Emissions Inventory for Air Quality Modeling Technical Support Document: Heavy-Duty Vehicle Greenhouse Gas Phase 2 Final Rule


Emissions Inventory for Air Quality
Modeling Technical Support Document:
Heavy-Duty Vehicle Greenhouse Gas
Phase 2 Final Rule

£%	United States
Environmental Protect
Agency

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Emissions Inventory for Air Quality
Modeling Technical Support Document:
Heavy-Duty Vehicle Greenhouse Gas
Phase 2 Final Rule
Air Quality Assessment Division
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
and
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
United States
Environmental Protection
^1	Agency
EPA-420-R-16-008
August 2016

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TABLE OF CONTENTS
LIST OF TABLES	HI
LIST OF FIGURES	V
LIST OF APPENDICES	VI
ACRONYMS	VI
1	INTRODUCTION	1
2	2011 EMISSION INVENTORIES AND APPROACHES	4
2.1	2011 NEI POINT SOURCES (PTEGU, PT_OILGAS AND PTNONIPM)	7
2.1.1	EGUsector (ptegu)	8
2.1.2	Point source oil and gas sector (pt oilgas)	9
2.1.3	Non-IPM sector (ptnonipm)	10
2.2	2011 NONPOINT SOURCES (AFDUST, AG, AGFIRE, NP_OILGAS, RWC, NONPT)	11
2.2.1	Area fugitive dust sector (afdust)	11
2.2.2	Agricultural ammonia sector (ag)	16
2.2.3	Agricultural fires (agfire)	17
2.2.4	Nonpoint source oil and gas sector (npoilgas)	17
2.2.5	Residential wood combustion sector (rwc)	18
2.2.6	Other nonpoint sources sector (nonpt)	18
2.3	2011 ONROAD MOBILE SOURCES (ONROAD)	19
2.3.1 Onroad (onroad)	19
2.4	2011 NONROAD MOBILE SOURCES (C1C2RAIL, C3MARINE, NONROAD)	22
2.4.1	Category 1/Category 2 Commercial Marine Vessels and Locomotives and (clc2rail)	22
2.4.2	Category 3 commercial marine vessels (c3marine)	23
2.4.3	Nonroad mobile equipment sources: (nonroad)	25
2.5	"Other Emissions": Offshore Category 3 commercial marine vessels and drilling platforms and non-U.S.
SOURCES	26
2.5.1	Point sources from offshore C3 CMV, drilling platforms, Canada and Mexico (othpt)	26
2.5.2	Area and nonroad mobile sources from Canada and Mexico (othar, othafdust)	27
2.5.3	Onroad mobile sources from Canada and Mexico (othon)	27
2.6	Fires (ptfire)	27
2.7	Biogenic sources (beis)	29
2.8	SMOKE-ready non-anthropogenic inventories for chlorine	31
3	EMISSIONS MODELING SUMMARY	32
3.1	Emis sions modeling Overview	32
3.2	Chemical Speciation	35
3.2.1	VOC speciation	37
3.2.1.1	The combination of HAP BAFM (benzene, acetaldehyde, formaldehyde and methanol) and VOC for VOC speciation37
3.2.1.2	County specific profile combinations (GSPRO_COMBO)	40
3.2.1.3	Additional sector specific details	41
3.2.1.4	Future year speciation	45
3.2.2	PM speciation	49
3.2.3	NOxspeciation	50
3.3	Temporal Allocation	51
3.3.1	Use of FF10 format for finer than annual emissions	52
3.3.2	Electric Generating Utility temporalization (ptegu)	52
3.3.2.1	Base year temporal allocation of EGUs	52
3.3.2.2	Future year temporal allocation of EGUs	56
3.3.3	Residential Wood Combustion Temporalization (rwc)	62
3.3.4	Agricultural Ammonia Temporal Profiles (ag)	66
3.3.5	Onroad mobile temporalization (onroad)	67
3.3.6	Additional sector specific details (afdust, beis, clc2rail, c3marine, nonpt, ptnonipm, ptfire, np oilgas)	73
3.3.7	Additional sector specific details (afdust, beis, clc2rail, c3marine, nonpt, ptnonipm, ptfire, np oilgas)	75
3.4	Spatial Allocation	76
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3.4.1	Spatial Surrogates for U.S. emissions	76
3.4.2	Allocation method for airport-related sources in the U.S.	82
3.4.3	Surrogates for Canada and Mexico emission inventories	82
DEVELOPMENT OF 2040 REFERENCE AND CONTROL CASE EMISSIONS	87
4.1	EGU SECTOR PROJECTIONS: PTEGU	92
4.2	Non-EGU Point and NEI Nonpoint sector projections: afdust, ag, c1c2rail, c3marine, nonpt, np_oilgas,
PTNONIPM, PT_OILGAS, RWC	93
4.2.1	CoST Background: Used for NEI non-EGU Point and Nonpoint sectors	94
4.2.2	CoST Plant CLOSURE packet (ptnonipm, ptoilgas)	98
4.2.3	CoST PROJECTION packets (afdust, ag, clc2rail, c3marine, nonpt, npoilgas, ptnonipm, ptoilgas, rwc) ...99
4.2.3.1	Paved and unpaved roads VMT growth (afdust)	99
4.2.3.2	Livestock population growth (ag)	100
4.2.3.3	Crude Production and Transport, Energy Production for Refineries, Refineries, and Distribution (nonpt, ptnonipm, pt_oilgas)
100
4.2.3.4	Category 3 commercial marine vessels (c3marine, othpt)	102
4.2.3.5	Locomotives and Category 1 & 2 commercial marine vessels (clc2rail, ptnonipm)	103
4.2.3.6	Oil and gas and industrial source growth from 2011 v6.0 NODA (nonpt, np_oilgas, ptnonipm, pt_oilgas)	106
Natural Gas Consumption and Crude Oil Production	112
Other Fuels	112
Oil/Gas Plays	113
Remaining Areas	114
4.2.3.7	Data from comments on previous platforms (nonpt, ptnonipm, pt_oilgas)	117
4.2.3.8	Aircraft (ptnonipm)	119
4.2.3.9	Cement manufacturing (ptnonipm)	120
4.2.3.10	Corn ethanol plants (ptnonipm)	122
4.2.3.11	Residential wood combustion (rwc)	123
4.2.4	CoST CONTROL packets (nonpt, np oilgas, ptnonipm, pt oilgas)	126
4.2.4.1	Oil and gas NSPS (np_oilgas, pt_oilgas)	127
4.2.4.2	RICE NESHAP (nonpt, np_oilgas, ptnonipm, pt_oilgas)	128
4.2.4.3	RICE NSPS (nonpt, np_oilgas, ptnonipm, pt_oilgas)	130
4.2.4.4	ICI Boilers (nonpt, ptnonipm, pt_oilgas)	132
4.2.4.5	Fuel sulfur rules (nonpt, ptnonipm, pt_oilgas)	136
4.2.4.6	Natural gas turbines NOx NSPS (ptnonipm, pt_oilgas)	137
4.2.4.7	Process heaters NOx NSPS (ptnonipm, pt_oilgas)	139
4.2.4.8	Arizona Regional Haze controls (ptnonipm)	141
4.2.4.9	CISWI (ptnonipm)	141
4.2.4.10	Data from comments on previous platforms (nonpt, ptnonipm, pt_oilgas)	141
4.2.5	Stand-alone future year inventories (nonpt, ptnonipm)	142
4.2.5.1	Portable fuel containers (nonpt)	142
4.2.5.2	Biodiesel plants (ptnonipm)	143
4.2.5.3	Cellulosic plants (nonpt)	145
4.2.5.4	New cement plants (nonpt, ptnonipm)	146
4.3	Mobile source projections	148
4.3.1 Onroad mobile (onroad)	148
4.3.1.1	Future activity data	148
4.3.1.2	Setup and Run MOVES to create EFs	149
4.3.1.3	National and California adjustments	150
4.4	Nonroad MOBILE SOURCE PROJECTIONS (NONROAD)	150
4.5	"Other Emissions": Offshore Category 3 commercial marine vessels and drilling platforms, Canada and
Mexico (othpt, othar, othafdust, and othon)	152
EMISSION SUMMARIES	153
REFERENCES	164
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List of Tables
Table 1-1. List of cases in the HDGHG Phase 2 Emissions Modeling Platform 2
Table 2-1. Platform sectors for the 2011 emissions modeling platform 4
Table 2-2. Summary of differences between 201 lv6.2 platform and 201 1NEIv2 emissions by sector 6
Table 2-3. Point source oil and gas sector NAICS Codes 9
Table 2-4. Corn Ethanol Plant Criteria Pollutant Emission Factors (grams per gallon produced) 10
Table 2-5. Toxic-to-VOC Ratios for Corn Ethanol Plants 11
Table 2-6. SCCs in the afdust platform sector 12
Table 2-7. Total Impact of Fugitive Dust Adjustments to Unadjusted 2011 Inventory 13
Table 2-8. Livestock SCCs extracted from the NEI to create the ag sector 16
Table 2-9. Fertilizer SCCs extracted from the NEI for inclusion in the "ag" sector 17
Table 2-10. SCCs in the Residential Wood Combustion Sector (rwc)* 18
Table 2-11. Onroad emission aggregate processes 22
Table 2-12. 201 1NEIv2 SCCs extracted for the starting point in clc2rail development 23
Table 2-13. Growth factors to project the 2002 ECA-IMO inventory to 2011 24
Table 2-14. 2011 Platform SCCs representing emissions in the ptprescfire and ptwildfire modeling sectors28
Table 2-15. Large fires apportioned to multiple grid cells 28
Table 2-16. Meteorological variables required by BEIS 3.61 31
Table 3-1. Key emissions modeling steps by sector 33
Table 3-2. Descriptions of the platform grids 34
Table 3-3. Emission model species produced for CB05 CMAQ MP-Lite* 35
Table 3-4. Integration approach for BAFM and EBAFM for each platform sector 40
Table 3-5. MOVES integrated species in M-profiles 42
Table 3-6. VOC profiles for WRAP Phase III basins 43
Table 3-7. National VOC profiles for oil and gas 43
Table 3-8. Counties included in the WRAP Dataset 44
Table 3-9. Select VOC profiles 2011 vs 2040 46
Table 3-10. Onroad M-profiles 47
Table 3-11. MOVES Process IDs 48
Table 3-12. MOVES Fuel subtype IDs 48
Table 3-13. MOVES Regclass IDs 49
Table 3-14. PM model species: AE5 versus AE6 49
Table 3-15. NOx speciation profiles 50
Table 3-16. Temporal settings used for the platform sectors in SMOKE 51
Table 3-17. Time zone corrections for US counties in 201 lv6.3 platform 75
Table 3-18. U.S. Surrogates available for the 2011 modeling platform 76
Table 3-19. Off-Network Mobile Source Surrogates 78
Table 3-20. Spatial Surrogates for Oil and Gas Sources 78
Table 3-21. Selected 2011 CAP emissions by sector for U.S. Surrogates* 79
Table 3-22. Canadian Spatial Surrogates 82
Table 3-23. CAPs Allocated to Mexican and Canadian Spatial Surrogates 84
Table 4-1. Control strategies and growth assumptions for creating the 2040 reference case emissions
inventories from the 2011 base case 89
Table 4-2. Ptegu upstream adjustments for the 2040 reference and 2040 control cases 93
Table 4-3. Subset of CoST Packet Matching Hierarchy 95
Table 4-4. Summary of non-EGU stationary projections subsections 96
Table 4-5. Reductions from all facility/unit/stack-level closures by modeling sector 99
Table 4-6. Increase in total afdust PM2.5 emissions from VMT projections 99
Table 4-7. NH3 projection factors and total impacts to years 2030 for animal operations 100
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Table 4-8. Petroleum pipelines & refineries and production storage and transport factors and reductions.. 101
Table 4-9. Growth factors to project the 2011 ECA-IMO inventory to 2030 102
Table 4-10. Difference in c3marine sector and othpt C3 CMV emissions between 2011 and 2030 103
Table 4-11. Non-California projection factors for locomotives and Category 1 and Category 2 Commercial
Marine Vessel Emissions 104
Table 4-12. Difference in clc2rail sector emissions between 2011 and future years 105
Table 4-13 Scalars Applied to 2025 C1/C2 Combustion Emissions 105
Table 4-14 Scalars Applied to 2025 Rail Combustion Emissions 106
Table 4-15. Sources of new industrial source growth factor data from the 201 lv6.0 NODA 107
Table 4-16. Summary of "EPA" Projection Approaches for IC Engines/Gas Turbines and ICI Boilers/Process
Heaters 109
Table 4-17. NAICS Codes for which NAICS/SCC-level Growth Factors were developed 110
Table 4-18. AEO Oil/Gas Plays 112
Table 4-19. Industrial source projections net impacts from 201 lv6.0 NODA 116
Table 4-20. Impact of 201 lv6.0 projection factors for Texas 118
Table 4-21. NEI SCC to FAA TAF ITN aircraft categories used for aircraft projections 119
Table 4-22. National aircraft emission projection summary 120
Table 4-23. U.S. Census Division ISMP-based projection factors for existing kilns 122
Table 4-24. ISMP-based cement industry projected emissions 122
Table 4-25. 2011 and 2030 corn ethanol plant emissions [tons] 123
Table 4-26. Non-West Coast RWC projection factors, including NSPS impacts 125
Table 4-27. Cumulative national RWC emissions from growth, retirements and NSPS impacts 125
Table 4-28. Assumed retirement rates and new source emission factor ratios for new sources for various
NSPS rules 127
Table 4-29. NSPS VOC oil and gas reductions from projected pre-control 2030 grown values 128
Table 4-30. Summary RICE NESHAP SI and CI percent reductions prior to 201 1NEIv2 analysis 129
Table 4-31. National by-sector reductions from RICE Reconsideration controls (tons) 130
Table 4-32. RICE NSPS Analysis and resulting 201 lv6.2 new emission rates used to compute controls.... 131
Table 4-33. National by-sector reductions from RICE NSPS controls (tons) 132
Table 4-34. Facility types potentially subject to Boiler MACT reductions 133
Table 4-35. National-lev el, with Wisconsin exceptions, ICI boiler adjustment factors by base fuel type .... 135
Table 4-36. New York and New Jersey NOx ICI Boiler Rules that supersede national approach 135
Table 4-37. Summary of ICI Boiler reductions 135
Table 4-38. State Fuel Oil Sulfur Rules data provided by MANE-VU 136
Table 4-39. Summary of fuel sulfur rule impacts on SO2 emissions 137
Table 4-40. Stationary gas turbines NSPS analysis and resulting 201 lv6.2 new emission rates used to
compute controls 138
Table 4-41. National by-sector NOx reductions from Stationary Natural Gas Turbine NSPS controls 139
Table 4-42. Process Heaters NSPS analysis and 201 lv6.2 new emission rates used to compute controls ... 140
Table 4-43. National by-sector NOx reductions from Process Heaters NSPS controls 140
Table 4-44. Summary of remaining ptnonipm and pt_oilgas reductions 142
Table 4-45. PFC emissions for 2011, 2018 and 2025 [tons] 143
Table 4-46. Emission Factors for Biodiesel Plants (Tons/Mgal) 144
Table 4-47. 2040 biodiesel plant emissions from sources not in the NEI [tons] 144
Table 4-48. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/RIN gallon) 145
Table 4-49. Toxic Emission Factors for Cellulosic Plants (Tons/RIN gallon) 145
Table 4-50. 2018 cellulosic plant emissions [tons] 146
Table 4-51. New cellulosic plants NOX emissions provided by Iowa DNR 146
Table 4-52. Locations of new ISMP-generated cement kilns 147
Table 4-53. ISMP-generated new permitted and non-permitted emissions 147
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Table 4-54. Projection factors for 2040 VMT) 148
Table 4-55. Inputs for MOVES runs for 2040 reference and control cases 149
Table 4-56. CA LEVIII program states 149
Table 5-1. National by-sector emissions summary for the 2011 evaluation case 154
Table 5-2. National by-sector CAP emissions summaries for the 2040 reference case 155
Table 5-3. National by-sector CAP emissions summaries for the 2040 control case 156
Table 5-4. National by-sector CO emissions (tons/yr) summaries and percent change 157
Table 5-5. National by-sector NH3 emissions (tons/yr) summaries and percent change 158
Table 5-6. National by-sector NOx emissions (tons/yr) summaries and percent change 159
Table 5-7. National by-sector PM2.5 emissions (tons/yr) summaries and percent change 160
Table 5-8. National by-sector PM10 emissions (tons/yr) summaries and percent change 161
Table 5-9. National by-sector SO2 emissions (tons/yr) summaries and percent change 162
Table 5-10. National by-sector VOC emissions (tons/yr) summaries and percent change 163
List of Figures
Figure 2-1. Impact of adjustments to fugitive dust emissions due to transport fraction, precipitation, and
cumulative 15
Figure 2-2. Illustration of regional modeling domains in ECA-IMO study 24
Figure 2-3. Annual NO emissions output from BEIS 3.61 for 2011 30
Figure 2-4. Annual isoprene emissions output from BEIS 3.61 for 2011 30
Figure 3-1. Air quality modeling domains 34
Figure 3-2. Process of integrating BAFM with VOC for use in VOC Speciation 38
Figure 3-3. Eliminating unmeasured spikes in CEMS data 53
Figure 3-4. Seasonal diurnal profiles for EGU emissions in a Virginia Region 54
Figure 3-5. IPM Regions for EPA Base Case v5.14 55
Figure 3-6. Month-to-day profiles for different fuels in a West Texas Region 56
Figure 3-7. Future year emissions follow pattern of base year emissions 60
Figure 3-8. Excess emissions apportioned to hours less than maximum 60
Figure 3-9. Adjustment to Hours Less than Maximum not Possible, Regional Profile Applied 61
Figure 3-10. Regional Profile Applied, but Exceeds Maximum in Some Hours 62
Figure 3-11. Example of RWC temporalization in 2007 using a 50 versus 60 °F threshold 63
Figure 3-12. RWC diurnal temporal profile 64
Figure 3-13. Diurnal profile for OHH, based on heat load (BTU/hr) 65
Figure 3-14. Day-of-week temporal profiles for OHH and Recreational RWC 65
Figure 3-15. Annual-to-month temporal profiles for OHH and recreational RWC 66
Figure 3-16. Example of animal NH3 emissions temporalization approach, summed to daily emissions 67
Figure 3-17. Example of SMOKE-MOVES temporal variability of NOx emissions 68
Figure 3-18. Previous onroad diurnal weekday profiles for urban roads 69
Figure 3-19. Use of submitted versus new national default profiles 70
Figure 3-20. Updated national default profiles for LDGV vs. HHDDV, urban restricted 71
Figure 3-21. Updated national default profiles for day of week 72
Figure 3-22. Combination long-haul truck restricted and hoteling profile 73
Figure 3-23. Agricultural burning diurnal temporal profile 74
Figure 4-1. Oil and gas plays with AEO projection data 114
Figure 4-2. Oil and Gas NEMS Regions 115
Figure 4-3. Cement sector trends in domestic production versus normalized emissions 121
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List of Appendices
Appendix A: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT
Appendix B: Future Animal Population Projection Methodology
Appendix C: Upstream Methodology
Acronyms
AE5
CMAQ Aerosol Module, version 5, introduced in CMAQ v4.7
AE6
CMAQ Aerosol Module, version 6, introduced in CMAQ v5.0
AEO
Annual Energy Outlook
BAFM
Benzene, Acetaldehyde, Formaldehyde and Methanol
BEIS
Biogenic Emissions Inventory System
BELD
Biogenic Emissions Land use Database
Bgal
Billion gallons
BPS
Bulk Plant Storage
BTP
Bulk Terminal (Plant) to Pump
C1/C2
Category 1 and 2 commercial marine vessels
C3
Category 3 (commercial marine vessels)
CAEP
Committee on Aviation Environmental Protection
CAIR
Clean Air Interstate Rule
CAMD
EPA's Clean Air Markets Division
CAMx
Comprehensive Air Quality Model with Extensions
CAP
Criteria Air Pollutant
CARB
California Air Resources Board
CB05
Carbon Bond 2005 chemical mechanism
CBM
Coal-bed methane
CEC
North American Commission for Environmental Cooperation
CEMS
Continuous Emissions Monitoring System
CEPAM
California Emissions Projection Analysis Model
CISWI
Commercial and Industrial Solid Waste Incineration
CI
Chlorine
CMAQ
Community Multiscale Air Quality
CMV
Commercial Marine Vessel
CO
Carbon monoxide
CSAPR
Cross-State Air Pollution Rule
EO, E10, E85
0%, 10% and 85% Ethanol blend gasoline, respectively
EBAFM
Ethanol, Benzene, Acetaldehyde, Formaldehyde and Methanol
ECA
Emissions Control Area
EEZ
Exclusive Economic Zone
EF
Emission Factor
EGU
Electric Generating Units
EIS
Emissions Inventory System
EISA
Energy Independence and Security Act of 2007
EPA
Environmental Protection Agency
EMFAC
Emission Factor (California's onroad mobile model)
FAA
Federal Aviation Administration
FAPRI
Food and Agriculture Policy and Research Institute
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FASOM
Forest and Agricultural Section Optimization Model
FCCS
Fuel Characteristic Classification System
FF10
Flat File 2010
FIPS
Federal Information Processing Standards
FHWA
Federal Highway Administration
HAP
Hazardous Air Pollutant
HC1
Hydrochloric acid
HDGHG
Heavy-Duty Vehicle Greenhouse Gas
Hg
Mercury
HMS
Hazard Mapping System
HPMS
Highway Performance Monitoring System
HWC
Hazardous Waste Combustion
HWI
Hazardous Waste Incineration
ICAO
International Civil Aviation Organization
ICI
Industrial/Commercial/Institutional (boilers and process heaters)
ICR
Information Collection Request
IDA
Inventory Data Analyzer
I/M
Inspection and Maintenance
IMO
International Marine Organization
IPAMS
Independent Petroleum Association of Mountain States
IPM
Integrated Planning Model
ITN
Itinerant
LADCO
Lake Michigan Air Directors Consortium
LDGHG
Light-Duty Vehicle Greenhouse Gas
LPG
Liquefied Petroleum Gas
MACT
Maximum Achievable Control Technology
MARAMA
Mid-Atlantic Regional Air Management Association
MATS
Mercury and Air Toxics Standards
MCIP
Meteorology-Chemistry Interface Processor
Mgal
Million gallons
MMS
Minerals Management Service (now known as the Bureau of Energy

Management, Regulation and Enforcement (BOEMRE)
MOVES
Motor Vehicle Emissions Simulator
MSA
Metropolitan Statistical Area
MSAT2
Mobile Source Air Toxics Rule
MTBE
Methyl tert-butyl ether
NCD
National County Database
NEEDS
National Electric Energy Database System
NEI
National Emission Inventory
NESCAUM
Northeast States for Coordinated Air Use Management
NESHAP
National Emission Standards for Hazardous Air Pollutants
NH3
Ammonia
NIF
NEI Input Format
NLCD
National Land Cover Database
NLEV
National Low Emission Vehicle program
nm
nautical mile
NMIM
National Mobile Inventory Model
NO A A
National Oceanic and Atmospheric Administration
NODA
Notice of Data Availability
NONROAD
OTAQ's model for estimation of nonroad mobile emissions
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NOx
Nitrogen oxides
NSPS
New Source Performance Standards
NSR
New Source Review
OAQPS
EPA's Office of Air Quality Planning and Standards
OHH
Outdoor Hydronic Heater
OTAQ
EPA's Office of Transportation and Air Quality
ORIS
Office of Regulatory Information System
OKI)
EPA's Office of Research and Development
ORL
One Record per Line
OTC
Ozone Transport Commission
PADD
Petroleum Administration for Defense Districts
PF
Projection Factor, can account for growth and/or controls
PFC
Portable Fuel Container
PM2.5
Particulate matter less than or equal to 2.5 microns
PM10
Particulate matter less than or equal to 10 microns
ppb, ppm
Parts per billion, parts per million
RBT
Refinery to Bulk Terminal
RFS2
Renewable Fuel Standard
RIA
Regulatory Impact Analysis
RICE
Reciprocating Internal Combustion Engine
RRF
Relative Response Factor
RWC
Residential Wood Combustion
RPO
Regional Planning Organization
RVP
Reid Vapor Pressure
see
Source Classification Code
SEMAP
Southeastern Modeling, Analysis, and Planning
SESARM
Southeastern States Air Resource Managers
SESQ
Sesquiterpenes
SMARTFIRE
Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation
SMOKE
Sparse Matrix Operator Kernel Emissions
SO2
Sulfur dioxide
SOA
Secondary Organic Aerosol
SI
Spark-ignition
SIP
State Implementation Plan
SPDPRO
Hourly Speed Profiles for weekday versus weekend
SPPD
Sector Policies and Programs Division
TAF
Terminal Area Forecast
TCEQ
Texas Commission on Environmental Quality
TOG
Total Organic Gas
TSD
Technical support document
ULSD
Ultra Low Sulfur Diesel
USD A
United States Department of Agriculture
VOC
Volatile organic compounds
VMT
Vehicle miles traveled
VPOP
Vehicle Population
WRAP
Western Regional Air Partnership
WRF
Weather Research and Forecasting Model
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1 Introduction
This document provides the details of emissions modeling done in support of the air quality modeling
completed for the Environmental Protection Agency (EPA) as part of the joint rulemaking effort under the
Clean Air Act (CAA) and the Energy Independence and Security Act of 2007 (EISA) to establish fuel
efficiency and greenhouse gas emissions standards for commercial medium-and heavy-duty on-highway
vehicles and work trucks beginning with the 2018 model year (MY). This rulemaking effort is hereafter
referred to in this technical support document (TSD) as the Heavy Duty Vehicle Greenhouse Gas (HDGHG)
Phase 2 rule.
Air quality modeling studies are based on an air quality modeling "platform", which consists of all the
emissions inventories and ancillary data files used for emissions modeling, as well as the meteorological,
initial condition, and boundary condition files needed to run the air quality model. This document focuses
on the emissions modeling component of the platform, which includes the emission inventories, the
ancillary data files, and the approaches used to transform inventories for use in air quality modeling. For
more information on emissions modeling, see https://www.epa.gov/air-emissions-modeling.
The air quality modeling platform used for this analysis is based on the U.S. Environmental Protection
Agency (EPA)'s 201 lv6.2 emissions modeling platform (EPA, 2015). The 201 lv6.2 platform was used to
support the 2015 National Ambient Air Quality Standards (NAAQS) for ozone, the proposed Cross-state
Air Pollution Update Rule, the focus of which is ozone transport modeling for the 2008 National Ambient
Air Quality Standards (NAAQS), the 2011 National Air Toxics Assessment (NATA), along with other
studies. Most of the emission inventories used in this modeling analysis and the 201 lv6.2 platform are
based on the 2011 National Emissions Inventory, version 2 (201 1NEIv2), although there are some
differences between the inventories used for modeling and the 201 1NEIv2 emissions, including
inventories specific to this rulemaking and not intended for general use. For more information on the
201 lv6.2 platform, see https://www.epa.gov/air-emissions-modeling/2011-version-62-platform. The
specific emission inventories used for this modeling analysis along with corresponding summaries of the
emissions are available upon request.
The air quality model used for this analysis is the Community Multiscale Air Quality (CMAQ) model,
version 5.02 multipollutant lite. CMAQ supports modeling ozone (O3) and particulate matter (PM), and
specific hazardous air pollutants (HAPs). It requires as input hourly and gridded emissions of chemical
species that correspond to specific criteria air pollutants (CAPs) and HAPs. For more information on
CMAQ, see https://www.epa.gov/air-research/communitv-multi-scale-air-qualitv-cmaq-modeling-svstem-
air-qualitv-management.
The emissions modeling platform for this analysis consists of three 'complete' emissions cases: the 2011
base case, the 2040 base case, and the 2040 control case. The purpose of the 2011 base case is to represent
a base year in a manner consistent with the methods used in the corresponding future-year cases. For
regulatory applications, the outputs from the 2011 base case are used in conjunction with the outputs from
the 2040 reference and control cases using relative response factor (RRF) calculations. The air quality
concentrations in the control case are then compared to the reference case to quantify the impact of the
rule. Table 1-1 has more information on these cases, including their names. For more information on the
use of RRFs and air quality modeling, see "Guidance on the Use of Models and Other Analyses for
Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5, and Regional Haze", available from
http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf.
1

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Table 1-1. List of cases in the HDGHG Phase 2 Emissions Modeling Platform
Case Name
Abbreviation
Description
2011 base case
201 lei v6 cb05v2
2011 case relevant for air quality model evaluation
purposes and for computing relative response factors
with the 2040 scenarios. Uses 201 1NEIv2 and some
other inventory data, with hourly 2011 continuous
emissions monitoring System (CEMS) data for
Electrical Generating Units (EGUs), hourly onroad
mobile emissions, and 2011 day-specific wild and
prescribed fire data.
2040 reference
case
2040ei v6 cb05v2 hdghg
p2_ref
2040 "reference case" scenario, representing the best
estimate for the future year that incorporates estimates
of the impact of current "on-the-books" regulations
without implementation of the HDGHG Phase 2
standards.
2040 control
case
2040ei v6 cb05v2 hdghg
p2_ctl
2040 "control case" scenario, representing "on-the-
books" regulations plus the implementation of the
HDGHG Phase 2 standards.
The case names are constructed using multiple parts to provide information about the cases:
•	the first part of the name is the year represented in the modeling (i.e., 2011 or 2040);
•	the "e" following the year indications that year-specific data for fires and electric generating
units (EGUs) are used;
•	the "i" represents that this was the ninth set of emissions modeled for a 2011-based modeling
platform (i.e., the first case for the 2011 platform was 201 lea, the second was 201 leb, and so
on);
•	the "v6" is used for all 2011 platform cases;
•	the "cb05v2" indicates that the speciation used was the Carbon Bond 2005 mechanism with
some updates to the species mappings
•	the "hdghgp2" in the future year case names is an abbreviation for the analysis name; and
•	the "ref" and "ctl" in the future year case names distinguish between the reference case ("ref')
and the control case ("ctl").
Note that all of the above cases use the same version of the 2011 meteorology and the cases are
sometimes referred to with "_1 lg" after the emissions portion of the case name shown above.
The primary emissions modeling tool used to create the air quality model-ready emissions was the
SMOKE modeling system (http://www.smoke-model.org/). SMOKE version 3.6.5 was used to create
emissions files for a 12-km national grid that includes all of the contiguous states "12US2", shown in
Figure 3-1.
The gridded meteorological model used to provide input data for the emissions modeling was developed
using the Weather Research and Forecasting Model (WRF, http ://wrf-model.org) version 3.4, Advanced
Research WRF core (Skamarock, et al., 2008). The WRF Model is a mesoscale numerical weather
prediction system developed for both operational forecasting and atmospheric research applications.
WRF was run for 2011 over a domain covering the continental United States at a 12km resolution with 35
vertical layers. The WRF data were collapsed to 25 layers prior to running the emissions and air quality
models. The run for this platform included high resolution sea surface temperature data from the Group
2

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for High Resolution Sea Surface Temperature (GHRSST) (see https://www.ghrsst.org/) and is given the
EPA meteorological case label "11 g" and are consistent with those used for the air quality modeling and
are described in more detail in the Air Quality Modeling TSD for this rulemaking (EPA, 2016).
This document contains five sections and several appendices. Section 2 describes the 2011 inventories
input to SMOKE. Section 3 describes the emissions modeling and the ancillary files used with the
emission inventories. Section 4, describes the development of the 2040 inventories (projected from
2011). Data summaries comparing the 2011 base case and 2040 reference and control cases are provided
in Section 5. Section 6 provides references. The Appendices provide additional details about specific
technical methods.
3

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2 2011 Emission Inventories and Approaches
This section describes the 2011 emissions data used for this analysis. The starting point for the 2011
stationary source emission inputs is the 201 1NEIv2. Documentation for the 201 1NEIv2, including a
Technical Support Document (TSD), is available at
http://www.epa.gOv/ttn/chief/net/2011inventorv.html#inventorvdoc.
The NEI data for CAPs are largely compiled from data submitted by state, local and tribal (S/L/T) air
agencies. HAP emissions data are also from the state data, but are often augmented by EPA because they
are voluntarily submitted. EPA collaborated extensively with S/L/T agencies to ensure a high quality of
data in the 201 1NEIv2. The 2011 NEI includes five data categories: point sources, nonpoint (formerly
called "stationary area") sources, nonroad mobile sources, onroad mobile sources, and fire events. As
explained below, the major differences between the 2011 NEIv2 and the 2011 modeling platform used for
this analysis include: a different version of MOVES-based onroad mobile source emissions,
meteorologically-adjusted road dust emissions, the use of CEMS data for EGUs, and the inclusion of
emissions for areas outside the U.S. such as Canada and Mexico. In addition, the modeling platform uses
more spatially and temporally-resolved emissions than the NEI for many sectors, although the totals
reflected in the NEI are often consistent with those in the modeling platform.
For the purposes of preparing the air quality model-ready emissions, the 201 1NEIv2 was split into finer-
grained sectors used for emissions modeling. The significance of an emissions modeling or "platform
sector" is that the data are run through all of the SMOKE programs except the final merge (Mrggrid)
independently from the other sectors. The final merge program then combines the sector-specific gridded,
speciated, hourly emissions together to create CMAQ-ready emission inputs. For CAMx applications, the
CMAQ-ready emissions are then converted into the format needed by CAMx using a converter program.
Table 2-1 presents the sectors in the 2011 platform and how they generally relate to the 201 1NEIv2 as a
starting point. As discussed in greater detail in Table 2-2, the emissions in some of these sectors were
modified from the 201 1NEIv2 emissions for the 201 lv6.2 modeling platform. The platform sector
abbreviations are provided in italics. These abbreviations are used in the SMOKE modeling scripts,
inventory file names, and throughout the remainder of this document.
Table 2-1. Platform sectors for the 2011 emissions modeling platform
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
EGU units:
ptegu
Point
201 1NEIv2 point source EGUs. The 201 1NEIv2 emissions are
replaced with hourly 2011 CEMS values for NOx and SO2, where the
units are matched to the NEI. Other pollutants are scaled from
201 1NEIv2 using CEMS heat input. Emissions for all sources not
matched to CEMS data come from 201 1NEIv2. For future year
emissions, these units are mapped to the Integrated Planning Model
(IPM) model results using a cross reference to the National Electric
Energy Database System (NEEDS) version 5.15. Annual resolution for
non-matched sources, hourly for CEMS sources.
Point source oil
and gas:
pt oilgas
Point
201 1NEIv2 point sources that include oil and gas production
emissions processes. Annual resolution.
4

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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Remaining non-
EGU point:
ptnonipm
Point
All 201 1NEIv2 point source records not matched to the ptegu or
pt_oilgas sectors, except for offshore point sources that are in the othpt
sector. Includes all aircraft emissions and some rail yard emissions.
Annual resolution.
Agricultural:
ag
Nonpoint
NH3 emissions from 201 1NEIv2 nonpoint livestock and fertilizer
application, county and annual resolution.
Agricultural fires:
agfire
Nonpoint
201 1NEIv2 agricultural fire sources. County and monthly resolution.
Area fugitive dust:
afdust
Nonpoint
PM10 and PM2 5 fugitive dust sources from the 201 1NEIv2 nonpoint
inventory; including building construction, road construction,
agricultural dust, and road dust; however, unpaved and paved road
dust emissions differ from the NEI in that do not have a precipitation
adjustment. Instead, the emissions modeling adjustment applies a
transport fraction and a meteorology-based (precipitation and snow/ice
cover) zero-out. County and annual resolution.
Biogenic:
Beis
Nonpoint
Year 2011, hour-specific, grid cell-specific emissions generated from
the BEIS3.61 model within SMOKE using BELD4 land use, including
emissions in Canada and Mexico.
Category 1 & 2
CMV and
locomotives:
clc2rail
Nonpoint
Locomotives and category 1 (CI) and category 2 (C2) commercial
marine vessel (CMV) emissions sources from the 201 1NEIv2
nonpoint inventory. County and annual resolution.
commercial
marine:
c3 marine
Nonpoint
Category 3 (C3) CMV emissions from the 2011NEIv2; see othpt
sector for all non-U.S. emissions. County and annual resolution.
Remaining
nonpoint:
nonpt
Nonpoint
201 1NEIv2 nonpoint sources not included in other platform sectors;
county and annual resolution.
Nonpoint source
oil and gas:
np oilgas
Nonpoint
201 1NEIv2 nonpoint sources from oil and gas-related processes with
corrections to sources in Utah. County and annual resolution.
Residential Wood
Combustion:
rwc
Nonpoint
201 1NEIv2 NEI nonpoint sources with Residential Wood Combustion
(RWC) processes. County and annual resolution.
Nonroad:
nonroad
Nonroad
201 1NEIv2 nonroad equipment emissions developed with the National
Mobile Inventory Model (NMIM) using NONROAD2008 version
NR08a. NMIM was used for all states except California and Texas,
which submitted their own emissions for the 201 1NEIv2. County and
monthly resolution.
Onroad:
onroad
Onroad
2011 onroad mobile source gasoline and diesel vehicles from parking
lots and moving vehicles. Includes the following modes: exhaust,
extended idle, auxiliary power units, evaporative, permeation,
refueling, and brake and tire wear. Based on monthly MOVES
emissions tables from an updated version of MOVES2014 specific to
this rulemaking. MOVES-based emissions computed for each hour
and model grid cell using monthly and annual activity data (e.g.,
VMT, vehicle population).
Point source fires:
ptjire
Fires
Point source day-specific wildfires and prescribed fires for 2011
computed using SMARTFIRE2, except for Georgia and Florida-
submitted emissions. Consistent with 201 1NEIv2.
5

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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Other dust
sources not from
the 2011 NEI:
Othafdust
N/A
Fugitive dust sources from Canada's 2010 inventory.
Other point
sources not from
the 2011 NEI:
othpt
N/A
Point sources from Canada's 2010 inventory and Mexico's 2008
inventory, annual resolution. Also includes all non-U.S. C3 CMV and
U.S. offshore oil production including corrections to double counting
of CMV in the St. Lawrence and Saguenay Rivers.
Other non-NEI
nonpoint and
nonroad:
othar
N/A
Monthly year 2010 Canada (province resolution) and annual year 2008
Mexico (municipio resolution) nonpoint and nonroad mobile
inventories.
Other non-NEI
onroad sources:
othon
N/A
Monthly year 2010 Canada (province resolution) and annual year 2008
Mexico (municipio resolution) onroad mobile inventories.
Table 2-2 provides a brief by-sector overview of the most significant differences between the 201 lv6.2
emissions platform used for the HDGHG2 air quality modeling analysis and the 201 1NEIv2. Only those
sectors with significant differences are listed. The specific by-sector updates to the 2011 platform are
described in greater detail later in this section under each sector subsection.
Table 2-2. Summary of differences between 201 lv6.2 platform and 201 1NEIv2 emissions by sector
Platform Sector
Summary of Significant Inventory Differences of 2011 Platform vs.
2011NEIv2
Area fugitive dust:
afdust
1) Replaced EPA-provided emission estimates for paved and unpaved road dust
with "non-meteorologically-adjusted" emissions, then adjusted emissions to
reflect land use (transport) and meteorological effects such as rain and snow
cover which significantly reduces PM emissions. These adjusted emissions are
known as the afdust adj sector.
Biogenic:
beis
1) Biogenic emissions changed from 3.60 to 3.61
Remaining
nonpoint sector:
nonpt
1) Split the 201 1NEIv2 nonpoint file into the platform sectors afdust, ag, agfire,
beis, np_oilgas, rwc, c3marine, and clc2rail.
2) Used agricultural fires emissions from a daily inventory aggregated to monthly
values, whereas the NEI only stores annual values.
Nonpoint oil and
gas sources:
np oilgas
1) Corrections made to nonpoint oil and gas sources in Utah as compared to those
in the 201 1NEIv2 and those in the 201 lv6.2 platform.
Nonroad sector:
nonroad
2) Monthly rather than annual emissions that match 201 1NEIv2 totals.
3) Texas: replaced with annual 2011 Texas data apportioned to months using
EPA's 2011 nonroad estimates.
Onroad sector:
Onroad
1) Year 2011 emissions based on emission factors from the released version of
MOVES2014, as opposed to the early MOVES2014 which was used for the
201 1NEIv2. Developed with SMOKE-MOVES using 2011 meteorology.
2) Platform contains correction to diesel refueling emissions
3) Platform uses E-85 activity data and emission factors, where for NEI E-
85 was not included.
6

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Platform Sector
Summary of Significant Inventory Differences of 2011 Platform vs.
2011NEIv2
EGUs:
ptegu
1) Based on 201 1NEIv2 and 2011 CEMS data analysis by EPA and states, added
ORIS Boiler IDs to some units (greater than 1,000 tons of NOx or SO2) with
missing or incorrect values to allow for hourly CEMS data processing.
2) Added CEMS matches to additional units identified as CEMS sources.
Hourly NOx and SO2 CEMS data replaces annual NOx and SO2 NEI data in
the air quality model inputs for 2011 and scaled hourly data in future years.
Non-EGUs
sector:
ptnonipm
1) Additional matches to IPM YN codes and ORIS facility codes caused several
sources to move out of ptnonipm and into the ptegu sector. The goal is to
prevent double counting of EGU emissions in the future years.
The remainder of Section 2 provides details about the data contained in each of the 201 lv6.2 platform
sectors. Different levels of detail are provided for different sectors depending on the availability of
reference information for the data, the degree of changes or manipulation of the data needed to prepare it
for input to SMOKE, and whether the 2011 platform emissions are significantly different from the
201 1NEIv2.
2.1 2011 NEI point sources (ptegu, pt_oilgas and ptnonipm)
Point sources are sources of emissions for which specific geographic coordinates (e.g.,
latitude/longitude) are specified, as in the case of an individual facility. A facility may have multiple
emission release points, which may be characterized as units such as boilers, reactors, spray booths,
kilns, etc. A unit may have multiple processes (e.g., a boiler that sometimes burns residual oil and
sometimes burns natural gas). With a couple of minor exceptions, this section describes only NEI point
sources within the contiguous United States. Offshore oil platform and category 3 CMV emissions, both
in the othpt sector, are processed by SMOKE as point source inventories, as described in Section 2.5.1
and Section 2.4.2, respectively. A comprehensive description of how EGU emissions were characterized
and estimated in the 2011 NEI can be found in Section 3.10 in the 201 1NEIv2 TSD.
The point source file used for the modeling platform is exported from the Emission Inventory System
(EIS) that is used to compile the NEI into the Flat File 2010 (FF10) format that is compatible with
SMOKE (see https://www.cmascenter.Org/smoke/documentation/3.7/html/ch08s02sl0.html#d0e44847).
After moving offshore oil platforms into the othpt sector, and dropping sources without true locations
(i.e., their FIPS code ends in 777), initial versions of the other four platform point source sectors were
created from the remaining 201 1NEIv2 point sources. The point sectors are: the EGU sector for non-
peaking units (ptegu), point source oil and gas extraction -related emissions (pt oilgas) and the
remaining non-EGU sector also called the non-IPM (ptnonipm) sector. The EGU emissions are split out
from the other sources to facilitate the use of distinct SMOKE temporal processing and future-year
projection techniques. The oil and gas sector emissions (pt oilgas) were processed separately for
summary tracking purposes and distinct future-year projection techniques from the remaining non-EGU
emissions (ptnonipm).
The inventory pollutants processed through SMOKE for all point source sectors were: CO, NOx, VOC,
SO2, NH3, PM10, and PM2.5 and the following HAPs: HC1 (pollutant code = 7647010), and CI (code =
7782505). The inventory BAFM from these sectors was not used, instead VOC was speciated to these
pollutants without any use (i.e., integration) of the VOC HAP pollutants from the inventory (VOC
integration is discussed in detail in Section 3.2.1).
7

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The ptnonipm and ptoilgas sector emissions were provided to SMOKE as annual emissions. For those
ptegu sources with CEMS data (that could be matched to the 201 1NEIv2), 2011 hourly CEMS NOx and
SO2 emissions were used rather than NEI emissions, and for all other pollutants annual emissions were
used as-is from the NEI, but were allocated to hourly values using heat input CEMS data. For the non-
CEMS sources in the ptegu sector, daily emissions were created using an approach described in Section
2.1.1, and IPM region- and pollutant-specific diurnal profiles were applied to create hourly emissions.
Changes made to the point-based sectors from the 201 1NEIv2 for the 2011 platform were described in
Table 2-2. Some of these changes involved splitting the stacks, units and facilities into the ptnonipm,
pt oilgas and ptegu sectors. Sources were included in the ptegu sector only when it was determined that
these sources were reflected in the future-year IPM output data. These changes and other updates to the
point source sectors for the 2011 platform are discussed in the following sections.
2.1.1 EGU sector (ptegu)
The ptegu sector contains emissions from EGUs in the 201 1NEIv2 point inventory that could be
matched to units found in the NEEDS v5.15 database. It was necessary to put these EGUs into separate
sectors in the platform because IPM projects future emissions for the EGUs defined in the NEEDS
database, and emissions for sources in the ptegu sector are replaced with IPM outputs in the future year
modeling case. Sources not matched to units found in NEEDS are placed into the pt oilgas (see Section
2.1.2) or ptnonipm (see Section 2.1.3) sectors and are projected to the future year using projection and
control factors. It is important that the matching between the NEI and NEEDS database be as complete
as possible because there can be double counting of emissions in the future year if emissions for units
projected by IPM are not properly matched to the units in the NEI. Note that additional matches were
identified for this analysis as compared to the 201 lv6.2 platform.
In the SMOKE point flat file, emission records for sources that have been matched to the NEEDS
database have a value filled into the IPM YN column. Many of these matches are stored within EIS. In
some cases, it was difficult to match the sources between the databases due to different facility names in
the two data systems and due to differences in how the units are defined, thereby resulting in matches
that are not always one-to-one. Some additional matches were made in the modeling platform to
accommodate some of these situations as described later in this section. The NEEDS v5.15 database can
be found at https://www.epa.gov/airmarkets/power-sector-modeling, along with additional information
about IPM. The updated version of the cross reference between NEEDS 5.15 units and those in the 2011
v6.2 platform can be found here: ftp://ftp.epa.gov/EmisInventorv/2011v6/v2platform/reports/
ipm to flat file xref 2011NEIv2 Updated 20150710.xlsx and was used when IPM outputs were
postprocessed to create flat files for modeling. This version of the cross reference includes updates to
stack parameters as compared to earlier versions.
Some units in the ptegu sector are matched to CEMS data via ORIS facility codes and boiler ID. For
matched units, SMOKE replaces the 2011 emissions of NOx and SO2 with the CEMS emissions,
thereby ignoring the annual values specified in the NEI. For other pollutants, the hourly CEMS heat
input data are used to allocate the NEI annual emissions to hourly values. All stack parameters, stack
locations, and SCC codes for these sources come from the NEI. Because these attributes are obtained
from the NEI, the chemical speciation of VOC and PM2.5 for the sources is selected based on the SCC or
in some cases, based on unit-specific data. If CEMS data exists for a unit, but the unit is not matched to
the NEI, the CEMS data for that unit is not used in the modeling platform. However, if the source exists
in the NEI and is not matched to a CEMS unit, the emissions from that source are still modeled using the
8

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annual emission value in the NEI. EIS stores many matches from EIS units to the ORIS facility codes
and boiler IDs used to reference the CEMS data.
Additional matches outside of those in EIS were made in the modeling platform as described in the
201 lv6.2 platform TSD. As a result of identifying additional matches through this analysis, many
EGUs that otherwise would have remained in the ptnonipm sector were moved to the ptegu sector. Note
that SMOKE can perform matches of CEMS data down to the stack or release point-level, which is finer
than the unit-level that is available in EIS.
In the SMOKE point flat file, emission records for point sources matched to CEMS data have values
filled into the ORIS FACILITY CODE and ORIS BOILER ID columns. The CEMS data in SMOKE-
ready format is available at http://ampd.epa.gov/ampd/ near the bottom of the "Prepackaged Data" tab.
Many smaller emitters in the CEMS program are not identified with ORIS facility or boiler IDs that can
be matched to the NEI due to inconsistencies in the way a unit is defined between the NEI and CAMD
datasets, or due to uncertainties in source identification such as inconsistent plant names in the two data
systems. Also, the NEEDS database of units modeled by IPM includes many smaller emitting EGUs
that are not included in the CAMD hourly CEMS programs. Therefore, there will be more units in the
NEEDS database than have CEMS data. The temporalization of EGU units matched to CEMS is based
on the CEMS data in the base and future years are based on the base year CEMS data for those units,
whereas regional profiles are used for the remaining units. More detail can be found in Section 3.3.2.
For sources not matched to CEMS data, daily emissions were computed from the NEI annual emissions
using average CEMS data profiles specific to fuel type, pollutant2, and IPM region. To allocate
emissions to each hour of the day, diurnal profiles were created using average CEMS data for heat input
specific to fuel type and IPM region. For future-year scenarios, there are no CEMS data available for
specific units, but the shape of the CEMS profiles is preserved as much as possible for sources that are
carried into the future year. This method keeps the temporal behavior of the base and future year cases
as consistent as possible. See Section 3.3.2 for more details on the temporalization approach for ptegu
sources.
2.1.2 Point source oil and gas sector (pt_oilgas)
The ptoilgas sector was separated from the ptnonipm sector by selecting sources with specific NAICS
codes shown in Table 2-3. The emissions and other source characteristics in the pt oilgas sector are
submitted by states, while EPA developed a dataset of nonpoint oil and gas emissions for each county in
the U.S. with oil and gas activity that was available for states to use. Nonpoint oil and gas emissions can
be found in the np oilgas sector. More information on the development of the 2011 oil and gas
emissions can be found in Section 3.20 of the 201 1NEIv2 TSD.
Table 2-3. Point source oil and gas sector NAICS Codes
NAICS
NAICS description
2111
Oil and Gas Extraction
2212
Natural Gas Distribution
4862
Pipeline Transportation of Natural Gas
21111
Oil and Gas Extraction
22121
Natural Gas Distribution
2 The year to day profiles use NOx and S02 CEMS for NOx and S02, respectively. For all other pollutants, they use heat
input CEMS data.
9

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NAICS
NAICS description
48611
Pipeline Transportation of Crude Oil
48621
Pipeline Transportation of Natural Gas
211111
Crude Petroleum and Natural Gas Extraction
211112
Natural Gas Liquid Extraction
213111
Drilling Oil and Gas Wells
213112
Support Activities for Oil and Gas Operations
221210
Natural Gas Distribution
486110
Pipeline Transportation of Crude Oil
486210
Pipeline Transportation of Natural Gas
2.1.3 Non-IPM sector (ptnonipm)
Except for some minor exceptions, the non-IPM (ptnonipm) sector contains the 201 1NEIv2 point
sources that are not in the ptegu or pt oilgas sectors. For the most part, the ptnonipm sector reflects the
non-EGU sources of the NEI point inventory; however, it is likely that some small low-emitting EGUs
not matched to the NEEDS database or to CEMS data are present in the ptnonipm sector. The ptnonipm
sector also includes some ethanol plants that have been identified by EPA and require special treatment
in the future cases as they are impacted by mobile source rules.
Sources with state/county FIPS code ending with "777" are in the 201 1NEIv2 but are not included in
any modeling sectors. These sources typically represent mobile (temporary) asphalt plants that are only
reported for some states, and are generally in a fixed location for only a part of the year and are therefore
difficult to allocate to specific places and days as is needed for modeling. Therefore, these sources are
dropped from the point-based sectors in the modeling platform.
EPA estimates for ethanol facilities
EPA developed a list of corn ethanol facilities for 2011. Ethanol facilities that were not in 201 INEIvl
were added into 201 1NEIv2. Locations and FIPS codes for these ethanol plants were verified using web
searches and Google Earth. EPA believes that some of these sources were not originally included in the
NEI as point sources because they do not meet the ton/year potential-to-emit threshold for NEI point
sources. Emission rates for the ethanol plants were obtained from EPA's updated spreadsheet model for
upstream impacts developed for the Renewable Fuel Standard (RFS2) rule (EPA, 2010a). Plant
emission rates for criteria pollutants used to estimate impacts for 2011 and 2040 are given in Table 2-4.
Table 2-4. Corn Ethanol Plant Criteria Pollutant Emission Factors (grams per gallon produced)
Corn Ethanol Plant Type
VOC
CO
NOx
PM10
pm25
SO2
nh3
Dry Mill Natural Gas (NG)
2.29
0.58
0.99
0.94
0.23
0.01
0.00
Dry Mill NG (wet distillers grains with solubles (DGS))
2.27
0.37
0.63
0.91
0.20
0.00
0.00
Dry Mill Biogas
2.29
0.62
1.05
0.94
0.23
0.01
0.00
Dry Mill Biogas (wet DGS)
2.27
0.39
0.67
0.91
0.20
0.00
0.00
Dry Mill Coal
2.31
2.65
4.17
3.81
1.71
4.52
0.00
Dry Mill Coal (wet DGS)
2.31
2.65
2.65
2.74
1.14
2.87
0.00
Dry Mill Biomass
2.42
2.55
3.65
1.28
0.36
0.14
0.00
Dry Mill Biomass (wet DGS)
2.35
1.62
2.32
1.12
0.28
0.09
0.00
10

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Corn Ethanol Plant Type
VOC
CO
NOx
PM10
pm25
SO2
nh3
Wet Mill NG
2.35
1.62
1.77
1.12
0.28
0.09
0.00
Wet Mill Coal
2.33
1.04
5.51
4.76
2.21
5.97
0.00
Air toxic inventories were estimated by applying the toxic to VOC ratios in Table 2-5. The toxic-to-
VOC ratios were developed using emission inventory data from the 2005 NEI (EPA, 2009a).
Table 2-5. Toxic-to-VOC Ratios for Corn Ethanol Plants

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meteorological adjustments are based on the gridded resolution of the platform (e.g., 12km grid cells);
therefore, different emissions will result if the process were applied to different grid resolutions. A
limitation of the transport fraction approach is the lack of monthly variability that would be expected
with seasonal changes in vegetative cover. While wind speed and direction are not accounted for in the
emissions processing, the hourly variability due to soil moisture, snow cover and precipitation is
accounted for in the subsequent meteorological adjustment.
The sources in the afdust sector are for SCCs and pollutant codes (i.e., PMio and PM2.5) that are
considered to be "fugitive" dust sources. These SCCs are provided in Table 2-6.
Table 2-6. SCCs in the afdust platform sector
see
SCC Description
2275085000
Mobile Sources;Aircraft;Unpaved Airstrips;Total
2294000000
Mobile Sources;Paved Roads;All Paved Roads;Total: Fugitives
2294000002
Mobile Sources;Paved Roads;All Paved Roads;Total: Sanding/Salting - Fugitives
2296000000
Mobile Sources;Unpaved Roads;All Unpaved Roads;Total: Fugitives
2296005000
Mobile Sources;Unpaved Roads;Public Unpaved Roads;Total: Fugitives
2296010000
Mobile Sources;Unpaved Roads;Industrial Unpaved Roads;Total: Fugitives
2311000000
Industrial Processes;Construction: SIC 15 - 17;A11 Processes;Total
2311010000
Industrial Processes;Construction: SIC 15 - 17;Residential;Total
2311020000
Industrial Processes;Construction: SIC 15 - 17;Industrial/Commercial/Institutional;Total
2311030000
Industrial Processes;Construction: SIC 15 - 17;Road Construction;Total
2311040000
Industrial Processes;Construction: SIC 15 - 17;Special Trade Construction;Total
2325000000
Industrial Processes;Mining and Quarrying: SIC 14;A11 Processes;Total
2325020000
Industrial Processes;Mining and Quarrying: SIC 14;Crushed and Broken Stone;Total
2325030000
Industrial Processes;Mining and Quarrying: SIC 14;Sand and Gravel;Total
2801000000
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Total
2801000002
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Planting
2801000003
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Tilling
2801000005
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture -
Crops;Harvesting
2801000008
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture -
Crops;Transport
2805001000
Miscellaneous Area Sources;Agriculture Production - Livestock;Beef cattle - finishing
operations on feedlots (drylots);Dust Kicked-up by Hooves (use 28-05-020, -001, -002, or
-003 for Waste
The dust emissions in the modeling platform are not the same as the 201 1NEIv2 emissions because the
NEI paved and unpaved road dust emissions include a built-in precipitation reduction that is based on
average meteorological data, which is at a coarser temporal and spatial resolution than the modeling
platform meteorological adjustment. Due to this, in the platform the paved and unpaved road emissions
data used did not include any precipitation-based reduction. This allows the entire sector to be processed
consistently so that the same grid-specific transport fractions and meteorological adjustments can be
applied. Where states submitted afdust data, it was assumed that the state-submitted data were not met-
adjusted and therefore the meteorological adjustments were still applied. Thus, it is possible that these
sources may have been adjusted twice. Even with that possibility, air quality modeling shows that in
general, dust is frequently overestimated in the air quality modeling results.
12

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The total impacts of the transport fraction and meteorological adjustments for the 201 1NEIv2 are shown
in Table 2-7, where the starting inventory numbers include a different type of adjustment. The amount of
the reduction ranges from about 93% in New Hampshire to about 29% in Nevada. The afdust emissions
did not change much between the 201 lv6.2 platform and this analysis. The largest change was in
Indiana with a reduction of 2000 tons of PMio, and 320 tons of PM2.5. The next largest change was in
North Dakota with a reduction of 115 tons of PM10 and 20 tons of PM2.5. All other changes were 30
tons or smaller.
Table 2-7. Total Impact of Fugitive Dust Adjustments to Unadjusted 2011 Inventory
State
Unadjusted
PM10
Unadjusted
PM2 5
Change in
PM10
Change in
PM2 5
PM10
Reduction
PM2_5
Reduction
Alabama
378,874
47,158
-310,750
-38,597
82%
82%
Arizona
237,361
30,015
-78,519
-9,778
33%
32%
Arkansas
421,958
58,648
-305,611
-40,757
72%
69%
California
255,889
38,664
-119,035
-17,930
47%
46%
Colorado
244,630
40,421
-130,598
-20,991
53%
52%
Connecticut
29,067
4,393
-25,877
-3,912
89%
89%
Delaware
11,548
1,968
-8,219
-1,396
71%
71%
District of
Columbia
2,115
337
-1,596
-254
75%
75%
Florida
292,797
39,637
-181,017
-24,333
62%
62%
Georgia
733,478
90,041
-593,644
-72,028
81%
80%
Idaho
432,116
49,294
-291,880
-32,897
68%
67%
Illinois
763,665
123,680
-472,816
-76,088
62%
61%
Indiana
603,152
85,151
-436,988
-60,981
72%
72%
Iowa
590,528
96,070
-339,349
-54,855
57%
57%
Kansas
747,242
118,726
-352,589
-54,766
47%
46%
Kentucky
199,744
29,496
-160,606
-23,504
80%
80%
Louisiana
236,787
35,730
-162,780
-24,086
69%
67%
Maine
50,547
7,016
-43,643
-6,078
86%
87%
Maryland
65,701
10,215
-49,481
-7,691
75%
75%
Massachusetts
205,561
22,444
-177,808
-19,370
87%
86%
Michigan
462,324
61,969
-353,229
-47,138
76%
76%
Minnesota
336,791
64,253
-217,036
-41,145
64%
64%
Mississippi
956,702
107,965
-782,249
-86,685
82%
80%
Missouri
1,063,992
130,995
-780,488
-94,576
73%
72%
Montana
385,541
50,583
-266,046
-33,521
69%
66%
Nebraska
591,457
85,206
-316,918
-45,198
54%
53%
Nevada
160,699
20,477
-47,147
-5,688
29%
28%
New Hampshire
25,540
3,766
-23,836
-3,515
93%
93%
New Jersey
24,273
5,412
-19,215
-4,255
79%
79%
New Mexico
924,497
95,871
-352,117
-36,344
38%
38%
New York
274,114
37,493
-236,431
-31,990
86%
85%
North Carolina
186,650
33,409
-146,918
-26,184
79%
78%
North Dakota
354,107
59,113
-218,744
-36,305
62%
61%
Ohio
414,902
64,609
-319,844
-49,300
77%
76%
Oklahoma
733,750
87,864
-385,344
-44,585
53%
51%
Oregon
348,093
40,596
-268,605
-30,516
77%
75%
Pennsylvania
208,246
30,344
-179,991
-26,158
86%
86%
Rhode Island
4,765
731
-3,628
-564
76%
77%
13

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State
Unadjusted
PM10
Unadjusted
PM2 5
Change in
PM10
Change in
PM2 5
PM10
Reduction
PM2_5
Reduction
South Carolina
259,350
31,494
-198,175
-24,002
77%
76%
South Dakota
262,935
44,587
-155,969
-26,220
59%
59%
Tennessee
139,731
25,357
-107,964
-19,514
77%
77%
Texas
2,573,687
304,551
-1,278,053
-146,122
50%
48%
Utah
196,551
21,589
-113,837
-12,464
58%
58%
Vermont
67,690
7,563
-61,423
-6,855
91%
91%
Virginia
131,798
19,374
-108,700
-15,895
82%
82%
Washington
174,969
27,999
-99,720
-15,425
57%
55%
West Virginia
85,956
10,652
-79,745
-9,888
93%
93%
Wisconsin
239,851
41,669
-164,113
-28,542
68%
68%
Wyoming
434,090
45,350
-264,580
-27,467
61%
61%
Domain Total
18,525,814
2,489,943
-11,792,873
-1,566,353
64%
63%
Figure 2-1 shows the impact of each step of the adjustment for 2011. The reductions due to the transport
fraction adjustments alone are shown at the top of Figure 2-1. The reductions due to the precipitation
adjustments are shown in the middle of Figure 2-1. The cumulative emission reductions after both
transport fraction and meteorological adjustments are shown at the bottom of Figure 2-1. The top plot
shows how the transport fraction has a larger reduction effect in the east, where forested areas are more
effective at reducing PM transport than in many western areas. The middle plot shows how the
meteorological impacts of precipitation, along with snow cover in the north, further reduce the dust
emissions.
14

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Figure 2-1. Impact of adjustments to fugitive dust emissions due to transport fraction, precipitation, and
cumulative

0 £

0 £
I
° £

2011eg Xportfrac - Unadjusted Annual Afdust PM2 5
2011eg Precip and Xportfrac Adjusted - Xportfrac Annual Afdust PM2 5
2011eg Precip and Xportfrac Adjusted - Unadjusted Annual Afdust PM2_5
15

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2.2.2 Agricultural ammonia sector (ag)
The agricultural NH3 (ag) sector includes livestock and agricultural fertilizer application emissions from
the 201 1NEIv2 nonpoint inventory. The livestock and fertilizer emissions in this sector are based only
on the SCCs listed in Table 2-8 and Table 2-9. The "ag" sector includes all of the NH3 emissions from
fertilizer from the NEI. However, the "ag" sector does not include all of the livestock ammonia
emissions, as there are also a small amount of NH3 emissions from livestock feedlots in the ptnonipm
inventory (as point sources) in California (175 tons) and Wisconsin (125 tons).
Table 2-8. Livestock SCCs extracted from the NEI to create the ag sector
see
SCC Description*
2805001100
Beef cattle - finishing operations on feedlots (drylots);Confinement
2805001200
Beef cattle - finishing operations on feedlots (drylots);Manure handling and storage
2805001300
Beef cattle - finishing operations on feedlots (drylots);Land application of manure
2805002000
Beef cattle production composite;Not Elsewhere Classified
2805003100
Beef cattle - finishing operations on pasture/range;Confinement
2805007100
Poultry production - layers with dry manure management systems;Confinement
2805007300
Poultry production - layers with dry manure management systems ;Land application of manure
2805008100
Poultry production - layers with wet manure management systems;Confinement
2805008200
Poultry production - layers with wet manure management systems;Manure handling and storage
2805008300
Poultry production - layers with wet manure management systems;Land application of manure
2805009100
Poultry production - broilers;Confinement
2805009200
Poultry production - broilers ;Manure handling and storage
2805009300
Poultry production - broilers;Land application of manure
2805010100
Poultry production - turkeys;Confinement
2805010200
Poultry production - turkeys;Manure handling and storage
2805010300
Poultry production - turkeys;Land application of manure
2805018000
Dairy cattle composite;Not Elsewhere Classified
2805019100
Dairy cattle - flush dairy;Confinement
2805019200
Dairy cattle - flush dairy;Manure handling and storage
2805019300
Dairy cattle - flush dairy;Land application of manure
2805020000
Cattle and Calves Waste Emissions;Milk Total
2805020002
Cattle and Calves Waste Emissions :Beef Cows
2805021100
Dairy cattle - scrape dairy;Confinement
2805021200
Dairy cattle - scrape dairy;Manure handling and storage
2805021300
Dairy cattle - scrape dairy;Land application of manure
2805022100
Dairy cattle - deep pit dairy;Confinement
2805022200
Dairy cattle - deep pit dairy;Manure handling and storage
2805022300
Dairy cattle - deep pit dairy;Land application of manure
2805023100
Dairy cattle - drylot/pasture dairy;Confinement
2805023200
Dairy cattle - drylot/pasture dairy;Manure handling and storage
2805023300
Dairy cattle - drylot/pasture dairy;Land application of manure
2805025000
Swine production composite;Not Elsewhere Classified (see also 28-05-039, -047, -053)
2805030000
Poultry Waste Emissions;Not Elsewhere Classified (see also 28-05-007, -008, -009)
2805030003
Poultry Waste Emissions;Layers
2805030004
Poultry Waste Emissions;Broilers
2805030007
Poultry Waste Emissions;Ducks
2805030008
Poultry Waste Emissions;Geese
2805030009
Poultry Waste Emissions;Turkeys
2805035000
Horses and Ponies Waste Emissions;Not Elsewhere Classified
2805039100
Swine production - operations with lagoons (unspecified animal age);Confinement
2805039200
Swine production - operations with lagoons (unspecified animal age);Manure handling and storage
16

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SCC
SCC Description*
2805039300
Swine production - operations with lagoons (unspecified animal age);Land application of manure
2805040000
Sheep and Lambs Waste Emissions;Total
2805045000
Goats Waste Emissions;Not Elsewhere Classified
2805045002
Goats Waste Emissions;Angora Goats
2805045003
Goats Waste Emissions;Milk Goats
2805047100
Swine production - deep-pit house operations (unspecified animal age);Confinement
2805047300
Swine production - deep-pit house operations (unspecified animal age);Land application of manure
2805053100
Swine production - outdoor operations (unspecified animal age);Confinement
* All SCC Descriptions begin "Miscellaneous Area Sources;Agriculture Production - Livestock"
Table 2-9. Fertilizer SCCs extracted from the NEI for inclusion in the "ag" sector
SCC
SCC Description*
2801700001
Anhydrous Ammonia
2801700002
Aqueous Ammonia
2801700003
Nitrogen Solutions
2801700004
Urea
2801700005
Ammonium Nitrate
2801700006
Ammonium Sulfate
2801700007
Ammonium Thiosulfate
2801700008
Other Straight Nitrate
2801700009
Ammonium Phosphates
2801700010
N-P-K (multi-grade nutrient fertilizers)
2801700011
Calcium Ammonium Nitrate
2801700012
Potassium Nitrate
2801700013
Diammonium Phosphate
2801700014
Monoammonium Phosphate
2801700015
Liquid Ammonium Polyphosphate
2801700099
Miscellaneous Fertilizers
* All descriptions include "Miscellaneous Area Sources;
Agriculture Production - Crops; Fertilizer Application" as
the beginning of the description.
2.2.3	Agricultural fires (agfire)
The agricultural fire (agfire) sector contains emissions from agricultural fires. These emissions were
placed into the sector based on their SCC code. All SCCs starting with 28015 were included. The first
three levels of descriptions for these SCCs are: Fires - Agricultural Field Burning; Miscellaneous Area
Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole field set on
fire. The SCC 2801500000 does not specify the crop type or burn method, while the more specific
SCCs specify field or orchard crops, and in some cases the specific crop being grown. For more
information on how emissions for agricultural fires were developed in the 201 1NEIv2, see Section 5.2
of the 201 1NEIv2 TSD.
2.2.4	Nonpoint source oil and gas sector (np_oilgas)
The nonpoint oil and gas (np oilgas) sector contains onshore and offshore oil and gas emissions. EPA
estimated emissions for all counties with 2011 oil and gas activity data with the Oil and Gas Tool, and
many S/L/T agencies also submitted nonpoint oil and gas data. The types of sources covered include
drill rigs, workover rigs, artificial lift, hydraulic fracturing engines, pneumatic pumps and other devices,
17

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storage tanks, flares, truck loading, compressor engines, and dehydrators. For more information on the
development of the oil and gas emissions in the 201 1NEIv2, see Section 3.20 of the 201 1NEIv2 TSD.
Note that some corrections to nonpoint oil and gas emissions in Utah were made for this analysis as
compared to those in the 201 lv6.2 platform and 201 1NEIv2. See the pt_oilgas sector (section 2.1.2) for
more information on point source oil and gas sources.
2.2.5 Residential wood combustion sector (rwc)
The residential wood combustion (rwc) sector includes residential wood burning devices such as
fireplaces, fireplaces with inserts (inserts), free standing woodstoves, pellet stoves, outdoor hydronic
heaters (also known as outdoor wood boilers), indoor furnaces, and outdoor burning in firepots and
chimneas. Free standing woodstoves and inserts are further differentiated into three categories:
conventional (not EPA certified); EPA certified, catalytic; and EPA certified, noncatalytic. Generally
speaking, the conventional units were constructed prior to 1988. Units constructed after 1988 had to
meet EPA emission standards and they are either catalytic or non-catalytic. For more information on the
development of the residential wood combustion emissions, see Section 3.14 of the 201 1NEIv2 TSD.
The SCCs in the rwc sector are shown in Table 2-10. Some reductions to Clark County, NV were
implemented in the modeling platform as a result of data review for NATA, but these are not reflected in
the 201 1NEIv2.
Table 2-10. SCCs in the Residential Wood Combustion Sector (rwc)*
see
SCC Description
2104008100
SSFC;Residential;Wood;Fireplace: general
2104008210
SSFC;Residential;Wood;Woodstove: fireplace inserts; non-EPA certified
2104008220
SSFC;Residential;Wood;Woodstove: fireplace inserts; EPA certified; non-catalytic
2104008230
SSFC;Residential;Wood;Woodstove: fireplace inserts; EPA certified; catalytic
2104008300
SSFC;Residential;Wood;Woodstove: freestanding, general
2104008310
SSFC;Residential;Wood;Woodstove: freestanding, non-EPA certified
2104008320
SSFC;Residential;Wood;Woodstove: freestanding, EPA certified, non-catalytic
2104008330
SSFC;Residential;Wood;Woodstove: freestanding, EPA certified, catalytic
2104008400
SSFC;Residential;Wood;Woodstove: pellet-fired, general (freestanding or FP insert)
2104008420
SSFC;Residential;Wood;Woodstove: pellet-fired, EPA certified (freestanding or FP
insert)
2104008510
SSFC;Residential;Wood;Furnace: Indoor, cordwood-fired, non-EPA certified
2104008610
SSFC;Residential;Wood;Hydronic heater: outdoor
2104008700
SSFC;Residential;Wood;Outdoor wood burning device, NEC (fire-pits, chimeas, etc)
2104009000
SSFC;Residential;Firelog;Total: All Combustor Types
* SSFC=Stationary Source Fuel Combustion
2.2.6 Other nonpoint sources sector (nonpt)
Stationary nonpoint sources that were not subdivided into the afdust, ag, np oilgas, or rwc sectors were
assigned to the "nonpt" sector. Locomotives and CMV mobile sources from the 201 1NEIv2 nonpoint
inventory are described in Section 2.4.1. There are too many SCCs to list all of them individually, but
the types of sources in the nonpt sector include:
•	stationary source fuel combustion, including industrial, commercial, and residential;
•	chemical manufacturing;
18

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•	industrial processes such as commercial cooking, metal production, mineral processes, petroleum
refining, wood products, fabricated metals, and refrigeration;
•	solvent utilization for surface coatings such as architectural coatings, auto refinishing, traffic
marking, textile production, furniture finishing, and coating of paper, plastic, metal, appliances,
and motor vehicles;
•	solvent utilization for degreasing of furniture, metals, auto repair, electronics, and
manufacturing;
•	solvent utilization for dry cleaning, graphic arts, plastics, industrial processes, personal care
products, household products, adhesives and sealants;
•	solvent utilization for asphalt application and roofing, and pesticide application;
•	storage and transport of petroleum for uses such as portable gas cans, bulk terminals, gasoline
service stations, aviation, and marine vessels;
•	storage and transport of chemicals;
•	waste disposal, treatment, and recovery via incineration, open burning, landfills, and composting;
•	agricultural burning and orchard heating;
•	miscellaneous area sources such as cremation, hospitals, lamp breakage, and automotive repair
shops.
2.3 2011 onroad mobile sources (onroad)
Onroad mobile sources include emissions from motorized vehicles that are normally operated on public
roadways. These include passenger cars, motorcycles, minivans, sport-utility vehicles, light-duty trucks,
heavy-duty trucks, and buses. The sources are further divided between diesel, gasoline, E-85, and
compressed natural gas (CNG) vehicles. The sector characterizes emissions from parked vehicle
processes (e.g. starts, hot soak, and extended idle) as well as from on-network processes (i.e., from
vehicles moving along the roads). All onroad emissions are generated using the SMOKE-MOVES
emissions modeling framework that leverages MOVES-generated outputs
(http://www.epa.gov/otaq/models/moves/index.htm) and hourly meteorology. The SCCs used in the
201 lv6.2 platform and in this analysis are significantly different than what was used in previous
platforms. The new onroad SCCs were designed to be more consistent with MOVES. For more details,
see the 201 1NEIv2 TSD. Informaion on the speciation, temporal allocation, and spatial allocation for the
onroad sector are available in Sections 3.2, 3.3, and 3.4.
2.3.1 Onroad (onroad)
For the continental U.S., EPA used a modeling framework that took into account the temperature
sensitivity of the on-road emissions. Specifically, EPA used MOVES inputs for representative counties,
vehicle miles traveled (VMT) and vehicle population (VPOP) data for all counties, along with tools that
integrated the MOVES model with SMOKE. In this way, it was possible to take advantage of the
gridded hourly temperature information available from meteorology modeling used for air quality
modeling. The "SMOKE-MOVES" integration tool was developed by EPA in 2010 and is used for
regional air quality modeling of onroad mobile sources.
SMOKE-MOVES requires that emission rate "lookup" tables be generated by MOVES which
differentiate emissions by process (i.e., running, start, vapor venting, etc.), vehicle type, road type,
temperature, speed, hour of day, etc. To generate the MOVES emission rates that could be applied
across the U.S., EPA used an automated process to run MOVES to produce emission factors by
19

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temperature and speed for a series of "representative counties," to which every other county is mapped.
Representative counties are used because it is impractical to generate a full suite of emission factors for
the more than 3,000 counties in the United States. Representative counties, for which emission factors
are generated are selected according to their state, elevation, fuels, age distribution, ramp fraction,
inspection & maintenance programs. Each county is then mapped to a representative county based on
its similarity with the representative county with respect to those attributes. For this modeling analysis,
there are 285 representative counties used.
Once representative counties have been identified, emission factors are generated with MOVES for each
representative county and for each "fuel month" - typically a summer month and a winter month. Using
the available MOVES emission rates for each representative county, SMOKE selects appropriate
emissions rates for each county, gridded hourly temperature, SCC, and speed bin and multiplies the
emission rate by activity (VMT (vehicle miles travelled), VPOP (vehicle population)), or HOTELING
(hours of extended idle) to produce emissions. These calculations were done for every county and grid
cell in the continental U.S. for each hour of the year.
Thus, the SMOKE-MOVES process for creating the model-ready emissions consists of the following
steps:
1) Determine which counties will be used to represent other counties in the MOVES runs.
2) Determine which months will be used to represent other month's fuel characteristics.
3) Create MOVES county databases (CDBs) to provide county-specific information on vehicle
populations, age distributions, and inspection-maintenance programs for each of the
representative counties.
4) Create inputs needed both by MOVES and by SMOKE, including temperatures and activity data.
5) Run MOVES to create emission factor tables for the temperatures found in each county.
6) Run SMOKE to apply the emission factors to activity data (VMT, VPOP, and HOTELING) to
calculate emissions based on the gridded hourly temperatures in the meteorological data.
7) Aggregate the results to the county-SCC level for summaries and quality assurance.
The onroad emissions are processed in four processing streams that are merged together into the onroad
sector emissions after processing:
• rate-per-distance (RPD) uses VMT as the activity data plus speed and speed profile information
to compute on-network emissions from exhaust, evaporative, permeation, refueling, and brake
and tire wear processes;
• rate-per-vehicle (RPV) uses vehicle population (VPOP) activity data to compute off-network
emissions from exhaust, evaporative, permeation, and refueling processes;
• rate-per-profile (RPP) uses VPOP activity data to compute off-network emissions from
evaporative fuel vaper venting including hot soak (immediately after a trip) and diurnal (vehicle
parked for a long period) emissions; and
• rate-per-hour (RPH) uses hoteling hours activity data to compute off-network emissions for
idling of long-haul trucks from extended idling and auxiliary power unit process.
The onroad emissions inputs are similar to the emissions in the onroad data category of the 201 1NEIv2,
described in more detail in Section 4.6 of the 201 1NEIv2 TSD. Specifically the platform for this
analysis, the 201 lv6.2 platform, and 201 1NEIv2 have nearly identical:
20

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• MOVES County databases (CDBs)
• Representative counties
• Fuel months
• Meteorology
• Activity data (VMT, VPOP, speed, HOTELING)
The key differences between the platform for this analysis and the 201 lv6.2 platform onroad inventories
are:
1. An updated version of MOVES2014 specific to this rulemaking was was used to generate
emission factors
2. The MOVES code and MOVES default database for this analysis were version 20150522 and
movesdb20150515, respectively. Also for the platform, EPA updated the internal speciation of
TOG to provide revised CB05 and CB6 model-species for air quality modeling.
3. Updates were made to CDBs and representative county mappings in Colorado and Pennsylvania
to correct issues with inspection and maintenance programs.
4. The platform for this analysis used the fuel database: movesdb20141021_fuelsupply.
Some additional key differences between the inventories for this analysis and the 201 1NEIv2 onroad
emission inventories are:
5. The 2011 platform used a different post-processor to create EFs for SMOKE because the
pollutants needed for speciation and running CMAQ are different than what is needed for the
NEI. For example, the NEI needs a much larger set of HAPs and the modeling platform requires
emissions for the components of PM2.5.
6. The 2011 platform had separate activity data for E-85. For the 2011 NEIv2, E-85 VMT and
VPOP were combined with gasoline VMT and VPOP because the CDBs were not generating a
complete set of EF for the E-85 sources. EPA corrected the seeding script for the platform and
regenerated the representative CDBs so that the EF tables had a complete set of EF for all fuel
types.
7. The treatment of California emissions differs between the two inventories (see below for more
details).
8. The NEI includes emissions for Alaska, Hawaii, Puerto Rico, and the Virgin Islands, where the
modeling platform does not.
9. The list of emission processes and SCCs differ between the two inventories. Both SMOKE-
MOVES runs were generated at the same level of detail, but the NEI emissions were aggregated
into 2 all-inclusive modes: refueling and all other modes. In addition, the NEI SCCs were
aggregated over roads to all parking and all road emissions. The list of modes (or aggregate
processes) used in the v6.2 platform and the corresponding MOVES processes mapped to them
are listed in Table 2-11.
21

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Table 2-11. Onroad emission aggregate processes
Aggregate process
Description
MOVES process IDs
40
All brake and tire wear
9; 10
53
All extended idle exhaust
17;90
62
All refueling
18; 19
72
All exhaust and evaporative except refueling and hoteling
1;2;11;12;13;15;16
91
Auxiliary Power Units
91
One reason that brake and tire wear was split out from the other processes was to allow for better
modeling of the impacts of electric vehicles in future years, since these vehicles still have brake and tire
wear emissions, but do not have exhaust, evaporative, or refueling emissions. For more detailed
information on methods used to develop the onroad emissions and input data sets and on running
SMOKE-MOVES, see the 201 1NEIv2 TSD.
Unlike in the 201 lv6.2 platform, California onroad emissions were not adjusted to match state-supplied
annual emissions (from the 201 1NEIv2) for this analysis meaning that unadjusted outputs taken directly
from SMOKE-MOVES were used as-is. As a result, there is no separate "onroad ca adj" sector in the
platform for this analysis and the "onroad" sector includes onroad emissions from all states, including
California.
An additional step was taken for the refueling emissions. Colorado submitted point emissions for
gasoline refueling for some counties3. For these counties, EPA zeroed out the onroad estimates of
gasoline refueling (SCC 2201*62) so that the states' point emissions would take precedence. The
onroad refueling emissions were zeroed out using the adjustment factor file (CFPRO) and Movesmrg.
2.4 2011 nonroad mobile sources (c1c2rail, c3marine, nonroad)
The nonroad mobile source emission modeling sectors consist of nonroad equipment emissions
(nonroad) and locomotive and commercial marine vessel (CMV) emissions divided into two nonroad
sectors: "clc2rail" and "c3marine".
2.4.1 Category 1/Category 2 Commercial Marine Vessels and Locomotives and
(c1c2rail)
The clc2rail sector contains locomotive and smaller CMV sources, except for railway maintenance
locomotives. Railway maintenance emissions are included in the nonroad sector. The "clc2" portion of
this sector name refers to the Category 1 and 2 CMV emissions, not the railway emissions. The C3
CMV emissions are in the c3marine and othpt sectors. All emissions in this sector are annual and at the
county-SCC resolution. As discussed in Table 2-1 and Table 2-2, the modeling platform emissions for
the clc2rail SCCs were extracted from the 201 1NEIv2 nonpoint inventory using the SCCs listed in
Table 2-12. The emissions include the offshore portion of the CI and C2 commercial marine sources,
including fishing vessels and oil rig support vessels in the Gulf of Mexico. Emissions that occur outside
of state waters are not assigned to states. For more information on CMV and locomotive sources in the
NEI, see Section 4.3 and Section 4.4 of the 201 1NEIv2 TSD, respectively.
3 There were 53 counties in Colorado that had point emissions for gasoline refueling. Outside Colorado, it was determined
that refueling emissions in the 2011 NEIv2 point did not significantly overlap the refueling emissions in onroad.
22

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Table 2-12. 201 1NEIv2 SCCs extracted for the starting point in clc2rail development
see
Description: Mobile Sources prefix for all
2280002100
Marine Vessels; Commercial; Diesel; Port
2280002200
Marine Vessels; Commercial; Diesel; Underway
2285002006
Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations
2285002007
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations
2285002008
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains (Amtrak)
2285002009
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines
2285002010
Railroad Equipment; Diesel; Yard Locomotives
2.4.2 Category 3 commercial marine vessels (c3marine)
The Category 3 (C3) CMV sources in the c3marine sector of the 201 lv6.2 platform run on residual oil
and use the SCCs 2280003100 and 2280003200 for port and underway emissions, respectively, and are
consistent with the 201 1NEIv2. Emissions for this sector use state-submitted values and EPA-developed
emissions in areas where states did not submit. A change in the 201 lv6.2 platform included in the
platform for this analysis is to restrict this sector only to include emissions in state-waters and to treat
the emissions as nonpoint sources instead of point sources. Thus, the c3marine emissions are placed in
layer 1 and allocated to grid cells using spatial surrogates. The development of the ECA-IMO-based C3
CMV inventory is discussed below. Canadian emissions, C3 CMV emissions outside of state waters,
and non-U.S. emissions farther offshore than U.S. waters are processed in the "othpt" sector (see Section
2.5.1).
The EPA-estimated C3 CMV emissions were developed based on a 4-km resolution ASCII raster format
dataset that preserves shipping lanes. This dataset has been used since the Emissions Control Area-
International Marine Organization (ECA-IMO) project began in 2005, although it was then known as the
Sulfur Emissions Control Area (SEC A). The ECA-IMO emissions consist of large marine diesel
engines (at or above 30 liters/cylinder) that until recently were allowed to meet relatively modest
emission requirements and as a result these ships would often burn residual fuel in that region. The
emissions in this sector are comprised of primarily foreign-flagged ocean-going vessels, referred to as
C3 CMV ships. The c3marine inventory includes these ships in several intra-port modes (i.e., cruising,
hoteling, reduced speed zone, maneuvering, and idling) and an underway mode, and includes near-port
auxiliary engine emissions.
An overview of the C3 ECA Proposal to the International Maritime Organization (EPA-420-F-10-041,
August 2010) project and future-year goals for reduction of NOx, SO2, and PM C3 emissions can be
found at: http://www.epa.gov/oms/regs/nonroad/marine/ci/420r09019.pdf. The resulting ECA-IMO
coordinated strategy, including emission standards under the Clean Air Act for new marine diesel
engines with per-cylinder displacement at or above 30 liters, and the establishment of Emission Control
Areas is available from http://www.epa.gov/oms/oceanvessels.htm. The base year ECA inventory is
2002 and consists of these CAPs: PM10, PM2.5, CO, CO2, NH3, NOx, SOx (assumed to be SO2), and
hydrocarbons (assumed to be VOC). EPA developed regional growth (activity-based) factors that were
applied to create the 2011 inventory from the 2002 data. These growth factors are provided in Table
2-13. The geographic regions listed in the table are shown in Figure 2-2. * The East Coast and Gulf
Coast regions were divided along a line roughly through Key Largo (longitude 80° 26' West).
Technically, the EEZ FIPS are not really "FIPS" state-county codes, but are treated as such in the
inventory and emissions processing.
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Table 2-13. Growth factors to project the 2002 ECA-IMO inventory to 2011
Region
EEZ FIPS
NOx
PMio
PM2.5
voc
CO
SO2
East Coast (EC)
85004
1.301
0.500
0.496
1.501
1.501
0.536
Gulf Coast (GC)
85003
1.114
0.428
0.423
1.288
1.288
0.461
North Pacific (NP)
85001
1.183
0.467
0.458
1.353
1.353
0.524
South Pacific (SP)
85002
1.367
0.525
0.521
1.565
1.562
0.611
Great Lakes (GL)
n/a
1.072
0.394
0.390
1.177
1.176
0.415
Outside ECA
98001
1.341
1.457
1.457
1.457
1.457
1.457
Figure 2-2. Illustration of regional modeling domains in ECA-IMO study
The emi ssions were converted to SMOKE point source inventory format as described in
http://www.epa.gov/ttn/chief/conference/ei l7/session6/mason.pdf allowing for the emissions to be
allocated to modeling layers above the surface layer. As described in the paper, the ASCII raster dataset
was converted to latitude-longitude, mapped to state/county FIPS codes that extended up to 200 nautical
miles (nm) from the coast, assigned stack parameters, and monthly ASCII raster dataset emissions were
used to create monthly temporal profiles. All non-US, non-EEZ emissions (i.e., in waters considered
outside of the 200 nm EEZ, and hence out of the U.S. and Canadian ECA-IMO controllable domain)
were simply assigned a dummy state/county FIPS code=98001, and were projected to year 2011 using
the "Outside EC A" factors in Table 2-13.
The assignment of U.S. state/county FIPS codes was restricted to state-federal water boundaries data
from the Mineral Management Service (MMS) that extend approximately 3 to 10 nautical miles (nm) off
shore. Emissions outside the 3 to 10 mile MMS boundary, but within the approximately 200 nm EEZ
boundaries in Figure 2-2, were projected to year 2011 using the same regional adjustment factors as the
U.S. emissions; however, the state/county FIPS codes were assigned as "EEZ" codes and those
emissions processed in the "othpt" sector (see Section 2.5.1). Note that state boundaries in the Great
Lakes are an exception, extending through the middle of each lake such that all emissions in the Great
24

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Lakes are assigned to a U.S. county or Ontario. This holds true for Midwest states and other states such
as Pennsylvania and New York. The classification of emissions to U.S. and Canadian FIPS codes is
needed to avoid double-counting of C3 CMV U.S. emissions in the Great Lakes because, as discussed in
the previous section, all CMV emissions in the Midwest RPO are processed in the "clc2rail" sector.
The SMOKE-ready data have been cropped from the original ECA-IMO entire northwestern quarter of
the globe to cover only the large continental U.S. 36-km "36US1" air quality model domain, the largest
domain used by EPA in recent years4.
The original ECA-IMO inventory did not delineate between ports and underway emissions (or other C3
modes such as hoteling, maneuvering, reduced-speed zone, and idling). However, a U.S. ports spatial
surrogate dataset was used to assign the ECA-IMO emissions to ports and underway SCCs 2280003100
and 2280003200, respectively. This had no effect on temporal allocation or speciation because all C3
CMV emissions, unclassified/total, port and underway, share the same temporal and speciation profiles.
See Section 3.2.1.3 for more details on c3marine speciation and Section 3.3.6 for details on temporal
allocation.
For California, the ECA-IMO 2011 emissions were scaled to match those provided by CARB for year
2011 because CARB has had distinct projection and control approaches for this sector since 2002.
These CARB C3 CMV emissions are documented in a staff report available at:
http://www.arb.ca.gov/regact/2010/offroadlsi 10/offroadisor.pdf. The CMV emissions obtained from the
CARB nonroad mobile dataset include the 2011 regulations to reduce emissions from diesel engines on
commercial harbor craft operated within California waters and 24 nautical miles of the California
shoreline. These emissions were developed using Version 1 of the California Emissions Projection
Analysis Model (CEPAM) that supports various California off-road regulations. The locomotive
emissions were obtained from the CARB trains dataset "ARMJ_RF#2002_ANNUAL_TRAINS.txt".
Documentation of the CARB offroad mobile methodology, including clc2rail sector data, is provided at:
http://www.arb.ca.gOv/msei/categories.htm#offroad motor vehicles.
2.4.3 Nonroad mobile equipment sources: (nonroad)
The nonroad equipment emissions are equivalent to the emissions in the nonroad data category of the
201 1NEIv2, with the exception that the modeling platform emissions also include monthly totals. The
NMIM County Database version was NCD20140620_nei201 lv2. This version includes updates to
population, spatial allocation, growth, and fuel data received from states as part of the 2011 NEI process
and improvements to fuel properties developed since the public release of NONROAD2008a. All
nonroad emissions are compiled at the county/SCC level. NMIM (EPA, 2005) creates the nonroad
emissions on a month-specific basis that accounts for temperature, fuel types, and other variables that
vary by month. The nonroad sector includes monthly exhaust, evaporative and refueling emissions from
nonroad engines (not including commercial marine, aircraft, and locomotives) that EPA derived from
NMIM for all states except California and Texas. Additional details on the development of the
201 1NEIv2 nonroad emissions are available in Section 4.5 the 201 1NEIv2 TSD. Note that the nonroad
emissions for 201 1NEIv2 are the same as those in the 201 INEIvl for all states except Delaware.
California year 2011 nonroad emissions were submitted to the 201 1NEIv2 and are also documented in a
staff report (ARB, 2010a). The nonroad sector emissions in California were developed using a modular
approach and include all rulemakings and updates in place by December 2010. These emissions were
4 The extent of the "36US1" domain is similar to the full geographic region shown in Figure 3-1. Note that this domain is not
specifically used in this 2011 platform, although spatial surrogates that can be used with it are provided.
25

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developed using Version 1 of the CEP AM which supports various California off-road regulations such
as in-use diesel retrofits (ARB, 2007), Diesel Risk-Reduction Plan (ARB, 2000) and 2007 State
Implementation Plans (SIPS) for the South Coast and San Joaquin Valley air basins (ARB, 2010b).
The CARB-supplied nonroad annual inventory emissions values were converted to monthly values by
using the aforementioned EPA NMIM monthly inventories to compute monthly ratios by county, SCC7
(fuel, engine type, and equipment type group), mode, and pollutant. SCC7 ratios were used because the
SCCs in the CARB inventory did not align with many of the SCCs in EPA NMIM inventory. By
aggregating up to SCC7, the two inventories had a more consistent coverage of sources. Some VOC
emissions were added to California to account for situations when VOC HAP emissions were included
in the inventory, but there were no VOC emissions. These additional VOC emissions were computed by
summing benzene, acetaldehyde, and formaldehyde for the specific sources.
Texas year 2011 nonroad emissions were also submitted to the NEI. The 201 1NEIv2 nonroad annual
inventory emissions values were converted to monthly values by using EPA's NMIM monthly
inventories to compute monthly ratios by county, SCC7, mode, and poll5.
2.5 "Other Emissions": Offshore Category 3 commercial marine vessels
and drilling platforms and non-U. S. sources
The emissions from Canada, Mexico, and non-U.S. offshore Category 3 Commercial Marine Vessels
(C3 CMV) and drilling platforms are included as part of four emissions modeling sectors: othpt, othar,
othafdust, and othon. The "oth" refers to the fact that these emissions are usually "other" than those in
the U.S. state-county geographic FIPS, and the remaining characters provide the SMOKE source types:
"pt" for point, "ar" for "area and nonroad mobile", and "on" for onroad mobile.
2.5.1 Point sources from offshore C3 CMV, drilling platforms, Canada and
Mexico (othpt)
As discussed in Section 2.4.2, the ECA-IMO-based C3 CMV emissions outside of state waters are
processed in the othpt sector. These C3 CMV emissions include those assigned to U.S. federal waters,
Canada, those assigned to the Exclusive Economic Zone (EEZ; defined as those emissions beyond the
U.S. Federal waters approximately 3-10 miles offshore, and extending to about 200 nautical miles from
the U.S. coastline), along with any other offshore emissions. These emissions are developed in the same
way as the EPA-dataset described in the c3marine sector (see Section 2.4.2). Emissions in U.S. waters
are aggregated into large regions and included in the 201 1NEIv2 using special FIPS codes. Because
these emissions are treated as point sources, shipping lane routes can be preserved and they may be
allocated to air quality model layers higher than layer 1.
For Canadian point sources, 2010 emissions provided by Environment Canada were used. Note that
VOC was not provided for Canadian point sources, but any VOC emissions were speciated into CB05
species. Temporal profiles and speciated emissions were also provided. Point sources in Mexico were
compiled based on the Inventario Nacional de Emisiones de Mexico, 2008 (ERG, 2014a). The point
source emissions in the 2008 inventory were converted to English units and into the FF10 format that
could be read by SMOKE, missing stack parameters were gapfilled using SCC-based defaults, and
5 If there was no match at county/SCC7/mode/poll, the allocation would fall back to state/SCC7/mode/poll. If that did not
find a match, then state/SCC7 was used. For a few situations, that would also fail to match and the monthly emissions were
allocated with a similar SCC7.
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latitude and longitude coordinates were verified and adjusted if they were not consistent with the
reported municipality. Note that there are no explicit HAP emissions in this inventory.
The othpt sector also includes point source offshore oil and gas drilling platforms that are beyond U.S.
state-county boundaries in the Gulf of Mexico. For these offshore emissions, data from the 201 1NEIv2
were used.
2.5.2 Area and nonroad mobile sources from Canada and Mexico (othar,
othafdust)
For Canada area and nonroad mobile sources, month-specific year-2010 emissions provided by
Environment Canada were used, including C3 CMV emissions. In this platform, an overlap between
these emissions and the U.S. C3 CMV emissions in the St. Lawrence River and Saguenay River was
corrected as compared to the 201 lv6.2 platform cases, which had double counting in this region. The
treatment of these sources as monthly was new in the modeling platform for this analysis. The Canadian
inventory included fugitive dust emissions that do not incorporate either a transportable fraction or
meteorological-based adjustments. To properly account for these issues, a separate sector called
othafdust was created and modeled using the same adjustments as are done for U.S. sources (see Section
2.2.1 for more details). Updated Shapefiles for creating spatial surrogates for Canada were also
provided.
Area and nonroad mobile sources in Mexico were compiled from the Inventario Nacional de Emisiones
de Mexico, 2008 (ERG, 2014a). The 2008 emissions were quality assured for completeness, SCC
assignments were made when needed, the pollutants expected for the various processes were reviewed,
and adjustments were made to ensure that PMio was greater than or equal to PM2.5. The resulting
inventory was written using English units to the nonpoint FF10 format that could be read by SMOKE.
Note that unlike the U.S. inventories, there are no explicit HAPs in the nonpoint or nonroad inventories
for Canada and Mexico, and therefore all HAPs are created from speciation.
2.5.3 Onroad mobile sources from Canada and Mexico (othon)
Onroad mobile sources in Mexico were compiled from the Inventario Nacional de Emisiones de
Mexico, 2008 (ERG, 2014a). SCCs compatible with the 201 1NEIv2 were assigned to the 2008 onroad
mobile source emissions in Mexico, and it was enforced that PM10 be greater than or equal to PM2.5.
Quality assurance of the onroad mobile source emissions data revealed that Baja California, Michoacan,
and Nuevo Leon had significantly high per capita emissions for all pollutants and should be considered
to be outliers. The emissions for these states were replaced with values computed based on the average
per capita emissions for the remaining states. The data were written using English units to the nonpoint
FF10 format that could be read by SMOKE.
For Canada, month-specific year-2010 emissions provided by Environment Canada were used. The
treatment of these sources as monthly was new in the modeling platform for this analysis. Note that
unlike the U.S. inventories, there are no explicit HAPs in the onroad inventories for Canada and Mexico,
and therefore all HAPs are created from speciation.
2.6 Fires (ptfire)
For this analysis, both the wildfires and prescribed burning emissions are contained in the ptfire sector.
In the 201 lv6.2 platform, wildfires are in the ptwildfire sector and prescribed burning emissions are
contained in the ptprescfire sector. Fire emissions are specified at geographic coordinates (point
locations) and have daily emissions values. The ptwildfire and ptprescfire sectors exclude agricultural
27

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burning and other open burning sources that are included in the nonpt sector. Emissions are day-specific
and include satellite-derived latitude/longitude of the fire's origin and other parameters associated with
the emissions such as acres burned and fuel load, which allow estimation of plume rise. Emissions for
the SCCs listed in Table 2-14 are treated as point sources and are consistent with the fires stored in the
Events data category of the 201 1NEIv2. Emissions for SCC 2811015000 are in the ptprescfire sector,
while the rest are in the ptwildfire sector. For more information on the development of the 201 1NEIv2
fire inventory, see Section 5.1 of the 201 1NEIv2 TSD.
Table 2-14. 2011 Platform SCCs representing emissions in the ptprescfire and ptwildfire modeling
sectors
SCC
SCC Description*
2810001000
Other Combustion; Forest Wildfires; Total
2810001001
Other Combustion; Forest Wildfires; Wildland fire use
2811015000
Other Combustion-as Event; Prescribed Burning for Forest Management; Total
* The first tier level of the SCC Description is "Miscellaneous Area Sources"
The point source day-specific emission estimates for 2011 fires rely on SMARTFIRE 2 (Sullivan, et al.,
2008), which uses the National Oceanic and Atmospheric Administration's (NOAA's) Hazard Mapping
System (HMS) fire location information as input. Additional inputs include the CONSUMEv3.0
software application (Joint Fire Science Program, 2009) and the Fuel Characteristic Classification
System (FCCS) fuel-loading database to estimate fire emissions from wildfires and prescribed burns on
a daily basis. The method involves the reconciliation of ICS-209 reports (Incident Status Summary
Reports) with satellite-based fire detections to determine spatial and temporal information about the
fires. A functional diagram of the SMARTFIRE 2 process of reconciling fires with ICS-209 reports is
available in the documentation (Raffuse, et al., 2007). Once the fire reconciliation process is completed,
the emissions are calculated using the U.S. Forest Service's CONSUMEv3.0 fuel consumption model
and the FCCS fuel-loading database in the BlueSky Framework (Ottmar, et al., 2007).
SMARTFIRE 2 estimates were used directly for all states except Georgia and Florida. For Georgia, the
satellite-derived emissions were removed from the ptfire inventory and replaced with a separate state-
supplied ptfire inventory. Adjustments were also made to Florida as described in Section 5.1.4 of the
201 1NEIv2 TSD. These changes made the data in the ptfire inventory consistent with the data in the
201 1NEIv2.
An update in the 201 lv6.2 platform was to split fires over 20,000 acres into the respective grid cells that
they overlapped. The idea of this was to prevent all emissions from going into a single grid cell, when
in reality the fire was more dispersed than a single point. The large fires were each projected as a circle
over the area centered on the specified latitude and longitude, and then apportioned into the grid cells
they overlapped. The area of each of the "subfires" was computed in proportion to the overlap with that
grid cell. These "subfires" were given new names that were the same as the original, but with "_a",
"_b", "_c", and "_d" appended as needed. The FIPS state and county codes and fire IDs for the fifteen
fires apportioned to multiple grid cells are shown in Table 2-15.
Table 2-15. Large fires apportioned to multiple grid cells
County FIPS
Fire ID
32007
SF11C1774898
32007
SF11C1775252
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County FIPS
Fire ID
32013
SF11C1774993
35027
SF11C1760072
35027
SF11C1760460
46065
SF11C1503125
48003
SF11C1718109
48081
SF11C1742329
48125
SF11C1749358
48243
SF11C1738273
48243
SF11C1747162
48353
SF11C1759082
48371
SF11C1750272
48415
SF11C1742358
56013
SF11C1791126
2.7 Biogenic sources (beis)
Biogenic emissions were computed based on the same 1 lg version of the 2011 meteorology data used
for the air quality modeling, and were developed using the Biogenic Emission Inventory System, version
3.61 (BEIS3.61) within SMOKE. This was an update from the emissions in the 201 lv6.1 platform that
used BEIS 3.14, and from the 201 1NEIv2 that used BEIS 3.60. Like BEIS 3.14, BEIS3.61 creates
gridded, hourly, model-species emissions from vegetation and soils. It estimates CO, VOC (most
notably isoprene, terpene, and sesquiterpene), and NO emissions for the contiguous U.S. and for
portions of Mexico and Canada.
BEIS3.61 has some important updates from BEIS 3.14. These include the incorporation of Version 4 of
the Biogenic Emissions Land use Database (BELD4), and the incorporation of a canopy model to
estimate leaf-level temperatures (Pouliot and Bash, 2015). BEIS version 3.61 includes a two-layer
canopy model. Layer structure varies with light intensity and solar zenith angle. Both layers of the
canopy model include estimates of sunlit and shaded leaf area based on solar zenith angle and light
intensity, direct and diffuse solar radiation, and leaf temperature (Bash et al., 2015). The new algorithm
requires additional meteorological variables over previous versions of BEIS. The variables output from
the Meteorology-Chemistry Interface Processor (MCIP) that are used to convert WRF outputs to CMAQ
inputs are shown in Table 2-16.
BELD4 is based on an updated version of the USDA-USFS Forest Inventory and Analysis (FIA)
vegetation speciation based data from 2002 to 2013 from the Forest Inventory and Analysis version 5.1.
Canopy coverage is based on the Landsat satellite NLCD product from 2001, since no canopy product
was developed for the 2006 NLCD. The FIA includes approximately 250,000 representative plots of
species fraction data that are within approximately 75 km of one another in areas identified as forest by
the NLCD canopy coverage. The 2006 NLCD provides land cover information with a native data grid
spacing of 30 meters. For land areas outside the conterminous United States, 500 meter grid spacing
land cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) is used.
29

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To provi de a sense of the scope and spatial di stribution of the emissions, plots of annual BEIS outputs
for isoprene and NO for 2011 are shown in Figure 2-3 and Figure 2-4, respectively.
Figure 2-3. Annual NO emissions output from BEIS 3.61 for 2011
Annual 2011 BEIS 3.6.1 NO

52
"lax 271 ?489 Min 0 0
Figure 2-4. Annual isoprene emissions output from BEIS 3.61 for 2011
Annual 2011 BEIS 3.6.1 ISOP
''¦'ax. 3032 0544 Min: 0.0
30

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Table 2-16. Meteorological variables required by BEIS 3.61
Variable
Description
LAI
leaf-area index
PRSFC
surface pressure
Q2
mixing ratio at 2 m
RC
convective pcpn per met TSTEP
RGRND
solar rad reaching sfc
RN
nonconvec. pcpn per met TSTEP
RSTOMI
inverse of bulk stomatal resistance
SLYTP
soil texture type by USDA category
SOIM1
volumetric soil moisture in top cm
SOIT1
soil temperature in top cm
TEMPG
skin temperature at ground
USTAR
cell averaged friction velocity
RADYNI
inverse of aerodynamic resistance
TEMP2
temperature at 2 m
2.8 SMOKE-ready non-anthropogenic inventories for chlorine
The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (Cb)
concentrations in oceanic air masses (Bullock and Brehme, 2002). Data at 36 km and 12 km resolution
were available and were not modified other than the model-species name "CHLORINE" was changed to
"CL2" to support CMAQ modeling.
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3 Emissions Modeling Summary
CMAQ requires hourly emissions of specific gas and particle species for the horizontal and vertical grid
cells contained within the modeled region (i.e., modeling domain). To provide emissions in the form
and format required by the model, it is necessary to "pre-process" the "raw" emissions (i.e., emissions
input to SMOKE) for the sectors described above in Section 2. In brief, the process of emissions
modeling transforms the emissions inventories from their original temporal resolution, pollutant
resolution, and spatial resolution into the hourly, speciated, gridded resolution required by the air quality
model. Emissions modeling includes temporal allocation, spatial allocation, and pollutant speciation. In
some cases, emissions modeling also includes the vertical allocation of point sources, but many air
quality models also perform this task because it greatly reduces the size of the input emissions files if the
vertical layers of the sources are not included.
As seen in Section 2, the temporal resolutions of the emissions inventories input to SMOKE vary across
sectors, and may be hourly, daily, monthly, or annual total emissions. The spatial resolution, may be
individual point sources, county/province/municipio totals, or gridded emissions and varies by sector.
This section provides some basic information about the tools and data files used for emissions modeling
as part of the modeling platform. In Section 2, the emissions inventories and how they differ from the
201 1NEIv2 were described. In Section 3, the descriptions of data are limited to the ancillary data
SMOKE uses to perform the emissions modeling steps. Note that all SMOKE inputs for the 201 lv6
platform are available from the CHIEF Emissions Modeling Clearinghouse website (see Section 1).
SMOKE version 3.6.5 was used to pre-process the raw emissions inventories into emissions inputs for
each modeling sector in a format compatible with CMAQ. For sectors that have plume rise, the in-line
emissions capability of the air quality models was used, which allows the creation of source-based and
two-dimensional gridded emissions files that are much smaller than full three-dimensional gridded
emissions files. For quality assurance of the emissions modeling steps, emissions totals by specie for the
entire model domain are output as reports that are then compared to reports generated by SMOKE on the
input inventories to ensure that mass is not lost or gained during the emissions modeling process.
3.1 Emissions modeling Overview
When preparing emissions for the air quality model, emissions for each sector are processed separately
through SMOKE, and then the final merge program (Mrggrid) is run to combine the model-ready,
sector-specific emissions across sectors. The SMOKE settings in the run scripts and the data in the
SMOKE ancillary files control the approaches used by the individual SMOKE programs for each sector.
Table 3-1 summarizes the major processing steps of each platform sector. The "Spatial" column shows
the spatial approach used: here "point" indicates that SMOKE maps the source from a point location
(i.e., latitude and longitude) to a grid cell; "surrogates" indicates that some or all of the sources use
spatial surrogates to allocate county emissions to grid cells; and "area-to-point" indicates that some of
the sources use the SMOKE area-to-point feature to grid the emissions (further described in Section
3.4.2). The "Speciation" column indicates that all sectors use the SMOKE speciation step, though
biogenics speciation is done within the Tmpbeis3 program and not as a separate SMOKE step. The
"Inventory resolution" column shows the inventory temporal resolution from which SMOKE needs to
calculate hourly emissions. Note that for some sectors (e.g., onroad, beis), there is no input inventory;
instead, activity data and emission factors are used in combination with meteorological data to compute
hourly emissions.
32

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Finally, the "plume rise" column indicates the sectors for which the "in-line" approach is used. These
sectors are the only ones with emissions in aloft layers based on plume rise. The term "in-line" means
that the plume rise calculations are done inside of the air quality model instead of being computed by
SMOKE. The air quality model computes the plume rise using the stack data and the hourly air quality
model inputs found in the SMOKE output files for each model-ready emissions sector. The height of
the plume rise determines the model layer into which the emissions are placed. The othpt sector has only
"in-line" emissions, meaning that all of the emissions are treated as elevated sources and there are no
emissions for those sectors in the two-dimensional, layer-1 files created by SMOKE. Day-specific point
fires are treated separately. For CMAQ modeling, fire plume rise is done within CMAQ itself. After
plume rise is applied, there will be emissions in every layer from the ground up to the top of the plume.
Table 3-1. Key emissions modeling steps by sector.
Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
afdust
Surrogates
Yes
annual

ag
Surrogates
Yes
annual

agfire
Surrogates
Yes
monthly

beis
Pre-gridded
land use
in BEIS3 .14
computed hourly

clc2rail
Surrogates
Yes
annual

c3 marine
Surrogates
Yes
annual
in-line
nonpt
Surrogates &
area-to-point
Yes
annual

nonroad
Surrogates &
area-to-point
Yes
monthly

np oilgas
Surrogates
Yes
annual

onroad
Surrogates
Yes
monthly activity,
computed hourly

othafdust
Surrogates
Yes
annual

othar
Surrogates
Yes
annual &
monthly

othon
Surrogates
Yes
annual &
monthly

othpt
Point
Yes
annual
in-line
pt oilgas
Point
Yes
annual
in-line
ptegu
Point
Yes
daily & hourly
in-line
ptfire
Point
Yes
daily
in-line
ptnonipm
Point
Yes
annual
in-line
rwc
Surrogates
Yes
annual

SMOKE has the option of grouping sources so that they are treated as a single stack when computing
plume rise. For the 2011 platform, no grouping was performed because grouping combined with "in-
line" processing will not give identical results as "offline" processing (i.e., when SMOKE creates 3-
dimensional files). This occurs when stacks with different stack parameters or lat/lons are grouped,
thereby changing the parameters of one or more sources. The most straightforward way to get the same
results between in-line and offline is to avoid the use of grouping.
33

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SMOKE was run for the smaller 12-km CONtinental United States "CONUS" modeling domain
(12US2) shown in Figure 3-1 and boundary conditions were obtained from a 2011 run of GEOS-Chem.
Figure 3-1. Air quality modeling domains
12US1 Continental US Domain
12US2 Continental US Domain
Both grids use a Lambert-Conformal projection, with Alpha = 33°, Beta = 45° and Gamma = -97°, with a
center of X = -97° and Y = 40°. Table 3-2 describes the grids for the two domains.
Table 3-2. Descriptions of the platform grids
Common
Name
Grid
Cell Size
Description
(see Figure 3-1)
Grid name
Parameters listed in SMOKE grid
description (GRIDDESC) file:
projection name, xorig, yorig,
xcell, ycell, ncols, nrows, nthik
Continental
12km grid
12 km
Entire conterminous
US plus some of
Mexico/Canada
12US1_459X299
T.AM 40N97W', -2556000, -1728000,
12.D3, 12.D3, 459, 299, 1
US 12 km or
"smaller"
CONUS-12
12 km
Smaller 12km
CONUS plus some of
Mexico/Canada
12US2
T.AM 40N97W', -2412000 , -
1620000, 12.D3, 12.D3, 396, 246, 1
Section 3.4 provides the details on the spatial surrogates and area-to-point data used to accomplish
spatial allocation with SMOKE.
34

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3.2 Chemical Speciation
The emissions modeling step for chemical speciation creates the "model species" needed by the air
quality model for a specific chemical mechanism. These model species are either individual chemical
compounds, i.e., "explicit species", or groups of species, i.e., "lumped species." The chemical
mechanism used for this analysis is CB05 with updated species mappings (i.e., "cb05v2) and includes
specific HAPs including acrolein, 1,3-butadiene, and naphthalene. In addition, this platform generates
the PM2.5 model species associated with the CMAQ Aerosol Module, version 6 (AE6). Table 3-3 lists
the model species produced by SMOKE in this platform.
The TOG and PM2.5 speciation factors that are the basis of the chemical speciation approach were
developed from the SPECIATE 4.4 database (https://www.epa.gov/air-emissions-modeling/speciate-
version-44-through-32). which is EPA's repository of TOG and PM speciation profiles of air pollution
sources. However, a few of the profiles used in the v6.2 platform will be published in later versions of
the SPECIATE database after the release of this documentation. The SPECIATE database development
and maintenance is a collaboration involving EPA's ORD, OTAQ, and the Office of Air Quality
Planning and Standards (OAQPS), in cooperation with Environment Canada (EPA, 2006a). The
SPECIATE database contains speciation profiles for TOG, speciated into individual chemical
compounds, VOC-to-TOG conversion factors associated with the TOG profiles, and speciation profiles
for PM2.5.
Some updates to speciation made in the 201 lv6.2 platform and also for this analysis include the
following (the subsections below contain more details on the specific changes):
•	VOC speciation profile cross reference assignments for nonpoint oil and gas sources were
updated;
•	VOC and PM speciation for onroad mobile sources occurs within MOVES2014;
•	only AE6 PM species are included, where previously both AE5 and AE6 species were generated;
•	the 2010 Canadian point source inventories in the othpt use CB05 speciation as it was provided
from Environment Canada; and
•	speciation for onroad mobile sources in Mexico was updated to be more consistent with that used
in the United States.
Totals of each model species by state and sector can be found in the state-sector totals workbooks for the
respective cases.
Table 3-3. Emission model species produced for CB05 CMAQ MP-Lite*
Inventory Pollutant
Model Species
Model species description
Cl2
CL2
Atomic gas-phase chlorine
HC1
HCL
Hydrogen Chloride (hydrochloric acid) gas
CO
CO
Carbon monoxide
NOx
NO
Nitrogen oxide
N02
Nitrogen dioxide
HONO
Nitrous acid
S02
S02
Sulfur dioxide
SULF
Sulfuric acid vapor
nh3
NH3
Ammonia
VOC
ALD2
Acetaldehyde
ALDX
Propionaldehyde and higher aldehydes
35

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Inventory Pollutant
Model Species
Model species description

BENZENE
Benzene
CH4
Methane6
ETH
Ethene
ETHA
Ethane
ETOH
Ethanol
FORM
Formaldehyde
IOLE
Internal olefin carbon bond (R-C=C-R)
ISOP
Isoprene
MEOH
Methanol
OLE
Terminal olefin carbon bond (R-C=C)
PAR
Paraffin carbon bond
TOL
Toluene and other monoalkyl aromatics
XYL
Xylene and other polyalkyl aromatics
VOC species from the biogenics
model that do not map to model
species above
SESQ
Sesquiterpenes
TERP
Terpenes
VOC HAPs for CMAQ MP-Lite
ALD2 PRIMARY
Primary acetaldehyde
FORM PRIMARY
Primary formaldehyde
ACROLEIN
Acrolein
BUTADIENE 13
1,3-Butadiene
NAPHTHALENE
Naphthalene
PMio
PMC
Coarse PM >2.5 microns and <10 microns
pm25
PAL
Aluminum
PCA
Calcium
PCL
Chloride
PEC
Particulate elemental carbon <2.5 microns
PFE
Iron
PK
Potassium
PH20
Water
PMG
Magnesium
PMN
Manganese
PMOTHR
PM2.5 not in other AE6 species
PNA
Sodium
PNCOM
Non-carbon organic matter
PN03
Particulate nitrate <2.5 microns
PNH4
Ammonium
POC
Particulate organic carbon (carbon only) <2.5
microns
PSI
Silica
PS04
Particulate Sulfate <2.5 microns
PTI
Titanium

6 Technically, CH4 is not a VOC but part of TOG. Although emissions of CH4 are derived, the AQ models do not use these
emissions because the anthropogenic emissions are dwarfed by the CH4 already in the atmosphere.
36

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3.2.1 VOC speciation
The concept of VOC speciation is to use emission source-related speciation profiles to convert VOC to
TOG, to speciate TOG into individual chemical compounds, and to use a chemical mechanism mapping
file to aggregate the chemical compounds to the chemical mechanism model species. The chemical
mechanism mapping file is typically developed by the developer of the chemical mechanism.
SMOKE uses profiles that convert inventory species and TOG directly to the model species. The
SMOKE-ready profiles are generated from the Speciation Tool which uses the "raw" (TOG to chemical
compounds) SPECIATE profiles and the chemical mechanism mapping file.
For the 201 lv6.2 platform and for this analysis, an updated chemical mapping file based on the latest
mechanism table for CB05 was used for all sectors (i.e., "cb05v2"), including onroad mobile sources.
This CB6 mapping file included some corrections to the CB05 profiles used in the 201 lv6.1 platform.
Similarly to previous platforms, HAP VOC inventory species were used in the VOC speciation process
for some sectors as described below.
3.2.1.1 The combination of HAP BAFM (benzene, acetaldehyde, formaldehyde and
methanol) and VOC for VOC speciation
The VOC speciation includes HAP emissions from the 201 1NEIv2 in the speciation process. Instead of
speciating VOC to generate all of the species listed in Table 3-3, emissions of four specific HAPs:
benzene, acetaldehyde, formaldehyde and methanol (collectively known as "BAFM") from the NEI
were "integrated" with the NEI VOC. The integration combines these HAPs with the VOC in a way
that does not double count emissions and uses the HAP inventory directly in the speciation process. The
basic process is to subtract the specified HAPs emissions mass from the VOC emissions mass, and to
then use a special "integrated" profile to speciate the remainder of VOC to the model species excluding
the specific HAPs. EPA believes that the HAP emissions in the NEI are often more representative of
emissions than HAP emissions generated via VOC speciation, although this varies by sector.
The BAFM HAPs (benzene, acetaldehyde, formaldehyde and methanol) were chosen for integration in
previous platforms because, with the exception of BENZENE7, they are the only explicit VOC HAPs in
the base version of the CMAQ 5.0.2 (CAPs only with chlorine chemistry) model. Explicit means that
they are not lumped chemical groups. The use of inventory HAP emissions along with VOC is called
"HAP-CAP integration".
For specific sources, especially within the nonpt sector, the integration included ethanol. To
differentiate when a source was integrating BAFM versus EBAFM (ethanol in addition to BAFM), the
speciation profiles that do not include ethanol are referred to as an "E-profile" and should be used when
ethanol comes from the inventory. For example, the E10 headspace gasoline evaporative speciation
profile 8763 should be used when ethanol is speciated from VOC, but 8763E should be used when
ethanol is obtained directly from the inventory.
The integration of HAP VOC with VOC is a feature available in SMOKE for all inventory formats other
than PTDAY (the format used for the ptfire sector). SMOKE allows the user to specify both the
particular HAPs to integrate via the INVTABLE and the particular sources to integrate via the
7 BENZENE was chosen to keep its emissions consistent between the multi-pollutant and base versions of CMAQ
37

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NHAPEXCLUDE file (which actually provides the sources to be excluded from integration8). For the
"integrated" sources, SMOKE subtracts the "integrated" HAPs from the VOC (at the source level) to
compute emissions for the new pollutant "NONHAPVOC." The user provides NONHAPVOC-to-
NONHAPTOG factors and NONHAPTOG speciation profiles9. SMOKE computes NONHAPTOG and
then applies the speciation profiles to allocate the NONHAPTOG to the other air quality model VOC
species not including the integrated HAPs. After determining if a sector is to be integrated, if all sources
have the appropriate HAP emissions, then the sector is considered fully integrated and does not need a
NHAPEXCLUDE file. If on the other hand, certain sources do not have the necessary HAPs, then an
NHAPEXCLUDE file must be provided based on the evaluation of each source's pollutant mix. EPA
considered CAP-HAP integration for all sectors and developed "integration criteria" for some of them
(see Section 3.2.1.3 for details).
The process of partial integration for BAFM is illustrated in Figure 3-2 that the BAFM records in the
input inventories do not need to be removed from any sources in a partially integrated sector because
SMOKE does this automatically using the INVTABLE configuration. For EBAFM integration, this
process is identical to that shown in the figure except for the addition of ethanol (E) to the list of
subtracted HAP pollutants. For full integration, the process would be very similar except that the
NHAPEXCLUDE file would not be used and all sources in the sector would be integrated.
Figure 3-2. Process of integrating BAFM with VOC for use in VOC Speciation
Emissions ready for SMOKE
SMOKE
Compute moles of each CBOS model species.
Use NONHAPTOG profilesappliedto NONHAPTOG
emissionsand B, F, A, M emissions for integrate sources.
Use TOG profiles applied to TOG for no-integrate sources
Assign speciation profile code to each emission source
Compute NONHAPVOC= VOC- (B + F + A+M)
emissionsfbr each integrate source
Retain VOC emissionsfbr each no-integrate source
Compute: NONHAPTOG emissions from NONHAPVOC for
each integrate source
Compute: TOG emissionsfrom VOC for each no-integrate
sou rce
: list of "no-i ntegrate" ¦
H sourres (NHAPEXCLUDE) I
Speciation Cross
j Reference File(GSREF) ¦
VOC-to-TOG fectors
| NONHAPVOC-to-NONHAPTOG
factors (GSCNV)
TOG and NONHAPTOG
speciati on factors
(G5PRO)
Speciated Emissionsfor VOC species
8 In SMOKE version 3.6.5, the options to specify sources for integration are expanded so that a user can specify the particular
sources to include or exclude from integration and there are settings to include or exclude all sources within a sector. In
addition, the error checking is significantly stricter for integrated sources. If a source is supposed to be integrated, but it is
missing BAFM or VOC, SMOKE will now raise an error.
9 These ratios and profiles are typically generated from the Speciation Tool when it is run with integration of a specified list
of pollutants, for example BAFM.
38

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In SMOKE, the INVTABLE allows the user to specify both the particular HAPs to integrate. Two
different types of INVTABLE files are included for use with different sectors of the platform. For
sectors that had no integration across the entire sector (see Table 3-4), EPA created a "no HAP use"
INVTABLE in which the "KEEP" flag is set to "N" for BAFM pollutants. Thus, any BAFM pollutants
in the inventory input into SMOKE are automatically dropped. This approach both avoids double-
counting of these species and assumes that the VOC speciation is the best available approach for these
species for sectors using this approach. The second INVTABLE, used for sectors in which one or more
sources are integrated, causes SMOKE to keep the inventory BAFM pollutants and indicates that they
are to be integrated with VOC. This is done by setting the "VOC or TOG component" field to "V" for
all four HAP pollutants. This type of INVTABLE is further differentiated into a version for those
sectors that integrate BAFM and another for those that integrate EBAFM.
39

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Table 3-4. Integration approach for BAFM and EBAFM for each platform sector
Platform
Sector
Approach for Integrating NEI emissions of Benzene (B), Acetaldehyde (A), Formaldehyde
(F), Methanol (M), and Ethanol (E)
ptegu
No integration
ptnonipm
No integration
ptfire
No integration
othafdust
No integration
othar
No integration
othon
No integration
ag
N/A - sector contains no VOC
afdust
N/A - sector contains no VOC
biog
N/A - sector contains no inventory pollutant "VOC"; but rather specific VOC species
agfire
Partial integration (BAFM)
clc2rail
Partial integration (BAFM)
c3marine
Partial integration (BAFM)
nonpt
Partial integration (BAFM and EBAFM)
nonroad
Partial integration (BAFM)
np oilgas
Partial integration (BAFM)
pt_oilgas
Partial integration (BAFM)
rwc
Partial integration (BAFM)
othpt
Partial integration (BAFM)
onroad
Full integration (internal to MOVES)1

'For the integration that is internal to MOVES, an extended list of HAPs are integrated, not just
BAFM. See 3.2.1.3
More details on the integration of specific sectors and additional details of the speciation are provided in
Section 3.2.1.3.
3.2.1.2 County specific profile combinations (GSPRO_COMBO)
SMOKE can compute speciation profiles from mixtures of other profiles in user-specified proportions.
The combinations are specified in the GSPROCOMBO ancillary file by pollutant (including pollutant
mode, e.g., EXH VOC), state and county (i.e., state/county FIPS code) and time period (i.e.,
month).This feature was used to speciate nonroad mobile and gasoline-related related stationary sources
that use fuels with varying ethanol content. In these cases, the speciation profiles require different
combinations of gasoline profiles, e.g. EO and E10 profiles. Since the ethanol content varies spatially
(e.g., by state or county), temporally (e.g., by month) and by modeling year (future years have more
ethanol) the GSPRO COMBO feature allows combinations to be specified at various levels for different
years. SMOKE computes the resultant profile using the fraction of each specific profile assigned by
county, month and emission mode.
The GSREF file indicates that a specific source uses a combination file with the profile code
"COMBO". Because the GSPRO COMBO file does not differentiate by SCC and there are various
levels of integration across sectors, sector-specific GSPRO COMBO files are used. Different profile
combinations are specified by the mode (e.g. exhaust, evaporative) and by changing the pollutant name
(e.g. EXH NONHAPTOG, EVP	NONHAPTOG). For the nonpt sector, a combination of BAFM
and EBAFM integration is used. Due to the lack of SCC-specificity in the GSPRO COMBO, the only
way to differentiate the sources that should use BAFM integrated profiles versus E-profiles is by
40

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changing the pollutant name. For example, EPA changed the pollutant name for the PFC future year
inventory so the integration would use EVP NONHAPVOC to correctly select the E-profile
combinations, while other sources used NONHAPVOC to select the typical BAFM profiles.
3.2.1.3 Additional sector specific details
The decision to integrate HAPs into the speciation was made on a sector by sector basis. For some
sectors there is no integration and VOC is speciated directly; for some sectors there is full integration
meaning all sources are integrated; and for other sectors there is partial integration meaning some
sources are not integrated and other sources are integrated. The integrated HAPs are either BAFM
(BAFM HAPs subtracted from VOC) or EBAFM (ethanol and BAFM HAPs subtracted from VOC).
Table 3-4 above summarizes the integration method for each platform sector.
For the clc2rail sector, EPA integrated BAFM for most sources from the 201 1NEIv2. There were a few
sources that had zero BAFM; therefore, they were not integrated. The CARB inventories (see Section
2.4.1) did not include HAPs; therefore, all non-NEI source emissions in the clc2rail sector were not
integrated. For California, the CARB inventory TOG was converted to VOC by dividing the inventory
TOG by the available VOC-to-TOG speciation factor.
For the othpt sector, the C3 marine sources (see Section 2.4.2) are integrated. HAPs in this sector are
derived identically to the U.S. C3 in the c3marine sector. The rest of the sources in othpt are not
integrated, thus the sector is partially integrated.
For the onroad sector, there are series of unique speciation issues. First, SMOKE-MOVES (see Section
2.3.1) is used to create emissions for these sectors and both the MEPROC and INVTABLE files are
involved in controlling which pollutants are processed. Second, the speciation occurs within MOVES
itself, not within SMOKE. The advantage of using MOVES to speciate VOC is that during the internal
calculation of MOVES, the model has complete information on the characteristics of the fleet and fuels
(e.g. model year, ethanol content, process, etc.), thereby allowing it to more accurately make use of
specific speciation profiles. This means that MOVES produces EF tables that include inventory
pollutants (e.g. TOG) and model-ready species (e.g. PAR, OLE, etc)10. SMOKE essentially calculates
the model-ready species by using the appropriate emission factor without further speciation11. Third,
MOVES' internal speciation uses full integration of an extended list of HAPs beyond EBAFM (called
"M-profiles"). The M-profiles integration is very similar to BAFM integration explained above except
that the integration calculation (see Figure 3-2) is performed on emissions factors instead of on
emissions. The list of integrated HAPs is described in Table 3-5. An additional run of the speciation
tool was necessary to create the M-profiles that were then loaded into the MOVES default database.
10 Because the EF table has the speciation "baked" into the factors, all counties that are in the county group (i.e. are mapped
to that representative county) will have the same speciation.
11 For more details on the use of model-ready EF, see the SMOKE 3.6.5 documentation:
https ://www. cmascenter. org/smoke/documentation/3.6.5/html/
41

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Table 3-5. MOVES integrated species in M-profiles
MOVES ID
Pollutant Name
5
Methane (CH4)
20
Benzene
21
Ethanol
22
MTBE
24
1,3-Butadiene
25
Formaldehyde
26
Acetaldehyde
27
Acrolein
40
2,2,4-Trimethylpentane
41
Ethyl Benzene
42
Hexane
43
Propionaldehyde
44
Styrene
45
Toluene
46
Xylene
185
Naphthalene gas
For the nonroad sector, CNG or LPG sources (SCCs beginning with 2268 or 2267) are not integrated
because NMIM computed only VOC and not any HAPs for these SCCs. All other nonroad sources were
integrated except in California. For California, the CARB inventory TOG was converted to VOC by
dividing the inventory TOG by the available VOC-to-TOG speciation factor. SMOKE later applies the
same VOC-to-TOG factor prior to computing speciated emissions. The CARB-based nonroad data
includes exhaust and evaporative mode-specific data for VOC, but does not contain refueling. The
CARB inventory does not include HAP estimates for all sources. Therefore, the sources which have
VOC but not BAFM, or for which BAFM is greater than VOC, are not integrated and the remaining
sources are integrated. The future year CARB inventories did not have BAFM so all sources for
California were not integrated. The gasoline exhaust profiles were updated to 8750a and 875la (this is
true nation-wide).
For the ptnonipm sector, the 2011, and 2040 runs were not integrated. This was an oversight— it should
have been partial integration in the 2040 runs because the biodiesel inventory (SCC 30125010) provided
by OTAQ includes BAFM. Aircraft emissions use the profile 5565. In previous versions of the
platform, a significant amount of VOC emissions associated with the pulp and paper and the chemical
manufacturing industries did not have specific profiles assigned to them (i.e. they had the default VOC
profile 0000). To address this, EPA and Environ developed industry wide average profiles to improve
the speciation of these significant sources of VOC. The two new composite profiles are "Composite
Profile - Chemical Manufacturing (95325)" and "Composite Profile - Pulp and Paper Mills" (95326)12
For most sources in the rwc sector, the VOC emissions were greater than or equal to BAFM, and BAFM
was not zero, so those sources were integrated, although a few specific sources that did not meet these
criteria could not be integrated.
12 These profiles are expected to be included in SPECIATE 4.5.
42

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For the oil and gas sources in the np oilgas and pt oilgas sectors, the basins studied in WRAP Phase III
have basin-specific VOC speciation that takes into account the distinct composition of gas. ENVIRON
developed these basin-specific profiles using gas composition analysis data obtained from operators
through surveys. ENVIRON separated out emissions and speciation from conventional/tight sands/shale
gas from coal-bed methane (CBM) gas sources. Table 3-6 lists the basin- and gas composition-specific
profiles used for the sources in the WRAP Phase III basins. For oil and gas sources outside of the
WRAP Phase III basins, the profiles did not vary by region or basin (see Table 3-7). Table 3-8 lists the
WRAP Phase III counties.
Table 3-6. VOC profiles for WRAP Phase III basins
Profile Code
Description
DJFLA
D-J Basin Flashing Gas Composition for Condensate
DJVNT
D-J Basin Produced Gas Composition
PNC01
Piceance Basin Gas Composition at Conventional Wells
PNC02
Piceance Basin Gas Composition at Oil Wells
PNC03
Piceance Basin Flashing Gas Composition for Condensate
PRBCO
Powder River Basin Produced Gas Composition for Conventional Wells
PRM01
Permian Basin Produced Gas Composition
SSJCO
South San Juan Basin Produced Gas Composition for Conventional Wells
SWFLA
SW Wyoming Basin Flash Gas Composition
SWVNT
SW Wyoming Basin Vented Gas Composition
UNT02
Uinta Basin Gas Composition at Conventional Wells
UNT03
Uinta Basin Flashing Gas Composition for Oil
UNT04
Uinta Basin Flashing Gas Composition for Condensate
WRBCO
Wind River Basin Produced Gas Composition for Conventional Wells
Table 3-7. National VOC profiles for oil and gas
profile
Description
0000
Over All Average
0001
External Combustion Boiler - Residual Oil
0002
External Combustion Boiler - Distillate Oil
0003
External Combustion Boiler - Natural Gas
0004
External Combustion Boiler - Refinery Gas
0007
Natural Gas Turbine
0008
Reciprocating Diesel Engine
0051
Flares - Natural Gas
0296
Fixed Roof Tank - Crude Oil Production
1001
Internal Combustion Engine - Natural Gas
1010
Oil and Gas Production - Fugitives - Unclassified
1011
Oil and Gas Production - Fugitives - Valves and Fittings - Liquid Service
1012
Oil and Gas Production - Fugitives - Valves and Fittings - Gas Service
1207
Well Heads (Water Flood) Composite
2487
Composite of 7 Emission Profiles from Crude Oil Storage Tanks - 1993
43

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profile
Description
2489
Composite of 15 Fugitive Emission Profiles from Petroleum Storage Facilities - 1993
8489
Natural Gas Production
8950
Natural Gas Transmission
Table 3-8. Counties included in the WRAP Dataset
MPS
State
County
08001
CO
Adams
08005
CO
Arapahoe
08007
CO
Archuleta
08013
CO
Boulder
08014
CO
Broomfield
08029
CO
Delta
08031
CO
Denver
08039
CO
Elbert
08043
CO
Fremont
08045
CO
Garfield
08051
CO
Gunnison
08059
CO
Jefferson
08063
CO
Kit Carson
08067
CO
La Plata
08069
CO
Larimer
08073
CO
Lincoln
08075
CO
Logan
08077
CO
Mesa
08081
CO
Moffat
08087
CO
Morgan
08095
CO
Phillips
08097
CO
Pitkin
08103
CO
Rio Blanco
08107
CO
Routt
08115
CO
Sedgwick
08121
CO
Washington
08123
CO
Weld
08125
CO
Yuma
30003
MT
Big Horn
30075
MT
Powder River
35005
NM
Chaves
35015
NM
Eddy
35015
NM
Lea
35031
NM
Mc Kinley
MPS
State
County
35039
NM
Rio Arriba
35041
NM
Roosevelt
35043
NM
Sandoval
35045
NM
San Juan
48003
TX
Andrews
48033
TX
Borden
48079
TX
Cochran
48081
TX
Coke
48103
TX
Crane
48105
TX
Crockett
48107
TX
Crosby
48109
TX
Culberson
48115
TX
Dawson
48125
TX
Dickens
48135
TX
Ector
48141
TX
El Paso
48151
TX
Fisher
48165
TX
Gaines
48169
TX
Garza
48173
TX
Glasscock
48219
TX
Hockley
48227
TX
Howard
48229
TX
Hudspeth
48235
TX
Irion
48263
TX
Kent
48269
TX
King
48301
TX
Loving
48303
TX
Lubbock
48305
TX
Lynn
48317
TX
Martin
48329
TX
Midland
48335
TX
Mitchell
48353
TX
Nolan
48371
TX
Pecos
MPS
State
County
48383
TX
Reagan
48389
TX
Reeves
48413
TX
Schleicher
48415
TX
Scurry
48431
TX
Sterling
48435
TX
Sutton
48445
TX
Terry
48451
TX
Tom Green
48461
TX
Upton
48475
TX
Ward
48495
TX
Winkler
48501
TX
Yoakum
49007
UT
Carbon
49009
UT
Daggett
49013
UT
Duchesne
49015
UT
Emery
49019
UT
Grand
49043
UT
Summit
49047
UT
Uintah
56001
WY
Albany
56005
WY
Campbell
56007
WY
Carbon
56009
WY
Converse
56011
WY
Crook
56013
WY
Fremont
56019
WY
Johnson
56023
WY
Lincoln
56025
WY
Natrona
56027
WY
Niobrara
56033
WY
Sheridan
56035
WY
Sublette
56037
WY
Sweetwater
56041
WY
Uinta
56045
WY
Weston
44

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Everywhere in the WRAP region (Table 3-8), WRAP speciation was applied instead of applying BAFM
integration. This is a correction from the 201 lv6.2 platform, in which a SMOKE processing error meant
that for select sources in select counties the national VOC profiles for oil and gas were used13.
For the biog sector, the speciation profiles used by BEIS are not included in SPECIATE. The 2011
platform uses BEIS3.61, which includes a new species (SESQ) that was mapped to the model species
SESQT. The profile code associated with BEIS3.61 for use with CB05 is "B10C5", while the profile for
use with CB6 is "B10C6". The main difference between the profiles is the explicit treatment of acetone
emissions in B10C6.
For the nonpt sector, where VOC emissions were greater than or equal to BAFM and BAFM was not
zero, the sources were integrated. For portable fuel containers (PFCs) and fuel distribution operations
associated with the bulk-plant-to-pump (BTP) distribution, ethanol may be mixed into the fuels; therefore,
county- and month-specific COMBO speciation was used (via the GSPROCOMBO file). Refinery to
bulk terminal (RBT) fuel distribution and bulk plant storage (BPS) speciation are considered upstream
from the introduction of ethanol into the fuel; therefore a single profile is sufficient for these sources. No
refined information on potential VOC speciation differences between cellulosic diesel and cellulosic
ethanol sources was available, therefore cellulosic diesel and cellulosic ethanol sources used the same
SCC (30125010: Industrial Chemical Manufacturing, Ethanol by Fermentation production) for VOC
speciation as was used for corn ethanol plants. For the future year, PFC and the cellulosic sources were
integrated EBAFM (i.e. used E-profiles) because ethanol was present in those inventories.
3.2.1.4 Future year speciation
The VOC speciation approach used for the future year case is customized to account for the impact of fuel
changes. These changes affect the onroad, nonroad, and parts of the nonpt and ptnonipm sectors.
Speciation profiles for VOC in the nonroad sector account for the changes in ethanol content of fuels
across years. A description of the actual fuel formulations for 2011 can be found in the 201 1NEIv2 TSD,
and for 2040 see Section 4.3. For 2011, EPA used "COMBO" profiles to model combinations of profiles
for E0 and E10 fuel use. For 2040, EPA assumed E10 fuel use for all nonroad gasoline processes.
The speciation changes from fuels in the nonpt sector are for PFCs and fuel distribution operations
associated with the BTP distribution. For these sources, ethanol may be mixed into the fuels, in which
case speciation would change across years. The speciation changes from fuels in the ptnonipm sector
include BTP distribution operations inventoried as point sources. RBT fuel distribution and BPS
speciation does not change across the modeling cases because this is considered upstream from the
introduction of ethanol into the fuel. For PFCs, ethanol was present in the future inventories and therefore
EBAFM profiles were used to integrate ethanol in the future year speciation. The mapping of fuel
distribution SCCs to PFC, BTP, BPS, and RBT emissions categories can be found in Appendix A.
Table 3-9 summarizes the different profiles utilized for the fuel-related sources in each of the sectors for
2011 and the future year cases. This table indicates when "E-profiles" were used instead of BAFM
13 The sources were accidentally not included in the list of non-integrated sources/counties. At the time of the 201 lv6.2
platform modeling, EPA only had no-integrate WRAP profiles (VOC); therefore the incorrectly assigned integrated sources
(NONHAPVOC) defaulted to the national integrated profiles for oil and gas. This impacted np oilgas but not pt oilgas. This
issue was corrected for the 201 lv6.3 platform.
45

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integrated profiles. The term "COMBO" indicates that a combination of the profiles listed was used to
speciate that subcategory using the GSPRO COMBO file.
Table 3-9. Select VOC profiles 2011 vs 2040
sector
Sub-
category
2011
2040
nonroad
gasoline
exhaust
COMBO
8750a Pre-Tier 2 E0 exhaust
8751a Pre-Tier 2 E10 exhaust
8751a Pre-Tier 2 E10 exhaust
nonroad
gasoline
evap-
orative
COMBO
8753 E0 evap
8754 E10 evap
8754 E10 evap
nonroad
gasoline
refueling
COMBO
8869 E0 Headspace
8870 E10 Headspace
8870 E10 Headspace
nonroad
diesel
exhaust
8774 Pre-2007 MY HDD exhaust
8774 Pre-2007 MY HDD exhaust
nonroad
diesel
evap.
4547 Diesel Headspace
4547 Diesel Headspace
nonroad
diesel
refueling
4547 Diesel Headspace
4547 Diesel Headspace
nonpt/
ptnonipm
PFC
COMBO
8869 E0 Headspace
8870 E10 Headspace
8870E E10 Headspace
nonpt/
ptnonipm
BTP
COMBO
8869 E0 Headspace
8870 E10 Headspace
COMBO
8870 E10 Headspace
8871 E15 Headspace
8934 E85 Evap
nonpt/
ptnonipm
BPS/RBT
8869 E0 Headspace
8869 E0 Headspace
The speciation of onroad VOC occurs within MOVES. MOVES takes into account fuel type and
properties, emission standards as they affect different vehicle types and model years, and specific
emission processes. A description of the actual fuel formulations for 2011 can be found in the 201 1NEIv2
TSD; for 2040 see Section 4.3. Table 3-10 describes all of the M-profiles available to MOVES depending
on the model year range, MOVES process (processID), fuel sub-type (fuelSubTypelD), and regulatory
class (regClassID). Table 3-11 to Table 3-13 describe the meaning of these MOVES codes. For a
specific representative county and future year, there will be a different mix of these profiles. For
example, for HD diesel exhaust, the emissions will use a combination of profiles 8774M and 8775M
depending on the proportion of HD vehicles that are pre-2007 model years (MY) in that particular county.
As that county is projected farther into the future, the proportion of pre-2007 MY vehicles will decrease.
A second example, for gasoline exhaust (not including E-85), the emissions will use a combination of
profiles 8756M, 8757M, 8758M, 8750aM, and 875laM. Each representative county has a different mix
of these key properties and therefore has a unique combination of the specific M-profiles.
46

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Table 3-10. Onroad M-profiles
Profile
Profile Description
Model Years
processID
fuelSubTypelD
regClassID
1001M
CNG Exhaust
1940-2050
1,2,15,16
30
48
4547M
Diesel Headspace
1940-2050
11
20,21,22
0
4547M
Diesel Headspace
1940-2050
12,13,18,19
20,21,22
10,20,30,40,41,
42,46,47,48
8753M
E0 Evap
1940-2050
12,13,19
10
10,20,30,40,41,42,
46,47,48
8754M
E10 Evap
1940-2050
12,13,19
12,13,14
10,20,30,40,41,
42,46,47,48
8756M
Tier 2 E0 Exhaust
2001-2050
1,2,15,16
10
20,30
8757M
Tier 2 E10 Exhaust
2001-2050
1,2,15,16
12,13,14
20,30
8758M
Tier 2 El5 Exhaust
1940-2050
1,2,15,16
15,18
10,20,30,40,41,
42,46,47,48
8766M
E0 evap permeation
1940-2050
11
10
0
8769M
E10 evap permeation
1940-2050
11
12,13,14
0
8770M
E15 evap permeation
1940-2050
11
15,18
0
8774M
Pre-2007 MY HDD
exhaust
1940-2006
1,2,15,16,17,9
0
20,21,22
40,41,42,46,47,48
8774M
Pre-2007 MY HDD
exhaust
1940-2050
9114
20,21,22
46,47
8774M
Pre-2007 MY HDD
exhaust
1940-2006
1,2,15,16
20,21,22
20,30
8775M
2007+ MY HDD exhaust
2007-2050
1,2,15,16
20,21,22
20,30
8775M
2007+ MY HDD exhaust
2007-2050
1,2,15,16,17,9
0
20,21,22
40,41,42,46,47,48
8855M
Tier 2 E85 Exhaust
1940-2050
1,2,15,16
50,51,52
10,20,30,40,41,
42,46,47,48
8869M
E0 Headspace
1940-2050
18
10
10,20,30,40,41,
42,46,47,48
8870M
E10 Headspace
1940-2050
18
12,13,14
10,20,30,40,41,
42,46,47,48
8871M
E15 Headspace
1940-2050
18
15,18
10,20,30,40,41,
42,46,47,48
8872M
E15 Evap
1940-2050
12,13,19
15,18
10,20,30,40,41,
42,46,47,48
8934M
E85 Evap
1940-2050
11
50,51,52
0
8934M
E85 Evap
1940-2050
12,13,18,19
50,51,52
10,20,30,40,41,
42,46,47,48
8750aM
Pre-Tier 2 E0 exhaust
1940-2000
1,2,15,16
10
20,30
8750aM
Pre-Tier 2 E0 exhaust
1940-2050
1,2,15,16
10
10,40,41,42,46,47,48
875 laM
Pre-Tier 2 E10 exhaust
1940-2000
1,2,15,16
11,12,13,14
20,30
875 laM
Pre-Tier 2 E10 exhaust
1940-2050
1,2,15,16
11,12,13,14,15,18
15
10,40,41,42,46,47,48
14	91 is the processid for APUs, which are diesel engines that are not covered by the 2007 Heavy-Duty Rule, so the older
technology applies to all years.
15	The profile assignments for pre-2001 gasoline vehicles fueled on E15/E20 fuels (subtypes 15 and 18) were corrected
MOVES2014a. This model year range, process, and fuelsubtype, and regclass combination is already assigned to profile 8758.
47

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Table 3-11. MOVES Process IDs
Process ID
Process Name
1
Running Exhaust
2
Start Exhaust
11
Evap Permeation
12
Evap Fuel Vapor Venting
13
Evap Fuel Leaks
15
Crankcase Running Exhaust
16
Crankcase Start Exhaust
17
Crankcase Extended Idle Exhaust
18
Refueling Displacement Vapor Loss
19
Refueling Spillage Loss
20
Evap Tank Permeation
21
Evap Hose Permeation
22
Evap RecMar Neck Hose Permeation
23
Evap RecMar Supply /Ret Hose Permeation
24
Evap RecMar Vent Hose Permeation
30
Diurnal Fuel Vapor Venting
31
HotSoak Fuel Vapor Venting
32
RunningLoss Fuel Vapor Venting
40
Nonroad
90
Extended Idle Exhaust
91
Auxiliary Power Exhaust
Table 3-12. MOVES Fuel subtype IDs
Fuel Subtype ID
Fuel Subtype Descriptions
10
Conventional Gasoline
11
Reformulated Gasoline (RFG)
12
Gasohol (E10)
13
Gasohol (E8)
14
Gasohol (E5)
15
Gasohol (El5)
18
Ethanol (E20)
20
Conventional Diesel Fuel
21
Biodiesel (BD20)
22
Fischer-Tropsch Diesel (FTD100)
30
Compressed Natural Gas (CNG)
50
Ethanol
51
Ethanol (E85)
Having it assigned to both caused errors in speciation in proportion to the contribution of E15 in an area. However the incorrect
assignment in MOVES2014 did not impact the 2011 and 2040 air quality runs used for the HD GHG Phase 2, as mentioned in
the Docket Memo: Updates to MOVES for Emissions Analysis of Greenhouse Gas Emissions and Fuel Efficiency Standards
for Medium- and Heavy-Duty Engines and Vehicles - Phase 2 FRM.
48

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52 Ethanol (E70)
Table 3-13. MOVES Regclass IDs
Reg. Class ID
Regulatory Class Description
0
Doesn't Matter
10
Motorcycles
20
Light Duty Vehicles
30
Light Duty Trucks
40
Class 2b Trucks with 2 Axles and 4 Tires (8,500 lbs < GVWR <= 10,000 lbs)
41
Class 2b Trucks with 2 Axles and at least 6 Tires or Class 3 Trucks (8,500 lbs < GVWR <= 14,000
lbs)
42
Class 4 and 5 Trucks (14,000 lbs < GVWR <= 19,500 lbs)
46
Class 6 and 7 Trucks (19,500 lbs < GVWR <= 33,000 lbs)
47
Class 8a and 8b Trucks (GVWR > 33,000 lbs)
48
Urban Bus (see CFR Sec 86.091 2)
3.2.2 PM speciation
In addition to VOC profiles, the SPECIATE database also contains the PM2.5 speciated into both
individual chemical compounds (e.g., zinc, potassium, manganese, lead), and into the "simplified" PM2.5
components used in the air quality model. We speciated PM2.5 into the AE6 species associated with
CMAQ 5.0.1 and later versions.
Table 3-14 shows the mapping of AE5 and AE6 for historical reference. The majority of the 2011
platform PM profiles come from the 911XX series which include updated AE6 speciation16.
Table 3-14. PM model species: AE5 versus AE6
Species name
Species description
AE5
AE6
POC
organic carbon
Y
Y
PEC
elemental carbon
Y
Y
PS04
Sulfate
Y
Y
PN03
Nitrate
Y
Y
PMFINE
unspeciated PM2.5
Y
N
PNH4
Ammonium
N
Y
PNCOM
non-carbon organic matter
N
Y
PFE
Iron
N
Y
PAL
Aluminum
N
Y
PSI
Silica
N
Y
PTI
Titanium
N
Y
PCA
Calcium
N
Y
PMG
Magnesium
N
Y
PK
potassium
N
Y
PMN
Manganese
N
Y
16 The exceptions are 5674 (Marine Vessel - Marine Engine - Heavy Fuel Oil) used for c3marine and 92018 (Draft Cigarette
Smoke - Simplified) used in nonpt.
49

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Species name
Species description
AE5
AE6
PNA
Sodium
N
Y
PCL
Chloride
N
Y
PH20
Water
N
Y
PMOTHR
PM2.5 not in other AE6 species
N
Y
For the onroad sector, for all processes except brake and tire wear, PM speciation occurs within MOVES
itself, not within SMOKE (similar to the VOC speciation described above). The advantage of using
MOVES to speciate PM is that during the internal calculation of MOVES, the model has complete
information on the characteristics of the fleet and fuels (e.g. model year, sulfur content, process, etc.) to
accurately match to specific profiles. This means that MOVES produces EF tables that include total PM
(e.g. PMio and PM2.5) and speciated PM (e.g. PEC, PFE, etc). SMOKE essentially calculates the PM
components by using the appropriate EF without further speciation17. For onroad brake and tire wear, the
PM is speciated in the moves2smk postprocessor that prepares the emission factors for processing in
SMOKE. The formulas for this are based on the standard speciation factors that would otherwise be used
in SMOKE via the profiles 91134 for brake wear and 91150 for tire wear:
POC = 0.4715 * PM25TIRE + 0.107 * PM25BRAKE
PEC = 0.22 * PM25TIRE + 0.0261 * PM25BRAKE
PN03 = 0.0015 * PM25TIRE + 0.0016 * PM25BRAKE
PS04 = 0.0311 * PM25TIRE + 0.0334 * PM25BRAKE
PNH4 = 0.00019 * PM25TIRE + 0.00003 * PM25BRAKE
PNCOM = 0.1886 * PM25TIRE + 0.0428 * PM25BRAKE
3.2.3 NOx speciation
NOx can be speciated into NO, N02, and/or HONO. For the non-mobile sources, EPA used a single
profile "NHONO" to split NOx into NO and NO2. For the mobile sources except for onroad (including
nonroad, clc2rail, c3marine, othon sectors) and for specific SCCs in othar and ptnonipm, the profile
"HONO" splits NOx into NO, NO2, and HONO. Table 3-15 gives the split factor for these two profiles.
The onroad sector does not use the "HONO" profile to speciate NOx. MOVES2014 produces speciated
NO, NO2, and HONO by source, including emission factors for these species in the emission factor tables
used by SMOKE-MOVES. Within MOVES, the HONO fraction is a constant 0.008 of NOx. The NO
fraction varies by heavy duty versus light duty, fuel type, and model year. The NO2 fraction = 1 - NO -
HONO. For more details on the NOx fractions within MOVES, see
http://www3.epa.gov/otaq/models/moves/documents/420rl2022.pdf.
Table 3-15. NOx speciation profiles
Profile
pollutant
species
split factor
HONO
NOX
N02
0.092
HONO
NOX
NO
0.9
HONO
NOX
HONO
0.008
NHONO
NOX
N02
0.1
NHONO
NOX
NO
0.9
17 Unlike previous platforms, the PM components (e.g. POC) are now consistently defined between MOVES2014 and CMAQ.
For more details on the use of model-ready EF, see the SMOKE 3.6.5 documentation:
https ://www. cmascenter. org/smoke/documentation/3.6.5/html/
50

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3.3 Temporal Allocation
Temporal allocation (i.e., temporalization) is the process of distributing aggregated emissions to a finer
temporal resolution, thereby converting annual emissions to hourly emissions. While the total emissions
are important, the timing of the occurrence of emissions is also essential for accurately simulating ozone,
PM, and other pollutant concentrations in the atmosphere. Many emissions inventories are annual or
monthly in nature. Temporalization takes these aggregated emissions and if needed distributes them to the
month, and then distributes the monthly emissions to the day and the daily emissions to the hours of each
day. This process is typically done by applying temporal profiles to the inventories in this order: monthly,
day of the week, and diurnal. A summary of emissions by temporal profile and sector for the 201 leh case
is available from ftp://ftp.epa.gov/EmisInventorv/2011v6/v2platform/reports/
201 leh emissions by temporal profile.xlsx.
In SMOKE 3.6.5 and in this platform, more readable and flexible file formats are used for temporal
profiles and cross references. The temporal factors applied to the inventory are selected using some
combination of country, state, county, SCC, and pollutant. Table 3-16 summarizes the temporal aspects
of emissions modeling by comparing the key approaches used for temporal processing across the sectors.
In the table, "Daily temporal approach" refers to the temporal approach for getting daily emissions from
the inventory using the SMOKE Temporal program. The values given are the values of the SMOKE
L TYPE setting. The "Merge processing approach" refers to the days used to represent other days in the
month for the merge step. If this is not "all", then the SMOKE merge step runs only for representative
days, which could include holidays as indicated by the right-most column. The values given are those
used for the SMOKE M TYPE setting (see below for more information).
Table 3-16. Temporal settings used for the platform sectors in SMOKE
Platform sector
short name
Inventory
resolutions
Monthly
profiles
used?
Daily
temporal
approach
Merge
processing
approach
Process Holidays
as separate days
afdust adj
Annual
Yes
week
all
Yes
ag
Annual
Yes
all
all
Yes
agfire
Monthly

week
week
Yes
beis
Hourly

n/a
all
Yes
clc2rail
Annual
Yes
mwdss
mwdss

c3marine
Annual
Yes
aveday
aveday

nonpt
Annual
Yes
week
week
Yes
nonroad
Monthly

mwdss
mwdss
Yes
np_°ilgas
Annual
yes
week
week
Yes
onroad
Annual & monthly1

all
all
Yes
othafdust
Annual
yes
week
all

othar
Annual & monthly
yes
week
week

othon
Annual & monthly
yes
week
week

othpt
Annual
yes
mwdss
mwdss

pt oilgas
Annual
yes
mwdss
mwdss
Yes
ptegu
Daily & hourly

all
all
Yes
ptnonipm
Annual
yes
mwdss
mwdss
Yes
ptfire
Daily

all
all
Yes
rwc
Annual
no
met-based
all
Yes
51

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1. Note the annual and monthly "inventory" actually refers to the activity data (VMT and VPOP) for onroad. The
actual emissions are computed on an hourly basis.
The following values are used in the table: The value "all" means that hourly emissions are computed for
every day of the year and that emissions potentially have day-of-year variation. The value "week" means
that hourly emissions computed for all days in one "representative" week, representing all weeks for each
month. This means emissions have day-of-week variation, but not week-to-week variation within the
month. The value "mwdss" means hourly emissions for one representative Monday, representative
weekday (Tuesday through Friday), representative Saturday, and representative Sunday for each month.
This means emissions have variation between Mondays, other weekdays, Saturdays and Sundays within
the month, but not week-to-week variation within the month. The value "aveday" means hourly
emissions computed for one representative day of each month, meaning emissions for all days within a
month are the same. Special situations with respect to temporalization are described in the following
subsections.
In addition to the resolution, temporal processing includes a ramp-up period for several days prior to
January 1, 2011, which is intended to mitigate the effects of initial condition concentrations. The ramp-up
period was 10 days (December 22-31, 2010). For most sectors, emissions from December 2011 were
used to fill in surrogate emissions for the end of December 2010. In particular, December 2011 emissions
(representative days) were used for December 2010. For biogenic emissions, December 2010 emissions
were processed using 2010 meteorology.
3.3.1 Use of FF10 format for finer than annual emissions
The Flat File 2010 format (FF10) inventory format for SMOKE provides a more consolidated format for
monthly, daily, and hourly emissions inventories than prior formats supported. Previously, processing
monthly inventory data required the use of 12 separate inventory files. With the FF10 format, a single
inventory file can contain emissions for all 12 months and the annual emissions in a single record. This
helps simplify the management of numerous inventories. Similarly, daily and hourly FF10 inventories
contain individual records with data for all days in a month and all hours in a day, respectively.
SMOKE prevents the application of temporal profiles on top of the "native" resolution of the inventory.
For example, a monthly inventory should not have annual-to-month temporalization applied to it; rather,
it should only have month-to-day and diurnal temporalization. This becomes particularly important when
specific sectors have a mix of annual, monthly, daily, and/or hourly inventories. The flags that control
temporalization for a mixed set of inventories are discussed in the SMOKE documentation. The
modeling platform sectors that make use of monthly values in the FF10 files are agfire, nonroad, onroad,
othar, othon, and ptegu.
3.3.2 Electric Generating Utility temporalization (ptegu)
3.3.2.1 Base year temporal allocation of EGUs
The 201 1NEIv2 annual EGU emissions not matched to CEMS sources are allocated to hourly emissions
using the following 3-step methodology: annual value to month, month to day, and day to hour. Several
updates were made to EGU temporalization in the 201 lv6.2 platform. First, the CEMS data were
processed using a tool that reviewed the data quality flags that indicate the data were not measured.
Unmeasured data can cause erroneously high values in the CEMS data. If the data were not measured at
specific hours, and those values were found to be more the 3 times the annual mean for that unit, the data
for those hours were replaced with annual mean values (Adelman, et al., 2012). These adjusted CEMS
data were then used for the remainder of the temporalization process described below (see Figure 3-3 for
52

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an example). Winter and summer seasons are included in the development of the diurnal profiles as
opposed to using data for the entire year because analysis of the hourly CEMS data revealed that there
were different diurnal patterns in winter versus summer in many areas. Typically a single mid-day peak is
visible in the summer, while there are morning and evening peaks in the winter as shown in Figure 3-4.
The temporal allocation procedure is differentiated by whether or not the source could be directly
matched to a CEMS unit via ORIS facility code and boiler ID. Prior to temporal allocation, as many
sources as possible were matched to CEMS data via ORIS facility code and boiler ID. Units were
considered matches if the FIPS state/county code matched, the facility name was similar, and the NOx
and SO2 emissions were similar. EIS stores a base set of previously matched units via alternate facility
and unit IDs. Additions to these matches were made for this platform due to additional matching
specificity available in SMOKE but not in EIS and also based on comments. For any units that are
matched, the ORIS facility and boiler ID columns of the point FF10 inventory files are filled with the
information on the rows for the corresponding NEI unit. Note that for units matched to CEMS data,
annual totals of their emissions may be different than the annual values in 201 1NEIv2 because the CEMS
data actually replaces the inventory data for the seasons in which the CEMS are operating. If a CEMS-
matched unit is determined to be a partial year reporter, as can happen for sources that run CEMS only in
the summer, emissions totaling the difference between the annual emissions and the total CEMS
emissions are allocated to the non-summer months.
Figure 3-3. Eliminating unmeasured spikes in CEMS data
2011 CEM of 5019 1 Month 11
Raw CEM
v2.1 Corrected
It/
Nov
2011
53

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Figure 3-4. Seasonal diurnal profiles for EGU emissions in a Virginia Region
Diurnal CEMS Profile for PJM_Dom Gas
o.io
Annual Average
Summer Average
Winter Average
0.08
C 0.06
O
u
(0
LL-
IU
3
Q 0.04
0.02
0.00
Hour
For sources not matched to CEMS units, the allocation of annual emissions to months and then days are
done outside of SMOKE and then daily emissions are output to day-specific inventory files. For these
units, the allocation of the inventory annual emissions to months is done using average fuel-specific
season-to-month factors generated for each of the 64 IPM regions shown in Figure 3-5. These factors are
based 2011 CEMS data only. In each region, separate factors were developed for the fuels: coal, natural
gas, and "other", where the types of fuels included in "other" vary by region. Separate profiles were
computed for NOx, SO2, and heat input. An overall composite profile was also computed and was used
when there were no CEMS units with the specified fuel in the region containing the unit. For both CEMS-
matched units and units not matched to CEMS, NOx and SO2 CEMS data are used to allocate NOx and
SO2 emissions to monthly emissions, respectively, while heat input data are used to allocate emissions of
all other pollutants to monthly emissions and to allocate all of the monthly emissions to daily emissions.
54

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Figure 3-5. 1PM Regions for EPA Base Case v5.14
WECC_PNW
NENG_ME,
WECC_MT
MAP_
WAUE
WECC_WY
NENG_CT
PJM_
PJM_ WMAC
PENE
WEC_CALN
PJM_
COMD
PJM_EMAC
PJM_SMAC
WECCSCE
S_VACA
WECCAZ
SPP_WEST
ERC_WEST
S_D_AMSO
Daily temporal allocation of units matched to CEMS was performed using a procedure similar to the
approach to allocate emissions to months in that the CEMS data replaces the inventory data for each
pollutant. For units without CEMS data, emissions were allocated from month to day using IPM-region
and fuel-specific average month-to-day factors based on the 2011 CEMS data. Separate month-to-day
allocation factors were computed for each month of the year using heat input for the fuels coal, natural
gas, and "other" in each region. For both CEMS and non-CEMS matched units, NOx and SO2 CEMS data
are used to allocate NOx and SO2 emissions, while CEMS heat input data are used to allocate all other
pollutants. An example of month-to-day profiles for gas, coal, and an overall composite for a region in
western Texas is shown in Figure 3-6.
55

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Figure 3-6. Month-to-day profiles for different fuels in a West Texas Region
Daily temporal fraction: ERC_WEST_NOX_7
o.io
0.08
:« 0.06
£ 0.04
0.02
0.00
day
For units matched to CEMS data, hourly emissions use the hourly CEMS values for NOx and S02, while
other pollutants are allocated according to heat input values. For units not matched to CEMS data,
temporal profiles from days to hours are computed based on the season-, region- and fuel-specific average
day-to-hour factors derived from the CEMS data for those fuels and regions using the appropriate subset
of data. For the unmatched units, CEMS heat input data are used to allocate all pollutants (including NOx
and SO2) because the heat input data was generally found to be more complete than the pollutant-specific
data. SMOKE then allocates the daily emissions data to hours using the temporal profiles obtained from
the CEMS data for the analysis base year (i.e., 2011 in this case).
3.3.2.2 Future year temporal allocation of EGUs
IPM provides unit-level emission projections of average winter (representing October through April) and
average summer (representing May through September) values. To use these data in an air quality model,
the unit-level data must first be converted to into hourly values through the temporal allocation process.
These annualized emissions are allocated to hourly emissions using a 3-step methodology: annualized
summer/winter value to month, month to day, and day to hour. The first two steps are done outside of
SMOKE and the third step is done by SMOKE using daily emissions files created from the first two steps.
The approach in the 201 lv6.2 platform maximizes the use of the CEMS data from the air quality analysis
year (e.g., 2011).
The goal of the temporal allocation process is to reflect the variability in the unit-level emissions that can
impact air quality over seasonal, daily, or hourly time scales, in a manner compatible with incorporating
future-year emission projections into future-year air quality modeling. The temporal allocation process is
applied to the seasonal emission projections obtained from an IPM modeling scenario. IPM represents
two seasons: summer (May through September) and winter (October through April). IPM unit-level
parsed files contain seasonal and annual totals of SO2, NOx, CO2, Hg, and HC1 emissions (computed
directly within IPM), while PM2.5, PM10, VOC, NH3, and CO emissions are calculated using a post-
56

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processing tool18 based on each unit's projected fuel use and configuration, coupled with pollutant-
specific emission factors.19 When calculating PM emissions, the post-processing tool utilizes specific data
assumptions such as the ash and sulfur content of the coal projected to be used at the unit. The tool creates
a Flat File (in a comma-separated value or .csv file format) that provides the starting point for developing
emission inputs to an air quality model.
The resulting Flat File contains all of the endogenously-determined and post-calculated unit-level
emissions combined with stack parameters (i.e., stack location and other characteristics consistent with
information found in the National Emissions Inventory (NEI)). A cross reference is used to map the units
in NEEDS to the stack parameter and facility, unit, release point, and process identifiers used in the
National Emissions Inventory (NEI). The cross reference also maps sources to the hourly Continuous
Emissions Monitoring System (CEMS) data used to temporally allocate the emissions in the base year air
quality modeling. This cross reference has been updated for the v5.14 platform through collaboration with
EPA, regional planning organizations, and states and is also used to determine which emissions sources in
the NEI sources have future year emissions predicted by IPM, and is available at
ftp://ftp.epa.gov/EmisInventorv/2011v6/v2platform/reports/ipm to flat file xref 2011NEIv2 Updated
20150710.xlsx.
Emissions from point sources for which emissions are not predicted by IPM are carried forward into the
future year modeling platform using other projection methods. Therefore, if the NEI and IPM sources are
not properly matched, double-counting could result because the future year emissions output from IPM in
the future year are treated as a full replacement for the base year emissions, although only for the
emissions processes estimated by IPM. Note: any feedback to EPA on future updates to the cross
reference should be provided based on this updated version that will be used with later versions of IPM:
ftp://ftp.epa.gov/EmisInventorv/2011v6/v2platform/reports/
ipm to flat file xref 2011NEIv2 Updated 20150710.xlsx.
In order to support the temporal allocation process and other requirements of modeling point sources, the
Flat File output from the IPM postprocessor specifies annual and monthly emissions for each stack;
however, since IPM projections are only modeled for two seasons comprising multiple months each,
monthly emissions cannot be precisely specified in the Flat File. Instead, the monthly values in the Flat
File output from the postprocessor are computed by multiplying the average summer day and average
winter day emissions predicted by IPM by the number of days in the respective month. In summary, the
monthly emission values shown in the Flat File are not intended to represent an actual month-to-month
emission pattern; instead, they are interim values that have translated IPM's seasonal projections into
month-level data that serve as a starting point for the temporal allocation process.
The monthly emissions within the Flat File undergo a multi-step temporal allocation process to yield the
hourly emission values at each unit, as is needed for air quality modeling: summer/winter value to month,
month to day, and day to hour. For sources not matched to unit-specific CEMS data, the first two steps are
done outside of SMOKE and the third step to get to hourly values is done by SMOKE using daily the
emissions files created from the first two steps. For each of these three temporal allocation steps, N0X
and SO2 CEMS data are used to allocate N0X and SO2 emissions, while CEMS heat input data are used to
allocate all other pollutants. The approach defined here gives priority to temporalization based on the base
year CEMS data to the maximum extent possible.
18	Documentation of this tool can be found at www.epa. gov/powersectormodeling
19	For more information on EPA emission factors see http://www.epa. gov/ttnchie l/ap42/
57

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Prior to using the 2011 CEMS data to develop monthly, daily, and hourly profiles, the CEMS data were
processed through a tool that found data quality flags that indicated the data were measured (see Section
3.3.2.1). These adjusted CEMS data were used to compute the monthly, daily, and hourly profiles
described below.
For units in NEEDS that are matched to units in the National Emission Inventory (NEI), and for which
CEMS data are available, the emissions are temporalized based on the CEMS data for that unit and
pollutant. For units that are not matched to the NEI or for which CEMS data are not available, the
allocation of the IPM seasonal emissions to months is done using average fuel-specific season-to-month
factors generated for each of the 64 IPM regions shown in Figure 3-5. These factors are based on a single
year of CEMS data for the modeling base year associated with the air quality modeling analysis being
performed, such as 2011. Note that IPM uses load data (reflecting the shape of demand) corresponding to
the load in each IPM region that occurred in the base year of the air quality modeling analysis, such as
2011. The fuels used for creating the profiles for a region are coal, natural gas, and other, where the other
fuels used include oil and wood and vary by region. Separate profiles are computed for NOx, SO2, and
heat input. An overall composite profile across all fuels is also computed and can be used in the event that
a region has too few units of a fuel type to make a reasonable average profile, or in the case when a unit
changes fuels between the base and future year and there were previously no units with that fuel in the
region containing the unit.
The monthly emission values in the Flat File are first reallocated across the months in that season to align
the month-to-month emission pattern at each stack with historic seasonal emission patterns.20 While this
reallocation affects the monthly pattern of each unit's future-year seasonal emissions, the seasonal totals
are held equal to the IPM projection for that unit and season. Second, the reallocated monthly emission
values at each stack are disaggregated down to the daily level consistent with historic daily emission
patterns in the given month at the given stack using separate profiles for NOx, SO2, and heat input. This
process helps to capture the influence of meteorological episodes that cause electricity demand to vary
from day-to-day, as well as weekday-weekend effects that change demand during the course of a given
week. Third, this data set of emission values for each day of the year at each unit is input into SMOKE,
which uses temporal profiles to disaggregate the daily values into specific values for each hour of the
year.
For units without or not matched to CEMS data, or for which the CEMS data are found to be unsuitable
for use in the future year, emissions are allocated from month to day using IPM-region and fuel-specific
average month-to-day factors based on CEMS data from the base year of the air quality modeling
analysis. These instances include units that did not operate in the base year or for which it may not have
been possible to match the unit in NEEDS with a specific unit in the NEI. EPA uses average emission
profiles for some units with CEMS data in the base year when one of the following cases is true: (1) units
are projected to have substantially increased emissions in the future year compared to its emissions in the
base (historic) year21; (2) CEMS data are only available for a limited number of hours in that base year or
IPM predicts emissions in a season for which there are no CEMS data; (3) units change fuels in the future
year; (4) the unit is new in the future year; or (5) units experienced atypical conditions during the base
20	For example, the total emissions for a unit in May would not typically be the same as the total emissions for the same unit in
July, even though May and July are both in the summer season and the number of days in those months is the same. This is
because the weather changes over the course of each season, and thus the operating behavior of a specific unit can also vary
throughout each season. Therefore, part of the temporal allocation process is intended to create month-specific emissions totals
that reflect this intra-seasonal variation in unit operation and associated emissions.
21	In such instances, EPA does not use that unit's CEMS data for temporal allocation in order to avoid assigning large increases
in emissions over short time periods in the unit's hourly emission profile.
58

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year, such as lengthy downtimes for maintenance or installation of controls. The temporal profiles that
map emissions from days to hours are computed based on the region and fuel-specific seasonal (i.e.,
winter and summer) average day-to-hour factors derived from the CEMS data for those fuels and regions
using only heat input data for that season. Only heat input is used because it is the variable that is the most
complete in the CEMS data. SMOKE uses these profiles to allocate the daily emissions data to hours.
The emissions from units for which unit-specific profiles are not deemed appropriate, and for units in the
IPM outputs that are not specifically matched to units in the base year, are temporally allocated to hours
reflecting patterns typical of the region in which the unit is located. Analysis of CEMS data for units in
each of the 64 IPM regions revealed that there were differences in the temporal patterns of historic
emission data that correlate with fuel type (e.g., coal, gas, and other), time of year, pollutant, season (i.e.
winter versus summer) and region of the country. The correlation of the temporal pattern with fuel type is
explained by the relationship of units' operating practices with the fuel burned. For example, coal units
take longer to ramp up and ramp down than natural gas units, and some oil units are used only when
electricity demand cannot otherwise be met. Geographically, the patterns were less dependent on state
location than they were on IPM regional location. For temporal allocation of emissions at these units,
Figure 3-6 provides an example of daily coal, gas, and composite profiles in one IPM region. EPA
developed seasonal average emission profiles, each derived from base year CEMS data for each season
across all units sharing both IPM region and fuel type.22 Figure 3-4 provides an example of seasonal
profiles that allocate daily emissions to hours in one IPM region. These average day-to-hour temporal
profiles were also used for sources during seasons of the year for which there were no CEMS data
available, but for which IPM predicted emissions in that season. This situation can occur for multiple
reasons, including how the CEMS was run at each source in the base year.
For units that do have CEMS data in the base year and are matched to units in the IPM output, the base
year CEMS data are scaled so that their seasonal emissions match the IPM-projected totals. In particular,
the fraction of the unit's seasonal emissions in the base year is computed for each hour of the season, and
then applied to the seasonal emissions in the future year. This is accomplished outside of SMOKE by
creating data in the same format as CEMS data for NOx, S02, and heat input. Any pollutants other than
NOX and S02 are temporalized within SMOKE using heat input as a surrogate. Distinct factors are used
for the fuels coal, natural gas, and "other". This procedure yields future-year hourly data that have the
same temporal pattern as the base year CEMS data while matching future-year seasonal total emissions
for each stack to IPM's future-year projections (see example in Figure 3-7).
In cases when the emissions for a particular unit are projected to be substantially higher in the future year
than in the base year, the proportional scaling method to match the emission patterns in the base year
described above can yield emissions for a unit that are much higher than the historic maximum emissions
for that unit. To address this issue for the 2040 case, the maximum measured emissions of NOx and SO2
in the period of 2011-2014 were computed. The temporalized emissions were then evaluated at each hour
to determine whether they were above this cumulative maximum. The amount of "excess emissions" over
the maximum was then computed. For units for which the "excess emissions" could be reallocated to
other hours, those emissions were distributed evenly to hours that were below the maximum. Those
hourly emissions were then reevaluated against the maximum, and the procedure of reallocating the
excess emissions to other hours was repeated until all of the hours had emissions below the maximum,
22 EPA also uses an overall composite profile across all fuels for each IPM region in instances where a unit is projected to burn
a fuel for which EPA cannot construct an average emission profile (because there were no other units in that IPM region whose
historic CEMS data represent emissions from burning that fuel).
59

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whenever possible (see example in Figure 3-8). Note: this reallocation technique was new in the 201 lv6.2
platform is used in the 2040ei cases.
Figure 3-7. Future year emissions follow pattern of base year emissions
2017 and 2011 Summer CEMs for 3 4
2011 CEMs
2017 CEMs
2017 Adjusted CEMs
Annual unit max
4000
3000
2000
1000
2011
Date
Figure 3-8. Excess emissions apportioned to hours less than maximum
2017 and 2011 Summer CEMs for 1356 4
5000
2011 CEMs
2017 CEMs
2017 Adjusted CEMs
Annual unit max
2000
60

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Using the above approach, it was not always possible to reallocate excess emissions to hours below the
historic maximum, such as when the total seasonal emissions of NOx or S02 for a unit divided by the
number of hours of operation are greater than the 2011-2014 maximum emissions level. For these units,
the regional fuel-specific average profile was applied to all pollutants, including heat input, for that
season (see example in Figure 3-9). An exception to this is if the fuel for that unit is not gas or coal. In
that case, the composite (non-fuel-specific) profile was used for that unit. This is because many sources
that used "other" fuel profiles had very irregular shapes due to a small number of sources in the region,
and the allocated emissions frequently still exceeded the 2011-2014 maximum. Note that it was not
possible for SMOKE to use regional profiles for some pollutants and adjusted CEMS data for other
pollutants for the same unit / season, therefore all pollutants are assigned to regional profiles when
regional profiles are needed. Also note that for some units, some hours still exceed the 2011-2014 annual
maximum for the unit even after regional profiles were applied (see example in Figure 3-10).
For more information on the development of IPM emission estimates and the temporalization of those,
see https://www.epa uov/airmarkets/power-sector-modelitm-pIatfonn-v515. in particular the Air Quality
Modeling Flat File Documentation and accompanying inputs.
Figure 3-9. Adjustment to Flours Less than Maximum not Possible, Regional Profile Applied
450
400
350
300
j= 250
A
O 200
150
100
50
May Jun Jul Aug Sep
2011
Date
2017 and 2011 Summer CEMs for 1396 6-1

2011 CEMs
2017 CEMs
2017 Average CEMs
Annual unit max

-4
—

1 !

fflfflji "H

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Figure 3-10. Regional Profile Applied, but Exceeds Maximum in Some Hours
2017 and 2011 Summer CEMs for 10525_ES5A
2011 CEMs
— 2017 CEMs
			2017 Average CEMs
Annual unit max
May	Jun	Jul	Aug	Sep
2011
Date
3.3.3 Residential Wood Combustion Temporalization (rwc)
There are many factors that impact the timing of when emissions occur, and for some sectors this includes
meteorology. The benefits of utilizing meteorology as method for temporalization are: (1) a
meteorological dataset consistent with that used by the AQ model is available (e.g., outputs from WRF);
(2) the meteorological model data are highly resolved in terms of spatial resolution; and (3) the
meteorological variables vary at hourly resolution and can therefore be translated into hour-specific
temporalization.
The SMOKE program GenTPRO provides a method for developing meteorology-based temporalization.
Currently, the program can utilize three types of temporal algorithms: annual-to-day temporalization for
residential wood combustion (RWC), month-to-hour temporalization for agricultural livestock ammonia,
and a generic meteorology-based algorithm for other situations. For the 2011 platform, meteorological-
based temporalization was used for porti ons of the rwc sector and for livestock within the ag sector.
GenTPRO reads in gridded meteorological data (output from MCIP) along with spatial surrogates, and
uses the specified algorithm to produce a new temporal profile that can be input into SMOKE. The
meteorological variables and the resolution of the generated temporal profile (hourly, daily, etc.) depend
on the selected algorithm and the run parameters. For more details on the development of these
algorithms and running GenTPRO, see the GenTPRO documentation and the SMOKE documentation at
http://www.cmascenter.Org/smoke/documentation/3.l/GenTPRO Technical Summary Aim2012 Final.pd
f and http://www.cmascenter.Org/smoke/documentation/3.5.l/html/ch05s03s07.htmk respectively.
As of the 201 lv6.2 platform and in SMOKE 3.6.5, the temporal profile format was updated. GenTPRO
now produces separate files including the monthly temporal profiles (ATPROMONTHLY) and day-of-
month temporal profiles (ATPRODAILY), instead of a single ATPRODAILY with day-of-year
temporal profiles as it did in SMOKE 3.5. The results are the same either way, so the temporal profiles
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themselves are effectively the same in 201 lv6.2 as they were in 201 lv6.0 since the meteorology is the
same, but they are formatted differently.
For the RWC algorithm, GenTPRO uses the daily minimum temperature to determine the temporal
allocation of emissions to days. GenTPRO was used to create an annual-to-day temporal profile for the
RWC sources. These generated profiles distribute annual RWC emissions to the coldest days of the year.
On days where the minimum temperature does not drop below a user-defined threshold, RWC emissions
for most sources in the sector are zero. Conversely, the program temporally allocates the largest
percentage of emissions to the coldest days. Similar to other temporal allocation profiles, the total annual
emissions do not change, only the distribution of the emissions within the year is affected. The
temperature threshold for rwc emissions was 50 °F for most of the country, and 60 °F for the following
states: Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and
Texas.
Figure 3-11 illustrates the impact of changing the temperature threshold for a warm climate county. The
plot shows the temporal fraction by day for Duval County, Florida for the first four months of 2007. The
default 50 °F threshold creates large spikes on a few days, while the 60 °F threshold dampens these spikes
and distributes a small amount of emissions to the days that have a minimum temperature between 50 and
60 °F.
Figure 3-11. Example of RWC temporalization in 2007 using a 50 versus 60 °F threshold
RWC temporal profile, Duval County, FL, Jan - Apr
0.035
60F, alternate formula
50F, default formula
The diurnal profile for used for most RWC sources (see Figure 3-12) places more of the RWC emissions
in the morning and the evening when people are typically using these sources. This profile is based on a
2004 MANE-VU survey based temporal profiles (see
http://www.marama.org/publications folder/ResWoodCombustion/Final report.pdf). This profile was
created by averaging three indoor and three RWC outdoor temporal profiles from counties in Delaware
and aggregating them into a single RWC diurnal profile. This new profile was compared to a
concentration based analysis of aethalometer measurements in Rochester, NY (Wang et al. 2011) for
various seasons and day of the week and found that the new RWC profile generally tracked the
concentration based temporal patterns.
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Figure 3-12. RWC diurnal temporal profile
Comparison of RWC diurnal profile
0.12
0.1
c
o
0.08
	NEW
0.06
o
g 0.04
	OLD
0.02
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
The temporalization for "Outdoor Hydronic Heaters" (i.e.,"OHH", SCC=2104008610) and "Outdoor
wood burning device, NEC (fire-pits, chimneas, etc.)" (i.e., "recreational RWC", SCC=21040087000)
were updated because the meteorological-based temporalization used for the rest of the rwc sector did not
agree with observations for how these appliances are used.
For OHH, the annual-to-month, day-of-week and diurnal profiles were modified based on information in
the New York State Energy Research and Development Authority (NYSERDA) "Environmental, Energy
Market, and Health Characterization of Wood-Fired Hydronic Heater Technologies, Final Report"
(NYSERDA, 2012) as well as a Northeast States for Coordinated Air Use Management (NESCAUM)
report "Assessment of Outdoor Wood-fired Boilers" (NESCAUM, 2006). A Minnesota 2008 Residential
Fuelwood Assessment Survey of individual household responses (MDNR, 2008) provided additional
annual-to-month, day-of-week and diurnal activity information for OHH as well as recreational RWC
usage.
The diurnal profile for OHH, shown in Figure 3-13 is based on a conventional single-stage heat load unit
burning red oak in Syracuse, New York. As shown in Figure 3-14, the NESCAUM report describes how
for individual units, OHH are highly variable day-to-day but that in the aggregate, these emissions have
no day-of-week variation. In contrast, the day-of-week profile for recreational RWC follows a typical
"recreational" profile with emissions peaked on weekends.
Annual-to-month temporalization for OHH as well as recreational RWC were computed from the MN
DNR survey (MDNR, 2008) and are illustrated in Figure 3-15. OHH emissions still exhibit strong
seasonal variability, but do not drop to zero because many units operate year round for water and pool
heating. In contrast to all other RWC appliances, recreational RWC emissions are used far more
frequently during the warm season.
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Figure 3-13. Diurnal profile for OHH, based on heat load (BTU/hr)
Heat Load (BTU/hr)
45,000
40,000
35,000
30,000
25,000
20,000
15,000
10,000
5,000

Figure 3-14. Day-of-week temporal profiles for OHH and Recreational RWC
Fire Pits/Chimineas Day-of-Week Profile
350
300
250
200
150
100
¦Fire Pit/Chimenea
Outdoor Hydronic Heater
65

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Figure 3-15. Annual-to-month temporal profiles for OHH and recreational RWC
Monthly Temporal Activity for OHH & Recreational RWC
¦Fire Pit/Chimenea
Outdoor Hydronic Heater
JAN FEB MARAPRMAYJUN JUL AUG SEP OCT NOV DEC
3.3.4 Agricultural Ammonia Temporal Profiles (ag)
For the agricultural livestock NFb algorithm, the GenTPRO algorithm is based on an equation derived by
Jesse Bash of EPA ORD based on the Zhu, Henze, et al. (2013) empirical equation. This equation is based
on observations from the TES satellite instrument with the GEOS-Chem model and its adjoint to estimate
diurnal NFB emission variations from livestock as a function of ambient temperature, aerodynamic
resistance, and wind speed. The equations are:
Ea = [161500/T,/; x e("1380/T,-/;)] x AR,/;
PE;,/; = Ea, / Sum(E, /,)
where
•	PE;,/; = Percentage of emissions in county i on hour h
•	Eij, = Emission rate in county i on hour h
•	Tij, = Ambient temperature (Kelvin) in county i on hour h
•	Vi,/; = Wind speed (meter/sec) in county i (minimum wind speed is 0.1 meter/sec)
•	AR;,/; = Aerodynamic resistance in county i
GenTPRO was run using the "BASH NH3" profile method to create month-to-hour temporal profiles for
these sources. Because these profiles distribute to the hour based on monthly emissions, the monthly
emissions are obtained from a monthly inventory, or from an annual inventory that has been temporalized
to the month. Figure 3-16 compares the daily emissions for Minnesota from the "old" approach (uniform
monthly profile) with the "new" approach (GenTPRO generated month-to-hour profiles). Although the
GenTPRO profiles show daily (and hourly variability), the monthly total emissions are the same between
the two approaches.
66

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Figure 3-16. Example of animal NH3 emissions temporalization approach, summed to daily emissions
MN ag NH3 livestock temporal profiles
12.0
10.0
old
— 6.0
4.0
2.0
0.0
1/1/2008
8/1/2008 9/1/2008 10/1/2008 11/1/2008 12/1/2008
3.3.5 Onroad mobile temporalization (onroad)
For the onroad sector, the temporal distribution of emissions is a combination of more traditional
temporal profiles and the influence of meteorology. This section will discuss both the meteorological
influences and the diurnal temporal profiles for this platform.
Meteorology is not used in the development of the temporal profiles, but rather it impacts the calculation
of the hourly emissions through the program Movesmrg. The result is that the emissions vary at the
hourly level by grid cell. More specifically, the on-network (RPD) and the off-network parked vehicle
(RPV, RPH, and RPP) processes use the gridded meteorology (MCIP) directly. Movesmrg determines
the temperature for each hour and grid cell and uses that information to select the appropriate emission
factor (EF) for the specified SCC/pollutant/mode combination. In the 2011 platform (and the
201 1NEIv2), RPP was updated to use the gridded minimum and maximum temperature for the day. This
more spatially resolved temperature range produces more accurate emissions for each grid cell. The
combination of these four processes (RPD, RPV, RPH, and RPP) is the total onroad sector emissions.
The onroad sector show a strong meteorological influence on their temporal patterns (see the 201 1NEIv2
TSD for more details).
Figure 3-17 illustrates the difference between temporalization of the onroad sector used in the 2005 and
earlier platforms and the meteorological influence via SMOKE-MOVES. In the plot, the "MOVES"
inventory is a monthly inventory that is temporalized by SCC to day-of-week and hour. Similar
temporalization is done for the VMT in SMOKE-MOVES, but the meteorologically varying EFs add an
additional variation on top of the temporalization. Note, the SMOKE-MOVES run is based on the 2005
platform and previous temporalization of VMT to facilitate the comparison of the results. In the figure,
the MOVES emissions have a repeating pattern within the month, while the SMOKE-MOVES shows
day-to-day (and hour-to-hour) variability. In addition, the MOVES emissions have an artificial jump
between months which is due to the inventory providing new emissions for each month that are then
temporalized within the month but not between months. The SMOKE-MOVES emissions have a
smoother transition between the months.
67

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Figure 3-17. Example of SMOKE-MOVES temporal variability of NOx emissions
80
75
70
- 65
w
c
o
- 60
V)
c
0
155
1
45
40
•HLnc^mr^rHino^mr>HLOO^roh«-rHijnc^rop^rHLno^for*-rHLn
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inininintnininifiinininmininininininininininininininLn
ooooooooooooooooooooooooooo
ooooooooooooooooooooooooooo
fMCslfNfMfNfNfNfNfNfNfMfNCNfNfMfMfMINlNfNfMfNJNPMCMfMfM
Julian date
For the onroad sector, the "inventories" referred to in Table 3-16 actually consist of activity data, not
emissions. For RPP and RPV processes, the VPOP inventory is annual and does not need temporalization.
For RPD, the VMT inventory is monthly and was temporalized to days of the week and then to hourly
VMT through temporal profiles. The RPD processes require a speed profile (SPDPRO) that consists of
vehicle speed by hour for a typical weekday and weekend day. Unlike other sectors, the temporal profiles
and SPDPRO will impact not only the distribution of emissions through time but also the total emissions.
Because SMOKE-MOVES (for RPD) calculates emissions from VMT, speed and meteorology, if one
shifted the VMT or speed to different hours, it would align with different temperatures and hence
different EF. In other words, two SMOKE-MOVES runs with identical annual VMT, meteorology, and
MOVES EF, will have different total emissions if the temporalization of VMT changes. For RPH, the
HOTEL ING inventory is monthly and was temporalized to days of the week and to hour of the day
through temporal profiles. This is an analogous process to RPD except that speed is not included in the
calculation of RPH.
In previous platforms, the diurnal profile for VMT23 varied by road type but not by vehicle type (see
Figure 3-18). These profiles were used throughout the nation.
BHM (Jefferson Co., AL) daily NOX
— MOVES
— SMOKE-MOVES
23 These profiles were used in the 2007 platform and proceeding platforms.
68

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Figure 3-18. Previous onroad diurnal weekday profiles for urban roads
Diurnal Weekday profiles - urban
0.1
0.09
0.08
SMOKE interstate
0.07
SMOKE other expwy
SMOKE major art.
SMOKE minor art.
0.06
0.05
0.04
0.03
SMOKE collector
0.02
SMOKE local
0.01
MOVES default
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Diurnal profiles that could differentiate by vehicle type as well as by road type and would potentially vary
over geography were desired. In the development of the 201 lv6.024 platform, the EPA updated these
profiles to include information submitted by states in their MOVES county databases (CDBs). The
201 1NEIv2 process provided an opportunity to update these diurnal profile with new information
submitted by states, to supplement the data with additional sources, and to refine the methodology.
States submitted MOVES county databases (CDBs) that included information on the distribution of VMT
by hour of day and by day of week25 (see the 201 1NEIv2 TSD for details on the submittal process for
onroad). EPA mined the state submitted MOVES CDBs for non-default diurnal profiles'6. The list of
potential diurnal profiles was then analyzed to see whether the profiles varied by vehicle type, road type,
weekday vs. weekend, and by county within a state. For the MOVES diurnal profiles, EPA only
considered the state profiles that varied significantly by both vehicle and road types. Only those profiles
that passed this criteria were used in that state or used in developing default temporal profiles. The
Vehicle Travel Information System (VTRIS) is a repository for reported traffic count data to the Federal
Highway Administration (FHWA). EPA used 2012 VTRIS data to create additional temporal profiles for
states that did not submit temporal information in their CDBs or where those profiles did not pass the
variance criteria. The VTRIS data were used to create state specific diurnal profiles by ITPMS vehicle and
road type. EPA created distinct diurnal profiles for weekdays, Saturday and Sunday along with day of the
week profiles2 '.
EPA attempted to maximize the use of state and/or county specific diurnal profiles (either from MOVES
or VTRIS). Where there was no MOVES or VTRIS data, then a new default profile would be used (see
below for description of new profiles). This analysis was done separately for weekdays and for
weekends, therefore some areas had submitted profiles for weekdays but defaults for weekends. The
24 These profiles that were generated from MOVES submittals only were used for the v6 and v6.1 platforms. See their
respective TSDs for more details.
35 The MOVES tables are the liourvmtfraction and the dayvmtfraction.
36 Further QA was done to remove duplicates and profiles that were missing two or more hours. If they were missing a single
hour, the missing hour could be calculated by subtracting all other hours fractions from 1.
27 Note, the day of the week profiles (ie. Monday vs Tuesday vs etc) are only from the VTRIS data. The MOVES CDBs only
have weekday vs weekend profiles so they were not included in calculating a new national default day of the week profile.
69

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result was a set of profiles that varied geographically depending on the source of the profile and the
characteristics of the profiles (see Figure 3-19).
Figure 3-19. Use of submitted versus new national default profiles
Temporal Sources for 2011v2 Mobile Emissions
- VTRIS state
MOVES VMT
CARB
I VTRIS/MOVES national
I average
A new set of diurnal profiles was developed from the submitted profiles that varied by both vehicle type
and road type. For the purposes of constructing the national default diurnal profiles, EPA created
individual profiles for each state (averaging over the counties within) to create a single profile by state,
vehi cle type, road type, and the day (i.e. weekday vs Saturday vs Sunday). The source of the underlying
profiles was either MOVES or VTRIS data (see Figure 3-19). The states individual profiles were averaged
together to create a new default profile28. Figure 3-20 shows two new national default profiles for light
duty gas vehicles (LDGV, SCC6 220121) and combination long-haul diesel trucks (HHDDV, SCC6
220262) on restricted urban roadways (interstates and freeways). The blue lines indicate the weekday
profile, the green the Saturday profile, and the red the Sunday profile. In comparison, the new default
profiles for weekdays places more LDGV VMT (upper plot) in the rush hours while placing 1THDDV
VMT (lower plot) predominately in the middle of the day with a longer tail into the evening hours and
early morning.
>ome counties may use national defaults for certain days
28 Note that the states were weighted equally in the average independent of the size of the state or the variation in submitted
county data.
70

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Figure 3-20. Updated national default profiles for LDGV vs. HHDDY, urban restricted
Hourly Day fraction: nationai_0_21_4_all
0.08
0.07
0.06
0.05
C
O
_i rahonai_0_21_4_weeltday
•M
u
£ 0.04
<4—
_ ratfona!_0_21_4_saturday
>
ftJ
Q
— nab-ona!_0_2 l_4_sunday
0.03
0.02
0.01
0.00
hour
Hourly Day fraction: national_0_62_4_all
0.07
0.06
0.05
c
o
rat! onal_0_62_i_weekday
4-J
| 0.04
_ nehonal_0_62_4_saturday
— rathonal_0_62_4_sunday
0.03
0.02
0.01
hour
In addition to creating diurnal profiles, EPA developed day of week profiles using the VTRIS data. The
creation of the state and national profiles was similar to the diurnal profiles (described above). Figure
71

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3-21 shows a set of national default profiles for rural restricted roads (top plot) and urban unrestricted
roads (lower plot). Each vehicle type is a different color on the plots.
Figure 3-21. Updated national default profiles for day of week
Daily Week fraction: national_0_all_2_0
0.18
0.16
0.14
0.12
¦a 0.10
£0.08
0.06
0.04
0.02
mon
tue
wed
day
thu
sat
Daily Week fraction: national_0_all_5_0
0.20
0.15
C
O
4-J
E 0.10
>•
(O
Q
0.05
mon
wed
day
thu
sat
tue
In addition to creating diurnal profiles for VMT, EPA developed a national profile for hoteling. EPA
averaged all the combination long-haul truck profiles on restricted roads (urban and rural) for weekdays to
create a single national restricted profile (blue line in Figure 3-22). This was then inverted to create a
profile for hoteling (green line in Figure 3-22). This single national profile was used for hoteling
72

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irrespective of location.
Figure 3-22. Combination long-haul truck restricted and hoteling profile
Hourly Day fraction: national_0_all_0_0
0.06
0.05
0.04
— retronai_0_l_0_0
0.03
0.02
0.01
0.00
1
hour
For California, CARB supplied diurnal profiles that varied by vehicle type, day of the week29, and air
basin. These CARB specific profiles were used in developing EPA estimates for California. The
temporalization of these emissions took into account both the state-specific VMT profiles and the
SMOKE-MOVES process of incorporating meteorology.
3.3.6 Additional sector specific details (afdust, beis, c1c2rail, c3marine, nonpt,
ptnonipm, ptfire, np_oilgas)
For the afdust sector, meteorology is not used in the development of the temporal profiles, but it is used to
reduce the total emissions based on meteorological conditions. These adjustments are applied through
sector-specific scripts, beginning with the application of land use-based gridded transport fractions and
then subsequent zero-outs for hours during which precipitation occurs or there is snow cover on the
ground. The land use data used to reduce the NEI emissions explains the amount of emissions that are
subject to transport. This methodology is discussed in (Pouliot, et a!., 2010,
http://www3.epa.gov/ttn/chief/conference/eil9/session9/pouliot pres.pdf and in "Fugitive Dust Modeling
for the 2008 Emissions Modeling Platform" (Adelman, 2012). The precipitation adjustment is applied to
remove all emissions for days where measureable rain occurs. Therefore, the afdust emissions vary day-
to-day based on the precipitation and/or snow cover for that grid cell and day. Both the transport fraction
and meteorological adjustments are based on the gridded resolution of the platform; therefore, somewhat
different emissions will result from different grid resolutions. Application of the transport fracti on and
29 California's diurnal profiles varied within the week. Monday. Friday, Saturday, and Sunday had unique profiles and
Tuesday, Wednesday, Thursday had the same profile.
73

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meteorological adjustments prevents the overestimation of fugitive dust impacts in the grid modeling as
compared to ambient samples.
Biogenic emissions in the beis sector vary by every day of the year because they are developed using
meteorological data including temperature, surface pressure, and radiation/cloud data. The emissions are
computed using appropriate emission factors according to the vegetation in each model grid cell, while
taking the meteorological data into account.
For the clc2rail and c3marine sectors, emissions are allocated with flat monthly and day of week profiles,
and most emissions are also allocated with flat hourly profiles.
For the agfire sector, the emissions were allocated to months by adding up the available values for each
day of the month. For all agricultural burning, the diurnal temporal profile used reflected the fact that
burning occurs during the daylight hours - see Figure 3-23 (McCarty et al., 2009). This puts most of the
emissions during the work day and suppresses the emissions during the middle of the night. A uniform
profile for each day of the week was used for all agricultural burning emissions in all states, except for the
following states that for which EPA used state-specific day of week profiles: Arkansas, Iowa, Kansas,
Louisiana, Minnesota, Missouri, Nebraska, Oklahoma, and Texas.
Updates were made to temporal profiles for the ptnonipm sector in the 201 lv6.2 platform based on
comments and data review by EPA staff. Temporal profiles for small airports (i.e., non-commercial) were
updated to eliminate emissions between 10pm and 6am due to a lack of tower operations. Industrial
process that are not likely to shut down on Sundays such as those at cement plants were assigned to other
more realistic profiles that included emissions on Sundays. This also affected emissions on holidays
because Sunday emissions are also used on holidays.
Figure 3-23. Agricultural burning diurnal temporal profile
Comparison of Agricultural Burning Temporal Profiles
0.18
0.16
0.14
New McCarty Profile
OLD EPA
0.12
0.1

10.08
0.06
0.04
0.02
12345678 9 10111213141516171819 202122 23 24
For the ptwildfire and ptprescfire sectors, the inventories are in the daily point fire format ORL PTDAY.
The ptfire sector is used in the model evaluation case (2011 eh and in the future base case (2040eh). The
74

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2007 and earlier platforms had additional regulatory cases that used averaged fires and temporally
averaged EGU emissions, but the 2011 platform uses base year-specific (i.e., 2011) data for both cases.
For the nonroad sector, while the NEI only stores the annual totals, the modeling platform uses monthly
inventories from output from NMIM. For California, a monthly inventory was created from CARB's
annual inventory using EPA-estimated NMIM monthly results to compute monthly ratios by pollutant and
SCC7 and these ratios were applied to the CARB inventory to create a monthly inventory.
Some cross reference updates for temporalization of the npoilgas sector were made as of the 201 lv6.2
platform to assign np oilgas sources to 24 hour per day, 7 days a week based on comments received.
3.3.7 Additional sector specific details (afdust, beis, c1c2rail, c3marine, nonpt,
ptnonipm, ptfire, np_oilgas)
Various time zone corrections/updates were made to the 201 lv6.3 platform, which affects the hourly
temporalization of emissions. Table 3-XX lists the time zone corrections for US counties. The other time
zone corrections made to Canada and Mexico are the following:
Canada
o Quebec: Seven census divisions moved from Atlantic time to Eastern time. Only one
Quebec census division remains in Atlantic time zone,
o Manitoba: Daylight Saving Time (DST) added. (Only affects entire province FIPS;
individual census divisions were already correct.)
o Saskatchewan: now Central time without DST; was previously a mix of Central time and
Mountain time with DST.
o Peace River, BC: changed from Pacific time with DST to Mountain time without DST.
o NW Territories: moved from Pacific time to Mountain time. (Only affects entire province
FIPS; individual census divisions were already correct.)
Mexico
o Almost the entire country needed to be corrected. Most of country is Central time zone
with DST, except for the six northwesternmost states. In the previous platform (201 lv6.2),
most of Mexico was Central time without DST.
Table 3-17. Time zone corrections for US counties in 201 lv6.3 platform
FIPS
State
Countv
201leh
201lek
ALL
Indiana
ALL
some with no daylight saving time
implemented (DST)
all changed to
implementing DST
20093
Kansas
Kearny Co
MT
CT
21087
Kentucky
Green Co
ET
CT
21225
Kentucky
Union Co
ET
CT
21233
Kentucky
Webster Co
ET
CT
38057
North Dakota
Mercer Co
MT
CT
38059
North Dakota
Morton Co
MT
CT
38065
North Dakota
Oliver Co
MT
CT
38085
North Dakota
Sioux Co
MT
CT
46075
South Dakota
Jones Co
MT
CT
46095
South Dakota
Mellette Co
MT
CT
75

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46121
South Dakota
Todd Co
MT
CT
3.4 Spatial Allocation
The methods used to perform spatial allocation are summarized in this section. For the modeling
platform, spatial factors are typically applied by county and SCC. As described in Section 3.1, spatial
allocation was performed for a national 12-km domain. To accomplish this, SMOKE used national 12-km
spatial surrogates and a SMOKE area-to-point data file. For the U.S., EPA updated surrogates to use circa
2010-2011 data wherever possible. For Mexico, updated spatial surrogates were used as described below.
For Canada surrogates provided by Environment Canada were used and are unchanged from the 2007
platform. The U.S., Mexican, and Canadian 12-km surrogates cover the entire CONUS domain 12US1
shown in Figure 3-1.
The details regarding how the 201 lv6.2 platform surrogates were created are available from
ftp://ftp.epa.gov/EmisInventory/2011v6/v2platform/spatial surrogates/ in the files
US SpatialSurrogate Workbook v072115.xlsx and US SpatialSurrogate Documentation v()70JI5.pdf
and SurrogateToolsScripts 2014.zip available. These same surrogates are used for this platform. The
remainder of this subsection provides further detail on the origin of the data used for the spatial surrogates
and the area-to-point data.
3.4.1 Spatial Surrogates for U.S. emissions
There are more than 100 spatial surrogates available for spatially allocating U.S. county-level emissions
to the 12-km grid cells used by the air quality model. As described in Section 3.4.2, an area-to-point
approach overrides the use of surrogates for a limited set of sources. Table 3-18 lists the codes and
descriptions of the surrogates. Surrogate names and codes listed in italics are not directly assigned to any
sources for the 201 lv6.2 platform, but they are sometimes used to gapfill other surrogates, or as an input
for merging two surrogates to create a new surrogate that is used.
Many surrogates use circa 2010-based data, including 2010 census data at the block group level, 2010
American Community Survey Data for heating fuels, 2010 TIGER/Line data for railroads and roads, the
2006 National Land Cover Database, 2011 gas station and dry cleaner data, and the 2012 National
Transportation Atlas Data for rail-lines, ports and navigable waterways. Surrogates for ports (801) and
shipping lanes (802) were developed based on the 201 1NEIv2 shapefiles: Ports_032310_wrf and
ShippingLanes l 11309FINAL_wrf, but also included shipping lane data in the Great Lakes and support
vessel activity data in the Gulf of Mexico. The creation of surrogates and shapefiles for the U.S. was
generated via the Surrogate Tool. The tool and documentation for it is available at
https://www.cmascenter.Org/sa-tools/documentation/4.2/SurrogateToolUserGuide 4 2.pdf.
Table 3-18. U.S. Surrogates available for the 2011 modeling platform.
Code
Surrogate Description
Code
Surrogate Description
N/A
Area-to-point approach (see 3.3.1.2)
507
Heavy Light Construction Industrial Land
100
Population
510
Commercial plus Industrial
110
Housing
515
Commercial plus Institutional Land
120
Urban Population
520
Commercial plus Industrial plus Institutional
130
Rural Population
525
Golf Courses + Institutional +Industrial +
Commercial
137
Housing Change
526
Residential Non-Institutional
76

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Code
Surrogate Description
Code
Surrogate Description
140
Housing Change and Population
527
Single Family Residential
150
Residential Heating - Natural Gas
530
Residential - High Density
160
Residential Heating - Wood
535
Residential + Commercial + Industrial +
Institutional + Government
165
0.5 Residential Heating - Wood plus 0.5 Low
Intensity Residential
540
Retail Trade
170
Residential Heating - Distillate Oil
545
Personal Repair
180
Residential Heating - Coal
550
Retail Trade plus Personal Repair
190
Residential Heating - LP Gas
555
Professional/Technical plus General
Government
200
Urban Primary Road Miles
560
Hospitals
205
Extended Idle Locations
565
Medical Offices/Clinics
210
Rural Primary Road Miles
570
Heavy and High Tech Industrial
220
Urban Secondary Road Miles
575
Light and High Tech Industrial
221
Urban Unrestricted Roads
580
Food, Drug, Chemical Industrial
230
Rural Secondary Road Miles
585
Metals and Minerals Industrial
231
Rural Unrestricted Roads
590
Heavy Industrial
240
Total Road Miles
595
Light Industrial
250
Urban Primary plus Rural Primary
596
Industrial plus Institutional plus Hospitals
255
0.75 Total Roadway Miles plus 0.25 Population
600
Gas Stations
256
Off-Network Short-Haul Trucks
650
Refineries and Tank Farms
257
Off-Network Long-Haul Trucks
675
Refineries and Tank Farms and Gas Stations
258
Intercity Bus Terminals
680
Oil & Gas Wells, IHS Energy, Inc. and
USGS (see updated surrogates in Table 3-19)
259
Transit Bus Terminals
700
Airport Areas
260
Total Railroad Miles
710
Airport Points
261
NT AD Total Railroad Density
720
Mili tary A irports
270
Class 1 Railroad Miles
800
Marine Ports
271
NT AD Class 1, 2, 3 Railroad Density
801
NEI Ports
280
Class 2 and 3 Railroad Miles
802
NEI Shipping Lanes
300
Low Intensity Residential
806
Offshore Shipping NEI NOx
310
Total Agriculture
807
Navigable Waterway Miles
312
Orchards/Vineyards
808
Gulf Tug Zone Area
320
Forest Land
810
Navigable Waterway Activity
330
Strip Mines/Quarries
812
Midwest Shipping Lanes
340
Land
820
Ports NEI NOx
350
Water
850
Golf Courses
400
Rural Land Area
860
Mines
500
Commercial Land
870
Wastewater Treatment Facilities
505
Industrial Land
880
Drycleaners
506
Education
890
Commercial Timber
For the onroad sector, the on-network (RPD) emissions were spatially allocated to roadways, and the off-
network (RPP and RPV) emissions were allocated to population. The refueling emissions were spatially
allocated to gas station locations (surrogate 600). On-network (i.e., on-roadway) mobile source emissions
were assigned to the following surrogates: rural restricted access to rural primary road miles (210), rural
unrestricted access to 231, urban restricted access to urban primary road miles (200), and urban
unrestricted access to 221. Off-network emissions were spatially allocated according to the mapping in
77

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Table 3-19. Starting with the 201 lv6.2 platform, emissions from the extended (i.e., overnight) idling of
trucks were assigned to a new surrogate 205 that is based on locations of overnight truck parking spaces.
Table 3-19. Off-Network Mobile Source Surrogates
Source type
Source Type name
Surrogate ID
11
Motorcycle
535
21
Passenger Car
535
31
Passenger Truck
535
32
Light Commercial Truck
510
41
Intercity Bus
258
42
Transit Bus
259
43
School Bus
506
51
Refuse Truck
507
52
Single Unit Short-haul Truck
256
53
Single Unit Long-haul Truck
257
54
Motor Home
526
61
Combination Short-haul Truck
256
62
Combination Long-haul Truck
257
For the oil and gas sources in the np oilgas sector, the spatial surrogates were updated to those shown in
Table 3-20 using 2011 data consistent with what was used to develop the 2011NEI nonpoint oil and gas
emissions. Note that the "Oil & Gas Wells, IHS Energy, Inc. and USGS" (680) is older and based on
circa-2005 data. These surrogates were based on the same GIS data of well locations and related
attributes as was used to develop the 201 1NEIv2 data for the oil and gas sector. The data sources include
Drilling Info (DI) Desktop's HPDI database (Drilling Info, 2012) aggregated to grid cell levels, along
with data from Oil and Gas Commission (OGC) websites. Well completion data from HPDI was
supplemented by implementing the methodology for counting oil and gas well completions developed for
the U.S. National Greenhouse Gas Inventory. Under that methodology, both completion date and date of
first production from HPDI were used to identify wells completed during 2011. In total, over 1.08 million
unique well locations were compiled from the various data sources. The well locations cover 33 states and
1,193 counties (ERG, 2014b). Although basically the same surrogates were used, some minor updates to
the oil and gas surrogates were made in the 201 lv6.2 platform to correct some mis-located emissions.
Table 3-20. Spatial Surrogates for Oil and Gas Sources
Surrogate Code
Surrogate Description
681
Spud count - Oil Wells
682
Spud count - Horizontally-drilled wells
683
Produced Water at all wells
684
Completions at Gas and CBM Wells
685
Completions at Oil Wells
686
Completions at all wells
687
Feet drilled at all wells
688
Spud count - Gas and CBM Wells
689
Gas production at all wells
692
Spud count - All Wells
78

-------
693
Well count - all wells
694
Oil production at oil wells
695
Well count - oil wells
697
Oil production at Gas and CBM Wells
698
Well counts - Gas and CBM Wells
Some spatial surrogate cross reference updates were made between the 201 lv6.1 platform and the
201 lv6.2 platform aside from the reworking of the onroad mobile source surrogates described above.
These updates included the following:
•	Nonroad SCCs using spatial surrogate 525 (50% commercial + industrial + institutional, 50% golf
courses) were changed to 520 (100% commercial + industrial + institutional). The golf course
surrogate 850, upon which 525 is partially based, is incomplete and subject to hot spots;
•	Some nonroad SCCs for commercial equipment in New York County had assignments updated to
surrogate 340;
•	Commercial lawn and garden equipment was updated to use surrogate 520; and
•	Some county-specific assignments for RWC were updated to use surrogate 300.
Not all of the available surrogates are used to spatially allocate sources in the modeling platform; that is,
some surrogates shown in Table 3-18 were not assigned to any SCCs, although many of the "unused"
surrogates are actually used to "gap fill" other surrogates that are used. When the source data for a
surrogate has no values for a particular county, gap filling is used to provide values for the surrogate in
those counties to ensure that no emissions are dropped when the spatial surrogates are applied to the
emission inventories. Table 3-21 shows the CAP emissions (i.e., NH3, NOx, PM2.5, SO2, and VOC) by
sector, with rows for each sector listed in order of most emissions to least CAP emissions.
Table 3-21. Selected 2011 CAP emissions by sector for U.S. Surrogates*
sector
srg
srgdesc
NH3
NOX
PM2_5
S02
VOC
afdust
130
Rural Population
0
0
1,089,422
0
0
afdust
140
Housing Change and Population
0
0
159,485
0
0
afdust
240
Total Road Miles
0
0
286,188
0
0
afdust
310
Total Agriculture
0
0
895,786
0
0
afdust
330
Strip Mines/Quarries
0
0
58,959
0
0
afdust
400
Rural Land Area
0
0
1
0
0
ag
310
Total Agriculture
3,502,246
0
0
0
0
agfire
310
Total Agriculture
3,287
42,326
92,754
15,470
73,858
agfire
312
Orchards/Vineyards
27
432
1,082
753
799
agfire
320
Forest Land
7
8
121
0
124
clc2rail
261
NT AD Total Railroad Density
2
16,636
379
260
925
clc2rail
271
NT AD Class 12 3 Railroad Density
332
734,683
22,636
7,390
38,304
clc2rail
280
Class 2 and 3 Railroad Miles
13
41,963
948
287
1,622
clc2rail
806
Offshore Shipping NEI2011 NOx
329
504,779
16,146
7,272
12,151
clc2rail
820
Ports NEI2011 NOx
19
56,363
1,866
834
1,666
c3 marine
806
Offshore Shipping NEI2011 NOx
27
77,281
5,143
50,309
3,150
c3 marine
820
Ports NEI2011 NOx
41
54,101
3,900
36,064
1,998
79

-------
sector
srg
srgdesc
NH3
NOX
PM2_5
S02
voc
nonpt
100
Population
4,137
0
0
0
1,196,465
nonpt
140
Housing Change and Population
3
23,423
65,897
29
134,887
nonpt
150
Residential Heating - Natural Gas
40,775
217,560
4,785
1,443
13,031
nonpt
170
Residential Heating - Distillate Oil
2,045
40,842
4,523
88,432
1,394
nonpt
180
Residential Heating - Coal
247
1,033
605
7,931
1,233
nonpt
190
Residential Heating - LP Gas
136
38,705
224
705
1,432
nonpt
240
Total Road Miles
0
27
602
0
32,152
nonpt
250
Urban Primary plus Rural Primary
0
0
0
0
102,207
nonpt
260
Total Railroad Miles
0
0
0
0
2,195
nonpt
300
Low Intensity Residential
3,847
18,334
90,706
3,048
40,003
nonpt
310
Total Agriculture
0
0
614
0
363,385
nonpt
312
Orchards/Vineyards
0
441
117
1,806
262
nonpt
320
Forest Land
0
85
287
0
97
nonpt
330
Strip Mines/Quarries
0
4
0
0
48
nonpt
400
Rural Land Area
2,855
0
0
0
0
nonpt
500
Commercial Land
2,367
2
85,404
585
26,183
nonpt
505
Industrial Land
35,360
195,282
124,150
111,849
114,391
nonpt
510
Commercial plus Industrial
4
178
27
109
224,110
nonpt
515
Commercial plus Institutional Land
1,408
177,903
18,637
58,287
21,915
nonpt
520
Commercial plus Industrial plus
Institutional
0
0
0
0
14,965
nonpt
527
Single Family Residential
0
0
0
0
153,528
nonpt
535
Residential + Commercial + Industrial +
Institutional + Government
23
366
1,283
0
327,986
nonpt
540
Retail Trade (COM1)
0
0
0
0
1,371
nonpt
545
Personal Repair (COM3)
0
0
93
0
60,289
nonpt
555
Professional/Technical (COM4) plus
General Government (GOVI)
0
0
0
0
2,865
nonpt
560
Hospital (COM6)
0
0
0
0
10
nonpt
575
Light and High Tech Industrial (IND2 +
IND5)
0
0
0
0
2,538
nonpt
580
Food, Drug, Chemical Industrial (IND3)
0
610
313
171
10,535
nonpt
585
Metals and Minerals Industrial (IND4)
0
23
140
8
443
nonpt
590
Heavy Industrial (IND1)
10
4,373
5,419
1,131
138,575
nonpt
595
Light Industrial (IND2)
0
1
244
0
79,169
nonpt
600
Gas Stations
0
0
0
0
416,448
nonpt
650
Refineries and Tank Farms
0
0
0
0
129,572
nonpt
675
Refineries and Tank Farms and Gas
Stations
0
0
0
0
1,203
nonpt
711
Airport Areas
0
0
0
0
1,956
nonpt
801
Port Areas
0
0
0
0
12,469
nonpt
870
Wastewater Treatment Facilities
1,003
0
0
0
4,671
nonpt
880
Drycleaners
0
0
0
0
7,053
nonroad
100
Population
40
39,475
2,824
85
5,030
nonroad
140
Housing Change and Population
554
537,250
45,058
1,255
78,526
nonroad
261
NT AD Total Railroad Density
2
2,673
310
5
568
80

-------
sector
srg
srgdesc
NH3
NOX
PM2_5
S02
voc
nonroad
300
Low Intensity Residential
106
26,637
4,324
138
202,928
nonroad
310
Total Agriculture
481
488,224
39,037
910
57,473
nonroad
350
Water
213
143,096
12,395
337
614,637
nonroad
400
Rural Land Area
157
25,658
16,711
194
620,786
nonroad
505
Industrial Land
452
146,871
5,809
411
32,978
nonroad
510
Commercial plus Industrial
382
131,572
9,888
348
139,291
nonroad
520
Commercial plus Industrial plus
Institutional
205
70,541
16,361
288
255,836
nonroad
850
Golf Courses
12
2,394
112
17
7,092
nonroad
860
Mines
2
2,931
341
5
594
nonroad
890
Commercial Timber
19
12,979
1,486
38
8,680
np oilgas
400
Rural Land Area
0
0
0
0
50
np oilgas
680
Oil and Gas Wells
0
10
0
0
55
np oilgas
681
Spud count - Oil Wells
0
0
0
0
6,700
npoilgas
682
Spud count - Horizontally-drilled wells
0
5,526
208
9
349
np oilgas
683
Produced Water at all wells
0
0
0
0
44,772
np oilgas
684
Completions at Gas and CBM Wells
0
1,532
46
1,497
11,706
np oilgas
685
Completions at Oil Wells
0
360
11
381
28,194
np oilgas
686
Completions at all wells
0
8,926
333
44
106,170
np oilgas
687
Feet drilled at all wells
0
44,820
1,449
119
9,714
np oilgas
688
Spud count - Gas and CBM Wells
0
0
0
0
16,115
np oilgas
689
Gas production at all wells
0
39,184
2,318
224
64,828
npoilgas
692
Spud count - all wells
0
30,138
445
556
4,598
np oilgas
693
Well count - all wells
0
23,437
436
137
48,209
np oilgas
694
Oil production at oil wells
0
1,847
0
12,602
729,483
np oilgas
695
Well count - oil wells
0
102,828
3,275
91
431,704
np oilgas
697
Oil production at gas and CBM wells
0
3,009
183
34
465,478
np oilgas
698
Well count - gas and CBM wells
0
379,989
6,691
2,643
580,186
onroad
200
Urban Primary Road Miles
25,551
963,216
37,833
5,597
159,249
onroad
205
Extended Idle Locations
915
329,990
6,886
116
78,449
onroad
210
Rural Primary Road Miles
12,289
803,356
24,538
2,771
77,670
onroad
221
Urban Unrestricted Roads
48,172
1,633,441
65,390
12,425
428,473
onroad
231
Rural Unrestricted Roads
30,466
1,267,729
41,899
6,872
230,381
onroad
256
Off-Network Short-Haul Trucks

11,521
273
10
17,071
onroad
257
Off-Network Long-Haul Trucks

456
38
2
1,430
onroad
258
Intercity Bus Terminals

18
1
0
27
onroad
259
Transit Bus Terminals

8
4
0
101
onroad
506
Education

348
28
1
1,058
onroad
507
Heavy Light Construction Industrial
Land

34
2
0
100
onroad
510
Commercial plus Industrial

144,713
2,295
153
222,758
onroad
526
Residential - Non-Institutional

751
19
1
2,445
onroad
535
Residential + Commercial + Industrial +
Institutional + Government

703,841
13,766
959
1,389,749
onroad
600
Gas Stations




199,375
81

-------
sector
srg
srgdesc
NH3
NOX
PM2_5
S02
voc
rwc
165
0.5 Residential Heating - Wood plus 0.5
Low Intensity Residential
19,260
33,660
374,085
8,838
433,797
rwc
300
Low Intensity Residential
412
745
6,742
107
8,301
3.4.2 Allocation method for airport-related sources in the U.S.
There are numerous airport-related emission sources in the NEI, such as aircraft, airport ground support
equipment, and jet refueling. The modeling platform includes the aircraft and airport ground support
equipment emissions as point sources. For the modeling platform, EPA used the SMOKE "area-to-point"
approach for only jet refueling in the nonpt sector. The following SCCs use this approach: 2501080050
and 2501080100 (petroleum storage at airports), and 2810040000 (aircraft/rocket engine firing and
testing). The ARTOPNT approach is described in detail in the 2002 platform documentation:
http://www3.epa.gov/scram001/reports/Emissions%20TSD%20Voll 02-28-08.pdf. The ARTOPNT file
that lists the nonpoint sources to locate using point data were unchanged from the 2005-based platform.
3.4.3 Surrogates for Canada and Mexico emission inventories
The surrogates for Canada to spatially allocate the 2010 Canadian emissions have been updated in the
201 lv6.2 platform. The spatial surrogate data came from Environment Canada, along with cross
references. The surrogates they provided were outputs from the Surrogate Tool (previously referenced).
The Canadian surrogates used for this platform are listed in Table 3-22. The leading "9" was added to the
surrogate codes to avoid duplicate surrogate numbers with U.S. surrogates. Surrogates for Mexico are
circa 1999 and 2000 and were based on data obtained from the Sistema Municpal de Bases de Datos
(SEVLBAD) de INEGI and the Bases de datos del Censo Economico 1999. Most of the CAPs allocated to
the Mexico and Canada surrogates are shown in Table 3-23. The entries in this table are for the othar
sector except for the "MEX Total Road Miles" and the "CAN traffic" rows, which are for the othon
sector.
Table 3-22. Canadian Spatial Surrogates
Code
Canadian Surrogate Description
Code
Description
9100
Population
92424
BARLEY
9101
total dwelling
92425
BUCWHT
9103
rural dwelling
92426
CANARY
9106
ALL INDUST
92427
CANOLA
9111
Farms
92428
CHICPEA
9113
Forestry and logging
92429
CORNGR
9211
Oil and Gas Extraction
92425
BUCWHT
9212
Mining except oil and gas
92430
CORNSI
9221
Total Mining
92431
DFPEAS
9222
Utilities
92432
FLAXSD
9233
Total Land Development
92433
FORAGE
9308
Food manufacturing
92434
LENTIL
9321
Wood product manufacturing
92435
MUSTSD
9323
Printing and related support activities
92436
MXDGRN
9324
Petroleum and coal products manufacturing
92437
OATS
9327
Non-metallic mineral product manufacturing
92438
ODFBNS
9331
Primary Metal Manufacturing
92439
OTTAME
9412
Petroleum product wholesaler-distributors
92440
POTATS
82

-------
Code
9416
9447
9448
9481
9482
9562
9921
9924
9925
9932
9941
9942
9945
9946
9948
9950
9955
9960
9970
9980
9990
9996
9997
91201
92401
92402
92403
92404
92405
92406
92407
92408
92409
92410
92412
92413
92414
92416
92417
92418
92419
92421
92422
Canadian Surrogate Description
Code
Description
Building material and supplies wholesaler-
distributors
92441
RYEFAL
Gasoline stations
92442
RYESPG
clothing and clothing accessories stores
92443
SOYBNS
Air transportation
92444
SUGARB
Rail transportation
92445
SUNFLS
Waste management and remediation services
92446
TOBACO
Commercial Fuel Combustion
92447
TRITCL
Primary Industry
92448
WHITBN
Manufacturing and Assembly
92449
WHTDUR
CANRAIL
92450
WHTSPG
PAVED ROADS
92451
WHTWIN
UNPAVED ROADS
92452
BEANS
Commercial Marine Vessels
92453
CARROT
Construction and mining
92454
GRPEAS
Forest
92455
OTHVEG
Combination of Forest and Dwelling
92456
SWCORN
UNPAVED ROADS AND TRAILS
92457
TOMATO
TOTBEEF
92430
CORNSI
TOTPOUL
92431
DFPEAS
TOTSWIN
92432
FLAXSD
TOTFERT
92433
FORAGE
urban area
92434
LENTIL
CHBOISQC
92435
MUSTSD
traffic bcw
92436
MXDGRN
BULLS
92437
OATS
BFCOWS
92438
ODFBNS
BFHEIF
92439
OTTAME
CALFU1
92440
POTATS
FDHEIF
92441
RYEFAL
STEERS
92442
RYESPG
MLKCOW
92443
SOYBNS
MLKHEIF
92444
SUGARB
MBULLS
92445
SUNFLS
MCALFU1
92446
TOBACO
BROILER
92447
TRITCL
LAYHEN
92448
WHITBN
TURKEY
92449
WHTDUR
BOARS
92450
WHTSPG
GRWPIG
92451
WHTWIN
NURPIG
92452
BEANS
SOWS
92453
CARROT
IMPAST
92454
GRPEAS
UNIMPAST
92455
OTHVEG
83

-------
Code
Canadian Surrogate Description
Code
Description
92423
ALFALFA
92456
SWCORN


92457
TOMATO
Table 3-23. CAPs Allocated to Mexican and Canadian Spatial Surrogates
Code
Mexican or Canadian Surrogate Description
nh3
NOx
pm25
so2
voc
12
MEX Housing
20,299
37,970
3,076
269
52,351
14
MEX Residential Heating - Wood
0
974
12,490
149
85,877
16
MEX Residential Heating - Distillate Oil
0
10
0
3
0
20
MEX Residential Heating - LP Gas
0
5,042
152
0
86
22
MEX Total Road Miles
7,977
243,883
1,624
4,921
319,740
24
MEX Total Railroads Miles
0
18,946
423
166
738
26
MEX Total Agriculture
141,820
104,270
22,804
5,073
8,876
28
MEX Forest Land
912
2,735
7,792
850
6,352
32
MEX Commercial Land
0
58
1,280
0
19,884
34
MEX Industrial Land
2
1,108
1,527
0
94,034
36
MEX Commercial plus Industrial Land
0
0
0
0
76,677
38
MEX Commercial plus Institutional Land
1
1,243
51
2
32
40
MEX Residential (RES1-
4)+Comercial+Industrial+Institutional+Government
0
3
8
0
59,870
42
MEX Personal Repair (COM3)
0
0
0
0
4,440
44
MEX Airports Area
0
2,552
68
321
799
46
MEX Marine Ports
0
7,677
487
3,843
78
50
MEX Mobile sources - Border Crossing - Mexico
4
142
1
2
262
9100
CAN Population
583
19
607
11
243
9101
CAN total dwelling
265
26,699
6,792
4,937
20,769
9103
CAN rural dwelling
1
426
68
2
2,491
9106
CAN ALL INDUST
6
8,999
348
8
2,738
9111
CAN Farms
26
27,674
2,409
39
3,212
9113
CAN Forestry and logging
576
6,506
352
632
15,352
9211
CAN Oil and Gas Extraction
1
1,640
98
2
141
9212
CAN Mining except oil and gas
0
0
2,074
0
0
9221
CAN Total Mining
37
11,269
41,316
1,217
987
9222
CAN Utilities
60
3,831
305
652
164
9233
CAN Total Land Development
13
12,742
1,362
20
1,983
9308
CAN Food manufacturing
0
0
4,323
0
7,548
9321
CAN Wood product manufacturing
0
0
537
0
0
9323
CAN Printing and related support activities
0
0
0
0
33,802
9324
CAN Petroleum and coal products manufacturing
0
784
835
410
2,751
9327
CAN Non-metallic mineral product manufacturing
0
0
4,362
0
0
9331
CAN Primary Metal Manufacturing
0
142
5,279
46
17
9412
CAN Petroleum product wholesaler-distributors
0
0
0
0
44,248
9448
CAN clothing and clothing accessories stores
0
0
0
0
132
9481
CAN Air transportation
5
7,692
130
787
6,112
9482
CAN Rail transportation
3
4,247
94
136
94
9562
CAN Waste management and remediation services
1,111
1,497
1,837
2,183
13,868
84

-------
9921
CAN Commercial Fuel Combustion
467
133,155
11,421
29,102
100,565
9924
CAN Primary Industry
0
0
0
0
220,312
9925
CAN Manufacturing and Assembly
0
0
0
0
71,912
9932
CAN CANRAIL
67
62,931
2,373
1,431
1,846
9941
CAN PAVED ROADS
2
1,261
158,418
2
2,269
9942
CAN UNPAVED ROADS
21
4,245
1,312
26
57,495
9945
CAN Commercial Marine Vessels
30
40,929
3,360
27,659
5,954
9946
CAN Construction and mining
0
1
9
0
78
9950
CAN Combination of Forest and Dwelling
267
2,899
31,312
424
44,339
9955
CAN UNPAVED ROADS AND TRAILS
0
0
242,538
0
0
9990
CAN TOTFERT
0
0
29,266
0
159,858
9996
CAN urban area
0
0
618
0
0
9997
CAN CHBOISQC
442
4,912
48,652
702
71,050
91201
CAN traffic bcw
18,654
345,837
12,226
1,702
178,466
92401
CAN BULLS
4,394
0
0
0
0
92402
CAN BFCOWS
46,101
0
0
0
0
92403
CAN BFHEIF
7,398
0
0
0
0
92404
CAN CALFU1
17,987
0
0
0
0
92406
CAN STEERS
24,551
0
0
0
0
92407
CAN MLKCOW
37,603
0
0
0
0
92408
CAN MLKHEIF
2,617
0
0
0
0
92409
CAN MBULLS
35
0
0
0
0
92410
CAN MCALFU1
11,988
0
0
0
0
92412
CAN BROILER
7,049
0
0
0
0
92413
CAN LAYHEN
8,044
0
0
0
0
92414
CAN TURKEY
3,220
0
0
0
0
92416
CAN BOARS
139
0
0
0
0
92417
CAN GRWPIG
51,078
0
0
0
0
92418
CAN NURPIG
13,047
0
0
0
0
92419
CAN SOWS
5,376
0
0
0
0
92421
CAN IMPAST
1,949
0
0
0
0
92422
CAN UNIMPAST
2,081
0
0
0
0
92423
CAN ALFALFA
1,622
0
0
0
0
92424
CAN BARLEY
7,576
0
0
0
0
92425
CAN BUCWHT
21
0
0
0
0
92426
CAN CANARY
282
0
0
0
0
92427
CAN CANOLA
7,280
0
0
0
0
92428
CAN CHICPEA
449
0
0
0
0
92429
CAN CORNGR
15,655
0
0
0
0
92430
CAN CORNSI
2,328
0
0
0
0
92431
CAN DFPEAS
703
0
0
0
0
92432
CAN FLAXSD
1,667
0
0
0
0
92433
CAN FORAGE
526
0
0
0
0
92434
CAN LENTIL
547
0
0
0
0
92435
CAN MUSTSD
722
0
0
0
0
92436
CAN MXDGRN
658
0
0
0
0
85

-------
92437
CAN OATS
4,452
0
0
0
0
92438
CAN ODFBNS
254
0
0
0
0
92439
CAN OTTAME
5,985
0
0
0
0
92440
CAN POT ATS
1,268
0
0
0
0
92441
CAN RYEFAL
153
0
0
0
0
92442
CAN RYESPG
7
0
0
0
0
92443
CAN SOYBNS
1,775
0
0
0
0
92444
CAN SUGARB
30
0
0
0
0
92445
CAN SUNFLS
383
0
0
0
0
92446
CAN TOBACO
72
0
0
0
0
92447
CAN TRITCL
73
0
0
0
0
92448
CAN WHITBN
288
0
0
0
0
92449
CAN WHTDUR
5,524
0
0
0
0
92450
CAN WHTSPG
13,929
0
0
0
0
92451
CAN WHTWIN
2,785
0
0
0
0
92452
CAN BEANS
109
0
0
0
0
92453
CAN CARROT
73
0
0
0
0
92454
CAN GRPEAS
113
0
0
0
0
92455
CAN OTHVEG
294
0
0
0
0
92456
CAN SWCORN
297
0
0
0
0
92457
CAN TOMATO
98
0
0
0
0
86

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4 Development of 2040 Reference and Control Case Emissions
The emission inventories for the future year of 2040 have been developed using projection methods that are
specific to the type of emission source. Future emissions are projected from the 2011 base case either by
running models to estimate future year emissions from specific types of emission sources (e.g., EGUs, and
onroad and nonroad mobile sources), or for other types of sources by adjusting the base year emissions
according to the best estimate of changes expected to occur in the intervening years (e.g., non-EGU point and
nonpoint sources). For some sectors, the same emissions are used in the base and future years, such as biogenic,
fire, and Canadian emissions. For the remaining sectors, rules and specific legal obligations that go into effect in
the intervening years, along with changes in activity for the sector, are considered when possible. For some
sectors where we do not expect there to be significant changes after 2030 due to federal and state measures, the
2030 inventories are used to represent 2040. The 2040 reference case emission inventories represent predicted
emissions that account for Federal and State measures promulgated by August, 2015.
Emissions inventories for neighboring countries used in our modeling are included in the 201 lv6.2 platform,
specifically 2008 and 2030 emissions inventories for Mexico, and 2010 emissions inventories for Canada. The
meteorological data used to create and temporalize emissions for the future year cases is held constant and
represents the year 2011. With the exception of speciation profiles for mobile sources and temporal profiles for
EGUs, the same ancillary data files are used to prepare the future year emissions inventories for air quality
modeling as were used to prepare the 2011 base year inventories.
Emission projections for EGUs for the year 2030 were developed using IPM version 5.15 and are reflected in an
air quality modeling-ready flat file taken from the EPA Base Case v.5.15, including the Clean Power Plan. The
NEEDS database is an important input to IPM in that contains the generation unit records used for the model
plants that represent existing and planned/committed units in EPA modeling applications of IPM. NEEDS
includes basic geographic, operating, air emissions, and other data on these generating units and has been
updated for the EPA's version 5.15 power sector modeling platform based on comments received on the notices
of data availability for the 2011 and 2018 emissions modeling platforms and through other sources of data. The
EGU emission projections in the flat file format, the corresponding NEEDS database, and user guides and
documentation are available with the information for the EPA's Power Sector Modeling Platform v.5.15 at
https://www.epa.gov/airmarkets/power-sector-modeling-platform-v515. The projected EGU emissions include
the Final Mercury and Air Toxics (MATS) rule announced on December 21, 2011 and the Cross-State Air
Pollution Rule (CSAPR) issued July 6, 2011.
To project future emissions for onroad and nonroad mobile sources to the year 2040, the EPA used an updated
version of MOVES2014 for this rule and NMIM, respectively. The EPA obtained future year projected
emissions for these sectors by running the MOVES and NMIM models using year-specific information about
fuel mixtures, activity data, and the impacts of national and state-level rules and control programs.
For non-EGU point and nonpoint sources, projections of year 2030 emissions were developed by starting with
the 2011 emissions inventories and applying adjustments that represent the impact of national, state, and local
rules coming into effect in the years 2012 through 2030, along with the impacts of planned shutdowns, the
construction of new plants, specific information provided by states, and specific legal obligations resolving
alleged environmental violations, such as consent decrees. Changes in activity are considered for sectors such as
oil and gas, residential wood combustion, cement kilns, livestock, aircraft, commercial marine vessels and
locomotives. Efforts were made to include some regional haze and state-reported local controls as part of a
larger effort to include more local control information on stationary non-EGU sources are described further in
Section 4.2.
87

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The following bullets summarize the projection methods used for sources in the various sectors, while
additional details and data sources are given in the following subsections and in Table 4-1.
• EGU sector (ptegu): Unit-specific estimates from IPM version 5.15, including CPP, CSAPR, Final
MATS, Regional Haze rule, and Cooling Water Intakes Rule. Upstream impacts from AEO fuel volume
to year 2040 are reflected in both the reference and control cases.
• Non-IPM sector (ptnonipm): Closures, projection factors and percent reductions reflect comments
received from the notices of data availability for the 2011 and 2018 emissions modeling platforms, along
with emission reductions due to national and local rules, control programs, plant closures, consent
decrees and settlements. Projections for corn ethanol and biodiesel plants, refineries and other upstream
impacts take into account Annual Energy Outlook (AEO) fuel volume projections. Airport-specific
terminal area forecast (TAF) data were used for aircraft to account for projected changes in
landing/takeoff activity.
• Point and nonpoint oil and gas sectors (pt oilgas and np oilgas): Regional projection factors by product
and consumption indicators using information from AEO 2014 projections to year 2030, as well as
comments received on the notices of data availability for the 2011 and 2018 emissions modeling
platforms. Cobenefits of stationary engines CAP-cobenefit reductions (RICE NESHAP) and controls
from New Source Performance Standards (NSPS) are reflected for select source categories. Both the
reference and control case emissions for these sectors include upstream adjustments to take into account
AEO 2040 fuel volume projections.
• Biogenic (beis): 2011 emissions are used for all future-year scenarios and are computed with the same
" 1 lg" meteorology as is used for the air quality modeling.
• Fires sectors (ptfire, agfire): No growth or control - 2011 estimates are used directly.
• Agricultural sector (ag): Year 2030 projection factors for livestock estimates based on expected changes
in animal population from 2005 Department of Agriculture data, updated according to EPA experts in
July 2012.
• Area fugitive dust sector (afdust): For livestock PM emissions, projection factors for dust categories
related to livestock estimates based on expected changes in animal population. For unpaved and paved
road dust, county-level VMT projections to 2040 were considered.
• Remaining Nonpoint sector (nonpt): Projection factors and percent reductions reflect comments received
from the notices of data availability for the 2011 and 2018 emissions modeling platforms, along with
emission reductions due to national and local rules/control programs. PFC projection factors reflecting
impact of the final Mobile Source Air Toxics (MSAT2) rule. Upstream impacts from AEO fuel volume,
including cellulosic ethanol plants, to year 2040 are reflected in both the reference and control cases.
• Residential Wood Combustion (rwc): Year 2030 projection factors reflect assumed growth of wood
burning appliances based on sales data, equipment replacement rates and change outs. These changes
include the 2-stage NSPS for Residential Wood Heaters, resulting in growth in lower-emitting stoves
and a reduction in higher emitting stoves.
• Locomotive, and non-Category 3 commercial marine sector (clc2rail): Year 2030 projection factors for
Category 1 and Category 2 commercial marine and locomotives reflect final locomotive-marine controls
are the basis for the projections. Upstream impacts from AEO fuel volume to year 2040 are reflected in
both the reference and control cases.
• Category 3 commercial marine vessel (c3marine): Base-year 2011 emissions grown and controlled to
2030, incorporating controls based on Emissions Control Area (ECA) and International Marine
Organization (IMO) global NOx and SO2 controls.
88

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• Nonroad mobile sector (nonroad): Other than for California and Texas, this sector uses data from a run
of NMIM that utilized NONROAD2008a, using future-year equipment population estimates and control
programs to year 2040. The NMIM County Database version was NCD2015081 l_nei2040vdl. This
version includes updates to population, spatial allocation, growth, and fuel data received from states as
part of the 2011 NEI process and improvements to fuel properties developed since the public release of
NONROAD2008a. Final controls from the final locomotive-marine and small spark ignition rules are
included. California data were provided by CARB, and Texas data were projected from the 2011
inventory provided by TCEQ.
• Onroad mobile (onroad): emissions factors from the updated version of MOVES2014 for year 2040
were developed using the same representative counties, state-supplied data, meteorology, and
procedures that were used to produce the 2011 emission factors described in Section 2.3.1. Year 2030
data for California were provided by the California Air Resources Board.
• Other point (othpt), nonpoint/nonroad (othar), onroad (othon): For Canada, year 2010 inventories were
used for 2040 because no future year projected inventories were available. Mexico inventory data were
projected from year 2008 to 2030. C3 CMV data were projected to year 2030 using the same
methodology as the c3marine sector. Offshore oil platforms emission held constant at 2011 levels.
Table 4-1 summarizes the growth and control assumptions by source type that were used to create the U.S. 2040
reference case emissions from the 2011 base year inventories, many of which represent year 2030 emissions.
The control, closures and projection packets (i.e., data sets) used to create the 2030 reference case scenario
inventories from the 2011 base case are provided on the EPA air emissions modeling web site and are discussed
in more detail in the sections listed in Table 4-1. These packets were processed through EPA's Control Strategy
Tool (CoST) to create future year emission inventories. CoST is described here:
http://www3.epa.gov/ttnecasl/cost.htm and discussed in context to this emissions modeling platform in Section
4.2.1. The last column in Table 4-1 indicates the order of the CoST strategy used for the source/packet type. For
some sectors (e.g., ptnonipm), multiple CoST strategies are needed to produce the future year inventory because
the same source category may be subject to multiple projection or control packets. For example, the "Loco-
marine" projection factors are applied in a second control strategy for the ptnonipm sector, while for the
clc2rail sector, these same projection factors can be applied in the first (and only) control strategy. Thus, in
Table 4-1, packets with a "1" in the CoST strategy column are applied in the first strategy, while packets with a
"2" in the CoST strategy column are applied in a second strategy that is run on an intermediate inventory output
from the first strategy.
The remainder of this section is organized by broad NEI sectors with further stratification by the types of
packets (e.g., projection, control, closure packets) and whether emissions are projected via a stand-alone model
(e.g., EGUs use the IPM model and onroad mobile uses MOVES), using CoST, or by other mechanisms. EGU
projections are discussed in Section 4.1. All NEI non-EGU Point and Nonpoint sector projections (including all
commercial marine vessels, locomotives and aircraft) are described in Section 4.2, along with some background
on CoST. Onroad and nonroad mobile projections are discussed in Sections 4.3 and 4.4, respectively. Finally,
projections for all "other" sources, primarily outside the U.S., are described in Section 4.5.
Table 4-1. Control strategies and growth assumptions for creating the 2040 reference case emissions
inventories from the 2011 base case
Description of growth, control, closure data, or, new
inventory
Sector(s)
Packet Type
CAPs
impacted
Section(s)
CoST
Strategy
Non-EGU Point (ptnonipm and ptoilgas sectors) Controls and Growth Assumptions
Facility, unit and stack closures, primarily from the Emissions
Inventory System (EIS)
ptnonipm,
ptoilgas
CLOSURE
All
4.2.2
1
89

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Description of growth, control, closure data, or, new
inventory
Sector(s)
Packet Type
CAPs
impacted
Section(s)
CoST
Strategy
"Loco-marine rule": Growth and control to year 2030 from
Locomotives and Marine Compression-Ignition Engines Less
than 30 Liters per Cylinder: March, 2008
ptnonipm,
clc2rail
PROJECTION
All
4.2.3.5
2,
1
Upstream RFS2/EISA/LDGHG impacts on gas distribution,
pipelines and refineries to year 2040
ptnonipm,
ptoilgas,
nonpt,
npoilgas,
clc2rail,
ptegu
PROJECTION
All
4.2.3.5
2
AEO growth to 2030: industrial sources, including oil and gas
play-level projections
ptnonipm,
ptoilgas,
nonpt,
np oilgas
PROJECTION
All
4.2.3.6
1
Aircraft growth via Itinerant (ITN) operations at airports to
2030
ptnonipm
PROJECTION
All
4.2.3.8
1
Corn Ethanol plants adjusted via AEO volume projections to
2030
ptnonipm
PROJECTION
All
4.2.3.10
1
NESHAP: Portland Cement census-division level based on
Industrial Sector Integrated Solutions (ISIS) policy emissions
to year 2025. The ISIS results are from the ISIS-Cement model
runs for the NESHAP and NSPS analysis of August 2013 and
include closures and growth.
ptnonipm,
nonpt
PROJECTION
& new
inventories for
new kilns
All
4.2.3.9 &
4.2.5.4
1&
n/a
NESHAP: RICE (reciprocating internal combustion engines)
with reconsideration amendments
ptnonipm,
ptoilgas,
nonpt,
np oilgas
CONTROL
CO,
NOx,
PM, S02,
voc
4.2.4.2
1
NSPS: oil and gas
ptoilgas,
np oilgas
CONTROL
voc
4.2.4.1
1
NSPS: RICE
ptnonipm,
ptoilgas,
nonpt,
np oilgas
CONTROL
CO,
NOx,
VOC
4.2.4.3
2
NSPS: Gas turbines
ptnonipm,
pt oilgas
CONTROL
NOx
4.2.4.6
1
NSPS: Process heaters
ptnonipm,
pt oilgas
CONTROL
NOx
4.2.4.7
1
Industrial/Commercial/Institutional Boiler MACT with
Reconsideration Amendments + local programs
nonpt,
ptnonipm,
pt oilgas
CONTROL
CO,
NOx,
PM, SO2,
VOC
4.2.4.4
1
State fuel sulfur content rules for fuel oil - via 2018 NOD A
comments, effective only in most northeast states
nonpt,
ptnonipm,
pt oilgas
CONTROL
so2
4.2.4.5
1
State comments: from previous platforms (including consent
decrees) and 2018 NODA (search for 'EPA-HQ-OAR-2013-
0809' at regulations.gov)
nonpt,
ptnonipm,
pt oilgas
PROJECTION
&
CONTROL
All
4.2.3.6,
4.2.3.7,
4.2.4.10
1
Commercial and Industrial Solid Waste Incineration (CISWI)
revised NSPS
ptnonipm
CONTROL
S02
4.2.4.9
1
Arizona Regional haze controls
ptnonipm
CONTROL
NOx,S02
4.2.4.8
1
New biodiesel plants in year 2040
ptnonipm
new inventory
All
4.2.5.2
n/a
Nonpoint (afdust, ag, nonpt, npoilgas and rwc sectors) Controls and Growth Assumptions
AEO-based VMT growth for paved and unpaved roads
afdust
PROJECTION
PM
4.2.3.1
1
Livestock emissions growth from year 2011 to year 2030
ag
PROJECTION
nh3
4.2.3.2
1
Upstream RFS2/EISA/LDGHG impacts on gas distribution,
pipelines and refineries to years 2040
ptnonipm,
ptoilgas,
PROJECTION
All
4.2.3.5
2
90

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Description of growth, control, closure data, or, new
inventory
Sector(s)
Packet Type
CAPs
impacted
Section(s)
CoST
Strategy

nonpt,
npoilgas,
clc2rail,
ptegu




AEO growth to 2030: industrial sources, including oil and gas
play-level projections
ptnonipm,
ptoilgas,
nonpt,
np oilgas
PROJECTION
All
4.2.3.6
1
NESHAP: RICE (reciprocating internal combustion engines)
with reconsideration amendments
ptnonipm,
ptoilgas,
nonpt,
np oilgas
CONTROL
CO,
NOx,
PM, S02,
voc
4.2.4.2
1
NSPS: oil and gas
ptoilgas,
np oilgas
CONTROL
voc
4.2.4.1
1
NSPS: RICE
ptnonipm,
ptoilgas,
nonpt,
np oilgas
CONTROL
CO,
NOx,
VOC
4.2.4.3
2
Residential wood combustion growth and change-outs from
year 2011 to year 2030
rwc
PROJECTION
All
4.2.3.11
1
Industrial/Commercial/Institutional Boiler MACT with
Reconsideration Amendments + local programs
nonpt,
ptnonipm,
pt oilgas
CONTROL
CO,
NOx,
PM, S02,
VOC
4.2.4.4
1
State fuel sulfur content rules for fuel oil - via 2018 NOD A
comments, effective only in most northeast states
nonpt,
ptnonipm,
pt oilgas
CONTROL
so2
4.2.4.5
1
State comments: from previous platforms (including consent
decrees) and 2018 NODA (search for 'EPA-HQ-OAR-2013-
0809' at regulations.gov)
nonpt,
ptnonipm,
pt oilgas
PROJECTION
&
CONTROL
All
4.2.3.6,
4.2.3.7,
4.2.4.10
1
MSAT2 and RFS2 impacts with state comments on PFC
(portable fuel container) growth and control from 2011 to years
2018 and 2025
nonpt
new inventory
All
4.2.5.1
n/a
New cellulosic plants in year 2018
nonpt
new inventory
All
4.2.5.3
n/a
Onroad Mobile (onroad sector) Controls and Growth Assumptions
All national in-force regulations are modeled. The list includes recent key mobile source regulations but is not exhaustive.
National Onroad Rules:
All onroad control programs finalized as of the date of the
model run including most recently:





Tier-3 Vehicle Emissions and Fuel Standards Program: March,
2014





2017 and Later Model Year Light-Duty Vehicle Greenhouse
Gas Emissions and Corporate Average Fuel Economy
Standards: October, 2012





Greenhouse Gas Emissions Standards and Fuel Efficiency
Standards for Medium- and Heavy-Duty Engines and
Vehicles: September, 2011
onroad
n/a
All
4.3
n/a
Regulation of Fuels and Fuel Additives: Modifications to
Renewable Fuel Standard Program (RFS2): December, 2010





Light-Duty Vehicle Greenhouse Gas Emission Standards and
Corporate Average Fuel Economy Standards;
Final Rule for Model-Year 2012-2016: May, 2010





Final Mobile Source Air Toxics Rule (MSAT2): February,
2007





Local Onroad Programs:
California LEVIII Program
onroad
n/a
All
4.3
n/a
91

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Description of growth, control, closure data, or, new
inventory
Sector(s)
Packet Type
CAPs
impacted
Section(s)
CoST
Strategy
Ozone Transport Commission (OTC) LEV Program:
January, 1995





Inspection and Maintenance programs
Fuel programs (also affect gasoline nonroad equipment)
Stage II refueling control programs
Nonroad Mobile (clc2rail, c3marine, nonroad sectors) Controls and Growth Assumptions
All national in-force regulations are modeled. The list includes recent key mobile source regulations but is not exhaustive.
National Nonroad Controls:
All nonroad control programs finalized as of the date of the
model run including most recently:
nonroad
n/a
All
4.4
n/a
Emissions Standards for New Nonroad Spark-Ignition Engines,
Equipment, and Vessels: October, 2008
Locomotives:
Growth and control to year 2030 from Locomotives and
Marine Compression-Ignition Engines Less than 30 Liters per
Cylinder: March 2008
clc2rail,
ptnonipm
PROJECTION
All
4.2.3.5
1,
2
Commercial Marine:
Category 3 marine diesel engines Clean Air Act and
International Maritime Organization standards: April, 2010
c3 marine
PROJECTION
All
4.2.3.4
1
Growth and control to years 2017 and 2025 from Locomotives
and Marine Compression-Ignition Engines Less than 30 Liters
per Cylinder: March, 2008
clc2rail,
ptnonipm
PROJECTION
All
4.2.3.5
1,
2
4.1 EGU sector projections: ptegu
The future-year data for the ptegu sector used in the air quality modeling were created by the Integrated
Planning Model (IPM) version 5.15 (v5.15). The IPM is a multiregional, dynamic, deterministic linear
programming model of the U.S. electric power sector. IPM version 5.15 reflects state rules, consent decrees
and announced shutdowns through February, 2015, in addition the NEEDS database was updated based on
comments received on the notices of data availability for the 2011 and 2018 emissions modeling platforms. IPM
5.15 was updated from the previous version 5.14 and represents electricity demand projections for the Annual
Energy Outlook (AEO) 2015. The scenario used for this modeling represents the implementation of the Clean
Power Plan (CPP), Cross-State Air Pollution Rule (CSAPR), the Mercury and Air Toxics Standards (MATS),
the final actions EPA has taken to implement the Regional Haze Rule, and the Cooling Water Intakes Rule and
Combustion Residuals from Electric Utilities (CCR). More details on the IPM v5.15 base case scenarios can be
found at https://www.epa.gov/airmarkets/power-sector-modeling-platform-v515.
Directly emitted PM emissions (i.e., PM2.5 and PM10) from the EGU sector are computed via a post processing
routine that applies emission factors to the IPM-estimated fuel throughput based on fuel, configuration and
controls to compute the filterable and condensable components of PM. This postprocessing step also apportions
the regional emissions down to the unit-level emissions used for air quality modeling.
From the unit-level parsed file, a flat file is created that is used as the input to SMOKE and processed into the
format needed by the air quality model. As part of the development of the flat file, a cross reference between the
201 1NEIv2 and IPM is used to populate stack parameters and other related information for matched sources.
The flat file creation methodology is documented in the air quality modeling flat file documentation available
here: https://www.epa.gov/sites/production/files/2015-07/documents/flatfile methodology.pdf with additional
information available in ftp://ftp.epa.gov/EmisInventorv/2011v6/v2platform/reports/
ipm to flat file xref 2011NEIv2 Updated 20150710.xlsx. The emissions in the flat file created based on the
92

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IPM outputs are temporalized into the hourly emissions needed by the air quality model as described in Section
3.3.2.
EGU emissions were adjusted for the control case to account for upstream impacts of the rule due to changes in
ethanol volumes. The projection factors for the reference case and control case are in Table 4-2.
Table 4-2. Ptegu upstream adjustments for the 2040 reference and 2040 control cases
Pollutant
2040 reference
2040 control
voc
1.0068
0.9897
1,3-Butadiene
1.0068
0.9897
Acetaldehyde
1.0068
0.9897
Acrolein
1.0068
0.9897
Benzene
1.0068
0.9897
Formaldehyde
1.0068
0.9897
CO
1.0004
0.9982
NOx
0.9988
0.9980
PM10
0.9925
0.9919
PM2.5
0.9976
0.9968
S02
0.9995
0.9988
4.2 Non-EGU Point and NEI Nonpoint sector projections: afdust, ag, c1c2raii,
c3marine, nonpt, npoilgas, ptnonipm, ptoilgas, rwc
To project all U.S. non-EGU stationary sources, facility/unit closures information, and growth (PROJECTION)
factors and/or controls were applied to certain categories within the afdust, ag, clc2rail, c3marine, nonpt,
np oilgas, ptnonipm, pt oilgas and rwc platform sectors. Some facility or sub-facility-level closure information
was also applied to the point sources. There are also a handful of situations where new inventories were
generated for sources that did not exist in the 2011 NEI (e.g., biodiesel and cellulosic plants, yet-to-be
constructed cement kilns). This subsection provides details on the data and projection methods used for these
sectors.
In recent platforms, EPA has assumed that emissions growth for most industrial sources did not track with
economic growth for most stationary non-IPM sources (EPA, 2006b). This "no-growth" assumption was based
on an examination of historical emissions and economic data. Recently however, EPA has received growth (and
control) data from numerous states and regional planning organizations for many industrial sources, including
the rapidly-changing oil and gas sector. EPA provided a Notice of Data Availability for the 201 lv6.0 emissions
modeling platform and projected 2018 inventory in January, 2014 (docket EPA-HQ-OAR-2013-0809). EPA
requested comment on the future year growth and control assumptions used to develop the 2018 inventories.
One of the most frequent comments EPA received was to use the growth factors information that numerous
states either provided or deferred to growth factors provided by broader region-level efforts. In an attempt to
make the projections approach as consistent as possible across all states, EPA decided to expand this effort to
all states for some of the most-significant industrial sources (see Section 4.2.3.6).
Because much of the projections and controls data are developed independently from how EPA defines its
emissions modeling sectors, this section is organized primarily by the type of projections data, with secondary
consideration given to the emissions modeling sector. For example, industrial source growth factors are
applicable to 4 emissions modeling sectors. The rest of this section is organized in the order that the EPA uses
CoST in combination with other methods to produce future year inventories: 1) for point sources, apply plant
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(facility or sub-facility-level) closure information via CoST, 2) apply all PROJECTION packets via CoST
(multiplicative factors that could cause increases or decreases), 3) apply all percent reduction-based CONTROL
packets via CoST, and 4) append all other future-year inventories not generated via CoST. This organization
allows consolidation of the discussion of the emissions categories that are contained in multiple sectors, because
the data and approaches used across the sectors are consistent and do not need to be repeated. Sector names
associated with the CoST packets are provided in parentheses.
4.2.1 CoST Background: Used for NEI non-EGU Point and Nonpoint sectors
CoST is used to apply most non-EGU projection/growth factors, controls and facility/unit/stack-level closures
to the 2011 NEI-based emissions modeling inventories to create inventories for year 2030 for the following
sectors: afdust, ag, clc2rail, c3marine, nonpt, np oilgas, ptnonipm, ptoilgas and rwc. The CoST training
manual is available at: https://www.cmascenter.Org/cost/documentation/2.10/html/. The CoST development
document, which is a more thorough albeit dated document of how to build and format CoST input files
(packets) is available from https://www3.epa.gov/ttnecasl/cost.htm.
CoST allows the user to apply projection (growth) factors, controls and closures at various geographic and
inventory key field resolutions. Each of these CoST datasets, also called "packets" or "programs", provides the
user with the ability to perform numerous quality assurance assessments as well as create SMOKE-ready future
year inventories. Future year inventories are created for each emissions modeling sector via a CoST "strategy"
and each strategy includes all 2011 inventories and applicable CoST packets. For reasons discussed later, some
emissions modeling sectors require multiple CoST strategies to account for the compounding of control
programs that impact the same type of sources. There are also available linkages to existing and user-defined
control measures databases and it is up to the user to determine how control strategies are developed and
applied. EPA typically creates individual CoST packets that represent specific intended purposes. For example,
aircraft projections for airports are in a separate PROJECTION packet from residential wood combustion
sales/appliance turnover-based projections. CoST uses three packet types as described below:
1. CLOSURE: Applied first in CoST. This packet can be used to zero-out (close) point source emissions at
resolutions as broad as a facility to as specific as a stack. EPA uses these types of packets for known
post-2011 controls as well as information on closures provided by states on specific facilities, units or
stacks. This packet type is only used in the ptnonipm and pt oilgas sectors.
2. PROJECTION: This packet allows the user to increase or decrease emissions for virtually any
geographic and/or inventory source level. Projection factors are applied as multiplicative factors to the
2011 emissions inventories prior to the application of any possible subsequent CONTROLS. A
PROJECTION packet is necessary whenever emissions increase from 2011 and is also desirable when
information is based more on activity assumptions rather than known control measures. EPA uses
PROJECTION packet(s) in every non-EGU modeling sector.
3. CONTROL: These packets are applied after any/all CLOSURE and PROJECTION packet entries. The
user has similar level of control as PROJECTION packets regarding specificity of geographic and/or
inventory source level application. Control factors are expressed as a percent reduction (0 to 100) and
can be applied in addition to any pre-existing inventory control, or as a replacement control where
inventory controls are first backed out prior to the application of a more-stringent replacement control.
All of these packets are stored as data sets within the Emissions Modeling Framework (EMF) and use comma-
delimited formats. As mentioned above, CoST first applies any/all CLOSURE information for point sources,
then applies PROJECTION packet information, followed by CONTROL packets. A hierarchy is used by CoST
to separately apply PROJECTION and CONTROL packets. In short, in a separate process for PROJECTION
and CONTROL packets, more specific information is applied in lieu of less-specific information in ANY other
packets. For example, a facility-level PROJECTION factor will be replaced by a unit-level, or facility and
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pollutant-level PROJECTION factor. It is important to note that this hierarchy does not apply between packet
types; for example, CONTROL packet entries are applied irrespective of PROJECTION packet hierarchies. A
more specific example: a state/SCC-level PROJECTION factor will be applied before a stack/pollutant-level
CONTROL factor that impacts the same inventory record. However, an inventory source that is subject to a
CLOSURE packet record is removed from consideration of subsequent PROJECTION and CONTROL packets.
The implication for this hierarchy and intra-packet independence is important to understand and quality assure
when creating future year strategies. For example, with consent decrees, settlements and state comments, the
goal is typically to achieve a targeted reduction (from the 2011NEI) or a targeted future-year emissions value.
Therefore, consent decrees and state comments for specific cement kilns (expressed as CONTROL packet
entries), needed to be applied instead of (not in addition to) the more general approach of the PROJECTION
packet entries for cement manufacturing. By processing CoST control strategies with PROJECTION and
CONTROL packets separated by the type of broad measure/program, it is possible to show actual changes from
the 2011 inventory to the future year inventory due to each packet.
Ultimately, CoST concatenates all PROJECTION packets into one PROJECTION dataset and uses a hierarchal
matching approach to assign PROJECTION factors to the inventory. For example, a packet entry with
Ranking=l will supersede all other potential inventory matches from other packets. CoST then computes the
projected emissions from all PROJECTION packet matches and then performs a similar routine for all
CONTROL packets. Therefore, when summarizing "emissions reduced" from CONTROL packets, it is
important to note that these reductions are not relative to the 2011 inventory, but rather, to the intermediate
inventory after application of any/all PROJECTION packet matches (and CLOSURES). A subset of the more
than 70 hierarchy options is shown in Table 4-3, although the fields in Table 4-3 are not necessarily named the
same in CoST, but rather are similar to those in the SMOKE FF10 inventories. For example, "REGIONCD" is
the county-state-county FIPS code (e.g., Harris county Texas is 48201) and "STATE" would be the 2-digit state
FIPS code with three trailing zeroes (e.g., Texas is 48000).
Table 4-3. Subset of CoST Packet Matching Hierarchy
Rank
Matching Hierarchy
Inventory Type
1
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID, SCC, POLL
point
2
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID, POLL
point
3
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, POLL
point
4
REGION CD, FACILITY ID, UNIT ID, POLL
point
5
REGION CD, FACILITY ID, SCC, POLL
point
6
REGION CD, FACILITY ID, POLL
point
7
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID, SCC
point
8
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID
point
9
REGION CD, FACILITY ID, UNIT ID, REL POINT ID
point
10
REGION CD, FACILITY ID, UNIT ID
point
11
REGION CD, FACILITY ID, SCC
point
12
REGION CD, FACILITY ID
point
13
REGION CD, NAICS, SCC, POLL
point, nonpoint
14
REGION CD, NAICS, POLL
point, nonpoint
15
STATE, NAICS, SCC, POLL
point, nonpoint
16
STATE, NAICS, POLL
point, nonpoint
17
NAICS, SCC, POLL
point, nonpoint
18
NAICS, POLL
point, nonpoint
19
REGION CD, NAICS, SCC
point, nonpoint
20
REGION CD, NAICS
point, nonpoint
21
STATE, NAICS, SCC
point, nonpoint
22
STATE, NAICS
point, nonpoint
95

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Rank
Matching Hierarchy
Inventory Type
23
NAICS, SCC
point, nonpoint
24
NAICS
point, nonpoint
25
REGION CD, SCC, POLL
point, nonpoint
26
STATE, SCC, POLL
point, nonpoint
27
SCC, POLL
point, nonpoint
28
REGION CD, SCC
point, nonpoint
29
STATE, SCC
point, nonpoint
30
SCC
point, nonpoint
31
REGION CD, POLL
point, nonpoint
32
REGION CD
point, nonpoint
33
STATE, POLL
point, nonpoint
34
STATE
point, nonpoint
35
POLL
point, nonpoint
The contents of the controls, local adjustments and closures for the 2040 reference case are described in the
following subsections. Year-specific projection factors (PROJECTION packets) for years 2030 (and for
upstream adjustments, 2040) were used for creating the reference case unless noted otherwise. The contents of a
few of these projection packets (and control reductions) are provided in the following subsections where
feasible. However, most sectors used growth or control factors that varied geographically and their contents
could not be provided in the following sections (e.g., facilities and units subject to the Boiler MACT
reconsideration has thousands of records). The remainder of Section 4.2 is divided into several subsections that
are summarized in Table 4-4. Note that future year inventories were used rather than projection or control
packets for some sources.
Table 4-4. Summary of non-EGU stationary projections subsections
Subsection
Title
Sector(s)
Brief Description
4.2.2
CoST Plant CLOSURE
packet
ptnonipm,
ptoilgas
All facility/unit/stack closures information,
primarily from Emissions Inventory System (EIS),
but also includes information from states and other
organizations.
4.2.3
CoST PROJECTION
packets
All
Introduces and summarizes national impacts of all
CoST PROJECTION packets
4.2.3.1
Paved and unpaved roads
VMT growth
Afdust
PROJECTION packet: county-level resolution,
based on VMT growth.
4.2.3.2
Livestock population
growth
Ag
PROJECTION packet: national, by-animal type
resolution, based on animal population projections.
4.2.3.5
Locomotives and Category
1 & 2 commercial marine
vessels
clc2rail,
ptnonipm
PROJECTION packet: national, by-equipment type
and pollutant, based on cumulative growth,
upstream impacts from mobile source rulemakings,
and control impacts from rulemaking.
4.2.3.4
Category 3 commercial
marine vessels
c3 marine,
othpt
PROJECTION packet: region-level by-pollutant,
based on cumulative growth and control impacts
from rulemaking.
4.2.3.5
OTAQ upstream
distribution, pipelines and
refineries
nonpt,
ptnonipm,
ptoilgas
PROJECTION packet: national, by-broad source
category, based on upstream impacts from mobile
source rulemakings.
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Subsection
Title
Sector(s)
Brief Description
4.2.3.6
Oil and gas and industrial
source growth
nonpt,
npoilgas,
ptnonipm,
pt oilgas
Several PROJECTION packets: varying geographic
resolutions from state, county, to oil/gas play-level
and by-process/fuel-type applications. Data derived
from AEO2014 with several modifications.
4.2.3.7
Data from comments on
previous platforms
nonpt,
ptnonipm
Several PROJECTION packets: varying geographic
resolutions and by-process/fuel-type applications.
Data derived from various sources in response to the
previous (201 lv6.0) version of the emissions
modeling platform.
4.2.3.8
Aircraft
ptnonipm
PROJECTION packet: by-airport for all direct
matches to FAA Terminal Area Forecast data, with
state-level factors for non-matching NEI airports.
4.2.3.9
Cement manufacturing
ptnonipm
PROJECTION packet: by-kiln projections based on
Industrial Sectors Integrated Solutions (ISIS) model
of demand growth and Portland Cement NESHAP.
4.2.3.10
Corn ethanol plants
ptnonipm
PROJECTION packet: national, based on 2014
AEO renewable fuel production forecast.
4.2.3.11
Residential wood
combustion
rwc
PROJECTION packet: national with exceptions,
based on appliance type sales growth estimates and
retirement assumptions and impacts of recent NSPS.
4.2.4
CoST CONTROL packets
nonpt,
npoilgas,
ptnonipm,
pt oilgas
Introduces and summarizes national impacts of all
CoST CONTROL packets
4.2.4.1
Oil and gas NSPS
npoilgas,
pt oilgas
CONTROL packet: national, oil and gas NSPS
impacting VOC only for some activities.
4.2.4.2
RICE NESHAP
nonpt,
npoilgas,
ptnonipm,
pt oilgas
CONTROL packet: national, reflects NESHAP
amendments on compression and spark ignition
stationary reciprocating internal combustion engines
(RICE).
4.2.4.3
RICE NSPS
nonpt,
npoilgas,
ptnonipm,
pt oilgas
CONTROL packet: state and county-level new
source RICE controls, whose reductions by-
definition, are a function of growth factors and also
equipment retirement assumptions.
4.2.4.4
ICI Boilers
nonpt,
ptnonipm,
ptoilgas
CONTROL packet: by-fuel, and for point sources,
by-facility-type controls impacting Industrial and
Commercial/Institutional boilers from rulemaking
and state-provided information.
4.2.4.5
Fuel sulfur rules
nonpt,
ptnonipm,
pt oilgas
CONTROL packet: state and MSA-level fuel sulfur
control programs provided by several northeastern
U.S. states.
4.2.4.6
Natural gas turbines NSPS
ptnonipm,
ptoilgas
CONTROL packet: state and county-level new
source natural gas turbine controls, whose
reductions by-definition, are a function of growth
factors and also equipment retirement assumptions.
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Subsection
Title
Sector(s)
Brief Description
4.2.4.7
Process heaters NSPS
ptnonipm,
ptoilgas
CONTROL packet: state and county-level new
source process heaters controls, whose reductions
by-definition, are a function of growth factors and
also equipment retirement assumptions.
4.2.4.8
Arizona Regional Haze
ptnonipm
CONTROL packet: Regional haze controls for
Arizona provided by Region 9.
4.2.4.9
CISWI
ptnonipm
CONTROL packet reflecting EPA solid waste rule
cobenefits.
4.2.4.10
Data from comments on
previous platforms
nonpt,
ptnonipm,
ptoilgas
CONTROL packets for all other programs,
including Regional Haze, consent
decrees/settlements, and other information from
states/other agencies in prior platforms.
4.2.5
Stand-alone future year
inventories
nonpt,
ptnonipm
Introduction to future-year inventories not generated
via CoST strategies/packets.
4.2.5.1
Portable fuel containers
nonpt
Reflects impacts of Mobile Source Air Toxics
(MSAT2) on PFCs.
4.2.5.2
Biodiesel plants
ptnonipm
Year 2040 new biodiesel plants provided by OTAQ
reflecting planned sited-plants production volumes.
4.2.5.3
Cellulosic plants
nonpt
Year 2018 new cellulosic ethanol plants based on
cellulosic biofuel refinery siting provided by OTAQ
and 2018 NOD A.
4.2.5.4
New cement plants
nonpt,
ptnonipm
Year 2030 ISIS policy case-derived new cement
kilns, permitted (point) and model-generated based
on shifted capacity from some closed units to open
units (nonpt)
4.2.2 CoST Plant CLOSURE packet (ptnonipm, pt_oilgas)
Packet: "CLOSURES_201 Iv6.2_v4fix_3laug2015.txt"
This packet contains facility, unit and stack-level closure information derived from the following sources:
1. Emissions Inventory System (EIS) facilities report from December 20, 2014 with closure status equal to
"PS" (permanent shutdown)
2. EIS unit-level report from November 29, 2014 with status = 'PS'
3. Concatenation of all 2011v6.0 closures information; see Section 4.2.11.3 at:
http://www.epa.gov/ttn/chief/emch/2011v6/outreach/2011v6 2018base EmisMod TSD 26feb2014.pdf
4. Comments from states and regional planning organizations.
The "fix" in the name of the packet refers to corrections that were made after the 201 lv6.2 platform.
Specifically, two fixes related to effective dates were implemented. First, EIS facilities with an erroneous
"default" closure effective date of 1/1/2012 were changed to a closure date of 1/1/2050. Second, any facility
with a closure effective date of 1/1/2011 or earlier was removed. Many facilities with closure dates prior to
2011 are actually in the 201 1NEIv2; in most cases, these are facilities that closed and then re-opened.
The EIS sources, accessible at http://www.epa.gov/ttnchiel/eis/gateway/. report post-2011 permanent
facility/unit shutdowns through the date of the EIS report, assuming reporting agencies provided the
information. The 201 lv6.0 closure information is from a concatenation of previous facility and unit-level
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closure information used in the 2008 NEI-based emissions modeling platform
(http://epa.gov/ttn/chief/emch/2007v5/2007v5 2020base EmisMod TSD 13dec2012.pdf). In addition,
comments on the 201 lv6.0 emissions modeling platform received by states and other agencies indicated the
removal of some closure information (keep open). Ultimately, all data were updated to match the SMOKE FF10
inventory key fields, with all duplicates removed, and a single CoST packet was generated. The cumulative
reductions in emissions from this packet are shown in Table 4-5. Note that these reductions are the same for all
future years.
Table 4-5. Reductions from all facility/unit/stack-level closures by modeling sector
Pollutant
ptnonipm
pt oilgas
Cumulative
CO
6,520
1,360
7,880
nh3
380
0
380
NOx
9,438
2,197
11,635
PM10
3,323
106
3,429
PM2.5
2,464
103
2,567
S02
28,686
182
28,868
voc
11,322
484
11,806
4.2.3 CoST PROJECTION packets (afdust, ag, c1c2rail, c3marine, nonpt, np_oilgas,
ptnonipm, pt_oilgas, rwc)
As previously discussed, for point inventories, after application of any/all CLOSURE packet information, the
next step in running a CoST control strategy is the application of all CoST PROJECTION packets. Regardless
of inventory type (point or nonpoint), the PROJECTION packets are applied prior to the CoST packets. For
several emissions modeling sectors (afdust, ag, clc2rail, c3marine and rwc), there is only one CoST
PROJECTION packet. For all other sectors, there are several different sources of PROJECTIONS data and
therefore there are multiple PROJECTION packets that are concatenated and quality-assured for duplicates and
applicability to the inventories in the CoST strategy. The PROJECTION (and CONTROL) packets were
separated into a few "key" control program types to allow for quick summaries of these distinct control
programs. The remainder of this section is broken out by CoST packet, with the exception of discussion of the
various packets used for oil and gas and industrial source projections; these packets are a mix of different
sources of data that targeted similar sources.
4.2.3.1 Paved and unpaved roads VMT growth (afdust)
Packet: "PROJECTION_2011_2040_AFDUST_VMT_2011 v6.2_31 aug2015.txt"
We received comments from the 2018 NODA (search for 'EPA-HQ-OAR-2013-0809' at www.regulations.gov)
suggesting we grow emissions from paved and unpaved road dust as a function of vehicle miles traveled
(VMT). The future year VMT used to project this sector was consistent with the VMT used for the onroad
mobile source modeling as described in Section 4.3.1.1. The resulting national sector-total increase in PM2.5
emissions are provided in Table 4-6. Note that this packet does not impact any other sources of fugitive dust
emissions in the afdust sector (e.g., no impact on construction dust, mining and quarrying, etc.).
Table 4-6. Increase in total afdust PM2.5 emissions from VMT projections
2011 Emissions
2040 Emissions
% Increase 2040
2,510,307
3,056,305
21.8%
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4.2.3.2 Livestock population growth (ag)
Packet: "PROJECTION_201 l_2030_ag_201 lv6.2_no_RFS2_23jan2015.txt"
EPA estimated animal population growth in ammonia (NH3) emissions from livestock in the ag sector. Except
for dairy cows and turkey production, the animal projection factors are derived from national-level animal
population projections from the U.S. Department of Agriculture (USDA) and the Food and Agriculture Policy
and Research Institute (FAPRI). This methodology was initiated in 2005 for the 2005 NEI, but was updated on
July 24, 2012 in support of the 2007v5 platform (EPA, 2012). For dairy cows, EPA assumed that there would
be no growth in emissions based on little change in U.S. dairy cow populations from year 2011 through 2030
according to linear regression analyses of the FAPRI projections. This assumption was based on an analysis of
historical trends in the number of such animals compared to production rates. Although productions rates have
increased, the number of animals has declined. Based on this analysis, EPA concluded that production forecasts
do not provide representative estimates of the future number of cows and turkeys; therefore, these forecasts
were not used for estimating future-year emissions from these animals. In particular, the dairy cow population
is projected to decrease in the future as it has for the past few decades; however, milk production will be
increasing over the same period. Note that the ammonia emissions from dairies are not directly related to animal
population but also nitrogen excretion. With the cow numbers going down and the production going up the
excretion value will change, but no change was assumed because a quantitative estimate was not available.
Appendix B provides the animal population data and regression curves used to derive the growth factors.
The projection factors by animal category and ag sector total impacts are provided in Table 4-7. As discussed
below, dairy cows are assumed to have no growth in animal population, and therefore the projection factor for
these animals is 1.0 (no growth). Impacts from the renewable fuels mandate are not included in projections for
this sector.
Table 4-7. NH3 projection factors and total impacts to years 2030 for animal operations
Animal Category
2030 Projection Factors
& Total Emissions
Dairy Cow
1.0
Beef
0.950
Pork
1.140
Broilers
1.130
Turkeys
0.921
Layers
1.094
Poultry Average
1.085
Overall Average
1.039
Total Emissions
3,610,711
% Increase from 2011
2.5%
4.2.3.3 Crude Production and Transport, Energy Production for Refineries, Refineries,
and Distribution (nonpt, ptnonipm, pt oilgas)
Packets:
Reference case: "PROJECTION 201 l_2040_OTAQ_upstream_201 Iv6.2_06aug2015.txt"
Control case: "PROJECTION 201 l_2040ctl_OTAQ_upstream_201 Iv6.2_17dec2015"
To account for projected increases in renewable fuel volumes and decreases in gasoline volumes as quantified
in AEO 2014 (http://www.eia. gov/forecasts/archive/aeo 14/). EPA developed county-level inventory
adjustments for gasoline and gasoline/ethanol blend transport and distribution for 2040 reference and control
cases. These adjustments account for losses during truck, rail and waterways loading/unloading and intermodal
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transfers such as highway-to-rail, highways-to-waterways, and all other possible combinations of transfers.
These emissions are entirely evaporative and therefore limited to VOC.
Pipeline usage and refinery emissions were also adjusted to account for impacts of vehicle greenhouse gas
emission standards, as well as renewable fuel volume projections. These adjustments were developed by EPA
OTAQ and impact processes such as process heaters, catalytic cracking units, blowdown systems, wastewater
treatment, condensers, cooling towers, flares and fugitive emissions. These impacts of decreased production
were assumed to be spread evenly across all U. S. refineries. Toxic emissions were estimated in SMOKE by
applying speciation to VOC emissions. It should be noted that the adjustment factors are estimated relative to
that portion of refinery emissions associated with gasoline and diesel fuel production. Production of jet fuel, still
gas and other products also produce emissions. If these emissions were included, the adjustment factors would
not be as large.
The 2040 reference and control case inventories were developed by applying adjustment factors to the
201 1NEIv2 inventory. These adjustments were made using an updated version of EPA's model for upstream
emission impacts, developed for the RFS2 rule (EPA, 2010d). The methodology used to compute these
adjustments is described in Appendix C, Upstream Methodology.
The resulting adjustments for pipelines, refineries and the gasoline distribution processes (RBT, BPS and BTP)
are provided in Table 4-8. Separate adjustments were applied to refinery to bulk terminal (RBT), bulk plant
storage (BPS), and bulk terminal to gasoline dispensing pump (BTP) components. Emissions for the BTP
component are greater than the RBT and BPS components. See Appendix A for the complete cross-walk
between SCC, for all component types of petroleum transport and storage components. An additional
adjustment was applied for 2025 at a national scale to account for impacts of gasoline volume reductions of the
2017-2025 light-duty greenhouse gas rule.
Notice that the "2011 Emissions" are not the same in Table 4-8. This is because these "2011" emissions are
actually an intermediate set up projections applied after a first CoST strategy used to apply most other
PROJECTION and CONTROL packets. We decided to first apply these other packets because we have multiple
PROJECTION and CONTROL programs that impact the same emission sources. For this example, we applied
year-specific industrial sector AEO-based growth (discussed in the next section) with our first CoST strategy,
then applied these "EISA" adjustments on the results of this first CoST strategy. Similarly, we have IC Engine
(RICE) existing (NESHAP) as well as new source (NSPS) controls that need to be applied in separate strategies.
Alternatively, we could have made "compound" CoST packets that combine these PROJECTION (and
CONTROL) factors, but preferred to keep these packets separate for transparency. If we tried to process the
multiple packets affecting the same sources in a single CoST strategy, CoST would either fail if the packet
entries were are the same key-field resolution (duplicate error), or, if packets were at a different key-field
resolution, CoST would only apply the packet entry with higher priority according to Table 4-3.
Table 4-8. Petroleum pipelines & refineries and production storage and transport factors and reductions
Poll
Year
Factors
2011
Emissions
Reduction
%
Reduction

Pipelines
Refineries
BTP
RBT/BPS

CO
2040 ref
0.9765
0.9188

98,428
5,213
5.3%

2040 ctl
0.9631
0.8472

98,428
11,236
11.4%
NOx
2040 ref
0.9097
0.9414

117,976
12,201
10.3%

2040 ctl
0.864
0.8896

117,976
19,769
16.8%
PMio
2040 ref
0.9847
0.9474

57,101
14,751
25.8%

2040 ctl
0.9778
0.9009

57,101
17,174
30.1%
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pm25
2040 ref
0.9881
0.9467

32,856
3,999
12.2%

2040 ctl
0.9831
0.8997

32,856
5,940
18.1%
so2
2040 ref
0.9873
0.9200

135,285
8,650
6.4%

2040 ctl
0.9811
0.8493

135,285
16,930
12.5%
voc
2040 ref
0.9931
0.9405
0.6770
0.7345
799,526
208,612
26.1%

2040 ctl
0.9927
0.8880
0.6716
0.7205
799,526
219,181
27.4%
The fuel volumes associated with the 2040 reference and control case inventories not only impact on-site
petroleum refinery emissions, but also emissions upstream of refineries associated with producing the energy
they use. Petroleum refineries rely on upstream energy from residual oil, natural gas, coal and electricity.
Although energy use at biofuel refineries would also be impacted, these impacts were not included. In addition
to impacts associated with producing energy for refineries, there are also impacts associated with crude
production, and evaporative losses during transport of crude to refineries. The methodology used to compute
these adjustment factors is described in Appendix C, Upstream Methodology. The factors are incorporated into
the projection packets referenced at the top of this section.
4.2.3.4 Category 3 commercial marine vessels (c3marine, othpt)
Packet: "PROJECTION_2011_2030_C3_CMV_ECA_IMO_201 Iv6.2_10feb2015.txt"
As discussed in Section 2.4.2, the EPA estimates for C3 CMV emissions data were developed for year 2002 and
projected to year 2011 for the 2011 base case and used where states did not submit data to Version 2 of the 2011
NEI. Pollutant and geographic-specific projection factors to year 2011 were applied, along with projection
factors to year 2030 that reflect assumed growth and final ECA-IMO controls. These emissions estimates reflect
EPA's coordinated strategy for large marine vessels. More information on EPA's coordinated strategy for large
marine vessels can be found in our Category 3 Marine Diesel Engines and Fuels regulation published in April
2010. That rule, as well as information about the North American and U.S. Caribbean Sea EC As, designated by
amendment to MARPOL Annex VI, can be found here: http://www.epa.gov/otaq/oceanvessels.htm.
Projection factors for creating the year 2030 c3marine inventories from the 2011 base case are provided in
Table 4-9. Background on the region and Exclusive Economic Zone (EEZ) FIPS is provided in the discussion
on the c3marine inventory for 2011 -Section 2.4.2. For example, Washington state emissions are grown the
same as all North Pacific offshore emissions.
Table 4-9. Growth factors to project the 2011 ECA-IMO inventory to 2030
Region
EEZ
(Offshore)
FIPS
Year
2030 Adjustments Relative to 2011
CO
NOx
PMio
pm25
so2
VOC
North Pacific (NP)
85001
2030
1.853
0.742
0.256
0.254
0.072
1.853
South Pacific (SP)
85002
2030
2.642
0.864
0.376
0.373
0.108
2.642
East Coast (EC)
85004
2030
2.308
0.778
0.314
0.311
0.083
2.308
Gulf Coast (GC)
85003
2030
1.721
0.582
0.236
0.234
0.062
1.721
Great Lakes (GL)
n/a
2030
1.378
0.957
0.182
0.181
0.049
1.378
Outside ECA
98001
2030
2.345
1.810
0.516
0.512
0.427
2.345
As discussed in Section 2.4.2, emissions outside the 3 to 10 mile coastal boundary but within the approximately
200 nm EEZ boundaries were projected to years 2017 and 2025 using the same regional adjustment factors as
the U.S. emissions; however, the FIPS codes were assigned as "EEZ" FIPS and these emissions are processed in
the "othpt" sector (see Section 2.5.1 and 4.5.1). Note that state boundaries in the Great Lakes are an exception,
102

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extending through the middle of each lake such that all emissions in the Great Lakes are assigned to a U.S.
county or Ontario. The classification of emissions to U.S. and Canadian FIPS codes is needed to avoid double-
counting of Canadian-provided C3 CMV emissions in the Great Lakes.
The cumulative impact of these ECA-IMO projections and controls to the U.S. + near-offshore (c3marine
sector) and far-offshore emissions (othpt sector) in 2030 is provided in Table 4-10.
Table 4-10. Difference in c3marine sector and othpt C3 CMV emissions between 2011 and 2030
Region
Pollutant
2011 emissions
2030 emissions
Difference
2030 - 2011
U.S. + near offshore
CO
13,705
27,695
13,990
U.S. + near offshore
NOx
145,729
114,254
-31,475
U.S. + near offshore
PM10
11,370
3,152
-8,218
U.S. + near offshore
PM25
10,148
2,814
-7,334
U.S. + near offshore
S02
95,306
7,247
-88,059
U.S. + near offshore
VOC
5,645
11,515
5,870
far-offshore (othpt sector)
CO
250,674
576,679
326,005
far-offshore (othpt sector)
NOx
2,923,929
4,584,955
1,661,025
far-offshore (othpt sector)
PM10
215,211
104,818
-110,393
far-offshore (othpt sector)
PM25
197,848
95,504
-102,344
far-offshore (othpt sector)
S02
1,610,619
612,244
-998,375
far-offshore (othpt sector)
VOC
106,444
244,882
138,438
4.2.3.5 Locomotives and Category 1 & 2 commercial marine vessels (c1c2rail, ptnonipm)
Base Packet: "PROJECTION_2011v6.2_2030_clc2rail_BASE_06feb2015.txt"
Projection Packet: PROJECTION 2011 v6_2_2040ctl_ptnonipm_railyards_including_OTAQ_l7dec2015
Upstream reference: "PROJECTION2011 v6_2_2040_clc2rail_OTAQ_upstream_06aug2015.txt"
Upstream control: "PRO JECTION2011 v6_2_2040ctl_clc2rail_OTAQ_upstream_l7dec2015.txt"
There are three components used to create projection factors for year 2030. The first component of the future
year clc2rail inventory is the non-California data projected from the 2011 base case. The second component is
the CARB-supplied year 2011 and 2030 data for California. Finally, upstream impacts based on 2040 AEO fuel
volumes are reflected in adjustment factors applied to 2025 emissions.
For all states outside of California, national projection factors by SCC and pollutant between 2011 and future
years reflect the May 2004 "Tier 4 emissions standards and fuel requirements"
(http://www.epa.gov/otaq/documents/nonroad-diesel/420r04007.pdf) as well as the March 2008 "Final
locomotive-marine rule" controls (http://www.epa.gov/otaq/regs/nonroad/420f08004.pdf). The future-year
clc2rail emissions account for increased fuel consumption based on Energy Information Administration (EIA)
fuel consumption projections for freight, and emissions reductions resulting from emissions standards from the
Final Locomotive-Marine rule (EPA, 2009d).29 For locomotives, EPA applied HAP factors for VOC HAPs by
using VOC projection factors to obtain 1,3-butadiene, acetaldehyde, acrolein, benzene, and formaldehyde.
Similar to locomotives, C1/C2 VOC HAPs were projected based on the VOC factor. C1/C2 diesel emissions
were projected based on the Final Locomotive Marine rule national-level factors. These non-California
projection ratios are provided in Table 4-11. Note that projection factors for "... Yard Locomotives"
29 This rule lowered diesel sulfur content and tightened emission standards for existing and new locomotives and marine diesel
emissions to lower future-year PM, SO2, and NOx, and is documented at: http://www.epa.gOv/otaa/marine.htm#2008final.
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(SCC=2285002010) are applied to the ptnonipm (point inventory) "yard locomotives" (SCC=28500201)
reported by a couple of states in the 2011 NEI.
Table 4-11. Non-California projection factors for locomotives and Category 1 and Category 2 Commercial
Marine Vessel Emissions
see
Description
Poll
2030
Factor
2280002XXX
Marine Vessels, Commercial; Diesel; Underway & port emissions
CO
0.975
2280002XXX
Marine Vessels, Commercial; Diesel; Underway & port emissions
NOx
0.425
2280002XXX
Marine Vessels, Commercial; Diesel; Underway & port emissions
PM
0.388
2280002XXX
Marine Vessels, Commercial; Diesel; Underway & port emissions
S02
0.068
2280002XXX
Marine Vessels, Commercial; Diesel; Underway & port emissions
VOC
0.416
2285002006
Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations
CO
1.352
2285002006
Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations
NOx
0.478
2285002006
Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations
PM
0.322
2285002006
Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations
S02
0.039
2285002006
Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations
VOC
0.327
2285002007
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations
CO
1.352
2285002007
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations
NOx
1.050
2285002007
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations
PM
0.978
2285002007
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations
S02
0.039
2285002007
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations
VOC
1.352
2285002008
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains (Amtrak)
CO
1.163
2285002008
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains (Amtrak)
NOx
0.341
2285002008
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains (Amtrak)
PM
0.212
2285002008
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains (Amtrak)
S02
0.033
2285002008
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains (Amtrak)
VOC
0.172
2285002009
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines
CO
1.163
2285002009
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines
NOx
0.341
2285002009
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines
PM
0.212
2285002009
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines
S02
0.033
2285002009
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines
VOC
0.172
2285002010
Railroad Equipment; Diesel; Yard Locomotives
CO
1.352
2285002010
Railroad Equipment; Diesel; Yard Locomotives
NOx
0.740
2285002010
Railroad Equipment; Diesel; Yard Locomotives
PM
0.717
2285002010
Railroad Equipment; Diesel; Yard Locomotives
S02
0.039
2285002010
Railroad Equipment; Diesel; Yard Locomotives
VOC
0.676
For California projections, CARB provided to EPA the locomotive, and Category 1 and 2 commercial marine
emissions used to reflect years 2011 and year 2030. These CARB inventories included nonroad rules reflected
in the December 2010 Rulemaking Inventory (http ://www.arb.ca.gov/regact/2010/offroadlsi 10/offroadisor.pdf).
those in the March 2011 Rule Inventory, the Off-Road Construction Rule Inventory for "In-Use Diesel", cargo
handling equipment rules in place as of 2011 (see http://www.arb.ca.gov/ports/cargo/cargo.htm), and the 2007
and 2010 regulations to reduce emissions diesel engines on commercial harbor craft operated within California
waters and 24 nautical miles of the California baseline.
The California C1/C2 CMV emissions were obtained from the CARB nonroad mobile dataset
"ARMJ_RF#2002_ANNU AL_MOBILE.txt". These emissions were developed using Version 1 of the CEP AM
which supports various California off-road regulations. The locomotive emissions were obtained from the
CARB trains dataset "ARMJ_RF#2002_ANNUAL_TRAINS.txt". Documentation of the CARB off-road
methodology, including clc2rail sector data, is provided here:
http://www.arb.ca.gOv/msei/categories.htm#offroad motor vehicles. EPA converted the CARB inventory TOG
to VOC by dividing the inventory TOG by the available VOC-to-TOG speciation factor. The CARB year-2011
inventory (provided with the 2017 and 2025 emissions) did not match the CARB-submitted inventory in
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Version 2 of the 2011 NEI; therefore, we used the CARB 2011/2030 data to compute projection ratios that were
then applied to the 2011 emissions modeling platform (201 1NEIv2). California projection factors were
"capped" at 2.5; we found that those counties/SCCs/pollutants with projection factors greater than 2 (100%
increase) were all under 100 tons for any given pollutant. The California projection factors are county-level and
therefore not provided here.
The non-California projection factors were applied to all "offshore" clc2 CMV emissions. These offshore
emissions, in the 2011 NEI, start at the end of state waters, and extend out to the Economic Exclusion Zone
(EEZ). A summary of the national impact for US (including California) and offshore clc2rail sector emissions
are provided in Table 4-12 and include upstream adjustments.
Table 4-12. Difference in clc2rail sector emissions between 2011 and future years
Region
Pollutant
2011
2040 ref
Difference
2040-2011
2040 ctl
Difference
ctl - ref
U.S.
CO
185,074
234,924
49,850
234,491
-433
U.S.
NOx
1,097,162
550,120
-547,043
548,825
-1,295
U.S.
PMio
36,079
13,686
-22,392
13,627
-59
U.S.
PM2.5
33,713
12,788
-20,925
12,731
-57
U.S.
S02
12,869
963
-11,905
959
-4
U.S.
VOC
48,810
20,301
-28,508
20,288
-13
Offshore
CO
66,395
64,566
-1,829
64,321
-244
Offshore
NOx
326,631
138,181
-188,451
137,234
-946
Offshore
PM10
10,795
4,153
-6,642
4,110
-43
Offshore
PM2.5
10,471
4,025
-6,446
3,982
-43
Offshore
S02
4,014
271
-3,743
269
-2
Offshore
VOC
7,472
3,066
-4,406
3,059
-7
Finally, the reference and control case fuel volume impacts on category 1 marine engine, category 2 marine
engine (CI, C2) and rail inventories were calculated by scaling the 2025 inventory using overall impacts on
combustion emissions associated with transport of fuel volumes between 2025 and 2040 reference and control
cases. The overall impacts on combustion emissions associated with transport were calculated using the RFS2
impacts spreadsheet, then nationwide emission fractions for combustion emission fractions associated with rail
and C1/C2 vessels from GREET 1.8.c were used to obtain the portion attributable to rail and C1/C2 vessels
(Argonne). In summary, the clc2rail inventories were projected to 2030 using the
PROJECTION201 lv6_2_2030_clc2rail_BASE packet. Then, a second control strategy was run which applied
the Table 4-13 factors on top of the already projected clc2rail inventories. The scalars are provided in Table
4-13 and Table 4-14.
Table 4-13 Scalars Applied to 2025 C1/C2 Combustion Emissions
Pollutant
2040 reference
2040 control
CO
0.997
0.993
NOx
0.994
0.988
PM10
0.990
0.980
PM2.5
0.990
0.979
S02
0.992
0.986
VOC
0.985
0.983
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Table 4-14 Scalars Applied to 2025 Rail Combustion Emissions
Pollutant
2040 reference
2040 control
CO
0.999
0.998
NOx
0.999
0.998
PM10
0.999
0.997
PM2.5
0.998
0.997
S02
0.967
0.966
VOC
0.999
0.999
4.2.3.6 Oil and gas and industrial source growth from 2011v6.0 NODA (nonpt, np oilgas,
ptnonipm, pt oilgas)
Packets:
"PROJECTION201 lv6.2_2030_nonpoint_SCC_SRAcapped_08jun2015"
"PROJECTION201 lv6.2_2030_SCC_NONPOINT_LADCO_08jun2015"
"PROJECTION201 lv6.2_2030_SCC_NONPOINT_SCA_orig_CAPPED_08jun2015"
"PROJECTION201 lv6.2_2030_SRAcapped_POINT_08jun2015"
"PROJECTION201 lv6.2_2030_SCC_POINT_LADC0 08jun2015"
"PROJECTION201 lv6.2_2030_SCC_POINT_SCA_orig_CAPPED_08jun2015"
"PROJECTION_201 lv6.2_2030_NAICS_SCC_SCA_orig_NEI_matched_CAPPED2.5_08jun2015"
The EPA provided a Notice of Data Availability (NODA, search for the docket 'EPA-HQ-OAR-2013-0809' on
regulations.gov) for the 201 lv6.0 emissions modeling platform and projected 2018 inventory in January, 2014.
A significant number of the comments were about the EPA's "no growth" assumption for industrial stationary
sources and about the current projection approach for oil and gas sources that was applied similarly to 5 broad
geographic (NEMS) regions and limited to only oil and gas drilling activities.
With limited exceptions, the EPA has used a no-growth assumption for all industrial non-EGU emissions since
the 2005 NEI-based emissions modeling platform (EPA, 2006). However, comments provided to the EPA for
this platform (via the NODA) and for previous modeling platforms suggested that this approach was
insufficient. In addition, the NOx Budget program, which had a direct impact on post-2002 emissions
reductions, is in full compliance in the 2011 NEI. This means that additional large-scale industrial reductions
should not be expected beyond 2011 in the absence of on-the-books state and federal rules.
In response to the comments about EPA's no-growth approach, the EPA developed industrial sector activity-
based growth factors. In response to the NODA, many states have additionally provided detailed activity-based
projection factors for industrial sources, including oil and gas sources. To develop the methods described here,
we have blended the state-provided growth factors with the EPA-developed industrial sector growth factors.
This approach has attempted to balance using the specific information that is available with EPA's interest in
consistency for a given sector and technical credibility. Table 4-15 lists the resulting data sources for industrial
sector non-EGU growth factors that EPA applied to this emissions modeling platform. Note that this list does
not include the revised "projection packets" from the 201 lv6.0-based platform, which were also sent to the EPA
via the docket process. That additional data were considered and included in our projections as well, and are
discussed separately in Section 4.2.3.7.
Ultimately, there were 3 broad sources of projection information for industrial sources, including oil and gas;
these sources are referenced as the following for simplicity (e.g., realizing that not all data from Mid-Atlantic
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Regional Air Management Association (MARAMA) states are limited to MARAMA states):
1) EPA: Reflects EPA-sponsored data provided by a contractor (SC&A, 2014a; SC&A, 2014b). Packet file
names for these data include "SCA".
2) MARAMA: Reflects data submitted on behalf of Atlantic seaboard states from North Carolina through
Maine, and extending west through Pennsylvania and West Virginia. Packet file names for these data
include "SRA" (SRA, 2014).
3) LADCO: Reflects data submitted on behalf of Lake Michigan Air Directors Consortium (LADCO)
states (MN, WI, MI, IL, IN, OH). Projection data from this data source are reflected in packet names
containing "LADCO" (Alpine Geophysics, 2014).
Table 4-15. Sources of new industrial source growth factor data from the 201 lv6.0 NODA
Abbrev.
Source
Geographic
Resolution
Inventory
Resolution
Use/Caveat
EPA
2014 AEO fuel
consumption/production
for EPA "priority"
categories: IC Engines,
Gas Turbines and ICI
Boilers/Process Heaters
Census
Division
NAICS/SCC
or SCC
Impacts almost all non-EGU stationary industrial and
commercial sources. These data are used where LADCO and
MARAMA data (below) are not also provided. Growth factors
are "capped" to 1.25 (25% cumulative growth) and outlier
values (e.g., commercial residual oil) set to no-growth,
consistent with MARAMA growth factor data (below).
EPA
2014 AEO Crude Oil
Production, Natural Gas
Production and Lease
plant fuel + pipeline fuel
natural gas consumption
AEO
Oil/Gas
Play-level
and "Rest of
Census
Division"
NAICS/SCC
or SCC
Impacts both point and nonpoint oil and gas sectors as well as
some non-EGU point sources not in the pt oilgas sector. These
data supersede any/all other projections data, including
LADCO/MARAMA information with minor exceptions (PA
drilling, NY state no-growth, point source MARAMA data).
These data also "cap" growth factors to 2.5 (150% cumulative
growth).
MARAMA
MARAMA/SRA/states
State or
county for
nonpoint and
facility and
below for
most point
sources
Facility and
sub-facility
for point,
SCC-level
for nonpoint
Many projection factors are unchanged from the 201 lv6.0
platform; however, many new data provided were included in
our 2011v6.2 platform. However, the provided oil and gas
projection data were not used, as we opted for single data
source "EPA" approach. Emissions assigned a cap of 1.25 for
many non-oil/gas production related sources (regardless of
future year); we retain this cap and also apply this cap to all
non-oil and gas sources.
LADCO
LADCO/ Alpine/states
State or
county
SCC
Most projection factors are unchanged from 201 lv6.0 platform.
Oil and Gas factors from 2014 AEO but state-level only. We
only used the new growth factor data, essentially limited to a
couple priority (LADCO) source categories and did NOT use
any oil and gas production-based projections data, opting
instead to use the single data source "EPA" approach. As a
result, these data have very limited impact on point and
nonpoint oil and gas projections.
A discussion of each projection component in Table 4-15 is discussed below.
As previously discussed, the EPA created a nationally-consistent set of industrial source projection factors for
several future years. We relied on 2014 AEO fuel consumption/production projections data to develop growth
factors for our first two "priority" source categories: IC Engines/Gas Turbines and ICI Boilers/Process Heaters.
We selected specific AEO energy consumption data sets that reflect the best available indicator of each source's
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emissions activity. The discussion below provides more details on the approaches summarized in Table 4-16.
This discussion is generally organized from the most straightforward approach, to the most complex. Growth
factors were only developed for the specific NAICS codes that the AEO identifies as associated with economic
sector-specific fuel consumption projections. These NAICS codes are listed in Table 4-17. The EPA mapped
and expand the resulting NAICS/SCC and SCC growth factors to all relevant SCCs in the 201 lv6.2 inventories.
In cases where the AEO data showed a fuel type with zero consumption in each year, we set the growth factors
for these fuels to "1.0" for each relevant year. This approach holds the growth flat for those fuels rather than
zeros out the emissions in future years.
For all applicable commercial/institutional fuel combustion priority category SCCs, we compiled 2014 AEO
regional commercial sector energy consumption projections data by fuel type for 2011, 2018, 2025 and 2030,
and computed growth factors for each projection year/fuel type as the ratio of each future year's energy
consumption to 2011 energy consumption. The AEO's commercial sector fuel consumption regions are
equivalent to Census Divisions (see www2.census.gov/geo/pdfs/maps-data/maps/reference/us regdiv.pdf).
Because Census Divisions are groups of states, commercial/institutional fuel combustion growth factors were
developed at the state-level, with each state in a given division assigned the same growth factors.
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Table 4-16. Summary of "EPA" Projection Approaches for IC Engines/Gas Turbines and ICI Boilers/Process Heaters
Source
Category
Industry Sector1
NAICS Code(s)
Fuel
Type(s)
Summary of Approach
Commercial/
Institutional


All
AEO Commercial sector energy consumption projections by fuel type by Census Division
Industrial
Food
311
All
Calculate national energy intensity factors by AEO industry sector and fuel type; multiply by projected industry sector output at
Census Division level to yield estimated regional energy consumption by sector/fuel type; and calculate regional growth factors
by fuel type from these estimates
Paper
322
Refining
32411
Bulk Chemical
3251,3252, 3253
Glass
3272
Cement
32731,32741
Iron and Steel
3311
Aluminum
3313
Metal Based Durables
332-336
Other Manufacturing
all other 31-33
Other
Agriculture
111,112,113,115
All
Calculate national energy intensity factors by AEO industry sector and fuel type; multiply by projected industry sector output at
Census Division level to yield estimated regional energy consumption by sector/fuel type; and calculate regional growth factors
by fuel type from these estimates
Construction
233-238
Mining
211,2121,2122-2123
Oil/Gas Production2
211,213111,213112,
2212,4861,4862,
Natural
Gas
AEO Oil/Gas Play-level. Calculate ratios of national oil/gas production to oil/gas sector constant dollar output; multiply ratios by
AEO Census Division-level oil/gas sector constant dollar output to estimate Census Division-level oil/gas production; calculate
Census Division-level ratios representing the sum of lease plant fuel + pipeline fuel natural gas consumption to the estimated
volume of oil/gas produced; multiply these ratios by the volume of oil/gas produced in each applicable oil/gas play to yield oil/gas
play-level projections of lease plant fuel + pipeline fuel natural gas consumption; finally, calculate play-level growth factors from
these estimates.
Rest of Nation: Calculate "rest-of-Census Division" consumption estimates for pipeline fuel natural gas + lease and plant fuel by
subtracting oil/gas play consumption estimates from the Census Division estimates published in the AEO. This yields nine sets of
growth factors for each year, corresponding to each of the nine Census Divisions.
All other
AEO Oil/Gas Plays: Calculate the national ratio of oil/gas sector constant dollar output to the volume of oil and gas produced for
each year; multiply these national ratios by AEO estimates of oil and gas production in each oil/gas play to yield estimates of
oil/gas sector constant dollar output in each play; multiply the oil/gas sector constant dollar output estimates for each play by
ratios of national Mining sector fuel consumption to national oil/gas sector constant dollar output (this procedure develops
estimates of mining sector energy consumption by fuel type within each oil/gas play); and calculate growth factors by play/fuel
type from these estimates.
Rest of Nation: develop "rest-of-Supply Region" oil/gas production estimates by subtracting the AEO's oil/gas production
estimates for these plays from AEO's total oil/gas Supply region production estimates (the residual production values are then
used to calculate the "rest-of-Supply Region" growth factors). This yields six sets of growth factors for each year, corresponding
to each of the AEO's six oil/gas Supply Regions.
All Other

All
AEO total energy consumption projections by fuel type and Census Division
Notes: Though not displayed in this table, a no-growth assumption was applied to 3 rocket engine SCCs, and applied AEO transportation sector fuel consumption projections to aircraft and railroad equipment-related SCCs.
1	Identified based either on NA1CS code or SCC/NA1CS code-see text for discussion.
2	In addition to priority category SCC records with oil/gas production NA1CS codes, factors will also be applied to natural gas production SCCs (e.g., 31000203-Natural Gas Production/Compressors).
109

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Table 4-17. NAICS Codes for which NAICS/SCC-level Growth Factors were developed
NAICS
Code
NAICS Code Description
111
Crop Production
112
Vegetable and Melon Farming
113
Fruit and Tree Nut Farming
115
Support Activities for Agriculture and Forestry
211
Oil and Gas Extraction
2121
Coal Mining
2122
Iron Ore Mining
2123
Nonmetallic Mineral Mining and Quarrying
213111
Drilling Oil and Gas Wells
213112
Support Activities for Oil and Gas Operations
2212
Natural Gas Distribution
233
Nonresidential Building Construction
234
Heavy Construction
235
Special Trade Contractors
236
Construction of Buildings
237
Heavy and Civil Engineering Construction
238
Specialty Trade Contractors
311
Food Manufacturing
312
Beverage and Tobacco Product Manufacturing
313
Textile Mills
314
Textile Product Mills
315
Apparel Manufacturing
316
Leather and Allied Product Manufacturing
321
Wood Product Manufacturing
322
Paper Manufacturing
323
Printing and Related Support Activities
32411
Petroleum Refineries
32412
Asphalt Paving, Roofing, and Saturated Materials Manufacturing
32419
Other Petroleum and Coal Products Manufacturing
3251
Basic Chemical Manufacturing
3252
Resin, Synthetic Rubber, and Artificial Synthetic Fibers and Filaments Manufacturing
3253
Pesticide, Fertilizer, and Other Agricultural Chemical Manufacturing
3254
Pharmaceutical and Medicine Manufacturing
3255
Paint, Coating, and Adhesive Manufacturing
3256
Soap, Cleaning Compound, and Toilet Preparation Manufacturing
3259
Other Chemical Product and Preparation Manufacturing
326
Plastics and Rubber Products Manufacturing
3272
Glass and Glass Product Manufacturing
32731
Cement Manufacturing
32732
Ready-Mix Concrete Manufacturing
32733
Concrete Pipe, Brick, and Block Manufacturing
32739
Other Concrete Product Manufacturing
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NAICS
Code
NAICS Code Description
32741
Lime Manufacturing
3279
Other Nonmetallic Mineral Product Manufacturing
3311
Iron and Steel Mills and Ferroalloy Manufacturing
3312
Steel Product Manufacturing from Purchased Steel
3313
Alumina and Aluminum Production and Processing
3314
Nonferrous Metal (except Aluminum) Production and Processing
3315
Foundries
332
Fabricated Metal Product Manufacturing
333
Machinery Manufacturing
334
Computer and Electronic Product Manufacturing
335
Electrical Equipment, Appliance, and Component Manufacturing
336
Transportation Equipment Manufacturing
337
Furniture and Related Product Manufacturing
339
Miscellaneous Manufacturing
4861
Pipeline Transportation of Crude Oil
4862
Pipeline Transportation of Natural Gas
For all IC Engine/Gas Turbines and ICI Boilers/Process Heaters SCCs in the 2011 NEI that have no NAICS
code, or a NAICS code for which there is no economic sector-specific AEO energy forecast available, we
compiled growth factors based on the SCC process description. For example, SCC 10200101 (External
Combustion Boilers; Industrial; Anthracite Coal; Pulverized Coal) is assigned to AEO's Total Industrial
sector coal consumption projections.
For all oil and gas production priority category SCCs (e.g., SCC 23100xxxxx) and all other priority category
SCCs with oil/gas production-related NAICS codes, we used two separate approaches for AEO oil/gas plays
(see Table 4-18). The first approach is specific to natural gas consumption, and the other approach is for
consumption of all other individual fuel types. Because the AEO does not have information on the counties
that comprise each of the oil/gas plays represented by the AEO data, we developed the necessary county lists
using an internet search of oil/gas play maps and other related information. For counties not included in one
of the AEO data's oil/gas plays, "rest-of-Census Division" oil/gas consumption estimates were computed to
develop growth factors for projecting energy consumption for all fuel types except natural gas (e.g., residual
and distillate oil, coal, LPG, kerosene). Details on the oil/gas production sector growth factor development
methods are described below.
Ill

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Table 4-18. AEO Oil/Gas Plays
Tight Oil Play*
Shale Gas Play
Austin Chalk
Antrim
Avalon/Bone Springs
Bakken
Bakken
Barnett
Eagle Ford
Eagle Ford
Monterey
Fayetteville
Niobrara
Haynesville/Bossier
Sprayberry
Marcellus
Wolfcamp

Woodford

* AEO also publishes estimates for an "Other" tight oil plays category, however, EIA was
unable to provide a list of what plays should be considered included in this "Other" category.
"Tight" oil (not to be confused with oil, or "kerogen" shale) refers to heterogeneous formations
of low-permeability that vary widely over relatively short distances.
For natural gas consumption-related growth factor records, we developed oil/gas production area ("play")-
level estimates of natural gas consumption and "rest-of-Census Division" estimates, which were applied to
counties outside these play areas. For each relevant year, we first calculated from AEO's projections, the
ratio of national oil/gas production (in BTUs) to national oil/gas sector constant dollar output. These values
were then multiplied by AEO projections of Census Division-level oil/gas sector constant dollar output to
yield Division-level estimates of oil/gas production by year (in BTUs). Next, we computed AEO Census
Division-level ratios of the sum of pipeline fuel natural gas + lease and plant fuel consumption31 to the
estimated volume of oil/gas produced in each year. Then we developed oil/gas play-level projections of
pipeline fuel natural gas + lease and plant fuel consumption by multiplying the Census Division-level ratios
by the AEO estimates of oil/gas production in each play by year. Growth factors for each play/forecast year
were developed by dividing each forecast year's estimated consumption of pipeline fuel natural gas + lease
and plant fuel by the estimated 2011 total volume of these fuels.
We calculated "rest-of-Census Division" consumption estimates for pipeline fuel natural gas + lease and
plant fuel by subtracting the oil/gas play consumption estimates from the Census Division estimates
published in the AEO. This yielded nine sets of growth factors for each year, corresponding to each of the
nine Census Divisions. These growth factors reflect "rest-of-Census Division" estimated consumption of
pipeline fuel natural gas + lease and plant fuel, and were applied to the non-AEO oil/gas play counties in
each Division.
Crude oil production growth factors were also generated for crude oil production-specific sources (SCCs).
These growth factors were generated at similar spatial resolution as natural gas consumption factors: oil/gas
production "play"-level and rest of Census Division resolution. These data are based on AEO reference table
"14" Crude Oil Production and Supply projections.
The AEO prepares fuel consumption projections for the mining sector, which is comprised of the Oil & Gas
Extraction & Support Activities, Coal Mining, and Other Mining and Quarrying sectors. On a national basis,
AEO data indicate that the Oil & Gas Extraction & Support Activities sector contributed 85 percent of total
31 While pipeline fuel natural gas should be self-explanatory, lease and plant fuel refers to "natural gas used in well, field, and lease
operations, in natural gas processing plant machinery, and for liquefaction in export facilities."
112

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mining sector output (NAICS codes 211,2121, 2122-2123) in the 2011 NEI, and is projected to account for a
similar contribution (86 percent) in 2030. Estimates of mining sector energy consumption by fuel type for the
AEO oil and gas plays were developed, and assumed that mining sector fuel consumption growth rates
approximate growth rates in the oil/gas sector for these plays. Because of concerns that oil/gas production
may not represent as significant a contribution to total mining activity in other areas (outside oil/gas plays),
we chose not to use estimated Mining sector fuel consumption data to project oil/gas production sector fuel
consumption in other areas of the country.
Oii/Gas Hlsys
As listed in Table 4-18 and displayed in Figure 4-1, the AEO reports oil and natural gas production
projections for a number of individual oil and gas plays. The first step in estimating oil/gas sector energy
consumption projections for these plays was to calculate, for each relevant year, the national ratio of Oil/Gas
sector constant dollar output to the volume of oil and gas produced (expressed in BTUs) from the AEO. We
then estimated Oil/Gas sector constant dollar output in each oil/gas play by multiplying these national ratios
by AEO estimates of oil and gas production (BTUs) in each oil/gas play. These oil/gas play-level output
estimates were then multiplied by year-specific national AEO ratios of Mining sector fuel consumption to
Oil/Gas sector output, which produced Mining sector fuel consumption estimates by play/year. As noted
above, these Mining sector estimates are deemed to be reasonable surrogates for Oil/Gas sector activity in
the AEO oil/gas play areas.
113

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Figure 4-1. Oil and gas plays with AEO projection data
Oil and Gas Plays
Marcellus
Antrim
Fayetteville
Haynesville/Bossier
Barnett
Wolfcamp
Monterey
Avalon/Bone Springs
Niobrara
Spra berry
Austin Chalk
Woodford
Eagle Ford
Bakken
Remaining Areas
For all non-AEO gas/oil play counties, we developed "rest-of-Supply Region" (National Energy Modeling
System, or "NEMS") oil/gas production estimates as the surrogate growth indicator for projecting fuel
consumption in the Oil/Gas sector. These NEMS regions are shown in Figure 4-2. Since we address oil/gas
play areas separately, this approach reflects oil/gas production occurring outside these areas. To accomplish
this, we subtracted the AEO's oil/gas production estimates for these plays from AEO's total Oil/Gas Supply
region production estimates, and used the result to calculate the growth factors for areas outside the oil/gas
plays. After implementing this procedure, we produced six sets of "rest-of-Oil/Gas Supply Region" growth
factors (one for each of the six Supply Regions) for application to counties in each supply region not in one
of the AEO oil/gas plays. All oil and gas production related growth factors were capped at 2.5 (150%
increase). Note that raw AEO growth factors of 4-6+ were not uncommon. The cap addresses concerns that
the uncertainty in these projections was too great to allow such dramatic growth.
114

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Figure 4-2. Oil and Gas NEMS Regions
Atlantic
ShallowJjulf of Mexico!
Deep Gulf of Mexico
Source: U.S. Energy Information Administration. Office of Energy Analysis.
"MARAMA "factors
The MARAMA states provided usable projections and controls data for 15 states (SRA, 2014). The growth
data for oil and gas-specific processes (SCC-level) were not used. Rather, we used the EPA approach for
nonpoint oil and gas emissions because it was more comprehensive and consistent nationally. We used the
MARAMA facility and sub-facility (point inventory) growth factors; only a small subset of these factors
impact oil and gas point sources (facilities). We capped growth factors related to employment growth for
both point and nonpoint factors at 1.25 (25% cumulative growth) to prevent excessive overestimation of
future-year emissions.
The MARAMA point inventory projection factors were included in our projection packets at facility and
sub-facility resolution and supersede any/all "EPA"-based SCC and NAICS/SCC projection factors (recall
the CoST hierarchy described in Section 4.2.1). We used the other (i.e., non-oil and gas) MARAMA
nonpoint projection factors which cover a large portion of the nonpoint sectors, including ICI fuel
combustion, construction and mining, surface coating and degreasing processes, dry cleaners and waste
water treatment.
"LADCO "factors
115

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Similar to the MARAMA data, the LADCO (Midwest RPO) states provided usable projections and controls
data for some states: MI, OH, IN. IL, WI, MN, IA, MO and KY (Alpine Geophysics, 2014). We did not use
the growth data for oil and gas-specific processes (SCC-level). Rather, we used the EPA approach for
nonpoint oil and gas emissions because it was more comprehensive and consistent nationally. The only
facility or sub-facility growth factors for LADCO's "priority" source categories that remained were limited
to (hydraulic) frac sanding mining and petroleum mining. Our use of the nonpoint factors were limited to
agriculture tilling and pesticide application, degreasing operations and residential wood combustion (RWC);
we split out RWC factors and merged with our "rest of the country" set of projection factors (discussed in
Section 4.2.3.11). These growth factors were all deemed reasonable, in that the values were well under 2.0 -a
100% increase and did not rely on employment growth projections, and as such these were used essentially
as-is. Note that the impact of these factors will be seen more for RWC than other stationary non-EGU
emissions modeling sectors.
Net impacts ofprojection factors
Net impacts of these projection packets for each of the modeling sectors is provided in Table 4-19. There are
a couple of items to note:
1)	All projection factors are for year 2030.
2)	The largest increases are to the nonpoint oil and gas sector; however, these are not the final future
year impacts, as significant new and existing controls, discussed in 4.2.4, have not yet been applied in
creating the future-year emissions values shown in this table.
Table 4-19. Industrial source projections net impacts from 201 lv6.0 NODA
Poll
Sector
2011 Emissions
Subject to
PROJECTION factors
Intermediate
Projected (not yet
controlled) Emissions
Difference
(Future - 2011)
% Difference
(Future -
2011)
For 2030
2030
2030
2030
CO
nonpt
677,225
731,998
54,773
8%
CO
np oilgas
531,395
802,327
270,931
51%
CO
pt oilgas
228,676
317,235
88,559
39%
CO
ptnonipm
563,685
622,482
58,797
10%
CO
Total
2,000,982
2,474,042
473,060
24%
NH,
nonpt
16,116
16,684
568
4%
NH,
pt oilgas
222
236
15
7%
NH,
ptnonipm
12,303
13,288
985
8%
nh3
Total
28,640
30,208
1,568
5%
NOx
nonpt
485,753
512,835
27,081
6%
NOx
np oilgas
571,088
896,296
325,208
57%
NOx
pt oilgas
522,165
674,755
152,590
29%
NOx
ptnonipm
595,409
643,406
47,997
8%
NOx
Total
2,174,415
2,727,292
552,876
25%
PMio
nonpt
261,098
299,532
38,434
15%
116

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Poll
Sector
2011 Emissions
Subject to
PROJECTION factors
Intermediate
Projected (not yet
controlled) Emissions
Difference
(Future - 2011)
% Difference
(Future -
2011)
For 2030
2030
2030
2030
PMio
np oilgas
15,277
23,478
8,201
54%
PMio
pt oilgas
13,246
16,735
3,488
26%
PMio
ptnonipm
130,949
152,649
21,701
17%
PMio
Total
420,570
492,394
71,825
17%
PM2.5
nonpt
209,383
241,456
32,073
15%
PM2.5
np oilgas
13,823
20,760
6,937
50%
PM2.5
pt oilgas
13,000
16,424
3,424
26%
PM2.5
ptnonipm
106,461
125,506
19,046
18%
pm25
Total
342,668
404,147
61,479
18%
S02
nonpt
252,948
247,705
-5,243
-2%
S02
np oilgas
17,631
37,687
20,056
114%
S02
pt oilgas
51,550
54,024
2,474
5%
S02
ptnonipm
509,260
500,629
-8,631
-2%
SO2
Total
831,389
840,045
8,656
1%
voc
nonpt
875,318
896,019
20,701
2%
voc
np oilgas
2,409,679
4,269,399
1,859,720
77%
voc
pt oilgas
131,802
169,368
37,566
29%
voc
ptnonipm
130,740
143,161
12,421
10%
voc
Total
3,547,539
5,477,947
1,930,408
54%
4.2.3.7 Data from comments on previous platforms (nonpt, ptnonipm, pt oilgas)
Packets:
"PROJECTION_2011v6.2_2030_TCEQ_v6_leftovers_NONPOINT_30jan2015_revised.txt"
"PROJECTION_VA_ME_TCEQ_AL_comments_2011 v6_2019.txt"
" PROJECTION_TCEQ_ptnonipm_NAICS_comments_2011 v6_2030_revisedl 5jul2015.txt"
These projection packets includes projection factors used in the development of the 201 lv6.0 emissions
modeling platform, specifically, those discussed in Section 4.2.9 in the 2011v6.1 emissions modeling
platform TSD (EPA, 2014b). Most of these data were originally received from the Texas Commission on
Environmental Quality (TCEQ).
TCEQ nonpoint projection data
Packet: PROJECTION_2011v6.2_20YY_TCEQ_v6_leftovers_NONPOINT_30jan2015.txt, where "YY" is
18 or 25 for year 2018 (2017 as well) and 2025 respectively.
Most of the "old" TCEQ nonpoint projections data are superseded by the 201 lv6.0 NODA data, particularly
the SCC4evel growth factors discussed in Section 4.2.3.6. We removed all TCEQ projection factors for
SCCs that overlapped. The remaining TCEQ nonpoint projection data are unchanged from the 201 lv6.0
emissions modeling platform.
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State comments from 2013
Packet: PROJECTION_VA_ME_TCEQ_AL_comments_201 lv6_2019.txt
This packet includes comments received prior to 201 lv6.0 emissions modeling platform processing from
Alabama, Maine, Texas and Virginia. These projections data target specific point sources in each of these
states, generally impacting only a couple of facilities/units in a couple of counties in each state.
TCEQ point countv/NAICS projections data
Packet: PROJECTION_TCEQ_ptnonipm_NAICS_comments_2011 v6_2030_revisedl 5jul2015.txt
This packet was provided by TCEQ for minor point source emissions. Projections are applied by county and
NAICS codes and are based on gross product projections for various types of industry, population and
economy.com data. We did not apply these to oil and gas sources, opting to instead use the approach
discussed in Section 4.2.3.6; in fact, most of these entries are not used because they are lower in the CoST
hierarchy than the county/NAICS/SCC projection factors discussed in the same section.
Summary impacts
A summary of the impacts of these three projection factors for the three affected sectors are provided in
Table 4-20. Most of these impacts are in Texas.
Table 4-20. Impact of 201 lv6.0 projection factors for Texas
Poll
Sector
2011 Emissions
Subject to
PROJECTION
factors
Projected
Emissions
for 2030
Tons Difference
(Future - 2011)
% Difference
(Future - 2011)
CO
nonpt
56,014
72,703
16,689
30%
CO
pt oilgas
6,194
5,856
-338
-5%
CO
ptnonipm
13,533
25,943
12,410
92%
CO
Total
75,742
104,503
28,761
38%
NH,
nonpt
2,265
2,302
37
2%
NH,
pt oilgas
32
25
-7
-23%
NH,
ptnonipm
400
657
257
64%
nh3
Total
2,697
2,984
287
11%
NOx
nonpt
13,666
14,244
578
4%
NOx
pt oilgas
4,552
4,088
-464
-10%
NOx
ptnonipm
17,297
32,824
15,527
90%
NOx
Total
35,515
51,156
15,641
44%
PMio
nonpt
19,835
28,316
8,481
43%
PMio
pt oilgas
1,101
1,004
-97
-9%
PMio
ptnonipm
3,555
5,285
1,730
49%
PMio
Total
24,491
34,605
10,114
41%
PM2.5
nonpt
15,478
22,964
7,486
48%
PM2.5
pt oilgas
1,067
967
-100
-9%
PM2.5
ptnonipm
2,462
3,712
1,249
51%
pm25
Total
19,007
27,642
8,635
45%
118

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Poll
Sector
2011 Emissions
Subject to
PROJECTION
factors
Projected
Emissions
for 2030
Tons Difference
(Future - 2011)
% Difference
(Future - 2011)
S02
nonpt
260
271
11
4%
S02
pt oilgas
9,120
8,092
-1,028
-11%
so2
ptnonipm
6,954
13,646
6,692
96%
so2
Total
16,334
22,009
5,675
35%
voc
nonpt
295,071
331,405
36,334
12%
voc
pt oilgas
14,453
12,444
-2,009
-14%
voc
ptnonipm
12,676
28,929
16,253
128%
voc
Total
322,200
372,778
50,578
16%
4.2.3.8 Aircraft (ptnonipm)
Packet: "PRO JECTION2011 2030_aircraft_ST_and_by_airport_22jan2015.txt"
Aircraft emissions are contained in the ptnonipm inventory. These 2011 point-source emissions are projected
to future years by applying activity growth using data on itinerant (ITN) operations at airports. The ITN
operations are defined as aircraft take-offs whereby the aircraft leaves the airport vicinity and lands at
another airport, or aircraft landings whereby the aircraft has arrived from outside the airport vicinity. EPA
used projected ITN information available from the Federal Aviation Administration's (FAA) Terminal Area
Forecast (TAF) System: http://www.apo.data.faa.gov/main/taf.asp (publication date March, 2014). This
information is available for approximately 3,300 individual airports, for all years up to 2040. The methods
that the FAA used for developing the ITN data in the TAF are documented in:
http://www.faa.gov/about/office org/headquarters offices/apl/aviation forecasts/taf reports/media/TAF Su
mmary Report FY2013-2040.pdf.
None of our aircraft emission projections account for any control programs. EPA considered the NOx
standard adopted by the International Civil Aviation Organization's (ICAO) Committee on Aviation
Environmental Protection (CAEP) in February 2004, which is expected to reduce NOx by approximately 3%
by 2020. However, this rule has not yet been adopted as an EPA (or U.S.) rule; therefore, its effects were not
included in the future-year emissions projections.
EPA developed two sets of projection factors for aircraft. The first set was a simple state-level aggregation,
used primarily for airports with very little activity, by ITN operation type (commercial, general aviation,
military and air taxi) to be used as a default method for projecting from 2011 to future years. The second set
of projection factors was by airport, where EPA projects emissions for each individual airport with
significant ITN activity.
Where NEI facility identifiers were not matched to FAA airport identifiers we simply summed the ITN
operations to state totals by year and aircraft operation and computed projection factors as future-year ITN to
year-2011 ITN. EPA assigned factors to inventory SCCs based on the operation type shown in Table 4-21.
Table 4-21. NEI SCC to FAA TAF ITN aircraft categories used for aircraft projections
SCC
Description
FAA ITN Type
2265008005
Commercial Aircraft: 4-stroke Airport Ground Support Equipment
Commercial
2267008005
Commercial Aircraft: LPG Airport Ground Support Equipment
Commercial
2268008005
Commercial Aircraft: CNG Airport Ground Support Equipment
Commercial
119

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see
Description
FAA ITN Type
2270008005
Commercial Aircraft: Diesel Airport Ground Support Equipment
Commercial
2275000000
All Aircraft Types and Operations
Commercial
2275001000
Military Aircraft, Total
Military
2275020000
Commercial Aviation, Total
Commercial
2275050011
General Aviation, Piston
General
2275050012
General Aviation, Turbine
General
2275060011
Air Taxi, Total: Air Taxi, Piston
Air Taxi
2275060012
Air Taxi, Total: Air Taxi, Turbine
Air Taxi
2275070000
Commercial Aircraft: Aircraft Auxiliary Power Units, Total
Commercial
27501015
Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust;
Military; Jet Engine: JP-5
Military
27502011
Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust;
Commercial; Jet Engine: Jet A
Commercial
27505001
Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Civil;
Piston Engine: Aviation Gas
General
27505011
Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Civil;
Jet Engine: Jet A
General
Most NEI airports matched FAA TAF identifiers and therefore use airport-specific projection factors. We
applied a cap on projection factors of 2.0 (100% increase) for state-level defaults and 5.0 for airport-specific
entries. None of the largest airports/larger-emitters had projection factors close to these caps. A national
summary of aircraft emissions between 2011 and future year 2030 are provided in Table 4-22.
Table 4-22. National aircraft emission projection summary

Emissions
Difference
% Difference
2011
2030
2030
2030
CO
496,159
627,356
131,225
26.4%
NOx
121,132
189,599
68,467
56.5%
PMio
9,246
10,838
1,592
17.2%
PM2.5
7,954
9,443
1,490
18.7%
S02
14,230
21,658
7,428
52.2%
voc
32,262
43,474
11,213
34.8%
4.2.3.9 Cement manufacturing (ptnonipm)
Packet: "PROJECTION_201 l_2025_ISIS_cement_by_CENSUS_DIVISION_25nov2013 .txt"
As indicated in Table 4-1, the Industrial Sectors Modeling Platform (ISMP) (EPA, 2010b) was used to
project the cement industry component of the ptnonipm emissions modeling sector to 2025. This approach
provided reductions of criteria and select hazardous air pollutants. The ISMP cement emissions were
developed in support for the Portland Cement NESHAPs and the NSPS for the Portland cement
manufacturing industry.
The ISMP model produced a Portland Cement NESHAP policy case of multi-pollutant emissions for
individual cement kilns (emission inventory units) that were relevant for years 2015 through 2025. These
ISMP-based emissions are reflected using a CoST packet for all existing kilns that are not impacted by more
local information from states (or consent decrees). ISMP also generates new cement kilns that are permitted
(point inventory) and not-permitted, but generated based on ISMP assumptions on demand and infrastructure
(nonpt inventory). These new cement kilns are discussed in Section 4.2.5.4.
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The PROJECTION packets contain U.S. census division level based projection factors for each NEI unit
(kiln) based on ISMP updated policy case emissions at existing cement kilns. The units that closed before
2025 are included in the 2025 base case but are included in other CoST packets that reflect state comments
and consent decrees (discussed in Section 4.2.4.10).
The ISMP model, version August 2013 was used for these projections. Recent data updates include updated
matching of kilns to better capture recent retirements, capacity additions and projections of capacity
additions from Portland Cement Association (PCA) Plant Information Summary of December 31, 2010 and
feedback from Portland Cement NESHAP reconsideration comments. Updated cement consumption
projections are based on a post-recession (July 2012) PCA long-term cement consumption outlook. Updated
emissions controls in 2015 from the NESHAP are also reflected. Overall, as seen in Figure 4-3, domestic
production of cement grows significantly between 2011 and 2015, then more slowly through 2018.
Meanwhile, emissions from NESHAP-regulated pollutants such as PM and SO2 drop significantly based on
regulated emissions rates. Emissions for NOx increase, though not as much as production because the ISMP
model continues the recent trend in the cement sector of the replacement of lower capacity, inefficient wet
and long dry kilns with bigger and more efficient preheater and precalciner kilns.
Figure 4-3. Cement sector trends in domestic production versus normalized emissions
Series 1
Senes4
Multiple regulatory requirements such as the NESHAP and NSPS currently apply to the cement industry to
reduce CAP and HAP emissions. Additionally, state and local regulatory requirements might apply to
individual cement facilities depending on their locations relative to ozone and PM2.5 nonattainment areas.
The ISMP model provides the emission reduction strategy that balances: 1) optimal (least cost) industry
operation, 2) cost-effective controls to meet the demand for cement, and 3) emission reduction requirements
over the time period of interest.
The first step in using ISMP 2025 projected emissions is matching the kilns in future years to those in the
2011 NEI. While ISMP provides by-kiln emissions for each future year, EPA cement kilns experts preferred
that the Agency project existing cement kilns based on a more-smooth geographic approach to reduce the
"on"/"off' switching that ISMP assigns to each kiln based on production and capacity demands. It would be
inefficient and unrealistic to project existing cement kilns to operate as essentially 0% or 100% capacity
based strictly on ISMP output. Therefore, EPA developed a U.S. Census Division approach where ISMP
emissions in 2011 and future years, that matched the 2011 NEI (e.g., not new ISMP kilns), were aggregated
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by pollutant for each year within each of the 9 census divisions in the contiguous U.S.
(http://www.eia.gov/consumption/commercial/images/cendivco.gif). These aggregate emissions were used to
create 2018/2011 and 2025/2011 emissions ratios for each pollutant and geographic area. The projection
ratios, provided in Table 4-23, were then applied to all 2011 NEI cement kilns -except for kilns where
specific local information (e.g., consent decrees/settlements/local information) was available.
Table 4-23. U.S. Census Division ISMP-based projection factors for existing kilns
Region
Division
2025
NOx
2025
PM
2025
SOi
2025
voc
Midwest
East North Central
2.053
0.144
3.034
0.670
Midwest
West North Central
1.279
0.673
1.262
0.492
Northeast
Middle Atlantic
1.221
0.119
0.867
0.569
Northeast
New England
2.560
0.004
3.563
0.713
South
East South Central
0.999
0.109
0.402
0.323
South
South Atlantic
1.077
0.339
0.936
0.420
South
West South Central
1.526
0.174
0.664
0.252
West
Mountain
1.321
1.032
1.366
0.345
West
Pacific
1.465
0.006
0.251
0.290
Table 4-24 shows the magnitude of the ISMP census division based projected cement industry emissions at
existing NEI facilities from 2011 to future year 2025. Additional new kiln emissions generated by ISMP are
discussed in Section 0. There are some local exceptions where EPA did not use ISMP-based projections for
cement kilns where local information from consent decrees/settlements and state comments were used
instead. Cement kilns projected using these non-ISMP information are not reflected here in Table 4-24.
Table 4-24. ISMP-based cement industry projected emissions

Emissions
Tons Difference
% Difference
2011
2025
2025
2025
NOx
53,240
75,680
22,440
42.1%
PM10
2,954
1,033
-1,921
-65.0%
PM2.5
1,709
657
-1,052
-61.6%
S02
15,876
25,579
9,702
61.1%
VOC
2,503
1,026
-1,477
-59.0%
4.2.3.10 Corn ethanol plants (ptnonipm)
Packet: "PROJECTION 201 l_2030_Corn_Ethanol_Plants_AE02014_Tablel7_201 Iv6.2_19feb2015.txt"
We used the AEO 2014 renewable forecast projections of "From Corn and Other Starch" to compute
national year 2030 growth in ethanol plant production. Per OTAQ direction, we exempted two facilities
('Highwater Ethanol LLC' in Redwood county MN and 'Life Line Foods LLC-St. Joseph' in Buchanan
county MO) from these projections; future year emissions were equal to their 2011 NEI v2 values for these
two facilities.
The 2011 corn ethanol plant emissions were projebest cted to account for the change in domestic corn
ethanol production between 2011 and future years, from approximately 13.9 Bgal (billion gallons) in 2011 to
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13.2 Bgal in 2030, based on AEO 2014 projections. The projection was applied to all pollutants and all
facilities equally. Table 4-25 provides the summaries of estimated emissions for the corn ethanol plants in
2011 and 2030.
Table 4-25. 2011 and 2030 corn ethanol plant emissions [tons]

Emissions
Difference
% Change
2011
2030
2030
2030
CO
877
833
-44
-5.0%
NOx
1,328
1,261
-67
-5.0%
PMio
1,259
1,196
-63
-5.0%
PM2.5
302
287
-15
-5.0%
S02
10
9
0
-5.0%
voc
3,084
2,929
-155
-5.0%
4.2.3.11 Residential wood combustion (rwc)
Packet: "PROJECTION_2011_2030_RWC_201 Iv6.2_03mar2015txt"
EPA applied the recently-promulgated national New Source Performance Standards (NSPS) for wood stoves
to the Residential Wood Combustion (RWC) projections methodology for this platform. To learn more about
the strengthened NSPS for residential wood heaters, see http://www2.epa.gov/residential-wood-
heaters/regulatory-actions-residential- wood-heaters. EPA projected residential wood combustion (RWC)
emissions to year 2030 based on expected increases and decreases in various residential wood burning
appliances. As newer, cleaner woodstoves replace some older, higher-polluting wood stoves, there will be an
overall reduction of the emissions from older "dirty" stoves but an overall increase in total RWC due to
population and sales trends in all other types of wood burning devices such as indoor furnaces and outdoor
hydronic heaters (OHH). It is important to note that our RWC projection methodology does not explicitly
account for state or local residential wood control programs. There are a number more-stringent state and
local rules in place in 2011, specifically in California, Oregon and Washington. However, at this time, EPA
does not have enough detailed information to calculate state specific or local area growth rates. Therefore,
with the exception of California, Oregon and Washington, EPA is using national level growth rates for each
RWC SCC category. After discussions with California air districts, regional office contacts and EPA experts,
EPA decided to hold RWC emissions flat (unchanged) for all SCCs in California, Oregon and Washington.
Assumed Appliance Growth and Replacement Rates
The development of projected growth in RWC emissions to year 2030 starts with the projected growth in
RWC appliances derived from year 2012 appliance shipments reported in the Regulatory Impact Analysis
(RIA) for Proposed Residential Wood Heaters NSPS Revision Final Report (EPA, 2013b), also available at:
http://www2.epa.gov/sites/production/files/2013-12/documents/ria-20140103.pdf. The 2012 shipments are
based on 2008 shipment data and revenue forecasts from a Frost & Sullivan Market Report (Frost &
Sullivan, 2010). Next, to be consistent with the RIA (EPA, 2013b), growth rates for new appliances for
certified wood stoves, pellet stoves, indoor furnaces and OHH were based on forecasted revenue (real GDP)
growth rate of 2.0% per year from 2013 through 2030 as predicted by the U.S. Bureau of Economic Analysis
(BEA, 2012). While this approach is not perfectly correlated, in the absence of specific shipment projections,
the RIA assumes the overall trend in the projection is reasonable. The growth rates for appliances not listed
in the RIA (fireplaces, outdoor wood burning devices (not elsewhere classified) and residential fire logs) are
123

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estimated based on the average growth in the number of houses between 2002 and 2012, about 1% (U.S.
Census, 2012).
In addition to new appliance sales and forecasts extrapolating beyond 2012, assumptions on the replacement
of older, existing appliances are needed. Based on long lifetimes, no replacement of fireplaces, outdoor wood
burning devices (not elsewhere classified) or residential fire logs is assumed. It is assumed that 95% of new
woodstoves will replace older non-EPA certified freestanding stoves (pre-1988 NSPS) and 5% will replace
existing EPA-certified catalytic and non-catalytic stoves that currently meet the 1988 NSPS (Houck, 2011).
EPA RWC NSPS experts assume that 10% of new pellet stoves and OHH replace older units and that
because of their short lifespan, that 10% of indoor furnaces are replaced each year; these are the same
assumptions used since the 2007 emissions modeling platform (EPA, 2012d). The resulting growth factors
for these appliance types varies by appliance type and also by pollutant because the emission rates, from
EPA RWC tool (EPA, 2013rwc), vary by appliance type and pollutant. For EPA certified units, the
projection factors for PM are lower than those for all other pollutants. The projection factors also vary
because the total number of existing units in 2011 varies greatly between appliance types.
NSPS Overview
The residential wood heaters NSPS Final Rule was promulgated on February 3, 2015. This rule does not
affect existing woodstoves or other wood burning devices; however, it does provide more stringent emissions
standards for new woodstoves, outdoor hydronic heaters and indoor wood-burning forced air furnaces. New
"Phase 1" less-polluting heater standards begin in 2015, with even more-stringent Phase 2 standards
beginning in 2020. The associated reduced emission rates for each appliance type (SCC) are applied to all
new units sold, some of which are assumed to replace retired units, since year 2015.
Currently the 1988 NSPS limits primary PM2.5 emissions from adjustable burn rate stoves, including
fireplace inserts and freestanding woodstoves, to 7.5 grams/hour (g/hr) for non-catalytic stoves and 4.1 g/hr
for catalytic stoves. The final NSPS limits PM2.5 emissions for room heaters, which include adjustable and
single burn rate stoves and pellet stoves to 4.5 g/hr in 2015 and 1.3 g/hr in 2020. In addition, the final NSPS
limits PM2.5 emissions from hydronic heaters to 0.32 lb/MMBtu heat output in 2015, and 0.06 lb/MMBtu in
2020. The final NSPS limits PM2.5 emissions from indoor furnaces to 0.93 lb/MMBtu in 2015 and
0.06/MMBtu in 2020.
Emission factors were estimated from the 201 lv2 NEI based on tons of emissions per appliance for PM2.5,
VOC and CO. This calculation was based on estimated appliance (SCC) population and total emissions by
SCC. EPA-certified wood stove emission factors are provided in the wood heaters NSPS RIA Tables 4-3, 4-
7 and 4-11 for PM2.5, VOC and CO, respectively. For all RWC appliances subject to the NSPS, baseline RIA
emission factors, when lower than the computed emission factors (2011 NEI), are used for new appliances
sold between 2012 and 2014. Starting in 2015, Phase 1 emission limits are 60% stronger (0.45 g/hr / 0.75
g/hr) than the RIA baseline emission factors. There are also different standards for catalytic versus non-
catalytic EPA-certified stoves. Similar calculations are performed for Phase 2 emission limits that begin in
2020 and for different emission rates for different appliance types. Because the 2011NEI and RIA baseline
(2012-2014) emission factors vary by pollutant, all RWC appliances subject to the NSPS have pollutant-
specific "projection" factors. We realize that these "projection" factors are a composite of growth,
retirements and potentially emission factors in 4 increments. More detailed documentation on the EPA RWC
Projection Tool, including information on baseline, new appliances pre-NSPS, and Phase 1 and Phase 2
emission factors, is available upon request.
Caveats and Results
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California, Oregon and Washington have state-level RWC control programs, including local burn bans in
place. Without an ability to incorporate significant local RWC control programs/burn bans for a future year
inventory, EPA left RWC emissions unchanged in the future for all three states. The RWC projections
factors for states other than California, Oregon and Washington are provided in Table 4-26. VOC HAPs use
the same projection factors as VOC, PMio uses the same factor as PM2.5, and all other pollutants use the CO
projection factor. Note that appliance types not subject to the wood heaters NSPS (e.g., fire pits, fire logs)
have pollutant-independent projection factors because there is no assumed change in future year emission
factors.
Table 4-26. Non-West Coast RWC projection factors, including NSPS impacts
see
SCC Description
Default
PM
VOC and
HAPs
CO and
remaining
HAPs
2104008100
Fireplace: general
1.208



2104008210
Woodstove: fireplace inserts; non-EPA certified
0.648



2104008220
Woodstove: fireplace inserts; EPA certified; non-
catalytic
1.307
1.128


2104008230
Woodstove: fireplace inserts; EPA certified;
catalytic
1.409
1.183


2104008310
Woodstove: freestanding, non-EPA certified
0.698
0.684
0.715
0.698
2104008320
Woodstove: freestanding, EPA certified, non-
catalytic
1.307
1.129


2104008330
Woodstove: freestanding, EPA certified, catalytic
1.409
1.184


2104008400
Woodstove: pellet-fired, general
2.197
2.26


2104008510
Furnace: Indoor, cordwood-fired, non-EPA
certified
0.025
0.025
0.025
0.025
2104008610
Hydronic heater: outdoor
1.019
1.055


2104008700
Outdoor wood burning device, NEC
1.208



2104009000
Residential Firelog Total: All Combustor Types
1.208



National emission summaries for the RWC sector in 2011 and 2030 are provided in Table 4-27. For direct
PM, the NSPS emission factor reductions mostly offset the growth in appliances by year 2017; by year 2025,
continued penetration of Phase 1 and 2 standards and replacement of non-EPA certified units with much
cleaner EPA-certified appliances result in a net reduction for primary PM.
Table 4-27. Cumulative national RWC emissions from growth, retirements and NSPS impacts
Pollutant
Emissions
Difference
% Difference
2011
2030
2030-2011
2030 -2011
CO
2,527,054
2,255,288
-271,766
-10.8%
nh3
19,759
17,737
-2,022
-10.2%
NOx
34,577
34,723
145
0.4%
PM10
382,817
344,568
-38,249
-10.0%
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PM2.5
382,591
344,305
-38,286
-10.0%
so2
8,977
7,382
-1,596
-17.8%
voc
444,349
391,708
-52,640
-11.8%
4.2.4 CoST CONTROL packets (nonpt, np_oilgas, ptnonipm, pt_oilgas)
The final step in a CoST control strategy, after application of any/all CLOSURE packet(s) (point inventories
only) and any/all PROJECTION packet(s) is the application of CoST CONTROL packets. While some
controls are embedded in our PROJECTION packets (e.g., NSPS controls for RWC and loco-marine controls
for rail and commercial marines vessels), we attempted to separate out the control (program) component in
our modeling platform where feasible. In our platform, CoST control packets only impact the nonpt,
npoilgas, ptnonipm and pt oilgas sectors.
There are several different sources of CONTROL data that are concatenated and quality-assured for
duplicates and applicability to the inventories in the CoST strategies. We broke up the CONTROL (and
PROJECTION) packets into a few "key" control program types to allow for quick summaries of these
distinct control programs. The remainder of this section is broken out by CoST packet, with the exception of
discussion of the various packets gathered from previous versions of the emissions modeling platform; these
packets are a mix of different sources of data, only some of which have not been replaced by more recent
information gathered for this platform.
For future-year NSPS controls (oil and gas, RICE, Natural Gas Turbines, and Process Heaters), we attempted
to control only new sources/equipment using the following equation to account for growth and retirement of
existing sources and the differences between the new and existing source emission rates.
Qn = Qo {[(l+Pf)t-l]Fn + (l-Ri)tFe + [l-(l-Ri)t]Fn]}	Equation 1
where:
Qn = emissions in projection year
Qo = emissions in base year
Pf = growth rate expressed as ratio (e.g., 1.5=50% cumulative growth)
t = number of years between base and future years
Fn = emission factor ratio for new sources
Ri = retirement rate, expressed as whole number (e.g., 3.3%=0.033)
Fe = emission factor ratio for existing sources
The first term in Equation 1 represents new source growth and controls, the second term accounts for
retirement and controls for existing sources, and the third term accounts for replacement source controls. For
computing the CoST % reductions (Control Efficiency), the simplified Equation 2 was used for 2025
projections:
Control_Efficiency2025(%) = 100 * (1- [(Pf2025-l)*Fn + (1-Ri)14 + (l-(l-Ri)14)*Fn]/ Pf202s) Equation 2
Here, the existing source emissions factor (Fe) is set to 1.0, 2025 (future year) minus 2011 (base year) is 14,
and new source emission factor (Fn) is the ratio of the NSPS emission factor to the existing emission factor.
Table 4-28 shows the values for Retirement rate and new source emission factors (Fn) for each NSPS
regulation and other conditions within; this table also provides the subsection where the CONTROL packets
are discussed.
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Table 4-28. Assumed retirement rates and new source emission factor ratios for new sources for various
NSPS rules
NSPS
Rule
TSD
Section
Retirement
Rate years
(%/year)
Pollutants
Impacted
Applied where?
New Source
Emission
Factor (Fn)
Oil and
Gas
4.2.4.1
No
assumption
VOC
Storage Tanks: 70.3% reduction in
growth-only (>1.0)
0.297
Gas Well Completions: 95%
control (regardless)
0.05
Pneumatic controllers, not high-
bleed >6scfm or low-bleed: 11%
reduction in growth-only (>1.0)
0.23
Pneumatic controllers, high-bleed
>6scfm or low-bleed: 100%
reduction in growth-only (>1.0)
0.00
Compressor Seals: 19.9%
reduction in growth-only (>1.0)
0.201
RICE
4.2.4.3
40, (2.5%)
NOx
Lean burn: PA, all other states
0.25, 0.606
Rich Burn: PA, all other states
0.1, 0.069
Combined (average) LB/RB: PA,
other states
0.175, 0.338
CO
Lean burn: PA, all other states
1.0 (n/a),
0.889
Rich Burn: PA, all other states
0.15, 0.25
Combined (average) LB/RB: PA,
other states
0.575, 0.569
VOC
Lean burn: PA, all other states
0.125, n/a
Rich Burn: PA, all other states
0.1, n/a
Combined (average) LB/RB: PA,
other states
0.1125, n/a
Gas
Turbines
4.2.4.6
45 (2.2%)
NOx
California and NOx SIP Call
states
0.595
All other states
0.238
Process
Heaters
4.2.4.7
30(3.3%)
NOx
Nationally to Process Heater
SCCs
0.41
4.2.4.1 Oil and gas NSPS (np_oilgas, pt oilgas)
Packet: "CONTROL_201 lv6.2_2030_OilGas_VOC_NSPS_12dec2014.txt"
For oil and gas NSPS controls, with the exception of gas well completions (a 95% control) the assumption of
no equipment retirements through year 2030 dictates that NSPS controls are applied to the growth
component only of any PROJECTION factors. For example, if a growth factor is 1.5 for storage tanks
(indicating a 50% increase activity), then, using Table 4-28, the 70.3% VOC NSPS control to this new
growth will result in a 23.4% control: 100 *(70.3 * (1.5 -1) / 1.5); this yields an "effective" growth rate
(combined PROJECTION and CONTROL) of 1.1485, or, a 70.3% reduction from 1.5 to 1.0. The impacts of
all non-drilling completion VOC NSPS controls are therefore greater where growth in oil and gas production
is assumed highest. Conversely, for oil and gas basins with assumed negative growth in activity/production,
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VOC NSPS controls will be limited to well completions only. Because these impacts are so geographically
varying, we are providing the VOC NSPS reductions by each of the 6 broad NEMS regions, with Texas and
New Mexico aggregated because these states include multiple NEMS regions (see Figure 4-2). These
reductions are year-specific because projection factors for these sources are year-specific.
Table 4-29. NSPS VOC oil and gas reductions from projected pre-control 2030 grown values
Region
Pre-NSPS
Emissions
Post-NSPS
Emissions
NSPS
Reductions
NSPS %
Reductions
2030
2030
2030
2030
Gulf Coast
149,986
124,610
25,377
17%
Midcontinent
129,939
105,180
24,758
19%
New Mexico/Texas
1,354,641
864,149
490,492
36%
Northeast
33,642
15,222
18,420
55%
Rocky Mountains
961,633
518,539
443,094
46%
West Coast
15
1
14
95%
Overall
2,629,856
1,627,701
1,002,155
38%
4.2.4.2 RICE NESHAP (nonpt, np oilgas, ptnonipm, pt oilgas)
Packet: "CONTROL_201 lv6.2_RICE_NESHAP_v2_30jan2015"
There are two rulemakings for National Emission Standards for Hazardous Air Pollutants (NESHAP) for
Reciprocating Internal Combustion Engines (RICE). These rules reduce HAPs from existing and new RICE
sources. In order to meet the standards, existing sources with certain types of engines will need to install
controls. In addition to reducing HAPs, these controls have co-benefits that also reduce CAPs, specifically,
CO, NOx, VOC, PM, and SO2. In 2014 and beyond, compliance dates have passed for both rules and are
thus included in emissions projections. These RICE reductions also reflect the Reconsideration Amendments
(proposed in January, 2012), which result in significantly less stringent NOx controls (fewer reductions) than
the 2010 final rules.
The rules can be found at http://www.epa.gov/ttn/atw/icengines/ and are listed below:
•	National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion
Engines; Final Rule (FR 9648) published 03/03/10
•	National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion
Engines; Final Rule (75 FR 51570) published 08/20/2010
The difference among these two rules is that they focus on different types of engines, different facility types
(major for HAPs, versus area for HAPs) and different engine sizes based on horsepower. In addition, they
have different compliance dates, though both are after 2011 and fully implemented prior to 2017. EPA
projects CAPs from the 201 1NEIv2 RICE sources, based on the requirements of the rule for existing sources
only because the inventory includes only existing sources. EPA estimates the NSPS (new source) impacts
from RICE regulations in a separate CONTROL packet and CoST strategy; the RICE NSPS is discussed in
the next section.
The Regulatory Impact Analysis (RIA) for the Reconsideration of the Existing Stationary Compression
Ignition (CI) Engines NESHAP: Final Report (EPA, 2013ci) is available at:
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http://www.epa.gov/ttn/ecas/regdata/RIAs/RICE NESHAPreconsideration Compression Ignition Engines
RIA final2013 EPA.pdf. The Regulatory Impact Analysis (RIA) for Reconsideration of the Existing
Stationary Spark Ignition (SI) RICE NESHAP: Final Report (EPA, 2013si) is available at:
http://www.epa.gov/ttn/ecas/regdata/RIAs/NESHAP RICE Spark Ignition RIA finalreconsideration2013
EPA.pdf. Together, EPA calls these the RICE NESHAP amendment RIA's for SI and CI engines. From
these RICE NESHAP RIA documents, EPA obtained cumulative RICE reductions for all SCCs represented
by CI and SI engines. These aggregate reductions and percent reductions from baseline emissions (not the
201 1NEIv2) are provided in Table 4-30.
Table 4-30. Summary RICE NESHAP SI and CI percent reductions prior to 201 1NEIv2 analysis

CO
NOx
PM
SO2
voc
RIA Baseline: SI engines
637,756
932,377

127,170
RIA Reductions: SI engines
22,211
9,648

9,147
RIA Baseline: CI engines
81,145

19,369
11,053
79,965
RIA Reductions: CI engines
14,238

2,818
5,100
27,142
RIA Cumulative Reductions
36,449
9,638
2,818
5,100
36,289
SI % reduction
3.5%
1.0%
n/a
n/a
7.2%
CI % reduction
17.5%
n/a
14.5%
46.1%
33.9%
These RIA percent reductions were used as an upper-bound for reducing emissions from RICE SCCs in the
201 1NEIv2 point and nonpoint modeling sectors (ptnonipm, nonpt, ptoilgas and npoilgas). To begin with,
the RIA inventories are based on the 2005 NEI, so EPA wanted to ensure that our 2011 reductions did not
exceed those in the RICE RIA documents. For the 2011 platform EPA worked with EPA RICE NESHAP
experts and developed a fairly simple approach to estimate RICE NESHAP reductions. Most SCCs in the
inventory are not broken down by horsepower size range, mode of operation (e.g., emergency mode), nor
major versus area source type. Therefore, EPA summed NEI emissions nationally by-SCC for RICE sources
and also for sources that were at least partially IC engines (e.g., "Boiler and IC engines"). Then, EPA applied
the RIA percent reductions to the 201 1NEIv2 for SCCs where national totals exceeded 100 tons; EPA chose
100 tons as a threshold, assuming there would be little to no application of RICE NESHAP controls on
smaller existing sources.
Next, EPA aggregated these national reductions by engine type (CI vs. SI) and pollutant and compared these
to the RIA reductions. As expected, for most pollutants and engine types, the cumulative reductions were
significantly less than those in the RIA. The only exception was for SO2 CI engines, where EPA scaled the
RIA percent reduction from 46.1% to 14.4% for four broad nonpoint SCCs that were not restricted to only
RICE engines. These four SCCs were the "Boilers and IC Engines" or "All processes" that would
presumably contain some fraction of non-RICE component. This had minimal impact as sulfur content in
distillate fuel for many IC engine types has decreased significantly since 2005. Reducing the SO2 percent
reduction for these four SCCs resulted in slightly less than 5,100 tons of SO2 reductions overall from only
RICE NESHAP controls. However, more specific CoST projection packets would later override these RICE
NESHAP reductions for SO2. Recall the CoST hierarchy discussed earlier; these RICE NESHAP reductions
are national by pollutant and SCC and thus easily overridden by more-specific information such as state-
level fuel sulfur rules (discussed in the next section).
Additional comments from the NODA were also implemented; specifically, CO controls were modified for a
couple of distillate-fueled industrial/commercial boiler sources. Impacts of the RICE NESHAP controls on
nonpt, ptnonipm, pt oilgas and np oilgas sector emissions are provided in Table 4-31.
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Table 4-31. National by-sector reductions from RICE Reconsideration controls (tons)
Pollutant
Year
Nonpoint
Oil & Gas
(npoilgas)
Point
Oil & Gas
(ptoilgas)
Nonpoint
(nonpt)
Point
(ptnonipm)
Total
CO
2018
7,993
4,533
3,214
6,146
21,886
CO
2025
8,783
6,146
3,215
6,394
24,538
CO
2030
9,044
6,194
3,202
6,535
24,976
NOx
2018
2,240
1,749
192
81
4,262
NOx
2025
2,441
2,411
197
84
5,133
NOx
2030
2,365
2,597
199
84
5,245
PMio
2018
0
8
929
305
1,242
PMio
2025
0
10
922
311
1,243
PMio
2030
0
10
916
314
1,240
PM2.5
2018
0
8
828
289
1,124
PM2.5
2025
0
10
821
294
1,124
PM2.5
2030
0
10
815
298
1,123
S02
2018
0
9
71
296
376
SO2
2025
0
14
71
294
378
SO2
2030
0
15
70
297
382
VOC
2018
1,989
2,874
586
942
6,392
VOC
2025
2,224
4,217
588
956
7,986
VOC
2030
2,080
4,182
588
961
7,811
4.2.4.3 RICE NSPS (nonpt, np oilgas, ptnonipm, pt oilgas)
Packet: CONTROL 2011v6.2 2030 RICE NSPS 29dec2014.txt
Controls for existing RICE source emissions were discussed in the previous section. This section discusses
control for new equipment sources, NSPS controls that impact CO, NOx and VOC. EPA emission
requirements for stationary engines differ according to whether the engine is new or existing, whether the
engine is located at an area source or major source, and whether the engine is a compression ignition or a
spark ignition engine. Spark ignition engines are further subdivided by power cycle, two vs. four stroke, and
whether the engine is rich burn or lean burn.
RICE engines in the NOx SIP Call area are covered by state regulations implementing those requirements.
EPA estimated that NOx emissions within the control region were expected to be reduced by about 53,000
tons per five month ozone season in 2007 from what they would otherwise be without this program. Federal
rules affecting RICE included the NESHAP for RICE 40 CFR Part 63, Subpart ZZZZ, NSPS for Stationary
Spark Ignition IC engines 40 CFR Part 60 Subpart JJJJ, and NSPS for Compression Ignition IC engines 40
CFR Part 60 Subpart IIII. SI engine operators were affected by the NSPS if the engine was constructed after
June 12, 2006, with some of the smaller engines affected by the NSPS 1-3 years later. The recommended
RICE equipment lifetime is 30 to 40 years depending on web searches. We chose 40 years as a conservative
estimate.
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The 2011 estimates of the RICE engine average emission rates for lean burn and rich burn engines was
developed using the stationary engine manufacturers data submitted to EPA for the NSPS analysis (Parise,
2005). Emission factors by pollutant for engines 500-1200 horsepower (hp) were used to develop the average
emission rates. The analysis was organized this way because lean vs. rich burn engine type is such a
significant factor in the NOx emissions rate. Any state emission regulations that require stationary RICE
engines to achieve emission levels lower than the 2012 NSPS could be included by using lower new source
emission ratios that account for the additional emission reductions associated with having more stringent
state permit rules. Information is provided for Pennsylvania in Table 4-32. That information shows that the
PA regulations have different emission standards for lean burn vs. rich burn engines, and that the emission
limits also vary by engine size (100-500 hp or greater than 500 hp). While some of the newer RICE SCCs
(oil and gas sector in particular) allow states to indicate whether engines are lean vs. rich burn, some SCCs
lump these two together. None of the RICE point source SCCs have information about engine sizes.
However, the EPA regulatory impact analysis for the RICE NSPS and NESHAP analysis (RTI, 2007)
provides a table that shows the NOx (CO, NMHC and HAP emission estimates are provided as well)
emissions in 2015 by engine size, along with engine populations by size. In the future, more rigorous
analysis can use this table to develop computations of weighted average emission reductions by rated hp to
state regulations like Pennsylvania's.
Table 4-32. RICE NSPS Analysis and resulting 201 lv6.2 new emission rates used to compute controls
Engine type & fuel
Max Engine
Power
Geographic
Applicability
Emission standards
g/HP-hr
NOx
CO
VOC
2011 pop lean burn
500-1200 hp

1.65
2.25
0.7
2011 pop rich burn
500-1200 hp

14.5
8
0.45
Non-Emerg. SI NG and Non-E. SI Lean
Burn LPG (except LB 500100
2006 NSPS
2.0
4.0
1.0
Non-Emerg. SI NG and Non-E. SI Lean
Burn LPG (except LB 500100
2012 NSPS
1.0
2.0
0.7

HP>100
PA (Previous GP-5)
2.0
2.0
2.0
New NG Lean Burn
100500
PA (New GP-5)
0.5
2.0
0.25
New NG Rich Burn
100500
PA (New GP-5)
0.2
0.3
0.2

HP>100
Maryland
1.5

HP>7500
Colorado
1.2-2

Wyoming
None
None
None
Notes: the above table compares the criteria pollutant emission standards from the recent NSPS with the emission limits from selected
states for stationary IC engines to determine whether future year emission rates are likely to be significantly lower than for the existing
engine population. States in the NOx SIP Call region instituted NOx emission limits for large engines well before 2011. Most of the
values in the above table come from an analysis posted on the PA DEP website. The state emission limits listed above are those in
place prior to 2011. Some states (like PA) have instituted tougher RICE emission limits for new and modified engines more recently.
Note 2: Wyoming exempts all but the largest RICE engines from emission limits.
Note 3: PA has had a size limit for new RICE engines of 1500 hp until recently (i.e., not engines bigger than 1500 hp can be installed).
Their new General Permit-5 removed the engines size cap, but requires new or modified larger engines to be cleaner (i.e., has emission
limits lower than the NSPS). PA expects that the new emission limits will result in an increase in larger engines being installed, and
bringing the average emission rate much lower than it is currently.
New source Emissions Rate (Fn): Controls % =100 * (1-Fn)
NOx
CO
VOC
Pennsylvania
NG-Comb. LB & RB
0.175
0.575
0.113
All other states
NG-Comb. LB & RB
0.338
0.569
1.278
Pennsylvania
NG-lean burn
0.250
1.000
0.125
131

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All other states
NG-lean burn
0.606
0.889
1.000
Pennsylvania
NG-rich burn
0.100
0.150
0.100
All other states
NG-rich burn
0.069
0.250
1.556
We applied NSPS reduction for lean burn, rich burn and "combined" (not specified). We also computed
scaled-down (less-stringent) NSPS controls for SCCs that were "IC engines + Boilers" because boiler
emissions are not subject to RICE NSPS. For these SCCs, we used the 201 1NEIv2 point inventory to
aggregate eligible (fuel and type) boiler and IC engine emissions for each pollutant. We found that for CI
engines, almost all emissions were boiler-related; therefore, there are no CI engine RICE NSPS reductions
for "IC engines + Boilers". For SI engines, we found that approximately 9% of NOX, 10% Of CO and 19%
of VOC "IC engines + Boilers" were IC engines; these splits were then applied to the NSPS reductions in
Table 4-32. Finally, we limited RICE NSPS-eligible sources (SCCs) to those that have at least 100 tons
nationally for NOx, CO or VOC, and ignored resulting controls that were under 1%.
PA DEP staff note that until recently they have limited RICE engines to a maximum of 1500 hp. That cap is
lifted under the new General Permit-5 regulations. With that cap lifting, PA expects that new applications
will choose to install larger engines which have lower emission limits. However, that potential effect will be
difficult to capture with no information about how this might occur. These controls were then plugged into
Equation 2 (see Section 4.2.4) as a function of the projection factor. Resulting controls greater than or equal
to 1% were retained. Note that where new Emissions Factors >=1.0 (uncontrolled, as represented by red cells
at the bottom of Table 4-32), no RICE NSPS controls were computed. National RICE NSPS reductions from
projected pre-NSPS 2018 and 2025 inventories are shown in Table 4-33.
Table 4-33. National by-sector reductions from RICE NSPS controls (tons)
Pollutant
Year
Nonpoint
Oil & Gas
(np oilgas)
Point Oil
& Gas
(pt oilgas)
Nonpoint
(nonpt)
Point
(ptnonipm)
Total
NSPS
Reductions
Pre-NSPS
Total
Emissions
NSPS %
Reduction
CO
2018
93,422
19,321
758
861
114,363
503,266
23%
CO
2025
161,075
51,249
2,277
1,366
215,968
762,028
28%
CO
2030
178,339
56,740
2,803
1,596
239,499
776,990
31%
NOX
2018
156,942
44,949
1,685
1,242
204,818
777,041
26%
NOX
2025
252,770
122,603
3,903
2,063
381,339
1,072,744
36%
NOX
2030
272,708
151,000
4,835
2,444
430,986
1,100,571
39%
VOC
2018
1,656
422
0
1
2,079
3,986
52%
VOC
2025
1,838
484
0
2
2,324
3,990
58%
VOC
2030
1,940
508
0
2
2,450
3,981
62%
4.2.4.4 ICI Boilers (nonpt, ptnonipm, pt oilgas)
Packets:
CONTROL2011 v6.2_20xx_BoilerMACT_POINT_v2_30j an2015 .txt
CONTROL2011 v6.2_20xx_BoilerMACT_NONPT_08j an2015 .txt
The Industrial/Commercial/Institutional Boilers and Process Heaters MACT Rule, hereafter simply referred
to as the "Boiler MACT" was promulgated on January 31, 2013 based on reconsideration. Background
information on the Boiler MACT can be found at: http://www.epa.gov/ttn/atw/boiler/boilerpg.html. The
Boiler MACT promulgates national emission standards for the control of HAPs (NESHAP) for new and
132

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existing industrial, commercial, and institutional (ICI) boilers and process heaters at major sources of HAPs.
The expected cobenefit for CAPs at these facilities is significant and greatest for SO2 with lesser impacts for
direct PM, CO and VOC. These packets address only the expected cobenefits to existing ICI boilers.
Boiler MACT reductions were computed from a non-NEI database of ICI boilers. As seen in the Boiler
MACT Reconsideration RIA (http://www.epa.gov/ttn/atw/boiler/boilersriaproposalreconl 11201 .pdf). this
Boiler MACT Information Collection Request (ICR) dataset computed over 558,000 tons of SO2 reductions
by year 2015. However, the Boiler MACT ICR database and reductions are based on the assumption that if a
unit could burn oil, it did burn oil, and often to capacity. With high oil prices and many of these units also
able to burn cheaper natural gas, the 201 1NEIv2 inventory has a lot more gas combustion and a lot less oil
combustion than the boiler MACT database. For this reason, EPA decided to target units that potentially
could be subject to the Boiler MACT and compute preliminary reductions for several CAPs prior to building
a control packet.
Step 1: Extract facilities/sources potentially subject to Boiler MACT
This step is only applicable to point inventory sources. EPA did not attempt to map each ICR unit to the NEI
units, instead choosing to use a more general approach to extract NEI sources that would be potentially
subject to, and hence have emissions reduced by the Boiler MACT. The NEI includes a field that indicates
whether a facility is a major source of HAPs and/or CAPs. This field in our FF10 point inventory modeling
file is called "FACIL CATEGORY CODE" and the possible values for that field are shown in Table 4-34.
Because the Boiler MACT rule applies to only major sources of HAPs, EPA restricted the universe of
facilities potentially subject to the Boiler MACT to those classified as HAP major or unknown (UNK). The
third column indicates whether the facility was a candidate for extraction as being potentially subject to the
Boiler MACT.
Table 4-34. Facility types potentially subject to Boiler MACT reductions
Code
Facility
Category
Subject
to Boiler
MACT?
Description
CAP
CAP Major
N
Facility is Major based upon 40 CFR 70 Major Source definition paragraph
2 (100 tpy any CAP. Also meets paragraph 3 definition, but NOT
paragraph 1 definition).
HAP
HAP Major
Y
Facility is Major based upon only 40 CFR 70 Major Source definition
paragraph 1 (10/25 tpy HAPs).
HAPCAP
HAP and
CAP Major
Y
Facility meets both paragraph 1 and 2 of 40 CFR 70 Major Source
definitions (10/25 tpy HAPs and 100 tpy any CAP).
HAPOZN
HAP and 03
n/a Major
Y
Facility meets both paragraph 1 and 3 of 40 CFR 70 Major Source
definitions (10/25 tpy HAPs and Ozone n/a area lesser tons for NOx or
VOC).
NON
Non-Major
N
Facility's Potential To Emit is below all 40 CFR 70 Major Source threshold
definitions without a FESOP.
OZN
03 n/a Major
N
Facility is Major based upon only 40 CFR 70 Major Source definition
paragraph 3 (Ozone n/a area lesser tons for NOx or VOC).
SYN
Synthetic
non-Major
N
Facility has a FESOP which limits its Potential To Emit below all three 40
CFR 70 Major Source definitions.
UNK
Unknown
N
Facility category per 40 CFR 70 Major Source definitions is unknown.
Step 2: Obtain "MARAMA" control information
From the (point inventory) facilities extracted in Step 1, we merged in ICI Boiler controls/adjustments
developed under MARAMA in support of the Ozone Transport Commission (OTC) 2007 modeling platform
133

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future year analyses. These adjustments are discussed in a White Paper "White Paper for ICI Blr Emissions
V6.doc" (available upon request). This white paper provides methodology and summary future year
adjustments and emission estimates based on the OTC 2007 platform for the purpose of estimating emissions
changes in ICI point and nonpoint sources due to the Boiler MACT, 1-hour S02 NAAQS and economic
factors related to natural gas prices. This MARAMA approach relies on Council of Industrial Boilers (CIBO)
analysis of cost-effectiveness of boiler controls and retrofits in order to maintain the Boiler MACT. In short,
the CIBO analyses showed that many ICI boilers were converting (or replacing coal units) to natural gas
rather than applying more costly controls. Specifically, CIBO determined that 63% of coal units found it
more economical to replace their coal boilers with natural gas boilers.
ICI boilers were categorized by fuel: Light Oil (distillate), Heavy Oil (residual), Pulverized Coal and
Stoker/Other Coal. Next, AP42 (http://www.epa.gov/ttn/chief/ap42/) emission factors for each fuel was
converted to consistent units (lb/MMBtu) via heat content and these normalized emission factors were used
to develop emission factor ratios of natural gas to non-natural gas fuel type. Finally, the estimated number of
ICI boiler replacements and retrofits were used to create weighted-average adjustment ("control") factors
from these normalized emission factor ratios. This methodology makes the following assumptions:
• Natural Gas NOx emissions rates: 0.10 lb/MMBtu for new boilers,
0.1961 lb/MMBtu for burner retrofits
• Natural gas emission rates for SO2, PM2.5 and VOC are the same for both boiler replacement and
burner retrofits
• Any unit that finds it economical to replace the entire boiler, will do so. Those that don't replace the
boiler but find it economical to retrofit the burner will do so. Other units remain unchanged for NOx,
SO2, PM2.5 and VOCs emissions.
• Analyses are based on OTC 2007 modeling platform and applied to 2011 emissions modeling
platform
Step 3: Merge control information with 2011 NEI and apply state NOD A comments
EPA analyzed the SCCs in the OTC 2007 inventories and tweaked the SCC mapping of these ICI boiler
adjustments to map to those in the 2011 NEI point and nonpoint inventory with non-zero emissions. EPA
also removed some duplicate and incorrect mappings and expanded the SCC mapping in some cases to SCCs
that were in the NEI but not the OTC inventory (and thus missing from the analysis). In addition, the
MARAMA approach only includes adjustments for NOX, PM and S02. Therefore, EPA merged in existing
VOC, CO, HC1 controls (applying VOC controls to VOC HAPs as well) from the 201 lv6.0 emissions
modeling platform (see Section 4.2.7 in the 201 lv6.0 Emissions Modeling TSD) to the same set of facilities
(point) and SCCs as those for the pollutants provided by the MARAMA approach.
Some states commented on the 201 lv6.0 ICI boiler controls via the 2018 NODA (docket # EPA-HQ-OAR-
2013-0809 on http://www.regulations.gov). Wisconsin provided alternative SO2, VOC and HC1 controls for
stoker and pulverized coal fueled units. The national-level and Wisconsin-specific ICI boiler adjustments,
applied at the unit-level for point sources and by SCC (and state for Wisconsin) are provided in Table 4-35;
note that we applied the same national-level adjustments to CO, NOx and PM for coal units in Wisconsin.
New York and New Jersey, via the MARAMA comment/data to the 2018 NODA, provided boiler rule NOx
reductions that also supersede these nationally-applied factors. The NJ and NY factors are provided in Table
4-36; note that NJ controls apply only to nonpoint sources and that NY controls vary by fuel for point
sources.
134

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The impacts of these ICI boiler reductions are provided in Table 4-37. Overall, the CO and PM2.5 reductions
are reasonably close to the year-2015 expected reductions in the Boiler MACT Reconsideration RIA:
http://www.epa.gov/ttn/atw/boiler/boilersriaproposalreconl 11201 .pdf. It is worth noting that the SO2
reductions in the preamble (http://www.epa.gov/ttn/atw/boiler/fr21mrllm.pdf) were estimated at 442,000
tons; the additional SO2 reductions in the reconsideration are from an additional co-benefit from more
stringent HC1 controls. The 201 1NEIv2 SO2 emissions are actually less than the estimated Boiler MACT
reductions, likely a result of numerous units undergoing fuel switching from coal or oil to natural gas.
Table 4-35. National-level, with Wisconsin exceptions, ICI boiler adjustment factors by base fuel type
Unit/Fuel Type
Default %
deduction (Adjustments)
CO
NOX
PM
S02
voc
HC1
Stoker Coal
98.9
70.7
96
97.4
98.9
95
Pulverized Coal
98.9
60.6
72.2
73
98.9
95
Residual Oil
99.9
57
92.4
97.1
99.9
95
Distillate Oil
99.9
38.8
68.4
99.9
99.9
88.6
Wisconsin: Stoker Coal
98.9
70.7
96
30
0
45
Wisconsin: Pulverized Coal
98.9
60.6
72.2
30
0
45
Table 4-36. New York and New Jersey NOx ICI Boiler Rules that supersede national approach
NJ and NY Boiler Rule controls
NOX %
Reduction
New Jersey Small Boiler Rule (nonpoint only): Default for Distillate, Residual, natural gas and LPG
25
New York Small Boiler Rule (nonpoint only): Default for Distillate, Residual, natural gas and LPG
10
NY Boiler Rule: Industrial /Distillate Oil /< 10 Million Btu/hr
10
NY Boiler Rule: Industrial /Residual Oil /10-100 Million Btu/hr
33.3
NY Boiler Rule: Electric Gen /Residual Oil /Grade 6 Oil: Normal Firing
40
NY Boiler Rule: Electric Gen /Natural Gas /Boilers, <100 Million Btu/hr except Tangent
50
NY Boiler Rule: Electric Gen /Natural Gas /Boilers, 100 Million Btu/hr except Tangent
60
NY Boiler Rule: Industrial /Bitum Coal /Cyclone Furnace
66.7
NY Boiler Rule: Industrial /Natural Gas /> 100 Million Btu/hr
70
NY Boiler Rule: Electric Gen /Bituminous Coal /Pulverized Coal: Dry Bottom
73.3
Table 4-37. Summary of ICI Boiler reductions
Year
Pollutant
Emissions Eligible
for Control
Controlled (Final)
Emissions
Reductions
(tons)
%
Reductions
2017
CO
35,118
336
34,782
99.0%
2017
NOx
128,943
64,596
64,347
49.9%
2017
PM10
34,592
7,401
27,191
78.6%
2017
PM2.5
13,973
2,504
11,469
82.1%
2017
SO2
273,449
29,451
243,998
89.2%
2017
VOC
1,778
43
1,735
97.6%
2025
CO
35,883
346
35,537
99.0%
135

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2025
NOx
130,862
65,259
65,603
50.1%
2025
PMio
35,729
7,700
28,030
78.4%
2025
PM2.5
14,273
2,572
11,701
82.0%
2025
S02
272,425
30,579
241,846
88.8%
2025
voc
1,791
43
1,747
97.6%
2030
CO
33,976
325
33,651
-99.0%
2030
NOx
126,547
64,034
62,513
-49.4%
2030
PM10
35,130
7,700
27,430
-78.1%
2030
PM2.5
14,380
2,590
11,790
-82.0%
2030
S02
267,470
26,096
241,374
-90.2%
2030
VOC
1,785
43
1,742
-97.6%
4.2.4.5 Fuel sulfur rules (nonpt, ptnonipm, pt oilgas)
Packet: CONTROL201 lv6.2_20xx_Fuel_Sulfur_Rules_09jan2015.txt
Fuel sulfur rules, based on web searching and the 2011 emissions modeling NODA comments are currently
limited to the following states: Connecticut, Delaware, Maine, Massachusetts, New Jersey, New York,
Pennsylvania, Rhode Island and Vermont. The fuel limits for these states are incremental starting after year
2012, but are fully implemented by July 1, 2018 in all of these states.
A summary of all fuel sulfur rules provided back to EPA by the 2011 emissions modeling NODA comments
is provided in Table 4-38. State-specific control factors were computed for distillate, residual and #4 fuel oil
using each state's baseline sulfur contents and the sulfur content in the rules. For most states, the baseline
sulfur content was 3,000 ppm (0.3%) for distillate oil, and 2.25% for residual and #4 oil. However, many
states had lower baseline sulfur contents for residual oil, which varied by state and county. SRA used state-
or county-specific baseline residual oil sulfur contents to calculate a state- or county-specific control factors
for residual oil (SRA, 2014).
Table 4-38. State Fuel Oil Sulfur Rules data provided by MANE-VU
State
Reference
Connecticut
Section 22a-174-19a. Control of sulfur dioxide emissions from power plants and other large stationary sources
of air pollution: Distillate and Residual: 3000 ppm effective April 15, 2014.
Section 22a - 174 - 19b. Fuel Sulfur Content Limitations for Stationary Sources (except for sources subject to
Section 22a-174-19a).
Distillate: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2018
Residual: 1.0% effective July 1, 2014; 0.3% effective July 1, 2018
Connecticut General Statute 16a-21a. Sulfur content of home heating oil and off-road diesel fuel.
Number 2 heating oil and off-road diesel fuel: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2018
See: htto ://www. ct. eov/deeo/cwo/view. aso?a=2684&0=322184&deet>Nav GID=1619
Delaware
1108 Sulfur Dioxide Emissions from Fuel Burning Equipment
Distillate: 15 ppm effective July 1, 2017
Residual: 0.5% effective July 1, 2017
#4 Oil: 0.25% effective July 1, 2017
See: htto://regulations.delaware.ao\/AdininCode/title7/1000/1100/1108.shtml
Maine
Chapter 106: Low Sulfur Fuel
Distillate: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2018
Residual: 0.5% effective July 1, 2018
See: htto://www.maineleeislature.ore/leeis/bills/bills 124th/billt>dfs/SP062701.t>df.
Massachusetts
310 CMR 7.05 (l)(a)l: Table 1 : Sulfur Content Limit of Liquid Fossil Fuel
136

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Distillate: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2018
Residual: 1.0% effective July 1, 2014; 0.5% effective July 1, 2018
See: htto://www.mass.eov/eea/docs/det>/service/reeulations/310cmr07.i3df
New Jersey
Title 7, Chapter 27, Subchapter 9 Sulfur in Fuels
Distillate: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2016
Residual: 0.5% or 0.3%, depending on county, effective July 1, 2014
#4 Oil: 0.25% effective July 1, 2014
See: htto://www.ni.aov/dco/aa 111/rules27.html
New York
Subpart 225-1 Fuel Composition and Use - Sulfur Limitations
Distillate: 15 ppm effective July 1, 2016
Residual: 0.3% in New York City effective July 1, 2014; 0.37% in Nassau, Rockland and Westchester
counties effective July 1, 2014; 0.5% remainder of state effective July 1, 2016
See: http://www.nvc.eov/html/dep/html/news/dep stories p3-109.shtml and
htto://sreen.bloss.nvtimes.com/2010/07/20/new-vork-mandates-cleaner-heatins-oil/? r= 1 and
htto://switchboard.nrdc.ore/bloes/rkassel/eovernor oatcrson siens new la.html
Pennsylvania
§ 123.22. Combustion units
Distillate: 500 ppm effective July 1, 2016
Residual: 0.5% effective July 1, 2016
#4 Oil: 0.25% effective July 1, 2016
See: htto://www.racode.com/secure/data/025/chaoterl23/sl23.22.html
Rhode Island
Air Pollution Control Regulations No. 8 Sulfur Content of Fuels
Distillate: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2018
Residual: 0.5% effective July 1, 2018
See: httt>://www.dem.ri.eov/t>ubs/rees/rees/air/air08 l-Uxlf
Vermont
5-221(1) Sulfur Limitations in Fuel
Distillate: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2018
Residual: 0.5% effective July 1, 2018
#4 Oil: 0.25% effective July 1, 2018
See: htto://www.era.eov/reeionl/tomcs/air/sit>s/vt/VT Section5 221 ,odf
A summary of the sulfur rules by state, with emissions reductions is provided in Table 4-39. Most of these
reductions (98+%) occur in the nonpt sector; a small amount of reductions occur in the ptnonipm sector
(approximately 1,600 tons in 2025 and 580 tons in 2017), and a negligible amount of reductions occur in the
pt oilgas sector. Note that these reductions are based on intermediate 2030 inventories, those grown from
2011 to the specific future years. In addition, with implementation (effective) dates after June 30, 2017 for
several of these rules, there are more eligible sources for control in 2030 than there were 2017.
Table 4-39. Summary of fuel sulfur rule impacts on SO2 emissions
Year
Emissions Eligible
for Control
Controlled (Final)
Emissions
Reductions
% Reductions
2017
76,177
12,849
63,328
83.1%
2025
99,975
21,172
78,803
78.8%
2030
95,914
20,453
75,461
78.7%
4.2.4.6 Natural gas turbines NOx NSPS (ptnonipm, pt_oilgas)
Packet: "CONTROL_201 lv6.2_2030_NOX_GasTurbines_16dec2014.txt"
These controls were generated based on examination of emission limits for stationary combustion turbines
that are not in the power sector. In 2006, EPA promulgated standards of performance for new stationary
combustion turbines in 40 CFR subpart KKKK. The standards reflect changes in NOx emission control
technologies and turbine design since standards for these units were originally promulgated in 40 CFR part
60, subpart GG. The 2006 NSPSs affecting NOx and SO2 were established at levels that bring the emission
137

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limits up-to-date with the performance of current combustion turbines. Stationary combustion turbines were
also regulated by the NOx SIP (State Implementation Plan) Call, which required affected gas turbines to
reduce their NOx emissions by 60 percent.
Table 4-40 compares the 2006 NSPS emission limits with the NOx RACT regulations in selected states
within the NOx SIP Call region. The map showing the states and partial-states in the NOx SIP Call Program
can be found at: http://www3.epa.gov/airmarkets/progress/reports/program basics.html. We assigned only
those counties in Alabama, Michigan and Missouri as NOx SIP call based on the map on page 8. The state
NOx RACT regulations summary (Pechan, 2001) is from a year 2001 analysis, so some states may have
updated their rules since that time.
Table 4-40. Stationary gas turbines NSPS analysis and resulting 201 lv6.2 new emission rates used to
compute controls
NOx Emission Limits for New Stationary Combustion Turbines

<50
50-850
>850

Firing Natural Gas
MMBTU/hr
MMBTU/hr
MMBTU/hr

Federal NSPS
100
25
15
ppm

5-100
100-250
>250

State RACT Regulations
MMBTU/hr
MMBTU/hr
MMBTU/hr

Connecticut
225
75
75
ppm
Delaware
42
42
42
ppm
Massachusetts
65*
65
65
ppm
New Jersey
50*
50
50
ppm
New York
50
50
50
ppm
New Hampshire
55
55
55
ppm
* Only applies to 25-100 MMBTU/hr
Notes: The above state RACT table is from a 2001 analysis. The current NY State regulations have the same
emission limits.

Vew source emission rate (Fn)
NOx ratio
Control (%)
NOx SIP Call states plus CA
= 25 / 42 =
0.595
40.5%
Other states
= 25/ 105 =
0.238
76.2%
Regarding stationary gas turbine lifetimes, the Integrated Planning Model (IPM) financial modeling
documentation lists the book life of combustion turbines as 30 years, with a debt life of 15 years, and a US
MACRS Depreciation Schedule of 15 years (EPA, 2013). This same documentation lists the book life of
nuclear units at 40 years. IPM uses a 60 year lifetime for nuclear units in its simulations of unit retirements.
Using the same relationship between estimated lifetime and book life for nuclear units of 1.5, the estimated
lifetime for a combustion turbine would be 45 years. This is the same as an annual retirement rate of 2.2
percent.
For projection factor development, the existing source emission ratio was set to 1.0 for combustion turbines.
The new source emission ratio for the NOx SIP Call states and California is the ratio of state NOx emission
limit to the Federal NSPS. A complicating factor in the above is the lack of size information in the stationary
source SCCs. Plus, the size classifications in the NSPS do not match the size differentiation used in state air
emission regulations. We accepted a simplifying assumption that most industrial applications of combustion
turbines are in the 100-250 MMBtu/hr size range, and computed the new source emission rates as the NSPS
138

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emission limit for 50-850 MMBtu/hr units divided by the state emission limits. We used a conservative new
source emission ratio by using the lowest state emission limit of 42 ppmv (Delaware). This yields a new
source emission ratio of 25/42, or 0.595 (40.5% reduction) for states with existing combustion turbine
emission limits. States without existing turbine NOx limits would have a lower new source emission ratio -
the uncontrolled emission rate (105 ppmv via AP-42) divided into 25 ppmv = 0.238 (76.2% reduction). This
control was then plugged into Equation 2 (see Section 4.2.4) as a function of the year-specific projection
factor. Resulting controls greater than or equal to 1% were included in our projections. National Process
Heaters NSPS reductions from projected pre-NSPS inventories are shown in Table 4-41.
Table 4-41. National by-sector NOx reductions from Stationary Natural Gas Turbine NSPS controls
Sector
Pre-NSPS
Emissions

NSPS
Reductions

NSPS %
Reductions

2018
2025
2030
2018
2025
2030
2018
2025
2030
Non-EGU
14,238
15,593
15,372
2,400
4,405
4,974
17%
28%
32%
Point

(ptnonipm)

Point Oil &
70,267
75,310
84,658
8,783
24,536
26,600
12%
33%
31%
Gas (pt oilgas)

Total
84,505
90,903
100,030
11,183
28,941
31,574
13%
32%
32%
4.2.4.7 Process heaters NOx NSPS (ptnonipm, pt_oilgas)
Packet: For 2030: "CONTROL_201 lv6.2_2030_NOX_Process_heaters_09dec2014.txt"
Process heaters are used throughout refineries and chemical plants to raise the temperature of feed materials
to meet reaction or distillation requirements. Fuels are typically residual oil, distillate oil, refinery gas, or
natural gas. In some sense, process heaters can be considered as emission control devices because they can
be used to control process streams by recovering the fuel value while destroying the VOC. The criteria
pollutants of most concern for process heaters are NOx and SO2.
In 2011, process heaters have not been subject to regional control programs like the NOx SIP Call, so most
of the emission controls put in-place at refineries and chemical plants have resulted from RACT regulations
that were implemented as part of SIPs to achieve ozone NAAQS in specific areas, and refinery consent
decrees. The boiler/process heater NSPS established NOx emission limits for new and modified process
heaters. These emission limits are displayed in Table 4-42.
In order to develop a relationship between the typical process heater emission rates in 2011 compared with
what the NSPS will require of new and modified sources, an analysis of the materials in the EPA docket
(EPA-HQ-OAR-2007-0011) for the NSPS was performed. This docket contained an EPA memorandum that
estimated the NOx emissions impacts for process heaters. Table 1 in that memo—titled: Summary of
Representative Baseline NOx Concentrations for Affected Process Heaters. That analysis can be used to
establish an effective 2011 process heater NOx emission rate, although the information that EPA-SPPD used
in the revised NOx impact estimates probably uses data from a few years before 2011. It is likely that the
data used are representative of 2011 emissions because the only wide-ranging program that has affected
process heater emission rates recently have been consent decrees, and the emission reductions associated
with these agreements should have been achieved before 2011. However, the compliance schedules are
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company-specific, and differ by company, so it is difficult to make overarching conclusions about when
compliance occurred.
Table 4-42. Process Heaters NSPS analysis and 201 lv6.2 new emission rates used to compute controls
NOx emission rate Existing (Fe)
Fraction at this rate

Natural
Forced

PPMV
Draft
Draft
Average
80
0.4
0

100
0.4
0.5

150
0.15
0.35

200
0.05
0.1

240
0
0.05

Cumulative, weighted: Fe
104.5
134.5
119.5
NSPS Standard
40
60

New Source NOx ratio (Fn)
0.383
0.446
0.414
NSPS Control (%)
61.7
55.4
58.6
EPA states that because it "does not have much data on the precise proportion of process heaters that are
forced versus natural draft, so the nationwide impacts are expressed as a range bounded by these two
scenarios". (Scenario 1 assumes all of the process heaters are natural draft process heaters and Scenario 2
assumes all of the process heaters are forced draft process heaters.)
For computations, the existing source emission ratio (Fe) was set to 1.0. The computed (average) NOx
emission factor ratio for new sources (Fn) is 0.41 (58.6% control). The retirement rate is the inverse of the
expected unit lifetime. There is limited information in the literature about process heater lifetimes. This
information was reviewed at the time that the Western Regional Air Partnership (WRAP) developed its
initial regional haze program emission projections, and energy technology models used a 20 year lifetime for
most refinery equipment. However it was noted that in practice, heaters would probably have a lifetime that
was on the order of 50 percent above that estimate. Therefore, a 30 year lifetime was used to estimate the
effects of process heater growth and retirement. This yields a 3.3 percent retirement rate. This control was
then plugged into Equation 2 (see Section 4.2.4) as a function of the year-specific projection factor.
Resulting controls greater than or equal to 1% were retained. National Process Heaters NSPS reductions
from projected pre-NSPS 2018 and 2025 inventories are shown in Table 4-43.
Table 4-43. National by-sector NOx reductions from Process Heaters NSPS controls
Sector
Pre-NSPS Emissions
NSPS Reductions
NSPS % Reductions

2018
2025
2030
2018
2025
2030
2018
2025
2030
Non-EGU Point
74,129
75,936
71,806
14,200
21,306
23,488
19%
28%
33%
(ptnonipm)

Point Oil & Gas
7,086
7,170
6,966
1,114
1,816
2,091
16%
25%
30%
(pt oilgas)

Total
81,215
83,106
78,772
15,313
23,123
25,579
19%
28%
33%
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4.2.4.8 Arizona Regional Haze controls (ptnonipm)
Packet: CONTROL201 lv6.2_20xx_AZ_Regional_Haze_PT_24feb2015.txt
U.S. EPA Region 9 provided regional haze Federal Implementation Plan (FIP) controls for a few industrial
facilities. Information on these controls are available in the Federal Register (EPA-R09-OAR-2013-0588;
FRL-9912-97-OAR) at http://www.federalregister.com . Emissions are reduced at 5 smelter and cement
units: NOx by 1,722 tons and SO2 by 26,423 tons.
4.2.4.9 CISWI (ptnonipm)
Packet: CONTROL CISWI 201 Iv6_22nov2013.txt
On March 21, 2011, EPA promulgated the revised NSPS and emission guidelines for Commercial and
Industrial Solid Waste Incineration (CISWI) units. This was a response to the voluntary remand that was
granted in 2001 and the vacatur and remand of the CISWI definition rule in 2007. In addition, the standards
re-development included the 5-year technology review of the new source performance standards and
emission guidelines required under Section 129 of the Clean Air Act (CAA). The history of the CISWI
implementation is documented here: http://www.epa.gov/ttn/atw/129/ciwi/ciwipg.html. Baseline and CISWI
rule impacts associated with the CISWI rule are documented here:
http://www.epa.gov/ttn/atw/129/ciwi/baseline emission reductions memo.pdf. EPA mapped the units from
the CISWI baseline and controlled dataset to the 2011 NEI inventory and because the baseline CISWI
emissions and the 2011 NEI emissions were not the same, EPA computed percent reductions such that our
future year emissions matched the CISWI controlled dataset values. CISWI controls are applied in Arkansas
and Louisiana only, totaling 3,866 tons of SO2 reductions in 2030.
4.2.4.10 Data from comments on previous platforms (nonpt, ptnonipm, pt_oilgas)
Packets:
"CONTROL2011 v6_2_20xx_CD_St_com_2018docket_pt_l 5j an2015_fixed.txt"
"CONTROL_2011 v6.2_20xx_State_comments_2018docket_nonpt_l 5j an2015 .txt"
All remaining non-EGU point and nonpoint controls are discussed in this section. For the nonpoint sector,
these controls are limited to comments/data-responses on the previous (201 lv6.0) emissions modeling
platforms, and the 2018 NODA process. For point sources, controls include data from the 2018 NODA
process as well as a concatenation of all remaining controls not already discussed. These controls are split
into separate packets for point and nonpoint sources.
Nonpoint packet: (CONTROL_2011 v6.2_20xx_State_comments_2018docket_nonpt_l 5jan2015.txt)
This packet contains all nonpoint controls not already discussed in previous sections (e.g., Fuel Sulfur rules,
ICI boilers) provided in response to the 2018 NODA, and is restricted to VOC controls for Delaware,
Massachusetts, Pennsylvania and Virginia, with the great majority of these controls restricted to Virginia.
These VOC controls cover various state programs and rules such as Auto refinishing, Adhesives and Surface
Coatings. Cumulatively, these VOC controls reduce nonpoint VOC by approximately 4,221 tons in 2030.
Point packet: CONTROL 2011 v6_2_20xx_CD_St_com_2018docket_pt_l 5j an2015_fixed.txt
This packet contains all point controls not already discussed in previous sections (e.g., Fuel Sulfur rules, ICI
boilers). This packet includes new controls information provided in response to the 2018 NODA as well as
"legacy" controls from the 201 lv6.0 emissions modeling platform from numerous sources such as settlement
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and consent decree data gathering efforts, comments received during the Cross-State Air Pollution Rule
rulemaking process, regional haze modeling, and stack-specific control information provided by TCEQ.
New control information from the 2018 NODA responses is primarily limited to VOC controls from several
states: Delaware, Massachusetts, New Jersey, Pennsylvania and Virginia. However, we also received
comments with revised compliance dates, removal of existing control information, and updated controls from
local settlements. The CONTROL packet comments field provides information on the source of new control
information, where available.
The "old" control information includes information discussed in previous emissions modeling platforms;
these CONTROL packet components are discussed in Section 4.2.9 in the 201 lv6.1 emissions modeling
platform TSD (EPA, 2014b).
Cumulative ptnonipm and ptoilgas reductions to 2017 and 2025 pre-controlled (projection factors already
applied) from this CONTROL packet are shown in Table 4-44. While these controls impact both the
ptnonipm and pt oilgas sector, almost all reductions are in the ptnonipm sector; pt oilgas NOx is reduced by
1,300 tons in 2025, and VOC by 172 tons in 2025. Reductions to pt_oilgas for PM and S02 are under 100
tons cumulatively.
Table 4-44. Summary of remaining ptnonipm and pt oilgas reductions
Year
Pollutant
Emissions
Eligible for
Control
Controlled
(Final)
Emissions
Reductions
%
Reductions
2030
CO
5,884
754
5,129
87.2%
2030
NH3
223
52
182
77.9%
2030
NOX
86,447
42,967
43,480
50.3%
2030
PM10
4,035
1,934
2,101
52.1%
2030
PM2.5
3,607
1,756
1,852
51.3%
2030
S02
122,180
26,708
95,472
78.1%
2030
VOC
3,107
2,324
782
25.2%
4.2.5 Stand-alone future year inventories (nonpt, ptnonipm)
This section discusses future year NEI non-EGU point and nonpoint emission inventories that were not
created via CoST strategies/programs/packets. These inventories are either new to the future years because
they did not exist in 2011 (e.g., new cement kilns, biodiesel and cellulosic plants), or are a complete
replacement to the year 2011 NEI inventory in the case of portable fuel containers. New non-EGU facilities
provided by South Carolina via the 2018 NODA on the 201 lv6.0 platform were mistakenly omitted from
both year 2017 and 2025 emissions modeling processing. Cumulatively, these new facilities would have
added approximately 200 tons of NOx, and under 100 tons of each of the remaining CAPs.
4.2.5.1 Portable fuel containers (nonpt)
Future year inventory: "pfc_2025_201 Iv6_2_ffl0_28jan2015_28aug2015_vl.csv"
EPA used future-year VOC emissions from Portable Fuel Containers (PFCs) from inventories developed and
modeled for EPA's MSAT2 rule (EPA, 2007a). The 10 PFC SCCs are summarized below (note that the full
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SCC descriptions for these SCCs include "Storage and Transport; Petroleum and Petroleum Product Storage"
as the beginning of the description).
2501011011	Residential Portable Fuel Containers:
2501011012	Residential Portable Fuel Containers:
2501011013	Residential Portable Fuel Containers:
2501011014	Residential Portable Fuel Containers:
2501011015	Residential Portable Fuel Containers:
2501012011	Commercial Portable Fuel Containers
2501012012	Commercial Portable Fuel Containers
2501012013	Commercial Portable Fuel Containers
2501012014	Commercial Portable Fuel Containers
2501012015	Commercial Portable Fuel Containers
Permeation
Evaporation
Spillage During Transport
Refilling at the Pump: Vapor Displacement
Refilling at the Pump: Spillage
: Permeation
: Evaporation
: Spillage During Transport
: Refilling at the Pump: Vapor Displacement
: Refilling at the Pump: Spillage
Note that emissions for the SCCs ending in 11, 12, and 14 are not affected by this rule and emissions for the
SCCs ending in 13 and 15 are carried forward from 2011. The future-year emissions reflect projected
increases in fuel consumption, state programs to reduce PFC emissions, standards promulgated in the
MSAT2 rule, and impacts of the RFS2 standards on gasoline volatility. EPA developed year 2025 PFC
emissions that include estimated Reid Vapor Pressure (RVP) and oxygenate impacts on VOC emissions, and
more importantly, large increases in ethanol emissions from RFS2. These emission estimates also include
gas can vapor displacement, tank permeation and diurnal emissions from evaporation. Because the future
year PFC inventories contain ethanol in addition to benzene, EPA developed a VOC E-profile that integrated
ethanol and benzene. Note that spillage emissions were not projected and were carried forward from 2011.
We received comments and PFC projections data for year 2018 from MARAMA as part of the 201 lv6.0
emissions modeling platform NODA (see: http://www.regulations.gov/#!docketDetail;D=EPA-HQ-OAR-
2013-0809). We used these projection factors to project MARAMA state PFC emissions to year 2018 and
then projected to year 2025 using the existing ratios 2025 to 2018 PFC emissions provided by OTAQ. We
used commercial software to blend the MARAMA projection factors and existing PFC inventories to create
year 2025 PFC inventories for this platform. A summary of the resulting PFC emissions for 2011, 2018
(used for 2017) and 2025 are provided in Table 4-45.
Table 4-45. PFC emissions for 2011, 2018 and 2025 [tons]

Emissions
Difference
% Change
2011
2018
2025
2018
2025
2018
2025
VOC
171,963
32,158
37,617
-139,805
-134,347
-81.3%
-78.1%
Benzene
742
654
758
-88
15
-11.9%
2.1%
Ethanol
0
3,719
4,448
n/a
4.2.5.2 Biodiesel plants (ptnonipm)
New Future year inventory: "Biodiesel_Plants_OTAQ_2040ei_ffl0"
EPA OTAQ developed an inventory for biodiesel plants for the 2040 reference and control cases. Plant
location and production volume data come from a database of 2014 biodiesel RINs. The biodiesel plants
included plants in the 201 1NEIv2, as well as a number of additional facilities added to the ptnonipm sector
using their location.
Total volume of biodiesel came from the AEO 2014 projections for the 2040 reference case, 1.4 BG (AEO,
2014), and the total volume for the 2040 control case was 1.14 BG. The 2014 production volume from all
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plants was 1.298 BG. To reach the 2040 reference case total volume of biodiesel (1.4 BG), plants that had
2030 production volumes that were within 90% of their capacity were assumed to hit 100% production for
2040 and the remaining volume was split among plants whose 2030 production volumes were less than 90%
of their capacity. To reach the 2040 control case total volume of biodiesel (1.14 BG), the 2014 plant
production volumes were scaled down proportionally. Once facility-level production capacities were scaled,
emission factors were applied based on soybean oil feedstock. These emission factors in Table 4-46 are in
tons per million gallons (Mgal) and were obtained from EPA's spreadsheet model for upstream EISA
impacts developed for the RFS2 rule (EPA, 2010a).
Twenty-one of the expected biodiesel plants were included in the 2011 NEI, so adjustment factors were
calculated to project their 2030 and 2040 emissions as described above using the packet
PROJECTION 201 l_2040ctl_OTAQ_biodiesel_facility_201 Iv6.2_24nov2015. The remaining 99 biodiesel
plants were modeled as point sources with Google Earth and web searching validating facility coordinates
and correcting state-county FIPS. Thus, the sources in this specific "add on" inventory are those that are not
in the 201 1NEIv2, while the sources that are in 201 1NEIv2 are projected.
Table 4-46. Emission Factors for Biodiesel Plants (Tons/Mgal)
Pollutant
Emission Factor
voc
4.3981E-02
CO
5.0069E-01
NOx
8.0790E-01
PMio
6.8240E-02
PM2.5
6.8240E-02
S02
5.9445E-03
nh3
0
Acetaldehyde
2.4783E-07
Acrolein
2.1290E-07
Benzene
3.2458E-08
1,3-Butadiene
0
Formaldehyde
1.5354E-06
Ethanol
0
Table 4-47. 2040 biodiesel plant emissions from sources not in the NEI [tons]
Pollutant
2040
reference
2040
control
CO
309
260
NOx
498
420
PM10
42
35
PM2.5
42
35
S02
4
3
VOC
27
23
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4.2.5.3 Cellulosic plants (nonpt)
New Future year inventories:
Primary inventory: "2018_cellulosic_inventory"
New Iowa inventory: "cellulosic_new_Iowa_plants_from2018docket_201 Iv6.2_ffl0_28jan2015"
Development of primary inventory
The cellulosic fuel production inventory for 2030 and 2040 is unchanged from what is included for 2018 in
the 2011 version 6.1 platform, as described in section 4.2.1.4 of the technical support document for the ozone
NAAQS emissions modeling platform (EPA, 2014b). This because AEO 2014 projections for cellulosic
volume for 2030 and 2040 are close to the volume assumptions used to develop 2018_cellulosic_inventory
Note that the Iowa inventory resulted from a docket comment and was held constant for 2030/2040 to keep
the facility operating at the capacity submitted in the Iowa comment.
Depending on available feedstock, cellulosic plants are likely to produce fuel through either a biochemical
process or a thermochemical process. EPA developed county-level inventories for biochemical and
thermochemical cellulosic fuel production for 2018 to reflect AEO2013er renewable fuel volumes.
Emissions factors for each cellulosic biofuel refinery reflect the fuel production technology used rather than
the fuel produced. Emission rates in Table 4-48 and Table 4-49 were used to develop cellulosic plant
inventories. Criteria pollutant emission rates are in tons per RIN gallon. Emission factors from the cellulosic
diesel work in the Tier 3 NPRM were used as the emission factors for the thermochemical plants. Cellulosic
ethanol VOC and related HAP emission factors from the Tier 3 NPRM were used as the biochemical VOC
and related HAP emission factors. Because the future year cellulosic inventory contains ethanol, a VOC E-
profile that integrated ethanol was used, see Sections 3.2.1.1 and 3.2.1.3 for more details.
Plants were treated as area sources spread across the entire area of whatever county they were considered to
be located in. Cellulosic biofuel refinery siting was based on utilizing the lowest cost feedstock, accounting
for the cost of the feedstock itself as well as feedstock storage and the transportation of the feedstock to the
cellulosic biofuel refinery. The total number of cellulosic biofuel refineries was projected using volumes
from AEO2013 (early release). The methodology used to determine most likely plant locations is described
in Section 1.8.1.3 of the RFS2 RIA (EPA, 2010a). Table 4-50 provides the year 2018 cellulosic plant
emissions estimates. Since the 2030 and 2040 inventories were developed to compare with control scenarios
which do not impact cellulosic volumes, an existing cellulosic inventory was used to maximize efficiency.
Biofuel volume projection estimates for 2030 and 2040 were similar to 2018 estimates, so the year 2018
cellulosic inventory was used for years 2030 and 2040.
Table 4-48. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/RIN gallon)
Cellulosic Plant
Type
VOC
CO
NOx
PMio
pm25
so2
nh3
Thermochemical
5.92E-07
8.7E-06
1.31E-05
1.56E-06
7.81E-07
1.17E-06
1.44E-10
Biochemical
1.82E-06
1.29E-05
1.85E-05
3.08E-06
1.23E-06
6.89E-07
0
Table 4-49. Toxic Emission Factors for Cellulosic Plants (Tons/RIN gallon)
Plant Type
Acetaldehyde
Acrolein
Benzene
1,3-Butadiene
Formaldehyde
Ethanol
Thermochemical
2.95E-08
1.27E-09
9.61E-10
0
5.07E-09
2.09E-07
Biochemical
3.98E-07
1.11E-08
1.39E-08
0
2.28E-08
6.41E-07
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Table 4-5
). 2018 cellulosic plant emissions [tons]
Pollutant
Emissions
Acrolein
1
Formaldehyde
4
Benzene
1
Acetaldehyde
21
CO
6,088
Ethanol
146
nh3
0.1
NOx
9,199
PMio
1,088
PM2.5
547
S02
819
voc
414
Development of new Iowa inventory
Iowa DNR (Department of Natural Resources), via the 2018 NOD A comments (see docket # EPA-HQ-
OAR-2013-0809 under http://www.regulations.gov.). provided information on new cellulosic ethanol
capacity information for three facilities. Emissions for these facilities were computed using the emission
factors previously discussed in Table 4-48 and Table 4-49. The resulting new facilities and NOx emissions,
used for both years 2017 and 2025, are provided in Table 4-51. Note that these facilities are in a nonpoint
inventory because latitude-longitude coordinates were not available.
Table 4-51. New cellulosic plants NOX emissions provided by Iowa DNR.
FIPS
County
Facility Name
Approximate
Production
Capacity
(Mgal/yr)
NOx
Emissions
19093
Ida
Quad County Corn Processors' Adding Cellulosic Ethanol (ACE)
2
26
19147
Palo Alto
POET-DSM Project Liberty
25
329
19169
Story
DuPont Cellulosic Ethanol
30
394
4.2.5.4 New cement plants (nonpt, ptnonipm)
Point Inventories: "cement_newkilns_year_2025_from_ISIS2013_NEI201 Ivl_08nov2013_v0.csv"
Nonpoint Inventories:
"cement_newkilns_year_2025_from_ISIS2013_NEI2011 v l_NONPOINT_l 2nov2013_v0. csv"
As discussed in Section 4.2.3.9, the ISMP model, was used to project the cement manufacturing sector to
future years. This section covers new ISMP-generated kilns that did not exist in the 2011 NEI. For kilns that
were new in 2025, EPA used two different approaches for modeling. For kilns already permitted, known
locations (coordinates) allowed us to process these as point sources. However, the ISMP model also created
"generic" kilns in specific geographically strategic locations (counties) to cover the need for increased
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production/capacity in future years. Because these generic kilns are not permitted and the location in these
counties is uncertain, EPA decided to model these as county-level to avoid placing large emissions sources
from a model (ISMP) artifact in one grid cell. These nonpoint source kilns were then spatially allocated
based on industrial land activity in the county. A list of all new point and nonpoint inventory cement kilns in
2018 and 2025 are provided in Table 4-52. Note that as production continues to increase beyond 2018, that
additional new kilns are needed in 2025. Not shown here is a cement kiln in Washington (King county) that
ISMP generates but according to Washington Department of Ecology, was not correct; this kiln was thus
removed from our emissions modeling platform.
Table 4-52. Locations of new ISMP-generated cement kilns
Year(s)
ISMP ID
Permitted?
Facility Name
FIPS
State
County
Both
FLNEW2
Y
Vulcan
12001
FL
Aluchua
2025
FLNEW1
Y
American Cement Company
12119
FL

Both
GANEW1
Y
Houston American Cement
13153
GA
Houston
Both
NCNEW1
Y
Titan America LLC
37129
NC
New Hanover
Both
NewGA2
N
n/a
13153
GA
Houston
Both
NewPA8
N
n/a
42011
PA
Berks
Both
NewSCl
N
n/a
45035
SC
Dorchester
Both
NewTXl
N
n/a
48029
TX
Bexar
Both
NewTXIO
N
n/a
48091
TX
Comal
2025
NewAZ2
N
n/a
04025
AZ
Yavapai
2025
NewC02
N
n/a
08043
CO
Freemont
2025
NewOK2
N
n/a
40123
OK
Pontotoc
2025
NewPA8
N
n/a
42095
PA
Northampton
2025
NewTX4
N
n/a
48029
TX
Bexar
2025
NewTX5
N
n/a
48091
TX
Comal
2025
NewTXl 2
N
n/a
48209
TX
Hays
For all ISMP future year emissions, PMio is assigned as 0.85 of total PM provided by ISMP, and PM2.5 is
assigned as 0.15 of total PM. All new ISMP-generated kilns, point and nonpoint format, are assigned as
Precalciner kilns (SCC=30500623). While ISMP provides emissions for mercury, EPA did not retain these in
our modeling. Table 4-53 shows the magnitude of the new ISMP-based cement kilns. We split out ISMP-
based new kilns in future years with permitted (as of August 2013) kilns modeled as point sources and
"generic" ISMP-generated kilns as nonpoint sources. Notee that the 2018 emissions are in the table for
context.
Table 4-53. ISMP-generated new permitted and non-permitted emissions

New kilns in 2018
New kilns in 2025
Total New IS
MP Emissions
Permitted
(point)
ISMP-
generated
(nonpoint)
Permitted
(point)
ISMP-
generated
(nonpoint)
2018
2025
NOx
3,751
5,697
4,795
13,673
8,546
19,370
PM2.5
8
24
11
57
19
81
SO2
1,775
13
2,004
30
3,779
43
VOC
91
2,969
117
7,115
208
10,084
147

-------
4.3 Mobile source projections
Mobile source monthly inventories of onroad and nonroad mobile emissions were created for 2040 using a
combination of the MOVES2014 version that was updated for this rule and the NMIM models. The future
year onroad emissions account for changes in activity data and the impact of on-the-books rules including
some of the recent regulations such as the Light Duty Vehicle GHG Rule for Model-Year 2017-2025, and
the Tier 3 Motor Vehicle Emission and Fuel Standards Rule (http://www.epa.gov/otaq/tier3.htm). Local
inspection and maintenance (I/M) and other onroad mobile programs are included such as California LEVIII,
the National Low Emissions Vehicle (LEV) and Ozone Transport Commission (OTC) LEV regulations
(http://www.epa.gov/otaq/lev-nlev.htm), local fuel programs, and Stage II refueling control programs. Table
4-1 provides references to many of these programs.
Nonroad mobile emissions reductions for these years include reductions to various nonroad engines such as
diesel engines and recreational marine engine types (pleasure craft), fuel sulfur content, and evaporative
emissions standards.
Onroad mobile sources are comprised of several components and are discussed in the next subsection (4.3.1).
Monthly nonroad equipment mobile emission projections are discussed in subsection 4.4. Locomotives and
Category 1 and Category 2 commercial marine vessel (C1/C2 CMV) projections were discussed in Section
Error! Reference source not found., and Category 3 (C3) CMV projections were discussed in Section 0.
4.3.1 Onroad mobile (onroad)
The onroad emissions for 2040 use the same SMOKE-MOVES system as for the base year (see Sections
2.3.1). Meteorology, speed, spatial surrogates and temporal profiles, representative counties, and fuel months
were the same as for 2011.
4.3.1.1 Future activity data
Estimates of total national Vehicle Miles Travelled (VMT) in 2040 came from DOE's Annual Energy
Outlook (AEO) 2014 (http://www.eia.gov/forecasts/aeo/) transportation projections, specifically the
reference case that was released on May 7, 2014. The starting for the 2040 VMT projections was the 2025
VMT dataset from the 201 lv6.2 platform. Trends were developed by calculating ratios between 2025 AEO
and 2040 AEO32 estimates and applying the trends to the 2025 VMT from the 201 lv6.2 platform. These
ratios were developed for light duty and heavy duty only, with no consideration given to fuel type. The
projection factors, the national 2025 VMT from the 201 lv6.2 platform ("VMT_2025") by broad vehicle and
fuel type, and the future VMT ("VMT_2040") are shown in Table 4-54.
Table 4-54. Projection factors for 2040 VMT)
Classification
MOVES source types
Ratio 2040
Light Duty
11,21,31,32
1.153346
Heavy Duty
41,42,43,51,52,53,54,61,62
1.212038
In the above table, light duty (LD) includes passenger cars, light trucks, and sometimes motorcycles, heavy
duty (HD) includes buses, single unit trucks, and combination trucks. The specific MOVES source type
codes are listed above. These national ratios were applied to the 2025 VMT to create an EPA estimate of
2040 VMT at the county, SCC level.
32 By "2025 AEO" and "2040 AEO," this refers to the AEO2014's estimates of national VMT in those specific calendar years.
148

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For 2040, MOVES does not output emission factors for gasoline single-unit long-haul trucks (220153) or
gasoline combination short-haul trucks (220161), under the assumption that these types of vehicles will no
longer be in operation in 2040. Therefore, all VMT associated with those fuel-vehicles was moved from
gasoline to diesel in the 2040 VMT projection.
Vehicle population (VPOP) was developed by creating VMT/VPOP ratios from the 201 1NEIv2 VMT and
201 1NEIv2 VPOP at the county, fuel and vehicle type (SCC6) level. These ratios were applied to the 2040
VMT to create the 2040 VPOP.
Hoteling (HOTELING) was developed by creating VMT/HOTELING ratios from the 2011 NEIv2 VMT and
2011 NEIv2 HOTELING at the county level. For these ratios, the VMT was limited to combination long-
haul trucks on restricted roads (SCC6 220262, road types 2 and 4). The HOTELING was the total of
auxiliary power units (APU) and extended idle (EXT). These ratios were applied to the 2040 VMT to create
2040 HOTELING data. To get the APU split, 30% of HOTELING was assumed to be APU in all counties
for 2010 and later model year vehicles, and zero percent for model year vehicles before 2010, This is
consistent with MOVES2014 default split for APU for calendar year 2040.
4.3.1.2 Set up and Run MOVES to create EFs
Emission factor tables were created by running SMOKE-MOVES using the same procedures and models as
described for 2011 (see the 201 1NEIv2 TSD and Section 2.3). The same meteorology and the same
representative counties were used. Changes between 2011 and future year 2040 are predominantly activity
data, fuels, national and local rules, and age distributions. Age (i.e., model year) distributions were projected
forward using the methodology described in the MOVES activity report (EPA, 2015), although some states
supplied age distributions in their CDBs. Fleet turnover resulted in a greater fraction of newer vehicles
meeting stricter emission standards. The similarities and differences between the two runs are described in
Table 4-55.
Table 4-55. Inputs for MOVES runs for 2040 reference and control cases
Element
2040 reference
2040 control
Code
MOVES20150507c
MOVES20150507c
Default
database
Movesdb20150515b
Movesdb20150515b
VMT and
VPOP
2025 projected to 2040
2025 projected to 2040
Hydrocarbon
speciation
Done inside MOVES
Done inside MOVES
Fuels
movesdb20150515b_fuelsupply
movesdb20150515b_fuelsupply
CA LEVIII
ca_standards_SS_20140903
(16 states)
ca_standards_SS_20140903
(16 states)
The following states were modeled as having adopted the California LEV III program (see Table 4-56):
Table 4-56. CA LEVIII program states
FIPS
State Name
06
California
09
Connecticut
149

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FIPS
State Name
10
Delaware
23
Maine
24
Maryland
25
Massachusetts
34
New Jersey
36
New York
41
Oregon
42
Pennsylvania
44
Rhode Island
50
Vermont
53
Washington
Note that County 48261 was remapped to use county 48047 due to negative emission factors in 28261.
Fuels were projected into the future using estimates from the AEO2014 (http://www.eia.gov/forecasts/aeo/).
release date May 7th 2014, as well as fuel properties changing as part of the Tier 3 Emissions and Fuel
Standards Program (http://www.epa.gov/otaq/tier3.htm). The AEO2014 projection includes market shares of
E10, E15, and E85 through 2040, as well as biodiesel market shares up to B5 (note that these values do not
assume full implementation of the RFS2 program). The regional fuel properties and renewable volumes in
2011 were projected to 2040 in order to preserve the regional variation present in these fuel supplies, with
total fuel volumes aligned to those in the AEO2014. For details on the future year speciation of onroad
mobile source emissions, which is dependent on the fuels, see Section 3.2.1.4.
4.3.1.3 National and California adjustments
A set of adjustments were done in SMOKE-MOVES to create 2040 emissions: 1) refueling, 2) extended idle
emissions.
The first set of adjustment factors was for refueling. This uses the same approach as was used in 2011 (see
the Section 2.3.1 for details) to take account of the few counties in Colorado that provided point source gas
refueling emissions. These adjustments essentially zero out the MOVES-based gasoline refueling emissions
(SCC 2201*62) in these counties so that the point estimates will be used instead.
The second set of adjustment factors was for extended idle emissions. In the 2040 reference case, the only
adjustment to extended idle emissions was the application of state-wide adjustment factors in California,
resulting in a large reduction in extended idle emissions for all pollutants except NH3 and S02, and the
elimination of all APU emissions in California. In the 2040 control case, an additional set of nationwide
adjustment factors was applied to all extended idle emissions outside of California. Within California,
extended idle adjustment factors were the same in the 2040 control case as in the reference case.
4.4 Nonroad mobile source projections (nonroad)
The projection of locomotives and Category 1 and 2 commercial marine vessels to 2040 is described in
Section 4.2.3.5. The projection of the larger Category 3 commercial marine vessels is described in Section
4.2.3.4. Most of the remaining sources in the nonroad sector are projected by running the NMIM model with
fuels and vehicle populations appropriate to 2040; this section describes the projection of these sources. The
same nonroad emissions are used in the reference and control cases.
150

-------
This sector includes monthly exhaust, evaporative and refueling emissions from nonroad engines (not
including commercial marine, aircraft, and locomotives) derived from NMIM for all states except California
and Texas. NMIM provides nonroad emissions for VOC by three emission modes: exhaust, evaporative and
refueling.
With the exception of California and Texas, U.S. emissions for the nonroad sector (defined as the equipment
types covered by the NONROAD model) were created using a consistent NMIM-based approach as was
used for 2011. Specifically, NMIM utilized the public release version of NONROAD2008a, NMIM
20090504, including future-year equipment population estimates, control programs to the year 2040, and
inputs were either state-supplied as part of the 201 INEIvl and 201 1NEIv2 process or national level inputs.
Fuels for 2040 were assumed to be E10 everywhere for nonroad equipment. The fuels were developed from
the MOVES2014 fuels (database movesdb20141021). The databases used in the 2040 run were NMIM
county database NCD2015081 l_nei2040vdl, which incorporates fuels database
"movesdb20141021_2040fuelsNMIM." The 2040 emissions account for changes in activity data (based on
NONROAD model default growth estimates of future-year equipment population) and changes in fuels and
engines that reflect implementation of national regulations and local control programs that impact each year
differently due to engine turnover. For details on the speciation of future year nonroad emissions, see Section
3.2.1.4.
The version of NONROAD used was the current public release, NR08a, which models all in-force nonroad
controls. The represented rules include:
• "Clean Air Nonroad Diesel Final Rule - Tier 4", published June, 2004:
http://www.epa.gov/otaq/nonroad-diesel.htm
• Control of Emissions from Nonroad Large Spark-Ignition Engines, and Recreational Engines (Marine
and Land-Based), November 8, 2002.
• Small Engine Spark Ignition Rule, October, 2008: http://www.epa.gov/otaq/smallsi.htm
Not included are voluntary local programs such as encouraging either no refueling or evening refueling on
Ozone Action Days.
California and Texas nonroad emissions
Similar to the 2011 base year nonroad mobile, NMIM was not used to generate future-year nonroad
emissions for California, other than for NH3. EPA used NMIM for California future nonroad NH3 emissions
because CARB did not provide these data for any nonroad vehicle types. For the rest of the pollutants, the
CARB-supplied 2030 nonroad annual inventories were distributed to monthly emissions values by using the
2030 NMIM monthly inventories to compute monthly ratios by county, SCC7, mode and pollutant, which
was consistent with the approach in 2011 (see Section 2.4.3). Some adjustments to the CARB inventory were
needed to convert the provided TOG to VOC. See Section 3.2.1.3 for details on speciation of California
nonroad data see Section 3.2.1.3)33. The CARB nonroad emissions include nonroad rules reflected in the
December 2010 Rulemaking Inventory (http://www.arb.ca.gov/regact/2010/offroadlsi 10/offroadisor.pdf) and
those in the March 2011 Rule Inventory, the Off-Road Construction Rule Inventory for "In-Use Diesel".
For Texas, EPA combined Texas' submitted estimates for 2011 with EPA projections of nonroad emissions
into 2040. Conceptually, EPA used the trend of 2011 to 2040 based on EPA's estimates to project Texas'
33 In addition, airport equipment was removed from CARB's inventory because these sources were modeled elsewhere.
151

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submitted emissions for 2011. Specifically, projections were based on state-wide SCC7, mode, poll ratios34
of 2040 NMIM to 2011 NMIM. These ratios were then applied to Texas' submitted 2011 nonroad emissions,
which had already been distributed to a monthly inventory (see Section 2.4.3), to create a 2040 monthly
nonroad inventory.
4.5 "Other Emissions": Offshore Category 3 commercial marine vessels
and drilling platforms, Canada and Mexico (othpt, othar, othafdust, and
othon)
As described in Section 2.5, emissions from Canada, Mexico, and non-U.S. offshore Category 3 Commercial
Marine Vessels (C3 CMV) and drilling platforms are included as part of four emissions modeling sectors:
othpt, othar, othafdust, and othon. For oil drilling platforms, EPA used emissions from the 201 1NEIv2 point
source inventory for 2011 and both future years. Environment Canada did not provide any future-year
emissions that were consistent with the 2010 base year emissions, therefore emissions for Canada were not
projected to future years and are the same as those used in the 2011 base case. Emissions for Mexico are
based on the Inventario Nacional de Emisiones de Mexico, 2008 projected to year 2030 (ERG, 2014a).
As discussed in Section 2.5.1, the ECA-IMO-based C3 CMV emissions outside of U.S. state waters are
processed in the othpt sector. This enables shipping lanes to be represented and for emissions to be treated as
elevated sources. These C3 CMV emissions include those assigned to the Exclusive Economic Zone (defined
as those emissions just beyond U.S. waters approximately 3-10 miles offshore, extending to about 200
nautical miles from the U.S. coastline), and all other offshore emissions. The projection factors for the othpt
C3 CMV emissions vary by geographic and region as shown in Table 4-9.
34 These ratios were initially attempted by county/SCC7/mode/pollutant, but due to significantly different distributions of certain
source types between EPA and TCEQ's emissions, this created unreasonable growth in certain areas. The above approach was
used except in the following, relatively limited conditions. If a state/SCC7/mode/pollutant was in EPA 2017 and 2025 emissions
but not in EPA's 2011 emissions, 2017 and 2025 EPA emissions were used in the final inventory. If a state/SCC7/mode/pollutant
was in TCEQ's 2011 emissions but was not in EPA's 2017 and 2025 emissions, then state/SCC3/mode/pollutant ratios were used
to project to 2017 and 2025.
152

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5 Emission Summaries
The following tables summarize emissions differences between the 2011 base case, the 2040 reference case,
and the 2040 control case. These summaries are provided at the national level by sector for the contiguous
U.S. and for the portions of Canada and Mexico inside the smaller 12km domain (12US2) discussed in
Section 3.1. The afdust sector emissions represent the summaries after application of both the land use
(transport fraction) and meteorological adjustments (see Section 2.2.1); therefore, this sector is called
"afdust adj" in these summaries. The onroad sector totals are post-SMOKE-MOVES totals, representing air
quality model-ready emission totals. The c3marine sector includes U.S. emissions within state waters only;
these extend to roughly 3-5 miles offshore and includes CMV emissions at U.S. ports. "Offshore to EEZ"
represents CMV emissions that are within the (up to) 200 nautical mile Exclusive Economic Zone (EEZ)
boundary but are outside of U.S. state waters along with the offshore oil platform emissions from the NEI.
Finally, the "Non-US SECA C3" represents all non-U. S. and non-Canada emissions outside of the (up to)
200nm offshore boundary, including all Mexican CMV emissions. Canadian CMV emissions are included in
the othar sector.
National emission totals by air quality model-ready sector are provided CAPs and key HAPs for the 2011
base case in Table 5-1. The total of all sectors in the 2011 base case are listed as "Con U.S. Total". Table 5-2
provides national emissions totals by sector for the 2040 reference case. Table 5-3 provides national
emissions totals by sector for the 2040 control case. In the tables, "Offshore to EEZ" includes both the
offshore point emissions, and the "Offshore to EEZ" c3marine emissions.
Table 5-4 provides national-by sector emission summaries for CO for all the cases: 2011 base case, 2040
reference case, and 2040 control case, with percent changes from 2040 reference to control. Tables 5-5
through Table 5-10 provide the same summaries for NH3, NOx, PM2.5, PM10, SO2 and VOC, respectively.
Note that the same fire emissions are used in all cases.
153

-------
Table 5-1. National by-sector emissions summary for the 2011 evaluation case
Sector
CO
MI;
NOx
PMio
PM2.5
SO2
voc
ACRO
LEIN
ACET
ALDE
HYDE
BENZE
NE
1,3BU
TADI
ENE
FORM
ALDE
HYDE
NAPH
THAL
ENE
afdustadj



6,732,941
923,590








ag

3,515,198











agfire
956,243
3,321
42,767
140,728
93,959
16,224
74,783
1,070
11,803
5,747
2,822
27,157

clc2rail
180,579
511
1,075,217
35,359
33,019
12,609
48,281
147
1,316
273
126
2,837
78
nonpt
1,645,989
94,242
720,454
491,825
404,258
275,655
3,672,249
576
4,284
17,142
43
3,505
906
npoilgas
626,764
0
641,611
16,850
15,395
18,338
2,548,410
831
1,156
18,028
162
7,919
29
nonroad
13,951,020
2,627
1,630,301
162,417
154,657
4,031
2,024,419
964
11,796
40,020
5,032
24,210
809
onroad
27,656,741
117,392
5,859,657
354,192
192,970
28,908
2,808,335
3,074
37,494
68,851
10,671
45,424
6,045
c 3 marine
12,532
68
131,382
10,168
9,043
86,373
5,149

1
1
1
9
0
ptfire
20,562,697
329,330
333,398
2,171,987
1,844,263
165,773
4,688,094
35,965
114,574
112,150
34,353
504,978

ptegu
787,103
24,765
2,029,880
275,422
203,036
4,652,958
37,967
308
6
1,436
2
6,217
48
ptnonipm
2,297,474
66,050
1,214,919
474,939
320,206
1,049,418
801,072
1,346
6,017
25,696
1,116
16,385
1,517
ptoilgas
231,972
5,942
500,938
14,242
13,806
66,441
161,659
1,824
2,261
1,708
137
12,281
27
rwc
2,517,844
19,693
34,436
381,476
381,252
8,954
442,541
987
9,107
20,303
2,740
18,011
2,435
Con U.S.
71,426,957
4,179,140
14,214,961
11,262,546
4,589,455
6,385,682
17,312,959
47,093
199,815
311,354
57,205
668,932
11,895
Off-shore
to EEZ*
175,353
185
899,986
26,247
24,544
139,169
81,602
161
306
250
0
855
8
Non-US
SECA C3
16,207
0
190,904
16,226
14,926
120,340
6,879

2
0

11

Canada
othafdust



780,453
112,597








Can. Othar
3,015,606
326,610
361,896
158,996
131,114
70,272
886,456

14,384
24,712

12,007

Can. othon
3,032,005
18,653
345,664
17,628
12,216
1,701
178,431

2,112
7,744

2,997

Can. othpt
496,083
13,069
266,912
70,005
29,165
544,502
129,119

390
13,239

2,668

Mex. othar
277,810
163,040
182,869
98,812
50,158
10,679
410,734

12,142
6,252

9,061

Mex othon
3,361,123
7,978
243,714
2,425
1,624
4,919
319,353

2,937
15,130

3,756

Mex. othpt
153,061
3,706
286,303
55,162
42,105
453,466
53,813

126
1,246

4,691

Non-US
Total
10,527,155
532,913
2,778,288
1,226,016
418,502
1,345,052
2,066,346
161
32,398
68,574
0
36,045
8
154

-------
Table 5-2. National by-sector CAP emissions summaries for the 2040 reference case
Sector
CO
MI;
NOx
PMio
PM2.5
SO2
voc
ACRO
LEIN
ACET
ALDE
HYDE
BENZE
NE
1,3-
BUTA
I) I I N
FORM
ALDE
HYDE
NAPH
THAL
ENE
afdustadj



8,413,026
1,111,843








ag

3,603,443











agfire
956,243
3,321
42,767
140,728
93,959
16,224
74,783
1,070
11,803
5,747
2,822
27,157

clc2rail
230,494
514
540,631
13,407
12,519
946
20,078
62
613
135
51
1,304
30
nonpt
1,703,173
94,851
746,049
516,744
437,006
94,229
3,405,755
597
4,587
16,146
43
3,784
947
npoilgas
710,367
0
691,536
25,050
22,332
38,395
3,403,731
1,058
1,494
25,809
210
9,906
45
nonroad
15,055,720
3,876
705,271
66,815
62,014
2,789
1,218,574
543
7,288
26,166
3,314
13,993
861
onroad
6,370,117
94,027
1,101,240
267,115
57,182
12,536
634,605
1,173
13,236
12,038
1,579
21,741
1,921
c 3 marine
25,345
68
95,936
2,825
2,516
6,378
10,519

3
1
1
18
0
ptfire
20,562,697
329,330
333,398
2,171,987
1,844,263
165,773
4,688,094
35,965
114,574
112,150
34,353
504,978

ptegu
730,109
41,900
956,224
185,832
141,675
972,580
31,030
0
1
1,274
0
10,288
0
ptnonipm
2,454,485
66,542
1,232,303
475,090
328,730
802,012
807,226
1,673
6,287
27,123
1,273
17,827
1,740
ptoilgas
256,926
5,949
476,022
17,484
17,017
66,586
191,024
2,573
3,216
1,901
179
15,849
38
rwc
2,247,367
17,680
34,583
343,423
343,163
7,363
390,193
932
8,694
16,722
2,442
17,650
2,520
Con U.S.
51,303,042
4,261,502
6,955,960
12,639,527
4,474,218
2,185,810
14,875,612
45,647
171,795
245,211
46,267
644,495
8,103
Off-shore
to EEZ*
231,552
185
598,534
9,293
8,716
14,306
102,357
6
136
202
0
541
3
Non-US
SECA C3
37,967
0
345,977
8,382
7,640
51,408
16,137

4
0

25

Canada
othafdust



780,453
112,597








Can. Othar
3,015,514
326,281
361,958
159,054
131,167
70,276
886,419

14,384
24,712

12,007

Can. othon
3,032,193
18,655
345,657
17,628
12,216
1,702
178,440

2,112
7,744

2,997

Can. othpt
496,083
13,069
266,912
70,009
29,166
544,504
129,119

390
13,239

2,668

Mex. othar
327,344
169,400
229,780
107,843
56,576
14,278
567,181

14,975
10,141

11,113

Mex othon
1,961,590
14,900
48,723
3,438
2,263
524
149,119

1,371
7,065

1,754

Mex. othpt
220,646
6,954
439,755
86,035
65,539
408,785
100,317

239
2,189

9,522

Non-US
Total
9,322,888
549,444
2,637,297
1,242,136
425,880
1,105,785
2,129,090
6
33,611
65,293
0
40,628
3
155

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Table 5-3. National by-sector CAP emissions summaries for the 2040 control case
Sector
CO
MI;
NOx
PMio
PM2.5
SO2
voc
ACRO
LEIN
ACET
ALDE
HYDE
BENZE
NE
1,3-
BUTA
I) I I N
FORM
ALDE
HYDE
NAPH
THAL
ENE
afdustadj



8,413,026
1,111,843








ag

3,603,443











agfire
956,243
3,321
42,767
140,728
93,959
16,224
74,783
1,070
11,803
5,747
2,822
27,157

clc2rail
230,077
514
539,397
13,351
12,465
942
20,065
62
613
134
51
1,303
30
nonpt
1,703,141
94,851
745,970
516,537
436,906
94,042
3,400,567
597
4,585
16,117
43
3,780
947
npoilgas
710,335
0
691,420
25,050
22,332
38,395
3,403,557
1,058
1,494
25,807
210
9,905
45
nonroad
15,055,720
3,876
705,271
66,815
62,014
2,789
1,218,574
543
7,288
26,166
3,314
13,993
861
onroad
6,319,854
93,704
856,326
270,291
58,856
11,645
605,398
875
11,077
11,642
1,577
14,655
1,418
c 3 marine
25,345
68
95,936
2,825
2,516
6,378
10,519

3
1
1
18
0
ptfire
20,562,697
329,330
333,398
2,171,987
1,844,263
165,773
4,688,094
35,965
114,574
112,150
34,353
504,978

ptegu
728,468
41,900
955,464
185,706
141,562
971,823
30,505
0
1
1,243
0
10,114
0
ptnonipm
2,449,829
66,420
1,226,380
472,853
326,867
795,846
803,051
1,673
6,278
26,908
1,271
17,695
1,740
ptoilgas
255,724
5,949
475,095
17,461
16,985
64,730
189,805
2,573
3,215
1,882
179
15,834
38
rwc
2,247,367
17,680
34,583
343,423
343,163
7,363
390,193
932
8,694
16,722
2,442
17,650
2,520
Con U.S.
51,244,799
4,261,056
6,702,006
12,640,054
4,473,731
2,175,948
14,835,110
45,349
169,623
244,519
46,263
637,081
7,601
Off-shore
to EEZ*
231,275
185
597,702
9,254
8,676
14,304
102,354
6
136
202
0
541
3
Non-US
SECA C3
37,967
0
345,977
8,382
7,640
51,408
16,137

4
0

25

Canada
othafdust



780,453
112,597








Can. Othar
3,015,514
326,281
361,958
159,054
131,167
70,276
886,419

14,384
24,712

12,007

Can. othon
3,032,193
18,655
345,657
17,628
12,216
1,702
178,440

2,112
7,744

2,997

Can. othpt
496,083
13,069
266,912
70,009
29,166
544,504
129,119

390
13,239

2,668

Mex. othar
327,344
169,400
229,780
107,843
56,576
14,278
567,181

14,975
10,141

11,113

Mex othon
1,961,590
14,900
48,723
3,438
2,263
524
149,119

1,371
7,065

1,754

Mex. othpt
220,646
6,954
439,755
86,035
65,539
408,785
100,317

239
2,189

9,522

Non-US
Total
9,322,611
549,444
2,636,465
1,242,096
425,841
1,105,783
2,129,087
6
33,610
65,293
0
40,628
3
156

-------
Table 5-4. National by-sector CO emissions (tons/yr) summaries and percent change
Sector
2011 CO
2040 ref.
CO
2040 ctrl.
CO
% change ctrl-ref
afdustadj




ag




agfire
956,243
956,243
956,243
0%
clc2rail
180,579
230,494
230,077
0%
nonpt
1,645,989
1,703,173
1,703,141
0%
np_°ilgas
626,764
710,367
710,335
0%
nonroad
13,951,020
15,055,720
15,055,720
0%
onroad
27,656,741
6,370,117
6,319,854
-1%
c3marine
12,532
25,345
25,345
0%
ptfire
20,562,697
20,562,697
20,562,697
0%
ptegu
787,103
730,109
728,468
0%
ptnonipm
2,297,474
2,454,485
2,449,829
0%
pt_oilgas
231,972
256,926
255,724
0%
rwc
2,517,844
2,247,367
2,247,367
0%
Con U.S. Total
71,426,957
51,303,042
51,244,799
0%
Off-shore to EEZ*
175,353
231,552
231,275
0%
Non-US SECA C3
16,207
37,967
37,967
0%
Canada othafdust




Canada othar
3,015,606
3,015,514
3,015,514
0%
Canada othon
3,032,005
3,032,193
3,032,193
0%
Canada othpt* *
496,083
496,083
496,083
0%
Mexico othar
277,810
327,344
327,344
0%
Mexico othon
3,361,123
1,961,590
1,961,590
0%
Mexico othpt
153,061
220,646
220,646
0%
Non-US Total
10,527,155
9,322,888
9,322,611
0%
157

-------
Table 5-5. National by-sector NH3 emissions (tons/yr) summaries and percent change
Sector
2011
nh3
2040 ref.
nh3
2040 ctrl.
nh3
% change ctrl-ref
afdustadj




ag
3,515,198
3,603,443
3,603,443

agfire
3,321
3,321
3,321
0%
clc2rail
511
514
514
0%
nonpt
94,242
94,851
94,851
0%
np_°ilgas
0
0
0

nonroad
2,627
3,876
3,876
0%
onroad
117,392
94,027
93,704
0%
c3marine
68
68
68
0%
ptfire
329,330
329,330
329,330
0%
ptegu
24,765
41,900
41,900
0%
ptnonipm
66,050
66,542
66,420
0%
pt_oilgas
5,942
5,949
5,949
0%
rwc
19,693
17,680
17,680
0%
Con U.S. Total
4,179,140
4,261,502
4,261,056
0%
Off-shore to EEZ*
185
185
185
0%
Non-US SECA C3
0
0
0

Canada othafdust




Canada othar
326,610
326,281
326,281
0%
Canada othon
18,653
18,655
18,655
0%
Canada othpt* *
13,069
13,069
13,069
0%
Mexico othar
163,040
169,400
169,400
0%
Mexico othon
7,978
14,900
14,900
0%
Mexico othpt
3,706
6,954
6,954
0%
Non-US Total
532,913
549,444
549,444
0%
158

-------
Table 5-6. National by-sector NOx emissions (tons/yr) summaries and percent change
Sector
2011 NOx
2040 ref.
NOx
2040 ctrl.
NOx
% change ctrl-
ref
afdustadj




ag




agfire
42,767
42,767
42,767
0%
clc2rail
1,075,217
540,631
539,397
0%
nonpt
720,454
746,049
745,970
0%
np_°ilgas
641,611
691,536
691,420
0%
nonroad
1,630,301
705,271
705,271
0%
onroad
5,859,657
1,101,240
856,326
-22%
c3marine
131,382
95,936
95,936
0%
ptfire
333,398
333,398
333,398
0%
ptegu
2,029,880
956,224
955,464
0%
ptnonipm
1,214,919
1,232,303
1,226,380
0%
pt_oilgas
500,938
476,022
475,095
0%
rwc
34,436
34,583
34,583
0%
Con U.S. Total
14,214,961
6,955,960
6,702,006
-4%
Off-shore to EEZ*
899,986
598,534
597,702
0%
Non-US SECA C3
190,904
345,977
345,977
0%
Canada othafdust




Canada othar
361,896
361,958
361,958
0%
Canada othon
345,664
345,657
345,657
0%
Canada othpt* *
266,912
266,912
266,912
0%
Mexico othar
182,869
229,780
229,780
0%
Mexico othon
243,714
48,723
48,723
0%
Mexico othpt
286,303
439,755
439,755
0%
Non-US Total
2,778,288
2,637,297
2,636,465
0%
159

-------
Table 5-7. National by-sector PM2.5 emissions (tons/yr) summaries and percent change
Sector
2011
pm25
2040 ref.
pm25
2040 ctrl.
pm25
% change ctrl-
ref
afdustadj
923,590
1,111,843
1,111,843

ag




agfire
93,959
93,959
93,959
0%
clc2rail
33,019
12,519
12,465
0%
nonpt
404,258
437,006
436,906
0%
np_°ilgas
15,395
22,332
22,332
0%
nonroad
154,657
62,014
62,014
0%
onroad
192,970
57,182
58,856
3%
c3marine
9,043
2,516
2,516
0%
ptfire
1,844,263
1,844,263
1,844,263
0%
ptegu
203,036
141,675
141,562
0%
ptnonipm
320,206
328,730
326,867
-1%
pt_oilgas
13,806
17,017
16,985
0%
rwc
381,252
343,163
343,163
0%
Con U.S. Total
4,589,455
4,474,218
4,473,731
0%
Off-shore to EEZ*
24,544
8,716
8,676
0%
Non-US SECA C3
14,926
7,640
7,640
0%
Canada othafdust
112,597
112,597
112,597

Canada othar
131,114
131,167
131,167
0%
Canada othon
12,216
12,216
12,216
0%
Canada othpt* *
29,165
29,166
29,166
0%
Mexico othar
50,158
56,576
56,576
0%
Mexico othon
1,624
2,263
2,263
0%
Mexico othpt
42,105
65,539
65,539
0%
Non-US Total
418,502
425,880
425,841
0%
160

-------
Table 5-8. National by-sector PMio emissions (tons/yr) summaries and percent change
Sector
2011 PMi„
2040 ref.
PMio
2040 ctrl.
PMio
% change ctrl-ref
afdustadj
6,732,941
8,413,026
8,413,026

ag




agfire
140,728
140,728
140,728
0%
clc2rail
35,359
13,407
13,351
0%
nonpt
491,825
516,744
516,537
0%
np_°ilgas
16,850
25,050
25,050
0%
nonroad
162,417
66,815
66,815
0%
onroad
354,192
267,115
270,291
1%
c3marine
10,168
2,825
2,825
0%
ptfire
2,171,987
2,171,987
2,171,987
0%
ptegu
275,422
185,832
185,706
0%
ptnonipm
474,939
475,090
472,853
0%
pt_oilgas
14,242
17,484
17,461
0%
rwc
381,476
343,423
343,423
0%
Con U.S. Total
11,262,546
12,639,527
12,640,054
0%
Off-shore to EEZ*
26,247
9,293
9,254
0%
Non-US SECA C3
16,226
8,382
8,382
0%
Canada othafdust
780,453
780,453
780,453

Canada othar
158,996
159,054
159,054
0%
Canada othon
17,628
17,628
17,628
0%
Canada othpt* *
70,005
70,009
70,009
0%
Mexico othar
98,812
107,843
107,843
0%
Mexico othon
2,425
3,438
3,438
0%
Mexico othpt
55,162
86,035
86,035
0%
Non-US Total
1,226,016
1,242,136
1,242,096
0%
161

-------
Table 5-9. National by-sector SO2 emissions (tons/yr) summaries and percent change
Sector
2011 S02
2040 ref.
SO2
2040 ctrl.
SO2
% change ctrl-ref
afdustadj




ag




agfire
16,224
16,224
16,224
0%
clc2rail
12,609
946
942
0%
nonpt
275,655
94,229
94,042
0%
np_°ilgas
18,338
38,395
38,395
0%
nonroad
4,031
2,789
2,789
0%
onroad
28,908
12,536
11,645
-7%
c3marine
86,373
6,378
6,378
0%
ptfire
165,773
165,773
165,773
0%
ptegu
4,652,958
972,580
971,823
0%
ptnonipm
1,049,418
802,012
795,846
-1%
pt_oilgas
66,441
66,586
64,730
-3%
rwc
8,954
7,363
7,363
0%
Con U.S. Total
6,385,682
2,185,810
2,175,948
0%
Off-shore to EEZ*
139,169
14,306
14,304
0%
Non-US SECA C3
120,340
51,408
51,408
0%
Canada othafdust




Canada othar
70,272
70,276
70,276
0%
Canada othon
1,701
1,702
1,702
0%
Canada othpt* *
544,502
544,504
544,504
0%
Mexico othar
10,679
14,278
14,278
0%
Mexico othon
4,919
524
524
0%
Mexico othpt
453,466
408,785
408,785
0%
Non-US Total
1,345,052
1,105,785
1,105,783
0%
162

-------
Table 5-10. National by-sector VOC emissions (tons/yr) summaries and percent change
Sector
2011 VOC
2040 ref.
VOC
2040 ctrl.
VOC
% change ctrl-ref
afdustadj




ag




agfire
74,783
74,783
74,783
0%
clc2rail
48,281
20,078
20,065
0%
nonpt
3,672,249
3,405,755
3,400,567
0%
np_°ilgas
2,548,410
3,403,731
3,403,557
0%
nonroad
2,024,419
1,218,574
1,218,574
0%
onroad
2,808,335
634,605
605,398
-5%
c3marine
5,149
10,519
10,519
0%
ptfire
4,688,094
4,688,094
4,688,094
0%
ptegu
37,967
31,030
30,505
-2%
ptnonipm
801,072
807,226
803,051
-1%
pt_oilgas
161,659
191,024
189,805
-1%
rwc
442,541
390,193
390,193
0%
Con U.S. Total
17,312,959
14,875,612
14,835,110
0%
Off-shore to EEZ*
81,602
102,357
102,354
0%
Non-US SECA C3
6,879
16,137
16,137
0%
Canada othafdust




Canada othar
886,456
886,419
886,419
0%
Canada othon
178,431
178,440
178,440
0%
Canada othpt* *
129,119
129,119
129,119
0%
Mexico othar
410,734
567,181
567,181
0%
Mexico othon
319,353
149,119
149,119
0%
Mexico othpt
53,813
100,317
100,317
0%
Non-US Total
2,066,346
2,129,090
2,129,087
0%
163

-------
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Appendix A: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT
The table below provides a crosswalk between fuel distribution SCCs and classification type for portable fuel containers (PFC), fuel
distribution operations associated with the bulk-plant-to-pump (BTP), refinery to bulk terminal (RBT) and bulk plant storage (BPS).
see
Type
Description
40301001
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 13:
Breathing Loss (67000 Bbl. Tank Size)
40301002
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 10:
Breathing Loss (67000 Bbl. Tank Size)
40301003
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 7:
Breathing Loss (67000 Bbl. Tank Size)
40301004
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 13:
Breathing Loss (250000 Bbl. Tank Size)
40301006
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 7:
Breathing Loss (250000 Bbl. Tank Size)
40301007
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Fixed Roof Tanks (Varying Sizes); Gasoline RVP 13:
Working Loss (Tank Diameter Independent)
40301101
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Gasoline RVP 13:
Standing Loss (67000 Bbl. Tank Size)
40301102
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Gasoline RVP 10:
Standing Loss (67000 Bbl. Tank Size)
40301103
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Gasoline RVP 7:
Standing Loss (67000 Bbl. Tank Size)
40301105
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Gasoline RVP 10:
Standing Loss (250000 Bbl. Tank Size)
40301151
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Floating Roof Tanks (Varying Sizes); Gasoline: Standing
Loss - Internal
40301202
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Variable Vapor Space; Gasoline RVP 10: Filling Loss
40301203
RBT
Petroleum and Solvent Evaporation; Petroleum Product Storage at Refineries; Variable Vapor Space; Gasoline RVP 7: Filling Loss
40400101
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Breathing Loss (67000
Bbl Capacity) - Fixed Roof Tank
40400102
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Breathing Loss (67000
Bbl Capacity) - Fixed Roof Tank
40400103
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Breathing Loss (67000
Bbl. Capacity) - Fixed Roof Tank
40400104
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Breathing Loss
(250000 Bbl Capacity)-Fixed Roof Tank
171

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see
Type
Description
40400105
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Breathing Loss
(250000 Bbl Capacity)-Fixed Roof Tank
40400106
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Breathing Loss (250000
Bbl Capacity) - Fixed Roof Tank
40400107
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Working Loss (Diam.
Independent) - Fixed Roof Tank
40400108
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Working Loss
(Diameter Independent) - Fixed Roof Tank
40400109
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Working Loss
(Diameter Independent) - Fixed Roof Tank
40400110
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss (67000
Bbl Capacity)-Floating Roof Tank
40400111
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss (67000
Bbl Capacity)-Floating Roof Tank
40400112
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss (67000
Bbl Capacity)- Floating Roof Tank
40400113
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss (250000
Bbl Cap.) - Floating Roof Tank
40400114
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss (250000
Bbl Cap.) - Floating Roof Tank
40400115
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss (250000
Bbl Cap.) - Floating Roof Tank
40400116
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13/10/7: Withdrawal Loss
(67000 Bbl Cap.) - Float RfTnk
40400117
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13/10/7: Withdrawal Loss
(250000 Bbl Cap.) - Float RfTnk
40400118
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Filling Loss (10500
Bbl Cap.) - Variable Vapor Space
40400119
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Filling Loss (10500
Bbl Cap.) - Variable Vapor Space
40400120
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Filling Loss (10500 Bbl
Cap.) - Variable Vapor Space
40400130
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Specify Liquid: Standing Loss - External
Floating Roof w/ Primary Seal
40400131
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss - Ext.
Floating Roof w/ Primary Seal
40400132
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss - Ext.
Floating Roof w/ Primary Seal
40400133
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss - External
Floating Roof w/ Primary Seal
172

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see
Type
Description
40400140
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Specify Liquid: Standing Loss - Ext. Float
Roof Tank w/ Secondy Seal
40400141
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss - Ext.
Floating Roof w/ Secondary Seal
40400142
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss - Ext.
Floating Roof w/ Secondary Seal
40400143
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss - Ext.
Floating Roof w/ Secondary Seal
40400148
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13/10/7: Withdrawal Loss
- Ext. Float Roof (Pri/Sec Seal)
40400149
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Specify Liquid: External Floating Roof
(Primary/Secondary Seal)
40400150
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Miscellaneous Losses/Leaks: Loading
Racks
40400151
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Valves, Flanges, and Pumps
40400152
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Vapor Collection Losses
40400153
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Vapor Control Unit Losses
40400160
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Specify Liquid: Standing Loss - Internal
Floating Roof w/ Primary Seal
40400161
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss - Int.
Floating Roof w/ Primary Seal
40400162
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss - Int.
Floating Roof w/ Primary Seal
40400163
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss - Internal
Floating Roof w/ Primary Seal
40400170
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Specify Liquid: Standing Loss - Int.
Floating Roof w/ Secondary Seal
40400171
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13: Standing Loss - Int.
Floating Roof w/ Secondary Seal
40400172
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 10: Standing Loss - Int.
Floating Roof w/ Secondary Seal
40400173
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 7: Standing Loss - Int.
Floating Roof w/ Secondary Seal
40400178
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Gasoline RVP 13/10/7: Withdrawal Loss
- Int. Float Roof (Pri/Sec Seal)
40400179
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; Specify Liquid: Internal Floating Roof
(Primary/Secondary Seal)
40400199
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Terminals; See Comment **
40400201
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13: Breathing Loss (67000 Bbl
Capacity) - Fixed Roof Tank
173

-------
see
Type
Description
40400202
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10: Breathing Loss (67000 Bbl
Capacity) - Fixed Roof Tank
40400203
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 7: Breathing Loss (67000 Bbl.
Capacity) - Fixed Roof Tank
40400204
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13: Working Loss (67000 Bbl.
Capacity) - Fixed Roof Tank
40400205
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10: Working Loss (67000 Bbl.
Capacity) - Fixed Roof Tank
40400206
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 7: Working Loss (67000 Bbl.
Capacity) - Fixed Roof Tank
40400207
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13: Standing Loss (67000 Bbl
Cap.) - Floating Roof Tank
40400208
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10: Standing Loss (67000 Bbl
Cap.) - Floating Roof Tank
40400210
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13/10/7: Withdrawal Loss
(67000 Bbl Cap.) - Float RfTnk
40400211
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13: Filling Loss (10500 Bbl
Cap.) - Variable Vapor Space
40400212
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10: Filling Loss (10500 Bbl
Cap.) - Variable Vapor Space
40400213
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 7: Filling Loss (10500 Bbl
Cap.) - Variable Vapor Space
40400230
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify Liquid: Standing Loss - External
Floating Roof w/ Primary Seal
40400231
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13: Standing Loss - Ext.
Floating Roof w/ Primary Seal
40400232
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10: Standing Loss - Ext.
Floating Roof w/ Primary Seal
40400233
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 7: Standing Loss - External
Floating Roof w/ Primary Seal
40400240
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify Liquid: Standing Loss - Ext. Floating
Roof w/ Secondary Seal
40400241
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13: Standing Loss - Ext.
Floating Roof w/ Secondary Seal
40400248
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10/13/7: Withdrawal Loss -
Ext. Float Roof (Pri/Sec Seal)
40400249
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify Liquid: External Floating Roof
(Primary/Secondary Seal)
40400250
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Loading Racks
40400251
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Valves, Flanges, and Pumps
174

-------
see
Type
Description
40400252
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Miscellaneous Losses/Leaks: Vapor
Collection Losses
40400253
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Miscellaneous Losses/Leaks: Vapor Control
Unit Losses
40400260
RBT
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify Liquid: Standing Loss - Internal
Floating Roof w/ Primary Seal
40400261
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13: Standing Loss - Int.
Floating Roof w/ Primary Seal
40400262
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10: Standing Loss - Int.
Floating Roof w/ Primary Seal
40400263
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 7: Standing Loss - Internal
Floating Roof w/ Primary Seal
40400270
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify Liquid: Standing Loss - Int. Floating
Roof w/ Secondary Seal
40400271
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 13: Standing Loss - Int.
Floating Roof w/ Secondary Seal
40400272
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10: Standing Loss - Int.
Floating Roof w/ Secondary Seal
40400273
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 7: Standing Loss - Int. Floating
Roof w/ Secondary Seal
40400278
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Gasoline RVP 10/13/7: Withdrawal Loss -
Int. Float Roof (Pri/Sec Seal)
40400279
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Bulk Plants; Specify Liquid: Internal Floating Roof
(Primary/Secondary Seal)
40400401
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; Gasoline RVP
13: Breathing Loss
40400402
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; Gasoline RVP
13: Working Loss
40400403
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; Gasoline RVP
10: Breathing Loss
40400404
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; Gasoline RVP
10: Working Loss
40400405
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; Gasoline RVP
7: Breathing Loss
40400406
BTP/BPS
Petroleum and Solvent Evaporation; Petroleum Liquids Storage (non-Refinery); Petroleum Products - Underground Tanks; Gasoline RVP
7: Working Loss
40600101
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Splash
Loading **
40600126
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Submerged
Loading **
175

-------
see
Type
Description
40600131
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Submerged
Loading (Normal Service)
40600136
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Splash
Loading (Normal Service)
40600141
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Submerged
Loading (Balanced Service)
40600144
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Splash
Loading (Balanced Service)
40600147
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Submerged
Loading (Clean Tanks)
40600162
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Loaded with
Fuel (Transit Losses)
40600163
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Gasoline: Return with
Vapor (Transit Losses)
40600199
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Tank Cars and Trucks; Not Classified **
40600231
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Tankers:
Cleaned and Vapor Free Tanks
40600232
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Tankers
40600233
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Barges:
Cleaned and Vapor Free Tanks
40600234
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Tankers:
Ballasted Tank
40600235
BTP/BPS
Petroleum and Solvent Evaporation;Transportation and Marketing of Petroleum Products;Marine Vessels;Gasoline: Ocean Barges Loading
- Ballasted Tank
40600236
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Tankers:
Uncleaned Tanks
40600237
RBT
Petroleum and Solvent Evaporation;Transportation and Marketing of Petroleum Products;Marine Vessels;Gasoline: Ocean Barges Loading
- Uncleaned Tanks
40600238
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Barges:
Uncleaned Tanks
40600239
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Tankers: Ballasted
Tank
40600240
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Loading Barges:
Average Tank Condition
40600241
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Gasoline: Tanker Ballasting
40600299
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Marine Vessels; Not Classified **
40600301
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; Splash
Filling
40600302
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; Submerged
Filling w/o Controls
176

-------
see
Type
Description
40600305
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; Unloading
**
40600306
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; Balanced
Submerged Filling
40600307
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage I;
Underground Tank Breathing and Emptying
40600399
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Gasoline Retail Operations - Stage I; Not
Classified **
40600401
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Filling Vehicle Gas Tanks - Stage II; Vapor
Loss w/o Controls
40600501
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline Petroleum Transport - General - All
Products; Pipeline Leaks
40600502
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline Petroleum Transport - General - All
Products; Pipeline Venting
40600503
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline Petroleum Transport - General - All
Products; Pump Station
40600504
RBT
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Pipeline Petroleum Transport - General - All
Products; Pump Station Leaks
40600602
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fleet Refueling - Stage
II; Liquid Spill Loss w/o Controls
40600701
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fleet Refueling - Stage I;
Splash Filling
40600702
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fleet Refueling - Stage I;
Submerged Filling w/o Controls
40600706
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fleet Refueling - Stage I;
Balanced Submerged Filling
40600707
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Consumer (Corporate) Fleet Refueling - Stage I;
Underground Tank Breathing and Emptying
40688801
BTP/BPS
Petroleum and Solvent Evaporation; Transportation and Marketing of Petroleum Products; Fugitive Emissions; Specify in Comments Field
2501050120
RBT
Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Terminals: All Evaporative Losses; Gasoline
2501055120
BTP/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Bulk Plants: All Evaporative Losses; Gasoline
2501060050
BTP/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage 1: Total
2501060051
BTP/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage 1: Submerged Filling
2501060052
BTP/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage 1: Splash Filling
2501060053
BTP/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage 1: Balanced Submerged Filling
2501060200
BTP/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Underground Tank: Total
2501060201
BTP/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Underground Tank: Breathing and Emptying
2501995000
BTP/BPS
Storage and Transport; Petroleum and Petroleum Product Storage; All Storage Types: Working Loss; Total: All Products
177

-------
see
Type
Description
2505000120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; All Transport Types; Gasoline
2505020120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline
2505020121
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Marine Vessel; Gasoline - Barge
2505030120
BTP/BPS
Storage and Transport; Petroleum and Petroleum Product Transport; Truck; Gasoline
2505040120
RBT
Storage and Transport; Petroleum and Petroleum Product Transport; Pipeline; Gasoline
2660000000
BTP/BPS
Waste Disposal, Treatment, and Recovery; Leaking Underground Storage Tanks; Leaking Underground Storage Tanks; Total: All Storage
Types
178

-------
Appendix B: Future Animal Population Projection Methodology, Updated 07/24/12
In the EPA's ammonia inventory for animal agricultural operations (National Emission Inventory -
Ammonia Emissions from Animal Agricultural Operations; Revised Draft Report; April 22, 2005),
population projections for the beef, dairy, swine, and poultry animal sectors were developed and used to
estimate future ammonia emissions from these animal sectors. To develop the 2005 population projections,
EPA used inventory data from the U.S. Department of Agriculture (USDA) and the Food and Agriculture
Policy and Research Institute (FAPRI).
Since completion of the 2005 ammonia emissions inventory, USDA and FAPRI have released updated
reports that contain animal population data and projections. These data were used to update the 2005 animal
inventory projections. The data sources and the methodology used to develop the population projections for
each animal type are discussed below. These future projections do not account for any changes in animal
populations or regional dislocations associated with EPA's revised effluent limitations guidelines and
standards for concentrated animal feeding operations promulgated in December 2002 (68 FR 7176, February
12, 2003). Due to insufficient data, animal population projections and future emission estimates were not
developed for sheep, goats, and horses.
Dairy Cattle. The 2010 FAPRI U.S. and World Agricultural Outlook (FAPRI 2010) report provides
estimated national milk cow inventory data and projections from 2009 through 2019 and shows an overall
decline in U.S. dairy cow populations. The FAPRI projections depict an essentially linear relationship
between 2001 milk cow populations and subsequent years. The EPA estimated future dairy cattle
populations using a linear regression analysis of the national population data available from the FAPRI
report, covering 1982 through 2019. Figure B-l illustrates the linear projection of the U.S. dairy cow
population and trend line.
Beef Cattle. The USDA Agricultural Projections to 2021 (USDAa) provides estimated national cattle
inventory data and projections from 2010 through 2021. Beef production has a clear cycle generated by
producers' expectations about future prices, grain market cycles, and other economic conditions. The pace
of the cycle is limited by the reproductive capacity of the animal. Cattle inventories can expand only as fast
as cows can reproduce. This has historically resulted in a 7- to 12-year cycle, from peak to peak (Kohls,
1998). Peaks and troughs of the cycle are 5 to 6 percent higher or lower than the general trend in cattle
populations so the stage of the cycle can make a significant difference in population at any given future date.
The EPA decomposed the beef cow inventory time series into a trend line, a cyclical component, and a
random error component (Bowerman, 1987). The trend line was estimated by linear regression of the
inventory data from 1990 to 2015 on a time variable. The cyclical component was then estimated as the
percentage deviation from the trend line in the historical data. A graph of that information appeared to show
a cyclic trend (trough to peak). The robust U.S. economy of the 1990s may explain the longer than average
cycle. With so little data, EPA assumed the down side of the cycle was symmetrical with the up side, so the
data set would contain three values for each stage of the cycle. The average of the absolute value of the three
observations represents the cyclical component. The EPA forecasted the trend line out to 2030 and adjusted
it by the average percentage deviation from the trend for that stage of the cycle, as illustrated in Figure B-2.
The projection data for the beef cattle inventory show some difference in growth cycle of beef cows versus
other beef cattle (e.g., steers, bulls). The EPA conducted a separate analysis of these animal populations.
Other beef cattle populations appear to follow similar cycles and were forecasted using the same technique
as beef cows (see Figure B-3).
179

-------
Figure B-l. Dairy Cow Inventory Projections
U.S. Dairy Population and Trend Line
12,000
11,000
y= -55.524X +120554
R2 = 0.7947
¦o 10,000
£ 9,000
6 8,000
7,000
6,000

Year
Figure B-2. Beef Cow Inventory Projections
U.S. Beef Cow Population
Raw data line: y = -28.545x + 90437
R2 = 0.0436
36,000
Adj. data line: y = -26.983x + 88178
R2 = 0.3417
35,000
34,000
000
-------
Figure B-3. Non-cow Beef Inventory Projections
U.S. Beef Non-cow Population
u
re
0)
.c
u
c
re
(A
3
O
0
-4-J
c
0)
>
c
1
o
o
c
o
60,000
59,000
58,000
57,000
56,000
55,000
54,000
53,000
52,000
51,000
50,000
Raw data line: y =-120.28x + 295808
R2 = 0.3119
Adj. data line: y =-145.51x + 347612
R2 = 0.9452
Year
Swine. Annual swine populations are categorized by breeding and market swine. The 2010 FAPRI U.S. and
World Agricultural Outlook (FAPRI 2010) report presents annual inventory data and projections from 2009
through 2019 for breeding swine and market swine inventories (rather than a combined total). The FAPRI
data show an overall increase in swine production over time. Due to increasing productivity (i.e., increased
number of pigs per litter), the population of breeding swine is expected to decline over the long term.
The EPA estimated future swine populations using a cycle and trend decomposition analysis. Breeding and
market swine population projections and inventory data from the FAPRI report capture the variability of the
swine production cycle. Changes in the pork industry in the 1990's have made recent data atypical and
inconsistent. For example, EPA replaced the 1996 market hog cyclical deviation with the average of all of
the other data because they were so far out of line with the hog cycle.
The EPA estimated the trend and deviations from the trend as in the beef cattle analysis. However, it was
not possible to apply the identical technique from the beef cattle industry to the hog industry because a well-
defined periodic cycle was not evident in the annual data. The EPA evaluated a 3-year moving average of
the deviation to further reduce the random component. As the smoothed cycle continued to appear irregular,
EPA assumed that the 2010's will repeat the pattern of the 1990's. Breeding hog populations were estimated
using a similar approach. See Figures B-4 and B-5 for an illustration of the swine projections for the market
hog and breeding hog inventories, respectively.
Poultry. Annual poultry populations in the EPA's ammonia emissions inventory for animal agriculture are
presented for broilers, turkeys, and layers. To project poultry populations, EPA used population and
181
-------
projection data from the annual summary of the USDA/NASS Poultry - Production and Value reports
(USDAb) for broilers and turkeys, and the Chickens and Eggs reports (USD Ac). With these data, EPA used
a linear regression analysis to predict the number of birds produced in the U.S. for years beyond 2011.
Figures B-6 and B-7 present the population projections for broilers and turkeys, respectively. Figure B-8
shows the population projections for egg layers.
Figure B-4. Market Hog Inventory Projections
U.S. Market Hog Population
Raw data line: y = 0.5161x - 978.44
R2 = 0.9284
75
70
o c
Adj. data line: y = 0.5276x - 1001.5
R2 = 0.982
45
40
O? ^ -Sp
* k<5° tSSr rtST n* rb* rf>y
Year
Figure B-5. Breeding Hog Inventory Projections
U.S. Breeding Hog Population
Raw data line: y =-0.0493x + 105.01
R2 = 0.8486
8.00
Adj. data line: y =-0.0458x + 98.155
R2 = 0.9277
7.50
^ 7.00
.50
T3 -5.00
.50
4.00 ^
rST r»? rSSr rSy rS> rS rvN3 »V _fL? -fl/
Year
182
-------
Figure B-6. Broiler Inventory Projection
U.S. Broiler Population
y = 135606x - 3E+08
R2 = 0.8276
14
12
10
8
6
4
2
0 4
Year
Figure B-7. Turkey Inventory Projection
U.S. Turkey Population
350,000
-2204.3x + 5E+06
R2 = 0.6536
300,000
:250,000
1.200,000
a>
•Q
E
=150,000
100,000
Year
183
-------
Figure B-8. Egg Layer Projection
U.S. Egg Layer Population
370,000
350,000
330,000
o
o
o
y = 3,757.24x - 7,197,845.17
R2 = 0.86
310,000
290,000
270,000
250,000
Year
References
Bowerman, Bruce L. and Richard T. O'Connell (1987) Time Series Forecasting: Unified Concepts and
Computer Implementation. Second Edition. Boston, MA. Duxbury Press.
FAPRI (2010). FAPRI 2010 U.S. and World Agricultural Outlook. January 2010. Food and Agricultural
Policy Research Institute. Iowa State University and University of Missouri-Columbia.
http://www.fapri.iastate.edu/outlook/2010/text/Qutlook 2010.pdf
Kohls, Richard L. and Joseph N. Uhl. (1998) Marketing Edition. Upper Saddle River, NJ Prentice Hall.
USDA (2012a). USDA Agricultural Projections to 2021. Interagency Agricultural Projections Committee.
February 2021.
http://www.usda.gov/oce/commoditv/archive projections/USDAAgriculturalProiections2021.pdf
USDA (2012b). National Agricultural Statistics Service (NASS). Poultry - Production and Value Reports.
http ://usda.mannlib. cornel!. edu/MannUsda/viewDocumentlnfo. do? documentID= 1130
USDA (2012c). NASS. Chickens and Eggs Annual Summary.
http://usda.mannlib.cornell.edU/MannUsda/viewDocumentInfo.do:i sessionid=0AFEA5A73F0092B7498AC
B64B 7C ABBED? documentID= 1509
184
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Appendix C: Upstream Methodology, June 2016
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
NATIONAL VEHICLE AND FUEL EMISSIONS LABORATORY
2585 PLYMOUTH ROAD
ANN ARBOR, MICHIGAN 48105-2498
OFFICE OF
AIR AND RAO WW ION
The purpose of this memorandum is to document the methods used to account for upstream impacts of
mobile sources in 2030 and 2040 reference case and 2040 control case inventories. The 2040 reference case
and control case inventories were used in air quality modeling for the final Phase 2 greenhouse gas emission
and fuel efficiency standards for medium and heavy-duty engines and vehicles.
Background
Upstream impacts occur upstream of the engine, e.g. fuel production and transport, and apply to sources that
can be point, nonpoint and/or mobile. Upstream emissions are impacted by economic and other factors,
including environmental regulations. Two key examples are as follows.
Renewable Fuel Requirements: Increased renewable fuel use can cause shifts in the fuel production and
transport/distribution methods that can have substantial impacts on emissions. These "upstream" emissions
are associated with all stages of biofuel production and distribution, including biomass production
(agriculture, forestry), fertilizer and pesticide production and transport, biomass transport, biomass refining
(corn or cellulosic ethanol production facilities), biofuel transport to blending/distribution terminals, and
distribution of finished fuels to retail outlets.
Greenhouse Gas Emission Standards: Greenhouse gas emission standards impact production, distribution,
and consumption of both renewable and petroleum-based fuels, as well as emissions associated with these
processes.
Reference Case Inventories
A significant number of modifications were made to the 201 lv.6.2 platform inventories for 2030 and 2040 to
account for upstream impacts of mobile sources. This section of the memo describes how expected impacts
of renewable fuel requirements under EISA, as estimated by Annual Energy Outlook (AEO),1 as well as
impacts of recent light-duty and heavy-duty vehicle greenhouse gas emission standards, were incorporated
into 2030 and 2040 reference case inventories.
Agricultural Sector
Changes in agricultural economics associated with increased biomass production can result in shifts in
related agricultural production. However, these impacts were not accounted for in the reference case
inventories for this rule, due to a lack of recent agricultural sector modeling, and small overall impact on
urban air quality.

%
m.
"»£
./
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Biofuel Production
Corn Ethanol Plants
The corn ethanol plant inventory is unchanged from the 2011 version 6.1 platform described in the technical
support document for the ozone NAAQS emissions modeling platform.11 As the document explained, the
inventory included emissions from plants in the 2011 NEIvl (National Emissions Inventory), as well as a
number of additional facilities not in the inventory but added to the ptnonipm sector. Locations and FIPS
codes for these plants were verified using web searches and Google Earth. EPA believes that some of these
sources were not included in the NEI because they do not meet the 1000 ton/year potential-to-emit threshold
for NEI point sources.
Emission rates in Table 1 were used to develop inventories (except for ethanol), for these facilities.
Emission rates in this table were obtained from an upstream impacts spreadsheet originally developed for the
RFS2 rule.111 For air toxics, except ethanol, toxic to VOC ratios were developed using emission inventory
data from the 2005 National Emissions Inventory (NEI). These ratios were then applied to VOC emission
rates in Table 1, to obtain toxic emission rates. Ratios for different plant types are given in Table 2. Ethanol
emission rates were obtained from SMOKE speciation profiles.
Table 11 Corn Ethanol Plant Criteria Pollutant Emission Factors (grams per gallon produced)
Corn Ethanol Plant
Year
VOC
CO
NOx
PMio
PM25
sox
nh3
Type

Dry Mill Natural
2007,2018
2.29
0.58
0.99
0.94
0.23
0.01
0.00
Gas (NG)
2030
2.29
0.58
0.94
0.94
0.23
0.01
0.00
Dry Mill NG (wet
2007,2018
2.27
0.37
0.63
0.91
0.20
0.00
0.00
distillers grains with
2030
2.27
0.37
0.60
0.91
0.20
0.00
0.00
soluble (DGS))

Dry Mill Biogas
2007,2018
2.29
0.62
1.05
0.94
0.23
0.01
0.00
2030
2.29
0.62
1.00
0.94
0.23
0.01
0.00
Dry Mill Biogas
2007,2018
2.27
0.39
0.67
0.91
0.20
0.00
0.00
(wet DGS)
2030
2.27
0.39
0.63
0.91
0.20
0.00
0.00
Dry Mill Coal
2007,2018
2.31
2.65
4.17
3.81
1.71
4.52
0.00
2030
2.31
2.65
3.68
3.64
1.54
3.48
0.00
Dry Mill Coal (wet
2007,2018
2.31
2.65
2.65
2.74
1.14
2.87
0.00
DGS)
2030
2.28
1.68
2.34
2.62
1.03
2.21
0.00
Dry Mill Biomass
2007,2018
2.42
2.55
3.65
1.28
0.36
0.14
0.00
2030
2.42
2.55
3.65
1.28
0.36
0.14
0.00
Dry Mill Biomass
2007,2018
2.35
1.62
2.32
1.12
0.28
0.09
0.00
(wet DGS)
2030
2.35
1.62
2.32
1.12
0.28
0.09
0.00
Wet Mill NG
2007,2018
2.35
1.62
1.77
1.12
0.28
0.09
0.00
2030
2.33
1.04
1.68
1.00
0.29
0.01
0.00
Wet Mill Coal
2007,2018
2.33
1.04
5.51
4.76
2.21
5.97
0.00
2030
2.33
3.50
4.86
4.53
1.98
4.60
0.00
Table 12 Toxic to VOC Ratios for Corn Ethanol Plants

Acetaldehyde
Acrolein
Benzene
1,3-Butadiene
Formaldehyde
Wet Mill NG
0.02580
0.00131
0.00060
2.82371E-08
0.00127
Wet Mill Coal
0.08242
0.00015
0.00048
2.82371E-08
0.00108
Dry Mill NG
0.01089
0.00131
0.00060
2.82371E-08
0.00127
Dry Mill Coal
0.02328
0.00102
0.00017
2.82371E-08
0.00119
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Cellulosic Biofuel Refineries
The cellulosic fuel production inventory for 2030 and 2040 is unchanged from what is included for 2018 in
the 2011 version 6.1 platform, described in section 4.2.1.4 of the technical support document for the ozone
NAAQS emissions modeling platform.1V
Depending on available feedstock, cellulosic plants are likely to produce fuel through either a biochemical
process or a thermochemical process. OTAQ developed county-level inventories for biochemical and
thermochemical cellulosic fuel production for 2018 to reflect renewable fuel requirements under EISA, as
estimated by AEO2013er renewable fuel volumes. Emissions factors for each cellulosic biofuel refinery reflect
the fuel production technology used rather than the fuel produced. Emission rates in Table 3 and Table 4 were
used to develop cellulosic plant inventories. Criteria pollutant emission rates are in tons per RIN gallon. Emission
factors from the cellulosic diesel work in the Tier 3 NPRM were used as the emission factors for the
thermochemical plants. Cellulosic ethanol VOC and related HAP emission factors from the Tier 3 FRM were
used as the biochemical VOC and related HAP emission factors. Because the future year cellulosic inventory
contains ethanol, a VOC E-profile that integrated ethanol was used.v
Plants were treated as area sources spread across the entire area of whatever county they were considered to be
located in. Cellulosic biofuel refinery siting was based on utilizing the lowest cost feedstock, accounting for the
cost of the feedstock itself as well as feedstock storage and the transportation of the feedstock to the cellulosic
biofuel refinery. The total number of cellulosic biofuel refineries was projected using volumes from AEO2013
(early release). The methodology used to determine most likely plant locations is described in Section 1.8.1.3 of
the RFS2 RIA (EPA, 2010a). Since the 2030 and 2040 inventories were developed to compare with control
scenarios which do not impact cellulosic volumes, an existing cellulosic inventory was used to maximize
efficiency. Biofuel volume projection estimates for 2030 and 2040 were similar to 2018 estimates, so the year
2018 cellulosic inventory was used for years 2030 and 2040.
Table 13 Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/RIN gallon)
Cellulosic Plant
Type
VOC
CO
NOx
PM10
PM2.5
SOx
NH3
Thermochemical
5.92E-07
8.7E-06
1.31E-05
1.56E-06
7.81E-07
1.17E-06
1.44E-10
Biochemical
1.82E-06
1.29E-05
1.85E-05
3.08E-06
1.23E-06
6.89E-07
0
Table 14 Toxic Emission Factors for Cellulosic Plants (Tons/RIN gallon)
Plant Type
Acetaldehyde
Acrolein
Benzene
1,3-
Butadie
ne
Formaldehyde
Ethanol
Thermochemical
2.95E-08
1.27E-09
9.61E-10
0
5.07E-09
2.09E-07
Biochemical
3.98E-07
1.11E-08
1.39E-08
0
2.28E-08
6.41E-07
Biodiesel Plants
An inventory for biodiesel plants was developed for 2030 and 2040. Plant location and production volume data
come from a database of 2014 biodiesel RINs.vl The biodiesel plants included plants in the 2011 NEIvl, as well
as a number of additional facilities added to the ptnonipm sector using their location. The plants that were not
included in the 2011 NEI were plants with unreported emissions.
Total volume of biodiesel came from the AEO 2014 projections, 1.3 BGfor 2030 and 1.4 BGfor 2040.vii The
2014 production volume from all plants was 1.298 BG so these volumes were held constant for 2030. To reach
the 2040 total volume of biodiesel (1.4 BG), plants that had 2030 production volumes that were within 90% of
their capacity were assumed to hit 100% production for 2040 and the remaining volume was split among plants
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whose 2030 production volumes were less than 90% of their capacity. Once facility-level production capacities
were scaled, emission factors were applied based on soybean oil feedstock. These emission factors in Table 5 are
in tons per million gallons (Mgal) and were obtained from EPA's spreadsheet model for upstream EISA impacts
developed for the RFS2 rule (EPA, 2010a). Twenty-one of the biodiesel plants were included in the 2011 NEI, so
adjustment factors were calculated to project their 2030 and 2040 emissions. The remaining 99 biodiesel plants
were modeled as point sources with Google Earth and web searching validating facility coordinates and
correcting state-county FIPS.
Table 15 Emission Factors for Biodiesel Plants (Tons/Mgal)
Pollutant
Emission Factor
voc
4.3981E-02
CO
5.0069E-01
NOx
8.0790E-01
PM10
6.8240E-02
PM2.5
6.8240E-02
SOx
5.9445E-03
NH3
0
Acetaldehyde
2.4783E-07
Acrolein
2.1290E-07
Benzene
3.2458E-08
1,3-Butadiene
0
Formaldehyde
1.5354E-06
Ethanol
0
Crude Production and Losses During Transport to Refineries
We assumed that (a) 68% of the change in gasoline and diesel supply was projected to come from domestic
refineries, and (b) 10% of the change in crude being used by domestic refineries would be domestic crude.
Using the assumption that 1.0 gallon less of gasoline and diesel fuel equates to approximately 1.0 gallon less
crude throughput, the reduction in crude extraction and transport in 2030 relative to 2011 would equal about
0.6% of the change in gasoline/diesel volume, and 0.7% of the change in gasoline/diesel volume in 2040.
Since the reduction in fuel consumption is estimated at 21.1 billion gallons in 2030 and 24.3 billion gallons
in 2040, the reduction in crude production is about 1.43 billion gallons in 2030 and 1.65 billion gallons in
2040. To generate the emission inventory adjustment factors for air quality modeling these reductions were
applied to the projected crude supply of 230 billion gallons to US refineries in 2011, per AEO 2014.VU1 Thus,
the adjustment factors are 0.62% in 2030 and 0.72% in 2040.
Petroleum Refineries
Since projection year inventories in the modeling platform (like 2030 and 2040) do not account for impacts
of EISA and greenhouse gas rules on gasoline and diesel fuel production, we had to adjust the 2030 and 2040
emissions for SCCs associated with gasoline and distillate production at refineries. In order to calculate
adjustment scalars we used an updated version of the impacts spreadsheet developed for the RFS2 rule and
ran it for 2011, 2030 and 2040, accounting for impacts of EISA and greenhouse gas rules on fuel volumes.1X
The resulting impacts were used to calculate scaling factors to apply to criteria pollutant emissions for SCCs
associated with refinery gasoline and distillate production. Scalars applied to petroleum refineries are
presented in Table 6.
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Table 16 Scalars Applied to Petroleum Refineries
Pollutant
2030
2040
voc
0.9485
0.9405
1,3-Butadiene
0.9680
0.9631
acetaldehyde
0.9669
0.9618
acrolein
0.9970
0.9965
benzene
0.9514
0.9438
formaldehyde
0.9596
0.9533
CO
0.9298
0.9188
NOx
0.9493
0.9414
PM10
0.9544
0.9474
PM2.5
0.9539
0.9467
SOx
0.9307
0.9200
NH3
0.9516
0.9440
Production of Energy for Petroleum Refinery Use
The EISA standards and GHG rules being included in the 2030 and 2040 reference case inventories not only
impact on-site petroleum refinery emissions, but also emissions upstream of refineries associated with
producing the energy they use. Petroleum refineries rely on upstream energy from residual oil, natural gas,
coal and electricity. Although energy use at biofuel refineries would also be impacted as well, these impacts
were not included.
GREET 1,8.c was used to determine the percent of refinery emissions attributable to each type of input
energy, which were applied to refinery upstream emission estimates from the impacts spreadsheet.
Emissions for refinery upstream emissions reflect the incremental mix of EGU energy feedstocks assumed in
the IPM analysis conducted for the final greenhouse gas emissions and fuel economy standards for 2017 and
later light-duty vehicles.x Table 7 summarizes the percent of emissions attributable to each type of input
energy.
Table 17 Refinery Energy Use

Percent of Emission Rates from Energy Feedstocks
Pollutant
Residual Oil
Natural Gas
Coal
Electric
VOC
5
45
26
24
CO
5
38
5
52
NOx
7
40
11
42
PMio
0
1
85
14
PM2.5
1
2
81
16
SOx
4
28
8
60
Table 8 presents the emission impacts for upstream refinery emissions in 2030. Along with nationwide
emissions for sources associated with producing these energy feedstocks in the modeling platform, these
impacts were used to develop nationwide scalars which were applied to county and facility level emission
estimates. The scalars used are given in Table 8 as well. Table 9 presents impacts and scalars for 2040. It
should be noted that the emission totals in the platform that scalars were applied to reflect only point and
nonpoint sources directly associated with producing these energy feedstocks, and do not include emissions
upstream of the feedstocks or from nonroad equipment used in mining or natural gas extraction that may be
accounted for in the impacts spreadsheet.
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Table 18 Upstream Refinery Emission Impacts in Tons and Inventory Scalars for 2030

Residual Oil
Natural Gas
Coal Production
Electricity

Production
Production

Production
Pollutant
Impact
Scalar
Impact
Scalar
Impact
Scalar
Impact
Scalar
voc
42
1.0008
398
1.0153
226
1.1209
211
1.0056
CO
25
1.0005
179
1.0100
26
1.0018
247
1.0003
NOx
-360
0.9952
-2063
0.8880
-565
0.9748
-2124
0.9989
PMio
-63
0.9974
-89
0.8448
-11,191
0.6437
-1804
0.9936
PM2.5
-24
0.9989
-54
0.9008
-2209
0.7767
-431
0.9979
SOx
-134
0.9984
-876
0.9829
-243
0.9133
-1860
0.9996
Table 19 Upstream Refinery Emission Impacts in Tons and Inventory Scalars for 2040

Residual Oil
Natural Gas
Coal Production
Electricity

Production
Production

Production
Pollutant
Impact
Scalar
Impact
Scalar
Impact
Scalar
Impact
Scalar
VOC
52
1.0010
485
1.0189
276
1.1475
258
1.0067
CO
31
1.0006
230
1.0128
33
1.0023
317
1.0004
NOx
-417
0.9945
-2391
0.8702
-655
0.9709
-2462
0.9988
PM10
-74
0.9987
-104
0.8185
-13,091
0.5832
-2111
0.9925
PM2.5
-27
0.9987
-63
0.8843
-2574
0.7398
-502
0.9976
SOx
-153
0.9982
-999
0.9805
-277
0.9011
-2123
0.9995
Fuel Distribution Chain
There are five types of facilities that make up this distribution chain for gasoline. Bulk gasoline terminals
are large storage facilities that receive gasoline directly from the refineries via pipelines, barges, or tankers
(or are collocated at refineries). Gasoline from the bulk terminal storage tanks is loaded into cargo tanks
(tank trucks or railcars) for distribution to smaller intermediate storage facilities (bulk plants), or directly to
gasoline dispensing facilities (retail public service stations and private service stations). When ethanol is
blended into gasoline it usually occurs in the pipes which supply cargo tanks at bulk terminals.
Bulk plants are intermediate storage and distribution facilities that normally receive gasoline or
gasoline/ethanol blends from bulk terminals via tank trucks or railcars. Gasoline and gasoline/ethanol blends
from bulk plants are subsequently loaded into tank trucks for transport to local dispensing facilities.
Gasoline and gasoline/ethanol blend dispensing facilities include both retail public outlets and private
dispensing operations such as rental car agencies, fleet vehicle refueling centers, and various government
motor pool facilities. Dispensing facilities receive gasoline and gasoline/ethanol blends via tank trucks from
bulk terminals or bulk plants. Inventory estimates for this source category only include the delivery of
gasoline at dispensing facilities and does not include the vehicle or equipment refueling activities.
2030 and 2040 inventories which included EISA and previous greenhouse gas rule impacts were developed
by adjusting the 2011 platform inventory. These adjustments were made using the previously described
updated version of EPA's spreadsheet model for upstream emission impacts. Below we describe how we
developed emission factors and fuel volumes to make these adjustments with the impacts spreadsheet.
Fuel Transport - Vapor Loss Emissions
Changes in volumes of fuels produced, due to EISA regulations or GHG rules, have a number of impacts on
upstream emissions including emissions associated with transport. These impacts include vapor losses of
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ethanol associated with storage and transport of ethanol, and vapor losses associated with storage and
transport of gasoline and gasoline ethanol blends.
Vapor Losses from Storage and Transport of Ethanol
Vapor emissions of ethanol occur during storage, as well as loading, unloading and transport. For this rule's
air quality inventory, however, these impacts were not accounted for. The expected impact on reference case
air quality results would likely be minor.
Vapor Losses from Transfer and Storage of Gasoline and Gasoline/Ethanol Blends
VOC evaporative emissions are also produced by transfer and storage activities associated with distribution
of gasoline and gasoline/ethanol blends. These are referred to as Stage I emissions. Stage I distribution
begins at the point the fuel leaves the production facility and ends when it is loaded into the storage tanks at
dispensing facilities.
Vapor loss VOC emission factors (EFs) for gasoline were based on inventory estimates from the 2005 NEI,
divided by total volume of gasoline.M Emissions were partitioned into a refinery to bulk terminal component
(RBT), a bulk plant storage (BPS), and a bulk terminal to gasoline dispensing pump (BTP) component.
Emissions for the BTP component are greater than the RBT and BPS components. Total nationwide
emissions for these components were divided by the energy content of the total volume of gasoline
distributed in 2005, to obtain emission factors in grams per million metric British Thermal Units
(g/mmBTU). Total volume of gasoline was based on gasoline sales as reported by the Energy Information
Administration.™ In addition to gasoline VOC emission factors for the RBT/BPS components, emission
factors were developed for the BTP component, for 10 percent ethanol, 15 percent ethanol, and 85% ethanol.
Emission factors for ethanol blends were calculated by applying adjustment factors to gasoline EFs. The
adjustment factors were based on an algorithm from the 1994 EPA On-Board Refueling Vapor Recovery
Rule™':
EF (g/gal) = exp[-1.2798 - 0.0049(AT) + 0.0203(Td) + 0.1315(RVP)]
where delta T is the difference in temperature between the fuel in the tank and the fuel being dispensed, and
Td is the temperature of the gasoline being dispensed. We assumed delta T is zero, and the temperature of
the fuel being dispensed averages 60 °F over the year.
Expected volumes were based on projections from Annual Energy Outlook 2014.XIV Nationwide volumes for
each fuel type and summer RVPs (from previous modeling for the Tier 3 rule) for 2011, 2030 and 2040 are
provided in Table 10.
Table 20 RVPs assumed for 2011, 2030 and 2040 with projected ethanol and gasoline volumes. Used to
Estimate Transfer and Storage VOC Emissions.

2011
2030
2040
Total Fuel Volume (Bgal)
135.02
106.16
100.96
Gasoline Volume (Bgal)
122.11
91.8
86.6
Ethanol Volume (Bgal)
12.92
14.36
14.36
E10 Only Volume (Bgal)
129
40
32.6
E15 Only Volume (Bgal)
0
56.67
60.73
E85(74) Only Volume (Bgal)
0.02
3.62
2.59
Weighted E0 RVP
8.4
8.5
8.5
Weighted E10 RVP
9.1
8.7
8.7
Weighted E15 RVP
n.a.
8.2
8.2
Weighted E85 RVP
7
7
7
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A benzene g/mmgal emission factor for 2018 and 2030 was based on benzene inventory projections used in
the 2011 Cross-State Air Pollution Rule and average 2015-2030 gasoline volumes obtained from the Annual
Energy Outlook 2011 Early Release Overview. This emission factor was used to estimate g/mmBTU
emission factors based on the energy content of E0, E10, and E15 gasoline. Aside from energy content, we
did not account for the effect of other fuel parameters on emission rates for E0, E10, and El 5 blends. Thus,
the E10 emission rate is slightly higher than the E0 rate due to the lower energy content of E10, and the E15
emission rate is higher still. The E85 emission rate was estimated for the RFS2 rule. Emission factors are
summarized in Table 11.
Table 21 Storage and Transport Vapor Loss Emission Factors (g/mmBtu)
Process
Fuel
Benzene
BTP
E0
0.250
E10
0.259
E15
0.264
E85
0.023
RBT/BPS
E0
0.059
Emission factors for VOC and benzene were used in conjunction with an updated version of EPA's
spreadsheet model for upstream emission impacts, developed for the RFS2 rule, to estimate national level
inventory changes of the changes in gasoline volume in 2030 and 2040 with 2011 ethanol volumes versus
projected volumes with EISA and previous greenhouse gas rules.^ VOC inventory changes were used to
develop nationwide adjustment factors that were applied to modeling platform inventory SCCs associated
with storage and transport processes (Table 12). This created the 2030 and 2040 reference case inventories.
Benzene emission estimates were obtained either by application of the adjustments in Table 12 or through
speciation of VOC in SMOKE, following criteria described in EPA's Technical Support Document for the
2005 modeling platform.
Ethanol emissions were estimated in SMOKE by applying the ethanol to VOC ratios from headspace profiles
to VOC emissions. These ratios are 0.065 for E10 and 0.272 for E15. The profiles were obtained from an
ORD analysis of fuel samples from the EPAct exhaust test programxyu and have been submitted for
incorporation into EPA's SPECIATE database. The E85 profile was obtained from data collected as part of
the CRC E-80 test program5™111 and has also been submitted for incorporation into EPA's SPECIATE
database.
It should be noted that these adjustment factors are based on summer RVP, but applied to emissions for the
whole calendar year. However, higher RVPs in winter corresponding to lower temperatures result in roughly
the same vapor pressure of the fuel and roughly the same propensity to evaporate. Significant evaporative
emissions are not expected from storage and transport of biodiesel, renewable or cellulosic diesel fuel due to
their low volatility.
Table 22 Adjustment Factors Applied to Storage and Transport Emissions
Year
Process
Pollutant
Adjustment
Factor
2030
BTP
VOC
0.7132

benzene
0.7612

RBT/BPS
VOC
0.7728

benzene
0.7518
2040
BTP
VOC
0.6770

benzene
0.7299

RBT/BPS
VOC
0.7345

benzene
0.5930
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Fuel Transport - Combustion Emissions
Changes in volumes of fuels produced, due to EISA regulations or GHG rules, have a number of impacts on
upstream emissions including emissions associated with transport. These impacts include combustion
emissions associated with transport of feedstocks from the refinery to fuel production facilities and bulk
terminals.
Emissions are produced by the vehicles and engines used to transport feedstocks such as crude oil, corn, and
cellulosic biomass to fuel production facilities, as well as transport/distribution of the finished fuels from the
production plants to distribution terminals and retail outlets. For example, corn would be transported from
farms and grain facilities to ethanol plants by truck and possibly rail. The finished ethanol would be
transported from there to bulk distribution terminals by truck, rail, or barge, and distribution from terminal to
retail outlet is by truck. We did not account for emissions associated with feedstocks, but did account for
non-truck transport of finished fuels.
Combustion Emissions from Water and Rail Transport of Fuels
Fuel can be transported from the refinery to bulk terminals via water using barges (category 1 and category 2
marine engines), and/or locomotives.
The 2030 and 2040 reference case category 1 marine engine, category 2 marine engine (C1,C2) and rail
inventories were calculated by scaling the 2025 inventory using overall impacts on combustion emissions
associated with transport of fuel volumes between 2025 and 2030 or 2040. The overall impacts on
combustion emissions associated with transport were calculated using the RFS2 impacts spreadsheet, then
nationwide emission fractions for combustion emission fractions associated with rail and C1/C2 vessels from
GREET 1.8.c were used to obtain the portion attributable to rail and C1/C2 vessels.^™ The scalars are
provided in Table 13 and Table 14.
Table 23 Scalars Applied to 2025 C1/C2 Combustion Emissions
Pollutant
2030
2040
CO
0.998
0.997
NOx
0.996
0.994
PM10
0.994
0.990
PM2.5
0.993
0.990
S02
0.995
0.992
VOC
0.991
0.985
Table 24 Scalars Applied to 2025 Rail Combustion Emissions
Pollutant
2030
2040
CO
0.999
0.999
NOx
0.999
0.999
PM10
0.999
0.999
PM2.5
0.999
0.998
S02
0.978
0.967
VOC
0.999
0.999
Combustion Emissions Associated with Transport of Gasoline by Pipeline
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In addition to combustion emissions associated with transport of gasoline by water, rail and tanker truck
transport, there are also combustion emissions associated with the pumps used to transport gasoline by
pipeline. The 2030 and 2040 reference case pipeline inventories were calculated by scaling the 2011
platform pipeline inventory using overall impacts on combustion emissions associated with transport of 2011
volumes and 2030 or 2040 volumes. The overall impacts on combustion emissions associated with transport
were calculated using the RFS2 impacts spreadsheet, then nationwide emission fractions for combustion
emission fractions associated with pipelines from GREET 1.8.c were used to obtain the portion attributable
to pipelines.™'™ The scalars are provided in Table 15.
Table 25 Scalars Applied to 2011 Pipeline Combustion Emissions to get 2030 and 2040 reference case
inventories
Pollutant
2030 reference case
2040 reference case
CO
0.979
0.976
NOx
0.922
0.910
PM10
0.987
0.985
PM2.5
0.990
0.988
S02
0.989
0.987
VOC
0.994
0.993
Combustion Emissions from Truck Transport of Fuels
Fuel can be transported from bulk terminals to dispensing facilities using trucks. These impacts were not
accounted for in the 2030 and 2040 reference case inventories. The expected impact on reference case air
quality results would likely be minor.
Portable Fuel Container Impacts
There are several sources of emissions associated with portable fuel containers (PFC) used for gasoline.
These sources include vapor displacement and spillage while refueling the gas can at the pump, spillage
during transport, permeation and evaporation from the gas can during transport and storage, and vapor
displacement and spillage while refueling equipment. Increased use of ethanol in gasoline fuels can increase
evaporative emissions from PFCs for a number of reasons. If, E10 fuels have higher volatility than
corresponding E0 fuels, that can increase evaporation and vapor displacement. Second, ethanol in gasoline
increases permeation of fuel through gas can materials. Finally, the lower energy content of ethanol fuels
leads to more frequent refueling, and, thus, greater emissions from spillage and displacement while filling
the gas can at the pump.
The use of ethanol also changes the mix of hydrocarbons in the evaporated fuel. In particular it can change
the fraction of ethanol, benzene and naphthalene as described below.
As part of the 2007 regulation controlling emissions of hazardous pollutants from mobile sources (MSAT2
rule), EPA promulgated requirements to control VOC emissions from gas cans. The methodology used to
develop emission inventories for gas cans is described in the regulatory impact analysis for the rule and in an
accompanying technical support document.™11' ^ The large majority of spillage emissions occur when
refueling equipment, and this is already included in the nonroad equipment inventory. Thus we did not
include these emissions in the PFC inventory for this rule.
New PFC inventories were not developed for this rule. For permeation, evaporation and vapor displacement
while refueling containers, 2025 inventories developed for the recent ozone transport rule were used.^
These inventory estimates take into account emission reductions from the MSAT2 rule. For spillage during
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transport and refueling at the pump, 2011 inventory estimates were used for future years, due to the lack of
projected air quality inventories for these emission types.
Control Case Inventories
Additional modifications were made to the 201 lv6.2 platform inventory to create control case inventories for
2040, which account for expected impacts of the phase 2 heavy-duty greenhouse gas standards.
The phase 2 heavy duty greenhouse gas emission standards impact production, distribution, and consumption
of fuel, as well as emissions associated with these processes.
Agricultural Sector
Changes in agricultural economics associated with changes in biomass production can result in shifts in
related agricultural production. However, these impacts were not accounted for in the control case
inventories for this rule, due to a lack of recent agricultural sector modeling, and small overall impact on
urban air quality.
Biofuel Production
Corn Ethanol Plants
We did not account for impacts of this rule on emissions corn ethanol plants. These impact are likely to be
small.
Cellulosic Biofuel Refineries
Changes in biomass production can result in changes in emissions. However, the impacts of this rule on
cellulosic biofuels were not accounted for in the control case inventories, due to small overall impact on
cellulosic fuel volumes.
Biodiesel Plants
The development of the 2040 inventory of biodiesel plants for the reference case is described above. Plant
location and production volume data come from a database of 2014 biodiesel RINs. The biodiesel inventory
included emissions from plants in the 2011 NEIvl, as well as a number of additional facilities not in the
inventory but added to the ptnonipm sector using their location.
The 2040 biodiesel reference case inventory was scaled down to reflect the control case volume of biodiesel,
1.14 BG. Once facility-level production capacities were scaled, emission factors were applied based on
soybean oil feedstock. These emission factors in Table 5 are in tons per million gallons (Mgal) and were
obtained from EPA's spreadsheet model for upstream EISA impacts developed for the RFS2 rule (EPA,
2010a). Twenty-one of the biodiesel plants were included in the 2011 NEI, so adjustment factors were
calculated to project their emissions. The remaining 99 biodiesel plants were modeled as point sources with
Google Earth and web searching validating facility coordinates and correcting state-county FIPS.
Crude Production and Losses During Transport to Refineries
Since the reduction in fuel consumption associated with this rule is estimated at 10.6 billion gallons in 2040,
the reduction in domestic crude production is about 0.72 billion gallons. To generate the emission inventory
adjustment factors for air quality modeling these reductions were applied to the projected crude supply of
230 billion gallons to US refineries in 2011, per AEO 2014.^ Thus, the adjustment factor is 0.32%.
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Petroleum Refineries
Phase 2 standards will reduce the output of gasoline and diesel fuel from petroleum refineries by about 13
Bgal and lOBgal, respectively. The impact of these reductions on refinery emissions was modeled using the
impacts spreadsheet. Resulting refinery scalars, applied to 2011 refinery emissions, are presented in Table
16.
Table 16 Scalars Applied to 2011 Refinery Emissions to Get 2040 Control Case Inventories
Pollutant
Scalar
voc
0.8880
1,3-Butadiene
0.9305
acetaldehyde
0.9281
acrolein
0.9934
benzene
0.8943
formaldehyde
0.9121
CO
0.8472
NOx
0.8896
PM10
0.9009
PM2.5
0.8997
Production of Energy for Refinery Use
Table 17 presents the emission impacts and scalars for upstream refinery emissions under the 2040 control
case.
Table 17 Upstream Refinery Emission Impacts in Tons and Inventory Scalars Applied to 2011 to Obtain
2040 Control Case Emissions

Residual Oil
Production
Natural Gas Production
Coal Production
Electricity
Production
Pollutan
t
Impact
Scalar
Impact
Scalar
Impact
Scalar
Impact
Scalar
VOC
-79
0.9985
-1219
0.9717
-417
0.77769
-390
0.9897
CO
-146
0.9970
-1292
0.9408
-151
0.9893
-1464
0.9982
NOx
695
0.9908
-1593
0.7837
-1092
0.9515
-4103
0.9980
PMio
-80
0.9967
-9
0.8022
-14,266
0.5457
-2301
0.9919
PM2.5
-37
0.9983
-21
0.8463
-84
0.6543
-667
0.9968
sox
-412
0.9952
-1689
0.9475
-2688
07339
-5710
0.9988
Fuel Distribution Chain
Fuel Transport - Vapor Loss Emissions
Vapor Losses from Storage and Transport of Ethanol
The expected impact on the control case on these emissions would likely be minor and was not accounted
for.
Vapor Losses from Transfer and Storage of Gasoline and Gasoline/Ethanol Blends
Table 18 Scalars Applied to 2011 Vapor Losses from Gasoline and Gasoline/Ethanol Blends to Get 2040
Control Case Inventories

Adjustment
Process
Pollutant
Factor
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BTP
VOC
0.6716

benzene
0.7241
RBT/BPS
VOC
0.7205

benzene
0.5887
Fuel Transport - Combustion Emissions
Changes in volumes of fuels produced, due to the phase 2 heavy-duty greenhouse gas standards, have a
number of impacts on upstream emissions including emissions associated with transport. These impacts
include combustion emissions associated with transport of feedstocks from the refinery to fuel production
facilities and bulk terminals. We did not account for emissions associated with feedstocks, but did account
for non-truck transport of finished fuels.
Combustion Emissions from Water and Rail Transport of Fuels
To develop the CI, C2 and rail 2040 control case inventories, CI, C2 and rail 2025 platform inventories were
adjusted to account for differences in CI, C2 and rail emission rates in a 2040 control scenario versus 2025.
The 2040 control case CI, C2 and rail inventories were calculated by scaling the 2025 platform inventory
using overall impacts on combustion emissions associated with transport of fuel volumes between 2025 and
a 2040 control scenario. The overall impacts on combustion emissions associated with transport were
calculated using the RFS2 impacts spreadsheet, then nationwide emission fractions for combustion emission
fractions associated with rail and C1/C2 vessels from GREET 1.8.c were used to obtain the portion
attributable to rail and C1/C2 vessels.*™1'*™11 The scalars are provided in Table 19 and Table 20.
Table 19 Scalars Applied to 2025 C1/C2 Combustion Emissions to get 2040 control case inventories
Pollutant
2040 control case scalar
CO
0.993
NOx
0.988
PM10
0.980
PM2.5
0.979
S02
0.986
VOC
0.983
Table 20 Scalars Applied to 2025 Rail Combustion Emissions to get 2040 control case inventories
Pollutant
2040 control case scalar
CO
0.998
NOx
0.998
PM10
0.997
PM2.5
0.997
S02
0.966
VOC
0.999
Combustion Emissions from Truck Transport of Fuels
These impacts were not accounted for in the control case inventories as the expected impact on reference
case air quality results would likely be minor.
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Combustion Emissions Associated with Transport of Gasoline by Pipeline
In addition to combustion emissions associated with transport of gasoline by water, rail and tanker truck
transport, there are also combustion emissions associated with transport of gasoline by pipeline. The 2040
control case pipeline inventories were calculated by scaling the 2011 platform pipeline inventory using
overall impacts on combustion emissions associated with transport of 2011 volumes and 2040 control case
volumes. The overall impacts on combustion emissions associated with transport were calculated using the
RFS2 impacts spreadsheet, then nationwide emission fractions for combustion emission fractions associated
with pipelines from GREET 1.8.c were used to obtain the portion attributable to pipelines.™*'™ The scalars
are provided in Table 21.
Table 21 Scalars Applied to 2011 Pipeline Combustion Emissions to get 2040 control case inventories
Pollutant
2040 control case scalar
CO
0.963
NOx
0.864
PM10
0.977
PM2.5
0.983
S02
0.981
VOC
0.993
1U. S. Energy Information Administration. 2014. Annual Energy Outlook, 2014. Report No. DOE/EIA-0383ER(2014).
Available at http://www.eia.gov/forecasts/archive/aeo 14/
11 Technical Support Document (TSD): Preparation of Emissions Inventories for the Version 6.1, 2011 Emissions Modeling
Platform. Prepared by the Office of Air and Radiation, Office of Air Quality Planning and Standards, Air Quality Assessment
Division. November 30, 2014. Available at <
http://www.epa.gOv/ttn/chief/emch/2011v6/2011v6.l 2018 2025 base EmisMod TSD nov2014 v6.pdf>
111U. S. EPA. 2010. Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. Assessment and Standards Division,
Office of Transportation and Air Quality, Ann Arbor, MI. Report No. EPA-420-R-10-006, February, 2010. Available at
.
1V Technical Support Document (TSD): Preparation of Emissions Inventories for the Version 6.1, 2011 Emissions Modeling
Platform. Prepared by the Office of Air and Radiation, Office of Air Quality Planning and Standards, Air Quality Assessment
Division. November 30, 2014. Available at <
http://www.epa.gOv/ttn/chief/emch/2011v6/2011v6.l 2018 2025 base EmisMod TSD nov2014 v6.pdr>
v Technical Support Document (TSD): Preparation of Emissions Inventories for the Version 6.1, 2011 Emissions Modeling
Platform. Prepared by the Office of Air and Radiation, Office of Air Quality Planning and Standards, Air Quality Assessment
Division. November 30, 2014. Available at <
http://www.epa.gOv/ttn/chief/emch/2011v6/2011v6.l 2018 2025 base EmisMod TSD nov2014 v6.pdf>. See Sections 3.2.1.1
and 3.2.1.3.
V1 US EPA, 2015. Spreadsheet "HDGHG2 biodiesel 112415.xlsx." Available in EPA-HQ-OAR-2014-0827
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Available at http://www.eia.gov/forecasts/archive/aeo 14/
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April 2015. http://205.254.135.7/forecasts/aeo/pdf/0383(20151.pdf
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xu Source: Energy Information Administration. 2006. Annual Energy Outlook 2006. Report #:DOE/EIA-0383(2006) Available at

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Development and Office of Transportation and Air Quality. Report No. EPA-420-R-11-018. EPA Docket EPA-HQ-OAR-2011-
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