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2011 National Emissions Inventory, Version 1
Technical Support Document - June 2014
DRAFT
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EPA-454/D-20-003
June 2014
2011 National Emissions Inventory, Version 1 Technical Support Document - June 2014
DRAFT
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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Contents
List of Tables vii
List of Figures x
Acronyms and Chemical Notations xii
1 Introduction 1
1.1 What data are included in the 2011 NEI? 1
1.2 What is included in this documentation? 1
1.3 Where can I obtain the 2011 NEI data? 2
1.3.1 Emission Inventory System Gateway 2
1.3.2 2011 NEI main webpage 2
1.3.3 Air Emissions and "Where you live" 3
1.3.4 Modeling files 3
1.4 Why is the NEI created? 3
1.5 How is the NEI created? 4
1.6 Who are the target audiences for the 2011 NEI? 5
1.7 What are appropriate uses of the 2011 NEI version 1 and what are the caveats about the data? ..6
2 2011 inventory contents overview 8
2.1 What are EIS Sectors and what list was used for this document? 8
2.2 What do the data show about the sources of data in the 2011 NEI? 10
2.3 What are the top sources of some key pollutants? 16
2.4 How does this NEI compare to past inventories? 18
2.4.1 Differences in approaches 18
2.4.2 Differences in emissions between 2011 and 2008 20
2.5 How well are tribal data and regions represented in the 2011 NEI? 25
2.6 What does this NEI tell us about mercury? 26
3 Stationary sources 32
3.1 Stationary source approaches 32
3.1.1 Sources of data overview and selection hierarchies 32
3.1.2 Particulate matter augmentation 36
3.1.3 Chromium augmentation 36
3.1.4 Use of the 2011 Toxics Release Inventory 37
3.1.5 HAP augmentation based on emission factor ratios 47
3.1.6 Priority Facility List 49
3.1.7 EPA nonpoint data 49
3.1.8 References for Stationary Sources 56
3.2 Agriculture - Crops & Livestock Dust 57
3.2.1 Sector Description 57
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3.2.2 Sources of data overview and selection hierarchy 57
3.2.3 Spatial coverage and data sources for the sector 58
3.2.4 EPA-developed agricultural crops and livestock dust emissions data 58
3.2.5 Summary of quality assurance methods 62
3.2.6 References for Agriculture - Crop & Livestock Dust 62
3.3 Agriculture - Fertilizer Application 63
3.3.1 Sector Description 63
3.3.2 Sources of data overview and selection hierarchy 63
3.3.3 Spatial coverage and data sources for the sector 65
3.3.4 EPA-developed agricultural fertilizer application emissions data 65
3.3.5 Summary of quality assurance methods 70
3.3.6 References 71
3.4 Agriculture - Livestock Waste 71
3.4.1 Sector Description 71
3.4.2 Sources of data overview and selection hierarchy 71
3.4.3 Spatial coverage and data sources for the sector 75
3.4.4 EPA-developed livestock waste emissions data 76
3.4.5 Summary of quality assurance methods 80
3.4.6 References 80
3.5 Bulk Gasoline Terminals 81
3.5.1 Sector Description 81
3.5.2 Sources of data overview and selection hierarchy 81
3.5.3 Spatial coverage and data sources for the sector 81
3.6 Commercial Cooking 81
3.6.1 Sector Description 81
3.6.2 Sources of data overview and selection hierarchy 82
3.6.3 Spatial coverage and data sources for the sector 83
3.6.4 EPA-developed commercial cooking emissions data 84
3.6.5 Summary of Quality Assurance Methods 87
3.6.6 References 88
3.7 Dust - Construction Dust 88
3.7.1 Sector Description 88
3.7.2 Sources of data overview and selection hierarchy 89
3.7.3 Spatial coverage and data sources for the sector 91
3.7.4 Construction - Non-Residential - EPA estimates 92
3.7.5 Construction - Residential -EPA estimates 94
3.7.6 Construction - Road- EPA estimates 97
3.7.7 Summary of Quality Assurance Methods 100
3.8 Dust - Paved Road Dust 100
3.8.1 Sector Description 100
3.8.2 Sources of data overview and selection hierarchy 100
3.8.3 Spatial coverage and data sources for the sector 101
3.8.4 EPA methodology for paved road dust 101
3.8.5 Summary of Quality Assurance Methods 106
3.8.6 References 106
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3.9 Dust - Unpaved Road Dust 107
3.9.1 Sector Description 107
3.9.2 Sources of data overview and selection hierarchy 107
3.9.3 Spatial coverage and data sources for the sector 108
3.9.4 EPA methodology for unpaved road dust 109
3.9.5 Summary of Quality Assurance Methods 112
3.9.6 References 112
3.10 Fuel Combustion - Electric Generation 113
3.10.1 Sector Description 113
3.10.2 Sources of data overview and selection hierarchy 114
3.10.3 Spatial coverage and data sources for the sector 117
3.10.4 PM Augmentation for EGUs 118
3.10.5 EPA-developed EGU emissions data 119
3.10.6 Alternative facility and unit IDs needed for matching with other databases 120
3.10.7 Summary of quality assurance methods 121
3.11 Fuel Combustion - Industrial Boilers, ICEs 121
3.11.1 Sector Description 121
3.11.2 Sources of data overview and selection hierarchy 122
3.11.3 Spatial coverage and data sources for the sector 126
3.11.4 EPA-developed fuel combustion -Industrial Boilers, ICEs emissions data 128
3.11.5 Summary of quality assurance methods 129
3.11.6 References 130
3.12 Fuel Combustion - Commercial/Institutional 131
3.12.1 Sector Description 131
3.12.2 Sources of data overview and selection hierarchy 131
3.12.3 Spatial coverage and data sources for the sector 134
3.12.4 EPA-developed commercial/institutional fuel combustion data 136
3.12.5 Summary of quality assurance methods 137
3.12.6 References 138
3.13 Fuel Combustion - Residential - Natural Gas, Oil, and Other 138
3.13.1 Source Category Description 139
3.13.2 Sources of data overview and selection hierarchy 139
3.13.3 Spatial coverage and data sources for the sector 141
3.13.4 EPA Residential Heating estimates 142
3.13.5 Summary of quality assurance methods 142
3.14 Fuel Combustion - Residential - Wood 142
3.14.1 Sector Description 142
3.14.2 Sources of data overview and selection hierarchy 142
3.14.3 Spatial coverage and data sources for the sector 144
3.14.4 EPA developed residential wood combustion estimates 144
3.14.5 Summary of quality assurance methods 147
3.14.6 References 147
3.15 Gas Stations 148
3.15.1 Sector Description 148
3.15.2 Sources of data overview and selection hierarchy 148
3.15.3 Spatial coverage and data sources for the sector 148
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3.16 Industrial Processes - Cement Manufacturing 148
3.16.1 Sector Description 148
3.16.2 Sources of data overview and selection hierarchy 149
3.16.3 Spatial coverage and data sources for the sector 149
3.17 Industrial Processes - Chemical Manufacturing 149
3.17.1 Sector Description 149
3.17.2 Sources of data overview and selection hierarchy 149
3.17.3 Spatial coverage and data sources for the sector 149
3.18 Industrial Processes - Ferrous Metals 150
3.18.1 Sector Description 150
3.18.2 Sources of data overview and selection hierarchy 150
3.18.3 Spatial coverage and data sources for the sector 150
3.19 Industrial Processes - Mining 150
3.19.1 Sector Description 150
3.19.2 Sources of data overview and selection hierarchy 152
3.19.3 Spatial coverage and data sources for the sector 154
3.19.4 EPA Emissions- Mining 154
3.19.5 Quality Assurance Procedures 160
3.19.6 References 160
3.20 Industrial Processes - Non-ferrous Metals 161
3.20.1 Sector Description 161
3.20.2 Sources of data overview and selection hierarchy 161
3.20.3 Spatial coverage and data sources for the sector 161
3.21 Industrial Processes - Oil & Gas Production 161
3.21.1 Sector Description 161
3.21.2 Sources of data overview and selection hierarchy 164
3.21.3 Spatial coverage and data sources for the sector 166
3.21.4 EPA Emissions Calculation Approach 166
3.21.5 Summary of data Quality Assurance Methods 168
3.22 Industrial Processes - Petroleum Refineries 169
3.22.1 Sector Description 169
3.22.2 Sources of data overview and selection hierarchy 169
3.22.3 Spatial coverage and data sources for the sector 169
3.23 Industrial Processes - Pulp & Paper 169
3.23.1 Sector Description 169
3.23.2 Sources of data overview and selection hierarchy 169
3.23.3 Spatial coverage and data sources for the sector 169
3.24 Industrial Processes - Storage and Transfer 170
3.24.1 Sector Description 170
3.24.2 Sources of data overview and selection hierarchy 170
3.24.3 Spatial coverage and data sources for the sector 170
3.25 Industrial Processes - NEC (Other) 170
3.25.1 Sector Description 170
3.25.2 Sources of data overview and selection hierarchy 170
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3.25.3 Spatial coverage and data sources for the sector 170
3.26 Miscellaneous Non-industrial NEC (Other) 171
3.26.1 Sector Description 171
3.26.2 Sources of data overview and selection hierarchy 171
3.26.3 Spatial coverage and data sources for the sector 171
3.27 Solvent - Consumer & Commercial Solvent Use 171
3.27.1 Sector Description 171
3.27.2 Sources of data overview and selection hierarchy 171
3.27.3 Spatial coverage and data sources for the sector 171
3.28 Solvent - Degreasing, Dry Cleaning, and Graphic Arts 172
3.28.1 Sector Description 172
3.28.2 Sources of data overview and selection hierarchy 172
3.28.3 Spatial coverage and data sources for the sector 172
3.29 Solvent - Industrial and Non-Industrial Surface Coating 173
3.29.1 Sector Description 173
3.29.2 Sources of data overview and selection hierarchy 173
3.29.3 Spatial coverage and data sources for the sector 173
3.30 Waste Disposal 174
3.30.1 Sector Description 174
3.30.2 Sources of data overview and selection hierarchy 174
3.30.3 Spatial coverage and data sources for the sector 174
4 Mobile sources 175
4.1 Mobile sources overview 175
4.2 Aircraft 175
4.2.1 Sector Description 175
4.2.2 Sources of data overview and selection hierarchy 176
4.2.3 Spatial coverage and data sources for the sector 177
4.2.4 EPA-developed aircraft emissions estimates 178
4.2.5 Summary of quality assurance methods 180
4.2.6 References for Airports 181
4.3 Commercial Marine Vessels 181
4.3.1 Sector Description 181
4.3.2 Sources of data overview and selection hierarchy 182
4.3.3 Spatial coverage and data sources for the sector 183
4.3.4 EPA-developed commercial marine vessel emissions data 183
4.3.5 Summary of quality assurance methods 185
4.3.6 References for Commercial Marine 186
4.4 Locomotives 187
4.4.1 Sector Description 187
4.4.2 Sources of data overview and selection hierarchy 187
4.4.3 Spatial coverage and data sources for the sector 188
4.4.4 EPA-developed locomotive emissions data 188
4.4.5 Summary of quality assurance methods 189
4.4.6 References for Locomotives 190
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4.5 Nonroad Equipment - Diesel, Gasoline and other 190
4.5.1 Sector Description 190
4.5.2 Sources of data overview and selection hierarchy 191
4.5.3 Spatial coverage and data sources for the sector 193
4.5.4 EPA-developed NMIM-based nonroad emissions data 193
4.5.5 Summary of quality assurance methods 197
4.6 On-road - all Diesel and Gasoline vehicles 198
4.6.1 Sector Description 198
4.6.2 Sources of data overview and selection hierarchy 198
4.6.3 Calculation of EPA Emissions 221
4.6.4 EPA-developed on-road mobile emissions data for Alaska, Hawaii, Puerto Rico and the Virgin
Islands 238
4.6.5 Summary of quality assurance methods 240
4.6.6 Summary of quality assurance methods on emissions once selected to 2011NEIvl 241
4.6.7 References for On-road vehicles 242
5 Fires 243
5.1 Wildfires and Prescribed burning 243
5.1.1 Sector Description 243
5.1.2 Sources of data overview and selection hierarchy 244
5.1.3 Spatial coverage and data sources for the sector 245
5.1.4 EPA-developed fire emissions estimates 245
5.1.5 Summary of quality assurance methods 253
5.1.6 References for Wildfires and Prescribed burning 254
5.2 Fires - Agricultural field burning 255
5.2.1 Sector Description 255
5.2.2 Sources of data overview and selection hierarchy 255
5.2.3 Spatial coverage and data sources for the sector 257
5.2.4 EPA-developed agricultural emissions data 260
5.2.5 Summary of quality assurance methods 263
5.2.6 References for Agricultural Burning 264
6 Biogenics - Vegetation and Soil 266
6.1 Sector Description 266
6.2 Sources of data overview and selection hierarchy 267
6.3 Spatial coverage and data sources for the sector 267
7 Quality assessment 269
7.1 What are the quality criteria used to assess the inventory? 269
7.2 How did the 2011 NEI compare to the quality criteria? 269
7.3 What EIS sectors seem to be incomplete and for which key pollutants? 269
7.4 How can the quality of the emissions data be further evaluated by users? 269
7.5 What improvements in the NEI and EIS submission process are planned for the future? 269
8 Supporting data and summaries 270
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List of Tables
Table 1: Point source reporting thresholds (potential to emit) for criteria pollutants in the Air Emissions
Reporting Rule 5
Table 2: Examples of major current uses of the NEI 6
Table 3: EIS sectors and associated emissions categories and document sections 8
Table 4: EIS sectors and associated CAP emissions and total HAP (1000 short tons/year) 17
Table 5: Emission differences for CAPs as shown in Figures 9 and 10 21
Table 6: Emission Sum Differences for HAP Emissions 2011-2008 as shown in Figure 11 through Figure 13 21
Table 7: Tribal Participation in the 2011 NEI 26
Table 8: Facilities on Tribal Lands with 2011 NEI emissions from EPA only 26
Table 9: Datasets, groups, and amount of Hg in 2011 NEI from each 28
Table 10: Trends in Mercury Emissions - 1990, 2005, 2008 and 2011 29
Table 11: Data sources and selection hierarchy used for point sources 33
Table 12: Data sources and selection hierarchy used for nonpoint sources 35
Table 13: Valid chromium pollutant codes 36
Table 14: Mapping of TRI Pollutant Codes to EIS Pollutant codes 42
Table 15: Pollutant Groups 45
Table 16: Nickel species in the NEI from the HAP-augmentation dataset which should not have been used 48
Table 17: Lead from HAP-augmentation from coal combustion that was not used 49
Table 18: EPA-estimated emissions sources expected to be exclusively nonpoint 50
Table 19: Emissions sources with potential nonpoint and point contribution 52
Table 20: 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
54
Table 21: 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 55
Table 22: SCCs used in past inventories that were not included in EPA's 2011 nonpoint estimates 56
Table 23: SCCs used in the 2011 NEI for the Agriculture - Crops & Livestock Dust Sector 57
Table 24: Agencies that Submitted Agricultural Crops and Livestock Dust Data 57
Table 25: 2011 NEI agricultural crops and livestock dust data selection hierarchy 58
Table 26: Silt Content for Soil Types in USDA Surface Soil Map 59
Table 27: Number of Passes or Tillings per Year 59
Table 28: Crosswalk between Crop Residue Management Category and USDA Data 60
Table 29: Acres Planted by Tillage Type, Fallow and Pasture in 2008 and 2011 61
Table 30: Agencies Tagged Values for Agriculture - Crop and Livestock Dust 62
Table 31: Source Categories for Agricultural Fertilizer Application 63
Table 32: Agencies that Submitted Agricultural Fertilizer Application Data 64
Table 33: 2011 NEI Agricultural Fertilizer Application Data Selection Hierarchy 64
Table 34: Fertilizers Assigned to Fertilizer Groups 66
Table 35: Fertilizer Nitrogen Content 68
Table 36: Fertilizer Emission Factors 68
Table 37: Agencies Tagged Values for Agriculture - Fertilizer 70
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Table 38: Nonpoint SCCs with 2011 NEI Emissions in the Livestock Waste Sector 71
Table 39: Point SCCs with 2011 NEI Emissions in the Livestock Waste Sector - reported only by States 74
Table 40: Agencies that Submitted Livestock Waste Data 74
Table 41: 2011 NEI agricultural livestock data selection hierarchy 75
Table 42: Emission Factors for NH3 emissions used for EPA's agricultural livestock data 78
Table 43: Agencies Tagged Values for Agriculture Livestock 80
Table 44: Source Classification Codes used in the Commercial Cooking sector 81
Table 45: Agencies that Submitted Commercial Cooking Data 82
Table 46: 2011 NEI Commercial Cooking Data Selection Hierarchy 83
Table 47: Ratio of filterable particulate matter to primary particulate matter for PM2.5 and PM10 by SCC 85
Table 48. Fraction of restaurants with source category equipment and average number of units per restaurant.
86
Table 49: Agencies Tagged Values for Commercial Cooking 88
Table 50: SCCs in the 2011 NEI in the Dust - Construction Dust Sector 89
Table 51: Agencies that Submitted Construction Dust Data 90
Table 52: 2011 NEI Construction Dust Data Selection Hierarchy 91
Table 53: SCC for Non-Residential Construction 92
Table 54: SCC for Residential Construction 94
Table 55: Surface Soil removed per unit type 95
Table 56: Emission Factors for Residential Construction 95
Table 57: SCC for Road Construction 97
Table 58: Spending per Mile and Acres Disturbed per Mile by Highway Type 98
Table 59: SCCs used for Paved Road Dust - 2011 NEI 100
Table 60: Agencies that Submitted Paved Road Dust Data 100
Table 61: 2011 NEI Paved Road Dust Data Selection Hierarchy 101
Table 62: Rule effectiveness was assumed to be 100% for all counties where this control was applied 103
Table 63: 2011 Silt Loadings by State and Roadway Class Modeled in Paved Road Emission Factor Calculations
(g/m2) 104
Table 64: Average Vehicle Weights by MOBILE6 Vehicle Class 105
Table 65: Penetration Rate of Paved Road Vacuum Sweeping 106
Table 66: SCCs used for Unpaved Road Dust - 2011 NEI 107
Table 67: Agencies that Submitted Unpaved Road Emissions Data 107
Table 68: 2011 NEI Unpaved Roads Data Selection Hierarchy 108
Table 69: Constants for Unpaved Roads Reentrained Dust Emission Factor Equation [ref 1] 110
Table 70: Speeds Modeled by Roadway Type on Unpaved Roads 110
Table 71: Assumed Values for Average Daily Traffic Volume (ADTV) by Volume Group Ill
Table 72: Agencies that submitted 2011 EGU data by EGU fuel groups 114
Table 73: 2011 NEI EGU data selection hierarchy by EGU fuel groups 116
Table 74: Agency-submitted, PM Augmentation, and total PM10 and PM2.5 emissions for EGU sectors 119
Table 75: Agencies that submitted data for the Fuel Combustion - Industrial Boilers, ICEs Sectors 122
Table 76: 2011 NEI selection hierarchy for datasets used by the Fuel Comb - Industrial Boilers, ICEs Sectors ... 124
Table 77: Algorithm to determine whether to augment state data with EPA data for Industrial Boilers sector. 125
Table 78: Agencies Tagged Values for Industrial Fuel Combustion 129
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Table 79: Agencies that Submitted Commercial/Institutional Fuel Combustion Data 132
Table 80: 2011 NEI Commercial/Institutional Fuel Combustion Data Selection Hierarchy 133
Table 81: Assumptions Used to Estimate Commercial/Institutional Sector Stationary Source Distillate Fuel
Consumption 137
Table 82: Agencies Tagged Values for Commercial/Institutional Fuel Combustion 138
Table 83: SCCs in the Residential Fuel Combustion Sectors (except Wood) in the 2011 NEI 139
Table 84: Agencies that submitted data for Fuel Combustion - Residential Heating - Natural Gas, Oil and Other
140
Table 85: SCCs in the Residential Wood Combustion Sector in the 2011 NEI 142
Table 86: Agencies that submitted data for the sector Fuel Combustion - Residential Heating - Wood 143
Table 87: Datasets Included in the Fuel Comb - Residential - Wood sector 143
Table 88: MSA's using updated AHS data for residential wood combustion 145
Table 89: SCCs for Industrial Processes- Mining 150
Table 90: Agencies that submitted data for the Industrial Processes - Mining Sector 152
Table 91: Summary of Emission Factors 157
Table 92: NAICS Codes for Metallic and Non-Metallic Mining 157
Table 93: 2006 County Business Pattern for NAICS 31-33 in Maine 158
Table 94: Point and nonpoint SCCs used for the Oil and Gas Production Sector 161
Table 95: Agencies that submitted data for the Industrial Processes - Oil and Gas Production Sector 164
Table 96: 2011 NEI Industrial Processes - Oil & Gas Production data selection hierarchy 166
Table 97: List of Comments and Resolution for Building the 2011 NEI for the Oil and Gas Production Sector... 168
Table 98: Source classification codes for the aircraft sector in the 2011 NEI 176
Table 99: Agencies that submitted 2011 aircraft emissions data 177
Table 100: 2011 NEI aircraft data selection hierarchy 177
Table 101: Agencies that submitted aircraft activity data for EPA's emissions calculation 178
Table 102: Commercial Marine SCCs and Emission Types in EPA Estimates 182
Table 103: Additional Commercial Marine SCCs used by Washington 182
Table 104: Agencies that Submitted Commercial Marine Emissions Data 182
Table 105: 2011 NEI commercial marine vehicle selection hierarchy 182
Table 106: Example of Selection Result in Merging EPA and S/L/T CMV (VOC in Tons) 186
Table 107: Locomotive SCCs, descriptions, and EPA estimation status 187
Table 108: Agencies that submitted Rail Emissions to the 2011 NEI 187
Table 109: Compare NOx among EPA, S/L/T, and 2011vlNEI Selection for Rail 189
Table 110: NMIM Equipment and Fuel Types 191
Table 111: Selection Hierarchy for the Nonroad Data Category 192
Table 112: S/L/T Agency Submitted Data for Nonroad 192
Table 113: NCD Tables Provided in State and Local NCD Submissions 194
Table 114: State-assisted NCD Table Updates 195
Table 115: SCC/Emissions Type with Missing VOC in CA submittal 197
Table 116: Agency Submission History for Onroad 199
Table 117: Selection Hierarchy for the On-road Data Category 201
Table 118: MOVES CDB Tables 202
Table 119: Summary of EPA's MOVES Technical Guidance for CDB Inputs 203
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Table 120: Number of counties with submitted data, by state and MOVES CDB input table 205
Table 121: Source of Defaults for Data Tables in MOVES CDB 211
Table 122: States adopting California LEV Standards, Start Years 212
Table 123: VMT Fraction by HPMS Vehicle Type 217
Table 124: Converting FHWA to MOVES Vehicle Types 218
Table 125: Mapping of MOVES Source Types to FHWA Vehicle Populations from Highway Statistics Tables MV-2
and MV-9 219
Table 126: Default State-level VMT:VPOP ratios derived from 2011 Highway Statistics 220
Table 127: Characteristics for grouping counties 223
Table 128: MOVES Vehicle Types (HMPSVtypelD) 226
Table 129: SCC Vehicle Types (SCC7) 227
Table 130: Ratio of HPMS to SCC Vehicle Types 227
Table 131: MOVES Road Types 228
Table 132: MOVES Source Types 228
Table 133: SCC Road Types 229
Table 134: Distribution of Trip Starts and Ends 231
Table 135: Distribution of Length of Extended Idle 232
Table 136: Fraction of Operating Hours Spent Idling 232
Table 137: Distribution of Demand for Overnight Parking by State 234
Table 138: Supplemental Input Data for MOVES2010b for 2011NEI 238
Table 139: Source classification codes for wildland fires 244
Table 140: Agency that submitted wildfire and prescribed burning emissions data 244
Table 141: 2011 NEI wildfire and prescribed fires selection hierarchy 244
Table 142: Pollutants estimated by EPA* for wildland fires and HAP emission factors 246
Table 143: SF2 and State-Submitted acres burned for FL WLFs 249
Table 144: Source Classification Codes in the NEI for Agricultural Burning 255
Table 145: Agencies that submitted agricultural fire emissions to the 2011 NEI 256
Table 146: Data source and selection hierarchy used for agricultural fire emissions 256
Table 147: Emission Estimates for Agricultural Burning (short tons/year) using EPA Methods 258
Table 148: Source classification codes for Biogenics - Vegetation and Soil 266
Table 149: State Summary of Biogenics - Vegetation and Soil Emissions (short tons/year) 267
List of Figures
Figure 1: Data sources for point and nonpoint emissions for criteria pollutants 11
Figure 2: Data sources for onroad and nonroad mobile emissions for criteria pollutants 12
Figure 3: Data sources of emissions for acid gases and HAP VOCs, by data category 12
Figure 4: Data sources of emissions for Pb and HAP metals, by data category 13
Figure 5: Point inventory - submission types - includes local agencies 14
Figure 6: Nonpoint inventory - submission types - includes local agencies 15
Figure 7: On-road inventory - submission types - does not include local agencies 15
Figure 8: Nonroad equipment inventory - submission types - does not include local agencies 16
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Figure 9: Comparison of CAP Emissions from 2008 to 2011, Excluding Wildfires and Biogenics 22
Figure 10: Comparison of Wildfire CAP Emissions from 2008 to 2011 23
Figure 11: Comparison of Select HAP Emissions from 2008 to 2011, Excluding Wildfires and Biogenics, Select
HAPs- Group 1 24
Figure 12: Comparison of Select HAP Emissions from 2008 to 2011, Excluding Wildfires and Biogenics, Select
HAPs- Group 2 24
Figure 13: Comparison of Wildfire HAP Emissions from 2008 to 2011 25
Figure 14: Data sources of Hg emissions in the 2011 NEI, by data category 27
Figure 15: States with state- or local-provided Hg emissions in the point data category of the 2011 NEI 29
Figure 16: Dark blue indicates States/Counties that submitted at least 1 CDB input 207
Figure 17: Breakdown of CDB inputs provided by States that submitted at least 1 input 208
Figure 18: The coverage of state-submitted fire activity data sets 247
Figure 19: Proportion of Fires by Type using EPA Methods 251
Figure 20: Acres burned using EPA Methods 252
Figure 21: 2011 PM2.5 Emissions using EPA methods 252
Figure 22: Difference map of PM2.5 Emissions, with and without large fires 253
Figure 23: 2011 PM2.5 wild land fire emissions using EPA methods 254
Figure 24: 2011 NEI state-total PM2.5 emissions from agricultural fires 259
Figure 25: States that submitted agricultural burning emissions to the NEI 260
Figure 26: EPA's Geospatial method for producing Cropland Burning Emissions for 2011 NEI 261
Figure 27: PM2.5 Emissions from Agricultural Burning, 2011 EPA Data 263
Figure 28: Comparison of percentage of PM2.5 emissions assigned to agricultural, prescribed and wild fires.... 264
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Acronyms and Chemical Notations
AERR Air Emissions Reporting Rule
APU Auxiliary power unit
BEIS Biogenics Emissions Inventory System
BSO Benzene Soluble Organics
CI Category 1 (commercial marine vessels)
C2 Category 2 (commercial marine vessels)
C3 Category 3 (commercial marine vessels)
CAIR Clean Air Interstate Rule
CAMD Clean Air Markets Division (of EPA Office of Air and Radiation)
CAP Criteria Air Pollutant
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
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
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FWS United States Fish and Wildlife Service
FRS Facility Registry System
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 Transboundary 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
MSO Methylene Chloride Soluble Organics
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
NH3 Ammonia
NMIM National Mobile Inventory Model
NO Nitrous oxide
N02 Nitrogen dioxide
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NOAA National Oceanic and Atmospheric Administration
NOx Nitrogen oxides
03 Ozone
OAQPS Office of Air Quality Standards and Planning (of EPA)
OEI Office of Environmental Information (of EPA)
ORIS Office of Regulatory Information Systems
OTAQ Office of Transportation and Air Quality (of EPA)
PADD Petroleum Administration for Defense Districts
PAH Polycyclic Aromatic Hydrocarbons
Pb Lead
PCB Polychlorinated Biphenyl
PM Particulate matter
PM25-CON Condensable PM2.5
PM25-FIL Filterable PM2.5
PM25-PRI Primary PM2.5 (condensable plus filterable)
PM2.5 Particulate matter 2.5 microns or less in diameter
PM 10 Particular matter 10 microns or less in diameter
PM10-FIL Filterable PM10
PM10-PRI Primary PM10
POM Polycyclic Organic Matter
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
S04 Sulfate
TAF Terminal Area Forecasts
TEISS Tribal Emissions Inventory Software Solution
TRI Toxics Release Inventory
UNEP United Nations Environment Programme
USDA United States Department of Agriculture
VMT Vehicle miles traveled
VOC Volatile organic compounds
xiv
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USFS
United States Forest Service
WebFIRE
Factor Information Retrieval System
WFU
Wildland fire use
WLF
Wildland fire
WRAP
Western Regional Air Partnership
WRF
Weather Research and Forecasting Model
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1 Introduction
1.1 What data are included in t] ?
The 2011 National Emissions Inventory (NEI), version 1 (hereafter referred to as the 2011 NEI) 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 related to implementation of 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), ammonia (NH3), particulate
matter 10 microns or less (PMio) and particulate matter 2.5 microns or less (PM2.5). The HAP pollutants include
the 187 remaining HAP pollutants 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 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 NEI 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 a quality assessment of the 2011 NEI. Finally, Section 8 provides instructions for
accessing supporting materials. A separate document contains the appendices.
1 The current list of HAPs is available at http://www.epa.gov/ttn/atw/188polls.html.
1
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1,3 Where can I obtain the a?
The 2011 NEI data are available in several different ways, as follows. EPA continues to review and streamline
the approach for accessing the NEI data.
1.3.1 Emission Inventory System Gateway
http;//www.epj,gov/tttv*chief/eiVgi>teway/
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 NEI vl in EIS is called "2011 NEI vl with biogenics". Note
that if you run facility, unit or process level reports in EIS, you will get the 2011 NEI vl emissions, but the facility
inventory, which is dynamic in 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 (Summer 2013), then that new Agency ID will
be in the Facility Inventory or a Facility Configuration report in EIS but not in the report on the public website
nor the Facility Emissions Summary reports run on the"2011 NEI vl with biogenics" in EIS.
1.3.2 2 011 1EI mal n we b page
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.
2
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1.3.3 Air Emissions and "Where you live"
Main: http://www.epa.gov/air/emissions/
Where you live: http://www.epa.gov/air/emissions/where.htm
NOTE: This site may not yet contain the 2011 NEI emissions, but will be updated by the end of calendar year
2013. 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.
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
http://www.epa.gOv/ttn/chief/emch/index.html#2011
The modeling files are provided in formats that can be read by the Sparse Matrix Operator Kernel Emissions
(SMOKE, http://www.smoke-model.org). These formats 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 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 U.S. public, and other countries the
U.S.'s 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
3
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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).
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 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.
4
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Table 1: Point source reporting thresholds (potential to emit) for criteria
pollutants in the Air Emissions Reporting Rule
Pollutant
2011 NEI thresholds: potential to emit (tons/yr)
Everywhere
(Type B sources)
NAA sources1
1 SOx
> 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 PMio
> 100
PMio (moderate) > 100
11 PMio
PMio (serious) > 70
12 PM25
> 100
> 100
13 NH3
> 100
> 100
1 NAA = Nonattainment Area. Special point source reporting thresholds apply for certain
pollutants by type of nonattainment area. The pollutants by nonattainment area are:
Ozone: VOC, NOx, CO; CO: CO; PMi0: PMi0
Based on the AERR requirements, S/L/T 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.
1,6 Who are the target audiences f IT!
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 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.
5
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Table 2: Examples of major current uses of the NEI
Audience
Purposes
Last NEI
data used
U.S. Public
Learn about sources of air emissions
2011NEI vl
EPA - NAAQS
Regulatory Impact Analysis - benefits estimates using air quality
modeling
Modified 2005 NEI v2, for
PM NAAQS Proposal,
Modified 2008 NEI v2, for
PM NAAQS Final
PM and SO2 NAAQS Implementation
2011 NEI vl
SO2 NAAQS Monitoring Implementation - Population Weighted
Emissions Index
2008 NEI v3 with some
2009 data
Pb Monitoring Rule
2005 NEI v2
Pb NAAQS final designations
2008 NEI v3
Pb NAAQS Policy Assessment
Modified 2008 NEI v3
Transport Rule air quality modeling (e.g., Clean Air Interstate Rule,
Cross-State Air Pollution Rule)
2005 NEI v2
State Implementation Plans - source of emissions data for regions
outside of the state jurisdiction
2011 NEI vl
EPA-Air toxics
National Air Toxics Assessment (NATA)
Modified 2005 NEI, v2;
will be updated with 2011
NEI
Mercury and Air Toxics Standard - mercury risk assessment and
Regulatory Impact Assessment
Modified 2005 NEI, v2
Residual Risk and Technology Review - starting point for inventory
development
2008 NEI v3
EPA - other
Inspector General - review of oil and gas industry
2008 NEI vl.5
NEI booklet - analysis of emissions inventory data
2002 NEI v2
Report on the Environment
2008 NEI v3
Air Emissions website for providing graphical access to CAP emissions
for state maps and Google Earth views of facility total emissions
2011 NEI vl
Department of Transportation, national transportation sector
summaries of CAPs
2008 NEI vl.5
Black Carbon Report to Congress
Modified 2005 NEI, v2
Other federal or
regional agencies
Western Regional Air Partnership - modeling in support of Regional
Haze SIPs and other air quality issues
Modified 2008 NEI 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)
2005 NEI v2
United Nations Environment Programme (UNEP) - global mercury
program
2008 NEI, v2
North American Commission for Environmental Cooperation (CEC) -
North American Regional Action Plan (NARAP) on Mercury
Modified 2005 NEI, v2
1,7 What are appropriate uses of t: " • •' ! . •, wrsi ¦ ¦' • 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 users 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
6
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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
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 2010b (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 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, 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 PM2.5 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.
The spreadsheet "2011neivl issues.xlsx" provides a detailed listing of the issues that were identified during the
course of the development of the 2011 NEI and the current status of those issues. This issues list is also available
from the main 2011 NEI data page listed in Section 1.3.2.
In addition to the 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, 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 PMio and PM2.5-
2 See http://www.epa.gov/otaq/models/moves/index.htm
3 See http://www.epa.gov/otaq/m6.htm
7
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2 2011 inventory contents overview
2,1 What are EIS Sectors ami what list was used for this document?
First used for the 2008 NEl, 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 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 3 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 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 3: EIS sectors and associated emissions categories and document sections
Sector name
Point
Nonpoint
On-road
Nonroad
Event
Document
Section
Agriculture - Crops & Livestock Dust
0
0
Agriculture - Fertilizer Application
0
3.3
Agriculture - Livestock Waste
0
0
3.4
Biogenics - Vegetation and Soil
0
6
Bulk Gasoline Terminals
0
0
0
8
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Sector name
Point
Nonpoint
On-road
Nonroad
Event
Document
Section
Commercial Cooking
0
3.6
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
3.13.4
Gas Stations
0
0
3.15
Industrial Processes - Cement Manufacturing
0
3.16
Industrial Processes - Chemical Manufacturing
0
0
3.17
Industrial Processes - Ferrous Metals
0
3.18
Industrial Processes - Mining
0
0
3.19
Industrial Processes - NEC
0
0
3.25
Industrial Processes - Non-ferrous Metals
0
0
3.20
Industrial Processes - Oil & Gas Production
0
0
3.21
Industrial Processes - Petroleum Refineries
0
0
3.22
Industrial Processes - Pulp & Paper
0
3.23
Industrial Processes - Storage and Transfer
0
0
3.24
Miscellaneous Non-Industrial NEC
0
0
3.26
Mobile - Aircraft
0
0
4.2
9
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Sector name
Point
Nonpoint
On-road
Nonroad
Event
Document
Section
Mobile - Commercial Marine Vessels
0
4.3
Mobile - Locomotives
0
0
4.4
Mobile - Non-Road Equipment - Diesel
0
0
4.5
Mobile - Non-Road Equipment - Gasoline
0
0
4.5
Mobile - Non-Road Equipment - 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
3.27
Solvent - Degreasing
0
0
3.28
Solvent - Dry Cleaning
0
0
3.28
Solvent - Graphic Arts
0
0
3.28
Solvent - Industrial Surface Coating & Solvent Use
0
0
3.29
Solvent - Non-Industrial Surface Coating
0
3.29
Waste Disposal
0
0
3.30
2,2 What do the iata show about the sources of iata in tin EI?
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 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 PMio 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.
10
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Figure 1: Data sources for point and nonpoint emissions for criteria pollutants
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EPA Nonpoint
I EPA PM Aug
EPA other
EPAEGU
I EPA Air/Rail/CMV
S/L/T
1 Nonpoint emission shown here exclude biogenic sources, which are all EPA data
The data sources for the emissions from nonroad, on-road and event data categories are shown in Figure 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).
11
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Figure 2: Data sources for onroad and nonroad mobile emissions for criteria pollutants
¦ EPA Onroad
¦ EPA Nonroad
In Figure 3, the nonpoint acid gases are very small, with 6,700 tons from S/L/T agencies and 3,500 tons from the
EPA nonpoint dataset. For point sources, the bulk of the acid gases emissions (primarily HCI) comes from two
EPA EGU datasets (75,000 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 3: Data sources of emissions for acid gases and HAP VOCs, by data category
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Figure 4 shows emissions sources for Pb and HAP metal emissions. For nonpoint sources, almost all of 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 (260 tons),
while the EPA nonroad dataset airport emissions makes up a substantial part of the rest (230 tons). For metals,
the point sources data has a significant portion from S/L/T agencies (1,600 tons), with the rest from the EPA EGU
dataset (800 tons), TRI (400 tons), and other EPA datasets (220 tons).
Figure 4: Data sources of emissions for Pb and HAP metals, by data category
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The figures below provide more detail about which states submitted data to the NEI for the stationary and
mobile categories. In Sections 3 through 5, we explain more about what data actually were used by EPA in
creating the NEI for each sector. Usually, but not always, EPA uses the data provided by the states. These
figures present the states for which data were used by EPA in compiling the 2011 NEI.
Figure 5 shows that all states submitted point source CAP emissions. All states except Utah, South Dakota and
Alaska submitted point source HAP emissions (at least one HAP pollutant). Though not shown in the figure,
Georgia submitted point HAPs only for airports and only a local agency in Nevada (not the state agency4)
submitted HAPs. Generally, when states submitted CAP emissions they submitted all of the CAPs, but for HAP
emissions there is more variability in the data provided. S/L/T generally report what they collect, and collection
varies depending on state, local, and tribal reporting regulations. Puerto Rico and the Virgin Islands are not
4 Though the Nevada Division of Environmental Protection does not submit HAPs to EIS, they do provide mercury emissions
data to EPA for gold mines from their annual emissions reporting program (EPA NV Gold Mines dataset listed in Table 11)
13
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shown in Figure 5. Puerto Rico submitted point source CAP emissions for 2011. Virgin Islands did not emissions
for any data category.
Figure 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 and Texas are based on the EPA's run of the
MOVES2010b 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. Texas provided emissions using
the MOVES2010b model. Figure 7 shows the states that submitted onroad model inputs, emissions or accepted
EPA estimates. Several states provided consultation on representative counties (i.e., groups of counties that
share emission factors), but those states were not counted in the map as having provided inputs in this figure.
Section 4.6 has more detail and identifies the local agencies that submitted inputs.
5 See "EMFAC Overview" link available at http://www.arb.ca.gov/msei/onroad/background.htm
Figure 5: Point inventory - submission types - includes local agencies
2011 Point Submissions
I I No Submission
~ CAP/HAP |
¦ CAP
14
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Figure 6: Noripoirit inventory - submission types - includes local agencies
2011 Nonpoint Submissions
| None
|CAPHAP
¦ CAP
Figure 7: On-road inventory - submission types - does not include local agencies
2011 Onroad Submissions
| Accepted EPA Data
| Emissions
| Inputs
15
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As seen in Figure 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 8: Nonroad equipment inventory - submission types - does not include local agencies
2011 Nonroad Submissions
| Accepted EPA Estimates
~ Emissions
| Inputs
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 of 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
6 The OFFROAD model and documentation are available at http://www.arb.ca.gov/msei/offroad/offroad.htm.
16
-------
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.
Table 4: EIS sectors and associated CAP emissions and total HAP (1000 short tons/year)
1000 short tons / year
Sector
CO
voc
NOx
SO,
PM,..
PMi„
NH:
Lead
Total
HAP1
Agriculture - Crops & Livestock Dust
897
4,506
Agriculture - Fertilizer Application
1,185
Agriculture - Livestock Waste
0.14
56
0.13
8.45E-03
0.22
0.50
2,344
0.04
Bulk GasolineTerminals
0.70
153
0.31
4.04E-03
0.02
0.02
0.02
8.33E-04
7.39
Commercial Cooking
31
13
84
88
5.26
Dust - Construction Dust
0.02
0.02
0.02
7.14E-04
163
1,509
0.05
Dust - Paved Road Dust
270
1,134
Dust - Unpaved Road Dust
832
8,329
Fires - Agricultural Field Burning
1,443
112
65
27
141
225
3.47
4.50E-04
80
Fires - Prescribed Fires
10,308
2,375
171
85
921
1,085
166
261
Fires - Wildfires
14,494
3,320
195
105
1,267
1,493
233
322
Fuel Comb - Comm/lnstitutional - Biomass
23
0.64
8.51
1.08
11
13
0.13
3.49E-04
0.26
Fuel Comb - Comm/lnstitutional - Coal
6.63
0.22
17
60
1.40
3.69
0.06
2.42E-03
1.70
Fuel Comb - Comm/lnstitutional - Natural Gas
112
10
153
1.61
5.94
6.17
1.55
2.36E-03
1.47
Fuel Comb - Comm/lnstitutional - Oil
17
2.03
70
58
6.77
8.09
0.75
8.39E-04
0.15
Fuel Comb - Comm/lnstitutional - Other
8.58
0.91
7.51
1.23
0.63
0.66
0.02
2.91E-04
0.12
Fuel Comb - Electric Generation - Biomass
21
0.75
11
2.36
1.87
2.18
0.97
0.001
2.14
Fuel Comb - Electric Generation - Coal
615
25
1,793
4,526
167
237
9.05
0.04
93
Fuel Comb - Electric Generation - Natural Gas
101
9.82
172
5.87
25
26
11
8.18E-04
3.63
Fuel Comb - Electric Generation - Oil
13
2.30
95
76
5.87
7.99
1.09
1.44E-03
0.51
Fuel Comb - Electric Generation - Other
34
3.25
26
20
2.55
2.89
2.94
1.58E-03
1.15
Fuel Comb - Industrial Boilers, ICEs- Biomass
247
8.69
106
23
105
123
2.48
8.16E-03
5.70
Fuel Comb - Industrial Boilers, ICEs - Coal
46
1.37
174
483
21
60
0.87
0.01
16
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
344
65
691
17
26
27
6.32
3.62E-03
22
Fuel Comb - Industrial Boilers, ICEs - Oil
29
3.17
105
100
8.80
11
0.61
3.23E-03
0.55
Fuel Comb - Industrial Boilers, ICEs - Other
122
7.77
57
53
24
26
1.09
3.84E-03
3.04
Fuel Comb - Residential - Natural Gas
94
13
219
1.44
4.24
4.41
41
1.06E-04
0.97
Fuel Comb - Residential - Oil
11
1.42
43
92
4.52
5.18
2.12
3.01E-03
0.10
Fuel Comb - Residential - Other
61
3.09
41
9.41
0.99
1.45
0.48
7.97E-06
0.27
Fuel Comb - Residential - Wood
2,588
449
36
9.01
390
390
20
2.10E-04
75
Gas Stations
0.03
644
0.02
1.51E-03
1.45E-03
1.54E-03
2.13E-04
3.75E-04
81
Industrial Processes - Cement Manuf
76
4.32
117
60
6.48
12
0.91
3.79E-03
2.31
Industrial Processes - Chemical Manuf
185
92
74
133
19
24
24
4.40E-03
29
Industrial Processes - Ferrous Metals
417
17
56
28
29
35
0.22
0.06
1.94
Industrial Processes - Mining
32
1.66
34
2.07
74
487
0.11
6.10E-03
0.75
Industrial Processes - NEC
207
195
177
137
89
148
47
0.06
46
Industrial Processes - Non-ferrous Metals
331
15
15
103
16
20
0.52
0.08
9.49
Industrial Processes - Oil & Gas Production
668
2,445
678
73
19
24
0.11
1.06E-04
54
Industrial Processes - Petroleum Refineries
50
56
74
85
22
25
2.82
2.94E-03
6.28
Industrial Processes - Pulp & Paper
107
117
71
32
33
41
5.83
3.63E-03
51
Industrial Processes - Storage and Transfer
19
231
15
8.79
18
50
5.72
0.01
14
Miscellaneous Non-Industrial NEC
11
202
2.71
0.23
2.05
2.19
2.74
5.32E-04
22
Mobile - Aircraft
424
30
111
14
7.34
8.63
0.49
8.24
Mobile - Commercial Marine Vessels
85
15
505
65
17
18
0.24
1.65E-03
1.95
Mobile - Locomotives
131
46
862
8.52
26
28
0.37
2.24E-03
5.01
Mobile - Non-Road Equipment - Diesel
726
130
1,291
2.88
101
104
1.19
1.05E-05
29
Mobile - Non-Road Equipment - Gasoline
12,886
1,912
253
1.10
54
59
0.86
429
Mobile - Non-Road Equipment - Other
708
27
112
0.70
2.08
2.08
0.61
0.09
Mobile - On-Road Diesel Heavy Duty Vehicles
887
213
2,774
4.26
130
149
6.00
42
Mobile - On-Road Diesel Light Duty Vehicles
44
8.98
64
0.12
3.92
4.46
0.35
1.57
Mobile - On-Road Gasoline Heavy Duty Vehicles
1,947
122
231
1.31
3.42
6.64
4.70
33
Mobile - On-Road Gasoline Light Duty Vehicles
23,929
2,069
2,717
23
71
130
109
564
Solvent - Consumer & Commercial Solvent Use
0.03
1,676
0.01
7.70E-03
0.01
0.02
809
Solvent - Degreasing
7.16E-03
144
4.94E-03
4.55E-04
0.06
0.06
0.03
7.48E-05
23
Solvent - Dry Cleaning
1.88E-04
11
5.73E-04
5.73E-04
4.15E-05
66
Solvent - Graphic Arts
0.14
75
0.15
0.01
0.17
0.18
0.08
7.21E-05
7.57
17
-------
1000 short tons / year
Sector
CO
VOC
NOx
so,
PM,..
PMi„
NHj
Lead
Total
HAP1
Solvent - Industrial Surface Coating & Solvent
Use
3.43
576
2.32
0.43
3.78
4.23
0.59
3.63E-03
199
Solvent - Non-Industrial Surface Coating
334
0.02
167
Waste Disposal
1,116
126
84
17
172
201
67
0.01
32
Sub Total (no federal waters)
75,760
18,169
14,574
6,557
6,306
20,907
4,316
0.82
3,641
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
78
1.42
64
0.04
0.38
0.38
Fuel Comb - Industrial Boilers, ICEs - Oil
1.83
0.35
7.55
0.72
0.33
0.34
Fuel Comb - Industrial Boilers, ICEs - Other
5.02E-03
3.06E-04
4.47E-03
2.84E-05
9.69E-05
9.69E-05
Industrial Processes - Oil & Gas Production
1.85
58
2.31
0.27
0.06
0.06
Industrial Processes - Storage and Transfer
0.91
Mobile - Commercial Marine Vessels
129
34
1,035
223
36
38
0.34
2.32E-03
1.66
Sub Total (federal waters)
211
95
1,110
224
36
39
0.34
2.32E-03
1.66
Sub Total (all but vegetation and soil)
75,971
18,264
15,684
6,781
6,342
20,946
4,316
0.82
3,643
Biogenics - Vegetation and Soil'
6,528
39,653
1,018
5,101
Total
82,499
57,917
16,702
6,781
6,342
20,946
4,316
0.82
8,744
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, and 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 PM2.5, and we required PMio to be reported if PM2.5 was reported for the same process. If either of
these criteria were not met (HAP group, or PMio vs PM2.5 magnitude) then none of the pollutants submitted for
18
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the process were allowed into EIS for that process. Another new check was to allow only certain pollutant-
emision type combinations to be reported for on-road and nonroad data categories.
We also implemented a data tagging process in EIS. This allowed EPA to tag suspect data and communicate it
using 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 11 and Table 12 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 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 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
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 EIS, S/L agencies submitted on-road inputs in the form of MOVES county database files. Prescribed and
wildfire inputs were collected outside of EIS. For nonroad mobile sources, we encouraged S/L agencies to
provide inputs to NMIM via 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 PM10
through PM Augmentation of the MATS PM2.5 data and used the resultant EFs along with 2011 heat input to
estimate PMio emissions for the tested units. The EPA data were used ahead of the S/L/T PM2.5 and PMio except
where the S/L/T PM data were indicated by the S/L/T agency to have been from measurement data.
19
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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
fugitive for the NEI and assigned to generic placeholder stack and fugitive processes in EIS. We assigned an SCC
code based on the SCC codes used for CAPS (see 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 arid 2008
This section presents a comparison from the 2008 NEI v3 to the 2011 NEI. Figure 9 through Figure 13 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
10 and in Figure 13. 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 NATA 2005.
In Figure 9 and Figure 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. Table 5 and Table 6 show the emission changes for CAPs and HAPs respectively, for each
pollutant/sector combination.
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.
20
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Table 5: Emission differences for CAPs as shown in Figures 9 and 10
Emissions Sum Difference (tons)
Total Sum Difference
excludes wildfire
(77,004)
(2,449,635)
(9,037)
(4,056,146)
(115,749)
(6,173,841)
Sector
NH3
NOx
PM2.5
SO2
VOC
CO
Miscellaneous
(60,341)
60,093
184,117
39,268
628,277
2,583,264
Fuel Combustion
1,325
(1,125,840)
(1,958)
(3,501,010)
78,299
265,022
Industrial Processes
507
172,529
(85,621)
(220,475)
201,381
246,710
Nonroad Mobile
(441)
(400,552)
(61,600)
(363,474)
(384,929)
(2,920,106)
Highway Vehicle
(18,054)
(1,155,865)
(43,974)
(10,454)
(638,778)
(6,348,731)
Fires - Wildfires
34,794
98,584
268,197
35,234
473,833
2,293,543
NH3
NOx
PM2.5
SO2
VOC
CO
Pollutant Percent Difference 2011 to 2008
Total % Difference
excludes wildfire
-2
-14
-0.2
-38
-1
-9
Ta
lie 6: Emission Sum Differences for HAP Emissions 2011-2008 as shown in Figure 11 through Figure 13
Emissions Sum Difference
tons)
Total Sum Difference
excludes wildfire
(17,460)
16,384
860
60,730
63,281
650
3,098
(98)
(28)
(130)
Sector
Ethyl Benzene
Acetaldehyde
Acrolein
Formaldehyde
Tetrachloroethylene
1,4-Dichlorobenzene
1,3-Butadiene
Chromium
rnmnnnnHc
Arsenic
Lead
Miscellaneous
2,404
20,513
391
64,279
19
(2)
7,773
(50)
0
0
Fuel Combustion
24
2,040
387
4,473
(11)
1
269
(54)
(20)
(30)
Industrial Processes
2,163
(1,025)
442
3,517
63,274
651
110
14
0
(32)
Nonroad Mobile
(8,502)
(2,847)
(31)
(6,851)
(1)
-
(2,380)
(8)
(8)
(68)
Highway Vehicle
(13,549)
(2,296)
(329)
(4,688)
-
-
(2,675)
(0)
0
-
Fires - Wildfires
-
7,388
7,673
46,599
-
-
7,329
-
-
-
Pollutant Percent Difference 2011 to 2008
Total % Difference
excludes wildfires
-19
19
3
28
1080
56
8
-17
-19
-14
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:
21
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Miscellaneous - agricultural field burning (PM2.5, 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).
Figure 9: Comparison of CAP Emissions from 2008 to 2011, Excluding Wildfires and Biogenics
Sector Emissions CAPs
Amount of Difference BetweenYears 2008 and 2011
2011-2008
Data Source: US EPA NEI 2008v3, 2011vl
NH3 NOX PM2.5 S02 VOC CO
¦ Miscellaneous ¦ Fuel Combustion In dust Processes DNon road Mobile ¦ Highway Vehicle
Emissions sectors exclude Biogenics, Wildfires; Prescribed Fires are included in Miscellaneous.
Geographic coverage includes PR, VI, federal waters; excludesTribal.
22
-------
Figure 10: Comparison of Wildfire CAP Emissions from 2008 to 2011
Wildfire Emissions CAPs
Amount of Difference BetweenYears 2008 and 2011
2011-2008
Data Source: US EPA NEI 2008v3, 2011vl
NH3 NOX PM2.5 S02 VOC CO
2,500 -|
2,000
1,500
1,000
500
Geographic coverage includes PR, VI, federal waters; excludesTribal.
For the select HAPs reviewed, Table 6 and Figure 12 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.
Note that Figure 11 and Figure 12 use different scales for emissions. Figure 11 is in thousands of tons and Figure
12 is in tons. Figure 13 is also in thousands of tons. HAP emission increases in sectors, include the following:
• Miscellaneous - agricultural field burning (formaldehyde, acetaldehyde, 1,3-butadiene); prescribed fires
(formaldehyde, acetaldehyde, 1,3-butadiene, acrolein); gas stations (ethyl benzene)
• Industrial Processes - solvent dry cleaning (tetrachloroethylene—which was found to be an error due to
EPA's HAP augmentation and is on the version 1 issues list); industrial surface coating and solvent use (ethyl
benzene); consumer and commercial solvent use (1,4-dichlorobenzene, tetrachloroethylene)
• Fuel Combustion - biomass and natural gas (formaldehyde, acrolein).
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
described in this draft technical support document, or will be included in the EPA's "2011 NEI Report".
23
-------
Figure 11: Comparison of Select HAP Emissions from 2008 to 2011,
Excluding Wildfires and Biogenics, Select HAPs- Group 1
Sector Emissions Select HAPs
Amount of Difference BetweenYears 2008 and 2011
2011-2008
Data Source: US EPA NEI 20Q8v3, 2011vl
Highway vehicle
Nonroad Mobile
Indust Processes
Fuel Combustion
Miscellaneous
Tonsx Hp
cV^°
Emissions sectors exclude Biogenics, Wildfires; Prescribed Fires are included in Miscellaneous.
Geographic coverage includes PR, VI, federal waters; excludesTribal.
Figure 12: Comparison of Select HAP Emissions from 2008 to 2011,
Excluding Wildfires and Biogenics, Select HAPs- Group 2
Sector Emissions Select HAPs
Amount of Difference BetweenYears2008and 2011
2011-2008
Data Source: US EPA NEI 2008v3, 2011vl
Tons
1,200
1.000
800
600
400
200
(200)
(400)
CO'
,oO'
,o^
PS&
¦ Highway Vehicle
¦ Nonroad Mobile
~ Indust Processes
¦ Fuel Combustion
¦ Miscellaneous
1
~
Emissions seciorsexclutie Biogenics. Wildfires. Prescribed firesare included in Miscellaneous.
Gecgraphiccoverase includes PR, VI federal waters. excludesTribal.
24
-------
Figure 13: Comparison of Wildfire HAP Emissions from 2008 to 2011
Wildfire Emissions Select HAPs
Amount of Difference BetweenYears 2008 and 2011
2011-2008
Data Source: US EPA NEI 2008v3, 2011vl
50 1
45
40
35
30
25
20
15
10
5
TonsxlO3 Acetaldehyde Acrolein Formaldehyde 1,3-Butadiene
Geographic coverage includes PR, VI, federal waters; excludesTribal.
2.5 How well are tribal data and regions represented in the 2011 NEI?
Thirteen tribes submitted data to EIS for 2011 as shown in Table 7. 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 7 with just a CAP flag will also have some HAP emissions in most cases.
Six additional tribes, shown in Table 8, 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
include 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.
25
-------
Table 7: Tribal Participation in t
ne 2011 NEI
Tribe
Point
Nonpoint
Onroad*
Nonroad*
Coeur d'Alene Tribe of the Coeur d'Alene
Reservation, Idaho
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
Kootenai Tribe of Idaho
CAP, HAP
CAP, HAP
CAP, HAP
Navajo Nation, Arizona, New Mexico & Utah
CAP
Nez Perce Tribe of Idaho
CAP, HAP
CAP, HAP
CAP, HAP
CAP, HAP
Northern Cheyenne Tribe of the Northern
Cheyenne Indian Reservation, Montana
CAP
Paiute-Shoshone Indians of the Bishop
Community of the Bishop Colony, California
CAP, HAP
Sac & Fox Nation of Missouri in Kansas and
Nebraska
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
* 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 EIS.
Table 8: Facilities on Tribal Lands with 2011 NEI emissions from EPA only
Tribe
EPA data used
Assiniboine and SiouxTribes 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 iocs this NEI tell lis 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.
Hg emission estimates in the 2011 NEI sum to 52 tons with 50.5 tons from stationary sources and 1.2 tons from
mobile sources. Of the stationary source emissions, the inventory shows that 25.5 tons come from coal,
petroleum coke or oil-fired EGUs with units larger than 25 megawatts (MW), with coal-fired units making up the
26
-------
vast majority (25.4 tons) of that total. The other sources of emissions are summarized below for the special Hg
sectors.
We used a variety of data sources to create the 2011 NEI Hg inventory, as shown Figure 14 below. The datasets
are described in more detail starting in Section 3.1.1, and we highlight some key datasets here. For EGUs, we
used an approach developed for the Mercury and Air Toxics Standards (MATS) rule during 2011s, and used 2011-
specific activity. The MATS-based data are labeled "EPA EGU" in the figure; all of 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 reported the calculation method
to be based either on continuous emissions monitors (CEMs) or test data. In addition, S/L/T 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 0), and other EPA data developed for gap filling (see Section 3.1.1).
Figure 14: Data sources of Hg emissions in the 2011 NEI, by data category
50
40
>• 30
(/)
C
o
+->
00 20
10
S/L/T
Other EPA
EPA TRI
EPA NV Goldmines
I EPA Mobile
EPA HAP aug
EPA EGU
EPA Air/Rail/CMV
0 -I-"
Nonpoint
Point
Nonroad
Onroad
In addition to Figure 14, Table 9 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.
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, available at
http://www.epa.gov/ttn/atw/utility/emis_overview_memo_matsfinal.pdf, or at Docket number EPA-HQ-OAR-2009-0234
27
-------
Table 9: Datasets, groups, and amount of Hg in 2011 NEI from each
Mercury
Data
Emissions
Grouped Data Source for
Category
Dataset Short name
(tons/yr)
Chart
2011EPA_NP_NoOvrlp
2.3
Other EPA
S/L/T
1.6
S/L/T
Nonpoint
2011EPA Rail
0.59
EPA Air/Rail/CMV
2011EPA_HAP-Aug
0.32
EPA HAPaug
2011EPA CMV
0.02
EPA Air/Rail/CMV
2011E PA_N P_0 vrl p
0.01
Other EPA
S/L/T
23.2
S/L/T
2011EPA EGU
17.2
EPA EGU
2011EPA TRI
3.7
EPA TRI
Point
2011 NVGLD
0.8
EPA NV Goldmines
2011EPA_Carry Forward
0.72
Other EPA
2011EPA_HAP-Aug
0.4
EPA HAPaug
2011EPA Other
0.39
Other EPA
2011EPA Rail
0.05
EPA Air/Rail/CMV
2011 EPA Landfills
0.01
Other EPA
Nonroad
S/L/T
0.03
S/L/T
2011 EPA Mobile
0.02
EPA Mobile
Onroad
2011 EPA Mobile
0.29
EPA Mobile
S/L/T
0.11
S/L/T
Since mercury is a HAP, it is reported voluntarily by S/L/T agencies. For the 2011 NEI, 42 states reported point
source Hg emissions; Figure 15 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 9 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 data except where the S/L/T 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 data were from a source test or CEMS.
28
-------
Figure 15: States with state- or local-provided Hg emissions in the point data category of the 2011 NEI
2011 Mercury Submissions
I No Point Mercuiy
^ Point Mercury
Table 10 shows the 2011 NEI 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 ® 2007
ACCESS ® database included in the zip file 2011nei_supdata_mercury.zip at
ftp://ftp.epa.gov/Emislnventorv/2011/doc 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 10: Trends in Mercury Emissions - 1990, 2005, 2008 and 2011
Source Category
1990 (tpy)
2005(tpy)
2008
2011
Categorization Approach
Baseline NEI for
MATS proposal
(tpy)
(tpy)
HAPs, 11/14/2005
3/15/2011
2008 NEIv3
2011 NEIvl
Utility Coal Boilers
Regulatory code, NESHAP: MATS
(Electricity Generation
rule and unit specific info on
Units - EGUs,
58.8
52.2
29.4
25.4
boiler config (from MATS rule) to
combusting coal)
assign fuel, SCC for units not in
MATS database
Hospital/Medical/
Regulatory code: Hospital,
Infectious Waste
51
0.2
0.1
0.1
Medical, Infectious Waste
Incineration
Incineration (HMIWI)
Municipal Waste
Regulatory codes: Section 129
Combustors
57.2
2.3
1.3
1.0
rules for Small Municipal Waste
Combustors (MWC) and Large
MWC
Industrial,Commercial
SCC list- chose only processes with
Institutional Boilers
14.4
6.4
4.2
3.8
these SCCs that were not already
and Process Heaters
tagged with rule or via manual
approach
Mercury Cell Chlor-
10
3,1
1.3
0.5
Regulatory code: NESHAP,
Alkali Plants
Mercury Cell Chlor-Alkali Plants.
29
-------
Source Category
1990 (tpy)
Baseline NEI for
HAPs, 11/14/2005
2005(tpy)
MATS proposal
3/15/2011
2008
(tpy)
2008 NEIv3
2011
(tpy)
2011 NEIvl
Categorization Approach
Electric Arc Furnaces
7.5
7.0
4.8
5.3
Regulatory code: Area Source rule
for "Stainless & Non-stainless
Steel Manufacturing: Electric Arc
Furnaces" plus 2 major sources
that have EAFs
Commercial/Industrial
Sold Waste
Incineration
Not available
1.1
0.02
0.01
Source Classification Code
(50200101) and Manually
assigned based on how it was
categorized in previous
inventories
Hazardous Waste
Incineration
6.6
3.2
1.3
0.8
Combination of regulatory code,
NESHAP: Hazardous Waste
Incineration, and manual
examination based on
examination of unit/process
description and how it was
categorized in 2008.
Portland Cement Non-
Hazardous Waste
5.0
7.5
4.2
2.9
Regulatory code: NESHAP,
Portland Cement Manufacturing
Gold Mining
4.4
2.5
1.7
0.8
Regulatory code: NESHAP, Gold
Mine Ore Processing and
Production
Sewage Sludge
Incineration
2
0.3
0.3
0.3
Source Classification Code:
50100506, 50100515, 50100516,
50382501, 50100701, 50100793
Mobile Sources
Not available
1.2
1.8
1.2
Sum of all of onroad, nonroad,
locomotives and commercial
marine vessels (locomotives and
marine used SCC code)
Other Categories
29.5
18
10.7
9.5
Total (all categories)
246
105
61
52
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 10, 2011 mercury emissions are 9 tons lower than in the 2008. Four tons of this difference is
due to lower mercury emissions from EGUs covered by MATS; two 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.
30
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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 chloralkali 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
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 EIS. The EFs are provided in the file electric_arc_furnace_testabased_efs.zip at
ftp://ftp.epa.gov/Emislnventorv/2011/doc/; 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
nonEGU 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 other datasets developed by
EPA.
The MWC 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 or TRI data were not available. These data were put into
the EIS dataset "2011EPA_CarryForward".
31
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3 Stationary sources
3.1 Stationary source approiicli.es
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 11 and Table 12 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 (2008 MMS 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-reported pollutant submissions for PM (Section 3.1.2) and to speciate S/L/T
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 data were tagged for two reasons: 1) if they were deemed to be likely outliers and
were not addressed during the S/L/T data reviews, 2) to set the hierarchy to use the Mercury and Air Toxics
Standard (MATS) data ahead of the S/L/T data where the S/L/T 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. 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.
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 or at: http://www.epa.gov/ttn/chief/net/neip/appendix 6.mdb.
32
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Table 11: 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
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.
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
emissions are not used is where tagged in 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 data except where S/L/T
data were from source test or continuous emission monitors and 2) where
S/L/T data were suspected outliers that were not addressed.
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.
2011EPA_
chrom_split
Hexavalent and trivalent chromium speciated from S/L/T 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 chromium. See Section 3.1.3.
EPA NV Gold Mines
(2011JWGLD)
2011 Mercury emissions from the Nevada Mercury Control Program - Annual
Emissions Reporting (http://ndep.nv.gov/bapc/hg/aer.html) -
early copy of the data emailed by Adele Malone, Nevada Division of
Environmental Protection, 11/05/2012
2011EPA Other
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 EIS by states
and EFs from the ICR test program or S/L/T 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.
33
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Dataset name
(Short name* provided
if different)
Description and Rationale for the Order of the Selected Datasets
Order
2011EPA_TRI
Toxics Release Inventory data for the year 2011. (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 12).
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 in the 2011 reporting year.
10
2011EPA_CarryForw
ard-
PreviousYearData
(2011EPA_CarryForw
ard)
Variety of estimates used to gap fill important sources/pollutants: 1) coke
oven missing from S/L/T 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 0. These data are selected below
theTRI 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
2008 MMS Data
Same data as were used in 2008: 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 (Wilson et. al, 2010) in the National Inventory
Input Format and converted to the CERS format by EPA. See also
http://www.gomr.boemre.gov/homepg/regulate/
13
environ/airaualitv/gulfwide emission inventorv/2008GulfwideEmission
lnventorv.html. The state code for data from this data set is "DM" (Federal
Waters).
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 EIS will list in its process-level reports
34
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Table 12: 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
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 emissions are not used is where tagged in 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 data where states
asked for EPA data to be used in place of their data and 2) where S/L/T data
were suspected outliers.
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. 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).
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
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 data so
that the S/L/T agency HAP data are used first.
4
2011EPA_CMV
EPA commercial marine vessel emissions estimates. See Section 4.3.
5
2011EPA_Rail
EPA locomotive (referred to as "rail" in this document) emissions estimates.
See Section 4.4.
6
2011EPA_NP_NoOve
rlap_w_Pt
(2011EPA NP NoOvr
lp)
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
includes 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,
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).
7
2011EPA_NP_Overla
p_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.
8
35
-------
Dataset name
(Short Name*
provided if different)
Description and Rationale for the Order of the Selected Datasets
Order
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.
9
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 PMio (called PM10-PRI in EIS and NEI) and
primary PM2.5 (PM25-PRI), filterable PM (PM10-FIL and PM25-FIL) and condensable PM (PM-CON). EPA needed
to augment the S/L/T 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 ® Access ® database, available under the "Emission
Inventory Tools" heading at http://www.epa.gov/ttn/chief/eiinformation.html.
3.1.3 Chromium augmentation
The 2011 reporting cycle has 5 valid pollutant codes for chromium, as shown in Table 13.
Table 13: 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
36
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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 13.
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.
We used the new EIS augmentation feature to speciate S/L/T reported chromium. 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. 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 11 and Table 12 and excludes the S/L/T
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 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.
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 11 selection hierarchy shown above. For 2011, all TRI emissions values that could
reasonably be matched to an EIS facility were loaded into 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 were considered for inclusion
in the 2011 NEI.
The basis of the 2011EPA_TRI dataset is the US EPA's 2011 Toxic Release Inventory (www.epa.gov/tri). 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
37
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1, 2012 from http://www2.epa.gov/toxics-release-inventory-tri-program/tri-basic-data-files-calendar-years-
1987-2011.
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 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 11) 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 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 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 QA reviews and
comparisons.
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 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
TRIJDs 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 EIS. Many TRI latitudes and longitudes were also found to be in error
38
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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 EIS_ID facility crosswalk, along with facility name and location
information and emissions levels from both TRI and 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 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 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 TRIJDs 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 14 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 14. 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 14 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 14. 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 EIS.
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
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".
39
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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 an SIC-to-NAICS assigned
split fraction. These factors were used for the remaining TRI-reported chromium. To calculate the
NAICS-based factors, we summed by NAICS the total amounts of chromium III and chromium VI for the
entire 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-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 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/Ts for any suspect facilities identified by that review.
Lastly, as part of the S/L/T review of the TRI-to-EIS facility matching described in step 1 above, we also
provided to the S/L/Ts 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
40
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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 NEI vl selection, the EIS tags on these two classes of TRI emissions values
were removed, allowing those TRI values to be used in the 2011 NEI wherever the S/L/T had not
reported that pollutant for that facility.
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 EIS, they were created.
6. Revise SCCs on the EIS Processes used for the TRI emissions
The 2002 and 2005 NEIs had assigned all of 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-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-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.
11 In fact, a "SMOKE" modeling file was used as the easiest way to get the file in the right format for this step.
41
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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 datasets or other EPA datasets that are higher in the EIS selection hierarchy, it is necessary to tag
any TRI emissions values stored in EIS wherever the same pollutant is already reported by a S/L/T 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 15 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_EGU dataset included "Xylenes (Mixed Isomers)" for a facility, any
of the related individual xylene isomers would be tagged in the 2011EPA_TRI dataset in EIS as well as
any "Xylenes (Mixed Isomers)". Tagging an emissions value in EIS in any dataset makes that emissions
value not available for selection to the NEI.
Table 14: Mapping of TRI Pollutant Coc
es to EIS Pollutant codes
TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
New in 2011
79345
1,1,2,2-TETRACHLOROETHANE
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-TRICHLORO BENZENE
120821
1,2,4-TRICHLOROBENZENE
96128
l,2-DIBROMO-3-CHLOROPROPANE
96128
l,2-DIBR0M0-3-CHL0R0PR0PANE
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-PROPANESULTONE
106467
1,4-DICHLOROBENZENE
106467
1,4-DICHLOROBENZENE
25321226
DICHLOROBENZENE (MIXED ISOMERS)
NA- pollutant not used
95954
2,4,5-TRICHLOROPHENOL
95954
2,4,5-TRICHLOROPHENOL
88062
2,4,6-TRICHLOROPHENOL
88062
2,4,6-TRICHLOROPHENOL
94757
2,4-DICHLOROPHENOXY ACETIC ACID
94757
2,4-DICHLOROPHENOXY ACETIC ACID
51285
2,4-DINITROPHENOL
51285
2,4-DINITROPHENOL
121142
2,4-DINITROTOLUENE
121142
2,4-DINITROTOLUENE
53963
2-ACETYLAMINOFLUORENE
53963
2-ACETYLAMINOFLUORENE
79469
2-NITROPROPANE
79469
2-NITROPROPANE
91941
3,3'-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-CHL0RANILINE)
101779
4,4'-METHYLENEDIANILINE
101779
4,4'-METHYLENEDIANILINE
534521
4,6-DINITRO-O-CRESOL
534521
4,6-DINITR0-0-CRES0L
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
42
-------
TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
New in 2011
62533
ANILINE
62533
ANILINE
7440360
ANTIMONY
7440360
ANTIMONY
N010
ANTIMONY COMPOUNDS
7440360
ANTIMONY
7440382
ARSENIC
7440382
ARSENIC
N020
ARSENIC COMPOUNDS
7440382
ARSENIC
1332214
ASBESTOS (FRIABLE)
1332214
ASBESTOS
71432
BENZENE
71432
BENZENE
92875
BENZIDINE
92875
BENZIDINE
98077
BENZOIC TRICHLORIDE
98077
BENZOTRICHLORIDE
100447
BENZYL CHLORIDE
100447
BENZYL CHLORIDE
7440417
BERYLLIUM
7440417
BERYLLIUM
N050
BERYLLIUM COMPOUNDS
7440417
BERYLLIUM
92524
BIPHENYL
92524
BIPHENYL
117817
DI(2-ETHYLHEXYL) PHTHALATE
117817
BIS(2-ETHYLHEXYL)PHTHALATE
542881
BIS(CHLOROMETHYL) ETHER
542881
Bis(Chloromethyl)Ether
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!EXCEPT CHROMITE
ORE MINED IN THE TRANSVAAL REGION)
7440473
CHROMIUM
7440484
COBALT
7440484
COBALT
N096
COBALT COMPOUNDS
7440484
COBALT
1319773
CRESOL (MIXED ISOMERS)
1319773
CRESOL/CRESYLIC ACID (MIXED ISOMERS)
108394
M-CRESOL
108394
M-CRESOL
95487
O-CRESOL
95487
O-CRESOL
106445
P-CRESOL
106445
P-CRESOL
98828
CUMENE
98828
CUMENE
N106
CYANIDE COMPOUNDS
57125
CYANIDE
74908
HYDROGEN CYANIDE
57125
Cyanide
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
43
-------
TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
New in 2011
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
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-HEXACHLOROCYCLOHEXANE
108316
MALEIC ANHYDRIDE
108316
MALEIC ANHYDRIDE
7439965
MANGANESE
7439965
MANGANESE
N450
MANGANESE COMPOUNDS
7439965
MANGANESE
7439976
MERCURY
7439976
MERCURY
N458
MERCURY COMPOUNDS
7439976
MERCURY
67561
METHANOL
67561
METHANOL
72435
METHOXYCHLOR
72435
METHOXYCHLOR
74839
BROMOMETHANE
74839
METHYL BROMIDE
74873
CHLOROMETHANE
74873
METHYL CHLORIDE
71556
1,1,1-TRICHLOROETHANE
71556
METHYL CHLOROFORM
74884
METHYL IODIDE
74884
METHYL IODIDE
108101
METHYL ISOBUTYL KETONE
108101
METHYL ISOBUTYL KETONE
624839
METHYL ISOCYANATE
624839
METHYL ISOCYANATE
80626
METHYL METHACRYLATE
80626
METHYL METHACRYLATE
1634044
METHYLTERT-BUTYL ETHER
1634044
METHYLTERT-BUTYL ETHER
75092
DICHLOROMETHANE
75092
METHYLENE CHLORIDE
60344
METHYL HYDRAZINE
60344
METHYLHYDRAZINE
121697
N,N-DIMETHYLANILINE
121697
N,N-DIMETHYLANILINE
68122
N,N-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 BlPHENYLS
1336363
POLYCHLORINATED BIPHENYLS
44
-------
TRI CAS
TRI Pollutant Name
EIS Pollutant
Code
EIS Pollutant Name
New in 2011
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-PHENYLENEDIAMINE
106503
P-PHENYLENEDIAMINE
123386
PROPION ALDEHYDE
123386
PROPIONALDEHYDE
114261
PROPOXUR
114261
PROPOXUR
78875
1,2-DICHLOROPROPANE
78875
PROPYLENE DICHLORIDE
75569
PROPYLENE OXIDE
75569
PROPYLENE OXIDE
91225
QUINOLINE
91225
QUINOLINE
106514
QUINONE
106514
QUINONE
7782492
SELENIUM
7782492
SELENIUM
N725
SELENIUM COMPOUNDS
7782492
SELENIUM
100425
STYRENE
100425
STYRENE
96093
STYRENE OXIDE
96093
STYRENE OXIDE
127184
TETRACHLOROETHYLENE
127184
TETRACHLOROETHYLENE
7550450
TITANIUM TETRACHLORIDE
7550450
TITANIUM TETRACHLORIDE
108883
TOLUENE
108883
TOLUENE
95807
2,4-DIAMINOTOLUENE
95807
TOLUENE-2,4-DI AMINE
8001352
TOXAPHENE
8001352
TOXAPHENE
79016
TRICHLOROETHYLENE
79016
TRICHLOROETHYLENE
121448
TRIETHYLAMINE
121448
TRIETHYLAMINE
1582098
TRIFLURALIN
1582098
TRIFLURALIN
108054
VINYL ACETATE
108054
VINYL ACETATE
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 15: Po
lutant Groups
Group Name
Pollutant Code
Pollutant
7440473
Chromium
1333820
Chromium Trioxide
Chromium
7738945
Chromic Acid (VI)
18540299
Chromium (VI)
16065831
Chromium III
1330207
Xylenes (Mixed Isomers)
Xylenes (Mixed
95476
o-Xylene
Isomers)
106423
p-Xylene
108383
m-Xylene
Cresol/Cresylic
Acid (Mixed
Isomers)
1319773
Cresol/Cresylic Acid (Mixed Isomers)
95487
o-Cresol
108394
m-Cresol
106445
p-Cresol
1336363
Polychlorinated Biphenyls (PCBs)
2050682
4,4'-Dichlorobiphenyl (PCB-15)
Polychlorinated
Biphenyls
2051243
Decachlorobiphenyl (PCB-209)
2051607
2-Chlorobiphenyl (PCB-1)
25429292
Pentachlorobiphenyl
26601649
Hexachlorobiphenyl
26914330
Tetrachlorobiphenyl
45
-------
Group Name
Pollutant Code
Pollutant
28655712
Heptachlorobiphenyl
53742077
Nonachlorobiphenyl
55722264
Octachlorobiphenyl
7012375
2,4,4'-Trichlorobiphenyl (PCB-28)
130498292
PAH, total
120127
Anthracene
129000
Pyrene
189559
Dibenzo[a,i]Pyrene
189640
Dibenzo[a,h] Pyrene
191242
Benzo[g,h,l,]Perylene
191300
Dibenzo[a,l]Pyrene
192654
Dibenzo[a,e] Pyrene
192972
Benzo[e]Pyrene
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
Polycyclic
218019
Chrysene
Organic Matter
224420
Dibenzo[a,j]Acridine
(POM)
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
46
-------
Group Name
Pollutant Code
Pollutant
779022
9-Methyl Anthracene
8007452
Coal Tar
832699
1-Methylphenanthrene
83329
Acenaphthene
85018
Phenanthrene
86737
Fluorene
86748
Carbazole
90120
1-Methylnaphthalene
91576
2-Methylnaphthalene
91587
2-Chloronaphthalene
Cyanide &
Compounds
57125
Cyanide
74908
Hydrogen 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 missing HAPs in the S/L/T-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 (http://www.epa.gov/ttn/chief/webfire/index.html) 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-submitted CAP to be the same as the HAP to CAP
ratios, and the HAP emissions to be consistent with the S/L/T-reported CAP data.
A HAP augmentation feature was built into EIS for the 2011 cycle, and the HAP EF ratios are available to 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
47
-------
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
data or other EPA datasets. The extra step of data tagging of the HAP augmentation dataset was taken to
ensure the NEI would not use the data from the HAP augmentation dataset for facilities where the HAP was
reported by an S/L/T agency at any process at the facility or where the HAP was included in the EPA TRI dataset.
For example, if a facility reported formaldehyde at process A only, and the WebFIRE emission factor database
yields formaldehyde emissions for processes A, B, and C, then we would not use any records from the HAP
augmentation dataset containing formaldehyde from any processes at the facility. If that facility had no
formaldehyde, but the TRI dataset had formaldehyde for any processes at that facility, then the NEI would still
not use formaldehyde from the HAP augmentation dataset for any of the processes (it would use the TRI data).
If the EPA EGU dataset contained formaldehyde for that facility we would use the HAP augmentation set but not
for any process at the same unit as EPA EGU dataset. If the EPA EGU dataset contained formaldehyde at process
A or any other process within the same unit as process A, then the HAP augmentation dataset would be used for
processes B and C, but not process A.
This approach was taken to be conservative in our attempt to prevent double counted emissions, which is
necessary because we know that some states aggregate their HAP emissions and assign to fewer or different
processes than their CAP emissions. These types of differences are expected since CAPs are required to be
submitted at the process level, but HAPs are entirely voluntary for the NEI's reporting rule. We used the EIS
tagging to tag records from the 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
15, 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 16.
Table 16: Nickel species in the NEI from the HAP-augmentation dataset which should not have been used
EIS
Nickel species in HAP
Potential
Facility
EIS
Augmentation
Emissions
Double Count
State
ID
Process ID
Dataset
(lbs)
Data Set
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
48
-------
SCC/pollutant combination, or if no S/L/T reported any values for the same SCC/pollutant, 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 17. 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 17: Lead from HAP-augmentation from coal combustion that was not used.
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
water
56037
Green River Trona Plant
0.1500
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 EIS ensured
that the S/L/T 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, 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 15.
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 version 1 (see issues list for details)
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. Therefore all augmented
tetrachloroethylene from dry cleaners is incorrect.
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
(http://www.epa.gov/ttn/chief/eis/2011nei/prioritv facility list.xls) 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 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
49
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Committee (ERTAC, http://www.ertac.us/). 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],
Table 18 and Table 19 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 18),
such as residential heating, from sectors that may overlap with the point sources (Table 19). 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 of 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 posted at ftp://ftp.epa.gov/Emislnventorv/2011nei/doc/. which is the
directory containing all supporting data files listed in Table 18 and Table 19. 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.
Table 18: 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
is
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
In
Residential Heating; liquefied
petroleum gas
Fuel Comb - Residential
-Other
residential consumption Ipg.zip
Residential Heating; Fireplaces,
woodstoves, fireplace inserts, pellet
stoves, indoor furnaces, outdoor
hydronic heaters, and firelogs.
Fuel Comb - Residential
-Wood
rwc estimation tool 2011vl 120612.zip
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
50
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EPA-estimated emissions source
description
Carried
Forward?
EIS Sector Name
Name of supporting data file or other
reference
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 auarrving.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
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
potw 2011 rev.zip
Agricultural Tilling
Agriculture - Crops &
Livestock Dust
agricultural tilling 2801000003 2011.zip
51
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EPA-estimated emissions source
description
Carried
Forward?
EIS Sector Name
Name of supporting data file or other
reference
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
X
Miscellaneous Non-
Industrial NEC
Documentation for the 1999 Base Year Nonpoint
area source National Emission Inventory for HAPs,
page A-30
General Laboratory Activities
X
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)
X
Miscellaneous Non-
Industrial NEC
Documentation for the Final 2002 Nonpoint
Sector (Feb 06 version) National Emission
Inventory for Criteria and HAPs, page A-109
Lamp (Fluorescent) Recycling
X
Miscellaneous Non-
Industrial NEC
Documentation for the Final 2002 Nonpoint
Sector (Feb 06 version) National Emission
Inventory for Criteria and HAPs, page A-107
"Carried Forward" indicates whether EPA data were carried forward from the 2008 or other previous year inventory.
Table 19: 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
EPA Oil and Gas Production Emission Esti
mation Tool 2011 NEI VI 4 20130919.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 eauip wha
ps2011.zip
Industrial Surface Coating - Large
Appliances
Solvent - Industrial Surface
Coating & Solvent Use
surface coating appliances 2011whaps.zip
52
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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 mfgwhaps2011.zip
Industrial Surface Coating -
Railroad
Solvent - Industrial Surface
Coating & Solvent Use
surface coating railroad whaps 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 stagel 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
X
Miscellaneous Non-Industrial
NEC
cremation 2011.zip
"Carried Forward" indicates whether EPA data were carried forward from the 2008 or other previous year inventory.
The file "2011nei np matrix submittals.xlsx" at ftp://ftp.epa.gov/Emislnventory/2011/doc/ has a list of
submitting S/L/T agencies and for what nonpoint sectors they submitted data.
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
53
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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
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 20
and Table 21.
Table 20: 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.
54
-------
Table 21: 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
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 are <20%
of the EPA estimate
for nonpoint),
augment their data.
We believe that the state filled out the
survey incorrectly. There have to 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
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
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.
55
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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 22 below. If a state within 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 22: SCCs used in past inventories that were not included in EPA's 2011 nonpoint estimates
see
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 found at, ftp://ftp.epa.gov/Emislnventorv/2008v3/doc/2008nei references.zip
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.
(http://www.epa.gov/ttn/chief/conference/eil2/point/strait.pdf)
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.
(http://www.epa.gov/ttn/chief/conference/eil9/session7/huntlev.pdf)
56
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3.2 Agriculture - Crops & Livestock Dust
3,2,1 Sector Description
The 2011 NEI has emissions from the following SCCs that belong to this sector.
Tab
e 23: SCCs used in the 2011 NEI for the Agriculture - Crops & Livestock Dust Sector
see
SCC 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 - Crops
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 feedlots
(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"
EPA estimates emissions for fugitive dust emissions from agricultural tilling (SCC 2801000003), highlighted in the
above table; the methodology is described in 3.2.4.
3,2,2 Sources of data overview and selection hierarchy
The agricultural crops and livestock dust sector includes data from the S/L/T agency submitted data and the
default EPA generated emissions. The agencies listed in Table 24 submitted emissions for this sector.
Table 24: Agencies that Submitted Agricultural Crops and Livestock Dust Data
Agency
Type
2801000000
2801000002
2801000003
2801000005
2801000008
2801600000
2805001000
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
57
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Agency
Type
2801000000
2801000002
2801000003
2801000005
2801000008
2801600000
2805001000
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
Table 25 shows the selection hierarchy for datasets included in the agricultural crops and livestock dust sector.
Table 25: 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
P - Point
N - Nonpoint
PN - P&N
All CAPS EPA SLT — EPA&SLT
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 PM10-PRI, PM10-FIL, PM25-PRI, and PM25-
58
-------
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.
Particulate emissions from agricultural tilling were computed by multiplying a crop specific emissions factor by
an activity factor.
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 l],[ref 2]:
EF = 4.8 x k x s0-6 x pcrop,t////ng tyPe
where:
k = dimensionless particle size multiplier (PMio = 0.21; PM2.5 = 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 (pim) 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 26 shows silt content assumed for each soil type.
Table 26: 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 27 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 27: 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
59
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Crop
Conservation Use
Conventional Use
Forage
3
3
Hay
3
3
Oats
3
5
Peanuts
3
3
Permanent Pasture
1
1
Potatoes
3
3
Rice
5
5
Rye
3
5
Sorghum
1
6
Soybeans
1
6
Spring Wheat
1
4
Sugarbeets
3
3
Sugarcane
3
3
Sunflowers
3
3
Tobacco
3
3
3,2.4,1 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 28 for how USDA and CRM categories were
matched.
Table 28: 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
60
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CRM Category
USDA Data Items
Soybeans
SOYBEANS - ACRES HARVESTED
Sugarbeets
SUGARBEETS - 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.
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 at: http://www.ctic.purdue.edu/CRM/. 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 29. Due to data nondisclosure agreements with CTIC, the EPA cannot
release the county-level tillage data by crop type.
Table 29: 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
61
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The following equation was used to determine the emissions from agricultural tilling [ref l],[ref 2], The county-
level activity data are the acres of land tilled for a given crop and tilling type. The equation is adjusted to
estimate PMio and PM2.5 emissions using the following parameters: a particle size multiplier, the silt content of
the surface soil, the number of tillings per year for a given crop and tilling type, and the acres of land tilled for a
given crop and tilling type.
f - Zc X /f X S X pcropjjjijng type X dCrop,tilling type
where: E = PM10-FIL or PM25-FIL emissions
c = constant 4.8 Ibs/acre-pass
k = dimensionless particle size multiplier (PMio=0.21; PM2.s=0.042)
s = percent silt content of surface soil, defined as the mass fraction of particles smaller than 75
pirn diameter 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)
3.2.4,2 Controls
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 30 summarizes the number of
tagged process-level emissions values from each agency affected by this QA.
Table 30: 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
http://www.ctic.purdue.edu/CTIC/CTIC.html .
5. USDA Quickstats 2.0, http://quickstats.nass.usda.gov/. Accessed April 2012.
62
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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 2011 NEI has emissions for the following SCCs that belong to this
category. The highlighted SCCs are those for which EPA estimates emissions as described in Section 3.3.4.
Table 31: Source Categories for Agricultural Fertilizer Application
see
Descriptor 2
Descriptor 4
Descriptor 5
Descriptor 10
Miscellaneous
Agriculture
Fertilizer
2801700001
Area Sources
Production - Crops
Application
Anhydrous Ammonia
Miscellaneous
Agriculture
Fertilizer
2801700002
Area Sources
Production - Crops
Application
Aqueous Ammonia
Miscellaneous
Agriculture
Fertilizer
2801700003
Area Sources
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
Agriculture
Fertilizer
2801700007
Area Sources
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 nutrient
2801700010
Area Sources
Production - Crops
Application
fertilizers)
Miscellaneous
Agriculture
Fertilizer
2801700011
Area Sources
Production - Crops
Application
Calcium Ammonium Nitrate
Miscellaneous
Agriculture
Fertilizer
2801700012
Area Sources
Production - Crops
Application
Potassium Nitrate
Miscellaneous
Agriculture
Fertilizer
2801700013
Area Sources
Production - Crops
Application
Diammonium Phosphate
Miscellaneous
Agriculture
Fertilizer
2801700014
Area Sources
Production - Crops
Application
Monoammonium Phosphate
Miscellaneous
Agriculture
Fertilizer
Liquid Ammonium
2801700015
Area Sources
Production - Crops
Application
Polyphosphate
Miscellaneous
Agriculture
Fertilizer
2801700099
Area Sources
Production - Crops
Application
Miscellaneous Fertilizers
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 Table 32 submitted emissions for this
sector. Note that not all agencies submitted all of the different fertilizer types.
63
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Table 32: Agencies that Submittec
Agricultural Fertilizer Application Data
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
Table 33 shows the selection hierarchy for the agricultural fertilizer application sector.
Table 33: 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
64
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3.3.3 Spatial coverage and data sources for the sector
Agriculture - Fertilizer Application
V
N —
P - Point
N - Nonpoint
PN - P&N
Ail 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.
3.3.4.1 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 Table 34 below. 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.
65
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Table 34: Fertilizers Assigned to Fertilizer Groups
Commercial
Fertilizers
CMU Ammonia Model
Report -
Fertilizer Group
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 Phosphates
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
66
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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
Miscellaneous
766
Soil Conditioner
Soil Cond
(cont.)
767
Potting Soil
Potting Soil
797
Sec./Micronut. - Cod
Sec/Mic No Code
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 35, 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.
67
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Table 35: 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
Monoammonium Phosphate
11
Nitrogen Solutions
29
Potassium Nitrate
14
Urea
46
3.3.4,2 Emission Factors
Emission factors for each fertilizer group were provided with the CMU Ammonia Model [ref 1] and are reported
in Table 36 below.
Tab
e 36: Fertilizer Emission Factors
Emission Factor
(varies by county for some
fertilizers)
Emission
Pollutant
Factor
Fertilizer Description
Code
Min
Max
Average
Emission Factor Unit
Reference
Ammonium Nitrate
NHs
1.0
3.0
1.91
% N volat
lized as NH3
1
Ammonium Sulfate
NHb
5.0
15.0
9.53
% N volat
lized as NH3
1
Ammonium Thiosulfate
NHs
2.5
2.5
2.5
% N volat
lized as NH3
1
Anhydrous Ammonia
NHs
4.0
4.0
4.0
% N volat
lized as NH3
1
Aqueous Ammonia
NHs
4.0
4.0
4.0
% N volat
lized as NH3
1
Calcium Ammonium
% N volat
lized as NH3
Nitrate
NHs
1.0
3.0
1.91
1
Diammonium Phosphate
NHs
5.0
5.0
5.0
% N volatilized as NH3
1
Liquid Ammonium
% N volatilized as NH3
Polyphosphate
NHs
5.0
5.0
5.0
1
Miscellaneous Fertilizers
NHs
6.0
8.0
6.59
% N volatilized as NH3
1
Monoammonium
% N volatilized as NH3
Phosphate
NHs
5.0
5.0
5.0
1
Nitrogen Solutions
NHs
8.0
8.0
8.0
% N volatilized as NH3
1
N-P-K (multi-grade
% N volatilized as NH3
nutrient fertilizers)
NHs
1.0
3.0
1.91
1
Potassium Nitrate
NHs
2.0
2.0
2.0
% N volatilized as NH3
1
Urea
NHs
15.0
20.0
15.8
% N volatilized as NH3
1
68
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3.3.4.3 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
3.3.4.4 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
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.
July:
3,250 kg x 1.09 = 3,543 kg N
August:
3,210 kg x 1.09 = 3,499 kg N
September:
9,640 kg x 1.09 = 10,508 kg N
October:
6,320 kg x 1.09 = 6,889 kg N
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
69
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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.
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 37 summarizes the number of tagged process-level emissions values
from each agency affected by this QA.
Table 37: Agencies Tagged Values for Agriculture - Fertilizer
Agency
Number of
Values Tagged
Tag Reason
Georgia Department of Natural
Resources
2226
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
70
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3,3,6 References
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, at
http://www.cmu.edu/ammonia/. 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, at
http://www.aapfco.Org/publications.html#comm. accessed 2 May 2009.
3. U.S. Department of Agriculture, 2002 Census of Agriculture, at http://www.agcensus.usda.gov/.
accessed 30 April 2009.
4. U.S. Department of Agriculture, 2007 Census of Agriculture, at http://www.agcensus.usda.gov/.
accessed 30 April 2009.
3.4 Agrlcul restock 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 ant! 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
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 38 shows the nonpoint SCCs covered by the EPA estimates (discussed in Section 0) and by the State/Local
and Tribal agencies that submitted data. Table 39 presents the two 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 38: 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
2805001100
Agriculture Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Confinement
X
X
X
X
2805001200
Agriculture Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Manure handling and
storage
X
X
X
2805001300
Agriculture Production -
Livestock
Beef cattle - finishing operations
on feedlots (drylots)
Land application of
manure
X
X
X
12 California does have some point sources appropriately assigned to 30202001
71
-------
see
SCC Level Two
SCC Level Three
SCC Level Four
EPA
Local
State
Tribe
Agriculture Production -
Not Elsewhere
2805002000
Livestock
Beef cattle production composite
Classified
X
X
X
Agriculture Production -
Beef cattle - finishing operations
2805003100
Livestock
on pasture/range
Confinement
X
X
X
Poultry production - layers with
Agriculture Production -
dry manure management
2805007100
Livestock
systems
Confinement
X
X
X
X
Poultry production - layers with
Agriculture Production -
dry manure management
Land application of
2805007300
Livestock
systems
manure
X
X
X
Poultry production - layers with
Agriculture Production -
wet manure management
2805008100
Livestock
systems
Confinement
X
X
X
Poultry production - layers with
Agriculture Production -
wet manure management
Manure handling and
2805008200
Livestock
systems
storage
X
X
X
Poultry production - layers with
Agriculture Production -
wet manure management
Land application of
2805008300
Livestock
systems
manure
X
X
X
Agriculture Production -
2805009100
Livestock
Poultry production - broilers
Confinement
X
X
X
Agriculture Production -
Manure handling and
2805009200
Livestock
Poultry production - broilers
storage
X
X
X
Agriculture Production -
Land application of
2805009300
Livestock
Poultry production - broilers
manure
X
X
X
Agriculture Production -
2805010100
Livestock
Poultry production - turkeys
Confinement
X
X
X
Agriculture Production -
Manure handling and
2805010200
Livestock
Poultry production - turkeys
storage
X
X
X
Agriculture Production -
Land application of
2805010300
Livestock
Poultry production - turkeys
manure
X
X
X
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 and
2805019200
Livestock
Dairy cattle - flush dairy
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 28-05-
2805020000
Livestock
Emissions
001, -002, -003)
X
Agriculture Production -
2805021100
Livestock
Dairy cattle - scrape dairy
Confinement
X
X
X
Agriculture Production -
Manure handling and
2805021200
Livestock
Dairy cattle - scrape dairy
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 and
2805022200
Livestock
Dairy cattle - deep pit dairy
storage
X
X
X
72
-------
see
SCC Level Two
SCC Level Three
SCC Level Four
EPA
Local
State
Tribe
Agriculture Production -
Land application of
2805022300
Livestock
Dairy cattle - deep pit dairy
manure
X
X
X
Agriculture Production -
2805023100
Livestock
Dairy cattle - drylot/pasture dairy
Confinement
X
X
X
Agriculture Production -
Manure handling and
2805023200
Livestock
Dairy cattle - drylot/pasture dairy
storage
X
X
X
Agriculture Production -
Land application of
2805023300
Livestock
Dairy cattle - drylot/pasture dairy
manure
X
X
X
Not Elsewhere
Agriculture Production -
Classified (see also 28-
2805025000
Livestock
Swine production composite
05-039, -047, -053)
0
X
0
Not Elsewhere
Agriculture Production -
Classified (see also 28-
2805030000
Livestock
Poultry Waste Emissions
05-007, -008, -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 than
2805030002
Livestock
Poultry Waste Emissions
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
Agriculture Production -
Horses and Ponies Waste
Not Elsewhere
2805035000
Livestock
Emissions
Classified
X
X
X
X
Swine production - operations
Agriculture Production -
with lagoons (unspecified animal
2805039100
Livestock
age)
Confinement
X
X
X
X
Swine production - operations
Agriculture Production -
with lagoons (unspecified animal
Manure handling and
2805039200
Livestock
age)
storage
X
X
X
Swine production - operations
Agriculture Production -
with lagoons (unspecified animal
Land application of
2805039300
Livestock
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-pit
Agriculture Production -
house operations (unspecified
2805047100
Livestock
animal age)
Confinement
X
X
X
73
-------
see
SCC Level Two
SCC Level Three
SCC Level Four
EPA
Local
State
Tribe
Swine production - deep-pit
Agriculture Production -
house operations (unspecified
Land application of
2805047300
Livestock
animal age)
manure
X
X
X
Swine production - outdoor
Agriculture Production -
operations (unspecified animal
2805053100
Livestock
age)
Confinement
X
X
X
Domestic Animals Waste
2806010000
Emissions
Cats
Total
X
X
Domestic Animals Waste
2806015000
Emissions
Dogs
Total
X
X
Wild Animals Waste
2807025000
Emissions
Elk
Total
X
Wild Animals Waste
2807030000
Emissions
Deer
Total
X
Table 39: Point SCCs with 2011 NEI Emissions in the Livestock Waste Sector - reported only by States
SCC
SCC Level One
SCC Level Two
SCC Level Three
SCC Level Four
CA
CO
Wl
30202001
Industrial
Processes
Food and
Agriculture
Beef Cattle Feedlots
Feedlots:
General
X
X
X
30202101
Industrial
Processes
Food and
Agriculture
Eggs and Poultry
Production
Manure
Handling: Dry
X
The agencies listed in Table 40 submitted emissions for this sector.
Table 40: 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
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
74
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Table 41 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 41: 2011 NEI agricultura
livestock 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
3.4.3 Spatial coverage and data sources for the sector
Agriculture - Livestock Waste
PN
PN
PN
P - Point
N - Noripoint
PN - P&N
All CAPS EPA SLT EPA&SLT
75
-------
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.
3.4,4,1 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-level 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 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., drylot, 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
76
-------
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.
3,4,4.3 Emission Factors
Table 42 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
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 Component.xls" (see ftp://ftp.epa.gov/Emislnventorv/2008v3/doc/
2008nei_supdata_3a.zip). 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.
77
-------
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" (see ftp://ftp.epa.gov/Emislnventory/2008v3/doc/2008nei_supdata_3a.zip).
Table 42: Emission Factors for NH3 emissions used for EPA's agricultural livestoc
< data
Emission Factor
Emission
Reference
Description
Factor
Emission Factor Unit
(see footnotes)
Beef Cattle - Composite
county
kg NHs/cow/month
Beef Cattle - Drylot Operation - Confinement
9.45E-01
kg NHs/cow/month
1
Beef Cattle - Drylot Operation - Land Application
state
kg NHs/cow/month
1
Beef Cattle - Drylot Operation - Manure Storage
3.78E-04
kg NHs/cow/month
1
Beef Cattle - Pasture Operation - Confinement
county
kg NHs/cow/month
1
Da
ry Cattle - Composite
county
kg NHs/cow/month
Da
ry Cattle - Deep Pit Dairy Confinement
2.42E+00
kg NHs/cow/month
1
Da
ry Cattle - Deep Pit Dairy Land Application
state
kg NHs/cow/month
1
Da
ry Cattle - Deep Pit Dairy Manure Storage
1.13E-01
kg NHs/cow/month
1
Da
ry Cattle - Drylot Dairy Confinement
state
kg NHs/cow/month
1
Da
ry Cattle - Drylot Dairy Land Application
state
kg NHs/cow/month
1
Da
ry Cattle - Drylot Dairy Manure Storage
state
kg NHs/cow/month
1
Da
ry Cattle - Flush Dairy Confinement
2.00E+00
kg NHs/cow/month
1
Da
ry Cattle - Flush Dairy Land Application
state
kg NHs/cow/month
1
Da
ry Cattle - Flush Dairy Manure Storage
state
kg NHs/cow/month
1
Da
ry Cattle - Scrape Dairy Confinement
state
kg NHs/cow/month
1
Da
ry Cattle - Scrape Dairy Land Application
state
kg NHs/cow/month
1
Da
ry Cattle - Scrape Dairy Manure Storage
state
kg NHs/cow/month
1
Ducks
7.67E-02
kg NHs/duck/month
1
Geese
7.67E-02
kg NHs/goose/month
1
Goats
5.29E-01
kg NHs/goat/month
1
Horses
1.02E+00
kg NHs/horse/month
1
Poultry - Broiler Operation - Confinement
8.32E-03
kg NHs/b
rd/month
1
Poultry - Broiler Operation - Land Application
6.80E-03
kg NHs/b
rd/month
1
Poultry - Broiler Operation - Manure Storage
1.51E-03
kg NHs/b
rd/month
1
Poultry - Composite
2.00E-02
kg NHs/b
rd/month
Poultry - Layers - Dry Manure Operation - Confinement
3.36E-02
kg NHs/b
rd/month
1
Poultry - Layers - Dry Manure Operation - Land Application
county
kg NHs/b
rd/month
1
Poultry - Layers - Wet Manure Operation - Confinement
9.45E-03
kg NHs/b
rd/month
1
Poultry - Layers - Wet Manure Operation - Land Application
county
kg NHs/b
rd/month
1
Poultry - Layers - Wet Manure Operation - Manure Storage
county
kg NHs/b
rd/month
1
Poultry - Turkey Operation - Confinement
3.78E-02
kg NHs/b
rd/month
1
Poultry - Turkey Operation - Land Application
3.40E-02
kg NHs/b
rd/month
1
Poultry - Turkey Operation - Storage
6.80E-03
kg NHs/b
rd/month
1
Sheep
2.65E-01
kg NHs/sheep/month
1
Swine - Composite
county
kg NHs/pig/month
1
Swine - Deep Pit Operation - Confinement
2.65E-01
kg NHs/pig/month
1
Swine - Deep Pit Operation - Land Application
county
kg NHs/pig/month
1
Swine - Lagoon Operation - Confinement
2.27E-01
kg NHs/pig/month
1
Swine - Lagoon Operation - Land Application
county
kg NHs/pig/month
1
78
-------
Description
Emission
Factor
Emission Factor Unit
Emission Factor
Reference
(see footnotes)
Swine - Lagoon Operation - Manure Storage
county
kg NHs/pig/month
1
Swine - Outdoor Operation - Confinement
county
kg NHs/pig/month
1
1 Davidson, et al., 2004
2 Dorn, 2009
3 US EPA, 2005
3.4.4.4 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 "animal_livestock_emissions_2011.zip" as listed in Table 18, Section 3.1.7.
3.4.4.5 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
Number of beef cattle under pasture management in 2007 = 7,939 x 0.9958 = 7,906
79
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"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 43 summarizes the number of tagged process-level emissions values
from each agency affected by this OA.
Table 43: Agencies Tagged Values for Agriculture Livestock
Agency
Number of
Values Tagged
Tag Reason
California Air Resources Board
1653
Extraneous pollutants (no other states
submitted)
California Air Resources Board
9
Outlier
Idaho Department of Environmental
Quality
11088
Extraneous pollutants (no other states
submitted)
Louisiana Department of
Environmental Quality
2944
State requested that we replace their data
with EPA estimates.
3,4,6 References
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, at
http://www.cmu.edu/ammonia/. accessed 25 April 2009.
2. U.S. Department of Agriculture, 2007 Census of Agriculture, at http://www.agcensus.usda.gov/,
accessed 30 April 2009.
3. U.S. Department of Agriculture, National Agricultural Statistics Service, at
http://www.nass.usda.gov/Data and Statistics/Quick Stats/, accessed 28 January 2010.
80
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4. U.S. Environmental Protection Agency, National Emission Inventory-Ammonia Emissions from Animal
Agricultural Operations, Revised Draft Report, 22 April 2005, p. 4-6, at
http://www.epa.gov/ttn/chief/net/2002inventory.htrnl, 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.
3.5 Bulk Gasoline Terminals
3.5.1 Sector Description
3.5.2 Sources of data overview and selection hierarchy
3.5.3 Spatial coverage and data sources for the sector
Bulk Gasoline Terminals
Bulk Gasoline Terminals
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
'N
3N
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
J?N
PN
PN
>N
PN
PN
PN
PN
PN
PN
PN
PN
PN PN PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
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
P - Point
N - Nonpoint
PN - P&N
PN
All CAPS EPA SLT ™ EPA & SLT All HAPs EPA SLT ""EPA&SLT
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 NEl has emissions for the SCCs in Table 44; EPA
computes emissions for all except the first one (2302002000), since it's a grouping of the two more detailed
SCCs for charbroiling.
Table 44: Source Classification Codes used in the Commercial Cooking sector
see
El Sector
SCC Level One
SCC Level Two
SCC Level Three
SCC Level Four
2302002000
Commercial
Cooking
Industrial
Processes
Food and Kindred
Products: SIC 20
Commercial Cooking
- Charbroiling
Charbroiling
Total
2302002100
Commercial
Cooking
Industrial
Processes
Food and Kindred
Products: SIC 20
Commercial Cooking
- Charbroiling
Conveyorized
Charbroiling
81
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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 45 submitted emissions for this sector. EPA datasets are individually listed.
Table 45: Agencies that Submitted Commercial Cooking Data
Agency
Type
Char-
broiling
Total
Convey-
orized
Char-
broiling
Under-
fired
Char-
broiling
Deep
Fat
Frying
Flat
Griddle
Frying
Clamshell
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
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
82
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Agency
Type
Char-
broiling
Total
Convey-
orized
Char-
broiling
Under-
fired
Char-
broiling
Deep
Fat
Frying
Flat
Griddle
Frying
Clamshell
Griddle
Frying
Nez Perce Tribe
T
X
X
X
X
X
Shoshone-Banriock Tribes of the Fort Hall
Reservation of Idaho
T
X
X
X
X
X
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 46 shows the selection hierarchy for the datasets included in the commercial cooking sector.
Table 46: 2011 NEI Commercial Cooking 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_spiit
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
3.6.3 Spatial coverage and data sources for the sector
Commercial Cooking
Commercial Cooking
P - Point
N - Nonpoint
PN-P&N
P - Point
N - Nonpoint
PN - P&N
All CAPS EPA SLT —EPA & SLT All HAPs EPA SLT — EPA&SLT
83
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3,6,4 EPA-developed commercial cooking emissions data
The approach to estimating emissions from commercial cooking in 2011 consists of three general 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.
3,6,4,1 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 = Ejjm,2002
1,2002 pRAC. x UNITS. x AVG_EMISSIONSjm [ '
where:
REST, 2002 — the total number of restaurants in county / in 2002
Eijm,2oo2 = the emissions of pollutant m from source category j in county i in 2002, as
calculated for the 2002 National Emissions Inventory
FRACj = the fraction of restaurants in those categories that have equipment in source j
UNITSj = the average number of units of source category j in each restaurant
AVG_EMISSIONSJm = 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 FRAC,> 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
84
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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^n = RESTi 2002 x GFj (2)
where GF, is the growth factor for county /'.
3,6,4.2 Emission Factors
Emission factors for each pollutant for each type of commercial cooking equipment (EFJm„) 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 47). The condensable particulate matter (PM-CON) emission factors were derived by
subtracting PMlO-FILfrom PM10-PRI.
Table 47: Ratio of filterable particulate matter to primary particulate matter for PM2.5 and PMio by SCC.
Cooking Device
SCC
PM25-FIL/ PM25-PRI
PM10-FIL/ PM10-PRI
Conveyorized Charbroiling
2302002100
0.00321
0.00331
Underfired Charbroiling
2302002200
0.00287
0.00297
Flat Griddle Frying
2302003100
0.00201
0.00264
Clamshell Griddle Frying
2302003200
0.00241
0.00283
3.6.4,3 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 = RE STL2or\ X FRACj X UNITSj X AVG_EMISSIONSjm (3)
where EiJm,2ou 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 (UNITSj) 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),
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 48. 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.
85
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Table 48. 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.
3.6,4,4 Sample Calculations
Determining the Number of Restaurants in Autauga County. AL in 2002
Ei
RESTi2 002
'ijm,2002
FRACj X UNITSj X AVG_EMlSSONSjm
100 restaurants =
8.76
PM25, Under fir ed-Charbr oilers
0.125 x 1.54 x 0.454
Emissions of PIVh.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 PM2.5). 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.
86
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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.
REST( 2011 = REST[ 2002 x GFi
138 restaurants = 100 restaurants x 1.38
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 REST^ou 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:
Eijm,20\i = SiTSX'j 2011 x ERACj X UNITSj X AVG_EMISSIONSjm
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 PIVh.s)- The
result shows that the emissions of PM2.5 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 vl 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 49 summarizes the number of tagged process-level emissions values from each agency
affected by this QA.
87
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Table 49: 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
1. County Business Patterns: http://www.census.gov/econ/cbp/index.html
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. ftp://ftp.epa.gov/Emislnventorv/2002finalnei/documentation/nonpoint/
2002nei final nonpoint documentation0206version.pdf
3. Potepan, M. 2001. Charbroiling Activity Estimation. Public Research Institute, report for the California
Air Resources Board and the California Environmental Protection Agency.
http://www.arb.ca.gov/research/apr/reports/l943.pdf
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. http://daq.state.nc.us/planning/triad maintenance plan/
Appendix%20B-Emissions%20lnventory%20Documentation.pdf
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 50 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.
88
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Table 50: 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/Commercial/Institutional
Total
2311030000
Industrial
Processes
Construction:
SIC 15 -17
Road Construction
Total
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 51 submitted emissions for this sector.
89
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Table 51: Agencies t
lat Su
Dmitted 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 dAlene 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
90
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Table 52 shows the selection hierarchy for datasets included in the construction dust sector.
Table 52: 2011 NEl
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
2011EPA_NP_NoOverlap_w_Pt
EPA-generated data
Point Data Category
1
2011EPA_PM-Augrnentation
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
EPA TRI 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
PN
PN
PN
P - Point
N - Nonpoint
PN - P&N
All CAPS EPA SLT ™EEPA& SLT
91
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3,7,4 Construction - Non-Residential - timates
3.7.4,1 Source Category Description
Emissions from non-residential construction activity are a function of the acreage disturbed for non-residential
construction.
For this source category, the following SCC was used:
Table 53: SCC for Non-Residential Construction
Source
Classification Code
SCC Level One
SCC Level Two
SCC Level Three
SCC Level Four
2311020000
Industrial
Processes
Construction: SIC
15 -17
Heavy Construction
Total
3.7.4.2 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 employees
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 inustry 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
3.7.4.3 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
92
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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 PMio emissions from non-residential construction to develop the
final emissions inventory.
To account for the soil moisture level, the PMio emissions are weighted using the 30-year average precipitation-
evaporation (PE) values from Thornthwaite's PE Index. Average precipitation evaporation values for each State
were estimated based on PE values for specific climatic divisions within a State [ref 4],
To account for the silt content, the PMio emissions are weighted using average silt content for each county. 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],
The equation for PMio emissions corrected for soil moisture and silt content is:
24 S
Corrected EPMW = Initial ^PMW x — x —
where: Corrected EPMio = PMio emissions corrected for soil moisture and silt content,
PE = precipitation-evaporation value for each State,
S = % dry silt content in soil for area being inventoried.
Once PMio adjustments have been made, PM2.5 emissions are set to 10% of PMio.
3.7.4,4 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 PMio emission factor (ton/acre-month)
M = duration of construction activity (months)
As an example, in Grand Traverse County, Michigan, 2011 acres disturbed and PMio 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 PMio
Where EFAdj is calculated as follows:
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EFAdj = 0.19 ton/acre-month * (24/103.6 * 12/9)
= 0.059 ton/acre-month
3.7,4.5 References
1. Annual Value of Construction Put in Place: http://www.census.gov/const/C30/priv2011.pdf
2. County Business Patterns: http://www.census.gov/econ/cbp/index.html
3. Bureau of Labor Statistics: http://data.bls.gov/pdq/SurvevOutputServlet 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 ofPM-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.
3,7,5 Construction - Residential -EPA estimates
3.7.5.1 Source Category 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.
For this source category, the following SCC was used:
Table 54: SCC for Residential Construction
Source
Classification Code
SCC Level One
SCC Level Two
SCC Level Three
SCC Level Four
2311010000
Industrial
Processes
Construction: SIC
15 -17
General Building
Construction
Total
3.7.5.Z Activity Data
There are two activity calculations performed for this SCC, acres of surface soil disturbed and volume of soil
removed for basements.
3.7.5.2.1 Surface soil disturbed
The US Census Bureau has 2010 data for Housing Starts - New Privately Owned Housing Units Started [ref 1]
which provides regional level housing starts based on the groupings of 1 unit, 2-4 units, 5 or more units. A
consultation with the Census Bureau in 2002 gave a breakdown of approximately 1/3 of the housing starts being
for 2 unit structures, and 2/3 being for 3 and 4 unit structures. The 2-4 unit category was then divided into 2-
units, and 3-4 units based on this ratio. 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 2] gives a conversion factor to determine the ratio of structures to units in the
5 or more unit category. For example if a county has one 40 unit apartment building, the ratio would be 40/1. If
there are 5 different 8 unit buildings in the same project, the ratio would be 40/5. Structures started by category
are then calculated at a regional level. The table Annual Housing Units Authorized by Building Permit [ref 3] has
94
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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 following surface areas were assumed disturbed for each unit type:
Table 55: Surface Soil removed per unit type
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.
3.7.5.2.2 Basement soil removal
To calculate basement soil removal, 2010 Characteristics of New Houses [ref 4] is used to estimate the
percentage of 1 unit structures that have a basement (on the regional level). The county level estimate of
number of 1 unit starts is multiplied by the percent of 1 unit houses in the region that have a basement to get
the number of basements in a county. Basement volume is calculated by assuming a 2000 square foot house
has a basement dug to a depth of 8 feet (making 16,000 ft3 per basement). An additional 10% is added for
peripheral dirt bringing the total to 17,600 ft3 per basement.
3,7,5,3 Emission Factors
Initial PMio emissions from construction of single family, two family, and apartments structures are calculated
using the emission factors given in Table 56 [ref 5], The duration of construction activity for houses is assumed
to be 6 months and the duration of construction for apartments is assumed to be 12 months.
Table 56: 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 5], 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
6],
95
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The equation for PMio emissions corrected for soil moisture and silt content is:
24 S
Corrected EPMl0 = Initial EPM10 x — x —
where: Corrected EPMio = PMio emissions corrected for soil moisture and silt content,
PE = precipitation-evaporation value for each State,
S = % dry silt content in soil for area being inventoried.
Once PMio adjustments have been made, PM2.5-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.
3,7,5.4 Example Calculation
PMio Emissions = £( Aunit X Tconstruction x EFunit ) x AdjpM
where Aunit = HSUnit x SMUnit
HSunit = Regional Housing Starts x (county building permits/Regional building permits)
SM unit - Area or volume of soil moved for the given unit type
Tconstruction = Construction time (in months) for given unit type
EFunit = Unadjusted emission factor for PMio for the given unit type
AdjpM = 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
PMio Emissions = (50.9 acres x 6 months x 0.032 tons PMio/acre-month) x 0.242 = 2.37 tons PMio
; References
6. New Privately Owned Housing Units Started for 2010 (Not seasonally adjusted), available at:
http://www.census.gov/const/startsua.pdf
7. Table 2au. New Privately Owned Housing Units Authorized - Unadjusted Units for Regions, Divisions, and
States, Annual 2010, available at: http://www.census.gov/const/C40/Table2/tb2u2010.txt
8. Annual Housing Units Authorized by Building Permits CO2010A, purchased from US Department of
Census
9. Type of Foundation in New One-Family Houses Completed, available at:
http://www.census.gov/const/C25Ann/sftotalfoundation.pdf
10. Midwest Research Institute. Improvement of Specific Emission Factors (BACM Project No. 1). Prepared
for South Coast Air Quality Management District. March 29, 1996.
96
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11. Campbell, 1996: Campbell, S.G., D.R. Shimp, and S.R. Francis. Spatial Distribution ofPM-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.
http://www.epa.gov/ttn/chief/conference/eil5/sessionl4/cowherd.pdf
3,7,6 Construction - load- 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 Category 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).
For this category, the following SCC was used:
Table 57: SCC for Road Construction
Source
Classification Code
SCC Level One
SCC Level Two
SCC Level Three
SCC Level Four
2311030000
Industrial
Processes
Construction: SIC
15 -17
Road Construction
Total
3.7,6.2 Activity Data
The Federal Highway Administration has Highway Statistics, Section IV - Highway Finance, Table SF-12A, State
Highway Agency Capital Outlay [ref 1] 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 the table below:
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Table 58: 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 58 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 2] 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.
3.7,6.3 Emission Factors
Initial PMio emissions from construction of roads are calculated using an emission factor of 0.42 tons/acre-
month [ref 3], 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.
Regional variances in construction emissions are corrected using soil moisture level and silt content. These
correction parameters are applied to initial PMio emissions from road construction to develop the final
emissions inventory.
To account for the soil moisture level, the PMio emissions are weighted using the 30-year average precipitation-
evaporation (PE) values from Thornthwaite's PE Index. Average precipitation evaporation values for each State
were estimated based on PE values for specific climatic divisions within a State [ref 4],
To account for the silt content, the PMio emissions are weighted using average silt content for each county. 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
3],
The equation for PMio emissions corrected for soil moisture and silt content is:
24 S
Corrected EPMW = Initial ^PMW x — x —
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where: Corrected EPMio = PMio emissions corrected for soil moisture and silt content,
PE = precipitation-evaporation value for each State,
S = % dry silt content in soil for area being inventoried.
Once PMio adjustments have been made, PM2.5 emissions are set to 10% of PM10. Primary PM emissions are
equal to filterable emissions since there are no condensable emissions from road construction.
3,7,6.4 Example Calculation
EmissionspMio = I(HDrt x MCrt x ACrt) x (HSCounty/ HSstate) x EFAdj x M
where HDrt = Highway Spending for a specific road type
MCrt = Mileage conversion for a specific road type
ACrt = Acreage conversion for a specific road type
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 = I(HDrt x MCrt x ACrt) 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)
where EFAdj is calculated as follows:
EFAdj = 0.42 ton/acre-month * (24/110.1 * 33/9)
= 0.28 ton/acre-month
3.7,6.5 References
1. 2008 Highway Spending : http://www.fhwa.dot.gov/policvinformation/statistics/2008/sfl2a.cfm
2. 2008 Building Permits data from US Census "BPS01",
http://www.census.gov/support/USACdataDownloads.html
3. Midwest Research Institute. Improvement of Specific Emission Factors (BACM Project No. 1). Prepared
for South Coast Air Quality Management District. March 29, 1996.
4. Campbell, 1996: Campbell, S.G., D.R. Shimp, and S.R. Francis. Spatial Distribution ofPM-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.
nocounty
HSstate
EFAdj
Housing Starts in a given county
Housing Starts in a given State
Adjusted PM10 Emission Factor
duration of construction activity
54 acres x 0.28ton/acre-month x 12 months
181.4 tons PM10
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3,7,7 Summary of Quality Assurance Methods
3.8 Dust-Paw st
3,8,1 Sector Description
The 2011 NEI has emissions for the following SCCs in this sector.
Table 59: 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
EPA estimates emissions for particulate matter for the first SCC in this table.
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 60 submitted emissions for this sector.
Table 60: 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 dAlene 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
Kootenai Tribe of Idaho
T
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
100
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All Other
Public
All
Paved
Paved
Interstate/
AGENCY
Type
Roads
Roads
Arterial
Northern Cheyenne Tribe
T
X
Sac and Fox Nation of Missouri in Kansas and Nebraska
Reservation
T
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 61 shows the selection hierarchy for the datasets included in the paved road dust sector.
Table 61: 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
5
2011EPA_NP_NoOverlap_w_Pt
EPA-generated data
P - Point
N - Nonpoint
3.8.3 Spatial coverage and data sources for the sector
Dust - Paved Road Dust
PN - P&N
All CAPs EPA SLT — EPA& SLT
3.8.4 EPA methodology for paved road dust
Fugitive dust emissions from paved road traffic were estimated by EPA for PM1Q-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.
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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 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, determination of paved road VMT, and
controls.
3.8.4,1. Emission Factor Equation
Reentrained 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)]
where: E = paved road dust emission factor (gram [g]/VMT)
k = particle size multiplier (1 g/VMT for PM10-PRI/-FIL and .25 g/VMT for PM25-PRI/-FIL)
sL = road surface silt loading (g/square meter [m2]) (dimensionless in eq.)
W = average weight (tons) of all vehicles traveling the road (dimensionless in eq.)
P = number of days in the year with at least 0.01 inches of precipitation
N = number of days in the year
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
ftp://ftp.epa.gov/Emislnventorv/2011/doc/roads paved 2011.zip. 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 63.
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 64. 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
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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.
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.
3.8.4.2 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 unpaved road VMT from total State/roadway class VMT. Federal Highway
Administration's (FHWA) annual Highway Statistics report was used to determine the unpaved 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.
3.8.4.3 Controls
Paved road dust controls were applied by county to urban and rural roads in serious PMio nonattainment areas
and to urban roads in moderate PMio nonattainment areas. The assumed control measure is vacuum sweeping
of paved roads twice per month. A control efficiency of 79 percent was assumed for this control measure [ref 4],
The assumed rule penetration varies by roadway class and PMio nonattainment area classification (serious or
moderate). The rule penetration rates are shown in Table 62.
Table 62: Rule effectiveness was assumed to be 100% for all counties where this control was applied.
PMio
Roadway Class
Vacuum Sweeping
Nonattainment
Penetration Rate
Status
(%)
Moderate
Urban Freeway & Expressway
67
Moderate
Urban Minor Arterial
67
Moderate
Urban Collector
64
Moderate
Urban Local
88
Serious
Rural Minor Arterial
71
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PMio
Roadway Class
Vacuum Sweeping
Nonattainment
Penetration Rate
Status
(%)
Serious
Rural Major Collector
83
Serious
Rural Minor Collector
59
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.
Table 63: 2011 Silt Loadings by State and Roadway Class Modeled in Paved Road Emission Factor Calculations
(g/m!)
Rural
Urban
State
Interstate
Other
Principal
Arterial
Minor Arterial
Major
Collector
Minor
Collector
Local
Interstate
Other
Freeways and
Expressways
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
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
104
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Rural
Urban
State
Interstate
Other
Principal
Arterial
Minor Arterial
Major
Collector
Minor
Collector
Local
Interstate
Other
Freeways and
Expressways
Other
Principal
Arterial
Minor Arterial
Collectors
Local
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
Table 64: Average Vehicle Weights by M0BILE6 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
105
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Vehicle Class
Vehicle Weight
Abbreviation
Vehicle Class Description
Estimate (lbs)
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
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
Table 65: Penetration Rate of Paved Road Vacuum Sweeping
PMio
Nonattainment
Status
Roadway Class
Vacuum Sweeping
Penetration Rate
Moderate
Urban Freeway & Expressway
0.67
Moderate
Urban Minor Arterial
0.67
Moderate
Urban Collector
0.64
Moderate
Urban Local
0.88
Serious
Rural Minor Arterial
0.71
Serious
Rural Major Collector
0.83
Serious
Rural Minor Collector
0.59
Serious
Rural Local
0.35
Serious
Urban Freeway & Expressway
0.67
Serious
Urban Minor Arterial
0.67
Serious
Urban Collector
0.64
Serious
Urban Local
0.88
3.8.5 Summary of Quality Assurance Methods
3.8.6 References
1. United States Environmental Protection Agency, Office of Air Quality Planning and Standards.
"Compilation of Air Pollutant Emission Factors, AP-42, Fifth Edition, Volume I: Stationary Point and Area
Sources, Section 13.2.1, Paved Roads." Research Triangle Park, NC. January 2011.
2. U.S. Department of Transportation, Federal Highway Administration. Highway Statistics 2010. Office of
Highway Policy Information. Washington, DC. 2011. Available at
106
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http://www.fhwa.dot.gov/policvinformation/statistics/2010/.
3. U.S. Department of Commerce, National Oceanic and Atmospheric Administration. "2011 Local
Climatological Data Annual Summaries with Comparative Data": Available at:
http://www7.ncdc.noaa.gov/IPS/lcd/lcd.html, 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 - Unpav st
3.9.1 Sector Description
The 2011 NEI has emissions for the SCCs shown in Table 66 for this sector. EPA estimates emissions for
particulate matter for the first SCC (2296000000) in Table 66.
Table 66: 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
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 67 submitted emissions for this sector.
Table 67: Agencies that Submitted Unpaved Road Emissions Data
Agency
Type
All
Unpaved
Roads
Industrial
Unpaved
Roads
Public
Unpaved
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
Management
L
X
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
107
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Agency
Type
All
Unpaved
Roads
Industrial
Unpaved
Roads
Public
Unpaved
Roads
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 68 shows the selection hierarchy for the datasets used in the unpaved roads sector.
Table 68: 2011 NEI Unpaved Roads 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-Augrnentation
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
3.9.3 Spatial coverage and data sources for the sector
Dust - Unpaved Road Dust
P-Point i!
N - Nonpoint
PN-P&N
All CAPs EPA SLT EPA & SLT
108
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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 level using 2010 rural 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.
1.1.1.1 Emission Factor Equation
Reentrained 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)0-5] h- (M/0.5)a2-C
where k and C are empirical constants given in Table 69, 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)
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 69 [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.
109
-------
Table 69: Constants for Unpaved Roac
s Reentrained 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 70 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 70: 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 resuspension. Monthly corrected emission factors by State and
roadway classification were calculated using the following equation:
ECOrr = Ex[(D-p)/D]
where: 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
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.
3,9,4,1 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
110
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the average daily traffic volume (ADTV) groups shown in Table 71: Assumed Values for Average Daily Traffic
Volume (ADTV) by Volume Group
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: VMTup = 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 71: 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.
3.9.4,2 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.
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
111
-------
EMISy = 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.
3.9,4.3 Controls
The controls assumed for unpaved roads varied by PMio nonattainment area classification and by urban and rural
areas. On urban unpaved roads in moderate PMio nonattainment areas, paving of the unpaved road was
assumed, and a control efficiency of 96 percent and a rule penetration of 50 percent were applied. Chemical
stabilization, with a control efficiency or 75 percent and a rule penetration of 50 percent, was assumed for rural
areas in serious PMio nonattainment areas. A combination of paving and chemical stabilization, with a control
efficiency of 90 percent and a rule penetration of 75 percent, was assumed for urban unpaved roads in serious
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
3.9.6 References
1. United States Environmental Protection Agency, Office of Air Quality Planning and Standards.
"Compilation of Air Pollutant Emission Factors, AP-42, Fifth Edition, Volume I: Stationary Point and Area
Sources, Section 13.2.2, Unpaved Roads." Research Triangle Park, NC. 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
Highway Policy Information. Washington, DC. 2009. Available at
http://www.fhwa.dot.gov/policvinformation/statistics/2007/.
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. Retrieved from http://www.census.gov/geo/www/ua/2010urbanruralclass.html 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
112
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Policy, Planning and Evaluation. Washington, DC. June 1995.
3.10 Fuel Combusl sctrlc 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
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.
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3,10,2 Sources of data overview and selection hierarchy
The primary sources of data for the EGU sectors were the S/L/T 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 submitted data except where the S/L/T 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 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 were also from the EPA Carry Forward, EPA
other, and EPA's Nevada Gold datasets.
The agencies listed in Table 72Error! Reference source not found, 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 72: 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
Kentucky Division for Air Quality
State
X
X
X
X
X
Lane Regional Air Pollution Authority
Local
X
114
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Agency
Type
Coal
Oil
Natural
Gas
Biomass
Other
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
Tennessee Department of Environmental Conservation
State
X
X
X
X
X
115
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Agency
Type
Coal
Oil
Natural
Gas
Biomass
Other
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 73 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 73: 2011 NEI EGU data selection hierarchy by EGU fuel groups
Priority
Data Set Name
Data Set Contents and Impact
Coal
Oil
Natural
Gas
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 4tribes(see
Section 0)
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 0)
X
X
X
X
X
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3.10.3 Spatial coverage and data sources for the sector
Fuel Comb - Electric Generation - Biomass Fuel Comb - Electric Generation - Biomass
p— P-Point
N - Nonpoint
PN-P&N
All CAPS EPA SLT ~EPA & SLT
p —~ P - Point
N - Nonpoint
PN-P&N
All HAPs EPA SLT ~EPA & SLT
Fuel Comb - Electric Generation - Coal Fuel Comb - Electric Generation - Coal
N - Nonpoint
PN - P&N
All CAPs EPA SLT "EPA & SLT
p— *
N - Nonpoint
PN - P&N
All HAPs EPA SLT "EPA & SLT
Fuel Comb - Electric Generation - Natural Gas Fuel Comb - Electric Generation - Natural Gas
N - Nonpoint
PN - P&N
All CAPs EPA SLT —EPA & SLT
^ - Nonpoint
PN-P&N
All HAPs EPA SLT —EPA & SLT
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Fuel Comb - Electric Generation - Oil
Fuel Comb - Electric Generation - Oil
P - Point
N - Nonpoint
P - Point
N - Nonpoint
PN - P&N
All CAPS EPA SLT —EPA & SLT
PN - P&N
All HAPs EPA SLT EPA & SLT
Fuel Comb - Electric Generation - Other Fuel Comb - Electric Generation - Other
P - Point
N - Nonpoint
P - Point
N - Nonpoint
PN - P&N
All CAPS EPA SLT "EPA & SLT
PN-P&N
All HAPs EPA SLT EPA & SLT
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 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 submitted filterable and
condensable pieces together to create the PMio and PM2.5 Primary species, or they may also include EPA
estimates for the condensable piece if not submitted by the S/L/T. 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 74 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.
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Table 74: Agency-submitted, PM Augmentation, and total PMio and PM2.5 emissions for EGU sectors
EIS Sector
PM10
Agency
(tons)
PM10
Aug
(tons)
PM10
Total
(tons)
PM2.5
Agency
(tons)
PM2.5
Aug
(tons)
PM2.5
Total
(tons)
Fuel Comb - Electric Generation - Biomass
1,439
743
2,182
1,010
863
1,873
Fuel Comb - Electric Generation - Coal
130,611
106,873
237,484
80,670
85,967
166,637
Fuel Comb - Electric Generation - Natural Gas
12,604
13,027
25,632
10,875
13,946
24,821
Fuel Comb - Electric Generation - Oil
6,931
1,054
7,985
4,455
1,416
5,871
Fuel Comb - Electric Generation - Other
1,759
1,134
2,893
1,165
1,383
2,549
153,344
122,832
276,176
98,176
103,575
201,751
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/Ts and in some cases to be used
instead of S/L/T submitted data. The 2011EPA_EGU 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 73, the selection hierarchy was set such that S/L/T-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 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 value, except where the S/L/T submittal indicated that the S/L/T value was from either
a CEM or a recent stack test. The selection of the MATS-based emissions over the S/L/T emissions was
accomplished by setting a "tag" on those S/L/T 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 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
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S/L/T agency for reporting the largest portion of the S/L/T NOx emissions. Wherever S/L/T 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 emissions for that pollutant be
tagged.
In summary, the 2011 NEI for EGUs is comprised of largely S/L/T-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.
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 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 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 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 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.
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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 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 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 and the value was exceptionally suspect, the
value was tagged to be excluded from selection for the NEI.
uel 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
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
Fuel Comb - Industrial Boilers, ICEs - Biomass
Fuel Comb - Industrial Boilers, ICEs - 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 liquified 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/Institutional 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 3.10. In
addition, while CO Boilers are in this sector, they are not included in the Industrial/Commercial/Institutional
Boilers and Process Heaters MACT category.
Also as described above in 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
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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 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 ant! selection hierarchy
The industrial fuel combustion sectors include data from S/L/T and 9 EPA datasets that cover both point and
nonpoint data categories. Table 75 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.
Table 75: 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
122
-------
Nonpoint
Point
Bio-
Natural
Bio-
Natural
Agency
TYPE
mass
Coal
Gas
Oil
Other
mass
Coal
Gas
Oil
Other
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
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 Department 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
123
-------
Nonpoint
Point
Agency
TYPE
Bio-
mass
Coal
Natural
Gas
Oil
Other
Bio-
mass
Coal
Natural
Gas
Oil
Other
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 76 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 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.
Table 76: 2011 NEI selection hierarchy for datasets used by the 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
2008EPA_MMS
Same data as were used in 2008: 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 (Wilson et. al, 2010) in the National Inventory
Input Format and converted to the CERS format by EPA.
9
2011EPA_N P_0 ve r 1 a p_w_Pt
EPA generated emissions for nonpoint sources
5
124
-------
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 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 77.
Table 77: Algorithm to determine whether to augment state data with EPA data for Industrial Boilers sector
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 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.
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
Don't augment
Assume that they filled out the survey
incorrectly, and that they meant that
the category is fully covered by
nonpoint.
State claims
that category
is fully covered
by their
nonpoint
inventory for
an SCC
No
Yes
Don't 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
Don't augment
Assume that they filled out the survey
incorrectly.
State claims
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
Don't 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
125
-------
Survey Data
State
Submitted
to Point?
State
Submitted
to
Nonpoint?
EPA Action
Rationale
category is not covered by the state at
all.
Yes
No
Augment
While there would be some double-
counting of point emissions, it would be
small, and we believe that there would
stiii 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
Don't 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.
3.11.3 Spatial coverage and data sources for the sector
Fuel Comb - Industrial Boilers, ICEs - Biomass Fuel Comb - Industrial Boilers, ICEs - Biomass
p
PN
PN
"PN
?tpn
-p
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
SLT
P - Point
N - Nonpoint
PN - P&N
SLT
All CAPS EPA
All HAPs
EPA & SLT
EPA & SLT
126
-------
P - Point
N - Nonpoint
P - Point
N - Nonpoint
_ , ^ ^ , Fuel Comb - Industrial Boilers, ICEs - Coal
Fuel Comb - Industrial Boilers, ICEs - Coal
PN - P&N PN - P&N
All CAPs EPA SLT ~EPA & SLT All HAPs EPA SLT "EPA & SLT
P - Point
N - Nonpoint
P - Point
N - Nonpoint
i- . « i . . ... n ~ Fuel Comb - Industrial Boilers, ICEs - Natural Gas
Fuel Comb - Industrial Boilers, ICEs - Natural Gas
PN-P&N
All CAPs EPA SLT "EPA & SLT
PN - P&N
All HAPs EPA SLT —EPA & SLT
Fuel Comb - Industrial Boilers, ICEs - Oil Fuel Comb - Industrial Boilers, ICEs - Oil
P - Point
N - Nonpoint
PN-P&N
SLT "EPA & SLT
P - Point
N - Nonpoint
PN - P&N
SLT ~EPA & SLT
All CAPs EPA
All HAPs EPA
127
-------
Fuel Comb - industrial Boilers, ICEs - Other Fuel Comb - Industrial Boilers, ICEs - Other
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN PN PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN PN
PN
PN
PN i
P - Point
N - Nonpoirit
PN - P&N
SLT
N — P - Point
N - Nonpoint
PN - P&N
All HAPs EPA SLT
All CAPS EPA
EPA & SLT
EPA & SLT
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, ail 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-level 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
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,
128
-------
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 can be found in
the documentation given in ftp://ftp.epa.gov/Emislnventory/2011nei/doc/. 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 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 vl NEI. Other states agreed that the tagged values seemed
incorrect, and that EPA should use the EPA generated estimates in its place. Table 78 summarizes the number of
tagged process-level emissions values from each agency affected by this QA.
Table 78: Agencies Tagged Values for Industrial Fuel Combustion
Number of
Agency
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
129
-------
Agency
Number of
Values Tagged
Tag Reason
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 plans to resubmit for version 2
Wisconsin Department of Natural
Resources
2
State did not report hex, so EPA data should
be used
3.11.6 References
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, accessed from
http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html. 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,
data available from http://tonto.eia.doe.gov/dnav/pet/pet cons 821use dcu nus a.htm. accessed
March, 2012.
5. Census, 2012: Bureau of the Census, U.S. Department of Commerce, County Business Patterns 2009,
Washington, DC, available from: http://www.census.gov/econ/cbp/index.html 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.
130
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3.12 Fuel Combusi lercial/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/Institutional 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 79 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.
131
-------
Table 79: 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
132
-------
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 80 shows the selection hierarchy for the commercial/institutional fuel combustion sector.
Table 80: 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
133
-------
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
2011EPAJTRI
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
2008EPA_MMS
Same data as were used in 2008: 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 (Wilson et. al, 2010) in the National Inventory
Input Format and converted to the CERS format by EPA.
8
2011EPA_N P_0 ve r 1 a p_w_Pt
EPA generated emissions for nonpoint sources
5
3.12.3 Spatial coverage and data sources for the sector
Fuel Comb - Comm/lnstitutional - Biomass Fuel Comb - Comm/lnstitutional - Biomass
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
134
-------
Fuel Comb - Comm/lnstitutional - Coal
Fuel Comb - Comm/lnstitutional - Coal
PN PN
p PN
P .«*>..
"n P- Point
N - Noripoint
PN-P&N
All CAPs1—1 EPA SLT *EPA & SLT
P-Point N
N - Nonpoint
PN - P&N
All HAPsa EPA SLT — EPA & SLT
Fuel Comb - Comm/lnstitutional - Natural Gas Fuel Comb - Comm/lnstitutional - Natural Gas
PN
PN
PN
PN
PN
PN
PN
PN
PN
-PM
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN PN PN
PN
PN
PN
PN
PN
PN PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN p I r'
PN
P - Point
N - Nonpoint
PN
PN
PN
->
PN ^
PN
PN
PN —^
P - Point
N - Nonpoint
PN
PN
PN PN
PN PN
PN
Point
N - Nonpoint
PN - P&N
All CAPs EPA SLT EPA & SLT
PN
P - Point
N - Nonpoint
PN - P&N
All HAPs EPA SLT ~ EPA & SLT
Fuel Comb - Comm/lnstitutional - Oil
Fuel Comb - Comm/lnstitutional - Oil
N - Nonpoint
PN - P&N
All CAPs EPA SLT ~EPA & SLT
J - Nonpoint
PN-P&N
All HAPs EPA SLT —EPA & SLT
135
-------
Fuel Comb - Comm/lnstitutional - Other Fuel Comb - Comm/lnstitutional - Other
P - Point
N - Nonpoirit
PN-P&N
SLT "EPA & SLT
P - Point
N - Nonpoint
PN-P&N
SLT "EPA & SLT
All HAPs EPA
3.12.4 EPA-developed commercial/institutional fuel combustion data
The approach in calculating nonpoint emissions for commercial/institutional fuel 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-level 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 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 81 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
136
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percentages shown in Table 81 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 can be found in the documentation given in
ftp://ftp.epa.gov/Emislnventory/2011nei/doc/. 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 81: Assumptions Used to Estimate Commercial/Institutional Sector 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.
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
137
-------
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 82 summarizes the number of
tagged process-level emissions values from each agency affected by this QA.
Table 82: Agencies Tagged Values for Commercial/Institutional Fuel Combustion
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
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,
data available from http://tonto.eia.doe.gov/dnav/pet/pet cons 821use dcu nus a.htm, 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, available from: http://www.census.gov/econ/cbp/index.html 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, available from:
http://www.census.gov/govs/www/apesloc06.html. 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.
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 Combusi sidential - 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 submitted data for it for the 2011 NEI.
138
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• "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 83 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 83: 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
84 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.
139
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Table 84: Agencies that submitted data for Fuel Combustion - Residential Heating - Natural Gas, Oil and Other
Natural
Gas
Oil
Other
Natural
Distillate
Kero-
Residual
Anthracite
Bituminous/
Subbitumi-
Liquified
Petroleum
Agency
Type
Gas
Oil
sene
Oil
Coal
nous Coal
Gas (LPG)
US Environmental Protection Agency (2011EPA_NP_NoOvrlp
EPA
X
X
X
X
X
X
dataset, to be described in 3.13.4)
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 dAlene 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
s
X
X
X
0
X
Environmental Control
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
X
Kansas
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
X
Reservation
Santee Sioux Nation
T
X
Shoshone-Bannock Tribes of the Fort Hall Reservation of
X
X
X
0
X
X
Idaho
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
140
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P - Point
N - Noripoint
P - Point
N - Nonpoint
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
Fuel Comb - Residential - Oil Fuel Comb - Residential - Oil
PN - P&N PN - P&N
All CAPS EPA SLT ~EPA & SLT All HAPs EPA SLT "EPA & SLT
3.13.3 Spatial coverage and data sources for the sector
Fuel Comb - Residential - Other
Fuel Comb -
Residential - Other
Fuel Comb - Residential - Natural Gas Fuel Comb - Residential - Natural Gas
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
141
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3.13.4 EPA Residential Heating estimates
3.13.5 Summary of quality assurance methods
3.14 Fuel Combusi slientlal - 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 85 shows the SCCs used in the 2011 NEI from in this sector. EPA estimates emission for all SCCs in Table
85 other than SCC=2104008300, which is a general woodstove SCC that provides no details on the category.
Only the California Air Resources Board and the Sac and Fox Nation of Missouri in Kansas and Nebraska
Reservation submit emissions for this general woodstove SCC.
Table 85: SCCs in the Residential Wood Combustion Sector in the 2011 NEI
SCC
SCC Level Three*
SCC Level Four
2104008100
Wood
Fireplace: general
2104008210
Wood
Woodstove: fireplace inserts; non-EPA certified
2104008220
Wood
Woodstove: fireplace inserts; EPA certified; non-catalytic
2104008230
Wood
Woodstove: fireplace inserts; EPA certified; catalytic
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, chimeas, 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 ant! selection hierarchy
The residential wood sector includes emissions from both S/L/T agencies and from the EPA no-overlap nonpoint
dataset. Table 86 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 87 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 or both types of data are used for this sector. In cases where an
agency is listed in Table 86 and "both" is shown in the figure, this means that one of the EPA augmentation
datasets was used in that state.
142
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Table 86: 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
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 87: 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
143
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3.14-.3 Spatial coverage and data sources for the sector
Fuel Comb - Residential - Wood Fuel Comb - Residential - Wood
N
P - Point
N - Nonpoint
PN - P&N
SLT ~ EPA & SLT
' n—p \ t p - Point
N - Nonpoint
PN-P&N
All CAPs EPA SLT ~EPA & SLT
All HAPs EPA
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
appliance listed in Table 85. 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 (http://www.census.gov/hhes/www/housing/ahs/ahs05/ahs05.htmi ), 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
144
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appliances is discussed below. Because the AHS does 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 88 lists the MSAs using updated AHS survey data for the 2011 NEI.
Table 88: 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 2011 NEI, 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 MARAMA (Mid-Atlantic Regional Air Management Association) survey that was later
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.
Decreases of emissions from RWC from 2008 occur in the southeast; we believe the 2008 version of the tool
overestimated emissions in those states.
145
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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 NEI version
2, 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 2008
version 3, and then this was the baseline data for the 2011 updates.
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.
146
-------
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 (http://www.epa.gov/hg/report.htm). 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.
3.14.5 Summary of quality assurance methods
3.14.6 References
1. Huntley, Roy; Van Bruggen, J., Coldner, S., Divita, F.; "New Methodology for Estimating Emissions from
Residential Wood Combustion", presented at the 17th International Emission Inventory Conference,
Portland, Oregon, June 2008, http://www.epa.gov/ttnchiel/conference/eil7/session2/huntley.pdf .
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, available at
http://www.hearthandhome.com/James%20Houck%20Articles/
HH 03 October.Wood%20or%20Gas.pdf, accessed June 23, 2014.
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.
(http://www.nvserda.nv.gov/Publications/Case-Studies/Biomass-Heating-Case-
Studies.aspx?sc database=web).
147
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3.15 Gas Stations
3.15.1 Sector Description
3.15.2 Sources of data overview and selection hierarchy
P - Point
N - Nonpoirit
P - Point
N - Nonpoint
3.15.3 Spatial coverage and data sources for the sector
Gas Stations Gas Stati0ns
PN - P&N PN - P&N
All CAPs EPA SLT "EPA & SLT All HAPs EPA SLT EPA & SLT
3.16 Industrial Processes - Cement Manufacturing
3.16.1 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.
148
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3.16.2 Sources of data overview and selection hierarchy
3.16.3 Spatial coverage and data sources for the sector
Industrial Processes - Cement Manuf
Industrial Processes - Cement Manuf
P - Point
N - Nonpoint
P - Point
N - Nonpoint
p p
p p
p p
P - Point
N - Nonpoint
PN - P&N
All CAPs EPA SLT "™EPA & SLT
p p
P - Point
N - Nonpoint
PN - P&N
All HAPs EPA SLT EPA & SLT
3.17 Industrial Processes - Chemical Manufacturing
3.17.1 Sector Description
3.17.2 Sources of data overview and selection hierarchy
3.17.3 Spatial coverage and data sources for the sector
Industrial Processes - Chemical Manuf
Industrial Processes - Chemical Manuf
Point
N - Nonpoint
PN - P&N
All CAPs EPA SLT EPA & SLT
N - Nonpoint
PN - P&N
All HAPs EPA SLT —EPA & SLT
149
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3.18 Industrial Processes - Ferrous Metals
3.18.1 Sector Description
3.18.2 Sources of data overview and selection hierarchy
3.18.3 Spatial coverage and data sources for the sector
Industrial Processes - Ferrous Metals Industrial Processes - Ferrous Metals
p
P - Point
N - Nonpoint
PN-P&N
SLT
P - Point
N - Nonpoint
PN-P&N
SLT
All CAPs EPA
EPA & SLT
EPA & SLT
All HAPs
3.19 Industrial Processes - Mining
3.19.1 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 emission for the SCCs shown in Table 89 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.
Table 89: SCCs for Industrial Processes- Mining
see
SCC Level Two
SCC Level Three
SCC Level Four
2325000000
Mining and Quarrying: SIC 14
AN 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
150
-------
see
see Level Two
SCC Level Three
SCC Level Four
30302408
Primary Metal Production
Metal Mining (General Processes)
Material Handling: High Moisture Ore
30302409
Primary Metal Production
Metal Mining (General Processes)
Dry Grinding with Air Conveying
30302410
Primary Metal Production
Metal Mining (General Processes)
Dry Grinding without Air Conveying
30302411
Primary Metal Production
Metal Mining (General Processes)
Ore Drying
30303102
Primary Metal Production
Leadbearing Ore Crushing and Grinding
Zinc Ore w/ 0.2% Lead Content
30303107
Primary Metal Production
Leadbearing Ore Crushing and Grinding
Copper-Lead-Zinc w/ 2% Lead Content
30501001
Mineral Products
Coal Mining, Cleaning, and Material Handling
Fluidized Bed Reactor
30501002
Mineral Products
Coal Mining, Cleaning, and Material Handling
Flash or Suspension Dryer
30501003
Mineral Products
Coal Mining, Cleaning, and Material Handling
Multilouvered Dryer
30501004
Mineral Products
Coal Mining, Cleaning, and Material Handling
Rotary Dryer
30501005
Mineral Products
Coal Mining, Cleaning, and Material Handling
Cascade Dryer
30501006
Mineral Products
Coal Mining, Cleaning, and Material Handling
Continuous Carrier/Conveyor
30501008
Mineral Products
Coal Mining, Cleaning, and Material Handling
Unloading
30501009
Mineral Products
Coal Mining, Cleaning, and Material Handling
Raw Coal Storage
30501010
Mineral Products
Coal Mining, Cleaning, and Material Handling
Crushing
30501011
Mineral Products
Coal Mining, Cleaning, and Material Handling
Coal Transfer
30501012
Mineral Products
Coal Mining, Cleaning, and Material Handling
Screening
30501013
Mineral Products
Coal Mining, Cleaning, and Material Handling
Coal Cleaning: AirTable
30501014
Mineral Products
Coal Mining, Cleaning, and Material Handling
Cleaned Coal Storage
30501015
Mineral Products
Coal Mining, Cleaning, and Material Handling
Coal Loading (For Clean Coal Loading USE
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
30501030
Mineral Products
Coal Mining, Cleaning, and Material Handling
Topsoil Removal (See also 305010 -33, -35, -
36, -37, -42, -45, -48)
30501031
Mineral Products
Coal Mining, Cleaning, and Material Handling
Scrapers: Travel Mode
30501032
Mineral Products
Coal Mining, Cleaning, and Material Handling
Topsoil Unloading
30501033
Mineral Products
Coal Mining, Cleaning, and Material Handling
Overburden (See also 305010 -30, -35, -36, -
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
30501037
Mineral Products
Coal Mining, Cleaning, and Material Handling
Truck Loading: Overburden
30501038
Mineral Products
Coal Mining, Cleaning, and Material Handling
Truck Loading: Coal
30501039
Mineral Products
Coal Mining, Cleaning, and Material Handling
Hauling: Haul Trucks
30501040
Mineral Products
Coal Mining, Cleaning, and Material Handling
Truck Unloading: End Dump - Coal
30501041
Mineral Products
Coal Mining, Cleaning, and Material Handling
Truck Unloading: Bottom Dump - Coal
30501043
Mineral Products
Coal Mining, Cleaning, and Material Handling
Open Storage Pile: Coal
30501044
Mineral Products
Coal Mining, Cleaning, and Material Handling
Train Loading: Coal
30501045
Mineral Products
Coal Mining, Cleaning, and Material Handling
Bulldozing: Overburden
30501046
Mineral Products
Coal Mining, Cleaning, and Material Handling
Bulldozing: Coal
30501047
Mineral Products
Coal Mining, Cleaning, and Material Handling
Grading
30501048
Mineral Products
Coal Mining, Cleaning, and Material Handling
Overburden Replacement
30501049
Mineral Products
Coal Mining, Cleaning, and Material Handling
Wind Erosion: Exposed Areas
30501050
Mineral Products
Coal Mining, 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
Mining and Quarrying of Nonmetallic Minerals
Open Pit Blasting
30504002
Mineral Products
Mining and Quarrying of Nonmetallic Minerals
Open Pit Drilling
30504003
Mineral Products
Mining and Quarrying of Nonmetallic Minerals
Open Pit Cobbing
151
-------
see
see Level Two
SCC Level Three
SCC Level Four
30504010
Mineral Products
Mining and Quarrying of Nonmetallic Minerals
Underground Ventilation
30504024
Mineral Products
Mining and Quarrying of Nonmetallic Minerals
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
3,19,2 Sources of data overview ant! selection hierarchy
The industrial processes-mining sector includes data from S/L/T and EPA datasets that cover both point and
nonpoint data categories. Table 90 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 90: 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 clay 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
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
152
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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 clay 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)
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
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
153
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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 clay 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)
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 data (see Section 3.1.2). EPA estimates for SCC 2325000000 is
described in Section 3.19.4
3.19.3 Spatial coverage and data sources for the sector
Industr ial Pi ocesses - Mir ling industrial Processes - Mining
PN
P - Point
N - Nonpoint
PN - P&N
All CAPS EPA SLT ~EPA & SLT
P - Point
N - Nonpoint
PN - P&N
All HAPs EPA SLT —EPA & SLT
3.19.4 EPA Emissions- Mining
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.
154
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3.19.4.1 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 PMio/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+(BxEFb) + EFi+EFd
where, EFmo = metallic ore mining emissions factor (lbs/ton)
EF0 = PM io 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 io drilling/blasting emission factor for copper ore (lbs/ton)
EFi = PM io loading emission factor for copper ore (lbs/ton)
EFd = PM io truck dumping emission factor for copper ore (lbs/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 lbs/ton
3.19.4.2 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)
where, EFnmo = non-metallic ore mining emissions factor (lbs/ton)
EFV = PM io open pit overburden removal emission factor at western surface coal mining
operations (lbs/ton)
D = fraction of total ore production that is obtained by blasting at non-metallic ore mines
EFr = PM io drilling/blasting emission factor at western surface coal mining operations (lbs/ton)
EFa = PM io loading emission factor at western surface coal mining operations (lbs/ton)
EFe = PM io truck unloading: end dump-coal emission factor at western surface coal mining
operations (lbs/ton)
EFt = PM io truck unloading: bottom dump-coal emission factor at western surface coal mining
operations (lbs/ton)
Applying the TSP 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
3.19.4.3 Cool 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
155
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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 + EFr +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 10 open pit overburden removal emission factor at western surface coal mining operations
(lbs/ton)
EFr = PM io drilling/blasting emission factor at western surface coal mining operations (lbs/ton)
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
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 PM2.5/PM10 ratios for several fugitive dust categories and
concluded that the PM2.5/PM10 ratios for fugitive dust categories should be in the range of 0.1 to 0.15 [ref 5],
Consequently, a ratio of 0.125 was applied to the PMio emissions factors to estimate PM2.5 emissions factors for
mining and quarrying. A summary of emissions factors is presented in Table 91.
156
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Table 91: 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
3.19.4.4 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.
3.19.4.5 Activity Allocation 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 92 for a list of NAICS codes).
Table 92: NAICS Codes for Metallic and Non-Metallic Mining
NAICS Code
Description
2122
Metal Ore Mining
212210
Iron Ore Mining
21222
Gold Ore and Silver Ore Mining
212221
Gold Ore Mining
212222
Silver Ore Mining
21223
Copper, Nickel, Lead, and Zinc Mining
212231
Lead Ore and Zinc Ore Mining
157
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NAICS Code
Description
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 93.
Table 93: 2006 County Business Pattern for NAICS 31-33 in Maine
fipsstate
fipscty
naics
empflag
emp
23
001
31—
6774
23
003
31—
3124
23
005
31—
10333
158
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fipsstate
fipscty
naics
empflag
emp
23
007
31—
1786
23
009
31—
1954
23
011
31—
2535
23
013
31—
1418
23
015
31—
F
0
23
017
31—
2888
23
019
31—
4522
23
021
31—
948
23
023
31—
1
0
23
025
31—
4322
23
027
31—
1434
23
029
31—
1014
23
031
31—
9749
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.
3.19.4.6 Controls
No controls were accounted for in the emissions estimation.
3.19.4.7 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 "t" En + Ec
where, E = PMio emissions from mining and quarrying operations
Em = PM io emissions from metallic ore mining operations
En = PM io emissions from non-metallic ore mining
Ec = PM io emissions from coal mining operations
159
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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 = EFxA
where, E = PMio 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:
EpM10-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.
3.19.5 Quality Assurance Procedures
3.19.6 References
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, available at
http://www.epa.gov/ttn/chief/ap42/chll/final/clls09.pdf (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, available at
http://www.epa.gov/ttnchiel/ap42/chl3/bgdocs/bl3s02.pdf (accessed December 2011).
6. United States Geologic Survey, "Minerals Yearbook 2009",
http://minerals.usgs.gov/minerals/pubs/commoditv/m&q/index.html#mvb (accessed April 2012).
7. Energy Information Administration, "Production by Company and Mine - 2009",
http://www.eia.gOv/coal/data.cfm#production (accessed April 2012).
8. U.S. Census Bureau, 2009 County Business Patterns, available at
http://www.census.gov/econ/cbp/download/index.htm (accessed April 2012)
160
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3.20 Industrial Processes - Non-ferrous Metals
3.20.1 Sector Description
3.20.2 Sources of data overview and selection hierarchy
3.20.3 Spatial coverage and data sources for the sector
Industrial Processes - Non-ferrous Metals Industrial Processes - Non-ferrous Metals
p
PN
PN
PN
PN
PN
P - Point
N - Nonpoint
PN - P&N
SLT EPA & SLT
P - Point
N - Nonpoint
PN - P&N
SLT
All CAPs EPA
All HAPs EPA
EPA & SLT
3.21 Industrial Processes - Oil & Gas Production
3.21.1 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 94 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.
Table 94: Point and nonpoint 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
through
2310002421
Off-Shore Oil & Gas Production;
Nonpoint
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
161
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Data
EPA
Category
uses
see
SCC Description (Abbreviated)
Nonpoint
Y
2310010100
Nonpoint
Y
2310010200
Nonpoint
Y
2310010300
Nonpoint
2310010700
Nonpoint
2310010800
Nonpoint
Y
2310011000
Nonpoint
2310011020
Nonpoint
2310011100
Nonpoint
Y
2310011201
Nonpoint
2310011450
Nonpoint
2310011500
Nonpoint
Y
2310011501
Nonpoint
Y
2310011502
Nonpoint
Y
2310011503
Nonpoint
2310011504
Nonpoint
Y
2310011505
Nonpoint
2310011506
2310012000
Nonpoint
through
2310012526
2310020000
Nonpoint
through
2310020800
Nonpoint
Y
2310021010
Nonpoint
2310021011
Nonpoint
Y
2310021030
Nonpoint
Y
2310021100
Nonpoint
2310021101
Nonpoint
2310021102
Nonpoint
2310021103
Nonpoint
2310021201
Nonpoint
Y
2310021202
Nonpoint
2310021203
Nonpoint
2310021209
Nonpoint
Y
2310021251
Nonpoint
Y
2310021300
Nonpoint
2310021301
Nonpoint
Y
2310021302
Nonpoint
2310021303
Crude Petroleum; Oil Well Heaters
Crude Petroleum; Oil Well Tanks - Flashing &
Standing/Working/Breathing
Crude Petroleum; Oil Well Pneumatic Devices
Crude Petroleum; Oil Well Fugitives
Crude Petroleum; Oil Well Truck Loading
On-shore oil production
On-shore oil production
On-shore oil production
On-shore oil production
On-shore oil production
On-shore oil production
On-shore oil production
On-shore oil production
On-shore oil production
On-shore oil production
On-shore oil production
On-shore oil production
Total: All Processes
Storage Tanks: Crude Oil
Heater Treater
Tank Truck/Railcar Loading: Crude Oil
Wellhead
Fugitives: All Processes
Fugitives: Connectors
Fugitives: Flanges
Fugitives: Open Ended Lines
Fugitives: Pumps
Fugitives: Valves
Fugitives: Other
Off-Shore Oil Production;
Total: All Processes, Storage Tanks: Crude Oil, Fugitives, Connectors:
Oil Streams, Fugitives, Flanges: Oil, Fugitives, Valves: Oil, Fugitives,
Other: Oil, Fugitives, Connectors: Oil/Water Streams, Fugitives,
Flanges: Oil/Water, Fugitives, Other: Oil/Water
Natural Gas; Total: All Processes, Compressor Engines, Gas Well Truck
Loading
On-Shore Gas Production; Storage Tanks: Condensate
On-Shore Gas Production; Condensate Tank Flaring
On-Shore Gas Production; Tank Truck/Railcar Loading: Condensate
On-Shore Gas Production; Gas Well Heaters
Natural Gas Fired 2Cycle Lean Burn Compressor Engines < 50 HP
Natural Gas Fired 2Cycle Lean Burn Compressor Engines 50 To 499 HP
Natural Gas Fired 2Cycle Lean Burn Compressor Engines 500+ HP
Natural Gas Fired 4Cycle Lean Burn Compressor Engines <50 HP
Natural Gas Fired 4Cycle Lean Burn Compressor Engines 50 To 499 HP
Natural Gas Fired 4Cycle Lean Burn Compressor Engines 500+ HP
Total: All Natural Gas Fired 4Cycle Lean Burn Compressor Engines
On-Shore Gas Production; Lateral Compressors 4 Cycle Lean Burn
On-Shore Gas Production; Gas Well Pneumatic Devices
Natural Gas Fired 4Cycle Rich Burn Compressor Engines <50 HP
Natural Gas Fired 4Cycle Rich Burn Compressor Engines 50 To 499 HP
Natural Gas Fired 4Cycle Rich Burn Compressor Engines 500+ HP
162
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Data
EPA
Category
uses
see
SCC Description (Abbreviated)
Nonpoint
Nonpoint
2310021309
2310021310
Nonpoint
Y
2310021351
Nonpoint
Y
2310021400
Nonpoint
2310021401
Nonpoint
2310021402
Nonpoint
2310021403
Nonpoint
2310021411
Nonpoint
2310021500
Nonpoint
Y
2310021501
Nonpoint
Y
2310021502
Nonpoint
Y
2310021503
Nonpoint
2310021504
Nonpoint
Y
2310021505
Nonpoint
Y
2310021506
Nonpoint
2310021509
Nonpoint
2310021600
Nonpoint
2310021601
Nonpoint
2310021602
Nonpoint
Y
2310021603
Nonpoint
2310021604
Nonpoint
2310021605
Nonpoint
2310021700
2310022000
Nonpoint
through
2310022506
2310030000
Nonpoint
through
2310030401
Nonpoint
Y
2310111100
Nonpoint
Y
2310111401
Nonpoint
Y
2310111700
Nonpoint
2310112401
Nonpoint
Y
2310121100
Nonpoint
Y
2310121401
Nonpoint
Y
2310121700
Nonpoint
2310122100
Total: All Natural Gas Fired 4Cycle Rich Burn Compressor Engines
On-Shore Gas Production; Gas Well Pneumatic Pumps
On-Shore Gas Production; Lateral Compressors 4 Cycle Rich Burn
On-Shore Gas Production; Gas Well Dehydrators
Nat Gas Fired 4Cycle Rich Burn Compressor Engines <50 HP w/NSCR
Nat Gas Fired 4Cycle Rich Burn Compressor Engines 50 To 499 HP
w/NSCR
Nat Gas Fired 4Cycle Rich Burn Compressor Engines 500+ HP w/NSCR
Gas Well Dehydrators - Flaring
Gas Well Completion - Flaring
Fugitives: Connectors
Fugitives: Flanges
Fugitives: Open Ended Lines
Fugitives: Pumps
Fugitives: Valves
Fugitives: Other
Fugitives: All Processes
Gas Well Venting
Gas Well Venting - Initial Completions
Gas Well Venting - Recompletions
Gas Well Venting - Blowdowns
Gas Well Venting - Compressor Startups
Gas Well Venting - Compressor Shutdowns
Miscellaneous Engines
On-Shore
Gas
Production
On-Shore
Gas
Production
On-Shore
Gas
Production
On-Shore
Gas
Production
On-Shore
Gas
Production
On-Shore
Gas
Production
On-Shore
Gas
Production
On-Shore
Gas
Production
On-Shore
Gas
Production
On-Shore
Gas
Production
On-Shore
Gas
Production
On-Shore
Gas
Production
On-Shore
Gas
Production
On-Shore
Gas
Production
On-Shore
Gas
Production
On-Shore
Gas
Production
Off-Shore Gas Production
Total: All Processes, Storage Tanks: Condensate, Turbines: Natural Gas
Boilers/Heaters: Natural Gas, Diesel Engines, Amine Unit
Dehydrator, Fugitives, Connectors: Gas Streams, Fugitives, Flanges:
Gas Streams, Fugitives, Valves: Gas, Fugitives, Other: Gas
Natural Gas Liquids;
Total: All Processes, Gas Well Tanks - Flashing & Standing/Working/
Breathing, Uncontrolled, Gas Well Water Tank Losses, Gas Plant Truck
Loading
On-shore Oil Exploration; Mud Degassing
On-shore Oil Exploration; Oil Well Pneumatic Pumps
On-shore Oil Exploration; Oil Well Completion: All Processes
On-shore Oil Exploration; Oil Well Pneumatic Pumps
Off-shore Oil Exploration; Mud Degassing
Off-shore Oil Exploration; Gas Well Pneumatic Pumps
Off-shore Oil Exploration; Gas Well Completion: All Processes
Off-shore Gas Exploration; Mud Degassing
163
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Data
EPA
Category
uses
see
SCC Description (Abbreviated)
Point
31000101
through
31000506,
31088801
Various descriptions;
Excludes 31000104 through 31000108 and 31000140 through
31000145, which are in the sector "Industrial Processes - Storage and
Transfer"
Point
through
31088811
Fugitive Emissions; Specify in Comments Field
Point
31700101
Natural Gas Transmission and Storage Facilities; Pneumatic
Controllers Low Bleed
3,21,2 Sources of data overview and selection hierarchy
The S/L/T agencies that submitted data to the EPA are listed in Table 95 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 95: 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
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
164
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Data Set Name
State
Dataset Short Name
Data Category
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
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
W V
2011WVDAQ
Point
Wyoming Department of Environmental Quality
WY
2011WYDEQ
Nonpoint
Wyoming Department of Environmental Quality
WY
2011WYDEQ
Point
Table 96 shows the selection hierarchy for datasets included in the Industrial Processes - Oil & Gas Production
sector.
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Table 96: 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
2011EPA_Other
New Mexico emissions that state was unable to
submit to EIS due to submittal issues
5
2011EPA_TR!
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
2011EPA_N P_0 ve r 1 a p_w_Pt
EPA-generated data
3.21,3 Spatial coverage and data sources for the sector
industrial Processes - Oil & Gas Production Industrial Processes - Oil & Gas Production
P - Point
Point
N - Nonpoint
PN - P&N
All CAPS EPA SLT *EPA & SLT
All HAPs EPA
N - Nonpoint
PN P&N
SLT —EPA & SLT
3.21.4- 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
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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 3.21.3, 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
More information about the tool and directions on how to use it can be found on the CHIEF website at
http://www.epa.gOv/ttn/chief/net/2011inventorv.html#inventorvdoc. At this page, the heading "2011 NEI
Version 1 Documentation" section contains a list for "Nonpoint Emissions Tools and Methods". There you will
find a file called "Oil & Gas Emission Estimation Tool.zip". 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. Recall that these emissions will not match the emissions in the NEI,
because the NEI is a merge of S/L/T agency and EPA data. Usually, when an S/L/T agency submits data, we use
the submitted data and use the EPA data as "back-fill".
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3,21,5 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 97 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 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 97: 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 one SCC (2310121401, gas pneumatic pumps)
from EPA tool to the NEI, at Texas staff request, since they did not cover
that process. 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 with California
and they have reviewed their data, and we are using the California-
submitted data in the 2011 NEI vl.
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 are not sure of why the differences exist, and we are continuing to work with states and the national
workgroup to reconcile the differences.
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3.22 Industrial Processes - Petroleum Refineries
3.22.1 Sector Description
3.22.2 Sources of data overview and selection hierarchy
3.22.3 Spatial coverage and data sources for the sector
Industrial Processes - Petroleum Refineries Industrial Processes - Petroleum Refineries
p
PN
PN
PN
PN
PN
P - Point
N - Nonpoint
PN-P&N
SLT
P - Point
N - Nonpoint
PN - P&N
SLT
All CAPS EPA
All HAPs EPA
EPA & SLT
EPA & SLT
3.23 Industrial Processes - Pulp & Paper
3.23.1 Sector Description
3.23.2 Sources of data overview and selection hierarchy
3.23.3 Spatial coverage and data sources for the sector
Industrial Processes - Pulp & Paper Industrial Processes - Pulp & Paper
p
-p
p
P - Point
N - Nonpoint
PN - P&N
SLT
P - Point
N - Nonpoint
PN - P&N
SLT
All CAPS EPA
All HAPs EPA
EPA & SLT
EPA & SLT
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3.24 Industrial Processes - Storage and Transfer
3.24.1 Sector Description
3.24.2 Sources of data overview and selection hierarchy
3.24.3 Spatial coverage and data sources for the sector
Industrial Processes - Storage arid Transfer Industrial Processes - Storage and Transfer
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
3.25 Industrial Processes - NEC (Other)
3.25.1 Sector Description
3.25.2 Sources of data overview and selection hierarchy
3.25.3 Spatial coverage and data sources for the sector
Industrial Processes - NEC
Industrial Processes - NEC
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
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3.26 Miscellaneous Non-industrial NEC (Other)
3.26.1 Sector Description
3.26.2 Sources of data overview and selection hierarchy
3.26.3 Spatial coverage and data sources for the sector
Miscellaneous Non-Industrial NEC Miscellaneous Non-Industrial NEC
PN
PN
N
PN
PN
PN
PN
PN
PN
PN
PN
P - Point
N - Nonpoint
PN - P&N
SLT
N - Nonpoint
PN - P&N
SLT
All CAPS EPA
All HAPs EPA
EPA & SLT
EPA & SLT
3.27 Solvent - Consumer & Commercial Solvent Use
3.27.1 Sector Description
3.27.2 Sources of data overview and selection hierarchy
3.27.3 Spatial coverage and data sources for the sector
Solvent - Consumer & Commercial Solvent Use Solvent - Consumer & Commercial Solvent Use
*
P - Point
N - Nonpoint
PN-P&N
SLT
N .
All CAPS EPA
EPA & SLT
P - Point
N - Nonpoint
PN-P&N
SLT
All HAPs EPA
EPA & SLT
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3.28 Solvent - Degreasing, Dry Cleaning, and Graphic Arts
3.28.1 Sector Description
3.28.2 Sources of data overview and selection hierarchy
3.28.3 Spatial coverage and data sources for the sector
Solvent - Degreasing Solvent - Degreasing
PN
PN
PN
PN
PN
PN
PN
>N
3N
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
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
P - Point
N - Nonpoint
PN - P&N
All CAPs EPA SLT —EPA & SLT All HAPs EPA SLT "EPA & SLT
P - Point
N - Nonpoint
P - Point
N - Nonpoint
PN - P&N
Solvent ¦ Dry Cleaning Solvent" Dry Cleanin9
All CAPs EPA SLT —EPA & SLT
PN - P&N
All HAPs EPA SLT EPA & SLT
172
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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
Solvent - Graphic Arts
Solvent - Graphic Arts
3.29 Solvent - Industrial and Non-Industrial Surface Coating
3.29.1 Sector Description
3.29.2 Sources of data overview and selection hierarchy
P - Point
N - Nonpoint
P - Point
N - Nonpoint
3.29.3 Spatial coverage and data sources for the sector
Solvent - Industrial Surface Coating & Solvent Solvent - Industrial Surface Coating & Solvent
PN-P&N PN-P&N
All CAPs EPA SLT —EPA&SLT All HAPs EPA SLT ™EPA & SLT
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Solvent - Non-Industrial Surface Coating
Solvent - Non-Industrial Surface Coating
P - Point
N - Nonpoint
PN - P&N
All CAPs EPA SLT —EPA & SLT
3.30 Waste Disposal
3.30.1 Sector Description
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
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 EPA ^SLT -EPA & SLT
P - Point
N - Nonpoint
PN - P&N
All HAPs EPA "=SLT -EPA & SLT
Waste Disposal
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
PN
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 HAPs EPA ^SLT "EPA & SLT
3.30.2 Sources of data overview and selection hierarchy
3.30.3 Spatial coverage and data sources for the sector
Waste Disposal
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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 of those sectors,
though without the benefit of local-specific model inputs in all cases. The sections below explain how we
created the initial 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 of the approximately 20,000 individual airports
are geographically located by latitude/longitude and stored in the NEI as point sources. As part of the
development process, S/L/T agencies had the opportunity to provide both activity data as well emissions to the
NEI. When activity data were provided, EPA used that data to calculate EPA's emissions estimates.
4,2,1 Sector Description
The aircraft sector includes all aircraft types used for public, private, and military purposes. This includes four
types of aircraft: (1) Commercial, (2) Air Taxis (AT), (3) General Aviation (GA), and (4) Military. A critical detail
about the aircraft is whether each aircraft is turbine- or piston-driven, which allows the emissions estimation
model to assign the fuel used, jet fuel or aviation gas, respectively. The fraction of turbine- and piston-driven
aircraft is either collected or assumed for all aircraft types.
Commercial aircraft include those used for transporting passengers, freight, or both. Commercial aircraft tend
to be larger aircraft powered with jet engines. Air Taxis carry passengers, freight, or both, but usually are
smaller aircraft and operate on a more limited basis than the commercial aircraft. General Aviation includes
most other aircraft used for recreational flying and personal transportation. Finally, military aircraft are
associated with military purposes, and they sometimes have activity at non-military airports.
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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 98 below:
Table 98: 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
4,2,2 Sources of data overview ant! 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 99. States that provided activity
data for use in the EPA method are listed in Section 4.2.4.
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Table 99: Agencies that submitted 2011 aircraft emissions data
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 QA section
Tennessee Department of Environmental Conservation
State
Texas Commission on Environmental Quality
State
The selection hierarchy used for aircraft is shown below in Table 100. This hierarchy pulls the relevant datasets
for this sector from the overall point sources hierarchy listed in Section 3, Table 11. The aircraft emissions also
have a nonpoint component (in-flight lead) which is discussed in Section 4.2.4.2 and uses only EPA data.
Table 100: 2011 NEI aircraft data selection hierarchy
Priority
Dataset Name
Dataset Content
1
2011EPA_PM-Augmentation
PM augmentation was inadvertently applied to state-
submitted aircraft data, creating values for PMXX-FIL and
PM-CON but not impacting the S/L/T-provided PMXX-
PRI. In all cases the PM augmentation value of PM-CON is
0 and the PMXX-FIL is equal to the S/L/T-provided PMXX-
PRI. In the future, we will not create values for PM-CON
or PMXX-FIL, as we do not calculate these for EPA aircraft
estimates, only primary PM is calculated.
2
State/Local/Tribal Data
Submitted aircraft emissions
3
2011EPA_Airports
EPA data (Section 4.2.4)
4.2.3 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
FN PN PN
P - Point rN\
N - Nonpoint
PN - P&N
SLT —EPA&SLT
P - Point
N - Nonpoint
PN - P&N
SLT —EPA&SLT
All HAPs EPA
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4,2,4 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 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 replacement/revisions. As shown in Table 101, the
following S/L/T agencies submitted aircraft activity data that EPA incorporated as inputs to the final EPA dataset
model run.
Table 101: 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 Dept 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
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4.2.4.1 Emissions Jbr 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.
4.2.4.2 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 (TAF) and 5010 forms (See
http://www.faa.gov/airports/airport safety/airportdata 5010/). 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.
4.2.4.3 Aviation lead emissions
Lead (Pb) emission estimates were handled differently from 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.xlsx" is
available at ftp://ftp.epa.gov/Emislnventory/2011/doc. 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, but the merged EPA and S/L/T data total to 237 for the 2011NEvl. EPA's estimate for
out-of-LTO or "in-flight" Pb is 238 tons.
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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.
4,2,5 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
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.
180
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4,2,6 References I sorts
1. Eastern Research Group (ERG), 2013. Memorandum: Development of 2011 Aircraft Component for
National Emissions Inventory, June 17, 2013.
ftp://ftp.epa.gov/Emislnventory/2011/doc/2011nei Aircraft 20130717.pdf
2. Federal Aviation Administration (FAA), 2011. Emissions and Dispersion Modeling System, Version 5.1.
September, 2011. http://www.faa.gov/about/office org/
headquarters offices/aep/models/edms model/
3. Federal Aviation Administration (FAA), 2012. General Aviation and Part 135 Activity Survey - Calendar
Year 2010. http://www.faa.gov/data research/aviation data statistics/general aviation/CY2010/
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.
ftp://ftp.epa.gov/Emislnventorv/2011/doc/2011nei AircraftLead 20130827.
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 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 was allocated to a shape file identifier in the nonpoint inventory. The allowed combinations are
shown in Table 102. The default values are those assumed when the actual emission type may be unknown; for
181
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example, emissions that occur in shipping lanes are assumed to be 'cruising' and cannot be 'hotelling', which
only occurs at ports.
Table 102: Commercial Marine SCCs and Emission Types in EPA Estimates
see
SCC Description
Allowed
Default
2280002100
Marine Vessels, Commercial Diesel Port
M
M
2280002200
Marine Vessels, Commercial Diesel Underway
C
C
2280003100
Marine Vessels, Commercial Residual Port
H
H
2280003100
Marine Vessels, Commercial Residual Port
M
H
2280003200
Marine Vessels, Commercial Residual Underway
C
C
2280003200
Marine Vessels, Commercial Residual Underway
Z
C
Shown in Table 103, gasoline CMV emissions were submitted by Washington State and included in the NEI.
Table 103: Additional Commercial Marine SCCs used by Washington
SCC
SCC Description
States
2280004000
Mobile Sources, Marine Vessels, Commercial, Gasoline, Total, All Vessel Types
WA
4,3,2 Sources of data overview and selection hierarchy
EPA received emissions data from the agencies identified in Table 104.
Table 104: Agencies that Submitted Commercial Marine 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
Table 105 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 12.
Table 105: 2011 NEI commercial marine vehicle selection hierarchy
Priority
Dataset Name
Dataset Content
1
State/Local/Tribal Data
Submitted commercial marine vessel emissions
2
2011EPA_PM-Augmentation
Completes PM species in WA submittal for additional SCC
3
2011EPA_HAP-Augmentation
Uses emission factors to calculate HAP values based on S/L/T
submitted criteria estimates (VOC or PM species)
4
2011EPA_chrom_split
Splits submitted unspeciated chromium into hexavalent
(chromium VI) and trivalent (chromium III) forms.
182
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Priority
Dataset Name
Dataset Content
5
2011EPAJZMV
EPA data (Section 4.3.4)
4.3.3 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.
Mobile - Commercial Marine Vessels Mobile - Commercial Marine Vessels
P - Point
N - Nonpoint
PN-P&N
SLT
P - Point
N - Nonpoint
PN - P&N
SLT
N
All CAPS EPA
All HAPs EPA
EPA & SLT
EPA & SLT
4.3.4 EPA-developed commercial marine vessel emissions data
EPA estimated CMV emission estimates [ref 11] as a collaborative effort between the Office of Transportation
and Air Quality (OTAQ) and QAQPS. 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 4.3.4.1) 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],
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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.
4,3,4,1 Allocation 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.
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 shapefile for 2011 NEI contained 237 ports
(including 76 additional ports from 2008 NEI) and 275 polygons, considering that a single port can cross county
boundaries and thus include multiple polygons. The set of port shapefile GIS data is posted at
http://www.epa.gov/ttn/chief/eis/2011nei/2011 ports shapefile.zip.
The shapefiles used for the underway emissions were unchanged from those used in the 2008 NEI and are
available in the file http://www.epa.gov/ttn/chief/eis/2011nei/shippinglanes_112812_shapefile.zip.
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 (deepwater, ferries, fishing, government, Great Lake, offshore, research, and tugs) available in
"Category 2 Vessel Census, Activity, and Spatial Allocation Assessment and Category 1 and Category 2 In-port/At-
sea Splits," (Census Report) February 16, 2007 [ref 2], This method, described in the August 22, 2012
Memorandum from Eastern Research Group [ref 3], shifts the distribution used in previous NEIs from majority in
ports to majority in underway.
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) Preparation of Emissions Inventories for the Version
5.0, 2007, December 14, 2012 (see
184
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http://epa.gov/ttn/chief/emch/2007v5/2Q07v5 2020base EmisMod TSD 13dec2012.pdf). 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 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:
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/Ts were encouraged to use the
EPA-provided shape-to-county fractions (http://www.epa.gov/ttn/chief/eis/2011nei/
cmv rail shape 20cntvfractions.zip) if they were unsure how to distribute county emissions to shapes. When
EPA and S/L/Ts did not use the same county/shape/SCC/emistype combinations, the resultant NEI selection gave
a value that combined EPA and S/L/T values and may not be equal to either EPA's or the S/L/T's.
1. In assisting California allocate their county emissions to the EPA shapes, the California-submitted
chromium was speciated with the EPA default speciation profiles. California did not agree with that
speciation, but did not provide speciated chromium emissions in their submittal.
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)
3. Where S/L/T county/shape/SCC/emistype combinations matched exactly, the resultant selection
matches the S/L/T submission; for example, see IL, NH and TX in the example in Table 106. When S/L/T
did not use exact matches, the state sum may be different than either the S/L/T or EPA value. It may be
intended (for example the S/L/T may have only had detailed data for a certain river system and not the
entire state) or not.
4. Although not submitted to the 2011NEI, the LADCO regional program had significantly different values
for CMV estimates in the Great Lakes region. EPA is re-evaluating CI C2 estimates that may over
estimate tug traffic in the Great Lakes, for example.
185
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Table 106: Example of Selection Result in Merging EPA and S/L/T CMV (VOC in Tons)
State
State
FIPs
see
Pollutant
EPA
S/L/T
2011NEIvl
Selection
DE
10
2280002100
VOC
28
12
7
DE
10
2280002200
VOC
130
40
40
DE
10
2280003100
VOC
24
8
10
DE
10
2280003200
VOC
63
77
77
IL
17
2280002100
VOC
7
1
1
IL
17
2280002200
VOC
102
154
154
MD
24
2280002100
VOC
13
3
3
MD
24
2280002200
VOC
137
29
51
MD
24
2280003100
VOC
64
26
30
MD
24
2280003200
VOC
121
236
307
NH
33
2280002100
VOC
4
0
0
NH
33
2280002200
VOC
1
0
0
NH
33
2280003100
VOC
12
12
NH
33
2280003200
VOC
2
2
NJ
34
2280002100
VOC
53
22
42
NJ
34
2280002200
VOC
122
213
227
NJ
34
2280003100
VOC
233
26
63
NJ
34
2280003200
VOC
65
93
103
OR
41
2280002100
VOC
30
59
76
OR
41
2280002200
VOC
40
64
95
SC
45
2280002100
VOC
15
9
10
SC
45
2280003100
VOC
93
37
39
SC
45
2280003200
VOC
18
57
79
TX
48
2280002100
VOC
301
2
2
TX
48
2280002200
VOC
954
161
161
TX
48
2280003100
VOC
240
339
338
TX
48
2280003200
VOC
171
282
282
WA
53
2280002100
VOC
149
243
251
WA
53
2280002200
VOC
283
0
32
WA
53
2280003100
VOC
261
59
91
WA
53
2280003200
VOC
646
341
391
4,3,6 References for Commercial Marine
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. http://www.epa.gOv/ttn/chief/net/2008inventorv.html#inventorydoc
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, EPA420-R-
03-004, January 2003. http://www.epa.gov/otaq/oceanvessels.htm
186
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3. Eastern Research Group (ERG), 2007. Project report: Category 2 Vessel Census, Activity, and Spatial
Allocation Assessment and Category 2 and Category 2 In-port/At-Sea Splits, February 16, 2007
ftp://ftp.epa.gov/Emislnventorv/2011/doc/Categorv%202%20vessel%20census.pclf
4. Eastern Research Group (ERG), 2012. Project report: Category 1 / Category 2 Commercial Marine
Activity Spatial Allocation, August 22, 2012
ftp://ftp.epa.gov/Emislnventory/2011/doc/2011nei CMV Catl%262 Activity Spatial Allocation 08221
2.pdf
4.4 Locomotives
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 107 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 107: 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
4,4,2 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 107. The
agencies listed in Table 108 also submitted emissions to locomotive SCCs.
Table 108: Agencies that submitted Rail 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
187
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Data Set
Agency FIP
Point
Nonpoint
Agency Name
Short Name
or Tribal Code
Rail
Yard
Yard
Maricopa Co Arizona
2011Maricopa
04013
X
Maryland
2011MDDOE
24
X
X
X
Massachusetts
2011MADEP
25
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
Washington
2011WADOE
53
X
Washoe Co Nevada
2011WashoeCty
32031
X
X
4.4.3 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
P - Point
N - Nonpoint
Mobile - Locomotives
PN-P&N
All CAPS EPA SLT EPA & SLT
Mobile - Locomotives
PN - P&N
All HAPs EPA SLT "EPA & SLT
4.4.4 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
Association for class II and III See ERG project report Development of 2011 Railroad Component for National
Emissions InventorySeptember 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.
4.4.4.1 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
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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.
4,4,5 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 2011NEI
• 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 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 and EPA estimates did not use identical county/shape/SCC combinations, the
resultant selection may equal to neither EPA's nor the S/L/T's value. For example, see AZ SCC =2285002006,
and MD SCC = 2285002007 in Table 109 below.
Table 109: Compare NOx among EPA, S/L/T, and 2011vlNEI Selection for Rail
NOx (tons)
Tribal Code
State Name
SCC
EPA
S/L/T
2011vl Selection
Alaska
2285002009
703
703
Arizona
2285002006
1,414
1,263
1,263
Arizona
2285002007
89
0
45
Arizona
2285002008
9
9
California
2285002008
5,488
5,488
California
2285002009
2,237
2,237
California
2285002010
4,745
4,745
Connecticut
2285002008
241
241
Connecticut
2285002009
358
358
Connecticut
2285002010
85
85
Illinois
2285002006
36,886
39,841
39,841
Illinois
2285002007
1,869
2,388
2,388
Maryland
2285002006
3,419
2,154
2,154
Maryland
2285002007
251
12
145
Maryland
2285002008
20
20
Maryland
2285002009
460
460
Maryland
2285002010
134
134
Massachusetts
2285002009
2,589
2,589
189
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NOx (tons)
Tribal Code
State Name
see
EPA
S/L/T
2011vl Selection
Nevada
2285002006
506
398
398
North Carolina
2285002008
448
448
Texas
2285002006
60,389
58,762
58,762
Texas
2285002007
2,168
2,633
2,633
Texas
2285002010
2,225
2,225
Utah
2285002006
6287
5,878
5,878
Washington
2285002006
14,445
12,420
12,420
Washington
2285002009
534
534
863
2285002006
6,789
6,789
4,4,6 References for Locomotives
1. Eastern Research Group (ERG), 2012. Memorandum: Development of 2011 Railroad Component for
National Emissions Inventory, September 5, 2012
ftp://ftp.epa.gov/Emislnventory/2011/doc/2011nei Locomotive.pdf
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.
ftp://ftp.epa.gov/Emislnventorv/2011/doc/2008nei locomotive report.pdf
4,5 Nonroad Equipment - Diesel, Gasoline ami 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.
4,5,1 Sector Description
This section deals specifically with emissions processes calculated by the EPA's NONROAD model
(http://www.epa.gov/otaq/nonrdmdl.htm) and the OFFROAD model
(http://www.arb.ca.gov/msei/offroad/offroad.htm) 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) (http://www.epa.gov/otaq/nmim.htm) 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 110.
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Table 110: NMIM 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.
4,5,2 Sources of data overview and selection hierarchy
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 112
submitted inputs and/or emissions to the 2011 NEI.
Table 111 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
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.
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Table 111: Selection Hierarchy for the Nonroad 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 112 and NMIM defaults where S/L
accepted EPA default.
California
1
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
1
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 112 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 112: S/L/T Agency Submitted Data for Nonroad
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
1/7/13
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
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
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Agency Organization
Nonroad
Emissions
Nonroad
NCD
Notes
Sac and Fox Nation of Missouri in Kansas and Nebraska
Reservation
10/5/12
Shoshone-BannockTribes 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
4.5.3 Spatial coverage and data sources for the sector
Nonroad equipment emissions are included in every state, DC, Puerto Rice, and the Virgin Islands.
4.5.4 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 the hierarchy decision
model is that EPA-supplied fuel and meteorological data were used for all 2011 NMIM modeling runs, as
explained below. A copy of NCD20130531_nei2011vl which includes all the state-supplied updates, as well as
EPA's fuel and meteorological data is named NCD20130531 and is provided in the same zip file as the NCD used
for the NEI. The zip file name is 2011nei_supdata_nonroad.zip, and it can be accessed at
ftp://ftp.epa.gov/Emislnventorv/2011vl/doc. The development of the NCD used for the 2011 NEI is explained in
the following sections.
4.5.4.1 Default NCD
The default 2011 NCD, NCD20130531_nei2011 is available at ftp://ftp.epa.gov/Emislnventorv/2011vl/doc in the
zip file "2011nei_supdata_nonroad.zip" and is based upon NCD20101201a.13 EPA updated fuel and
meteorological data for inclusion in the new 2011 NCD.
4.5.4.2 State-Submitted NCDs
NCD activity data submitted by state and local agencies 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
13 NCD20101201a is the NCD that is included in the current download of NMIM.
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EPA, the default values for these data parameters were retained. NCD tables updated using state and local NCD
submissions are presented in Table 113.
Table 113: NCD Tables Provided in State and Local NCD Submissions
State Name
DataSource
CountyNRFile
County
CountyYearMonth*
Diesel*
Gasoline*
External Files
CountyYearMonthHour
*
Maryland
X
X
X
New Jersey
X
X
X
X
X
X
Connecticut
X
X
X
X
X
Delaware
X
X
X
X
Georgia
X
X
X
Idaho
X
X
Illinois
X
Nevada
X
X
X
X
X
New Hampshire
X
X
X
North Carolina
X
X
X
Washington
X
X
X
Wisconsin
X
X
X
*Updates to these tables were not used to develop the 2011 NEI NCD. Instead EPA-supplied data were used.
4.5.4,3 State-Assisted NCD 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, EPA 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 114.
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Table 114: State-assisted NCD Table Updates
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X
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State Name
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0
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01
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0
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Diese
o
A3
iD
01
X
LU
C
3
O
u
Davidson County (Tennessee)
X
X
X
X
New York
X
X
X
X
X
X
Texas
X
X
X
X
X
X
*Updates to these tables were not used to develop the 2011 NEI NCD. Instead EPA-supplied data was used.
4,5,4,4 Nashville/Davidson County Tennessee
Nashville Pollution Control Division provided all of 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 Reid
Vapor Pressure (RVP) and sulfur values in the fuel data tables within NMIM. However, these data were not used
because EPA fuel data was used for the whole country. However, these updates are contained in NCD20130531.
4,5,4,E> 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. These
data were updated using the U.S. Census data.14 These updates were made to the NMIM database
NCD20130531_nei2011vl.
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. However, these data were not used because EPA fuel data was used for the
whole country. However, these updates are contained in NCD20130531.
4,5,4.6 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
equipment is found in many different types of construction. However, their equipment population and use
profiles are unique within each of the sectors defined by the TexN model. TexN processes each of these
14 U.S. Census data file dc_acs_2009_5yr_g00 datal.txt, which is based on the 2005-2009 American Community Survey 5-
Year Estimates (http://factfinder.census.gov/servlet/DTTable?_bm=y&-geo_id=01000US&-
ds_name=ACS_2009_5YR_G00_&-mt_name=ACS_2009_5YR_G2000_B25024).
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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.
Equipment 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 NCD20130531_nei2011dvl.
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 NCD20130531_nei2011dvl.
The activity data from TexN were 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
NCD20130531_nei2011dvl.
The geographic allocation of equipment populations were 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 (known as "XRF") value. These values were then used to build new allocation files for
inclusion into NMIM and are included in NCD20130531_nei2011dvl.
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.
The final Texas dataset used EPA fuel data instead of state-supplied fuel inputs, though the state updates were
provided to EPA in NCD20130531.
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Note that this final Texas dataset was used in computing the EPA emissions (2011EPA_MOBILE EIS dataset) but
these emissions were selected for the 2011 NEI behind the Texas emissions as was shown in Table 111.
4,5,5 Summary of quality assurance methods
4,5,5.1. Summary of quality assurance on NCDs
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.
4.5.5.2 Summary of quality assurance on S/L/T 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 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 115.
Table 115: SCC/Emissions Type with Missing VOC in CA submittal
see
Emissions Type
2260001020
Evaporation
2260001020
Exhaust
2265001010
Evaporation
2265001010
Exhaust
2265001030
Evaporation
2265001030
Exhaust
2265001060
Evaporation
2265001060
Exhaust
2270001060
Evaporation
2270001060
Exhaust
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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 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 115 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
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 EIS in
the dataset "2011EPA_CAmodelerdata".
4,6 On-roai bscI ami Gasoline vehicles
This section includes the description of four EIS sectors:
• Mobile - On-road - Diesel Heavy Duty Vehicles
• Mobile - On-road - Diesel Light Duty Vehicles
• Mobile - On-road - Gasoline Heavy Duty Vehicles
• Mobile - On-road - Gasoline Light Duty Vehicles
They are treated here in a single section because the methods used are the same across all sectors.
4.6.1 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.
SCCs starting with 22010 define the light duty gasoline vehicles including motorcycles, with the exception of
SCCs starting with 220107, which define the heavy duty gasoline vehicles. SCCs starting with 22300 define the
light duty diesel vehicles, with the exception of SCCs starting with 223007 that define the heavy duty diesel
vehicles.
The 2008 NEI vl and past NEIs included emissions from the MOBILE6 model. The 2008 NEI v2 and v3 are the
first NEI to include emissions from the MOVES model. The 2011 NEI vl is the first NEI to accept MOVES inputs
directly as part of the NEI submittal process.
4.6.2 Sources of data overview ant! selection hierarchy
All of EPA's 2011 NEI on-road estimates were calculated by EPA using MOVES, except in California. Table 116
shows a list of all submissions to EIS for onroad sources. Agencies other than California submitted complete
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emissions using MOVES2010b, or inputs that enabled EPA to generate emissions estimates using MOVES.
California submitted complete emissions generated using the EMFAC2011 model.15 For counties in the
contiguous 48 for which onroad emission data was not submitted, EPA used SMOKE-MOVES to generate
emission inventories from on-road sources; The SMOKE-MOVES process is described later in this section. For
AK, HI, PR and VI EPA used MOVES directly (in inventory mode) to estimate emissions.
Table 116: Agency Submission History for Onroad
Onroad Emissions
Onroad CDB
Agency Organization
Submission Date
Submission Date
Notes
Alaska Department of
Environmental Conservation
12/11/2012
12/18/2012
California Air Resources
CA uses a CA-specific model (EMFAC).
Board
4/16/2013
N/A
CA emissions are included in NEI.
EPA does not currently break out tribal
areas in the EPA estimates, however,
tribal emissions submittals are included
Coeur d'Alene Tribe
11/28/2012
N/A
in EIS.
CO supplied updated IM coverage data
Colorado
N/A
N/A
directly to EPA staff.
In addition to submitting CDBs, CT
Connecticut Department Of
5/10/2013;
supplied updated CDBs directly to EPA
Environmental Protection
N/A
6/7/2013
staff.
DC-District Department of
the Environment
N/A
1/8/2013
Delaware Department of
Natural Resources and
Environmental Control
N/A
1/7/2013
EPA does not currently include tribal
Eastern Band of Cherokee
areas in EPA estimates; however, tribal
Indians
10/23/2012
N/A
emissions are included in the NEI.
In addition to submitting CDBs, GA
supplied activity data (by SMOKE SCCs)
for all counties and hourly speed
Georgia Department of
5/31/2013;
profiles for Atlanta counties directly to
Natural Resources
N/A
6/6/2013
EPA staff.
ID submitted both input and emissions.
ID emissions included only a subset of
HAPs and had SCC-emistype
combinations that do not occur in EPA
Idaho Department of
estimates. ID CDB was used in NEI
Environmental Quality
12/18/2012
12/5/2012
estimates instead of emis submittal.
Illinois Environmental
Protection Agency
N/A
2/19/2013
Knox County Department of
Air Quality Management
N/A
1/7/2013
EPA does not currently include tribal
areas in EPA estimates; however, tribal
Kootenai Tribe of Idaho
12/14/2012
N/A
emissions are included in the NEI.
15 The EMFAC2011 model the supporting documentation can be found at http://www.arb.ca.gov/msei/modeling.htm
199
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Agency Organization
Onroad Emissions
Submission Date
Onroad CDB
Submission Date
Notes
Louisville Metro Air Pollution
Control District
N/A
2/19/2013
Maine Department of
Environmental Protection
N/A
11/19/2012
Maricopa County Air Quality
Department
N/A
12/18/2012
Maryland Department of the
Environment
N/A
12/24/2012
Massachusetts Department
of Environmental Protection
N/A
6/5/2013
CDB was submitted late after deadline
to EIS, but was available to EPA prior to
submittal and used in EPA NEI
estimates.
Metro Public Health of
Nashville/Davidson County
12/18/2012
N/A
EPA assisted Metro in creating CDB
from their inputs to EPA estimation.
Submitted emissions were not used in
NEI.
Michigan Department of
Environmental Quality
N/A
1/8/2013
Minnesota Pollution Control
Agency
N/A
12/13/2012;
5/20/2013
In addition to submitting CDBs, MN
supplied updated age bin distribution
data directly to EPA staff, and VMT for
Kanabec county and VPOP for Otter
Tail County.
Missouri Department of
Natural Resources
N/A
12/21/2012
New Hampshire Department
of Environmental Services
N/A
10/31/2012
New Jersey Department of
Environment Protection
N/A
5/1/2013
Nez Perce Tribe
11/29/2012
N/A
EPA does not currently include tribal
areas in EPA estimates; however, tribal
emissions are included in the NEI.
North Carolina Department
of Environment and Natural
Resources
N/A
1/8/2013
Northern Cheyenne Tribe
1/28/2013
N/A
EPA does not currently include tribal
areas in EPA estimates; however, tribal
emissions are included in the NEI.
Ohio Environmental
Protection Agency
N/A
5/16/2013
Oregon Department of
Environmental Quality
1/7/2013
1/26/2013
Pennsylvania Department of
Environmental Protection
N/A
12/31/2012
Rhode Island Department of
Environmental Management
N/A
1/10/2013
Shoshone-Bannock Tribes of
the Fort Hall Reservation of
Idaho
11/27/2012
N/A
EPA does not currently include tribal
areas in EPA estimates; however, tribal
emissions are included in the NEI.
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Onroad Emissions
Onroad CDB
Agency Organization
Submission Date
Submission Date
Notes
South Carolina Department
of Health and Environmental
Control
N/A
12/13/2012
EPA assisted TX in creating CDB from
their inputs to EPA estimation.
However, TX emissions were used in
the NEI because differences in model
techniques resulted in significant
Texas Commission on
differences between EPA and TX
Environmental Quality
12/21/2012
N/A
results.
In addition to submitting CDBs, UT
supplied an updated CDB for
Washington County directly to EPA
Utah Division of Air Quality
N/A
1/10/2013
staff.
Vermont Department of
Environmental Conservation
N/A
12/14/2012
In addition to submitting CDBs, VA
Virginia Department of
12/5/2012;
supplied activity data (by SMOKE SCCs)
Environmental Quality
N/A
7/29/2013
for all counties directly to EPA staff.
Washington State
Department of Ecology
N/A
12/19/2012
Washoe County Health
District
12/26/2012
1/8/2013
West Virginia Division of Air
Quality
N/A
1/4/2013
Wisconsin Department of
Natural Resources
N/A
1/9/2013
The selection hierarchy for the on-road data category is shown in Table 117. EPA's MOVES estimates using S/L
inputs are used other than in California and Texas. For California, California-submitted emissions were used. For
Texas, Texas-submitted data were used ahead of the EPA's (MOVES) estimates, which were used second to gap
fill any missing data/pollutants from the Texas dataset.
Table 117: Selection Hierarchy for the On-road Data Category
Priority
Dataset
Everywhere except California and Texas
1
2011_EPA_Mobile
Contains emissions from EPA's MOVES
run using S/L-provided inputs as shown in
Table 116 and MOVES defaults where S/L
accepted EPA default
California
1
California Air Resources Board
California does not use the EPA MOVES
model. They use the EMFAC model and
provided 2011 emissions based on this
model.
Texas
1
Texas Commission on Environmental Quality
2
2011_EPA_Mobile
EPA estimates (same dataset described
above)
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4.6.2.1 Agencies submitting completed on-mad emissions data
California and Texas 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. Texas' emissions were generated
by TTI under contract with TCEQ using MOVES. The methodology for developing emission inventories was
adapted from SIP-level modeling at the roadway link level for the major urban areas in Texas. Though TCEQ
submitted final emissions through EIS, they also provided MOVES inputs and documentation used in the
generation of their emission estimates.
4.6.2.2 Agencies Submitting MOVES inputs
For states that did not submit completed on-road emissions results, EPA generated emissions using the latest
publically released version of the EPA highway emissions model, MOVES2010b (20120410 version). For states
within the contiguous 48, the SMOKE-MOVES emissions processing tool was used with output from MOVES in
emission rate mode; for Alaska, Hawaii, Puerto Rico and Virgin Islands the MOVES model was used in emission
inventory mode. EPA established a standard format for states to submit inputs into MOVES; submission results,
OA and corrections are discussed in the next section. For states that did not submit emissions or MOVES inputs,
default data were applied, as discussed in Section 4.6.3.
MOVES Input Submissions - Overview
MOVES emissions were generated using the "County Scale" option, which allows users to replace the defaults in
the MOVES database with local county-level information, as encouraged by EPA's technical guidance. These
data are entered through the County Data Manager (CDM) interface, which defines a subset of the entire
MOVES database generally available to local areas and most relevant for constructing local onroad emission
inventories. The result of the process is a unique MOVES County Database (CDB) for each county submitting
data. The data tables that make up a CDB are shown in Table 118.
Table 118: MOVES CDB Tables
CDB Table
Description of Content
auditlog
Information about the creation of the database
avft
Diesel 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
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CDB Table
Description of Content
year
Year of the database
zone
Allocations of starts, extended idle and vehicle hours parked
to the county
zonemonthhour
Temperature and relative humidity values
zoneroadtype
Allocation of 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]
Sccroadtypedistribution
Allocation of results to SCC categories
As part of the 2011 NEI plan, EPA provided guidance to agencies on the process and format of submitting local
CDB data through EIS. This guidance did not include detailed technical specifications on how local data should
be developed. However, for the development of State Implementation Plan (SIP) inventories, EPA has written a
technical guidance document that discusses preferred data sources for each of these inputs, and fallback
options where data isn't available.16 Responsible agencies are not required to follow this guidance for the NEI,
but because the guidance encompasses best practice and identifies the most readily available data sources for
CDB inputs, many states submitting data follow EPA's technical guidance. A summary of the best practice SIP
guidance for the primary CDB inputs (i.e. not informational inputs) is shown in Table 119.
Table 119: Summary of EPA's MOVES Technical Guidance for CDB Inputs
Input
Best Practice
Suggested Fallbacks
Source Type Population
Direct data from state registration
data, local transit agencies, school
districts, bus companies, refuse
haulers
Ratio to VMT based on MOVES default
VMT/Population ratios; MOVES
default source type split; MOBILE6
inputs
Age Distribution
Unique registration data for each
source type
Applying the same distribution to
multiple source types; MOVES
defaults; MOBILE6 inputs
VMT
State DOT data and/or Travel Demand
Models calibrated to HPMS; EPA
convertor to adjust to annual
Count-based programs, MOBILE6
inputs
Average Speed
Distribution
Post-processing of Travel Demand
Model output into each MOVES speed
bin, by hour
Applying the same distribution to
multiple source types (expected);
single average speed; peak/off-peak or
daily; MOBILE6 inputs
Speeds on Local Roads
Include local road speeds as part of
unrestricted distributions
Highways
Reflect speeds on the highway only,
not ramps (handled separately)
Road Type Distribution
"Consistent with transportation
planning" (same sources as VMT)
Applying the same distribution to
multiple source types within same
16UsingMOVES to Prepare Emission Inventories in State Implementation Plans and Transportation Conformity: Technical
Guidance for MOVES2010, 2010a and 2010b, U.S EPA, Report No. EPA-420-B-012-028, April 2012
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Input
Best Practice
Suggested Fallbacks
HPMS class (expected), or even across
HPMS classes; MOBILE6 inputs
Ramp Fraction
Optional input, no specific guidance
on data sources given
MOVES defaults
Fuel Type & Technology
VMT by fuel type
Population (expected); MOVES
(gas/diesel/CNG mix)
defaults; MOBILE6 inputs
Fuel Formulation (fuel
Reformulated Gasoline (RFG) fuel
Modify fuel properties where data
properties)
property info for RFG areas;
available, use defaults for others;
regulatory RVP level in RVP control
using fuel with desired properties in
areas, accounting for 1 psi waiver; fuel
same PADD/year; MOVES defaults
survey data for non-RFG areas (e.g.
NIPER, AAM)
Fuel Supply (market
Volume data
MOVES defaults
share)
Inspection/Maintenance
Modify defaults to make consistent
MOVES defaults
(l/M) Program
with actual program
Compliance Rate
Operating program data: sticker
Should not use 100%; automatic
surveys, license plate surveys, no. of
registration denial program can
tests vs. potential tests
assume 96%, but should update based
on operating data when available
Waiver Rate
Historical waiver rates
Meteorology
Local data via sources such as
National Climatic Data Center
MOVES default
Month, Day & Hour
No explicit guidance
MOVES defaults
VMT Fractions
A summary of the agency-submitted data is below, along with the QA steps and corrections made to submitted
data before use in developing NEI emissions.
Agencies Submitted CDB data
County level data were obtained from state-supplied county databases (CDBs) submitted to the Emission
Inventory System (EIS) as part of the 2011 National Emission Inventory (NEI) project. For the initial submissions,
a total of 1,342 CDBs were submitted by agencies; during the second round of submission, New Jersey also
provided CDBs, which brought the total to 1,363. A summary providing a count of counties with submitted data
by CDB input and state is contained in Table 120.
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Table 120: N um
Der of counties with submitted data, by state anc
State/County
avft
Avgspeed distribution
dayvmtfraction
fuelformulation
fuelsupply
hourvmtfraction
hpmsvtypeyear
imcoverage
monthvmtfraction
roadtype
Roadtype distribution
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
*
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
21
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
*
87
Missouri
110
115
5
115
115
115
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
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
7
72
72
72
Total
218
712
813
699
825
734
1327
355
821
346
1151
1157
1325
219
MOVES CDB input table
Agencies submitting data through the Emissions Inventory System (EIS) filled out a checklist of CDB data tables
for which local data was provided, along with documentation for how the local data were developed. A review
of the documentation showed a considerable variety in state sources and approaches to producing the data.
The benchmark for such submissions is technical guidance for MOVES developed by EPA discussed above. Some
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key inputs can rely on readily available data available in each state; examples would include VMT data from
federal or state transportation departments, and age distribution data from state vehicle registration databases.
Other inputs are less uniform, so the sources and quantify of data states submit vary more. Average speed is a
primary example - some states rely on travel demand modeling at the link-level, in conjunction with SIP-level
modeling, while others rely on more aggregate sources, including posted speed limits.
For many states, some inputs are available and provided, while if an input was more difficult to get states simply
provided MOVES defaults. Some states submitted a subset of CDB inputs (for example, only data related to
inspection/maintenance programs, or population data for a subset of source types); defaults were used for any
CDB data not provided by states. This occurred even within a single field; for example, states may provide local
data for a subset of source types (e.g., passenger cars), while using MOVES defaults for other source types. In
the extreme cases, some states only provided a single input, such as l/M inputs, while submitting defaults for all
other inputs.
Figure 16 shows the states/local agencies submitting data at the county level in dark blue. In some cases, only a
subset of states was supplied. This map includes states that did not submit full local data, i.e. relied on defaults
for some data.
Texas did not provide CDBs through EPA's submission process. They developed their own inventories using an
approach that was unique to Texas, so the MOVES inputs didn't correspond directly to county-level inputs
needed for the NEI. EPA's contractor did procure these databases from TCEQ, and provide these data to EPA for
use in developing representing counties for Version 1. The data from TCEQ was used to inform the choice of
representing counties for Texas for Version 1, but data from the CDBs was not used directly.
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Figure 16: Dark blue indicates States/Counties that submitted at least 1 CDB input
Washoe County, NV
Louisville, KY (Jefferson County)
Knox County, TN
Maricopa County. AZ J
Evaluation of Submissions vs EPA guidance
Under contract with the Coordinating Research Council (CRC), Eastern Research Group, Inc. (ERG) did a detailed
assessment of the first round of MOVES data submitted by states, quantifying the distribution of inputs and their
impact on MOVES emission estimates.17 Part of this assessment was an evaluation of the sources of state-
submitted data against best practice, as defined by the EPA technical guidance summarized in Table 119.
Documentation provided by responsible agencies along with the MOVES CDBs submitted for the 30 states
during the first round of submission was analyzed to determine whether states followed EPA's best practice
guidance or suggested fallbacks, or used other approaches. This summary is presented in Figure 17, and
provides insight into what states are doing to gather data for submission. While several states generated local
data for the majority of inputs, no state provided custom data for all inputs. Where custom data wasn't
provided, MOVES defaults were submitted (as noted, for some fields MOVES defaults are mentioned as a
"fallback" option in the EPA guidance). The likelihood of states submitting custom data depends on the field,
and is an indication of how readily available local data is for that field. State transportation departments collect
17 "Study of MOVES information for the National Emissions Inventory", Report for Coordinating Research Council (CRC)
by Eastern Research Group, Inc under CRC Project A-84; expected publication 2014
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detailed activity information, providing a ready source for VMT and allocations by road type, month, day and
hour. State vehicle registration databases are a ready source for vehicle population and age distribution inputs.
The prevalence of local data for these inputs is therefore higher. Average speed distribution data required by
MOVES is more challenging to obtain, particularly for rural areas and urban areas not employing travel demand
models; VMT by fuel technology is also difficult to obtain. As a result, these inputs have lower prevalence of
local data submitted.
Figure 17: Breakdown of CDB inputs provided by States that submitted at least 1 input
a 20
^ J? N^-
~ MOVES Default
~ Local Data - Not Determined
¦ Local Data - Guidance Fallback
~ Local Data - Guidance Best Practice
* Fields for which any local data
was counted as best practice
t MOVES Default listed as fallback
option in guidance
Resubmission Process
Following posting of first round inputs, EIS was opened to a second round of submission ending in May 2013.
Several states submitted new or corrected data during this time (or provided data directly to EPA staff), as
follows:
• New Jersey provided CDBs for all 21 counties in the state
• Utah resubmitted a CDB for county 49053
• Colorado submitted an IMCoverage table for counties 08031, 08123, 08001, 08005, 08013, 08014,
08035, 08041, 08059, 08069, and 08097
• Connecticut submitted HPMSVMTBaseYear (VMT) data for counties 09003 09001, 09005, 09007, 09009,
09011, 09013, and 09015 and replaced sccroadtypedistribution data with EPA default data
• Minnesota submitted:
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o sourceTypeAgeDistribution for counties 27053, 27003, 27111, and 27137
o HPMSVMTBaseYear (VMT) and sourceTypePopulation data for county 27065
o sourceTypePopulation data for county 27111
• Virginia resubmitted HPMSVMTBaseYear (VMT) and sourceTypePopulation data for county 51103
QA Checks on MOVES inputs
A quality assurance (QA) process in the form of a MySQL script developed by EPA and supplemented by the EPA
contractor ERG was implemented for each of the state-submitted MOVES CDBs. This tool generated a QA report
that was submitted to EIS with any CDB input submission. The tool must confirm that all inputs passed QA in
order for the submission to proceed. This process was also performed on any re-submitted or newly submitted
data that followed draft release of the CDBs (including data submitted directly to EPA staff rather than through
EIS). The initial QA checks repeated the checks required during submittal to EIS ensuring basic integrity of the
input files. These checks focused on range checks for submitted data, making sure distributions summed to
unity (one), and making sure that no empty tables were submitted. Submitting agencies ran this QA check at
the time of submission.
Following loading into EIS, an additional set of checks were developed to further assess the "reasonableness" of
the submitted data. These checks were meant to flag entries that contained more extreme values for further
investigation, though not necessarily in error. A summary of these reasonableness checks is below:
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.
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].
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These checks set several flags; for example, many counties had individual age distribution values = 0, individual
hour VMT fraction values =0, and VMT/Population ratios that varied from national defaults by more than 50
percent. After review of all of the reasonableness check results, for Version 1 the majority of flagged data were
deemed reasonable and as intended by the submitting agency. For a few cases, identified errors in submissions
were corrected by states during the resubmission process.
A particular concern with data submission were cases where states submitted updated VMT data, but did not
update vehicle population data accordingly. Check #12 was devised to flag unreasonable VMT/population
ratios, but was too coarse to catch this specific case. Cases where states supplied local VMT data but default
population (based on the previous 2008 NEI) data were identified through the submitted documentation and
check of data against the national defaults from MOVES. The submissions were:
• Oregon
• Ohio (source types 41 and higher)
• Knox County, TN
• Washoe County, NV
For these areas, 2011 default population data as discussed in the following section was used instead of the
submitted default data. Other results required correction by EPA prior to development of the emissions
inventory, as discussed in the following sections.
Case-by-case corrections
Utah counties 49043 (Summit) and 49045 (Tooele) appeared to have AvgSpeedDistribution data incorrectly
transposed from other counties. To address this, the 49045 AvgSpeedDistribution table was replace with the
one originally submitted for county 49043, while the AvgSpeedDistribution table for county 49043 was replaced
with the one from county 49041 (Sevier).
Updating submissions where Road Type VMT=0
Several corrections were made to representing counties where states submitted placeholder
averageSpeedFraction or hour VMTfraction data for roadtypes which did not have any VMT. While these were
technically not errors from the state perspective, for SMOKE-MOVES application it was necessary to have
realistic values for all road types in a representing county, so that emission rates calculated for these counties
could be applied to all road types over a broader set of non-representing counties. To ensure consistency, for all
representing counties, averageSpeedFraction and hourVMTFraction values for roadTypes where
roadTypeVMTFraction = 0 were set to national defaults. This fix was not made for non-representing counties, as
it wasn't a needed correction for application in the SMOKE-MOVES framework.
Ramp fraction corrections
62 counties had cases where RampFraction for restricted roadways (MOVES road type 2 or 4) was submitted as
zero. For some of these cases the roadTypeVMTFraction for restricted roadways was submitted as zero as noted
above, meaning that a zero RampFraction wasn't consequential. However, because a non-zero RampFraction is
required for representing counties in the application in SMOKE-MOVES, values were updated using the national
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default RampFraction (0.08) where rampFraction=0 and roadtypeid in (2,4). [Note: For 2 counties in GA (Fulton
13121 and Floyd 13115), EPA incorrectly gave them 0.8 ramp fraction instead of 0.08. Also, when GA
resubmitted 13121, it actually had a non-zero ramp fraction but they previously had a 0. EPA erroneously
replaced it as if it was 0 and replaced it with 0.8. These errors were not found in time for correction in
2011NEIvl but will be fixed in version 2.]
4,6,2,3 Default Inputs used for counties with no submitted CDBs
For counties which did not submit CDB data, default CDBs were developed. Table 121 describes the source of
the defaults used for Version 1 of the 2011 NEI for each table in the CDB that states have the option to supply
alternate data for (there are additional tables in the CDB that are informational only, i.e. state, county, year etc.
that EPA prepopulated).
Table 121: Source of Defaults for Data Tables in MOVES CDB
CDB Table
Description of Content
Default CDB Table Content
avgspeeddistribution
Average speed distributions
MOVES2010b national default
dayvmtfraction
VMT distribution across the
type of day
2008 NEI v3
fuelformulation
Fuel properties
Based on EPA estimates for each county based on
calendar year 2011 refinery data.
Fuelsupply
Fuel differences by month of
the year
Based on EPA estimates for each county based on
calendar year 2011 refinery data.
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
2008 NEI v3
monthvmtfraction
VMT distribution across the
months of the year
MOVES2010b national default
roadtypedistribution
VMT distribution across the
road types
2008 NEI v3
Sourcetypeagedistribu
tion
Distribution of vehicle ages
MOVES2010b national default for 2011
Sourcetypeyear
Vehicle populations
Calculated from county-level VMT based on ratios
of VPOP:VMT from state-level FHWA data (see
below)
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.
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Defaults California Emissions Standards
In addition to these CDB tables, the EmissionRateByAge table for some counties were populated using the
appropriate data described in the guidance for states adopting California emission standards. The states that
were given California LEV programs are shown in Table 122, along with the program date start years:
Table 122: 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
41
Oregon
2009
42
Pennsylvania
2008
44
Rhode Island
2008
50
Vermont
2000
53
Washington
2009
The alternative base emission rates are available in the file "2011nei_supdata_or_CALEV.zip" (see Section 4.6.5
for access information).
Default VMT
The default 2011 National Emissions Inventory (NEI) vehicle miles traveled (VMT) data are developed primarily
from information provided by the Federal Highway Administration (FHWA), along with the FHWA's published
Highway Statistics 2011, data from the Bureau of Census, and information provided by the US Environmental
Protection Agency (EPA). This 2011 full VMT database was developed to provide default VMT at the county,
road type, and vehicle type level of detail, to be used in the 2011 NEI in areas for which no state or local agency
provided VMT data. The road types included were the 12 Highway Performance Monitoring System (HPMS)
functional roadway types while the vehicle types used were the six MOVES HPMS vehicle types, for a total of 72
records per county. To be compatible with MOVES inputs and for easy import into the MOVES HPMSVtypeYear
table, the data were also provided at the level of county and HPMSVtypelD. In both cases, the VMT data
provided are annual data in units of millions of vehicle miles.
Data Sources Used to Develop Default VMT
The 2011 VMT database was developed using data supplied directly by FHWA and as well as publicly available
data from the 2011 version of FHWA's Highway Statistics data series
(http://www.fhwa.dot.gov/policvinformation/statistics/2011/). The FHWA data sets that were provided include
a subset of fields from the 2011 HPMS database.
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The HPMS database is at the link level of detail. Each link is coded with the corresponding state, county, and, if
applicable, urban area code. The roadway functional system code is included. The beginning point and ending
point mileage of the link are included, from which the length of the link can be calculated. In addition, the total
average annual daily traffic (AADT) volume is included along with AADT for single-unit trucks and combination
trucks. Annual VMT can then be calculated by multiplying the link length by the AADT and multiplying by 365.
The Highway Statistics data used in the VMT development include:
• Table VM-2, "Functional System Travel, Annual Vehicle-Miles,"
• Table VM-1, "Annual Vehicle Distance Traveled in Miles and Related Data by Highway Category and
Vehicle Type," and
• Table HM-71, "Urbanized Areas, Miles and Daily Vehicle-Miles of Travel."
Highway Statistics Table VM-2 contains State-level summaries of 2011 miles of annual travel in each State. Rural
VMT and urban VMT are provided at the state level for seven HPMS functional roadway types: interstate, other
freeways and expressways, other principal arterial, minor arterial, major collector, minor collector, and local.
Highway Statistics Table VM-1 provides annual VMT summarized by five roadway groups (interstate rural, other
arterial rural, other rural, interstate urban, and other urban) and by the following vehicle categories: light duty
vehicles-short wheelbase, motorcycles, buses, light duty vehicles-long wheelbase, single-unit trucks, and
combination trucks.
Highway Statistics Table HM-71 provides daily VMT data summarized by urban area for urban areas with a
population of 50,000 or more in each of the seven urban functional roadway classes.
In addition to the FHWA data, Census population estimates were used in developing the VMT database. This
includes estimates of 2011 population by county. Two additional Census files were used that include 2010
Census data, as these were data tables only available from the decennial census. One of these files provides the
percentage of each county's population that fell within specific urban areas in 2010. The other included file
provides the percentage of urban and rural population in each county. Ratios based on the 2010 population
data, including the county-level percentage of population classified as urban versus rural and the percentage of
a county's total population falling within a specific urban area, were applied to the 2011 county-level population
data.
Default VMT Development Procedures
The procedures used in the development of the 2011 NEI default VMT are broken down into four parts:
1. Extracting the county and roadway type VMT from interstates, other freeways and expressways, and
other principal arterials from the HPMS database;
2. Allocating rural VMT for minor arterial, major collector, minor collector, and local roadways from the
state/roadway type level to the county/roadway type level using Highway Statistics Table VM-2 and
Census data;
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3. Allocating urban VMT for minor arterial, major collector, minor collector, and local roadways from the
urban area/roadway type and state/roadway type level to the county/roadway type level using Highway
Statistics Tables VM-2 and HM-71 and Census data; and
4. Allocating the county/roadway type VMT by vehicle type using Highway Statistics Tab\e VM-1 and data
from EPA.
Each of these procedures in the development of the 2011 VMT is discussed separately below.
Extracting VMT from HPMS database
The HPMS database is essentially complete for the three highest functional roadway classes—interstate, other
freeways and expressways, and other principal arterial. In other words, the sum of the VMT for each of these
three roadway classes is less than 0.0001 percent different than the national total for the same roadway class
published in Highway Statistics Table VM-2. For the remaining roadway classes, the VMT estimated from the
HPMS 2011 database represents only a small fraction of the total Highway StatisticsTab\e VM-2 VMT for these
roadway classes. Based on this assessment, the HPMS database was used to develop the county level VMT for
the 2011 NEI for VMT on intestates, other freeways and expressways, and other principal arterial roadways. For
the remaining roadway classes, Highway Statistics Tables VM-2 and HM-71 were used in combination with
population data to allocate the VMT by county. This procedure is discussed below.
There were some cases in the HPMS database where county codes were null. In these cases, the VMT from
counties with a null county code was allocated to any missing counties according to rural or urban population,
depending on the roadway type. This occurred to some extent in nine states. The worst case of this was Iowa
where only the null county code was used.
Allocating Rural VMT to Road Types
State-level rural VMT from Highway Statistics Tab\e VM-2 was allocated to the county level for these four
roadway types: minor arterial, major collector, minor collector, and local roadways. The allocation used rural
population and rural functional system roadway length as surrogates. Counties with a roadway length of zero
for a specified roadway type were not assigned any VMT for that roadway type. The allocation of VMT by county
was essentially a two-step process. First, the rural population was estimated for each county by multiplying the
2011 county-level population by the 2010 rural population fraction for that county. In addition, using a
database of county-level roadway lengths by functional system, the functional system roadway length was
assigned to each county and roadway type combination. For each state and roadway type, the rural population
from each county with a non-zero roadway length for the specified roadway type was then totaled. Next, to
obtain the county/roadway type VMT, the state-level VMT total for a given roadway type was multiplied by the
ratio of the rural population of the county (if the county had non-zero roadway length for the specified roadway
type) divided by the total state-level rural population from counties with a non-zero roadway length for the
specified roadway type. This is shown the equation below:
RVMTr,c =RVMTr,s * (RPOPc / RPOPs)
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where:
RVMTr c = VMT on rural roadtype R in county C (calculated)
RVMTr s = VMT on rural roadtype R, State total (Highway Statistics Table VM-2)
RPOPc = Rural population in county C (0 if zero mileage from rural roadway type R in county C)
(Census)
RPOPs = Rural population, State total of all counties with nonzero mileage from rural roadway type R
(Census)
Allocating Urban VMT to Road Types
The allocation procedure for roadway types in urban areas differs from the procedure used for rural areas. The
difference in the procedures is due to the incorporation of the urban area VMT data from Highway Statistics
Table HM-71. This table contains 2011 VMT by functional roadway type for each urban area with a population
greater than 50,000. Note that for multistate urban areas, the 2011 version of the Highway Statistics Table HM-
71 data does not provide separate totals for each state portion of a multistate urban area. The general
procedure used to allocate urban VMT was to first allocate the Highway Statistics Tab\e HM-71 data to the
counties included in each urban area and then allocate the remaining state-level urban VMT to counties with
urban populations not covered by these urban areas. This procedure is discussed in more detail below.
Step 1. Allocate Urban Area VMT from Highway Statistics Table HM-71
These were allocated to county based on county population percentage within the specific urban area.
Remaining state-level urban VMT from Highway StatisticsTab\e VM-2 was allocated to the remaining urban
areas in the state based on urban population. Data from the 2010 Census provides the relation between each
urban area and the counties that are included within the urban area. Note, this Census data file accounts for all
urban population, not just the urban areas with population greater than 50,000. For each county included at
least in part in an urban area, the Census data provides the percentage of the urban area's total population that
falls within each county making up the urban area. This percentage was then multiplied by the 2011 urban area
VMT on each of the four roadway classes in the following equation:
UVMTr,ua,c = UVMTr,ua * (UAPOPPCTua,c/100)
where:
UVMTr, ua,c — VMT on urban roadtype R in urban area UA in county C (calculated)
UVMTr, ua — VMT on urban roadtype R in urban area UA [Highway Statistics Table HM-71)
UAPOPPCT ua,c — Percent of urban area UA population within county C (Census)
Step 2. Allocate Remaining Urban VMT from Highway Statistics Table VM-2
The 2011 VMT allocated to large urban areas was totaled by state and roadway type. These state/roadway type
VMT totals were then subtracted from the Highway Statistics Table VM-2 state urban VMT for each of the four
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roadway types (see Equation 1). The remainder is the rest-of-state urban VMT to be allocated according to
urban population not in large urban areas.
Similarly, the total 2010 urban population in urban areas with population greater than 50,000 was subtracted
from each county's 2010 total urban population to obtain the urban population not accounted for by any of the
large urban areas (see Equation 2). This remaining urban population was then totaled at the state level to
obtain the rest-of-state urban population total in areas outside of large urban areas.
Once the portion of the state-level urban population that falls outside of large urban areas is estimated, the
county-level fraction of the rest-of-state population is calculated for each county with an urban population that
exceeds the total of the county urban population within large urban areas. This ratio of the county level urban
population not included in large urban areas to the total state urban population outside of large urban areas is
multiplied by the state/roadway type VMT total of urban VMT not in large urban areas (see Equation 3).
UVMTr,ros = UVMTr,s - I UVMTr,ua,c,s (Eq. 1)
UPOProc = UPOPc - I UPOPUA,c (Eq. 2)
UVMTr,roc = UVMTr,ros *( UPOProc / I UPOProc,s) (Eq. 3)
where:
UVMTr,ros
UVMTr,s
I UVMTr,ua,c,s =
UPOProc
UPOPc
I UPOPua,c
UVMTr,roc
I UPOProc,s
Step 3. Combine Urban VMT
The urban VMT estimated from large urban areas on minor arterial, major collector, minor collector, and local
roadways was then added to the remaining urban VMT outside of large urban areas for each of these four
roadway classes and each county to obtain the county/roadtype level urban VMT.
Rest-of-state urban VMT on urban roadtype R in state S not included in a large
urban area (calculated)
Urban VMT on urban roadtype R in state S (Highway Statistics Tab\e VM-2)
Sum of urban VMT on urban roadtype R in a large urban area in county C in
state S (from Equation 2)
Rest-of county urban population in county C not included in a large urban area
Urban population in county C (Census)
Sum of urban population within large urban areas in county C (Census)
Rest-of-county urban VMT on urban roadtype R not included in a large urban
area (calculated)
Sum of UPOProc in state S
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Allocating VMT to Vehicle Type
Step 1. Allocate VMT by Table VM-1 HPMS Vehicle Categories
After allocating the 2011 VMT by county and roadtype, the VMT then needed to be allocated by the six vehicle
types used in MOVES, known as the MOVES HPMSVtypes. Highway Statistics Table VM-1 provides a summary of
annual VMT for five groupings (interstate rural, other arterial rural, other rural, interstate urban, and other
urban) and by six HPMS vehicle categories: light duty vehicles-short wheelbase, motorcycles, buses, light duty
vehicles-long wheelbase, single-unit trucks, and combination trucks. The first step in allocating the 2011
county/road type VMT by vehicle type was to multiply the VMT at the county/road type level of detail by the
fraction of Highway Statistics Table VM-1 VMT attributed to each vehicle type for the selected roadtype
category. Table 123 shows the Highway Statistics Table VM-1 VMT for 2011 as fractions of 2011 VMT in each of
the FHWA HPMS vehicle categories by the five roadway type categories, along with the roadway functional
classes that are included in each roadway type category.
For the roadtypes for which the county-level VMT was available from the HPMS database (interstates, other
freeways and expressways, and other principal arterials, the HPMS also included VMT for single-unit trucks plus
buses and for combination trucks. In cases where these values were nonzero, these county-specific VMT data
were used in place of the values derived as discussed above. In such cases, these VMT data were subtracted
from the county/roadtype VMT total and the remaining VMT was renormalized among the remaining vehicle
categories.
Table 123: VMT Fraction by HPMS Vehicle Type
Roadway
Functional
System
Table
VM-1
Road
Category
Light Duty
Vehicles -
Short
Wheelbase
Motorcycles
Buses
Light Duty
Vehicles -
Long
Wheelbase
Single-
Unit
Trucks
Combination
Trucks
-Rural
Interstate
Interstate
Rural
0.577
0.005
0.007
0.176
0.039
0.195
-Rural Other
Freeways
and
Expressways
Other
Arterial
Rural
0.623
0.008
0.005
0.240
0.045
0.079
-Rural Other
Principal
Arterial
-Rural Minor
Arterial
-Rural Major
Collector
Other
Rural
0.633
0.008
0.006
0.261
0.051
0.041
-Rural Minor
Collector
-Rural Local
-Urban
Interstate
Interstate
Urban
0.717
0.004
0.004
0.173
0.030
0.071
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Roadway
Functional
System
Table
VM-1
Road
Category
Light Duty
Vehicles -
Short
Wheelbase
Motorcycles
Buses
Light Duty
Vehicles -
Long
Wheelbase
Single-
Unit
Trucks
Combination
Trucks
-Urban Other
Freeways
and
Expressways
-Urban Other
Principal
Arterial
-Urban Minor
Arterial
-Urban Major
Collector
-Urban Minor
Collector
-Urban Local
Other
Urban
0.737
0.006
0.004
0.197
0.030
0.026
Step 2. Convert from Table VM-1 HPMS Vehicle Categories to MOVES HPMS Vehicle Categories
Although these HPMS vehicle categories shown in Table 123 are similar to the MOVES HPMS vehicle categories,
FHWA has changed the definition of these categories over time, such that there is no longer a one-to-one match
between the FHWA vehicle categories and the MOVES vehicle categories. Thus, EPA developed adjustment
ratios to convert VMT from the FHWA vehicle categories to the MOVES vehicle categories (see Table 124). The
VMT data by county, roadway type, and HPMS vehicle type derived in Step 1, above, were divided by the
corresponding FHWA to MOVES Adjustment Factors shown in Table 124. The resulting VMT from Step 2,
summed nationally, is also shown in Table 124.
Table 124: Converting FHWA to MOVES Ve
nicle Types
FHWA to
Step 2. Initial
Step 3.
MOVES
MOVES
2011 VMT* by
Normalized 2011
FHWA HPMS
HPMS
MOVES
Adjustment
MOVES HPMS
VMT* by MOVES
Vehicle Type Name
VtypelD
HPMSVtypeName
Factor
VtypelD
HPMS VtypelD
Motorcycles
10
Motorcycles
1.0000
18,659
18,519
Light Duty Vehicles
20
Passenger Cars
1.2627
1,632,912
1,630,613
-Short Wheelbase
Light Duty Vehicles
30
Other 2-Axle, 4-Tire
0.5441
1,118,093
1,111,198
- Long Wheelbase
Vehicles
Buses
40
Buses
2.0270
6,122
6,131
Single-Unit Trucks
50
Single Unit Trucks
1.5052
61,761
61,595
Combination Trucks
60
Combination Trucks
1.2963
120,102
121,894
* million of miles
Step 3. Normalize VMT to Original County-level VMT Totals
The application of the conversion from FHWA to MOVES HPMS categories in Step 2 changes the total VMT.
Thus, the final step in the VMT allocation by vehicle type is to normalize the VMT resulting from Step 2 at the
county/roadway type level of detail. This was accomplished by multiplying the ratio of the VMT by MOVES
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HPMS vehicle category for an individual county/roadway type/MOVES vehicle category to the total of the VMT
from the six MOVES HPMS vehicle categories for that county/roadway type by the total county/roadway type
VMT prior to allocating the VMT by vehicle type. The resulting, final 2011 VMT, summarized by MOVES HPMS
VtypelD nationally is shown in the column to the far right of Table 124.
Default Source Type Population
Default Source type population estimates were developed based on FHWA's 2011 Highway Statistics summaries
by state. Population for each state was derived from Highway Statistics Tables MV-2 and MV-9, and in some
cases required additional processing to develop population estimates by MOVES source type. One exception
was single unit trucks, for which state-level data could not be estimated from these tables. Estimates of
population for each source type were derived as shown in Table 125.
Table 125: Mapping of MOVES Source Types to FHWA Vehicle Populations from Highway Statistics Tables MV-2
and MV-9
MOVES Source Type
Method for estimating population from
2011 Highway Statistics
MOVES default factor for
splitting HPMS level data into
source type
Motorcycle
Table MV-1, Private/Commercial +
Publicly Owned
1.00
Passenger Car
Table MV-1, Automobiles, Total
1.00
Passenger Truck
Table MV-9, Pickups, Vans, SUVs, Other
Light
0.75
Light Commercial Truck
0.25
Intercity Bus
Table MV-1, Buses, Total
0.12
Transit Bus
0.07
School Bus
0.81
Refuse Truck
Not able to break out single unit truck
populations from MV-1 or MV-9 tables.
0.01
Motorhome
0.72
Single Unit Short Haul
0.10
Single Unit Long Haul
0.17
Combination Short Haul
Table VM-9, Truck Tractors
0.45
Combination Long Haul
0.55
Resulting state-level populations were paired with VMT data for two different applications in the NEI. For
MOVES runs necessary to produce emission factors for use in SMOKE-MOVES, MOVES tables containing the
state-level vehicle population data as well as state-level VMT compiled from Table VM-2 of FHWA's 2011
Highway Statistics. VMT for each state was taken directly from the vehicle classes reported out in Highway
Statistics Table VM-2. These classes are the basis for vehicle classes in MOVES (HPMSVtype ID). These state-
level databases were used for MOVES runs to update the VMT to vehicle population ratios (hence forward
referred to as "VMT:VPOP" ratios) used in MOVES emission rate calculations, overriding county-level VMT and
source type population data in the default CDBs. For single unit trucks, MOVES national default population and
VMT was used in the absence of good state-level data.
Vehicle Population is also needed by SMOKE to calculate emissions for processes that rely on emission rates per
vehicle (mainly start and evaporative emissions). Populations provided by states were used directly for these;
for states not submitting data, default populations were estimate based on the same state-level VMT and
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populations described above. Default source type population inputs were developed by multiplying default
county-level VMT estimates by ratios of VPOP:VMT derived from the FHWA data. VMT:VPOP ratios were
calculated at the HPMS class level for each state, and are shown in Table 126. Because population data were
not published by FHWA for Puerto Rico and the U.S. Virgin Islands, MOVES national default VMT:VPOP ratios
were applied in these territories. These calculated VMT:VPOP ratios are used only for states that did not supply
data.
Table 126: Default State-level VMT:VPOP ratios derived from 2011 Highway Statistics
State
Motorcycle
Pass Car
Light Truck
Bus
Single Unit
Truck*
Combination
Truck
Alabama
3174
15130
11624
87909
11989
78464
Alaska
922
11454
3857
32805
11989
45004
Arizona
2072
13561
10367
84890
11989
64589
Arkansas
2688
17346
10584
164823
11989
38415
California
2439
11859
9806
145760
11989
58678
Colorado
1674
13935
8600
78054
11989
73310
Connecticut
1982
9759
13269
69736
11989
198235
Delaware
1842
9896
9234
48362
11989
123403
Dist. of Columbia
6275
8047
21832
21900
11989
3865699
Florida
2079
12929
11530
92001
11989
134811
Georgia
3381
17006
11763
66744
11989
71240
Hawaii
2054
10124
7038
72391
11989
162602
Idaho
1584
14038
7326
58650
11989
33730
Illinois
1834
9980
9852
51747
11989
32211
Indiana
2329
14274
11612
59559
11989
16346
Iowa
1117
10220
8322
58544
11989
19442
Kansas
2296
14145
11545
98964
11989
34665
Kentucky
3037
13726
12861
75694
11989
72716
Louisiana
4228
13888
8930
58045
11989
61615
Maine
1762
14459
10501
51553
11989
74308
Maryland
2913
14482
14483
64709
11989
204918
Massachusetts
2145
9268
9614
79214
11989
188891
Michigan
1912
10746
9538
78564
11989
66008
Minnesota
1468
13558
10211
54216
11989
38208
Mississippi
8611
19883
18012
72235
11989
77529
Missouri
3037
15159
11446
71650
11989
45656
Montana
1544
14398
7265
40795
11989
24000
Nebraska
2313
13400
9979
86187
11989
15023
Nevada
2183
12057
10069
121316
11989
139362
New Hampshire
999
11500
9131
66478
11989
108101
New Jersey
1376
8902
9665
59503
11989
81320
New Mexico
2448
19115
10996
101717
11989
98059
New York
2298
11710
13151
44269
11989
149930
North Carolina
2892
17098
16818
93575
11989
67473
North Dakota
1739
14950
11299
58483
11989
12849
Ohio
1785
11130
10943
66244
11989
51963
Oklahoma
2323
16767
13023
95935
11989
38327
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State
Motorcycle
Pass Car
Light Truck
Bus
Single Unit
Truck*
Combination
Truck
Oregon
1918
12192
9277
124267
11989
59742
Pennsylvania
1525
9729
9677
65095
11989
58130
Rhode Island
1486
8056
9156
65316
11989
181401
South Carolina
2798
13213
11809
91951
11989
86336
South Dakota
804
12591
7346
58351
11989
17346
Tennessee
2614
14429
12241
92828
11989
68193
Texas
3340
14246
10073
74150
11989
48981
Utah
2746
16439
12143
81154
11989
41146
Vermont
1478
13425
10792
56202
11989
89911
Virginia
2573
11381
11574
64018
11989
97998
Washington
1605
10086
9455
79634
11989
60754
West Virginia
2050
15374
11003
61400
11989
102895
Wisconsin
1066
12096
9581
61113
11989
40006
Wyoming
1894
20550
7680
43833
11989
40550
MOVES Default
1701
13959
9918
6518
11989
51271
^National average estimate used for Single Unit Trucks
4,6,3 Calculation of EPA Emissions
4.6,3.1 EPA-developed on-mad mobile emissions data for the continental U.S.
For the 2011 NEl, EPA estimated emissions for every county in the U.S. except for California and Texas. 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 SMOKE18 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 163 "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) or vehicle population) 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 available in the file "2011nei_supdata_or_RepCounty_Runspecs.zip" (see Section 4.6.5 for access
information). A full listing of datasets available as supporting information for the on-road MOVES runs is
available in Section 4.6.5 and these are referenced in the subsections below.
18 A beta version of SMOKE v3.5 was used for the 2011 NEI vl. The current version is available at: http://www.smoke-
model.org/index.cfm
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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 NEI vl, EPA used the
latest publically released version: MOVES2010b (http://www.epa.gov/otaq/models/moves/index.htm). 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
4.6.3.3)
• Create MOVES inputs needed only for the MOVES runs (see Section 4.6.3.4). 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 4.6.3.5 and 4.6.3.6).
• Create a set of adjustment factors for distributing extended idle emissions from long haul trucks (see
Section 4.6.3.7)
• Run MOVES to create emission factor tables (see Section 4.6.3.8)
• Run SMOKE to apply the emission factors to activities to calculate emissions (see Section 4.6.3.9)
• Aggregate the results at the county-SCC level for the NEI, summaries, and quality assurance (see Section
4.6.3.10)
4,6.3.2 Representative counties
Although EPA compiles county-specific databases for all counties in the nation, actual county-specific data is
rare. Instead, much of our "county" data is based on state-wide estimates or national defaults. For the NEI,
rather than explicitly modeling every county in the nation, we have done detailed modeling for some counties
and less detailed estimates for the other counties. This approach dramatically reduces the number of modeling
runs required to generate inventories and still takes into account important differences between counties.
In this approach, we group counties that have similar properties that would result in similar emission rates. We
explicitly model only one county in the group (the "representative" county) to determine emission rates. These
rates are then used in combination with county-specific activity and meteorology data, to generate inventories
for all of the counties in the group. The grouping of counties was based on several characteristics as
summarized in Table 127 below.
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Table 127: Characteristics for grouping counties
County Grouping Characteristic
Description
Fuel Parameters
Weighted average gasoline fuel properties for January and July
2011, including RVP, sulfur level, ethanol fraction and percent
benzene
Emission Standards
Some states have adopted California highway vehicle emission
standards or plan to adopt them. Since implementation of the
standards varies, each state with California standards is
treated separately.
Inspection/Maintenance Programs
Counties were grouped within a state according to whether or
not they had an inspection and maintenance (l/M) program.
All l/M programs within a state were considered as a single
program, even though each county may be administered
separately and have a different program design.
Altitude
Counties were categorized as high or low altitude based on the
criteria set forth by EPA certification procedures (4,000 feet
above sea level).
Fleet Age
The weighted average age of passenger cars.
State
A county group, including the representative county, can only
consist of counties from the same state
The result is a set of 163 county groups with similar fuel, emission standards, altitude, l/M programs and fleet
age. For each group, the county with the highest total VMT was chosen as the representative county for the
group (this VMT is not used to calculate the emissions however). The representative counties for the 2011 NEI
vl match those that were used for the "2011 emissions modeling platform" (2011v6 platform, available at:
http://www.epa.gOv/ttn/chief/emch/index.html#2011). However, the representative counties have a different
mapping from what was used in the 2008 NEI v3. A summary of the representative counties is available in the
spreadsheet included in "2011nei_supdata_or_RepCnty.zip" and the MOVES County Database Manager
databases are available in the file "2011nei_supdata_or_CDB_164RepCnty.zip" (see Section 4.6.5 for access
information).
For each county group, SMOKE-MOVES generated a set of emission rates that varied by SCC (vehicle type and
road type), fuel, speed, temperature, and humidity; thus, we did not need to consider the fleet mix, speed,
temperature range, or humidity in our grouping characteristics. This greatly increased the number of counties
that can be grouped, and reduced the number of MOVES runs required.
With help from the RPO's, EPA solicited comments from the states on the set of representative counties used in
the 2011 draft run. The following states provided with comments on the representative counties, and in most
cases, any suggested changes were incorporated into the list of representative counties for Version 1 of the
2011 NEI: CO, CT, GA, IL, IN, MA, MD, ME, MN, MO, NC, NH, NJ, NY, OH, PA, TX and VA.
4.6,3.3 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
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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 available in the spreadsheet included in
"2011nei_supdata_or_FuelCR.zip" (see Section 4.6.5 for access information).
4.6.3.4 Fuels
Although state-submitted NMIM and MOVES input data may have included information about fuel properties,
the MOVES runs for the 2011 NEI were run using a set of fuel properties for a set of fuel regions generated by
EPA. We 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.
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 2010, 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 available in the file "2011nei_supdata_or_RegFuel.zip" (see Section 4.6.5 for
access information).
4.6.3.5 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
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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,
http://wrf-model.org). 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
(http://www.cmascenter.org/help/model docs/mcip/4.1/ReleaseNotes) 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 (Section 4.6.3.8).
The temperature lists were organized based on the representative counties and fuel months as described in
Sections 4.6.3.2 and 4.6.3.3, 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
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 4.6.3.8), 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
and RPV.
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.
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The resulting temperatures provided to the representative counties are available in the file
"2011nei_supdata_or_RepCounty_temperatures .zip" (see Section 4.6.5 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.chief@epa.gov').
4,6.3,6 VMT, vehicle population, and speed for SMOKE
SMOKE requires county-specific VMT, vehicle population (VPOP), and average speed by SCC to calculate the
gridded or county emissions. Unlike the other inputs that are needed just for the representative counties, these
inputs are needed for every county. In cases where this data was supplied by states in the CDBs, there needs to
be a mapping between MOVES source and road types (the data classification within the CDBs) and SMOKE SCCs.
All three types of data (VMT, VPOP, and speed) need to be translated into SCCs for use in SMOKE. The data for
VMT, VPOP and speed were obtained either from EPA default estimate or from state supplied data. The state
submitted input data are described in Section4.6.2.2.
SMOKE requires estimates of VMT by county, month, and SCC. The annual VMT values were derived from the
state supplied MOVES inputs or the EPA defaults (see Section 4.6.2.2 and 4.6.2.3, respectively). The conversion
from MOVES source and road types is described below. In addition to state supplied VMT in MOVES CDB
format, two states provided data already converted into SCCs: Virginia and Georgia. These state supplied annual
VMT were distributed to monthly VMT by using EPA estimated month to annual ratios by county and SCC.
The CDBs provided by states and the EPA defaults do not contain vehicle miles traveled (VMT), vehicle
population, road type or average speed data divided by SCC categories. MOVES normally allocates the user
supplied data to the SCC categories internally when MOVES is run. It is not practical to run MOVES for each
county to obtain the vehicle activity data by SCC. For the 2011 NEI vl, the data provided by the states in the
CDBs was allocated to the SCC categories manually using the methods described in this section for use by
SMOKE.
Calculating VMT by SCC from State Supplied County Databases
VMT is provided in the CDBs using the six vehicle classifications used in the Federal Highway Administration
(FHWA) Highway Performance Monitoring System (HPMS). This data is provided annually to FHWA by the
states. FHWA has changed the classification system they used in 2011, but MOVES2010b continues to use the
old definition shown in Table 128.
Table 128: MOVES Vehicle Types (HMPSVtypelD)
HPMSVtypelD
HPMSVtypeName
10
Motorcycles
20
Passenger Cars
30
Other 2 axle-4 tire vehicles
40
Buses
50
Single Unit Trucks
60
Combination Trucks
Portions of each of the HPMS vehicle types can be matched to each of the 12 SCC vehicle types. The fraction of
the HPMS vehicle type VMT that maps to each SCC vehicle type depends on the calendar year, since the mix of
226
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vehicle types varies by model year. The twelve SCC vehicle types are shown in Table 129 with the first seven
characters of their SCC (SCC7) which indicate their vehicle type.
Table 129: SCC Vehicle Types (SCC7)
SCCVtypelD
SCC7
SCCVtype
SCC Vtype Description
1
2201001
LDGV
Light Duty Gasoline Vehicles (LDGV)
2
2201020
LDGT1
Light Duty Gasoline Trucks 1 & 2
3
2201040
LDGT2
Light Duty Gasoline Trucks 3 and 4
4
2201070
HDGV
Heavy Duty Gasoline Vehicles 2B thru 8B and Gasoline Buses
5
2201080
MC
Motorcycles (MC)
6
2230001
LDDV
Light Duty Diesel Vehicles (LDDV)
7
2230060
LDDT
Light Duty Diesel Trucks 1 thru 4 (LDDT)
8
2230071
2BHDDV
Heavy Duty Diesel Vehicles (HDDV) Class 2B
9
2230072
LHDDV
Heavy Duty Diesel Vehicles (HDDV) Class 3, 4, and 5
10
2230073
MHDDV
Heavy Duty Diesel Vehicles (HDDV) Class 6 and 7
11
2230074
HHDDV
Heavy Duty Diesel Vehicles (HDDV) Class 8A and 8B
12
2230075
BUSES
Heavy Duty Diesel Buses (Intercity, School and Transit)
In order to obtain factors to map the HPMS to each of the SCC vehicle classes, MOVES was run using national
totals for calendar year 2011 separately for each HPMS vehicle type (with the VMT for other HPMS vehicle types
set to zero) and with the results reported by SCC. Using this method, the aggregate 2011 calendar year fraction
of total national HPMS vehicle type VMT that is mapped to each of the SCC classifications by model year used by
MOVES can be calculated from the MOVES output. These totals are used to calculate the fraction of each HPMS
vehicle type total that is mapped to each SCC vehicle type (see Table 130). Using these fractions, the VMT by
HPMS vehicle type in each CDB can be converted to VMT by SCC vehicle type.
Table 130: Ratio of HPMS to SCC Vehicle Types
HPMSVTypelD
SCC7
SCC Vehicle Type Miles
HPMS Total Miles
HPMS/SCC Fraction
10
2201080
15,795,702,944
15,795,702,944
1.00000
20
2201001
1,599,816,994,816
1,604,541,324,792
0.99706
20
2230001
4,724,329,976
1,604,541,324,792
0.00294
30
2201020
662,010,531,072
1,125,283,986,520
0.58831
30
2201040
341,035,309,568
1,125,283,986,520
0.30307
30
2201070
73,609,382,400
1,125,283,986,520
0.06541
30
2230060
13,005,392,016
1,125,283,986,520
0.01156
30
2230071
5,902,273,160
1,125,283,986,520
0.00525
30
2230072
29,721,098,304
1,125,283,986,520
0.02641
40
2201070
321,909,878
7,005,147,462
0.04595
40
2230075
6,683,237,584
7,005,147,462
0.95405
50
2201070
28,540,914,206
88,486,798,750
0.32254
50
2230073
43,362,661,178
88,486,798,750
0.49005
50
2230074
16,583,223,366
88,486,798,750
0.18741
60
2201070
36,810,973
133,753,812,445
0.00028
60
2230073
15,428,808,640
133,753,812,445
0.11535
60
2230074
118,288,192,832
133,753,812,445
0.88437
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MOVES distributes the total VMT by HPMS vehicle type supplied in the state supplied CDB to the four MOVES
road types, as shown in Table 131.
Table 131: MOVES Road Types
roadTypelD
MOVES Road Type Description
2
Rural Restricted Access
3
Rural Unrestricted Access
4
Urban Restricted Access
5
Urban Unrestricted Access
The distribution of VMT by source use type to road types is found in the RoadTypeDistribution table provided as
part of the CDB. The distributions are by source type and not HPMS vehicle type or SCC vehicle type. However,
the source types map cleanly into the six HPMS vehicle classifications, as shown in Table 132.
Table 132: MOVES Source Types
sourceTypelD
HPMSVtypelD
Source Type Description
11
10
Motorcycle
21
20
Passenger Car
31
30
Passenger Truck
32
30
Light Commercial Truck
41
40
Intercity Bus
42
40
Transit Bus
43
40
School Bus
51
50
Refuse Truck
52
50
Single Unit Short-haul Truck
53
50
Single Unit Long-haul Truck
54
50
Motor Home
61
60
Combination Short-haul Truck
62
60
Combination Long-haul Truck
The values in the state supplied RoadTypeDistribution tables were collapsed into the HPMS vehicle classes by
grouping by HPMS vehicle types. This assumes that the road type distributions are the same for any of the
subgroups of the HPMS vehicle types.
Once the road type distributions by HPMS vehicle type are known, the road type fraction information can be
combined with the VMT by SCC vehicle type using the HPMS vehicle type to calculate the road type VMT for
each SCC/HPMS vehicle type combination. When state supplied data was not provided, MOVES national
average default values were used.
Although the SCCRoadTypeDistribution table is not part of the County Data Manager and is not normally
included in the CDBs, states were encouraged to provide this table as part of their submissions to the 2011 NEI.
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The SCCRoadTypeDistribution table has the fraction of each of the four MOVES road types that map into one of
the 12 SCC road types, as shown in Table 133.
Table 133: SCC Road Types
SCCRoadTypelD
SCC Road Type Description
11
Rural Interstate
13
Rural Principal Arterial
15
Rural Minor Arterial
17
Rural Major Collector
19
Rural Minor Collector
21
Rural Local
23
Urban Interstate
25
Urban Freeway/Expressway
27
Urban Principal Arterial
29
Urban Minor Arterial
31
Urban Collector
33
Urban Local
These fractions are joined with the table that contains the VMT by HPMS vehicle type, SCC vehicle type and
MOVES road type to allocate the VMT in each row to one of the 12 SCC road types. The VMT can now be
aggregated to sum the VMT in each SCC vehicle type and road type category across all HPMS vehicle types and
MOVES road types to provide VMT by the full SCC (vehicle type and road type) for each county. In those cases
where the SCCRoadTypeDistribution table was not provided, the default MOVES distributions were used.
Calculating VMT by SCC from Default VMT Estimates
In cases where states did not provide CDBs, VMT by SCC was estimated from a nationwide set of county-specific
VMT estimates developed by a contractor for EPA (see Section 4.6.2.3). The default VMT values by county are
by HPMS vehicle type and by the 12 road types used in the HPMS. The road types used by HPMS are the same
road types used in the SCC. Since only default SCC vehicle type distributions and road type distributions were
used, the fractions to allocate the HPMS vehicle type VMT to the SCC categories was done in a single step using
fractions developed from the national MOVES run for calendar year 2011. These fractions were applied to VMT
for each HPMS vehicle type and SCC road type combination in each county. The distribution from HPMS vehicle
type and road type to SCC was the same in all counties in cases where states did not provide CDBs.
The average speeds provided to SMOKE for each county were derived from the default national average speed
distributions found in the default MOVES2010b database AvgSpeedDistribution table. These average speeds are
the average speeds developed for the previous EPA highway vehicle emission factor model, MOBILE6. EPA used
the MOVES distribution of average speeds for each hour of the day for each road type to calculate an overall
average speed for each hour of the day. The default EPA average speed data does not vary by source type
(vehicle type). These hourly speeds were used to create the speed profile (SPDPRO) input for SMOKE. In
addition, these hourly average speeds were weighted together using the default national average hourly VMT
distribution found in the MOVES default database HourlyVMTFraction table, to calculate an average speed for
each road type. This average speed by road type was provided to SMOKE for each county as the speed
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inventory19. In addition to state supplied speeds in MOVES CDB format, two states supplied data already
converted into SCCs: Virginia supplied a state-wide speed inventory and Georgia supplied a speed profile
(SPDPRO) for the greater Atlanta area.
SMOKE also requires vehicle population (VPOP) estimates for each county by SCC vehicle type. The VPOP values
were derived from the state supplied MOVES inputs or the EPA defaults (see Sections 4.6.2.2 and 4.6.2.3,
respectively). The process of converting MOVES vehicle types into SCC vehicle types is similar to the process for
VMT described above20. In addition to state supplied VPOP in MOVES CDB format, two states provided data
already translated into SCCs: Virginia and Georgia.
The MOVES MySQL databases that include the VMT and vehicle population used for the representative counties
are listed in Section 4.6.3.2. The SMOKE input VMT, vehicle population, speed data, and hourly speed profiles
used to estimate emissions for every county are available in the files "2011nei_supdata_or_VMT.zip",
"2011nei_supdata_or_VPOP.zip", "2011nei_supdata_or_Speed.zip", and "2011nei_supdata_or_SpdProf.zip"
(see Section 4.6.5 for access information).
4,6,3,7 Extended idle adjustments
The extended idling of long-haul trucks are a subset of the exhaust emissions. Although the NEI only reports
total exhaust emissions (emis_type = E), the development of the emissions tracks extended idle separately and
sums it with other forms of exhaust to create total exhaust. These extended idle emissions are typically from
trucks which have traveled across county and state boundaries. Federal rules require that truck drivers may not
drive more than 10 hours without rest. These long-haul trucks are known to stop for these rest periods at truck
stops along their routes and idle21 their trucks for hours while they rest. The MOVES model generates an
estimate of the total number of extended idling hours and emissions for every county. However, when MOVES
is run using the County scale, the extended idling rate (in grams per hour per truck) is not adjusted to account
for allocation of the extended idling to counties where interstate travel occurs.
MOVES calculates the total number of hours nationally that long haul heavy duty diesel trucks
(SourceTypelD=62) are expected to spend stopped at rest areas, truck stops and other parking locations during
operation based on the estimated number of miles traveled by this type of vehicle and average speeds. Long
haul trucks typically travel interstate, so individual counties or even states cannot adequately account for the
overall behavior of these vehicles. Instead, MOVES calculates the total hours of extended idling in the nation
and these total hours of extended idle are allocated to each county in each state.
A national estimate of total long haul truck extended idle hours is estimated from the total hours of long haul
truck operation calculated from total long haul truck vehicle miles traveled. The total national hours of
extended idle operation are a function of these total operation hours. The extended idle hours are allocated to
hours of the day based on the distribution of vehicle miles traveled to hours of the day for these long haul
19 The speed inventory has annual average and monthly average speed by county/SCC. It is in SMOKE FF10 format.
20 For VPOP, there is no need to allocate to road type because all vehicle population for SMOKE is characterized as off-
network (SCC3 390).
21 For this section, all references to "idle" should be assumed to be extended idle from long haul trucks. This does not
include idling from other types of trucks and/or other types of idling (e.g. buses idling outside schools).
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trucks. The extended idle hours are then distributed to counties using estimates of demand for truck parking by
state and the activity on interstate highways in counties.
The total hours of extended idling calculation begins with a calculation of the distribution of the number of trips
that begin in each hour of the day. By federal law, long haul truck drivers are only allowed to travel 10 hours
without a break, so all trips are assumed to be 10 hours long.
For each hour of the day, the calculation determines the number of trips that end in that hour. This is the same
as the number of trips that started 10 hours earlier and the number of trucks that are eligible to begin extended
idling in that hour. The hours of extended idling in that hour is the number that begin in that hour, plus the
number that began in the previous hour, plus the number that began in the hour before that, and so on, until all
of the idling is accounted for.
In the sample of trucks used for this calculation, the distribution of trip starts vs trip ends over the hours of the
day is shown in Table 134.
Table 134: Distribution of Trip Starts and Ends
Hour of the day
trip starts
trip ends*
1
78
171
2
76
167
3
65
144
4
94
98
5
107
71
6
131
73
7
194
71
8
230
52
9
279
85
10
267
48
11
275
78
12
240
76
13
201
65
14
211
94
15
171
107
16
167
131
17
144
194
18
98
230
19
71
279
20
73
267
21
71
275
22
52
240
23
85
201
24
48
211
* Assumes all trips are 10 hours long.
The distribution calculated above by this method is similar to the behavior observed in the study, "Effects of
Heavy-Duty Diesel Vehicle Idling Emissions on Ambient Air Quality at a Truck Travel Center and Air Quality
Benefits Associated with Advanced Truck Stop Electrification Technology," a dissertation presented for the
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Doctor of Philosophy Degree at the University of Tennessee, Knoxville, for Guenet Tilahun Indale (May 2005) [ref
2], This study observed the trucks parking at the Petro truck travel center located at the 140/175 and Watt Road
interchange performed between mid-December 2003 and August 2004.
Not all trucks idle for the same amount of time. Another study, shown in Table 135, provided the distribution of
idle time per stop for long haul trucks:
Table 135: Distribution of Length of Extended Id
Idle Time (hours)
Fraction of time
2
0.227
4
0.135
6
0.199
8
0.191
10
0.156
12
0.057
14
0.014
16
0.021
Total
1.000
Using these fractions, EPA then calculates an estimate of the fraction of all trip ends in each hour to determine
the number of trucks that begin extended idling in each hour and the number still idling in any hour of the day.
For each idle time bin (2, 4, 6, etc.), EPA calculates the number of trips of that length affecting that hour. For
example, the first bin (trucks idling 2 hours) is the sum of the number of trips ending in the current hour and
40% of the previous hour trips. The 40% value accounts for the fact that not all idling in this bin was exactly 2
hours long. This assumes that all trucks are idling, so this fraction must be further adjusted by the fraction of all
idling in that bin from the table above. Similarly, in the 4 hour bin, sum the number of trips ending in the
previous four hours, but reduce the trip ends from 4 hours ago by 40%. These values represent the trucks that
may be idling in the current hour as the result of trips ending in recent hours. EPA then performs similar
calculations for all of the idle time bins for each hour of the day. After the results in each bin have been
weighted by the fraction of all idling that fall into the bin using the above table, the sum is the total number of
extended idle hours calculated for this sample in each hour of the day.
In order to use this number in MOVES, EPA calculates fractions that relate the extended idle hours to the hours
operating calculated using vehicle miles traveled. The extended idle hours in each hour of the day are divided
by the total vehicle operation hours based on travel from the sample. These fractions are the percent of total
operating hours spent idling (long haul heavy duty diesel combination trucks only) in that hour. The resulting
fractions of total source hours operating (SHO) are shown in Table 136.
Table 136: Fraction of Operating Hours Spent Idling
Hour
Fraction of SHO
1
0.037
2
0.036
3
0.034
4
0.031
5
0.027
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Hour
Fraction of SHO
6
0.024
7
0.021
8
0.018
9
0.017
10
0.015
11
0.014
12
0.013
13
0.013
14
0.013
15
0.014
16
0.015
17
0.019
18
0.022
19
0.027
20
0.031
21
0.035
22
0.037
23
0.038
24
0.038
The total number of extended idle hours calculated using the method above amounts to a value about 59% of
the total number of vehicle operation hours during travel measured in the sample. Driving schedules for heavy
duty trucks do not include extended idle behavior, so extended idling hours are in addition to the hours
accounted for by measurements of vehicle miles traveled. The MOVES model calculates the total hours of
operation based on the vehicle miles traveled and the average speeds. These fractions are then used to
calculate the additional hours of extended idle operation that occurs in each hour of the day. The same hourly
allocation is used for all counties.
The above national calculations assume that all of the idling occurs somewhere in the nation, but does not
determine where the trucks will stop and idle. It was necessary to take the total extended idle times calculated
nationally and allocate them to states and then to counties.
The state allocations were obtained using data from the technical report, "Study of Adequacy of Commercial
Truck Parking Facilities" (FHWA-RD-01-158, March 2002) written by Stephen A. Fleger, Robert P. Haas, Jeffrey W.
Trombly, Rice H. Cross III, Juan E. Noltenius, Kelley K. Pecheux and Kathryn J. Chen of the Federal Highway
Administration Office of Safety Research and Development (HRDS) [ref 3],
This report documents the findings of a study to investigate the adequacy of commercial truck parking facilities
serving the National Highway System (NHS). Table 7 of the report, "Commercial truck parking demand," details
peak hour demand along interstates and other NHS routes carrying more than 1,000 trucks per day in calendar
year 2000. Using these values, EPA calculated the distribution of demand across states, shown Table 137.
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Table 137: Distribution of Demanc
FIPS Code
State
Rest Stop
Truck Stop
Total
Distribution
1
Alabama
1,634
5,473
7,107
0.024736
2
Alaska
25
88
113
0.000393
4
Arizona
1,052
3,523
4,575
0.015923
5
Arkansas
1,783
5,968
7,751
0.026977
6
California
4,539
15,183
19,722
0.068642
8
Colorado
760
2,546
3,306
0.011506
9
Connecticut
616
2,060
2,676
0.009314
10
Delaware
206
694
900
0.003132
11
DC
0
0
0
0.000000
12
Florida
1,694
5,665
7,359
0.025613
13
Georgia
2,188
7,324
9,512
0.033106
15
Hawaii
0
0
0
0.000000
16
Idaho
734
2,462
3,196
0.011124
17
Illinois
3,338
11,172
14,510
0.050502
18
Indiana
4,299
14,400
18,699
0.065082
19
Iowa
688
2,302
2,990
0.010407
20
Kansas
566
1,907
2,473
0.008607
21
Kentucky
2,206
7,380
9,586
0.033364
22
Louisiana
2,060
6,910
8,970
0.031220
23
Maine
205
691
896
0.003119
24
Maryland
592
1,983
2,575
0.008962
25
Massachusetts
863
2,894
3,757
0.013076
26
Michigan
1,275
4,262
5,537
0.019271
27
Minnesota
872
2,925
3,797
0.013215
28
Mississippi
1,254
4,194
5,448
0.018962
29
Missouri
2,643
8,841
11,484
0.039970
30
Montana
462
1,550
2,012
0.007003
31
Nebraska
251
837
1,088
0.003787
32
Nevada
682
2,285
2,967
0.010327
33
New Hampshire
72
243
315
0.001096
34
New Jersey
457
1,528
1,985
0.006909
35
New Mexico
1,218
4,083
5,301
0.018450
36
New York
1,801
6,034
7,835
0.027270
37
North Carolina
1,270
4,262
5,532
0.019254
38
North Dakota
188
635
823
0.002864
39
Ohio
3,301
11,059
14,360
0.049980
40
Oklahoma
1,078
3,610
4,688
0.016317
41
Oregon
1,139
3,819
4,958
0.017256
42
Pennsylvania
2,360
7,903
10,263
0.035720
44
Rhode Island
167
566
733
0.002551
45
South Carolina
1,265
4,236
5,501
0.019146
46
South Dakota
199
666
865
0.003011
47
Tennessee
1,214
4,073
5,287
0.018401
48
Texas
8,305
27,797
36,102
0.125653
for Overnight Parking by State
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FIPS Code
State
Rest Stop
Truck Stop
Total
Distribution
49
Utah
391
1,307
1,698
0.005910
50
Vermont
27
91
118
0.000411
51
Virginia
1,772
5,932
7,704
0.026814
53
Washington
815
2,724
3,539
0.012317
54
West Virginia
468
1,572
2,040
0.007100
55
Wisconsin
633
2,115
2,748
0.009564
56
Wyoming
440
1,475
1,915
0.006665
72
Puerto Rica
0
0
0
0.000000
78
Virgin Islands
0
0
0
0.000000
TOTAL
66,067
221,249
287,316
1.000000
The total number of extended idle hours over the nation are allocated to each state based on the distributions
of demand shown above. The state specific number of extended idle hours are further allocated to individual
counties within the state using EPA estimates of the total vehicle miles traveled (VMT). EPA starts with the VMT
for long haul heavy duty diesel combination trucks (SourceTypelD=62) on rural or urban interstate roadways in
each county and divide it by the state-wide VMT on rural or urban interstates, respectively, to get a distribution
within the state. The distribution of interstate VMT by long haul diesel combination trucks within each state is
used to distribute the portion of total national extended idle hours that has been allocated to the state.
This methodology predicts no extended idling hours in Washington D.C., Hawaii, the Virgin Islands or Puerto
Rico. This is to be expected on the islands, since no overnight trips are needed and there is little opportunity to
park overnight in Washington DC.
When running MOVES using the County scale (inventory mode), the hours of extended idle are calculated from
the source hours operating (SHO) calculated for the long haul heavy duty diesel combination trucks as described
above. However, the allocation used to scale the total hours to counties is not applicable. Instead, the total
amount of extended idle calculated for the SHO in the county is assumed to result in extended idle hours in that
county, without any allocation adjustments. This will conflict with estimates made using the National scale,
which account for truck parking demand and the presence of interstate VMT by long haul heavy duty diesel
combination trucks. Currently, these adjustments will need to be done manually outside the model for any
MOVES runs using County scale.
For air quality analysis, the 2011 NEIvl, and other purposes emissions are calculated through SMOKE-MOVES
instead of the inventory mode. As discussed earlier, MOVES results for extended idle using the County scale are
not the same as the extended idle estimates made by MOVES at National scale, since only VMT for a single
county is available, and the national allocation adjustment is not properly applied. In addition, even if the
extended idle emissions for the representative county were correct, it would not be proper to use the same
extended idle emission rates for all of the counties being represented, since the amount of extended idle will
vary by county and some counties without interstate highways would have no extended idle emissions.
This problem was addressed by developing an adjustment to the extended idle emissions calculated in the
representative counties that would appropriately adjust the extended idle emission rate to reflect the impact of
the national allocation factors. The emission rate for extended idle reported by MOVES has units of grams per
vehicle, where "vehicle" is all diesel long haul trucks and not just idling trucks. The extended idle emission rate
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is based on vehicle populations, so the adjustment factor is based on national vehicle populations by county and
will vary by calendar year.
MOVES stores the allocation factor used to allocate the national extended idle hours to counties in the Zone
table. The sum of the idleAllocFactor fractions is one (1.0) for all of the counties in the nation. Knowing the
national population and the population in a county, a new factor can be calculated which can be used to adjust
the grams per vehicle in the representative county for each county in the county group. The calculation is:
ElAdjust = (National Population/County Population)*idleAllocFactor
where:
ElAdjust = multiplicative factor for adjusting the extended idle grams per vehicle.
national population = total national population of diesel long haul trucks.
county population = population of long haul trucks in the county.
idleAllocFactor = extended idle allocation factor from MOVES Zone table.
The extended idle adjustment factor is calculated for each county and is used to calibrate the grams per vehicle
emission rate in each county before multiplying the emissions rate and the number of diesel long haul vehicles
in the county.
Inventory = Emission Rate (grams per vehicle) * EDAdjust * County Population
SMOKE has an optional input that adjusts emissions (CFPRO) by county, SCC, and mode. To account for the
extended idle adjustment, EPA created an adjustment file that applies these allocation factors (idleAllocFactor)
by county for extended idle and for the long-haul type SCCs (SCC7 2230073 and 2230074) only. This SMOKE
adjustment factor is available in the file "2011nei_supdata_or_Extldle.zip" (See Section 4.6.5 for access
information)
4.6.3.8 Run MOVES to create emission factors
EPA used the SMOKE-MOVES driver scripts to run MOVES for each of the representative counties, fuel-months,
and the listed temperatures and temperature profiles. 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 each
representative county. The SMOKE-MOVES driver scripts resulted in three emission factors (EF) tables for each
representative county and fuel month: rate per distance (RPD), rate per vehicle (RPV), 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:
http://www.cmascenter.Org/smoke/documentation/3.5.l/html/ch05s02s04.html.
4.6.3.9 Run SMOKE to create emissions
Lastly, EPA generated air quality model ready emissions at a gridded and hourly resolution. The Movemrg
SMOKE-MOVES program performs this function by combining activity data, meteorological data, and emission
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factors to produce gridded, hourly emissions. EPA ran Movesmrg for each of the three sets of emission factor
tables (RPD, RPV, and RPP). During the Movesmrg run, the program used the hourly, gridded temperature (for
RPD and RPV) or daily 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 on-network emissions. This includes the following modes: vehicle
exhaust, evaporation, evaporative permeation, 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)22. 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 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 off-network emissions. This includes the following modes: vehicle
exhaust, extended idle exhaust, evaporative, and evaporative permeation. 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. For extended idle, an additional adjustment is applied
to account for the allocation of these emissions across county and state boundaries. Movesmrg reads in the
adjustment file (CFPRO, see Section 4.6.3.7) and multiples the extended idle specific emissions by the
appropriate adjustment factor (by county, SCC, and mode).
The emission process RPP is for modeling the off-network emissions for parked vehicles. 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 three processing streams (RPD, RPV, and RPP). The results include emissions for every
county in the continental U.S., rather than just for the representative counties.
22 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.
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4.6,3,10 Post-Processing to Generate Annual Inventory
For the purposes of the NEI, EPA needed emissions data by county, SCC, pollutant, and emission type (exhaust,
evaporative, brake wear, and tire wear)23. EPA developed and used a set of scripts to combine the emissions
from the three 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"24.
4,6,4 EPA-developed 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 for all months of the year using the inventory scale mode
of the MOVES model. This approach directly calculates the inventory in each month using the inputs provided in
each of the county databases.
The MOVES inputs used for these emissions are available as described in the file "AKHIPRVI_Counties.zip"
contains the MOVES county database manager databases, and the file
"2011nei_supdata_or_AKHIPRVI_Runspecs .zip" contains the run specifications used to run MOVES. Lastly, the
file "2011nei_supdata_or_akhiprvi_temperatures.zip" contains the MySQL database containing the tables that
describe the temperatures and relative humidity values used for these states and territories. Supplemental
Input Data for MOVES2010b, used for the 2011NEI are listed in Table 138 and are available at
ftp://ftp.epa.gov/Emislnventorv/2011/doc.
Table 138: Supplemental Input Data for MOVES2010b for 2011NEI
Contents
Name for posting
1
MOVES county database inputs. Includes all
agency submittals thru EIS, but not all
subsequent revisions and EPA replacements
(e.g., fuel and met)
2011nei_supdata_or_CDB.zip
23 EPA ran SMOKE-MOVES at a more detailed level of emission modes (e.g. evaporative permeation and extended idle) and
combined these detailed emission modes to create the 4 emission types.
24 The corresponding EMF datasets are 2011ec_NEIvl_onroad_norfl_SMOKE-MOVES_2010b_forNEI (v2) and
2011_NEIvl_onroad_AK_HI_PR_VI_MOVES_2010b_forNEI (vO). Although SMOKE-MOVES estimated refueling, these
emissions are in a separate EMF dataset and are in the EIS as part of the nonpoint estimates.
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Contents
Name for posting
2
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.
2011nei_supdata_or_RepCnty.zip
3
MOVES county databases (CDBs) for the
representative counties. These CDBs include all
agency submittals through EIS in addition to
subsequent revisions and EPA replacements.
2011nei_supdata_or_CDB_164RepCnty.zip
4
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.
2011nei_supdata_or_VPOP.zip
5
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.
2011nei_supdata_or_VMT.zip
6
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.
2011nei_supdata_or_Speed.zip
7
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.
2011nei_supdata_or_SpdProf.zip
8
Extended idle adjustments factors to reallocate
extended idle emissions. Factors are by county,
heavy duty diesel vehicle SCCs (SCC7 2230073
and 2230074), and extended idle mode only
and covers every county in the continental US.
Data is a control factor file (CFPRO) for SMOKE-
MOVES
2011nei_supdata_or_Extldle.zip
9
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.
2011nei_supdata_or_RegFuel.zip
10
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.
2011nei_supdata_or_CALEV.zip
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Contents
Name for posting
11
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.
2011nei_supdata_or_FuelCR.zip
12
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.
2011nei_supdata_or_VMTsrpt.zip
13
The MOVES2010b run specifications (runspecs)
for the representative counties. This is for
running MOVES in emissions rate mode (for
SMOKE-MOVES).
2011nei_supdata_or_RepCounty_Runspecs.zip
14
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
2011nei_supdata_or_RepCounty_temperatures.zip
15
The MOVES2010b run specifications (runspecs)
for all counties in Alaska, Hawaii, Puerto Rico
and the Virgin Islands. This is for running
MOVES in inventory mode
2011nei_supdata_or_AKHIPRVI_Runspecs.zip
16
The temperature and relative humidity values
for all counties in Alaska, Hawaii, Puerto Rico
and the Virgin Islands necessary for running
MOVES in inventory mode.
2011nei_supdata_or_akhiprvi_temperatures.zip
4,6,5 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
4.6.2.2). The following is a list of the more significant checks and resulting corrections:
• Checked the VMT data by comparing the 2011 with 2008 NEIv3 based activity data. Compared the VMT
at various resolutions including: state, county, vehicle type (SCC7), and road type (ending SCC3). Also
analyzed the ratio of VMT to vehicle population to look for extreme values.
• Checked the VMT data by comparing the 2011 NEIvl with 2011 draft based activity data. Compared the
VMT at various resolutions including: state, county, vehicle type (SCC7), and road type (ending SCC3).
Also analyzed the ratio of VMT to vehicle population to look for extreme values.
• Checked the VMT data by comparing the 2011 NEIvl (state supplied) with 2011 NEIvl (default) for those
states that submitted activity data. Compared the VMT at various resolutions including: state, county,
and road type (ending SCC3).
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• Checked the VPOP data by comparing the 2011 with 2008 NEIv3 based activity data. Compared the
VPOP at various resolutions including: state, county and vehicle type (SCC7).
• Checked the VPOP data by comparing the 2011 NEIvl with 2011 draft based activity data. Compared
the VPOP at various resolutions including: state, county, and vehicle type (SCC7).
• 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.
• Identified one county in Virginia with unreasonable VMT (FIPS 51103). Virginia supplied updated VMT
and VPOP which was included in the final run of 2011 NEIvl.
• Identified six counties in Utah that had no VMT on any of their rural roads, FIPS 49003, 49011, 49035,
49045, 49049 and 49057. Augmented the state supplied VMT with the EPA default VMT for those rural
road types.
• Compared the on-road results to similar results from the previous version of the 2011 NEI draft. As
expected, found numerous differences between the two sets of results. Detailed comparisons by state,
county and SCC vehicle type showed that most of the differences were due to updated input data from
the states or due to differences between how the two models were run in terms of representative
counties.
• Compared the 2011 NEI vl emissions with a similar run done for 2008 NEIv3 using 2008 inputs.
Compared the results at various resolutions including: state, county, fuel type (SCC3) and vehicle type
(SCC7). Additionally, compared the results using difference maps both at the county level and gridded
(after spatially allocating the emissions to grid cells using SMOKE).
• Air toxic results were quality assured by back-calculating toxics ratios from inventory outputs to ensure
they were consistent with inventory inputs.
• 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.
4,6,6 Summary of quality assurance methods on emissions once selected to 20'ilNEIvl
Onroad emissions came from EPA estimates exclusively, except in CA and TX.
California emissions estimates were used without any merging with EPA data. Because of mapping differences,
the SCCs used in CA are not consistent with those in the rest of US.
Texas onroad emissions may have been overestimated by adding EPA-estimated emissions data to SCCs not
reported by Texas. Texas provided CDBs for EPA estimates, but did they did not include VMT for every county.
When EPA applied default VMT for those counties, County/SCC combinations that were not in TX emissions
submittal were created, and then added to TX's in the selection process that merged EPA and TX emission
estimates. 45 pollutants impacted. Potential overestimates for some key pollutants are as follows: 20 tons
formaldehyde (out of 2,685 total); 20 tons benzene (out of 3,716 tons total); 12 tons acetaldehyde (out of 1,693
tons total);17,837 tons CO (out of 1.8 million tons total); 134 tons NH3 (out of 8,801 tons total), 5,657 tons NOx
(out of 474,137 tons total); 275 tons PMio (out of 21,823 tons total); 205 tons PM2.5 (out of 16,297 tons total); 35
tons S02 (out of 2,022 tons total); 849 tons VOC (out of 149,235 tons total).
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4,6,7 References for On-road vehicles
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, available at http://www2.mmm.ucar.edu/wrf/users/docs/arw v3.pdf
2. Guenet, T.I., et al., University of Tennessee -Knoxville, May 2005, Effects of Heavy-Duty Diesel Vehicle
Idling Emissions on Ambient Air Quality at a Truck Travel Center and Air Quality Benefits Associated with
Advanced Truck Stop Electrification Technology, available at
http://trace.tennessee.edu/utk graddiss/2085/
3. Fleger, S.A., et al., Federal Highway Administration, March 2002, FHWA-RD-01-158, Study of Adequacy
of Commercial Truck Parking Facilities, available at
http://www.fhwa.dot.gov/publications/research/safetv/01158/index.cfm
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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 3.30) 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 3.26), 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 Wildfir cribed 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 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 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 to estimate wild land fire emission estimates.
Significant improvements were made from 2005 to 2008 to SF2 as documented in the 2008 NEI TSD. In going
from 2008 to 2011, smaller improvements and refinements were made to the SF2 system as outlined in Reid [ref
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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 139 lists the SCCs that define the different types of WLFs in the 2011 NEI, both for EPA data and for S/L/T
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 data for the 2011 NEI considers all SCCs that define any one type of fire and
appropriately combines emissions from those SCCs.
Table 139: 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 just Georgia, 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 EIS.
S/L/T agency data were received in event format from only one agency (GA) as listed in Table 140.
Table 140: Agency that submitted wildfire and prescribed burning emissions data
Agency
Agency
Type
Rx provided
Wildfire
provided
Georgia
State
as event
as event
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 141 shows the selection hierarchy for the wildfire and Rx burning sectors.
Table 141: 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
There were no overlapping data in the above datasets. Georgia was excluded from the 2011EPA_Event dataset
and the State/Local/Tribal Data contained only Georgia.
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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 way we blended these emissions is
summarized in above. As discussed above, only GA reported wildfire and prescribed fire emissions to the NEI in
2011. GA 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 Raffuse [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/Bluesky process:
http://getblueskv.org/smartfire/. 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 142 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
submitted their own emissions data. GA used the same FEPS system as EPA did to estimate all the CAP
emissions. EPA sent to GA 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 did not submit C02 nor CH4 (GHGs) so these pollutants are not available in GA.
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Table 142: Po
lutants estimated by EPA* for wildland fires and HAP emission factors
Pollutant
HAP Emission factor
(lb/ton fuel consumed)
PM2.5
PM10
CO
CO2
cm
N/A
NOx
NHs
S02
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
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
246
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along with other traditional or new fire data sets at national or regional scales25 to reconcile the various fire
information data sets. The NFEI data are available in EIS.
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. (http://www.airsci.com) 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 18 shows the
states that submitted fire activity data and identifies states that provided usable data and states covered by the
FETS data set.
Figure 18: The coverage of state-submitted fire activity data sets
-WA'
ND
MN
•or:
wi
SD
.WY-
NE
OH
;nv.
UT
iWV
.CO-
CA
VA
KS
MO
KY
TN
OK
:az'
AR
:nm
sc
AL \ GA
MS
TX
LA
FL
J 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 the 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
recommended removing these prescribed burns from the final 2011 NFEI since HI submitted these
emissions as part of their nonpoint agricultural fires.
25 Additional details on these other data sets are provided in the "Other Data Sources" section that follows.
247
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• 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 http://satepsanone.nesdis.noaa.gov/FIRE/fire.html. 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, STI 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 (https://fam.nwcg.gov/fam-web/sit/). .
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 (https://fam.nwcg.gov/fam-web/).
Forest Service Activity Tracking System (FACTS) fire information data were supplied by the USFS.
GeoMACfire perimeter data were downloaded via the USGS GeoMAC wildland fire support website
(http://rmgsc.cr.usgs.gov/outgoing/GeoMAC/).
Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data were downloaded via the
USFS Remote Sensing Applications Center website (http://activefiremaps.fs.fed.us/gisdata.php).
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 (FCCS) 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 (http://www.fs.fed.us/pnw/fera/fccs/maps.shtml).
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],
248
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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 143 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 143: 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.
249
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A caveat to add to the 2011 NEI data is 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 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 this version of the 2011
NEI, 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 2011 NEI.
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, which submitted their own data. These
data also reflect the changes made to the Florida data as detailed above. These data thus reflect what is in the
NEI for wild and prescribed fires.
First, Figure 19 shows the proportion of acres burned for each type of fire by state. In the West, there are more
wildfires than in the East, 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. In the 2011 NEI, there are an estimated 22.7 million acres burned
from prescribed and wildfires. Of these 22.7 million acres, about 12.5 million are estimated to be prescribed
fires and the remaining 10.2 million acres wildfires.
250
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Figure 19: Proportion of Fires by Type using EPA Methods
Legend
Acres Burned_2011_Forest Fire Type_,
Sum of Fields
| Wildfires
1 Prescribed
Figure 20 shows the total acres burned on a county-by-county basis. Active areas are seen in northern California
and in some southeastern parts of the US. Shown immediately below the "acres burned" map is Figure 21,
which shows PM2.5 emissions. In the 2011 NEI, there is an estimated total of 6.31 million tons of PM2.5
emissions. Of this total, 1.27 million is estimated to be from wildfires and about 921,000 tons from prescribed
fires. The total of 2.19 million tons of PM2.5 from these fires are mapped in Figure 21 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.
251
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Figure 20: Acres burned using EPA Methods
Legend
CountyFiies_2011 _AcresBumed
Sum Of area
0-2376
Hi 2377-6257
6258- 11260
11261 - 17766
17767 - 26650
I I 26651 - 39916
I I 39917 - 63408
I | 63409 - 93400
93401 - 181114
¦ 181115 - 2842376
Figure 21: 2011 PM2.5 Emissions using EPA methods
Legend
Cou nty F ires_2011_ P M2.5E i
SurrPM25E missions
HI : '200
201 -628
629-1350
1351 -2516
2517 - 4606
r 1 4607 - 84SD
I 1 8461 - 17498
I 31 17499 - 30828
30829 - 56485
H 564S8 - 131648
252
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1.5 Summary of quality assurance methods
WLFs' 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. 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 was 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.
We created, as an example, a difference map for PM2.s emissions, in which we took the difference on a
county by county basis of total (all sector) PM2.5 emissions density (per square mile) in the 2011 NEI to
PM2.5 emissions density 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.
That difference map is shown below in Figure 22. The areas identified in this map align well with known
areas of very high fire activity in 2011.
Figure 22: Difference map of PM2.5 Emissions, with and without large fires
PM25 Emissions DensityDiffeiencewwo Fires
Tons SqMi Difference
m 0.0 - 0.6
1 10.7 • 1.1
i 11.2 -1.8
j 1.9 - 2.9
¦ 3.0 - 190.1
253
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• As shown in Figure 23 below, we compared total mass of emissions (the sum of all WLFs) to past EPA
inventories which used SF2 to estimate emissions. This generally shows that all 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 still 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.
Figure 23: 2011 PM2.5 wild land fire emissions using EPA methods
2,500
g 2,000
rH 1,500
« 1,000
500
WF
RX
2007
2008
2009
2010
2011
Year
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.
2. Pollard E.K., Du Y., Raffuse 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. Raffuse, 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., Raffuse 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.
254
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6. Du Y., Raffuse 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 - Agrlct rning
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 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 below. 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 144. These other SCCs have more specific
details about the type of crop burned.
Table 144: Source Classification Codes 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
5,2,2 Sources of data overview ant! 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 145 lists the state and tribal agencies that submitted agricultural fire emissions.
255
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Table 145: Agencies that submitted agricultural fire emissions to the 2011 NEI
Agency
Agency Type
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
Kootenai Tribe of Idaho
Tribal
Louisiana Department of Environmental Quality
State
New Jersey Department of Environment Protection
State
Nez Perce Tribe
Tribal
Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho
Tribal
Washington State Department of Ecology
State
Coeur d'Alene Tribe of Idaho
Tribal
South Carolina Department of Health and Environmental Control
State
Oregon Department of Environmental Quality
State
Kansas Department of Health and Environment
State
Indiana Department of Environmental Management
State
Arizona Department of Environmental Quality
State
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 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 below 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 0, 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 146.
Table 146: 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
Table 12 for additional details.
2
256
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Dataset name
(Short Name provided if
different)
Description and Rationale for the Order of the Selected Datasets
Order
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 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 data in
order that the S/L/T agency HAP data are used first.
4
2011EPA_NP_NoOverla
p_w_Pt
(2011EPA_NP_NoOvrlp)
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 147 summarizes these CAP estimates by state. For example, total PM2.5 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.
257
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Table 147: 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
68,107.1
29,917.6
148,515.8
247,668.0
116,307.5
258
-------
As an example of data contained in the 2011 NEI for this sector, the PM2.5 emissions data in Table 147 are
combined (using the hierarchy discussed earlier) with the S/L agency submissions (excluding tribal) shown in
Table 145 and summarized in Figure 24 below. For this sector, the states in the upper Midwest (Minnesota,
North Dakota, South Dakota, and Kansas) as well as Louisiana and Arkansas all show high levels of emissions
compared to areas in the Northeast and Western US.
Figure 24: 2011 NEI state-total PM2.5 emissions from agricultural fires
Legend
StateAg F ir es_2 011 _P M? .5 E mission s
Agricultural Field Burning_NEI
°-
74 -185
^ 186-466
H 467 - 719
'] 720 - 977
| 978 - 2084
|2085- 3166
1 3167- 4668
1 4569- 10002
¦[ 10003 - 16839
Figure 25 below shows states that submitted agricultural burning data to the NEI, corresponding to the list
shown in Table 145. States in blue submitted some data to the NEI for this sector, while states in red submitted
none and were reliant on emission estimates based on EPA methods. For the states in yellow (all LADCO states,
except for MN), 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. 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).
259
-------
Figure 25: States that submitted agricultural burning emissions to the NEI
Legend
Submittals I Estimates for Ag Fires_2011
AgFires_EPA/State
EPA
| State
| | 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 version, 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
(http://earthdata.nasa.gov/data/nrt-data/firms/active-fire-data) were also used for visual comparison with the
260
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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
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. Information and data can be found here at
http://nassgeodata.gmu.edu/CropScape/. Users of these emission estimates should note that Conservation
Reserve Program (CRP: http://www.fsa.usda.gov/FSA/webapp?area=home&subiect=copr&topic=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 provided by Dr. Tesh Rao at the U.S. EPA. With the aid of a flow diagram,
Figure 26 shows the overall geospatial method for producing the remote sensing-based cropland emission
estimates.
Figure 26: 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 dNBR for temporally consecutive MODIS pairs:
NBR = (R2-R7)/(R2 + R7);
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)
261
-------
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
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 (http://www.exelisvis.com/), the MODIS
Reprojection Tool (MRT: https://lpdaac.usgs.gov/tools/modis reproiection tool), and Arc Python within ESRI
ArcGIS (http://resources.arcgis.com/en/communities/python/).
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, and Ohio) based on the emission
estimates submitted by the State of Indiana. In comparing the Indiana estimates to EPA estimates, it was
discovered that the EPA estimates were unrealistically high, and information from local agencies in the Indiana
vicinity (LADCO report, Private communication with Region 5 emissions inventory staff) indicated that EPA
estimates are likely too high. Because we only received state-submissions from Indiana, a ratio of the state
submitted emissions for Indiana to the estimates developed by EPA were applied to the other states listed
above. For MN, we had confirmation from Minnesota staff that EPA-based estimates were accurate so we did
not alter those emissions. This ratio approach led to a reduction of between 90-96% of emissions for Wisconsin,
Michigan, Ohio, and Illinois. These changes are reflected in the results shown in Table 147 and in Figure 24.
Figure 27 below shows the resulting PM2.5 emissions for the lower 48 states based on these EPA methods (it can
be compared to Figure 24 which is a combination of EPA results and state submitted data).
262
-------
Figure 27: 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 P Adat a
PM25AgBumE PA
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 145.
5.2.5 Summary of quality assurance me thods
• 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 NEI. 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
263
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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 145), 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 SLTs submitted too many different SCCs (see Table 144) 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 28 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 28 confirms this. Further,
the figure shows the highest emissions in states known to have significant cropland burning activity.
Figure 28: Comparison of percentage of PM2.5 emissions assigned to agricultural, prescribed and wild fires
5.2.6 References for Agricultural 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.
264
Legend
StateAgFireTypes_2011_PM2.5Emissions
Sum of Fields ¦ >
Agricultural Field Burning
Prescribed Fires
| Wildfires
-------
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.
265
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6 Biogenics - Vegetation and. Soil
Biogenic emission sources 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 148 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 148: Source classification codes for Biogenics - Vegetation and Soil
Source
Classification
Code
El Sector
SCC
Level
One
SCC
Level
Two
SCC Level
Three
SCC Level
Four
Tier 1
Description
Tier 2
Description
Tier 3
Description
2701200000
Biogenics -
Vegetation
and Soil
Natural
Sources
Biogenic
Vegetation
Total
Natural
Resources
Biogenic
Vegetation |
!
j
2701220000
Biogenics -
Natural
Biogenic iVegetation/ (Total
Natural
Biogenic
Vegetation j
Vegetation
Sources
Agriculture
Resources
and Soil
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 3.14
(BEIS3.14) model within SMOKE. The BEIS3.14 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 other
biogenic species except CO, NO, SESQ. Mapping of BEIS pollutants to NEI pollutants is as follows:
266
-------
• NO maps to NOx
• FORM maps to formaldehyde;
• ALD2 maps to acetaldehyde;
• MEOH maps to methanol;
• VOC is the sum of all other biogenic species except CO, NO, SESQ.
The BEIS3.14 model is described further in:
http://www.cmascenter.org/conference/2011/slides/pouliot tale two cmas08.ppt
The inputs to BEIS include:
• Temperature data at 2 meters which were obtained from the WRF input files to the air quality model,
• Land-use data from the Biogenic Emissions Land use Database, version 3 (BELD3). BELD3 data provides
data on the 230 vegetation classes at 1-km resolution over most of North America. These data are
available at http://www.epa.gov/ttnchiel/emch/biogenic/.
6.2 Sources of data overview and selection hierarchy
The only source of data for this sector is the EPA-estimated emissions from BEIS3.14. States are neither
required nor encouraged to report emissions, and no state has done this. The name of the EPA dataset in 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 is available at
http://www.epa.gOv/ttn/chief/emch/index.html#2011.
Table 149 shows state emissions summaries for the biogenic emissions sector and the contribution of biogenics
to the total inventory 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 N0X.
More detailed summaries of the BEIS model species at county level and monthly are available as a supporting
summary on the 2011 web page (ftp://ftp.epa.gov/Emislnventory/2011/2011 biogenic reports.zip).
Table 149: State Summary of Biogenics - Vegetation and Soil Emissions (short tons/year)
formaldehyde
methanol
acetaldehyde
CO
NOx
VOC
State
biogenics
(tons)
percent
of tota1
biogenics
(tons)
percent
of tota 1
biogenics
(tons)
percent
of total
biogenics
(tons)
percent
of total
biogenics
(tons)
percent
of total
biogenics
(tons)
percent
of total
AL
24,601
69%
89,095
91%
18,040
86%
172,499
10%
11,415
3%
1,682,519
82%
AR
22,490
67%
82,504
95%
16,492
85%
157,639
10%
19,060
8%
1,303,104
80%
AZ
51,351
75%
221,217
98%
37,656
92%
359,527
13%
18,596
7%
1,769,969
78%
CA
56,359
70%
203,816
98%
41,329
84%
394,658
9%
36,558
5%
2,552,499
75%
CO
21,814
74%
79,259
95%
15,996
87%
152,726
10%
26,089
8%
841,461
63%
CT
1,073
53%
2,733
56%
787
59%
7,524
2%
456
1%
48,071
37%
DC
21
16%
79
18%
16
20%
149
0%
16
0%
1,346
14%
DE
528
62%
2,005
87%
387
70%
3,703
2%
794
2%
27,737
51%
FL
32,733
64%
127,019
90%
24,004
82%
229,319
5%
33,994
5%
1,703,874
67%
267
-------
formaldehyde
methanol
acetaldehyde
CO
NOx
voc
State
biogenics
(tons)
percent
of tota1
biogenics
(tons)
percent
of tota 1
biogenics
(tons)
percent
of total
biogenics
(tons)
percent
of total
biogenics
(tons)
percent
of total
biogenics
(tons)
percent
of total
GA
30,130
62%
119,535
91%
22,095
82%
211,210
7%
18,304
4%
2,026,130
83%
IA
10,626
62%
43,198
96%
7,793
72%
74,456
8%
32,994
13%
337,518
64%
ID
23,908
76%
60,715
98%
17,532
91%
167,368
13%
11,754
11%
864,610
77%
IL
12,870
73%
52,826
87%
9,438
80%
90,196
5%
34,905
6%
479,020
56%
IN
8,216
75%
31,374
88%
6,025
79%
57,602
4%
20,063
5%
316,079
54%
KS
22,242
54%
101,091
98%
16,310
78%
155,758
9%
55,495
15%
599,015
57%
KY
11,407
71%
40,322
89%
8,365
84%
80,038
7%
14,783
4%
561,513
68%
LA
22,042
58%
85,431
90%
16,164
80%
154,508
6%
18,031
3%
1,314,747
66%
MA
1,657
49%
4,269
98%
1,215
56%
11,618
1%
939
1%
77,172
34%
MD
2,679
63%
9,166
85%
1,965
62%
18,788
2%
2,822
2%
150,670
54%
ME
9,778
92%
16,540
91%
7,170
93%
68,496
19%
1,943
3%
328,860
84%
Ml
12,616
70%
35,615
79%
9,252
75%
88,430
4%
13,932
3%
546,264
55%
MN
16,616
50%
49,585
93%
12,185
68%
116,455
5%
27,088
8%
770,780
61%
MO
19,816
62%
77,941
96%
14,531
79%
138,954
7%
28,311
6%
1,168,254
75%
MS
22,636
76%
82,927
94%
16,599
89%
158,705
15%
14,279
7%
1,485,664
85%
MT
32,663
77%
97,384
99%
23,952
92%
228,651
15%
42,065
26%
1,129,984
78%
NC
18,767
51%
66,914
88%
13,762
75%
131,579
3%
12,437
3%
1,113,082
59%
ND
9,929
65%
35,839
96%
7,281
78%
69,531
13%
32,938
18%
248,782
57%
NE
15,100
75%
63,927
98%
11,073
85%
105,745
16%
44,439
18%
449,183
79%
NH
2,358
80%
5,032
100%
1,729
83%
16,529
6%
459
1%
94,681
68%
NJ
2,025
52%
6,307
97%
1,485
60%
14,199
1%
1,539
1%
125,687
41%
NM
39,351
76%
173,337
99%
28,857
92%
275,468
17%
28,739
12%
1,295,424
75%
NV
27,862
93%
114,094
99%
20,432
97%
195,033
28%
9,496
9%
882,412
91%
NY
9,801
68%
25,265
67%
7,187
74%
68,725
3%
7,597
2%
339,306
45%
OH
8,649
66%
30,887
81%
6,343
70%
60,660
2%
16,475
3%
314,846
44%
OK
24,890
59%
106,566
96%
18,253
84%
174,382
9%
40,464
9%
1,079,843
63%
OR
29,794
67%
69,906
95%
21,849
87%
208,584
9%
11,130
7%
1,086,280
69%
PA
9,423
64%
27,132
77%
6,910
74%
66,103
3%
8,244
1%
432,095
53%
Rl
242
49%
626
50%
178
57%
1,698
1%
144
1%
12,358
36%
SC
14,251
76%
54,251
89%
10,450
86%
99,897
9%
8,872
4%
941,035
81%
SD
13,642
66%
55,153
99%
10,004
81%
95,535
12%
36,640
34%
428,859
74%
TN
13,879
79%
49,347
90%
10,178
86%
97,371
8%
12,929
4%
838,457
76%
TX
130,744
80%
602,237
97%
95,877
92%
915,571
14%
199,169
13%
4,252,162
66%
UT
20,528
92%
79,541
98%
15,054
96%
143,712
19%
8,527
4%
692,038
74%
VA
12,427
71%
41,680
88%
9,113
82%
87,151
6%
7,931
2%
761,044
72%
VT
2,378
84%
5,120
93%
1,744
86%
16,672
9%
1,001
5%
77,837
73%
WA
20,886
77%
41,336
89%
15,316
86%
146,219
8%
12,109
4%
624,247
67%
Wl
10,817
75%
35,435
87%
7,932
79%
75,831
5%
18,230
6%
493,604
64%
WV
5,837
74%
16,849
93%
4,280
87%
40,987
8%
2,791
2%
344,906
72%
WY
17,425
68%
63,285
99%
12,778
88%
121,982
10%
10,704
5%
638,249
68%
268
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7 Quality assessment
[This section will be included in future versions of this documentation]
7.1 What are the quality criteria used to assess the Inventory?
7.2 How did tl . i-J: : i<-\ compare to the quality criteria?
7.3 What EIS sectors seem to be Incomplete and for which key pollutants?
7.4 How can the quality of the emissions data be further evaluated by users?
7.5 What Improvements In the NEI and EIS submission process are planned for the
future?
269
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8 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 at:
ftp://ftp.epa.gov/Emislnventory/2011/doc/
or, on the 2011 webpage:
http://www.epa.gov/ttn/chief/net/2011inventory.html
270
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/D-20-003
Environmental Protection Air Quality Assessment Division June 2014
Agency Research Triangle Park, NC
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