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Technical Support Document (TSD):
Preparation of Emissions Inventories for the
Version 6.1, 2011 Emissions Modeling Platform
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EPA-454/B-20-009
November 2014
Technical Support Document (TSD): Preparation of Emissions Inventories for the Version 6.1,
2011 Emissions Modeling Platform
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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TABLE OF CONTENTS
LIST OF TABLES IV
LIST OF FIGURES VI
LIST OF APPENDICES VI
ACRONYMS VII
1 INTRODUCTION 1
2 2011 EMISSION INVENTORIES AND APPROACHES 4
2.1 2011 NEI POINT SOURCES (PTEGU, PTEGU_PK, PT_OILGAS AND PTNONIPM) 8
2.1.1 EGU non-peaking units sector (ptegu) 9
2.1.2 EGU peaking units sector (ptegu_pk) 11
2.1.3 Point source oil and gas sector (pt oilgas) 12
2.1.4 Non-IPM sector (ptnonipm) 13
2.2 2011 NONPOINT SOURCES (AFDUST, AG, NP_OILGAS, RWC, NONPT) 15
2.2.1 Area fugitive dust sector (afdust) 16
2.2.2 Agricultural ammonia sector (ag) 21
2.2.3 Nonpoint source oil and gas sector (np oilgas) 22
2.2.4 Residential wood combustion sector (rwc) 22
2.2.5 Other nonpoint sources sector (nonpt) 23
2.3 2011 ONROAD MOBILE SOURCES (ONROAD, ONROAD_RFL) 24
2.3.1 Onroad non-refueling (onroad) 24
2.3.2 Onroad refueling (onroadrfl) 27
2.4 2011 NONROAD MOBILE SOURCES (C1C2RAIL, C3MARINE, NONROAD) 27
2.4.1 Class 1/Class 2 Commercial Marine Vessels and Locomotives and (clc2rail) 27
2.4.2 Class 3 commercial marine vessels (c3marine) 28
2.4.3 Nonroad mobile equipment sources: (nonroad) 31
2.5 "Other Emissions": Offshore Class 3 commercial marine vessels and drilling platforms and non-U.S.
SOURCES 32
2.5.1 Point sources from offshore C3 CMV and drilling platforms and Canada and Mexico (othpt) 33
2.5.2 Area and nonroad mobile sources from Canada and Mexico (othar) 33
2.5.3 Onroad mobile sources from Canada and Mexico (othon) 34
2.6 Fires (PTFiRE) 34
2.7 Biogenic sources (biog) 35
2.8 SMOKE-ready non-anthropogenic inventories for chlorine 35
3 EMISSIONS MODELING SUMMARY 37
3.1 Emissions modeling Overview 37
3.2 Chemical Speciation 40
3.2.1 VOC speciation 41
3.2.2 PM speciation 51
3.2.3 NO x speciation 53
3.3 Temporal Allocation 53
3.3.1 Use of FF10 format for finer than annual emissions 55
3.3.2 Electric Generating Utility temporalization (ptegu, ptegu_pk) 55
3.3.3 Residential Wood Combustion Temporalization (rwc) 57
3.3.4 Agricultural Ammonia Temporal Profiles (ag) 61
3.3.5 Onroad mobile temporalization (onroad, onroad rfl) 62
3.3.6 Additional sector specific details (afdust, beis, clc2rail, c3marine, nonpt, ptfire) 68
3.4 Spatial Allocation 69
3.4.1 Spatial Surrogates for U.S. emissions 70
3.4.2 Allocation method for airport-related sources in the U.S. 75
3.4.3 Surrogates for Canada and Mexico emission inventories 75
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4 DEVELOPMENT OF 2018 AND 2025 BASE-CASE EMISSIONS 80
4.1 Stationary source projections: EGU sectors (ptegu, ptegu_pk) 86
4.2 Stationary source projections: non-EGU sectors (afdust, ag, nonpt, np_oilgas, ptnonipm, pt_oilgas, rwc)
86
4.2.1 Mobile source upstream future year inventories and adjustments (nonpt, ptnonipm) 88
4.2.2 Upstream agricultural and Livestock adjustments (afdust, ag, nonpt, ptnonipm) 95
4.2.3 Residential wood combustion growth (nonpt) 97
4.2.4 Oil and Gas projections (npoilgas, ptoilgas) 98
4.2.5 RICE NESHAP (nonpt, ptnonipm, np oilgas, pt oilgas) 102
4.2.6 Fuel sulfur rules (nonpt, ptnonipm) 103
4.2.7 Industrial Boiler MACT reconsideration (ptnonipm) 105
4.2.8 Portland Cement NESHAP projections (ptnonipm) 107
4.2.9 State comments and consent decrees/settlements (nonpt, ptnonipm) 110
4.2.10 Aircraft projections (ptnonipm) 115
4.2.11 Remaining non-EGU controls and closures (ptnonipm) 117
4.3 Mobile source projections 118
4.3.1 Onroad mobile (onroad and onroad rfl) 118
4.4 Nonroad mobile source projections (C1C2RAIL, C3MARINE, nonroad) 121
4.4.1 Locomotives and Class 1 & 2 commercial marine vessels (clc2rail) 121
4.4.2 Class 3 commercial marine vessels (c3marine) 124
4.4.3 Other nonroad mobile sources (nonroad) 125
4.5 "Other Emissions": Offshore Class 3 commercial marine vessels and drilling platforms, Canada and
Mexico (othpt, othar, and othon) 126
4.5.1 Point sources from offshore C3 CMV and drilling platforms and Canada and Mexico (othpt) 126
4.5.2 Area, nonroad mobile and onroad mobile sources from Canada and Mexico (other, othon) 127
5 EMISSION SUMMARIES 128
6 REFERENCES 139
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List of Tables
Table 1-1. List of cases in the 2011 Version 6 Emissions Modeling Platform 2
Table 2-1. Platform sectors for the 2011 emissions modeling platform 5
Table 2-2. Summary of significant changes between 2011 platform and 201 INEIvl by sector 7
Table 2-3. Point source oil and gas sector SCCs 12
Table 2-4. Corn Ethanol Plant Criteria Pollutant Emission Factors (grams per gallon produced) 14
Table 2-5. Toxic-to-VOC Ratios for Corn Ethanol Plants 15
Table 2-6. 201 INEIvl nonpoint sources removed from the 2011 platform 15
Table 2-7. SCCs in the afdust platform sector 17
Table 2-8. Total Impact of Fugitive Dust Adjustments to Unadjusted 2011 Inventory 18
Table 2-9. Livestock SCCs extracted from the NEI to create the ag sector 21
Table 2-10. Fertilizer SCCs extracted from the NEI for inclusion in the "ag" sector 22
Table 2-11. SCCs in the Residential Wood Combustion Sector (rwc)* 23
Table 2-12. Onroad emission modes 26
Table 2-13. 201 INEIvl SCCs extracted for the starting point in clc2rail development 27
Table 2-14. Growth factors to project the 2002 ECA-IMO inventory to 2011 30
Table 2-15. 2011 Platform SCCs representing emissions in the ptfire modeling sector 34
Table 3-1. Key emissions modeling steps by sector 38
Table 3-2. Descriptions of the platform grids 39
Table 3-3. Emission model species produced for CB05 with SOA for CMAQ5.0.1 and CAMx* 41
Table 3-4. Integration approach for BAFM and EBAFM for each platform sector 44
Table 3-5. VOC profiles for WRAP Phase III basins 46
Table 3-6. National VOC profiles for oil and gas 46
Table 3-7. Counties included in the WRAP Dataset 47
Table 3-8. Select VOC profiles 2011 versus 2018 and 2025 49
Table 3-9. PM model species: AE5 versus AE6 51
Table 3-10. MOVES exhaust PM species versus AE5 species 52
Table 3-11. NOx speciation profiles 53
Table 3-12. Temporal settings used for the platform sectors in SMOKE 54
Table 3-13. Mapping of MOVES to SMOKE road types 66
Table 3-14. U.S. Surrogates available for the 2011 modeling platform 70
Table 3-15. Spatial Surrogates for Oil and Gas Sources 71
Table 3-16. Selected 2011 CAP emissions by sector for U.S. Surrogates* 72
Table 3-17. Canadian Spatial Surrogates 75
Table 3-18. CAPs Allocated to Mexican and Canadian Spatial Surrogates 77
Table 4-1. Control strategies and growth assumptions for creating the 2018 and 2025 base-case emissions
inventories from the 2011 base case 82
Table 4-2. Subset of CoST Packet Matching Hierarchy 85
Table 4-3. Summary of non-EGU stationary projections subsections 87
Table 4-4. Renewable Fuel Volumes Assumed for Stationary Source Adjustments 88
Table 4-5. 2011 and 2018/2025 corn ethanol plant emissions [tons] 89
Table 4-6. Emission Factors for Biodiesel Plants (Tons/Mgal) 89
Table 4-7. 2018/2025 biodiesel plant emissions [tons] 90
Table 4-8. PFC emissions for 2011, 2018 and 2025 [tons] 90
Table 4-9. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/RIN gallon) 91
Table 4-10. Toxic Emission Factors for Cellulosic Plants (Tons/RIN gallon) 91
Table 4-11. 2018/2025 cellulosic plant emissions [tons] 91
Table 4-12. 2018/2025 VOC working losses (Emissions) due to ethanol transport [tons] 92
Table 4-13. Adjustment factors applied to storage and transport emissions 93
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Table 4-14. 2018 and 2025 adjustment factors applied to petroleum pipelines and refinery emissions
associated with gasoline and diesel fuel production 95
Table 4-15. Adjustments to modeling platform agricultural emissions for 2018 and 2025 96
Table 4-16. Composite NH3 projection factors to years 2018 and 2025 for animal operations 96
Table 4-17. Non-West Coast RWC projection factors 98
Table 4-18. AEO-based Projection Factors 100
Table 4-19. Oil and Gas sector VOC Projection Factors for NSPS sources 101
Table 4-20. Summary RICE NESHAP SI and CI percent reductions prior to 201 INEIvl analysis 102
Table 4-21. National by-sector reductions from RICE Reconsideration Controls 103
Table 4-22. Summary of fuel sulfur rules by state 105
Table 4-23. Facility types potentially subject to Boiler MACT reductions 106
Table 4-24. Default Boiler MACT fuel percent % reductions by ICR fuel type 106
Table 4-25. Summary of Boiler MACT reductions (tons) compared to Reconsideration RIA reductions.... 107
Table 4-26. Locations of new ISIS-generated cement kilns 109
Table 4-27. U.S. Census Division ISIS-based projection factors for existing kilns 109
Table 4-28. ISIS-based cement industry change (tons/yr) 110
Table 4-29. Impacts of most non-EGU point source state comments received in 2013 112
Table 4-30. Minor source ptnonipm sector NAICS-level projections for Texas 112
Table 4-31. Minor source nonpt sector projections for Texas 113
Table 4-32. Target company-wide reductions from OECA consent decree information 114
Table 4-33. Default national-level factors used to project 2011 base-case aircraft emissions to 2018 and
2025 116
Table 4-34. Reductions from all ElS-based and remaining information facility/unit-level closures 118
Table 4-35. Projection factors for 2018 and 2025 VMT (in millions of miles) 119
Table 4-36. Comparison of MOVES runs for 2018 and 2025 120
Table 4-37. CA LEVIII program states 120
Table 4-38. Non-California intermediate projection factors for locomotives and Class 1 and Class 2
Commercial Marine Vessel Emissions 122
Table 4-39. C1/C2 and locomotive emission adjustments in 2018 and 2025 123
Table 4-40. Difference in clc2rail sector emissions between 2011 and future years 124
Table 4-41. Growth factors to project the 2011 ECA-IMO inventory to 2018 and 2025 124
Table 5-1. National by-sector CAP emissions summaries for the 2011 evaluation case 129
Table 5-2. National by-sector CAP emissions summaries for the 2018 base case 130
Table 5-3. National by-sector CAP emissions summaries for the 2025 base case 131
Table 5-4. National by-sector CO emissions (tons/yr) summaries and percent change 132
Table 5-5. National by-sector NH3 emissions (tons/yr) summaries and percent change 133
Table 5-6. National by-sector NOx emissions (tons/yr) summaries and percent change 134
Table 5-7. National by-sector PM2.5 emissions (tons/yr) summaries and percent change 135
Table 5-8. National by-sector PM10 emissions (tons/yr) summaries and percent change 136
Table 5-9. National by-sector SO2 emissions (tons/yr) summaries and percent change 137
Table 5-10. National by-sector VOC emissions (tons/yr) summaries and percent change 138
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List of Figures
Figure 2-1. Example of January PM2.5 afdust emissions: raw 2008 NEI (top), after application of transport
fraction (middle) and final post-meteorological adjusted (bottom) 20
Figure 2-2. Illustration of regional modeling domains in ECA-EVK) study 31
Figure 2-3. Annual NO emissions output from BEIS 3.14 for 2011 36
Figure 2-4. Annual isoprene emissions output from BEIS 3.14 for 2011 36
Figure 3-1. Air quality modeling domains 39
Figure 3-2. Process of integrating BAFM with VOC for use in VOC Speciation 43
Figure 3-3. IPM Regions for EPA Base Case v5.13 56
Figure 3-4. Example of RWC temporalization in 2007 using a 50 versus 60 °F threshold 58
Figure 3-5. RWC diurnal temporal profile 59
Figure 3-6. Diurnal profile for OHH, based on heat load (BTU/hr) 60
Figure 3-7. Day-of-week temporal profiles for OHH and Recreational RWC 60
Figure 3-8. Annual-to-month temporal profiles for OHH and recreational RWC 61
Figure 3-9. Example of new animal NH3 emissions temporalization approach, summed to daily emissions 62
Figure 3-10. Example of SMOKE-MOVES temporal variability of NOx emissions 63
Figure 3-11. Previous onroad diurnal weekday profiles for urban roads 64
Figure 3-12. Variation in MOVES diurnal profiles 64
Figure 3-13. Use of submitted versus new national default profiles 65
Figure 3-14. Updated national default profiles for LDGV vs. HHDDV, urban restricted weekday 67
Figure 3-15. Agricultural burning diurnal temporal profile 69
Figure 4-1. Map of Petroleum Administration for Defense Districts (PADD) 94
Figure 4-2. Oil and Gas NEMS Regions 99
Figure 4-3. Cement sector trends in domestic production versus normalized emissions 108
List of Appendices
Appendix A: Nonpoint Oil and Gas NEI SCCs
Appendix B: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT
Appendix C: Memo Describing the Differences in MOVES speciated PM and CMAQ PM
Appendix D: Future Animal Population Projection Methodology
Appendix E: Boiler MACT ICR Fuels Cross-Reference to NEI SCCs
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Acronyms
ACI
Activated Carbon Injection
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
AIM
Architectural and Industrial Maintenance (coatings)
ARW
Advanced Research WRF
BAFM
Benzene, Acetaldehyde, Formaldehyde and Methanol
BEIS3.14
Biogenic Emissions Inventory System, version 3.14
BELD3
Biogenic Emissions Land use Database, version 3
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
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
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HAP
Hazardous Air Pollutant
HC1
Hydrochloric acid
HDGHG
Heavy-Duty Vehicle Greenhouse Gas
Hg
Mercury
HMS
Hazard Mapping System
HPMS
Highway Performance Monitoring System
IIWC
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 (2010b) — OTAQ's model for estimation
of onroad mobile emissions - replaces the use of the MOBILE model
MSA
Metropolitan Statistical Area
MSAT2
Mobile Source Air Toxics Rule
MTBE
Methyl tert-butyl ether
MWRPO
Mid-west Regional Planning Organization
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
NOx
Nitrogen oxides
NSPS
New Source Performance Standards
NSR
New Source Review
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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
ORD
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
WGA
Western Governors' Association
WRAP
Western Regional Air Partnership
WRF
Weather Research and Forecasting Model
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1 Introduction
The U.S. Environmental Protection Agency (EPA) developed an air quality modeling platform for 2011
based on the 2011 National Emissions Inventory, version 1 (201 INEIvl). The air quality modeling
platform 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 2011 modeling platform,
which includes the emission inventories, the ancillary data files, and the approaches used to transform
inventories for use in air quality modeling. Many emissions inventory components of this air quality
modeling platform are based on the 201 INEIvl, although there are some differences between the
platform inventories and the 201 INEIvl emissions.
This 2011 modeling platform includes all criteria air pollutants and precursors (CAPs) and the following
hazardous air pollutants (HAPs): chlorine (CI), hydrogen chloride (HC1), benzene, acetaldehyde,
formaldehyde and methanol. The latter four HAPs are also abbreviated as BAFM. This platform is called
the "CAP-BAFM 2011-Based Platform, version 6.1" because it is primarily a CAP platform with BAFM
species included. Here, "version 6.1" denotes an evolution from the 2011-based platform, version 6, with
improvements due to the use of newer data and methods. For the rest of this document, the platform that
is described is referred to as the "2011 platform" or "201 lv6.1". Future updates to the 2011 platform will
include a version qualifier such as "2011 Platform v6.2", and so on.
The 201 lv6.1 platform was used to support the 2015 National Ambient Air Quality Standards (NAAQS)
for ozone along with other special studies. The air quality model used for this rule is the Comprehensive
Air Quality Model with Extensions (CAMx) model version 6.10; however, emissions are first processed
for the Community Multiscale Air Quality (CMAQ) model, version 5.0.1 and then converted to CAMx-
ready format. Both CAMx and CMAQ support modeling ozone (O3) and particulate matter (PM), and
require as input hourly and gridded emissions of chemical species that correspond to CAPs and specific
HAPs. The chemical mechanism used by CAMx for this platform is called Carbon Bond 2005 (CB05)
with chlorine chemistry. CB05 allows explicit treatment of BAFM and includes HAP emissions of HC1
and CI.
The 2011v6.1 platform consists of three 'complete' emissions cases: the 2011 base case (i.e., 2011ef_v6),
the 2018 base case (i.e., 2018ef_v6) and the 2025 base case (i.e., 2025ef_v6). In the case abbreviations,
the 2011, 2018 and 2025 are the year represented by the emissions; the "e" stands for evaluation, meaning
that year-specific data for fires and EGUs are used, and the "f" represents that this was the sixth set of
emissions modeled for the 201 lv6.x platform, where "x" represents "1" for the set of emissions
documented in this technical support document (TSD). Table 1-1 provides more information on these
emissions cases. The purpose of the 2011 base case is to represent the year 2011 in a manner consistent
with the methods used in corresponding future-year cases, including the 2018 and 2025 future year base
cases, as well as any additional future year control and source apportionment cases.
For regulatory applications, the outputs from the 2011 base case are used in conjunction with the outputs
from the 2018 and 2025 base cases in the relative response factor (RRF) calculations to identify future
areas of nonattainment. 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". This document is available on EPA's Emissions Modeling
Clearinghouse website, under the section entitled "2011-based Modeling Platform (201 lv6 Platform)".
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Table 1-1. List of cases in the 2011 Version 6 Emissions Modeling Platform
Case Name
Abbreviation
Description
2011 base case
201lef_v6
2011 case relevant for air quality model evaluation purposes
and for computing relative response factors with 2018 and
2025 scenario(s). Uses 201 INEIvl 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.
2018 base case
2018ef_v6
2018 "base case" scenario, representing the best estimate for
the future year that incorporates estimates of the impact of
current "on-the-books" regulations, without including
implementation of controls needed to attain current PM2.5
annual and 24-hour standards (12 |ig/m3 and 35 |ig/m3,
respectively) and ozone 8-hour standard (75 ppb).
2025 base case
2025ef_v6
2025 "base case" scenario, representing the best estimate for
the future year that incorporates estimates of the impact of
current "on-the-books" regulations, without including
implementation of controls needed to attain current PM2.5
annual and 24-hour standards (12 |ig/m3 and 35 |ig/m3,
respectively) and ozone 8-hour standard (75 ppb).
A brief summary of the emissions data used in the 201 lv6.1 platform follows:
1) Point and nonpoint sources are based on the 201 INEIvl.
2) Onroad mobile sources are based on year 2011 emissions computed using the Sparse Matrix
Operator Kernel Emission (SMOKE) interface to emission factors developed with the version of
Motor Vehicle Emissions Simulator (MOVES) that represents the final Tier 3 Vehicle Emission
and Fuel Standards.
3) Nonroad mobile sources are based on the 201 INEIvl, except for some additions of VOC in
California where there were HAP emissions but no VOCs in 201 INEIvl.
4) Commercial marine vessels (CMV) are based on the 201 INEIvl, 2010 regional planning
organization (RPO) inventories in the Midwest, and a separate year-2002-based (projected to
2011) inventory for Class 3 CMV vessels. Additional minor changes were made to point sources
as described in Section 2.1.
The 201 lv6.1 platform cases are very similar to the 201 lv6 cases 201 led and 2018ed that were released
for public comment via the Federal Register notices 78 FR 70935 and 79 FR 2437, respectively. The
differences in the 201 lef and 2018ef cases were: the commercial marine vessel emissions in California
used state-provided values, MOVES Tier3FRM was used for onroad mobile source emissions, updated
spatial surrogates were used for oil and gas emissions, and the meteorological fields used. The latter of
which affected biogenic emissions, the meteorologically-adjusted values of fugitive dust emissions, and
the temporal profiles for agricultural and residential wood emissions. Due to the timing of the modeling,
these cases do not reflect incorporation of any comments provided from the Federal Register notices.
The primary emissions modeling tool used to create the air quality model-ready emissions was the
SMOKE modeling system. SMOKE version 3.5.1 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. Boundary conditions for this
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grid were obtained from a 2011 run of GEOS-Chem. Electronic copies of the data used as input to
SMOKE for the 2011 Platform are available from the Emissions Modeling Clearinghouse.
The gridded meteorological model used for the emissions modeling was developed using the Weather
Research and Forecasting Model (WRF). version 3.4, Advanced Research WRF (ARW) 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 was
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 for High Resolution Sea Surface
Temperature (GHRSST) and is given the EPA meteorological case label "1 lg".
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 2018 and 2025 inventories (projected
from 2011). Data summaries comparing the 2011, 2018 and 2025 base cases are provided in Section 5.
Section 6 provides references. The Appendices provide additional details about specific technical
methods.
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2 2011 Emission Inventories and Approaches
This section describes the 2011 emissions data that make up the 2011 platform. The starting point for the
2011 stationary source emission inputs is the 201 INEIvl. Emissions of NOx, SO2, VOC and PM
emissions decrease from values in the 2008 NEI version 3 for most source sectors, with a couple of
notable exceptions including increased industrial NOx, VOC and CO associated with increased oil and
gas sector emissions and improved emission estimates; slightly increased VOC, CO and NH3 from fuel
combustion; and increased wildfire emissions. Documentation for the 201 INEIvl. including a Technical
Support Document (TSD).
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 uses the Emissions Inventory System (EIS) to compile the NEI. EIS
includes hundreds of automated QA checks to help improve data quality, and also supports tracking
release point (e.g., stack) coordinates separately from facility coordinates. EPA collaborated extensively
with S/L/T agencies to ensure a high quality of data in the 201 INEIvl. Tangible benefits of this
collaboration are seen in improved data quality from past first version inventories, improved completeness
and avoided duplication between point and nonpoint source categories such as industrial boilers. Onroad
mobile source emissions in the 201 INEIvl were developed using MOVES2010b; however, the 2011
emissions modeling platform used a different version of MOVES, hence forth referred to as
"MOVESTier3FRM" that facilitated the representation of the final Tier 3 standards in future years. When
given the same inputs, these two versions of MOVES produce similar emissions estimates for the year
2011.
The 2011 NEI includes five data categories: point sources, nonpoint (formerly called "stationary area")
sources, nonroad mobile sources, onroad mobile sources, and events consisting of fires. The 201 INEIvl
uses 60 sectors to further describe the emissions, with an additional biogenic sector generated from a
summation of the gridded, hourly 2011 biogenic data used in the modeling platform. In addition to the
NEI data, emissions from the Canadian and Mexican inventories and several other non-NEI data sources
are included in the 2011 platform. As explained below, the non-NEI emissions component to the 2011
platform primarily includes: different version of MOVES-based onroad mobile source emissions, non-
meteorologically-adjusted road dust, year-2010 commercial marine vessel (CMV) emissions in the
Midwest (LADCO), and Class 3 CMV data developed by EPA.
Fire emissions in 201 1NEIv2 were developed based on Version 2 of the Satellite Mapping Automated
Reanalysis Tool for Fire Incident Reconciliation (SMARTFIRE) system (Sullivan, et al., 2008).
SMARTFIRE2 was the first version of SMARTFIRE to assign all fires as either prescribed burning or
wildfire categories. In past inventories, a significant number of fires were published as unclassified,
which impacted the emissions values and diurnal emissions pattern. Recent updates to SMARTFIRE
include improved emission factors for prescribed burning.
For the purposes of preparing the air quality model-ready emissions, the 201 INEIvl 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 by a convertor program.
4
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Table 2-1 presents the sectors in the 2011 platform and how they generally relate to the 201 INEIvl 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 INEIvl emissions for the 2011 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 non-peaking
units:
ptegu
Point
2011 NEI point source EGUs determined to operate as non-peaking
units based on criteria discussed in Section 2.1. For future year
emissions, these units are mapped to the Integrated Planning Model
(IPM) model using the National Electric Energy Database System
(NEEDS) version 5.13. The 201 INEIvl emissions are replaced with
hourly 2011 CEMS values for NOx and SO2, where the units match.
Other pollutants are scaled from 201 INEIvl using CEMS heat input.
Emissions for all sources not matched to CEMS data come from
201 INEIvl. Annual resolution for non-matched sources, hourly for
CEMS sources.
EGU peaking
units:
ptegu pk
Point
Same as ptegu sector, but limited to EGUs that are determined to
operate as peaking units, as discussed in Section 2.1. All sources in
this sector have CEMS data for 2011 and are therefore hourly.
Point source oil
and gas:
pt oilgas
Point
201 INEIvl point sources with oil and gas production emissions
processes. Annual resolution.
Remaining non-
EGU point:
ptnonipm
Point
All 201 INEIvl point source records not matched to the ptegu,
ptegu_pk, and 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 INEIvl nonpoint livestock and fertilizer
application, county and annual resolution.
Area fugitive dust:
afdust
Nonpoint
PM10 and PM2 5 from fugitive dust sources from the 201 INEIvl
nonpoint inventory including building construction, road construction,
and 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.
Nonpoint source
oil and gas:
np oilgas
Nonpoint
201 INEIvl nonpoint sources from oil and gas-related processes.
County and annual resolution.
Residential Wood
Combustion:
rwc
Nonpoint
201 INEIvl NEI nonpoint sources with Residential Wood Combustion
(RWC) processes. County and annual resolution.
Class 1 & 2 CMV
and locomotives:
clc2rail
Nonpoint
Locomotives and primarily category 1 (CI) and category 2 (C2)
commercial marine vessel (CMV) emissions sources from the
201 INEIvl nonpoint inventory. Midwestern states' CMV emissions,
including Class 3 sources, are from a separate year 2010 emissions
inventory. County and annual resolution.
5
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
commercial
marine:
c3marine
Nonpoint
Category 3 (C3) CMV emissions projected to 2011 from year 2002
values. These emissions are not from the NEI, but rather were
developed for the rule called "Control of Emissions from New Marine
Compression-Ignition Engines at or Above 30 Liters per Cylinder",
usuallv described as the Emissions Control Area- International
Maritime Organization (ECA-IMO) studv. (EPA-420-F-10-041.
August 2010). U.S. states-only emissions (zero in Midwest); see othpt
sector for all non-U.S. emissions. Treated as point sources to reflect
shipping lanes, annual resolution.
Remaining
nonpoint:
nonpt
Nonpoint
201 INEIvl nonpoint sources not otherwise removed from modeling or
included in other platform sectors; county and annual resolution.
Nonroad:
nonroad
Nonroad
201 INEIvl 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 to the 201 INEIv 1. County and
monthly resolution.
Onroad non-
refueling:
onroad
Onroad
2011 onroad mobile source gasoline and diesel vehicles from parking
lots and moving vehicles. Includes the following modes: exhaust,
extended idle, evaporative, permeation, and brake and tire wear. For
all states except California and Texas, based on monthly MOVES
emissions tables from MOVESTier3FRM. Texas emissions are from
the 201 INEIvl and are based on MOVES 2010b, and California
emissions are based on Emission Factor (EMFAC). MOVES-based
emissions computed for each hour and model grid cell using monthly
and annual activity data (e.g., VMT, vehicle population).
Onroad refueling:
onroadrfl
Onroad
2011 onroad mobile gasoline and diesel vehicle refueling emissions.
For all states (including Texas and California), based on
MOVESTier3FRM emissions tables. 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-submitted
emissions. Consistent with 201 INEIvl.
Other point
sources not from
the 2011 NEI:
othpt
N/A
Point sources from Canada's 2006 inventory and Mexico's Phase III
2012 inventory, annual resolution. Mexico's inventory is year 2012
and grown from year 1999 (ERG, 2009; Wolf, 2009). Also includes
all non-U.S. C3 CMV and U.S. offshore oil production, which are
unchanged from the 2008 NEI point source annual emissions.
Other non-NEI
nonpoint and
nonroad:
othar
N/A
Annual year 2006 Canada (province resolution) and year 2012 (grown
from 1999) Mexico Phase III (municipio resolution) nonpoint and
nonroad mobile inventories.
Other non-NEI
onroad sources:
othon
N/A
Year 2006 Canada (province resolution) and year 2012 (grown from
1999) Mexico Phase III (municipio resolution) onroad mobile
inventories, annual resolution.
Biogenic:
beis
Nonpoint
Year 2011, hour-specific, grid cell-specific emissions generated from
the BEIS3.14 model with SMOKE, including emissions in Canada and
Mexico.
6
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Table 2-2 provides a brief by-sector overview of the most significant differences between the 2011
emissions platform and the 201 INEIvl. Only those sectors with significant differences between the
201 INEIvl and the 2011 emissions modeling platform are listed. For some sectors, such as non-EGU
point (ptnonipm), these changes are very minor and localized. In contrast, other sectors such as C3
commercial marine (c3marine) are either completely replaced or have significant and detailed edits based
on review of available alternative data. The specific by-sector updates to the 2011 platform are described
in greater detail later in this section under each by-sector subsection.
Table 2-2. Summary of significant changes between 2011 platform and 201 INEIvl by sector
Platform Sector
Summary of Significant Inventory Differences of 2011 Platform vs.
201 INEIvl
IPM sectors:
ptegu & ptegu_pk
1) Based on 201 INEIvl and 2011 CEMS data analysis, 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.
3) Hourly NOx and SO2 CEMS data replaces annual NOx and SO2 NEI data in
the air quality model inputs.
Remaining non-
EGU (IPM)
sector:
ptnonipm
1) Based on items above (ptegu & ptegu_pk), made additional matches to
IPM_YN codes and ORIS facility codes that caused several sources to move
into the ptegu and ptegu_pk sectors. This edit prevents double counting of
EGU emissions in the future years.
2) Included 2011 ethanol plant facilities from EPA's Office of Transportation and
Air Quality (OTAQ) that were not identified in the 2011 NEIvl.
Area fugitive dust:
afdust
1) Replaced EPA-provided emission estimates for paved and unpaved road dust
with "non-met-adjusted" emissions; i.e., the meteorology/precipitation
reduction included in the 201 INEIvl is backed-out.
2) All emissions in this sector are processed (adjusted) to reflect land use
(transport) and meteorological effects such as rain and snow cover that
significantly reduce PM emissions input to the air quality model. These
adjusted emissions are known as the afdust adj emissions.
Remaining
nonpoint sector:
nonpt
1) Split the 201 INEIvl nonpoint file into the platform sectors afdust, ag,
npoilgas, rwc, c3marine, and clc2rail.
2) Used agricultural fires emissions from daily inventory aggregated to monthly
values, whereas the NEI only stores annual values.
Class 1 & 2 CMV
and locomotives:
clc2rail
Replaced Midwest RPO states clc2 CMV emissions with comprehensive year
2010 RPO inventory.
C3 commercial
marine:
c3marine
1) Used non-201 INEIvl-based data. Rather used year-2011 point sources as
projected from 2002 from the ECA-IMO project.
2) Midwest RPO states replaced with 2010 RPO inventory (see clc2rail sector).
Nonroad sector:
nonroad
1) States other than Texas: monthly rather than annual + small VOC adjustments
in California.
2) Texas: replaced with annual 2011 Texas data apportioned to months using
EPA's 2011 nonroad estimates.
7
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Platform Sector
Summary of Significant Inventory Differences of 2011 Platform vs.
201 INEIvl
Onroad non-
refueling:
onroad
1) For all states except California and Texas: Year 2011 emissions for all
pollutants and modes (exhaust, evaporative, permeation, extended idle, tire and
brake wear) from all vehicle types are based on emission factors from the
version of MOVESTier3FRM, as opposed to MOVES 2010b which was used
for the 201 INEIvl. Processed with 2011 meteorology using SMOKE-
MOVES (discussed later).
2) For California and Texas: merged in 2011 California and Texas data to post-
adjust SMOKE-MOVES data (discussed later).
Onroad non-
refueling:
onroadrfl
For all states including California: Year 2011 emissions for all pollutants AND all
vehicle types are based on MOVESTier3FRM emission factor tables processed
with 2011 meteorology using SMOKE-MOVES (discussed later). Therefore, these
emissions are identical to the 201 INEIvl for states that did not submit refueling
emissions, but are inconsistent with 201 INEIvl for states that did submit point and
nonpoint refueling since the 201 INEIvl kept state-submissions over EPA data.
The emission inventories in SMOKE input format for the 2011 base case are available from the Emissions
Modeling Clearinghouse website. The inventories "readme" file indicates the particular zipped files
associated with each platform sector. A number of reports were developed for the 2011 platform.
Descriptions of the available data and reports are available from the FTP site at. The types of reports
include state summaries of inventory pollutants and model species by modeling platform sector for 2011,
2018 and 2025 in the Microsoft® Excel® files "201 lef_v6_l lg_state_sector_totals.xlsx",
"2018ef_v6_llg_state_sector_totals.xlsx", and "2025ef_v6_llg_state_sector_totals.xlsx", with a
comparison of the emissions in the three cases in the file
"201 lef_2018ef_2025ef_state_sector_comparison.xlsx". Annual and summer NOx and VOC emission
totals by county and modeling platform sector are available in the files
"201 lef_2025ef_county_sector_comparisons_NOX.xlsx" and
"2011ef_2025ef_county_sector_comparisons_VOC.xlsx". Summaries by state and source classification
code (SCC), including SCC descriptions, by modeling sector for anthropogenic 2025 emissions are
available in the file "2025ef_state_scc_summaries.zip". A comparison of the complete list of inventory
files, ancillary files, and parameter settings for the 2011, 2018 and 2025 modeling cases is available in the
file "201 lef_2018ef_2025ef_case_inputs.xlsx".
The remainder of Section 2 provides details about the data contained in each of the 2011 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 INEIvl.
2.1 2011 NEI point sources (ptegu, ptegu_pk, 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. The offshore oil platform (othpt sector) and category 3
CMV emissions (c3marine and othpt sectors) are processed by SMOKE as point source inventories, as
described in Section 2.5.1 and Section 2.4.2, respectively. A comprehensive description on how EGU
emissions were characterized and estimated in the 2011 NEI can be found in Section 3.10 in the
201 INEIvl TSD.
8
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The point source file used for the modeling platform is exported from EIS into the Flat File 2010 (FF10)
format that is compatible with SMOKE. After moving offshore oil platforms into the othpt sector, and
dropping sources without true locations (i.e., FIPS code ends in 777), initial versions of the other four
platform point source sectors were created from the remaining 201 INEIvl point sources. The point
sectors are: the EGU sector for non-peaking units (ptegu), the EGU sector for peaking units (ptegu pk),
point source oil and gas extraction -related emissions (ptoilgas) 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
EGU sectors are further split into "peaking" (ptegu_pk) and non-peaking units to allow for better
analysis of the impact of peaking units. 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).
In addition to the emissions summaries described in Section 1, two other specialized point source
summaries are available on the Emissions Modeling Clearinghouse website. A summary report of stack
parameters for the point source sectors, including cross references to CEMS data via ORIS IDs, can be
found in the file
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/reports/2011 emissions/201 lec stack parameter re
port.xlsx. Although this report was created for the older 201 lec emissions case, this part of the inventory
was unchanged for the 201 lef case. A comparison of the 201 INEIvl EGU emissions with the 2011
CEMS data is available in the same directory in the file "201 lEGUsNEICEMS.xlsx".
The inventory pollutants processed through SMOKE for both 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) and the VOC HAP pollutants from the inventory
were ignored (VOC integration is discussed in detail in Section 3.2.1.1).
The ptnonipm and pt oilgas sector emissions were provided to SMOKE as annual emissions. For those
ptegu and ptegu_pk sources with CEMS data (that could be matched to the 201 INEIvl), 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 and ptegu_pk sectors, 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 INEIvl for the 2011 platform were briefly
described in Table 2-2. One of these changes involved splitting the stacks, units and facilities into the
ptnonipm, pt oilgas, ptegu and ptegu_pk sectors. Sources were included in the ptegu or ptegu_pk
sectors 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 non-peaking units sector (ptegu)
The ptegu and ptegu_pk (see Section 2.1.2) sectors contain emissions from EGUs in the 201 INEIvl
point inventory that could be matched to units found in the NEEDS v5.13 database. It was necessary to
put these EGUs into separate sectors in the platform because IPM projects future emissions for the
9
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EGUs defined in the NEEDS database, and emissions for sources in the ptegu and ptegu_pk sectors 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 2.1.3) or ptnonipm (see Section 2.1.4) 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 are projected by IPM are not properly matched to
the units in the NEI.
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 v5.13 database
along with additional information about IPM.
Some units in the ptegu and ptegu_pk sectors are matched to CEMS data via ORIS facility codes and
boiler ID. For these 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 just not matched to a CEMS unit, the emissions from that
source would still be modeled using the 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. Some
additional matches were made in the modeling platform as described later in this section.
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 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.
For sources not matched to CEMS data ("non-CEMS" sources), 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 for sources that are
carried into the future year. This method keeps the temporal behavior of the base and future year cases
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.
10
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as consistent as possible. See Section 3.3.2 for more details on the temporalization approach for ptegu
sources.
Finding additional matches between the NEI. NEEDS, and CEMS data
Several analytical steps were performed to better link the NEEDS units to the 201 INEIvl, along with
implementing better matching to the CEMS data cross-referenced using "ORIS" facility and boiler IDs.
The steps described in the 201 INEIvl TSD have some detail on how the values in the IPMYN column
were assigned. For the modeling platform, an initial ptipm/ptnonipm split was determined using the
values in the SMOKE point source flat file variable "IPM YN", which is populated based on an EIS
alternative facility identifier. Because EIS expects the matches to be one-to-one for an entire unit, if the
units are not defined in the same way in EIS and NEEDS, one-to-many or many-to-many matches can
only be stored in EIS with specified "end dates" and will not export directly to the flat file. However,
one-to-many and many-to-many matches to the IPM YN values were placed into the SMOKE input file
through a postprocessing step. This requires the additional of additional "dummy" records in the
SMOKE file that will be overlaid with CEMS data when SMOKE is run.
Additional matches between the NEI and NEEDS were identified by identifying units in IPM outputs
that were not yet matched to NEI data, and by looking for units identified in the NEI with facility type
codes identifying them as EGUs or facility names that indicated they were EGUs. In each case, priority
was given to units with larger emissions (e.g., > 300TPY of NOx or SO2). The units in each data set that
did not yet have matches within the same county were compared to one another on the basis of their
plant names and locations. In some cases, IDs were similar but were mismatched only due to a missing
leading zero in one of the databases. In other cases, a facility level match was specified, but a unit/boiler
level match was not yet identified and therefore the units at the facility were compared to one another on
the basis of design capacity and naming. For any new matches that were found, values that represented
the NEEDS IDs were filled in to the IPM YN in the modeling platform flat files. When possible, these
matches were loaded into EIS.
A similar matching process was used to identify additional matches between the 201 INEIvl and CEMS
data. To determine whether a NEI unit matched a CEMS unit, the CEMS units were compared to
facilities in the NEI that were not yet identified as a CEMS unit on the basis of their county FIPS codes,
locations, and total emissions of NOx and SO2. Additional CEMS matches that were found were applied
to the FF10 file by specifying values for ORIS FACILITY CODE, ORIS BOILER ID. Because IPM
uses a concatenation of the ORIS facility code and boiler ID, values were also filled in to the IPM YN
field for these units.
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. Many new CEMS assignments
were loaded into EIS for use in future inventories. Note that SMOKE can perform matches of CEMS
data down to the stack or release point level.
2.1.2 EGU peaking units sector (ptegu_pk)
The ptegu_pk sector includes sources identified by EPA as peaking units. The units were separated into
this sector to facilitate analyses of the impact of peaking units. Aside from their inclusion in this sector,
in all other ways they are treated in the same way as CEMS sources in the ptegu sector because all of
them are matched to CEMS data. To identify units for inclusion in this sector, EPA required them to
satisfy two tests: (1) the capacity factor was less than 10% over a 3 year average (2010-2012), and (2)
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the capacity factor was less than 20% in each of the 3 years. Here, "capacity factor" means either: (1)
The ratio of a unit's actual annual electric output (expressed in MWe/hr) to the unit's nameplate capacity
(or maximum observed hourly gross load (in MWe/hr) if greater than the nameplate capacity) times
8760 hours; or (2) The ratio of a unit's annual heat input (in million BTUs or equivalent units of
measure) to the unit's maximum rated hourly heat input rate (in million BTUs per hour or equivalent
units of measure) times 8,760 hours. The list of units in the ptegu_pk sector is provided in the file (see
the file
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/reports/2011 emissions/Peakers CAMP 2011.0802
13 NEI IPM match.xls).
2.1.3 Point source oil and gas sector (pt_oilgas)
The ptoilgas sector includes sources with the SCCs specified in the list 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. The nonpoint 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 3.21 of the
201 INEIvl TSD.
Table 2-3. Point source oil and gas sector SCCs
see
SCC Description*
31000309
IP;OGP;Natural Gas Processing Facilities;Compressor Seals
31000310
IP;OGP;Natural Gas Processing Facilities;Pump Seals
31000311
IP;OGP;Natural Gas Processing Facilities;Flanges and Connections
31000321
IP;OGP;Natural Gas Processing Facilities;Glycol Dehydrators: Niagaran Formation (Mich.)
31000322
IP;OGP;Natural Gas Processing Facilities;Glycol Dehydrators: Prairie du Chien Formation (Mich.)
31000323
IP;OGP;Natural Gas Processing Facilities;Glycol Dehydrators: Antrim Formation (Mich.)
31000324
IP;OGP;Natural Gas Processing Facilities;Pneumatic Controllers Low Bleed
31000325
IP;OGP;Natural Gas Processing Facilities;Pneumatic Controllers High Bleed >6 scfm
31000401
IP;OGP;Process Heaters;Distillate Oil (No. 2)
31000402
IP;OGP;Process Heaters;Residual Oil
31000403
IP;OGP;Process Heaters;Crude Oil
31000404
IP;OGP;Process Heaters;Natural Gas
31000405
IP;OGP;Process Heaters;Process Gas
31000406
IP;OGP;Process Heaters;Propane/Butane
31000411
IP;OGP;Process Heaters;Distillate Oil (No. 2): Steam Generators
31000412
IP;OGP;Process Heaters;Residual Oil: Steam Generators
31000413
IP;OGP;Process Heaters;Crude Oil: Steam Generators
31000414
IP;OGP;Process Heaters;Natural Gas: Steam Generators
31000415
IP;OGP;Process Heaters;Process Gas: Steam Generators
31000502
IP;OGP;Liquid Waste Treatment;Liquid - Liquid Separator
31000503
IP;OGP;Liquid Waste Treatment;Oil-Water Separator
31000504
IP;OGP;Liquid Waste Treatment;Oil-Sludge-Waste Water Pit
31000506
IP;OGP;Liquid Waste Treatment;Oil-Water Separation Wastewater Holding Tanks
31088801
IP;OGP;Fugitive Emissions;Specify in Comments Field
12
-------
see
SCC Description*
31088802
IP;OGP;Fugitive Emissions;Specify in Comments Field
31088803
IP;OGP;Fugitive Emissions;Specify in Comments Field
31088804
IP;OGP;Fugitive Emissions;Specify in Comments Field
31088805
IP;OGP;Fugitive Emissions;Specify in Comments Field
31088811
IP;OGP;Fugitive Emissions;Fugitive Emissions
31700101
Industrial Processes;NGTS;Natural Gas Transmission and Storage Facilities;Pneumatic Controllers
Low Bleed
40400300
PSE;PLS;OGFSWT;Fixed Roof Tank: Flashing Loss
40400301
PSE;PLS;OGFSWT;Fixed Roof Tank: Breathing Loss
40400302
PSE;PLS;OGFSWT;Fixed Roof Tank: Working Loss
40400303
PSE;PLS;OGFSWT;External Floating Roof Tank with Primary Seals: Standing Loss
40400304
PSE;PLS;OGFSWT;External Floating Roof Tank with Secondary Seals: Standing Loss
40400305
PSE;PLS;OGFSWT;Internal Floating Roof Tank: Standing Loss
40400306
PSE;PLS;OGFSWT;External Floating Roof Tank: Withdrawal Loss
40400307
PSE;PLS;OGFSWT;Internal Floating Roof Tank: Withdrawal Loss
40400311
PSE;PLS;OGFSWT;Fixed Roof Tank, Condensate, working+breathing+flashing losses
40400312
PSE;PLS;OGFSWT;Fixed Roof Tank, Crude Oil, working+breathing+flashing losses
40400313
PSE;PLS;OGFSWT;Fixed Roof Tank, Lube Oil, working+breathing+flashing losses
40400314
PSE;PLS;OGFSWT;Fixed Roof Tank, Specialty Chem-working+breathing+flashing losses
40400315
PSE;PLS;OGFSWT;Fixed Roof Tank, Produced water, working+breathing+flashing losses
40400316
PSE;PLS;OGFSWT;Fixed Roof Tank, Diesel, working+breathing+flashing losses
40400321
PSE;PLS;OGFSWT;External Floating Roof Tank, Condensate, working+breathing+flashing losses
40400322
PSE;PLS;OGFSWT;External Floating Roof Tank, Crude Oil, working+breathing+flashing losses
40400323
PSE;PLS;OGFSWT;External Floating Roof Tank, Lube Oil, working+breathing+flashing losses
40400324
PSE;PLS;OGFSWT;External Floating Roof Tank, Specialty Chem-working+breathing+flashing losses
40400325
PSE;PLS;OGFSWT;External Floating Roof Tank, Produced water, working+breathing+flashing losses
40400326
PSE;PLS;OGFSWT;External Floating Roof Tank, Diesel, working+breathing+flashing losses
40400331
PSE;PLS;OGFSWT;Internal Floating Roof Tank, Condensate, working+breathing+flashing losses
40400332
PSE;PLS;OGFSWT;Internal Floating Roof Tank, Crude Oil, working+breathing+flashing losses
40400334
PSE;PLS;OGFSWT;Internal Floating Roof Tank, Specialty Chem-working+breathing+flashing losses
40400335
PSE;PLS;OGFSWT;Internal Floating Roof Tank, Produced water, working+breathing+flashing losses
*IP;OGP = Industrial Processes;Oil and Gas Production and
PSE;PLS;OGFSWT=Petroleum and Solvent Evaporation;Petroleum Liquids Storage (non-Refinery);Oil and Gas
Field Storage and Working Tanks
2.1.4 Non-IPM sector (ptnonipm)
Except for some minor exceptions, the non-IPM (ptnonipm) sector contains the 201 INEIvl point
sources that are not in the ptegu, ptegu_pk, 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 sector also includes some ethanol plants that have been identified by EPA but are not in
201 INEIvl.
13
-------
The ptnonipm sector contains a small amount of fugitive dust PM emissions from vehicular traffic on
paved or unpaved roads at industrial facilities, coal handling at coal mines, and grain elevators. Some
point sources in the 201 INEIvl that are not included in any modeling sectors are:
• Sources with state/county FIPS code ending with "777". These sources 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 for modeling. Therefore, these sources are dropped from the point-based sectors in the
modeling platform.
• Offshore oil records with FIPS=85000 were not updated from the 2008NEIv3 and are processed
in the othpt sector as discussed in Section 2.5.1.
Additional Ethanol facilities
Another difference between the 201 INEIvl data and the modeling platform is the addition of some
ethanol production facilities identified by EPA but were not found in the NEI. For some rule
development work, EPA developed a list of corn ethanol facilities for 2011. Many of these ethanol
facilities were included in the 201 INEIvl, but those that were not matched were added to the ptnonipm
sector in a separate FFlO-format inventory file. Locations and FIPS codes for these ethanol plants were
verified using web searches and Google Earth. EPA believes that some of these sources are not included
in the NEI as point sources because they do not meet the 100 ton/year potential-to-emit threshold for
NEI point sources. In other cases, EPA is following up with states to evaluate whether the state data
should include these 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 years 2011 (assumed the same in 2018 and 2025) 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
PMio
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
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 emission rates were estimated by applying toxic to VOC ratios in Table 2-5 were multiplied by
facility production estimates for 2011 and 2018 based on analyses performed for the industry
characterization described in Chapter 1 of the RFS2 final rule regulatory impact analysis. For air toxics
14
-------
except ethanol, 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
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
2.2 2011 nonpoint sources (afdust, ag, npoilgas, rwc, nonpt)
Several modeling platform sectors were created from the 201 INEIvl nonpoint inventory. This section
describes the stationary nonpoint sources. Locomotives, CI and C2 CMV, and C3 CMV are also
included the 201 INEIvl nonpoint data category, but are mobile sources that are described in Sections
2.4.1 and 2.4.2 as the clc2rail and c3marine sectors, respectively. The 201 INEIvl TSD includes
documentation for the nonpoint sector of the 201 INEIvl.
The nonpoint tribal-submitted emissions are dropped during spatial processing with SMOKE due to the
configuration of the spatial surrogates. Part of the reason for this is to prevent possible double-counting
with county-level emissions and also because spatial surrogates for tribal data are not currently
available. These omissions are not expected to have an impact on the results of the air quality modeling
at the 12-km scales used for this platform.
The emissions modeling sector inventories start with the NEI data. Several source categories were not
included in the modeling platform inventories for the following reasons: 1) these sources are only
reported by a small number of states or agencies, 2) these sources are 'atypical' and have small
emissions, and/or 3) EPA has have other data the Agency believes to be more accurate. Table 2-6
provides a list of SCCs removed from the nonpoint sectors, justification for their removal, and the
national annual NOx, VOC and NH3 emission totals. The following subsections describe how the
remaining sources in the 201 INEIvl nonpoint inventory were separated into 2011 modeling platform
sectors, along with any data that were updated replaced with non-NEI data.
Table 2-6. 201 INEIvl nonpoint sources removed from the 2011 platform
see
Description
Reason for
Removal
NOx*
VOC*
MI;
2280003100
Marine Vessels, Commercial; Residual; Port
emissions
Replaced with
OTAQ ECA-
IMO dataset -see
Section 2.4.2
62,906
2,411
23
2280003200
Marine Vessels, Commercial; Residual; Underway
emissions
817,367
30,846
151
2294000000
Paved Roads; All Paved Roads; Total: Fugitives
Replaced with
emissions NOT
reduced via
precipitation -
see Section 2.2.1
2294010000
Paved Roads; All Other Public Paved Roads; Total:
Fugitives
2501060100
Gasoline Stage 2 refueling: Total
Replaced with
MOVES
T3FRM-based
estimates -see
Section 2.3.2
154,349
2501060101
Gasoline Stage 2 refueling: Displacement
Loss/Uncontrolled
6,731
2501060102
Gasoline Stage 2 refueling: Displacement
Loss/Controlled
6,890
15
-------
see
Description
Reason for
Removal
NOx*
voc*
MI;
2501060103
Gasoline Stage 2 refueling: Spillage
2,771
2810005001
Managed Burning, Slash (Logging Debris) ;Pile
Burning
Replaced with
SMARTFIRE 2
estimates -see
Section 2.6
84.5
95
2810005002
Managed Burning, Slash (Logging Debris);Broadcast
Burning
0
0
2810020000
Prescribed Rangeland Burning; Unspecified
2810090000
Open Fire; Not categorized
2275087000
Aircraft; In-flight (non-Landing-Takeoff cycle);Total
Dropped because
they are atypical
and sparsely-
reported
categories with
small emissions
2806010000
Domestic Animals Waste Emissions; Cats; Total
294
2806015000
Domestic Animals Waste Emissions; Dogs; Total
1,674
2807020001
Wild Animals Waste Emissions; Bears; Black Bears
3
2807020002
Wild Animals Waste Emissions; Bears; Grizzly Bears
0
2807025000
Wild Animals Waste Emissions; Elk; Total
1,425
2807030000
Wild Animals Waste Emissions; Deer; Total
1,431
2807040000
Wild Animals Waste Emissions; Birds; Total
0
2810003000
Cigarette Smoke; Total
2
43
4
2810010000
Human Perspiration and Respiration; Total
2,742
2830000000
Catastrophic/Accidental Releases; All; Total
0
167
0
2830010000
Catastrophic/Accidental Releases; Transportation
Accidents; Total
0
2862000000
Swimming Pools; Total (Commercial, Residential,
Public);Total
198
* Emission units are short tons
2.2.1 Area fugitive dust sector (afdust)
The area-source fugitive dust (afdust) sector contains PMio and PM2.5 emission estimates for nonpoint
SCCs identified by EPA staff as dust sources. Categories included in the afdust sector are paved roads,
unpaved roads and airstrips, construction (residential, industrial, road and total), agriculture production,
and mining and quarrying. It does not include fugitive dust from grain elevators, coal handling at coal
mines, or vehicular traffic on paved or unpaved roads at industrial facilities because these are treated as
point sources so they are properly located.
The afdust sector is separated from other nonpoint sectors to allow for the application of a "transport
fraction," and meteorological/precipitation reductions. These adjustments are applied with a script that
applies land use-based gridded transport fractions followed by another script that zeroes out emissions
for days on which at least 0.01 inches of precipitation occurs or there is snow cover on the ground. The
land use data used to reduce the NEI emissions determines the amount of emissions that are subject to
transport. This methodology is discussed in (Pouliot, et. al., 2010), and in "Fugitive Dust Modeling for
the 2008 Emissions Modeling Platform" (Adelman, 2012). The purpose of applying the transport
fraction and meteorological adjustments is to reduce the overestimation of fugitive dust in the grid
modeling as compared to ambient observations. Both the transport fraction and 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.
16
-------
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 2-7.
Table 2-7. SCCs in the afdust platform sector
see
SCC Description
2275085000
Industrial Processes;Construction: SIC 15 - 17;A11 Processes;Vehicle Traffic
2294000000
Industrial Processes;Construction: SIC 15 - 17;Industrial/Commercial/Institutional;Total
2294005000
Industrial Processes;Construction: SIC 15 - 17;Residential;Total
2294010000
Industrial Processes;Construction: SIC 15 - 17;Road Construction;Total
2296000000
Industrial Processes;Construction: SIC 15 - 17;Special Trade Construction;Total
2296005000
Industrial Processes;Mining and Quarrying: SIC 14;A11 Processes;Total
2296010000
Industrial Processes;Mining and Quarrying: SIC 14;Crushed and Broken Stone;Total
2311000070
Industrial Processes;Mining and Quarrying: SIC 14;Sand and Gravel;Total
2311010000
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture -
Crops;Harvesting
2311020000
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Planting
2311030000
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Tilling
2311040000
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Total
2325000000
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Transport
2325020000
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
2325030000
Mobile Sources;Aircraft;Unpaved Airstrips;Total
2801000000
Mobile Sources;Paved Roads;All Other Public Paved Roads;Total: Fugitives
2801000002
Mobile Sources;Paved Roads;All Paved Roads;Total: Fugitives
2801000003
Mobile Sources;Paved Roads;Interstate/Arterial;Total: Fugitives
2801000005
Mobile Sources;Unpaved Roads;All Unpaved Roads;Total: Fugitives
2801000008
Mobile Sources;Unpaved Roads;Industrial Unpaved Roads;Total: Fugitives
2805001000
Mobile Sources;Unpaved Roads;Public Unpaved Roads;Total: Fugitives
The dust emissions in the modeling platform are not the same as the 201 INEIvl 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 paved and unpaved road emissions
data was used that 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.
The total impacts of the transport fraction and meteorological adjustments for 201 INEIvl are shown in
Table 2-8, where the starting inventory numbers include unadjusted paved and unpaved road dust, so
they do not match the NEI values which include a different type of adjustment. The amount of the
reduction ranges from about 6% in New Hampshire to almost 73% in Nevada. Figure 2-1 shows the
impact of each step of the adjustment for January 2008, using the 2008 NEI as an example. The raw
17
-------
NEI afdust PM2.5 emissions - prior to transport fraction meteorological adjustments - are shown at the
top of Figure 2-1. The afdust emissions after the application of the transport fraction, but prior to
meteorological adjustments are shown in the middle of Figure 2-1. Finally, the resulting emissions after
both transport fraction and meteorological adjustments are shown at the bottom of Figure 2-1. The top
and middle plots show 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. Comparing the bottom
and middle plots shows how the meteorological impacts of precipitation, along with snow cover in the
north, further reduce the dust emissions.
Table 2-8. 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
378,873
47,158
-310,750
-38,597
18.0%
18.2%
Arizona
237,361
30,015
-78,519
-9,778
66.9%
67.4%
Arkansas
421,958
58,648
-305,611
-40,757
27.6%
30.5%
California
255,889
38,664
-119,035
-17,930
53.5%
53.6%
Colorado
244,630
40,421
-130,598
-20,991
46.6%
48.1%
Connecticut
29,067
4,393
-25,877
-3,912
11.0%
10.9%
Delaware
11,477
2,046
-7,968
-1,431
30.6%
30.1%
District of Columbia
2,115
337
-1,596
-254
24.5%
24.6%
Florida
292,797
39,636
-181,017
-24,333
38.2%
38.6%
Georgia
733,478
90,041
-593,644
-72,027
19.1%
20.0%
Idaho
432,116
49,294
-291,880
-32,897
32.5%
33.3%
Illinois
763,665
123,680
-472,806
-76,086
38.1%
38.5%
Indiana
603,153
85,151
-435,027
-60,660
27.9%
28.8%
Iowa
590,528
96,070
-339,349
-54,855
42.5%
42.9%
Kansas
748,652
118,902
-353,311
-54,854
52.8%
53.9%
Kentucky
199,744
29,496
-160,640
-23,511
19.6%
20.3%
Louisiana
236,787
35,730
-162,780
-24,086
31.3%
32.6%
Maine
50,547
7,016
-43,643
-6,078
13.7%
13.4%
Maryland
49,225
8,361
-37,192
-6,287
24.4%
24.8%
Massachusetts
205,561
22,444
-177,808
-19,370
13.5%
13.7%
Michigan
462,324
61,969
-353,225
-47,137
23.6%
23.9%
Minnesota
336,791
64,253
-217,036
-41,145
35.6%
36.0%
Mississippi
956,702
107,965
-782,249
-86,685
18.2%
19.7%
Missouri
1,064,146
130,995
-780,595
-94,576
26.6%
27.8%
Montana
385,541
50,583
-266,046
-33,521
31.0%
33.7%
Nebraska
591,457
85,206
-316,917
-45,198
46.4%
47.0%
Nevada
152,191
19,538
-43,681
-5,307
71.3%
72.8%
New Hampshire
25,540
3,766
-23,836
-3,515
6.7%
6.7%
New lersey
24,273
5,412
-19,215
-4,255
20.8%
21.4%
New Mexico
924,497
95,871
-352,117
-36,344
61.9%
62.1%
New York
274,114
37,493
-236,431
-31,990
13.7%
14.7%
North Carolina
186,650
33,409
-146,918
-26,184
21.3%
21.6%
18
-------
State
Unadjusted
PM10
Unadjusted
PM2 5
Change in
PM10
Change in
PM2 5
PM10
Reduction
PM2 5
Reduction
North Dakota
354,107
59,113
-218,630
-36,286
38.3%
38.6%
Ohio
414,902
64,609
-319,831
-49,298
22.9%
23.7%
Oklahoma
733,749
87,864
-385,344
-44,585
47.5%
49.3%
Oregon
348,093
40,596
-268,605
-30,516
22.8%
24.8%
Pennsylvania
208,246
30,344
-179,990
-26,158
13.6%
13.8%
Rhode Island
4,765
731
-3,628
-564
23.9%
22.8%
South Carolina
259,350
31,494
-198,175
-24,002
23.6%
23.8%
South Dakota
262,935
44,587
-155,937
-26,215
40.7%
41.2%
Tennessee
139,732
25,357
-107,964
-19,514
22.7%
23.0%
Texas
2,573,682
304,550
-1,278,048
-146,122
50.3%
52.0%
Utah
196,554
21,589
-113,838
-12,464
42.1%
42.3%
Vermont
67,690
7,563
-61,423
-6,855
9.3%
9.4%
Virginia
131,797
19,374
-108,701
-15,895
17.5%
18.0%
Washington
174,969
27,999
-99,720
-15,425
43.0%
44.9%
West Virginia
85,956
10,652
-79,745
-9,888
7.2%
7.2%
Wisconsin
239,851
41,669
-164,113
-28,542
31.6%
31.5%
Wyoming
434,090
45,350
-264,580
-27,467
39.0%
39.4%
CONUS Total
18,502,317
2,487,403
12,698,646
-1,614,445
-68.6%
-64.9%
19
-------
Figure 2-1. Example of January PM2.5 afdust emissions: raw 2008 NEI (top), after application of
transport fraction (middle) and final post-meteorological adjusted (bottom)
tons ]
16.0299 r
20
-------
2.2.2 Agricultural ammonia sector (ag)
The agricultural NH3 (ag) sector includes livestock and agricultural fertilizer application emissions from
the 201 INEIvl nonpoint inventory. The livestock and fertilizer emissions in this sector are based only
on the SCCs listed in Table 2-9 and Table 2-10. 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 2-9. 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
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)
2805030001
Poultry Waste Emissions;Pullet Chicks and Pullets less than 13 weeks old
2805030002
Poultry Waste Emissions;Pullets 13 weeks old and older but less than 20 weeks old
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
2805039300
Swine production - operations with lagoons (unspecified animal age);Land application of manure
21
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SCC
SCC Description*
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 2-10. 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 Nonpoint source oil and gas sector (np_oilgas)
The nonpoint oil and gas (npoilgas) 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,
storage tanks, flares, truck loading, compressor engines, and dehydrators. For more information on the
development of the oil and gas emissions in the 201 INEIvl, see Section 3.21 of the 201 INEIvl TSD.
A complete list of SCCs for the np oilgas modeling platform sector is provided in Appendix A. See the
pt oilgas sector (section 2.1.3) for more information on point source oil and gas sources.
2.2.4 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
chimineas. Free standing woodstoves and inserts are further differentiated into three categories:
22
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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 INEIvl TSD.
The SCCs in the rwc sector are shown in Table 2-11.
Table 2-11. 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)
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, chimineas, etc)
2104009000
SSFC;Residential;Firelog;Total: All Combustor Types
* SSFC=Stationary Source Fuel Combustion
2.2.5 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 INEIvl 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;
• 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;
23
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• 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.
Most sources in this sector have annual emissions that are temporally allocated to hourly values using
temporal profiles. The annual agricultural burning estimates are treated as monthly values. The annual
values in the 201 INEIvl were split into monthly emissions by aggregating the data up to monthly values
from daily estimates of emissions.
2.3 2011 onroad mobile sources (onroad, onroad_rfl)
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 and gasoline vehicles.
The sector characterizes emissions from off-network processes (e.g. starts, hot soak, and extended idle)
as well as from on-network processes (i.e., from vehicles moving along the roads). For the 2011
platform, as indicated in Table 2-1, the 2011 onroad emissions are separated into two sectors: (1)
"onroad" and (2) "onroadrfl". The onroad and onroadrfl sectors are processed separately to allow for
different spatial allocation to be applied to onroad refueling, which is allocated using a gas station
surrogate, versus onroad vehicles, which are allocated using surrogates based on roads and population.
Except for California and Texas, all onroad and onroad refueling emissions are generated using the
SMOKE-MOVES emissions modeling framework that leverages MOVES generated outputs and hourly
meteorology. All tribal data from the mobile sectors have been dropped because the emissions are
small, the emissions could be double-counted with state-provided onroad emissions, all tribal data was
developed using the older model MOBILE6, and because spatial surrogate data is not currently
available.
2.3.1 Onroad non-refueling (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 in use by
states and regional planning organizations 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
temperature and speed for a series of "representative counties," to which every other county was
mapped. Using the MOVES emission rates, SMOKE selects appropriate emissions rates for each
county, hourly temperature, SCC, and speed bin and multiplied the emission rate by activity (VMT
(vehicle miles travelled) or VPOP (vehicle population)) to produce emissions. These calculations were
done for every county and grid cell, in the continental U.S. for each hour of the year.
Using SMOKE-MOVES for creating the model-ready emissions requires numerous steps:
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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 inputs needed only by MOVES. MOVES requires 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 a list of temperatures and
activity data
5) Run MOVES to create emission factor tables
6) Run SMOKE to apply the emission factors to activity data (VMT and VPOP) to calculate
emissions
7) Aggregate the results to the county-SCC level for summaries and quality assurance
The onroad emissions inputs are similar to the emissions in the onroad data category of the 201 INEIvl,
described in more detail in Section 4.6 of the 201 INEIvl TSD. Specifically the platform and
201 INEIvl have identical:
• MOVES County databases (CDBs)
• Fuels
• Representative counties
• Fuel months
• Meteorology
• Activity data (VMT, VPOP, speed)
• Extended idle adjustments
Despite the commonalities, there are some key differences between the two onroad emission inventories:
• The 201 INEIvl used MOVES2010b to create the emission factor (EF) tables, while the
2011v6.1 platform used the MOVESTier3FRM (specifically, model "Moves 20121002P and
the default database "movesdb201210021_truncatedgfre") for the EFs.
• 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.
• The treatment of Texas and California emissions differs between the two inventories (see below
for more details).
• The list of emission modes differ between the two inventories. Both SMOKE-MOVES runs
were generated at the same level of detail, but the NEI emissions were aggregated into 4 all-
inclusive modes: exhaust (including extended idle), evaporative (including permeation), brake
wear, and tire wear. The list of modes and the corresponding MOVES processes mapped to
them are listed in Table 2-12.
25
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Table 2-12. Onroad emission modes
Mode
Description
MOVES process IDs
EXH
Exhaust, including running and starts, excluding extended idle
1;2;15;16
EXT
Extended idle exhaust from long-haul trucks
17;90
APU3
Auxiliary Power Unit exhaust from long-haul trucks
91
EVP
Evaporative emissions, including vapor venting and fuel leaks, excluding
permeation
12; 13
EPM
Evaporative permeation
11
RFL
Refueling
18; 19
BRK
Brake wear
9
TIR
Tire wear
10
For more detailed information on methods used to develop the onroad emissions and input data sets and
on running SMOKE-MOVES, see the 201 INEIvl TSD.
The California and Texas onroad emissions were created through a hybrid approach of combining state-
supplied annual emissions (from the 201 INEIvl) with EPA developed SMOKE-MOVES runs. Through
this approach, the platform was able to reflect California's unique rules and Texas' detailed modeling,
while leveraging the more detailed SCCs and the highly resolved spatial patterns, temporal patterns, and
speciation from SMOKE-MOVES. The basic steps involved in temporally allocating California's and
Texas' onroad emissions based on SMOKE-MOVES results were:
1) Run CA/TX using EPA inputs through SMOKE-MOVES to produce hourly 2011 emissions
hereafter known as "EPA estimates". These EPA estimates for CA/TX are run in a separate
sector called "onroad catx".
2) Calculate ratios between state-supplied emissions and EPA estimates4. For Texas, these ratios
were calculated for each county/SCC7 (fuel and vehicle type)/pollutant combination. For
California, these were calculated for each county/SCC3 (fuel type)/pollutant combination. These
were not calculated at a greater resolution because California's emissions did not provide data
for all vehicle types.
3) Create an adjustment factor file (CFPRO) that includes EPA-to-state estimate ratios. For
extended idle adjustments, each specific state ratio (county/SCC Group (7 or 3)/pollutant) was
multiplied by the extended idle adjustment factor (see the 201 INEIvl TSD for details).
4) Rerun CA/TX through SMOKE-MOVES using EPA inputs and the new adjustment factor file.
Through this process, adjusted model-ready files were created that sum to California's and Texas'
annual totals, but have the temporal and spatial patterns reflecting the highly resolved meteorology and
SMOKE-MOVES. After adjusting the emissions, this sector is called "onroad catx adj". Note that in
3 APU emissions are only in the future year projections of the MOVESTier3FRM version of the model. As part of the HD
GHG rule, a certain percentage of long-haul combination trucks will start to use APU's instead of extended idle for hoteling
overnight.
4 These ratios were created for all matching pollutants. These ratios were duplicated for all appropriate modeling species.
For example, EPA used the NOx ratio for NO, NO2, HONO and used the PM2 5 ratio for PEC, PNO3, POC, PSO4, and
PMFINE (For more details on NOx and PM speciation, see Sections 3.2.3 and 3.2.2). For VOC model-species, if there was
an exact match (e.g., BENZENE), EPA used that HAP pollutant ratio. For other VOC-based model-species that didn't exist
in the NEI inventory, EPA used VOC ratios.
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emission summaries, the emissions from the "onroad" and "onroad catx adj" sectors are summed and
designated as the emissions for the onroad sector.
2.3.2 Onroad refueling (onroad_rfl)
Onroad refueling is modeled very similarly to other onroad emissions, and were generated using
MOVESTier3FRM. The onroadrfl emissions are spatially allocated to gas station locations (see Section
3.4.1). Because the refueling emission factors use the same SCCs as the other onroad models, refueling
was run in a separate sector from the other onroad mobile sources to allow for the different spatial
allocation. To facilitate this, the refueling EFs were separated from the other emission factors into rate-
per-distance (RPD) refueling and rate-per-vehicle (RPV) refueling tables5. SMOKE-MOVES was run
using these EF tables as inputs, and spatially allocated using a gas stations spatial surrogate. Lastly, the
SMOKE program Mrggrid combined RPD refueling and RPV refueling into a single onroad rfl model-
ready output for final processing with the other sectors prior to use in CMAQ. EPA SMOKE-MOVES
generated emissions for onroad refueling were used without any adjustments for all states, including
California and Texas. These emissions were used instead of state submissions to provide a consistent
approach nationwide and also because most states did not submit refueling emissions for diesel fuel.
Since the 201 INEIvl includes the state-submitted emissions, the platform and the NEI refueling
emissions in the nonpoint category are inconsistent for states that submitted refueling emissions. For
states that didn't submit emissions, the approaches are similar but not identical because of differences in
the MOVES version, specifically 2010b for the NEI and Tier3FRM for the modeling platform.
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 Class 1/Class 2 Commercial Marine Vessels and Locomotives and
(c1c2rail)
The clc2rail sector contains locomotive and smaller CMV sources, except for railway maintenance
locomotives and C3 CMV sources outside of the Midwest states. The "clc2" portion of this sector name
refers to the Class 1 and 2 CMV emissions, not the railway emissions. Railway maintenance emissions
are included in the nonroad sector. The C3 CMV emissions are in the c3marine sector. All emissions in
this sector are annual and at the county-SCC resolution.
The starting point for the clc2rail sector is the 201 INEIvl nonpoint inventory for all but specific
Midwest states, which are instead derived from the Great Lakes 2010 CMV inventory. As discussed in
Table 2-1 and Table 2-2, the modeling platform emissions for the clc2rail SCCs were extracted from the
NEI nonpoint inventory. For more information on CMV sources in the NEI, see Section 4.3 of the
201 INEIvl TSD. For more information on locomotives, see Section 4.4 of the 201 INEIvl TSD. Table
2-13 lists the NEI SCCs included in the clc2rail sector of the modeling platform.
Table 2-13. 201 INEIvl 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
5 The Moves2smk post-processing script has command line arguments that will either consolidate or split out the refueling
EF.
27
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SCC
Description: Mobile Sources prefix for all
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
The difference between the 201 INEIvl and the modeling platform for this sector is due to the
availability of alternative data from the Midwest RPO. Year-2010 emissions were received from the
Lake Michigan Air Directors Consortium for tug boats, Great Lakes vessels ("Lakers") and inland
waterways for states within the Midwest RPO and Minnesota, hereafter simply referred to as
"MWRPO" (LADCO). The states in the MWRPO are: Illinois, Indiana, Michigan, Minnesota, Ohio and
Wisconsin. These MWRPO CMV emissions include coverage for bordering states/counties along the
inland waterways such as the Mississippi and Ohio rivers in Iowa, Missouri, Kentucky, West Virginia,
Pennsylvania and New York. The LADCO 2010 inventory was used to replace EPA-estimated CMV
emissions in the MWRPO states, but was not used to replace the 201 INEIvl emissions in the bordering
non-MWRPO states.
Some modifications to the MWRPO CMV data were made prior to SMOKE processing:
• Emissions provided at the level of NEI Shape IDs were aggregated to county-level.
• The 201 INEIvl was used to determine which counties had ports; for those counties that had
ports, 90% of emissions in the MWPRO inventory were assigned as underway
(SCC=2280002200) and 10% were assigned as port emissions (SCC=2280002100).
• Emissions were converted to short tons and PM2.5 was added by assuming it is equal to 92% of
PM10 at the suggestion of the MWRPO.
• Tugs were assigned a unique SCC (2280002021) to allow for unique spatial allocation (see
Section 3.4.1).
• Tugs were assigned from MWRPO total to counties based on 201 INEIvl county-level activity
information for tug vessels.
Because the Great Lakes vessels include all CMV activity on the Great Lakes, EPA-estimated C3 CMV
(c3marine) sector emissions (discussed in the following section) in the MWRPO states were removed to
avoid potential double-counting of C3 CMV with the LADCO inventory in the MWRPO states.
2.4.2 Class 3 commercial marine vessels (c3marine)
The U.S. C3 CMV inventory was developed based on a 4-km resolution ASCII raster format dataset
used since the Emissions Control Area-International Marine Organization (ECA-IMO) project began in
2005, then known as the Sulfur Emissions Control Area (SECA). The ECA-IMO data are used instead
of the 201 INEIvl data for the modeling platform because accompanying estimates of emission
projections for future years are available. In addition, the inventory preserves shipping lanes in federal
waters while these are not stored within the NEI data. Keeping the sources in this sector separate from
smaller CMV sources allows for the emissions to be elevated above the surface layer within the AQ
model. The ECA-IMO data are used for all states with C3 CMV emissions. For the MWPRO states, the
ECA-IMO C3 CMV emissions in the Great Lakes are assumed to be misclassified as C3 vessels for
which emissions are included in the clc2rail sector as part of the LADCO inventory, therefore the ECA-
IMO emissions are not included in the c3marine sector.
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The development of this ECA-IMO-based C3 CMV inventory is discussed below; however, all non-U. S.
emissions (Canadian emissions and emissions farther offshore than U.S. waters) are processed in the
"othpt" sector, discussed later in Section 2.5.1. This splitting of the C3 CMV emissions from the farther
offshore emissions allows for easier summaries of U.S.-only and state or county total emissions.
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 often burn residual fuel.
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 EC A 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: Vehicles and Engines. 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.
The ECA-IMO emissions data were converted to SMOKE point-source ORL input format. 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. Counties were
assigned as extending up to 200nm from the coast because this was the distance to the edge of the U.S.
Exclusive Economic Zone (EEZ), a distance that defines the outer limits of ECA-IMO controls for these
vessels.
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 2-14. The emissions were converted to SMOKE point source inventory
format, allowing for the emissions to be allocated to modeling layers above the surface layer. 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 ECA" factors in Table 2-14. 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 years.
For California, we scaled the resulting ECA-IMO 2011 emissions by county 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. 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_A1S[NUAL_TRAINS.txt". Documentation of the CARB offroad mobile
methodology, including clc2rail sector data.
29
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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).
The Canadian near-shore emissions were assigned to province-level FIPS codes and paired those to
region classifications for British Columbia (North Pacific), Ontario (Great Lakes) and Nova Scotia (East
Coast).
Table 2-14. Growth factors to project the 2002 ECA-IMO inventory to 2011
Region
EEZ
FIPS
NOx
PMio
PM2.5
voc
(HC)
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
* Technically, these are not really "FIPS" state-county codes, but are treated as such in the
inventory and emissions processing.
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 these
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
Lakes are assigned to a U.S. county or Ontario. This holds true for MWRPO 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.
30
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Figure 2-2. Illustration of regional modeling domains in ECA-IMQ study
SP
The emissions were converted to SMOKE point source inventory format, allowing for the emissions to
be allocated to modeling layers above the surface layer. 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 ECA" factors in Table 2-14. 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 years6.
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.
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 INEIvl, with the exception that the modeling platform emissions also include monthly totals. 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 INEIvl nonroad emissions are available in Section 4.5 the 201 INEIvl TSD.
6 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.
31
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California year 2011 nonroad emissions were submitted to the 201 INEIvl 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
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 201 INEIvl 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 INEIvl 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 poll7.
2.5 "Other Emissions": Offshore Class 3 commercial marine vessels and
drilling platforms and non-U.S. sources
The emissions from Canada, Mexico, and non-U.S. offshore Class 3 Commercial Marine Vessels (C3
CMV) and drilling platforms are included as part of three emissions modeling sectors: othpt, othar, 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 third and fourth characters provide the SMOKE source types: "pt" for point,
"ar" for "area and nonroad mobile", and "on" for onroad mobile.
For Canada, year-2006 Canadian emissions were the starting point with the addition of several
modifications to these inventories. The SCCs in these inventories were changed to the generic
39999999 and the industrial code information was removed to preserve confidentiality. The Canadian
point sources are split into three inventory files:
• ptinv_canada_point_2006_orl_13aug2013_v3_orl.txt: contains point sources for all pollutants
except VOC;
• ptinv_canada_point_cb5_2006_orl_13aug2013_vl_orl.txt: contains VOC emissions split into
CB05 species;
• ptinv_canada_point_uog_2006_orl_02mar2009_v0_orl.txt: contains oil and gas-related sources.
For Mexico, emissions for year 2012 are projections of their 1999 inventory originally developed by
Eastern Research Group Inc., (ERG, 2006; ERG, 2009; Wolf, 2009) as part of a partnership between
7 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.
32
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Mexico's Secretariat of the Environment and Natural Resources (Secretaria de Medio Ambiente y
Recursos Naturales-SEMARNAT) and National Institute of Ecology (Instituto Nacional de Ecologia-
INE), the U.S. EPA, the Western Governors' Association (WGA), and the North American Commission
for Environmental Cooperation (CEC). This inventory includes emissions from all states in Mexico. A
background on the development of year-2012 Mexico emissions from the 1999 inventory is available at:
WRAP.
2.5.1 Point sources from offshore C3 CMV and drilling platforms and Canada and
Mexico (othpt)
As discussed in Section 2.4.2, the ECA-IMO-based C3 CMV emissions for non-U.S. states are
processed in the othpt sector. These C3 CMV emissions include those assigned to Canada, those
assigned to the Exclusive Economic Zone (EEZ, 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 -far offshore and non-U.S. These emissions are included in the othpt sector
for simplicity of creating U.S.-only emissions summaries. Otherwise, these emissions are developed in
the same way as the U.S. C3 CMV emissions in the c3marine sector.
For Canadian point sources, other than some minor formatting changes, the Canada-provided year-2006
emissions were modified as follows:
i. Speciated VOC emissions from the Acid Deposition and Oxidant Model (ADOM) chemical
mechanism were not included because EPA modeling uses speciated emissions from the CB5
chemical mechanism, which Canada also provided.
ii. Excessively high CO emissions were removed from Babine Forest Products Ltd (British
Columbia SMOKE plantid='5188') in the point inventory. This change was made at EPA's
discretion because the value of the emissions was impossibly large.
iii. The county part of the state/county FIPS code field in the SMOKE inputs were modified in the
point inventory from "000" to "001" to enable matching to existing temporal profiles.
iv. An update to the 2007 platform version was the removal of three units that closed in 2010: Grand
Lake Generating Station in New Brunswick (PLANTID=' 1708', POINTID=' 130011'),
Raffinerie de Montreal-Est in Quebec (PLANTID='3127', POINITD='53202982') and Kidd
Metallurgical Site in Ontario (PLANTID='2815', POINTID='ON500004').
Mexico point-format year-2012 inventories projected from the 1999 Mexico NEI were used essentially
as-is with only minor formatting changes. 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, the 2008 NEI version 3 point source inventory data were used because the 2011 data
were not yet available. This is consistent with the 201 INEIvl. Updated offshore oil and gas drilling
emissions are expected to be incorporated into version 2 of the 2011 NEI. The 2008-based offshore
emission sources were provided by the Mineral Management Services (MMS).
2.5.2 Area and nonroad mobile sources from Canada and Mexico (othar)
For Canada, year-2006 emissions provided by Canada and unchanged from EPA 2007 platform were
used. Inventory files were provided for area fugitive dust, agricultural, commercial marine, railroad,
nonroad, aircraft, and other area sources. The following adjustments were made to the original files:
i. Wildfires or prescribed burning were not included because Canada does not include these
inventory data in their modeling. Note that SMARTFIRE 2 is used for U.S. sources only.
33
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ii. In-flight aircraft emissions were not included because these sources are not included in the U.S.
modeling.
iii. A 75% reduction ("transport fraction") was applied to PM for the road dust, agricultural, and
construction emissions in the Canadian "afdust" inventory. This approach is more simplistic
than the county-specific approach used for the U.S., but a comparable approach was not
available for Canada.
iv. Wind erosion (SCC=2730100000) and cigarette smoke (SCC=2810060000) emissions were
removed from the nonpoint (nonpt) inventory because these emissions are not modeled in the
U.S. inventory.
v. Quebec PM2.5 emissions (2,000 tons/yr) were removed for one SCC (2305070000) for Industrial
Processes, Mineral Processes, Gypsum, Plaster Products due to corrupt fields after conversion to
SMOKE input format.
vi. C3 CMV SCCs (22800030X0) records were removed because, as discussed in Section 2.5.1,
these emissions are included in the (ECA-IMO derived) othpt sector, which covers not only
emissions close to Canada but also emissions far at sea. Canada was involved in the inventory
development of the ECA-IMO C3 CMV inventory.
For Mexico nonpoint-format year-2012 inventories, the only significant modification was the removal of
domestic ammonia (SCC=5555555555) (ERG, 2009; Wolf, 2009).
2.5.3 Onroad mobile sources from Canada and Mexico (othon)
Both year-2006 Canada and year-2012 Mexico inventories (ERG, 2009; Wolf, 2009) were converted
from their original SMOKE One-Record per Line (ORL) and Inventory Data Analyzer (IDA) formats,
respectively, into the SMOKE Flat File 2010 (FF10) inventory format. Otherwise, these inventories
were used as-is. The emission values in the Canada-provided Canadian inventories were unchanged
from the 2007 platform.
2.6 Fires (ptfire)
Wildfire and prescribed burning emissions are contained in the ptfire sector. The ptfire sector has
emissions provided at geographic coordinates (point locations) and has daily emissions values. The
ptfire sector excludes agricultural 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 2-15 are treated as point sources and
are consistent with the fires stored in the Events data category of the 201 INEIvl. For more information
on the development of the 201 INEIvl fire inventory, see Section 5.1 of the 201 INEIvl TSD.
Table 2-15. 2011 Platform SCCs representing emissions in the ptfire modeling sector
SCC
SCC Description*
2810001000
Other Combustion; Forest Wildfires; Total
2810015000
Other Combustion; Prescribed Burning for Forest Management; Total
2811015000
Other Combustion-as Event; Prescribed Burning for Forest Management; Total
2811090000
Other Combustion-as Event; Prescribed Forest Burning ;Unspecified
* 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
34
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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 INEIvl TSD. These changes made the data in the ptfire inventory consistent with the data in the
201 INEIvl.
2.7 Biogenic sources (biog)
The biogenic emissions were computed based on the 1 lg version of the 2011 meteorology data using the
Biogenic Emission Inventory System, version 3.14 (BEIS3.14) model within SMOKE. The BEIS3.14
model creates gridded, hourly, model-species emissions from vegetation and soils. It estimates CO,
VOC (most notably isoprene, terpene, and sesquiterpene), and NO emissions for the U.S., Mexico, and
Canada. The BEIS3.14 model.
The inputs to BEIS include:
• Temperature data at 2 meters, which were obtained from the meteorological input files to the air
quality model,
• Land-use data from the Biogenic Emissions Land use Database, version 3 (BELD3). BELD3
data provides data on the 230 vegetation classes at 1-km resolution over most of North America.
To provide a sense of the scope and spatial distribution 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.
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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Figure 2-3. Annual NO emissions output from BEIS 3.14 for 2011
Biogenic NO
2011
1 57 113 169 225 281 337 393
January 1, 2011 00:00:00 UTC
Min (1,1)= 0., Max (192, 3) = 138.
Figure 2-4. Annual isoprene emissions output from BEIS 3.14 for 2011
Biogenic Isoprene
2011
1 50 99 148 197 246 295 344 393
January 1, 2011 00:00:00 UTC
Min (1,1)= 0., Max (210, 58) = 2258.
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3 Emissions Modeling Summary
Both the CMAQ and CAMx models require 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 layer of the sources does not need to be 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, which also
can be different for different sectors, may be individual point sources, county/province/municipio totals,
or gridded emissions. 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 INEIvl 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 3.5.1 was used to pre-process the raw emissions inventories into emissions inputs for
CMAQ. For projects that used CAMx, the CMAQ emissions were converted into the CAMx formats
using CAMx converter programs. 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.
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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 c3marine, othpt, and
ptfire sectors are the only sectors with only "in-line" emissions, meaning that all of the emissions are
placed in aloft layers and there are no emissions for those sectors in the two-dimensional, layer-1 files
created by SMOKE.
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
(some monthly)
beis
Pre-gridded
land use
in BEIS3 .14
computed hourly
clc2rail
Surrogates
Yes
annual
c3 marine
Point
Yes
annual
in-line
nonpt
Surrogates &
area-to-point
Yes
annual
(some monthly)
nonroad
Surrogates &
area-to-point
Yes
monthly
np oilgas
Surrogates
Yes
annual
onroad
Surrogates
Yes
monthly activity,
computed hourly
onroadrfl
Surrogates
Yes
monthly activity,
computed hourly
othar
Surrogates
Yes
annual
othon
Surrogates
Yes
annual
othpt
Point
Yes
annual
in-line
pt oilgas
Point
Yes
annual
in-line
ptegu
Point
Yes
daily & hourly
in-line
ptegu pk
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.
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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 Contutental ITS 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 gnd
12 km
Entire conterminous
US plus some of
Mexico/Canada
12US1_459X299
'LAM 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
'LAM 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.
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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 or groups of species, called "model species." The chemical mechanism used for the 2011
platform is the CB05 mechanism (Yarwood, 2005). The same base chemical mechanism is used within
both CMAQ and CAMx, but the implementation differs slightly between the two models. The specific
versions of CMAQ and CAMx used in applications of this platform include secondary organic aerosol
(SOA) and HONO enhancements.
From the perspective of emissions preparation, the CB05 with SOA mechanism is the same as was used
in the 2007 platform. Table 3-3 lists the model species produced by SMOKE for use in CMAQ and
CAMx. It should be noted that the BENZENE model species is not part of CB05 in that the
concentrations of BENZENE do not provide any feedback into the chemical reactions (i.e., it is not
"inside" the chemical mechanism). Rather, benzene is used as a reactive tracer and as such is impacted
by the CB05 chemistry. BENZENE, along with several reactive CB05 species (such as TOL and XYL)
plays a role in SOA formation.
The TOG and PM2.5 speciation factors that are the basis of the chemical speciation approach were
developed from the SPECIATE 4.3 database, 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 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.
Speciation profiles and cross-references for 201 lv6 platform are available in spreadsheet form from
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/reports/speciation profiles/. The profiles are in the
Excel files "gspro_2011 .xlsx" and "gspro_combo_2011 .xlsx, gsref_2011 .xlsx". The cross reference
information is in "gsref_201 l.xlsx", and differences between 2011 and 2018 speciation profiles are
shown in "201 led_2018ed_gspro_differences.xlsx". A spreadsheet showing emission totals for each
speciation profile for the 201 led case by modeling sector is available in the file
"201 led_speciation_profile_CAPs_febl 12014.xlsx". Note that the emissions totals differ slightly from
the 201 lef case, as do some of the VOC to TOG conversion factors. However, the reports still convey
the relative importance of each speciation profile in terms of emissions affected.
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Table 3-3. Emission model species produced for CB05 with SOA for CMAQ5.0.1 and CAMx*
Inventory Pollutant
Model Species
Model species description
Ch
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
so2
S02
Sulfur dioxide
SULF
Sulfuric acid vapor
nh3
NH3
Ammonia
VOC
ALD2
Acetaldehyde
ALDX
Propionaldehyde and higher aldehydes
BENZENE
Benzene (not part of CB05)
CH4
Methane8
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
SESQ
Sesquiterpenes
model that do not map to model
species above
TERP
Terpenes
PMio
PMC
Coarse PM >2.5 microns and <10 microns
PM2.59
PEC
Particulate elemental carbon <2.5 microns
PN03
Particulate nitrate <2.5 microns
POC
Particulate organic carbon (carbon only) <2.5 microns
PS04
Particulate Sulfate <2.5 microns
PMFINE
Other particulate matter <2.5 microns
Sea-salt species (non -
PCL
Particulate chloride
anthropogenic)10
PNA
Particulate sodium
*The same species names are used for the CAMX model with exceptions as follows:
1. CL2 is not used in CAMx
2. CAMx particulate sodium is NA (in CMAQ it is PNA)
3. CAMx uses different names for species that are both in CB05 and SOA for the following: TOLA=TOL, XYLA=XYL,
ISP=ISOP, TRP=TERP. They are duplicate species in CAMX that are used in the SOA chemistry. CMAQ uses the same
names in CB05 and SOA for these species.
4. CAMx uses a different name for sesquiterpenes: CMAQ SESQ = CAMX SQT
5. CAMx particulate species have different names for organic carbon, coarse particulate matter and other particulate mass:
CMAQ uses POC, PMC, PMFINE, and PMOTHR, while CAMx uses POA, CPRM, FCRS, and FPRM, respectively.
3.2.1 VOC speciation
3.2.1.1 The combination of HAP BAFM (benzene, acetaldehyde, formaldehyde and
methanol) and VOC for VOC speciation
41
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The VOC speciation includes HAP emissions from the 201 INEIvl 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 process (described in more detail below)
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 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 generally, the
HAP emissions from the NEI are more representative of emissions of these compounds than their
generation via VOC speciation.
The BAFM HAPs (benzene, acetaldehyde, formaldehyde and methanol) were chosen because, with the
exception of BENZENE, they are the only explicit VOC HAPs in the base version of CMAQ 5.0.1
(CAPs only with chlorine chemistry) model. Explicit means that they are not lumped chemical groups
like the other CB05 species. These "explicit VOC HAPs" are model species that participate in the
modeled chemistry using the CB05 chemical mechanism. The use of these HAP emission estimates
along with VOC is called "HAP-CAP integration". BENZENE was chosen because it is a model
species in the base version of CMAQ 5.0.1, and there was a desire to keep its emissions consistent
between multi-pollutant and base versions of CMAQ.
For specific sources, especially within the onroad and onroad rfl sectors, 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" (to be used
when the ethanol comes from the inventory pollutant). For example, use E10 headspace gasoline
evaporative speciation profile 8763 when ethanol is speciated from VOC, but use 8763E 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
NHAPEXCLUDE file (which actually provides the sources to be excluded from integration11). 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 profiles12. 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
8 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.
9 For CMAQ 5.0, PM2 5 is speciated into a finer set of PM components. Listed in this table are the AE5 species
10 These emissions are created outside of SMOKE
11 In SMOKE version 3.5, 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.
12 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.
42
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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
f *
I I
: ii st "ncH niegrsie' 1
I sources iNhAPEXCLUDE) I
• Speciati on Lross J
• -«Srenc« FilefGSREF) :
VOil-to-TOG factors
NON-AF v'OC-t c-N O N K APTQG
factors (GSOWJ
TOG and NONHAPTOG
ipeci&ti on "actors
(GSPRC)
Speciateci Emssionsfor VOC species
! Emissions ready for SMOKE i
I...
i
4- SMOKE
f Compute NONHAFVOC- VOC- |B+ F+ A+M)
1 emissionsfor each iintegrale source
1 Retain VOC emissions far each no-integrate source
Assign speciation profile code to each emission source
¦
s
I Compute: NONRAPTtXS emissions from NOlNHAPVOC for
I each integrate source
1 Compute: TOG emissions from VOC for each no-integrate
I source
1 Compute moles of each CBOS rrode! species.
J Use NONHAPTOG profiles applied to NONHAPTOG
J emissions and B, F, A, M emissions far integrate sources.
I Use TOG profiles appiied to TOG far no-integrate sources
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, such as the onroad and
onroad rfl sectors.
43
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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
ptegu_pk
No integration
ptnonipm
No integration
ptfire
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
nonpt
Partial integration (BAFM and EBAFM)
np oilgas
Partial integration (BAFM)
pt oilgas
Partial integration (BAFM)
rwc
Partial integration (BAFM)
nonroad
Partial integration (BAFM)
clc2rail
Partial integration (BAFM)
othpt
Partial integration (BAFM)
c3marine
Full integration (BAFM)
onroad
Full integration (EBAFM and BAFM)
onroad rfl
Full integration (EBAFM and BAFM)
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 onroad and 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. For the onroad and
onroad rfl sectors, the GSPRO COMBO uses E-profiles (i.e. there is EBAFM integration). Different
profile combinations are specified by the mode (e.g. exhaust, evaporative, refueling, etc.) by changing
the pollutant name (e.g. EXH NONHAPTOG, EVP NONHAPTOG, RFL 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 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
44
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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 (VOC is speciated directly), for some sectors there is full integration (all
sources are integrated), and for other sectors there is partial integration (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 summarizes the
integration for each platform sector.
For the clc2rail sector, EPA integrated BAFM for most sources from the 201 INEIvl. There were a few
sources that had zero BAFM; therefore, they were not integrated. The MWRPO and 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. c3marine sector. The rest of the sources in othpt are not integrated, thus
the sector is partially integrated.
For the onroad and onroadrfl sectors, there are series of unique speciation issues. First, SMOKE-
MOVES (see Sections 2.3.1 and 2.3.2) is used to estimate these sectors, meaning both the MEPROC
and INVTABLE files are involved in controlling which pollutants are ingested and speciated. Second,
these sectors have estimates of TOG as well as VOC; therefore, TOG can be speciated directly. Third,
the gasoline sources use full integration of EBAFM (i.e. use E-profiles) and the diesel sources use full
integration of BAFM. Fourth, the onroad sector utilizes 7 different modes for speciation: exhaust,
extended idle, auxiliary power units, evaporative, permeation (gasoline vehicles only), brake wear, and
tire wear (See Table 2-12 for more details). The onroad rfl sector utilizes an additional mode: refueling.
Fifth, the gasoline exhaust profiles were updated to 8750a (revision to Gasoline Exhaust - Reformulated
gasoline) and 8751a (revision to Gasoline Exhaust - E10 ethanol gasoline)13. Sixth, for California and
Texas, EPA applied adjustment factors to SMOKE-MOVES to produce California and Texas adjusted
model-ready files (see Section 2.3.1 for details). By applying the ratios through SMOKE-MOVES, the
CARB and Texas inventories are essentially speciated to match EPA estimated speciation grid cell by
grid cell. The future year CARB inventories did not have BAFM, so EPA estimates of BAFM were
adjusted using VOC adjustment factors for California only.
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 do not have BAFM or BAFM is greater than VOC are not integrated. The remaining sources
are integrated. The future year CARB inventories did not have BAFM so all sources for California were
13 These revised profiles are expected to be in the yet to be released SPECIATE 4.4.
45
-------
not integrated. Similar to onroad, the gasoline exhaust profiles were updated to 8750a and 8751a (this is
true nation-wide).
For the ptnonipm sector, the 2011, 2018 and 2025 runs were not integrated. This was an oversight— it
should have been partial integration because the 2011 ethanol inventory (SCC 30125010) provided by
OTAQ includes BAFM. In the future year, ptnonipm should be partially integrated because both the
ethanol and biodiesel inventories (SCC 30125010) provided by OTAQ include BAFM. Aircraft
emissions use the profile 5565b which is chemically equivalent to 5565 (aircraft exhaust) in SPECIATE
4.3 database. The profile numbers are differentiated from each other because a draft version of 5565
was used in previous modeling platforms.
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.
For the oil and gas sources in np oilgas and pt oilgas, 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 3-5 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 3-6). Table 3-7 lists the
WRAP Phase III counties.
Table 3-5. 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
SWE01
Wyoming Flashing Gas Composition
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 3-6. National VOC profiles for oil and gas
profile
Description
0000
Over All Average
0001
External Combustion Boiler - Residual Oil
46
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profile
Description
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
2489
Composite of 15 Fugitive Emission Profiles from Petroleum Storage Facilities - 1993
Table 3-7. 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
MPS
State
County
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
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
MPS
State
County
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
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
47
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MPS
State
County
48495
TX
Winkler
48501
TX
Yoakum
49007
UT
Carbon
49009
UT
Daggett
49013
UT
Duchesne
49015
UT
Emery
49019
UT
Grand
49043
UT
Summit
MPS
State
County
49047
UT
Uintah
56001
WY
Albany
56005
WY
Campbell
56007
WY
Carbon
56009
WY
Converse
56011
WY
Crook
56013
WY
Fremont
56019
WY
Johnson
MPS
State
County
56023
WY
Lincoln
56025
WY
Natrona
56027
WY
Niobrara
56033
WY
Sheridan
56035
WY
Sublette
56037
WY
Sweetwater
56041
WY
Uinta
56045
WY
Weston
For the biog sector, the speciation profiles used by BEIS are not included in SPECIATE. The 2011
platform uses BEIS3.14, which includes a new species (SESQ) that was mapped to the model species
SESQT. The profile code associated with BEIS3.14 profiles for use with CB05 uses the profile:
"B10C5."
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, onroadrfl, nonroad, and parts of the nonpt and ptnonipm
sectors.
Speciation profiles for VOC in the nonroad, onroad and onroad rfl sectors 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 INEIvl TSD, and for 2018 and 2025 see Section 4.3. For 2011, EPA used "COMBO" profiles
to model combinations of profiles for E0 and E10 fuel use. For 2018 and 2025, EPA used "COMBO"
profiles to model combinations of E10, E15, and E85 fuel use. The speciation of onroad exhaust VOC
also accounts for a portion of the vehicle fleet meeting Tier 2 standards in that different exhaust profiles
are available for pre-Tier 2 versus Tier 2 vehicles. Thus for onroad gasoline, VOC speciation uses
different COMBO profiles to take into account both the increase in ethanol use, and the increase in Tier 2
vehicles in the future case.
48
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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 B.
Table 3-8 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
integrated profiles. The term "COMBO" indicates that a combination of the profiles listed was used to
speciate that subcategory using the GSPROCOMBO file. Although some of the component profiles are
the same between 2018 and 2025, for example "onroad, gasoline exhaust", the proportion of each profile
within the GSPRO COMBO differs between the two future years.
Table 3-8. Select VOC profiles 2011 versus 2018 and 2025
Sub-
sector
category
2011
2018
2025
COMBO
Pre-Tier 2 E0
COMBO
Pre-Tier 2 E10
COMBO
Pre-Tier 2 E10
8750aE
exhaust
8751aE
exhaust
8751aE
exhaust
onroad
gasoline
exhaust
8751aE
Pre-Tier 2
E10 exhaust
8757E
Tier 2 E10
Exhaust
8757E
Tier 2 E10 Exhaust
8756E
Tier 2 E0
Exhaust
Tier 2 E10
8758E
Tier 2 E15
Exhaust
Tier 2 E85
8758E
Tier 2 E15 Exhaust
8757E
Exhaust
8855 E
Exhaust
8855 E
Tier 2 E85 Exhaust
onroad
gasoline
evap-
orative
COMBO
8753E
8754E
E0 Evap
E10 Evap
COMBO
8754E
8872E
8934E
E10 Evap
E15 Evap
E85 Evap
COMBO
8754E
8872E
8934E
E10 Evap
E15 Evap
E85 Evap
COMBO
COMBO
COMBO
onroad
gasoline
perm-
eation
8766E
8769E
E0 evap
perm
E10 evap
perm
8769E
8770E
8934E
E10 evap perm
E15 evap perm
E85 Evap
8769E
8770E
8934E
E10 evap perm
E15 evap perm
E85 Evap
COMBO
E0
Headspace
COMBO
COMBO
onroad_
gasoline
8869E
8870E
E10 Headspace
8870E
E10 Headspace
rfl
refueling
E10
8870E
Headspace
8871E
8934E
E15 Headspace
E85 Evap
8871E
8934E
E15 Headspace
E85 Evap
49
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sector
Sub-
category
2011
2018
2025
onroad
diesel
exhaust
14
Weighted
diesel exh
87710 0.94
Weighted diesel
877P0 exh 0.78
Weighted diesel
877EIT3 exh 0.52
onroad
diesel
extende
d idle
Weighted
diesel exh
877P0 0.78
Weighted diesel
877EIT3 exh 0.52
Weighted diesel
877T3 exh 0.69
onroad
diesel
APU
N/A
Pre-2007 MY
8774 HDD exhaust
Pre-2007 MY HDD
8774 exhaust
onroad
diesel
evap-
orative
Diesel
4547 Headspace
Diesel
4547 Headspace
4547 Diesel Headspace
onroad_
rfl
diesel
refueling
Diesel
4547 Headspace
Diesel
4547 Headspace
4547 Diesel Headspace
nonroad
gasoline
exhaust
COMBO
Pre-Tier 2 E0
8750a exhaust
Pre-Tier 2
8751a E10 exhaust
Pre-Tier 2 E10
8751a exhaust
Pre-Tier 2 E10
8751a exhaust
nonroad
gasoline
evap-
orative
COMBO
8753 E0 evap
8754 E10 evap
8754 E10 evap
8754 E10 evap
nonroad
gasoline
refueling
COMBO
E0
8869 Headspace
E10
8870 Headspace
8870 E10 Headspace
8870 E10 Headspace
nonroad
diesel
exhaust
Pre-2007 MY
8774 HDD exhaust
Pre-2007 MY
8774 HDD exhaust
Pre-2007 MY HDD
8774 exhaust
nonroad
diesel
evap-
orative
Diesel
4547 Headspace
Diesel
4547 Headspace
4547 Diesel Headspace
nonroad
diesel
refueling
Diesel
4547 Headspace
Diesel
4547 Headspace
4547 Diesel Headspace
nonpt/
ptnonip
m
PFC
COMBO
E0
8869 Headspace
E10
8870 Headspace
COMBO
8870E E10 Headspace
8871E E15 Headspace
8934E E85 Evap
COMBO
8870E E10 Headspace
8871E E15 Headspace
8934E E85 Evap
BTP
COMBO
COMBO
COMBO
14 For the weighted diesel exhaust and extended idle profiles, the fraction in the description refers to the fraction of profile 8774
vs profile 8775. For example, profile "877P0" is made of 0.78 profile 8774 and 0.22 profile 8775.
50
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sector
Sub-
category
2011
2018
2025
nonpt/
ptnonip
m
E0
8869 Headspace
E10
8870 Headspace
8870 E10 Headspace
8871 E15 Headspace
8934 E85 Evap
8870 E10 Headspace
8871 E15 Headspace
8934 E85 Evap
nonpt/
ptnonip
m
BPS/RBT
E0
8869 Headspace
8869 E0 Headspace
8869 E0 Headspace
3.2.2 PM speciation
3.2.2.1 AE5 versus AE6 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. For CMAQ 4.7.1 modeling, these "simplified" components
(AE5) are all that is needed. For CMAQ 5.0.1, there is a new thermodynamic equilibrium aerosol
modeling tool (ISORROPIA) v2 mechanism that needs additional PM components (AE6), which are
further subsets of PMFINE (see Table 3-9). EPA speciated PM2.5 so that it included both AE5 and AE6
PM model species without causing any double counting. Therefore, emissions from this platform can be
used with either CMAQ 4.7.1 or CMAQ 5.0.1.
Table 3-9. 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
PNA
sodium
N
Y
PCL
chloride
N
Y
PH20
Water
N
Y
PMOTHR
PM2.5 not in other AE6 species
N
Y
51
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The majority of the 2011 platform PM profiles come from the 911XX series which include updated AE6
speciation15. The 201 lef, 2018ef, and 2025ef state-sector totals workbooks include state totals of the PM
emissions for each state for most sectors that include PM.
3.2.2.2 Onroad PM speciation
Unlike other sectors, the onroad sector has pre-speciated PM. This speciated PM comes from the
MOVES model and is processed through the SMOKE-MOVES system (see Section 2.3.1).
Unfortunately, the MOVES speciated PM does not map 1-to-l to the AE5 speciation (nor the AE6
speciation) needed for CMAQ modeling. Table 3-10 shows the relationship between MOVES16 exhaust
PM2.5 related species and CMAQ AE5 PM species.
Table 3-10. MOVES exhaust PM species versus AE5 species
MOVES2010b Pollutant Name
Variable name
for Equations
Relation to AE5 model species
Primary Exhaust PM2.5 - Total
PM25 TOTAL
Primary PM2 5 - Organic Carbon
PM250M
Sum of POC, PN03 and PMFINE
Primary PM2 5 - Elemental Carbon
PM25EC
PEC
Primary PM2 5 - Sulfate Particulate
PM25S04
PS04
MOVES species are related as follows:
PM25TOTAL = PM25EC + PM250M + PS04
The five CMAQ AE5 species also sum to total PM2.5:
PM2.5 = P0C+PEC+PN03+PS04+PMFINE
The basic problem is to differentiate MOVES species "PM250M" into the component AE5 species (POC,
PN03 and PMFINE). The Moves2smkEF post-processor script takes the MOVES species (EF tables)
and calculates the appropriate AE5 PM2.5 species and converts them into a format that is appropriate for
SMOKE for details on the Moves2smkEF script). For a more detailed discussion of the derivation of
these equations, see Appendix C.
For brake wear and tire wear PM, total PM2.5 (not speciated) comes directly from MOVES. These PM
modes are speciated by SMOKE. PMFINE from onroad exhaust is further speciated by SMOKE into the
component AE6 species.
For California and Texas, adjustment factors were applied to SMOKE-MOVES to produce California and
Texas adjusted model-ready files (see Section 2.3.1 for details). California and Texas did not supply
speciated PM, therefore the adjustment factors applied to PM2.5 were also applied to the speciated PM
components. By applying the ratios through SMOKE-MOVES, the CARB and Texas inventories are
essentially speciated to match EPA estimated speciation grid cell by grid cell.
15 The exceptions are 5674 (Marine Vessel - Marine Engine - Heavy Fuel Oil) used for c3marine and 92018 (Draft Cigarette
Smoke - Simplified) used in nonpt.
16 The Tier3 FRM MOVES model has the same PM components as MOVES2010b. MOVES2014 has a one-to-one mapping
of PM species to CMAQ PM species.
52
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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 3-11 gives the split factor for these two profiles.
Table 3-11. 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
The onroad sector does not use the "HONO" profile to speciate NOx. MOVES2010b 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. HONO is not calculated directly by the
Tier 3 proposal version of MOVES. For these EF tables, the calculation of HONO and the NO2 fraction
are calculated externally by the moves2smk script17. The SMOKE-MOVES system then models these
species directly without further speciation.
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 hour. This
process is typically done by applying temporal profiles to the inventories in this order: monthly, day of
the week, and diurnal.
The temporal profiles and associated cross references used to create the hourly emissions inputs for the
2011 air quality modeling platform were similar to those used for the 2007 platform. The temporal
factors applied to the inventory are selected using some combination of country, state, county, SCC, and
pollutant. Table 3-12 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).
17 A specific version of the moves2smk script was developed to do this calculation of HONO. The typical version assumes that
HONO was calculated directly by MOVES2010b.
53
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Table 3-12. 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
ptegu
Daily & hourly
all
all
Yes
ptegu_pk
Daily & hourly
all
all
Yes
ptnonipm
Annual
yes
mwdss
mwdss
Yes
ptoilgas
Annual
yes
mwdss
mwdss
Yes
ptfire
Daily
all
all
Yes
othpt
Annual
yes
mwdss
mwdss
nonroad
Monthly
mwdss
mwdss
Yes
othar
Annual
yes
week
week
clc2rail
Annual
yes
mwdss
mwdss
c3 marine
Annual
yes
aveday
aveday
onroad
Annual & monthly1
all
all
Yes
onroadrfl
Annual & monthly2
all
all
Yes
othon
Annual
yes
week
week
nonpt
Annual & monthly
yes
all
all
Yes
npoilgas
Annual
yes
mwdss
mwdss
Yes
rwc
Annual
no
met-based
All
Yes
ag
Annual
yes
all
all
Yes
afdustadj
Annual
yes
week
all
Yes
beis
Hourly
n/a
all
Yes
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.
2. Note the annual and monthly "inventory" actually refers to the activity data (VMT and VPOP) for onroadrfl. The
actual emissions are computed on an hourly basis.
The following values are used in the table: The value "all" means that hourly emissions 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
54
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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 previous formats supported. Previously, to process
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 3.5.1 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 (e.g. the
nonpt sector). 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 nonroad, onroad, and the ag burning inventory within the nonpt sector.
3.3.2 Electric Generating Utility temporalization (ptegu, ptegu_pk)
3.3.2.1 Base year temporal allocation of EGUs
The 201 INEIvl annual EGU emissions are allocated to hourly emissions using the following 3-step
methodology: annual value to month, month to day, and day to hour. 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. EIS stores a base set of previously matched units via alternate
facility and unit IDs. For any units not yet matched, reports were generated by unit to identify potential
matches with the NEI. The reports included FIPS state/county code, facility name, and NOx and SO2
emissions. Units were considered matches if the FIPS state/county code matched, the facility name was
similar, and the NOx and SO2 emissions were similar.
For sources not matched to CEMS measurements, the first two steps of the allocation are done outside of
SMOKE. For sources in the ptegu and ptegu_pk sectors that are matched to CEMS data, annual totals of
the emissions may be different than the annual values in 201 INEIvl because the CEMS data actually
replaces the inventory data. All units in the ptegu_pk sector with non-zero emissions for 2011 were
matched to CEMS data.
For units not matched to CEMS data, 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 3-3. 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 and SO2, and heat input. An overall composite profile
was also computed and was used in a few cases in which the fuel-specific profile was too irregular, or
there were no CEMS units with the specified fuel in the region containing the unit. 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 is used to allocate all other pollutants.
55
-------
Daily "temporal allocation" of units with CEMS was performed using a procedure similar to that in the
first step 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 allocation factors were computed for NOx, SO2, and
heat input for the fuels coal, natural gas, and other. 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 is used to
allocate all other pollutants.
For units with associated CEMS data, hourly emissions use the hourly CEMS values as described above
for NOx and S02, while other pollutants are allocated according to heat input values. For units without
CEMS data, temporal profiles from days to hours are computed based on the region- and fuel-specific
average day-to-hour factors derived from the CEMS data for those fuels and regions using data from the
entire year. For NEI units not matched to specific CEMS units, CEMS heat input data is used to allocate
all pollutants (including NOs and SO2). SMOKE then allocates the daily emissions data to hours using
the profiles obtained from the CEMS data for the analysis base year.
Figure 3-3. EPM Regions for EPA Base Case v5.13
_WECC_PNW
NENG_ME
MAP_
WAUE
NENG_CT
WECCALN
PJM_
COMD
SPPNEBR
PJM_EMAC
PJM_SMAC
WECC_SCE
S_VACA
WECC_AZ
WECCJID
S_D_WOTA
S_D_AMSO
EPA Base Case v5.12 U.S. Regions
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. These annualized emissions are allocated
to hourly emissions using a 3-step methodology: annualized summer/winter value to month, month to
56
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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. This approach maximizes the use of
the CEMS data from the air quality analysis year (e.g., 2011).
For CEMS-matched units, the 2011 based CEMS were scaled so that their seasonal emissions matched
IPM totals. In other words, EPA created a set of artificial CEMS data which had the same temporal
pattern as 2011, but for which the seasonal total emissions matched IPM's predictions for 2018 and 2025.
Except for the scaling of CEMS data, the procedure for allocating the emissions of CEMS matched units
is the same as the base year (see Section 3.3.2.1). For sources not matched to CEMS units, the allocation
of the IPM seasonal emissions to months was done using average fuel-specific season-to-month factors
generated for each of the 64 IPM regions shown in Figure 3-3. These factors are based on a single year of
CEMS data consistent with the modeling base year, in this case 2011. Similar to the base year, profiles
were created for coal, natural gas, and "other" fuel. For each fuel, separate profiles were computed for
NOx, SO2, and heat input. An overall composite profile was also computed in the event that a fuel-specific
profile was too irregular or in the case when a unit changed fuels between the base and future year and
there were previously no units with that fuel in the specific region. Except for the season-to-month
allocation, the procedure for allocating the emissions of units not matched to CEMS units is the same as
the base year.
Units with year-specific impacts in the season-to-month allocations, such as long-duration downtimes for
maintenance or installation of controls that occur only in one year were temporalized using average
profiles instead of using the anomalous profile for the base year. These situations are determined by
analysis of the base and future year data. 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.
Some refinements to the above approach were made in some special cases:
• When emissions were substantially higher for units with limited hours of operation in the base
year, an averaged profile was used.
• When a unit switched fuels in the future year to a fuel not used in the base year, the profile was
selected according to the new type of fuel. If the unit was a CEMS unit in the base year, it was
treated as a non-matched unit in the future years.
• When a CEMS unit operated in only one season in the base year, but IPM predicted that there
were emissions in both seasons, an average profile was used for the future year unit during both
seasons.
• New units coming on line used the appropriate region and fuel-specific profiles
• Units that are not new but had no emissions in 2011 were treated like new units.
For more information on the development of IPM emission estimates and the temporalization of those, in
particular, the Air Quality Modeling Flat File Documentation and accompanying inputs.
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 is 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.
57
-------
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 portions 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,
respectively.
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 3-4 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 3-4. Example of RWC temporalization in 2007 using a 50 versus 60 °F threshold
RWC temporal profile, Duval County, FL, Jan - Apr
60F, alternate formula
50F, default formula
Z 0.015
0.005
The diurnal profile for used for most RWC sources (see Figure 3-5) 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. 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
58
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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.
Figure 3-5. 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, chimineas, 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 3-6 is based on a conventional single-stage heat load unit
burning red oak in Syracuse, New York. As shown in Figure 3-7, 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 3-8. 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.
59
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Figure 3-6. 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 3-7. 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
60
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Figure 3-8. Annual-to-month temporal profiles for OHH and recreational RWC
Monthly Temporal Activity for OHH & Recreational RWC
Fire Pit/Chimenea
Outdoor Hydronic Heater
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/V] 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 month18.
18 SMOKE v3.5.1 will correctly read in a monthly inventory and apply GenTPRO ag NH3 month-to-hour temporalization.
However, SMOKE v3.5 beta incorrectly applied an annual-to-month temporal profile on top of a monthly inventory when
61
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Figure 3-9 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.
Figure 3-9. Example of new animal NFb emissions temporalization approach, summed to daily
emissions
MN ag NH3 livestock temporal profiles
0.0
1/1/2008 2/1/2008 3/1/2008 4/1/2008 5/1/2008 6/1/2008 7/1/2008 8/1/2008 9/1/2008 10/1/2008 11/1/2008 12/1/2008
-old
-new
3.3.5 Onroad mobile temporalization (onroad, onroad_rfl)
For the onroad and onroadrfl sectors, 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 updates to the diurnal temporal profiles for the 2011 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 (RPV 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 previous platform, RPP used county level minimum and
maximum temperature ranges for the day to determine the appropriate EF. This potentially overestimated
the temperature range for any particular grid cell, which would result in increased emissions for vapor-
venting. In the 2011 platform (and the 201 INEIvl), 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 three processes (RPD, RPV, and RPP) is
the total onroad sector emissions, while the combination of the two processes (RPD, RPV) for the
refueling mode only is the total onroad rfl sector emissions. Both sectors show a strong meteorological
influence on their temporal patterns (see the 201 INEIvl TSD for more details).
Figure 3-10 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
temporalizing with GenTPRO ag NH3 profiles. As an interim solution a flat monthly profile was applied to the states with a
monthly ag NH3 inventory.
62
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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.
Figure 3-10. Example of SMOKE-MOVES temporal variability of NOx emissions
BHM (Jefferson Co., AL) daily NOX
I II I
MOVES
SMOKE-MOVES
OrHfN^-inr^ooarHrMrj-inioooc^iHfNjroLnvoooworvimLn'X)
OOOOOOOOfHrHiHHrHrHpHrNIINfNfNfNfNfNmmmmm
LninifitntninmtnintnininLnifiinLnifiininmtnininmininin
ooooooooooooooooooooooooooo
ooooooooooooooooooooooooooo
fNrMfM(N(NfSlfN(Nf\rMr\J(N(Nrs|fNfM(NfNfMrslfM(N(N(NfN(N(N
Julian date
For the onroad and onroad_rfl sectors, the "inventories" referred to in Table 3-12 actually consist of
activity data. 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' process 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.
In previous platforms, the diurnal profile for VMT19 varied by road type but not by vehicle type (see
Figure 3-11). These profiles were used throughout the nation.
13 These same profiles were used for onroad emissions in the 2005 platform.
63
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Figure 3-11. Previous onroad diurnal weekday profiles for urban roads
Diurnal Weekday profiles - urban
1 2 3 4 5 6
n 1 1 1 1 1 1 1 1 1 1 1 r
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
-SMOKE interstate
-SMOKE other expwy
-SMOKE major art.
-SMOKEminorart.
-SMOKE collector
-SMOKE local
MOVES default
EPA wanted to create new diurnal profiles that could differentiate by vehicle type as well as by road type
and would potentially vary over geography. The 2011NEIvl process provided an opportunity to update
the diurnal profile with information submitted by states. States submitted MOVES county databases
(CDBs) that included information on the distribution of VMT by hour of day and by day of week20 (see
the 201 INEIvl TSD for details on the submittal process for onroad). EPA decided not to update the day
of week profile because MOVES only differentiated weekday versus weekend while the default SMOKE
profiles differentiated each of the 7 days. EPA mined the state submitted MOVES CDBs for non-default
diurnal profiles21. 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 (see Figure 3-12).
Figure 3-12. Variation in MOVES diurnal profiles
varies by vehicle,
but not road or day
varies by road and day,
but not vehicle
MOVES temporal profile diurnal submittals
| California (not MOVES)
varies by vehicle, road, and day
varies by vehicle and road,
but not day
varies by road,
but not vehicle or day
varies by day,
but not vehicle or road
no variation
(1 profile for county)
2" The MOVES tables are the hourvmtfraction and the dayvmi Tract ion.
21 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.
64
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EPA attempted to maximize the use of state and/or county specific diurnal profiles. If a specific state or
county's profiles varied by vehicle type or/and road type, then the submitted profile was used. If the
profile had less variability than the old SMOKE defaults (i.e. neither varied by vehicle type nor road
type), 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 result was a set of profiles that varied geographically
depending on whether or not the profile was submitted and the characteristics of the profiles (see Figure
3-13).
Figure 3-13. Use of submitted versus new national default profiles
MOVES temporal profile diurnal use
ndicates where we are using MOVES-submitted diurnal profiles, and where we are using national defaults.
I
I
S/L/T weekday and weekend
S/L/T weekday,
national weekend
national weekday and weekend
A new set of diurnal profiles was developed from the submitted profiles that varied by both vehicle type
and road type. Before developing the national profiles, there needs to be a mapping between MOVES
road types and SMOKE road types (i.e., the last three digits of the SCC) and between MOVES source
types and SMOKE vehicle types. The mapping between road types is relatively straight forward (see
Table 3-13). Basically the road types are consolidated into 4 types in MOVES, therefore the new profiles
will not differentiate at the level of the SMOKE road type. For example, the SMOKE "urban interstate"
(SCCLAST3=230) will have the same profile as the SMOKE "urban other freeways and expressways"
(SCCLAST3=250). The mapping between MOVES source type and SMOKE vehicle type is more
complicated; it is a many-to-many mapping (see the 201 INEIvl TSD for more details).
65
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Table 3-13. Mapping of MOVES to SMOKE road types
MOVES
roadtype ID
Description
SMOKE
SCCLAST3
Description
2
Rural Restricted Access
110
Rural Interstate: Total
3
Rural Unrestricted Access
130
Rural Other Principal Arterial: Total
150
Rural Minor Arterial: Total
170
Rural Major Collector: Total
190
Rural Minor Collector: Total
210
Rural Local: Total
4
Urban Restricted Access
230
Urban Interstate: Total
250
Urban Other Freeways and Expressways: Total
5
Urban Unrestricted Access
270
Urban Other Principal Arterial: Total
290
Urban Minor Arterial: Total
310
Urban Collector: Total
330
Urban Local: Total
For the purposes of constructing the SMOKE diurnal profiles, all MOVES profiles for the road type and
for any overlapping source types are averaged together to create a single diurnal profile for a specific
county, SMOKE road type, SMOKE vehicle type, and weekday or weekend . This process is also used
for creating SMOKE versions of the submitted profile in the non-default regions (described above). The
states that submitted profiles that varied by vehicle and road types for weekdays were: Idaho, Maine,
Michigan, New Jersey, Ohio, and Pennsylvania. The states that submitted profiles that varied by vehicle
and road types for weekends were: Idaho, Maine, and Michigan. EPA created individual profiles for
each state (averaging over the counties within) to create a single profile by state, vehicle type, road type,
and weekday or weekend. The states individual profiles were averaged together to create a new default
profile22. Figure 3-14 shows two new default profiles for light duty gas vehicles (LDGV, SCC7 2201001)
and heavy, heavy duty diesel vehicles (HHDDV, SCC7 2230074) on restricted urban roadways
(interstates and freeways, SCCLAST3=230 and 250) for weekdays. The grey lines are the individual state
profiles, the black line is the new default profile, and the 2 colored lines are the previous SMOKE default
profiles. Note that there are two previous SMOKE profiles for this road type, but that they don't vary by
vehicle. In contrast, the new default profile does vary by vehicle and places more LDGV VMT (left plot)
in the rush hours while placing HHDDV VMT (right plot) predominately in the middle of the day with a
longer tail into the evening hours and early morning. For a full list of the default profiles, see
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/reports/temporal profiles/
onroad_default_hourly_profile_plots_2011ed.zip.
22 Note that the states were weighted equally in the average independent of the size of the state or the variation in submitted
county data.
66
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Figure 3-14. Updated national default profiles for LDGV vs. HHDDV, urban restricted weekday
Hourly VMT fraction: multi_comp_2201001_230-250_weekday
0.08
c 0.06
> 0.04
0.02
0.00
hour
Hourly VMT fraction: multi_comp_2230074_230-250_weekday
0.08
c 0.06
multi_comp_2230O74_230-25
0_weekday
Freeways Profile 2008
> 0.04
0.02
0.00
hour
For California, CARB supplied diurnal profiles that varied by vehicle type, day of the week23, and air
basin. These CARB specific profiles were used in developing EPA estimates for California. For Texas,
the profiles used were a combination of state supplied (via MOVES CDBs) and new national defaults.
Although EPA adjusted the total emissions to match California's and Texas' submittals to the
* California's diurnal profiles varied within the week. Monday, Friday, Saturday, and Sunday had unique profiles and
Tuesday, Wednesday. Thursday had the same profile.
67
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201 INEIvl, the temporalization of these emissions took into account both the state specific VMT profiles
and the SMOKE-MOVES process of incorporating meteorology. For more details on the adjustments to
California's and Texas' onroad emissions, see the 201 INEIvl TSD.
3.3.6 Additional sector specific details (afdust, beis, c1c2rail, c3marine, nonpt,
ptfire)
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. al.. 2010). 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 fraction and 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 nonpt sector, most the inventories are annual except for the agricultural burning (SCC
2801500000) inventory which was 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 3-15 (McCarty et al., 2009). This puts most of the emissions
during the work day and suppresses the emissions during the middle of the night. All states used a
uniform profile for each day of the week for all agricultural burning emissions, except for the following
states that for which EPA used state-specific day of week profiles: Arkansas, Kansas, Louisiana,
Minnesota, Missouri, Nebraska, Oklahoma, and Texas.
68
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Figure 3-15. Agricultural burning diurnal temporal profile
Comparison of Agricultural Burning Temporal Profiles
0.18
0.16
0.14
New McCarty Profile
OLD EPA
0.12
c
o
0.1
10.08
0.06
0.04
0.02
12345678 9 10111213141516171819 20 2122 23 24
For the ptfire sector, the inventories are in the daily point fire format ORL PTDAY. The ptfire sector is
used in the model evaluation case (201 led and in the future base case (2018ed). The 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.
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 remainder of this subsection provides further detail on the origin of the
data used for the spatial surrogates and the area-to-point data.
Additional documentation on the 2011 spatial surrogates is available at
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/reports/spatial surrogates/ in the files
US_SpatialSurrogate_Documentation_v091113.pdf and US_SpatialSurrogate_Workbook_v093013.xlsx.
The spatial cross reference file is in gsref_201 l.xlsx. Plots of the spatial surrogates are available in
all_surrogate_maps_201 lplatform_12USl_v2.pdf. Note that these are plots of the surrogate fractions
summed by grid cell, so grid cells that overlap multiple counties can show values greater than one. These
69
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maps are only to give an idea of the spatial distribution of the surrogates. Allocations of CAP emissions to
each of the surrogate codes is given in 201 led_spatial_surrogate_CAPs_febl 12014.xlsx.
3.4.1 Spatial Surrogates for U.S. emissions
There are more than 70 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 some sources. Table 3-14 lists the codes and descriptions of
the surrogates. The surrogates in bold have been updated with 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 INEIvl
shapefiles: Ports_032310_wrf and ShippingLanes_111309FINAL_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 UNC.
Table 3-14. U.S. Surrogates available for the 2011 modeling platform.
Code
Surrogate Description
I Code
Surrogate Description
N/A
Area-to-point approach (see 3.3.1.2)
520
Commercial plus Industrial plus Institutional
Golf Courses + Institutional +Industrial +
100
Population
1 525
Commercial
110
Housing
527
Single Family Residential
120
Urban Population
530
Residential - High Density
Residential + Commercial + Industrial +
130
Rural Population
1 535
Institutional + Government
137
Housing Change
540
Retail Trade
140
Housing Change and Population
545
Personal Repair
150
Residential Heating - Natural Gas
550
Retail Trade plus Personal Repair
Professional/Technical plus General
160
Residential Heating - Wood
555
Government
165
0.5 Residential Heating - Wood plus 0.5 Low
Intensity Residential
560
Hospital
170
Residential Heating - Distillate Oil
565
Medical Office/Clinic
180
Residential Heating - Coal
570
Heavy and High Tech Industrial
190
Residential Heating - LP Gas
575
Light and High Tech Industrial
200
Urban Primary Road Miles
580
Food, Drug, Chemical Industrial
210
Rural Primary Road Miles
585
Metals and Minerals Industrial
220
Urban Secondary Road Miles
590
Heavy Industrial
230
Rural Secondary Road Miles
595
Light Industrial
240
Total Road Miles
596
Industrial plus Institutional plus Hospitals
250
Urban Primary plus Rural Primary
600
Gas Stations
255
0.75 Total Roadway Miles plus 0.25 Population
650
Refineries and Tank Farms
260
Total Railroad Miles
675
Refineries and Tank Farms and Gas Stations
Oil & Gas Wells, IHS Energy, Inc. and
270
Class 1 Railroad Miles
680
USGS
261
NT AD Total Railroad Density
700
Airport Areas
271
NTAD Class 1, 2, 3 Railroad Density
710
Airport Points
70
-------
Code
Surrogate Description |
1 Code
Surrogate Description
280
Class 2 and 3 Railroad Miles f
720
Military Airports
300
Low Intensity Residential
800
Marine Ports
310
Total Agriculture
801
NEI Ports
312
Orchards/Vineyards j
802
NEI Shipping Lanes
320
Forest Land j
807
Navigable Waterway Miles
330
Strip Mines/Quarries i
808
Gulf Tug Zone Area
340
Land 1
810
Navigable Waterway Activity
350
Water
812
Midwest Shipping Lanes
400
Rural Land Area
850
Golf Courses
500
Commercial Land
860
Mines
505
Industrial Land
870
Wastewater Treatment Facilities
510
Commercial plus Industrial
880
Drycleaners
515
Commercial plus Institutional Land
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. For the onroad rfl sector, the emissions
were spatially allocated to gas station locations. For the oil and gas sources in the np oilgas sector, the
spatial surrogates were updated to those shown in Table 3-15 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 INEIvl data for the
oil and gas sector. The data sources included 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, 2014).
Table 3-15. 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
693
Well count - all wells
694
Oil production at oil wells
71
-------
695
Well count - oil wells
697
Oil production at Gas and CBM Wells
698
Well counts - Gas and CBM Wells
Not all of the available surrogates are used to spatially allocate sources in the modeling platform; that is,
some surrogates shown in Table 3-14 were not assigned to any SCCs, although many of the "unused"
surrogates are actually used to "gap fill" other surrogates that are assigned. 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 3-16 shows the total of CAP emissions (i.e., NH3, NOx, PM2.5, SO2, and
VOC) by sector.
Table 3-16. Selected 2011 CAP emissions by sector for U.S. Surrogates*
Sector
Srg.
Code
Description
NH3
NOX
PM2 5
S02
VOC
afdust
130
Rural Population
0
0
1,102,192
0
0
afdust
140
Housing Change and Population
0
0
162,157
0
0
afdust
240
Total Road Miles
0
0
287,531
0
0
afdust
310
Total Agriculture
0
0
896,741
0
0
afdust
330
Strip Mines/Quarries
0
0
59,782
0
0
afdust
400
Rural Land Area
0
0
1
0
0
ag
310
Total Agriculture
3,524,607
0
0
0
0
clc2rail
261
NTAD Total Railroad Density
2
13,840
16,621
249
861
clc2rail
271
NTAD Class 12 3 Railroad
Density
332
733,500
896,099
7,388
38,881
clc2rail
280
Class 2 and 3 Railroad Miles
13
42,220
48,316
293
1,632
clc2rail
802
Shipping Lanes
335
529,920
662,303
11,490
12,970
clc2rail
808
Gulf Tug Zone Area
0
4,031
5,742
1,247
145
clc2rail
820
Ports NEI2011 NOx
24
69,021
86,742
2,492
2,165
nonpt
100
Population
0
0
0
0
1,221,647
nonpt
140
Housing Change and Population
1
23,368
66,271
8
134,851
nonpt
150
Residential Heating - Natural Gas
41,132
218,591
4,235
1,441
12,721
nonpt
170
Residential Heating - Distillate
Oil
2,122
42,645
4,519
91,994
1,420
nonpt
180
Residential Heating - Coal
325
1,388
796
8,658
1,624
nonpt
190
Residential Heating - LP Gas
151
39,636
195
752
1,462
nonpt
240
Total Road Miles
0
0
0
0
6,825
nonpt
250
Urban Primary plus Rural
Primary
0
0
0
0
102,793
nonpt
260
Total Railroad Miles
0
0
0
0
2,195
nonpt
300
Low Intensity Residential
3,849
18,563
96,738
3,082
40,575
nonpt
310
Total Agriculture
3,435
64,432
140,559
26,212
474,539
nonpt
312
Orchards/Vineyards
27
874
1,199
2,559
1,061
nonpt
320
Forest Land
7
21
165
0
154
nonpt
400
Rural Land Area
0
1,036
43
30
79
72
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Sector
Srg.
Code
Description
NH3
NOX
PM2 5
S02
voc
nonpt
500
Commercial Land
2,367
1
86,448
585
26,503
nonpt
505
Industrial Land
86,938
235,940
108,508
198,90
9
117,339
nonpt
510
Commercial plus Industrial
5
178
27
109
224,947
nonpt
515
Commercial plus Institutional
Land
1,438
188,184
21,307
62,460
21,329
nonpt
520
Commercial plus Industrial plus
Institutional
0
0
0
0
11,252
nonpt
525
Golf Courses plus Institutional
plus Industrial plus Commercial
0
0
0
0
0
nonpt
527
Single Family Residential
0
0
0
0
0
nonpt
535
Residential + Commercial +
Industrial + Institutional +
Government
23
2
145
0
334,081
nonpt
540
Retail Trade (COM1)
0
0
0
0
1,375
nonpt
545
Personal Repair (COM3)
0
0
93
0
62,913
nonpt
555
Professional/Technical (COM4)
plus General Government
(GOV1)
0
0
0
0
2,872
nonpt
560
Hospital (COM6)
0
0
0
0
9
nonpt
575
Light and High Tech Industrial
(IND2 + IND5)
0
0
0
0
2,554
nonpt
580
Food, Drug, Chemical Industrial
(IND3)
0
610
313
171
10,532
nonpt
585
Metals and Minerals Industrial
(IND4)
0
23
140
8
443
nonpt
590
Heavy Industrial (IND1)
10
4,362
5,441
1,131
145,088
nonpt
595
Light Industrial (IND2)
0
1
238
0
80,245
nonpt
600
Gas Stations
0
0
0
0
413,518
nonpt
650
Refineries and Tank Farms
0
0
0
0
130,222
nonpt
675
Refineries and Tank Farms and
Gas Stations
0
0
0
0
1,203
nonpt
700
Airport Areas
0
0
0
0
32,030
nonpt
801
Port Areas
0
51
1
0
12,526
nonpt
870
Wastewater Treatment Facilities
1,015
13
1
1
4,988
nonpt
880
Drycleaners
0
0
0
0
10,026
nonroad
100
Population
40
39,475
2,824
85
5,030
nonroad
140
Housing Change and Population
554
537,249
45,058
1,255
78,526
nonroad
261
NTAD Total Railroad Density
2
2,673
310
5
568
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
143,196
12,397
337
614,849
nonroad
400
Rural Land Area
157
25,667
16,711
194
620,788
73
-------
Sector
Srg.
Code
Description
NH3
NOX
PM2 5
S02
voc
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
42
21,395
7,569
65
93,164
nonroad
525
Golf Courses plus Institutional
plus Industrial plus Commercial
163
49,146
8,792
223
162,672
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
1
0
0
50
np_oilgas
680
Oil and Gas Wells
0
24
1
0
85
np_oilgas
681
Spud count - Oil Wells
0
0
0
0
6,244
np_oilgas
682
Spud count - Horizontally-drilled
wells
0
2,297
87
4
145
np_oilgas
683
Produced Water at all wells
0
0
0
0
44,469
np_oilgas
684
Completions at Gas and CBM
Wells
0
257
7
580
7,460
np_oilgas
685
Completions at Oil Wells
0
19
0
205
28,017
np_oilgas
686
Completions at all wells
0
3,801
112
50
63,924
np_oilgas
687
Feet drilled at all wells
0
33,433
1,409
41
9,576
np_oilgas
688
Spud count - Gas and CBM Wells
0
0
0
0
1,810
np_oilgas
689
Gas production at all wells
0
50,926
3,859
89,370
153,277
np_oilgas
692
Spud count
0
35,655
972
1,816
4,414
np_oilgas
693
Well count - all wells
0
26,838
509
258
89,423
np_oilgas
694
Oil production at Oil wells
0
1,018
0
9,254
618,190
np_oilgas
695
Well count - oil wells
0
107,011
3,429
68
422,416
np_oilgas
697
Oil production at gas wells
0
244
0
0
319,117
np_oilgas
698
Well count - gas wells
0
391,705
6,816
4,615
504,599
onroad
100
Population
0
1,217,387
20,480
1,207
1,503,878
onroad
120
Urban Population
11,021
383,680
17,175
2,820
107,083
onroad
130
Rural Population
5,614
219,432
8,260
1,289
47,988
onroad
200
Urban Primary Road Miles
59,212
1,928,303
85,642
13,115
447,741
onroad
210
Rural Primary Road Miles
26,058
1,328,031
49,969
5,770
199,185
onroad
220
Urban Secondary Road Miles
6,321
207,553
9,305
1,536
56,296
onroad
230
Rural Secondary Road Miles
9,899
382,320
14,314
2,179
83,071
onroad_
rfl
600
Gas Stations
0
0
0
0
157,629
rwc
165
0.5 Residential Heating - Wood
plus 0.5 Low Intensity
Residential
20,415
35,818
389,655
9,010
448,753
Note: Onroad emissions numbers are from the 201 led case, but the distribution for 20
lef is similar
74
-------
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 emissions as point sources. For
the modeling platform, EPA used the SMOKE "area-to-point" approach for only airport ground support
equipment (nonroad sector), and jet refueling (nonpt sector). The approach is described in detail in the
2002 platform documentation. The ARTOPNT file that lists the nonpoint sources to locate using point
data was unchanged from the 2005-based platform.
3.4.3 Surrogates for Canada and Mexico emission inventories
The surrogates for Canada to spatially allocate the 2006 Canadian emissions are unchanged from the 2007
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). Per Environment
Canada, the surrogates are based on 2001 Canadian census data. The Canadian surrogates used for this
platform are listed in Table 3-17. The leading "9" was added to the surrogate codes to avoid duplicate
surrogate numbers with U.S. surrogates. Some new surrogates for Mexico became available in the 2011
platform. The surrogates are circa 1999 and 2000 and were based on data obtained from the Sistema
Municpal de Bases de Datos (SIMBAD) 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 3-18. 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 3-17. Canadian Spatial Surrogates
Code
Description
Code
Description
9100
Population
9493
Warehousing and storage
9101
Total dwelling
9494
Total Transport and warehouse
9102
Urban dwelling
9511
Publishing and information services
9103
Rural dwelling
9512
Motion picture and sound recording
industries
9104
Total Employment
9513
Broadcasting and telecommunications
9106
ALL INDUST
9514
Data processing services
9111
Farms
9516
Total Info and culture
9113
Forestry and logging
9521
Monetary authorities - central bank
9114
Fishing hunting and trapping
9522
Credit intermediation activities
9115
Agriculture and forestry activities
9523
Securities commodity contracts and other
financial investment activities
9116
Total Resources
9524
Insurance carriers and related activities
9211
Oil and Gas Extraction
9526
Funds and other financial vehicles
9212
Mining except oil and gas
9528
Total Banks
9213
Mining and Oil and Gas Extract activities
9531
Real estate
9219
Mining-unspecified
9532
Rental and leasing services
9221
Total Mining
9533
Lessors of non-financial intangible assets
(except copyrighted works)
9222
Utilities
9534
Total Real estate
9231
Construction except land subdivision and
land development
9541
Professional scientific and technical
services
9232
Land subdivision and land development
9551
Management of companies and enterprises
75
-------
Code
Description
Code
Description
9233
Total Land Development
9561
Administrative and support services
9308
Food manufacturing
9562
Waste management and remediation
services
9309
Beverage and tobacco product manufacturing
9611
Education Services
9313
Textile mills
9621
Ambulatory health care services
9314
Textile product mills
9622
Hospitals
9315
Clothing manufacturing
9623
Nursing and residential care facilities
9316
Leather and allied product manufacturing
9624
Social assistance
9321
Wood product manufacturing
9625
Total Service
9322
Paper manufacturing
9711
Performing arts spectator sports and related
industries
9323
Printing and related support activities
9712
Heritage institutions
9324
Petroleum and coal products manufacturing
9713
Amusement gambling and recreation
industries
9325
Chemical manufacturing
9721
Accommodation services
9326
Plastics and rubber products manufacturing
9722
Food services and drinking places
9327
Non-metallic mineral product manufacturing
9723
Total Tourism
9331
Primary Metal Manufacturing
9811
Repair and maintenance
9332
Fabricated metal product manufacturing
9812
Personal and laundry services
9333
Machinery manufacturing
9813
Religious grant-making civic and
professional and similar organizations
9334
Computer and Electronic manufacturing
9814
Private households
9335
Electrical equipment appliance and
component manufacturing
9815
Total other services
9336
Transportation equipment manufacturing
9911
Federal government public administration
9337
Furniture and related product manufacturing
9912
Provincial and territorial public
administration (9121 to 9129)
9338
Miscellaneous manufacturing
9913
Local municipal and regional public
administration (9131 to 9139)
9339
Total Manufacturing
9914
Aboriginal public administration
9411
Farm product wholesaler-distributors
9919
International and other extra-territorial
public administration
9412
Petroleum product wholesaler-distributors
9920
Total Government
9413
Food beverage and tobacco wholesaler-
distributors
9921
Commercial Fuel Combustion
9414
Personal and household goods wholesaler-
distributors
9922
TOTAL DISTRIBUTION AND RETAIL
9415
Motor vehicle and parts wholesaler-
distributors
9923
TOTAL INSTITUTIONAL AND
GOVERNEMNT
9416
Building material and supplies wholesaler-
distributors
9924
Primary Industry
9417
Machinery equipment and supplies
wholesaler-distributors
9925
Manufacturing and Assembly
76
-------
Code
Description
Code
Description
9418
Miscellaneous wholesaler-distributors
9926
Distribution and Retail (no petroleum)
9419
Wholesale agents and brokers
9927
Commercial Services
9420
Total Wholesale
9928
Commercial Meat cooking
9441
Motor vehicle and parts dealers
9929
HIGHJET
9442
Furniture and home furnishings stores
9930
LOWMEDJET
9443
Electronics and appliance stores
9931
OTHERJET
9444
Building material and garden equipment and
supplies dealers
9932
CANRAIL
9445
Food and beverage stores
9933
Forest fires
9446
Health and personal care stores
9941
PAVED ROADS
9447
Gasoline stations
9942
UNPAVED ROADS
9448
clothing and clothing accessories stores
9943
HIGHWAY
9451
Sporting goods hobby book and music stores
9944
ROAD
9452
General Merchandise stores
9945
Commercial Marine Vessels
9453
Miscellaneous store retailers
9946
Construction and mining
9454
Non-store retailers
9947
Agriculture Construction and mining
9455
Total Retail
9950
Intersection of Forest and Housing
9481
Air transportation
9960
TOTBEEF
9482
Rail transportation
9970
TOTPOUL
9483
Water Transportation
9980
TOTSWIN
9484
Truck transportation
9990
TOTFERT
9485
Transit and ground passenger transportation
9993
Trail
9486
Pipeline transportation
9994
ALLROADS
9487
Scenic and sightseeing transportation
9995
3 0UNPA VED7 Otrail
9488
Support activities for transportation
9996
Urban area
9491
Postal service
9997
CHBOISQC
9492
Couriers and messengers
9991
Traffic
Table 3-18. CAPs Allocated to Mexican and Canadian Spatial Surrogates
Srg code
Description
nh3
NOx
pm25
so2
VOC
22
MEX Total Road Miles
15,965
370,867
34,396
13,713
375,276
10
MEX Population
0
0
0
0
431,231
12
MEX Housing
0
161,013
17,483
2,123
452,685
14
MEX Residential Heating - Wood
0
20,093
211,525
2,859
380,572
16
MEX Residential Heating - Distillate Oil
0
38
0
11
2
20
MEX Residential Heating - LP Gas
0
25,303
787
63
614
22
MEX Total Road Miles
0
0
0
0
3,513
24
MEX Total Railroads Miles
0
74,969
1,669
663
2,824
26
MEX Total Agriculture
679,212
164,144
72,372
2,127
43,958
28
MEX Forest Land
0
16,224
67,683
660
79,018
32
MEX Commercial Land
0
125,211
7,726
0
286,982
34
MEX Industrial Land
0
45,831
5,684
59,201
133,440
77
-------
Srg code
Description
nh3
NOx
pm25
so2
voc
36
MEX Commercial plus Industrial Land
0
0
0
0
332,495
38
MEX Commercial plus Institutional Land
0
6,400
216
84
28,293
40
Residential (RES 1 -
4)+Commercial+Industrial+Institutional+
Government
0
8
20
0
241,710
42
MEX Personal Repair (COM3)
0
0
0
0
33,616
44
MEX Airports Area
0
14,639
0
1,149
6,857
46
MEX Marine Ports
0
124,951
2,991
1,482
1,099
48
Brick Kilns - Mexico
0
776
6,691
0
10,244
50
Mobile sources - Border Crossing - Mexico
0
454
0
0
2,668
9100
CAN Population
603
0
276
0
304
9101
CAN total dwelling
643
46,256
12,783
14,698
32,944
9106
CAN ALL INDUST
133
21,526
381
3,921
2
9113
CAN Forestry and logging
1,582
8,561
28,622
1,809
36,114
9115
CAN Agriculture and forestry activities
160
239,553
25,318
9,092
26,526
9116
CAN Total Resources
0
17
0
0
5
9212
CAN Mining except oil and gas
0
0
5,391
0
0
9221
CAN Total Mining
42
2,292
45,374
728
26
9222
CAN Utilities
189
14,882
369
1,124
255
9233
CAN Total Land Development
17
20,789
1,928
981
2,551
9308
CAN Food manufacturing
0
0
0
0
4,535
9323
CAN Printing and related support activities
0
0
0
0
25,203
9324
CAN Petroleum and coal products
manufacturing
0
0
2,402
0
0
9327
CAN Non-metallic mineral product
manufacturing
0
238
7,708
2,941
1,218
9331
CAN Primary Metal Manufacturing
0
98
5,062
12
6
9412
CAN Petroleum product wholesaler-distributors
0
0
0
0
70,125
9416
CAN Building material and supplies
wholesaler-distributors
2
0
1,461
3,259
560
9448
CAN clothing and clothing accessories stores
0
0
0
0
328
9562
CAN Waste management and remediation
services
165
893
1,596
1,998
16,551
9921
CAN Commercial Fuel Combustion
494
33,816
2,750
35,471
850
9924
CAN Primary Industry
0
0
0
0
219,282
9925
CAN Manufacturing and Assembly
0
0
0
0
139,227
9931
CAN OTHERJET
9
14,388
548
1,139
7,629
9932
CAN CANRAIL
109
122,694
4,093
5,737
3,304
9942
CAN UNPAVED ROADS
40
3,462
3,499
48
152,674
9945
CAN Commercial Marine Vessels
28
45,454
6,404
14,325
61,139
78
-------
Srg code
Description
nh3
NOx
pm25
so2
voc
9946
CAN Construction and mining
247
156,770
10,070
5,667
17,180
9947
CAN Agriculture Construction and mining
19
37,452
536
26
32,683
9950
CAN Intersection of Forest and Housing
1,053
11,700
120,045
1,671
173,130
9960
CAN TOTBEEF
176,156
0
7,420
0
317,394
9970
CAN TOTPOUL
74,204
0
2
0
264
9980
CAN TOTS WIN
122,094
0
996
0
3,186
9990
CAN TOTFERT
178,791
0
9,279
0
0
9991
CAN traffic
22,294
550,896
10,888
5,548
285,104
9994
CAN ALLROADS
0
0
55,468
0
0
9995
CAN 30UNPAVED 70trail
0
0
106,707
0
0
9996
CAN urban area
0
0
284
0
0
79
-------
4 Development of 2018 and 2025 Base-Case Emissions
This section describes the methods used for developing the 2018 and 2025 future-year base-case emissions.
The future base-case projection methodologies vary by sector. With the exceptions discussed in Section 4.2,
the 2018 and 2025 base cases represent predicted emissions in the absence of any further controls beyond
those Federal and State measures already promulgated or under reconsideration before emissions processing
began in November, 2013. The future base-case scenario reflects projected economic changes and fuel usage
for EGU and mobile sectors. The 2018 and 2025 EGU projected inventories represent demand growth, fuel
resource availability, generating technology cost and performance, and other economic factors affecting
power sector behavior. They also reflect the expected 2018 and 2025 emissions effects due to environmental
rules and regulations, consent decrees and settlements, plant closures, control devices updated since 2011,
and forecast unit construction through the calendar years 2018 and 2025, respectively. In this analysis, the
projected EGU emissions include the Final Mercury and Air Toxics (MATS) rule announced on December
21, 2011 and the Clean Air Interstate Rule (CAIR) issued March 10, 2005. More information on the EGU
base case.
For mobile sources (onroad, onroad rfl, nonroad, clc2rail and c3marine sectors), all national measures for
which data were available at the time of modeling have been included. The Tier 3 standards finalized in
March, 2014 are represented (Vehicles and Engines). Efforts 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. 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 Table 4-1.
• EGU sector (ptegu and ptegu pk): Unit-specific estimates from IPM version 5.13, including CAIR
and Final MATS.
• Non-IPM sector (ptnonipm): Projection factors and percent reductions reflect comments received
during the development of the Cross-State Air Pollution Rule (CSAPR) along with emission
reductions due to national and local rules, control programs, plant closures, consent decrees and
settlements. Projection for corn ethanol and biodiesel plants, refineries and 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 type using AEO 2013 projections to years 2018 and 2025. Cobenefits of stationary engines
CAP-cobenefit reductions (RICE NESHAP) and New Source Performance Standards (NSPS) VOC
controls reflected for select source categories.
• Fires sector (ptfire): No growth or control - 2011 estimates used directly.
• Agricultural sector (ag): 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; fertilizer application NH3 emissions projections include upstream impacts from EISA.
• Area fugitive dust sector (afdust): Projection factors for dust categories related to livestock estimates
based on expected changes in animal population and upstream impacts from EISA.
• Residential Wood Combustion (rwc): Projection factors that reflect assumed growth of wood
burning appliances based on sales data, equipment replacement rates and change outs. These changes
include a growth in lower-emitting stoves and a reduction in higher emitting stoves.
80
-------
• Remaining Nonpoint sector (nonpt): Projection factors implement comments received during Cross
State Air Pollution Rule development and emission reductions due to 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, are reflected.
• 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 years 2018 and 2025. The inputs were either state-supplied as part of the
201 INEIvl process or using national level inputs. Final controls from the final locomotive-marine
and small spark ignition OTAQ rules are included. California and Texas-specific data were provided
by CARB and TCEQ, respectively.
• Locomotive, and non-Class 3 commercial marine sector (clc2rail): Projection factors for Class 1 and
Class 2 commercial marine and locomotives reflect final locomotive-marine controls and fuel volume
projections from AEO.
• Class 3 commercial marine vessel (c3marine): Base-year 2011 emissions grown and controlled to
2018 and 2025, incorporating controls based on Emissions Control Area (ECA) and International
Marine Organization (IMO) global NOx and SO2 controls.
• Onroad mobile, not including refueling (onroad): MOVESTier3FRM-based emissions factors for
years 2018 and 2025 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. California and TCEQ-specific data were provided by CARB and TCEQ, respectively.
This sector includes all non-refueling onroad mobile emissions (exhaust, extended idle, auxiliary
power units, evaporative, evaporative permeation, brake wear and tire wear modes).
• Onroad refueling mode (onroad rfl): the same projection approach is used as for the onroad sector
and processing is described in Section 2.3.2, in that emission factors are from MOVESTier3FRM and
that California and Texas did not include state supplied emissions.
• Other onroad (othar): No growth or control for Canada because data are not available. Mexico
inventory data were grown from year 1999 to 2018 and retained at year 2018 values for 2025.
• Other nonroad/nonpoint (othon): No growth or control for Canada. Mexico inventory data were
grown from year 1999 to 2018 and retained at year 2018 values for 2025.
• Other point (othpt): No growth or control for Canada and offshore oil. Mexico inventory data were
grown from 1999 to year 2018 and retained at year 2018 values for 2025. Non-U. S. C3 CMV data
projected using the same methodology as the c3marine sector.
• Biogenic: 2011 emissions computed with "1 lg" meteorology are used for all future-year scenarios.
Table 4-1 summarizes the control strategies and growth assumptions by source type that were used to create
the U.S. 2018 and 2025 base-case emissions from the 201 lv6.1 base-case inventories. Lists of the control,
closures, projection packets (datasets) used to create 2018 and 2025 future year base-case scenario
inventories from the 2011 base case are provided on the FTP site. These packets were processed through
EPA's Control Strategy Tool (CoST) to create future year inventories. CoST is described here: CoST. The
CoST packets are formatted in the same way as those needed for SMOKE. .
81
-------
Table 4-1. Control strategies and growth assumptions for creating the 2018 and 2025 base-case emissions
inventories from the 2011 base case
Control Strategies and/or growth assumptions
(grouped by standard and approach used to apply to the inventory)
CAPs
affected
Section
\on-K(il Point (ptnonipm and pi oilgas sectors) Controls and (irowlli Assumptions
Ethanol plants adjustments for AEO volumes
All
4.2.1.1
Biodiesel plants adjustments for AEO volumes
All
4.2.1.2
Ethanol distribution vapor losses adjustments due to AEO volumes
VOC
4.2.1.6
Refinery upstream adjustments for AEO volumes
All
4.2.1.7
Livestock emissions growth from year 2011 to years 2018 and 2025, also including upstream RFS2
impacts on agricultural-related activities such as pesticide and fertilizer production
All
4.2.2
Oil and gas production AEO-based regional growth factors and VOC NSPS controls
All
4.2.4
Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsiderations
NOx,
CO, PM,
S02
4.2.3
State fuel sulfur content rules for fuel oil - as of July, 2012, effective only in Maine, Massachusetts,
New Jersey, New York and Vermont
S02
4.2.6
Industrial/Commercial/Institutional Boilers and Process Heaters MACT with Reconsideration
Amendments
CO, PM,
so2,
VOC
4.2.7
NESHAP: Portland Cement census-division level based on Industrial Sector Integrated Solutions
(ISIS) policy emissions to years 2018 and 2025. The ISIS results are from the ISIS-Cement model
runs for the NESHAP and NSPS analysis of August 2013 and include closures.
All
4.2.8
Future baseline inventory improvements received from states and a 2005 platform NODA and
comments from the CSAPR proposal, including local controls, fuel switching, unit closures and
consent decrees
All
4.2.9
Facility and unit closures obtained from various sources such as states, industry and web posting,
EPA staff and post-2011 inventory submittals
All
4.2.10
Aircraft growth via Itinerant (ITN) operations at airports to 2018 and 2025
All
4.2.10
Lafarge and Saint Gobain consent decrees
NOx,
PM, S02
4.2.9.3
Consent decrees on companies (based on information from the Office of Enforcement and
Compliance Assurance - OECA) apportioned to plants owned/operated by the companies
CO,
NOx,
PM, S02,
VOC
4.2.9.3
Refinery Consent Decrees: plant/unit controls
NOx,
S02
4.2.9.3
Commercial and Industrial Solid Waste Incineration (CISWI) revised NSPS
PM, S02
4.2.11.1
Nonpoint (al'dust, ag. nonpl np oilgas. and rwc sectors) Controls and (Irowth Assumptions
MSAT2 and RFS2 impacts on portable fuel container growth and control from 2011 to years 2018
and 2025
VOC
4.2.1.3
Cellulosic ethanol and diesel emissions from AEO volumes
All
4.2.1.4
Ethanol transport working losses inventory from AEO volumes
VOC
4.2.1.5
Ethanol distribution vapor losses adjustments from AEO volumes
VOC
4.2.1.6
Livestock emissions growth from year 2011 to years 2018 and 2025, also including upstream RFS2
impacts on agricultural-related activities such as pesticide and fertilizer production
All
4.2.2
Oil and gas production AEO-based regional growth factors and VOC NSPS controls
All
4.2.4
Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsiderations
NOx,
CO, PM,
S02
4.2.3
State fuel sulfur content rules for fuel oil - as of July, 2012, effective only in Maine, Massachusetts,
New Jersey, New York and Vermont
S02
4.2.6
Residential wood combustion growth and change-outs from year 2011 to years 2018 and 2025
All
4.2.3
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Control Strategies and/or growth assumptions
(grouped by standard and approach used to apply to the inventory)
CAPs
affected
Section
Future baseline inventory improvements received from states
NOx,
voc
4.2.9
Onroad Mobile Controls
(All national in-lorce regulations are modeled. The list includes key recent mobile control strategies but is
not exhauslne.)
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
Light-Duty Vehicle Greenhouse Gas Rule for Model-Year 2017-2025: October, 2012
Heavy (and Medium)-Duty Greenhouse Gas Rule: September, 2011
Renewable Fuel Standard Program (RFS2): March, 2010
Light Duty Vehicle Greenhouse Gas Rule for Model-Year 2012-2016: May, 2010
Final Mobile Source Air Toxics Rule (MSAT2): February, 2007
2007 Heavy-Duty Highway Rule: January, 2001
Tier 2 Vehicle and Gasoline Sulfur Program: February, 2000
National Low Emission Vehicle Program (NLEV): March, 1998
All
4.3
Local Onroad Programs:
California LEVIII Program
Ozone Transport Commission (OTC) LEV Program: January, 1995
Inspection and Maintenance programs
Fuel programs (also affect gasoline nonroad equipment)
Stage II refueling control programs
VOC
4.3
Nonroad Mobile Controls
(All national in-l'orcc regulations are modeled. I lie list includes recent key mobile control strategies but is
not cxhauslne.)
National Nonroad Controls:
All nonroad control programs finalized as of the date of the model run, including most recently:
Emissions Standards for New Nonroad Spark-Ignition Engines, Equipment, and Vessels: October,
2008
Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition Engines
Less than 30 Liters per Cylinder: March, 2008
Clean Air Nonroad Diesel Final Rule - Tier 4: May, 2004
All
4.4
Locomotives:
Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition Engines
Less than 30 Liters per Cylinder: March, 2008
Clean Air Nonroad Diesel Final Rule - Tier 4: May, 2004
All
4.4.1
Commercial Marine:
Category 3 marine diesel engines Clean Air Act and International Maritime Organization standards:
April, 2010
Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition Engines
Less than 30 Liters per Cylinder: March, 2008
Clean Air Nonroad Diesel Final Rule - Tier 4: May, 2004
All
4.4.2
A quick background on the Control Strategy Tool (CoST)
CoST is used to apply most non-EGU projection/growth factors, controls and facility/unit/stack-level
closures to the 2011 emissions modeling inventories to create inventories for years 2018 and 2025 for the
following sectors: afdust, ag, clc2rail, nonpt, npoilgas, ptnonipm, pt oilgas and rwc. The CoST training
manual. The CoST development document, which is a more thorough but dated document of how to build
and format CoST input files (packets).
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CoST allows the user to apply projection factors, controls and closures at various geographic and inventory
key field resolutions. CoST provides the user with the ability to perform numerous quality assurance
routines as well as create SMOKE-ready future year inventories. 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 datasets or "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 used 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 sector in the 2011
platform.
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 simple scalars 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 controls. EPA used
PROJECTION packet(s) in every non-EGU modeling sector in the 2011 platform.
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.
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 pollutant-level PROJECTION factor. It is important to note that this hierarchy does not apply intra-
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 201 INEIvl) or a targeted 2018 (or
2025) emissions value. Therefore, as encountered with this 2018 base case, 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 type of measure/program and also by consent decree and state comments, it is possible to show
actual changes from the 2011 inventory to the 2018 and 2025 inventory for each packet.
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Ultimately, CoST concatenates all PROJECTION packets into one PROJECTION dataset and uses a
hierarchal matching approach, a sample subset of which is shown in Table 4-2, 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. It is also important not all 70+ hierarchy options are shown. The fields listed
in Table 4-2 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 4-2. 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, SCC, POLL
point
4
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, POLL
point
5
REGION CD, FACILITY ID, UNIT ID, SCC, POLL
point
6
REGION CD, FACILITY ID, UNIT ID, POLL
point
7
REGION CD, FACILITY ID, SCC, POLL
point
8
REGION CD, FACILITY ID, POLL
point
9
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID
point
10
REGION CD, FACILITY ID, UNIT ID, REL POINT ID
point
11
REGION CD, FACILITY ID, UNIT ID
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, POLL
point, nonpoint
16
REGION CD, NAICS
point, nonpoint
17
NAICS
point, nonpoint
18
REGION CD, SCC, POLL
point, nonpoint
19
STATE, SCC, POLL
point, nonpoint
20
SCC, POLL
point, nonpoint
21
REGION CD, SCC
point, nonpoint
22
STATE, SCC
point, nonpoint
23
SCC
point, nonpoint
24
REGION CD, POLL
point, nonpoint
25
REGION CD
point, nonpoint
26
STATE, POLL
point, nonpoint
27
STATE
point, nonpoint
28
POLL
point, nonpoint
The remainder of this section is organized either by source sector or by specific emissions category within a
source sector for which a distinct set of data were used or developed for the purpose of projections for the
2018 and 2025 base cases. 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 emissions categories are
provided in parentheses.
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A list of inventory datasets used for this and all cases is provided on the FTP site. The ancillary input data in
the future-year scenarios are very similar to those used in the 2011 base case except for the speciation
profiles used for gasoline-related sources, which change in the future to account for increased ethanol usage
in gasoline. The specific speciation profile changes are discussed in Section 3.2.1.
4.1 Stationary source projections: EGU sectors (ptegu, ptegu_pk)
The future-year data for the ptipm sector used in the air quality modeling were created by the Integrated
Planning Model (IPM) version 5.13 (v5.13) Final MATS (Mercury and Air Toxics Standards) (Clean Air
Markets). The IPM is a multiregional, dynamic, deterministic linear programming model of the U.S. electric
power sector. Version 5.13 reflects state rules, consent decrees and announced shutdowns through August,
2013. IPM 5.13 was significantly updated from the previous version 4.10 and represents electricity demand
projections for the Annual Energy Outlook (AEO) 2013. The scenario used for this modeling represents the
implementation of the Clean Air Interstate Rule, the Mercury and Air Toxics Standards, and the final actions
EPA has taken to implement the Regional Haze Rule. More details on the IPM v5.13 base case scenarios.
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 methodology
is documented in the air quality modeling flat file documentation. This postprocessing step also apportions
the regional emissions down to the unit-level emissions used for air quality modeling. A single IPM run is
postprocessed once for each output year to get results for both 2018 and 2025. As part of the development of
the flat file, a cross reference between the 201 INEIvl and IPM is used to help populate stack parameters and
other related information. The emissions in the flat file created from the IPM outputs are temporalized into
the hourly emissions needed by the air quality model as described in Section 3.3.2.
4.2 Stationary source projections: non-EGU sectors (afdust, ag, nonpt,
npoiigas, ptnonipm, pt_oiigas, rwc)
To project U.S. stationary sources other than the ptipm sector, growth factors and/or controls were applied to
certain categories within the afdust, ag, nonpt, np oiigas, ptnonipm, ptoilgas and rwc platform sectors. This
subsection provides details on the data and projection methods used for these sectors. In estimating future-
year emissions, EPA assumed that emissions growth does not track with economic growth for many
stationary non-IPM sources. This "no-growth" assumption is based on an examination of historical
emissions and economic data. While EPA is working toward improving the projection approach in future
emissions platforms, the Agency is still using the no-growth assumption for the 2011 platform unless states
provided specific growth factors for 2018 or other years beyond 2018. More details on the rationale for this
approach can be found in Appendix D of the Regulatory Impact Assessment for the PM NAAQS rule (EPA,
2006b).
For many sources, EPA applied emissions reduction factors (CONTROL packets) to the 2011 base case
emissions for particular sources in the ptnonipm, nonpt and two oil and gas sectors (np oiigas and pt oilgas)
to reflect the impact of stationary-source national and local-scale control programs including consent
decrees. Information on plant, unit and stack closures (CLOSURE packets) is restricted to the ptnonipm
sector. Some of the controls described in this section were obtained from comments on the Cross-State Air
Pollution Rule (CSAPR) proposal.
The contents of the controls, local adjustments and closures for the 2018 and 2025 base cases are described
in the following subsections. Year-specific projection factors (PROJECTION packets) for years 2018 and
2025 were used for creating the 2018 and 2025 base cases unless noted otherwise. The contents of these
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projection packets (and control reductions) are provided in the following sections where feasible. However,
some 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). This section is divided into several subsections that are summarized in Table 4-3.
Note that future year inventories were used rather than projection or control packets for some sources.
Table 4-3. Summary of non-EGU stationary projections subsections
Subsection
Title
Sector(s)
Brief Description
4.2.1
Mobile source upstream
future year inventories and
adjustments
nonpt
ptnonipm
1) Point and non-point inventories received from
OTAQ that account for the upstream impact of
AEO fuel volume projections.
2) Point and non-point adjustment factors that EPA
applied to the 2011 inventory to reflect AEO
fuel volumes in 2018, with 2025 held at year
2018 values.
3) LDGHG adjustments made for year 2025
4.2.2
Upstream agricultural and
livestock adjustments
afdust, ag,
nonpt,
ptnonipm
Adjustment factors reflect impacts on agriculture
related processes due to increased ethanol use under
the EISA mandate.
4.2.3
Residential wood
combustion projections
rwc
Adjustment factors that reflect the change in RWC
emissions by appliance type, including wood stove
change-outs and accounting for estimated future
sales and replacement rates.
4.2.4
Oil and Gas projections
npoilgas,
ptoilgas
Projection packet reflecting regional AEO-based
growth for oil and gas production as well as VOC
NSPS controls for select sources.
4.2.3
RICE NESHAP controls
nonpt,
npoilgas,
ptnonipm,
pt oilgas
Control packet reflecting RICE NESHAP with
reconsideration amendments.
4.2.6
Fuel sulfur rule controls
nonpt
ptnonipm
Control packet reflecting state and local fuel sulfur
rules, including ULSD.
4.2.7
Industrial Boiler MACT
reconsideration controls
ptnonipm
Control packet reflecting ICI Boiler MACT
reconsideration reductions.
4.2.8
Portland cement NESHAP
projections
ptnonipm
Year 2018 and 2025 ISIS policy cases reflecting the
Portland Cement NESHAP, including closures,
controls at existing kilns and an inventory
containing new kilns constructed after 2011 that
account for shifting capacity from some closed units
to open units.
4.2.3
State comments and
consent
decrees/settlements
nonpt,
ptnonipm
Projection and control packets reflecting numerous
sources of consent decree/settlement information as
well as state comments and data regarding 2018,
with limited information beyond year 2018.
4.2.9
Aircraft projections
ptnonipm
Airport-specific projections to years 2018 and 2025
based on FAA itinerary activity estimates.
4.2.10
Remaining non-EGU
controls and closures
ptnonipm
All other controls and plant/unit/stack closures
information not covered in previous subsections
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4.2.1 Mobile source upstream future year inventories and adjustments (nonpt,
ptnonipm)
EPA incorporated adjustments for some stationary source categories to account for expected impacts of
renewable fuel requirements under EISA, as estimated by Annual Energy Outlook (AEO) 2013, as well as
impacts of recent 2017-2025 light duty vehicle greenhouse gas emission standards and heavy-duty greenhouse
gas standards. These fuel requirements not only impact emissions associated with highway vehicles and
nonroad engines, but also emissions associated with point and nonpoint sources. The "upstream" emission
impacts of the renewable fuels mandate 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. These impacts are
accounted for in the 2018 inventories. Except for cellulosic diesel, there was not a significant change in
biofuel volumes between 2018 and 2025 (Table 4-4); thus the same biofuel adjustments were used for 2025.
There are also impacts on domestic crude emissions upstream of petroleum refineries, due to displacement of
gasoline and diesel fuel with biofuels, but these are not accounted for in these projections as these data were
not available. Greenhouse gas standards also affect production and distribution of gasoline and diesel fuels,
but the impacts of these rules will be very small in 2018 and were not accounted for in this analysis.
However, the effects are substantial for 2025 and were thus accounted for in the inventories for that year.
Based on the Annual Energy Outlook 2013 (early release) energy use of 15.47 quad (1015 BTU) (Department
of Energy, 2012), EPA estimated the 2011 ethanol volume as 11.1 billion gallons (Bgal). EPA assumed that
an unadjusted 2018 inventory, which does not account for the impacts of the EISA renewable fuel mandate,
would have comparable ethanol volumes to 2011. However, analyses done to support the RFS2 rule (EPA,
2010a) suggested a significant increase in renewable fuel volumes in 2018 (see Table 4-4). Adjustments
applied to the inventories (described in the following subsections) reflect the impacts on emissions due to the
difference between the 2011 ethanol volumes and the renewable fuel volumes shown in Table 4-4. In 2018
and 2025, EPA assumed 1 Bgal of ethanol would be used as E85, 10 Bgal as E10, and about 4 Bgal as E15.
Table 4-4. Renewable Fuel Volumes Assumed for Stationary Source Adjustments.
Renewable Fuel
2018 Volume (Bgal)
2025 Volume (Bgal)
AEO 2013
AEO 2013
Corn Ethanol
14.7
14.7
Cellulosic Ethanol
0.235
0.235
Imported Ethanol
1.1
0.94
Biodiesel
1.9
1.9
Renewable Diesel
0.236
0.236
Cellulosic Diesel
0.118
0.472
4.2.1.1 Corn Ethanol plants inventory (ptnonipm)
Future year inventories: "ethanol plants 2018ed NET and "ethanol_plants_2018ed_OTAQ"
As discussed in Section 2.1.4, EPA supplemented the 2011 NEI with corn ethanol plants that EPA OTAQ
identified. The 2011 emissions were projected to account for the increased domestic corn ethanol production
assumed in this modeling, specifically an increase from 13.9 Bgal in 2011 to 14.7 Bgal by 2018. Since
biofuels were not projected to change significantly between 2018 and 2025 the year 2018 inventory was used
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for year 2025. The projection was applied to all pollutants and all facilities equally. Table 4-5 provides the
summaries of estimated emissions for the corn ethanol plants in year 2011 and 201824.
Table 4-5. 2011 and 2018/2025 corn ethanol plant emissions [tons]
Pollutant
2011
2018/2025
CO
15,934
16,858
nh3
726
768
NOx
18,048
19,095
PMio
10,602
11,217
PM2.5
5,995
6,343
S02
34,608
36,294
VOC
19,654
21,115
4.2.1.2 Biodiesel plants inventory (ptnonipm)
New Future year inventory: "Biodiesel_Plants_2018_ffl0"
EPA OTAQ developed an inventory of biodiesel plants for 2018. Plant location and production volume data
came from the Tier 3 proposed rule.25'26 The total volume of biodiesel came from the AEO 2013 early
release, 1.3 BG for 2018. To reach the total volume of biodiesel, plants that had current production volumes
were assumed to be at 100% production and the remaining volume was split among plants with planned
production. Once facility-level production capacities were scaled, emission factors were applied based on
soybean oil feedstock. These emission factors in Table 4-6 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). Inventories were modeled as point sources with Google Earth and web searching validating facility
coordinates and correcting state-county FIPS. Table 4-7 provides the 2018 biodiesel plant emissions
estimates. Since biofuels were not projected to change significantly between 2018 and 2025 the year 2018
inventory was used for year 2025. Emissions in 2011 are assumed to be near zero, and HAP emissions in
2018 and 2025 are nearly zero.
Table 4-6. 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
S02
5.9445E-03
nh3
0
Acetaldehyde
2.4783E-07
Acrolein
2.1290E-07
Benzene
3.2458E-08
1,3-Butadiene
0
24 The 2011 emissions are the sum of the NEI and OTAQ facilities. The same is true for 2018 and 2025.
25 US EPA 2014.Regulatory Impact Analysis for Tier 3 Vehicle Emission and Fuel Standards Program. EPA-420-RD-143-0052.
26 Cook, R. 2014. Development of Air Quality Reference Case Upstream and Portable Fuel Container Inventories for Tier 3 Final
Rule. Memorandum to Docket EPA-HQ-OAR-2010-0162.
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Pollutant
Emission Factor
Formaldehyde
1.5354E-06
Ethanol
0
Table 4-7. 2018/2025 biodiesel plant emissions [tons]
Pollutant
2018
CO
649
NOx
1048
PMio
89
PM2.5
89
S02
8
VOC
57
4.2.1.3 Portable fuel container inventory (nonpt)
Future year inventory: "2018_PFC_inventory_FF 10_revision2"
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
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
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 EISA on gasoline volatility.
OTAQ provided year 2018 and 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 refueling from the NONROAD model for gas can vapor
displacement, changes in 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; see Section 3.2.1.1 for more details. Emissions for 2011, 2018 and 2025 are provided
in Section 5.
Table 4-8. PFC emissions for 2011, 2018 and 2025 [tons]
Pollutant
2011
2018
2025
VOC
198,395
29,119
34,269
Benzene
786
645
752
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Ethanol
0
3,719
4,448
4.2.1.4 Cellulosic fuel production inventory (nonpt)
New Future year inventory: "2018_cellulosic_inventory"
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 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 4-9 and Table 4-10 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.27 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 4-11 provides the year 2018 cellulosic plant
emissions estimates. Since biofuels were not projected to change significantly between 2018 and 2025 the
year 2018 inventory was used for year 2025.
Table 4-9. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/RIN gallon)
Cellulosic Plant
Type
VOC
CO
NOx
PMio
pm25
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 4-10. 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
Table 4-11. 2018/2025 cellulosic plant emissions [tons]
Pollutant
Emissions
Acrolein
1
Formaldehyde
4
Benzene
1
27 It should be noted that in the Tier 3 NPRM we meant to use different cellulosic ethanol non-VOC CAP emission factors
depending on which feedstock the plant was using but instead used the same emission factors (based on a forest waste feedstock)
for all the plants. This was corrected by using emission factors for the non-VOC CAPS that were based on a stover feedstock for
the biochemical plants.
91
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Acetaldehyde
21
CO
6,088
Ethanol
146
nh3
0.1
NOx
9,199
PMio
1,088
PM2.5
547
S02
819
VOC
414
4.2.1.5 Ethanol working loss inventory (nonpt)
New Future year inventory: "Ethanol_transport_vapor_2018rg_ref_v 1"
The year 2018inventory was provided by OTAQ to represent upstream impacts of loading and unloading at
ethanol terminals. Since biofuels were not projected to change significantly between 2018 and 2025, the
2018 inventory was used for year 2025. Emissions are entirely evaporative and were computed by county
for truck, rail and waterway loading and unloading and intermodal transfers (e.g., highway to rail).
Inventory totals are summarized in Table 4-12. The leading descriptions are "Industrial Processes; Food and
Agriculture; Ethanol Production" for each SCC.
Table 4-12. 2018/2025 VOC working losses (Emissions) due to ethanol transport [tons]
SCC
Description
Emissions
30205031
Denatured Ethanol Storage Working Loss
23,420
30205052
Ethanol Loadout to Truck
14,425
30205053
Ethanol Loadout to Railcar
10,484
4.2.1.6 Vapor losses from transport and distribution of gasoline and gasoline/ethanol
blends (nonpt, ptnonipm)
Packet: "PROJECTION 201 l_2018_distribution_upstream_OTAQ_Tier3FRM" and
"PROJECTION2011 v6_2025_distribution_upstream.csv"
OTAQ developed county-level inventory adjustments for gasoline and gasoline/ethanol blend transport and
distribution for 2018 and 2025, to account for losses for the processes such as truck, rail and waterways
loading/unloading and intermodal transfers such as highway-to-rail, highways-to-waterways, and all other
possible combinations of transfers. Adjustments for 2018 account for impacts of the EISA mandate, and the
2025 adjustments account for additional impacts of greenhouse gas emission standards for motor vehicles on
transported volumes. These emissions are entirely evaporative and therefore limited to VOC.
A 2018 inventory which included impacts of the EISA mandate was developed by adjusting the 2007
platform inventory. These adjustments were made using an updated version of EPA's spreadsheet model for
upstream emission impacts, developed for the RFS2 rule28. The methodology used to make these
28 U.S. EPA. 2013. Spreadsheet "upstream emissions rev T3.xls.
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adjustments is described in a 2014 memorandum included in the docket for the EPA Tier 3 rule.29 The
resulting adjustments are provided in Table 4-13. 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 B for the complete cross-walk between SCC, and state-SCC for BTP components, and each type
of petroleum transport and storage. 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.
Table 4-13. Adjustment factors applied to storage and transport emissions
Process
PADD
Pollutant
2018 Adjustment
Factor
2025 Adjustment
Factor30
BTP
1
voc
0.9515
0.87843
benzene
0.9905
0.87843
2
VOC
0.9619
0.87843
benzene
0.9882
0.87843
3
VOC
0.9778
0.87843
benzene
0.9879
0.87843
4
VOC
0.8983
0.87843
benzene
0.9885
0.87843
5
VOC
0.9430
0.87843
benzene
0.9901
0.87843
RBT/BPS
All
VOC
0.9553
0.87843
benzene
0.9893
0.87843
29 U. S. EPA. Development of Air Quality Reference Case Upstream and Portable Fuel Container Inventories for the Tier 3 Final
Rule. Memorandum from Rich Cook, Margaret Zawacki and Zoltan Jung to the Docket. February 25, 2014. Docket EPA-HQ-
OAR-2011-0135.
30 The 2025 adjustment factors are in addition to the 2018 adjustment factors, i.e. to go from 2011 to 2025, one would need to
apply both adjustments.
93
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Figure 4-1. Map of Petroleum Administration for Defense Districts (PADD)
SratDe
PADD 5: , :
West Coast,
AK, HI
San
Francisco
NV
Lc* Angela!
PADD 4:
Rocky
Mountain
HI
PADD 2*
Midwest
PADD 3: Gulf Coast
Source: U.S. Energy Information Administration
TN
PADD 1 A:
N
-------
Calculation of the emission inventory impacts of decreased gasoline and diesel production, due to renewable
fuel volume projections, on nationwide refinery emissions was done in EPA's spreadsheet model for
upstream emission impacts (EPA, 2009b). Emission inventory changes reflecting these impacts were used to
develop adjustment factors that were applied to inventories for each petroleum refinery in the U.S. (Table
4-14). 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 in Table 4-14 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.
Table 4-14. 2018 and 2025 adjustment factors applied to petroleum pipelines and refinery emissions
associated with gasoline and diesel fuel production.
2018 Factors
2025 Factors
Pollutant
Pipelines
Refineries
Both
Pipelines
Refineries
Both
CO
0.9964
0.9776
0.9741
0.9875
0.8603
0.8495
NOx
0.9819
0.9867
0.9688
0.9286
0.8683
0.8063
PMio
0.9967
0.9839
0.9806
0.9899
0.8659
0.8571
PM2.5
0.9975
0.9789
0.9765
0.9930
0.8615
0.8555
S02
0.9981
0.9781
0.9763
0.9910
0.8608
0.8530
nh3
n/a
0.9517
0.9517
n/a
0.8376
0.8376
VOC
0.999
0.9719
0.9710
0.9963
0.8554
0.8522
4.2.2 Upstream agricultural and Livestock adjustments (afdust, ag, nonpt, ptnonipm)
Packet: "PROJECTION201 l_2018_ag_including_upstream_OTAQ_25nov2013_vl" and
"PROJECTION201 l_2025_ag_including_upstream_OTAQ_25nov2013 .txt"
Inventory adjustments were previously developed for 2017 and 2030 as part of final RFS2 rule modeling32.
Although 2018 and 2025 were modeled for this rule rather than 2017 and 2030, EPA continued to use the
2017 and 2030 adjustments. Impacts on farm equipment emissions were not accounted for, however.
Emission rates from the GREET model (fertilizer and pesticide production)33 or based on the 2002 National
Emissions Inventory (fertilizer and pesticide application, agricultural dust, livestock waste) were combined
with estimates of agricultural impacts from FASOM (Forest and Agricultural Section Optimization Model).
Since FASOM modeling used a reference case of 13.2 billion gallons of ethanol, impacts used in the
modeling for this rule are underestimates.
Adjustment factors are provided in Table 4-15. These adjustments were applied equally to all counties
having any of the affected sources. This is an area of uncertainty in the inventories, since there would likely
be variation from one county to another depending on how much of the predicted agricultural changes
occurred in which counties. By using percent change adjustments rather than attempting to calculate
absolute ton changes in each county, EPA has attempted to minimize the inventory distortions that could
occur if the calculated change for a given county was out of proportion to the reference case emissions for
that county. For instance, a different approach could estimate reductions that were larger than the reference
case emissions, since there was no linkage between the 201 INEIvl inventories and the FASOM modeling.
32 U. S. Environmental Protection Agency. 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.
33 GREET, version 1.8c.
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The specific sources (SCCs) and affected pollutants that these adjustments were applied to are listed in a
docket reference34.
Table 4-15. Adjustments to modeling platform agricultural emissions for 2018 and 2025
Source Description
2018 Adjustment
2025 Adjustment
Nitrogen fertilizer application
1.0242
1.0573
Fertilizer production, mixing/blending
1.0603
1.0603
Pesticide production
0.9544
0.9954
Agricultural tilling/loading dust
1.0079
1.0265
Agricultural burning
1.000
1.000
Livestock dust
0.9868
0.9983
Livestock waste
0.9901
0.9983
For the animal waste sources, EPA also estimated animal population growth in ammonia (NH3) and dust
(PM10 and PM2.5) emissions from livestock in the ag, afdust, and ptnonipm sectors. Therefore, a composite
set of projection factors is needed for animal operations that also reflect the minor 0.99% decrease resulting
from the EISA mandate. These composite projection factors by animal category are provided in Table 4-16.
As discussed below, dairy cows and turkeys are assumed to have no growth in animal population, and
therefore the projection factor for these animals is the same as the upstream agriculture-related projection
factor.
Table 4-16. Composite NH3 projection factors to years 2018 and 2025 for animal operations
Animal Category
2018 Factor
2025 Factor
Dairy Cow
0.9901
0.9743
Beef
0.9851
0.9727
Pork
1.0582
1.1164
Broilers
1.0904
1.1283
Turkeys
0.9290
0.9190
Layers
1.0629
1.0926
Poultry Average
1.0557
1.0826
Overall Average
1.0310
1.0408
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) and 2011 to 2018 and
2025 animal population projections were computed for these 201 lv6 projections those future years. 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 2025 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
34 U. S. EPA. 2011. Spreadsheet "agricultural sector adjustments.xls." Docket EPA-HQ-OAR-2011-0135.
96
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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 D provides
the animal population data and regression curves used to derive the growth factors.
4.2.3 Residential wood combustion growth (nonpt)
Packet: "PROJECTION2011 v6_2018bau_RWC_25nov2013 .txt" and
"PROJECTION2011 v6_2025bau_RWC_25nov2013 .txt"
EPA used a "business as usual" (BAU) approach to Residential Wood Combustion (RWC) projections that
does not account for national New Source Performance Standards (NSPS) for wood stoves, since they are
currently in the comment-seeking process from proposal (EPA, 2013a). EPA projected residential wood
combustion (RWC) emissions to years 2018 and 2025 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 state and local rules in place, 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 simply hold RWC emissions flat (unchanged) for all SCCs in California,
Oregon and Washington.
The development of projected growth in RWC emissions to years 2018 and 2025 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). 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 2025 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 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 in 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 our non-NSPS projection approach,
97
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the projection factors are the same for all pollutants except for EPA certified woodstoves of all types. 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.
California did not report detailed SCCs in the 201 INEIvl, simply reporting emissions from general
fireplaces (SCC=2104008100) and general woodstoves (SCC=2104008300). California, Oregon and
Washington also have state-level RWC control programs, including local burn bans in place. Without
appliance counts in California at specific appliance types (e.g., certified versus non-certified), and an
inability to incorporate significant local RWC control programs/burn bans for a future year inventory, EPA
decided to leave all 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 4-17Table 4-18. EPA-
certified woodstoves (inserts and freestanding) utilize different projection factors for direct PM than all other
pollutants.
Table 4-17. Non-West Coast RWC projection factors
Pollutant
see
Description
2018
Factor
2025
Factor
All
2104008100
Fireplace: general
1.072
1.149
All
2104008210
Woodstove
fireplace inserts; non-EPA certified
0.897
0.78
PM
2104008220
Woodstove
fireplace inserts; EPA certified; non-catalytic
1.076
1.162
All other
2104008220
Woodstove
fireplace inserts; EPA certified; non-catalytic
1.181
1.389
PM
2104008230
Woodstove
fireplace inserts; EPA certified; catalytic
1.081
1.174
All other
2104008230
Woodstove
fireplace inserts; EPA certified; catalytic
1.181
1.389
All
2104008300
Woodstove
freestanding, general
1.171
1.368
All
2104008310
Woodstove
freestanding, non-EPA certified
0.98
0.957
PM
2104008320
Woodstove
freestanding, EPA certified, non-catalytic
1.076
1.162
All other
2104008320
Woodstove
freestanding, EPA certified, non-catalytic
1.181
1.389
PM
2104008330
Woodstove
freestanding, EPA certified, catalytic
1.081
1.174
All other
2104008330
Woodstove
freestanding, EPA certified, catalytic
1.181
1.389
All
2104008400
Woodstove
pellet-fired, general (freestanding or FP insert)
1.645
2.385
All
2104008510
IF: Indoor Furnaces: cordwood-fired, non-EPA certified
1.103
1.315
All
2104008610
OHH: Outdoor Hvdronic heaters
1.237
1.509
All
2104008700
Outdoor wood burning device, NEC (e.g., fire-pits,
chimineas)
1.072
1.149
All
2104009000
Residential firelog total; all combustor types
1.072
1.149
4.2.4 Oil and Gas projections (np_oilgas, pt_oilgas)
Packet: "PROJECTION 201 Iv6_2018_oilgas_27nov2013.txt" and
"PROJECTION_2011v6_2025_oilgas_06mar2014.txt"
The oil and gas point (pt oilgas) and nonpoint (np oilgas) sectors are modeled separately from the remaining
point (ptnonipm) and nonpoint (nonpt) sector emissions primarily to better track/isolate and summarize the
oil and gas projections from 2011 to future years. EPA is aware that these emissions inventories are subject
to much scrutiny in the base year (2011) as well as growth and control assumptions in the coming years. Our
initial approach at projecting these emissions is a simple regional-level Annual Energy Outlook (AEO) 2013-
based methodology with some associated VOC reduction factors for sources that would be subject to New
98
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Source Performance Standards (NSPS). The methodology EPA describes here was a result of a coordinated
effort between EPA OAQPS and EPA Office of Atmospheric Programs (OAP) Climate Change Division
(CCD).
The AEO-2013 regional growth factors are based on 2011 to 2018 and 2025 oil production, gas production
and combined oil and gas production trends, available in Supplemental tables for regional detail, Table 131
and Table 132 at: Annual Energy Outlook. These National Energy Modeling System (NEMS) regions are
shown in Figure 4-2 and demonstrate one of the many limitations of this projection strategy: projections are
not based on oil/gas basin but rather, much larger geographic regions. A county-NEMS region cross-walk
was developed to assign counties in New Mexico and Texas to specific NEMS regions.
Figure 4-2. Oil and Gas NEMS Regions
Atlantic
ShallowGulf of Mexicol
Deep Gulf of Mexico
Source; U.S. Energy Information Administration, Office of Energy Analysis.
The AEO-2013 provides regional growth factors for oil production and gas production; however, numerous
sources (SCCs) in the 2011 platform are ambiguous regarding the type of product being extracted/produced.
These sources were assigned to a combined oil and gas category set of factors where oil and natural gas
production levels were summed using a barrel-of-oil equivalent of 0.178 barrels of crude oil to 1000 cubic
feet of natural gas. The AEO-based projection factors for each products type and NEMS region, provided in
Table 4-18, are applied to for all pollutants and SCCs in the point and nonpoint oil and gas sector
inventories, with the exception of VOC for select SCCs. The two character region codes (e.g., "NE" for
Northeast region) are relevant in the following discussion on VOC projection factors.
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Table 4-18. AEO-based Projection Factors
Region
2018
2025
Oil
Gas
Oil/Gas
Oil
Gas
Oil/Gas
Northeast (NE)
1.238
1.596
1.572
1.301
2.059
2.009
Gulf Coast (GC)
1.853
1.246
1.368
1.743
1.486
1.538
Midcontinent (MC)
1.165
0.910
0.955
1.272
0.890
0.958
Southwest (SW)
1.391
1.043
1.173
1.190
0.985
1.062
Rocky Mountains (RM)
1.642
1.098
1.243
1.588
1.097
1.228
West Coast (WC)
0.865
0.993
0.888
0.879
0.828
0.870
For select VOC processes, SCCs were identified that were likely to be affected by NSPS and verified with
EPA OAP and OAQPS oil and gas sector experts. NSPS reductions for VOC-only were applied in
composite with AEO-based regional growth factors to create a set of "net" growth factors. These NSPS
VOC reductions are consistent with EPA OAP-led Climate Action Report. The VOC NSPS reductions
specifically, are discussed in Section 2 of the "Methodologies for U.S. Greenhouse Gas Emissions
Projections" document available at: US Department of State. These composite projection factors for VOC
NSPS sources are provided in Table 4-19.
There were several assumptions in the application of NSPS VOC reductions. NSPS VOC reductions were
only applied to increases (if any) of emissions from 2011 to future years as provided by the AEO projection
factor. If AEO-based gas or oil production was projected to decrease in future years versus 2011, then NSPS
reductions had no impact. One exception, highlighted in Table 4-19, is for natural gas well completions;
these "one-shot" activities are generally short-term year to year processes and therefore NSPS reductions are
applied to the entire future year projected estimates. Other important assumptions are:
• Emissions change linearly with production-level changes (AEO projections)
• In the absence of local/state rules, existing equipment will continue to be used and there is no
replacement of capital that would be affected by the NSPS; the NSPS only affects growth for
processes other than natural gas well completions.
• Engine-related regulatory impacts are accounted for separately (see RICE NESHAP in the following
section)
• EPA did not attempt to account for or quantify the potential reductions due to the oil and natural gas
NESHAP
• Secondary emissions related to NSPS reductions were not accounted for (e.g., NOx emissions arising
from the combustion of VOC emissions)
EPA acknowledges that these assumptions are not ideal, particularly the linear scaling of production changes
to emissions for all processes. EPA hopes that future refinement of this methodology, particularly for large
processes with highly-reactive pollutants such as glycol dehydrators, improve this aspect of oil and gas
projections. Note, reductions from the RICE NESHAP impact some oil and gas sources (see next Section).
EPA is also aware that early release AEO 2014 projections became available in December 2013. Overall, it
appears that oil production increases significantly in the draft AEO 2014 compared to the AEO 2013
projections, about 22% higher by 2018 in the draft AEO 2014 projection versus the AEO 2013 projection.
There appears to be less significant increase, about 11%, in projections for natural gas in the draft 2014 AEO
versus AEO 2013.
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Table 4-19. Oil and Gas sector VOC Projection Factors for NSPS sources
SCCs
SCC Level 4
NSPS Source
NSPS
Reduction
Resource
Region
2018
AEO
Factor
2018
VOC
NSPS
(Final)
Factor
2025
AEO
Factor
2025
VOC
NSPS
(Final)
Factor
2310030210;
2310030300;
2310021010
Gas Well Tanks - Flashing &
Standing AV orking/Breathing,
Uncontrolled;
Gas Well Water Tank Losses;
Storage Tanks: Condensate
Storage
Tanks
70.3%
Gas
NE
1.596
1.177
2.059
1.315
GC
1.246
1.073
1.486
1.144
MC
0.910
0.910
0.890
0.890
SW
1.043
1.013
0.985
0.985
RM
1.098
1.029
1.097
1.0288
WC
0.993
0.993
0.828
0.828
2310010200;
2310011020
Oil Well Tanks - Flashing &
Standing AV orking/Breathing;
Storage Tanks: Crude Oil
Oil
NE
1.238
1.071
1.301
1.089
GC
1.853
1.253
1.743
1.221
MC
1.165
1.049
1.272
1.0378
SW
1.391
1.116
1.190
1.056
RM
1.642
1.191
1.588
1.175
WC
0.865
0.865
0.879
0.879
31000222;
2310121700;
2310021601;
2310021602
Drilling and Well Completion;
Gas Well Completion: All
Processes;
Gas Well Venting - Initial
Completions;
Gas Well Venting - Recompletions
Gas Well
Completions
95.0%
Gas
NE
1.596
0.080
2.059
0.1030
GC
1.246
0.062
1.486
0.0743
MC
0.910
0.045
0.890
0.0445
SW
1.043
0.052
0.985
0.0493
RM
1.098
0.055
1.097
0.0985
WC
0.993
0.050
0.828
0.0429
2310021300
Gas Well Pneumatic Devices
Pneumatic
controllers
77.0%
Gas
NE
1.596
1.137
2.059
1.244
GC
1.246
1.056
1.486
1.112
MC
0.910
0.910
0.890
0.890
SW
1.043
1.010
0.985
0.985
RM
1.098
1.023
1.097
1.022
WC
0.993
0.993
0.828
0.828
31000325;
31000324
Pneumatic Controllers High Bleed
>6 scfm;
Pneumatic Controllers Low Bleed
100.0%
Gas
NE
1.596
1.000
2.059
1.000
GC
1.246
1.000
1.486
1.000
MC
0.910
0.910
0.890
0.890
SW
1.043
1.000
0.985
0.985
RM
1.098
1.000
1.097
1.000
WC
0.993
0.993
0.828
0.828
2310010300
Oil Well Pneumatic Devices
77.0%
Oil
NE
1.238
1.055
1.301
1.069
GC
1.853
1.196
1.743
1.171
MC
1.165
1.038
1.272
1.063
SW
1.391
1.090
1.190
1.044
RM
1.642
1.148
1.588
1.135
WC
0.865
0.865
0.879
0.879
31000309
Compressor Seals
Compressor
Seals
79.9%
Gas
NE
1.596
1.120
2.059
1.213
GC
1.246
1.049
1.486
1.098
MC
0.910
0.910
0.890
0.890
SW
1.043
1.009
0.985
0.985
RM
1.098
1.020
1.097
1.019
WC
0.993
0.993
0.828
0.828
101
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4.2.5 RICE NESHAP (nonpt, ptnonipm, np_oilgas, pt_oilgas)
Packet: CONTROL_RICE_incl_SO2_2007v5_27nov2013 .txt
There are three 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 all three rules; thus
all three rules are included in the emissions projection. These RICE reductions also reflect the recent
(proposed January, 2012) Reconsideration Amendments, which results in significantly less stringent NOx
controls (fewer reductions) than the 2010 final rules.
The rules are listed below:
• National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion
Engines; Final Rule (69 FR 33473) published 06/15/04
• 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 three 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 all are after 2011 and fully implemented prior to 2018. EPA
projects CAPs from the 201 INEIvl RICE sources, based on the requirements of the rule for existing sources
only because the inventory includes only existing sources and the current projection approach does not
estimate emissions from new sources.
The Regulatory Impact Analysis (RIA) for the Reconsideration of the Existing Stationary Compression
Ignition (CI) Engines NESHAP: Final Report (EPA. 2013ci).
. The Regulatory Impact Analysis (RIA) for Reconsideration of the Existing Stationary Spark Ignition (SI)
RICE NESHAP: Final Report (EPA. 2013si). 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 INEIvl) are provided in Table 4-20.
Table 4-20. Summary RICE NESHAP SI and CI percent reductions prior to 201 INEIvl 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%
102
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These RIA percent reductions were used as an upper-bound for reducing emissions from RICE SCCs in the
201 INEIvl 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 INEIvl for SCCs where national totals exceeded 100 tons;
EPA chose 100 tons as a threshold arbitrarily, assuming there would be little to no application of RICE
NESHAP controls on smaller 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, our cumulative reductions were significantly less than those in the RIA. The only exception was for
S02 CI engines, where EPA opted to scale the RIA percent reduction from 46.1% to 10.2% 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.
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. 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). Impacts of the RICE
NESHAP controls on nonpt, ptnonipm, pt oilgas and np oilgas sector emissions are provided in Table 4-21.
Table 4-21. National by-sector reductions from RICE Reconsideration Controls
Pollutant
Nonpoint
Oil & Gas
(np oilgas)
Point
Oil & Gas
(pt oilgas)
Nonpoint
(nonpt)
Point
(ptnonipm)
Total
CO
1,865
95
8,838
7,167
17,964
NOx
1,101
94
1,976
2,033
5,205
PM10
0
0
1,201
300
1,501
PM2.5
0
0
1,120
282
1,402
S02
1,699
0
1,571
1,049
4,319
VOC
6,249
52
1,304
4,074
11,679
4.2.6 Fuel sulfur rules (nonpt, ptnonipm)
Packet: CONTROL_SULF_2011 v6_2018_27nov2013 .txt
Fuel sulfur rules that were signed by November, 2013 are limited to Connecticut, Maine, Massachusetts,
New Jersey, New York, Pennsylvania and Vermont. The fuel limits for these states are incremental starting
after year 2012, but are fully implemented before June 30, 2018 in all of these states. Other states in the
Northeast and Mid-Atlantic had pending sulfur rules but were not finalized prior to November, 2013 -the
completion date of the 2011 platform projections. Background on most of these enforceable and pending
fuel sulfur rules can be found at ILTA. A more recent update to the status of fuel sulfur rules.
Connecticut
103
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A public hearing on proposed regulations on fuel sulfur limits for heating oil via Connecticut State Agencies
section 221-174-19b was held on October 9. Effective July 1, 2018 maximum fuel sulfur content limits for
distillate, residual and kerosene fuels go into effect. For distillate fuel oil or distillate fuel oil blended with
biodiesel, these new limits must not exceed 15 ppm, a 99.5% reduction from 3000 ppm in the baseline and
down from 500 ppm effective July 1, 2014. Residual oil or residual fuel oil blended with biodiesel fuel must
not exceed 3000 ppm, a 70% reduction from today's 1% fuel content assumption for smaller stationary
sources. For kerosene, a 15 ppm limit replaces the existing 500 ppm limit, a 97% reduction.
Maine
The Maine Law Legislative Document (LD) 1662 sets a fuel sulfur rule effective January 1, 2014 that
reduces sulfur to 15 ppm for distillate fuel, resulting in a 99.5% reduction from 3,000 ppm assumed in year
2008. Maine Law LD 1662 also states that #5 and #6 fuel oils must not exceed 0.5% by weight (500 ppm),
which is a 75% reduction from an assumed 2% baseline sulfur content in 2008. These Maine sulfur content
reductions.
Massachusetts
The Massachusetts Department of Environmental Protection issued a commitment in their State
Implementation Plan (SIP) to adopt Phase 2 ultra-low sulfur diesel (ULSD) limits by year 2016. Similar to
Maine, this will reduce the sulfur content in distillate fuel to 15 ppm, a 99.5% reduction from the 3,000 ppm
baseline. Additional details on the phase-in of ULSD can be found at: Massachusetts State government.
New Jersey
The New Jersey Department of Environmental Protection adopted sulfur fuel content rules for kerosene and
home heating distillate oil. For distillate oil, the ULSD limit of 15 ppm yields a 99.5% reduction from the
3,000 ppm baseline. For kerosene, the same 15 ppm limit is adopted, resulting in a 97% reduction from an
assumed 2,000 ppm baseline. More details on these fuel sulfur limits in New Jersey.
New York
New York also signed a law requiring ULSD to replace distillate heating oil #2, which results in a fuel sulfur
content limit of 15 ppm, a 99.5% reduction from the 3,000 ppm baseline. The ULSD law (A.8642-
A/S.l 145-C) can be found here: NR.DC and here: The New York Times. New York City also includes limits
by year 2015 on #4 and #6 residual oils, where fuel sulfur content must not exceed 0.5% by weight (500
ppm), a 75% reduction from an assumed 2% baseline sulfur content in 2008. By 2030, these sources must
burn ULSD (15 ppm). The NYC updated Air Code, updated from the NY DEP.
Pennsylvania
Legislation has been proposed in Pennsylvania that would reduce allowable sulfur levels to 15 ppm for
distillate oil, a 99.5% reduction from the 3,000 ppm baseline. While EPA typically do not include proposed
rulemakings in our base projection scenarios without direction from state agencies, the existence of similar,
finalized standards in neighboring Northeast census region states such as New Jersey and New York suggest
this will become finalized prior to 2018. EPA can revise this, and potential application to other fuels, based
on state comment or regulatory changes.
Vermont
Vermont ULSD fuel and date requirements for home heating oil are similar to those adopted in
Massachusetts: a 99.5% reduction to 15 ppm from the 3,000 ppm baseline.
A summary of the sulfur rules by state, with emissions reductions is provided in Table 4-22.
104
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Table 4-22. Summary of fuel sulfur rules by state
State/
Fuel
%
2011
2018/2025
2018/2025
Metro
reduction
Emissions
Emissions
Reductions
CT
Distillate
99.5
CT
Kerosene
97
12,535
347
12,188
CT
Residual
70
ME
Distillate
99.5
7,041
706
6,335
ME
Residual
75
MA
Distillate
99.5
19,540
98
19,443
NJ
Distillate
99.5
6,146
31
6,115
NJ
Kerosene
96.25
NY
Distillate
99.5
32,984
1,027
31,957
NYC
Residual
75
PA
Distillate
99.5
14,634
73
14,561
VT
Distillate
99.5
997
5
992
4.2.7 Industrial Boiler MACT reconsideration (ptnonipm)
Packet: CONTROL_BlrMACT_ptnonipm_20XX_201 lv6
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. The Boiler MACT promulgates national emission standards for the
control of HAPs (NESHAP) for new and 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.
Boiler MACT reductions were computed from a non-NEI database of ICI boilers. As seen in the Boiler
MACT Reconsideration RIA, 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 INEIvl 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
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 4-23. 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.
105
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Table 4-23. 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.
From these facilities EPA extracted records (process level / release point level emissions) from our modeling
file with industrial, commercial, institutional boiler or process heater SCCs. A complete list of these SCCs is
provided in Appendix E. The resultant data are the NEI sources potentially subject to the Boiler MACT.
Step 2: Match fuel types and control reductions to the NEI SCCs
After obtaining the subset of 201 INEIvl sources potentially subject to the Boiler MACT, EPA assigned each
inventory SCC to a fuel type. The reductions are based on the ICR fuel types and associated controls from
an April 2010 "Baseline Memo.pdf' memorandum available on the Regulations.gov website under docket #
EPA-HQ-OAR-2002-0058-0802. These ICR fuel types and associated default controls were mapped to
SCCs in our inventory using the cross-walk provided in Table 4-24. The previously-mentioned Appendix E
also maps the complete list of inventory SCCs to these ICR fuel categories.
Table 4-24. Default Boiler MACT fuel percent % reductions by ICR fuel type
ICR Fuel Category
SCC Fuel Category(s)
CO
PM2.5
SO2
VOC
Coal
coal, petroleum coke, waste coal
98.9
95.8
95
98.9
gas 1 (other)
gasified coal, hydrogen, liquefied petroleum gas
(LPG), propane/butane, refinery gas
1
1
1
1
gas 2
digester gas, gas, landfill gas, process gas
99.97
0
95
99.97
Bagasse
Bagasse
95.3
90
95
95.3
dry biomass
Wood
95.8
99.1
95
95.8
gas 1 (natural gas)
natural gas, unknown
1
1
1
1
heavy liquid
coal-based Synfuel, crude oil, liquid waste,
methanol, residual oil, waste oil
99.9
98.3
95
99.9
light liquid
distillate oil, gasoline, kerosene, oil, other oil
99.9
93
95
99.9
wet biomass
solid waste, wood/bark waste
85.5
99.2
95
85.5
106
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The impacts of these Boiler MACT reductions on the controllable facilities and units are provided in Table
4-25. Controls were applied as "replacement" controls to prevent over-control of units that had existing
controls. However, this assumes that the inventory correctly reflects units with controls, so it is likely that
some units that are not recorded as controlled in the 201 INEIvl but are actually controlled were reduced
more than they should have. Overall, the CO and PM2.5 reductions are reasonably close to the year-2015
expected reductions in the Boiler MACT Reconsideration RIA. It is worth noting that the SO2 reductions in
the preamble (Clean Air Act Standards and Guidelines for Energy. Engines, and Combustion) were
estimated at 442,000 tons; the additional SO2 reductions in the reconsideration are from an additional
cobenefit from more stringent HC1 controls. The 201 INEIvl 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 via changing energy prices between the Boiler MACT RIA analyses and the 201 INEIvl. It
is also worth noting that EPA did not attempt to quantify the reductions of nonpoint ICI boiler emissions
from Boiler MACT controls.
Table 4-25. Summary of Boiler MACT reductions (tons) compared to Reconsideration RIA reductions
Pollutant
2011 Emissions
Controlled Emissions
Reductions
RIA Reductions
CO
267,685
66,682
201,003
187,000
PM2.5
34,586
10,819
24,654
25,601
S02
301,748
35,553
276,195
558,430
voc
19,295
6,984
12,311
n/a
4.2.8 Portland Cement NESHAP projections (ptnonipm)
As indicated in Table 4-1, the Industrial Sectors Integrated Solutions (ISIS) model (EPA, 2010b) was used to
project the cement industry component of the ptnonipm emissions modeling sector to 2018 and 2025. This
approach provided reductions of criteria and select hazardous air pollutants. The ISIS cement emissions
were developed in support for the Portland Cement NESHAPs and the NSPS for the Portland cement
manufacturing industry.
The ISIS 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 2030. These
ISIS-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) -see next section- and two cement inventories for new
kilns:
1) Inventories: "cement newkilns vear2018 from ISIS2013 NEI201 1 v 1" and
"cement_newkilns_year_2025_from_ISIS2013_NEI2011 vl_08nov2013_v0.csv"
Contains information on new cement kilns constructed after year 2011,
2) Inventory: "cement newkilns year 2018 from ISIS2013 NEI2011vl NONPOINT vO.csv" and
"cement_newkilns_year_2025_from_ISIS2013_NEI2011 vl_NONPOINT_12nov2013_v0.csv"
Contains information ISIS-generated, but not-permitted, new cement kilns constructed after year
2011,
3) Packet: "PROJECTION 2011 2018_ISIS_cement_by_CENSUS_DIVISION_04dec2013 .txt" and
"PROJECTION201 l_2025_ISIS_cement_by_CENSUS_DIVISION_25nov2013 .txt"
Contains U.S. census division level based projection factors for each NEI unit (kiln) based on ISIS
updated policy case emissions at existing cement kilns. The units that closed before 2018 (and 2025)
are included in the 2018 (and 2025) base case but are included in other CoST packets that reflect state
comments and consent decrees (discussed in the next section).
107
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The ISIS 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 ISIS
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
Domestic Production
NOX trend
•S02 trend
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 ISIS 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 ISIS 2018 and 2025 projected emissions is matching the kilns in future years to those
in the 201 INEIvl. For kilns that were new in 2018 and/or 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 ISIS model also created "generic" kilns in specific geographically strategic locations
(counties) to cover the need for increased 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 (ISIS) 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 4-26. Note that as production
continues to increase beyond 2018, that additional new kilns are needed in 2025.
108
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Table 4-26. Locations of new ISIS-generated cement kilns
Year(s)
ISIS 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
Both
NewWAl
N
n/a
53033
WA
King
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
While ISIS 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 ISIS 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 ISIS output. Therefore, EPA developed a U.S. Census Division approach where ISIS emissions in 2011
and future years, that matched the 201 INEIvl (e.g., not new ISIS kilns), were aggregated by pollutant for
each year within each of the 9 census divisions in the contiguous U.S.. 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 4-27, were then applied to all 201 INEIvl cement kilns -except for kilns where
specific local information (e.g., consent decrees/settlements/local information).
Table 4-27. U.S. Census Division ISIS-based projection factors for existing kilns
NOx
PM2.5
SO2
voc
Region
Division
2018
2025
2018
2025
2018
2025
2018
2025
Midwest
East North Central
2.024
2.053
0.106
0.144
1.800
3.034
0.527
0.670
Midwest
West North Central
0.930
1.279
0.614
0.673
0.695
1.262
0.317
0.492
Northeast
Middle Atlantic
1.853
1.221
0.058
0.119
0.904
0.867
0.561
0.569
Northeast
New England
2.560
2.560
0.004
0.004
3.563
3.563
0.713
0.713
South
East South Central
0.999
0.999
0.109
0.109
0.402
0.402
0.323
0.323
South
South Atlantic
1.042
1.077
0.284
0.339
0.911
0.936
0.413
0.420
South
West South Central
1.220
1.526
0.079
0.174
0.484
0.664
0.225
0.252
West
Mountain
1.453
1.321
2.542
1.032
1.917
1.366
0.310
0.345
West
Pacific
1.465
1.465
0.001
0.006
0.300
0.251
0.321
0.290
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For all ISIS future year emissions, PMio is assigned as 0.85 of total PM provided by ISIS, and PM2.5 is
assigned as 0.45 of total PM. All new ISIS-generated kilns, point and nonpoint format, are assigned as
Precalciner kilns (SCC=30500623). While ISIS provides emissions for mercury, EPA did not retain these in
our modeling.
Table 4-28 shows the magnitude of the ISIS-based cement industry emissions changes between the
201 INEIvl and future year projection scenarios. Kilns that matched the 201 INEIvl were simply projected
to future years based on U.S. census division aggregate changes in ISIS predictions. There are some local
exceptions where EPA did not use ISIS-based projections for cement kilns where local information from
consent decrees/settlements and state comments were used instead. Cement kilns projected using these non-
ISIS information are not reflected here in Table 4-28. EPA also split out ISIS-based new kilns in future
years with permitted (as of August 2013) kilns modeled as point sources and "generic" ISIS-generated kilns
as nonpoint sources.
Table 4-28. ISIS-based cement industry change (tons/yr)
Poll
2011
NEIvl
2018
projected
2025
projected
New kilns in 2018
New kilns in 2025
Total
2018
Total
2025
Diff
2018-
2011
Diff
2025-
2011
Permitted
(point)
ISIS-
generated
(nonpoint)
Permitted
(point)
ISIS-
generated
(nonpoint)
NOx
53,874
71,205
76,647
3,751
6,836
4,795
14,812
81,792
96,254
27,919
42,380
PM2.5
1,772
722
668
8
15
11
33
745
712
-1,027
-1,060
S02
17,065
18,629
26,368
1,775
3,263
2,004
7,409
23,667
32,781
6,602
15,716
voc
2,690
903
1,073
91
167
117
361
1,161
1,551
-1,529
-1,139
4.2.9 State comments and consent decrees/settlements (nonpt, ptnonipm)
This subsection describes the numerous (12 in all) CoST PROJECTION and CONTROL packets developed
to reflect a wide range of information on future year non-EGU point and nonpoint source projections. In
general, this information is derived from:
• comments received from the Cross-State Air Pollution proposal
• local and state comments over the past several years,
• consent decrees and settlements, and
• EPA staff data mining and analyses
4.2.9.1 Comments from Cross-State Air Pollution Rule (2010)
EPA released a Notice of Data Availability (NOD A) after the CSAPR proposal to seek comments and
improvements from states and outside agencies. The goal was to improve the future baseline emissions
modeling platform prior to processing the Final CSAPR. EPA received several control programs and other
responses that were used for future year projections. However, this effort was performed on a version of the
2005 modeling platform, which used the 2005NEIv2 as a base year starting point for future year projections.
Now with the 2011 platform using the 201 INEIvl for most non-EGU point and nonpoint sources, many of
these controls and data improvements were removed from the 2018 and 2025 base case projections. But for
those controls, closures and consent decree information that are implemented after 2011, EPA used these
controls/data after EPA mapped them to the correct SCCs and/or facilities in the 2011 NEI. This subsection
breaks down the controls used for the nonpt and ptnonipm sectors separately, and also describes the consent
decrees separately. EPA used July 1, 2011 as the cut-off date for assuming whether controls were included
in the 2011 NEI. For example, if a control had a compliance date of December 2011 EPA would assume that
the 2011 NEI emissions did not reflect this control and EPA would need to reflect this control in our future
110
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base cases. It is important to note that these controls are not comprehensive for all state/counties and source
categories. These only represent post-year 2011 controls for those areas and categories where EPA received
usable feedback from the CSAPR comments and related 2005 platform NOD A.
Packet: "CONTROLS_CSAPR_consent_201 lv6.csv"
These controls reflect consent decree and settlements that were identified in our preparation of the Final
CSAPR emissions modeling platform. These controls generally consist of one or more facilities and target
future year reductions. After EPA removed all consent decrees with compliance dates prior to late-2011,
EPA matched the remaining controls to the 2011 NEI using a combination of EIS facility codes,
"agy facility id", "agy_point_id" and searching the EIS. Then, EPA recomputed the percent reductions
such that the future year emissions would match those for facilities originally projected from the 2005 NEI-
based platform -these consent decrees were released from 2007 through 2010, when the 2005 NEI was the
general baseline. EPA did not retain consent decree controls if the emissions in the 2011NEI were less than
the controlled future year emissions based on the 2005 platform. EPA were left with consent decree controls
in twelve states (AL, CA, IN, KS, KY, LA, MI, MS, OH, TN, TX, WY) that accounted for 2,731 tons of
NOx and 10,891 tons of SO2 cumulative reductions in 2025.
Packet: "CONTROL_CSAPR_ptnonipm_201 Iv6_22nov2013.txt"
EPA created a CONTROL packet for the ptnonipm sector that contains reductions needed to achieve post
year-2011 emissions values from the CSAPR response to comments. These reductions reflect fuel
switching, cleaner fuels, and permit targets via specific information on control equipment and unit and
facility zero-outs in the following states: Georgia, New Hampshire, New York and Virginia. Cumulatively,
these controls reduce NOx by 655 tons and SO2 by 7,221 tons.
Packet: "PROJECTION_CSAPR_WVunit_ptnonipm_2012_201 Iv6_21nov2013.txt"
This packet contains the only post-2011 unit-level growth projection resulting from CSAPR comments. The
Sunoco Chemicals Neal Plant in Wayne County West Virginia replaced a 155MM Btu/hour coal-fired boiler
with a 96.72 MM Btu/hour natural gas-fired unit in 2010. This closure is already reflected in the 2011 NEI;
however, in 2012, a new natural gas unit was slated to operate and therefore EPA scaled emissions at an
existing natural gas boiler to match these 2012 emission targets provided to us by West Virginia via CSAPR
comments. This packet simply results in an extra 22.5 tons of NOx and minimal increased emissions for PM
and SO2.
4.2.9.2 State comments since spring of 2013
The following packets were derived from information received from several states since the spring of 2013
regarding point and nonpoint projections to year 2018.
Packets:
For 2018:
"PRO JECTION_VA_ME_TCEQ_AL_comments_2011 v6_2018 03 dec2013 .txt"
"CONTROL_VA_ME_TCEQ_comments_2011 v6_2018_03 dec2013 .txt"
For 2025:
"PRO JECTION_VA_ME_TCEQ_AL_comments_2011 v6_2019 03 dec2013 .txt"
"CONTROL_VA_ME_TCEQ_comments_201 Iv6_2019_03dec2013 .txt"
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These packets represent primarily local closures and expected changes in future year emissions, in some
cases, specified as year 2018 or 2019 (furthest out-year for Texas), but otherwise simply to be used rather
than the 2011 NEI values for general future year modeling. These comments from Alabama, Maine, Texas
and Virginia were received in the spring through early fall of 2013. The CONTROL packet was used for
specific stack/unit closures and emissions reductions. Deciding which packet type to use (PROJECTION or
CONTROL) for applying state comments in CoST is fairly subjective. EPA is forced to use PROJECTION
packets when emissions increase, and if EPA can get away with using only 1 type of packet (PROJECTION
or CONTROL) for a single source of comments, AND, the choice does not result in different final projected
values, then the packet type that best fits was used. For example, if a set of state comments results in
emissions increasing and decreasing at various stacks and other CoST packets do not apply, then the packet
type choice does not matter. If, however, EPA chose to represent emission decreases as a PROJECTION
packet entry, and another CoST CONTROL packet applies to that source, then EPA are applying two
different sources of reductions -not ideal. Our goal is for state comments to pass through to the final future
year inventory as-is. For this reason, EPA does not quantify emission changes for these packets separately.
The cumulative impact of these emissions is shown in Table 4-29. Note that the widespread Texas NAICS-
level economic-based growth factors and impacts are discussed separately.
Table 4-29. Impacts of most non-EGU point source state comments received in 2013
State
Pollutant
2011NEIvl
2018
2025
2018
2025
Projection
Projection
Change
Change
Alabama
NOx
2,941
3,062
3,062
120
120
Alabama
S02
1,156
1,168
1,168
12
12
Maine
NOx
178
45
45
-134
-134
Maine
S02
2,069
666
666
-1,463
-1,463
Texas
NOx
3,337
712
712
-2,625
-2,625
Texas
S02
8,461
229
220
-8,233
-8,242
Texas
VOC
469
65
65
-404
-404
Virginia
NOx
8,065
4,531
4,531
-3,534
-3,534
Virginia
S02
1,646
2
2
-1,644
-1,644
Packet:
For 2018: "PROJECTION_TCEQ_ptnonipm_NAICS_comments_201 Iv6_2018_04dec2013.txt"
For 2025: "PROJECTION_TCEQ_ptnonipm_NAICS_comments_201 lv6_2025_llfeb2014.txt"
This packet represents county-specific economic-based NAICS-level projections provided by the Texas
Commission on Environmental Quality (TCEQ) for minor source emissions. Growth factors are based on
projections of gross product for various types of industry, population and various economy data. EPA did
not apply these projections to oil and gas sources, opting to use the consistent regional/fuel-based approach
discussed in Section 4.2.4. A summary of these minor source ptnonipm sector projection impacts for Texas
are provided in Table 4-30. Note that there are 2 values for 2011 emissions. This is because no-growth in
2018 was replaced with growth not equal to 1.000 in 2025 for some source categories. Therefore, the 2025
projections impact more source categories than the 2018 projections.
Table 4-30. Minor source ptnonipm sector NAICS-level projections for Texas
2011 NEIvl
2011 NEIvl
2018
2025
Pollutant
for 2018
for 2025
Increase
Increase
CO
114,817
130,957
21,879
40,506
nh3
2,099
2,506
520
959
NOx
138,389
161,498
19,609
37,159
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PM10
21,146
28,493
4,898
9,531
PM2.5
17,301
23,833
4,084
7,786
S02
21,432
44,198
6,601
13,406
voc
62,386
81,824
17,285
32,858
Packet:
For 2018: "PROJECTION_TCEQ_AREA_comments_201 Iv6_2018_04dec2013.txt"
For 2025: "PROJECTION_TCEQ_AREA_comments_201 Iv6_2025_04dec2013.txt"
This packet represents nonpt sector 2011-based projections for years 2018 and 2025 for Texas as provided
by TCEQ. These county-level and SCC-specific projections are based on a combination of economy.com
and Annual Energy Outlook (AEO) data. EPA did not apply these projections to oil and gas sources, opting
to use the consistent regional/fuel-based approach discussed in Section 4.2.4. EPA also did not apply these
projections to the Residential Wood Combustion sector which were the same for every RWC SCC and
county, opting instead to use our national-based but SCC-specific approach discussed in Section 4.2.3. A
summary of these nonpt sector changes in Texas is provided in Table 4-31.
Table 4-31. Minor source nonpt sector projections for Texas
Pollutant
201 INEIvl
2018 Projection
2025 Projection
2018 Increase
2025 Increase
CO
68,967
83,299
85,760
14,333
16,793
nh3
2,659
2,720
2,742
60
83
NOx
32,581
34,329
34,752
1,748
2,171
PM10
19,999
24,416
26,835
4,416
6,836
PM2.5
15,520
19,268
21,465
3,747
5,944
S02
9,099
8,805
8,795
-293
304
VOC
239,657
256,046
264,750
16,389
25,093
4.2.9.3 Consent decrees and settlements
These packets were derived in prior emissions modeling platforms, dating back to the 2005 NEI and 2008
NEI. EPA updated this information based on information in the 201 INEIvl and analysis for compliance
dates. Many of these consent decrees were already in place in 2011 and therefore removed from
consideration for projections. New information (e.g., Cabot Corporation) has also been obtained since the
spring of 2013 and has been included in our projections. Consent decrees or settlements released after
November are not included. EPA also does not reflect consent decrees that do not have obvious quantifiable
reductions for important emissions modeling pollutants (CAPs).
Packet: "CONTROL_ConsentDecree_Cabot_BlackPowderPlants_03dec2013_v0.txt"
This Cabot Corporation Clean Air Act settlement (release date of November 19, 2013) targets NOx and SO2
reductions of 1,975 and 12,380 tons, respectively, from three carbon black manufacturing plants in Louisiana
and Texas. More information on this settlement can be found at:
Cabot Corporation Clean Air Act Settlement.
Because EPA did not have specific stack-level information on this settlement, the Agency apportioned the
total reductions proportionally to each of the three facilities such that each process in all the facilities was
assigned the same percent reduction and that the cumulative NOx and SO2 reductions would be achieved.
Packet: "CONTROLS Refineries additional consent 2011NEI vl 25nov2013 vl.txt"
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This packet consists of two settlements. The BP Whiting settlement (released May 23. 2012. The Marathon
Petroleum Company. Detroit Refinery environmental mitigation project (released April 5, 2012.
The initial application of these settlements was to the 2008 NEI. Therefore, to be consistent with previous
future year estimates for these facilities, EPA modified existing computed reductions from the 2008 such that
future year estimates from the 201 INEIvl matched those done with the 2008 NEI. These settlements reduce
NOx by 78 tons at the Detroit Refinery and NOx and SO2 by 780 and 150 tons, respectively, at the Indiana
BP Whiting facility.
Packet: "CONTROL_OECA_201 Iv6_25nov2013.txt"
The Office of Enforcement and Compliance Assurance (OECA) provided emission reduction information for
several consent decrees while EPA was preparing emissions for the 2005 NEI-based modeling platform. The
press releases for these consent decrees are available on EPA's enforcement website and some were
available with quantitative emission reductions that EPA was able to convert into a control packet. These
petroleum refinery settlements are available at:
Petroleum Refinery National Case Results. These settlements were released in the 2003-2010 time period
and include information for a few corporations but with aggregate reductions over numerous facilities under
these companies and subsidiaries. Therefore, EPA developed an initial table of 2008 NEI emissions summed
over all affected facilities for each company. Then EPA merged the multi-facility expected reductions from
each of these consent decrees to develop an overall future year (post-compliance date) emissions estimate for
each company after all controls/reductions are implemented. Using this methodology, the emissions
reductions were apportioned to each plant owned/operated by each company using the same percent
reduction from the 2005 NEI emissions.
Now that EPA is using the 2011 NEIvl, EPA expected that some of these consent decree controls/reductions
would have already been applied by 2011. EPA did not want to over-control any particular plant. Therefore,
EPA computed facility-specific reductions based on the controlled emissions from the 2008 NEI. For
example, as seen in Table, SO2 emissions at all Cargill facilities were reduced about 24% in the 2008 NEI:
from 6,921 tons to 5,280 tons. In the 201 INEIvl, SO2 emissions at these same Cargill facilities totaled
6,263 tons, so only approximately 1,000 tons, a 16% cumulative reduction over all Cargill facilities, were
needed to achieve the 5,280 consent decree target.
The column "2008 NEI Controlled" in Table 4-32 was our target for year 2018 emissions. However, many
of these facilities are ethanol plants and are therefore projected separately using EPA OTAQ's national
projections for ethanol plants (see Section 4.2.1). This is a biggest issue for the Cargill facilities, a majority
of which are defined as ethanol plants. Note in Table 4-32, the "applicable" (non-ethanol plants) 201 INEIvl
emissions available for OECA consent decree controls is significantly less than the sum of all Cargill facility
emissions. The discrepancies between actual and applicable 2011 NEI emissions for the other OECA
facilities are primarily a result of CoST hierarchy assignments. In short, more-specific (more resolved than
facility/pollutant of the OECA packet) control information from other CoST packets are used for some of
these stacks/units/facilities.
Table 4-32. Target company-wide reductions from OECA consent decree information
Corporation
Pollutant
2008 NEI
(tons)
2008 NEI
Controlled
(tons)
Reductions
from 2008
(tons)
201 INEIvl
Emissions
(tons)
201 INEIvl
applicable
(tons)
Actual
2018/2015
Reductions
Cargill
CO
10,889
262
10,627
6,045
401
394
NOx
2,265
1,478
787
1,714
806
111
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S02
6,921
5,280
1,642
6,263
849
172
Conoco Phillips
NOx
14,331
7,334
6,997
9,391
9,070
2,932
Sunoco
NOx
4,506
1,975
2,531
3,235
3,154
1,231
PM2.5
1,030
585
445
1,072
714
379
Valero
NOx
8,212
6,109
2,103
6,676
4,913
966
PM2.5
2,554
1,955
599
2,338
1,883
718
S02
11,479
2,903
8,575
6,040
4,807
3,367
Total
CO
10,889
262
10,627
6,045
401
394
NOx
29,314
16,896
12,418
21,016
17,943
5,240
PM2.5
3,584
2,540
1,044
3,410
2,597
1,097
S02
18,400
8,183
10,217
12,303
5,656
3,539
Packet: "CONTROL_LaFarge_StGobain_ptnonipm_201 Iv6_22nov2013.txt"
This control packet includes settlements for all 15 U.S. plants owned by Saint-Gobain Containers, Inc., the
nation's second largest container glass manufacturer, and all 13 U.S. plants owned by the Lafarge Company
and two subsidiaries, the nation's second largest manufacturer of Portland cement. These settlements,
released January 21, 2010, are the first system-wide settlements for these sectors under the Clean Air Act and
require pollution control upgrades, acceptance of enforceable emission limits, and payment of civil penalties.
The settlements require various NOx and SO2 controls, some of which (SO2 scrubbers) also reduce PM
emissions. A couple of Lafarge kilns were also scheduled to be shut down. One of these units was
shutdown prior to 2011 and as expected, is not in the 201 INEIvl. However, a Lafarge kiln in Joppa, Illinois
was unexpectedly found in the 201 INEIvl and communication with the Illinois DEP indicated that this unit
was not closed as of the summer of 2012. More information on the Lafarge settlement. More information on
the Saint-Gobain settlement. Many of the controls for the units at these facilities were implemented prior to
2011 and were therefore removed from the CONTROL packet; however, cumulatively, there is still
significant reductions post-2011: 9,210 tons of NOx, 214 tons of PM2.5 and 11,777 tons of SO2.
4.2.9.4 EPA staff data mining
Packet: "CONTROLS_Regional_Haze_201 lv6.csv"
This packet includes a set of NOx and SO2 reductions provided by EPA's OAQPS Air Quality Policy
Division (AQPD) visibility experts. These reductions reflect expected emissions reductions and future year
caps for facilities of various industries (e.g., cement kilns, taconite, steel, pulp and paper and mining
industries) in the following states: Georgia, Idaho, Michigan, Minnesota, Montana, New York, Ohio,
Tennessee, Virginia and Wisconsin. Cumulatively, 28,618 tons of NOx and 20,686 tons of SO2 are reduced
by these controls in 2025.
4.2.10 Aircraft projections (ptnonipm)
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 (publication date March, 2013). This information is available for approximately
3,300 individual airports, for all years up to 2030. The methods that the FAA used for developing the ITN
data in the TAF.
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
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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 national (U.S.)
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 2018. The second
set of projection factors was by airport, where EPA projected project emissions for each individual airport
with significant ITN activity.
Packet:
For 2018: "PROJECTION 201 l_2018_aircraft_21nov2013.txt"
For 2025: "PROJECTION_201 l_2025_aircraft_21nov2013.txt"
In this case, EPA simply summed the ITN operations to national totals by year and aircraft operation and
computed projection factors as future-year ITN by 2011-year ITN. EPA assigned factors to inventory SCCs
based on the operation type shown in Table 4-33.
Table 4-33. Default national-level factors used to project 2011 base-case aircraft emissions to 2018 and
2025
see
Description
2018 Factor
2025 Factor
2265008005
Commercial Aircraft: 4-stroke Airport Ground Support Equipment
1.1741
1.3796
2267008005
Commercial Aircraft: LPG Airport Ground Support Equipment
1.1741
1.3796
2268008005
Commercial Aircraft: CNG Airport Ground Support Equipment
1.1741
1.3796
2270008005
Commercial Aircraft: Diesel Airport Ground Support Equipment
1.1741
1.3796
2275000000
All Aircraft Types and Operations
1.1741
1.3796
2275001000
Military Aircraft, Total
0.9972
0.9973
2275020000
Commercial Aviation, Total
1.1741
1.3796
2275050000
General Aviation, Total
1.0199
1.0515
2275050011
General Aviation, Piston
1.0199
1.0515
2275050012
General Aviation, Turbine
1.0199
1.0515
2275060000
Air Taxi, Total
0.9417
0.9402
2275060011
Air Taxi, Total: Air Taxi, Piston
0.9417
0.9402
2275060012
Air Taxi, Total: Air Taxi, Turbine
0.9417
0.9402
2275070000
Commercial Aircraft: Aircraft Auxiliary Power Units, Total
1.1741
1.3796
27501015
Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust;
Military; Jet Engine: JP-5
0.9972
0.9973
27502011
Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust;
Commercial; Jet Engine: Jet A
1.1741
1.3796
27505001
Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Civil;
Piston Engine: Aviation Gas
1.0199
1.0515
27505011
Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Civil;
Jet Engine: Jet A
1.0199
1.0515
Packet:
For2018: "PROJECTION 201 l_2018_aircraft_by_airport_21nov2013.txt"
For2025: "PROJECTION_201 l_2025_aircraft_by_airport_21nov2013.txt"
The second set of projection factors was by airport, where EPA projected emissions for each individual
airport based on the following criteria:
• ITN activity in year 2011 are greater than 1000 for any of the four available modes: commercial,
general aviation, military and air taxi;
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• ITN airport matched to 201 INEIvl
• ITN activity is not the same for 2011, 2018 and 2025 AND 2035. The rational here is that these ITN
data add no value if 2011 ITN data are used for all future years. These airports were projected based
on the national default method.
• A hierarchal assignment was applied when the airport emissions in the NEI did not match the type of
ITN information. For example, if an airport in the 201 INEIvl contained only general aviation
emissions (based on NEI SCC), and the ITN data for that airport did not contain general aviation,
then commercial aviation activity was used to project these emissions. There were 11 of 15 possible
hierarchal assignments used in our projection methodology where EPA assigned a "fallback" ITN
projection method to an NEI airport SCC, and most of these assignments were linked to very small
NEI emissions.
Most of the significant airports, and hence increased emissions, are projected via the airport-specific
projection packet. Overall, aircraft NOx emissions increase approximately 17% between 2011 and 2018 and
37% by 2025.
4.2.11 Remaining non-EGU controls and closures (ptnonipm)
This section describes all remaining non-EGU stationary source reductions and closures not already
discussed. These CONTROL packets and CLOSURE packets generally have lesser national-level impact on
future year projections than many of the items above. However, these impacts can be significant locally -
particularly plant closures.
4.2.11.1 CISWI controls (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. Baseline and CISWI rule impacts associated with the CISWI rule. EPA mapped the units
from the CISWI baseline and controlled dataset to the 201 INEIvl inventory and because the baseline CISWI
emissions and the 201 INEIvl emissions were not the same, EPA computed percent reductions such that our
future year emissions matched the CISWI controlled dataset values. CISWI reductions limited to SO2
reductions of 1,427 and 1,413 tons in Arkansas and Louisiana, respectively.
4.2.11.2 Remaining facility closures
Packets:
"CLOSURES_EIS_201 lNEIvl_sep2013_25nov2013_vl.txt" &
"CLOSURES_2008_Merged_12nov2013_v0.txt"
This section describes two CLOSURE packets. The first "EIS" packet is from a September 11, 2013
Emissions Inventory System report of post-2011 permanent facility shutdowns, based on facility status code
"PS". The second "Merged" packet is from a concatenation of previous facility and unit-level closure
information used in the 2008 NEI-based emissions modeling platform. The "EIS" closures impact facilities
in 12 states while the "Merged" packet closures are spread out over 34 states. The cumulative reductions in
emissions from this packet are shown in Table 4-34.
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Table 4-34. Reductions from all ElS-based and remaining information facility/unit-level closures
Pollutant
Reductions
CO
1,420
nh3
441
NOx
3,117
PMio
1,858
PM2.5
1,613
S02
26,073
voc
2,207
4.3 Mobile source projections
Mobile source monthly inventories of onroad and nonroad mobile emissions were created for 2018 and 2025
using a combination of the SMOKE-MOVES and the NMIM models. The 2018 and 2025 onroad emissions
account for changes in activity data and the impact of on-the-books rules including: the Light-Duty Vehicle
Tier 2 Rule (EPA, 2000), the 2007 Heavy Duty Diesel Rule (Transportation. Air Pollution, and Climate
Change), the Mobile Source Air Toxics (MSAT2) Rule (EPA, 2007a), the Renewable Fuel Standard (RFS2)
(EPA, 2010a), the Light Duty Vehicle GHG/CAFE standards for Model-Year 2012-2016 (EPA, 2010c), the
Heavy-Duty Vehicle Greenhouse Gas Rule (EPA, 201 la), the Light Duty Vehicle GHG Rule for Model-
Year 2017-2025, and the Tier 3 Motor Vehicle Emission and Fuel Standards (Tier3 FRM) Rule. 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
(Transportation. Air Pollution, and Climate Change), local fuel programs, and Stage II refueling control
programs.
Nonroad mobile emissions reductions for these years include reductions to locomotives, various nonroad
engines including diesel engines and various marine engine types, 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 mobile emission projections are discussed in subsection 4.4. Locomotives and Class 1 and
Class 2 commercial marine vessel (C1/C2 CMV) projections are discussed in subsection 4.5, and Class 3
(C3) CMV projected emissions are discussed in subsection 4.4.2.
4.3.1 Onroad mobile (onroad and onroad_rfl)
The onroad emissions for 2018 and 2025 use the same SMOKE-MOVES system as for the base year (see
Sections 2.3.1 and 2.3.2). Meteorology, speed, spatial and temporal surrogates, representative counties, and
fuel months were the same as for 2011, discussed above.
4.3.1.1 VMT and vehicle population
Estimates of total national Vehicle Miles Travelled (VMT) in 2018 and 2025 came from DOE's Annual
Energy Outlook (AEO) 2013 transportation projections, specifically the reference case (release dates April
15th-May 2nd 2013). Trends were developed by calculating ratios between 2011 AEO and 2018 AEO35
estimates and renormalizing the trends so that a projection of the 201 INEIvl VMT would match the AEO's
2018 total VMT (across all vehicle types). These ratios were developed for light versus heavy duty and for
gasoline versus diesel vehicle types. This same method was used to project 2011 NEIvl VMT to 2025 with
35 By "2011 AEO," "2018 AEO," and "2025 AEO," this refers to the AEO2013's estimates of national VMT in those specific
calendar years.
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the incorporation of 2025 AEO estimates. The projection factors, the national 201 INEIvl VMT
("VMT_2011") by vehicle type (SCC7), and the default future VMT ("VMT_2018" and VMT_2025") by
vehicle type are show in Table 4-35.
Table 4-35. Projection factors for 2018 and 2025 VMT (in millions of miles)
Classification
SCC7
Description
VMT
2011
Ratio
2018
VMT
2018
Ratio
2025
VMT
2025
lightgas
2201001
Light Duty Gasoline Vehicles
(LDGV)
1,595,751
1.0226
1,631,840
1.1206
1,828,577
lightgas
2201020
Light Duty Gasoline Trucks 1 & 2
(M6) = LDGT1 (M5)
682,930
1.0226
698,375
1.1206
782,572
lightgas
2201040
Light Duty Gasoline Trucks 3 & 4
(M6) = LDGT2 (M5)
351,812
1.0226
359,768
1.1206
403,143
heavygas
2201070
Heavy Duty Gasoline Vehicles 2B
thru 8B & Buses (HDGV)
98,334
1.1056
108,714
1.2641
137,428
lightgas
2201080
Motorcycles (MC)
19,744
1.0226
20,190
1.1206
22,624
lightdiesel
2230001
Light Duty Diesel Vehicles (LDDV)
4,764
3.8885
18,526
5.9148
109,581
lightdiesel
2230060
Light Duty Diesel Trucks 1 thru 4
(M6) (LDDT)
13,389
3.8885
52,063
5.9148
307,946
heavydiesel
2230071
Heavy Duty Diesel Vehicles (HDDV)
Class 2B
6,080
1.2753
7,753
1.4378
11,148
heavydiesel
2230072
Heavy Duty Diesel Vehicles (HDDV)
Class 3, 4, & 5
30,625
1.2753
39,055
1.4378
56,156
heavydiesel
2230073
Heavy Duty Diesel Vehicles (HDDV)
Class 6 & 7
48,998
1.2753
62,486
1.4378
89,846
heavydiesel
2230074
Heavy Duty Diesel Vehicles (HDDV)
Class 8A & 8B
131,503
1.2753
167,704
1.4378
241,133
heavydiesel
2230075
Heavy Duty Diesel Buses (School &
Transit)
8,938
1.2753
11,399
1.4378
16,390
These national SCC7 ratios were applied to the 201 INEIvl VMT to create an EPA estimate of 2018 and
2025 VMT at the county, SCC level36.
Vehicle population (VPOP) was developed by creating VMT/VPOP ratios from the 201 INEIvl VMT and
201 INEIvl VPOP at the county, vehicle type (SCC7) level. These ratios were applied to the 2018 VMT to
create a 2018 VPOP. This process was repeated using the 2025 VMT to create the 2025 VPOP.
4.3.1.2 Set up and Run MOVES to create EF
Emission factor tables were created by running SMOKE-MOVES using the same procedures and models as
described for 2011 (see the 201 INEIvl TSD and Section 2.3). The same meteorology and the same
representative counties were used. Changes between 2011 and future years (2018 or 2025) are
predominantly VMT, fuels, national and local rules, and the model-year distribution of the fleet, which is
built into MOVES. 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 4-36.
36 A few states/regional organizations provided 2018 projections. Those were incorporated into the 2018ed modeling but were not
consistently available for 2025. Therefore, these state/regional organization projections were not incorporated into the 2018ef nor
the 2025ef cases because EPA wanted to keep the projections of VMT consistent between 2018ef and 2025ef.
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Table 4-36. Comparison of MOVES runs for 2018 and 2025
Element
2018 T3FRM
2025 T3FRM
Code
MOVES20121002f
MOVES20121002f
Default database
movesdb201210021_truncatedgfre
movesdb201210021_truncatedgfreim
VMT and VPOP
CDBs and state DBs for 26 states for
2018 dated 9/23/2013
CDBs and state DBs for 26 states for
2025 dated 1/17/2014
Hydrocarbon
speciation
T3FRM2018_natinv_HCspec_SS_M
T3FRM203 0_natinv_HCspec_S S_M
Fuels
tier3frm2018ctrlfiiels 03152013
oaqps2025fuels_20140116
CA LEVIII
ca_standards_SS_20130905
(16 states)
ca_standards_SS_20130905
(16 states)
Tier 3 controls
tier3ctldbs 090513
tier3ctldbs 090513
The following states were modeled as having adopted the California LEV III program (see Table 4-37)
Table 4-37. CA LEVIII program states
FIPS
State Name
06
California
09
Connecticut
10
Delaware
23
Maine
24
Maryland
25
Massachusetts
34
New lersey
36
New York
41
Oregon
42
Pennsylvania
44
Rhode Island
50
Vermont
53
Washington
Fuels were projected into the future using estimates from the AEQ2013. release dates April 15th-May 2nd
2013. The AEO2013 projection includes partial implementation of RFS2 in 2018 and assumes that all fuels
have an ethanol content of E10 or greater. The regional fuels in 2011 were projected to 2018 so that some of
the regional variation is preserved but the totals match AEO2013. The 2025 fuels were identical to the 2018
fuels. For details on the 2018 and 2025 speciation of onroad, which is strongly dependent on the fuels, see
Section 3.2.1.4.
4.3.1.3 National, California, and Texas adjustments
A set of adjustments were done in SMOKE-MOVES to create 2018 and 2025 emissions: extended idle,
California emissions, and Texas emissions.
The first set of adjustment factors was for extended idle (EXT) and auxiliary power units (APU). This uses
the same approach as was used in 2011 (see the 201 INEIvl TSD for details) except for the VPOP was
updated to be consistent with 2018 or 2025, depending on the future year case. These adjustments were by
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county, vehicle type (long-haul truck SCCs only), and mode (EXT or APU) and impacted the RPV process
only.
The second set of adjustment factors was meant to incorporate future year emissions provided by California.
The same approach as was used in 2011 was used to match the emissions totals provided by CARB (see
Section 2.3.1). The only differences between the 2011 approach and that applied for 2018 are that the latter
uses the 2018 emissions from CARB and the 2018 SMOKE-MOVES output (EPA estimates), where the
2018 "CARB emissions" were created by interpolating between the 2017 and 2020 CARB emissions. For
2025, the process was repeated using 2025 emissions provided by CARB and the 2025 SMOKE-MOVES
output (EPA estimates). The provided CARB emissions were produced from working draft versions of
EMFAC2011-LD and EMFAC2011-HD and include the following heavy duty regulations: chip reflash,
extended idling, public fleet, trash trucks, drayage trucks, and trucks and buses. It does not include the
GHG/smartway regulations for trucks, or the low carbon fuel standard. These adjustment factors are by
county, SCC3, pollutant and impact all processes (RPD, RPV, and RPP).
The third set of adjustment factors was meant to incorporate emissions provided by Texas. Conceptually,
EPA used the trend of 2011 to 2018 based on EPA's estimates to project Texas' submitted emissions for
2011. Mathematically, this is equivalent to taking the Texas adjustment factors derived for 2011 (see
Section 2.3.1 for details) and applying them directly to EPA's 2018 run. These adjustment factors are by
county, SCC7, pollutant and impact all processes. The same process was repeated for 2025 by taking the
Texas adjustment factors derived for 2011 and applying them to the EPA's 2025 run.
Because these adjustment factors are multiplicative, a single set of adjustment factors may be created by
multiplying the three adjustment factors together taking care to match process (RPD, RPV, or RPP), mode,
pollutant, SCC, and county. Movesmrg uses the composite adjustment factor file (CFPRO) to estimate 2018
or 2025 emissions that incorporates each of these adjustments (or a subset of them depending on county,
mode, and process).
4.4 Nonroad mobile source projections (c1c2raii, c3marine, nonroad)
The projection of locomotives and Class 1 and 2 commercial marine vessels to 2018 and 2025 is described in
Section 4.4.1. These sources are treated in shapes in the NEI but are considered at the county-level in the
modeling platform. The projection of the larger Class 3 commercial marine vessels, treated as point sources
in the modeling platform, is described in Section 4.4.2. Most of the remaining sources in the nonroad sector
are projected by running the NMIM model with fuels and vehicle populations appropriate to 2018 and 2025,
as described in Section 4.4.3.
4.4.1 Locomotives and Class 1 & 2 commercial marine vessels (c1c2rail)
There are three distinct components used to craft year 2018 and 2025 inventories from the 2011 base case.
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 2017 and 2025 data for California. The third
component is a set of EPA OTAQ-provided county-specific emissions adjustments that account for different
fuel transport characteristics resulting from AEO2013 renewable fuel projections and the 2017-2025 light
duty vehicle greenhouse gas standards.
Step 1: Project non-California CMV and rail emissions
Packet:
For 2018: "PROJECTION 2011 2018 clc2rail BASE noRFS2 05dec2013.txt"
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For 2025: "PROJECTION201 l_2025_clc2rail_BASE_noRFS2_llfeb2014.txt"
This packet creates an intermediate set of future year emissions for all states except California. This packet
does not reflect emission impacts from projected renewable fuel volumes, these impacts are applied for all
states in Step 3. This packet consists of national projection factors by SCC and pollutant between 2011 and
future years that reflect the May 2004 "Tier 4 emissions standards and fuel requirements" (Vehicles and
Engines) as well as the March 2008 "Final locomotive-marine rule" controls (Vehicles and Engines). These
projection ratios are provided in Table 4-38.
Table 4-38. Non-California intermediate projection factors for locomotives and Class 1 and Class 2
Commercial Marine Vessel Emissions
SCC
Description
Poll
2018
Factor
2025
Factor
2280002XXX
Marine Vessels, Commercial;Diesel;Underway & port emissions
CO
0.9525
0.9547
2280002XXX
Marine Vessels, Commercial;Diesel;Underway & port emissions
NOx
0.7623
0.5372
2280002XXX
Marine Vessels, Commercial;Diesel;Underway & port emissions
PM
0.6755
0.4906
2280002XXX
Marine Vessels, Commercial;Diesel;Underway & port emissions
S02
0.1275
0.0691
2280002XXX
Marine Vessels, Commercial;Diesel;Underway & port emissions
VOC
0.7715
0.5233
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
CO
1.175
1.2489
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
NOx
0.8123
0.6207
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
PM
0.6764
0.4574
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
S02
0.0319
0.0356
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
VOC
0.6116
0.4299
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
CO
1.175
1.2489
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
NOx
1.0576
1.0646
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
PM
1.0241
1.0118
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
S02
0.0319
0.0357
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
VOC
1.1175
1.2489
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
CO
1.0574
1.1180
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
NOx
0.6635
0.4582
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
PM
0.6052
0.3369
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
S02
0.0303
0.0331
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
VOC
0.5316
0.2751
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
CO
1.0574
1.1180
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
NOx
0.6635
0.4582
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
PM
0.6052
0.3369
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
S02
0.0303
0.0330
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
VOC
0.5316
0.2751
2285002010
Railroad Equipment;Diesel;Yard Locomotives
CO
1.175
1.2489
2285002010
Railroad Equipment;Diesel;Yard Locomotives
NOx
0.9767
0.8366
2285002010
Railroad Equipment;Diesel;Yard Locomotives
PM
0.9436
0.8096
2285002010
Railroad Equipment;Diesel;Yard Locomotives
S02
0.0320
0.0356
2285002010
Railroad Equipment;Diesel;Yard Locomotives
VOC
0.9388
0.7666
The future-year locomotive emissions account for increased fuel consumption based on Energy Information
Administration (EIA) fuel consumption projections for freight rail, and emissions reductions resulting from
emissions standards from the Final Locomotive-Marine rule (EPA, 2009d). 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.
EPA applied HAP factors for VOC HAPs by using the VOC projection factors to obtain 1,3-butadiene,
acetaldehyde, acrolein, benzene, and formaldehyde. C1/C2 diesel emissions (SCC = 2280002100 and
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2280002200) were projected based on the Final Locomotive Marine rule national-level factors. Similar to
locomotives, VOC HAPs were projected based on the VOC factor.
Step 2: Intermediate California year 2018 and 2025 inventories
Obtained from CARB, the locomotive, and class 1 and 2 commercial marine emissions used for California
reflect year 2017 (used for 2018) and year 2025 and include nonroad rules reflected in the December 2010
Rulemaking Inventory, 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, 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 C1/C2 CMV emissions were obtained from the CARB nonroad mobile dataset
"ARMJ_RF#2002_AN]SrUAL_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
offroad methodology, including clc2rail sector data. EPA converted the CARB inventory TOG to VOC by
dividing the inventory TOG by the available VOC-to-TOG speciation factor.
Step 3: Adjusting 2018 and 2025 clc2rail emissions to reflect 2017-2025 light duty vehicle greenhouse gas
standards and renewable fuel volume projections
Rail and barges are used to transport fuel from production facilities to bulk terminals. To account for
emissions associated with this transport, C1/C2 and rail inventories were adjusted to account for differences
in ethanol volumes and emission rates between the base year and future years.
In EPA's Tier 3 final rule, impacts of these modes of transport of fuel on combustion emissions from the CI
and C2 CMV and rail inventories were estimated for 2018, based on AEO 2013 projections.37 The adjusted
national inventory impacts were allocated to individual counties using factors developed from the Oak Ridge
analysis of ethanol transport (Oak Ridge National Laboratory, 2009). EPA OTAQ was unable to provide
year 2025-specfic impacts; therefore, these year-2018 impacts were then applied to the unadjusted inventory
for the 2025 projected inventories.
Emissions from updated renewable fuel volume projections are not included in the previously-discussed non-
California loco-marine rule-based projections (Step 1) and CARB 2017 and 2025 inventories (Step 2).
Nationally, these additional emissions are modest and are shown in Table 4-39. In addition, for year 2025
projections, very minor scalar adjustments (a decrease of a fraction of a percent) were applied to rail and
clc2 CMV emissions to reflect the minor impact of the Light-Duty Vehicle Greenhouse Gas (LDGHG) Rule
(EPA, 2012b). The overall differences between 2011 and future year clc2rail sector emissions, reflecting
final rules (Loco-Marine, and LDGHG 2017-2025), and renewable fuel volume projections, are provided in
Table 4-40. These sector totals include all U.S. states as well as offshore and Puerto Rico.
Table 4-39. C1/C2 and locomotive emission adjustments in 2018 and 2025
Pollutant
C1/C2 CMV
Locomotives
CO
-855
1,715
nh3
-2
5
37 Cook, R. 2014. Development of Air Quality Reference Case Upstream and Portable Fuel Container Inventories for Tier 3 Final
Rule. Memorandum to Docket EPA-HQ-OAR-2010-0162.
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NOx
-3,635
8,346
PMio
-139
198
PM2.5
-155
-10
S02
-296
80
VOC
-136
357
Table 4-40. Difference in clc2rail sector emissions between 2011 and future years
Pollutant
2011
2018
2025
Difference
2018-2011
Difference
2025-2011
CO
242,771
255,496
274,511
12,725
31,740
nh3
707
712
709
5
2
NOx
1,392,532
1,129,284
849,112
-263,248
-543,420
PM10
46,142
31,963
22,704
-14,179
-23,438
PM2.5
43,491
29,893
21,186
-13,598
-22,305
S02
23,160
3,161
1,514
-19,999
-21,646
VOC
56,543
33,334
28,077
-23,209
-28,466
4.4.2 Class 3 commercial marine vessels (c3marine)
As discussed in Section 2.4.2, the c3marine sector emissions data were developed for year 2002 and
projected to year 2011 for the 2011 base case. The ECA-IMO project provides pollutant and geographic-
specific projection factors to year 2011, and also projection factors to years 2018 and 2025 that reflect
assumed growth and final ECA-IMO controls. The ECA-IMO rule, published in December 2009, applies to
Category 3 (C3) diesel engines (engines with per cylinder displacement at or above 30 liters) installed on
U.S. vessels. The ECA-IMO rule includes an implementation of Tier 2 and Tier 3 NOx limits for C3 engines
beginning in 2011 and 2016, respectively. The ECA-IMO rule also imposes fuel sulfur limits of 1,000 ppm
(0.1%) by 2015 in the ECA region -generally within 200 nautical miles of the U.S. and Canadian coastlines,
as well as 5,000 ppm (0.5%) for "global" areas -those areas outside the ECA region. For comparison, with
the exception of some local areas, year 2011 sulfur content limits are as high as 15,000 ppm (1.5%) in U.S.
waters and 45,000 ppm (4.5%) in global areas. More information on the ECA-IMO rule can be found in the
Category 3 marine diesel engines Regulatory Impact Assessment.
Projection factors for creating the year 2018 and 2025 c3marine inventories from the 2011 base case are
provided in Table 4-41. 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. The impact of the Tier 2 and Tier 3 NOx
engine standards is less noticeable because of the inevitable delay in fleet turnover for these new engines;
however, the immediate and drastic cuts in fuel sulfur content are obvious. VOC and CO are mostly
unaffected by the engine and fuel standards, thus providing an idea on how much these emissions would
have grown without ECA-IMO controls. VOC HAPs are assigned the same growth rates as VOC.
Table 4-41. Growth factors to project the 2011 ECA-IMO inventory to 2018 and 2025
Region
EEZ
FIPS
Year
2018 and 2025 Adjustments Re
ative to 2
Oil
NOx
PM10
PM2.5
VOC
(HC)
CO
SO2
East Coast (EC)
85004
2018
1.068
0.556
0.556
1.361
1.361
0.136
2025
0.890
0.756
0.756
1.852
1.852
0.185
Gulf Coast (GC)
85003
2018
0.960
0.504
0.504
1.222
1.222
0.122
2025
0.721
0.615
0.615
1.492
1.492
0.149
2018
1.014
0.501
0.501
1.255
1.255
0.126
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North Pacific (NP)
85001
2025
0.846
0.629
0.629
1.575
1.575
0.158
South Pacific (SP)
85002
2018
1.121
0.593
0.593
1.421
1.420
0.144
2025
0.965
0.858
0.858
2.028
2.027
0.211
Great Lakes (GL)
n/a
2018
1.027
0.444
0.444
1.125
1.125
0.113
2025
0.998
0.500
0.500
1.266
1.266
0.127
Outside ECA
98001
2018
1.217
1.356
1.356
1.356
1.356
1.356
2025
1.463
0.409
0.405
1.858
1.858
0.337
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 2018 and 2025 using the same regional
adjustment factors as the U.S. emissions; however, the FIPS codes were assigned as "EEZ" FIPS and these,
as well as Canada C3 CMV, emissions are processed in the "othpt" sector (see Section 2.5.1 and 4.4.1).
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 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 C3 CMV U.S. emissions
in the Great Lakes because, as discussed in Section 2.4.1, all CMV emissions in the Midwest RPO are
processed in the "clc2rail" sector.
4.4.3 Other nonroad mobile sources (nonroad)
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. Similar to the onroad emissions, 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 NONROAD2008a including future-year equipment population
estimates, control programs to the years 2018 and 2025, and inputs either state-supplied as part of the
201 INEIvl process or national level inputs. Fuels for 2018 and 2025 were assumed to be E10 everywhere
for nonroad equipment. The fuels were developed from the MOVES fuels, which in turn were developed to
be consistent with AEO2013 projections for 2018 and 2025. The databases used in the 2018 run were
NMIM county database "NCD20130731_nei2018dvl" and fuels database "tier3frm2018ctrlfuels_03152013_
elOfuelsNMIM." EPA inadvertently used a 2025 inventory from an earlier platform. The 2018 and 2025
emissions account for increases in activity (based on NONROAD model default growth estimates of future-
year equipment population), 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 2018
and 2025 speciation of nonroad, see Section 3.2.1.4.
The version of NONROAD used was the current public release, NR08a, which models all in-force nonroad
controls. Recent rules include:
• "Clean Air Nonroad Diesel Final Rule - Tier 4", published June, 2004: Vehicles and Engines
• Control of Emissions from Nonroad Large Spark-Ignition Engines, and Recreational Engines (Marine
and Land-Based), November 8, 2002 ("Pentathalon Rule").
• OTAQ's Small Engine Spark Ignition ("Bond") Rule, October, 2008: Vehicles and Engines
Not included are voluntary local programs such as encouraging either no refueling or evening refueling on
Ozone Action Days.
125
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California and Texas nonroad emissions
Similar to the 2011 base 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 2017 and 2025 nonroad annual inventories were distributed to monthly emissions values by using
the respective year 2018 and 2025 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)38. . The CARB nonroad emissions include
nonroad rules reflected in the December 2010 Rulemaking Inventory 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 2018 and 2025. Conceptually, EPA used the trend of 2011 to 2018 or 2025 based on EPA's estimates to
project Texas' submitted emissions for 2011. Specifically, projections were based on state-wide SCC7,
mode, poll ratios39 of 2018 and 2025 NMIM to 2011 NMIM. These ratios were then applied to Texas'
submitted 2011 emissions inventory, which had already been distributed to a monthly inventory (see Section
2.4.3), to create a 2018 and 2025 monthly nonroad inventories.
4.5 "Other Emissions": Offshore Class 3 commercial marine vessels
and drilling platforms, Canada and Mexico (othpt, othar, and othon)
Recall from Section 2.5, that emissions from Canada, Mexico, and non-U.S. offshore Class 3 Commercial
Marine Vessels (C3 CMV) and drilling platforms are included as part of three emissions modeling sectors:
othpt, othar, and othon. Non C3 CMV emissions for Canada and offshore sources were not projected to
future years, and are therefore the same as those used in the 2011 base case. Canada did not provide future-
year emissions that were consistent with the base year emissions. The Mexico emissions are based on year
1999 but projected to year 2018 for both the 2018 and 2025 future base cases. A background on the
development of year-2018 Mexico emissions from the 1999 inventory is available at: WRAP.
4.5.1 Point sources from offshore C3 CMV and drilling platforms and Canada and
Mexico (othpt)
As discussed in Section 2.5.1, the ECA-IMO-based C3 CMV emissions for non-U.S. states are processed in
the othpt sector. These C3 CMV emissions include those assigned to Canada, 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 -
far offshore and non-U.S. EPA processed these emissions in the othpt sector for simplicity of creating U.S.-
only emissions summaries. Otherwise, these emissions are processed in the same way as the U.S. C3 CMV
emissions in the c3marine sector. The projection factors for the othpt C3 CMV emissions vary by
geographic and region as shown in Table 4-41. C3 CMV emissions in British Columbia were assigned as
North Pacific, Ontario as Great Lakes, and all other eastern Canada provinces as East Coast.
38 In addition, airport equipment was removed from CARB's inventory because these sources were modeled elsewhere.
39 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 2018 and 2025 emissions
but not in EPA's 2011 emissions, 2018 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 2018 and 2025 emissions, then state/SCC3/mode/pollutant ratios were used
to project to 2018 and 2025.
126
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Mexico point-format year-2018 inventories are used essentially as-is with only minor formatting changes.
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. EPA used emissions from the 2008NEIv2 point source inventory
for both 2011 and 2018. EPA expects updated offshore oil and gas drilling emissions in the next version of
the 2011 NEI (Version 2).
4.5.2 Area, nonroad mobile and onroad mobile sources from Canada and Mexico
(other, othon)
Both year-2006 Canada and year-2018 Mexico inventories were converted from their original SMOKE One-
Record per Line (ORL) and Inventory Data Analyzer (IDA) formats, respectively, to SMOKE Flat File 10
(FF10) inventory format. Otherwise, these inventories were used as-is.
127
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5 Emission Summaries
The following tables summarize emissions differences between the 2011 evaluation case, the 2018 base case,
and the 2025 base 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 and onroad refueling (onroad rfl) sector totals are post-SMOKE-MOVES
totals, representing air quality model-ready emission totals, and the onroad portion include CARB emissions
for California. The "c3marine-US" sector represents c3marine sector emissions with U.S. FIPS only; these
extend to roughly 3-5 miles offshore and all U.S. waters in the Great Lakes and also include all U.S. ports.
The "c3marine, EEZ component" represents all non-U.S. c3marine emissions that are within the (up to) 200
nautical mile Exclusive Economic Zone (EEZ) boundary but outside of U.S. state waters. Finally, the
"c3marine, non-US non-EEZ component" represents all non-U.S. emissions outside of the (up to) 200nm
offshore boundary, including all Canadian and Mexican c3marine emissions. The c3marine sector is
discussed in Section 2.4.2. The "Off-shore othpt" sector is the non-Canada, non-Mexico component of the
othpt sector - i.e., the offshore oil platform emissions from the 2008 NEI.
National emission totals by air quality model-ready sector are provided for all CAP emissions for the 2011
evaluation case in Table 5-1. The total of all sectors in the 2011 evaluation case are listed as "Con U.S.
Total w/ ptfire". Table 5-2 provides national emissions totals by sector for all CAPs in the 2018 base case.
Table 5-3 provides national emissions totals by sector for all CAPs in the 2025 base case.
Table 5-4 provides national-by sector emission summaries for CO for all the cases: 2011 evaluation case,
2018 base case, and 2025 base case, with percent change from 2011 to 2018 and 2011 to 2025. Table 5-5
through Table 5-10 provide the same summaries for NH3, NOx, PM2.5, PM10, SO2 and VOC, respectively.
Note that the same ptfire emissions are used in all cases.
128
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Table 5-1. National by-sector CAP emissions summaries for the 2011 evaluation case
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj
18,502,317
2,487,403
ag
3,517,371
clc2rail
173,437
481
1,046,095
34,670
32,367
17,651
47,714
nonpt
3,046,375
142,323
832,166
715,709
533,248
392,638
3,792,612
np oilgas
642,182
0
653,219
21,756
17,200
17,195
2,273,214
nonroad
13,952,389
2,627
1,630,409
162,420
154,660
4,031
2,024,633
onroad adi
25,230,444
118,130
5,591,695
287,540
207,517
28,475
2,576,504
onroad rfl
161,415
c3marine
12,425
124,725
4,279
3,909
38,645
4,954
ptfire
22,580,113
362,910
347,103
2,362,132
2,005,142
177,107
5,174,593
ptegu
719,414
21,644
1,925,742
259,011
188,811
4,596,656
32,288
ptegu pk
8,662
425
21,941
2,159
1,886
28,476
783
ptnonipm
2,565,936
74,841
1,767,748
491,837
338,447
1,071,950
872,433
pt oilgas
20,579
112
17,026
1,833
1,810
55,142
87,842
rwc
2,578,229
20,343
35,672
389,019
388,288
8,986
446,972
Con U.S. Total
71,530,185
4,261,207
13,993,540
23,234,681
6,360,688
6,436,952
17,495,956
Off-shore to EEZ*
130,419
0
610,664
16,961
15,525
133,606
81,286
Non-US SECA C3
17,169
0
202,516
17,199
15,823
127,563
7,297
Canada othar
2,810,350
386,147
462,996
810,747
248,907
61,179
932,322
Canada othon
3,303,239
17,572
392,209
11,075
7,712
4,046
199,939
Canada othpt* *
560,661
15,543
369,993
65,782
39,828
825,675
157,170
Mexico othar
439,901
109,861
189,592
69,523
23,600
26,559
499,145
Mexico othon
423,978
3,247
76,880
7,593
6,970
1,413
73,888
Mexico othpt
116,609
0
414,399
137,512
101,884
828,418
83,838
Non-US Total
7,802,326
532,370
2,719,249
1,136,392
460,249
2,008,459
2,034,885
* "Offshore to EEZ" inc udes both the offshore point emissions, and the "Offshore to EEZ" c3marine emissions
** Canadian c3 emissions are included in "Canada othpt"
129
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Table 5-2. National by-sector CAP emissions summaries for the 2018 base case
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj
6,804,937
932,732
as
3,596,908
clc2rail
189,355
489
869,089
24,346
22,508
2,628
33,334
nonpt
3,058,148
142,384
847,975
720,106
536,477
304,514
3,634,506
np oilgas
782,408
0
795,491
27,248
21,565
25,488
2,555,021
nonroad
12,377,375
2,900
1,071,612
107,005
100,949
1,868
1,360,554
onroad adj
16,063,457
87,336
2,684,537
208,304
124,876
12,597
1,341,243
onroad rfl
78,655
c3marine
17,518
136,147
2,338
2,129
5,354
6,678
ptfire
22,580,113
362,910
347,103
2,362,132
2,005,142
177,107
5,174,593
ptegu
748,085
39,366
1,434,376
249,897
194,123
1,424,574
38,701
ptegu pk
11,253
439
9,959
248
215
3,432
315
ptnonipm
2,417,844
75,816
1,764,777
463,765
315,535
720,649
869,495
pt oilgas
23,683
159
20,450
2,002
1,973
63,868
104,268
rwc
2,736,854
21,485
38,434
413,597
412,852
10,018
466,259
Con U.S. Total
61,006,094
4,330,193
10,019,951
11,385,923
4,671,078
2,752,096
15,663,623
Off-shore to EEZ*
146,323
635,570
9,630
8,841
18,746
88,045
Non-US SECA C3
23,318
246,579
23,327
21,462
173,124
9,896
Canada othar
2,810,350
386,147
462,996
810,747
248,907
61,179
932,322
Canada othon
3,303,239
17,572
392,209
11,075
7,712
4,046
199,939
Canada othpt
561,438
15,543
370,944
65,276
39,370
818,374
157,501
Mexico othar
527,917
109,840
226,341
70,916
47,191
19,286
577,078
Mexico othon
397,197
4,465
46,794
9,420
8,591
659
62,948
Mexico othpt
148,758
544,690
170,910
127,734
1,066,482
94,351
Non-US Total
7,918,540
533,567
2,926,123
1,171,301
509,808
2,161,896
2,122,080
130
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Table 5-3. National by-sector CAP emissions summaries for the 2025 base case
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj
6,848,008
940,823
as
3,676,962
clc2rail
208,308
487
666,150
17,188
15,835
1,228
24,012
nonpt
3,060,603
142,406
856,371
722,626
538,703
308,654
3,605,078
np oilgas
866,014
0
874,359
29,135
23,493
26,761
2,550,615
nonroad
13,352,920
3,548
796,408
77,632
72,801
2,843
1,188,117
onroad adj
12,540,692
86,413
1,491,639
181,686
91,249
11,843
1,004,875
onroad rfl
55,129
c3marine
21,017
105,421
2,979
2,724
6,647
8,448
ptfire
22,580,113
362,910
347,103
2,362,132
2,005,142
177,107
5,174,593
ptegu
856,897
44,731
1,497,728
274,311
209,690
1,499,936
41,947
ptegu pk
11,323
484
10,358
275
241
4,057
280
ptnonipm
2,480,398
75,640
1,802,732
472,733
322,368
751,697
881,162
pt oilgas
24,742
196
22,370
2,093
2,061
69,621
106,744
rwc
2,932,569
22,924
41,716
443,780
443,021
11,373
489,136
Con U.S. Total
58,935,596
4,416,700
8,512,357
11,434,578
4,668,151
2,871,765
15,130,136
Off-shore to EEZ*
146,323
635,570
9,630
8,841
18,746
88,045
Non-US SECA C3
23,318
246,579
23,327
21,462
173,124
9,896
Canada othar
2,810,350
386,147
462,996
810,747
248,907
61,179
932,322
Canada othon
3,303,239
17,572
392,209
11,075
7,712
4,046
199,939
Canada othpt
561,438
15,543
370,944
65,276
39,370
818,374
157,501
Mexico othar
527,917
109,840
226,341
70,916
47,191
19,286
577,078
Mexico othon
397,197
4,465
46,794
9,420
8,591
659
62,948
Mexico othpt
148,758
544,690
170,910
127,734
1,066,482
94,351
Non-US Total
7,918,540
533,567
2,926,123
1,171,301
509,808
2,161,896
2,122,080
131
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Table 5-4. National by-sector CO emissions (tons/yr) summaries and percent change
Sector
2011 CO
2018 CO
2025 CO
% change
2011 to 2018
% change
2011 to 2025
afdust adi
0
0
0
0%
0%
ag
0
0
0
0%
0%
clc2rail
230,407
243,619
262,605
6%
14%
c3marine
12,426
17,518
21,017
41%
69%
nonpt
3,046,776
3,058,568
3,061,022
0%
0%
nonroad
13,993,701
12,409,684
13,385,224
-11%
-4%
np oilgas
642,179
782,405
866,011
22%
35%
onroad
25,230,442
16,063,457
12,540,692
-36%
-50%
pt oilgas
22,217
25,493
26,544
15%
19%
ptegu
724,448
752,505
856,945
4%
18%
ptegu pk
8,661
11,258
11,316
30%
31%
ptfire
22,584,187
22,580,113
22,580,113
0%
0%
ptnonipm
2,567,765
2,419,697
2,482,236
-6%
-3%
rwc
2,583,182
2,742,131
2,938,191
6%
14%
Grand Total
71,646,391
61,106,449
59,031,918
-15%
-18%
Off-shore to EEZ*
130,419
146,323
167,853
12%
29%
Non-US SECA C3
17,169
23,318
31,925
36%
86%
Canada othar
2,810,350
2,810,350
2,810,350
0%
0%
Canada othon
3,303,239
3,303,239
3,303,239
0%
0%
Canada othpt* *
560,661
561,438
561,438
0%
0%
Mexico othar
439,901
527,917
527,917
20%
20%
Mexico othon
423,978
397,197
397,197
-6%
-6%
Mexico othpt
116,609
148,758
148,758
28%
28%
Non-US Total
7,802,326
7,918,540
7,948,677
1%
1%
132
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Table 5-5. National by-sector NH3 emissions (tons/yr) summaries and percent change
Sector
2011 NH3
2018 NH3
2025 NH3
% change
2011 to 2018
% change
2011 to 2025
afdust adi
0
0
0
0%
0%
ag
3,522,231
3,601,811
3,681,929
2%
2%
clc2rail
667
675
672
1%
1%
c3marine
0
0
0
0%
0%
nonpt
142,329
142,388
142,411
0%
0%
nonroad
2,616
2,886
3,528
10%
35%
np oilgas
0
0
0
0%
0%
onroad
118,129
87,336
86,413
-26%
-27%
pt oilgas
113
159
196
40%
73%
ptegu
21,947
39,548
44,644
80%
103%
ptegu pk
428
436
480
2%
12%
ptfire
362,979
362,910
362,910
0%
0%
ptnonipm
74,781
75,754
75,578
1%
1%
rwc
20,402
21,549
22,992
6%
13%
Grand Total
4,266,622
4,335,451
4,421,754
2%
4%
Off-shore to EEZ*
0
0
0
0%
0%
Non-US SECA C3
0
0
0
0%
0%
Canada othar
386,147
386,147
386,147
0%
0%
Canada othon
17,572
17,572
17,572
0%
0%
Canada othpt* *
15,543
15,543
15,543
0%
0%
Mexico othar
109,861
109,840
109,840
0%
0%
Mexico othon
3,247
4,465
4,465
38%
38%
Mexico othpt
0
0
0
0%
0%
Non-US Total
532,370
533,567
533,567
0%
0%
133
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Table 5-6. National by-sector NOx emissions (tons/yr) summaries and percent change
Sector
2011 NOx
2018 NOx
2025 NOx
% change
2011 to 2018
% change
2011 to 2025
afdust adi
0
0
0
0%
0%
ag
0
0
0
0%
0%
clc2rail
1,326,348
1,082,712
816,311
-18%
-38%
c3marine
124726
136,147
105,421
9%
-15%
nonpt
831,254
847,062
855,457
2%
3%
nonroad
1,620,552
1,067,278
795,064
-34%
-51%
np oilgas
653214
795,488
874,356
22%
34%
onroad
5,591,694
2,684,537
1,491,639
-52%
-73%
pt oilgas
22,091
25,970
27,885
18%
26%
ptegu
2,001,241
1,467,773
1,497,784
-27%
-25%
ptegu pk
22,591
9,966
10,351
-56%
-54%
ptfire
347,109
347,103
347,103
0%
0%
ptnonipm
1,771,516
1,768,543
1,806,483
0%
2%
rwc
35,758
38,527
41,814
8%
17%
Grand Total
14,348,094
10,271,108
8,669,670
-28%
-40%
Off-shore to EEZ*
610,664
635,570
533,545
4%
-13%
Non-US SECA C3
202,516
246,579
296,490
22%
46%
Canada othar
462,996
462,996
462,996
0%
0%
Canada othon
392,209
392,209
392,209
0%
0%
Canada othpt* *
369,993
370,944
370,944
0%
0%
Mexico othar
189,592
226,341
226,341
19%
19%
Mexico othon
76,880
46,794
46,794
-39%
-39%
Mexico othpt
414,399
544,690
544,690
31%
31%
Non-US Total
2,719,249
2,926,123
2,874,009
8%
6%
134
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Table 5-7. National by-sector PM2.5 emissions (tons/yr) summaries and percent change
Sector
2011 PM25
2018 PM2 s
2025 PM2 5
% change
2011 to 2018
% change
2011 to 2025
afdust adi
923,058
932,732
940,823
1%
2%
ag
0
0
0
0%
0%
clc2rail
41,348
28,578
20,232
-31%
-51%
c3marine
3,910
2,129
2,724
-46%
-30%
nonpt
533,058
536,289
538,516
1%
1%
nonroad
154,053
100,522
72,568
-35%
-53%
np oilgas
17,200
21,566
23,494
25%
37%
onroad
207,521
124,876
91,249
-40%
-56%
pt oilgas
1,853
2,026
2,114
9%
14%
ptegu
193,877
199,191
209,695
3%
8%
ptegu pk
1,884
215
241
-89%
-87%
ptfire
2,005,508
1,872,281
1,872,281
-7%
-7%
ptnonipm
339,398
316,128
322,957
-7%
-5%
rwc
389,086
413,700
443,924
6%
14%
Grand Total
4,811,754
4,550,235
4,540,817
-5%
-6%
Off-shore to EEZ*
15,525
8,841
11,594
-43%
-25%
Non-US SECA C3
15,823
21,462
6,411
36%
-59%
Canada othar
248,907
248,907
248,907
0%
0%
Canada othon
7,712
7,712
7,712
0%
0%
Canada othpt* *
39,828
39,370
39,370
-1%
-1%
Mexico othar
23,600
47,191
47,191
100%
100%
Mexico othon
6,970
8,591
8,591
23%
23%
Mexico othpt
101,884
127,734
127,734
25%
25%
Non-US Total
460,249
509,808
497,510
11%
8%
135
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Table 5-8. National by-sector PMio emissions (tons/yr) summaries and percent change
Sector
2011
PM10
2018
PM10
2025
PM10
% change
2011 to 2018
% change
2011 to 2025
afdust adi
6,726,726
6,804,937
6,848,008
1%
2%
ag
0
0
0
0%
0%
clc2rail
43,930
30,603
21,719
-30%
-51%
c3marine
4,278
2,338
2,979
-45%
-30%
nonpt
715,771
720,171
722,692
1%
1%
nonroad
161,816
106,582
77,403
-34%
-52%
np oilgas
21,753
27,249
29,136
25%
34%
onroad
287,541
208,304
181,686
-28%
-37%
pt oilgas
1,885
2,062
2,153
9%
14%
ptegu
266,641
256,685
274,316
-4%
3%
ptegu pk
2,161
247
275
-89%
-87%
ptfire
2,362,550
2,209,292
2,209,292
-6%
-6%
ptnonipm
495,912
466,201
475,323
-6%
-4%
rwc
389,815
414,444
444,683
6%
14%
Grand Total
11,480,779
11,249,116
11,289,665
-2%
-2%
Off-shore to EEZ*
16,961
9,630
12,648
-43%
-25%
Non-US SECA C3
17,199
23,327
7,033
36%
-59%
Canada othar
810,747
810,747
810,747
0%
0%
Canada othon
11,075
11,075
11,075
0%
0%
Canada othpt* *
65,782
65,276
65,276
-1%
-1%
Mexico othar
69,523
70,916
70,916
2%
2%
Mexico othon
7,593
9,420
9,420
24%
24%
Mexico othpt
137,512
170,910
170,910
24%
24%
Non-US Total
1,136,392
1,171,301
1,158,026
3%
2%
136
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Table 5-9. National by-sector SO2 emissions (tons/yr) summaries and percent change
Sector
2011 S02
2018 SO2
2025 S02
% change
2011 to 2018
% change
2011 to 2025
afdust adi
0
0
0
0%
0%
ag
0
0
0
0%
0%
clc2rail
21,093
3,068
1,465
-85%
-93%
c3marine
38,645
5,354
6,647
-86%
-83%
nonpt
392,004
303,955
308,094
-22%
-21%
nonroad
4,010
1,861
2,845
-54%
-29%
np oilgas
17,196
25,488
26,761
48%
56%
onroad
28,472
12,597
11,843
-56%
-58%
pt oilgas
55,272
64,076
69,823
16%
26%
ptegu
4,636,758
1,443,845
1,500,061
-69%
-68%
ptegu pk
28,584
3,433
4,058
-88%
-86%
ptfire
177,122
177,107
177,107
0%
0%
ptnonipm
1,071,820
720,578
751,617
-33%
-30%
rwc
9,003
10,033
11,388
11%
26%
Grand Total
6,479,979
2,771,394
2,871,708
-57%
-56%
Off-shore to EEZ*
133,606
18,746
24,872
-86%
-81%
Non-US SECA C3
127,563
173,124
43,084
36%
-66%
Canada othar
61,179
61,179
61,179
0%
0%
Canada othon
4,046
4,046
4,046
0%
0%
Canada othpt* *
825,675
818,374
818,374
-1%
-1%
Mexico othar
26,559
19,286
19,286
-27%
-27%
Mexico othon
1,413
659
659
-53%
-53%
Mexico othpt
828,418
1,066,482
1,066,482
29%
29%
Non-US Total
2,008,459
2,161,896
2,037,982
8%
1%
137
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Table 5-10. National by-sector VOC emissions (tons/yr) summaries and percent change
Sector
2011 VOC
2018 VOC
2025 VOC
% change
2011 to 2018
% change
2011 to 2025
afdust adi
0
0
0
0%
0%
ag
0
0
0
0%
0%
clc2rail
54,122
38,280
27,337
-29%
-49%
c3marine
4,954
6,678
8,448
35%
71%
nonpt
3,792,586
3,663,326
3,605,003
-3%
-5%
nonroad
2,049,724
1,374,906
1,197,404
-33%
-42%
np oilgas
2,273,193
2,555,006
2,550,596
12%
12%
onroad
2,576,504
1,341,243
1,004,875
-48%
-61%
onroad rfl
161,415
78,655
55,129
-51%
-66%
pt oilgas
89,753
106,346
108,811
18%
21%
ptegu
32,376
39,228
41,948
21%
30%
ptegu pk
783
313
280
-60%
-64%
ptfire
5,174,593
5,174,593
5,174,593
0%
0%
ptnonipm
872,643
869,688
881,341
0%
1%
rwc
447,599
466,927
489,848
4%
9%
Grand Total
17,530,245
15,715,190
15,145,614
-10%
-14%
Off-shore to EEZ*
81,286
88,045
97,216
8%
20%
Non-US SECA C3
7,297
9,896
13,550
36%
86%
Canada othar
932,322
932,322
932,322
0%
0%
Canada othon
199,939
199,939
199,939
0%
0%
Canada othpt* *
157,170
157,501
157,501
0%
0%
Mexico othar
499,145
577,078
577,078
16%
16%
Mexico othon
73,888
62,948
62,948
-15%
-15%
Mexico othpt
83,838
94,351
94,351
13%
13%
Non-US Total
2,034,885
2,122,080
2,134,905
4%
5%
138
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