United States Office of Air Quality
Environmental Protection Planning and Standards EPA-454/R-01-006
Agency Research Triangle Park NC 27711 March 2001
Air
PROCEDURES DOCUMENT FOR
NATIONAL EMISSION INVENTORY,
CRITERIA AIR POLLUTANTS
1985-1999
-------
-------
CONTENTS
Page
TABLES AND FIGURES xii
ACRONYMS AND ABBREVIATIONS xvii
SECTION 1.0
INTRODUCTION 1-1
1.1 WHAT IS THE NATIONAL EMISSIONS INVENTORY (NEI) PROCEDURES
DOCUMENT? 1-1
1.2 HOW IS THIS DOCUMENT ORGANIZED? 1-1
1.3 REFERENCES 1-2
SECTION 2.0
CHANGES IN METHODOLOGY 2-1
2.1 HOW DO CURRENT METHODOLOGIES RELATE TO PREVIOUS METHODS ? ... 2-1
2.2 1900 - 1939 METHODOLOGY 2-1
2.3 1940 - 1984 METHODOLOGY 2-1
2.4 REFERENCES 2-1
SECTION 3.0
FUTURE CHANGES TO THIS DOCUMENT 3-1
3.1 NEW FORMAT 3-1
3.2 EMPHASIS ON MOST CURRENT YEAR BEING DEVELOPED 3-1
SECTION 4.0
NATIONAL CRITERIA POLLUTANT ESTIMATES
1985 - 1999 METHODOLOGY 4-1
4.1 WHAT CATEGORIES AND POLLUTANTS ARE REVIEWED IN THIS SECTION? . . 4-1
4.1.1 What Significant Methodology Changes Have Occurred? 4-1
4.1.2 What Methodologies Does EPA Use to Develop Emissions Estimates? 4-2
4.1.3 References 4-3
4.2 FUEL COMBUSTION - ELECTRIC UTILITY 4-7
4.2.1 Which sources does EPA include in the Fuel Combustion - Electric Utility
category? 4-7
4.2.2 What emissions data for electric utilities are included in the Trends inventory? . . . 4-7
4.2.3 How does EPA develop emission estimates for fossil-fuel fired steam electric
utilities? 4-7
4.2.4 Where does EPA obtain the utility data necessary for emissions estimates? 4-8
4.2.4.1 What data does Form EIA-767 contain? 4-8
4.2.4.2 What information does FormEIA-759 provide? 4-9
4.2.5 How does EPA develop the necessary data not supplied by the EIA forms? .... 4-10
4.2.6 What EIA data have been replaced with data from other sources? 4-11
m
-------
4.2.7 How does EPA calculate ozone season daily emissions? 4-13
4.2.8 1998 projected fossil-fuel steam emission inventory 4-13
4.2.9 What additional emissions estimates adjustments does EPA make? 4-14
4.2.10 How does EPA perform its calculations? 4-14
4.2.11 References 4-15
4.3 INDUSTRIAL 4-25
4.3.1 What Source Categories Does the Industrial Sector Include? 4-25
4.3.2 What Information Does This Section Provide? 4-25
4.3.3 How did EPA Develop the 1990 Interim Inventory? 4-26
4.3.3.1 What Control Efficiency Revisions did EPA Make? 4-27
4.3.3.2 What Rule Effectiveness Assumptions did EPA Make? 4-28
4.3.3.3 What Emission Factor Changes Occurred? 4-28
4.3.3.4 What Emissions Calculations Did EPA Use? 4-29
4.3.3.5 For What Source Categories Did EPARevise VOC and SO2 Emissions? . . 4-30
4.3.3.6 How Did EPA Grow Point Source Emissions? 4-32
4.3.3.7 How Did EPA Grow Area Source Emissions? 4-32
4.3.4 How Did EPA Develop Emissions for 1985 to 1989? 4-33
4.3.5 What is the 1990 NET Inventory? 4-33
4.3.5.1 OTAG 4-33
4.3.5.2 Grand Canyon Visibility Transport Commission Inventory 4-37
4.3.5.3 AIRS/FS 4-38
4.3.5.4 Data Gaps 4-38
4.3.6 How Did EPA Develop Emissions for 1991 to 1994? 4-39
4.3.6.1 Grown Estimates 4-40
4.3.6.2 AIRS/FS 4-40
4.3.7 How were 1995 Emissions Prepared? 4-41
4.3.7.1 GrownEstimate 4-41
4.3.7.2 NOX RACT 4-41
4.3.7.3 Rule Effectiveness 4-42
4.3.7.4 Cotton Ginning 4-42
4.3.8 How Did EPA Develop the 1996 NET Inventory? 4-46
4.3.8.1 Grown Estimates 4-46
4.3.8.2 1996 VOC Controls 4-46
4.3.8.3 NOX Controls 4-49
4.3.8.4 How Did EPA Incorporate State/Local Emissions Inventory Data Into
the 1996 NET? 4-49
4.3.9 How Were Nonutility Point and Area Source Emissions Prepared for the 1997
through 1999 NET? 4-59
4.3.9.1 Growth Factors 4-59
4.3.9.2 Control Factors 4-61
4.3.10 References 4-61
4.4 OTHER COMBUSTION 4-120
4.4.1 What Source Categories Does the Other Combustion Sector Include? 4-120
4.4.2 What Information Does This Section Provide? 4-120
4.4.3 How did EPA Develop the 1990 Interim Inventory? 4-121
IV
-------
4.4.3.1 What Control Efficiency Revisions did EPA Make? 4-122
4.4.3.2 What Rule Effectiveness Assumptions did EPA Make? 4-123
4.4.3.3 What Emissions Calculations Did EPA Use? 4-123
4.4.3.4 How Did EPA Grow Point Source Emissions? 4-124
4.4.3.5 How Did EPA Grow Area Source Emissions? 4-125
4.4.4 How Did EPA Develop Emissions for 1985 to 1989? 4-125
4.4.5 What is the 1990 NET Inventory? 4-126
4.4.5.1 OTAG 4-126
4.4.5.2 Grand Canyon Visibility Transport Commission Inventory 4-129
4.4.5.3 AIRS/FS 4-130
4.4.5.4 Data Gaps 4-130
4.4.6 How Did EPA Develop Emissions for 1991 to 1994? 4-132
4.4.6.1 Grown Estimates 4-132
4.4.6.2 AIRS/FS 4-133
4.4.7 How were 1995 Emissions Prepared? 4-133
4.4.7.1 GrownEstimate 4-134
4.4.7.2 NOX RACT 4-134
4.4.7.3 Rule Effectiveness 4-134
4.4.8 How Did EPA Develop the 1996 NET Inventory? 4-134
4.4.9 Alternative Methodologies for Area Source Categories 4-135
4.4.9.1 Forest Fires/Wildfires 4-136
4.4.9.2 Prescribed/Slash and Managed Burning 4-138
4.4.9.3 Residential Wood 4-149
4.4.9 A SO2 andPM ResidentialNonwood Combustion 4-141
4.4.9.5 Structure Fires 4-142
4.4.9.6 Open Burning Emission Estimates for the Year 1999 4-144
4.4.10 How Were Nonutility Point and Area Source Emissions Prepared for the 1997
through 1999 NET? 4-148
4.4.11 References 4-148
4.5 SOLVENT UTILIZATION 4-177
4.5.1 What sources are included in this category? 4-177
4.5.2 What is EPA's Current Methodology for Estimating Emissions from Solvent
Utilization? 4-177
4.5.3 Are Pollutants Other than VOC Estimated for Solvent Utilization Sources? . . . 4-177
4.5.4 How Did EPA Prepare Solvent Utilization Emissions for Point and Area Sources
When Not Provided by State/Local Agencies? 4-177
4.5.5 How did EPA Develop the Solvent Portion of the 1990 NET Inventory? 4-178
4.5.5.1 How did EPA use the OTAG Inventory? 4-178
4.5.5.2 How did EPA use the GCVTC Inventory? 4-180
4.5.5.3 What AIRS/FS Data did EPA Use? 4-180
4.5.5.4 How did EPA Fill the Data Gaps Remaining from these Inventories? .... 4-181
4.5.6 How did EPA Prepare the 1996 NET Inventory for Solvent Utilization Sources?4-181
4.5.7 How Were Nonutility Point and Area Source Emissions Prepared for the 1997
through 1999 NET? 4-181
4.5.8 References 4-182
-------
4.6 ON-ROAD VEHICLES 4-192
4.6.1 Which Sources Does EPA Include in the On-road Vehicle Category? 4-192
4.6.2 What Is EPA's Current Methodology for Developing Emission Estimates for On-road
Vehicles? 4-192
4.6.3 How Does EPA Estimate Vehicle Mies Traveled (VMT)? 4-193
4.6.3.1 How Does EPA Develop 1970 to 1979 VMT Data? 4-193
4.6.3.2 How Does EPA Develop 1980 to 1995 VMT Data? 4-193
4.6.3.3 What States Provided 1990 VMT Data? 4-197
4.6.3.4 What Changes Did EPA Make to the VMT Estimation Methodology for the
Years 1996 through 1998? 4-197
4.6.3.5 How Did EPA Project 1999 VMT Data? 4-198
4.6.3.6 How Did EPA Modify 1990 through 1995 VMT Allocations? 4-198
4.6.4 How Does EPA Develop Emission Factors for VOC, NOX, and CO? 4-199
4.6.4.1 What Temperature Data Does EPA Input to the MO BILE Model? 4-199
4.6.4.2 How Does EPA Calculate the Monthly RVP Inputs? 4-199
4.6.4.3 How Does EPA Develop Speed Inputs? 4-202
4.6.4.4 What Operating Mode Inputs Does EPA Use? 4-203
4.6.4.5 What Altitude Inputs Does EPA Use? 4-203
4.6.4.6 How Does EPA Develop Registration Distribution Data? 4-203
4.6.4.7 Which MONTH Flag(s) Does EPA Use in the MOBILE Model? 4-207
4.6.4.8 What Additional Area-Specific Inputs from OTAG are Used? 4-207
4.6.4.9 How Does EPA Model On-road Control Programs? 4-208
4.6.5 How Does EPA Develop PM-10 and SO2 Emission Factors? 4-211
4.6.5.1 How are Registration Distributions Developed for the PART5 Model? . . . 4-212
4.6.5.2 How is Speed Modeled in the PART5 Model? 4-212
4.6.5.3 How Does EPA Develop VMT for the Five PART5 HDDV Vehicle
Classes? 4-212
4.6.5.4 How Does EPA Calculate Exhaust PM Emissions? 4-212
4.6.5.5 How Does EPA Calculate Exhaust SO2 Emissions? 4-212
4.6.5.6 How Does EPA Calculate PM Brake Wear Emissions? 4-212
4.6.5.7 How Does EPA Calculate PM Tire Wear Emissions? 4-212
4.6.5.8 How does EPA calculate PM and SO2 Emissions For 1970 to 1984? 4-213
4.6.6 How Does EPA Calculate Pre-1996 Ammonia (NH3) Emission Factors? 4-215
4.6.7 How Does EPA Calculate 1996 through 1999 Ammonia Emission Factors? ... 4-216
4.6.8 References 4-216
4.7 NONROAD ENGINES AND VEHICLES 4-252
4.7.1 What Sources Do We Include in the Nonroad Engines and Vehicles Category? 4-252
4.7.2 What Information Does This Section Provide? 4-252
4.7.3 What Methodologies Did We Use to Develop Nonroad Emission Estimates? . . 4-252
4.7.4 How Was the Nonroad Mo del Used to Develop Emission Estimates? 4-253
4.7.4.1 What Emissions Does the NONROAD Model Measure? 4-253
4.7.4.2 What Equipment Categories Are Included in the NONROAD Model? . . . 4-253
4.7.4.3 Do We Use Different Methods to Calculate Nonroad Emissions for Different
Years? 4-254
4.7A A Were Nonroad Model Runs Performed for Any Specific States? 4-256
VI
-------
4.7.5 How Did We Update Aircraft Emissions for 1997, 1998, and 1999? 4-256
4.7.5.1 How Did We Update Commercial Aircraft and General Aviation
Emissions? 4-256
4.7.5.2 How Did We Update Emissions for Military Aircraft, Unpaved Airstrips, and
Aircraft Refueling? 4-256
4.7.6 How Did We Update Commercial Marine Emissions? 4-256
4.7.6.1 How Did We Develop Commercial Marine Diesel Vessel Emission Estimates for
1996 Through 1999? 4-256
4.7.6.2 How Did We Develop Historic Year Estimates for Commercial Marine Diesel
Vessels? 4-257
4.7.6.3 How Did We Update Emission Estimates for Non-Diesel Commercial Marine
Vessels and Military Marine? 4-257
4.7.7 How Did We Update Locomotive Emissions for 1996 Through 1999? 4-257
4.7.8 How Did We Develop NH3 Emission Estimates? 4-257
4.7.8.1 How Did We Calculate NH3 Emissions for NONROAD Model Categories for
1996 Through 1999? 4-257
4.7.8.2 How Did We Calculate NH3 Emissions for Aircraft, Commercial Marine, and
Aircraft Categories? 4-258
4.7.9 References 4-258
4.8 MISCELLANEOUS SOURCES (FUGITIVE DUST AND AMMONIA) 4-274
4.8.1 What Source Categories Does the Miscellaneous Sector Include? 4-274
4.8.1.1 Agricultural Crops (1985-1989) 4-274
4.8.1.2 Agricultural Crops (1990-1999) 4-275
4.8.1.3 Agricultural Livestock and Fertilizer Application 4-277
4.8.1.4 PM Emissions fromReentrained Road Dust from Unpaved Roads 4-280
4.8.1.5 PM Emissions from Reentrained Road Dust from Paved Roads 4-284
4.8.1.6 Calculation of PM-2.5 Emissions from Paved and Unpaved Roads 4-286
4.8.1.7 Other Fugitive Dust Sources 4-286
4.8.1.8 Grown Emissions 4-295
4.8.2 References 4-303
4.9 BIOGENICS 4-326
4.9.1 How are biogenic emissions estimated? 4-326
4.9.2 What factors affect biogenic emissions? 4-326
4.9.3 What is the uncertainty associated with these estimates? 4-326
4.9.4 References 4-326
SECTION 5.0
LEAD EMISSIONS METHODOLOGY 5-1
5.1 INTRODUCTION 5-1
5.1 INTRODUCTION 5-1
5.1.1 Background 5-1
5.1.2 General Procedure 5-1
5.1.3 Organization of Procedures 5-2
5.2 FUEL COMBUSTION ELECTRIC UTILITY - COAL: 01-01 5-7
5.2.1 Technical Approach 5-7
Vll
-------
5.2.2 Activity Indicator 5-7
5.2.3 Emission Factor 5-7
5.2.4 Control Efficiency 5-7
5.2.5 References 5-8
5.3 FUEL COMBUSTION ELECTRIC UTILITY - OIL: 01-02 5-9
5.3.1 Technical Approach 5-9
5.3.2 Activity Indicators 5-9
5.3.3 Emission Factors 5-9
5.3.4 Control Efficiency 5-9
5.3.5 References 5-10
5.4 FUEL COMBUSTION INDUSTRIAL - COAL: 02-01 5-11
5.4.1 Technical Approach 5-11
5.4.2 Activity Indicator 5-11
5.4.3 Emission Factors 5-11
5.4.4 Control Efficiency 5-11
5.4.5 References 5-12
5.5 FUEL COMBUSTION INDUSTRIAL - OIL: 02-02 5-13
5.5.1 Technical Approach 5-13
5.5.2 Activity Indicator 5-13
5.5.3 Emission Factor 5-13
5.5.4 Control Efficiency 5-14
5.5.5 References 5-14
5.6 FUEL COMBUSTION OTHER - COMMERCIAL/INSTITUTIONAL COAL: 03-01 ..5-15
5.6.1 Technical Approach 5-15
5.6.2 Activity Indicator 5-15
5.6.3 Emission Factors 5-16
5.6.4 Control Efficiency 5-16
5.6.5 References 5-17
5.7 FUEL COMBUSTION OTHER - COMMERCIAL/INSTITUTIONAL OIL: 03-02 5-18
5.7.1 Technical Approach 5-18
5.7.2 Activity Indicator 5-18
5.7.3 Emission Factor 5-18
5.7.4 Control Efficiency 5-18
5.7.5 References 5-19
5.8 FUEL COMBUSTION OTHER - MISCELLANEOUS FUEL COMBUSTION (EXCEPT
RESIDENTIAL): 03-04 5-20
5.8.1 Technical Approach 5-20
5.8.2 Activity Indicator 5-20
5.8.3 Emission Factor 5-20
5.8.4 Control Efficiency 5-20
5.8.5 References 5-20
5.9 FUEL COMBUSTION OTHER - RESIDENTIAL OTHER: 03-06 5-22
5.9.1 Technical Approach 5-22
5.9.2 Activity Indicator 5-22
5.9.3 Emission Factors 5-23
Vlll
-------
5.9.4 Control Efficiency 5-23
5.9.5 References 5-23
5.10 CHEMICAL AND ALLIED PRODUCT MANUFACTURE - INORGANIC CHEMICAL
MANUFACTURE: 04-02 5-25
5.10.1 Technical Approach 5-25
5.10.2 Activity Indicator 5-25
5.10.3 Emission Factor 5-25
5.10.4 Control Efficiency 5-25
5.10.5 References 5-25
5.11 METALS PROCESSING - NONFERROUS: 05-01 5-26
5.11.1 Technical Approach 5-26
5.11.2 Activity Indicator 5-26
5.11.2.1 Nonferrous Metals 5-26
5.11.2.2 Secondary Metals 5-27
5.11.2.3 Miscellaneous Process Sources 5-27
5.11.3 Emission Factor 5-28
5.11.3.1 Nonferrous Metals 5-28
5.11.3.2 Secondary Metals 5-28
5.11.3.3 Miscellaneous Process Sources 5-28
5.11.4 Control Efficiency 5-28
5.11.4.1 Nonferrous Metals 5-28
5.11.4.2 Secondary Metals 5-28
5.11.4.3 Miscellaneous Process Sources 5-28
5.11.5 References 5-29
5.12 METALS PROCESSING - FERROUS: 05-02 5-30
5.12.1 Technical Approach 5-30
5.12.2 Activity Indicator 5-30
5.12.2.1 Iron and Steel 5-30
5.12.2.2 Nonferrous Metals 5-31
5.12.2.3 Secondary Metals 5-31
5.12.3 Emission Factor 5-31
5.12.3.1 Iron and Steel 5-31
5.12.3.2 Nonferrous Metals 5-31
5.12.3.3 Secondary Metals - Grey Iron Foundries 5-31
5.12.4 Control Efficiency 5-32
5.12.5 References 5-32
5.13 METALS PROCESSING - NOT ELSEWHERE CLASSIFIED: 05-03 5-34
5.13.1 Technical Approach 5-34
5.13.2 Activity Indicator 5-34
5.13.3 Emission Factor 5-34
5.13.4 Control Efficiency 5-34
5.13.5 References 5-35
5.14 OTHER INDUSTRIAL PROCESSES - MINERAL PRODUCTS: 07-05 5-36
5.14.1 Technical Approach 5-36
5.14.2 Activity Indicator 5-36
IX
-------
5.14.3 Emission Factor 5-36
5.14.4 Control Efficiency 5-36
5.14.5 References 5-37
5.15 OTHER INDUSTRIAL PROCESSES - MISCELLANEOUS INDUSTRIAL PRODUCTS:
07-10 5-38
5.15.1 Technical Approach 5-38
5.15.2 Activity Indicator 5-38
5.15.3 Emission Factor 5-39
5.15.4 Control Efficiency 5-39
5.15.5 References 5-39
5.16 WASTE DISPOSAL AND RECYCLING : 10-01 5-40
5.16.1 Technical Approach 5-40
5.16.2 Activity Indicator 5-40
5.16.3 Emission Factor 5-41
5.16.4 Control Efficiency 5-41
5.16.5 References 5-41
5.17 ON-ROAD VEHICLES: 11 5-43
5.17.1 Technical Approach 5-43
5.17.2 Activity Indicator 5-43
5.17.3 Emission Factor 5-43
5.17.4 Control Efficiency 5-44
5.17.5 Allocation of Emissions to the Tier II Categories 5-44
5.17.6 References 5-44
5.18 NON-ROAD ENGINES AND VEHICLES - NONROAD GASOLINE: 12-01 5-47
5.18.1 Technical Approach 5-47
5.18.2 Activity Indicator 5-47
5.18.3 Emission Factor 5-49
5.18.4 Control Efficiency 5-49
5.18.5 References 5-49
SECTION 6.0
OVERVIEW OF PROJECTION METHODS USED BY EPA 6-1
6.1 EMISSION PROJECTIONS 6-1
6.2 GROWTH FACTORS 6-2
6.2.1 Growth Data Sets Used by EPA 6-2
6.3 CONTROL FACTORS/EMISSION FACTORS 6-3
6.3.1 Conditions & Influences on Determining Controls and Emission Rates 6-4
6.4 MEASURING THE EFFECTIVENESS OF CONTROL PROGRAMS 6-4
6.5 USE OF SCCS AND SICS TO ASSOCIATE GROWTH AND CONTROL
INFORMATION 6-5
6.6 OTHER CONSIDERATIONS 6-6
6.6.1 Spatial Considerations 6-6
6.6.2 Temporal Considerations 6-6
6.6.3 Speciation Considerations 6-6
6.7 QUALITY ASSURANCE 6-7
-------
XI
-------
TABLES AND FIGURES
Tables Page
1-1. Emission Estimating Methods For Criteria Air Pollutants and Ammonia 1-4
4.1-1. Section4.0 Structure 4-4
4.1-2. Major Source Categories 4-5
4.2-1. Methods for Developing Annual Emission Estimates for Steam Generated Fossil-Fuel Utility
Boilers for the Years 1989-1999 4-17
4.2-2. Steam Electric Utility Unit Source Classification Code Relationships 4-19
4.2-3. Boiler Emissions Data Sources (Other than EIA-767)
for NOX, SO2, and Heat Input Data by Year 4-21
4.2-4. Algorithms Used to Estimate EIA-Based VOC, NOX, CO, SO2, PM-10, PM-2.5, and NH3
Annual Emissions from Electric Utility Boilers 4-22
4.2-5. Algorithms Used to Estimate EIA-Based Condensible PM, Total PM-10, Total PM-2.5, and
CO2 Annual Emissions for Electric Utility Boilers 4-23
4.2-6. Algorithms Used to Disaggregate ETS/CEM Boiler Data to the Boiler-SCC Level 4-24
4.3-1. Methods for Developing Annual Emission Estimates for Industrial Nonutility Point and Area
Sources for the Years 1985-1999 4-65
4.3-2. SCCs With 100 Percent CO Rule Effectiveness 4-66
4.3-3. July RVPs Used to Model Motor Vehicle Emission Factors 4-67
4.3-4. 1990 SeasonalRVP (psi) by State 4-68
4.3-5. Seasonal Maximum and Minimum Temperatures (°F) by State 4-69
4.3-6. Average Annual Service Station Stage II VOC Emission Factors 4-70
4.3-7. TSDF Area Source Emissions Removed from the Inventory (1985-1996) 4-70
4.3-8. Bureau of Economic Analysis's SA-5 National Changes in Earnings by Industry 4-71
4.3-9. Area Source Growth Indicators 4-72
4.3-10. SEDS National Fuel Consumption 4-73
4.3-11. AMS to NAPAP Source Category Correspondence 4-74
4.3-12. Point Source Data Submitted by OTAG States 4-76
4.3-13. Area Source Data Submitted by OTAG States 4-78
4.3-14. Ad Hoc Report 4-79
4.3-15. SEDS National Fuel Consumption, 1990-1996 (trillion Btu) 4-80
4.3-16. BEA SA-5 National Earnings by Industry, 1990-1996 4-81
4.3-17. Area Source Listing by SCC and Growth Basis 4-83
4.3-18. Emission Estimates Available from AIRS/FS by State, Year, and Pollutant 4-86
4.3-19. NOX and VOC Major Stationary Source Definition 4-87
4.3-20. Summary of Revised NOX Control Efficiencies 4-87
4.3-21. Cotton Ginning Emission Factors 4-88
4.3-22. Estimated Percentage of Crop By Emission Control Method 4-88
4.3-23. Cotton Ginnings: Running Bales Ginned By
County, District, State, and United States 4-89
4.3-24. Point Source Controls by Pod and Measure 4-90
4.3-25. Point Source SCC to Pod Match-up 4-92
4.3-26. AreaSource VOC Controls by SCC and Pod 4-99
4.3-27. Counties in the United States with Stage II Programs that use Reformulated Gasoline . . 4-100
4.3-28. NOX Area Source RACT 4-101
xii
-------
TABLES AND FIGURES (continued)
Tables Page
4.3-29. Sources of Point and Area Source Emissions Data for the 1996 NET Inventory After
Incorporating State/Local Agency Data Received in 1999 and 2000 4-102
4.3-30. State/Local Point Source Inventories Used to Update the 1996 NET Inventory 4-104
4.3-31. State/Local Area Source Inventories Used to Update the 1996 NET Inventory 4-105
4.3-32. Non-Utility Point Source Data Augmentation Methods 4-106
4.3-33. Stationary Area Source Data Augmentation Methods 4-107
4.3-34. MACT Control Efficiencies Applied to 1996 VOC Emissions for Point and Area Industrial
Sources 4-108
4.4-1. Bureau ofEconomic Analysis's SA-5National Changes in Earnings by Industry 4-152
4.4-2. Area Source Growth Indicators 4-153
4.4-3. SEDS National Fuel Consumption 4-154
4.4-4. AMS to NAPAP Source Category Correspondence 4-155
4.4-5. Point Source Data Submitted by OTAG States 4-156
4.4-6. Area Source Data Submitted by OTAG States 4-158
4.4-7. Ad Hoc Report 4-159
4.4-8. SEDS National Fuel Consumption, 1990-1996 (trillion Btu) 4-160
4.4-9. BEA SA-5 National Earnings by Industry, 1990-1996 4-161
4.4-10. Area Source Listing by SCC and Growth Basis 4-163
4.4-11. Emission Estimates Available from AIRS/FS by State, Year, and Pollutant 4-164
4.4-12. NOX and VOC Major Stationary Source Definition 4-165
4.4-13. Summary of Revised NOX Control Efficiencies 4-165
4.4-14. Methods for Developing Annual Emission Estimates for Other Combustion Sources for the
Years 1989-1999 4-166
4.4-15. Comparison of Methodologies Used to Develop 1996 Base Year Emissions
for Other Combustion Area Source Categories for Versions 1 through 4 of the NET
Inventory 4-169
4.4-16. Other Combustion Area Source Categories: Summary of State-Submitted Emissions for 1996
Included in Versions 3 and 4 of the NET Inventory 4-171
4.4-17. Wildfires 4-173
4.4-18. Emission Factors for Residential Wood Combustion by Pollutant 4-174
4.4-19. PM Control Efficiencies for 1991 through 1999 4-174
4.4-20. Basis for 1996 Structure Fire Emission Estimates 4-175
4.4-21. Criteria Pollutant Emission Factors For Open Burning 4-176
4.5-1. Point Source Data Submitted by OTAG States 4-183
4.5-2. Area Source Data Submitted by OTAG States 4-185
4.5-3. Ad Hoc Report 4-186
4.5-4. MACT Control Efficiencies Applied to 1996 VOC Emissions for Point and Area Solvent
Emission Sources 4-187
4.6-1. Methods for Developing Annual Emission Estimates for On-road Highway Vehicles for the
Years 1989-1999 4-220
4.6-2. Comparison of Methodologies Used to Develop 1996 Base Year Emissions
for On-road Sources in Versions 1 through4 of the NEI 4-221
4.6-3. Data Components of HPMS 4-223
xiii
-------
TABLES AND FIGURES (continued)
Tables Page
4.6-4. Apportionment Percentages for Conversion of HPMS Vehicle Type Categories to MOBILESa
Categories (through 1995) 4-224
4.6-5. VMT Seasonal and Monthly Temporal Allocation Factors 4-225
4.6-6. State-level Daily VMT Totals in the OTAG Inventory 4-226
4.6-7. Cities Used for Temperature Data Modeling from 1970 through 1999 4-227
4.6-8. Surrogate City Assignment 4-238
4.6-9. Substitute Survey City Assignment 4-234
4.6-10. Monthly RVP Values Modeled in 1996 4-233
4.6-11. HPMS Average Overall Travel Speeds for 1990 4-235
4.6-12. Average Speeds by Road Type and Vehicle Type 4-236
4.6-13. State-Supplied Operating Mode Inputs 4-237
4.6-14. Default Values for the 1998 National Registration Distribution 4-238
4.6-15. State-Supplied Trip Length Distribution Inputs 4-239
4.6-16. State-Supplied Alcohol Fuels Data 4-239
4.6-17. Counties Included in 1996 and 1997 I/M Programs 4-240
4.6-18. Counties Include din 1998 and 1999 I/M Programs 4-242
4.6-19. Counties Modeled with Federal Reformulated Gasoline 4-244
4.6-20. Oxygenated Fuel Modeling Parameters 4-247
4.6-21. PART5 HDDV Vehicle Classes 4-248
4.6-22. Average Speeds by Road Type and Vehicle Type 4-248
4.6-23. PM-10 Emission Factors used in the Emission Trends Inventory 4-249
4.6-24. Fuel Economy Values Used in Calculation of SO2 Emission Factors for the Emission Trends
Inventory 4-249
4.6-25. SO2 Emission Factors used in the Emission Trends Inventory 4-250
4.6-26. Fractions of Vehicles Equipped with 3-Way Catalysts by Vehicle Type and Model Year . 4-250
4.7-1. Methods for Developing Annual Emission Estimates for Nonroad Sources for the Years 1989-
1999 4-260
4.7-2. Comparison of Methodologies Used to Develop 1996 Base Year Emissions
for Nonroad Sources in Versions 1 through 4 of the NET 4-262
4.7-3. Seasonal RVP Values Modeled for 1996 NONROAD Model Runs 4-264
4.7-4. Counties Modeled with Federal Reformulated Gasoline 4-266
4.7-5. Oxygenated Fuel Modeling Parameters 4-269
4.7-6. Summary of Input Values for National NONROAD Model Runs 4-271
4.7-7. Surrogate SCC Assignments for New SCCs in June 2000 NONROAD Model 4-272
4.7-8. National Seasonal RVP Averages for NONROAD Model Runs 4-273
4.7-9. National Seasonal Temperatures for NONROAD Model Runs 4-273
4.7-10. Growth Indicators for Nonroad Sources 4-273
4.8-1. Particle Size Ratios 4-308
4.8-2. Methods for Developing Annual Emission Estimates for Miscellaneous Area Sources for the
Years 1989-1999 4-309
4.8-3. Comparison of Methodologies Used to Develop 1996 Base Year Emissions for Miscellaneous
Area Source Categories for Versions 1 through 4 of the NET Inventory 4-312
xiv
-------
TABLES AND FIGURES (continued)
Tables Page
4.8-4. Miscellaneous Area Source Categories: Summary of State-Submitted Emissions for 1996
Included in Versions 3 and 4 of the NET Inventory 4-315
4.8-5. Silt Content by Soil Type, 1985 to 1989 4-316
4.8-6. Silt Content by Soil Type, 1990 to 1998 4-316
4.8-7. Number of Tilings by Crop Type 4-316
4.8-8. Livestock Operations Ammonia Emission Factors 4-317
4.8-9. Assumed Values for Average Daily Traffic Volume by Volume Group 4-317
4.8-10. PM-2.5 to PM-10 Ratios for Paved and Unpaved Roads 4-317
4.8-11. List of Grown Sources 4-318
4.8-12. Point Source Data Submitted 4-319
4.8-13. Area Source Data Submitted 4-321
4.8-14. Ad Hoc Report 4-322
4.8-15. Bureau of Economic Analysis's SA-5 National Changes in Earnings by Industry 4-323
4.8-16. Emission Estimates Available from AIRS/FS by State, Year, and Pollutant 4-324
4.8-17. SEDS National Fuel Consumption, 1990-1996 (trillion Btu) 4-325
4.8-18. BEA SA-5 National Earnings by Industry, 1990-1996 4-325
4.8-19. Area Source Listing by SCC and Growth Basis 4-325
5.1-1. Correspondence Between Tier II Categories and Lead Emissions Methodology
Categories 5-4
5.1-2. Method Used for Estimating 1996 ActivityData 5-6
5.8-1. Annual Percentage Lead Content 5-21
5.17-1. Number of Grams of Lead/Gasoline (Y) 5-45
5.17-2. Relative VMT Fractions for Each Tier II Category 5-46
6.2-1. Projection (Growth) Resources 6-3
xv
-------
TABLES AND FIGURES (continued)
Figures Page
4.3-1. OTAG Inventory Data Source - Area Sources 4-118
4.3-2. OTAG Inventory Data Source - Point Sources 4-119
4.6-1. OTAG Inventory Source of Data - VMT 4-251
xvi
-------
ACRONYMS AND ABBREVIATIONS
AADT annual average daily traffic
AAMA American Automotive Manufacturer's Association
AAR Association of American Railroads
ACT Alternative Control Technology
AD TV average daily traffic volume
AIRS Aerometric Information Retrieval System
AIRS/AMS AIRS Area and Mobile Source Subsystem
AIRS/FS AIRS Facility Subsystem
ARE) Acid Rain Division
ASTM American Society for Testing and Materials
BEA U.S. Department of Commerce, Bureau of Economic Analysis
BLS U.S. Bureau of Labor Statistics
CAAA Clean Air Act Amendments of 1990
CEM continuous emissions monitor(ing)
CNOI Census number of inhabitants
CO carbon monoxide
CTG Control Techniques Guidelines
CTIC Conservation Information Technology Center
DOE U.S. Department of Energy
DOT Department of Transportation
DVMT daily vehicle miles traveled
EIA U.S. DOE, Energy Information Administration
EFIG EPA, OAQPS, Emission Factor and Inventory Group
EG earnings growth
EPA U.S. Environmental Protection Agency
ERCAM Emission Reductions and Cost Analysis Model
BSD EPA, OAQPS, Emission Standards Division
ETS/CEM Emissions Tracking System/Continuous Emissions Monitoring
FAA Federal Aviation Adrrrinstration
FCC fluid catalytic cracking unit
FGD flue gas desulfurization
FHWA U.S. Federal Highway Adminstration
FID Flame lonization Detector
FREDS Flexible Regional Emissions Data System
FTP Federal Test Procedure
GCVTC Grand Canyon Visibility Transport Commission
GT gas turbines
HC hydrocarbon
HCPREP FREDS Hydrocarbon Preprocessor
HDV heavy duty vehicle
hp horsepower
HPMS Highway Performance Monitoring System
1C internal combustion (engine)
I/M inspection and maintenance
LOT light duty truck
xvii
-------
ACRONYMS AND ABBREVIATIONS (continued)
LDV light duty vehicle
LTO landing and takeoff
MACT maximum available control technology
MRI Midwest Research Institute
MW megawatts
NAA nonattainment area
NADB National Allowance Data Base
NAPAP National Acid Precipitation Assessment Program
NEDS National Emission Data System
NESHAP National Emission Standards for Hazardous Air Pollutants
NET National Emissions Trends (inventory)
NH3 ammonia
NOX oxides of nitrogen
NPI National Particulates Inventory
NSPS New Source Performance Standards
OAQPS EPA, Office of Air Quality Standards and Planning
OSD ozone season daily
OTAG Ozone Transport Assessment Group
OTAQ Office of Air Transportation and Quality
OTR ozone transport region
Pb lead
PCE personal consumption expenditures
PM particulate matter
PM-2.5 particulate matter less than 2.5 microns in diameter
PM-10 particulate matter less than 10 microns in diameter
ppm parts per million
QA quality assurance
QC quality control
RACT Reasonably Available Control Technology
RCRA Resource Conservation and Recovery Act
ROM Regional Oxidant Model
RVP Reid vapor pressure
SCC source classification code
SEDS State Energy Data System
SIC Standard Industrial Classification (code)
SIP State Implementation Plan
SO2 sulfur dioxide
SO4 sulfates
SUPROXA Super Regional Oxidant A
TOG total organics
tpy tons per year
TSDF hazardous waste treatment, storage, and disposal facility
TSP total suspended particulate matter
USDA U.S. Department of Agriculture
USFS USDA Forest Service
xvm
-------
ACRONYMS AND ABBREVIATIONS (continued)
VMT vehicle miles traveled
VOC volatile organic compound(s)
xrx
-------
SECTION 1.0
INTRODUCTION
1.1 WHAT IS THE NATIONAL EMISSIONS INVENTORY (NEI) PROCEDURES
DOCUMENT?
The Emission Factors and Inventory Group (EFIG) of the U.S. Environmental Protection Agency
(EPA) is responsible for compiling and maintaining national emission data for the criteria and hazardous
air pollutants. Topromote the consolidation of criteria and toxic pollutant data in one national inventory,
in 1999 EFIG combined the National Emission Trends (NET) criteria air pollutant inventory, and the
National Toxics Inventory (NTI) into one "National Emission Inventory." The procedures EPA applies
to prepare criteria air pollutant emissions (plus ammonia) for the NEI are documented separately from
the procedures EPA uses to prepare hazardous air pollutant emissions for the NEI. It is expected that in
the future, the methodology descriptions for developing both the criteria and toxic emissions data, will be
consolidated into one document.
This document includes methodologies for estimating emissions , 1985-1999, for the following
criteria pollutants: carbon monoxide (CO), nitrogen oxides (NOX), lead (Pb), particulate matter less than
10 and 2.5 microns in diameter (PM-10/PM-2.5), sulfur dioxide (SO2), volatile organic compounds
(VOC), and ammonia (NH3). This document does not include the data results, only method descriptions.
EPA has published criteria emission estimates for years prior to 1999 in the "National Air Pollutant
Emission Trends" and 'National Air Quality and Emission Trends" Reports. Collectively, these are
known as the Trends Reports. Beginning with the 1999 data, it is expected that EPA will summarize and
publish emission trends annually in the one combined "National Air Quality and Emission Trends"
Report, including an electronic distribution on EPA Internet sites.
1.2 HOW IS THIS DOCUMENT ORGANIZED?
The emission estimating methodologies presented in this document are organized by the following
categories: 1985-1989 Methodology, 1990-1999 Methodology, Pb Methodology, and Projections
Methodology. The methodology used to make specific estimates depends on the pollutant and the year
of the estimated emissions. Emission estimates presented in the Trends Reports are summarized using the
EPA's Tier structure. Table 1-1 provides an overview of the Tier I categories, time periods, pollutants,
and methodologies covered by sections 2 through 6. A description of the correspondence between the
emission source categories, the methodology descriptions, and the Tier structure is included in each
section of this document.
Section 2 distinguishes the current methods from those used for historical years, specifically for
1900-1939 and 1940-1984, respectively. The emission estimation methods used for these historical years
are considered 'top-down approaches', e.g., pollutant emissions were estimated by using national average
emission characterization techniques. Only SO2, NOX, and VOC emissions were developed for the
historical time period before 1940. For the time period 1940-1984, methods were used to estimate all
criteria emissions, e.g., SO2, NOX, and VOC, CO, Pb, PM-10, and TSP.
1-1
-------
Section 3 discusses some changes that are expected to be made to the format of this document in the
future. Expected changes include the consolidation the procedures documentation for both the criteria
and toxic emissions data development.
Section 4 describes the methodologies for estimating emissions for 1985-1999. The 1990 Clean Air
Act Amendments included provision that 'base year' inventories be prepared periodically by state and
local agencies - for areas not attaining the National Ambient Air Quality Standards (NAAQS). In
anticipation of receiving data results from the states, the EPA reassessed its historical emission estimation
practices, and with the help of state / local agency input, developed improved methods, and an improved
national emission inventory which is referred to as the Interim Inventory.! The Interim Inventory
represents an evolution of methods at that time, for the years 1985-1989. Those methodologies are also
discussed in Section 4, and were used to estimate emissions for all pollutants, except Pb and total
suspended particulate (TSP) matter. (TSP estimates were last developed for the year 1992, after which
particulate matter emissions have been characterized as PM10, or more recently, PM2.5). Beginning
with the 1990 NET inventory, EPA placed emphasis on incorporating emissions data submitted by
State/local agencies, and any improved methods available at the time, for filling in gaps when State/local
agency data were not available.
To navigate through Section 4, be aware that for a given source category, the estimating procedure
is described for all pollutants collectively, unless differences exist in the methods used for different
pollutants. In this case, the methods used for each pollutant are described separately. This allows each
section to be used independently.
Section 5 discusses the methodology used to estimate the lead emissions that are included in the
National Emission Trends (NET) criteria air pollutant inventory. Lead emissions for 1996 are also
estimated in the National Toxics Inventory (NTI)2 and were used in the nationwide dispersion modeling
as part of EPA's National Air Toxics Assessment (NATA). For 1996, the NTI estimates would be the
preferred source for data. Section 6 presents general information and procedures that EPA uses in
projecting air pollutant emissions for the: point, area, and mobile sectors of the inventory, emphasizing
the objective to account for as many of the important variables that affect future year emissions as
possible.
One of the distinct and natural occurrences in inventory development is the evolution and
improvement of emission estimate methods over time. In some cases, an improved estimation method for
a source category may be applied 'backwards' to previous year estimates for that same category. It is
unlikely that the methodologies and references presented in this document for estimating emissions for
period 1900-1984 will change. However, this Procedures Document does note method updates that have
been made over time to improve emission estimates for the year 1985 to the current year reported. Any
changes in the data or methodologies used to estimate the emissions for this time period will continue to
be noted in future updates to this document.
1.3 REFERENCES
1. Regional Interim Emission Inventories (1987-1991), Volume I: Development Methodologies.
EPA-454/R-93-021 a. Source Receptor Analysis Branch, U.S. Environmental Protection Agency,
Research Triangle Park, NC. May 1993.
1-2
-------
2. U.S. Environmental Protect!on Agency. National Toxics Inventory, 1996.
http://www.epa. gov/ttn/chiei7nti (March 2001) .
1-3
-------
Table 1-1. Emission Estimating Methods For Criteria Air Pollutants and Ammonia
Tier Category
Fuel Combustion - Electric Utilities
Fuel Combustion - Industrial
Fuel Combustion - Other
Chemical & Allied Product Mfg.
Metals Processing
Petroleum & Related Industries
Other Industrial Processes
Solvent Utilization
Storage & Transport
Waste Disposal & Recycling
Natural Sources (Biogenic)
Miscellaneous
Onroad Vehicles
Nonroad Vehicles and Engines
Time Period
1900-1969, excluding
1940, 1950, and 1960
1940, 1950, 1960, and
1970 through 1984
1985 through 1989 and
1990 through 1999
Post-1999
1900-1939
1940 through 1969
1970 through 1999
Post- 1999
Pollutant(s)
VOC, S02, and NOX
VOC, SO2, NOX, CO,
and PM-10
Pb
VOC, S02, NOX, CO,
and PM-10
VOC, S02, NOX, CO,
PM-10, PM-2.5, and
NH3
Pb
VOC, S02, NOX, CO,
PM-10, PM-2.5, and
NH3
VOC, SO2, NOX
VOC, S02, NOX, CO,
and PM-10
Pb
VOC, S02, NOX, CO,
and PM-10
PM-2.5 and NH3
Pb
VOC, S02, NOX, CO,
PM-10, PM-2.5, and
NH3
Methodology
1900-1939
Methodology
1940-1984
Methodology
Lead
Methodology
1985-1989
Methodology
1990-1999
Methodology
Lead
Methodology
Projection
Methodology
1900-1939
Methodology
1940-1984
Methodology
Lead
Methodology
1985-1999
Methodology
1990-1999
Methodology
Lead
Methodology
Projection
Methodology
Section
2
2
5
4
4
5
6
2
2
5
4
4
5
6
NOTE(S): SO2, VOC, and NOxestimated 1900-1999.
CO and PM-10 estimated 1940-1999.
Lead estimated 1970-1998.
PM-10 fugitive dust estimated 1985-1999.
PM-2.5 and NH3 estimated 1990-1999.
Biogenb 1998, 1990, 1991, 1995-1997.
1-4
-------
SECTION 2.0
CHANGES IN METHODOLOGY
2.1 HOW DO CURRENT METHODOLOGIES RELATE TO PREVIOUS METHODS ?
Each year the EPA produces a National Emissions Inventory, using improved estimation methods
where available and practical Section 4 describes significant method changes that have occurred over
time for the inventory years 1985-1999. All of these changes are a broad effort to update and improve
the emission estimates. As estimation methods change and improve over time, EPA may re-compute
emission estimates for specific categories. This document describes the most recent data development
procedures for the NEI and indicates where previous year estimates have been recalculated as part of a
methodology change. No such changes are planned for emission estimates for the years prior to 1985.
Updates are likely to be made however, to the emissions for the years 1985 to the current year of the
report. Any changes in the data or methodologies used to estimate emissions for this time period will be
discussed in future revisions to this procedures document.
2.2 1900 - 1939 METHODOLOGY
Only SO2, NOx, and VOC emissions were estimated for the time period prior to 1940. The
methodology used to produce SO2, NOx estimates included some use of state-level data where it was
available. The basic technique for estimating VOC emissions was a 'top-down' method using national
activity indicators and national emission factors. In addition, interpolation methods were used to derive
national emissions data for some years. These top-down estimation techniques used to generate
emissions for historic years, while generally no longer employed, are discussed in previous versions of the
National Emission Trends Procedures Documentl.
2.3 1940 - 1984 METHODOLOGY
The methodology used to estimate emissions for the time period 1940-1984 was based on a top-
down approach where national information was used to create national emission estimates. For these
historic years, emissions were generated for all the criteria pollutants, e.g., CO, NOX, PM-10, SO2, VOC,
Pb, and TSP. These top-down estimation techniques used to generate emissions for historic years, while
generally no longer employed, are discussed in previous versions of the National Emission Trends
Procedures Document1.
2.4 REFERENCES
1. National Air Pollutant Emission Trends Procedures Document, 1900-1996, EPA-454/R-98-008a.
U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle
Park,NC. June 1998.
2-1
-------
SECTION 3.0
FUTURE CHANGES TO THIS DOCUMENT
3.1 NEW FORMAT
It is expected that future updates and distribution of this procedures document will include a new
'look and feel' to improve how the information is organized. The new format is expected to provide
consistency across category methodology descriptions. The existing Procedures Document(s) will be
divided into the following volumes:
An "archive years" volume and will contain emissions information for the historical years of 1900-1984.
It is anticipated that this volume would not change once developed, as there are no plans on revising any
of these data based on updated activity, factors, or methods.
Our "current years" volume will contain emissions information for the historical years of 1985 through
our most recent year inventory of data development. It is anticipated that the information in this volume
may change as updated activity, factors, or methods become available and recent historical 'anchor' years
(i.e., 1990, 1996, 1999, etc.) require modification.
Our "anchor year" volume and will contain emissions information for all versions of the most recent base
year inventory we are developing (i.e., NEI99 v.l, NEI99 v.2, etc.) and the projection year inventories
based on this anchor year (i.e., 2000, 2001, 2002). It is anticipated that for any given year, it is this
volume where the majority of documented procedure changes would occur.
3.2 EMPHASIS ON MOST CURRENT YEAR BEING DEVELOPED
The primary objective of re-formatting the Procedures Document is to fully concentrate on the
activity, factors, and methods used to develop to the current base anchor year data set that is being
developed or updated. This may include any related future or previous year emissions inventories that are
projected or backcast from the current base year inventory that is being improved and updated. Most of
the interest inEPA's methodologies is directed toward the most recent base year of data under
development. As the base, or anchor year changes, ie., 1999 to 2002, the procedural description for
estimating emissions for the last anchor year inventory will be added into the "current years' volume.
3-1
-------
SECTION 4.0
NATIONAL CRITERIA POLLUTANT ESTIMATES
1985 -1999 METHODOLOGY
4.1 WHAT CATEGORIES AND POLLUTANTS ARE REVIEWED IN THIS SECTION?
Section 4 describes the methodologies used to generate emissions for the years 1985 through 1999
for carbon monoxide (CO), oxides of nitrogen (NOX), volatile organic compounds (VOC), sulfur dioxide
(SO2), and particulate matter less than 10 microns in diameter (PM-10). Section 4 also describes the
methodologies used to generate emissions for the years 1990 through 1999 for particulate matter less
than 2.5 microns in diameter (PM-2.5) and ammonia (NH3). Categories reviewed in this section include
fuel combustion - electric utility, industrial, other combustion (i.e., commercial/institutional and
residential), solvent utilization, on-road vehicles, nonroad engines and vehicles, fugitive dust, and
biogenic sources. The descriptions are divided into subsections based on similar approaches in estimating
the emissions. The beginning of each subsection lists the Tier I category, as well as other Tier categories,
if necessary. Table 4.1-1 shows the subsection/Tier I and II category relationships. If a Tier II category
is not listed, it is currently not estimated within the National Emissions Trends (NET) Inventory.
In 2000, EPA combined the NET and National Toxics Inventories into one inventory called the
National Emissions Inventory (NEI). However, due to resource constraints associated with revising
section 4, we have continued to use "NET inventory" to refer to the criteria pollutant (and ammonia)
component of the NEI.
4.1.1 What Significant Methodology Changes Have Occurred?
Each year, the U.S. Environmental Protection Agency (EPA) prepares national emissions estimates
to assess trends in criteria air pollutant emissions. Historically, EPA prepared these estimates by using
consistent top-down methodologies that employed national statistics on economic activity, material flows,
etc. for the years ranging from 1940 to the year of the report. Although useful for evaluating year-to-
year changes, these estimates did not provide a geographically detailed measure of emissions for any
given year.
Over the past several years, EPA has revised its methodologies to incorporate bottom-up inventories
and allow for an evaluation of changes in emissions from year to year. Bottom-up inventories, in which
emissions are derived at the plant or county level, are extremely useful in many applications, such as
providing inputs into atmospheric models. Starting with the National Air Pollutant Emission Trends,
1900-19961 (Emission Trends) report, EPA began to incorporate these methodological changes. EPA
now derives its emissions estimates starting at the county level, which enables it to incorporate more
detailed State/local agency data, including emissions estimates.
For most source categories, EPA developed emission estimates at the county and Source
Classification Code (SCC) level and then summed these emissions to the Tier level The Tier
categorization contains four levels. The first and second level, referred to as Tier I and Tier II,
respectively, are the same for each of the seven pollutants and are listed in Table 4.1-2. The third level,
Tier III, is unique for each of the seven pollutants. The fourth level, Tier 4, is the SCC level For a
4-1
-------
current list of the SCCs assigned to Tier levels I through III, contact EPA's Emission Factor and
Inventory Group (EFIG).
4.1.2 What Methodologies Does EPA Use to Develop Emissions Estimates?
EPA estimated the 1985 through 1989 CO, NOX, SO2, and VOC emissions according to the
methodologies presented in the Regional Interim Emission Inventories (1987-1991),2 although with
several exceptions. EPA developed a similar methodology for preparing a national 1990 particulate
matter inventory as documented in the Development of the OPPE Particulate Programs Implementation
Evaluation System:1 To generate the necessary emissions for the Emissions Trends report, EPA
expanded the methodology used in the Regional Interim Emission Inventories to generate 1985 and 1986
emissions estimates for CO, NOX, SO2, and VOC; and PM-10 emission estimates for the years 1985
through 1989.
After preparing the 1990 Interim Inventory, EPA developed a new 1990 base year for the NET
inventory. EPA revised the 1990 Interim Inventory with State/local agency emissions when available.
The Ozone Transport Assessment Group (OTAG), the Grand Canyon Visibility Transport Commission
(GCVTC), and the Aerometric Information Retrieval System/Facility Subsystem (AIRS/FS) provided
data on State non-utility point source emissions. OTAG and the States of California and Oregon
provided area source emissions. EPA calculated on-road emissions from State-provided emission factor
inputs and vehicle miles traveled (VMT), and it used the 1990 Interim Inventory emissions to fill all gaps
in emissions. The 1990 State/local agency emissions serve as the basis for the 1991 through 1996
emissions.
Starting with the 1996 Emission Trends report, EPA added PM-2.5 and NH3 to the list of pollutants
inventoried by EPA's EFIG. Emissions and associated data for these two pollutants are available for the
years 1990 through 1999.
Since the 1996 NET was initially completed in December 1997, EPA has revised the 1996 NET to
include base year emissions data submitted by State/local agencies to comply with the CAAA
requirements to submit (1) periodic emissions inventories (PEI) every 3 years for ozone nonattainment
areas (NAAs), and (2) emissions data for major point sources annually. States with ozone NAAs needed
to submit their PEI for 1996 by July 1997. While the CAAA only require submittal of ozone precursor
pollutant data for the PEI requirements, annual point source reporting covers all criteria air pollutants.
EPA began assigning version numbers to the 1996 through 1999 NET inventories in EPA fiscal year
1997 to track revisions to the 1996 base year inventory as it was updated each year to incorporate
emissions data submitted by State/local agencies, and the effects of changes to emission estimation
methodologies. The first 1996 NET inventory is Version I.1 Version 2 of the NET contains revised
1996 emissions and anew inventory for 1997.4 Version 3 contains revisions to the 1996 and 1997
inventories and a new inventory for 1998.5 Version 4 contains revisions to the 1996 through 1998
inventories and a new inventory for 1999.6 The method descriptions in this section, include those applied
to prepare 1996 through 1999 emissions included in Version 4 of the NET inventories.
To develop 1997 through 1999 emission estimates for nonutiKty point sources and the majority of
area sources, EPA compiled a set of emission growth and control factors for each year that were applied
to the 1996 NET inventory. EPA prepared Version 2 of the 1997 NET using growth factors developed
fromU.S. Department of Energy's (DOE) State Energy Data System (SEDS) annual fuel consumption
4-2
-------
data or Bureau of Economic Analysis (BEA) historic earnings by industry. For Versions 3 and 4, the
growth factors for estimating 1997 through 1999 emissions for the continental United States were
developed using the inputs developed for EGAS 4.0. BEA data were used to prepare growth factors for
Alaska and Hawaii in Versions 2 through 4 of the NET inventory. Energy efficiency factors compiled
from DOE energy projections data were applied to the growth factors for fuel combustion sources in all
three versions of the NET inventory. For several point and area VOC emission sources, EPA
incorporated the effects of maximum achievable control technology (MACT) controls implemented from
1997 through 1999. For some area source categories, EPA compiled current activity data to estimate
emissions for 1997 through 1999.
4.1.3 References
1. National Air Pollutant Emission Trends, 1900-1996, EPA-454/R-97-011. U.S. Environmental
Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC.
December 1997.
2. Regional Interim Emission Inventories (1987-1991), Volume I: Development Methodologies.
EPA-4 54/R- 93-0 21 a. U.S. Environment al Pro tect ion Agency, O ffice of Air Quality P lanning and
Standards, Research Triangle Park, NC. May 1993.
3. Development of the OPPE Paniculate Programs Implementation Evaluation System, Final,
Prepared for the Office of Policy, Planning and Evaluation/Office of Policy Analysis, U.S.
Environmental Protection Agency, under EPA Contract No. 68-D3-0035, Work Assignment
No. 0-10, Washington, DC. July 1994.
4. National Air Pollutant Emission Trends, 1900-1997, EPA-454/E-98-007. U.S. Environmental
Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC.
December 1998.
5. National Air Pollutant Emission Trends, 1900-1998, EPA-454/R-00-002. U.S. Environmental
Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC.
March 2000.
6. A National Air Pollutant Emission Trends report for 1900-1999 was not available when Volume I,
Section 4 of the NEI Procedures Document was revised in March 2001.
4-3
-------
Table 4.1 -1. Section 4.0 Structure
Subsection
Tier I
Tier II
4.1 Introduction
4.2 Fuel Combustion
Electric Utility
Fuel Combustion - Electric
Utility (01)
Majority of Coal (01), Oil (02), Gas
(03 ), and Other (04). The po int level -
steam generated fossil fuel sources.
4.3 Industrial
Fuel Combustion - Electric
Utility (01)
Fuel Combustion - Industrial
(02)
Chemical & Allied Product
Manufacturing (04)
Metals Processing (05)
Petroleum & Related Industries
(06)
Other Industrial Processes (07)
Storage & Transport (09)
Waste Disposal & Recycling
(10)
Miscellaneous (14)
Gas Turbines and Internal Combustion
(05). The area source level - steam
generated Coal (01), Oil (02), Gas (03).
All
All
All
All
All
All
All
Health services (05)
4.4 Other Combustion
Other Combustion (03)
Miscellaneous (14)
All
Other combustion (02)
4.5 Solvents
Solvent Utilization (08)
All
4.6 On-road Vehicles
On-road Vehicles (11)
All
4.7 Nonroad Engines and Nonroad Engines and Vehicles
Vehicles (12)
Storage & Transport (09)
Miscellaneous (14)
All
Petroleum & Petroleum Product
Storage (02)
Fugitive dust (07)
4.8 Fugitive Dust
Miscellaneous (14)
Agriculture & Forestry [(01),
agricultural crops and livestock only]
Fugitive dust (07)
4.9 Natural Sources
Natural Sources (13)
Biogenic (01)
NOTE:
Numbers in parentheses after Tier name are the T ier category codes.
4-4
-------
Tier I
Code Category
Table 4.1-2. Major Source Categories
Tier II
Code Category
01 FUEL COMBUSTION-ELECTRIC UTILITI ES
02 FUEL COMBUSTION-INDUSTRIAL
03 FUEL COMBUSTION-OTHER
04 CHEMICAL &ALLIED PRODUCT MFG.
05 METALS PROCESSING
06 PETROLEUM & RELATED INDUSTRIES
07 OTHER INDUSTRIAL PROCESSES
08 SOLVENT UTILIZATION
01
02
03
04
05
01
02
03
04
05
01
02
03
04
05
06
01
02
03
04
05
06
07
01
02
03
01
02
03
01
02
03
04
05
06
07
08
09
10
01
02
03
04
05
06
07
Coal
Oil
Gas
Other
Internal Combustion
Coal
Oil
Gas
Other
Internal Combustion
Commercial / Institutional Coal
Commercial / Institutional Oil
Commercial / Institutional Gas
Misc. Fuel Combustion (except residential)
Residential Wood
Residential Other
Organic Chemical Mfg.
Inorganic Chemical Mfg.
Polymer & Resin Mfg.
Agricultural Chemical Mfg.
Paint, Varnish, Lacquer, Enamel Mfg.
Pharmaceutical Mfg.
Other Chemical Mfg.
Nonferrous
Ferrous
Not elsewhere classified (NEC)
Oil & Gas Production
Petroleum Refineries & Related Industries
Asphalt Manufacturing
Agriculture, Food, & Kindred Products
Textiles, Leather, & Apparel Products
Wood, Pulp & Paper, & Publishing Products
Rubber & Miscellaneous Plastic Products
Mineral Products
Machinery Products
Electronic Equipment
Transportation Equipment
Construction
Miscellaneous Industrial Processes
Degreasing
Graphic Arts
Dry Cleaning
Surface Coating
Other Industrial
Nonindustrial
Solvent Utilization NEC
4-5
-------
Table 4.1-2. (continued)
Tier I
Code Category
Tier II
Code Category
09 STORAGE & TRANSPORT
10 WASTE DISPOSAL & RECYCLING
11 ON-ROAD VEHICLES
12 NONROAD ENGINES AND VEHICLES
13 NATURAL SOURCES
14 MISCELLANEOUS
01 Bulk Terminals & Plants
02 Petroleum & Petroleum Product Storage
03 Petroleum & Petroleum Product Transport
04 Service Stations: Stage I
05 Service Stations: Stage II
06 Service Stations: Breathing & Emptying
07 Organic Chemical Storage
08 Organic Chemical Transport
09 Inorganic Chemical Storage
10 Inorganic Chemical Transport
11 Bulk Materials Storage
12 Bulk Materials Transport
01 Incineration
02 Open Burning
03 Publicly Owned Treatment Works
04 Industrial Waste Water
05 Treatment Storage and Disposal Facility
06 Landfills
07 Other
01 Light-Duty Gas Vehicles & Motorcycles
02 Light-Duty Gas Trucks
03 Heavy- Duty G as Vehi cles
04 Diesels
01 Non-road Gasoline
02 Non-road Diesel
03 Aircraft
04 Marine Vessels
05 Railroads
06 Other
01 Biogenic
02 Geogenic
03 Miscellaneous (lightning, freshwater, saltwater)
01 Agricultures Forestry
02 Other Combustion (forest fires)
03 Catastrophic/Accidental Releases
04 Repair Shops
05 Health Services
06 Cooling Towers
07 Fugitive Dust
NOTE(S): For the purposes of this report, forest fires are considered anth ropogenic sources although many fires do occur naturally.
4-6
-------
4.2 FUEL COMBUSTION - ELECTRIC UTILITY
4.2.1 Which sources does EPA include in the Fuel Combustion - Electric Utility category?
The point and area source categories under the "Electric Utility" heading include the following Tier I
and Tier II categories:
Tier I Category Tier II Category
(01) FUEL COMBUSTION - ELECTRIC UTILITY (01) Coal
(02) Oil
(03) Gas
(04) Other
The emissions from the combustion of fuel by electric utilities are divided into two classifications:
(1) steam generated fossil-fuel units (boilers) with SCCs = 1 Olxxxxx; and (2) non-steam generated fossil-
fuel units such as gas turbines (GT) and internal combustion (1C) engines with SCCs =201xxxxx.
Estimating emissions for these two classes requires two very different methodologies, each of which is
described separately. Section 4.2 describes the methodology for fossil-fuel steam utility boilers. The
methodology used to estimate emissions for nonsteam generated fossil-fuel units is described in
section 4.3.
4.2.2 What emissions data for electric utilities are included in the Trends inventory?
The Trends data bases for fossil-fuel steam electric utility boilers include emission estimates of VOC,
NOX, CO, SO2, PM-10, and PM-2.5 for the years 1985 through 1999. In addition, NH3 emissions were
added in 1996 and CO2 emissions were added in 1997. Table 4.2-1 summarizes the methods applied to
estimate emissions for each pollutant for 1989 through 1999. Table 4.2-2 identifies the SCCs by fuel type
and boiler firing and bottom type for which emissions were estimated. Estimates for fossil-fuel steam
electric utilities do not include emissions from the combustion of anthracite coal because anthracite coal
accounts for less than 1 percent of the overall emissions from fuel combustion by fossil-fuel steam electric
utility units. EPA does not develop emissions estimates for sulfates (SO4) because no known utility
emission factors exist for this pollutant.
4.2.3 How does EPA develop emission estimates for fossil-fuel fired steam electric utilities?
Six basic factors are used to estimate emissions for fossil-fuel steam electric utility units for the years
1985 through 1998: (1) fuel consumption; (2) emission factor, which relates the quantity of fuel
consumed to the quantity of pollutant emitted; (3) fuel characteristics, such as sulfur content, ash content,
and heating value of fuels; (4) control efficiency, which indicates the percent of pollutant emissions not
removed through control methods; (5) rule effectiveness (which, according to EPA, measures a
regulatory program's ability to achieve all the emissions reductions that could be achieved by full
compliance with the applicable regulations at all sources at all times); and (6) whether Emissions
Tracking System/Continuous Emissions Monitoring (ETS/CEM) data exist for SO2, NOX, and heat input.
Fuel consumption characteristics and control efficiencies are determined at the boiler-level, whereas
emission factors are specified at the SCC-level.
4-7
-------
To derive 1999 emissions estimates, EPA extrapolates the 1999 emissions and heat input from the
1998 boiler-level emissions based on the ratio of plant-level 1999 fuel consumption to 1998 fuel
consumption. If the ratio is unknown, perhaps because this methodology does not account for fuels other
than coal, oil, or gas, the ratio is defaulted to 1. Finally, ETS/CEM SO2, NOX, and heat input values, if
they exist, are overlaid. Note that if a boiler reports ETS/CEM data but does not report to the EIA-767,
its ETS/CEM data are not used.
4.2.4 Where does EPA obtain the utility data necessary for emissions estimates?
Primary utility data collected by the Department of Energy's (DOE) Energy Information
Administration (EIA) serves as the basis for the fossil-fuel-fired steam electric utility component of the
Trends inventory. The EIA uses Form EIA-767 (Steam-Electric Plant Operation and Design Report1) to
collect monthly boiler-level data on a yearly basis and Form EIA-759 (Monthly Power Plant Report2} to
collect plant-level fossil-fuel steam data from all filing electric utility plants. Currently, data from Form
EIA-767 are available for the years 1985 through 1998, while data from Form EIA-759 are available
through the year 1999. The fossil-fuel steam electric utility component of the Trends emission
inventories for 1985 through 1999 includes data derived from the two EIA forms. Additionally,
beginning in 1998, EIA has determined that plants that have previously reported to Form EIA-767 must
continue to do so — even if they have been sold to anonutility, so that this file does contain some fossil-
fuel steam utility boilers that are presently owned by nonutiMes. This steam component does not include
data from GT or 1C engines (which account for a very small share of electric utility fuel use and
corresponding emissions) unless companies report that data to EIA.
The steam emission inventory data for 1985 through 1998 are initially based on the aggregated
monthly electric utility steam boiler-level data provided by Form EIA-767. All plants of at least
10 megawatts (MW) that have at least one operating boiler are required to provide this information to
EIA, although the amount of data required from plants with less than 100 MW of steam-electric
generating capacity is not as extensive as the amount required from those plants of at least 100 MW. For
plants with a generator nameplate rating from 10 MW to less than 100 MW, only those pages of Form
EIA-767 containing identification (ID) information (ie., plant ORIS code, State name, county name,
plant name, operator name, boiler ID), boiler fuel quantity and quality, and flue gas desulfurization (FGD)
information must be completed. Other sources of data for NOX, SO2, and heat input are used in place of
the EIA-based estimated data when the data are known to be better: EPA's ETS/CEM annual Scorecard
NOX and SO2 emissions and heat input overky the EIA-based data for affected acid rain utility boilers
beginning in 1995 (the data are also available for Phase 1 units for 1994).3 These sources are summarized
in Table 4.2-3.
4.2.4.1 What data does Form EIA-767 contain?
The EIA requires that the operating utility for each plant with fossil-fuel steam utility boilers of
10 MW or greater submit at least some sections of Form EIA-767. This form is designed so that
information for each plant is reported on separate pages that relate to different levels of data. The
relevant levels of data include the following:
• Plant-level: Delineation of the plant configuration, which establishes the number of boilers and
the IDs for each boiler, as well as the associated generator(s), FGD unit(s) (SO2 scrubbers), flue
4-8
-------
gas particulate collectors, flue(s), and stack(s). These do not necessarily have a one-to-one
correspondence. In addition, plant name, location, and operating utility are provided.
• Boiler-level: Monthly fuel consumption and quality data (for coal, oil, gas, and other),
regulatory data, and design parameters (including NOX control device and annual SO2 operating
efficiency).
• Gen era tor-level: Monthly generator and maximum nameplate capacity.
• FGD-level: One page per five FGD units for annual operating data (including SO2 control
efficiency) and design parameter data (including type of SO2 control device).
• Flue gas particulate collector-level: One page each for (up to five) collectors with annual
operating data [including total particulate (TSP) control efficiency] and design specifications
(including type of particulate control device).
• Flue- and stack-level: Design parameter data.
Form EIA-767 data for 1985 through 1997 are processed in a series of steps aimed at converting the
mainframe-level computerized data into usable data base form Only certain information is extracted.
For example, Form EIA-767 includes fuel-related boiler data such as monthly values for each fuel burned,
along with the fuel's associated sulfur, ash, and heat content. Only information regarding coal, oil, and
gas fuel type data is processed for the Trends inventory and only data from the first stack associated with
a boiler is used. Beginning with the 1998 data, EIA provided 15 data base files to include the EIA-767
data, and for the first time, all fuel types' data were processed.
The data are aggregated for each fuel to produce annual estimates for each boiler before they are
combined with other data (such as control devices and efficiencies, plant location data, associated
generator generation, and associated stack parameters). Once SCCs are assigned to each boiler's fuel
data in a given plant, the SCC-specific data are then separated so that each new data base record is on the
plant-boiler-SCC level.
4.2.4.2 What information does Form EIA-'/'59provide?
Form EIA-759 provides information on electric power generation, energy source consumption, and
end-of-month fossil fuel stock from all electric utilities that operate electric power generators and provide
electric power for public use. The Form EIA-759 data are also processed in a series of steps, although it
uses a less intricate method than for Form EIA-767, since the data for each plant are not reported at the
boiler level but instead are reported by fuel type and prime mover (for example, steam, hydro, 1C, and
GT).
For each plant-prime mover combination (in this case, for the steam prime mover), plant ID data, as
well as monthly fuel-specific generation and consumption data, are reported. EPA aggregates the
monthly plant steam prime mover data to annual estimates for each fuel reported and categorized as coal,
residual oil, distillate oil, and natural gas only, and combines to produce a single annual steam plant-level
data observation. (Beginning with 1996, EIA collects only annual, not monthly, data for small (less than
25 MW) plants, making the intermediate aggregation of monthly data unnecessary.)
-------
Since actual 1999 EIA-767 data are unavaikble, Form EIA-759 data is used to "grow" the 1998 fuel
and emissions data for 1999, as described later in section 4.2.8.
4.2.5 How does EPA develop the necessary data not supplied by the EIA forms?
To obtain data not contained in the computerized EIA data files, or converted to other measurement
units, algorithms (utilized since the 1980s) are used to develop values for SCC, heat input, pollutant
emissions, and NOX control efficiency.
Although Form EIA-767 reports generator nameplate capacity, this information cannot be used to
represent the boiler size when a one-to-one correspondence does not exist between boiler and generator
(referred to as a multiheader situation-for example, if one boiler is associated with two or more
generators or if several boilers are reciprocally associated with several generators). Therefore, EPA
developed a boiler design capacity variable (in MMBtu/hr) based on the reported maximum continuous
boiler steam flow at 100 percent load (in thousand pounds per hour) by multiplying the steam flow value
by a units conversion of 1.36. (EPA revised the boiler capacity methodology and updated the previous
value of 1.25 to 1.36 beginning with the 1997 data year.)
AP-424 emission factors are used to calculate emissions. The emission factor used depends upon the
SCC and pollutant, as explained below.
• The appropriate SCC is assigned to each source based on its fuel and boiler characteristics. For
sources using coal, the SCC is based on the American Society for Testing and Materials
(ASTM) criteria for moisture, mineral-free matter basis (if greater than 11,500 Btu/lb, coal type
is designated to be bituminous; if between 8,300 and 11,500 Btu/lb, coal type is designated to
be subbituminous; and if less than 8,300 Btu/lb, coal type is designated to be lignite) and the
boiler type (firing configuration and bottom type) as specified by AP-42. Fluidized bed
combustion boilers have SCCs assigned based on the fuel type. If both coal and oil are burned
in the same boiler, it is assumed that the oil is distillate; if coal is not burned, the oil burned is
assumed to be residual. See Table 4.2-2 for a complete list of the relationships among fuel
type, firing type, bottom type, and SCC.
Since Form EIA-767 does not provide control efficiencies for NOX, PM-10, and PM-2.5, control
efficiencies are derived using the following methods:
• NOX control efficiency is based on the assumption that the boiler would be controlled so that its
emission rate would equal its emission limit, expressed on an annual equivalent basis. After
calculating the heat input, EPA back-calculates controlled emissions assuming compliance with
the applicable standard. The NOX net control efficiency is calculated by dividing the controlled
by the uncontrolled NOX emissions.
• Since Form EIA-767 only reports TSP control efficiency, EPA uses the (updated) PM-10
Calculator5 to derive PM-10 and PM-2.5 control efficiencies. (The PM Calculator estimates
PM-10 and PM-2.5 control efficiencies based on the SCC and the primary and secondary
control devices. The control efficiencies from the PM Calculator are based on particle size
distribution data from AP-42 for specific SCCs, where available. These control efficiencies
were revised beginning with the 1998 data file.)
4-10
-------
EPA computes the SO2 emissions as controlled emissions assuming 100 percent rule effectiveness
and using the sulfUr content of the fuel as specified in the EIA-767 data. The PM-10 and PM-2.5
emissions are also computed as controlled emissions assuming 100 percent rule effectiveness. The ash
content of the fuel used to calculate uncontrolled PM-10 and PM-2.5 emissions is also specified in the
EIA-767 data. The NOX emissions are computed as controlled emissions assuming 80 percent rule
effectiveness for 1985-1994 data; beginning with 1995 data, NOX rule effectiveness is assumed to be
100 percent. The CO and VOC emissions are calculated as uncontrolled emissions. Although no NH3
AP-42 emission factors officially exist for utility fossil-fuels, in 1998 EPA developed coal, oil, and gas
NH3 emission factors that are applied to the specified quantity of fuel used. Thus, beginning with the
1996 data year, NH3 estimates are included in the Trends data base.
Due to EP A's increased interest in CO2 emissions, CO2 emissions were estimated for the data year
1997. Although it is possible to overlay EIA-based calculations with ETS/CEM data, EPA made a policy
decision to not do this until such time that the ETS/CEM data undergo thorough QA/QC review by
EPA's Clean Air Markets Division (CAMD), formerly known as the Acid Rain Division. Therefore, CO2
emission estimates are calculated using a methodology recommended by the Intergovernmental Panel on
Climate Change (IPCC)8 and used by both El A and EPA in the annual report on CO2 emissions in
response to the April 15, 1999 Presidential Directive.9 This methodology includes using fuel
consumption, carbon content co efficient, and conversion factors to yield CO2 tons. The algorithms to
compute all pollutant emissions are presented in Tables 4.2-4 and 4.2-5.
The PM-10 and PM-2.5 emissions included in the Trends inventory for all years through the 1999
data year represent filterable PM-10 and PM-2.5 emissions. For data years 1996 through 1998,
condensible PM (PMCD) emissions were estimated and summed with filterable PM-10 and PM-2.5
emissions to eatimate total PM-10 and total PM-2.5 emissions. To keep the basis for the PM-10 and
total PM-2.5 emissions for steam generated fossil-fuel utility boilers consistent with all other source
categories, EPA did not include PMCD or total PM-10 and total PM-2.5 emissions for steam generated
fossil-fuel utility boilers in the Trends inventory.
Since fewer required data elements (identification data, boiler fuel quantity and quality data, and
FGD data, if applicable) exist for those plants with a total capacity between 10 MW and 100 MW, many
values are missing. Most data elements are assigned a default value of zero; however, if values for boiler
firing and bottom type are missing (these are needed in the SCC assignment), the default values for wall-
fired and dry bottom types are assigned. In the past, discrepancies have occurred in the boiler bottom
and firing type data as reported to EIA and CAMD. Based on a coordinated effort in 1996, all
differences in bottom and firing types for coal boilers were resolved for previous years (i.e., 1985 through
1995).
4.2.6 What EIA data have been replaced with data from other sources?
EPA replaced the 1985 SO2 emissions and heat input calculated from the 1985 Form EIA-767 data
with corresponding boiler-level data (disaggregated to the SCC level) from the National Allowance Data
Base Version 3.11 (NADBV311).6 These data underwent two public comment periods in 1991 and 1992
and are considered the best available data for 1985. Aggregations at the fuel levels (Tier III) are
approximations only and are based on the methodology described in Section 4.2.1.
4-11
-------
In 1996, CAMD completed research on utility coal boiler-levelNOX rates. Approximately
90 percent of the rates were based on relative accuracy tests performed in 1993 and 1994 as a
requirement for continuous emissions monitor (CEM) certification, while the remaining boilers' rates
were obtained from utility stack tests from various years. These coal boiler-specific NOX rates were
considered, on the whole, to be significantly better than those calculated from EPA's NOX AP-42 emission
factors, which are SCC-category averages.
Thus, whenever these new NOX rates were available, EPA recalculated NOX coal emissions at the
coal SCC level, using the heat input (EIA's 767 fuel throughput multiplied by the fuel heat content) and
adjusting units, according to the following equation:
NOXCOALSCC = NOXRTcoal * HTISCC * JL (Eq. 4.2_i)
where: NOXCO AL = NOX emissions for the boiler coal SCC (in tons)
NOXRT = CAMD's coal NOX rate for the given boiler (in Ibs/MMBtu)
HTI = heat input for the boiler's coal SCC (in MMBtu)
These new NOX SCC-level coal emissions replaced the AP-42 calculated emissions for most of the coal
SCCs in the 1985-1994 data years (when ETS/CEM data were unavailable).
As of January 1, 1994, Title IV (Acid Deposition Control) of the Clean Air Act Amendments of
1990 (CAAA) required Phase I affected utility units to report heat input, SO2, and NOX data to EPA.
Beginning January 1, 1995, all affected units were required to report heat input and SO2 emissions; most
also had to report NOX emissions, although some units received extensions until July 1,1995 or
January 1, 1996 for NOX reporting.
The ETS/CEM data contain actual, rather than estimated, data. Thus, if a complete set of
ETS/CEM annual SO2 and/or NOX emissions and/or heat input data existed for 1994 and 1995, those data
values replaced the data estimated from EIA-767 data. This process involved the following steps:
• Aggregation of ETS/CEM hourly or quarterly data to annual data.
• Assignment of ETS/CEM data, reported on a monitoring stack or pipe level, to the boiler level.
• Matching the ETS/CEM boiler-level annual data to the processed EIA-767 annual data.
• Disaggregating the boiler-level ETS/CEM data to the boiler SCC level based on each SCC's
fractional share of the boiler EIA-based heat input, SO2, and NOX, respectively. The algorithms
used are included in Table 4.2-6.
Beginning with 1996 data, the ETS/CEM annual Scorecard data replaced EIA-derived SO2 and NOX
emissions and heat input for all boilers included in EIA-767 and in ETS/CEM. For those records in
which the ETS/CEM heat input replaces the EIA-calculated value, the heat input does not equal the
product of the EIA-reported fuel throughput and heat content. Additionally, CO2 and PMCD values are
recalculated using the ETS/CEM heat input value, thus also changing the values of TOTPM10 and
TOTPM25.
4-12
-------
4.2.7 How does EPA calculate ozone season daily emissions?
Ozone season daily (OSD) emissions are estimated for data years 1990-1997 by assuming the day to
be a typical or average summer July day. Emissions for VOC, NOX, CO, SO2, PM-10, PM-2.5, and NH3
(SO4 is zero) are calculated at the SCC level by taking the ratio of the Form EIA-767 July monthly to
annual heat input, dividing it by 31, and then multiplying this value by the already calculated annual
emissions. Beginning in data year 1998, a weighted average of the heat inputs for the five ozone season
months (July-September) was used in pkce of the July month heat input. The equation is:
HTISUM~rr
EOSD~rr = —— * EANN~rr (Ea 42-2)
scc 31 * HTIANNSCC scc ( ^ >
where: EOSD = Ozone season daily emissions for a given pollutant at the SCC level (in tons)
HTISUM = July monthly or ozone season monthly average Form EIA-767 calculated
heat input for the given boiler's SCC (in MMBtu)
HTIANN = annual Form EIA-767 calculated heat input for the given boiler's SCC (in
MMBtu)
EANN = Annual emissions for a given pollutant at the SCC level (in tons) for that
year
For the OSD emissions for projected 1999, the projected 1999 annual emissions are used, but the Form
EIA-767 calculated 1998 average summer month to annual heat input ratio is also used in the above
equation since the 1999 ratio is unknown.
4.2.8 1998 projected fossil-fuel steam emission inventory
The 1999 computerized fossil-fuel steam utility plant-level data from Form EIA-759 are used in
conjunction with the 1998 fossil-fuel steam electric utility component data to develop the 1999 steam
emission inventory file, since the 1999 Form EIA-767 data are not available. The fuel quantity, heat
input, and emissions values are grown by a factor based on the ratio of the 1999 Form EIA-759 plant-
level, fuel-specific data to the data for 1998.
The projected 1999 fossil-fuel steam utility inventory includes the same records that are in the 1998
file. That is, no new plants are added or subtracted from the 1998 steam inventory to produce the
projected 1999 steam inventory. However, the 1999 Form EIA-759 plant-level data should reflect boiler
retirement or additions for plants in 1999 and their fuel data would be incorporated in the growth ratios
and should be reflected in the 1999 data for the other boilers at a plant. As a result, the 1999 figures
should be considered to be preliminary estimates only.
4.2.9 What additional emissions estimates adjustments does EPA make?
To derive VOC emissions estimates, an adjustment is made due to the underestimation of aldehydes
which are not accounted for in the VOC emission factors for the following SCCs: 10100401, 10100404,
10100501, 10100601, and 10100604. The VOC emissions are augmented according to the methodology
used in the Hydrocarbon Preprocessor (HCPREP) of the Flexible Regional Emissions Data System
(FREDS).7 This augmentation was made on steam emission inventories for the years 1985 through
projected 1999.
-------
4.2.10 How does EPA perform its calculations?
The following provides an example calculation for estimating SO2 emissions for a tangentially-fired
dry-bottom utility boiler burning bituminous coal. This example shows how the emissions are initially
calculated using data reported to EIA-767 and an AP-42 emission iactor, and then overlaid with SO2
emissions reported to ETS/CEM. The methods shown in the example calculation are used to estimate
emissions for all steam generated fossil-fuel boilers and pollutants. See section 4.2.7 for details on what
EIA-767 data are replaced with ETS/CEM data for calculating emissions.
• 1995 boiler SCC data:
Variable Description
Variable
Name
Value
Units
Source classification code SCC
Annual fuel throughput thruput
Heat co ntent of fuel heatcon
Sulfur con tent of fuel sulfcon
SO2 control efficiency coneff4
Final emissions for inventory emiss4
Final heat input for inventory htinpt
Annual heat input calculated from EIA-767 data eiahti
Annual SO2 emissions calculated from EIA 767 eiaso2
data
SO2 emission factor emf4
Annual SO2 emissions reported to ETS/CEM so2ets
Annual heat input reported to ETS/CEM htiets
10100212
1300000
23.18 (really 23.1849046)
3.17 (really 3.1716)
89.30
9332.5590
31782453.38
30140376.00
8382.2216
39 (38 beginning with 1996 data)
9332.5590
31782453.38
SCC units
MMBtu/SCC units
tons
MMBtu
MMBtu
tons
Ibs SO2/ton coal
tons
MMBtu
Equation:
FJAVH -
2 ~
thruput * EMF4 * sulfcon * (1- (coneff4l\Qty)
2000
(Eq. 4.2-3)
4-14
-------
Calculation:
Emo = (1,300,000) (38) (3.1716) (1-0.893)
2 2000
Result:
EIASO2 = 8,382 (tons/year) to nearest integer
But replaced by 1995 ETSICEM SO2 emissions (SO2ets) = 9,332.5590 (tons/year) = final emissions (EMISS4)
Therefore EIASO2 = 8,382 (tons/year); and SO2ets = EMISS4 = 9,333 (tons/year) in the Inventory
Note that the AP-42 SO2 emission factor for SCC 10100212 was changed from 39 to 38 Ibs/ton of coal
beginning with data year 1996, reflecting the updated emission factor value.
4.2.11 References
1. Monthly Power Plant Report, Form EIA-759, data files for 1990 - 1999, U.S. Department of
Energy, Energy Information Administration, Washington, DC, 2000.
2. Steam-Electric Plant Operation and Design Report, Form EIA-767, data files for 1985 - 1998, U.S.
Department of Energy, Energy Information Administration, Washington, DC, 2000.
3. Acid Rain Program CEMS Submissions Instructions for Monitoring Plans, Certification Test
Notifications, and Quarterly Reports, U.S. Environmental Protection Agency, Washington, DC,
May 1995.
4. Compilation of Air Pollutant Emission Factors, Volume I: Stationary Point and Area Sources,
Fifth Edition, AP-42, U.S. Environmental Protection Agency, Research Triangle Park, NC.
5. Enhanced Paniculate Matter Controlled Emissions Calculator, Draft User's Manual, Emission
Factor and Inventory Group, Emissions Monitoring and Analysis Division, Office of Air Quality
Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC.
Prepared by E.H. Pechan & Associates, Inc., Durham, NC under EPA Contract No. 68-D7-0067,
Work Assignment No. 3-09, November 1999.
6. The National Allowance Data Base Version 3.11: Technical Support Document, Acid Rain
Division, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington,
DC, March 1993.
7. The Flexible Regional Emissions Data System (FREDS) Documentation for the 1985 NAPAP
Emission Inventory: Preparation for the National Acid Precipitation Assessment Program.
Appendix A. EPA-6 00/9-89-047. U.S. Environmental Protection Agency, Office of Research and
Development, Air and Energy Engineering Research Laboratory, Research Triangle Park, NC, May
1989.
4-15
-------
8. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-1998. U.S. Environmental Protection
Agency, Washington, DC, 2000.
9. Carbon Dioxide Emissions from the Generation of Electric Power in the United States;
http://www.epa.gov/globalwarming/publications/emissions/co2emiss.pdf.
4-16
-------
Table 4.2-1. Methods for Developing Annual Emission Estimates for Steam Generated Fossil-Fuel
Utility Boilers for the Years 1989-1999
For the data years For the pollu tant(s)
EPA estimated emissions by
1989-1993
NOV
If coal is burned, EIA data and EPA/ARD emission factors and heat input are used; if coal is not
burned, EIA data and AP-42 emission factors applied to fuel quantity are used.
1994-1995
NOV
If the boiler reports to both EIA-767 and ETS/CEM, and the ETS/CEM NOX data are complete for
the year, then the ETS/CEM data are used. Otherwise, if a boiler burned coal, EIA data and
EPA/ARD emission factors and heat input are used; if coal is not burned, EIA data and AP-42
emission factors applied to fuel quantity are used.
1996-1998
N0y
If the boiler reports to both EIA-767 and ETS/CEM, then the ETS/CEM data are used. Otherwise,
EIA data and AP-42 emission factors applied to fuel quantity are used. Note that AP-42 emission
factors for some SCCs changed from data years 1985-1995 to data year 1996, and again in data
year 1997.
1989-1993
SO,
EIA data and AP-42 emission factors applied to fuel quantity are used.
1994-1998
SO,
If the boiler reports to both EIA-767 and ETS/CEM, then the ETS/CEM data are used. Otherwise,
EIA data and AP-42 emission factors applied to fuel quantity are used. Note that AP-42 emission
factors for some SCCs changed from data years 1985-1995 to data year 1996.
1989-1998
VOC, CO
EIA data and AP-42 emission factors applied to fuel quantity are used. Note that AP-42 emission
factors for some SCCs changed from data years 1985-1995 to data year 1996 for VOC and CO.
1989-1997
PM-10, PM-2.5
(Filterable)
EIA data and AP-42 emission factors applied to fuel quantity are used. Note that AP-42 emission
factors for some SCCs changed from data years 1985-1995 to data year 1996 for PM10.
1998
PM-10, PM-2.5
(Filterable)
EIA data and AP-42 emission factors applied to fuel quantity are used. Note that AP-42
emission factors for some SCCs changed from data year 1996 to data year 1998 for PM10 and
PM25. Since the PM10 Calculator Program was updated in 1999-2000, updated PM efficiencies
are derived for emissions calculations.
1996-1998
PM Condensible
(PMCD), Total PM-10,
Total PM-2.5
EIA data and AP-42 emission factors applied to heat input are used to estimate PMCD. PMCD is
summed with filterable PM10 and PM25, respectively, to estimate total PM10 and PM25. However,
if the boiler reports to both EIA-767 and ETS/CEM, then the ETS/CEM heat input overlays EIA-
based heat input, PM condensible is recalculated, and total PM10 and PM25 emissions are
updated. Note that filterable PM10 and PM25 emissions for utility boilers are included in the
National Emissions Inventory to keep the basis for PM10 and PM25 emissions for utility boilers
consistent with all other source categories.
-------
Table 4.2-1 (continued)
For the data years For the pollutant(s) EPA estimated emissions by
1996-1998 NH3 EIA data and emission factors applied to heat input are used to estimate ammonia emissions.
However, if the boiler reports to both EIA-767 and ETS/CEM, then the ETS/CEM heat input
overlays ElA-based heat input, NH3 is recalculated, and the emissions are updated. For data
years prior to 1996, NH3 emissions were not estimated for utility boilers.
1997-1998 CO2 EIA data and carbon coefficients (as emission factors) are applied to heat input to estimate CO2.
However, if the boiler reports to both EIA-767 and ETS/CEM, then the ETS/CEM heat input
overlays ElA-based heat input, CO2 is recalculated, and the emissions are updated, too. Note
than for boilers burning coal, carbon coefficients changed slightly from data year 1997 to data
year 1998.
1999 NOX, SO2, VOC, CO, Projecting 1998 boiler-level emissions using ratio of plant-level 1999 fuel consumption to 1998
CO2, PM-10, PM-2.5, fuel consumption.
NH3
-------
Table 4.2-2. Steam Electric Utility Unit Source Classification Code Relationships
Fossil-Fuel
Firing Type
Bottom Type
SCC
Coal
Bituminous No data
Wall*
Opposed
Tangential
Stoker
Cyclone
Fluidized Bed
Subbituminous No data
Wall
Opposed
Tangential
Stoker
Cyclone
Fluidized Bed
Lignite No data
Wall
Opposed
Tangential
Stoker
Cyclone
Fluidized Bed
No data
Wet
Dry
No data
Wet
Dry
No data
Wet
Dry
No data
Wet
Dry
All
All
N/A
No data
Wet
Dry
No data
Wet
Dry
No data
Wet
Dry
No data
Wet
Dry
All
All
N/A
All
All
All
All
All
All
N/A
10100202
10100201
10100202
10100202
10100201
10100202
10100202
10100201
10100202
10100212
10100201
10100212
10100204
10100203
10100217
10100222
10100221
10100222
10100222
10100221
10100222
10100222
10100221
10100222
10100226
10100221
10100226
10100224
10100223
10100238
10100301
10100301
10100301
10100302
10100306
10100303
10100317
4-19
-------
Table 4.2-2 (continued)
Fossil-Fuel
Residual Oil
Distillate Oil
Natural Gas
Process Gas
Petroleum Coke
Biomass/Wood/Wood Waste
Propane
Refuse/Solid Waste
Other Liquid Oil
Firing Type
No data
Wall
Opposed
Tangential
Stoker
Cyclone
No data
Wall
Opposed
Tangential
Stoker
Cyclone
No data
Wall
Opposed
Tangential
Stoker
Cyclone
N/A
N/A
N/A
N/A
N/A
N/A
Bottom Type
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
N/A
N/A
N/A
N/A
N/A
N/A
sec
10100401
10100401
10100401
10100404
10100401
10100401
10100501
10100501
10100501
10100501
10100501
10100501
10100601
10100601
10100601
10100604
10100601
10100601
10100701
10100801
10100902
10101002
10101202
10101302
*Wall firing includes front, arch, concentric, rear, side, vertical, and duct burner firing.
4-20
-------
Table 4.2-3. Boiler Emissions Data Sources (Other than EIA-767)
for NOX, SO2, and Heat Input Data by Year
Year
NO,
SO,
1985 Overlaid CAMD coal NOX rate calculations
when possible
1986 Overlaid CAMD coal NOX rate calculations
when possible
1987 Overlaid CAMD coal NOX rate calculations
when possible
1988 Overlaid CAMD coal NOX rate calculations
when possible
1989 Overlaid CAMD coal NOX rate calculations
when possible
1990 Overlaid CAMD coal NOX rate calculations
when possible
1991 Overlaid CAMD coal NOX rate calculations
when possible
1992 Overlaid CAMD coal NOX rate calculations
when possible
1993 Overlaid CAMD coal NOX rate calculations
when possible
1994 Overlaid CAMD coal NOX rate calculations
when possible; overlaid ETS/CEM data
when possible
1995 Overlaid ETS/CEM data when possible
1996 Overlaid ETS/CEM data when possible
1997 Overlaid ETS/CEM data when possible
1998 Overlaid ETS/CEM data when possible
1999 Grew from 1998 data and overlaid the
ETS/CEM data, when possible, for the given
1998 universe of boilers.
NADBV311 data
Calculated from EIA-767 data
Calculated from EIA-767 data
Calculated from EIA-767 data
Calculated from EIA-767 data
Calculated from EIA-767 data
Calculated from EIA-767 data
Calculated from EIA-767 data
Calculated from EIA-767 data
Overlaid ETS/CEM data when possible
Overlaid ETS/CEM data when possible
Overlaid ETS/CEM data when possible
Overlaid ETS/CEM data when possible
Overlaid ETS/CEM data when possible
Grew from 1998 data and overlaid the
ETS/CEM data, when possible, for the
given 1998 universe of boilers.
CAMD = EPA's Clean Air Markets Division
NADBv311 = National Allowance Data Base Version 3.11
ETS/CEM = Emissions Tracking System/Continuous Emissions Monitoring data
4-21
-------
4.2-4. Algorithms Used to Estimate ElA-Based VOC, NOX, CO, SO2, PM-10, PM-2.5, and
NH3 Annual Emissions from Electric Utility Boilers
NOX,SCC = FCSCC * EFNOX,SCC * 0 ~ (RENOX * CENOx,b^ * UCF
•¥ = J7C1 * fi'fi' *
or VOC, \ SCC r^SCC ^r CO or VOC, SCC 'CO or VOC
PMW or PM25, SCC = **^SCC * PM10 or PM25, SCC * f* ^ ~ PM10 or PM25,b' *
Eso2,scc = FCscc * EFso2,scc * Sf * (I - CEso^b) * UCF
, SCC SCC NH3, SCC
where: £
FC
EF
S
A
RE
CE
b
f
UCF
Unitcoal
unito!l
unitgas
= annual estimated emission (in
tons/year)
= annual fuel consumption (in units/yearf )
= emission factor (in Ibs/unitf )
= sulfur content (expressed as a
decimal)
= ash content (expressed as a decimal)
= rule effectiveness (expressed as a decimal: 0.8 for CO2 and VOC; 0.8
for NOX until 1995, then 1.0; 1.0 for all other pollutants)
= control efficiency (expressed as a decimal)
= boiler
= fuel type
= units conversion factor (1 ton/2000 Ibs)
= tons burned
= 1000 gallons burned
= million cubic feet burned
t Note that VOC also undergoes an augmentation procedure.
4-22
-------
Table 4.2-5. Algorithms Used to Estimate ElA-Based Condensible PM, Total PM-10,
Total PM-2.5, and CO2 Annual Emissions for Electric Utility Boilers
= ffTT * J7J7 * C'ff
, SCO n±±SCC ^rPMCD, SCO ^r
= 7T * 7T
or TotPM25, SCO PMlo or PM2S, SCC £'PMCD, SCO
Eco2, sec = HTISCC * CCSCC * .99 * * CF
where: PMCD
E
HTI
EF
CC
.99
44
12
CF
= particulate matter condensible
= annual estimated emissions (in tons/year)
= annual heat input (in MMBtu/year)$
= emission factor (i n tons/MMBtu)
= carbon content coefficient in million metric tons of carbon equivalent
per quad (in MMTCE/1015 Btu)
= fraction oxidized to yield carbon
= ratio of CO2 molecular weight to carbon molecular weight
= units conversion factor to convert to short tons
$ Calculate using ElAfuel consumption and heat content values, but use ETS/CEM heat input data if available
and recalculate PMCD, TOTPM10, TOTPM25, and CO2.
4-23
-------
Table 4.2-6. Algorithms Used to Disaggregate ETS/CEM
Boiler Data to the Boiler-SCC Level
, 161SO2,rfni
CEMSO2^ = ^^ * CEMSO2.
~scc
767SO2,
, 767NOXf,rrni
CEMNOX^ = ^^ * CEMNOX.
'-sec
767NOX,
CEMHTL
767HTI,
sec
767HTI
* CEMHTL
where: b
CEMSO2, CEMNOX, CEMHTI
767SO2, 767NOX, 767HTI
= boiler-level
= ETS/CEM annual boiler data for given parameter
= Form EIA-767-based calculated data for given parameter
4-24
-------
4.3 INDUSTRIAL
4.3.1 What Source Categories Does the Industrial Sector Include?
The point and area source categories under the "Industrial" heading include the following Tier I and
Tier II categories:
Tier I Category Tier II Category
(01) FUEL COMBUSTION - ELECTRIC UTILITY (05) Gas Turbines and Internal
Combustion
(02) FUEL COMBUSTION-INDUS TRIAL All
(04) CHEMICAL & ALLIED PRODUCT MANUFACTURING All
(05) METALS PROCESSING All
(06) PETROLEUM & RELATED INDUSTRIES All
(07) OTHER INDUSTRIAL PROCESSES All
(09) STORAGE & TRANSPORT All
(10) WASTE DISPOSAL & RECYCLING All
(14) MISCELLANEOUS (05) Health Services
The methodologies for estimating emissions for gas turbines and internal combustion engines at
electric utilities are included in this section because they are the same as the methodologies for the
industrial sector.
See section 4.1.3 for instructions on how to identify the SCCs for the point and area source
categories assigned to these tier categories.
4.3.2 What Information Does This Section Provide?
This section describes the methods used to estimate 1985 through 1989 emissions, 1990 emissions
for the 1990 Interim Inventory, and 1990 through 1999 emissions in the National Emission Trends (NET)
inventory. Table 4.3-1 summarizes the methods applied and the pollutants for which emissions were
estimated for each year. Section 4.3.3 explains the methods for preparing the 1990 Interim Inventory.
Section 4.3.4 explains how emissions for 1985 through 1989 were developed from the 1990 Interim
Inventory.
After preparing the 1990 Interim Inventory, EPA developed a new 1990 base year inventory called
the NET) inventory. The NET inventory was prepared by combining State/local agency data from the
Ozone Transport Assessment Group (OTAG) emission inventory, the Grand Canyon Visibility Transport
Commission (GCVTQ emission inventory, and Aerometric Information Retrieval System/Facility
Subsystem (AIRS/FS). Data gaps were filled with information from the 1990 Interim Inventory. In
1997, PM-2.5 and NH3 emissions were added to the 1990 inventory. This 1990 inventory was then used
to grow emissions to 1991 through 1995. Subsequently, EPA has updated the 1990 to 1995 NET
inventories with data submitted by State and local agencies to comply with the CAAA requirement to
submit emissions data for major point sources every year. Section 4.3.5 provides details on how the 1990
NET inventory was developed. The methodologies for the 1991 through 1994 and the 1995 NET
emissions are presented in section 4.3.6 and 4.3.7, respectively.
4-25
-------
Initially, EPA prepared the 1996 emission inventory by merging the 1995 AIRS/FS emissions with
1995 emissions grown from 1990 emissions for the States that did not submit emissions data to AIRS/FS.
Sections 4.3.8.1 through 4.3.8.3 provide details on how this initial 1996 inventory was prepared.
Subsequently, EPA has been revising the 1996 NET to include base year emissions data submitted by
State/local agencies to comply with the CAAA requirements to submit (1) periodic emissions inventories
(PEI) every 3 years for ozone nonattainment areas (NAAs), and (2) emissions data for major point
sources annually. States with ozone NAAs needed to submit their PEI for 1996 by July 1997. While the
CAAA only require submittal of ozone precursor pollutant data for the PEI requirements, annual point
source reporting covers all criteria air pollutants. In its guidance provided to the State/local agencies on
the PEI submittal process, EPA encouraged State/local agencies to submit emission estimates for all
pollutants because the NET contains estimates for all criteria pollutants and is to be the ultimate
repository of the State/local agency data. To reduce the burden of preparing this inventory, EPA gave
each State/local agency a copy of the 1996 NET inventory as a starting point in preparing their 1996 base
year emissions. The methodologies used to prepare and revise the 1996 NET emissions are presented in
section4.3.8.4.
EPA developed 1997, 1998, and 1999 emissions for the NET inventory. Emissions for nonutiity
point sources and many area sources were developed using growth and control factors. Section 4.3.9
describes the methodologies used to prepare the 1997 through 1999 NET emissions.
4.3.3 How did EPA Develop the 1990 Interim Inventory?
The 1990 Interim Inventory is based on the 1985 NAPAP Inventory. The database includes annual
and average summer day emission estimates for 48 States and the District of Columbia. Five pollutants
(CO, NOX, VOC, SO2, and PM-10) were estimated for 1990.
The 1985 NAPAP Emission Inventory estimates for the point sources were projected to 1990 based
on the growth in Bureau of Economic Analysis (BEA) historic earnings for the appropriate State and
industry, as identified by the 2-digit SIC code.1 To remove the effects of inflation, the earnings data were
converted to 1982 constant dollars using the implicit price deflator for personal consumption
expenditures (PCE).2 State and SIC code-level growth factors were calculated as the ratio of the 1990
earnings data to the 1985 earnings data. Additional details on point source growth indicators are
presented in section 4.3.3.6.
The area source emissions from the 1985 NAPAP Emission Inventory were projected to 1990 based
on BEA historic earnings data, BEA historic population data, DOE SEDS data, or other growth
indicators. The specific growth indicator was assigned based on the source category. The BEA earnings
data were converted to 1982 dollars as described above. The 1990 SEDS data were extrapolated from
data for the years 1985 through 1989.3 All growth factors were calculated as the ratio of the 1990 data
to the 1985 data for the appropriate growth indicator. Additional details on area source growth
indicators are presented in section 4.3.3.7.
When creating the 1990 emission inventory, changes were made to emission factors, control
efficiencies, and emissions from the 1985 inventory for all sources. The PM-10 control efficiencies were
obtained from the PM Calculator.4 In addition, rule effectiveness, which was not applied in the 1985
NAPAP Emission Inventory, was applied to the 1990 emissions estimated for the point sources. The CO,
4-26
-------
NOX, and VOC point source controls were assumed to be 80 percent effective; PM-10 and SO2 controls
were assumed to be 100 percent effective.
The 1990 emissions for CO, NOX, SO2, and VOC were calculated using the following steps:
(1) projected 1985 controlled emissions to 1990 using the appropriate growth factors, (2) calculated the
uncontrolled emissions using control efficiencies from the 1985 NAPAP Emission Inventory, and
(3) calculated the final 1990 controlled emissions using revised control efficiencies and the appropriate
rule effectiveness. The 1990 PM-10 emissions were calculated using the TSP emissions from the 1985
NAPAP Emission Inventory. The 1990 uncontrolled TSP emissions were estimated in the same manner
as the other pollutants. The 1990 uncontrolled PM-10 estimates were calculated from these uncontrolled
TSP emissions by applying SCC-specific uncontrolled particle size distribution factors.5 The controlled
PM-10 emissions were estimated in the same manner as the other pollutants. Because the majority of
area source emissions for all pollutants represented uncontrolled emissions, the second and third steps
were not required to estimate the 1990 area source emissions.
4.3.3.1 What Control Efficiency Revisions did EPA Make?
In the 1985 NAPAP point source estimates, control efficiencies for VOC, NOX, CO, and SO2
sources in Texas were judged to be too high for their process/control device combination. These high
control efficiencies occurred because Texas did not ask for control efficiency information, and simply
applied the maximum efficiency for the reported control device.6 High control efficiencies lead to high
future growth in modeling scenarios based on uncontrolled emissions (which are based on the control
efficiency and reported actual emissions). High control efficiencies also lead to extreme increases in
emissions when rule effectiveness is incorporated.
Revised VOC control efficiencies were developed for Texas from the Emission Reduction and Cost
Analysis Model for VOC (ERCAM-VOC).7 For this analysis, revised efficiencies were also developed by
SCC and control device combination for NOX, SO2, and CO using engineering judgement. These revised
control efficiencies were applied to sources in Texas. A large number of point sources outside of Texas
had VOC and CO control efficiencies that were also judged to be too high The VOC and CO control
efficiencies used for Texas were also applied to these sources.
Control efficiencies not applied in the 1985 NAPAP Emission Inventory were incorporated in the
data files for VOC emissions from gasoline marketing (Stage I and vehicle refueling) and bulk gasoline
plants and terminals, since many areas already have regulations in place for controlling Stage I and Stage
II gasoline marketing emissions. Many current State regulations require the use of Stage I controls
(except at small volume service stations) to reduce emissions by 95 percent. Emissions were revised to
reflect these controls in areas designated as having these requirements as part of their SIPs.8 Stage II
vapor recovery systems are estimated to reduce emissions by 84 percent.9 Stage n controls are already in
place in the District of Columbia, St. Louis, Missouri, and parts of California. Stage II controls also
reduce underground tank breathing/emptying losses. Emissions in these area were revised to reflect these
controls.
Gasoline bulk plants and terminals are covered by existing Control Techniques Guidelines (CTGs)
and are included in many State regulations. Emissions were revised to reflect these controls in areas with
regulations.8 Control efficiencies assumed for these area source categories were 51 percent for gasoline
bulk plants and terminals. The 1985 NAPAP area source estimates have control levels built into these
4-27
-------
emissions. These control levels were first backed out of the emissions. In areas with no controls, the
emissions remained at uncontrolled levels. In areas with regulation, the uncontrolled emissions were
reduced to reflect the above efficiencies.
4.3.3.2 What Rule Effectiveness Assumptions did EPA Make?
Controlled emissions for each inventory year were recalculated, assuming that reported VOC, NOX,
and CO controls were 80 percent effective. Sulfur dioxide and PM-10 controls were assumed to be
100 percent effective. The 80 percent rule effectiveness assumption was judged to be unreasonable for
several VOC and CO source categories. The VOC rule effectiveness was changed to 100 percent for
bulk storage tank sources that had VOC control devices codes 90, 91, or 92. These three codes
represent conversion to variable vapor space tank, conversion to floating roof tank, and conversion to
pressurized tank, respectively. These controls were judged to be irreversible process modifications (there
are SCCs which represent these type of tanks), and, therefore, 100 percent rule effectiveness was applied.
VOC and CO rule effectiveness was changed to 100 percent for all Petroleum Industry - Fluid Catalytic
Cracking Units (FCCs), SCC 30600201. AP-42 lists CO waste heat boilers as a control for these units
with both CO and hydrocarbon emissions reduced to negligible levels. Since these boilers handle VOC
and CO as fuels rather than as emissions, they are treated as a process instead of as control device, and,
therefore, are not subject to rule effectiveness.
There is no control device code for CO boilers in the 1985 NAPAP Inventory. To implement this
set of revisions, all FCCs were assumed to have CO boilers. In addition, the CO rule effectiveness was
changed to 100 percent for sources in five other SCCs that burn CO as a fuel. The CO rule effectiveness
was also changed to 100 percent for sources with In-Process Fuel Use SCCs. According to AP-42, there
should be no CO emissions from these sources. Emissions were not deleted from the inventory, however
applying 80 percent rule effectiveness resulted in CO emissions of up to 36,000 short tons from some In-
Process Fuel Use sources. Changing the rule effectiveness to 100 percent for sources in these SCCs
retains the emissions, but at more reasonable levels. Table 4.3-2 lists the SCCs for which the CO rule
effectiveness was changed to 100 percent.
Rule effectiveness was also adjusted for all chemical and allied product point sources from 80 to
100 percent.
4.3.3.3 What Emission Factor Changes Occurred?
The VOC emission factors for vehicle refueling were updated to reflect changes in gasoline Reid
vapor pressure (RVP). The 1985 NAPAP gasoline marketing service station emissions were divided into
two components: evaporative losses from underground tanks (Stage I) and Stage II vehicle refueling
(including spillage). The 1985 NAPAP emissions were derived based on gasoline usage combined with
the following uncontrolled emissions factors from AP-42:
Stage 1: 7.3 Ibs/1,00 0 gallons
Stage II: 11.0 lbs/1,000 gallons
Spillage: 0.7 Ibs/1,000 gallons
4-28
-------
These emission factors were used to calculate the fraction of total emissions attributable to each of the
components above. The total percentage is 38.4 percent for Stage I emissions and 61.6 percent for
Stage II emissions, plus spillage.
The Stage II emissions were also revised to reflect changes in emission factors. Stage II emission
factors are a function of gasoline RVP and temperature. Gasoline RVPs have decreased since 1985 in
response to the phase I and phase II RVP regulations. MOBILES was used to calculate Stage II
emission factors for five sample States (Maryland, Illinois, New York, Texas, and North Carolina).
Factors for each season were calculated based on the seasonal RVP and temperature (see Tables 4.3-3 to
4.3-5) based on engineering judgement. The national average annual factors for each inventory year are
shown in Table 4.3-6. The 1987 value was used to estimate the 1985 and 1986 emissions.
In addition to updating the emission factor for Stage II, underground tank breathing/emptying losses
were also added to the inventory. The AP-42 emission factor of 1.0 lbs/1,000 gallons was used to
estimate emissions for each inventory year. Gasoline usage was back-calculated from the Stage II VOC
emissions and emission factor.
4.3.3.4 What Emissions Calculations Did EPA Use?
A three-step process was used to calculate emissions incorporating rule effectiveness. First, base
year controlled emissions are projected to the inventory year using the following formula
(Equation 4.3-1):
CEi = CEBY + (CEBY x EG) (Eq. 4.3-1)
where: CE; = controlled emissions for inventory year i
CEBY = controlled emissions for base year
EG; = earnings growth for inventory year i
Earnings growth (EG) is calculated using Equation 4.3-2:
DAT,
EG, - 1 - — (Eq. 4.3-2)
where: DAT; = earnings data for inventory year i
DATBY = earnings data in the base year
Second, uncontrolled emissions in the inventory year are back-calculated from the controlled emissions
based on the control efficiency with the following formula (Equation 4.3-3):
4-29
-------
CE
f i - CEFF\ (Eq" 43'3)
I 100 J
where: UE; = uncontrolled emissions for inventory year i
CE; = controlled emissions for inventory year I
CEFF = control efficiency (%)
Third, controlled emissions are recalculated incorporating rule effectiveness using the following equation
(Equation 4. 3 -4):
CER = UC
t
1 100 100 EFBY
where: CER; = controlled emissions incorporating rule effectiveness
UC; = uncontrolled emissions
REFF = rule effectiveness (%)
CEFF = control efficiency (%)
EF; = emission factor for inventory year i
EFBY = emission factor for base year
In many cases, the PM-10 emissions calculated based on the particle size distribution and PM-10
control efficiency were higher than the TSP emissions because of inconsistencies between the TSP
control efficiencies from the 1985 NAPAP inventory and the control efficiencies determined using the PM
Calculator. This error may have been compounded in the ibllowing steps with the values selected for
particle size distribution and efficiency. In the instances where the controlled PM-10 emissions were
calculated to be higher than the controlled TSP emissions, the controlled PM-10 emissions were replaced
with the controlled TSP emissions. The uncontrolled PM-10 was then recalculated using the revised
PM-10 emissions and the control efficiency from the PM Calculator. It was assumed that in these
instances, virtually all of the particles above 10 microns are being controlled and that particles emitted
after the control device are all particles of 10 microns or less.
The basis for rep lacing the PM-10 emissions with the TSP emissions in these cases is the assumption
that the controlled TSP emissions from the 1985 NAPAP inventory are the best data that are available as
a measure of point source particulate emissions. If it is assumed that the uncontrolled emissions were the
best data available, then an adjustment to the TSP control efficiency (resulting in an increase to actual
TSP emissions) would be performed rather than replacing the PM-10 emissions.
4. 3. 3. 5 For What Source Categories Did EPA Revise VOC and SO2 Emissions?
The EPA revised the NAPAP projected VOC emissions for hazardous waste treatment, storage, and
disposal facilities (TSDF) in the point source inventory, petroleum refinery fugitive emissions in the area
source inventory, and point source SO2 emissions for a copper smelter based on current data available for
these categories.
4-30
-------
Hazardous waste TSDF emissions were updated using an April 1989 file from EPA's Emission
Standards Division (ESD).10a This file provided estimates of TSDF emissions with longitude and ktitude
as the geographical indicator for each facility. The longitude and latitude were used to match emissions
to the appropriate State and county. The emissions were generated by using the Hazardous Waste Data
Management System (HWDMS)10b which includes data on facility-specific process descriptions, waste
characterization and quantities, and VOC speciation. HWDMS generated national emissions estimates by
summing emissions from each plant process at a TSDF. Speciated emissions from each plant process
were calculated as the quantity of a specific waste handled, multiplied by a process-specific emission
factor. Emission factors were taken from the Background Information Documents for TSDFs.Wc The
emission estimates displayed in Table 4.3-7 for eight counties were removed based on comments EPA
received from various State and Regional Emission Inventory personnel.
Area source petroleum refinery fugitive emissions were re-estimated based on a revised estimate of
national petroleum refinery emissions. The national petroleum refinery emissions used to estimate area
source emissions in the 1985 NAPAP were obtained from the Emissions Trends report.11 The emissions
for blowdown systems were revised to reflect the high level of control as shown in the point source
inventory.
The area source petroleum refinery fiigitive emissions were re-estimated using the revised national
emission total by applying the methodology used to develop the 1985 NAPAP estimate.12 Total county
fugitive petroleum refinery emissions were determined by distributing the revised Emission Trends
estimate (excluding process heaters and catalytic cracking units) based on 1985 county refinery capacity
from the DOE Petroleum Supply Annual.13 Refinery capacity from this publication was allocated to
counties based on the designated location of the refinery. The 1985 NAPAP Emission Inventory was
used to aid in the matching of refineries to location.
Total area source petroleum refinery fugitive emissions were then estimated by subtracting the point
source emissions (SCCs 3-06-004 through 3-06-888) from the total county-level emissions. Negative
values (indicating higher point source emissions than the totals shown for the county), were re-allocated
to counties exhibiting positive emission values based on the proportion of total refinery capacity for each
county to avoid double-counting of emissions. This resulted in an estimate of 351,000 short tons for
1985 compared with the earlier 1985 NAPAP estimate of 728,000 short tons (area source refinery
fugitives). This revised 1985 estimate was projected to the inventory years, as described in section
4.3.3.1.
The SO2 emissions for 1987 through 1989 were adjusted to correct for the permanent closing of the
Phelps Dodge copper smelter in Arizona in January 1987. This adjustment was made by subtracting the
1985 emissions for State=04, County=003, and NEDS ID=0013 from the inventory for 1987 through
1989.
4-31
-------
4.3.3.6 How Did EPA Grow Point Source Emissions?
The changes in the point source emissions were equated with the changes in historic earnings by
State and industry. Emissions from each point source in the 1985 NAPAP Emissions Inventory were
projected to the years 1985 through 1990 based on the growth in earnings by industry (2-digit SIC code).
Historical annual State and industry earnings data from BEA's Table SA-51 were used to represent
growth in earnings from 1985 through 1990.
The 1985 through 1990 earnings data in Table SA-5 are expressed in nominal dollars. To be used to
estimate growth, these values were converted to constant do liars to remove the effects of inflation.
Earnings data for each year were converted to 1982 constant dollars using the implicit price deflator for
PCE.2 The PCE deflators used to convert each year's earnings data to 1982 dollars are:
Year 1982 PCE Deflator
1985 111.6
1987 114.3
1988 124.2
1989 129.6
1990 136.4
Several BEA categories did not contain a complete time series of data for the years 1985 through
1990. Because the SA-5 data must contain 1985 earnings and earnings for each inventory year (1985
through 1990) to be useful for estimating growth, a log linear regression equation was used where
possible to fill in missing data elements. This regression procedure was performed on all categories that
were missing at least one data point and which contained at least three data points in the time series.
Each record in the point source inventory was matched to the BEA earnings data based on the State
and the 2-digit SIC. Table 4.3-8 shows the BEA earnings category used to project growth for each of
the 2-digit SICs found in the 1985 NAPAP Emission Inventory. No growth in emissions was assumed
for all point sources for which the matching BEA earnings data were not complete. Table 4.3-8 also
shows the national average growth and earnings by industry from Table SA-5.
4.3.3.7 How Did EPA Grow Area Source Emissions?
Emissions from the 1985 NAPAP Inventory were grown to the Emission Trends years based on
historical BEA earnings data (section 4.3.3.6), historical estimates of fUel consumption, or other
category-specific growth indicators. Table 4.3-9 shows the growth indicators used for each area source
by 1985 NAPAP category.
The SEDS data were used as an indicator of emissions growth for the area source fUel combustion
categories and for the gasoline marketing categories shown in Table 4.3-10. (SEDS reports fuel
consumption by sector and fuel type.) Since fuel consumption was the activity level used to estimate
emissions for these categories, fuel consumption was a more accurate predictor of changes in emissions,
compared to other surrogate indicators such as earnings or population SEDS fuel consumption data
were available through 1989 at the time the emission estimates were developed. The 1990 values were
extrapolated from the 1985 through 1989 data using a log linear regression technique. In addition to
4-32
-------
projecting 1990 data for all fuel consumption categories, the regression procedure was used to fill in
missing data points for fuel consumption categories if at least three data points in the time series (1985 to
1989) were available.
The last step in the creation of the area source inventory was matching the 1985 NAPAP categories
to the new AIRS Area and Mobile Source Subsystem (AMS) categories. This matching is provided in
Table 4.3-11. Note that there is not always a one-to-one correspondence between 1985 NAPAP and
AMS categories. For example, the gasoline marketing NAPAP category was split into two separate
AMS categories representing Stage I and Stage II emissions. In addition, three 1985 NAPAP SCCs are
not included in the AMS system of codes. Therefore, AMS codes were created for process emissions
from pharmaceutical manufacture, synthetic fiber manufacture, and SOCMI fugitive emissions.
4.3.4 How Did EPA Develop Emissions for 1985 to 1989?
The 1990 Interim Inventory was used as the base year from which emissions for 1985 to 1989 were
estimated. As discussed under section 4.3.3, the 1985 NAPAP controlled emissions were grown to 1990
to serve as the starting point for preparing the 1990 Interim Inventory emissions. However, several
changes were made to the 1990 emissions to improve the inventory prior to backcasting the emissions to
1985 through 1989. Consequently, the 1985 emissions estimated by this method do not match the 1985
NAPAP Emission Inventory. The factors used to backcast 1990 emissions to prior years are the same as
the factors used to grow 1985 NAPAP emissions to 1990.
4.3.5 What is the 1990 NET Inventory?
The 1990 NET inventory is based primarily on State data, with the 1990 Interim Inventory data
filling in the gaps. The database houses U. S. annual and average summer day emission estimates for the
50 States and the District of Columbia. Seven pollutants (CO, NOX, VOC, SO2, PM-10, PM-2.5, and
NH3) were estimated for 1990. The State data were extracted from three sources, the OTAG inventory,
the GCVTCinventory, and AIRS/FS. Sections 4.3.5.1, 4.3.5.2, and4.3.5.3 give brief descrptions of
these efforts. Section 4.3.5.4 describes the efforts necessary to supplement the inventory gaps that are
either temporal, spacial, or pollutant. Since EPA did not receive documentation on how these inventories
were developed, this section only describes the effort to collect the data and any modifications or
additions made to the data.
4.3.5.1 OTAG
The OTAG inventory for 1990 was completed in December 1996. The database houses emission
estimates for those States in the Super Regional Oxidant A (SUPROXA) domain. The estimates were
developed to represent average summer day emissions for the ozone pollutants (VOC, NOX, and CO).
This section gives a background of the OTAG emission inventory and the data collection process.
4.3.5.1.1 Inventory Components —
The OTAG inventory contains data for all States that are partially or fully in the SUPROXA
modeling domain The SUPROXA domain was developed in the late 1980s as part of the EPA regional
oxidant modeling (ROM) applications. EPA had initially used three smaller regional domains (Northeast,
Midwest, and Southeast) for ozone modeling, but wanted to model the full effects of transport in the
eastern United States without having to deal with estimating boundary conditions along relatively high
433
-------
emission areas. Therefore, these three domains were combined and expanded to form the Super Domain.
The western extent of the domain was designed to allow for coverage of the largest urban areas in the
eastern United States without extending too far west to encounter terrain difficulties associated with the
Rocky Mountains. The Northern boundary was designed to include the major urban areas of eastern
Canada. The southern boundary was designed to include as much of the United States as possible, but
was limited to latitude 26°N, due to computational limitations of the photochemical models. (Emission
estimates for Canada were not extracted from OTAG for inclusion in the NET inventory.)
The current SUPROXA domain is defined by the following coordinates:
North: 47.0 0°N East: 67.0 0°W
South: 26.0 0°N West: 99.0 0°W
Its eastern boundary is the Atlantic Ocean and its western border runs from north to south through North
Dakota, South Dakota, Nebraska, Kansas, Oklahoma, and Texas. In total, the OTAG Inventory
completely covers 37 States and the District of Columbia.
The OTAG inventory is primarily an ozone precursor inventory. It includes emission estimates of
VOC, NOX, and CO for all applicable source categories throughout the domain. It also includes a small
amount of SO2 and PM-10 emission data that was sent by States along with their ozone precursor data.
No quality assurance (QA) was performed on the SO2 and PM-10 emission estimates for the OTAG
inventory effort.
Since the underlying purpose of the OTAG inventory is to support photochemical modeling for
ozone, it is primarily an average summer day inventory. Emission estimates that were submitted as
annual emission estimates were converted to average summer day estimates using operating schedule data
and default temporal profiles and vice versa.
The OTAG inventory is made up of three major components: (1) the point source component,
which includes segment/pollutant level emission estimates and other relevant data (e.g., stack parameters,
geographic coordinates, and base year control information) for all stationary point sources in the domain;
(2) the area source component, which includes county level emission estimates for all stationary area
sources and non-road engines; and (3) the on-road vehicle component, which includes county/roadway
functional class/vehicle type estimates of VMT and MOBILESa input files for the entire domain. Of
these three components, the NET inventory extracted all but the utility emissions. (See section 4.2 for a
description of the utility NET emissions and section4.6 for the on-road mobile NET emissions.)
4.3.5.1.2 Interim Emissions Inventory (OTAG Default) —
The primary data sources for the OTAG inventory were the individual States. Where States were
unable to provide data, the 1990 Interim Inventory was used for default inventory data.14 A more
detailed description of the 1990 Interim Inventory is presented in section 4.3.3.
4-34
-------
4.3.5.1.3 State Data Collection Procedures —
Since the completion of the Interim Inventory in 1992, many States had completed 1990 inventories
for ozone nonattainment areas as required for preparing SIPs. In addition to these SIP inventories, many
States had developed more comprehensive 1990 emission estimates covering their entire State. Since
these State inventories were both more recent and more comprehensive than the Interim Inventory, a new
inventory was developed based on State inventory data (where available) in an eifort to develop the most
accurate emission inventory to use in the OTAG modeling.
On May 5, 1995, a letter from John Seitz (Director of EPA's Office of Air Quality Planning and
Standards [OAQPS]) and MaryGade (Vice President of EGOS) to State Air Directors, States were
requested to supply available emission inventory data for incorporation into the OTAG inventory.15
Specifically, States were requested to supply all available point and area source emissions data for VOC,
NOX, CO, SO2, andPM-10, with the primary focus on emissions of ozone precursors. Some emission
inventory data were received from 36 of the 38 States in the OTAG domain. To minimize the burden to
the States, there was no specified format for submitting State data. The majority of the State data was
submitted in one of three formats:
1) an Emissions Preprocessor System Version 2.0 (EPS2.0) Workfile
2) an ad hoc report from AIRS/FS
3) data files extracted from a State emission inventory database
The origin of data submitted by each State is described in section 4.3.5.1.4.1 for point sources and
4.3.5.1.4.2 for area sources.
4.3.5.1.4. State Data Incorporation Procedures/Guidelines —
The general procedure for incorporating State data into the OTAG Inventory was to take the data
"as is" from the State submissions. There were two main exceptions to this policy. First, any inventory
data for years other than 1990 was back cast to 1990 using BEA Industrial Earnings data by State and
2-digit SIC code.1 This conversion was required for five States that submitted point source data for the
years 1992 through 1994. All other data submitted were for 1990.
Second, any emission inventory data that included annual emission estimates but not average
summer day values were temporally allocated to produce average summer day values. This temporal
allocation was performed for point and area data supplied by several States. For point sources, the
operating schedule data, if supplied, were used to temporally allocate annual emissions to average
summer weekday using the following equation:
EMSSIONSASD = EMISSIONSANNUAL * SUMTHRU * 1/(13 * DPW) (Eq. 4.3-5)
where: EMISSIGNSASD = average summer day emissions
EMIS SIGNSANNUAL = annual emissions
SUMTHRU = summer throughput percentage
DPW = days per week in operation
4-35
-------
If operating schedule data were not supplied for the point source, annual emissions were temporally
allocated to an average summer weekday using EPA's default Temporal Allocation file. This computer
file contains default seasonal and daily temporal profiles by SCC. The following equation was used:
EMISSIONSASD = EMISSIONSANNUAL I (SUMFACSCC * WDFACSCC) (Eq. 4.3-6)
where: EMISSIONSASD = average summer day emissions
EMISSIONSANNUAL = annual emissions
SUMFACSCC = default summer season temporal factor for SCC
WDFACSCC = default summer weekday temporal factor for SCC
There were a small number of SCCs that were not in the Temporal Allocation file. For these SCCs,
average summer weekday emissions were assumed to be the same as those for an average day during the
year and were calculated using the following equation:
EMISSIONSASD = EMISSIONSANNUAL I 365 (Eq. 4.3-7)
where: EMISSIGNSASD = average summer day emissions
EMIS SIGNSANNUAL = annual emissions
4.3.5.1.4.1 Point. For stationary point sources, 36 of the 38 States in the OTAG domain supplied
emission estimates covering the entire State. Data from the Interim Inventory were used for the two
States (Iowa and Mississippi) that did not supply data. Most States supplied 1990 point source data,
although some States supplied data for later years because the later year data reflected significant
improvements over their 1990 data. Inventory data for years other than 1990 were backcast to 1990
using BEA historical estimates of industrial earnings at the 2-digit SIC level. Table 4.3-12 provides a
brief description of the point source data supplied by each State. Figure 4.3-1 shows the States that
supplied point source data and whether the data were for 1990 or a later year.
4.3.5.1.4.2 Area, For area sources, 17 of the 38 States in the OTAG domain supplied 1990 emission
estimates covering the entire State, and an additional nine States supplied 1990 emission estimates
covering part of their State (partial coverage was mostly in ozone nonattainment areas). Interim
Inventory data were the sole data source for 12 States. Where the area source data supplied included
annual emission estimates, the default temporal factors were used to develop average summer daily
emission estimates. Table 4.3-13 provides a brief description of the area source data supplied by each
State. Figure 4.3-2 shows the States that supplied area source data.
4.3.5.1.4.3 Rule Effectiveness. For the OTAG inventory, States were asked to submit their best
estimate of 1990 emissions. There was no requirement that State-submitted point source data include
rule effectiveness for plants with controls in place in that year. States were instructed to use their
judgment about whether to include rule effectiveness in the emission estimates. As a result, some States
submitted estimates that were calculated using rule effectiveness, while other States submitted estimates
that were calculated without using rule effectiveness.
4-36
-------
The use of rule effectiveness in estimating emissions can result in emission estimates that are much
higher than estimates for the same source calculated without using rule effectiveness, especially for
sources with high control efficiencies (95 percent or above). Because of this problem, there was concern
that the OTAG emission estimates for States that used rule effectiveness would be biased to larger
estimates relative to States that did not include rule effectiveness in their computations.
To test if this bias existed, county level maps of point source emissions were developed for the
OTAG domain If this bias did exist, one would expect to see sharp differences at State borders between
States using rule effectiveness and States not using rule effectiveness. Sharp State boundaries were not
evident in any of the maps created. Based on this analysis, it was determined that impact of rule
effectiveness inconsistencies was not causing large biases in the inventory.
4.3.5.2 Grand Canyon Visibility Transport Commission Inventory
The GCVTC inventory includes detailed emissions data for 11 States: Arizona, California,
Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.16 This
inventory was developed by compiling and merging existing inventory databases. The primary data
sources used were State inventories for California and Oregon, AIRS/FS for VOC, NOX, and SO2 point
source data for the other nine States, the 1990 Interim Inventory for area source data for the other nine
States, and the 1985 NAPAP inventory for NH3 and TSP data. In addition to these existing data, the
GCVTC inventory includes newly developed emission estimates for forest wildfires and prescribed
burning.
After a detailed analysis of the GCVTC inventory, it was determined that the following portions of
the GCVTC inventory would be incorporated into the PM inventory:
• complete point and area source data for California
• complete point and area source data for Oregon
• forest wildfire data for the entire 11 State region
• prescribed burning data for the entire 11 State region
State data from California and Oregon were incorporated because they are complete inventories
developed by the States and are presumably based on more recent, detailed and accurate data than the
Interim Inventory (some of which is still based on the 1985 NAPAP inventory). The wildfire data in the
GCVTC inventory represent a detailed survey of forest fires in the study area and are clearly more
accurate than the wildfire data in the Interim Inventory. The prescribed burning data in the GCVTC
inventory are the same as the data in the Interim Inventory at the state level, but contain more detailed
county-level data.
Non-utility point source emission estimates in the GCVTC inventory from States other than
California and Oregon came from AIRS/FS. Corrections were made to this inventory to the VOC and
PM emissions. The organic emissions reported in GCVTC inventory for California are total organics
(TOG). These emissions were converted to VOC using the profiles fromEPA's SPECIATE17 database.
Since the PM emissions in the GCVTC were reported as both TSP and PM-2.5, EPA estimated PM-10
from the TSP in a similar manner as described in section 4.3.3.4.
4-37
-------
4.3.5.3 AIRS/FS
SO2 and PM-10 (or PM-10 estimated from TSP) sources of greater than 250 tons per year as
reported to AIRS/FS that were not included in either the OTAG or GCVTC inventories were appended
to the NET inventory. The data were extracted from AIRS/FS using the data criteria set listed in
Table 4.3-14. The data elements extracted are also listed in Table 4.3-14. The data were extracted in
late November 1996. It is important to note that estimated emissions were extracted.
4.3.5.4 Data Gaps
As stated above, the starting point for the 1990 NET inventory is the OTAG, GCVTC, AIRS, and
1990 Interim inventories. Data added to these inventories include estimates of SO2, PM-10, PM-2.5, and
NH3, as well as annual or ozone season daily (depending on the inventory) emission estimates for all
pollutants. This section describes the steps taken to fill in the gaps from the other inventories.
4.3.5.4.1 SO2 and PM Emissions —
For SO2 and PM-10, State data from OTAG were used where possible. (The GCVTC inventory
contained SO2 andPM annual emissions.) In most cases, OTAG data for these pollutants were not
available. For point sources, data for plants over 250 tons per year for SO2 and PM-10 were added from
AIRS/FS. The AIRS/FS data were also matched to the OTAG plants and the emissions were attached to
existing plants from the OTAG data where a match was found. Where no match was found to the plants
in the OTAG data, new plants were added to the inventory. For OTAG plants where there were no
matching data in AIRS/FS and for all area sources of SO2 and PM-10, emissions were calculated based
on the emission estimates for other pollutants.
The approach to developing SO2 and PM-10 emissions from unmatched point and area sources
involved using uncontrolled emission factor ratios to calculate uncontrolled emissions. This method used
SO2 or PM-10 ratios to NOX. NOX was the pollutant utilized to calculate the ratio because (1) the types
of sources likely to be important SO2 and PM-10 emitters are likely to be similar to important NOX
sources and (2) the generally high quality of the NOX emissions data. Ratios of SO2/NOX and PM-10/NOX
based on uncontrolled emission factors were developed. These ratios were multiplied by uncontrolled
NOX emissions to determine either uncontrolled SO2 or PM-10 emissions. Once the uncontrolled
emissions were calculated, information on VOC, NOX, and CO control devices was used to determine if
they also controlled SO2 and/or PM-10. If this review determined that the control devices listed did not
control SO2 and/or PM-10, plant matches between the OTAG and Interim Inventory were performed to
ascertain the SO2 and PM-10 controls applicable for those sources. The plant matching component of
this work involved only simple matching based on information related to the State and county Federal
Information Processing Standards (FIPS) code, along with the plant and point IDs.
There was one exception to the procedures used to develop the PM-10 point source estimates. For
South Carolina, PM-10 emission estimates came from the Interim Inventory. This was because South
Carolina had no PM data in AIRS/FS for 1990 and using the emission factor ratios resulted in
unrealistically high PM-10 emissions.
There were no PM-2.5 data in either OTAG or AIRS/FS. Therefore, the point and area PM-2.5
emission estimates were developed based on the PM-10 estimates using source-specific uncontrolled
particle size distributions and particle size specific control efficiencies for sources with PM-10 controls.
438
-------
To estimate PM-2.5, uncontrolled PM-10 was first estimated by removing the impact of any PM-10
controls on sources in the inventory. Next, the uncontrolled PM-2.5 was calculated by multiplying the
uncontrolled PM-10 emission estimates by the ratio of the PM-2.5 particle size multiplier to the PM-10
particle size multiplier. (These particle size multipliers represent the percentage to total particulates
below the specified size.) Finally, controls were reapplied to sources with PM-10 controls by multiplying
the uncontrolled PM-2.5 by source/control device particle size specific control efficiencies.
4.3.5.4.2 NH3 Emissions —
A11NH3 emission estimates incorporated into the NET Inventory came directly from EPA's National
Particukte Inventory (NPI).18 This methodology is the same as that reported in section 4.3.3 for the
1990 Interim Inventory, with the exception of agricultural sources. The NPI contained the only NH3
emissions inventory available. (Any NH3 estimates included in the OTAG or AIRS/FS inventory were
eliminated due to sparseness of data.) As with SO2 and PM-10, plant matching was performed for point
sources. Emissions were attached to existing plants where there was a match. New plants were added
for plants where there was no match.
4.3.5.4.3 Other Modifications —
Additional data were also used to fill data gaps for residential wood combustion and prescribed
burning. Although these categories were in the OTAG inventory, the data from OTAG were not usable
since the average summer day emissions were often very small or zero. Therefore, annual and average
summer day emission estimates for these two sources were taken from the NET.
Additional Q A/quality control (QC) of the inventory resulted in the following changes:
• Emissions with SCCs of fewer than eight digits or starting with a digit greater than the number "6"
were deleted because they are invalid codes.
• Area source PM-10 and PM-2.5 utility emissions were deleted.
• A correction was made to a point (State 13/county 313/plant 0084) where the ozone season daily
value had been revised but not the annual value.
• Tier assignments were made for all SCCs.
• Checked and fixed sources with PM-2.5 emissions which were greater than their PM-10 emissions.
• Checked and fixed sources with PM-10 emissions greater than zero and PM-2.5 emissions equal to
zero.
• TSDFs - The 1990 TSDF emission estimates provided by the States through the OTAG effort were
replaced with the 1990 emission estimates modified as described in section 4.3.3.5.
4.3.6 How Did EPA Develop Emissions for 1991 to 1994?
The 1991 through 1994 area source emissions were grown in a similar manner as the 1985 through
1989 estimates, except for using a different base year inventory. The base year for the 1991 through
1994 emissions is the 1990 NET inventory. The point source inventory was also grown for those States
that did not want their AIRS/FS data used. (The list of States are detailed in the AIRS/FS subsection,
4.3.6.2.). For those States requesting that EPA extract their data from AIRS/FS, the years 1990 through
1995 were downloaded from the EPA IBM Mainframe. The 1996 emissions were not extracted since
States are not required to have the 1996 data uploaded into AIRS/FS until July 1997.
4-39
-------
4.3.6.1 Grown Estimates
The 1991 through 1994 point and area source emissions were grown using the 1990 NET inventory
as the basis. The algorithm for determining the estimates is detailed in section 4.3.3.4. The 1990 through
1996 SEDS and BEA data are presented in Tables 4.3-15 and 4.3-16. The 1996BEAandSEDS data
were determined based on linear interpretation of the 1988 through 1995 data. Point sources were
projected using the first two digits of the SIC code by State. Area source emissions were projected using
either BEA or SEDS. Table 4.3-17 lists the SCC and the source for growth.
The 1990 through 1996 earnings data in BEA Table SA-5 (or estimated from this table) are
expressed in nominal dollars. In order to be used to estimate growth, these values were converted to
constant dollars to remove the effects of inflation. Earnings data for each year were converted to 1992
constant dollars using the implicit price deflator for PCE. The PCE deflators used to convert each year's
earnings data to 1992 dollars are:
Year 1992 PCE Deflator
1990 93.6
1991 97.3
1992 100.0
1993 102.6
1994 104.9
1995 107.6
1996 109.7
4.3.6.2 AIRS/FS
Several States responded to EPA's survey and requested that their 1991 through 1995 estimates
reflect their emissions as reported in AIRS/FS. The list of these States, along with the years available in
AIRS/FS is given in Table 4.3-18. As described in section 4.3.5.3, default estimated annual and ozone
season daily emissions (where available) were extracted from AIRS/FS. Some changes were made to
these AIRS/FS files. For example, the default emissions for some States contain rule effectiveness and
the emissions were determined to be too high by EPA. The emissions without rule effectiveness were
extracted from AIRS/FS and replaced the previously high estimates. The changes made to select State
and/or plant AIRS/FS data are listed below.
• Louisiana All VOC source emissions were re-extracted to obtain emissions
without rule effectiveness for the year 1994.
• Colorado - Mastercraft The VOC emissions were reported as ton/year in the initial
download from AIRS. The units were changed to pounds/
year in AIRS.
• Wisconsin - Briggs and Stratton The VOC emissions for two SCCs were changed from with
rule effectiveness to without rule effectiveness for the years
1991, 1993, and 1994.
4-40
-------
As noted in Table 4.3-18, several States did not report emissions for all pollutants for all years for
the 1990 to 1995 time period. To fill these data gaps, EPA applied linear interpolation or extrapolated
the closest 2 years worth of emissions at the plant level. If only 1 year of emissions data were available,
the emission estimates were held constant for all the years. The segment-SCC level emissions were
derived using the average split for all available years. The non-emission data gaps were filled by using the
most recent data available for the plant.
As described in section 4.3.5.4.1, many States do not provide PM-10 emissions to AIRS. These
States' TSP emissions were converted to PM-10 emissions using uncontrolled particle size distributions
and AP^12 derived control efficiencies. The PM-10 emissions are then converted to PM-2.5 in the same
manner as described in section 4.3.3.4. The State of South Carolina provided its own conversion factor
for estimating PM-10 from TSP.18
For all sources that did not report ozone season daily emissions, these emissions were estimated
using the algorithm described in section 4.3.5.1.4 and equations 4.3-5 through 4.3-7.
4.3.7 How were 1995 Emissions Prepared?
The 1995 emission estimates were derived in a similar manner as the 1991 through 1994 emissions.
The estimates were either extracted from AIRS/FS for 1995, estimated using AIRS/FS data for the years
1990 through 1994, or projected using the 1990 NET inventory. The method used depended on the
States' responses to a survey conducted by EPA early in 1997. A description of the AIRS/FS
methodology is described in section 4.3.6.2. The following three subsections describe the projected
emissions. In addition, EPA has added the source category cotton ginning to the NET area source
inventory. The methodology is detailed in section 4.3.7.4.
4.3.7.1 Grown Estimate
The 1995 point and area source emissions were grown using the 1990 NET inventory as the basis.
Growth factors were prepared for each year using either SEDS annual fuel consumption data or BEA
national earnings by industry. The 1990 through 1996 SEDS and BEA data are presented in Tables 4.3-
15 and 4.3-16. The algorithm for determining the estimates is detailed in section 4.3.3.4.
4,3,7,2 NOXRACT
Major stationary source NOX emitters in marginal and above nonattainment areas and in ozone
transport regions (OTRs) are required to install RACT-level controls under the ozone nonattainment
related provisions of Title I of the CAAA. The definition of major stationary source for NOX differs by
the severity of the ozone problem as shown in Table 4.3-19.
NOX RACT controls for non-utility sources that were modeled for the 1995 NET emissions are
shown in Table 4.3-20. These RACT-level controls were applied to point source emitters with emissions
at or above the major source size definition for each area. The application of NOX RACT controls was
only applied to grown sources.
4-41
-------
4.3.7.3 Rule Effectiveness
Rule effectiveness was revised in 1995 for all grown sources using the information in the 1990
database file. If the rule effectiveness value was between 0 and 100 percent in 1990 and the control
efficiency was greater than 0 percent, the uncontrolled emissions were calculated for 1990. The 1995
emissions were calculated by multiplying the growth factor by the 1990 uncontrolled emissions and the
control efficiency and a rule effectiveness of 100 percent. The adjustment for rule effectiveness was only
applied to grown sources.
4.3.7.4 Cotton Ginning
Emissions for cotton ginning are classified under SCC 2801000000. Cotton ginning estimates for
1995 through 1999 were calculated using the following methodology. Ginning activity occurs from
August/September through March, covering parts of two calendar years,19 with the majority of ginning
activity occurring between September and January. Ginning activity occurs in the 16 States where cotton
is grown, i.e., Alabama, Arizona, Arkansas, California, Florida, Georgia, Louisiana, Mississippi, Missouri,
New Mexico, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, and Virginia. The majority
of the ginning facilities are located in Arkansas, California, Louisiana, Mississippi, and Texas.
The general equation for estimating emissions from this category is given below.
E = (Pc*£) * EFc + (PfE) * EFf (Eq. 4.3-8)
where: E = annual county emissions (Ibs/year)
B = number of bales ginned in the county
Pc = frac tio n of t ot al bale s at gins w ith c onv ent iona 1 con tr ols
EFC = emission factor for gins with conventional controls (Ibs/bale)
Pf = fraction of total bales at gins with full controls
EFf = emission factor for gins with full controls (Ibs/bale)
4.3.7.4.1 Activity Indicator —
The activity factor for this category is the number of bales of cotton ginned. The U. S. Department
of Agriculture (USD A) compiles and reports data on the amount of cotton ginned by State, district, and
county for each crop year in its Cotton Ginnings reports.20 (A crop year runs from September through
March.) These reports are published once or twice per month during the crop year and give the amount
of cotton ginned as running totals.
The number of bales ginned in a county can be obtained from Reference 19. However, since these
data are reported as running totals for the growing season (which spans parts of two calendar years), the
number of bales ginned for a calendar year will need to be determined using data from two crop years.
The amount of cotton ginned from January 1 to the end of the season (March) for calendar year x (crop
year*) and the amount of cotton ginned from the beginning of the season (August/ September) for
calendar year x (crop year y) should be summed to get the calendar year x total. To determine the
amount ginned from January 1 to the end of the season, subtract the amount ginned by January 1 (in the
early January Cotton Ginnings report) from the total reported in the March or end of season Cotton
4-42
-------
Ginnings report. To determine the amount ginned from the beginning of the season to January 1, use the
total recorded by January 1 in the early January Cotton Ginnings report.
It should be noted that for confidentiality purposes, the Cotton Ginnings report may not show
detailed data for a county, but may include those data in the district, State, or U. S. totals. Data for a gin
maybe considered confidential if (1) there are fewer than three gins operating in the county, or (2) more
than 60 percent of the cotton ginned in the county is ginned at one mill. The standard Cotton Ginnings
report lists the following four footnotes to its table of running bales ginned:
I/ withheld to avoid disclosing individual gins
21 withheld to avoid disclosing individual gins, but included in State total
3/ excludes some gins' data to avoid disclosing individual gins, but included in the State total
4/ withheld to avoid disclosing individual gins but included in the U.S. total
The following methodology can be used for estimating the number of bales ginned from those
counties with confidential data.
(1) If all counties in the district show confidentiality, but there is a district total, divide district total by
the number of counties to get individual county estimates.
(2) If some (but not all) counties in a district show confidentiality and there is a district total, subtract
county totals from district total and divide the remainder by the number of counties showing
confidentiality to get estimates for the "confidential" counties.
(3) If both county and district totals are considered confidential within a State, divide the State total by
the number of counties to get individual county estimates.
(4) If some (but not all) districts show confidentiality, subtract recorded district totals from the State
total and divide the remainder by the number of counties showing confidentiality to get estimates ibr
the "confidential" counties.
Although this method of apportioning is time consuming, it is preferable to using the ginning
distribution from previous years to determine current estimates of number of bales ginned in confidential
counties. The variability of the cotton harvest from year to year, the possibility of past claims of
confidentiality, and the industry trend from numerous small gins to fewer, large gins makes distribution
based on past activity unreliable. In addition, if the estimates generated by the methodology above does
not meet with State approval, the State may submit more accurate data for those counties and the
apportioning methodology can be revised.
The March report, produced at the end of the crop year, contains the final totals (including revisions
and updates) for the crop year. Data in the report may differ from earlier reports for the crop year in both
total number of bales ginned and counties where ginning occurred. In fact, for crop year 1995, the
January reports showed higher totals for some counties than did the final report. Subtracting the January
totals from the March totals for these counties yielded a negative number. In these cases, the activity for
the county for that time period was considered zero. For this methodology, in instances where counties
are recorded in the March finalreport, but not in earlier (e.g., January) reports, the activity is assumed to
4-43
-------
have occurred sometime before January. These counties were then added to the January listing as
confidential counties, and distribution of ginning activity was then performed.
Kansas has only one small gin operating in the State, and this gin does not operate every year. Since
the amount of cotton ginned at this facility is considered insignificant (less than 0.005 percent of the total
cotton ginned in the United States in 1995), no emissions for Kansas were calculated.
4.3.7.4.2 Emission Factor —
AP-4221 presents total PM and PM-10 emission factors (in Ibs/bale) for gins with high-efficiency
cyclones on all exhaust streams (i.e., full controls) and for gins with screened drums or cages on the lint
cleaners and battery condenser and high-efficiency cyclones on all other exhaust streams ( i.e.,
conventional controls). PM-2.5 emissions were assumed to be 1 percent of the total PM emissions, as
given in Table B.2.2. in AP-42 for Grain Handling. Table 4.3-21 shows the AP-42 emission factors.
Additional information obtained from EPA includes the estimated percent of cotton baled at gins using
each type of control by State. These data were developed by the National Cotton Council and are shown
in Table 4.3-22.22 Emission factors are controlled emissions factors as indicated.
4.3.7.4.3 Sample Calculation —
Using the data for Alabama from the 03/25/96 Cotton Ginnings report:
• District 10 shows data for three counties, confidential data for two counties and a district total.
(1) Subtract District 10 county data from District 10 total.
144,250 - (35,200 + 59,300 + 25,750) = 24,000 bales
(2) Divide the remaining total by two (two counties claimed confidentiality) to estimate amount for
each confidential county.
24,000/2= 12,000 bales per confidential county
This procedure can also be used for District 40.
• Districts 50 and 60 show district totals only (i.e., all counties within these districts claim
confidentiality). To estimate individual county totals, divide each district total by the number of
counties within that district.
District 50 District 60
122,300/4 = 30,575 bales per county 153,650/6 = 25,608 bales per county
• Districts 20 and 30 claim county and district confidentiality. To estimate county totals,
(1) Subtract available district totals from State total.
491,150 - (144,250 + 34,650 + 122,300 + 153,650) = 36,300 bales
4-44
-------
(2) Divide remainder by the number o f counties claiming confidentiality in the two remaining
districts.
36,300/8 = 4,538 bales per confidential county
Using the data in Table 4.3-23 and data from Cotton Ginnings reports, PM-10 emissions can be
calculated for Madison County, Alabama, as shown in the following example.
(1) Determine total running bales ginned in Madison County in 1996
(a) For the period January 1, 1996 until the end of the crop season, subtract the running total as of
January 1, 1996 from the 01/25/96 Cotton Ginnings report from the final crop season total
from the 03/25/96 Cotton Ginnings report.
25,750 bales - 25,700 bales = 50 bales
(b) For the period from the beginning of the 1996 crop year until the end of calendar year 1996, use
the running total as of January 1, 1997 from the 01/24/97 Cotton Ginnings report. Add this to
the total from (a) above to get calendar year 1996 total.
50 bales + 40,500 bales = 40,550 bales ginned in calendar year 1996
(2) Determine the percent of crop ginned by emission control method using Table 4.3-23.
(3) Use the emission factors from AP-42 as shown in Table 4.3-21 , the results of (1) and (2) above, and
the general equation to estimate emissions.
E = [(Pc*£) * EFc} + [(Pf*B) * EFf] (Eq. 4.3-9)
where: Pc = 0.8
Pf = 0.2
B = 40,550 bales
EFC = 1.21b/balePM-10
EFf = 0.82 Ib/bale PM-10
Emissions = [(0.8 * 40,550 bales) * 1.2 Ib/bale] + [(0.2 * 40,550 bales) * 0.82 Ib/bale]
= 38,928 lbs + 6, 650 Ibs
= 45,578 Ibs or 23 tons of PM-10
4-45
-------
4.3.8 How Did EPA Develop the 1996 NET Inventory?
Initially, the 1 996 emission inventory was developed by merging the 1995 AIRS/FS emissions with
1 995 emissions grown from 1 990 emissions for the States that did not submit 1995 emissions to
AIRS/FS. No 1996 AIRS/FS data were available for use. The following three subsections describe the
projected 1996 emissions. Subsequently, the merged data set was replaced with new emissions data
submitted by State/local agencies. Section 4.3.8.4 explains how EPA incorporated State/local data into
the 1996 NET.
4. 3. 8. 1 Grown Estimates
The 1996 point and area source emissions were grown using the 1995 NET inventory as the basis.
The algorithm for determining the estimates is detailed in section 4. 3. 3. 4 and is described by the equation
below. The 1990 through 1 996 SEDS and BEA data are presented in Tables 4.3-15 and 4.3-16. The
1996 BEA and SEDS data were determined using linear interpretation of the 1988 through 1995 data.
Rule effectiveness was updated to 100 percent as described in section 4.3.7.3 for the AIRS/FS sources
that reported rule effectiveness of less than 100 percent in 1995.
The following equation describes the calculation used to estimate the 1996 emissions:
nw rr^ ™>6 ^ REFF CEFF RP
CER.^,= UC.^, x — — x 1- - x - x -
1996 1995
.^,
1996 100 100 lOO
where: CER1996 = controlled emissions incorporating rule effectiveness
UC1995 = uncontrolled emissions
GS = growth surrogate (either BEA or SEDS data)
REFF = rule effectiveness (percent)
CEFF = control efficiency (percent)
RP = rule penetration (percent)
The rule effectiveness for 1996 was always assumed to be 100 percent. The control efficiencies and rule
penetrations are detailed in the following subsections.
4.3.8.2 1996 VOC Controls
This section discusses VOC stationary source controls (except those for electric utilities). These
controls were developed to represent the measures mandated by the CAAA and in place in 1996. Title I
(specifically the ozone nonattainment provisions) affects VOC stationary sources. Title III hazardous air
pollutant regulations will also affect VOC source categories. The discussion for each source category-
specific control measure includes the regulatory authority, CAAA provisions relating to the control
measure, and relevant EPA guidance.
Table 4.3-24 list the point source controls by pod. (A pod is a group of SCCs with similar emissions
and process characteristics for which common control measures, i.e., cost and emission reductions, can
be applied. It is used for control measure application/costing purposes.) Table 4.3-25 fists the POD to
4-46
-------
SCC match. Table 4.3-26 lists the area source control efficiencies, and rule effectiveness and rule
penetration if not 100 percent. A description of the controls is detailed below by measure.
4.3.8.2.1 Hazardous Waste Treatment, Storage, and Disposal Facilities —
Control assumptions for TSDF reflect application of Phase I and Phase II standards, as described
below. Regulatory authority for these rules falls under the Resource Conservation and Recovery Act
(RCRA). The Phase I rule for hazardous waste TSDFs restricts emissions from equipment leaks and
process vents.23 Process vent emissions must be below 3 Ib/hr and 3.1 tons per year (tpy) or control
devices must be installed. The control device must reduce emissions by 95 percent from uncontrolled
levels or, if enclosed combustion devices are used, reduce the vent stream to 20 parts per million (ppm)
by volume. The choice of control is not limited; condensers, absorbers, incinerators, and flares are
demonstrated control techniques.
The equipment leak standards apply to emissions from valves, pumps, compressors, pressure relief
devices, sampling connection systems, and open-ended valves or lines. Streams with organic
concentrations equal to or greater than ten percent by weight are subject to the standards. Record
keeping and monitoring are required for affected devices, in addition to the equipment standards, such as
dual mechanical seals for compressors.
The Phase II rule will restrict emissions from tanks, containers, and surface impoundments.24 The
rule will affect an estimated 2,300 TSDFs. The proposed rule also requires generators with 90-day
accumulation tanks (tanks holding waste for a period of 90 days or more) to install controls in order to
retain RCRA permit exempt status. An estimated 7,200 generators will be affected. Controls specified
for the Phase II rule are covers vented to a 95 percent destruction device, such as incinerators or carbon
absorbers.
4.3.8.2.2 Municipal Solid Waste Landfills —
Emission reductions for landfills reflect the proposed rule and guidelines published in the Federal
Register.25 Regulatory authority for this control measure falls under RCRA. The proposed rule requires
installation of gas collection systems and combustion (open flare) of the captured gases for all existing
landfills emitting greater than 150 mg/year, or 167 tpy, of nonmethane organic compounds. A new
source performance standard (NSPS) requires the same controls on all new facilities. The control device
efficiency is estimated to be 82 percent. A rule effectiveness of 100 percent was applied. The
penetration rate for existing facilities is estimated at 84 percent. A 100 percent penetration was applied
to new sources.
4.3.8.2.3 New Control Technique Guidelines (CTGs) —
Section 183 of the CAAA mandated EPA to establish 11 new CTGs by November 1993. Controls
following these guidelines must be implemented in moderate, serious, severe, and extreme nonattainment
areas. The majority of these documents are in draft form or still in the analysis stages. Clean-up solvents
will also be regulated through a negotiated rulemaking; however, implementation is not expected by
1996. Both of these control measures would apply nationwide. Control efficiency information was not
available for many of the source categories, so default assumptions were made.
4-47
-------
4.3.8.2.4 Existing CTGs —
EPA has issued three groups of CTG documents to be implemented in ozone nonattainment areas.
These controls should already be included in areas designated as nonattainment prior to 1990. These
controls, however, must also be implemented in newly designated nonattainment areas and over the entire
OTR. Not all CTGs are included in Table 4.3-26 because of the difficulty, in some cases, of matching the
document to the appropriate sources within the inventory. It is assumed that all existing CTGs are
implemented by 1996.
4.3.8.2.5 Reasonably Available Control Technology —
The CAAA direct moderate and above ozone nonattainment areas to require reasonably available
control technology (RACT)-level controls to VOC major stationary sources. The definition of major
source varies, depending on the severity of the ozone nonattainment classification, as listed in
Table 4.3-19.
Point source RACT control assumptions are based on EPA documents, including background
documents for New Source Performance Standards (NSPSs) and National Emission Standards for
Hazardous Air Pollutants (NESHAPs), Alternative Control Technology (ACT) documents, and other
compilations of VOC control techniques.
Area source RACT control information was taken from similar sources. The complicating factor for
area source RACT controls is the major stationary source size cutoff. A penetration iactor was
developed that accounts for the fraction of emissions within the area source category that are expected to
be emitted from major stationary sources. The penetration rate varies according to the major stationary
source size cutoff and, therefore, the ozone nonattainment classification.
4.3.8.2.6 Vehicle Refueling Controls-Stage II Vapor Recovery—
The CAAA and Title I General Preamble include the following specifications for Stage II vapor
recovery programs.
• Stage II is required in serious and above nonattainment areas. Moderate areas must implement
Stage II if onboard is not promulgated, and are also encouraged to implement Stage II
(regardless of whether onboard is promulgated) in order to achieve early reductions. (Onboard
controls require fleet turnover to become fully effective.)
• Stage II must be installed at facilities that sell more than 10,000 gallons of gasoline per month
(the cutoff is 50,000 gallons per month for independent small business marketers). There is
nothing to preclude States from adopting lower source size cutoffs.26
• A study must be conducted to analyze comparable measures in the OTR. Implementation plans
for OTRs must be modified within 1 year after issuance of the comparability study to include
Stage II or comparable measures.27
• States must prescribe the use of Stage II systems that are certified to achieve at least 95 percent
control of VOC and that are properly installed and operated.28
EPA has issued two guidance documents related to Stage II:
4-48
-------
• Technical Guidance - Stage II Vapor Recovery Systems for Control of Vehicle Refueling
Emissions at Gasoline Dispensing Facilities - Volume 1 (EPA-450/3-91-022, November
1991)29
• Enforcement Guidance for Stage II Vehicle Refueling Programs (December 1991)30
Table 4.3-27 list the areas with Stage II programs in place as of January 1996.
4.3.8.2.7 New Source Performance Standards —
For new sources subject to NSPS controls, these standards apply regardless of location. New
sources in nonattainment areas are also subject to New Source Review (NSR)/offsets. A 100 percent
rule effectiveness is assumed, consistent with that for other VOC stationary source controls.
4.3.8.2.8 Title III—
The source categories affected by Title III maximum achievable control technology (MACT)
standards were identified by using EPA's timetable for regulation development under Title III.
Applicability of the anticipated regulations in various projection years was also derived from this draft
timetable.
Control technology efficiencies were estimated for the expected MACT standards based on available
information. The information used depended on the status of specific standards in their development
timetable. For standards that have already been proposed or promulgated, efficiencies were estimated
using information presented in preambles to the appropriate regulations.
Rule effectiveness was estimated at 100 percent for all Title III standards, in accordance with current
EPA guidelines for rule effectiveness. Rule penetration is not applicable for any of the MACT categories,
since it is included in the average "control technology efficiency parameter.
4.3.8.3 NOX Controls
For the 1996 emissions, reductions were made in areas of the country that did not put RACT
controls into place until January 1996. Area combustion sources were reduced in 1996 according to the
control efficiencies and rule penetration values listed in Table 4.3-28.
4.3.8.4 How Did EPA Incorporate State/Local Emissions Inventory Data Into the 1996 NET?
The incorporation of the 1996 State/local emission inventory data is a five step process:
• Data Collection;
Quality Control (QQ;
• Data Augmentation (2 steps);
• Quality Assurance (Q A); and
• Data Loading.
In the data collection step, EPA solicited point and area source PEI data and annual point source
data from the State/local agencies. There were four acceptable formats State/local agencies could use to
4-49
-------
submit their data: (1) the NET Input Format; (2) through AIRS/FS; (3) the Electronic Data Interchange
X. 12 format; and (4) the NET Overwrite Format.
In the QC step, EPA evaluated the data received to ensure that each State/local agency had correctly
characterized, on the 1996 Emission Inventory Submittal Form, the data they submitted (e.g., geographic
coverage, pollutants, SCCs, annual and daily emissions), that the data were formatted correctly, that
mandatory data elements were included, and the priority SCCs needed to incorporate the data were
present (e.g., non-utility point and stationary area source SCCs). Each data element was characterized as
"mandatory submission" or "data can be augmented." As part of the QC step, all data received were
checked to ensure that data elements classified as "mandatory submission" were included in the data
supplied by the State/local agencies. Any problems found were followed-up by a phone call to the
State/local agency for review and resolution, and the data set was updated with data provided by the
State/local agency. If basic problems could not be resolved, the data were not included in the NET.
EPA needs a complete inventory containing annual and daily emissions for VOC, NOX, CO, SO2,
PM-10, PM-2.5, andNH3. Thus, in the first data augmentation step, EPA added annual emissions for
each pollutant not included in the State/local agency's inventory. The procedures for augmenting
inventories to add pollutants is expkined insection4.3.8.4.3.
In the QA step, data were checked for reasonableness. QA reports highlighting questionable data
were developed and sent to the State/local agencies for review. Questionable data were either confirmed
by the State/local agency as correct, corrected by the State/local agency, or in the case where the
State/local agency did not respond, replaced using the data augmentation methods. The following QA
reports were sent to the State/local agencies for review:
• Tier 2 summary;
• Top 20 plants for each pollutant with comparison to current data;
• NET plants not in the State/local agency data;
• Geographic coordinates falling outside State or NAA borders;
• Stack parameter exceptions;
• Large sources without emission controls; and
• Segments with emissions and control efficiency values of 100 percent or more.
For State/local agencies that submitted data in the NET input format, and had data tables with missing
records (e.g., from the Emission Release Point table), QAreports were prepared to show the segments
missing from some tables but not others.
After incorporating comments from the State/local agencies, EPA conducted a second data
augmentation step to add or modify data in the State/local agency inventory because the data were
missing or did not meet QA criteria. The augmentation step focused on the data required for regional
scale modeling or the Trends report. For example, for point sources, data augmentation involved
correcting stack, throughput, and operating time values that were missing or fell outside of typical ranges
required for air quality modeling. The procedures for augmenting inventories to add or modify the
required data elements is explained insection4.3.8.4.3.
In the data loading step, EPA loaded State/local agency data that met the QA/QC criteria into the
NET data base. This resulted in a fully revised 1996 point and area source file. For data incorporated
4-50
-------
into Version 4.0 of the 1996 NET, EPA prepared a QA/QC plan defining the procedures for correcting
missing or out-of-range values.31 Computer programs were developed to apply the procedures to the
entire point and area source files after incorporating State/local agency data. By doing this, the QA
procedures have been applied consistently to all data included in the 1996 NET, as well as the 1997
through 1999 emissions prepared from the 1996 NET.
4.3.8.4.1 How Many States Submitted Data for 1996? —
Table 4.3-29 summarizes the sources of inventory data included in the 1996 NET point and area
source inventories after incorporating inventories received in 1999 and 2000. For the State/local agencies
that submitted point and area source inventories in 1999 and 2000, Tables 4.3-30 and 4.3-31 identify the
pollutants for which data were submitted, the temporal basis o f the emissions (i. e., annual or daily
emissions), and the version of the NET in which the data were incorporated. Inventories submitted in
1999 were incorporated into Version 3.0 of the 1996 NET, and inventories submitted in 2000 were
incorporated into Version 4.0 of the 1996 NET. For the majority of States, point source inventory
submittals were made to AIRS/FS. Some States submitted data in alternative formats, primarily using the
NET input format.
4.3.8.4.2 Were Any State-Supplied Data Rejected in the QC Phase? —
Yes. A few States' data were rejected either due to problems with data completeness, data format,
or both. EPA is working to resolve these problems with the individual States and will include the data in
the NET when the problems are resolved.
4.3.8.4.3 What Types of Data Were Augmented in the Data Augmentation Step? —
As mentioned above, the NET contains emission estimates for all criteria pollutants (except Pb).
Thus, data elements and/or pollutant emissions missing from State/local agency data needed to be
augmented. The following explains how the State/local agency emission inventories were augmented to
add the data elements required for the NET.
Annual emissions for pollutants were added to State/local agency data sets, and then QA summary
reports of the entire data set were prepared and submitted to each State/local agency for review and
comment. EPA only added pollutants if the pollutants were completely missing from a State/local
agency's data set. If a State/local agency's data set appeared to contain incomplete coverage of VOC,
NOX, CO, PM-10, or SO2, EPA did not add any emissions for these pollutants to the data set. For
example, if a data set contained SO2 emissions for some but not all coal-fired external combustion
sources, EPA did not add any SO2 emissions to the data set. Note that for inventories submitted in 1999,
no agency provided PM-2.5 or NH3 emissions. For the inventories submitted in 2000, no agency
provided PM-2.5, and three State agencies provided very limited NH3 emissions. Thus, EPA added PM-
2.5 and NH3 emissions to the inventories.
State/local agency comments on the QA summary reports were incorporated into the data sets. For
point sources, the revised data sets were incorporated into the 1996 NET by replacing the existing data in
the NET by State and county. For area sources, the data sets were incorporated into the NET by State,
county, and SCC.
4.3.8.4.3.1 Non-utility point sources. Table 4.3-32 lists the minimum set of the data elements EPA
needs in order to add non-utility point source data into the NET. Each data element is coded "mandatory
submission" (MS) or "data can be augmented" (DA). Data elements coded MS must be supplied by the
-------
State/local agency for EPA to process the data Data elements coded DA are elements that EPA adds to
the State/local agency's data if they were not supplied by the State/local agency. Table 4.3-32 also
includes a brief description of the method EPA uses to augment the necessary DA data elements.
PM-2.5 Augmentation: Inventory missing PM-2.5 emissions, but contains PM-10 emissions.
• Identify segment records with PM-10 emissions >0 but no PM-2.5, create PM-2.5 record and
combine with PM-10 control code(s) from control file, and back calculate uncontrolled PM-10
annual emissions as follows where the control efficiency (CE) or rule effectiveness (RE) values
must be >0:
(PM-10 Annual Emissions)/(l-(CE (decimal) * (RE (decimal))
For data supplied in NET input format, use CE value in "Total Capture Control Efficiency"
field to calculate uncontrolled PM-10.
If the record has PM-10 emissions >0 but PM-10 RE=0, assume State did not report PM-10
RE and set PM-10 RE=100. For area sources, use same procedure if rule penetration (RP)=0
and emissions are >0.
• Run uncontrolled PM-10 annual emissions through PM Calculator to get PM-2.5 annual
emissions and control efficiency. Input fields needed for PM Calculator are:
• Uncontrolled PM-10 annual emissions;
• Primary and secondary control device codes;
• SCC; and
• Comments field for State/County/plant identification (ID) code/point ID/segment ID
information to identify segment level records.
• To prepare inputs to PM Calculator, assume the following if control device code and associated
CE data are incomplete:
• If no PM-10 control device code(s) and no control efficiency value provided in State/local
inventory, assume source is uncontrolled.
• If have PM-10 control device code but no control efficiency value, run PM-10 emissions as
uncontrolled through PM Calculator to estimate PM-2.5 emissions. Make control device
record for PM-2.5 same as for PM-10 (i.e., include control code but no control efficiency).
• If have PM-10 control efficiency value but no control device code, calculate uncontrolled
PM-10, run PM-10 emissions through PM Calculator to estimate uncontrolled PM-2.5,
and then apply PM-10 control efficiency to PM-2.5 to estimate controlled PM-2.5
emissions. Assume PM-10 and PM-2.5 control efficiency is the same.
• For some PEI data supplied in the NET input format, incomplete CE data were provided.
The following assumptions were applied to interpret the PM-10 control efficiency provided
in the Control Equipment table for back calculating uncontrolled PM-10 emissions:
4-52
-------
PrimaryPCT Control
Efficiency (PPCE)
Data
Data
No data
No data
No data
Data
PCT Capture Efficiency
(PCE)
Data
No data
Data
Data
No data
No data
Total Capture Control
Efficiency (TCCE)
No data
Data
Data
No data
Data
No data
Assumption
TCCE=PPCE x PCE
Leave as is
Leave as is
Not enough information,
assumed uncontrolled
Leave as is
TCCE=PPCE
• Review PM Calculator output and identify records with PM-2.5 annual emissions that exceed
PM-10 annual emissions, and set PM-2.5 emissions equal to PM-10 emissions.
• Calculate PM-2.5 daily emissions as ratio of PM-10 daily to PM-10 annual times PM-2.5 annual
emissions.
• Update data tables containing pollutant and control information, including PM-2.5 CE from PM
Calculator. Set PM-2.5 control codes and RE equal to PM-10 control codes and RE.
PM-10 and PM-2.5 Augmentation: Inventory submittal missing PM-10 and PM-2.5 emissions,
but contains total PM emissions.
Applied the procedures previously described but used uncontrolled total PM emissions as input to
PM Calculator to calculate PM-10 and PM-2.5 emissions from total PM emissions. Then,
• Review PM Calculator output and identify records with PM-2.5 annual emissions that exceed
PM-10 annual emissions, and set PM-2.5 emissions equal to PM-10 emissions.
• Calculate PM-10 and PM-2.5 daily emissions as ratio of total PM daily to total PM annual times
PM-10 and PM-2.5 annual emissions.
• Update data tables containing pollutant and control information, including PM-10 and PM-2.5
CE from PM Calculator. Set PM-10 and PM-2.5 control codes and RE equal to total PM
control codes and RE.
• Remove all records for total PM.
PM-10 and PM-2.5 Augmentation: Inventory submittal does not contain any PM emissions (i.e.,
total PM, PM-10, or PM-2.5).
The following steps were applied sequentially to estimate PM-10 and PM-2.5 emissions for
State/local agency inventories that did not contain any PM emissions:
4-53
-------
• Perform plant, point, and segment-level match of State/local agency inventory to current NET
data. If a match, add PM-10 and PM-2.5 emissions from NET to segment in State/local
inventory.
• If no segment match but is a match at plant and point, distribute point-level PM-10 and PM-2.5
emissions in NET to segments for same point in State/local agency inventory based on segment-
level distribution of known pollutant (NOX) in State/local inventory.
• If no point-level match but is a match at plant-level, distribute point-level PM-10 and PM-2.5
emissions in NET to segments for same plant in State/local inventory based on segment-level
distribution of known pollutant (NOX) in State/local inventory.
• If no plant-level match to current NET data, develop emissions using uncontrolled PM-10-to-
NOX emission factor ratio by SCC, and multiply ratio by uncontrolled NOX emissions in
State/local inventory, to calculate uncontrolled PM-10 emissions. NOX was used to calculate
the ratio because: (1) the types of sources likely to be important PM-10 emitters are likely to
be similar to important NOX sources, and (2) the generally high quality of the NOX emissions
data. After calculating PM-10 emissions, the PM Calculator was used to estimate uncontrolled
PM-2.5 emissions.
SO2 and CO Augmentation: Inventory does not contain any SO2 or CO emissions.
The procedures previously described for State/local inventories that did not contain total PM,
PM-10, or PM-2.5 emissions were used to add SO2 and CO emissions to the inventories.
NH3 Augmentation
In addition to criteria pollutants, the NET also houses estimates of NH3 emissions. In 1999, none of
the State/local agencies submitted NH3 emissions. As a consequence, the NH3 emissions from the 1996
NET were added. Two steps were taken to perform this augmentation. First, plant-level total NOX
emissions were calculated for the data submitted by State/local agencies. Then plant-level summaries of
NH3 from the NET were developed. Where amatch could be made using the State FIPS code, county
FIPS code, and plant ID code, segment-level emissions for NH3 were calculated using the following
equation:
NH^seg = (NOxseglNOxplant) * NH^plant
where: NH3seg = segment-level NH3 emissions added to State/local inventory
NOxseg = segment-level NOX emissions in State/local inventory
NOjlant = plant-level NOX emissions in State/local inventory
NH3plant = plant-level NH3 emissions in NET inventory
To maintain the NH3 totals currently in the NET, NH3-only pknt/segment-level records were added
for those facilities that did not match plants in the State/local inventory.
4-54
-------
In 2000, Arkansas, New Mexico, and West Virginia submitted NH3 emissions for some point
sources. For these States, the procedures for adding NH3 emissions in the NET to the State submitted
data were applied to maintain the NH3 totals currently in the NET, with the following exceptions:
• If a State provided NH3 emissions for a plant that matched with a plant in the 1996 NET, the
NH3 emissions for the plant in the State/local inventory replaced the plant's NH3 emissions in
the 1996 NET.
• If a State provided NH3 emissions for a plant that could not be matched to a plant in the NET,
the NH3 emissions for the plant in the State/local inventory were maintained. This procedure
resulted in adding the emissions for the plant to the State's total NH3 emissions currently in the
NET.
Louisiana provided revisions to their NH3 emissions in the NET. Therefore, the NH3 emissions
provided by Louisiana replaced the emissions in the NET.
Augment Temporal Emissions
If daily or annual emissions (whichever) is not known, EPA calculates the emissions using equations
4.3-1 1 and4.3-12 with the Summer Throughput Percentage and the Days Per Week in Operation
provided in the State/local inventory.
To calculate daily from annual -
EMISASD = (EMISANN * (SUMTHRU/100)) I (13 * DPW) (Eq. 4.3-11)
To calculate annual from daily -
= EMISASD I ((SUMTHRU/WO) * (1/(13 * DPWjj) (Eq. 4.3-12)
where: EMISANN = Annual Emissions
EMISASD = Typical Summer Day Emissions
SUMTHRU = Summer Throughput Percentage
DPW = Days Per Week in Operation
13 = Number of Weeks in Summer
If the State/local inventory does not contain values for Summer Throughput Percentage or the Days
Per Week in Operation, then the SCC in the State/local inventory is matched with a default profile in the
TAFF. If there is no profile in the TAFF for an SCC, or the SCC is missing or invalid in the State/local
inventory, then daily emissions are calculated by dividing annual emissions by the number of days in the
year. If only daily emissions are provided, daily emissions are multiplied by the number of days in the
year to estimate annual emissions.
4. 3. 8. 4. 3. 2 Stationary area sources. Table 4.3-33 lists the minimum set of the data elements EPA
needs in order to add area source data into the NET. Each data element is coded "mandatory
4^55
-------
submission" (MS) or "data can be augmented" (DA). Data elements coded MS must be supplied by the
State/local agency for EPA to process the data Data elements coded DA are elements that EPA adds to
the State/local agency's data if they were not supplied by the State/local agency. Table 4.3-33 also
includes a brief description of the method EPA used to augment the necessary DA data elements.
• Perform State/county/SCC-level match to current NET data. If there is a match, use emissions
from current NET.
• If there is no State/county/SCC match to current NET data, develop emissions using
uncontrolled emission factor ratios to calculate uncontrolled emissions. This method applies
SO2 or PM-10 ratios to NOX. NOX was used to calculate the ratio because: (1) the types of
sources likely to be important SO2 and PM-10 emitters are likely to be similar to important NOX
sources, and (2) the generally high quality of the NOX emissions data. Ratios of SO2/NOX and
PM-10/NOX based on uncontrolled emission factors were developed. These ratios were
multiplied by uncontrolled NOX emissions to determine either uncontrolled SO2 or PM-10
emissions.
• PM-2.5 emission estimates were developed based on the PM-10 estimates using source-specific
uncontrolled particle size distributions and particle size specific control efficiencies for sources
with PM-10 controls. To estimate PM-2.5, uncontrolled PM-10 was first estimated by
removing the impact of any PM-10 controls on sources in the inventory. Next, the uncontrolled
PM-2.5 was calculated by multiplying the uncontrolled PM-10 emission estimates by the ratio
of the PM-2.5 particle size multiplier to the PM-10 particle size multiplier. (These particle size
multipliers represent the percentage to total particulates below the specified size.) Finally,
controls were reappKed to sources with PM-10 controls by multiply ing the uncontrolled PM-2.5
by source/control device particle size specific control efficiencies.
Augment Temporal Emissions
If daily or annual emissions (whichever) is not known, EPA calculates the emissions equations
4.3-13 and 4.3-14 and EPA's default TAFF. The TAFF contains national default temporal factors by
sec.
To calculate daily from annual -
EMISASD = EMIS^ * (SUMFAC * WKDYFAC) (Eq. 4.3-13)
To calculate annual from daily -
EMISAm = EMISASD I (SUMFAC * WKDYFAC) (Eq. 4.3-14)
where: EMISANN = Annual Emissions
EMISASD = Typical Summer Day Emissions
SUMFAC = Summer Season Factor from TAFF (by SCC)
WKDYFAC = Summer Weekday Factor from TAFF (by SCC)
4-56
-------
4.3.8.4.4 What Quality Assurance Steps Were Taken to Ensure That State/Local Data Were
Incorporated Correctly? —
Quality assurance was an essential element of the data incorporation process. Extensive internal
review of the data was performed to ensure that the data were retrieved and formatted correctly and that
the data augmentation process was performed correctly. EPA conducted QA review of stack parameters
(height, diameter, velocity, flow, temperature), location information (latitude and longitude), operating
schedule (hours per day, days per week, hours per year, seasonal throughput), and emission estimates for
pollutants not included in the State submittals. On-going reviews were made of the data to ensure that
there were not duplicate records, that emissions values were not "out of range," and that the values for
stack parameters were within normal operational values.
The most important part of the QA program was State/local agency review of the retrieved and
augmented data. EPA prepared a review package for each State/local agency that submitted data. The
review package consisted of a number of reports and tables showing a variety of information about the
preliminary data set.
In the past, QA of the NET inventory focused almost exclusively on the emission estimates. Due to
the NET's change in focus to a modeling inventory, QA of the NET was expanded to cover additional
data elements including stack parameters, geographic coordinates, emission control data, and operating
schedule data.
To QA stack parameters, upper and lower limits were developed for each stack parameter carried in
the NET. The upper and lower limits define the acceptable range for stack parameter inputs to air quality
models. The Stack Exception Report in the QA package listed stacks in the NET where one or more of
the parameters was above the upper bound or below the lower bound. High and low values not corrected
by the States were replaced with the corresponding upper or lower bound value. The acceptable ranges
for each stack parameter are listed below:
Height <0 ft or >1,250 ft
Diameter <0 ft or >50 ft
Temperature <32°F or >2,250°F
Velocity <0 ft/sec or >98.4 ft/sec
Flow Rate <0 ftVsec or >16,666 ftVsec
To QA geographic coordinates, maps were generated for each State showing any facilities that were
located outside of their State borders when plotted using the geographic coordinates supplied by the
State. For NAA inventories, any facilities located outside of the county borders were identified by
plotting coordinates. Coordinates not corrected by the State/local agencies were replaced with the
coordinates for the county centroid based on the State and county FIPS codes provided by the State/local
agency.
4.3.8.4.5 What Did EPA Do With Comments Received From the State/Local Agencies? —
In the early review of State/local data downloaded from AIRS/FS in 1999, several agencies indicated
that the emissions for their ozone precursor pollutants were not correct. The original downloads from
AIRS/FS were designed to retrieve the default emissions value. However, several States indicated that
they typically stored emissions data in one of the alternative emission fields. As a consequence, EPA
contacted the States that submitted data to determine which States submitted emissions data in something
4^57
-------
other than the default emissions field. Data for those States was retrieved a second time and augmented
as required. The emissions for those States were re-summarized and sent back to the States for a final
review.
Once comments from all of the review packages were received, modifications to the emissions or
process data were made based on the State/local agency comments. Modification to the AIRS/FS data
were made to reflect either new data from the additional downloads, modifications based on the review
packages sent out to the State/local agencies, or based on data that remained anomalous (e.g., stack flow
rates).
The State review package included a table of plants that a State/local agency did not include in its
inventory, but the plants were in the 1996 NET that EPA provided to the State/local agency as a starting
point for its inventory. Several States provided comments on that table indicating that: (1) some or all of
these facilities should be maintained, and (2) indicating that while they should be maintained, the
emissions should be modified to reflect more accurate State-supplied values. The data for these plants
were extracted from the NET and maintained in a separate file. Since the review packages only provided
plant totals, ratios of old to new plant emissions were used to adjust the values of each segment's
emissions and then the data were updated.
4.3.8.4.6 Were There Emissions From Any Sources Submitted by State/Local Agencies That Were
Not Incorporated into the NET? —
A few source categories were not updated using State/local agency data. These source categories
were not updated because EPA feels that the consistent methodology and the quality of the data involved
in the calculation of emissions from these categories is at or above that provided by the States. For point
and area sources, State-supplied utility emissions data for segments with SCCs beginning with 101 were
not retained. Section 4.2 of this document explains the methodologies used to prepare point source
utility emissions data for segments with SCCs beginning with 101. Area source SCCs beginning with
2101 for utilities were not retained to avoid double counting of emissions in the point source inventory.
In addition, some States submitted aggregated emissions for portable sources under a general county
code 777. The emissions associated with county code 777 were not retained because they cannot be
allocated to actual counties for modeling purposes.
Some of the State/local inventories contain electric utilities with SCCs for industrial or commercial/
institutional fuel combustion (i.e., starting with 201/3 01, 202/302, and 203/303). In one case, this
resulted in double counting of emissions when the PEI data were combined with the utility emissions that
EPA prepared using the procedures in section 4.2. Thus, for the update of the 1996 NET (Version 4),
EPA removed industrial and commercial/institutional fuel combustion SCCs for electric utilities identified
with SIC code 4911. In addition, for plants having electric utility SCCs but an SIC code other than 4911,
EPA excluded the electric utility SCCs (i.e., starting with 101, 102, and 103). EPA excluded these
records from the NET to avoid double counting of emissions.
4-58
-------
4.3.9 How Were Nonutility Point and Area Source Emissions Prepared for the 1997 through
1999 NET?
To develop 1997 through 1999 emission estimates, EPA compiled a set of emission growth and
control factors for each year that was applied to the 1996 NET inventory. This section explains the
methods applied to prepare the growth and control factors for Versions 2, 3, and 4 of the NET inventory.
4.3.9.1 Growth Factors
Version 2 of the NET Inventory
Emissions for 1997 were first included in Version 2 of the NET inventory. Growth factors were
prepared for each year using either SEDS annual fuel consumption data or BEA national earnings by
industry. The 1990 through 1996 SEDS and BEA data are presented in Tables 4.3-15 and 4.3-16. The
algorithm ibr determining the estimates is detailed in section 4.3.3.4.
Versions 3 and 4 of the NET Inventory
Point and area source emissions for 1998 and 1999 are included in Versions 3 and 4, respectively.
As a result of updates to the 1996 base year inventory to incorporate State/local agency emission
inventories, Version 3 includes revisions to the 1997 inventory, and Version 4 includes revisions to the
1997 and 1998 inventories. For Versions 3 and 4, the growth factors for developing 1997 through 1999
estimates for the continental United States were developed using the inputs developed for EGAS 4.0.
BEA data were used to prepare growth fectors for Alaska and Hawaii
As part of the EGAS 4.0 development effort, EPA obtained more recent data/models and updated
some of the underlying files in the previous version (i.e., EGAS 3.0).32 Two of the major changes are: (1)
incorporating new economic models from Regional Economic Models, Inc. (REMI); and (2) revising the
crosswalk that is used to assign REMI model-derived growth factors to SCCs. The REMI models, which
included 72 modeling regions in EGAS 3.0, cover the continental United States. While many modeling
regions cover an entire State, some States have separate models ibr ozone NAAs and rest-of-state areas.
For this effort, updated REMI models were available that provide historical (through 1996) and forecast
(through 2035) socioeconomic data for each of 75 modeling regions in the United States (three new
modeling regions were added inNorth Carolina).33 As part of the revisions to the EGAS 3.0 crosswalk,
EPA reviewed each of the previous SCC assignments and incorporated new assignments for over 2,600
additional SCCs.
For point sources, REMI model-derived growth factors were assigned to each unique State, county,
and SIC code combination whenever SIC code information was available in the inventory. These growth
factors are based on REMI projections of socioeconomic activity. For most emission sectors, REMI
constant dollar output (total sales) by economic sector are used as the surrogate growth indicator.
Because REMI's models provide output for 172 economic sectors, which are roughly equivalent to
3-digit SIC codes, REMI output was first directly matched to the SIC code information available from
the point source component of the 1996 NET inventory. For some point source records, SIC code
information was missing, available at less than a 3-digit SIC code level, or invalid (did not represent a
valid SIC code). For these point source records, EPA assigned REMI model-derived growth fectors to
SCCs using the revised EGAS crosswalk.
4^59
-------
The 1996 area source inventory does not contain SIC code information. Thus, REMI model-derived
growth factors were assigned to each unique State/county/SCC combination in the inventory using the
revised EGAS crosswalk.
Because the REMI models do not include Alaska and Hawaii, a different source of projections data
were used for these States. The BEA released a set of gross State product (GSP) projections in 1995.34
These projections, which are generally available at a2-digit SIC code level, were used to develop growth
factors for Alaska and Hawaii. The BEA-derived growth factors were first matched with point sources in
the inventory at the 2-digit SIC code level. For point sources with missing/invalid SIC code information,
and for all area sources, EPA matched BEA data with emission sources using an updated EGAS
crosswalk matching BEA sectors with SCCs.
EGAS includes several models that project energy consumption by sector and fuel type (e.g.,
residential natural gas consumption). The revisions to the energy consumption modules in EGAS were
not completed when Versions 2, 3, and 4 of the NET inventory were prepared. Because the revisions
incorporate the use of Department of Energy (DOE) energy projections data, EPA compiled the DOE's
forecast data for use in adjusting the REMI/BEA data for projected changes in energy intensity.35
Specifically, the EPA calculated the following national energy intensity factors for 1996, 1997, 1998, and
1999:
• Residential fuel combustion - projected delivered energy by fuel type divided by projected
residential floor space;
• Commercial/institutional fuel combustion - projected delivered energy by fuel type divided by
projected commercial floor space; and
• Industrial fuel combustion - projected delivered energy by fuel type for both specific industries
(e.g., refining industry) and for total industrial fuel use divided by projected constant dollar
industrial output (specific industry or total industrial output).
Next, EPA calculated the ratios of national 1996 energy intensity to the national 1997, 1998, and
1999 energy intensity for each sector/fuel type (each sector/fuel type combination has a different SCC in
the point and area source inventories). The energy intensity ratios were then applied to the growth
factors for each fuel combustion SCC. For industrial natural gas consumption, for example, EPA
developed 1996:1997, 1996:1998, and 1996:1999 ratios of industrial natural gas consumption per
constant dollar of industrial output. These ratios were then used to adjust the EGAS modeling region-
specific REMI/BEA output-based industrial fuel consumption growth factors. Note that in the point
source inventory, fuel combustion sources (e.g., industrial boilers) may burn more than one fuel type
(identified by a different SCC for each fuel type) within a year. Although the same REMI-derived growth
factor would be assigned to each SCC, the source may have different composite growth factors resulting
from applying a different fuel-specific energy intensity factor to the growth factor for each SCC.
4-60
-------
4.3.9.2 Control Factors
For VOC emissions, controls were applied for several maximum achievable control technology
(MACT) sources. Table 4.3-32 presents the SCCs and the MACT control efficiencies applied for 1997,
1998, and 1999 for point and area sources. The control efficiencies were applied in Versions 2, 3, and 4
of the NET inventory. If a source category was subject to MACT in either 1997, 1998, or 1999, the
1996 control efficiency ibr that source was compared with the control efficiency that the MACT control
would have on VOC. If the 1996 control efficiency was greater than or equal to the MACT control
efficiency, then the data was maintained at the 1996 level. If the 1996 control efficiency was lower than
the MACT control efficiency, then uncontrolled emissions were back-calculated using the 1996 control
efficiency and then controlled emissions were calculated from the uncontrolled levels using the MACT
control efficiency. The MACT control efficiency value was also inserted into the data base field for
control efficiency. It was assumed that the MACT controls operated for the entire year, even if they were
not scheduled to come on-line until the middle to latter part of the year.
4.3.10 References
1. Table SA-5 — Total Personal Income by Major Sources 1969-1990. Data files. Bureau of
Economic Analysis, U.S. Department of Commerce, Washington. DC. 1991.
2. Survey of Current Business. Bureau of Economic Analysis, U.S. Department of Commerce,
Washington, DC. 1986, 1987, 1988, 1989, 1990, 1991.
3. State Energy Data Report — Consumption Estimates 1960-1989, DOE/EIA-0214(89), U.S.
Department of Energy, Energy Information Administration, Washington, DC. May 1991.
4. Enhanced Particulate Matter Controlled Emissions Calculator, Draft User's Manual, Emission
Factor and Inventory Group, Emissions Monitoring and Analysis Division, Office of Air Quality
Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC.
Prepared by E.H. Pechan & Associates, Inc., Durham, NC under EPA Contract No. 68-D7-0067,
Work Assignment No. 3-09, November 1999.
5. Barnard, W.R., and P. Carlson, PM-10 Emission Calculation, Tables 1 and 4, E.H. Pechan &
Associates, Inc. Contract No. 68-DO-1020, U.S. Environmental Protection Agency, Emission
Factor and Methodologies Section. June 1992.
6. Gill, W., Texas Air Control Board personal communication with D. Solomon. April 23, 1992.
7. E.H. Pechan & Associates, Inc., National Assessment of VOC, CO, and NOx Controls, Emissions,
and Costs, prepared for Office of Policy Planning and Evaluation, U.S. Environmental Protection
Agency. September 1988.
8. Battye, W, Alliance Technologies Corporation, Chapel Hil, NC, Ozone Cost Study Files,
memorandum and computer files to Jim Wilson, E.H. Pechan & Associates, Inc. April 3, 1987.
9. Shedd, S., U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards,
personal communication. November 13, 1991.
4-61
-------
lOa. TSDF Inventory File, computer file transferred to E.H. Pechan & Associates, Inc., from Emission
Standards Division, U.S. Environmental Protection Agency, via Alliance Technologies
Corporation, Research Triangle Park, NC. April 1989.
lOb. 1985 Hazardous Waste Data Management System, U.S. Environmental Protection Agency, Office
of Solid Waste, Washington, DC. 1985.
lOc. (Draft) Background Information Document for Chapter 1-6, Hazardous Waste Treatment,
Storage and Disposal Facilities, U.S. Environmental Protection Agency, Office of Air Quality
Planning and Standards, Emission Standards and Engineering Division, Research Triangle Park,
NC. February 6, 1986.
11. National Air Pollutant Emission Estimates, 1940-1985, U.S. Environmental Protection Agency,
Office of Air Quality Planning and Standards, Research Triangle Park, NC. 1986.
12. Area Source Documentation for the 1985 National Acid Precipitation Assessment Program
Inventory, EPA-600/8-88-106, U.S. Environmental Protection Agency, Air and Energy
Engineering Research Laboratory, Research Triangle Park, NC. December 1988.
13. 1985 Petroleum Supply Annual, DOE/EIA-0340, U.S. Department of Energy, Energy Information
Administration, Office of Oil and Gas, Washington, DC. May 1986.
14. Regional Interim Emission Inventories (1987-1991), Volume I: Development Methodologies,
EPA-454/R-23-021a, U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards, Research Triangle Park, NC. May 1993.
15. Seitz, John, U.S. Environmental Protection Agency, Research Triangle Park, NC, Memorandum to
State Air Directors. May 5, 1995.
16. An Emission Inventory for Assessing Regional Haze on the Colorado Plateau, Grand Canyon
Visibility Transport Commission, Denver, CO. January 1995.
17. Volatile Organic Compound (VOC)/Particulate Matter (PM) Speciation Data System
(SPECIATE) User's Manual, Version 1.5, FinalReport, Radian Corporation, EPA Contract No.
68-DO-0125, Work Assignment No. 60, Office of Air Quality Planning and Standards, U.S.
Environmental Protection Agency, Research Triangle Park, NC. February 1993.
18. Internet E-mail from J. Nuovo to J. Better of the Department of Health and Environmental Control
(DHEC), Columbia, South Carolina, entitled Total Suspended Paniculate (TSP)/PM-10 Ratio.
Copy to P. Carlson, E.H. Pechan & Associates, Inc., Durham, NC. April 10, 1997.
19. Telecon. SharonKersteter, E.H. Pechan & Associates, Inc., Durham, NC, with Roger Latham,
U.S. Department of Agriculture, Cotton Statistics. March 6, 1997.
20. Cotton Ginnings, PCG, U.S. Department of Agriculture, National Agricultural Statistics Service,
Agricultural Statistics Board, Washington, DC. (13 issues, mailed approximately twice per month
during August-March ginning season)
4-62
-------
21. Compilation of Air Pollutant Emissions Factors and Supplements, Fifth Edition and Supplements,
AP-42, U.S. Environmental Protection Agency, Research Triangle Park, NC. 1997.
22. Memorandum. Fred Johnson, National Cotton Council, Memphis, TN, to Bill Mayfield, U.S.
Department of Agriculture, Memphis, TN, Estimated Percent of Crop by Emission Control
Method, My 23, 1996.
23. 55 FR 25454, 1990 Federal Register, Vol. 55, No. 120, p. 25454, Hazardous Waste TSDFs -
Organic Air Emission Standards for Process Vents and Equipment Leaks. June 21,1990.
24. Lacy, Gail. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards,
Emission Standards Division, personal communication, June 1991.
25. Federal Register, Vol. 55, No. 104, p. 24468, Standards of Performance for New Stationary
Sources and Guidelines for Control of Existing Sources: Municipal Solid Waste Landfills.
May 30, 1991.
26. 57 FR 13498, 1992 Federal Register, General Preamble, Implementation of Title I, Clean Air Act
Amendments of 1990. April 16, 1992.
27. Public Law 101-549, Clean Air Act Amendments of 1990, Section 182(b)(3). November 15,
1990.
28. Public Law 101-549, Clean Air Act Amendments of 1990, Section 184(b)(2). November 15,
1990.
29. Technical Guidance - Stage II Vapor Recovery Systems for Control of Vehicle Refueling
Emissions at Gasoline Dispensing Facilities - Volume 1, EPA-450/3-91-022a, U.S. Environmental
Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC,
November 1991.
30. Enforcement Guidance for Stage II Vehicle Refueling Programs, U.S. Environmental Protection
Agency, Office of Air and Radiation, Washington, DC, December 1991.
31. Memorandum The Pechan-Avanti Group, Durham, NC, to Solomon, D, U.S. EPA, Research
Triangle Park, NC, Development of Quality Assurance (QA) Plan for the National Emission
Trends (NET) Data for FY2000, March 2000. EPA Contract Number 68-D7-0067, Work
Assignment Number 3-12, Task 2.
32. "Economic Growth Analysis System: Version 3.0," software, reference manual, and user's guide,
U.S. Environmental Protection Agency. Available for download from
http://www.epa.gov/ttn/chief/ei_data.html#EGAS. August 1995.
33. "EPA EGAS EDFS-14 Multi-Region County Models," Nine DOS Models Covering the U.S., Last
History Year 1996, Regional Economic Models, Inc., CD-ROM. February 18, 1999.
4-63
-------
34. "Regional Projections to 2045," Volumes 1, 2, and 3, Bureau of Economic Analysis, U.S.
Department of Commerce, Washington DC. July 1995.
35. "Annual Energy Outlook 1999, with Projections through 2020," DOE/EIA-0383(99), Office of
Integrated Analysis and Forecasting, Energy Information Administration, U.S. Department of
Energy. December 1998.
4-64
-------
Table 4.3-1. Methods for Developing Annual Emission Estimates for Industrial
Nonutility Point and Area Sources for the Years 1985-1999
For the
years
1985-1989
1990
(Interim
Inventory)
1990
(NET)
1991-1996
1996
1997-1999
For the
pollutant(s)
VOC, NOX,
CO, SO2,
PM-10
VOC, NOX,
CO, SO2,
PM-10
VOC, NOX,
CO, SO2,
PM-10,
PM-2.5,
NH3
VOC, NOX,
CO, SO2,
PM-10,
PM-2.5,
NH3
VOC, NOX,
CO, SO2,
PM-10,
PM-2.5,
NH3
VOC, NOX,
CO, SO2,
PM-10,
PM-2.5,
NH3
EPA estimated emissions by
Backcasting 1990 Interim Inventory Emissions with historical Bureau of
Economic Analysis (BEA) earnings data or fuel consumption data from
the State Energy Data System (SEDS).
Projecting 1985 National Acid Precipitation Assessment Program
(NAPAP) emissions to 1990 and revising the 1990 emissions to:
(1) update VOC emission factors for Stage I and II vehicle refueling;
(2) revise point source emissions for hazardous waste treatment, storage,
and disposal facilities and closing of a copper smelter; (3) revise
petroleum refinery area source fugitive emissions; (4) apply VOC
controls to Stage I and II vehicle refueling and gasoline bulk plants and
terminals where required by State implementation plans; (5) update point
source control efficiencies for VOC, NOX, CO, and SO2 where judged to
be too high in NAPAP; (6) apply rule effectiveness assumptions to VOC,
NOX, and CO emissions; and (7) add PM-10 emissions calculated from
total suspended particulate emissions.
Combining State/local agency data from the Ozone Transport
Assessment Group (OTAG) emission inventory, the Grand Canyon
Visibility Transport Commission (GCVTC) emission inventory, and
Aerometric Information Retrieval System/Facility Subsystem (AIRS/FS).
Filled data gaps with information from the 1990 Interim Inventory, and
added PM-2.5 and NH3 emissions.
Projecting 1990 NET emissions to the appropriate year using BEA or
SEDS data, and replacing projected emissions with data from OTAG,
GCVTC, or AIRS/FS, as directed by State/local agencies.
Updating projected emissions with data received from State/local
agencies or State/local agency data downloaded from AIRS/FS.
Projecting through 1999 based on the 1996 emissions using growth
factors derived from the Economic Growth Analysis System (EGAS) and
BEA growth factors, where applicable.
4-65
-------
sec
Table 4.3-2. SCCs With 100 Percent CO Rule Effectiveness
Process
30300801 Primary Metals Production - Iron Production - Blast Furnaces
30300913 Primary Metals Production -Steel Production - Basic Oxygen Furnace: Open Hood-Stack
30300914 Primary Metals Production -Steel Production - Basic Oxygen Furnace: Closed Hood-Stack
30500401 Mineral Products - Calcium Carbide - Electric Furnace (Hoods and Main Stack)
30600201 Petroleum Industry - Fluid Catalytic Cracking Units
31000205 Oil and Gas Production - Natural Gas Production - Flares
31000299 Oil and Gas Production - Natural Gas Production - Other Not Classified
39000689 In-Process Fuel Use - Natural Gas - General
39000797 In-Process Fuel Use - Process Gas - General
4-66
-------
Table 4.3-3. July RVPs Used to Model Motor Vehicle Emission Factors
State Reid Vapor Pressure (psi)
State
AL
AZ
AR
CA
CO
CT
DE
DC
FL
GA
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
Ml
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
Rl
SC
SD
TN
TX
UT
VT
VA
WA
WV
Wl
WY
1987
10.8
8.6
10.2
8.6
9.7
10.9
11.3
11.0
10.2
10.5
10.1
11.1
11.6
10.5
9.8
11.3
10.4
10.8
11.2
10.8
11.7
10.5
10.2
10.0
9.3
10.2
8.6
10.8
11.3
9.0
11.2
10.5
10.5
11.6
9.9
9.7
11.4
10.8
10.5
10.5
10.4
9.8
9.7
10.8
10.9
10.8
11.4
11.4
9.5
1988
10.9
8.3
9.8
8.5
9.4
11.0
10.8
10.8
10.5
10.7
9.9
10.6
11.1
10.3
9.6
10.9
11.0
11.0
10.8
11.0
11.0
10.3
9.8
9.7
9.5
9.9
8.5
11.0
10.9
8.5
11.0
10.7
10.3
11.4
9.7
9.4
10.9
11.0
10.7
10.3
10.5
9.6
9.4
11.0
10.8
10.2
11.2
10.9
9.4
1989
8.9
8.2
9.4
8.4
8.7
8.6
9.2
9.1
9.0
8.6
9.5
9.5
9.6
9.7
9.1
9.5
8.6
8.6
9.1
8.6
9.8
9.7
9.4
9.3
9.3
9.4
8.3
8.6
9.0
8.2
8.7
8.6
9.7
9.8
8.7
9.1
9.3
8.6
8.6
9.7
8.8
8.4
8.7
8.6
9.0
9.7
9.6
9.6
9.0
1990
8.5
8.1
8.7
8.1
8.3
8.3
8.4
8.2
9.1
8.5
9.1
8.6
8.7
9.6
8.5
8.7
8.3
8.3
8.3
8.3
9.1
9.6
8.7
8.6
8.6
9.1
8.2
8.3
8.4
8.1
8.3
8.5
9.6
9.6
8.2
8.9
8.6
8.3
8.5
9.6
8.5
8.0
8.3
8.3
8.3
9.6
9.1
8.8
8.4
1991
8.5
8.2
8.5
8.2
8.4
8.3
8.3
8.1
9.1
8.3
9.4
8.8
9.0
9.8
8.6
8.8
8.4
8.3
8.2
8.3
9.3
9.8
8.5
8.6
9.2
9.2
8.3
8.3
8.3
8.1
8.4
8.3
9.8
9.7
8.4
9.0
8.5
8.3
8.3
9.8
8.3
8.2
8.4
8.3
8.1
9.7
9.1
9.0
8.8
Source: Developed from J uly MV MA Fuel Volatility Su rveys
4-67
-------
Table 4.3-4. 1990 Seasonal RVP (psi) by State
State Winter Soring Summer Fall
AL
AZ
AR
CA
CO
CT
DE
DC
FL
GA
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
Ml
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
Rl
SC
SD
TN
TX
UT
VT
VA
WA
WV
Wl
WY
12.8
10.1
13.4
12.3
11.5
13.2
13.9
12.2
11.9
12.5
12.5
13.7
13.8
13.4
12.5
12.9
12.2
13.1
13.4
13.1
13.8
13.4
13.4
12.4
13.1
13.0
10.9
13.1
13.8
11.6
13.4
12.5
13.4
13.9
13.1
12.4
13.9
13.1
12.5
13.0
12.7
12.4
11.5
13.1
12.1
13.6
13.5
13.7
12.2
10.3
8.5
10.7
10.1
9.6
10.2
10.5
9.1
9.1
10.2
10.5
10.5
10.6
11.2
9.5
9.6
10.0
10.1
10.2
10.1
10.9
11.0
10.7
10.7
10.1
10.5
8.8
10.1
10.5
9.0
10.2
11.0
11.8
11.2
9.6
10.4
10.6
10.1
11.0
10.9
11.1
9.9
10.0
10.1
9.1
11.1
10.8
10.7
9.8
9.1
8.1
8.7
8.1
8.5
8.3
8.4
8.2
9.1
9.1
9.1
8.6
8.7
10.0
8.5
8.7
8.9
8.3
8.3
8.3
9.1
9.6
9.4
8.6
8.6
9.1
8.2
8.3
8.4
8.1
8.3
9.1
9.6
9.6
8.2
8.8
8.6
8.3
9.1
9.6
9.1
8.0
8.5
8.3
8.2
9.6
9.1
8.8
8.4
9.7
8.3
10.9
8.7
9.3
10.2
9.4
9.1
9.1
9.6
9.5
9.6
9.7
11.2
9.0
9.6
9.4
10.1
9.3
10.1
10.9
10.3
10.0
10.2
10.1
9.5
8.5
10.1
10.5
9.3
10.2
10.4
10.9
10.4
8.9
9.6
10.6
10.1
10.4
10.0
10.5
8.6
9.3
10.1
9.1
10.4
9.9
9.7
8.8
Source: Based on RVP s from the January and July MV MA F uel Volatility Surveys interpolated to
spring and fall.
4-68
-------
Table 4.3-5. Seasonal Maximum and Minimum Temperatures (°F) by State
Winter
State
AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HI
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
Ml
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
Rl
SC
SD
TN
TX
UT
VT
VA
WA
WV
Wl
WY
Min
42
20
41
32
45
18
19
25
29
52
34
66
25
17
21
15
23
27
44
14
26
25
14
5
36
22
14
15
21
12
25
24
21
32
1
22
28
35
24
22
34
7
31
37
22
11
31
30
26
15
17
Max
62
31
67
53
61
45
36
42
45
72
54
81
40
33
37
31
44
44
64
33
43
38
30
24
59
40
33
35
47
33
43
49
36
54
23
38
50
47
39
38
58
27
50
61
40
28
49
42
44
29
40
Spring
Min Max
57
32
54
50
50
34
38
42
47
62
50
69
37
39
41
39
44
45
59
33
43
41
33
32
53
44
31
40
31
32
41
40
39
48
30
40
48
42
41
38
51
34
50
54
37
33
47
39
43
35
30
78
46
83
73
67
61
59
62
66
77
72
83
61
59
62
59
67
66
78
52
64
56
53
51
77
65
54
62
64
56
61
70
57
72
53
61
71
61
61
57
76
56
71
78
62
52
68
57
66
53
54
Summer
Min
72
46
76
70
59
56
60
64
68
73
68
73
56
62
63
64
68
66
73
55
65
63
55
56
70
66
52
64
45
54
62
62
61
67
54
61
69
55
62
61
69
59
69
71
58
56
67
53
62
59
52
Max
91
63
103
92
78
85
83
84
86
89
87
87
86
83
84
84
91
86
90
76
85
79
77
78
92
87
80
86
87
80
82
91
81
88
82
82
91
77
83
80
91
84
89
95
89
78
86
76
84
78
80
Fall
Min
58
36
59
51
54
37
42
47
51
65
52
71
39
43
44
42
47
47
60
38
47
48
39
36
53
52
35
42
31
36
46
43
45
51
31
44
50
45
45
44
52
36
51
55
40
39
51
41
45
41
34
Max
79
47
86
75
73
66
63
66
69
82
73
86
64
63
65
63
69
68
79
59
68
62
57
54
78
67
58
65
69
60
66
71
62
73
57
64
73
64
65
63
76
60
73
79
66
57
71
59
67
59
60
U.S. NOAA "Climatology of theUnited States", 198212
4-69
-------
Table 4.3-6. Average Annual Service Station Stage IIVOC Emission Factors
Emission Factor
Year
1985
1986
1987
1988
1989
1990
1991
1992
1993
grams/gallon
4.6
4.6
4.6
4.6
3.9
3.6
3.6
3.6
3.6
lbs/1,000
gallons
10.0
10.0
10.0
10.0
8.5
8.0
8.0
8.0
8.0
Table 4.3-7. TSDF Area Source Emissions Removed from the Inventory (1985-1996)
48
45
54
22
13
54
48
01
State
Texas
South Carolina
West Virginia
Louisiana
Georgia
West Virginia
Texas
Alabama
County
071
005
073
047
051
079
039
129
Chambers
Allendale
Pleasants
Iberville
Chatham
Putnum
B razor! a
Washington
VOC Annual
Emissions
372,295
364,227
252,128
100,299
84,327
60,568
59,951
49,296
4-70
-------
Table 4.3-8. Bureau of Economic Analysis's SA-5 National Changes
in Earnings by Industry
Percent Growth from:
Industry
Farm
Agricultural services, forestry,
fisheries, and other
Coal mining
Metal mining
Nonmetallic minerals, except fuels
Construction
Food and kindred products
Textile mill products
Apparel and other textile products
Paper and allied products
Printing and publishing
Chemicals and allied products
Petroleum and coal products
Tobacco manufactures
Rubber and miscellaneous plastic
products
Leather and leather products
Lumber and wood products
Furniture and fixtures
Primary metal industries
Fabricated metal products
Machinery, except electrical
Electric and electronic equipment
Transportation equipment,
excluding motor vehicles
Motor vehicles and equipment
Stone, clay, and glass products
Instruments and related products
Miscellaneous manufacturing
industries
Railroad transportation
Trucking and warehousing
Water transportation
Local and interurban passenger
transit
Transportation by air
Pipelines, except natural gas
Transportation services
Communication
Electric, gas, and sanitary services
SIC
01, 02
07, 08, 09
11, 12
10
14
15, 16, 17
20
22
23
26
27
28
29
21
30
31
24
25
33
34
35
36
37
371
32
38
39
40
42
44
41
45
46
47
48
49
1985 to 1987
14.67
23.58
-17.46
-3.03
2.33
7.27
1.67
8.50
-1.72
2.62
7.44
1.75
-10.82
-1.97
5.27
-9.39
10.03
6.82
-9.09
-4.72
-5.72
-3.17
8.44
-6.45
-0.23
-0.04
1.84
-14.13
5.63
-8.92
13.45
12.01
-5.21
15.92
1.94
0.07
1987 to 1988
-2.73
5.43
-6.37
18.01
3.74
4.81
1.34
-0.64
1.25
0.94
5.67
6.94
-3.22
2.43
5.51
-1.64
5.15
2.35
5.32
2.55
6.02
-18.01
-1.57
2.20
-1.61
60.65
6.92
-2.53
3.26
0.07
0.51
4.63
3.67
8.52
0.68
3.05
1988 to 1989
14.58
1.01
-4.16
8.94
-2.79
-1.36
-1.20
-1.39
-1.62
-0.14
-0.81
0.32
-3.02
-2.43
0.68
-3.58
-3.54
-1.46
-0.34
-0.86
-0.32
-1.91
0.55
-2.96
-1.96
-0.82
-2.21
-3.83
-0.20
-1.02
2.14
4.94
-4.93
4.60
-2.81
0.63
1989 to 1990
-3.11
2.48
4.73
4.56
-0.45
-3.80
-0.24
-4.97
-4.22
-0.39
0.43
1.61
1.06
-5.01
-0.14
-2.55
-3.71
-2.98
-3.03
-1.91
-1.92
-3.22
-1.07
-5.43
-3.19
-2.91
-2.54
-6.03
0.99
2.83
1.44
4.36
3.53
4.97
2.07
0.39
4-71
-------
Table 4.3-9. Area Source Growth Indicators
NAPAP
sec
Category Description
Data
Source
Growth Indicator
13 Industrial Fuel-Anthracite Coal SEDS
14 Industrial Fuel - Bituminous Coal SEDS
15 Industrial Fuel - Coke BEA
16 Industrial Fuel - Distillate Oil SEDS
17 Industrial Fuel - Residual Oil SEDS
18 Industrial Fuel - Natural Gas SEDS
19 Industrial Fuel-Wood BEA
20 Industrial Fuel - Process Gas SEDS
21 On-Site Incineration - Residential BEA
22 On-Site Incineration - Industrial BEA
23 On-Site Incineration-Commercial/lnstitutional BEA
24 Open Burning - Residential BEA
25 Open Burning - Industrial BEA
26 Open Burning - Commercial/Institutional BEA
54 Gasoline Marketed SEDS
63 Frost Control - Orchard Heaters BEA
99 Minor Point Sources BEA
100 Publicly Owned Treatment Works BEA
102 Fugitive Emissions From Synthetic Organic BEA
Chemical Manufacturing
103 Bulk Terminal and Bulk Plants BEA
104 Fugitive Emissions From Petroleum Refinery
105 Process Emissions From Bakeries BEA
106 Process Emissions From Pharmaceutical BEA
Manufacturing
107 Process Emissions From Synthetic Fiber BEA
Manufacturing
108 Crude Oil and Natural Gas Production Fields BEA
109 Hazardous Waste Treatment, Storage, and BEA
Disposal Facilities (TSDFs)
Ind - Anthracite
Ind - Bituminous
Total Manufacturing
Ind - Distillate oil
Ind - Residual oil
Ind - Natural gas
Total Manufacturing
Ind - LPG
Population
Total Manufacturing
Services
Population
Total Manufacturing
Services
Trans - Motor gasoline
Farm
Population
Electric, Gas, and Sanitary Services
Mfg - Chemicals and Allied Products
Trucking and Warehousing
Refinery operating cap
Mfg - Food and Kindred Products
Mfg - Chemicals and Allied Products
Mfg - Textile Mill Products
Oil and Gas Extraction
Total Manufacturing
4-72
-------
Table 4.3-10. SEDS National Fuel Consumption
Category 1985 1986 1987 1988 1989 1990
Anthracite Coal (thousand short tons)
Industrial 575 470 437 434 392 387
Bituminous Coal (thousand short tons)
Industrial 115,854 111,119 111,695 117,729 117,112 118,322
Distillate Fuel (thousand barrels)
Industrial 203,659 206,108 210,699 209,553 197,035 205,856
Liquefied Petroleum Gases (thousand barrels)
Industrial 437,964 411,451 447,120 453,599 441,784 457,013
Motor Gasoline (thousand barrels)
Transportation 2,433,592 2,507,936 2,570,047 2,627,331 2,617,450 2,703,666
All Sectors 2,493,361 2,567,436 2,630,089 2,685,145 2,674,669 2,760,414
Natural Gas (million cubic feet)
Industrial 6,867 6,502 7,103 7,479 7,887 8,120
Residual Fuel (thousand barrels)
Industrial 120,002 132,249 107,116 105,448 95,646 118,122
4-73
-------
Table 4.3-11. AMS to NAPAP Source Category Correspondence
AMS
NAPAP
SCC
Category
SCC
Category
Stationary Source Fuel Combustion
2102001000 Industrial - Anthracite Coal (Total: All Boiler
Types)
2102002000 Industrial - Bituminous/Subbituminous Coal
(Total: All Boiler Types)
2102004000 Industrial-DistillateOil (Total: Boilers & 1C
Engines)
2102005000 Industrial - Residual Oil (Total: All Boiler
Types)
2102006000 Industrial -Natural Gas (Total: Boilers & 1C
Engines)
2102008000 Industrial - Wood (Total: All Boiler Types)
2102009000 Industrial - Coke (Total: All Boiler Types)
2102010000 Industrial - Process Gas (Total: All Boiler
Types)
Industrial Processes
2301020000 Process Emissions from Pharmaceuticals
(PECHAN)
2301030000 Process Emissions from Synthetic Fiber
(PECHAN)
2301040000 SOCMI Fugitives (PECHAN)
2302050000 Food & Kindred Products: SIC 20 - Bakery
Products (Total)
2306000000 Petroleum Refining: SIC 29 - All Processes
(Total)
2310000000 Oil & Gas Production: SIC 13-All
Processes (Total)
2399000000 Industrial Processes: NEC
13 Industrial Fuel - Anthracite Coal
14 Industrial Fuel - Bituminous Coal
16 Industrial Fuel - Distillate Oil
17 Industrial Fuel - Residual Oil
18 Industrial Fuel - Natural Gas
19 Industrial Fuel - Wood
15 Industrial Fuel - Coke
20 Industrial Fuel - Process Gas
106 Process Emissions from
Pharmaceutical Manufacturing
107 Process Emissions from
Synthetic Fibers Manufacturing
102 Fugitive Emissions From
Synthetic Organic Chemical
Manufacturing
105 Process Emissions From
Bakeri es
104 Fugitive Emissions From
Petroleum Refinery Operations
108 Crude Oil and Natural Gas
Production Fields
99 Minor point sources
Storage & Transport
2501050120 Petroleum & Petroleum Product Storage -
Bulk Stations/Terminals: Breathing Loss
(Gasoline)
2501060050 Petroleum & Petroleum Product Storage -
Gasoline Service Stations (Stage I: Total)
2501060100 Petroleum & Petroleum Product Storage -
Gasoline Service Stations (Stage II: Total)
2501060201 Petroleum & Petroleum Product Storage -
Gasoline Service Stations (Underground
Tank: Breathing & Emptying)
103 Bulk Terminal and Bulk Plants
54 Gasoline Marketed (Stage I)
54 Gasoline Marketed (Stage II)
54 Gasoline Marketed (Breathing &
Emptying)
(continued)
4-74
-------
Table 4.3-11 (continued)
AMS
NAPAP
SCC
Category
SCC
Category
Waste Disposal, Treatment, & Recovery
2601010000 On-Site Incineration - Industrial (Total) 22
2601020000 On-Site Incineration - 23
Commercial/Institutional (Total)
2601030000 On-Site Incineration - Residential (Total) 21
2610010000 Open Burning - Industrial (Total) 25
2610020000 Open Burning - Commercial/Institutional 26
(Total)
2610030000 Open Burning - Residential (Total) 24
2630020000 Wastewater Treatment - Public Owned 100
(Total)
2640000000 TSDFs - All TSDF Types (Total: All 109
Processes)
On-Site Incineration - Industrial
On-Site Incineration -
Commercial/Institutional
On-Site Incineration - Residential
Open Burning - Industrial
Open Burning - Commercial/Institutional
Open Burning - Residential
Publicly-Owned Treatment Works
(POTWs)
Hazardous Waste Treatment, Storage,
and Disposal Facilities (TSDF)
4-75
-------
Table 4.3-12. Point Source Data Submitted by OTAG States
State
Alabama
Arkansas
Connecticut
Delaware
District of Columbia
Florida
Georgia - Atlanta
Urban Airshed (47
counties) domain
Georgia - Rest of
State
Illinois
Indiana
Kansas
Kentuc ky - Jeffers on
County
Kentucky - Rest of
State
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Missouri
Nebraska
New Hamps hire
New J ersey
New York
North Carolina
North Dakota
Ohio
Oklahoma
Pennsylvania -
Allegheny County
Pennsylvania -
Philadelphia County
Pennsylvania- Rest
of State
Rhode Island
South Carolina
Data Source/Format
AIRS/FS - Ad hoc retrievals
AIRS/FS - Ad hoc retrievals
State -E PS Workfile
State -E PS Workfile
AIRS/FS - Ad hoc retrievals
AIRS/FS -Ad hoc retrievals
State - State form at
AIRS/FS - Ad hoc retrievals
State - EPS Workfiles
AIRS/FS - Ad hoc retrievals
AIRS/FS -Ad hoc retrievals
Jefferson C ounty - E PS W orkfile
State -EPS Workfile
State - State Format
State- EPS Workfile
State - EPS W orkfile
State- EPS Workfile
State -State Format
AIRS/FS - Ad hoc retrievals
AIRS/FS -Ad hoc retrievals
AIRS/FS -Ad hoc retrievals
State -EPS Workfile
State -EPS Workfile
State - EPS Workfile
State- EPS Workfiles
AIRS/FS - Ad hoc retrievals
State - State Format
State - State Format
Allegheny County - Coun ty Format
Philadelphia County - County Format
State -EPS Workfile
State - EPS Workfile
AIRS/FS - Ad hoc retrievals
Temporal
Resolution
Annual
Annual
Daily
Daily
Annual
Annual
Daily
Annual
Daily
Annual
Annual
Daily
Daily
Annual
Daily
Daily
Daily
Annual
Annual
Annual
Annual
Daily
Daily
Daily
Daily
Annual
Annual
Annual
Daily
Daily
Daily
Daily
Annual
Year of Data
1994
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1993
1990
1990
1990
1990
1990
1990
1990
1994
1990
1990
1990
1990
1991
Adjustments to Data
Backcast to 1990 using BEA. Average Sum mer
Day estimated using methodology described above.
Average Summer Day es timated using d efault
temporal factors.
None
None
AverageSummer Day estimated using methodobgy
described above.
AverageSummer Day estimated using methodology
described above.
None
Average Summer Day es timated using d efault
temporal factors.
None
AverageSummer Day estimated using methodobgy
described above.
AverageSummer Day estimated using methodology
described above.
None
None
AverageSummer Day estimated using methodobgy
described above.
None
None
None
AverageSummer Day estimated using methodology
described above.
AverageSummer Day estimated using methodobgy
described above.
Backcast to 1990 using BEA. AverageSummer
Day estimated using m ethodology desc ribed above.
AverageSummer Day estimated using methodology
described above.
None
None
None
None
AverageSummer Day estimated using methodobgy
described above.
AverageSummer Day estimated using methodology
described above.
Backcast to 1990 using BEA. AverageSummer
Day estimated using m ethodology desc ribed above.
None
None
None
None
Average Summer Day es timated using d efault
South Dakota
AIRS/FS - Ad hoc retrievals
temporal factors.
Annual 1990 AverageSummer Day estimated using methodobgy
described above.
4-76
-------
Table 4.3-12 (continued)
State
Data Source/Format
Temporal
Resolution Year of Data Adjustments to Data
Tennessee
Texas
Vermont
Virginia
West Virginia
Wisconsin
AIRS/FS - Ad hoc retrievals
State - State Format
State - EPS W orkfile
AIRS/FS - Ad hoc retrievals
AIRS/FS -Ad hoc retrievals
State - State Format
Annual 1990 Average S ummer Day estimated using default
temporal factors.
Daily 1992 Backcast to 1990 using BEA.
Daily 1990 None
Annual 1990 AverageSummer Day estimated using methodobgy
described above.
Annual 1990 AverageSummer Day estimated using methodology
described above.
Daily
1990 None
4-77
-------
Table 4.3-13. Area Source Data Submitted by OTAG States
State
Connecticut
Delaware
District of Columbia
Florida
Georg ia
Data Source/Format1
State -E PS Workfile
State -E PS Workfile
State -Hard copy
AIRS/AM S - Ad hoc retrievals
State - State form at
Temporal
Resolution
Daily
Daily
Daily
Daily
Daily
Geoaraohic Coveraae
Entire State
Entire State
Entire State
Jacksonville, Miami/
Ft. Lauderdale, Tampa
Atlanta Urban Airshed
Adjustments to Data
None
None
None
Added Non-road emission estimates
from Int. Inventory to Jacksonville
(Duval Cou nty)
None
Illinois
Indiana
Kentucky
Louisiana
Maine
Maryland
Michigan
Missouri
New Hamps hire
New J ersey
New York
North Carolina
Ohio
Pennsylvania
Rhode Island
Tennessee
Texas
Vermont
Virginia
West Virginia
Wisconsin
State - State form at
State - State form at
State - State Format
State - State Format
State- EPS Workfile
State- EPS Workfile
State -State Format
AIRS/AM S- Ad hoc retrievals
State- EPS Workfile
State- EPS Workfile
State- EPS Workfile
State -EPS Workfiles
State -Hard copy
State - EPS W orkfile
State-EPS Workfile
State - State form at
State - State Format
State- EPS Workfile
State- EPS Workfile
AIRS/AM S - Ad hoc retrievals
State - State Format
(47 Counties)
Daily Entire State
Daily Entire State
Daily Kentucky Ozone Nonattainment
Areas
Daily Baton Rouge Nonattainment
Area (20 Parishes)
Daily Entire State
Daily Entire State
Daily 49 Southern Mich igan Counties
Daily St. Louis area (25 counties)
Daily Entire State
Daily Entire State
Daily Entire State
Annual Entire State
Daily Canton, Cleveland Columbus,
Dayton, Toledo, and Youngstown
Daily Entire State
Daily Entire State
Daily 42 Counties in Midd le
Tennessee
Annual Entire State
Daily Entire State
Daily Entire State
Daily Charleston, Huntington/Ashland,
and Parkers burg (5 counties
total)
Daily Entire State
None
Non-road emissions subm itted were
county totals. Non-road emissions
distributed to specific SCCs based
on Int. Inventory
None
None
None
None
None
Only area sou rce com bustion d ata
was provided. All other area source
data came from Int. Inventory
None
None
None
Average Su mmer D ay estimated
using default temporal factors.
Ass igned SC Cs an d converted from
kgs to tons. NOX and CO from Int.
Inventory added to Canton, Dayton,
and Tofedo counties.
Non-road emissions subm itted were
county totals. Non-road emissions
distributed to specific SCCs based
on Int. Inventory
None
No non-road data submitted. Non-
road emissions added from Int.
Inventory
Average Su mmer D ay estimated
using default temporal factors.
None
None
None
None
1 AIRS/AMS = AIRS Area and Mobile Subsystem.
4-78
-------
Table 4.3-14. Ad Hoc Report
Criteria
Regn
PLL4
PLL4
3LL4
3LL4
3LL4
PLL4
DES4
DUE4
YINV
GT 0
CE VOC
CE CO
CE SO2
CE NO2
CE PM-10
CE PT
GE 0
VIE TY
ME 90
Plant Output
YINV
SITE
CNTY
CYCD
ZIPC
3NED
PNME
LAT1
_ON1
SIC1
OPST
SIRS
YEAR OF INVENTORY
STATE FIPS CODE
COUNTY FIPS CODE
CITY CODE
ZIP CODE
\IEDS POINT ID
PLANT NAME
LATITUDE PLANT
.ONGITUDEPLANT
STANDARD INDUSTRIAL
CODE
OPERATING STATUS
STATE REGISTRATION
NUMBER
Point Output
STTE
CNTY
PNED
=>NUM
CAPC
CAPU
PAT1
PAT2
=>AT3
PAT4
NOHD
NODW
NOHY
STATE FIPS CODE
COUNTY FIPS CODE
NEDS POINT ID
= OINT NUMBER
DESIGN CAPACITY
DESIGN CAPACITY
UNITS
WINTER
THROUGHPUT
SPRING
THROUGHPUT
SUMMER
THROUGHPUT
FALLTHROUGHPUT
NUMBER HOURS/DAY
NUMBER DAYS/WEEK
NUMBER
HOURS/YEAR
Stack Output
STTE
CNTY
=>NED
STNB
_AT2
LON2
STHT
STDM
STET
STEV
STFR
PLHT
STATE FIPS CODE
COUNTY FIPS CODE
\IEDS POINT ID
STACK NUMBER
.ATITUDE STACK
.ONGITUDESTACK
STACK HEIGHT
STACK DIAMETER
STACK EXIT
TEMPERATURE
STACK EXIT
VELOCITY
STACK FLOW RATE
PLUME HEIGHT
Segment Output
General
STTE
CNTY
3NED
STNB
3NUM
SEGN
SCC8
HEAT
= PRT
SULF
ASHC
PODP
STATE FIPS CODE
COUNTY FIPS CODE
\IEDS POINT ID
STACK NUMBER
= OINT NUMBER
SEGMENT NUMBER
sec
HEAT CONTENT
ANNUAL FUEL
THROUGHPUT
SULFUR CONTENT
ASH CONTENT
PEAK OZONE
SEASON DAILY
PROCESS RATE
Segment Output
Pollutant
STTE
CNTY
=>NED
STNB
=>NUM
SEGN
SCC8
=>LL4
D034
DU04
DES4
DUE4
CLEE
CLT1
CTL2
REP4
DME4
Emfa
STATE FIPS CODE
COUNTY FIPS CODE
\IEDS POINT ID
STACK NUMBER
3OINT NUMBER
SEGMENT NUMBER
sec
3OLLUTANT CODE
OSD EMISSIONS
OSD EMISSION
UNITS
DEFAULT
ESTIMATED
EMISSIONS
DEFAULT
ESTIMATED
EMISSIONSUNITS
CONTROL
EFFICIENCY
PRIMARY CONTROL
DEVICECODE
SECONDARY
CONTROL DEVICE
CODE
RULE
EFFECTIVENESS
METHOD CODE
Emission factor
-------
Table 4.3-15. SEDS National Fuel Consumption, 1990-1996 (trillion Btu)
Fuel Tvoe
End-User
Code
1990
1991
1992
1993
1994
1995
1996
Anthracite Coal
Bituminous
Commercial
Electric utility
Industrial
Residential
Coal
Commercial
Electric utility
Industrial
Residential
ACCCB
ACEUB
ACICB
ACRCB
BCCCB
BCEUB
BCICB
BCRCB
12
17
10
19
80
16,071
2,744
43
15
2
11
16
8
17
72
,997
,592
39
16
2
11
17
7
17
75
,175
,505
40
11
16
11
16
72
16,825
2,489
40
11
15
10
16
70
16,995
2,434
40
11
15
10
16
69
17,164
2,379
39
11
15
10
16
68
17,333
2,333
39
Distillate Fuel
Commercial
Industrial
Residential
Total
DFCCB
DFICB
DFRCB
DFTCB
Distillate Fuel including Kerosene jet
Kerosene
Electric utility
Commercial
Industrial
Residential
Total
DKEUB
KSCCB
KSICB
KSRCB
KSTCB
487
1,181
837
6,422
fuel
86
12
12
64
88
1
6
482
,139
832
,210
80
12
11
72
96
1
6
464
,144
865
,351
67
11
10
65
86
464
1,100
913
6,466
77
14
13
76
103
450
1,090
887
6,417
64
13
10
67
89
435
1,080
862
6,368
58
12
9
59
76
422
1,071
836
6,319
54
11
9
51
65
Liquid Petroleum Gas
Commercial
Industrial
Residential
Total
LGCCB
LGICB
LGRCB
LGTCB
64
1,608
365
2,059
1
2
69
,749
389
,227
1
2
67
,860
382
,328
70
1,794
399
2,282
70
1,804
398
2,290
70
1,813
397
2,298
70
1,823
397
2,306
Natural Gas
Commercial
Electric utility
Industrial
Residential
Total
NGCCB
NGEUB
NGICB
NGRCB
NGTCB
2,698
2,861
8,520
4,519
19,280
2
2
8
4
19
,808
,854
,637
,685
,605
2
2
8
4
20
,884
,829
,996
,821
,139
2,996
2,744
9,387
5,097
20,868
3,035
2,720
9,635
5,132
21,164
3,074
2,698
9,883
5,166
21,461
3,114
2,675
10,131
5,201
21,757
Residual Fuel
Population
Commercial
Electric utility
Industrial
Total
RFCCB
RFEUB
RFICB
RFTCB
TPOPP
233
1,139
417
2,820
248,709
1
2
252
213
,076
336
,657
,131
2
255
191
854
391
,518
,025
175
939
452
2,479
257,785
170
823
459
2,346
259,693
168
726
469
2,213
261,602
167
650
481
2,080
263,510
4-80
-------
Table 4.3-16. BEA SA-5 National Earnings by Industry, 1990-1996 (million $)
Industrv
Total population as of July 1 (thousands)
Total population as of July 1 (thousands)
Total population as of July 1 (thousands)
Total population as of July 1 (thousands)
Total population as of July 1 (thousands)
Total population as of July 1 (thousands)
Total population as of July 1 (thousands)
Total population as of July 1 (thousands)
Tola I population as of July 1 (thousands)
Tola I population as of July 1 (thousands)
Tola I population as of July 1 (thousands)
Tola I population as of July 1 (thousands)
Farm
Farm
Farm
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Metal mining
Coal mining
Oil and gas extraction
Nonm etallic min erals, exce pt fuels
Construction
Construction
Construction
Construction
Manufacturing
Durable goods
Lumb er and wood p roducts
Furniture and fixtures
Stone, clay, and g lass prod ucts
Primary m etal industries
Fabricated m etal products
Machinery, except electrical
Electric and electronic equipment
Motor vehicles and equipment
Transportation equipment, excluding m otor vehicles
Instruments and related products
Miscellaneous manufacturing industries
Nondurablegoods
Food and kindred products
Tobacco manufactures
Textile mill products
Apparel and other textile products
Paper and allied products
Printing and publishing
Chem icals and allied prod ucts
Petroleum and coal products
Rubber and miscellaneous plastic products
Leather and leather products
LNUM
020
030
040
041
045
046
047
050
060
070
071
072
081
082
090
100
110
120
121
122
123
200
210
220
230
240
300
310
320
330
400
410
413
417
420
423
426
429
432
435
438
441
444
450
453
456
459
462
465
468
471
474
477
480
SIC
999
999
999
999
999
999
999
999
999
999
999
999
1, 2
1, 2
1, 2
7-9
7-9
7-9
7-9
7-9
7-9
7-9
10
11, 12
13
14
15-17
15-17
15-17
15-17
998
996
24
25
32
33
34
35
36
371
37
38
39
997
20
21
22
23
26
27
28
29
30
31
1990
0
1
3,634
238
3,395
971
735
2,932
321
381
34
347
48
3,586
3,001
24
20
4
1
2
1
36
2
8
20
4
218
54
29
135
710
437
22
13
20
33
51
86
63
41
54
43
11
273
51
3
16
20
28
54
61
9
27
3
1991
0
1
3,593
242
3,350
947
791
2,891
331
370
28
342
41
3,552
2,957
24
20
3
1
2
1
37
3
8
22
4
197
47
28
123
690
418
21
12
18
30
48
83
62
38
52
42
11
272
51
3
16
20
27
54
63
9
26
3
1992
0
1
3,732
248
3,483
907
858
2,975
351
405
34
372
46
3,686
3,079
24
21
3
1
2
1
36
3
8
21
4
195
46
28
121
705
423
22
13
19
31
49
83
62
42
50
42
11
281
52
3
17
20
28
55
66
10
28
2
1993
0
1
3,785
253
3,531
914
888
3,003
371
410
32
378
45
3,740
3,126
24
22
3
0
2
1
34
2
6
21
4
199
47
27
125
705
424
22
13
19
30
49
84
63
46
45
40
12
282
52
2
17
19
28
56
65
9
29
3
1994
0
1
3,891
265
3,626
934
912
3,082
383
426
29
396
42
3,849
3,228
26
23
3
1
2
1
35
2
6
21
4
216
51
29
136
725
440
24
14
20
32
51
86
65
53
43
40
12
285
53
2
17
19
29
57
65
10
30
3
1995
0
1
4,011
273
3,737
980
951
3,182
394
436
18
418
31
3,980
3,353
27
24
3
1
2
1
35
2
6
21
4
219
51
29
138
740
452
25
14
20
33
53
90
68
56
42
40
12
288
53
3
17
19
29
58
67
9
31
2
1996
0
1
4,086
280
3,805
981
994
3,231
408
447
16
432
29
4,058
3,423
27
25
3
1
1
1
35
3
6
21
4
219
50
29
139
747
456
25
14
20
32
53
91
69
60
39
39
12
291
54
3
17
19
29
59
68
9
31
2
4-81
-------
Table 4.3-16 (continued)
Industry
LNUM
SIC
1990 1991 1992 1993 1994 1995 1996
Leather and leather products
Railroad transportation
Trucking and warehousing
Water transportation
Water transportation
Local and interurban passenger transit
Transportation by air
Pipelines, except natural gas
Transportation services
Comm unication
Electric, gas, and sanitary services
Wholesale trade
Retail trade
Retail trade
Retail trade
Retail trade
Retail trade
Retail trade
Retail trade
Retail trade
Retail trade
Retail trade
Banking and credit agencies
Banking and credit agencies
Banking and credit agencies
Insurance
Insurance
Real estate
Holding companies and investment services
Services
Hotels and other lodging places
Personal services
Private households
Business and miscellaneous repair services
Auto repair, services, and garages
Auto repair, services, and garages
Amusement and recreation services
Amusement and recreation services
Health services
Legal services
Educational services
Social services and membership organizations
Social services and membership organizations
Social services and membership organizations
Social services and membership organizations
Miscellaneous professional services
Governm ent and governm ent enterprises
Federal, civilian
Federal, military
State and local
State and local
State and local
500
510
520
530
540
541
542
543
544
560
570
610
620
621
622
623
624
625
626
627
628
700
710
730
731
732
733
734
736
800
805
810
815
820
825
830
835
840
845
850
855
860
865
870
875
880
900
910
920
930
931
932
31
40
42
44
44
41
45
46
47
48
49
50, 51
52-59
52-59
52-59
52-59
52-59
52-59
52-59
52-59
52-59
52-59
60, 61
60, 61
60, 61
63, 64
63, 64
65, 66
62, 67
995
70
72
88
76
75
75
78, 79
78, 79
80
81
82
83, 86
83, 86
83, 86
83, 86
84, 87, 89
995
43, 91, 97
992
92-96
92-96
92-96
243
12
59
7
48
8
30
1
12
63
49
236
342
18
40
56
55
18
22
76
57
246
82
163
38
56
34
28
8
946
31
33
10
170
29
15
29
16
290
80
39
29
1
35
125
14
585
118
50
417
125
292
245
12
58
7
49
8
30
1
13
63
52
231
335
18
38
56
54
18
20
78
54
247
81
166
40
59
33
25
10
951
31
32
9
162
28
13
30
16
304
80
41
31
1
36
121
14
594
120
50
425
128
297
251
13
60
7
50
9
31
1
14
64
53
238
342
18
39
57
54
18
19
80
57
280
86
194
50
61
33
36
14
1,008
32
33
10
175
28
13
34
16
325
85
42
34
1
36
127
15
607
123
51
433
128
305
260
12
62
6
51
9
31
1
14
67
56
235
347
19
39
56
56
18
19
82
57
290
89
201
53
62
34
43
10
1,032
33
36
10
180
30
14
33
17
330
84
44
35
1
38
130
15
613
124
48
441
130
311
269
12
66
6
50
9
31
1
15
71
56
242
359
20
40
57
60
18
21
85
59
291
89
202
51
63
36
44
9
1,066
33
36
10
191
31
14
35
18
341
84
45
38
2
40
132
17
621
125
45
451
134
317
277
12
69
6
52
10
31
1
16
75
56
255
372
21
41
58
62
18
22
88
62
302
90
212
55
63
37
47
10
1,128
35
36
11
213
33
15
37
20
355
85
46
40
2
41
141
18
626
123
44
459
136
323
283
12
71
6
53
10
31
1
17
78
57
258
378
21
41
58
64
18
22
90
63
313
91
221
58
65
38
51
10
1,164
36
37
11
221
34
15
39
20
368
86
48
42
2
42
145
19
635
124
43
468
138
330
4-82
-------
Table 4.3-17. Area Source Listing by SCC and Growth Basis
sec
FILE
CODE '
SCC
FILE
CODE
SCC
FILE
CODE
FILE
CODE
SCC
FILE
CODE
2101002000
2101004001
2101004002
2101006001
2101006002
2102001000
2102002000
2102004000
2102005000
2102006000
2102006001
2102006002
2102007000
2102008000
2102010000
2102011000
2103001000
2103002000
2103004000
2103005000
2103006000
2103007000
2103008000
2103011000
2104001000
2104002000
2104004000
2104005000
2104006000
2104007000
2104008000
2104008001
2104008010
2104008030
2104008050
2104008051
2104011000
2110030000
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
BEA
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
BEA
SEDS
SEDS
SEDS
SEDS
NG
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
NG
ACEUB
DKEUB
DKEUB
NGEUB
NGEUB
ACICB
BCICB
DFICB
RFICB
NGICB
NGICB
NGICB
LGICB
400
LGICB
KSICB
ACCCB
BCCCB
DFCCB
RFCCB
NGCCB
LGCCB
400
KSCCB
ACRCB
BCRCB
DFRCB
NGRCB
LGRCB
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
KSRCB
2199005000
2199006000
2199007000
2199011000
2260000000
2260001000
2260001010
2260001020
2260001030
2260001050
2260001060
2260002000
2260002006
2260002009
2260002021
2260002033
2260003000
2260003010
2260003020
2260003030
2260003040
2260004000
2260004010
2260004015
2260004020
2260004025
2260004030
2260004035
2260004050
2260004075
2260005000
2260006000
2260006005
2260006010
2260006015
2260006020
2260007000
2260007005
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
RFTCB
NGTCB
LGTCB
KSTCB
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
300
300
300
300
300
400
400
400
400
400
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
81
400
400
400
400
400
100
100
2260008010
2265000000
2265001000
2265001010
2265001030
2265001040
2265001050
2265001060
2265002000
2265002003
2265002006
2265002009
2265002015
2265002021
2265002024
2265002027
2265002030
2265002033
2265002039
2265002042
2265002045
2265002054
2265002057
2265002060
2265002066
2265002072
2265002078
2265002081
2265003000
2265003010
2265003020
2265003030
2265003040
2265003050
2265004000
2265004010
2265004015
2265004025
BEA
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
SEDS
SEDS
SEDS
SEDS
542
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
400
400
400
400
400
400
TPOPP
TPOPP
TPOPP
TPOPP
2265004035
2265004040
2265004045
2265004050
2265004055
2265004060
2265004065
2265004070
2265004075
2265005000
2265005010
2265005015
2265005020
2265005030
2265005035
2265005040
2265005045
2265005050
2265005055
2265006000
2265006005
2265006010
2265006015
2265006025
2265006030
2265007000
2265007010
2265008000
2265008005
2265008010
2270000000
2270001000
2270001010
2270001050
2270001060
2270002000
2270002003
2270002009
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
SEDS
SEDS
SEDS
SEDS
SEDS
BEA
BEA
BEA
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
81
81
81
81
81
81
81
81
81
81
400
400
400
400
400
400
100
100
542
542
542
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
300
300
300
2270002015
2270002018
2270002021
2270002027
2270002030
2270002033
2270002036
2270002039
2270002042
2270002045
2270002048
2270002051
2270002054
2270002057
2270002060
2270002063
2270002066
2270002069
2270002072
2270002075
2270002078
2270002081
2270003000
2270003010
2270003020
2270003030
2270003040
2270003050
2270004000
2270004010
2270004040
2270004055
2270004060
2270004065
2270004070
2270004075
2270005000
2270005015
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
BEA
BEA
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
400
400
400
400
400
400
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
81
81
-------
Table 4.3-17 (continued)
sec
FILE
CODE '
SCC
FILE
CODE
SCC
FILE
CODE
SCC
FILE
CODE
SCC
FILE
CODE
2199004000
2270005025
2270005035
2270005045
2270005050
2270005055
2270006000
2270006005
2270006010
2270006015
2270006025
2270006030
2270007000
2270007015
2270007020
2270008000
2270008005
2270008010
2275000000
2275001000
2275020000
2275020021
2275050000
2275060000
2275070000
2275900000
2275900101
2275900102
2280000000
2280001000
2280002000
2280002010
2280002020
2280002040
2280003000
2280003010
2280003020
2280003030
2280004020
2282000000
2501050000
SEDS
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
SEDS
BEA
DFTCB
81
81
81
81
81
400
400
400
400
400
400
100
100
100
542
542
542
542
920
542
542
542
542
542
542
542
542
530
530
530
530
530
530
530
530
530
530
530
TPOPP
610
2260008000
2282005000
2282005010
2282005015
2282005025
2282010000
2282010005
2282010010
2282010015
2282010020
2282010025
2282020000
2282020005
2282020010
2282020020
2282020025
2283002000
2285000000
2285002000
2285002005
2285002010
2301000000
2301010000
2301020000
2301030000
2301040000
2302000000
2302002000
2302010000
2302050000
2302070000
2302070001
2302070005
2302070010
2303020000
2304000000
2304050000
2305000000
2305070000
2306000000
2501995000
BEA
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
542
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
920
510
510
510
510
471
471
471
471
471
453
453
453
453
453
453
453
453
423
423
423
240
240
474
230
2265004030
2306010000
2308000000
2309000000
2309100230
2310000000
2310010000
2310020000
2312000000
2325030000
2390004000
2390005000
2390006000
2390007000
2390010000
2399000000
2401000000
2401001000
2401002000
2401005000
2401008000
2401015000
2401020000
2401025000
2401030000
2401035000
2401040000
2401045000
2401045999
2401050000
2401055000
2401060000
2401065000
2401070000
2401075000
2401080000
2401085000
2401090000
2401100000
2401200000
2601020000
SEDS
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
SEDS
SEDS
NG
BEA
SEDS
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
TPOPP
474
477
426
426
230
230
230
429
210
400
400
400
400
400
400
TPOPP
TPOPP
825
TPOPP
413
417
417
465
477
426
426
426
426
429
432
432
435
438
438
438
444
400
400
570
2270002012
2401990000
2415000000
2415000385
2415000999
2415035000
2415045000
2415065000
2415100000
2415105000
2415110000
2415120000
2415125000
2415130000
2415135000
2415140000
2415145000
2415200000
2415230000
2415245000
2415260000
2415300000
2415305000
2415310000
2415315000
2415320000
2415325000
2415330000
2415335000
2415340000
2415345000
2415350000
2415355000
2415360000
2415365000
2420000000
2420000055
2420000370
2420000999
2420010000
2810015000
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
300
400
400
400
400
438
444
413
400
417
423
426
429
432
438
441
444
438
432
444
825
438
417
423
423
426
429
432
438
441
444
510
620
825
820
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
2270005020
2420010055
2420010370
2420010999
2420020000
2420020055
2425000000
2425000999
2425010000
2425030000
2425040000
2430000000
2440000000
2440000999
2440020000
2460000000
2460000385
2461000000
2461020000
2461021000
2461022000
2461023000
2461050000
2461160000
2461600000
2461800000
2461850000
2465000000
2465100000
2465200000
2465400000
2465600000
2465800000
2465900000
2500000000
2501000000
2501000030
2501000090
2501000150
2501010000
2495000000
BEA
SEDS
SEDS
SEDS
SEDS
SEDS
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
SEDS
SEDS
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
SEDS
NG
BEA
BEA
BEA
BEA
BEA
SEDS
81
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
820
820
820
820
820
477
444
444
444
TPOPP
TPOPP
300
300
300
300
300
300
300
300
300
300
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
230
230
230
230
230
TPOPP
-------
Table 4.3-17 (continued)
sec
FILE
CODE '
SCC
FILE
CODE
SCC
FILE
CODE
SCC
FILE
CODE
SCC
FILE
CODE
2501050030
2501050060
2501050090
2501050120
2501050150
2501050180
2501060000
2501060050
2501060051
2501060052
2501060053
2501060100
2501060101
2501060102
2501060103
2501060200
2501060201
2501070000
2501070051
2501070052
2501070101
2501070103
2501070201
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
610
610
610
610
610
610
620
620
620
620
620
620
620
620
620
620
620
620
620
620
620
620
620
2501995030
2501995060
2501995090
2501995120
2501995150
2501995180
2505000000
2505000120
2505010120
2505020000
2505020030
2505020060
2505020090
2505020120
2505020150
2505020180
2505020900
2505030000
2505030120
2510000000
2510995000
2601000000
2601010000
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
230
230
230
230
230
230
474
474
474
474
474
474
474
474
474
474
474
474
474
471
471
570
570
2601030000
2610000000
2610010000
2610020000
2610030000
2620000000
2620030000
2630000000
2630010000
2630020000
2630030000
2640000000
2640000001
2640000004
2640010001
2640010004
2660000000
2801000005
2801500000
2810001000
2810003000
2810005000
2810010000
BEA
BEA
BEA
BEA
SEDS
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
NG
SEDS
BEA
BEA
570
570
570
570
TPOPP
570
570
570
570
570
570
570
570
570
570
570
570
100
100
TPOPP
100
100
2810025000
2810030000
2810035000
2810050000
2810060000
2830000000
2830001000
2850000010
2102009000
2275085000
2280004000
2294000000
2296000000
2302080000
2307060000
2309100010
2310030000
2311000100
2325000000
2401010000
2415045999
2415060000
2461800999
SEDS
SEDS
SEDS
SEDS
SEDS
NG
NG
NG
BEA
BEA
BEA
NG
NG
BEA
BEA
BEA
BEA
NG
NG
BEA
BEA
BEA
SEDS
TPOPP
TPOPP
TPOPP
TPOPP
TPOPP
400
542
530
453
413
426
230
459
400
400
TPOPP
2505010000
2710020030
2730050000
2730100000
2801000003
2801520000
2801700001
2801700002
2801700003
2801700004
2801700005
2801700006
2801700007
2801700008
2801700009
2801700010
2805000000
2805001000
2805020000
2805025000
2805030000
2805040000
2805045001
BEA
BEA
NG
NG
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
BEA
474
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
NOT E(S): * BEA Code is eq ual to LNU M on previous table.
-------
Table 4.3-18. Emission Estimates Available from AIRS/FS by State, Year, and Pollutant
State
1990
T V
1991
C N
T V
1992
C N
T V
1993
C N
T V
1994
C N
T V
1995
C N
T V
Alabama
Alaska
Arizona
California
Colorado
Connecticut
Hawaii
Illinois
Louisiana
Michigan
Minnesota
Montana
Nebraska
Nevada
New Hampshire
New Mexico
North Dakota
Oregon
Pennsylvania
South Carolina
South Dakota
Texas
Utah
Vermont
Virginia
Washington
Wisconsin
Wyoming
Notes:
C = CO N = NO2 S = SO2 P = PM-10 T = TSP V = VOC
Pennsylvania only includes Allegheny County (State 42, County 003); N ew Mexico only includes Albuquerque (State 35, County 001); W ashington only includes Puget
Sound (State 53, County 033, 053, or 061); Nebraska includes all except Omaha City (State 31 , County 055); the CO emissions in NET were maintained for South Dakota
(State 46).
-------
Page Intentionally Blank
-------
Table 4.3-21. Cotton Ginning Emission Factors22
Control Type
Full controls (high-efficiency cyclone)
Conventional controls (screened drums or
cages)
Total PM
(Ib/bale)
2.4
3.1
PM-10
(Ib/bale)
0.82
1.2
PM-2.5
(Ib/bale)
0.024
0.031
Table 4.3-22. Estimated Percentage of Crop By Emission Control Method
(By State and U.S. Average)29
State
Alabama
Arizona
Arkansas
California
Florida
Georgia
Louisiana
Mississippi
Missouri
New Mexico
North Carolina
Oklahoma
South Carolina
Tennessee
Texas
Virginia
U.S. Average3
Percent Crop -
Full Controls
20
50
30
72
20
30
20
20
20
20
30
20
20
20
30
20
35
Percent Crop -
Conventional Controls
80
50
70
28
80
70
80
80
80
80
70
80
80
80
70
80
65
"Average is based on the average crop (average total bales ginned per year) from 1991 to
1995 for these States.
4-88
-------
Table 4.3-23. Cotton Ginnings: Running Bales Ginned By
County, District, State, and United States3
State/County/
District
UNITED STATES
Alabama
Colbert 1/
Lauderdale 1/
Lawrence
Limestone
Madison
District 10
Blount 1/
Cherokee 1/
District 20
Chilton 1/
Fayette 1/
Pickens 1/
Shelby 1/
Tallapoosa 1/
Tuscal oosa 1 /
District 30 21
Autauga 1/
Dallas 1/
Elmore
Greene 1/
Hale 1/
Lowndes 1/
Macon 1/
Marengo 1/
District 40
Running Bales
Ginned
17,498,800
72,000
72,000
35,200
59,300
25,750
144,250
4,538
4,538
4,538
4,538
4,538
4,538
4,538
4,538
4,079
4,079
6,100
4,079
4,079
4,079
4,079
4,079
34,650
State/County/
District
Alabama (Cont'd)
Baldwin 1/
Escambial/
Mobile 1/
Monroe 1/
District 50
Covington 1/
Crenshaw 1/
Geneva 1/
Henry 1/
Houston 1/
Russell 1/
District 60
AL Total
Arizona
Mohave 1/
District 20 21
Maricopa
Final
District 50
La Paz 1/
Yuma
Running Bales
Ginned
30,575
30,575
30,575
30,575
122,300
25,608
25,608
25,608
25,608
25,608
25,608
153,650
491,150
354,050
266,900
620,950
74,100
"The data in and format of this table were taken from the 03/25/96 Cotton Ginnings report.
1/ Withheld to avoid disclosing indwidual gins.
21 W ithheld to avoid disclosing individual gins, but included in State total.
3/ Excludes some gins' data to avoid disclosing individual gins, but included in State total.
41 W ithheld to avoid disclosing individual gins, but included in U .S. total.
4-89
-------
Table 4.3-24. Point Source Controls by Pod and Measure
POD PODNAME
MEASNAME
SOURCE
PTFYCE
4 Fixed roof petroleum product tanks
5 Fixed roof gasoline tanks
6 EFR petroleum product tanks
7 EFR gasoline tanks
15 Ethylene oxide manufacture
16 Phenol manufacture
17 Terephthalic acid manufacture
Acrylonitrile manufacture
Cellulose acetate manufacture
23 Polypropylene manufacture
24 Polyethylene manufacture
25 Ethylene manufacture
26 Petroleum refinery wastewater treatment
27 Petroleum refinery vacuum distillation
28 Vegetable oil manufacture
29 Paint and varnish manufacture
32 Carbon black manufacture
42 Surface coating-thinning solvents
47 Ferrosilicon production
48 By-product coke manufacture - other
49 By-product coke manufacture - oven charging
50 Coke ovens - door and topside leaks
51 Coke oven by-product plants
53 Whiskey fermentation - aging
54 Charcoal manufacturing
56 SOCMI reactor
57 SOCMI distillation
61 Open top degreasing
62 In-line degreasing
63 Cold cleaning
65 Open top degreasing - halogenated
66 In-line degreasing - halogenated
CTG
CTG
CTG
CTG
SOCMIHON
SOCMIHON
Incineration (RACT)
SOCMIHON
Carbon adsorber (RACT)
Flare (RACT)
Flare (RACT)
Flare (RACT)
Benzene NESHAP/CTG
CTG
Stripper and equipment (RACT)
RACT
Flare (RACT)
RACT
RACT
NESHAP
NESHAP
NESHAP
NESHAP
Carbon adsorption (RACT)
Incineration (RACT)
New CTG
New CTG
MACT
MACT
MACT
MACT
MACT
Fixed roof petroleum tanks 98
Fixed roof gasoline tanks 96
EFR petroleum tanks 90
EFR gasoline tanks 95
Ethylene oxide manufacture 79
Phenol manufacture 79
Terephthalic acid manufacture 98
Acrylonitrile manufacture 79
Cellulose acetate manufacture 54
Polypropylene manufacture 98
Polyethylene manufacture 98
Ethylene manufacture 98
Petroleum ref wastewater treatment 95
Petroleum ref vacuum distillation 100
Vegetable oil manufacture 42
Paint and varnish manufacture 70
Carbon black manufacture 90
Surface coating-thinning solvents 90
Ferrosilicon production 88
By-product coke manufacture - other 94
By-product coke mfg - oven charging 94
Coke ovens - door and topside leaks 94
Coke oven by-product plants 94
Whiskey fermentation - aging 85
Charcoal manufacturing 80
SOCMI reactor 98
SOCMI distillation 98
Open top degreasing 63
In-line degreasing 63
Cold cleaning 63
Open top degreasing - halogenated 63
In-line degreasing - halogenated 63
-------
Table 4.3-24 (continued)
POD PODNAME
MEASNAME
SOURCE
PTFYCE
68 SOCMI fugitives
69 SOCMI wastewater
71 SOCMI processes - pharmaceutical
73 SOCMI processes - gum and wood
74 SOCMI processes - cyclic crudes
75 SOCMI processes- industrial chemicals
77 SOCMI processes - crudes & agricultural
80 SOCMI fugitives - cyclic crudes
81 SOCMI fugitives - industrial organics
82 SOCMI - process vents
84 VOL storage
85 Misc organic solvent evaporation
86 Single chamber incinerators
91 Dry cleaning - perchloroethylene
93 Dry cleaning - other
95 Bakeries
96 Urea resins - general
97 Organic acids manufacture
98 Leather products
114 Petroleum refineries - Slowdown w/o control
199 Miscellaneous non-combustion
401 By-product coke mfg
402 By-product coke - flushing-l iquor circulation tank
403 By-product coke - excess-ammonia liquor tank
404 By-product coke mfg - tar storage
405 By-product coke mfg - light oil sump
406 By-product coke mfg - light oil dec/cond vents
407 By-product coke mfg - tar bottom fi nal cooler
408 By-product coke mfg - naphthalene processing
409 By-product coke mfg - equipment leaks
HON - Equipment Leak and
Detection
SOCMIHON
SOCMI HON/Pharmaceuticals
SOCMI reactor CTG
SOCMIHON
SOCMIHON
SOCMI reactor CTG
SOCMIHON
SOCMIHON
SOCMIHON
SOCMIHON
SOCMIHON
RACT
MACT
MACT
Incineration (RACT)
RACT
RACT
RACT
RACT/CTG
RACT
Benzene NESHAP
Benzene NESHAP
Benzene NESHAP
Benzene NESHAP
Benzene NESHAP
Benzene NESHAP
Benzene NESHAP
Benzene NESHAP
Benzene NESHAP
SOCM I fugitives 79
SOCMI wastewater 79
SOCMI processes - pharmaceutical 79
SOCMI processes - gum and wood 98
SOCMI processes - cyclic crudes 79
SOCMI processes - industrial chem 79
SOCMI processes - crudes & agricul 98
SOCMI fugitives - cyclic crudes 79
SOCMI fugitives - ind organics 79
SOCMI - process vents 79
VOL storage 79
Misc organic solvent evaporation 79
Single chamber incinerators 90
Dry cleaning - perchloroethylene 44
Dry cleaning - other 44
Bakeries 95
Urea resins - general 90
Organic acids manufacture 90
Leather products 90
Petroleum ref- blowdown 98
Miscellaneous non-combustion 90
By-product coke mfg 85
By-prod coke - flush-liq circ tank 95
By-prod coke - ex nh3 liquor tank 98
By-product coke mfg - tar storage 98
By-product coke - light oi I sum 98
By-prod coke- oil dec/cond vents 98
By-prod coke - tar bottom cooler 81
By-prod coke - naphtha processing 100
By-product coke - equipment leaks 83
NOTE:
A pod is a group of SCCs with similar emissions and process characteristics for which common control measures (i.e., cost and ernes! on reductions) can be applied.
-------
Table 4.3-25. Point Source SCC to Pod Match-up
sec
POD
sec
POD
sec
POD
sec
POD
SCC
POD
SCC
POD
SCC
POD
30100101
30100103
30100104
30100180
30100199
30100504
30100509
30100601
30100603
30100604
30100699
30101012
30101013
30101021
30101022
30101030
30101099
30101401
30101402
30101403
30101404
30101499
30101501
30101502
30101503
30101505
30101599
30101603
30101801
30101802
30101803
75
17
56
81
75
32
68
54
54
54
73
116
116
116
116
116
116
29
29
29
29
29
29
29
29
29
29
145
140
23
23
30101842
30101847
30101849
30101852
30101860
30101861
30101863
30101864
30101865
30101866
30101870
30101872
30101880
30101881
30101882
30101885
30101890
30101891
30101892
30101893
30101894
30101899
30101901
30101902
30101904
30101907
30102001
30102002
30102003
30102004
30102005
70
136
143
70
24
24
24
24
24
24
136
136
136
136
136
136
104
104
104
104
104
104
74
74
74
57
29
29
29
29
29
30102630
30102699
30103101
30103102
30103103
30103104
30103105
30103199
30103301
30103311
30103312
30103399
30103402
30103405
30103406
30103410
30103412
30103420
30103425
30103499
30104204
30106001
30106002
30106003
30106004
30106005
30106006
30106007
30106008
30106009
30106010
22
22
134
134
134
134
134
134
76
76
76
78
75
82
82
75
75
75
75
75
75
71
71
71
71
71
71
71
71
71
71
30112021
30112099
30112199
30112480
30112501
30112502
30112509
30112510
30112512
30112514
30112520
30112524
30112525
30112526
30112533
30112534
30112535
30112540
30112541
30112547
30112550
30112599
30112699
30112701
30112702
30112730
30112780
30113201
30113210
30113221
30113227
56
75
56
81
75
75
81
75
82
75
75
81
75
82
75
81
75
75
75
75
81
75
75
75
75
75
81
75
75
75
75
30116780
30116799
30116901
30116906
30116980
30117401
30117421
30117480
30117617
30117680
30118101
30118102
30118103
30118110
30118180
30119001
30119013
30119014
30119080
30119501
30119580
30119701
30119705
30119707
30119708
30119709
30119710
30119741
30119742
30119743
30119744
81
75
74
74
80
15
15
15
75
75
74
74
74
74
80
74
74
74
80
75
81
25
25
75
75
75
75
75
75
75
75
30125003
30125004
30125005
30125010
30125015
30125020
30125099
30125101
30125180
30125201
30125301
30125302
30125306
30125315
30125325
30125326
30125380
30125401
30125405
30125406
30125409
30125413
30125415
30125420
30125499
30125801
30125802
30125803
30125805
30125807
30125810
82
81
56
56
56
56
56
75
81
56
75
82
82
75
75
82
81
75
18
75
81
75
75
81
56
75
75
57
75
57
75
30181001
30182001
30182002
30182003
30182004
30182005
30182006
30182007
30182008
30182009
30182010
30182011
30183001
30184001
30188801
30188802
30188803
30188804
30188805
30190001
30190002
30190003
30190004
30201003
30201401
30201902
30201903
30201906
30201907
30201908
30201911
77
69
69
69
69
69
69
69
69
69
69
69
68
57
68
68
68
68
68
88
88
88
88
53
94
28
28
28
28
28
28
-------
Table 4.3-25 (continued)
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
30101805
30101807
30101808
30101809
30101810
30101811
30101812
30101813
30101814
30101815
30101816
30101817
30101818
30101819
30101820
30101821
30101822
30101827
30101832
30101837
30101838
30101839
30101840
30300335
30300336
30300341
30300342
30300343
30300344
30300351
30300353
30300361
30300813
137
24
24
24
24
24
24
24
24
136
136
138
136
136
136
136
138
136
96
144
143
143
143
402
404
405
406
406
406
401
408
409
46
30102099
30102401
30102402
30102410
30102416
30102423
30102424
30102426
30102427
30102499
30102501
30102505
30102601
30102602
30102608
30102609
30102612
30102613
30102614
30102615
30102616
30102617
30102625
3060081 1
30600812
30600813
30600814
30600815
30600816
30600817
30600818
30600819
30600821
29
142
104
141
21
21
21
21
21
21
139
21
22
22
22
22
22
22
22
22
22
22
22
20
20
20
20
20
20
20
20
20
20
30106011
30106012
30106099
30109101
30109105
30109151
30109152
30109153
30109154
30109180
30109199
30110002
30110003
30110080
30110099
30112001
30112002
30112005
30112006
30112007
30112011
30112013
30112014
30700703
30700704
30700705
30700706
30700707
30700708
30700709
3070071 1
30700713
30700715
71
71
79
75
75
75
75
57
57
81
75
75
82
81
75
75
75
82
82
81
75
82
82
117
117
117
117
117
117
117
117
117
117
30113299
30113301
30113302
30113701
30113710
30113799
30114001
30114005
30115201
30115301
30115311
30115380
30115601
30115604
30115701
30115704
30115780
30115802
30115803
30115822
30116701
30116703
30116704
31000205
31000206
31000207
31000299
31000401
31000403
31000404
31000405
31088801
31088802
97
75
75
75
75
75
75
56
75
75
82
81
74
74
74
74
80
75
75
57
75
82
75
112
112
112
112
88
88
88
88
112
112
30119745
30119749
30119799
30120201
30120202
30120204
30120205
30120206
30120280
30120501
30120502
30120521
30120530
30120545
30120580
30120601
30120603
30120680
30121001
30121002
30121101
30125001
30125002
40100101
40100102
40100103
40100104
40100105
40100198
40100201
40100202
40100203
40100204
75
75
25
16
16
82
16
16
81
75
75
82
82
82
81
74
74
80
75
82
75
75
75
91
92
91
92
93
93
61
65
65
65
30125815
30125880
30125899
30130101
30130102
30130103
30130104
30130105
30130106
30130107
30130108
30130180
30130301
30130380
30130402
30130480
30130501
30130502
30130580
30180001
30180002
30180003
30180006
40188898
40199999
40200101
40200110
40200301
40200310
40200401
40200410
40200501
40200510
75
75
75
74
74
74
74
74
82
74
74
80
75
81
75
81
75
75
81
68
68
68
68
63
63
33
33
34
34
33
40
33
33
30201912
30201914
30201915
30201916
30201917
30201918
30201919
30201999
30203201
30203202
30203299
30300302
30300303
30300304
30300306
30300308
30300313
30300314
30300315
30300331
30300332
30300333
30300334
40201505
40201531
40201599
40201601
40201602
40201603
40201604
40201605
40201606
40201607
28
28
28
28
28
28
28
28
95
95
95
49
48
48
48
50
48
50
51
401
402
403
402
37
37
37
33
33
33
33
33
33
33
-------
Table 4.3-25 (continued)
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
30300825
30390003
30390004
30490001
30490003
30490004
30490031
30490033
30490034
30600101
30600102
30600103
30600104
30600105
30600106
30600107
30600111
30600201
30600202
30600204
30600301
30600401
30600402
30600503
30600504
30600505
30600506
30600508
30600514
30600516
30600517
30600519
30600520
46
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
109
109
109
109
113
114
26
26
26
26
26
26
26
26
26
26
30600903
30600904
30600905
30600999
30601001
30601101
30601201
30601401
30609902
30609903
30609904
30610001
30688801
30688802
30688803
30688804
30688805
30700101
30700102
30700103
30700104
30700105
30700106
30700107
30700108
30700109
30700110
30700199
30700203
30700214
30700215
30700221
30700222
110
110
110
110
110
110
110
110
110
110
110
110
20
20
20
20
20
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
60
30700798
30700799
30701199
30790001
30790002
30790003
30800101
30800102
30800103
30800104
30800105
30800106
30800107
30800108
30800109
30800120
30800121
30800122
30800123
30800197
30800198
30800199
30800501
30800699
30800701
30800702
30800703
30800704
30800705
30800720
30800721
30800722
30800723
117
117
36
88
88
88
30
30
30
30
30
31
30
30
30
30
30
30
31
30
30
30
30
123
123
123
123
123
123
123
123
123
123
31088803
31088804
31088805
32099997
32099998
32099999
39000201
39000203
39000289
39000299
39000402
39000403
39000489
39000499
39000501
39000502
39000503
39000589
39000598
39000599
39000602
39000603
39000605
39000689
39000699
39000701
39000702
39000789
39000797
39000799
39000801
39000889
39000899
112
112
112
98
98
98
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
40100205
40100206
40100207
40100221
40100222
40100223
40100224
40100225
40100235
40100236
40100251
40100252
40100253
40100254
40100255
40100256
40100257
40100258
40100259
40100275
40100295
40100296
40100297
40100298
40100299
40100301
40100302
40100303
40100304
40100305
40100306
40100307
40100308
65
61
65
62
66
66
66
66
62
62
61
65
65
65
65
61
65
61
61
61
62
62
61
62
61
63
63
63
63
63
61
63
63
40200601
40200610
40200701
40200706
40200707
40200710
40200801
40200802
40200803
40200810
40200898
40200998
40201001
40201002
40201003
40201004
40201101
40201103
40201105
40201112
40201113
40201114
40201115
40201116
40201199
40201201
40201210
40201301
40201303
40201304
40201305
40201399
40201401
33
33
36
36
36
36
35
35
35
35
35
33
88
88
88
88
41
41
41
41
41
41
41
41
41
41
41
36
36
36
36
36
37
40201608
40201609
40201619
40201620
40201621
40201622
40201623
40201625
40201626
40201627
40201628
40201629
40201631
40201632
40201699
40201702
40201703
40201704
40201705
40201721
40201722
40201723
40201724
40201725
40201726
40201727
40201728
40201731
40201732
40201734
40201735
40201799
40201801
33
33
33
33
33
33
33
33
33
33
33
33
33
33
33
34
34
34
34
34
34
34
34
34
34
34
34
34
34
34
34
34
37
-------
Table 4.3-25 (continued)
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
30600602
30600603
30600701
30600702
30600801
30600802
30600803
30600804
30600805
30600806
30600807
40202031
40202033
40202099
40202101
40202103
40202104
40202105
40202106
40202107
40202108
40202109
40202131
40202132
40202133
40202199
40202201
40202202
40202203
40202205
40202299
40202301
40202302
27
27
111
111
20
20
20
20
20
20
20
37
37
37
40
40
40
40
40
40
40
40
40
40
40
40
38
38
38
38
38
132
132
30700223
30700234
30700299
30700301
30700303
30700401
30700402
30700501
30700597
30700599
30700701
40300106
40300107
40300108
40300109
40300111
40300112
40300115
40300116
40300150
40300151
40300152
40300153
40300154
40300156
40300157
40300159
40300160
40300161
40300198
40300199
40300201
40300202
60
60
60
60
60
60
60
115
115
115
117
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
7
7
30800724
30800799
30800901
30901601
31000104
31000105
31000199
31000201
31000202
31000203
31000204
40301068
40301078
40301097
40301098
40301099
40301101
40301102
40301103
40301104
40301105
40301106
40301107
40301108
40301109
40301110
40301111
40301112
40301113
40301114
40301115
40301116
40301117
123
123
123
108
112
112
112
112
112
112
112
4
4
4
4
4
7
7
7
7
7
7
7
7
6
6
6
6
6
6
6
6
6
39000989
39000999
39001089
39001099
39001299
39001389
39001399
39990001
39990002
39990003
39990004
40388802
40388803
40388804
40388805
40399999
40400101
40400102
40400103
40400104
40400105
40400106
40400107
40400108
40400109
40400110
40400111
40400112
40400113
40400114
40400115
40400116
40400117
87
87
87
87
98
87
87
88
88
88
88
110
110
110
110
110
150
150
150
150
150
150
151
151
151
152
152
152
152
152
152
153
153
40100309
40100310
40100335
40100336
40100398
40100399
40100499
40100550
40188801
40188802
40188805
40400240
40400241
40400250
40400251
40400254
40400260
40400261
40400271
40400301
40400302
40400303
40400304
40400305
40400401
40400402
40400403
40400404
40400406
40400408
40400410
40400412
40400413
63
63
63
63
63
63
63
63
63
63
63
173
173
155
155
155
174
174
174
156
157
158
158
158
159
160
159
160
160
160
160
160
159
40201404
40201405
40201406
40201431
40201432
40201433
40201435
40201499
40201501
40201502
40201503
40500510
4050051 1
40500512
40500513
40500514
40500598
40500599
40500601
40500701
40500801
4050081 1
40500812
40588801
40588802
40588803
40588804
40588805
40600101
40600126
40600130
40600131
40600132
37
37
37
37
37
37
37
37
37
37
37
186
183
183
183
183
183
183
184
187
188
188
188
188
188
188
188
188
161
163
166
163
166
40201803
40201805
40201806
40201899
40201901
40201903
40201904
40201999
40202001
40202002
40202005
40600243
40600244
40600245
40600246
40600248
40600249
40600250
40600251
40600253
40600257
40600259
40600298
40600299
40600301
40600302
40600306
40600307
40600399
40700401
40700402
40700497
40700498
37
37
37
37
39
39
39
39
37
37
37
55
55
55
55
55
55
55
55
55
55
55
55
55
168
169
170
171
170
84
84
84
84
-------
Table 4.3-25 (continued)
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
40202305
40202306
40202399
40202401
40202402
40202403
40202405
40202406
40202499
40202501
40202502
40202503
40202504
40202505
40202531
40202532
40202533
40202534
40202537
40202598
40202599
40202601
40202605
40202606
40202607
40202699
40290013
40300101
40300102
40300103
40300104
40300105
40703202
132
132
132
52
52
52
52
52
52
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
88
5
4
5
4
4
84
40300203
40300204
40300205
40300207
40300208
40300209
40300210
40300212
40300216
40300299
40300302
40301001
40301002
40301003
40301004
40301005
40301006
40301007
40301008
40301009
40301010
40301011
40301012
40301013
40301014
40301015
40301016
40301017
40301018
40301019
40301020
40301021
40704498
6
6
6
6
6
6
6
6
6
6
6
5
5
5
5
5
5
5
5
5
4
4
4
4
4
4
4
4
4
4
4
4
84
40301118
40301119
40301120
40301130
40301131
40301132
40301133
40301134
40301135
40301140
40301141
40301142
40301143
40301144
40301145
40301150
40301151
40301152
40301153
40301154
40301155
40301197
40301198
40301199
40301201
40301202
40301203
40301204
40301205
40301206
40301299
40388801
40707698
6
6
6
6
7
6
6
6
6
8
9
8
8
8
8
8
9
8
8
8
8
6
6
6
7
7
7
6
6
6
6
110
84
40400118
40400119
40400120
40400130
40400131
40400140
40400141
40400150
40400151
40400152
40400153
40400154
40400160
40400161
40400170
40400171
40400178
40400199
40400201
40400202
40400203
40400204
40400205
40400206
40400207
40400208
40400209
40400210
4040021 1
40400212
40400230
40400231
40787201
154
154
154
173
173
173
173
155
155
155
155
155
174
174
174
174
174
155
150
150
150
151
151
151
152
152
152
154
154
154
173
173
84
40400414
40400497
40400498
40500101
40500199
40500201
40500202
40500203
4050021 1
40500212
40500299
40500301
40500303
40500304
40500305
40500306
40500307
4050031 1
40500312
40500314
40500401
4050041 1
40500412
40500413
40500414
40500416
40500418
40500501
40500502
40500503
40500506
40500507
50200301
160
159
160
189
189
180
186
186
180
180
180
181
186
186
186
186
186
181
181
181
182
182
182
182
182
182
182
183
183
186
186
186
89
40600133
40600134
40600135
40600136
40600137
40600138
40600139
40600140
40600141
40600143
40600144
40600145
40600146
40600147
40600148
40600149
40600161
40600162
40600163
40600197
40600198
40600199
40600231
40600232
40600233
40600234
40600235
40600236
40600237
40600238
40600239
40600240
166
166
166
161
164
164
164
164
162
165
165
165
165
163
166
166
166
167
167
172
172
172
55
55
55
55
55
55
55
55
55
55
40700801
40700802
40700803
40700805
40700806
40700807
40700808
40700809
40700810
4070081 1
40700812
40700813
40700814
40700815
40700816
40700817
40700818
40700897
40700898
40701605
40701606
40701608
40701611
40701612
40701613
40701614
40701697
40701698
40702003
40702097
40702098
40703201
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
-------
Table 4.3-25 (continued)
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
40703203
40703204
40703205
40703206
40703297
40703298
40703601
40703602
40703603
40703605
40703606
40703608
40703609
40703610
40703613
40703614
40703615
40703616
40703617
40703618
40703619
40703620
40703622
40703623
40703624
40703697
40703698
40704001
40704002
40704003
40704004
40704008
40704009
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
40704801
40704802
40704897
40704898
40705203
40705208
40705210
40705211
40705213
40705216
40705297
40705298
40705603
40705604
40705605
40705606
40705607
40705609
40705610
40705697
40705698
40706005
40706006
40706007
40706008
40706009
40706010
40706011
40706012
40706013
40706015
40706017
40706018
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
40708097
40708098
40708401
40708403
40708404
40708497
40708498
40715809
40717205
40717206
40717207
40717208
40717209
40717211
40717297
40717298
40717601
40717602
40717603
40717604
40717697
40717698
40718097
40720801
40720897
40720898
40722001
40722003
40722005
40722009
40722010
40722097
40722098
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
40787299
40799997
40799998
40899995
40899997
40899999
49000101
49000103
49000105
49000199
49000201
49000202
49000203
49000204
49000205
49000206
49000299
49000399
49000401
49000499
49000501
49000599
49090013
49090023
49099998
49099999
50100101
50100103
50100201
50100401
50100505
50100506
50100507
84
84
84
85
55
85
85
85
85
85
85
85
85
85
85
85
85
85
85
85
85
85
85
85
85
85
89
89
89
89
89
89
89
50200302
50200505
50200506
50200601
50200602
50290005
50290006
50290099
50300101
50300102
50300103
50300104
50300105
50300106
50300201
50300202
50300204
50300501
50300506
50300599
50300601
50300602
50300603
50300701
50300801
50300810
50300820
50300830
50300899
50390005
50390006
50390010
62540010
89
89
89
128
128
88
88
88
89
89
89
89
89
89
89
89
89
89
89
89
128
128
128
89
129
129
129
129
129
89
89
89
138
-------
Table 4.3-25 (continued)
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
sec
POD
40704097
40704098
40704401
40704402
40704403
40704404
40704405
40704406
40704407
40704408
4070441 1
40704412
40704414
40704416
40704418
40704419
40704420
40704421
40704422
40704497
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
40706019
40706020
40706021
40706022
40706023
40706024
40706097
40706098
40706401
40706402
40706403
40706497
40706801
40706802
40706814
40706897
40706898
40707601
40707602
40707697
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
40722801
40722802
40722803
40722804
40722805
40722806
40722897
40722898
40781602
40781605
40781699
40782001
40782003
40782006
40782009
40782099
40783203
40784899
40786004
40786099
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
50100510
50100515
50100516
50100601
50100603
50100701
50100702
50100703
50100704
50190005
50190006
50200101
50200103
50200104
50200105
50200106
50200116
50200117
50200201
50200202
89 62540020
89 62540022
89 64630016
88 64630040
89
127
127
127
127
87
87
89
89
89
89
89
89
89
89
89
138
138
138
138
NOTE:
A pod is a group of SCCs with similar emissions and process characteristics for which common control measures (i.e., cost and emission reductions) can beapplied.
-------
Table 4.3-26. Area Source VOC Controls by SCC and Pod
POD
SCC
SOURCE
MEASURE
PCTRD96
211 2420010055 Dry Cleaning - perchloroethylene
211 2420000055 Dry Cleaning -perchloroethylene
217 2501050120 Bulk Terminals
217 2501050000 Bulk Terminals
217 2501995000 Bulk Terminals
241 2415305000 Cold cleaning
241 2415310000 Cold cleaning
241 2415320000 Cold cleaning
241 2415325000 Cold cleaning
241 2415330000 Cold cleaning
241 2415335000 Cold cleaning
241 2415340000 Cold cleaning
241 2415345000 Cold cleaning
241 2415355000 Cold cleaning
241 2415360000 Cold cleaning
241 2415365000 Cold cleaning
250 2401075000 Aircraft surface coating
251 2401080000 marine surface coating
259 2301040001 SOCMI batch reactor processes
270 2640000000 TSDFs
270 2640000004 TSDFs
272 2461021000 Cutback Asphalt
272 2461020000 Cutback Asphalt
274 2301040000 SOCMI fugitives
276 2306000000 Petrol eum refinery fugi tives
277 2301030000 Pharmaceutical manufacture
278 2301020000 Synthetic fiber manufacture
279 2310000000 Oil & natural gas fields
279 2310010000 Oil & natural gas fields
279 2310020000 Oil & natural gas fields
279 2310030000 Oil & natural gas fields
280 2501060050 Service stations - stage I
281 2501060101 Service stations - stage II
281 2501060103 Service stations - stage II
283 2501060201 Service stations - underground tank
283 2501060201 Service stations - underground tank
284 2620000000 Municipal solid waste landfills
284 2620030000 Municipal solid waste landfills
MACT 44.0
MACT 44.0
RACT 51.0
RACT 51.0
RACT 51.0
MACT 35.0
MACT 35.0
MACT 35.0
MACT 35.0
MACT 35.0
MACT 35.0
MACT 35.0
MACT 35.0
MACT 35.0
MACT 35.0
MACT 35.0
MACT 0.0
MACT 0.0
New CTG 78.0
Phase I & II rules 94.0
Phase I &II rules 94.0
Switch to emulsified (CTG) 100.0
Switch to emulsified (CTG) 100.0
RACT 37.0
RACT 43.0
RACT 37.0
RACT (adsorber) 54.0
RACT (equipment/maintenance) 37.0
RACT (equipment/maintenance) 37.0
RACT (equipment/maintenance) 37.0
RACT (equipment/maintenance) 37.0
Vapor balance (CTG) 95.0
Vapor balance (stage II) 70.0
Vapor balance (stage II) 70.0
Vapor balance (stage II) 84.0
Vapor balance (stage II) 86.0
RCRA standards 82.0
RCRA standards 82.0
POD VOC PODNAME
APPLICABLE
211 Dry Cleaning -perchloroethylene
217 Bulk Terminals
241 Cold cleaning
250 Aircraft surface coating
251 marine surface coating
259 SOCMI batch reactor processes
270 Treatment, storage and disposal facilities
272 Cutback Asphalt
274 SOCMI fugitives
276 Petrol eum refinery fugi tives
277 Pharmaceutical manufacture
278 Synthetic fiber manufacture
279 Oil and natural gas production fields
280 Service stations - stage l-truck unloading
284 Municipal solid waste landfills
National
National
National
National
National
Moderate*
National
Marginal*
National
National
National
National
Moderate*
National
National
NOTE:
A pod is a group of SCCs with similar emissions and process characteristics for which common control measures (i.e., cost and
emission reductions) can be applied.
4-99
-------
Table 4.3-27. Counties in the United States with Stage II Programs
that use Reformulated Gasoline
State
6
6
6
6
6
6
6
9
9
9
9
9
9
9
9
10
10
10
11
17
17
17
17
17
17
17
17
18
18
21
21
21
21
21
21
23
23
23
23
23
23
23
24
24
24
24
24
24
24
24
24
24
24
24
24
Californ ia
Californ ia
Californ ia
Californ ia
Californ ia
Californ ia
California
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Delaware
Delaware
Delaware
Dist. Colum bia
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Indiana
Indiana
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Countv
19
29
37
55
67
73
75
1
3
5
7
9
11
13
15
1
3
5
1
31
43
63
89
93
97
111
197
89
127
15
29
37
111
117
185
1
5
11
13
15
23
31
3
5
9
13
15
17
21
25
27
29
31
33
35
Fresno Co
Kern Co
Los Angeles Co
NapaCo
SacramentoCo
San Diego Co
San Francisco Co
Fairfield Co
Hartford Co
Lifchfield Co
MiddlesexCo
New Haven Co
New London Co
Tolland Co
Windham Co
Kent Co
New Castle Co
Sussex Co
Washington
Cook Co
Du Page Co
Grundy Co
Kane Co
Kendall Co
Lake Co
McHenry Co
WilICo
Lake Co
Porter Co
Boone Co
Bull ill Co
Campbell Co
Jefferson Co
Kenton Co
Oldham Co
Androscoggin Co
Cumberland Co
Kennebec Co
KNOxCo
Lincoln Co
SagadahocCo
York Co
AnneArundeICo
Baltimore Co
CaK/ertCo
CarrolICo
Cecil Co
Charles Co
FrederickCo
Harford Co
Howard Co
Kent Co
Montgomery Co
Prince George's Co
Queen Annes Co
State
24
25
25
25
25
25
25
25
25
25
25
25
25
25
25
33
33
33
33
34
34
34
34
34
34
34
34
34
34
34
34
34
34
34
34
34
34
34
34
34
36
36
36
36
36
36
36
36
36
36
36
36
42
42
42
Maryland
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
New Hamps hire
New Hamps hire
New Hamps hire
New Hamps hire
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New Jersey
New Jersey
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Pennsylvania
Pennsylvania
Pennsylvania
Countv
510
1
3
5
7
9
11
13
15
17
19
21
23
25
27
11
13
15
17
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
41
5
27
47
59
61
71
79
81
85
87
103
119
17
29
45
Baltimore
Bamstabfe Co
Berkshire Co
Bristol Co
Dukes Co
Essex Co
Franklin Co
Hampden Co
Hampshire Co
MiddlesexCo
Nantucket Co
Norfolk Co
Plymouth Co
Suffolk Co
Worcester Co
Hi Us bo rough Co
MerrimackCo
Rockingham Co
Strafford Co
Atlantic Co
Bergen Co
Burlington Co
Camden Co
CapeMayCo
Cumberland Co
Essex Co
Gloucester Co
Hudson Co
Hunterdon Co
MercerCo
MiddlesexCo
Monmouth Co
Morris Co
Ocean Co
Passaic Co
Salem Co
Somerset Co
Sussex Co
Union Co
Warren Co
BronxCo
Dutch ess Co
Kings Co
Nassau Co
New York Co
Orange Co
Putnam Co
Queens Co
Richmond Co
RocWand Co
Suffolk Co
Westchester Co
Bucks Co
Chester Co
Delaware Co
State
42
42
44
44
44
44
44
48
48
48
48
48
48
48
48
48
48
48
48
51
51
51
51
51
51
51
51
51
51
51
51
51
51
51
51
51
51
51
51
51
51
51
51
51
51
51
51
55
55
55
55
55
55
Pennsylvania
Pennsylvania
Rhode Island
Rhode Island
Rhode Island
Rhode Island
Rhode Island
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Virginia
Virginia
Virgin ia
Virgin ia
Virgin ia
Virgin ia
Virgin ia
Virgin ia
Virgin ia
Virgin ia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Countv
91
101
1
3
5
7
9
39
71
85
113
121
157
167
201
291
339
439
473
13
36
41
85
87
95
107
153
159
179
199
510
550
570
600
610
650
670
683
685
700
710
735
740
760
800
810
830
59
79
89
101
131
133
Monlg ornery Co
Philadelphia Co
Bristol Co
Kent Co
Newport Co
Providence Co
Washington Co
Brazoria Co
Chambers Co
Collin Co
Dallas Co
Denton Co
Fort Bend Co
Galveston Co
Harris Co
Liberty Co
Montgomery Co
Tarrant Co
WallerCo
Arlington Co
Charles City Co
Chesterfield Co
Hanover Co
HenricoCo
James City Co
Loudoun Co
Prince William Co
Richmond Co
Stafford Co
York Co
Alexandria
Chesapeake
Colonial Heights
Fairfax
Falfe Church
Hampton
Hopewell
Manassas
Manassas Park
NewportNews
Norfolk
Poquoson
Portsmouth
Richmond
Suffolk
Virginia Beach
Williamsburg
Kenosha Co
MiNivaukeeCo
OzaukeeCo
Racine Co
Washington Co
Waukesha Co
4-100
-------
Table 4.3-28. NO, Area Source RACT
sec
POD PODNAME
ATTAINMENT RULPEN96 CONEFF96
2102001000 22 Industrial Bituminous Coal Combustion Moderate
2102001000 22 Industrial Bituminous Coal Combustion Serious
2102001000 22 Industrial Bituminous Coal Combustion Severe
2102001000 22 Industrial Bituminous Coal Combustion Extreme
2102002000 22 Industrial Anthracite Coal Combustion Moderate
2102002000 22 Industrial Anthracite Coal Combustion Serious
2102002000 22 Industrial Anthracite Coal Combustion Severe
2102002000 22 Industrial Anthracite Coal Combustion Extreme
2102004000 23 Industrial Distillate Oil Combustion Moderate
2102004000 23 Industrial Distillate Oil Combustion Serious
2102004000 23 Industrial Distillate Oil Combustion Severe
2102004000 23 Industrial Distillate Oil Combustion Extreme
2102005000 23 Industrial Residual Oil Combustion Moderate
2102005000 23 Industrial Residual Oil Combustion Serious
2102005000 23 Industrial Residual Oil Combustion Severe
2102005000 23 Industrial Residual Oil Combustion Extreme
2102006000 24 Industrial Natural Gas Combustion Moderate
2102006000 24 Industrial Natural Gas Combustion Serious
2102006000 24 Industrial Natural Gas Combustion Severe
2102006000 24 Industrial Natural Gas Combustion Extreme
23
45
45
45
23
45
45
45
8
16
16
16
8
16
16
16
11
22
22
22
21
21
21
21
21
21
21
21
36
36
36
36
42
42
42
42
31
31
31
31
4-101
-------
Table 4.3-29. Sources of Point and Area Source Emissions Data for the 1996 NET
Inventory After Incorporating State/Local Agency Data Received in 1999 and 2000
State
Alabama
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
District of
Columbia
Delaware
Florida
Georgia
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Missouri
Montana
Point
Source2
BY96
AIRS/FS
1994
AIRS/FS
1995
BY96
BY96
BY96
BY96
BY96
BY96
BY96
BY96
OTAG
BY96
NAPAP
BY96
BY96
NAPAP
BY96
BY96
BY96
BY96
BY96
BY96
BY96
AIRS/FS
1995
BY96
BY96
OTAG
BY96
Sources
Comments
Emissions grown to 1996
Only Atlanta not statewide
Average summer day emissions
estimated using default temporal
factors
Area
Source2
BY96
NAPAP
NAPAP
NAPAP
BY96
NAPAP
BY96
OTAG
BY96
OTAG
BY96
OTAG
Sources1
Comments
Birmingham NAA Only
Rest of State
Only Atlanta not statewide
No emissions data available for
Statewide data submitted in 1999
but not incorporated into NET
inventory
Only partial state.
Backcast to 1990 using BEA.
Average summ er day emissions
estimated using methodology
described
NAPAP
OTAG
BY96
NAPAP
NAPAP
OTAG
BY96
OTAG
BY96
NAPAP
OTAG/
BY96
NAPAP
NAPAP
BY96
OTAG
NAPAP
industrial category
Statewide data submitted
but not incorporated into
inventory
in 1999
NET
Includes average summer day
emissions for VOC, NOX, and CO
St. Louis NAA Only
Rest of State grown from
Interim Inventory data
1990
4-102
-------
Table 4.3-29 (continued)
Point Sources
Area Sources
State
Source Comments
Source2 Comments
Nebraska
BY96
Nevada BY96
Nevada AIRS/FS
1995
New Hampshire BY96
New Jersey OTAG
New Mexico BY96
New York BY96
North Carolina BY96
North Dakota
Ohio
BY96
OTAG
Oklahoma BY96
Oregon AIRS/FS
1995
Pennsylvania BY96
Pennsylvania BY96
Rhode Island OTAG
South Carolina BY96
South Dakota BY96
Tennessee OTAG
Texas
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
BY96
BY96
BY96
BY96
BY96
BY96
BY96
AIRS/FS
1995
Statewide data submitted in 1999
but not incorporated into NET
inventory
Washoe County only
Rest of State
Average summer day emissions
estimated using methodology
described
Allegheny and Philadelphia
Counties only incorporated in
1999
Statewide incorporated in 2000
Average summ er day emissions
estimated using default temporal
factors
NAPAP
NAPAP Statewide
OTAG
OTAG
NAPAP
OTAG
OTAG
NAPAP
OTAG
BY96
GCVTC
BY96
OTAG
OTAG
NAPAP
NAPAP
OTAG
BY96
OTAG
NAPAP
OTAG
BY96
BY96
OTAG
OTAG
NAPAP
Average summer day emissions
estimated using default temporal
factors.
Assigned SCCs and converted
from kgs to tons. NOX and CO
from 1990 Interim Inventory added
to Canton, Dayton and Toledo.
Allegheny and Philadelphia
Counties only incorporated in
1999
NAAs Only (Houston, Beaumont,
Dallas, El Paso)
Rest of State. Average summer
day emissions estimated using
default temporal factors.
1 EPA has developed 1996 emissions for many area source categories. These estimates are prepared for all Slates and counties.
2 BY96= State/local agencies that submitted 1996 base year inventories in calendar years 1 999 or 2000 that are incorporated into the 1996 NET.
Year of Inventory is 1990 for OTAG and 1985 for NAPAP . AIRS/FS identifies State/local agency inventories downloaded from AIRS/FS in the year
specified.
4-103
-------
Table 4.3-30. State/Local Point Source Inventories
Used to Update the 1996 NET Inventory
State
AL
AR
AZ
CO
CT
DC
DE
FL
GA
HI
ID
IL
IN
KY
LA
MA
MD
Ml
MO
MS
MT
NC
ND
NE
NH
NM
NV
NY
OK
PA
SC
SD
TX
UT
VA
VT
WA
WA
Wl
WY
WV
Geographic Coverage
Statewide
Statewide
Maricopa County Nonattainment Area (NAA)
Statewide
Statewide
Statewide
Statewide
Statewide
Atlanta Ozone NAA (34 counties)
Statewide
Pollutants
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
CO,
CO,
CO,
CO,
CO,
CO,
CO,
CO,
CO
CO,
S02
S02,
S02,
S02,
S02,
S02
S02,
S02,
S02,
PM10, NH3
PM10, NH3
PM10
PM10
PM10
PM10
PM10
Annual/Daily
Emissions
Annual
Annual
Annual
Annual
Both
Annual
Both
Annual
Both
Annual
Included in 1996
NET Version?
3
3
3
3
3
3
and
4
and
and
and
4
and
and
4
4
4
4
4
4
4
4
Statewide, but did not meet EPA QA critieriafor incorporating data into NEI
Statewide
Statewide
Statewide
Statewide
Statewide
Statewide
Statewide
Statewide
Statewide
Statewide
Statewide
Statewide
Statewide Except for Lancaster County and City of
Omaha
Statewide
Statewide
Washoe Cou nty
Statewide
Statewide
Statewide
Statewide
Statewide
Statewide
Statewide
Statewide
Statewide
Statewide except for counties under the jurisdiction
of Puget Sound Air Pollution Control Agency
(APCA)
Puget Sound APCA
Statewde
Statewde
Statewde
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
voc,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
CO,
CO,
CO,
CO,
CO,
CO,
CO,
CO,
CO,
CO,
CO,
CO,
CO,
CO
CO,
CO,
CO,
CO,
CO,
SO2;
CO,
CO,
CO,
CO,
CO,
CO,
CO,
CO,
CO,
PM,
S02,
S02,
S02,
S02,
S02,
S02,
S02,
S02,
S02,
S02,
S02,
S02,
S02,
S02,
S02
S02,
S02,
PM10
PM10, PM25, NH3
PM10
PM10
PM10
PM10
PM10
PM10
PM10
PM10
PM10, NH3
PM10
PM10
PM10
PM10
PM10, NH3
PM10
PM10
S02,
S02,
S02,
S02,
S02,
S02,
S02,
S02,
S02;
PM10
PM10
PM10
PM10
PM10, PM25
PM10, PM25
PM10
PM10, NH3
and PM10 andNH3for
Annual
Both
Annual
Both
Annual
Annual
Both
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Both
Both
Annual
Annual
Both
Annual
Annual
Both
Annual
Both
Both
Annual
Annual
Both
Annual
Both
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
and
and
and
and
and
and
4
and
4
and
and
and
and
and
4
4
4
and
4
and
and
and
4
and
and
and
and
and
and
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
some sources
4-104
-------
Table 4.3-31. State/Local Area Source Inventories
Used to Update the 1996 NET Inventory
State
Geographic Coverage?
Pollutant(s):
Annual/Daily Included in 1996
Emissions NET Version?
AL
CA
CO
CT
DE
GA
IN
KS
LA
MD
MO
OK
TX
VA
WA
Birmingham Ozone Nonattainment Area (NAA) only (2 VOC, NOX, CO
counties)
Statewide
Statewide for Residential Woodburning only
Statewde
Statewde
Atlanta Ozone NAA ony (13counties)
Statewde
Statewid e for W ildfires only
Statewde
Statewde
St. Louis NAA ony (6 counties)
Statewde
16 counties
Statewde
Cons umer s olvents and presc ribed bu rning for all
counties except those un der the jurisd iction of Pu get
SoundAir Pollution Control Agency (APCA)
WA Puget Sound APCA
VOC,
VOC,
VOC,
VOC,
VOC,
VOC,
VOC,
VOC,
VOC,
VOC,
VOC,
VOC,
VOC,
VOC,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NOX,
NO,,
CO,
CO,
CO
CO
CO
CO,
CO,
CO,
CO
CO,
CO,
CO
CO
CO,
SO2, PM10
SO,, PM,n
S02, PM10, PM25
S02, PM10
SO2, PM10
SO2, PM10, NH3
SO2, PM10, PM25, NH.
VOC, NOX, CO, SO2, PM10, PM25, NH3
Both
Annual
Annual
Both
Both
Both
Both
Both
Both
Both
Both
Both
Both
Both
Annual
Annual
3 and 4
3 and 4
3 and 4
3 and 4
4
4
3 and 4
3 and 4
3 and 4
3 and 4
3 and 4
3 and 4
3 and 4
3 and 4
3 and 4
3 and 4
4-105
-------
Table 4.3-32. Non-Utility Point Source Data Augmentation Methods
Data Element
State FIPS
County FIPS
Plant ID
Plant Name
SIC
Point ID
Stack ID
Stack Height
Stack Diameter
Stack Temperature
Stack Flow Rate
Stack Exit Gas Velocity
Stack Coordinates
(Latitude/Longitude or
UTM)
Segment ID
sec
Winter Throughput PCT
Spring Throughput PCT
Summer Throughput PCT
Fall Throughput PCT
Days Per Week
Hours Per Day
Start Date Time
End Date Time
Emissions
Element
Type*
MS
MS
MS
MS
DA
DA
DA
DA
DA
DA
DA
DA
DA
DA
MS
DA
DA
DA
DA
DA
DA
DA
DA
DA**
Augmentation Method
If SIC code is a 1977 or 1972 code, update to 1987 code
Else
Contact State/local agency for SIC code
Else
Match to current NET plant
Else
Leave blank in NET, but assign code based on processes identified by
SCCs and/or plant name for purposes of assigning growth factor to
prepare future year inventories. Note that this augmentation will be
fully implemented in future versions of the NET to correct invalid and
outdated SIC codes.
Number sequentially within plant - start with "X" (e.g., X1 , X2, ...)
Number sequentially within plant - start with "Y" (e.g., Y1 , Y2, ...)
If value is <0 ft or >1 ,250 ft, set value to 0 or 1 ,250 ft.
If value is <0 ft or >50 ft, set value to 0 or 50 ft.
If value is <32°For >2,250°F, set value to 32°For 2,250°F.
If value is<0ft3/secor>16,666ft3/sec, set value to 0 or 16,666
ft3/sec.
If value is <0 ft/sec or >98.4 ft/sec, set value to 0 or 98.4 ft/sec.
Match to current NET
Else
County centroid
Number sequentially within point - start with "Z" (e.g., Z1 , Z2, ...)
Temporal Allocation Factor File (TAFF) by SCC
TAFF by SCC
TAFF by SCC
TAFF by SCC
Default to 7
Default to 24
19960101
19961231
See text, section 4. 3. 8. 4. 3.1
MS - States must submit these dab elements
DA - Data can be augmented if not submitted
States must submit annual or daily emissions
Confirmation of submission of these fields will be performed in QC Step.
by the States.
for at least 1 pollutant.
4-106
-------
Table 4.3-33. Stationary Area Source Data Augmentation Methods
Data Element
State FIPS
County FIPS
sec
Emissions
Element
Type*
MS
MS
MS
DA**
Augmentation Method
See text, section 4. 3. 8. 4. 3. 2
MS - States must submit these data elements. Confirmation of submission of these fields will be performed in QC Step.
DA - Data can be augmented if not submitted by the States.
States m ust su bmit annu al or daily emissions for at least 1 pollutant.
4-107
-------
Table 4.3-34. MACT Control Efficiencies Applied to 1996 VOC Emissions for Point and Area Industrial Sources
sec
POD
MACT Control
Efficiency (%)1
1997
1998
1999
SCC1 DESC
SCC3 DESC
SCC6 DESC
SCC8 DESC
Point Sources
30101815
30101816
30101817
30101818
30101819
30101820
30101821
30101822
30101827
30101847
30101870
30101871
30101872
30101880
30101881
30101882
30101885
30102601
30102602
30102608
30102609
30102611
30102612
30102613
30102614
30102615
30102616
30102617
30102625
30102630
30102699
30600402
30600801
30600802
30600803
30600804
30600805
30600806
30600807
30600811
136
136
138
136
136
136
136
138
136
136
136
136
136
136
136
136
136
22
22
22
22
22
22
22
22
22
22
22
22
22
22
114
20
20
20
20
20
20
20
20
0
0
47
0
0
0
0
47
0
0
0
0
0
0
0
0
0
48
48
48
48
48
48
48
48
48
48
48
48
48
48
0
0
0
0
0
0
0
0
0
0
0
47
0
0
0
0
47
0
0
0
0
0
0
0
0
0
48
48
48
48
48
48
48
48
48
48
48
48
48
48
0
0
0
0
0
0
0
0
0
78
78
47
78
78
78
78
47
78
78
78
78
78
78
78
78
78
48
48
48
48
48
48
48
48
48
48
48
48
48
48
78
72
72
72
72
72
72
72
72
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Chemical Manufacturing
Petroleum Industry
Detroleum Industry
Detroleum Industry
Detroleum Industry
Detroleum Industry
Detroleum Industry
Detroleum Industry
Detroleum Industry
Detroleum Industry
Plastics Production
Plastics Production
Plastics Production
Plastics Production
Plastics Production
Plastics Production
Plastics Production
Plastics Production
Plastics Production
Plastics Production
Plastics Production
Plastics Production
Plastics Production
Plastics Production
Plastics Production
Plastics Production
Plastics Production
Synthetic Rubber (Ma nufa cturing O nly)
Synthetic Rubber (Ma nufa cturing O nly)
Synthetic Rubber (Ma nufa cturing O nly)
Synthetic Rubber (Ma nufa cturing O nly)
Synthetic Rubber (Ma nufa cturing O nly)
Synthetic Rubber (Ma nufa cturing O nly)
Synthetic Rubber (Ma nufa cturing O nly)
Synthetic Rubber (Ma nufa cturing O nly)
Synthetic Rubber (Ma nufa cturing O nly)
Synthetic Rubber (Ma nufa cturing O nly)
Synthetic Rubber (Ma nufa cturing O nly)
Synthetic Rubber (Ma nufa cturing O nly)
Synthetic Rubber (Ma nufa cturing O nly)
Synthetic Rubber (Ma nufa cturing O nly)
Slowdown Systems
Fugitive Emissions
= ugitive Emissions
= ugitive Emissions
= ugitive Emissions
= ugitive Emissions
= ugitive Emissions
= ugitive Emissions
= ugitive Emissions
Pellet Silo
Transferring/Handling/Loading/Packing
General
Reactor
Solvent Recovery
Polymer Drying
Extru d in g /Pe I letiz ing/Convey ing/Storage
Acrylic Resins
Polyamide Resins
Epoxy Resins
Reactor (Polyether Resins)
Blowing Agent: Freon (Polyether Resins)
Miscellaneous (Potyether Resins)
Reactor (Polyurethane)
Blowing Agent: Freon (Polyurethane)
Blowing Agent: Methylene Chloride
(Polyurethane)
Other Not Classified (Polyurethane)
General
Butyl (Isobutylene)
Acrylonitrile
Dryers
Steam Stripper
Pre-storage Tank
Monomer Recovery: Absorber Vent
Blending T anks
Isoprene
Latex: Monomer Removal
Latex: Blending Tank
Chloroprene
Silicone Rubber
Other Not Classifed
Blowdow n System w/o Controls
Pipeline Valves and Flanges
Vessel Relief VaK/es
Pump Seals w/o Controls
Com pressor S eals
Miscellaneous: Sam pling/Non-Asphalt
Blowing/Purgingfetc.
Pump Seals with C ontrols
Blind Changing
Pipeline Valves: Gas Streams
-------
Table 4.3-34 (continued)
sec
POD
MACT Control
Efficiency (%)1
1997
1998
1999
SCC1 DESC
SCC3 DESC
SCC6 DESC
SCC8 DESC
Point Sources
30600812
30600813
30600814
30600815
30600816
30600817
30600818
30600819
30600821
30600822
30688801
30688802
30688803
30688804
30688805
40100550
40201904
40202404
40300101
40300102
40300103
40300104
40300105
40300106
40300107
40300108
40300109
40300111
40300112
40300115
40300116
40300150
40300151
40300152
40300153
40300154
40300156
40300157
40300158
40300159
40300160
40300161
40300197
40300198
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
63
39
52
5
4
5
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
63
60
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
72
72
72
72
72
72
72
72
72
72
72
72
72
72
72
63
60
60
98
98
96
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
Petroleum Industry
Petroleum Industry
Petroleum Industry
Petroleum Industry
Petroleum Industry
Petroleum Industry
Petroleum Industry
Petroleum Industry
Petroleum Industry
Petroleum Industry
Petroleum Industry
Petroleum Industry
3etroleum Industry
3etroleum Industry
3etroleum Industry
Organic Solvent Evaporation
Surface Coating Operations
Surface Coating Operations
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
Fugitive Emissions
Fugitive Emissions
Fugitive Emissions
Fugitive Emissions
Fugitive Emissions
Fugitive Emissions
Fugitive Emissions
Fugitive Emissions
Fugitive Emissions
Fugitive Emissions
Fugitive Emissions
Fugitive Emissions
= ugitive Emissions
= ugitive Emissions
= ugitive Emissions
SolventStorage
Wood Furniture Surface Coating
_arge A ircraft
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-010 and 4-07)
Pipeline Valves: Light Lquid/Gas Streams
Pipeline Valves: HeavyLiquid Streams
Pipeline Valves: Hydrogen Streams
Open-ended Valves:AII Streams
Flanges: All Streams
Pump Seals: Light Liquid/Gas Streams
Pump Seals: Heavy Lquid Streams
Compressor Seals: Gas Streams
Drains: All Streams
Vessel Relief VaK/es: AllStreams
Specify in Com ments Field
Specify in Com ments Field
Specify in Com ments Field
Specify in Com ments Field
Specify in Com ments Field
General Processes: Drum Storage - Pure
Organic Chem icals
Coating Storage
Coating Storage
Gaso line **
Crude **
Gaso line **
Crude **
Jet Fuel "
Kerosen e **
Dist Fuel "
Benzene **
Cyclohexane **
Heptane **
Hexane **
3entane **
Toluen e **
Jet Fuel **
-------
Table 4.3-34 (continued)
sec
POD
MACT Control
Efficiency (%)1
1997
1998
1999
SCC1 DESC
SCC3 DESC
SCC6 DESC
SCC8 DESC
Point Sources
40300199
40300201
40300202
40300203
40300204
40300205
40300207
40300208
40300209
40300210
40300212
40300216
40300299
40300302
40301001
40301002
40301003
40301004
40301005
40301006
40301007
40301008
40301009
40301010
40301011
40301012
40301013
40301014
40301015
40301016
40301017
4
7
7
6
6
6
6
6
6
6
6
6
6
6
5
5
5
5
5
5
5
5
5
4
4
4
4
4
4
4
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
98
95
95
90
90
90
90
90
90
90
90
90
90
90
96
96
96
96
96
96
96
96
96
98
98
98
98
98
98
98
98
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
3etroleum and Solvent Evaporation
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
3etroleum Product Storage at Refineries
Deleted - Do Not Use (See 4-03-010 and 4-07)
Deleted - Do Not Use (See 4-03-01 1 and 4-07)
Deleted - Do Not Use (See 4-03-01 1 and 4-07)
Deleted - Do Not Use (See 4-03-01 1 and 4-07)
Deleted - Do Not Use (See 4-03-011 and 4-07)
Deleted - Do Not Use (See 4-03-011 and 4-07)
Deleted - Do Not Use (See 4-03-01 1 and 4-07)
Deleted - Do Not Use (See 4-03-01 1 and 4-07)
Deleted - Do Not Use (See 4-03-01 1 and 4-07)
Deleted - Do Not Use (See 4-03-011 and 4-07)
Deleted - Do Not Use (See 4-03-011 and 4-07)
Deleted - Do Not Use (See 4-03-011 and 4-07)
Deleted - Do Not Use (See 4-03-01 1 and 4-07)
Deleted - Do Not Use (See 4-03-01 1 and 4-07)
= ixed Roof Tanks (Varying Sizes)
= ixed Roof Tanks (Varying Sizes)
= ixed Roof Tanks (Varying Sizes)
= ixed Roof Tanks (Varying Sizes)
= ixed Roof Tanks (Varying Sizes)
= ixed Roof Tanks (Varying Sizes)
= ixed Roof Tanks (Varying Sizes)
= ixed Roof Tanks (Varying Sizes)
= ixed Roof Tanks (Varying Sizes)
rixed Roof Tanks (Varying Sizes)
rixed Roof Tanks (Varying Sizes)
rixed Roof Tanks (Varying Sizes)
rixed Roof Tanks (Varying Sizes)
rixed Roof Tanks (Varying Sizes)
rixed Roof Tanks (Varying Sizes)
rixed Roof Tanks (Varying Sizes)
rixed Roof Tanks (Varying Sizes)
See Com ment **
Gaso line **
Product **
Crude **
Crude **
Jet Fuel "
Dist Fuel "
Benzene **
Cyclohexane **
Cyclopentan e **
Hexane **
Toluen e **
Specify Liquid **
Gaso line **
Gasoline RVP 1 3: Breathing Loss (67000 Bbl.
Tank Size)
Gasoline RVP 1 0: Breathing Loss (67000 Bbl.
Tank Size)
Gasoline RVP 7: Breathing Loss (67000 B bl.
Tank Size)
Gasoline RVP 13: Breathing Loss (250000
Bbl. Tank Size)
Gasoline RVP 10: Breathing Loss (250000
Bbl. Tank Size)
Gaso line RVP 7 : Breathing Los s (250000 Bbl.
Tank Size)
Gasoline RVP 13: Working Loss (Tank
Diam eter Indepe ndent)
Gasoline RVP 10: Working Loss (Tank
Diam eter Indepe ndent)
Gasoline RVP 7: Working Loss (Tank
Diam eter Indepe ndent)
Crude O il RVP 5: Breathing Loss (67000 Bbl.
Tank Size)
Crude Oil RVP 5: Breathing Loss (250000
Bbl. Tank Size)
Crude Oil RVP 5: Working Loss (Tank
Diam eter Indepe ndent)
Jet Naphtha (JP-4): Breathing Loss (67000
Bbl. Tank Size)
Jet Naphtha (JP-4): Breathing Loss (250000
Bbl. Tank Size)
Jet Naphtha (JP-4): Working Loss (Tank
Diam eter Indepe ndent)
Jet Kerose ne: Breathing L oss (670 00 Bbl.
Tank Size)
Jet Kerosene: Breathing Loss (250000 Bbl.
Tank Size)
-------
Table 4.3-34 (continued)
sec
POD
MACT Control
Efficiency (%)1
1997
1998
1999
SCC1 DESC
SCC3 DESC
SCC6 DESC
SCC8 DESC
Point Sources
40301018
40301019
40301020
40301021
40301068
40301078
40301097
40301098
40301099
40301101
40301102
40301103
40301104
40301105
40301106
40301107
40301108
40301109
40301110
40301111
40301112
40301113
40301114
40301115
40301116
4
4
4
4
4
4
4
4
4
7
7
7
7
7
7
7
7
6
6
6
6
6
6
6
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
98
98
98
98
98
98
98
98
98
95
95
95
95
95
95
95
95
90
90
90
90
90
90
90
90
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Detroleum Product Storage at Refineries
Detroleum Product Storage at Refineries
Detroleum Product Storage at Refineries
Detroleum Product Storage at Refineries
Fixed Roof Tanks (Vaiying Sizes)
Fixed Roof Tanks (Vaiying Sizes)
Fixed Roof Tanks (Vaiying Sizes)
Fixed Roof Tanks (Vaiying Sizes)
Fixed Roof Tanks (Vaiying Sizes)
Fixed Roof Tanks (Vaiying Sizes)
Fixed Roof Tanks (Vaiying Sizes)
Fixed Roof Tanks (Vaiying Sizes)
Fixed Roof Tanks (Vaiying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
= loating Roof Tanks (Varying Sizes)
= loating Roof Tanks (Varying Sizes)
= loating Roof Tanks (Varying Sizes)
= loating Roof Tanks (Varying Sizes)
Jet Kerosene: Working Loss (Tank Diameter
Independ ent)
Distillate Fuel #2: Breathing Loss (67000 Bbl.
Tank Size)
Distillate Fuel #2: Breathing Loss (250000
Bbl. Tank Size)
Distillate Fuel #2: Working Loss (Tank
Diam eter Indepe ndent)
Grade 2 Fuel Oil: Breathing Loss (250000
Bbl. Tank Size)
Grade 2 Fuel Oil: Working Loss (Independent
Tank Diam eter)
Specify Liquid: Breathing Loss (67000 Bbl.
Tank Size)
Specify Liquid: Breathing Loss (250000 Bbl.
Tank Size)
Specify Lquid: Working Loss (Tank Diameter
Independ ent)
Gasoline RVP 1 3: Standing Loss (67000 Bbl.
Tank Size)
Gasoline RVP 1 0: Standing Loss (67000 Bbl.
Tank Size)
Gaso line RVP 7 : Standing Los s (67000 B bl.
Tank Size)
Gasoline RVP 13: Standing Loss (250000
Bbl. Tank Size)
Gasoline RVP 10: Standing Loss (250000
Bbl. Tank Size)
Gasoline RVP 7: Standing Loss (250000 Bbl.
Tank Size)
Gasoline RVP 1 3/1 0/7: Withdrawal Loss
(67000 Bbl. Tank Sfee)
Gasoline RVP 1 3/1 0/7: Withdrawal Loss
(250000 Bbl. Tank See)
Crude O il RVP 5: Stand ing Loss (6 7000 Bbl.
Tank Size)
Crude O il RVP 5: Stand ing Loss (2 50000 Bbl.
Tank Size)
Jet Naphtha (JP-4): Standing Loss (67000
Bbl. Tank Size)
Jet Naphtha (JP-4): Standing Loss (250000
Bbl. Tank Size)
Jet Kerosene: Standing Loss (67000 Bbl.
Tank Size)
Jet Kerosene: Standing Loss (250000 Bbl.
Tank Size)
Distillate Fuel #2: Standing Loss (67000 Bbl.
Tank Size)
Distillate Fuel #2: Standing Loss (25 0000 Bbl.
Tank Size)
-------
Table 4.3-34 (continued)
sec
POD
MACT Control
Efficiency (%)1
1997
1998
1999
SCC1 DESC
SCC3 DESC
SCC6 DESC
SCC8 DESC
Point Sources
40301117
40301118
40301119
40301120
40301130
40301131
40301132
40301133
40301134
40301135
40301197
40301198
40301199
40301201
40301202
40301203
40301204
40301205
40301206
40301299
40400101
40400102
40400103
40400104
40400105
40400106
40400107
40400108
40400109
40400110
6
6
6
6
6
7
6
6
6
6
6
6
6
7
7
7
6
6
6
6
150
150
150
150
150
150
151
151
151
152
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
5
5
5
5
5
5
5
5
5
90
90
90
90
90
95
90
90
90
90
90
90
90
95
95
95
90
90
90
90
5
5
5
5
5
5
5
5
5
5
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Product Storage at Refineries
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Floating Roof Tanks (Varying Sizes)
Variable Vapor Space
Variable Vapor Space
Variable Vapor Space
Variable Vapor Space
Variable Vapor Space
Variable Vapor Space
Variabfe Vapor Space
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Crude Oil RVP 5: Withdrawal Loss
Jet Naphtha (JP-4): Withdrawal Loss
Jet Kerosene: Withdrawal Loss
Distillate Fuel#2: Withdrawal Loss
Specify Liquid: Standing Loss - External -
Primary Seal
Gasoline: Standing Loss - External - Primary
Seal
Crude Oil: Standing Los s - External - Primary
Seal
Jet Naphtha (JP-4): Standing Loss - External
- Primary Seal
Jet Kerose ne: Standing Loss - External -
Primary Seal
Distillate Fu el #2: Standing Loss - External -
Primary Seal
Specify Liquid: Withdrawal Loss
Specify Liquid: Standing Loss (67000 Bbl.
Tank Size)
Specify Liquid: Stan ding Loss (250000 B bl.
Tank Size)
Gasoline RVP 13:Filling Loss
Gasoline RVP 10:Filling Loss
Gasoline RVP 7: Filling Loss
Jet Naphtha (JP-4): Filling Loss
Jet Kerosene: Filling Loss
Distillate Fuel#2: Filling Loss
Specify Liquid: Filling Loss
Gasoline RVP 13: Breathing Loss (67000 Bbl
Capacity) -Fixed Roof Tank
Gasoline RVP 10: Breathing Loss (67000 Bbl
Capacity) -Fixed Roof Tank
Gasoline RVP 7: Breathing Loss (67000 B bl.
Capacity) -Fixed Roof Tank
Gasoline RVP 13: Breathing Loss (250000
Bbl Capacity)-Fixed RoofTank
Gasoline RVP 10: Breathing Loss (250000
Bbl Capacity)-Fixed RoofTank
Gasoline RVP 7: Breathing Loss (250000 Bbl
Capacity) -Fixed Roof Tank
Gasoline RVP 13: Working Loss (Diam.
Independent) - Fixed RoofTank
Gasoline RVP 10: Working Loss (Diameter
Independent) - Fixed RoofTank
Gasoline RVP 7: Working Loss (Diameter
Independent) - Fixed RoofTank
Gasoline RVP 13: Standing Loss (67000 Bbl
Capacity)-Floating RoofTank
-------
Table 4.3-34 (continued)
sec
POD
MACT Control
Efficiency (%)1
1997
1998
1999
SCC1 DESC
SCC3 DESC
SCC6 DESC
SCC8 DESC
Point Sources
40400111
40400112
40400113
40400114
40400115
40400116
40400117
40400118
40400119
40400120
40400130
40400131
40400140
40400141
40400150
40400151
40400152
40400153
40400154
40400160
40400161
40400170
40400171
40400178
40400199
152
152
152
152
152
153
153
154
154
154
173
173
173
173
155
155
155
155
155
174
174
174
174
174
155
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Bulk Terminals
Gasoline RVP 10: Standing Loss (67000 Bbl
Capacity)-Floating Roof Tank
Gasoline RVP 7: Standing Loss (67000 Bbl
Capacity)- Floating RoofTank
Gasoline RVP 13: Standing Loss (250000 Bbl
Cap.) - Floating Roof Tank
Gasoline RVP 10: Standing Loss (250000 Bbl
Cap.) - Floating Roof Tank
Gasoline RVP 7: Standing Loss (250000 Bbl
Cap.) - Floating Roof Tank
Gasoline RVP 1 3/1 0/7: Withdrawal Loss
(67000 Bbl Cap.)- Float Rf Tnk
Gasoline RVP 1 3/1 0/7: Withdrawal Loss
(250000 Bbl Cap.)- Float Rf Tnk
Gasoline RVP 13: Filling Loss (10500 Bbl
Cap.) - Variable Vapor Space
Gasoline RVP 10: Filling Loss (10500 Bbl
Cap.) - Variable Vapor Space
Gasoline RVP 7: Filling Loss (10500 Bbl
Cap.) - Variable Vapor Space
Specify Lquid: Standing Loss - External
Floating Roof w/ Primary Seal
Gasoline RVP 13: Standing Loss - Ext.
Floating Roof w/ Primary Seal
Specify Lquid: Standing Loss - Ext Float
Roof Tank w/ Second'ySeal
Gasoline RVP 13: Standing Loss - Ext.
Floating Roof w/Secondary Seal
Miscellaneous Losses/Leaks: Loading Racks
Valves, Flanges, and Pumps
Vapor Collection Losses
Vapor Control UnitLosses
Tank T ruck Vapo r Leaks
Specify Lquid: Standing Loss - Internal
Floating Roof w/ Primary Seal
Gasoline RVP 13: Standing Loss - Int.
Floating Roof w/Primary Seal
Specify Lquid: Standing Loss - Int. Floating
Roof w/ Secondary Seal
Gasoline RVP 13: Standing Loss - Int.
Floating Roof w/Secondary Seal
Gasoline RVP 13/1 0/7: W ithdrawal Loss - Int.
Float Roof (Pri/Sec Seal)
See Com ment **
-------
Table 4.3-34 (continued)
sec
POD
MACT Control
Efficiency (%)1
1997
1998
1999
SCC1 DESC
SCC3 DESC
SCC6 DESC
SCC8 DESC
Point Sources
40400201
40400202
40400203
40400204
40400205
40400206
40400207
40400208
40400209
40400210
40400211
40400212
40400230
40400231
40400240
40400241
40400250
40400251
40400254
40400260
40400261
40400271
40400301
40400302
40400303
150
150
150
151
151
151
152
152
152
154
154
154
173
173
173
173
155
155
155
174
174
174
156
157
158
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Bulk Plants
Oil and G as Field S torage and W orking Ta nks
Oil and G as Field S torage and W orking Ta nks
Oil and G as Field S torage and W orking Ta nks
Gasoline RVP 13: Breathing Loss (67000 Bbl
Capacity) -Fixed Roof Tank
Gasoline RVP 10: Breathing Loss (67000 Bbl
Capacity) -Fixed Roof Tank
Gaso line RVP 7 : Breathing Los s (67000 B bl.
Capacity) -Fixed Roof Tank
Gasoline RVP 1 3: Working Loss (67000 Bbl.
Capacity) -Fixed Roof Tank
Gasoline RVP 1 0: Working Loss (67000 Bbl.
Capacity) -Fixed Roof Tank
Gaso line RVP 7 : W orking Loss (67000 Bbl.
Capacity) -Fixed Roof Tank
Gasoline RVP 13: Standing Loss (67000 Bbl
Cap.) - Floating Roof Tank
Gasoline RVP 10: Standing Loss (67000 Bbl
Cap.) - Floating Roof Tank
Gasoline RVP 7: Standing Loss (67000 Bbl
Cap.) - Floating Roof Tank
Gasoline RVP 1 3/1 0/7: Withdrawal Loss
(67000 Bbl Cap.)- Float Rf Tnk
Gasoline RVP 13: Filling Loss (10500 Bbl
Cap.) - Variable Vapor Space
Gasoline RVP 10: Filling Loss (10500 Bbl
Cap.) - Variable Vapor Space
Specify Lquid: Standing Loss - External
Floating Roof w/ Primary Seal
Gasoline RVP 13: Standing Loss - Ext.
Floating Roof w/ Primary Seal
Specify Lquid: Standing Loss - Ext Floating
Roof w/ Secondary Seal
Gasoline RVP 13: Standing Loss - Ext.
Floating Roof w/Secondary Seal
Loading R acks
Valves, Flanges, and Pumps
Tank Truck Vapor Losses
Specify Lquid: Standing Loss - Internal
Floating Roof w/ Primary Seal
Gasoline RVP 13: Standing Loss - Int.
Floating Roof w/ Primary Seal
Gasoline RVP 13: Standing Loss - Int.
Floating Roof w/Secondary Seal
Fixed Roof Tank: Breathing Loss
Fixed Roof Tank: Working Loss
External Floating Roof Tank w ith Primary
Seals: Standing Loss
-------
Table 4.3-34 (continued)
sec
POD
MACT Control
Efficiency (%)1
1997
1998
1999
SCC1 DESC
SCC3 DESC
SCC6 DESC
SCC8 DESC
Point Sources
40400304
40400305
40400401
40400402
40400403
40400404
40400405
40400406
40400407
40400408
40400409
40400410
40400411
40400412
40400413
40400414
40400497
40400498
40600101
40600126
40600131
40600136
40600137
40600138
40600139
158
158
159
160
159
160
159
160
159
160
159
160
159
160
159
160
159
160
161
163
163
161
164
164
164
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Petroleum Liquids Storage (non-
Refinery)
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Petroleum Products
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Petroleum Products
Oil and G as Field S torage and W orking Ta nks
Oil and Gas Field Storage and W orking Ta nks
Petroleum Products - U ndergroun d Tanks
Petroleum Products - U ndergroun d Tanks
Petroleum Products - U ndergroun d Tanks
Petroleum Products - U ndergroun d Tanks
Petroleum Products - U ndergroun d Tanks
Petroleum Products - U ndergroun d Tanks
Petroleum Products - U ndergroun d Tanks
Petroleum Products - U ndergroun d Tanks
Petroleum Products - U ndergroun d Tanks
Petroleum Products - U ndergroun d Tanks
Petroleum Products - U ndergroun d Tanks
Petroleum Products - U ndergroun d Tanks
Petroleum Products - U ndergroun d Tanks
Petroleum Products - U ndergroun d Tanks
Petroleum Products - U ndergroun d Tanks
Petroleum Products - U ndergroun d Tanks
Tank C ars and T rucks
Tank C ars and T rucks
Tank C ars and T rucks
Tank C ars and T rucks
Tank C ars and T rucks
Tank C ars and T rucks
Tank C ars and T rucks
External Floating Roof Tank with Secondary
Seals: Standing Loss
Internal Floating Roof Tank: Standing Loss
Gasoline RVP 13:Breathing Loss
Gasoline RVP 13:Working Loss
Gasoline RVP 10:Breathing Loss
Gasoline RVP 10:Working Loss
Gasoline RVP 7: Breathing Loss
Gasoline RVP 7:Working Loss
Crude Oil RVP 5:Breathing Loss
Crude Oil RVP 5:Working Loss
Jet Naphtha (JP-4): Breathing Loss
Jet Naphtha (JP-4): Working Loss
Jet Kerosene: Breathing Loss
Jet Kerosene: Working Loss
Distillate Fuel#2: Breathing Loss
Distillate Fuel#2: Working Loss
Specify Liquid: Breathing Loss
Specify Liquid: Working Loss
Gasoline: Splash Loading **
Gasoline: Submerged Loading **
Gasoline: Submerged Loading (Normal
Service)
Gasoline: Splash Loading (Normal Service)
Crude Oil: Splash Loading (Normal Service)
Jet Naphtha: Spbsh Loading (Normal
Service)
Kerosene: Splash Loading (Normal Service)
-------
Table 4.3-34 (continued)
sec
POD
MACT Control
Efficiency (%)1
1997
1998
1999
SCC1 DESC
SCC3 DESC
SCC6 DESC
SCC8 DESC
Point Sources
40600140
40600141
40600142
40600143
40600144
40600145
40600146
40600147
40600162
40600163
40600197
40600198
40600199
40600301
40600302
40600305
40600306
40600307
40600399
50300801
50300810
50300820
50300830
50300899
62540010
62540020
62540022
64630016
164
162
165
165
162
165
165
163
167
167
172
172
172
168
169
170
170
171
170
129
129
129
129
129
138
138
138
138
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
47
47
47
47
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
0
0
0
0
0
47
47
47
47
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
96
96
96
96
96
47
47
47
47
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Waste Disposal
Waste Disposal
Waste Disposal
Waste Disposal
Waste Disposal
MACT Source Categories
MACT Source Categories
MACT Source Categories
MACT Source Categories
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Petroleum Products
Transportation and Marketing of
Petroleum Products
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Petroleum Products
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Petroleu m Products
Transportation and Marketing of
Detroleu m Products
Transportation and Marketing of
Detroleu m Products
Transportation and Marketing of
Detroleu m Products
Solid Waste Disposal - Industrial
Solid Waste Disposal - Industrial
Solid Waste Disposal - Industrial
Solid Waste Disposal - Industrial
Solid Waste Disposal - Industrial
=ood and Agricultural Processes
=ood and Agricultural Processes
=ood and Agricultural Processes
Vinyl-based Resins
Tank C ars and T rucks
Tank C ars and T rucks
Tank C ars and T rucks
Tank C ars and T rucks
Tank C ars and T rucks
Tank C ars and T rucks
Tank C ars and T rucks
Tank C ars and T rucks
Tank C ars and T rucks
Tank C ars and T rucks
Tank C ars and T rucks
Tank C ars and T rucks
Tank C ars and T rucks
Gasoline Retail Operations - Stage I
Gasoline Retail Operations - Stage I
Gasoline Retail Operations - Stage I
Gasoline Retail Operations - Stage I
Gasoline Retail Operations - Stage I
Gasoline Retail Operations - Stage I
Treatment, Storage, Disposal/TSDF
Treatment, Storage, Disposal/TSDF
Treatment, Storage, Disposal/TSDF
Treatment, Storage, Disposal/TSDF
Treatment, Storage, Disposal/TSDF
Cellulose Food Casing Manufacture
Cellulose Food Casing Manufacture
Cellulose Food Casing Manufacture
Dolyvinyl Chloride and Copolymers Production -
Suspension Process
Distillate Oil: Splash Loading (Normal
Service)
Gasoline: Submerged Loading (Balanced
Service)
Crude Oil: Submerged Loading (Balanced
Service)
Jet Naphtha: Submerged Loading (Balanced
Service)
Gasoline: Splash Loading (Balanced Service)
Crude Oil: Splash Loading (Balanced
Service)
Jet Naphtha: Spbsh Loading (Balanced
Service)
Gasoline: Submerged Loading (Clean Tanks)
Gasoline: Loaded with Fuel (Transfc Losses)
Gasoline: Return with Vapor (Transit Losses)
Not Classified "
Not Classified "
Not Classified "
Splash Filling
Subm erged Filling w/o Contro Is
Unloadin g **
Balanced Submerged Filling
Underground Tank Breathing and Emptying
Not Classified "
Surface Impoundment: Fugitive Emissions
Waste Piles: Fugitive Emissions
Land Treatment: Fugitive Emissions
Containers: Fugitive Emissions
General: Fugitwe Emissions
Cellulose Xanthate Formation: Barattees
Viscose Processing
Viscose Processing: Extrusion and
Coagulation
Proce ss Ve nts, Rea ctor: Safe ty Valve Vents
-------
Table 4.3-34 (continued)
sec
POD
MACT Control
Efficiency (%)1
1997
1998
1999
SCC1 DESC
SCC3 DESC
SCC6 DESC
SCC8 DESC
Point Sources
64630040
138
47
47
47
VIACT Source Categories
Vinyl-based Resins
Polyvinyl Chloride and Copolymers Production -
Suspension Process
Process Vents: Rotaiy Dryer
Area Sources
2501000000
2501010000
2501050000
2501050120
2501995000
2501995120
217
217
217
217
217
217
0
0
0
0
0
0
0
0
0
0
0
0
51
51
51
51
51
51
Storage and Transport
Storage and Transport
Storage and Transport
Storage and Transport
Storage and Transport
Storage and Transport
Petroleum and Petroteum Product
Petroleum and Petroteum Product
Petroleum and Petroteum Product
Petroleum and Petroteum Product
Petroleum and Petroteum Product
Petroleum and Petroteum Product
Storage
Storage
Storage
Storage
Storage
Storage
All Storage Types: Breathing Loss
Commercial/Industrial: Breathing Loss
Bulk Stations/Terminals: Breathing Loss
Bulk Stations/Terminals: Breathing Loss
All Storage Types: Working Loss
All Storage Types: Working Loss
Total: All Products
Total: All Products
Total: All Products
Gasoline
Total: All Products
Gasoline
1 Percen t red ucti on from uncontrolled em iss ions in1996NET inventory.
-------
Figure 4.3-1. OTAG Inventory Data Source - Area Sources
STATE SUBMITTED
INTERIM INVENTORY
-------
Figure 4.3-2. OTAG Inventory Data Source - Point Sources
.STATE SUBMITTED- 1990
DSTATE SUBMITTED - OTHER YEAR
INTERIM INVENTORY
-------
4.4 OTHER COMBUSTION
4.4.1 What Source Categories Does the Other Combustion Sector Include?
The source categories falling under "Other Combustion" include the following Tier I and Tier II
categories:
Tier I Category Tier II Category
(03) OTHER COMBUSTION (01-06) All
(14) MISCELLANEOUS (02) Other Combustion
The Tier I "Other Combustion" category includes point and area source emissions associated with
commercial/institutional and residential burning of all fuels (i.e., coal, oil, natural gas, and liquified
petroleum gas) except solid waste. This category accounts for emissions associated with fuel combustion
in external combustion boilers, space heaters, reciprocating internal combustion engines, and turbines.
The Tier 1 "Miscellaneous" category includes burning of agricultural crops, forest fires/wildfires,
prescribed/slash and managed burning, structure fires, and open burning.
See section 4.1.3 for instructions on how to identify the SCCs for the point and area source
categories assigned to these tier categories.
4.4.2 What Information Does This Section Provide?
Section 4.4 describes the methods used to estimate 1985 through 1989 emissions, 1990 emissions
for the 1990 Interim Inventory, and 1990 through 1999 emissions in the National Emission Trends (NET)
inventory. Except for certain area source categories, the methods used to prepare emissions for the
"Other Combustion" categories are essentially the same as those used to prepare emissions for the
"Industrial" categories discussed in section 4.3. Table 4.3-1 of section 4.3 summarizes the methods
applied and the pollutants for which emissions were estimated for each year.
The 1990 Interim Inventory emissions for the majority of the source categories were generated from
both the point source and area source portions of the 1985 National Acid Precipitation Assessment
Program (NAPAP) inventory, except for emissions from wildfires, residential wood combustion, and
prescribed burning. The 1990 Interim Inventory emissions served as the base year from which the
emissions for the years 1985 through 1989 were estimated. The emissions for the years 1985 through
1989 were estimated using historical data compiled by the BEA1 or historic estimates of fuel consumption
based on the DOE's SEDS.2 Section 4.4.3 explains the methods for preparing the 1990 Interim
Inventory. Section 4.4.4 explains how emissions for 1985 through 1989 were developed from the 1990
Interim Inventory.
The 1990 National Emission Trends (NET) emissions were revised to incorporate as much State-
supplied data as possible. Sources of State data include the Ozone Transport Assessment Group
(OTAG) emission inventory, the Grand Canyon Visibility Transport Commission (GCVTC) emission
inventory, and Aerometric Information Retrieval System/Facility Subsystem (AIRS/FS). For most point
sources, these emissions were projected from the revised 1990 NET inventory to the years 1991 through
1996 using BEA and SED S data. States were surveyed to determine whether EPA should project their
4-120
-------
1990 non-utility point source emissions or extract them from AIRS/FS. For all States that selected
AIRS/FS option, the emissions in the NET inventory reflect their AIRS/FS data for the years 1991
through 1995. Additional controls were added to the projected (or grown) emissions for the year 1996.
Sections 4.4.5 through4.4.8 explain how emissions were prepared for 1990 through 1996.
Section4.4.10 explains how emissions for 1997 through 1999 were grown from the 1996 NET inventory.
The methodologies for estimating emissions for 1990 through 1999 for forest fires/wildfires,
prescribed/slash and managed burning, residential wood combustion, and structure fires are described in
section 4.4.9. Section 4.4.9 also explains the methodologies applied to estimate 1999 emissions for the
open burning of residential municipal solid waste (MSW), leaves, and brush; and open burning of land
clearing debris. Prior to 1996, emissions for these open burning categories were either grown from 1990
emissions, or not estimated. In addition, section 4.4.9 discusses how 1990-1994 PM and SO2 emissions
for residential nonwood combustion sources were estimated. The methodologies for these source
categories are based on methodologies that are different from the general methodologies for Other
Combustion sources discussed in sections 4.4.3 through4.4.8.
4.4.3 How did EPA Develop the 1990 Interim Inventory?
The 1985 NAPAP inventory estimates for the point sources have been projected to the year 1990
based on the growth in BEA historic earnings for the appropriate State and industry, as identified by the
two-digit SIC code. To remove the effects of inflation, the earnings data were converted to 1982
constant dollars using the implicit price deflator for personal consumption expenditures.3 State and SIC-
level growth factors were calculated as the ratio of the 1990 earnings data to the 1985 earnings data
Additional information on point source growth indicators is presented in section 4.4.3.4.
For the 1990 Interim inventory, the emissions from agricultural burning and open burning were
based on the 1985 NAPAP inventory. The emissions estimation methodologies for these categories are
described individually below.
The agricultural burning category includes emissions from burning practices routinely used to
clear and/or prepare land for planting. Specific operations include grass stubble burning, burning of
agricultural crop residues, and burning of standing field corps as part of harvesting activities (e.g., sugar
cane). Emissions are estimated by multiplying the number of acres burned in each county by a fuel
loading factor and the an emission factor for each pollutant.
The original emissions estimation methodology for agricultural burning was developed by IIT
Research4 and estimated the 1974 activity level in terms of acres burned per State. It was assumed that
the total quantity of agricultural products burned in 1974 was the same quantity which was consumed by
fire each year. If no specific crop data were available, it was assumed that the number of acres burned
annually was divided equally between sugar cane and other field crops.5 Fuel loadings for grass burning
were 1 to 2 tons per acre; fuel loadings for sugar cane burning were 6 to 12 tons per acre.6 Emission
factors were taken from the 1985 Procedures Documenf and AP-42.7
NAPAP defined open burning as the uncombined burning of wastes such as leaves, landscape
refuse, and other rubbish. The activity factor for open burning was the quantity of solid waste burned,
which was computed for the year of interest by updating the previous year's waste generation for each
4-121
-------
sector. The update factor was determined using engineering judgement. Estimates of the quantity of
solid waste burned in the most recent year were obtained from the National Emissions Data System
(NEDS) point source data.8 Generation factors were originally obtained from data in the 1968 Survey of
Solid Waste Practices, Interim Report9 and the Preliminary Data Analysis.10 Allocations were based on
county population and emission factors for open burning or refuse and organic materials were taken
directly from AP-42.7
The area source emissions from the 1985 NAPAP inventory were projected to 1990 based on BEA
historic earnings data, BEA historic population data, DOE SEDS data, or other growth indicators. The
specific growth indicator was assigned based on the source category. The BEA earnings data were
converted to 1982 dollars as described above. The 1990 SEDS data were extrapolated from data for the
years 1985 through 1989. All growth factors were calculated as the ratio of the 1990 data to the 1985
data for the appropriate growth indicator. Additional information on area source growth indicators is
presented in section 4.4.3.5.
When creating the 1990 emission inventory, changes were made to emission factors, control
efficiencies, and emissions from the 1985 inventory for some sources. The PM-10 control efficiencies
were obtained from the PM Calculator.11 In addition, rule effectiveness, which was not applied in the
1985 NAPAP inventory, was applied to the 1990 emissions estimated for the point sources. The CO,
NOX, and VOC point source controls were assumed to be 80 percent effective; PM-10 and SO2 controls
were assumed to be 100 percent effective.
The 1990 emissions for CO, NOX, SO2, NH3, and VOC were calculated using the following steps:
(1) projected 1985 controlled emissions to 1990 using the appropriate growth factors, (2) calculated the
uncontrolled emissions using control efficiencies from the 1985 NAPAP Emission Inventory, and
(3) calculated the final 1990 controlled emissions using revised control efficiencies and the appropriate
rule effectiveness. The 1990 PM-10 and PM-2.5 emissions were calculated using the TSP emissions from
the 1985 NAPAP inventory. The 1990 uncontrolled TSP emissions were estimated in the same manner
as the other pollutants. The 1990 uncontrolled PM-10 estimates were calculated from these TSP
emissions by applying SCC-specific uncontrolled particle size distribution factors. The controlled PM-10
emissions were estimated in the same manner as the other pollutants. Because the majority of area source
emissions for all pollutants represented uncontrolled emissions, the second and third steps were not
required to estimate the 1990 area source emissions.
4.4.3.1 What Control Efficiency Revisions did EPA Make?
In the 1985 NAPAP point source estimates, control efficiencies for VOC, NOX, CO, and SO2
sources in Texas were judged to be too high for their process/control device combination. These high
control efficiencies occurred because Texas did not ask for control efficiency information, and simply
applied the maximum efficiency for the reported control device.12 High control efficiencies lead to high
future growth in modeling scenarios based on uncontrolled emissions (which are based on the control
efficiency and reported actual emissions). High control efficiencies also lead to extreme increases in
emissions when rule effectiveness is incorporated.
Revised VOC control efficiencies were developed for Texas for the ERCAM-VOC.13 For this
analysis, revised efficiencies were also developed by SCC and control device combination for NOX, SO2,
and CO using engineering judgement. These revised control efficiencies were applied to sources in
4-122
-------
Texas. A large number of point sources outside of Texas had VOC and CO control efficiencies that were
also judged to be too high. The VOC and CO control efficiencies used for Texas were also applied to
these sources.
4.4.3.2 What Rule Effectiveness Assumptions did EPA Make?
Controlled emissions for each inventory year were recalculated, assuming that reported VOC, NOX,
and CO controls were 80 percent effective. Sulfur dioxide and PM-10 controls were assumed to be
100 percent effective.
4. 4. 3. 3 What Emissions Calculations Did EPA Use?
A three-step process was used to calculate emissions incorporating rule effectiveness. First, base
year controlled emissions are projected to the inventory year using Equation 4.4-1.
CEi = CEBY + (CEBY x EG) (Eq. 4.4-1)
where: CE; = controlled emissions for inventory year I
CEBY = controlled emissions for base year
EG; = earnings growth for inventory year I
Earnings growth is calculated using Equation 4.4-2:
DAT
where: EG = earnings growth
DAT; = earnings data for inventory year I
DATBY = earnings data in the base year
Second, uncontrolled emissions in the inventory year are back- calculated from the controlled emissions
based on the control efficiency with Equation 4.4-3.
CE
US, =
j_ CEFF] (Eq. 4.4-3)
I " 100 J
where: UE; = uncontrolled emissions for inventory year I
CE; = controlled emissions for inventory year I
CEFF = control efficiency (percent)
Third, controlled emissions are recalculated incorporating rule effectiveness using Equation 4.4-4:
4-123
-------
^D TT^ f, (REFF} (CEFF}} ( EF<
CER. = UCf x 1 - x x L
1 100 100
where: CEP^ = controlled emissions incorporating rule effectiveness
UQ = uncontrolled emissions
REFF = rule effectiveness (percent)
CEFF = control efficiency (percent)
EF; = emission factor for inventory year I
EFBY = emission factor for base year
4.4.3.4 How Did EPA Grow Point Source Emissions?
The changes in the point source emissions were equated with the changes in historic earnings by
State and industry. Emissions from each point source in the 1985 NAPAP Emissions Inventory were
projected to the years 1985 through 1990 based on the growth in earnings by industry (2-digit SIC code).
Historical annual State and industry earnings data from BEA's Table SA-51 were used to represent
growth in earnings from 1985 through 1990.
The 1985 through 1990 earnings data in Table SA-5 are expressed in nominal do liars. To be used to
estimate growth, these values were converted to constant dollars to remove the effects of inflation.
Earnings data for each year were converted to 1982 constant dollars using the implicit price deflator for
PCE.3 The PCE deflators used to convert each year's earnings data to 1982 dollars are:
Year 1982 PCE Deflator
1985 111.6
1987 114.3
1988 124.2
1989 129.6
1990 136.4
Several BEA categories did not contain a complete time series of data for the years 1985 through
1990. Because the SA-5 data must contain 1985 earnings and earnings for each inventory year (1985
through 1990) to be useful for estimating growth, a log linear regression equation was used where
possible to fill in missing data elements. This regression procedure was performed on all categories that
were missing at least one data point and which contained at least three data points in the time series.
Each record in the point source inventory was matched to the BEA earnings data based on the State
and the 2-digit SIC. Table 4.4-1 shows the BEA earnings category used to project growth for each of
the 2-digit SICs found in the 1985 NAPAP Emission Inventory. No growth in emissions was assumed
for all point sources for which the matching BEA earnings data were not complete. Table 4.4-1 also
shows the national average growth and earnings by industry from Table SA-5.
4-124
-------
4.4.3.5 How Did EPA Grow Area Source Emissions?
Emissions from the 1985 NAPAP Inventory were grown to the Emission Trends years based on
historicalBEA earnings data (section 4.4.3.4), historical estimates of iuel consumption, or other
category-specific growth indicators. Table 4.4-2 shows the growth indicators used for each area source
by 1985 NAPAP category.
Due to the year-to-year volatility in the SEDS fuel consumption data for the commercial residual oil
fuel use category, the regression technique used above did not yield realistic projections for 1990 for this
category. Therefore, a different procedure was used to project 1990 data for commercial residual oil iuel
use. State-level sales volumes of residual fuel to the commercial sector were obtained from Fuel OH and
Kerosene Sales 199014 for 1989 and 1990. Each State's growth in sales of residual fuel to the
commercial sector from 1989 to 1990 was applied to that State's 1989 SEDS commercial residual fuel
consumption to yield a 1990 consumption estimate. A summary of SEDS national fuel consumption by
fuel and sector can be found in Table 4.4-3.
The SEDS data were used as an indicator of emissions growth for the area source iuel combustion
categories and for the gasoline marketing categories shown in Table 4.4-3. (SEDS reports fuel
consumption by sector and fuel type.) Since fuel consumption was the activity level used to estimate
emissions for these categories, fuel consumption was a more accurate predictor of changes in emissions,
compared to other surrogate indicators such as earnings or population SEDS fuel consumption data
were available through 1989 at the time the emission estimates were developed. The 1990 values were
extrapolated from the 1985 through 1989 data using alog linear regression technique. In addition to
projecting 1990 data for all fuel consumption categories, the regression procedure was used to fill in
missing data points for fuel consumption categories if at least three data points in the time series (1985 to
1989) were available.
The last step in the creation of the area source inventory was matching the 1985 NAPAP categories
to the new AIRS Area and Mobile Source Subsystem (AMS) categories. This matching is provided in
Table 4.4-4. Note that there is not always a one-to-one correspondence between 1985 NAPAP and
AMS categories. For example, the gasoline marketing NAPAP category was split into two separate
AMS categories representing Stage I and Stage II emissions. In addition, three 1985 NAPAP SCCs are
not included in the AMS system of codes. Therefore, AMS codes were created for process emissions
from pharmaceutical manufacture, synthetic fiber manufacture, and synthetic organic chemical
manufacturing industry (SOCMI) fugitive emissions.
4.4.4 How Did EPA Develop Emissions for 1985 to 1989?
The 1990 Interim Inventory was used as the base year from which emissions for 1985 to 1989 were
estimated. As discussed under section 4.4.3, the 1985 NAPAP controlled emissions were grown to 1990
to serve as the starting point for preparing the 1990 Interim Inventory emissions. However, several
changes were made to the 1990 emissions to improve the inventory prior to backcasting the emissions to
1985 through 1989. Consequently, the 1985 emissions estimated by this method do not match the 1985
NAPAP Emission Inventory. The factors used to backcast 1990 emissions to prior years are the same as
the factors used to grow 1985 NAPAP emissions to 1990.
4-125
-------
4.4.5 What is the 1990 NET Inventory?
The 1990 National Emission Trends is based primarily on State data, with the 1990 Interim data
filling in the gaps. The data base houses U. S. annual and average summer day emission estimates for the
50 States and the District of Columbia. Seven pollutants (CO, NOX, VOC, SO2, PM-10, PM-2.5, and
NH3) were estimated in 1990. The State data were extracted from three sources, the OTAG inventory,
the GCVTC inventory, and AIRS/FS. Sections 4.4.5.1, 4.4.5.2, and 4.4.5.3 give brief descrptions of
these efforts. Section 4.4.5.4 describes the efforts necessary to supplement the inventory gaps that are
either temporal, spacial, or pollutant. Since EPA did not receive documentation on how these inventories
were developed, this section only describes the effort to collect the data and any modifications or
additions made to the data.
4,4,5,1 OTAG
The OTAG inventory for 1990 was completed in December 1996. The database houses emission
estimates for those States in the Super Regional Oxidant A (SUPROXA) domain. The estimates were
developed to represent average summer day emissions for the ozone pollutants (VOC, NOX, and CO).
This section gives a background of the OTAG emission inventory and the data collection process.
4.4.5.1.1 Inventory Components —
The OTAG inventory contains data for all States that are partially or fully in the SUPROXA
modeling domain The SUPROXA domain was developed in the late 1980s as part of the EPA regional
oxidant modeling (ROM) applications. EPA had initially used three smaller regional domains (Northeast,
Midwest, and Southeast) for ozone modeling, but wanted to model the full effects of transport in the
eastern United States without having to deal with estimating boundary conditions along relatively high
emission areas. Therefore, these three domains were combined and expanded to form the Super Domain.
The western extent of the domain was designed to allow for coverage of the largest urban areas in the
eastern United States without extending too far west to encounter terrain difficulties associated with the
Rocky Mountains. The Northern boundary was designed to include the major urban areas of eastern
Canada. The southern boundary was designed to include as much of the United States as possible, but
was limited to latitude 26°N, due to computational limitations of the photochemical models. (Emission
estimates for Canada were not extracted from OTAG for inclusion in the NET inventory.)
The current SUPROXA domain is defined by the following coordinates:
North: 47.0 0°N East: 67.0 0°W
South: 26.0 0°N West: 99.0 0°W
Its eastern boundary is the Atlantic Ocean and its western border runs from north to south through North
Dakota, South Dakota, Nebraska, Kansas, Oklahoma, and Texas. In total, the OTAG Inventory
completely covers 37 States and the District of Columbia.
The OTAG inventory is primarily an ozone precursor inventory. It includes emission estimates of
VOC, NOX, and CO for all applicable source categories throughout the domain. It also includes a small
amount of SO2 and PM-10 emission data that was sent by States along with their ozone precursor data.
No quality assurance (QA) was performed on the SO2 and PM-10 emission estimates for the OTAG
inventory effort.
4-126
-------
Since the underlying purpose of the OTAG inventory is to support photochemical modeling for
ozone, it is primarily an average summer day inventory. Emission estimates that were submitted as
annual emission estimates were converted to average summer day estimates using operating schedule data
and default temporal profiles and vice versa.
The OTAG inventory is made up of three major components: (1) the point source component,
which includes segment/pollutant level emission estimates and other relevant data (e.g., stack parameters,
geographic coordinates, and base year control information) for all stationary point sources in the domain;
(2) the area source component, which includes county level emission estimates for all stationary area
sources and non-road engines; and (3) the on-road vehicle component, which includes county/roadway
functional class/vehicle type estimates of VMT and MOBILESa input files for the entire domain. Of
these three components, the NET inventory extracted all but the utility emissions. (See section 4.2 for a
description of the utility NET emissions and section4.6 for the on-road mobile NET emissions.)
4.4.5.1.2 Interim Emissions Inventory (OTAG Default) —
The primary data sources for the OTAG inventory were the individual States. Where States were
unable to provide data, the 1990 Interim Inventory 15 and National Particukte Inventory (NPI)16 was used
for default inventory data. A more detailed description of the 1990 Interim Inventory is presented in
section 4.4.3.
4.4.5.1.3 State Data Collection Procedures —
Since the completion of the Interim Inventory in 1992, many States had completed 1990 inventories
for ozone nonattainment areas as required for preparing SIPs. In addition to these SIP inventories, many
States had developed more comprehensive 1990 emission estimates covering their entire State. Since
these State inventories were both more recent and more comprehensive than the 1990 Interim Inventory,
a new inventory was developed based on State inventory data (where available) in an effort to develop
the most accurate emission inventory to use in the OTAG modeling.
On May 5, 1995, a letter from John Seitz (Director of EP A's Office of Air Quality Planning and
Standards [OAQPS]) and MaryGade (Vice President of EGOS) to State Air Directors, States were
requested to supply available emission inventory data for incorporation into the OTAG inventory.17
Specifically, States were requested to supply all available point and area source emissions data for VOC,
NOX, CO, SO2, andPM-10, with the primary focus on emissions of ozone precursors. Some emission
inventory data were received from 36 of the 38 States in the OTAG domain. To minimize the burden to
the States, there was no specified format for submitting State data. The majority of the State data was
submitted in one of three formats:
1) an Emissions Preprocessor System Version 2.0 (EPS2.0) Workfile
2) an ad hoc report from AIRS/FS
3) data files extracted from a State emission inventory data base
The origin of data submitted by each State is described in section 4.4.5.1.4.1 for point sources and
4.4.5.1.4.2 for area sources.
4.4.5.1.4. State Data Incorporation Procedures/Guidelines —
The general procedure for incorporating State data into the OTAG Inventory was to take the data
"as is" from the State submissions. There were two main exceptions to this policy. First, any inventory
4-127
-------
data for years other than 1990 was back cast to 1990 using BEA Industrial Earnings data by State and
2-digit SIC code. This conversion was required for five States that submitted point source data for the
years 1992 through 1994. All other data submitted were for 1990.
Second, any emission inventory data that included annual emission estimates but not average
summer day values were temporally allocated to produce average summer day values. This temporal
allocation was performed for point and area data supplied by several States. For point sources, the
operating schedule data, if supplied, were used to temporally allocate annual emissions to average
summer weekday using Equation 4.4-5
EMISSIONSASD = EMISSIONSANNUAL * SUMTHRU * 1/(13 * DPW) (Eq. 4.4-5)
where:
EMISSIGNSASD = average summer day emissions
EMIS SIGNSANNUAL = annual emissions
SUMTHRU = summer throughput percentage
DPW = days per week in operation
If operating schedule data were not supplied for the point source, annual emissions were temporally
allocated to an average summer weekday using EPA's default Temporal Allocation file. This computer
file contains default seasonal and daily temporal profiles by SCC. Equation 4.4-6 was used.
EMISSIONSASD = EMISSIONSANNUAL I (SUMFACSCC * WDFACSCC) (Eq. 4.4-6)
where:
EMIS SIGNS ASD = average summer day emissions
EMIS SIGNS ANNUAL = annual emissions
SUMFACSCC = default summer season temporal factor for SCC
WDFACSCC = default summer weekday temporal factor for SCC
There were a small number of SCCs that were not in the Temporal Allocation file. For these SCCs,
average summer weekday emissions were assumed to be the same as those for an average day during the
year and were calculated using Equation 4.4-7.
EMISSIONSASD = EMISSIONSANNUAL I 365 (Eq. 4.4-7)
where:
EMIS SIGNS ASD = average summer day emissions
EMIS SIGNS ANNUAL = annual emissions
4-128
-------
4.4.5.1.4.1 Point. For stationary point sources, 36 of the 38 States in the OTAG domain supplied
emission estimates covering the entire State. Data from the 1990 Interim Inventory were used for the
two States (Iowa and Mississippi) that did not supply data. Most States supplied 1990 point source data,
although some States supplied data for later years because the later year data reflected significant
improvements over their 1990 data. Inventory data for years other than 1990 were backcast to 1990
using BEA historical estimates of industrial earnings at the 2-digit SIC level. Table 4.4-5 provides a brief
description of the point source data supplied by each State.
4.4.5.1.4.2 Area. For area sources, 17 of the 38 States in the OTAG domain supplied 1990 emission
estimates covering the entire State, and an additional nine States supplied 1990 emission estimates
covering part of their State (partial coverage was mostly in ozone nonattainment areas). 1990 Interim
Inventory data were the sole data source for 12 States. Where the area source data supplied included
annual emission estimates, the default temporal factors were used to develop average summer daily
emission estimates. Table 4.4-6 provides a brief description of the area source data supplied by each
State.
4.4.5.1.4.4 Rule Effectiveness. For the OTAG inventory, States were asked to submit their best
estimate of 1990 emissions. There was no requirement that State-submitted point source data include
rule effectiveness for plants with controls in place in that year. States were instructed to use their
judgment about whether to include rule effectiveness in the emission estimates. As a result, some States
submitted estimates that were calculated using rule effectiveness, while other States submitted estimates
that were calculated without using rule effectiveness.
The use of rule effectiveness in estimating emissions can result in emission estimates that are much
higher than estimates for the same source calculated without using rule effectiveness, especially for
sources with high control efficiencies (95 percent or above). Because of this problem, there was concern
that the OTAG emission estimates for States that used rule effectiveness would be biased to larger
estimates relative to States that did not include rule effectiveness in their computations.
To test if this bias existed, county-level maps of point source emissions were developed for the
OTAG domain If this bias did exist, one would expect to see sharp differences at State borders between
States using rule effectiveness and States not using rule effectiveness. Sharp State boundaries were not
evident in any of the maps created. Based on this analysis, it was determined that impact of rule
effectiveness inconsistencies was not causing large biases in the inventory.
4.4.5.2 Grand Canyon Visibility Transport Commission Inventory
The GCVTC inventory includes detailed emissions data for 11 States: Arizona, California,
Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.18 This
inventory was developed by compiling and merging existing inventory data bases. The primary data
sources used were State inventories for California and Oregon, AIRS/FS for VOC, NOX, and SO2 point
source data for the other nine States, the 1990 Interim Inventory for area source data for the other nine
States, and the 1985 NAPAP inventory for NH3 and TSP data. In addition to these existing data, the
GCVTC inventory includes newly developed emission estimates for forest wildfires and prescribed
burning.
4-129
-------
After a detailed analysis of the GCVTC inventory, it was determined that the following portions of
the GCVTC inventory would be incorporated into the PM inventory:
• complete point and area source data for California
• complete point and area source data for Oregon
• forest wildfire data for the entire 11-State region
• prescribed burning data for the entire 11 -State region
State data from California and Oregon were incorporated because they are complete inventories
developed by the States and are presumably based on more recent, detailed and accurate data than the
Interim Inventory (some of which is still based on the 1985 NAPAP inventory). The wildfire data in the
GCVTC inventory represent a detailed survey of forest fires in the study area and are clearly more
accurate than the wildfire data in the Interim Inventory. The prescribed burning data in the GCVTC
inventory are the same as the data in the Interim Inventory at the State level, but contain more detailed
county-level data.
Point source emission estimates in the GCVTC inventory from States other than California and
Oregon came from AIRS/FS. Corrections were made to this inventory to the VOC and PM emissions.
The organic emissions reported in GCVTC inventory for California are total organics (TOG). These
emissions were converted to VOC using the profiles fromEPA's SPECIATE19 data base. Since the PM
emissions in the GCVTC were reported as both TSP and PM-2.5, EPA estimated PM-10 from the TSP in
a similar manner as described in section 4.4.3.
4.4.5.3 AIRS/FS
SO2 and PM-10 (or PM-10 estimated from TSP) sources of greater than 250 tons per year as
reported to AIRS/FS that were not included in either the OTAG or GCVTC inventories were appended
to the NET inventory. The data were extracted from AIRS/FS using the data criteria set listed in
Table 4.4-7. The data elements extracted are also listed in Table 4.4-7. The data were extracted in late
November 1996. It is important to note that default estimated emissions were extracted.
4.4.5.4 Data Gaps
As stated above, the starting point for the 1990 NET inventory is the OTAG, GCVTC, AIRS, and
1990 Interim Inventory. Data added to these inventories include estimates of SO2, PM-10, PM-2.5, and
NH3, as well as annual or ozone season daily (depending on the inventory) emission estimates for all
pollutants. This section describes the steps taken to fill in the gaps from the other inventories.
4.4.5.4.1 SO2 and PM Emissions —
For SO2 and PM-10, State data from OTAG were used where possible. (The GCVTC inventory
contained SO2 andPM annual emissions.) In most cases, OTAG data for these pollutants were not
available. For point sources, data for plants over 250 tons per year for SO2 and PM-10 were added from
AIRS/FS. The AIRS/FS data were also matched to the OTAG plants and the emissions were attached to
existing plants from the OTAG data where a match was found. Where no match was found to the plants
in the OTAG data, new plants were added to the inventory. For OTAG plants where there were no
matching data in AIRS/FS and for all area sources of SO2 and PM-10, emissions were calculated based
on the emission estimates for other pollutants.
4-130
-------
The approach to developing SO2 and PM-10 emissions from unmatched point and area sources
involved using uncontrolled emission factor ratios to calculate uncontrolled emissions. This method used
SO2 or PM-10 ratios to NOX. NOX was the pollutant utilized to calculate the ratio because (1) the types
of sources likely to be important SO2 and PM-10 emitters are likely to be similar to important NOX
sources and (2) the generally high quality of the NOX emissions data. Ratios of SO2/NOX and PM-10/NOX
based on uncontrolled emission factors were developed. These ratios were multiplied by uncontrolled
NOX emissions to determine either uncontrolled SO2 or PM-10 emissions. Once the uncontrolled
emissions were calculated, information on VOC, NOX, and CO control devices was used to determine if
they also controlled SO2 and/or PM-10. If this review determined that the control devices listed did not
control SO2 and/or PM-10, plant matches between the OTAG and Interim Inventory were performed to
ascertain the SO2 and PM-10 controls applicable for those sources. The plant matching component of
this work involved only simple matching based on information related to the State and county FIPS code,
along with the plant and point IDs.
There were two exceptions to the procedures used to develop the SO2 and PM-10 point source
estimates. For South Carolina, PM-10 emission estimates came from the Interim Inventory. This was
because South Carolina had no PM data in AIRS/FS for 1990 and using the emission factor ratios
resulted in unreaKstically high PM-10 emissions. The residential nonwood SO2 and PM emissions were
also deemed too high for all States based on the above calculation. The emission estimates reverted to an
earlier method as outlined in section 4.4.9.4.
There were no PM-2.5 data in either OTAG or AIRS/FS. Therefore, the point and area PM-2.5
emission estimates were developed based on the PM-10 estimates using source-specific uncontrolled
particle size distributions and particle size specific control efficiencies for sources with PM-10 controls.
To estimate PM-2.5, uncontrolled PM-10 was first estimated by removing the impact of any PM-10
controls on sources in the inventory. Next, the uncontrolled PM-2.5 was calculated by multiplying the
uncontrolled PM-10 emission estimates bythe ratio of the PM-2.5 particle size multiplier to the PM-10
particle size multiplier. (These particle size multipliers represent the percentage to total particulates
below the specified size.) Finally, controls were reapplied to sources with PM-10 controls by multiplying
the uncontrolled PM-2.5 by source/control device particle size specific control efficiencies.
4.4.5.4.2 NH3 Emissions —
A11NH3 emission estimates incorporated into the NET Inventory came directly from EPA's NPI.16
This methodology is the same as that reported in section 4.4.3 for the 1990 Interim Inventory. The NPI
contained the only NH3 emissions inventory available. (Any NH3 estimates included in the OTAG or
AIRS/FS inventory were eliminated due to sparseness of data.) As with SO2 and PM-10, plant matching
was performed for point sources. Emissions were attached to existing plants where there was a match
New plants were added for plants where there was no match.
4.4.5.4.4 Other Modifications —
Additional data were also used to fill data gaps for residential wood combustion and prescribed
burning. Although these categories were in the OTAG inventory, the data from OTAG were not usable
since the average summer day emissions were often very small or zero. Therefore, annual and average
summer day emission estimates for these two sources were taken from the NET (detailed in sections
4.4.9.3 and 4.4.9.2).
4-131
-------
Additional Q A/quality control (QC) of the inventory resulted in the following changes:
• Emissions with SCCs of fewer than eight digits or starting with a digit greater than the number
"6" were deleted because they are invalid codes.
• Tier assignments were made for all SCCs.
• Checked and fixed sources with PM-2.5 emissions which were greater than their PM-10
emissions.
• Checked and fixed sources with PM-10 emissions greater than zero and PM-2.5 emissions
equal to zero.
4.4.6 How Did EPA Develop Emissions for 1991 to 1994?
The 1991 through 1994 area source emissions were grown in a similar manner as the 1985 through
1989 estimates, except for using a different base year inventory. The base year for the 1991 through
1994 emissions is the 1990 NET inventory. The point source inventory was also grown for those States
that did not want their AIRS/FS data used. (The list of States are detailed in the AIRS/FS subsection,
4.4.6.2.) For those States requesting that EPA extract their data from AIRS/FS, the years 1990 through
1995 were downloaded from the EPA IBM Mainframe. The 1996 emissions were not extracted since
States are not required to have the 1996 data uploaded into AIRS/FS until July 1997.
4.4.6.1 Grown Estimates
The 1991 through 1994 point and area source emissions were grown using the 1990 NET inventory
as the basis. The algorithm for determining the estimates is detailed in section 4.4.3.3. The 1990 through
1996 SEDS and BEA data are presented in Tables 4.4-8 and 4.4-9. The 1996 BEA and SEDS data were
determined based on linear interpretation of the 1988 through 1995 data. Point sources were projected
using the first two digits of the SIC code by State. Area source emissions were projected using either
BEA or SEDS. Table 4.4-10 lists the SCC and the source for growth.
The 1990 through 1996 earnings data in BEA Table SA-5 (or estimated from this table) are
expressed in nominal dollars. In order to be used to estimate growth, these values were converted to
constant dollars to remove the effects of inflation. Earnings data for each year were converted to 1992
constant dollars using the implicit price deflator for PCE. The PCE deflators used to convert each year's
earnings data to 1992 dollars are:
Year 1992 PCE Deflator
1990 93.6
1991 97.3
1992 100.0
1993 102.6
1994 104.9
1995 107.6
1996 109.7
4-132
-------
4.4.6.2 AIRS/FS
Several States responded to EPA's survey and requested that their 1991 through 1995 estimates
reflect their emissions as reported in AIRS/FS. The list of these States, along with the years available in
AIRS/FS is given in Table 4.4-11. As described in section 4.4.5.3, default estimated annual and ozone
season daily emissions (where available) were extracted from AIRS/FS. Some changes were made to
these AIRS/FS files. For example, the default emissions for some States contain rule effectiveness and
the emissions were determined to be too high by EPA. The emissions without rule effectiveness were
extracted from AIRS/FS and replaced the previously high estimates. The changes made to select State
and/or plant AIRS/FS data are listed below.
• Louisiana All VOC source emissions were re-extracted to obtain emissions
without rule effectiveness for the year 1994.
• Colorado - Mastercraft The VOC emissions were reported as ton/year in the initial
download from AIRS. The units were changed to
pounds/year in AIRS.
• Wisconsin - Briggs and Stratton The VOC emissions for two SCCs were changed from with
rule effectiveness to without rule effectiveness for the years
1991, 1993, and 1994.
As noted in Table 4.4-11, several States did not report emissions for all pollutants for all years for
the 1990 to 1995 time period. To fill these data gaps, EPA applied linear interpolation or extrapolated
the closest two years worth of emissions at the pknt level. If only one year of emissions data were
available, the emission estimates were held constant for all the years. The segment-SCC level emissions
were derived using the average split for all available years. The non-emission data gaps were filled by
using the most recent data available for the plant.
As described in section 4.4.5.4.1, many States did not provide PM-10 emissions to AIRS. These
States' TSP emissions were converted to PM-10 emissions using uncontrolled particle size distributions
and AP-42 derived control efficiencies. The PM-10 emissions are then converted to PM-2.5 in the same
manner as described in section 4.4.3.3. The State of South Carolina provided its own conversion factor
for estimating PM-10 from TSP.20
For all sources that did not report ozone season daily emissions, these emissions were estimated
using the algorithm described in section 4.4.5.1.4 and equations 4.4-5 through 4.4-7.
4.4.7 How were 1995 Emissions Prepared?
The 1995 emission estimates were derived in a similar manner as the 1991 through 1994 emissions.
The estimates were either extracted from AIRS/FS for 1995, estimated using AIRS/FS data for the years
1990 through 1994, or projected using the 1990 NET inventory. The method used depended on the
States' responses to a survey conducted by EPA early in 1997. A description of the AIRS/FS
methodology is described in section 4.4.6. The following three subsections describe the projected
emissions.
4-133
-------
4.4.7.1 Grown Estimate
The 1995 point and area source emissions were grown using the 1990 NET inventory as the basis.
Growth factors were prepared for each year using either SEDS annual fuel consumption data or BEA
national earnings by industry. The 1990 through 1996 SEDS and BEA data are presented in Tables 4.4-8
and 4.4-9. The algorithm for determining the estimates is detailed insection4.4.3.3.
4,4,7,2 NOXRACT
Major stationary source NOX emitters in marginal and above nonattainment areas and in ozone
transport regions (OTRs) are required to install Reasonably Available Control Technology (RACT)-level
controls under the ozone nonattainment related provisions of Title I of the 1990 Clean Air Act
Amendments (CAAA). The definition of major stationary source for NOX differs by the severity of the
ozone problem as shown in Table 4.4-12.
NOX RACT controls for non-utility sources that were modeled for the 1995 NET emissions are
shown in Table 4.4-13. These RACT-level controls were applied to point source emitters with emissions
at or above the major source size definition for each area. The application of NOX RACT controls was
only applied to grown sources.
4,4,7,3 Rule Effectiveness
Rule effectiveness was revised in 1995 for all grown sources using the information in the 1990 data
base file. If the rule effectiveness value was between 0 and 100 percent in 1990 and the control efficiency
was greater than 0 percent, the uncontrolled emissions were calculated for 1990. The 1995 emissions
were calculated by multiplying the growth factor by the 1990 uncontrolled emissions and the control
efficiency and a rule effectiveness of 100 percent. The adjustment for rule effectiveness was only applied
to grown sources.
4.4.8 How Did EPA Develop the 1996 NET Inventory?
The 1996 emission estimates were derived in a similar manner as the 1995 emissions. For point
sources, the 1995 AIRS/FS emissions and 1995 emissions grown from 1990 emissions were merged. The
following describes the projected 1996 emissions. No controls were added to the 1996 emissions.
The 1996 point and area source emissions were grown using the 1995 NET inventory as the basis.
The algorithm for determining the estimates is described by Equation 4.4-8. The 1990 through 1996
SEDS and BEA data are presented in Tables 4.4-8 and 4.4-9. The 1996 BEA and SEDS data were
determined using linear interpretation of the 1988 through 1995 data Rule effectiveness was updated to
100 percent as described in section 4.4.7.3 for the AIRS/FS sources that reported rule effectiveness of
less than 100 percent in 1995.
The following equation describes the calculation used to estimate the 1996 emissions:
4-134
-------
^70 rrn 1996 i REFF CEFF RP
CER.^,= UC.^, x — — x 1- - x - x
1996 1995
.^,
1996 QS 100 100 iQO
where: CER1996 = controlled emissions incorporating rule effectiveness
UC1995 = uncontrolled emissions
GS = growth surrogate (either BEA or SEDS data)
REFF = rule effectiveness (percent)
CEFF = control efficiency (percent)
RP = rule penetration (percent)
The rule effectiveness for 1996 was always assumed to be 100 percent. The control efficiencies and rule
penetrations are 100 percent since no additional controls were applied.
Subsequently, EPA has been revising the 1996 NET to include base year emissions data submitted
by State/local agencies to comply with the CAAA requirements to submit (1) periodic emissions
inventories (PEI) every 3 years for ozone nonattainment areas (NAAs), and (2) emissions data for major
point sources annually. States with ozone NAAs needed to submit their PEI for 1996 by July 1997.
While the CAAA only require submittal of ozone precursor pollutant data for the PEI requirements,
annual point source reporting covers all criteria air pollutants. In its guidance provided to the State/local
agencies on the PEI submittal process, EPA encouraged State/local agencies to submit emission estimates
for all pollutants because the NET contains estimates for all criteria pollutants and is to be the ultimate
repository of the State/local agency data. To reduce the burden of preparing this inventory, EPA gave
each State/local agency a copy of the 1996 NET inventory as a starting point in preparing their 1996 base
year emissions. Except for the source category methodologies discussed in section 4.4.9, the
methodologies used to update the 1996 NET emissions are presented in section4.3.8.4 of section 4.3 for
"Industrial" sources.
4.4.9 Alternative Methodologies for Area Source Categories
The EPA methodologies for estimating emissions for the area source categories discussed in this
section are different from the methodologies previously described. This section explains the
methodologies applied to estimate emissions for 1990 through 1999 for forest fires/wildfires,
prescribed/slash and managed burning, residential wood combustion, and structure fires. This section
also explains the methodologies applied to estimate 1999 emissions for the open burning of residential
MSW, leaves, and brush; and open burning of land clearing debris. Prior to 1996, emissions for these
open burning categories were either grown from 1990 emissions as discussed in sections 4.4.3 through
4.4.8, or were not estimated. Table 4.4-14 summarizes the methods applied to estimate emissions for
1989-1999 for these area source categories. Table 4.4-15 summarizes the methods applied to prepare the
1996 base year inventory from 1996 through 1999 for each of the categories. Table 4.4-16 identifies the
State/local agencies that submitted 1996 base year emissions for these categories. The State/local agency
emissions replaced the EPA estimates in Versions 3 and 4 of the 1996 NET inventory.
Finally, this section discussed how 1990-1994 PM and SO2 emissions for residential nonwood
combustion sources were estimated. For these categories, sections 4.4.7 and 4.4.8 discuss the
4-135
-------
methodologies applied to prepare 1995 and 1996 emissions, respectively. The methodologies for
estimating 1997 through 1999 emissions are covered in section 4.4.10.
4.4.9.1 Forest Fires/Wildfires
Forest fire/wildfire emissions are classified under SCC 2810001000. EPA developed separate
methodologies to estimate emissions for the non-GCVTC States and the 11 States included in the
GCVTC inventory.
4.4.9.1.1 Non-Grand Canyon States —
4.4.9.1.1.1 Non-Grand Canyon States (1985-1998). Forest fire/wildfire emissions were generated
for the years 1985 through 1998 using the data on the number of acres burned (obtained from the U.S.
Department of Interior [DOI]21'22 and the U.S. Forest Service [USFS]23'24), AP-42 emission factors, and
AP-42 fuel loading factors.25 Equation4.4-9 summarizes the calculation.
Estate = Activity x Fuel Loading x EF x UCF (Eq. 4.4-9)
where: Estate = annual State emissions (tons)
Activity = sum of DOI, USFS, and State and private land acres burned (acres)
Fuel Loading = average fuel loading for State (tons/acre)
EF = emission factor (Ibs/ton)
UCF = unit conversion factor (1 ton 72,000 Ibs)
Table 4.4-17 shows the emission factors and fuel loading factors developed fromAP-42. PM-2.5
emissions were estimated by multiplying the PM-10 emissions by State-level ratios of PM-2.5 to PM-10
developed from the 1990 inventory for non-GCVTC States.
The EPA estimates for 1996 were replaced with emissions provided by the States identified in Table
4.4-16. At Kansas' request, the 1996 emissions submitted by Kansas were held constant for 1997
through 1999. For Florida, 1998 emissions for VOC, NOX, CO, SO2 and PM-10 were replaced with
county-level emissions provided by Florida in 1999. Florida did not provide estimates for PM-2.5;
therefore, PM-2.5 emissions were estimated by multiptying the PM-10 emissions by State-level ratios of
PM-2.5 to PM-10 developed from the 1990 inventory for Florida.
4.4.9.1.1.2 Non-Grand Canyon States (1999). Emissions for 1998 were held constant for 1999
because complete activity data on the number of acres burned were not available for 1999.
4.4.9.1.2 Grand Canyon States —
4.4.9.1.2.1 Grand Canyon States (1986-1993). For the years 1986 through 1993, for the States of
Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and
Wyoming, the CO, NOX, VOC, and PM-10 emissions calculated using the methodology described above
were replaced by those included in the GCVTC inventory.18 The GCVTC inventory provided county
level emissions for forest fires. PM-2.5 emissions for 1990 were also replaced by those in the GCVTC
inventory. For other years, PM-2.5 emissions were estimated using State-level ratios developed from
4-136
-------
1990 emission estimates in the GCVTC inventory. The SO2 emissions for these States were calculated
using the AP-42 emission factor ratio equation shown below. The emission factors are shown in Table
4.4-17.
SO.EF
SO2 Emissions = * M9X Emissions (Eq 4.4-10)
EF
where: SO2 Emissions = annual county SO2 emissions (tons)
SO2 EF = AP-42 emission factor for SOX (Ibs/ton)
NOX EF = AP-42 emission fector for NOX (Ibs/ton)
NOX Emissions = annual NOX emissions (tons)
4.4.9.1.2.2 Grand Canyon States (1985,1994-1998). For the years 1985, and 1994 through 1998,
for the States of Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah,
Washington, and Wyoming, CO, NOX, VOC, PM-10 and PM-2.5 emissions were calculated using
Equation 4.4-11.
State Activity
County Emissions = y— * County Emissions.990 (Eq.4.4-11)
-^ \f-t-s*-t-f* A s^-J-i* t-f^-i i Lyy\j \ TL /
State Activity
1990
where: County Emissionsyear = annual county emissions (tons)
State Activity = DOI, State and private, and National Forest Lands burned (acres)
County Emissions]990 = annual county emissions provided by the GCVTC (tons)
4.4.9.1.2.3 Grand Canyon States (1999). Emissions for 1998 were held constant for 1999 because
complete activity data on the number of acres burned were not available for 1999.
4.4.9.1.3 Activity —
The activity factor for wildfires is land acres burned. There are three sources of data for this
activity USFS acres burned, State and private acres burned,23'24 and DOI acres burned.21'22 Data from
these three sources were summed to get the total acres burned for each State.
4.4.9.1.4 Fuel Loading and Emission Factors —
AP-42 fuel loading and emission factors are shown in Table 4.4-17.25 An average fuel loading was
determined for five regions in the United States. Emission factors for SO2, NOX, VOC, CO, and PM-10
were used.
4.4.9.1.5 County Distribution —
All non-GCVTC States were distributed to the county-level using the same county-level distribution
as was used in the 1985 NAPAP Inventory. GCVTC provided county-level emissions for 1986 through
1993. GCVTC emissions were calculated for 1985, and 1994 through 1998 using the 1990 GCVTC
emissions, as described above. For 1999, the 1998 emissions were held constant.
4-137
-------
4. 4. 9. 2 Prescribed/Slash and Managed Burning
EPA's estimates for prescribed/slash and managed burning are reported under SCC 281 001 5000 in
the 1990-1999 NET inventories. Some State/local agencies have submitted emission estimates for
prescribed burning under SCC 2810015000 and emissions for slash burning under SCC 2810005000.
The emissions supplied by State/ local agencies are included in the NET inventory and replace the EPA
estimates.
The prescribed burning emissions were based on a 1989 USFS inventory of PM and air toxics from
prescribed burning.26 The USFS inventory contains State-level totals for total PM, PM-10, PM-2.5, CO,
carbon dioxide, methane, non-methane, and several air toxics. This inventory also contains county-level
emissions for PM-10 and VOC. The NOX, CO, and SO2 emissions were calculated by assuming the ratio
between the VOC emissions to either the NOX, CO or SO2 emissions in the USFS inventory was equal to
the corresponding ratio using the 1985 NAPAP inventory. Equation 4.4-12 was used.
NAPAP
(Eq. 4.4-12)
where: FSPOL = prescribed burning (NOX, CO, or SO2) emissions from USFS
FSVOC = prescribed burning VOC emissions from USFS
NAPAPpQL = prescribed burning (NOX, CO, or SO2) emissions from 1985 NAPAP
NAPAPVOC = prescribed burning VOC emissions from 1985 NAPAP
The resulting 1989 emissions for CO, NOX, PM-10, SO2, and VOC are used for all years between 1985
and 1990.
4.4.9.2.1 1991-1999 Methodology
Emissions for 1990-1999 were estimated using a ratio method as developed for the Section 812
Prospective Analysis which held the number of acres burned on private lands constant, and projected
growth for public knds based on development of a national growth factor of national statistics for acres
burned. Using this data, a State-level ratio of public to total lands was calculated for 1987 using U.S.
Forest Service Data.26 The 1990 State-level emissions were then multiplied by this ratio to get a State-
level distribution of emissions for public lands. Growth factors were then developed from the summation
of USFS and DOI acres burned for each year. The number of acres burned on DOI lands was obtained
from the following agencies: Bureau of Indian Affairs (BIA), Bureau of Land Management (BLM), Fish
and Wildlife Service (FWS), and National Park Service (NPS). Using 1990 as the base year, national
growth factors were calculated for years 1991-1995. Using 1996 as the base year, growth factors were
calculated for years 1997-1 999. If a calculated growth factor resulted in a value greater than 2.0, the
growth factor was set at 2.0. These growth factors were then applied to the fraction of acres burned
attributed to public lands. The calculated value was then added to the acreage burned on private lands
(i.e., the acreage held constant) to obtain the emissions for each year through 1999. The emissions for
each year were then distributed from the State level to the county level using the existing distribution for
prescribed burning that exists in the 1990 NET.
4-138
-------
4.4.9.3 Residential Wood
EPA emission estimates for residential wood combustion are classified under SCC 2104008001
(Residential Wood Combustion: Fireplaces). Currently, information is not available to determine how to
distribute wood consumption between fireplaces and wood stoves. Therefore, emissions for this category
are placed with one SCC. Note that when this methodology was first implemented, SCC 2104008000
(Total: Woodstoves and Fireplaces) was not available on which emissions for this category could be
placed. For consistency reasons, it was decided to continue to report total residential wood combustion
emissions under SCC 2104008001 after the SCC 2104008000 became avaikble. Some State/local
agencies reported 1996 emissions under SCCs for woodstoves. Table 4.4-17 identifies the agencies that
submitted emissions, and the SCCs they used to report emissions. The emissions submitted by these
agencies replaced the EPA estimates in the 1996 NET inventory.
Emissions from residential wood combustion were estimated for 1985 through 1999 using annual
wood consumption and an emission factor. The following general equation (Equation 4.4-13) was used
to calculate emissions:
CE
100,
If M I
1 - 77d (Eq. 4.4-13)
where: Eyear = county emissions (tons)
Activity = wood consumption (cords)
EF = emission factor (tons/cord)
CE = control efficiency (percent)
Activity was based on EPA's County Wood Consumption Estimation Model.27 This model was adjusted
with heating degree day information,28 and normalized with annual wood consumption estimates.29
AP-42 emission factors for CO, NOX, PM-10, PM-2.5, SO2 and VOC were used. PM-2.5 emissions are
assumed to be the same as PM-10 emissions.
4.4.9.3.1 Activity - County Model —
EPA's County Wood Consumption Estimation Model is based on 1990 data and provides county
level estimates of wood consumption, in cords.27 Model F of the overall Model was used to estimate the
amount of residential wood consumed per county, using a sample set of 91 counties in the northeast and
northwestern United States. Model F calculates estimates of cords of wood consumed per household as
a function of the number of homes heating primarily with wood with a forced intercept of zero. Using
the Model F results, the percentage of the population heating with wood, the number of households in a
county, land area per county, and heating degree days, county-level wood consumption for 1990 was
estimated.
4-139
-------
The counties listed below show no residential wood consumption activity. The emissions for these
18 counties for the years 1985 through 1999 are zero.
State (FIPS)
Alaska (02)
Hawaii (15)
Kansas (20)
Montana (30)
Texas (48)
County (FIPS)
Aleutians East Borough (013)
Kalawao (005)
Kearny (093)
Stanton(187)
Yellowstone National Park (113)
Cochran (079)
Crockett (105)
Crosby (107)
Garza(169)
Hartley (205)
Jim Hogg (247)
Loving (301)
Moore (341)
Reagan (383)
Sterling (431)
Swisher (437)
Terrell (443)
Yoakum(501)
4.4.9.3.2 Heating Degree Days —
A heating degree day is the number of degrees per day the daily average temperature is below
65 degrees Fahrenheit. These data were collected for one site in all States (except Texas and California
where data were collected for two sites) for each month and summed for the year. An average of the two
sites was used for Texas and California. This information is used to adjust the model, which is partially
based on 1990 heating degree days, to the appropriate year's heating degree data. Equation 4.4-14 was
used.
Adjusted Model,
State hdd Total
year
year State hdd Total
County Model
1990
(Eq. 4.4-14)
1990
where: Adjusted Model
State hdd Total
County Model
county wood consumption (cords)
total heating degree days (degrees Fahrenheit)
EPA model consumption (cords)
4.4.9.3.3 National Wood Consumption —
The Adjusted Model wood consumption estimate was normalized on a national level using the U.S.
Department of Energy (DOE) estimate of residential U.S. wood consumption. This value is reported in
trillion British thermal units (Btu) and is converted to cords by multiptying by 500,000. Consumption for
4-140
-------
the years 1985, 1986, and 1988 were unavailable from the DOE. Known year's consumption and heating
degree days were used to estimate these years. The 1985 DOE estimate was calculated using the ratio of
1985 total heating degree days to 1984 total heating degree days multiplied by the 1984 DOE wood
consumption estimate. The 1986 DOE estimate was calculated using the ratio of 1986 total heating
degree days to 1985 total heating degree days multiplied by the "calculated" 1985 DOE wood
consumption estimate. The 1988 DOE estimate was calculated using the ratio of 1988 total heating
degree days to 1987 total heating degree days multiplied by the 1987 DOE wood consumption estimate.
Equation 4.4-15 shows the normalization of the Adjusted Model.
DOE
Activity = Adjusted Model x —— yeaj — (Eq. 4.4-15)
^Adjusted Model ear
where: Activity = normalized county consumption (cords)
Adjusted Model = county wood consumption (cords)
DOE = DOE national estimate of residential wood consumption (cords)
4.4.9.3.4 Emission Factors —
Emission factors were obtained from Table 1.10-1 of AP-42, Emission Factors for Residential
Wood Combustion, for conventional wood stoves,25 and are shown here in Table 4.4-18. Table 4.4-18
also shows the emission factors expressed in tons per cord consumed.
4.4.9.3.5 Control Efficiency —
A control efficiency was applied nationally to PM-10 and PM-2.5 residential wood combustion for
the years 1991 through 1999.30 The control efficiency for all pollutants for 1985 through 1990, and for
VOC, NOX, CO, and SO2 for 1991 through 1999 is zero. Table 4.4-19 shows the control efficiencies for
PM-10 and PM-2.5 for 1991 through 1999.
4.4.9.4 SO2 and PM Residential Nonwood Combustion
The residential nonwood category includes SCCs 2104001000 (anthracite coal), 2104002000
(bituminous/subbituminous coal), 2104004000 (distillate oil), 2104005000 (residual oil), 21040060xx
(natural gas), 2104007000 (liquified petroleum gas (LPG)), and 2104011000 (Kerosene) for all
combustor or heater types.
The 1990 SO2 and PM NET emissions are the same as the 1990 Interim Inventory emissions. EPA
estimated 1991 through 1994 emissions by applying growth factors to the 1990 emissions. The growth
factors were obtained from the prereleased E-GAS, version 2.O.31 The EGAS generates growth factors at
the SCC-level for counties representative of all counties within each ozone nonattainment area classified
as serious and above, and for counties representative of all counties within both the attainment portions
and the marginal and moderate nonattainment areas within each State. The appropriate growth factors
were applied by county and SCC to the 1990 emissions as shown by Equation 4.4-16.
Emissions(coun^scc^ar) = Growth{county^CCyear) x Emissions(county^l990) (Eq. 4.4-16)
4-141
-------
There are approximately 150 representative counties in EGAS 2.0 and 2000 SCCs present in the
base year inventory. This yields amatrix of 300,000 growth factors generated to determine a single
year's inventory. To list all combinations would be inappropriate.
4.4.9.5 Structure Fires
Structure fire emissions are reported under SCC 2810030000.
4,4,9.5.11985-1989 Methodology —
Structure fires were included in the 1985 NAPAP inventory because these fires can be sources of
high-level, short-term emissions of air contaminants. The activity factor for this category was the total
number of fires per county, and was multiplied by a fuel loading factor and emission factors to obtain the
emission estimates. For the 1985 NAPAP inventory, the total national number of building fires was
obtained from the 1985 statistics from the National Fire Protection Association (NFPA).32 Since there
were no data available to allocate the number of fires to the county level, an average of four fires per
1,000 population was assumed to occur each year.32 The fiiel loading factor was 6.8 tons per fire5 and
emission factors were taken from the OAQPS Technical Tables.5
4.4.9.5.2 1990 Methodology for County-Level Emissions Provided by OTAG States —
During development of the OTAG inventory, several States provided 1990 emissions for structure
fires by county. Some States provided emissions for only a portion of the State (e.g., for nonattainment
area counties). These emissions were included in the 1990 NET inventory when provided. The States
did not provide any information on the methodologies they used to prepare the county-level emission
estimates.
4.4.9.5.3 1990 Methodology for All Other Counties —
Work by the Emission Inventory Improvement Program (EIIP) identified a revision to the loading
factor used to estimate emissions from structure fires. The revised loading factor of 1.15 tons per fire33
was obtained from the California Air Resources Board (CARB).33 For the non-OTAG States, and the
counties for which OTAG States did not provide 1990 emissions, the 1990 emissions were prepared
using the revised loading factor, county population in 1990, an average of four fires per 1,000 population,
and pollutant-specific emission factors for VOC, NOX, CO, SO2, and PM-10. For PM-2.5, emissions are
estimated by multiplying PM-10 emissions by 0.91.
4.4.9.5.4 1991-1995 Methodology for All States —
Using 1990 as the base year, 1991-1995 estimates were calculated using a growth factor calculated
from a regression equation developed from EGAS. This equation was develop by relating national
estimates of tons of material burned to population for 1972 through 1992. State-level population was
then used as an input to predict the amount of material burned in each State using the regression
equation. Both non-OTAG and OTAG States were grown using the equation. The equation is as
follows:
GFrf = b + m^ + /M2x2 (Eq. 4.4-17)
where: GFsf = growth factor for structure fires
b (y intercept) = -66809.3
4-142
-------
nij (slope) = 0.721
x = State population (year)
m2 (slope) = -0.000001744
4.4.9.5.5 1996 Methodology for County-Level Emissions Provided by OTAG States —
Unless a State/local agency provided 1996 base year emissions, the 1990 county-level emissions
provided by OTAG States were grown using Equation 4.4-17 and year-specific population. The grown
estimates were replaced with county-level emissions included in a State/local agency's inventory.
4.4.9.5.6 1996 Methodology for All Other Counties —
For 1996, estimates were developed using updated activity data and the revised loading factor for
non-OTAG States. The U.S. Fire Administration maintains the National Fire Incident Reporting System
(NFIRS). The NFIRS represents the most comprehensive data base of fire incident information currently
available at a State level. However, since State participation in NFIRS is voluntary, it is incomplete.
Currently, 42 States and the District of Columbia re port data to NFIRS, and within these States, not all
fire stations report data. Using the number of structure fires each State reported to NFIRS, and the
percentage of fire stations reporting relative to the total number of fire stations within each State, the
number of fires for each State was scaled up to estimate the actual number (i.e., reported and unreported)
of fires occurring within a State for 1996. From these data, and from State population, State-specific per
capita factors were developed and multiplied by the emission factors used for 1990 emissions to estimate
Statewide emissions. State-level emissions were allocated to the county level using the ratio of county-
to-State population for 1990.
The number of fires reported to NFIRS is scaled as follows to account for fire departments that did
not report to NFIRS in 1996:
Percentage of Fire Departments Reporting= Number of Fire Departments Reporting/Number of Fire
Departments
Scaled Number of Fires=Number of Fires/(Percentage of Fire Departments Reporting)
Then, the average number of fires/1,000 population is calculated using the scaled number of fires and
State-level population for the current year obtained from the Census.
If the information needed to calculate the data was unavailable, the national NFP A default value of
2.18 fires/1,000 population was substituted. In addition, use of the NFIRS data for Alabama, Hawaii,
and Washington State resulted in estimates of 0.28, 0.68, and 0.31 fires per 1,000 population,
respectively. For Oklahoma, use of the NFIRS data yielded an estimate of 10.99 fires/1,000 population.
Because the estimates for these four States were significantly outside of the range of the estimates for the
other States reporting to NFIRS, the national NFPA default value of 2.18 fires/1,000 population was
used to calculate emissions for each of the four States.
The average number of fires/1,000 population is multiplied by the State population to get the
number of fires per State for that year. Table 4.4-20 shows the reference for the number of fires reported
by State, and the average number of fires per 1,000 population used to estimate emissions for each State.
4-143
-------
4.4.9.5.71997-1999 Methodology for All States —
Equation 4.4-17 was used to grow 1996 county-level emissions to 1997 through 1999, using year-
specific population.
4.4.9.6 Open Burning Emission Estimates for the Year 1999
Emission estimates for open burning categories were updated for 1999 by obtaining more recent
activity data, applying updated emission factors, and making further adjustments based on expected open
burning practices. The open burning categories for which updated emission estimates were developed
include:
SCC SCC Name
2610030000 Residential municipal solid waste burning
2610000100 Residential leaf burning
2610000400 Residential brush burning
2610000500 Land clearing debris burning
Residential MSW refers to nonhazardous refuse produced by households (e.g., paper, plastics,
metals, wood, glass, rubber, leather, textiles, and food wastes). Residential yard waste refers to materials
such as leaves, trimmings from trees and shrubs, and grass. Land clearing debris refers to the clearing of
land for new construction and the burning of organic material (ie., trees, shrubs and other vegetation).
The SCCs for residential brush burning and land clearing debris burning are new SCCs. Previous years
estimates for open burning were reported under SCC 2610000000 (Total for all open burning
categories). In the 1999 NET, this SCC was removed and the 1999 emissions were reported on the
SCCs listed above to avoid double-counting of emissions.
4.4.9.6.1 How Did We Estimate Emissions for Residential Municipal Solid Waste (MSW)
Burning? —
Emission estimates for residential MSW burning were developed by first estimating the amount of
waste generated for each county in the United States. The method assumes that the amount of waste
open burned can be estimated based on the total amount of waste generated. The amount of waste
generated was estimated using a national average per capita waste generation factor, as reported in
Characterization of Municipal Solid Waste in the United States: 1998 Update.34 To better reflect the
actual amount of household residential waste subject to being burned, non-combustibles (glass and
metals) are subtracted out. In addition, since yard waste is considered a separate op en burning category,
yard waste generation was subtracted out as well The latest avaikble per capita waste generation factor
was for 1997, and was estimated to be 3.28 Ibs/person/day. This factor was then applied to the portion
of the county's total population that is considered rural based on 1990 Census data on rural and urban
population, since open burning is generally not practiced in urban areas.
The percentage of total waste generated that is burned was estimated from survey data as reported
in Emission Characteristics of Burn Barrels?5 This study estimated that for rural populations, 25 to
4-144
-------
32 percent of the municipal waste generated is burned A median value of 28 percent was assumed for
the nation, and this correction factor was applied to the total amount of waste generated.
Controls (or burning bans) were accounted for by assuming that no burning takes place in counties
where the urban population exceeds 80 percent of the total population (i.e., urban plus rural). Zero open
burning emissions were attributed to these counties.
To summarize, the following steps were taken:
Step 1 - Estimate the amount of waste generated for each county in the United States using a national
average per capita waste generation factor applied to the portion of the county's total population that is
considered rural. Rural versus urban percentages for each county were obtained from 1990 Census data.
Step 2 - Estimate the amount of waste generated by rural populations that is burned using a correction
factor of 0.28.
Step 3 - Estimate the emissions from MS W burning by applying emission factors as presented in
Table 4.4-21.
4.4.9.6.2 How Did We Estimate Emissions for Residential Yard Waste Burning? —
Similar to residential MSW, a national per capita waste generation value was used as the basis for
yard waste emissions for 1999. EPA's report Characterization of Municipal Solid Waste in the United
States: 1998 Update?* reports an average daily value of 0.57 Ibs yard waste/person/day. Of the total
amount of yard waste generated, the yard waste composition was assumed to be 25 percent leaves,
25 percent brush, and 50 percent grass by weight. It was determined that open burning of grass clippings
is not typically practiced by homeowners, and as such only estimates for leaf burning and brush burning
were developed. Emissions for leaves and residential brush were calculated separately, since emission
factors vary by yard waste type. It was assumed that 28 percent of the total yard waste generated is
burned and that burning occurs primarily in rural areas, similar to the assumptions used for residential
MSW burning.
To adjust for variations in vegetation, we obtained data on the percentage of forested acres from
Version 3.1 of the Biogenic Emissions Landcover Database (BELD3) within EPA's Biogenic Emission
Inventory System (BEIS). This database contains the number of acres of rural forest, urban forest,
agricultural land, and miscellaneous vegetation per county. We first determined the percentage of
forested acres per county (including rural forest and urban forest). To better account for the native
vegetation that would likely be occurring in the residential yards of farming States, we subtracted out the
agricultural lands before calculating the percentage of forested acres. We then used the following ranges
to make adjustments to the amount o f yard waste that is assumed to be generated per county:
4-145
-------
Percent forested acres per county Adjustment for yard waste generated
< 10% Zero out
>=10%, and <50% Multiply by 50%
>=50% Assume 100%
To summarize, the following steps were taken:
Step 1 - Estimate the amount of waste generated for each county in the United States using a national
average per capita waste generation factor applied to the portion of the county's total population that is
considered rural. Rural versus urban percentages for each county are obtained from 1990 Census data.
Step 2 - Estimate the amount of leaves, brush and grass waste generated by rural populations (by
assuming 25 percent leaves, 25 percent brush, and 50 percent grass). Zero out the yard waste portion
that corresponds to grass since it is assumed that grass is not burned.
Step 3 - Adjust the amount of yard waste generated per county using data on forested acres per county,
discounting the number of acres of agricultural land.
Step 4 - Estimate the amount of brush and leaves that is burned using a correction factor of 0.28.
Step 5 - Estimate the emissions from each yard waste type using emission factors as presented in
Table 4.4-21.
4.4.9.6.3 How Did We Estimate Emissions for Land Clearing Debris Burning? —
Activity data for this category are the acres cleared multiplied by a fuel loading fector. National data
on the number of acres cleared for all States are not avaikble from known data sources. As such, a value
for the acres disturbed by construction activity must be estimated using surrogate data, which is then
converted to acres using an appropriate conversion factor.36 Three general types of construction are
accounted for to estimate land clearing activity: 1) residential construction; 2) nonresidential
construction; and 3) roadway construction. This approach assumes that all land clearing debris that is
cleared is then burned.
The formula for calculating the county-level emissions from land clearing debris is:
Emissions = Acres x LF x EF
where: Acres = total acres disturbed by construction
LF = weighted loading factor to convert acres to tons of available fuel
EF = emission factor in Ibs pollutant/ton of fuel
4.4.9.6.3.1 Residential Construction. For residential construction, housing permit data for single-
family units, two-family units, and apartments were obtained at the county level from the U.S.
Department of Commerce's (DOC) Bureau of the Census.37 County permit data were then adjusted to
4-146
-------
equal regional housing start data, which would more accurately reflect actual construction, also obtained
from the Bureau of the Census.38 Once the number of buildings in each category were estimated, the
total acres disturbed by construction can be estimated by applying conversion factors to the available
activity data for each category as follows:
• Single family - 1/4 acre/bldg
• Two-family - 1/3 acre/bldg
• Apartment - Vz acre/bldg
4.4.9.6.3.2 Nonresidential Construction. The emissions produced from the construction of
nonresidential buildings are calculated using the value of construction put in place. The national value of
construction put in place was obtained from the Bureau of the Census,39 and was allocated to counties
using construction employment data for SIC 154.40 A conversion factor of 1.6 acres/106 dollars ($) was
applied to the construction valuation data. This conversion factor was developed by adjusting the 1992
value of 2 acres/$106 to 1999 constant dollars using the Price and Cost Indices for Construction.
4.4.9.6.3.3 Road Construction. To estimate the acres disturbed by road construction, we first
obtained Federal Highway Administration (FHWA) State expenditure data for capital outlay according to
the following sk classifications:41
• Interstate, urban
• Interstate, rural;
• Other arterial, urban;
• Other arterial, rural;
• Collectors, urban; and
• Collectors, rural
We obtained data from the North Carolina Department of Transportation (NCDOT) on the $/mile
spent on various road construction projects.42 For interstate expenditures, we used an average of
$4 million/mile corresponding to freeways and interstate projects listed for: 1) new location; 2) widen
existing 2-lane shoulder section; and 3) widen existing 4-lane w/ median. For expenditures on other
arterial and collectors, we used an average of $1.9 million/mile corresponding to all other projects
(excluding freeways and interstate projects) listed for: 1) new location; 2) widen existing 2-lane shoulder
section; and 3) widen existing 4-lane w/ median.
Once the new miles of road constructed were estimated using the above $/mile conversions, then the
miles were converted to acres for each of the 6 road types using the following estimates of acres
disturbed per mie:
• Interstate, urban and rural; Other arterial, urban - 15.2 acres/mile
• Other arterial, rural - 12.7 acres/mile
• Collectors, urban - 9.8 acres/mile
• Collectors, rural - 7.9 acres/mile
State-level estimates of acres disturbed were then distributed to counties according to the housing
starts per county (similar to residential construction). Once the number of acres disturbed per county was
4-147
-------
estimated for each construction type, these values were added together to obtain a county-level estimate
of total acres disturbed by land clearing.
The fuel loading at any given location will vary depending on the predominant vegetation in the area
being cleared. Ideally, one would account for where within the county the land clearing is actually
occurring, and what type of vegetation is being cleared. In the absence of these data, we used the
BELD3 data base in BEIS to determine the number of acres of hardwoods, softwoods, and grasses in
each county. Average loading factors43 are weighted according to the percent contribution of each type
of vegetation class to the total land area for each county. The loading factors for slash hardwood and
slash softwood were further adjusted by a factor of 1.5 to account for the mass of tree that is below the
soil surface that would also be subject to burning once the land is cleared. Average loading factors are as
follows:
Fuel loading
Fuel type (tons/acre)1
Hardwood 99
Softwood 572
Grass 4.5
'Original values for hardwood and softwood slash were adjusted by a factor of 1.5
to account for the mass of tree that is below the soil surface.
2This value represents the average of a bading factor value reported for long-
needle pine slash (21 tons/acre) and mixed conifer slash (54 tons/acre).
4.4.10 How Were Nonutility Point and Area Source Emissions Prepared for the 1997 through
1999 NET?
Emissions for 1997 through 1999 for the Other Combustion categories were grown from the 1996
NET inventory. Section 4.3.9.1 for Industrial nonutility point and area sources explains how the growth
factors and energy intensity factors were prepared and applied to estimate emissions for Versions 2, 3,
and 4 1997 through 1999 of the NET. No control factors were applied to the 1996 emissions when
preparing the 1997 through 1999 emissions for Other Combustion sources. As previously discussed,
section 4.4.9 explains the methodologies applied to estimate 1996 through 1999 emissions for forest
fires/wildfires, prescribed/slash and managed burning, residential wood combustion, and structure fires;
and 1999 emissions for open burning.
4.4.11 References
1. Table SA-5 — Total Personal Income by Major Sources 1969-1990. Data files. Bureau of
Economic Analysis, U.S. Department of Commerce, Washington. DC. 1991.
2. State Energy Data Report — Consumption Estimates 1960-1989, DOE/EIA-0214(89), U.S.
Department of Energy, Energy Information Administration, Washington, DC. May 1991.
3. Survey of Current Business. Bureau of Economic Analysis, U.S. Department of Commerce,
Washington, DC. 1986, 1987, 1988, 1989, 1990, 1991.
4-148
-------
4. Area Source Documentation for the 1985 National Acid Precipitation Assessment Program
Inventory, EPA-600/8-88-106, U.S. Environmental Protection Agency, Air and Energy Engineering
Research Laboratory, Research Triangle Park, NC. December 1988.
5. Procedures Document for Development of National Air Pollutant Emissions Trends Report, U.S.
Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle
Park, NC, December 1985.
6. AEROS Manual Series Volume II: AEROS User's Manual, U.S. Environmental Protection Agency,
Office of Air Quality Planning and Standards, Research Triangle Park, NC, July 1984.
7. Compilation of Air Pollutant Emission Factors - Volume I: Stationary Point and Area Sources,
AP-42 (GPO 055-000-00251-7), Fourth Edition, U.S. Environmental Protection Agency, Research
Triangle Park, NC. 1985.
8. 1985 National Emissions Data System Point Source Data, U.S. Environmental Protection Agency,
Office of Air Quality Planning and Standards, Research Triangle Park, NC, 1987.
9. 1968 National Survey or Community Solid Waste Practices, Interim Report, U.S. Department of
Health, Education and Welfare, Public Health Services, Cincinnati, OH, 1968.
10. 1968 National Survey of Community Solid Waste Practices, Preliminary Data Analysis, U.S.
Department of Health, Education and Welfare, Public Health Services, Cincinnati, OH, 1968.
11. Enhanced Paniculate Matter Controlled Emissions Calculator, Draft User's Manual, Emission
Factor and Inventory Group, Emissions Monitoring and Analysis Division, Office of Air Quality
Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC.
Prepared by E.H. Pechan & Associates, Inc., Durham, NC under EPA Contract No. 68-D7-0067,
Work Assignment No. 3-09, November 1999.
12. Barnard, W.R., and P. Carlson, PM-10 Emission Calculation, Tables 1 and 4, E.H. Pechan &
Associates, Inc. Contract No. 68-DO-1020, U.S. Environmental Protection Agency, Emission
Factor and Methodologies Section. June 1992.
13. Gill, W., Texas Air Control Board personal communication with D. Solomon. April 23, 1992.
14. "Fuel Oil and Kerosene Sales 1990," U.S. Department of Energy, Energy Information
Administration, Washington, DC, October 1991.
15. Regional Interim Emission Inventories (1987-1991), Volume I: Development Methodologies, EPA-
454/R-23-021a, U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards, Research Triangle Park, NC. May 1993.
16. E.H. Pechan & Associates, Inc., National Particulates Inventory: Phase IIEmission Estimates,
Draft Report. June 1995.
4-149
-------
17. Seitz, John, U.S. Environmental Protection Agency, Research Triangle Park, NC, Memorandum to
State Air Directors. May 5, 1995.
18. An Emission Inventory for Assessing Regional Haze on the Colorado Plateau, Grand Canyon
Visibility Transport Commission, Denver, CO. January 1995.
19. Volatile Organic Compound (VOC)/Particulate Matter (PM) Speciation Data System (SPECIATE)
User's Manual, Version 1.5, Final Report, Radian Corporation, EPA Contract No. 68-DO-0125,
Work Assignment No. 60, Office of Air Quality Planning and Standards, U.S. Environmental
Protection Agency, Research Triangle Park, NC. February 1993.
20. Internet E-mail from J. Nuovo to J. Better of the Department of Health and Environmental Control
(DHEC), Columbia, South Carolina, entitled Total Suspended Paniculate (TSP)/PM-10 Ratio.
Copy to P. Carlson, E.H. Pechan & Associates, Inc., Durham, NC. April 10, 1997.
21. Annual Wildland Fire Report. U.S. Department of the Interior. Internal Publication. 1994.
22. Wildfires by State. U.S. Department of the Interior. 1995.
23. Report to the U.S. Forest Service, Fiscal Year 1992. ISBN 0-16-041707-4. Forest Service, U.S.
Department of Agriculture. 1993.
24. National Forest Fire Report. Annual. Forest Service, U.S. Department of Agriculture. 1993-1995.
25. Compilation of Air Pollutant Emission Factors, AP-42, U.S. Environmental Protection Agency, 4th
Edition. July 1993.
26. An Inventory of Particulate Matter and Air Toxic Emissions from Prescribed Fires in the United
States for 1989. Forest Service, U.S. Department of Agriculture, Seattle, WA. 1989.
27. Phillips, Breda M. County Wood Consumption Estimation Model, U.S. Environmental Protection
Agency, Research Triangle Park, NC, March 1995.
28. Local Climatology Data, National Climatological Center, U.S. Environmental Protection Agency,
Research Triangle Park, NC, Monthly, 1985-1996.
29. Estimates of U.S. Biofuels Consumption. DOE/EIA-0548. Energy Information Administration, U.S.
Department of Energy, Washington, DC. Annual.
30 E.H. Pechan & Associates, Inc. 2010 Clean Air Act Baseline Emission Projections for the
Integrated Ozone, P articulate Matter, and Regional Haze Cost Analysis. Prepared for U.S.
Environmental Protection Agency, Research Triangle Park, NC. May 1997.
31. Economic Growth Analysis System: User's Guide, Version 2.0. EPA-600/R-94-139b. Joint
Emissions Inventory Oversight Group, U.S. Environmental Protection Agency, Research Triangle
Park,NC. August 1994.
4-150
-------
32. Structural Fires Statistics 1985, National Fire Protection Association, Boston, MA, 1986.
33. "Emission Inventory Procedural Manual, Volume III: Methods for Assessing Area Source
Emissions," California EPA: Air Resources Board. 1994.
34. Characterization of Municipal Solid Waste in the United States: 1998 Update. Prepared by
Franklin Associates Ltd. for the U.S. Environmental Protection Agency. July 1999.
35. Emission Characteristics of Burn Barrels. Prepared by Two Rivers Regional Council of Public
Officials and Patrick Engineering, Inc. for the U.S. Environmental Protection Agency, Region V.
June 1994.
36. Estimating Particulate Matter Emissions from Construction Operations. Prepared by Midwest
Research Institute for the U.S. Environmental Protection Agency, Office of Air Quality Planning
Standards, Research Triangle Park, NC. September 1999.
37. Building Permits Survey, 1999. U.S. Department of Commerce, Bureau of the Census,
Manufacturing and Construction Division, Residential Construction Branch 1999
38. Housing Starts Report, 1999. U.S. Department of Commerce, Bureau of the Census, Construction
Statistics. 1999
39. Value of Construction Put in Place, 1999. U.S. Department of Commerce, Bureau of the Census,
Construction Statistics. 1999.
40. Annual Average Employment for SIC 154, Data Series ES202. U.S. Department of Labor, Bureau
of Labor Statistics. 1998.
41. Highway Statistics 1998, Section IV - Finance, Table SF-12A "State Highway Agency Capital
Outlay-1998." Federal Highway Administration. 1998.
42. Facsimile from D. Lane, North Carolina Department of Transportation, to Roy Huntley, U.S.
Environmental Protection Agency, Office of Air Quality Planning Standards, Emission Factor and
Inventory Group. September 2000.
43. Ward, D.E., C.C Hardy, D.V. Sandberg, and T.E. Reinhardt. Mitigation of prescribed Fire
Atmospheric Pollution Through Increased Utilization of Hardwoods, Piled Residues, and Long-
Needled Conifers. Final Report. USDA Forest Service, Pacific Northwest Research Station, Fire
and Air Resource Management Project. 1989.
44. Evaluation of Emissions from the Open Burning of Household Waste in Barrels. Prepared by Paul
Lemieux of the U.S. Environmental Protection Agency, Office of Research and Development.
November 1997.
4-151
-------
Table 4.4-1. Bureau of Economic Analysis's SA-5 National Changes in
Earnings by Industry
Percent Growth from:
Industry
Wholesale trade
Retail trade
Banking and credit agencies
Insurance
Real estate
Holding companies and
investment services
Hotels and other lodging places
Personal services
Private households
Business and miscellaneous
repair services
Auto repair, services, and garages
Amusement and recreation
services and motion pictures
Health services
Legal services
Educational services
Social services and membership
organizations
Miscellaneous professional
services
Federal, civilian
Federal, military
State and local government
SIC
50, 51
52 to 59
60,61
63, 64
65,66
67
70
72
88
76
75
78,79
80
81
82
83
84
91
97
92 to 96
1985 to 1987
5.01
5.19
12.44
14.09
92.14
39.05
12.65
7.17
-5.68
17.05
6.65
17.93
15.15
20.14
9.35
17.39
11.28
-0.54
1.96
7.88
1987 to 1988
5.87
4.39
2.45
4.20
-6.98
-34.86
5.59
2.35
2.41
-17.34
2.46
16.43
7.08
9.92
7.17
8.45
5.04
3.79
-1.07
3.63
1988 to 1989
2.44
0.65
-0.33
1.52
-7.87
-12.18
1.71
7.44
0.83
5.79
3.00
4.06
5.11
4.09
3.88
7.95
7.08
1.21
-1.58
3.19
1989 to 1990
-1.02
-0.94
-0.49
2.71
-0.48
16.91
2.29
5.41
-3.69
4.34
3.93
7.59
6.28
4.80
2.60
7.37
4.12
1.96
-3.19
3.04
4-152
-------
Table 4.4-2. Area Source Growth Indicators
NAPAP
sec
Category Description
Data
Source
Growth Indicator
1 Residential Fuel-Anthracite Coal SEDS
2 Residential Fuel - Bituminous Coal SEDS
3 Residential Fuel - Distillate Oil SEDS
4 Residential Fuel - Residual Oil
5 Residential Fuel - Natural Gas SEDS
6 Residential Fuel-Wood BEA
7 Commercial/Institutional Fuel-Anthracite SEDS
Coal
8 Commercial/Institutional Fuel - Bituminous SEDS
Coal
9 Commercial/Institutional - Distillate Oil SEDS
10 Commercial/Institutional - Residual Oil SEDS
11 Commercial/Institutional - Natural Gas SEDS
12 Commercial/Institutional-Wood BEA
60 Forest Wild Fires
61 Managed Burning - Prescribed
62 Agricultural Field Burning BEA
64 Structural Fires
99 Minor Point Sources BEA
Res - Anthracite
Res - Bituminous
Res - Distillate oil
Zero growth
Res - Natural gas
Population
Comm -Anthracite
Comm - Bituminous
Comm - Distillate oil
Comm - Residual oil
Comm - Natural gas
Services
Zero growth
Zero growth
Farm
Zero growth
Population
4-153
-------
Table 4.4-3. SEDS National Fuel Consumption
Category 1985 1986
Anthracite Coal (thousand short tons)
Commercial 524 494
Residential 786 740
Bituminous Coal (thousand short tons)
Commercial 4,205 4,182
Residential 2,264 2,252
Distillate Fuel (thousand barrels)
Commercial 107,233 102,246
Residential 171,339 173,736
1987
478
717
3,717
2,002
101,891
176,822
1988
430
646
3,935
2,119
98,479
182,475
1989
422
633
3,323
1,789
91,891
178,629
1990
410
615
3,470
1,869
95,385
184,501
Motor Gasoline (thousand barrels)
All Sectors 2,493,361 2,567,436 2,630,089 2,685,145 2,674,669 2,760,414
Natural Gas (million cubic feet)
Commercial 2,432 2,318 2,430 2,670 2,719 2,810
Residential 4,433 4,314 4,315 4,630 4,777 4,805
Residual Fuel (thousand barrels)
Commercial 30,956 39,480 41,667 42,256 35,406 27,776
4-154
-------
Table 4.4-4. AMS to NAPAP Source Category Correspondence
AMS
NAPAP
SCC
Category
SCC Category
Stationary
2103001000
2103002000
2103004000
2103005000
2103006000
2103008000
2104001000
2104002000
2104004000
2104005000
2104006000
2104008000
Source Fuel Combustion
OmiTHd£Mr^itilicnal-MrraciteQd(rot£l:^IBder
Types)
GcrriTTeraaMnstitutiiTial-am
(Total: Al Boier Types)
GbnTTBraal/lnstitutioral -Dstillate CH (Tdal: Bdlers& I.C
FJignes)
Types)
GbmTBrdal/lnstitutional -Natiral GisObta1: Bdlers& I.C
FJignes)
Cormraal'hstlutional-Woad(roa:/ajlBdler Types)
Readertial-/adh^teGbal (Total: /aiCarTtustor Types)
Ccrrtustor Types)
Readertial-Ostl late Q (Tea i/aJIContxistor Types)
Readential-ReadualOiCTdal: All Ccntustor Types)
Readertial-NalLial(fe(r(^:^ICOrTlxistor Types)
Residential -Weed (Tdat Wcodsta/esand [replaces)
7 Ccmredal^ nstituticnal Fuel -Mhacite Coal
8 Ccmrredal^ nstituticnal Fuel - Htumhous Ccal
9 GCrmH-ciayinstitutioral - Dstillde Q
10 CcmrEraal^nstituticnal- Resdual Ql
11 CcmrEraal^nstituticnal- Natual QSB
12 OrrrrEraal^nstituticnal-Wtod
1 Residential Fuel-MhaciteCcal
2 Ftesdertial Fid-Btuircus Goal
3 Residential Fuel-Distillate a
4 Residential Fuel-Residual a
5 Residential Fuel-Natual G=s
6 Residential Fuel-V\tod
IVf scell aneous Aea Sources
2801500000 AgicJtureRcducticn- Ocps -AgicJturd Reid BLrring
(Total)
2801520000 AgicJtureRcducticn- Ocps -OchBrdheaters(Tctal)
2810001000 QherCcntusticn-FaestVycriies (Total)
2810015000 Qha-CarlxBti on- Managed (3ash/Ftescribed) Buning
(Total)
2810030000 Qha-CorrlxBtion-amctuiBRiBS
62 Ajioltual FieldBuring
63 Frost Ccnlrd-Crchard Heaters
60 FoestVUdFres
61 Managed Bunng-Resciibed
64 Stiuctual Fres
4-155
-------
Table 4.4-5. Point Source Data Submitted by OTAG States
State
Alabama
Arkansas
Connecticut
Delaware
District of Columbia
Florida
Georgia - Atlanta
Urban Airshed (47
counties) dom ain
Georgia - Rest of
State
Illinois
Indiana
Kansas
Kentuc ky - Jeffers on
County
Kentucky - Rest of
State
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Missouri
Nebraska
New Hamps hire
New J ersey
New York
North Carolina
North Dakota
Ohio
Oklahoma
Pennsylvania -
Allegheny County
Pennsylvania -
Philadelphia County
Pennsylvania- Rest
of State
Rhode Island
South Carolina
Data Source/Format
AIRS/FS - Ad hoc retrievals
AIRS/FS - Ad hoc retrievals
State - E PS W orkfile
State - E PS W orkfile
AIRS/FS -Ad hoc retrievals
AIRS/FS -Ad hoc retrievals
State - State form at
AIRS/FS -Ad hoc retrievals
State - EPS Workfiles
AIRS/FS -Ad hoc retrievals
AIRS/FS -Ad hoc retrievals
Jefferson C ounty - E PS W orkfile
State -E PS W orkfile
State -State Format
State -E PS W orkfile
State - EPS W orkfile
State -E PS W orkfile
State -State Format
AIRS/FS - Ad hoc retrievals
AIRS/FS - Ad hoc retrievals
AIRS/FS - Ad hoc retrievals
State -E PS W orkfile
State -E PS W orkfile
State - EPS W orkfile
State -EPS Workfiles
AIRS/FS - Ad hoc retrievals
State - State Format
State - State Format
Allegheny County - County Format
Philadelphia County - County Format
State -E PS W orkfile
State - EPS W orkfile
AIRS/FS - Ad hoc retrievals
Temporal
Resolution
Annual
Annual
Daily
Daily
Annual
Annual
Daily
Annual
Daily
Annual
Annual
Daily
Daily
Annual
Daily
Daily
Daily
Annual
Annual
Annual
Annual
Daily
Daily
Daily
Daily
Annual
Annual
Annual
Daily
Daily
Daily
Daily
Annual
Year of Data
1994
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1993
1990
1990
1990
1990
1990
1990
1990
1994
1990
1990
1990
1990
1991
Adjustments to Data
Backcast to 1990 using BEA. Average Sum mer
Day estimated using m ethodology desc ribed above.
Average Summer Day es timated using d efault
temporal factors.
None
None
Average Summer Day estimated using methodology
described above.
Average Summer Day estimated using methodology
described above.
None
Average S umm er Day estimated using default
temporal factors.
None
AverageSummer Day estimated using methodology
described above.
AverageSummer Day estimated using methodology
described above.
None
None
AverageSummer Day estimated using methodobgy
described above.
None
None
None
AverageSummer Day estimated using methodobgy
described above.
AverageSummer Day estimated using methodobgy
described above.
Backcast to 1990 using BEA. AverageSummer
Day estimated using methodology described above.
AverageSummer Day estimated using methodobgy
described above.
None
None
None
None
AverageSummer Day estimated using methodobgy
described above.
AverageSummer Day estimated using methodobgy
described above.
Backcast to 1990 using BEA. AverageSummer
Day estimated using methodology described above.
None
None
None
None
Average Summer Day es timated using d efault
South Dakota
AIRS/FS - Ad hoc retrievals
temporal factors.
Annual 1990 AverageSummer Day estimated using methodobgy
described above.
4-156
-------
Table 4.4-5 (continued)
State
Data Source/Format
Temporal
Resolution
Year of Data Adjustments to Data
Tennessee
Texas
Vermont
Virgin ia
West Virginia
Wisconsin
AIRS/FS - Ad hoc retrievals
State - State Format
State - E PS W orkfile
AIRS/FS -Ad hoc retrievals
AIRS/FS -Ad hoc retrievals
State - State Format
Annual 1990 Average S ummer Day estimated using default
temporal factors.
Daily 1992 Backcast to 1990 using BEA.
Daily 1990 None
Annual 1990 Average Summer Day estimated using methodology
described above.
Annual 1990 Average Summer Day estimated using methodology
described above.
Daily 1990 None
4-157
-------
Table 4.4-6. Area Source Data Submitted by OTAG States
State
Connecticut
Delaware
District of Columbia
Florida
Georg ia
Data Source/Format
State -E PS Workfile
State - EPS W orkfile
State -Hard copy
AIRS/AM S - Ad hoc retrievals
State - State form at
Temporal
Resolution
Daily
Daily
Daily
Daily
Daily
Geoaraohic Coveraae
Entire State
Entire State
Entire State
Jacksonville, Miami/
Ft. Lauderdale, Tampa
Atlanta Urb an Airsh ed
Adjustments to Data
None
None
None
Added N on-road emis sion
estimates
from Int. Inventory to J acksonville
(Duval Cou nty)
None
Illinois
Indiana
Kentucky
Louisiana
Maine
Maryland
Michigan
Missouri
New Hamps hire
New J ersey
New York
North Carolina
Ohio
Pennsylvania
Rhode Island
Tennessee
Texas
Vermont
Virginia
West Virginia
Wisconsin
State - State form at
State - State form at
State -State Format
State - State Format
State-EPS Workfile
State-EPS Workfile
State -State Format
AIRS/AM S- Ad hoc retrievals
State-EPS Workfile
State-EPS Workfile
State-EPS Workfile
State- EPS Workfiles
State -Hard copy
State-EPS Workfile
State-EPS Workfile
State - State form at
State - State Format
State-EPS Workfile
State-EPS Workfile
AIRS/AM S - Ad hoc retrievals
State - State Format
(47 Counties)
Daily Entire State
Daily Entire State
Daily Kentucky Ozone Nonattainment
Areas
Daily Baton Rouge Nonattainment
Area(20 Parishes)
Daily Entire State
Daily Entire State
Daily 49 Southern Mich igan Counties
Daily St. Louis area (25 counties)
Daily Entire State
Daily Entire State
Daily Entire State
Annual Entire State
Daily Canton, Cleveland Columbus,
Dayton, Toledo, and Youngstown
Daily Entire State
Daily Entire State
Daily 42 Counties in Middle
Tennessee
Annual Entire State
Daily Entire State
Daily Entire State
Daily Charleston, Huntington/Ashland,
and Parkers burg (5 counties
total)
Daily Entire State
None
Non-road emissions subm itted were
county totals. Non-road emissions
distributed to specific SCCs based
on Int. Inventory
None
None
None
None
None
Only area sou rce com bustion d ata
was provided. All other area source
data came from Int. Inventory
None
None
None
Average Su mmer D ay estimated
using default temporal factors.
Ass igned SCCs and converted from
kgs to tons. NOX and CO from Int.
Inventory added to Canton, Dayton,
and Tofedo counties.
Non-road emissions subm itted were
county totafe. Non-road emissions
distributed to specific SCCs based
on Int. Inventory
None
No non-road data submitted. Non-
road emissions added from Int.
Inventory
Average Su mmer D ay estimated
using default temporal factors.
None
None
None
None
4-158
-------
Table 4.4-7. Ad Hoc Report
Criteria
Regn
PLL4
PLL4
PLL4
PLL4
PLL4
PLL4
DES4
DUE4
YINV
GT 0
CE VOC
CE CO
CE SO2
CE NO2
CE PM-10
CE PT
GE 0
ME TY
ME 90
Plant Output
YINV
SITE
CNTY
CYCD
ZIPC
PNED
PNME
LAT1
LON1
SIC1
OPST
SIRS
YEAR OF INVENTORY
STATE FIPS CODE
COUNTY FIPS CODE
CITY CODE
ZIP CODE
NEDS POINT ID
PLANT NAME
LATITUDE PLANT
LONGITUDEPLANT
STANDARD INDUSTRIAL
CODE
OPERATING STATUS
STATE REGISTRATION
NUMBER
Point Output
STTE
CNTY
PNED
PNUM
CAPC
CAPU
PAT1
PAT2
PATS
PAT4
NOHD
NODW
NOHY
STATE FIPS CODE
COUNTY FIPS CODE
NEDS POINT ID
POINT NUMBER
DESIGN CAPACITY
DESIGN CAPACITY
UNITS
WINTER
THROUGHPUT
SPRING
THROUGHPUT
SUMMER
THROUGHPUT
FALLTHROUGHPUT
NUMBER HOURS/DAY
NUMBER DAYS/WEEK
NUMBER
HOURS/YEAR
Stack Output
STTE
CNTY
PNED
STNB
LAT2
LON2
STHT
STDM
STET
STEV
STFR
PLHT
STATE FIPS CODE
COUNTY FIPS CODE
NEDS POINT ID
STACK NUMBER
LATITUDE STACK
LONGITUDESTACK
STACK HEIGHT
STACK DIAMETER
STACK EXIT
TEMPERATURE
STACK EXIT VELOCITY
STACK FLOW RATE
PLUME HEIGHT
Segment Output
General
STTE
CNTY
PNED
STNB
PNUM
SEGN
SCC8
HEAT
FPRT
SULF
ASHC
PODP
STATE FIPS CODE
COUNTY FIPS CODE
NEDS POINT ID
STACK NUMBER
POINT NUMBER
SEGMENT NUMBER
sec
HEAT CONTENT
ANNUAL FUEL
THROUGHPUT
SULFUR CONTENT
ASH CONTENT
PEAK OZONE SEASON
DAILY PROCESS RATE
Segment Output
Pollutant
STTE
CNTY
PNED
STNB
PNUM
SEGN
SCC8
PLL4
D034
DU04
DES4
DUE4
CLEE
CLT1
CTL2
REP4
DME4
Emfa
STATE FIPS CODE
COUNTY FIPS CODE
NEDS POINT ID
STACK NUMBER
POINT NUMBER
SEGMENT NUMBER
sec
POLLUTANT CODE
OSD EMISSIONS
OSD EMISSION UNITS
DEFAULT ESTIMATED
EMISSIONS
DEFAULT ESTIMATED
EMISSIONSUNITS
CONTROL
EFFICIENCY
PRIMARY CONTROL
DEVICECODE
SECONDARY
CONTROL DEVICE
CODE
RULE
EFFECTIVENESS
METHOD CODE
Emission factor
-------
Table 4.4-8. SEDS National Fuel Consumption, 1990-1996 (trillion Btu)
Fuel Type End-User
Code
1990
1991
1992
1993
1994
1995
1996
Anthracite Coal
Commercial
Residential
Bituminous Coal
Commercial
Residential
Distillate Fuel
Commercial
Residential
Kerosene
Commercial
Residential
Liquid Petroleum Gas
Commercial
Residential
Natural Gas
Commercial
Residential
Residual Fuel
Commercial
Population
ACCCB
ACRCB
BCCCB
BCRCB
DFCCB
DFRCB
KSCCB
KSRCB
LGCCB
LGRCB
NGCCB
NGRCB
RFCCB
TPOPP
12
19
80
43
487
837
12
64
64
365
2,698
4,519
233
248,709
11
17
72
39
482
832
12
72
69
389
2,808
4,685
213
252,131
11
17
75
40
464
865
11
65
67
382
2,884
4,821
191
255,025
11
16
72
40
464
913
14
76
70
399
2,996
5,097
175
257,785
11
16
70
40
450
887
13
67
70
398
3,035
5,132
170
259,693
11
16
69
39
435
862
12
59
70
397
3,074
5,166
168
261,602
11
16
68
39
422
836
11
51
70
397
3,114
5,201
167
263,510
4-160
-------
Table 4.4-9. BEASA-5 National Earnings by Industry, 1990-1996 (million $)
Industry
LNUM
SIC
1990 1991 1992 1993 1994 1995 1996
Tola I population as of July 1 (thousands)
Tola I population as of July 1 (thousands)
Tola I population as of July 1 (thousands)
Total population as of July 1 (thousands)
Total population as of July 1 (thousands)
Total population as of July 1 (thousands)
Total population as of July 1 (thousands)
Total population as of July 1 (thousands)
Total population as of July 1 (thousands)
Total population as of July 1 (thousands)
Total population as of July 1 (thousands)
Total population as of July 1 (thousands)
Farm
Farm
Farm
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Metal mining
Coal mining
Oil and gas extraction
Nonm etallic min erals, exce pt fuels
Construction
Construction
Construction
Construction
Manufacturing
Durable goods
Lumber and wood products
Furniture and fixtures
Stone, clay, and glass products
Primary metal industries
Fabricated m etal products
Machinery, except electrical
Electric and electronic equipment
Motor vehicles and equipment
Transportation equipment, excluding m otor vehicles
Instruments and related products
Miscellaneous manufacturing industries
Nondurablegoods
Food and kindred products
Tobacco manufactures
Textile mill products
Apparel and other textile products
Paper and allied products
Printing and publehing
Chem icals and allied prod ucts
Petroleum and coal products
Rubb er and mis cellaneous p lastic produ cts
Leather and leather products
020
030
040
041
045
046
047
050
060
070
071
072
081
082
090
100
110
120
121
122
123
200
210
220
230
240
300
310
320
330
400
410
413
417
420
423
426
429
432
435
438
441
444
450
453
456
459
462
465
468
471
474
477
480
999
999
999
999
999
999
999
999
999
999
999
999
1, 2
1, 2
1, 2
7-9
7-9
7-9
7-9
7-9
7-9
7-9
10
11,12
13
14
15-17
15-17
15-17
15-17
998
996
24
25
32
33
34
35
36
371
37
38
39
997
20
21
22
23
26
27
28
29
30
31
0
1
3,634
238
3,395
971
735
2,932
321
381
34
347
48
3,586
3,001
24
20
4
1
2
1
36
2
8
20
4
218
54
29
135
710
437
22
13
20
33
51
86
63
41
54
43
11
273
51
3
16
20
28
54
61
9
27
3
0
1
3,593
242
3,350
947
791
2,891
331
370
28
342
41
3,552
2,957
24
20
3
1
2
1
37
3
8
22
4
197
47
28
123
690
418
21
12
18
30
48
83
62
38
52
42
11
272
51
3
16
20
27
54
63
9
26
3
0
1
3,732
248
3,483
907
858
2,975
351
405
34
372
46
3,686
3,079
24
21
3
1
2
1
36
3
8
21
4
195
46
28
121
705
423
22
13
19
31
49
83
62
42
50
42
11
281
52
3
17
20
28
55
66
10
28
2
0
1
3,785
253
3,531
914
888
3,003
371
410
32
378
45
3,740
3,126
24
22
3
0
2
1
34
2
6
21
4
199
47
27
125
705
424
22
13
19
30
49
84
63
46
45
40
12
282
52
2
17
19
28
56
65
9
29
3
0
1
3,891
265
3,626
934
912
3,082
383
426
29
396
42
3,849
3,228
26
23
3
1
2
1
35
2
6
21
4
216
51
29
136
725
440
24
14
20
32
51
86
65
53
43
40
12
285
53
2
17
19
29
57
65
10
30
3
0
1
4,011
273
3,737
980
951
3,182
394
436
18
418
31
3,980
3,353
27
24
3
1
2
1
35
2
6
21
4
219
51
29
138
740
452
25
14
20
33
53
90
68
56
42
40
12
288
53
3
17
19
29
58
67
9
31
2
0
1
4,086
280
3,805
981
994
3,231
408
447
16
432
29
4,058
3,423
27
25
3
1
1
1
35
3
6
21
4
219
50
29
139
747
456
25
14
20
32
53
91
69
60
39
39
12
291
54
3
17
19
29
59
68
9
31
2
4-161
-------
Table 4.4-9 (continued)
Industry
LNUM
SIC
1990 1991 1992 1993 1994 1995 1996
Leather and leather products
Railroad transportation
Trucking and warehousing
Water transportation
Water transportation
Local and interurban passenger transit
Transportation by air
Pipelines, except natural gas
Transportation services
Comm unication
Electric, gas, and sanitary services
Wholesale trade
Retail trade
Retail trade
Retail trade
Retail trade
Retail trade
Retail trade
Retail trade
Retail trade
Retail trade
Retail trade
Banking and credit agencies
Banking and credit agencies
Banking and credit agencies
Insurance
Insurance
Real estate
Holding companies and investment services
Services
Hotels and other lodging places
Personal services
Private households
Business and miscellaneous repair services
Auto repair, services, and garages
Auto repair, services, and garages
Amusement and recreation services
Amusement and recreation services
Health services
Legal services
Educational services
Social services and membership organizations
Social services and membership organizations
Social services and membership organizations
Social services and membership organizations
Miscellaneous professional services
Governm ent and governm ent enterprises
Federal, civilian
Federal, military
State and local
State and local
State and local
500
510
520
530
540
541
542
543
544
560
570
610
620
621
622
623
624
625
626
627
628
700
710
730
731
732
733
734
736
800
805
810
815
820
825
830
835
840
845
850
855
860
865
870
875
880
900
910
920
930
931
932
31
40
42
44
44
41
45
46
47
48
49
50, 51
52-59
52-59
52-59
52-59
52-59
52-59
52-59
52-59
52-59
52-59
60, 61
60, 61
60, 61
63, 64
63, 64
65, 66
62, 67
995
70
72
88
76
75
75
78, 79
78, 79
80
81
82
83, 86
83, 86
83, 86
83, 86
84, 87, 89
995
43, 91, 97
992
92-96
92-96
92-96
243
12
59
7
48
8
30
1
12
63
49
236
342
18
40
56
55
18
22
76
57
246
82
163
38
56
34
28
8
946
31
33
10
170
29
15
29
16
290
80
39
29
1
35
125
14
585
118
50
417
125
292
245
12
58
7
49
8
30
1
13
63
52
231
335
18
38
56
54
18
20
78
54
247
81
166
40
59
33
25
10
951
31
32
9
162
28
13
30
16
304
80
41
31
1
36
121
14
594
120
50
425
128
297
251
13
60
7
50
9
31
1
14
64
53
238
342
18
39
57
54
18
19
80
57
280
86
194
50
61
33
36
14
1,008
32
33
10
175
28
13
34
16
325
85
42
34
1
36
127
15
607
123
51
433
128
305
260
12
62
6
51
9
31
1
14
67
56
235
347
19
39
56
56
18
19
82
57
290
89
201
53
62
34
43
10
1,032
33
36
10
180
30
14
33
17
330
84
44
35
1
38
130
15
613
124
48
441
130
311
269
12
66
6
50
9
31
1
15
71
56
242
359
20
40
57
60
18
21
85
59
291
89
202
51
63
36
44
9
1,066
33
36
10
191
31
14
35
18
341
84
45
38
2
40
132
17
621
125
45
451
134
317
277
12
69
6
52
10
31
1
16
75
56
255
372
21
41
58
62
18
22
88
62
302
90
212
55
63
37
47
10
1,128
35
36
11
213
33
15
37
20
355
85
46
40
2
41
141
18
626
123
44
459
136
323
283
12
71
6
53
10
31
1
17
78
57
258
378
21
41
58
64
18
22
90
63
313
91
221
58
65
38
51
10
1,164
36
37
11
221
34
15
39
20
368
86
48
42
2
42
145
19
635
124
43
468
138
330
4-162
-------
Table 4.4-10. Area Source Listing by SCC and Growth Basis
SCC
SCC DESC
FILE CODE
2103001000 Stationary Source Fuel Combustion Commercial/Institutional Anth racite Coal Total: All Boiler SEDS ACCCB
Types
2103002000 Stationary Source Fuel Combustion Commercial/Institutional Bituminous/Subbituminous Coal SEDS BCCCB
Total: All Boiler Types
2103004000 Stationary Source Fuel Combustion Commercial/Institutional Distillate Oil Total: Boilers and 1C SEDS DFCCB
Engines
2103005000 Stationary Source Fuel Com bustion Commercial/Institutional ResidualOil Total: All Boiler Types SEDS RFCCB
2103006000 Stationary Source Fuel Combustion Commercial/Institutional NaturalGas Total: Boilers and 1C SEDS NGCCB
Engines
2103007000 Stationary Source Fuel Combustion Commercial/Institutional Liquified Petroleum Gas (LPG) SEDS LGCCB
Total: All Combus tor Types
2103008000 Stationary Source Fuel Combustion Commercial/Institutional Wood Total: All Boiler Types BEA 400
2103011000 Stationary Source Fuel Combustion Commercial/Institutional Kerosene Total: All Combus tor Types SEDS KSCCB
2199004000 Stationary Source Fuel Combustion Total Area Source Fuel Combustion Distillate Oil Total: Boilers SEDS DFTCB
and 1C E ngines
2199005000 Stationary Source Fuel Combustion Total Area Source F uel Com bustion Res idual Oil Total: All SEDS RFTCB
Boiler Types
2199006000 Stationary Source Fuel Combustion Total Area Source Fuel Combustion Natural Gas Total: Boilers SEDS NGTCB
and 1C E ngines
2199007000 Stationary Source Fuel Com bustion Total Area Source Fuel Com bustion Liquified Petroleum Gas SEDS LGTCB
(LPG) Total: All Boiler Types
2199011000 Stationary Source Fuel Com bustion Total Area Source Fuel Com bustion Kerosene Total: All Heater SEDS KSTCB
Types
2810001000 Miscellaneous Area Sources O ther Com bustion Forest W ildfires Total NG
2810003000 Miscellaneous Area Sources O ther Com bustion C igarette Smoke Total SEDS TPOPP
2810005000 Miscellaneous Area Sources Other Combustion Managed Burning, Slash - (Use A28-1 0-01 5-000) BEA 100
Total
2810010000 Miscellaneous Area Sources Other Combustion [unknown] BEA 100
2810025000 Miscellaneous Area Sources O ther Com bustion C harcoal Grilling Total SEDS TPOPP
2810035000 Miscellaneous Area Sources O ther Com bustion F irefighting T raining Total SEDS TPOPP
2810050000 Miscellaneous Area Sources O ther Com bustion M otor Vehicle Fires Total SEDS TPOPP
2810060000 Miscellaneous Area Sources O ther Com bustion _ SEDS TPOPP
NOT E(S): * BEA Code is eg ual to LNU M on previous table. _
4-163
-------
Table 4.4-11. Emission Estimates Available from AIRS/FS by State, Year, and Pollutant
State
1990
C N S P T V
1991
C N S P T V
1992
C N S P T V
1993
C N S P T V
1994
C N S P T V
1995
C N S P T V
Alabama
Alaska
Arizona
California
Colorado
Connecticut
Hawaii
Illinois
Louisiana
Michigan
Minnesota
Montana
Nebraska
Nevada
New Hampshire
New Mexico
North Dakota
Oregon
Pennsylvania
South Carolina
South Dakota
Texas
Utah
Vermont
Virginia
Washington
Wisconsin
Wyoming
Notes:
C = CO N = NO2 S = SO2 P = PM-10 T = TSP V = VOC
Pennsylvania only includes AlleghenyCounty (State 42, County 003); New Mexico ony includes Albuquerque(State35, County001); Washington ony includes Puget Sound
(State 53, County 033, 053, or 061); Nebraska includes all except Omaha City (State 31, County 055); the CO emissions in NET were maintained for South Dakota (State 46).
-------
Table 4.4-12. NOX and VOC Major Stationary Source Definition
Ozone Nonattainment Status
Major Stationary Source (tons)
Marginal/Moderate
Serious
Severe
Extreme
Ozone Transport Region
100
50
25
10
50
Pod
ID
Table 4.4-13. Summary of Revised NOX Control Efficiencies
Pod Name
Estimated
Efficiency
Control
58 Commercial/Institutional - Coal
59 Commercial/Institutional - Oil
60 Commercial/Institutional - Gas
50
50
50
LNB
LNB
LNB
Controls: LNB - Low NOV Burner
4-165
-------
Table 4.4-14. Methods for Developing Annual Emission Estimates for
Other Combustion Sources for the Years 1989-1999
For the category
For the years For the pollu tant(s)
EPA estimated emissions by
Forest Fires/Wildfires
1989-1998 VOC, NOX, CO, SO2, PIVL
1) Obtaining acres burned data at the State level for DOI, USFS, and
State/private lands; 2) Applying AP-42 emission factors and fuel loading
factors; 3) Distributing emissions to the county level. County distribution for
non-GCVTC States and GCVTC States performed differently. Non-GCVTC
States were distributed to the county level using 1985 NAPAP distribution.
Emissions were distributed to counties using 1990 county-to-State level
emissions in GCVTC inventory.
1990-1998 PM,
Multiplying PM10 emissions by State-level ratios of PM25/PM10 developed from
1990 inventory for non-GCVTC States. For GCVTC States, use State-level
ratios developed from 1990 emission estimates they supplied. Emissions
data supplied by State/local agencies replaced EPA default emission
estimates.
Prescribed/Slash and
Managed Burning
1999
1989
1989
1990
1991-1996
PM10, PM25
VOC, PM10
NOX, CO, SO2
VOC, NOX, CO,
SO2, PM10, PM25
VOC, NOX, CO,
SO2, PM10, PM25
Holding 1998 emissions constant.
Obtaining county level emissions from the 1989 USDA Forest Service
inventory of particulate matter and air toxics from prescribed burning.
Assuming the ratio between VOC emissions to either NOX, CO, and SO2
emissions in the Forest Service inventory was equal to the corresponding
emission ratios in the 1985 NAPAP prescribed burning inventory.
Holding 1989 emissions constant, but incorporating State-supplied data into
emission estimates.
Growing 1990 emissions to each year using growth factors developed from
national acres burned data, and distributing the State-level emissions to the
county-level using the existing distribution for prescribed burning in the 1990
NET. Emissions data supplied by State/local agencies replaced EPA default
emission estimates.
1997-1999 VOC, NOX, CO,
SO2, PM10, PM25
Growing 1996 emissions to each year using growth factors developed from
national acres burned data, and distributing the State-level emissions to the
county-level using the existing distribution for prescribed burning in the 1990
NET.
-------
Table 4.4-14 (continued)
For the category
For the years For the pollu tant(s)
EPA estimated emissions by
Residential Wood
Combustion
1989 VOC, NOX, CO, SO2, PIVL
Updating county-level wood consumption estimates using national total for
residential wood consumption, heating degree day data and AP-42 emission
factor.
1990
VOC, NOX, CO, S02, PM10,
PM,
'2.5
Running the County Wood Consumption Estimation Model, which was
adjusted with heating degree day information and normalized with annual
wood consumption estimates.
1991-1999 VOC, NOX, CO, S02, PM10,
PM2.5
Updating activity estimates using national total for residential wood
consumption and heating degree days. AP-42 emission factors and a control
efficiency are applied to emissions for counties classified as nonattainment
areas. Emissions data supplied by State/local agencies replaced EPA default
emission estimates.
Structure Fires
1989
VOC, NOX, CO, S02, PM,
Backcasting 1990 Interim Inventory Emissions with historical BEAdata.
1990
(Interim
Inventory)
VOC, NOX, CO, S02, PM10,
PM2.5
Projecting 1985 NAPAP emissions.
1990
(NET)
VOC, NOX, CO, PM10, PM25 Supplementing 1990 Interim Inventory data with State-supplied data.
1991-1996 VOC, NOX, CO, PM10, PM2
Using updated activity data of number of fires per State, and the California Air
Resources Board's revised loading factor of 1.15 tons/fire to develop
emissions for non-OTAG States. For OTAG States, emissions are grown from
1990 using EGAS growth factors. Emissions data supplied by State/local
agencies replaced EPA default emission estimates.
1997-1999 VOC, NOX, CO, PM10, PM25
Growing 1996 emissions to each year using EGAS regression equation that
relates population to number of structure fires.
-------
Table 4.4-14 (continued)
For the category
For the years For the pollu tant(s)
EPA estimated emissions by
Open Burning (Non-Ag,
Non-Wildland)
1989
VOC, CO, S02, PIVL
Backcasting 1990 Interim Inventory Emissions with historical BEAdata.
1990
(Interim
Inventory)
1990
(NET)
VOC,
VOC,
CO,
CO,
S02,
SO2,
PM10. PM2.5
PM10, PM25
Projecting
1985
Supplementing
NAPAP emissions.
1990
Interim
Inventory data with
State-supplied data.
1991-1995 VOC, CO, SO2, PIVL
Projecting 1990 NET emissions to the appropriate year using BEA or SEDS
data, and replacing projected data with State data where provided under
OTAG or GCVTC, or State directed EPA to use AIRS/FS data.
1996-1998 VOC, CO, SO2, PM10, PM2 5 Projecting 1995 NET emissions to the appropriate year using BEA or SEDS
data, and replacing projected data with data supplied by State/local agencies.
1999
VOC, CO, SO2, PM10, PM
25 Updating emissions for residential municipal solid waste and yard waste by
1) obtaining 1999 population and waste generation activity data; 2)
multiplying resulting activity by more current emission factors; and 3) making
further adjustments based on expected open burning practices.
Updating emissions for land clearing debris burning by 1) estimating the acres
of land cleared due to residential, commercial, and road construction based
on surrogate activity data, including residential units built, commercial
valuation, and State highway expenditures; 2) applying vegetation-specific
fuel loading factors (in tons/acre) to the acres cleared; and; 3) multiplying tons
of fuel by more current emission factors.
-------
Table 4.4-15. Comparison of Methodologies Used to Develop 1996 Base Year Emissions
for Other Combustion Area Source Categories for Versions 1 through 4 of the NET Inventory
For the Category
Forest Fires /Wildfires
Prescribed /Slash anc
Managed Burning
For the
Pollutant(s)
VOC, NOX,
CO, SO2,
PM10
PM25
VOC, NOX,
CO, SO2,
PM10, PM25
EPA estimated 1996 Base Year emissions for
Version 1 by
For all States 1) Obtaining acres burned
data at the State level for DOI, USFS, and
State/private lands; 2) Applying AP-42
emission factors and fuel loading factors;
and 3) D istributing em issions to the county
level. County distribution for non-GCVTC
States and GCVTC States performed
differently. State-level emissions for non-
GCVTC States were distributed to the
county level using 1985 NAPAP
distribution. State-level emissions for
GCVTC States were d istributed to counties
using ratio of county-to-State em issions for
1990 in GCVTC inventory.
Multiplying PM10 emissions byO.23.
Growing 1990 em issions to each year
using g rowth factors developed from
national acres burned data, and distributing
the S tale-level em issio ns to t he cou nty-
level using the existing distribution for
prescribed burning in the 1990 NET.
Emissions data s upplied by State/local
agencies replaced EPA default em ission
estimates.
Version 2 by
Usin g sam e method ology as us ed in
Version 1 .
Usin g sam e method ology as us ed in
Version 1 .
Usin g sam e method ology as us ed in
Version 1 .
Version 3 by
Usin g sam e method ology as us ed in
Version 1. Emissions datasupplied by
State/bcal agencies replaced EPA
defaultestimates.
Using Slate-level ratios for PM2 5
multiplied by county-level PM10
emissions togetcurrentyearPM25
emiss ion becaus e 0.23 was not an
accuratemultiplierto estimatePM25
emissions from PM10. Emissions data
supplied by State/local agencies
replaced EPA default estimates.
Calcu lating a State-level ratio of pu blic
to total lands using U.S. Forest
Service Data. Multiplying 1990 State-
level emissions by this ratio to estimate
prescribed burning emissions
attributabb topublb lands.
Holding prwateland acres constant
and projecting growth forpublic lands
based on national growth factor
developed from national statistics for
acres burned. Em issions data
supplied by State/local agencies
replaced EPA default estimates.
Version 4 by
Usin g sam e method ology as us ed in
Version 1. Emissions datasupplied by
State/local agencies replaced EPA
defaultestimates.
Usin g sam e method ology as us ed in
Version 3. Emissions datasupplied by
State/bcal agencies replaced EPA
defaultestimates.
Usin g sam e method ology as us ed in
Version 3. Emissions datasupplied by
State/local agencies replaced EPA
defaultestimates.
-------
Table 4.4-15 (continued)
For the Category
Residential Wood
Structure Fires
Open Burning
(Non-Ag.,
Non-Wildland)
For the
Pollutant(s)
VOC, NOX,
CO, SO2,
PM10, PM25
VOC, NOX,
CO, SO2,
PM10, PM25
VOC, NOX,
CO, SO2,
PM10, PM25
EPA estimated 1996 Base Year emissions for
Version 1 by
Using annual wood consum ption data for
1990 from EPA's County Wood
Consumption Estimation Model. Adjusting
1990 model results using 1996 heating
degree day inform ation from the N ational
Climatic Data Center, and normalizing it
with U.S . DO E national estim ate of
residential wood consumption. Applying
AP-42 emission factors and a control
efficiency. Applying a national control
efficiency of 10.8% forPM10 and PM25.
For all other pollutants, th e control
efficiency was zero.
Estimating 1996 BEA and SEDS data
using linear interpolation of 1988 to 1995
data and growing from 1995 N ET.
Using 1985 NAPAP inventory and SEDS
or BEA data. Replacing any projected data
with State data where provided under
OTAG or GCVTC, orState directed EPA
to use AIRS/FS data.
Version 2 by
Usin g sam e method ology as us ed in
Version 1 .
1) Revising methodology for 42 States
and the D istrict of C olumbia us ing data
from the National Fire Incident
Reporting System (NFIRS) to develop
a State-specific per capita factor. 2)
Using this factor to allocate activity to
the county level. 3) Using a national
estimate of structure fires from the
National Fire Protection Agency
(NFP A) for any S tale that did not
reportto NFIRS. 4) Applying
appropriate loading and emission
factors. 5) Growing 1990 data, that
was su pplied by the rem aining States
under OTAG, to the current year using
population as a s urrogate.
Usin g sam e method ology as us ed in
Version 1 .
Version 3 by
Usin g sam e method ology as us ed in
Version 1. Emissions datasupplied by
State/local agencies replaced EPA
default estimates.
Growing OTAG State emissions using
an EGAS regression equation using
1990 as the base year. For non-
OT AG S tales, applied loading factor of
1 .15 ton s/fire from the Californ ia Air
Resources Board (GARB). Emissions
data supplied by State/local agencies
replaced EPA default estimates.
Usin g sam e method ology as us ed in
Version 1. Emissions datasupplied by
State/local agencies replaced EPA
default estimates.
Version 4 by
Usin g sam e method ology as us ed in
Version 1. Emissions datasupplied by
State/bcal agencies replaced EPA
defaultestimates.
Usin g sam e method ology as us ed in
Version 3. Emissions datasupplied by
State/bcal agencies replaced EPA
defaultestimates.
Usin g sam e method ology as us ed in
Version 1. Emissions datasupplied by
State/bcal agencies replaced EPA
defaultestimates.
Notes:
Version 1 corresponds toDecember 1997 Trends report, Version 2 estimates correspond to December 1998 Trends report, Version 3 corresponds to March 2000 Trends report,and
Version 4 is for report yet to be published.
-------
Table 4.4-16. Other Combustion Area Source Categories: Summary of State-Submitted
Emissions for 1996 Included in Versions 3 and 4 of the NET Inventory
Source
Category/
SCC State
Geo graph ic
Coverage
Wildfires (2810001000)
CA Statewide
DE Statewide
KS Statewide
LA Statewide
MD Statewide
VA Statewide
AL 2 Counties
GA 13 Counties
TX 16 Counties
Temporal VOC
Annual/D aily x
Annual x
Annual x
Annual/D aily x
Annual/D aily x
Annual/D aily x
Annual/D aily x
Annual x
Annual/D aily x
NOV
x
x
X
X
X
X
X
X
X
1996 NET
CO SO3 PM-10 PM-25 NH3 Version
x
X
X
X
X
X
X
X
X
X X
XXX
XXX
XXX
XXX
XXX
XXX
3
3
3
3
3
3
3
and 4
4
and 4
and 4
and 4
and 4
and 4
4
and 4
Com ments
Prescribed Burning (2810015000 and 2810005000)*
CA
DE
LA
MD
VA
WA
AL
GA
MO
TX
Statewide
Statewide
Statewide
Statewide
Statewide
Statewide
2 Counties
13 Counties
4 Counties
1 6 Counties
Annual x
Annual x
Annual/D aily x
Annual/D aily x
Annual/D aily x
Annual/Daily x
Annual/Daily x
Annual x
Annual/Daily x
Annual/D aily x
Residential Wood (2104008000,2104008001, 2104008010,
CA
CT
Statewide
Statewide
Annual/Daily x
Annual/D aily x
X
X
X
X
X
X
X
X
2104008030,
X
X
X
X
X
X
X
X
X
X
X
X
2104008050
X
X
X X
XXX
X
XXX
XXX
X
X
XXX
, 2104008051)
XXX
XXX
3
3
3
3
3
3
3
3
3
3
and 4
4
and 4
and 4
and 4
and 4
and 4
4
and 4
and 4
and 4
and 4
Emissions
Emissions
Emissions
E m i s s i on s
E m i s s i on s
E m i s s i on s
E m i s s i on s
Emissions
Emissions
reported
reported
reported
reported
reported
reported
reported
reported
reported
under both
under both
under both
under both
under both
under both
under both
SCCs
SCCs
SCCs
SCCs
SCCs
SCCs
SCCs
under SCCs 2104008001 and 2104008010
under SCC
2104008001, 2104008030,2104008050, and
2104008051
DE
IN
LA
OK
VA
AL
GA
TX
Statewide
Statewide
Statewide
Statewide
Statewide
2 Counties
1 3 Counties
1 6 Counties
Annual x
Annual/Daily x
Annual/D aily x
Annual/D aily x
Annual/D aily x
Annual/D aily x
Annual x
Annual/Daily x
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
XXX
XXX
X X
X X
X X
3
3
3
3
3
3
4
and 4
and 4
and 4
and 4
and 4
4
and 4
Emissions
Emissions
Emissions
Emissions
Emissions
Emissions
Emissions
Emissions
reported
reported
reported
reported
reported
reported
reported
reported
under SCC
under SCC
under SCC
under SCC
under SCC
under SCC
under SCC
under SCC
2104008051
2104008050
2104008001
2104008001
2104008000
2104008000
2104008000
2104008000
Structure Fires (2810030000)
CA
CT
DE
LA
MD
VA
AL
GA
Statewide
Statewide
Statewide
Statewide
Statewide
Statewide
2 Counties
13 Counties
Annual/Daily x
Annual/Daily x
Annual x
Annual/Daily x
Annual/D aily x
Annual/D aily x
Annual/D aily x
Annual x
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X X
X X
X X
X X
X X
X X
3
3
3
3
3
3
and 4
and 4
4
and 4
and 4
and 4
and 4
4
-------
Table 4.4-16 (continued)
Source
Category/
SCC
Open Burning
State
TX
(Non-Ag
CA
CT
DE
IN
LA
OK
VA
AL
GA
MO
TX
Geo graph ic
Coverage
16 Counties
Temporal
Annual/D aily
, Non-Wildland) (2610000000,
Statewide
Statewide
Statewide
Statewide
Statewide
Statewide
Statewide
2 Counties
13 Counties
6 Counties
1 6 Counties
Annual/D aily
Annual/D aily
Annual
Annual/D aily
Annual/D aily
Annual/Daily
Annual/Daily
Annual/D aily
Annual
Annual/Daily
Annual/Daily
VOC NOV CO
XXX
2610030000,2610000100,
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
XXX
so,
PM-10
X
PM-25
X
1996 NET
NH, Version
3 and 4
Com ments
2610000400, 2610000500)**
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
3 and 4
3 and 4
4
3 and 4
3 and 4
3 and 4
3 and 4
3 and 4
4
3 and 4
3 and 4
Emissions
Emissions
Emissions
Emissions
Emissions
Emissions
Emissions
Emissions
Emissions
Emissions
Emissions
reported
reported
reported
reported
reported
reported
reported
reported
reported
reported
reported
under SCC 2610000000
under SCC 2610000000
underSCC 2610030000
under SCC 2610030000
under SCCs 261 0000000 and 2610030000
underSCC 2610030000
under SCCs 261 0000000 and 2610030000
underSCC 2610030000
under SCC 2610030000
underSCC 2610030000
underSCC 2610000000
* EPA emission estimates are for both prescribed and slash burn ing a re reported under SCC 281 001 5000. Some State/local agencies have submitted emission estimates for prescribed burning under SCC 281 001 5000
and emissions for slash burning under SCC 2810005000.
** First two SCCs in Open Burning category a re in 1996-1 998 NET. In the 1999 NET, SCCs 2610000000 (Total for all open burning categores) was removed and emissions calculated by EPA were reported under SCCs
2610030000, 2610000100, 261 0000400, and 2610000500.
-------
Table 4.4-17. Wildfires
Region
Rocky Mountain
Pacific
North Central
South
East
Fuel loading
Tons/Acre
Burned
Pollutant
37
19
11
9
11
Emission Factor
Ibs/ton
TSP
SO2
NOX
VOC
CO
PM-10
17
0.15
4
19.2
140
13
States Comprising Regions
South
Alabama
Arkansas
Florida
Georgia
Kentucky
Louisiana
Mississippi
North Carolina
Oklahoma
South Carolina
Tennessee
Texas
Virginia
East
Connecticut
Delaware
Maine
Maryland
Massachusetts
New Hampshire
New Jersey
New York
Pennsylvania
Rhode Island
Vermont
West Virginia
Rocky Mountain
Arizona
Colorado
Idaho
Kansas
Montana
Nebraska
Nevada
New Mexico
North Dakota
South Dakota
Utah
Wyoming
North Central
Illinois
Indiana
Iowa
Michigan
Minnesota
Missouri
Ohio
Wisconsin
Pacific
Alaska
California
Guam
Hawaii
Oregon
Washington
4-173
-------
Table 4.4-18. Emission Factors for Residential Wood Combustion by Pollutant
Pollutant
CO
NOX
voc
SO2
PM-103
PM-2.53
Emission Factor
(Ibs/ton)
230.80
2.80
43.80
0.40
30.60
30.60
Emission Factor
(tons/cord)
1.342E-1
1 .628 E-3
2.547 E-2
2.326 E-4
1 .779 E-2
1 .779 E-2
aAII PM is considered to be less than 2.5 microns.
Table 4.4-19. PM Control Efficiencies for 1991 through 1999
Year
1991
1992
1993
1994
1995
1996-
1999
Control Efficiency
(%)
1.4
2.8
4.8
6.8
8.8
10.8
4-174
-------
Table 4.4-20. Basis for 1996 Structure Fire Emission Estimates
Reference for Activity Number of Fires per
State or Emissions Data 1,000 Population
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine*
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
NFPA
NFIRS
NFPA
NFIRS
NFIRS
NFIRS
OTAG
OTAG
OTAG
NFIRS
NFIRS
NFPA
NFIRS
OTAG
OTAG
NFIRS
NFIRS
NFIRS
NFIRS
OTAG
OTAG
NFIRS
NFIRS
NFIRS
NFPA
NFPA
NFIRS
NFIRS
NFPA
OTAG
OTAG
NFIRS
OTAG
OTAG
NFPA
NFIRS
NFPA
NFPA
OTAG
OTAG
NFIRS
NFIRS
NFIRS
OTAG
NFIRS
2.18
2.08
2.18
4.40
1.96
4.43
NA
NA
NA
1.74
3.70
2.18
2.61
NA
NA
2.69
2.69
1.66
2.58
NA
NA
2.13
2.23
1.79
2.18
2.18
3.00
2.32
2.18
NA
NA
2.83
NA
NA
2.18
2.55
2.18
2.18
NA
NA
2.02
2.35
4.14
NA
1.49
4-175
-------
Table 4.4-20 (continued)
State
Vermont*
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Reference for Activity
or Emissions Data
OTAG
OTAG
NFPA
NFIRS
OTAG
NFPA
Number of Fires per
1,000 Population
NA
NA
2.18
2.48
NA
2.18
United States
NFPA=National Fire Protection Association; OTAG =Ozone TransportAssessment Group;
and NFIRS = National Fire Incident Reporting System.
NA = Not appi cable. Grew 1990 emissions supplied during devebpment of 1990 OTAG
inventory.
Table 4.4-21. Criteria Pollutant Emission Factors For Open Burning, Ib/ton
sec
2610030000
2610000100
2610000400
2610000500
Residential MSW
Yard waste - leaves
Yard waste - brush
Land clearing debris
voc
30
28
19
11.6
NOX
6
NA
NA
NA
CO SO2 PM-10 PM-2.5
85
112
140
169
1
NA
NA
NA
381
38
17
17
34. 81
38
17
17
Source
AP-42, Table 2.5-125
AP-42, Table 2.S-625
AP-42, Table 2.5-S25
Ward, 1989
1 PM-1 0 and PM-2.5 emission factors for residential MSW were obtained from a report, entitled "Evaluation of E missions from the Open Burning of
Household Waste in Barrels ."44
NA = Not available
4-176
-------
4.5 SOLVENT UTILIZATION
4.5.1 What sources are included in this category?
The point and area source categories under the "Solvent Utilization" heading include the following
Tier I and Tier II categories:
Tier I Category Tier II Category
(08) SOLVENT UTILIZATION (01-07) All
4.5.2 What is EPA's Current Methodology for Estimating Emissions from Solvent Utilization?
EPA's methodologies for estimating emissions from solvent utilization apply to the years 1985
through 1999. EPA's current methodology for estimating solvent utilization emissions is to use
emissions data submitted by State/local agencies wherever possible. However, for some State/local
agencies that have either not supplied or not estimated emissions from some or all solvent utilization
sources, the EPA uses the 1990 National Emissions Trends (NET) inventory as the base year from which
emissions are grown through 1999.
The 1990 Interim Inventory was used as the base year from which emissions for 1985 to 1989 were
estimated. As discussed under section 4.3.3 for "Industrial"point and area sources, the 1985 National
Acid Precipitation Assessment Program (NAPAP) controlled emissions were grown to 1990 to serve as
the starting point for preparing the 1990 Interim Inventory emissions. However, several changes were
made to the 1990 emissions to improve the inventory prior to backcasting the emissions to 1985 through
1989. Consequently, the 1985 emissions estimated by this method do not match the 1985 NAPAP
emission inventory. The factors used to backcast 1990 emissions to prior years are the same as the
factors used to grow 1985 NAPAP emissions to 1990.
4.5.3 Are Pollutants Other than VOC Estimated for Solvent Utilization Sources?
Yes. Although VOC is the primary pollutant associated with solvent utilization, EPA includes
estimates for other pollutants when information is avaikble. For example, Version 4 of the 1996 through
1999 point source NET contains emissions for CO, NOX, SO2, PM-10, PM-2.5, and NH3 for about half of
the 383 Source Classification Codes (SCCs) grouped under the solvent utilization Tier I category.
4.5.4 How Did EPA Prepare Solvent Utilization Emissions for Point and Area Sources When
Not Provided by State/Local Agencies?
The estimates in the 1990 NET inventory were developed using as much State/local agency data as
possible. To understand the basis for emission estimates for 1991-1995, one needs to understand how
the 1990 estimates were developed. For solvents, the 1990 NET inventory estimates were derived from
one of four sources: 1) estimates prepared for the 1990 Ozone Transport Assessment Group (OTAG)
inventory, 2) estimates prepared as part of the Grand Canyon Visibility Transport Commission (GCVTC)
inventory, 3) Aerometric Information Retrieval System/Facility Subsystem (AIRS/FS), or 4) a mass
balance approach that was used to develop the 1990 Interim Inventory.
4-177
-------
The majority of emission estimates for 1991-1995 are derived by using growth factors to project the
1990 NET inventory to the appropriate year. The methodologies used to prepare 1991 through 1995
emissions for "Industrial" point and area sources are the same methodologies that were used to prepare
point and area source solvent utilization emissions. To avoid duplication of the methodologies in this
section, the reader is referred to sections 4.3.6 and 4.3.7 of section 4.3 for the methodologies applied to
estimate solvent utilization emissions for 1991 through 1995.
The emissions in the NET for 1996 are a mixture of data received from the State/local agencies as
part of their 1996 periodic emission inventory (PEI) submittals or annual submittal for major point
sources, coupled with grown emissions from the previous version of the 1996 NET inventory. Growth
factors used to project the 1996 inventory to 1997 through 1999 were developed using the Economic
Growth Analysis System (EGAS) version 4 prototype. Additional details on the use of growth factors to
project emissions for the years 1997-1999 are given in section 4.5.7.
4.5.5 How did EPA Develop the Solvent Portion of the 1990 NET Inventory?
EPA developed the NET by using State/local agency data and filling in the data gaps with the 1990
Interim Inventory data. (See section 4.5.1, page 140 of Reference 1 for details on how solvent emissions
were developed for the 1990 Interim Inventory.) EPA obtained State data for the NET from the OTAG
inventory, GCVTC inventory, and AIRS/FS.
4.5.5.1 How did EPA use the OTAG Inventory?
The 1990 OTAG inventory houses average summer day VOC, NOX, and CO emission estimates for
those States that are either partially or fully in the Super Regional Oxidant A (SUPROXA) domain. The
OTAG SUPROXA domain is defined by the following coordinates:
North: 47.0 0°N East: 67.0 0°W
South: 26.0 0°N West: 99.0 0°W
The SUPROXA domain's eastern boundary is the Atlantic Ocean and its western boundary runs north to
south through North Dakota, South Dakota, Nebraska, Kansas, Oklahoma, and Texas. The western
extent of the domain allows for coverage of the largest urban areas in the eastern United States without
extending too far west to encounter terrain difficulties associated with the Rocky Mountains. The
northern boundary includes the major urban areas of eastern Canada. The southern boundary includes as
much of the United States as possible, but is limited to latitude 26°N due to computational limitations of
the photochemical models. In total, the OTAG inventory completely includes 37 States and the District
of Columbia.
The OTAG inventory is primarily an ozone precursor inventory and includes emission estimates of
VOC, NOX, and CO for all applicable source categories throughout the domain. It also includes a small
amount of SO2 and PM-10 emission data as well as ozone precursor data submitted by State/local
agencies. The OTAG inventory effort did not undertake any quality assurance (QA) procedures on the
SO2 and PM-10 emission estimates.
Since the underlying purpose of the OTAG inventory was to support photochemical modeling for
ozone, it is primarily an average summer day inventory. EPA used operating schedule data and default
4-178
-------
temporal profiles to convert any annual emission estimates submitted by the States to average summer
day estimates.
The OTAG inventory has three major components: (1) the point source component, which includes
segment/pollutant level emission estimates and other relevant data (e.g., stack parameters, geographic
coordinates, and base year control information) for all stationary point sources in the domain; (2) the area
source component, which includes county-level emission estimates for all stationary area sources; and (3)
the on-road vehicle component, which includes county/roadway functional class/vehicle type estimates of
VMT and MOBILE 5a input files for the entire domain. The NET inventory extracted all point sources
except utilities.
The general procedure for incorporating State data from the OTAG inventory into the NET
inventory involved using the data "as is" from the State submissions, with two main exceptions. First, for
the five States that submitted point source data for the years 1992 through 1994, EPA backcast the
inventory data to 1990 using BEA Industrial Earnings by State and two-digit SIC code.2 Second, EPA
temporally allocated any emission inventory data that only included annual emission estimates in order to
produce average summer day values. EPA performed this allocation for point and area source data
supplied by several States. For point sources, EPA used the operating schedule data, if supplied, to
temporally allocate annual emissions to average summer data emissions using Equation 4.5-1.
EMISSIONSASD = EMISSIONSAmUAL * SUMTHRU * 1/(13 * DPW) (Eq. 4.5-1)
where: EMISSIONSASD = average summer day emissions
EMISSIONSANNUAL = annual emissions
SUMTHRU = summer throughput percentage
DPW = days per week in operation
If a State did not supply operating schedule data for a point source, then EPA used its default Temporal
Allocation file and Equation 4.5-2 to temporally allocate annual emissions to an average summer
weekday. The Temporal Allocation file contains default seasonal and daily temporal profiles by SCC.
EMISSIONSASD = EMISSIONSAmUAL I (SUMFACSCC * WDFACSCC) (Eq. 4.5-2)
where: EMISSIGNSASD = average summer day emissions
EMIS SIGNSANNUAL = annual emissions
SUMFACSCC = default summer season temporal factor for SCC
WDFACSCC = default summer weekday temporal factor for SCC
For the small number of SCCs not included in the Temporal Allocation file, EPA assumed that the
average summer weekday emissions equaled those for an average day during the year. EPA used
Equation 4.5-3 to calculate these emissions.
EMISSIONSASD = EMISSIONSANNUAL I 365 (Eq. 4.5-3)
where: EMISSIONSASD = average summer day emissions
EMISSIONSANNUAL = annual emissions
4-179
-------
For stationary point sources, 36 of the 38 States in the OTAG domain supplied emission estimates
for their entire State. EPA used data from the 1990 Interim Inventory for the two States (Iowa and
Mississippi) that did not supply data. Table 4.5-1 provides a brief description of the point source data
supplied by each State, including information on temporal resolution, year of data, and EPA adjustments
to the data.
For area sources, 17 of the 38 States in the OTAG domain supplied emission estimates for their
entire State, and an additional 9 States supplied emission estimates covering part of their State (partial
coverage primarily covered ozone nonattainment areas). The 1990 Interim Inventory served as the sole
data source for 12 States. In those cases where the area source data supplied included annual emission
estimates, EPA used the default Temporal Allocation file to develop average summer daily emission
estimates. Table 4.5-2 provides a brief description of the area source data supplied by each State,
including information on temporal resolution, geographic coverage, and EPA adjustments to the data.
4.5.5.2 How did EPA use the GCVTC Inventory?
The 1990 GCVTC inventory includes detailed emissions data for the following 11 States: Arizona,
California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and
Wyoming.3 The GCVTC compiled this inventory by merging existing inventory data bases. The primary
data sources used were State-supplied inventories for California and Oregon, AIRS/FS for VOC, NOX,
and SO2 point source data for the other nine States, the 1990 Interim Inventory for area source data for
the other nine States, and the 1985 NAPAP inventory for NH3 and total suspended particulate (TSP)
data.
With respect to solvent emissions, EPA incorporated the following portions of the GCVTC
inventory into the 1990 NET inventory:
• complete point and area source data for California
• complete point and area source data for Oregon
The organic compound emissions reported in the GCVTC inventory for California are total organics
(TOG). EPA converted these emissions to VOC using the profiles from EPA's SPECIATE data base.4
Since the PM emissions in the GCVTC were reported as both TSP and PM-2.5, EPA estimated PM-10
from the TSP by applying SCC-specific uncontrolled particle size distribution factors.5 For solvent
utilization, PM and SO2 emissions are relatively minor.
4.5.5.3 What AIRS/FS Data did EPA Use?
EPA appended to the NET inventory those SO2 and PM-10 (or PM-10 estimated from TSP) sources
of greater than 250 tons per year as reported to AIRS/FS that were not included in either the OTAG or
GCVTC inventories. In late 1996, EPA extracted the data from AIRS/FS using the data criteria set
shown in Table 4.5-3. Table 4.5-3 also lists the data elements that were extracted. Note that EPA
extracted the estimated emissions. As mentioned above, PM and SO2 emissions are relatively minor for
this source category, so few data were derived from AIRS.
4-180
-------
4.5.5.4 How did EPA Fill the Data Gaps Remaining from these Inventories?
For SO2 and PM-10, EPA used the State data from OTAG, where possible. (The GCVTC inventory
contained SO2 andPM annual emissions.) In most cases, OTAG data for these pollutants were not
available. For point sources, EPA added data from AIRS/FS for plants over 250 tons per year of SO2
and PM-10. EPA also matched the AIRS/FS data to the OTAG plants and attached the emissions to the
existing OTAG plants when a match was found. If no match to the OTAG plant data was found, EPA
added new plants to the inventory. For OTAG plants where there were no matching data in AIRS/FS
and for all area sources of SO2 and PM-10, EPA calculated emissions based on the emission estimates for
other pollutants.
This approach to developing SO2 and PM-10 emissions from unmatched point and area sources
involved using uncontrolled emission factor ratios of SO2 to NOX or PM-10 to NOX to calculate
uncontrolled emissions. EPA used NOX to calculate the ratio because (1) the types of sources likely to be
important SO2 and PM-10 emitters are likely to be similar to important NOX sources, and (2) the generally
high quality of the NOX emissions data. EPA developed the SO2/NOX and PM-10/NOX ratios based on
uncontrolled emission factors. It then multiplied these ratios by uncontrolled NOX emissions to determine
the uncontrolled SO2 and PM-10 emissions. EPA thenreviewed information on VOC, NOX, and CO
control devices to determine if these devices also controlled SO2 and/or PM-10. If this review showed
that the listed control devices did not control SO2 and/or PM-10, EPA matched the OTAG and Interim
Inventory plants to determine the SO2 and PM-10 control applicable for those sources. The plant
matching component of this task involved only simple matching based on the State and county FIPS
codes and plant and point IDs. Because solvent utilization sources are relatively minor emitters of PM or
SO2, there were few sources that had significant emissions of these pollutants added via this method.
4.5.6 How did EPA Prepare the 1996 NET Inventory for Solvent Utilization Sources?
Initially, the 1996 emission inventory was developed by merging the 1995 AIRS/FS emissions with
1995 emissions grown from 1990 emissions for the States that did not submit emissions data to AIRS/FS.
Subsequently, EPA has been revising the 1996 NET to include base year emissions data submitted by
State/local agencies to comply with the CAAA requirements to submit (1) a PEI every 3 years for ozone
nonattainment areas (NAAs), and (2) emissions data for major point sources annually. States with ozone
NAAs needed to submit their PEI for 1996 by July 1997. To reduce the burden of preparing this
inventory, EPA gave each State/local agency a copy of the 1996 NET inventory as a starting point in
preparing their 1996 base year emissions. The methodologies used to prepare and revise the 1996 NET
emissions are presented in section 4.3.8.4 of section 4.3 for "Industrial" sources.
4.5.7 How Were Nonutility Point and Area Source Emissions Prepared for the 1997 through
1999 NET?
Emissions for 1997 through 1999 for the Solvent Utilization categories were grown from the 1996
NET inventory. Section 4.3.9.1 for Industrial nonutility point and area sources explains how the growth
and control factors were prepared and applied to estimate emissions for Versions 2, 3, and 4 1997
through 1999 of the NET. The methods EPA used to prepare and apply growth and control factors for
Solvent Utilization point and area sources are the same as those described in section 4.3.9.1. Table 4.5^1
presents the MACT control efficiencies applied to uncontrolled 1996 VOC emissions for Solvent
Utilization sources to prepare 1997 through 1999 emissions.
4-181
-------
4.5.8 References
1. National Air Pollutant Emission Trends Procedures Document, Sections 1, 4, and 6 1985-1996,
Projections 1999-2010, E PA-454/R-98-008, U.S. Environmental Protection Agency, Office of Air
Quality Planning and Standards, Research Triangle Park, NC, June 1998.
2. Table SA-5 - Total Personal Income by Major Sources 1969-1990, Data files, U.S. Department of
Commerce, Bureau of the Census, Washington, DC, 1991.
3. An Emission Inventory for Assessing Regional Haze on the Colorado Plateau, Grand Canyon
Visibility Transport Commission, Denver, CO, January 1995.
4. Volatile Organic Compound (VOC)/Particulate Matter (PM) Speciation Data System (SPECIATE)
User's Manual, Version 1.5, Final Report, Radian Corporation, EPA Contract No. 68-DO-0125,
Work Assignment No. 60, Office of Air Quality Planning and Standards, U.S. Environmental
Protection Agency, Research Triangle Park, NC, February 1993.
5. Barnard, W.R., and P. Carlson. PM-10 Emission Calculation, Table 1 and 4, E.H. Pechan &
Associates, Inc., Contract No. 68-DO-1-2-, U.S. Environmental Protection Agency, Research
Triangle Park, NC, June 1992.
4-182
-------
Table 4.5-1. Point Source Data Submitted by OTAG States
State
Alabama
Arkansas
Connecticut
Delaware
District of Columbia
Florida
Georgia - Atlanta
Urban Airshed (47
counties) domain
Georgia - Rest of
State
Illinois
Indiana
Kansas
Kentuc ky - Jeffers on
County
Kentucky - Rest of
State
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Missouri
Nebraska
New Hamps hire
New J ersey
New York
North Carolina
North Dakota
Ohio
O k I ah o m a
Pennsylvania -
Allegheny County
Pennsylvania -
Philadelphia County
Pennsylvania- Rest
of State
Rhode Island
South Carolina
Data Source/Format
AIRS/FS -Ad hoc retrievals
AIRS/FS -Ad hoc retrievals
State - EPS W orkfile
State- EPS W orkfile
AIRS/FS -Ad hoc retrievals
AIRS/FS -Ad hoc retrievals
State - State form at
AIRS/FS - Ad hoc retrievals
State - EPS Workfiles
AIRS/FS -Ad hoc retrievals
AIRS/FS -Ad hoc retrievals
Jefferson C ounty - E PS W orkfile
State -E PS W orkfile
State - State Format
State -E PS W orkfile
State -E PS W orkfile
State- EPS W orkfile
State - State Format
AIRS/FS -Ad hoc retrievals
AIRS/FS -Ad hoc retrievals
AIRS/FS -Ad hoc retrievals
State - EPS W orkfile
State- EPS W orkfile
State - EPS W orkfile
State -EPS Workfiles
AIRS/FS -Ad hoc retrievals
State - State Format
State -State Format
Allegheny County - County Format
Philadelphia County - County Format
State- EPS W orkfile
State - E PS W orkfile
AIRS/FS -Ad hoc retrievals
Temporal
Resolution
Annual
Annual
Daily
Daily
Annual
Annual
Daily
Annual
Daily
Annual
Annual
Daily
Daily
Annual
Daily
Daily
Daily
Annual
Annual
Annual
Annual
Daily
Daily
Daily
Daily
Annual
Annual
Annual
Daily
Daily
Daily
Daily
Annual
Year of Data
1994
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1993
1990
1990
1990
1990
1990
1990
1990
1994
1990
1990
1990
1990
1991
Adjustments to Data
Backcast to 1990 using BEA. Average Sum mer
Day estimated using m ethodology desc ribed above.
Average S umm er Day estimated using default
temporal factors.
None
None
AverageSummer Day estimated using methodology
described above.
AverageSummer Day estimated using methodology
described above.
None
Average Summer Day es timated using d efault
temporal factors.
None
AverageSummer Day estimated using methodology
described above.
AverageSummer Day estimated using methodology
described above.
None
None
AverageSummer Day estimated using methodobgy
described above.
None
None
None
AverageSummer Day estimated using methodology
described above.
AverageSummer Day estimated using methodology
described above.
Backcast to 1990 using BEA. AverageSummer
Day estimated using m ethodology desc ribed above.
AverageSummer Day estimated using methodology
described above.
None
None
None
None
AverageSummer Day estimated using methodology
described above.
AverageSummer Day estimated using methodology
described above.
Backcast to 1990 using BEA. AverageSummer
Day estimated using m ethodology desc ribed above.
None
None
None
None
Average S umm er Day estimated using default
temporal factors.
4-183
-------
Table 4.5-1 (continued)
State
Data Source/Format
Temporal
Resolution Year of Data Adjustments to Data
South Dakota
Tennessee
Texas
Vermont
Virgin ia
West Virginia
Wisconsin
AIRS/FS - Ad hoc retrievals
AIRS/FS - Ad hoc retrievals
State - State Format
State - E PS W orkfile
AIRS/FS -Ad hoc retrievals
AIRS/FS -Ad hoc retrievals
State - State Format
Annual
Annual
Daily
Daily
Annual
Annual
Daily
1990 AverageSummer Day estimated using methodobgy
described above.
1990 Average Summer Day estimated using default
temporal factors.
Backcast to 1990 us ing BE A.
None
1992
1990
1990
AverageSummer Day estimated using methodology
described above.
1990 AverageSummer Day estimated using methodology
described above.
1990 None
4-184
-------
Table 4.5-2. Area Source Data Submitted by OTAG States
State
Connecticut
Delaware
District of Columbia
Florida
Georgia
Data Source/Format
State -E PS Workfile
State -E PS Workfile
State -Hard copy
AIRS/AM S - Ad hoc retrievals
State - State form at
Temporal
Resolution
Daily
Daily
Daily
Daily
Daily
Geoaraohic Coveraae
Entire State
Entire State
Entire State
Jacksonville, Miami/
Ft. Lauderdale, Tampa
Atlanta Urban Airshed
Adjustments to Data
None
None
None
Added Nonroad emission
estimates
from Int. Inventory to Jacksonville
(Duval County)
None
Illinois
Indiana
Kentucky
Louisiana
Maine
Maryland
Michigan
Missouri
New Hamps hire
New J ersey
New York
North Carolina
Ohio
Pennsylvania
Rhode Island
Tennessee
Texas
Vermont
Virginia
West Virginia
Wisconsin
State - State form at
State - State form at
State - State Format
State - State Format
State - E PS W orkfile
State - E PS W orkfile
State - State Format
AIRS/AM S- Ad hoc retrievals
State - EPS W orkfile
State - EPS W orkfile
State - EPS W orkfile
State - EPS Workfiles
State -Hard copy
State - E PS W orkfile
State - E PS W orkfile
State - State form at
State - State Format
State- EPS Workfile
State- EPS Workfile
AIRS/AM S - Ad hoc retrievals
State - State Format
(47 Counties)
Daily Entire State
Daily Entire State
Daily Kentucky OzoneNonattainment
Areas
Daily Baton Rouge Nonattainment
Area (20 Parishes)
Daily Entire State
Daily Entire State
Daily 49 Southern Mich igan Counties
Daily St. Louis area (25 counties)
Daily Entire State
Daily Entire State
Daily Entire State
Annual Entire State
Daily Canton, Cleveland Columbus,
Dayton, Toledo, and Youngstown
Daily Entire State
Daily Entire State
Daily 42 Counties in Middle
Tennessee
Annual Entire State
Daily Entire State
Daily Entire State
Daily Charleston, Huntington/Ashland,
and Parkers burg (5 counties
total)
Daily Entire State
None
Nonroad emissions submitted were
county totate. Nonroad emissions
distributed to specific SCCs based
on Int. Inventory
None
None
None
None
None
Only area sou rce com bustion d ata
was provided. All other area source
data came from Int. Inventory
None
None
None
Average Su mmer D ay estimated
using default temporal factors.
Assigned SCCs and converted from
kgs to tons. NOX and CO from Int.
Inventory added to Canton, Dayton,
and Tofedo counties.
Nonroad emissions subm itted were
county totals. Nonroad emissions
distributed to specific SCCs based
on Int. Inventory
None
No nonroad data subm itted. Nonroad
emissions added from Int. Inventory
Average Su mmer D ay estimated
using default temporal factors.
None
None
None
None
4-185
-------
Table 4.5-3. Ad Hoc Report
Criteria
Regn
PLL4
PLL4
PLL4
PLL4
PLL4
PLL4
DES4
DUE4
YINV
GT 0
CE VOC
CE CO
CE SO2
CE NO2
CE PM-10
CE PT
GE 0
ME TY
ME 90
Plant Output
YINV
SITE
CNTY
CYCD
ZIPC
PNED
PNME
LAT1
LON1
SIC1
OPST
SIRS
YEAR OF INVENTORY
STATE FIPS CODE
COUNTY FIPS CODE
CITY CODE
ZIP CODE
NEDS POINT ID
PLANT NAME
LATITUDE PLANT
LONGITUDEPLANT
STANDARD INDUSTRIAL
CODE
OPERATING STATUS
STATE REGISTRATION
NUMBER
Point Output
STTE
CNTY
PNED
PNUM
CAPC
CAPU
PAT1
PAT2
PATS
PAT4
NOHD
NODW
NOHY
STATE FIPS CODE
COUNTY FIPS CODE
NEDS POINT ID
POINT NUMBER
DESIGN CAPACITY
DESIGN CAPACITY
UNITS
WINTER
THROUGHPUT
SPRING
THROUGHPUT
SUMMER
THROUGHPUT
FALLTHROUGHPUT
NUMBER HOURS/DAY
NUMBER DAYS/WEEK
NUMBER
HOURS/YEAR
Stack Output
STTE
CNTY
PNED
STNB
LAT2
LON2
STHT
STDM
STET
STEV
STFR
PLHT
STATE FIPS CODE
COUNTY FIPS CODE
NEDS POINT ID
STACK NUMBER
LATITUDE STACK
LONGITUDESTACK
STACK HEIGHT
STACK DIAMETER
STACK EXIT
TEMPERATURE
STACK EXIT VELOCITY
STACK FLOW RATE
PLUME HEIGHT
Segment Output
General
STTE
CNTY
PNED
STNB
PNUM
SEGN
SCC8
HEAT
FPRT
SULF
ASHC
PODP
STATE FIPS CODE
COUNTY FIPS CODE
NEDS POINT ID
STACK NUMBER
POINT NUMBER
SEGMENT NUMBER
sec
HEAT CONTENT
ANNUAL FUEL
THROUGHPUT
SULFUR CONTENT
ASH CONTENT
PEAK OZONE SEASON
DAILY PROCESS RATE
Segment Output
Pollutant
STTE
CNTY
PNED
STNB
PNUM
SEGN
SCC8
PLL4
D034
DU04
DES4
DUE4
CLEE
CLT1
CTL2
REP4
DME4
Emfa
STATE FIPS CODE
COUNTY FIPS CODE
NEDS POINT ID
STACK NUMBER
POINT NUMBER
SEGMENT NUMBER
sec
POLLUTANT CODE
OSD EMISSIONS
OSD EMISSION UNITS
DEFAULT ESTIMATED
EMISSIONS
DEFAULT ESTIMATED
EMISSIONSUNITS
CONTROL
EFFICIENCY
PRIMARY CONTROL
DEVICECODE
SECONDARY
CONTROL DEVICE
CODE
RULE
EFFECTIVENESS
METHOD CODE
Emission factor
-------
Table 4.5-4. MACT Control Efficiencies Applied to 1996 VOC Emissions for Point and Area Solvent Emission Sources
sec
POD
MACT Control
Efficiency (%)1
1997
1998
1999
SCC1 DESC
SCC3 DESC
SCC6 DESC
SCC8 DESC
Point Sources
40100201
40100202
40100203
40100204
40100205
40100206
40100207
40100209
40100221
40100222
40100223
40100224
40100225
40100235
40100236
40100251
40100252
40100253
40100254
40100255
40100256
40100257
40100258
40100259
40100275
40100295
61
65
65
65
65
61
65
61
62
66
66
66
66
62
62
61
65
65
65
65
61
65
61
61
61
62
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Organic Solvent Evaporation
Organic Solvent Evaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Stoddard (Petroleum Solvent): Open-top
Vapor Degreasing
1 ,1 ,1 -Trichloroethane (Methyl Chloroform ):
Open-top Vapor Degreasing
Perchloroethyiene: Open-top Vapor
Degreasing
Methylene Chloride: Open-top Vapor
Degreasing
Trichloroethyiene: Open-top Vapor
Degreasing
Toluene: Open-top Vapor Degreasing
Trichlorotrifluoroethane (Freon): Open-top
Vapor Degreasing
Butyl Acetate
Stoddard (Petroleum Solvent): Conveyorized
Vapor Degreasing
1 ,1 , 1 -T rich loro etha ne ( Meth yl
Chloroform):Conveyorized Vapor Degreaser
Perchloroethyiene: Conveyorized Vapor
Degreasing
Methylene Chloride: Conveyorized Vapor
Degreasing
Trichloroethyiene: Conveyorized Vapor
Degreasing
Entire U nit: with Va porized Solvent:
Conveyorfeed Vapor Degreasing
Entire U nit: with N on-boil ing Solven t:
Conveyorfeed Vapor Degreasing
Stoddard (Petroleum Solvent): General
Degre asing U nits
1 ,1 ,1 -Trichloroethane (Methyl Chloroform ):
Gene ral Deg reasin g Units
Perch loroethylen e: Gen eral De greas ing Un its
Methylene Chlorid e: Gen eral De greas ing Un its
Trie hloroeth ylene: Ge neral D egreas ing Un its
Toluene: General Degreasing Units
Trichlorotrifluoroethane (Freon): General
Degre asing U nits
Trichlorofluoromethane: General Degreasing
Units
1 ,1 ,1 -Trichloroethane (Methyl Chloroform ):
Gene ral Deg reasin g Units
Other Not Classifed: General Degreasing
Units
-------
Table 4.5-4 (continued)
sec
POD
MACT Control
Efficiency (%)1
1997
1998
1999
SCC1 DESC
SCC3 DESC
SCC6 DESC
SCC8 DESC
Point Sources
40100296
40100297
40100298
40100299
40100301
40100302
40100303
40100304
40100305
40100306
40100307
40100308
40100309
40100310
40100335
40100336
40100398
40100399
40100401
40100499
40188801
40188802
40188805
40188898
40199999
40201901
40201903
40201999
40202301
40202302
40202305
40202306
40202399
40202401
40202402
40202403
40202405
62
61
62
61
63
63
63
63
63
61
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
39
39
39
132
132
132
132
132
52
52
52
52
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
60
60
60
24
24
24
24
24
0
0
0
0
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
60
60
60
24
24
24
24
24
60
60
60
60
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Organic Solvent Evaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Organic SolventEvaporation
Surface Coating Operations
Surface Coating Operations
Surface Coating Operations
Surface Coating Operations
Surface Coating Operations
Surface Coating Operations
Surface Coating Operations
Surface Coating Operations
Surface Coating Operations
Surface Coating Operations
Surface Coating Operations
Surface Coating Operations
Degreasing
Degreasing
Degreasing
Degreasing
Cold SolventCleaning/Stripping
Cold SolventCleaning/Stripping
Cold SolventCleaning/Stripping
Cold SolventCleaning/Stripping
Cold SolventCleaning/Stripping
Cold SolventCleaning/Stripping
Cold SolventCleaning/Stripping
Cold SolventCleaning/Stripping
Cold SolventCleaning/Stripping
Cold SolventCleaning/Stripping
Cold SolventCleaning/Stripping
Cold SolventCleaning/Stripping
Cold SolventCleaning/Stripping
Cold SolventCleaning/Stripping
Knit Fabric Scouring with Chlorinated
Solvent
Knit Fabric Scouring with Chlorinated
Solvent
Fugitive Emissions
Fugitive Emissions
Fugitive Emissions
Fugitive Emissions
Fugitive Emissions
Wood Furniture Surface Coating
Wood Furniture Surface Coating
Wood Furniture Surface Coating
Large Ships
Large Ships
Large Ships
Large Ships
Large Ships
Large Aircraft
Large Aircraft
Large A ircraft
Large A ircraft
Other Not Classifed: General Degreasing
Units
Other Not Classifed: Open-top Vapor
Degreasing
Other Not Classified: Conveyorized Vapor
Degreasing
Other Not Classified: Open-top Vapor
Degreasing
Methanol
Methylene Chloride
Stodda rd (Petro leum Solvent)
Perchloroethylene
1 ,1 ,1 -Trichloroethane (Methyl Chloroform )
Trichloroethylene
Isopropyl Alcohol
Methyl Ethyl Ketone
Freon
Acetone
Entire Unit
Degreaser: Entire Unit
Other Not Classified
Other Not Classified
Perchloroethylene
Other Not Classified
Specify in Com ments Field
Specify in Com ments Field
Specify in Com ments Field
Specify in Com ments Field
Coating Operation
Coating Mixing
Other Not Classifed
Prime Coating Operation
Clean ing/P retreat ment
Equipment Cleanup
Topcoat Operation
Other Not Classifed
Prime Coating Operation
Cleaning/Pretreatment
Coating Mixing
Equipment Cleanup
-------
Table 4.5-4 (continued)
sec
POD
MACT Control
Efficiency (%)1
1997
1998
1999
SCC1 DESC
SCC3 DESC
SCC6 DESC
SCC8 DESC
Point Sources
40202406
40202499
40500301
40500311
40500312
40500313
40500314
40500501
40500511
40500512
40500513
40500514
40500598
40500599
52
52
181
181
181
181
181
183
183
183
183
183
183
183
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
60
60
32
32
32
32
32
27
27
27
27
27
27
27
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Petroleum and Solvent Evaporation
Surface Coating Operations
Surface Coating Operations
Printing/Publishing
Printing/Publishing
Printing/Publishing
Printing/Publishing
Printing/Publishing
Printing/Publishing
Printing/Publishing
Printing/Publishing
PrintingPublishing
Printing/Publishing
PrintingPublishing
PrintingPublishing
Large A ircraft
Large Aircraft
General
General
General
General
General
General
General
General
General
General
General
General
Topcoat Operation
Other Not Classifed
Printing: Flexographic
Printing: Flexographic
Printing: Flexographic
Printing: Flexographic: Propyl Alcohol Cleanup
Gravure: 2754
Gravure: 2754
Gravure: 2754
Gravure: 2754
Gravure: Cleanup Solvent
Ink Thinning Solvent Other Not Specified
Ink Thinning Solvent Other Not Specified
-------
Table 4.5-4 (continued)
sec
POD
MACT Control
Efficiency (%)1
1997
1998
1999
SCC1 DESC
SCC3 DESC
SCC6 DESC
SCC8 DESC
Area Sources
2401005000
2401020000
2401075000
2415000000
2415000385
2415000999
2415035000
2415045000
2415045999
2415060000
2415065000
2415100000
2415105000
2415110000
2415120000
2415125000
2415130000
2415135000
2415140000
2415145000
2415200000
2415230000
2415245000
2415260000
2415300000
2415305000
2415310000
2415315000
2415320000
246
225
250
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
241
241
241
241
241
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
30
0
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
36
30
59
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
Solvent Utilization
Solvent Utilization
Solvent Utilization
Solvent Utilization
Solvent Utilization
Solvent Utilization
Solvent Utilization
SolventUtilizafon
Solvent Utilization
SolventUtilizafon
SolventUtilizafon
Solvent Utilizaf on
Solvent Utilizaf on
SolventUtilizafon
SolventUtilizafon
SolventUtilizafon
Solvent Utilizaf on
Solvent Utilizaf on
Solvent Utilizaf on
Solvent Utilizaf on
Solvent Utilizaf on
SolventUtilizafon
SolventUtilizafon
SolventUtilizafon
Solvent Utilizaf on
Solvent Utilizaf on
Solvent Utilizaf on
SolventUtilizafon
SolventUtilizafon
Surface Coating
Surface Coating
Surface Coating
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Auto Refinishing: SIC 7532
Wood Furniture: SIC 25
Aircraft: SIC 372
All Processes/All Industries
All Processes/All Industries
All Processes/All Industries
Trans portation Equip ment (S 1C 37): All
Processes
Miscellaneous Man ufacturing (SIC 39): All
Processes
Miscellan eous Man ufacturing (SIC 3 9): All
Processes
Miscellan eous Re pair Services (SIC 76): All
Processes
Auto Repa ir Services (SIC 75): All
Processes
All Industries: Open Top Degreasing
Furniture and Futures (SIC 25): Open Top
Degreasing
Primary Metal Industries (SIC 33):Open
Top Degreasing
Fabricated Metal Products (SIC 34): Open
Top Degreasing
Indu strial Mac hin ery a nd E qui pm ent (SIC
35): Open Top Degreasing
Electronic and Other Elec. (SIC 36): Open
Top Degreasing
Transportation Equipment (SIC 37):Open
Top Degreasing
Instruments and Related Products (SIC 38):
Open Top Degreasing
Miscellaneous Manufacturing (SIC 39):
Open Top Degreasing
All Industries: Conveyerized Degreasing
Electronic and Other Elec. (SIC 36):
Conveyerfeed Degreasing
Miscellaneous Manufacturing (SIC 39):
Conveyerfeed Degreasing
Auto Repair Services (SIC 75):
Conveyerfeed Degreasing
All Industries: ColdCleaning
Furniture an d Fixtures (SIC 25 ): Cold
Cleaning
Primary Metal Industries (SIC 33): Cold
Cleaning
Secondary Metal Industries (SIC 33): Cold
Cleaning
Fabricated Metal Products (SIC 34): Cold
Cleaning
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Trichloroethylene
Solvents: NEC
Total: All SoK/ent Types
Total: All SoK/ent Types
Solvents: NEC
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All Sok/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All Sok/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All Sok/ent Types
Total: All SoK/ent Types
-------
Table 4.5-4 (continued)
sec
POD
MACT Control
Efficiency (%)1
1997
1998
1999
SCC1 DESC
SCC3 DESC
SCC6 DESC
SCC8 DESC
Area Sources
2415325000
2415330000
2415335000
2415340000
2415345000
2415350000
2415355000
2415360000
2415365000
2440020000
2460000000
2465000000
2465100000
2465200000
2465400000
2465600000
2465800000
2465900000
2495000000
241
241
241
241
241
241
241
241
241
226
249
249
249
249
249
269
249
249
249
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
63
63
63
63
63
63
63
63
63
63
0
0
0
0
0
0
0
0
0
63
63
63
63
63
63
63
63
63
63
20
20
20
20
20
20
20
20
20
Solvent Utilization
SolventUtilizafon
Solvent Utilization
Solvent Utilization
Solvent Utilization
Solvent Utilization
Solvent Utilization
SolventUtilizafon
SolventUtilizafon
Solvent Utilizaf on
SolventUtilizafon
Solvent Utilizaf on
SolventUtilizafon
Solvent Utilizaf on
SolventUtilizafon
SolventUtilizafon
SolventUtilizafon
Solvent Utilizaf on
SolventUtilizafon
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Degreasing
Miscellaneous Industrial
Miscellaneous Non-industrial: Consumer
and Commercial
Miscellaneous Non-industrial: Consumer
Miscellaneous Non-industrial: Consumer
Miscellaneous Non-industrial: Consumer
Miscellaneous Non-industrial: Consumer
Miscellaneous Non-industrial: Consumer
Miscellaneous Non-industrial: Consumer
Miscellaneous Non-industrial: Consumer
All Solvent User Categories
Industrial Mac hin ery a nd E qui pm ent (SIC
35): Cold Cleaning
Electronic and Other Elec. (SIC 36): Cold
Cleaning
Trans portation Equip ment (S 1C 37): Cold
Cleaning
Instruments and Related Products (SIC 38):
Cold Cleaning
Miscellaneous Man ufacturing (SIC 39): Cold
Cleaning
Trans portati on M ainten anc e Fa cil ities (S 1C
40-45): Cold Cleaning
Automotive Dealers (SIC 55):Cold Cleaning
Auto Repa ir Services (SIC 75): Cold
Cleaning
Miscellaneous Repair Services (SIC 76):
Cold Cleaning
Adhesive (Industrial) Application
All Processes
All Products/Processes
Perso nal Ca re Prod ucts
Household Products
Autom otive Afterm arket Products
Adhesives and Sealants
Pesticide Applcation
Miscellaneous Products: NEC
All Processes
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All Sok/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
Total: All SoK/ent Types
1 Percent red ucti on from uncontrolled em iss ions in 19 96 NET inventory.
-------
4.6 ON-ROAD VEHICLES
4.6.1 Which Sources Does EPA Include in the On-road Vehicle Category?
The "on-road vehicles" category includes 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. Section 4.6 discusses the methodologies EPA uses to calculate on-road
vehicle emissions. The on-road vehicle category includes all on-road vehicles from the following Tier I
and Tier II categories:
Tier I Category Tier II Category
(11) On-road Vehicles All
4.6.2 What Is EPA's Current Methodology for Developing Emission Estimates for On-road
Vehicles?
EPA uses a consistent methodology to calculate on-road vehicle emissions for all years from 1970
through!999. On-road emissions inventories for all pollutants (CO, NOX, VOC, PM-10, PM-2.5, SO2,
and NH3) are calculated by multiplying an appropriate emission factor in grams per mile by the
corresponding VMT in millions of miles, and then converting the product to units of tons of emissions.
Emission estimates for all years 1970 through 1999 include calculations by month, county, road type, and
vehicle type. Table 4.6-1 summarizes the current methodologies used to calculate on-road emissions
from 1989 through 1999. In addition, Table 4.6-2 tracks how the methodology used to calculate the
1996 on-road emissions has changed from Version 1 of the NEI through Version 4.
EPA uses its MOBILESa model for the years 1970 to 1994 and its MOBILESb model for the years
1995 through 1999 to calculate monthly state-level emission factors by vehicle type for VOC, NOX, and
CO. The PARTS model is used to calculate emission factors PM-10, PM-2.5, and SO2. These emission
factors from PARTS do not vary by month, so the same emission factors are multiplied by the monthly
VMT at the county, roadway type, and vehicle type (for the twelve PARTS vehicle types) level of detail
NH3 emission factors vary only by vehicle type, so the eight emission factors by vehicle type are
multiplied by VMT representing the same vehicle type at the monthly, county, and roadway type level of
detail. The NH3 emission factors used from 1990 through 1995 were based on test data from
Volkswagen, while the factors from 1996 through 1999 are based on emission test data from EPA's
Office of Air Transportation and Quality's (OTAQ), formerly the Office of Mobile Sources, which
capture the impact of catalytic converters on NH3 emission.
EPA does not calculate emission factors separately for every county. To determine the emission
factor sets to be modeled in each State, EPA prepared a county-level database for each year modeled.
The data base includes information on non-default inputs to be modeled, such as registration distributions
and other State-supplied data from OTAG, for each county. For each county, the control programs
applicable in that year were indicated. Next, EPA determined for each State all unique combinations of
control programs and other non-default inputs for each modeled year. MOBILES model runs were then
made modeling each of these unique combinations. Each combination was identified using the county
code of one of the counties with this combination of controls and inputs. To apply the emission factors
to the appropriate counties, EPA developed a county correspondence file which mapped all counties with
4-192
-------
the same unique set of input data and control programs to the MOBILES emission factors modeled for
the county representing that unique combination of inputs and control programs. For some States, EPA
applied a single set of emission factors to all counties in the State, while for other States, EPA calculated
a separate set of emission factors for each county. Most States, though, had several sets of emission
factors calculated for the State, with each set applying to one or more counties within the State. A
similar process was followed in mapping the PARTS emission factors to the appropriate counties.
4.6.3 How Does EPA Estimate Vehicle Miles Traveled (VMT)?
Vehicle niles traveled (VMT) is the activity factor EPA uses to estimate on-road vehicle emissions;
therefore, the development of a VMT database is critical to the estimation process. Using State VMT
totals for each year, EPA allocates VMT by county, roadway type, and vehicle type for each year
between 1970 and 1999. Each State and county combination in the output files contains 96 assigned
source classification codes (SCCs) representing the 6 rural and 6 urban roadway types and 8 vehicle
types. This section describes how the VMT estimation procedure described in the previous Trends
procedure document was modified for the years 1996 through 1999. In addition, the VMT estimated
previously for 1990 through 1995 was modified in the allocation procedure by vehicle type. These two
sets of VMT modifications are described here. The reader should refer to the earlier procedures
document to understand the VMT estimation methodology prior to 1990 and for the basis of the VMT
calculations prior to the 1990 through 1999 adjustments, (see
http://www.epa.tov/ttn/chief/trends/procedures/)1
4,6.3.1 How Does EPA Develop 1970 to 1979 VMT Data?
EPA's current methodology for allocating VMT totals for 1970 through 1979 is based on State
totals published in the Department of Transportation's (DOT) Federal Highway Administration's
(FHWA) Highway Statistics.2 For each year, EPA allocates State totals by county, roadway type, and
vehicle type using a ratio from the 1980 VMT file for each State/county/SCC combination expressed as a
percentage of the 1980 State total. Quality assurance is performed by comparing statewide totals for
each year's output to the FHWA's State totals.
4.6.3.2 How Does EPA Develop 1980 to 1995 VMT Data?
To develop VMT for the period 1980 through 1995, EPA relies on data supplied by the FHWA
regarding the latest mileage and daily travel summary areawide records reported to the Highway
Performance Monitoring System (HPMS).3 These records contain state-level summaries of miles of daily
travel by functional system and by rural, small urban (population of 5,000 to 49,999), and individual
urban (population of 50,000 and more) areas. Rural daily VMT is provided on a state level for the
following six roadway types: principal arterial-interstate, other principal arterial, minor arterial, major
collector, minor collector, and local. Small urban and urban area daily VMT are provided for the
following six roadway types: principal arterial - interstate, principal arterial - other freeways and
expressways, other principal arterial, minor arterial, collector, and local.
What is the Highway Performance Monitoring System? The HPMS is a national data collection
and reporting system administered by the FHWA in cooperation with State highway programs. The
HPMS contains data on the following: mileage, extent, and usage of various functional road systems; the
condition and performance of pavements; physical attributes of roads; road capacity and improvement
4-193
-------
needs; and other data important to the structural integrity and operation of the nation's road systems.
Each State highway program submits the data that make up HPMS to the FHWA on an annual basis.
The HPMS consists of three main data components: a universe database, a sample database, and an
areawide database. The universe data base contains a complete inventory of all mileage for all functional
systems except local roads. The sample data base contains more detailed information for a subset of the
highway sections in the universe data base. Each record in the sample data base composes part of a
sample panel which can be expanded to represent the universe of highway mileage. The areawide data
base contains annual state-level summaries of the major HPMS components. Most of the state-level data
in the areawide data base are divided into rural, small urban, and individualized urban area components.
Table 4.6-3 illustrates the main data components of HPMS and the type of data contained within each
component.
The HPMS travel data, which is based on samples of daily traffic counts taken at various points in a
State's roadway network, is critical to estimating VMT. These daily traffic counts are expanded to
annual average daily traffic (AADT). To calculate VMT for a specific section of road, EPA multiplies
the AADT for that section of road by the road length.4 EPA uses the HPMS data to create the county/
roadway type/vehicle type level VMT data file necessary for it to calculate emissions from 1980 to 1995.
What Problems Exist with Using HPMS to Estimate VMT? While the HPMS is an important
tool for EPA, several difficulties are associated with using its data to estimate VMT for the Trends
inventory. First, the geographic scope of HPMS data differs from that of the Trends inventory. All data
in HPMS are divided into rural, small urban, and individualized urban geographic areas, whereas the
Trends inventory relies on the county as its basic geographic unit. Because of this difference, EPA had to
develop a mechanism to distribute VMT from rural, small urban, and individual urban area levels to
county levels in order to use the HPMS data. Second, the level of detail of reporting in the sample data
base (the most detailed database containing VMT information) varies from State to State. In the sample
data base, some States report data for each individual urban area, some States report combined data for
all individual urban areas, and some States report data separately for some individual urban areas and
combined for the remaining individual urban areas. These variations complicate the task of distributing
VMT from the sample data base to counties. As a result, EPA relied on the areawide data base to
generate county-level VMT estimates. Unlike the sample date base, all States reported data for individual
urban areas separately to the areawide data base and only the area wide data base contains travel data for
local road systems.
How did EPA Calculate County-level VMT for 1980 to 1995? VMT from the HPMS areawide
data base is distributed to counties based on each county's rural, small urban, and urban area population.
The EPA relied upon two tables in the Bureau of the Census 1980 Number of Inhabitants (CNOI)
documents5 as the source for population data for the years 1980 to 1994. EPA had to use the 1980
population data to allocate the VMT because the Census Urbanized Area boundaries were changed for
the 1990 census. Because of this change, use of the 1990 Census Urbanized Area boundaries would
prevent a one-to-one match between HPMS large, urban-area VMT and urbanized area population,
making VMT distribution difficult. Although not exactly the same, the large urban area boundaries used
in HPMS are based on the 1980 Census Urbanized Area boundaries. 1990 Census data were used
starting with the 1995 inventory.6
The two CNOI tables used to distribute VMT to counties are:
4-194
-------
Table 3: Population of Counties by Urban and Rural
Residence. This table lists the urban population
living inside census-defined urban areas, the urban
population living outside census-defined urban areas,
and the rural population for each county.
Table 13: Population of Urban Areas. This table divides an
urban area's population among the counties that
contain portions of that urban area.
EPA calculates county-level rural VMT, small urban VMT, and urbanized area VMT separately
using the following methodologies.
How Does EPA Estimate Rural VMT from 1980 to 1995? To calculate rural VMT by county
from 1980 to 1995, EPA follows two steps. First, we calculate the percentage of the State's rural
population by county using county rural population data from CNOI Table 3. Next, we calculate each
county's rural VMT by distributing State rural VMT from the HPMS areawide data base, based on the
percentage of the State's rural population in each county. Equation 4.6-1 shows this calculation.
POPRC
VMTRC = VMTR^ x __^ (Eq
R,S
where: VMTRC = Rural VMT in county C (calculated)
VMT^g = Rural VMT, State total (HPMS)
POPR c = Rural population in county C (CNOI)
POPR'S = Rural population, State total (CNOI)
How Does EPA Estimate Small Urban VMT from 1980 to 1995? To calculate each county's
small urban VMT from 1980 to 1995, EPA uses a methodology similar to that used to calculate rural
VMT. First, EPA uses data from CNOI Table 3 on the urban population living outside census-defined
urban areas to calculate the percentage of the State's small urban population living in each county. Next,
EPA distributes the State small urban VMT from the HPMS areawide data base based on the percentage
of the State's small urban population living in each county. Equation 4.6-2 shows this calculation.
VMTSUC = VMTSU^ x - (Eq. 4>6.2)
""
where: VMTSUC = Small urban VMT in county C (calculated)
VMTSU'S = Small urban VMT, State total (HPMS)
POPSU c = Small urban population in county C (CNOI)
POPsu!s = Small urban population, State total (CNOI)
4-195
-------
How Does EPA Estimate Urban Area VMT from 1980 to 1995? EPA's approach to allocating
HPMS urban area Daily VMT from 1980 to 1995 differs slightly from the approach used to allocate rural
and small urban Daily VMT. EPA assigns each urban area in the HPMS file a unique 3-digit code. To
allocate Daily VMT totals by road type for each individual urban area, an urban area population file is
used to link a given urban area code to the corresponding population in each component county.
Because the boundaries of urban and small urban areas change from year to year, some urban areas in the
HPMS input files do not contain population figures for component counties. In these cases, the VMT for
these urban areas is added to the HPMS small urban VMT total by road category and allocated by small
urban population ratios.
For each urban area, EPA uses data from CNOI Table 13 to calculate the percentage of population
in each county containing a portion of the urban area. As shown in Equation 4.6-3, EPA then calculates
each county's share of an urban area's VMT by distributing urban area VMT from the HPMS areawide
data base based on the percentage of the urban area's population in each county.
(Eq. 4>6.3)
where: VMTUAC = Urban area's VMT in county C (calculated)
VMTUA|S = Urban area's VMT, State total (HPMS)
POPUA c = Urban area's population in county C (CNOI)
POPUA!S = Urban area's population, State total (CNOI)
In a few cases, a single county contains parts of more than one urban area. For those counties,
urban VMT equals the sum of the county's proportion of VMT from each of the large urban areas in the
county and the county's small urban VMT.
How Does EPA Determine 1980 to 1995 VMT by Roadway Type and Vehicle Type? To
calculate 1980 to 1995 VMT at the county/roadway type/vehicle type level, the Daily VMT totals for the
12 rural and urban roadway categories need to be allocated among the 8 MOBILE model vehicle type
categories. For each year between 1980 and 1995, EPA calculates a percentage distribution ibr each
vehicle type for both the rural and urban classifications. To develop this percentage distribution, EPA
first obtained VMT totals by vehicle type and by year from FHWA's Highway Statistics.2 Highway
Statistics provides rural and urban VMT for the following vehicles types: passenger cars, motorcycles,
buses, two-axle/four-tire single-unit trucks, other single-unit trucks, and combination trucks. (In the
years prior to 1990, FHWA did not provide a VMT breakdown between passenger cars and motorcycles;
instead, it provided a total VMT for Personal Passenger Vehicles is provided. EPA assumes that the
division between passenger car VMT and motorcycle VMT prior to 1990 matches that reported for
1990.) For each of the six vehicle type categories for which VMT is reported in Highway Statistics, a
percentage of the total is calculated for both rural and urban VMT. To convert these percentages for the
six HPMS categories to the eight MOBILE vehicle type categories, EPA provides a breakdown that
reconciles the vehicle class categories used in the HPMS to those used in EPA's MOBILE model.7 This
method of conversion from HPMS categories to MOBILE categories is based on a matching scheme that
allows States to apportion VMT as it is reported in HPMS categories to the eight MOBILE model
vehicle class categories. Table 4.6-4 shows the apportionment percentages supplied by EPA.
4-196
-------
After allocating HPMS Daily VMT totals by county, roadway category, and vehicle type, EPA
converts the Daily VMT values to millions of annual VMT by multiptying the Daily VMT values by 365.
Quality assurance was performed on the output files for each of the years by comparing State totals to the
HPMS data provided by State. (It is important to note that for certain years, slight discrepancies exist
between the HPMS totals and the totals reported in Highway Statistics^) The resulting annual
county-level, vehicle, and roadway type-specific VMT data were temporally allocated to months. EPA
used seasonal 1985 National Acid Precipitation Assessment Program (NAPAP) temporal allocation
factors8 were used to apportion the VMT to the four seasons. Monthly VMT data were obtained using a
ratio between the number of days in a month and the number of days in the corresponding season. These
temporal factors are shown in Table 4.6-5.
4.6.3.3 What States Provided 1990 VMT Data?
For 1990 VMT data, thirteen of the 38 Ozone Transport Assessment Group (OTAG) States
supplied VMT estimates for their entire State, an additional 3 States supplied VMT estimates covering
part of their State, and the remaining 22 plus parts of the 3 States used Emission Trends VMT data.
These State-provided data replaced the 1990 VMT data calculated as discussed above. Table 4.6-6 lists
the state-level daily VMT totals in the OTAG Inventory. Figure 4.6-1 shows which States supplied
VMT.
4.6.3.4 What Changes Did EPA Make to the VMT Estimation Methodology for the Years 1996
through 1998?
The primary changes EPA made to the 1996 through 1998 VMT estimation procedure fall into two
categories. These are changes in (1) the allocation of State or metropolitan area VMT by roadway type
to the counties within that State or metropolitan area, and (2) the allocation of VMT from the HPMS
vehicle classes to the MOBILE5b vehicle classes.
In prior years, population was the sole surrogate for allocating State-level or metropolitan area-level
VMT to the counties within that area. This allocation was modified for all road types except rural local
roadways and urban local roadways. The modified procedure differed for rural interstates and for the
remaining nine roadway types. The surrogate for allocating VMT from rural interstates was changed
from population to interstate mileage within a county. The Federal Highway Administration provided
data on roadway mileage by State, county, and roadway type. Rural interstate VMT totals for each State
were then multiplied by the fraction of rural interstate mileage within a given county divided by the total
State rural interstate mileage to give rural interstate VMT by county.
For the remaining nine roadway types (all roadway types except rural interstate, rural local, and
urban local), a combination of population and the existence of roadway mileage in the given county of the
specified roadway type were used to allocate State-level or metropolitan-level VMT. For each State or
metropolitan area, the VMT from a given roadway type were allocated by population to all counties
within that State or metropolitan area that had mileage greater than 0 of the given roadway type. In other
words, for each of these nine roadway types, the State or metropolitan area VMT was multiplied by the
fraction of population within a given county divided by the total population of counties in that State or
area with mileage of the specified roadway type.
4-197
-------
The second category of VMT methodology change for 1996 through 1998 involves the allocation of
VMT by vehicle type. The allocation of HP MS to MOBILES vehicle categories was updated by OTAQ
and varied by year from. This updated HPMS to MOBILES vehicle type mapping was prepared by EPA
in its development of VMT estimates that were used in the 2007 heavy-duty vehicle final rule analyses.9
As was done previously, the allocations of VMT by vehicle type in a given year differed for rural and
urban roadway types, but did not differ by State. OTAQ developed data showing how to allocate the
total VMT from each of the HPMS vehicle categories to the MOBILES vehicle categories. HPMS VMT
totals by vehicle category, summed separately for total urban roadway types and total rural roadway
types, were multiplied by the OTAQ allocation factor for that year and vehicle category. Once VMT
totals for each of the MOBILES vehicle types were calculated for total urban roadways and total rural
roadways, these values were converted to the fraction of total VMT by vehicle type and urban or rural
area. Thus, each VMT value by county and roadway type was then multiplied by the eight MOBILES
vehicle type fractions for either rural or urban roadways, depending on whether the VMT was from an
urban or rural roadway type.
4.6.3.5 How Did EPA Project 1999 VMT Data?
The 1999 VMT data should be considered a projection of the 1998 VMT data. FHWA VMT data
for 1999 are preliminary data and are not available at the same level of detail as the historical VMT data.
FHWA provided preliminary 1999 VMT totals by State and roadway type. The 1998 VMT data were
totaled by State and roadway type, and then the ratio of 1999 VMT by State and roadway type to the
1998 VMT by State and roadway type were calculated. Each State and roadway type 1999 to 1998
fraction was then multiplied by the 1998 VMT at the county, roadway type, and vehicle type level of
detail.
4.6.3.6 How Did EPA Modify 1990 through 1995 VMT Allocations?
Modifications were made to the 1990 through 1995 VMT calculated in prior years. These changes
were made only to the final step of allocating VMT from the county and roadway type level to the
county/roadway type/vehicle type level of detail. In 1997, FHWA updated their data showing VMT by
the HPMS vehicle type categories. This shift accounted for the more accurate classification of minivans
and sport utility vehicles from the passenger car category to the 2-axle, 4-tire single-unit truck category.
VMT fractions by vehicle type were calculated as done previously, except with the updated HPMS data.
The VMT data in the previous 1990 through 1995 VMT databases were then multiplied by the ratio of
the new MOBILES vehicle type fraction to the previous MOBILES vehicle type fraction for the given
year.
4.6.4 How Does EPA Develop Emission Factors for VOC, NOX, and CO?
As mentioned previously, EPA relied upon its MOBILESa and MOBILESb models to calculate
VOC, NOX, and CO emission factors for on-road sources for the years 1970 through 1994 and the years
1995 through 1999, respectively.10 More specifically, EPA modeled exhaust VOC, evaporative VOC
(which includes resting loss, running loss, and evaporative emissions), exhaust NOX, and exhaust CO.
VOC emissions include aldehydes and hydrocarbons measured by Flame lonization Detector (FID)
testing. These emission factors are expressed as grams of pollutant per vehicle mile traveled (VMT).
The MOBILE model takes into consideration a number of parameters in tailoring emission factor
4-198
-------
calculations. A discussion of how EPA develops these parameters follows. Where applicable, EPA used
State-supplied MOBILE model inputs for 1990 and 1995 and later years.
4.6.4.1 What Temperature Data Does EPA Input to the MOBILE Model?
The temperature data inputs to the MOBILE model for the Emission Trends inventory include
monthly average daily maximum and minimum temperature for each State for each year from 1970 to
1999. These data were obtained from The National Climatic Data Center.11 EPA selected one city from
each State to represent that particular State's temperature conditions. Each chosen city is thought to be
the most representative of the average conditions within the State. Generally this means either centrally
located cities or, in States with a majority of VMT clustered in one area, the most populous cities. Due
to the great temperature variation and the wide VMT distribution throughout California, EPA divides
California into two geographic regions, with Los Angeles representing the southern and interior portions
of the State and San Francisco representing the northern coastal region of the State. Table 4.6-7 lists the
cities used to represent each State's temperature conditions from 1970 to 1999.
In cases where temperature data is missing for a month or more, EPA relies on 30-year average
monthly maximum and minimum temperature values reported by the Department of Commerce's
Statistical Abstracts.12 The temperature range for input to the MOBILE model is 0°F to 100°F for the
minimum daily temperatures and 10°F to 110°F for the maximum daily temperatures. In the few cases
where temperatures fall outside of these ranges, EPA substitutes the endpoint of the range for the actual
temperatures.
4.6.4.2 How Does EPA Calculate the Mon thly R VP Inputs?
Allocating monthly RVP values for each State is an important part of the MOBILE modeling
process. To determine these values, EPA first assigned a weighted January and July RVP for each year
to each State and then estimated the RVP for each State for the remaining ten months. For some areas,
EPA adjusted the calculated RVP values for those areas not receiving reformulated gasoline (RFG) in
order to eliminate the effects of lower RVP values associated with the use of RFG. In addition, some
States provided summer RVP data to OTAG that differed from the values calculated for the emissions
inventory. The procedures used to account for these discrepancies are described below.
To help with assigning the weighted January and July RVP values, OTAQ provided historic RVP
data for the years 1970 through 1999. This historic data includes the average January and July RVP
values weighted by the market share of each type of gasoline (regular unleaded, intermediate unleaded,
premium unleaded, etc.) from each of the 23 cities included in the American Automobile Manufacturer's
Association (AAMA) (replaced in 1999 by the Alliance of Automobile Manufacturers (AAM)) fuel
surveys.13'14 The OTAQ also provided a listing that matches each nonattainment area and many
Metropolitan Statistical Areas (MSAs) throughout the United States with the corresponding AAMA
survey city with which the RVP should be used to represent that nonattainment areas. Using these data,
EPA assigns January and July RVP values to each State for each year. These assignments were based on
pipeline distribution maps and are shown in Table 4.6-8. EPA then assigns the corresponding January
and July weighted RVP values to each of the nonattainment areas. EPA averages the January or July
RVP values for a given year for all nonattainment areas and listed MSAs within a State to estimate a
single statewide January or July RVP value. For those States that had no nonattainment areas or MSAs
4-199
-------
included in the OTAQ cross reference listing, OTAQ assigned survey cities to these States based on a
combination of location and pipeline maps. These assignments are as follows:
State Survey City
Idaho Billings, MT and Seattle, WA
Iowa Minneapolis, MN
Nebraska Kansas City, MO and Minneapolis, MN
North Dakota Minneapolis, MN
South Dakota Minneapolis, MN
Wyoming Billings, MT and Denver, CO
For States with two or more survey cities assigned to its nonattainment areas and MSAs, EPA averaged
the RVP values assigned to each of the nonattainment areas or MSAs within that State. Alaska and
Hawaii are not matched with survey cities; instead, they are assigned winter and summer RVP values
based on guidance from OTAQ. Based on this guidance, Alaska received a winter RVP value of 14.5 psi
and a summer RVP value of 12.5 psi while Hawaii received a winter RVP value of 10.0 psi and a summer
RVP value of 9.5 psi. These assignments apply to each year from 1970 through 1999. An Alaskan city
has been included as a survey city in the RVP surveys in recent years.
The next step in the process of allocating RVP values is to estimate statewide RVP values for the
remaining months based on the January and July RVP values. The ASTM schedule of seasonal and
geographical volatility classes provides the basis for the RVP allocation by month.15 This schedule
assigns one or two volatility classes to each State for each month of the year. Volatility classes are
designated by a letter (A through E), with A being the least volatile. The ASTM schedule divides several
States into two or more regions, with each region having its own set of volatility class guidelines. The
MOBILE4 User's Guide16 provides guidance on which ASTM class to assign to each State for each
month when more than one region is included for a State, or when two ASTM classes are listed for a
given State in a given month. EPA followed this guidance to select a single ASTM class for each State
and month. The MOBILE4 User's Guide also lists RVP limits that correspond to each ASTM class.
These RVP limits are as follows:
ASTM class A = 9.0 psi
ASTM class B = 10.0psi
- ASTM class C = 11.5 psi
ASTM class D = 13.5 psi
ASTM class E= 15.0psi
EPA assigns the January ASTM class designation to the calculated January RVP value for each
State and the July ASTM class designation to the calculated July RVP value for each State. Those
months with the same ASTM class designation as either January or July are assigned the January or July
RVP value for that State. The RVP values for months with intermediate ASTM class designations are
calculated by interpo lation using the January and July RVP values and the ASTM class RVP limits. This
interpolation uses Equation 4.6-5.
IM = [(IA - SA) x (WM - SM) I (WA - SA)} + SM (Eq. 4.6-5)
4-200
-------
where: IM = Intermediate month's (not January or July) RVP value
WM = Winter (January) RVP value
SM = Summer (July) RVP value
IA = Intermediate month's (not-January or July) ASTM RVP limit
WA = Winter (January) ASTM RVP limit
SA = Summer (July) ASTM RVP limit
EPA makes calculations for each intermediate month for each State. Starting in 1989, summer RVP
values were limited by EPA's Phase I RVP limits and in 1995 by the Phase II RVP limits. After the May
through September RVP values are calculated for each State using the procedure above, the values are
replaced by the State-specific monthly Phase I (for 1989 to 1991) or the Phase II (for 1992 and later
years) limit if the corresponding limit was lower than the calculated monthly RVP value.
How does EPA Eliminate the RVP Effects of Reformulated Gasoline? Several of the AAMA
survey cities sold RFG starting in 1995. The July RVP of RFG sold in a particular geographic area is
almost always lower than the July RVP of regular gasoline sold in that same geographic area As a result,
using an RFG survey city to represent RVP values for areas receiving regular gasoline results in
inappropriately low RVP values for these areas. To correct this situation, OTAQ provided each of the
AAMA survey cities receiving reformulated gasoline in 1995 and later years with a substitute survey city
to use when calculating the July RVP values of areas without reformulated gasoline.17 This substitute
survey city assignment is shown in Table 4.6-9. The procedure discussed above for determining
state-level July RVP values in States that receive both RFG and regular gasoline was modified to
determine separate RVP values for both types of areas. To calculate the July RVP of regular gasoline in
the State, the RVP of the substitute survey cities replaced the RVP of the original survey cities and the
RVP was recalculated. This value was then used for areas in the State that did not receive reformulated
gasoline.
How Was State-supplied RVP Data Used? Some States supplied summer 1995 RVP data to
OTAG that differed from the values calculated using the methodology discussed above. In these cases,
EPA used the State-supplied RVP data instead of the calculated 1995 through 1997 RVP values for the
months from May through September. In some cases, the State-supplied data varied within a State. EPA
maintained these distinctions in the Trends modeling. The resultant 1996 monthly RVP data for all areas
are shown in Table 4.6-10.
How Did EPA Calculate Ozone Season 1998 and 1999 RVP Values? The procedure discussed
above was NOT applied to the ozone season months in 1998 and 1999 because most of the cities in the
RVP surveys by 1998 were implementing either a low RVP program or reformulated gasoline.
Therefore, the RVP values from these cities would not be applicable to a majority of the remaining areas
in the United States. For 1998 and 1999, Reid vapor pressure (RVP) data for the ozone season months
(May through September) was based on data from OTAQ showing RVP throughout the ozone season by
State or county, if a particular county's RVP varied from the remainder of the State's RVP. This
information can be found at: http://www.epa.gov/oms/regs/fuels/rfg/sumrvp4.pdf. The July RVP value
from this table was applied in all five of the ozone season months for a given county. These data were
then superceded by actual July RVP survey data for areas included in American Automobile
Manufacturer's Association (AAMA) fuel survey (1998)13 or the Alliance of Automobile Manufacturers
(AAM) fuel survey (1999)14. RVP values for the remaining months were calculated at the State level,
based on the AAMA 1998 and AAM 1999 January RVP survey data. To estimate RVP values for the
4-201
-------
remaining months in 1998 and 1999, EPA first assigned a weighted January RVP for each year to each
State as discussed above for the earlier years. However, the July RVP value used in this procedure for
estimating the values for the non-ozone season months, was the area's Phase II RVP limit (with 8.7 psi
used to represent the 9.0 psi limit in most areas to account for the typical margin of safety used by most
refiners) rather than the July values from the RVP survey data.
4.6.4.3 How Does EPA Develop Speed Inpu ts?
Speed is another input to the MOBILE model calculations. EPA has developed representative
national speeds for each vehicle type/roadway type combination. Average overall speed data, output
from the HPMS impact analysis were obtained for the years 1987 through 1990.3 The average overall
speed for each vehicle type varied less than one mile per hour (mph) over the four-year span. Therefore,
EPA used 1990 speed data for all years from 1970 to 1999. Table 4.6-11 lists the average overall speed
output for 1990 from the HPMS impact analysis. To determine the actual speeds to use in modeling the
emission factors, EPA used the following HPMS vehicle types to represent the speeds for each MOBILE
model vehicle type:
• Passenger cars — correspond to the MOBILE model's light-duty gasoline vehicles (LDGVs),
light-duty diesel vehicles (LDDVs) and motorcycles (speeds for small and large cars were the
same)
• Pick-ups and vans — correspond to the MOBILE model's light-duty gasoline trucks [LDGTls
(pick-ups, minivans, passenger vans, and sport-utility vehicles) up to 6,000 Ibs gross vehicle
weight (GVW)], LDGT2s (LDGTs of 6,000 to 8,500 Ib GVW), and light-duty diesel trucks
(LDDTs) up to 8500 Ib GVW
• Multi-trailer trucks with five or more axles — correspond to the MOBILE model's heavy-duty
gasoline vehicles (HDGVs) and heavy-duty diesel vehicles (HDDVs), both of which include
vehicles weighing 8501 Ib or more GVW
To reduce the number of speeds that need to be modeled, EPA rounds the HPMS speeds to the
nearest 5 mph. Speeds on local roads are not included in the HPMS impact analysis output. To make up
for this omission, EPA assumes that speeds on local rural roads are the same as speeds on minor collector
roads and that speeds on local urban roads are the same as speeds on collector roads. Table 4.6-12 lists
the average speed used for each road type/vehicle type combination. EPA does not use State-supplied
speed data in making its Trends calculations.
EPA recognizes that the abolition of the national speed limit in 1995 may have caused overall speeds
to increase, particularly on rural interstates. Unfortunately, little data is currently available to assess the
impacts of the speed limit change on actual travel speeds. In addition, the maximum speed that can be
modeled in MOBILE5b is 65 mph, so even if speed data were available, emission factors for these higher
speeds could not currently be modeled with MOBILE5b.
4.6.4.4 What Operating Mode Inputs Does EPA Use?
EPA uses the operating mode assumptions of the Federal Test Procedure (FTP) for all MOBILE
runs at all speeds, with the exception of Maryland and Texas, as described below. According to FTP
4-202
-------
results, 20.6 percent of all VMT is accumulated in the cold start mode (or Bag 1 of the FTP), 27.3
percent of all VMT is accumulated in the hot start mode (or Bag 3 of the FTP), and 52.1 percent of all
VMT is accumulated in the hot stabilized mode (or Bag 2 of the FTP).
Maryland and Texas supplied their own operating mode data. EPA substituted these State-supplied
operating modes for the default FTP operating mode in the 1995 and later MOBILESb input files for
these States. The operating mode data modeled for these two States are shown in Table 4.6-13.
4.6.4.5 What Altitude Inputs Does EPA Use?
The States of Colorado, Nevada, New Mexico, and Utah were all modeled as high altitude areas; all
other States are treated as low altitude areas in the MOBILES modeling.
4.6.4.6 How Does EPA Develop Registration Distribution Data?
All of the MOBILE input files include a national vehicle registration distribution. These registration
distributions vary by calendar year and show the fraction of vehicles registered in the given calendar year
by model year. Each vehicle type has a separate registration distribution, although single registration
distributions are used for LDGVs and LDDVs and for LDGTls and LDDTs. Registration distributions
developed under earlier Emission Trends work assignments were used for calendar years 1970 through
1994. EPA developed new registration distributions for each year thereafter.
The specific procedures used in developing the national annual registration distributions are
discussed in detail in the following sections. In some cases, the methods used for this version of Emission
Trends inventory correspond to procedures used in previous years, while in other cases, EPA has made
improvements to the estimation procedure.
EPA developed a computer program to calculate vehicle registration distributions for 1991 through
1999. (This program performs the computations that had been done in a spreadsheet model for earlier
Emission Trends inventories.) This registration distribution program estimates the distribution of vehicles
operating by model year for calendar years 1991 and later for each of the eight MOBILE vehicle types.
For automobiles, the registration distribution is based on the number of cars in operation by model year as
reported in AAMA's (and in 1999, Ward's) Facts and Figures™'19 and sales data from Automotive News'
Market Data Book.20 For each of the five MOBILE truck classes, the distribution is based on sales
figures from AAMA and Automotive News, as well as the number of trucks in operation by model year
from AAMA. For motorcycles, the registration distribution for these three years did not change from
previous years; this distribution was taken from the default distribution from the previous Emission
Trends procedures, which covered a 12-model-year range. The specific procedure used to calculate the
registration distribution for automobiles and trucks is discussed below.
How Does EPA Calculate the Registration Distribution for Automobiles? The 1998 national
registration distribution was calculated starting with Ward's Motor Vehicle Facts and Figures 1999
tables showing the number of cars in operation by model year.19 1998 is the most recent calendar year for
which data are available from this source. EPA uses the number of cars in operation in 1998 for each
model year from 1983 through 1999 as a preliminary estimate of the number of cars from these model
years operating in 1999. (These will be updated in the next version of Emission Trends inventory by
4-203
-------
Ward's actual estimates for the 1999 calendar year.) Table 4.6-14 shows the 1998 national registration
distribution
The 1983 model year is the earliest model year for which data are provided on the number of cars
operating in 1998. An aggregate estimate of the number of cars in operation in 1998 from model years
prior to 1983 is also given. EPA developed a methodology to distribute the cars operating from model
year 1983 and earlier years over the remaining 9 years required for developing a 25-year registration
distribution. To do this, EPA derived a formula using automobile survival rates to project estimates of
operation for these older cars by model year to 1999.21 Based on AAMA data for previous years, the
number of cars from each model year from 1974 through 1982 still in operation in 1999 was estimated
using Equation 4.6-6.
C
Model YearN Cars in Operation in Yearl999= A x — (Eq 4.6-6)
B
where: A = AAMA number of Model YearN Cars Operating in YearY
B = Survival rate for ageY. N
C = Survivalrate for age 1999.N
Year = Last calendar year for which an estimate is available for this particular
model year (as of July 1)
N = Most current model year for which 'Number of Automobiles in Operation' are
available
For example, in calculating the 1995 registration distribution, 1990 is the most recent calendar year
for which data on the number of 1976 model year cars still in operation is available. Facts and Figures™
indicates that 2.981 million 1976 model year cars were operating in 1990. The car survival rate from
1976 to 1995 (19 years of survival) is 0.10130.21 The car survival rate from 1976 to 1990 (14years of
survival) is 0.32221.21 Thus, of the 2.981 million 1976 model year cars that survived to 1990, it is
expected that 31 percent (0.10130/0.32221) or 0.937 million will survive to 1995.
To develop an estimate of the number of 1999 model year cars were operating in 1999, the number
of 1998 registrations of model year 1998 automobiles was multiplied by 0.75, since by July 1, three-
quarters of the car mo del year had passed (new mo del year automobiles are generally re leased in
October).
Using this complete set of automobile registrations by model year for the 25-year period from 1975
to 1999, EPA calculated the registration distribution by dividing the number of cars in operation by model
year by the total number of cars operating over the 25-year period. EPA repeats this process to develop
a registration distribution for other years back to 1991. The only difference for these years is that the
number of cars in operation in the most recent model year is available from AAMA for these previous
years and therefore, no projections of the number of cars in operation were made for the latest model
year.
How Does EPA Calculate the Registration Distribution for Trucks? For each truck type, the
1998 registration distribution is calculated using truck sales figures by type and model year, which are
weighted by the distribution of truck registrations (the total over all truck types) from Ward's Motor
Vehicle Facts and Figures 1999.19
4-204
-------
EPA first determines 1998 truck sales by MOBILESb truck category. (Sales figures for years prior
to 1998 did not change from those used in calculating previous years' registration distributions.) Because
Ward's truck categories do not directly correspond to the categories used in MOBILESb, EPA uses the
method described below to allocate sales from Ward's weight class categories to the MOBILE truck
categories. The data needed for the 1998 model year for each of the formulas listed below were obtained
from Facto and Figures 1999.19 The sales data for the earlier model years needed fora 1998 registration
distribution were already calculated for registration distributions prepared for previous Trends
inventories, and used similar data from earlier versions of Facts and Figures.™ The equations used to
estimate sales for each MOBILESb truck category are listed below. Equations 4.6-7 through 4.6-11
show the formulas used for the 1991 through 1999 distribution.
LDGT1 = Retail Sales (domestic + import),Q _ 6 ooolbs) - Diesel Factory Sales,Q _ 6 ooolbs)
(Eq. 4.6-7)
LDGT2 =
where: VCC
M
CP
Retail
Sales
- VCC - M - (0.05 x CP) -
Diesel
Factory
Sales )
(Eq. 4.6-8)
(6,000- 10,000/fo)
= Retail sales of van cutaway chassis
= Retail sales of multi-stops
= Retail sales of conventional pickups
,.,„„ ,, rt rt , „„ ( Heavy- Duty] (Retail]
HDGT - (VCC + M + [0.05 x CP])(6j000_ Wfl00lbs) - (j^j^j - ( Sales)
4-6'9)
LDDT = Diesel Factory Sales(0_60()()lhs) + (0-l°xDieselFactorySales\6,ooo-w,oooibS)
(Eq. 4.6-10)
HDDT = p.9 x DieselFactorySaleS)^^^^ + S Diesel Factory SaleS)(>WKlbs) (Eq. 4.6-11)
Once EPA converted AAMA sales data for the 1998 model year into sales data for the MOBILESb
truck categories, it calculated the fraction of total 1998 truck sales in each of these five MOBILESb truck
categories. EPA did this for each model year from 1974 through 1997, using data from earlier versions
of Facts and Figures.
Next, EPA calculated a fii!125-year distribution of trucks in operation in 1998 by model year from
the 1974 through the 1998 model years. The AAMA lists the total number of trucks (of all types) in
operation by model year in 1998 back to 1983. All trucks in operation from model years 1982 and earlier
were provided as an aggregate figure. The total number of trucks in operation from 1982 and earlier
model years was distributed to each model year from 1974 to 1982 using the method described above for
distributing the figure of cars in operation from the 1982 and earlier model years to the same set of model
4-205
-------
years. The survival rates used for distributing the number of trucks in operation were specific to trucks,
rather than cars.
Using the fraction of truck sales by truck type for each of the 25 model years needed and the number
of total trucks in operation in 1998 for each of the 25 model years needed, separate 1998 registration
distributions were calculated for each truck type. This was accomplished by multiplying the total number
of trucks in operation in 1998 in a given model year by the fraction of truck sales of the specified truck
type in the given model year. Equation 4.6-12 shows how EPA calculated the number of 1990 model
year LDGTls operating in 1995.
1990 Model Year LDGTls = Total Model Year 1990 x 1990 Model Year LDGTls Sold
Operating in 1995 Trucks Operating in 1995 X Tota[ 1990 Model Year Trucks Sold (Eq> 4'6'12)
EPA applied this process to all five truck types for model years 1974 through 1998. With the number of
trucks in operation 1998 by truck type and model year, the 1998 registration distribution for each truck
type was calculated by dividing the number of trucks operating in 1998 from a given model year by the
total number of trucks operating in 1998 for that particular truck category.
EPA projected the 1999 truck registration distributions from the calculations made for the 1998
truck registration distributions. EPA multiplied the number of trucks in operation in 1998 by truck
survival rates21 to obtain the corresponding numbers that would have survived to 1999. This is the same
as the process used to project the 1998 car registration distribution to 1999. As with the procedure for
cars, estimates of the number of 1998 and 1999 model year trucks operating in 1999 were calculated
separately. All of the 1998 model year trucks would not have been sold by the end of the 1998 calendar
year. Therefore, the number of 1998 model year trucks operating in 1999 should represent an increase
over the number of 1998 trucks operating in 1998, and a survival rate of 1998 cars to 1999 should be
factored in. Truck sales for 1999 were estimated as 50 percent of the 1998 sales figures for each of the
truck categories. (The truck model year is assumed to start in January, so half of the model year trucks
would be sold by July 1.) As with the development of the 1998 truck registration distributions, the last
step in calculating the 1999 truck registration distribution was to divide the number of trucks in operation
in each model year by the total number of estimated trucks in operation in 1999.
Registration distributions input to MOBILE5a should be expressed as a July 1 registration
distribution Internally, the model can then adjust this registration distribution to represent either a
January 1 or a July 1 registration distribution, depending on the user selected setting of the month flag.
When modeling months from January through June, EPA set the month flag within the MOBILE5a input
files to "1" to simulate January registration distributions. For months from July through December, EPA
set the month flag to "2" to model July registration distributions.
What Does EPA do with Local Registration Distributions for 1990,1995, and Later Years?
For the 1990, 1995, and later years MOBILE5b modeling, EPA replaced the national registration
distributions in some States with State-provided data. The State-provided data were extracted from the
registration distributions provided by the States to OTAG. In some States, a single registration
distribution applied to the entire State. In other States, different registration distributions applied to
different groupings of counties, such as nonattainment areas or MSAs. Since these State-provided
registration distributions did not vary by year, EPA applied the same distributions in 1990, 1995, and later
4-206
-------
years. All of the State-supplied registration distributions included only a single distribution for HDDVs,
since they were all created for use with MOBILE.
The following States supplied their own registration distribution: Delaware, Washington DC,
Louisiana, Maryland, Massachusetts, Michigan, Missouri, New Jersey, New York, North Carolina,
Pennsylvania, Texas, Virginia, and Wisconsin.
The following counties in Illinois supplied their own distribution: Cook County, Du Page County,
Lake County, Grundy County, Kane County, Kendall County, McHenry County, Will County, Madison
County, St. Clair County, and Monroe County.
4.6.4.7 Which MONTH Flag(s) Does EPA Use in the MOBILE Model?
Registration distribution inputs to MOBILESb are expressed as July 1 registration distributions.
Internally, the model then adjusts this re gist rat ion distribution to represent either a January 1 or a July 1
registration distribution, depending on the user selected setting of the MONTH flag. When modeling
months from January through June, the MONTH flag within the MOBILESb input files is set to "1" to
simulate January registration distributions. For months from July through December, the flag is set to "2"
to model July registration distributions.
4,6.4.8 What Additional Area-Specific Inputs from OTAG are Used?
In addition to the inputs discussed above, States supplied several additional MOBILESb inputs for
the OTAG modeling. This data has been incorporated into the Trends MOBILESb input files. These
inputs are listed below followed by the States that provided the inputs:
• trip length distributions (DC, MD, TX, and VA) (see Table 4.6-15)
• alcohol fuel market shares (GA, IL, IN, MI, MO, and WI) (see Table 4.6-16)
• diesel sales shares (DE, MD, and VA)
For all other States, EPA assumed the MOBILESb model defaults for these variables.
4.6.4.9 How Does EPA Model On-road Con trol Programs ?
The MOBILE model also allows for the modeling of several area-specific on-road contro 1 programs,
such as inspection and maintenance (I/M) programs, reformulated gasoline (RFG), oxygenated fuels, the
national low emission vehicle program (NLEV), heavy-duty diesel engine corrections and controls, and
California emission standards.
How Does EPA Model Inspection and Maintenance (I/M) Programs? Modeling an Inspection
and Maintenance (I/M) program in MOBILE requires the most complex set of inputs of any highway
vehicle control program. The sources used for developing the necessary I/M program inputs include the
I/M program inputs supplied by States to the OTAG process, a summary prepared by OTAQ showing the
basic characteristics of I/M programs planned by the States,22 past OTAQ I/M program summaries
showing characteristics of historical or current I/M programs in each State, and inputs prepared for
previous Trends inventories.
4-207
-------
For States that had an I/M program in place in one or more counties in the year being modeled, EPA
created at least one additional MOBILE input file to model the characteristics of the I/M program in that
State. All other inputs (such as temperature, RVP, speeds, etc.) are identical to the input file without I/M
modeled for the State in the year being analyzed. The determination of whether or not a county has an
I/M program in place in a given year is based on a series of I/M program summaries released by OTAQ.
I/M program characteristics are also included in the I/M program summaries. These program
characteristics vary by State and in some cases by nonattainment area or county within a particular State.
The effectiveness statistics used as MOBILES inputs varied by State based on the characteristics of
representative I/M programs in that State. For States where I/M programs varied within a given State, a
single set of effectiveness statistics, based on a combination of characteristics of all the I/M programs
within the State, was used as an I/M input to the model. In some cases, the characteristics of the
different programs within a specific State could not be adequately modeled using some average of the
I/M program characteristics. In these cases, multiple I/M programs were modeled for these States, with
the appropriate I/M programs applied to the corresponding counties. Tables 4.6-17 and 4.6-18 show the
counties included in the 1996 through 1999 I/M programs by test type.
A number of States provided data to OTAG that included MOBILE I/M program inputs and the
counties that these inputs should be applied to. These State-provided I/M inputs replaced the OTAQ I/M
program data for 1996 and 1999. States with I/M programs outside of the OTAG domain were modeled
according to the I/M program parameters supplied by OTAQ.
How Does EPA Account for the Reformulated Gasoline Program? Phase I of the federal RFG
program began on January 1, 1995. Phase I RFG provides year-round toxic emission reductions and
additional VOC emission reductions during the ozone season (May through September). The Clean Air
Act Amendments of 1990 (CAAA) mandates that RFG be used in the nine most severe ozone
nonattainment areas and allows additional nonattainment areas to opt in to the program. OTAQ provided
a list of areas that participated in this program. This list can be found at: http://www.epa.gov/
oms/regs/fuels/rfg/rfgarea.pdf Table 4.6-19 shows the counties modeled with Federal RFG in 1996.
RFG was modeled in the appropriate MOBILESb input files by setting the RFG flag to "2",
including the appropriate ASTM class of the area being modeled (B for Southern RFG areas or C for
Northern RFG areas), and setting WINFLG (a hidden MOBILESb flag) to "1". Setting WINFLG to "1"
guarantees that the summer RFG reductions are modeled regardless of the setting of the MONTH flag.
For all other months, and for areas not included in the RFG program, WINFLG is either set to "2" or not
included (in which case the model defaults to a setting of "2").
How Does EPA Account for Oxygenated Fuels? The oxygenated fuel requirements of the 1990
CAAA began to take effect in late 1992. Therefore, oxygenated fuel was modeled in the areas indicated
by OTAQ, using the oxygenated fuel flag and the oxygenated fuel market share and oxygen content
inputs in MOBILE. OTAQ provided a listing of areas participating in the oxygenated fuel program,23 the
months that each area used oxygenated fuel, and market share data indicating the percentage of ether
blends versus alcohol blends in each oxygenated fuel area. EPA assumed the average oxygen content of
ether blend fuels for all areas, except California, to be 2.7 percent while alcohol blend fuels were assumed
to have an oxygen content of 3.5 percent. For California, the oxygen content of both ether blends and
alcohol blends was modeled as 2 percent, based on documentation from OTAQ on how to model
reformulated and oxygenated fuels in the CALK model Table 4.6-20 lists the areas modeled with
oxygenated fuels and the corresponding inputs used for these areas.
4-208
-------
How Does EPA Account for the National Low Emission Vehicle (NLEV) Program? On
March 2, 1998, EPA's voluntary National Low Emission Vehicle (NLEV) program came into effect.
This program was modeled as starting in the Northeast Ozone Transport Commission (OTC) States in
1999. States in the OTC that had already adopted a LEV program on their own were modeled with the
characteristics of their own program. These States included Massachusetts, New York, Vermont,
Maine, and Connecticut. The implementation schedule of the NLEV program is shown below.
Model Year
1999
2000
2001 and later
Federal Tier 1
Standards
30%
Transitional LEV
Standards
40%
40%
LEV Standards
30%
60%
100%
These LEV implementation schedules differ from the MOBILESb default LEV implementation
schedule, which was designed to model the California LEV program. For the model to access the
implementation schedule of these other LEV programs, the PROMPT flag in the applicable MOBILESb
input files was set to '5' and the name of the file containing the corresponding LEV implementation
schedule was entered when prompted by MOBILESb. In addition to setting the PROMPT flag, the
REGION flag was set to '4' to properly model the LEV program in the MOBILESb input files. The
setting of'4' for the REGION flag indicates that an additional line is being added to the input file to
model a LEV program. The necessary inputs for this additional program line include the start year of the
LEV program and whether an "appropriate" I/M program will be implemented in conjunction with the
LEV program. The start year of the LEV program was set to "95" for input files modeling
Massachusetts, "96" for modeling New York, "98" for input files modeling Connecticut, and "99" for
input files modeling all other States within the OTC (including the Washington DC nonattainment area
portion of Virginia). With an "appropriate" I/M program, maximum benefits of the LEV program are
modeled by MOBILESb, implementing a lower set of deterioration rates.
How Does EPA Account for Heavy-Duty Vehicle Emission Rate Corrections? A correction
was made to the basic emission rates (BERs) for HDDVs and HDGVs as specified by OTAQ. This
correction modifies the default MO BILES b zero mile level (ZML) (the ZML is the emission rate at the
beginning of a vehicle's life) and DR (the DR reflects how quickly the emission rate of a vehicle increases
with time) for NOX for HDDVs and NOX and VOC for HDGVs. EPA believes that these default ZMLs
and DRs in MOBILESb are not reflective of actual heavy-duty vehicle emissions.24 The corrected BERs
input to MOBILESb are shown below. These inputs were included in all of the 1995 and later
MOBILE5b input files, for both low and high altitude areas. In addition, the NEWFLG in the
MOBILESb input files was set to "2" to incorporate these additional input lines.
Vehicle Category
HDGV
HDGV
HDDV
Model Year
1998 +
1994 +
1994-2003
ZML
(g/bhp-hr)
3.19
NOX
DR
(g/bhp-hr/ 10k
mi)
0.045
VOC
ZML
(g/bhp-hr)
0.364
0.283
DR
(g/bhp-hr/10k
mi)
0.023
0.000
Note(s): g/bhp-hr = grams per brake horsepower-hour; k = 1,000
4-209
-------
How Does EPA Account for California's Vehicle Program? California's highway vehicle fleet
has been subject to different emission standards than the rest of the country. To account for these
differences in basic emission rates, EPA used an EPA-modified version of MOBILESa, referred to as
CALI5, for California. Input files used with this model are essentially identical to MOBILESa input files.
The model internally handles the different emission standards. EPA developed temperature, RVP, speed,
registration distribution, and operating mode inputs for California in the same manner as for the rest of
the nation. The primary difference in inputs is the modeling of the California RFG program. Using
CALI5, EPA modeled the RFG program in the summer months for 1995 by setting the RFG flag to "4".
Phase II of California's RFG program began on June 1, 1996. EPA modeled this by setting the RFG flag
to "5" starting with the June 1996 scenarios in the CALI5 input files. As mentioned earlier, EPA divided
California into two temperature regions to account for the differences in climate throughout the State.
California's low emission vehicle (LEV) program began in 1994. This was modeled in the CALI5
input files indicating a start year of 1994 for this program and minimum LEV credits. Because
MOBILESa did not include LDGT2s in the LEV modeling, this was carried forward to CALI5.
However, California's LEV program does include LDGT2s. To model the LDGT2s in the LEV
program, additional BER input lines were added that model the zero mile level (ZML) and deterioration
rate (DR) of the California LEV program standard for LDGT2s. Two sets of basic emission rates
(BERs) were developed—one modeling the maximum LEV benefits for LDGT2s and the other modeling
the minimum benefits.
How Does EPA Account for the HDDV NOX Excess Emissions? On October 22, 1998, EPA
reached a settlement agreement with seven manufacturers of diesel truck engines. EPA had found that
the engines in as many as 1.3 million trucks built over the last 10 years contained devices that defeated
pollution controls. Federal officials considered such engine control software to be "defeat devices,"
which are illegal under the federal laws. These devices allow for excessive NOX emissions during
highway driving but prohibit high emissions during engine certification testing.
Certain engine manufacturers built these devices into heavy-duty diesel vehicles beginning in the
1988 model year. In the late 1980's and early 1990's these devices were being phased into the fleet,
mostly confined to the heavy end of the heavy-duty diesels (8a and 8b vehicles). However, by the mid to
late 1990's such devices were widespread on virtually all of the heavy end engines and most of the
medium and light end heavy-duty diesels.
Because EPA's MOBILE model is designed based on engine certification testing, these excess in-use
emissions from heavy-duty diesels caused the emission factors calculated by the MOBILE model to
underestimate actual emissions from these vehicles. In order to estimate actual in-use emissions from
HDDVs, OTAQ developed a series of spreadsheet models to provide emission factor adjustments to
apply to MOBILESb HDDV emission factors.25 These spreadsheets contain multiplicative factors
representing the ratio of HDDV NOX emissions with the defeat devices to the HDDV NOX emissions
without the defeat devices. These factors differ by calendar year, roadway type, and vehicle speed. The
HDDV NOX emissions, calculated using the MOBILESb HDDV NOX emission factors, were revised by
multiplying the appropriate factor at the State/county/roadway type level of detail for the years 1990
through 1999.
4-210
-------
4.6.5 How Does EPA Develop PM-10 and SO2 Emission Factors?
In 1994, EPA released a particulate emission factor model, known as PARTS,26 that calculates
particle emission factors in grams per mile from on-road automobiles, trucks, and motorcycles, for
particle sizes up to 10 microns. PARTS calculates on-road vehicle PM-10 and PM-2.5 emission factors
for vehicle exhaust, brake wear, and tire wear; reentrained road dust from paved and unpaved roads; and
SO2 vehicle exhaust emission factors.
EPA makes the following basic assumptions regarding inputs to PARTS that apply to all PARTS
model runs:
• The transient speed cycle is used.
• Any county with an existing I/M program receives I/M credit from PARTS, regardless of the
details of the I/M program. PARTS gives credit based on the assumption that high emitting
vehicles will be forced to make emission reducing repairs and that an existing I/M program will
deter tampering. This only affects lead and sulfate emissions from gasoline-powered vehicles.
• Using the input parameter BUSFLG in PARTS, EPA used the PARTS transit bus emission
factors to model bus emission factors for all rural road types, urban interstates, and other
freeways and expressways road types, while the PARTS Central Business District bus emission
factors are used to model bus emission factors for all other urban road types.
4.6.5.1 How are Registration Distributions Developed for the PAR T5 Model?
Registration distributions for PARTS include distributions for 12 vehicle categories. The MOBILES
HDDV category is subdivided into five subclasses (2BHDDV, LHDDV, MHDDV, HHDDV, and
BUSES) in PARTS. Table 4.6-21 lists the PARTS HDDV vehicle classes along with the corresponding
FHWA class and gross vehicle weight. The national MOBILES year-specific vehicle registration
distributions were modified to distribute the MOBILE HDDV vehicle class distribution among the five
PARTS HDDV subclasses. This was accomplished using HDDV subclass-specific sales, survival rates,
and diesel market shares. The table below shows how EPA calculated the sales for each of these five
HDDV categories. All of the relevant sales data came from. Facts and Figures. Once the sales data are
extracted for each of these HDDV categories, EPA applies the procedures described above individually
to each category to obtain the five separate HDDV registration distributions required by PARTS.
Truck Class Data Used to Calculate Truck Sales
2B HDDVs 0.90 *U.S. Factory Sales of Diesel Trucks 6,001 to 10,000 Ib GVWR
Light HDDVs U.S. Factory Sales of Diesel Trucks 10,001 to 19,500 Ib GVWR
Medium HDDVs U.S. Factory Sales of Diesel Trucks 19,501 to 33,000 Ib GVWR
Heavy HDDVs U.S. Factory Sales of Diesel Trucks 33,001 Ib GVWR - Factory Bus Sales
Buses Factory Bus Sales
For all other vehicle categories, the national MOBILESb and PARTS registration distributions are
identical. For areas that used local registration distributions in the MOBILESb modeling, the HDDV
category was applied to all five of the corresponding PARTS HDDV subcategory registration
4-211
-------
distributions since the local data did not contain sufficient information to split the distributions according
to HDD V subcategory.
4.6.5.2 How is Speed Modeled in the PAR T5 Model?
The speed inputs outlined above for use in the MOBILE model are used in the PARTS model as
well, except that the maximum allowable speed in PARTS is 55 mph. Therefore, the rural interstate
speed was changed from 60 mph to 55 mph for the PART5 modeling (see Table 4.6-22).
4,6.5.3 How Does EPA Develop VMTfor th e Five PARTS HDD V Vehicle Classes?
The HDDV VMT data developed as described above are broken down into the five PART5
subcategories for use with the PART5 PM and SO2 emission factors. This is done by multiplying the
HDDV VMT by a weighting factor for each of the five subcategories. These weighting factors are based
on truck VMT by weight and truck class from the Truck Inventory and Use Survey27 and FHWA's
Highway Statistics.2 The fractional weighting factors are shown in Table 4.6-21. After PART5 emission
factors are generated, EPA then multiplies the PART5 HDDV subclass emission factors (2BHDDV,
LHDDV, MHDDV, HHDDV, and BUSES) by the corresponding subclass VMT value.
4.6.5.4 How Does EPA Calculate Exhau st PM Emissions ?
EPA calculates monthly, county-level, SCC-specific PM emissions from on-road vehicle exhaust
components by multiplying year specific monthly, county-level, SCC-specific VMT by state-level,
SCC-specific exhaust PM emission factors generated using PART5. Since none of the inputs affecting
the calculation of the PM exhaust emission factors varies by month, EPA only calculates annual PM
exhaust emission factors. PART5 total exhaust emission factors are the sum of lead, soluble organic
fraction, remaining carbon portion, and direct SO4 (sulfates) emission factors.
4.6.5.5 How Does EPA Calculate Exhaust SO2 Emissions?
EPA uses the PART5 model to calculate national annual SO2 on-road vehicle exhaust emission
factors by vehicle type and speed. These emission factors vary according to fuel density, the weight
percent of sulfur in the fuel, and the fuel economy of the vehicle (which varies by speed). None of these
parameters varies by month or State. EPA calculates monthly/county/SCC-specific SO2 emissions by
multiplying each county's monthly VMT at the road type and vehicle type level by the SO2 emission
factor (calculated for each vehicle type and speed) that corresponds to the vehicle type and road type.
4.6.5.6 How Does EPA Calculate PM Brake Wear Em issions?
The PART5 model generates PM emission factors for brake wear of 0.013 grams per mile for
PM-10 and 0.005 grams per mile for PM-2.5. These values are used to estimate brake wear emissions
for all vehicle types.
4.6.5.7 How Does EPA Calculate PM Tire Wear Emissions?
The emission factors for tire wear generated by the PART5 model are proportional to the average
number of wheels per vehicle. The emission factor is 0.002 grams per mile per wheel for PM-10 and
4-212
-------
0.0005 grams permile per wheelfor PM-2.5. Therefore, EPA calculates separate tire wear emission
factors for each vehicle type. Estimates of the average number of wheels per vehicle by vehicle class
were developed using information from the Truck Inventory and Use Survey.21 Tire wear PM emissions
were then calculated at the monthly/county/SCC level by multiplying the monthly/county/SCC level VMT
by the tire wear emission factor for the appropriate vehicle type.
4.6.5.8 How does EPA calculate PM and SO2 Emissions For 1970 to 1984?
The EPA did not use the PARTS model to calculate PM-10 and SO2 emission factors from 1970 to
1984. (EPA did not calculate PM2 5 emissions for any years prior to 1990.) Instead, it relies on data from
AP-42 and other applicable EPA documents to develop PM-10 and SO2 emission factors. EPA
developed emission factors for both PM-10 and SO2 on a national basis by vehicle type for each year.
The procedure followed for developing these emission factors is discussed below.
How Does EPA Calculate PM-10 Emission Factors for 1970 to 1984? EPA relied on the
methodology used to develop the Regional Particulate Inventory [RPI] for 199028 to calculate on-road
vehicle PM-10 emission factors for 1970 to 1984. The Regional Particulate Inventory calculated national
annual 1990 PM-10 emission factors by vehicle type. With regard to gasoline PM-10 exhaust emission
factors, the RPI based the factors on exhaust particulate emission iactors specific to the technology type
of the vehicle (i.e., catalyst vs. no catalyst) and model year group.29 EPA then applied these basic exhaust
emission factors to the corresponding portion of the vehicle fleet for each model year from age 1 to 25
comprising the 1990 fleet. Model year specific data indicating the fraction of vehicles with catalysts were
obtained from the MOBILESa source code.10 After obtaining the model year weighted emission factor
for each of the gasoline vehicle types, the model year specific emission factors were then weighted by the
model year travel fraction, obtained using the by-model-year option in MOBILES a that lists VMT
fractions for each model year for the calendar year specified. These model year-weighted emission
factors were then summed to obtain the fleet average exhaust particulate emission factor for each of the
gasoline vehicle types. These particulate emission factors were then multiplied by the PM-10 particle size
multiplier from AP-42. The PM-10 emission factors calculated for LDGVs were also applied to
motorcycles.
EPA used the RPI procedure to obtain 1970 and 1984 PM-10 exhaust emission factors for gasoline-
fueled vehicles, and then used straight line interpolation to calculate the PM-10 exhaust emission factors
for the years between 1970 and 1984. Total PM-10 emission factors were then calculated by adding the
brake and tire wear PM-10 emission factors from AP-42 (which do not vary by year).
EPA calculated PM-10 emission factors fromdiesel vehicles using a similar methodology, however,
EPA used data by model year and vehicle type for diesel particulate emission factors and diesel travel
fractions.30 Again, EPA multiplied the particulate emission factors by the AP-42 particle size multipliers
to obtain PM-10 exhaust emission factors, and PM-10 brake and tire wear emission factors were added to
the exhaust emission factors.
The PM-10 emission factors by vehicle type and year used in Emission Trends inventory are shown
in Table 4.6-23. These emission factors include the exhaust, brake, and tire wear components of PM-10.
How Does EPA Calculate SO2 Emission Factors for 1970 to 1984? EPA used Equation 4.6-13
to calculate the on-road vehicle SO2 emission factors by vehicle type.
4-213
-------
SO^EFx = SULFCONT x 0.98 x FUELDENSZ x 453.59 x
FUELECON
where: SO2EFX y = SO2 emission factor for vehicle type x in year y (grams per mie)
SULFCONTy z = Sulfur content in year y for fuel type z (fractional value)
FUELDENSZ = Fuel density of fuel type z (pounds per gallon)
FUELECONxy = Fuel economy for vehicle type x in year y (miles per gallon)
The factor of 0.98 in the above equation represents the fraction of sulfur in the iuelthat is converted to
SO231 while the 2 represents the weight molecular ratio of sulfur to SO2. The remaining term (453.59) is
the conversion from pounds to grams.
The value used for fuel sulfur content depends on whether a vehicle is gasoline-fueled or diesel-
fueled. EPA relied upon a fuel sulfur content of 0.000339 for gasoline-fueled vehicles, which is based on
the fuel sulfur content of EPA baseline fuel, while a fuel sulfur content of 0.00227 was used for diesel-
fueled vehicles through September 1993. EPA used fuel density values of 6.17 pounds per gallon for
gasoline and 7.05 pounds per gallon for diesel for all years.32
Fleet average iuel economy varies slightly from year to year for each vehicle type. The values used
for fuel economy from 1982 to 1984 were obtained from output from the draft MOBILE4.1 Fuel
Consumption Model33 for all vehicle types except motorcycles. 1982 was the earliest model year
included in this output. EPA estimated fuel economy values for 1970 through 198 1 using fuel economy
data from Highway Statistics.2 Because the vehicle classes included in Highway Statistics differ from the
MOBILE vehicle classes, EPA needed to make adjustments to the Highway Statistics fuel economy data
in order to smooth out the discontinuity in fuel economy estimates between the two sources from 1981 to
1982. This was done using Equation 4.6-14.
(Eq. 4.6-14)
where: FExy = Fuel economy value for vehicle type x in year y used SO2 emission factor
calculations (mpg)
FE(HS)X y = Highway Statistics fuel economy for vehicle type x in year y (mpg)
FE(FCM)X 1982 = MOBILE4.1 Fuel Consumption Model fuel economy for vehicle type x in
1982
FE(HS)X 1982 = Highway Statistics fuel economy for vehicle type x in 1982
Differences in vehicle class definitions used in the MOBILE4.1 Fuel Consumption Model versus those
used in Highway Statistics proved difficult when using the above equation. To resolve this, EPA
calculated a single light duty vehicle and a single light duty truck fuel economy value for each year. EPA
also used the same OTAQ apportionment used in allocating HP MS VMT to the diesel and gasoline
categories in weighing gasoline and diesel vehicles. Because the MOBILE4.1 Fuel Consumption Model
does not include motorcycles, EPA used a fuel economy value of 50 mpg for motorcycles in all years
4-214
-------
from 1970 through 1984 based on AAMA motorcycle fuel economy data.11 The fuel economy values
used for each vehicle type and year are shown in Table 4.6-24.
The resulting SO2 emission factors by vehicle type and year are shown in Table 4.6-25.
4.6.6 How Does EPA Calculate Pre-1996 Ammonia (NH3) Emission Factors?
Little research has been done to date on NH3 emission factors from motor vehicles. The NH3
emission factors used by EPA for years from 1990 through 1995 were calculated from vehicle test data
including NH3 emission factors summarized in a report by Volkswagen AG.34 In the testing program
described in that report, 18 different Volkswagen/Audi vehicles from the 1978 through 1986 model years
were tested. These 18 vehicles represented a cross-section of the Volkswagen/Audi passenger car
production program. The vehicles all had either 4 or 5 cylinder gasoline or diesel engines. Seven of the
gasoline vehicles were equipped with 3-way catalysts with oxygen sensors, seven of the vehicles were
diesel-fueled, and the remaining four vehicles were gasoline vehicles with no catalysts.
The Volkswagen test measured emissions from each of these vehicles using a chassis dynamometer
over three different test procedures: the U.S. FTP, the U.S. Sulfate Emission Test (SET), and the U.S.
Highway Driving Test. The FTP includes both cold and hot engine starts with a cumulative mileage of
11.1 miles over 505 seconds. The SET simulates 13.5 miles of travel on a freeway in Los Angeles with
heavy traffic over a time of 1,398 seconds. The Highway Driving Test, also known as the Highway Fuel
Economy Test (HFET), results in an average speed of 48.1 mph over 10.2 miles with a maximum speed
of 59.9 mph. Both the SET and the HFET are hot start tests (no cold starts are included). The test ran
each vehicle on all three test cycles on the same day, with three to five repeated measurements carried out
for each vehicle on consecutive days.
The Volkswagen report includes the mean results of the emissions testing program for each of the 18
vehicles tested and for each of the test cycles. The report shows the total mean value over all three tests
by engine type (gasoline with catalyst, gasoline without catalyst, and diesel). These total mean values
were used in Trends analysis to calculate NH3 emission factors, given that most types of driving would be
included in one of the three test cycles studied (that is., the FTP would represent urban driving; the SET
would represent stop and go driving on expressways; and the HFET would represent freeway driving).
These mean emission factors are shown below.
Engine Type
Mean NH3 Emission Factor (grams/mile)
Gasoline Engine without Catalyst
Gasoline Engine with 3-Way Catalyst
Diesel Engine
0.00352
0.13743
0.00188
Using the NH3 emission factors listed above, EPA calculated emission factors by vehicle type and
model year using MOBILE5b data listing the fraction of vehicles with 3-way catalysts by vehicle type and
travel fractions from MOBILE5b output by model year and vehicle type. For the Trends analysis, EPA
assigned the non-catalyst gasoline engine emission factor to motorcycles and the diesel engine emission
factor to all diesel vehicle types.
4-215
-------
To calculate the LDGV emission factor for 1995, a MOBILESb run was made to produce
by-model-year output for LDGVs in 1995. The by-model-year travel fractions were extracted from the
resulting MOBILESb output file. Then, for each of the 25 model years included in the by-model-year
output, a weighted emission factor was calculated by multiptying the fraction of LDGVs with 3-way
catalysts in that model year by the emission factor listed above for gasoline engines with 3-way catalysts
(i.e., 0.13743 g/mi) and adding to this the product of the fraction of LDGVs without 3-way catalysts in
that model year and the emission factor for gasoline engines without 3-way catalysts (i.e., 0.00352 g/mi).
This weighted emission factor was then multiplied by the LDGV travel fraction for that model year,
giving a model year-weighted emission factor. This procedure was repeated for each of the 25 model
years included in the by-model-year output for 1995 and the 25 model-year weighted emission factors
were then summed to give the composite 1995 LDGV NH3 emission factor.
EPA repeated the above procedure for each calendar year from 1990 through 1994 for LDGVs,
LDGT1 s, LDGT2s, and HDGVs. Table 4.6-26 summarizes the catalyst fractions used in this analysis by
model year and vehicle type.
4.6.7 How Does EPA Calculate 1996 through 1999 Ammonia Emission Factors?
EPA used a different data set to estimate NH3 emission factors starting in 1996. These emission
factors are based on data contained in a report supplied by OTAQ that allows EPA to capture the effect
of catalytic converters on vehicles.35 These numbers are, in general, consistent with more recent studies
on motor vehicle emissions. MOBILE5b travel fractions are then generated by model year for each
calendar year to weight the emission factors according to the fraction of vehicles with different catalyst
types.
4.6.8 References
1. "National Air Pollutant Emission Trends, Procedures Document, 1900-1996," EPA-454/R-98-008,
U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research
Triangle Park, NC, May 1998.
2. Highway Statistics. Federal Highway Administration, U.S. Department of Transportation,
Washington, DC, published annually.
3. "Highway Performance Monitoring System Field Manual," Federal Highway Administration, U.S.
Department of Transportation, Washington, DC, December 1987.
4. "Traffic Monitoring Guide," Federal Highway Administration, U.S. Department of Transportation,
Washington, DC, June 1985.
5. "1980 Census of Population, Volumel Characteristics of Population, Chapter B Number of
Inhabitants," Bureau of the Census, U.S. Department of Commerce, Washington, DC, April 1983.
6. "1990 Census of Population, Volumel Characteristics of Population, Chapter B Number of
Inhabitants," Bureau of the Census, U.S. Department of Commerce, Washington, DC, July 1992.
4-216
-------
7. Letter from Mark Wolcott, Technical Support Branch, Office of Mobile Sources, U.S.
Environmental Protection Agency, to E.H. Pechan & Associates, Inc., dated Januarys, 1994
8. "The 1985 NAPAP Emissions Inventory: Development of Temporal Allocation Factors," EPA-
600/7-89-01 Od, Air & Energy Engineering Research Laboratory, U.S. Environmental Protection
Agency, Research Triangle Park, NC, April 1990.
9. "VMT Estimates for the 2007 Heavy-Duty Final Rule Analyses," memorandum from Penny Carey
and Michael Sklar, U.S. Environmental Protection Agency, Assessment and Standards Division, to
Docket A-99-06, May 9, 2000.
10. "User's Guide to MOBILES (Mobile Source Emission Factor Model)," U.S. Environmental
Protection Agency, EPA-AA-AQAB-94-01, Office of Mobile Sources, Ann Arbor, MI, May 1994.
11. National Climatic Center, data files to E.H. Pechan & Associates, Inc., Asheville, NC, 1994, and
annually thereafter.
12. "National Data Book and Guide to Sources, Statistical Abstract of the United States - 1993," U.S.
Department of Commerce, Bureau of the Census, Washington, DC. 1994.
13. "Fuel Volatility Survey," American Automobile Manufacturers Association, Washington, DC,
published annually through 1998.
14. "Fuel Volatility Survey 1999," Alliance of Automobile Manufacturers, Washington, DC, 1999.
15. "1988 Annual Book of ASTM Standards," American Society for Testing and Materials, (Section 5:
Petroleum Products, Lubricants, and Fossil Fuels; Volume 05.01: Petroleum Products and
Lubricants (I): D 56 - D 1947), Philadelphia, PA, 1988.
16. "User's Guide to MOBILE4 (Mobile Source Emission Factor Model)," EPA-AA-TEB-89-01, U.S.
Environmental Protection Agency, Office of Mobile Sources, Ann Arbor, MI, February 1989.
17. Table provided by Greg Janssen, Office of Mobile Sources, U.S. Environmental Protection Agency,
to E.H. Pechan & Associates, Inc., May 11, 1996.
18. "Motor Vehicle Facts and Figures," American Automobile Manufacturers Association, Washington,
DC, published annually through 1998.
19. "Ward's Motor Vehicle Facts and Figures 1999," Ward's Communications, Southfield, MI, 1999.
20. "19XX Market Data Book," Automotive News, Detroit, MI, published annually.
21. "Study of Vehicle Scrappage Rates," Miaou, Shaw-Pin, ORNL, Oak Ridge National Laboratories,
Oak Ridge, TN, August 1990.
4-217
-------
22. "Major Modeling Elements for Operating I/M Programs," table provided by Joseph Somers, Office
of Mobile Sources, U.S. Environmental Protection Agency, Ann Arbor, MI, to E.H. Pechan &
Associates, Inc., July 10, 1997, updated annually.
23. "State Winter Oxygenated Fuel Programs," table provided by Joseph Somers, Office of Mobile
Sources, U.S. Environmental Protection Agency, Ann Arbor, MI, to E.H. Pechan & Associates,
Inc., February 25, 1997, updated annually.
24. "Draft Regulatory Impact Analysis: NMHC+NOX Emission Standards for 2004 and Later Model
Year On-Highway Heavy Duty Engines," Office ofMobfle Sources, U.S. Environmental Protection
Agency, Ann Arbor, MI, January 26, 1996.
25. U.S. Environmental Protection Agency, "Development and Use of Heavy-Duty NOX Defeat Device
Emission Effects for MOBILES and MOBILE 6," EPA420-P-99-030, October 1999.
26. "Draft User's Guide to PART 5: AProgram For Calculating Particle Emissions From Motor
Vehicles,"EPA-AA-AQAB-94-2, U.S. Environmental Protection Agency, Office of Mo bile
Sources, Ann Arbor, MI, July 1994.
27. 1987 Census of Transportation: Truck Inventory and Use Survey - United States, TC87-T-52, U.S.
Department of Commerce, Bureau of the Census, August 1990.
28. E.H. Pechan & Associates, Inc., "Regional Particulates Inventory for the National Particukte Matter
Study," prepared for U.S. Environmental Protection Agency, Office of Policy, Planning and
Evaluation/Office of Policy Analysis, June 1994.
29. "Air Toxics Emissions from Motor Vehicles," U.S. Environmental Protection Agency, Office of
Mobile Sources, EPA-AA-TSS-PA-86-5, Ann Arbor, MI, September 1987.
30. "Motor Vehicle-Related Air Toxics Study," U.S. Environmental Protection Agency, Office of
Mobile Sources, Public Review Draft, Ann Arbor, MI, December 1992.
31. "Regulatory Impact Analysis: Control of Sulfur and Aromatics Contents of On-Highway Diesel
Fuel," U.S. Environmental Protection Agency, Office of Mobile Sources, 1990.
32. Compilation of Air Pollutant Emission Factors, AP-42, U S. Environmental Protect ion Agency,
1975.
33. "MOBILE4.1 Fuel Consumption Model (Draft)," U.S. Environmental Protection Agency, Office of
Mobile Sources, Ann Arbor, MI, August 1991.
34. "Unregulated Motor Vehicle Exhaust Gas Components," Volkswagen AG Research and
Development, Wolfsburg, Germany, March 1989.
35. "A Study of the Potential Impact of Some Unregulated Motor Vehicle Emissions," Craig Harvey,
Robert Garbe, Thomas Baines, Joseph Somers, KarlHellman, and Penny Carey, SAE Paper 830897,
June 1983.
4-218
-------
4-219
-------
Table 4.6-1. Methods for Developing Annual Emission Estimates for On-road Highway
Vehicles for the Years 1989-1999
For the
category
Highway
Vehicles
For the
years
1989-1994
For the
pollutant(s)
VOC, NOX, CO
EPA estimated emissions by
Using monthly, area-specific MOBILESa emission
factors except for California. For California, using
monthly, area-specific CALI5 emission factors.
1995-1999 VOC, NOX, CO
Using monthly, area-specific MOBILESb emission
factors except for California. For California, using
monthly, area-specific CALI5 emission factors.
1989-1999 SO2, PM10, PM25 Using annual, area-specific PARTS emission factors.
1990-1995 NH,
Using annual Volkswagen-based emission factors by
vehicle type.34
1996-1999 NH,
Using annual OTAQ-based emission factors by vehicle
type.35
Heavy Duty
Diesel
Vehicles
1990-1999 NOV
Applying excess emission adjustment factors to
emissions calculated with MOBILESb or CALI5.25
Highway 1989 VMT (affects all
Vehicles pollutants)
Using HPMS VMT data by urban area and rest-of-state
rural and small urban areas, also by roadway type.
Converting HPMS VMT by vehicle classes to MOBILES
vehicle classes based on OTAQ 1994 mapping
scheme.7 Allocating VMT to county based on
population.
1990 VMT (affects all
pollutants)
Using State-supplied VMTfrom OTAG where available
to replace HPMS-based VMT.
1990-1995 VMT (affects all
pollutants)
Adjusting previous VMT (calculated as discussed for
1989) to account for shifts from passenger cars to light-
duty trucks; using HMPS to MOBILES vehicle category
allocation from 1994.
1996-1999 VMT (affects all
pollutants)
Using updated annual HPMS to MOBILESb vehicle
category allocations provided by OTAQ;9 modifying
roadway type allocations to only place VMT in counties
with roadway mileage of that type. Local road VMT
allocated by population. Rural interstate VMT allocated
by interstate mileage.
1999 VMT (affects all
pollutants)
Starting with preliminary FHWA VMT totals by State and
roadway type and allocating to county/roadway type by
applying 1999 to 1998 ratio of State/roadway type VMT
to 1998 VMT data.
4-220
-------
Table 4.6-2. Comparison of Methodologies Used to Develop 1996 Base Year Emissions
for On-road Sources in Versions 1 through 4 of the NEI
For the Category
For the
estimation of
EPA estimated 1996 Base Year emissions for
Version 1 by
Version 2 by
Version 3 by
Version 4 by
Light Duty Gasoline
Vehicles, Light Duty
GasolineTrucks,
Heavy Duty
Gasoline Vehicbs,
Light Duty Diesel
Vehicles, Light Duty
Diesel Trucks,
Heavy Duty D iesel
Vehicles
VMT (same
VMT data is
used in
calculating
emissions
from all
pollutants)
Growing 1995 V MT to 1 996 bas ed on
preliminary 1996 HP MS state/roadway
type VMT data. Using MOBILESb
emission factors developed at monthly
level of detail by control area, vehicle type,
and roadway type.
Calculating 1996 VMT by county,
vehicle type, and roadway type based on
1996 final VMT data from HPMS by
urban area, and state totals for small
urban and rural areas, all by roadway
type. Apportioning VMT to county level
for all roadway types bas ed on coun ty
population. Converting HPMS vehicle
type distributions to MOBILESb vehicle
type distributbns based on 1994EPA
guidance.7
Usin g sam e method ology as us ed in
Version 2.
Apportioning Rural Interstate VMT to
county level using Rural Interstate
mileage as activity surrogate;
apportioning local roadway VMT to
county level based on county
population; and apportioning VMT to
county level for remaining roadway
types based on county population for
counties with m ileage from specific
roadwaytype. Reallocating Version 2
VMT by vehicle class , based on
OTAQ u pdate of the conversion of
HPMS vehicle lypes to MOBILESb
vehicle types.9
Light Duty Gasoline
Vehicles, Light Duty
GasolineTrucks,
Heavy Duty
Gasoline Vehicbs,
Light Du ty Diesel
Vehicles, Light Du ty
Diesel Trucks
VO C, N Ox,
CO
Heavy Duty D iesel
Vehicbs
VOC, CO
Calculating emission factors with
MOBILESb using a national registration
distribution projected from 1995 to 1996,
or locallysupplied registration detributbns
where available, state-level monthly
temperature data, monthly RVP data by
State and non attainment area, area-
specific I/M inputs, area-specific
reformulated gasoline and oxygenated fuel
program inputs, and other state-suppled
inputs, where provided. Cabulating
emiss ions by cou nty, month, vehic le type,
and roadway type.
Updating national 1996 registration
distribution based on actual 1996 data.
Updating control program inputs to
MOBILESb. Recalculating MOBILESb
emission factors with updated inputs
and other unc hanged inp uts from
Version 1. Recalculating emissions by
county, month, vehicle type, and
roadway type.
Usin g sam e method ology as us ed in
Version 2.
Applying V ersion 2 em ission fac tors to
updated VMT.
Heavy Duty D iesel
Vehicbs
NO,
Calculating emission factors with
MOBILESb using a national registration
distribution projected from 1995 to 1996,
or locallysupplied registration detributbns
where available, state-level monthly
temperature data, monthly RVP data by
State and non attainment area, area-
specific I/M inputs, area-specific
reformulated gasoline and oxygenated fuel
program inputs, and other state-supplbd
inputs, where provided. Cabulating
emissions by county, month, vehicle type,
and roadway type.
Updating national 1996 reg istration
distribution based on actual 1996 data.
Updating control program inputs to
MOBILESb. Recalculating MOBILESb
emission factors with updated inputs
and other unc hanged inp uts from
Version 1. Recalculating emissions by
county, month, vehicle type, and
roadway type.
Applying excess emission adjustment
factors to Version 2 emissions.25
Applying Version 2 emission factors
and Vers ion 3 excess emiss ion
adjustment factors to updated VMT.
-------
Table 4.6-2 (continued)
For the Category
Light Duty Gasoline
Vehicles, Light Duty
Gasoline Trucks,
Heavy Duty
Gasoline Vehicfes,
Light Duty Diesel
Vehicles, Light Duty
Diesel Trucks,
Heavy Duty D iesel
Vehicles
Gasoline Vehicfes,
Light Duty Gasoline
Trucks, Heavy Duty
Gasoline Vehicfes,
Light Duty Diesel
Vehicles, Light Duty
Diesel Trucks,
Heavy Duty D iesel
Vehicles
Gasoline Vehicfes,
Light Duty Gasoline
True ks, Heavy D uty
Gasoline Vehicfes,
Light Duty Diesel
Vehicles, Light Duty
Diesel Trucks,
Heavy Duty D iesel
Vehicles
c ** r t u Q
r or me
estimation of
S02, PM10
PM25
NH3
EPA estimated 1 996 Base Year emissions for
Version 1 by
Calculating emission factors with PARTS
using a national registration distribution
projected from 1 995 to 1 996, or locally
supplied registration distributions where
available, and area-specific I/M and
reformulated gasoline inputs. Calculating
emiss ions by cou nty, month, vehic le type,
and roadway type.
Not estimated. PM2 5 and NH3 were not
included in the Trends inventories (for any
sources) until 1998 (i.e., version 2).
Not estimated. PM25 and NH3 were not
included in the Trends inventories (for any
sources) until 1998 (i.e., version 2).
Version 2 by
Updating national 1996 registration
distribution based on actual 1996 data
and recalculating PARTS emiss ion
factors u sing other u nchang ed inputs
from Version 1. Calculating emissions
by county, month, vehicle type, and
roadway type.
Updating national 1996 reg istration
distribution based on actual 1996 data
and recalculating PARTS emiss ion
factors u sing other u nchang ed inputs
from Version 1 . Calculating emissions
by county, month, vehicletype, and
roadway type.
Calculating natbnal emission factors by
vehicle type.35 Calculating emissions by
county, month, vehicle type, and
roadway type.
Version 3 by
Usin g sam e method ology as us ed in
Version 2.
Usin g sam e method ology as us ed in
Version 2.
Usin g sam e method ology as us ed in
Version 2.
Version 4 by
Applying V ersion 2 em ission fac tors to
updated VMT.
Applying Version 2 emission factors to
updated VMT.
Applying V ersion 2 em ission fac tors to
updated VMT.
NOTES: Version 1 corresponds to December 1997 Trends report, Version 2 estimates correspond to December 1998 report, Version 3 corresponds to March 2000 report, and Version 4 is for report
yet to be published.
4-222
-------
Table 4.6-3. Data Components of HPMS
Universe -All Road Mileage
Identification
System
Jurisdiction
Operation
Other
Contains State, county, and rural/small urbanized codes and a unique
identification of location reference.
Optionally, the latitude and longitude coordinates for the beginning and
ending points of universe and sample sections are provided.
Provides for coding of functional system and federal-aid system.
Provides for coding of State or local highway system and special funding
category.
Includes type of facility, truck prohibition, and toll.
Contains length of highway section and fields for the coding of AADT
and the number of through lanes.
Sample - Statistical Sample of Universe
Identification
Computational
Elements
Pavement Attributes
Improvements
Geometries/
Configuration
Traffic/Capacity
Environment
Supplemental Data
Contains unique identification for the sample section portion of the
record.
Provides data items used to expand sample information to universe
val ues.
Contains data items used to evaluate the physical characteristics of
pavement, pavement performance, and the need for pavement overlays.
Describes the improvement type for the year of the improvement
completion.
Describes the physical attributes used to evaluate the capacity and
operating characteristics of the facility.
Provides operational data items used to calculate the capacity of a
section and the need for improvements.
Contains items that marginally affect the operation of a facility but are
important to its structural integrity.
Provides linkage to existing structure and railroad crossing information
systems.
Areawide - State Summaries
Mileage
Travel
Accidents
Injuries
Population
Road mileage
Vehicle miles traveled, percent travel by vehicle type
Number of accidents
Number of injuries
Area population
4-223
-------
Table 4.6-4. Apportionment Percentages for Conversion of HPMS Vehicle Type
Categories to MOBILESa Categories (through 1995)
HPMS Vehicle Type Category MOBILESa Vehicle Type Category and Apportionment Percentages
Motorcycle
Passenger Car
Other 2-Axle, 4-tire
Buses
Other Single Unit Trucks
Combination Trucks
MC
LDGV
LDDV
LDGT1
LDGT2
LDDT
HDGV
HDDV
HDGV
HDDV
HDDV
1.0000
0.9864
0.0136
0.6571
0.3347
0.0082
0.1028
0.8972
0.7994
0.2006
1.0000
4-224
-------
Table 4.6-5. VMT Seasonal and Monthly Temporal Allocation Factors
Vehicle Type
LDV, LOT, MC
LDV, LOT, MC
HDV
Vehicle Type
LDV, LOT, MC
LDV, LOT, MC
HDV
Roadway
Type
Rural
Urban
All
Roadway
Type
Rural
Urban
All
Seasonal VMT Factors
Winter
0.2160
0.2340
0.2500
Spring
0.2390
0.2550
0.2500
Summer
0.2890
0.2650
0.2500
Fall
0.2560
0.2450
0.2500
Monthly VM T Factors: Non-Leap Years--1995, 1999,
Jan
0.0744
0.0806
0.0861
Feb
0.0672
0.0728
0.0778
Mar
0.0805
0.0859
0.0842
Apr
0.0779
0.0832
0.0815
May
0.0805
0.0859
0.0842
Jun
0.0942
0.0864
0.0815
Jul
0.0974
0.0893
0.0842
2002,2005, 2007, 2010
Aug
0.0974
0.0893
0.0842
Sep
0.0844
0.0808
0.0824
Oct
0.0872
0.0835
0.0852
Nov
0.0844
0.0808
0.0824
Dec
0.0744
0.0806
0.0861
Vehicle Type
LDV, LOT, MC
LDV, LOT, MC
HDV
Roadway
Type
Rural
Urban
All
Jan
0.0736
0.0797
0.0852
Feb
0.0688
0.0746
0.0797
Mar
0.0805
0.0859
0.0842
Apr
0.0779
0.0832
0.0815
Monthly VMT
May
0.0805
0.0859
0.0842
Factors: Leap
Jun
0.0942
0.0864
0.0815
Years--1996,
Jul
0.0974
0.0893
0.0842
2000, 2008
Aug
0.0974
0.0893
0.0842
Sep
0.0844
0.0808
0.0824
Oct
0.0872
0.0835
0.0852
Nov
0.0844
0.0808
0.0824
Dec
0.0736
0.0797
0.0852
-------
Table 4.6-6. State-level Daily VMT Totals in the OTAG Inventory
STATF
Alabama
Arkansas
Connecticut
Delaware
District of Columbia
Florida
Georg ia
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Nebraska
New Hamps hire
New J ersey
New York
North Carolina
North D akota
Ohio
Oklahoma
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Vermont
Virginia
West Virginia
Wisconsin
TOTAL
1990 VMT
(VMT/SIIMMFR HAY)
130,293,139
64,893,375
80,795,439
21,688,232
9,512,227
301,401,066
215,733,554
254,405,708
146,238,700
70,914,717
70,274,093
103,468,764
85,036,022
36,687,471
124,790,087
128,906,395
244,651,250
119,486,368
75,306,141
144,836,950
42,949,068
30,337,965
177,882,767
327,206,333
159,748,582
18,241,880
249,268,477
101,777,917
262,877,528
22,482,474
106,001,636
21,648,546
143,924,247
456,338,143
18,055,581
184,879,090
47,716,623
116,510,029
4,917,166,586
4-226
-------
Table 4.6-7. Cities Used for Temperature Data Modeling from 1970 through 1999
State
Alabama
Alaska
Arizona
Arkansas
Californ la
Californ la
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hamps hire
New J ersey
New Mexico
New York
North Carolina
North D akota
Ohio
O k I ah o m a
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
City
Birming ham
Anchorage
Phoenix
Little Rock
Los Angeles
San Francisco
Denver (1970-1997)
Colorado Springs (1998-1999)
Hartford
Dover
Washington
Orlando
Atlanta
Honolu lu
Boise
Springfield
Indianapolis
Des Moines
Topeka
Louisville
Baton Rouge
Portland
Baltimore
Boston
Detroit
Minneapolis
Jackson
Springfield
Billings
Lincoln
Las Veg as
Concord
Newark
Albuquerque
New York City
Greensboro
Bismarck
Columbus
Oklahom a City
Eugene
Harrisburg (1970-1991 ),
Middletown (1991-1999)
Providence
Colum bia
Pierre
Nashville
Dallas/Fort Worth
Salt Lake City
Montpelier
Richmond
Seattle
Charleston
Milwaukee
Casper
4-227
-------
Table 4.6-8. Surrogate City Assignment
Nonattainment Area/MSA
Albany-Schenectady-Troy, NY MSA
Albuquerque, NM MSA
Allentown-Bethlehem, PA-NJ MSA
Altoona, PA MSA
Anchorage, AK MSA
Anderson, SC MSA
Appleton-Oshkosh-Neenah, Wl MSA
Atlanta
Atlantic City, NJ MSA
Bakersfield, CA MSA
Baltimore, MD MSA
Baton Rouge
Beaumont-Port Arthur, TX MSA
Bennington Co., VT
Birmingham, AL MSA
Boston Metropolitan Area
Boston Metropolitan Area
Bowling Green, KY
Buffalo-Niagara Falls, NY CMSA
Canton, OH MSA
Charleston, WV MSA
Charlotte-Gastonia-Rock Hill, NC-SC MSA
Chattanooga, TN-GA MSA
Cherokee Co., SC
Chester Co., SC
Chicago-Gary-Lake County, IL-IN-WI CMSA
Chico, CA MSA
Cincinnati-Hamilton, OH-KY-IN CMSA
Cleveland Metropolitan Area
Clinton Co., OH
Colorado Springs, CO MSA
Columbia, SC MSA
Columbus, OH MSA
Dallas-Ft. Worth, TX CMSA
Dayton-Springfield, OH MSA
Denver-Boulder, CO CMSA
Detroit-Ann Arbor, Ml CMSA
Door Co., Wl
Duluth, MN-WI MSA
Edmonson Co., KY
El Paso, TX MSA
Erie, PA MSA
Essex Co., NY
Evansville, IN-KY MSA
Fairbanks, AK
Fayetteville, NC MSA
Flint, Ml MSA
Fort Collins-Loveland, CO MSA
State
NY
NM
PA-NJ
PA
AK
SC
Wl
GA
NJ
CA
MD
LA
TX
VT
AL
MA
MA-NH
KY
NY
OH
WV
NC
GA-TN
SC
SC
IL-IN-WI
CA
OH-KY-IN
OH
OH
CO
SC
OH
TX
OH
CO
Ml
Wl
MN
KY
TX
PA
NY
IN-KY
AK
NC
Ml
CO
Survey City
New York City
Albuquerque
Philadelphia
Philadelphia
Cleveland
Atlanta
Chicago
Atlanta
Philadelphia
San Francisco
Washington, DC
New Orleans
Dallas
Boston
Atlanta
Boston
Boston
Chicago
New York City
Cleveland
Washington, DC
Atlanta
Atlanta
Atlanta
Atlanta
Chicago
San Francisco
Cleveland
Cleveland
Cleveland
Denver
Atlanta
Cleveland
Dallas
Cleveland
Denver
Detroit
Chicago
Minneapolis
Chicago
Albuquerque
Cleveland
New York City
Chicago
Cleveland
Atlanta
Detroit
Denver
4-228
-------
Table 4.6-8 (continued)
Nonattainment Area/MSA
Fresno, CA MSA
Glens Falls, NY MSA
Grand Rapids, Ml MSA
Great Falls, MT MSA
Greater Connecticut Metropolitan Area
Greeley, CO MSA
Greenbrier Co., WV
Greensboro-Winston-Salem-High Point PMSA
Greenville-Spartanburg, SC MSA
Hancock Co., ME
Harrisburg-Lebanon-Carlisle, PA MSA
Hartford-New Britain-Middletown, CT
Houston-Galveston-Brazoria, TX CMSA
Huntington-Ashland, WV-KY-OH MSA
Huntsville, AL MSA
Indianapolis, IN MSA
Jacksonville, FL MSA
Janesville-Beloit, Wl MSA
Jefferson Co., NY
Jersey Co., IL
Johnson City-Kingsport-Bristol, TN-VA MSA
Johnstown, PA MSA
Josephine Co., OR
Kansas City, MO-KS MSA
Kent and Queen Anne's Cos., MD
Kewaunee Co., Wl
Kings Co., CA
Klamath Co., OR
Knox Co., ME
Knoxville, TN MSA
Lafayette-West Lafayette, IN MSA
Lake Charles, LA MSA
Lake Tahoe South Shore, CA
Lancaster, PA MSA
Las Vegas, NV MSA
Lawrence Co., PA
Lewiston, ME
Lexington-Fayette, KY MSA
Lincoln Co., ME
Livingston Co., KY
Longmont, CO
Longview-Marshall, TX MSA
Los Angeles-Anaheim-Riverside, CA CMSA
Los Angeles-South Coast Air Basin, CA
Louisville, KY-IN MSA
Manchester, NH MSA
Manitowoc Co., Wl
Medford, OR MSA
State
CA
NY
Ml
MT
CT
CO
WV
NC
SC
ME
PA
CT
TX
WV-KY-OH
AL
IN
FL
Wl
NY
IL
TN
PA
OR
MO
MD
Wl
CA
OR
ME
TN
IN
LA
CA
PA
NV
PA
ME
KY
ME
KY
CO
TX
CA
CA
KY-IN
NH
Wl
OR
Survey City
San Francisco
New York City
Chicago
Billings
Boston
Denver
Washington, DC
Atlanta
Atlanta
Boston
Philadelphia
Boston
Dallas
Washington, DC
Chicago
Chicago
Miami
Chicago
Philadelphia
Chicago
Atlanta
Philadelphia
Seattle
Kansas City
Philadelphia
Chicago
San Francisco
San Francisco
Boston
Atlanta
Chicago
New Orleans
San Francisco
Philadelphia
Las Vegas
Cleveland
Boston
Chicago
Boston
St. Louis
Denver
Dallas
Los Angeles
Los Angeles
Chicago
Boston
Chicago
San Francisco
4-229
-------
Table 4.6-8 (continued)
Nonattainment Area/MSA
State
Survey City
Memphis, TN-AR-MS MSA TN-AR-MS
Miami-Fort Lauderdale, FL CMSA FL
Milwaukee Metropolitan Area Wl
Minneapolis-St. Paul, MN-WI MSA MN-WI
Missoula, MT MT
Mobile, AL MSA AL
Modesto, CA MSA CA
Montgomery, AL MSA AL
Muskegon, Ml MSA Ml
Nashville, TN MSA TN
New Orleans, LA MSA LA
New York-Northern New Jersey-Long Island CMSA NY-NJ-CT
Norfolk-Virginia Beach-Newport News, VA MSA VA
Northampton Co., VA VA
Oklahoma City, OK MSA OK
Owensboro, KY MSA KY
Paducah, KY KY
Parkersburg, WV WV
Parkersburg-Marietta, WV-OH MSA OH-WV
Philadelphia Metropolitan Area PA-NJ-DE-
Phoenix, AZ MSA AZ
Pittsburgh-Beaver Valley, PA CMSA PA
Portland, ME ME
Portland-Vancouver, OR-WA CMSA OR-WA
Portsmouth-Dover-Rochester, NH-ME MSA ME-NH
Poughkeepsie, NY MSA NY
Providence-Pawtucket-Fall River, RI-MA CMSA MA-RI
Provo-Orem, UT MSA UT
Raleigh-Durham, NC MSA NC
Reading, PA MSA PA
Reno, NV MSA NV
Richmond-Petersburg VA
Rochester, NY MSA NY
Sacramento, CA MSA CA
Salt Lake City-Ogden, UT MSA UT
San Antonio, TX MSA TX
San Diego, CA MSA CA
San Francisco-Oakland-San Jose, CA CMSA CA
San Joaquin Valley, CA CA
Santa Barbara-Santa Maria-Lompoc, CA MSA CA
Scranton-Wilkes-Barre, PA MSA PA
Seattle-Tacoma, WA WA
Sheboygan, Wl MSA Wl
Smyth Co., VA VA
South Bend-Elkhart, IN IN
South Bend-Mishawaka, IN MSA IN
Southeast Desert Modified AQMA, CA CA
Spokane, WA MSA WA
St. Louis
Miami
Chicago
Minneapolis
Billings
New Orleans
San Francisco
Atlanta
Chicago
Atlanta
New Orleans
New York City
Washington, DC
Washington, DC
Dallas
Atlanta
Chicago
Cleveland
Cleveland
MD Philadelphia
Phoenix
Philadelphia
Boston
Seattle
Boston
New York City
Boston
Denver
Atlanta
Philadelphia
San Francisco
Washington, DC
Philadelphia
San Francisco
Denver
San Antonio
Los Angeles
San Francisco
San Francisco
Los Angeles
Philadelphia
Seattle
Chicago
Washington, DC
Chicago
Chicago
Los Angeles
Seattle
4-230
-------
Table 4.6-8 (continued)
Nonattainment Area/MSA
State
Survey City
Springfield, MA MSA
St. Louis, MO-IL MSA
Steubenville-Weirton, OH-WV MSA
Stockton, CA MSA
Sussex Co., DE
Syracuse, NY MSA
Tampa-St. Petersburg-Clearwater, MSA
Toledo, OH MSA
Tulsa, OK MSA
Ventura Co., CA
Visalia-Tulare-Porterville, CA MSA
Waldo Co., ME
Walworth Co., Wl
Washington, DC-MD-VA MSA
Wheeling, WV-OH MSA
Winnebago Co., Wl
Winston-Salem, NC
Worcester, MA MSA
Yakima, WA MSA
York, PA MSA
Youngstown-Warren, OH MSA
Yuba City, CA MSA
MA
MO-IL
OH-WV
CA
DE
NY
FL
OH
OK
CA
CA
ME
Wl
DC-MD-VA
WV-OH
Wl
NC
MA
WA
PA
OH
CA
Boston
St. Louis
Cleveland
San Francisco
Philadelphia
New York City
Miami
Detroit
Kansas City
Los Angeles
San Francisco
Boston
Chicago
Washington, DC
Cleveland
Chicago
Atlanta
Boston
Seattle
Philadelphia
Cleveland
San Francisco
4-231
-------
Table 4.6-9. Substitute Survey City Assig nment
Nonattainment Area/MSA
Albany-Schenectady-Troy, NY MSA
Allentown-Bethlehem, PA-NJ MSA
Altoona, PA MSA
Appleton-Oshkosh-Neenah, Wl MSA
Beaumont-Port Arthur, TX MSA
Bennington Co., VT
Bowling Green, KY
Buffalo-Niagara Falls, NY CMSA
Charleston, WV MSA
Door Co., Wl
Edmonson Co., KY
Essex Co., NY
Evansville, IN-KY MSA
Glens Falls, NY MSA
Grand Rapids, Ml MSA
Greenbrier Co., WV
Harrisburg-Lebanon-Carlisle, PA MSA
State
NY
PA-NJ
PA
Wl
TX
VT
KY
NY
WV
Wl
KY
NY
IN-KY
NY
Ml
WV
PA
Huntington-Ashland, WV-KY-OH MSAWV-KY-OH
Huntsville, AL MSA
Indianapolis, IN MSA
Jefferson Co., NY
Jersey Co., IL
Johnstown, PA MSA
Kewaunee Co., Wl
Lafayette-West Lafayette, IN MSA
Lancaster, PA MSA
Longview-Marshall, TX MSA
Louisville, KY-IN MSA
Manitowoc Co., Wl
Muskegon, Ml MSA
Northampton Co., VA
Oklahoma City, OK MSA
Paducah, KY
Pittsburgh-Beaver Valley, PA CMSA
Reading, PA MSA
Rochester, NY MSA
Sheboygan, Wl MSA
Smyth Co., VA
South Bend-Elkhart, IN
South Bend-Mishawaka, IN MSA
Syracuse, NY MSA
Waldo Co., ME
Walworth Co., Wl
York, PA MSA
AL
IN
NY
IL
PA
Wl
IN
PA
TX
KY-IN
Wl
Ml
VA
OK
KY
PA
PA
NY
Wl
VA
IN
IN
NY
ME
Wl
PA
Original Survey
New York City
Philadelphia
Philadelphia
Chicago
Dallas
Boston
Chicago
New York City
Washington, DC
Chicago
Chicago
New York City
Chicago
New York City
Chicago
Washington, DC
Philadelphia
Washington, DC
Chicago
Chicago
Philadelphia
Chicago
Philadelphia
Chicago
Chicago
Philadelphia
Dallas
Chicago
Chicago
Chicago
Washington, DC
Dallas
Chicago
Philadelphia
Philadelphia
Philadelphia
Chicago
Washington, DC
Chicago
Chicago
New York City
Boston
Chicago
Philadelphia
City New Survey City
Cleveland
Cleveland
Cleveland
Minneapolis
New Orleans
Minneapolis
Cleveland
Cleveland
Cleveland
Minneapolis
Cleveland
Cleveland
Cleveland
Cleveland
Detroit
Cleveland
Cleveland
Cleveland
Atlanta
Cleveland
Cleveland
Cleveland
Cleveland
Minneapolis
Cleveland
Cleveland
New Orleans
Cleveland
Minneapolis
Detroit
Atlanta
St. Louis
Cleveland
Cleveland
Cleveland
Cleveland
Minneapolis
Atlanta
Cleveland
Cleveland
Cleveland
Minneapolis
Minneapolis
Cleveland
4-232
-------
Table 4.6-10. Monthly RVP Values Modeled in 1996
State
AL
AK
AZ
AR
CA
CA
CO
CT
DE
DC
FL
GA
HI
ID
IL
IL
IN
IA
KS
KY
KY
LA
ME
MD
MA
Ml
MN
MN
MS
MO
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
Applicable
Counties
Entire State
Entire State
Entire State
Entire State
Los Angeles Region
San Francisco Region
Entire State
Entire State
Entire State
Entire State
Entire State
Entire State
Entire State
Entire State
Madison, Monroe, St. Clair
Rest of State
Entire State
Entire State
Entire State
Boone, Campbell, Kenton
Rest of State
Entire State
Entire State
Entire State
Entire State
Entire State
Anoka, Carver, Dakota, Hennepin,
Ramsey, Scott, Washington, Wright
Rest of State
Entire State
Franklin, Jefferson, St. Charles, St.
Louis, St. Louis City
Rest of State
Entire State
Entire State
Entire State
Entire State
Entire State
Entire State
Entire State
Entire State
Entire State
Butler, Cuyahoga, Hamilton, Lake,
Lorain
1996 Monthly RVP (psi)
Jan
12.4
14.1
8.7
13.7
11.9
11.7
13.2
13.0
13.5
12.8
11.8
12.4
10.0
13.9
14.1
14.1
14.5
14.9
14.0
14.2
14.2
12.4
13.2
13.2
12.9
14.1
14.9
14.9
13.7
13.9
13.9
13.8
14.5
10.5
12.9
13.7
11.7
14.3
12.4
14.9
14.6
Feb Mar
12.4 9.5
14.1 14.1
7.9 7.2
13.7 9.8
11.9 11.9
11.7 11.7
12.1 10.7
13.0 10.8
13.5 11.1
10.3 10.3
11.8 7.4
12.4 9.4
10.0 10.0
12.3 12.3
14.1 11.4
141 114
14.5 12.0
14.9 13.3
12.1 9.5
11.7 11.7
11.7 11.7
12.4 9.6
13.2 11.0
13.2 10.8
12.9 10.7
14.1 11.2
14.9 12.6
14.9 12.6
13.7 9.8
11.9 11.9
11.9 11.9
13.8 12.3
145 127
9.2 8.2
12.9 10.7
13.7 11.3
11.7 10.2
14.3 11.9
12.4 12.4
14.9 13.3
14.6 12.1
Apr
9.5
14.1
7.2
9.8
9.0
11.7
10.7
10.8
11.1
7.0
7.4
9.4
10.0
10.2
11.4
11 4
12.0
11.2
9 5
8.4
8.4
9.6
11.0
10.8
10.7
11.2
12.6
12.6
9.8
9.2
9.2
10.2
104
8.2
10.7
11.3
9.1
11.9
9.4
13.3
12.1
May
7.8
13.0
6.8
7.1
6.9
9.0
9.0
8.6
8.5
7.5
7.4
7.6
10.0
8.6
7.1
84
9.0
9.0
74
9.3
8.6
7.3
8.6
7.8
8.6
8.9
9.3
9.0
7.1
7.1
7.3
8.7
84
7.6
8.6
8.6
8.4
8.7
7.6
9.0
9.3
Jun
7.8
13.0
6.8
7.1
6.9
6.9
7.8
8.6
8.5
7.5
7.4
7.6
10.0
8.6
7.1
84
9.0
9.0
7.4
9.3
8.6
7.3
8.6
7.8
8.6
8.9
9.3
9.0
7.1
7.1
7.3
8.7
84
7.6
8.6
8.6
7.8
8.7
7.6
9.0
9.3
Jul
7.8
13.0
6.8
7.1
6.9
6.9
7.8
8.6
8.5
7.5
7.4
7.6
9.5
8.6
7.1
84
9.0
9.0
74
9.3
8.6
7.3
8.6
7.8
8.6
8.9
9.3
9.0
7.1
7.1
7.3
8.7
84
7.6
8.6
8.6
7.8
8.7
7.6
9.0
9.3
Aug
7.8
13.0
6.8
7.1
6.9
6.9
7.8
8.6
8.5
7.5
7.4
7.6
10.0
8.6
7.1
84
9.0
9.0
74
9.3
8.6
7.3
8.6
7.8
8.6
8.9
9.3
9.0
7.1
7.1
7.3
8.7
84
7.6
8.6
8.6
7.8
8.7
7.6
9.0
9.3
Sep
7.8
13.0
6.8
7.1
6.9
6.9
7.8
8.6
8.5
7.5
7.4
7.6
10.0
8.6
7.1
84
9.0
9.0
74
9.3
8.6
7.3
8.6
7.8
8.6
8.9
9.3
9.0
7.1
7.1
7.3
8.7
84
7.6
8.6
8.6
7.8
8.7
7.6
9.0
9.3
Oct
9.5
14.1
6.8
9.8
6.9
7.0
9.6
10.8
7.9
7.0
7.4
9.4
10.0
8.6
7.8
7 8
8.7
11.2
7 6
8.4
8.4
9.6
11.0
7.5
10.7
11.2
9.6
9.6
9.8
9.2
9.2
10.2
8 6
7.6
10.7
11.3
9.1
11.9
9.4
11.2
8.7
Nov Dec
9.5 12.4
14.1 14.1
7.2 7.9
13.7 13.7
9.0 11.9
9.0 11.7
10.7 12.1
10.8 13.0
11.1 13.5
10.3 12.8
7.4 11.8
9.4 12.4
10.0 10.0
10.2 12.3
11.4 14.1
114 14 1
12.0 14.5
13.3 14.9
9.5 12.1
11.7 14.2
11.7 14.2
9.6 12.4
11.0 13.2
10.8 13.2
10.7 12.9
11.2 14.1
12.6 14.9
12.6 14.9
9.8 13.7
11.9 11.9
11.9 11.9
12.3 13.8
104 127
8.2 9.2
10.7 12.9
11.3 13.7
10.2 11.7
11.9 14.3
12.4 12.4
13.3 14.9
12.1 14.6
4-233
-------
Table 4.6-10 (continued)
State
OH
OK
OR
PA
PA
Rl
SC
SD
TN
TX
TX
TX
UT
VT
VA
WA
WV
Wl
WY
Applicable
Counties
Rest of State
Entire State
Entire State
Clarion, Crawford, Elk, Erie, Forest,
Jefferson, Lawrence, McKean, Mercer,
Venango, Warren
Rest of State
Entire State
Entire State
Entire State
Entire State
El Paso
Hardin, Harris, Jefferson, Orange
Rest of State
Entire State
Entire State
Entire State
Entire State
Entire State
Entire State
Entire State
1996 Monthly RVP (psi)
Jan
14.6
13.9
13.1
14.4
14.4
12.9
12.4
14.9
12.7
12.2
12.2
12.2
13.2
14.9
12.6
14.0
14.6
14.6
13.5
Feb Mar
14.6 12.1
13.9 10.1
10.8 10.8
14.4 12.0
14.4 12.0
12.9 10.7
12.4 12.4
14.9 13.3
12.7 12.7
12.2 10.0
12.2 10.0
12.2 10.0
12.1 12.1
14.9 12.6
10.2 10.2
14.0 11.6
14.6 12.1
14.6 12.2
13.5 12.1
Apr
12.1
10.1
10.8
12.0
12.0
10.7
9.4
11.2
9.5
10.0
10.0
10.0
10.7
12.6
7.1
11.6
12.1
12.2
10.2
May
9.0
7.4
7.7
9.3
8.5
8.6
7.6
9.0
7.5
8.2
7.4
7.7
9.0
9.0
7.5
8.5
8.8
9.0
8.8
Jun
9.0
7.4
7.7
9.3
8.5
8.6
7.6
9.0
7.5
8.2
7.4
7.7
7.8
9.0
7.5
8.5
8.8
9.0
8.8
Jul
9.0
7.4
7.7
9.3
8.5
8.6
7.6
9.0
7.5
8.2
7.4
7.7
7.8
9.0
7.5
8.5
8.8
9.0
8.8
Aug
9.0
7.4
7.7
9.3
8.5
8.6
7.6
9.0
7.5
8.2
7.4
7.7
7.8
9.0
7.5
8.5
8.8
9.0
8.8
Sep
9.0
7.4
7.7
9.3
8.5
8.6
7.6
9.0
7.5
8.2
7.4
7.7
7.8
9.0
7.5
8.5
8.8
9.0
8.8
Oct
8.7
7.2
7.7
12.0
12.0
10.7
9.4
9.6
9.5
8.3
8.3
8.3
9.6
12.6
7.1
8.5
8.8
9.0
8.8
Nov Dec
12.1 14.6
10.1 13.9
10.8 13.1
12.0 14.4
12.0 14.4
10.7 12.9
12.4 12.4
11.2 13.3
12.7 12.7
10.0 12.2
10.0 12.2
10.0 12.2
10.7 12.1
12.6 14.9
10.2 12.6
11.6 14.0
12.1 14.6
12.2 14.6
10.2 12.1
Note: May through September RVP values modeled for areas receiving reformulated gasoline are set within
MOBILESb and are not reflected here.
4-234
-------
s
.3
O
4-1
O
"o
O
re
o.
Q. <
O
CM
O
CO
u
c
O
o>
o>
V)
•D
0)
Q)
Q.
(O
"55
re
re
o
O
a)
D)
re
V)
2
Q.
CO
re
Q.
E
4-235
-------
Table 4.6-12. Average Speeds by Road Type and Vehicle Type
(mph)
Rural
LDV
LOT
HDV
Interstate
60
55
40
Principal
Arterial
45
45
35
Minor
Arterial
40
40
30
Major
Collector
35
35
25
Minor
Collector
30
30
25
Local
30
30
25
Urban
LDV
LOT
HDV
Interstate
45
45
35
Other Freeways
& Expressways
45
45
35
Principal
Arterial
20
20
15
Minor
Arterial
20
20
15
Collector
20
20
15
Local
20
20
15
4-236
-------
Table 4.6-13. State-Supplied Operating Mode Inputs
State
County
Percent of VMT Accumulated by:
Non-catalyst
Vehicles in
Cold Start Mode
Catalyst Equipped
Vehicles in
Hot Start Mode
Catalyst Equipped
Vehicles in
Cold Start Mode
Texas Brazoria Co
Chambers Co
Fort Bend Co
Galveston Co
Harris Co
Liberty Co
Montgomery Co
Waller Co
16.0
14.3
23.3
Texas
Collin Co
Dallas Co
Denton Co
Tarrant Co
16.5
14.6
24.9
Maryland
Allegany Co
Anne Arundel Co
Baltimore Co
Caroline Co
Carroll Co
Cecil Co
Dorchester Co
Garrett Co
Harford Co
Howard Co
Kent Co
Queen Annes Co
St. Mary's Co
Somerset Co
Talbot Co
Washington Co
Wicomico Co
Worcester Co
Baltimore
22.3
14.6
22.3
4-237
-------
Table 4.6-14. Default Values for the 1998 National Registration Distribution
AGE
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
LDV
0.044
0.064
0.06
0.071
0.063
0.063
0.058
0.06
0.061
0.065
0.064
0.059
0.055
0.046
0.037
0.031
0.021
0.018
0.014
0.015
0.011
0.008
0.005
0.005
0.003
LOT
0.062
0.089
0.075
0.082
0.076
0.065
0.055
0.054
0.05
0.054
0.051
0.046
0.046
0.037
0.029
0.02
0.016
0.014
0.012
0.017
0.012
0.011
0.009
0.009
0.008
LDGT2
0.062
0.074
0.064
0.071
0.062
0.048
0.037
0.035
0.038
0.051
0.048
0.036
0.039
0.035
0.031
0.034
0.026
0.023
0.022
0.043
0.043
0.032
0.021
0.018
0.009
HDGV
0.018
0.036
0.033
0.026
0.049
0.04
0.031
0.036
0.037
0.052
0.056
0.04
0.038
0.027
0.024
0.023
0.021
0.026
0.03
0.091
0.081
0.064
0.047
0.052
0.021
HDDV
0.076
0.084
0.072
0.102
0.069
0.055
0.044
0.04
0.043
0.052
0.049
0.04
0.039
0.041
0.039
0.027
0.022
0.019
0.014
0.023
0.016
0.013
0.007
0.006
0.008
MC
0.133
0.152
0.149
0.115
0.083
0.08
0.065
0.049
0.033
0.029
0.022
0.09
0
0
0
0
0
0
0
0
0
0
0
0
0
4-238
-------
Table 4.6-15. State-Supplied Trip Length Distribution Inputs
State
Georgia
Illinois
Illinois
Indiana
Michigan
Missouri
Wisconsin
Wisconsin
Nonattainment
Area
Washington, DC/MD/VA
Baltimore
Houston
Dallas
Table 4.6-16
Applicable Area
Entire State
Percentage of Total VMT Accumulated in Trips of:
<10 11 to 20 21 to 30 31 to 40 41 to 50 > 50
Minutes Minutes Minutes Minutes Minutes Minutes
16.6 33.9 23.4 13.3 6.1 6.7
15.1 31.7 26 13.3 6.5 7.4
14.8 27.9 22.4 14.3 8.5 12.1
9.8 19 23.8 19.4 13.6 14.4
. State-Supplied Alcohol Fuels Data
Ether Alcohol Oxygen Oxygen
Blends Blends Content of Content of
Market Market Ether Alcohol
Share (%) Share (%) Blend s (%) Blend s (%)
0.0 2.5 3.5
Chicago Nonattainment Area 17.0 83.0 2.1 3.5
Rest of State
Entire State excl uding RFC
Entire State
Entire State
0.0 33.0 3.5
Counties 0.0 19.0 3.5
0.0 12.7 3.5
0.0 33.0 3.5
Milwaukee Nonattainment Area 17.0 83.0 2.1 3.5
Rest of State excluding St.
Croix County 0.0 10.0 3.5
1.0 psi
RVP
Waiver
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
4-239
-------
Table 4.6-17. Counties Included in 1996 and 1997 I/M Programs
(P) Indicates a Pressure and/or Purge test was also included.
I/M Program Name
Included Counties
COUNTIES WITH IDLE/2 SPEED IDLE TESTING
ALASKA
ARIZONA
CALIFORNIA
COLORADO
CONNECTICUT
DELAWARE
FLORIDA
GEORGIA
IDAHO
ILLINOIS
INDIANA
KENTUCKY (P)
LOUISIANA
MASSACHUSETTS
MARYLAND
MINNESOTA
MISSOURI
NEVADA
NEW JERSEY
NEW MEXICO
Anchorage Ed, Fairbanks Ed
Pima Co
Butte Co, El Dorado Co, Madera Co, Merced Co, Orange Co, Placer Co, Riverside Co,
San Bernardino Co, San Joaquin Co, Santa Clara Co, Stanislaus Co, Tulare Co, Ventura
Co, Yolo Co, Monterey Co, San Luis Obispo Co, Santa Barbara Co, Santa Cruz Co,
Sonoma Co, Fresno Co, Kern Co, Los Angeles Co, Sacramento Co, San Diego Co,
Alameda Co, Contra Costa Co, Solano Co, Marin Co, San Mateo Co, Napa Co, San
Francisco Co
The following CA counties were added in 1997 - Colusa Co, Glenn Co, Kings Co,
Nevada Co, San Benito Co, Shasta Co, Sutler Co, Tehama Co, Yuba Co
Pitkin Co, Larimer Co, Weld Co
Fairfield Co, Hartford Co, Litchf ield Co, Middlesex Co, New Haven Co, New London,
Tolland Co, Windham Co
New Castle Co, Kent Co, Sussex Co
Broward Co, Dade Co, Duval Co, Hillsborough Co, Palm Beach Co, Pinellas Co
Cobb Co, De Kalb Co, Fulton Co, Gwinnett Co
Ada Co
Cook Co, Du Page Co, Lake Co, Madison Co, St. Clair Co, Grundy Co, Kane Co,
Kendall Co, McHenry Co, Will Co
Clark Co, Floyd Co
Boone Co, Campbell Co, Kenton Co, Jefferson Co
Ascension Par, Calcasieu Par, East Baton Rouge Par, Iberville Par, Livingston Par,
Pointe Coupee Par, West Baton Rouge Par
Barnstable Co, Berkshire Co, Bristol Co, Dukes Co, Essex Co, Franklin Co, Hampden
Co, Hampshire Co, Middlesex Co, Nantucket Co, Norfolk Co, Plymouth Co, Suffolk Co,
Worcester Co
Anne Arundel Co, Baltimore Co, Carroll Co, Harford Co, Howard Co, Baltimore City,
Montgomery Co, Prince Georges Co, Washington Co, Calvert Co, Cecil Co, Queen
Annes Co, Charles Co, Frederick Co
Anoka Co, Carver Co, Dakota Co, Hennepin Co, Ramsey Co, Scott Co, Washington Co,
Wright Co,
Franklin Co, Jefferson Co, St. Charles Co, St. Louis Co, St. Louis
Clark Co, Washoe Co
Atlantic Co, Cape May Co, Warren Co, Bergen Co, Essex Co, Hudson Co, Hunterdon
Co, Mercer Co, Middlesex Co, Monmouth Co, Morris Co, Ocean Co, Passaic Co,
Somerset Co, Sussex Co, Union Co, Burlington Co, Camden Co, Cumberland Co,
Gloucester Co, Salem Co
Bernalillo Co
4-240
-------
Table 4.6-17 (continued)
(P) Indicates a Pressure and/or Purge test was also included.
I/M Program Name
Included Counties
COUNTIES WITH IDLE/2 SPEED IDLE TESTING (cont'd.)
NEW YORK
NORTH CAROLINA
OKLAHOMA
OREGON (P)
PENNSYLVANIA
RHODE ISLAND
TENNESSEE
TEXAS
UTAH
VERMONT
VIRGINIA
COUNTIES WITH ASM
PENNSYLVANIA (P)
UTAH
WASHINGTON
Bronx Co, Kings Co, Nassau Co, New York Co, Queens Co, Richmond Co, Rockland
Co, Suffolk Co, Westchester Co
Mecklenburg Co, Wake Co, Davidson Co, Davie Co, Forsyth Co, Guilford Co, Durham
Co, Granville Co, Gaston Co
Canadian Co, Cleveland Co, Kingfisher Co, Lincoln Co, Logan Co, McClain Co,
Oklahoma Co, Pottawatomie Co, Creek Co, Osage Co, Rogers Co, Tulsa Co, Wagoner
Co
Jackson Co, Clackamas Co, Multnomah Co, Washington Co
Lehigh Co, Northampton Co, Allegheny Co, Beaver Co, Washington Co, Westmoreland
Co
Bristol Co, Kent Co, Newport Co, Providence Co, Washington Co
Shelby Co, Davidson Co, Rutherford Co, Sumner Co, Williamson Co, Wilson Co
Dallas Co, Tarrant Co, El Paso Co, Harris Co
Weber Co, Utah Co
Addison Co, Bennington Co, Caledonia Co, Chittenden Co, Essex Co, Franklin Co,
Grand Isle Co, Lamoille Co, Orange Co, Orleans Co, Rutland Co, Washington Co,
Windham Co, Windsor Co
Arlington Co, Fairfax Co, Fairfax, Prince William Co, Alexandria, Manassas, Manassas
Park, Falls Church
TESTING
Bucks Co, Chester Co, Delaware Co, Montgomery Co, Philadelphia Co
Salt Lake Co
King Co, Snohomish Co, Spokane Co, Clark Co, Pierce Co
COUNTIES WITH IM240 TESTING
ARIZONA (P)
COLORADO
DC
INDIANA
OHIO (P)
UTAH
WISCONSIN
Maricopa Co
Adams Co, Arapahoe Co, Boulder Co, Douglas Co, Jefferson Co, Denver Co, El Paso
Co
Washington
Lake Co, Porter Co
Clark Co, Greene Co, Montgomery Co, Clermont Co, Geauga Co, Medina Co, Portage
Co, Summit Co, Warren Co, Butler Co, Hamilton Co, Lake Co, Lorain Co, Cuyahoga Co
Davis Co
Kenosha Co, Milwaukee Co, Ozaukee Co, Racine Co, Washington Co, Waukesha Co,
Sheboygan Co
4-241
-------
Table 4.6-18. Counties Included in 1998 and 1999 I/M Programs
(P) indicates a Pressure and/or Purge test was also included.
* Indicates the state was added in 1999
I/M Program Name
Included Counties
COUNTIES WITH IDLE/2 SPEED IDLE TESTING
ALASKA
ARIZONA (P)
CALIFORNIA (P)
Anchorage Ed, Fairbanks Ed
Pima Co
Butte Co, Colusa Co, El Dorado Co, Glenn Co, Kings Co, Madera Co, Merced Co,
Nevada Co, Orange Co, Placer Co, Riverside Co, San Benito Co, San Bernardino Co,
San Joaquin Co, Santa Clara Co, Shasta Co, Stanislaus Co, Sutler Co, Tehama Co,
Tulare Co, Ventura Co, Yolo Co, Yuba Co, Monterey Co, San Luis ObispoCo, Santa
Barbara Co, Santa Cruz Co, Sonoma Co, Fresno Co, Kern Co, Los Angeles Co,
Sacramento Co, San Diego Co, Alameda Co, Contra Costa Co, Solano Co, Marin Co,
San Mateo Co, Napa Co, San Francisco Co
Pitkin Co, Larimer Co, Weld Co
Washington
New Castle Co, Kent Co, Sussex Co
Broward Co, Dade Co, Duval Co, Hillsborough Co, Palm Beach Co, Pinellas Co
Cobb Co, De Kalb Co, Fulton Co, Gwinnett Co
Ada Co
Cook Co, Du Page Co, Lake Co, Madison Co, St. Clair Co, Grundy Co, Kane Co,
Kendall Co, McHenry Co, Will Co
KENTUCKY (P)Boone Co, Campbell Co, Kenton Co, Jefferson Co
COLORADO (P)
DC
DELAWARE (P)
FLORIDA
GEORGIA
IDAHO?
ILLINOIS
LOUISIANA
MASSACHUSETTS
MARYLAND
*MAINE (P)
MINNESOTA
MISSOURI
NORTH CAROLINA
*NEW HAMPSHIRE
NEW JERSEY
NEW MEXICO (P)
Ascension Par, Calcasieu Par, East Baton Rouge Par, Iberville Par, Livingston Par,
Pointe Coupee Par, West Baton Rouge Par
Barnstable Co, Berkshire Co, Bristol Co, Dukes Co, Essex Co, Franklin Co, Hampden
Co, Hampshire Co, Middlesex Co, Nantucket Co, Norfolk Co, Plymouth Co, Suffolk Co,
Worcester Co
Anne Arundel Co, Baltimore Co, Carroll Co, Harford Co, Howard Co, Baltimore,
Montgomery Co, Prince Georges Co, Washington Co, Calvert Co, Cecil Co, Queen
Annes Co, Charles Co, Frederick Co
Cumberland Co
Anoka Co, Carver Co, Dakota Co, Hennepin Co, Ramsey Co, Scott Co,
Washington Co, Wright Co
Franklin Co, Jefferson Co, St. Charles Co, St. Louis Co, St. Louis
Mecklenburg Co, Wake Co, Davidson Co, Davie Co, Forsyth Co, Guilford Co, Durham
Co, Granville Co, Gaston Co
Hillsborough Co, Rockingham Co
Atlantic Co, Cape May Co, Warren Co, Bergen Co, Essex Co, Hudson Co, Hunterdon
Co, Mercer Co, Middlesex Co, Monmouth Co, Morris Co, Ocean Co, Passaic Co,
Somerset Co, Sussex Co, Union Co, Burlington Co, Camden Co, Cumberland Co,
Gloucester Co, Salem Co
Bernalillo Co
4-242
-------
Table 4.6-18 (continued)
(P) indicates a Pressure and/or Purge test was also included.
* Indicates the state was added in 1999
I/M Program Name
Included Counties
COUNTIES WITH IDLE/2 SPEED IDLE TESTING (cont'd.)
NEVADA Clark Co, Washoe Co
NEW YORK Bronx Co, Kings Co, Nassau Co, New York Co, Queens Co, Richmond Co, Rockland
Co, Suffolk Co, Westchester Co
OKLAHOMA Canadian Co, Cleveland Co, Kingfisher Co, Lincoln Co, Logan Co, McClain Co,
Oklahoma Co, Pottawatomie Co, Creek Co, Osage Co, Rogers Co, Tulsa Co, Wagoner
Co
PENNSYLVANIA Lehigh Co, Northampton Co, Allegheny Co, Beaver Co, Washington Co, Westmoreland
Co
RHODE ISLAND Bristol Co, Kent Co, Newport Co, Providence Co, Washington Co
TENNESSEE Shelby Co, Davidson Co, Rutherford Co, Sumner Co, Williamson Co, Wilson Co
TEXAS (P) Dallas Co, Tarrant Co, El Paso Co, Harris Co
UTAH (P) Davis Co, Weber Co, Utah Co
VERMONT (P) Addison Co, Bennington Co, Caledonia Co, Chittenden Co, Essex Co, Franklin Co,
Grand Isle Co, Lamoille Co, Orange Co, Orleans Co, Rutland Co, Washington Co,
Windham Co, Windsor Co
COUNTIES WITH ASM TESTING
CONNECTICUT (P) Fairfield Co, Hartford Co, Litchfield Co, Middlesex Co, New Haven Co, New London Co,
Tolland Co, Windham Co
OHIO (P) Clark Co, Greene Co, Montgomery Co
PENNSYLVANIA (P) Bucks Co, Chester Co, Delaware Co, Montgomery Co, Philadelphia Co
UTAH (P) Salt Lake Co
VIRGINIA (P) Arlington Co, Fairfax Co, Fairfax, Prince William Co, Alexandria, Manassas, Manassas
Park, Falls Church
WASHINGTON (P) King Co, Snohomish Co, Spokane Co, Clark Co, Pierce Co
COUNTIES WITH IM240 TESTING
ARIZONA (P) Maricopa Co
COLORADO (P) Adams Co, Arapahoe Co, Boulder Co, Douglas Co, Jefferson Co, Denver Co, El Paso
Co
INDIANA (P) Clark Co, Floyd Co, Lake Co, Porter Co
OHIO (P) Clermont Co, Geauga Co, Medina Co, Portage Co, Summit Co, Warren Co, Butler Co,
Hamilton Co, Lake Co, Lorain Co, Cuyahoga Co
OREGON (P) Jackson Co, Clackamas Co, Multnomah Co, Washington Co
WISCONSIN (P) Kenosha Co, Milwaukee Co, Ozaukee Co, Racine Co, Washington Co, Waukesha Co,
Sheboygan Co
4-243
-------
Table 4.6-19. Counties Modeled with Federal Reformulated Gasoline
State (ASTM Class)/
Nonattainment Area County
State (ASTM Class)/
Nonattainment Area County
Arizona (B)
Phoenix
Maricopa Co
Connecticut (C)
Greater Connecticut
Hartford Co
Litchfield Co
Middlesex Co
New Haven Co
New London Co
Tolland Co
Windham Co
New York-Northern New Jersey-Long Island
Fairfield Co
District of Columbia (B)
Washington DC
Washington
Delaware (C)
Philadelphia-Wilmington-Trenton
Kent Co
New Castle Co
Sussex County
Sussex Co
Illinois (C)
Chicago-Gary-Lake County
Cook Co
Du Page Co
Grundy Co
Kane Co
Kendall Co
Lake Co
McHenry Co
Will Co
Indiana (C)
Chicago-Gary-Lake County
Lake Co
Porter Co
Kentucky (C)
Cincinnati-Ham ilton
Boone Co
Campbell Co
Kenton Co
Maine (C)
Knox & Lincoln Counties
Knox Co
Lincoln Co
Lewiston-Auburn
Androscoggin Co
Kennebec Co
Portland
Cumberland Co
Sagadahoc Co
York Co
Maryland (B)
Baltimore
Anne Arundel Co
Baltimore
Baltimore Co
Carroll Co
Harford Co
Howard Co
Kent & Queen Annes Counties
Kent Co
Queen Annes Co
Philadelphia-Wilmington-Trenton
Cecil Co
Washington DC
Calvert Co
Charles Co
Frederick Co
Montgomery Co
Prince Georges Co
Massachusetts (C)
Boston-Lawrence-Worcester-Eastem MA
Barnstable Co
Bristol Co
Dukes Co
Essex Co
Middlesex Co
Nantucket Co
Norfolk Co
Plymouth Co
Suffolk Co
Worcester Co
4-244
-------
Table 4.6-19 (continued)
State (ASTM Class)/
Nonattainment Area County
State (ASTM Class)/
Nonattainment Area County
Louisville
Bullitt Co
Jefferson Co
Oldham Co
New Hampshire (C)
Manchester
Hillsborough Co
Merrimack Co
Portsmouth-Dover-Rochester
Rockingham Co
Strafford Co
New Jersey (C)
Allentown-Bethlehem-Easton
Warren Co
Atlantic City
Atlantic Co
Cape May Co
New York-Northern New Jersey-Long Island
Bergen Co
Essex Co
Hudson Co
Hunterdon Co
Middlesex Co
Monmouth Co
Morris Co
Ocean Co
Passaic Co
Somerset Co
Sussex Co
Union Co
Philadelphia-Wilmington-Trenton
Burlington Co
Camden Co
Cumberland Co
Gloucester Co
Mercer Co
Salem Co
New York (C)
New York-Northern New Jersey-Long Island
Bronx Co
Kings Co
Nassau Co
New York Co
Orange Co
Springfield/Pittsfield-Western MA
Berkshire Co
Franklin Co
Hampden Co
Hampshire Co
New York (C)
Poughkeepsie
Dutchess Co
Putnam Co
Pennsylvania (C)
Philadelphia-Wilmington-Trenton
Bucks Co
Chester Co
Delaware Co
Montgomery Co
Philadelphia Co
Rhode Island (C)
Providence
Bristol Co
Kent Co
Newport Co
Providence Co
Washington Co
Texas (B)
Dallas-Fort Worth
Collin Co
Dallas Co
Denton Co
Tarrant Co
Houston-Galveston-Brazoria
Brazoria Co
Chambers Co
Fort Bend Co
Galveston Co
Harris Co
Liberty Co
Montgomery Co
Waller Co
Virginia (B)
Norfolk-Virginia Beach-Newport News
Chesapeake
Hampton
James City Co
Newport News
Norfolk
4-245
-------
Table 4.6-19 (continued)
State (ASTM Class)/
Nonattainment Area County
State (ASTM Class)/
Nonattainment Area County
Queens Co
Richmond Co
Rockland Co
Suffolk Co
Westchester Co
Poquoson
Portsmouth
Suffolk
Virginia Beach
Williamsburg
York Co
Virginia (B)
Richmond-Petersburg
Washington DC
Charles City Co
Chesterfield Co
Colonial Heights
Hanover Co
Henrico Co
Hopewell
Richmond
Alexandria
Arlington Co
Fairfax
Fairfax Co
Falls Church
Loudoun Co
Manassas
Manassas Park
Prince William Co
Stafford Co
Wisconsin (C)
Milwaukee-Racine
Kenosha Co
Milwaukee Co
Ozaukee Co
Racine Co
Washington Co
Waukesha Co
Notes: Reformulated gasoline was only modeled in Phoenix during 1997 and 1998. California reformulated gasoline was
modeled statewide in California.
4-246
-------
Table 4.6-20. Oxygenated Fuel Modeling Parameters
Market Shares (%)
State
Alaska
Arizona
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Connecticut
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Montana
Nevada
Nevada
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Texas
Utah
Washington
Washington
Wisconsin
County
Anchorage Ed
Maricopa Co
Adams Co
Arapahoe Co
Boulder Co
Douglas Co
Jefferson Co
Denver Co
El Paso Co
Larimer Co
Fairfield Co
Anoka Co
Carver Co
Dakota Co
Hennepin Co
Ramsey Co
Scott Co
Washington Co
WrightCo
Chisago Co
IsantiCo
Missoula Co
Clark Co
WashoeCo
Bergen Co
Essex Co
Hudson Co
Hunterdon Co
MiddlesexCo
Mon mouth Co
Morris Co
Ocean Co
Passaic Co
Somerset Co
Sussex Co
Union Co
Bronx Co
Kings Co
Nassau Co
New York Co
Queens Co
Richmond Co
Rockland Co
Suffolk Co
Westchester Co
Orange Co
Putnam Co
Clackamas Co
Jackson Co
Multnomah Co
Washington Co
Josephine Co
Klamath Co
Yamhill Co
El Paso Co
Utah Co
Clark Co
Spokane Co
St.CroixCo
MTBE
0
80
75
75
75
75
75
75
75
75
90
10
10
10
10
10
10
10
10
10
10
0
0
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
1
1
1
1
1
1
1
15
20
1
1
10
Alcohol Blends
100
20
25
25
25
25
25
25
25
25
10
90
90
90
90
90
90
90
90
90
90
100
100
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
99
99
99
99
99
99
99
85
80
99
99
90
Oxy gen Co nte nt (%)
MTBE
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
Alcohol Blends
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
Oxygenated
Fuel Season
NOV -
OCT -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
OCT -
OCT -
OCT -
OCT -
OCT -
OCT -
OCT -
OCT -
OCT -
OCT -
NOV -
OCT -
OCT -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
NOV -
SEP -
OCT -
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
JAN
JAN
JAN
JAN
JAN
JAN
JAN
JAN
JAN
JAN
FEE
MAR
JAN
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
FEE
JAN
4-247
-------
Table 4.6-21. PARTS HDDV Vehicle Classes
Vehicle Class
FHWA Class
Gross Vehicle
Weight (\bs)
Fraction of Total
HDDV VMT
2BHDDV class 2B heavy-duty diesel vehicles 2B
LHDDV light heavy-duty diesel vehicles 3,4,5
MHDDV medium heavy-duty diesel vehicles 6,7,8A
HHDDV heavy heavy-duty diesel vehicles 8B
BUSES buses
8,501-10,000 0.001
10,001-19,500 0.026
19,501-33,000 0.102
33,000+ 0.820
0.051
Table 4.6-22. Average Speeds by Road Type and Vehicle Type
Rural Road Speeds (mph)
Vehicle
Tvoe
LDV
LOT
HDV
Vehicle
Tvoe
LDV
LOT
HDV
Interstate
60
55
40
Interstate
45
45
35
Principal
Arterial
45
45
35
Urban
Other Freeways
& Expressways
45
45
35
Minor
Arterial
40
40
30
Major
Collector
35
35
25
Minor
Collector
30
30
25
Local
30
30
25
Road Speeds (mph)
Principal
Arterial
20
20
15
Minor
Arterial
20
20
15
Collector
20
20
15
Local
20
20
15
4-248
-------
Table 4.6-23. PM-10 Emission Factors used in the Emission Trends Inventory
Emission Factor (grams per mile)
Year
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
LDGV LDGT1
0.070
0.066
0.063
0.060
0.057
0.054
0.051
0.048
0.045
0.042
0.039
0.036
0.033
0.030
0.026
Table
0.069
0.066
0.063
0.060
0.057
0.054
0.051
0.049
0.046
0.043
0.040
0.037
0.034
0.032
0.029
LDGT2
0.070
0.067
0.064
0.062
0.059
0
0
0
0
0
0
0
0
0
0
.057
.054
.052
.049
.047
.044
.042
.039
.037
.034
HDGV
0.062
0.062
0.062
0.062
0.062
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
062
062
062
062
062
062
062
062
062
062
LDDV
0.615
0.615
0.615
0.615
0.615
0.615
0.615
0.585
0.555
0.525
0.495
0.465
0.435
0.405
0.375
4.6-24. Fuel Economy Values Used
Emission
Facto rs
for
Fuel
Year
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
LDGV
12.68
12.70
12.57
12.48
12.59
12.68
12.69
12.94
13.17
13.52
14.50
14.95
15.49
16.13
16.78
LDGT
10.18
10.39
10.51
10.69
11.15
11.40
11.39
11.63
11.81
12.00
12.54
12.72
12.96
13.42
13.90
the
LDDT
0.615
0.615
0.615
0.615
0.615
0.615
0.615
0.583
0.552
0.520
0.489
0.457
0.426
0.395
0.363
HDDV MC
2.367
2.367
2.367
2.367
2.351
2.335
2.319
2.303
2.287
2.271
2.255
2.239
2.223
2.207
2.191
in Calculation of
0.070
0.066
0.063
0.060
0.057
0
0
0
0
0
0
0
0
0
0
SO2
.054
.051
.048
.045
.042
.039
.036
.033
.030
.026
Emission Trends Inventory
Economy (miles/gallon)
HDGV
6.79
6.85
6.86
6.90
7.11
7.16
7.05
7.05
6.97
6.94
7.13
7.07
7.65
7.96
8.15
LDDV
12.68
12.70
12.57
12.48
12.59
12.68
12.69
12.94
13.17
13.52
14.50
14.95
24.90
25.10
25.21
LDDT
10.18
10.39
10.51
10.69
11.15
11.40
11.39
11.63
11.81
12.00
12.54
12.72
24.59
24.85
24.96
HDDV
5.05
5.17
5.27
5.32
5.47
5.62
5.47
5.47
5.45
5.45
5.64
5.56
5.30
5.44
5.57
MC
50.00
50.00
50.00
50.00
50.00
50.00
50.00
50.00
50.00
50.00
50.00
50.00
50.00
50.00
50.00
4-249
-------
Table 4.6-25. SO2 Emission Factors used in the Emission Trends Inventory
Emission Factor (grams per mile)
Year
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
LDGV
0.147
0.146
0.148
0.149
0.148
0.147
0.147
0.144
0.141
0.138
0.128
0.124
0.120
0.115
0.111
LDGT1
0.183
0.179
0.177
0.174
0.167
0.163
0.163
0.160
0.158
0.155
0.148
0.146
0.144
0.139
0.134
LDGT2
0.183
0.179
0.177
0.174
0.167
0.163
0.163
0.160
0.158
0.155
0.148
0.146
0.144
0.139
0.134
HDGV
0.274
0.272
0.271
0.270
0.262
0.260
0.264
0.264
0.267
0.268
0.261
0.263
0.243
0.234
0.228
LDDV
0.989
0.987
0.997
1.004
0.996
0.989
0.988
0.969
0.952
0.927
0.865
0.839
0.503
0.499
0.497
LDDT
1.231
1.207
1.193
1.173
1.124
1.100
1.101
1.078
1.061
1.045
1.000
0.986
0.510
0.504
0.502
HDDV
2.482
2.425
2.379
2.356
2.292
2.231
2.292
2.292
2.300
2.300
2.223
2.255
2.365
2.304
2.251
MC
0.037
0.037
0.037
0.037
0.037
0.037
0.037
0.037
0.037
0.037
0.037
0.037
0.037
0.037
0.037
Table 4.6-26. Fractions of Vehicles Equipped with 3-Way Catalysts by
Vehicle Type and Model Year
Model
Year
1990 and
later
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979 and
earlier
LDGVs
With
Catalyst
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.88
0.86
0.07
0.07
0.00
Without
Catalyst
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.12
0.14
0.93
0.93
1.00
LDGT1
With Without
Catalyst Catalyst
0.95
0.95
0.95
0.95
0.50
0.40
0.30
0.20
0.10
0.05
0.00
0.00
0.05
0.05
0.05
0.05
0.50
0.60
0.70
0.80
0.90
0.95
1.00
1.00
LDGT2
With Without
Catalyst Catalyst
0.85
0.85
0.85
0.85
0.50
0.40
0.30
0.10
0.00
0.00
0.00
0.00
0.15
0.15
0.15
0.15
0.50
0.60
0.70
0.90
1.00
1.00
1.00
1.00
HDGVs
With Without
Catalyst Catalyst
0.25
0.15
0.15
0.15
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.75
0.85
0.85
0.85
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
4-250
-------
Figure 4.6-1. OTAG Inventory Source of Data - VMT
4-251
-------
4.7 NONROAD ENGINES AND VEHICLES
4.7.1 What Sources Do We Include in the Nonroad Engines and Vehicles Category?
"Nonroad engines and vehicles" includes the following Tier I and Tier II categories:
Tier I Category Tier II Category
(09) STORAGE AND TRANSPORT (02) Petroleum and Petroleum Product
Storage
(12) NONROAD ENGINES AND VEHICLES All
(14) MISCELLANEOUS (07) Fugitive Dust
The "nonroad engines and vehicles" category includes motorized vehicles and equipment that
provide transportation but are not usually operated on public roadways. This includes aircraft,
commercial marine vessels, railroads, and all other nonroad vehicles and equipment. In addition,
although not technically nonroad engines or vehicles, unpaved airstrips and aircraft refueling categories
are included in this category.
4.7.2 What Information Does This Section Provide?
This section describes the methods used to estimate nonroad emissions for 1985 through 1999.
Table 4.7-1 summarizes the methods applied and the pollutants for which emissions were estimated for
each year. Section4.7.3 provides an overview ofrecent updates made to nonroad estimates. Section
4.7.4 explains how EPA's draft NONROAD model was used for those nonroad equipment categories
included in the model. Finally, sections 4.7.5, 4.7.6, and 4.7.7 provide a description of the emission
estimation methodologies for aircraft, commercial marine, and locomotive categories. Finally, section
4.7.8 discusses the methods used to estimate NH3 emissions for nonroad engines.
4.7.3 What Methodologies Did We Use to Develop Nonroad Emission Estimates?
For several years OTAQ has been developing an emissions model (NONROAD) to estimate
emissions fromnonroad sources. In large part, we used the draft version of the NONROAD model to
generate emission inventories for volatile organic compounds (VOCs), oxides of nitrogen (NOX), carbon
monoxide (CO), sulfur dioxide (SO2), primary particulate matter with an aerodynamic diameter less than
or equal to 10 micrometers (PM-10), and primary particulate matter with an aerodynamic diameter less
than or equal to 2.5 micrometers (PM-2.5) for all gasoline, diesel, compressed natural gas (CNG), and
liquefied petroleum gas (LPG) nonroad equipment types at the 10-digit Source Classification Code
(SCC) level
We did not use the NONROAD model to calculate emissions for aircraft, commercial marine
vessels, and locomotives. For these sources, the draft version of the NONROAD model does not
currently include estimation methods for these categories.
The NONROAD model does not contain emission factors to calculate NH3 emissions; therefore, we
used fuel consumption estimates generated by the NONROAD model and applied NH3 emission factors
for diesel and noncatalyst gasoline vehicles as appropriate. NH3 estimates for aircraft, commercial
marine, and locomotives are also calculated based on fuel consumption estimates for these categories.
4-252
-------
For the NET Version 4, we generally updated nonroad emission estimates for most nonroad
categories for the years 1996-1999. For previous years' Trends estimates, we have made adjustments to
years prior to 1996, every year back to 1985, and for 1970, 1975, 1980, and 1985. Table 4.7-1 presents
a summary of how nonroad emission estimates have been developed over the most recent 10-year period
(i.e., 1989-1999). Table 4.7-2 summarizes the methods applied to prepare the 1996 base year inventory
from 1996 through 1999 for each of the general nonroad categories.
4.7.4 How Was the Nonroad Model Used to Develop Emission Estimates?
The majority of nonroad mobile source emission estimates in the NET inventory are based on the
NONROAD model. For the latest version of the NONROAD model, the reader is referenced to
http://www.epa.gov/oms/nonrdmdl.htm. Criteria pollutant emission estimates in the NET Version 3
were based on the NONROAD model all categories included in the model, with the exception of
recreational gasoline-fueled equipment. For the NET Version 4, the nonroad emission estimates are
based on estimates for all categories in NONROAD, including recreational gasoline.
4.7.4.1 What Emissions Does the NONROAD Model Measure?
The NONROAD model provides emission estimates for hydrocarbon (HC), NOX, CO, SO2, PM-10,
and PM-2.5. The model reports various hydrocarbon species, including VOC, and breaks out the
hydrocarbon emissions according to exhaust and evaporative components. PM-10 is assumed to be
equivalent to total PM, and PM-2.5 is assumed to 92 percent of PM-10 for gasoline and diesel-fueled
engines, and 100 percent of PM-10 for LPG and CNG-fueled engines.
4.7.4.2 What Equipment Categories Are Included in the NONROAD Model?
The NONROAD model includes the following general equipment categories:
• agricultural;
• airport support;
• light commercial;
• construction and mining;
• industrial;
• lawn and garden;
logging;
• pleasure craft;
• railroad; and
• recreational equipment.
The model generates emissions at subcategory levels lower than the general categories listed above.
The subcategories are equivalent to 10-digit SCCs, and correspond to specific nonroad applications
within a category.
4.7.4.3 Do We Use Different Methods to Calculate Nonroad Em issionsfor Different Years?
Yes. We describe the different methods used for various years below.
4-253
-------
4.7.4.3.1 How Did We Develop 1996 Base Year Emissions? —
We estimated 1996 nonroad emissions from two emission inventories including: 1) a 1996 county-
level inventory, developed using EPA's April 1999 draft NONROAD model; and 2) an updated national
inventory, based on EPA's June 2000 draft version of the NONROAD model. The recreational gasoline
equipment category was an exception, since estimates for this category in EPA's NET Version 3 were
not based on the April 1999 model, but were based on NEVES (Nonroad Engine and Vehicle Emission
Study). Emission estimates for recreational gasoline developed for the NET Version 4 series, as well as
all other categories, are based on the June 2000 draft NONROAD model.
To develop the original 1996 county-level inventory, we used the April 1999 draft NONROAD
model adapted to run on a DEC Alpha UNIX workstation. We prepared NONROAD model input files
for each State to account for the average statewide temperatures and Reid vapor pressure (RVP) for four
seasons, including summer, fall, winter, and spring. We used these default state input files to calculate
emissions for all counties in the United States. Estimates for particular counties were replaced with
county-specific estimates, if those counties had significant differences in their RVP, fuel characteristics
due to reformulated gasoline and oxygenated fuel requirements, and Stage II controls. Typical summer
season daily (SSD) emissions were estimated by dividing total summer season emissions by 92 days.
Table 4.7-3 presents the statewide seasonal default RVP values used as input to the NONROAD
model. For areas subject to Phase 1 of the Federal reformulated gasoline (RFG) program, separate RVP
values were mode led in the 1996 NONROAD inputs for May through September (values not shown).
Table 4.7-4 presents the areas and counties modeled with RFG. Oxygenated fuel was modeled in the
areas participating in this program in 1996, as presented in Table 4.7-5. Emissions calculated for counties
with fuel characteristic data that varied from statewide average values replaced emissions for these same
counties generated by running the default input files. Four seasonal emissions files for each run were then
added together to estimate annual emissions.
We then updated this 1996 county-level emissions inventory to reflect revisions made to the
NONROAD model since the April 1999 version. Using the June 2000 draft NONROAD model, OTAQ
generated national, seasonal emissions at the SCC level for the following pollutants: VOC, NOX, SO2,
CO, PM-10, and PM-2.5. The results for three seasonal runs (i.e., summer, winter, fall/spring combined)
were summed to calculate annual emissions. Additional NONROAD model runs were performed to
estimate typical summer weekday emissions as well. Table 4.7-6 presents a summary of the input values
used for the national NONROAD model runs.
We calculated SCC-specific ratios by dividing the updated national, annual emission estimates by the
previous 1996 national values (i.e., based on the April 1999 version of NONROAD). We then calculated
county-level emissions by multiplying each record in the 1996 inventory by the appropriate ratio for each
SCC. In this manner, we normalized the county-level distribution of the 1996 estimates in Version 3 of
the NET inventory to the updated national, SCC-level totals for 1996.
Similar to annual emission ratios, SSD ratios were first developed by dividing updated SSD
emissions by previous SSD emissions. However, when SSD ratios were applied to certain records, new
SSD emission values were calculated that were larger than the corresponding annual emissions. This was
occurring because SSD values had been calculated differently in the April 1999 data base (i.e., they had
previously been calculated by dividing summer season emissions by 92, as opposed to performing
separate runs for a typical summer day). To adjust this result, and to ensure that the total national SSD
4-254
-------
emissions were equivalent to what OTAQ had originally provided, county-level SSD emissions were
calculated by multiptying national SSD emissions by the ratio of county level annual emissions to national
level annual emissions. This is shown in the formula below:
SSDoounty = SSDnational X (Annualoounty + AnnU3>'national
The most recent version of NONROAD includes emission estimates for new SCCs. Since the April
1999 county-level data base did not include these SCCs, we assigned surrogate SCCs to the new SCCs to
use in allocating national emissions to the county-level. The additional SCCs and each corresponding
surrogate SCC are shown in Table 4.7-7.
4.7.4.3.2 How Did We Calculate 1997,1998,1999 Inventories? —
For NONROAD model categories, we used similar procedures to update 1997, 1998, and 1999
emission estimates that were used to develop the most recently updated 1996nonroad estimates. The
steps we took included:
1. Perform three seasonal (i.e., summer, winter, and fall/spring combined) NONROAD model runs at
the national level, to account for differences in average seasonal temperature, as well as RVP.
2. Calculate year-specific ratios by dividing national SCC-level emission estimates for 1997, 1998, and
1999 by the 1996 national values.
3. Calculate county-level estimates for 1997, 1998, and 1999 by multiplying each ratio times the 1996
county-level emissions inventory.
By following these steps, the county-level distribution assumed for the 1996 inventory is normalized
to the updated national, SCC-level totals for each alternative year. This approach ensures that the sum of
all county-level emissions for any year are equivalent to the national-level estimates and are distributed to
the counties according to the 1996 distribution.
Because the NONROAD model estimates growth in local equipment populations using one national
average growth rate, the effects of growth should be reflected in the national-level runs for each alternate
year aside from the base year 1996. The effects of federal nonroad emission standards in future years
(e.g., years beyond 1996) would also be accounted for. Because the model uses one average growth rate
for the whole nation, the approach of using the 1996 county-level inventory as a basis for geographically
allocating national inventories for other years was assumed to be reasonable. However, temperature and
fuel inputs to reflect local conditions cannot be accounted for when doing a national-level run for a
specified year. We used this approach due to time and resource constraints.
4.7.4.3.3 How Did We Calculate Historic Year Inventories? —
For the inventory years 1985-1996, we ran the April 1999 draft NONROAD model at the national
level for all relevant inventory years. Each national run included three seasonal NONROAD model runs
per year to estimate annual criteria pollutant emissions. The seasonal runs help to account for differences
in average seasonal temperature, as well as RVP. Tables 4.7-8 and 4.7-9 present the RVP and
temperature inputs used for each inventory year, respectively.
4-255
-------
4.7.4.4 Were Nonroad Model Runs Perform edfor Any Specific States?
Yes, we performed separate runs for California for the years 1996, 1997, 1998, and 1999 using the
June 2000 draft model We generated new results for diesel-fueled equipment SCCs to account for lower
diesel fuel sulfur levels in California. Based on the results of the separate California NONROAD model
runs, we calculated SCC emission ratios by dividing the updated California diesel emission estimates by
the previous 1996 California values. We applied these ratios to county-level records for California, and
incorporated the resulting emissions into the 1996 emissions inventory.
4.7.5 How Did We Update Aircraft Emissions for 1997,1998, and 1999?
The following discusses the procedures we used to grow aircraft emissions for the 1997, 1998, and
1999 year NET inventories. We based all aircraft emissions for 1996 on Version 3 of the NET inventory.
In addition, we made no changes to any historical year NET estimates for aircraft prior to 1996 (i.e., the
estimates are consistent with Version 1 of the NET).
4,7,5.1 How Did We Update Comm ercial Aircraft and General Aviation Emissions?
We revised commercial aircraft and general aviation emission estimates for 1997, 1998, and 1999
using updated landing-takeoff operations (LTO) data from the Federal Aviation Administration (FAA).
We developed growth factors using 1996 operations data and operations data for the year in question.
4.7.5.2 How Did We Update Emissions for Military Aircraft, Unpaved Airstrips, and Aircraft
Refueling?
We grew military aircraft, unpaved airstrips, and aircraft refueling emissions from 1996 to 1997,
1998, and 1999 using growth factors consistent with the current draft version of the Economic Growth
Analysis System (EGAS). See Table 4.7-10 for a list of growth indicators used for aircraft categories.
4.7.6 How Did We Update Commercial Marine Emissions?
4.7.6.1 How Did We Develop Commercial Marine Diesel Vessel Emission Estimates for 1996
Through 1999?
We obtained revised 1996 HC, NOX, CO, and total PM national emission estimates for commercial
marine engines from OTAQ. These national estimates were those used in EPA rulemaking documents,
and reflect the effect of Federal emission standards promulgated for new diesel-fueled commercial marine
vessels. We calculated VOC by multiplying HC by a factor of 1.053. We assumed PM-10 to be
equivalent to PM, and PM-2.5 was estimated by multiplying PM-10 emissions by a factor of 0.92. We
developed new pollutant-specific ratios by dividing new/old emissions at a national level. This ratio was
applied to the county level emissions using the geographic distribution for 1996 estimates in Version 2 of
the NET so that the sum of the county-level emissions now equaled the new national total. We
established the distribution based on emissions for SCC 2280002000. Revised emissions for SO2 were
not developed by OTAQ; therefore, SO2 estimates from the current NET inventory were used.
In addition, records for several states contained emissions data for some pollutants, such as SO2 and
PM-10, but contained no data on VOC, NOX, or CO emissions. To estimate emissions for these
4-256
-------
pollutants, we calculated national average ratios of VOC/PM-10, NOX/PM-10, and CO/PM-10 from the
available inventory data. These ratios were then applied to the PM-10 emissions to estimate the missing
VOC, NOX, and CO emissions.
For 1997, 1998, and 1999, we developed diesel commercial marine estimates similar to the 1996
base year estimates. We distributed national commercial marine diesel emissions provided by OTAQ to
counties according to the 1996 county level distribution. For 1997 through 1999, we grew the 1996 SO2
annual emissions using the BEA GSP growth factors (since revised national SO2 emissions were not
available).
4.7,6.2 How Did We Develop Historic Year Estimates for Com mercial Marine Diesel Vessels?
For Version 3 of the NET, OTAQ provided revised commercial marine diesel emissions back to the
year 1995, consistent with estimates presented in EPA rulemaking documents. We used the following
methodology to adjust historic year emissions for this category, to avoid a large disconnect in previous
Trends estimates and revised OTAQ estimates. For each pollutant, we calculated the ratio of the 1995
revised OTAQ commercial marine emissions to the 1995 emissions in Version 3 of the NET. This ratio
was then applied to emission estimates for the following SCCs: commercial marine diesel (2280002),
commercial marine residual (2280003), and commercial marine unspecified fuel (2280000). We did not
perform any additional data augmentation for these years. For the NET Version 4, no further
adjustments were made prior to 1996 to reflect revised national commercial marine diesel emissions.
4.7.6.3 How Did We Update Emission Estimates for Non-Diesel Commercial Marine Vessels and
Military Marine?
We estimated commercial gasoline, commercial coal, and military marine emissions for the years
1997 through 1999 by applying EGAS growth factors to 1996 emission estimates for these same
categories. See Table 4.7-10 for a list of the growth indicators used for each category.
4.7.7 How Did We Update Locomotive Emissions for 1996 Through 1999?
As a first step, we developed 1999 county-level emission estimates for all pollutants using 1996 as a
base year and applying 1999 growth factors from EGAS. We then adjusted the 1999 grown emissions
using national locomotive emissions for 1999 as reported in the "Locomotive Emission Standards-
Regulatory Support Document (RSD)."1 This report included emission projections for all criteria
pollutants except for SO2; therefore, SO2 estimates from Version 2 of the NET inventory were used. We
developed new/old pollutant specific ratios for 1999, and for each record we added emissions for three
SCCs (2285002000, 2285002005, 2285002010). We applied these ratios to county-level emission
estimates for the same SCCs for 1996, 1997, 1998, and for 1999 as well.
4.7.8 How Did We Develop NH3 Emission Estimates?
4.7.8.1 How Did We Calculate NH3 Emissions for NONROAD Model Categories for 1996 Through
1999?
We estimated NH3 emissions based on updated national, SCC-level fuel consumption estimates for
diesel and gasoline engines, as reported by the June 2000 draft version of NONROAD. Fuel
4-257
-------
consumption estimates were not avaikble for LPG- and CNG-fueled equipment (however, fuel
consumption can be estimated from the CO2 emissions provided by NONROAD for these engines). As
with the criteria pollutant emission estimates, we developed SCC-specific ratios by dividing updated fuel
consumption values (i.e., from June 2000 draft NONROAD) by previous fuel consumption values (i.e.,
from April 1999 draft NONROAD). NH3 emissions for California were also recalculated using updated
diesel fuel consumption values generated for California-specific runs. Once a county-level data base of
fuel consumption was developed, we multiplied these activity data by emission factors provided by
OTAQ to estimate NH3 emissions. OTAQ derived the emission factors primarily from light-duty onroad
vehicle emission measurements,2 and extrapolated to nonroad engines on a fuel consumption basis. For
diesel engines, we applied an emission factor of 165.86 milligrams (mg)/gallon. For gasoline engines
(without catalysts) we applied an emission factor of 153.47 milligrams/gallon These emission factor
values are, in general, consistent with more recent studies on motor vehicle NH3 emissions.
4.7.8.2 How Did We Calculate NH3 Emissions for Aircraft, Commercial Marine, and Aircraft
Categories?
Similar to the NONROAD categories, we calculated NH3 emissions for aircraft, commercial marine,
and locomotives based on fuel consumption estimates for these categories. We obtained 1996 national
fuel consumption estimates for aircraft, commercial marine, and locomotive categories from various
sources. Jet fuel and aviation gasoline consumption for general aviation and commercial aircraft come
from the "FAA Aviation Forecasts Fiscal Years, 1998-2009."3 For aircraft categories, we applied NH3
emission factors developed for diesel engines to all fuel consumption estimates, since aviation gasoline
consumption was determined to be relatively small compared to jet fuel, and the aircraft SCCsare not
defined by fuel type. We obtained diesel consumption estimates for locomotives from "Locomotive
Emission Standards - Regulatory Support Document (RSD)".1 For commercial marine, data for distillate
and residual fuel oil were reported in "Fuel Oil and Kerosene Sales."4
To develop 1997, 1998, and 1999 NH3 emissions for aircraft, commercial marine, and locomotives,
we projected 1996 base year NH3 emissions for these categories using the growth indicators listed in
Table 4.7-10. NH3 emissions were reported in the NET database for commercial marine and locomotive
categories for historic years (i.e., 1990-1995); no changes were made to these historic estimates. Historic
NH3 emissions were not available for aircraft, so a disconnect occurs between 1995 and 1996 for NH3
emissions for this category.
4.7.9 References
1. "Locomotive Emission Standards - Regulatory Support Document (RSD)," U.S. Environmental
Protection Agency, Office of Mobile Sources, Ann Arbor, MI, April 1997.
2. Craig Harvey, Robert Garbe, Thomas Baines, Joseph Somers, Karl Hellman, and Penny Carey, "A
Study of the Potential Impact of Some Unregulated Motor Vehicle Emissions," SAE Paper 830987,
June 1983.
3. "FAA Aviation Forecasts Fiscal Years, 1998-2009," Federal Aviation Administration, Office of
Aviation Policy and Plans. March 1998.
4-258
-------
4. "Fuel Oil and Kero sene Sales," U. S. Department of Energy, Energy Information Administration,
DOE/EIA-0380, Washington, DC. 1996.
4-259
-------
Table 4.7-1. Methods for Developing Annual Emission
Estimates for Nonroad Sources for the Years 1989-1999
For the
For the category years
For the
pollutant(s)
EPA est imated em issi ons by
NONROAD Model Categories
Nonroad Gasoline,
Nonroad Diesel,
Nonroad LPG,
Nonroad CNG
1997-1999 VOC, NOX, CO, Running the June 2000 draft NONROAD model at a
SO2, PM-10, PM-2.5 national level foreach year, and distributing to counties
based on 1996 inventory
1996 VOC, NOX, CO, Calculating SCC-specific ratios by dividing the updated
SO2, PM-10, PM-2.5 national, annual emission estimates (based on the June
2000 draft NONROAD model) by the previous 1996
national values (based on the April 1999 draft NONROAD
model), and applying these ratios to the 1996 county-level
emissions (i.e., as reported in EPA, 1998)1
1989-1995 VOC, NOX, CO, Running the April 1999 draft NONROAD model at a
SO2, PM-10, PM-2.5 national level, and distributing to counties based on 1996
inventory
Nonroad Gasoline,
Nonroad Diesel
1997-1999 NH,
Obtaining national fuel consumption estimates from the
June 2000 draft NONROAD model, multiplying by NH3
emission factors, and distributing to counties based on
1996 inventory
1996
NH,
Obtaining county fuel consumption estimates from the April
1999 draft NONROAD model, normalizing to new national
fuel consumption values from June 2000 draft NONROAD
model, and multiplying by NH3 emission factors
1990-1995 NH,
Running the April 1999 draft NONROAD model at a
national level, and distributing to counties based on 1996
inventory
Aircraft
All Ai rcraft Categories 1989-1996
VOC, NOX, CO,
SO2, PM-10, PM
I-2.5
Using emissions from NET (i.e., as reported in EPA, 2000)
Commercial Aircraft, 1997-1999
General Aviation
VOC, NOX, CO,
SO2, PM-10,
PM-2.5, NH3
Growing 1996 emissions using landing-takeoff operations
(LTD) data from the FAA
1996
NH,
Applying NH3 emissions factors to 1996 national jet fuel
and aviation gasoline consumption estimates
Military Aircraft
1997-1999
VOC, NOX, CO,
SO2, PM-10, PM-
Growing emissions each year using SIC992-Federal,
2.5 Military growth factors consistent with the current draft
version of Economic Growth Analysis System (EGAS)
Unpaved Airstrips
1997-1999 PM-10, PM-2.5
Growing emissions each yearusing SIC45-Air
Transportation growth factors consistent with the current
draft version of EGAS
Aircraft Refueling
1997-1999 VOC
Growing emissions for each year using SIC45-Air
Transportation growth factors consistent with the current
draft version of EGAS
4-260
-------
Table 4.7-1 (continued)
For the category
Commercial Marine
CM Diesel
For the
years
(CM)
1996-1999
For the
pollutant(s)
VOC, NOX, CO,
PM-10, PM-2.5
EPA estimated em issions by
Distributing national commercial marine diesel
provided by OTAG to counties according to the
county level distribution
emissions
1996
1997-1999 SO,, NH,
Growing the 1996 SO2 and NH3 emissions using Bureau of
Economic Analysis (BEA) Gross State Product (GSP)
growth factors
1996
NH,
Applying NH3 emissions factors to 1996 distillate and
residual fuel oil estimates (i.e., as reported in EIA, 1996)
1989-1995 VOC, NOX, CO, Calculating the ratio of the 1995 revised national OTAQ
SO2, PM-10, PM-2.5 commercial marine emissions to 1995 Trends emissions
(as reported in EPA, 1998) and applying the ratio to
emissions estimates for the specified SCC.
1990-1995 NH,
Using emissions from NET (i.e., as reported in EPA, 2000)
CM Coal, CM Residual 1997-1999
Oil, CM Gasoline, Military
Marine
VOC, NOX, CO, Applying EGAS growth factors to 1996 emissions
SO2, PM-10, PM-2.5 estimates for this category
CM Coal, CM Residual 1989-1996
Oil, CM Gasoline, Military
Marine
VOC, NOX, CO, Using emissions from NET (i.e., as reported in EPA, 2000)
SO2, PM-10, PM-2.5
Locomotives
1996-1999 VOC, NOX, CO, Developing 1999 county-level emissions estimates for all
PM-10, PM-2.5 pollutants using 1996 as a base year and applying 1999
growth factors from EGAS and adjusting the 1999 grown
emissions using national locomotive emissions for 1999,
then developing pollutant specific ratios for 1999 and
applying these ratios to county-level emissions estimates
for each year
1989-1995 VOC, NOX, CO, Using emissions from NET (i.e., as reported in EPA, 2000)
SO2, PM-10, PM-2.5
1996
SO2
Using emissions from NET (i.e., as reported in EPA, 2000)
1997-1999 SO2, NH3
Growing 1996 base year emissions using EGAS growth
indicators
1996
NH3
Applying NH3 emissions factors to diesel consumption
estimates for 1996
1990-1995 NH,
Using emissions from NET (i.e., as reported in EPA, 2000)
Notes:
1 Exception for recreation gasoline equipment; recreational gasoline estimates from the April 1999 version of N ON ROAD were not incorporated into
the NET Version 3 DataBase. Estimates for the NET Version 4 were based on June 2000 draft NONROAD.
References: EIA, 1996: "FuelOil and Kerosene Sales," U.S. Department of Energy, Energy Informatbn Administration, DOE/EIA-0380,
Washington, DC, 1996.
EPA, 1998: "National Air Pollutant Emission Trends Update, 1970-1997," E PA-454/E-9 8-007, U .S. Environmental Protection
Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC, December, 1998.
EPA, 2000: "National Air Pollutant Emission Trends, 1900-1998," E PA-454/R-0 0-00 2, U.S. Environmental Protection Agency,
Office of Air Quality Planning and Standards, Research Triangle Park, NC, March 2000.
4-261
-------
Table 4.7-2. Comparison of Methodologies Used to Develop 1996 Base Year Emissions
for Nonroad Sources in Versions 1 through 4 of the NET
For the Category
Nonroad Gasoline
Nonroad D iesel
Nonroad Gasoline,
Nonroad D iesel
Nonroad LPG,
Nonroad CNG
Comm ercial
Aircraft,
General Aviation
Military Aircraft
For the
Pollutant(s)
VOC, NOX,
CO, SO2,
PM10, PM25
VOC, NOX,
CO, SO2,
PM10, PM25
NH3
VOC, NOX,
CO, SO2,
PM10, PM25
VOC, NOX,
CO, SO2,
PM10, PM25
NH3
VOC, NOX,
CO, SO2,
PM10, PM25
EPA estimated 1996 Base Year
emissions for Version 1 by
Growing from 1995 NET using appropriate
surrogates. Updating NOX, and VOC
emission estimates for recreational marine
only using national estimates from OTAQ
so that the su m of the county/SCC level
NET estimates eq ual the new national
estimates.
Growing from 1995 NET us ing appropriate
surrogates. Updating emission estimates
forCO, NOX, VOC, and PM10 using
national estimates from OTAQ so thatthe
sum of the county/SCC level NET
estimates eq ual the new national
estimates.
Not estimated.
Not estimated.
Growing 1995 emissions using landing-
takeoff operations (LTOs) data obtained
from frie FAA.
Growing 1995 emissions using landing-
takeoff operations (LTO s) data obtained
from the FAA instead of BE A data.
Estimating 1996 BEA and SEDS data
using linear interpolatbn of 1988 to 1995
data and growingfrom 1995 NET.
EPA estimated 1996 Base Year emissions for
Version 2 by
Usin g sam e method ology as us ed in
Version 1 .
Obtaining national level emission
estimates from OTAQ's draft
NO NRO AD m odel for all categories
except airport service. Distributing to
counties based on 1996 estimates in
Version 1 of NET.
Not estimated.
Not estimated.
Using same methodology as used in
Version 1 .
Usin g sam e method ology as us ed in
Version 1 .
Using same methodology as used in
Versbn 1 , but using actual 1996 BEA
and SEDS data.
Version 3 by
Running the April 1999 NONROAD
model at th e coun ty level for all
categories except recreational
gasoline. Recreational gasoline
emissions were based on Version 1
estimates.
Running the April 1999 NONROAD
model at th e coun ty level for all
categorbs.
Obtaining county-level fuel
consumption estimates from the April
1999 NONROAD model. Multiplying
by NH3 emissions factors.
Running the April 1999 NONROAD
model at th e coun ty level for all
categories.
Using same methodology as used in
Version 1, but using updated LTO
data.
Appying NH3 emission factors to 1996
national jet fuel and aviation gasoline
consumption estimates. Allocating to
counties based on PM-10 emissbns
distribution.
Using same methodology as used for
Version 2.
Version 4 by
Calculating SCC-specific ratios by
dividing the updated national, annual
emission estimates (based on June
2000 draft NONROAD m odel) by the
previous 1996 national values (based
on the April 1999 draftNONROAD
model). Applying these ratios to 1996
county level emissions.
Calculating SCC-specific ratios by
dividing the updated national, annual
emission estimates (based on June
2000 draft NONROAD m odel) by the
previous 1996 national values (based
on the April 1999 draftNONROAD
model). Applying these ratios to 1996
county level emissions.
Obtaining county fu el estimates from
theApril 1999 draft NONROAD
model. Normalizing to new national
fuel consumptbn values from June
2000 d raft NO NRO AD m odel.
Multiplying by NH 3 emissions factors.
Calculating SCC-specific ratios by
dividing the updated national, annual
emission estimates (based on June
2000 draft NONROAD model) by the
previous 1996 national values (based
on the April 1999 draftNONROAD
model). Applying these ratios to 1996
county levelemissions.
Using same m ethodology as us ed for
Version 3.
Appying NH3 emission factors to 1996
national jet fuel and aviation gasoline
consumption estimates.
Using same m ethodology as us ed for
Version 2.
-------
Table 4.7-2 (continued)
For the Category
Unpaved Airstrips
Aircraft Refueling
Commercial Marine
(CM) Diesel
CM Coal,
CM Residual Oil,
CM G asoline,
Military Marine
Locomotives
For the
Pollutant(s)
PM10, PM25
voc
VOC, NOX,
CO, SO2 PM10,
PM25
NH3
VOC, NOX,
CO, SO2,
PM10, PM25
VOC, NOX,
CO, PM10,
PM25
S02
NH3
EPA estimated 1996 Base Year
emissions for Version 1 by
Estimating 1996 BEA and SEDS data
using linear interpolatbn of 1988 to 1995
data and growingfrom 1995 NET.
Estimating 1996 BEA and SEDS data
using linear interpolatbn of 1988 to 1995
data and growing from 1995 N ET.
Estimating 1996 BEA and SEDS data
using linear interpolatbn of 1988 to 1995
data and growing from 1995 N ET.
Estimating 1996 BEA and SEDS data
using linear interpolation of 1988 to 1995
data and growing from 1995 N ET.
Estimating 1996 BEA and SEDS data
using linear interpolatbn of 1988 to 1995
data and growing from 1995 N ET.
Estimating 1996 BEA and SEDS data
using linear interpolatbn of 1988 to 1995
data and growing from 1995 N ET.
Updating PM10and PM25 emissions
estimates using national estimates from
OTAQ so thatthesum of the county /SCC
level NET estimates equal the new
natbnalestimates.
Estimating 1996 BEA and SEDS data
using linear interpolatbn of 1988 to 1995
data and growing from 1995 N ET.
Estimating 1996 BEA and SEDS data
using linear interpolatbn of 1988 to 1995
data and growingfrom 1995 NET.
EPA estimated 1996 Base Year emissions for
Version 2 by
Usin g sam e method ology as us ed in
Versbn 1 , but using actual 1996 BEA
and SEDS data.
Usin g sam e method ology as us ed in
Versbn 1 , but using actual 1996 BEA
and SEDS data.
Using same m ethodology as us ed for
Version 1 , but using actual 1996 BEA
and SEDS data.
Using same methodology as used for
Versbn 1 , but using actual 1996 BEA
and SEDS data.
Using same m ethodology as us ed for
Versbn 1 , but using actual 1996 BEA
and SEDS data.
Using same methodology as used for
Version 1, but using actual 1996 BEA
and SEDS data.
Using same methodology as used for
Version 1, but using actual 1996 BEA
and SEDS data.
Using same methodology as used for
Version 1, but using actual 1996 BEA
and SEDS data.
Version 3 by
Using same m ethodology as us ed for
Version 2.
Using same m ethodology as us ed for
Version 2.
Distributing updated national
commercial marine diesel emissions
(with exception of SO2) provided by
OTAQ to counties us ing 1996 county
level distribution in Version 1 of NET.
Applying NH3 emissions factors to
1996 distillate and residual fuel
estimates. Allocating to counties
based on PM-10 emissions
distribution.
Using same m ethodology as us ed for
Version 2.
Applying E-GAS growth factors to
1996 emissions to estimate 1999
county-level emissions.
Using same methodology as used for
Version 2.
Apptying NH3 emissions factors to
1996 diesel consumption estimates.
Allocating to counties based on PM-10
emissions distributbn.
Version 4 by
Using same methodology as used for
Version 2.
Using same methodology as used for
Version 2.
Using same m ethodology as us ed for
Version 3, but with revised national
emission estimates provided by
OTAQ.
Using same methodology as used for
Version 3.
Using same methodology as used for
Version 2.
Applying E-GAS growth factors to
1996 emissions to estimate 1999
county-tevel emissions. Adjusting
1999 g rown emis sions us ing national
locomotive emissions for 1999.
Developing pollutant-specific ratios for
1999/1996, and applying ratios to
county-level 1996 emissions.
Using same m ethodology as us ed for
Version 2.
Using same m ethodology as us ed for
Version 3.
NOTES: Version 1 corresponds to December 1997 Trends report, Version 2 estimates correspond to December 1998 report, Version 3 corresponds to March 2000 report, and Version 4 series is for
report yet to be published.
-------
Table 4.7-3. Seasonal RVP Values Modeled for 1996 NONROAD Model Runs
Seasonal RVP (psi)
State
AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HI
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
Ml
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
FIPS State
Code
01
02
04
05
06
08
09
10
11
12
13
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
Winter
12.4
14.1
8.2
13.7
11.9
12.5
13.0
13.5
12.0
11.8
12.4
10.0
12.8
14.1
14.5
14.9
12.7
13.4
12.4
13.2
13.2
12.9
14.1
14.9
13.7
12.6
13.8
13.9
9.6
12.9
13.7
11.7
14.3
12.4
14.9
14.6
13.9
Spring Summer Autumn
9.3
13.7
7.1
9.5
9.3
10.1
9.8
10.0
8.1
7.4
9.3
10.0
10.4
10.2
10.9
11.2
8.9
9.5
9.4
10.3
9.7
9.7
9.9
11.4
9.5
10.0
10.4
10.6
8.0
9.7
10.5
9.2
10.9
10.3
11.9
11.0
9.1
7.5
13.0
6.8
6.8
6.9
7.8
7.9
7.9
7.0
7.4
7.4
9.8
8.6
7.8
8.8
9.0
7.6
8.4
7.6
9.0
7.5
7.8
7.4
9.0
7.1
7.2
8.7
8.6
7.6
7.8
8.8
7.8
8.8
7.4
9.0
8.7
7.2
8.8
13.7
6.9
10.1
7.6
9.4
9.8
9.0
8.1
7.4
8.7
10.0
9.1
9.0
9.8
11.2
8.2
9.5
8.9
10.3
8.6
9.7
9.9
10.4
8.8
9.4
10.4
9.2
7.8
9.7
10.5
9.0
10.9
9.7
11.2
9.8
8.2
4-264
-------
Table 4.7-3 (continued)
Seasonal RVP (psi)
State
OR
PA
Rl
SC
SD
TN
TX
UT
VT
VA
WA
WV
Wl
WY
CA
FIPS State
Code
41
42
44
45
46
47
48
49
50
51
53
54
55
56
57
Winter
12.3
14.4
12.9
12.4
14.4
12.7
12.2
12.5
14.9
11.8
14.0
14.6
14.6
13.0
11.7
Spring Summer Autumn
9.8
10.9
9.7
10.3
11.2
10.4
9.7
10.6
11.4
8.2
10.6
11.0
11.1
10.4
10.8
7.7
8.8
7.8
7.4
9.0
7.3
7.8
7.8
9.0
7.2
8.5
8.8
9.0
8.8
6.9
8.7
10.9
9.7
9.7
9.9
9.8
8.7
9.4
11.4
8.2
9.5
9.9
10.1
9.3
7.6
Note: For areas receiving reformulated gasoline May through September,
RVP values were modeled in place of the values shown here.
4-265
-------
Table 4.7-4. Counties Modeled with Federal Reformulated Gasoline
State (American Society for Testing and
Materials (ASTM) Class)/
Nonattainment Area County
State (ASTM Class)/
Nonattainment Area
County
Arizona (B)
Phoenix
Maricopa Co
Connecticut (C)
Greater Connecticut
Hartford Co
Litchfield Co
Middlesex Co
New Haven Co
New London Co
Tolland Co
Windham Co
New York-Northern New Jersey-Long Island
Fairfield Co
District of Columbia (B)
Washington DC
Washington
Delaware (C)
Philadelphia-Wilmington-Trenton
Kent Co
New Castle Co
Sussex County
Sussex Co
Illinois (C)
Chicago-Gary-Lake County
Cook Co
Du Page Co
Grundy Co
Kane Co
Kendall Co
Lake Co
McHenry Co
Will Co
Indiana (C)
Chicago-Gary-Lake County
Lake Co
Porter Co
Kentucky (C)
Cincinnati-Ham ilton
Boone Co
Campbell Co
Kenton Co
Louisville
Bullitt Co
Jefferson Co
Oldham Co
Maine (C)
Knox & Lincoln Counties
Knox Co
Lincoln Co
Lewiston-Auburn
Androscoggin Co
Kennebec Co
Portland
Cumberland Co
Sagadahoc Co
York Co
Maryland (B)
Baltimore
Anne Arundel Co
Baltimore
Baltimore Co
Carroll Co
Harford Co
Howard Co
Kent & Queen Annes Counties
Kent Co
Queen Annes Co
Philadelphia-Wilmington-Trenton
Cecil Co
Washington DC
Calvert Co
Charles Co
Frederick Co
Montgomery Co
Prince Georges Co
Massachusetts (C)
Boston-Lawrence-Worcester-Eastern MA
Barnstable Co
Bristol Co
Dukes Co
Essex Co
Middlesex Co
Nantucket Co
Norfolk Co
Plymouth Co
Suffolk Co
Worcester Co
Springfield/Pittsfield-Western MA
Berkshire Co
Franklin Co
Hampden Co
Hampshire Co
4-266
-------
Table 4.7-4 (continued)
State (American Society for Testing and
Materials (ASTM) Class)/
Nonattainment Area County
State (ASTM Class)/
Nonattainment Area
County
New Hampshire (C)
Manchester
Hillsborough Co
Merrimack Co
Portsmouth-Dover-Rochester
Rockingham Co
Strafford Co
New Jersey (C)
Allentown-Bethlehem-Easton
Warren Co
Atlantic City
Atlantic Co
Cape May Co
New York-Northern New Jersey-Long Island
Bergen Co
Essex Co
Hudson Co
Hunterdon Co
Middlesex Co
Monmouth Co
Morris Co
Ocean Co
Passaic Co
Somerset Co
Sussex Co
Union Co
Philadelphia-Wilmington-Trenton
Burlington Co
Camden Co
Cumberland Co
Gloucester Co
Mercer Co
Salem Co
New York (C)
New York-Northern New Jersey-Long Island
Bronx Co
Kings Co
Nassau Co
New York Co
Orange Co
Queens Co
Richmond Co
Rockland Co
Suffolk Co
Westchester Co
New York (C)
Poughkeepsie
Dutchess Co
Putnam Co
Pennsylvania (C)
Philadelphia-Wilmington-Trenton
Bucks Co
Chester Co
Delaware Co
Montgomery Co
Philadelphia Co
Rhode Island (C)
Providence
Bristol Co
Kent Co
Newport Co
Providence Co
Washington Co
Texas (B)
Dallas-Fort Worth
Collin Co
Dallas Co
Denton Co
Tarrant Co
Houston-Gal veston-Brazoria
Brazoria Co
Chambers Co
Fort Bend Co
Galveston Co
Harris Co
Liberty Co
Montgomery Co
Waller Co
Virginia (B)
Norfolk-Virginia Beach-Newport News
Chesapeake
Hampton
James City Co
Newport News
Norfolk
Poquoson
Portsmouth
Suffolk
Virginia Beach
Williamsburg
York Co
4-267
-------
Table 4.7-4 (continued)
State (American Society for Testing and
Materials (ASTM) Class)/
Nonattainment Area County
State (ASTM Class)/
Nonattainment Area
County
Virginia (B)
Richmond-Petersburg
Washington DC
Charles City Co
Chesterfield Co
Colonial Heights
Hanover Co
Henrico Co
Hopewell
Richmond
Alexandria
Arlington Co
Fairfax
Fairfax Co
Falls Church
Loudoun Co
Manassas
Manassas Park
Prince William Co
Stafford Co
Wisconsin (C)
Milwaukee-Racine
Kenosha Co
Milwaukee Co
Ozaukee Co
Racine Co
Washington Co
Waukesha Co
NOTE:
California reform ulated gasoline was modeled statewide in California.
4-268
-------
Table 4.7-5. Oxygenated Fuel Modeling Parameters
Market Shares (%)
State
Alaska
Alaska
Arizona
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Connecticut
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Montana
Nevada
Nevada
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New J ersey
New Mexico
New York
New York
New York
County
Anchorage Ed
Anchorage Ed
Maricopa Co
Adams Co
Arapahoe Co
Boulder Co
Douglas Co
Jefferson Co
Denver Co
El Paso Co
Larimer Co
F airfield Co
AnokaCo
Carver Co
Dakota Co
Hennepin Co
Ramsey Co
Scott Co
Washington Co
WrightCo
Chisago Co
Isanti Co
Missoula Co
Clark Co
WashoeCo
Bergen Co
Essex Co
Hudson Co
Hunterdon Co
MercerCo
Middles ex Co
Monmouth Co
Morris Co
Ocean Co
Passaic Co
Somerset Co
Sussex Co
Union Co
BemailloCo
BronxCo
Kings Co
Nassau Co
MTBE
0
0
80
75
75
75
75
75
75
75
75
90
10
10
10
10
10
10
10
10
10
10
0
0
95
95
95
95
95
95
95
95
95
95
95
95
95
95
15
95
95
95
Alcohol Blends
100
100
20
25
25
25
25
25
25
25
25
10
90
90
90
90
90
90
90
90
90
90
100
100
5
5
5
5
5
5
5
5
5
5
5
5
5
5
85
5
5
5
Oxy gen Co nte nt (%)
MTBE
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
Alcohol Blends
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
Oxygenated
Fuel Season
NOV-FEB (2007 & 2030)
NOV-DEC (199 6 only)
OCT-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
OCT-JAN
OCT-JAN
OCT-JAN
OCT-JAN
OCT-JAN
OCT-JAN
OCT-JAN
OCT-JAN
OCT-JAN
OCT-JAN
NOV-FEB
OCT-MAR
OCT-JAN
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
JAN -FEE (1996 only)
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
JAN -FEE (1996 only)
NOV-FEB
NOV-FEB
NOV-FEB
4-269
-------
Table 4.7-5 (continued)
Market Shares (%)
State
New York
New York
New York
New York
New York
New York
New York
New York
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Texas
Utah
Washington
Washington
Washington
Washington
Wisconsin
County
New York Co
Queens Co
Richmond Co
Rockland Co
Suffolk Co
WestchesterCo
Orange Co
Putnam Co
Clackamas Co
Jackson Co
Multnomah Co
Washington Co
Josephine Co
Klamath Co
Yamhill Co
El Paso Co
Utah Co
Clark Co
King Co
Snohomeh Co
Spokane Co
St.CroixCo
MTBE
95
95
95
95
95
95
95
95
1
1
1
1
1
1
1
15
20
1
1
1
1
10
Alcohol Blends
5
5
5
5
5
5
5
5
99
99
99
99
99
99
99
85
80
99
99
99
99
90
Oxy gen Co nte nt (%)
MTBE
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
Alcohol Blends
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
Oxygenated
Fuel Season
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
NOV-FEB
JAN -FEE (1996 only)
JAN -FEE (1996 only)
SEP-FEB
OCT-JAN
4-270
-------
Table 4.7-6. Summary of Input Values for National NONROAD Model Runs1
Season
Summer
Fall/Spring
Winter
Typical Summer Weekday
Input2
RVP (psi)
Min Temp
Max Temp
Average Temp
RVP (psi)
Min Temp
Max Temp
Average Temp
RVP (psi)
Min Temp
Max Temp
Average Temp
RVP (psi)
Min Temp
Max Temp
Average Temp
Value3
8.1
62
82
72
9.7
43
63
53
13.1
24
44
34
8.1
62
82
72
1 The base case input values presented were the sam e for 1996, 1997, 1998, and 1999. The control case input values
were the same for all three projection years (no control case was developed for 1996).
2 Values for minimum, maximum, and average temperature are expressed in degrees Fahrenheit (°F).
3 For California runs, a diesel fuel sulfur con tent of 120 ppm was used for all seasons.
4-271
-------
Table 4.7-7. Surrogate SCC Assignments for New SCCs in June 2000 NONROAD Model
Additional
SCCs
2260002054
2260005050
2265001020
2265007015
2267005055
2268002081
2268003020
2268003040
2268003070
2268005050
2268005055
2268006015
Description
Gasoline, 2-Stroke Construction
Equipment Crushing/Processing
Equipment
Gasoline, 2-Stroke Farm
Equipment Hydro Power Units
Gasoline, 4-Stroke Recreational
Vehicles Snowmobiles
Gasoline, 4-Stroke Logging
Equipment Skidders
LPG Farm Equipment Other
Agricultural Equipment
CNG Construction Equipment
Other Construction Equipment
CNG Industrial Equipment
Forklifts
CNG Industrial Equipment Other
General Industrial Equipment
CNG Industrial Equipment
Terminal Tractors
CNG Farm Equipment Hydro
Power Units
CNG Farm Equipment Other
Agricultural Equipment
CNG Light Commercial Air
Comp ressors
Surrogate
SCC
2265002054
2265005050
2260001020
2270007015
2265005055
2265002081
2265003020
2265003040
2265003070
2265005050
2265005055
2265006015
Description
Gasoline, 4-Stroke Construction
Equipment Crushing/Processing
Equipment
Gasoline, 4-Stroke Farm
Equipment Hydro Power Units
Gasoline, 2-Stroke Recreational
Vehicles Snowmobiles
Diesel Logging Equipment
Skidders
Gasoline, 4-Stroke Farm
Equipment Other Agricultural
Equipment
Gasoline, 4-Stroke Construction
Equipment Other Construction
Equipment
Gasoline, 4-Stroke Industrial
Equipment Forklifts
Gasoline, 4-Stroke Industrial
Equipment Other General Industrial
Equipment
Gasoline, 4-Stroke Industrial
Equipment Terminal Tractors
Gasoline, 4-Stroke Farm
Equipment Hydro Power Units
Gasoline, 4-Stroke Farm
Equipment Other Agricultural
Equipment
Gasoline, 4-Stroke Light
Commercial Air Compressors
4-272
-------
Table 4.7-8. National Seasonal RVP Averages for NONROAD Model Runs, in psi
Year
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
Winter
13.7
13.4
13.5
13.6
12.8
12.8
12.7
12.8
12.8
13.0
12.8
Spring/Fall
11.9
11.6
11.5
11.4
10.3
10.1
10.1
9.6
9.7
9.8
9.8
Summer
11.0
10.8
10.5
10.4
9.2
8.8
8.9
8.1
8.1
8.3
8.2
Table 4.7-9. National Seasonal Temperatures for NONROAD Model Runs"
Summer
Winter
Spring/Fall
Min (°C)
62
24
43
Max (°C)
82
44
63
Average (°C)
72
34
53
"Assumed same temperature inputs for all inventoryyears, exceptfor 1996
Table 4.7-10. Growth Indicators for Nonroad Sources
Nonroad SCC
SCC Description
Growth Indicator
2275050000, 2275060000
2275020000, 2275070000
General Aviation and Air Taxis
Commercial Aircraft and Auxiliary
Power Units
Landing-Takeoff Operations
(LTOs) for total ai rcraft operations
2275001000
Military Aircraft
992 - Federal, Military
2275085000
Unpaved Airstrips
SIC 45 - Air Transportation
2275900xxx
Aircraft Refueling
SIC 45 - Air Transportation
2280002xxx
Commercial Marine - Diesel Vessels SIC 44 - Water Transportation1
2280001xxx, 2280003xxx,
2280004xxx
Commercial Marine - Coal, Residual SIC 44 - Water Transportation
Oil, and Gas-fired Vessels
2283xxxxxx
Military Marine Vessels
992 - Federal, Military
2285002xxx
Locomotives
SIC - Rail Transportation
1 SO2 and NH3 emissions were estimated using growth factors; estimates for all other pollutants provided by
OTAQ.
4-273
-------
4.8 MISCELLANEOUS SOURCES (FUGITIVE DUST AND AMMONIA)
4.8.1 What Source Categories Does the Miscellaneous Sector Include?
The point and area source categories under the "Miscellaneous Sources" heading include the
following Tier I and Tier II categories:
Tier I Category Tier II Category
(14) Miscellaneous (01) Agriculture and Forestry
(07) Fugitive Dust
The methodologies discussed in Section 4.8.1 for the Miscellaneous Sources sector cover PM-10
and PM2.5 emissions associated with the following fugitive dust categories: agricultural crops,
agricultural livestock, paved road resuspension, unpaved roads, construction activities, and mining and
quarrying. Methodologies are also discussed for estimating ammonia (NH3) emissions for agricultural
livestock operations and the application of fertilizer to agricultural lands. The methodologies discussed in
sections 4.8.2 through 4.8.7 for these categories are for area sources. There are a few point sources
associated with the Tier 14/07 categories. The methodologies for the point sources are discussed in
section 4.8.1.8.
For the fugitive dust categories, PM-10 emissions are estimated for 1985 through 1999. However,
PM-2.5 emissions are calculated only for the years 1990 through 1999. Although several of the source
categories listed above have information concerning the PM-2.5 particle size multiplier that should be
applied to the AP-42 emission factor to calculate PM-2.5 emissions, much of that data is fairly old. As a
consequence, EPA, Pechan, and Midwest Research Institute (MRI) performed an evaluation of more
recent particle size distribution information.1 That review indicated that the PM-2.5/PM-10 ratio for
several of the fugitive dust source categories should be reduced. Table 4.8-1 shows the particle size
ratios used to calculate PM-2.5 particle size multipliers from the PM-10 particle size multipliers used to
develop PM-10 emissions for each fugitive dust category in this section.
Table 4.8-2 summarizes the methods applied and the pollutants for which emissions were estimated
for 1989 through 1999. Table 4.8-3 summarizes the methods applied to prepare Versions 1 through 4 of
the 1996 base year inventory for each of the categories discussed in sections 4.8.2 through 4.8.7.
Table 4.8-4 identifies the State/local agencies that submitted 1996 base year emissions for these
categories. The State/local agency emissions replaced the EPA estimates in Versions 3 and 4 of the 1996
NET inventory. Inventories submitted in 1999 were incorporated into Version 3.0 of the 1996 NET, and
inventories submitted in 2000 were incorporated into Version 4.0 of the 1996 NET.
4.8.1.1 Agricultural Crops (1985-1989)
Agricultural crops are classified under Source Classification Code (SCC) 2801000003.
EPA estimated PM-10 emissions for the years 1985 through 1989 by using an equation for
agricultural tilling.2'3 The activity data for this calculation is the acres of land planted. The emission
factor, developed to estimate the mass of total suspended particukte (TSP) emissions produced per acre-
4-274
-------
tilled, is adjusted to estimate PM-10 using the following constant parameters: the silt content of the
surface soil, a particle size multiplier, and the number of tilings per year.
EPA used the following equation (Equation 4.8-1) to determine State PM-10 emissions from
agricultural tilling for 1985 through 1989:
E = c x k x s°'6 x p x a (Eq. 4.8-1)
where: E = PM-10 emissions
c = constant 4.8 Ibs/acre-pass
k = dimensionless particle size multiplier (PM-10=0.21)
s = silt content of surface soil, defined as the mass fraction of particles smaller than
75 |-im diameter found in soil to a depth of 10 cm (%)
p = number of passes or fillings in a year
a = acres of land planted
4.8.1.1.1 Determination of Correction Parameters —
4.8.1.1.1.1 Silt contends). By comparing the USDA4 surface soil map with the USDA5 county map,
soil types are assigned to all counties of the continental United States. Silt percentages are determined by
using a soil texture classification triangle.6 For those counties with organic material as its soil type, EPA
used the silt percentages presented by Cowherd et al.7 The weighted mean State silt values are
determined by weighing the county value by the number of hectares within the county and summing
across the entire State. Table 4.8-5 shows the silt percentages used for 1985 through 1989. These silt
values are assumed constant for the 5-year period examined.
4.8.1.1.1.2 Number of Tillingsper year (p). Cowherd et al.7 reported that crops are tilled three times
each year, on average, and this value is used for p.
4.8.1.1.2 Activity Data —
The acres of crops planted (a) in each State is obtained for each of the 5 years from the USDA.8
4.8.1.1.3 County Distribution —
State-level PM-10 estimates are distributed to the county-level using county estimates of cropland
harvested from the 1987 Census of Agriculture.9 Equation 4.8-2 is used.
~ , „ . . [ County Cropland Harvested] 0, . „ . .
County Emissions = — * State Emissions (Eq 4 8-2)
^ State Croplant Harvested
4.8.1.2 Agricultural Crops (1990-1999)
The methodology to determine agricultural crop emissions for the years 1990 through 1998 is similar
to the methodology for the years 1985 through 1989, with several exceptions. The PM-10 and PM-2.5
emissions for the years 1990 through 1998 are also estimated using the equation for agricultural tilling.2'3
4-275
-------
The activity data for this calculation is the acres of land tilled. The emission factor, developed to estimate
the mass of TSP produced per acre-tilled, is adjusted to estimate PM-10 and PM-2.5 using the following
constant parameters: the silt content of the surface soil, a particle size multiplier, and the number of
tillings per year.
The following equation (Equation 4.8-3) is used to determine regional PM-10 emissions from
agricultural tilling for 1990 through 1998:
E = c x k x s0'6 x p x a (Eq. 4.8-3)
where: E = PM emissions
c = constant 4.8 Ibs/acre-pass
k = dimensionless particle size multiplier
(PM-10=0.21; PM-2.5=0.042)
s = silt content of surface soil, defined as the mass fraction of particles smaller than
75 |-im diameter found in soil to a depth of 10 cm (%)
p = number of passes or tillings in a year
a = acres of land tilled
Emissions ere estimated for the year 1999 by using a trend analysis of national level tillage type data
for the years 1990, 1992, 1994, 1996 and 1998 to project estimates by tillage type for 1999. Then,
national level estimates by tillage type for 1999 are divided by the national level estimates by tillage type
for 1998 to get national growth factors by tillage type for 1999. These growth factors are then applied to
county level data for 1998 to estimate county level emissions for 1999.
4.8.1.2.1 Determination of Correction Parameters —
4.8.1.2.1.1 Silt contends). By comparing the USDA4 surface soil map with the USDA5 county map,
soil types are assigned to all counties of the continental U.S. Silt percentages are determined by using a
soil texture classification triangle.6 For those counties with organic material as its soil type, EPA uses the
silt percentages presented by Cowherd et al.7 These silt factors are then corrected using information from
Spatial Distribution of PM-10 emissions from Agricultural Tilling in the San Joaquin Valley.10
Information in that report indicates that silt contents determined from the classification triangle are
typically based on wet sieving techniques. Wet sieving tends to desegregate finer materials thus leading
to a higher than expected silt content based on the soil triangle estimates. The over estimation is
dependent upon the soil type. As a consequence, the values for silt loam and loam were reduced by a
factor of 1.5. The values for clay loam and clay were reduced by a factor of 2.6. The values for sand,
loamy sand, sandy loam and organic material remained the same. Table 4.8-6 shows the percent silt used
for each soil type for 1990 through 1998. These sit values were assumed constant for the 8-year period
examined. This differs from the 1989 through 1985 methodology in that the silt factors are applied on
the county level, and are corrected values.
4.8.1.2.1.2 Number of Tillinss per year (p). The number of tillings for 1990 through 1998 were
determined for each crop type, and for conservation and conventional use using information from
Agricultural Activities Influencing Fine Particulate Matter Emissions.11 The tillage emission factor ratio
column in the tables in that report are totaled by crop type when the agricultural implement code is not
4-276
-------
blank. Harvesting is not included in this total. When the tilling instrument is felt to deeply disturb the
soil, the value of the tillage emission factor ratio is equal to one. However, other field instruments are
not felt to disturb the soil to the extent of the instruments used to develop the original emission factor and
thus have an emission factor ratio of less than one. Discussions with the organization that developed the
original emission factor and the report referenced above indicated that these values should be used to
calculate the number of tillings rather than a single value for each implement usage.12 Where there is data
from more than one region for a single crop, an average value is used. Information for both conservation
and convention tillage methods are developed. The tallies are rounded to the nearest whole number,
since it is not physically possible to have a partial tillage event.
These totals are tallied for corn, cotton, rice, sorghum, soybeans, spring wheat, and winter wheat.
Table 4.8-7 shows the number of tilling used for each crop type, and for conservation and conventional
use included in the database provided by the Conservation Information Technology Center (CTIC).13
The number of tillings for categories not included in Agricultural Activities Influencing Fine Particulate
Matter Emissions were determined by contact with the CTIC.14
Rice and spring wheat are included in the category "spring-seeded small grain" in the database
provided by the CTIC.13 Winter wheat is assumed to prevail in all States except Arkansas, Louisiana,
Mississippi and Texas. Rice is assumed to prevail in these four States, and the number of tillings for rice
are applied to the acres harvested in these States. Both rice and winter wheat are grown in California. A
ratio of rice to winter wheat acres harvested for 1990 through 1998 is obtained from the U.S. Land Use
Summary.8 This ratio is used to calculate a modified number of tillings for spring-seeded small grain in
California for each year.
Acres reported in the CTIC database for no till, mulch till, and ridge till are considered conservation
tillage. Those with 0 to 15 percent residue, and 15 to 30 percent residue were considered conventional
tillage.
4.8.1.2.2 Activity Data —
The acres of crops tilled (a) in each county for each crop type and tilling method is obtained for each
of the 6 years from the CTIC.13
4.8.1.2.3 County Distribution —
All emissions for agricultural crops for 1990-1998 are calculated on a county basis.
4.8.1.3 Agricultural Livestock and Fertilizer Application
This subsection discusses the methodologies applied to estimate PM-10 and PM-2.5 emissions for
beef cattle feedlots, andNH3 emissions for livestock operations and fertilizer application.
4.8.1.3.1 Beef Cattle Feedlots —
Emissions for beef cattle feedlots are classified under SCC 2805001000. This subsection discusses
the methodology applied to estimate PM-10 and PM-2.5 emissions for 1990-1999. EPA estimated
emissions for the years 1985 through 1989 using the methodology described in section 4.8.1.8.3.
The 1990-1999 PM-10 emissions from beef cattle feedlots are estimated using the number of head of
beef cows published by the Census of Agriculture and a national PM-10 emission factor.15'16 The activity
4-277
-------
data reported for the beef cow category is believed to provide the best indicator of feedlot activity. Other
categories include animals not kept in feedlots and, if used, could result in overestimating emissions for
this category. The PM-2.5 emissions for the years 1990 through 1999 are determined by multiplying the
PM-10 emission for each year by the size adjustment factor of 0.15, shown in Table 4.8-1. Equation 4.8-
4 is used to estimate county level emissions.
( County Head of Beef Cows'
1,000
County Emissions = —»"v~^»J^~J — | x 17 (Eq. 4.8-4)
4.8.1.3.2 Livestock Operations —
The SCCs for which NH3 emissions are estimated for livestock operations are as follows:
Category
Cattle
Goats
Hogs and Pigs
Horses and Ponies
Poultry
Sheep
sec
2805020000
2805045001
2805025000
2710020030
2805030000
2805040000
The NH3 emissions are estimated using activity data published by the Census of Agriculture and NH3
emission factors. The Census of Agriculture publishes county-level estimates of number of head for the
following livestock: cattle and calves, goats, hogs and pigs, horses and ponies, poultry, and sheep. The
activity data used to determine NH3 emissions from poultry includes activity data for broilers, ducks,
geese, layers and pullets, turkeys, and other poultry. The activity data used to determine NH3 emissions
from sheep include activity data for sheep and lambs. The activity data used to determine NH3 emissions
from cattle used in animal husbandry (SCC 2805020000) correspond to the inventory of all cattle and
calves, which includes beef and milk cows, heifer and heifer calves, and steer and bulls and their calves.
This differs from the activity data used to calculate PM-10 emissions for beef cattle feedlots, which is
discussed in section 4.8.1.3.1.
The emission factors used to calculate emissions are taken from a study of NH3 emissions conducted
in the Netherlands,17 and are listed in Table 4.8-8. Before applying the emission factors, activity data for
livestock operations is divided by 2000, since the emission factors are in units of pounds per head
(Ib/head).
Two SCCs each are available for reporting total emissions for dairy, hog, and poultry operations.
To avoid the potential for double counting of emissions for these categories, the following SCCs have
been deleted from the NET: 2805010000 (Dairy Operations: Total), 2805015000 (Hog Operations:
Total), and 2805005000 (Poultry Operations: Total).
4-278
-------
In 1999, EPA changed the method for estimating beef cattle feedlots and agricultural livestock
operations emissions as a result of additional activity data published by the Census of Agriculture. The
current methods, previously described, are used to revise and update previous estimates back to 1990.
As a result of this method change, emissions for total livestock operations for SCC 2805000000
(Agriculture - Livestock: Total) have been removed from the NET because the newly calculated
emissions are thought to include any emissions that may have previously been reported under this SCC.
This SCC also had no specific emission factor associated with it, and was simply grown from 1985
NAPAP estimates.
4.8.1.3.3 Activity Data for Beef Cattle Feedlots and Livestock Operations —
The activity data used to estimate emissions for 1990 through 1999 for beef cattle feedlots and
livestock operations are county-level activity data (head of livestock) published by the USDA National
Agricultural Statistics Service (NASS) 1997 Census of Agriculture,16 which contains activity data for the
years 1987, 1992 and 1997. In some States, county activity data are not reported or were withheld, but
the State total is reported. To accurately reflect the total activity for a specific category and State, such
data are apportioned to each county equally within a State that had withheld or not reported data.
Further, there are also cases where the data is reported under a general county code designation of all
other counties. Data reported under this county code are added to the withheld totals for the State
before distributing the State totals to counties.
However, there are several States that withheld state-level activity data. In these cases, State totals
are first estimated by calculating the total activity corresponding to all States combined that withheld
data This value is calculated by subtracting the category-specific totals from all States that reported data
from the national total. The remaining activity data is then equally distributed to the States that had
withheld data, and then evenly distributed to each county in that State based upon the number of counties
in the State.
Once county-level activity data are estimated for 1987, 1992 and 1997, activity data for the interim
years (1988-1991 and 1993-1996) are estimated using linear interpolation. To estimate activity data for
1998 and 1999, linear interpolation is also used using activity estimates for the years 1992 through 1997,
and applying a fraction as a multiplier for each of these two years. For 1998, (6/5) is the multiplier used,
and for 1999, (7/5) is the multiplier used. For example, the equation to estimate 1998 activity data is
[Activity data (1992) + (Activity data (1997) -Activity Data (1992)) *(6/5)]. To estimate activity for
1999, the fraction (7/5) is substituted for (6/5). In certain cases, this method returned a negative result.
This is usually due to either activity data being reported in 1992 but not in 1997, or declining activity
from 1992 to 1997. In these cases, an average of the 1996 and 1997 activity data is used to estimate
1998 activity data, and an average of the 1997 and 1998 activity data is used to estimate 1999 activity
data (i.e., 1998 activity data = [1997+(1997-1996)/2]). In a few cases, this equation also produced a
negative result. In these cases, a default value of zero is assigned.
4.8.1.3.4 Fertilizer Application —
The activity data used to estimate NH3 emissions were obtained from the Commercial Fertilizers
Data Base compiled by the Tennessee Valley Authority (TVA) which is now maintained by Association
of American Plant Food Control Officials.18 This data base includes county-level usage of over 100
different types of fertilizers, including those that emit NH3.
4-279
-------
The emission factors used for fertilizer application are also obtained from the Netherlands NH3
study.17 This source lists emission factors for the following 10 different types of fertilizers:
Fertilizer Type
Anhydrous Ammonia
Aqua Ammonia
Nitrogen Solutions
Urea
Ammonium Nitrate
Ammonium Sulfate
Ammonium Thiosulfate
Other Straight Nitrogen
Ammonium Phosphates
N-P-K
sec
2801700001
2801700002
2801700003
2801700004
2801700005
2801700006
2801700007
2801700008
2801700009
2801700010
Emissions for 1999 are estimated by using a linear trend analysis on national level emissions by SCC for
1990, 1996, 1997 and 1998 to project national level emissions by SCC for 1999. Then, countyto
national ratios for 1998 are calculated. These ratios are then applied to the 1999 national estimates to
obtain county level emissions estimates for 1999.
4.8.1.4 PM Emissions from Reentrained Road Dust from Unpaved Roads
Estimates of PM emissions from reentrained road dust on unpaved roads are developed for each
county. An updated AP-42 emission factor equation replaced PARTS reentrained road dust emission
factors for the years 1996 through 1999 (http://www.epa.gov/ttn/chief/ap42pdf/cl3s02-2.pdf). This
emission factor equation depends upon the surface material sit content, the mean weight of vehicles
traveling on the unpaved roads, the surface material moisture content, and the number of days with
measurable precipitation. Emissions are calculated by month at the State/road type level for the average
vehicle fleet and then allocated to the county/road type level. The activity factor for calculating
reentrained road dust emissions on unpaved roads is the VMT accumulated on these roads. The specifics
of the emission estimates for reentrained road dust from unpaved roads are discussed in more detail
below.
4.8.1.4.1 PM Emission Factor Calculation —
Equation 4.8-5, is the AP-42 equation that is used to calculate PM-10 emission factors from
reentrained road dust on unpaved roads, adapted to calculate a monthly rather than an annual emission
factor.
*/2000*
(A^/0.2)'
(Eq. 4.8-5)
4-280
-------
where: Eext = monthly PM-10 emission factor extrapolated for natural mitigation (tons per
mile)
k = empirical constant (2.6 Ib/rrile)
2000 = conversion factor, number of pounds per ton
s = surface material silt content (%)
a = empirical constant (0.8)
W = mean vehicle weight (tons)
b = empirical constant (0.4)
M^ = surface material moisture content under dry, uncontrolled conditions (%)
c = empirical constant (0.3)
p = number of days in a given month with greater than 0.01 inches of precipitation
The above equation is representative of a fleet average emission factor rather than a vehicle-specific
emission factor. A default value of 2.2 tons is used nationally as the mean vehicle weight, as
recommended in the AP-42 documentation for travel on publicly accessible unpaved roads. The value of
1 percent for "M^" was chosen to be representative ofnational conditions.
4.8.1.4.1.1 Silt Content Inputs. Average state-level, unpaved road silt content values developed as
part of the 1985 NAPAP Inventory, are obtained from the Illinois State Water Survey.19" Silt contents of
over 200 unpaved roads from over 30 States are obtained. Average silt contents of unpaved roads are
calculated for each State 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 is substituted.
4.8.1.4.1.2 Precipitation Inputs. Rain data input to the original AP-42 emission factor equation is in
the form of the total number of rain days in the year. However, the equation uses the number of days
simplyto calculate apercentage ofrain days. Equation4.8-5 above modifies the original equation to
calculate a monthly emission factor for each State. Data from the National Climatic Data Center19b>19c
showing the number of days per month with more than 0.01 inches ofrain were used. Precipitation event
accumulation data were collected from a meteorological station selected to be representative of rural
areas within that state.
4.8.1.4.2 Unpaved Road VMT—
The unpaved road VMT calculation methodology starting in 1993 is performed in two parts.
Separate calculations are performed for county and noncounty (State or federally) maintained roadways.
Equation 4.8-6 is used to calculate unpaved road VMT.
VMTUP = ADTV * FSRM * DPY (Eq. 4.8-6)
where: VMTUP = VMT on unpaved roads (miles/year)
ADTV = average daily traffic volume (vehicles/day/mile)
FSRM = functional system roadway mileage (miles)
DPY = number of days in a year
4.8.1.4.2.1 Estimating Local Unpaved VMT. Unpaved roadway mileage estimates are retrieved from
the FHWA's annual Highway Statistics10 report. State-level, county-maintained roadway mileage
4-281
-------
estimates are organized by surface type, traffic volume, and population category. From these data, state-
level unpaved roadway mileage estimates are derived for the volume and population categories listed in
Table 4.8-9. This is done by first assigning an average daily traffic vo lume (ADTV) to each volume
category, as shown in Table 4.8-9.
The above equation is then used to calculate state-level unpaved road VMT estimates for the volume
and population categories listed in Table 4.8-9. These detailed VMT data are then summed to develop
state-level, county-maintained unpaved roadway VMT.
4.8.1.4.2.2 Estimation of Federal and State-Main tained Unpaved Roadway VMT. The calculation
of noncounty (State or federally) maintained unpaved road VMT differed from the calculation of county-
maintained unp aved ro ad VMT. This is re quired since nonco unty unpaved ro ad mileage is cat egor ized by
arterial classification, not roadway traffic volume.
To calculate noncounty, unpaved road VMT, state-level ADTV values for urban and rural roads are
multiplied by state-level, rural and urban roadway mileage estimates. Assuming the ADTV does not vary
by roadway maintenance responsibility, the county-maintained ADTV values are assumed to apply to
noncounty-maintained roadways as well. To develop noncounty unpaved road ADTV estimates, county-
maintained roadway VMT is divided by county-maintained roadway mileage estimates, as shown in
Equation 4.8-7.
ADTV = VMT I MILEAGE (Eq. 4.8-7)
where: ADTV = average daily traffic volume for State and federally maintained roadways
VMT = VMT on county-maintained roadways (miles/year)
MILEAGE = state-level roadway mileage of county-maintained roadways (mies)
Federal and state-maintained roadway VMT is calculated by multiplying the state-level roadway
mileage of federal and state-maintained unpaved roads20 by the state-level ADTV values calculated as
discussed above for locally-maintained roadways. Equation 4.8-8 illustrates.
VMT = ADTV * RM * 365 (Eq. 4.8-8)
where: VMT = VMT at the state level for federally and state-maintained unpaved roadways
(miles/year)
ADTV = average daily traffic volume derived from local roadway data
RM = sta te -lev el fed era lly and s tate -maint ained ro adw ay mileag e (mi)
4.8.1.4.2.3 Unpaved VMT For 1993 and L ater Years. The unpaved road VMT calculation
methodology starting in 1993 differs from the procedure discussed above due to a difference in the data
reported in the annual Highway Statistics.
Unpaved VMT for 1993 and later years is calculated by multiplying the total number of miles of
unpaved road by State and functional class by the annualized traffic volume, where the annualized traffic
4-282
-------
volume is calculated as the average daily traffic volume multiplied by the total number of days per year.
This calculation is illustrated in Equation 4.8-9.
UnpaVedmTRoadtype= MileageRoadtype* ADTV* DPY (Eq. 4.8-9)
where: Unpaved VMT = road type specific unpaved Vehicle Mies Traveled (miles/year)
Mileage = total number of miles of unpaved roads by functional class (miles)
ADTV = Average daily traffic volume (vehicle/day)
DPY = number of days per year
The total number of unpaved road miles by State and functional class is retrieved from the federal
Highway Administrations Highway Statistics.20 In Highway Statistics, state-level local functional class
unpaved mileage is broken out by ADTV category. The ADTV categories differed for urban and rural
areas. Table HM-67 of Highway Statistics shows unpaved road mileage by ADTV categories for rural
and urban local functional classes and the assumed traffic volume for each category. Local functional
class unpaved VMT is calculated for each of these ADTV categories using the equation illustrated above.
Unpaved road mileage for functional classes other than Local (ruralminor collector, rural major
collector, rural minor arterial, rural other principal arterial, urban collector, urban minor arterial, urban
other principal arterial) are not broken out by ADTV in Highway Statistics. An average ADTV is
calculated for these functional classes by dividing state level unpaved Local VMT by the total number of
miles of Local unpaved road. Separate calculations are preformed for urban and rural areas. The
resulting state level urban and rural ADTV is then multiplied by the total number of unpaved miles in each
of the non-local functional classes.
EPA made one modification to the local functional class mileage reported in Highway Statistics.
The distribution of mileage between the ADTV categories for Mississippi resulted in unrealistic
emissions. Total unpaved road mileage in Mississippi is redistributed within the ADTV categories based
on the average distributions found in Alabama, Georgia, and Louisiana.
Starting with the 1997 version of Highway Statistics, the table that shows state-level unpaved road
mileage by ADTV categories (Table HM-67) was no longer published. Therefore, for 1997 and later
years, the 1996 state-level distribution of unpaved roadway mileage by ADTV category and functional
class was substituted. The remainder of the unpaved road VMT calculation methodology for 1997 and
later years is the same as that described above for 1993 through 1996.
4.8.1.4.3 Calculation of State-Level Emissions —
The State and federally maintained unpaved road VMT were added to the county- maintained VMT
for each State and road type to determine each State's total unpaved road VMT by road type. The state-
level unpaved road VMT by road type are then temporally allocated by month using the same NAPAP
temporal allocation factors used to allocate total VMT. These monthly state-level, road type-specific
VMT are then multiplied by the corresponding monthly, state-level, road type-specific emission factors
developed as discussed above. These state-level emission values are then allocated to the county level
using the procedure discussed below.
4-283
-------
4.8.1.4.4 Allocation of State-Level Emissions to Counties —
The State/road type- level unpaved road PM emission estimates are then allocated to each county in
the State using estimates of county rural and urban land area from the U.S. Census Bureau21 for the years
1985 through 1989 . Equation 4 .8-1 0 is used for this allocation.
PMXY = (CNTYLANDURBj/STATLANDURB) * >>
+ (CNTYLANDRURj/STATLANDRUR) * PM^jm ^ }
where: PMX y = unpaved road PM emissions (tons) for county x and road type y
CNTYLANDURB x = urban land area in county x
STATLANDURB = urban knd area in entire State
ST URB Y = unpaved road PM emissions in entire State for urban road type y
CNTYLANDRURX = rural land are a in county x
STATLANDRUR = rural land are a in entire State
PMSTRURY = unpaved road PM emissions in entire State for rural road type y
For the years 1990 through 1999, 1990 county-level rural population is used to distribute the state-level
emissions instead of land area.
4.8.1.4.5 Nonattainment Area 1995 and Later Unpaved Road Controls —
PM control measures are applied to the unpaved road emission estimates for 1995 and later years.
The level of control assumed varied by PM nonattainment area classification and by rural and urban areas.
On urban unpaved roads in moderate PM nonattainment areas, the assumed control was paving the
unpaved roads. This control is applied with a 96 percent control efficiency and a 50 percent penetration
rate. On rural roads in serious PM nonattainment areas, chemical stabilization is the assumed control.
This control is applied with a 75 percent control efficiency and a 50 percent penetration rate. On urban
unpaved roads in serious PM nonattainment areas, paving and chemical stabilization are the controls
assumed to be applied. This combination of controls is applied with an overall control efficiency of 90
percent and a penetration rate of 75 percent.
4.8.1.5 PM Emissions from Reentrained Road Dust from Paved Roads
Estimates ofPM emissions from reentrained road dust onpaved roads are developed at the county
level in a manner similar to that for unpaved roads. PM10 emission factors for reentrained road dust
from paved roads were calculated using EPA's PART5 model.22 PART5 reentrained road dust emission
factors for paved roads depend on the road surface silt loading and the average weight of all of the
vehicles traveling on the paved roadways. The equation used in PART5 to calculate PM emission factors
from reentrained road dust on paved roads is a generic paved road dust calculation formula from AP-42,
shown in Equation 4.8-1 1 ,22
PAVED = PSDPVD * (PVSILT/2?65 * (WEIGHT/3)15 (Eq. 4.8-11)
where: PAVED = paved road dust emission factor for all vehicle classes combined (grams per
mile)
4-284
-------
PSDPVD = base emission factor for particles of less than 10 microns in diameter from
paved road dust (7.3 g/mi for PM-10)
PVSILT = road surface silt loading (g/m2)
WEIGHT = average weight of all vehicle types combined (tons)
Paved road silt loadings are assigned to each of the twelve functional roadway classifications (six urban
and six rural) based on the average annual traffic volume of each functional system by State. One of
three values are assigned to each of these road classes: 1 (gm/m2) is assigned local functional class roads,
and either 0.20 (gm/nf) or 0.04 (gm/m2) are assigned to each of the other functional roadway classes. A
silt loading of 0.20 (gm/m2) is assigned to a road types that had an ADTV less than 5,000 vehicles per
day and 0.04 (gm/m2) is assigned to road types that had an ADTV greater than or equal to 5,000 vehicles
per day. ADTV is calculated by dividing annual VMT by State and functional class (from Highway
Statistics, Table VM-220)by State specific functional class roadway mileage (from Highway Statistics,
Table HM-2020).
As with the PART5 emission factor equation for unpaved roads, the above PM emission factor
equation for paved roads is representative of a fleet average emission factor rather than a vehicle-specific
emission factor and it includes particulate matter from tailpipe exhaust, brake wear, tire wear, and
ambient background particulate concentrations. Therefore, the PART5 fleet average PM emission factors
for the tailpipe, tire wear, and brake wear components are subtracted from the paved road fugitive dust
emission factors before calculating emissions from reentrained road dust on paved roads. Estimates of
average vehicle weight over the entire vehicle fleet on paved road are based on data provided in the Truck
Inventory and Use Survey^ MVMA Motor Vehicle Facts and Figures '91,24 and the 1991 Market Data
Book.25 Using these data sources, a fleet average vehicle weight of 6,360 pounds is modeled
The emission factors obtained from PART5 are modified to account for the number of days with a
sufficient amount of precipitation to prevent road dust resuspension. The PART5 emission factors are
multiplied by the fraction of days in a month with less than 0.01 inches of precipitation. This is done by
subtracting data from the National Climatic Data Center showing the number of days per month with
more than 0.01 inches of precipitation from the number of days in each month and dividing by the total
number of days in the month. These emission factors are developed by month at the State and road type
level for the average vehicle fleet.
For the years 1990 to 1999 the rain correction factor applied to the paved road fugitive dust
emission factors is reduced by 50 percent (i.e., the rain correction factor is calculated as: (365 -p * 12 *
0.5) / 365, where/? represents the number of days in a given month with greater than 0.01 inches of
precipitation). It should be noted that the precipitation data used in the paved road emission factor
calculations were taken from stations representative of urban areas in each state, and as such, the
precipitation data used for the paved road emission factor calculations differ in most cases from the data
used in the unpaved road emission factor calculations.
VMT from paved roads is calculated at the State/ro ad type level by subtracting the State/road type-
level unpaved road VMT from total State/road type-level VMT. Because there are differences in
methodology between the calculation of total and unpaved VMT there are instances where unpaved
VMT is higher than total VMT. For these instances, unpaved VMT is reduced to total VMT and paved
road VMT is assigned a value of zero. The paved road VMT are then temp orally allocated by month
4-285
-------
using the NAPAP temporal allocation factors for VMT. These monthly/State/road type-level VMT are
then multiplied by the corresponding paved road emission factors developed at the same level.
These paved road emissions are allocated to the county level according to the fraction of total VMT
in each county for the specific road type. Equation 4.8-12 illustrates this allocation.
PVDEMISXY = PVDEMISSTY * VMTXYIVMTSTY (Eq. 4.8-12)
where: PVDEMISX Y = paved road PM emissions (tons) for county x and road type y
PVDEMISST Y = paved road PM emissions (tons) for the entire State for road type y
VMTX Y = total VMT (million miles) in county x and road type y
VMTST Y = total VMT (million niles) in entire State for road type y
PM control measures are applied to the paved road emission estimates for the years 1995 and later.
The control assumed is vacuum sweeping on paved roads twice per month to achieve an control level of
79 percent. This control is applied to urban and rural roads in serious PM nonattainment areas and to
urban roads in moderate PM nonattainment areas. The penetration factor used varies by road type and
NAA classification (serious or moderate).
4.8.1.6 Calculation ofPM-2.5 Emissions from Paved and Unpaved Roads
EPA, Pechan, and MRI performed an evaluation of more recent particle size distribution
information.1 That review indicated that the PM-2.5/PM-10 ratio for reentrained road dust from paved
and unpaved roads should be reduced from the older AP-42 particle size multipliers. Table 4.8-10 shows
the particle size ratios used to calculate PM-2.5 emissions from the PM-10 emissions for these sources.
Thus, all PM-2.5 emissions from paved and unpaved roads are calculated by multiplying the final
PM-10 emissions at the county/road type/month level by 0.25 for paved roads and by 0.15 for unpaved
roads.
4.8.1.7 Other Fugitive Dust Sources
The other fugitive dust sources are from construction and mining and quarrying activities. Methods
for estimating construction emissions for the years 1985 through 1998 are explained in section 4.8.1.7.1,
and a revised procedure for estimating 1999 construction emissions is described in section 4.8.1.7.2.
Mining and quarrying methodology is detailed in section 4.8.1.7.3.
4.8.1.7.1 Construction Activities (1985-1998) —
Area source emissions for construction activities are classified under SCC 2311000100. PM-10
emissions for the years 1985 through 1995, and the PM-2.5 emission for the years 1990 through 1995 are
calculated from an emission factor, an estimate of the acres of land under construction, and the average
duration of construction activity.26 The acres of land under construction are estimated from the dollars
spent on construction.27 The PM-10 emission factor for the years 1985 through 1989 is calculated from
the TSP emission factor for construction obtained from AP-42 and data on the PM-10/TSP ratio for
various construction activities.15 The PM-10 emission factor for the years 1990 through 1995 is obtained
from Improvement of Specific Emission Factors.28
4-286
-------
EPA extrapolated 1996 emissions from the 1995 emissions using the ratio between the number of
residential construction permits issued in 1996 and the number issued in 1995.27 PM-10 emissions for the
years 1997 and 1998 are estimated from state-level annual permit data published annually by the U.S.
Census Bureau for "New Privately Owned Housing Units Authorized Unadjusted Units for Regions,
Divisions, and States." These data are obtained from the U.S. Census web site at
www.census.gov/const/www/C40/TabIe2.html# annual. State-level growth factors are calculated for the
1997 and 1998 using 1996 permit data as the base year. The growth factors are then applied to the 1996
county level estimates to estimate county-level emissions for the years 1997 and 1998. EPA then applied
a control efficiency to emissions for 1995 through 1998 for counties classified as PM nonattainment
70
areas.
4.8.1.7.1.1 1985- 1989 Emission Factor Equation. The following AP^12 particulate emission factor
equation (Equation 4.8-13) for heavy construction is used to determine regional PM-10 emissions from
construction activities for 1985 through 1989.
E = rx$x/x^xp (Eq. 4.8-13)
where: E = PM-10 emissions
T = TSP emission factor (1.2 ton/ acre of construction/month of activity)
$ = dollars spent on construction ($ million)
f = factor for converting dollars spent on construction to acres of construction (varies by
type of construction, acres/$ million)
m = months o f activity (varies by type o f construct ion)
P = dimensionlessPM-10/TSP ratio (0.22).
4.8. 1. 7.1.2 1990 through 1995 Emission Factor Equation . Equation 4 .8-1 4 is a variation of the
AP-42 particulate emission factor equation for heavy construction and was used to determine regional
PM-10andPM-2.5 emissions from construction activities for 1990 through 1995. The PM-2. 5 emission
factor used for the years 1990 through 1995 is the PM-10 emission iactor multiplied by the particle size
adjustment factor of 0.2, shown in Table 4.8-1. A control efficiency is applied to PM nonattainment
areas for 1995 and 1996.
(Eq 4 8_14)
100
where: E = PM emissions
P = PM emission factor (ton/acre of construction/month of activity)
(PM-10 = 0.11; PM-2.5 = 0.022)
$ = dollars spent on construction ($ million)
f = factor for converting dollars spent on construction to acres of construction (varies by
type of construction, acres/$ million)
m = months o f activity (varies by type o f construction)
CE = control efficiency (percent)
4-287
-------
4.8.1.7.1.2.1 Dollars spent on construction ($). Estimates of the dollars spent on the various types of
construction by EPA region for 1987 are obtained from the Census Bureau.30 The fraction of total U.S.
dollars spent in 1987 for each region for each construction type is calculated. Since values from the
Census Bureau are only available every five years, the Census dollars spent for the United States for
construction are normalized using estimates of the dollars spent on construction for the United States as
estimated by the F.W. Dodge27 corporation for the other years. This normalized Census value is
distributed by region and construction type using the above calculated fractions. An example of how this
procedure is applied for SIC 1521 (general contractor, residential building: single family) is shown in
Equation 4.8-15.
$
19S7Jfation,CenSuS
*
19V7Jleel0nl,CenaU,®£1
(Eq. 4.8-15)
where:
$
1988
1987
Region I
SIC 1521
Nation
Census
Dodge
dollar amount of const ruction spent
year 1988
year 1987
U.S. EPA Region I
Standard Industrial Code for general contractor, residential building; single
family
United States
Census Bureau
F.W. Dodge
4.8.1.7.1.2.2 Determination of construction acres (f) . Information developed by Cowherd et al.26
determined that for different types of construction, the number of acres is proportional to dollars spent on
that type construction. This information (proportioned to constant dollars using the method developed
by Heisler31) is utilized along with total construction receipts to determine the total number of acres of
each construction type.
4.8.1.7.1.2.3 Months of construction (m). Estimates of the duration (in months) for each type
construction are derived from Cowherd et al.26
4.8.1.7.1.2.4 PM-10/TSP Ratio (P) (1985-1989). The PM-1 0/TSP ratio for construction activities is
derived fromMRI.15 In MRI's report, the data in Table 9, "Net Particulate Concentrations and Ratios" is
cited from Kinsey et al.32 That table included the ratios of PM- 10/TSP for 19 test sites for three different
construction activities. MRI suggests averaging the ratios for the construction activity of interest. Since
EPA was looking at total construction emissions from all sources, EPA averaged the PM-10/TSP ratios
for all test sites and construction activities.
4.8.1.7.1.2.5 PM-10 and PM-2.5 Ratio (P) (1990-1998). The PM-10 emission factor used for the
years 1990 through 1995 for construction activities is obtained from Improvement of Specific Emission
Factors.28 This study reports an emission factor of 0.1 1 ton PM- 10/ac re-mo nth. This value is the
geometric mean of emission factors for 7 different sites considered in the study. Emission inventories for
the sites are prepared for the construction activities observed at each site. The PM-2.5 emission factor
4-288
-------
used for the years 1990-1995 is the PM-10 emission factor (0.11 ton PM-10/acre-month) multiplied by
the particle size adjustment factor of 0.2, shown in Table 4.8-1.
4.8.1.7.1.2.6 Control Efficiency (1990-1998). The control efficiency for the years 1990 through 1994
is zero for all counties. However, starting in 1995, a control efficiency is applied to emissions for
counties classified as PM nonattainment areas.29 The PM-10 control efficiency used for 1995 through
1998 for PM nonattainment areas is 62.5. The PM-2.5 control efficiency for these years and areas is
37.5.
4.8.1.7.1.2.7 County Distribution. Regional-level PM-10 estimates are distributed to the county-level
using county estimates of payroll for construction (SICs 15, 16, 17) from County Business Patterns.33
Equation 4.8-16 is used.
„ . r . . County Construction Payroll n . .
County Emissions = x Regional Emissions (En 48-16)
Regional Construction Payroll
4.8.1.7.2 How Did We Update Construction Emission Estimates for the Year 1999? —
We updated 1999 fugitive dust emission estimates from construction by obtaining more recent
activity data correspond ing to various subcate go ries of construction, and applying category-specific
emission factors. The final emission estimates are adjusted to account for variations in soil silt and
moisture content, as well as control efficiency. The construction categories for which updated emission
estimates are developed include:
Construction Category SCC SCC Name
Residential 2311010000 Industrial Processes Construction: SIC codes 15 - 17 General
Building Construction Total
Commercial 2311020000 Industrial Processes Construction: SIC codes 15-17 Heavy
Construction Total
Roadway 2311030000 Industrial Processes Construction: SIC codes 15 - 17
Road Construction Total
Construction emissions are estimated using two basic construction parameters, the acres of land
disturbed by the construction activity and the duration of the activity. The actual acres disturbed by the
various types of construction are generally not available, and must be estimated using surrogate data,
which must be converted to acres using the appropriate conversion factor. The methodology is based
upon procedures documented in the U.S. EPA report, "Estimating Particulate Matter Emissions from
Construction Operations,"34 with some adjustments.
4.8.1.7.2.1 How Did We Estimate Emissions for Residential Construction? For residential
construction, housing permit data for single-family units, two-family units, and apartments were obtained
at the county level from the U.S. Department of Commerce's (DOC) Bureau of the Census.35 We then
adjusted county permit data to equal regional housing start data, which would more accurately reflect
actual construction, also available from the Bureau of the Census.36 Once the number of buildings in each
4-289
-------
category is estimated, the total acres disturbed by construction is estimated by applying conversion
factors to the housing start data for each category as follows:
• Single family - 1/4 acre/building
• Two-family - 1/3 acre/building
• Apartment - 1/2 acre/building
Housing construction emissions are calculated using an emission factor of 0.032 tons PM-10/acre/
month, the number of housing units created, aunits-to-acres conversion factor, and the duration of
construction activity. The duration of construction activity for houses is assumed to be 6 months. The
formula for calculating emissions from residential construction is:
Emissions = (0.032 tons PM-\Q I acre I month) x B x / x m
where: B = the number of single or two-family houses constructed
f = buildings-to-acres conversion factor
m = the duration of construction activity in months
Apartment construction emissions are calculated separately using an emission factor that is more
representative of emissions due to construction of apartment buildings (0.11 tons PM-10/acre/month). A
duration of 12 months was assumed for apartment construction.
Basement Adjustment
For areas in which basements are constructed or the amount of dirt moved at a residential
construction site is known, an alternative formula is used. An average value of 2000 square feet is
assumed for both single-family and two-family homes. This value is used to estimate the cubic yards of
dirt moved per house. Multiplying the average total square feet by an average basement depth of 8 feet
and adding in 10 percent of the cubic feet calculated for peripheral dirt removed produces an estimate of
the cubic yards of earth moved during residential construction. The added 10 percent accounts for the
footings, space around the footings, and other backfilled areas adjacent to the basement.
The cubic yards of earth moved per house along with the number of houses constructed is used with
the best available control measures (BACM) Level 2 equation (emission factor of 0.011 tons
PM-10/acre/month plus 0.059 tons PM-10/1000 cubic yards of on-site cut/fill) to calculate emissions for
regions in which basements are constructed or a large amount of dirt is moved during most residential
construction. The percentage of one-family houses with basements was obtained from the DOC report,
Characteristics of New Houses?1 The percentage of houses per Census region (Northeast, Midwest,
South, and West) that contain full or partial basements is applied to the housing start estimates for each
of these respective regions. The BACM Level 2 equation is applied once the number of acres disturbed
due to the estimated number of houses built with basements was determined.
4.8.1.7.2.2 How Did We Estimate Emissions for Non-Residential Construction? The emissions
produced from the construction of nonresidential buildings is calculated using the value of construction
put in place. The national value of construction put in place is obtained from the Bureau of the Census,38
and is allocated to counties using construction employment data for SIC 154.39 A conversion factor of
1.6 acres/106 dollars ($) is applied to the construction valuation data. This conversion factor is developed
4-290
-------
by adjusting the 1992 value of 2 acres/$106 to 1999 constant dollars using the Price and Cost Indices for
Construction.
The duration of construction activity for nonresidential construction is estimated to be 11 months.
The formula for calculating the emissions from nonresidential construction is:
Emissions = (0.19 tons PMWI acre I month) x $ x / x m
where: $ = dollars spent on nonresidential construction in millions
f = dolkrs-to-acres conversion factor
m = durat ion of co nstru ction a ctivity in months
4.8.1.7.2.3 How Did We Estimate Emissions for Road Construction? The PM-10 emissions
produced by road construction are estimated using an emission factor for heavy construction and the
State capital outlay for new road construction. To estimate the acres disturbed byroad construction, we
obtained FHWA State expenditure data for capital outlay according to the following six classifications:40
• Interstate, urban
• Interstate, rural;
• Other arterial, urban;
• Other arterial, rural;
• Collectors, urban; and
• Collectors, rural
We obtained data from the North Carolina Department of Transportation (NCDOT) on the $/mile
spent on various road construction projects.41 For interstate expenditures, we used an average of
$4 million/mile corresponding to freeways and interstate projects listed for: 1) new location; 2) widen
existing 2-lane shoulder section; and 3) widen existing 4-lane w/ median. For expenditures on other
arterial and collectors, we used an average of $1.9 million/mile corresponding to all other projects
(excluding freeways and interstate projects) listed for: 1) new location; 2) widen existing 2-lane shoulder
section; and 3) widen existing 4-lane w/ median.
After new miles of road constructed are estimated using the above $/mile conversions, miles are
converted to acres for each of the 6 road types using the following estimates of acres disturbed per mile:
• Interstate, urban and rural; Other arterial, urban - 15.2 acres/mile
• Other arterial, rural - 12.7 acres/mile
• Collectors, urban - 9.8 acres/mile
• Collectors, rural - 7.9 acres/mile
State-level estimates of acres disturbed are distributed to counties according to the housing starts
per county (similar to residential construction).
An emission factor of 0.42 tons/acre/month is used to account for the large amount of dirt moved
during the construction of roadways. Since most road construction consists of grading and leveling the
land, the higher emission factor more accurately reflects the high level of cut and fill activity that occurs
4-291
-------
at road construction sites. The duration of construction activity for road construction is estimated to be
12 months. The formula for calculating roadway construction emissions is:
Emissions = (0.42 tons PMWI acre I month) x$xflxf2xd
where: $ = State expenditures for capital outlay on road construction
fl = $-to-miles conversion factor
f2 = miles-to-acres conversion factor
m = durat ion of ro adway co nstru ction a ctivity in months
Regional variances in construction activity are accounted for by using correction parameters
including soil moisture level, silt content, and control efficiency. The recommended emission factors are
representative of uncontrolled emissions.
4.8.1.7.2.3.1 Soil Moisture Level. To account for the soil moisture level, the folio wing equation is
used:
Moisture Level Corrected Emissions = Base Emissions * (24/PE)
where: PE = Precipitation-Evaporation value for county
Precipitation-Evaporation (PE) values are obtained from Thornthwaite's PE Index. We determined
the average Thornethwaite PE value for each State based on a map presenting PE values for specific
climatic divisions within a State.34 Alaska and Hawaii were assigned default average PE values by
examining rainfall data, and using PE values from those States whose 30-year average statewide rainfall
was most comparable to Alaska and Hawaii.
4.8.1.7.2.3.2 Silt Content. To account for the silt content, the following equation is used:
Silt Content Corrected Emissions = Base Emissions * (s/9%)
where: s = % dry silt content in soil for area being inventoried
County-level dry silt values are applied to PM-10 emissions for each county. The development of
the dry silt content values applied to construction emissions is discussed in section 4.8.1.2.1.1, under the
procedures for estimating agricultural tilling emissions.
4.8.1.7.2.3.3 Control Efficiency. For 1999 construction emissions, a control efficiency of 50 percent is
used for both PM-10 and PM-2.5 for PM nonattainment areas. According to EPA's Green Book,42 we
identified additional nonattainment counties that should be assumed to have BACM controls on their
fugitive dust construction emissions. These included Gila County, AZ, Arapahoe, Douglas, and Jefferson
Counties in Colorado, and Lake County, OR. Control efficiencies are applied to 1999 emission estimates
for these additional counties.
4.8.1.7.2.3.4 PM-2.5 Emissions. The method describes emission factors for calculating PM-10
emissions. Once PM-10 estimates are developed PM-2.5 emissions are estimated by applying a particle
size multiplier of 0.20 to PM-10 emissions.
4-292
-------
4.8.1.7.3 Mining and Quarrying —
Area source emissions for mining and quarrying are classified under SCC 2325000000.
The PM-10 emissions for the years 1985 through 1998 are the sum of the emissions from metallic
ore, nonmetallic ore, and coal mining operations. The 1999 PM-10 emissions are produced through a
linear projection of the emissions for the previous 5 years of data (i.e., 1994 through 1998 inclusive).
The PM-2.5 emissions for the years 1990 through 1999 are determined by multiplying the PM-10
emissions for that year by the particle size adjustment factor of 0.2, represented in Table 4.8-1.
PM-10 emissions estimates from mining and quarrying operations include only the following sources
of emissions: 1) overburden removal, 2) drilling and blasting, 3) loading and unloading and 4) overburden
replacement. Transfer and conveyance operations, crushing and screening operations and storage are not
included. Travel on haul roads is also omitted. These operations are not included in order to be
consistent with previous TSP emissions estimates from these sources,43 because they represent activities
necessary for ore processing, but not necessary for actual extraction of ore from the earth, and because
these activities are the most likely to have some type of control implemented.
EPA's emissions of mining and quarrying operations is a summation of three types of mining
(metallic, non-metallic and coal) which are expressed in Equation 4.8-17.
E = Em + En + Ec (Eq. 4.8-17)
where: E = PM-10 emissions from mining and quarrying operations
Em = PM-10 emissions from metallic mining operations
En = PM-10 emissions from non-metallic mining operations
Ec = PM-10 emissions from coalmining operations
4.8.1.7.3.1 Determination of Correction Param eters. It was assumed that, for the four operations
listed above, the TSP emission factors utilized in developing copper ore processing Emission Trends
estimates applied to all metallic minerals. PM-10 emission factors are determined for each of the four
operations listed above by making the following assumptions. Table 11.2.3-2 of AP-422'3 is used to
determine that 35 percent of overburden removal TSP emissions were PM-10. For drilling and blasting
and truck dumping, 81 percent of the TSP emissions were assumed to be PM-10.44 For loading
operations, 43 percent of TSP emissions were assumed to be PM-10.44
Non-metallic mineral emissions are calculated by assuming that the PM-10 emission factors for
western surface coal mining45 applied to all non-metallic minerals.
Coal mining includes two additional sources of PM-10 emissions compared to the sources
considered for metallic and non-metallic minerals. The two additional sources are overburden
replacement and truck loading and unloading of that overburden. EPA assumes that tons of overburden
was equal to ten times the tons of coal mined.43
4.8.1.7.3.2 Activity Data. The regional metallic and non-metallic crude ore handled at surface mines
for 1985 through 1998 are obtained from the U.S. Geological Survey.46 Some State-level estimates are
4-293
-------
withheld by the U.S. Geological Survey to avoid disclosing proprietary data. Known distributions from
past years are used to estimate these withheld data.
The regional production figures for surface coal mining operations are obtained from the Coal
Industry Annual47 for 1985 through 1998.
4.8.1.7.3.2.1 Metallic Mining Operations. The following PM-10 emissions estimate equation
(Equation 4.8-18) calculates the emissions from overburden removal, drilling and blasting, and loading
and unloading during metallic mining operations.
Em = Am x EFo + B x EFh + EFl + EFd (Eq. 4.8-18)
where: A^ = metallic crude ore handled at surface mines (1000 short tons)
EF0 = PM-10 open pit overburden removal emission factor for copper ore processing
(Ibs/ton)
B = fraction of total ore production that is obtained by blasting at metallic nines
EFb = PM-10 drilling/blasting emission factor for copper ore processing (Ibs/ton)
EFl = PM-10 loading emission factor for copper ore processing (Ibs/ton)
EFd = PM-10 truck dumping emission factor for copper ore processing (Ibs/ton)
4.8.1.7.3.2.2 Non-metallic Mining Operations. The following PM-10 emissions estimate equation
(Equation 4.8-1 9) calculates the emissions from overburden removal, drilling and blasting, and loading
and unloading during non-metallic mining operations.
= An x (EFV + DxEFr + EFa+ V2x EFe + EFt)) (Eq. 4.8-19)
where: A,, = non-metallic crude ore handled at surface mines (1000 short tons)
EFV = PM-10 open pit overburden removal emission factor at western surface coal
mining operations (Ibs/ton)
D = fraction of total ore production that is obtained by blasting at non-metallic
mines
EFr = PM-10 drilling/blasting emission factor at western surface coalmining
operations (Ibs/ton)
EFa = PM-10 loading emission factor at western surface coalmining operations
(Ibs/ton)
EFe = PM-10 truck unloading: end dump-coal emission factor at western surface
coal mining operations (Ibs/ton)
EFt = PM-10 truck unloading: bottom dump-coal emission factor at western
surface coal mining operations (Ibs/ton)
4.8.1.7.3.2.3 Coal Mining. The following PM-10 emissions estimate equation (Equation 4.8-20)
calculates the emissions from overburden removal, drilling and blasting, loading and unloading, and
overburden replacement during coal mining operations.
4-294
-------
Ec = Ac x (10x EFto + EFor + EF^ + EFv + EFr + EFa + V4x EF, + EFf)) (Eq. 4.8-20)
where: Ac = coal production at surface mines (1000 short tons)
Ef,0 = PM-10 emission factor for truck loading overburden at western surface coal
mining operations (Ibs/ton of overburden)
Efor = PM-10 emission factor for overburden replacement at western surface coal
mining operations (Ibs/ton of overburden)
Efdt = PM-10 emission factors for truck unloading: bottom dump-overburden at
western surface coal mining operations (Ibs/ton of overburden)
EFV = PM-10 open pit overburden removal emission factor at western surface coal
mining operations (Ibs/ton)
EFr = PM-10 drilling/blasting emission factor at western surface coalmining
operations (Ibs/ton)
EFa = PM-10 loading emission factor at western surface coal mining operations
(Ibs/ton)
EFe = PM-10 truck unloading: end dump-coal emission factor at western surface
coal mining operations (Ibs/ton)
EF, = PM-10 truck unloading: bottom dump-coal emission factor at western
surface coal mining operations (Ibs/ton)
4.8.1.7.3.3 1999 Emissions Methodology. For the year 1999 PM-10 emissions from mining and
quarrying operations are projected based on linear regression of the previous 5 years. EPA was unable to
obtain regional metallic and non-metallic crude ore handled at surface mines for 1999. The U.S.
Geological Survey publishes summary statistics on mining and quarrying with a one year delay.
4.8.1.7.3.4 County Distribution. Regional-level emissions are distributed equally among counties
within each region (Equation 4.8-21).
County Emissions = x Regional Emissions (Eq. 4.8-21)
Number of Counties in Region
4.8.1.8 Grown Emissions
Point and area fugitive dust sources in the 1990 NET inventory were wind erosion, unpaved roads,
and paved roads. (A complete list of source categories is presented in Table 4.8-11.) Emissions from
these sources were grown from the 1990 NET inventory based on BEA earnings.
4.8.1.8.1 Emissions Calculations —
Base year controlled emissions are projected to the inventory year using Equation 4.8-22.
CEt = CEBY + (CEBY x EG,) (Eq. 4.8-22)
4-295
-------
where: CE; = Controlled Emissions for inventory year I
CEBY = Controlled Emissions for base year
EG; = Earnings Growth for inventory year I
Earnings growth (EG) is calculated as shown in Equation 4.8-23.
DATt
= 1- — (Eq. 4.8-23)
where: DAT; = Earnings data for inventory year I
DATBY = Earnings data in the base year
4.8.1.8.2 1990 Emissions —
The 1990 NET inventory is based primarily on State data, with the 1990 interim data filling in the
gaps. The database houses U. S. annual and average summer day emission estimates for the 50 States and
the District of Columbia. Seven pollutants (CO, NOX, VOC, SO2, PM-10, PM-2.5, and NH3) were
estimated in 1990. The State data were extracted from three sources, the OTAG inventory, the GCVTC
inventory, and AIRS/FS.
Since EPA did not receive documentation on how these inventories were developed, this section
only describes the effort to collect the data and any modifications or additions made to the data.
4.8.1.8.2.1 OTAG. The OTAG inventory for 1990 was completed in December 1996. The database
houses emission estimates for those States in the Super Regional Oxidant A (SUPROXA) domain. The
estimates were developed to represent average summer day emissions for the ozone pollutants (VOC,
NOX, and CO). This section gives a background of the OTAG emission inventory and the data collection
process.
4.8.1.8.2.1.1 Inventory Components. The OTAG inventory contains data for all States that are
partially or fully in the SUPROXA modeling domain. The SUPROXA domain was developed in the late
1980s as part of the EPA regional oxidant modeling (ROM) applications. EPA had initially used three
smaller regional domains (Northeast, Midwest, and Southeast) for ozone modeling, but wanted to model
the full effects of transport in the eastern United States without having to deal with estimating boundary
conditions along relatively high emission areas. Therefore, these three domains were combined and
expanded to form the Super Domain The western extent of the domain was designed to allow for
coverage of the largest urban areas in the eastern United States without extending too far west to
encounter terrain difficulties associated with the Rocky Mountains. The Northern boundary was
designed to include the major urban areas of eastern Canada. The southern boundary was designed to
include as much of the United States as possible, but was limited to latitude 26°N, due to computational
limitations of the photochemical models. (Emission estimates for Canada were not extracted from OTAG
for inclusion in the NET inventory.)
The current SUPROXA domain is defined by the following coordinates:
North: 47.0 0°N East: 67.0 0°W
South: 26.0 0°N West: 99.0 0°W
4-296
-------
Its eastern boundary is the Atlantic Ocean and its western border runs from north to south through North
Dakota, South Dakota, Nebraska, Kansas, Oklahoma, and Texas. In total, the OTAG Inventory
completely covers 37 States and the District of Columbia.
The OTAG inventory is primarily an ozone precursor inventory. It includes emission estimates of
VOC, NOX, and CO for all applicable source categories throughout the domain. It also includes a small
amount of SO2 and PM-10 emission data that was sent by States along with their ozone precursor data.
No quality assurance (QA) was performed on the SO2 and PM-10 emission estimates for the OTAG
inventory effort.
Since the underlying purpose of the OTAG inventory is to support photochemical modeling for
ozone, it is primarily an average summer day inventory. Emission estimates that were submitted as
annual emission estimates were converted to average summer day estimates using operating schedule data
and default temporal profiles and vice versa.
The OTAG inventory is made up of three major components: (1) the point source component,
which includes segment/pollutant level emission estimates and other relevant data (e.g., stack parameters,
geographic coordinates, and base year control information) for all stationary point sources in the domain;
(2) the area source component, which includes county level emission estimates for all stationary area
sources and non-road engines; and (3) the on-road vehicle component, which includes county/roadway
functional class/vehicle type estimates of VMT and MOBILESa input files for the entire domain.
4.8.1.8.2.1.2 Interim Emissions Inventory (OTAG Default). The primary data sources for the
OTAG inventory were the individual States. Where States were unable to provide data, the 1990 Interim
Inventory48 was used for default inventory data.
4.8.1.8.2.1.3 State Data Collection Procedures. Since the completion of the Interim Inventory in
1992, many States had completed 1990 inventories for ozone nonattainment areas as required for
preparing SIPs. In addition to these SIP inventories, many States had developed more comprehensive
1990 emission estimates covering their entire State. Since these State inventories were both more recent
and more comprehensive than the Interim Inventory, a new inventory was developed based on State
inventory data (where available) in an effort to develop the most accurate emission inventory to use in the
OTAG modeling.
On May 5, 1995, a letter from John Seitz (Director of EPA's Office of Air Quality Planning and
Standards [OAQPS]) and MaryGade (Vice President of ECOS) to State Air Directors, States were
requested to supply available emission inventory data for incorporation into the OTAG inventory.49
Specifically, States were requested to supply all available point and area source emissions data for VOC,
NOX, CO, SO2, and PM-10, with the primary focus on emissions of ozone precursors. Some emission
inventory data were received from 36 of the 38 States in the OTAG domain. To minimize the burden to
the States, there was no specified format for submitting State data. The majority of the State data was
submitted in one of three formats:
1) an Emissions Preprocessor System Version 2.0 (EPS2.0) Workfile
2) an ad hoc report from AIRS/FS
3) data files extracted from a State emission inventory database
4-297
-------
4.8.1.8.2.1.4 State Data Incorporation Procedures/Guidelines. The general procedure for
incorporating State data into the OTAG Inventory was to take the data "as is" from the State
submissions. There were two main exceptions to this policy. First, any inventory data for years other
than 1990 was backcast to 1990 using BEA Industrial Earnings data by State and two-digit SIC code.
This conversion was required for five States that submitted point source data for the years 1992 through
1994. All other data submitted were for 1990.
Second, any emission inventory data that included annual emission estimates but not average
summer day values were temporally allocated to produce average summer day values. This temporal
allocation was performed for point and area data supplied by several States. For point sources, the
operating schedule data, if supplied, were used to temporally allocate annual emissions to average
summer weekday using Equation 4.8-24:
EMISSIONSASD = EMISSIONSANNUAL * SUMTHRU * 1/(13 * DPW) (Eq. 4.8-24)
where:
EMISSIGNSASD = average summer day emissions
EMIS SIGNSANNUAL = annual emissions
SUMTHRU = summer throughput percentage
DPW = days per week in operation
If operating schedule data were not supplied for the point source, annual emissions were temporally
allocated to an average summer weekday using EPA's default Temporal Allocation file. This computer
file contains default seasonal and daily temporal profiles by SCC. The following equation was used:
EMISSIONSASD = EMISSIONSANNUAL I (SUMFACSCC * WDFACSCC) (Eq. 4.8-25)
where:
EMISSIONSASD = average summer day emissions
EMISSIONSANNUAL = annual emissions
SUMFACSCC = default summer season temporal factor for SCC
WDFACSCC = default summer weekday temporal factor for SCC
There were a small number of SCCs that were not in the Temporal Allocation file. For these SCCs,
average summer weekday emissions were assumed to be the same as those for an average day during the
year and were calculated using the following equation:
EMISSIONSASD = EMISSIONSANNUAL I 365 (Eq. 4.8-26)
where:
EMISSIONSASD = average summer day emissions
4-298
-------
EMISSIONSANNUAL = annual emissions
4.8.1.8.2.1.5 Point. For stationary point sources, 36 of the 38 States in the OTAG domain supplied
emission estimates covering the entire State. Data from the Interim Inventory were used for the two
States (Iowa and Mississippi) that did not supply data. Most States supplied 1990 point source data,
although some States supplied data for later years because the later year data reflected significant
improvements over their 1990 data. Inventory data for years other than 1990 were backcast to 1990
using BEA historical estimates of industrial earnings at the 2-digit SIC level. Table 4.8-12 provides a
brief description of the point source data supplied by each State.
4.8.1.8.2.1.6 Area. For area sources, 17 of the 38 States in the OTAG domain supplied 1990 emission
estimates covering the entire State, and an additional nine States supplied 1990 emission estimates
covering part of their State (partial coverage was mostly in ozone nonattainment areas). Interim
Inventory data were the sole data source for 12 States. Where the area source data supplied included
annual emission estimates, the default temporal factors were used to develop average summer daily
emission estimates. Table 4.8-13 provides a brief description of the area source data supplied by each
State.
4.8.1.8.2.1.7 Rule Effectiveness. For the OTAG inventory, States were asked to submit their best
estimate of 1990 emissions. There was no requirement that State-submitted point source data include
rule effectiveness for plants with controls in place in that year. States were instructed to use their
judgment about whether to include rule effectiveness in the emission estimates. As a result, some States
submitted estimates that were calculated using rule effectiveness, while other States submitted estimates
that were calculated without using rule effectiveness.
The use of rule effectiveness in estimating emissions can result in emission estimates that are much
higher than estimates for the same source calculated without using rule effectiveness, especially for
sources with high control efficiencies (95 percent or above). Because of this problem, there was concern
that the OTAG emission estimates for States that used rule effectiveness would be biased to larger
estimates relative to States that did not include rule effectiveness in their computations.
To test if this bias existed, county level maps of point source emissions were developed for the
OTAG domaia If this bias did exist, one would expect to see sharp differences at State borders between
States using rule effectiveness and States not using rule effectiveness. Sharp State boundaries were not
evident in any of the maps created. Based on this analysis, it was determined that impact of rule
effectiveness inconsistencies was not causing large biases in the inventory.
4.8.1.8.2.2 Grand Canyon Visibility Transport Commission Inventory. The GCVTC inventory
includes detailed emissions data for eleven States: Arizona, California, Colorado, Idaho, Montana,
Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.50 This inventory was developed by
compiling and merging existing inventory databases. The primary data sources used were State
inventories for California and Oregon, AIRS/FS for VOC, NOX, and SO2 point source data for the other
nine States, the 1990 Interim Inventory for area source data for the other nine States, and the 1985
NAPAP inventory for NH3 and TSP data. In addition to these existing data, the GCVTC inventory
includes newly developed emission estimates for forest wildfires and prescribed burning.
4-299
-------
After a detailed analysis of the GCVTC inventory, it was determined that the following portions of
the GCVTC inventory would be incorporated into the PM inventory:
• complete point and area source data for California
• complete point and area source data for Oregon
• forest wildfire data for the entire eleven State region
• prescribed burning data for the entire eleven State region
State data from California and Oregon were incorporated because they are complete inventories
developed by the States and are presumably based on more recent, detailed and accurate data than the
Interim Inventory (some of which is still based on the 1985 NAPAP inventory). The wildfire data in the
GCVTC inventory represent a detailed survey of forest fires in the study area and are clearly more
accurate than the wildfire data in the Interim Inventory. The prescribed burning data in the GCVTC
inventory are the same as the data in the Interim Inventory at the State level, but contain more detailed
county-level data.
Non-utility point source emission estimates in the GCVTC inventory from States other than
California and Oregon came from AIRS/FS. Corrections were made to this inventory to the VOC and
PM emissions. The organic emissions reported in GCVTC inventory for California are total organics
(TOG). These emissions were converted to VOC using the profiles from EPA's SPECIATE51 database.
4.8.1.8.2.3 AIRS/FS. SO2 and PM-10 (or PM-10 estimated from TSP) sources of greater than
250 tons per year as reported to AIRS/FS that were not included in either the OTAG or GCVTC
inventories were appended to the NET inventory. The data were extracted from AIRS/FS using the data
criteria set listed in Table4.8-14. The data elements extracted are also listed in Table 4.8-14. The data
were extracted in late November 1996. It is important to note that estimated emissions were extracted.
4.8.1.8.2.4 Data Gaps. As stated above, the starting point for the 1990 NET inventory is the OTAG,
GCVTC, AIRS, and 1990 Interim inventories. Data added to these inventories include estimates of SO2,
PM-10, PM-2.5, andNH3, as well as annual or ozone season daily (depending on the inventory) emission
estimates for all pollutants. This section describes the steps taken to fill in the gaps from the other
inventories.
4.8.1.8.2.4.1 SO2, PM-10, and PM-2.5 Emissions. For SO2 and PM-10, State data from OTAG were
used where possible. (The GCVTC inventory contained SO2 and PM annual emissions.) In most cases,
OTAG data for these pollutants were not available. For point sources, data for plants over 250 tons per
year for SO2 and PM-10 were added from AIRS/FS. The AIRS/FS data were also matched to the OTAG
plants and the emissions were attached to existing plants from the OTAG data where a match was found.
Where no match was found to the plants in the OTAG data, new plants were added to the inventory. For
OTAG plants where there were no matching data in AIRS/FS and for all area sources of SO2 and PM-10,
emissions were calculated based on the emission estimates for other pollutants.
The approach to developing SO2 and PM-10 emissions from unmatched point and area sources
involved using uncontrolled emission factor ratios to calculate uncontrolled emissions. This method used
SO2 or PM-10 ratios to NOX. NOX was the pollutant utilized to calculate the ratio because (1) the types
of sources likely to be important SO2 and PM-10 emitters are likely to be similar to important NOX
sources and (2) the generally high quality of the NOX emissions data. Ratios of SO2/NOX and PM-10/NOX
4-300
-------
based on uncontrolled emission factors were developed. These ratios were multiplied by uncontrolled
NOX emissions to determine either uncontrolled SO2 or PM-10 emissions. Once the uncontrolled
emissions were calculated, information on VOC, NOX, and CO control devices was used to determine if
they also controlled SO2 and/or PM-10. If this review determined that the control devices fisted did not
control SO2 and/or PM-10, plant matches between the OTAG and Interim Inventory were performed to
ascertain the SO2 and PM-10 controls applicable for those sources. The plant matching component of
this work involved only simple matching based on information related to the State and county FIPS code,
along with the plant and point IDs.
There was one exception to the procedures used to develop the PM-10 point source estimates. For
South Carolina, PM-10 emission estimates came from the Interim Inventory. This was because South
Carolina had no PM data in AIRS/FS for 1990 and using the emission factor ratios resulted in
unrealistically high PM-10 emissions.
There were no PM-2.5 data in either OTAG or AIRS/FS. Therefore, the point and area PM-2.5
emission estimates were developed based on the PM-10 estimates using source-specific uncontrolled
particle size distributions and particle size specific control efficiencies for sources with PM-10 controls.
To estimate PM-2.5, uncontrolled PM-10 was first estimated by removing the impact of any PM-10
controls on sources in the inventory. Next, the uncontrolled PM-2.5 was calculated by multiplying the
uncontrolled PM-10 emission estimates bythe ratio of the PM-2.5 particle size multiplier to the PM-10
particle size multiplier. (These particle size multipliers represent the percentage to total particulates
below the specified size.) Finally, contro Is were reapplied to sources with PM-10 contro Is by multiplying
the uncontrolled PM-2.5 by source/control device particle size specific control efficiencies.
4.8.1.8.3 Growth Indicators, 1985-1989 —
The changes in the point and area source emissions were equated with the changes in historic
earnings by State and industry. Emissions from each point source in the 1985 NAPAP inventory were
projected to the years 1985 through 1990 based on the growth in earnings by industry (two-digit SIC
code). Historical annual State and industry earnings data from BEA's Table SA-552 were used to
represent growth in earnings from 1985 through 1990.
The 1985 through 1990 earnings data in Table SA-5 are expressed in nominal dollars. To estimate
growth, these values were converted to constant dollars to remove the effects of inflation. Earnings data
for each year were converted to 1982 constant dollars using the implicit price deflator for PCE.53 The
PCE deflators used to convert each year's earnings data to 1982 dollars are:
Year 1982 PCE Deflator
1985 111.6
1987 114.8
1988 124.2
1989 129.6
1990 136.4
Several BEA categories did not contain a complete time series of data for the years 1985 through
1990. Because the SA-5 data must contain 1985 earnings and earnings for each inventory year (1985
through 1990) to be useful for estimating growth, a log linear regression equation was used where
4-301
-------
possible to fill in missing data elements. This regression procedure was performed on all categories that
were missing at least one data point and which contained at least three data points in the time series.
Each record in the inventory was matched to the BEA earnings data based on the State and the two-
digit SIC. Table 4.8-15 shows the BEA earnings category used to project growth for each of the two-
digit SICs found in the 19 85 NAPAP Emission Inventory. No growth in emissions was assumed for all
point sources for which the matching BEA earnings data were not complete. Table 4.8-15 also shows the
national average growth and earnings by industry from Table SA-5.
4.8.1.8.4 Growth Indicators, 1991 through 1996 —
The 1991 through 1996 area source emissions were grown in a similar manner as the 1985 through
1989 estimates, except for using a different base year inventory. The point source inventory was also
grown for those States that did not want their AIRS/FS data used. (See Table 14 for a list of States that
chose AIRS/FS.) For those States requesting that EPA extract their data from AIRS/FS, the years 1990
through 1995 were downloaded from the EPA IBM Mainframe. The 1996 emissions were not extracted
since States are not required to have the 1996 data uploaded into AIRS/FS until July 1997.
4.8.1.8.4.1 Grown Estimates. The 1991 through 1996 point and area source emissions were grown
using the 1990 NET inventory as the basis. The algorithm for determining the estimates is detailed in
section4.8.1.8. The 1990 through 1996 SEDS and BEA data are presented in Tables 4.8-17 and 4.8-18.
The 1996 BEA and SEDS data were determined based on linear interpretation of the 1988 through 1995
data. Point sources were projected using the first two digits of the SIC code by State. Area source
emissions were projected using either BEA or SEDS. Table 4.8-19 lists the SCC and the source for
growth.
The 1990 through 1996 earnings data in BEA Table SA-5 (or estimated from this table) are
expressed in nominal dollars. In order to be used to estimate growth, these values were converted to
constant dollars to remove the effects of inflation. Earnings data for each year were converted to 1992
constant dollars using the implicit price deflator for PCE. The PCE deflators used to convert each year's
earnings data to 1992 dollars are:
Year 1992 PCE Deflator
1990 93.6
1991 97.3
1992 100.0
1993 102.6
1994 104.9
1995 107.6
1996 109.7
4.8.1.8.4.2 AIRS/FS. Several States responded to EPA's survey and requested that their 1991
through 1995 estimates reflect their emissions as reported in AIRS/FS. The list of these States, along
with the years avaikble in AIRS/FS is given in Table 4.8-16.
As noted in Table 4.8-16, several States did not report emissions for all pollutants for all years for
the 1990 to 1995 time period. To fill these data gaps, EPA applied linear interpolation or extrapolated
the closest two years worth of emissions at the pknt level. If only one year of emissions data were
4-302
-------
available, the emission estimates were held constant for all the years. The segment-SCC level emissions
were derived using the average split for all available years. The non-emission data gaps were filled by
using the most recent data available for the plant.
Many States do not provide PM-10 emissions to AIRS. These States' TSP emissions were
converted to PM-10 emissions using uncontrolled particle size distributions and AP-42 derived control
efficiencies. The PM-10 emissions are then converted to PM-2.5 in the same manner as described in
section 4.8.1.8.2.4.1. The State of South Carolina provided its own conversion factor for estimating PM-
10 from TSP.54
4.8.1.8.5 Growth Indicators, 1997 through 1999 —
Except for the source categories discussed in sections 4.8.1.2 through 4.8.1.7, the methods applied
to prepare 1997 through 1999 emissions for point and area source fugitive dust categories are the same
as those described in Section 4.3.9 for Industrial nonutiHty point and area sources. Sections 4.8.1.2
through 4.8.1.7 provide the methodologies for preparing 1997 through 1999 emissions for the area
source categories discussed in those sections.
4.8.2 References
1. Memorandum from Chatten Cowherd of Midwest Research Institute, to Bill Kuykendal of the U.S.
EPA, Emission Factors and Inventories Group, and W.R. Barnard of E.H. Pechan and Associates,
Inc., September 1996.
2. T. A. Cuscino, Jr., et al, "The Role of Agricultural Practices in Fugitive Dust Emissions", California
Air Resources Board, Sacramento, CA, June 1981.
3. G. A. Jutze et al., "Investigation of Fugitive Dust - Sources Emissions And Control", EPA-450/3-
74-03 6a, U. S. Environmental Protection Agency, Research Triangle Park, NC, June 1974.
4. Soil Conservation Service Soil Geography - NATSGO Map Series Dominant Surface Soil Texture,
Data Source: USDA-SCS 1982 NRI & Sofl-5 Databases & 1984 MLRA Map: U.S. Department of
Agriculture, Sept 1988: L.D. Spivey, Jr. & R.L. Glenn. 1988.
5. Major Land Resource Areas of the United States Adjusted to County Boundaries for Compilations
of Statistical Data, U.S. Department of Soil Conservation Service. USGS National Atlas Base.
Agricultural Handbook 296. 1978.
6. Brady, Nyle C., The Nature & Properties of Soils, 8th Edition, New York, MacMilkn, 1974. p 48.
7. Cowherd, C.C. Jr., K. Axtell, CM. Guenther, & G.A. Jutze, Development of Emission Factors for
Fugitive Dust Sources. U.S. Environmental Protection Agency, Research Triangle Park, NC. June
1974. EPA-450/3-74-037.
8. U.S. Land Use Summary, from the Feed Grains and Oil Seeds Section of ASCS-U.S. Department of
Agriculture, 1985-1996, annual.
4-303
-------
9. "1987 Census of Agriculture, Volume 1: Geographic Area Series," county data file, Bureau of the
Census, U.S. Department of Commerce, Washington, DC, 1987.
10. Shimp, D.R. Campbell, S.G., and Francis, S.R "Spatial Distribution of PM-10 emissions from
Agricultural TilKngin the San Joaquin Valley," California Air Resources Board, 1996.
11. Woodard, Kenneth R. "Agricultural Activities Influencing Fine Particulate Matter Emissions,"
Midwest Research Institute, March 1996.
12. Cowherd, C.C., Midwest Research Institute, personal communication with W.R. Barnard of E.H.
Pechanand Associates, Inc., Durham, NC, 1997.
13. National Crop Residue Management Survey, Conservation Technology Information Center,
1990-1996.
14. Towery, D., Conservation Information Technology Center (CTIC), Purdue University, personal
communication with W.R. Barnard of E.H. Pechan and Associates, Inc., Durham, NC, 1997.
15. Midwest Research Institute, "Gap Filling PM-10 Emission Factors for Selected Open Area Dust
Sources," U.S. EPA Rept. No. EPA-450/4-88-003, February, 1988.
16. 1997 Census of Agriculture - Geographic Area Series 1A, IB, and 1C, (CD-ROM), U.S.
Department of Agriculture, National Agricultural Statistics Service, Washington, DC, 1997.
17. Asman, William, A.H., Ammonia Emissions in Europe: Updated Emission and Emission Variations,
National Institute of Public Health and Environmental Protection, Biltoven, The Netherknds, May
1992.
18. Commercial Fertilizers Data - 1989 and 1990, National Fertilizer Research Center, Tennessee
Valley Authority, Muscle Shoals, AL, 1990.
19a. Stensland, G., Illinois State Water Survey, personal communication with W. Barnard of E.H. Pechan
& Associates, Inc., Durham, NC, 1989.
19b. Local ClimatologicalData, National Climatic Data Center, Monthly, 1985-1996.
19c. Local ClimatologicalData, National Climatic Data Center, Daily, 1997-1999.
20. Highway Statistics, ISBN 0-16-035995-3, U.S. Department of Transportation, Federal Highway
Administration, annually from October 1990 to 1999.
21. "Rural and Urban Land Area by County Data," Bureau of Census, CPHL79.DAT, Query Request by
E.H. Pechan & Associates, Inc., 1992.
22. "Draft User's Guide to PART5: A Program for Calculating Particle Emissions from Motor
Vehicles,"EPA-AA-AQAB-94-2, U.S. Environmental Protection Agency, Office of Mo bile
Sources, Ann Arbor, MI, July 1994.
4-304
-------
23. 1987 Census of Transportation: Truck Inventory and Use Survey- United States, TC87-T-52, U.S.
Department of Commerce, Bureau of Census, August 1990.
24. MVMA Motor Vehicle Facts and Gifures'91, Motor Vehicle Manufacturers Association, Detroit,
MI, 1991.
25. 1991 Market Data Book, Automotive News, Grain Communications, Inc., May 19, 1991.
26. Cowherd, C. C. Jr., C. Guenther and D. Walkce, Emission Inventory of Agricultural Tilling,
Unpaved Roads and Airstrips and Construction Sites, MRI, U.S. EPA Rept. No. EPA-450/3-74-
085, NTIS PB-238 919, November 1974.
27. Construction Review. Bureau of the Census, U.S. Department of Commerce, Washington, DC,
annual
28. Improvement of Specific Emission Factors. Midwest Research Institute, BACM Project No. 1,
March 1996.
29. 2010 Clean Air Act Baseline Emission Projections for the Integrated Ozone, Particulate Matter, and
Regional Haze Cost Analysis. E.H. Pechan & Associates, Inc., May 1997.
30. U.S. DOC, Bureau of Census, Industrial Series Census of Construction, Table 10, Value of
Construction Work for Establishments with Payroll by Location of Construction Work. 1987.
31. Heisler, S.L. "Interim Emissions Inventory for Regional Air Quality Studies," Electric Power
Research Institute Report EPRI EA-6070, November 1988.
32. Kinsey, J.S., et al. Study of Construction Related Dust Control, Contract No. 32200-07976-01,
Minnesota Pollution Control Agency, Roseville, MN, April 19, 1983.
33. "1990 County Business Patterns," Bureau of the Census, U.S. Department of Commerce,
Washington, DC, 1992.
34. EPA, 1999: U.S. Environmental Protection Agency, Office of Air Quality Planning Standards,
Estimating Particulate Matter Emissions from Construction Operations. Prepared by Midwest
Research Institute. Research Triangle Park, NC. September 1999.
35. DOC, 1999a: U.S. Department of Commerce, Bureau of the Census, Manufacturing and
Construction Division, Residential Construction Branch Building Permits Survey, 1999. 1999.
36. DOC, 1999b: U.S. Department of Commerce, Bureau of the Census, Construction Statistics.
Housing Starts Report, 1999. 1999.
37. DOC, 1999c: U.S. Department of Commerce, Bureau of the Census. Characteristics of New
Houses - Table 9. Type of Foundation by Category of House and Location, 1998. 1998.
4-305
-------
38. DOC, 1999d: U.S. Department of Commerce, Bureau of the Census. Value of Construction Put in
Place, 1999. 1999.
39. BLS, 1998: Bureau of Labor Statistics. Annual Average Employment for SIC 154, Data Series
ES202. 1998.
40. FHWA, 1999: Federal Highway Administration. Highway Statistics, Section IV - Finance, Table
SF-12A, "State Highway Agency Capital Outlay - 1998. " 1999.
41. NCDOT, 2000: Facsimile from D. Lane, North Carolina Department of Transportation, to R.
Huntley, U.S. Environmental Protection Agency, Office of Air Quality Planning Standards, Emission
Factor and Inventory Group, Research Triangle Park, NC. September 2000.
42. EPA, 2000: The Green Book, Nonattainment Areas for Criteria Pollutants - Particulate Matter,
downloaded from http://www.epa.gov/oar/oaqps/greenbk/pindex.htmL June 5, 2000.
43. Evans, J.S. and D.W. Cooper, "An Inventory of Particulate Emissions from Open Sources," Journal
Air Pollution Control Association, Vol. 30, #12, pp. 1298-1303, December 1980.
44. U.S. EPA, 'Generalized Particle Size Distributions for Use in Preparing Size-Specific Particulate
Emissions Inventories," U.S. EPA Rept. No. EPA-450/4-86-013, July 1986.
45. AIRS Facility Subsystem Source Classification Codes and Emission Factor Listing for Criteria Air
Pollutants. EPA-450/4-90-003. Office of Air Quality Planning and Standards, U.S. Environmental
Protection Agency, Research Triangle Park, NC. March 1990.
46. Correspondence with Jean Moore of the US Geological Survey, U.S. Department of Interior,.
March 1997.
47. "Coal Industry Annual" DOE/EIA-0584, U.S. Department of Energy, November, 1985-1996.
48. Regional Interim Emission Inventories (1987-1991), Volume I: Development Methodologies, EPA-
454/R-23-021a, U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards, Research Triangle Park, NC. May 1993.
49. Seitz, John, U.S. Environmental Protection Agency, Research Triangle Park, NC, Memorandum to
State Air Directors. May 5, 1995.
50. An Emission Inventory for Assessing Regional Haze on the Colorado Plateau, Grand Canyon
Visibility Transport Commission, Denver, CO. January 1995.
51. Volatile Organic Compound (VOC)/Particulate Matter (PM) Speciation Data System (SPECIA TE)
User's Manual, Version 1.5, Final Report, Radian Corporation, EPA Contract No. 68-DO-0125,
Work Assignment No. 60, Office of Air Quality Planning and Standards, U.S. Environmental
Protection Agency, Research Triangle Park, NC. February 1993.
4-306
-------
52. Table SA-5 — Total Personal Income by Major Sources 1969-1990. Data files. Bureau of
Economic Analysis, U.S. Department of Commerce, Washington, DC. 1991.
53. Survey of Current Business. Bureau of Economic Analysis, U.S. Department of Commerce,
Washington, DC. 1988, 1987, 1988, 1989, 1990, 1991.
54. Internet E-mail from J. Nuovo to J. Better of the Department of Health and Environmental Control
(DHEC), Columbia, South Carolina, entitled Total Suspended Paniculate (TSP)/PM-10 Ratio.
Copy to P. Carlson, E.H. Pechan & Associates, Inc., Durham, NC. April 10, 1997.
4-307
-------
Table 4.8-1. Particle Size Ratios
Source Category Ratio of PM-2.5 to PM-10
Wind Erosion -Agricultural Land 0.15
Agricultural Crops 0.20
Agricultural Livestock 0.15
Paved Roads 0.25
Unpaved Roads 0.15
Construction Activities 0.20
Mining and Quarrying 0.20
4-308
-------
Table 4.8-2. Methods for Developing Annual Emission Estimates for
Miscellaneous Area Sources for the Years 1989-1999
For the category
For the years
For the pollutant(s)
EPA estimated emissions by
Agricultural Tilling
(Crops)
1989
PM-10
Using State-level acres of crops planted from U.S. Department of Agriculture
(USDA) to calculate emissions, and then distributing emissions to the county-
level using acres of cropland harvested from the USDA. Also by using a
particle size multiplier for the fraction of PM-10 in total particulate of 0.21.
1990-1998
PM-10
Using county-level acres of land tilled by crop and tillage types from the
Conservation Technology Information Center at Purdue University to
calculate emissions. Relative to 1989 methodology, improving method for
determining silt content of surface soil and the num ber of till ings per year by
crop type which are variables in the equation for calculating emissions.
Replaced EPA estimates for 1996 with State data when available.
1999
PM-10
Dividing national number of acres by tillage type for 1999 by national number
of acres tilled by tillage type for 1998 to get national growth factors by tillage
type, and applying these factors to the 1998 county-level emissions to get
1999 emissions.
1990-1999
PM-2.5
Using the same method as used to calculate PM-10 emissions, but
substituting 0.042 as particle size multiplier. Replaced EPA estimates for
1996 with State data when available.
Beef Cattle Feedlots
1989-1999
PM-10
1) Obtaining county-level number of head of livestock for 1987, 1992, and
1997 from the 1997 Census of Agriculture; 2) estimating interim years
activity data forl988-1991, 1993-1996, 1998, and 1999 using linear
interpolation; and 3) applying national average PM-10 emissions factor to
updated county activity data for each year.
1990- 1999
PM-2.5
Multiplying the PM-10 emissions for each year by particle size adjustment
factor of 0.15.
Animal Husbandry
1990- 1999
NH,
1) Obtaining county-level num ber of head of cattle, goats, hogs and pigs,
horses, poultry and sheep for 1987, 1992, 1997 from the 1997 Census of
Agriculture; 2) estimating interim years activity data (1988-1991, 1993-1996,
1998, 1999) using linear interpolation; 3) dividing the activity data by 2000 to
convert from pounds to tons, and apply a national average NH3 emission
factor to updated county activity data for each year.
-------
Table 4.8-2 (continued)
For the category
For the years
For the pollutant(s)
EPA estimated emissions by
Fertilizer Application 1990-1998
NH,
Applying NH3 emission factors to county-level fertilizer consumption by
fertilizer type obtained from the Commercial Fertilizers Data Base
maintained by the Association of American Plant Food Control Officials.
Replaced EPA estimates for 1996 with State data when available.
1999
NH,
1) Using national-level emissions by SCC for years 1990, 1996, 1997 and
1998 to project 1999 national emissions; 2) Calculating a county to national-
level ratio of 1998 data; 3) Applying this ratio to 1999 national emission
estimate to distribute 1999 emissions to the county level.
Unpaved Road
Fugitive Dust
Emissions
1989-1995
PM-10
Running the PARTS model to estimate emission factors with State-specific
monthly precipitation data and State-specific silt content values.
1996-1999
PM-10
Using 9/98 AP-42 Equation 2 emission factor with State-specific monthly
precipitation data and State-specific silt content values.
1989-1999
PM-2.5
Multiplying unpaved road fugitive dust emissions by 0.15.
Paved Road Fugitive
Dust Emissions
1989-1999
PM-10
Running the PARTS model to estimate emission factors with State-specific
monthly precipitation data and State/roadway type-specific silt loading
val ues.
1989-1999
PM-2.5
Multiplying paved road fugitive dust emissions by 0.25.
Construction
1989
PM-10
Obtaining an TSP emission factor for construction, an estimate of the acres
of land under construction, and the average duration of construction activity
to calculate regional-level emissions; Distributing emissions to the county-
level using county estimates of payroll for construction (SICs 15, 16, 17)
from County Business Patterns.
1990-1995
PM-10
1) Obtaining an emission factor for construction from Improvement of
Specific Emission Factors, an estimate of the acres of land under
construction, and the average duration of construction activity to calculate
regional-level emissions; 2) Distributing to the county-level using county
estimates of payroll for construction (SICs 15, 16, 17) from County Business
Patterns; 3) Applying a control efficiency to 1995 emissions for counties
classified asnonattainment areas.
-------
Table 4.8-2 (continued)
For the category
For the years
For the pollutant(s)
EPA estimated emissions by
1996-1998
PM-10
Growing emissions to current year by calculating ratio of number of permits
issued in current year to number of perm its issued in prior year. A control
efficiency was applied to emissions for counties classified as nonattainment
areas. Replaced EPA estimates for 1996 with State data when available.
1999
PM-10
1) Obtaining 1999 activity data corresponding to three subcategories of
construction, including residential, commercial and road. 2) Applying
category-specific emission factors that account for the duration of
construction; 3) Accounting for regional variances in construction activity by
correcting for soil moisture level, silt content, and control efficiency.
1990-1999
PM-2.5
Multiplying the PM-10 emissions by particle size adjustment factor of 0.20.
Replaced EPA estimates for 1996 with State data when available.
Mining and Quarrying 1989-1998
PM-10
1) Obtaining regional metallic and non-metallic crude ore handled at surface
mines from the U.S. Geologic Survey; 2) Estimating State withheld data
using known distributions from past years; 3) Applying PM-10 emission
factors to activity data to develop regional emissions for metallic ore,
nonmetallic ore, and coal mining operations; 4) Distributing total emissions
from the regional to county level by dividing regional emissions by the
number of counties in that region. Replaced EPA estimates for 1996 with
State data when available.
1999
PM-10
Projecting emissions based on linear regression of emissions for the years
1990 to 1998.
1990-1999
PM-2.5
Applying a particle size distribution factor of 0.20 to PM-10 emission
estimates. Replaced EPA estimates for 1996 with State data when
available.
-------
Table 4.8-3. Comparison of Methodologies Used to Develop 1996 Base Year Emissions
for Miscellaneous Area Source Categories for Versions 1 through 4 of the NET Inventory
For the Category
Agricultural Tilling
(Crops)
Beef Cattle Feedlots
Animal Husbandry
For the
Pollutant(s)
PM-10
PM-2.5
PM-10
PM-2.5
NH3
EPA estimated 1996 Base Year emissions for
Version 1 by
Using county-level acres of land tilled by
crop and tilagetypes from the
Conservation Technology Information
Center at P urdue U niversity. Equation
multiplied acres of land tilled, nu mber of
tilings in a year, silt con tent of surface soil,
particle size multiplier of 0.21 for PM-10,
and a constant of 4.8 Ibs of PM-10/acre
pass to obtain county-level emissions.
Using same method as used to calculate
PM-10, but substituting 0.042 as particle
size multiplier.
Growing from 1990 NET using surrogate
indicator (B EA earn ings data).
Multiplying PM-10 by particle size
adjustmentfactor of 0.15.
Obtaining county-level activity data from
1992 Census of Agriculture and multiplying
by national average NH3 emission factor.
Version 2 by
Usin g sam e method ology as us ed in
Version 1 .
Usin g sam e method ology as us ed in
Version 1 .
Usin g sam e method ology as us ed in
Version 1 .
Usin g sam e method ology as us ed in
Version 1 .
Usin g sam e method ology as us ed in
Version 1 .
Version 3 by
Usin g sam e method ology as us ed in
Version 1. Emissions datasupplied by
State/bcal agencies replaced EPA
default estimates.
Usin g sam e method ology as us ed in
Version 1 .
1) Obtaining county-level head of beef
cattle in feedlots for 1987, 1992, 1997
from the 1 997 Census of Agriculture;
2) estimating interim years activity data
(1988-1991, 1993-1996, 1998, 1999)
using linear interpolation; 3) applying
national average PM-10 emissions
factor to updated county activity data
for each year.
Usin g sam e method ology as us ed in
Version 1 .
1) Obtaining county-level head of
livestock for1987, 1992, 1997 from the
1997 Census of Agriculture; 2)
estimating interim years activity data
(1988-1991, 1993-1996, 1998, 1999)
using linear interpolatbn; 3) applying
national average NH3 emissions factor
to updated county acfvly datafor each
year.
Version 4 by
Using EPA estimates for 1999 but with
following methodology change:
Dividing national tillage type estimates
for 1999 by national tilagetype
estimates for 1998 to get national
growth factors by tillage type, and
applying these factors to the 1998
county-tevel estimates to get 1999
estimates. Emissions data supplied by
State/local agencies replaced EPA
default estimates.
Usin g sam e method ology as us ed in
Version 1 .
Usin g sam e method ology as us ed in
Version 3.
Usin g sam e method ology as us ed in
Version 1 .
Usin g sam e method ology as us ed in
Version 3.
-------
Table 4.8-3 (continued)
For the Category
Fertilizer App lication
Construction
Mining and Quarrying
For the
Pollutant(s)
NH3
PM-10
PM-2.5
PM-10
PM-2.5
EPA estimated 1996 Base Year emissions for
Version 1 by
Applying NH3 emission factors to county-
level fertilizer consumption by fertilizer type
obtained from the Commercial Fertilzers
Data Base maintained by the Association
of America nPlantFood Control Offbiate.
1) Using AP-42 particulate emission factor,
estimate of ac res of land u nder
construction, and average duration of
construction activity to determine 1995
emissions. 2) Determining em ission factor
from dollars spent on construction from
Cens us Bu reau, determinin g cons truction
acres and duration of activity from
Cowherd, and deriving PM-10 to TSP ratio
from MRI. 3) Estimating 1996 emissions
by extrapolating from 1 995 using the ratio
between the nu mber of res idential
construction permits issued in 1996 and
the number issued in 1995.
4) App lying a control effic iency to counties
classified as nonattainmentareas. 5)
Distributing regional emissions to the
county level using county estim ales of
payroll forconstruction (SIC 15, 16, 17)
from County Business Patterns.
Multiplying PM-10 em ission s by particle
size adjustment factor of 0.2.
1) Obtaining regional metallic and non-
metallb crude ore handled at surface
mines from the U .S. Geologic Survey.
2) Estimating State wlhheld data using
known distributions from past years.
3) App lying activity data to PM-1 0 emiss ion
factors to d evelop regional emiss ions for
metallic ore, nonm etallic ore, and coal
mining operations. 4) Distributing the
emissions from the regional to county level
by multiplying reg ional emission s by 1 over
number of counties in that region.
Applying particle size distribution factor of
0.20 to PM-10 emission estimates.
Version 2 by
Usin g sam e method ology as us ed in
Version 1 .
Usin g sam e method ology as us ed in
Version 1 .
Usin g sam e method ology as us ed in
Version 1 .
Usin g sam e method ology as us ed in
Version 1 .
Usin g sam e method ology as us ed in
Version 1 .
Version 3 by
Usin g sam e method ology as us ed in
Version 1. Emissions datasupplied by
State/local agencies replaced EPA
default estimates.
Chang ing AP-42 emission factor used.
Obtaining new factorfrom AP-42
findings report "Improvement of
Specific Emission Factors."
Methodology did not change.
Emis sions data s upplied by S tale/local
agencies replaced EPA default
estimates.
Usin g sam e method ology as us ed in
Version 1 .
Usin g sam e method ology as us ed in
Version 1. Emissions datasupplied by
State/bcal agencies replaced EPA
default estimates.
Usin g sam e method ology as us ed in
Version 1 .
Version 4 by
Using EPA estimates for 1999 but with
following methodologychange: 1)
Using natbnal-tevd emesions bySCC
for years 1990, 1996, 1997 and 1998
to project 1999 nationalemissions; 2)
calculating a county to national-level
ratio of 1998 data; 3) applying this ratio
to 1999 national emission estimate to
distribute 1999 emissions to the county
level. Emissions data supplied by
State/local agencies replaced EPA
default estimates.
Usin g sam e method ology as us ed in
Version 3. Emissions datasupplied by
State/local agencies replaced EPA
default estimates.
Usin g sam e method ology as us ed in
Version 1 .
Projecting em issions based on linear
regression of emissions for the years
1990 to 1998. Em issions data
supplied by State/local agencies
replaced EPA default estimates.
Usin g sam e method ology as us ed in
Version 1 .
-------
Table 4.8-3 (continued)
Notes:Version 1 corresponds toDecember 1997 Trends report, Version 2 estimates correspond to December 1998 Trends report, Version 3 corresponds to March 2000 Trends report,and Version 4
is for report yet to be published.
-------
Table 4.8-4. Miscellaneous Area Source Categories: Summary of State-Submitted Emissions for 1996 Included in
Versions 3 and 4 of the NET Inventory
Source
Category/ Geographic 1996 NET
SCC State Coverage Temporal VOC NOV CO SO, PM-10 PM-25 NH, Version Comments
Agricultural Tilling (Crops) (2801000003)
CA Statewide Annual/Daily x x 3 and 4
LA Statewide Annual/Daily x x 3 and 4
OK Statewide Annual/Daily x x 3 and 4
Fertilizer Application (2801700001, 2801700002, 2801700003, 2801700004,2801700005, 2801700006, 2801700007,2801700008, 2801700009, 2801700010)
LA Statewide Annual/Daily x 3 and 4
Construction (2311000100,2311010000, 2311020000, 2311030000)
CA Statewide Annual/Daily x x 3 and 4 Emissions reported under SCCs 2311010000,2311020000, 2311030000
Mining and Quarrying (2325000000)
CA Statewide Annual/Daily x x x x x x 3 and 4
LA Statewide Annual/D ally x x 3 and 4
-------
Table 4.8-5. Silt Content by Soil Type, 1985 to 1989
Soil Type
Si It Con ten t(%
Silt Loam
Sandy Loam
Sand
Loamy Sand
Clay
Clay Loam
Organic Material
Loam
78
33
12
12
75
75
10-82
60
Table 4.8-6. Silt Content by Soil Type, 1990 to 1998
Soil Type
Silt Loam
Sandy Loam
Sand
Loamy Sand
Clay
Clay Loam
Organic Material
Loam
Si It Con ten t(%)
52
33
12
12
29
29
10-82
40
Crop
Table 4.8-7. Number of Tillings by Crop Type
Number of Tillings
Conservation Use
Conventional Use
Corn
Spring Wheat
Rice
Fall-Seeded Small Grain
Soybeans
Cotton
Sorghum
Forage
Permanent Pasture
Other Crops
Fallow
Annual Conservation Use
2
1
5
3
1
5
1
3
1
3
1
6
4
5
5
6
8
6
3
1
3
1
(No method, not used after 1995; num ber of till ings = 1)
4-316
-------
Table 4.8-8. Livestock Operations Ammonia Emission Factors
Category
AMS SCC
Emission Factor
(Ib NH3/Head)
Cattle and Calves
Pigs and Hogs
Poultry
Sheep
Horses
Goats
Mink
2805020000
2805025000
2805030000
2805040000
2710020030
2805045001
2205045002
50.5
20.3
0.394
7.43
26.9
14.1
1.28
Table 4.8-9. Assumed Values for Average Daily Traffic Volume by Volume Group
Vehicles Per Day Per Mile
Volume Category for Rural Roads
Assumed ADTV Value for Rural
Roads
Volume Category for Urban Roads
Assumed ADTV Value for Urban
Roads
Less than 50
5*
Less than 200
20*
50-199
125"
200 - 499
350"
200 - 499
350"
500-1999
1250"
500 and over
550"*
2000 and over
2200*"
NOT E(S): '10% of volume group's maximu m range en dpoint.
"Average of volume group's rangeendpoints.
"°110% of volume group's minimum.
Table 4.8-10. PM-2.5 to PM-10 Ratios for Paved and Unpaved Roads
Source Category
Ratio of PM-2.5 to PM-10
Paved Roads
Unpaved Roads
0.25
0.15
4-317
-------
Table 4.8-11. List of Grown Sources
sec
SCC Description
TIER1 TIER2
2307010000 Industrial Processes Wood Products: SIC 24 Logging Operations Total 14 01
2650000005 Waste Dis posal, Treatment, & Recovery Scrap & W aste Materials Scrap & Waste Materials StoragePiles 14 07
30300519 Primary Metal Production Primary Metal Production Primary Copper Smelting Unpaved Road Traffic: Fugitive 14 07
Emissions
30300831 Primary Metal Production Iron Productbn Fugitwe Emissions: Roads Unpaved Roads: LDV 14 07
30300832 PrimaryMetal Production Iron Production Fugitive Emissions: Roads Unpaved Roads: MDV 14 07
30300833 Primary Metal Production Iron Production Fug itive Emissions: Roads Unpaved Roads: HDV 14 07
30300834 Primary Metal Production Iron Production Fug itive Emissions: Roads Paved Roads: All Vehicle Types 14 07
30302321 Primary Metal Production Primary Metal Production Taconite Iron Ore Processing Haul Road: Rock 14 07
30302322 Primary Metal Production Primary Metal Production Tacon ite Iron Ore Processing Haul Road: Tacon ite 14 07
30501024 Mineral Products Mineral Products Surface Mining Operations Hauling 14 07
30501031 Mineral Products Mineral Products Surface Mining Operations Scrapers: Travel Mode 14 07
30501039 Mineral Products Mineral Products Surface M ining Operations Hauling: Haul Trucks 14 07
30501045 Mineral Prod ucts M ineral Products Surface Min ing Operations Bu lldozing: Overburden 14 07
30501046 Mineral Prod ucts M ineral Products Surface Min ing Operations Bu lldozing: Coal 14 07
30501047 Mineral Products Mineral Products Surface Mining Operations Grading 14 07
30501049 Mineral Prod ucts M ineral Products Surface Mining Operations W ind Erosion: Exposed Areas 14 07
30501050 Mineral Prod ucts M ineral Products Surface Min ing Operations Vehicle Traffic: Light/Med ium Veh icles 14 07
30501090 Mineral Prod ucts M ineral Products Surface Min ing Operations Hau I Roads: G eneral 14 07
30502011 Mineral Products Mineral Products Stone Quarrying/Processing Hauling 14 07
30502504 Mineral Products Mineral Products Sand/Gravel Hauling 14 07
31100101 Building Construction Building Construction Construction: Building Contractors S ite Preparation: Topsoil Rem oval 14 07
31100102 BuildingConstruction Building Construction Construction: Building Contractors Site Preparatbn: Earth Moving (Cut 14 07
&FIII)
31100103 BuildingConstruction BuildingConstruction Construction: Building Contractors Site Preparatbn: Aggregate Hauling 14 07
(on dirt)
31100205 BuildingConstruction BuildingConstruction Construction: D emolition of Structures O n-Site T ruck Traffic 14 07
31100206 BuildingConstruction BuildingConstruction Construction: D emolition of Structures O n-Site T ruck Traffic 14 07
4-318
-------
Table 4.8-12. Point Source Data Submitted
State
Alabama
Arkansas
Connecticut
Delaware
District of Columbia
Florida
Georgia - Atlanta
Urban Airshed (47
counties) domain
Georg ia - Rest of
State
Illinois
Indiana
Kansas
Kentucky - Jefferson
County
Kentucky - Rest of
State
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Missouri
Nebraska
New Hamps hire
New J ersey
New York
North Carolina
North Dakota
Ohio
O k I ah o m a
Pennsylvania
Allegheny County
Pennsylvania
Philadelphia County
Pennsylvania - Restof
State
Rhode Island
South Carolina
Data Source/Format
AIRS/FS - Ad hoc retrievals
AIRS/FS -Ad hoc retrievals
State- EPS Workfile
State - E PS W orkfile
AIRS/FS - Ad hoc retrievals
AIRS/FS - Ad hoc retrievals
State - State form at
AIRS/FS - Ad hoc retrievals
State- EPS Workfiles
AIRS/FS - Ad hoc retrievals
AIRS/FS - Ad hoc retrievals
Jefferson C ounty - E PS W orkfile
State -EPS Workfile
State -State Format
State -EPS Workfile
State - EPS W orkfile
State - E PS W orkfile
State - State Format
AIRS/FS - Ad hoc retrievals
AIRS/FS - Ad hoc retrievals
AIRS/FS -Ad hoc retrievals
State- EPS Workfile
State - E PS W orkfile
State -EPS Workfile
State - EPS Workfiles
AIRS/FS -Ad hoc retrievals
State - State Format
State - State Format
Allegheny C ounty - Coun ty Format
Philadelphia County - County Format
State -EPS Workfile
State- EPS Workfile
AIRS/FS -Ad hoc retrievals
Temporal
Resolution
Annual
Annual
Daily
Daily
Annual
Annual
Daily
Annual
Daily
Annual
Annual
Daily
Daily
Annual
Daily
Daily
Daily
Annual
Annual
Annual
Annual
Daily
Daily
Daily
Daily
Annual
Annual
Annual
Daily
Daily
Daily
Daily
Annual
Year of Data
1994
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1990
1993
1990
1990
1990
1990
1990
1990
1990
1994
1990
1990
1990
1990
1991
Adjustments to Data
Backcast to 1990 using BEA. Average Sum mer
Day estimated using m ethodology desc ribed above.
Average Summer Day es timated using d efault
temporal factors.
None
None
AverageSummer Day estimated using methodobgy
described above.
AverageSummer Day estimated using methodobgy
described above.
None
Average Summer Day es timated using d efault
temporal factors.
None
AverageSummer Day estimated using methodobgy
described above.
AverageSummer Day estimated using methodobgy
described above.
None
None
AverageSummer Day estimated using methodobgy
described above.
None
None
None
AverageSummer Day estimated using methodobgy
described above.
AverageSummer Day estimated using methodobgy
described above.
Backcast to 1990 using BEA. AverageSummer
Day estimated using m ethodology desc ribed above.
AverageSummer Day estimated using methodology
described above.
None
None
None
None
AverageSummer Day estimated using methodology
described above.
AverageSummer Day estimated using methodology
described above.
Backcast to 1990 using BEA. AverageSummer
Day estimated using methodology described above.
None
None
None
None
Average S umm er Day estimated using default
temporal factors.
4-319
-------
Table 4.8-12 (continued)
State
Data Source/Format
Temporal
Resolution Year of Data Adjustments to Data
South Dakota
Tennessee
Texas
Vermont
Virginia
West Virginia
Wisconsin
AIRS/FS - Ad hoc retrievals
AIRS/FS - Ad hoc retrievals
State - State Format
State-E PS Workfile
AIRS/FS -Ad hoc retrievals
AIRS/FS -Ad hoc retrievals
State -State Format
Annual 1990 AverageSummer Day estimated using methodobgy
described above.
Annual 1990 Average S ummer Day estimated using default
temporal factors.
Daily 1992 Backcast to 1990 using BEA.
Daily 1990 None
Annual 1990 AverageSummer Day estimated using methodology
described above.
Annual 1990 AverageSummer Day estimated using methodology
described above.
Daily 1990 None
4-320
-------
Table 4.8-13. Area Source Data Submitted
State
Connecticut
Delaware
District of Columbia
Florida
Georg ia
Data Source/Format
State - EPS W orkfile
State - E PS W orkfile
State -Hard copy
AIRS-AMS - Ad hoc retrievals
State - State form at
Temporal
Resolution
Daily
Daily
Daily
Daily
Daily
Geoaraohic Coveraae
Entire State
Entire State
Entire State
Jacksonville, Miami/
Ft. Lauderdale, Tampa
Atlanta Urb an Airsh ed
Adjustments to Data
None
None
None
Added Non-road emission
estimates
from Int. Inventory to Jacksonville
(Duval Cou nty)
None
Illinois
Indiana
Kentucky
Louisiana
Maine
Maryland
Michigan
Missouri
New Hamps hire
New J ersey
New York
North Carolina
Ohio
Pennsylvania
Rhode Island
Tennessee
Texas
Vermont
Virginia
West Virginia
Wisconsin
State - State form at
State - State form at
State - State Format
State -State Format
State - EPS W orkfile
State - E PS W orkfile
State - State Format
AIRS-AMS- Ad hoc retrievals
State - EPS W orkfile
State - E PS W orkfile
State - EPS W orkfile
State - EPS Workfiles
State -Hard copy
State - E PS W orkfile
State - E PS W orkfile
State - State form at
State - State Format
State - EPS W orkfile
State - EPS W orkfile
AIRS-AMS - Ad hoc retrievals
State - State Format
(47 Counties)
Daily Entire State
Daily Entire State
Daily Kentucky Ozone Nonattainment
Areas
Daily Baton Rouge Nonattainment
Area (20 Parishes)
Daily Entire State
Daily Entire State
Daily 49 Southern Mich igan Counties
Daily St. Louis area (25 counties)
Daily Entire State
Daily Entire State
Daily Entire State
Annual Entire State
Daily Canton, Cleveland Columbus,
Dayton, Toledo, and Youngstown
Daily Entire State
Daily Entire State
Daily 42 Counties in Middle
Tennessee
Annual Entire State
Daily Entire State
Daily Entire State
Daily Charleston, Huntington/Ashland,
and Parkers burg (5 counties
total)
Daily Entire State
None
Non-road emissions submitted were
county totafe. Non-road emissions
distributed to specific SCCs based
on Int. Inventory
None
None
None
None
None
Only area sou rce com bustion d ata
was provided. All other area source
data came from Int. Inventory
None
None
None
Average Su mmer D ay estimated
using default temporal factors.
Assigned SCCs and converted from
kgs to tons. NOX and CO from Int.
Inventory added to Canton, Dayton,
and Toledo counties.
Non-road emissions subm itted were
county totals. Non-road emissions
distributed to specific SCCs based
on Int. Inventory
None
No non-road data submitted. Non-
road emiss ions added from Int.
Inventory
Average Su mmer D ay estimated
using default temporal factors.
None
None
None
None
4-321
-------
Table 4.8-14. Ad Hoc Report
Criteria
Regn
PLL4
PLL4
PLL4
PLL4
PLL4
PLL4
DES4
DUE4
YINV
GT 0
CE VOC
CE CO
CE SO2
CE NO2
CE PM-10
CE PT
GE 0
ME TY
ME 90
Plant Output
YINV
SITE
CNTY
CYCD
ZIPC
PNED
PNME
LAT1
LON1
SIC1
OPST
SIRS
YEAR OF INVENTORY
STATE FIPS CODE
COUNTY FIPS CODE
CITY CODE
ZIP CODE
NEDS POINT ID
PLANT NAME
LATITUDE PLANT
LONGITUDEPLANT
STANDARD INDUSTRIAL
CODE
OPERATING STATUS
STATE REGISTRATION
NUMBER
Point Output
STTE
CNTY
PNED
PNUM
CAPC
CAPU
PAT1
PAT2
PATS
PAT4
NOHD
NODW
NOHY
STATE FIPS CODE
COUNTY FIPS CODE
NEDS POINT ID
POINT NUMBER
DESIGN CAPACITY
DESIGN CAPACITY
UNITS
WINTER
THROUGHPUT
SPRING
THROUGHPUT
SUMMER
THROUGHPUT
FALLTHROUGHPUT
NUMBER HOURS/DAY
NUMBER DAYS/WEEK
NUMBER
HOURS/YEAR
Stack Output
STTE
CNTY
PNED
STNB
LAT2
LON2
STHT
STDM
STET
STEV
STFR
PLHT
STATE FIPS CODE
COUNTY FIPS CODE
NEDS POINT ID
STACK NUMBER
LATITUDE STACK
LONGITUDESTACK
STACK HEIGHT
STACK DIAMETER
STACK EXIT
TEMPERATURE
STACK EXIT VELOCITY
STACK FLOW RATE
PLUME HEIGHT
Segment Output
General
STTE
CNTY
PNED
STNB
PNUM
SEGN
SCC8
HEAT
FPRT
SULF
ASHC
PODP
STATE FIPS CODE
COUNTY FIPS CODE
NEDS POINT ID
STACK NUMBER
POINT NUMBER
SEGMENT NUMBER
sec
HEAT CONTENT
ANNUAL FUEL
THROUGHPUT
SULFUR CONTENT
ASH CONTENT
PEAK OZONE
SEASON DAILY
PROCESS RATE
Segment Output
Pollutant
STTE
CNTY
PNED
STNB
PNUM
SEGN
SCC8
PLL4
D034
DU04
DES4
DUE4
CLEE
CLT1
CTL2
REP4
DME4
Emfa
STATE FIPS CODE
COUNTY FIPS CODE
NEDS POINT ID
STACK NUMBER
POINT NUMBER
SEGMENT NUMBER
sec
POLLUTANT CODE
OSD EMISSIONS
OSD EMISSION
UNITS
DEFAULT
ESTIMATED
EMISSIONS
DEFAULT
ESTIMATED
EMISSIONSUNITS
CONTROL
EFFICIENCY
PRIMARY CONTROL
DEVICECODE
SECONDARY
CONTROL DEVICE
CODE
RULE
EFFECTIVENESS
METHOD CODE
Emission factor
-------
Table 4.8-15. Bureau of Economic Analysis's SA-5 National Changes in Earnings by
Industry
Percent Growth from:
Industry SIC 1985 to 1987 1987 to 1988 1988 to 1989 1989 to 1990
Farm
Agricultural services, forestry,
01,02
07, 08, 09
14.67
23.58
-2.73
5.43
14.58
1.01
-3.11
2.48
fisheries, and other
Coalmining 11,12 -17.46 -6.37 -4.16 4.73
Metal mining 10 -3.03 18.01 8.94 4.56
Nonmetallic minerals, except fuels 14 2.33 3.74 -2.79 -0.45
Construction 15,16,17 7.27 4.81 -1.36 -3.80
4-323
-------
Table 4.8-16. Emission Estimates Available from AIRS/FS by State, Year, and Pollutant
State
1990
C N S P T V
1991
C N S P T V
1992
C N S P T V
1993
C N S P T V
1994
C N S P T V
1995
C N S P T V
Alabama
Alaska
Arizona
California
Colorado
Connecticut
Hawaii
Illinois
Louisiana
Michigan
Minnesota
Montana
Nebraska
Nevada
New Hampshire
New Mexico
North Dakota
Oregon
Pennsylvania
South Carolina
South Dakota
Texas
Utah
Vermont
Virginia
Washington
Wisconsin
Wyoming
Notes:
C = CO N = NO2 S = SO2 P = PM-10 T = TSP V = VOC
Pennsylvania only includes AlleghenyCounty (State 42, County 003); New Mexico only includes Albuquerque(State35,
County 001); Washington onlyincludes Puget Sound (State 53, County 033, 053, or 061); Nebraska includes all except
Omaha City (State 31, County 055); the CO emiss ions in NET were maintained for South Dakota (State 46).
4-324
-------
Table 4.8-17. SEDS National Fuel Consumption, 1990-1996 (trillion Btu)
Fuel Type End-User
Code
1990
1991
1992
1993
1994
1995
1996
Population
TPOPP 248,709 252,131 255,025 257,785 259,693 261,602 263,510
Table 4.8-18. BEA SA-5 National Earnings by Industry, 1990-1996 (million $)
Industry
LNUM
SIC
1990 1991 1992 1993 1994 1995 1996
Farm
Farm
Farm
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Agricultural services, forestry, fisheries, and other
Nonm etallic minerals, except fuels
Construction
Construction
Construction
Construction
Primary metal industries
Transportation by air
81
82
90
100
110
120
121
122
123
200
240
300
310
320
330
423
542
1, 2
1, 2
1, 2
7-9
7-9
7-9
7-9
7-9
7-9
7-9
14
15-17
15-17
15-17
15-17
33
45
48
3,586
3,001
24
20
4
1
2
1
36
4
218
54
29
135
33
30
41
3,552
2,957
24
20
3
1
2
1
37
4
197
47
28
123
30
30
46
3,686
3,079
24
21
3
1
2
1
36
4
195
46
28
121
31
31
45
3,740
3,126
24
22
3
0
2
1
34
4
199
47
27
125
30
31
42
3,849
3,228
26
23
3
1
2
1
35
4
216
51
29
136
32
31
31
3,980
3,353
27
24
3
1
2
1
35
4
219
51
29
138
33
31
29
4,058
3,423
27
25
3
1
1
1
35
4
219
50
29
139
32
31
Table 4.8-19. Area Source Listing by SCC and Growth Basis
sec
SCC DESCRIPTION
FILE CODE
2275000000 Mobile Sources Aircraft All Aircraft Types and O perations Total BEA 542
2275001000 Mobile Sources Aircraft Military Aircraft Total BEA 920
2275020000 Mobile Sources Aircraft Commercial Aircraft Total: All Types BEA 542
2275020021 Mobile Sources Aircraft Commercial Aircraft BEA 542
2275050000 Mobile Sources Aircraft General Aviation Total BEA 542
2275060000 Mobile Sources Aircraft Air Taxi Total BEA 542
2275070000 Mobile Sources Aircraft Aircraft Auxiliary Power Units Total BEA 542
2275085000 Mobile Sources Aircraft U npaved Airstrips Total BEA 542
2275900000 Mobile Sources Aircraft Refueling: All Fuels All Processes BEA 542
2275900101 Mobile Sources Aircraft Refueling: All Fuels Displacement Loss/U ncontrolled BEA 542
2275900102 Mobile Sources Aircraft Refueling: All Fuels Displacement Loss/Controlled BEA 542
2301000000 Industrial Processes Chemical Manufacturing: SIC 28 All Processes Total BEA 471
2301010000 Industrial Processes Chemical Man ufacturing: SIC 28 Industrial Inorganic Chemical Manufactu ring Total BEA 471
2301020000 Industrial Processes Chemical Manufacturing: SIC 28 Process Emissions from Synthetic Fibers Manuf BEA 471
(NAP AP cat. 107) Total
2301030000 Industrial Processes Chemical Manufacturing: SIC 28 Process Emissions from Pharmaceutical Manuf BEA 471
(NAP AP cat. 106) Total
2301040000 Industrial Processes Chemical Manufacturing: SIC 28 Fugitive Emissions from Synthetic Organic Chem Manuf BEA 471
(NAPAP cat. 102) Total
2801000005 Miscellaneous Area Sources Agriculture Productbn - Crops Agricuture - Crops Harvesting BEA 100
4-325
-------
4.9 BIOGENICS
This section explains EPA's methodologies for estimating volatile organic compounds (VOC) and
nitric oxides (NO) from natural sources for the years 1988, 1990, 1991, 1995, 1996, and 1997. Biogenic
emissions from natural sources are classified under SCC 2701000000 and the following Tier I and II
categories:
Tier I Category Tier II Category
(13) Natural S ources (01) Bio genie
4.9.1 How are biogenic emissions estimated?
EPA calculated bio genie emissions for 1988, 1991, 1995, 1996 and 1997 using the Biogenic
Emissions Inventory System-Version 2 (BEIS-2).1'2'3 EPA used a slightly different version ofBEIS-2 to
generate the 1990 estimates, based on an interim version of processed land use data and spatial
interpolation of meteorological data. BEIS-2 estimates VOC emissions from vegetation and NO
emissions from soil. Biogenic VOC emissions are comprised of isoprene, monoterpenes, and other
nonmethane hydrocarbons. BEIS-2 calculates VOC emissions for 75 tree genera, 17 agricultural crops,
and urban grasses, and calculates emissions of NOX as NO based on crop type and fertilizer use. The
BEIS model continues to evolve and is expected to result in new versions of the model.
4.9.2 What factors affect biogenic emissions?
Biogenic emission estimates are strongly affected by differences in climatology and land use. The
highest emission levels occur in the summer when temperatures rise the highest. An increase of
10 degrees Celsius (°C) can result in over a two-fold increase in both VOC and NO. Variations in land
use can also greatly affect spatial variation in biogenic emissions densities. For example, higher densities
of VOC in the southern United States and Missouri can be attributed to large areas of high-emitting oak
trees, while high densities of NO in the midwestern United States are associated with areas of fertilized
crop land.
4.9.3 What is the uncertainty associated with these estimates?
These estimates have an uncertainty factor of two. However, continuous improvements in these
emission estimates are expected over the next few years.
4.9.4 References
1. Birth, T., "User's Guide to the PC Version of the Biogenic Emissions Inventory System (PC-
BEIS2), "EPA-600/R-95-091, U.S. Environmental Protection Agency, Research Triangle Park, NC.
1995.
2. Geron, C, A. Guenther, and T. Pierce, "An Improved Model for Estimating Emissions of Volatile
Organic Compounds from Forests in the Eastern United States," Journal of Geophysical Research,
vol. 99, pp. 12773-12791. 1994.
4-326
-------
3. Williams, E., A. Guenther, and F. Fehsenfeld, "An Inventory of Nitric Oxide Emissions from Soils in
the United States," Journal of Geophysical Research, vol. 97, pp. 7511-7519. 1992.
4-327
-------
SECTION 5.0
LEAD EMISSIONS METHODOLOGY
5.1 INTRODUCTION
The methodology used to estimate the lead emissions presented in the Trends reports for the years
1970 to 1996 was based on the 1940-1984 Methodology. This section describes, in detail, the
procedures used to create these estimates.
5.1.1 Background
The lead emissions methodology was based on a "top-down" approach where national information
was used to create a national inventory of lead emissions. The emissions were estimated based on the
source of the emissions and, in the case of combustion sources, the fuel type. The national activity of a
process producing lead emissions was measured by the consumption of fuel, the throughput of raw
materials, or an alternative production indicator. An emission factor was then applied to activity data to
determine the amount of lead emitted from a specific process. For some categories, the lead content of
the fuel was incorporated into the estimating procedure as part of the emission factor. The final element
used to estimate emissions was the control efficiency, which quantifies the amount of lead not emitted
due to the presence of control devices.
The lead emissions were presented in the 1997 Trends report by Tier categories, but in the lead
emissions methodology, emissions were estimated by a different set of source categories. The source
categories or subcategories contributing to lead emissions were regrouped into the Tier categories. The
estimation procedures are presented in this section by Tier II category. The correspondence between the
Tier II categories and the lead emissions methodology source categories is presented in Table 5.1-1.
Within the description of the procedures for each Tier II category, the correlation between the categories
is reiterated.
5.1.2 Genera 1 Procedu re
Lead emissions were calculated according to Equation 5.1-1.
Lead Emissions.; = At ; x EF( ; x [1 - CEi ;] (Eq. 5.1-1)
where: A = activity
EF = emission factor
CE = control efficiency
I = year
j = source category
As an aid in the calculation of emissions by the lead methodology, two Excel spreadsheets were
created for each year and are collectively referred to as the Trends spreadsheets. The spreadsheets were
5-1
-------
entitled TRENDSxx.XLS and MGTMPxx.XLS, where xx represents the year. The required data were
entered into the TRENDSxx.XLS spreadsheet, after which the MGTMPxx.XLS spreadsheet was opened
and the necessary calculations were made to estimate the national emissions. This procedure was
designed to simplify the process of estimating emissions for a new year. By using the TRENDSxx.XLS
spreadsheets from the previous year as templates, the spreadsheets for the new year were created by
editing only the data requiring updating.
The calculations utilized within the TRENDSxx.XLS spreadsheets required specific units for the
activity indicators and the emission factors. The required units are specified within the procedures for
each Tier II category. In general, the units for activity indicators were short tons for solids, gallons for
liquids, and cubic feet for gases. Emission factors were expressed in units of metric pounds of pollutant
per unit consumption or throughput. Contro 1 efficiencies were expressed as a dimensionless decimal
fraction. By using these units, the emissions calculated within the spreadsheets were expressed in metric
tons. Raw data used as the basis for activity indicators or emission factors were often expressed in units
which required conversion to the appropriate units. The following conversion factors were used in many
cases.
1 ton (metric) = 1.1023 tons (short)
1 ton (long) = 1.1016 tons (short)
1 ton (short) = 0.9072 tons (metric)
1 bbl = 42 gal
The emission factors used to estimate lead emissions were based on the most recent information
available. For many categories, the most recent emission factor was used to estimate the emissions for all
years.
When the emissions were estimated for 1996, not all of the activity information was available. In
order to make a preliminary emissions estimate, activity data from preceding years were used to estimate
the activity data for 1996. This was done using several different methods. The first method used a
quadratic equation and the past 20 years of activity data. Data for 1976-1995 were used, and the previous
ten year's data (1986-1995) was repeated. The second method used a linear regression and the past 7
years of activity data. Data from 1989-1995 were used, 1993-1995 data were repeated, and the 1995 data
were repeated a third time. The third method, used in cases where the first method resulted in a negative
activity value, calculated the average of the activity data over the past 5 years. Table 5.1-2 presents by
general source category the method used to estimate activity data for generating 1996 emissions. For
general source categories not listed, activity data for the current year were available at the time the
emissions were estimated.
5.1.3 Organization of Procedures
The methodology used to estimate lead emissions is described by Tier II category except for the On-
road vehicles category which is described at the Tier I level. For each category, the procedure is divided
into four sections, reflecting the data required to generate the estimates: (1) technical approach, (2)
activity indicator, (3) emission factor, and (4) control efficiency. The procedures for obtaining activity
indicators, emission factors or control efficiencies are arranged in a variety of ways, depending on the
5-2
-------
specific requirements of the category. The procedures could be arranged by process, fuel type, or other
sub category.
References are provided at the end of the description of the procedure for each Tier II category.
Many of the references are published annually as part of a series. In some cases, several references are
provided for the same information, reflecting a change or discontinuation of one source and its
replacement by another. The specific source used would depend on the specific year for which
information is needed. All tables and supporting data immediately follow the description of the procedure
for each Tier II category.
5-3
-------
Table 5.1-1. Correspondence Between Tier II Categories and Lead Emissions Methodology Categories
Tier I Category
Fuel Combustion -
Electric Utility
Fuel Combustion -
Industrial
Fuel Combustion -
Other
Tier II Category
Coal
Oil
Coal
Oil
Commercial and
Institutional Coal
Commercial and
Institutional Oil
Miscellaneous Fuel
Combustion (except
residential)
Residential Other
Tier I/Tier II Code
01-01
01-02
02-01
02-02
03-01
03-02
03-04
03-06
Lead Emissions
Methodology
Category
Bituminous Coal and
Lignite
Anthracite Coal
Residual Oil
Distillate Oil
Bituminous Coal and
Lignite
Anthracite Coal
Residual Oil
Distillate Oil
Bituminous Coal and
Lignite
Anthracite Coal
Residual Oil
Distillate Oil
Residual Oil
Bituminous Coal and
Lignite
Anthracite Coal
Residual Oil
Distillate Oil
Lead Emissions Methodology Subcategory
Electric Utility
Electric Utility
Electric Utility
Electric Utility
Industrial
Industrial
Industrial
Industrial
Commercial and Institutional
Commercial and Institutional
Commercial and Institutional
Commercial and Institutional
Waste Oil
Residential
Residential
Residential
Residential
-------
Table 5.1-1 (continued)
Tier I Category
Chemical and Allied
Product Manufacture
Metals Processing
Other Industrial
Processes
Waste Disposal and
Recycling
On-road vehicles
Non-road engines
and vehicles
Tier II Category
Inorganic Chemical
Manufacturing
Nonferrous
Ferrous
Not Elsewhere
Classified
Mineral Products
Miscellaneous
Industrial Processes
Incineration
All Categories (Light-
Duty Gas Vehicles
and Motorcycles,
Light-Duty Gas
Trucks, and
Heavy-Duty Gas
Vehicles)
Nonroad Gasoline
Ai rcraft
Tier I/Tier II Code
04-02
05-01
05-02
05-03
07-05
07-10
10-01
11
12-01
12-03
Lead Emissions
Methodology
Category
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Industrial Processes
Solid Waste
Disposal
On-road vehicles
Other Non-road
engines and
vehicles
Vessels
Aircraft
Lead Emissions Methodology Subcategory
Secondary Metals (lead oxide/pigment)
Nonferrous Metals (copper, zinc, and lead production)
Secondary Metals (lead, copper, and battery production)
Miscellaneous Process Sources [miscellaneous products
(can soldering and cable covering)]
Iron and Steel Industry
Nonferrous Metals (ferroalloy production)
Secondary Metals Industry (grey iron foundries)
Mineral Products (ore crushing)
Miscellaneous Process Sources [miscellaneous products
(type metal production)]
Mineral Products (cement manufacturing and glass
production, lead-glass)
Miscellaneous Process Sources (lead alkyl production -
electrolytic process, sodium lead alloy, and miscellaneous
products (ammunition)]
Incineration
Gasoline (leaded and unleaded)
Gasoline
Gasoline
Aviation Gasoline
-------
Table 5.1-2. Method Used for Estimating 1996 Activity Data
General Source Category
Activity Data Estimation Method
Non-road engines and vehicles
All Anthracite Coal Categories
Fuel Combustion, excluding Electric Utility
Bituminous Coal
Residual Oil
Distillate Oil
Solid Waste
Industrial Process Sources
Quadratic equation method
Linear regression method
Linear regression method
Quadratic equation method
Linear regression method
Quadratic equation method
Linear regression method
5-6
-------
5.2 FUEL COMBUSTION ELECTRIC UTILITY - COAL: 01-01
The emissions for this Tier II category were determined by the Lead Emissions Methodology for the
following source categories (See Table 5.1-1 for Tier correspondence):
Category: Subcategory:
Bituminous Coal and Lignite Electric Utility
Anthracite Coal Electric Utility
5.2.1 Technical Approach
The lead emissions included in this Tier category were the sum of the emissions from the source
categories listed above. Emissions were estimated from an activity indicator and an emissions factor. In
order to utilize these values in the Trends spreadsheets, activity indicators were expressed in million short
tons for bituminous coal, and in thousand short tons for anthracite coal. Emission factors were expressed
in metric pounds/thousand short tons.
The following procedures for determining activity indicators and emission factors were used for the
years 1970 through 1995.
5.2.2 Activity Indicator
The activity indicator for the combustion of coal at electric Utility was the anthracite coal receipts at
electric Utility obtained from Reference la or Ib.
The activity indicator for the combustion of bituminous coal and lignite was calculated as the
difference between the total national consumption of coal by electric Utility and the anthracite coal
consumption at electric Utility as determined above. The total national consumption of coal was obtained
from Reference 2a or Reference 3.
5.2.3 Emission Factor
The emission factors for the combustion of anthracite coal and of bituminous coal and lignite were
obtained from Reference 4a.
5.2.4 Control Efficiency
No control efficiencies were applied to activity data to estimate emissions from the sources included
in this Tier II category.
5-7
-------
5.2.5 References
1. Cost and Quality of Fuels for Electric Utility Plants. DOE/EIA-0191(xx). Energy Information
Administration, U.S. Department of Energy, Washington, DC. Annual.
a. Appendix A
b. Table entitled, "Receipts and Average Delivered Cost of Coal By Rank, Census Division, and
state, 19xx"
2. Electric Power Annual. DOE/EOA-0348(xx). Energy Information Administration, U.S.
Department of Energy, Washington, DC. Annual.
a. Volume I. Table entitled, "Consumption of Fossil Fuels and End-year Stocks of Coal and
Petroleum at U.S. Utility."
3. Quarterly Coal Report: January - March. DOE/EIA-0121 (xx/1Q). Energy Information
Administration, U.S. Department of Energy, Washington, DC. Quarterly.
4. Compilation of Air Pollutant Emission Factors, Third Edition, Supplements 1 through 14, AP-42.
NTIS PB-275525. U.S. Environmental Protection Agency, Research Triangle Park, NC.
September 1977.
a. Appendix E
5-8
-------
5.3 FUEL COMBUSTION ELECTRIC UTILITY - OIL: 01-02
The emissions for this Tier II category were determined by the Lead Emissions Methodology for the
following source categories (see table 5.1-1 for Tier correspondence):
Category: Subcategory:
Residual Oil Electric Utility
Distillate Oil Electric Utility
5.3.1 Technical Approach
The lead emissions included in this Tier category were the sum of the emissions from the source
categories listed above. Emissions were estimated from an activity indicator and an emissions factor. In
order to utilize these values in the Trends spreadsheets, activity indicators were expressed in million
gallons and emission factors were expressed in metric pounds/million gallons.
The following procedures for determining activity indicators and emission factors were used for the
years 1970 through 1995.
5.3.2 Activity Indicators
The activity indicators for the combustion of residual and distillate oils were the consumption of
these fuel types by electric Utility. The distillate oil consumption was assumed to be equal to the
"adjusted" distillate fuel oil sales to electric Utility obtained from Reference la or Reference 2. The
residual fuel oil consumption was obtained from "adjusted" residual fuel sales in Reference la. When this
reference was unavailable, the residual oil consumption was calculated as the difference between the total
oil consumption and the distillate oil consumption The total annual oil consumption was obtained from
Reference 3.
5.3.3 Emission Factors
The emission iactors for the combustion of residual oil and of distillate oil by electric Utility were
obtained from Reference 4a.
5.3.4 Control Efficiency
No control efficiencies were applied to activity data to estimate emissions from the sources included
in this Tier II category.
5-9
-------
5.3.5 References
1. Fuel Oil and Kerosene Sales 19xx. DOE/EIA-0535(xx). Energy Information Administration, U.S.
Department of Energy, Washington, DC. Annual.
a. Table entitled, "Adjusted Sales of Distillate Fuel Oil By End Use in the U.S."
b. Table entitled, "Adjusted Sales of Residual Fuel Oil By End Use in the U.S."
2. Petroleum Marketing Annual. DOE/EIA-0389(xx/07). Energy Information Administration, U.S.
Department of Energy, Washington, DC. Annual.
3. Electric Power Annual. DOE/EOA-0348(xx). Energy Information Administration, U.S.
Department of Energy, Washington, DC. Annual.
4. Compilation of Air Pollutant Emission Factors, Third Edition, Supplements 1 through 14, AP-42.
NTIS PB-275525. U.S. Environmental Protection Agency, Research Triangle Park, NC.
September 1977.
a. Appendix E
5-10
-------
5.4 FUEL COMBUSTION INDUSTRIAL - COAL: 02-01
The emissions for this Tier II category were determined by the Lead Emissions Methodology for the
following source categories (see table 5.1-1 for Tier correspondence):
Category: Subcategory:
Anthracite Coal Industrial
Bituminous Coal and Lignite Industrial
5.4.1 Technical Approach
The lead emissions included in this Tier category were the sum of the emissions from the source
categories listed above. Emissions were estimated from an activity indicator and an emissions factor. In
order to utilize these values in the Trends spreadsheets, the activity indicators were expressed in million
short tons for bituminous coal, and in thousand short tons for anthracite coal The emission factors were
expressed in metric pounds/thousand short tons.
The following procedures for determining activity indicators and emission factors were used for the
years 1970 through 1995.
5.4.2 Activity Indicator
The activity indicator for the industrial combustion of anthracite coal was the distribution of
anthracite coal from Pennsylvania (i.e. District 24) obtained from Reference launder the category
"Industrial Plants (except coke)."
The activity indicator for the combustion of bituminous coal and lignite was based on total national
coal consumption obtained from Reference 2a under the category "Industrial Plants (except coke)." The
sum of coal consumption by cement plants and lime pknts was subtracted from the total coal
consumption. The coal consumption by cement plants was obtained from Reference 3 or Reference 4a
The coal consumption by lime pknts was estimated by multiplying the lime production value obtained
from Reference 5 by the conversion frictor, 0.1 tons coal/ton lime produced. If Reference 4 was
unavailable, the previous year's data was used.
5.4.3 Emission Factors
The emission factors for the industrial combustion of anthracite coal and of bituminous coal and
lignite were obtained from Reference 6a.
5.4.4 Control Efficiency
No control efficiencies were applied to activity data to estimate emissions from the sources included
in this Tier n category.
5-11
-------
5.4.5 References
1. Coal Distribution January-December 19xx. DOE/EIA-0125(xx/4Q). Energy Information
Administration, U.S. Department of Energy, Washington, DC. Annual.
a. Table entitled, "Domestic Distribution of U.S. Coal by Origin, Destination, and Consumer:
January-December 19xx."
2. Quarterly Coal Report: January - March. DOE/EIA-0121(xx/lQ). Energy Information
Administration, U.S. Department ofEnergy, Washington, DC. Quarterly.
a. Table entitled, "U.S. Coal Recepts By End-Use Sector"
3. Minerals Industry Surveys, Cement. Bureau of Mines, U.S. Geological Survey, Washington, DC.
Monthly.
4. Minerals Yearbook, Cement. US Geological Survey (formerly Bureau of Mines), Washington,
DC. Annual
a. Table entitled, "Clinker Produced and Fuel Consumed by the Portland Cement Industry the
U.S. byprocess."
5. Chemical and Engineering News, Facts and Figures Issue. American Chemical So ciety,
Washington, DC. Annual.
6. Compilation of Air Pollutant Emission Factors, Third Edition, Supplements 1 through 14, AP-42.
NTIS PB-275525. U.S. Environmental Protection Agency, Research Triangle Park, NC.
September 1977.
a. Appendix E
5-12
-------
5.5 FUEL COMBUSTION INDUSTRIAL - OIL: 02-02
The emissions for this Tier II category were determined by the Lead Emissions Methodology for the
following source categories (see table 5.1-1 for Tier correspondence):
Category: Subcategory:
Residual Oil Industrial
Distillate Oil Industrial
5.5.1 Technical Approach
The lead emissions included in this Tier category were the sum of the emissions from the source
categories listed above. Emissions were estimated from an activity indicator and an emissions factor. In
order to utilize these values in the Trends spreadsheets, activity indicators were expressed in million
gallons and emission factors were expressed in metric pounds/million gallons.
The following procedures for determining activity indicators and emission factors were used for the
years 1970 through 1995.
5.5.2 Activity Indicator
The activity indicator for industrial combustion of residual oil was based on the adjusted quantity of
residual oil sales for industrial and oil company use obtained from Reference 1 or 2a. The total of three
statistics was subtracted from this value to obtain the activity indicator. The first statistic was two-thirds
of the quantity of oil consumed by cement plants reported in Reference 3 or 4a. The second statistic was
the quantity of residual oil consumed by petroleum refineries reported in Reference 5a. The third statistic
was the quantity of residual oil consumed by steel mills; this value was calculated by multiplying the
quantity of raw steel production obtained from Reference 6a or 7, by 0.00738 * 106 gal/103 ton steel.
The conversion factor between the gallons of oil and the tons of steel was updated in 1982 based on
Reference 8.
The activity indicator for industrial combustion of distillate oil was based on the adjusted quantity of
distillate oil sales to industrial and oil companies obtained from Reference 1 or 2a. The total of two
statistics was subtracted from this value to obtain the activity indicator for distillate oil. The first statistic
was one-third of the quantity of oil consumed by cement plants, expressed in gallons, reported in
Reference 3 or 4a. The second statistic was the quantity of distillate oil consumed by petroleum
refineries, expressed in gallons, reported in Reference 5a or 5b.
5.5.3 Emission Factor
The lead emission factor for the industrial combustion of residual oil and of distillate oil were
obtained from Reference 9a.
5-13
-------
5.5.4 Control Efficiency
No control efficiencies were applied to activity data to estimate emissions from the sources included
in this Tier II category.
5.5.5 References
1. Petroleum Marketing Monthly. DOE/EIA-0380(xx/01). Energy Information Administration, U.S.
Department of Energy, Washington, DC. Annual.
2. Fuel Oil and Kerosene Sales 19xx. DOE/EIA-0535(xx). Energy Information Administration, U.S.
Department of Energy, Washington, DC. Annual.
a. Table entitled, "Adjusted Sales of Residual Fuel Oil by End-Use in the U.S."
3. Minerals Industry Surveys, Cement. Bureau of Mines, U.S. Department of the Interior,
Washington, DC. Monthly.
a. Table entitled, "Clinker Produced and Fuel Consumed by the Portland Cement Industry in the
U.S. By Process."
4. Minerals Yearbook, Cement. US Geological Survey (formerly Bureau of Mines), Washington, DC.
Annual
a. Table entitled, "Clinker Produced and Fuel Consumed by the Portland Cement Industry in the
U.S. By Process."
5. Petroleum Supply Annual. DOE/EIA-0340(xx/07). Energy Information Administration, U.S.
Department of Energy, Washington, DC. Annual.
a. Table entitled, "Fuel Consumed at Refineries by PAD District."
b. Table entitled, "Refinery Fuel Use and Losses by PAD District."
6. Survey of Current Business. Bureau of Economic Analysis, U.S. Department of Commerce,
Washington, DC.
a. Table containing information on metals and manufactures.
7. Mineral Industry Surveys. Iron and Steel. US Geological Survey (formerly Bureau of Mines).
a. Table entitled, "Salient Iron and Steel Statistics."
8. Census of Manufactures (Fuels and Electric Energy Consumed). Bureau of the Census, U.S.
Department of Commerce, Washington, DC. 1982.
9. Compilation of Air Pollutant Emission Factors, Third Edition, Supplements 1 through 14, AP-42.
NTIS PB-275525. U.S. Environmental Protection Agency, Research Triangle Park, NC.
September 1977.
a. Appendix E
5-14
-------
5.6 FUEL COMBUSTION OTHER - COMMERCIAL/INSTITUTIONAL COAL: 03-01
The emissions for this Tier II category were determined by the Lead Emissions Methodology for the
following source categories (see table 5.1-1 for Tier correspondence):
Category: Subcategory:
Anthracite Coal Commercial / Institutional
Bituminous Coal and Lignite Commercial / Institutional
5.6.1 Technical Approach
The lead emissions included in this Tier category were the sum of the emissions from the source
categories listed above. Emissions were estimated from an activity indicator and an emissions factor. In
order to utilize these values in the Trends spreadsheets, the activity indicators were expressed in million
short tons for bituminous coal, and in thousand short tons for anthracite coal The emission factors were
expressed in metric pounds/thousand short tons.
The following procedures for determining activity indicators and emission factors were used for the
years 1970 through 1995.
5.6.2 Activity Indicator
The activity indicators for the combustion of anthracite and bituminous coal and lignite were the
consumption of each coal type by commercial and institutional users. Determination of these activity
indicators required activity data for both anthracite and bituminous residential coal combustion.
The commercial/institutional consumption of anthracite coal was obtained by subtracting the
residential anthracite consumption from residential and commercial/institutional anthracite consumption.
Residential and commercial/institutional consumption of anthracite coal was obtained from Reference la
for District 24 only. This calculation is shown in Equation 5.6-1.
Anthracite CoalCII = Anthracite CoalRandCII - Anthracite CoalR (Eq. 5.6-1)
where: R = residential consumption
C / 1= commercial/institutional consumption
Residential consumption of anthracite coal was determined by extrapolating the consumption of the
previous year based on the change in the number of dwelling units in the Northeastern United States
having coal as the main fuel for space heating. Data concerning the number of dwelling units were
obtained from Reference 2. The calculation of the residential anthracite coal consumption is summarized
in Equation 5.6-2.
5-15
-------
Dwelling Units.
Anthracite CoalD = Anthracite Coal0 , x fpa 5 6-
*'' R'!~l Dwelling Units._ l { q'
where: R = residential consumption
I = year under study
Commercial/institutional consumption of bituminous coal was obtained by subtracting the residential
bituminous consumption from the residential and commercial/institutional bituminous consumption.
Residential and commercial/institutional consumption of bituminous coal was calculated by subtracting
residential and commercial/institutional consumption of anthracite coal from residential and
commercial/institutional consumption of all types of coal. These two consumption values were obtained
from Reference la and excluded coal from District 24 which represents anthracite coal consumption.
This calculation is summarized in Equation 5.6-3.
Bituminous Coalcll = (All CoalRandclI - Anthracite CoalRandc/I) - Bituminous CoalR (Eq. 5.6-3)
where: R = residential consumption
C / I = commercial/institutional consumption
The residential consumption of bituminous coal was determined by estimating the quantity of all coal
consumed by all dwelling units using coal as the main fuel and subtracting from this value the residential
consumption of anthracite coal calculated above. The quantity of all coal consumed was calculated using
the number of dwelling units using coal as the main fuel for space heating obtained from Reference 2 and
a factor estimating the average annual consumption of coal per dwelling unit. This calculation is
summarized in Equation 5.6-4.
Bituminous CoalR = (Dwelling Units x 6.73 tons burned/dwelling/year) - Anthracite CoalR (Eq. 5.6-4)
where: R = residential consumption
5.6.3 Emission Factors
The emission factors for the commercial/institutional combustion of anthracite coal and of
bituminous coal and lignite were obtained from Reference 3 a.
5.6.4 Control Efficiency
No control efficiencies were applied to activity data to estimate emissions from the sources included
in this Tier II category.
5-16
-------
5.6.5 References
1. Coal Distribution January-December 19xx. DOE/EIA-0125(xx/4Q). Energy Information
Administration, U.S. Department of Energy, Washington, DC. Annual.
a. Table entitled, "Domestic Distribution of U.S. Coal to the Residential and Commercial Sector
by Origin."
2. American Housing Survey, Current Housing Reports, Series H-l50-83. Bureau of the Census, U.S.
Department of Commerce, Washington DC. Biennial.
3. Compilation of Air Pollutant Emission Factors, Third Edition, Supplements 1 through 14, AP-42.
NTIS PB-275525. U.S. Environmental Protection Agency, Research Triangle Park, NC.
September 1977.
a. Appendix E
5-17
-------
5.7 FUEL COMBUSTION OTHER - COMMERCIAL/INSTITUTIONAL OIL: 03-02
The emissions for this Tier II category were determined by the Lead Emissions Methodology for the
following source categories (see table 5.1-1 for Tier correspondence):
Category: Subcategory:
Residual Oil Commercial / Institutional
Distillate Oil Commercial / Institutional
5.7.1 Technical Approach
The lead emissions included in this Tier category were the sum of the emissions from the source
categories listed above. Emissions were estimated from an activity indicator and an emissions factor. In
order to utilize these values in the Trends spreadsheets, activity indicators were expressed in million
gallons and emission factors were expressed in metric pounds/million gallons.
The following procedures for determining activity indicators and emission factors were used for the
years 1970 through 1995.
5.7.2 Activity Indicator
The activity indicator for the commercial/institutional combustion of residual oil was the "adjusted"
total quantity of residual oil sales for commercial and military use obtained from Reference 1 or
Reference 2a.
The activity indicator for the combustion of distillate oil was the "adjusted" total quantity of distillate
oil sales for commercial and military use (not including military diesel fuel) obtained from Reference 1, or
commercial and military use obtained from Reference 2b minus military diesel fuel use obtained from
Reference 2c.
5.7.3 Emission Factor
The emission factors for the commercial/institutional combustion of residual oil and of distillate oil
were obtained from Reference 3 a.
5.7.4 Control Efficiency
No control efficiencies were applied to activity data to estimate emissions from the sources included
in this Tier II category.
5-18
-------
5.7.5 References
1. Petroleum Marketing Monthly. DOE/EIA-0380(xx/01). Energy Information Administration, U.S.
Department of Energy, Washington, DC. Annual.
2. Fuel Oil and Kerosene Sales 19xx. DOE/EIA-0535(xx). Energy Information Administration, U.S.
Department of Energy, Washington, DC. Annual.
a. Table entitled, "Adjusted Sales of Residual Fuel Oil by End Use in the US."
b. Table entitled, "Adjusted Sales of Distillate Fuel Oil by End Use in the US."
c. Table entitled, "Adjusted Sales for Military, Non-road engines and vehicles, and All Other Uses:
Distillate Fuel Oil, Residual Fuel Oil and Kerosene."
3. Compilation of Air Pollutant Emission Factors, Third Edition, Supplements 1 through 14, AP-42.
NTIS PB-275525. U.S. Environmental Protection Agency, Research Triangle Park, NC.
September 1977.
a. Appendix E
5-19
-------
5.8 FUEL COMBUSTION OTHER - MISCELLANEOUS FUEL COMBUSTION (EXCEPT
RESIDENTIAL): 03-04
The emissions for this Tier II category were determined by the Lead Emissions Methodology for the
following source categories (see table 5.1-1 for Tier correspondence):
Category: Subcategory:
Residual Oil Waste Oil
5.8.1 Technical Approach
The lead emissions included in this Tier category were the sum of the emissions from the source
categories listed above. Emissions were estimated from an activity indicator and an emissions factor. In
order to utilize these values in the Trends spreadsheets, the activity indicator was expressed in million
gallons and the emission factor was expressed in metric pounds/million gallons.
The following procedures for determining activity indicators and emission factors were used for the
years 1970 through 1996.
5.8.2 Activity Indicator
The activity indicator for the combustion of residual waste oil was assumed to be a constant annual
consumption of 500 x 106 gallons of waste oil.
5.8.3 Emission Factor
The emission iactor for the combustion of residual waste oil was calculated as 75 lb/1,000 gal
multiplied by the average percentage of lead. It was assumed that the percentage of lead had a constant
value ofO.5333 up to the year 1975; afterwhich, itwas assumed that the lead percentage steadily
decreased. After 1984, the value has remained constant at 0.0213. The average lead percentage values
are presented in Table 5.8-1.
5.8.4 Control Efficiency
No control efficiency was applied to activity data to estimate lead emissions from the combustion of
waste oil.
5.8.5 References
None.
5-20
-------
Table 5.8-1. Annual Percentage Lead Content
Percent
Year Lead
1975 0.5333
1976 0.4702
1977 0.407
1978 0.3439
1979 0.2807
1980 0.2176
1981 0.1545
1982 0.0913
1983 0.0282
1984 0.0213
5-21
-------
5.9 FUEL COMBUSTION OTHER - RESIDENTIAL OTHER: 03-06
The emissions for this Tier II category were determined by the Lead Emissions Methodology for the
following source categories (see table 5.1-1 for Tier correspondence):
Category: Subcategory:
Anthracite Coal Residential
Bituminous Coal and Lignite Residential
Residual Oil Residential
Distillate Oil Residential
5.9.1 Technical Approach
The lead emissions included in this Tier category were the sum of the emissions from the source
categories listed above. Emissions were estimated from an activity indicator and an emissions factor. In
order to utilize these values in the Trends spreadsheets, the activity indicators were expressed in million
tons for bituminous coal and in thousand tons for anthracite coal. The emission factors for these
categories were expressed in metric pounds/thousand tons. Activity indicators for residual and distillate
oils were expressed in million gallons and emission factors were expressed in metric pounds/million
gallons.
The following procedures for determining activity indicators and emission factors were used for the
years 1970 through 1995.
5.9.2 Activity Indicator
The activity indicator for the residential combustion of anthracite coal was the residential
consumption of anthracite coal. This value was determined by extrapolating the residential consumption
of anthracite coal during the previous year based on the change in the number of dwelling units in the
Northeastern United States having coal as the main fuel for space heating. Data concerning the number
of dwelling units were obtained from Reference 1. The calculation of the residential anthracite coal
consumption is summarized in Equation 5.9-1.
Dwelling Units
Anthracite CoaL = Anthracite CoaL . x /ga 5 o.n
*' *''-' Dwelling Unitsi_l {q' '
where: R = residential consumption
I = year under study
The activity indicator for the combustion of bituminous coal and lignite was the residential
consumption of bituminous coal and lignite. This value was determined by estimating the quantity of all
coal consumed by all dwelling units using coal as the main fuel and subtracting from this value the
5-22
-------
residential consumption of anthracite coal calculated above. The quantity of all coal consumed was
calculated using the number of dwelling units using coal as the main fuel for space heating obtained from
Reference 1 and a factor estimating the average annual consumption of coal per dwelling unit. This
calculation is summarized in Equation 5.9-2.
Bituminous CoalR = (Dwelling Units x 6.73 tons burned/dwelling/year) - Anthracite CoalR (Eq. 5.9-2)
where: R = residential consumption
The activity indicator for the residential combustion of residual oil was assumed to be zero. The
activity indicator for the combustion of distillate oil was the sum of the "adjusted" sales (or deliveries) for
residential use of distillate oil and for farm use of other distillates as reported in Reference 2 or Reference
3aand 3b.
5.9.3 Emission Factors
The emission factor for the residential combustion of anthracite coal was obtained from Reference
4.
The emission iactor for the combustion of bituminous coal and lignite and for distillate oil was
obtained from Reference 5a.
No emission factor was required for the combustion of residual oil because the activity was assumed
to be zero.
5.9.4 Control Efficiency
No control efficiencies were applied to activity data to estimate emissions from the sources included
in this Tier II category.
5.9.5 References
1. American Housing Survey, Current Housing Reports, Series H-l50-83. Bureau of the Census, U.S.
Department of Commerce, Washington DC. Biennial.
2. Petroleum Marketing Monthly. DOE/EIA-0380(xx/01). Energy Information Administration, U.S.
Department of Energy, Washington, DC. Annual.
3. Fuel Oil and Kerosene Sales 19xx. DOE/EIA-0535(xx). Energy Information Administration, U.S.
Department of Energy, Washington, DC. Annual.
a. Table entitled, "Adjusted Sales of Distillate Fuel Oil by End Use in the U.S."
b. Table entitled, "Adjusted Sales for Gram Use: Distillate Fuel Oil and Kerosene; Sales for
Electric Utility and Oil Company Uses; Distillate Fuel Oil and Residual Fuel Oil."
4. Development ofHATREMS Data Base and Emission Inventory Evaluation. EPA-450/3-77-011.
U.S. Environmental Protection Agency, Research Triangle Park, NC. April 1977.
5-23
-------
5. Compilation of Air Pollutant Emission Factors, Third Edition, Supplements 1 through 14, AP-42.
NTIS PB-275525. U.S. Environmental Protection Agency, Research Triangle Park, NC.
September 1977.
a. Appendix E
5-24
-------
5.10 CHEMICAL AND ALLIED PRODUCT MANUFACTURE - INORGANIC CHEMICAL
MANUFACTURE: 04-02
The emissions for this Tier II category were determined by the Lead Emissions Methodology for the
following source categories (see table 5.1-1 for Tier correspondence):
Category: Subcategory:
Industrial Processes - Lead Emissions Secondary Metals (lead oxide/pigment)
5.10.1 Technical Approach
The lead emissions included in this Tier category were the sum of the emissions from the source
categories listed above. Emissions were estimated from an activity indicator and an emissions factor. In
order to utilize these values in the Trends, spreadsheets, activity indicators were expressed in thousand
tons and emission factors were expressed in metric pounds/tons.
The following procedures for determining activity indicators and emission factors were used for the
years 1970 through 1995.
5.10.2 Activity Indicator
Activity indicators for the of barton pot (litharge and leady oxide), red lead, and white lead were the
respective quantities of each produced (using the lead content) as reported in Reference 1. If the litharge
and red lead are reported together, the last known distribution was used to distribute the activity. If the
value for white lead was withheld, the previous year's data was used.
5.10.3 Emission Factor
The lead emission factors for barton pot, red lead, and white lead were obtained from Reference 2a.
5.10.4 Control Efficiency
No control efficiencies were applied to activity data to estimate lead emissions from the sources
included in this Tier II category.
5.10.5 References
1. Minerals Yearbook, Lead. US Geological Survey (formerly Bureau of Mines), Washington, DC.
Annual.
a. Table entitled, "Production & Shipments of Lead Pigments and Oxides in the U.S."
2. Compilation of Air Pollutant Emission Factors, Fourth Edition, Supplements A through D, AP-42.
U.S. Environmental Protection Agency, Research Triangle Park, NC. September 1991.
a. Table 7.16-1
5-25
-------
5.11 METALS PROCESSING - NONFERROUS: 05-01
The emissions for this Tier II category were determined by the Lead Emissions Methodology for the
following source categories (see table 5.1-1 for Tier correspondence):
Category:
Industrial Processes - Lead Emissions
Subcategory:
Nonferrous Metals (copper, zinc, and lead
production)
Secondary Metals (lead, copper, and battery
production)
Miscellaneous Process Sources [miscellaneous
products (can soldering and cable covering)]
5.11.1 Technical Approach
The lead emissions included in this Tier category were the sum of the emissions from the source
categories listed above. Emissions were estimated from an activity indicator, emissions factor, and
control efficiency, where applicable. In order to utilize these values in the Trends, spreadsheets, activity
indicators were expressed in thousand tons and emission factors were expressed in metric pounds/tons.
All control efficiencies were expressed as dimensionless fractions.
The following procedures for determining activity indicators, emission factors, and applicable
control efficiencies were used for the years 1970 through 1995.
5.11.2 Activity Indicator
5.11.2.1 Nonferrous Metals
The activity indicator for copper roasting was based on the primary copper smelter production from
domestic and foreign ores from Reference la Copper smelter production was expressed in units of
blister copper produced. It was assumed that of the 4 tons of copper concentrate/ton of blister, only half
was roasted. Therefore, the amount of blister copper produced multiplied by 2 resulted in the activity
indicator for the roasting process.
Activity indicators for copper smelting and converting were assumed to be equivalent. Activity data
were calculated in the same manner as for the roasting process, except it was assumed that all of the
blister copper produced was smelted and converted. Therefore, units of blister copper produced
multiplied by 4 resulted in the activity indicators for the smelting and converting process.
Activity data for zinc sintering was based on the redistilled slab zinc production obtained from
Reference 2a. The activity indicator for the horizontal retort process was assumed to be zero. The
activity indicator for the vertical retort process was assigned the same value as used for zinc sintering.
5-26
-------
The activity indicators for lead sintering, blast furnaces, and reverberatory furnaces were assumed to
be equal to the primary refined lead production from domestic and foreign ores as listed in Reference 3.
5.11.2.2 Secondary Metals
Activity data for three copper-producing processes were obtained from Reference Ib. The
production level of high-leaded tin bronze was used as the basis for high Lead (5 8%) activity. The
production level of yellow brass was used as the basis for red-yellow brass (15%) activity. Other alloys
(7%) activity was based on the production level of leaded red brass and semi-red brass.
Activity indicators for three lead-producing furnace types and fugitive lead processes were obtained
from Reference 3 or 4a. The pot furnace activity was estimated as 90 percent of the total consumption of
lead scrap by all consumers obtained from Reference 4a. The activity indicator for reverberatory furnaces
was estimated by multiplying the total consumption of lead scrap by the ratio between the quantity of lead
recovered as soft lead (obtained from Reference 3b) and the total lead recovered from scrap. The activity
indicator for blast furnaces was estimated by multiplying the total consumption of lead scrap by the ratio
between lead recovered as antimonial lead and the total lead recovered from scrap. Fugitive lead activity
was assumed to be equal to the total quantity of lead recovered.
Battery production consists of five processes: (1) grid casting, (2) paste mixing, (3) lead oxide mill,
(4) three process operations, and (5) lead reclamation furnace. The number of batteries produced was
used as the activity indicator for each process. The total weight of lead used to produce storage batteries
was obtained from Reference 3c. This value was converted from metric tons to English units and was
used to calculate the number of batteries produced, expressed in thousands of batteries, as shown in
Equation 5.11-1.
Weightp, x 1.10231 x 2,000 Iblton
Number of Batteries = — (Eq. 5.11 -1)
1,000 x 26 Iblbattery
The activity indicator for lead reclamation furnaces was 1 percent of the number of batteries
produced as calculated above.
5.11.2.3 Miscellaneous Process Sources
The activity indicator for can soldering was the can soldering consumption as listed in Reference 3c.
If this activity indicator was not available, the previous year's value was used. The activity indicator for
cable covering was based on the value for cable covering consumption, also obtained from Reference 3c,
which was multiplied by 10 to account for recycling.
5-27
-------
5.11.3 Emission Factor
5. 11.3.1 Nonferrous Metals
The emission factors for primary copper and lead smelting processes were obtained from References
5a and 5b, respectively. The emission factors for processes associated with primary zinc smelting were
obtained from Reference 6a. Values for these emission factors were established as the midpoint of the
emission factor ranges reported in the references cited.
5.11.3.2 Secondary Metals
The emission factors for secondary lead processing were obtained from Reference 6a. The emission
factors for secondary copper processing were obtained from Reference 5c. Battery production emission
factors were reported in Reference 5d.
5. 11.3.3 Miscellaneous Process Sources
The emission factors for can soldering and can covering were obtained from Reference 5e.
5.11.4 Control Efficiency
5. 11.4.1 Nonferrous Metals
The control efficiencies for all copper, zinc, and lead production processes for the years 1970
through 1984 were equivalent to the TSP control efficiencies for the same processes. The TSP control
efficiencies were derived from Reference 7 or Reference 8 using Equation 5.11-2. Values for the control
efficiency were assumed constant after the year 1984.
where: CE = control efficiency
UE = emissions before control
AE = emissions after control
5.11.4.2 Secondary Metals
The control efficiencies for the secondary lead production processes were obtained from Reference
9.
5.11.4.3 Miscellaneous Process Sources
The control efficiencies for can soldering and cable covering were obtained from Reference 9.
5-28
-------
5.11.5 References
1. Minerals Yearbook, Copper. US Geological Survey (formerly Bureau of Mines), Washington, DC.
Annual.
a. Table entitled, "Copper: World Smelter Production, by Country."
b. Table entitled, "Production of Secondary Copper & Copper Alloy Products in the U.S. by Item
Produced From Scrap."
2. Minerals Yearbook, Zinc. US Geological Survey (formerly Bureau of Mines), Washington, DC.
Annual.
a. Table entitled, "Salient Zinc Statistics" (production of slab zinc from scrap).
3. Minerals Yearbook, Lead. US Geological Survey (formerly Bureau of Mines), Washington, DC.
Annual.
a. Table entitled, "Salient Lead Statistics."
b. Table entitled, "Pb Recovered from Scrap Processed in the U.S., by Rind of Scrap and Form of
Recovery."
c. Table entitled, "U.S. Consumption of Lead, by Product."
4. Minerals Yearbook, Recycling of Nonierrous Materials. US Geological Survey (formerly Bureau of
Mines), Washington, DC. Annual.
a. Table entitled, "Stocks and Consumption of New and Old Lead Scrap in the U.S. by Type of
Scrap."
5. Compilation of Air Pollutant Emission Factors, Fourth Edition, Supplements A through D, AP-42.
U.S. Environmental Protection Agency, Research Triangle Park, NC. September 1991.
a. Table 7.3-10
b. Table 7.6-1
c. Table 7.9-1
d. Table 7.15-1
e. Table 7.17-1
6. Compilation of Air Pollutant Emission Factors, Third Edition, Supplements 1 through 14, AP-42.
NTIS PB-275525. U.S. Environmental Protection Agency, Research Triangle Park, NC.
September 1977.
a. Appendix E
7. Standard Computer Retrievals, AFP650 report, from the AIRS Facility Subsystem. Unpublished
computer reports. National Air Data Branch, Office of Air Quality Planning and Standards, U.S.
Environmental Protection Agency, Res ear ch Triangle Park, NC. Annual.
8. Standard Computer Retrievals, NE257 report, from the National Emissions Data System (NEDS).
Unpublished computer reports. National Air Data Branch, Office of Air Quality Planning and
Standards, U.S. Environmental Protection Agency, Res ear ch Triangle Park, NC. Annual.
9. Control Techniques for Lead Air Emissions, Volumes 1 and 2. U.S. Environmental Protection
Agency, Research Triangle Park, NC. December 1977.
5-29
-------
5.12 METALS PROCESSING - FERROUS: 05-02
The emissions for this Tier II category were determined by the Lead Emissions Methodology for the
following source categories (see table 5.1-1 for Tier correspondence):
Category: Subcategory:
Industrial Processes - Lead Emissions Iron and Steel Industry (coke, blast furnace, sintering,
open hearth, BOF (Basic Oxygen Furnace), and electric
arc furnace)
Nonferrous Metals (ferroalloy production)
Secondary Metals Industry (grey iron foundries)
5.12.1 Technical Approach
The lead emissions included in this Tier category were the sum of the emissions from the source
categories listed above. Emissions were estimated from an activity indicator, emissions factor, and
control efficiency, where applicable. In order to utilize these values in the Trends spreadsheets, activity
indicators for all source categories, except those in the iron and steel industry, were expressed in
thousand tons. For the iron and steel industry source categories, activity indicators were expressed in
million tons. All emission factors were expressed in metric pounds/tons. All control efficiencies were
expressed as dimensionless fractions.
The following procedures for determining activity indicators, emission factors, and applicable
control efficiencies were used for the years 1970 through 1995.
5.12.2 Activity Indicator
5.12.2.1 Iron and Steel
The activity indicator for coke production was the oven production figure obtained from Reference
la. The activity for coke production was assumed to be zero for all years including and following 1994.
The activity indicator for blast furnaces was the total pig iron production as reported in Reference Ib,
Reference 2a, or Reference 3. This value included exports. The activity indicator for the windbox
sintering process was the total production of pig iron, divided by 3 (two other processes [discharge,
sinter-fugitive] to not contribute to Pb emissions).
The activity indicators for open hearth, basic oxygen, and electric arc furnaces were based on the
total scrap and pig iron consumption. Reference 4 contained the total scrap and pig iron consumed by
each furnace type by manufacturers of pig iron and raw steel and castings. The fraction of the combined
quantity of scrap and pig iron consumed by each of the three furnace types was calculated. Total raw
steel production reported in Reference Ib or Reference 2a was multiplied by each fraction to obtain the
raw steel production for each furnace type.
5-30
-------
5.12.2.2 Non ferrous Metals
The activity indicator for ferrosilicon production was the net gross weight production obtained from
Reference 5a or 6a. Silicon manganese activity was assumed to be 42.1 percent of the net production of
ferrosilicon. Production of ferro manganese by electric furnaces was assumed to be 57.9 percent of the
net production of ferrosilicon. Production of silicon metal was obtained from Reference 6a. For
ferro manganese from blast furnaces and for Ferro-Mang (std), the activity indicators were assumed to be
zero.
Ferrochrome-silicon activity was obtained from Reference 5a or 7, and activity data for High Carbon
Ferro production was obtained from Reference 5a or 8. If these data were not available, values for the
previous year were used.
5.12.2.3 Secon dary Metals
The activity indicator for cupola furnaces in grey iron foundries was based on the combined quantity
of scrap and pig iron consumed by cupola furnaces. This value was obtained from Reference 4a under
the category of iron foundries and miscellaneous users. The final activity was determined by adjusting
this production value to account for this category's respective emission factor, which was expressed in
terms of the charged quantity, and not the fresh feed quantity. This adjustment required dividing the
production value by 0.78.
The activity indicator for electric induction was based on the combined quantity of iron and steel
scrap and pig iron consumed in electric furnaces. This value was obtained from Reference 4a under the
category of iron foundries and miscellaneous users. The amount consumed was adjusted to account for
recycling by dividing the consumption value by 0.78.
5.12.3 Emission Factor
5.12.3.1 Iron and Steel
The emission factors for all processes were obtained from Reference 9a. The emission factor used
for by-product coke was the same as that established for metallurgical coke manufacturing.
5.12.3.2 Nonferrous Metals
The emission factors for all processes were set equal to the midpoint of the emission factor ranges
reported in Reference lOa
5.12.3.3 Secondary Metals - Grey Iron Foundries
The emission factors for all processes were reported in Reference lOb.
5-31
-------
5.12.4 Control Efficiency
The control efficiencies for all processes included in this Tier II category for the years 1970 through
1984 were equivalent to the TSP control efficiencies for the same processes. The TSP control
efficiencies were derived from Reference 11 or Reference 12 using Equation 5.12-1. Values after the year
1984 were assumed constant.
where: CE = control efficiency
UE = emissions before control
AE = emissions after control
5.12.5 References
1. Survey of Current Business. Bureau of Economic Analysis, U.S. Department of Commerce,
Washington, DC.
a. Table containing information on "Petroleum, Coal, and Products." SCC = 3-03-003
b. Table containing information on "Metals and Manufactures."
2. Minerals Yearbook, Iron and Steel U.S. Geological Survey (formerly Bureau of Mines),
Washington, DC. Annual.
a. Table entitled, "Salient Iron and Steel Statistics."
b. Table entitled, "U.S. Consumption of Iron and Steel Scrap, Pig Iron, and Direct-Reduced Iron
(DRI) in 19xx, by Type of Furnace and Other Use."
3. Minerals Industry Surveys, Iron Ores. U.S. Geological Survey (formerly Bureau of Mines),
Washington, DC. Monthly.
4. Minerals Industry Surveys, Iron and Steel Scrap. U.S. Geological Survey (formerly Bureau of
Mines), Washington, DC. Monthly.
a. Table on consumption of iron and steel scrap and pig iron in the United States by type of
furnace or other use.
5. Minerals Yearbook, Ferroalloys. U.S. Geological Survey (formerly Bureau of Mines), Washington,
DC. Annual.
a. Table entitled, 'Table 2. Ferroalloys Produced and Shipped from Furnaces in the U.S."
6. Minerals Yearbook, Silicon. U.S. Geological Survey (formerly Bureau of Mines), Washington, DC.
Annual.
a. "Table 1. Production, Shipments, and Stocks of Silvery Pig Iron, Ferrosflicon, and Silicon
Metal in the U.S. in 19xx"
5-32
-------
7. Minerals Yearbook, Chromium. U.S. Geological Survey (formerly Bureau of Mines), Washington,
DC. Annual.
8. Minerals Yearbook, Iron and Steel. US Geological Survey (formerly Bureau of Mines),
Washington, DC. Annual.
9. Compilation of Air Pollutant Emission Factors, Third Edition, Supplements 1 through 14, AP-42.
NTIS PB-275525. U.S. Environmental Protection Agency, Research Triangle Park, NC.
September 1977.
a. Appendix E
10. Compilation of Air Pollutant Emission Factors, Fourth Edition, Supplements A through D, AP-42.
U.S. Environmental Protection Agency, Research Triangle Park, NC. September 1991.
a. Table 7.4-5
b. Table 7.10-3
11. Standard Computer Retrievals, AFP650 report, from the AIRS Facility Subsystem. Unpublished
computer reports. National Air Data Branch, Office of Air Quality Planning and Standards, U.S.
Environmental Protection Agency, Research Triangle Park, NC. Annual.
12. Standard Computer Retrievals, NE257 report, from the National Emissions Data System (NEDS).
Unpublished computer reports. National Air Data Branch, Office of Air Quality Planning and
Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. Annual.
5-33
-------
5.13 METALS PROCESSING - NOT ELSEWHERE CLASSIFIED: 05-03
The emissions for this Tier II category were determined by the Lead Emissions Methodology for the
following source categories (see table 5.1-1 for Tier correspondence):
Category:
Industrial Processes - Lead Emissions
Subcategory:
Mineral Products (ore crushing)
Miscellaneous Process Sources [miscellaneous
products (type metal production)]
5.13.1 Technical Approach
The lead emissions included in this Tier category were the sum of the emissions from the source
categories listed above. Emissions were estimated from an activity indicator, emissions factor, and
control efficiency, where applicable. In order to utilize these values in the Trends spreadsheets, activity
indicators were expressed in thousand tons and emission factors were expressed in metric pounds/tons.
All control efficiencies were expressed as dimensionless fractions.
The following procedures for determining activity indicators, emission factors, and applicable
control efficiencies were used for the years 1970 through 1995.
5.13.2 Activity Indicator
The activity indicator for lead ore production was the gross weight of lead ore produced on a dry
weight basis as reported in Reference la or Ib. If this value is not reported on a dry weight basis, the dry
weight is estimated from the Pb ore production, in terms of recoverable Pb content, divided by 0.0799.
The activity indicator for Zn, Cu, Cu-Zn ores was estimated as the sum of the "ore produced" listed in
Reference 2a, and "all other sources" listed in Reference la. The activity data for Pb-Zn, Cu-Pb, Cu-Pb-
Zn ores was assumed to be zero. If Reference la is not available, Zn, Cu, Cu-Zn ores are estimated using
the following equation:
1.4291(x) - 49736.557
(Eq. 5.13-1)
where: x = value for copper ore produced, in short tons.
The activity indicator for type metal production was based on the consumption of lead for type metal
pro duction obtained from Reference 1. In accordance with procedures provided in Reference 3, this
value was multiplied by 330 to account for recycling. If the value is withheld, use the most recent
available year.
5-34
-------
5.13.3 Emission Factor
The emission factors for ore crushing and grinding processes were obtained from Reference 4a. The
emission factors for type metal production were obtained from Reference 4b.
5.13.4 Control Efficiency
The control efficiencies for ore crushing and grinding processes and type metal production were
obtained from Reference 3. No control efficiencies were applied to the activity data to estimate emissions
from type metal production.
5.13.5 References
1. Minerals Yearbook, Lead. U.S. Geological Survey (formerly Bureau of Mines), Washington, DC.
Annual.
a. Table entitled, "Production of Lead and Zinc in Terms of Recoverable Metals, in U. S. in 19xx,
by State."
b. Table Entitled, "Salient Lead Statistics."
2. Minerals Yearbook, Copper. U.S. Geological Survey (formerly Bureau of Mines), Washington, DC.
Annual.
a. Table entitled, "Salient Copper Statistics."
3. Control Techniques for Lead Air Emissions, Volumes 1 and 2. U.S. Environmental Protection
Agency, Research Triangle Park, NC. December 1977.
4. Compilation of Air Pollutant Emission Factors, Fourth Edition, Supplements A through D, AP-42.
U.S. Environmental Protection Agency, Research Triangle Park, NC. September 1991.
a. Table 7.6-1
b. Table 7.17-1
5-35
-------
5.14 OTHER INDUSTRIAL PROCESSES - MINERAL PRODUCTS: 07-05
The emissions for this Tier II category were determined by the Lead Emissions Methodology for the
following source categories (see table 5.1-1 for Tier correspondence):
Category:
Industrial Processes - Lead Emissions
Subcategory:
Mineral Products [Cement Manufacturing (wet
kiln/cooler, wet dryer/grinder, dry kiln/cooler and
dry dryer/grinder) and Glass Production (lead-
glass)]
5.14.1 Technical Approach
The lead emissions included in this Tier category were the sum of the emissions from the source
categories listed above. Emissions were estimated from an activity indicator, emissions factor, and
control efficiency, where applicable. In order to utilize these values in the Trends spreadsheets, activity
indicators were expressed in thousand tons and emission factors were expressed in metric pounds/tons.
All control efficiencies were expressed as dimensionless fractions.
The following procedures for determining activity indicators, emission factors, and applicable
control efficiencies were used for the years 1970 through 1995.
5.14.2 Activity Indicator
The activity indicators for wet kiln/cooler and wet dryer/grinder used in cement manufacturing were
assumed to be equal. The value used was the sum of two categories: "wet" clinker produced and "both"
clinker produced, reported in Reference la or Reference 2a. The activity indicators for dry kiln/cooler
and dry dryer/grinder were both estimated to be the sum of "dry" clinker produced and "both" clinker
produced, as reported in Reference 1 a The activity indicator for lead-glass production was assumed to
be zero.
5.14.3 Emission Factor
The emission factors for cement manufacturing processes were obtained from Reference 3a. The
emission factor for glass production was obtained from Reference 3b.
5.14.4 Control Efficiency
The control efficiencies for the wet and dry kiln/cooler used in cement manufacturing for the years
1970 through 1984 were equivalent to the TSP control efficiencies for kilns. The control efficiencies for
the wet and dry dryer/grinders for the years 1970 through 1984 were equivalent to the TSP control
efficiencies for grinders. These TSP control efficiencies were derived from Reference 4 or Reference 5
using Equation 5.14-1. All control efficiencies for the years following 1984 were assumed constant.
5-36
-------
where: CE = control efficiency
UE = emissions before control
AE = emissions after control
No contro 1 efficiencies were applied to activity data to estimate emissions from lead-glass
production.
5.14.5 References
1. Minerals Industry Surveys, Cement. US Geological Survey (formerly Bureau of Mines),
Washington, DC. Monthly.
a. Table entitled, "Clinker Produced and Fuel Consumed by the Portland Cement Industry."
2. Minerals Yearbook, Cement. US Geological Survey (formerly Bureau of Mines), Washington,
DC. Annual
a. Table entitled, "Clinker Produced and Fuel Consumed by the Portland Cement Industry in the
U.S. byprocess."
3. Compilation of Air Pollutant Emission Factors, Fourth Edition, Supplements A through D, AP-42.
U.S. Environmental Protection Agency, Research Triangle Park, NC. September 1991.
a. Table 8.6-1
b. Table 8.13-1
4. Standard Computer Retrievals, AFP650 report, from the AIRS Facility Subsystem. Unpublished
computer reports. National Air Data Branch, Office of Air Quality Planning and Standards, U.S.
Environmental Protection Agency, Research Triangle Park, NC. Annual.
5. Standard Computer Retrievals, NE257 report, from the National Emissions Data System (NEDS).
Unpublished computer reports. National Air Data Branch, Office of Air Quality Planning and
Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. Annual.
5-37
-------
5.15 OTHER INDUSTRIAL PROCESSES - MISCELLANEOUS INDUSTRIAL PRODUCTS:
07-10
The emissions for this Tier II category were determined by the Lead Emissions Methodology for the
following source categories (see table 5.1-1 for Tier correspondence):
Category:
Industrial Processes - Lead Emissions
Subcategory:
Miscellaneous Process Sources [Lead Alkyl
Production (electrolytic process), Sodium Lead
Alloy (recovery furnace, TEL process vents, TML
process vents, and sludge pits), and Miscellaneous
Products (ammunition)]
5.15.1 Technical Approach
The lead emissions included in this Tier category were the sum of the emissions from the source
categories listed above. Emissions were estimated from an activity indicator, emissions factor, and
control efficiency, where applicable. In order to utilize these values in the Trends spreadsheets, activity
indicators were expressed in thousand tons and emission factors were expressed in metric pounds/tons.
All control efficiencies were expressed as dimensionless fractions.
The following procedures for determining activity indicators, emission factors, and applicable
control efficiencies were used for the years 1970 through 1995.
5.15.2 Activity Indicator
The activity indicator for lead alky 1 production by the electrolytic process was based on the quantity
of lead consumed in anti-knock manufacturing obtained from Reference la. This quantity of lead was
converted to a quantity of additive by multiplying by 1.76. The activity indicator for this category was
assumed to be 10 percent of the quantity of additive consumed based on Reference 2. As of 1992, it was
assumed that there were no producers of lead alkyl products in the United States. All emissions after
1992 for this category are zero.
The activity indicator for sodium lead alloy production processes was based on the remaining 90
percent of the quantity of additive consumed as determined above for lead alkyl production. The activity
for recovery furnaces and sludge pits was assumed to be equal to the remaining quantity of additive. The
activity of TEL (TetraEthyl Lead) process vents and TML (TetraMethyl Lead) process vents was 63
percent and 37 percent, respectively, of the remaining quantity of additive. These apportionments were
based on Reference 2. As of 1992, it was assumed that there were no producers of sodium lead alloy
products in the US. All emissions after 1992 for this category are zero.
The activity indicator for ammunition production was the sum of lead consumption for the following
uses: (1) caulking lead (building construction), (2) total pipes, traps, and other extruded products, (3)
total sheet lead, and (4) other metal pro ducts. The consumption information was obtained from
Reference 1.
5-38
-------
5.15.3 Emission Factor
The emission factors for lead alkyl and sodium lead alloy production processes were obtained from
Reference 3a. The emission factors for ammunition production were obtained from Reference 3b.
5.15.4 Control Efficiency
The control efficiencies for ammunition production were obtained from Reference 2. No control
efficiencies were applied to estimate emissions from the other sources included in this Tier II category.
5.15.5 References
1. Minerals Yearbook, Lead. U.S. Geological Survey (formerly Bureau of Mines), Washington, DC.
Annual.
a. Table entitled, "U.S. Consumption of Lead, by Product."
2. Control Techniques for Lead Air Emissions, Volumes 1 and 2. U.S. Environmental Protection
Agency, Research Triangle Park, NC. December 1977.
3. Compilation of Air Pollutant Emission Factors, Fourth Edition, Supplements A through D, AP-42.
U.S. Environmental Protection Agency, Research Triangle Park, NC. September 1991.
a. Table 5.22-1
b. Table 7.17-1
5-39
-------
5.16 WASTE DISPOSAL AND RECYCLING : 10-01
The emissions for this Tier II category were determined by the Lead Emissions Methodology for the
following source categories (see table 5.1-1 for Tier correspondence):
Category: Subcategory:
Solid Waste Disposal Incineration (Municipal, Residential,
Commercial/Institutional, and Conical
Woodwaste)
5.16.1 Technical Approach
The lead emissions included in this Tier category were the sum of the emissions from the source
categories listed above. Emissions were estimated from an activity indicator and an emissions factor. In
order to utilize these values in the Trends spreadsheets, activity indicators were expressed in million tons
and emission factors were expressed in metric pounds/thousand tons.
The following procedures for determining activity indicators, emission factors, and applicable
control efficiencies were used for the years 1970 through 1995.
5.16.2 Activity Indicator
The activity indicator for municipal incineration was the sum of the operating rates for the SCCs 5-
01-001-01 and 5-01-001-02 obtained from Reference 1 or 2. The activity for 1995 was calculated by
multiplying the 1990 activity by the ratio of 1995 combustion to 1990 combustion from Reference 3.
The activity indicator for residential incineration was the operating rate for residential on-site
incineration obtained from Reference 4. The activity for 1995 and 1996 was calculated by multiplying the
1994 activity obtained from reference 4 by the ratio of 1994 activity to 1995 or 1996 activity obtained
from Reference 5.
Commercial/industrial incineration was based on the sum of the operating rates provided in
Reference 1 or 2 for the following SCCs: 5-02-001-01, 5-02-001-02, 5-03-001-01, and 5-03-001-02.
The previous year's activity data reported in the Trends spreadsheet was scaled based on the ratio of the
total operating rate for the current year to the total for the previous year. This calculation is shown in
Equation 5.16-1.
/
£U0&
(Eq. 5.16-1)
sccs°s'->
5-40
-------
where: A = activity indicator
I = year
OR = operating rates for SCCs 5-02-001-01, 5-02-001-02, 5-03-001-01, and 5-03-001-02
The activity for commercial/industrial incineration for the years 1995 and 1996 was calculated by
multiplying the 1994 activity obtained from Reference 1 by the ratio of 1994 emissions to 1995 or 1996
emissions obtained from Reference 5.
The activity indicator for conical woodwaste incineration was the sum of the operating rates for the
SCCs 5-02-001-05 and 5-03-001-05 obtained from Reference 1 or 2.
5.16.3 Emission Factor
The emission factors for municipal, residential, and commercial/institutional incineration were
obtained from Reference 6a or Reierence 7a.
The emission factor for conical woodwaste incineration (SCC 5-02-001-05) was assumed to be zero.
5.16.4 Control Efficiency
The control efficiency associated with municipal incineration was obtained from Reference 1 or 2 for
SCC 5-01-001.
No control efficiencies were applied to the activity data to estimate emissions from the remaining
types ofincineration (ie., residential, commercial/institutional, and conical woodwaste).
5.16.5 References
1. Standard Computer Retrievals, AFP650 report, from the AIRS Facility Subsystem. Unpublished
computer reports. National Air Data Branch, Office of Air Quality Planning and Standards, U.S.
Environmental Protection Agency, Research Triangle Park, NC. Annual.
2. Computer Retrieval, NE257 report, by Source Classification Code (SCC) from the National
Emission Data System (NEDS). Unpublished computer report. National Air Data Branch, Office of
Air Quality Planning and Standards, U. S. Environmental Protection Agency, Research Triangle
Park,NC. February 9, 1980.
3. Characterization of Municipal Solid Waste in the United States. (1996 Update) Municipal and
Industrial Solid Waste Division, U. S. Environmental Protection Agency, Washington, DC. June
1997.
4. Computer Retrieval, NE260 report, by Source Classification Code (SCC) from the National
Emission Data System (NEDS). Unpublished computer report. National Air Data Branch, Office of
Air Quality Planning and Standards, U. S. Environmental Protection Agency, Research Triangle
Park,NC. February 9, 1980.
5-41
-------
5. National Emission Trends Report. Draft Report. Prepared by E.H. Pechan and Associates, Inc.
under contract No. 68-D3-0035, work assignment III-102 for Emission Factor and Inventory Group,
U.S. Environmental Protection Agency, Research Triangle Park, NC. September 1997.
6. Compilation of Air Pollutant Emission Factors, Fourth Edition, Supplements A through D, AP-42.
U.S. Environmental Protection Agency, Research Triangle Park, NC. September 1991.
a. Table 2.1-1.
7. Compilation of Air Pollutant Emission Factors, Third Edition, Supplements 1 through 14, AP-42.
NTIS PB-275525. U.S. Environmental Protection Agency, Research Triangle Park, NC.
September 1977.
a. Appendix E
5-42
-------
5.17 ON-ROAD VEHICLES: 11
The emissions for all Tier II categories under this Tier I category were determined by the Lead
Emissions Methodology for the following source categories (see table 5.1-1 for Tier correspondence):
Category: Subcategory:
On-road vehicles Gasoline (leaded, unleaded)
5.17.1 Technical Approach
The lead emissions included in these Tier II categories were the sum of the emissions from the
source categories listed above. Emissions were estimated from an activity indicator and an emissions
factor. In order to utilize these values in the Trends spreadsheets, activity indicators were expressed in
million gallons and emission factors were expressed in metric pounds/gallons. The total lead emissions
for the Tier I category were allocated to the Tier II categories by the relative fraction of vehicle miles
traveled (VMT) for the appropriate vehicle types.
The following procedures for determining activity indicators, emission factors, and allocation to the
Tier II categories were used for the years 1970 through 1996.
5.17.2 Activity Indicator
The activity indicator for On-road vehicles was the gasoline consumption by all On-road vehicles as
reported in Reference la If this consumption value was not available, the previous year's consumption
was adjusted based on the vehicle miles traveled (VMT) obtained from Reference 2a using Equation
5.17-1:
VMT,
(Eq. 5.17-1)
where: GC = total gasoline consumption by all On-road vehicles
I = year of interest
VMT = vehicle miles traveled
The percentage of total unleaded gasoline was obtained from Reference 3 a, and this value was
applied to the total consumption of gasoline, resulting in unleaded gasoline use. This procedure was
repeated to obtain leaded gasoline activity.
5.17.3 Emission Factor
The lead emission factors for On-road vehicles were reported in Reference 4 to be 1.5(Y) Ib/ton,
where Y is the number of grams of lead/gasoline. Y values are shown in Table 5.17-1. The values for Y
were obtained from Reference 5.
5-43
-------
5.17.4 Control Efficiency
No control efficiencies were applied to activity data to estimate emissions from On-road vehicles.
5.17.5 Allocation of Emissions to the Tier II Categories
The total lead emissions were the sum of the emissions from leaded gasoline and from unleaded
gasoline. Lead emissions from these two types of gasolines were calculated by multiply ing the activity
indicator by the emission factor. In order to allocate the total lead emissions to the Tier II categories, the
relative fraction of the VMT for each of the three vehicle classifications was determined. The VMT data
for this purpose were obtained from a variety of sources. Relative VMT fractions used for the years
1940 through 1993 for each of the vehicle classifications are given in Table 5.17-2.
5.17.6 References
1. On-road vehicles Statistics. Federal On-road vehicles Administration, U.S. Department of
Transportation, Washington, DC. Annual.
a. Table MF-21, "Motor Fuel Use"
2. Welty, K. On-road vehicles Information Management, Federal On-road vehicles Administration, US
Department of Transportation, personal communications withE.H. Pechan and Associates, Inc.,
Durham, NC, 1997. (Information received on floppy diskette.)
3. Petroleum Supply Annual. DOE/EIA-0340(xx/07). Energy Information Administration, U.S.
Department of Energy, Washington, DC. Annual.
a. Table entitled, "Finished Motor Gasoline Supply and Disposition."
4. Control Techniques for Lead Air Emissions, Volumes 1 and 2. U.S. Environmental Protection
Agency, Research Triangle Park, NC. December 1977.
5. Motor Gasolines. National Institute for Petroleum and Energy Research, IIT Research Institute,
Barltesville, OK. Summer 1987 and Summer 1990.
5-44
-------
Table 5.17-1. Number of Grams of Lead/Gasoline (Y)
Year
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Leaded Gasoline
2.43
2.59
2.63
2.2
2.07
1.82
2.02
2.03
1.76
1.76
1.33
1.01
1.02
0.83
0.84
0.59
0.37
0.15
0.15
0.08
0.08
0.0002
0.0002
0.0002
0.0002
0.0002
0.0002
Unleaded
Gasoline
NA
NA
NA
0.014
0.014
0.014
0.014
0.014
0.01
0.016
0.028
0.009
0.005
0.003
0.006
0.002
0.002
0.001
0.001
0.002
0.0004
0.0002
0.0002
0.0002
0.0002
0.0002
0.0002
5-45
-------
Table 5.17-2. Relative VMT Fractions for Each Tier II Category
Light-Duty Gas Vehicles Light-Duty
Year and Motorcycles Gas Trucks
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
0.83
0.83
0.82
0.82
0.82
0.82
0.81
0.80
0.80
0.79
0.78
0.76
0.79
0.78
0.77
0.76
0.75
0.74
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.13
0.13
0.14
0.14
0.15
0.15
0.16
0.17
0.17
0.18
0.19
0.21
0.19
0.20
0.21
0.22
0.23
0.24
0.24
0.24
0.24
0.24
0.24
0.24
0.24
0.24
0.24
Heavy-Duty Gas
Trucks
0.04
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
5-46
-------
5.18 NON-ROAD ENGINES AND VEHICLES - NONROAD GASOLINE: 12-01
The emissions for this Tier II category were determined by the Lead Emissions Methodology for the
following source categories (see table 5.1-1 for Tier correspondence):
Category:
Other Non-road engines and vehicles
Vessels
Aircraft
Subcategory:
Gasoline (Farm Tractors, Other Farm Equipment,
construction, Snowmobiles, Small Utility Engines,
Heavy Duty General Utility Engines,
Motorcycles)
Gasoline
Aviation Gasoline
5.18.1 Technical Approach
The lead emissions included in this Tier category were the sum of the emissions from the source
categories listed above. Emissions were estimated from an activity indicator and an emissions factor. In
order to utilize these values in the Trends spreadsheets, activity indicators were expressed in million
gallons and emission factors were expressed in metric pounds/thousand gallons.
The following procedures for determining activity indicators, emission factors, and applicable
control efficiencies were used for the years 1970 through 1995.
5.18.2 Activity Indicator
The activity indicator for gasoline-powered farm tractors was based on the 1973 gasoline
consumption by farm tractors reported in Reference 1. The adjustment factor applied to the 1973 data
was the ratio of the quantity of gasoline consumed by all agricultural equipment in 1973 and in the year
under study as reported in Reference 2a It is assumed that this procedure was used for the years both
before 1973 and after 1973. Equation 5.18-1 summarizes this procedure.
Tractor, i
GC
Agriculture, i
Tractor, 1973
Agriculture, 1973
(Eq. 5.18-1)
where: GC = gasoline consumption
I = year under study
The activity indicator for other gasoline-powered farm equipment was also based on gasoline
consumption. It was assumed that the gasoline consumption by other farm equipment was equivalent to
8.52 percent of the quantity of gasoline consumed by farm tractors as determined by the preceding
5-47
-------
procedure. Activity for other farm equipment is considered zero for the year 1991 and all subsequent
years.
The activity indicator for gasoline-powered construction equipment was the total gasoline
consumption by construction equipment as reported in Reference 2.
Activity data for snowmobiles were based on the 1973 gasoline consumption by snowmobiles, as
reported in Reference 1. An adjustment factor was applied to the 1973 value to account for the ratio of
the number of snowmobile registrations in 1973 and in the year under study as reported in Reference 3.
It is assumed that this procedure was used for the years both before 1973 and after 1973. Equation 5.18-
2 summarizes this procedure.
N
* — CT1 x Snowmobiles, i
Snowmobiles, i ~ ^^Snowmobiles, 1973 T7 (Eq. 5.18-2)
Snowmobiles, 1973
where: GC = gasoline consumption
I = year under study
N = number of registered vehicles
Activity data for small utility gasoline engines was based on the 1980 value for gasoline consumption
by small engines (533 x 106 gallons). An adjustment factor was applied to the 1980 data to account for
the ratio of the number of single unit dwellings in 1980 and in the year under study. The number of
single unit dwellings in 1980 was obtained from Reference 4. For the year under study, the number of
single unit dwellings was estimated by adding or subtracting the number of new one-family structures
started each year between 1980 and the year under study to the number of single unit dwellings in 1980.
The number of new one- family structures started was obtained from Reference 5 for each year. It is
assumed that this procedure was used for the years both before 1973 and after 1973. Equation 5.18-3
summarizes this procedure.
Single Unit Dwellings
Single Uni,
where: GC = gasoline consumption
I = year under study
The activity indicator for heavy duty general gasoline utility engines was the total gasoline consumed
by the industrial/commercial category obtained from Reference 2.
The activity indicator for motorcycles was calculated from the number of motorcycles, the average
annual Non-road engines and vehicles mileage traveled, and the median estimated average miles per
gallon The motorcycle population and the Non-road engines and vehicles mileage were obtained from
Reference 6. The average miles per gallon (MPG) was assumed to be 44.0 miles/gallon. Activity for
motorcycles was considered zero for the year 1995 and all subsequent years because no leaded gasoline
was consumed by motorcycles after this year. Equation 5.18-4 summarizes this calculation.
5-48
-------
1 \oior
~ MPG
= AT x Motorcyles, Off-highway ^ ^ i o /i\
'Motorcycles ^Motorcycles , fn/~, ^r,q. O.lO-t;
where: GC = gasoline consumption
N = number of motorcycles
M = mileage
MPG = miles/gallon
The activity indicator for aircraft was the total national quantity of aviation gasoline supplied as
reported in Reference 7 a, Reference 8a, or Reference 9a. Reference 7a was used for the years 1970
through 1978. Reference 8a was used for the years 1979 and 1980. Reference 9a was used for the years
1981 through 1995.
5.18.3 Emission Factor
The lead emission factor for the combustion of gasoline in Non-road engines and vehicles was
reported in Reference 10 to be 1.5(Y) Ib/ton, where Y is the number of grams of lead/gasoline. It was
assumed that all gasoline used for these engines was leaded. The value of Y was obtained from
Reference 11 for the years 1970 to 1988 and Reference 12 for the years 1989 to 1996.
The lead emission factor for aircraft was reported in Reference 13 to be the lead content of aviation
gasoline multiplied by the percent of lead emitted. Therefore, the emission factor is 2g/gal times 0.75.
5.18.4 Control Efficiency
No control efficiencies were applied to activity data to estimate emissions from Non-road engines
and vehicles.
5.18.5 References
1. Exhaust Emissions from Uncontrolled Vehicles and Related Equipment Using Internal Combustion
Engines. U.S. Environmental Protection Agency. Prepared by Southwest Research Institute, San
Antonio, TX, under Contract No. EHS-70-108. October 1973.
2. On-road vehicles Statistics. Federal On-road vehicles Administration, U.S. Department of
Transportation, Washington, DC. Annual.
a. Table MF-24
3. International Snowmobile Industry Association, 7535 Little River Turnpike, Suite 330, Annandale,
VA.
4. American Housing Survey, Current Housing Reports, Series H-l50-83. Bureau of the Census, U.S.
Department of Commerce, Washington DC. Biennial.
5-49
-------
5. Survey of Current Business. Bureau of Economic Analysis, U.S. Department of Commerce,
Washington, DC.
6. 19xx Motorcycle Statistical Annual. Motorcycle Industry Council, Inc., Costa Mesa, CA. Annual.
7. Annual Energy Review. DOE/EIA-0384(xx). Energy Information Administration, U.S. Department
of Energy, Washington, DC. Annual.
a. Table Entitled, "Petroleum Products Supplied to the Transportation Sector, Electric Utilities,
and Total, 1949-19xx."
8. Energy Data Report. DOE/EIA-0109(80/12). Energy Information Administration, U. S. Department
of Energy, Washington, DC. Annual.
a. Table entitled, "Comparative Supply of Disposition Statistics."
9. Petroleum Supply Annual. DOE/EIA-0340(xx/07). Energy Information Administration, U.S.
Department of Energy, Washington, DC. Annual.
a. Table Entitled, "U.S. Supply, Disposition, and Ending Stocks of Crude Oil and Petroleum
Products, 19xx."
10. Control Techniques for Lead Air Emissions, Volumes 1 and 2. U.S. Environmental Protection
Agency, Research Triangle Park, NC. December 1977.
11. Gray, C.L. Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency.
"Transmittal of Revised Lead Mobile Source Emission Factors." Internal Memorandum to D.
Tyler.
12. Motor Gasolines. National Institute for Petroleum and Energy Research, IIT Research Institute,
Barltesville, OK. Summer 1987 and Summer 1990.
13. Locating and Estimating Air Emissions from Sources of Lead and Lead Compounds. Draft Report.
U.S. Environmental Protection Agency, Research Triangle Park, NC, July 1996.
-------
SECTION 6.0
OVERVIEW OF PROJECTION METHODS USED BY EPA
The EPA projects emissions for many reasons. Typically, the reason is to evaluate benefits or
determine cost-effective control strategies of potential regulations and policies. The purpose of the
EPA's emission projection may influence the methodology selected and the data that is developed. If
control cost analyses will be performed, source-specific information may need to be retained. If the
projected inventory will be used in grid-based air quality models, source-specific information, including
location and stack parameters, is required. Other efforts such as benefits analysis, may also require
county/source category level information so that emissions can be aggregated for projection purposes.
The EPA's National Emission Inventory (NEI) of criteria and toxic air pollutants is generally the starting
point for EPA's projected inventories. A recent example includes EPA projection inventories to support
evaluation and analysis of controls for onroad mobile and nonroad mobile emission source sectors under
the Tier 2 Tailpipe rulemaking.
The purpose of this chapter is to describe information and procedures that EPA uses in projecting air
pollutant emissions for various regulatory purposes. Included in this discussion is general information for
projecting future emissions for the following sectors: point, area, onroad mobile, and nonroad mobile. A
more thorough description of some of the methods noted maybe found in projection guidance documents
by the Emission Inventory Improvement Program (EIIP) located at http://www.epa.gov/ttn/chief/eiip.
EPA generally includes documentation with each projection inventory to describe specific assumptions,
models, etc. that were used for a particular analysis.
6.1 EMISSION PROJECTIONS
The goal in developing emission projections is to account for as many of the important variables that
affect future year emissions as possible. Emission projections are a function of change in activity (growth
or decline) combined with changes in the emission rate or controls applicable to the source. To a large
extent, projection inventories are based on forecasts of industrial growth, population growth, changes in
land use patterns, and transportation growth. Changes in emission rates can be influenced by such causes
as technological advances, environmental regulations, age or deterioration of process and control
equipment, how the source is operated and maintained, and fuel formulations.
In general, stationary point and area source projections are based on the following equation:
Efy = Eby*G*C (Eq. 6.1-1)
where: Efy = projection year emissions
Eby = base year emissions
G = growth factor
C = control factor, accounting for changes in emission factors or controls
For onroad and nonroad mobile sources, the general equation is:
Efy = Aby*G*F (Eq. 6.1-2)
6-1
-------
where: Efy = projection year emissions
Aby = base year activity
G = growth factor
F = projection year emission factor
In equation 6.1-2, the projection year emission factor accounts for the effect of any new regulations as
well as technological changes.
There are complicating issues which go beyond the parameters explained in these two equations, so
a specific projection calculation should be developed for each sector. For example, within the point
source sector, industry growth and the addition of new plants are often accompanied by the retirement of
aging facilities. Projections should reflect this because net growth can only be determined after
retirement is defined, and emission rates often differ for the new sources that replace existing ones. Other
sectors may also require such adjustments to the generalized equations listed above.
6.2 GROWTH FACTORS
The growth factor accounts for changes (increases or decreases) in the emissions-generating activity.
In selecting growth factors, the most important considerations are how closely the surrogate data
approximates or relates to changes in the emission-generating activity; how closely it relates to the
activity indicator used to develop the base year emissions; and the locality (how well it characterizes the
activity in the area of interest versus a larger geographical area). Potential growth indicators include
employment, earnings, value added, and product output. Each of these growth indicators are described in
more detail in the EIIP projection/guidance documents located at http://www.epa.gov/ttn/chief/eiip.
6.2.1 Growth Data Sets Used by EPA
The data used to project activity growth depend on the sector of analysis. Onroad mobile
projections often use VMT data. EPA generally bases point and area source projections on U.S.
Department of Commerce's Bureau of Economic Analysis (BEA), Economic Growth Analysis System
(EGAS), or Regional Economic Models, Inc. (REMI) data. Table 6.2-1 contains references for several
data sets containing regional-level forecasts of growth.
Future changes in activity level will be the result of complex interactions between human population
growth, changes in national and local economic factors, and changes in the markets for the sector being
examined and the products it produces. Historically, EPA has often used projections of economic
indicators as surrogates for growth in activity for the purpose of estimating future emissions. In addition
to the data sets above, projections based on historical economic time-series data are also used. The most
simplistic method is through extrapolations of the historic data. Projections based on historic
extrapolations capture long-term trends and may not accurately represent year-to-year fluctuations in
activity. Projections of economic activity should be carried out using accepted statistical and economic
techniques, such as multiple regression analysis, moving averages, or autoregression.
6-2
-------
Table 6.2-1. Projection (Growth) Resources
Resource
Where To Go
Brief Description
National
Economic Growth Analysis System
(E-GAS)
http://www.epa.gov/ttn/chief/
ei data.html#EGAS
Provides emission growth factors
based on various methods and data
sources, including regional
economic models.
BEA Data (from U.S. Department
of Commerce's Bureau of
Economic Analysis)
http://www.bea.doc.gov/
BEA's national and regional
economic accounts present basic
information on issues such as U.S.
economic growth and regional
economic development.
Standard & Poor's DRI Regional
Economic Service
http://www.dri.mcgraw-hill.com/
regional/index.htm
Standard & Poor's forecasts of key
economic and demographic
concepts for 50 states, 310
metropolitan areas, and over 3000
counties, along with U.S. regional
models which provide current
projections of interest rates, GDP,
inflation, and other economic
indicators.
DOE/EIA's Annual Energy Outlook
http://www.eia.doe.gov/oiaf/
forecasting.html
Overview forecasts of annual
energy supply, demand, and prices
based on results from ElA's National
Energy Modeling System (NEMS).
Site also contains information on
climate change and other
projections.
WE FA
http://www.wefa.com/
Provides data at the state, MSA,
county, and census tract level for
the United States. Statist! cs range
from general macroeconomic
indicators to company-specific
detail.
Regional Economic Models, Inc.
(REMI)
http://www.remi.com/
REMI constructs regional and
national economic forecasting
models; REMI models are included
within EGAS.
The use of economic indicators to predict growth in an emissions sector has its drawbacks.
Economic indicators generally predict growth for broad economic sectors, and therefore cannot identify
trends within individual emission sectors. Another drawback is that economic indicators may not be able
to adequately predict the effects of substitution of equipment for labor in the market.
6.3 CONTROL FACTORS/EMISSION FACTORS
Control strategy projections are estimates of future year emissions that also include the expected
impact of modified or additional control regulations. To the extent possible, EPA incorporates the effect
of future scheduled regulations in their control strategy projections. Future year emissions may also be
6-3
-------
affected by fuel switching, fuel efficiency improvements, improvements in performance due to economic
influences, or any occurrence that alters the emissions-producing process. Programs other than those
aimed at reducing the emissions of the criteria pollutants of interest may affect future year emissions.
These may include energy efficiency programs, pollution prevention programs, and greenhouse gas or
global warming initiatives. These programs generally are reflected in the projections through the future
year control factor, emission factor, or in some cases, by adjusting the activity growth forecast.
6.3.1 Conditions & Influences on Determining Controls and Emission Rates
Several conditions are accounted for when developing control strategy information. Control factors
and emission factors vary by source category and are continuously being revised and improved based on
field and laboratory measurements. Future year control factors or emission factors are examined in
relation to the base year values to ensure any existing controls are not double-counted by taking
additional credit in the future year, noting that the control factor and/or emission factor may also be a
weighted composite. For mobile sources where emission factors are generally used in the projections,
models are available which calculate the future year emission factor (i.e., EPA's NONROAD and
MOBILE models).
In determining the future year control factor or emission factor, three basic parameters are
quantified: regulation control, rule effectiveness (RE), and rule penetration (RP). Regulation control is
the level of reduction expected from full compliance with a control measure. Rule effectiveness accounts
for the level of expected compliance with the regulation Rule penetration indicates the fraction of
emissions within a source category which are subject to the regulation, accounting for size cutoffs and
other exemptions.
When accounting for regulation control, RE, and RP, the control factor can be described as:
C = 1 - [(RC/100) * (RE/100) * (RP/100)] (Eq. 6.3-1)
where: C = control factor
RC = regulation control
RE = rule effectiveness
RP = rule penetration
More than one control measure can affect the emissions in a single emission category. The
methodology addressing the effects of multiple control measures must reflect each control measure's
level of control, and how many pollutants will be affected. In some cases, a new measure can be adopted
on top of the control measure already in place, leading to a greater combined emission reduction In
these cases, it is imperative that any reductions credited by the new control measure configuration reflect
the emission reductions due to new controls. In all cases, emission reductions should be correctly
assigned to the control measures.
6.4 MEASURING THE EFFECTIVENESS OF CONTROL PROGRAMS
The previous sections discussed the two major elements of an emission projection: the growth or
activity projection and the future year control or emission factor. It is important to note that control
programs may in some cases affect either or both of these parameters. Installing a control device or
6-4
-------
making other modifications may impact the control factor. Nonroad engine standards may impact the
future year emission factor. Seasonal bans may reduce the level of emission generating activity and,
therefore, should be incorporated into the projection by either adjusting the activity projection, or
including a control factor. Efficiency improvements in a plant may reduce the need for steam, thereby
reducing the amount of fuel which must be burned to supply the steam. This can also be reflected
through adjustments to the activity projection, or through the use of a control factor which will account
for the reduced fuel usage (though the former is preferred).
In many cases, it is also necessary to account for multiple programs which affect the same source
category. Industrial boiler emission projections may be affected by both new regulations requiring the
installation of controls as well as efficiency improvements. Onroad mobile emissions may be affected by
tailpipe standards, inspection and maintenance programs, as well as transportation initiatives aimed at
reducing vehicle miles traveled. Therefore, expected controls should be calculated for each action and
applied appropriately based on their implementation dates.
Other programs are complex, and determining appropriate control factors or adjustments to activity
forecasts for specific source categories is not straightforward. For example, initiatives such as the EPA
Green Lights program are aimed at reducing energy use by reducing electricity demand. This, in turn, is
tied to reductions in emissions from individual utility boilers. Emission caps or allowance programs set
overall constraints on future emission levels, but this must also be translated into reductions at individual
units inmost cases. For trading programs, a simplified approach may be used to constrain emissions at
individual units to the level used to calculate the emission budget. More complex approaches would
examine how individual units will respond - by controlling emissions or purchasing credits.
6.5 USE OF SCCS AND SICS TO ASSOCIATE GROWTH AND CONTROL INFORMATION
The EPA's Source Classification Code value (SCC) is a key emission inventory field used in
developing emission projections. SCCs describe the types of processes within each point, area, nonroad,
and onroad mobile source sector. The SCCs are used to link the type of emission process controls, and
may also be used to identify appropriate emission growth factors. The latest posted SCC code lists are
available in various format s at: http://www .ep a.gov/ttn/chief/scccodes.html.
Another key emission inventory field used in developing emission projections are the Standard
Industrial Classification (SIC) (and NAICS) codes which are published by the U.S. Office of Management
and Budget (OMB). These codes describe the type of activity in which businesses are engaged. SIC
codes identify establishments using a coding system that ranges from 2 to 4 digits. SIC codes indicate the
type of industry and are often used in selecting appropriate growth factors.
Some area and mobile source categories do not have associated SIC codes. For these categories,
surrogates such as population, vehicle miles traveled (VMT), and engine populations are used to estimate
activity growth. For certain area source categories, such as wood furniture surface coating, the link
between SCCs and SIC codes is straightforward. Others, such as open top vapor degreasing, may be a
combination of several industries, so the link is not straightforward and may require using surrogate data
representing a cross-section of industries.
The EPA national point source inventory generally includes SIC codes for individual plants and
points. In cases where SIC codes are not provided, SCCs may be linked to SIC code forecast data. As
6-5
-------
for area sources, there is no perfect mapping scheme between the SCC and SIC code, particularly for the
industrial fuel combustion SCCs, which can be associated with many industries.
The OMB has recently produced a new industry classification system. The North American Industry
Classification System (NAICS) identifies industries by NAICS codes, which are defined using a 6-digit
coding system. Because of the larger number of digits, the NAICS accommodates more sectors and
provides additional flexibility in designating subsectors versus the SIC system. Although Federal
government agencies are in the process of transitioning from the SIC system to the NAICS, the EPA's
National Emission Inventory currently contains only SIC code information. The NAICS web site
provides information on this new classification system: http://www.naics.com. The following site
provides links to pages maintained by OSHA and other agencies regarding the SIC codes and their
replacement NAICS codes: http://www. epa.gov/ttn/chief/eiip/eicrepts.htm#techpapers.
6.6 OTHER CONSIDERATIONS
There are several other factors which should be considered in performing emission projections,
particularly when air quality modeling will be performed using the projection. This includes potential
changes in the spatial, temporal, and/or speciation profiles of the emissions. Additional information on
spatial, temporal, and speciation considerations can be found in respective EIIP emission inventory
development documents (http://www.epa.gov/ttn/chief/eip/techrep.htm).
6.6.1 Spatial Considerations
In performing emission projections, it is important to account for any geographic shifts in emissions.
Changes in land use patterns may lead to shifts in the location of emissions or may result in higher growth
in some areas as opposed to others.
Changes in land use patterns may also influence the types of sources emitting in an area For
example, suburbanization of rural areas may result in decreases in the agricultural sector activities and
increases in activity of population-based emission sources such as lawn and garden equipment, consumer
solvents, and highway vehicles.
6.6.2 Temporal Considerations
The temporal profile (when the pollution is emitted, including seasonal, monthly, daily, and hourly
differences) is important, because meteorology also impacts the dispersion of pollution and the chemical
transformations to species of concern (ozone, fine particles). Control strategies should be reviewed to
determine whether any will have a seasonal impact, or result in shifts in the time period of emissions.
6.6.3 Speciation Considerations
Emission modeling systems speciate criteria pollutant emissions. VOC emissions are dispersed into
many different compounds with varying degrees of reactivity. In projecting emissions, changes in fuel
and solvent formulations should be reviewed to identify changes in the projection year speciation profiles.
Changes may be the result of regulations such as the control of toxic pollutants (especially VOC) or
economic incentives (e.g., cost of solvents).
6-6
-------
6.7 QUALITY ASSURANCE
The functions of quality assurance during development of projection inventories include the
following:
1. Ensure reasonableness of the emission projections and data used,
2. Ensure validity of the assumptions and methods used,
3. Ensure mathematical correctness (e.g., ensure calculations were performed correctly),
4. Ensure valid data were used,
5. Assess the accuracy of the estimates.
Projected emissions are generally compared with base year emissions to identify any anomalies that
might indicate calculation or data errors, and to verify reasons for trends towards higher or lower
emissions. For example, if projected emissions are lower than those in the base year, the activity
projection data may be examined versus the change in projection year emission factors, to ensure that the
magnitude of these changes support the overall change in emissions from the base year. Comparisons of
contributions of different source categories to total emissions in the base year and in the projection year
are also reviewed and any significant changes investigated and expkined.
6-7
-------
TECHNICAL REPORT DATA
(Please read Instructions on reverse before completing)
1. REPORT NO.
EPA-454/R-01-006
3. RECIPIENT'S ACCESSION NO.
4. TITLE AND SUBTITLE
PROCEDURES DOCUMENT FOR NATIONAL EMISSION
INVENTORY, CRITERIA AIR POLLUTANTS
1985-1999
5. REPORT DATE (of preparation)
March 2001
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
Rebecca Lee Tooly
8. PERFORMING ORGANIZATION REPORT NO.
9. PE RFORMING O RGANIZATIO N NAM E AND ADDRE SS
U. S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
10. PROGRAM ELEMENT NO.
( EMAD / EFIG )
11. CONTRACT/GRANT NO.
68-D7-0067
12. SPONSORING AGENCY NAME AND ADDRESS
Director
Office of Air Quality Planning and Standards
Office of Air and Radiation
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
13. TYPE OF REPORT AND PERIOD COVERED
Final
14. SPONSORING AGENCY CODE
EPA/200/04
15. SUPPLEMENTARY NOTES
Supersedes EPA-454/R-00-002, March 2000
16. ABSTRACT
This document includes methodologies for estimating emissions, for years 1985-1999, for the following
criteria pollutants: carbon monoxide (CO), nitrogen oxides (NOx), lead (Pb), particulate matter less than
10 and 2.5 microns in diameter (PM-10/ PM2.5), sulfur dioxide (SO2), volatile organic compounds
(VOC), and ammonia (NH3). This document does not include data results, only method descriptions.
This document and the data results are distributed electronically on EPA Internet sites.
17.
KEY WORDS AND DO CUMENT ANALYSIS
DESCRIPTORS
b. IDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
6-8
-------
Air Pollution control
18. DISTRIBUTION STATEMENT
Release Unlimited
19. SECURITY CLASS (Report)
Unclassified
21. NO. OF PAGES
405
20. SECURITY CLASS (Page)
Unclassified
22. PRICE
EPA Form 2220-1 (Rev. 4-77)PREVIOUS EDITION IS OBSOLETE
6-9
------- |