/L/T agency submitted data and the default
EPA generated agricultural fertilizer emissions. The agencies listed in
68
-------
Table 3-23 submitted emissions for this sector. Note that not all agencies submitted all the different fertilizer
types. Where only zero emissions were submitted (sum across all pollutants submitted), these are shown as
zeroes ("0") in the table. Table 3-24 shows the selection hierarchy for the agricultural fertilizer application
sector.
69
-------
Table 3-23: Agencies that su
amitted Agricultural Fertilizer Application c
AGENCY
Type
Ammonium Nitrate
Ammonium
Ammonium Sulfate
Ammonium
Anhydrous Ammonia
Aqueous Ammonia
Calcium
Ammonium Nitrate
Diammonium
Phosphate
Liquid Ammonium
Polyphosphate
Miscellaneous
Mono-Ammonium
Phosphate
Nitrogen Solutions
N-P-K (multi-grade
nutrient fertilizers)
Other Straight
Potassium Nitrate
Urea
EPA estimates (section
3.3.4)
EPA
X
X
X
X
X
X
X
X
X
X
X
X
X
X
California Air Resources
Board
S
X
Connecticut Department
of Environmental
Protection
S
X
X
X
X
X
X
X
X
Delaware Department of
Natural Resources and
Environmental Control
S
X
Hawaii Department of
Health Clean Air Branch
S
0
0
X
0
0
0
0
X
X
0
X
X
Illinois Environmental
Protection Agency
S
X
X
X
X
0
0
X
X
X
X
X
X
0
X
Kansas Department of
Health and Environment
S
X
X
X
X
X
X
X
X
X
X
X
X
X
Kickapoo Tribe of Indians
of the Kickapoo
Reservation in Kansas
T
X
X
X
X
Sac and Fox Nation of
Missouri in Kansas and
Nebraska Reservation
T
X
Virginia Department of
Environmental Quality
S
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Washington State
Department of Ecology
S
X
X
X
X
X
X
X
X
X
X
X
X
X
X
West Virginia Division of
Air Quality
s
X
X
0
0
0
0
X
0
X
0
X
X
0
X
ata
Table 3-24: 2011 NEI Agricultural Fertilizer Application data selection hierarchy
Priority
Dataset Name
Dataset Content
1
Responsible Agency Data Set
State and Local Agency submitted emissions
2
2011EPA_NP_NoOverlap_w_Pt
EPA-generated data
70
-------
3.3.3 Spatial coverage and data sources for the sector
Agriculture - Fertilizer Application
P - Point
N - Noripoint
PN - P&N
All CAPs [=EPA <=SLT — EPA&SLT
3.3.4 EPA-developed agricultural fertilizer application emissions data
The approach to calculating emissions from this sector consisted of three general steps, as follows:
• Calculating the percent change in county-level fertilizer quantities applied between 2002 and
2007.
• Using the percent change in applied fertilizer quantity to grow the fertilizer activity files
provided with the CMU Ammonia Model v.3.6. [ref 1]
• Running the CMU Ammonia Model to calculate ammonia emissions based on the updated
county-level fertilizer quantities.
Activity Data
County-level fertilizer consumption data for 2002 and 2007 were obtained from the Fertilizer Institute's
Commercial Fertilizers 2002 and 2007 reports [ref 2], The consumption data includes total fertilizer sales or
shipments for farm and non-farm use and is reported semi-annually for the fiscal year. To make the fertilizer
types listed in the Commercial Fertilizers reports match the activity input files from the CMU Ammonia Model,
the fertilizer types were grouped according to
71
-------
Table 3-25. For any state in 2002 reporting fertilizer quantities from unknown counties, the quantities were
apportioned to every county in the state based on cropland area obtained from the U.S. Department of
Agriculture's 2002 Census of Agriculture [ref 3], Similarly, for 2007, fertilizer quantities from unknown counties
were apportioned based on cropland area reported in the 2007 Census of Agriculture [ref 4], For each fertilizer
group, the percent difference in fertilizer consumption between 2002 and 2007 was calculated for each county.
These percentages were used to grow the 2002 county-level nitrogen quantities from the fertilizer activity files
provided with the CMU Ammonia Model v.3.6.
72
-------
Table 3-25: Fertilizers assigned to fertilizer groups
CMU Ammonia Model
Fertilizer Group
Commercial
Fertilizers
Report -
Fertilizer Code
Description 1
Description 2
Ammonium Nitrate
10
Ammonium Nitrate
Ammoniumnitrate
Ammonium Sulfate
24
Ammonium Sulfate
Ammoniumsulfate
Ammonium Thiosulfate
31
Ammonium Thiosulfate
Ammoniumthiosul
Anhydrous Ammonia
2
Anhydrous Ammonia
Anhy Ammonia
Aqueous Ammonia
6
Aqua Ammonia
Aqua Ammonia
Calcium Ammonium
Nitrate
35
Calcium Ammonium Nit
Calcium Amm Nit
Diammonium Phosphate
203
Diammonium Phosphate
DAP
Liquid Ammonium
Polyphosphate
249
Liquid Ammonium Poly
Liq Amm Poly
Miscellaneous
12
Ammonium Nitrate Sol
Amm Nit Solution
13
Ammonium Nitrate-Lim
Amm Nit Lime Mix
16
Ammonium Nitrate-Sul
Ammoniumnit-Sul
20
Ammonium Polysulfide
Ammoniumpolysulf
25
Ammonium Sulfate Sol
Amm Sul Solution
27
Ammonium Sulfate-Nit
Ammoniumsul-Nit
29
Ammonium Sulfate-Ure
Ammoniumsul-Urea
46
Calcium Nitrate-Urea
Calcium Nit-Urea
52
Magnesium Nitrate
Magnesium Nit
54
Nitric Acid
Nitric Acid
62
Sodium Nitrate
Sodium Nitrate
64
Sulfur Coated Urea
Sul Ctd Urea
67
Urea Solution
Urea Solution
68
Urea-Formaldehyde
Urea-Form
97
Nitrogen Product - C
Nitrogen No Code
98
Nitrogen Product - C
Nitrogen No Id
201
Ammonium Metaphospha
Ammoniummetaphos
202
Ammonium Phosphate
Ammoniumphos
204
Ammonium Polyphospha
Ammoniumpoly
206
Ammonium Phosphate N
Amm Phosnitrate
207
Ammonium Phosphate S
Amm Phossulfate
241
Nitric Phosphate
Nitric Phos
413
Manure Salts
Manure Salts
458
Potassium-Sodium Nit
Pot-Sod Nitrate
617
Fish Scrap
Fish Scrap
629
Guano
Guano
649
Manure
Manure
652
Peat
Peat
661
Sewage Sludge, Activ
Act Sew Sludge
663
Sewage Sludge, Diges
Dig Sew Sludge
665
Sewage Sludge, Heat
Ht Driedsew Slge
667
Sewage Sludge, Other
Oth Sew Sludge
671
Soybean Meal
Soybean Meal
673
Tankage, Animal
Animal Tankage
675
Tankage, Process
Process Tankage
73
-------
Commercial
Fertilizers
CMU Ammonia Model
Report -
Fertilizer Group
Fertilizer Code
Description 1
Description 2
697
Natural Organic Prod
Nat Org No Code
698
Nat Organic Product
Nat Org No Id
764
Soil Amendment
Soil Amendmnt
766
Soil Conditioner
Soil Cond
767
Potting Soil
Potting Soil
797
Sec./Micronut. - Cod
Sec/Mic No Code
Miscellaneous (cont.)
798
Sec./Micronut. - Cod
Sec/Mic No Id
978
Fertilizer Product -
Fert No Id
988
Single Nutrient - Co
Sgle-Nu No Id
Mix
0
Identified By Grade
Ident. By Grade
998
Multiple Nutrient -
Mult-Nut No Grade
Monoammonium
Phosphate
209
Monoammonium Phosphate
Monoamm Phos
Nitrogen Solutions
56
Nitrogen Solution <28%
Nitrogensol <28%
58
Nitrogen Solution 28%
Nitrogensol 28%
59
Nitrogen Solution 30%
Nitrogensol 30%
60
Nitrogen Solution 32%
Nitrogensol 32%
61
Nitrogen Solution >32%
Nitrogensol >32%
Potassium Nitrate
453
Potassium Nitrate
Pot Nitrate
Urea
66
Urea
Urea
The average nitrogen content for each fertilizer group, reported in Table 3-26, was calculated by summing the
county-level fertilizer quantities for all counties from the CMU Ammonia Model activity files to generate total
nitrogen applied. For each fertilizer group, the total nitrogen applied was then divided by the 2002 fertilizer
consumption data from the 2002 Commercial Fertilizers report to obtain the percent nitrogen content for each
fertilizer group. For any county with fertilizer consumption in 2007, but not in 2002, the fertilizer quantity
obtained from the 2007 Commercial Fertilizer's report was multiplied by the percent nitrogen content of each
fertilizer group to determine tons of nitrogen. The tons of nitrogen were then converted to kilograms and
allocated temporally by month according to the state-level percentage of total fertilizer in that group applied
each month. The state-level percentage was calculated using data in the CMU Ammonia Model input files.
Table 3-26: Fertilizer Nitrogen content
Nitrogen
Content
Fertilizer
(percent)
Ammonium Nitrate
36
Ammonium Sulfate
22
Ammonium Thiosulfate
12
Anhydrous Ammonia
82
Aqueous Ammonia
21
Calcium Ammonium Nitrate
17
Diammonium Phosphate
18
Liquid Ammonium Polyphosphate
10
Miscellaneous
8
Mix
12
74
-------
Nitrogen
Content
Fertilizer
(percent)
Monoammonium Phosphate
11
Nitrogen Solutions
29
Potassium Nitrate
14
Urea
46
Emission Factors
NH3 emission factors for each fertilizer group were provided with the CMU Ammonia Model [ref 1] and are
reported in Table 3-27.
Table 3-27: Fertilizer NH3 emission factors
Emission Factor
(varies by county for
Emission
some fertilizers)
Factor
Fertilizer Description
Min
Max
Average
Emission Factor Unit
Reference
Ammonium Nitrate
1.0
3.0
1.91
% N volatilized as NH3
1
Ammonium Sulfate
5.0
15.0
9.53
% N volatilized as NH3
1
Ammonium Thiosulfate
2.5
2.5
2.5
% N volatilized as NH3
1
Anhydrous Ammonia
4.0
4.0
4.0
% N volatilized as NH3
1
Aqueous Ammonia
4.0
4.0
4.0
% N volatilized as NH3
1
Calcium Ammonium Nitrate
1.0
3.0
1.91
% N volatilized as NH3
1
Diammonium Phosphate
5.0
5.0
5.0
% N volatilized as NH3
1
Liquid Ammonium
% N volatilized as NH3
Polyphosphate
5.0
5.0
5.0
1
Miscellaneous Fertilizers
6.0
8.0
6.59
% N volatilized as NH3
1
Monoammonium Phosphate
5.0
5.0
5.0
% N volatilized as NH3
1
Nitrogen Solutions
8.0
8.0
8.0
% N volatilized as NH3
1
N-P-K (multi-grade nutrient
% N volatilized as NH3
fertilizers)
1.0
3.0
1.91
1
Potassium Nitrate
2.0
2.0
2.0
% N volatilized as NH3
1
Urea
15.0
20.0
15.8
% N volatilized as NH3
1
Emissions
The fertilizer activity files provided with the CMU Ammonia Model v.3.6 were replaced with the updated county-
level fertilizer files. County-level ammonia emissions were then calculated by running the model. The model
corrects for the difference in mass between nitrogen and ammonia.
N applied x % N volatilized as NH3 x 17 g /14 g = NH3 emissions
Sample Calculations
Allocation of Fertilizer Quantities from Unknown Counties
From the 2007 Commercial Fertilizers report, Colorado reported 4,774,000 kg of ammonium nitrate
from unknown counties for January through June of 2007. This quantity was distributed to counties based on
75
-------
the percent of cropland in the state located in each county. For example, Colorado has 11,484,000 acres of
cropland. Adams County, Colorado has 547,000 acres of cropland.
Percent of cropland in CO located in Adams County = (547,000 / 11,484,000) x 100 = 4.76
Ammonium nitrate allocated to Adams County = 4,774,000 kg x .0476 = 227,240 kg
Growing the CMU Ammonia Model Input Files
After allocating fertilizer data from unknown counties for 2002 and 2007, the county-level percent
difference between fertilizer quantity applied in 2002 and 2007 was used to grow the data in the activity files
provided with the CMU Ammonia Model. For example, Autauga County, Alabama applied 473,180 kg of
ammonium nitrate from July 2001 through December 2001 and 516,240 kg from July 2006 through December
2006.
Percent change in ammonium nitrate applied = (516,240 kg / 473,180 kg) x 100 = 109
The quantity of nitrogen, in the form of ammonium nitrate, applied per month from July through
December 2002 in Autauga County was extracted from the CMU Ammonia Model activity files and multiplied by
the percent change.
November: 2,600 kg x 1.09 = 2,834 kg N
December: 1,380 kg x 1.09 = 1,504 kg N
Calculation of Nitrogen Content in a Fertilizer Group
The sum of all nitrogen applied in the form of ammonium nitrate from the CMU Ammonia Model
ammonium nitrate activity file was 508,000,000 kg. From the 2002 Commercial Fertilizers report, the total
quantity of ammonium nitrate applied in 2002 was 1,420,000,000 kg.
N content of ammonium nitrate = (508,000,000 kg / 1,420,000,000 kg) x 100 = 36 %
County Where Fertilizer was Applied in 2007, but not in 2002
In Meade County, Kentucky, there was no ammonium nitrate applied from January to June of 2002, but
there were 356,705 kg applied from January to June of 2007. To convert to kg of nitrogen, the quantity of
ammonium nitrate applied in 2007 was multiplied by the nitrogen content of ammonium nitrate.
N applied = 356,705 kg x 0.36 = 128,414 kg
The quantity of nitrogen was then allocated temporally by month from January to June based on the state-level
distribution of nitrogen applied in the form of ammonium nitrate from the CMU Ammonia Model ammonium
nitrate activity file. Total nitrogen in the form of ammonium nitrate applied in Kentucky from January through
June of 2002 was 17,000,000 kg. The total for January was 289,000 kg. The total for February was 745,000 kg.
January: (289,000 kg / 17,000,000 kg) x 128,414 kg = 2,183 kg N applied in Meade County
February: (745,000 kg / 17,000,000 kg) x 128,414 kg = 5,600 kg N applied in Meade County
March - June: calculated same as above.
July:
August:
September:
October:
3,250 kg x 1.09 = 3,543 kg N
3,210 kg x 1.09 = 3,499 kg N
9,640 kg x 1.09 = 10,508 kg N
6,320 kg x 1.09 = 6,889 kg N
76
-------
3.3.5 Summary of quality assurance methods
A comparison was performed between emissions from 2011 and 2008. There were no large discrepancies in
emissions from this sector between the two years. In fact, two states, Georgia and Louisiana, had data that were
remarkably similar to their 2008 submissions, so these states were called for clarification on their submissions.
Contact with these states revealed that Georgia and Louisiana had pulled 2008 data forward for this sector, and
both states requested that we use EPA data for 2011 for these emissions instead. Therefore, these state values
were tagged. In addition, one value from West Virginia was determined to be an outlier (greater than 2008 by a
factor of 10). Table 3-28 summarizes the number of tagged process-level emissions values from each agency
affected by this QA.
Table 3-28: Agencies tagged values ¦
or Agriculture - Fertilizer
Agency
Number of
Values Tagged
Tag Reason
Georgia Department of Natural
Resources
2,226
State requested that we replace their
submitted data with EPA's estimates.
Louisiana Department of
Environmental Quality
256
State requested that we replace their data
with EPA estimates.
West Virginia Division of Air Quality
1
Outlier
3.3.6 References for Agriculture - Fertilizer Application
1. Cliff Davidson, Peter Adams, Ross Strader, Rob Pinder, Natalie Anderson, Marian Goebes, and Josh
Ayers. The Environmental Institute, Carnegie Mellon University, CMU Ammonia Model v.3.6., 2004,
accessed 25 April 2009.
2. Association of American Plant Food Control Officials in partnership with The Fertilizer Institute,
Commercial Fertilizers 2002 and Commercial Fertilizers 2007. accessed 2 May 2009.
3. U.S. Department of Agriculture, 2002 Census of Agriculture, accessed 30 April 2009.
4. U.S. Department of Agriculture, 2007 Census of Agriculture, accessed 30 April 2009.
3.4 Agriculture - Livestock Waste
3.4.1 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. As
discussed in Section 3.4.2, 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 defined above.
3.4.2 Sources of data overview and selection hierarchy
The agricultural livestock waste sector includes data from three datasets from the nonpoint data category: the
S/L/T agency submitted data, the PM Augmentation dataset, and the default EPA generated livestock emissions.
It also includes data from the point data category the S/L/T agency submitted data, the PM Augmentation
dataset, TRI, chromium speciation and EPA EGU. The TRI, chromium speciation and EPA EGU datasets in this
77
-------
sector result from the use of an erroneous SCC code (30202001) submitted by California for approximately 40
facilities that are unrelated to this category12.
Table 3-29 shows the nonpoint SCCs covered by the EPA estimates (discussed in Section 3.4.4) and by the
State/Local and Tribal agencies that submitted data. Table 3-30 presents the two "Industrial Processes" point
SCCs reported by 3 states: California, Wisconsin and Colorado. Point emissions from this sector are negligible
compared to the nonpoint emissions (3 orders of magnitude lower).
Table 3-29: Nonpoint SCCs with 2011 NEI emissions in the Livestock Waste sector
SCC
SCC Level Two
SCC Level Three
SCC Level Four
EPA
Local
State
Tribe
Beef cattle - finishing
Agriculture Production
operations on feedlots
2805001100
- Livestock
(drylots)
Confinement
X
X
X
X
Beef cattle - finishing
Agriculture Production
operations on feedlots
Manure handling
2805001200
- Livestock
(drylots)
and storage
X
X
X
Beef cattle - finishing
Agriculture Production
operations on feedlots
Land application of
2805001300
- Livestock
(drylots)
manure
X
X
X
Agriculture Production
Beef cattle production
Not Elsewhere
2805002000
- Livestock
composite
Classified
X
X
X
Beef cattle - finishing
Agriculture Production
operations on
2805003100
- Livestock
pasture/range
Confinement
X
X
X
Poultry production - layers
Agriculture Production
with dry manure
2805007100
- Livestock
management systems
Confinement
X
X
X
X
Poultry production - layers
Agriculture Production
with dry manure
Land application of
2805007300
- Livestock
management systems
manure
X
X
X
Poultry production - layers
Agriculture Production
with wet manure
2805008100
- Livestock
management systems
Confinement
X
X
X
Poultry production - layers
Agriculture Production
with wet manure
Manure handling
2805008200
- Livestock
management systems
and storage
X
X
X
Poultry production - layers
Agriculture Production
with wet manure
Land application of
2805008300
- Livestock
management systems
manure
X
X
X
Agriculture Production
Poultry production -
2805009100
- Livestock
broilers
Confinement
X
X
X
Agriculture Production
Poultry production -
Manure handling
2805009200
- Livestock
broilers
and storage
X
X
X
Agriculture Production
Poultry production -
Land application of
2805009300
- Livestock
broilers
manure
X
X
X
Agriculture Production
Poultry production -
2805010100
- Livestock
turkeys
Confinement
X
X
X
Agriculture Production
Poultry production -
Manure handling
2805010200
- Livestock
turkeys
and storage
X
X
X
Agriculture Production
Poultry production -
Land application of
2805010300
- Livestock
turkeys
manure
X
X
X
12 California does have some point sources appropriately assigned to 30202001
78
-------
see
SCC Level Two
SCC Level Three
SCC Level Four
EPA
Local
State
Tribe
Agriculture Production
Not Elsewhere
2805018000
- Livestock
Dairy cattle composite
Classified
X
X
X
Agriculture Production
2805019100
- Livestock
Dairy cattle - flush dairy
Confinement
X
X
X
X
Agriculture Production
Manure handling
2805019200
- Livestock
Dairy cattle - flush dairy
and storage
X
X
X
Agriculture Production
Land application of
2805019300
- Livestock
Dairy cattle - flush dairy
manure
X
X
X
Agriculture Production
Cattle and Calves Waste
Total (see also 28-
2805020000
- Livestock
Emissions
05-001, -002, -003)
X
Agriculture Production
2805021100
- Livestock
Dairy cattle - scrape dairy
Confinement
X
X
X
Agriculture Production
Manure handling
2805021200
- Livestock
Dairy cattle - scrape dairy
and storage
X
X
X
Agriculture Production
Land application of
2805021300
- Livestock
Dairy cattle - scrape dairy
manure
X
X
X
Agriculture Production
2805022100
- Livestock
Dairy cattle - deep pit dairy
Confinement
X
X
X
Agriculture Production
Manure handling
2805022200
- Livestock
Dairy cattle - deep pit dairy
and storage
X
X
X
Agriculture Production
Land application of
2805022300
- Livestock
Dairy cattle - deep pit dairy
manure
X
X
X
Agriculture Production
Dairy cattle -
2805023100
- Livestock
drylot/pasture dairy
Confinement
X
X
X
Agriculture Production
Dairy cattle -
Manure handling
2805023200
- Livestock
drylot/pasture dairy
and storage
X
X
X
Agriculture Production
Dairy cattle -
Land application of
2805023300
- Livestock
drylot/pasture dairy
manure
X
X
X
Not Elsewhere
Classified (see also
Agriculture Production
Swine production
28-05-039, -047, -
2805025000
- Livestock
composite
053)
0
X
0
Not Elsewhere
Classified (see also
Agriculture Production
28-05-007, -008, -
2805030000
- Livestock
Poultry Waste Emissions
009)
X
X
X
Pullet Chicks and
Agriculture Production
Pullets less than 13
2805030001
- Livestock
Poultry Waste Emissions
weeks old
0
Pullets 13 weeks old
Agriculture Production
and older but less
2805030002
- Livestock
Poultry Waste Emissions
than 20 weeks old
0
Agriculture Production
2805030003
- Livestock
Poultry Waste Emissions
Layers
0
Agriculture Production
2805030004
- Livestock
Poultry Waste Emissions
Broilers
0
Agriculture Production
2805030007
- Livestock
Poultry Waste Emissions
Ducks
X
X
X
Agriculture Production
2805030008
- Livestock
Poultry Waste Emissions
Geese
X
X
X
Agriculture Production
2805030009
- Livestock
Poultry Waste Emissions
Turkeys
0
79
-------
see
SCC Level Two
SCC Level Three
SCC Level Four
EPA
Local
State
Tribe
Agriculture Production
Horses and Ponies Waste
Not Elsewhere
2805035000
- Livestock
Emissions
Classified
X
X
X
X
Swine production -
Agriculture Production
operations with lagoons
2805039100
- Livestock
(unspecified animal age)
Confinement
X
X
X
X
Swine production -
Agriculture Production
operations with lagoons
Manure handling
2805039200
- Livestock
(unspecified animal age)
and storage
X
X
X
Swine production -
Agriculture Production
operations with lagoons
Land application of
2805039300
- Livestock
(unspecified animal age)
manure
X
X
X
Agriculture Production
Sheep and Lambs Waste
2805040000
- Livestock
Emissions
Total
X
X
X
X
Agriculture Production
Not Elsewhere
2805045000
- Livestock
Goats Waste Emissions
Classified
X
X
X
X
Agriculture Production
2805045002
- Livestock
Goats Waste Emissions
Angora Goats
0
Agriculture Production
2805045003
- Livestock
Goats Waste Emissions
Milk Goats
0
Swine production - deep-
Agriculture Production
pit house operations
2805047100
- Livestock
(unspecified animal age)
Confinement
X
X
X
Swine production - deep-
Agriculture Production
pit house operations
Land application of
2805047300
- Livestock
(unspecified animal age)
manure
X
X
X
Swine production -
Agriculture Production
outdoor operations
2805053100
- Livestock
(unspecified animal age)
Confinement
X
X
X
Domestic Animals
2806010000
Waste Emissions
Cats
Total
X
X
Domestic Animals
2806015000
Waste Emissions
Dogs
Total
X
X
Wild Animals Waste
2807025000
Emissions
Elk
Total
X
Wild Animals Waste
2807030000
Emissions
Deer
Total
X
Table 3-30: Point SCCs with 2011 NEI emissions in the Livestock Waste sector - reportec
SCC
SCC Level Two
SCC Level Three
SCC Level Four
CA
CO
ws
30202001
Food and Agriculture
Beef Cattle Feedlots
Feedlots: General
X
X
X
30202101
Food and Agriculture
Eggs and Poultry
Production
Manure
Handling: Dry
X
only by States
The agencies listed in Table 3-31 submitted emissions for this sector.
Table 3-31: Agencies that submitted Livestock Waste data
Agency
Type
California Air Resources Board
State
Clark County Department of Air Quality and Environmental Management
Local
Connecticut Department of Environmental Protection
State
Delaware Department of Natural Resources and Environmental Control
State
80
-------
Agency
Type
Georgia Department of Natural Resources
State
Hawaii Department of Health Clean Air Branch
State
Idaho Department of Environmental Quality
State
Illinois Environmental Protection Agency
State
Kansas Department of Health and Environment
State
Maine Department of Environmental Protection
State
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribal
Coeur d'Alene Tribe
Tribal
Nez Perce Tribe
Tribal
Kootenai Tribe of Idaho
Tribal
Sac and Fox Nation of Missouri in Kansas and Nebraska Reservation
Tribal
Utah Division of Air Quality
State
West Virginia Division of Air Quality
State
Kickapoo Tribe of Indians of the Kickapoo Reservation in Kansas
Tribal
Table 3-32 shows the selection hierarchy that applies to the nonpoint datasets included in this sector. The point
source datasets are not included in the table. The point hierarchy includes the EPA PM-Augmentation dataset
first, the Responsible Agency Data Set second, and the other EPA datasets behind the Responsible Agency Data
Set.
Table 3-32: 2011 NEI Agricultural Livestock Waste data selection hierarchy
Priority
Dataset Name
Dataset Content
1
Responsible Agency Data Set
State and Local Agency submitted emissions
2
2011EPA_PM-Augmentation
Augments PM emissions
3
2011EPA_NP_NoOverlap_w_Pt
EPA-generated data
81
-------
3.4.3 Spatial coverage and data sources for the sector
Agriculture - Livestock Waste
N
PN
PN
PN
P - Point
N - Nonpoint
PN - P&N
All CAPs I I EPA 1 1 OIT EPA&SLT
3.4.4 EPA-developed livestock waste emissions data
Due to resource constraints at EPA, 2011 emissions are assumed to be the same as 2008 emissions.
EPA's approach to calculating 2008 emissions for this sector consisted of four general steps, as follows:
• Determine county-level activity data, i.e., the population of animals for 2007.
• For beef, dairy, poultry, and swine, apportion animal populations to a manure management train (MMT)
for each county. Animal populations for ducks, geese, goats, horses, and sheep were not apportioned to
MMTs.
• Modify the emission factor files provided with the Carnegie Mellon University (CMU) Ammonia Model v.
3.6 [ref 1] to ensure that every county had an assigned emission factor.
• Use the CMU Ammonia Model v. 3.6 to calculate ammonia emissions based on the updated county-level
animal populations and emission factor.
Activity Data
County-level animal population numbers for 2007 were obtained from the U.S. Department of Agriculture's
2007 Census of Agriculture report [ref 2]). 2007 data were used because they were the most recent available at
the time these estimates were prepared (in 2008). For Virginia, the county-level census data includes animal
populations from Virginia's 39 independent cities. For some counties and states, census data were withheld to
avoid disclosing data for individual farms. However, the total national-ievel animal numbers and most state-level
animal numbers for each livestock type reported in the Census include those animal numbers not disclosed at
the county-level. When available, state-level animal numbers from the United States Department of Agriculture
(USDA) National Agriculture Statistical Service (NASS) online database [ref 3], were used for states with
undisclosed animal numbers in the 2007 Census of Agriculture. To determine the total number of undisclosed
animals, we summed and subtracted disclosed county-level animal numbers for each livestock type from the
total state animal numbers. The total undisclosed animal population for a specific livestock type was then
allocated to those counties reporting undisclosed data proportionally based on the number of farms raising that
livestock in each county. If the state-level data were undisclosed and not available in the NASS database, then
82
-------
national animal numbers were used to determine undisclosed state numbers in a manner similar to the case
where counties had undisclosed data. We then summed and subtracted the disclosed county-level data from the
state-level data to determine animal numbers not disclosed at the county-level. We then allocated the
difference to those counties with undisclosed data proportionally based on the number of farms raising that
livestock in each county. States that had undisclosed data at the state level are as follows: for broilers,
Massachusetts and Rhode Island; for layers, Arizona, Connecticut, Delaware, Idaho, Kansas, Maine and New
Mexico; for turkeys, Colorado and Oklahoma; for pullets, Arizona, Connecticut, Delaware, Hawaii, Idaho, Kansas,
Massachusetts, New Mexico, North Dakota, and South Dakota; and for ducks, New Jersey and Utah.
Apportion activity data to manure management trains
To run the model using 2007 animal population, it was necessary to match the 2007 animal information to the
CMU model's (v3.6) input files, which were based on 2002 animal population and MMTs. We apportioned the
2007 county-level animal population data to MMTs based on data available in the model. A MMT consists of an
animal confinement area (e.g., dry lot, pasture, flush, scrape); components used to store, process, or stabilize the
manure (e.g., anaerobic lagoons, deep pits); and a land application site where manure is used as a fertilizer
source [ref 4], It is important to apportion the animal populations to MMTs because it has a large impact on the
emissions estimates in the CMU model for the animals using that approach. Not all animal types were
apportioned to MMTs. MMTs for ducks, geese, goats, horses, and sheep are not a part of the model. Also, some
animal category names did not match the category names currently in the model. See the example of "Other
Cattle" described below.
The apportionment was based on county-level MMT percentages derived from the CMU Ammonia Model v3.6,
which was originally developed for a 2002 inventory year. For each livestock type, we divided the CMU Model's
2002 county-level number of animals in each MMT by the total county-level animal population for that livestock
type to calculate the percentage of total animals managed by each MMT. In cases where the county-level
numbers were zero in the CMU Ammonia Model and the county animal population in 2007 for that MMT was
not zero, we assigned the county state-level MMT percentages. We then multiplied the county-level animal
population for each livestock type by the MMT percentages to apportion the 2007 animal populations to each
MMT. The result of this approach is that the proportion of animals in each MMT is unchanged from the CMU
model's 2002-based approach to the 2011 NEI.
Cattle reported as "Other Cattle" in the 2007 Census of Agriculture were divided between dairy cattle and beef
cattle at the county-level using percent allocations derived from county-level dairy and beef cattle reported in
the 2007 Census of Agriculture and corrected for undisclosed data. The animal numbers from "Other Cattle"
apportioned to dairy and beef cattle were used to grow the "Dairy Cattle - Composite and Beef Cattle -
Composite" activity input files from 2002 to 2007 for input to the CMU Ammonia Model.
County-level pullet numbers reported in the 2007 Census of Agriculture were used to grow the "Poultry -
Composite" activity input file from 2002 to 2007 for input to the CMU Ammonia Model.
Emission Factors
Table 3-33 provides information on emission factors used in the EPA emissions estimate. The table lists "county"
for county-specific emission factors, and "state" for state-specific emission factors. The emission factor for the
poultry composite categories was obtained from an EPA report [ref 4], The county-level emission factors for the
beef composite and dairy composite categories were developed using beef and dairy cattle emission factors
provided with the CMU Model. Specifically, weighted average emission factors were calculated based on the
number of beef or dairy cattle in each MMT from the CMU Model's 2002 activity files and the emission factor
83
-------
assigned to each MMT. The calculations made for the beef composite are available in the file "County-Level
Emission Factors for Beef Composite.xls", and the calculations for the dairy composite are available in the file
"County-level Emission factors for Diary Coriiponent.xls". All other emission factors are consistent with those
included in the CMU Ammonia Model v.3.6.
The emission factors for some counties in the CMU Ammonia Model files were zero. To ensure that all counties
with animal populations were assigned emissions factors, the emission factor input files provided with the CMU
Ammonia Model were modified. For all counties with an emission factor of zero, the emission factor was
replaced with the state average emission factor. If all counties in the state had emission factors of zero, then the
county emission factor was replaced with the national average emission factor.
The state average emission factor was calculated by summing the counties with non-zero emission factors in the
state and dividing the total by the number of counties in that state with non-zero emission factors. The national
average emission factors listed in the table were calculated by summing the counties with non-zero emission
factors in the nation and dividing the total by the number of counties in the nation with non-zero emission
factors. The final county-specific and state-specific emission factors are available in the file "Emission Factors for
Ag animal husbandry 2008v2.xlsx".
Table 3-33: Emission factors for NH3 emissions usee
for EPA's Agricultural Livestock Waste data
Emission
Emission Factor
Description
Factor
Emission Factor Unit
Reference
Beef Cattle - Composite
county
kg NHs/cow/month
ref 5
Beef Cattle - Drylot Operation - Confinement
9.45E-01
kg NHs/cow/month
ref 1
Beef Cattle - Drylot Operation - Land Application
state
kg NHs/cow/month
ref 1
Beef Cattle - Drylot Operation - Manure Storage
3.78E-04
kg NHs/cow/month
ref 1
Beef Cattle - Pasture Operation - Confinement
county
kg NHs/cow/month
ref 1
Dairy Cattle - Composite
county
kg NHs/cow/month
ref 5
Dairy Cattle - Deep Pit Dairy Confinement
2.42E+00
kg NHs/cow/month
ref 1
Dairy Cattle - Deep Pit Dairy Land Application
state
kg NHs/cow/month
ref 1
Dairy Cattle - Deep Pit Dairy Manure Storage
1.13E-01
kg NHs/cow/month
ref 1
Dairy Cattle - Drylot Dairy Confinement
state
kg NHs/cow/month
ref 1
Dairy Cattle - Drylot Dairy Land Application
state
kg NHs/cow/month
ref 1
Dairy Cattle - Drylot Dairy Manure Storage
state
kg NHs/cow/month
ref 1
Dairy Cattle - Flush Dairy Confinement
2.00E+00
kg NHs/cow/month
ref 1
Dairy Cattle - Flush Dairy Land Application
state
kg NHs/cow/month
ref 1
Dairy Cattle - Flush Dairy Manure Storage
state
kg NHs/cow/month
ref 1
Dairy Cattle - Scrape Dairy Confinement
state
kg NHs/cow/month
ref 1
Dairy Cattle - Scrape Dairy Land Application
state
kg NHs/cow/month
ref 1
Dairy Cattle - Scrape Dairy Manure Storage
state
kg NHs/cow/month
ref 1
Ducks
7.67E-02
kg NHs/duck/month
ref 1
Geese
7.67E-02
kg NHs/goose/month
ref 1
Goats
5.29E-01
kg NHs/goat/month
ref 1
Horses
1.02E+00
kg NHs/horse/month
ref 1
Poultry - Broiler Operation - Confinement
8.32E-03
kg NHs/bird/month
ref 1
Poultry - Broiler Operation - Land Application
6.80E-03
kg NHs/bird/month
ref 1
Poultry - Broiler Operation - Manure Storage
1.51E-03
kg NHs/bird/month
ref 1
Poultry - Composite
2.00E-02
kg NHs/bird/month
ref 4
Poultry - Layers - Dry Manure Operation - Confinement
3.36E-02
kg NHs/bird/month
ref 1
Poultry - Layers - Dry Manure Operation - Land Application
county
kg NHs/bird/month
ref 1
Poultry - Layers - Wet Manure Operation - Confinement
9.45E-03
kg NHs/bird/month
ref 1
Poultry - Layers - Wet Manure Operation - Land Application
county
kg NHs/bird/month
ref 1
84
-------
Emission
Emission Factor
Description
Factor
Emission Factor Unit
Reference
Poultry - Layers - Wet Manure Operation - Manure Storage
county
kg NHs/bird/month
ref 1
Poultry-Turkey Operation - Confinement
3.78E-02
kg NHs/bird/month
ref 1
Poultry -Turkey Operation - Land Application
3.40E-02
kg NHs/bird/month
ref 1
Poultry-Turkey Operation - Storage
6.80E-03
kg NHs/bird/month
ref 1
Sheep
2.65E-01
kg NHs/sheep/month
ref 1
Swine - Composite
county
kg NHs/pig/month
ref 1
Swine - Deep Pit Operation - Confinement
2.65E-01
kg NHs/pig/month
ref 1
Swine - Deep Pit Operation - Land Application
county
kg NHs/pig/month
ref 1
Swine - Lagoon Operation - Confinement
2.27E-01
kg NHs/pig/month
ref 1
Swine - Lagoon Operation - Land Application
county
kg NHs/pig/month
ref 1
Swine - Lagoon Operation - Manure Storage
county
kg NHs/pig/month
ref 1
Swine - Outdoor Operation - Confinement
county
kg NHs/pig/month
ref 1
Emissions
The livestock activity files provided with the CMU Ammonia Model v.3.6 were replaced with the updated
county-level animal population files and modified emission factors files. We then ran the CMU Ammonia Model
v.3.6 to create county/SCC ammonia emissions. EPA's county-level emissions can be found in the supporting
materials in the file "animaljivestock_emissions_2011.zip" as listed in Table 3-9, Section 3.1.7.
Sample Calculations
Allocation of Undisclosed Data
From the 2007 Census of Agriculture, the total national number of beef cattle in Alabama is 678,949. The total
number of beef cattle disclosed at the county-level is 388,827.
Total number of beef cattle undisclosed at the county-level = 678,949 - 338,827 = 340,122
From the 2007 Census of Agriculture, the total number of farms in Alabama not disclosing beef cattle numbers is
10,518.
Average beef cattle per farm not disclosing data = 340,122 / 10,518 = 32.3
For 2007, Baldwin County, Alabama beef cattle data were not disclosed. The total number of farms with beef
cattle in Baldwin County is 343.
Estimated number of beef cattle in Baldwin County = 32.3 x 343 = 11,092
Manure Management Train
From the 2002 CMU Ammonia Model input files, Chilton County, Alabama had 79 beef cattle under drylot
management and 18,900 beef cattle under pasture management in 2002.
Total beef cattle = 79 + 18,900 = 18,979
% of beef cattle under drylot management = 79 / 18,979 = 0.42
% of beef cattle under pasture management = 18,900 / 18,979 = 99.58
The total number of beef cattle for Chilton County reported in the 2007 Census of Agriculture is 7,939.
Number of beef cattle under drylot management in 2007 = 7,939 x 0.0042 = 33
85
-------
Number of beef cattle under pasture management in 2007 = 7,939 x 0.9958 = 7,906
Other Cattle
For Clay County, Alabama, the 2007 Census of Agriculture reports the number of "Other Cattle" as 5,471, the
number of dairy cattle as 216, and the number of beef cattle as 9,096.
Total beef and dairy cattle reported = 216 + 9,096 = 9,312
% of other cattle assigned to beef cattle = (9,096/9,312)*100 = 97.68
% of other cattle assigned to dairy cattle = (216/9,312)*100 = 2.32
Other cattle allocated to beef cattle = 5,471 x .9768 = 5,344
Other cattle allocated to dairy cattle = 5,471 x 0.0232 = 127
3.4.5 Summary of quality assurance methods
Data analyses involving comparison of emissions between 2011 and 2008 showed some large discrepancies in
emissions from this sector between the two years. Values submitted by S/L/T agencies that were larger than 10
times the 2008 submitted values were tagged as outliers and were not used in the 2011 NEI (unless the agency
corrected the values prior to the final 2011 selection). Furthermore, California and Idaho submitted some
pollutants for this sector that EPA did not estimate nor did any other states, so for consistency, these values
were tagged and not used in the 2011 NEI. In addition, Louisiana requested that some values be tagged and not
used, because Louisiana had pulled 2008 data forward for this sector and requested that we use EPA data for
2011 for these emissions instead. Table 3-34 summarizes the number of tagged process-level emissions values
from each agency affected by this QA.
Table 3-34: Agencies tagged values for Agriculture Livestock Waste
Agency
Number of
Values Tagged
Tag Reason
California Air Resources Board
1,653
Extraneous pollutants (no other states
submitted)
California Air Resources Board
9
Outlier
Idaho Department of Environmental
Quality
11,088
Extraneous pollutants (no other states
submitted)
Louisiana Department of
Environmental Quality
2,944
State requested that we replace their data
with EPA estimates.
3.4.6 References for Agriculture - Livestock Waste
1. Cliff Davidson, Peter Adams, Ross Strader, Rob Pinder, Natalie Anderson, Marian Goebes, and Josh
Ayers. The Environmental Institute, Carnegie Mellon University, CMU Ammonia Model v.3.6., 2004,
accessed 25 April 2009.
2. U.S. Department of Agriculture, 2007 Census of Agriculture, accessed 30 April 2009.
3. U.S. Department of Agriculture, National Agricultural Statistics Service, accessed 28 January 2010.
4. U.S. Environmental Protection Agency, National Emission Inventory - Ammonia Emissions from Animal
Agricultural Operations, Revised Draft Report, 22 April 2005, p. 4-6, accessed 5 May 2009.
5. Jonathan Dorn, E.H. Pechan & Associates. 2009. A weighted average emission factor calculated using
data from the 2002 CMU Ammonia Model v.3.6.
86
-------
3.5 Bulk Gasoline Terminals and Gas Stations
3.5.1 Sector description
This section covers the creation of the EIS sectors "Bulk Gasoline Terminals" and "Gas Stations". In composite,
we refer to these sources as "Stage I gasoline distribution".
Stage I gasoline distribution includes the following gasoline emission points: 1) bulk terminals; 2) pipeline
facilities; 3) bulk plants; 4) tank trucks; and 5) service stations. Emissions from Stage I gasoline distribution occur
as gasoline vapors are released into the atmosphere. These Stage I processes are subject to EPA's maximum
available control technology (MACT) standards for gasoline distribution [ref 1],
Emissions from gasoline distribution at bulk terminals and bulk plants take place when gasoline is loaded into a
storage tank or tank truck, from working losses (for fixed roof tanks), and from working losses and roof seals (for
floating roof tanks). Working losses consist of both breathing and emptying losses. Breathing losses are the
expulsion of vapor from a tank vapor space that has expanded or contracted because of daily changes in
temperature and barometric pressure; these emissions occur in the absence of any liquid level change in the
tank. Emptying losses occur when the air that is drawn into the tank during liquid removal saturates with
hydrocarbon vapor and expands, thus exceeding the fixed capacity of the vapor space and overflowing through
the pressure vacuum valve [ref 2],
Emissions from tank trucks in transit occur when gasoline vapor evaporates from (1) loaded tank trucks during
transportation of gasoline from bulk terminals/plants to service stations, and (2) empty tank trucks returning
from service stations to bulk terminals/plants [ref 3], Pipeline emissions result from the valves and pumps found
at pipeline pumping stations and from the valves, pumps, and storage tanks at pipeline breakout stations. Stage
I gasoline distribution emissions also occur when gasoline vapors are displaced from storage tanks during
unloading of gasoline from tank trucks at service stations (Gasoline Service Station Unloading) and from gasoline
vapors evaporating from service station storage tanks and from the lines going to the pumps (Underground
Storage Tank Breathing and Emptying).
3.5.2 Source of data overview and selection hierarchy
The Stage I gasoline distribution sources -bulk gasoline terminals and gasoline stations EIS sectors- include
emissions from both S/L/T agencies and from the EPA overlap nonpoint dataset. Table 3-35 lists the various
datasets used in the 2011 NEI for this sector. Table 3-36 shows the agencies that submitted data used by the
2011 NEI. In some cases, the EPA PM and HAP augmentation datasets were used to fill in PM species and HAP
pollutants based on S/L/T agency data. The figures shown in Section 3.5.3 illustrate where S/L/T agency data are
used for this sector. EPA data is used where S/L/T agency data were not provided.
Table 3-35: 2011 NEI selection hierarchy for datasets used in Bulk Terminals sector
Priority
Dataset Name
Dataset Content
1
2011EPA_PM-Augmentation
Augments PM data in 47 states and some tribes
2
Responsible Agency Data Set
State and Local Agency submitted emissions
4
2011EPA_HAP-Augmentation
Adds Pb and other HAP emissions in 46 states
5
2011EPA_NP_Overlap_w_Pt
EPA-generated data
87
-------
Table 3-36: Agencies that submitted data for the sector Bulk Gasoline Terminals and Gasoline Stations
Agency Name
Bulk Gasoline
Terminals
Gasoline Stations
Point
Point
Nonpoint
Alabama Department of Environmental Management
X
Alaska Department of Environmental Conservation
X
X
City of Albuquerque
X
Allegheny County Health Department
X
Arkansas Department of Environmental Quality
X
California Air Resources Board
X
X
X
Chattanooga Air Pollution Control Bureau (CHCAPCB)
X
X
Clark County Department of Air Quality and Environmental Management
X
Colorado Department of Public Health and Environment
X
Connecticut Department of Environmental Protection
X
DC-District Department of the Environment
X
X
Delaware Department of Natural Resources and Environmental Control
X
HAP Augmentation EPA
X
X
No Overlap EPA
X
Overlap EPA
X
PM Augmentation EPA
X
X
TRI EPA
X
Florida Department of Environmental Protection
X
Georgia Department of Natural Resources
X
X
X
Hawaii Department of Health Clean Air Branch
X
X
Iowa Department of Natural Resources
X
X
Idaho Department of Environmental Quality
X
X
Illinois Environmental Protection Agency
X
X
X
Indiana Department of Environmental Management
X
Jefferson County (AL) Department of Health
X
Knox County Department of Air Quality Management
X
Kansas Department of Health and Environment
X
X
X
Kentucky Division for Air Quality
X
Louisiana Department of Environmental Quality
X
Louisville Metro Air Pollution Control District
X
Massachusetts Department of Environmental Protection
X
X
Maricopa County Air Quality Department
X
X
X
Maryland Department of the Environment
X
X
Mecklenburg County Air Quality
X
Maine Department of Environmental Protection
X
Memphis and Shelby County Health Department - Pollution Control
X
Michigan Department of Environmental Quality
X
X
X
Minnesota Pollution Control Agency
X
88
-------
Agency Name
Bulk Gasoline
Terminals
Gasoline Stations
Point
Point
Nonpoint
Missouri Department of Natural Resources
X
Mississippi Dept of Environmental Quality
X
Metro Public Health of Nashville/Davidson County
X
North Carolina Department of Environment and Natural Resources
X
New Hampshire Department of Environmental Services
X
X
New Jersey Department of Environment Protection
X
X
X
New Mexico Environment Department Air Quality Bureau
X
Nevada Division of Environmental Protection
X
New York State Department of Environmental Conservation
X
X
X
Ohio Environmental Protection Agency
X
X
Oklahoma Department of Environmental Quality
X
Oregon Department of Environmental Quality
X
Pennsylvania Department of Environmental Protection
X
Philadelphia Air Management Services
X
Pinal County
X
Rhode Island Department of Environmental Management
X
South Carolina Department of Health and Environmental Control
X
X
Southwest Clean Air Agency
X
Tennessee Department of Environmental Conservation
X
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
X
Coeur d'Alene Tribe
X
Nez Perce Tribe
X
Kootenai Tribe of Idaho
X
Bishop Paiute Tribe
X
Washoe Tribe of California and Nevada
X
Texas Commission on Environmental Quality
X
X
X
Utah Division of Air Quality
X
X
X
Virginia Department of Environmental Quality
X
X
X
Vermont Department of Environmental Conservation
X
X
Washington State Department of Ecology
X
Washoe County Health District
X
Wisconsin Department of Natural Resources
X
West Virginia Division of Air Quality
X
X
Wyoming Department of Environmental Quality
X
Kickapoo Tribe of Indians of the Kickapoo Reservation in Kansas
X
89
-------
P - Point
N - Nonpoint
PN-P&N
P - Point
N - Nonpoint
3.5.3 Spatial coverage and data sources for the sector
Bulk Gasoline Terminals
Bulk Gasoline Terminals
All CAPs — =p.
AH HAPS
Gas Stations
Gas Stations
P - Point
N - Nonpoint
PN-P&N
PN —PN
P - Point
N - Nonpoint
All CAPs
PN - P&N
All HAPS ssli mm lm.su
3.5.4 EPA-deveioped emission estimates
The nonpoint SCCs that comprise the Stage I Gasoline Distribution source category are provided in Table 3-37;
SCC level 1 and 2 descriptions for all SCCs are "Storage and Transport; Petroleum and Petroleum Product
Storage".
Table 3-37: Nonpoint Stage I Gasoline Distribution SCCs
SCC
SCC Level 3
SCC Level 4
2501050120
Bulk Terminals: All Evaporative Losses
Gasoline
2501055120
Bulk Plants: All Evaporative Losses
Gasoline
2501060051
Gasoline Service Stations
Stage 1: Submerged Filling
2501060052
Gasoline Service Stations
Stage 1: Splash Filling
2501060053
Gasoline Service Stations
Stage 1: Balanced Submerged Filling
2501060201
Gasoline Service Stations
Underground Tank: Breathing and Emptying
2505030120
Truck
Gasoline
2505040120
Pipeline
Gasoline
90
-------
Bulk Terminals and Pipelines
For 2011, EPA used 2008 emission estimates due to resource constraints. This section describes the method
used in 2008. There is no generally accepted activity-based VOC emission factors for the pipelines and bulk
terminals sectors because they are generally treated as point sources whose emissions are estimated using
site-specific information. For example, emission estimates for bulk terminal storage tanks are typically derived
from tank specific parameters that are input into the TANKS program [ref 4] Therefore, for bulk terminals and
pipelines, EPA estimated 2008 national VOC emissions by multiplying 1998 national estimates developed in
support of the Gasoline Distribution MACT standard [ref 5] by the 2008 to 1998 ratio of the national volume of
wholesale gasoline supplied (see Table 3-38). The gasoline supply information was obtained from Table 2 in
Volume I of Petroleum Supply Annual 2008 [ref 6],
Ta
)le 3-38: Estimation of national 2008 VOC emissions for Pipelines and Bulk Terminals
Category
1998 Post-
MACT Control
Emissions (Mg)
Mg to Ton
Conversion
Factor
1998
Emissions
(tons)
Ratio of 2008 to 1998
Gasoline Supplied
2008
Emissions
(tons)
Pipelines
79,830
1.1023
87,997
1.089
= (8,989,000 barrels per day /
8,253,000 barrels per day)
95,844
Bulk
Terminals
137,555
1.1023
151,627
165,149
To estimate HAP emissions, EPA applied national average speciation profiles to the VOC emission estimates [ref
7], Table 3-39 presents these speciation profiles and the national bulk terminal and pipeline HAP emission
estimates (note that unless otherwise noted, all emission values reported in this section exclude estimates for
Puerto Rico and the U.S. Virgin Islands). EPA used total VOC emission estimates, so emissions represent total
emissions. Where necessary, States should perform point source subtractions to obtain nonpoint emissions. The
following describes how total national VOC estimates were allocated to counties.
Table 3-39: HAP speciation profiles and 2008 Bulk Terminal and Pipeline emissions
HAP
Pollutant
Code
Percentage of
VOC Emissions
Reference
2008 National Emissions
(tons)
Bulk Terminals
Pipelines
Benzene
71432
0.27
7
4.46E+02
2.59E+02
2,2,4-
Trimethylpentane
540841
0.75
7
1.24E+03
7.19E+02
Cumene
98828
0.012
7
1.98E+01
1.15E+01
Ethyl Benzene
100414
0.053
7
8.75E+01
5.08E+01
n-Hexane
110543
1.8
7
2.97E+03
1.73E+03
Naphthalene
91203
0.00027
7
4.46E-01
2.59E-01
Toluene
108883
1.4
7
2.31E+03
1.34E+03
Xylenes
1330207
0.56
7
9.25E+02
5.37E+02
For both categories, EPA allocated national VOC and HAP emissions for these categories in a two-step manner.
First, EPA allocated emissions based on 2008 gasoline supply data reported by the U.S. Department of Energy
(DOE). Next, EPA allocated emissions based on employment data reported in the 2007 County Business Patterns
[ref 8],
91
-------
For pipelines, EPA allocated emissions to Petroleum Administration for Defense (PAD) Districts based on the
total amount of finished motor gasoline moved by pipeline in each PAD in year 2008. There are five PAD Districts
across the United States: PAD District 1 comprises seventeen states plus the District of Columbia along the
Atlantic Coast; PAD District 2 comprises fifteen states in the Midwest; PAD District 3 comprises six states in
South Central U.S.; PAD District 4 comprises five states in the Rocky Mountains; and PAD District 5 comprises
seven states along the West Coast. These data, which are displayed below in Table 3-40, are reported in Table
35 of Volume 1 of Petroleum Supply Annual 2008 [ref 9], Next, EPA allocated pipeline emissions in each PAD
District to counties based on County Business Patterns employment data. Because employment data for NAICS
code 48691 (Pipeline Transportation of Refined Petroleum Products) are often withheld due to confidentiality
reasons, EPA used the number of employees in NAICS code 42471 (Petroleum Bulk Stations and Terminals) for
this allocation. To better account for the location of refined petroleum pipelines, however, EPA did not allocate
any activity to States which had employees in this NAICS code, but, did not have employees in NAICS code 48691
(i.e., District of Columbia, Idaho, Maine, New Hampshire, Vermont, and West Virginia).
Table 3-40: Movement of finished motor gasoline by pipeline between PAD Districts, 2008
From 1
From II
From III
From IV
From V
To 1
n/a
393
333,462
0
0
To II
70,895
n/a
99,167
7,442
0
To III
0
9,193
n/a
0
0
To IV
0
8,680
5,778
n/a
0
To V
0
0
25,453
9,287
n/a
For bulk terminals, EPA first allocated national emissions to States based on the 2008 refinery, bulk terminal,
and natural gas plant stocks of motor gasoline reported for each State in Table 33 of Volume 1 of DOE's
Petroleum Supply Annual 2008 (see Table 3-41) [ref 9], Next, EPA allocated emissions in each State to counties
based on the number of NAICS code 42471 (Petroleum Bulk Stations and Terminals) employees reported in the
2007 County Business Patterns [ref 8],
Table 3-41: Refinery, BulkTerminal, and Natural Gas Plant Stocks of Motor Gasoline, 2008
State
Motor Gasoline
State
Motor Gasoline
(Thousand Barrels)
(Thousand Barrels)
Alabama
1,090
Montana
872
Alaska
616
Nebraska
658
Arizona
470
Nevada
102
Arkansas
819
New Hampshire
0
California
460
New Jersey
2,956
Colorado
748
New Mexico
350
Connecticut
0
New York
1,469
Delaware
105
North Carolina
1,724
District of Columbia
0
North Dakota
291
Florida
1,877
Ohio
2,724
Georgia
1,724
Oklahoma
1,245
Hawaii
12
Oregon
525
Idaho
181
Pennsylvania
3,595
Illinois
1,940
Rhode Island
0
Indiana
2,464
South Carolina
720
Iowa
1,090
South Dakota
283
92
-------
State
Motor Gasoline
(Thousand Barrels)
State
Motor Gasoline
(Thousand Barrels)
Kansas
2,347
Tennessee
923
Kentucky
1,045
Texas
9,530
Louisiana
5,209
Utah
793
Maine
374
Vermont
31
Maryland
31
Virginia
1,285
Massachusetts
0
Washington
1,902
Michigan
1,772
West Virginia
183
Minnesota
1,305
Wisconsin
704
Mississippi
1,580
Wyoming
910
Missouri
491
It is important to reiterate that the above discussion addresses the calculation of total VOC emissions. The 2008
point source NEI reports VOC emissions related to bulk terminal and pipeline processes. To obtain nonpoint
emissions, States should subtract the 2008 point source VOC emission estimates from the total VOC emission
estimates reported here. The relevant point source SCCs are listed in Table 3-42 and Table 3-43; the SCC level 1
description for all SCCs in both tables is "Petroleum and Solvent Evaporation".
Table 3-42: Pipeline Point Source SCCs
SCC
SCC Level 2
SCC Level 3
SCC Level 4
40600501
Transportation and Marketing
of Petroleum Products
Pipeline Petroleum Transport
- General - All Products
Pipeline Leaks
40600502
Transportation and Marketing
of Petroleum Products
Pipeline Petroleum Transport
- General - All Products
Pipeline
Venting
40600503
Transportation and Marketing
of Petroleum Products
Pipeline Petroleum Transport
- General - All Products
Pump Station
40600504
Transportation and Marketing
of Petroleum Products
Pipeline Petroleum Transport
- General - All Products
Pump Station
Leaks
Table 3-43: Bulk Terminal Point Source SCCs
SCC
SCC Level 2
SCC Level 3
SCC Level 4
40400101
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 13: Breathing Loss (67000
Bbl Capacity) - Fixed Roof Tank
40400102
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 10: Breathing Loss (67000
Bbl Capacity) - Fixed Roof Tank
40400103
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 7: Breathing Loss (67000
Bbl. Capacity) - Fixed Roof Tank
40400104
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 13: Breathing Loss (250000
Bbl Capacity)-Fixed Roof Tank
40400105
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 10: Breathing Loss (250000
Bbl Capacity)-Fixed Roof Tank
40400106
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 7: Breathing Loss (250000
Bbl Capacity) - Fixed Roof Tank
40400107
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 13: Working Loss (Diam.
Independent) - Fixed Roof Tank
40400108
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 10: Working Loss (Diameter
Independent) - Fixed Roof Tank
93
-------
see
see Level 2
SCC Level 3
SCC Level 4
40400109
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 7: Working Loss (Diameter
Independent) - Fixed Roof Tank
40400110
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 13: Standing Loss (67000
Bbl Capacity)-Floating Roof Tank
40400111
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 10: Standing Loss (67000
Bbl Capacity)-Floating Roof Tank
40400112
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 7: Standing Loss (67000 Bbl
Capacity)- Floating Roof Tank
40400113
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 13: Standing Loss (250000
Bbl Cap.) - Floating Roof Tank
40400114
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 10: Standing Loss (250000
Bbl Cap.) - Floating Roof Tank
40400115
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 7: Standing Loss (250000
Bbl Cap.) - Floating Roof Tank
40400116
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 13/10/7: Withdrawal Loss
(67000 Bbl Cap.) - Float RfTnk
40400117
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 13/10/7: Withdrawal Loss
(250000 Bbl Cap.) - Float RfTnk
40400118
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 13: Filling Loss (10500 Bbl
Cap.) - Variable Vapor Space
40400119
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 10: Filling Loss (10500 Bbl
Cap.) - Variable Vapor Space
40400120
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 7: Filling Loss (10500 Bbl
Cap.) - Variable Vapor Space
40400131
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 13: Standing Loss - Ext.
Floating Roof w/ Primary Seal
40400132
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 10: Standing Loss - Ext.
Floating Roof w/ Primary Seal
40400133
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 7: Standing Loss - External
Floating Roof w/ Primary Seal
40400141
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 13: Standing Loss - Ext.
Floating Roof w/ Secondary Seal
40400142
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 10: Standing Loss - Ext.
Floating Roof w/ Secondary Seal
40400143
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 7: Standing Loss - Ext.
Floating Roof w/ Secondary Seal
40400148
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 13/10/7: Withdrawal Loss -
Ext. Float Roof (Pri/Sec Seal)
40400150
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Miscellaneous Losses/Leaks: Loading
Racks
40400151
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Valves, Flanges, and Pumps
40400152
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Vapor Collection Losses
40400153
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Vapor Control Unit Losses
40400161
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 13: Standing Loss - Int.
Floating Roof w/ Primary Seal
94
-------
see
see Level 2
SCC Level 3
SCC Level 4
40400162
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 10: Standing Loss - Int.
Floating Roof w/ Primary Seal
40400163
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 7: Standing Loss - Internal
Floating Roof w/ Primary Seal
40400171
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 13: Standing Loss - Int.
Floating Roof w/ Secondary Seal
40400172
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 10: Standing Loss - Int.
Floating Roof w/ Secondary Seal
40400173
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 7: Standing Loss - Int.
Floating Roof w/ Secondary Seal
40400178
Petroleum Liquids
Storage (non-Refinery)
Bulk Terminals
Gasoline RVP 13/10/7: Withdrawal Loss -
Int. Float Roof (Pri/Sec Seal)
Bulk Plants
EPA calculated VOC emissions from bulk plants by developing an average emission factor from the bulk plant
motor gasoline VOC emissions and throughput data developed in support of the Gasoline Distribution MACT
standards [ref 2, ref 5], To estimate 2008 national VOC emissions, the VOC emission factor (8.62 pounds of VOC
per 1,000 gallons) was applied to the estimated national volume of gasoline passing through bulk plants in 2008.
The volume of bulk plant gasoline throughput was assumed to be 9 percent of total gasoline consumption [ref
10], Total gasoline consumption for 2008 was assumed to be the same as the volume of finished motor gasoline
supplied as reported on the U.S. Energy Information Administration's Petroleum Navigator website [ref 11], The
resulting national VOC emission estimate was then allocated to counties based on employment data for NAICS
code 42471 (Petroleum Bulk Stations and Terminals). To estimate benzene emissions from bulk plants, EPA
multiplied VOC emission estimates by county-level speciation profiles calculated from the annual onroad
refueling (Stage 2) emissions from the 2008 NEI NMIM results [ref 12], All other HAPs were estimated by
multiplying VOC emissions by the national average speciation profiles displayed in Table 3-44.
Table 3-44: Bulk Plant HAP Speciation Profiles and Tota
Emission Estimates
Pollutant
Pollutant
Code
Emission Factor
Reference
National
Emissions (tpy)
VOC
VOC
8.62 Ib./l,000 gallons
2 and 5
5.35E+04
2,2,4-Trimethylpentane
540841
0.75% of VOC
7
4.01E+02
Cumene
98828
0.012% of VOC
7
6.41E+00
Ethyl Benzene
100414
0.053% of VOC
7
2.83E+01
n-Hexane
110543
1.8% of VOC
7
9.62E+02
Naphthalene
91203
0.00027% of VOC
7
1.44E-01
Toluene
108883
1.4% of VOC
7
7.48E+02
Xylenes
1330207
0.56% of VOC
7
2.99E+02
Benzene
71432
county-specific % of VOC
12
3.94E+02
It is important to reiterate that the above discussion addresses the calculation of total VOC emissions. The 2008
point source NEI reports VOC emissions related to bulk plants. To obtain nonpoint emissions, States should
subtract the 2008 point source VOC emission estimates from the total VOC emission estimates reported here.
95
-------
The relevant point source SCCs are listed in Table 3-45; SCC level 1 descriptions are "Petroleum and Solvent
Evaporation" for all SCCs.
Table 3-45: Bulk Plant Point Source SCCs
SCC
SCC Level 2
SCC Level 3
SCC Level 4
40400201
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 13: Breathing Loss
(67000 Bbl Capacity) - Fixed Roof
Tank
40400202
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 10: Breathing Loss
(67000 Bbl Capacity) - Fixed Roof
Tank
40400203
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 7: Breathing Loss
(67000 Bbl. Capacity) - Fixed Roof
Tank
40400204
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 13: Working Loss
(67000 Bbl. Capacity) - Fixed Roof
Tank
40400205
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 10: Working Loss
(67000 Bbl. Capacity) - Fixed Roof
Tank
40400206
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 7: Working Loss (67000
Bbl. Capacity) - Fixed Roof Tank
40400207
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 13: Standing Loss
(67000 Bbl Cap.) - Floating Roof Tank
40400208
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 10: Standing Loss
(67000 Bbl Cap.) - Floating Roof Tank
40400209
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 7: Standing Loss (67000
Bbl Cap.) - Floating Roof Tank
40400210
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 13/10/7: Withdrawal
Loss (67000 Bbl Cap.) - Float RfTnk
40400211
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 13: Filling Loss (10500
Bbl Cap.) - Variable Vapor Space
40400212
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 10: Filling Loss (10500
Bbl Cap.) - Variable Vapor Space
40400213
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 7: Filling Loss (10500
Bbl Cap.) - Variable Vapor Space
40400231
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 13: Standing Loss - Ext.
Floating Roof w/ Primary Seal
40400232
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 10: Standing Loss - Ext.
Floating Roof w/ Primary Seal
40400233
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 7: Standing Loss -
External Floating Roof w/ Primary
Seal
40400241
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 13: Standing Loss - Ext.
Floating Roof w/ Secondary Seal
40400242
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 10: Standing Loss - Ext.
Floating Roof w/ Secondary Seal
40400243
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 7: Standing Loss - Ext.
Floating Roof w/ Secondary Seal
96
-------
see
see Level 2
SCC Level 3
SCC Level 4
40400248
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 10/13/7: Withdrawal
Loss - Ext. Float Roof (Pri/Sec Seal)
40400250
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Loading Racks
40400251
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Valves, Flanges, and Pumps
40400252
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Miscellaneous Losses/Leaks: Vapor
Collection Losses
40400253
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Miscellaneous Losses/Leaks: Vapor
Control Unit Losses
40400261
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 13: Standing Loss - Int.
Floating Roof w/ Primary Seal
40400262
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 10: Standing Loss - Int.
Floating Roof w/ Primary Seal
40400263
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 7: Standing Loss -
Internal Floating Roof w/ Primary
Seal
40400271
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 13: Standing Loss - Int.
Floating Roof w/ Secondary Seal
40400272
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 10: Standing Loss - Int.
Floating Roof w/ Secondary Seal
40400273
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 7: Standing Loss - Int.
Floating Roof w/ Secondary Seal
40400278
Petroleum Liquids
Storage (non-Refinery)
Bulk Plants
Gasoline RVP 10/13/7: Withdrawal
Loss - Int. Float Roof (Pri/Sec Seal)
40400401
Petroleum Liquids
Storage (non-Refinery)
Petroleum Products -
Underground Tanks
Gasoline RVP 13: Breathing Loss
40400402
Petroleum Liquids
Storage (non-Refinery)
Petroleum Products -
Underground Tanks
Gasoline RVP 13: Working Loss
40400403
Petroleum Liquids
Storage (non-Refinery)
Petroleum Products -
Underground Tanks
Gasoline RVP 10: Breathing Loss
40400404
Petroleum Liquids
Storage (non-Refinery)
Petroleum Products -
Underground Tanks
Gasoline RVP 10: Working Loss
40400405
Petroleum Liquids
Storage (non-Refinery)
Petroleum Products -
Underground Tanks
Gasoline RVP 7: Breathing Loss
40400406
Petroleum Liquids
Storage (non-Refinery)
Petroleum Products -
Underground Tanks
Gasoline RVP 7: Working Loss
40600101
Transportation and
Marketing of Petroleum
Products
Tank Cars and Trucks
Gasoline: Splash Loading **
40600126
Transportation and
Marketing of Petroleum
Products
Tank Cars and Trucks
Gasoline: Submerged Loading **
40600131
Transportation and
Marketing of Petroleum
Products
Tank Cars and Trucks
Gasoline: Submerged Loading
(Normal Service)
97
-------
see
see Level 2
SCC Level 3
SCC Level 4
40600136
Transportation and
Marketing of Petroleum
Products
Tank Cars and Trucks
Gasoline: Splash Loading (Normal
Service)
40600141
Transportation and
Marketing of Petroleum
Products
Tank Cars and Trucks
Gasoline: Submerged Loading
(Balanced Service)
40600144
Transportation and
Marketing of Petroleum
Products
Tank Cars and Trucks
Gasoline: Splash Loading (Balanced
Service)
40600147
Transportation and
Marketing of Petroleum
Products
Tank Cars and Trucks
Gasoline: Submerged Loading (Clean
Tanks)
Tank Trucks in Transit
The EPA calculated VOC emissions from Tank Trucks in Transit by multiplying county-level tank truck gasoline
throughput by a 0.06 lb of VOC per 1,000 gallon emission factor. As noted in Table 3-46, this emission factor is
the sum of the individual emission factors reported in the Gasoline Distribution EIIP guidance document for
gasoline-filled trucks (traveling to service station/bulk plant for delivery) and vapor-filled trucks (traveling to bulk
terminal/plant for reloading) [ref 3], County-level gasoline consumption was estimated by summing county-level
onroad and nonroad estimates. County-level onroad consumption was estimated by subtracting the NMIM-
derived national nonroad consumption from the ElA's estimate of finished motor gasoline supplied and then
allocating to counties using NMIM-derived onroad county-level C02 emissions [ref 11, ref 13], County-level
nonroad consumption was estimated by allocating NMIM-derived state/SCC-level nonroad gasoline
consumption to the county-level based on nonroad county/SCC-level C02 emissions [ref 13], Gasoline
throughput for tank trucks was computed by multiplying the county-level gasoline consumption estimates by a
factor of 1.09 to account for gasoline that is transported more than once in a given area (i.e., transported from
bulk terminal to bulk plant and then from bulk plant to service station) [ref 10], Benzene emission estimates
were calculated by multiplying county-level NMIM speciation profiles by the VOC emission estimates [ref 12],
Emissions for the remaining HAPs were calculated by multiplying VOC emissions by the national speciation
profiles presented in Table 3-47.
Table 3-46: Tank Trucks in Transit VOC Emission Factors
VOC Emission Factor
Vapor-Filled Trucks
0.055 lb/1,000 gallons
Gasoline Filled Trucks
0.005 lb/1,000 gallons
Total
0.06 lb/1,000 gallons
Table 3-47: Tan
< Trucks in Transit HAP Speciation Profiles and Total Emission Estimates
Pollutant
Pollutant
Code
Emission Factor
Reference
National Emissions
(tpy)
VOC
VOC
0.06 Ib./l,000 gallons
3
4.51E+03
2,2,4-Trimethylpentane
540841
0.75% of VOC
7
3.38E+01
Cumene
98828
0.012% of VOC
7
5.41E-01
Ethyl Benzene
100414
0.053% of VOC
7
2.39E+00
98
-------
Pollutant
Pollutant
Emission Factor
Reference
National Emissions
Code
(tpy)
n-Hexane
110543
1.8% of VOC
7
8.11E+01
Naphthalene
91203
0.00027% of VOC
7
1.22E-02
Toluene
108883
1.4% of VOC
7
6.31E+01
Xylenes
1330207
0.56% of VOC
7
2.52E+01
Benzene
71432
county-specific % of VOC
12
3.13E+01
It is important to reiterate that the above discussion addresses the calculation of total VOC emissions. The 2008
point source NEI reports VOC emissions related to tank trucks in transit. To obtain nonpoint emissions, States
should subtract the 2008 point source VOC emission estimates from the total VOC emission estimates reported
here. The relevant point source SCCs are listed in Table 3-48; the SCC level 1 description is "Petroleum and
Solvent Evaporation" for all SCCs.
Table 3-48: Tank Trucks in Transit Point Source SCCs
SCC
SCC Level 2
SCC Level 3
SCC Level 4
40400154
Petroleum Liquids Storage
(non-Refinery)
Bulk Terminals
Tank Truck Vapor Leaks
40400254
Petroleum Liquids Storage
(non-Refinery)
Bulk Plants
Tank Truck Vapor Losses
40600162
Transportation and Marketing
of Petroleum Products
Tank Cars and
Trucks
Gasoline: Loaded with
Fuel (Transit Losses)
40600163
Transportation and Marketing
of Petroleum Products
Tank Cars and
Trucks
Gasoline: Return with
Vapor (Transit Losses)
Underground Storage Tank (UST) Breathing and Emptying
The EPA calculated VOC emissions from UST breathing and emptying by multiplying county-level total gasoline
consumption, calculated as described above in the Tank Trucks in Transit section, by the 1 lb/1,000 gallons
emission factor recommended by the Gasoline Distribution EIIP guidance document [ref 3], With the exception
of benzene, HAP emissions were estimated by multiplying VOC emissions by the national HAP speciation profiles
listed in Table 3-49. To estimate benzene emissions, EPA multiplied VOC emissions by county-level speciation
profiles from NMIM [ref 12],
Table 3-49: Underground Storage Tank (UST) Breathing and Emptying Emissions
Pollutant
Pollutant
Code
Emission Factor
Reference
National Emissions
(tpy)
VOC
VOC
1 lb./l,000 gallons
3
6.89E+04
2,2,4-Trimethylpentane
540841
0.75% of VOC
7
5.17E+02
Cumene
98828
0.012% of VOC
7
8.27E+00
Ethyl Benzene
100414
0.053% of VOC
7
3.65E+01
n-Hexane
110543
1.8% of VOC
7
1.24E+03
Naphthalene
91203
0.00027% of VOC
7
1.86E-01
Toluene
108883
1.4% of VOC
7
9.65E+02
Xylenes
1330207
0.56% of VOC
7
3.86E+02
Benzene
71432
county-specific % of VOC
12
4.78E+02
99
-------
It is important to reiterate that the above discussion addresses the calculation of total VOC emissions. The 2008
point source NEI reports VOC emissions related to UST breathing and emptying. To obtain nonpoint emissions,
States should subtract the 2008 point source VOC emission estimates from the total VOC emission estimates
reported here. The relevant point source SCCs are listed in Table 3-50; SCC level 1 and 2 descriptions are
"Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products" for both SCCs.
Table 3-50: UST Breathing and Emptying Point Source SCCs
SCC
SCC Level 3
SCC Level 4
40600307
Gasoline Retail Operations - Stage 1
Underground Tank Breathing and Emptying
40600707
Consumer (Corporate) Fleet
Refueling - Stage 1
Underground Tank Breathing and Emptying
Gasoline Service Station Unloading
Stage I gasoline distribution emissions also occur when gasoline vapors are displaced from storage tanks during
unloading of gasoline from tank trucks at service stations (Gasoline Service Station Unloading). States vary in
whether these emissions are reported to point or nonpoint. The gasoline service station unloading sector
includes data from the S/L/T agency submitted data and the default EPA generated emissions. The agencies
listed in Table 3-36 submitted emissions for this sector.
The EPA estimated uncontrolled VOC emissions from unloading of gasoline into service station tanks from
county-level total gasoline consumption estimates, calculated as described above in the Tank Trucks in Transit
section, and the following AP-42 equation:
L = (12.46 x S x P x M)/T
where:
L = uncontrolled loading loss of liquid loaded (in lb/1,000 gallons)
S = saturation factor;
P = true vapor pressure of liquid loaded (pounds per square inch absolute);
M = molecular weight of vapors (lbs per lb/mole); and
T = temperature of liquid loaded (Rankine) [ref 14].
This equation requires geographic-specific information. This information includes the saturation factor, which
differs by method of loading (e.g., submerged filling), Reid vapor pressure (RVP), temperature, and true vapor
pressure of gasoline.
Gasoline RVP values were obtained from the NMIM 2008 database. Because NMIM is a county-level database
that reports RVP values by month, EPA developed county-level monthly gasoline consumption estimates by
multiplying annual county gasoline consumption by monthly allocation factors. State-level monthly allocation
factors were developed from monthly gasoline sales data reported in the Federal Highway Administration's
Highway Statistics 2008 [ref 15], Geographic-specific information on the temperature of gasoline and the
method of loading were obtained from a Stage I and II gasoline emission inventory study prepared for the EIIP
[ref 16],
The true vapor pressure of gasoline was estimated for each county/month using the following equation:
100
-------
0 "553 -
413 0
P — exp
where:
P
T
RVP
S
+
¦vJ-459 6.
2,416
-459.6
2.013
|5'"log m(MVP)~
log i: \K1T ) —
1.85- -
i ,742 1
1.0-^2
J ^ 459 .6
i -t- 459 .6
+ 15.64
.S'
Stock true vapor pressure, in pounds per square inch absolute.
Stock temperature, in degrees Fahrenheit.
Reid vapor pressure, in pounds per square inch.
Slope of the ASTM distillation curve at 10 percent evaporated, in degrees Fahrenheit per percent
(assumed that S = 3.0 for gasoline per Figure 7.1-14a of AP-42) [ref 17],
This equation was used to calculate monthly county-level true vapor pressure estimates. In cases where more
than one filling method was assumed to apply in a county (e.g., due to vapor balancing requirement applying to
a portion of a county's total gasoline throughput due to a throughput exemption), EPA developed two sets of
calculations for each month, one for each filling method.
The EIIP study regional stock temperature information was used to estimate the temperature of gasoline in each
county in each month (see Table 3-51) [ref 16],
Table 3-51: Temperature Data Used in Estimating True Vapor Pressure (§F)
Region
Jan
Feb
Mar
Apr
May
June
July
Aug
Sep
Oct
Nov
Dec
1 (Northeast)
46
44
44
48
57
64
70
73
70
64
60
51
2 (Southeast)
66
67
69
74
78
81
80
81
80
77
69
60
3 (Southwest)
60
61
62
66
73
78
81
84
82
78
71
62
4 (Midwest)
33
35
40
47
55
62
71
73
68
65
64
63
5 (West)
50
52
62
66
73
76
80
83
86
84
73
60
6 (Northwest)
49
50
50
52
57
62
67
72
68
60
49
42
Region 1: Alaska, Connecticut, Delaware, DC, Illinois, Indiana, Kentucky, Maine, Maryland, Massachusetts, Michigan, New Hampshire,
New Jersey, New York, Ohio, Pennsylvania, Rhode Island, Vermont, Virginia, West Virginia, Wisconsin
Region 2: Alabama, Arkansas, Florida, Georgia, Hawaii, Louisiana, Mississippi, N. Carolina, S. Carolina, Tennessee
Region 3: Arizona, New Mexico, Oklahoma, Texas
Region 4: Colorado, Iowa, Kansas, Minnesota, Missouri, Montana, Nebraska, N. Dakota, S. Dakota, Wyoming
Region 5: California, Nevada, Utah
Region 6: Idaho, Oregon, Washington
The EPA incorporated the effect of Stage I Gasoline Service Station vapor balancing controls based on the
county-level control efficiency values (either 90 or 95 percent) that were compiled for the EIIP study [ref 16],
Table 3-52 presents the HAP speciation profiles and total VOC and HAP emission estimates calculated using
these procedures.
Emissions are reported by SCC based on the filling methods used in each county as determined from the EIIP
study: SCC 2501060051 (Submerged Filling); SCC 2501060052 (Splash Filling); and SCC 2501060053 (Balanced
Submerged Filling).
101
-------
Table 3-52: Stage I Service Station Unloading HAP Speciation Profiles and Total Emission Estimates
Pollutant
Pollutant
Code
Emission Factor
Reference
National Emissions
(tpy)
VOC
VOC
Equation 1
14
3.82E+05
2,2,4-Trimethylpentane
540841
0.75% of VOC
7
2.86E+03
Cumene
98828
0.012% of VOC
7
4.58E+01
Ethyl Benzene
100414
0.053% of VOC
7
2.02E+02
n-Hexane
110543
1.8% of VOC
7
6.87E+03
Naphthalene
91203
0.00027% of VOC
7
1.03E+00
Toluene
108883
1.4% of VOC
7
5.35E+03
Xylenes
1330207
0.56% of VOC
7
2.14E+03
Benzene
71432
county-specific % of VOC
12
2.97E+03
It is important to reiterate that the above discussion addresses the calculation of total VOC emissions. The 2008
point source NEI reports VOC emissions related to service station unloading. To obtain nonpoint emissions,
States should subtract the 2008 point source VOC emission estimates from the total VOC emission estimates
reported here. The relevant point source SCCs are listed in Table 3-53, Table 3-54 and Table 3-55; the SCC level 1
and 2 description for all SCCs in these tables is "Petroleum and Solvent Evaporation; Transportation and
Marketing of Petroleum Products".
Table 3-53: Service Station Unloading: Submergec
Fill Point Source SCCs
SCC
SCC Level 3
SCC Level 4
40600302
Gasoline Retail Operations - Stage 1
Submerged Filling w/o Controls
40600702
Consumer (Corporate) Fleet Refueling - Stage 1
Submerged Filling w/o Controls
Table 3-54: Service Station Unloading: Splash Fill Point Source SCCs
SCC
SCC Level 3
SCC Level 4
40600301
Gasoline Retail Operations - Stage 1
Splash Filling
40600701
Consumer (Corporate) Fleet Refueling - Stage 1
Splash Filling
Table 3-55: Service Station Unloading: Balanced Submerged Fill Point Source SCCs
SCC
SCC Level 3
SCC Level 4
40600305
Gasoline Retail Operations - Stage 1
Unloading **
40600306
Gasoline Retail Operations - Stage 1
Balanced Submerged Filling
40600706
Consumer (Corporate) Fleet Refueling - Stage 1
Balanced Submerged Filling
""Unloading emissions might also be reported in the point source inventory under SCC 40600399 (Gasoline Retail Operations - Stage I,
Not Classified).
Example Emission Calculations
Bulk Terminals
2008 national benzene emissions = VOC emissions x HAP speciation factor
1.65E+05 tons x 0.0027
4.46E+02 tons
Pipelines
2008 national cumene emissions = VOC emissions x HAP speciation factor
102
-------
9.58E+04 tons x 0.00012
1.15E+01tons
Bulk Plants
2008 national VOC emissions
= national gasoline consumption x proportion passing through bulk plants x VOC emission factor
= 137,801,370 thousand gallons x 0.09 x 8.62 lbs. VOC/thousand gallons
= 1.07E+08 lbs. / 2000 lbs.
= 5.35E+04 tons
Tank Trucks in Transit
2008 Alamance County, North Carolina VOC emissions
= total county gasoline consumption x (1+proportion of gasoline transported twice) x VOC emission factor
= 61,446 thousand gallons x (1+0.09) x 0.06 lbs. VOC/thousand gallons
= 4.02E+03 lbs. / 2000 lbs.
= 2.01E+00 tons
UST Breathing and Emptying
2008 Alamance County, North Carolina VOC emissions
= total county gasoline consumption x VOC emission factor
= 61,466 thousand gallons x 1 lb. VOC/thousand gallons
= 6.15E+04 lbs. / 2000 lbs.
= 30.73E+00 tons
Stage I Gasoline Service Station Unloading - uncontrolled VOC emissions in July for balanced submerged fill
unloading in Alamance County, NC
= annual county consumption x proportion of annual gasoline sold in July x VOC emission factor
= 61,466 thousand gallons x 0.1087 x VOC emission factor
= 6,681 thousand gallons x ((12.46 x saturation factor x true vapor pressure x vapor molecular weight) /
temperature))
= 6,681 thousand gallons x ((12.46 x 1.0 x 6.309 x 67.811) / 540)
= 65,950 lbs
Incorporate effect of control (vapor balancing requirement)
= Uncontrolled emissions x ((100-CE)/100)
= 65,950 lbs x ((100-90)/100)
= 6,595 lbs/2,000 lbs
= 3.30E+00 tons
3.5.5 References for Bulk Gasoline Terminals and Gas Stations
1. U.S. Environmental Protection Agency, "National Emission Standards for Source Categories: Gasoline
Distribution (Stage I), 40 CFR Part 63." Office of Air Quality Planning and Standards, February 28, 1997.
Pages 9087-9093.
2. U.S. Environmental Protection Agency, "Gasoline Distribution Industry (Stage l)-Background Information
for Proposed Standards," EPA-453/R94-002a, Office of Air Quality Planning and Standards, January 1994.
103
-------
3. Eastern Research Group, Inc., "Volume III: Chapter 11, Gasoline Marketing (Stage I and Stage II), Revised
Final," prepared for the Emission Inventory Improvement Program, January 2001.
4. U.S. Environmental Protection Agency, "TANKS Emission Estimation Software," Office of Air Quality
Planning and Standards, Emission Inventory Group, last updated October 29, 2004.
5. 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.
6. U.S. Department of Energy, Energy Information Administration, "U.S. Daily Average Supply and
Distribution of Crude Oil and Petroleum Product e 2 in Petroleum Supply Annual 2008, Volume 1,
released June 2009.
7. Hester, Charles, MACTEC, Inc. Memorandum from Charles Hester, MACTEC, Inc., to Stephen Shedd, U.S.
Environmental Protection Agency, Office of Air Quality Planning and Standards, Emission Standards
Division, "Review of Data on HAP Content in Gasoline," May 18, 2006.
8. U.S. Department of Commerce, Bureau of the Census, County Business Patterns 2007, released July
2009.
9. U.S. Department of Energy, Energy Information Administration, "Refinery, Bulk Terminal, and Natural
Gas Plant Stocks of Selected Petroleum Products by PAD District and State, 2008" and "Movements of
Crude Oil and Petroleum Products by Pipeline Between PAD Districts, 2008," Tables 33 and 35 in
Petroleum Supply Annual 2008, Volume 1. released June 2009.
10. 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.
11. U.S. Department of Energy, Energy Information Administration, Petroleum Navigator - Product
Supplied, accessed January 2010.
12. Benzene speciation profiles calculated by Jonathan Dorn, E.H. Pechan and Associates, Inc. from county-
level VOC and benzene emissions developed from a 2008 NMIM run. The NMIM run was performed by
John Van Bruggen, E.H. Pechan and Associates, Inc., January 2010.
13. 2008 NMIM runs performed by John Van Bruggen and Melissa Spivey, E.H. Pechan and Associates, Inc.,
January 2010.
14. U.S. Environmental Protection Agency, "Compilation of Air Pollutant Emission Factors, AP-42, Fifth
Edition, Volume I: Stationary Point and Area Sources, Section 5.2 Transportation and Marketing of
Petroleum Liquids," Office of Air Quality Planning and Standards, January 1995.
15. Federal Highway Administration, "Monthly Gasoline/Gasohol Reported by States," Table MF-33GA in
Highway Statistics 2008, Office of Highway Policy Information, accessed January 2010.
16. Pacific Environmental Services, Inc., "Draft Summary of the Analysis of the Emissions Reported in the
1999 NEI for Stage I and Stage II Operations at Gasoline Service Stations," prepared for the U.S.
Environmental Protection Agency and the Emission Inventory Improvement Program, September 2002.
17. U.S. Environmental Protection Agency, "Compilation of Air Pollutant Emission Factors, AP-42, Fifth
Edition, Volume I: Stationary Point and Area Sources, Chapter 7: Liquid Storage Tanks," Office of Air
Quality Planning and Standards, Emission Inventory Group, September 1997.
104
-------
3.6 Commercial Cooking
3.6.1 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. The 2011 NEI has emissions for the SCCs in Table 3-56; EPA
computes emissions for all except the first one (2302002000), since it's a grouping of the two more detailed
SCCs for charbroiling.
Table 3-56: SCCs used in the Commercial Cooking sector
see
El Sector
SCC Level One
SCC Level Two
SCC Level Three
SCC Level Four
Commercial
Industrial
Food and Kindred
Commercial Cooking
Charbroiling
2302002000
Cooking
Processes
Products: SIC 20
- Charbroiling
Total
Commercial
Industrial
Food and Kindred
Commercial Cooking
Conveyorized
2302002100
Cooking
Processes
Products: SIC 20
- Charbroiling
Charbroiling
Commercial
Industrial
Food and Kindred
Commercial Cooking
Under-fired
2302002200
Cooking
Processes
Products: SIC 20
- Charbroiling
Charbroiling
Commercial
Industrial
Food and Kindred
Commercial Cooking
Flat Griddle
2302003100
Cooking
Processes
Products: SIC 20
- Frying
Frying
Commercial
Industrial
Food and Kindred
Commercial Cooking
2302003000
Cooking
Processes
Products: SIC 20
- Frying
Deep Fat Frying
Commercial
Industrial
Food and Kindred
Commercial Cooking
Clamshell
2302003200
Cooking
Processes
Products: SIC 20
- Frying
Griddle Frying
3.6.2 Sources of data overview and selection hierarchy
The commercial cooking sector includes data from the S/L/T agency submitted data, the EPA PM Augmentation
data, the EPA Chromium Split data, the EPA HAP Augmentation data, and the default EPA generated commercial
cooking emissions. This sector is only present in the nonpoint data category. The agencies listed in Table 3-57
submitted emissions for this sector. EPA datasets are individually listed. Where only zero emissions were
submitted (sum across all pollutants submitted), these are shown as zeroes ("0") in the table.
Table 3-57: Agencies t
lat submitted Commercial Cooking c
ata
Char-
Convey-
-orized
Char-
broiling
Under-
fired
Char-
broiling
Deep
Flat
Clamshell
Agency
Type
broiling
Total
Fat
Frying
Griddle
Frying
Griddle
Frying
EPA Chromium Speciation
EPA
X
EPA HAP Augmentation
EPA
X
X
X
X
EPA Commercial Cooking data (Section 3.6.4)
EPA
X
X
X
X
X
EPA PM Augmentation
EPA
X
X
X
0
X
X
California Air Resources Board
S
X
Clark County Department of Air Quality and
Environmental Management
L
X
X
X
X
X
Coeur d'Alene Tribe
T
X
X
X
X
X
DC-District Department of the Environment
S
X
X
X
X
X
Delaware Department of Natural Resources and
Environmental Control
S
X
X
X
X
X
105
-------
Char-
Convey-
-orized
Char-
broiling
Under-
fired
Char-
broiling
Deep
Flat
Clamshell
Agency
Type
broiling
Total
Fat
Frying
Griddle
Frying
Griddle
Frying
Hawaii Department of Health Clean Air Branch
S
X
X
X
X
X
Idaho Department of Environmental Quality
S
X
X
X
X
X
Illinois Environmental Protection Agency
s
X
X
X
X
X
Kansas Department of Health and Environment
s
X
X
X
X
X
Kootenai Tribe of Idaho
T
X
X
X
X
X
Maricopa County Air Quality Department
L
X
X
X
X
X
Maryland Department of the Environment
S
X
X
X
X
X
Memphis and Shelby County Health
Department - Pollution Control
L
X
X
Minnesota Pollution Control Agency
S
X
X
X
X
X
New Jersey Department of Environment
Protection
S
X
X
X
X
X
New York State Department of Environmental
Conservation
s
X
X
X
X
X
Nez Perce Tribe
T
X
X
X
X
X
Shoshone-Bannock Tribes of the Fort Hall
Reservation of Idaho
Texas Commission on Environmental Quality
s
X
X
X
X
X
Virginia Department of Environmental Quality
s
X
X
X
X
X
West Virginia Division of Air Quality
s
X
X
X
X
X
Table 3-58 shows the selection hierarchy for the datasets included in the commercial cooking sector.
Table 3-58: 2011 NEI Commercial Cooking data selection hierarchy
Priority
Dataset Name
Dataset Content
l
Responsible Agency Data Set
State and Local Agency submitted emissions
2
2011EPA_PM-Augmentation
Augments PM data in 47 states and some tribes
3
2011EPA_chrom_split
Splits total chromium into speciated chromium in 37
states
4
2011EPA_HAP-Augmentation
Adds Pb and other HAP emissions in 46 states
5
2011EPA_NP_NoOverlap_w_Pt
EPA-generated data
106
-------
3.6.3 Spatial coverage and data sources for the sector
Commercial Cooking Commercial Cooking
v
In V
1 n ;
P - Point
N - Nonpoint y
PN - P&N
P - Point
N - Nonpoint
PN - P&N
All CAPS
I I CTA
All HAPs EPA SLT EPA & SLT
3.6.4 EPA-developed commercial cooking emissions data
The approach to estimating emissions from commercial cooking in 2011 consists of three genera! steps, as
follows:
• Determine county-level activity, i.e., the number of restaurants in each county in 2011;
• 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.
Activity Data
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 full-service restaurants (NAICS 722110) and limited-
service restaurants (722211) in each county. The 2002 NEI, which is the most recent inventory in which the
emissions from commercial cooking were estimated using restaurant-level data, rather than population data,
used the Dun and Bradstreet industry database, which contains more specific information on the type of
restaurant in each county. The documentation from the 2002 NEI [ref 2] identifies five specific categories of
restaurants that are likely to have the equipment that matches 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 2011 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 equation 1:
REST 2002= Eijm,2002 ^
' FRACjXUNlTSjXAVG_EMIS SI ONSjm y '
where:
/?£5T,;2002
Eijm,2002
FRACj
-------
UNITS, = the average number of units of source category j in each restaurant
AVG_EMISSIONS^ = the average emissions of pollutant m from food cooked on source category j,
based on summing the average amount of food cooked on source category j
multiplied by the emission factor for pollutant m from source category j
The values of FRACir and UNITS,, as well as the average amount of food cooked on each type of source category
equipment used to calculate AVG_EMISSIONSJm, came from Potepan [ref 3], The emission factors used to
calculate AVG_EMISSIONSJm are from the 2002 NEI documentation [ref 2],
Next the change in the number of restaurants in each county between 2002 and 2011 was determined using
data from the U.S. Census Bureau County Business Patterns database [ref 1] to create a growth factor. For
example, if the number of restaurants in a particular county increased from 100 to 125 between 2002 and 2011,
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 2011:
REST,,2oii = RESTi,2002 XGF, (2)
where 6F,-is the growth factor for county /'.
Emission Factors
Emission factors for each pollutant for each type of commercial cooking equipment (EFJmn) came from the 2002
NEI documentation [ref 1], This information remains the most complete catalog of emission factors for
commercial cooking; a recent review of the literature on emissions from cooking [ref 4] revealed no new studies
with a similar breadth of pollutants analyzed. The particulate matter (PM) emission factors from the 2002
documentation only contain primary PM. The emission factors for filterable PM were derived by applying ratios
to primary PM (Table 3-59). The condensable particulate matter (PM-CON) emission factors were derived by
subtracting PM10-FIL from PM10-PRI.
Table 3-59: Ratio o
filterable PM to primary PM for PM2.s and PMW by SCC.
Cooking Device
see
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
Emissions
After determining the number of establishments in 2011 using Equation 2, Equation 3 provides the amount of
emissions in 2011 by rearranging Equation 1:
Eijm,2011 = RESTii20u X FRACj x UNlTSj x AV G _E miss ions jm (3)
where £,ym,2on is the emissions of pollutant m from commercial equipment j in county i in 2011.
The fraction of restaurants with commercial cooking equipment (FRAQ) and the average units of equipment per
restaurant (UNlTSj) were obtained from Potepan [ref 3], Because Potepan reports the fraction of restaurants
with commercial cooking equipment broken down by subcategories of restaurant types (Ethnic food
restaurants, Fast food restaurants, Family restaurants, Seafood restaurants, and Steak & Barbecue restaurants),
108
-------
a weighted average of these fractions was calculated 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 3-60. The percentage of restaurants with under-fired
charbroilers (12.5%) is similar to a more recent survey [ref 5] in North Carolina, 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 3-60: 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
The number of restaurants in 2011 estimated using Equation 2 was then used in Equation 3 to determine the
quantity of emissions in 2011.
Sample Calculations
Determining the Number of Restaurants in Autauga County. AL in 2002
REST — Ejjm,2002
12002 ~~ FRACj x UNITSj x AVG_EMlSSIONSjm
. ,,,, . . 8.7 6 pM2S,Under fired-Charbroilers
100 restaurants = ————— ——
0.125 x 1.54 x 0.454
Emissions of PM2.sfrom underfired charbroilers in county Autauga County, AL 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), and the average emissions from each establishment from underfired charbroilers
(0.454 tons PM2 s). The result shows that there were approximately 100 restaurants in Autauga County, AL in
2002. This process is repeated for each SCC across all counties.
Determining the Number of Restaurants in Each County in 2011
Using the estimated number of restaurants in 2002, the number of restaurants in 2011 was determined by
employing a growth factor based on the change in the number of restaurants between 2002 and 2011 as
determined by the U.S. Census Bureau County Business Statistics Database.
RESTj,2011 = RESTi.2002 X GF,
138 restaurants = 100 restaurants x 1.38
109
-------
There were 100 restaurants estimated to be in Autauga County, AL in 2002. Data from the U.S. Census Bureau
show that there was a 38% increase in the number of restaurants in Autauga between 2002 and 2011. The
growth factor (1.38) was multiplied by 100 to estimate that there were 138 restaurants in Autauga in 2011. Note
that the actual number of restaurants in 2011 as determined from the U.S. Census Bureau County Business
Statistics database is not equal to RESTuon 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.
Determining the Emissions in 2011
The emissions in 2011 were determined using the following equation:
E_(ijm, 2011) = [REST] _(i, 2011) x [FRACj J x [UNITS] J x [AVG EMISSIONS] Jm
12.06 tons PM2.5 = 138 x 0.125 x 1.54 x 0.454
There were 138 restaurants in Autauga County, AL in 2011. 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), and the average emissions from each establishment from underfired charbroilers (0.454 tons PM2 s). The
result shows that the emissions of PM2 s in Autauga County, AL were 12.06 tons in 2011.
3.6.5 Summary of quality assurance methods
Data analyses involving comparison of emissions between 2011 and 2008 showed no large discrepancies in
emissions from this sector between the two years. However, California submitted some pollutants for this sector
that EPA did not estimate nor did any other states; so, for consistency, these values were tagged and not used in
the 2011 NEI. In addition, Louisiana requested that some values be tagged and not used, because Louisiana had
pulled 2008 data forward for this sector and requested that we use EPA data for 2011 for these emissions
instead. Table 3-61 summarizes the number of tagged process-level emissions values from each agency affected
by this QA. EPA data for CA were tagged to avoid double counting with state data because CA used different
SCCs than EPA did. We noticed a problem with the HAP augmentation applied to commercial cooking in the VA
dataset. In several counties, the selection used some erroneous PM augmentation data instead of the state
submitted data. The errors are small, and these emissions were also not tagged out of 2011 v2; the PM
augmentation methodology should be revised for these SCCs for the next (2014 NEI) inventory cycle.
Table 3-61: Agencies tagged values for Commercial Cooking
Agency
Number of
Values Tagged
Tag Reason
California Air Resources Board
57
Extraneous pollutants (no other states
submitted)
Louisiana Department of Environmental
Quality
988
State requested that we replace their data
with EPA estimates.
3.6.6 References for Commercial Cooking
1. County Business Patterns
2. Environmental Protection Agency (EPA). 2002. Commercial Cooking. From: Documentation for the Final
2002 Nonpoint Sector (FEB 06 version) National Emission Inventory for Criteria and Hazardous Air
Pollutants.
3. Potepan, M. 2001. Charbroiling Activity Estimation. Public Research Institute, report for the California
Air Resources Board and the California Environmental Protection Agency.
110
-------
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. North Carolina Division of Air Quality. 2013. Supplement Section 110(a)(1) Maintenance Plan - February
2013, Appendix B, Section 4.4.4.
3.7 Dust - Construction Dust
3.7.1 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 3-62 lists the SCCs associated with this sector in the 2011 NEI. EPA estimates emissions for the SCCs
covered by the shaded rows in the table.
Table 3-62: SCCs in the 2011 NEI in the Dust - Construction Dust sector
see
see Level
One
SCC Level
Two
SCC Level Three
SCC Level Four
NONPOINT
2311010000
Industrial
Processes
Construction:
SIC 15 -17
Residential
Total
2311020000
Industrial
Processes
Construction.
SIC 15 -17
Industrial/Cominercial/lnstitutional
Totril
2311030000
Industrial
Processes
Construction:
SIC 15 -17
Ro3d Co nsti'U ct 1 o 11
! Ol. d 1
2311040000
Industrial
Processes
Construction:
SIC 15 -17
Special Trade Construction
Total
POINT
31100101
Industrial
Processes
Building
Construction
Construction: Building Contractors
Site Preparation: Topsoil
Removal
31100102
Industrial
Processes
Building
Construction
Construction: Building Contractors
Site Preparation: Earth
Moving (Cut and Fill)
31100103
Industrial
Processes
Building
Construction
Construction: Building Contractors
Site Preparation:
Aggregate Hauling (On
Dirt)
31100199
Industrial
Processes
Building
Construction
Construction: Building Contractors
Other Not Classified
31100202
Industrial
Processes
Building
Construction
Demolitions/Special Trade
Contracts
Mechanical or Explosive
Dismemberment
31100206
Industrial
Processes
Building
Construction
Demolitions/Special Trade
Contracts
On-site Truck Traffic
31100299
Industrial
Processes
Building
Construction
Demolitions/Special Trade
Contracts
Other Not Classified:
Construction/Demolition
3.7.2 Sources of data overview and selection hierarchy
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 3-63 submitted emissions for this sector.
Ill
-------
Table 3-63: Agencies that submitted Construction Dust data
Nonpoint SCCs
Point SCCs
Agency
Type
2311010000
2311020000
2311030000
2311040000
31100101
31100102
31100103
31100199
31100202
31100206
31100299
Allegheny County Health Department
L
X
X
California Air Resources Board
S
X
X
X
X
X
X
Chattanooga Air Pollution Control Bureau
(CHCAPCB)
L
X
Clark County Department of Air Quality and
Environmental Management
L
X
X
X
Coeur d'Alene Tribe
T
X
X
X
Delaware Department of Natural Resources and
Environmental Control
S
X
X
X
Florida Department of Environmental Protection
S
X
Georgia Department of Natural Resources
s
X
X
Hawaii Department of Health Clean Air Branch
s
X
X
X
Idaho Department of Environmental Quality
s
X
X
X
0
X
X
Illinois Environmental Protection Agency
s
X
X
X
X
X
Indiana Department of Environmental
Management
s
X
X
Kansas Department of Health and Environment
s
X
X
X
X
Kentucky Division for Air Quality
s
X
Kootenai Tribe of Idaho
T
X
0
X
Maricopa County Air Quality Department
L
X
X
X
X
Maryland Department of the Environment
s
X
X
X
Metro Public Health of Nashville/Davidson County
L
X
X
X
X
Michigan Department of Environmental Quality
S
X
Missouri Department of Natural Resources
s
X
Nevada Division of Environmental Protection
s
X
New Hampshire Department of Environmental
Services
s
X
New Jersey Department of Environment Protection
s
X
X
X
Nez Perce Tribe
T
X
X
X
Ohio Environmental Protection Agency
s
X
Pennsylvania Department of Environmental
Protection
s
X
Philadelphia Air Management Services
L
X
Puget Sound Clean Air Agency
L
X
Shoshone-Bannock Tribes of the Fort Hall
Reservation of Idaho
T
X
X
X
Texas Commission on Environmental Quality
S
X
Utah Division of Air Quality
s
X
Virginia Department of Environmental Quality
s
X
X
X
X
West Virginia Division of Air Quality
s
X
X
X
Table 3-64 shows the selection hierarchy for datasets included in the construction dust sector.
112
-------
Table 3-64: 2011 NEI Construction Dust data selection hierarchy
Priority
Dataset Name
Dataset Content
Nonpoint Data Category
1
Responsible Agency Data Set
State and Local Agency submitted emissions
2
2011EPA_PM-Augmentation
Augments PM data
5
2011E P A_ N P_ NoOverl a p_w_Pt
EPA-generated data
Point Data Category
1
2011EPA_PM-Augmentation
Augments PM data
2
Responsible Agency Data Set
State and Local agency submitted emissions
3
2011EPA_chrom_split
Speciates S/L/T agency submitted chromium
4
EPA NV Gold Mines
Mercury emissions at Nevada gold mine facilities (likely incorrect
SCC used)
5
2011EPA_TRI
EPATRI data (likely incorrect SCC used)
3.7.3 Spatial coverage and data sources for the sector
Dust - Construction Dust
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
* -
PN
P - Point
N - Nonpoint
PN - P&N
All CAPS EPA SLT ™!EPA& SLT
3.7.4 Construction - Non-Residential - EPA estimates
3.7.4.1 Source ca tegory description
Emissions from non-residential construction activity are a function of the acreage disturbed for non-residential
construction. The SCC that belongs to this sector is provided in Table 3-65.
113
-------
Table 3-65: SCC for Non-Residential Construction
SCC
SCC Level One
SCC Level Two
SCC Level Three
SCC Level Four
2311020000
Industrial Processes
Construction: SIC 15 -17
Heavy Construction
Total
Activity Data
Annual Value of Construction Put in Place in the U.S. [ref 1] has the 2011 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 2], Because some counties employment data were withheld due to privacy
concerns, the following procedure was adopted:
1. State totals for the known county level employees was 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 guess of the midpoint of the range code was used (so for instance in the 1-19 employee range,
a guess of 10 employees would be used) and a state total of the withheld counties was computed.
3. A ratio of guessed employees (Step 2) to withheld employees (Step 1) was then used to adjust the
county level guesses 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 3] lists
costs of the construction industry from 1999-2011.
2011 acres per $106 = 1999 acres per $106 x (1999 PPI / 2011 PPI)
=2 acres/$106 * (132.9 / 229.3)
= 1.159 acres per $106
Emission Factors
Initial PMio emissions from construction of non-residential buildings are calculated using an emission factor of
0.19 tons/acre-month [ref 4], 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
emissions. Once PMlO-xx emissions are developed, PM25-xx emissions are estimated by applying a particle size
multiplier of 0.10 to PMlO-xx emissions.
Regional variances in construction emissions are corrected using soil moisture level and silt content. These
correction parameters are applied to initial PMW 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. A
data base containing county-level dry silt values was complied. These values were derived by applying a
correction factor developed by the California Air Resources Board to convert wet silt values to dry silt values [ref
5],
114
-------
The equation for PMio emissions corrected for soil moisture and silt content is:
24 S
CorrectedEpm 10 = Initial EWI„ x -7-7: •:
where:
Corrected Epmio = PMw emissions corrected for soil moisture and silt content,
PE = precipitation-evaporation value for each State,
S = % dry silt content in soil for area being inventoried.
Once PM10 adjustments have been made, PM2.5 emissions are set to 10% of PM10.
Example Calculation
EmiSSiOnSpMlO = 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 PM10 emission factor (ton/acre-month)
M = duration of construction activity (months)
As an example, in Grand Traverse County, Michigan, 2011 acres disturbed and PM10 emissions from non-
residential construction are calculated as follows:
EmissionspMio = 269,045 x 10s $ x (130/651,996) x 1.159 acres/106$ x EFAdj x M
= 62.2 acres x 0.059 ton/acre-month x 11 months
= 40.4 tons PM10
where EFAdj is calculated as follows:
EFAdj = 0.19 ton/acre-month * (24/103.6 * 12/9)
= 0.059 ton/acre-month
3.7.4.2 References for Construction - Non-Residential
1. US Census Bureau, 2011. Annual Value of Construction Put in Place
2. County Business Patterns
3. Bureau of Labor Statistics: Table BMNR
4. Midwest Research Institute. Improvement of Specific Emission Factors Emission Factors (BACM Project
No. 1). Prepared for South Coast Air Quality Management District. March 29, 1996.
5. Campbell, 1996: Campbell, S.G., D.R. Shimp, and S.R. Francis. Spatial Distribution of PM-10 Emissions
from Agricultural Tilling in the San Joaquin Valley, pp. 119-127 in Geographic Information Systems in
Environmental Resources Management, Air and Waste Management Association, Reno, NV. 1996.
115
-------
3.7.5 Construction - Residential -EPA estimates
3.7,5,1 Source ca tegory description
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. The SCC that belongs to this sector is provided
in Table 3-66.
Table 3-66: SCC for Residential Construction
SCC
SCC Level One
SCC Level Two
SCC Level Three
SCC Level Four
2311010000
Industrial Processes
Construction: SIC 15 -17
General Building
Construction
Total
Activity Data
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 2010 data for Housing Starts - New Privately Owned Housing Units Started [ref 6]
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. To get 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 listed may be made up of more than one structure. New Privately Owned Housing Units
Authorized Unadjusted Units [ref 7] 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 8] has
2010 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 3-67 provides surface areas that were assumed disturbed for each unit
type.
Table 3-67: 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, 2010 Characteristics of New Houses [ref 9] 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 in the region that have a basement to get
116
-------
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 per basement.
Emission Factors
Initial PMio emissions from construction of single family, two family, and apartments structures are calculated
using the emission factors given in Table 3-68 [ref 10], 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 3-68: 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 w/o Basements
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
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 [ref 11], 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. A
data base containing county-level dry silt values was compiled. These values were derived by applying a
correction factor developed by the California Air Resources Board to convert wet silt values to dry silt values [ref
12].
The equation for PMio emissions corrected for soil moisture and silt content is:
24 S
Corrected EPM10 = Initial EPM10 x — x —
rh 9%
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-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 = ]>( Aunit xTCOnstruction x EFunit) X AdjpM
where Aunit = HSjnit x SMUnit
HSunit = Regional Housing Starts x (county building permits/Regional building permits)
117
-------
SMunit
Construction
Area or volume of soil moved for the given unit type
Construction time (in months) for given unit type
Unadjusted emission factor for PMiofor the given unit type
PM Adjustment factor
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 = 247,000 x (211/232,280) x 0.907{Fraction without basement) * 0.25 acres/unit
= 203 units * 0.25 acres/unit = 50.9 acres
AdjpM =(24/110.1) *(10/9) = 0.242
PM 10 Emissions - (50.9 acres x 6 months x 0.032 tons PMio/acre-month) x 0.242 - 2.37 tons PMio
Summary of Quality Assurance Methods
Data analyses involving comparison of emissions between 2011 and 2008 showed no large discrepancies in
emissions from this sector between the two years.
3,7.5.2 References for Construction - Residential
6. New Privately Owned Housing Units Started for 2010 (Not seasonally adjusted).
7. Table 2au. New Privately Owned Housing Units Authori; ' adjusted Units for Regions, Divisions, and
States, Annual 2010.
8. Annual Housing Units Authorized by Building Permits CO2010A, purchased from US Department of
10. Midwest Research Institute. Improvement of Specific Emission Factors (BACM Project No. 1). Prepared
for South Coast Air Quality Management District. March 29, 1996.
11. Campbell, 1996: Campbell, S.G., D.R. Shimp, and S.R. Francis. Spatial Distribution of PM-10 Emissions
from Agricultural Tilling in the San Joaquin Valley, pp. 119-127 in Geographic Information Systems in
Environmental Resources Management, Air and Waste Management Association, Reno, NV. 1996.
12. "Proposed Revisions to Fine Fraction Ratios Used for AP-42 Fugitive Dust Emission Factors," C. Cowherd,
J. Donaldson and R. Hegarty, Midwest Research Institute; D. Ono, Great Basin UAPCD.
3.7.6 Construction - Road- EPA estimates
Activity data for 2011 were not yet available when developing the 2011 NEI. Therefore, emissions from road
construction were not recalculated for the 2011 NEI. Instead, emissions in 2011 are assumed to be the same as
emissions in 2008. The methodology for estimating road construction emissions in 2008 is presented below.
3.7.6.1 Source ca tegory description
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). The
SCC that belongs to this sector is provided in Table 3-69.
Census
9. Type of Foundation in New One-Family Houses Completed.
Tab e 3-69: SCC for Road Construction
SCC
SCC Level One
SCC Level Two SCC Level Three SCC Level Four
2311030000 Industrial Processes
Construction: SIC 15 -17 Road Construction Total
118
-------
Activity Data
The Federal Highway Administration has Highway Statistics, Section IV - Highway Finance, Table SF-12A, State
Highway Agency Capital Outlay [ref 13] for 2008 which outlines spending by state in several different categories.
For this SCC, the following columns are used: New Construction, Relocation, Added Capacity, Major Widening,
and Minor Widening. These columns are also differentiated according to the following six classifications:
1. Interstate, urban
2. Interstate, rural
3. Other arterial, urban
4. Other arterial, rural
5. Collectors, urban
6. Collectors, rural
The State expenditure data are then converted to new miles of road constructed using $/mile conversions
obtained from the North Carolina Department of Transportation (NCDOT) in 2000. A conversion of $4
million/mile is applied to the interstate expenditures. For expenditures on other arterial and collectors, a
conversion factor of $1.9 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 3-70.
Table 3-70: Spending per mile and acres disturbed per mile by highway type
Road Type
Thousand
Dollars per
mile
Total Affected
Roadway Width
(ft)* [ref 3]
Acres Disturbed
per mile [ref 3]
Urban Areas, Interstate
4,000
125
15.2
Rural Areas, Interstate
4,000
125
15.2
Urban Areas, Other Arterials
1,900
125
15.2
Rural Areas, Other Arterials
1,900
105
12.7
Urban Areas, Collectors
1,900
81
9.8
Rural Areas, Collectors
1,900
65
7.9
*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 3-70 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 14] 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.
Emission Factors
Initial PMio emissions from construction of roads are calculated using an emission factor of 0.42 tons/acre-
month [ref 15], 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.
119
-------
Regional variances in construction emissions are corrected using soil moisture level and silt content. These
correction parameters are applied to initial PMw emissions from road construction to develop the final
emissions inventory.
To account for the soil moisture level, the PMw 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 16],
To account for the silt content, the PMW emissions are weighted using average silt content for each county. A
data base containing county-level dry silt values was complied. These values were derived by applying a
correction factor developed by the California Air Resources Board to convert wet silt values to dry silt values [ref
15].
The equation for PMw emissions corrected for soil moisture and silt content is:
24 S
Corrected EPMW = Initial EPM10 x — x ^7
where: Corrected Epmio = PM10 emissions corrected for soil moisture and silt content,
PE = precipitation-evaporation value for each State,
S = % dry silt content in soil for area being inventoried.
Once PMw adjustments have been made, PM2.5 emissions are set to 10% of PM10. Primary PM emissions are
equal to filterable emissions since there are no condensable emissions from road construction.
Example Calculation
EmissionspMio = £(HDrt x MC,t x AC„) 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
m
-n
>
,0.
1!
Adjusted PM10 Emission Factor
M
duration of construction activity
As an example in 2010, in Newport County, Rhode Island, acres disturbed and PM10 emissions from urban
interstate and urban other arterial road construction are calculated as follows:
EmissionspMio = ]>(HD„ x MC„ x AC„) x (HSCounty / HSstate) x EFAdj x M
= ($35,474/$4,000/mi x 15.2 acres/mi) * (187/1058) + ($21,332/$l,600/mi x 15.2
acres/mi) * (187/1058)
= 54 acres x 0.28ton/acre-month x 12 months
= 181.4 tons PM10
where EFAdj is calculated as follows:
EFa^ = 0.42 ton/acre-month * (24/110.1 * 33/9)
= 0.28 ton/acre-month
120
-------
3.7.6.2 References for Construction - Road
13. 2008 Highway Spending.
14. 2008 Building Permits data from US Census "BPS01".
15. Midwest Research Institute. Improvement of Specific Emission Factors (BACM Project No. 1). Prepared
for South Coast Air Quality Management District. March 29, 1996.
16. Campbell, 1996: Campbell, S.G., D.R. Shimp, and S.R. Francis. Spatial Distribution of PM-10 Emissions
from Agricultural Tilling in the San Joaquin Valley, pp. 119-127 in Geographic Information Systems in
Environmental Resources Management, Air and Waste Management Association, Reno, NV. 1996.
3.8 Dust - Paved Road Dust
3.8.1 Sector description
The SCCs that belong to this sector are provided in Table 3-71. EPA estimates emissions for particulate matter for
the first SCC in this table.
Table 3-71: SCCs used for Paved Road Dust - 2011 NEI
SCC
SCC Level 1
SCC Level 2
SCC Level 3
SCC Level 4
2294000000
Mobile Sources
Paved Roads
All Paved Roads
Total: Fugitives
2294005000
Mobile Sources
Paved Roads
Interstate/Arterial
Total: Fugitives
2294010000
Mobile Sources
Paved Roads
All Other Public Paved Roads
Total: Fugitives
3.8.2 Sources of data overview and selection hierarchy
The paved road dust sector includes data from the S/L/T agency submitted data and the default EPA generated
paved road dust emissions. The agencies listed in Table 3-72 submitted emissions for this sector. Table 3-73
shows the selection hierarchy for the datasets included in the paved road dust sector.
Table 3-72: Agencies that submitted Paved Road Dust data
All Other
Public
All
Paved
Paved
Interstate/
AGENCY
Type
Roads
Roads
Arterial
EPA- paved road estimates
EPA
X
EPA- PM-augmentation
EPA
X
X
X
Bishop Paiute Tribe
T
X
California Air Resources Board
S
X
Clark County Department of Air Quality and
Environmental Management
L
X
Coeur d'Alene Tribe
T
X
Colorado Department of Public Health and Environment
S
X
Delaware Department of Natural Resources and
Environmental Control
S
X
Hawaii Department of Health Clean Air Branch
s
X
Idaho Department of Environmental Quality
s
X
Kansas Department of Health and Environment
s
X
Kickapoo Tribe of Indians of the Kickapoo Reservation in
Kansas
T
X
X
121
-------
All Other
Public
All
Paved
Paved
Interstate/
AGENCY
Type
Roads
Roads
Arterial
Kootenai Tribe of Idaho
*r
X
Maricopa County Air Quality Department
L
X
Maryland Department of the Environment
s
X
Metro Public Health of Nashville/Davidson County
L
X
New Hampshire Department of Environmental Services
s
X
New Jersey Department of Environment Protection
s
X
Nez Perce Tribe
T
X
Northern Cheyenne Tribe
T
X
Sac and Fox Nation of Missouri in Kansas and Nebraska
Reservation
*r
X
Shoshone-Bannock Tribes of the Fort Hall Reservation
of Idaho
T
X
Virginia Department of Environmental Quality
s
X
Washington State Department of Ecology
s
X
West Virginia Division of Air Quality
s
X
Table 3-73: 2011 NEI Paved Road Dust data selection hierarchy
Priority
Dataset Name
Dataset Content
1
Responsible Agency Data Set
State and Local Agency submitted emissions
2
2011EPA_PM-Augmentation
Augments PM data in 47 states and some tribes
3
2011EPA_NP_NoOverlap_w_Pt
EPA-generated data
3.8.3 Spatial coverage and data sources for the sector
Dust - Paved Road Dust
r-r-J
P - Point
N - Nonpoint
PN - P&N
All CAPs EPA SLT — EPA&SLT
122
-------
3.8.4 EPA methodology for paved road dust
Fugitive dust emissions from paved road traffic were estimated by EPA 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.
Uncontrolled paved road emissions were calculated by EPA at the county level by roadway type and year. This
was done by multiplying the county/roadway class paved road VMT by the appropriate paved road emission
factor. Next, control factors were applied to the paved road emissions in PMW nonattainment area counties.
Emissions and VMT 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.
Emission Factor Equation
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 = [k x (sL)a91x (W)102] x [l-P/(4 x N)]
paved road dust emission factor (gram [g]/VMT)
particle size multiplier (1 g/VMT for PM10-PRI/-FIL and .25 g/VMT for PM25-PRI/-FIL)
road surface silt loading (g/square meter [m2]) (dimensionless in eq.)
average weight (tons) of all vehicles traveling the road (dimensionless in eq.)
number of days in the year with at least 0.01 inches of precipitation
number of days in the year
where:
E
k
sL
W
P
N
The uncontrolled PM10-PRI/-FIL and PM25-PRI/-FIL emission factors by county, roadway class, and year are
provided in the tab "Emission Factors" in the calculation workbook
"2011 paved roads 2294000000 cap emissions.xlsx", available at. They are provided both utilizing the
precipitation correction and without it, as needed for emissions modeling.
Paved road silt loadings were assigned to each of the twelve functional roadway classes (six urban and six rural)
based on the average annual traffic volume of each functional system by State [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 was calculated by dividing an estimate of VMT by functional road length. The resulting paved
road silt loadings calculated from the average annual traffic volume data are shown in Table 3-74.
Table 3-74: 2011 Si
t loadings by state and roadway class used in paved road emission factor calculations (g/m2
Rural
Urban
State
Interstate
Other
Principal
Arterial
Minor
Arterial
Major
Collector
Minor
Collector
Local
Interstate
Other
Freeways
and
Other
Principal
Arterial
Minor
Arterial
Collectors
Local
Alabama
0.015
0.06
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Alaska
0.015
0.2
0.2
0.2
0.6
0.6
0.015
0.015
0.03
0.03
0.2
0.6
Arizona
0.015
0.06
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.03
0.06
0.2
Arkansas
0.015
0.06
0.2
0.2
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.6
California
0.015
0.03
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.03
0.2
0.2
123
-------
Rural
Urban
State
Interstate
Other
Principal
Arterial
Minor
Arterial
Major
Collector
Minor
Collector
Local
Interstate
Other
Freeways
and
Other
Principal
Arterial
Minor
Arterial
Collectors
Local
Colorado
0.015
0.2
0.2
0.2
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Connecticut
0.015
0.06
0.06
0.2
0.2
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Delaware
0.015
0.03
0.06
0.2
0.2
0.6
0.015
0.015
0.03
0.03
0.06
0.2
Dist. of Columbia
0.015
0.6
0.6
0.6
0.6
0.6
0.015
0.015
0.03
0.03
0.06
0.2
Florida
0.015
0.06
0.2
0.2
0.2
0.2
0.015
0.015
0.03
0.03
0.06
0.2
Georgia
0.015
0.06
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Hawaii
0.015
0.03
0.06
0.2
0.2
0.2
0.015
0.015
0.03
0.03
0.06
0.2
Idaho
0.015
0.2
0.2
0.2
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Illinois
0.015
0.2
0.2
0.2
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Indiana
0.015
0.06
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.06
0.06
0.2
Iowa
0.015
0.2
0.2
0.2
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Kansas
0.015
0.2
0.2
0.6
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Kentucky
0.015
0.06
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Louisiana
0.015
0.06
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.06
0.2
0.6
Maine
0.015
0.06
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.03
0.2
0.2
Maryland
0.015
0.03
0.06
0.2
0.2
0.6
0.015
0.015
0.03
0.03
0.06
0.2
Massachusetts
0.015
0.06
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Michigan
0.015
0.2
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Minnesota
0.015
0.06
0.2
0.2
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Mississippi
0.015
0.06
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Missouri
0.015
0.2
0.2
0.2
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Montana
0.015
0.2
0.2
0.6
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Nebraska
0.015
0.2
0.2
0.6
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Nevada
0.015
0.2
0.2
0.2
0.6
0.6
0.015
0.015
0.03
0.03
0.06
0.2
New Hampshire
0.015
0.06
0.06
0.2
0.2
0.6
0.015
0.015
0.03
0.03
0.2
0.2
New Jersey
0.015
0.03
0.06
0.2
0.2
0.6
0.015
0.015
0.03
0.06
0.2
0.2
New Mexico
0.015
0.2
0.2
0.2
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.2
New York
0.015
0.2
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.06
0.2
0.2
North Carolina
0.015
0.03
0.06
0.2
0.2
0.6
0.015
0.015
0.03
0.03
0.06
0.2
North Dakota
0.015
0.2
0.2
0.6
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Ohio
0.015
0.06
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Oklahoma
0.015
0.06
0.2
0.2
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Oregon
0.015
0.2
0.2
0.2
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Pennsylvania
0.015
0.06
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Rhode Island
0.015
0.06
0.06
0.2
0.2
0.6
0.015
0.015
0.03
0.06
0.2
0.6
South Carolina
0.015
0.06
0.2
0.2
0.6
0.6
0.015
0.015
0.03
0.03
0.2
0.2
South Dakota
0.015
0.2
0.2
0.6
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.6
Tennessee
0.015
0.06
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Texas
0.015
0.06
0.2
0.2
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.6
Utah
0.015
0.2
0.2
0.2
0.6
0.6
0.015
0.015
0.03
0.03
0.2
0.2
Vermont
0.015
0.06
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.06
0.2
0.2
Virginia
0.015
0.03
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.03
0.2
0.2
Washington
0.015
0.2
0.2
0.2
0.2
0.6
0.015
0.015
0.03
0.06
0.2
0.2
West Virginia
0.015
0.06
0.2
0.2
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.6
Wisconsin
0.015
0.06
0.2
0.2
0.6
0.6
0.015
0.015
0.03
0.06
0.2
0.6
Wyoming
0.015
0.2
0.2
0.2
0.6
0.6
0.015
0.015
0.06
0.06
0.2
0.2
124
-------
To better estimate paved road fugitive dust emissions, the average vehicle weight was estimated by road type
for each county in the U.S. (plus Puerto Rico and the U.S. Virgin Islands) based on the mix of VMT by vehicle type
from the 2008 onroad NEI. For state and local agencies that provided VMT data to EPA for use in the 2008 NEI,
those data are included in this data set. Additionally, if a state/local agency did not provide VMT data for the
2008 NEI, but, had provided information for either the 2005 or 2002 NEI, the state/local-supplied data were
grown to 2008 based on 2008 VMT data from the Federal Highway Administration (FHWA). The VMT data for
the remaining counties were based on 2008 Federal Highway Administration data. (See the NEI onroad
documentation for more details on how the default VMT data were calculated from the FHWA data set.)
The 2008 VMT data set from the NEI included in EPA's National Mobile Inventory Model (NMIM) BaseYearVMT
table includes 2008 VMT for each county by road type and 28 MOBILE6 vehicle types. An average vehicle weight
was estimated for each of these 28 vehicle types, as shown in Table 3-75. For the heavy-duty Class 2B through
Class 7 vehicle classes, the average of the gross vehicle weight rating (GVWR) range was selected as the average
weight of the vehicle class. More detailed information for the heavy-duty Class 8A and 8B vehicle classes were
available from the U.S. Bureau of the Census Vehicle Inventory and Use Survey (VIUS). The Class 8A and 8B
subcategories by weight from VIUS were weighted by annual mileage to estimate the average 8A and 8B
average vehicle class weights. For the light-duty vehicle and truck classes, data from the U.S. Department of
Energy Annual Energy Outlook 2010 were used to represent the average vehicle weights. The average weight of
motorcycles and the three bus categories were estimated using professional judgment based on information
about existing model weights for these vehicle classes. Once the average vehicle weight was assigned to each of
the 28 MOBILE6 vehicle classes, these averages were then assigned to each VMT record in the NMIM
BaseYearVMT table, corresponding to the vehicle class that the VMT represented. A VMT-weighted average
vehicle weight was then calculated by county and road type for each county/road type combination in the
database.
Table 3-75: Average vehicle weights by MOBILE6 vehicle class
Vehicle Class
Vehicle Weight
Abbreviation
Vehicle Class Description
Estimate (lbs)
LDGV
Light-Duty Gasoline Vehicles (Passenger Cars)
3,369
LDGT1
Light-Duty Gasoline Trucks 1 (0-6,000 lbs. GVWR, 0-3750 lbs. LVW)
4,150
LDGT2
Light-Duty Gasoline Trucks 2 (0-6,000 lbs. GVWR, 3751-5750 lbs. LVW)
4,150
LDGT3
Light-Duty Gasoline Trucks 3 (6,001-8,500 lbs. GVWR, 0-5750 lbs. ALVW)
5,327
Light-Duty Gasoline Trucks 4 (6,001-8,500 lbs. GVWR, 5751 lbs. and greater
LDGT4
ALVW)
5,327
HDGV2B
Class 2b Heavy-Duty Gasoline Vehicles (8501-10,000 lbs. GVWR)
9,250
HDGV3
Class 3 Heavy-Duty Gasoline Vehicles (10,001-14,000 lbs. GVWR)
12,000
HDGV4
Class 4 Heavy-Duty Gasoline Vehicles (14,001-16,000 lbs. GVWR)
15,000
HDGV5
Class 5 Heavy-Duty Gasoline Vehicles (16,001-19,500 lbs. GVWR)
17,750
HDGV6
Class 6 Heavy-Duty Gasoline Vehicles (19,501-26,000 lbs. GVWR)
22,750
HDGV7
Class 7 Heavy-Duty Gasoline Vehicles (26,001-33,000 lbs. GVWR)
29,500
HDGV8A
Class 8a Heavy-Duty Gasoline Vehicles (33,001-60,000 lbs. GVWR)
48,000
HDGV8B
Class 8b Heavy-Duty Gasoline Vehicles (>60,000 lbs. GVWR)
71,900
LDDV
Light-Duty Diesel Vehicles (Passenger Cars)
3,369
LDDT12
Light-Duty Diesel Trucks 1 and 2 (0-6,000 lbs. GVWR)
4,150
HDDV2B
Class 2b Heavy-Duty Diesel Vehicles (8501-10,000 lbs. GVWR)
9,250
HDDV3
Class 3 Heavy-Duty Diesel Vehicles (10,001-14,000 lbs. GVWR)
12,000
HDDV4
Class 4 Heavy-Duty Diesel Vehicles (14,001-16,000 lbs. GVWR)
15,000
HDDV5
Class 5 Heavy-Duty Diesel Vehicles (16,001-19,500 lbs. GVWR)
17,750
125
-------
Vehicle Class
Vehicle Weight
Abbreviation
Vehicle Class Description
Estimate (lbs)
HDDV6
Class 6 Heavy-Duty Diesel Vehicles (19,501-26,000 lbs. GVWR)
22,750
HDDV7
Class 7 Heavy-Duty Diesel Vehicles (26,001-33,000 lbs. GVWR)
29,500
HDDV8A
Class 8a Heavy-Duty Diesel Vehicles (33,001-60,000 lbs. GVWR)
48,000
HDDV8B
Class 8b Heavy-Duty Diesel Vehicles (>60,000 lbs. GVWR)
71,900
MC
Motorcycles (Gasoline)
500
HDGB
Gasoline Buses (School, Transit and Urban)
32,500
HDDBT
Diesel Transit and Urban Buses
32,500
HDDBS
Diesel School Buses
25,000
LDDT34
Light-Duty Diesel Trucks 3 and 4 (6,001-8,500 lbs. GVWR)
5,327
The AP-42 equation listed above includes a correction factor to adjust for the number of days with measurable
precipitation in the year. The factor of "4" in the precipitation adjustment accounts for the fact that paved roads
dry more quickly than unpaved roads and that precipitation may not occur over the entire 24-hour day period.
The number of days with at least 0.01 inches of precipitation in each month by State was obtained from the
National Climatic Data Center by state [ref 3], Data were collected from a meteorological station selected to be
representative of urban areas within each State.
Activity
Total annual VMT estimates by county and roadway class were derived from the 2008 NMIM run described
above, totaling all vehicle types and speeds for each county and road type. Paved road VMT was estimated using
a ratio of state-level paved road VMT to total VMT. State level paved road VMT was calculated by subtracting
the State/roadway class un paved road VMT from total State/roadway class VMT. Federal Highway
Administration's (FHWA) annual Highway Statistics report was used to determine the un paved VMT in each
state [ref 2], Once the paved road VMT were calculated for 2008, these numbers were grown to 2010 using the
ratio of the 2010 to 2008 VMT estimates by state and road type from the highway statistics series table VM2
Annual Vehicle-Miles.
Controls
Paved road dust controls were applied by county to urban and rural roads in serious PMio nonattainment areas
and to urban roads in moderate PMW nonattainment areas. The assumed control measure is vacuum sweeping
of paved roads twice per month. A control efficiency of 79 percent was assumed for this control measure [ref 4],
The assumed rule penetration varies by roadway class and PMi0 nonattainment area classification (serious or
moderate). The rule penetration rates are shown in Table 3-76. Rule effectiveness was assumed to be 100% for
all counties where this control was applied.
Table 3-76: Penetration rates of paved road vacuum sweeping
PMio Nonattainment
Roadway Class
Vacuum Sweeping
Status
Penetration Rate (%)
Moderate
Urban Freeway & Expressway
67
Moderate
Urban Minor Arterial
67
Moderate
Urban Collector
64
Moderate
Urban Local
88
Serious
Rural Minor Arterial
71
Serious
Rural Major Collector
83
Serious
Rural Minor Collector
59
126
-------
PMio Nonattainment
Status
Roadway Class
Vacuum Sweeping
Penetration Rate (%)
Serious
Rural Local
35
Serious
Urban Freeway & Expressway
67
Serious
Urban Minor Arterial
67
Serious
Urban Collector
64
Serious
Urban Local
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 CERS submission, the emissions for all roadway classes were summed to the
county level. Therefore, the emissions at the county level can represent several different control efficiency, rule
effectiveness, and rule penetration levels. As a result, the control efficiency values were reported in the
ControlPollutant table as a composite, overall control efficiency for each county; the rule effectiveness and rule
penetration values were not reported separately in the ControlApproach table.
3.8.5 Summary of quality assurance methods
The EPA compared 2008 to the estimates for 2011 and found one issue with the state of Colorado and paved
road emissions. Colorado submitted a reasonable dataset that contained both species of filterable and primary
PM, but the EPA PM-Aug methodology did not work as expected and produced some erroneous PM10-FIL and
PM25-FIL data. This data is currently in the 2011 v2 and should be disregarded. The PM10-PRI and the PM25-PRI
data appear to be reasonable estimates.
3.8.6 References for Dust - 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 2010. Office of
Highway Policy Information. Washington, DC. 2011.
3. U.S. Department of Commerce, National Oceanic and Atmospheric Administration. "2011 Local
Climatological Data Annual Summaries with Comparative Data", retrieved April 2012.
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.
3.9 Dust - Unpaved Road Dust
3.9.1 Sector description
The 2011 NEI has emissions for the SCCs shown in Table 3-77 for this sector. EPA estimates emissions for
particulate matter for the first SCC (2296000000) in Table 3-77.
Table 3-77: SCCs used for Unpaved Road Dust - 2011 NEI
SCC
SCC Level 1
SCC Level 2
SCC Level 3
SCC Level 4
2296000000
Mobile Sources
Unpaved Roads
All Unpaved Roads
Total: Fugitives
2296005000
Mobile Sources
Unpaved Roads
Public Unpaved Roads
Total: Fugitives
2296010000
Mobile Sources
Unpaved Roads
Industrial Unpaved Roads
Total: Fugitives
127
-------
3.9.2 Sources of data overview and selection hierarchy
The unpaved road emissions sector includes data from the S/L/T agency submitted data and the default EPA
generated unpaved road emissions. The agencies listed in Table 3-78 submitted emissions for this sector.
Table 3-78: Agencies that submitted Unpaved Road Dust emissions data
All
Industrial
Public
Agency
Type
Unpaved
Unpaved
Unpaved
Roads
Roads
Roads
2011EPA Unpaved Road estimates
EPA
X
EPA PM Augmentation
EPA
X
0
X
Bishop Paiute Tribe
T
X
California Air Resources Board
S
X
Clark County Department of Air Quality and Environmental
I
y
Management
Colorado Department of Public Health and Environment
s
X
Eastern Band of Cherokee Indians
T
X
Hawaii Department of Health Clean Air Branch
S
X
Kansas Department of Health and Environment
s
X
Kickapoo Tribe of Indians of the Kickapoo Reservation in Kansas
T
X
Maricopa County Air Quality Department
L
X
X
Maryland Department of the Environment
S
X
New Jersey Department of Environment Protection
s
X
Northern Cheyenne Tribe
T
X
Sac and Fox Nation of Missouri in Kansas and Nebraska Reservation
T
X
Santee Sioux Nation
T
X
Vermont Department of Environmental Conservation
S
X
Virginia Department of Environmental Quality
s
X
Washington State Department of Ecology
s
X
West Virginia Division of Air Quality
s
X
Table 3-79 shows the selection hierarchy for the datasets used in the unpaved roads sector.
Table 3-79: 2011 NEI Unpaved Road Dust data selection hierarchy
Priority
Dataset Name
Dataset Content
1
Responsible Agency Data Set
State and Local Agency submitted emissions
2
2011EPA_PM-Augmentation
Augments PM data in 47 states and some tribes
3
2011EPA_chrom_split
Splits total chromium into speciated chromium in 37 states
4
2011EPA_HAP-Augmentation
Adds Pb and other HAP emissions in 46 states
5
2011EPA_NP_NoQverlap_w_Pt
EPA-generated data, including agricultural crops and livestock
dust emissions
128
-------
3.9.3 Spatial coverage and data sources for the sector
Dust - Unpaved Road Dust
v r - r uii u
N - Nonpoint
PN - P&N
All CAPs EPA hSLT — EPA & SLT
3.9.4 EPA methodology for unpaved road dust
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.
Uncontrolled unpaved road emissions were calculated at the State level by roadway class and month. This was
done by multiplying the State/roadway class unpaved roadway VMT by the appropriate monthly temporal
allocation factor and by the monthly unpaved road emission factor. After the unpaved road dust emissions were
calculated at the State/roadway class/monthly level of detail, the uncontrolled emissions were then allocated to
the county ievel using 2010 rurai population data as a surrogate. Next, control factors were applied to the
unpaved road emissions in PMio nonattainment area counties. Emissions and VMT 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, temporal and spatial allocation procedures, and controls.
Emission Factor Equation
Re-entrained road dust emissions for unpaved roads were estimated using unpaved road VMT and the emission
factor equation for public roads from AP-42 [ref 1]:
E =[ k * (s/12)1x (SPD/30)05] ~ (M/O.5)02 - C
where k and C are empirical constants given in Table 3-80, with
k
particle size multiplier (Ib/VMT)
E
size specific emission factor (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)
129
-------
The uncontrolled emission factors without precipitation corrections are in the worksheet "Emission Factors" by
State 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 3-80 [ref 1] 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 2], Silt contents of over 200 unpaved roads from over 30
States were obtained. Average silt contents of unpaved roads were calculated for each sate that had three or
more samples for that State. For States that did not have three or more samples, the average for all samples
from all States was used as a default value. The silt content values by State, and, identifies if the values were
based on a sample average or default value.
Table 3-80: Constants for Unpaved Roads re-entrained dust emission factor Equation [ref 1]
Constant
PM25-PRI/
PM25-FIL
PM10-PRI/
PM10-FIL
k (Ib/VMT)
0.18
1.8
C
0.00036
0.00047
Table 3-81 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.
Table 3-81: 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 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.
Correction factors were applied to the emission factors to account for the number of days with a sufficient
amount of precipitation to prevent road dust re-suspension. Monthly corrected emission factors by State and
roadway classification were calculated using the following equation:
where:
Ecorr=Ex[(D-P)/D]
Ecorr = unpaved road dust emission factor corrected for precipitation effects
E = uncorrected emission factor
D = number of days in the month
p = number of days in the month with at least 0.01 inches of precipitation
130
-------
The number of days with at least 0.01 inches of precipitation in each month was obtained from the National
Climatic Data Center [ref 3], Data were collected from a meteorological station selected to be representative of
rural areas within the State.
Activity
Unpaved roadway mileage estimates were obtained from the FHWA's annual Highway Statistics report Table HM-
51 [ref 4], Unpaved mileage data for 2008 were used, as data for 2009-2011 were not available.
Separate calculations of VMT were performed for locally and non-locally- (State or federally) maintained
roadways. State-level, locally-maintained roadway mileage was organized by surface type (rural and urban) and
the average daily traffic volume (ADTV) groups shown in Table 3-82.
From these data, State-level unpaved roadway mileage estimates were made. The following equation was then
used to calculate State-level unpaved road VMT estimates:
VMTup = ADTV * FSRM * 365 days/year
where:
VMT up = VMT on unpaved roads (miles/year)
ADTV = average daily traffic volume (vehicles/day/mile)
FSRM = functional system roadway mileage (miles)
State and federally maintained roadway mileage was categorized by arterial classification, not roadway traffic
volume; therefore, the VMT was calculated differently than for county-maintained roadways. The ADTV was
assumed to not vary by roadway maintenance responsibility, so the ADTV calculated from county-maintained
VMT and mileage (ADTV = VMT/Mileage) was used with non-locally-maintained roadway mileage to calculate
VMT in the above equation. The following roadway types do not have unpaved road segments and therefore
had zero VMT calculated: rural and urban interstates and other principal arterial roadways, rural minor arterial
roadways, and urban other freeways and expressways.
Table 3-82: Assumed values for average daily traffic volume (ADTV) by volume group
Rural Roads
Volume Category (vehicles per dav per mile)
<50
50-199
200-499
>500
Assumed ADTV
5*
125**
350**
550***
Urban Roads
Volume Category (vehicles per dav per mile)
<200
200-499
500-1999
> 2000
Assumed ADTV
20*
350**
1250**
2200***
Notes: *10% of volume group's maximum range endpoint.
** Average of volume group's range endpoints.
*** 110% of volume group's minimum range endpoint.
Allocation
The unpaved road VMT estimates by State/roadway class were first temporally allocated by season using the
NAPAP inventory seasonal temporal allocations factors for VMT [ref 5], These factors are provided in the
worksheet "NAPAP Temporal VMT Adjustment". The seasonal VMT values were then multiplied by the ratio of
the number of days in a month to the number of days in a season to adjust to monthly VMT. The emission
factors were then applied to estimate emissions by month.
131
-------
The State/roadway class unpaved road emissions were then spatially allocated to each county using estimates of
the ratio of 2010 county rural population to the State rural population from the U.S. Census Bureau as shown by
the following equation:
EMISx,y=(CLx/SL) * EMIS,y
where:
EMISx.y = unpaved road emissions (tons) for county x and roadway class y
CLX = rural population in county x SL = rural population in the State
EMIS.y = unpaved road emissions in entire State for roadway class y
The county-level allocation factors are provided in the worksheet "State to County Emis Allocation." The factors
are derived from the 2010 census rural population [ref 6], An exception was made for the District of Columbia,
where 100% of households were considered urban, but it there is only one "county" in the district, so no
allocation was necessary.
Controls
The controls assumed for unpaved roads varied by PMw nonattainment area classification and by urban and rural
areas. On urban unpaved roads in moderate PMi0 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. Chemical
stabilization, with a control efficiency or 75 percent and a rule penetration of 50 percent, was assumed for rural
areas in serious PMi0 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
PM io nonattainment areas [ref 7].
Note that the controls were applied at the county/roadway class level, and the controls differ by roadway class.
In the NIF 3.0 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, rule effectiveness, and rule penetration
levels. As a result, the control efficiency, rule effectiveness, and rule penetration values were reported in the
control equipment table as a composite, overall control level for each county; the rule effectiveness and rule
penetration values were not reported separately in the emissions table.
3.9.5 Summary of quality assurance methods
The EPA compared emissions from unpaved roads to previous inventories and found no significant issues. The
EPA also compared state submitted data to EPA data and found no significant issues
3.9.6 References for Dust - Unpaved 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 13.2.2, Unpaved Roads." Research Triangle Park, NC. 2003.
2. W. Barnard, G. Stensland, and D. Gatz, Illinois State Water Survey, "Evaluation of Potential Improvements
in the Estimation of Unpaved Road Fugitive Emission Inventories," paper 87-58.1, presented at the 80th
Annual Meeting of the APCA. New York, New York. June 21-26, 1987.
3. U.S. Department of Commerce, National Oceanic and Atmospheric Administration. Summary of the Day
Element TD-3200, 2008 data provided via FTP. National Climatic Data Center, 2009.
4. U.S. Department of Transportation, Federal Highway Administration. Highway Statistics 2007. Office of
132
-------
Highway Policy Information. Washington, DC. 2009.
5. U.S. Environmental Protection Agency. "The 1985 NAPAP Emissions Inventory: Development of Temporal
Allocation Factors," EPA-600/7-89-010d. Air & Energy Engineering Research Laboratory. Research
Triangle Park, NC. April 1990.
6. U.S. Census Bureau. "2010 Census Urban and Rural Classification," Bureau of the Census. Washington,
DC, August 2012.
7. 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.
3.10 Fuel Combustion - Electric Generation
This section includes the description of five EIS sectors:
• Fuel Comb - Electric Generation - Coal
• Fuel Comb - Electric Generation - Oil
• Fuel Comb - Electric Generation - Natural Gas
• Fuel Comb - Electric Generation - Biomass
• Fuel Comb - Electric Generation - Other
They are treated here in a single section because the methods used are the same across all sectors.
3.10.1 Sector description
These five sectors are defined by the point source SCCs beginning with 101 (primarily boilers) and 201 (primarily
turbines and engines). There are no nonpoint contributions to this sector. These SCCs include boilers,
combustion gas turbines, combined cycle units, and reciprocating engines firing any type of fuel for the purpose
of turning a generator connected to the electrical grid. The primary fuels used by the boilers are coal and natural
gas. A much smaller number of oil and wood-fired boilers are also included in the oil and natural gas sectors.
Various waste or by-products such as municipal waste, bagasse, petroleum coke, and tires are also used in some
boilers. The primary fuel used by the combustion gas turbines and combined cycle units is natural gas, although
some distillate oil is also used. The reciprocating engines are generally much smaller in terms of generating
capacity and also much less efficient than either the boilers and steam turbines or the combustion gas turbines.
The engines are primarily fired by natural gas or diesel oil, but there are some which use various available waste
gases, such as landfill gas.
The SCC-based EIS sector definitions will cause a different universe of units to be included in these sectors than
would other definitions of EGUs. For example, the EIS sector definitions do not include a heat input or generator
output size threshold. In contrast, some EPA regulatory applications define EGUs to include only units with
capacity greater than 25 MW. Many of the engines and some of the combustion gas turbines in the EIS sectors
for EGUs are well below 25 MW generating capacity. The boilers and steam turbine-generators, and particularly
those fired on coal, are almost always greater than 25 MW capacity, except for some older units.
The use of SCCs in the NEI by S/L/T agencies impacts the units included in these EIS sectors. There are some
boilers and gas turbines in industrial facilities which cogenerate both electricity for distribution to the public
power grid and process steam for their internal use. Some S/L/T agencies reporting to the NEI use an SCC (1-01
or 2-01) that would include these units in one of the EGU sectors, while others use an Industrial (1-02 or 2-02) or
a Commercial/Institutional (1-03 or 2-03) SCC. This can result in boilers or gas turbines not connected to the
133
-------
public power grid being included in these EGU sectors, with the SCC assigned based upon either strictly their
large size (some EPA references to utility boilers have cited them as greater than 100 mmBTU/hr heat input) or
because they may generate electrical power for internal consumption.
3.10.2 Sources of data overview and selection hierarchy
The primary sources of data for the EGU sectors were the S/L/T agency-submitted data and EPA's EGU dataset.
The EPA EGU dataset emissions for a suite of 15 HAP pollutants that were tested as part of the Mercury and Air
Toxics Standard (MATS) rule development were used ahead of S/L/T agency-submitted data except where the
S/L/T agency submittal indicated that it was based on either a CEM or recent stack testing. Additional emissions
data in the EPA EGU dataset from either CAMD's S02 and NOx CEM data or from AP-42 emissions factors were
only used where the responsible S/L/T agency did not report a pollutant for a given unit. In addition to these two
primary sources of data, the EGU sectors also have contributions from the EPA PM Augmentation, EPA
Chromium Split, EPA TRI, and EPA HAP Augmentation datasets. A smaller amount of contributions was also
from the EPA Carry Forward, EPA other, and EPA's Nevada Gold datasets.
The agencies listed in Table 3-83 submitted emissions for these sectors. A box with an "X" means that the
agency submitted data for EGU units included in that EGU fuel group for the individual EIS Sectors.
Table 3-83: Agencies that submitted 2011 EGU data by EGU fuel groups
Agency
Type
Coal
Oil
Natural
Gas
Biomass
Other
Alabama Department of Environmental Management
State
X
X
X
X
X
Alaska Department of Environmental Conservation
State
X
X
X
Allegheny County Health Department
Local
X
X
X
X
Arizona Department of Environmental Quality
State
X
X
X
X
X
Arkansas Department of Environmental Quality
State
X
X
X
X
X
California Air Resources Board
State
X
X
X
X
X
City of Albuquerque
Local
X
X
X
Clark County Dept of Air Quality and Environmental
Management
Local
X
X
X
Colorado Department of Public Health and Environment
State
X
X
X
X
Connecticut Department Of Environmental Protection
State
X
X
X
X
DC Department of Health Air Quality Division
State
X
Delaware Dept of Natural Resources and Environmental
Control
State
X
X
X
X
Florida Department of Environmental Protection
State
X
X
X
X
X
Forsyth County Environmental Affairs Department
Local
X
Georgia Department of Natural Resources
State
X
X
X
X
X
Hawaii Department of Health Clean Air Branch
State
X
X
X
X
Idaho Department OF Environmental Quality
State
X
X
X
X
Illinois Environmental Protection Agency
State
X
X
X
X
X
Indiana Department of Environmental Management
State
X
X
X
X
Iowa Department of Natural Resources
State
X
X
X
X
X
Jefferson County (AL) Department of Health
Local
X
X
X
Kansas Department of Health and Environment
State
X
X
X
X
134
-------
Agency
Type
Coal
Oil
Natural
Gas
Biomass
Other
Kentucky Division for Air Quality
State
X
X
X
X
X
Lane Regional Air Pollution Authority
Local
X
Lincoln/Lancaster County Health Department
Local
X
Louisiana Department of Environmental Quality
State
X
X
X
X
Louisville Metro Air Pollution Control District
Local
X
X
X
Maine Department of Environmental Protection
State
X
X
X
X
Maricopa County Air Quality Department
Local
X
X
Maryland Department of the Environment
State
X
X
X
X
Massachusetts Department of Environmental Protection
State
X
X
X
X
X
Mecklenburg County Air Quality
Local
X
Memphis and Shelby County Health Dept - Pollution Control
Local
X
X
X
X
X
Metro Public Health of Nashville/Davidson County
Local
X
X
X
Michigan Department of Environmental Quality
State
X
X
X
X
X
Minnesota Pollution Control Agency
State
X
X
X
X
X
Mississippi Department of Environmental Quality
State
X
X
X
X
Missouri Department of Natural Resources
State
X
X
X
X
X
Montana Department of Environmental Quality
State
X
X
X
X
Navajo Nation
Tribal
X
Nebraska Environmental Quality
State
X
X
X
X
X
Nevada Division of Environmental Protection
State
X
X
X
X
New Hampshire Department of Environmental Services
State
X
X
X
X
X
New Jersey Department of Environment Protection
State
X
X
X
X
New Mexico Environment Department Air Quality Bureau
State
X
X
X
New York State Department of Environmental Conservation
State
X
X
X
X
X
North Carolina Dept of Environment and Natural Resources
State
X
X
X
X
X
North Dakota Department of Health
State
X
X
X
Ohio Environmental Protection Agency
State
X
X
X
X
X
Oklahoma Department of Environmental Quality
State
X
X
X
X
Olympic Region Clean Air Agency
Local
X
X
Omaha Air Quality Control Division
Local
X
Oregon Department of Environmental Quality
State
X
X
X
X
Pennsylvania Department of Environmental Protection
State
X
X
X
X
X
Philadelphia Air Management Services
Local
X
X
Pinal County
Local
X
X
X
Puerto Rico
State
X
X
X
X
Puget Sound Clean Air Agency
Local
X
X
X
X
Rhode Island Department of Environmental Management
State
X
X
X
South Carolina Dept of Health and Environmental Control
State
X
X
X
X
X
South Dakota Dept of Environment and Natural Resources
State
X
X
X
X
Southern Ute Indian Tribe
Tribal
X
X
X
Southwest Clean Air Agency
Local
X
X
X
X
135
-------
Agency
Type
Coal
Oil
Natural
Gas
Biomass
Other
Tennessee Department of Environmental Conservation
State
X
X
X
X
X
Texas Commission on Environmental Quality
State
X
X
X
X
Utah Division of Air Quality
State
X
X
X
X
Vermont Department of Environmental Conservation
State
X
X
X
X
Virginia Department of Environmental Quality
State
X
X
X
X
X
Washington State Department of Ecology
State
X
X
X
X
Washoe County Health District
Local
X
X
West Virginia Division of Air Quality
State
X
X
X
X
X
Western North Carolina Regional Air Quality Agency
Local
X
X
X
X
Wisconsin Department of Natural Resources
State
X
X
X
X
X
Wyoming Department of Environmental Quality
State
X
X
X
X
Table 3-84 shows the selection hierarchy for the EGU sectors. A box with an "X" means that the dataset
contributed to the EGU sector for that fuel group.
Table 3-84: 2011 NEI EGU data selection hierarchy by EGU fuel groups
Priority
Data Set Name
Data Set Contents and Impact
Coal
Oil
Natural
Biomass
Other
1
2011EPA_PM-Augmentation
Augments PM data in 47
states and some tribes (see
Section 3.1.2)
X
X
X
X
X
2
2011 Responsible Agency
Selection
S/L/T agency submitted
emissions
X
X
X
X
X
3
2011EPA_EGU
Overwrites Hg, other metals,
and acid gases to use data
from the MATS rule in 49
states and some tribes (see
Section 3.10.5)
X
X
X
X
X
4
2011EPA_chrom_split
Splits total chromium into
speciated chromium in 37
states (see Section 3.1.3)
X
X
X
X
X
5
EPA NV Gold Mines
EPA-generated data
X
6
2011EPA_Other
EPA-generated data
X
7
2011EPA_TRI
Adds Pb and HAP emissions in
53 states and 4 tribes (see
Section 3.1.4)
X
X
X
X
8
2011EPA_CarryForward-
PreviousYearData
EPA-generated data
X
9
2011EPA_HAP -Augmentation
Adds Pb and HAP emissions in
26 states (see Section 3.1.5)
X
X
X
X
X
136
-------
3.10.3 Spatial coverage and data sources for the sector
Fuel Comb - Electric Generation - Biomass
Fuel Comb - Electric Generation - Biomass
F V -
All CAPs
\ P - Point
V_j N - Nonpoint
PN - P&N
P - Point
N - Nonpoint
All HAPS
P - Point
Nonpoint
PN-P&N
Fuel Comb - Electric Generation - Coal
Fuel Comb - Electric Generation - Coal
Nonpoint
PN-P&N
A CAPs
;lt ¦Hcf>as
-------
Fuel Comb - Electric Generation - Oil
Fuel Comb - Electric Generation - Oil
P - Point
N - Nonpoint
PN-P&N
All CAPS
Nonpoint
PN-P&N
All HAPS
Fuel Comb - Electric Generation - Other
Fuel Comb - Electric Generation - Other
P - Point
N - Nonpoint
PN - P&N
All CAPs
N - Nonpoint
PN-P&N
All HAPS
3.10.4 PM Augmentation for EGUs
As described above in section 3.1.2; EPA performs various steps starting from the S/L/'T agency submitted
emissions for the various pieces of PM emissions in order to complete a consistent representation for both
PMIO-Primary and PM2.5-Primary emissions from all sectors. These steps may be a simple as adding S/L/'T
agency submitted filterable and condensable pieces together to create the PMi0 and PM2.5 Primary species, or
they may also include EPA estimates for the condensable piece if not submitted by the S/L/T agency. For the five
EGU sectors as a whole, the 2011EPA_PM-Augmentation dataset contributed 44% of the total PMIO-Primary
mass and 51% of the total PM2.5-Primary mass. Table 3-85 provides the emissions contribution from all S/L/T
agencies and from the EPA PM Augmentation data for each of the EIS sectors associated with EGUs.
Table 3-85: Agency-submitted, PM Augmentation, and total PM10 and PM2.5 emissions for EGU sectors
PM10
PM10
PM10
PM2.5
PM2.5
PM2.5
Agency
Aug
Total
Agency
Aug
Total
EIS Sector
(tons)
(tons)
(tons)
(tons)
(tons)
(tons)
Fuel Comb - Electric Generation - Biomass
1,440
735
2,174
1,010
866
1,877
Fuel Comb - Electric Generation - Coal
131,218
110,472
241,690
80,808
89,556
170,364
Fuel Comb - Electric Generation - Natural Gas
12,374
13,027
25,401
10,641
13,945
24,586
Fuel Comb - Electric Generation - Oil
6,985
1,053
8,038
4,508
1,415
5,922
Fuel Comb - Electric Generation - Other
1,680
1,178
2,858
1,086
1,427
2,513
153,696
126,464
280,161
98,054
107,209
205,263
138
-------
3.10.5 EPA-developed EGU emissions data
In addition to the S/L/T-reported data, 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 and in some cases to be
used instead of S/L/T agency submitted data. The 2011EPA_EGll dataset was developed from three separate
estimation sources. The three sources were: the 2010 MATS testing program emission factors for 15 HAPs with
annual throughputs primarily from EPA's Clean Air Market Division (CAMD) but also from the Department of
Energy's Energy Information Administration (EIA) and data provided by Puerto Rico; S02 and NOx emissions from
CAMD's CEM database; and emission factors used in the 2008 NEI that were built from AP-42 emission factors
and 2008 fuel heat and sulfur contents with 2011 annual throughputs from CAMD. A small number of the AP-42
based estimates were not included in the 2011EPA-EGU dataset because the primary fuel burned, or the control
devices used by the units in 2011 were found to be different than in 2008, which would render the 2008
emission factors non-representative of 2011 operations for these emission units.
As shown above in Table 3-84, the selection hierarchy was set such that S/L/T agency-submitted data would be
used ahead of the values in the 2011EPA_EGU dataset. However, the emissions values in the 2011EPA_EGU
dataset that were derived from the MATS testing program were believed to be based on more up-to-date and
more reliable emissions factors than what EPA had previously made available for S/L/T agency use via AP-42.
Therefore, wherever a MATS-based emissions estimate was available in the 2011EPA_EGU dataset, it was used
for the 2011 NEI rather than the S/L/T agency value, except where the S/L/T agency submittal indicated that the
S/L/T agency value was from either a CEM or a recent stack test. The selection of the MATS-based emissions
over the S/L/T agency emissions was accomplished by setting a "tag" on those S/L/T agency emissions values to
exclude them from being available for selection even though they remain in the EIS data system. The purpose of
this approach was to use the best available data, with either the unit-specific MATS-tested data or the more
recent MATS-based bin emission factors assumed to be more representative of current operations than the
published AP-42 emissions factors.
For the 2011EPA_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.
In making the estimates, EPA assumed that all heat input came from the primary fuel, and the emission factors
used reflected only that primary fuel. The resultant unit-level estimates had to be loaded into the EIS at the
process-level to meet the EIS requirement that emissions can only be associated with that most detailed level.
For the EGU sectors, the unit-level represents the boiler or gas turbine unit as a whole, while the process level
represents the individual fuels burned within the units. EPA therefore assigned all of the calculated unit-level
emissions to a single process representing the primary fuel, which EPA determined to be the process used by the
S/L/T agency for reporting the largest portion of the S/L/T agency NOx emissions. Wherever S/L/T agency
emissions values were to be excluded from the 2011 NEI because there was an available EPA MATS-based
emissions value, it was therefore necessary that all processes at that emission unit that had S/L/T agency
emissions for that pollutant be tagged.
In summary, the 2011 NEI for EGUs is comprised of largely S/L/T agency-reported data for the CAPs and any
HAPs that the S/L/T agencies reported other than the fifteen MATS-estimated pollutants. For those fifteen
MATS-estimated pollutants, the 2011 NEI is comprised largely of the EPA estimates, except S/L/T agency data
were used where it was believed to be based upon use of a CEM or unit-specific test. Other HAPs for the MATS-
regulated units, and all HAPs for units not part of MATS, include S/L/T agency emissions values where they were
reported (with PM and Chromium augmentation, if needed), or include the 2011EPA_EGU emissions where no
S/L/T agency emissions were reported.
139
-------
The matching of the 2011EPA_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, and revisions were made wherever discrepancies were noted. As a final confirmation that the
correct emissions unit and a reasonable process ID in the 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. Several discrepancies were identified and resolved from this emissions
comparison.
3.10.6 Alternative facility and unit IDs needed for matching with other databases
The 2011 NEI data contains two sets of alternate unit identifiers related to the ORIS plant ID and CAMD unit IDs.
The first set is stored in the 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 unit 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 the 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 IPM model. The storage format of these alternate unit IDs,
in both the EIS and in the exported SMOKE file, replicates the IDs as found in the NEEDS database used as input
to the IPM model. The NEEDS IDs are a concatenation of the ORIS plant ID and a unit ID, with either a "_B_" or a
"_G_" between the two IDs, indicating "Boiler" or "Generator". Note that the ORIS plant IDs and the unit IDs as
stored in the CAMD dataset and in the NEEDS database are almost always the same, but that there are
occasional differences for the same unit. The EPACAMD alternate unit IDs available in the 2011 NEI are believed
to be a complete set of all those that can safely be used for the purpose of substituting hourly CEM values
during SMOKE processing. The EPAIPM alternate unit IDs in the 2011 NEI are not a complete listing of all the
NEEDS/IPM units, although almost all of the larger emitters, including all of the EPACAMD CEM units, do have an
EPAIPM alternate unit ID. The NEEDS database includes a much larger set of smaller, non-CEM units.
3.10.7 Summary of quality assurance methods
The S/L/T agency-reported data were subject to the same overall emissions outlier analysis that was performed
on the S/L/T agency point source emissions datasets as a whole. That outlier analysis included a comparison of
the facility-level sums for each of the key pollutants to the corresponding values seen in the 2008 NEI v3 and to
the facility's Toxics Release Inventory reports for 2011. New facility-pollutant values, missing facility-pollutant
values, and significant increases or decreases in facility-pollutant values compared to the 2008 NEI v3 values
were identified in a comparison file provided to S/L/T agencies for review. Significance levels were established
separately for each key pollutant. The identified S/L/T agency values were either revised or confirmed as
accurate by the responsible S/L/T agency or if no action was taken by the S/L/T agency and the value was
exceptionally suspect, the value was tagged to be excluded from selection for the NEI.
3.11 Fuel Combustion - Industrial Boilers, ICEs
This section includes the description of five EIS sectors:
• Fuel Comb - Industrial Boilers, ICEs - Coal
• Fuel Comb - Industrial Boilers, ICEs - Oil
140
-------
• Fuel Comb -
• Fuel Comb -
• Fuel Comb -
Industrial Boilers, ICEs
Industrial Boilers, ICEs
Industrial Boilers, ICEs
- Natural Gas
- Biomass
- Other
They are treated here in a single section because the methods used are the same across all sectors.
3.11.1 Sector description
These five sectors are defined by the point source SCCs beginning with 102105, 202, 2040 (engine testing
including aircraft engines) and SCC 28888801 (engine fugitive emissions). It also includes the nonpoint SCCs
starting with 2102 (boilers, engines or total across boilers and engines) and 280152 (orchard heaters). These
SCCs include boilers, internal combustion engines (ICE), including reciprocating and turbines, industrial space
heaters and orchard heaters (nonpoint) firing any type of fuel. The primary fuels used by the boilers are coal, oil
and natural gas. Other fuels used by industrial boilers include biomass, waste products and process gases. The
primary fuels used by the ICE are natural gas and oil, but there are some which use various available process
gases and liquefied petroleum gas (LPG).
The SCC-based EIS sector definitions will cause a different universe of units to be included in these sectors than
would other definitions of boilers, turbines or reciprocating internal combustion engines. For example, the
Industrial/Commercial/lnstitutional Boilers and Process Heaters MACT include 25 MW and smaller boilers used
to generate electricity; these boilers are not included in the sectors described here because they have SCCs
beginning with 1-01. Thus, the EIS sector definition would put these units, which are considered industrial
boilers for the purpose of the MACT, in the Fuel Combustion - Electric Generation sector described in section
3.10. In addition, while CO Boilers are in this sector, they are not included in the
Industrial/Commercial/lnstitutional Boilers and Process Heaters MACT category.
Also, as described in section 3.10 the use of SCCs in the NEI by S/L/T agencies impacts the units included in these
EIS sectors. There are some boilers and gas turbines in industrial facilities which cogenerate electricity for
distribution to the public power grid and process steam for their internal use. Some S/L/T agencies reporting to
the NEI use an SCC starting with 101 or 201 that would include these units in one of the EGU sectors, while
others use an Industrial (102 or 202) or a Commercial/Institutional (103 or 203) SCC. This can result in boilers or
gas turbines not connected to the public power grid being included in these EGU sectors and not the Industrial
sectors.
In addition to the potential of ambiguity in assigning SCCs to industrial boiler units that may be used to generate
electricity, there is also miss-assignment, where the wrong SCC is applied to clearly defined units, based on
description fields such as the unit description in the EIS. For this reason, when looking at individual units, these
other description fields may be useful in accurately categorizing the unit.
3.11.2 Sources of data overview and selection hierarchy
The industrial fuel combustion sectors include data from S/L/T agencies and 9 EPA datasets that cover both
point and nonpoint data categories. Table 3-86 shows the agencies that submitted data in each of the data
categories for each of the fuel combustion - industrial boilers and ICE sectors. Where only emission values of
zero were submitted (sum across all pollutants submitted), these are shown as zeroes in the table. No "X" or "0"
indicates that nothing was submitted by the agency for that data category and fuel combination for the
industrial boilers sector.
141
-------
Table 3-86: Agencies that submitted data for the Fuel Combustion - Industrial Boilers, ICEs sectors
Nonpoint
Point
Bio-
Natural
Bio-
Natural
Agency
TYPE
mass
Coal
Gas
Oil
Other
mass
Coal
Gas
Oil
Other
US Environmental Protection Agency
EPA
X
X
X
X
X
X
X
X
X
X
Alabama Department of Environmental Management
S
X
X
X
X
X
X
X
X
X
X
Alaska Department of Environmental Conservation
S
X
X
X
X
X
X
Allegheny County Health Department
L
X
X
X
X
Arizona Department of Environmental Quality
S
X
X
X
X
Arkansas Department of Environmental Quality
S
X
X
X
X
X
California Air Resources Board
S
X
X
X
X
X
X
X
X
Chattanooga Air Pollution Control Bureau (CHCAPCB)
L
0
X
0
X
0
X
X
X
City of Albuquerque
L
X
X
X
Clark County Department of Air Quality and
Environmental Management
L
X
X
X
X
X
X
X
Coeur d'Alene Tribe
T
0
X
X
X
X
X
X
X
Colorado Department of Public Health and
Environment
S
X
X
X
X
X
Connecticut Department of Environmental Protection
S
X
0
X
X
X
X
X
X
DC-District Department of the Environment
S
0
0
X
X
X
X
Delaware Department of Natural Resources and
Environmental Control
S
0
X
X
X
X
X
X
X
Eastern Band of Cherokee Indians
T
X
Florida Department of Environmental Protection
S
X
X
X
X
X
X
X
X
X
X
Forsyth County Office of Environmental Assistance and
Protection
L
X
X
X
X
X
Georgia Department of Natural Resources
S
0
0
X
X
X
X
X
X
X
X
Hawaii Department of Health Clean Air Branch
S
0
X
X
X
X
X
Idaho Department of Environmental Quality
S
X
X
X
X
X
X
X
X
X
X
Illinois Environmental Protection Agency
S
0
0
X
X
0
X
X
X
X
X
Indiana Department of Environmental Management
S
X
0
X
X
X
X
X
X
X
X
Iowa Department of Natural Resources
S
X
0
X
X
X
X
X
X
X
X
Jefferson County (AL) Department of Health
L
X
X
X
X
Kansas Department of Health and Environment
S
X
0
X
X
X
X
X
X
X
Kentucky Division for Air Quality
S
X
X
X
X
X
Kickapoo Tribe of Indians of the Kickapoo Reservation
in Kansas
T
X
X
Knox County Department of Air Quality Management
L
0
0
X
X
X
X
Kootenai Tribe of Idaho
T
X
X
X
X
X
Lane Regional Air Pollution Authority
L
X
X
0
0
Lincoln/Lancaster County Health Department
L
X
X
Louisiana Department of Environmental Quality
S
X
X
0
X
X
X
X
X
X
Louisville Metro Air Pollution Control District
L
X
0
X
X
X
X
X
X
X
X
Maine Department of Environmental Protection
S
0
0
X
0
X
X
X
X
X
Maricopa County Air Quality Department
L
0
X
X
X
X
Maryland Department of the Environment
S
X
X
X
X
Massachusetts Department of Environmental
Protection
S
X
0
X
X
X
X
X
X
X
X
Mecklenburg County Air Quality
L
X
X
X
Memphis and Shelby County Health Department -
Pollution Control
L
X
X
X
Metro Public Health of Nashville/Davidson County
L
X
X
X
X
Michigan Department of Environmental Quality
S
X
X
X
X
X
X
X
X
X
Minnesota Pollution Control Agency
S
X
X
X
X
X
X
X
X
X
X
Mississippi Dept of Environmental Quality
S
X
X
X
X
X
Missouri Department of Natural Resources
S
X
0
X
X
X
X
X
X
X
X
Montana Department of Environmental Quality
S
X
X
X
X
X
Navajo Nation
T
X
142
-------
Nonpoint
Point
Bio-
Natural
Bio-
Natural
Agency
TYPE
mass
Coal
Gas
Oil
Other
mass
Coal
Gas
Oil
Other
Nebraska Environmental Quality
S
X
X
X
X
X
Nevada Division of Environmental Protection
S
X
X
X
New Hampshire Department of Environmental Services
s
X
X
X
X
X
X
X
X
New Jersey Department of Environment Protection
s
0
0
X
X
X
X
X
X
New Mexico Environment Department Air Quality
Bureau
s
X
X
X
New York State Department of Environmental
Conservation
s
X
X
X
X
X
X
X
X
Nez Perce Tribe
T
X
X
X
X
X
X
X
North Carolina Department of Environment and
Natural Resources
S
X
X
X
0
X
X
X
X
X
North Dakota Department of Health
S
X
X
X
X
Ohio Environmental Protection Agency
S
X
0
X
X
X
X
X
X
X
X
Oklahoma Department of Environmental Quality
s
X
X
X
X
0
X
X
X
X
X
Olympic Region Clean Air Agency
L
X
X
X
X
Omaha Air Quality Control Division
L
X
X
Oregon Department of Environmental Quality
S
X
X
X
X
X
X
0
X
X
X
Pennsylvania Department of Environmental Protection
S
X
X
0
X
X
X
X
X
X
X
Philadelphia Air Management Services
L
X
X
X
Pinal County
L
X
X
X
Puerto Rico
S
0
X
X
Puget Sound Clean Air Agency
L
X
X
X
X
Rhode Island Depart, of Environmental Management
S
X
X
X
X
Shoshone-Bannock Tribes of the Fort Hall Reservation
of Idaho
T
X
X
X
X
X
South Carolina Department of Health and
Environmental Control
S
X
X
X
X
0
X
X
X
X
X
South Dakota Department of Environment and Natural
Resources
S
X
X
X
Southern Ute Indian Tribe
T
X
Southwest Clean Air Agency
L
X
X
X
X
Tennessee Department of Environmental Conservation
S
X
X
X
X
X
X
X
X
X
X
Texas Commission on Environmental Quality
S
0
X
X
X
X
X
X
X
Utah Division of Air Quality
S
X
X
X
X
Vermont Department of Environmental Conservation
S
X
0
X
X
X
X
X
X
X
Virginia Department of Environmental Quality
S
X
0
X
X
0
X
X
X
X
X
Washington State Department of Ecology
S
X
X
X
X
X
Washoe County Health District
L
X
West Virginia Division of Air Quality
S
X
X
X
X
X
X
X
X
Western North Carolina Regional Air Quality Agency
(Buncombe Co.)
L
X
X
X
Wisconsin Department of Natural Resources
S
0
0
X
X
X
X
X
X
X
X
Wyoming Department of Environmental Quality
S
X
X
X
X
X
Table 3-87 shows the selection hierarchy for all datasets contributing emissions to the Fuel Comb - Industrial
Boilers, ICEs Sectors. This selection hierarchy combines the S/L/T agency data with the EPA datasets. As can be
seen, most of the datasets used for this selection have data for the point source data category only.
143
-------
Table 3-87: 2011 NEI selection hierarchy for datasets used by Fuel Comb - Industrial Boilers, ICEs sectors
Data Set Name
Description
Point
Non-
point
2011EPA_PM-Augmentation
PM species added to gap fill missing S/L/T agency data or
make corrections where S/L/T agency have inconsistent PM
species' emissions.
1
2
Responsible Agency Data Set
S/L/T agency submitted data
2
1
2011EPA_EGU
EPA MATS EGU data developed from CAMD heat input and
EFs.
3
2011EPA_chrom_split
Contains corrected and speciated hexavalent and trivalent
chromium emissions derived from the S/L/T agency data for
sources in which S/L/T agency reports the total
(unspeciated) chromium pollutant.
4
3
2011EPA_Other
Data added to boiler and ICE SCCs resulting mercury
emissions for a boiler in Missouri using state-provided data
5
2011EPA_TRI
Toxics Release Inventory data for the year 2011.
6
2011EPA_CarryForward-
PreviousYear Data
Variety of estimates used to gap fill important
sources/pollutants.
7
2011EPA_HAP-Augmentation
HAP data computed from S/L/T agency criteria pollutant
data using HAP/CAP emission factor ratios.
8
4
2011EPA_BOEM
CAP Emissions from Offshore oil platforms located in
9
Federal Waters in the Gulf of Mexico developed bv the U.S.
Department of the Interior, Bureau of Ocean and Energy
Management, Regulation, and Enforcement.
2011E PA_N P_Overlap_w_Pt
EPA generated emissions for nonpoint sources
5
EPA requested feedback from states and local agencies on the extent of their inventories, including details on
whether they had performed point/nonpoint reconciliation, whether they did nonpoint estimates for each SCC,
whether the state had any nonpoint sources in a category or whether a state preferred to use EPA estimates.
This survey was used, in conjunction with a few assumptions, to determine whether EPA should potentially
augment the data submitted by the S/L/T agency with EPA generated data. Because the EPA generated data
were based on activity data that would cover all industrial combustion sources (both point and nonpoint), it was
necessary to use this methodology so that double counting of emissions would not occur. For this sector, the
algorithm for determining whether to augment data in the 2011 NEI is given in Table 3-88.
Table 3-88: Algorithm to determine whether to augment state data with EPA data for Industrial Boilers
Survey Data
State
Submitted
to Point?
State
Submitted
to
Nonpoint?
EPA Action
Rationale
State claims that
category is fully
covered by their
point inventory
for an SCC
Yes
Yes or No
Don't augment
their nonpoint
data. Tag EPA
data so that it
doesn't get put
into the EIS
The nonpoint inventory is based on EIA
numbers, which takes all fuel combustion
into account. The EIA makes no distinction
between point and nonpoint. Augmenting
would double-count point emissions.
144
-------
State
State
Submitted
Submitted
to
Survey Data
to Point?
Nonpoint?
EPA Action
Rationale
Augment with
EPA estimates
for nonpoint
category
The EIA data tracks fuel usage by state.
No
No
There will be a gap in the data if this
category is not covered by the state at all.
Assume that they filled out the survey
No
Yes
Don't augment
incorrectly, and that they meant that the
category is fully covered by nonpoint.
State claims that
No
Yes
Don't augment
Augmenting would double-count nonpoint
category is fully
covered by their
nonpoint
inventory for an
see
The EIA data tracks fuel usage by state.
No
No
Augment
There will be a gap in the data if this
category is not covered by the state at all.
Yes
Yes or No
Don't augment
Assume that they filled out the survey
incorrectly.
We believe that they intended to submit
nonpoint. Though there will be some
Yes
No
Augment
double-counting, we believe that their
submitted emissions for point would be
lower than if they claimed that their
category was covered fully in point.
State claims that
No augmentation is necessary, since either
they do
Yes or No
Yes
Don't augment
both point and nonpoint were submitted,
point/nonpoint
or nonpoint would be double-counted.
reconciliation
The EIA data tracks fuel usage by state.
No
No
Augment
There will be a gap in the data if this
category is not covered by the state at all.
While there would be some double-
Yes
No
Augment
counting of point emissions, it would be
small, and we believe that there would still
be nonpoint emissions for this category.
Assume that they intended to submit
nonpoint. Though there will be some
Yes
No
Augment
double-counting, we believe that their
submitted emissions for point would be
State claims that
lower than if they claimed that their
they do
category was covered fully in point.
point/nonpoint
No augmentation is necessary, since either
reconciliation
Yes or No
Yes
Don't augment
both point and nonpoint were submitted,
or nonpoint would be double-counted.
The EIA data tracks fuel usage by state.
No
No
Augment
There will be a gap in the data if this
category is not covered by the state at all.
145
-------
3.11.3 Spatial coverage and data sources for the sector
Fuel Comb - Industrial Boilers, ICEs - Biomass Fuel Comb - Industrial Boilers, ICEs - Biomass
P - Point
N - Nonpoint
PN-P&N
P - Point
N - Nonpoint
PN - P&N
All CAPS All HAPs
P - Point
N - Nonpoint
PN-P&N
P - Point
N - Nonpoint
PN - P&N
Fuel Comb - Industrial Boilers, ICEs - Coal
All CAPS
Fuel Comb - Industrial Boilers, ICEs - Coal
All HAPs — =„
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
Nonpoint
PN - P&N
All CAPs
P - Point
N - Nonpoint
PN-P&N
All HAPs
146
-------
Fuel Comb - Industrial Boilers, ICEs - Oil
Fuel Comb - Industrial Boilers, ICEs - Oil
P - Point
N - Nonpoint
PN-P&N
P - Point
N - Nonpoint
PN-P&N
All CAPs
All HAPs
Fuel Comb - Industrial Boilers, ICEs - Other
P - Point
N - Nonpoint
PN - P&N
P - Point
N - Nonpoint
PN-P&N
All CAPs
Fuel Comb - Industrial Boilers, ICEs - Other
All HAPs
3.11.4 EPA-developed fuel combustion -industrial Boilers, ICEs emissions data
Nonpoint industrial fuel combustion emissions were computed for the following fuel types: coal, distillate oil,
residual oil, natural gas, liquefied petroleum gas (LPG), kerosene, and wood.
There are additional types of energy that are consumed in the industrial sector: asphalt and road oil; feedstocks,
naphtha (less than 401°F); feedstocks, other oils (greater than 401°F); lubricants; motor gasoline; miscellaneous
petroleum products; pentanes plus; special naphthas; and waxes. With the exception of motor gasoline, all of
these additional fossil fuels are not actually combusted (oxidized) but are used as chemical feedstocks,
construction materials, lubricants, solvents, or reducing agents. Therefore, there are no industrial sector
combustion emissions from these fuel types. As described in more detail later, most of the fuel types that are
included in the industrial combustion sector also have a non-fuel use component. Therefore, it is necessary to
exclude this component in calculating nonpoint source industrial fuel combustion activity/emissions. Motor
gasoline is not inventoried as a nonpoint source because it is expected that gasoline combustion in this sector is
included in the nonroad inventory.
The EPA approach used in calculating emissions for industrial fuel combustion is to first develop state-levei fuel
consumption estimates, then to allocate these to the county-level, and then to multiply the resulting county-
level consumption estimates by appropriate emission factors.
Total state-level industrial sector energy consumption data are available from the Energy Information
Administration (EIA)'s State Energy Data System (SEDS) [ref 1] and were used for most source categories. In
147
-------
calculating the emission activity for industrial fuel combustion, EPA excluded all SEDS fuel types for which EIA
assumes 100 percent of consumption is non-fuel use. For fuel types for which non-fuel use occurs, but is less
than 100 percent, EPA reviewed two information sources to identify the non-fuel use percentage to apply in the
NEI: ElA's 2002 Manufacturing Energy Consumption Survey (MECS) [ref 2] and ElA's GHG emissions inventory for
2005 [ref 3], Further adjustments were made to the SEDS data for the coal and LPG sectors, and a separate EIA
data source, Fuel Oil and Kerosene Sales [ref 4], was used for distillate oil. These adjustments were necessary in
order to avoid double counting between the point, nonroad, and nonpoint inventories. For example, coal
consumed by coke plants is accounted for in the point source inventory, so when estimating nonpoint emissions,
this consumption should be removed. Similarly, for distillate oil and LPG, the SEDS data includes consumption
estimates for equipment that EPA includes in the nonroad sector inventory. Therefore, the SEDS data should be
adjusted so that these emissions are not double counted. More details on these adjustments. Year 2009 SEDS
data were used to estimate 2011 emissions because these were the most recent consumption data available at
the time this work was performed in 2012.
County-level activity estimates were developed by allocating the state-level adjusted EIA data. To do this, the
EPA compiled 2009 estimates of manufacturing sector employment from the Bureau of Census' County Business
Patterns 2009 [ref 5] for use in this procedure. We allocated state-level industrial fuel combustion by fuel type
to each county using the ratio of the number of manufacturing sector (NAICS codes 31-33) employees in each
county to the total number of manufacturing sector employees in the state. A separate document describes how
withheld County Business Patterns employment data were estimated [ref 6],
The EPA has compiled and used criteria and hazardous air pollutant emission factors for nonpoint source
industrial fuel combustion categories [ref 7], These emission factors, which are too numerous to list here, are
included in a spreadsheet within the ICI fuel combustion workbook. In most cases, these are the same emission
factors that were used in preparing the 2002 nonpoint source NEI [ref 8], Industrial LPG and wood combustion
emission factors were obtained from an ICI fuel combustion study being performed for the Central Regional Air
Planning Association (CENRAP) [ref 9],
3.11.5 Summary of quality assurance methods
Data analyses involving comparison of emissions between 2011 and 2008 showed some large discrepancies in
emissions from this sector between the two years. Values submitted by S/L/T agencies that were larger than 10
times the 2008 submitted values were tagged as outliers and were not used in the 2011 NEI (unless the agency
corrected the values prior to the final 2011 selection). Furthermore, some lead values from California were more
than 2 times the highest value of the EPA dataset for this sector, and these values were tagged as outliers and
not used in the 2011 NEI. In addition, some states requested that some values be tagged and not used, because
they realized errors after submission.
The OA process included the release of a draft to data submitters that showed where tagged data values needed
to be reexamined and possibly revised. State submitters were given the chance to resubmit tagged data during
this period of time. Some states, like Minnesota, resubmitted some data, but it still did not pass the second QA
check, and therefore remains tagged in the 2011 v2 NEI. Other states agreed that the tagged values seemed
incorrect, and that EPA should use the EPA generated estimates in its place. Table 3-89 summarizes the number
of tagged process-level emissions values from each agency affected by this OA in 2011 vl. This analysis was not
repeated for the 2011 v2 but any differences in number of tags are suspected to be minor.
148
-------
Table 3-89: Agencies tagged values for Industrial Fuel Combustion in 2011 NEI vl
Agency
Number of
Values Tagged
Tag Reason
California Air Resources Board
6
Duplicated facility
California Air Resources Board
6
Outlier
Minnesota Pollution Control Agency
311
Outlier
Nebraska Environmental Quality
1
Outlier
New York State Department of
Environmental Conservation
61
Outlier
Ohio Environmental Protection
Agency
33
State requested that these be tagged
because values were off by a factor of 1000
Pennsylvania Department of
Environmental Protection
2
State requested that these records be tagged
because state submitted incorrect values
Pennsylvania Department of
Environmental Protection
1
Outlier
Wisconsin Department of Natural
Resources
1
State planned to resubmit for 2011 v2
Wisconsin Department of Natural
Resources
2
State did not report hex, so EPA data should
be used
3.11.6 References for Fuel Combustion - Industrial Boilers, ICEs
1. EIA, 2012a: Energy Information Administration, U.S. Department of Energy, State Energy Data System -
Consumption, Physical Units, 1960-2009, available from http://205.254.135.7/state/seds/, accessed
March 2012.
2. EIA, 2007a: Energy Information Administration, U.S. Department of Energy, 2002 Manufacturing Energy
Consumption Survey, U.S. Department of Energy, Energy Information Administration, issued
January 2007.
3. EIA, 2007b: Energy Information Administration, US Department of Energy, Documentation for Emissions
of Greenhouse Gases in the United States 2005, DOE/EIA-0638 (2005), October 2007.
4. EIA, 2012b: Energy Information Administration, U.S. Department of Energy, Fuel Oil and Kerosene Sales,
accessed March 2012.
5. Census, 2012: Bureau of the Census, U.S. Department of Commerce, County Business Patterns 2009,
Washington, DC, accessed March 2012.
6. Divita, 2008: Divita, Frank, E.H. Pechan & Associates, Inc., memorandum to Roy Huntley, U.S.
Environmental Protection Agency, "County Business Patterns Calculations," December 4, 2008.
7. Huntley, 2009: Huntley, Roy, U.S. Environmental Protection Agency, "SCCs & emission factors to be used
in 2008 NEI to Bollman May 1 2009.mdb [electronic file]," May 1, 2009.
8. Pechan, 2006: E.H. Pechan & Associates, Inc. "Documentation for the Final 2002 Nonpoint Sector (Feb
06 Version) National Emission Inventory for Criteria and Hazardous Air Pollutants," prepared for U.S.
Environmental Protection Agency, July 2006.
9. Pechan, 2009a: E.H. Pechan & Associates, Inc., "Area Combustion Source Emissions Inventory
Improvement Methodology, Technical Memorandum," E.H. Pechan & Associates, Inc., prepared for
Central Regional Air Planning Association, March 20, 2009.
149
-------
3.12 Fuel Combustion - Commercial/Institutional
This section includes the description of five EIS sectors:
• Fuel Comb - Commercial/Institutional Boilers, ICEs - Coal
• Fuel Comb - Commercial/Institutional Boilers, ICEs - Oil
• Fuel Comb - Commercial/Institutional Boilers, ICEs - Natural Gas
• Fuel Comb - Commercial/Institutional Boilers, ICEs - Biomass
• Fuel Comb - Commercial/Institutional Boilers, ICEs - Other
They are treated here in a single section because the methods used are the same across all sectors.
3.12.1 Sector description
These five sectors are defined by the point source SCCs beginning with 103, 105 and 2030 and the nonpoint SCCs
starting with 2103. These SCCs include boilers, internal combustion engines (ICE), including reciprocating and
turbines, and space heaters. The primary fuels used by the boilers are coal, oil and natural gas. Other fuels used
by commercial/institutional boilers include biomass, waste products and process gases. The primary fuels used
by the ICE are natural gas and oil, but there are some which use various available process gases and LPG.
The SCC-based EIS sector definitions will cause a different universe of units to be included in these sectors than
would other definitions of boilers, turbines or reciprocating internal combustion engines. For example, the
Industrial/Commercial/lnstitutional Boilers and Process Heaters MACT include 25 MW and smaller boilers used
to generate electricity; these boilers are not included in the sectors described here because they may have SCCs
beginning with 101. Thus, the EIS sector definition would put these units in the Fuel Combustion - Electric
Generation sector described in Section 3.10.
The use of SCCs in the NEI by S/L/T agencies impacts the units included in these EIS sectors. There are some
boilers and gas turbines in commercial/institutional facilities which cogenerate electricity for distribution to the
public power grid and process steam for their internal use. Some S/L/T agencies reporting to the NEI use an SCC
(e.g., starting with 101 or 201) that would include these units in one of the EGU sectors, while others use an
Industrial (starting with 102 or 202) SCC. This can result in boilers or gas turbines not connected to the public
power grid being included in these EGU sectors and not the commercial/institutional boiler sectors.
3.12.2 Sources of data overview and selection hierarchy
The commercial/institutional fuel combustion sector includes data from the S/L/T agency submitted data and
the default EPA generated emissions. The agencies listed in Table 3-90 submitted emissions for this sector.
Where only emission values of zero were submitted (sum across all pollutants submitted), these are shown as
zeroes in the table. No "X" or "0" indicates that nothing was submitted by the agency for that data category and
fuel combination for this sector.
150
-------
Table 3-90: Agencies that submitted Commercial/Institutional Fuel Combustion data
Nonpoint
Point
Bio-
Natural
Bio-
Natural
Agency
Type
mass
Coal
Gas
Oil
Other
mass
Coal
Gas
Oil
Other
US Environmental Protection Agency
EPA
X
X
X
X
X
X
X
X
X
X
Alabama Department of Environmental Management
S
X
0
X
X
X
X
X
X
0
Alaska Department of Environmental Conservation
S
X
X
X
X
Allegheny County Health Department
L
X
X
X
Arizona Department of Environmental Quality
S
X
X
X
X
Arkansas Department of Environmental Quality
S
X
X
X
California Air Resources Board
S
X
X
X
X
X
X
X
X
Chattanooga Air Pollution Control Bureau (CHCAPCB)
L
0
X
0
X
X
City of Albuquerque
L
X
X
X
City of Huntsville Division of Natural Resources and
Environmental Mgmt
L
X
Clark County Department of Air Quality and
Environmental Management
L
0
X
X
X
X
X
Coeur d'Alene Tribe
T
X
X
X
X
X
Colorado Department of Public Health and Environment
S
X
X
X
X
X
Connecticut Department Of Environmental Protection
S
X
0
X
X
X
X
X
X
DC-District Department of the Environment
S
0
X
X
X
X
X
X
Delaware Department of Natural Resources and
Environmental Control
S
0
X
X
X
X
X
X
X
Eastern Band of Cherokee Indians
T
X
X
X
Florida Department of Environmental Protection
S
X
0
X
X
X
X
X
X
X
Forsyth County Office of Environmental Assistance and
Protection
L
X
X
X
X
Georgia Department of Natural Resources
S
0
0
X
X
X
X
X
X
X
X
Hawaii Department of Health Clean Air Branch
S
0
X
X
X
X
X
Idaho Department of Environmental Quality
S
X
X
X
X
X
X
X
X
X
Illinois Environmental Protection Agency
S
0
0
X
X
X
X
X
X
X
Indiana Department of Environmental Management
S
X
0
X
X
X
X
X
X
X
X
Iowa Department of Natural Resources
S
X
0
X
X
X
X
X
X
X
X
Jefferson County (AL) Department of Health
L
X
X
X
X
X
Kansas Department of Health and Environment
S
X
0
X
X
X
X
X
X
X
Kentucky Division for Air Quality
S
X
X
X
X
X
Kickapoo Tribe of Indians of the Kickapoo Reservation
in Kansas
T
X
Knox County Department of Air Quality Management
L
X
X
X
X
X
X
X
Kootenai Tribe of Idaho
T
X
X
X
X
X
Lane Regional Air Pollution Authority
L
X
X
Louisiana Department of Environmental Quality
S
X
0
X
X
X
X
X
X
X
Louisville Metro Air Pollution Control District
L
X
0
X
X
X
X
X
X
Maine Department of Environmental Protection
S
X
0
X
X
X
X
X
X
Maricopa County Air Quality Department
L
X
X
X
X
X
Maryland Department of the Environment
S
X
X
X
X
X
0
X
X
X
Massachusetts Department of Environmental
Protection
S
0
X
X
X
X
X
X
X
Mecklenburg County Air Quality
L
X
Memphis and Shelby County Health Department -
Pollution Control
L
0
0
X
X
X
X
X
X
X
Metro Public Health of Nashville/Davidson County
L
X
X
X
X
X
Michigan Department of Environmental Quality
S
X
X
X
X
X
X
X
X
X
Minnesota Pollution Control Agency
S
X
X
X
X
X
X
X
X
X
Mississippi Dept of Environmental Quality
S
X
X
0
Missouri Department of Natural Resources
S
X
0
X
X
X
X
X
X
X
X
Montana Department of Environmental Quality
S
X
X
X
Nebraska Environmental Quality
S
X
X
X
X
151
-------
Nonpoint
Point
Bio-
Natural
Bio-
Natural
Agency
Type
mass
Coal
Gas
Oil
Other
mass
Coal
Gas
Oil
Other
Nevada Division of Environmental Protection
S
X
X
X
X
New Hampshire Department of Environmental Services
S
X
X
X
X
X
X
X
New Jersey Department of Environment Protection
s
0
X
X
X
X
X
X
X
New Mexico Environment Department Air Quality
Bureau
s
X
New York State Department of Environmental
Conservation
s
X
X
X
X
X
X
X
X
X
Nez Perce Tribe
T
X
X
X
X
X
North Carolina Department of Environment and Natural
Resources
S
X
X
X
X
X
X
X
X
X
X
North Dakota Department of Health
S
X
X
Northern Cheyenne Tribe
T
X
X
X
X
Ohio Environmental Protection Agency
S
X
X
X
X
X
X
X
X
X
X
Oklahoma Department of Environmental Quality
S
X
0
X
X
X
X
X
X
Olympic Region Clean Air Agency
L
X
Omaha Air Quality Control Division
L
X
X
Oregon Department of Environmental Quality
S
X
0
X
X
X
X
X
X
X
Pennsylvania Department of Environmental Protection
S
X
0
X
X
X
X
X
X
X
X
Philadelphia Air Management Services
L
X
X
X
Pinal County
L
X
X
X
Puerto Rico
S
X
X
Puget Sound Clean Air Agency
L
X
X
X
Rhode Island Department of Environmental
Management
S
X
X
X
Shoshone-Bannock Tribes of the Fort Hall Reservation
of Idaho
T
X
X
X
X
X
South Carolina Department of Health and
Environmental Control
S
X
X
X
X
X
X
0
X
X
X
South Dakota Department of Environment and Natural
Resources
S
X
X
X
X
X
Southern Ute Indian Tribe
T
X
Southwest Clean Air Agency
L
X
0
Tennessee Department of Environmental Conservation
S
X
X
X
X
X
X
X
X
0
Texas Commission on Environmental Quality
S
0
X
X
X
X
X
X
X
Utah Division of Air Quality
S
X
X
X
X
X
X
X
Vermont Department of Environmental Conservation
S
X
0
X
X
X
X
X
X
Virginia Department of Environmental Quality
S
X
X
X
X
X
X
X
X
X
X
Washington State Department of Ecology
S
X
X
X
Washoe County Health District
L
X
X
West Virginia Division of Air Quality
S
X
X
0
X
X
X
Wisconsin Department of Natural Resources
S
X
X
X
X
X
X
X
X
X
X
Wyoming Department of Environmental Quality
S
X
X
X
X
X
Table 3-91 shows the selection hierarchy for the commercial/institutional fuel combustion sector.
Table 3-91: 2011 NEI Commercial/Institutional Fuel Combustion data selection hierarchy
Data Set Name
Description
Point
Non-
point
2011EPA_PM-Augmentation
PM species added to gap fill missing S/L/T agency data or
make corrections where S/L/T agency have inconsistent PM
species' emissions.
1
2
Responsible Agency Data Set
S/L/T agency submitted data
2
1
152
-------
Data Set Name
Description
Point
Non-
point
2011EPA_EGU
EPA MATS EGU data developed from CAMD heat input and
EFs.
3
2011EPA_chrom_split
Contains corrected and speciated hexavalent and trivalent
chromium emissions derived from the S/L/T agency data for
sources in which S/L/T agency reports the total
(unspeciated) chromium pollutant.
4
3
2011EPA_TRI
Toxics Release Inventory data for the year 2011.
5
2011EPA_CarryForward-
PreviousYear Data
Variety of estimates used to gap fill important
sources/pollutants.
6
2011EPA_HAP-Augmentation
HAP data computed from S/L/T agency criteria pollutant
data using HAP/CAP emission factor ratios.
7
4
2011EPA_BOEMS
CAP Emissions from Offshore oil platforms located in
Federal Waters in the Gulf of Mexico developed by the U.S.
8
Department of the Interior, Bureau of Ocean and Energy
Management, Regulation, and Enforcement.
2011E P A_N P_Overla p_w_Pt
EPA generated emissions for nonpoint sources
5
3.12.3 Spatial coverage and data sources for the sector
P - Point
N - Nonpoint
PN - P&N
Fuel Comb - Comm/lnstitutional - Biomass
All CAPs
Fuel Comb - Comm/lnstitutional - Biomass
All HAPs
P - Point
N - Nonpoirit
PN - P&N
P - Point
N - Nonpoint
PN - P&N
lN r 's~'"
P - Point
N - Nonpoint
PN - P&N
Fuel Comb - Comm/lnstitutional - Coal
All CAPs
Fuel Comb - Comm/lnstitutional - Coal
All HAPs
153
-------
Fuel Comb - Comm/lnstitutional - Natural Gas
Fuel Comb - Comm/lnstitutional - Natural Gas
P - Point
N - Nonpoint
PN - P&N
All CAPs I I EP* r=n S_T ¦¦ £Pa & SlI
Nonpoint
PN - P&N
All HAPs
Fuel Comb - Comm/lnstitutional - Oil
Fuel Comb - Comm/lnstitutional - Oil
ST
Nonpoint
PN - P&N
All CAPs
Fuel Comb - Comm/lnstitutional - Other
PN
PN
PN
PN
PN
PN
PN
PN
PN PN PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
P - Point
N - Nonpoint
PN - P&N
All CAPs ¦>, .
3.12.4 EPA-developed commercial/institutional fuel combustion data
The approach in calculating nonpoint emissions for commercial/institutional fuei combustion is to first develop
state-level fuel consumption estimates, then to allocate these to the county-level, and then to multiply the
resulting county-levei consumption estimates by appropriate emission factors.
Total state-level commercial sector energy consumption data are available from the Energy Information
Administration (EIA)'s State Energy Data System (SEDS) [ref 1] and were used for most source categories. Several
154
P - Point
N - Nonpoint
PN-P&N
All HAPs t=]EP« BBUMISLT
P - Point
N - Nonpoint
PN-P&N
Fuel Comb - Comm/lnstitutional - Other
All HAPs
-------
adjustments were made to the SEDS data. These adjustments were necessary in order to avoid double counting
between the nonroad and nonpoint inventories. Furthermore, for the coal sector, SEDS data do not provide coal
consumption estimates by type of coal (i.e., anthracite versus bituminous/subbituminous), and this level of data
is needed because of differing emission factors for these coal types.
For LPG and distillate oil, the SEDS data includes consumption estimates for equipment that EPA includes in the
nonroad sector inventory. Therefore, the SEDS data should be adjusted so that these emissions are not double
counted.
To estimate the volume of commercial/institutional sector LPG consumption that should not be included in the
nonpoint source inventory, EPA subtracted 18 percent from each state's commercial sector LPG consumption
estimate reported in SEDS. EPA ran the National Mobile Inventory Model (NMIM) for 2006 and calculated the
national volume of nonroad LPG consumption from commercial sector source categories. This estimate was then
divided into the SEDS total commercial sector LPG consumption estimate to yield the proportion of total
commercial/institutional sector LPG consumption attributable to the nonroad sector in that year (approximately
18 percent).
To avoid double-counting of distillate oil consumption between the nonpoint and nonroad sector emission
inventories, EPA relied on a source other than SEDS to estimate consumption. The approach uses more detailed
distillate oil consumption estimates reported in ElA's Fuel Oil and Kerosene Sales [ref 2], and assumptions from
the regulatory impact analysis (RIA) for EPA's nonroad diesel emissions rulemaking [ref 3], Table 3-92 displays
the assumptions that were applied to the state-level distillate oil consumption estimates reported in Fuel Oil and
Kerosene Sales to estimate total stationary source commercial/institutional sector consumption. The
percentages shown in Table 3-92 come from p 7-8 of EPA's RIA for the nonroad diesel emissions rulemaking [ref
3], Note, a very small portion of total commercial/institutional diesel is consumed by point sources (SCC
203001xx).
More details on these adjustments. Year 2009 SEDS data were used to estimate 2011 emissions because these
were the latest year consumption data available at the time this work was performed in 2012.
Table 3-92: Assumptions used to estimate Commercial/Institutional stationary source distillate fuel consumption
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
Year 2009 county-level activity estimates were developed by allocating the state-level activity resulting from the
adjustments to the SEDS data described above. The EPA compiled 2006 estimates of commercial sector (NAICS
codes 42 through 81) employment from the Bureau of Census' County Business Patterns 2009 [ref 4] for use in
this procedure. A separate document [ref 5] describes how withheld County Business Patterns employment data
were estimated. The EPA also developed 2006 county-level estimates of institutional sector (NAICS code 92)
employment from 2007 local government employment data in the 2007 Census of Governments [ref 6] and
adjustments reflecting each state's 2006/2007 local government employment ratio. State-level
commercial/institutional fuel combustion by fuel type was allocated to each county using the ratio of the
number of commercial/institutional sector employees in each county to the total number of
commercial/institutional sector employees in the state.
155
-------
The EPA has compiled criteria and hazardous air pollutant emission factors for nonpoint source
commercial/institutional fuel combustion categories [ref 7], These emission factors, which are too numerous to
list here, are included in a spreadsheet within the ICI fuel combustion workbook. In most cases, these are the
same emission factors that were used in preparing the 2002 nonpoint source NEI [ref 8],
Commercial/institutional wood combustion emission factors were obtained from an ICI fuel combustion study
being performed for the Central Regional Air Planning Association (CENRAP) [ref 9],
3.12.5 Summary of quality assurance methods
Data analyses involving comparison of emissions between 2011 and 2008 showed some large discrepancies in
emissions from this sector between the two years. Emissions values submitted by S/L/T agencies that were
larger than 10 times the 2008-submitted values were tagged as outliers and were not used in the 2011 NEI,
unless the agency corrected or confirmed the value. Furthermore, some lead values from Clark County, Nevada
were more than 2 times the highest value of the EPA dataset for this SCC, and these values were tagged as
outliers and not used in the 2011 NEI.
The QA process included the release of a draft to data submitters that showed where tagged data values needed
to be reexamined and possibly revised. State submitters were given the chance to resubmit tagged data during
this period of time. Some states, like Minnesota, resubmitted some data, but it still did not pass the second QA
check, and therefore remains tagged in the 2011 NEI. Other states agreed that the tagged values seemed
incorrect, and that EPA should use the EPA generated estimates in its place. Table 3-93 summarizes the number
of tagged process-level emissions values from each agency affected by this QA in vl of the 2011 NEI. This
analysis was not repeated for the v2 NEI but any differences in number of tags are suspected to be minor.
Table 3-93: Agencies tagged values for Commercia
/Institutional Fuel Combustion in vl of the 2011 NEI.
Agency
Number of Values Tagged
Tag Reason
Clark County Department of Air Quality
and Environmental Management
1
Outlier
Minnesota Pollution Control Agency
67
Outlier
Nebraska Environmental Quality
1
Outlier
3.12.6 References for Fuel Combustion - Commercial/Institutional
1. EIA, 2012a: Energy Information Administration, U.S. Department of Energy, State Energy Data System -
Consumption, Physical Units, 1960-2009, available from http://205.254.135.7/state/seds/, accessed
March 2012.
2. EIA, 2012b: Energy Information Administration, U.S. Department of Energy, Fuel Oil and Kerosene Sales,
accessed March 2012.
3. EPA, 2003: U.S. Environmental Protection Agency, "Draft Regulatory Impact Analysis: Control of
Emissions from Nonroad Diesel Engines," EPA420-R-03-008, Office of Transportation and Air Quality,
April 2003.
4. Census, 2012a: Bureau of the Census, U.S. Department of Commerce, County Business Patterns 2009,
Washington, DC, accessed March 2012.
5. Divita, 2008: Divita, Frank, E.H. Pechan & Associates, Inc., memorandum to Roy Huntley, U.S.
Environmental Protection Agency, "County Business Patterns Calculations," December 4, 2008.
6. Census, 2009b: Bureau of the Census, U.S. Department of Commerce, "Local Government Employment
and Payroll, March 2006," 2007 Census of Governments, accessed March 2009.
7. Huntley, 2009: Huntley, Roy, U.S. Environmental Protection Agency, "SCCs & emission factors to be used
in 2008 NEI to Bollman May 1 2009.mdb [electronic file]," May 1, 2009.
156
-------
8. Pechan, 2006: E.H. Pechan & Associates, Inc. "Documentation for the Final 2002 Nonpoint Sector (Feb
06 Version) National Emission Inventory for Criteria and Hazardous Air Pollutants," prepared for U.S.
Environmental Protection Agency, July 2006.
9. Pechan, 2009a: E.H. Pechan & Associates, Inc., "Area Combustion Source Emissions Inventory
Improvement Methodology, Technical Memorandum," E.H. Pechan & Associates, Inc., prepared for
Central Regional Air Planning Association, March 20, 2009.
3.13 Fuel Combustion - Residential - Natural Gas, Oil, and Other
The EIS sectors to be documented here are:
• "Fuel Comb - Residential - Other" which includes the fuels: (1) coal, (2) liquid petroleum gas 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 2011 NEI.
• "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 the only S/L that submitted data for this category
in 2011 submitted emissions of 0 (zero).
• "Fuel Comb - Residential - Natural Gas" which includes the fuel natural gas only.
3.13.1 Source category description
Table 3-94 shows the SCCs used in the 2011 NEI from the sectors: "Fuel Comb - Residential - Other", "Fuel Comb
- Residential - Oil" and "Fuel Comb - Residential - Natural Gas". EPA estimates emission for all SCCs other than
SCC=2104005000 and SCC=2104006010.
Tab
e 3-94: SCCs in the Residential Fuel Combustion sectors (except Wood) in the 2011 NEI
see
SCC Level Three
SCC Level Four
El Sector
2104001000
Anthracite Coal
Total: All Combustor Types
Fuel Comb - Residential - Other
2104002000
Bituminous/Subbituminous Coal
Total: All Combustor Types
Fuel Comb - Residential - Other
2104004000
Distillate Oil
Total: All Combustor Types
Fuel Comb - Residential - Oil
2104005000
Residual Oil
Total: All Combustor Types
Fuel Comb - Residential - Oil
2104006000
Natural Gas
Total: All Combustor Types
Fuel Comb - Residential - Natural Gas
2104006010
Natural Gas
Residential Furnaces
Fuel Comb - Residential - Natural Gas
2104007000
Liquified Petroleum Gas (LPG)
Total: All Combustor Types
Fuel Comb - Residential - Other
2104011000
Kerosene
Total: All Heater Types
Fuel Comb - Residential - Oil
3.13.2 Sources of data overview and selection hierarchy
The residential fuel combustion sectors include data from the S/L/T agency submitted data and the default EPA
generated emissions. This sector is contained solely in the nonpoint data category. The agencies listed in Table
3-95 submitted emissions for this sector. Where only emission values of zero were submitted (sum across all
pollutants submitted), these are shown as zeroes in the table. No "X" or "0" indicates that nothing was
submitted by the agency for that data category and fuel combination for this sector.
157
-------
Table 3-95: Agencies that submitted data for Fuel Combustion - Residential Heating - Natural Gas, Oil and Other
Natural
Gas
Oil
Other
Agency
Type
Natural
Gas
Distillate
Oil
Kero-
sene
Residual
Oil
Anthracite
Coal
Bituminous/
Subbitumi-
nous Coal
Uquified
Petroleum
Gas (LPG)
US Environmental Protection Agency (2011EPA_NP_NoOvrlp
dataset, to be described in 3.13.4)
EPA
X
X
X
X
X
X
California Air Resources Board
S
X
X
X
Chattanooga Air Pollution Control Bureau (CHCAPCB)
L
0
0
Clark County Department of Air Quality and Environmental
Management
L
X
X
X
0
0
X
Coeur d'Alene Tribe
T
X
X
X
0
X
X
DC-District Department of the Environment
S
X
X
0
X
Delaware Department of Natural Resources and
Environmental Control
S
X
X
X
0
X
Eastern Band of Cherokee Indians
T
X
X
X
X
Hawaii Department of Health Clean Air Branch
S
X
X
X
0
X
Idaho Department of Environmental Quality
S
X
X
X
0
X
X
Illinois Environmental Protection Agency
S
X
X
X
0
X
X
Iowa Department of Natural Resources
S
X
X
X
X
X
X
Kansas Department of Health and Environment
S
X
X
X
0
0
0
X
Kickapoo Tribe of Indians of the Kickapoo Reservation in
Kansas
T
X
Kootenai Tribe of Idaho
T
X
X
X
0
0
X
Louisiana Department of Environmental Quality
S
X
X
X
0
0
X
Maine Department of Environmental Protection
S
X
X
X
X
Maricopa County Air Quality Department
L
X
X
Maryland Department of the Environment
S
X
X
X
X
X
Massachusetts Department of Environmental Protection
S
X
X
X
0
X
Memphis and Shelby County Health Department - Pollution
Control
L
X
X
X
0
0
X
Metro Public Health of Nashville/Davidson County
L
X
X
0
X
Michigan Department of Environmental Quality
S
X
X
X
X
X
X
Minnesota Pollution Control Agency
S
X
X
X
X
X
X
Missouri Department of Natural Resources
S
X
X
X
0
0
0
X
New Hampshire Department of Environmental Services
S
X
X
X
New Jersey Department of Environment Protection
S
X
X
X
0
0
X
New York State Department of Environmental Conservation
S
X
X
X
X
Nez Perce Tribe
T
X
X
X
0
X
X
Northern Cheyenne Tribe
T
X
X
X
X
Oklahoma Department of Environmental Quality
S
X
X
X
0
0
0
X
Sac and Fox Nation of Missouri in Kansas and Nebraska
Reservation
T
X
Santee Sioux Nation
T
X
Shoshone-Bannock Tribes of the Fort Hall Reservation of
Idaho
T
X
X
X
0
X
X
Texas Commission on Environmental Quality
S
X
X
Utah Division of Air Quality
S
X
Vermont Department of Environmental Conservation
S
X
X
X
Virginia Department of Environmental Quality
S
X
X
X
0
X
X
Washington State Department of Ecology
S
X
X
X
West Virginia Division of Air Quality
S
X
X
X
X
X
X
158
-------
3.13.3 Spatial coverage and data sources for the sector
Fuel Comb - Residential - Natural Gas
P - Point
N - Nonpoint
PN-P&N
P-Point \
N - Nonpoint y
PN - P&N
All CAPS SLT H EPA & SIT
Fuel Comb - Residential - Natural Gas
All HAPs
Fuel Comb - Residential - Oil
P - Point
N - Nonpoint
P - Point
N - Nonpoint
PN - P&N
All CAPs
Fuel Comb - Residential - Oil
PN-P&N
All HAPS C3H CZISLI MEMIILT
Fuel Comb - Residential - Other
P - Point
N - Nonpoint
PN - P&N
P - Point
N - Nonpoint
PN - P&N
All CAPS
Fuel Comb - Residential - Other
All HAPs — =
3.13.4 EPA Residential Heating estimates for oil, natural gas and other fuels
Documentation on residential heating emissions estimates are provided for coal, natural gas, distillate oil,
kerosene and liquefied petroleum gas (LPG) are provided on the main 2011 NEI website under "2011 NEI
159
-------
Documentation" and then under the "Data and documentation" FTP link under "Nonpoint Emissions Tools and
Methods". Specific links to each fuel type for this category are provided below:
Residential Consumption Natural Gas
Residential Consumption Oil
Residential Consumption Coal
Residential Consumption Kerosene
Residential Consumption IPG
3.13.5 Summary of quality assurance methods
Comparisons of the EPA estimates for 2011 to previous inventories, and comparison of EPA estimates to state
submitted data indicated no issues.
3.14 Fuel Combustion - Residential - Wood
3.14.1 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 speaking, 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.
Table 3-96 shows the SCCs used in the 2011 NEI from in this sector. EPA estimates emission for all SCCs in Table
3-96 other than SCC=2104008300, which is a general woodstove SCC that provides no details on the category.
Only the Tohono O'Odham Nation of Arizona, the Washoe Tribe of California and Nevada, the Prairie Band
Potawatomi Nation and Sac and Fox Nation of Missouri in Kansas and Nebraska Reservation submitted
emissions for this general woodstove SCC.
160
-------
Table 3-96: SCCs in the Residential Wood Combustion sector in the 2011 NEI
see
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
2104008300
Wood
Woodstove: freestanding, general
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
2104008700
Wood
Outdoor wood burning device, NEC (fire-pits, chimeneas, etc)
2104009000
Firelog
Total: All Combustor Types
*SCC Level One is "Stationary Source Fuel Combustion" and SCC Level Two is "Residential"
3.14.2 Sources of data overview and selection hierarchy
The residential wood sector includes emissions from both S/L/T agencies and from the EPA no-overlap nonpoint
dataset. Table 3-97 shows the selection hierarchy for all datasets contributing to the residential wood heating
sector. Table 3-98 shows the agencies that submitted data used by the 2011 NEI. In some cases, the EPA PM and
HAP augmentation as well as chromium split datasets were used to fill in PM species and HAP pollutants based
on S/L/T agency data. Table 3-99 lists the various datasets used in the 2011 NEI for this sector. The figures
shown in Section 3.14.3 illustrate where EPA, S/L/T agency or both types of data are used for this sector. In cases
where an agency is listed in Table 3-98 and "both" is shown in the figure, this means that one of the EPA
augmentation datasets was used in that state.
Table 3-97: 2011 NEI selection hierarchy for datasets used by the residential wood heating sector
Priority
Dataset Name
Dataset Content
1
2011EPA_PM-Augmentation
Augments PM data in 47 states and some tribes
2
Responsible Agency Data Set
State and Local Agency submitted emissions
3
2011EPA_chrom_split
Splits total chromium into speciated chromium in 37 states
4
2011EPA_HAP-Augmentation
Adds Pb and other HAP emissions in 46 states
5
2011EPA_NP_NoOverlap_w_Pt
EPA-generated data, including agricultural crops and livestock
dust emissions
Table 3-98: Agencies that submitted data for the sector Fuel Combustion - Residential Heating - Wood
Agency Name
Agency Type
Bishop Paiute Tribe
Tribal
California Air Resources Board
State
Clark County Department of Air Quality and Environmental Management
Local Agency
Eastern Band of Cherokee Indians
Tribal
Illinois Environmental Protection Agency
State
Kootenai Tribe of Idaho
Tribal
Maine Department of Environmental Protection
State
161
-------
Agency Name
Agency Type
Maryland Department of the Environment
State
Metro Public Health of Nashville/Davidson County
Local Agency
Minnesota Pollution Control Agency
State
Nez Perce Tribe
Tribal
Northern Cheyenne Tribe
Tribal
Oregon Department of Environmental Quality
State
Sac and Fox Nation of Missouri in Kansas and Nebraska Reservation
Tribal
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribal
Washington State Department of Ecology
State
West Virginia Division of Air Quality
State
Table 3-99: Datasets Included in the Fuel Comb - Residential - Wood sector
Dataset Short Name
Order
2011 Responsible Agency Selection
1
2011EPA_PM-AUG
2
2011EPA_chrom_split
3
2011EPA_HAP-Aug
4
2011EPA_NP_NoOvrlp
6
3.14.3 Spatial coverage and data sources for the sector
Fuel Comb - Residential - Wood
Fuel Comb - Residential - Wood
P - Point
N - Nonpoint
P - Point
N - Nonpoint
PN - P&N
All CAPS
PN - P&N
All HAPS
3.14.4 EPA-developed residential wood combustion estimates
Emission estimates were developed using a tool in Microsoft® Access®, developed by EPA. This tool computes
county- and SCC-level emissions of criteria and HAPs for the entire country. EPA updated the inputs to the tool
for the 2011 NEI in partnership with ERTAC. Details about the development of the tool can be found in a
conference paper [ref 1], and details on the updates made for 2011 are provided here.
Updated AHS appliance profile data
The tool developed to estimate emissions from residential wood combustion relies on "appliance profiles/'
which include estimates of the fraction of homes in each county that have and use each type of wood-burning
162
-------
appliance listed in Table 3-96. The appliance profiles used for most counties (approximately 83%) are
constructed using data from the American Housing Survey (AHS), while other state- and local-level surveys are
used for the other counties, as described below. Appliance profiles are constructed by dividing the number of
survey respondents that use a particular appliance into the total number of respondents. The appliance profiles
are used with Census data on the number of occupied homes in each county to estimate the number of
appliances in use in each county.
The AHS conducts national and metropolitan area surveys on the Nation's housing, including household
characteristics and heating equipment and fuels. Both the national and metropolitan statistical area (MSA)
surveys are conducted during a 3- to 7- month period. The national survey, which gathers information on
housing throughout the country, conducts interviews at about 55,000 housing units every 2 years, in odd-
numbered years. The metropolitan area survey consists of 47 metropolitan areas, where householders are
interviewed every 6 years. Data is gathered for about 14 metropolitan areas on an even numbered year until all
47 metropolitan areas are surveyed. Data are also gathered for non-MSA counties in 4 bins: West, South,
Northeast, and Midwest. We used the non-MSA information as defaults where we did not have any other
information. We used the data in Table 2-4: "Selected Equipment and Plumbing," which provides information on
the number of respondents that use fireplaces (with or without inserts) or woodstoves. The methodology for
constructing the appliance profiles for the other appliances is discussed below. Because the AHS do not
differentiate between fireplaces that burn wood with those that burn gas, we applied an adjustment factor to
the AHS data that assumes that 30% of fireplaces burn gas, based on Houck [ref 2], Table 3-100 lists the MSAs
using updated AHS survey data for the 2011 NEI.
Table 3-100: MSA's using updated AHS data for residential wood combustion
MSA
Year of American Housing Survey Data
Seattle
2009
Philadelphia
2009
New Orleans
2009
New York City
2009
Detroit
2009
Chicago
2009
Northeast
2009
Midwest
2009
West
2009
South
2009
The area contained in a MSA will usually contain an urban core and surrounding areas that are more sub-urban
than urban. One of the problems noted in previous versions of the tool is that applying the MSA information to
all the counties in the MSA usually results in the overestimation of residential wood combustion emissions in the
urban core and underestimation in the suburban counties. For future versions of the NEI (2014), we plan to
address this by separating the urban core county from the sub-urban counties and allocating a higher proportion
of the emissions to the suburban counties.
In addition to the appliance profiles used to estimate the number of appliances in each county, the tool uses
"burn rates," which are the estimated amount of wood burned in each appliance. The burn rates are
constructed using a mixture of local surveys, fuel sales data, and expert judgment. For the non-MSA counties,
the tool uses a mix of resources to establish burn rates and appliance profiles. Information on burn rates can be
found in the conference paper referenced earlier [ref 1], For appliance counts, for many of the New England
States, the tool uses a MA RAM A (Mid-Atlantic Regional Air Management Association) survey that was later
163
-------
adjusted by ERTAC. In addition, we used a 2008 Vermont (VT) survey [ref 3], We used the VT data as a reality
check on the other New England states (the survey was released in 2011 so it was not available for the 2008
NEI). The VT survey showed strong wood use (32% of household's burn wood for space heating) and a general
increase from the last survey which was in 1998. There were also news reports of higher wood use. Surveys from
other states (MN and OR) also showed strong wood use. According to the OR survey, 36% of household use
wood to heat as backup heat and 34.7% of all households burned wood in at least one wood burning device. In
MN, 45% use wood as primary source of heat, based on a 2008 survey. In order to get the tool to calculate the
expected increase in emissions from 2008, the appliance percentage for fireplaces, woodstoves, and inserts was
adjusted.
EPA added additional state- and regional-level survey data, which are deemed more accurate and specific than
the survey data used in most counties in the tool. The main sources of data are the American Housing Survey
(AHS),13 various state-level surveys (Minnesota,14 Oregon,15 and Vermont16) and regional-level surveys (Mid-
Atlantic Regional Air Management Association [MARAMA]17 and the tri-state area of Washington, Oregon, and
Idaho), and expert judgment. These survey data are used to estimate the number of each type of wood-burning
appliance and the amount of wood burned in each appliance in each county. The source of the data and the
specific location within the data source where these data can be found are now listed in the Burn Rates,
Appliance Profiles, and Other Appliance Populations tables in the accompanying Excel workbook.
The counties for which EPA added data include the following:
• All counties in California;
• All counties in Washington;
• Ada, Canyon, and Elmore Counties, Idaho;
• Silver Bow County and Lincoln Counties, Montana;
• Klamath and Lane Counties, Oregon; and
• Washoe County, Nevada.
In all, this represents 163 counties. EPA attempted to collect recent survey data from Alaska but, were unable to
make contact with the state agency staff. EPA also received data from Minnesota from their 2011-2012 wood
combustion survey, but the data arrived too late to incorporate into the tool. However, these data are available
to analyze and include in the tool for the 2014 National Emissions Inventory.
Using the survey data obtained, EPA updated the appliance fractions and burn rates for all appliances for which
these surveys collected data. For any appliances for which the surveys did not specifically ask questions, which
13 U.S. Census Bureau. American Housing Survey.(accessed July 2014).
14 Minnesota Department of Natural Resources. Residential Fuelwood Assessment: 2002-2003 Heating Season, (accessed
July 2014) (Note: Minnesota conducted another residential wood combustion survey in 2012, but these data were not
available for analysis in time to include in the tool.)
15 Johnson, A.B., T. Conklin, and D. Elliot. 2009. Department of Environmental Quality Residential Wood Combustion Survey:
Results Report. Prepared by Portland State University Survey Research Lab for the Oregon Department of Environmental
Quality, (accessed July 2014).
16 Data provided by Vermont Division of Forestry (accessed July 2014).
17 Houck, J.E. and B.N. Eagle. 2006. Control Analysis and Documentation for Residential Wood Combustion in the MANE-VU
Region. Technical Memorandum prepared by OMNI Environmental Services Inc. for the Mid-Atlantic Regional Air
Management Association, (accessed July 2014).
164
-------
typically included outdoor wood boilers (OWBs), indoor furnaces, and outdoor appliances not elsewhere
classified (NEC), EPA kept the existing appliance and burn rate data.
Decreases of emissions from RWC from 2008 occur in the southeast; we believe the 2008 version of the tool
overestimated emissions in those states.
Other appliance profile - outdoor wood boilers (OWBs) and indoor furnaces
Because the AHS and, in some cases, other local survey data do not include information on OWBs or indoor
furnaces, the populations for these appliances had to be estimated using a separate methodology. Projecting
growth for OWBs and indoor furnaces was a challenge due to conflicting data. For OWBs, the last good year of
sales is 2005 in which 67,564 of these units were sold. In 2004, 24,560 were sold. In 2003, 15,342 units were
sold. These data indicate a significant increasing trend. In EPA's earlier estimates for 2008, it was assumed that
sales did not increase in 2006 or 2007; we held sales constant at 67,564 units sold per year, which we thought
was a conservative estimate at the time. Since then, we have decreased the assumed sales, based partly on the
Frost and Sullivan report dated 2010 which reported declining growth since 2008 due to the weak economy,
decline in residential new construction, and the lack of credit. However, Ellen Burkhard with the New York State
Energy Research and Development Authority has higher estimates for NY than the EPA tool. She estimates that
there are 49,000 units in 2010 in NY, versus tool's 2011 estimates of 28,626. Also, we have 2033 OWB units in
the state of Vermont in 2005 and 4014 units in 2008, an almost 100% increase in 3 years from 2005 to 2008
(Note: the source for the 2008 number is the Vermont Residential Fuel survey for the 2007-2008 heating season,
released in August 2011 by the VT Department of Forestry, Parks and Recreation; the source for the 2005
number is the cumulative sales data from NESCAUM). In MN, a 9% increase in OWB population from 2002 to
2008 is reported, which is about a 1.6% increase per year. EPA based its growth projection on this and the Frost
and Sullivan report. Consequently, for the 2011 NEI, we grew the OWB county population from 2008 to 2011 by
a factor of 1.1 for the following states; IL, IN, ME, MA, MN, Ml, NH, NY, OH, VT, and Wl. We assumed no growth
for WA, OR, and HI. All other states were grown from 2008 to 2011 by a factor of 1.067. The factor 1.067 was
chosen because it was 50% of the growth rate we used to grow 2005 to 2008. The 1.1 factor was chosen
because it was conservative, which was in line with comments provided by Ml. For the 2011 v2, we expect to
change the growth rate using sales data reported to EPA by vendors. This sales data shows that sales were
stronger than expected, so this will result in higher emissions from OWBs.
We did not have sales data for Indoor furnaces. Based on a conversation with an industry representative who
indicated that that sales were not good, we assumed no growth from 2008.
Allocating OWBs and Indoor Furnaces to the county level
ERTAC devised two approaches. One was to allocate by an inverse population density, and the other was to
allocate by rural population and to zero out the counties where housing density was above a certain threshold.
Inverse density takes into account the area of the county. So, this normalizes the procedure for the physical size
of the county. The threshold we choose was 300 households/square mile. The ERTAC states that participated in
this exercise also had the opportunity to zero out any additional counties they wanted. The idea was to minimize
the number of these units in the urban counties where we thought they should not be as numerous. OWB and
indoor furnaces are typically used in rural settings, although they do exist in some suburban settings. The units
that were zeroed out were reallocated to other counties, not deleted. This was done on the NEI 2008 v3, and
then this was the baseline data for the 2011 updates.
165
-------
The other appliance types (fireplaces, woodstoves, and inserts) did not need to be allocated to the county level,
because the data from the AHS and other surveys allowed the populations of these appliances to be estimated
at the county level.
Outdoor wood boiler emission factors
For 2011, we updated emission factors for OWB. The factors for all other SCCs which were not updated were a
mix of factors used by MARAMA and for non-certified conventional wood stoves. The emission factor for
mercury was from the EPA's Report to Congress on Mercury. The emission factors are documented in the tool.
The full report title is listed in the references [ref 4], The testing was done by EPA. In general, the emissions for
PM increased. Prior to the 2011 NEI, in lieu of specific data, EPA used the emissions factors for the conventional
woodstoves. For the 2011 NEI, EPA used the emission factors developed in reference 4. Essentially, the emission
factor for outdoor wood boilers for primary PM2 s doubled from 30.6 to 64 lbs primary PM2 s/ton wood burned.
Tool Interface
EPA created a user-friendly interface that simplifies the process of running the RWC Tool. This interface allows
users to select the states for which they would like to estimate emissions. This feature reduces the run time if
the user is only interested in the emissions from one or a few states. Once the desired states are selected, the
user needs only to click a single button to calculate the inventory.
The interface includes easy options for displaying the following:
• County-level input data and Primary PM2.s emissions by SCC and burn type;
• County-level number of appliances by appliance type;
• State-level number of appliances by appliance type;
• Emission factors by SCC; and
• A flow diagram of the calculation methodology.
The ease-of-use provided by this interface could allow for a public release of the tool so that state, local, and
tribal agencies could use it to estimate residential wood combustion emissions in their own locales.
Hazardous Air Pollutant Emission Factors
There are several emission factors for hazardous air pollutants that were not listed uniformly across wood stove
types. For example, some of the emission factors were listed for EPA-certified wood stoves, but not for
conventional (uncertified) wood stoves. Following discussion with EPA, EPA updated the emissions factors listed
in Table 3-101 from all freestanding wood stove and fireplace insert categories with emission factors derived
from Hays et al.18 These emission factors included factors for seven pollutants that were not previously included
in the tool. They are marked as "n/a" in Table 3-101. EPA did not change the emission factors, or add new
emission factors, for any of these pollutants for any of the other SCCs.
18 Hays, M.D., N.D. Smith, J. Kinsey, Y. Dong, P. Kariher. 2003. Polycyclic aromatic hydrocarbon size distributions in aerosols
from appliances of residential wood combustion as determined by direct thermal desorption—GC/MS. Aerosol Science, 34:
1061-1084.
166
-------
Table 3-101: Emission factors for selected hazardous air pollutants in the RWC tool. The emission factors
updated or added for woodstoves (freestanding and inserts)
jut were left unchanged for all other SCCs.
Pollutant
Code
Original
Emission
Factor
Updated
Emission
Factor
Benzo[a]anthracene
56553
n/a
0.000577
Benzo[a]fluoranthene
203338
n/a
0.000321
Benzo[a]Pyrene
50328
0.00248
0.000979
Benzo[b]fluoranthene
205992
n/a
0.000592
Benzo[e]Pyrene
192972
0.00745
0.000589
Benzo[g,h,i,]Perylene
191242
0.00248
0.000201
Benzo[k]Fluoranthene
207089
0.00124
0.000509
Chrysene
218019
0.00745
0.000472
Dibenzo[ah]anthracene
53703
n/a
0.000039
Fluoranthene
206440
0.0124
0.000249
lndeno[l; 2; 3 . cdjpyrene
193395
n/a
0.000408
Methylchrysene
41637905
n/a
0.000058
Perylene
198550
n/a
0.000155
Pyrene
129000
0.0149
0.000217
Changes to Appliance Fractions and Burn Rates for Densely Populated Counties
Following discussion with EPA on the estimation of emissions in densely populated urban areas, EPA made
adjustments to the appliance fractions and burn rates of certain counties based on their population density to
ensure that the tool does not overestimate emissions in those areas.
Specifically, EPA zeroed the burn rates and appliance fractions for all appliances in New York County (FIPS
36061). For counties with more than 1,500 but less than 4,000 homes per square mile, EPA zeroed the burn rate
and appliance fractions for OWBs, indoor furnaces, and outdoor burning (NEC). The burn rates and appliance
fractions for all other appliances were left unchanged for these counties.
For counties with more than 4,000 homes per square mile (except New York County), EPA made several
changes, summarized in the Table 3-102. All counties affected by these changes are shown in Table 3-103.
Table 3-102: Updates to burn rates and appliance fractions in counties with more than 4,000 homes per square
mile (except New York County).
Appliance
Burn Type
Updated Burn Rate
Updated Appliance Fraction
Main
0
0
Fireplaces
Secondary
0.5 (a>
kept as is
Pleasure
0.069 (b>
kept as is
Noncertified
Main
0
0
Woodstoves/
Secondary
1.5 (c>
kept as is
Inserts
Pleasure
0
kept as is
Certified
Main
0
0
Woodstoves/
Secondary
1.2 (d»
kept as is
Inserts
Pleasure
0
kept as is
Main
0
0
Pellet Stoves
Secondary
1.5 (c)
kept as is
Pleasure
0
kept as is
167
-------
Appliance
Burn Type
Updated Burn Rate
Updated Appliance Fraction
Main
0
0
Firelogs
Secondary
0
kept as is
Pleasure
kept as is
kept as is
(a) Assumes approximately one fire per week for 7 months
(b) Assumes approximately four fires per year
(c) Based on engineering judgment
(d) Scaled using the difference in efficiency from AP-42
Emissions for New York County were zeroed out entirely. All other counties with more than 4,000 housing units
per square mile were updated with the appliance fractions and burn rates shown in Table 3-102, and the burn
rates and appliance populations of OWBs, indoor furnaces, and other outdoor burning were zeroed. For
counties with between 1,500 and 4,000 housing units per square mile, the burn rates and appliance populations
of OWBs, indoor furnaces, and other outdoor burning were zeroed, and the burn rates and appliance fractions
for all other appliances were left untouched.
Table 3-103: Densely populated counties subject to updated appliance fractions and burn rates.
County
State
Occupied
Housing Units
Area (mi2)
Density
(Housing Units/mi2)
New York
NY
763,846
25
30,554
Kings
NY
916,856
62
14,788
Bronx
NY
483,449
39
12,396
Queens
NY
780,117
111
7,028
San Francisco
CA
345,811
52
6,650
Hudson
NJ
246,437
54
4,564
Suffolk
MA
292,767
65
4,504
Philadelphia
PA
599,736
148
4,052
Washington
DC
266,707
66
4,041
Alexandria
VA
68,082
18
3,782
Arlington
VA
98,050
26
3,771
Richmond
NY
165,516
53
3,123
Baltimore
MD
249,903
81
3,085
Denver
CO
263,107
104
2,530
Manassas Park
VA
4,507
2
2,254
Essex
NJ
283,712
130
2,182
Cook
IL
1,966,356
961
2,046
St. Louis
MO
142,057
73
1,946
Union
NJ
188,118
106
1,775
Nassau
NY
448,528
278
1,613
Bristol
VA
7,879
5
1,576
Milwaukee
Wl
383,591
244
1,572
Norfolk
VA
86,485
56
1,544
Outdoor Wood Boiler Distribution
168
-------
The OWB populations in the RWC tool were originally based on a combination of data from the Northeast States
for Coordinated Air Use Management (NESCAUM) report Assessment of Outdoor Wood-fired Boilers,19 the 2008
Minnesota Residential Fuelwood Assessment,20 and the 2008 Vermont Residential Fuel Assessment.21
In November, EPA supplied EPA with sales data from 80% of the manufacturers of OWBs showing that 28,075
boilers were sold over a three-year period ending in July 2012 (Table 3-104).22 Scaling these numbers to
estimate 100% of OWB sales (by dividing the total number of OWBs sold by 0.8) suggests that there have been
approximately 35,000 OWBs added to the national population since the 2008 National Emissions Inventory.
Because the data were rolled up to the national level, EPA distributed the OWBs to counties using the
methodology described below, which was developed and approved by the Eastern Regional Technical Advisory
Committee (ERTAC).
Table 3-104: Outdoor wooc
boilers sold from 80% of manufacturers between August 2009 and July 2012.
Time Period
Number of OWBs Sold
8/2009 - 7/2010
7,163
8/2010-7/2011
10,469
8/2011-7/2012
10,754
Total
28,386
First, EPA distributed the 35,000 boilers to all states except Connecticut, Hawaii, Oregon, and Washington,23
based on their existing proportion of OWBs. For example, if a state had 3% of all OWBs in 2008, then it received
3% of the new OWBs, or 1,050 boilers.
Once the boilers were distributed to the states, EPA then distributed the state-level OWBs to counties based on
a county's proportion of rural households in the state. Note that this is slightly different from the method used
to distribute OWBs to counties for the 2008 NEI, in which they were distributed based on rural population,
rather than households.
The U.S. Census Bureau collects information at the county level on the urban and rural population, and the total
households, but it does not break the household data down into urban and rural data. Therefore, EPA estimated
the number of rural households by multiplying the total number of households in each county by the percentage
of the rural population in each county. For example, if 60% of the county's total population is listed as rural,
then the number of households would be multiplied by 0.6 to estimate the number of rural households.
Then EPA distributed each state's population of OWBs to each county based on that county's proportion of rural
households. OWBs were only distributed to counties with an average population density of less than 300 people
per square mile.
EPA used a different methodology to distribute OWBs in the states of Michigan and Ohio, which was also
developed and approved by ERTAC. In keeping with the previous methodology used for the 2008 NEI, state-level
OWBs in Michigan and Ohio were distributed to counties based on inverse population density. Therefore, in
19 NESCAUM. 2006, Assessment of Outdoor Wood-fired Boilers, (accessed July 2014).
20 Barzen, M,, R, Piva, C.Y. Wu, R. Dahlman. 2008. Residential Fuelwood Assessment, State of Minnesota: 2007-2008
Heating Season, (accessed July 2014).
21 See Vermont Division of Forestry, (accessed July 2014).
22 EPA's Burnwise Program has established partnerships with approximately 80% of OWB manufacturers in which the
manufacturers voluntarily report sales data to EPA, (accessed July 2014).
23 These states were excluded based on conversations with the states suggesting no growth in OWBs.
169
-------
these states, the counties with the lowest population density received the highest number of OWBs, but in
keeping with the previous methodology, a cap was employed to ensure that no county would be allocated more
OWBs than 10% of its population. In other words, if a county has a population of 1,000 people and if the inverse
population density method would distribute more than 100 boilers to that county, then the number of boilers in
that county would be set to 100. To ensure that all OWBs estimated for Michigan and Ohio were distributed to
the counties, the boilers in the counties with numbers below the cap were adjusted using the inverse population
density method.
Gas Log Adjustments
After reviewing the AHS questionnaire, EPA determined that the AHS do not distinguish between gas and wood-
burning fireplaces in the data it collects. For this reason, the appliance fractions constructed from AHS data are
likely overestimating the number of wood-burning fireplaces in use. Based on data from Houck (2003), Abt
estimated that approximately 30% of fireplaces use gas. Queries were constructed in the RWC Tool to adjust the
AHS appliance fractions to reflect the number of gas-burning fireplaces. These queries can be adjusted so that
the fraction of gas-burning fireplaces can be changed in the future, and the appliance fractions will be updated
accordingly.
Urban Core Pleasure Burning Adjustments
Many of the appliance profiles in the RWC tool are based on AHS data from Metropolitan Statistical Area (MSA)
surveys. These appliance profiles are typically applied equally across all counties within the relevant MSA. For
example, the appliance profile for Denver was applied equally to all counties in the MSA, even though Denver
County itself is much more densely populated than many of the outlying counties in the MSA.
To address this issue, EPA identified the "urban core" of the MSA based on the county in the MSA with the
highest proportion of multi-family homes (defined here as buildings with three or more living units). EPA then
adjusted the pleasure burning profiles in those counties to account for the proportion of multi-family homes. For
example, if the urban core of the county had 30% of its occupied units in multi-family homes, then EPA
multiplied the appliance fraction by 0.7. EPA also zeroed out the populations of OWBs and indoor furnaces in
the urban core counties.
St. Louis, MO, Adjustments
Following discussions over the high level of RWC emissions in St. Louis, Missouri, EPA revisited the assumptions
about that county. The appliance fractions in the tool were exactly double what they should be using AHS data.
EPA corrected this issue by returning the appliance profile value to the values that agree with AHS data.
3.14.5 Summary of quality assurance methods
EPA expected to see an increase in RWC emissions due to the slow economy and an increase in the price of
alternative heating fuels, like fuel oil and natural gas. Additionally, there were numerous articles in the
newspapers about the increased use of home heating with wood. The RWC tool generates a spreadsheet that
shows the burn rates (cords/year) and the appliance counts for every SCC in every county. That spreadsheet was
sent to ERTAC and other EPA offices for review. The 2011 v2 RWC inventory was compared to 2008 values. One
comment that we received was that emissions were too high in the urban centers in some cities. Additionally,
we were told that CA had some detailed county-level RWC emission data. Adjustments were made to address
the urban core issue (described earlier in this document), and we were able to obtain the CA and put it in our
tool. The EPA also looked for double counting caused by the inconsistent use of SCCs. If a state submitted data
170
-------
using an SCC that was different than the one EPA used, then the EIS could select both estimates, causing a
double count of emissions. This was the situation for CA. CA submitted RWC data to two SCCs; 2104008100 for
fireplaces and 2104008300 for woodstoves and neither SCC is used by the EPA. The EPA used 12 SCCs. The CA
data do not have the detail that the EPA has, so EPA tagged the CA data and used the EPA tool data. The state
level emission totals were similar, plus the underlying EPA RWC tool data had been revised with data from CA,
so EPA believes the use of the RWC tool data is reasonable. The EPA also tagged the RWC data from UT (per a
request from UT) and used the RWC data generated from the EPA RWC tool for UT. UT preferred the EPA
estimates to their own. The EPA also tagged RWC data submitted by CT, ID, MO, and KS because the data was
actually EPA Tool data that the state submitted back to EPA. We believe it better to use EPA data so that the
data source is correctly seen to be generated by EPA. The EPA also tagged numerous PMxx-FIL and PM-CON data
that were erroneously generated by the EPA's PM augmentation tool. The EPA does not have the information to
determine filterable or condensable emissions from primary PM.
3.14.6 References for Fuel Combustion - Residential - Wood
1. Huntley, Roy; Van Bruggen, J., Coldner, S., Divfc 5W Methodology for Estimating Emissions from
Residential Wood Combustion", presented at the 17th International Emission Inventory Conference,
Portland, Oregon, June 2008.
Vermont Residential Fuel Assessment for the 2007-2008 Heating Season, Paul Frederick, Wood Vermont
Residential Fuel Assessment for the 2007-2008 Heating Season, Paul Frederick, Wood Utilization
Forester, August 2011
2. Houck, J. and P. Tiegs, Wood or Gas Fireplaces?, Hearth & Home, October, 2003.
3. Vermont Residential Fuel Assessment for the 2007-2008 Heating Season, Paul Frederick, Wood
Utilization Forester, August 2011.
4. Environmental, Energy Market and Health Characterization of Wood-Fired Hydronic Heater
Technologies, Final Report, Prepared for The New York State Energy Research and Development
Authority, Albany, NY, Ellen Burkhard, Ph.D., Senior Project Manager.
3.15 Industrial Processes - Cement Manufacturing
3.15.1 Sector description
This sector is defined by some, but not all SCCs beginning with 305006, 305007 plus 39000201 (In-Process Fuel
Use /Bituminous Coal /Cement Kiln/Dryer), 39000402 (In-Process Fuel Use /Residual Oil /Cement Kiln/Dryer),
39000502 (In-Process Fuel Use /Distillate Oil /Cement Kiln/Dryer) and 39000602 (In-Process Fuel Use /Natural
Gas /Cement Kiln/Dryer). The processes associated with this sector from 305006 (dry process) and 305007 (wet
process) include the kilns including preheater and pre-calciner kilns, coal kiln feed units, crushing, screening, raw
material grinding and drying, clinker cooler, clinker grinding, cement loadout, pre-dryer, and raw mill processes.
3.15.2 Sources of data overview and selection hierarchy
Cement Manufacturing is covered fully in point. EPA did not provide estimates for nonpoint for this sector. The
selection hierarchy for all datasets contributing to this sector are provided in Table 3-1: Data sources and
selection hierarchy used for point sources.
171
-------
3.15.3 Spatial coverage and data sources for the sector
Industrial Processes - Cement Manuf
P - Point
N - Nonpoint
PN - P&N
P - Point
N - Nonpoint
All CAPs
Industrial Processes - Cement Manuf
PN-P&N
All HAPs I 1 EPA ea SLT ¦¦ rPA&SLT
3.16 Industrial Processes - Chemical Manufacturing
3.16.1 Sector description
This sector involves creating products by transforming organic and inorganic raw materials with chemical
processes. More information on chemical manufacturing can be found on the US EPA Chemical Manufacturing
Sector Information web site. This sector is defined by most point SCCs beginning with 301 and 302, and most
"MACT Source Category" SCCs (beginning with 631, 641, 646, 645, 646, 648, 649, 651, 684 and 685). Most non-
chemical manufacturing SCCs in these ranges deal with "Storage and Transfer" processes (see Section 3.23). This
sector also includes a handful of nonpoint SCCs (beginning with 230100, 230101, 230102, 230103 and 231004).
3.16.2 Sources of data overview and selection hierarchy
Chemical Manufacturing is covered almost completely in point. EPA did not provide estimates for nonpoint for
this sector. The selection hierarchy for all point inventory datasets contributing to this sector are provided in
Table 3-1: Data sources and selection hierarchy used for point sources. The selection hierarchy for all nonpoint
inventory datasets contributing to this sector are provided in Table 3-2: Data sources and selection hierarchy
used for nonpoint sources.
172
-------
3.16.3 Spatial coverage and data sources for the
Industrial Processes - Chemical Manuf
P - Point
N - Nonpoint
P - Point
N - Nonpoint
sector
Industrial Processes - Chemical Manuf
PN - P&N
All CAPs 1 I EPA H=l 3..T ¦¦ EPAiSLT
PN-P&N
All HAPs L J EPA SLT ¦¦ EPA S 8LT
3.17 Industrial Processes - Ferrous Metals
3.17.1 Sector description
This sector is defined by the processing of iron ores to metals. This sector includes primary and secondary metal
production processes such as taconite iron ore processing (SCCs beginning with 303023), grey iron foundries
(SCCs beginning with 304003), steel foundries (SCCs beginning with 304007) and malleable iron (SCCs beginning
with 304009). Most non-ferrous metals SCCs in these SCC ranges deal with "Storage and Transfer" processes
(see Section 3.23).
3.17.2 Sources of data overview and selection hierarchy
Ferrous Metals is covered fully in the point data category. EPA did not provide estimates for nonpoint data
category for this sector. The selection hierarchy for all point inventory datasets contributing to this sector are
provided in Table 3-1: Data sources and selection hierarchy used for point sources.
3.17.3 Spatial coverage and data sources for the sector
Industrial Processes - Ferrous Metals
P - Point
N - Nonpoint
PN-P&N
P - Point
N - Nonpoint
PN - P&N
All CAPS 1—™ ™';T
Industrial Processes - Ferrous Metals
All HAPs
173
-------
3.18 Industrial Processes - Mining
3.18.1 Sector description
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 2011 NEI has emissions for the SCCs shown in Table 3-105 for this sector. The first 4 SCCs are in the
nonpoint data category and the remaining are point. The EPA-estimated emissions cover only SCC 2325000000
(first row of the table). Emissions for all other SCCs were submitted by S/L/T agency.
Table 3-105: SCCs for Industrial Processes- Mining
SCC
SCC level Two
SCC Level Three
SCC Level Four
2325000000
Mining and Quarrying: SIC 14
All Processes
Total
2325020000
Mining and Quarrying: SIC 14
Crushed and Broken Stone
Total
2325030000
Mining and Quarrying: SIC 14
Sand and Gravel
Total
2325060000
Mining and Quarrying: SIC 14
Lead Ore Mining and Milling
Total
30302401
Primary Metal Production
Metal Mining (General Processes)
Primary Crushing: Low Moisture Ore
30302402
Primary Metal Production
Metal Mining (General Processes)
Secondary Crushing: Low Moisture Ore
30302403
Primary Metal Production
Metal Mining (General Processes)
Tertiary Crushing: Low Moisture Ore
30302404
Primary Metal Production
Metal Mining (General Processes)
Material Handling: Low Moisture Ore
30302405
Primary Metal Production
Metal Mining (General Processes)
Primary Crushing: High Moisture Ore
30302406
Primary Metal Production
Metal Mining (General Processes)
Secondary Crushing: High Moisture Ore
30302407
Primary Metal Production
Metal Mining (General Processes)
Tertiary Crushing: High Moisture Ore
30302408
Pr
mary Metal Production
Metal Mining (General Processes)
Material Handling: High Moisture Ore
30302409
Pr
mary Metal Production
Metal Mining (General Processes)
Dry Grinding with Air Conveying
30302410
Pr
mary Metal Production
Metal Mining (General Processes)
Dry Grinding without Air Conveying
30302411
Pr
mary Metal Production
Metal Mining (General Processes)
Ore Drying
30303102
Pr
mary Metal Production
Leadbearing Ore Crushing and Grinding
Zinc Ore w/ 0.2% Lead Content
30303107
Pr
mary Metal Production
Leadbearing Ore Crushing and Grinding
Copper-Lead-Zinc w/ 2% Lead Content
30501001
M
neral Products
Coal Mining, Cleaning, and Material Handling
Fluidized Bed Reactor
30501002
M
neral Products
Coal Mining, Cleaning, and Material Handling
Flash or Suspension Dryer
30501003
M
neral Products
Coal Mining, Cleaning, and Material Handling
Multilouvered Dryer
30501004
M
neral Products
Coal Mining, Cleaning, and Material Handling
Rotary Dryer
30501005
M
neral Products
Coal Mining, Cleaning, and Material Handling
Cascade Dryer
30501006
M
neral Products
Coal Mining, Cleaning, and Material Handling
Continuous Carrier/Conveyor
30501008
M
neral Products
Coal Mining, Cleaning, and Material Handling
Unloading
30501009
M
neral Products
Coal Mining, Cleaning, and Material Handling
Raw Coal Storage
30501010
M
neral Products
Coal Mining, Cleaning, and Material Handling
Crushing
30501011
M
neral Products
Coal Mining, Cleaning, and Material Handling
Coal Transfer
30501012
M
neral Products
Coal Mining, Cleaning, and Material Handling
Screening
30501013
M
neral Products
Coal Mining, Cleaning, and Material Handling
Coal Cleaning: AirTable
30501014
M
neral Products
Coal Mining, Cleaning, and Material Handling
Cleaned Coal Storage
Coal Loading (For Clean Coal Loading USE
30501015
Mineral Products
Coal Mining, Cleaning, and Material Handling
30501016)
30501016
Mineral Products
Coal Mining, Cleaning, and Material Handling
Clean Coal Loading
30501017
Mineral Products
Coal Mining, Cleaning, and Material Handling
Secondary Crushing
30501022
Mineral Products
Coal Mining, Cleaning, and Material Handling
Drilling/Blasting
30501024
Mineral Products
Coal Mining, Cleaning, and Material Handling
Hauling
Topsoil Removal (See also 305010 -33, -35, -
30501030
M
neral Products
Coal Mining, Cleaning, and Material Handling
36, -37, -42, -45, -48)
30501031
M
neral Products
Coal Mining, Cleaning, and Material Handling
Scrapers: Travel Mode
30501032
M
neral Products
Coal Mining, Cleaning, and Material Handling
Topsoil Unloading
Overburden (See also 305010 -30, -35, -36, -
30501033
Mineral Products
Coal Mining, Cleaning, and Material Handling
37, -42, -45, -48)
30501034
Mineral Products
Coal Mining, Cleaning, and Material Handling
Coal Seam: Drilling
30501035
Mineral Products
Coal Mining, Cleaning, and Material Handling
Blasting: Coal Overburden
30501036
Mineral Products
Coal Mining, Cleaning, and Material Handling
Dragline: Overburden Removal
174
-------
see
SCC level Two
SCC Level Three
SCC Level Four
30501037
M
neral Products
Coal M
n
ng, Cleaning, and Material Handling
Truck Loading: Overburden
30501038
M
neral Products
Coal M
n
ng, Cleaning, and Material Handling
Truck Loading: Coal
30501039
M
neral Products
Coal M
n
ng, Cleaning, and Material Handling
Hauling: Haul Trucks
30501040
M
neral Products
Coal M
n
ng, Cleaning, and Material Handling
Truck Unloading: End Dump - Coal
30501041
M
neral Products
Coal M
n
ng, Cleaning, and Material Handling
Truck Unloading: Bottom Dump - Coal
30501043
M
neral Products
Coal M
n
ng, Cleaning, and Material Handling
Open Storage Pile: Coal
30501044
M
neral Products
Coal M
n
ng, Cleaning, and Material Handling
Train Load
ng: Coal
30501045
M
neral Products
Coal M
n
ng, Cleaning, and Material Handling
Bulldozing
Overburden
30501046
M
neral Products
Coal M
n
ng, Cleaning, and Material Handling
Bulldozing
Coal
30501047
M
neral Products
Coal M
n
ng, Cleaning, and Material Handling
Grading
30501048
M
neral Products
Coal M
n
ng, Cleaning, and Material Handling
Overburden Replacement
30501049
M
neral Products
Coal M
n
ng, Cleaning, and Material Handling
Wind Erosion: Exposed Areas
30501050
M
neral Products
Coal M
n
ng, Cleaning, and Material Handling
Vehicle Traffic: Light/Medium Vehicles
30501051
Mineral Products
Coal Mining, Cleaning, and Material Handling
Surface Mining Operations: Open Storage Pile:
Spoils
30501060
Mineral Products
Coal Mining, Cleaning, and Material Handling
Surface Mining Operations: Primary Crusher
30501061
Mineral Products
Coal Mining, Cleaning, and Material Handling
Surface Mining Operations: Secondary
Crusher
30501062
Mineral Products
Coal Mining, Cleaning, and Material Handling
Surface Mining Operations: Screens
30501090
Mineral Products
Coal Mining, Cleaning, and Material Handling
Haul Roads: General
30501099
Mineral Products
Coal Mining, Cleaning, and Material Handling
Other Not Classified
30501640
Mineral Products
Lime Manufacture
Vehicle Traffic
30501650
Mineral Products
Lime Manufacture
Quarrying Raw Limestone
30502009
Mineral Products
Stone Quarrying - Processing (See also 305320)
Blasting: General
30502010
Mineral Products
Stone Quarrying - Processing (See also 305320)
Drilling
30502513
Mineral Products
Construction Sand and Gravel
Excavating
30502514
Mineral Products
Construction Sand and Gravel
Drilling and Blasting
30504001
Mineral Products
M
n
ng and Quarry
ng of Nonmetall
c M
nerals
Open P
t Blasting
30504002
Mineral Products
M
n
ng and Quarry
ng of Nonmetall
c M
nerals
Open P
t Drilling
30504003
Mineral Products
M
n
ng and Quarry
ng of Nonmetall
c M
nerals
Open P
t Cobbing
30504010
Mineral Products
M
n
ng and Quarry
ng of Nonmetall
c M
nerals
Underground Ventilation
30504024
Mineral Products
M
n
ng and Quarry
ng of Nonmetall
c M
nerals
Overburden Stripping
30504401
Mineral Products
Clay processing: Bentonite
Mining
30504601
Mineral Products
Clay processing: Common clay and shale, NEC
Mining
SCC Level 1 is "Industrial Processes" for all SCCS
3.18.2 Sources of data overview and selection hierarchy
The industrial processes-mining sector includes data from S/L/T agency and EPA datasets that cover both point
and nonpoint data categories. Table 3-106 shows the agencies that submitted data in each of the data
categories for the Industrial Processes - Mining sector. Where only zero emissions were submitted (sum across
all pollutants submitted), these are shown as zeroes ("0") in the table.
Table 3-106: Agencies that submitted data for the Industrial Processes - Mining sector
NONPOINT
POINT
Nonpoint: Mining and
quarrying
SIC 24
Mineral products
Primary
metal
production
AGENCY DESCRIPTION
Lead Ore Mining and
Milling
All Processes
Crushed and Broken
Stone
Sand and Gravel
Clay processing:
Bentonite
Clay processing:
Common day and shale,
NEC
Coal Mining, Cleaning,
and Material Handling
Construction Sand and
Gravel
Lime Manufacture
Mining and Quarrying
of Nonmetallic Minerals
Stone Quarrying -
Processing
Leadbearing Ore
Crushing and Grinding
Metal Mining (General
Processes)
US Environmental Protection Agency *
EPA
X
X
X
X
X
X
X
X
X
X
X
X
Alabama Department of Environmental
Management
S
X
X
X
175
-------
NONPOINT
POINT
Nonpoint: Mining and
quarrying
SIC 24
Mineral products
Primary
metal
production
AGENCY DESCRIPTION
Lead Ore Mining and
Milling
All Processes
Crushed and Broken
Stone
Sand and Gravel
Clay processing:
Bentonite
Clay processing:
Common day and shale,
NEC
Coal Mining, Cleaning,
and Material Handling
Construction Sand and
Gravel
Lime Manufacture
Mining and Quarrying
of Nonmetallic Minerals
Stone Quarrying -
Processing
Leadbearing Ore
Crushing and Grinding
Metal Mining (General
Processes)
Alaska Department of Environmental
Conservation
S
X
X
X
Allegheny County Health Department
L
X
X
Arizona Department of Environmental Quality
S
X
X
X
X
Arkansas Department of Environmental
Quality
S
X
X
California Air Resources Board
S
X
X
X
X
X
X
X
Chattanooga Air Pollution Control Bureau
(CHCAPCB)
L
0
Clark County Department of Air Quality and
Environmental Management
L
X
X
Colorado Department of Public Health and
Environment
S
X
X
X
X
X
X
X
Florida Department of Environmental
Protection
S
X
X
Georgia Department of Natural Resources
S
X
X
Idaho Department of Environmental Quality
S
X
0
Illinois Environmental Protection Agency
S
0
X
X
X
X
Indiana Department of Environmental
Management
S
X
X
X
X
Iowa Department of Natural Resources
S
X
X
X
0
X
Jefferson County (AL) Department of Health
L
X
X
Kansas Department of Health and
Environment
S
X
X
X
Kentucky Division for Air Quality
S
X
X
0
Lincoln/Lancaster County Health Department
L
X
Louisiana Department of Environmental
Quality
S
X
X
Louisville Metro Air Pollution Control District
L
X
Maricopa County Air Quality Department
L
X
Maryland Department of the Environment
S
X
X
0
Massachusetts Department of Environmental
Protection
S
X
Memphis and Shelby County Health
Department - Pollution Control
L
X
Metro Public Health of Nashville/Davidson
County
L
0
Michigan Department of Environmental
Quality
S
X
X
X
X
X
Minnesota Pollution Control Agency
S
X
X
X
Mississippi Dept of Environmental Quality
S
X
Missouri Department of Natural Resources
S
X
X
X
X
Montana Department of Environmental
Quality
S
X
X
X
X
Navajo Nation
T
X
Nebraska Environmental Quality
S
X
X
176
-------
NONPOINT
POINT
Nonpoint: Mining and
quarrying
SIC 24
Mineral products
Primary
metal
production
AGENCY DESCRIPTION
Lead Ore Mining and
Milling
All Processes
Crushed and Broken
Stone
Sand and Gravel
Clay processing:
Bentonite
Clay processing:
Common day and shale,
NEC
Coal Mining, Cleaning,
and Material Handling
Construction Sand and
Gravel
Lime Manufacture
Mining and Quarrying
of Nonmetallic Minerals
Stone Quarrying -
Processing
Leadbearing Ore
Crushing and Grinding
Metal Mining (General
Processes)
Nevada Division of Environmental Protection
S
X
X
New Hampshire Department of
Environmental Services
S
0
New Jersey Department of Environment
Protection
s
0
X
X
X
New Mexico Environment Department Air
Quality Bureau
s
X
X
New York State Department of Environmental
Conservation
s
X
X
North Carolina Department of Environment
and Natural Resources
s
X
Ohio Environmental Protection Agency
s
X
X
X
X
Oklahoma Department of Environmental
Quality
s
X
X
Oregon Department of Environmental Quality
s
X
Pennsylvania Department of Environmental
Protection
s
X
X
X
Pinal County
L
X
X
X
Puerto Rico
S
X
Santee Sioux Nation
T
X
South Carolina Department of Health and
Environmental Control
S
X
X
X
X
South Dakota Department of Environment
and Natural Resources
S
X
Southwest Clean Air Agency
L
X
Tennessee Department of Environmental
Conservation
S
X
Texas Commission on Environmental Quality
S
X
X
X
X
X
Utah Division of Air Quality
S
X
X
X
X
X
Virginia Department of Environmental Quality
S
X
X
X
X
West Virginia Division of Air Quality
S
X
X
X
X
Wisconsin Department of Natural Resources
S
X
X
X
X
Wyoming Department of Environmental
Quality
S
X
X
X
X
X
EPA data for most categories is due to PM augmentation of S/L/T agency data (see Section 3.1.2). EPA estimates for SCC 2325000000 is
described in Section 3.18.4.
177
-------
3.18.3 Spatial coverage and data sources for the sector
Industrial Processes - Mining
P - Point
N - Nonpoint
PN-P&N
P - Point
N - Nonpoint
All CAPs
PN - P&N
B SLT ¦¦ EPA 5 sir
Industrial Processes - Mining
All HAPs
3.18.4 EPA-deveioped emissions
The below sections explain how the PMio and PM2.5 emissions for the EPA data (SCC 2325000000; Industrial
Processes; Mining and Quarrying: SiC 14; All Processes; Total) were developed.
3.18.4.1 Metallic Ore Mining- emission factors and equations
The emissions factor for metallic ore mining includes overburden removal, drilling and blasting, and loading and
unloading activities. The 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)
EFi = PM 10 loading emission factor for copper ore (lbs/ton)
EFd = PM 10 truck dumping emission factor for copper ore (ibs/ton)
Applying the copper ore mining TSP emissions factors [ref 2] and PM10/TSP ratios yields the following metallic
ore mining emissions factor:
EFmo = 0.0003 + (0.57625 x 0.0008) + 0.022 + 0.032 = 0.0548 Ibs/ton
3.18.4.2 Non-Metallic Ore Mining- emission factors and equations
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)
178
-------
where,
EFnmo = non-metallic ore mining emissions factor (lbs/ton)
EFV = PM 10 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
EF, = PM 10 drilling/blasting emission factor at western surface coal mining operations (lbs/ton)
EFa = PM 10 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 emissions 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
3.18.4.3 Coal Mining- emission factors and equations
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:
EFC = (10 x (EFto + EFor + EFdt)) + EFV + EF, +EFa + (0.5 x (EFe + EFt))
where,
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)
EF, = 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 PMio emissions 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
179
-------
PM-FIL emissions factors are assumed to be the same as PM-PRI emissions factors; however, in reality, 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 s/PMw ratios for several fugitive dust categories and
concluded that the PM2 s/PMio 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 PMW emissions factors to estimate PM2s emissions factors for
mining and quarrying. A summary of emissions factors is presented in Table 3-107.
Table 3-107: Summary of 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
3.18.4.4 EPA activity data
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
Energy Information Administration (EIA) [ref 7], 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 is 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.
3.18.4.5 A ctivity aiioca tion procedure
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 was multiplied by the ratio of county- to state-level
number of employees in the metallic and non-metallic mining industries (see Table 3-108 for a list of NAICS
codes).
Table 3-108: NAICS codes for Metallic and Non-Metallic Mining
NAICS Code
Description
2122
Metal Ore Mining
212210
Iron Ore Mining
180
-------
NAICS Code
Description
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 was obtained from the U.S. Census Bureau's 2009 County Business Patterns (CBP) [ref 8], Due
to concerns with releasing confidential business information, the CBP does not release exact numbers for a
given NAICS code if there are enough data that individual businesses could be identified. Instead a series of
range codes is used. To estimate withheld counties the following procedure was used for each NAICS code being
computed.
1. County level data for counties with known employment were 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: 1-19 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.
For example, take the 2006 CBP data for NAICS 31-33 (Manufacturing) in Maine provided in Table 3-109.
181
-------
Table 3-109: 2006 County Business Pattern for NAICS 31-33 in Maine
State
County
Mid-
Total
FIPS
FIPS
NAICS
point flag
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 59322 employees for NAICS 31—. The difference is 6521.
3. County 015 is given a midpoint of 1750 (since range code F is 1000-2499) and County 023 is given a
midpoint of 17500.
4. State total for these two counties is 19250.
5. 6521/19250 = 0.33875.
6. The adjusted employment for county 015 is 1750*0.33875 = 592.82. County 023 has an adjusted
employment of 17500*0.33875 = 5928.18.
In the event that data at the state level is 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
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.
3.13.4.6 Controls
No controls were accounted for in the emissions estimation.
3.18.4.7 EPA approach - emissions equation and sample calculation
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 + En + Ec
where,
E = PM io emissions from mining and quarrying operations
Em = PM io emissions from metallic ore mining operations
182
-------
En = PM 10 emissions from non-metallic ore mining
Ec = PM 10 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:
E = EF x A
where,
E = PM 10 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 2009 Autauga County, Alabama handled 456,346 tons of metallic ore and associated waste,
714,718 tons of non-metallic ore and associated waste, and 0 tons of coal. Mining and quarrying PM10-PRI
emissions for Autauga County are:
Epmio-pri, Autauga county = [(456,346x0.0548) + (714,718x0.293) + (0x0.513)]/2000 = 117 tons
The division by 2000 is to convert from pounds to tons.
3.18.5 References for Industrial Processes - Mining
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 November 2011).
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 December 2011).
6. United States Geologic Survey, "Minerals Yearbook 2009", (accessed April 2012).
7. Energy Information Administration, "Production by Company and Mine - 2009", (accessed April 2012).
8. U.S. Census Bureau, 2009 County Business Patterns, (accessed April 2012)
183
-------
3.19 Industrial Processes - Non-ferrous Metals
3.19.1 Sector description
This sector is defined by the processing of non-iron various types of metals. This sector includes, but is not
limited to: primary and secondary metal production processes such as alumina electrolytic reduction (SCCs
beginning with 303001 and 303040), primary copper smelting (303005x), lead production (303010x), gold
processing (303013x), barium ore processing (303014x), zinc production (303030x), aluminum (304001) copper
(304002x), lead (3040Q4x), lead battery manufacture (304005x), magnesium (304006x), zinc (304008x) and
nickel (304010x) and numerous other primary and secondary metal production processes with SCCs beginning
with 303 and 304. Most other SCCs in these ranges not related to non-ferrous metals deal with "Storage and
Transfer" processes (see Section 3.23).
3.19.2 Sources of data overview and selection hierarchy
Non-ferrous metals are covered mostly in the point data category. EPA did not provide estimates for nonpoint
for this sector. The selection hierarchy for all point inventory datasets contributing to this sector are provided in
Table 3-1: Data sources and selection hierarchy used for point sources. The selection hierarchy for all nonpoint
inventory datasets contributing to this sector are provided in Table 3-2: Data sources and selection hierarchy
used for nonpoint sources.
3.19.3 Spatial coverage and data sources for the sector
Industrial Processes - Non-ferrous Metals
Industrial Processes - Non-ferrous Metals
P - Point
N - Nonpoint
PN - P&N
All CAPs
3.20 Industrial Processes - Oil & Gas Production
3.20.1 Sector description
This sector includes processes associated with the exploration and drilling at oil and gas 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. Table 3-110 lists the processes below with their corresponding SCCs; the SCCs used by EPA
to estimate nonpoint emissions marked in second column. 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 2011 NEI. All of the SCCs that the EPA oil
and gas tool uses are nonpoint SCCs.
P - Point
N - Nonpoint
PN - P&N
All HAPs
184
-------
Table 3-110: SCCs used for the Oil and Gas Production sector
Data
EPA
Category
uses
see
SCC Description (Abbreviated)
Nonpoint
2310000000
Total: All Processes (doesn't distinguish oil or gas)
Nonpoint
Y
2310000220
Drill Rigs
Nonpoint
2310000230
Workover Rigs
Nonpoint
Y
2310000330
Artificial Lift
Nonpoint
Y
2310000550
Produced Water
Nonpoint
Y
2310000660
Hydraulic Fracturing Engines
2310002000
Off-Shore Oil & Gas Production;
Nonpoint
through
2310002421
Total: All Processes, Flares: Continuous Pilot Light, Flares: Flaring Operations,
Pneumatic Pumps: Gas and Oil Wells, Pressure/Level Controllers, Cold Vents
Nonpoint
2310010000
Crude Petroleum; Total: All Processes
Nonpoint
Y
2310010100
Crude Petroleum; Oil Well Heaters
Nonpoint
Y
2310010200
Crude Petroleum; Oil Well Tanks - Flashing & Standing/Working/Breathing
Nonpoint
Y
2310010300
Crude Petroleum; Oil Well Pneumatic Devices
Nonpoint
2310010700
Crude Petroleum; Oil Well Fugitives
Nonpoint
2310010800
Crude Petroleum; Oil Well Truck Loading
Nonpoint
Y
2310011000
On-shore oil production; Total: All Processes
Nonpoint
2310011020
On-shore oil production; Storage Tanks: Crude Oil
Nonpoint
2310011100
On-shore oil production; Heater Treater
Nonpoint
Y
2310011201
On-shore oil production; Tank Truck/Railcar Loading: Crude Oil
Nonpoint
2310011450
On-shore oil production; Wellhead
Nonpoint
2310011500
On-shore oil production; Fugitives: All Processes
Nonpoint
Y
2310011501
On-shore oil production; Fugitives: Connectors
Nonpoint
Y
2310011502
On-shore oil production; Fugitives: Flanges
Nonpoint
Y
2310011503
On-shore oil production; Fugitives: Open Ended Lines
Nonpoint
2310011504
On-shore oil production; Fugitives: Pumps
Nonpoint
Y
2310011505
On-shore oil production; Fugitives: Valves
Nonpoint
2310011506
On-shore oil production; Fugitives: Other
Off-Shore Oil Production:
2310012000
Total: All Processes, Storage Tanks: Crude Oil, Fugitives, Connectors: Oil
Nonpoint
through
2310012526
Streams, Fugitives, Flanges: Oil, Fugitives, Valves: Oil, Fugitives, Other: Oil,
Fugitives, Connectors: Oil/Water Streams, Fugitives, Flanges: Oil/Water,
Fugitives, Other: Oil/Water
2310020000
Nonpoint
through
2310020800
Natural Gas; Total: All Processes, Compressor Engines, Gas Well Truck Loading
Nonpoint
Y
2310021010
On-Shore Gas Production; Storage Tanks: Condensate
Nonpoint
2310021011
On-Shore Gas Production; Condensate Tank Flaring
Nonpoint
Y
2310021030
On-Shore Gas Production; Tank Truck/Railcar Loading: Condensate
Nonpoint
Y
2310021100
On-Shore Gas Production; Gas Well Heaters
Nonpoint
2310021101
Natural Gas Fired 2Cycle Lean Burn Compressor Engines < 50 HP
Nonpoint
2310021102
Natural Gas Fired 2Cycle Lean Burn Compressor Engines 50 To 499 HP
Nonpoint
2310021103
Natural Gas Fired 2Cycle Lean Burn Compressor Engines 500+ HP
185
-------
Data
EPA
Category
uses
see
SCC Description (Abbreviated)
Nonpoint
2310021201
Natural Gas Fired 4Cycle Lean Burn Compressor Engines <50 HP
Nonpoint
Y
2310021202
Natural Gas Fired 4Cycle Lean Burn Compressor Engines 50 To 499 HP
Nonpoint
2310021203
Natural Gas Fired 4Cycle Lean Burn Compressor Engines 500+ HP
Nonpoint
2310021209
Total: All Natural Gas Fired 4Cycle Lean Burn Compressor Engines
Nonpoint
Y
2310021251
On-Shore Gas Production; Lateral Compressors 4 Cycle Lean Burn
Nonpoint
Y
2310021300
On-Shore Gas Production; Gas Well Pneumatic Devices
Nonpoint
2310021301
Natural Gas Fired 4Cycle Rich Burn Compressor Engines <50 HP
Nonpoint
Y
2310021302
Natural Gas Fired 4Cycle Rich Burn Compressor Engines 50 To 499 HP
Nonpoint
2310021303
Natural Gas Fired 4Cycle Rich Burn Compressor Engines 500+ HP
Nonpoint
2310021309
Total: All Natural Gas Fired 4Cycle Rich Burn Compressor Engines
Nonpoint
2310021310
On-Shore Gas Production; Gas Well Pneumatic Pumps
Nonpoint
Y
2310021351
On-Shore Gas Production; Lateral Compressors 4 Cycle Rich Burn
Nonpoint
Y
2310021400
On-Shore Gas Production; Gas Well Dehydrators
Nonpoint
2310021401
Nat Gas Fired 4Cycle Rich Burn Compressor Engines <50 HP w/NSCR
Nonpoint
2310021402
Nat Gas Fired 4Cycle Rich Burn Compressor Engines 50 To 499 HP w/NSCR
Nonpoint
2310021403
Nat Gas Fired 4Cycle Rich Burn Compressor Engines 500+ HP w/NSCR
Nonpoint
2310021411
On-Shore Gas Production; Gas Well Dehydrators - Flaring
Nonpoint
2310021500
On-Shore Gas Production; Gas Well Completion - Flaring
Nonpoint
Y
2310021501
On-Shore Gas Production; Fugitives: Connectors
Nonpoint
Y
2310021502
On-Shore Gas Production; Fugitives: Flanges
Nonpoint
Y
2310021503
On-Shore Gas Production; Fugitives: Open Ended Lines
Nonpoint
2310021504
On-Shore Gas Production; Fugitives: Pumps
Nonpoint
Y
2310021505
On-Shore Gas Production; Fugitives: Valves
Nonpoint
Y
2310021506
On-Shore Gas Production; Fugitives: Other
Nonpoint
2310021509
On-Shore Gas Production; Fugitives: All Processes
Nonpoint
2310021600
On-Shore Gas Production; Gas Well Venting
Nonpoint
2310021601
On-Shore Gas Production; Gas Well Venting - Initial Completions
Nonpoint
2310021602
On-Shore Gas Production; Gas Well Venting - Recompletions
Nonpoint
Y
2310021603
On-Shore Gas Production; Gas Well Venting - Blowdowns
Nonpoint
2310021604
On-Shore Gas Production; Gas Well Venting - Compressor Startups
Nonpoint
2310021605
On-Shore Gas Production; Gas Well Venting - Compressor Shutdowns
Nonpoint
2310021700
On-Shore Gas Production; Miscellaneous Engines
Off-Shore Gas Production;
2310022000
Total: All Processes, Storage Tanks: Condensate, Turbines: Natural Gas
Nonpoint
through
2310022506
Boilers/Heaters: Natural Gas, Diesel Engines, Amine Unit
Dehydrator, Fugitives, Connectors: Gas Streams, Fugitives, Flanges: Gas
Streams, Fugitives, Valves: Gas, Fugitives, Other: Gas
2310030000
Natural Gas Liquids;
Nonpoint
through
2310030401
Total: All Processes, Gas Well Tanks - Flashing & Standing/Working/
Breathing, Uncontrolled, Gas Well Water Tank Losses, Gas Plant Truck Loading
Nonpoint
Y
2310111100
On-shore Oil Exploration; Mud Degassing
Nonpoint
Y
2310111401
On-shore Oil Exploration; Oil Well Pneumatic Pumps
186
-------
Data
Category
EPA
uses
see
SCC Description (Abbreviated)
Nonpoint
Y
2310111700
On-shore Oil Exploration; Oil Well Completion: All Processes
Nonpoint
2310112401
On-shore Oil Exploration; Oil Well Pneumatic Pumps
Nonpoint
Y
2310121100
Off-shore Oil Exploration; Mud Degassing
Nonpoint
Y
2310121401
Off-shore Oil Exploration; Gas Well Pneumatic Pumps
Nonpoint
Y
2310121700
Off-shore Oil Exploration; Gas Well Completion: All Processes
Nonpoint
2310122100
Off-shore Gas Exploration; Mud Degassing
Point
31000101
through
31000506,
Various descriptions;
Excludes 31000104 through 31000108 and 31000140 through 31000145, which
are in the sector "Industrial Processes - Storage and Transfer"
Point
31088801
through
31088811
Fugitive Emissions; Specify in Comments Field
Point
31700101
Natural Gas Transmission and Storage Facilities; Pneumatic Controllers Low
Bleed
3.20.2 Sources of data overview and selection hierarchy
The S/L/T agencies that submitted data to the EPA are listed in Table 3-111 below, as well as in the charts. A
number of states submitted both point and nonpoint emissions. In all cases, the majority of emissions are in the
nonpoint data category.
Table 3-111: Agencies that submitted data for the Industrial Processes - Oil and Gas Production sector
Data Set Name
State
Dataset Short Name
Data Category
Shoshone-Bannock Tribes of the Fort Hall Reservation of
Idaho
2011TR180
Point
Navajo Nation, Arizona, New Mexico & Utah
2011TR780
Point
Southern Ute Indian Tribe
2011TR750
Point
Alaska Department of Environmental Conservation
AK
2011AKDEC
Point
Alabama Department of Environmental Management
AL
2011ADEM
Point
Jefferson County (AL) Department of Health
AL
2011JeffCty
Point
Arkansas Department of Environmental Quality
AR
2011ARDEQ
Point
Pinal County
AZ
2011Pinal
Point
Arizona Department of Environmental Quality
AZ
2011AZDEQ
Point
California Air Resources Board
CA
2011CARB
Nonpoint
California Air Resources Board
CA
2011CARB
Point
Colorado Department of Public Health and Environment
CO
2011CODPHE
Nonpoint
Colorado Department of Public Health and Environment
CO
2011CODPHE
Point
Connecticut Department Of Environmental Protection
CT
2011CTBAM
Point
Florida Department of Environmental Protection
FL
2011FLDEP
Point
Georgia Department of Natural Resources
GA
2011GADNR
Nonpoint
Georgia Department of Natural Resources
GA
2011GADNR
Point
Iowa Department of Natural Resources
IA
2011IADNR
Point
Illinois Environmental Protection Agency
IL
2011ILEPA
Point
Indiana Department of Environmental Management
IN
2011INDEM
Point
187
-------
Data Set Name
State
Dataset Short Name
Data Category
Kansas Department of Health and Environment
KS
2011KSDOHE
Nonpoint
Kansas Department of Health and Environment
KS
2011KSDOHE
Point
Kentucky Division for Air Quality
KY
2011KYDAQ
Point
Louisiana Department of Environmental Quality
LA
2011LADEQ
Nonpoint
Louisiana Department of Environmental Quality
LA
2011LADEQ
Point
Maryland Department of the Environment
MD
2011MDDOE
Point
Maine Department of Environmental Protection
ME
2011MEDEP
Point
Michigan Department of Environmental Quality
Ml
2011MIDEQ
Point
Michigan Department of Environmental Quality
Ml
2011MIDEQ
Nonpoint
Missouri Department of Natural Resources
MO
2011MODNR
Nonpoint
Missouri Department of Natural Resources
MO
2011MODNR
Point
Mississippi Dept of Environmental Quality
MS
2011MSDEQ
Point
Montana Department of Environmental Quality
MT
2011MTDEQ
Point
North Dakota Department of Health
ND
2011NDDOH
Point
Omaha Air Quality Control Division
NE
2011Omaha
Point
Nebraska Environmental Quality
NE
2011NEDEQ
Point
New Jersey Department of Environment Protection
NJ
2011NJDEP
Point
New Mexico Environment Department Air Quality Bureau
NM
2011NMED
Point
Nevada Division of Environmental Protection
NV
2011NVBAQ
Point
New York State Department of Environmental Conservation
NY
2011NYDEC
Nonpoint
New York State Department of Environmental Conservation
NY
2011NYDEC
Point
Ohio Environmental Protection Agency
OH
2011OHEPA
Nonpoint
Ohio Environmental Protection Agency
OH
2011OHEPA
Point
Oklahoma Department of Environmental Quality
OK
2011OKDEQ
Nonpoint
Oklahoma Department of Environmental Quality
OK
2011OKDEQ
Point
Pennsylvania Department of Environmental Protection
PA
2011PADEP
Nonpoint
Pennsylvania Department of Environmental Protection
PA
2011PADEP
Point
Allegheny County Health Department
PA
2011Alleg
Point
South Carolina Department of Health and Environmental
Control
SC
2011SCDHEC
Point
Texas Commission on Environmental Quality
TX
2011TXCEQ
Nonpoint
Texas Commission on Environmental Quality
TX
2011TXCEQ
Point
Utah Division of Air Quality
UT
2011UTDAQ
Nonpoint
Utah Division of Air Quality
UT
2011UTDAQ
Point
Virginia Department of Environmental Quality
VA
2011VADEQ
Point
Southwest Clean Air Agency
WA
2011SWCAA
Point
Wisconsin Department of Natural Resources
Wl
2011WIDNR
Point
West Virginia Division of Air Quality
WV
2011WVDAQ
Nonpoint
West Virginia Division of Air Quality
WV
2011WVDAQ
Point
Wyoming Department of Environmental Quality
WY
2011WYDEQ
Nonpoint
Wyoming Department of Environmental Quality
WY
2011WYDEQ
Point
188
-------
Table 3-112 shows the selection hierarchy for datasets included in the Industrial Processes - Oil & Gas
Production sector.
Tab
e 3-112: 2011 NEI Industrial Processes - Oil & Gas Production data selection hierarchy
Priority
Dataset Name
Dataset Content
Point Hierarchy
1
2011EPA_PM-Augmentation
Augments PM emissions
2
Responsible Agency Data Set
State and Local Agency submitted emissions
3
2011EPA_chrom_split
Speciates chromium
4
2QllEPA_Other
New Mexico emissions that state was unable to
submit to the EIS due to submittal issues
5
2011EPA_TRI
Toxics Release Inventory data for the year 2011.
6
2011EPA_HAP-Augmentation
Augments HAP emissions
7
2008 MMS Data
Off shore Platforms from the Bureau of Ocean and
Energy Management, carried forward from 2008
Nonpoint Hierarchy
1
Responsible Agency Data Set
State and Local Agency submitted emissions
2
2011EPA_PM-Augmentation
Augments PM emissions
3
2011E P A_N P_Overla p_w_Pt
EPA-generated data
3.20.3 Spatial coverage and data sources for the sector
P-Point
\ 1
N - Nonpoint j
PN - P&N
P - Point
N - Nonpoint
PN - P&N
Industrial Processes - Oil & Gas Production
All CAPs
Industrial Processes - Oil & Gas Production
All HAPs
189
-------
3.20.4 EPA emissions calculation approach
The EPA developed a methodology to estimate nonpoint emissions for the oil and gas production sector. This
development started in April 2012 and was done in collaboration with a national workgroup, which includes
state and regional emissions developers. The tool can produce county-level emissions for calendar year 2011 for
criteria pollutants and their precursors including volatile organic compounds and ammonia, as well as for
hazardous air pollutants (HAPs). This methodology was used by EPA to estimate emissions for use in the NEI for
field exploration, production, and gathering activities. The tool allows the S/L/T agency inventory developers to
subtract out point source emissions from the nonpoint estimates to avoid double counted emissions.
For the 2011 NEI, the tool was used by both states and EPA to estimate emissions. As was the case in previous
NEI cycles, states can use their own methodologies to estimate oil & gas emissions. States can also use the tool
by either using the default tool inputs, or by providing their own basin- and/or county-specific inputs. Custom
inputs to the tool allows for customized emissions from the tool. The tool is pre-populated with basin- and state-
specific inputs where those are available, and it uses default EPA inputs when nothing else is available. The EPA
default inputs are based on data developed during the recent rulemaking for this industry.
In the maps provided in Section 3.20.3, EPA data are considered as "EPA" even when they are based on state-
specific inputs from the tool. The EPA tool contains within its database descriptions of the sources for all data
used. So, the tool is the best place to better understand the underlying origin of the emissions data (see below
for tool access information).
The EPA oil and gas tool considers all significant sources of oil and gas industry emissions, such as:
• Drill rigs
• Workover rigs
• Well completions (flaring/venting for both conventional and green completions)
• Well hydraulic fracturing and completion engines
• Heaters (separator, line, tank, reboilers)
• Storage tanks (condensate, black oil, produced water)
• Mud degassing
• Dehydration units
• Pneumatics (pumps, all other devices)
• Well venting/blow downs (liquid unloading)
• Fugitives
• Truck loading
• Wellhead engines
• Pipeline compressor engines
• Flaring
• Artificial lifts
• Gas actuated pumps
The file contains the tool, directions on how to use the tool, documentation regarding the calculations with
sample calculations and national county level tool-generated emissions from this sector.
3.20.5 Summary of data quality assurance methods
We reviewed data comparisons between the 2008 and 2011 NEIs and between state-submitted data and EPA
generated data. Table 3-113 below lists some comments and the resolution. Many more comments were
received through the national workgroup while building the oil & gas tool. Generally speaking, emissions
190
-------
comparisons between 2008 and 2011 were not very informative because not many states submitted to EPA in
2008, and the industry is changing so fast that 3 years can make a big difference.
Table 3-113: List of comments and resolution for building the 2011 NEI for the Oil and Gas Production sector
State
EIS Sector
Pollutant
Comment
UT
Oil and gas
all
We added emissions from 7 counties that Utah did not submit for. Utah
only submitted data for 2 counties to EPA, the counties done by WRAP.
This was done per in coordination with Utah staff.
TX
Oil and gas
all
We added emissions from 5 SCCs from EPA tool to the NEI, at Texas staff
request, since they did not cover that process. The SCCs are 2310000660
(hydro-fracturing engines), 2310111100 (oil well mud degassing),
2310111401 (Oil well pneumatic pumps), 2310121100 (mud degassing),
and 2310121401 (gas well pneumatic pumps). Since Texas had submitted
emissions values of zero for this process, they asked EPA to tag the state
data, so the EPA data would be selected ahead of the Texas-provided zero
values.
CA
Oil and gas
all
We noted that California estimates look very different compared to EPA's
estimates. Emissions are lower (about one tenth of EPA estimates) and
SCC coverage is different than EPA's. We have discussed this with
California and they have reviewed their data, and we are using the
California-submitted data in the 2011 vl NEI.
We also tagged EPA's oil well completions data, which blocked them from merging to the NEI. These data were
not ready for use in the NEI because the available emission factors are not known to be applicable to oil well
completions. There are no emission factors that are specific to oil-well completions available from EPA at this
time.
We also noticed that in the raw data used by EPA's tool, there was one well that had a wrong latitude/longitude
and was actually supposed to be located in Kansas, not Minnesota, when allocating to counties. To resolve this,
we tagged the data, so it would not appear in Minnesota. Emissions were small enough that we believed it was
not worth the effort to add the well emissions back into the Kansas data (3.3 tons of VOC and 1.7 tons of NOx).
We noted several states where there were large differences between EPA's estimates and the state submittals.
We selected 2 states that had good emission inventory programs and therefore, the staff at each state (WY and
CO) have a lot of confidence in their own estimates. We believed it would be a good calculation check on the
tool if we compared emissions submitted by these states to emissions from the tool.
We compared county level EPA tool data to state submitted data for Sublette County in WY. We picked Sublette
County because of the high activity in that county plus some large differences in emission estimates between
the EPA tool and the state. In the tool for Sublette County, we populated some of the basin factors with data
from the WRAP III study, which the committee considered good data and certainly better than default data from
the CenSARA (Central States Air Resources Agencies) states. In several instances, this turned out not to be true.
For condensate tanks, according to the WRAP III data, none of the emissions from condensate tanks were
controlled by flares, and VOC emissions were calculated at 67,985 tons for condensate tanks for just Sublette
County. That is much higher than the emissions reported by WY (453 tons VOC). WY informed us that all
condensate tanks in Sublette County were controlled by flares. When we changed the basin factor in the tool to
match this new information, the tool calculated 1,622 tons VOC, which is still higher than what was reported by
the state but much more in line with the states estimates. For well completions, again for Sublette County, the
191
-------
WRAP III data had no green completions in Sublette County and the tool calculated emissions of 4240 tons of
VOC. The state submitted emissions of 54 tons of VOC. WY informed us that all well completions in Sublette
County were green, so with this new information, the tool now calculates zero VOC emissions from green
completions. This change brought the tool pretty much in line with the state estimates.
One of the problems with comparing the tool data to WY data is that WY submits a significant portion of their oil
and gas production emissions to the point source sector and it is not trivial to query and analyze. Currently the
tool still estimates about 12,000 more tons of VOC in Sublette County than the state submitted in the nonpoint,
and the discrepancy may be the emission submitted by WY in the point source sector. Another case in point, for
wellhead compressor engines, we noticed that the tool estimates 4,561 tons of NOx for Sublette County and WY
submitted zero emissions to the nonpoint sector. WY told us that all of their emissions from wellhead
compressors were submitted to the point source inventory.
In Natrona County, WY, the tool has little condensate production, so emissions are low, but WY reports high
condensate tank emissions in Natrona County. The discrepancy was traced to the fact that the HPDI database
called the liquid produced in Natrona County "oil" and WY called the liquid produced "condensate". The
difference is that the emission factor for condensate is about 10 times higher than the emission factor for oil, so
emissions for condensate are going to be a lot higher for condensate. We made the appropriate adjustments in
the tool and then the tool calculation more closely matched WY data, the emissions from the tool matched the
state submitted emissions for condensate tanks a lot better.
3.21 Industrial Processes - Petroleum Refineries
3.21.1 Sector description
This sector includes petroleum industry processes except non-storage and handling processes (see Section 3.23)
with SCCs beginning with 3060x. A couple of nonpoint SCCs for "Petroleum Refining: SIC 29" (2306000000 and
2306010000) are also assigned to this sector. Petroleum refinery processes include but are not limited to:
process heaters, catalytic cracking units, wastewater treatment, cooling towers, flares, distillation, blending and
treating units, incineration, and various fugitive sources at locations such as pipelines, drains and compressors.
3.21.2 Sources of data overview and selection hierarchy
This sector is covered almost completely in the point data category. EPA does not provide estimates for this
sector in nonpoint. The selection hierarchy for all point inventory datasets contributing to this sector are
provided in Table 3-1: Data sources and selection hierarchy used for point sources. The selection hierarchy for
all nonpoint inventory datasets contributing to this sector are provided in Table 3-2: Data sources and selection
hierarchy used for nonpoint sources.
192
-------
3.21.3 Spatial coverage and data sources for the sector
Industrial Processes - Petroleum Refineries
P - Point
N - Nonpoint
PN - P&N
P - Point
N - Nonpoint
All CAPs
Industrial Processes - Petroleum Refineries
PN - P&N
All HAPs 1 I EPA IS—M SIT EPA & SLT
3.22 Industrial Processes - Pulp & Paper
3.22.1 Sector description
This sector includes pulp and paper wood products processes except non-storage and handling processes (see
Section 3.23) with SCCs beginning with 307x. Pulp and paper processes include but are not limited to: sulfate
(Kraft) pulping, sulfite pulping, neutral sulfite semi-chemical pulping, semi-chemical (non-sulfur), soda, wood
pressure treating, particleboard manufacture, plywood and sawmill operations, medium density fiberboard
(MDF), oriented strand board (OSB), laminated strand lumber, fiberboard and hardboard (HB) manufacture, and
miscellaneous wood working operations.
3.22.2 Sources of data overview and selection hierarchy
This sector covered completely in point. The selection hierarchy for all point inventory datasets contributing to
this sector are provided in Table 3-1: Data sources and selection hierarchy used for point sources.
3.22.3 Spatial coverage and data sources for the sector
Industrial Processes - Pulp & Paper Industrial Processes - Pulp & Paper
P - Point
N - Nonpoint
PN - P&N
P - Point
N - Nonpoint
PN - P&N
All CAPs 1 I EPA I II SI r EPA S SLT All HAPs
193
-------
3.23 Industrial Processes-Storage and Transfer
3.23.1 Sector description
This sector includes storage and transport activities at industrial sources and includes emissions categorized as
nonpoint and point. Much of the emissions in this sector are related to working/breathing loss of various fuels
and inorganic and organic chemicals -both liquid and solid. Processes in this sector include those at chemical
manufacturing, primary and secondary metal production and cement mineral processing (e.g., cement
manufacturing) facilities. There is considerable overlap in emissions calculations and methodology with the
processes in the Bulk Gasoline Terminals and Gas Stations sector, particularly for bulk terminals and pipelines
discussed in Section 3.5.4.
3.23.2 Sources of data overview and selection hierarchy
The wide range of processes that define this sector impact most types of industrial facilities and therefore, most
states report both (at least some) point and nonpoint emissions for both CAPs and HAPs. The selection hierarchy
for all point inventory datasets contributing to this sector are provided in Table 3-1: Data sources and selection
hierarchy used for point sources. The selection hierarchy for all nonpoint inventory datasets contributing to this
sector are provided in Table 3-2: Data sources and selection hierarchy used for nonpoint sources.
3.23.3 Spatial coverage and data sources for the sector
Industrial Processes - Storage and Transfer Industrial Processes - Storage and Transfer
P - Point
N - Nonpoint
PN - P&N
P - Point
N - Nonpoint
PN - P&N
All CAPs
All HAPs
3.24 Industrial Processes - NEC (Other)
3.24.1 Sector description
This Industrial Processes NEC (not elsewhere classified) sector includes all industrial processes not covered in
other NEI/EIS sectors (i.e., sectors discussed in Section 3.15 through Section 3.23. These processes are
ubiquitous in the point and nonpoint data categories. Some point inventory processes (SCCs) include: internal
combustion engines wastewater and equipment leaks (2CS18x, 2CS28x, 2038x, 2048x), food and agriculture coffee
roasting, cotton ginning, feed and grain terminal elevators, grain millings, beer production, meat smokehouses,
sugar cane refining, and vegetable oil processing (3020x), by-product coke manufacturing (303003x), asphalt
roofing manufacture (305001x), brick manufacture (305003x), fiberglass manufacture (30501x), glass
manufacture (305014x), lime manufacture (305016x), mineral wood manufacturing (305017x), phosphate rock
194
-------
(305019x), industrial sand and gravel (305027x), tire manufacture (308001x), plastic products manufacturing
(308010x), vinyl floor tile manufacturing (308050x) and hundreds of other industrial processes. Some nonpoint
inventory processes (SCCs) include: food and kindred products (23020x), wood products (23070x) and fabricated
metals (2309x).
3.24.2 Sources of data overview and selection hierarchy
Most of the data in this sector is point sources. EPA does not generate nonpoint emissions for this sector. The
selection hierarchy for all point inventory datasets contributing to this sector are provided in Table 3-1: Data
sources and selection hierarchy used for point sources. The selection hierarchy for all nonpoint inventory
datasets contributing to this sector are provided in Table 3-2: Data sources and selection hierarchy used for
nonpoint sources.
3.24.3 Spatial coverage and data sources for the sector
Industrial Processes - NEC
Industrial Processes - NEC
p PN
P - Point
N - Nonpoint
PN - P&N
All CAPs
p-Point
N - Nonpoint
PN - P&N
AH HAPs
3.25 Miscellaneous Non-industrial NEC (Other)
3.25.1 Sector description
This sector includes primarily nonpoint processes and 4 point processes (waste disposal...firefighting,
SCCs=5010060x). The nonpoint sources include portable fuel containers (SCCs like 250101101x and
250101201x), structure and motor vehicle fires, catastrophic/accidental releases, and human and animal
cremation (28Q100x), automotive repair shops (28400x), miscellaneous repair shops (28410x), health services
(285000x), fluorescent lamp breakage (28610000x) and swimming pools (286200x).
3.25.2 Sources of data overview and selection hierarchy
The miscellaneous non-industrial not elsewhere classified (NEC) sector includes data from S/L/T agency and EPA
datasets that cover both point and nonpoint data categories. Table 3-114 shows the data categories and SCCs
submitted by each agency in this sector. Note that there are a wide range of sources in this sector, including new
(to 2011 v2) nonpoint mercury emissions provided by the EPA. Much of the EPA nonpoint data in this table are
discussed in section 3.1.7. The only EPA data in the point inventory in this sector is limited to PM and chromium
augmentation (see Section 3.1.2 and Section 3.1.3). The selection hierarchy for all point inventory datasets
contributing to this sector are provided in Table 3-1: Data sources and selection hierarchy used for point
195
-------
sources. The selection hierarchy for all nonpoint inventory datasets contributing to this sector are provided in
Table 3-2: Data sources and selection hierarchy used for nonpoint sources.
Table 3-114: Agencies and the SCCs submitted for t
Data Set Name
Data
Category
see
SCC Level One
SCC Level Two
SCC Level Three
SCC Level
Four
EPA Nonpoint Mercury
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
EPA Nonpoint Mercury
Nonpoint
2810060200
Miscellaneous
Area Sources
Other Combustion
Cremation
Animals
EPA Nonpoint Mercury
Nonpoint
2850001000
Miscellaneous
Area Sources
Health Services
Dental Alloy Production
Overall
Process
EPA Nonpoint Mercury
Nonpoint
2861000000
Miscellaneous
Area Sources
Fluorescent Lamp
Breakage
Non-recycling Related
Emissions
Total
EPA Nonpoint Mercury
Nonpoint
2861000010
Miscellaneous
Area Sources
Fluorescent Lamp
Breakage
Recycling Related
Emissions
Total
2011EPA_chrom_split
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
2011EPA_chrom_split
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
2011EPA_chrom_split
Nonpoint
2810060200
Miscellaneous
Area Sources
Other Combustion
Cremation
Animals
2011EPA_chrom_split
Point
50100601
Waste
Disposal
Solid Waste Disposal -
Government
Fire Fighting
Structure:
Jet Fuel
2011EPA_HAP-
Augmentation
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
2011EPA_NP_NoOverlap_w_
Pt
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
2011EPA_NP_NoOverlap_w_
Pt
Nonpoint
2851001000
Miscellaneous
Area Sources
Laboratories
Bench Scale Reagents
Total
2011E PA_N P_Ove rl a p_w_Pt
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
2011E PA_N P_Ove rl a p_w_Pt
Nonpoint
2810060200
Miscellaneous
Area Sources
Other Combustion
Cremation
Animals
2011E PA_N P_Ove rl a p_w_Pt
Nonpoint
2850000010
Miscellaneous
Area Sources
Health Services
Hospitals
Sterilization
Operations
2011EPA_PM-Augmentation
Nonpoint
2810003000
Miscellaneous
Area Sources
Other Combustion
Cigarette Smoke
Total
2011EPA_PM-Augmentation
Nonpoint
2810025000
Miscellaneous
Area Sources
Other Combustion
Charcoal Grilling -
Residential (see 23-02-
002-xxx for Commercial)
Total
2011EPA_PM-Augmentation
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
2011EPA_PM-Augmentation
Nonpoint
2810035000
Miscellaneous
Area Sources
Other Combustion
Firefighting Training
Total
2011EPA_PM-Augmentation
Nonpoint
2810040000
Miscellaneous
Area Sources
Other Combustion
Aircraft/Rocket Engine
Firing and Testing
Total
2011EPA_PM-Augmentation
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
ie Miscellaneous Non-Industrial - NEC sector
196
-------
Data Set Name
Data
Category
see
SCC Level One
SCC Level Two
SCC Level Three
SCC Level
Four
2011EPA_PM-Augmentation
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
2011EPA_PM-Augmentation
Nonpoint
2810060200
Miscellaneous
Area Sources
Other Combustion
Cremation
Animals
2011EPA_PM-Augmentation
Nonpoint
2830001000
Miscellaneous
Area Sources
Catastrophic/Accident
al Releases
Industrial Accidents
Total
2011EPA_PM-Augmentation
Nonpoint
2850000000
Miscellaneous
Area Sources
Health Services
Hospitals
Total: All
Operations
2011EPA_PM-Augmentation
Point
50100601
Waste
Disposal
Solid Waste Disposal -
Government
Fire Fighting
Structure:
Jet Fuel
2011EPA_PM-Augmentation
Point
50100602
Waste
Disposal
Solid Waste Disposal -
Government
Fire Fighting
Structure:
Distillate Oil
2011EPA_PM-Augmentation
Point
50100603
Waste
Disposal
Solid Waste Disposal -
Government
Fire Fighting
Structure:
Kerosene
2011EPA_PM-Augmentation
Point
50100604
Waste
Disposal
Solid Waste Disposal -
Government
Fire Fighting
Structure:
Wood
Pallets
California Air Resources
Board
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
California Air Resources
Board
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
California Air Resources
Board
Point
50100601
Waste
Disposal
Solid Waste Disposal -
Government
Fire Fighting
Structure:
Jet Fuel
California Air Resources
Board
Point
50100602
Waste
Disposal
Solid Waste Disposal -
Government
Fire Fighting
Structure:
Distillate Oil
Chattanooga Air Pollution
Control Bureau (CHCAPCB)
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
Clark County Department of
Air Quality and
Environmental Management
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Clark County Department of
Air Quality and
Environmental Management
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
Coeur d'Alene Tribe
Nonpoint
2810025000
Miscellaneous
Area Sources
Other Combustion
Charcoal Grilling -
Residential (see 23-02-
002-xxx for Commercial)
Total
Coeur d'Alene Tribe
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Coeur d'Alene Tribe
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
Coeur d'Alene Tribe
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
Coeur d'Alene Tribe
Nonpoint
2810060200
Miscellaneous
Area Sources
Other Combustion
Cremation
Animals
Connecticut Department Of
Environmental Protection
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
DC Department of Health Air
Quality Division
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
197
-------
Data Set Name
Data
Category
see
SCC Level One
SCC Level Two
SCC Level Three
SCC Level
Four
DC Department of Health Air
Quality Division
Nonpoint
2810035000
Miscellaneous
Area Sources
Other Combustion
Firefighting Training
Total
DC Department of Health Air
Quality Division
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
DC Department of Health Air
Quality Division
Nonpoint
2810060200
Miscellaneous
Area Sources
Other Combustion
Cremation
Animals
Delaware Department of
Natural Resources and
Environmental Control
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Delaware Department of
Natural Resources and
Environmental Control
Nonpoint
2810035000
Miscellaneous
Area Sources
Other Combustion
Firefighting Training
Total
Delaware Department of
Natural Resources and
Environmental Control
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
Eastern Band of Cherokee
Indians
Nonpoint
2810025000
Miscellaneous
Area Sources
Other Combustion
Charcoal Grilling -
Residential (see 23-02-
002-xxx for Commercial)
Total
Georgia Department of
Natural Resources
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Georgia Department of
Natural Resources
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
Hawaii Department of
Health Clean Air Branch
Nonpoint
2810010000
Miscellaneous
Area Sources
Other Combustion
Human Perspiration and
Respiration
Total
Hawaii Department of
Health Clean Air Branch
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Hawaii Department of
Health Clean Air Branch
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
Idaho Department of
Environmental Quality
Nonpoint
2810025000
Miscellaneous
Area Sources
Other Combustion
Charcoal Grilling -
Residential (see 23-02-
002-xxx for Commercial)
Total
Idaho Department of
Environmental Quality
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Idaho Department of
Environmental Quality
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
Idaho Department of
Environmental Quality
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
Idaho Department of
Environmental Quality
Nonpoint
2810060200
Miscellaneous
Area Sources
Other Combustion
Cremation
Animals
Illinois Environmental
Protection Agency
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Illinois Environmental
Protection Agency
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
Illinois Environmental
Protection Agency
Nonpoint
2810060200
Miscellaneous
Area Sources
Other Combustion
Cremation
Animals
Illinois Environmental
Protection Agency
Nonpoint
2850001000
Miscellaneous
Area Sources
Health Services
Dental Alloy Production
Overall
Process
198
-------
Data Set Name
Data
Category
see
SCC Level One
SCC Level Two
SCC Level Three
SCC Level
Four
Illinois Environmental
Protection Agency
Nonpoint
2851001000
Miscellaneous
Area Sources
Laboratories
Bench Scale Reagents
Total
Illinois Environmental
Protection Agency
Nonpoint
2861000000
Miscellaneous
Area Sources
Fluorescent Lamp
Breakage
Non-recycling Related
Emissions
Total
Illinois Environmental
Protection Agency
Nonpoint
2861000010
Miscellaneous
Area Sources
Fluorescent Lamp
Breakage
Recycling Related
Emissions
Total
Iowa Department of Natural
Resources
Point
50100601
Waste
Disposal
Solid Waste Disposal -
Government
Fire Fighting
Structure:
Jet Fuel
Kootenai Tribe of Idaho
Nonpoint
2810025000
Miscellaneous
Area Sources
Other Combustion
Charcoal Grilling -
Residential (see 23-02-
002-xxx for Commercial)
Total
Kootenai Tribe of Idaho
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Kootenai Tribe of Idaho
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
Kootenai Tribe of Idaho
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
Kootenai Tribe of Idaho
Nonpoint
2810060200
Miscellaneous
Area Sources
Other Combustion
Cremation
Animals
Louisiana Department of
Environmental Quality
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Maine Department of
Environmental Protection
Nonpoint
2810010000
Miscellaneous
Area Sources
Other Combustion
Human Perspiration and
Respiration
Total
Maine Department of
Environmental Protection
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Maine Department of
Environmental Protection
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
Maine Department of
Environmental Protection
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
Maine Department of
Environmental Protection
Nonpoint
2810060200
Miscellaneous
Area Sources
Other Combustion
Cremation
Animals
Maine Department of
Environmental Protection
Nonpoint
2850000010
Miscellaneous
Area Sources
Health Services
Hospitals
Sterilization
Operations
Maine Department of
Environmental Protection
Nonpoint
2861000000
Miscellaneous
Area Sources
Fluorescent Lamp
Breakage
Non-recycling Related
Emissions
Total
Maricopa County Air Quality
Department
Nonpoint
2810010000
Miscellaneous
Area Sources
Other Combustion
Human Perspiration and
Respiration
Total
Maricopa County Air Quality
Department
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Maricopa County Air Quality
Department
Nonpoint
2810040000
Miscellaneous
Area Sources
Other Combustion
Aircraft/Rocket Engine
Firing and Testing
Total
Maricopa County Air Quality
Department
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
Maricopa County Air Quality
Department
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
Maricopa County Air Quality
Department
Nonpoint
2830001000
Miscellaneous
Area Sources
Catastrophic/Accident
al Releases
Industrial Accidents
Total
199
-------
Data Set Name
Data
Category
see
SCC Level One
SCC Level Two
SCC Level Three
SCC Level
Four
Maricopa County Air Quality
Department
Nonpoint
2850000000
Miscellaneous
Area Sources
Health Services
Hospitals
Total: All
Operations
Maryland Department of the
Environment
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Maryland Department of the
Environment
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
Maryland Department of the
Environment
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
Maryland Department of the
Environment
Nonpoint
2810060200
Miscellaneous
Area Sources
Other Combustion
Cremation
Animals
Maryland Department of the
Environment
Nonpoint
2830000000
Miscellaneous
Area Sources
Catastrophic/Accident
al Releases
All
Catastrophic/Accidental
Releases
Total
Maryland Department of the
Environment
Nonpoint
2850000010
Miscellaneous
Area Sources
Health Services
Hospitals
Sterilization
Operations
Maryland Department of the
Environment
Nonpoint
2851001000
Miscellaneous
Area Sources
Laboratories
Bench Scale Reagents
Total
Maryland Department of the
Environment
Nonpoint
2861000000
Miscellaneous
Area Sources
Fluorescent Lamp
Breakage
Non-recycling Related
Emissions
Total
Maryland Department of the
Environment
Nonpoint
2861000010
Miscellaneous
Area Sources
Fluorescent Lamp
Breakage
Recycling Related
Emissions
Total
Massachusetts Department
of Environmental Protection
Nonpoint
2810025000
Miscellaneous
Area Sources
Other Combustion
Charcoal Grilling -
Residential (see 23-02-
002-xxx for Commercial)
Total
Massachusetts Department
of Environmental Protection
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Massachusetts Department
of Environmental Protection
Nonpoint
2810040000
Miscellaneous
Area Sources
Other Combustion
Aircraft/Rocket Engine
Firing and Testing
Total
Massachusetts Department
of Environmental Protection
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
Massachusetts Department
of Environmental Protection
Nonpoint
2830000000
Miscellaneous
Area Sources
Catastrophic/Accident
al Releases
All
Catastrophic/Accidental
Releases
Total
Metro Public Health of
Nashville/Davidson County
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Metro Public Health of
Nashville/Davidson County
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
Metro Public Health of
Nashville/Davidson County
Nonpoint
2840010000
Miscellaneous
Area Sources
Automotive Repair
Shops
Auto Top and Body
Repair
Total
Minnesota Pollution Control
Agency
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Minnesota Pollution Control
Agency
Nonpoint
2850001000
Miscellaneous
Area Sources
Health Services
Dental Alloy Production
Overall
Process
Minnesota Pollution Control
Agency
Nonpoint
2851001000
Miscellaneous
Area Sources
Laboratories
Bench Scale Reagents
Total
Minnesota Pollution Control
Agency
Nonpoint
2862000000
Miscellaneous
Area Sources
Swimming Pools
Total (Commercial,
Residential, Public)
Total
200
-------
Data Set Name
Data
Category
see
SCC Level One
SCC Level Two
SCC Level Three
SCC Level
Four
Missouri Department of
Natural Resources
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
Missouri Department of
Natural Resources
Nonpoint
2810060200
Miscellaneous
Area Sources
Other Combustion
Cremation
Animals
Nevada Division of
Environmental Protection
Point
50100603
Waste
Disposal
Solid Waste Disposal -
Government
Fire Fighting
Structure:
Kerosene
Nevada Division of
Environmental Protection
Point
50100604
Waste
Disposal
Solid Waste Disposal -
Government
Fire Fighting
Structure:
Wood
Pallets
New Hampshire Department
of Environmental Services
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
New Hampshire Department
of Environmental Services
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
New Jersey Department of
Environment Protection
Nonpoint
2810003000
Miscellaneous
Area Sources
Other Combustion
Cigarette Smoke
Total
New Jersey Department of
Environment Protection
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
New Jersey Department of
Environment Protection
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
New York State Department
of Environmental
Conservation
Nonpoint
2810060200
Miscellaneous
Area Sources
Other Combustion
Cremation
Animals
New York State Department
of Environmental
Conservation
Point
50100602
Waste
Disposal
Solid Waste Disposal -
Government
Fire Fighting
Structure:
Distillate Oil
Nez Perce Tribe
Nonpoint
2810025000
Miscellaneous
Area Sources
Other Combustion
Charcoal Grilling -
Residential (see 23-02-
002-xxx for Commercial)
Total
Nez Perce Tribe
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Nez Perce Tribe
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
Nez Perce Tribe
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
Nez Perce Tribe
Nonpoint
2810060200
Miscellaneous
Area Sources
Other Combustion
Cremation
Animals
Ohio Environmental
Protection Agency
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Ohio Environmental
Protection Agency
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
Oregon Department of
Environmental Quality
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Oregon Department of
Environmental Quality
Nonpoint
2850000010
Miscellaneous
Area Sources
Health Services
Hospitals
Sterilization
Operations
Shoshone-Bannock Tribes of
the Fort Hall Reservation of
Idaho
Nonpoint
2810025000
Miscellaneous
Area Sources
Other Combustion
Charcoal Grilling -
Residential (see 23-02-
002-xxx for Commercial)
Total
201
-------
Data Set Name
Data
Category
see
SCC Level One
SCC Level Two
SCC Level Three
SCC Level
Four
Shoshone-Bannock Tribes of
the Fort Hall Reservation of
Idaho
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Shoshone-Bannock Tribes of
the Fort Hall Reservation of
Idaho
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
Shoshone-Bannock Tribes of
the Fort Hall Reservation of
Idaho
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
Shoshone-Bannock Tribes of
the Fort Hall Reservation of
Idaho
Nonpoint
2810060200
Miscellaneous
Area Sources
Other Combustion
Cremation
Animals
Texas Commission on
Environmental Quality
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Texas Commission on
Environmental Quality
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
Texas Commission on
Environmental Quality
Point
50100601
Waste
Disposal
Solid Waste Disposal -
Government
Fire Fighting
Structure:
Jet Fuel
Utah Division of Air Quality
Nonpoint
2810010000
Miscellaneous
Area Sources
Other Combustion
Human Perspiration and
Respiration
Total
Utah Division of Air Quality
Nonpoint
2810040000
Miscellaneous
Area Sources
Other Combustion
Aircraft/Rocket Engine
Firing and Testing
Total
Utah Division of Air Quality
Point
50100604
Waste
Disposal
Solid Waste Disposal -
Government
Fire Fighting
Structure:
Wood
Pallets
Vermont Department of
Environmental Conservation
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Vermont Department of
Environmental Conservation
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
Vermont Department of
Environmental Conservation
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
Virginia Department of
Environmental Quality
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Virginia Department of
Environmental Quality
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
Washington State
Department of Ecology
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Washington State
Department of Ecology
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
Washington State
Department of Ecology
Point
50100601
Waste
Disposal
Solid Waste Disposal -
Government
Fire Fighting
Structure:
Jet Fuel
Washoe County Health
District
Nonpoint
2810030000
Miscellaneous
Area Sources
Other Combustion
Structure Fires
Unspecified
Washoe County Health
District
Nonpoint
2810035000
Miscellaneous
Area Sources
Other Combustion
Firefighting Training
Total
Washoe County Health
District
Nonpoint
2810050000
Miscellaneous
Area Sources
Other Combustion
Motor Vehicle Fires
Unspecified
202
-------
Data Set Name
Data
Category
see
SCC Level One
SCC Level Two
SCC Level Three
SCC Level
Four
Washoe County Health
District
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
Washoe County Health
District
Nonpoint
2810060200
Miscellaneous
Area Sources
Other Combustion
Cremation
Animals
Washoe County Health
District
Nonpoint
2840000000
Miscellaneous
Area Sources
Automotive Repair
Shops
Automotive Repair Shops
Total
Washoe County Health
District
Nonpoint
2850000000
Miscellaneous
Area Sources
Health Services
Hospitals
Total: All
Operations
Washoe County Health
District
Nonpoint
2851001000
Miscellaneous
Area Sources
Laboratories
Bench Scale Reagents
Total
West Virginia Division of Air
Quality
Nonpoint
2810060100
Miscellaneous
Area Sources
Other Combustion
Cremation
Humans
West Virginia Division of Air
Quality
Nonpoint
2850001000
Miscellaneous
Area Sources
Health Services
Dental Alloy Production
Overall
Process
West Virginia Division of Air
Quality
Nonpoint
2851001000
Miscellaneous
Area Sources
Laboratories
Bench Scale Reagents
Total
West Virginia Division of Air
Quality
Nonpoint
2861000000
Miscellaneous
Area Sources
Fluorescent Lamp
Breakage
Non-recycling Related
Emissions
Total
West Virginia Division of Air
Quality
Nonpoint
2861000010
Miscellaneous
Area Sources
Fluorescent Lamp
Breakage
Recycling Related
Emissions
Total
3.25.3 Spatial coverage and data sources for the sector
Miscellaneous Non-Industrial NEC Miscellaneous Non-Industrial NEC
P - Point
N - Nonpoint
P - Point
N - Nonpoint
PN - P&N PN - P&N
All CAPS E3l» ¦¦SIT ¦¦FP..5LT All HAPS CJEPA ¦¦ f PA ISLT
3.26 Solvent - Consumer & Commercial Solvent Use
3.26.1 Sector description
Consumer products are those products used around the home, office, institution, or similar settings. The
commercial and institutional use of these products is included under "consumer products." The solvent-
containing products in this category include personal care products, household products, automotive
aftermarket products, adhesives and sealants, pesticides, some coatings, and other commercial and consumer
products that may emit VOCs. Products not included in this category are products used as non-aerosol traffic
203
-------
markings, architectural and industrial maintenance coatings, autobody refinishing coatings, and products used in
industrial processes.
Volatile organic compounds (VOC) are ingredients of consumer and commercial products that serve as
propellants, aid in product drying (through evaporation), act as co-solvents and cleaning agents, and are emitted
during product use. Typically, these VOC sources are large in number, highly dispersed, and individually emit
relatively small amounts of VOC. It is important to note here that not all organic compounds contained in
consumer and commercial products are considered reactive VOCs by the EPA due to their negligible
photochemical reactivity. For more information on Consumer Solvents, see the EIIP document, Consumer and
Commercial Solvent Use, Final Report, 1996.
SCCs that are used by state, local and tribal agencies are provided in Table 3-115. The SCCs that EPA estimates
emissions for are marked in column 2. Because of the different nature of the activity inputs, the methodology
description for estimating emissions for this sector will be divided into three parts: 1) Personal Care, Household,
Automotive Aftermarket, Coatings, Adhesives and Sealants Products, NEC and FIFRA Related Products
(Household Pesticide); 2) Asphalt Paving, and 3) Agricultural Pesticides. SCC level 1 descriptions for all SCCs in
this table are "Solvent Utilization". SCC level 2 descriptions are "Miscellaneous Non-industrial: ", and one the
following: "Commercial", "Consumer" or "Consumer and Commercial".
Table 3-115: SCCs used by S/L/T agencies for Solvent - Consumer & Commercial Solvent Use sector
SCC
EPA uses
SCC short description
2460000000
All Processes
2460100000
Y
All Personal Care Products
2460110000
Personal Care Products: Hair Care Products
2460120000
Personal Care Products: Deodorants and Antiperspirants
2460130000
Personal Care Products: Fragrance Products
2460150000
Personal Care Products: Nail Care Products
2460160000
Personal Care Products: Facial and Body Treatments
2460170000
Personal Care Products: Oral Care Products
2460180000
Personal Care Products: Health Use Products (External Only)
2460190000
Personal Care Products: Miscellaneous Personal Care Products
2460200000
Y
All Household Products
2460210000
Household Products
Hard Surface Cleaners
2460220000
Household Products
Laundry Products
2460230000
Household Products
Fabric and Carpet Care Products
2460250000
Household Products
Waxes and Polishes
2460270000
Household Products
Shoe and Leather Care Products
2460290000
Household Products
Miscellaneous Household Products
2460400000
Y
All Automotive Aftermarket Products
2460410000
Automotive Aftermarket Products: Detailing Products
2460420000
Automotive Aftermarket Products: Maintenance and Repair Products
2460500000
Y
All Coatings and Related Products
2460510000
Coatings and Related Products: Aerosol Spray Paints
2460520000
Coatings and Related Products: Coating Related Products
2460600000
Y
All Adhesives and Sealants
2460610000
Adhesives and Sealants: Adhesives
204
-------
see
EPA uses
SCC short description
2460800000
Y
All FIFRA Related Products
2460810000
FIFRA Related Products: Insecticides
2460820000
FIFRA Related Products: Fungicides and Nematicides
2460900000
Y
Miscellaneous Products (Not Otherwise Covered)
2461000000
All Processes
2461020000
Asphalt Application: All Processes
2461021000
Y
Cutback Asphalt
2461022000
Y
Emulsified Asphalt
2461023000
Asphalt Roofing
2461100000
Solvent Reclamation: All Processes
2461160000
Tank/Drum Cleaning: All Processes
2461800000
Pesticide Application: All Processes
2461800001
Pesticide Application: All Processes, surface application
2461800002
Pesticide Application: All Processes, soil incorporation
2461850000
Y
Pesticide Appl
cation: Agr
cultural
2461850001
Pesticide Appl
cation: Agr
cultural, herbicides, corn
2461850002
Pesticide Appl
cation: Agr
cultural, herbicides, apples
2461850003
Pesticide Appl
cation: Agr
cultural, herbicides, grapes
2461850004
Pesticide Appl
cation: Agr
cultural, herbicides, potatoes
2461850005
Pesticide Appl
cation: Agr
cultural, herbicides, soy beans
2461850006
Pesticide Appl
cation: Agr
cultural, herbicides, hay & grains
2461850009
Pesticide Appl
cation: Agr
cultural, herbicides, NEC
2461850051
Pesticide Appl
cation: Agr
cultural, other pesticides, corn
2461850052
Pesticide Appl
cation: Agr
cultural, other pesticides, apples
2461850053
Pesticide Appl
cation: Agr
cultural, other pesticides, grapes
2461850054
Pesticide Appl
cation: Agr
cultural, other pesticides, potatoes
2461850055
Pesticide Appl
cation: Agr
cultural, other pesticides, soy beans
2461850056
Pesticide Appl
cation: Agr
cultural, other pesticides, hay & grains
2461850099
Pesticide Application: Agricultural, other pesticides, NEC
2461870999
Pesticide Application: Non-Agricultural, NEC
2465000000
All Products/Processes
2465100000
Personal Care Products
2465200000
Household Products
2465400000
Automotive Aftermarket Products
2465800000
Pesticide Application
3.26.2 Sources of data overview and selection hierarchy
The S/L/T agencies that submitted data to the EPA are listed in Table 3-116. A number of states submitted
nonpoint emissions for this sector. Table 3-117 shows the selection hierarchy included in the Solvent -
Commercial and Consumer sector.
205
-------
Table 3-116: Agencies that submitted data for Consumer & Commercial Solvents
Data Set Short Name
State
Data Set Name
Data Category
2011CARB
CA
California Air Resources Board
Nonpoint
2011ClarkCty
Clark County Department of Air Quality and Environmental Management
Nonpoint
2011DDOE
DC
DC Department of Health Air Quality Division
Nonpoint
2011DEDNR
DE
Delaware Deparment of Natural Resources and Environmental Control
Nonpoint
2011GADNR
GA
Georgia Department of Natural Resources
Nonpoint
2011HIDOH
HI
Hawaii Department of Health Clean Air Branch
Nonpoint
2011IADNR
IA
Iowa Department of Natural Resources
Nonpoint
2011IDDEQ
ID
Idaho Department of Environmental Quality
Nonpoint
20111LEPA
IL
Illinois Environmental Protection Agency
Nonpoint
2011KSDOHE
KS
Kansas Department of Health and Environment
Nonpoint
2011LADEQ
LA
Louisiana Department of Environmental Quality
Nonpoint
2011MADEP
MA
Massachusetts Department of Environmental Protection
Nonpoint
2011Vlaricopa
Maricopa County Air Quality Department
Nonpoint
2011MDDOE
MD
Maryland Department of the Environment
Nonpoint
2011MEDEP
ME
Maine Department of Environmental Protection
Nonpoint
2011MIDEQ
Ml
Michigan Department of Environmental Quality
Nonpoint
2011MNPCA
MN
Minnesota Pollution Control Agency
Nonpoint
2011MODNR
MO
Missouri Department of Natural Resources
Nonpoint
2011Nashville
Metro Public Health of Nashville/Davidson County
Nonpoint
2011NHDES
NH
New Hampshire Department of Environmental Services
Nonpoint
2011NJDEP
NJ
New jersey Department of Environment Protection
Nonpoint
2011NYDEC
NY
New York State Department of Environmental Conservation
Nonpoint
2011OKDEQ
OK
Oklahoma Department of Environmental Quality
Nonpoint
2011ORDEQ
OR
Oregon Department of Environmental Quality
Nonpoint
2011PADEP
PA
Pennsylvania Department of Environmental Protection
Nonpoint
2011TR001
Eastern Band of Cherokee Indians
Nonpoint
2011TR180
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Nonpoint
2011TR181
Coeur d!Alene Tribe
Nonpoint
2011TR182
Nez Perce Tribe
Nonpoint
2011TR183
Kootenai Tribe of Idaho
Nonpoint
2011TR207
Northern Cheyenne Tribe
Nonpoint
2011TR863
Sac and Fox Nation of Missouri in Kansas and Nebraska Reservation
Nonpoint
2011TXCEQ
TX
Texas Commission on Environmental Quality
Nonpoint
2011UTDAQ
UT
Utah Division of Air Quality
Nonpoint
2011VADEQ
VA
Virginia Department of Environmental Quality
Nonpoint
2011VTDEC
VT
Vermont Department of Environmental Conservation
Nonpoint
2011WADOE
WA
Washington State Department of Ecology
Nonpoint
2011WashoeCty
Washoe County Health District
Nonpoint
2011WVDAQ
WV
West Virginia Division of Air Quality
Nonpoint
TR382
Santee Sioux Nation, Nebraska
Nonpoint
TR861
Kickapoo Tribe of Indians of the Kickapoo Reservation in Kansas
Nonpoint
206
-------
Tab
e 3-117: Data selection hierarchy
or the Solvent -Commercial and Consumer Solvent Use sector
Priority
Dataset Name
Dataset Content
1
Responsible Agency Data Set
State and Local Agency submitted emissions
2
2011EPA_HAP-Augmentation
Adds Pb and other HAP emissions in 46 states
3
2011EPA_NP_NoOverlap_w_Pt
EPA-generated data, including agricultural crops and livestock
dust emissions
3.26.3 Spatial coverage and data sources for the sector
Solvent - Consumer & Commercial Solvent Use Solvent - Consumer & Commercial Solvent Use
N
,
N '
P - Point
N - Nonpoirit
PN - P&N
SLT
P - Point
N - Nonpoint
PN - P&N
SLT
All CAPs EPA
All HAPs EPA
EPA & SLT
EPA & SLT
3.26.4 Development of EPA Emissions for Consumer and Commercial Solvents
EPA developed emission estimates for the 10 SCCs given in Table 3-118. SCC level 1 descriptions for these SCCs
are "Solvent Utilization". SCC level 2 descriptions are "Miscellaneous Non-industrial: and one the following:
"Commercial" or "Consumer and Commercial".
Table 3-118: Nonpoint SCC estimates developed by EPA for Consumer & Commercial Solvents sector
SCC
SCC Description
2460100000
All Personal Care Products
2460200000
All Household Products
2460400000
All Automotive Aftermarket Products
2460500000
Ail Coatings and Related Products
2460600000
All Adhesives and Sealants
2460900000
Miscellaneous Products (Not Otherwise Covered)
2460800000
All FIFRA Related Products
2461021000
Cutback Asphalt
2461022000
Emulsified Asphalt
2461850000
Pesticide Application: Agricultural
Because of the different nature of the activity factors that go into the methodology, this sector's methodology
description for estimating emissions will be divided into three parts: 1) Personal Care, Household, Automotive
Aftermarket, Coatings, Adhesives and Sealants Products, NEC and FIFRA Related Products (Household Pesticide);
2) Asphalt Paving, and 3) Agricultural Pesticides.
207
-------
3.26.4.1 Personal Care, Household, Automotive Aftermarket, Coatings, Adhesives and Sealants Products, NEC
and FIFRA Related Products (Household Pesticide)
Emissions were calculated in accordance with the alternative method in EIIP Volume 3, Chapter 5 [ref 2],
Emissions are calculated for each county using emission factors and activity as:
E = A x EF where:
x,p X x,p
E = annual emissions for category x and pollutant p
x,p
A = population data associated with category x
EF = emission factor for category x and pollutant p
x,p
Example:
According to the U.S. Census Bureau, Ada County had a total population of 392,365 people. The emission factor
for personal care products VOC is 1.9 lb/ person:
Evqc= 392,365 people x 1.9 lb VOC/ person
= 372.7 tons VOC
Activity Data
This category uses population and emissions factors to calculate emissions. National population data were
collected from the 2010 Census Bureau Interactive Population Search [ref 1] for each county.
Emission Factors
EPA, through the ERTAC committee process, chose emission factors for consumer and commercial solvent use
from the Emission Inventory Improvement Program (EIIP) [ref 2] and a Freedonia 2007 report. The emission
factors are based on national population (lb/person). Two of the VOC factors were updated with information
from the EPA ERTAC 2008 calculations for this category [ref 3], Information about the EIIP can be found on the
CHIEF website. Emission factors EPA used in the 2011 NEI are provided in Table 3-119. The emission factors from
Fredonia were taken from the Freedonia report for 2007. For all SCCs, the leading SCC description is "Solvent
Utilization; Miscellaneous Non-Industrial: Consumer and Commercial" and the level 4 description is "Total: All
Solvent Types".
Table 3-119: Consumer and Commercial Solvent Use emission factors
SCC
Description
Pollutant
Code
Pollutant
Description
Emissio
n Factor
EF units
source
2460100000
All Personal Care Products
108883
Toluene
0.5092
LB/person
HAP speciation profile
2460100000
All Personal Care Products
67561
Methanol
0.2546
LB/person
HAP speciation profile
2460100000
All Personal Care Products
VOC
VOC
1.9
LB/person
Freedonia, 2007
2460200000
All Household Products
108883
Toluene
0.4824
LB/person
HAP speciation profile
2460200000
All Household Products
67561
Methanol
0.2412
LB/person
HAP speciation profile
2460200000
All Household Products
VOC
VOC
1.8
LB/person
Freedonia, 2007
2460400000
All Automotive Aftermarket
Products
107211
Ethylene
Glycol
0.39712
LB/person
HAP speciation profile
2460400000
All Automotive Aftermarket
Products
108883
Toluene
0.36448
LB/person
HAP speciation profile
208
-------
see
Description
Pollutant
Code
Pollutant
Description
Emissio
n Factor
EF units
source
2460400000
All Automotive Aftermarket
Products
67561
Methanol
0.18224
LB/person
HAP speciation profile
2460400000
All Automotive Aftermarket
Products
VOC
VOC
1.36
LB/person
EIIP [ref 2]
2460500000
All Coatings and Related
Products
108883
Toluene
0.2546
LB/person
HAP speciation profile
2460500000
All Coatings and Related
Products
67561
Methanol
0.1273
LB/person
HAP speciation profile
2460500000
All Coatings and Related
Products
VOC
VOC
0.95
LB/person
EIIP [ref 2]
2460600000
All Adhesives and Sealants
108883
Toluene
0.15276
LB/person
HAP speciation profile
2460600000
All Adhesives and Sealants
67561
Methanol
0.07638
LB/person
HAP speciation profile
2460600000
All Adhesives and Sealants
VOC
VOC
0.57
LB/person
EIIP [ref 2]
2460900000
Miscellaneous Products NEC
108883
Toluene
0.1876
LB/person
HAP speciation profile
2460900000
Miscellaneous Products NEC
67561
Methanol
0.00938
LB/person
HAP speciation profile
2460900000
Miscellaneous Products NEC
VOC
VOC
0.07
LB/person
EIIP [ref 2]
3.26,4.2 Asphalt Paving- Cutback and Emulsified
While Asphalt Paving 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.
Asphalt paving is the process of applying asphalt concrete to seal or repair the surface of roads, parking lots,
driveways, walkways, or airport runways. Asphalt concrete is a composite material comprised of a binder and a
mineral aggregate. The binder, referred to as asphalt cement, is a byproduct of petroleum refining and contains
the semi-solid residual material left after the more volatile chemical fractions have been distilled off.
Asphalt cements thinned with petroleum distillates are known as cutback asphalts (SCC=246102100). The
primary uses of cutback asphalt include tack and seal operations, priming roadbeds, and paving operations for
pavements up to several inches thick. Cut-back asphalt is produced by thinning the binder in a diluent containing
25 to 45 percent petroleum distillates by volume prior to mixing with the aggregate. This reduces the viscosity of
the asphalt making it easier to work with the mixture. Emissions from cutback asphalt result from the
evaporation of VOCs and HAPS after the mixture is laid down. Of all asphalt types, cutback asphalt has the
highest diluent content and, as a result, emits the highest levels of VOCs per ton used. The timeframe and
quantity of VOC and HAP emissions depend on the type and the quantity of organic solvent used as a diluent.
Asphalt cements thinned with water and an emulsifying agent are known as emulsified asphalts
(SCC=2461022000). This thinning reduces the viscosity of the asphalt making it easier to work with the mixture.
The primary uses of emulsified asphalt include tack and seal operations, priming roadbeds, and paving
operations for pavements up to several inches thick. Emulsified asphalt may contain up to 12 percent organic
solvents by volume. Emissions from emulsified asphalt result from the evaporation of VOCs after the mixture is
laid down. Compared to cutback asphalt, emulsified asphalt has lower VOCs emissions per ton used.
209
-------
Emissions were calculated by multiplying the county-level asphalt usage (barrels) by the emission factors listed
in Table 3-120 [ref 4] and then dividing by 2000 to convert pounds to tons.
Emissionsxy = (Asphalt Usage* * EFy) / 2000
where:
Emissionsxy = emissions (tons) of pollutant y in county x
Asphalt Usage* = emulsified asphalt (barrels) used in county x
EFy = emission factor for pollutant y
To convert tons of asphalt reported in the 2008 Asphalt Usage Survey to barrels, it was assumed that the density
of asphalt is similar to that of water, 8.34 lbs/gal, and that one barrel equals 42 gallons.
Barrels of Asphalt = (tons of asphalt * 2000 lbs / 8.34 lbs per gal) / 42 gal per barrel
Note that one barrel of asphalt weights approximately 350 pounds.
Example:
Nez Perce County was allocated 3,413.16 barrels of emulsified asphalt for 2011. The emission factor for VOC is
9.2 lb/Barrel of emulsified asphalt.
Eyoc = 3,413.16 barrels of emulsified asphalt x 9.2 lb VOC/Barrel.
= 15.7 Tons VOC for Nez Perce County.
Activity Data
The activity data required to calculate the emissions from asphalt paving are the number of barrels of cutback
asphalt and emulsified asphalt used in each county. To determine the amount of each kind of asphalt used in
each county, the total number of barrels of asphalt used in each state was required. The amount of cutback and
emulsified asphalt used was obtained from the 2008 Asphalt Usage Survey, from the Asphalt Institute [ref 5],
The 2008 data was used for 2011 due to the Asphalt Institute no longer publishing a state by state report, and
no other data was found for a more recent year due to time constraints. The barrels of asphalt used per state
were then allocated to county-level according to the fraction of paved road vehicle miles traveled (VMT) in each
county.
Total annual VMT estimates by State and roadway class were obtained from the Federal Highway
Administration's (FHWA) annual Highway Statistics report [ref 6], Paved road VMT was calculated by subtracting
the State/roadway class unpaved road VMT from total State/roadway class VMT. State-level paved road VMT
was spatially allocated to counties according to the fraction of total VMT in each county for the specific roadway
class as shown by the following equation:
VMTx,total = IVMTSTy * VMTx.y / VMTSTy
where:
VMTX,total = VMT (million miles) in county x on all paved roadways
VMTSTy = paved road VMT for the entire State for roadway class y
210
-------
VMTx.y = total VMT (million miles) in county x and roadway class y
VMTSTy = total VMT (million miles) in entire State for roadway class y
The county-level total VMT by roadway class used in this calculation was previously developed by E.H. Pechan
and Associates, Inc. to support the onroad national emissions inventory [ref 7], To convert tons of asphalt
reported in the 2008 Asphalt Usage Survey to barrels, it was assumed that the density of asphalt is similar to
that of water, 8.34 lbs/gal, and that one barrel equals 42 gallons.
Emission Factors
Emission factors for cutback and emulsified asphalt usage, provided in Table 3-120, were obtained from the EIIP
Technical Report Series produced by the U.S. EPA's Emission Inventory Improvement Program and are reported
in [ref 4],
Table 3-120: Criteria and HAP emission factors ¦
or Asphalt Paving
Source Category
Pollutant
Emission Factor (Ib/bbl)
Emulsified Asphalt
VOC
9.2
Cutback Asphalt
VOC
88.00
Ethylbenzene
2.02
Toluene
5.63
Xylenes (mix of o, m, p isomers)
10.74
3.26.4.3 Agricultural Pesticide Application
While Agricultural Pesticide Application (SCC=246185000, "Solvent Utilization; Miscellaneous Non-industrial:
Commercial; Pesticide Application: Agricultural; All Processes") 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 as a result of
evaporation.
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 crop to crop and from region to region, the crop-specific, regional application rates should be
considered when estimating potential VOC emissions.
Emissions Factors
The default emissions factor for pesticide application (0.751) is expressed as the pounds of VOC that evaporate
per pound of pesticide active ingredient (Al) applied and was calculated using the following equation:
EF = ER x VOC
211
-------
where:
EF = emissions factor (lb VOC / lb Al)
ER = evaporation rate of applied pesticide (expressed as a fraction)
VOC = weighted pesticide VOC content (lb VOC / lb Al)
The evaporation rate was assumed to be 0.9 (or 90 percent) and is based on EPA recommendations provided in
the Emissions Inventory Improvement Program guidance [ref 8], As discussed below in the section on activity
data, The Crop Life Foundation (CLF) has compiled a state-level dataset of fungicide, herbicide, and insecticide
use based on survey data from 1999 to 2004 [ref 9], A default VOC content was calculated as the weighted
average VOC content for all pesticides reported in the Crop Life Foundation database for which there were
pesticide matches to the California Department of Pesticide Regulation's (DPR) Pesticide Product Emission
Potential database [ref 10], Each record in the DPR database is for a specific pesticide product, and provides
product name, primary active ingredient, emission potential (EP), registration number, and method used to
estimate the EP. The pesticide specific VOC EP of reactive organic gases (i.e., the weight percentage of product
that contributes to VOC emissions) and the weight percent of active ingredient from the DPR database were
used to calculate the weighted average VOC content.
VOC = ZPesticides[((AI/(%AI/100))*(EP/100))/AI]*[(AI/(%AI/100))/T]
where:
VOC = weighted pesticide VOC content (lb VOC / lb Al)
Al = active ingredient applied (lb)
%AI = weight percent of Al in pesticide mixture
EP = emissions potential of reactive organic gases (expressed as % of pesticide weight)
T = total weight of all pesticides applied (lb)
The active ingredient applied (Al) was calculated from the active ingredient application rates reported in the CLF
database and the harvested acres reported in the 2007 Census of Agriculture [ref 11]. The national pesticide
usage (T), reported as pounds of pesticides applied, was calculated using the following equation:
T — £ pesticides AI/(%AI/100)
Activity
The activity for pesticide application is the pounds of active ingredient applied and is calculated using the
following equation:
A = HA x R x | x AT
where:
A = pounds of active ingredient applied by pesticide by county
HA = crop-specific harvested acres in county
R = crop-specific pounds of pesticide applied per year per harvested acre
I = pounds of active ingredient per pound of pesticide
AT = percent of crop acres in the state treated with the active ingredient
212
-------
The application rate, R x I, is simply the pounds of active ingredient per harvested acre per year. This rate data,
as well as the percent of crop acres in a state treated with the active ingredient, are available in the CLF
database [ref 9], The county-level harvested acres per crop in 2007 are available in the Department of
Agriculture's 2007 Census of Agriculture [ref 11], In cases where there was not a direct match between the crop
type provided in the CLF and the Census of Agriculture databases, the crop type from the CLF database was
matched to a general crop category from the Census of Agriculture using the crosswalk provided in Table 3-121.
This crosswalk enabled the assignment of pesticides to certain crops or crop types and allowed estimation of the
quantity of pesticide applied by crop at the county level by linking the rate and AT data from the CLF database
with the harvested acreage data from the Census of Agriculture.
Activity Allocation Procedure
To prevent disclosing proprietary data, some crop-specific harvested acre information in the Census of
Agriculture is withheld. Estimates for these withheld data were developed in a three-step process, starting with
estimating values for data withheld at the national-level, then at the state-level and finally at the county-level.
Where data are withheld at the national-level for a given crop, the average harvested acres per farm from all
disclosed farms at the national-level was multiplied by the total national-level number of undisclosed farms
harvesting that crop and added to the national disclosed number of acres to estimate the national total. If a
value is withheld at the state-level, the difference between the national total and the sum of disclosed state
totals was evenly distributed among withheld states. Similarly, if a value is withheld at the county level, the
difference between the state total and the sum of disclosed county totals was evenly distributed among
withheld counties.
For example, as shown in Table 2, the data on total harvested acres of bentgrass seed are withheld at the
national level. Taking the disclosed harvested acres of bentgrass seed at the national-level (6,374) and dividing
by the total number of disclosed harvested farms at the national-level (58) yields an average of ~110 harvested
acres per farm. This value was then applied to the total number of undisclosed farms harvesting bentgrass seed
at the national level (6) and the result added to the national-level disclosed acres (6,374) to estimate the total
number of acres of bentgrass at the national level (7,033). Subtracting the total number of bentgrass acres
associated with disclosed state totals (6,809) from the estimated national total (7,033) yields 224 acres which
were then distributed evenly across the undisclosed states.
Table 3-121: Estimation of national-level total harvested acres of
sentgrass seed
Estimated Harvested
Acres (Total)
Harvested Acres
(Disclosed)
Farms
(Total)
Farms
(Disclosed)
Farms
(Undisclosed)
Average Harvested Acres
per Disclosed Farm
7,033
6,374
64
58
6
110
Bentgrass seed is only grown in two states (Oregon and Illinois). The allocation procedure for Oregon is
discussed and presented in Table 3-122. The state-level data from the Census of Agriculture indicate that there
are 6,809 harvested acres in Oregon associated with 63 total harvested farms. At the county-level there are
6,374 harvested acres associated with 58 disclosed farms. To fill in values for the undisclosed farms, the sum of
the disclosed county values (6,374) was subtracted from the total state value (6,809) yielding a difference of 435
harvested acres. Dividing these remaining 435 acres by the 5 undisclosed farms gives an average value of 87
harvested acres per farm.
213
-------
Table 3-122: Estimation of county-level harvested acres of bentgrass seed
State-level Harvested
Acres (Total)
County-level Harvested
Acres (Disclosed)
State-level
Farms (Total)
County-level
Farms (Disclosed)
Farms
(Undisclosed)
Difference
6,809
6,374
63
58
5
435
Note: The difference is then allocated evenly to the undisclosed farms, in this case 87 acres perform.
Controls
No controls were accounted for in the emissions estimation.
Emissions Equation and Sample Calculation
Emissions were estimated by summing the product of the activity data and the emissions factor for each
pesticide and crop type at the county-level:
Total VOC EmiSSiOnScounty = I (Apesticide,crop x EF)
Taking Autauga County, Alabama as an example, the first step was to determine the amount of active ingredient
per pesticide being applied in the county by multiplying each crop type by pesticide specific application rates
and the percent of acres treated. For Trifluralin application to green lima beans in Autauga County, there were 5
acres harvested and 50 percent of those acres had pesticide applied. Taking the number of acres to which
Trifluralin was applied (2.5) and multiplying by the Trifluralin application rate of 0.5 lbs of Al applied per acre
yields 1.25 lbs of Al due to Trifluralin application to green lima beans in Autauga County.
5 acres harvested x 50% (acres treated) x 0.5 (lbs of Al per acre) = 1.25 lbs of Al
This process was then repeated for every crop and pesticide combination present in the county (~600 for
Autauga County) and the values were summed to determine the amount of Al applied across the county. For
Autauga County this aggregate value was determined to be 60,125 lbs of Al. This value was then multiplied by
the emissions factor of 0.751 lb VOC per lb Al to estimate VOC emissions.
60,125 (lbs of Al applied in Autauga County) x 0.751 (lb VOC per lb Al) = 45,179 lb of VOC
This is equivalent to approximately 23 tons of VOC emitted due to agricultural pesticide application in Autauga
County.
3.26.5 Summary of data quality assurance methods
The EPA compared EPA generated emissions from this category to previous inventories and found an error that
was noted and corrected in 2011 v2. Emissions for toluene for consumer/commercial solvent emissions were
mistakenly calculated too high in the EPA dataset. This error was caused by a bad emission factor that other
states used. VA noted some errors and EPA assisted VA in resubmitting and the toluene error was corrected at
that time. This problem could still be an issue for other states in the cases where they used the bad emission
factor for toluene.
The EPA also compared state submitted data to EPA data and found some overlap and instances where possible
double counting could occur. To eliminate the double counting in Clarke County, NV, EPA tagged emissions in
the EPA dataset for a number of SCCs: 2460100000, 2460200000, 2460400000, 2460500000, 2460600000, and
214
-------
2460800000. Similar tagging was done for CA (SCCs 2460100000 and 2460600000), and NH and NJ (SCCs
2460100000, 2460200000, 2460400000, 2460500000, 2460600000, 2460800000, and 2460900000).
Ethylene Glycol (pollutant code = 107211) emissions were erroneously applied to all consumer& commercial
solvents (this pollutant should only be applied to automotive aftermarket), and these EPA emissions were
tagged and removed for 2011 v2.
3.26.6 References for Solvent -Consumer & Commerical Solvent Use
1. U.S. Census Bureau. 2010 Interactive Population Search, Census 2010, accessed November 2011.
2. U.S. EPA Technology Transfer Network, Clearinghouse for Inventories & Emissions Factors, Technical
Report Series, Volume 3: Area Sources and Area Source Method Abstracts, Chapter 5 "Consumer and
Commercial Solvent Use.", accessed February 2011.
3. ERTAC 2008. Consumer solvents epa data.zip, accessed November 2011.
4. U.S. Environmental Protection Agency, Emissions Inventory Improvement Program, Technical Report
Series, Volume 111 - Area Sources, Chapfr \sphalt Paving," prepared by Eastern Research Group,
Inc. for EPA, Research Triangle Park, NC, 2001.
5. Asphalt Institute, 2008 Asphalt Usage Survey for the United States and Canada,
6. U.S. Department of Transportation, Federal Highway Administration, Highway Statistics 2007, Office of
Highway Policy Information, Washington, DC, 2008.
7. E.H. Pechan & Associates, Inc. "Documentation for the Onroad National Emission Inventory (NEI) for
Base Years 1970 - 2002," report prepared for U.S. Environmental Protection Agency, Office of Air Quality
Planning and Standards, Research Triangle Park, NC. January 2004.
8. 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.
9. Crop Life Foundation, "National Pesticide Use Database", (accessed July 2011).
10. California Department of Pesticide Regulation, "Pesticide Emission Potential Database", (accessed
August 5, 2011).
11. United States Department of Agriculture, "Census of Agriculture 2007". (accessed July 2011).
3.27 Solvent - Non-Industrial Surface Coating
3.27.1 Sector description
Architectural coating (AC) operations consist of applying a thin layer of coating such as paint, paint primer,
varnish, or lacquer to architectural surfaces, and the use of solvents as thinners and for cleanup. Architectural
surface coatings protect the substrates to which they are applied from corrosion, abrasion, decay, ultraviolet
light damage, and/or the penetration of water. Some architectural coatings also increase the aesthetic value of a
structure by changing the color or texture of its surface. Architectural coatings are also important in
construction of structures. Examples of the latter are concrete form release compounds, which prevent concrete
from sticking to forms, and concrete curing compounds, which allow concrete to cure properly. It should be
noted that this category does not include auto refinishing, traffic marking, surface coating during manufacturing,
industrial maintenance coatings, special purpose coatings, or paints used in graphic arts applications.
Volatile organic compounds (VOCs) that are used as solvents in the coatings are emitted during application of
the coating and as the coating dries. The amount of coating used, and the VOC content of the coating are the
factors that primarily determine emissions from architectural surface coating operations. Secondary sources of
VOC emissions are from the solvents used to clean the architectural coating application equipment and VOC
released as reaction byproducts while the coating dries and hardens. VOC emitted from this chemical reaction is
215
-------
determined by the resins used in a particular coating. The VOC emitted from any of these sources could include
HAPs. The 2011 NEI does not include any byproduct emissions.
Table 3-123 lists the SCCs that are included in the 2011 NEI v2. EPA estimates use the highlighted SCC below.
Table 3-123: Non-Industrial Architectural Coatings SCCs in the 2011 NEI
SCC
SCC Level One SCC Level Two
SCC Level Three
SCC Level Four
2401001000 Solvent Utilization Surface Coating Architectural Coatings Total: All Solvent Types
2401002000 Solvent Utilization Surface Coating Architectural Coatings - Solvent-based Total: All Solvent Types
2401003000 Solvent Utilization Surface Coating Architectural Coatings - Water-based Total: All Solvent Types
3.27.2 Sources of data overview and selection hierarchy
Table 3-124 shows the selection hierarchy for all datasets contributing to the architectural coatings sector. Table
3-125 shows the agencies that submitted data used by the 2011 NEI. In some cases, the EPA PM and HAP
augmentation were used to fill in PM species and HAP pollutants based on S/L/T agency data. There was no
point data submitted to this category.
Table 3-124: 2011 NEI Arc
litectural Coatings sector data selection hierarchy
Priority
Dataset Name
Dataset Content
1
2011EPA_PM-Augmentation
Augments PM data in 47 states and some tribes
2
Responsible Agency Data Set
State and Local Agency submitted emissions
3
2011EPA_HAP-Augmentation
Adds Pb and other HAP emissions in 46 states
4
2011EPA_NP_NoOverlap_w_Pt
EPA-generated data
Table 3-125: Agencies that submitted data for the Architectural Coatings sector
Data Set Name
SCC
2011EPA_HAP-Augmentation
2401001000
2011EPA_HAP-Augmentation
2401002000
2011EPA_HAP-Augmentation
2401003000
2011EPA_NP_NoOverlap_w_Pt
2401001000
2011EPA_PM-Augmentation
2401002000
2011EPA_PM-Augmentation
2401003000
California Air Resources Board
2401001000
California Air Resources Board
2401002000
California Air Resources Board
2401003000
Clark County Department of Air Quality and Environmental Management
2401002000
Clark County Department of Air Quality and Environmental Management
2401003000
Coeur d'Alene Tribe
2401001000
DC Department of Health Air Quality Division
2401001000
Delaware Department of Natural Resources and Environmental Control
2401002000
Delaware Department of Natural Resources and Environmental Control
2401003000
Hawaii Department of Health Clean Air Branch
2401002000
Hawaii Department of Health Clean Air Branch
2401003000
216
-------
Data Set Name
see
Idaho Department of Environmental Quality
2401001000
Illinois Environmental Protection Agency
2401001000
Kansas Department of Health and Environment
2401001000
Kootenai Tribe of Idaho
2401001000
Maine Department of Environmental Protection
2401001000
Maricopa County Air Quality Department
2401001000
Maryland Department of the Environment
2401002000
Maryland Department of the Environment
2401003000
Massachusetts Department of Environmental Protection
2401001000
Metro Public Health of Nashville/Davidson County
2401001000
Michigan Department of Environmental Quality
2401001000
Minnesota Pollution Control Agency
2401001000
New Hampshire Department of Environmental Services
2401002000
New Hampshire Department of Environmental Services
2401003000
New Jersey Department of Environment Protection
2401001000
New York State Department of Environmental Conservation
2401001000
Nez Perce Tribe
2401001000
Northern Cheyenne Tribe
2401002000
Northern Cheyenne Tribe
2401003000
Shoshone-Bannock Tribes of the Fort Hail Reservation of Idaho
2401001000
Texas Commission on Environmental Quality
2401001000
Virginia Department of Environmental Quality
2401001000
Washington State Department of Ecology
2401001000
Washoe County Health District
2401001000
West Virginia Division of Air Quality
2401001000
3.27.3 Spatial coverage and data sources for the sector
Solvent - Non-Industrial Surface Coating
N - Nonpoint
PN - P&N
All CAPs EPA SLT -EPA & SLT
217
-------
3.27.4 EPA-developed emissions
EPA uses the SCC code 2401001000 for its emissions estimation development. EPA calculated emissions in
accordance with the alternative method in EIIP Volume 3, Chapter 3 [ref 1], Emissions are calculated for each
county using emission factors and activity as:
E.v.,p = Ax x E Fx,p
where:
Ex,p = annual emissions for category x and pollutant p
A.v = population data associated with category x
EF.v;, = emission factor for category x and pollutant p
Example:
According to the U.S. Census Bureau, Ada County had a total population of 392,365 people. The emission factor
for VOC is 2.3 lb/ person:
Evoc = 392,365 x 2.3 lb VOC/ person
= 461.03 tons VOC
Activity Data
Since this category is so pervasive, this category uses population and emissions factors to calculate emissions.
US population data were collected from the 2010 Census Bureau Interactive Population Search [ref 2] on each
county.
Emission Factors
The emission factors for 2011, shown in Table 3-126, were derived by adjusting the 2008 emission factor by the
change in the amount of solvent sold from 2007 to 2010 according to the US Census (see the "AC" tab in the
spreadsheet "2011 NEI EFs Revision v2 JCS 062612.xlsx"). The 2008 emission factor was derived by using the
amount of solvents sold for architectural coating in the US in 2007 (Freedonia). That amount was scaled
upwards by 19% to account for solvents used in cleanup, thinning, and additives (CARB, 2005). ERTAC used a
value of 2.41 lb VOC/person to calculate the solvents used (aka emissions) in the 16 states (see Table 3-127) that
have rules that limit the VOC in coatings. That number was subtracted from the total used in the US, and then
the remainder population of the 34 states that do not have VOC limits on architectural coatings (AC) was used to
derive an emission factor for the states with no rules.
Table 3-126: Emission Factors for Arc
litectural Coatings used in the 2011 NEI
2008 NEI
2011 NEI
States with architectural coatings rules
2.41 lb VOC/person
1.88 lb VOC/person
States without architectural coatings rules
3.09 lb VOC/person
2.35 lb VOC/person
218
-------
Table 3-127: States with Architectural Coatings rules
Region
States
1
CT, MA, ME, NH, Rl, VT
2
NJ, NY
3
DC, DE, MD, PA, VA
6
TX
9
AZ, CA
3.27.5 Summary of quality assurance methods
For a number of states, plus Clark County, NV, it was necessary to tag EPA data to avoid a double count for AC.
This is a case of the states using different SCCs. The SCCs that were used by these states are 2401002000
(solvent based) and 2401003000 (water based). EPA uses the more general SCC of 2401001000. The agencies
that submitted using these more detailed SCCs are CA, DE, HI, MD, NH, and Clark County, NV.
3.27.6 References for Solvent - Non-Industrial Surface Coating
1. U.S. Environmental Protection Agency, Emissions Inventory Improvement Program, Technical Report
Series, Volume 111 - Area Sources, Chapter 3, "Architectural Surface Coating" prepared by Eastern
Research Group, Inc. for EPA, Research Triangle Park, NC, 1995.
2. U.S. Census Bureau. 2010 Interactive Population Search, Census 2010.
3.28 Solvent - Degreasing
3.28.1 Sector description
Solvent cleaning (degreasing) operations are an integral part of many industries and involve the use of solvents
or solvent vapor to remove water-insoluble contaminants such as grease, oils, waxes, carbon deposits, fluxes,
and tars from metal, plastic, glass, and other surfaces. Solvent cleaning is usually performed prior to painting,
plating, inspection, repair, assembly, heat treating, and machining. For this sector, the EPA developed estimates
for the nonpoint general SCC, 2415000000, highlighted in Table 3-128. The nonpoint SCC descriptions begin with
"Solvent Utilization; Degreasing;" and the point SCC descriptions begin with "Petroleum and Solvent
Evaporation; Organic Solvent Evaporation".
Table 3-128: SCCs for Solvent Cleaning and Degreasing
Data
Category
SCC
SCC level 3 & 4 Description
nO'U/f;
lit
7.41^000000
AM Soluenrs/AM Industries; A!! Processes
nonpo
nt
2415005000
Furniture and Fixtures; All Processes
nonpo
nt
2415010000
Primary Metal Industries; All Processes
nonpo
nt
2415020000
Fabricated Metal Products; All Processes
nonpo
nt
2415025000
Industrial Machinery and Equipment; All Processes
nonpo
nt
2415030000
Electronic and Other Elec; All Processes
nonpo
nt
2415035000
Transportation Equipment; All Processes
nonpo
nt
2415040000
Instruments and Related Products; All Processes
nonpo
nt
2415045000
Miscellaneous Manufacturing; All Processes
nonpo
nt
2415050000
Transportation Maintenance Facilities; All Processes
nonpo
nt
2415055000
Automotive Dealers; All Processes
nonpo
nt
2415060000
Miscellaneous Repair Services; All Processes
nonpo
nt
2415065000
Auto Repair Services; All Processes
219
-------
Data
Category
see
SCC level 3 & 4 Description
point
40100201
Degreasing; Stoddard (Petroleum Solvent): Open-top Vapor Degreasing
point
40100202
Degreasing; 1,1,1-Trichloroethane (Methyl Chloroform): Open-top Vapor Degreasing
point
40100203
Degreasing; Perchloroethylene: Open-top Vapor Degreasing
point
40100204
Degreasing; Methylene Chloride: Open-top Vapor Degreasing
point
40100205
Degreasing; Trichloroethylene: Open-top Vapor Degreasing
point
40100206
Degreasing; Toluene: Open-top Vapor Degreasing
point
40100207
Degreasing; Trichlorotrifluoroethane (Freon): Open-top Vapor Degreasing
point
40100209
Degreasing; Butyl Acetate: Open-top Vapor Degreasing
point
40100215
Degreasing; Entire Unit: Open-top Vapor Degreasing
point
40100221
Degreasing; Stoddard (Petroleum Solvent): Conveyorized Vapor Degreasing
point
40100222
Degreasing; 1,1,1-Trichloroethane (Methyl Chloroform):Conveyorized Vapor Degreaser
point
40100223
Degreasing; Perchloroethylene: Conveyorized Vapor Degreasing
point
40100224
Degreasing; Methylene Chloride: Conveyorized Vapor Degreasing
point
40100225
Degreasing; Trichloroethylene: Conveyorized Vapor Degreasing
point
40100235
Degreasing; Entire Unit: with Vaporized Solvent: Conveyorized Vapor Degreasing
point
40100236
Degreasing; Entire Unit: with Non-boiling Solvent: Conveyorized Vapor Degreasing
point
40100251
Degreasing; Stoddard (Petroleum Solvent): General Degreasing Units
point
40100252
Degreasing; 1,1,1-Trichloroethane (Methyl Chloroform): General Degreasing Units
point
40100253
Degreasing; Perchloroethylene: General Degreasing Units
point
40100254
Degreasing; Methylene Chloride: General Degreasing Units
point
40100255
Degreasing; Trichloroethylene: General Degreasing Units
point
40100256
Degreasing; Toluene: General Degreasing Units
point
40100257
Degreasing; Trichlorotrifluoroethane (Freon): General Degreasing Units
point
40100296
Degreasing; Other Not Classified: General Degreasing Units
point
40100298
Degreasing; Other Not Classified: Conveyorized Vapor Degreasing
point
40100299
Degreasing; Other Not Classified: Open-top Vapor Degreasing
point
40100301
Cold Solvent Cleaning/Str
pping; Methanol
point
40100302
Cold Solvent Cleaning/Str
pping; Methylene Chloride
point
40100303
Cold Solvent Cleaning/Str
pping; Stoddard (Petroleum Solvent)
point
40100304
Cold Solvent Cleaning/Str
pping; Perchloroethylene
point
40100305
Cold Solvent Cleaning/Str
pping; 1,1,1-Trichloroethane (Methyl Chloroform)
point
40100306
Cold Solvent Cleaning/Str
pping; Trichloroethylene
point
40100307
Cold Solvent Cleaning/Str
pping; Isopropyl Alcohol
point
40100308
Cold Solvent Cleaning/Str
pping; Methyl Ethyl Ketone
point
40100309
Cold Solvent Cleaning/Str
pping; Freon
point
40100310
Cold Solvent Cleaning/Str
pping; Acetone
point
40100311
Cold Solvent Cleaning/Str
pping; Glycol Ethers
point
40100335
Cold Solvent Cleaning/Str
pping; Entire Unit
point
40100336
Cold Solvent Cleaning/Str
pping; Degreaser: Entire Unit
point
40100399
Cold Solvent Cleaning/Str
pping; Other Not Classified
point
40188898
Fugitive Emissions; Specify in Comments Field
3.28.2 Sources of data overview and selection hierarchy
The degreasing sector includes emissions from both S/L/T agencies and from the EPA overlap nonpoint dataset.
The hierarchy of datasets used in the 2011 NEI for this sector is provided in Table 3-129. In some cases, the EPA
PM and HAP augmentation as well as chromium split datasets were used to fill in PM species and HAP pollutants
based on S/L/T agency data. The S/L/T agencies that submitted data to the EPA are listed in Table 3-116Table
3-130. Several agencies submitted nonpoint emissions for this sector.
220
-------
Table 3-129: Data selection hierarchy for the Solvent -Degreasing sector
Priority
Dataset Name
Dataset Content
1
2011EPA_PM-Augmentation
Augments PM data in 47 states and some tribes
2
Responsible Agency Data Set
State and Local Agency submitted emissions
3
2011EPA_chrom_split
Splits total chromium into speciated chromium in 37 states
4
2011EPA_HAP-Augmentation
Adds Pb and other HAP emissions in 46 states
5
2011EPA_NP_Overlap_w_Pt
EPA-generated data
Table 3-130: Agencies that submitted data for Solvent -Degreasing sector
Data Set Name
Point
Nonpoint
2011EPA_chrom_split
X
2011EPA_HAP-Augmentation
X
X
2011E PA_ N P_0 ve r 1 a p_w_Pt
X
2011EPA_PM-Augmentation
X
X
2011EPA_TRI
X
Alabama Department of Environmental Management
X
Allegheny County Health Department
X
Arizona Department of Environmental Quality
X
Arkansas Department of Environmental Quality
X
California Air Resources Board
X
X
Chattanooga Air Pollution Control Bureau (CHCAPCB)
X
X
Clark County Department of Air Quality and Environmental Management
X
Coeur d'Alene Tribe
X
Colorado Department of Public Health and Environment
X
Connecticut Department of Environmental Protection
X
X
Delaware Department of Natural Resources and Environmental Control
X
X
Florida Department of Environmental Protection
X
Forsyth County Environmental Affairs Department
X
Georgia Department of Natural Resources
X
X
Hawaii Department of Health Clean Air Branch
X
Idaho Department of Environmental Quality
X
Illinois Environmental Protection Agency
X
X
Indiana Department of Environmental Management
X
Iowa Department of Natural Resources
X
X
Jefferson County (AL) Department of Health
X
Kansas Department of Health and Environment
X
X
Kentucky Division for Air Quality
X
Knox County Department of Air Quality Management
X
X
Kootenai Tribe of Idaho
X
Louisiana Department of Environmental Quality
X
Louisville Metro Air Pollution Control District
X
Maine Department of Environmental Protection
X
X
221
-------
Data Set Name
Point
Nonpoint
Maricopa County Air Quality Department
X
X
Maryland Department of the Environment
X
X
Massachusetts Department of Environmental Protection
X
X
Memphis and Shelby County Health Department - Pollution Control
X
Metro Public Health of Nashville/Davidson County
X
X
Michigan Department of Environmental Quality
X
X
Minnesota Pollution Control Agency
X
X
Mississippi Dept of Environmental Quality
X
Missouri Department of Natural Resources
X
X
Montana Department of Environmental Quality
X
Nebraska Environmental Quality
X
New Hampshire Department of Environmental Services
X
X
New Jersey Department of Environment Protection
X
X
New York State Department of Environmental Conservation
X
X
Nez Perce Tribe
X
North Carolina Department of Environment and Natural Resources
X
Ohio Environmental Protection Agency
X
X
Oklahoma Department of Environmental Quality
X
Olympic Region Clean Air Agency
X
Omaha Air Quality Control Division
X
Oregon Department of Environmental Quality
X
X
Pennsylvania Department of Environmental Protection
X
X
Philadelphia Air Management Services
X
Puerto Rico
X
Puget Sound Clean Air Agency
X
Rhode Island Department of Environmental Management
X
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
X
South Carolina Department of Health and Environmental Control
X
Southwest Clean Air Agency
X
Tennessee Department of Environmental Conservation
X
X
Texas Commission on Environmental Quality
X
X
Utah Division of Air Quality
X
Vermont Department of Environmental Conservation
X
Virginia Department of Environmental Quality
X
X
Washington State Department of Ecology
X
Washoe County Health District
X
West Virginia Division of Air Quality
X
Western North Carolina Regional Air Quality Agency (Buncombe Co.)
X
Wisconsin Department of Natural Resources
X
X
Wyoming Department of Environmental Quality
X
222
-------
3.28.3 Spatial coverage and data sources for the sector
N
P - Point
N - Nonpoint
PN - P&N
P - Point
N - Nonpoint
Solvent - Degreasing
All CAPs
Solvent - Degreasing
PN - P&N
All HAPs 1 I EPA BS—M SIT EPA & SLT
3.28.4 EPA-deveioped emissions
Activity Data
The activity data needed for this category is the number of employees from several categories of industry listed
by the North American Industrial Classification Standard (NAICS) code(s) to determine county-level employment
for the category. County data were gathered from NAICS categories: 331, 332, 333, 334, 335, 336, 337, 339,
441, 483, 484, 485, 488, 8111, and 8112. NAICS data was gathered from the 2010 Census County Business
Patterns (CBP) [ref 1],
Due to concerns with releasing confidential business information, the Census CBP does not release exact
numbers for a given NAICS code if there is enough data that individual businesses could be identified. Instead a
series of range codes are used. Because employment data is a key factor in determining emissions, it is
important to estimate the number of employees for each county.
To estimate the number of employees in counties where data was withheld, EPA used the following procedure
for each NAICS code being computed:
1. County level data for each NAICS were obtained and any numerical values were summed.
2. The sum generated in step 1 was subtracted from the state total number of employees in that NAICS
reported in the state-level CBP.
3. The county level CBP report includes the number of establishments in the county within a specific
employee range. For each of the counties with withheld data, EPA multiplied the number of
establishments in a particular employee range (1 - 4, 5 - 9, etc.) by the midpoint of the range code (5 -
9 employees would be assigned 7) and summed the results.
4. An adjustment factor (to ensure the total number of estimated employees matches the state reported
totai) is calculated by dividing the sum of all the county level generated in step 2 by the sum of the
county calculations in step 3. If there are no numerical values at the county level the adjustment factor
is calculated by dividing the state total number of employees by the sum of the calculations in step 3.
5. The estimated number of employees, in counties where data was withheld, is calculated by multiplying
the sum from step 3 by the adjustment factor calculated in step 4.
Emissions are calculated for each county using emission factors and activity as:
223
-------
= Av x EFx,P
where:
E.v.;, = annual emissions for category x and pollutant p
A.v = employment data associated with category x
EF.v;, = emission factor for category x and pollutant p
Example:
According to the U.S. Census Bureau, Kootenai County had a total of 557 employees in NAICS 335 - Electrical
Equipment/Appliance/Component industry.
The emission factor for VOC is 36.97 lb /employee.
Evoc = 557 employees x 36.97 lb VOC/employee
= 10.296 tons VOC
3.28.5 References for Solvent - Degreasing
1. U.S. Census Bureau, 2010 County Business Patterns, accessed September 2012.
3.29 Solvent - Dry Cleaning
3.29.1 Sector description
The dry cleaning industry is a service industry for the cleaning of garments, draperies, leather goods, and other
fabric items. Dry cleaning operations do not use water that can swell textile fibers, but typically use either
synthetic halogenated or petroleum distillate organic solvents for cleaning purposes. Use of solvents rather than
water prevents wrinkles and shrinkage of fabrics. The dry cleaning industry is the most significant emission
source of perchloroethylene (PERC) in the United States.
The two major types of dry cleaning operations are coin-operated (coin-op) and commercial. Industrial
launderers are usually associated with soap and detergent cleaning, but also use large capacity dry cleaning
units. Coin-operated dry cleaning units are self-service machines that are usually found in laundromats.
Commercial dry cleaners are independent small businesses that offer dry cleaning services to the public. Some
commercial dry cleaning businesses provide numerous drop-off/pick-up outlet stores that are serviced by a
single dry cleaning plant, and thus some sites identified as dry cleaners may not be emissions sources. Industrial
launderers who use dry cleaning solvents are usually part of a business operation that generates soiled fabrics,
where it is convenient or cost-effective to perform dry cleaning on site. Industrial launderers can also be large
businesses that provide uniform and other rental services to business, industrial, and institutional customers.
For this sector, the EPA developed estimates for the nonpoint general SCC, 2420000000, highlighted in Table
3-131. The nonpoint SCC descriptions begin with "Solvent Utilization;" and the point SCC descriptions begin
with "Petroleum and Solvent Evaporation".
Table 3-131: SCCs for Solvent Utilization - Dry Cleaners
Data
Category
SCC
SCC Level 2, 3 & 4 Description
Nonpolii:
/470000000
Dry Cleaning: Al! Processes Total: All .'olvent Types
Nonpoint
2420000055
Dry Cleaning; All Processes; Perchloroethylene
Nonpoint
2420000370
Dry Cleaning; All Processes; Special Naphthas
224
-------
Nonpoint
2420010000
Dry Cleaning; Commercial/Industrial Cleaners; Total: All Solvent Types
Nonpoint
2420010055
Dry Cleaning; Commercial/Industrial Cleaners; Perchloroethylene
Nonpoint
2420010370
Dry Cleaning; Commercial/Industrial Cleaners; Special Naphthas
Nonpoint
2420020000
Dry Cleaning; Coin-operated Cleaners; Total: All Solvent Types
Point
40100101
Organic Solvent Evaporation; Dry Cleaning; Perchloroethylene
Point
40100102
Organic Solvent Evaporation; Dry Cleaning; Stoddard (Petroleum Solvent) ** (Use 4-10-
001-01 or 4-10-002-01)
Point
40100104
Organic Solvent Evaporation; Dry Cleaning; Stoddard (Petroleum Solvent) ** (Use 4-10-
001-02 or 4-10-002-02)
Point
40100146
Organic Solvent Evaporation; Dry Cleaning; Stoddard:Filtr Disp/Cooked
Muck(Drained)**(Use 4-10-001-61 or 002-61)
Point
40100198
Organic Solvent Evaporation; Dry Cleaning; Other Not Classified
Point
40100199
Organic Solvent Evaporation; Dry Cleaning; See Comment **
Point
41000101
Dry Cleaning; Petroleum Solvent - Industrial; Stoddard
Point
41000130
Dry Cleaning; Petroleum Solvent - Industrial; Dryer
Point
41000143
Dry Cleaning; Petroleum Solvent - Industrial; Filtration, Diatomite: Regenerative
Point
41000202
Dry Cleaning; Petroleum Solvent - Commercial; Stoddard
Point
41000230
Dry Cleaning; Petroleum Solvent - Commercial; Dryer
Point
41000231
Dry Cleaning; Petroleum Solvent - Commercial; Dryer: Loading/Unloading
Point
41000244
Dry Cleaning; Petroleum Solvent - Commercial; Filtration, Cartridge, Carbon Core, Batch
Operation
Point
41082001
Dry Cleaning; Petroleum Solvent - Wastewater, Aggregate; Process Area Drains
3.29.2 Sources of data overview and selection hierarchy
The dry cleaning sector includes emissions from both S/L/T agencies and from the EPA overlap nonpoint dataset.
The hierarchy of datasets used in the 2011 NEI for this sector is provided in Table 3-132. In some cases, the EPA
PM and HAP augmentation datasets were used to fill in PM species and HAP pollutants based on S/L/T agency
data. The S/L/T agencies that submitted data to the EPA are listed in Table 3-116Table 3-133. Several agencies
submitted nonpoint emissions for this sector.
Table 3-132: Data selection hierarchy for the Solvent -Dry Cleaning sector
Priority
Dataset Name
Dataset Content
1
2011EPA_PM-Augmentation
Augments PM data in 47 states and some tribes
2
Responsible Agency Data Set
State and Local Agency submitted emissions
3
2011EPA_HAP-Augmentation
Adds Pb and other HAP emissions in 46 states
4
2011E PA_N P_Overlap_w_Pt
EPA-generated data
Table 3-133: Agencies that submitted data for Solvent -Dry Cleaning sector
Data Set Name
Point
Nonpoint
Delaware Department of Natural Resources and Environmental Control
X
Florida Department of Environmental Protection
X
Georgia Department of Natural Resources
X
Hawaii Department of Health Clean Air Branch
X
Idaho Department of Environmental Quality
X
Illinois Environmental Protection Agency
X
X
Indiana Department of Environmental Management
X
Iowa Department of Natural Resources
X
225
-------
Data Set Name
Point
Nonpoint
Kansas Department of Health and Environment
X
Kentucky Division for Air Quality
X
Kootenai Tribe of Idaho
X
Louisville Metro Air Pollution Control District
X
Maine Department of Environmental Protection
X
Maricopa County Air Quality Department
X
Maryland Department of the Environment
X
Massachusetts Department of Environmental Protection
X
X
Metro Public Health of Nashville/Davidson County
X
X
Michigan Department of Environmental Quality
X
X
Minnesota Pollution Control Agency
X
X
New Hampshire Department of Environmental Services
X
New Jersey Department of Environment Protection
X
New York State Department of Environmental Conservation
X
X
Nez Perce Tribe
X
North Carolina Department of Environment and Natural Resources
X
X
Ohio Environmental Protection Agency
X
X
Oregon Department of Environmental Quality
X
Pennsylvania Department of Environmental Protection
X
Rhode Island Department of Environmental Management
X
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
X
South Carolina Department of Health and Environmental Control
X
Texas Commission on Environmental Quality
X
X
Vermont Department of Environmental Conservation
X
Virginia Department of Environmental Quality
X
X
Washington State Department of Ecology
X
Washoe County Health District
X
West Virginia Division of Air Quality
X
Wisconsin Department of Natural Resources
X
X
226
-------
3.29.3 Spatial coverage and data sources for the sector
P - Point
N - Nonpoint
P - Point
N - Nonpoint
Solvent - Dry Cleaning
PN - P&N
All CAPs 1—EPA ¦™«T
Solvent - Dry Cleaning
PN - P&N
All HAPs i i epa Sslt epa i sir
3.29.4 EPA-developed emissions
Activity Data
This category uses dry cleaning employees per county from NAICS 81232 and emissions factors to calculate
emissions. National dry cleaning employee data were collected from the 2010 Census Bureau County Business
Patterns [ref 1] for each county.
Due to concerns with releasing confidential business information, the Census CBP does not release exact
numbers for a given NAICS code if there is enough data that individual businesses could be identified. Instead, a
series of range codes are used. Because employment data is a key factor in determining emissions, it is
important to estimate the number of employees for each county.
To estimate the number of employees in counties where data was withheld, EPA used the following procedure
for each NAICS code being computed:
• County level data for each NAICS were obtained and any numerical values were summed.
• The sum generated in step 1 was subtracted from the state total number of employees in that NAICS
reported in the state-level CBP.
• The county level CBP report includes the number of establishments in the county within a specific
employee range. For each of the counties with withheld data, Idaho multiplied the number of
establishments in a particular employee range (1 - 4, 5 - 9, etc.) by the midpoint of the range code (5-9
employees would be assigned 7) and summed the results.
• An adjustment factor (to ensure the total number of estimated employees matches the state reported
total) is calculated by dividing the sum of all the county level generated in step 2 by the sum of the
county calculations in step 3. If there are no numerical values at the county level the adjustment factor
is calculated by dividing the state total number of employees by the sum of the calculations in step 3.
• The estimated number of employees, in counties where data was withheld, is calculated by multiplying
the sum from step 3 by the adjustment factor calculated in step 4.
227
-------
Emission Factors
The VOC and tetrachloroethylene emissions factors are from EPA ERTAC 2011 calculations [ref 2], A VOC factor
is computed based on national population (lb/person).
Calculations
EPA calculated emissions in accordance with the alternative method in EIIP Volume 3, Chapter 4, alternative
method two [ref 3], Emissions are calculated for each county using emission factors and activity as:
E x,p =AxxEF x,p
where:
E,.p = annual emissions for category x and pollutant p
Ax = employee data associated with category x
EF,.P = emission factor for category x and pollutant p
Example:
According to the U.S. Census Bureau County Business Patterns, Ada County had a total of 239 dry cleaning
employees. The emission factor for VOC is 10 lb/employee:
Evoc = 239 dry cleaning employees x 10 lb VOC/employee
= 1.195 tons VOC in Ada County
3.29.5 References for Solvent - Dry Cleaning
1. U.S. Census Bureau. 2010 County Business Patterns, accessed September 2012.
2. ERTAC 2011. Dry Cleaning, accessed December 2012.
3. U.S. EPA Technology Transfer Network. Clearinghouse for Inventories & Emissions Factors. Technical
Report Series. Volume 3: Area Sources and Area Source Method Abstracts, accessed February 2011.
3.30 Solvent - Graphic Arts
3.30.1 Sector description
Graphic arts operations are performed on printing presses that are made up of one or more "units." Each unit
can print only one color. The substrate in graphic arts operations is either individual pieces of substrate called
"sheets", or continuous and called a "web" [ref 1; ref 2], The pattern that is printed on the substrate is called
the "image". For this source category, the following SCCs were used in the 2011 NEI, with the highlighted SCC
used for EPA's estimates:
For this source category, the EPA developed estimates for the nonpoint general SCC, 2425000000, highlighted
in Table 3-134. The nonpoint SCC descriptions begin with "Solvent Utilization;" and the point SCC descriptions
begin with "Petroleum and Solvent Evaporation".
Table 3-134: Graphic Arts SCCs used in the 2011 NEI
SCC
SCC Level 2, 3 & 4 Description
2425000000
2425010000
Gr:;pmc Arts. Ail Processes: Totd!: AN Solvent "lyi^es
Graphic Arts; Lithography; Total: All Solvent Types
2425020000
Graphic Arts; Letterpress; Total: All Solvent Types
228
-------
see
SCC Level 2, 3 & 4 Description
2425030000
Graphic Arts; Rotogravure; Total: All Solvent Types
2425040000
Graphic Arts; Flexography; Total: All Solvent Types
40500101
Pr
nt
ng/Publishing; Drying; Dryer
40500201
Pr
nt
ng/Publishing; Letter Press; Printing
40500203
Pr
nt
ng/Publishing; Letter Press; Ink Thinning Solvents, Mineral Solvents
40500215
Pr
nt
ng/Publishing; Letter Press; Cleaning Solution
40500301
Pr
nt
ng/Publishing; Flexographic; Printing
40500302
Pr
nt
ng/Publishing; Flexographic; Ink Thinning Solvent, Carbitol
40500303
Pr
nt
ng/Publishing; Flexographic; Ink Thinning Solvent, Cellosolve
40500304
Pr
nt
ng/Publishing; Flexographic; Ink Thinning Solvent, Ethyl Alcohol
40500305
Pr
nt
ng/Publishing; Flexographic; Ink Thinning Solvent, Isopropyl Alcohol
40500306
Pr
nt
ng/Publishing; Flexographic; Ink Thinning Solvent, n-Propyl Alcohol
40500307
Pr
nt
ng/Publishing; Flexographic; Ink Thinning Solvent, Naphtha
40500314
Pr
nt
ng/Publishing; Flexographic; Propyl Alcohol Cleanup
40500315
Pr
nt
ng/Publishing; Flexographic; Steam: Water-based
40500318
Pr
nt
ng/Publishing; Flexographic; Steam: Water-based in Ink
40500401
Pr
nt
ng/Publishing; Lithographic; Printing
40500413
Pr
nt
ng/Publishing; Lithographic; Isopropyl Alcohol Cleanup
40500415
Pr
nt
ng/Publishing; Offset Lithography; Dampening Solution with Alcohol Substitute
40500416
Pr
nt
ng/Publishing; Offset Lithography; Dampening Solution with High Solvent Content
40500417
Pr
nt
ng/Publishing; Offset Lithography; Cleaning Solution: Water-based
40500418
Pr
nt
ng/Publishing; Offset Lithography; Dampening Solution with Isopropyl Alcohol
40500421
Pr
nt
ng/Publishing; Offset Lithography; Heatset Ink Mixing
40500422
Pr
nt
ng/Publishing; Offset Lithography; Heatset Solvent Storage
40500431
Pr
nt
ng/Publishing; Offset Lithography; Nonheated Lithographic Inks
40500502
Pr
nt
ng/Publishing; Gravure; Ink Thinning Solvent, Dimethylformamide
40500503
Pr
nt
ng/Publishing; Gravure; Ink Thinning Solvent, Ethyl Acetate
40500506
Pr
nt
ng/Publishing; Gravure; Ink Thinning Solvent, Methyl Ethyl Ketone
40500510
Pr
nt
ng/Publishing; Gravure; Ink Thinning Solvent, Toluene
40500511
Pr
nt
ng/Publishing; Gravure; Printing
40500514
Pr
nt
ng/Publishing; Gravure; Cleanup Solvent
40500597
Pr
nt
ng/Publishing; General; Other Not Classified
40500599
Pr
nt
ng/Publishing; Printing; Ink Thinning Solvent
40500601
Pr
nt
ng/Publishing; Printing; Ink Mixing
40500801
Pr
nt
ng/Publishing; Screen Printing; Screen Printing
40500802
Printing/Publishing; Screen Printing; Fugitive Emissions: Cleaning Rags
40588801
Printing/Publishing; Fugitive Emissions; SCC Needs to be Assigned
3.30.2 Sources of data overview and selection hierarchy
The graphic arts sector includes emissions from both S/L/T agencies and from the EPA overlap nonpoint dataset.
The hierarchy of datasets used in the 2011 NEI for this sector is provided in Table 3-135. In some cases, the EPA
229
-------
PM and HAP augmentation as well as TRI and chromium split datasets were used to fill in PM species and HAP
pollutants based on S/L/T agency data. The S/L/T agencies that submitted data to the EPA are listed in Table
3-116Table 3-136. Several agencies submitted nonpoint emissions for this sector.
Table 3-135: Data selection hierarchy for the Solvent -Graphic Arts sector
Priority
Dataset Name
Dataset Content
1
2011EPA_PM-Augmentation
Augments PM data in 47 states and some tribes
2
Responsible Agency Data Set
State and Local Agency submitted emissions
3
2011EPA_chrom_split
Splits total chromium into speciated chromium in 37 states
4
2011EPA_TRI
Toxics Release Inventory data for the year 2011. These data are
selected for a facility only when alternative emissions are not
included in the S/L/T agency data.
5
2011EPA_HAP-Augmentation
Adds Pb and other HAP emissions in 46 states
6
2011EPA_NP_Overlap_w_Pt
EPA-generated data
Table 3-136: Agencies that submitted data for Solvent -Graphic Arts sector
Data Set Name
Point
Nonpoint
2011EPA_chrom_split
X
2011EPA_HAP-Augmentation
X
2011E PA_ N P_0 ve r 1 a p_w_Pt
X
2011EPA_PM-Augmentation
X
X
2011EPA_TRI
X
Alabama Department of Environmental Management
X
Allegheny County Health Department
X
Arkansas Department of Environmental Quality
X
California Air Resources Board
X
X
Chattanooga Air Pollution Control Bureau (CHCAPCB)
X
X
City of Albuquerque
X
Clark County Department of Air Quality and Environmental Management
X
Coeur d'Alene Tribe
X
Colorado Department of Public Health and Environment
X
Connecticut Department of Environmental Protection
X
X
DC Department of Health Air Quality Division
X
X
Delaware Department of Natural Resources and Environmental Control
X
X
Florida Department of Environmental Protection
X
Forsyth County Environmental Affairs Department
X
Georgia Department of Natural Resources
X
X
Hawaii Department of Health Clean Air Branch
X
Idaho Department of Environmental Quality
X
Illinois Environmental Protection Agency
X
X
Indiana Department of Environmental Management
X
X
Iowa Department of Natural Resources
X
X
Jefferson County (AL) Department of Health
X
Kansas Department of Health and Environment
X
X
230
-------
Data Set Name
Point
Nonpoint
Kentucky Division for Air Quality
X
Kootenai Tribe of Idaho
X
Louisiana Department of Environmental Quality
X
Louisville Metro Air Pollution Control District
X
Maine Department of Environmental Protection
X
X
Maricopa County Air Quality Department
X
Maryland Department of the Environment
X
X
Massachusetts Department of Environmental Protection
X
X
Mecklenburg County Air Quality
X
Memphis and Shelby County Health Department - Pollution Control
X
Metro Public Health of Nashville/Davidson County
X
Michigan Department of Environmental Quality
X
X
Minnesota Pollution Control Agency
X
X
Mississippi Dept of Environmental Quality
X
Missouri Department of Natural Resources
X
X
Nevada Division of Environmental Protection
X
New Hampshire Department of Environmental Services
X
X
New Jersey Department of Environment Protection
X
X
New York State Department of Environmental Conservation
X
X
Nez Perce Tribe
X
North Carolina Department of Environment and Natural Resources
X
North Dakota Department of Health
X
Ohio Environmental Protection Agency
X
X
Oklahoma Department of Environmental Quality
X
Omaha Air Quality Control Division
X
Oregon Department of Environmental Quality
X
X
Pennsylvania Department of Environmental Protection
X
X
Philadelphia Air Management Services
X
Pinal County
X
Puget Sound Clean Air Agency
X
Rhode Island Department of Environmental Management
X
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
X
South Carolina Department of Health and Environmental Control
X
X
Tennessee Department of Environmental Conservation
X
X
Texas Commission on Environmental Quality
X
X
Utah Division of Air Quality
X
Vermont Department of Environmental Conservation
X
Virginia Department of Environmental Quality
X
X
Washington State Department of Ecology
X
Washoe County Health District
X
West Virginia Division of Air Quality
X
231
-------
Data Set Name
Point
Nonpoint
Western North Carolina Regional Air Quality Agency (Buncombe Co.)
X
Wisconsin Department of Natural Resources
X
X
3.30.3 Spatial coverage and data sources for the sector
P - Point
N - Nonpoint
PN - P&N
i3r
P - Point "
N - Nonpoint
Solvent - Graphic Arts
All CAPs
Solvent - Graphic Arts
PN - P&N
All HAPs I ' EPA "" SLT EPA S SLT
3.30.4 EPA-developed emissions
EPA calculated emissions using EPA's EIIP Volume 3, Chapter 7 Alternate Method 1 [ref 3], Emissions are
calculated for each county using emission factors and activity as:
Ev p = A x E F,r,p
where:
Enp = annual emissions for category x and pollutant p
A.: = employment data associated with category x
EFXrP = emission factor for category x and pollutant p
Activity Data
Graphic arts employment data is listed by the North American Industrial Classification Standard (NAICS) code(s)
that were used to determine county-level employment for the category. County data were gathered from
NAICS categories: 32311, and 3222. NAICS data was gathered from the 2010 Census County Business Patterns
(CBP) [ref 4],
Due to concerns with releasing confidential business information, the Census CBP does not release exact
numbers for a given NAICS code if there is enough data that individual businesses could be identified. Instead, a
series of range codes are used. Because employment data is a key factor in determining emissions, it is
important to estimate the number of employees for each county.
To estimate the number of employees in counties where data was withheld, EPA used the following procedure
for each NAICS code being computed:
1. County level data for each NAICS were obtained and any numerical values were summed.
232
-------
2. The sum generated in step 1 was subtracted from the state total number of employees in that NAICS
reported in the state-level CBP.
3. The county level CBP report includes the number of establishments in the county within a specific
employee range. For each of the counties with withheld data, EPA multiplied the number of
establishments in a particular employee range (1 - 4, 5 - 9, etc.) by the midpoint of the range code (5 -
9 employees would be assigned 7) and summed the results.
4. An adjustment factor (to ensure the total number of estimated employees matches the state reported
total) is calculated by dividing the sum of all the county level generated in step 2 by the sum of the
county calculations in step 3. If there are no numerical values at the county level the adjustment factor
is calculated by dividing the state total number of employees by the sum of the calculations in step 3.
5. The estimated number of employees, in counties where data was withheld, is calculated by multiplying
the sum from step 3 by the adjustment factor calculated in step 4.
Emission Factors
The VOC emission factor is from EPA's ERTAC Penna Graphic Arts study for 2011 [ref 5], Additional emission
factors were developed by ERTAC in 2011 [ref 6],
Sample Calculation
According to the U.S. Census Bureau, Kootenai County had a total of 80 employees in the graphic arts industry.
The emission factor for VOC is 200.82 lb/employee
Evoc
= 80 x 200.82 lb VOC/ employee
= 8.033 tons VOC
3.30.5 References for Solvent - Graphic Arts
1. Graphic Arts & Printing Inks. An example of specific gravity differences in ink, accessed April 2013.
2. Offset Printing Inks, Fillers for printing inks. Accessed April 2013.
3. U.S. Environmental Protection Agency, Emissions Inventory Improvement Program, Technical Report
Series, Volume 111 - Area Sources, Chapter 7, "Graphic Arts," prepared by Eastern Research Group, Inc.
for EPA, Research Triangle Park, NC, 2001.
4. U.S. Census Bureau, 2010 County Business Patterns for Idaho Counties, accessed September 2012.
5. ERTAC 2011 Final Penna Graphic Arts El Study, Final Penna Graphic Arts EF Study.xlsx, from an email
from Roy Huntley on 2/29/12.
6. ERTAC 2011 graphic arts calculations for Idaho, graphic arts 24250QQC ployment 2011 .xls.
accessed September, 2012.
3.31 Solvent - Industrial Surface Coating
3.31.1 Sector description
Surface coating operations involve applying a thin layer of coating (e.g., paint, lacquer, enamel, varnish, etc.) to
an object for decorative or protective purposes. The surface coating products include either a water-based or
solvent-based liquid carrier that generally evaporates in the drying or curing process.
Emissions result from the evaporation of the paint solvent and any additional solvent used to thin the coating.
Emissions also result from the use of solvents in cleaning the surface prior to coating and in cleaning coating
equipment after use.
233
-------
Ideally, all industrial surface coating facilities would be inventoried as point sources. Preferred and alternative
methods for estimating point source emissions from industrial surface coating operations are given in EIIP
Volume II, Chapter 7 [ref 1], That chapter also includes more detailed discussion of surface coatings technology
and controls, as well as process descriptions for industries having significant point source emissions. As a
practical matter, it is not usually possible to account for all industrial surface coating facilities as point sources.
Although the majority of industrial surface coating emissions may be inventoried as point sources, remaining
emissions of volatile organic compounds (VOCs) and hazardous air pollutants (HAPs) from industrial surface
coating operations must be accounted for as nonpoint sources. Since the use of surface coatings by
manufacturing industries is so widespread, it is extremely difficult to identify all of the industries in which
coating materials are consumed.
The SCCs in this sector are listed in Table 3-137; SCC descriptions do not include level 4 descriptions for many
point SCCs. The "x" in several point source SCCs indicates that all SCCs are included; this was done to avoid
listing 300+ point source SCCs in this sector.
Table 3-137: Industrial Solvent Use SCCs in the 2011 NEI
SCC
SCC Description
2401075000
Solvent Utilization; Surface Coating; All Solvent Types
Aircraft
2401060000
Solvent Ut
lization; Surface Coating; All Solvent Types
Appliances
2401065000
Solvent Ut
lization; Surface Coating; All Solvent Types
Electronic and Other Electrical
2401015000
Solvent Ut
lization; Surface Coating; All Solvent Types
Factory Finished Wood
2401100000
Solvent Ut
lization; Surface Coating; All Solvent Types
Industrial Maintenance Coatings
2401055000
Solvent Ut
lization; Surface Coating; All Solvent Types
Machinery and Equipment
2401080000
Solvent Ut
lization; Surface Coating; All Solvent Types
Marine
2401040000
Solvent Ut
lization; Surface Coating; All Solvent Types
Metal Cans
2401025000
Solvent Ut
lization; Surface Coating; All Solvent Types
Metal Furniture
2401045000
Solvent Ut
lization; Surface Coating; All Solvent Types
Metal Sheet/Strip/Coil
2401050000
Solvent Ut
lization; Surface Coating; All Solvent Types
Miscellaneous Finished Metals:
2401090000
Solvent Ut
lization; Surface Coating; All Solvent Types
Miscellaneous Manufacturing
2401070000
Solvent Ut
lization; Surface Coating; All Solvent Types
Motor Vehicles
2401200000
Solvent Ut
lization; Surface Coating; All Solvent Types
Other Special Purpose Coatings
2401030000
Solvent Ut
lization; Surface Coating; All Solvent Types
Paper, Film, and Foil
2401085000
Solvent Ut
lization; Surface Coating; All Solvent Types
Railroad
2401020000
Solvent Ut
lization; Surface Coating; All Solvent Types
Wood Furniture
40100499
Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Knit Fabric Scouring with Chlorinated
Solvent; Other Not Classified
40200x01
Petroleum and Solvent Evaporation
Surface Coating Operations; Surface Coating Application - General
402007xx
Petroleum and Solvent Evaporation
Surface Coating Operations; Surface Coating Application - General
402008xx
Petroleum and Solvent Evaporation
Surface Coating Operations; Coating Oven - General
402009xx
Petroleum and Solvent Evaporation
Surface Coating Operations; Thinning Solvents - General
40201004
Petroleum and Solvent Evaporation
Surface Coating Operations; Coating Oven Heater
402011xx
Petroleum and Solvent Evaporation
Surface Coating Operations; Fabric Coating/Printing
40201201
Petroleum and Solvent Evaporation
Surface Coating Operations; Fabric Dyeing
402013xx
Petroleum and Solvent Evaporation
Surface Coating Operations; Paper Coating
402014xx
Petroleum and Solvent Evaporation
Surface Coating Operations; Large Appliances
402015xx
Petroleum and Solvent Evaporation
Surface Coating Operations; Magnet Wire Surface Coating
402016xx
Petroleum and Solvent Evaporation
Surface Coating Operations; Automobiles and Light Trucks
402017xx
Petroleum and Solvent Evaporation
Surface Coating Operations; Metal Can Coating
402018xx
Petroleum and Solvent Evaporation
Surface Coating Operations; Metal Coil Coating
234
-------
see
SCC Description
402019xx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Wood Furniture Surface Coating
402020xx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Metal Furniture Operations
40202lxx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Flatwood Products
402022xx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Plastic Parts
402023xx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Large Ships
402024xx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Large Aircraft
402025xx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Miscellaneous Metal Parts
402026xx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Steel Drums
40202701
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Glass Mirrors
402028xx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Glass Optical Fibers
40203001
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Semiconductors
402040xx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Fabric Printing
40204lxx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Fabric Coating, Knife Coating
402042xx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Fabric Coating, Roller Coating
402043xx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Fabric Coating, Dip Coating
402044xx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Fabric Coating, Transfer Coating
402045xx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Fabric Coating, Extrusion Coating
40204630
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Fabric Coating, Melt Roll Coating
402047xx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Fabric Coating, Coagulation Coating
402060xx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Fabric Dyeing
40280001
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Equipment Leaks
40282001
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Wastewater, Aggregate
40282599
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Wastewater, Points of Generation
402888xx
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Fugitive Emissions
402900x3
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Fuel Fired Equipment
40299998
Petroleum and Solvent Evaporation; Surface Coat
ng Operations; Miscellaneous
490001xx
Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Solvent Extraction Process
490002xx
Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Waste Solvent Recovery Operations
49000399
Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Rail Car Cleaning
490004xx
Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Tank Truck Cleaning
490005xx
Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Air Stripping Tower
49000601
Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Freon Recovery/Recycling Operations
490900xx
Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Fuel Fired Equipment
49099998
Petroleum and Solvent Evaporation; Organic Solvent Evaporation; Miscellaneous Volatile Organic
Compound Evaporation
3.31.2 Sources of data overview and selection hierarchy
The industrial surface coating sector includes emissions from both S/L/T agencies and from the EPA overlap
nonpoint dataset. This sector is present in the point and nonpoint data category. The hierarchy of datasets used
in the 2011 NEI for this sector is provided in Table 3-138. In some cases, the EPA PM and HAP augmentation
datasets were used to fill in PM species and HAP pollutants based on S/L/T agency data. All S/L/T agencies that
submitted data to the EPA are listed in Table 3-139Table 3-116. Several agencies submitted nonpoint emissions
for this sector; these nonpoint sources are broken out by different types of categories in this table. EPA datasets
are individually listed.
235
-------
Table 3-138: Data selection hierarchy for the Solvent -Industrial Surface Coating sector
Priority
Dataset Name
Dataset Content
1
2011EPA_PM-Augmentation
Augments PM data in 47 states and some tribes
2
Responsible Agency Data Set
State and Local Agency submitted emissions
3
2011EPA_HAP-Augmentation
Adds Pb and other HAP emissions in 46 states
4
2011E PA_N P_Overlap_w_Pt
EPA-generated data
Table 3-139: EPA and S/L/T agency-submitted point and nonpoint data for Industrial Surface Coating sector
Data Set Name
Point
Aircraft
Auto Refinishing
Electronic & Other Electrical
Factory Finished Wood
Ind. Maintenance Coatings
Large Appliances
Machinery & Equipment
Marine
Metal Cans
Metal Coils
Metal Furniture
Misc. Finished Metals
Misc. Manufacturing
Motor Vehicles
Other Special Purpose
Paper
Plastic Products
Railroad
Textile Products
Traffic Markings
Wood Furniture
2011EPA_chrom_split
X
2011EPA_PM-Augmentation
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
2011EPA_HAP-Augmentation
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
2011EPA_NP_NoOverlap_w_Pt
X
X
2011EPA_NP_Overlap_w_Pt
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
2011EPA_TRI
X
Alabama Department of Environmental
Management
X
Alaska Department of Environmental
Conservation
X
Allegheny County Health Department
X
Arizona Department of Environmental Quality
X
Arkansas Department of Environmental
Quality
X
California Air Resources Board
X
X
X
X
X
X
X
X
X
X
Chattanooga Air Pollution Control Bureau
(CHCAPCB)
X
X
X
X
X
X
X
X
X
X
City of Albuquerque
X
Clark County Department of Air Quality and
Environmental Management
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Coeur d'Alene Tribe
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Colorado Department of Public Health and
Environment
X
Connecticut Department Of Environmental
Protection
X
X
X
X
X
X
X
DC Department of Health
X
X
X
Delaware Department of Natural Resources
and Environmental Control
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Florida Department of Environmental
Protection
X
236
-------
Data Set Name
Point
Aircraft
Auto Refinishing
Electronic & Other Electrical
Factory Finished Wood
Ind. Maintenance Coatings
Large Appliances
Machinery & Equipment
Marine
Metal Cans
Metal Coils
Metal Furniture
Misc. Finished Metals
Misc. Manufacturing
Motor Vehicles
Other Special Purpose
Paper
Plastic Products
Railroad
Textile Products
Traffic Markings
Wood Furniture
Forsyth County Environmental Affairs
Department
X
Georgia Department of Natural Resources
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Hawaii Department of Health Clean Air Branch
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Idaho Department of Environmental Quality
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Illinois Environmental Protection Agency
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Indiana Department of Environmental
Management
X
X
X
X
X
X
X
X
X
X
X
X
X
Iowa Department of Natural Resources
X
X
X
X
X
X
X
X
X
Jefferson County (AL) Department of Health
X
Kansas Department of Health and
Environment
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Kentucky Division for Air Quality
X
Knox County Department of Air Quality
Management
X
X
X
Kootenai Tribe of Idaho
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Lane Regional Air Pollution Authority
X
Lincoln/Lancaster County Health Department
X
Louisiana Department of Environmental
Quality
X
Louisville Metro Air Pollution Control District
X
Maine Department of Environmental
Protection
X
X
X
X
X
X
X
X
X
X
Maricopa County Air Quality Department
X
X
X
X
X
X
X
Maryland Department of the Environment
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Massachusetts Department of Environmental
Protection
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Mecklenburg County Air Quality
X
Memphis and Shelby County Health
Department - Pollution Control
X
Metro Public Health of Nashville/Davidson
County
X
X
Michigan Department of Environmental
Quality
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Minnesota Pollution Control Agency
X
X
X
X
X
X
X
X
X
X
X
X
X
Mississippi Dept of Environmental Quality
X
Missouri Department of Natural Resources
X
X
X
X
X
X
X
X
X
X
X
X
X
X
237
-------
Data Set Name
Point
Aircraft
Auto Refinishing
Electronic & Other Electrical
Factory Finished Wood
Ind. Maintenance Coatings
Large Appliances
Machinery & Equipment
Marine
Metal Cans
Metal Coils
Metal Furniture
Misc. Finished Metals
Misc. Manufacturing
Motor Vehicles
Other Special Purpose
Paper
Plastic Products
Railroad
Textile Products
Traffic Markings
Wood Furniture
Montana Department of Environmental
Quality
X
Navajo Nation
X
Nebraska Environmental Quality
X
Nevada Division of Environmental Protection
X
New Hampshire Department of Environmental
Services
X
X
X
X
X
X
X
X
X
X
X
X
New Jersey Department of Environment
Protection
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
New Mexico Environment Department Air
Quality Bureau
X
New York State Department of Environmental
Conservation
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Nez Perce Tribe
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
North Carolina Department of Environment
and Natural Resources
X
North Dakota Department of Health
X
Ohio Environmental Protection Agency
X
X
X
X
X
X
X
X
X
X
X
X
X
Oklahoma Department of Environmental
Quality
X
Olympic Region Clean Air Agency
X
Omaha Air Quality Control Division
X
Oregon Department of Environmental Quality
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Pennsylvania Department of Environmental
Protection
X
X
X
X
X
X
X
X
Philadelphia Air Management Services
X
Pinal County
X
Puerto Rico
X
Puget Sound Clean Air Agency
X
Rhode Island Department of Environmental
Management
X
Shoshone-Bannock Tribes
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
South Carolina Department of Health and
Environmental Control
X
X
X
X
X
X
X
X
South Dakota Department of Environment and
Natural Resources
X
Southwest Clean Air Agency
X
Tennessee Department of Environmental
Conservation
X
X
X
X
X
X
X
X
X
X
238
-------
Data Set Name
Point
Aircraft
Auto Refinishing
Electronic & Other Electrical
Factory Finished Wood
Ind. Maintenance Coatings
Large Appliances
Machinery & Equipment
Marine
Metal Cans
Metal Coils
Metal Furniture
Misc. Finished Metals
Misc. Manufacturing
Motor Vehicles
Other Special Purpose
Paper
Plastic Products
Railroad
Textile Products
Traffic Markings
Wood Furniture
Texas Commission on Environmental Quality
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Utah Division of Air Quality
X
Vermont Department of Environmental
Conservation
X
Virginia Department of Environmental Quality
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Washington State Department of Ecology
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Washoe Tribe of California and Nevada
X
Washoe County Health District
X
X
X
X
X
X
X
X
West Virginia Division of Air Quality
X
X
Western North Carolina Regional Air Quality
Agency (Buncombe Co.)
X
Wisconsin Department of Natural Resources
X
X
X
Wyoming Department of Environmental
Quality
X
3.31.3 Spatial coverage and data sources for the sector
Solvent - Industrial Surface Coating & Solvent Use Solvent - Industrial Surface Coating & Solvent Use
N - Nonpoint
PN-P&N
All CAPs
Nori point
PN-P&N
All HAPs
3.31.4 EPA-developed emissions
Emissions are calculated for each county using emission factors and activity as:
E.r,p — Ar X EF- .P
where:
239
-------
Ex,p = annual emissions for category x and pollutant p
A.v = employment or population data associated with category x
EFx,p = emission factor for category x and pollutant p
Activity Data
There are two types of activity data that are used in this category. The first is employment data listed by the
North American Industrial Classification Standard (NAICS) codes used to determine county-level employment for
the various categories. NAICS data was gathered from the 2010 Census County Business Patterns (CBP) for the
US [ref 2],
Due to concerns with releasing confidential business information, the Census CBP does not release exact
numbers for a given NAICS code if individual businesses could be identified. Instead a series of range codes are
used. Because employment data is a key factor in determining emissions it is important to estimate the number
of employees for each county.
To estimate the number of employees in counties where data was withheld, EPA used the following procedure
for each NAICS code being computed:
1. County level data for each NAICS were obtained and any numerical values were summed.
2. The sum generated in step 1 was subtracted from the state total number of employees in that NAICS
reported in the state-level CBP.
3. The county level CBP report includes the number of establishments in the county within a specific
employee range. For each of the counties with withheld data, EPA multiplied the number of
establishments in a particular employee range (1 - 4, 5 - 9, etc.) by the midpoint of the range code (5 -
9 employees would be assigned 7) and summed the results.
4. An adjustment factor (to ensure the total number of estimated employees matches the state reported
total) is calculated by dividing the sum of all the county level generated in step 2 by the sum of the
county calculations in step 3. If there are no numerical values at the county level the adjustment factor
is calculated by dividing the state total number of employees by the sum of the calculations in step 3.
5. The estimated number of employees, in counties where data was withheld, is calculated by multiplying
the sum from step 3 by the adjustment factor calculated in step 4.
The second category of activity data used to estimate emissions from industrial solvent use was 2010 county-
level population data, which was obtained from the US Census Bureau's interactive population search for the
2010 Census [ref 3], The per capita emission factors were then multiplied by the 2010 county-level population
estimates. This method was used when there was no applicable NAICS category or enough employees in a NAICS
category (Industrial Maintenance Coatings, Miscellaneous Finished Metals, Metal Sheet/Strip/Coil, and Other
Special Purpose Coatings).
Emission Factors
EPA emission factors for Industrial Surface Coatings, available in the SPECIATE v4.3 database [ref 4], are
provided in Table 3-140.
Table 3-140: EPA emission factors for Industrial Surface Coating used in 2011 NEI
see
Description
Pollutant
Code
Pollutant Description
Emission
Factor
Units
EF source
2401075000
Aircraft
108383
m-Xylene
0.1626394
LB/employee
HAP Speciation
2401075000
Aircraft
100414
Ethyl Benzene
0.0780098
LB/employee
HAP Speciation
2401075000
Aircraft
106423
p-Xylene
0.0724284
LB/employee
HAP Speciation
240
-------
Pollutant
Emission
see
Description
Code
Pollutant Description
Factor
Units
EF source
2401075000
Aircraft
95476
o-Xylene
0.07139
LB/employee
HAP Speciation
2401075000
Aircraft
108101
Methyl Isobutyl Ketone
0.3117796
LB/employee
HAP Speciation
2401075000
Aircraft
110543
Hexane
3.0676932
LB/employee
HAP Speciation
2401075000
Aircraft
121448
Triethylamine
0.0063602
LB/employee
HAP Speciation
2401075000
Aircraft
540885
Tert-butyl Acetate
0.329043
LB/employee
HAP Speciation
2401075000
Aircraft
108883
Toluene
1.682857
LB/employee
HAP Speciation
2401075000
Aircraft
VOC
VOC
12.98
LB/employee
2010 Fredonia
2401060000
Appliances
540885
Tert-butyl Acetate
5.22082
LB/employee
HAP Speciation
2401060000
Appliances
121448
Triethylamine
0.09823
LB/employee
HAP Speciation
2401060000
Appliances
110543
Hexane
49.39506
LB/employee
HAP Speciation
2401060000
Appliances
108383
m-Xylene
3.01796
LB/employee
HAP Speciation
2401060000
Appliances
108101
Methyl Isobutyl Ketone
14.41473
LB/employee
HAP Speciation
2401060000
Appliances
106423
p-Xylene
1.34596
LB/employee
HAP Speciation
2401060000
Appliances
100414
Ethyl Benzene
1.39821
LB/employee
HAP Speciation
2401060000
Appliances
108883
Toluene
26.36953
LB/employee
HAP Speciation
2401060000
Appliances
95476
o-Xylene
1.32506
LB/employee
HAP Speciation
2401060000
Appliances
VOC
VOC
209
LB/employee
2010 Freedonia
2401005000
Auto Refinishing
108883
Toluene
12.6269115
LB/employee
HAP Speciation
2401005000
Auto Refinishing
106423
p-Xylene
0.6495734
LB/employee
HAP Speciation
2401005000
Auto Refinishing
540885
Tert-butyl Acetate
4.9106234
LB/employee
HAP Speciation
2401005000
Auto Refinishing
67561
Methanol
0.2727072
LB/employee
HAP Speciation
2401005000
Auto Refinishing
100414
Ethyl Benzene
0.6637769
LB/employee
HAP Speciation
2401005000
Auto Refinishing
110543
Hexane
24.5673205
LB/employee
HAP Speciation
2401005000
Auto Refinishing
2807309
Propyl Cellosolve
0.6306354
LB/employee
HAP Speciation
2401005000
Auto Refinishing
112072
2-Butoxyethyl Acetate
0.8569445
LB/employee
HAP Speciation
2401005000
Auto Refinishing
108101
Methyl Isobutyl Ketone
5.0857999
LB/employee
HAP Speciation
2401005000
Auto Refinishing
108383
m-Xylene
1.5396594
LB/employee
HAP Speciation
2401005000
Auto Refinishing
95476
o-Xylene
0.7613076
LB/employee
HAP Speciation
2401005000
Auto Refinishing
121448
Triethylamine
0.0463981
LB/employee
HAP Speciation
2401005000
Auto Refinishing
80626
Methyl Methacrylate
0.09469
LB/employee
HAP Speciation
2401005000
Auto Refinishing
VOC
VOC
94.69
LB/employee
2010 Freedonia
Factory Finished
2401015000
Wood
1330207
Xylenes (Mixed Isomers)
2.3847527
LB/employee
HAP Speciation
Factory Finished
2401015000
Wood
VOC
VOC
48.07
LB/employee
2010 Freedonia
Machinery and
2401055000
Equipment
108383
m-Xylene
0.6754512
LB/EACH
HAP Speciation
Machinery and
2401055000
Equipment
110543
Hexane
12.2045976
LB/EACH
HAP Speciation
Machinery and
2401055000
Equipment
106423
p-Xylene
0.3010612
LB/EACH
HAP Speciation
Machinery and
2401055000
Equipment
100414
Ethyl Benzene
0.3258484
LB/EACH
HAP Speciation
Machinery and
2401055000
Equipment
540885
Tert-butyl Acetate
1.2899672
LB/EACH
HAP Speciation
Machinery and
2401055000
Equipment
108883
Toluene
6.4560328
LB/EACH
HAP Speciation
Machinery and
2401055000
Equipment
95476
o-Xylene
0.29693
LB/EACH
HAP Speciation
241
-------
Pollutant
Emission
see
Description
Code
Pollutant Description
Factor
Units
EF source
Machinery and
2401055000
Equipment
121448
Triethylamine
0.0242708
LB/EACH
HAP Speciation
Machinery and
2401055000
Equipment
108101
Methyl Isobutyl Ketone
2.0459768
LB/EACH
HAP Speciation
Machinery and
2401055000
Equipment
VOC
VOC
51.64
LB/EACH
2010 Freedonia
Diethylene Glycol
2401080000
Marine
111900
Monoethyl Ether
0
LB/employee
HAP Speciation
2401080000
Marine
2807309
Propyl Cellosolve
2.61
LB/employee
HAP Speciation
2401080000
Marine
1330207
Xylenes (Mixed Isomers)
0
LB/employee
HAP Speciation
2401080000
Marine
VOC
VOC
225
LB/employee
2010 Freedonia
2401025000
Metal Furniture
106423
p-Xylene
5.17704
LB/employee
HAP Speciation
2401025000
Metal Furniture
110543
Hexane
209.86992
LB/employee
HAP Speciation
2401025000
Metal Furniture
108883
Toluene
111.01776
LB/employee
HAP Speciation
2401025000
Metal Furniture
540885
Tert-butyl Acetate
22.18224
LB/employee
HAP Speciation
2401025000
Metal Furniture
100414
Ethyl Benzene
5.60328
LB/employee
HAP Speciation
2401025000
Metal Furniture
121448
Triethylamine
0.41736
LB/employee
HAP Speciation
2401025000
Metal Furniture
95476
o-Xylene
5.106
LB/employee
HAP Speciation
2401025000
Metal Furniture
108383
m-Xylene
11.61504
LB/employee
HAP Speciation
2401025000
Metal Furniture
108101
Methyl Isobutyl Ketone
35.18256
LB/employee
HAP Speciation
2401025000
Metal Furniture
VOC
VOC
888
LB/employee
2010 Freedonia
Miscellaneous
2401090000
Manufacturing
95476
o-Xylene
0.5313
LB/employee
HAP Speciation
Miscellaneous
2401090000
Manufacturing
100414
Ethyl Benzene
0.583044
LB/employee
HAP Speciation
Miscellaneous
2401090000
Manufacturing
108383
m-Xylene
1.208592
LB/employee
HAP Speciation
Miscellaneous
2401090000
Manufacturing
108101
Methyl Isobutyl Ketone
3.660888
LB/employee
HAP Speciation
Miscellaneous
2401090000
Manufacturing
106423
p-Xylene
0.538692
LB/employee
HAP Speciation
Miscellaneous
2401090000
Manufacturing
540885
Tert-butyl Acetate
2.308152
LB/employee
HAP Speciation
Miscellaneous
2401090000
Manufacturing
108883
Toluene
11.551848
LB/employee
HAP Speciation
Miscellaneous
2401090000
Manufacturing
110543
Hexane
21.837816
LB/employee
HAP Speciation
Miscellaneous
2401090000
Manufacturing
121448
Triethylamine
0.043428
LB/employee
HAP Speciation
Miscellaneous
2401090000
Manufacturing
VOC
VOC
92.4
LB/employee
2010 Freedonia
2401070000
Motor Vehicles
121448
Triethylamine
0.09555
LB/employee
HAP Speciation
2401070000
Motor Vehicles
80626
Methyl Methacrylate
0.195
LB/employee
HAP Speciation
2401070000
Motor Vehicles
108101
Methyl Isobutyl Ketone
10.47345
LB/employee
HAP Speciation
2401070000
Motor Vehicles
95476
o-Xylene
1.5678
LB/employee
HAP Speciation
2401070000
Motor Vehicles
110543
Hexane
50.59275
LB/employee
HAP Speciation
2401070000
Motor Vehicles
112072
2-Butoxyethyl Acetate
1.76475
LB/employee
HAP Speciation
2401070000
Motor Vehicles
540885
Tert-butyl Acetate
10.1127
LB/employee
HAP Speciation
2401070000
Motor Vehicles
106423
p-Xylene
1.3377
LB/employee
HAP Speciation
242
-------
Pollutant
Emission
see
Description
Code
Pollutant Description
Factor
Units
EF source
2401070000
Motor Vehicles
2807309
Propyl Cellosolve
1.2987
LB/employee
HAP Speciation
2401070000
Motor Vehicles
67561
Methanol
0.5616
LB/employee
HAP Speciation
2401070000
Motor Vehicles
100414
Ethyl Benzene
1.36695
LB/employee
HAP Speciation
2401070000
Motor Vehicles
108883
Toluene
26.00325
LB/employee
HAP Speciation
2401070000
Motor Vehicles
108383
m-Xylene
3.1707
LB/employee
HAP Speciation
2401070000
Motor Vehicles
VOC
VOC
195
LB/employee
2010 Freedonia
2401030000
Paper Film and Foil
110543
Hexane
143.93106
LB/employee
HAP Speciation
2401030000
Paper Film and Foil
540885
Tert-butyl Acetate
15.21282
LB/employee
HAP Speciation
2401030000
Paper Film and Foil
106423
p-Xylene
3.55047
LB/employee
HAP Speciation
2401030000
Paper Film and Foil
108883
Toluene
76.13718
LB/employee
HAP Speciation
2401030000
Paper Film and Foil
100414
Ethyl Benzene
3.84279
LB/employee
HAP Speciation
2401030000
Paper Film and Foil
95476
o-Xylene
3.50175
LB/employee
HAP Speciation
2401030000
Paper Film and Foil
108383
m-Xylene
7.96572
LB/employee
HAP Speciation
2401030000
Paper Film and Foil
108101
Methyl Isobutyl Ketone
24.12858
LB/employee
HAP Speciation
2401030000
Paper Film and Foil
121448
Triethylamine
0.28623
LB/employee
HAP Speciation
2401030000
Paper Film and Foil
VOC
VOC
609
LB/employee
2010 Freedonia
2401085000
Railroad
100414
Ethyl Benzene
3.12832
LB/employee
HAP Speciation
2401085000
Railroad
108883
Toluene
41.44816
LB/employee
HAP Speciation
2401085000
Railroad
110543
Hexane
73.8608
LB/employee
HAP Speciation
2401085000
Railroad
1330207
Xylenes (Mixed Isomers)
12.03488
LB/employee
HAP Speciation
2401085000
Railroad
VOC
VOC
208
LB/employee
2010 Freedonia
2401200000
Special Purpose
112072
2-Butoxyethyl Acetate
0.0009216
LB/Person
HAP Speciation
2401200000
Special Purpose
108907
Chlorobenzene
0.000288
LB/Person
HAP Speciation
2401200000
Special Purpose
540885
Tert-butyl Acetate
0.00186112
LB/Person
HAP Speciation
2401200000
Special Purpose
108883
Toluene
0.0097856
LB/Person
HAP Speciation
2401200000
Special Purpose
106423
p-Xylene
0.00013952
LB/Person
HAP Speciation
Diethylene Glycol
2401200000
Special Purpose
111900
Monoethyl Ether
0.00043904
LB/Person
HAP Speciation
2401200000
Special Purpose
108101
Methyl Isobutyl Ketone
0.00013824
LB/Person
HAP Speciation
2401200000
Special Purpose
98828
Cumene
0.000128
LB/Person
HAP Speciation
2401200000
Special Purpose
108383
m-Xylene
0.00033088
LB/Person
HAP Speciation
2401200000
Special Purpose
95476
o-Xylene
0.00017152
LB/Person
HAP Speciation
2401200000
Special Purpose
100414
Ethyl Benzene
0.00027392
LB/Person
HAP Speciation
2401200000
Special Purpose
67561
Methanol
0.00037184
LB/Person
HAP Speciation
2401200000
Special Purpose
VOC
VOC
0.064
LB/Person
2010 Freedonia
2401008000
Traffic Markings
540885
Tert-butyl Acetate
0.000899
LB/Person
HAP Speciation
2401008000
Traffic Markings
106990
1,3-Butadiene
0.00029
LB/Person
HAP Speciation
2401008000
Traffic Markings
1330207
Xylenes (Mixed Isomers)
0.0015022
LB/Person
HAP Speciation
2401008000
Traffic Markings
91203
Naphthalene
0.000145
LB/Person
HAP Speciation
2401008000
Traffic Markings
108883
Toluene
0.0246674
LB/Person
HAP Speciation
2401008000
Traffic Markings
100414
Ethyl Benzene
0.0008033
LB/Person
HAP Speciation
2401008000
Traffic Markings
VOC
VOC
0.29
LB/Person
2010 Freedonia
2401020000
Wood Furniture
1330207
Xylenes (Mixed Isomers)
25.99564
LB/Employee
HAP Speciation
2401020000
Wood Furniture
VOC
VOC
524
LB/Employee
2010 Freedonia
The total volume of coatings sold was obtained from the Census Bureau, Paint and Allied Products, 2010 [ref 5],
The volume of architectural and powder coatings was subtracted from the total to obtain the total non-
architectural coating volume. The volume of coatings sold for a particular category, like Automotive, Other
243
-------
Transportation and Machinery Refinish Paints and Enamels Including Primers, was obtained from the same
source and used to determine a percentage of category coatings to total non-architectural coating. This
percentage was applied to the amount of solvents in tons used for non-architectural Paint and Coatings,
obtained from the Freedonia Group (Report #2357, Solvents to 2012, June 2008) [ref 6], The result is the tons of
solvents sold for the particular category. An assumption is made that all the solvent is eventually emitted, so the
result is considered VOC emissions in tons. The emission factor units need to be lb of VOC per employee, so
employment data is obtained from the National American Industry Classification System (NAICS) for the
appropriate NAICS employment codes and a value per employee is determined. The HAP emission factors were
determined using HAP speciation profiles obtained from EPA's SPPD. EIAG received a database from SPPD,
which originated from the Aerosol Coatings Rule that EPA promulgated on March 24, 2008. Manufacturers,
importers, and distributors of aerosol coatings were required to submit initial notifications of product
formulations by July 1, 2009 to their EPA Regional Offices. From this database, EIAG developed speciated HAPs
[ref 4] for industrial surface coating categories.
Example Calculation
According to the U.S. Census Bureau, Kootenai County had a total of 492 employees in the factory finished wood
industry. According to EPA's 2011 calculations, the solvent use emission factor for VOC is 48.07 lb/employee.
Evoc = 492 x 48.07 lb VOC/ employee
= 11.83 tons VOC
3.31.5 Summary of data quality assurance methods
The EPA compared the 2011 dataset to previous year EPA dataset and found no significant issues. Since this
source category overlaps with the point source inventory, the submitting agency has the responsibility for
reconciling the emissions and submitting nonpoint data to EPA that has the point sources emissions accounted
for. Some effort was made by EPA to determine if point sources were properly accounted for. The EPA used
state responses to EPA surveys and personal communication, if necessary, to determine the status of point
source reconciliation for these categories.
Colorado asked EPA to tag the EPA nonpoint emissions for industrial surface coating, because they determined
that they had these sources covered in the point data category.
3.31.6 References for Solvent - Industrial Surface Coating
1. U.S. E inology Transfer Network, Clearinghouse for Inventories & Emissions Factors, Technical
Report Series, Volume 3, Chapter 8: Industrial Surface Coatings., accessed February 2011.
2. U.S. Census Bureau, 2010 County Business Patterns, accessed September 2012.
3. U.S. Census Bureau. 2010 Interactive Population Search, Census 2010.
4. U.S. EPA Technology Transfer Network, Clearinghouse for Inventories and Emissions Factors, Software
and Tools, Speciate 4.3 September 2011., accessed September 2012.
5. U.S. Census Bureau. Paints and Allied Products, 2010.
6. Freedonia Group. Study #2357, $4600. Solvents to 2012, June 2008.
244
-------
3.32 Waste Disposal
3.32.1 Sector description
Waste disposal covers a wide range of source categories, from incineration, open burning, landfills, wastewater
treatment, soil and groundwater remediation, scrap and waste materials, hazardous waste treatment storage
and disposal facilities (TSDFs) and leaking underground storage tanks. SCCs that are included in the 2011 NEI v2
in the Waste Disposal sector are provided in Table 3-141. The leading SCC description is "Waste Disposal,
Treatment, and Recovery" for nonpoint SCCs and "Waste Disposal" for point source SCCs. EPA estimates
emissions from the highlighted SCCs below, which include in the nonpoint category: open burning of municipal
solid waste, land clearing debris, and yard waste; publicly owned treatment works (POTW); and a few specific
mercury sources in landfills. The column "Hg only?" denotes categories where only mercury was estimated by
EPA. EPA also estimated landfill emissions in point, where S/L/T agencies did not include landfill emissions in
their point source submissions. The methodologies for the select source categories in the Waste Disposal sector
that EPA estimates are provided in separate subsections (reflected in the table) within this chapter. SCCs with an
"x" denote that all SCC level 4 descriptions have been removed and that the "Remaining SCC Description" covers
all SCCs under the Level 3 SCC description.
Table 3-141: Waste Disposal sector SCCs with
ocations of section discussion where available
SCC Level One
Section
Hg only?
SCC
Remaining SCC Description
Waste Disposal,
Treatment, and Recovery
2601000000
On-site Incineration; All Categories; Total
Waste Disposal,
Treatment, and Recovery
2601010000
On-site Incineration; Industrial; Total
Waste Disposal,
Treatment, and Recovery
2601020000
26J.0000.L00
On-site Incineration; Commercial/Institutional; Total
Waste Dispell.
TrC3l; t, am! Recovery
3,32.4
Open Burning, Ail Categories: Yard Waste - Leaf >pedes
Unsiietmeu
Waste Disposal,
Treatment, and Recovery
2610000300
Open Burning; All Categories; Yard Waste - Weed Species
Unspecified (incl Grass)
Waste Disposal.
M eslment, and Remove: y
3,32,4
?{jioooo.aoo
Open tUii'nli'ig; All Categories; Yard Waste - Rrush Species
Unspecified
Open Burning; AH Cdtegones; Land Clearing Debris (use 28-
10 00l>000 to: -..egging Debris burning)
Waste Disposal,
i '"eauneric. and Recovery
3.32.6
2&.I0000?00
Wo-o.p- Disposal,
Treatment, and Reioverv
3.32 .!>
26.1.0030000
Open Gnrning; Residential; Household Waste (use 20- .1.0-000-
xxx for Yard Wastes)
Waste Disposal,
Treatment, and Recovery
2610040400
Open Burning; Municipal (collected from residences, parks,
other for central burn); Yard Waste - Total (includes Leaves,
Weeds, and Brush)
Waste Disposal,
Treatment, and Recovery
2620000000
Landfills; All Categories; Total
Waste Disposal,
Treatment, and Recovery
Waste Disposal,
TVejcment. and Recovery
;i.;j2.s
y
2620030000
2ri2u03000.L
Landfills; Municipal; Total
i<-:ndfil!s; Municipal: Dumping/Crushmg/Spreading of New
Materials (working face)
Waste Disposal,
Treatment, and Recovery
2630000000
Wastewater Treatment; All Categories; Total Processed
Waste Disposal,
Treatment, and Recovery
2630010000
Wastewater Treatment; Industrial; Total Processed
Waste Dispell.
TrC3l; t, am! Recovery
3.32.7
2630020000
Wastewater Treatment; "ublu: Owner!; Total '-'recessed
245
-------
SCC Level One
Section
Hg only?
SCC
Remaining SCC Description
Waste Disposal,
Wastewater Treatment; Public Owned; Biosolids Processes
Treatment, and Recovery
2630020020
Total
Waste Disposal,
Treatment, and Recovery
2630040000
Wastewater Treatment; Public Owned; Ammonia pH Control
Waste Disposal,
Treatment, and Recovery
2635000000
Soil and Groundwater Remediation; All Categories; Total
Waste Disposal,
Treatment, and Recovery
2640000000
TSDFs; All TSDF Types; Total: All Processes
W.-.'-'U? DI?posal.
Scrap and Waste Materials; Scrap and Wasle Maferlais; Total:
Ti eat;"n;'nr. and Recovery
3.32.8
Y
2&50000000
All Processes
Wasr? Disposal,
Scie-o ano Waste Material?; Scrap and Waste Materials;
Treatment, and Rer.o«eiy
Y
?.6S00C)000/?
Shredding
Waste Disposal,
Leaking Underground Storage Tanks; Leaking Underground
Treatment, and Recovery
2660000000
Storage Tanks; Total: All Storage Types
Waste Disposal,
Composting; 100% Biosolids (e.g., sewage sludge, manure,
Treatment, and Recovery
2680001000
mixtures of these matls); All Processes
Waste Disposal,
Composting; Mixed Waste (e.g., a 50:50 mixture of biosolids
Treatment, and Recovery
2680002000
and green wastes); All Processes
Waste Disposal,
Composting; 100% Green Waste (e.g., residential or
Treatment, and Recovery
2680003000
municipal yard wastes); All Processes
Miscellaneous Area
Other Combustion; Managed Burning, Slash (Logging Debris);
Sources
2810005001
Pile Burning
Miscellaneous Area
Other Combustion; Managed Burning, Slash (Logging Debris);
Sources
2810005002
Broadcast Burning
Waste Disposal
501001xx
Solid Waste Disposal - Government; Municipal Incineration
Waste Disposal
501002xx
Solid Waste Disposal - Government; Open Burning Dump
So!ir! WsMe Disposal ¦ Government; Landfill Dumn; Fugitive
Waste P!spo?.j!
!)0jl0P402
(.-mission?
Solid Waste Disposal - Government; Landfill Dump;
Waste Disposal
501005xx
Solid Waste Disposal - Government; Other Incineration;
Waste Disposal
501007xx
Solid Waste Disposal - Government; Sewage Treatment
Solid Waste Disposal - Government; Equipment Leaks;
Waste Disposal
50180001
Equipment Leaks
Waste Disposal
5018200X
Solid Waste Disposal - Government; Wastewater, Aggregate
Solid Waste Disposal - Government; Wastewater, Points of
Waste Disposal
50182599
Generation; Specify Point of Generation
Solid Waste Disposal - Government; Auxiliary Fuel/No
Waste Disposal
501900xx
Emissions
Waste Disposal
5020010X
Solid Waste Disposal - Commercial/Institutional; Incineration
Solid Waste Disposal - Commercial/Institutional; Open
Waste Disposal
5020020X
Burning
Solid Waste Disposal - Commercial/Institutional; Incineration:
Waste Disposal
502005xx
Special Purpose
Solid Waste Disposal - Commercial/Institutional; Landfill
Waste Disposal
50200601
Dump; Waste Gas Flares ** (Use 5-01-004-10)
jo!!d Wa?te Dlspo?al ¦ CcMmneidal/lnsriuMional; Landfill
Woste Disposal
:>02006ui'
Dunsp; Municipal: Fugitive Emissions ; : (Use S-0i-004-02)
Solid Waste Disposal - Commercial/Institutional; Equipment
Waste Disposal
50280001
Leaks; Equipment Leaks
Solid Waste Disposal - Commercial/Institutional; Wastewater,
Waste Disposal
5028200X
Aggregate
246
-------
SCC Level One
Section
Hg only?
SCC
Remaining SCC Description
Solid Waste Disposal - Commercial/Institutional; Wastewater,
Waste Disposal
50282599
Points of Generation; Specify Point of Generation
Solid Waste Disposal - Commercial/Institutional; Auxiliary
Waste Disposal
502900xx
Fuel/No Emissions
Waste Disposal
503001xx
Solid Waste Disposal - Industrial; Incineration
Waste Disposal
503002xx
Solid Waste Disposal - Industrial; Open Burning;
Solid Waste Disposal - Industrial; Incineration; Hazardous
Waste Disposal
503005xx
Waste Incinerators
Waste Disposal
503005xx
Solid Waste Disposal - Industrial; Incineration
Solid Waste Disposal - Industrial; Landfill Dump; Waste Gas
Waste Disposal
50300601
Flares
Solid Waste Disposal - Industrial; Landfill Dump; Liquid Waste
Waste Disposal
50300602
Disposal
Soils J Waste Disposal - Industrial; landfill Du;isp; Hazardous:
\A/ysle Disposal
S0:J00
-------
3.32.2 Spatial coverage and data sources for the sector
P - Point
N - Nonpoint
PN - P&N
Waste Disposal
Waste Disposal
P - Point
N - Nonpoint
PN-P&N
All HAPs
All CAPs
3.32.3 Selection hierarchy
The waste disposal sector includes data from the S/L/T agency submitted data and the default EPA generated
emissions. The agencies listed in Table 3-142 submitted emissions for this sector.
Table 3-142: Agencies that submitted Waste Disposal c
Agency
Data Category
Composting
Landfills
Leaking Storage Tanks
Onsite Incineration
Open Burning
Other Combustion
Scrap & Waste Materials
Site Remediation
Soil & Groundwater Remediation
Solid Waste Commercial/lnstit.
Solid Waste Government
Solid Waste Industrial
TSDFs
Wastewater Treatment
2011EPA_CarryForward-PreviousYearData
P
2011EPA_HAP-Augmentation
NP
X
X
2011EPA_HAP-Augmentation
P
X
X
2011EPA LF
P
X
X
2011EPA_NP_NoOverlap_w_Pt
NP
X
X
2011EPA Other
P
X
2011EPA_PM-Augmentation
NP
X
X
X
X
X
X
X
X
2011EPA_PM-Augmentation
P
X
X
2011EPA TR1
P
X
X
2011EPA_chrom_split
NP
X
X
X
X
2011EPA_chrom_split
P
X
Alabama Department of Environmental Management
P
X
X
Alaska Department of Environmental Conservation
NP
X
X
Alaska Department of Environmental Conservation
P
X
X
Allegheny County Health Department
P
X
Arizona Department of Environmental Quality
P
X
Arkansas Department of Environmental Quality
P
X
X
ata
248
-------
Agency
Data Category
Composting
Landfills
Leaking Storage Tanks
Onsite Incineration
M
C
'£
3
CO
c
CD
SL
o
Other Combustion
Scrap & Waste Materials
Site Remediation
Soil & Groundwater Remediation
Solid Waste Commercial/lnstit.
Solid Waste Government
Solid Waste Industrial
TSDFs
Wastewater Treatment
Bishop Paiute Tribe
NP
X
California Air Resources Board
NP
X
X
X
X
X
California Air Resources Board
P
X
X
Chattanooga Air Pollution Control Bureau (CHCAPCB)
NP
X
Chattanooga Air Pollution Control Bureau (CHCAPCB)
P
X
City of Albuquerque
P
X
X
City of Huntsville Division of Natural Resources and
Environmental Mgmt
P
X
Clark County Department of Air Quality and Environmental
Management
NP
X
X
Clark County Department of Air Quality and Environmental
Management
P
X
Coeur d'Alene Tribe
NP
X
X
Colorado Department of Public Health and Environment
P
X
X
Connecticut Department Of Environmental Protection
NP
X
X
Connecticut Department Of Environmental Protection
P
X
DC Department of Health Air Quality Division
NP
X
Delaware Department of Natural Resources and
Environmental Control
NP
X
Delaware Department of Natural Resources and
Environmental Control
P
X
Delaware Department of Natural Resources and
Environmental Control
P
X
Eastern Band of Cherokee Indians
NP
X
X
Florida Department of Environmental Protection
P
X
X
Forsyth County Environmental Affairs Department
P
X
X
Georgia Department of Natural Resources
NP
X
X
X
X
X
X
Georgia Department of Natural Resources
P
X
X
Hawaii Department of Health Clean Air Branch
NP
X
X
Hawaii Department of Health Clean Air Branch
P
X
Idaho Department of Environmental Quality
NP
X
X
X
Idaho Department of Environmental Quality
P
X
X
Illinois Environmental Protection Agency
NP
X
X
X
Illinois Environmental Protection Agency
P
X
X
Indiana Department of Environmental Management
P
X
X
Iowa Department of Natural Resources
NP
X
Iowa Department of Natural Resources
P
X
X
Jefferson County (AL) Department of Health
P
X
Kansas Department of Health and Environment
NP
X
X
Kansas Department of Health and Environment
P
X
X
249
-------
Agency
Data Category
Composting
Landfills
Leaking Storage Tanks
Onsite Incineration
Open Burning
Other Combustion
Scrap & Waste Materials
c
o
V*
.5
TS
cd
£
CD
tr
CD
±2
V)
Soil & Groundwater Remediation
Solid Waste Commercial/lnstit.
Solid Waste Government
Solid Waste Industrial
TSDFs
Wastewater Treatment
Kentucky Division for Air Quality
P
X
X
Knox County Department of Air Quality Management
NP
X
Kootenai Tribe of Idaho
NP
X
X
Lane Regional Air Pollution Authority
P
X
Louisiana Department of Environmental Quality
NP
X
X
Louisiana Department of Environmental Quality
P
X
X
Louisville Metro Air Pollution Control District
P
X
Maine Department of Environmental Protection
NP
X
X
X
Maine Department of Environmental Protection
P
X
Maricopa County Air Quality Department
NP
X
X
X
X
X
Maricopa County Air Quality Department
P
X
Maryland Department of the Environment
NP
X
X
X
X
X
Maryland Department of the Environment
P
X
X
Massachusetts Department of Environmental Protection
NP
X
X
Massachusetts Department of Environmental Protection
P
X
X
Mecklenburg County Air Quality
P
X
Memphis and Shelby County Health Department - Pollution
Control
P
X
X
Metro Public Health of Nashville/Davidson County
NP
X
X
Metro Public Health of Nashville/Davidson County
P
X
X
Michigan Department of Environmental Quality
NP
X
X
X
Michigan Department of Environmental Quality
P
X
X
Minnesota Pollution Control Agency
NP
X
Minnesota Pollution Control Agency
P
X
X
Mississippi Dept of Environmental Quality
P
X
X
Missouri Department of Natural Resources
NP
X
Missouri Department of Natural Resources
P
X
X
Montana Department of Environmental Quality
P
X
Nebraska Environmental Quality
P
X
X
Nevada Division of Environmental Protection
P
X
X
New Hampshire Department of Environmental Services
NP
X
X
New Hampshire Department of Environmental Services
P
X
New Jersey Department of Environment Protection
NP
X
X
X
X
X
New Jersey Department of Environment Protection
P
X
X
New Mexico Environment Department Air Quality Bureau
P
X
X
New York State Department of Environmental Conservation
NP
X
X
New York State Department of Environmental Conservation
P
X
X
Nez Perce Tribe
NP
X
X
250
-------
Agency
Data Category
Composting
Landfills
Leaking Storage Tanks
Onsite Incineration
Open Burning
Other Combustion
Scrap & Waste Materials
Site Remediation
Soil & Groundwater Remediation
Solid Waste Commercial/lnstit.
Solid Waste Government
Solid Waste Industrial
TSDFs
Wastewater Treatment
North Carolina Department of Environment and Natural
Resources
P
X
X
North Dakota Department of Health
P
X
X
Northern Cheyenne Tribe
NP
X
Ohio Environmental Protection Agency
NP
X
Ohio Environmental Protection Agency
P
X
X
Oklahoma Department of Environmental Quality
P
X
X
Olympic Region Clean Air Agency
P
X
Omaha Air Quality Control Division
P
X
Oregon Department of Environmental Quality
NP
X
X
X
Oregon Department of Environmental Quality
P
X
X
Pennsylvania Department of Environmental Protection
P
X
Philadelphia Air Management Services
P
X
Pinal County
P
X
Puerto Rico
P
X
Puget Sound Clean Air Agency
P
X
Rhode Island Department of Environmental Management
P
X
Sac and Fox Nation of Missouri in Kansas and Nebraska
Reservation
NP
X
Shoshone-Bannock Tribes of the Fort Hall Reservation of
Idaho
NP
X
X
South Carolina Department of Health and Environmental
Control
P
X
X
South Dakota Department of Environment and Natural
Resources
P
X
Southern Ute Indian Tribe
P
X
Tennessee Department of Environmental Conservation
P
X
X
Texas Commission on Environmental Quality
NP
X
Texas Commission on Environmental Quality
P
X
X
Utah Division of Air Quality
NP
X
X
Utah Division of Air Quality
P
X
X
Vermont Department of Environmental Conservation
NP
X
X
Virginia Department of Environmental Quality
NP
X
X
X
X
X
Virginia Department of Environmental Quality
P
X
X
Washington State Department of Ecology
NP
X
X
X
Washington State Department of Ecology
P
X
Washoe County Health District
NP
X
X
X
Washoe County Health District
P
X
West Virginia Division of Air Quality
NP
X
X
West Virginia Division of Air Quality
P
X
251
-------
Agency
Data Category
Composting
Landfills
Leaking Storage Tanks
Onsite Incineration
Open Burning
Other Combustion
Scrap & Waste Materials
Site Remediation
Soil & Groundwater Remediation
Solid Waste Commercial/lnstit.
Solid Waste Government
Solid Waste Industrial
TSDFs
Wastewater Treatment
Western North Carolina Regional Air Quality Agency
(Buncombe Co.)
P
X
Wisconsin Department of Natural Resources
P
X
Wyoming Department of Environmental Quality
P
X
Wyoming Department of Environmental Quality
P
X
Table 3-143 shows the selection hierarchy for datasets included in the waste disposal sector. The waste disposal
sector includes emissions from both S/L/T agencies and from the EPA no overlap nonpoint dataset. The table
below lists the hierarchy of datasets used in the 2011 NEI for this sector. In some cases, the EPA PM and HAP
augmentation as well as TRI and chromium split datasets were used to fill in PM species and HAP pollutants
based on S/L/T agency data. In addition, if states did not report landfill emissions to their point source
inventories, EPA estimated these emissions and gap-filled the NEI to account for these in the dataset called
2011EPA_LF. Finally, EPA also estimated mercury emissions that end up in landfills and in shredding and
crushing operations, and if states did not include emissions of this nature, EPA gap-filled these data as well.
Table 3-143: 2011 NEI Waste Disposal data selection hierarchy
Priority
Dataset Name
Dataset Content
1
2011EPA_PM-Augmentation
Augments PM data in 47 states and some tribes
2
Responsible Agency Data Set
State and Local Agency submitted emissions
3
2011EPA_chrom_split
Splits total chromium into speciated chromium in 37 states
4
2011EPA_TRI
Toxics Release Inventory data for the year 2011. These data are
selected for a facility only when alternative emissions are not
included in the S/L/T agency data.
5
2011EPA LF
Landfills generated from GHG data
6
2011EPA_HAP-Augmentation
Adds Pb and other HAP emissions in 46 states
7
2011EPA_NP_NoOverlap_w_Pt
EPA-generated data
8
2011EPA_Mercury
Mercury only data for certain nonpoint categories
The following sections explain the EPA methodologies for those source categories for which EPA estimated
emissions.
3.32.4 EPA-developed emissions of Open Burning of Leaf and Brush Species
County-level criteria pollutant and HAP emissions were calculated by multiplying the total amount of yard waste
(either leaf or brush) burned per year by an emission factor. Emissions for leaves and residential brush were
calculated separately, since emission factors vary by yard waste type.
Source Category Description
252
-------
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. For this source category, the SCCs provided in Table 3-144
were assigned and estimated by EPA for the 2011 NEI.
Table 3-144: Open Burning, Leaf and Brush Species SCCs estimated by EPA in the 2011 NEI
see
see Level 1
SCC Level 2
SCC Level 3
SCC Level 4
2610000100
Waste Disposal,
Treatment, and Recovery
Open Burning
All Categories
Yard Waste - Leaf Species
Unspecified
2610000400
Waste Disposal,
Treatment, and Recovery
Open Burning
All Categories
Yard Waste - Brush
Species Unspecified
Activity Data
The amount of leaf and brush waste burned was estimated using data from EPA's report Municipal Solid Waste
Generation, Recycling, and Disposal in the United States: Facts and Figures for 2010 [ref 1], The report presents
the total mass of waste generated from the residential and commercial sectors, including yard waste, in the
United States by type of waste for the calendar year 2010. According to the EPA report, residential waste
generation accounts for 55-65 percent of the total waste from the residential and commercial sectors [ref 2], 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 3],
Open burning of grass clippings is not typically practiced by homeowners, and as such 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 3], 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 2010 population in each county that is expected to burn
waste. Since open burning is generally not practiced in urban areas, only the rural population of each county was
assumed to practice open burning. The ratio of urban to rural population was obtained from 2010 U.S. Census
data [ref 4], This ratio was then multiplied by the 2010 U.S. Census Bureau estimate of the population in each
county to obtain the county-level rural population for 2010 [ref 5],
The percentage of forested acres from Version 2 of BELD2 within BEIS was used to adjust for variations in
vegetation. 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 3-145 presents the ranges that were used to make adjustments to the amount of yard waste that is
assumed to be generated per county. All municipalities 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 3-145: Adjustment for percentage of forested acres
Percent Forested
Acres per County
Adjustment for Yard
Waste Generated
< 10%
0% generated
>= 10%, and < 50%
50% generated
>= 50%
100% generated
253
-------
Controls
Controls for yard waste burning are generally in the form of a ban on open burning of waste in a given
municipality or county. Counties that were more than 80% urban 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
Emission factors for CAPs were developed by the U.S. Environmental Protection Agency (EPA) in consultation
with the Eastern Regional Technical Advisory Committee [ref 6], For leaf burning, emission factors for PM2 s were
calculated by multiplying the PMW leaf burning emission factors by the PM2 s to PMio emission factor ratio for
brush burning (0.7709). Emission factors for HAPs are from an EPA Control Technology Center report [ref 7],
Forest fire simulation emission factors were used to estimate emissions for 17 dioxin congeners [ref 8],
Example Calculations
VOC emissions in Autauga County, Alabama from open burning of leaf waste:
Population of Autauga County in 2010 = 54,571
Rural fraction of Autauga County population = 0.42
Per capita waste yard waste generated (Ib/person/day) = 0.3557
Leaf fraction of waste = 0.25
Fraction of rural population that burns yard waste = 0.28
Adjustment factor based on % forested acres = 1
Number of days in a year = 365
Factor to convert from lbs to tons = 1/2000
2010 leaf burning activity in Autauga County = 54,571 * 0.42 * 0.3557 * 0.25 * 0.28 * 1 * 365 /2000
2010 leaf burning activity in Autauga County = 104.15 tons
VOC emissions = tons of leaves burned * VOC emission factor
VOC emission factor = 28 lb/ton
VOC emissions in Autauga County in 2010 = 104.15 tons * 28 lbs/ton * 1 ton/2000 lbs
VOC emissions in Autauga County in 2010 = 1.46 tons
3.32.5 EPA-developed emissions of Open Burning of Municipal Solid Waste (MSW)
County-level criteria pollutant and HAP emissions were calculated by multiplying the total amount of residential
municipal solid waste burned per year by an emission factor.
Source Category Description
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.
254
-------
For this source category, the following SCC was assigned, and emissions were estimated for the 2011 NEI:
SCC=2610030000, SCC description=" Waste Disposal, Treatment, and Recovery; Open Burning; Residential;
Household Waste (use 26-10-000-xxx for Yard Wastes)".
Activity Data
The amount of household MSW burned was estimated using data from EPA's report Municipal Solid Waste
Generation, Recycling, and Disposal in the United States: Facts and Figures for 2010 [ref 1 ]. The report presents
the total mass of waste generated from the residential and commercial sectors in the United States by type of
waste for the calendar year 2010. According to the EPA report, residential waste generation accounts for 55-65
percent of the total waste from the residential and commercial sectors [ref 2], 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 the per capita household waste subject to burning of 1.94 Ibs./person/day. Non-
combustible waste, such as glass and metals, was not considered to be waste subject to burning. Burning of yard
waste is included in SCC 2610000100 and SCC 2610000400; therefore, it is not part of residential MSW.
Approximately 25 to 32 percent of all waste that is subject to open burning is actually burned [ref 4, ref 9], A
median value of 28 percent is assumed to be burned in all counties in the United States.
Since open burning is generally not practiced in urban areas, only the rural population of each county was
assumed to practice open burning. The ratio of urban to rural population was obtained from 2010 U.S. Census
data [ref 4], This ratio was then multiplied by the 2010 U.S. Census Bureau estimate of the population in each
county to obtain the county-level rural population for 2010 [ref 5], 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 2010.
Controls
Controls for residential MSW burning are generally in the form of a ban on open burning of waste in a given
municipality or county. Counties that were more than 80% urban were assumed not to practice any open
burning. Therefore, criteria pollutant and HAP emissions from residential municipal solid 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 waste in all Colorado counties were assumed to be zero.
Emission Factors
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 10],
Emission factors for HAPs are from an EPA Control Technology Center report and emission factors for 17 dioxin
congeners were obtained from an EPA dioxin report [ref 11], These emission factors are provided in Table 3-146.
Table 3-146: Emission factors for Open Burning of Residential MSW (2610030000)
Pollutant
Pollutant
Code
Emission Factor
(lb/ton)
Emission Factor
Reference
CO
CO
8.50E+01
Reference 9
NOX
NOX
6.00E+00
Reference 9
PM10-FIL
PM10-FIL
3.80E+01
Reference 8
PM10-PRI
PM10-PRI
3.80E+01
Reference 8
PM25-FIL
PM25-FIL
3.48E+01
Reference 8
PM25-PRI
PM25-PRI
3.48E+01
Reference 8
SO 2
S02
1.00E+00
Reference 9
255
-------
Pollutant
Emission Factor
Emission Factor
rOiiUiaiil
Code
(lb/ton)
Reference
voc
VOC
8.56E+00
Reference 8
1,2,3,4,6,7,8-heptachlorodibenzofuran
67562394
2.48E-07
Reference 11
1,2,3,4,6,7,8-heptachlorodibenzo-p-dioxin
35822469
7.96E-08
Reference 11
1,2,3,4,7,8,9-heptachlorodibenzofuran
55673897
3.00E-08
Reference 11
1,2,3,4,7,8-hexachlorodibenzofuran
70648269
2.28E-07
Reference 11
1,2,3,4,7,8-hexachlorodibenzo-p-dioxin
39227286
1.28E-08
Reference 11
1,2,3,6,7,8-hexachlorodibenzofuran
57117449
7.70E-08
Reference 11
1,2,3,6,7,8-hexachlorodibenzo-p-dioxin
57653857
1.94E-08
Reference 11
1,2,3,7,8,9-hexachlorodibenzofuran
72918219
5.00E-09
Reference 11
1,2,3,7,8,9-hexachlorodibenzo-p-dioxin
19408743
3.80E-08
Reference 11
1,2,3,7,8-pentachlorodibenzofuran
57117416
7.44E-08
Reference 11
1,2,3,7,8-pentachlorodibenzo-p-dioxin
40321764
1.62E-08
Reference 11
1,2,4-trichlorobenzene
120821
1.95E-04
Reference 10
1,4-dichlorobenzene
106467
6.65E-05
Reference 10
2,3,4,6,7,8-hexachlorodibenzofuran
60851345
1.24E-07
Reference 11
2,3,4,7,8-pentachlorodibenzofuran
57117314
1.30E-07
Reference 11
2,3,7,8-tetrachlorodibenzofuran
51207319
9.12E-08
Reference 11
2,3,7,8-tetrachlorodibenzo-p-dioxin
1746016
5.40E-09
Reference 11
Acenaphthene
83329
1.54E-03
Reference 10
Acenaphthylene
208968
2.26E-02
Reference 10
Acetaldehyde
75070
8.57E-01
Reference 10
Acrolein
107028
6.19E-02
Reference 10
Anthracene
120127
3.66E-03
Reference 10
Benz[a]anthracene
56553
4.48E-03
Reference 10
Benzene
71432
2.48E+00
Reference 10
Benzo[a]pyrene
50328
4.24E-03
Reference 10
Benzo[b]fluoranthene
205992
5.26E-03
Reference 10
Benzo[g,h,i,]Perylene
191242
3.95E-03
Reference 10
Benzo[k]fluoranthene
207089
2.05E-03
Reference 10
Chlorobenzene
108907
8.48E-04
Reference 10
Chrysene
218019
5.07E-03
Reference 10
Dibenzo[a,h]anthracene
53703
6.46E-04
Reference 10
Fluoranthene
206440
8.14E-03
Reference 10
Fluorene
86737
7.31E-03
Reference 10
Hexachlorobenzene
118741
4.40E-05
Reference 10
Hydrochloric Acid
7647010
5.68E-01
Reference 10
Hydrogen Cyanide
74908
9.36E-01
Reference 10
lndeno[l,2,3-c,d]pyrene
193395
3.75E-03
Reference 10
Naphthalene
91203
3.51E-02
Reference 10
Octachlorodibenzofuran
39001020
7.28E-08
Reference 11
Octachlorodibenzo-p-dioxin
3268879
9.94E-08
Reference 11
Pentachlorophenol
87865
1.06E-04
Reference 10
Phenanthrene
85018
1.46E-02
Reference 10
Phenol
108952
2.80E-01
Reference 10
Polychlorinated Biphenyls
1336363
5.72E-03
Reference 10
256
-------
Pollutant
Pollutant
Code
Emission Factor
(lb/ton)
Emission Factor
Reference
Pyrene
129000
9.66E-03
Reference 10
Styrene
100425
1.48E+00
Reference 10
Example Calculations
VOC emissions in Autauga County, Alabama from open burning of residential MSW:
Population of Autauga County in 2010 = 54,571
Rural fraction of Autauga County population = 0.42
Per capita MSW generated (Ib/person/day) = 1.9435
Fraction of rural population that burns MSW = 0.28
Number of days in a year = 365
Factor to convert from lbs to tons = 1/2000
2010 MSW burning activity in Autauga County = 54,571 * 0.42 * 1.9435 * 0.28 * 365 /2000
2010 MSW activity in Autauga County = 2,276 tons
VOC emissions = MSW burned * VOC emission factor
VOC emission factor = 8.56 lb/ton
VOC emissions in Autauga County = 2,276 tons * 8.56 lbs/ton * 1 ton/2000 lbs
VOC emissions in Autauga County in 2010 = 9.74 tons
3.32.6 EPA-developed emissions of Open Burning of Land Clearing Debris
County-level criteria pollutant and HAP emissions were calculated by multiplying the total mass of land clearing
debris burned per year by an emission factor.
Source Category Description
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.
For this source category, the following SCC was assigned and estimated by EPA for the 2011 NEI:
SCC=2610000500, SCC description=" Waste Disposal, Treatment, and Recovery; Open Burning; All Categories;
Land Clearing Debris (use 28-10-005-000 for Logging Debris Burning)".
Activity Data
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 2010 data for Housing Starts - New Privately Owned Housing Units Started [ref 12]
which provides regional level housing starts based on the groupings of 1 unit, 2-4 units, 5 or more units. A
257
-------
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 13] 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 14] has 2010 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. The surface areas were assumed disturbed for each unit type
shown in Table 3-147.
Table 3-147: 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 15] has the 2011 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 16], Because some county employment data was withheld due to privacy
concerns, the following procedure was adopted:
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.
A starting estimate of the midpoint of the range code was used (so for instance in the 1-19 employee range, an
estimate of 10 employees would be used) and a state total of the withheld counties was computed.
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/$l million ($106) was developed. The Bureau of Labor Statistics Producer Price Index
[ref 17] lists costs of the construction industry from 1999-2011.
2011 acres per $106 = 1999 acres per $106 x (1999 PPI / 2011 PPI)
= 2 acres/$106 (132.9 / 229.3)
= 1.159 acres per $106
Acres Disturbed by Road Construction
258
-------
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 18] for 2008. (Note that this table has not been available in subsequent versions of
Highway Statistics. Thus, 2008 is the latest data currently available.) 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 are 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 North Carolina Department of Transportation (NCDOT) in 2000. A conversion of $4
million/mile was applied to the interstate expenditures. For expenditures on other arterial and collectors, a
conversion factor of $1.9 million/mile was 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 was 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 3-148.
Table 3-148: Spending per mile and acres disturbed per mile by highway type
Road Type
Dollars per mile
Acres Disturbed per mile
Urban Areas, Interstate
$4,000,000
15.2
Rural Areas, Interstate
$4,000,000
15.2
Urban Areas, Other Arterials
$1,900,000
15.2
Rural Areas, Other Arterials
$1,900,000
12.7
Urban Areas, Collectors
$1,900,000
9.8
Rural Areas, Collectors
$1,900,000
7.9
County-level building permits data are used to allocate the state-level acres disturbed by road construction to
the county [ref 19], 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 Land Cover Database (BELD2) within EPA's Biogenic Emission Inventory
System (BEIS) was used to identify the acres of hardwoods, softwoods, and grasses in each county. Table 3
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 20], Weighted average county-level loading
factors, provided in Table 3-149, 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.
259
-------
Table 3-149: Fuel loading factors by vegetation type
Vegetation Type
Unadjusted Average
Fuel Loading Factor
(Tons/acre)
Adjusted Average
Fuel Loading Factor
(Tons/acre)
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-1 eve I total acres disturbed were then multiplied
by the weighted average loading factor to derive tons of land clearing debris.
Controls
Controls for land clearing debris burning are generally in the form of a ban on open burning of waste in a given
municipality or county. Counties that were more than 80% urban 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 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.
Emission Factors
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 6, ref 10],
The PM2.s to PMio emission factor ratio for brush burning (0.7709) was multiplied by the PMio emission factors
for land clearing debris burning to develop PM2 s emission factors.
Emission factors for HAPs are from an EPA Control Technology Center report [ref 7] and emission factors for 17
dioxin congeners were obtained from an EPA dioxin report [ref 8], The dioxin emission factors were multiplied
by 0.002 to convert from mg/kg to lb/ton. Emission factors for open burning land clearing debris are provided in
Table 3-150.
Table 3-150: Emission factors for Open Burning of Land Clearing Debris (SCC 2610000500)
Pollutant
Pollutant Code
Emission Factor
(lb/ton)
Emission Factor Reference
voc
VOC
11.6
Reference 10
NOX
NOX
5
Reference 10
CO
CO
169
Reference 10
PM10-FIL
PM10-FIL
17
Reference 10
PM25-FIL
PM25-FIL
13.1
PM10-FIL multiplied by 0.7709
PM10-PRI
PM10-PRI
17
Reference 10
PM25-PRI
PM25-PRI
13.1
PM10-PRI multiplied by 0.7709
1,2,3,4,6,7,8-HpCDD
35822469
3.33E-07
Reference 13
1,2,3,4,6,7,8-HpCDF
67562394
5.08E-08
Reference 13
1,2,3,4,7,8,9-HpCDF
55673897
6.12E-09
Reference 13
1,2,3,4,7,8-HxCDD
39227286
1.14E-08
Reference 13
1,2,3,4,7,8-HxCDF
70648269
3.34E-08
Reference 13
1,2,3,6,7,8-HxCDD
57653857
2.14E-08
Reference 13
1,2,3,6,7,8-HxCDF
57117449
1.43E-08
Reference 13
260
-------
Pollutant
Pollutant Code
Emission Factor
(lb/ton)
Emission Factor Reference
1,2,3,7,8,9-HxCDD
19408743
3.47E-08
Reference 13
1,2,3,7,8,9-HxCDF
72918219
2.23E-09
Reference 13
1,2,3,7,8-PeCDD
40321764
7.66E-09
Reference 13
1,2,3,7,8-PeCDF
57117416
1.27E-08
Reference 13
2,3,4,6,7,8-HxCDF
60851345
1.96E-08
Reference 13
2,3,4,7,8-PeCDF
57117314
2.02E-08
Reference 13
2,3,7,8-TCDD
1746016
2.30E-09
Reference 13
2,3,7,8-TCDF
51207319
1.40E-08
Reference 13
Cumene
98828
1.33E-02
Reference 12
Dibenzofuran
132649
6.75E-03
Reference 12
Ethyl Benzene
100414
4.80E-02
Reference 12
OCDD
3268879
1.33E-06
Reference 13
OCDF
39001020
2.05E-08
Reference 13
Phenol
108952
1.15E-01
Reference 12
Styrene
100425
1.02E-01
Reference 12
Example Calculations
VOC emissions in Autauga County, Alabama from open burning of land clearing debris:
Rural fraction of Autauga County population = 0.42, so no emission controls
Acres disturbed by residential, non-residential, and road construction in Autauga County = 84.83
Weighted average fuel loading factor for Autauga County = 65.48 tons/acre
Mass of land clearing debris burned = 84.83 acres * 65.48 tons/acre = 5,555 tons
VOC emission factor = 11.6 lbs/ton
Factor to convert from lbs to tons = 1/2000
VOC emissions = tons of land clearing debris burned * VOC emission factor
VOC emissions = 5,555 tons * 11.6 lbs/ton * 1 ton /2000 lbs
VOC emissions (from land clearing debris burning in Autauga County in 2010) = 32 tons
3.32.7 EPA-developed emissions of Publicly Owned Treatment Works (POTW)
Source Category Description
Due to resource constraints, POTW emissions were not estimated for the 2011 NEI. The emissions from 2008
NEI were assumed to be similar in nature and were used in lieu of recalculated emissions. The below describes
the methods used in the 2008 NEI EPA estimates for POTWs.
Publicly Owned Treatment Works (POTWs) means a treatment works that is 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.
261
-------
The general approach to calculating emissions for POTWs is to estimate the POTW flow rate using methods
described below and then multiply the estimated flow rate by the emission factors for VOCs, ammonia, and
numerous HAPs. The emissions are allocated to the county level using methods described below. It is important
to note that the emission estimates for this category represent total emissions. It may be necessary to
determine whether there are point source emissions in SCCs 50100701 through 50100781 and 50100791
through 50182599 that need to be subtracted to yield the nonpoint source emission estimates for this category.
For this source category, the following SCC was assigned, and EPA estimated emissions for the NEI:
SCC=2630020000, SCC description=" Waste Disposal, Treatment and Recovery - Wastewater Treatment - Public
Owned - Total Processed".
Activity Data
A nationwide projected flow rate in 2010 of 39,780 million gallons per day (MMGD) was available from an EPA
report [ref 21], Of this, POTWs account for 98.5 percent of the flow rate or 39,180 MMGD, with privately owned
treatment works accounting for the rest. The EPA Clean Watersheds Needs Survey reports the existing flow rate
in 2004 for POTWs as 34,370 MMGD [ref 22], The interpolated 2008 nationwide flow rate (using a linear
regression) was calculated at 37,580 MMGD, or 13,754,280 million gallons annually. The nationwide flow rate
includes Puerto Rico and the U.S. Virgin Islands.
Emissions were allocated to the county-level by the county proportion of the U.S. population [ref 23],
Emission Factors
The ammonia emission factor was obtained from a report to EPA [ref 24], while the VOC emission factor was
based on a TriTAC (technical advisory committee representing three California associations) study [ref 25],
Emission factors for the 53 HAPs were derived using 1996 area source emissions estimates that were provided
by the EPA Sector Policies and Programs Division [ref 26] and the 1996 nationwide flow rate [ref 27], 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 2008 inventory.
Example Calculations
The 1996 flow rate per day was 32,175 MMGD. (1996 was a leap year.) Annually, this computes to:
32,175 MMGD treated * 366 days = 11,776,050 million gallons treated
Benzene emissions in 1996 for area source POTWs were estimated to be 461.44 tons per year. The derived
benzene emission factor is calculated as follows:
Benzene emission factor = ((461.44 tons * 2000 lb/ton) / (11,776,050 million gallons treated)) * (0.85/9.9)
Benzene emission factor = 0.0067287 lb/million gallons treated
National total benzene emissions for 2008 for area source POTWs are calculated as follows:
2008 benzene emissions = (37,580 MMGD * 366 days) * (0.0067287 lb/million gallons treated)
2008 benzene emissions = 92,548 pounds / 2,000 pounds = 46.27 tons/year
Total national 2008 benzene emissions from area source POTWs are allocated to county-level by the county
proportion of the U.S. population. The total U.S. population in 2008 is 308,123,578. Benzene emissions for
Autauga County, Alabama (2008 population of 50,364) are calculated as follows:
262
-------
2008 benzene emissions
= 46.27 tons/year * 50,364/308,123,578 = 0.0076 tons/year
3.32.8 EPA-developed emissions of Landfills
Source Category Description
Most landfill emissions are developed by EPA using methane data from the EPA's Greenhouse Gas reporting rule
program. This dataset is called 2011 EPA Landfills, and was presumed to contain landfills only for which no
pollutants were reported by S/L/T in the 2011 reporting year.
Mercury emissions for landfills are accounted for with an EPA estimated dataset called 2011EPA_NP_Mercury.
This methodology was not developed until 2011 v2, so these emissions are not accounted for in 2011 vl. These
Hg-only SCCs are provided in Table 3-151; the SCC Level 1 description is "Waste Disposal, Treatment, and
Recovery".
Table 3-151. Hg-only EPA-generated SCCs for Landfills
Subcategory
SCC
SCC Description
Landfill working face
2620030001
Landfills; Municipal; Dumping/Crushing/Spreading of New Materials
(working face)
Thermostats and
thermometers
2650000000
Scrap and Waste Materials; Scrap and Waste Materials; Total: All
Processes
Switches and Relays
2650000002
Scrap and Waste Materials; Scrap and Waste Materials; Shredding
Mercury from the Working Face of Landfills
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 28], Emissions from LFG systems are considered point sources and are already
included in the NEI as submissions from SLT agencies or from the point source dataset that gap fills these landfill
emissions (2011EPA_LF). Lindberg et al. (2005) [ref 28] 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.
Activity Data
The US 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 29], 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 2011 are included in the analysis.
Allocation Approach
The EPA LMOP database provides data at the county level.
Emission Factor
Lindberg et al. (2005) [ref 29], 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"6 lbs. / ton of waste.
263
-------
Example Calculation
The City of Durham landfill in Durham County, NC is estimated to receive approximately 144,000 tons of waste
annually.
144,000 tons of waste x 5.51 x 10"6 lbs. Hg/ton of waste = 0.79 lbs. Hg emissions
3.32.8.1 Quality Assurance
EPA noted some issues with point and nonpoint overlap for landfills after the 2011 vl was published. EPA
estimates landfill emissions for the point source inventory, and, believed that nonpoint SCCs were not being
used by the S/L/T agencies. However, approximately 15 states or tribes do use these nonpoint SCCs, and, when
using the EIS report for QA, some potential overlap was noted. Some tribal agencies submitted nonpoint landfill
emissions after the 2011 vl, after this EPA point landfills dataset was created, so this was not resolved until
2011 v2.
EPA has proposed to resolve this in future inventories by retiring the nonpoint SCCs, and, using EPA's point
inventory landfill dataset to fill in where S/L/T agencies do not report these as point sources. This would remove
the need for point-nonpoint reconciliation in the future. However, EPA created a new nonpoint SCC for working
face of landfills (currently restricted to Hg), so EPA is struggling with this question: does it really make sense to
retire the other nonpoint SCCs for landfills?
EPA's short-term solution has been to propose tagging out any point landfills where agencies report landfills to
the nonpoint inventory. This solution means that EPA would not retire the nonpoint landfill SCCs, which would
be consistent with the fact that we are adding a nonpoint landfill SCC.
322.E.2EPA-DevelopedEmissions of 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.
Activity Data
The 2002 EPA report estimated that 2-3 million thermostats came out of service in 1994 [ref 30], 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 31], Therefore, using this
estimate, there are approximately 2.3 million thermostats that are not recycled each year.
Allocation Approach
The national-level mercury emissions are apportioned to each county based on population.
Emission Factor
The 2002 EPA report estimates that there are 3 grams of mercury per thermostat [ref 30], Cain et al. (2007) [ref
32] 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"5 lbs. per thermostat.
264
-------
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 933,902 people, or 0.3% of the national population. The mercury emissions from
thermostats in Shelby County, TN are estimated by the following:
228 lbs. national mercury emissions x 0.3% = 0.684 lbs. mercury emissions
3.32.8.3 EPA-Devebped Emissions of Thermometers
Mercury thermometers have all but been phased out in the United States, with the USEPA and National Institute
of Standards and Technology (NIST) working to phase out mercury thermometers in industrial and laboratory
settings. NIST issued notice in 2011 that it would no longer calibrate mercury-in-glass thermometers for
traceability purposes. EPA issued a rule in 2012 that provides flexibility to use alternatives to mercury
thermometers when complying with certain regulations pertaining to petroleum refining, power generation, and
PCB waste disposal [ref 33], Furthermore, thirteen states have laws that limit the manufacture, sale, and/or
distribution of mercury-containing fever thermometers [ref 33],
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.
Activity Data
Data from the Northeast Waste Management Officials' Association (NEWMOA) Interstate Mercury Education &
Reduction Clearinghouse (IMERC) database suggests that there were 713 lbs. of mercury used in thermometers
in 2007 [ref 34], We assume that this value is held constant each year through 2011.
The US EPA assumes that the average lifespan of a glass thermometer is 5 years, and that 5% of glass
thermometers are broken each year [ref 30],24 Therefore, if 713 lbs. of mercury are used in thermometers each
year there would be an estimated 3,228 lbs. of mercury remaining in thermometers in 2011 (accounting for the
breakage rate each year).
NEWMOA [ref 34] estimates that during the period 2000-2006 there were 350 lbs. of mercury from
thermometers collected in recycling programs.
Therefore, there were 2,878 lbs. (1.44 tons) of mercury available for release in 2011.
Allocation Approach
The national-level mercury emissions from thermometers are allocated to the county level based on population.
Emission Factor
Cain et al. (2007) [ref 32] estimates that 10% of mercury from thermometers is emitted to the air before
disposal in a landfill, and Leopold (2002) [ref 30] 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.
24 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.
265
-------
Example Calculation
1.44 tons of mercury in broken thermometers x 10 lbs. emissions per ton = 14.4 lbs. of emissions
Boise County, ID has 7,028 people, or 0.0023% of the national population. The mercury emissions from broken
thermometers for Boise County are estimated by the following:
14.4 lbs. national emissions x 0.0023% = 0.00033 lbs. emissions
3.32,8.4 EPA-Developed Emissions of Switches and Relays
Switches and relays make up the largest potential source of mercury from products that intentionally contain
mercury. Mercury is an excellent electrical conductor and is liquid at room temperature, making it useful in a
variety of products, including switches used to indicate motion or tilt, as the mercury will flow when the switch
is in a certain position, completing the circuit.
While mercury switches in cars were phased out as of the 2002 model year, there are still millions of cars on the
road that contain them, which are potential emissions sources when the cars are recycled at the end of their
useful lives, which involves crushing and shredding cars. 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/shredding operations.
Activity Data
A 2011 report from the North Carolina Department of Environment and Natural Resources [35] provides
information on the estimated number of switches available for recovery in each state and the amount of
switches actually recovered in 2011. There were 3.4 million mercury-containing automobile switches available
nationwide in 2011 and 664,690 switches collected for recycling, for a collection rate of 19.4%. These
nationwide estimates are supported by similar data from the Quicksilver Caucus [36], Therefore, there were
approximately 2.7 million unrecycled automotive switches in 2011.
Allocation Approach
The number of unrecovered switches is apportioned to each county based on the number of car recycling
facilities (NAICS 423930) from the 2011 US Census Bureau County Business Patterns.
Emission Factor
The response to comments for the 2007 EPA Significant New Use Rule on Mercury Switches (72 Fed. Reg.
56903), suggests that the weighted average amount of mercury in switches is 1.2 grams (0.0026 lbs.). A 201
report by Griffith et al. [ref 37] 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.
Example Calculation
Alabama had 80,892 unrecovered vehicle switches in 2011. Baldwin County, AL has 3 car recycling facilities,
which represents 1.53% of the facilities in the state. Therefore, that county is apportioned switches as follows:
80,892 switches in AL x 1.53% = 1,238 switches in Baldwin County, AL
Emissions are estimated as follows:
266
-------
1,238 switches x 0.00156 Ibs./switch = 1.93 lbs. Hg emissions
3.32.9 References for Waste Disposal
1. U.S. Environmental Protection Agency, Municipal Solid Waste Generation, Recycling, and Disposal in the
United States: Facts and Figures for 2010, "Tables 1 and 2. Materials Generated in the Municipal Waste
Stream, 1960 to 2010," December 2011. (accessed April 2012).
2. U.S. Environmental Protection Agency, Municipal Solid Waste Generation, Recycling, and Disposal in the
United States: Facts and Figures for 2010—Fact Sheet," p. 4, December 2011, (accessed April 2012).
3. 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. U.S. Census Bureau, Decennial Censuses, 2010 Census: SF1, Table P2
5. U.S. Census Bureau. Annual Estimates of the Resident Population for the United States, Regions, States,
and Puerto Rico: April 1, 2010 to July 1, 2011 (NST-EST2011 -01), accessed April 2012.
6. Huntley, Roy, U.S. Environmental Protection Agency, "state_comparison ERTAC SS_version7_3 Oct 2009
[electronic file]," November 5, 2009.
7. U.S. Environmental Protection Agency, Evaluation of Emissions from the Open Burning of Household
Waste in Barrels, EPA-600/R-97-134a, Control Technology Center. November 1997.
8. Gullet, B.K. and T. Abderrahmne, "PCDD/F Emissions from Forest Fire Simulations," Atmospheric
Environment, Vol. 37, No. 6, pp. 803-813. February 2003.
9. Garbage Burning in Rural Minnesota: Key Results and Findings, prepared by Zenith Research Group for
Minnesota Pollution Control Agency, June 2010, (accessed June 10, 2011).
10. 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 2.5 Open Burning. Research Triangle Park, NC. October 1992.
11. United States Environmental Protection Agency, Office of Research and Development. Exposure and
Human Health Reassessment of 2,3,7,8-TetrachIorodibenzeno-p-Dioxin (TCCD) and Related Compounds.
Part I: Estimating Exposure to Dioxin-Like Compounds. Volume 2: Sources of Dioxin-Like Compounds in
the United States. EPA/600/P-00/001Ab. Washington D.C. March 2001.
12. U.S. Census Bureau, "New Privately Owned Housing Units Started for 2010 ( Not seasonally adjusted)".
13. U.S. Census Bureau, "Table 2au. New Privately Owned Housing Units Authorized Unadjusted Units for
Regions, Divisions, and States, Annual 2010".
14. Annual Housing Units Authorized by Building Permits CO2010A, purchased from US Department of
Census
15. U.S. Census Bureau, "Annual Value of Construction Put in Place".
16. U.S. Census Bureau, "County Business Patterns"
17. Bureau of Labor Statistics, Producer Price Index, Table BMNR
18. Federal Highway Administration, 2008 Highway Spending.
19. U.S. Census Bureau, 2008 Building Permits data "BPS01"
20. 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.
21. U.S. Environmental Protection Agency, "Wastewater Flow Projections for POTWs and Privately and
Federally Owned Treatment Works in 2000, 2005, and 2010," Table A-8 in Biosolids Generation, Use,
and Disposal in the United States, EPA530-R-99-009, September 1999.
22. U.S. Environmental Protection Agency, Clean Watersheds Needs Survey, Ask WATERS Online Database
Query Tool, accessed 19 May 2009.
23. U.S. Census Bureau, "Population Estimates", released 14 May 2009 with population estimates as of 1
July 2008. Note: The U.S. Census Bureau estimate does not include the U.S. Virgin Islands, so the Census
267
-------
Bureau estimate was supplemented with Virgin Island population data from U.S. Department of
Commerce, National Oceanic and Atmospheric Administration, Demographic Baseline Report of U.S.
Territories and Counties Adjacent to Coral Reef Habitats, June 2008, accessed 9 June 2009.
24. Stephen M. Roe, Melissa D. Spivey, Holly C. Lindquist, Kirstin B. Thesing, and Randy P. Strait, E.H. Pechan
& Associates, Inc., Estimating Ammonia Emissions from Anthropogenic Nonagricultural Sources - Draft
Final Report, prepared for U.S. Environmental Protection Agency, Emission Inventory Improvement
Program, April 2004.
25. Prakasam Tata, Jay Witherspoon, Cecil Lue-Hing (eds.), VOC Emissions from Wastewater Treatment
Plants: Characterization, Control, and Compliance, Lewis Publishers, 2003, p. 261.
26. Memorandum from Bob Lucas, U.S Environmental Protection Agency to Greg Nizich, U.S. Environmental
Protection Agency, "Review of Baseline Emissions Inventory," 16 October 1998.
27. U.S. Environmental Protection Agency, "Facilities Database (Needs Survey) - Frequently Asked
Questions", accessed 22 May 2009.
28. 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.
29. US EPA. 2014a. Landfill Methane Outreach Program, last accessed May 2014.
30. Leopold, B.R. 2002. Use and Release of Mercury in the United States. U.S. Environmental Protection
Agency. Report EPA/600/R-02/104.
31. 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.
32. 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.
33. US EPA. 2014b. Phase-Out of Mercury Thermometers Used in Industrial and Laboratory Settings.
34. Northeast Waste Management Officials' Association (NEWMOA). 2008. Trends in Merc in
Products: Summary of the Interstate Mercury Education and Reduction Clearinghouse (IMERC) Mercury-
added Products Database.
35. NC Department of Environment and Natural Resources. 2011. Mercury Switch Removal Program 2011
Annual Report.
36. Quicksilver Caucus. 2012. Third Compendium of States' Mercury Activities. The Environmental Council of
the States.
37. 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.
268
-------
4 Mobile sources
4.1 Mobile sources overview
Mobile sources are sources of pollution caused by vehicles transporting goods or people (e.g., highway vehicles,
aircraft, rail, and marine vessels) and other nonroad engines and equipment, such as lawn and garden
equipment, construction equipment, engines used in recreational activities, and portable industrial, commercial,
and agricultural engines.
EPA created a comprehensive set of mobile source emissions data for criteria, hazardous air pollutants, and
greenhouse gases for all states, Puerto Rico, and US Virgin Islands as a starting point for the NEI. EPA uses
models to estimate emissions for most of the mobile sources' categories. During training for their 2011 NEI
cycle, EPA encouraged S/L/T agencies to submit model inputs, where applicable, rather than emissions, so that
EPA could use those inputs beyond the 2011 NEI for future year projections. Agencies had the option to accept
EPA's estimates or submit new emissions or emission inputs to replace or enhance EPA's data.
For development and documentation purposes, the major groups of mobile sources are aircraft (Section 4.2),
commercial marine vessels (Section 4.3), locomotives (Section 4.4), nonroad equipment (Section 4.5) and on-
road vehicles (Section 4.6). In addition, EPA developed nationally consistent datasets for all those sectors,
though without the benefit of local-specific model inputs in all cases. The sections below explain how we
created the EPA estimates, which S/L/T agencies provided model inputs or emissions data for each sector, and
how the EPA data and S/L/T agency data were blended to produce the NEI.
In general, EPA used the data submitted by S/L/T agencies unless EPA determined that the data caused double
counting or invalid pollutant or pollutant/emission type combinations inclusion.
4.2 Aircraft
EPA estimated emissions related to aircraft activity for all known US airports, including seaplane ports and
heliports, in the 50 states, Puerto Rico, and US Virgin Islands. All the approximately 20,000 individual airports are
geographically located by latitude/longitude and stored in the NEI as point sources. As part of the development
process, S/L/T agencies had the opportunity to provide both activity data as well emissions to the NEI. When
activity data were provided, EPA used that data to calculate EPA's emissions estimates.
4.2.1 Revisions for the NEI 2011 v2
There were minimal aircraft sector changes between 2011 vl and 2011 v2. Military aircraft emissions for one
airport in Virginia were updated. One airport in Chicago was removed.
4.2.2 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
269
-------
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 fleet includes both jet- and piston-powered aircraft. Most of the Air Taxi and General
Aviation fleet 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-powered and piston-powered planes of varying sizes.
The 2011 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. However, these emissions are
included in the EIS Sectors for Non-road equipment (gasoline, diesel, and other), described in Section 4.5. This
sector includes the SCCs listed in Table 4-1.
Table 4-1: Source classification codes for the aircraft sector in the 2011 NEI
see
Data Category
SCC Description
EPA estimates
2275001000
Point
Mobile Sources; Aircraft; Military Aircraft; Total
X
2275020000
Point
Mobile Sources; Aircraft; Commercial Aircraft; Total: All Types
X
2275050011
Point
Mobile Sources; Aircraft; General Aviation; Piston
X
2275050012
Point
Mobile Sources; Aircraft; General Aviation; Turbine
X
2275060011
Point
Mobile Sources; Aircraft; Air Taxi; Piston
X
2275060012
Point
Mobile Sources; Aircraft; Air Taxi; Turbine
X
2260008005
Point
Mobile Sources; Off-highway Vehicle Gasoline 2-Stroke; Aircraft
Ground Support Equipment
X
2265008005
Point
Mobile Sources; Off-highway Vehicle Gasoline 4-Stroke; Aircraft
Ground Support Equipment
X
2267008005
Point
Mobile Sources; LPG; Aircraft Ground Support Equipment
X
2268008005
Point
Mobile Sources; CNG; Aircraft Ground Support Equipment
X
2270008005
Point
Mobile Sources; Off-highway Vehicle Diesel; Aircraft Ground
Support Equipment
X
2275070000
Point
Mobile Sources; Aircraft; Aircraft Auxiliary Power Total
X
2275085000
Nonpoint
Mobile Sources; Aircraft; Unpaved Airstrips; Total
2275087000
Nonpoint
Mobile Sources; Aircraft; In-flight (non-Landing-Takeoff cycle)
X
4.2.3 Sources of data overview and selection hierarchy
The aircraft sector includes data from two data components: S/L/T agency-provided emissions data, and an EPA
dataset that is enhanced with state- and local-provided model inputs. The S/L/T agency emissions data were
received from agencies listed in Table 4-2. States that provided activity data for use in the EPA method are listed
in Section 4.2.5
270
-------
Table 4-2: Agencies that submitted 2011 Aircraft emissions or emissions at facilities identified as "Airports"
Agency
Agency Type
Notes
California Air Resources Board
State
1 county, 20 airports included
Illinois Environmental Protection Agency
State
Michigan Department of Environmental Quality
State
Pinal County
Local
Non-aircraft SCCs: see Section 4.2.6
Tennessee Department of Environmental Conservation
State
Texas Commission on Environmental Quality
State
The selection hierarchy used for aircraft is shown below in Table 4-3. This hierarchy pulls the relevant datasets
for this sector from the overall point sources hierarchy listed in Section 3. The aircraft emissions also have a
nonpoint component (in-flight lead) which is discussed in 4.2.5.3 and uses only EPA data.
Table 4-3: 2011 NEI Aircraft data selection hierarchy
Priority
Dataset Name
Dataset Content
1
State/Local/Tribal Data
Submitted aircraft emissions
2
2011EPA_Airports
EPA data (Section 4.2.5)
4.2.4 Spatial coverage and data sources for the sector
The aircraft sector includes emissions in every state, Puerto Rico, and the US Virgin Islands as well as six tribes.
Mobile - Aircraft Mobile - Aircraft
} PN m ¦
P - Point
N - Nonpoint
P - Point
N - Nonpoint
PN-P&N PN-P&N
All CAPs EPA SLT — EPA & SLT All HAPs EPA SLT —EPA & SLT
4.2.5 EPA-developed aircraft emissions estimates
EPA developed emissions estimates associated with aircrafts' landing and takeoff (LTO) cycle. The cycle begins
when the aircraft approaches the airport on its descent from cruising altitude, lands, taxis to the gate, and idles
during passenger deplaning. It continues as the aircraft idles during passenger boarding, taxis back out onto the
runway for subsequent takeoff, and ascent (climb out) to cruising altitude. Thus, the five specific operating
modes in an LTO are (1) Approach, (2) Taxi/idle-in, (3) Taxi/idle-out, (4) Takeoff, and (5) Climbout.
The LTO cycle provides a basis for calculating aircraft emissions. During each mode of operation, an aircraft
engine operates at a fairly standard power setting for a given aircraft category. Emissions for one complete cycle
271
-------
are calculated using emission factors for each operating mode for each specific aircraft engine combined with
the typical period of time the aircraft is in the operating mode.
In fall of 2012, the EPA posted preliminary LTO data for review prior to developing the aircraft inventory. EPA
encouraged the S/L/T agencies to review the materials and provide comments on any necessary corrections to:
Airport names and locations for airports to be included in the EIS facility inventory;
LTO information that will be used to estimate emissions for each airport;
Aircraft/engine combinations to link to FAA LTO data including default assumptions and
AircraftEngineCodeTypes for EIS submittals; and
Refer to Development of 2011 Aircraft Component for National Emissions Inventory, June 17, 2013 [ref 1]
for more detail on preparing the LTO data and running the Emissions and Dispersion Modeling System (EDMS),
including a summary of EPA default values and S/L/T agency replacement/revisions. As shown in Table 4-4, the
following S/L/T agencies submitted aircraft activity data that EPA incorporated as inputs to the final EPA dataset
model run.
Table 4 4: Agencies that submitted Aircraft activity data for EPA's emissions calculation
State
Affiliation
CA
Planning & Evaluation Division, Ventura County APCD
CT
Technical Services Group, Bureau of Air Management, Connecticut Department
of Energy and Environmental Protection
GA
Air Branch, Planning & Support GA Environmental Protection Division
KS
Air Inventory Modeling Unit, Kansas Department of Health & Environment
MD
Maryland Department of the Environment
NH
New Hampshire Department of Environmental Services
NJ
NJ Department of Environmental Protection
NV
Air Quality Management Division, Washoe County Health District
VA
Virginia Department of Environmental Quality
VT
Vermont Air Pollution Control Division
WA
Air Quality Program, Department of Ecology
Wl
Regional Pollutant and Mobile Sources Section, Bureau of Air Management,
Wisconsin Department of Natural Resources
4,2,5.1 Emissions for aircraft with detailed aircraft-specific activity data
For airports where the available LTO, from agencies or FAA data bases, included detailed aircraft-specific make
and model information (e.g., Boeing 747-200 series), EPA used the FAA's EDMS, Version 5.1 [ref 2], This type of
detail is available for most LTOs at approximately three thousand larger airports that have commercial air traffic,
Smaller and most general-aviation-only airports would not have aircraft specific activity detail available.
Emissions for GSE and APUs associated with aircraft-specific activity were also estimated by EDMS, using the
assumptions and defaults incorporated in the model. EPA's NONROAD model also estimates GSE emissions, but
that method is deemed less accurate than EDMS's LTO-based estimates and an EIS critical error check prohibits
GSE SCCs from being submitted to the non-road equipment data category which would duplicate emissions.
More on Non-road equipment is described in Section 4.5. Thus, the 2011 NEI uses only data for GSEs and APUs
from EDMS.
272
-------
4.2.5.2 Emissions for airports without detailed aircraft-specific activity data
EPA estimated emissions for aircraft where detailed aircraft-specific activity data were not available by
combining aircraft operations data from FAA's Terminal Area Forecasts (TAR and 5010 forms. These sources
provide LTO estimates for general aviation airports. Because the aircraft make and models were not available,
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. These fractions were developed based on FAA's
General Aviation and Part 135 Activity Surveys - CY 2010 [ref 3], Then EPA estimated emissions based on the
percent of each aircraft type, LTOs, and emission factors.
4.2.5.3 A viation lead emissions
Lead (Pb) emission estimates were handled differently than the other pollutants. Lead emissions are associated
with leaded aviation fuel used in piston driven aircraft associated with general aviation. EDMS has a limited
number of piston engine aircraft in its aircraft data and is currently not set up to calculate metal emissions;
therefore, we did not use it to estimate aircraft lead emissions. Lead emissions are instead based on per-LTO
emissions factors, assumptions about lead content in the fuel, and lead retention rates in the piston engines and
oil. The general equation is:
LTO Pb (tons) = (piston - engine LTOHavgas Pb g/LTO)(l-Pb retention)
907,180 g/ton
The LTO estimate requires assumptions about the number of piston engines per plane, and number of LTOs
necessary to account for US average fuel usage. The assumptions are detailed in a project report Calculating
Piston-Engine Aircraft Airport Inventories for Lead for the 2011 National Emissions Inventory, September 2013
[ref 4], In addition, a summary of the EPA-only airport lead emissions "2011nei subdata airportPb.xIsx". This
summary is not the same as any summaries of the 2011 NEI, which would include Pb emissions data from S/L/T
agencies. The EPA-only estimate for total LTO-based Pb emissions is 245 short tons nationwide, but the merged
EPA and S/L/T data total to 237 tons for the 2011NEvl. EPA's estimate for out-of-LTO or "in-flight" Pb is 238
tons. A summary of national EPA-only lead estimates is available [ref 5],
In-flight lead emissions were calculated based on national aviation gasoline consumption and similar
assumptions noted above about lead fuel content and retention rates. These emissions are included in the
nonpoint data category under SCC 227508700 (Mobile Sources; Aircraft; In-flight non-Landing-Takeoff cycle;
Total). Lead emissions associated with airport LTO activities were subtracted from the national fuel-based lead
emissions to approximate in-flight lead emissions which were allocated to individual states and noted with the
county code 777. This county code is not used to identify any actual counties and; therefore, county code 777
provides a way of uniquely identifying all in-flight emissions from other sources in the nonpoint data category in
the NEI.
4.2.6 Summary of quality assurance methods
The agency-submitted aircraft emission estimates were compared to EPA's estimates by pollutant and SCC at
the unit (e.g., commercial, general aviation, military, air taxi) and process (SCC).
Findings and impacts:
1. Aircraft-related records were tagged (and excluded from the NEI selection) as follows:
California records with outlier high values:
o 10 records for PM25-PRI and PM10-PRI in SCC 2265008005
273
-------
o 2 records for PM25-PRI and PM10-PRI in SCC 2275001000
Illinois records that duplicated EPA estimates by using generic equipment emissions factors, rather
than detailed ones that EPA calculated via EDMS. Also 53 Illinois airports that were not in the EPA
data set, which are submitted with emissions totaling zero for all submitted pollutants,
o includes all aircraft SCCs and criteria and HAP pollutants. 40948 records.
Texas records zero emission records intended to overwrite EPA records, but actually lead to
undercounts of piston general aviation and air taxi lead and other criteria and HAP values
o 12992 records for SCC 2275050011
o 64 records for SCC 2275060011
Michigan records that duplicated EPA estimates by duplicating processes and 33 Airport Facilities
that EPA does not, 31 of which are submitted with emissions totaling zero for all submitted
pollutants.
o 18017 criteria pollutant records for all aircraft (not GSE or APU) SCCs
2. Pinal County's single process submittal at one airport was for a fuel tank, not aircraft-related process
(FIP 04021, EIS Facility ID 12342611, SCC 40600307). No change was made.
3. Pinal CA reports non-aircraft process SCC 20200102 (Internal combustion engines) at Airport EIS Facility
ID 10026511. No change was made.
4. Pinal TN reports military aircraft SCC 2275001000 at EIS Facility ID 6670811 (ARNOLD ENGINEERING
DEVELOPMENT CENTER (AEDC) in FIP 47031 (Coffee County). Other point source emissions processes
are located there. If the aircraft processes are correct, the facility should be split into airport and
nonairport and given facility type "Airport". Currently these emissions are not captured in a Facility Type
= airport query. No change was made.
4.2.7 References for Aircraft
1. Eastern Research Group (ERG), 2013. Memorandum: Development of 2011 Aircraft Component for
National Emissions Inventory, June 17, 2013.
2. Federal Aviation Administration (FAA), 2011. Emissions and Dispersion Modeling System, Version 5.1.
September. 2011.
3. Federal Aviation Administration (FAA), 2012. General Aviation and Part 135 Activity Survey - Calendar
Year 2010.
4. U.S. Environmental Protection Agency (US EPA), 2013. Calculating Piston-Engine Aircraft Airport
Inventories for Lead for the 2011 National Emissions Inventory, EPA-420-B-13-040, September 2013.
5. Spreadsheet of EPA lead estimates "2011nei supdata airportPb 20140306.xlsx".
4.3 Commercial Marine Vessels
The 2011 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.
4.3.1 Revisions for the NEI 2011 v2
Substantial revisions were made for 2011 v2:
274
-------
All EPA CMV CI and C2 estimates were revised with geographic allocation updates (national totals
remained the same)
All EPA CMV C3 estimates within Emission Control Areas (ECA) were revised because vl had been
calculated as if the sulfur ECA was in effect, but it did not actually take effect until August 2012. This
change in fuel type increased S02, PM, and NOx emissions for C3 vessels in these areas.
SLT emissions were resubmitted to prohibit double counting where EPA and SLT locations/shapes
were in conflict and became additive when they were merged.
California VOC-HAPs were found to be erroneously high and were tagged and replaced using "HAP-
augmentation" that calculates VOC-HAPs from the California VOC submittals.
Port of Angeles (Washington State) emissions were revised, including a port shape file addition.
Alaska emissions in retired FIP counties were reallocated to existing counties.
Oregon had their marine vessel submission deleted in favor of EPA-only estimates for that state.
4.3.2 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 (see Section 4.3.4).
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 NONROAD model; they reside in the nonroad data category and EIS "Mobile - Non-
Road Equipment" sectors of the 2011 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 were allocated to a shape file identifier in the nonpoint inventory. The allowed combinations are
shown in Table 4-5. 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.
Table 4-4: Commercial Marine Vessel SCCs and emission types in EPA estimates
SCC
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
275
-------
2280003100
Marine Vessels, Commercial Residual Port
M
H
2280003200
Marine Vessels, Commercial Residual Underway
C
C
2280003200
Marine Vessels, Commercial Residual Underway
Z
C
Shown in Table 4-6, gasoline CMV emissions were submitted by Washington State and included in the NEI.
Table 4-5: Additional Commercial Marine Vessel SCC used by Washington
SCC
SCC Description
States
2280004000
Mobile Sources, Marine Vessels, Commercial, Gasoline, Total, All Vessel Types
WA
4.3.3 Sources of data overview and selection hierarchy
EPA received emissions data from the agencies identified in Table 4-7.
Table 4-6: Agencies that submitted Commercial Marine Vessels emissions data
Agency
Agency Type
California Air Resources Board
State
Delaware Department of Natural Resources and Environmental Control
State
Illinois Environmental Protection Agency
State
Maryland Department of the Environment
State
New Hampshire Department of Environmental Services
State
New Jersey Department of Environment Protection
State
Oregon Department of Environmental Quality*
State
South Carolina Department of Health and Environmental Control
State
Texas Commission on Environmental Quality
State
Washington State Department of Ecology
State
*Oregon estimate were removed for 2011 v2
Table 4-8 shows the selection hierarchy for the CMV sector. This hierarchy pulls the relevant datasets for this
sector from the overall nonpoint sources hierarchy listed in Section 3.
Table 4-7: 2011 NEIv2 commercial marine vehicle selection hierarchy
Priority
Dataset Name
Dataset Content
1
State/Local/Tribal Data
Submitted commercial marine vessel emissions
2
2011EPA_HAP-Augmentation
Uses emission factors to calculate HAP values based on S/L/T
agency submitted criteria estimates (VOC or PM species)
3
2011EPA_CMVLADCO
Submitted by LADCO for state's that approved
4
2011EPA_CMV
EPA data (Section 4.3.5)
4.3.4 Spatial coverage and data sources for the sector
The commercial marine vessel sector includes emissions in every US state except Arizona, Colorado, Montana,
Nevada, New Mexico, North Dakota, South Dakota, Utah, Vermont, and Wyoming. It also includes emissions for
Puerto Rico and US Virgin Islands, as well as emissions in federal waters.
276
-------
P - Point
N - Nonpoint
PN-P&N
P - Point
N - Nonpoint
Mobile - Commercial Marine Vessels
PN - P&N
All CAPs I I EPA ir isi ¦¦ftPAASLT
Mobile - Commercial Marine Vessels
All HAPs
4.3.5 EPA-developed commercial marine vessel emissions data
EPA estimated CMV emission estimates as a collaborative effort between the Office of Transportation and Air
Quality (OTAQ) and OAQPS. EPA developed the Category 3 commercial marine inventories for a base year of
2002 and then projected to 2011 by applying regional adjustment factors to account for growth. In addition, EPA
developed and applied NOx adjustment factors to account for implementation of the NOx Tier 1 standard. The
C3 growth factors, NOx adjustment factors by tier and calendar year, and NOx adjustment factors by engine type
and speed are defined in Appendix A of the project report Documentation for the Commercial Marine Vessel
Component of the National Emissions Inventory Methodology, March 30, 2010 [ref 1], For Category 1 and 2
marine diesel engines, the emission estimates were consistent with the 2011 Locomotive and Marine federal
rule making [ref 2]). EPA derived HAP estimates by applying toxic fractions to VOC or PM estimates.
EPA then allocated these emissions to individual GIS polygons (see Section 4.3.5.1) using methods that varied by
operating mode (i.e., hotelling, maneuvering, reduced speed zone, and underway). For example, port emissions
appear only in port polygons, federal water emissions in federal waters. HAP emissions were estimated by
applying speciation profiles to each polygon's VOC and PM estimates; see also Appendix B of the 2008 NEI CMV
documentation [ref 1],
EPA allocated emissions estimates based on activity to GIS polygons representing port and waterway. GIS
polygons allowed the estimation/allocation of emissions to defined port, waterway, and coastal areas.
4.3.5.1 Alloca tion of port and underway emissions
EPA developed port boundaries using a variety of resources to identify the most accurate port boundaries. First,
GIS data or maps provided directly from the port were used. Next, maps or port descriptions from local port
authorities, port districts, etc. 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. We placed primary emphasis on mapping the 117 ports with Category 3 vessel activity
using available shape files of the port area. The shape file used for 2011 incorporated the efforts made in 2008.
During the 2008 NEI development, the Port of Huntington was developed independently, given its large extent
and limited available map data. The state of West Virginia provided a revised shape file of US Army Corps of
Engineers port terminals reported to be part of the Port of Huntington-Tristate area. The revised shape that
includes a 200-meter buffer of the water features near these port terminals was created to identify the port
area.
277
-------
In all cases, polygons were created on land, bordering waterways and coastal areas, and were split by county
boundary, such that no shape file crosses county lines and county total emission can be easily summed. Each
polygon was identified by the port name and state and county FIPS in addition to a unique ShapelD. Smaller
ports with Category 1 and 2 activities were mapped as small circles, such that the port is much like a point
source, but without the complication of emissions appearing in both point and nonpoint inventories. Note that
no Category 3 emissions were mapped to small circles. The final set of port and underway shapefile GIS data.
To develop emissions for the Category 1 and 2 part of the inventory, EPA started with criteria emissions and
activity as a single national number. EPA allocated category 1 and 2 vessels based on activity for the underlying
vessel types (deep water, ferries, fishing, government, Great Lake, offshore, research, and tugs). See ref 3, ref 4
and ref 5.
These updates changed the allocation fractions of emissions to underway and port county/shapelD
combinations. Agencies were given an opportunity to resubmit their emissions allocated in proportion to EPA's.
The C3 estimates were grown in gridded Emissions Control Area (ECA) model data from 2002 to 2011. The 2002
data are documented in Technical Support Document (TSD'l Preparation of Emissions Inventories for the Version
5.0, 2007, December 14, 2012. Emissions Modeling Platform Criteria pollutant estimates from combined C3 SCCs
from model platform were allocated to shapes by ratio to 2008 county/shape/emistype. HAP speciation
fractions based on VOC and PM were employed to calculate HAPs. Alaska and Hawaii are outside of the model
domain and used OTAQ ECA estimates allocated based on previous NEI.
In cases where model files had emissions in counties for which we had no shape ids, the model file emissions
were dropped. In all these cases, emissions were very small and considered to be negligible. In cases where
model files had emissions in counties with shape IDs that had no 2008 C3 estimates, emissions were allocated to
shapes in those counties proportionately to shape area.
4,3.5.1 LADCO emissions
The regional organization Lake Michigan Air Directors Consortium (LADCO) provided an alternative data set,
labeled in the NEI as 2011EPA_CMVLADCO. For state's that approved the use of these estimates, they were used
as the highest priority. Those states are Indiana, Michigan, Minnesota, Ohio, and Wisconsin.
4.3.6 Summary of quality assurance methods
EPA compared shape-, state-, and county-level sums in (1) EPA default data, (2) S/L/T agency submittals and (3)
the resultant 2011 NEI selection by
• Included pollutants, SCCs, SCC-Emission Types
• Emissions summed to agency and SCC level
Findings:
The EIS generated a critical error and did not allow county-wide emission records for CMV, except when the
S/L/T submitted to counties for which EPA had no shape ID available for that SCC. S/L/T agencies were
encouraged to use the EPA-provided shape-to-county fractions if they were unsure how to distribute county
emissions to shapes. For 2011 v2, all SLT emissions were updated to insure no duplication (additive results)
when EPA and SLT data were merged in the selection.
1. California VOC HAPs were found to be out of agreement and erroneously high in comparison to their
submitted VOCs. EPA used "HAP Augmentation" to create HAP species from CA's submitted VOC values.
278
-------
2. California submitted CMV values also to counties for which EPA had no shape IDs or emissions. CA
submitted several HAPs, and only some CAP (no VOC)
4.3.7 References for Commercial Marine Vessels
1. Eastern Research Group (ERG), 2010. Project report: Documentation for the Commercial Marine Vessel
Component of the National Emissions Inventory Methodology. ERG No. 0245.02.302.001, March 30,
2010.
2. 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, EPA42Q-R-
03-004, January 2003.
3. Eastern Research Group (ERG), 2013. 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.
4. Eastern Research Group (ERG), 2012. Project report: Category 1 / Category 2 Commercial Marine Activity
Spatial Allocation, August 22, 2012.
5. Eastern Research Group (ERG), 2013. Project report: Disaggregation of Category 1 / Category 2
Commercial Marine Vessel Emissions for 2011, "2011neiv2 CMV Cat 12 Reallocation.pdf", November
20, 2013
4.4 Locomotives
4.4.1 Revisions for the NEI 2011 v2
Changes to this sector were limited to new SLT submittal updates from Virginia, New Jersey, and Washoe County
NV. Alaska emissions in retired FIP counties were reallocated to existing counties.
4.4.2 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 4-9 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 4-8: Locomotive SCCs, descriptions, and EPA estimation status
SCC
Description
EPA Estimated?
Data Category
2285002006
Mobile Sources Railroad Equipment Diesel Line
Haul Locomotives: Class 1 Operations
Yes - 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
Railroad Equipment Diesel Yard Locomotives
No
Nonpoint
28500201
Internal Combustion Engines Railroad
Equipment Diesel Yard
Yes - as point
sources
Point
279
-------
4.4.3 Sources of data overview and selection hierarchy
The locomotives sector includes data from S/L/T agency-provided emissions data, and an EPA dataset of
locomotive emissions. EPA estimated emissions from select locomotive SCCs as indicated in Table 4-9. The
agencies listed in Table 4-10 also submitted emissions to locomotive SCCs.
Table 4-9: Agencies that submitted Locomotives emissions to the 2011 NEI
Agency Name
Data Set
Short Name
Agency FIP
or Tribal Code
Rail
Point
Yard
Nonpoint
Yard
Alaska
2011AKDEC
02
X
California
2011CARB
06
X
X
Connecticut
2011CTBAM
09
X
X
Illinois
2011ILEPA
17
X
Maricopa Co Arizona
2011Maricopa
04013
X
Maryland
2011MDDOE
24
X
X
X
Massachusetts
2011MADEP
25
X
New Jersey
2011NJDEP
34
X
North Carolina
2011NCDAQ
37
X
Sac & Fox Nation of Missouri
in Kansas and Nebraska
2011TR863
863
X
Texas
2011TXCEQ
48
X
X
X
Utah
2011UTDAQ
49
X
Virginia
2011VADEQ
51
X
Washington
2011WADOE
53
X
Washoe Co Nevada
2011WashoeCty
32031
X
X
4.4.4 Spatial coverage and data sources for the sector
The locomotives sector includes emissions in all states, DC, Puerto Rice, and some tribes.
P - Point
N - Nonpoint
PN-P&N
P - Point
N - Nonpoint
PN - P&N
Mobile - Locomotives
All CAPs
Mobile - Locomotives
All HAPs
4.4.5 EPA-developed locomotive emissions data
EPA's 2011 national rail estimates were developed by applying growth factors to the 2008NEI values based on
railroad freight traffic data from the 2008 and 2011 R-l reports submitted by all Class I rail lines to the Surface
Transportation Board and employment statistics from the American Short Lines and Regional Railroad
280
-------
Association for class II and III. See ERG project report Development of 2011 Railroad Component for National
Emissions Inventory, September 5, 2012 [ref 1] for details. For more information on the 2008 methodology, refer
to the 2008 documentation [ref 2], The emissions were allocated to line haul shape IDs and yard locations based
on 2008 allocations.
4.4.5,1 Hazardous Air Pollutant emissions estimates
HAP emissions were estimated by applying speciation profiles to the VOC or PM estimates. Since 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 states. HAP estimates were calculated at the yard
and link level, after the criteria emissions had been allocated.
4.4.6 Summary of quality assurance methods
EPA and Agency submitted emissions were compared at shape, state, and county to EPA default values.
Findings:
• California rail emissions had suspiciously high HAP values. These HAP data were tagged and therefore
are not included in the 2011 v2.
• California submitted rail records that duplicated identical CA submittal but with the addition of an
emission type = C (which is intended only for cruising CMV records). These records were tagged.
• Though EPA's estimates are intended to include activity in all tribe and non-tribal areas, the EPA dataset
does not break out the data into tribal areas. Therefore the 2011 NEI emissions in tribal areas are equal
to the tribal submission only, and do not have consistent SCCs and pollutants as are present in counties.
EPA and Agency rail yard emissions were compared. All EPA's rail yard estimates are point sources. S/L/T
agencies were allowed to submit nonpoint county-level estimates but were asked to verify they did not conflict
with EPA's, or they could submit point estimates that would be chosen over EPA's. No obvious conflicts were
noted.
As with CMV, where S/L/T agency and EPA estimates did not use identical county/shape/SCC combinations, the
resultant selection may equal to neither EPA's nor the SLT agencies value. For example, see AZ and NJ SCC
=2285002007, and MD SCC = 2285002007 in Table 4-11.
Ta
ble 4-10:
Comparison of NOx emissions (tons) among EPA, S/L/T agency, and 2011vlNEI selection for Rail
State
Tribal Code
SCC
EPA
SLT
2011vl Selection
2011v2 Selection
863
2285002006
3
3
3
AK
2285002007
417
417
417
AK
2285002009
703
703
703
AZ
2285002006
22,181
1,263
22,030
22,030
AZ
2285002007
529
0
485
485
AZ
2285002008
9
9
9
CA
2285002006
29,642
31,225
31,225
31,225
CA
2285002007
1,714
0
0
0
CA
2285002008
2,667
2,667
2,667
CA
2285002009
1,078
1,078
1,078
CA
2285002010
2,280
2,280
2,280
CT
2285002006
0
0
0
281
-------
State
Tribal Code
see
EPA
SLT
2011vl Selection
2011v2 Selection
CT
2285002007
639
639
639
CT
2285002008
241
241
241
CT
2285002009
358
358
358
CT
2285002010
85
85
85
IL
2285002006
36,886
39,841
39,841
39,841
IL
2285002007
1,869
2,388
2,388
2,388
MA
2285002006
882
882
882
MA
2285002007
686
686
686
MA
2285002009
2,589
2,589
2,589
MD
2285002006
3,419
2,154
2,154
2,154
MD
2285002007
251
12
145
145
MD
2285002008
20
20
20
MD
2285002009
460
460
460
MD
2285002010
134
134
134
NJ
2285002006
1,194
1,194
1,194
NJ
2285002007
652
738
652
815
NJ
2285002009
2,606
2,606
NY
2285002006
12,070
12,070
12,070
NY
2285002007
1,922
1,922
1,922
TX
2285002006
60,389
58,762
58,762
58,762
TX
2285002007
2,168
2,633
2,633
2,633
TX
2285002010
2,225
2,225
2,225
UT
2285002006
6,287
5,878
5,878
5,878
UT
2285002007
244
244
244
VA
2285002006
15,603
15,603
15,603
VA
2285002007
387
387
387
VA
2285002008
622
622
VA
2285002009
267
267
WA
2285002006
14,445
12,420
12,420
12,420
WA
2285002007
978
978
978
WA
2285002009
534
534
534
4.4.7 References for Locomotives
1. Eastern Research Group (ERG), 2012. Memorandum: Development of 2011 Railroad Component for
National Emissions Inventory, September 5, 2012
2. Eastern Research Group (ERG], 2011. Project report: Documentation for Locomotive Component of the
National Emissions Inventory Methodology, ERG No. 0245.03.402.001, May 3, 2011.
4.5 Nonroad Equipment - Diesel, Gasoline and other
Although "nonroad" is used to refer to all transportation sources that are not on-highway, these EIS sectors and
this section address nonroad equipment other than locomotives, aircraft, or commercial marine vehicles.
282
-------
4.5.1 Revisions for the NEI 2011 v2
Only Delaware was updated for 2011 v2, to reflect revised inputs provided by the state.
4.5.2 Sector description
This section deals specifically with emissions processes calculated by the EPA's NONROAD model and the
OFFROAD model used by California. 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.
The National Mobile Inventory Model (NMIM) is EPA's consolidated mobile emissions estimation system that
allows EPA to produce nonroad mobile emissions in a consistent and automated way for the entire country. EPA
encouraged agencies to submit NMIM inputs to the EIS for the 2011 NEI for inclusion in the National County
Database (NCD). The NCD contains all the county-specific information needed to run NONROAD. It also contains
the ratios that are applied to NONROAD outputs to estimate emissions of HAPs, dioxins/furans (not part of the
NEI), and some metals. Although NMIM was designed to also estimate onroad emissions, it is no longer used,
and we now use the MOVES model described in Section 4.6. Eventually MOVES will be revised to also estimate
nonroad emissions and NMIM will be retired.
Nonroad mobile source emissions are generated by a diverse collection of equipment from lawn mowers to
locomotive support. NMIM estimates emissions from nonroad mobile sources using a variety of fuel types as
shown in Table 4-12.
Table 4-11: NMIM Nonroad Equipment and fuel types
Equipment Types
Fuel Types
Recreational
Construction
Industrial
Lawn and Garden
Agriculture
CNG
Commercial
Diesel
Logging
Gasoline
Airport Support (GSE) (excludes aircraft)
LPG
Underground Mining
Oilfield
Pleasure Craft (recreational marine) (excludes commercial marine vessels)
Railroad (excludes locomotives)
NMIM estimates monthly emissions for total hydrocarbons (THC), nitrogen oxides, carbon monoxide, particulate
matter, and sulfur dioxide, as well as calculating monthly fuel consumption. NMIM uses ratios from some of
these emissions to calculate emissions for an additional 33 hazardous air pollutants (HAPs) and 17 dioxin/furan
congeners. All of the input and activity data required to run NMIM are contained within the NCD, which is
distributed with the model. S/L/T agencies are able to update the data within the NCD to create emissions
estimates that accurately reflect local conditions and equipment usage.
4.5.3 Sources of data overview and selection hierarchy
Table 4-13 shows the selection hierarchy for the nonroad data category. EPA's NMIM estimates using S/L inputs
are used other than in California and Texas. California-submitted emissions were used along with an EPA
283
-------
correction dataset containing only VOC. For Texas, Texas-submitted data were used ahead of the EPA's NMIM
estimates, which were used second to gap fill any missing data/pollutants from the Texas dataset.
EPA asked S/L/T agencies to provide model inputs (NCDs) instead of emissions for 2011. However, some
agencies also submitted nonroad emissions. In addition to EPA's estimates, the agencies included in Table 4-14
submitted inputs and/or emissions to the 2011 NEI.
Table 4-12: Selection hierarchy for the Nonroad mobile Equipment data category
¦¦Priority"
Dataset
Notes
Everywhere except California and Texas
1
2011_EPA_Mobile
Contains emissions from EPA's NMIM run using S/L-provided
inputs as shown in Table 4-14 and NMIM defaults where S/L
accepted EPA default.
California
California Air Resources Board
Uses CA-specific model, OFFROAD
2
2011EPA_CAmodelerdata
Correction dataset (see QA): EPA added VOC emissions for
several SCCs which were missing in the California data due to an
error. These data were obtained by the modeling group at CARB.
Texas
Texas Commission on
Environmental Quality
Emissions based on Texas NONROAD (TexN) model. TexN allows
Texas to calculate emissions at a more granular level than what
NMIM is able to accommodate.
2
2011_EPA_Mobile
EPA estimates (same dataset described above)
Table 4-14 shows the submission dates for the S/L/T agency-submitted nonroad emissions and/or NCD activity
data for the 2011 NEI via the Emission Inventory System (EIS) Gateway.
Table 4-13: S/L/T agency-submitted data for Nonroad mobile Equipment
Agency Organization
Nonroad
Emissions
Nonroad
NCD
Notes
California Air Resources Board
4/23/13
Uses model
specific to CA
Coeur d'Alene Tribe
12/7/12
Connecticut Department Of Environmental Protection
1/8/13
Delaware Department of Natural Resources and Environmental
Control*
4/1/13
Eastern Band of Cherokee Indians
10/23/12
Georgia Department of Natural Resources
12/12/12
Idaho Department of Environmental Quality
12/5/12
Illinois Environmental Protection Agency
10/24/12
10/24/12
Submitted NCD
was used rather
than emissions
Kootenai Tribe of Idaho
12/14/12
Maryland Department of the Environment
12/21/12
2/22/13
Metro Public Health of Nashville/Davidson County
12/18/12
Accepted EPA
Emission
Estimates
Nevada Division of Environmental Protection
12/31/12
284
-------
Agency Organization
Nonroad
Emissions
Nonroad
NCD
Notes
New Hampshire Department of Environmental Services
10/17/12
New Jersey Department of Environment Protection
5/14/13
Nez Perce Tribe
12/10/12
North Carolina Department of Environment and Natural
Resources
12/19/12
Sac and Fox Nation of Missouri in Kansas and Nebraska
Reservation
10/5/12
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
12/6/12
Texas Commission on Environmental Quality
12/11/12
Utah Division of Air Quality
1/7/13
Accepted EPA
Emission
Estimates
Washington State Department of Ecology
1/9/13
Washoe County Health District
12/26/12
Accepted EPA
Emission
Estimates
Wisconsin Department of Natural Resources
1/9/13
^Original Nonroad NCD submission was January 7, 2013. The updated NCD to reflect this update is named
NCD20140620 nei2011v2.
4.5.4 Spatial coverage and data sources for the sector
Nonroad equipment emissions are included in every state, DC, Puerto Rice, and the Virgin Islands.
4.5.5 EPA-developed NMIM-based nonroad emissions data
EPA uses the activity data within NIMIM as a basis for air quality modeling, rule development, international
reporting, air quality trends analysis, and other activities. To that end, a single NCD for the 2011 NEI was
developed to represent, as accurately as possible, the activity data upon which the 2011 NEI emissions are
based. This newly developed NCD, named NCD20130531_nei2011vl, was created using the approach discussed
in the following sections. Like the emissions, the updates to the NCD were determined using a hierarchy decision
model, where defaults were replaced with S/L-supplied data. The exception to hierarchy decision model is that
EPA-supplied fuel and meteorological data were used for all 2011 NMIM modeling runs, as explained below.
However, as a matter of record, a copy of NCD20130531_nei2011vl which includes all the state-supplied
updates, including fuel and meteorological data was provided to EPA and is named NCD20130531. Once 2011 vl
was posted, S/L/T agencies had the opportunity to submit updates. The state of Delaware submitted an update
for the activity data used for developing the nonroad emissions. The final version of the NCD, reflecting all the
updates for the 2011 NEI are reflected in the NCD named NCD20140620_nei2011v2. The development of the
NCD for the 2011 NEI is explained in the following sections. See file
"2011NEIv2_supdata_nr_RunNotesChangeLog" for a description of the update history of the NMIM NCD for the
most recent updates made to NMIM. A comprehensive history of updates is recorded in the file Change
Log.docx, which is included in the NCD Readme folder.
285
-------
4,5.5.1 Default NCD
The default 2011 NCD, NCD20130531_nei2011vl, is based upon NCD20101201a." EPA provided updated fuel
and meteorological data for inclusion in the new 2011 NCD. Using the fuel data provided by EPA in the file
named RegionalFuels_2011_20130208fuelsNMIM.zip, the countyyearmonth, gasoline, and diesel tables were
replaced. However, the fuel updates provided by EPA did not contain fuel data for Alaska, Hawaii, Puerto Rico,
or the U.S. Virgin. For these areas, fuel data from the original NCD20101201a was retained. The meteorological
data provided by EPA in the file named countymonthhour2011.zip were used to replace the countymonthhour
table.
The NCD for 2011 v2 is a copy of the 2011 vl NCD, NCD20130531_nei2011vl, but includes the second round
updated submitted by the state of Delaware. This new NCD is titled NCD20140620_nei2011v2. The following
sections describe all the updates made to create the 2011 NEI v02.
4.5.5.2 State-submittedNCDs
NCD activity data submitted by state and local agencies via the EIS Gateway were used to replace default data,
except for fuel and meteorological data. Even if an agency submitted fuel and meteorological data, per the
instructions provided by EPA, the default values for these data parameters were retained. NCD tables updated
using state and local NCD submissions are presented in Table 4-15. Again, more detailed information regarding
specific updates can be found in the abbreviated NCD update history presented in
"2011NEIv2_supdata_nr_RunNotesChangeLog", which also contains a table of external files updated using state-
specific data.
Table 4-14: NCD tables updated based on State anc
State Name
DataSource
CountyNRFile
County
CountyYearMonth*
Diesel*
Gasoline*
External Files
CountyYearMonthHour*
Maryland
~
~
~
New Jersey
~
~
~
~
~
~
Connecticut
~
~
~
~
~
Delaware
~
~
~
~
Georgia
~
~
~
Idaho
~
~
Illinois
~
Nevada
~
~
~
~
~
New Hampshire
~
~
~
North Carolina
~
~
~
Washington
~
~
~
Wisconsin
~
~
~
Local NCD submissions
Updates to these tables were not used to develop the 2011 NEI NCD. Instead EPA-supplied data was used.
NCD20101201a is the NCD that is included in the current download of NMIM.
286
-------
4,5.5.3 State-assistedNCD development
Some State and Local agencies possessed activity data that could be incorporated into the 2011 NCD. However,
the data were not formatted appropriately for inclusion into the NCD. In these instances, ERG worked with the
state and local agencies to obtain the data and incorporate as much as possible into the 2011 NCD. A summary
of the tables updated using this approach is presented in Table 4-16.
Table 4-15: State-assisted NCD table updates
*
*
JO
3
O
X
JE
State Name
DataSource
CountyNRFile
County
CountyYearMonl
Diesel*
Gasoline*
External Files
£
0
2
hm
m
01
%
£
3
O
u
Davidson County (Tennessee)
~
~
~
~
New York
~
~
~
~
~
~
Texas
~
~
~
~
~
~
* Updates to these tables were not used to develop the 2011 NEI NCD. Instead EPA-supplied data were used.
4.5.5.4 NashviHe/Davidson County Tennessee
Nashville Pollution Control Division provided all the NONROAD option files used to create their 2011 emissions
inventory. The fuel data contained within the option files were extracted and used to update the RVP and sulfur
values in the fuel data tables within NMIM. Using EPA fuel data instead of agency-supplied fuel inputs for the
2011 NEI NCD, these updates were provided as a matter of record to EPA in NCD20130531.
4.5.5.5 New York
The New York Department of Environmental Conservation provided a state-specific allocation file for new
housing developments (36000hou.alo). These data represent single and double (duplex) family homes. This data
was updated using the U.S. Census data.26 These updates are reflected in the NMIM database
NCD20140620_nei2011v2.
New York also provided copies of their NONROAD option files used to create their 2011 emissions inventory. The
fuel data contained within the option files were extracted and used to update the RVP and sulfur values in the
fuel data tables within NMIM. Using EPA fuel data instead of agency-supplied fuel inputs for the 2011 NEI NCD,
these updates were provided as a matter of record to EPA in NCD20130531.
4.5.5.6 Texas
The Texas Commission on Environmental Quality (TCEQ) uses the Texas NONROAD (TexN) model to create their
emissions estimates. TexN allows Texas to calculate emissions at a more granular level than what NMIM is able
to accommodate. In addition to including state-specific climate and fuel profiles, TexN contains a separate
activity profile for 25 different subsectors of diesel construction equipment (DCE). Diesel construction
U.S. Census data file dc_acs_2009_5yr_g00 data I .txt. which is based on the 2005-2009 American Community
Survey 5-Year Estimates (http://factfindcr.census.gov/se rvlct/DTTablc?_bm=y&-gco_id=01<>(X)US&-
ds_name=ACS_2009_5YR_G00_&-mt_name=ACS_2009_5YR_G2000_B25024).
287
-------
equipment is found in many different types of construction. However, their population and use profiles are
unique within each of the sectors defined by the TexN model. TexN processes each of these subsectors
separately and sums the emissions across all subsectors at the end of the processing. Furthermore, TexN applies
post-processing adjustments to the calculated emissions based on several factors such as Texas Low Emission
Diesel (TxLED) use, ground cover variation, altitude, and humidity corrections to name a few. Furthermore,
Texas has done studies specific to certain areas within the state and have compiled activity data specific to
specific areas (e.g., Houston-Galveston-Brazoria and Dallas-Fort Worth). These activity values are denoted using
a county flag within TexN. In order to create the NCD activity tables for Texas, data from TexN was queried and
used to create an NCD that approximates Texas emissions. The approach used to develop the NCD for Texas is
presented below.
Population data were extracted for the year 2011 for all sectors contained within TexN. The population data
were then summed by SCC and horsepower bin. Average horsepower values within the TexN population data,
weighted by equipment population, were calculated by SCC and horsepower bin. These data were used to
update the external population file and are included in NCD20140620_nei2011v2.
The external growth file for the 2011 NCD was updated using population profiles from TexN. Population data
from TexN was summed by year and SCC and assigned the appropriate indicator code, according to the default
indicator code mapping with the NONROAD model. These data were used to update the external growth file and
are included in NCD20140620_nei2011v2.
The activity data from TexN was processed using a statistical analysis software program (SAS©). A weighted
average activity value was calculated for each equipment SCC using horsepower-hours as the weighting factor.
(HP-hours were selected as the weighting factor as this value should correlate reasonably closely with total
exhaust emissions.) The first step in this process was to calculate the cumulative hp-hrs over the entire
population. Next, the population and hp-hrs were summed over each unique SCC-DCE Subsector-County Flag-
Load Factor combination. Then, the fraction of hp-hrs for each SCC within each DCE Subsector and County Flag
was calculated and applied to the total activity value. The resulting SAS outputs were then formatted according
to the external file format for activity used by NMIM. These updates are included in NCD20140620_nei2011v2.
The geographic allocation of equipment populations was also updated using county-specific population values
from TexN. The population values were summed by county and SCC, then each SCC was assigned the correct
allocation indicator (XRF) value. These values were then used to build new allocation files for inclusion into
NMIM and are included in NCD20140620_nei2011v2.
The fuel data within TexN contains fuel properties specific to Texas obtained through multiple fuel sampling
surveys conducted by the State. These fuel properties were used to update the fuel data within NMIM for:
• gasoline RVP,
• diesel sulfur,
• gasoline sulfur,
• marine diesel sulfur,
• CNG and LPG sulfur,
• MTBE volume, ETBE volume, TAME volume, EtOH volume, and
• MTBE, ETBE, TAME, and EtOH market share.
Once again, the final NEI used EPA fuel data instead of agency-supplied fuel inputs, though the state updates
were provided as a matter of record to EPA in NCD20130531.
288
-------
4.5.5.7 Quality assurance
After the NMIM completed its execution, the resulting output databases were checked to ensure that no error
messages were created during the runs for each geographical area. Furthermore, the NMIM generates the same
number of output records for each RunlD-FIPSCountylD-FIPSStatelD-Year-Month combination. Therefore, each
of the output tables was checked to ensure the number of records for this combination of fields summed to the
correct record count. As expected, zero error messages were recorded by NMIM and every county produced the
same number of output records.
Once the NMIM outputs were exported from the NMIM database, ERG created SAS programs to read in the
detailed NMIM outputs and produce emissions summaries, plots, and charts to help identify outliers in
emissions. As a part of this process, ERG also created programs to compare the 2011 emissions generated under
this effort against other emission datasets. Comparisons were made between the 2011 emissions generated
under this effort, 2011 emission estimates generated using all default input, the 2011 emissions submitted by
state and local agencies for the 2011 NEI, as well as the 2008 NEI emissions.
Upon completion of the review and approval by EPA, ERG generated MOVES SMOKE-formatted files using the
emissions generated by NMIM using the NCD20130531_nei2011vl, which includes all the required updates
(excluding state-submitted fuel and meteorological data) submitted for 2011 vl. Later, updated SMOKE files
were generated to reflect Delaware's update for 2011 v2 using the NCD20140620_nei2011v2.
4.5.5.8 Summary of quality assurance on S/L/T agency emissions
Because EPA emphasized the submittal of inputs and helped agencies develop those inputs, there were only 2
states (TX and CA) and no tribes that submitted emissions data. Tribal emissions are accepted as is into the EIS
but are not included in the 2011 NEI because they may duplicate emissions already accounted for at the county-
level.
For Texas, we compared state and county EPA defaults, agency submittals and selection results by (1) included
pollutants, SCCs, SCC-Emission Types (nonroad emission types are R=refueling, E=evap, X=exhaust), and (2)
emissions summed to agency level.
Findings
Texas-submitted SCC/emission type/county/pollutant records account for all the NEI emissions in Texas, except
for mercury and arsenic, which were not in Texas' submittal. For those two pollutants, EPA values are used.
For California, because a state-specific model was run, EPA NMIM/NONROAD emissions estimates are not
merged with the state-supplied data. However, we found that VOC estimates were missing from the
SCC/emission type combinations provided in Table 4-17.
Table 4-16: SCC and emissions type with missing VOC in CA submittal
SCC
Emissions Type
2260001020
Evaporation
2260001020
Exhaust
2265001010
Evaporation
2265001010
Exhaust
2265001030
Evaporation
2265001030
Exhaust
2265001060
Evaporation
289
-------
SCC
Emissions Type
2265001060
Exhaust
2270001060
Evaporation
2270001060
Exhaust
Separately from the EIS submittal, the California Air Resources Board (CARB) modeling group provided nonroad
emissions data to EPA's emissions modeling group in July 2012. This CARB "modelers" dataset was different than
the data the CARB inventory group submitted to the EIS in that it contained total organic gases (TOG) instead of
VOC, and TOG was present where the VOC was missing from the EIS CARB data. We chose to compute VOC for
Table 4-17 SCC/emission types using the TOG from the "modelers" dataset. The original format of the
"modelers" dataset was a text file with annual mobile emissions totals at the county level and for California
source categories. The nonroad emissions were extracted from this file based on a California source category
crosswalk to EPA's SCCs. TOG was converted to VOC using VOC/TOG factors based on the SCC and emission
type. Prior to using the "modelers" -based VOC for the missing SCCs, we compared VOC between the
"modelers" dataset (after the conversion from TOG to VOC) and the EIS CARB data for SCCs with non-missing
VOC. Because they were not identical, we chose to adjust the "modelers" VOC before adding submitting it to the
EIS. The "modelers" data were adjusted by multiplying by the ratio of EIS CARB VOC to "modelers" VOC from
common non-missing SCCs in both datasets. Ratios were computed for each county using VOC from the non-
missing SCCs at the "SCC7" level (first 7 digits of the SCC). We submitted this adjusted "modelers" VOC to the EIS
in the dataset "2011EPA_CAmodelerdata".
4.5.6 References for Nonroad Equipment
1. My SQL file of NCD inputs for 2011 v2 "2011neiv2 supdata nonroad".
2. Run specifications and Change log for 2C 2011NEIv2 supdata nr RunNotesChangeLog" On-road
mobile -All Vehicles and Refueling
4.5.7 Sector description
The four sectors for on-road 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 sectors include emissions from parking areas as well as emissions
while the vehicles are moving.
The 2008 NEI vl and past NEIs included emissions from the MOBILE6 model. The 2011 NEI vl included emissions
from the MOVES2010b model. The 2011 NEI v2 used the latest available model, MOVES2014.
4.5.8 Sources of data overview, selection hierarchy, and changes to default data in NEI 2011 v2
EPA calculated the on-road emissions for the 2011 v2 for all states using MOVES. California emissions were later
replaced with estimates based on California's emissions submittal, as described in Section 4.6.2.2. Many states
submitted county level input data for MOVES. The following states or counties provided inputs for v2: NY, Clark
County NV, GA, NC, NH, NJ, OR, UT, VA, and Wl. Table 4-25 lists the agencies who submitted 2011 data and their
submittal dates to the EIS. This agency submission list includes the previous vl submittals as well as the new and
revised data states provided for 2011 v2. For counties in the lower 48 states, EPA used the SMOKE-MOVES
integration tools (SMOKE-MOVES) to generate emission inventories sources. Section 4.6.3.7 describes SMOKE-
MOVES processing steps. EPA ran MOVES in "inventory mode" to directly estimate county level emissions for
290
-------
states and territories outside the lower 48 states (i.e., AK, HI, PR, and VI). California provided EPA with complete
emissions based on the EMFAC2011 model.27
The selection hierarchy for v2 favored local input data over EPA default input data. For California, EPA used the
California ARB-provided emissions. For other states, EPA preferentially selected submitted local data over
default data for use in MOVES runs.
As part of v2 updates of default data, EPA introduced new nationwide datasets of recent county-specific data to
replace the older NEI EPA-default inputs. The Coordinating Research Council (CRC) and ERG conducted CRC
project A-88 to compile and develop improved on-road datasets to improve the defaults used the NEI.28 The NEI
default data updates focused on three specific areas: light-duty age distribution, light-duty population, and long-
haul VMT fractions. Section 4.6.2.3 (EPA Default MOVES Inputs) describes these new data in detail.
EPA generated emissions using the latest available version of MOVES2014 (code version 20140925 and database
version movesdb20140918).
4.5.8.1 Updated Source Classification Codes (SCCJ
For 2011 NEIv2, EPA revised the source classification codes (SCCs) for the on-road sector. Previous inventories'
SCCs were consistent with the MOBILE6 model, while this model-ready inventory utilizes detailed SCCs that are
more consistent with the source types and fuels that are in MOVES. The new SCCs have the form:
220FSSRRPP
Where "F" is the fuel type, "SS" is the source type, "RR" is the road type, and "PP" is the process type. For
example, gas passenger cars on urban unrestricted roads running exhaust has SCC 2201210501 and diesel
combination long-haul trucks parked in extended idle has SCC 2202620190.
For the underlying modeling (described below), EPA used these more detailed SCCs29. For the NEI, the results
were aggregated to more general SCCs which do not include road type and have more aggregated processes. For
example, in the posted annual 2011 v2 emissions data, gas commercial trucks for all roads and parked emissions
for all process (except refueling) has SCC 2201320080.
The previous SCCs from 2011 vl do not map directly to the current set of SCCs. Therefore, it was necessary to
create a third set of SCCs, comparison SCCs, which would allow for comparison across the inventories. The
MOBILE6 era SCCs need to be aggregated to these comparison SCCs and the MOVES2014 based SCCs need to be
aggregated to these comparison SCCs to create an equivalent set of aggregate source types. Detailed mappings
between both set of SCCs and the comparison SCCs are provided in the supplementary material (see Table 4-26
for access information).
4.5.8.2 California submitted on-road emissions
California submitted on-road emissions data directly according to SCC-level formatting requirements. EPA
instituted a quality assurance process to ensure the submitted data were complete and correctly formatted.
California's submissions were generated by ARB using the EMFAC2011 model.
27 The EMFAC2011 model the supporting documentation
28 "MOVES Input Improvements for the 2011 NEI" Report for the Coordinating Research Council (CRC) by Eastern Research
Group, Inc. under CRC Project A 88; October 2014.
29 For the modeling, EPA used a set of aggregate processes: 62 (all refueling), 91 (Auxiliary Power Units), 53 (all extended
idle), and 81 (all exhaust, evaporative, brake, and tire except refueling and hotelling).
291
-------
California submissions were based on the older MOBILE6 SCCs. To maintain consistency with the rest of the
county, EPA converted these emissions to the new SCCs through a two-step process. First, EPA estimated
California emissions using MOVES2014 (same process as the rest of the lower 48 states). Second, EPA
aggregated California's submissions to comparison SCC6 (aggregate fuel and source type) and then redistributed
those emissions to the new SCCs based on EPA's distribution of emissions. This distribution from comparison
SCC6 to new SCCs was done by county, SCC, and pollutant. All VOC HAPs used the VOC adjustment factor to
convert from EPA estimates to CARB estimates for that county/SCC. This preserved the speciation in
MOVES2014 (i.e. the relationship between each of the VOC HAPs and total VOC was consistent with EPA
estimates). EPA estimated PAHs were summed and adjusted to match CARB submitted total PAH. The
distribution between the individual PAHs was preserved to match EPA estimates.30
4.5.8.3 Agency-submitted MOVES inputs
State and local (S/L) agencies provided inputs for MOVES at the county level in the form of county databases
(CDBs). This established format requirement in which states must submit data (as a CDB) enables EPA to more
efficiently identify errors and manage the input datasets. EPA screened the submitted data using several quality
assurance (QA) scripts that analyze the individual tables in each CDB to look for missing data or unrealistic
values.
Overview of MOVES Input Submissions
S/L agencies prepared complete sets of MOVES input data in the form of one CDB per county using the MOVES
county data manager (CDM). Table 4-18 lists each table in a MOVES CDB and describes its content.
Table 4-17: MOVES CDB tables
CDB Table
Description of Content
auditlog
Information about the creation of the database
avft
Fuel type sales fractions
avgspeeddistribution
Average speed distributions
county
Description of the county
dayvmtfraction
VMT distribution across the type of day
fuelformulation
Fuel properties
fuelsupply
Fuel differences by month of the year
fuelsupplyyear
Year for the fuel properties
hourvmtfraction
VMT distribution across the hours of the day
hpmsvtypeyear
Total annual VMT by HPMS vehicle type
imcoverage
Description of the Inspection and Maintenance program
monthvmtfraction
VMT distribution across the months of the year
roadtype
Description of the road types
roadtypedistribution
VMT distribution across the road types
sourcetypeagedistribution
Distribution of vehicle ages
sourcetypeyear
Vehicle populations
state
Description of the state
year
Year of the database
zone
Allocations of starts, extended idle and vehicle hours parked to the county
30 Chromium in MOVES2014 is chromium trivalent only. CARB submitted chromium total. EPA calculated the faction of
chromium trivalent as 0.18*chromium total. The emissions in the NEI therefore represent the portion of California's
submission that is approximately chromium trivalent.
292
-------
CDB Table
Description of Content
zonemonthhour
Temperature and relative humidity values
zoneroadtype
Allocation of road types to the county
countyyear
Description of the Stage 2 program
emissionratebyage
Implementation of California standards [not part of CDB but included for
NEI since state-specific data is applicable]
Previously during vl, agencies submitted 1,363 CDBs. Adding in the new submittals for v2, the total number of
submitted CDBs became 1,426. Agencies submitting data through the EIS provided complete CDBs with all
database tables filled as well as documentation and a submission checklist indicating which of CDB tables
contained local data.
Table 4-19 summarizes these submission checklists, showing the number of counties within each State/County
submission for which the information was local data. Empty records in the table below indicate that the
State/County did not provide local data for that particular CDB table. The grand totals of submittals across all
states show that VMT and population ('hpmsvtypeyear' and 'sourcetypeyear' tables, respectively) were the
most commonly provided local data.
Table 4-18: Number of counties with submitted data, by state and MOVES CDB input tab
State/County
avft
avgspeeddistribution
dayvmtfraction
fuelformulation
fuelsupply
hourvmtfraction
S.
(0
0)
>
0)
(/)
E
SL
SZ
imcoverage
monthvmtfraction
roadtype
roadtypedistribution
sourcetypeagedistribution
sourcetypeyear
emissionratebyage
Alaska
29
29
29
29
29
29
29
2
29
29
29
29
Arizona (Maricopa County)
1
1
1
1
1
1
1
1
1
1
1
1
1
Colorado
11**
Connecticut
8
8
8
8
8
8
8
8
8
8
8
8
Delaware*
3
3
3
3
3
3
3
3
3
3
Dist. of Columbia
1
1
1
1
1
1
1
1
1
1
1
1
1
Georgia
21
159
21
159
13
159
20
159
159
159
Idaho
44
44
44
44
44
44
2
44
44
44
44
44
Illinois
102
102
102
102
102
102
11
102
102
102
102
102
Kentucky (Jefferson
County)
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Maine
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
Massachusetts*
14
14
14
14
14
14
14
14
14
14
14
14
Michigan
83
83
83
83
83
83
83
83
83
83
83
76
Minnesota
87
87
87
87
4**
87
Missouri
110
115
5
115
115
115
Nevada (Clark County)
1
1
1
1
1
1
1
Nevada (Washoe County)
1
1
1
New Hampshire
10
10
10
10
10
10
New Jersey
21
21
21
21
21
21
21
21
21
21
21
21
New York
62
62
62
62
62
62
62
62
62
62
62
62
62
62
293
-------
State/County
avft
avgspeeddistribution
dayvmtfraction
fuelformulation
fuelsupply
hourvmtfraction
S.
(0
0)
>
0)
£L
t
E
SL
SZ
imcoverage
monthvmtfraction
roadtype
roadtypedistribution
sourcetypeagedistribution
sourcetypeyear
emissionratebyage
North Carolina
19
100
100
100
100
100
100
100
Ohio
88
88
88
1
1
88
88
14
88
23
88
88
88
Oregon
36
6
Pennsylvania
67
67
67
67
67
67
67
67
67
67
67
67
Rhode Island
5
5
South Carolina
46
46
46
46
46
Tennessee (Knox County)
1
1
1
1
Utah
29
29
29
29
29
29
29
29
29
29
29
29
29
29
Vermont
14
14
Virginia
134
40
34
34
134
10
40
134
134
134
Washington
1
39
39
39
39
39
5
39
39
39
39
West Virginia
13
13
13
13
13
Wisconsin
7
6
72
72
7
72
72
72
Total
280
774
875
761
959
797
1390
429
884
428
1214
1224
1388
281
* EIS checklist submitted blank, determined from documentation
** Submitted directly to EPA staff, not through the EIS
As shown above, some states supplied local data for only a subset of CDB tables. The other tables contained old
or default information.
Figure 4-1 shows geographic coverage of CDB submissions the S/L agencies submitting any local data at the
county level in dark blue. The light blue areas indicate counties where the MOVES runs used EPA default data.
294
-------
Figure 4-1: Dark blue indicates States/Counties that submitted at least 1 CDB input table
j Washoe County, NV
Louisville, KY (Jefferson County)
Clark County, NV
Knox County, TN
I Maricopa County, AZ
OA checks on MOVES CDB tables
EPA developed a QA process in the form of MySQL scripts which EPA's contractor ERG supplemented with
additional scripted checks to evaluate the reasonableness of data values compared to expected ranges in user
inputs. EPA's QA scripts read the database tables in each agency-submitted CDB and recorded warnings and
errors indicating the table's completeness and reasonableness. The EIS submission process required agencies to
run one of the QA scripts on each CDB and report results, but EPA performed the supplemental QA checks using
a second QA script to evaluate on reasonableness of data after receiving the submitted CDBs. The second QA
script that checked data reasonableness included the following:
1. Calculate average speeds by RoadType and SourceType using avgSpeedFraction values in the
AvgSpeedDistribution table; compare to the national average values in the MOVES default database
table. Flag differences > 10 miles per hour.
2. Flag RVP values in the FuelFormulation table if > 9 psi in the summer months (monthlD=5 through
9); or > 10 psi for E10.
3. Flag hourVMTFraction in the HourVMTFraction table if the sum of HourlD=6 through 18 (daytime
hours) if < 0.5; or if values for individual hours = 0, or > 0.8.
4. Flag monthVMTFraction in the MonthVMTFraction table if the sum of summer months
(4 0.8.
295
-------
5. Flag rampFraction in the RoadType table for roadTypelD=2 and 4 if = 0, or > 0.2; or > 0
roadTypelD=l, 3 and 5.
6. Flag ageFractions in the SourceTypeAgeDistribution table for SourceTypes where the sum across
agelD 0-15 is < 0.5; or for individual ageFraction = zero or > 0.8.
7. Flag DayVMT values where weekday VMT > weekend VMT
8. Flag gasoline sulfur in FuelFormulation for values > 80ppm
9. Flag EtOH Volume in FuelFormulation for values > 10
10. Flag sourceType Population in SourceTypeYear table where sum of population for SourceTypelDs
21 and 31 is < 0.5
11. Flag HPMSBaseYearVMT in HPMSVTypeYear table where sum of VMT for SourceTypelDs 21 and 31
is < 0.5
12. Calculate VMT/Population ratios by sourcetype and compare to national default ratios. Flag ratios
that differ from default > 50% [note: this was increased from original flag of 10% after this
threshold flagged most of the submitted data].
During v2 development, EPA refined a number of the vl QA checks including the screening of the
Inspection/Maintenance (l/M) coverage table. The l/M Coverage QA check flagged errors related to sequence,
gaps, and overlaps in model year coverage of exhaust and evaporative l/M programs. For example, the l/M
checking script flagged counties where two exhaust l/M programs were applied to the same set of model years
for passenger cars. EPA's contractor identified these errors, recommended specific corrections, and EPA
confirmed the proposed corrections with individual states when possible prior to implementation. As a result of
these efforts, the l/M tables were corrected for many counties in several states, including Rhode Island, Oregon,
Virginia, and Indiana.
Aside from l/M checks, the general QA scripts flagged errors in the new v2 data. For example, several counties
had speeds that were unrealistically low for restricted access road types (for example, 15 mph for all hours of
day). In these cases, EPA contacted the responsible agency for CDB submission and requested an additional
review. The outcome of review resulted in either the S/L agency opting to use EPA default speeds in the NEI in
place of submitted data or correcting their data.
Another common category of error was distributions that did not sum to 1. For example, age distributions for
specific source types summed to 0.96 instead of 1. EPA corrected this type of data problem by renormalizing the
distribution. The QA scripts also flagged distributions with atypical patterns, such as hourly VMT fractions with a
higher fraction in nighttime hours than daytime. EPA evaluated and addressed these potential errors on a case-
by-case basis.
4,5,8.4 EPA default MOVES inputs
EPA developed the CDBs for counties that did not submit any input data. Table 4-20 describes the source of
default data used for 2011 v2 for each table in a CDB for which states have the option to supply alternate data.
There are additional tables in a CDB, not listed below, that are informational only (i.e., state, county, year etc.)
that EPA populated. The new EPA default data in v2 applies to light-duty source type data in the age distribution
and vehicle population tables.
Tab
e 4-19: Source of defaults for data tables in MOVES CDBs
CDB Table
Description of Content
Default CDB Table Content
avgspeeddistribution
Average speed distributions
MOVES2010b national default
dayvmtfraction
VMT distribution across the
type of day
2011 NEI vl
296
-------
CDB Table
Description of Content
Default CDB Table Content
fuelformulation
Fuel properties
Based on EPA estimates for each county from
calendar year 2011 refinery data
fuelsupply
Fuel differences by month of
the year
Based on EPA estimates for each county from
calendar year 2011
hourvmtfraction
VMT distribution across the
hours of the day
MOVES2010b national default
hpmsvtypeyear
Total annual VMT by HPMS
vehicle type
2011 county-level data from FHWA
imcoverage
Description of the
Inspection and Maintenance
program
2011 NEI vl
monthvmtfraction
VMT distribution across the
months of the year
MOVES2010b national default
roadtype
Ramp fractions by road type
0.08 fraction (8 percent) of vehicle operating
hours on urban and rural restricted access roads
roadtypedistribution
VMT distribution across the
road types
2011 NEI vl
sourcetypeagedistribution
Distribution of vehicle ages
For source types 21, 31, and 32: CRC A-88
estimates for each county;
For all other source types: MOVES2010b national
default for 2011
sourcetypeyear
Vehicle populations
For source types 21, 31, and 32: CRC A-88
estimates for each county;
For all other source types: Calculated from county-
level VMT based on ratios of population to VMT
from state-level FHWA data
zonemonthhour
Temperature and relative
humidity values
Temperature and humidity data are EPA provided
data for each county from calendar year 2011
emissionratebyage
Implementation of
California standards
The EmissionRateByAge tables for some counties
have been populated using the appropriate data
described in the guidance for states adopting
California emission standards.
Default California Emission Standards
EPA populated an alternative MOVES database table 'EmissionRateByAge' for some counties in states that
adopted emission standards California's Low Emission Vehicle (LEV) program. Table 4-21 shows which states
adopted the California standards and the year it began.
Table 4-20: States adopting California LEV standards, start years
FIPS State ID
State Name
LEV Program Start Year
6
California
1994
9
Connecticut
2008
10
Delaware
2014
23
Maine
2001
24
Maryland
2011
25
Massachusetts
1995
34
New Jersey
2009
36
New York
1996
297
-------
FIPS State ID
State Name
LEV Program Start Year
41
Oregon
2009
42
Pennsylvania
2008
44
Rhode Island
2008
50
Vermont
2000
53
Washington
2009
Updated defaults from CRC A-88
Light Duty Age Distribution and Population
EPA updated light-duty default data in 2011 v2 CDBs for two specific inputs— age distribution (the
'sourceTypeAgeDistribution' table) and population (the 'sourceTypeYear' table). The affected light-duty source
types included passenger cars, passenger trucks, and light-duty commercial trucks (source types 21, 31, and 32).
Historically, EPA's default data source for fleet age has been a nationwide average age distribution applied to all
counties. For light-duty vehicles in 2011, the default data average age was 9 years old. The updated default age
distributions from CRC A-88 show a range in average age of 4 to 16 years old by county. EPA previously
determined default data population using a single national ratio of population to VMT for each source type; the
ratio did not vary geographically. The CRC project A-88 population data replacing this default is based on state-
reported vehicle registrations.
In order to improve the county resolution and to use more recent data, EPA incorporated county-level data from
CRC project A-88. The CRC project team procured vehicle populations from IHS Automotive (formerly R.L. Polk).
IHS compiled their data from state vehicle registrations provided to IHS by state departments of motor vehicles.
The IHS database provided vehicle population for each county separately for cars and light trucks by model years
1981 through 2012. A limitation of the IHS data is that it did not include vehicles for model years 1980 or earlier
as these models did not have a standardized Vehicle Identification Number (VIN) schema. To adjust for this, the
CRC project team added population to the oldest age category (1981 representing the 30+ vehicles) by until the
"tail" of the age distribution reached the median of data provided by states. CRC normalized the modified by-
model-year populations to produce the light-duty age distributions for cars (applicable to source type 21) and
light trucks (applicable to both source types 31 and 32). CRC summed the populations over the same set of
modified population data to calculate the total population for passenger cars and light trucks. The light-duty
truck population was split into source types 31 and 32 using the MOVES national average split of 75% and 25%,
respectively.
Updated fraction of Long-Haul Truck VMT
CRC data improvements also addressed a third default data parameter in the on-road NEI—-the fraction of long-
haul truck VMT. EPA's approach for determining the default allocations of truck VMT to the long-haul categories
has relied on national average rates of annual mileage accumulation and the relative vehicle population by
source types within an HPMS vehicle group, listed in Table 4-22
298
-------
Table 4-21: HPMS truck categories and their MOVES source types
HPMS Vehicle
HPMS Vehicle
Source
Type ID
Name
Type ID
Source Type Name
50
Single Unit
51
Refuse Truck
Trucks
52
Single Unit Short-haul Truck
53
Single Unit Long-haul Truck
54
Motor Home
60
Combination
61
Combination Unit Short-haul Truck
Unit Trucks
62
Combination Unit Long-haul Truck
These default methods resulted in a static value of 59 percent of long-haul VMT from combination unit trucks
and 12 percent long-haul VMT from single unit trucks nationwide with no geographic variability. The CRC A-88
analysis of the Freight Analysis Framework (FAF) data set suggested variability in the allocations of long-haul
VMT by region of the U.S. and by road type. The updated allocations show a range of 30 to 90 percent long-haul
VMT from combination unit trucks and a range of 2 to 50 percent long-haul VMT from single unit trucks,
depending on region and road type31.
Because a MOVES CDB input table does not exist for long-haul VMT, EPA implemented the updated VMT
fractions by post-processing the SMOKE-ready activity files (see Section 4.6.3.4). EPA also estimated the
hotelling hours from the combination unit long-haul trucks based on their updated VMT values resulting from
the CRC A-88 data.
4.5.9 Calculation of EPA Emissions
4.5.9.1 EPA -developed on-road emissions data for the continental U.S.
For the 2011 NEI, EPA estimated emissions for every county as discussed below. California had additional
processing (see Sections 4.6.2.2 for details). For the continental U.S., EPA used a modeling framework that took
into account the strong temperature sensitivity of the on-road emissions. Specifically, EPA used county-specific
inputs and tools that integrated the MOVES model with the SMOKE32 emission inventory model to take
advantage of the gridded hourly temperature information available from meteorology modeling used for air
quality modeling. This integrated "SMOKE-MOVES" tool was developed by EPA in 2010 and is in use by states
and regional planning organizations 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 that could
be applied across the U.S., EPA used an automated process to run MOVES to produce emission factors by
temperature and speed for 284 "representative counties," to which every other county could be mapped, as
detailed below. Using the 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 miles
travelled), vehicle population, or hotelling hours) to produce emissions. These calculations were done for every
county, grid cell, and hour in the continental U.S. and aggregated to produce continental U.S. emissions. The
MOVES "RunSpec" files (that tells MOVES what to run for each representative county) are provided in the
supplementary materials (see Table 4-26 for access information).
31 The explicit regions and long-haul splits are the in the CRC A 88 report, specifically Figures 17,18, and 19. See "MOVES
Input Improvements for the 2011 NEI" Report for the Coordinating Research Council (CRC) by Eastern Research Group, Inc.
under CRC Project A-88; October 2014.
32 SMOKE v3,6 was used for the 2011 v2. The current version of SMOKE
299
-------
EPA used a different approach for states and territories outside the lower 48 states. For Alaska, Hawaii, Puerto
Rico and the Virgin Islands, EPA ran MOVES in "inventory mode" for each county and month, using county-
specific inputs. More information is provided Section 4.6.4.
SMOKE-MOVES can be used with different versions of the MOVES model. For the 2011 v2, EPA used the latest
publicly released version: MOVES2Q14. Using SMOKE-MOVES for creating the NEI 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
4.6.3.2)
• Determine which months will be used to represent other month's fuel characteristics (see Section
4.6.1.1)
• Create MOVES inputs needed only for the MOVES runs (see Section 4.6.2.4). MOVES requires county-
specific information on vehicle populations, 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 Sections 4.6.3.3 and 4.6.3.4).
• Run MOVES to create emission factor tables (see Section 4.6.3.6)
• Run SMOKE to apply the emission factors to activities to calculate emissions (see Section 4.6.3.7)
• Aggregate the results at the county-SCC level for the NEI, summaries, and quality assurance (see Section
4.6.3.8)
4.5.9.2 Representative counties
Although EPA compiles county-specific database for all counties in the nation, EPA runs MOVES for a subset of
these because the important emissions-determining differences among counties can be accounted for by
assigning counties to groups with similar properties (e.g., similar fleet age, shared l/M programs, shared specific
fuel controls such as low RVP for summer gasoline, same state). This approach of running representative
counties helps manage computation time by reducing the number of MOVES runs needed to generate a
nationwide inventory.
Within the SMOKE-MOVES framework, lookup tables of representative county emission factors are multiplied
with the county-level activity for all counties within the representative country group. The activity specific to
each county in the inventory includes VMT, population, speed distributions, and hotelling hours.
EPA increased the number of representative counties for v2. The first update to the vl representative county
groups was to accommodate requests from five states, including CO, ME, MD, NC, and AK. EPA then undertook
new analysis to further subdivide the approximately 164 county groups based on ramp fractions and updated
default age distributions resulting from CRC A-88 data. After the conclusion of EPA's v2 representative county
analysis, other states requested changes including GA and AL, and EPA implemented these minor changes. The
final number of representative counties for 2011 v2 increased to 284. Figure 4-2 is a map of the representative
counties by state and their corresponding county groups.
300
-------
Figure 4-2: Representative county groups for NEI 2011 v2
Reference counties are outlined in black.
Number of counties assigned to each
reference county are labelled.
Reference County Groups 2011 V2
Ramp Fractions
During the 2011 on-road NEI development cycle, agencies had the option to provide the CDB table "roadType'
which specifies the fraction of restricted access road operating time that occurs on ramps. The >oadType* table
is optional in a CDB; if the CDB table is empty, MOVES will revert to its nationwide default value of a 0.08
fraction (8 percent) of vehicle operation time that occurs on ramps.
A ramp fraction vaiue of 0 is possible for a county where a single highway passes through the edge of the county
without having any exits. Conversely, a busy urban county with many flyovers and entrance/exit ramps could
have a much higher ramp fraction than the average of 8 percent. Because emission factors are higher on ramps
compared to highway driving and there exists a potential for wide variation in the county data, EPA added this
parameter in the consideration for county groups in 2011 v2.
S/L agencies provided ramp fractions for 716 counties out of the approximately 1,400 submitted CDBs. The ramp
fraction values ranged from 0 to 1 although most (97 percent) of the values were less than 0.13. After examining
the distribution of the data, EPA grouped the ramp fractions values according to the 5-bin scheme shown below
in Table 4-23.
301
-------
Table 4-22: Binning scheme for submitted ramp fraction data
Bin
Description
(Fractions from 0 to 1)
Number of
Counties
1
0 < ramp fraction < 0.05
244
2
0.05 < ramp fraction < 0.09
336
3
0.09 < ramp fraction < 0.13
120
4
0.13 < ramp fraction < 0.17
7
5
0.17 < ramp fraction
9
EPA assigned counties to one of the 5 bins according to the ramp fraction on either road type 2 (Rural Restricted
Access) or road type 4 (Urban Restricted Access), selecting the road type that had the higher VMT. Next, EPA
split the county groups on the basis of the new ramp fraction bin assignments. This process resulted in the
addition of more than 30 new representative counties in v2.
Mean Age of Light Duty Vehicles
Age distribution was previously a factor in the selection of representative counties in the 2011 vl, but the
binning at that time effectively only distinguished among submitted data because the default age distributions
did not vary by county in the states that did not submit data. Given the introduction of new nationwide county
specific age distributions from CRC A-88 to replace the default, the mean age parameter needed to be re-
evaluated in 2011 v2.
Just as for the ramp fraction analysis, EPA evaluated mean age with the intent to further subdivide existing
county groups where differences would likely affect emission factors within the group. The counties in v2 using
the default age distributions from CRC A-88 are those that either did not submit a CDB (see light blue in Figure
4-1) or did submit but elected to use the CRC age distributions or a modified version thereof instead of
submitted data. The latter category includes the following states and/or counties: GA, ME, MN, Washoe County
NV, Rl, SC, VT, and WV33. In total, EPA binned 2,082 counties based on light-duty mean age of the IHS-derived
data from CRC A-88. Table 4-24 shows the definitions of the 6 bins. The mean age binning process added nearly
70 new representative counties to the NEI.
Ta
lie 4-23: Binning scheme for CRC A-88 age distribution data
Bin
Description (Mean age in
number of years old in 2011)
Number of
Counties
1
0.0 < Mean Age < 7.0
1
2
7.0 < Mean Age < 9.0
140
3
9.0 < Mean Age < 11.0
994
4
11.0 < Mean Age < 13.0
920
5
13.0 < Mean Age < 15.0
25
6
15.0 < Mean Age
2
Validation of the average-age approach to binning similar counties
Following the analysis of grouping counties based on mean light duty vehicle age, EPA examined the full age
distribution of each representative county to determine how similar it was to the age distributions of its
33 Rl, SC, and VT used their own state supplied population data and used CRC data only for age distribution. WV did not use
CRC data for the following 13 counties: Berkeley (54003), Brook (54009), Cabell (54011), Hancock (54029), Kanawha
(54039), Marshall (54051), Mason (54053), Monongalia (54061), Ohio (54069), Pleasants (54073), Putnam (54079), Wayne
(54099), and Wood (54107). For all other counties, WV elected to use CRC data.
302
-------
member counties. Unlike the ramp fraction or mean age analysis, the purpose of examining the full age
distributions was not to add any new county groups, but rather identify whether any atypical age distribution
shapes that exist in the representative county set. The parameter used to analyze the age distribution, termed
"vector angle," indicates how similar a particular age distribution (or vector) is to a reference vector by
calculating their "angle," resulting in a value between 0° (very similar distribution) and 90° (maximum
difference). In general, most county age distributions matched well with their representative county age
distribution in overall shape, with approximately 95 percent of all vector angles below 7°, a value which upon
visual inspection corresponded to reasonable agreement between the full distributions. The remaining 5 percent
of county vector angles were mostly clustered around the range of 8 to 10°, with exception of one county vector
angle of 19.6° (the maximum angle). These relatively high vector angles for particular counties occurred in a
distributed fashion, among various county groups, in groups where other member counties showed good
agreement with the representative county. Therefore, EPA made no changes to the representative county
selection based on the vector angle validation check.
Fuel Months
The concept of a fuel month is used to indicate when a particular set of fuel properties should be used in a
MOVES simulation. Similar to the reference county, the fuel month reduces the computational time of MOVES
by using a single month to represent a set of months. Because there are winter fuels and summer fuels, 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 reference 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. 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 represent the other month sufficiently. The
fuel months used for each representative county are provided in the supplementary materials (see Table 4-26
for access information).
Fuels
Although state-submitted MOVES input data may have included information about fuel properties, the MOVES
runs for the 2011 NEI v2 were run using a set of fuel properties for a set of fuel regions generated by EPA. EPA
developed these data using a combination of purchased fuel survey data, proprietary fuel refinery information
and known federal and local regulatory constraints.
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 CBOB, RBOB or OBO fuel batches) produced
in 2010, excluding 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% ethanol (E10, no MTBE).
4) Refinery modeling results generated for the RFS2 rulemaking were used to adjust the regional
conventional gasoline fuel properties to account for ethanol blending up to E10, for a given month.
303
-------
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 2011, 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 ppm nationally with no significant biodiesel penetration.
The regional fuel supply database is provided in the supplementary materials (see Table 4-26 for access
information).
4.5.9.3 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 2011 covering the continental United States
were derived from simulations of version 3.4 of the Weather Research and Forecasting Model (WRF), Advanced
Research WRF core [ref 1], The WRF Model is a mesoscale numerical weather prediction system developed for
both operational forecasting and atmospheric research applications. The Meteorology-Chemistry Interface
Processor (MCIP) version 4.1.3
CMAS was used as the software for maintaining dynamic consistency between the meteorological model, the
emissions model, and air quality chemistry model.
EPA applied the SMOKE-MOVES tool Met4moves to the gridded, hourly meteorological data (output from MCIP)
to generate a list of the maximum temperature ranges, average relative humidity, and temperature profiles that
are needed for MOVES to create the emission-factor lookup tables. "Temperature profiles" are arrays of 24
temperatures that describe how temperatures change over a day, and they are used by MOVES to estimate
vapor venting emissions. The hourly gridded meteorological data (output from MCIP) was also used directly by
SMOKE (4.6.3.6).
The temperature lists were organized based on the representative counties and fuel months as described in
Sections 4.6.3.2 and 4.6.1.1, respectively. 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. 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 idealized temperature profiles using the minimum and maximum
temperatures and 10 °F intervals. In addition to the meteorological data, the representative counties and the
fuel months, Met4moves uses spatial surrogates to determine which grid cells from the meteorological data to
collect temperature and relative humidity statistics. For example, if a county had a mountainous area with no
roads, this would be excluded from the meteorological statistics.
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
304
-------
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 (Section 4.6.3.6), the appropriate temperature bin and fuel month
specific relative humidity was used for all runs of the county group. EPA used a 5 °F temperature bin size for
RPD, RPV, and 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 (RPP) based emissions. EPA ran Met4moves in daily mode for 2011 NEI.
The resulting temperatures provided to the representative counties are provided in the supplementary
materials (see Table 4-26 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 (contact
info. chiefPepa.gov).
4,5,9.4 VMT, vehicle population, speed, and hotelling for SMOKE
EPA prepared SMOKE-ready activity files in FF10 formats for all the activity types used by SMOKE-MOVES. The
activity files include FF10 tables for VMT, population, average speed, and hotelling. The script also produced
weekday and weekend hourly speed profiles, an optional input to SMOKE-MOVES.
EPA and its contractor ERG developed scripts that automated the creation of FF10 tables based on submitted
CDBs and supplemental information in the MOVES database. For clarity, it should be noted that the speed
profile input to SMOKE (spdpro) is not an FF10 file, but it was generated by the same script that produces the
FFlOs. Regardless of activity type, the objective of the script was to transform all user-supplied activity from CDB
input table format into the level of detail required for SMOKE input. SMOKE inputs require activity by SCC which
includes detail of MOVES source type, fuel type, and road type34. The script looped through the submitted CDBs
and reported results to each FF10 table, collating results for all counties.
VMT FF10 file creation
EPA's script included several calculation steps to produce SCC-level VMT. First, the script calculated travel
fractions by source type and model year that sum to one (1) for each HPMS vehicle type. The script generated
these travel fractions using the CDB tables 'sourceTypeAgeDistribution' and 'sourceTypeYear' and the MOVES
database table of annual mileage accumulation rates. Next, the script further divided the travel fractions by
model year into fuel types of gasoline, diesel, ethanol (E85), and compressed natural gas (CNG) based on MOVES
database table containing national sales of these engine types by model year and source type. Following that
step, the script multiplied the travel fractions with the corresponding HPMS vehicle type's VMT in the CDB table
'HPMSVtypeYear' resulting in VMT disaggregated into source type, fuel type and model year. The script then
aggregated over model years and multiplied the resulting VMT at the source type level by road type fractions of
34 The activity is by county and SCC8 (i.e. fuel, source type, and road type). The activity did not need to be by process as well
(last 2 digits of the SCC). For example, the VMT for exhaust would be identical to the VMT for brake and tire wear. Because
the EF tables are by full SCC (including process), SMOKE internally maps the activity by SCC8 to full SCCs using the SCCXREF
file. Note, for hotelling the activity is by full SCC because the number of hours for extended idle is different than the
number of hours for APU.
305
-------
VMT using the CDB table VoadTypeDistributiorf. The end result of these various calculations and table joins was
the annual total VMT by SCC consistent with the CDB tables. The script also used the CDB table
'monthVMTFraction' to divide annual totals into monthly VMT for January through December in the FF10 table.
Population FF10 file creation
The script's calculation of vehicle population (POP) was simpler than for VMT because the CDB table of
population was much closer to the SCC format - it already contained source type detail and road types are not
relevant for population activity SCCs. The only change needed was to incorporate fuel type detail into the source
type population.
In order to augment the fuel type information into population data, the script first disaggregated the source
type populations from the CDB table 'sourceTypeYear' into model years using the CDB table
*sourceTypeAgeDistribution\ Next, the script split the population into fuel types using the same MOVES national
fuel type fractions of gasoline, diesel, E85, and CNG by model year previously described for the VMT FF10 file
creation. In a third and final step, the script aggregated over model year for each source type and fuel type to
arrive at the SCC level populations. Unlike the VMT FF10, population is only available at the annual level, without
variation by month, because the registered populations are considered to be constant over the year.
Speed FF10 file creation
The script calculates average speed (SPEED) by SCC for each month and an annual average using primarily the
CDB table 'averageSpeedDistribution' which contains fractions of VMT by 16 speed bins for each source type by
hour of weekday and weekend day types. The script first calculated the weighted average speed for each hour
and then aggregated over hours up to the annual and month level using the various CDB tables for VMT
distributions (i.e., 'hourVMTFraction', 'dayVMTFraction', and 'monthVMTFraction').
Hotelling FF10 file creation
"Hotelling" is the time spent by long-haul combination trucks during federally required rest periods during long-
haul trips. The MOVES model assumes that only diesel combination unit trucks are used in long-haul operations
that result in hotelling. EPA calculated the national rate of hotelling to be 0.033807 hours per mile on all
restricted access roads (urban and rural together), and the script applied this rate to combination unit long-haul
VMT in each county to estimate county-level hotelling.
The MOVES model database includes a "HotellingActivityDistribution" table that identifies whether the main
engine or an auxiliary power unit (APU) was used during the hotelling activity. This engine description of the
activity is a function of model year because not all trucks are equipped with APUs. MOVES2014 and the NEI
assume that 100 percent of the hotelling hours from pre-2010 model year trucks use the main engine, but only
70 percent of hotelling hours are main engine beginning with model year 2010 into the future. The other 30
percent are assumed to operate on APUs with the main engine turned off. EPA's FF10 creation script calculated
the main engine hours of extended idle (EXT) and APU hours for each county according to the equations below:
Hotelling Hours = 0.003807 * VMTrestricted
EXT Hours = Hotelling Hours * EXT Fraction
APU Hours = Hotelling Hours *APU Fraction
where:
Hotelling Hours = total extended idle hours (hours)
0.003807 = national rate of hotelling (hours/mile)
306
-------
VMTrestricted = vehicle-miles traveled by diesel combination unit long-haul trucks on both urban and
rural restricted access road types (miles)
EXT Hours = extended idle hours operating the main engine as the power source (hours)
APU Hours = extended idle hours operating an APU as the power source (hours)
EXT Fraction = weighted fraction of main engine hotelling hours, a value ranging from 0.7 to 1
depending on the age distribution (dimensionless)
APU Fraction = weighted fraction of APU hotelling hours, a value ranging from 0 to 0.3 depending on
the age distribution (dimensionless)
A few states provided their own estimates of total hotelling hours based on their own analysis. These states
include: GA, NC, PA, and VA35. EPA used the state provided hotelling hours over EPA's estimates. Due to the
possibility of mismatches at the county level, if a state provided hotelling hours, the state-submitted data was
used for all counties in the state. States did not provide separate extended idle vs APU hours. EPA used the APU
fraction based on EPA estimates for each county to calculate the APU hours in the state submitted data, while
conserving the total number of hotelling hours (EXT + APU).36
Speed Profile file creation
The speed profile (SPDPRO) input to SMOKE is optional and allows the user to provide SMOKE with hour-specific
speeds by SCC and weekday/weekend day types. Similar to the SPD FF10 file creation, EPA's script calculated a
weighted average speed over the 16 speed bins for each hour by day type. For the 2011 NEI v2, the SPDPRO file
contained speed profiles for every county, source type, and road type in the country and it takes precedence
over the SPEED FF10 input.
VMT adjustments based on CRC A-88
As previously described for NEI v2 default data inputs, EPA updated the long-haul fractions of VMT based on
data from CRC project A-88. The CRC A-88 data resulted from an analysis of the FAF dataset for single unit
(source types 52 and 53) and combination unit (source types 61 and 62) trucks and found significant regional
differences in the relative amount of long-haul activity. EPA implemented the long-haul VMT reallocation by
updating the VMT activity files for SMOKE in a processing step prior to running SMOKE. Specifically, EPA's
contractor processed the SMOKE-ready VMT files using a script that summed VMT over the affected source
types (i.e., 52+53, and 61+62) and reapportioned the combined VMT totals to the constituent source types using
a lookup table of relative VMT fractions that varied by region and summed to 1 for single unit (52+53) and
combination unit (61+62) trucks. After calculating new long-haul VMT, EPA re-calculated the hotelling hours in
order to be consistent with the revised long-haul VMT resulting from the CRC A-88 data incorporation.
Population adjustments based on CRC A-88
EPA incorporated the county level CRC A-88 light duty vehicle populations for source types 21, 31, and 32 in
areas using "EPA default" data or where S/L/T agencies elected to use CRC data over their own previously
submitted data. The CRC A-88 population and age distribution data impacts the distribution of light duty vehicle
VMT between the three MOVES source types (21, 31, and 32). Total light duty is conserved and would match the
35 CT and NJ also provided data. After consultation with the states, they accepted EPA's revised estimates for 2011 NEIv2
hotelling based on rural + urban restricted VMT.
36 Additional modifications were made to the state data. For GA, NC, and VA, the states provided annual data. EPA used
the EPA estimates to distribute the annual to monthly data by county. PA did provide monthly data, but the sum of the
months was slightly different than the annual estimates. EPA renormalized their monthly estimates so that it equaled the
annual value. After consulting with the state, NC accepted EPA's revised 2011 NEIv2 estimate for the statewide total
hotelling hours. EPA distributed the statewide total to county using NC's distribution.
307
-------
HPMS vehicle type (25) VMT, but the distribution between the MOVES light duty source types depends on both
the age distribution and population tables.
Default data for SMOKE
The data for SMOKE obtained from state provided CDBs is the source for much of the data used for the 2011
NEI. However, CDBs were not provided for all counties in all states. The necessary information for the SMOKE
FF10 files for these counties was derived from EPA default information used in the MOVES model tables and
other EPA sources. All of the EPA default data was processed in a similar manner to the state supplied data and
added to the state supplied data in the FF10 formatted files for use with SMOKE.
The average speeds, speed profiles, road type distribution and day type, hour and monthly distributions of VMT
default values are all taken from the default MOVES database (movesdb20141021). VMT is obtained from the
2011 NEI Version 1 analysis and vehicle populations were derived from those VMT values. The source and
handling of VMT and populations is described in the documentation for the 2011 NEI Version 1.
The age distributions and populations used for the default case were obtained from the CRC A-88 for source
types 21, 31 and 32. The default MOVES database age distributions were used for all other source types. Similar
to the submitted data, the incorporation of the CRC A-88 age distribution and population impacts the
distribution of VMT between the three light duty source types.
The same VMT adjustments to account for long haul fractions that were applied to state supplied data were also
applied to VMT in the default case. All hotelling hours and extended idle and APU usage fractions were derived
in the same manner as for state supplied data from the VMT estimates.
The SMOKE-ready activity data used for the 2011 NEIv2 are provided in the supplementary materials (see Table
4-26 for access information).
4.5.9.5 Public release of the NEI county databases
Two sets of 2011 CDBs are available for download: (1) the representative county CDBs and (2) all county CDBs.
See Table 4-26 for access details. EPA converted all submitted and default CDBs to MOVES2014 formats using
the database conversion script available in the MOVES GUI, described in the MOVES model user guide37.
Representative CDBs
The representative counties are the counties for which EPA ran the MOVES model to generate emission factor
lookup tables for SMOKE-MOVES. EPA performed special processing on the CDBs for these counties to prepare
them for MOVES modeling. This processing "seeded" the databases to produce emission factors for every SCC
regardless of whether the representative county has all of the categories. The seeding step was necessary
because counties mapping to this representative county may require the emission factor. The seeding script
updated every 0 value to le-15, and also added missing categories to the various tables and set their data values
to le-15.
The following describes how the seeding process might affect a representative CDB. For example, a particular
submitted representative county may have only gasoline school buses (i.e., no diesel ones). A submitted CDB
would reflect this local information through a fraction of value set to 0 for diesel fuel in the alternative vehicle
and fuel technologies (AVFT) table. EPA's seeding script updated this 0 value to a small value of le-15 so that
MOVES could calculate an emission rate for diesel school buses. The small value of le-15 ensures that all
37 MOVES; r Guide
308
-------
distributions still sum to very close to one (1). The fact that this particular county in reality has no diesel school
buses would be incorporated in the NEI, but on the SMOKE side of processing. The SCC for diesel school buses
for the county would have zero activity because EPA created the FF10 SMOKE activity files for each county based
on the unseeded CDBs provided by S/L agencies or EPA default.
All county CDBs
The full set of CDBs includes both S/L-submitted CDBs as well as EPA default data CDBs. The submitted CDBs
include minor changes in some counties resulting from the QA process described previously. All CDBs, submitted
or default, were converted to MOVES2014 format, which altered the format of same tables and added some
new ones including the 'hotellinghours' CDB table. EPA inserted the final FF10 hotelling activity into the new
'hotellinghours' table from the FF10 format so that users would have access to the same activity used in the NEI
already incorporated into a MOVES input database format. EPA also inserted the default population and VMT
into the EPA default CDBs, so these are consistent with the FF10 files used in the NEI with one exception - the
long-haul vs. short-haul VMT. The long-haul fractions could not straightforwardly be put into a MOVES CDB.
However, the long-haul VMT allocations are available from the CRC project A-88 report or alternatively could be
derived from the VMT FF10 files provided with the NEI modeling platform.
4.5.9.6 Run MOVES to create emission factors
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).
The runspec generator created a series of runspecs (MOVES jobs) based on the outputs from Met4moves.
Specifically, the script used a 5-degree bin and 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 (EF) 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 Moves2smk converted the MySQL tables into EF files that can be read by
SMOKE. For more details, see the SMOKE documentation.
4.5.9.7 Run SMOKE to create emissions
Lastly, EPA generated air quality model ready emissions at a gridded and hourly resolution. The Movesmrg
SMOKE-MOVES program performs this function by combining activity data, meteorological data, and emission
factors to produce gridded, hourly emissions. EPA ran Movesmrg for each of the four sets of emission factor
tables (RPD, RPV, RPH, and RPP). During the Movesmrg run, the program used the hourly, gridded temperature
(for RPD, RPV, and RPH) or daily, gridded temperature profile (for RPP) to select the proper emissions rates and
compute emissions. These calculations were done for all counties and SCCs in the SMOKE inputs, covering the
continental U.S.
The emissions process RPD is for modeling the driving emissions. This includes the following modes: vehicle
exhaust, evaporation, evaporative permeation, refueling, brake wear, and tire wear. For RPD, the activity data is
monthly VMT, monthly speed (SPEED), and hourly speed profiles for weekday versus weekend (SPDPRO)38. The
SMOKE program Temporal takes vehicle and roadtype specific temporal profiles 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
38 If the SPDPRO file is available, the hourly speed takes precedence over the average monthly speed. For the NEI, the
SPDPRO covered all county and SCC combinations.
309
-------
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 emission process RPV is for modeling 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 process RPH is for modeling the parked emissions for combination long-haul trucks (source type
62) that are hotelling39. This includes the following modes: extended idle and auxiliary power units (APU). For
RPH, the activity data is monthly HOTELLING hours. The SMOKE program Temporal takes a temporal profile and
distributes the monthly HOTELLING 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 (EFs) from the
representative county's EF table. It then multiplies this EF by temporalized and gridded HOTELLING 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 process RPP is for modeling 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, gridded data suitable for use in air quality modeling 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., rather than just for the representative counties.
4 5.9.8 Post-processing to create annual inventory
For the purposes of the NEI, EPA needed emissions data by county, SCC, pollutant40. 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 on-road emissions for Alaska, Hawaii, Puerto Rico and the Virgin Islands, which EPA generated via MOVES in
inventory mode (see Section 4.6.4) were appended to the on-road inventory generated from SMOKE-MOVES to
create the final emissions. This complete inventory was submitted to the EIS as the EPA estimates for the on-
road sector. The resulting EIS dataset is named "2011_EPA_MOBILE"41.
39 The hotelling emissions is differentiated from simple idling emissions. These are the emissions for trucks that are parked
for an extended period of time while the driver rests.
40 EPA ran SMOKE-MOVES at a more detailed level including road type and emission processes (e.g. extended idle) and
summed over the road types and processes to create the more aggregate NEI SCCs.
41 The corresponding EMF datasets are 2011eg_NEIv2_onroad_SMOKE-MOVES_MOVES2014_forNEI (v4) and
2011_NEIv2_onroad_AK_HI_PR_VI_MOVES2014_forNEI (v2).
310
-------
4.5.10 On-road mobile emissions data for Alaska, Hawaii, Puerto Rico and the Virgin Islands
Since the meteorology domain used by EPA for running SMOKE-MOVES covers only the continental U.S., 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 from calendar year 2011. These emissions characterized all
pollutants including a full set of metals and dioxins.
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 scale mode of the MOVES2014 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 January and July only 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 county database manager 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 (Table 4-26 for access information).
4.5.11 Summary of quality assurance methods
EPA did a series of checks and comparisons against both the inputs and the resulting emissions to quality assure
the on-road inventory. These checks are in addition to the ones described on the underlying CDBs (see Section
4.6.2). The following is a list of the more significant checks and resulting corrections:
• Checked the VMT data by comparing the 2011v2 with 2011vl based activity data. Compared the VMT at
various resolutions including: state, county, vehicle type (comparison SCC6), and road type. Also
analyzed the ratio of VMT to vehicle population to look for extreme values.
• Checked the VMT data by comparing the 2011 NEIv2 (state supplied) with 2011 NEIv2 (default) for those
states that submitted activity data. Compared the VMT at various resolutions including: state, county,
and road type.
• Checked the VPOP data by comparing the 2011v2 with 2011vl based activity data. Compared the VPOP
at various resolutions including: state, county, and vehicle type (comparison SCC6).
• Checked the consistency of VMT with vehicle population to ensure that all counties with VMT for a
vehicle type also had VPOP for that vehicle type.
• Compared the on-road emission results to similar results for 2011 NEIvl. As expected, found numerous
differences between the two sets of results. Detailed comparisons by state, county and vehicle type
(comparison SCC6) showed that most of the differences were due to updated input data from the states,
updated age distributions for the EPA defaults, updated hotelling data, or due to differences between
how the two models were run in terms of representative counties. Additionally, compared the results
using difference maps both at the county level and gridded (after spatially allocating the emissions to
grid cells using SMOKE).
• Identified that E-85 emission factors were missing from a significant number of representative counties.
Decided to reclassify E-85 vehicles to gas vehicles for the purposes of the NEIv2. Added the E-85 VMT to
gas VMT by county/source type (repeated for VPOP) and re-estimated the emissions for the new set of
gas activity.
311
-------
• Some Idaho representative counties were missing RPD emission factors for CNG, and so emission
factors for CNG needed to be appended from other counties' emission factor tables. For the initial run,
an error was made when adding these CNG emission factors. This was corrected, and then the affected
counties were rerun through SMOKE.
• In the initial run, NH3 emissions were dropped from RPH due to an error in the SMOKE pollutant list
(MEPROC). This was corrected in a SMOKE rerun.
• Diesel refueling included both evaporative headspace and spillage emissions. It should only have
included spillage.
• Diesel refueling emission factors for benzene were all zero. To correct this, benzene for diesel refueling
was calculated using a constant emission factor multiplied by VOC (benzene = VOC * 0.00410).
• The l/M programs were incorrectly characterized in the following counties: CO (for county FIPS 8001,
8005, 8013, 8014, 8031, 8035, 8041, 8059, 8069, 8097, and 8123), LA (for county FIPS 22005, 22047, and
22063), and PA (for county FIP 42073). This was identified too late to be corrected in v2.
• Worked with the states as part of the MOVES working group to evaluate these national runs in
comparison to individual states' runs using MOVES in inventory mode.
4.5.12 Supporting data
Onroad 2011 v2 emissions came from EPA estimates exclusively, except in CA. Emissions and/or county
database submittal history and notes are provided in Table 4-25. Onroad reference data files are listed in Table
4-26.
Table 4-24: Agency submittal history for onroad inputs and emissions
Onroad
Emissions
Onroad CDB
Submission
Submission
Agency Organization
Date
Date
Notes
Alaska Department of
12/11/2012
12/18/2012
Environmental
Conservation
Alabama Department of
N/A
N/A
AL supplied county level VMT directly to EPA
Environmental
staff.
Management
California Air Resources
4/16/2013
N/A
CA uses a CA-specific model (EMFAC). CA
Board
emissions are included in NEI.
Clark County Department
N/A
6/3/2014
of Air Quality and
Environmental
Management
Coeur d'Alene Tribe
11/28/2012
N/A
EPA does not currently break out tribal areas in
EPA estimates; however, tribal emissions
submittals are included in the NEI.
Colorado Department of
N/A
?
CO supplied updated IM coverage data directly
Public Health and
to EPA staff.
Environment
Connecticut Department
N/A
5/10/2013
In addition to submitting CDBs, CT supplied
Of Environmental
updated CDBs directly to EPA staff.
Protection
312
-------
Onroad
Emissions
Onroad CDB
Submission
Submission
Agency Organization
Date
Date
Notes
DC-District Department of
N/A
1/8/2013
the Environment
Delaware Department of
N/A
1/7/2013
Natural Resources and
Environmental Control
Eastern Band of Cherokee
10/23/2012
N/A
EPA does not currently break out tribal areas in
Indians
EPA estimates; however, tribal emissions
submittals are included in the NEI.
Florida Department of
N/A
N/A
FL requested directly from EPA staff to replace
Environmental Protection
their default data for l/M coverage and Stage II
refueling to effectively turn off all programs.
Georgia Department of
N/A
In addition to submitting CDBs for v2, GA
Natural Resources
6/10/2014
provided age distributions and populations for
source types 21, 31, and 32 for each county in
GA directly to EPA staff.
Idaho Department of
12/18/2012
12/5/2012
ID submitted both input and emissions. ID
Environmental Quality
emissions included only a subset of HAPs and
had SCC-emtype combinations that do not occur
in EPA estimates. ID CDB was used in NEI
estimates instead of emission submittal.
Illinois Environmental
N/A
2/19/2013
Protection Agency
Knox County Department
N/A
1/7/2013
of Air Quality
Management
Kootenai Tribe of Idaho
12/14/2012
N/A
EPA does not currently break out tribal areas in
EPA estimates; however, tribal emissions
submittals are included in the NEI.
Louisville Metro Air
N/A
2/19/2013
Pollution Control District
Maine Department of
N/A
11/19/2012
Environmental Protection
Maricopa County Air
N/A
12/18/2012
Quality Department
Maryland Department of
N/A
12/24/2012
the Environment
Massachusetts
N/A
6/5/2013
CDB was submitted late after deadline to the NEI
Department of
but was available to EPA prior to submittal and
Environmental Protection
used on EPA NEI estimates.
Metro Public Health of
12/18/2012
N/A
EPA assisted Metro in creating CDB from their
Nashville/Davidson
inputs to EPA estimation. Submitted emissions
County
were not used in NEI
Michigan Department of
N/A
1/8/2013
Environmental Quality
313
-------
Onroad
Emissions
Onroad CDB
Submission
Submission
Agency Organization
Date
Date
Notes
Minnesota Pollution
N/A
12/13/2012;
In addition to submitting CDBs, MN supplied
Control Agency
5/20/2013
updated age bin distribution data directly to EPA
staff, and VMT for Kanabec county and VPOP for
Otter Tail County.
Missouri Department of
N/A
12/21/2012
Natural Resources
New Hampshire
N/A
3/26/2014
Department of
Environmental Services
New Jersey Department of
N/A
6/20/2014
Environment Protection
New York State
N/A
4/9/2014
Department of
Environmental
Conservation
Nez Perce Tribe
11/29/2012
N/A
EPA does not currently break out tribal areas in
EPA estimates; however, tribal emissions
submittals are included in the NEI.
North Carolina
N/A
3/25/2014
Department of
Environment and Natural
Resources
Northern Cheyenne Tribe
1/28/2013
N/A
EPA does not currently break out tribal areas in
EPA estimates; however, tribal emissions
submittals are included in the NEI.
Ohio Environmental
N/A
5/16/2013
Protection Agency
Oregon Department of
1/7/2013
8/12/2014
Environmental Quality
Pennsylvania Department
N/A
12/31/2012
of Environmental
Protection
Rhode Island Department
N/A
1/10/2013
of Environmental
Management
Shoshone-Bannock Tribes
11/27/2012
N/A
EPA does not currently break out tribal areas in
of the Fort Hall
EPA estimates; however, tribal emissions
Reservation of Idaho
submittals are included in the NEI.
South Carolina
N/A
12/13/2012
Department of Health and
Environmental Control
Texas Commission on
12/21/2012
N/A
Texas' vl submissions used MOVES2010b. For
Environmental Quality
v2, EPA estimated the emissions using
MOVES2014 to provide consistency with the
other states.
314
-------
Onroad
Emissions
Onroad CDB
Submission
Submission
Agency Organization
Date
Date
Notes
Utah Division of Air
N/A
4/14/2014
Quality
Vermont Department of
N/A
12/14/2012
Environmental
Conservation
Virginia Department of
N/A
4/18/2014
In addition to submitting CDBs, VA supplied
Environmental Quality
activity data (by SMOKE SCCs) for all counties
directly to EPA staff.
Washington State
N/A
12/19/2012
Department of Ecology
Washoe County Health
12/26/2012
1/8/2013
District
West Virginia Division of
N/A
1/4/2013
Air Quality
Wisconsin Department of
N/A
4/1/2014
Natural Resources
315
-------
Table 4-25: Onroad data file references for 2011 v2 NEI
NEI 2011 v2 Supporting Data File Name
Description of Contents
2011neiv2_supdata_or_RepCounty_Runspecs.zip
The MOVES2014 run specifications (runspecs) for the
representative counties. This is for running MOVES in
emissions rate mode (for SMOKE-MOVES).
2011neiv2_supdata_or_FuelCR.zip
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.
2011neiv2_supdata_or_RegFuel.zip
Regional fuels contain the fuel properties used for
each county in each month and replace all fuel
descriptions contained in the individual county
databases. These fuel properties were developed by
EPA.
2011neiv2_supdata_or_RepCounty_temperatures.zip
The temperature and relative humidity bins for
running MOVES to create the full range of emissions
factors necessary to run SMOKE-MOVES. Generated
from running met4moves
2011neiv2_supdata_or_AKHIPRVI_Runspecs.zip
The MOVES2014 run specifications (runspecs) for all
counties in Alaska, Hawaii, Puerto Rico and the Virgin
Islands. This is for running MOVES in inventory mode.
2011neiv2_supdata_or_CountyCR.zip
County cross reference file (MCXREF) is a table that
shows every US county along with the representing
county used as its surrogate. The MCXREF is an input
to SMOKE.
MOVES_CDBs_by_State Directory
MOVES2014 county database. Includes all agency
submittals thru the EIS, and all subsequent revisions
and EPA replacements (e.g., fuel and age
distributions)
2011neiv2_supdata_or_CDB_RepCnty.zip
MOVES county databases (CDBs) for the
representative counties. These CDBs include all
agency submittals through the EIS in addition to
subsequent revisions and EPA replacements to
prepare counties to run in rates mode.
2011neiv2_supdata_or_VPOP.zip
Vehicle population (VPOP) by county and SCC
covering every county in the US. Data is in FF10
format for SMOKE and is a combination of EPA
estimates, agency submittals, and corrections.
2011neiv2_supdata_or_VMT.zip
Vehicle miles traveled (VMT) annual and monthly by
county and SCC covering every county in the US. Data
is in FF10 format for SMOKE and is a combination of
EPA estimates, agency submittals, and corrections.
2011neiv2_supdata_or_Speed.zip
Average speed in miles per hour, annual and monthly
values, by county and SCC covering every county in
the US. Data is in FF10 format for SMOKE and is a
combination of EPA estimates, agency submittals,
and corrections.
316
-------
NEI 2011 v2 Supporting Data File Name
Description of Contents
2011neiv2_supdata_or_SpdProf.zip
Weekend and weekday hourly speed profiles
(SPDPRO) in miles per hour, by county and SCC
covering every county in the US. Data is for SMOKE
and is a combination of EPA estimates, agency
submittals, and corrections.
2011neiv2_supdata_or_Hotelling.zip
Hotelling hours (HOTELLING) annual and monthly by
county covering every county in the US. This includes
hours of extended idle and hours of auxiliary power
units for combination long-haul trucks only. Data is in
FF10 format for SMOKE and is a combination of EPA
estimates, agency submittals, and corrections.
2011neiv2_supdata_or_CALEV.zip
California LEV data contain the alternate base
emission rates that reflect the adoption of California
emission standards and replaces the emission rates
based on federal standards. A separate file exists for
each state which has adopted California standards to
reflect the different years of adoption.
2011neiv2_supdata_or_MySQL.zip
MySQL scripts contain the commands that translate
MOVES formatted inputs from the state supplied
county databases to SMOKE input format. These
include the vehicle populations, VMT, average
speeds and allocation of heavy-duty truck extended
idling. The translation includes a mapping of MOVES
vehicle and road type classifications to the SCC
classifications.
2011neiv2_supdata_or_SCC.zip
A set of tables that describe the new MOVES2014
based SCCs. Additional tables show the cross
reference between MOBILE6 SCCs and a set of
comparison SCCs as well as a cross reference
between MOVES2014 SCCs and a set of comparison
SCCs.
4.5.13 References for On-road Mobile
1. Skamarock, W.C., et al.. National Center for Atmospheric Research, Mesoscale and Microscale
Meteorology Division, Boulder CO, June 2008, NCAR/TN-475+STR, A Description of the Advanced
Research WRF Version 3.
317
-------
5 Fires
Fire sources in this section are sources of pollution caused by the inadvertent or intentional burning of biomass
including forest, rangeland (e.g., grasses and shrubs), and agricultural vegetative residue. This section describes
the 2011 NEI wildfires (Section 5.1), prescribed burning (also Section 5.1), and agricultural burning (Section 5.2).
Other types of fires are included in other EIS sectors, such as "Fuel Combustion - Residential - Wood" (Section
3.13.4), the "Waste Disposal" (Section 3.32) sector, which includes fires from burning yard waste, land clearing,
residential household waste, logging debris, and commercial, institutional, industrial, and "open dump" burning
of biomass and other refuse; and "Miscellaneous Non-Industrial NEC" sector (Section 3.25), which includes
structure fires, firefighting as part of waste disposal, firefighting training fires, motor vehicle fires, and other
open fires.
Collectively, the fires data included in this section have come to be known by the fire emissions community as
the National Fire Emissions Inventory (NFEI). This inventory is not a separate product, but rather the highest-
emitting fires component of the NEI.
5.1 Wildfires and Prescribed Burning
This section describes the 2011 NEI approach for wildfires, prescribed burning, and wild land fire use, collectively
called "wild land" fires (WLFs). Precise definitions of these types of fires are provided below in Section 5.1.1.
These are included in the same section because the approach used is exactly the same.
Unlike in the 2008 NEI, when the EIS database contained wildfires and prescribed fires as both event-based
(point source, day-specific) data and nonpoint data, the 2011 contains all of these data in day-specific events-
based format. The 2011 NEI website (see Section 1.3.2) provides separately wildfire and prescribed fire data at
the county-SCC resolution, it can also be obtained in the EIS through a summary of the "2011 NEI vl with
biogenics" EIS selection for the EVENT data category. A day-specific events summary is also available in the EIS;
however, it should only be run for a small geographic area such as one or two counties due to the size of the
data.
5.1.1 Sector description
WLFs are generally defined as any non-structural fire that occurs in wild lands. Included in WLFs are the
following types of fires:
• Prescribed (Rx) fire: Any fire ignited by management actions to meet specific objectives, generally
related to the reduction of the biomass potentially available for wildfires.
• Wildfire (WF): An unplanned, unwanted WLF including unauthorized human-caused fires, escaped
prescribed fire projects, or other inadvertent fire situation where objective is to put the fire out.
• Wildland Fire Use (WFU): The application of appropriate management response to naturally-ignited
WLFs to accomplish specific resource management objective in pre-designated areas outlined in fire
management plans. In other words, an unplanned fire that is subsequently controlled and used as a Rx
fire to meet specific objectives. This category existed in 2008, but no longer is used as a way to classify
fires in 2011, and thus will not be discussed further in this section.
For 2011, EPA continues to use the SMARTFIRE2 (SF2) system (which includes the BlueSky modeling framework)
to estimate wild land fire emission estimates. Significant improvements were made from 2005 to 2008 to SF2 as
318
-------
documented in the 2008 NEI TSD. From 2008 to 2011, smaller improvements and refinements were made to the
SF2 system as outlined in Reid [ref 1], In 2011, the most significant improvement made was in collecting local
activity data (acres burned, types of fuels, fuel consumption values, etc.) to make emission estimates for both
wild and prescribed fires more accurate in the 2011 NEI. This is documented further in section 5.1.4. Also, in
2011, EPA estimates included the states of AK and HI, unlike in previous NEI cycles.
Table 5-1 lists the SCCs that define the different types of WLFs in the 2011 NEI, both for EPA data and for S/L/T
agency data. Note that EPA data have only one unique SCC for each of these types of fires. Data submitted by
S/L/T agencies can have several different SCCs that define prescribed fires. As described below, EPA's approach
to combine EPA data with S/L/T agency data for the 2011 NEI considers all SCCs that define any one type of fire
and appropriately combines emissions from those SCCs.
Table 5-1: Source classification codes for wildland fires
Data Origin
Wildfires
Prescribed Burns
EPA
2810001000
2811015000
States/Locals/T ribes
2810001000
2810001000 ("wildland fire use")
2811015000 ("forested")
2811020000 ("rangeland")
5.1.2 Sources of data overview and selection hierarchy
The wildfire and Rx fire EIS sectors include data only from two components: S/L/T agency-provided emissions
data (day specific data in Events format) for Georgia and North Carolina, and the EPA dataset created from SFv2
(see Section 5.1.4) which used available state inputs. Only the combination (rather than the individual datasets)
of these data are available as summary information on the 2011 NEI website and in the EIS.
S/L/T agency data were received in event format from two agencies (GA and NC) as listed in Table 5-2.
Table 5-2: Agency that submitted wildfire and prescribed burning emissions d;
Agency
Agency Type
Rx provided
Wildfire provided
Georgia
State
as event
as event
North Carolina
State
as event
as event
ata
In 2011, no tribes submitted wild land fire emissions data, and 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. Table 5-3 shows the
selection hierarchy for the wildfire and Rx burning sectors. There were no overlapping data in the above
datasets. Georgia and North Carolina were excluded from the 2011EPA_Event dataset and the State/Local/Tribal
Data contained only Georgia and North Carolina.
319
-------
Table 5-3: 2011 NEI wildfire and prescribed fires selection hierarchy
Priority
Dataset Name
Dataset Content
Is Dataset in
EIS?
1
State/Local/Tribal Data
Submitted data as listed above.
Yes
2
2011EPA_Event
Emissions from SF2
Yes
5.1.3 Spatial coverage and data sources for the sector
The 2011 NEI includes wildfire and Rx fire emissions for all continental US states, Alaska, and Hawaii. These
emissions represent a combination of state-submitted information and EPA-estimated emissions from these
fires. The EPA methods are described in Section 5.1.4 below. The simple way we blend these emissions is
summarized in Table 5-3 above. As discussed above, only GA and NC reported wildfire and prescribed fire
emissions to the NEI in 2011. GA and NC data were used as submitted, and no backfilling was done with EPA
data for any counties that were missing or null.
5.1.4 EPA-developed fire emissions estimates
For the dataset developed by EPA for the 2011 NEI, we used the following general equation to estimate wildfires
and prescribed fires. Accurate estimates of fire emissions rely on accurate estimates of the terms in the
equation below.
Emissions = Area burned * Fuel Load Available * Fuel Consumed (Burn Efficiency) * Emission Factors
Daily CAP emission estimates were prepared using the software SF2 [ref 2], which include fire estimation
algorithms and is built within a database. Additional information on the approaches specific to the NEI are
available in Raff use [ref 3], SF2 estimates the "Area burned" term in the above equation, in conjunction with the
BlueSky framework model that estimates the last three terms in the above equation. The "fuel load available"
term is estimated using the Fuel Characteristic Classification System (FCCS) maps in the BlueSky model. The "fuel
consumed" term is estimated from BlueSky using the CONSUME3 model, which predicts the fraction of fuel that
burns based on many parameters including fuel moisture. Finally, the "Emission Factors" term is estimated in
BlueSky using the Fire Emissions Prediction Simulator which relies on EFs from the literature apportioned by
flaming and smoldering combustion. Since SF2 was recently developed, direct references to its development in
conjunction with updated BlueSky methods are not yet available; however, the following reference can be used
in general for past applications of these process models in the SF/BlueSkv process. Reid [ref 1] provides more
exacting details on the specific procedures used in developing the 2011 prescribed and wildfires.
The EPA data include emissions estimates for 38 pollutants. These pollutants are listed in Table 5-4 below. CAPs
were estimated via SF2 as just described. In addition, a set of 29 HAPs are estimated by applying the activity
levels estimated from the methods above with the emission factors shown in the table [ref 4], These same 29
HAPs have been estimated for fires over the past 10 years or so for the NEI by EPA. In 2011, only GA and NC
submitted their own emissions data. Both agencies used the same FEPS system as EPA did to estimate all the
CAP emissions. EPA sent to GA and NC the HAP EFs to use, so that the set of HAPs reported from WLFs is
consistent throughout the US. Thus, there was no need to do any further HAP augmentation as had been done
with previous NEIs. GA nor NC submitted C02 nor CH4 (GHGs) so these pollutants are not available for these two
320
-------
states but in general are avai
emissions.
lable for all the other states for which we used EPAs methods to estimate WLF
Table 5-4: Pol
utants estimated by EPA* for wildland fires and HAP emission factors
Pollutant
HAP Emission factor
(lb/ton fuel consumed)
PM2.s
PM10
CO
n
O
N)
ch4
N/A
NOx
nh3
so2
voc
1,3-butadiene
0.405
Acrolein
0.424
Toluene
0.56825
n-hexane
0.0164025
Anthracene
0.005
Pyrene
0.00929
o,m,p-xylene
0.242
benzo(ghi)perlyene
0.00508
benzo(e)pyrene
0.00266
indeno(l,2,3-cd)pyrene
0.00341
Benzo(c)phenanthrene
0.0039
Perylene
0.000856
benzo(a)fluoranthene
0.0026
Fluoranthene
0.00673
benzo(k)fluoranthene
0.0026
Chrysene
0.0062
methylpyrene,-fluoranthene
0.00905
Methylbenzopyrenes
0.00296
Methylchrysene
0.0079
Methylanthracene
0.00823
Carbonyl Sulfide
0.000534
Formaldehyde
2.575
benzo(a)pyrene
0.00148
benz(a)anthracene
0.0062
Benzofluoranthenes
0.00514
Benzene
1.125
Methylchloride
0.128325
Acetaldehyde
0.40825
Phenanthrene
0.005
* Other than CO2 and CH4, these pollutants were also
submitted by GA, the only state that submitted its own data
for wildfires and prescribed burning
321
-------
One of the big improvements made in the 2011 process was the collection and use of WLF activity data
submitted by State and Local Agencies. Through funding supplied by the USDA Forest Service (USFS), states were
invited to submit fire occurrence data in any format for use in developing the 2011 NEI for WLFs. The spatial and
temporal qualities of each data set were assessed to determine the usability of the data. A written assessment
of each data set was sent to each submitting state, regardless of whether the data set was ultimately included in
the NFEI. Suitable data sets were processed through the SF2 fire information system along with other traditional
or new fire data sets at national or regional scales42 to reconcile the various fire information data sets. .
EPA assessed a total of 50 data sets from 20 individual states and one regional data set from the Fire Emissions
Tracking System (FETS). The FETS data set was provided by Air Sciences Inc.. and contains data for 10 of the
states that make up the Western Regional Air Partnership (WRAP). Overall, additional fire activity data from 24
states were used in the development of the final NFEI. Figure 5-1 shows the states that submitted fire activity
data and identifies states that provided usable data and states covered by the FETS data set.
Figure 5-1: The coverage of state-submitted fire activity data sets
WA'
'///,
;mtZ
jME
ND
MN
or:
SD
,vyY/
NE
OH
;nv.
UT
iWV,
.CO-
CA
VA
MO
KY
NC
TN
OK
;az'
AR
;nm
sc
AL \ GA
MS
TX
FL
| | State-submitted data used
Data submitted; not usable
Covered by national data
Y//A States with FETS data
;ak:
In addition to submitting fire activity data, the following states provided comments on the on the draft version
of the NFEI, all of these comments were addressed in the final version of the 2011 NFEI. Details of how these
comments were addressed can be found in Reid [ref 1],
• The Lake States (Michigan, Minnesota, and Wisconsin) recommended using the boreal equation in
Consume instead of the western equation for all fires in these states.
• Minnesota recommended the use of local values for duff depth for the state's two largest wildfires (7
inches for the Pagami Creek fire and 5 inches for the Juneberry 3 fire).
• The Hawaii Department of Health Clean Air Branch (HIDOHCAB) determined that certain prescribed
burns in the draft NFEI are Hawaiian Commercial and Sugar (HC&S) agricultural burns. They
42 Additional details on these other data sets are provided in the "Other Data Sources" section that follows.
322
-------
recommended removing these prescribed burns from the final 2011 NFEI since HI submitted these
emissions as part of their nonpoint agricultural fires.
• Colorado found a discrepancy in fire size for a prescribed burn between draft NFEI and their data, and
recommended use of the latter.
Other Supporting Data Sources
In addition to the data provided by state, local, and tribal agencies, fire information from the following data
sources was also used to develop the final 2011 NFEI:
• Inputs to SmartFire2
Hazard Mapping System (HMS) data were acquired daily from the National Oceanic and
Atmospheric Administration's (NOAA) HMS via FTP as part of a routine process. Data were acquired
in ASCII text format from. Before input to SF2, the HMS detects in the conterminous United States
were intersected with the U.S. Geological Survey (USGS) 2006 30-m National Land Cover Dataset
(NLCD), while those in Alaska, Hawaii, and Puerto Rico were intersected with the 2001 30-m NLCD.
The NLCD classifies all land area in the United States into one of 19 land cover types, as outlined in
Huang [ref 5], The HMS detects that fell within land cover types 81 (Pasture/Hay) or 82 (Cultivated
Crops) were treated as agricultural burns and removed from the final HMS data input to SF2. In
addition, ST I was advised by the USFS that Texas implemented a no-burn requirement in 2011 as a
result of hazardous drought conditions. Based on this information, HMS detects that fell in the state
of Texas were all assigned as wildfires.
ICS-209 Reports were acquired as a Microsoft® Access® database via the Fire and Aviation
Management Web Applications website.
U.S. Fish and Wildlife Service (FWS) fire information data were provided by the U.S. FWS.
National Association of State Foresters (NASF) fire information data were downloaded from the
National Fire and Aviation Management Web Applications.
Forest Service Activity Tracking System (FACTS) fire information data were supplied by the USFS.
GeoMAC fire perimeter data were downloaded via the USGS Geo MAC wildland fire support website.
Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data were downloaded via the
USFS Remote Sensing Applications Center website. Data were converted from a shapefile to an ASCII
text file and used to fill in blank dates from HMS.
• Fuel moistures - Fire weather observation files (fdr_obs.dat) were acquired for each analysis day from
the USFS archive. Files were acquired and used as inputs to the Fuel_Moisture_WIMS module
implemented in the latest BlueSky Framework build [ref 6],
• Fuel loading - Fuel Characteristic Classification System fFCCS) 1-km fuels shapefile and lookup table for
the conterminous United States were provided by the AirFire Team. The Alaskan FCCS 1-kilometer fuels
shapefile and lookup table were acquired from the Fire and Environmental Research Applications
Team's website.
For all other details on how the data process streams were coalesced, the emissions processing that was done,
and the QA/QC used to develop final emission estimates, the reader is referred to Reid et al. [ref 1],
323
-------
Adjustments made to and comments on final EPA Data
After EPA developed the final SF2 estimates, Florida staff requested that we rescale their emissions so that we
exactly match the total acres burned for prescribed and wildfires as they reported in the data they sent to EPA
for processing through SF2. Table 5-5 lists the acres burned the SF2 process arrived at for FL (which took into
account the activity data FL sent as well as some ancillary data) and the amount of acres burned FL reported as
activity data (FL did not want us to supplement that data in any way and wanted us to match it exactly for wild
and prescribed fires). EPA scaled the information by computing the acres burned difference between what EPA
estimated using SF2 and what the FL activity data indicated it should be. EPA apportioned the difference on a
fire by fire basis, separately for prescribed and wildfires. Then, fire-by-fire, the resulting percentage difference in
acres burned was applied to each fire to arrive at the correct total. More specifics are given below on the
algorithm used, separately for prescribed and wildfires.
Table 5-5: SF2 and State-submitted acres burned for FL WLFs
2011 Final SF2
estimates (EPA)
2011 Activity acreage from
Florida Database
Prescribed Fires
897,833
1,314,868 (Silvicuture, authorized)
Wildfires
398,357
221,756
TOTAL
1,296,191
1,536,624
For Prescribed fires (an increase in total acres as requested by Florida staff):
• Add 65.182 acres to each fire (then fire-by-fire increase emissions by the amount that adding 65.182
acres increases acres by on a percentage basis)
For Wildfires, we applied the following factors as a function of area burned:
• For fires 2000 acres or bigger, adjust each fire's acres burned by the factor [(old acres * 0.4)-495.6],
Then, fire by fire adjust emissions accordingly down.
• For fires 1000 acres or bigger, adjust each fire's acres burned by [(old acres)*0.4)]. Then, use the same
adjustment to revise emissions.
• For fires 500 acres or bigger, adjust each fire's acres burned by [(old acres) * 0.6)]. Then, use the same
adjustment to revise emissions.
• For fires 100 acres or bigger, adjust each fire's acres burned by {(Old acres)*0.75)]. Then, use the same
adjustment to revise emissions.
• For fires 10 acres or bigger, adjust each fire's acres burned by [(old acres) * 0.9)]. Then, use the same
adjustment to revise emissions.
• For all other remaining fires (many), adjust each fires acres burned by [(old acres) * 0.5)]. Adjust
emissions accordingly fire by fire.
In sum, the adjustments to the Florida data caused acres burned to go up by about 19% and total emissions by
about 12% (due to varying Rx and WF changes). We confirmed with Florida staff that they were satisfied with
this scaling algorithm.
We have caveated the 2011 NEI data for Maryland. Well after the final estimates were developed and released
to the public, Maryland staff commented that EPA's estimate of acres burned for prescribed fires in 2011 is too
324
-------
high. They are satisfied with EPA's estimates for wildfires. EPA estimates that in Maryland there was about
10,925 acres burned for prescribed fires; whereas, Maryland staff have data that show this should be closer to
700 acres. Because this information came to EPA late in the process, we could not include these Maryland-
specific activity data into the final SF2 model runs. Instead we are reporting in the documentation that Maryland
believes that acres burned in 2011 for prescribed fires should be reduced by 90% from what EPA estimates. It is
expected that the emissions associated with prescribed fires using Maryland-reported acres burned in the fire
emissions models, would also decrease by a significant amount. We could use a scaling approach (as done for
Florida above) to estimate the decreased emissions; however, for 2011 v2, EPA was unable to make this revision
prior to releasing the data.
Washington state staff accepted all of our wild and prescribed fire data to help maintain consistency nationally.
However, they provided comments which indicated they are not in total agreement with how the county
distribution of acres burned compares with their own data. They indicated that they expected a closer match
since at the county level since FETS data for WA were used in EPA's processing. Note that statewide total acres
burned match well between their estimates and EPS's estimates. In future inventories, Washington staff have
indicated they will set aside extra time to understand why the differences in county allocation of acres burned.
Kansas state staff provided a comment that all of their prescribed fires identified using EPA methodology should
correctly be stored in the EIS/NEI using SCC 2811020000 (which is "prescribed rangeland burning"). We currently
store all EPA-estimated prescribed fires under SCC 2811015000 (which is "prescribe forest burning"). We have
indicated that we will fix this in future versions of the NEI (2014).
After vl was complete, DE commented that we have misidentified one of the large fires as wildfire when in fact
it should be a prescribed fire (with much lower emissions). The name of the fire in question in the SF2 dataset is
"Phragmites fire" and per DE's comment that fire has been moved from wildfire to prescribed fire in v2, and
since we did not rerun SF2 to compute emissions, we used emissions generated by DE for this fire (as a
prescribed fire), which result in much lower emissions for DE in general for wild land fires in the 2011 v2. In the
example of PM2 s emissions, when this fire was moved over to a prescribed fire in v2, the emission went from
502 tons as wildfire to 19.9 tons as a prescribed fire (and all other pollutants were decreased by a similar
percentage amount in v2 for this fire). In addition, in accordance with DE comments, we removed all the 100-
acre fires that were identified by EPA methods in Sussex county, since the comments indicated these fires did
not occur at all (false detects by satellites due to small size of fires). These changes result in PM2 s emissions for
prescribed fires going up from 92 to 96 tons for DE and Wild fire PM2 s emissions going down from 502 to 8.5
tons for DE in going from vl to v2.
Using the SF2 approach, EPA's 2011 emissions data are shown in several summary maps below. In each of these
maps, all of the data reflect output from SF2 other than for Georgia and North Carolina, which submitted their
own data. These data also reflect the changes made to the Florida and Delaware data as detailed above. These
data thus reflect what is in the NEI for wild and prescribed fires.
First, Figure 5-2 shows the proportion of acres burned for each type of fire by state. In the West, there are more
wildfires than in the East (with AK showing almost entirely wild fire activity), where most of the burning is seen
to be from prescribed burning. Kansas and Oklahoma also show a high level of acres burned for prescribed fires.
Texas, Oklahoma, Georgia, and Kansas have among the highest total acres burned (width of circles). In the 2011
NEI, there are an estimated 24.6 million acres burned from prescribed and wildfires. Of these 24.6 million acres,
about half is estimated to be prescribed fires and half wild fires. .
325
-------
Figure 5-2: Proportion of acres burned by type of fire
o
^ RX
A WF
345.33 2080722.23 4161099.13
In the 2011 NEI, there is an estimated total of 6.1 million tons of PM2.5 emissions. Of this total, 1.13 million is
estimated to be from wildfires and about 903,000 tons from prescribed fires. The total of ~2.1 million tons of
PM2.5 from these fires are mapped in Figure 5-3 on a county basis. For emissions, the pattern is based on not
only on acres burned, but also on fuel consumption, fuel loading, and how emission factors vary by fire type and
other dynamic processes that occur in a given type of fire. Wildfire PM2.5 emissions account for 58% of the total
emissions and prescribed burns account for 42%. Certain areas in the country (eastern NC, northern MN and
northern CA) stand out for emissions but not necessarily for acres burned. This is likely due to the relationship
between fire characteristics and emission factors: prescribed fires likely have lower amounts of emissions on a
per-acre basis due to lower burn temperatures than wildfires; prescribed fires have less smoldering than
wildfires, which causes wildfire emissions to accumulate over time; peat type wildfires burning extensive duff;
and wildfires burning very hot and for a long duration causing higher emissions.
326
-------
Figure 5-3: 2011 PM2.5 wildfire and prescribed burning emissions using EPA methods
5.1.5 Summary of quality assurance methods
• WLF emissions developed using the methods above were compared to EPA's 2008 estimates, since the
models used are very consistent. The spatial (and temporal) patterns seen in the data correspond to
what was expected in 2011, and how the domains changed from 2008 -Alaska and Hawaii are new to
the domain in 2011. 2011 was a "worse" fire year than 2008, as more acres were burned (about 30%
more), so the emissions are expected to be higher in 2011 compared to 2008.
• Georgia and NC were the only state to submit emissions data. A comparison of the data between the GA
submitted and SF2-generated emissions for GA showed a very good match for wildfires, but a marginal
match for prescribed fires. Due to that concern and some concerns that GA had on the spatial extent of
emissions estimate on a county basis for GA in SF2, they submitted their own emissions in 2011. In
future NEI cycles, the methods used by SF2 to estimate emissions from prescribed fires deserve
additional review and improvement. A comparison of the data between the NC submitted and SF2-
generated emissions for NC shows that SF2 estimates are much higher (emissions an order of magnitude
higher for both wild and prescribed fires). Although for the 2011 NEI v2, we decided to accept NC's
estimates over EPA estimates, in the future a closer evaluation will be done between state-submitted
data and EPA estimates to ensure that the State-submitted data is in accordance with known activity
data for the state as well as to check whether the state-submitted data covers the entire domain of the
state as well as all fires that occur over this area.
327
-------
• In Figure 5-4, we show a county by county map of PM2.5 emissions density (per square mile) that reflects
the difference in total PM2.5 emissions in 2011 NEI with and without wild and prescribed fire PM2.5
emissions. The resulting density difference map highlights those counties in which these large fires
dominate the PM2.5 emissions load. The areas identified in this map align well with known areas of very
high fire activity in 2011.
Figure 5-4: Difference map of 2011 NEI v2 PM2.5 emissions, with and without large fires
Legend
PM25 Emissions DensityDifferencew woFires
Tons SqMi Difference
H00~ 06
i 10.7 • 1.1
I 1.2 - 1.8
11.9- 2.9
¦ 3.0 - 190.1
• As shown in Figure 5-5, we compared total mass of PM2.5 emissions (the sum of all WLFs) to past EPA
inventories which used SF2 to estimate emissions. This generally shows that ail pollutants were in a
reasonable range that would be expected from these types of fires, given the expected year to year
variability. The figure shows SF2-based PM2.5 emissions from 2007 to 2011. Though the SF2 model has
undergone improvements over this time frame, the overall model is the same and, as such, the
agreement across years for total emissions is stiil relevant. As shown in the figure, the total of 2.1 million
tons of PM2.5 estimated in 2011 is in line with past estimates. However, 2011 had more fires than did
2008, and 2011 has the second highest emissions in the time frame shown. As expected, wildfires are
seen to drive most of the variation year-to-year.
328
-------
Figure 5-5: 2011 PM2.5 wild land fire emissions using EPA methods
2,500
§ 2,000
+¦>
o
o
o
tH 1,500
in
s 1,000
g 500
CL
WF
RX
2007 2008 2009 2010
Year
2011
• Changes between vl and v2 for wild land fires: In accordance with the changes made between vl and v2
identified above (North Carolina submitting their own emissions in v2 and Delaware data being altered
based on comments), Table 5-6 highlights the PM2.5 emission changes (other pollutants will behave the
same way) in going from vl to v2 of the 2011 wild land fire inventory
Table 5-6: PM2.5 Emission differences (tons) for WLFs between 2011 vl and 2011 v2
Prescribed Fires
Wild Fires
2011 vl
2011 v2
2011 vl
2011 v2
Alabama
50,537
50,537
11,035
11,035
Alaska
2,647
2,643
181,161
181,161
Arizona
7,218
7,218
121,112
121,112
Arkansas
55,057
55,057
9,907
9,907
California
25,866
25,866
65,116
53,487
Colorado
24,233
24,233
8,029
8,029
Connecticut
13
13
37
37
Delaware
92
96
502
9
Florida
47,030
69,583
32,236
19,372
Georgia
73,485
48,686
58,201
84,174
Hawaii
674
674
127
127
Idaho
20,098
19,284
41,585
40,878
Illinois
4,817
4,817
744
744
Indiana
2,124
2,124
151
151
Iowa
6,435
6,435
399
399
Kansas
81,560
81,560
2,675
2,675
Kentucky
8,078
8,078
7,898
7,898
329
-------
Prescribed Fires
Wild Fires
2011 vl
2011 v2
2011 vl
2011 v2
Louisiana
83,493
83,493
21,672
21,672
Maine
346
346
21
21
Maryland
1,042
1,042
1,562
1,562
Massachusetts
413
413
0
0
Michigan
1,689
1,689
1,005
1,005
Minnesota
16,358
16,358
51,811
51,811
Mississippi
27,783
27,783
2,022
2,022
Missouri
45,055
45,055
8,556
8,556
Montana
22,472
22,472
62,265
62,265
Nebraska
9,792
9,792
979
979
Nevada
628
628
7,381
7,381
New Hampshire
47
47
0
0
New Jersey
1,215
1,215
200
200
New Mexico
3,796
3,796
81,100
81,100
New York
462
462
201
201
North Carolina
21,245
2,871
138,376
8,873
North Dakota
13,857
13,857
384
384
Ohio
830
830
46
46
Oklahoma
66,628
66,628
26,439
26,439
Oregon
83,490
83,490
38,142
38,142
Pennsylvania
1,753
1,753
114
114
Rhode Island
59
59
5
5
South Carolina
15,028
15,028
3,235
3,235
South Dakota
14,728
14,728
17,675
17,675
Tennessee
8,626
8,626
2,654
2,654
Texas
5,253
5,253
188,970
188,970
Utah
4,446
4,446
2,312
2,312
Vermont
50
50
5
5
Virginia
7,193
7,193
7,506
7,506
Washington
18,797
18,797
3,706
3,706
West Virginia
4,723
4,723
2,772
2,772
Wisconsin
3,135
3,135
43
43
Wyoming
30,086
30,086
42,296
42,319
Total
924,482
903,048
1,254,370
1,125,170
5.1.6 References for Wildfires and Prescribed Burning
1. Reid, S.B., Technical Memorandum, Sonoma Technology, Inc., Preparation of Version 2 of the Wildland
Fire Emissions Inventory for 2011, April 26, 2013.
330
-------
2. Pollard E.K., Du Y., Raff use S.M., and Reid S.B. (2011) Preparation of wildland and agricultural fire
emissions inventories for 2009. Technical memorandum prepared for the U.S. Environmental Protection
Agency, Research Triangle Park, NC, by Sonoma Technology, Inc., Petaluma, CA, STI-910221-4231,
October 6.
3. Raff use, S., 2012. Sonoma Technical Inc. Technical Memorandum: AirFire/STI National Wildland Fire
Emission Inventory for 2011, DRAFT, April 2012.
4. Pace, T., Attachment 1 in Work Assignment #3-18. Tom Pace to Sonoma Technologies, Preparation of
Wildland and Agricultural Fire Emission Inventories for 2003-2006, April 2007
5. Huang S., Du Y., Raff use S.M., and Reid S.B. (2012) Preparation of wildland fire emissions inventories for
2009. Technical memorandum prepared for the U.S. Environmental Protection Agency, Research
Triangle Park, NC by Sonoma Technology, Inc., Petaluma, CA, STI-910321-5446, August 15.
6. Du Y., Raff use S.M., and Reid S.B. (2013) Technical guidance for using SmartFire2 / BlueSky Framework
to develop national wildland fire emissions inventories. Draft user's guide prepared for the U.S.
Environmental Protection Agency, Research Triangle Park, NC by Sonoma
5.2 Fires - Agricultural field Burning
An EPA approach to estimate agricultural fire emissions was developed for the first time for the 2008 NEI. In the
2008 effort, only CAPs were estimated for this sector. In 2011, EPA changed its methods for this sector to those
based on the peer-reviewed approach of Jessica McCarthy [ref 1], In 2011, 17 HAPs were also included in the
suite of pollutants estimated for this sector in the EPA data. In addition to the data submitted by S/L/T agencies,
EPA developed a nationally consistent agricultural fires estimate based on the McCarthy methods, which relies
on remote sensing, crop-usage maps and appropriate emission factors to estimate CAP (all CAPs except for
ammonia) and 17 HAPs for this sector. Within the EIS, the EPA annual agricultural fire estimates are county-
totals and are included in the dataset "2011EPA_NP_NoOverlap_w_Pt." They are also available outside of the
EIS as monthly totals upon request.
5.2.1 Sector description
Agricultural burning refers to fires that occur over lands used for cultivating crops and agriculture. The SCCs that
pertain to this source in the NEI are listed in Table 5-7. EPA data are all put into one SCC, while state-submitted
data are entered into one or more of 25 different SCCs shown in Table 5-7. These other SCCs have more specific
details about the type of crop burned.
Table 5-7: SCCs in the NEI for Agricultural Burning
Data Origin
Agricultural Fires - SCCs used
EPA
2801500000
States/Locals/T ribes
2801500000, 2801500100, 2801500111,2801500130, 2801500150, 2801500170,
2801500181, 2801500191, 2801500220, 2801500250, 2801500261, 2801500262,
2801500300, 2801500320, 2801500330, 2801500350, 2801500350, 2801500390,
2801500410, 2801500420, 2801500430, 2801500500, 2801500600, 2801520000,
2801500141
331
-------
5.2.2 Sources of data overview and selection hierarchy
The agricultural fire sector includes data from the following: S/L/T agency-provided emissions data, the
2011EPA_chrom_split dataset (see Section 3.1.3), 2011EPA_PM-Aug, and an EPA dataset created from the
McCarthy methods (see Section 5.2.4) and stored in the dataset 2011EPA_NP_NoOverlap_w_Pt.
The chromium speciation data were used only to speciate California total chromium to hexavalent and trivalent
chromium. The PM augmentation data had no impact on the primary PM emissions; it added filterable PM by
setting it equal to primary PM and condensable PM by setting it equal to zero. The EPA dataset includes
emissions from the pollutants VOC, NOx, S02, CO, PM25, C02 and methane because we had emission factors
available for these. The C02 and methane emissions were not included in the final 2011 NEI but are available
upon request. Table 5-8 lists the state and tribal agencies that submitted agricultural fire emissions.
Table 5-8: Agencies that submitted agricultural fire emissions to the 2011 NEI
Agency
Agency Type
Arizona Department of Environmental Quality
State
California Air Resources Board
State
Delaware Department of Natural Resources and Environmental Control
State
Georgia Department of Natural Resources
State
Hawaii Department of Health Clean Air Branch
State
Idaho Department of Environmental Quality
State
Indiana Department of Environmental Management
State
Kansas Department of Health and Environment
State
Louisiana Department of Environmental Quality
State
New Jersey Department of Environment Protection
State
North Carolina Department of Environmental Quality
State
Oregon Department of Environmental Quality
State
South Carolina Department of Health and Environmental Control
State
Washington State Department of Ecology
State
Coeur d'Alene Tribe of Idaho
Tribal
Kootenai Tribe of Idaho
Tribal
Nez Perce Tribe
Tribal
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribal
When we created the 2011 NEI, the EPA data were combined with the other data in such a way that any
counties or pollutants that were null in the S/L/T agency data were backfilled with EPA-based county estimates.
Any "zero" submissions were left as zero in the 2011 NEI for those counties and pollutants. In addition, EPA
augmented HAPs for those states that did not submit any of the HAPs (listed in the first paragraph of Section
5.2.3) using a simple ratio of state-based VOC to the HAP in question in the EPA emissions database. These ratios
were applied to the state submitted VOC emission values (all counties in a given state used the same EPA-data
based VOC:HAP ratio to estimate HAP emissions). The actual EPA-data based ratios provided along with all of
the other HAP augmentation ratios described in Section 3.1.5 and can be accessed via the supplemental data file
described in that section. For states that reported any of the HAPs that EPA estimates or any other HAPs, they
were left as is in the final NEI (as long as they passed the QA checks). The hierarchy used to select data for this
sector is outlined in Table 5-9.
332
-------
Table 5-9: Data source and selection hierarchy used for agricultural fire emissions
Dataset name
(Short Name provided if
different)
Description and Rationale for the Order of the Selected Datasets
Order
2011 Responsible
Agency Selection
S/L/T agency submitted data for agricultural burning; multiple datasets -
one for each reporting agency. These data are selected ahead of other
datasets.
1
2011EPA_PM-
Augmentation
(2011EPA_PM-AUG)
Adds 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 calculator for processes covered by that database. See
Section 3.1.1 for additional details.
2
2011EPA_
chrom_split
Hexavalent and trivalent chromium speciated from S/L/T agency
reported chromium. New EIS augmentation function creates the dataset
by applying multiplication factors by SCC, facility, process or NAICS code
to S/L/T agency chromium. See 3.1.3.
3
2011EPA_HAP-
Augmentation
(2011EPA_HAP-Aug)
HAP data computed from S/L/T agency criteria pollutant data using
HAP/CAP emission factor ratios based on ratios of HAP to CAP emission
factors used in the EPA estimates. This dataset is below the S/L/T agency
data in order that the S/L/T agency HAP data are used first.
4
2011EPA_AgBurningSF2
Contains data for categories primarily for which there was no or unlikely
possibility of point source contribution (or overlap). Agricultural burning
is one such category.
5
5.2.3 Spatial coverage and data sources for the sector
Using the methods described below in section 5.2.4, EPA developed county-by-county agriculture burning
estimates for the contiguous United States (no EPA estimates were developed for AK, HI, PR or VI). HI submitted
CAPs only; thus, there are no data for AK, PR or VI in the 2011 NEI. All CAPs other than NH3 were estimated with
EPA methods. Table 5-10 summarizes these CAP estimates by state. For example, total PM2 s emissions for the
48 contiguous states in the US based on EPA methods is about 148,000 tons. EPA also estimated emissions for
the following 17 HAPs: 1,3-butadiene, acetaldehyde, anthracene, benz(a)anthracene, benzo(a)pyrene, benzene,
benzo(e)pyrene, benzo(ghi)perylene, benzo(k)fluoroanthene, chrysene, fluoroanthene, formaldehyde,
indeno(l,2,3-cd)pyrene, perylene, phenanthrene, pyrene, and toluene.
333
-------
Table 5-10: Emission estimates for Agricultural Burning (short tons/year) using EPA methods
State
CO
NOx
S02 PM2.5
PM10
VOC
Alabama
4,065.5
152.3
60.3
420.4
644.5
284.6
Arizona
7,600.2
339.4
142.8
684.0
1,079.5
603.6
Arkansas
74,423.7
3,673.4
1,721.1
7,291.5
9,774.6
5,987.2
California
78,693.4
3,560.1
1,385.0
7,134.4
11,499.6
5,434.6
Colorado
33,958.2
1,427.8
615.0
3,165.8
5,940.6
2,337.6
Connecticut
50.4
1.9
0.8
5.3
9.6
3.1
Delaware
848.8
37.5
17.6
79.1
149.9
60.6
Florida
32,324.5
1,497.7
746.8
2,799.8
3,512.6
2,434.2
Georgia
15,343.7
656.6
294.0
1,431.8
2,353.8
1,130.1
Idaho
51,079.7
2,042.2
735.5
4,904.8
7,864.3
3,830.8
Illinois
16,139.2
741.1
373.9
1,532.7
2,817.3
1,218.3
Indiana
87,776.5
4,011.5
2,001.8
8,386.7
15,118.4
6,685.0
Iowa
132,324.8
6,071.7
3,074.3
12,588.6
23,175.6
9,969.6
Kansas
131,752.6
5,296.8
2,059.0
12,828.9
21,516.4
9,390.6
Kentucky
10,077.9
452.1
213.9
977.3
1,648.0
788.8
Louisiana
49,115.0
2,361.2
1,105.8
4,758.4
6,839.1
3,747.1
Maine
22.8
0.7
0.2
2.7
4.1
1.4
Maryland
1,605.0
67.5
30.6
156.4
280.6
113.3
Massachusetts
25,814.7
670.3
155.1
3,200.4
4,375.2
1,615.1
Michigan
1,305.2
56.7
26.0
125.0
221.3
96.4
Minnesota
180,964.6
8,259.1
3,776.6
16,838.7
28,923.8
14,297.5
Mississippi
47,915.5
2,276.7
1,083.0
4,567.7
6,975.8
3,926.2
Missouri
74,587.9
3,268.5
1,531.1
7,420.6
12,111.0
5,757.9
Montana
23,296.4
967.8
297.7
2,083.5
3,208.3
1,828.1
Nebraska
81,242.6
3,598.3
1,747.1
7,604.8
14,704.6
5,711.0
Nevada
6,625.1
174.6
39.4
811.2
1,120.1
411.3
New Hampshire
167.3
6.1
2.7
17.7
32.0
10.2
New Jersey
191.2
8.2
3.9
18.6
34.1
13.5
New Mexico
6,555.3
283.7
115.2
585.2
1,072.8
476.4
New York
3,949.6
149.0
65.5
411.3
728.0
255.4
North Carolina
18,678.2
841.9
399.2
1,724.2
3,130.3
1,375.3
North Dakota
110,207.0
4,902.2
1,902.0
10,001.7
16,048.0
8,810.1
Ohio
1,771.1
78.3
36.8
173.4
291.9
136.9
Oklahoma
15,520.1
661.1
229.8
1,373.1
2,326.2
1,123.1
Pennsylvania
3,050.6
119.2
53.8
314.4
553.2
204.8
Rhode Island
7.6
0.2
0.0
0.9
1.2
0.5
South Carolina
4,064.7
177.3
82.8
381.8
688.3
292.1
South Dakota
119,293.1
5,058.6
2,219.9
11,479.8
20,281.2
8,543.5
Tennessee
8,508.6
390.2
185.5
828.9
1,303.1
708.1
Texas
42,269.2
1,779.4
725.6
3,962.5
6,759.4
2,913.6
Utah
5,719.3
186.9
61.1
631.1
978.0
369.8
Vermont
331.2
10.4
3.8
38.0
60.5
20.2
Virginia
2,852.3
112.9
48.2
291.4
483.5
201.3
Washington
33,475.5
1,261.3
383.8
3,277.8
5,032.2
2,428.0
West Virginia
620.5
18.8
6.3
72.9
108.9
39.2
Wisconsin
4,246.8
181.4
87.4
411.9
772.3
295.0
Wyoming
6,564.0
216.3
70.0
718.9
1,114.5
426.4
Totals: 1,556,997.1 58,107.1 29,917.6 148,515.8 247,668.0 116,307.5
As an example of data contained in the 2011 NEI for this sector, the PM2 s emissions data in Table 5-10 are
combined (using the hierarchy discussed earlier) with the S/L agency submissions (excluding tribal) shown in
Table 5-8 and summarized in Figure 5-6 below. For this sector, Louisiana, Kansas, and the Dakotas, all show high
334
-------
levels of emissions compared to areas in the Northeast and Western US. The Midwest region shows very low
agricultural burning emissions due to very limited activity.
Figure 5-6: 2011 NEI state-total PM2.5 emissions from agricultural fires
PM2.5 Emissions (Tons)
NEI v2
I 0 to 2
I I 2 to 31
] 31 to 314
| 314 to 719
| 719 to 1290
| 1 290 to 2800
| 2800 to 8000
¦ 8000 to 14253
Figure 5-7 below shows states that submitted agricultural burning data to the NEI, corresponding to the list
shown in Table 5-8. States in gray submitted some data to the NEI for this sector, while states in yellow
submitted none and were reliant on emission estimates based on EPA methods. For the states in blue (all LADCO
states plus MO, NE and I A), the EPA data were adjusted to be more compliant with local information we got on
amounts of agricultural burning occurring in these states [ref 2], This adjustment procedure is discussed in more
detail in Section 5.2.4. AK is not shown, because AK does not have any agricultural burning activity. In addition,
states that submitted other pollutants not in the list of EPA-based HAPs and CAPs discussed in Section 5.2.3,
were left as is in the NEI (this mainly included other PAHs, phenol, ethyl benzene, some trace metals, ammonia,
and lead emissions).
335
-------
Figure 5-7: States that submitted agricultural burning emissions to the NEI
Adjusted EPA Data
5.2.4 EPA-developed agricultural emissions data
In the 2008 NEI for this sector, a method similar to that used for estimating wild land fires (relying on the
"SMARTFIRE" model) was used to develop emission estimates. In the current 2011 NEI, a different method was
used to estimate emissions for this sector. This caused the EPA-based emission estimates to be significantly
higher in 2011 (a factor of 2-3 times higher) for many states. The 2011 approach is based on the peer-reviewed
methods of Dr. Jessica McCarthy. This method relies mainly on satellite-based methods to develop the burned
area and then uses an assigned crop type to estimate final emissions. Readers should consult the references
provided at the end of this section for in-depth details on this method.
Burned Area: A differenced Normalized Burned Ratio (dNBR) was used to map potential cropland burned area
using 500 m MOD09A1 8-day surface reflectance of the MODIS. This method was published in McCarty et al. [ref
1] with results published in McCarty et al. [ref 3] and McCarty [ref 4], This product represents a weekly product,
not a daily product. For the 2011 v2, a higher difference Normalized Burn Ratio ("dNBR;" Key and Benson, 2006)
[ref 5] threshold of 425 was applied across the CONUS. This threshold was set based on burn scars in cropland
areas derived from 2011 Landsat data. These burn scars were digitized in cropland areas of Florida. Minnesota.
North Dakota. California, and Wyoming. Active fire data from the MODIS sensor were also used for visual
comparison with the cropland dNBR. The visual comparison was an analysis of spatio-temporal similarity, which
is the same approach used by Roy et al. [ref 6] when the MODIS Burned Area Product MCD45A1 was validated
336
-------
Crop Type: The agricultural area map and specific crop type of each burned area polygon was derived from the
U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL)
product. This is a 30 meter product created for the CONUS annually. Users of these emission estimates should
note that Conservation Reserve Program (CRP) lands are included in this estimate. CRP lands tend to be native
grasses, reeds/wetlands, shrubs, and trees in areas prone to soil erosion or lacking in nutrients within or
adjacent to actively farmed croplands.
Emissions: All emissions are crop-type specific and were calculated using the Seiler and Crutzen [ref 7] method
of multiplying burned area, combustion completeness, fuel loadings, and atmospheric species-specific emission
factors. For this analysis this equation included burn area as acres burned from the MODIS Cropland Burned
Area product, crop-type specific combustion completeness taken from McCarty [ref 4], fuel loading in tons/acre
representing the crop residue biomass per acre of cropland taken from McCarty [ref 4], and emission factors in
lbs/ton taken from McCarty [ref 4] or retained from previous NEI development. With the aid of a flow diagram,
Figure 5-8 shows the overall geospatial method for producing the remote sensing-based cropland emission
estimates.
Figure 5-8: EPA's Geospatial method for producing Cropland Burning emissions for 2011 NEI
MOD09A1: MODIS 8-day surface reflectance composites for CONUS
MODIS Reprojection Tool:
Puts all data in same geographic
projection of Albers Equal Area
NAD83
Apply MODIS data quality bits:
Mask out clouds, water, snow,
shadows, aerosols
Calculate dWBR for temporally consecutive MODIS pairs:
NBR = (R2 - R7) / (R2 + Re-
calculate NBR for each swath dNBR = NBR_pre - NBR_post;
calculate dNBR for each swath pair
Apply dNBR threshold to
detect burn scars and
assign Julian date to
burned pixels
Convert to shapefiles -
calculate areas and apply
emission coefficients
Merge burn detections
with annual Cropland
Data Layer to assign crop
type
Merge shapefiles with
state and county layers
(emissions by FIPS)
The initial version of the emissions database was shared by Dr. McCarthy with EPA for consideration and initial
dissemination to the states in July 2012. From July 2012 to January 2013, based on state partner comments, we
further analyzed Wyoming and Indiana results using other satellite sources of burned area at higher resolution
(30-meter Landsat and very high-resolution commercial datasets) to determine if this dataset was appropriately
quantifying burn conditions on the ground. For the corn belt portion of the U.S (Iowa, Indiana, Illinois), state-
level feedback and the analysis of Indiana led to a reduction of 20% in all cropland burning emissions as there
337
-------
was an initial overestimation of the burn scars in which dark soils (i.e., plowed and/or irrigated black soils) were
incorrectly classified as burned areas. The EPA emission estimates in the 2011 NEI reflects these changes: the
emission estimates for the states of Indiana, Illinois, and Iowa were all lowered by 20% based on the "dark soil"
issue. All satellite data processing was performed using ENVI IDL, the MODIS Reprojecti (MRT), and Arc
Python within ESRI ArcGIS.
In addition to the application of a 20% emissions reduction for these midwestern states as stated above, EPA
decreased the emissions for other nearby states (Wisconsin, Illinois, Michigan, Iowa, Missouri, and Ohio) based
on comments received from LAD CO that questioned the quality of satellite data's ability to detect small
agricultural fires in the mid-western region of the US. When the states confirmed this information, EPA reduced
all emissions by a factor of 0.000189 for all these states, resulting in near-nil emissions. For MN, we had
different reduction rates they supplied based on their information: MN emissions were reduced by 87%. This
ratio approach led to a reduction of between 95-99% of emissions for Wisconsin, Michigan, Ohio, Missouri, and
Illinois. These changes are reflected in the results shown in Figure 5-6. Figure 5-9 below shows the resulting
PM2.5 emissions for the lower 48 states based on EPA methods (it can be compared to Figure 5-6 which is a
combination of EPA results and state submitted data). Table 5-11 below outlines the changes in ag burning PM2.5
emissions state-by-state in going from NEI vl to NEI v2. Nationwide there is about a 34% reduction in PM2.5
emissions (other pollutants will show similar reductions) with the states outlined about showing much larger
reductions and many states staying unchanged.
Table 5-11: Agricultural Burning PM2.5 emission differences between NEI 2011 vl and 2011 v2
State
2011 vl
2011 v2
Difference (v2 - vl)
AL
466
466
0
AR
7,292
7,292
0
AZ
557
557
0
CA
3,933
3,933
0
CO
3,166
3,166
0
CT
5
5
0
DE
70
26
-44
FL
2,800
2,800
0
GA
3,583
3,583
0
HI
1,441
1,441
0
IA
13,065
2
-13,063
ID
876
876
0
IL
1,533
0
-1,533
IN
31
31
0
KS
14,253
14,253
0
KY
977
977
0
LA
8,278
8,278
0
MA
9
9
0
MD
156
156
0
ME
3
3
0
Ml
125
0
-125
MN
16,839
2,189
-14,650
MO
7,421
1
-7,420
338
-------
State
2011 vl
2011 v2
Difference (v2 - vl)
MS
4,568
4,568
0
MT
2,084
2,084
0
NC
1,724
1,290
-434
ND
10,002
10,002
0
NE
7,605
1
-7,604
NH
18
18
0
NJ
185
185
0
NM
585
585
0
NV
811
811
0
NY
411
411
0
OH
173
0
-173
OK
1,373
1,373
0
OR
869
869
0
PA
314
314
0
Rl
1
1
0
SC
1,896
1,896
0
SD
11,480
11,480
0
TN
829
829
0
TX
3,963
3,963
0
UT
631
631
0
VA
291
291
0
VT
38
38
0
WA
2,923
2,923
0
Wl
412
0
-412
wv
73
73
0
WY
719
719
0
Total
140,857
95,399
-45,458
339
-------
Figure 5-9: PM2.5 Emissions from Agricultural Burning, 2011 EPA data
Legend \
Stat e Ag F i res_2011 _P M2.5 E m i ssi on s_E PAdat a
PM25AgBumE PA
I0003 - I6339
The McCarthy methodology used by EPA only included emission estimates for the lower 48 contiguous States
(no agricultural burning activity was detected in Oregon based on these methods), Alaska does not have any
agricultural burning activity, and Hawaii submitted their own emissions as noted in Table 5-8.
5.2.5 Summary of quality assurance methods
• We compared EPA estimates to State submitted estimates, and discovered discrepancies in the
Midwestern States, where EPA emission estimates were too high. A report by LADCO [ref 2] provided
additional corroboration that EPA estimates may be too high for some of these states. We corrected by
applying a ratio based on state submitted information for Indiana after confirming that the state-based
estimates are likely more accurate. Similarly, for the state of Idaho, EPA estimates were much higher
than those submitted by the state; however, Idaho submitted a complete set of emissions which was
used in the final 2011 NEl Most of the states that had noted discrepancies between its estimates and
EPA-based estimates have large areas of "dark soils" which can spectrally be confused with burned areas
and thus produce overestimations of cropland burned area due to soil properties as well as tillage and
irrigation practices. In the future, if the McCarthy methods are to be used further, this area of
uncertainty has to be further investigated.
• 2011 EPA methods differed from the methods used by EPA in 2008, causing emissions in 2011 to be
significantly higher overall and in some major crop burning areas. While there could have been some
340
-------
increase in activity between 2008 and 2011, it is likely these new methods contributed most to the
increased emissions noted.
• For other states that submitted agricultural burning data (see Table 5-8), we compared those data to
EPA estimates in the same counties. The matches between state and EPA data varied, with Eastern
states generally matching better. It is difficult to arrive at major conclusions because we have limited
information on the methods used by states in estimating agricultural burning emissions. We tagged one
emission value submitted by California in Santa Barbara County (2,040.47 tons of acrolein) because it
was suspected to be incorrect. No other pollutants were reported for agricultural burning in this county,
and this value is 6 times higher than all other county emissions for this pollutant reported by California.
In addition, EPA data were tagged to avoid double counting with SLT-submitted data (this was needed
because SLT agencies submitted too many different SCCs (see Table 5-7) and EPA reported to only one
SCC as shown in the same table). EPA data in DE, KS, LA, NJ, OR, WA, and ID were all tagged to avoid
double counting with SLT-submitted data for those states.
• Finally, as a very rough check, Figure 5-10 below shows the percentage of PM2.5 emissions associated
with agricultural fires vs. wild vs. prescribed fires. Even though EPA methods in 2011 caused agricultural
fire acres burned (and emissions) to increase significantly, the agricultural fires still should be very small
in emissions magnitude compared to the large wild and prescribed fires. Figure 5-10 confirms this.
Further, the figure shows the highest emissions in states known to have significant cropland burning
activity.
Figure 5-10: Comparison of percentage of PM2.5 emissions assigned to agricultural, prescribed and wild fires
f
A
Ag Fires
*
Rx Fires
A
Wild Fires
[u
55
99121 198186
V
y
341
-------
5.2.6 References for Agricultural Field Burning
1. McCarty, J.L., Loboda, T., Trigg, S., 2008. A hybrid approach to quantifying crop residue burning in the US
based on burned area and active fire data. Appl. Eng. Agric. 24: 515-527.
2. Boyer, L, Battye, W., Fudge, S., and R. Barrows, 2004. Fire Emissions Inventory Development for the
Midwest Regional Planning Organization, Final Report, EC/R Incorporated, available upon request.
3. McCarty, J.L., Korontzi, S., Jutice, C.O., and Loboda, T., 2009. The spatial and temporal distribution of
crop residue burning in the contiguous United States. Science of the Total Environment. 407 (21): 5701-
5712.
4. McCarty, J.L., 2011. Remote sensing-based estimates of annual and seasonal emissions from crop
residue burning in the contiguous United States. JAPCA J Air Waste Ma. 61, 22-34.
5. Key, C.H., Benson, N.C., 2006. Landscape Assessment (LA). In 'FIREMON: Fire Effects Monitoring and
Inventory System'. (Eds DC Lutes, RE Keane, JF Carati, CH Key, NC Benson, LJ Gangi) USDA Forest Service,
Rocky Mountains Research Station General Technical Report RMRS-GTR-164-CD. p. LA-1-55. (Fort
Collins, CO).
6. Roy, D.P., Boschetti, L., Justic, C.O., and Ju, J. 2008. The collection 5 MODIS burned area product - Global
evaluation by comparison with the MODIS active fire product. Remote Sensing of Environment. 112:
3690-3707.
7. Seiler, W., and Crutzen, P. J., 1980. Estimates of gross and net fluxes of carbon between the biosphere
and the atmosphere from biomass burning, Clim. Change, 2, 207-247.
342
-------
6 Biogenics - Vegetation and Soil
Biogenic emissions are emissions that come from natural sources. They need to be accounted for in
photochemical grid models, as most types are widespread and ubiquitous contributors to background air
chemistry. In the NEI, only the emissions from vegetation and soils are included, but other relevant sources
include volcanic emissions, lightning, and sea salt.
Biogenic emissions from vegetation and soils are computed using a model which utilizes spatial information on
vegetation and 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.
6.1 Sector description
In the 2011 NEI, biogenic emissions are included in the nonpoint data category, in the EIS sector "Biogenics -
Vegetation and Soil." Table 6-1 lists the two SCCs used in the 2011 NEI that comprise this sector. These 2 SCCs
have distinct pollutants: SCC 2701220000 has only NOx emissions, and SCC 2701200000 has emissions for CO,
VOC and 3 VOC HAPs: formaldehyde, acetaldehyde and methanol.
Table 6-1: SCCs for Biogenics - Vegetation and Soil
Source
Classification
Code
SCC
Level
One
SCC
Level
Two
SCC Level
Three
SCC Level
Four
Tier 1
Description
Tier 2
Description
Tier 3
Description
2701200000
Natural
Sources
Biogenic
Vegetation
Total
Natural
Resources
Biogenic
Vegetation
2701220000
Natural
Biogenic Vegetation/ Total
Natural
Biogenic
Vegetation
Sources
(Agriculture
Resources
The biogenic emissions for the 2011 NEI were computed based on 2011 meteorology data from the
Weather Research and Forecasting (WRF) Model using the Biogenic Emission Inventory System, version 3.6
(BEIS3.6) model within SMOKE. The BEIS3.6 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 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, NO, SESQ. Mapping of BEIS pollutants to NEI pollutants is as follows:
343
-------
• 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.
An older version of the BEIS model. BEIS 3.6 will be described in more detail in Bash, J.O., Baker, K.R., Beaver,
M.R., Park, J.-H., Goldstein, A.H., Evaluation of improved land use and canopy representation in BEIS with
biogenic VOC measurements in California (in preparation, July 2015).
The inputs to BEIS include:
• Land-use data from the Biogenic Emissions Land use Database, version 4 (BELD4). BELD4 is derived from
the 2006 National Land Cover Database (NLCD) and Moderate Resolution Imaging Spectoradiometer
(MODIS) satellite data. Vegetation speciation information is based on data from 2002 to 2013 from the
Forest Inventory and Analysis (FIA) version 5.1.6.
• The following meteorological variables that are also inputs to the air quality model are provided in Table
6-2.
Table 6-2: Meteorological variables used by BEIS and air quality modeling
BEIS Meteorological Inputs
Met Variable
Description
LAI
leaf-area index
PRSFC
surface pressure
Q2
mixing ratio at 2 m
RC
convective pcpn per met TSTEP
RGRND
solar rad reaching sfc
RN
nonconvec. pcpn 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 one cm
TEMPG
skin temperature at ground
USTAR
cell averaged friction velocity
RADYNI
inverse of aerodynamic resistance
TEMP2
temperature at 2 meters
6.2 Sources of data overview and selection hierarchy
The only source of data for this sector is the EPA-estimated emissions from BEIS3.6. States are neither required
nor encouraged to report emissions, and no state has done this. The name of the EPA dataset in the EIS is:
2011EPA_biogenics.
6.3 Spatial coverage and data sources for the sector
The spatial coverage of the biogenics emissions is governed by the "2011 platform" modeling domain which
covers all counties in the lower 48 states. More information on this modeling platform.
344
-------
Table 6-3 shows state emissions summaries for the biogenic emissions sector and the contribution of biogenics
to the total 2011v2 NEI in that state. Biogenic emissions are a very large fraction of the total NEI VOC, methanol,
formaldehyde and acetaldehyde emissions but a very small fraction of the CO and NOx.
More detailed summaries of the BEIS model species at county level and monthly are available as a supporting
summary "2011 biogenic reports.zip" on the 2011 web page.
Table 6-3: State summary of Biogenics - Vegetation and Soil emissions (short tons/year)
formaldehyde
methanol
acetaldehyde
CO
NOx
VOC
state
biogenics
% of total
biogenics
% of total
biogenics
% of total
biogenics
% of total
biogenics
% of total
biogenics
% of total
AL
26,766
74%
114,495
85%
19,628
74%
187,956
9%
13,989
4%
2,041,217
84%
AR
23,930
71%
97,626
82%
17,548
71%
168,551
11%
25,331
10%
1,668,285
83%
AZ
54,769
79%
237,270
86%
40,163
79%
383,525
14%
13,924
5%
2,125,466
80%
CA
56,594
75%
221,121
89%
41,501
75%
396,812
10%
45,581
6%
2,412,727
74%
CO
22,570
76%
93,240
89%
16,551
76%
158,241
10%
32,910
10%
902,706
62%
CT
1,027
42%
3,308
59%
753
42%
7,328
2%
531
1%
66,784
45%
DC
18
12%
65
15%
13
12%
126
0%
15
0%
1,114
12%
DE
520
56%
2,004
85%
381
56%
3,826
3%
920
3%
25,080
52%
FL
31,438
62%
130,409
79%
23,054
62%
234,615
5%
22,534
4%
1,861,911
68%
GA
31,404
77%
137,126
90%
23,029
77%
221,820
7%
22,147
5%
2,197,186
84%
IA
10,492
74%
40,743
91%
7,694
74%
73,760
9%
34,354
13%
284,361
60%
ID
19,904
73%
65,397
79%
14,596
73%
139,504
11%
16,669
15%
787,965
75%
IL
12,423
67%
49,370
83%
9,110
67%
87,797
5%
36,678
7%
417,236
53%
IN
7,968
61%
30,689
86%
5,843
61%
56,148
3%
22,566
5%
270,734
49%
KS
23,741
65%
100,459
82%
17,410
65%
166,306
9%
57,224
14%
558,912
55%
KY
11,068
69%
43,006
84%
8,116
69%
78,021
7%
17,390
5%
611,525
69%
LA
23,769
59%
99,859
72%
17,430
59%
174,991
7%
16,831
3%
1,494,761
68%
MA
1,867
41%
5,687
70%
1,369
41%
13,501
2%
836
1%
108,787
42%
MD
2,789
55%
10,332
80%
2,045
55%
20,323
3%
3,451
2%
155,650
55%
ME
8,797
88%
20,432
95%
6,451
88%
62,334
18%
2,848
5%
355,085
84%
Ml
11,813
59%
37,928
84%
8,663
59%
84,430
4%
16,767
4%
524,136
54%
MN
14,361
54%
46,247
68%
10,531
54%
103,663
5%
27,597
8%
617,109
56%
MO
19,504
68%
79,531
81%
14,303
68%
137,236
7%
35,050
9%
1,226,623
77%
MS
23,942
78%
101,648
90%
17,557
78%
168,706
14%
17,971
8%
1,752,773
86%
MT
28,990
75%
97,545
80%
21,259
75%
203,185
13%
47,324
28%
1,030,794
75%
NC
19,143
76%
77,308
90%
14,038
76%
135,569
7%
16,628
4%
1,228,396
78%
ND
9,803
69%
33,210
89%
7,189
69%
69,640
12%
32,261
16%
214,839
43%
NE
15,033
85%
61,881
94%
11,024
85%
105,754
17%
52,775
20%
352,591
74%
NH
2,209
71%
6,211
92%
1,620
71%
15,628
6%
717
2%
104,534
69%
NJ
1,924
41%
6,852
65%
1,411
41%
13,986
1%
1,482
1%
122,017
40%
NM
43,642
82%
203,642
90%
32,004
82%
305,612
17%
30,991
12%
1,647,455
79%
NV
30,022
94%
132,715
98%
22,016
94%
210,305
28%
7,588
7%
1,093,557
93%
NY
9,872
58%
30,884
70%
7,239
58%
69,826
3%
9,936
3%
415,115
50%
OH
8,321
50%
31,772
80%
6,102
50%
58,523
2%
19,143
3%
332,886
43%
345
-------
formaldehyde
methanol
acetaldehyde
CO
NOx
voc
state
biogenics
% of total
biogenics
% of total
biogenics
% of total
biogenics
% of total
biogenics
% of total
biogenics
% of total
OK
29,022
71%
117,296
81%
21,282
71%
203,403
9%
42,428
9%
1,221,367
65%
OR
26,257
64%
76,670
67%
19,255
64%
184,245
7%
12,188
7%
963,526
66%
PA
8,892
57%
31,011
78%
6,521
57%
63,027
3%
11,107
2%
477,800
56%
R!
255
38%
793
55%
187
38%
1,867
1%
142
1%
17,896
43%
SC
15,025
74%
63,849
87%
11,018
74%
106,736
9%
9,872
4%
1,012,624
81%
SD
13,412
69%
52,126
84%
9,835
69%
94,424
12%
37,933
33%
335,805
68%
TN
13,722
71%
55,858
88%
10,062
71%
96,460
7%
16,506
5%
861,902
75%
TX
152,960
83%
670,246
91%
112,169
83%
1,074,822
16%
113,563
8%
6,052,447
73%
UT
19,865
90%
84,927
96%
14,567
90%
139,219
20%
8,148
4%
761,463
76%
VA
13,085
69%
51,092
84%
9,596
69%
92,614
7%
10,790
3%
893,208
75%
VT
2,048
75%
5,851
94%
1,502
75%
14,442
9%
1,408
7%
78,595
74%
WA
18,090
72%
43,492
80%
13,266
72%
127,103
7%
15,069
5%
594,115
66%
Wl
9,781
63%
34,050
87%
7,172
63%
70,517
5%
16,841
6%
469,051
62%
WV
5,543
72%
19,926
86%
4,065
72%
39,192
8%
4,620
3%
414,018
75%
WY
17,717
68%
70,655
77%
12,992
68%
124,325
10%
16,880
7%
672,959
64%
346
-------
7 Supporting data and summaries
The previous sections provide number references to both supporting data and key output summaries. All
supporting input data and summaries referenced in the sections above can be obtained through the CHIEF ftp
site or, on the 2011 webpaee.
347
-------
United States Office of Air Quality Planning and Standards Publication No. EPA-454/B-19-029
Environmental Protection Air Quality Assessment Division August 2015
Agency Research Triangle Park, NC
------- |