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Technical Support Document (TSD): Updates to
Emissions Inventories for the Version 6.3, 2011
Emissions Modeling Platform for the Year 2023
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EPA-454/B-20-012
December 2016
Technical Support Document (TSD): Updates to Emissions Inventories for the Version 6.3,
2011 Emissions Modeling Platform for the Year 2023
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
1 INTRODUCTION 1
2 2011 EMISSION INVENTORIES AND APPROACHES 4
2.1 2011 ONROAD MOBILE SOURCES (ONROAD) 5
2.2 Category 1, Category 2, Category 3 Commercial Marine Vessels (cmv) 6
2.3 "OtherEmissions": Emissions from Non-U.S. sources 6
2.3.1 Point Sources from Offshore C3 CMV, Drilling platforms, Canada and Mexico (othpt) 7
2.3.2 Area and Nonroad Mobile Sources from Canada and Mexico (othar) 7
2.3.3 Onroad Mobile Sources from Canada and Mexico (othon) 8
2.4 Non-U. S. Fires (ptfire_mxca) 9
3 EMISSIONS MODELING SUMMARY 10
3.1 Emis sions Modeling Overview 10
3.2 Chemical Speciation 14
3.3 Temporal Allocation 14
3.4 Spatial Allocation 16
3.4.1 Spatial Surrogates for U.S. Emissions 17
3.4.2 Allocation Method for Airport-related Sources in the U.S. 23
3.4.3 Surrogates for Canada and Mexico Emission Inventories 23
4 DEVELOPMENT OF 2023 BASE-CASE EMISSIONS 28
4.1 EGU SECTOR PROJECTIONS (PTEGU) 33
4.2 Non-EGU Point and NEI Nonpoint Sector Projections 34
4.2.1 Background on the Control Strategy Tool (CoST) 34
4.2.2 CoST Plant CLOSURE Packet (ptnonipm) 39
4.2.3 CoST PROJECTION Packets (afdust, ag, cmv, rail, nonpt, npoilgas, ptnonipm, ptoilgas, rwc) 40
4.2.3.1 Paved and unpaved roads VMT growth (afdust) 40
4.2.3.2 Livestock population growth (ag) 41
4.2.3.3 Locomotives and category 1, 2, & 3 commercial marine vessels (cmv, rail, ptnonipm, othpt) 41
4.2.3.4 Upstream distribution, pipelines and refineries (nonpt, ptnonipm, pt_oilgas) 45
4.2.3.5 Oil and gas and industrial source growth (nonpt, np_oilgas, ptnonipm, pt_oilgas) 47
4.2.3.6 Aircraft (ptnonipm) 52
4.2.3.7 Cement manufacturing (ptnonipm) 53
4.2.3.8 Corn ethanol plants (ptnonipm) 55
4.2.3.9 Residential wood combustion (rwc) 56
4.2.4 CoST CONTROL Packets (nonpt, np oilgas, ptnonipm, pt oilgas) 59
4.2.4.1 Oil and Gas NSPS (np_oilgas, pt_oilgas) 60
4.2.4.2 RICE NESHAP (nonpt, np_oilgas, ptnonipm, pt_oilgas) 61
4.2.4.3 RICE NSPS (nonpt, np_oilgas, ptnonipm, pt_oilgas) 63
4.2.4.4 ICI boilers (nonpt, ptnonipm, pt_oilgas) 65
4.2.4.5 Fuel sulfur rules (nonpt, ptnonipm, pt_oilgas) 68
4.2.4.6 Natural gas turbines NOx NSPS (ptnonipm, pt_oilgas) 69
4.2.4.7 Process heaters NOx NSPS (ptnonipm, pt_oilgas) 71
4.2.4.8 Arizona regional haze controls (ptnonipm) 72
4.2.4.9 CISWI (ptnonipm) 73
4.2.4.10 Data from comments on previous platforms and recent comments (nonpt, ptnonipm, pt_oilgas) 73
4.2.5 Stand-alone future year inventories (nonpt, ptnonipm) 74
4.2.5.1 Portable fuel containers (nonpt) 74
4.2.5.2 Biodiesel plants (ptnonipm) 75
4.2.5.3 Cellulosic plants (nonpt) 76
4.2.5.4 New cement plants (nonpt) 78
4.3 Mobile source projections 79
4.3.1 Onroad mobile (onroad) 79
4.3.1.1 Future activity data 79
4.3.1.2 Set up and run MOVES to create emission factors 81
4.3.1.3 California and Texas adjustments 82
4.3.2 Nonroad Mobile Source Projections (nonroad) 83
4.4 Projections of "Other Emissions" : Offshore Category 3 Commercial Marine Vessels and Drilling
Platforms, Canada and Mexico (othpt, othar, and othon) 84
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5 EMISSION SUMMARIES 85
6 REFERENCES 100
List of Figures
Figure 3-1. Air quality modeling domains 13
Figure 4-1. Oil and Gas NEMS Regions 50
Figure 4-2. Cement sector trends in domestic production versus normalized emissions 54
Figure 4-3. Light Duty VMT growth rates based on AEO2014 81
List of Tables
Table 1-1. List of cases in this update to the 2011 Version 6.3 Emissions Modeling Platform for 2023 2
Table 2-1. Platform sectors updated since the original 2011v6.3 emissions modeling platform 4
Table 2-2. Onroad CAP emissions in the 201 lv6.3 and updated platforms (tons) 5
Table 2-3. California CMV CAP emissions in the 201 lv6.3 and updated platforms (tons) 6
Table 2-4. Mexico CAP emissions in the 201 lv6.3 and updated platforms (tons) 6
Table 2-5. 2011 Platform SCCs representing emissions in the ptfire modeling sectors 9
Table 3-1. Key emissions modeling steps by sector 11
Table 3-2. Descriptions of the platform grids 13
Table 3-3. Temporal settings used for the platform sectors in SMOKE 15
Table 3-4. U.S. Surrogates available for the 2011 modeling platform 17
Table 3-5. Off-Network Mobile Source Surrogates 18
Table 3-6. Spatial Surrogates for Oil and Gas Sources 19
Table 3-7. Selected 2011 CAP emissions by sector for U.S. Surrogates* 20
Table 3-8. Canadian Spatial Surrogates 23
Table 3-9. CAPs Allocated to Mexican and Canadian Spatial Surrogates 25
Table 4-1. Growth and control methodologies used to create 2023 emissions inventories 31
Table 4-2. Subset of CoST Packet Matching Hierarchy 36
Table 4-3. Summary of non-EGU stationary projections subsections 37
Table 4-4. Reductions from all facility/unit/stack-level closures 39
Table 4-5. Increase in total afdust PM2.5 emissions from VMT projections 40
Table 4-6. NH3 projection factors and total impacts to years 2023 for animal operations 41
Table 4-7. Non-California projection factors for locomotives and Category 1 and Category 2 CMV
Emissions 42
Table 4-8. Difference in Category 1& 2 cmv and rail sector emissions between 2011 and 2023, 43
Table 4-9. Growth factors to project the 2011 ECA-IMO inventory to 2023 44
Table 4-10. Difference in Category 3 cmv sector and othpt C3 CMV emissions between 2011 and 2023 45
Table 4-11. Petroleum pipelines & refineries and production storage and transport factors and reductions.. 47
Table 4-12. Sources of new industrial source growth factor data for year 2023 in the 201 lv6.3 platform 49
Table 4-13. Year 2023 projection factors derived from AEO2016 for each EIA Supply Region 49
Table 4-14. Industrial source projections net impacts for 2023 51
Table 4-15. NEI SCC to FAA TAF ITN aircraft categories used for aircraft projections 52
Table 4-15. National aircraft emission projection summary 53
Table 4-17. U.S. Census Division ISMP-based projection factors for existing kilns 55
Table 4-18. ISMP-based cement industry projected emissions 55
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Table 4-19. 2011 and 2025 corn ethanol plant emissions [tons] 56
Table 4-20. Non-West Coast RWC projection factors, including NSPS impacts 58
Table 4-21. Cumulative national RWC emissions from growth, retirements and NSPS impacts 58
Table 4-22. Assumed retirement rates and new source emission factor ratios for various NSPS rules 60
Table 4-23. NSPS VOC oil and gas reductions from projected pre-control 2023 grown values 61
Table 4-24. Summary RICE NESHAP SI and CI percent reductions prior to 201 1NEIv2 analysis 62
Table 4-25. National by-sector reductions from RICE Reconsideration controls (tons) 63
Table 4-26. RICE NSPS Analysis and resulting 201 lv6.2 emission rates used to compute controls 64
Table 4-27. National by-sector reductions from RICE NSPS controls (tons) 65
Table 4-28. Facility types potentially subject to Boiler MACT reductions 66
Table 4-29. National-level, with Wisconsin exceptions, ICI boiler adjustment factors by base fuel type 67
Table 4-30. New York and New Jersey NOx ICI Boiler Rules that supersede national approach 67
Table 4-31. Summary of ICI Boiler reductions 68
Table 4-32. State Fuel Oil Sulfur Rules data provided by MANE-VU 68
Table 4-33. Summary of fuel sulfur rule impacts on SO2 emissions 69
Table 4-34. Stationary gas turbines NSPS analysis and resulting emission rates used to compute controls... 70
Table 4-35. National by-sector 2023 NOx reductions from Stationary Natural Gas Turbine NSPS controls .71
Table 4-36. Process Heaters NSPS analysis and 201 lv6.2 new emission rates used to compute controls 72
Table 4-37. National by-sector NOx reductions from Process Heaters NSPS controls 72
Table 4-38. Summary of remaining ptnonipm and pt_oilgas reductions 74
Table 4-39. PFC emissions for 2011 and 2023 [tons] 75
Table 4-40. Emission Factors for Biodiesel Plants (Tons/Mgal) 76
Table 4-41. 2018 biodiesel plant emissions [tons] 76
Table 4-41. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/RIN gallon) 77
Table 4-42. Toxic Emission Factors for Cellulosic Plants (Tons/RIN gallon) 77
Table 4-43. 2017 cellulosic plant emissions [tons] 77
Table 4-45. New cellulosic plants NOx emissions provided by Iowa DNR 78
Table 4-46. ISMP-generated nonpoint cement kiln emissions 78
Table 4-47. Projection factors for 2023 (in millions of miles) 79
Table 4-48. Inputs for MOVES runs for 2023 81
Table 4-49. CA LEVIII program states 82
Table 5-1. National by-sector CAP emissions summaries for the 2011 evaluation case 86
Table 5-2. National by-sector CAP emissions summaries for the 2023 base case 87
Table 5-3. National by-sector CO emissions (tons/yr) summaries and percent change 88
Table 5-4. National by-sector NH3 emissions (tons/yr) summaries and percent change 89
Table 5-5. National by-sector NOx emissions (tons/yr) summaries and percent change 90
Table 5-6. National by-sector PM2.5 emissions (tons/yr) summaries and percent change 91
Table 5-7. National by-sector PM10 emissions (tons/yr) summaries and percent change 92
Table 5-8. National by-sector SO2 emissions (tons/yr) summaries and percent change 93
Table 5-9. National by-sector VOC emissions (tons/yr) summaries and percent change 94
Table 5-10. Canadian province emissions changes from 2011 to 2023 for othon sector 95
Table 5-11. Canadian province emissions changes from 2011 to 2023 for othar sector 95
Table 5-12. Canadian province emissions changes from 2011 to 2023 for othpt sector 96
Table 5-13. Mexican state emissions changes from 2011 to 2023 for othon sector 97
Table 5-14. Mexican state emissions changes from 2011 to 2023 for othar sector 98
Table 5-15. Mexican state emissions changes from 2011 to 2023 for othpt sector 99
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Acronyms
AE5
CMAQ Aerosol Module, version 5, introduced in CMAQ v4.7
AE6
CMAQ Aerosol Module, version 6, introduced in CMAQ v5.0
AEO
Annual Energy Outlook
BAFM
Benzene, Acetaldehyde, Formaldehyde and Methanol
BEIS
Biogenic Emissions Inventory System
BELD
Biogenic Emissions Landuse Database
Bgal
Billion gallons
BPS
Bulk Plant Storage
BTP
Bulk Terminal (Plant) to Pump
C1/C2
Category 1 and 2 commercial marine vessels
C3
Category 3 (commercial marine vessels)
CAEP
Committee on Aviation Environmental Protection
CAIR
Clean Air Interstate Rule
CAMD
EPA's Clean Air Markets Division
CAMx
Comprehensive Air Quality Model with Extensions
CAP
Criteria Air Pollutant
CARB
California Air Resources Board
CB05
Carbon Bond 2005 chemical mechanism
CBM
Coal-bed methane
CEC
North American Commission for Environmental Cooperation
CEMS
Continuous Emissions Monitoring System
CEPAM
California Emissions Projection Analysis Model
CISWI
Commercial and Industrial Solid Waste Incinerators
CI
Chlorine
CMAQ
Community Multiscale Air Quality
CMV
Commercial Marine Vessel
CO
Carbon monoxide
CSAPR
Cross-State Air Pollution Rule
CWFIS
Canadian Wildland Fire Information System
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
FEPS
Fire Emission Production Simulator
FF10
Flat File 2010
FINN
Fire INventory from NCAR
FIPS
Federal Information Processing Standards
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FHWA
Federal Highway Administration
HAP
Hazardous Air Pollutant
HC1
Hydrochloric acid
HDGHG
Heavy-Duty Vehicle Greenhouse Gas
Hg
Mercury
HMS
Hazard Mapping System
HPMS
Highway Performance Monitoring System
HWC
Hazardous Waste Combustion
HWI
Hazardous Waste Incineration
ICAO
International Civil Aviation Organization
ICI
Industrial/Commercial/Institutional (boilers and process heaters)
ICR
Information Collection Request
IDA
Inventory Data Analyzer
I/M
Inspection and Maintenance
IMO
International Marine Organization
IPAMS
Independent Petroleum Association of Mountain States
IPM
Integrated Planning Model
ITN
Itinerant
LADCO
Lake Michigan Air Directors Consortium
LDGHG
Light-Duty Vehicle Greenhouse Gas
LPG
Liquefied Petroleum Gas
MACT
Maximum Achievable Control Technology
MARAMA
Mid-Atlantic Regional Air Management Association
MATS
Mercury and Air Toxics Standards
MCIP
Meteorology-Chemistry Interface Processor
Mgal
Million gallons
MMS
Minerals Management Service (now known as the Bureau of Energy
Management, Regulation and Enforcement (BOEMRE)
MOVES
Motor Vehicle Emissions Simulator
MSA
Metropolitan Statistical Area
MSAT2
Mobile Source Air Toxics Rule
MTBE
Methyl tert-butyl ether
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
EPA 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
OKI)
EPA's Office of Research and Development
ORL
One Record per Line
OTC
Ozone Transport Commission
PADD
Petroleum Administration for Defense Districts
PF
Projection Factor, can account for growth and/or controls
PFC
Portable Fuel Container
PM2.5
Particulate matter less than or equal to 2.5 microns
PM10
Particulate matter less than or equal to 10 microns
ppb, ppm
Parts per billion, parts per million
RBT
Refinery to Bulk Terminal
RFS2
Renewable Fuel Standard
RIA
Regulatory Impact Analysis
RICE
Reciprocating Internal Combustion Engine
RRF
Relative Response Factor
RWC
Residential Wood Combustion
RPO
Regional Planning Organization
RVP
Reid Vapor Pressure
see
Source Classification Code
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
U. S. Department of Agriculture
VOC
Volatile organic compound
VMT
Vehicle miles traveled
VPOP
Vehicle Population
WRAP
Western Regional Air Partnership
WRF
Weather Research and Forecasting Model
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1 Introduction
In support of the Final Cross-state Air Pollutant Update Rule that addresses the transport of ozone as it
relates to the 2008 Ozone National Ambient Air Quality Standards (NAAQS), the U.S. Environmental
Protection Agency (EPA) developed an air quality modeling platform based on the 2011 National
Emissions Inventory (NEI), version 2 (201 1NEIv2). 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. The emissions
modeling component of the modeling platform includes the emission inventories, the ancillary data files,
and the approaches used to transform inventories for use in air quality modeling. The emissions modeling
platform that corresponded to the air quality modeling platform for ozone transport related to the 2008
ozone NAAQS is known as the 201 lv6.3 platform.
This document focuses on the updates made to the 201 lv6.3 platform to support analyses of transport of
zone related to the 2015 Ozone NAAQS. Much of the 2011 data from the 201 lv6.3 platform was
unchanged for this updated platform, although a different future year was used for the two analyses. For
more information on the 201 lv6.3 platform, see the data files and the technical support document (TSD)
Preparation of Emission Inventories for the version 6.3, 2011 Emissions Modeling Platform, available
from EPA's Air Emissions Modeling website for the version 6.3 platform (EPA, 2016).
This 2011-based modeling platform includes all criteria air pollutants (CAPs) and precursors 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. The air
quality model used for this study is the Comprehensive Air Quality Model with Extensions (CAMx)
model, version 6.32. However, emissions are first processed into a format compatible with for the
Community Multiscale Air Quality (CMAQ) model version 5.0.2, and those emissions are 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 version 6 revision 4
(CB6r4). This version includes updated reactions, but the emissions species needed to drive this version
are unchanged from the Carbon Bond version 6 revision 2 (CB6r2), which includes important reactions
for simulating ozone formation, nitrogen oxides (NOx) cycling, and formation of secondary aerosol
species (Hildebrant Ruiz and Yarwood, 2013). CB6 provides several revisions to the previous carbon
bond version (CB05) through inclusion of four new explicit organic species: benzene, propane, acetylene
and acetone, along with updates to reaction chemistry for those species and several other volatile organic
chemicals (VOCs).
This update to the 201 lv6.3 platform consists of two 'complete' emissions cases: the 2011 base case (i.e.,
201 Iel_cb6v2_v6), and the 2023 base case (i.e., 2023el_cb6v2_v6). Most of the 2011 emissions in this
update to the 201 lv6.3 platform are the same as those used in the 201 lv6.3 platform, thus this platform
has not been given a new version number. In the case abbreviations, 2011 and 2023 are the years
represented by the emissions; the "e" stands for evaluation, meaning that year-specific data for fires and
electric generating units (EGUs) are used; and the "1" represents that this was the twelfth set of emissions
modeled for a 2011-based modeling platform (i.e., the first case for the 2011 platform was 201 lea, the
second was 201 leb, and so on). 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
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in corresponding future-year cases, including the 2023 future year base case, 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 2023 base case 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, "Guidance on the Use
of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5,
and Regional Haze".
Table 1-1. List of cases in this update to the 2011 Version 6.3 Emissions Modeling Platform for 2023
Case Name
Abbreviation
Description
2011 base case
201 lei cb6v2 v6
2011 case relevant for air quality model evaluation purposes
and for computing relative response factors with 2023
scenario(s). Uses 201 1NEIv2 along with some other inventory
data, with hourly 2011 continuous emissions monitoring
system (CEMS) data for electric generating units (EGUs),
hourly onroad mobile emissions, and 2011 day-specific wild
and prescribed fire data. Wildfire inventories for Canada and
Mexico were also added.
2023 base case
2023el cb6v2 v6
2023 "base case" scenario, representing the best estimate for
2023 that incorporates estimates of the impact of current "on-
the-books" regulations.
All of the above cases use the same version of the 2011 meteorology and the cases are sometimes referred
to with "_1 lg" after the emissions portion of the case name where "g" corresponds to the 7th configuration
of the meteorological modeling platform, although the configuration is not exclusive to modeling of the
year 2011. A special version of the 2023el_cb6v2_v6 case called 2023el_ussa_cb6v2_v6_l lg was
prepared for use with the CAMx OSAT/APCA feature that allowed the contribution of 2023 base case
NOx and VOC emissions from all sources in each state to projected 2023 ozone concentrations at air
quality monitoring sites to be quantified. The emissions for the case are equivalent to those in the
2023el_cb6v2_v6 case, except that the emission sources are tagged according to their origin by state or
sector. The steps for setting up the 2023el_ussa_cb6v2_v6 source apportionment case include:
1) prepare files for the source groups to track (e.g., anthropogenic emissions from each state, non-
geographic sector-specific tags for biogenic, fugitive dust, fire, and non-U.S. emissions);
2) run all sectors in Sparse Matrix Operator Kernel Emissions (SMOKE) using the specified source
groups (note that emissions for both source apportionment and for a regular CAMx run can be
developed simultaneously);
3) create CAMx point source files for source groups tracked only by sector;
4) convert SMOKE outputs to CAMx point source files using the tags assigned by SMOKE; and
5) merge all of the point source files together into a single CAMx mrgpt file for each day.
More information on processing for source apportionment is available with the scripts provided for the
2011v6.3 platform at ftp://newftp.epa.gov/air/emismod/201 l/v3platform/.
The EPA has adopted 2023 as the analytic year for this effort because it is the year by which moderate
areas need to be in attainment for the 2015 Ozone NAAQS. The emissions data in this platform are
primarily based on the 201 1NEIv2 for point sources, nonpoint sources, commercial marine vessels
(CMV), nonroad mobile sources and fires. The onroad mobile source emissions are similar to those in the
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201 1NEIv2, but were generated using the released 2014a version of the Motor Vehicle Emissions
Simulator (MOVES2Q14a).
The primary emissions modeling tool used to create the air quality model-ready emissions was the
SMOKE modeling system. SMOKE version 3.7 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. Electronic copies of the data
used as input to SMOKE for the cases for this update to the 2011v6.3 platform are available from the
corresponding section of the EPA Air Emissions Modeling website.
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 core (Skamarock, et al.,
2008). The WRF Model is a mesoscale numerical weather prediction system developed for both
operational forecasting and atmospheric research applications. The WRF was run for 2011 over a domain
covering the continental U.S. at a 12km resolution with 35 vertical layers. The WRF data were collapsed
to 25 layers prior to running the emissions and air quality models. The run for this platform included high
resolution sea surface temperature data from the Group for High Resolution Sea Surface Temperature
(GHRSST) and is given the EPA meteorological case label "1 lg" and are consistent with those used for
the original 2011v6.3 platform cases.
This document contains five sections. Section 2 describes the changes made to the 2011 inventories input
to SMOKE in this update to the 201 lv6.3 platform. Section 3 describes the updates to emissions
modeling and the ancillary files used to convert the emission inventories into air quality model-ready
formats. Section 4 describes the development of the 2023 inventory (projected from 2011). Data
summaries comparing the 2011 and 2023 base cases are provided in Section 5. Section 6 provides
references.
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2 2011 Emission Inventories and Approaches
This section describes the updates to the 2011 emissions data as compared to the 2011 case known as
201 Iek_cb6v2_v6 in the 201 lv6.3 platform. Table 2-1 presents the sectors in this update to the 2011
platform that differ from the original 201 lv6.3 platform. The platform sector abbreviations are provided
in italics. These sector abbreviations are used in the SMOKE modeling scripts, inventory file names, and
throughout the remainder of this document.
Table 2-1. Platform sectors updated since the original 201 lv6.3 emissions modeling platform
Platform Sector:
abbreviation
Description and resolution of the data input to SMOKE
Category 1, 2
and 3 CMV:
cmv
Category 1 (CI), category 2 (C2) and category 3 (C3) commercial marine
vessel (CMV) emissions sources from the 201 1NEIv2 nonpoint inventory.
County and annual resolution; see othpt sector for all non-U.S. C3
emissions. Includes updated cmv emissions for California.
Onroad:
onroad
2011 onroad mobile source gasoline and diesel vehicles from parking lots
and moving vehicles. Includes the following modes: exhaust, extended
idle, auxiliary power units, evaporative, permeation, refueling, and brake
and tire wear. For all states, except California and Texas, based on
monthly MOVES emissions tables produced by MOVES2014a.
California emissions are based on Emission Factor (EMFAC) and were
updatedfrom the original 2011v6.3platform. MOVES emissions for
Texas provided by TCEQ for year 2012 were backcast to year 2011.
MOVES-based emissions computed for each hour and model grid cell
using monthly and annual activity data (e.g., VMT, vehicle population).
Ethanol-85 usage in 2011 VMT was reduced to reflect actual percentage
of E-85 used.
Non-US. fires:
ptfiremxca
New Sector added: Point source day-specific wildfires and prescribed
fires for 2011 provided by Environment Canada with data for missing
months and for Mexico filled in using fires from the Fire INventory from
NCAR (FINN) fires.
Other point
sources not from
the 2011 NEI:
othpt
Point sources from Canada's 2010 inventory and Mexico's 2008
inventory projected to 2011, annual resolution. Also includes all non-
U.S. C3 CMV and U.S. offshore oil production.
Other non-NEI
nonpoint and
nonroad:
othar
Monthly year 2010 Canada (province resolution) and Mexico's 2008
nonpoint and nonroad mobile inventories projected to 2011 (municipio
resolution).
Other non-NEI
onroad sources:
othon
Monthly year 2010 Canada (province / annual resolution) onroad mobile
inventories and MOVES-Mexico emissions for 2011 (municipio / monthly
resolution).
The emissions for the remaining sectors are unchanged from those in the 201 lek case and documentation
for these sectors can be found in the 201 lv6.3 TSD:
ptegu - electric generating units
pt oilgas - point oil and gas sources
ptnonipm - remaining non-EGU point sources
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ag- agricultural ammonia emissions
agfire - agricultural fire emissions
afdust - area fugitive dust emissions
othafdust - area fugitive dust emissions for Canada
beis - biogenic emissions
rail - locomotive emissions
nonpt - remaining nonpoint source emissions
np oilgas - nonpoint sources from oil and gas-related processes
rwc - residential wood combustion emissions
nonroad- emissions from nonroad mobile source equipment
The emission inventories in SMOKE input format for the 2011 base case are available from the EPA's
Air Emissions Modeling website for the version 6.3 platform. A number of reports (i.e., summaries) are
available with the data files for the updated 201 lv6.3 platform. The types of reports include state
summaries of inventory pollutants and model species by modeling platform sector, county annual totals
by modeling platform sector, daily NOx and VOC emissions by sector and total, and state-SCC-sector
summaries. A comparison of the complete list of inventory files, ancillary files, and parameter settings
with those for the 201 lv6.3 platform is also available in 201 lei vs 2011 ekcaseinputs.xlsx.
The remainder of Section 2 provides details about the data contained in each of the 2011 platform sectors
that were modified from the original 201 lv6.3 platform.
2.1 2011 onroad mobile sources (onroad)
Onroad mobile sources include emissions from motorized vehicles that are normally operated on public
roadways. These include passenger cars, motorcycles, minivans, sport-utility vehicles, light-duty trucks,
heavy-duty trucks, and buses. The sources are further divided between diesel, gasoline, E-85, and
compressed natural gas (CNG) vehicles. The sector characterizes emissions from parked vehicle
processes (e.g., starts, hot soak, and extended idle) as well as from on-network processes (i.e., from
vehicles moving along the roads). Except for California and Texas, all onroad emissions are generated
using the SMOKE-MOVES emissions modeling framework that leverages MOVES-generated outputs
and hourly meteorological data. For more information on the preparation of onroad mobile source
emissions with SMOKE-MOVES, see the 201 lv6.3 platform TSD.
The primary change to the onroad mobile source sector made for this update to the 201 lv6.3 platform
concerns the penetration of E-85 fuels. Specifically, the percentage of E-85 in the activity data used to
compute the EPA-default emissions for the 201 lei case was updated to reflect actual usage of E-85 fuel,
instead of reflecting activity from all "flex-fuel" vehicles which could use E-85. In the 201 lek case, 5.14
percent of all passenger vehicle VMT activity was allocated to E-85. That percentage reflects all flex-fuel
vehicles on the road, whether or not those vehicles are actually using E-85. In the 201 lei case, only 0.016
percent of total passenger vehicle VMT was allocated to E-85 fuel, reflecting the actual amount of E-85
fuel consumed. Table 2-2 shows the total onroad U.S. CAP emissions in the 201 lv6.3 and updated
platforms, rounded to the nearest thousand tons. The slight increase in some pollutants is due to the fact
the E-85 emission factors are somewhat cleaner than those of regular gasoline. Thus, with the percent of
E-85 reduced, the emissions increase slightly.
Table 2-2. Onroad CAP emissions in the 201 lv6.3 and updated platforms (tons)
Pollutant
2011ek
2011el
% change
CO
25,380,000
25,992,000
2%
5
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NH3
112,000
121,000
8%
NOX
5,609,000
5,708,000
2%
PM10
326,000
327,000
0%
PM2 5
188,000
189,000
1%
S02
27,000
28,000
3%
VOC
2,657,000
2,713,000
2%
California onroad emissions were also updated for this update to the 201 lv6.3 platform. The new
California onroad inventory includes an updated vehicle type and road type distribution, so that they are
estimated in a consistent way with the state-provided 2023 emissions. The new vehicle type and road
type distribution is based on the latest mapping between EMFAC Emissions Inventory Codes (EICs) and
EPA source classification codes (SCCs), and unlike prior ElC-to-SCC mappings, distinguishes on-
network emissions from off-network emissions.
2.2 Category 1, Category 2, Category 3 Commercial Marine Vessels (cmv)
The cmv sector contains Category 1, 2 and 3 CMV emissions. All emissions in this sector are annual and
at the county-SCC resolution. The Category 3 (C3) CMV sources in the cmv sector of the 201 lv6.3
platform run on residual oil and use the SCCs 2280003100 and 2280003200 for port and underway
emissions, respectively, and are consistent with the 201 1NEIv2. Emissions for this sector use state-
submitted values and EPA-developed emissions in areas where states did not submit. The change in this
update to the 201 lv6.3 platforms is to incorporate updated CMV emissions in California so that they are
estimated in a consistent way with the state-provided 2023 emissions. The CMV CAP emissions for
California in the original and updated cases are shown in Table 2-3.
Table 2-3. California CMV CAP emissions in the 201 lv6.3 and updated platforms (tons)
Pollutant
2011ek
2011el
CO
6,572
5,082
NH3
8
6
NOX
21,622
21,055
PM10
495
808
PM2 5
462
752
S02
255
1,827
VOC
1,675
1,375
2.3 "Other Emissions": Emissions from Non-U.S. sources
The emissions from Canada, Mexico, and non-U.S. offshore Category 3 Commercial Marine Vessels (C3
CMV) and drilling platforms are included as part of four emissions modeling sectors: othpt, othar,
othafdust, and othon. The "oth" refers to the fact that these emissions are usually "other" than those in the
U.S. state-county geographic Federal Information Processing Standards (FIPS), and the remaining
characters provide the SMOKE source types: "pt" for point; "ar" for "area and nonroad mobile;" and
"on" for onroad mobile. Only the emissions for Mexico have changed in this update to the platform. The
changes in emissions for the entire country of Mexico for each sector are shown in Table 2-4.
Table 2-4. Mexico CAP emissions in the 201 lv6.3 and updated platforms (tons)
CO
NH3
NOX
PM10
PM2 5
S02
VOC
6
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Mexico 201 lek
othpt
694,173
31,569
606,442
233,158
160,911
2,393,790
290,676
Mexico 201 lei othpt
683,482
32,773
651,521
241,496
168,144
2,276,770
303,905
Mexico 201 lek
othar
3,081,442
852,041
721,690
628,158
454,385
47,290
3,488,075
Mexico 201 lei othar
2,579,614
875,696
706,612
574,293
404,291
44,083
3,564,949
Mexico 201 lek
othon
23,220,743
53,309
1,650,448
16,582
12,002
25,449
2,159,346
Mexico 201 lei
othon
5,887,937
9,170
1,411,830
57,782
43,576
22,470
541,390
2.3.1 Point Sources from Offshore C3 CMV, Drilling platforms, Canada and Mexico
(othpt)
The othpt sectors includes offshore oil and gas drilling platforms that are beyond U.S. state-county
boundaries in the Gulf of Mexico, point sources for Canada and Mexico along with the ECA-IMO-based
C3 CMV emissions outside of state waters. Point sources in Mexico were compiled based on the
Inventario Nacional de Emisiones de Mexico, 2008 (ERG, 2014a) and in this updated case, they were
projected to the year 2011 by interpolating between 2008 emissions and projected 2014 emissions (ERG,
2016). The point source emissions in the 2008 inventory were converted to English units and into the
FF10 format that could be read by SMOKE, missing stack parameters were gapfilled using SCC-based
defaults, and latitude and longitude coordinates were verified and adjusted if they were not consistent with
the reported municipality. Note that there are no explicit HAP emissions in this inventory.
The remaining sources in the sector were unchanged in this update. The point source offshore oil and gas
drilling platforms from the 201 1NEIv2 were used. For Canadian point sources, 2010 emissions provided
by Environment Canada were used. Note that VOC was not provided for Canadian point sources, but any
VOC emissions were speciated into CB05 species. Temporal profiles and speciated emissions were also
provided.
The C3 CMV emissions in this sector include those assigned to U.S. federal waters, Canada, those
assigned to the Exclusive Economic Zone (EEZ) (defined as those emissions beyond the U.S. Federal
waters approximately 3-10 miles offshore, and extending to about 200 nautical miles from the U.S.
coastline), along with any other offshore emissions. These emissions are developed in the same way as
the EPA-dataset for the cmv sector. Emissions in U.S. waters are aggregated into large regions and
included in the 201 1NEIv2 using special FIPS codes. Because these emissions are treated as point
sources, shipping lane routes can be preserved and they may be allocated to air quality model layers
higher than layer 1.
2.3.2 Area and Nonroad Mobile Sources from Canada and Mexico (othar)
The othar sector includes nonpoint and nonroad mobile source emissions in Canada and Mexico. The
Canadian sources are unchanged from the 201 lv6.3 platform and are based on month-specific year-2010
emissions provided by Environment Canada, including C3 CMV emissions.
The change in this sector in this update to the platform was in the Mexico emissions. Area and nonroad
mobile sources in Mexico for 2008 were compiled the Inventario Nacional de Emisiones de Mexico, 2008
(ERG, 2014a). The 2008 emissions were quality assured for completeness, SCC assignments were made
when needed, the pollutants expected for the various processes were reviewed, and adjustments were
7
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made to ensure that PMio was greater than or equal to PM2.5. The resulting inventory was written using
English units to the nonpoint FF10 format that could be read by SMOKE, projected to the year 2014
(ERG, 2016), and then linearly interpolated back to 2011. Also, wildfire and agricultural fire emissions
were removed from the Mexico nonpoint inventory to prevent double counting emissions with the new
ptfiremxca sector. Note that unlike the U.S. inventories, there are no explicit HAPs in the nonpoint or
nonroad inventories for Canada and Mexico and, therefore, all HAPs are created from speciation.
2.3.3 Onroad Mobile Sources from Canada and Mexico (othon)
The othon sector includes onroad mobile source emissions in Canada and Mexico. The Canadian sources
are unchanged from the 201 lv6.3 platform and are based on month-specific year-2010 emissions
provided by Environment Canada. Note that unlike the U.S. inventories, there are no explicit HAPs in the
onroad inventories for Canada and, therefore, all HAPs are created from speciation.
The update to this sector was for the onroad mobile sources in Mexico. These emissions were based on a
run of MOVES-Mexico for 2011 and is described in Development of Mexico Emission Inventories for the
2014 Modeling Platform (ERG, 2016). This document includes a comparison of emissions from
MOVES-Mexico with other recent inventories of onroad mobile sources in Mexico. Please see the
document for more information. The following information about MOVES-Mexico and how the 2011
inventory was developed is a collection of excerpts from that document:
"Under the sponsorship of USAID, through the Mexico Low Emissions Development Program
(MLED), in early 2016 ERG adapted MOVES2014a (https://www.epa.gov/moves) to Mexico
(USAID, 2016). As with the U.S. version of the model, "MOVES-Mexico" has the capability to
produce comprehensive national vehicle emission inventories, and to provide a framework for
users to create detailed regional emission inventories and microscale emission assessments. The
approach for adapting MOVES was determined based on Mexico's available vehicle fleet and
activity data, and to account for significant differences in vehicle emissions standards between
Mexico and the U.S. To aid this, the Mexican government agency National Institute of Ecology
and Climate Change (Institute) National de Ecologiay Cambio Climatico or INECC) provided data
for fundamental model inputs such as vehicle kilometers travelled, vehicle population, age
distribution, and emission standards. INECC also provided data on over 250,000 roadside remote
sensing device (RSD) measurements across 24 Mexican cities, which were analyzed to help
calibrate MOVES-Mexico emission rates. The data from INECC and other government sources
have been synthesized to create a national Mexico-specific MOVES database that can be used
directly with MOVES2014a as an alternate default database, replacing the U.S. default database
that comes with the U.S. model download. MOVES-Mexico can estimate vehicle emissions for
calendar years 1990 through 2050 at the nation, state or municipio (county-equivalent) level."
"[The 2011] on-road mobile source emissions inventory was developed using output from
MOVES-Mexico. Emissions were generated for each municipio; for a typical weekday and typical
weekend by month; for the pollutant set used for the U.S. NEI. Total annual emissions were
compiled into a single Flat File 10 (FF10) format file. MOVES-Mexico was run in default mode,
which reflects Mexico-specific data for key inputs such as vehicle population, VMT, fuels,
inspection and maintenance (I/M) programs and Mexico's emission standards."
"The outputs of the MOVES-Mexico runs were processed to obtain total annual emissions by
pollutant and EPA Source Classification Code (SCC) and compiled into a single FF10 format file.
This involved looping through the output databases for all the individual municipios; extracting the
emissions for a particular pollutant from both the evaporative and non-evaporative output
8
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databases; and summing the emissions across all hours to obtain total emissions by day type
(weekend and weekday) for each month. The total monthly emissions were then calculated as the
product of the daily weekend (weekday) emissions and the number of weekends (weekdays) in
each month. The monthly emissions were then summed to obtain annual emissions and converted
to U.S. short tons."
2.4 Non-U.S. Fires (ptfire_mxca)
In this update to the 201 lv6.3 platform, a new sector of fire emissions in Mexico and Canada was added.
Note that unlike the other sectors, the ptfiremxca sector emissions were processed with SMOKE 4.0
because it has better support for processing FFlO-formatted fire inventories. Fire emissions are specified
at geographic coordinates (point locations) and have daily emissions values. 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.
Table 2-5. 2011 Platform SCCs representing emissions in the ptfire modeling sectors
SCC
SCC Description*
2810001000
Other Combustion; Forest Wildfires; Total
2810001001
Other Combustion; Forest Wildfires; Wildland fire use
2811015000
Other Combustion-as Event; Prescribed Burning for Forest Management;
Total
* The first tier level of the SCC Description is "Miscellaneous Area Sources."
The fire inventory for Canada was obtained from Environment Canada. This point source fire inventory
was generated using the Canadian Wildland Fire Information System (CWFIS). Area burned and daily
fire spread estimates are derived from satellite products. CWFIS integrates multi-source data for national-
level products. These data include a fuels database, fire weather, topography, moisture content, and fire
type and duration information. CWFIS also uses the BlueSky module Fire Emission Production
Simulator (FEPS) (Anderson, 2004) to generate day-specific SMOKE-ready emissions data. The
CWFIS fire inventory can also include agricultural burns, however all CWFIS fires are labeled with SCC
2810001000. The output format from CWFIS currently only supports older versions of SMOKE. The
CWFIS data were converted to SMOKE FF10 format for use in this modeling effort.
The Fire INventory from NCAR (FINN) (Wiedinmyer, 2011) version 1.5 was used to supply a fire
inventory for Mexico. FINN provides daily, 1 km resolution, global estimates of the trace gas and
particle emissions from open burning of biomass, which includes wildfire, agricultural fires, and
prescribed burning and does not include biofuel use and trash burning. This day-specific FINN data was
downloaded and was converted to SMOKE FF10 format for use in this modeling effort.
9
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3 Emissions Modeling Summary
In Section 3, the descriptions of data are limited to updates to the ancillary data SMOKE uses to perform
the emissions modeling steps. Note that all SMOKE inputs for the updated 201 lv6.3 platform are
available from the Air Emissions Modeling ftp site. While an overview of emissions modeling is given
below, the details of the emissions modeling for the platform can be found in the 201 lv6.3 TSD.
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 0. In brief, the
process of emissions modeling transforms the emissions inventories from their original temporal
resolution, pollutant resolution, and spatial resolution into the hourly, speciated, gridded resolution
required by the air quality model. Emissions modeling includes temporal allocation, spatial allocation,
and pollutant speciation. In some cases, emissions modeling also includes the vertical allocation of point
sources, but many air quality models also perform this task because it greatly reduces the size of the input
emissions files if the vertical layers of the sources are not included.
SMOKE version 3.7 was used to pre-process the raw emissions inventories into emissions inputs for each
modeling sector in a format compatible with CMAQ. For projects that used CAMx, the CMAQ-
formatted emissions were converted into the required CAMx formats using CAMx convertor 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 for all species across 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.
The changes made to the ancillary emissions modeling files in this platform update are the following and
are described in more detail in the subsections that follow:
updates related to the processing of MOVES-Mexico inventory data, including speciation,
temporal, and gridding cross-references, speciation profiles, and inventory table;
updates to the speciation cross reference to support fires in Canada and Mexico;
development of speciation cross reference and GSPRO COMBO files for 2023;
updates to monthly temporal profiles and the temporal cross reference for processing 2023
California nonroad emissions;
development of MRCLIST files for 2023 onroad emission factors;
development of CFPRO files for 2011 and 2023 onroad California and Texas adjustments; and
updates to NHAPEXCLUDE files for some 2023 sectors.
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
10
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summarizes the major processing steps of each platform sector. The "Spatial" column shows the spatial
approach used: "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.
Finally, the "plume rise" column indicates the sectors for which the "in-line" approach is used. These
sectors are the only ones with emissions in aloft layers based on plume rise. The term "in-line" means
that the plume rise calculations are done inside of the air quality model instead of being computed by
SMOKE. The air quality model computes the plume rise using the stack data and the hourly air quality
model inputs found in the SMOKE output files for each model-ready emissions sector. The height of the
plume rise determines the model layer into which the emissions are placed. The othpt sector has only "in-
line" emissions, meaning that all of the emissions are treated as elevated sources and there are no
emissions for those sectors in the two-dimensional, layer-1 files created by SMOKE. Day-specific point
fires are treated separately. For CMAQ modeling, fire plume rise is done within CMAQ itself, but for
CAMx, the plume rise is done by running SMOKE to create a three-dimensional output file and then
those emissions are postprocessed into a point source format that CAMx can read. In either case, after
plume rise is applied, there will be emissions in every layer from the ground up to the top of the plume.
Table 3-1. Key emissions modeling steps by sector.
Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
afdust
Surrogates
Yes
annual
ag
Surrogates
Yes
annual
agfire
Surrogates
Yes
monthly
beis
Pre-gridded
land use
in BEIS3 .61
computed hourly
rail
Surrogates
Yes
annual
cmv
Surrogates
Yes
annual
nonpt
Surrogates &
area-to-point
Yes
annual
nonroad
Surrogates &
area-to-point
Yes
monthly
np oilgas
Surrogates
Yes
annual
onroad
Surrogates
Yes
monthly activity,
computed hourly
othafdust
Surrogates
Yes
annual
othar
Surrogates
Yes
annual &
monthly
othon
Surrogates
Yes
monthly
othpt
Point
Yes
annual
in-line
pt oilgas
Point
Yes
annual
in-line
ptegu
Point
Yes
daily & hourly
in-line
11
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Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
ptfire
Point
Yes
daily
in-line
ptfire mxca
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 latitudes/longitudes 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.
To prepare fires for CAMx using a plume rise algorithm that is consistent with the algorithms in SMOKE
and CMAQ, the following steps are performed:
1) The ptfire inventories are run through SMOKE programs to read the inventories, speciate,
temporalize, and grid the emissions.
2) The SMOKE program laypoint is used to estimate the plume height and layer fractions for
each fire.
3) The emissions are gridded and layered, and then written as three-dimensional netCDF CMAQ
ready files.
4) Species in the CMAQ-formatted file are converted to CAMx species using the spcmap
program.
5) The netCDF fire files are converted to a CAMx "PTSOURCE" type file where each grid cell
centroid represents one stack using the cmaq2uam program. Note that each virtual stack has
default stack parameters of 1 m height, 1 m diameter, 273 K temperature, and 1 m/s velocity.
Also, an individual virtual stack point (grid cell centroid) will have all of the emissions for the
grid cell divided up into layers with an effective plume height at each layer. Only the layers
that contain emissions are kept for each virtual stack.
6) The programpthtq is run to add an effective plume height based on the cell center height from
the METCR03D (ZH).
7) The resulting PTSOURCE files have emissions as a stack at (x, y) that to up to layer z that is
derived from the CMAQ 3D file, and are merged with the PTSOURCE sector files from other
sectors into a single PTSOURCE file with stacks for all point sources. This file, along with
the 2D emissions file, is input into the CAMx model.
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.
12
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Figure 3-1. Air quality modeling domains
12US1 Continental US Domain
12US2 C on tine 11 tal US I) om ain
Both grids use a Lambert-Conformal projection, with Alpha = 33°, Beta = 45° and Gamma = -91°, 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 (GRTDDESC) file:
projection name, xorig, yorig,
xcell, ycell, ncols, nrows, nthik
Continental
12km grid
12 km
Entire
conterminous US
plus some of
Mexico/Canada
12US1 459X29
9
'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 (i.e., "explicit species") or groups of species (i.e., "lumped species"). The chemical
mechanism used for the 2011 platform is the CB6 mechanism (Yarwood, 2010). The 201 lv6.2 platform
was the first EPA modeling platform to use CB6; previous platforms used CB05 and earlier versions of
the carbon bond mechanism. The key difference in CB6 from CB05 from an emissions modeling
perspective is that it has additional lumped and explicit model species. The specific version of CAMx
used in applications of this platform include secondary organic aerosol (SOA) and nitrous acid (HONO)
enhancements. In addition, this platform generates the PM2.5 model species associated with the CMAQ
Aerosol Module version 6 (AE6), though many are not used by CAMx. Table 3-3 of the 201 lv6.3
platform TSD lists the model species produced by SMOKE in the 201 lv6.2 platform Table 3-4 of the
201 lv6.3 platform TSD provides the cmaq2camx mapping file used to convert the SMOKE generated
model species to the appropriate inputs for CAMx.
The total organic gas (TOG) and PM2.5 speciation factors that are the basis of the chemical speciation
approach were developed from the SPECIATE 4.4 database, which is the EPA's repository of TOG and
PM speciation profiles of air pollution sources. However, a few of the profiles used in the v6.3 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 the EPA's Office of
Research and Development (ORD), Office of Transportation and Air Quality (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.
Only minor changes were made to the speciation cross reference in this update to the 201 lv6.3 platform.
Speciation for the updated 2011 emissions is the same as in the 2011 emissions from the 201 lv6.3
platform, with the new ptfiremxca sector emissions receiving the same speciation as the ptfire sector.
Speciation for the 2023 emissions is the same as in the 2017 emissions from the 201 lv6.3 platform,
except for the VOC speciation COMBO profiles for bulk plant terminal-to-pump (BTP) emissions.
COMBO profiles for 2023 were interpolated based on 2017 and 2025 COMBO profiles from the
201 lv6.2 and 201 lv6.3 emissions platforms.
The speciation cross reference and inventory table for the othon sector were configured so that VOC,
PM2.5 and NOx are speciated in Canada only. In Mexico, pre-speciated VOC, PM2.5, and NOx emissions
from MOVES-Mexico are used.
3.3 Temporal Allocation
Temporal allocation (i.e., temporalization) is the process of distributing aggregated emissions to a finer
temporal resolution, thereby converting annual emissions to hourly emissions. While the total emissions
are important, the timing of the occurrence of emissions is also essential for accurately simulating ozone,
PM, and other pollutant concentrations in the atmosphere. Many emissions inventories are annual or
monthly in nature. Temporalization takes these aggregated emissions and, if needed, distributes them to
the month, and then distributes the monthly emissions to the day and the daily emissions to the hours of
each day. This process is typically done by applying temporal profiles to the inventories in this order:
monthly, day of the week, and diurnal. A summary of emissions by temporal profile and sector for the
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201 lek case is available from the reports area of the FTP site for the original 201 lv6.3 platform
ftp://newftp.epa.gov/air/emismod/2011/v3platform/.
In SMOKE 3.7 and in the 201 lv6.3 platform, more readable and flexible file formats are used for
temporal profiles and cross references. The temporal factors applied to the inventory are selected using
some combination of country, state, county, SCC, and pollutant. Table 3-3 summarizes the temporal
aspects of emissions modeling by comparing the key approaches used for temporal processing across the
sectors. In the table, "Daily temporal approach" refers to the temporal approach for getting daily
emissions from the inventory using the SMOKE Temporal program. The values given are the values of
the SMOKE L TYPE setting. The "Merge processing approach" refers to the days used to represent
other days in the month for the merge step. If this is not "all," then the SMOKE merge step runs only for
representative days, which could include holidays as indicated by the right-most column. The values
given are those used for the SMOKE M TYPE setting (see below for more information).
Table 3-3. Temporal settings used for the platform sectors in SMOKE
Platform
sector short
name
Inventory
resolutions
Monthly
profiles
used?
Daily
temporal
approach
Merge
processing
approach
Process
Holidays as
separate days
afdust adj
Annual
Yes
week
all
Yes
ag
Annual
Yes
all
all
Yes
agfire
Monthly
week
week
Yes
beis
Hourly
n/a
all
Yes
cmv
Annual
Yes
aveday
aveday
rail
Annual
Yes
aveday
aveday
nonpt
Annual
Yes
week
week
Yes
nonroad
Monthly
mwdss
mwdss
Yes
np oilgas
Annual
yes
week
week
Yes
onroad
Annual &
monthly1
all
all
Yes
onroad ca adj
Annual &
monthly1
all
all
Yes
othafdust ad]
Annual
yes
week
all
othar
Annual & monthly
yes
week
week
othon
Monthly
week
week
othpt
Annual
yes
mwdss
mwdss
pt oilgas
Annual
yes
mwdss
mwdss
Yes
ptegu
Daily & hourly
all
all
Yes
ptnonipm
Annual
yes
mwdss
mwdss
Yes
ptfire
Daily
all
all
Yes
ptfire mxca
Daily
all
all
Yes
rwc
Annual
no
met-based
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.
The following values are used in the table. The value "all" means that hourly emissions are computed for
every day of the year and that emissions potentially have day-of-year variation. The value "week" means
15
-------
that hourly emissions computed for all days in one "representative" week, representing all weeks for each
month. This means emissions have day-of-week variation, but not week-to-week variation within the
month. The value "mwdss" means hourly emissions for one representative Monday, representative
weekday (Tuesday through Friday), representative Saturday, and representative Sunday for each month.
This means emissions have variation between Mondays, other weekdays, Saturdays and Sundays within
the month, but not week-to-week variation within the month. The value "aveday" means hourly
emissions computed for one representative day of each month, meaning emissions for all days within a
month are the same. Special situations with respect to temporalization are described in the following
subsections.
In addition to the resolution, temporal processing includes a ramp-up period for several days prior to
January 1, 2011, which is intended to mitigate the effects of initial condition concentrations. The ramp-up
period was 10 days (December 22-31, 2010). For most sectors, emissions from December 2011 were
used to fill in surrogate emissions for the end of December 2010. In particular, December 2011 emissions
(representative days) were used for December 2010. For biogenic emissions, December 2010 emissions
were processed using 2010 meteorology.
The only change to the temporal allocation process in this updated 201 lv6.3 platform concerns monthly
temporalization of California nonroad emissions in 2023. In prior platforms, annual nonroad emissions in
California were allocated to monthly values based on monthly distributions of the National Mobile
Inventory Model (NMIM) emissions at the SCC7 level. This resulted in unrealistic monthly
temporalization for some sub-SCC7 categories, for example, snowmobile emissions in the summer. A
different set of monthly temporal profiles was applied to California nonroad emissions for 2023 with
assignments based on full SCC, not SCC7, so that snowmobiles and other specific categories receive a
more realistic monthly profile.
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 0, 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., the EPA updated surrogates to
use circa 2010-2011 data wherever possible. For Mexico and Canada, updated spatial surrogates were
used as described below. The U.S., Mexican, and Canadian 12-km surrogates cover the entire CONUS
domain 12US1 shown in Figure 3-1.
The changes to spatial allocation in this updated platform were limited to the addition of SCCs from the
MOVES-Mexico inventory to the spatial cross reference for Canada and Mexico. In addition, with the
exception of some updates to the spatial surrogate cross reference, the spatial surrogates for the U.S. and
Mexico used in the 201 lv6.3 platform are the same as the surrogates used for the 201 lv6.2 platform
(EPA, 2015b). The details regarding how the 201 lv6.2 platform surrogates were created are available
from ftp://newftp.epa.gov/air/emismod/2011/v2platform/spatial surrogates/ in the files
US SpatialSurrogate Workbook v072115.xlsx and US SpatialSurrogate Documentation v()70II5.pdf
and Surrogate!oolsScripts 2014.zip available. 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.
16
-------
3.4.1 Spatial Surrogates for U.S. Emissions
There are more than 100 spatial surrogates available for spatially allocating U.S. county-level emissions
to the 12-km grid cells used by the air quality model. Table 3-4 lists the codes and descriptions of the
surrogates. Surrogate names and codes listed in italics are not directly assigned to any sources for the
201 lv6.3 platform, but they are sometimes used to gapfill other surrogates, or as an input for merging two
surrogates to create a new surrogate that is used.
Many surrogates use circa 2010-based data, including: 2010 census data at the block group level; 2010
American Community Survey Data for heating fuels; 2010 TIGER/Line data for railroads and roads; the
2006 National Land Cover Database; 2011 gas station and dry cleaner data; and the 2012 National
Transportation Atlas Data for rail-lines, ports and navigable waterways. Surrogates for ports (801) and
shipping lanes (802) were developed based on the 201 1NEIv2 shapefiles: Ports_032310_wrf and
ShippingLanes l 11309FINAL_wrf, but also included shipping lane data in the Great Lakes and support
vessel activity data in the Gulf of Mexico. The creation of surrogates and shapefiles for the U.S. was
generated via the Surrogate Tool. The tool and documentation.
Table 3-4. U.S. Surrogates available for the 2011 modeling platform.
Code
Surrogate Description
Code
Surrogate Description
N/A
Area-to-point approach (see 3.3.1.2)
507
Heavy Light Construction Industrial
Land
100
Population
510
Commercial plus Industrial
110
Housing
515
Commercial plus Institutional Land
Commercial plus Industrial plus
120
Urban Population
520
Institutional
130
Rural Population
525
Golf Courses + Institutional
+Industrial + Commercial
137
Housing Change
526
Residential Non-Institutional
140
Housing Change and Population
527
Single Family Residential
150
Residential Heating - Natural Gas
530
Residential - High Density
Residential + Commercial +
Industrial + Institutional +
160
Residential Heating - Wood
535
Government
0.5 Residential Heating - Wood plus 0.5
165
Low Intensity Residential
540
Retail Trade
170
Residential Heating - Distillate Oil
545
Personal Repair
180
Residential Heating - Coal
550
Retail Trade plus Personal Repair
Professional/Technical plus General
190
Residential Heating - LP Gas
555
Government
200
Urban Primary Road Miles
560
Hospitals
205
Extended Idle Locations
565
Medical O ffices/Clinics
210
Rural Primary Road Miles
570
Heavy and High Tech Industrial
220
Urban Secondary Road Miles
575
Light and High Tech Industrial
221
Urban Unrestricted Roads
580
Food, Drug, Chemical Industrial
230
Rural Secondary Road Miles
585
Metals and Minerals Industrial
231
Rural Unrestricted Roads
590
Heavy Industrial
240
Total Road Miles
595
Light Industrial
17
-------
Code
Surrogate Description j
ICode
Surrogate Description
250
Urban Primary plus Rural Primary 3
596
Industrial plus Institutional plus
Hospitals
255
0.75 Total Roadway Miles plus 0.25 j
Population \
600
Gas Stations
256
Off-Network Short-Haul Trucks
650
Refineries and Tank Farms
257
Off-Network Long-Haul Trucks
675
Refineries and Tank Farms and Gas
Stations
258
!
Intercity Bus Terminals
680
Oil & Gas Wells circa 2005 (replaced
by newer surrogates in Table 3-6)
259
Transit Bus Terminals
710
Airport Points
260
Total Railroad Miles
711
Airport Areas
261
NT AD Total Railroad Density J
720
Military Airports
270
Class 1 Railroad Miles j
800
Marine Ports
271
NT AD Class 1, 2, 3 Railroad Density
801
NEI Ports
280
Class 2 and 3 Railroad Miles f
802
NEI Shipping Lanes
300
Low Intensity Residential 1
806
Offshore Shipping NEI NOx
310
Total Agriculture |
807
Navigable Waterway Miles
312
Orchards/Vineyards 1
808
Gulf Tug Zone Area
320
Forest Land
810
Navigable Waterway Activity
330
Strip Mines/Quarries
812
Midwest Shipping Lanes
340
Land
820
Ports NEI NOx
350
Water
850
Golf Courses
400
Rural Land Area
860
Mines
500
Commercial Land
870
Wastewater Treatment Facilities
505
Industrial Land
880
Dry cleaners
506
Education
890
Commercial Timber
For the onroad sector, the on-network (RPD) emissions were spatially allocated to roadways. The
refueling emissions were spatially allocated to gas station locations (surrogate 600). On-network (i.e., on-
roadway) mobile source emissions were assigned to the following surrogates: rural restricted access to
rural primary road miles (210); rural unrestricted access to 231; urban restricted access to urban primary
road miles (200); and urban unrestricted access to 221. Off-network (RPP and RPV) emissions were
spatially allocated according to the mapping in Table 3-5. Starting with the 201 lv6.2 platform, emissions
from the extended (i.e., overnight) idling of trucks were assigned to a new surrogate 205 that is based on
locations of overnight truck parking spaces.
Table 3-5. Off-Network Mobile Source Surrogates
Source type
Source Type name
Surrogate
ID
11
Motorcycle
535
21
Passenger Car
535
31
Passenger Truck
535
32
Light Commercial Truck
510
41
Intercity Bus
258
42
Transit Bus
259
18
-------
Source type
Source Type name
Surrogate
ID
43
School Bus
506
51
Refuse Truck
507
52
Single Unit Short-haul Truck
256
53
Single Unit Long-haul Truck
257
54
Motor Home
526
61
Combination Short-haul Truck
256
62
Combination Long-haul Truck
257
For the oil and gas sources in the np oilgas sector, the spatial surrogates were updated to those shown in
Table 3-6 using 2011 data consistent with what was used to develop the 2011NEI nonpoint oil and gas
emissions. Note that the "Oil & Gas Wells, IHS Energy, Inc. and USGS" (680) is older and based on
circa-2005 data. These surrogates were based on the same GIS data of well locations and related
attributes as was used to develop the 201 1NEIv2 data for the oil and gas sector. The data sources include
Drilling Info (DI) Desktop's HPDI database (Drilling Info, 2012) aggregated to grid cell levels, along
with data from Oil and Gas Commission (OGC) websites. Well completion data from HPDI was
supplemented by implementing the methodology for counting oil and gas well completions developed for
the U.S. National Greenhouse Gas Inventory. Under that methodology, both completion date and date of
first production from HPDI were used to identify wells completed during 2011. In total, over 1.08 million
unique well locations were compiled from the various data sources. The well locations cover 33 states
and 1,193 counties (ERG, 2014b).
Table 3-6. 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
695
Well count - oil wells
697
Oil production at Gas and CBM Wells
698
Well counts - Gas and CBM Wells
Some spatial surrogate cross reference updates were made between the 201 lv6.2 platform and the
201 lv6.3 platform aside from the reworking of the onroad mobile source surrogates described above.
These updates included the following:
19
-------
Nonroad SCCs using spatial surrogate 525 (50 percent commercial + industrial + institutional, 50
percent golf courses) were changed to 520 (100 percent commercial + industrial + institutional).
The golf course surrogate 850, upon which 525 is partially based, is incomplete and subject to hot
spots;
Some nonroad SCCs for commercial equipment in New York County had assignments updated to
surrogate 340;
Commercial lawn and garden equipment was updated to use surrogate 520; and
Some county-specific assignments for residential wood combustion (RWC) were updated to use
surrogate 300.
For the 201 lv6.3 platform, the CMV underway emissions were changed to use surrogate 802. RWC
fireplaces in all counties, and other RWC emissions in select counties, were changed to use surrogate 300.
Not all of the available surrogates are used to spatially allocate sources in the modeling platform; that is,
some surrogates shown in Table 3-4 were not assigned to any SCCs, although many of the "unused"
surrogates are actually used to "gap fill" other surrogates that are used. When the source data for a
surrogate has no values for a particular county, gap filling is used to provide values for the surrogate in
those counties to ensure that no emissions are dropped when the spatial surrogates are applied to the
emission inventories. Table 3-7 shows the CAP emissions (i.e., ammonia (NH3), NOx, PM2.5, SO2, and
VOC) by sector, with rows for each sector listed in order of most emissions to least CAP emissions.
Table 3-7. Selected 2011 CAP emissions by sector for U.S. Surrogates*
Sector
ID
Description
NH3
NOX
PM2 5
S02
VOC
afdust
130
Rural Population
0
0
1,089,422
0
0
afdust
140
Housing Change and Population
0
0
159,485
0
0
afdust
240
Total Road Miles
0
0
286,188
0
0
afdust
310
Total Agriculture
0
0
895,786
0
0
afdust
330
Strip Mines/Quarries
0
0
58,959
0
0
afdust
400
Rural Land Area
0
0
1
0
0
ag
310
Total Agriculture
3,502,246
0
0
0
0
agfire
310
Total Agriculture
3,287
45,594
100,174
17,001
79,615
agfire
312
Orchards/Vineyards
27
432
1,082
753
799
agfire
320
Forest Land
7
8
121
0
124
cmv
801
Port Areas
38
48,093
3,687
34,683
1,738
cmv
802
Shipping Lanes
360
589,625
21,516
57,679
15,493
cmv
820
Ports NEI2011 NOx
23
61,823
2,072
2,354
1,883
nonpt
100
Population
4,137
0
0
0
1,196,465
nonpt
140
Housing Change and Population
3
23,423
65,897
29
134,887
nonpt
150
Residential Heating - Natural Gas
40,775
217,560
4,785
1,443
13,031
nonpt
170
Residential Heating - Distillate Oil
2,045
40,842
4,523
88,432
1,394
nonpt
180
Residential Heating - Coal
247
1,033
605
7,931
1,233
nonpt
190
Residential Heating - LP Gas
136
38,705
224
705
1,432
nonpt
240
Total Road Miles
0
27
602
0
32,152
nonpt
250
Urban Primary plus Rural Primary
0
0
0
0
102,207
nonpt
260
Total Railroad Miles
0
0
0
0
2,195
nonpt
300
Low Intensity Residential
3,847
18,334
90,706
3,048
40,003
20
-------
Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
nonpt
310
Total Agriculture
0
0
614
0
363,385
nonpt
312
Orchards/Vineyards
0
441
117
1,806
262
nonpt
320
Forest Land
0
85
287
0
97
nonpt
330
Strip Mines/Quarries
0
4
0
0
48
nonpt
400
Rural Land Area
2,855
0
0
0
0
nonpt
500
Commercial Land
2,367
2
85,404
585
26,183
nonpt
505
Industrial Land
35,360
195,282
124,150
112,01
6
114,391
nonpt
510
Commercial plus Industrial
4
178
27
109
224,110
nonpt
515
Commercial plus Institutional Land
1,408
177,903
18,637
58,798
21,915
nonpt
520
Commercial plus Industrial plus
Institutional
0
0
0
0
14,965
nonpt
527
Single Family Residential
0
0
0
0
153,528
nonpt
535
Residential + Commercial + Industrial +
Institutional + Government
23
366
1,283
0
327,986
nonpt
540
Retail Trade (COM1)
0
0
0
0
1,371
nonpt
545
Personal Repair (COM3)
0
0
93
0
60,289
nonpt
555
Professional/Technical (COM4) plus
General Government (GOVI)
0
0
0
0
2,865
nonpt
560
Hospital (COM6)
0
0
0
0
10
nonpt
575
Light and High Tech Industrial (IND2 +
IND5)
0
0
0
0
2,538
nonpt
580
Food, Drug, Chemical Industrial (IND3)
0
610
313
171
10,535
nonpt
585
Metals and Minerals Industrial (IND4)
0
23
140
8
443
nonpt
590
Heavy Industrial (IND1)
10
4,373
5,419
1,131
138,575
nonpt
595
Light Industrial (IND2)
0
1
244
0
79,169
nonpt
600
Gas Stations
0
0
0
0
416,448
nonpt
650
Refineries and Tank Farms
0
0
0
0
129,221
nonpt
675
Refineries and Tank Farms and Gas
Stations
0
0
0
0
1,203
nonpt
711
Airport Areas
0
0
0
0
1,956
nonpt
801
Port Areas
0
0
0
0
12,469
nonpt
870
Wastewater Treatment Facilities
1,003
0
0
0
4,671
nonpt
880
Drycleaners
0
0
0
0
7,053
nonroad
100
Population
40
39,475
2,824
85
5,030
nonroad
140
Housing Change and Population
554
537,250
45,058
1,255
78,526
nonroad
261
NT AD Total Railroad Density
2
2,673
310
5
568
nonroad
300
Low Intensity Residential
106
26,637
4,324
138
202,928
nonroad
310
Total Agriculture
481
488,224
39,037
910
57,473
nonroad
350
Water
213
143,096
12,395
337
614,637
nonroad
400
Rural Land Area
157
25,658
16,711
194
620,786
nonroad
505
Industrial Land
452
146,871
5,809
411
32,978
nonroad
510
Commercial plus Industrial
382
131,572
9,888
348
139,291
nonroad
520
Commercial plus Industrial plus
Institutional
205
70,541
16,361
288
255,836
nonroad
850
Golf Courses
12
2,394
112
17
7,092
21
-------
594
,680
50
55
,700
349
,772
,706
,194
,803
,714
,322
,828
,598
,205
,483
,306
,478
,201
,352
,756
,013
,908
,468
,456
,421
39
123
,037
157
,186
,122
,131
,012
925
,304
,622
,325
,858
ID
Description
NH3
NOX
860
Mines
2,931
890
Commercial Timber
19
12,979
400
Rural Land Area
0
680
Oil and Gas Wells
10
681
Spud count - Oil Wells
0
682
Spud count - Horizontally-drilled wells
5,526
683
Produced Water at all wells
0
684
Completions at Gas and CBM Wells
2,579
685
Completions at Oil Wells
360
686
Completions at all wells
45,044
687
Feet drilled at all wells
44,820
688
Spud count - Gas and CBM Wells
0
689
Gas production at all wells
39,184
692
Spud count - all wells
30,138
693
Well count - all wells
23,437
694
Oil production at oil wells
2,332
695
Well count - oil wells
96,244
697
Oil production at gas and CBM wells
3,579
698
Well count - gas and CBM wells
0
373,808
200
Urban Primary Road Miles
27,650
972,477
205
Extended Idle Locations
792
287,139
210
Rural Primary Road Miles
12,380
812,492
221
Urban Unrestricted Roads
49,327
1,574,451
231
Rural Unrestricted Roads
30,711
1,271,368
256
Off-Network Short-Haul Trucks
0
13,769
257
Off-Network Long-Haul Trucks
458
258
Intercity Bus Terminals
168
259
Transit Bus Terminals
43
506
Education
633
507
Heavy Light Construction Industrial Land
558
510
Commercial plus Industrial
121,163
526
Residential - Non-Institutional
658
535
Residential + Commercial + Industrial +
Institutional + Government
652,562
600
Gas Stations
0
261
NT AD Total Railroad Density
16,536
271
NT AD Class 12 3 Railroad Density
332
732,956
280
Class 2 and 3 Railroad Miles
13
41,886
0.5 Residential Heating - Wood plus 0.5
165 Low Intensity Residential
15,162
27,530
300
Low Intensity Residential
4,520
6,883
22
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3.4.2 Allocation Method for Airport-related Sources in the U.S.
There are numerous airport-related emission sources in the NEI, such as aircraft, airport ground support
equipment, and jet refueling. The modeling platform includes the aircraft and airport ground support
equipment emissions as point sources. For the modeling platform, the EPA used the SMOKE "area-to-
point" approach for only jet refueling in the nonpt sector. The following SCCs use this approach:
2501080050 and 2501080100 (petroleum storage at airports), and 2810040000 (aircraft/rocket engine
firing and testing). The ARTOPNT approach is described in detail in the 2002 platform documentation.
The ARTOPNT file that lists the nonpoint sources to locate using point data were unchanged from the
2005-based platform.
3.4.3 Surrogates for Canada and Mexico Emission Inventories
The surrogates for Canada to spatially allocate the 2010 Canadian emissions have been updated in the
201 lv6.2 platform. The spatial surrogate data came from Environment Canada, along with cross
references. The surrogates they provided were outputs from the Surrogate Tool (previously referenced).
The Canadian surrogates used for this platform are listed in Table 3-8. The leading "9" was added to the
surrogate codes to avoid duplicate surrogate numbers with U.S. surrogates. Surrogates for Mexico are
circa 1999 and 2000 and were based on data obtained from the Sistema Municpal de Bases de Datos
(SEVLBAD) de INEGI and the Bases de datos del Censo Economico 1999. Most of the CAPs allocated to
the Mexico and Canada surrogates are shown in Table 3-9. 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 3-8. Canadian Spatial Surrogates
Code
Canadian Surrogate Description
Code
Description
9100
Population
92424
BARLEY
9101
total dwelling
92425
BUCWHT
9103
rural dwelling
92426
CANARY
9106
ALL INDUST
92427
CANOLA
9111
Farms
92428
CHICPEA
9113
Forestry and logging
92429
CORNGR
9211
Oil and Gas Extraction
92425
BUCWHT
9212
Mining except oil and gas
92430
CORNSI
9221
Total Mining
92431
DFPEAS
9222
Utilities
92432
FLAXSD
9233
Total Land Development
92433
FORAGE
9308
Food manufacturing
92434
LENTIL
9321
Wood product manufacturing
92435
MUSTSD
9323
Printing and related support activities
92436
MXDGRN
9324
Petroleum and coal products manufacturing
92437
OATS
9327
Non-metallic mineral product manufacturing
92438
ODFBNS
9331
Primary Metal Manufacturing
92439
OTTAME
9412
Petroleum product wholesaler-distributors
92440
POTATS
9416
Building material and supplies wholesaler-
distributors
92441
RYEFAL
9447
Gasoline stations
92442
RYESPG
9448
clothing and clothing accessories stores
92443
SOYBNS
9481
Air transportation
92444
SUGARB
23
-------
Code
9482
9562
9921
9924
9925
9932
9941
9942
9945
9946
9948
9950
9955
9960
9970
9980
9990
9996
9997
91201
92401
92402
92403
92404
92405
92406
92407
92408
92409
92410
92412
92413
92414
92416
92417
92418
92419
92421
92422
92423
Canadian Surrogate Description
Code
Description
Rail transportation
Waste management and remediation services
92445
SUNFLS
92446
TOBACO
Commercial Fuel Combustion
92447
TRITCL
Primary Industry
92448
WHITBN
Manufacturing and Assembly
92449
WHTDUR
CANRAIL
92450
WHTSPG
PAVED ROADS
92451
WHTWIN
UNPAVED ROADS
92452
BEANS
Commercial Marine Vessels
92453
CARROT
Construction and mining
92454
GRPEAS
Forest
92455
OTHVEG
Combination of Forest and Dwelling
92456
SWCORN
UNPAVED ROADS AND TRAILS
92457
TOMATO
TOTBEEF
92430
CORNSI
TOTPOUL
92431
DFPEAS
TOTSWIN
92432
FLAXSD
TOTFERT
92433
FORAGE
urban area
92434
LENTIL
CHBOISQC
92435
MUSTSD
traffic bcw
92436
MXDGRN
BULLS
92437
OATS
BFCOWS
92438
ODFBNS
BFHEIF
92439
OTTAME
CALFU1
92440
POTATS
FDHEIF
92441
RYEFAL
STEERS
92442
RYESPG
MLKCOW
92443
SOYBNS
MLKHEIF
92444
SUGARB
MBULLS
92445
SUNFLS
MCALFU1
92446
TOBACO
BROILER
92447
TRITCL
LAYHEN
92448
WHITBN
TURKEY
92449
WHTDUR
BOARS
92450
WHTSPG
GRWPIG
92451
WHTWIN
NURPIG
92452
BEANS
SOWS
92453
CARROT
IMPAST
92454
GRPEAS
UNIMPAST
92455
OTHVEG
ALFALFA
92456
SWCORN
92457
TOMATO
24
-------
Table 3-9. CAPs Allocated to Mexican and Canadian Spatial Surrogates
Code
Mexican or Canadian Surrogate Description
nh3
NOx
pm25
so2
voc
10
MEX Population
0
169
5
1
342
12
MEX Housing
21,275
91,275
3,631
389
117,405
14
MEX Residential Heating - Wood
0
1,010
12,952
155
89,051
16
MEX Residential Heating - Distillate Oil
0
11
0
3
0
20
MEX Residential Heating - LP Gas
0
5,042
152
0
86
22
MEX Total Road Miles
2,154
306,924
8,198
4,305
68,105
24
MEX Total Railroads Miles
0
18,710
418
164
729
26
MEX Total Agriculture
146,737
105,222
22,250
5,106
8,400
32
MEX Commercial Land
0
61
1,343
0
19,436
34
MEX Industrial Land
3
1,055
1,626
0
98,576
36
MEX Commercial plus Industrial Land
0
1,559
26
4
83,144
38
MEX Commercial plus Institutional Land
2
1,427
64
3
42
40
MEX Residential (RES1-
4)+Comercial+Industrial+Institutional+Government
0
4
9
0
63,021
42
MEX Personal Repair (COM3)
0
0
0
0
4,637
44
MEX Airports Area
0
2,521
68
319
796
46
MEX Marine Ports
0
8,291
526
4,150
84
50
MEX Mobile sources - Border Crossing - Mexico
4
136
1
2
252
9100
CAN Population
583
19
607
11
243
9101
CAN total dwelling
265
26,700
6,793
4,937
20,769
9103
CAN rural dwelling
1
426
68
2
2,491
9106
CAN ALL INDUST
6
8,999
348
8
2,738
9111
CAN Farms
26
27,674
2,409
39
3,212
9113
CAN Forestry and logging
576
6,506
352
632
15,352
9211
CAN Oil and Gas Extraction
1
1,640
98
2
141
9212
CAN Mining except oil and gas
0
0
2,074
0
0
9221
CAN Total Mining
37
11,269
41,316
1,217
987
9222
CAN Utilities
60
3,831
305
652
164
9233
CAN Total Land Development
13
12,742
1,362
20
1,983
9308
CAN Food manufacturing
0
0
4,323
0
7,548
9321
CAN Wood product manufacturing
0
0
537
0
0
9323
CAN Printing and related support activities
0
0
0
0
33,802
9324
CAN Petroleum and coal products manufacturing
0
784
835
410
2,751
9327
CAN Non-metallic mineral product manufacturing
0
0
4,362
0
0
9331
CAN Primary Metal Manufacturing
0
142
5,279
46
17
9412
CAN Petroleum product wholesaler-distributors
0
0
0
0
44,248
9448
CAN clothing and clothing accessories stores
0
0
0
0
132
9481
CAN Air transportation
5
7,692
130
787
6,112
9482
CAN Rail transportation
3
4,247
94
136
94
9562
CAN Waste management and remediation services
1,111
1,497
1,837
2,183
13,868
9921
CAN Commercial Fuel Combustion
467
133,157
11,421
29,102
100,571
9924
CAN Primary Industry
0
0
0
0
220,312
9925
CAN Manufacturing and Assembly
0
0
0
0
71,912
9932
CAN CANRAIL
67
62,931
2,373
1,431
1,846
25
-------
Code
Mexican or Canadian Surrogate Description
nh3
NOx
pm25
so2
voc
9941
CAN PAVED ROADS
2
1,261
158,418
2
2,269
9942
CAN UNPAVED ROADS
21
4,245
1,311
26
57,493
9945
CAN Commercial Marine Vessels
30
40,929
3,360
27,659
5,954
9946
CAN Construction and mining
0
1
9
0
78
9950
CAN Combination of Forest and Dwelling
267
2,899
31,312
424
44,339
9955
CAN UNPAVED ROADS AND TRAILS
0
0
242,537
0
0
9990
CAN TOTFERT
0
0
29,266
0
159,858
9996
CAN urban area
0
0
618
0
0
9997
CAN CHBOISQC
442
4,912
48,652
702
71,050
91201
CAN traffic bcw
18,654
345,838
12,226
1,702
178,467
92401
CAN BULLS
4,394
0
0
0
0
92402
CAN BFCOWS
46,101
0
0
0
0
92403
CAN BFHEIF
7,398
0
0
0
0
92404
CAN CALFU1
17,987
0
0
0
0
92406
CAN STEERS
24,551
0
0
0
0
92407
CAN MLKCOW
37,603
0
0
0
0
92408
CAN MLKHEIF
2,617
0
0
0
0
92409
CAN MBULLS
35
0
0
0
0
92410
CAN MCALFU1
11,988
0
0
0
0
92412
CAN BROILER
7,049
0
0
0
0
92413
CAN LAYHEN
8,044
0
0
0
0
92414
CAN TURKEY
3,220
0
0
0
0
92416
CAN BOARS
139
0
0
0
0
92417
CAN GRWPIG
51,078
0
0
0
0
92418
CAN NURPIG
13,047
0
0
0
0
92419
CAN SOWS
5,376
0
0
0
0
92421
CAN IMPAST
1,949
0
0
0
0
92422
CAN UNIMPAST
2,081
0
0
0
0
92423
CAN ALFALFA
1,622
0
0
0
0
92424
CAN BARLEY
7,576
0
0
0
0
92425
CAN BUCWHT
21
0
0
0
0
92426
CAN CANARY
282
0
0
0
0
92427
CAN CANOLA
7,280
0
0
0
0
92428
CAN CHICPEA
449
0
0
0
0
92429
CAN CORNGR
15,655
0
0
0
0
92430
CAN CORNSI
2,328
0
0
0
0
92431
CAN DFPEAS
703
0
0
0
0
92432
CAN FLAXSD
1,667
0
0
0
0
92433
CAN FORAGE
526
0
0
0
0
92434
CAN LENTIL
547
0
0
0
0
92435
CAN MUSTSD
722
0
0
0
0
92436
CAN MXDGRN
658
0
0
0
0
92437
CAN OATS
4,452
0
0
0
0
92438
CAN ODFBNS
254
0
0
0
0
92439
CAN OTTAME
5,985
0
0
0
0
26
-------
Code
Mexican or Canadian Surrogate Description
nh3
NOx
pm25
so2
voc
92440
CAN POT ATS
1,268
0
0
0
0
92441
CAN RYEFAL
153
0
0
0
0
92442
CAN RYESPG
7
0
0
0
0
92443
CAN SOYBNS
1,775
0
0
0
0
92444
CAN SUGARB
30
0
0
0
0
92445
CAN SUNFLS
383
0
0
0
0
92446
CAN TOBACO
72
0
0
0
0
92447
CAN TRITCL
73
0
0
0
0
92448
CAN WHITBN
288
0
0
0
0
92449
CAN WHTDUR
5,524
0
0
0
0
92450
CAN WHTSPG
13,929
0
0
0
0
92451
CAN WHTWIN
2,785
0
0
0
0
92452
CAN BEANS
109
0
0
0
0
92453
CAN CARROT
73
0
0
0
0
92454
CAN GRPEAS
113
0
0
0
0
92455
CAN OTHVEG
294
0
0
0
0
92456
CAN SWCORN
297
0
0
0
0
92457
CAN TOMATO
98
0
0
0
0
27
-------
4 Development of 2023 Base-Case Emissions
The emission inventories for the future year of 2023 have been developed using projection methods that are
specific to the type of emission source. Future emissions are projected from the 2011 base case either by
running models to estimate future year emissions from specific types of emission sources (e.g., EGUs, and
onroad and nonroad mobile sources), or for other types of sources by adjusting the base year emissions
according to the best estimate of changes expected to occur in the intervening years (e.g., non-EGU point and
nonpoint sources). For some sectors, the same emissions are used in the base and future years, such as
biogenic, fire, and stationary nonpoint source Canadian emissions. For the remaining sectors, rules and specific
legal obligations that go into effect in the intervening years, along with changes in activity for the sector, are
considered when possible.
Emissions inventories for neighboring countries used in our modeling are included in this platform, specifically
2011 and 2023 emissions inventories for Mexico, and 2010 emissions inventories for Canada adjusted to
approximate 2023 levels. The meteorological data used to create and temporalize emissions for the future year
cases is held constant and represents the year 2011. With the exception of speciation profiles for mobile
sources and temporal profiles for EGUs, the same ancillary data files are used to prepare the future year
emissions inventories for air quality modeling as were used to prepare the 2011 base year inventories.
Emission projections for EGUs were developed using IPM version 5.16 and are reflected in an air quality
modeling-ready flat file taken from the EPA Base Case v.5.16. The NEEDS database is an important input to
IPM in that contains the generation unit records used for the model plants that represent existing and
planned/committed units in EPA modeling applications of IPM. NEEDS includes basic geographic, operating,
air emissions, and other data on these generating units and has been updated for the EPA's version 5.16 power
sector modeling platform. The EGU emission projections in the flat file format, the corresponding NEEDS
database, and user guides and documentation are available with the information for the EPA's Power Sector
Modeling Platform v.5.16. The projected EGU emissions include the Final Mercury and Air Toxics (MATS)
rule announced on December 21, 2011, the Cross-State Air Pollution Rule (CSAPR) issued July 6, 2011, and
the CSAPR Update Rule issued October 26, 2016. Note that the Clean Power Plan (CPP) is included in the
2023 base case.
To project future emissions for onroad and nonroad mobile sources, the EPA used MOVES2014a and NMIM,
respectively. The EPA obtained future year projected emissions for these sectors by running the MOVES and
NMIM models using year-specific information about fuel mixtures, activity data, and the impacts of national
and state-level rules and control programs. For this platform, the mobile source emissions for 2023 were
generated by using year 2023 activity data coupled with emission factors for a MOVES run for the year 2023.
For non-EGU point and nonpoint sources, projections of 2023 emissions were developed by starting with the
2011 emissions inventories and applying adjustments that represent the impact of national, state, and local rules
coming into effect in the intervening years, along with the impacts of planned shutdowns, the construction of
new plants, specific information provided by states, and specific legal obligations resolving alleged
environmental violations, such as consent decrees. Changes in activity are considered for sectors such as oil and
gas, residential wood combustion, cement kilns, livestock, aircraft, commercial marine vessels and locomotives.
Efforts were made to include some regional haze and state-reported local controls as part of a larger effort to
include more local control information on stationary non-EGU sources as described further in Section 4.2.
28
-------
The Mid-Atlantic Regional Air Management Association (MARAMA) provided projection and control data for
year 2023 for most non-point and point sectors of the year 2011 inventory. The sectors affected are afdust, ag,
cmv, nonpt, npoilgas, pt oilgas, ptnonipm, rail, rwc, and also portable fuel containers a subsector of nonpt.
These MARAMA data consisted of projection and control packets used by EPA's Control Strategy Tool
(CoST) and SMOKE to develop emissions for the following states: Virginia, North Carolina, New Hampshire,
New York, Pennsylvania, New Jersey, West Virginia, Connecticut, Delaware, Vermont, Maine, Rhode Island,
Maryland, Massachusetts, and District of Columbia. These MARAMA packets will be made available as part
of the Data Files and Summaries. They were developed using methods similar to those documented in the TSD
Inventory Growth and Control Factors based on EPA 201 INEIvl Emissions Modeling Platform (SRA, 2014)
The following bullets summarize the projection methods used for sources in the various sectors, while
additional details and data sources are given in the following subsections and in Table 4-1.
EGU sector (ptegu): Unit-specific estimates from IPM version 5.16, including CPP, CSAPR Update,
CSAPR, MATS rule, Regional Haze rule, and the Cooling Water Intakes Rule.
Non-IPM sector (ptnonipm): Closures, projection factors and percent reductions reflect comments
received from the notices of data availability for the 2011, 2017, and 2018 emissions modeling
platforms, 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. The year represented for this sector is 2025, except that MARAMA
factors for the year 2023 were used, where applicable.
Point and nonpoint oil and gas sectors (pt oilgas and np oilgas): Regional projection factors by
production indicators using information from AEO 2016 projections to year 2023. Co-benefits of
stationary engines CAP-cobenefit reductions (RICE NESHAP) and controls from New Source
Performance Standards (NSPS) are reflected for select source categories. MARAMA factors for the year
2023 were used where applicable.
Biogenic (beis): 2011 emissions are used for all future-year scenarios and are computed with the same
" 1 lg" meteorology as is used for the air quality modeling.
Fires sectors (ptfire, agfire): No growth or control - 2011 estimates are used directly.
Agricultural sector (ag): Year 2023 projection factors for livestock estimates based on expected changes
in animal population from 2005 USDA data, updated according to EPA experts in July 2012.
Area fugitive dust sector (afdust): For livestock PM emissions, projection factors for dust categories
related to livestock estimates based on expected changes in animal population. For unpaved and paved
road dust, county-level VMT projections to 2023 were considered.
Remaining Nonpoint sector (nonpt): Projection factors and percent reductions reflect comments received
from the notices of data availability for the 2011, 2017, and 2018 emissions modeling platforms, along
with emission reductions due to national and local rules/control programs. PFC projection factors
reflecting impact of the final Mobile Source Air Toxics (MSAT2) rule. Upstream impacts from AEO
fuel volume, including cellulosic ethanol plants, are reflected. The year represented for this sector is
2025, except that MARAMA factors for the year 2023 were used, where applicable.
Residential Wood Combustion (rwc): Year 2023 projection factors reflect assumed growth of wood
burning appliances based on sales data, equipment replacement rates and change outs. These changes
include the 2-stage NSPS for Residential Wood Heaters, resulting in growth in lower-emitting stoves
and a reduction in higher emitting stoves.
29
-------
Locomotive, and non-Category 3 commercial marine sector (cmv and rail): Year 2023 projection factors
for Category 1 and Category 2 commercial marine and locomotives reflect final locomotive-marine
controls.
Category 3 commercial marine vessel (cmv): Base-year 2011 emissions grown and controlled to 2023,
incorporating controls based on Emissions Control Area (ECA) and International Marine Organization
(IMO) global NOx and SO2 controls.
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 2023. The inputs were either state-supplied as part of the 201 1NEIv2 process or using
national level inputs, with only minor updates for 201 1NEIv2. Final controls from the final locomotive-
marine and small spark ignition rules are included. California data for 2023 were provided by the
California Air Resources Board (CARB). For Texas, the Texas Commission on Environmental Quality
(TCEQ) data were projected from 2011 to 2023 using trends based on NMIM data.
Onroad mobile (onroad): MOVES2014a-based emissions factors for year 2023 were developed using
the same representative counties, state-supplied data, meteorology, and procedures as were used to
produce the 2011 emission factors. See section 4.3.1.1 for details about future year activity data used in
generating emissions estimates.
Onroad emissions data for California were provided by CARB.
Other point (othpt), nonpoint/nonroad (othar, othafdust), onroad (othon): For Canada, year 2010
inventories were projected for the othon and for the nonroad part of the othar sectors using projection
factors derived from U.S. emissions changes from 2011 to 2023 by SCC and pollutant. In the othpt
sector, the Canadian point sources were modified by removing any remaining EGU facilities using coal.
For Mexico, the othon inventory data were based on a 2023 run of MOVES-Mexico, while othar and
othpt inventory data were interpolated to 2023 between 2018 and 2025. C3 CMV data was projected
using the same methodology as the cmv sector. Offshore oil platform emissions were held constant at
2011 levels.
Table 4-1 summarizes the growth and control assumptions by source type that were used to create the U.S. 2023
base-case emissions from the base year inventories. The control, closures and projection packets (i.e., data sets)
used to create the 2023 future year base-case scenario inventories from the 2011 base case are provided on the
EMCH website and are discussed in more detail in the sections listed in Table 4-1. These packets were
processed through CoST to create future year emission inventories. CoST is described and discussed in context
to this emissions modeling platform in Section 4.2.1. The last column in Table 4-1 indicates the order of the
CoST strategy used for the source/packet type. For some sectors (e.g., ptnonipm), multiple CoST strategies are
needed to produce the future year inventory because the same source category may be subject to multiple
projection or control packets. For example, the "Loco-marine" projection factors are applied in a second
control strategy for the ptnonipm sector, while for the cmv and rail sectors, these same projection factors can be
applied in the first (and only) control strategy. Thus, in Table 4-1, packets with a "1" in the CoST strategy
column are applied in the first strategy, while packets with a "2" in the CoST strategy column are applied in a
second strategy that is run on an intermediate inventory output from the first strategy.
The remainder of this section is organized by broad NEI sectors with further stratification by the types of
packets (e.g., projection, control, closure packets) and whether emissions are projected via a stand-alone model
(e.g., EGUs use the IPM model and onroad mobile uses MOVES), using CoST, or by other mechanisms. The
EGU projections are discussed in Section 4.1. Non-EGU point and nonpoint sector projections (including all
commercial marine vessels, locomotives and aircraft) are described in Section 4.2, along with some background
on CoST. Onroad and nonroad mobile projections are discussed in Section 4.3. Finally, projections for all
"other" sources, primarily outside the U.S., are described in Section 4.44. Section 5 contains summaries of the
30
-------
2011 and 2023 emissions the emissions changes between the years for emissions both within and outside of the
U.S.
Table 4-1. Growth and control methodologies used to create future year emissions inventories
Description of growth, control, closure data, or, new
inventory
Sector(s)
Packet Type
CAPs
impacted
Section(s)
CoST
Strategy
\on-ll(;i Point (plnonipm and pioilgas sectors) Growth and Control Assumptions
Facility, unit and stack closures, primarily from the Emissions
Inventory System (EIS)
ptnonipm,
pt oilgas
CLOSURE
All
4.2.2
1
"Loco-marine rule": Growth and control to years 2023 from
Locomotives and Marine Compression-Ignition Engines Less
than 30 Liters per Cylinder: March, 2008
ptnonipm,
cmv, rail
PROJECTION
All
4.2.3.3
2,
1
Upstream RFS2/EISA/LDGHG impacts on gas distribution,
pipelines and refineries to future years
ptnonipm,
ptoilgas,
nonpt
PROJECTION
All
4.2.3.4
2
AEO-based growth for industrial sources, including oil and gas
regional projections
ptnonipm,
ptoilgas,
nonpt,
np oilgas
PROJECTION
All
4.2.3.5
1
Aircraft growth via Itinerant (ITN) operations at airports
ptnonipm
PROJECTION
All
4.2.3.6
1
Corn Ethanol plants adjusted via AEO volume projections to
2025
ptnonipm
PROJECTION
All
4.2.3.8
1
NESHAP: Portland Cement projects. These results are from
model runs associated with the NESHAP and NSPS analysis of
August, 2013 and include closures and growth.
ptnonipm,
nonpt
PROJECTION
& new
inventories for
new kilns
All
4.2.3.7 &
4.2.5.4
1&
n/a
NESHAP: RICE (reciprocating internal combustion engines)
with reconsideration amendments
ptnonipm,
ptoilgas,
nonpt,
CO,
NOx,
PM, S02,
np oilgas
CONTROL
voc
4.2.4.2
1
NSPS: oil and gas
ptoilgas,
np oilgas
CONTROL
voc
4.2.4.1
1
NSPS: RICE
ptnonipm,
ptoilgas,
nonpt,
np oilgas
CONTROL
CO,
NOx,
VOC
4.2.4.3
2
NSPS: Gas turbines
ptnonipm,
pt oilgas
CONTROL
NOx
4.2.4.6
1
NSPS: Process heaters
ptnonipm,
pt oilgas
CONTROL
NOx
4.2.4.7
1
Industrial/Commercial/Institutional Boiler MACT with
nonpt,
CO,
NOx,
Reconsideration Amendments + local programs
ptnonipm,
pt oilgas
CONTROL
PM, S02,
VOC
4.2.4.4
1
State fuel sulfur content rules for fuel oil - via 2018 NOD A
nonpt,
ptnonipm,
pt oilgas
comments, effective only in most northeast states
CONTROL
so2
4.2.4.5
1
State comments: from previous platforms (including consent
decrees) and 2018 NODA (search for 'EPA-HQ-OAR-2013-
0809' at regulations.gov)
nonpt,
ptnonipm,
pt oilgas
PROJECTION
&
CONTROL
All
4.2.3.5,
4.2.4.10
1
Commercial and Industrial Solid Waste Incineration (CISWI)
revised NSPS
ptnonipm
CONTROL
S02
4.2.4.9
1
Arizona Regional haze controls
ptnonipm
CONTROL
NOx,S02
4.2.4.8
1
New biodiesel plants for year 2018
ptnonipm
new inventory
All
4.2.5.2
n/a
31
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Description of growth, control, closure data, or, new
inventory
Sector(s)
Packet Type
CAPs
impacted
Section(s)
CoST
Strategy
Nonpoint (aldusl, ag, nonpl, npoilgas and rwc sectors) Growth and Control Assumptions
AEO-based VMT growth for paved and unpaved roads
afdust
PROJECTION
PM
4.2.3.1
1
Livestock emissions growth from year 2011 to year 2023
ag
PROJECTION
nh3
4.2.3.2
1
Upstream RFS2/EISA/LDGHG impacts on gas distribution,
pipelines and refineries to years 2018
ptnonipm,
ptoilgas,
nonpt
PROJECTION
All
4.2.3.4
2
AEO-based growth: industrial sources, including oil and gas
regional projections
ptnonipm,
ptoilgas,
nonpt,
np oilgas
PROJECTION
All
4.2.3.5
1
NESHAP: RICE (reciprocating internal combustion engines)
with reconsideration amendments
ptnonipm,
ptoilgas,
nonpt,
np oilgas
CONTROL
CO,
NOx,
PM, S02,
voc
4.2.4.2
1
NSPS: oil and gas
ptoilgas,
np oilgas
CONTROL
voc
4.2.4.1
1
NSPS: RICE
ptnonipm,
ptoilgas,
nonpt,
np oilgas
CONTROL
CO,
NOx,
VOC
4.2.4.3
2
Residential wood combustion growth and change-outs
rwc
PROJECTION
All
4.2.3.9
1
Industrial/Commercial/Institutional Boiler MACT with
Reconsideration Amendments + local programs
nonpt,
ptnonipm,
pt oilgas
CONTROL
CO,
NOx,
PM, SO2,
VOC
4.2.4.4
1
State fuel sulfur content rules for fuel oil - via 2018 NOD A
comments, effective only in most northeast states
nonpt,
ptnonipm,
pt oilgas
CONTROL
so2
4.2.4.5
1
State comments: from previous platforms (including consent
decrees) and 2018 NODA (search for 'EPA-HQ-OAR-2013-
0809' at regulations.gov)
nonpt,
ptnonipm,
pt oilgas
PROJECTION
&
CONTROL
All
4.2.3.5,
4.2.4.10
1
MSAT2 and RFS2 impacts with state comments on portable
fuel container growth and control from 2011 to years 2018
nonpt
new inventory
All
4.2.5.1
n/a
New cellulosic plants in year 2018
nonpt
new invent* n \
Ml
4.2.5.3
n/a
Oni'oad Mobile (onrnad sector) Growth and Control Assumptions
All national in-lorce regulations are modeled. The list includes recent kej mobile source regulations hut is not e\hausti\e.
National Onroad Rules:
All onroad control programs finalized as of the date of the
model run, including most recently:
Tier-3 Vehicle Emissions and Fuel Standards Program: March,
2014
2017 and Later Model Year Light-Duty Vehicle Greenhouse
Gas Emissions and Corporate Average Fuel Economy
Standards: October, 2012
Greenhouse Gas Emissions Standards and Fuel Efficiency
Standards for Medium- and Heavy-Duty Engines and
Vehicles: September, 2011
onroad
n/a
All
4.3
n/a
Regulation of Fuels and Fuel Additives: Modifications to
Renewable Fuel Standard Program (RFS2): December, 2010
Light-Duty Vehicle Greenhouse Gas Emission Standards and
Corporate Average Fuel Economy Standards;
Final Rule for Model-Year 2012-2016: May, 2010
32
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Description of growth, control, closure data, or, new
inventory
Sector(s)
Packet Type
CAPs
impacted
Section(s)
CoST
Strategy
Final Mobile Source Air Toxics Rule (MSAT2): February,
2007
Local Onroad Programs:
California LEVIII Program
onroad
n/a
All
4.3
n/a
Ozone Transport Commission (OTC) LEV Program:
January, 1995
Inspection and Maintenance programs
Fuel programs (also affect gasoline nonroad equipment)
Stage II refueling control programs
Nonroad Mobile (cim. rail, nonroad scclors) (irowili and Conirol Assu 111 pi ions
All nalional in-lorce regulations a re modeled. The lisl includes rcccnl kej mobile source regulations hul is not exhaust i\ 0.
National Nonroad Controls:
All nonroad control programs finalized as of the date of the
model run, including most recently:
nonroad
n/a
All
4.3.2
n/a
Emissions Standards for New Nonroad Spark-Ignition Engines,
Equipment, and Vessels: October, 2008
Growth and control 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
Locomotives:
Growth and control from Locomotives and Marine
Compression-Ignition Engines Less than 30 Liters per
Cylinder: March, 2008
cmv, rail
ptnonipm
PROJECTION
All
4.2.3.3
1,
2
Clean Air Nonroad Diesel Final Rule - Tier 4: May, 2004
cmv, rail
n/a
All
4.3.2
n/a
Commercial Marine:
Category 3 marine diesel engines Clean Air Act and
International Maritime Organization standards: April, 2010
cmv
PROJECTION
All
4.2.3.3
1
Growth and control from Locomotives and Marine
Compression-Ignition Engines Less than 30 Liters per
Cylinder: March, 2008
cmv, rail,
ptnonipm
PROJECTION
All
4.2.3.3
1,
2
Clean Air Nonroad Diesel Final Rule - Tier 4: May, 2004
nonroad
n/a
All
4.3.2
n/a
4.1 EGU sector projections (ptegu)
The future-year data for the ptegu sector used in the air quality modeling were created by IPM version 5.16. The
IPM is a multiregional, dynamic, deterministic linear programming model of the U.S. electric power sector.
IPM version 5.16 reflects state rules, consent decrees and announced shutdowns forecast through calendar year
2023. The NEEDS database was updated based on comments received on the notice of data availability for the
emissions modeling platform issued prior to the proposal. IPM version 5.16 was updated from the previous
version 5.15 and represents electricity demand projections for the AEO 2016. The scenario used for this
modeling represents the implementation of the CSAPR Update, CSAPR, MATS, CPP and the final actions the
EPA has taken to implement the Regional Haze Rule, the Cooling Water Intakes Rule, and Combustion
Residuals from Electric Utilities (CCR).
Directly emitted PM emissions (i.e., PM2.5 and PM10) from the EGU sector are computed via a post processing
routine that applies emission factors to the IPM-estimated fuel throughput based on fuel, configuration and
controls to compute the filterable and condensable components of PM. This postprocessing step also apportions
33
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the regional emissions down to the unit-level emissions used for air quality modeling. A single IPM run was
postprocessed to get results for 2023.
From the unit-level parsed file, a flat file is created that is used as the input to SMOKE and processed into the
format needed by the air quality model. As part of the development of the flat file, a cross reference between
the 201 1NEIv2 and IPM is used to populate stack parameters and other related information for matched
sources. The flat file creation methodology is documented in the air quality modeling flat file documentation.
The cross reference is available from the reports directory of the 201 lv6.3 platform FTP site:
ftp://newftp.epa.gov/air/emismod/2011/v3platform/. The emissions in the flat file created based on the IPM
outputs are temporalized into the hourly emissions needed by the air quality model.
4.2 Non-EGU Point and NEIN on point Sector Projections
To project all U.S. non-EGU stationary sources, facility/unit closures information and growth (PROJECTION)
factors and/or controls were applied to certain categories within the afdust, ag, cmv, rail, nonpt, npoilgas,
ptnonipm, ptoilgas and rwc platform sectors. Some facility or sub-facility-level closure information was also
applied to the point sources. There are also a handful of situations where new inventories were generated for
sources that did not exist in the 2011 NEI (e.g., biodiesel and cellulosic plants, yet-to-be constructed cement
kilns). This subsection provides details on the data and projection methods used for these sectors.
In recent platforms, the EPA has assumed that emissions growth for most industrial sources did not track with
economic growth for most stationary non-IPM sources (EPA, 2006b). This "no-growth" assumption was based
on an examination of historical emissions and economic data. Recently however, the EPA has received growth
(and control) data from numerous states and regional planning organizations for many industrial sources,
including the rapidly-changing oil and gas sector. The EPA provided a Notice of Data Availability for the
201 lv6.0 emissions modeling platform and projected 2018 inventory in January, 2014 (Docket Id. No. EPA-
HQ-OAR-2013-0809). The EPA requested comment on the future year growth and control assumptions used to
develop the 2018 inventories. One of the most frequent comments the EPA received was to use the growth
factors information that numerous states either provided or deferred to growth factors provided by broader
region-level efforts. In an attempt to make the projections approach as consistent as possible across all states,
the EPA decided to expand this effort to all states for some of the most-significant industrial sources (see
Section 4.2.3).
Because much of the projections and controls data are developed independently from how the EPA defines its
emissions modeling sectors, this section is organized primarily by the type of projections data, with secondary
consideration given to the emissions modeling sector (e.g., industrial source growth factors are applicable to
four emissions modeling sectors). The rest of this section is organized in the order that the EPA uses CoST in
combination with other methods to produce future year inventories: 1) for point sources, apply plant (facility or
sub-facility-level) closure information via CoST; 2) apply all PROJECTION packets via CoST (multiplicative
factors that could cause increases or decreases); 3) apply all percent reduction-based CONTROL packets via
CoST; and 4) append all other future-year inventories not generated via CoST. This organization allows
consolidation of the discussion of the emissions categories that are contained in multiple sectors, because the
data and approaches used across the sectors are consistent and do not need to be repeated. Sector names
associated with the CoST packets are provided in parentheses.
4.2.1 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 NEI-based emissions modeling inventories to create future year inventories for the following
sectors: afdust, ag, cmv, rail, nonpt, np oilgas, ptnonipm, pt oilgas and rwc. Information about CoST and
related data sets.
34
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CoST allows the user to apply projection (growth) factors, controls and closures at various geographic and
inventory key field resolutions. Each of these CoST datasets, also called "packets" or "programs," provides the
user with the ability to perform numerous quality assurance assessments as well as create SMOKE-ready future
year inventories. Future year inventories are created for each emissions modeling sector via a CoST "strategy"
and each strategy includes all base year 2011 inventories and applicable CoST packets. For reasons discussed
later, some emissions modeling sectors require multiple CoST strategies to account for the compounding of
control programs that impact the same type of sources. There are also available linkages to existing and user-
defined control measures databases and it is up to the user to determine how control strategies are developed
and applied. The EPA typically creates individual CoST packets that represent specific intended purposes (e.g.,
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. The EPA uses these types of packets for
known post-2011 controls as well as information on closures provided by states on specific facilities,
units or stacks. This packet type is only used in the ptnonipm and pt oilgas sectors.
2. PROJECTION: This packet allows the user to increase or decrease emissions for virtually any
geographic and/or inventory source level. Projection factors are applied as multiplicative factors to the
2011 emissions inventories prior to the application of any possible subsequent CONTROLS. A
PROJECTION packet is necessary whenever emissions increase from 2011 and is also desirable when
information is based more on activity assumptions rather than known control measures. The EPA uses
PROJECTION packet(s) in every non-EGU modeling sector.
3. CONTROL: These packets are applied after any/all CLOSURE and PROJECTION packet entries. The
user has similar level of control as PROJECTION packets regarding specificity of geographic and/or
inventory source level application. Control factors are expressed as a percent reduction (0 to 100) and
can be applied in addition to any pre-existing inventory control, or as a replacement control where
inventory controls are first backed out prior to the application of a more-stringent replacement control.
All of these packets are stored as data sets within the Emissions Modeling Framework and use comma-
delimited formats. As mentioned above, CoST first applies any/all CLOSURE information for point sources,
then applies PROJECTION packet information, followed by CONTROL packets. A hierarchy is used by CoST
to separately apply PROJECTION and CONTROL packets. In short, in a separate process for PROJECTION
and CONTROL packets, more specific information is applied in lieu of less-specific information in ANY other
packets. For example, a facility-level PROJECTION factor will be replaced by a unit-level, or facility and
pollutant-level PROJECTION factor. It is important to note that this hierarchy does not apply between packet
types (e.g., CONTROL packet entries are applied irrespective of PROJECTION packet hierarchies). A more
specific example: a state/SCC-level PROJECTION factor will be applied before a stack/pollutant-level
CONTROL factor that impacts the same inventory record. However, an inventory source that is subject to a
CLOSURE packet record is removed from consideration of subsequent PROJECTION and CONTROL packets.
The implication for this hierarchy and intra-packet independence is important to understand and quality assure
when creating future year strategies. For example, with consent decrees, settlements and state comments, the
goal is typically to achieve a targeted reduction (from the 2011NEI) or a targeted future-year emissions value.
Therefore, as encountered with this future year 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 the type of broad measure/program,
35
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it is possible to show actual changes from the base year inventory to the future year inventory as a result of
applying each packet.
Ultimately, CoST concatenates all PROJECTION packets into one PROJECTION dataset and uses a hierarchal
matching approach to assign PROJECTION factors to the inventory. For example, a packet entry with
Ranking=l will supersede all other potential inventory matches from other packets. CoST then computes the
projected emissions from all PROJECTION packet matches and then performs a similar routine for all
CONTROL packets. Therefore, when summarizing "emissions reduced" from CONTROL packets, it is
important to note that these reductions are not relative to the 2011 inventory, but rather to the intermediate
inventory after application of any/all PROJECTION packet matches (and CLOSURES). A subset of the more
than 70 hierarchy options is shown in Table 4-2, although the fields 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 includes corrections to matching
hierarchy made in 201 lv6.3 platform modeling. These corrections did cause emissions changes from the
201 lv6.2 platform to 201 lv6.3 platform for the np oilgas, pt oilgas, ptnonipm and nonpt sectors.
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, POLL
point
4
REGION CD, FACILITY ID, UNIT ID, POLL
point
5
REGION CD, FACILITY ID, SCC, POLL
point
6
REGION CD, FACILITY ID, POLL
point
7
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID, SCC
point
8
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID
point
9
REGION CD, FACILITY ID, UNIT ID, REL POINT ID
point
10
REGION CD, FACILITY ID, UNIT ID
point
11
REGION CD, FACILITY ID, SCC
point
12
REGION CD, FACILITY ID
point
13
REGION CD, NAICS, SCC, POLL
point, nonpoint
14
REGION CD, NAICS, POLL
point, nonpoint
15
STATE, NAICS, SCC, POLL
point, nonpoint
16
STATE, NAICS, POLL
point, nonpoint
17
NAICS, SCC, POLL
point, nonpoint
18
NAICS, POLL
point, nonpoint
19
REGION CD, NAICS, SCC
point, nonpoint
20
REGION CD, NAICS
point, nonpoint
21
STATE, NAICS, SCC
point, nonpoint
22
STATE, NAICS
point, nonpoint
23
NAICS, SCC
point, nonpoint
24
NAICS
point, nonpoint
25
REGION CD, SCC, POLL
point, nonpoint
26
STATE, SCC, POLL
point, nonpoint
27
SCC, POLL
point, nonpoint
28
REGION CD, SCC
point, nonpoint
29
STATE, SCC
point, nonpoint
30
SCC
point, nonpoint
31
REGION CD, POLL
point, nonpoint
36
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Rank
Matching Hierarchy
Inventory Type
32
REGION CD
point, nonpoint
33
STATE, POLL
point, nonpoint
34
STATE
point, nonpoint
35
POLL
point, nonpoint
The contents of the controls, local adjustments and closures for the future year base case are described in the
following subsections. Year-specific projection factors (PROJECTION packets) for the future year were used
to create the future year base case, unless noted otherwise in the specific subsections. The contents of a few of
these projection packets (and control reductions) are provided in the following subsections where feasible.
However, most sectors used growth or control factors that varied geographically and their contents could not be
provided in the following sections (e.g., facilities and units subject to the Boiler MACT reconsideration has
thousands of records). The remainder of Section 4.2 is divided into several subsections that are summarized in
Table 4-3. 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
4.2.2
CoST Plant CLOSURE
packet
ptnonipm,
ptoilgas
All facility/unit/stack closures information,
primarily from Emissions Inventory System (EIS),
but also includes information from states and other
organizations.
4.2.3
CoST PROJECTION
packets
All
Introduces and summarizes national impacts of all
CoST PROJECTION packets to the future year.
4.2.3.1
Paved and unpaved roads
VMT growth
afdust
PROJECTION packet: county-level resolution,
based on VMT growth.
4.2.3.2
Livestock population
growth
ag
PROJECTION packet: national, by-animal type
resolution, based on animal population projections.
4.2.3.3
Locomotives
rail,
ptnonipm
PROJECTION packet: Rail projections are by
FIPS/SCC/poll for Calif. And SCC/poll for rest of
US. NC rail projection packet was added for NOD A,
by FIPS/SCC/poll.
4.2.3.3
Category 1, 2, and 3
commercial marine vessels
cmv
PROJECTION packet: Category 1 & 2: CMV uses
SCC/poll for all states except Calif.
Category 3: region-level by-pollutant, based on
cumulative growth and control impacts from
rulemaking.
4.2.3.4
OTAQ upstream
distribution, pipelines and
refineries
nonpt,
ptnonipm,
pt oilgas
PROJECTION packet: national, by-broad source
category, based on upstream impacts from mobile
source rulemakings.
4.2.3.5
Oil and gas and industrial
source growth
nonpt,
npoilgas,
ptnonipm,
pt oilgas
Several PROJECTION packets: varying geographic
resolutions from state, county, to oil/gas play-level
and by-process/fuel-type applications. Data derived
from AEO2016 with several modifications.
4.2.3.6
Aircraft
ptnonipm
PROJECTION packet: by-airport for all direct
matches to FAA Terminal Area Forecast data, with
state-level factors for non-matching NEI airports.
37
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Subsection
Title
Sector(s)
Brief Description
4.2.3.7
Cement manufacturing
ptnonipm
PROJECTION packet: by-kiln projections based on
Industrial Sectors Integrated Solutions (ISIS) model
of demand growth and Portland Cement NESHAP.
4.2.3.8
Corn ethanol plants
ptnonipm
PROJECTION packet: national, based on 2014
AEO renewable fuel production forecast.
4.2.3.9
Residential wood
combustion
rwc
PROJECTION packet: national with exceptions,
based on appliance type sales growth estimates and
retirement assumptions and impacts of recent NSPS.
4.2.4
CoST CONTROL packets
nonpt,
npoilgas,
ptnonipm,
pt oilgas
Introduces and summarizes national impacts of all
CoST CONTROL packets in the future year.
4.2.4.1
Oil and gas NSPS
npoilgas,
pt oilgas
CONTROL packet: national, oil and gas NSPS
impacting VOC only for some activities.
4.2.4.2
RICE NESHAP
nonpt,
npoilgas,
ptnonipm,
pt oilgas
CONTROL packet: national, reflects NESHAP
amendments on compression and spark ignition
stationary reciprocating internal combustion engines
(RICE).
4.2.4.3
RICE NSPS
nonpt,
npoilgas,
ptnonipm,
pt oilgas
CONTROL packet: state and county-level new
source RICE controls, whose reductions by-
definition, are a function of growth factors and also
equipment retirement assumptions.
4.2.4.4
ICI Boilers
nonpt,
ptnonipm,
ptoilgas
CONTROL packet: by-fuel, and for point sources,
by-facility-type controls impacting Industrial and
Commercial/Institutional boilers from rulemaking
and state-provided information.
4.2.4.5
Fuel sulfur rules
nonpt,
ptnonipm,
pt oilgas
CONTROL packet: state and MSA-level fuel sulfur
control programs provided by several northeastern
U.S. states.
4.2.4.6
Natural gas turbines NSPS
ptnonipm,
ptoilgas
CONTROL packet: state and county-level new
source natural gas turbine controls, whose
reductions by-definition, are a function of growth
factors and also equipment retirement assumptions.
4.2.4.7
Process heaters NSPS
ptnonipm,
ptoilgas
CONTROL packet: state and county-level new
source process heaters controls, whose reductions
by-definition, are a function of growth factors and
also equipment retirement assumptions.
4.2.4.8
Arizona Regional Haze
ptnonipm
CONTROL packet: Regional haze controls for
Arizona provided by Region 9.
4.2.4.9
CISWI
ptnonipm
CONTROL packet reflecting EPA solid waste rule
cobenefits.
4.2.4.10
Data from comments on
previous platforms
nonpt,
ptnonipm,
ptoilgas
CONTROL packets for all other programs,
including Regional Haze, consent
decrees/settlements, and other information from
states/other agencies in prior platforms.
4.2.5
Stand-alone future year
inventories
nonpt,
ptnonipm
Introduction to future-year inventories not generated
via CoST strategies/packets.
38
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Subsection
Title
Sector(s)
Brief Description
4.2.5.1
Portable fuel containers
nonpt
Reflects impacts of Mobile Source Air Toxics
(MSAT2) on PFCs.
4.2.5.2
Biodiesel plants
ptnonipm
Year 2018 new biodiesel plants provided by OTAQ
reflecting planned sited-plants production volumes.
4.2.5.3
Cellulosic plants
nonpt
Year 2018 new cellulosic ethanol plants based on
cellulosic biofuel refinery siting provided by OTAQ
and 2018 NODA.
4.2.5.4
New cement plants
nonpt,
ptnonipm
Year 2018 policy case-derived new cement kilns,
permitted (point) and model-generated based on
shifted capacity from some closed units to open
units (nonpt)
4.2.2 CoST Plant CLOSURE Packet (ptnonipm)
Packet: "CLOSURES_2011 v6_2_v4fix_31 aug2015_08jan2016_v5.txt" (ptnonipm)
The CLOSURES packet contains facility, unit and stack-level closure information derived from the following
sources:
1. Emissions Inventory System (EIS) facilities report from December 20, 2014 with closure status equal to
"PS" (permanent shutdown)
2. EIS unit-level report from November 29, 2014 with status = 'PS' (i.e., post-2011 permanent facility/unit
shutdowns known in EIS as of the date of the report).
3. Concatenation of all 2011v6.0 closures information; see Section 4.2.11.3 from the 2011v6.0 platform
TSD.
4. Comments from states and regional planning organizations on the 201 lv6.2 platform.
5. Closures provided by MARAMA with 201 lv6.3 2023 CoST packets.
Note that no ptoilgas sources are affected by the current CLOSURES packet. The 201 lv6.0 closure
information is from a concatenation of previous facility and unit-level closure information used in the 2008
NEI-based emissions modeling platform used for 2007 air quality modeling. In addition, comments on the
201 lv6.0 emissions modeling platform received by states and other agencies indicated that some previously
specified closures should remain open. Ultimately, all data were updated to match the SMOKE FF10 inventory
key fields, with all duplicates removed, and a single CoST packet was generated. The closures packets include
changes to closure dates for North Carolina, West Virginia and Oklahoma facilities and other changes received
as comments on the NODA for the 201 lv6.2 platform. These changes impact sources in the ptnonipm and
pt oilgas sectors. The cumulative reductions in emissions for ptnonipm are shown in Table 4-4.
Table 4-4. Reductions from all facility/unit/stack-level closures.
Pollutant
ptnonipm
CO
18,180
NH3
489
NOX
14,023
PM10
4,348
39
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PM2.5
3,114
S02
36,206
VOC
15,792
4.2.3 CoST PROJECTION Packets (afdust, ag, cmv, rail, nonpt, np_oilgas, ptnonipm,
pt_oilgas, rwc)
As previously discussed, for point inventories, after application of any/all CLOSURE packet information, the
next step in running a CoST control strategy is the application of all CoST PROJECTION packets. Regardless
of inventory type (point or nonpoint), the PROJECTION packets applied prior to the CoST packets. For several
emissions modeling sectors (i.e., afdust, ag, cmv, rail and rwc), there is only one CoST PROJECTION packet.
For all other sectors, there are several different sources of PROJECTIONS data and, therefore, there are
multiple PROJECTION packets that are concatenated and quality-assured for duplicates and applicability to the
inventories in the CoST strategy. The PROJECTION (and CONTROL) packets were separated into a few
"key" control program types to allow for quick summaries of these distinct control programs. The remainder of
this section is broken out by CoST packet, with the exception of discussion of the various packets used for oil
and gas and industrial source projections; these packets are a mix of different sources of data that targeted
similar sources.
MARAMA provided PROJECTION and CONTROL packets for year 2023 for states including: Connecticut,
Delaware, Maryland, Massachusetts, New Hampshire, New York, New Jersey, North Carolina, Pennsylvania,
Rhode Island, Vermont, Virginia, West Virginia, Maine, and the District of Columbia. MARAMA only
provided pt oilgas and np oilgas packets for Rhode Island, Maryland and Massachusetts. For states not covered
by the MARAMA packets, projection factors for 2023 were generated by interpolating from the 2017 and 2025
packets, except for the nonpt and ptnonipm sectors that represent 2025 levels. The 2025 CoST packets are
documented in the TSD Preparation of Emissions Inventories for the Version 6.2, 2011 Emissions Modeling
Platform (USEPA, 2015b).
4.2.3.1 Paved and unpaved roads VMT growth (afdust)
Packet:
" PROJECTION 201 lel_2023el_AFDUST_VMT_CPP_19sep2016_v0.txt"
"BETA Proj ections_AFDUST_2023_2ljul2016_emf_csv_02sep2016_v0.txt" (MARAMA)
These packets consist of county-level VMT projection factors for paved/unpaved roads and are based on county
comparison of projected year 2023 VMT versus year 2011 VMT. The method for projection VMT to year 2023
can be found in section 4.3.
We received comments from the 2018 NODA (EPA-HO-OAR-2Q13-0809) suggesting we grow emissions from
paved and unpaved road dust as a function of VMT. The resulting national sector-total increase from year 2011
to 2023 in PM2.5 emissions are provided in Table 4-5. Note that this packet does not impact any other sources
of fugitive dust emissions in the afdust sector (e.g., no impact on construction dust, mining and quarrying, etc.).
Table 4-5. Increase in total afdust PM2.5 emissions from VMT projections
2011 Emissions
2023 Emissions
percent Increase
2023
2,510,246
2,753,900
9.71%
40
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4.2.3.2 Livestock population growth (ag)
Packet:
"PROJECTION_201 l_2023_ag_201 lv6_2_no_RFS2_3 Iaug2016_v0.txt"
"BETAProj ections_AG_2023_2ljul2016" (MARAMA)
The EPA estimated animal population growth in NH3 emissions from livestock in the ag sector. Except for
dairy cows and turkey production, the animal projection factors are derived from national-level animal
population projections from the USDA and the Food and Agriculture Policy and Research Institute (FAPRI).
This methodology was initiated in 2005 for the 2005 NEI, but was updated on July 24, 2012, in support of the
2007v5 platform (EPA, 2012). For dairy cows, the EPA assumed that there would be no growth in emissions
based on little change in U.S. dairy cow populations from years 2011 through 2023, 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, the 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 NH3 emissions from dairies are not directly related to animal
population, but also nitrogen excretion. With the cow numbers going down and the production going up, the
excretion value will change, but no change was assumed because a quantitative estimate was not available.
Appendix C provides the animal population data and regression curves used to derive the growth factors.
The national projection factors by animal category and ag sector total impacts are provided in Table 4-6, while
the projection factors for MARAMA states varied by state. As discussed below, dairy cows are assumed to
have no growth in animal population and, therefore, the projection factor for these animals is 1.0 (no growth).
Impacts from the renewable fuels mandate are not included in projections for this sector. The overall average
factor was 1.037 resulting in a 2.47% increase over 2011 and total emissions of 3,609,331 tons.
Table 4-6. NH3 projection factors and total impacts to years 2023 for animal operations
Animal Category
Projection Factors
Dairy Cow
1.000
Beef
0.978
Pork
1.106
Broilers
1.119
Turkeys
0.927
Layers
1.087
Poultry Average
1.078
4.2.3.3 Locomotives and category 1, 2, & 3 commercial marine vessels (cmv, rail,
ptnonipm, othpt)
Packets for rail cmv and ptnonipm:
"PROJECTION2011 v6_3_2023_cl c2rail_B ASE_02sep2016_v0.txt"
"PROJECTION2011_2023_C3_CMV_EC AIMO2011 v6_3_02sep2016_v0.txt"
"BETA Proj ections_C 1 C2RAIL_2023_2ljul2016_emf_csv_02sep2016_v0.txt" (MARAMA)
There are two components used to create projection factors for year 2023. The first component of the future
year cmv and rail inventories is the non-California data projected from the 2011 base case. The second
component is the CARB-supplied year 2011 and 2023 data for California.
41
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For all states outside of California, national projection factors by SCC and pollutant between 2011 and future
years reflect the May 2004 "Tier 4 emissions standards and fuel requirements" as well as the March 2008 "Final
locomotive-marine rule" controls. The future-year cmv and rail emissions account for increased fuel
consumption based on Energy Information Administration (EIA) fuel consumption projections for freight, and
emissions reductions resulting from emissions standards from the Final Locomotive-Marine rule (EPA,
2009d)1. For locomotives, the EPA applied HAP factors for VOC HAPs by using VOC projection factors to
obtain 1,3-butadiene, acetaldehyde, acrolein, benzene, and formaldehyde. Similar to locomotives, C1/C2 VOC
HAPs were projected based on the VOC factor. C1/C2 diesel emissions were projected based on the Final
Locomotive Marine rule national-level factors. These non-California projection ratios are provided in Table
4-7. Note that projection factors for "... Yard Locomotives" (SCC=2285002010) are applied to the ptnonipm
(point inventory) "yard locomotives" (SCC=28500201) reported by a couple of states in the 2011 NEI. Note
that the factors for MARAMA states are similar to those below, but county-specific factors were provided for
North Carolina and those are not reflected in the table.
Table 4-7. Non-California projection factors for locomotives and Category 1 and Category 2 CMV Emissions
SCC
Description
Poll
2023
Factor
2280002XXX
Marine Vessels, Commercial; Diesel; Underway & port emissions
CO
0.955
2280002XXX
Marine Vessels, Commercial; Diesel; Underway & port emissions
NOx
0.603
2280002XXX
Marine Vessels, Commercial; Diesel; Underway & port emissions
PM
0.546
2280002XXX
Marine Vessels, Commercial; Diesel; Underway & port emissions
S02
0.091
2280002XXX
Marine Vessels, Commercial; Diesel; Underway & port emissions
VOC
0.596
2285002006
Railroad Equipment; Diesel; Line Haul Locomotives: Class I
Operations
CO
1.212
2285002006
Railroad Equipment; Diesel; Line Haul Locomotives: Class I
Operations
NOx
0.676
2285002006
Railroad Equipment; Diesel; Line Haul Locomotives: Class I
Operations
PM
0.522
2285002006
Railroad Equipment; Diesel; Line Haul Locomotives: Class I
Operations
S02
0.035
2285002006
Railroad Equipment; Diesel; Line Haul Locomotives: Class I
Operations
VOC
0.486
2285002007
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III
Operations
CO
1.212
2285002007
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III
Operations
NOx
1.062
2285002007
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III
Operations
PM
1.015
2285002007
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III
Operations
S02
0.035
2285002007
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III
Operations
VOC
1.212
2285002008
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger
Trains (Amtrak)
CO
1.101
1 This rule lowered diesel sulfur content and tightened emission standards for existing and new locomotives and marine diesel
emissions to lower future-year PM, SO2, and NOx, and is documented at Vehicles and Engines.
42
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2285002008
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger
Trains (Amtrak)
NOx
0.519
2285002008
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger
Trains (Amtrak)
PM
0.418
2285002008
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger
Trains (Amtrak)
S02
0.032
2285002008
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger
Trains (Amtrak)
VOC
0.356
2285002009
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter
Lines
CO
1.101
2285002009
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter
Lines
NOx
0.519
2285002009
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter
Lines
PM
0.418
2285002009
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter
Lines
S02
0.032
2285002009
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter
Lines
VOC
0.356
2285002010
Railroad Equipment; Diesel; Yard Locomotives
CO
1.212
2285002010
Railroad Equipment; Diesel; Yard Locomotives
NOx
0.873
2285002010
Railroad Equipment; Diesel; Yard Locomotives
PM
0.845
2285002010
Railroad Equipment; Diesel; Yard Locomotives
S02
0.035
2285002010
Railroad Equipment; Diesel; Yard Locomotives
VOC
0.812
For California projections, the CARB provided to the EPA the locomotive, and C1/C2 commercial marine
emissions used to reflect years 2011 and 2023. These CARB inventories included 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 (nm) of the California baseline.
The California C1/C2 CMV and locomotive year-specific 2023 emissions were obtained from the CARB in the
form of Excel workbooks. These data were converted to SMOKE FF10 format. These emissions were
developed using Version 1 of the CEP AM, which supports various California off-road regulations.
Documentation of the CARB off-road methodology, including cmv and rail sector data.
The non-California projection factors were applied to all "offshore" CI and C2 CMV emissions. These
offshore emissions, in the 2011 NEI, start at the end of state waters and extend out to the EEZ. A summary of
the national impact for the U.S. (including California) and rail and offshore CI &C2 cmv sector emissions are
provided in Table 4-8.
Table 4-8. Difference in Category 1& 2 cmv and rail sector emissions between 2011 and 2023,
Region
Pollutant
2011
2023
Difference 2023 -
2011
U.S. CMV
CO
70,408
76,265
5,857
U.S. CMV
NOx
413,314
280,626
-132,688
U.S. CMV
PMio
19,629
7,513
-12,116
U.S. CMV
PM2.5
18,099
7,039
-11,060
43
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U.S. CMV
S02
91,045
6,811
-84,234
U.S. CMV
voc
12,578
12,880
302
Offshore CMV
CO
66,395
63,421
-2,974
Offshore CMV
NOx
326,631
197,021
-129,610
Offshore CMV
PMio
10,795
5,894
-4,901
Offshore CMV
PM2.5
10,471
5,717
-4,754
Offshore CMV
S02
4,014
366
-3,648
Offshore CMV
VOC
7,472
4,453
-3,019
U.S. rail
CO
122,703
145,627
22,924
U.S. rail
NOx
791,381
563,382
-227,999
U.S. rail
PM10
25,898
14,236
-11,662
U.S. rail
PM2.5
23,963
13,165
-10,798
U.S. rail
S02
7,936
340
-7,596
U.S. rail
VOC
40,851
21,384
-19,467
As discussed in Section 2.4.1 of the 201 lv6.3 platform TSD, the EPA estimates for C3 CMV, emissions data
were developed for year 2002 and projected to year 2011 for the 2011 base case, and used where states did not
submit data to Version 2 of the 2011 NEI. Pollutant and geographic-specific projection factors to year 2011
were applied, along with projection factors to years 2023 that reflect assumed growth and final ECA-IMO
controls. These emissions estimates reflect the EPA's coordinated strategy for large marine vessels. More
information on the EPA's coordinated strategy for large marine vessels can be found in our Category 3 Marine
Diesel Engines and Fuels regulation published in April 2010. That rule, as well as information about the North
American and U.S. Caribbean Sea ECAs, designated by amendment to MARPOL Annex VI.
Projection factors for creating the year 2023 cmv inventory from the 2011 base case are provided in Table 4-9.
For more information on the mapping of the states to each EEZ, see Section 2.4.1 of the 201 lv6.3 platform
TSD. For example, Washington state emissions are grown the same as all North Pacific offshore emissions.
Table 4-9. Growth factors to project the 2011 ECA-IMO inventory to 2023
Region
EEZ
(offshore)
FIPS
CO
NOx
PM10
PM25
SO2
VOC
North Pacific
(NP)
85001
1.49
0.85
0.2
0.2
0.06
1.49
South Pacific (SP)
85002
1.86
0.95
0.26
0.26
0.07
1.86
East Coast (EC)
85004
1.71
0.89
0.23
0.23
0.06
1.71
Gulf Coast (GC)
85003
1.42
0.75
0.19
0.19
0.05
1.42
Great Lakes (GL)
n/a
1.23
0.95
0.16
0.16
0.04
1.23
Outside ECA
98001
1.72
1.39
0.63
0.63
0.58
1.72
Packet for othpt:
"PROJECTION2011_2023_C3_CMV_ECA_IMO_2011 v6_3_02sep2016_v0.txt"
Note that the MARAMA packet provided in
BETA_Projections_C3Marine_2023_20feb2016_emf_csv_02sep2016_v0.txt was not used because the offshore
44
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emissions were not in a MARAMA state. As discussed in Section 2.4.2 of the 201 lv6.3 platform TSD,
emissions outside the 3 to 10-mile coastal boundary, but within the approximately 200 nm EEZ boundaries,
were projected to year 2023 using the same regional adjustment factors as the U.S. emissions; however, the
FIPS codes were assigned as "EEZ" FIPS and these emissions are processed in the "othpt" sector. 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 Canadian-provided C3 CMV emissions in the
Great Lakes.
The cumulative impact of these ECA-IMO projections and controls to the U.S. + near-offshore (cmv sector) and
far-offshore emissions (othpt sector) in 2023 is provided in Table 4-10.
Table 4-10. Difference in Category 3 cmv sector and othpt C3 CMV emissions between 2011 and 2023
Region
Pollutant
2011
emissions
2023
emissions
Difference
2023-2011
Offshore to EEZ*
CO
133,574
173,938
40,364
Offshore to EEZ*
NOX
798,258
728,724
-69,534
Offshore to EEZ*
PM10
28,451
6,854
-21,597
Offshore to EEZ*
PM25
26,113
6,293
-19,820
Offshore to EEZ*
S02
222,113
16,509
-205,604
Offshore to EEZ*
VOC***
81,593
98,753
17,160
Non-US SECA C3
CO
187,439
321,978
134,539
Non-US SECA C3
NOX
2,209,800
3,078,374
868,574
Non-US SECA C3
PM10
187,587
118,375
-69,212
Non-US SECA C3
PM2 5
172,580
108,413
-64,167
Non-US SECA C3
S02
1,391,702
803,736
-587,966
Non-US SECA C3
VOC***
79,575
136,692
57,117
* - Offshore to EEZ includes both c3marine, and the offshore oil rigs/etc from the US point inventory
*** - INCLUDES pre-speciated inventory VOC in Canada, so post-SMOKE VOC_INV < VOC
4.2.3.4 Upstream distribution, pipelines and refineries (nonpt, ptnonipm, ptoilgas)
Packet:
ptnonpim and nonpt sectors only:
"PROJECTION201 l_2025_OTAQ_upstream_GasDist_pipelines_refineries_201 Iv6_2_05feb2015_05feb2015_v0.txt"
pt_oilgas sector only: "PROJECTION_201 lv6_2025_pipelines_refineries
"BETA_Projections_OTAQ_Upstream_GasDist_2023_20feb2016_emf_csv_02sep2016_v0.txt" (MARAMA)
To account for projected increases in renewable fuel volumes due to the Renewable Fuel Standards
(RFS2)/EISA (EPA, 2010a) and decreased gasoline volumes due to RFS2 and light-duty greenhouse gas
standards as quantified in AEO 2014. the EPA developed county-level inventory adjustments for gasoline and
gasoline/ethanol blend transport and distribution. Here, for non-MARAMA states, year 2025 factors are used
for year 2023. MARAMA provided year 2023-specific factors. These adjustments account for losses during
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 only account for impacts of
RFS2, and the 2025 adjustments also account for additional impacts of greenhouse gas emission standards for
45
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motor vehicles (EPA, 2012b) on transported volumes.. These emissions are entirely evaporative and, therefore,
limited to VOC.
A 2018 inventory that included impacts of the EISA mandate was developed by applying adjustment factors to
the 201 1NEIv2 inventory. These adjustments were made using an updated version of the EPA's model for
upstream emission impacts, developed for the RFS2 rule2. The methodology used to make these adjustments is
described in a 2014 memorandum included in the docket for the EPA Tier 3 rule (EPA, 2014)3.
Ethanol emissions were estimated in SMOKE by applying the ethanol to VOC ratios from headspace profiles to
VOC emissions for E10 and E15, and an evaporative emissions profile for E85. These ratios are 0.065 for E10,
0.272 for E15, and 0.61 for E85. The E10 and E15 profiles were obtained from an ORD analysis of fuel
samples from EPAct exhaust test program4 and were submitted for incorporation into the EPA's SPECIATE
database. The E85 profile was obtained from data collected as part of the CRC E-80 test program (Environ,
2008) and was also submitted into the EPA's SPECIATE database. For more details on the change in
speciation profiles between the base and future years, see Section 3.2 of the 201 lv6.3 platform TSD.
Pipeline usage and refinery emissions were adjusted to account for impacts of the 2017-2025 light duty vehicle
greenhouse gas emission standards, as well as renewable fuel volume projections. These adjustments were
developed by the EPA's OTAQ and impact processes such as process heaters, catalytic cracking units,
blowdown systems, wastewater treatment, condensers, cooling towers, flares and fugitive emissions.
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 the 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. These
impacts of decreased production were assumed to be spread evenly across all U.S. refineries. Toxic emissions
were estimated in SMOKE by applying speciation to VOC emissions. It should be noted that the adjustment
factors are estimated relative to that portion of refinery emissions associated with gasoline and diesel fuel
production. Production of jet fuel, still gas and other products also produce emissions. If these emissions were
included, the adjustment factors would not be as large.
The resulting adjustments for pipelines, refineries and the gasoline distribution processes (RBT, BPS and BTP)
are provided in Table 4-11. 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, for all component types of petroleum transport and storage components. An additional
adjustment was applied for 2025 at a national scale to account for impacts of gasoline volume reductions of the
2017-2025 light-duty greenhouse gas rule.
Notice that the "2011 Emissions" are not the same in Table 4-11. This is because these "2011" emissions are
actually an intermediate set up projections applied after a first CoST strategy used to apply most other
PROJECTION and CONTROL packets. We decided to first apply these other packets because we have
2 U.S. EPA. 2013. Spreadsheet "upstreamemissionsrev T3.xls.
3 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.
4 U.S. EPA. 2011. Hydrocarbon Composition of Gasoline Vapor Emissions from Enclosed Fuel Tanks. Office of Research and
Development and Office of Transportation and Air Quality. Report No. EPA-420-R-11-018. EPA Docket EPA-HQ-OAR-2011-
0135.
46
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multiple PROJECTION and CONTROL programs that impact the same emission sources. For this example, we
applied year-specific industrial sector AEO-based growth (discussed in the next section) with our first CoST
strategy, then applied these "EISA" adjustments on the results of this first CoST strategy. Similarly, we have
RICE existing NESHAP, as well as NSPS, controls that need to be applied in separate strategies. Alternatively,
we could have made "compound" CoST packets that combine these PROJECTION (and CONTROL) factors,
but preferred to keep these packets separate for transparency. If we tried to process the multiple packets
affecting the same sources in a single CoST strategy, CoST would either fail if the packet entries were are the
same key-field resolution (duplicate error), or, if packets were at a different key-field resolution, CoST would
only apply the packet entry with higher priority according to Table 4-2.
Table 4-11. Petroleum pipelines & refineries and production storage and transport factors and reductions
Poll
Year
Factors
2011
Emissions
Reduction
%
Reduction
Pipelines &
Refineries
RBT
BTP/BPS
CO
2023
0.9445
n/a
n/a
53,501
2,969
5.55%
NOX
2023
0.9348
n/a
n/a
68,354
4,454
6.52%
PM10
2023
0.9668
n/a
n/a
24,484
813
3.32%
PM2.5
2023
0.9679
n/a
n/a
21,599
694
3.21%
S02
2023
0.9517
n/a
n/a
78,944
3,815
4.83%
VOC
2023
0.9650
n/a
n/a
750,025
26,266
3.50%
4.2.3.5 Oil and gas and industrial source growth (nonpt, npoilgas, ptnonipm, ptoilgas)
Packets:
ptnonipm and nonpt sectors:
"PROJECTION201 lv6_2_2025_SCC_POINT_LADCO_09dec2014_09dec2014_v0.txt"
"PROJECTION201 lv6_2_2025_NAICS_SCC_SCA_orig_NEI_matched_CAPPED2_5_04dec2014_04dec2014_v0.txt"
"PROJECTION201 lv6_2_2025_SCC_POINT_SCA_orig_CAPPED_09dec2014_04feb2015_v 1 .txt"
"PROJECTION_2011v6_2_2025_SRAcapped_POINT_05dec2014_05dec2014_v0.txt"
'PROJECTION_TCEQ_ptnonipm_NAICS_comments_2011v6_2025_revised_16jul2015_v0.txt"
"PROJECTION_2011v6_2_2025_SCC_NONPOINT_LADCO_09dec2014_09dec2014_v0.txt"
"PROJECTION201 lv6_2_2025_SCC_NONPOINT_SCA_orig_CAPPED_09dec2014_09dec2014_v0.txt"
"PROJECTION201 lv6_2_2025_nonpoint_SCC_SRAcapped_05dec2014_05dec2014_v0.txt"
" PROJECTION_2011_2025_aircraft_ST_and_by_airport_22jan2015"
pt oilgas and np oilgas sectors:
"proj ections_np_oilgas_2023_csv_ 19sep2016_v0 .txt"
"proj ections_pt_oilgas_2023_csv_ 19sep2016_v0 .txt"
"PROJECTION2011v6.3: 2017_Oklahoma_source_NODA_lljan2016_vl.txt"
"PROJECTION_VA_ME_TCEQ_AL_comments_201 lv6_2019"
"PROJECTION_2011v6.2_2025_TCEQ_v6_leftovers_NONPOINT_30jan2015"
MARAMA states:
"BETA_Projections_NP_OILGAS_2023_22apr2016_emf.csv" (MARAMA)
"BETA_Projections_PT_OILGAS_2023_24aug2016_emf.csv" (MARAMA)
"BETA_Projections_PT_NonERTAC_2023_24aug2016_emf.csv" (MARAMA)
"BETA_Projections_PT_Small_EGU_2023_25jul2016_emf.csv" (MARAMA)
"BETA_Projections_NonPoint_2023_2016_08_24_emf.csv" (MARAMA)
"BETA_Projections_NONPT_REFUELING_2023_25jul2016_emf.csv" (MARAMA)
"BETA_Projections_Aircraft_Engine_GSE_APU_2023_10aug2016_emf.csv" (MARAMA)
47
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The EPA provided a NODA EPA-HQ-QAR-2013-0809 for the 201 lv6.0 emissions modeling platform and
projected 2018 inventory in January, 2014. A significant number of the comments were about the EPA's "no
growth" assumption for industrial stationary sources and about the current projection approach for oil and gas
sources that was applied similarly to five broad geographic (NEMS) regions and limited to only oil and gas
drilling activities.
With limited exceptions, the EPA has used a no-growth assumption for all industrial non-EGU emissions since
the 2005 NEI-based emissions modeling platform (EPA, 2006). However, comments provided to the EPA for
this platform (via the NODA) and for previous modeling platforms suggested that this approach was
insufficient. In addition, the NOx Budget program, which had a direct impact on post-2002 emissions
reductions, is in full compliance in the 2011 NEI. This means that additional large-scale industrial reductions
should not be expected beyond 2011 in the absence of on-the-books state and federal rules.
In response to the comments about the EPA's no-growth approach, the EPA developed industrial sector
activity-based growth factors. In response to the NODA, many states have additionally provided detailed
activity-based projection factors for industrial sources, including oil and gas sources. To develop the methods
described here, we have blended the state-provided growth factors with the EPA-developed industrial sector
growth factors. This approach has attempted to balance using the specific information that is available with the
EPA's interest in consistency for a given sector and technical credibility. Table 4-11 lists the new resulting data
sources for industrial sector non-EGU growth factors that the EPA applied to estimate year 2023 emissions for
this emissions modeling platform. That additional data were considered and included in our projections as well,
and are discussed separately in Section 4.2.3Error! Reference source not found..
Ultimately, there were three broad sources of projection information for industrial sources, including oil and
gas; these sources are referenced as the following for simplicity:
1) EPA:
a. (NEW) Reflects EPA-generated factors based on AEO2016 reference case production data
(label dated "19sep2016").
b. Reflects EPA-sponsored data provided by a contractor (SC&A, 2014a; SC&A, 2014b). Packet
file names for these data include "SCA."
2) MARAMA:
a. Reflects data submitted on behalf of Atlantic seaboard states from North Carolina through
Maine, and extending west through Pennsylvania and West Virginia. Packet file names for these
data include "SRA" (SRA, 2014).
b. (NEW) Reflects data submitted on behalf of Atlantic seaboard states from North Carolina
through Maine, and extending west through Pennsylvania and West Virginia. Packet file names
that begin with "BETA" (MARAMA, 2016).
3) LADCO: Reflects data submitted on behalf of Lake Michigan Air Directors Consortium (LADCO)
states (MN, WI, MI, IL, IN, OH). Projection data from this data source are reflected in packet names
containing "LADCO" (Alpine Geophysics, 2014).
48
-------
Table 4-12. Sources of new industrial source growth factor data for year 2023 in the 201 lv6.3 platform
Abbrev.
Source
Geographic
Resolution
Inventory
Resolution
Use/Caveat
MARAMA "BETA" packets
MARAMA/states
using 2015 AEO
data and other
data sources
State or county
for nonpoint and
facility and
below for most
point sources
Facility and
sub-facility
for point,
SCC-level for
nonpoint
Provided by
MARAMA (2016) for
year-2023 specific
projection purposes.
EPA
New projection packets for 2023:
"projections_np_oilgas_2023_csv_
19sep2016 v0.txt"
"projections_pt_oilgas_2023_csv_
19sep2016 v0.txt"
Non-MARAMA
states using 2016
AEO Crude Oil
Production and
Natural Gas
Production data
EIA Supply
Region
State or
county/ SCC
Impacts both point
and nonpoint oil and
gas sectors as well as
some non-EGU point
sources not in the
pt oilgas sector.
Table 4-12 above lists only the new projection packets used to estimate year 2023 emissions for this modeling
effort. MARAMA provided year-2023 specific factors for all sectors mentioned in this section. The EPA
generated factors using AEO2016 data were also year-2023 specific emissions. The previous TSDs for
201 lv6.2 and 201 lv6.3 describe the other packets mentioned earlier in this section. Specifically, year 2025
packets mentioned in this section are described in the 201 lv6.2 TSD (EPA, 2015b).
Natural Gas Consumption and Crude Oil Production
The oil and gas sector is rapidly changing in various regions throughout the U.S. To better capture these recent
trends and to forecast to year 2023, the AEO 2016 reference case data was used to project production-related oil
and gas sources. The AEO2016 tables used include the National Oil and Gas Supply Table #14, Lower 48
Crude Oil Production Table #60, and Lower 48 Natural Gas Production Table #61. The National Oil and Gas
Supply Table was used to project emissions nationally related to Coalbed Methane and Natural Gas Plant
Liquids production. The Lower 48 Crude Oil Production was used to project emissions related to oil production
for the six EIA Supply Regions (Figure 4-1) plus offshore regions. The Lower 48 Natural Gas Production
Table was used to project emissions related to natural gas dry production for the six EIA Supply Regions plus
offshore regions. Table 4-13 shows the projection factors for year 2023 for these EIA Supply Regions for
Natural Gas Dry and Oil production. An average of the two factors is also provided. These projection factors
were applied to appropriate production related SCCs in the NEI201 lv2 inventory. In cases where a SCC
description listed both oil and gas production processes may be involved, the average projection factor was used
for that EIA Supply Region. The states and counties that are part of each EIA Supply Region were defined so
that the projection packets generated would include the appropriate FIPS codes.
The MARAMA states provided similar projection packets for oil and gas sectors but used the AEO2015
reference case and used Eastern EIA Supply Region data. MARAMA also assumed no growth for the State of
New York in the npoilgas sector. The net impacts of these projection packets for each of the modeling sectors
is provided in Table 4-14.
Table 4-13. Year 2023 projection factors derived from AEO2016 for each EIA Supply Region.
EIA Supply Region
Natural
Gas Dry
Production
Oil
Production
Average Oil
and Gas
East
4.777
1.901
3.339
49
-------
Gulf Coast
1.702
2.069
1.885
Midcontinent
0.855
0.933
0.894
Southwest
0.858
2.071
1.465
Dakotas/Rockv Mtns
0.961
2.997
1.979
West Coast
0.750
0.774
0.762
OFFSHORE
0.652
1.281
0.966
Figure 4-1. Oil and Gas NEMS Regions
Coast (6)
ocky Mo
Midcontinent (3)
Southwest (4)
Shallow Gulf olMexico (9)
Pacific (8)
Atlantic
/ Deep Gulf of Mexico (10)
Source U S Energy Information Administration
50
-------
Table 4-14. Industrial source projections net impacts for 2023
Pollutant
Sector
2011 Emissions
Subject to
projection
Intermediate
Projected
Emissions
Difference
(Future - 2011)
% Difference
(Future - 2011)
CO
nonpt
733,239
790,635
57,396
8%
CO
np_°ilgas
634,109
1,128,796
494,687
78%
CO
pt_oilgas
233,454
293,484
60,030
26%
CO
ptnonipm
1,053,603
1,178,631
125,027
12%
CO
Total
2,654,405
3,391,546
737,140
28%
\ll
nonpl
IX.3XI
18.830
449
1)
\ll
pi oilijas
257
244
-13
n'I
^ 1)
Ml
plnonipni
I2>75
13.569
894
7%,
Ml
Total
31.314
32.644
1,330
4%
NOx
nonpt
499,419
517,606
18,187
4%
NOx
np_°ilgas
666,560
1,138,413
471,853
71%
NOx
pt_oilgas
525,974
593,058
67,084
13%
NOx
ptnonipm
781,910
888,425
106,515
14%
NOx
Total
2,473,863
3,137,502
663,638
27%
I'M
nonpl
315.788
34.856
12",.
I'M
up oilijas
17.7X2
31.508
13.726
77%
I'M
pi oilijas
14.22S
16.058
1.830
1 "»<>
1 o
I'M
plnonipni
147.37^
1 M.544
17.168
12",.
I'M in
Total
460.319
527,898
67,579
15%
PM2.5
nonpt
224,860
254,129
29,268
13%
PMz.5
np_oilgas
16,331
28,631
12,299
75%
PMz.5
pt_oilgas
13,955
15,748
1,794
13%
PMz.5
ptnonipm
118,527
134,163
15,636
13%
pm25
Total
373,673
432,670
58,997
16%
SO
nonpl
253.XX5
237.039
-7%
so
np oilijas
17.232
42.312
25.080
I4ft"
so
pi oilijas
NI.X32
78.892
1X.()(¦>()
.Ml o
so
plnonipni
540.547
546.037
5.490
1",.
SO;
Total
872.495
904.280
31,785
4%
voc
nonpt
1,098,831
1,151,852
53,021
5%
voc
np_oilgas
2,483,828
4,542,456
2,058,628
83%
voc
pt_oilgas
147,389
184,212
36,823
25%
voc
ptnonipm
177,562
202,823
25,262
14%
voc
Total
3,907,609
6,081,343
2,173,734
56%
51
-------
4.2.3.6 Aircraft (ptnonipm)
Packet:
"PRO JECTION201 l_2025_aircraft_ST_and_by_airport_22j an2015_v0 .txt"
"BETA_Proj ections_Aircraft_Engine_GSE_APU_2023_l 0aug2016_emf.csv" (MARAMA)
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 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. The EPA used projected ITN
information available from the Federal Aviation Administration's (FAA) Terminal Area Forecast (TAF) System
(publication date March, 2014). This information is available for approximately 3,300 individual airports, for
all years up to 2040. The methods that the FAA used for developing the ITN data in the TAF.
None of our aircraft emission projections account for any control programs. The EPA considered the NOx
standard adopted by the International Civil Aviation Organization's (ICAO) Committee on Aviation
Environmental Protection (CAEP) in February 2004, which is expected to reduce NOx by approximately 3
percent by 2020. However, this rule has not yet been adopted as an EPA (or U.S.) rule and, therefore, its effects
were not included in the future-year emissions projections.
The EPA developed two sets of projection factors for aircraft. The first set was a simple state-level aggregation,
used primarily for airports with very little activity, by ITN operation type (commercial, general aviation,
military and air taxi) to be used as a default method for projecting from 2011 to future years. The second set of
projection factors was by airport, where the EPA projects emissions for each individual airport with significant
ITN activity.
Where NEI facility identifiers were not matched to FAA airport identifiers, we simply summed the ITN
operations to state totals by year and aircraft operation and computed projection factors as future-year ITN to
year-2011 ITN. The EPA assigned factors to inventory SCCs based on the operation type shown in Table 4-15.
Table 4-15. NEI SCC to FAA TAF ITN aircraft categories used for aircraft projections
FAA ITN
SCC
Description
Type
Commercial Aircraft: 4-stroke Airport Ground Support
2265008005
Equipment
Commercial
2267008005
Commercial Aircraft: LPG Airport Ground Support Equipment
Commercial
Commercial Aircraft: CNG Airport Ground Support
Commercial
2268008005
Equipment
Commercial Aircraft: Diesel Airport Ground Support
Commercial
2270008005
Equipment
2275000000
All Aircraft Types and Operations
Commercial
2275001000
Military Aircraft, Total
Military
2275020000
Commercial Aviation, Total
Commercial
2275050011
General Aviation, Piston
General
2275050012
General Aviation, Turbine
General
2275060011
Air Taxi, Total: Air Taxi, Piston
Air Taxi
2275060012
Air Taxi, Total: Air Taxi, Turbine
Air Taxi
52
-------
see
Description
FAA ITN
Type
2275070000
Commercial Aircraft: Aircraft Auxiliary Power Units, Total
Commercial
27501015
Internal Combustion Engines; Fixed Wing Aircraft L & TO
Exhaust; Military; Jet Engine: JP-5
Military
27502011
Internal Combustion Engines; Fixed Wing Aircraft L & TO
Exhaust; Commercial; Jet Engine: Jet A
Commercial
27505001
Internal Combustion Engines; Fixed Wing Aircraft L & TO
Exhaust; Civil; Piston Engine: Aviation Gas
General
27505011
Internal Combustion Engines; Fixed Wing Aircraft L & TO
Exhaust; Civil; Jet Engine: Jet A
General
Most NEI airports matched FAA TAF identifiers and, therefore, use airport-specific projection factors. We
applied a cap on projection factors of 2.0 (100 percent increase) for state-level defaults and 5.0 for airport-
specific entries. None of the largest airports/larger-emitters had projection factors close to these caps. A
national summary of aircraft emissions between 2011 and future year 2023 are provided in Table 4-16.
Table 4-16. National aircraft emission projection summary
Emissions
Difference
%
Difference
2011
2025
2025-2011
2025
CO
489,867
559,797
69,930
4.05%
NOx
120,968
157,610
36,642
8.85%
PMio
9,165
10,039
874
2.27%
PM2.5
7,891
8,709
818
2.46%
S02
14,207
18,139
3,932
7.38%
voc
32,023
38,077
6,054
4.93%
4.2.3.7 Cement manufacturing (ptnonipm)
Packet:
"PROJECTION201 l_2025_ISIS_cement_by_CENSUS_DIVISION.txt"
As indicated in Table 4-1, the Industrial Sectors Modeling Platform (ISMP) (EPA, 2010b) was used to project
the cement industry component of the ptnonipm emissions modeling sector to 2025; we used year 2025
emissions for year 2023. This approach provided reductions of criteria and select hazardous air pollutants. The
ISMP cement emissions were developed in support for the Portland Cement NESHAPs and the NSPS for the
Portland cement manufacturing industry.
The ISMP model produced a Portland Cement NESHAP policy case of multi-pollutant emissions for individual
cement kilns (emission inventory units) that were relevant for years 2015 through 2030. These ISMP-based
emissions are reflected using a CoST packet for all existing kilns that are not impacted by more local
information from states (or consent decrees). ISMP also generates new cement kilns that are permitted (point
inventory) and not-permitted, but generated based on ISMP assumptions on demand and infrastructure (nonpt
inventory). These new cement kilns are discussed in Section 4.2.5.4.
53
-------
The PROJECTION packets contain U.S. census division level based projection factors for each NEI unit (kiln)
based on ISMP updated policy case emissions at existing cement kilns. The units that closed before 2025 are
included in the 2025 base case but are included in other CoST packets that reflect state comments and consent
decrees (discussed in Section 4.2.4.10).
The ISMP model, version August 2013, was used for these projections. Recent data updates include updated
matching of kilns to better capture recent retirements, capacity additions and projections of capacity additions
from Portland Cement Association (PC A) 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 4-2, domestic production of
cement grows significantly between 2011 and 2015, then more slowly through 2018. Meanwhile, emissions
from NESHAP-regulated pollutants such as PM and SO2 drop significantly based on regulated emissions rates.
Emissions for NOx increase, though not as much as production because the ISMP model continues the recent
trend in the cement sector of the replacement of lower capacity, inefficient wet and long dry kilns with bigger
and more efficient preheater and precalciner kilns.
Figure 4-2. Cement sector trends in domestic production versus normalized emissions
250
200
150
100
50
/y \P hfp ^ ftV rtb ^ (A
Domestic Production
PM trend
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
ISMP model provides the emission reduction strategy that balances: 1) optimal (least cost) industry operation;
2) cost-effective controls to meet the demand for cement; and 3) emission reduction requirements over the time
period of interest.
The first step in using ISMP 2025 projected emissions is matching the kilns in future years to those in the 2011
NEI. While ISMP provides by-kiln emissions for each future year, the EPA cement kilns experts preferred that
the agency project existing cement kilns based on a more-smooth geographic approach to reduce the "on/off'
switching that ISMP assigns to each kiln based on production and capacity demands. It would be inefficient
and unrealistic to project existing cement kilns to operate as essentially 0 percent or 100 percent capacity based
strictly on ISMP output. Therefore, the EPA developed a U.S. Census Division approach where ISMP
54
-------
emissions in 2011 and future years, that matched the 2011 NEI (e.g., not new ISMP kilns), were aggregated by
pollutant for each year within each of the nine census divisions in the contiguous U.S. These aggregate
emissions were used to create 2025/2011 emissions ratios for each pollutant and geographic area. The
projection ratios, provided in Table 4-17, were then applied to all 2011 NEI cement kilns, except for kilns
where specific local information (e.g., consent decrees/settlements/local information) was available.
Table 4-17. U.S. Census Division ISMP-based projection factors for existing kilns
Region
Division
NOx
PM
SOi
voc
2025
2025
2025
2025
Midwest
East North Central
2.053
0.144
3.034
0.67
Midwest
West North Central
1.279
0.673
1.262
0.492
Northeast
Middle Atlantic
1.221
0.119
0.867
0.569
Northeast
New England
2.56
0.004
3.563
0.713
South
East South Central
0.999
0.109
0.402
0.323
South
South Atlantic
1.077
0.339
0.936
0.42
South
West South Central
1.526
0.174
0.664
0.252
West
Mountain
1.321
1.032
1.366
0.345
West
Pacific
1.465
0.006
0.251
0.29
Table 4-18 shows the magnitude of the ISMP census division based projected cement industry emissions at
existing NEI facilities from 2011 to future year 2025; we use 2025 projected emissions for year 2023.
Additional new kiln emissions generated by ISMP are discussed in Section 4.2.5.4. There are some local
exceptions where the EPA did not use ISMP-based projections for cement kilns where local information from
consent decrees/settlements and state comments were used instead. Cement kilns projected using these non-
ISMP information are not reflected here in Table 4-18.
Table 4-18. ISMP-based cement industry projected emissions
Emissions
Tons
Difference
%
Difference
2011
2025
2025
2025
NOx
53,240
75,680
22,440
42.10%
PM10
2,954
1,033
-1,921
-65.00%
PM2.5
1,709
657
-1,052
-61.60%
S02
15,876
25,579
9,702
61.10%
VOC
2,503
1,026
-1,477
-59.00%
4.2.3.8 Corn ethanol plants (ptnonipm)
Packet:
"PROJECTION 2011 2025 Corn Ethanol Plants AEO2014 Tablel7 2011v6.2 19feb2015 v0.txt"
55
-------
We used the AEO 2014 renewable forecast projections of "From Corn and Other Starch" to compute national
year 2025 growth in ethanol plant production. Per OTAQ direction, we exempted two facilities ('Highwater
Ethanol LLC' in Redwood county MN and 'Life Line Foods LLC-St. Joseph' in Buchanan county MO) from
these projections; future year emissions were equal to their 2011 NEI v2 values for these two facilities.
The 2011 corn ethanol plant emissions were projected to account for the change in domestic corn ethanol
production between 2011 and future years, from approximately 13.9 Bgal (billion gallons) in 2011 to 13.2 Bgal
by 2025 based on AEO 2014 projections. The projection was applied to all pollutants and all facilities equally.
Table 4-19 provides the summaries of estimated emissions for the corn ethanol plants in 2011 and future year
2025.
Table 4-19. 2011 and 2025 corn ethanol plant emissions [tons]
Emissions
Difference
%
Change
2011
2025
2025
2025
CO
877
831
-46
-5.19%
NOx
1,328
1,259
-69
-5.19%
PMio
1,259
1,194
-65
-5.19%
PM2.5
302.243
286.545
-16
-5.19%
S02
9.52755
9.03272
0
-5.19%
voc
3,084
2,924
-160
-5.19%
4.2.3.9 Residential wood combustion (rwc)
Packet:
"PROJECTION_2011_2023_RWC_201 lv6.3.csv"
"BETAProj ections_RWC_2023_l 8apr2016_emf.csv" (MARAMA)
The EPA applied the recently-promulgated national NSPS for wood stoves to the RWC projections
methodology for this platform. To learn more about the strengthened NSPS for residential wood heaters. The
EPA projected RWC emissions to year 2017 and 2025 based on expected increases and decreases in various
residential wood burning appliances. The EPA linearly interpolated these factors to year 2023 for this modeling
platform. As newer, cleaner woodstoves replace some older, higher-polluting wood stoves, there will be an
overall reduction of the emissions from older "dirty" stoves but an overall increase in total RWC due to
population and sales trends in all other types of wood burning devices such as indoor furnaces and outdoor
hydronic heaters (OHH). It is important to note that our RWC projection methodology does not explicitly
account for state or local residential wood control programs. There are a number more-stringent state and local
rules in place in 2011, specifically in California, Oregon and Washington. However, at this time, the 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, the EPA is using national level growth rates for each RWC
SCC category. After discussions with California air districts, regional office contacts and EPA experts, the
EPA decided to hold RWC emissions flat (unchanged) for all SCCs in California, Oregon and Washington.
Assumed Appliance Growth and Replacement Rates
The development of projected growth in RWC emissions to year 2017 and 2025 starts with the projected growth
in RWC appliances derived from year 2012 appliance shipments reported in the Regulatory Impact Analysis
56
-------
(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 percent 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
percent (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 percent of
new woodstoves will replace older non-EPA certified freestanding stoves (pre-1988 NSPS) and 5 percent will
replace existing EPA-certified catalytic and non-catalytic stoves that currently meet the 1988 NSPS (Houck,
2011).
The EPA RWC NSPS experts assume that 10 percent of new pellet stoves and OHH replace older units and that
because of their short lifespan, that 10 percent of indoor furnaces are replaced each year; these are the same
assumptions used since the 2007 emissions modeling platform (EPA, 2012d). The resulting growth factors for
these appliance types varies by appliance type and also by pollutant because the emission rates, from EPA RWC
tool (EPA, 2013rwc), vary by appliance type and pollutant. For EPA certified units, the projection factors for
PM are lower than those for all other pollutants. The projection factors also vary because the total number of
existing units in 2011 varies greatly between appliance types.
NSPS Overview
The residential wood heaters NSPS final rule was promulgated on February 3, 2015. This rule does not affect
existing woodstoves or other wood burning devices; however, it does provide more stringent emissions
standards for new woodstoves, outdoor hydronic heaters and indoor wood-burning forced air furnaces. New
"Phase 1" less-polluting heater standards began in 2015, with even more-stringent Phase 2 standards beginning
in 2020. The associated reduced emission rates for each appliance type (SCC) are applied to all new units sold,
some of which are assumed to replace retired units, since year 2015.
Currently the 1988 NSPS limits primary PM2.5 emissions from adjustable burn rate stoves, including fireplace
inserts and freestanding woodstoves, to 7.5 grams/hour (g/hr) for non-catalytic stoves and 4.1 g/hr for catalytic
stoves. The final NSPS limits PM2.5 emissions for room heaters, which include adjustable and single burn rate
stoves and pellet stoves to 4.5 g/hr in 2015 and 1.3 g/hr in 2020. In addition, the final NSPS limits PM2.5
emissions from hydronic heaters to 0.32 lb/MMBtu heat output in 2015, and 0.06 lb/MMBtu in 2020. The final
NSPS limits PM2.5 emissions from indoor furnaces to 0.93 lb/MMBtu in 2015 and 0.06/MMBtu in 2020.
Emission factors were estimated from the 201 lv2 NEI based on tons of emissions per appliance for PM2.5, VOC
and CO. This calculation was based on estimated appliance (SCC) population and total emissions by SCC.
EPA-certified wood stove emission factors are provided in the wood heaters NSPS RIA Tables 4-3, 4-7 and 4-
11 for PM2.5, VOC and CO, respectively. For all RWC appliances subject to the NSPS, baseline RIA emission
factors, when lower than the computed emission factors (2011 NEI), are used for new appliances sold between
2012 and 2014. Starting in 2015, Phase 1 emission limits are 60 percent stronger (0.45 g/hr / 0.75 g/hr) than the
RIA baseline emission factors. There are also different standards for catalytic versus non-catalytic EPA-
certified stoves. Similar calculations are performed for Phase 2 emission limits that begin in 2020 and for
57
-------
different emission rates for different appliance types. Because the 2011NEI and RIA baseline (2012-2014)
emission factors vary by pollutant, all RWC appliances subject to the NSPS have pollutant-specific "projection"
factors. We realize that these "projection" factors are a composite of growth, retirements and potentially
emission factors in 4 increments. More detailed documentation on the EPA RWC Projection Tool, including
information on baseline, new appliances pre-NSPS, and Phase 1 and Phase 2 emission factors, is available upon
request.
Caveats and Results
California, Oregon and Washington have state-level RWC control programs, including local burn bans in place.
Without an ability to incorporate significant local RWC control programs/burn bans for a future year inventory,
the EPA left RWC emissions unchanged in the future for all three states. The RWC projections factors for
states other than California, Oregon and Washington are provided in Table 4-20. VOC HAPs use the same
projection factors as VOC; PMio uses the same factor as PM2.5; and all other pollutants use the CO projection
factor. Note that appliance types not subject to the wood heaters NSPS (e.g., fire pits, fire logs) have pollutant-
independent projection factors because there is no assumed change in future year emission factors.
Table 4-20. Non-West Coast RWC projection factors, including NSPS impacts
see
Description
Default if
pollutant not
defined
PM
VOC and
VOC
HAPs
CO and
remaining
CAPs
2104008100
Fireplace: general
1.127
2104008210
Woodstove: fireplace inserts; non-
EPA certified
0.791
2104008220
Woodstove: fireplace inserts; EPA
certified; non-catalytic
1.238
1.103
2104008230
Woodstove: fireplace inserts; EPA
certified; catalytic
1.281
1.128
2104008310
Woodstove: freestanding, non-EPA
certified
0.829
0.828
0.842
0.829
2104008320
Woodstove: freestanding, EPA
certified, non-catalytic
1.238
1.103
2104008330
Woodstove: freestanding, EPA
certified, catalytic
1.281
1.129
2104008400
Woodstove: pellet-fired, general
1.852
1.898
2104008510
Furnace: Indoor, cordwood-fired, non-
EPA certified
0.277
0.318
0.276
0.277
2104008610
Hydronic heater: outdoor
1.044
1.079
2104008700
Outdoor wood burning device, NEC
1.127
2104009000
Residential Firelog Total: All
Combustor Types
1.127
National emission summaries for the RWC sector in 2011 and 2023 are provided in Table 4-21. For direct PM,
the NSPS emission factor reductions mostly offset the growth in appliances by year 2023.
Table 4-21. Cumulative national RWC emissions from growth, retirements and NSPS impacts
Pollutant
Emissions
Difference
% Difference
58
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2011
2023
2023-2011
2023-2011
CO
2,526,548
2,376,924
149,624
5.92%
nh3
19,759
18,560
1,199
6.07%
NOx
34,518
35,000
-483
-1.40%
PMio
382,754
364,067
18,687
4.88%
PM2.5
382,528
363,818
18,710
4.89%
S02
8,975
7,926
1,049
11.68%
VOC
444,269
417,315
26,954
6.07%
4.2.4 CoST CONTROL Packets (nonpt, npoilgas, ptnonipm, ptoilgas)
The final step in a CoST control strategy, after application of any/all CLOSURE packet(s) (point inventories
only) and any/all PROJECTION packet(s) is the application of CoST CONTROL packets. While some controls
are embedded in our PROJECTION packets (e.g., NSPS controls for RWC and loco-marine controls for rail and
commercial marine vessels), we attempted to separate out the control (program) component in our modeling
platform where feasible. In our platform, CoST control packets only impact the nonpt, np oilgas, ptnonipm and
pt oilgas sectors.
There are several different sources of CONTROL data that are concatenated and quality-assured for duplicates
and applicability to the inventories in the CoST strategies. We broke up the CONTROL (and PROJECTION)
packets into a few "key" control program types to allow for quick summaries of these distinct control programs.
The remainder of this section is broken out by CoST packet, with the exception of discussion of the various
packets gathered from previous versions of the emissions modeling platform; these packets are a mix of
different sources of data, only some of which have not been replaced by more recent information gathered for
this platform.
For future-year NSPS controls (oil and gas, RICE, Natural Gas Turbines, and Process Heaters), we attempted to
control only new sources/equipment using the following equation to account for growth and retirement of
existing sources and the differences between the new and existing source emission rates.
Qn = Qo {[(l+Pf)t-l]Fn + (l-Ri)tFe + [l-(l-Ri)t]Fn]} Equation 1
where:
Qn = emissions in projection year
Qo = emissions in base year
Pf = growth rate expressed as ratio (e.g., 1.5=50 percent cumulative growth)
t = number of years between base and future years
Fn = emission factor ratio for new sources
Ri = retirement rate, expressed as whole number (e.g., 3.3 percent=0.033)
Fe = emission factor ratio for existing sources
The first term in Equation 1 represents new source growth and controls, the second term accounts for retirement
and controls for existing sources, and the third term accounts for replacement source controls.
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Table 4-22 shows the values for Retirement rate and new source emission factors (Fn) for new sources with
respect to each NSPS regulation and other conditions within; this table also provides the subsection where the
CONTROL packets are discussed.
Table 4-22. Assumed retirement rates and new source emission factor ratios for various NSPS rules
NSPS
Rule
TSD
Section
Retirement
Rate years
(%/year)
Pollutants
Impacted
Applied where?
New Source
Emission
Factor (Fn)
Oil and
Gas
4.2.4.1
No
assumption
voc
Storage Tanks: 70.3% reduction in
growth-only (>1.0)
0.297
Gas Well Completions: 95%
control (regardless)
0.05
Pneumatic controllers, not high-
bleed >6scfm or low-bleed: 77%
reduction in growth-only (>1.0)
0.23
Pneumatic controllers, high-bleed
>6scfm or low-bleed: 100%
reduction in growth-only (>1.0)
0.00
Compressor Seals: 19.9%
reduction in growth-only (>1.0)
0.201
Fugitive Emissions: 60% Valves,
flanges, connections, pumps,
open-ended lines, and other
0.40
Pneumatic Pumps: 71.3%
Oil and Gas
0.287
RICE
4.2.4.3
40, (2.5%)
NOx
Lean burn: PA, all other states
0.25, 0.606
Rich Burn: PA, all other states
0.1, 0.069
Combined (average) LB/RB: PA,
other states
0.175, 0.338
CO
Lean burn: PA, all other states
1.0 (n/a),
0.889
Rich Burn: PA, all other states
0.15, 0.25
Combined (average) LB/RB: PA,
other states
0.575, 0.569
VOC
Lean burn: PA, all other states
0.125, n/a
Rich Burn: PA, all other states
0.1, n/a
Combined (average) LB/RB: PA,
other states
0.1125, n/a
Gas
Turbines
4.2.4.6
45 (2.2%)
NOx
California and NOx SIP Call
states
0.595
All other states
0.238
Process
Heaters
4.2.4.7
30(3.3%)
NOx
Nationally to Process Heater
SCCs
0.41
4.2.4.1 Oil and Gas NSPS (npoilgas, ptoilgas)
Packet:
"CONTROL_2023_OILGAS_VOC_NSPS.csv"
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"BETA_Controls_OilGas_NSPS_2023_29apr2016.csv" (MARAMA)
For oil and gas NSPS controls, with the exception of gas well completions (a 95 percent control), the
assumption of no equipment retirements through year 2023 dictates that NSPS controls are applied to the
growth component only of any PROJECTION factors. For example, if a growth factor is 1.5 for storage tanks
(indicating a 50 percent increase activity), then, using Table 4-22, the 70.3 percent VOC NSPS control to this
new growth will result in a 23.4 percent control: 100 *(70.3 * (1.5 -1) / 1.5); this yields an "effective" growth
rate (combined PROJECTION and CONTROL) of 1.1485, or a 70.3 percent reduction from 1.5 to 1.0. The
impacts of all non-drilling completion VOC NSPS controls are therefore greater where growth in oil and gas
production is assumed highest. Conversely, for oil and gas basins with assumed negative growth in
activity/production, VOC NSPS controls will be limited to well completions only. Because these impacts are so
geographically varying, we are providing the VOC NSPS reductions by each of the 6 broad NEMS regions,
with Texas and New Mexico aggregated because these states include multiple NEMS regions (see Figure 4-1).
These reductions are year-specific because projection factors for these sources are year-specific.
Table 4-23. NSPS VOC oil and gas reductions from projected pre-control 2023 grown values
Region
Pre-NSPS
emissions
Post-NSPS
emissions
NSPS
Reductions
NSPS %
reductions
Gulf Coast
241,981
69,078
172,903
71%
Midcontinent
203,306
63,180
140,126
69%
New
Mexico/Texas*
1,492,201
427,779
1,064,421
71%
Northeast
362,847
116,824
246,024
68%
Rocky Mountains
1,120,805
312,627
808,179
72%
West Coast
106,700
31,432
75,269
71%
Overall
3,527,840
1,020,920
2,506,921
71%
4.2.4.2 RICE NESHAP (nonpt, npoilgas, ptnonipm, ptoilgas)
Packet:
"CONTROL2011 v6.2_RICE_NESHAP_v2_30j an2015_v0.txt"
"BETA_Controls_RICE_NESHAP_29apr2016" (MARAMA)
There are two rulemakings for National Emission Standards for Hazardous Air Pollutants (NESHAP) for
Reciprocating Internal Combustion Engines (RICE). These rules reduce HAPs from existing and new RICE
sources. In order to meet the standards, existing sources with certain types of engines will need to install
controls. In addition to reducing HAPs, these controls have co-benefits that also reduce CAPs, specifically, CO,
NOx, VOC, PM, and SO2. In 2014 and beyond, compliance dates have passed for both rules and are thus
included in emissions projections. These RICE reductions also reflect the Reconsideration Amendments
(proposed in January, 2012), which result in significantly less stringent NOx controls (fewer reductions) than
the 2010 final rules.
The rules are listed below:
National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion
Engines; Final Rule (FR 9648) published 03/03/10.
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National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion
Engines; Final Rule (75 FR 51570) published 08/20/2010.
The difference among these two rules is that they focus on different types of engines, different facility types
(major for HAPs, versus area for HAPs) and different engine sizes based on horsepower. In addition, they have
different compliance dates, though both are after 2011 and fully implemented prior to 2017. The EPA projects
CAPs from the 201 1NEIv2 RICE sources, based on the requirements of the rule for existing sources only
because the inventory includes only existing sources. The EPA estimates the NSPS (new source) impacts from
RICE regulations in a separate CONTROL packet and CoST strategy; the RICE NSPS is discussed in the next
section.
The "Regulatory Impact Analysis (RIA) for the Reconsideration of the Existing Stationary Compression
Ignition (CI) Engines NESHAP: Final Report" (EPA, 2013ci). The "Regulatory Impact Analysis (RIA) for
Reconsideration of the Existing Stationary Spark Ignition (SI) RICE NESHAP: Final Report" (EPA, 2013si) is
available at:
Together, the EPA calls these the RICE NESHAP amendment RIA's for SI and CI engines. From these RICE
NESHAP RIA documents, the EPA obtained cumulative RICE reductions for all SCCs represented by CI and
SI engines. These aggregate reductions and percent reductions from baseline emissions (not the 201 1NEIv2)
are provided in Table 4-24. This table reflects the impacts of both the MARAMA and non-MARAMA packets.
Table 4-24. Summary RICE NESHAP SI and CI percent reductions prior to 201 1NEIv2 analysis
CO
NOx
PM
SO2
VOC
RIA Baseline: SI engines
637,756
932,377
127,170
RIA Reductions: SI engines
22,211
9,648
9,147
RIA Baseline: CI engines
81,145
19,369
11,053
79,965
RIA Reductions: CI engines
14,238
2,818
5,100
27,142
RIA Cumulative Reductions
36,449
9,638
2,818
5,100
36,289
SI % reduction
3.5%
1.0%
n/a
n/a
7.2%
CI % reduction
17.5%
n/a
14.5%
46.1%
33.9%
These RIA percent reductions were used as an upper-bound for reducing emissions from RICE SCCs in the
201 1NEIv2 point and nonpoint modeling sectors (ptnonipm, nonpt, pt oilgas and np oilgas). To begin with,
the RIA inventories are based on the 2005 NEI, so the EPA wanted to ensure that our 2011 reductions did not
exceed those in the RICE RIA documents. For the 2011 platform, the 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, the 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, the EPA applied the
RIA percent reductions to the 201 1NEIv2 for SCCs where national totals exceeded 100 tons; the EPA chose
100 tons as a threshold, assuming there would be little to no application of RICE NESHAP controls on smaller
existing sources.
Next, the EPA aggregated these national reductions by engine type (CI vs. SI) and pollutant and compared these
to the RIA reductions. As expected, for most pollutants and engine types, the cumulative reductions were
significantly less than those in the RIA. The only exception was for SO2 CI engines, where the EPA scaled the
RIA percent reduction from 46.1 percent to 14.4 percent for four broad nonpoint SCCs that were not restricted
to only RICE engines. These four SCCs were the "Boilers and IC Engines" or "All processes" that would
presumably contain some fraction of non-RICE component. This had minimal impact as sulfur content in
62
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distillate fuel for many IC engine types has decreased significantly since 2005. Reducing the SO2 percent
reduction for these four SCCs resulted in slightly less than 5,100 tons of SO2 reductions overall from only RICE
NESHAP controls. However, more specific CoST projection packets would later override these RICE
NESHAP reductions for SO2. Recall the CoST hierarchy discussed earlier; these RICE NESHAP reductions are
national by pollutant and SCC and thus easily overridden by more-specific information such as state-level fuel
sulfur rules (discussed in the next section).
Additional comments from the NODA were also implemented; specifically, CO controls were modified for a
couple of distillate-fueled industrial/commercial boiler sources. Impacts of the RICE NESHAP controls on
nonpt, ptnonipm, ptoilgas and npoilgas sector emissions are provided in Table 4-25. This table reflects the
impacts of both the MARAMA and non-MARAMA packets.
Table 4-25. National by-sector reductions from RICE Reconsideration controls (tons)
Pollutant
Year
Nonpoint
Oil & Gas
(np oilgas)
Point Oil
& Gas
(pt oilgas)
Nonpoint
(nonpt)
Point
(ptnonipm)
Total
CO
2023
9,934
5,546
3,505
6,443
25,429
NOX
2023
2,500
2,225
216
83
5,025
PM10
2023
0
9
1,038
308
1,355
PM2.5
2023
0
9
913
292
1,214
S02
2023
0
12
2,951
311
3,274
VOC
2023
2,053
3,710
625
951
7,339
4.2.4.3 RICE NSPS (nonpt, np oilgas, ptnonipm, pt oilgas)
Packet:
"CONTROL2011 v6_3_2023_RICE_NSPS_l 8oct2016"
"BETA_Controls_RICE_NSPS_2023_30jul2016.csv" (MARAMA)
Controls for existing RICE source emissions were discussed in the previous section. This section discusses
control for new equipment sources, NSPS controls that impact CO, NOx and VOC. The EPA emission
requirements for stationary engines differ according to whether the engine is new or existing, whether the
engine is located at an area source or major source, and whether the engine is a compression ignition or a spark
ignition engine. Spark ignition engines are further subdivided by power cycle, two versus four stroke, and
whether the engine is rich burn or lean burn.
RICE engines in the NOx SIP Call area are covered by state regulations implementing those requirements. EPA
estimated that NOx emissions within the control region were expected to be reduced by about 53,000 tons per
5month ozone season in 2007 from what they would otherwise be without this program. Federal rules affecting
RICE included the NESHAP for RICE (40 CFR part 63, Subpart ZZZZ), NSPS for Stationary Spark Ignition IC
engines (40 CFR part 60, Subpart JJJJ), and NSPS for Compression Ignition IC engines (40 CFR part 60,
Subpart IIII). SI engine operators were affected by the NSPS if the engine was constructed after June 12, 2006,
with some of the smaller engines affected by the NSPS 1-3 years later. The recommended RICE equipment
lifetime is 30 to 40 years depending on web searches. We chose 40 years as a conservative estimate.
The 2011 estimates of the RICE engine average emission rates for lean burn and rich burn engines was
developed using the stationary engine manufacturers data submitted to the EPA for the NSPS analysis (Parise,
2005). Emission factors by pollutant for engines 500-1200 horsepower (hp) were used to develop the average
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emission rates. The analysis was organized this way because lean versus rich burn engine type is such a
significant factor in the NOx emissions rate. Any state emission regulations that require stationary RICE
engines to achieve emission levels lower than the 2012 NSPS could be included by using lower new source
emission ratios that account for the additional emission reductions associated with having more stringent state
permit rules. Information is provided for Pennsylvania in Table 4-26. That information shows that the
Pennsylvania regulations have different emission standards for lean burn versus rich burn engines, and that the
emission limits also vary by engine size (100-500 hp or greater than 500 hp). While some of the newer RICE
SCCs (oil and gas sector in particular) allow states to indicate whether engines are lean versus rich burn, some
SCCs lump these two together. None of the RICE point source SCCs have information about engine sizes.
However, the EPA RIA for the RICE NSPS and NESHAP analysis (RTI, 2007) provides a table that shows the
NOx (CO, NMHC and HAP emission estimates are provided as well) emissions in 2015 by engine size, along
with engine populations by size. In the future, more rigorous analysis can use this table to develop
computations of weighted average emission reductions by rated hp to state regulations like Pennsylvania's.
Table 4-26. RICE NSPS Analysis and resulting 201 lv6.2 emission rates used to compute controls
Engine type & fuel
Max Engine
Power
Geographic
Applicability
Emission standards
g/HP-hr
NOx
CO
VOC
2011 pop lean burn
500-1200 hp
1.65
2.25
0.7
2011 pop rich burn
500-1200 hp
14.5
8
0.45
Non-Emerg. SI NG and Non-E. SI
Lean Burn LPG (except LB
500100
2006 NSPS
2.0
4.0
1.0
Non-Emerg. SI NG and Non-E. SI
Lean Burn LPG (except LB
500100
2012 NSPS
1.0
2.0
0.7
HP>100
PA (Previous GP-
5)
2.0
2.0
2.0
New NG Lean Burn
100500
PA (New GP-5)
0.5
2.0
0.25
New NG Rich Burn
100500
PA (New GP-5)
0.2
0.3
0.2
HP>100
Maryland
1.5
HP>7500
Colorado
1.2 -
2
Wyoming
None
None
None
Notes: the above table compares the criteria pollutant emission standards from the recent NSPS with the emission limits from selected
states for stationary IC engines to determine whether future year emission rates are likely to be significantly lower than for the existing
engine population. States in the NOx SIP Call region instituted NOx emission limits for large engines well before 2011. Most of the
values in the above table come from an analysis posted on the PA DEP website. The state emission limits listed above are those in
place prior to 2011. Some states (like PA) have instituted tougher RICE emission limits for new and modified engines more recently.
Note 2: Wyoming exempts all but the largest RICE engines from emission limits.
Note 3: PA has had a size limit for new RICE engines of 1500 hp until recently (i.e., not engines bigger than 1500 hp can be installed).
Their new General Permit-5 removed the engines size cap, but requires new or modified larger engines to be cleaner (i.e., has emission
limits lower than the NSPS). PA expects that the new emission limits will result in an increase in larger engines being installed, and
bringing the average emission rate much lower than it is currently.
New source Emissions Rate (Fn): Controls % =100 * (1-Fn)
NOx
CO
VOC
Pennsylvania
NG-Comb. LB & RB
0.175
0.575
0.113
All other states
NG-Comb. LB & RB
0.338
0.569
1.278
Pennsylvania
NG-lean burn
0.250
1.000
0.125
64
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All other states
NG-lean burn
0.606
0.889
1.000
Pennsylvania
NG-rich burn
0.100
0.150
0.100
All other states
NG-rich burn
0.069
0.250
1.556
We applied NSPS reduction for lean burn, rich burn and "combined" (not specified). We also computed scaled-
down (less-stringent) NSPS controls for SCCs that were "IC engines + Boilers" because boiler emissions are
not subject to RICE NSPS. For these SCCs, we used the 201 1NEIv2 point inventory to aggregate eligible (fuel
and type) boiler and IC engine emissions for each pollutant. We found that for CI engines, almost all emissions
were boiler-related; therefore, there are no CI engine RICE NSPS reductions for "IC engines + Boilers." For SI
engines, we found that approximately 9 percent of NOx, 10 percent Of CO and 19 percent of VOC "IC engines
+ Boilers" were IC engines; these splits were then applied to the NSPS reductions in Table 4-26. Finally, we
limited RICE NSPS-eligible sources (SCCs) to those that have at least 100 tons nationally for NOx, CO or
VOC, and ignored resulting controls that were under 1 percent.
Pennsylvania DEP staff note that until recently they have limited RICE engines to a maximum of 1500 hp. That
cap is lifted under the new General Permit-5 regulations. With that cap lifting, Pennsylvania expects that new
applications will choose to install larger engines which have lower emission limits. However, that potential
effect will be difficult to capture with no information about how this might occur. These controls were then
plugged into Equation 2 (see Section 4.2.4) as a function of the projection factor. Resulting controls greater
than or equal to 1 percent were retained. Note that where new emissions factors >=1.0 (uncontrolled, as
represented by red cells at the bottom of Table 4-26), no RICE NSPS controls were computed. National RICE
NSPS reductions from projected pre-NSPS 2023 inventory is shown in Table 4-27. This table reflects the
impacts of both the MARAMA and non-MARAMA packets.
Table 4-27. National by-sector reductions from RICE NSPS controls (tons)
Pollutant
Year
Nonpoint
Oil & Gas
(np oilgas)
Point Oil
& Gas
(pt oilgas)
Nonpoint
(nonpt)
Point
(ptnonipm)
Total NSPS
reductions
Pre-
NSPS
total
emissions
NSPS %
reduction
CO
2023
284,741
47,013
2,278
99
334,131
994,100
34%
NOX
2023
363,537
113,599
3,903
172
481,211
1,272,286
38%
VOC
2023
2,641
209
0
2
2,852
4,662
61%
4.2.4.4 ICI boilers (nonpt, ptnonipm, ptoilgas)
Packets:
CONTROL2011 v6.2_20xx_BoilerMACT_POINT_v2_30j an2015_v0.txt
CONTROL2011 v6.2_20xx_BoilerMACT_NONPT_08j an2015_1 lj an2016_nf_vl .txt
NCDAQCONTROL2011 v6_2_2017_BoilerMACT_POINT_revised_07j an2016_v0.txt
BET AC ontrol s_B OILERM AC T_24 aug2 016.csv (MARAMA)
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. These packets address only the expected cobenefits to
existing ICI boilers. MARAMA supplied their own control packet that covers the MACT Rule impacts for their
states.
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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 1NEIv2 inventory has a lot more gas combustion and a
lot less oil combustion than the boiler MACT database. For this reason, the EPA decided to target units that
potentially could be subject to the Boiler MACT and compute preliminary reductions for several CAPs prior to
building a control packet.
Step 1: Extract facilities/sources potentially subject to Boiler MACT
This step is only applicable to point inventory sources. The 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 4-28.
Table 4-28. 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.
Because the Boiler MACT rule applies to only major sources of HAPs, the 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.
Step 2: Merge control information with 2011 NEI and apply state NOD A comments
The EPA analyzed the SCCs in the OTC 2007 inventories and tweaked the SCC mapping of these ICI boiler
adjustments to map to those in the 2011 NEI point and nonpoint inventory with non-zero emissions. The EPA
66
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also removed some duplicate and incorrect mappings and expanded the SCC mapping in some cases to SCCs
that were in the NEI, but not the OTC inventory (and thus missing from the analysis).
Some states commented on the 201 lv6.0 ICI boiler controls via the 2018 NODA (docket # EPA-HQ-OAR-
2013-0809). Wisconsin provided alternative SO2, VOC and HC1 controls for stoker and pulverized coal fueled
units. The national-level and Wisconsin-specific ICI boiler adjustments, applied at the unit-level for point
sources and by SCC (and state for Wisconsin) are provided in Table 4-29; note that we applied the same
national-level adjustments to CO, NOx and PM for coal units in Wisconsin. New York and New Jersey, via the
MARAMA comment/data to the 2018 NODA, provided boiler rule NOx reductions that also supersede these
nationally-applied factors. The New Jersey and New York factors are provided in Table 4-30; note that New
Jersey controls apply only to nonpoint sources and that New York controls vary by fuel for point sources.
Table 4-29. National-level, with Wisconsin exceptions, ICI boiler adjustment factors by base fuel type
Unit/Fuel Type
Default % Reduct
ion (Adjustments)
CO
NOX
PM
S02
VOC
HC1
Stoker Coal
98.9
70.7
96
97.4
98.9
95
Pulverized Coal
98.9
60.6
72.2
73
98.9
95
Residual Oil
99.9
57
92.4
97.1
99.9
95
Distillate Oil
99.9
38.8
68.4
99.9
99.9
88.0
Wisconsin: Stoker Coal
98.9
70.7
90
30
0
45
Wisconsin: Pulverized Coal
98.9
60.6
72.2
30
i)
45
Table 4-30. New York and New Jersey NOx ICI Boiler Rules that supersede national approach
NJ and NY Boiler Rule controls
NOX %
Reduction
New Jersey Small Boiler Rule (nonpoint only): Default for Distillate, Residual, natural gas and LPG
25
New York Small Boiler Rule (nonpoint only): Default for Distillate, Residual, natural gas and LPG
10
NY Boiler Rule: Industrial /Distillate Oil /< 10 Million Btu/hr
10
NY Boiler Rule: Industrial /Residual Oil /10-100 Million Btu/hr
33.3
NY Boiler Rule: Electric Gen /Residual Oil /Grade 6 Oil: Normal Firing
40
NY Boiler Rule: Electric Gen /Natural Gas /Boilers, <100 Million Btu/hr except Tangent
50
NY Boiler Rule: Electric Gen /Natural Gas /Boilers, 100 Million Btu/hr except Tangent
60
NY Boiler Rule: Industrial /Bitum Coal /Cyclone Furnace
66.7
NY Boiler Rule: Industrial /Natural Gas /> 100 Million Btu/hr
70
NY Boiler Rule: Electric Gen /Bituminous Coal /Pulverized Coal: Dry Bottom
73.3
The impacts of these ICI boiler reductions are provided in Error! Not a valid bookmark self-reference.. This
table reflects the impacts of both the MARAMA and non-MARAMA packets. 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 were estimated at 442,000 tons; the additional SO2
reductions in the reconsideration are from an additional co-benefit from more stringent HC1 controls. The
201 1NEIv2 SO2 emissions are actually less than the estimated Boiler MACT reductions, likely a result of
numerous units undergoing fuel switching from coal or oil to natural gas.
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Table 4-31. Summary of ICI Boiler reductions
Year
Pollutant
Emissions
Eligible for
Control
Controlled
(Final)
Emissions
Reductions
(tons)
%
Reductions
CO
2023
72,391
32,305
40,086
55.4%
NOX
2023
118,692
68,865
49,827
42.0%
PM10
2023
66,097
41,687
24,411
36.9%
PM2.5
2023
37,717
26,669
11,048
29.3%
S02
2023
265,390
53,062
212,328
80.0%
VOC
2023
2,929
1,110
1,819
62.1%
4.2.4.5 Fuel sulfur rules (nonpt, ptnonipm, ptoilgas)
Packet:
"CONTROL2011 v6.2_20xx_Fuel_Sulfur_Rules_09j an2015_v0.txt"
"BETA_Controls_MANEVU_SULFUR_2016_08_24.csv" (MARAMA)
Fuel sulfur rules, based on web searching and the 2011 emissions modeling NODA comments, are currently
limited to the following states: Connecticut, Delaware, Maine, Massachusetts, New Jersey, New York,
Pennsylvania, Rhode Island and Vermont. The fuel limits for these states are incremental starting after year
2012, but are fully implemented by July 1, 2018, in all of these states.
A summary of all fuel sulfur rules provided back to the EPA by the 2011 emissions modeling NODA comments
is provided in Table 4-32. State-specific control factors were computed for distillate, residual and #4 fuel oil
using each state's baseline sulfur contents and the sulfur content in the rules. For most states, the baseline
sulfur content was 3,000 ppm (0.3 percent) for distillate oil, and 2.25 percent for residual and #4 oil. However,
many states had lower baseline sulfur contents for residual oil, which varied by state and county. The SRA
used state- or county-specific baseline residual oil sulfur contents to calculate a state- or county-specific control
factors for residual oil (SRA, 2014).
A summary of the sulfur rules by state, with emissions reductions is provided in Table 4-33. This table reflects
the impacts of the MARAMA packet only, as these reductions are not estimated in non-MARAMA states. Most
of these reductions (98+ percent) occur in the nonpt sector; a small amount of reductions occur in the ptnonipm
sector, and a negligible amount of reductions occur in the pt oilgas sector. Note that these reductions are based
on intermediate 2023 inventories, those grown from 2011 to the specific future years.
Table 4-32. State Fuel Oil Sulfur Rules data provided by MANE-VU
State
Reference
Connecticut
Section 22a-174-19a. Control of sulfur dioxide emissions from power plants and other large stationary sources
of air pollution: Distillate and Residual: 3000 ppm effective April 15, 2014.
Section 22a - 174 - 19b. Fuel Sulfur Content Limitations for Stationary Sources (except for sources subject to
Section 22a-174-19a).
Distillate: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2018
Residual: 1.0% effective July 1, 2014; 0.3% effective July 1, 2018
Connecticut General Statute 16a-21a. Sulfur content of home heatins oil and off-road diesel fuel.
Number 2 heating oil and off-road diesel fuel: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2018
Delaware
1108 Sulfur Dioxide Emissions from Fuel Burnine Eciui Dine lit
Distillate: 15 ppm effective July 1, 2017
Residual: 0.5% effective July 1, 2017
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#4 Oil: 0.25% effective July 1, 2017
Maine
Chanter 106: Low Sulfur Fuel
Distillate: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2018
Residual: 0.5% effective July 1, 2018
Massachusetts
310 CMR 7.05 (1 )(a) 1: Table 1 : Sulfur Content Limit of Liauid Fossil Fuel
Distillate: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2018
Residual: 1.0% effective July 1, 2014; 0.5% effective July 1, 2018
New Jersey
Title 7. Chanter 27. Subchapter 9 Sulfur in Fuels
Distillate: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2016
Residual: 0.5% or 0.3%, depending on county, effective July 1, 2014
#4 Oil: 0.25% effective July 1, 2014
New York
Subpart 225-1 Fuel Composition and Use - Sulfur Limitations
Distillate: 15 ppm effective July 1, 2016
Residual: 0.3% in New York City effective July 1, 2014; 0.37% in Nassau, Rockland and Westchester
counties effective July 1, 2014; 0.5% remainder of state effective July 1, 2016
New York Times and NRDC
Pennsylvania
§ 123.22. Combustion units
Distillate: 500 ppm effective July 1, 2016
Residual: 0.5% effective July 1, 2016
#4 Oil: 0.25% effective July 1, 2016
Rhode Island
Air Pollution Control Regulations No. 8 Sulfur Content of Fuels
Distillate: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2018
Residual: 0.5% effective July 1, 2018
Vermont
5-221(1) Sulfur Limitations in Fuel
Distillate: 500 ppm effective July 1, 2014; 15 ppm effective July 1, 2018
Residual: 0.5% effective July 1, 2018
#4 Oil: 0.25% effective July 1, 2018
Table 4-33. Summary of fuel sulfur rule impacts on SO2 emissions
Year
Emissions Eligible
for Control
Controlled (Final)
Emissions
Reductions
% Reductions
2023
90,866
10,064
80,802
88.9%
4.2.4.6 Natural gas turbines NOx NSPS (ptnonipm, ptoilgas)
Packet:
"CONTROL_2011v6.2_2025_NOX_GasTurbines_16dec2014_v0.txt"
"BETA_Controls_GasTurbines_NSPS_2023_30jul2016.csv" (MARAMA)
These controls were generated based on examination of emission limits for stationary combustion turbines that
are not in the power sector. In 2006, the EPA promulgated standards of performance for new stationary
combustion turbines in 40 CFR part 60, subpart KKKK. The standards reflect changes in NOx emission control
technologies and turbine design since standards for these units were originally promulgated in 40 CFR part 60,
subpart GG. The 2006 NSPSs affecting NOx and SO2 were established at levels that bring the emission limits
up-to-date with the performance of current combustion turbines. Stationary combustion turbines were also
regulated by the NOx SIP (State Implementation Plan) Call, which required affected gas turbines to reduce their
NOx emissions by 60 percent.
Table 4-34 compares the 2006 NSPS emission limits with the NOx RACT regulations in selected states within
the NOx SIP Call region. The map showing the states and partial-states in the NOx SIP Call Program. We
assigned only those counties in Alabama, Michigan and Missouri as NOx SIP call based on the map on page 8.
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The state NOx RACT regulations summary (Pechan, 2001) is from a year 2001 analysis, so some states may
have updated their rules since that time.
Table 4-34. Stationary gas turbines NSPS analysis and resulting emission rates used to compute controls
NOx Emission Limits for New Stationary Combustion Turbines
Firing Natural Gas
<50
MMBTU/hr
50-850
MMBTU/hr
>850
MMBTU/hr
Federal NSPS
100
25
15
ppm
State RACT Regulations
5-100
MMBTU/hr
100-250
MMBTU/hr
>250
MMBTU/hr
Connecticut
225
75
75
ppm
Delaware
42
42
42
ppm
Massachusetts
65*
65
65
ppm
New Jersey
50*
50
50
ppm
New York
50
50
50
ppm
New Hampshire
55
55
55
ppm
* Only applies to 25-100 MMBTU/hr
Notes: The above state RACT table is from a 2001 analysis. The current NY State regulations have the same
emission limits.
Vew source emission rate (Fn)
NOx ratio
Control (%)
NOx SIP Call states plus CA
= 25 / 42 =
0.595
40.5%
Other states
= 25/ 105 =
0.238
76.2%
Regarding stationary gas turbine lifetimes, the IPM financial modeling documentation lists the book life of
combustion turbines as 30 years, with a debt life of 15 years, and a U.S. MACRS Depreciation Schedule of 15
years (EPA, 2013). This same documentation lists the book life of nuclear units at 40 years. IPM uses a 60-
year lifetime for nuclear units in its simulations of unit retirements. Using the same relationship between
estimated lifetime and book life for nuclear units of 1.5, the estimated lifetime for a combustion turbine would
be 45 years. This is the same as an annual retirement rate of 2.2 percent.
For projection factor development, the existing source emission ratio was set to 1.0 for combustion turbines.
The new source emission ratio for the NOx SIP Call states and California is the ratio of state NOx emission
limit to the Federal NSPS. A complicating factor in the above is the lack of size information in the stationary
source SCCs. Plus, the size classifications in the NSPS do not match the size differentiation used in state air
emission regulations. We accepted a simplifying assumption that most industrial applications of combustion
turbines are in the 100-250 MMBtu/hr size range, and computed the new source emission rates as the NSPS
emission limit for 50-850 MMBtu/hr units divided by the state emission limits. We used a conservative new
source emission ratio by using the lowest state emission limit of 42 ppmv (Delaware). This yields a new source
emission ratio of 25/42, or 0.595 (40.5 percent reduction) for states with existing combustion turbine emission
limits. States without existing turbine NOx limits would have a lower new source emission ratio -the
uncontrolled emission rate (105 ppmv via AP-42) divided into 25 ppmv = 0.238 (76.2 percent reduction). This
control was then plugged into Equation 2 (see Section 4.2.4) as a function of the year-specific projection factor.
Resulting controls greater than or equal to 1 percent were included in our projections. National Process Heaters
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NSPS reductions from projected pre-NSPS 2023 inventory are shown in Table 4-35. This table reflects the
impacts of both the MARAMA and non-MARAMA packets.
Table 4-35. National by-sector 2023 NOx reductions from Stationary Natural Gas Turbine NSPS controls
Sector
Pre-NSPS Emissions
NSPS
Reductions
NSPS % Reductions
Non-EGU Point
(ptnonipm)
15,588
4,225
27%
Point Oil & Gas
(pt oilgas)
71,318
23,253
33%
Total
86,906
27,478
32%
4.2.4.7 Process heaters NOx NSPS (ptnonipm, ptoilgas)
Packet:
"CONTROL_2011v6.2_2025_NOX_Process_heaters_09dec2014_v0.txt"
"BETA_Controls_ProcessHeaters_NSPS_2023_30jul2016.csv" (MARAMA)
Process heaters are used throughout refineries and chemical plants to raise the temperature of feed materials to
meet reaction or distillation requirements. Fuels are typically residual oil, distillate oil, refinery gas, or natural
gas. In some sense, process heaters can be considered as emission control devices because they can be used to
control process streams by recovering the fuel value while destroying the VOC. The criteria pollutants of most
concern for process heaters are NOx and SO2.
In 2011, process heaters have not been subject to regional control programs like the NOx SIP Call, so most of
the emission controls put in-place at refineries and chemical plants have resulted from RACT regulations that
were implemented as part of SIPs to achieve ozone NAAQS in specific areas, and refinery consent decrees. The
boiler/process heater NSPS established NOx emission limits for new and modified process heaters. These
emission limits are displayed in Table 4-36.
In order to develop a relationship between the typical process heater emission rates in 2011 compared with what
the NSPS will require of new and modified sources, an analysis of the materials in the EPA docket (EPA-HQ-
OAR-2007-0011) for the NSPS was performed. This docket contained an EPA memorandum that estimated the
NOx emissions impacts for process heaters. Table 1 in that memo titled, "Summary of Representative Baseline
NOx Concentrations for Affected Process Heaters," analysis can be used to establish an effective 2011 process
heater NOx emission rate, although the information that EPA used in the revised NOx impact estimates
probably uses data from a few years before 2011. It is likely that the data used are representative of 2011
emissions because the only wide-ranging program that has affected process heater emission rates recently have
been consent decrees, and the emission reductions associated with these agreements should have been achieved
before 2011. However, the compliance schedules are company-specific, and differ by company, so it is difficult
to make overarching conclusions about when compliance occurred.
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Table 4-36. Process Heaters NSPS analysis and 201 lv6.2 new emission rates used to compute controls
NOx emission rate Existing (Fe)
Fraction at this rate
Natural
Forced
PPMV
Draft
Draft
Average
80
0.4
0
100
0.4
0.5
150
0.15
0.35
200
0.05
0.1
240
0
0.05
Cumulative, weighted: Fe
104.5
134.5
119.5
NSPS Standard
40
60
New Source NOx ratio (Fn)
0.383
0.446
0.414
NSPS Control (%)
61.7
55.4
5S.6
The EPA states that because it "does not have much data on the precise proportion of process heaters that are
forced versus natural draft, so the nationwide impacts are expressed as a range bounded by these two
scenarios." (Scenario 1 assumes all of the process heaters are natural draft process heaters and Scenario 2
assumes all of the process heaters are forced draft process heaters.)
For computations, the existing source emission ratio (Fe) was set to 1.0. The computed (average) NOx emission
factor ratio for new sources (Fn) is 0.41 (58.6 percent control). The retirement rate is the inverse of the expected
unit lifetime. There is limited information in the literature about process heater lifetimes. This information was
reviewed at the time that the Western Regional Air Partnership (WRAP) developed its initial regional haze
program emission projections, and energy technology models used a 20-year lifetime for most refinery
equipment. However, it was noted that in practice, heaters would probably have a lifetime that was on the order
of 50 percent above that estimate. Therefore, a 30-year lifetime was used to estimate the effects of process
heater growth and retirement. This yields a 3.3 percent retirement rate. This control was then plugged into
Equation 2 (see Section 4.2.4) as a function of the year-specific projection factor. Resulting controls greater
than or equal to 1 percent were retained. National Process Heaters NSPS reductions from projected pre-NSPS
2023 inventory are shown in Table 4-37. This table reflects the impacts of both the MARAMA and non-
MARAMA packets.
Table 4-37. National by-sector NOx reductions from Process Heaters NSPS controls
Sector
Pre-NSPS
Emissions
NSPS Reductions
NSPS %
Reductions
Non-EGU Point
(ptnonipm)
73,057
20,225
28%
Point Oil & Gas
(pt oilgas)
9,398
2,246
24%
Total
82,455
22,501
27%
4.2.4.8 Arizona regional haze controls (ptnonipm)
Packet:
"CONTROL2011 v6.2_20xx_AZ_Regional_Haze_PT_24feb2015_v0.txt"
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U.S. EPA Region 9 provided regional haze FIP controls for a few industrial facilities. Information on these
controls are available in the Federal Register (EPA-R09-OAR-2013-0588; FRL-9912-97-0AR). These non-
EGU controls have implementation dates between September 2017 and December 2018 and, therefore, do not
reduce emissions in year 2017 projections. Year 2025 emissions are reduced at 5 smelter and cement units:
NOx by 1,722 tons and SO2 by 26,423 tons.
4.2.4.9 CISWI (ptnonipm)
Packet:
"CONTROLCISWI2011 v6_22nov2013_v0.txt"
On March 21, 2011, the 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. The history of the CISWI implementation.
Baseline and CISWI rule impacts associated with the CISWI rule. The EPA mapped the units from the CISWI
baseline and controlled dataset to the 2011 NEI inventory and because the baseline CISWI emissions and the
2011 NEI emissions were not the same, the EPA computed percent reductions such that our future year
emissions matched the CISWI controlled dataset values. CISWI controls are applied in Arkansas and Louisiana
only, totaling 3,100 and 3,552 tons of SO2 reductions in years 2017 and 2025 respectively. The reductions are
greater in year 2025 because they are applied to year-specific projected (grown) emissions.
4.2.4.10 Data from comments on previous platforms and recent comments (nonpt,
ptnonipm, ptoilgas)
Packets:
"CONTROL_2011 v6.2_20xx_State_comments_2018docket_nonpt_l 5j an2015_v0.txt"
"CONTROL2011 v6_2_20xx_CD_St_com_2018docket_pt_l 5j an2015_fixed_01 sep2015_v0.txt"
"BETA_Controls_STATE_RULES_AND_CONSENT_DECREES_2016_08_l 1 .csv" (MARAMA)
"BETA_Controls_OTC_RULES_2016 08 13.csv" (MARAMA)
All remaining non-EGU point and nonpoint controls are discussed in this section. For the nonpoint sector, these
controls are limited to comments/data-responses on the previous emissions modeling platforms, and the 2018
NODA process. For point sources, controls include data from the 2018 NODA process as well as a
concatenation of all remaining controls not already discussed. These controls are split into separate packets for
point and nonpoint sources.
Nonpoint packet: (CONTROL_2011 v6.2_20xx_State_comments_2018docket_nonpt_l 5jan2015_v0.txt)
This packet contains all nonpoint controls not already discussed in previous sections (e.g., Fuel Sulfur rules, ICI
boilers) provided in response to the 2018 NODA, and is restricted to VOC controls for Delaware,
Massachusetts, Pennsylvania and Virginia, with the great majority of these controls restricted to Virginia. These
VOC controls cover various state programs and rules such as auto refinishing, adhesives and surface coatings.
Cumulatively, these VOC controls reduce nonpoint VOC by approximately 3,900 tons in 2017 and 4,100 tons
in 2025.
Point packet: CONTROL 2011 v6_2_20xx_CD_St_com_2018docket_pt_l 5j an2015_fixed.txt
This packet contains all point controls not already discussed in previous sections (e.g., Fuel Sulfur rules, ICI
boilers). This packet includes new controls information provided in response to the 2018 NODA as well as
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"legacy" controls from the 201 lv6.0 emissions modeling platform from numerous sources such as settlement
and consent decree data gathering efforts, comments received during the CSAPR rulemaking process, regional
haze modeling, and stack-specific control information provided by TCEQ.
New control information from the 2018 NODA responses is primarily limited to VOC controls from several
states: Delaware, Massachusetts, New Jersey, Pennsylvania and Virginia. However, we also received
comments with revised compliance dates, removal of existing control information, and updated controls from
local settlements. The CONTROL packet comments field provides information on the source of new control
information, where available.
The "old" control information includes information discussed in previous emissions modeling platforms; these
CONTROL packet components are discussed in Section 4.2.9 in the 201 lv6.1 emissions modeling platform
TSD (EPA, 2014b).
Cumulative ptnonipm and ptoilgas reductions to 2023 pre-controlled (projection factors already applied) from
this CONTROL packet are shown in Table 4-38. This table reflects the impacts of both the MARAMA and non-
MARAMA packets.
Table 4-38. Summary of remaining ptnonipm and pt oilgas reductions
Year
Pollutant
Emissions
Eligible for
Control
Controlled
(Final)
Emissions
Reductions
%
Reductions
2023
CO
5,885
757
5,128
87.14%
2023
NH3
233
52
182
77.88%
2023
NOX
101,368
50,429
50,938
50.25%
2023
PM10
4,047
1,942
2,105
52.01%
2023
PM2.5
3,619
1,764
1,855
51.26%
2023
S02
122,115
26,741
95,374
78.10%
2023
VOC
3,104
2,326
778
25.05%
4.2.5 Stand-alone future year inventories (nonpt, ptnonipm)
This section discusses future year NEI non-EGU point and nonpoint emission inventories that were not created
via CoST strategies/programs/packets. These inventories are either new to the future years because they did not
exist in 2011 (e.g., new cement kilns, biodiesel and cellulosic plants), or are a complete replacement to the year
2011 NEI inventory in the case of portable fuel containers. New non-EGU facilities provided by South
Carolina via the 2018 NODA on the 201 lv6.0 platform were mistakenly omitted from both year 2017 and 2025
emissions modeling processing. Cumulatively, these new facilities would have added approximately 200 tons
of NOx, and under 100 tons of each of the remaining CAPs.
4.2.5.1 Portable fuel containers (nonpt)
Future year inventory: "pfc_2025_201 Iv6.2_ffl0_28jan2015_13sep2016_v2.csv"
The EPA used future-year VOC emissions from Portable Fuel Containers (PFCs) from inventories developed
and modeled for EPA's MSAT2 rule (EPA, 2007a). The six PFC SCCs are summarized below (note that the
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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: Permeation
2501011012 Residential Portable Fuel Containers: Evaporation
2501011014 Residential Portable Fuel Containers: Refilling at the Pump: Vapor Displacement
2501012011 Commercial Portable Fuel Containers: Permeation
2501012012 Commercial Portable Fuel Containers: Evaporation
2501012014 Commercial Portable Fuel Containers: Refilling at the Pump: Vapor Displacement
The future-year emissions reflect projected increases in fuel consumption, state programs to reduce PFC
emissions, standards promulgated in the MSAT2 rule, and impacts of the RFS2 standards on gasoline volatility.
The EPA developed year 2025 PFC emissions that include estimated Reid Vapor Pressure (RVP) and oxygenate
impacts on VOC emissions, and more importantly, large increases in ethanol emissions from RFS2. These
emission estimates also include gas can vapor displacement, tank permeation and diurnal emissions from
evaporation. Because the future year PFC inventories contain ethanol in addition to benzene, the EPA
developed a VOC E-profile that integrated ethanol and benzene (see Section 3.2.1.2 of the 201 lv6.3 platform
TSD for more details). Note that spillage emissions were not projected and were carried forward from 2011.
We received projection and control packets from MARAMA in August 2016. We applied these packets to the
PFC inventory to obtain year 2023 emissions for the MARAMA states. The names of these packets were the
following:
BETAProj ections_PFC_2023_l 0aug2016_emf. csv
BETA_Controls_PFC_28jul2016.csv
A summary of the resulting PFC emissions for 2011 and 2025 (used for 2023) for MARAMA and non-
MARAMA states are provided in Table 4-39. Note that for MARAMA states, PFCs were projected from 2011,
with separate projections for 2023 and 2028. For non-MARAMA states, the EPA 2025 PFC inventory was used
for 2023. Note that the EPA PFC inventory includes ethanol, but MARAMA inventories do not because they
were projected from the 201 1NEIv2.
Table 4-39. PFC emissions for 2011 and 2023 [tons]
MARAMA Emissions
Difference
% Change
2011
2023
2023
2023
VOC
38,152
12,595
-25,557
-67.0%
Benzene
463
474
10
2.3%
non-MARAMA
Emissions
Difference
% Change
2011
2025
2025
2025
VOC
160,051
46,498
-113,553
-70.9%
Benzene
323
613
290
89.8%
Ethanol
0
3,294
n/a
4.2.5.2 Biodiesel plants (ptnonipm)
New Future year inventory: "Biodiesel Plants 2018 fflO"
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The EPA's OTAQ developed an inventory of biodiesel plants for 2018. Plant location and production volume
data came from the Tier 3 proposed rule5,6. 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 percent production and the remaining volume was split among plants with planned
production. Once facility-level production capacities were scaled, emission factors based on soybean oil
feedstock were applied. These emission factors in Table 4-40 are in tons per million gallons (Mgal) and were
obtained from the 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 4-40. Emission Factors for Biodiesel Plants (Tons/Mgal)
Pollutant
Emission Factor
voc
4.3981E-02
CO
5.0069E-01
NOx
8.0790E-01
PMio
6.8240E-02
PM2.5
6.8240E-02
S02
5.9445E-03
nh3
0
Acetaldehyde
2.4783E-07
Acrolein
2.1290E-07
Benzene
3.2458E-08
1,3-Butadiene
0
Formaldehyde
1.5354E-06
Table 4-41provides the 2018 biodiesel plant emissions estimates. Since biofuels were not projected to change
significantly between 2018 and 2023 the year 2018 inventory was used for year 2023. Emissions in 2011 are
assumed to be near zero, and HAP emissions in 2023 are nearly zero. The emission factor for ethanol is 0.
Table 4-41. 2018 biodiesel plant emissions [tons]
Pollutant
2018
CO
649
NOx
1048
PM10
89
PM2.5
89
S02
8
VOC
57
4.2.5.3 Cellulosic plants (nonpt)
New Future year inventories:
Primary inventory: "2018_cellulosic_inventory"
5 U.S. EPA 2014.Regulatory Impact Analysis for Tier 3 Vehicle Emission and Fuel Standards Program. EPA-420-RD-143-0052.
6 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.
76
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New Iowa inventory: "cellulosic_new_Iowa_plants_from2018docket_201 Iv6.2_ffl0_28jan2015"
Development of primary inventory
Depending on available feedstock, cellulosic plants are likely to produce fuel through either a biochemical
process or a thermochemical process. The EPA developed county-level inventories for biochemical and
thermochemical cellulosic fuel production for 2018 to reflect AEO2013 energy 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 4-42 and Table 4-43 were used to develop cellulosic plant inventories.
Criteria pollutant emission rates are in tons per RIN gallon. Emission factors from the cellulosic diesel work in
the Tier 3 NPRM were used as the emission factors for the thermochemical plants. Cellulosic ethanol VOC and
related HAP emission factors from the Tier 3 NPRM were used as the biochemical VOC and related HAP
emission factors. Because the future year cellulosic inventory contains ethanol, a VOC E-profile that integrated
ethanol was used; see Section 3.2 of the 201 lv6.3 platform TSD 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 4-44 provides the year 2018 cellulosic plant emissions estimates that
were used in this year 2023 modeling platform.
Table 4-42. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/RIN gallon)
Cellulosic Plant
Type
VOC
CO
NOx
PMio
pm25
so2
nh3
Thermochemical
5.92E-07
8.7E-06
1.31E-05
1.56E-06
7.81E-07
1.17E-06
1.44E-10
Biochemical
1.82E-06
1.29E-05
1.85E-05
3.08E-06
1.23E-06
6.89E-07
0
Table 4-43. 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 4-44. 2017 cellulosic plant emissions [tons]
Pollutant
Emissions
Acrolein
1
Formaldehyde
3
Benzene
0
Acetaldehyde
15
CO
4,435
Ethanol
106
nh3
0
NOx
6,702
PMio
793
PM2.5
398
77
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S02
596
voc
302
Development of new Iowa inventory
The Iowa DNR (Department of Natural Resources), via the 2018 NOD A comments (docket # EPA-HQ-OAR-
2013-08091 provided information on new cellulosic ethanol capacity information for three facilities. Emissions
for these facilities were computed using the emission factors previously discussed in Table 4-42 and Table 4-43.
The resulting new facilities and NOx emissions used for year 2023 are provided in Table 4-45. Note that these
facilities are in a nonpoint inventory because latitude-longitude coordinates were not available.
Table 4-45. New cellulosic plants NOx emissions provided by Iowa DNR.
FIPS
County
Facility Name
Approximate
Production
Capacity
(Mgal/yr)
NOx
Emissions
19093
Ida
Quad County Corn Processors' Adding Cellulosic Ethanol (ACE)
2
26
19147
Palo Alto
POET-DSM Project Liberty
25
329
19169
Story
DuPont Cellulosic Ethanol
30
394
4.2.5.4 New cement plants (nonpt)
Nonpoint Inventories: "cement_newkilns_year_2025_from_ISIS2013_NEI201 lvl_NONPOINT_v0.csv"
As discussed in Section 4.2.3.7, the ISMP model, was used to project the cement manufacturing sector to future
years. This section covers new ISMP-generated kilns that did not exist in the 2011 NEI. For kilns that were
new in 2018, the EPA used two different approaches for modeling. The ISMP model 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, these
are modeled at the county-level to avoid placing new large modeled emissions sources into one grid cell. These
nonpoint source kilns were then spatially allocated based on industrial land activity in the county.
For all ISMP future year emissions, PMio is assigned as 0.85 of total PM provided by ISMP, and PM2.5 is
assigned as 0.15 of total PM. New ISMP-generated kilns are assigned as Precalciner kilns (SCC=30500623).
While ISMP provides emissions for mercury, the EPA did not retain these in our modeling. Table 4-46 shows
the magnitude of the new ISMP-based cement kilns. ISMP-generated kilns as nonpoint sources only.
Table 4-46. ISMP-generated nonpoint cement kiln emissions
Pollutant
Nonpoint Emissions
NOx
10,255
PM2.5
23
S02
5,311
VOC
250
78
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4.3 Mobile source projections
Mobile source monthly inventories of onroad and nonroad mobile emissions were created for 2023 using a
combination of the MOVES2014a and the NMIM models. The 2023 onroad emissions account for changes in
activity data and the impact of on-the-books rules including some of the recent regulations such as the Light
Duty Vehicle GHG Rule for Model-Year 2017-2025, and the Tier 3 Motor Vehicle Emission and Fuel
Standards Rule. 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, local fuel programs, and Stage II refueling control programs. Table 4-1 provides references to
many of these programs.
Nonroad mobile emissions reductions for these years include reductions to various nonroad engines such as
diesel engines and recreational marine engine types (pleasure craft), fuel sulfur content, and evaporative
emissions standards.
Onroad mobile sources are comprised of several components and are discussed in Section 4.3.1. Monthly
nonroad equipment mobile emission projections are discussed in Section 4.3.2. Locomotives and CMV
projections were discussed in Section 4.2.3.3.
4.3.1 Onroad mobile (onroad)
The onroad emissions for 2023 use the same SMOKE-MOVES system as for the base year (see Section 2.1).
Meteorology, speed, spatial surrogates and temporal profiles, representative counties, and fuel months were the
same as for 2011. For the 201 lv6.3 platform, the EPA developed activity data and emissions factors directly
for 2023.
4.3.1.1 Future activity data
Estimates of total national VMT in 2023 came from AEO 2016 transportation projections. Trends were
developed by calculating ratios between 2017 AEO and 2023 AEO7 estimates and applying the trends to the
2017 VMT from the 201 lv6.3 emissions platform. In states for which we received 2018 VMT for use in the
201 lv6.2 and 201 lv6.3 emissions platforms, 2018 state-submitted VMT was projected using AEO trends from
2018 to 2023, rather than from 2017 to 2023. These ratios were developed for light versus heavy duty and for
four fuel types: gasoline, diesel, E-85, and CNG. The projection factors, the national 2017 VMT from the
201 lv6.3 platform ("VMT 2017") by broad vehicle and fuel type, and the default future VMT ("VMT 2023")
are shown in Table 4-47. Note that where states provided 2018 VMT, the 2023 VMT does not exactly equal the
2017 VMT times the ratio.
Table 4-47. Projection factors for 2023 (in millions of miles)8
Classification
MOVES source types
VMT 2017
Ratio 2023
VMT 2023
LD gas
11,21,31,32
2,894,984
1.02357
2,958,777
HD gas
42,43,51,52,53,54
22,600
1.10173
25,018
HHD gas
61
835
1.83151
1,528
LD diesel
21,31,32
93,339
2.33508
212,725
HD diesel
41,42,43,51,52,53,54
73,374
1.10235
80,857
7 By "2017 AEO" and "2023 AEO," this refers to the AEO2016's estimates of national VMT in those specific calendar years.
8 Note: The LD ratios were further adjusted to take into account of high vs low growth of human population (discussed below). On
average, the LD ratios match those in this table. For the actual VMT, see the inventory packaged with the cases. In addition, areas for
which we incorporated state-submitted VMT for 2018 into the 201 lv6.3 emissions platform were projected from 2018 to 2023, rather
than from 2017.
79
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HHD diesel
61,62
151,984
1.05092
159,783
Bus CNG
42
480
1.00496
487
LD E-85
21,31,32
14,784
1.16852
17,245
Total
N/A
3,252,378
N/A
3,456,420
In the above table, light duty (LD) includes passenger cars, light trucks, and sometimes motorcycles, heavy duty
(HD) includes buses and single unit trucks, and heavy-heavy duty (HHD) includes combination trucks. The
specific MOVES source type codes are listed above. These national SCC6 ratios were applied to the 2017ek
VMT to create an EPA estimate of 2023 VMT at the county, SCC level.
Two additional steps were incorporated into the VMT projections. First, a set of states provided 2018 VMT
projections for use in the 201 lv6.2 and 201 lv6.3 emissions platforms: Alabama, Connecticut, Georgia, Maine,
Maryland, Massachusetts, Michigan, Missouri, Nevada, New York, New Jersey, North Carolina, Utah,
Vermont, Virginia, and Wyoming9. For these states, 2018 VMT was projected to 2023 using AEO2016-based
trends from 2018 to 2023, similarly to how the rest of the country was projected using AEO2016-based trends
from 2017 to 2023. This was done so that the 2018-to-2017 backcasting performed in the 201 lv6.3 emissions
platform, which is based on older AEO estimates (AEO2014), would not affect these new 2023 projections.
Second, the EPA adjusted the national LD ratios so that it would reflect regional differences in growth rate.
The EPA analyzed LD VMT and corroborated that it had a high correlation with human population. Therefore,
if a region has strong human population growth in the future, it will likely have larger VMT growth than the
national average. To take account of this spatial difference in growth, the EPA used human population to adjust
the national LD VMT growth rate so that on average the growth rate matched the national average, but any
specific county growth rate was adjusted by the human population growth for that county:
VMTprojFactorsc = AEOprojFactors *
/ humanProjFactorc \
(1 + ^(\jiatiflurnanprojpciCtor)
-1))
where
s = source type/fuel
c = county
VMTprojFactor = county VMT projection factor (by source/fuel)
AEOprojFactor = national VMT projection factor from AEO (by source/fuel)
humanProjFactor = human projection factor for the county (year specific)
natlhumanProjFactor = national human projection factor (year specific)
D = damping factor, 0 = no county adjustment, 1 = full county variation
The specific value of D used for EPA projections was 0.5. This was based on an analysis of the growth of LD
vehicles over time as compared to human population, which was found to be about 0.5 vehicles per person. The
LD growth rates will vary by county, fuel, and year. The range of these growth rates are shown in Figure 4-3.
9 For many of these states, we used the county total from the state data and distributed those totals to EPA's SCCs based on default
projected VMT. For Michigan, SEMCOG provided the Detroit projections and the rest of the counties came from the state. For
Missouri, the state provided the 5 counties around St Louis. For Nevada, the EPA received projections only for Clark County. For
Georgia, the state agreed with our default projection method but they wanted to use Georgia-provided human population projections
for distributing the LD VMT growth rates to counties. They provided the human population for the 21 Atlanta counties. For the
remaining counties, Georgia asked to use EPA defaults.
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Vehicle population (VPOP) was developed by creating VMT/VPOP ratios from the 201 1NEIv2 VMT and
201 1NEIv2 VPOP at the county, fuel and vehicle type (SCC6) level. These ratios were applied to the 2023
VMT to create a 2023 VPOP.
Hoteling (HOTELING) was developed by creating VMT/HOTELING ratios from the 2011 NEIv2 VMT and
2011 NEIv2 HOTELING at the county level. For these ratios, the VMT was limited to combination long-haul
trucks (SCC6 220262) on restricted access roads. The HOTELING was the total of auxiliary power units
(APU) and extended idle (EXT). These ratios were applied to the 2023 VMT to create a 2023 HOTELING. To
get the APU split, 22.62 percent of HOTELING was assumed to be APU in all counties. This is consistent with
MOVES2014a default splits for APU for calendar years 2017 and 2025, interpolated to 2023.
Figure 4-3. Light Duty VMT growth rates based on AEO2014
Q
Range of LD VMT growth rates
8 -
7 -
i
1
0)
+»
CO
-C
%
o
i
9 -
i
i
. i
n -
i i
s i i 1 1 I
2018 gas 2025 gas 2018diesel 2025 diesel 2018 e85 2025 e85
4.3.1.2 Set up and run MOVES to create emission factors
Emission factor tables were created by running SMOKE-MOVES using the same procedures and models as
described for 2011 (see the 201 1NEIv2 TSD and Section 2.1). The same meteorology and the same
representative counties were used. Changes between 2011 and future years (2023) are predominantly due to
activity data, fuels, national and local rules, and age distributions. Age (i.e., model year) distributions were
projected forward using the methodology described in the MOVES activity report (EPA, 2015), although some
states supplied age distributions in their CDBs. Fleet turnover resulted in a greater fraction of newer vehicles
meeting stricter emission standards. The similarities and differences between the two runs are described in
Table 4-48.
Table 4-48. Inputs for MOVES runs for 2023
Element
2023 MOVES Inputs
Code
MOVES20151201 (MOVES2014a)
81
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Rep. county database
285RepCos2023_M2014_20160520
Default database
movesdb20151028
VMT and VPOP
2023 el
Hydrocarbon speciation
CB6v2 done inside MOVES
Fuels
M2014a_fuel supply AND
regioncountytrnoda 20151203
CA LEVIII
ca_standards_SS_20140903 (16 states)
The following states were modeled as having adopted the California LEV III program (see Table 4-49):
Table 4-49. CA LEVIII program states
FIPS
State Name
06
California
09
Connecticut
10
Delaware
23
Maine
24
Maryland
25
Massachusetts
34
New Jersey
36
New York
41
Oregon
42
Pennsylvania
44
Rhode Island
50
Vermont
53
Washington
Fuels were projected into the future using estimates from the AEQ2014 release date May 7th 2014, as well as
fuel properties changing as part of the Tier 3 Emissions and Fuel Standards Program. The AEO2014 projection
includes market shares of E10, E15, and E85 in 2018, as well as biodiesel market shares up to B5 (note that
these values do not assume full implementation of the RFS2 program). The regional fuel properties and
renewable volumes in 2011 were projected to 2018 in order to preserve the regional variation present in these
fuel supplies, with total fuel volumes aligned to those in the AEO2014.
4.3.1.3 California and Texas adjustments
A set of adjustments were done in SMOKE-MOVES to create 2023 emissions: 1) refueling, and 2) California
and Texas emissions.
The first set of adjustment factors was for refueling. This uses the same approach as was used in 2011 (see the
Section 2.1 for details) to account for the few counties in Colorado that provided point source gas refueling
emissions. These adjustments essentially zero out the MOVES-based gasoline refueling emissions (SCC
2201*62) in these counties so that the point estimates will be used instead and, thus, refueling emissions will
not be double-counted.
The second set of adjustment factors was used 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. The only
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differences between the 2011 approach and that applied for 2023 are that the latter uses the 2023 emissions
provided by CARB and the 2023 EPA SMOKE-MOVES output to apportion and temporalize the emissions.
The third set of adjustment factors was meant to incorporate emissions provided by Texas. Conceptually, the
EPA used the trend of 2017 to 2023 based on the EPA's estimates to project Texas' submitted emissions for
2017. Mathematically, this is equivalent to taking the Texas adjustment factors derived for 2017 and applying
them directly to EPA's 2023 run.
4.3.2 Nonroad Mobile Source Projections (nonroad)
The projection of locomotive and CMV emissions to 2023 is described in Section 4.2.3.3. Most of the
remaining sources in the nonroad sector are projected by running the NMIM model with fuels and vehicle
populations appropriate to 2023; this section describes the projection of these sources.
The nonroad sector includes monthly exhaust, evaporative and refueling emissions from nonroad engines (not
including commercial marine, aircraft, and locomotives) derived from NMIM for all states except California
and Texas. NMIM provides nonroad emissions for VOC by three emission modes: exhaust, evaporative and
refueling.
With the exception of California and Texas, U.S. emissions for the nonroad sector (defined as the equipment
types covered by the NONROAD model) were created using a consistent NMIM-based approach as was used
for 2011. Specifically, NMIM version 20090504 utilized NONROAD2008a including future-year equipment
population estimates, control programs to the year 2023, and inputs were either state-supplied as part of the
201 INEIvl and 201 1NEIv2 process or national level inputs. Fuels for 2023 were assumed to be E10
everywhere for nonroad equipment. The databases used in the 2023 run were NMIM county database
"NCD20160627_nei2023vl" and fuels for the year 2023. The 2023 emissions account for changes in activity
data (based on NONROAD model default growth estimates of future-year equipment population) and changes
in fuels and engines that reflect implementation of national regulations and local control programs that impact
each year differently due to engine turnover.
The version of NONROAD used was the current public release, NR08a, which models all in-force nonroad
controls. The represented rules include:
"Clean Air Nonroad Diesel Final Rule - Tier 4", published June, 2004
Control of Emissions from Nonroad Large Spark-Ignition Engines, and Recreational Engines (Marine
and Land-Based), November 8, 2002 ("Pentathalon Rule").
Small Engine Spark Ignition ("Bond") Rule, October, 2008
Not included are voluntary local programs such as encouraging either no refueling or evening refueling on
Ozone Action Days.
California and Texas nonroad emissions
Similar to the 2011 base year nonroad mobile, NMIM was not used to generate future-year nonroad emissions
for California. The CARB-supplied 2023 nonroad annual inventories, which included all CAPs including NH3,
were distributed to monthly emissions values by using monthly temporal profiles assigned by SCC. This is a
change from future year California nonroad inventories in prior emissions platforms, in which NMIM monthly
inventories were used to compute monthly ratios by county, SCC7, mode and pollutant. See Section 3.2 of the
201v6.3 TSD for details on speciation of California nonroad data. The CARB nonroad emissions include
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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, the EPA combined Texas' submitted estimates for 2011 with EPA projections of nonroad emissions
into 2023. The EPA used the trend of 2011 to 2023 based on EPA's estimates to project Texas' submitted
emissions for 2011. The projections were based on state-wide SCC7, mode, poll ratios10 of 2023 NMIM to
2011 NMIM. These ratios were then applied to Texas' submitted 2011 nonroad emissions, which had already
been distributed to a monthly inventory to create 2023 monthly nonroad inventories. Please refer to the
201 lv6.3 TSD (EPA, 2016) for more information on the year 2011 data obtained from Texas.
4.4 Projections of "Other Emissions": Offshore Category 3 Commercial
Marine Vessels and Drilling Platforms, Canada and Mexico (othpt, othar,
and othon)
As described in Section 2.3, emissions from Canada, Mexico, and non-U.S. offshore Category 3 Commercial
Marine Vessels (C3 CMV) and drilling platforms are included as part of three emissions modeling sectors:
othpt, othar, and othon. For oil drilling platforms, the EPA used emissions from the 201 1NEIv2 point source
inventory for 2011 and both future years. The Canadian onroad (othon) and nonroad emissions in othar sector
were projected using U.S. emissions changes by SCC and pollutant (see Tables 5-11 and 5-12). The Canadian
point sources in othpt sectors were modified for 2023 by removing the remaining coal EGU plants (see Table 5-
13). Area, nonroad, and point emissions for Mexico are based on the Inventario Nacional de Emisiones de
Mexico, 2008 projected to years 2018 and 2025, then interpolated to 2023 (ERG, 2014a). Onroad emissions for
Mexico are based on run of MOVES-Mexico for 2023 (ERG, 2016).
As discussed in Section 2.5.1 of the 201 lv6.3 platform TSD, the ECA-IMO-based C3 CMV emissions outside
of U.S. state waters are processed in the othpt sector. This enables shipping lanes to be represented and for
emissions to be treated as elevated sources. These C3 CMV emissions include those assigned to the 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. The projection factors for the othpt C3
CMV emissions vary by geographic and region as shown in Table 4-9.
10 These ratios were initially attempted by county/SCC7/mode/pollutant, but due to significantly different distributions of certain
source types between the 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 the EPA's 2023 emissions but not in
the EPA's 2011 emissions; 2023 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 2023 emissions, then state/SCC3/mode/pollutant ratios were used to project to 2023.
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5 Emission Summaries
The following tables summarize emissions differences between the 2011 evaluation case and the 2023 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 0. The afdust sector
emissions represent the summaries after application of both the land use (transport fraction) and meteorological
adjustments; therefore, this sector is called "afdust adj" in these summaries. The onroad sector totals are post-
SMOKE-MOVES totals, representing air quality model-ready emission totals, and include CARB emissions for
California and TCEQ emissions for Texas. The cmv sector includes U.S. emissions within state waters only;
these extend to roughly 3-5 nautical miles offshore and includes CMV emissions at U.S. ports. "Offshore to
EEZ" represents CMV emissions that are within the (up to) 200 nautical mile EEZ boundary but are outside of
U.S. state waters along with the offshore oil platform emissions from the NEI. Finally, the "Non-US SECA
C3" represents all non-U. S. and non-Canada emissions outside of the (up to) 200nm offshore boundary,
including all Mexican CMV emissions. Canadian CMV emissions are included in the othar sector.
National emission totals by air quality model-ready sector are provided 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."
Table 5-2 provides national emissions totals by sector for CAPs in the 2023 base case.
Table 5-3 provides national-by sector emission summaries for CO for the 2011 evaluation case and 2023 base
case, along with percent change from 2011 to 2023. Table 5-4 through Table 5-9 provide the same summaries
for NH3, NOx, PM2.5, PM10, SO2 and VOC, respectively. Note that the same fire emissions are used in all
cases. Tables 5-10 through Table 5-12 provide summaries of the Canadian emissions for the entire country
used in the 2011 and 2023 base cases for onroad, area, and point source emissions. Tables 5-13 through Table
5-15 provide summaries of the Mexican emissions for the entire country used in the 2011 and 2023 base cases
for onroad, area, and point source emissions
85
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Table 5-1. National by-sector CAP emissions summaries for the 2011 evaluation case
Sector
CO
NHs
NOx
PMio
PM2S
SO2
VOC
afdust adj
6,732,941
923,590
ag
3,515,198
agfire
1,030,817
3,321
46,035
152,837
101,379
17,755
80,540
cmv
70,498
232
414,099
19,658
18,124
91,209
12,584
nonpt
1,645,989
94,242
720,454
491,825
404,258
276,332
3,671,898
np oilgas
635,942
0
667,068
17,784
16,333
17,232
2,482,590
nonroad
13,951,020
2,627
1,630,301
162,417
154,657
4,031
2,024,419
onroad
25,981,557
120,859
5,708,150
326,900
188,925
28,195
2,713,181
ptfire
20,562,697
329,330
333,398
2,171,987
1,844,263
165,773
4,688,094
ptegu
792,397
25,066
2,095,119
283,072
208,129
4,670,569
38,062
ptnonipm
2,297,650
66,051
1,213,528
477,387
320,857
1,049,424
801,188
pt oilgas
235,162
5,947
509,856
14,585
13,935
66,577
164,098
rail
122,703
347
791,381
25,898
23,963
7,936
40,851
rwc
2,517,844
19,693
34,436
381,476
381,252
8,954
442,541
Con U.S. Total
69,844,278
4,182,913
14,163,826
11,258,767
4,599,665
6,403,986
17,160,045
Offshore to EEZ
176,645
189
906,088
26,451
24,741
139,246
81,749
Non-US SECA C3
16,207
0
190,904
16,226
14,926
120,340
6,879
Canada othafdust
780,456
112,597
Canada othar
3,015,514
326,281
361,958
159,054
131,167
70,276
886,419
Canada othon
3,032,193
18,655
345,657
12,216
5,412
1,702
178,440
Canada othpt
496,083
13,069
266,912
70,009
29,166
544,504
129,119
Canada ptfire mxca
798,710
13,037
14,048
87,398
73,401
6,481
194,844
Mexico othar
185,229
168,021
181,716
90,559
42,491
10,173
419,249
Mexico othon
1,466,960
2,154
361,626
8,772
3,252
4,428
134,867
Mexico othpt
153,387
3,945
333,368
59,325
45,963
471,847
57,090
Mexico ptfire mxca
736,810
13,583
31,403
104,125
87,025
6,394
172,196
Non-US Total
10,077,739
558,933
2,993,679
1,414,590
570,141
1,375,391
2,260,852
* "Offshore to EEZ" includes both the offshore point emissions, and the "Offshore to EEZ" c3marine emissions.
86
-------
Table 5-2. National by-sector CAP emissions summaries for the 2023 base case
Sector
CO
nh3
NOx
PMio
pm25
so2
voc
afdust ad)
7,498,365
1,009,616
ag
3,602,039
agfire
1,030,817
3,321
46,035
152,837
101,379
17,755
80,540
cmv
76,265
235
280,626
7,513
7,039
6,811
12,880
nonpt
1,682,696
94,695
735,016
509,892
427,719
96,043
3,454,250
np oilgas
835,955
0
772,886
31,510
28,632
42,313
2,114,826
nonroad
12,627,798
3,228
856,831
84,153
78,858
2,380
1,177,147
onroad
11,300,137
82,106
1,786,856
232,752
79,527
12,114
987,796
ptfire
20,562,697
329,330
333,398
2,171,987
1,844,263
165,773
4,688,094
ptegu
710,281
41,879
888,542
181,229
136,612
1,165,674
30,745
ptnonipm
2,376,516
66,243
1,211,582
482,565
327,502
797,587
808,390
pt oilgas
243,841
5,934
448,133
16,551
15,865
84,942
187,955
rail
145,627
376
563,382
14,236
13,165
340
21,384
rwc
2,368,934
18,499
34,918
362,897
362,651
7,908
415,748
Con U.S. Total
53,961,563
4,247,885
7,958,204
11,746,487
4,432,829
2,399,640
13,979,755
Offshore to EEZ
205,441
189
717,820
9,658
9,152
11,619
92,477
Non-US SECA C3
27,810
0
266,354
10,233
9,372
69,593
11,843
Canada othafdust
780,456
112,597
Canada othar
3,130,776
326,337
290,025
151,257
123,523
70,176
824,416
Canada othon
1,348,633
12,001
116,704
5,110
6,146
983
65,716
Canada othpt
489,410
13,060
247,646
68,377
28,291
497,429
129,119
Canada ptfire mxca
798,710
13,037
14,048
87,398
73,401
6,481
194,844
Mexico othar
215,759
166,718
209,943
95,309
46,135
12,144
503,620
Mexico othon
1,533,904
2,853
374,074
9,571
4,584
6,364
141,276
Mexico othpt
199,007
5,669
376,422
71,542
54,940
361,230
80,922
Mexico ptfire mxca
736,810
13,583
31,403
104,125
87,025
6,394
172,196
Non-US Total
8,686,260
553,446
2,644,438
1,393,035
555,166
1,042,413
2,216,430
87
-------
Table 5-3. National by-sector CO emissions (tons/yr) summaries and percent change
Sector
2011 CO
2023 CO
% change 2011
to 2023
afdust adj
0
0
0%
ag
0
0
0%
aft fire
1,030,817
1,030,817
0%
cmv
70,498
76,265
8%
nonpt
1,645,989
1,682,696
2%
np oilgas
635,942
835,955
31%
nonroad
13,951,020
12,627,798
-9%
onroad
25,981,557
11,300,137
-57%
ptfire
20,562,697
20,562,697
0%
ptegu
792,397
710,281
-10%
ptnonipm
2,297,650
2,376,516
3%
pt oilgas
235,162
243,841
4%
rail
122,703
145,627
19%
rwc
2,517,844
2,368,934
-6%
Con U.S. Total
69,844,278
53,961,563
-23%
Offshore to EEZ
176,645
205,441
16%
Non-US SECA C3
16,207
27,810
72%
Canada othafdust
0
0
0%
Canada othar
3,015,514
3,130,776
4%
Canada othon
3,032,193
1,348,633
-56%
Canada othpt
496,083
489,410
-1%
Canada ptfire mxca
798,710
798,710
0%
Mexico othar
185,229
215,759
16%
Mexico othon
1,466,960
1,533,904
5%
Mexico othpt
153,387
199,007
30%
Mexico ptfire mxca
736,810
736,810
0%
Non-US Total
10,077,739
8,686,260
-14%
88
-------
Table 5-4. National by-sector NH3 emissions (tons/yr) summaries and percent change
Sector
2011 NHs
2023 NHs
% change 2011
to 2023
afdust adj
0
0
0%
ag
3,515,198
3,602,039
2%
aft fire
3,321
3,321
0%
cmv
232
235
2%
nonpt
94,242
94,695
0%
np oilgas
0
0
0%
nonroad
2,627
3,228
23%
onroad
120,859
82,106
-32%
ptfire
329,330
329,330
0%
ptegu
25,066
41,879
67%
ptnonipm
66,051
66,243
0%
pt oilgas
5,947
5,934
0%
rail
347
376
8%
rwc
19,693
18,499
-6%
Con U.S. Total
4,182,913
4,247,885
2%
Offshore to EEZ
189
189
0%
Non-US SECA C3
0
0
0%
Canada othafdust
0
0
0%
Canada othar
326,281
326,337
0%
Canada othon
18,655
12,001
-36%
Canada othpt
13,069
13,060
0%
Canada ptfire mxca
13,037
13,037
0%
Mexico othar
168,021
166,718
-1%
Mexico othon
2,154
2,853
32%
Mexico othpt
3,945
5,669
44%
Mexico ptfire mxca
13,583
13,583
0%
Non-US Total
558,933
553,446
-1%
89
-------
Table 5-5. National by-sector NOx emissions (tons/yr) summaries and percent change
Sector
2011 NOx
2023 NOx
% change 2011
to 2023
afdust adj
0
0
0%
ag
0
0
0%
aft fire
46,035
46,035
0%
cmv
414,099
280,626
-32%
nonpt
720,454
735,016
2%
np oilgas
667,068
772,886
16%
nonroad
1,630,301
856,831
-47%
onroad
5,708,150
1,786,856
-69%
ptfire
333,398
333,398
0%
ptegu
2,095,119
888,542
-58%
ptnonipm
1,213,528
1,211,582
0%
pt oilgas
509,856
448,133
-12%
rail
791,381
563,382
-29%
rwc
34,436
34,918
1%
Con U.S. Total
14,163,826
7,958,204
-44%
Offshore to EEZ
906,088
717,820
-21%
Non-US SECA C3
190,904
266,354
40%
Canada othafdust
0
0
0%
Canada othar
361,958
290,025
-20%
Canada othon
345,657
116,704
-66%
Canada othpt
266,912
247,646
-7%
Canada ptfire mxca
14,048
14,048
0%
Mexico othar
181,716
209,943
16%
Mexico othon
361,626
374,074
3%
Mexico othpt
333,368
376,422
13%
Mexico ptfire mxca
31,403
31,403
0%
Non-US Total
2,993,679
2,644,438
-12%
90
-------
Table 5-6. National by-sector PM2.5 emissions (tons/yr) summaries and percent change
Sector
2011 PM2.5
2023 PM2 s
% change 2011
to 2023
afdust adj
923,590
1,009,616
9%
ag
0
0
0%
aft fire
101,379
101,379
0%
cmv
18,124
7,039
-61%
nonpt
404,258
427,719
6%
np oilgas
16,333
28,632
75%
nonroad
154,657
78,858
-49%
onroad
188,925
79,527
-58%
ptfire
1,844,263
1,844,263
0%
ptegu
208,129
136,612
-34%
ptnonipm
320,857
327,502
2%
pt oilgas
13,935
15,865
14%
rail
23,963
13,165
-45%
rwc
381,252
362,651
-5%
Con U.S. Total
4,599,665
4,432,829
-4%
Offshore to EEZ
24,741
9,152
-63%
Non-US SECA C3
14,926
9,372
-37%
Canada othafdust
112,597
112,597
0%
Canada othar
131,167
123,523
-6%
Canada othon
5,412
6,146
14%
Canada othpt
29,166
28,291
-3%
Canada ptfire mxca
73,401
73,401
0%
Mexico othar
42,491
46,135
9%
Mexico othon
3,252
4,584
41%
Mexico othpt
45,963
54,940
20%
Mexico ptfire mxca
87,025
87,025
0%
Non-US Total
570,141
555,166
-3%
91
-------
Table 5-7. National by-sector PMio emissions (tons/yr) summaries and percent change
Sector
2011 PMio
2023 PMio
% change 2011
to 2023
afdust adj
6,732,941
7,498,365
11%
ag
0
0
0%
aft fire
152,837
152,837
0%
cmv
19,658
7,513
-62%
nonpt
491,825
509,892
4%
np oilgas
17,784
31,510
77%
nonroad
162,417
84,153
-48%
onroad
326,900
232,752
-29%
ptfire
2,171,987
2,171,987
0%
ptegu
283,072
181,229
-36%
ptnonipm
477,387
482,565
1%
pt oilgas
14,585
16,551
13%
rail
25,898
14,236
-45%
rwc
381,476
362,897
-5%
Con U.S. Total
11,258,767
11,746,487
4%
Offshore to EEZ
26,451
9,658
-63%
Non-US SECA C3
16,226
10,233
-37%
Canada othafdust
780,456
780,456
0%
Canada othar
159,054
151,257
-5%
Canada othon
12,216
5,110
-58%
Canada othpt
70,009
68,377
-2%
Canada ptfire mxca
87,398
87,398
0%
Mexico othar
90,559
95,309
5%
Mexico othon
8,772
9,571
9%
Mexico othpt
59,325
71,542
21%
Mexico ptfire mxca
104,125
104,125
0%
Non-US Total
1,414,590
1,393,035
-2%
92
-------
Table 5-8. National by-sector SO2 emissions (tons/yr) summaries and percent change
Sector
2011 SO2
2023 SO2
% change 2011
to 2023
afdust adj
0
0
0%
ag
0
0
0%
aft fire
17,755
17,755
0%
cmv
91,209
6,811
-93%
nonpt
276,332
96,043
-65%
np oilgas
17,232
42,313
146%
nonroad
4,031
2,380
-41%
onroad
28,195
12,114
-57%
ptfire
165,773
165,773
0%
ptegu
4,670,569
1,165,674
-75%
ptnonipm
1,049,424
797,587
-24%
pt oilgas
66,577
84,942
28%
rail
7,936
340
-96%
rwc
8,954
7,908
-12%
Con U.S. Total
6,403,986
2,399,640
-63%
Offshore to EEZ
139,246
11,619
-92%
Non-US SECA C3
120,340
69,593
-42%
Canada othafdust
0
0
0%
Canada othar
70,276
70,176
0%
Canada othon
1,702
983
-42%
Canada othpt
544,504
497,429
-9%
Canada ptfire mxca
6,481
6,481
0%
Mexico othar
10,173
12,144
19%
Mexico othon
4,428
6,364
44%
Mexico othpt
471,847
361,230
-23%
Mexico ptfire mxca
6,394
6,394
0%
Non-US Total
1,375,391
1,042,413
-24%
93
-------
Table 5-9. National by-sector VOC emissions (tons/yr) summaries and percent change
Sector
2011 VOC
2023 VOC
% change 2011
to 2023
afdust adj
0
0
0%
ag
0
0
0%
aft fire
80,540
80,540
0%
cmv
12,584
12,880
2%
nonpt
3,671,898
3,454,250
-6%
np oilgas
2,482,590
2,114,826
-15%
nonroad
2,024,419
1,177,147
-42%
onroad
2,713,181
987,796
-64%
ptfire
4,688,094
4,688,094
0%
ptegu
38,062
30,745
-19%
ptnonipm
801,188
808,390
1%
pt oilgas
164,098
187,955
15%
rail
40,851
21,384
-48%
rwc
442,541
415,748
-6%
Con U.S. Total
17,160,045
13,979,755
-19%
Offshore to EEZ
81,749
92,477
13%
Non-US SECA C3
6,879
11,843
72%
Canada othafdust
0
0
0%
Canada othar
886,419
824,416
-7%
Canada othon
178,440
65,716
-63%
Canada othpt
129,119
129,119
0%
Canada ptfire mxca
194,844
194,844
0%
Mexico othar
419,249
503,620
20%
Mexico othon
134,867
141,276
5%
Mexico othpt
57,090
80,922
42%
Mexico ptfire mxca
172,196
172,196
0%
Non-US Total
2,260,852
2,216,430
-2%
94
-------
Table 5-10. Canadian province emissions changes from 2011 to 2023 for othon sector
2023 othon emissions
(tons)
201 lei
2023el
% diff
(2023el-
2011el)
2011el
2023el
% diff
(2023el-
201 lei)
2011el
2023el
% diff
(2023el-
201 lei)
Province
CO
CO
CO
NOX
NOX
NOX
voc
VOC
VOC
Newfoundland
70,094
30,838
-56.0%
7,915
2,677
-66.2%
3,333
1,200
-64.0%
Prince Edward Island
24,124
10,745
-55.5%
3,319
1,173
-64.7%
1,390
508
-63.5%
Nova Scotia
119,570
53,151
-55.5%
13,799
4,731
-65.7%
6,593
2,411
-63.4%
New Brunswick
129,867
57,582
-55.7%
18,604
6,672
-64.1%
7,621
2,803
-63.2%
Quebec
885,568
402,179
-54.6%
106,445
37,055
-65.2%
48,478
18,206
-62.4%
Ontario
1,189,550
530,264
-55.4%
124,063
41,172
-66.8%
61,637
23,130
-62.5%
Manitoba
226,661
98,746
-56.4%
27,249
9,459
-65.3%
14,285
5,101
-64.3%
Saskatchewan
353,836
152,184
-57.0%
41,393
14,230
-65.6%
25,123
8,791
-65.0%
Alberta
658,481
287,868
-56.3%
94,080
32,364
-65.6%
48,414
17,262
-64.3%
British Columbia
588,527
256,809
-56.4%
67,944
21,711
-68.0%
45,044
16,189
-64.1%
Yukon
7,590
3,352
-55.8%
686
223
-67.6%
476
171
-64.0%
N W Territories
6,617
2,957
-55.3%
754
264
-64.9%
410
149
-63.7%
Nunavut
1,920
804
-58.1%
155
50
-67.8%
104
35
-65.9%
Canada Total
4,262,403
1,887,476
-55.7%
506,406
171,781
-66.1%
262,908
95,956
-63.5%
Table 5-11. Canadian province emissions changes from 2011 to 2023 for othar sector
2023 othar emissions
(tons)
201 lei
2023el
% diff
(2023el-
2011el)
2011el
2023el
% diff
(2023el-
201 lei)
201 lei
2023el
% diff
(2023el-
2011el)
Province
CO
CO
CO
NOX
NOX
NOX
VOC
VOC
VOC
Newfoundland
71,720
67,816
-5.4%
32,106
29,590
-7.8%
24,884
19,857
-20.2%
Prince Edward Island
27,420
28,106
2.5%
1,309
1,117
-14.6%
7,459
6,156
-17.5%
Nova Scotia
108,892
113,205
4.0%
34,093
31,813
-6.7%
31,588
30,246
-4.2%
New Brunswick
76,757
78,695
2.5%
12,057
10,813
-10.3%
27,446
26,575
-3.2%
Quebec
923,750
953,313
3.2%
96,533
81,444
-15.6%
274,657
261,356
-4.8%
Ontario
1,537,669
1,607,612
4.5%
169,367
138,266
-18.4%
388,132
355,105
-8.5%
Manitoba
153,099
158,768
3.7%
16,943
15,131
-10.7%
67,697
61,035
-9.8%
Saskatchewan
470,108
484,491
3.1%
53,501
37,220
-30.4%
132,559
112,022
-15.5%
Alberta
339,458
324,040
-4.5%
141,209
94,161
-33.3%
205,096
195,910
-4.5%
British Columbia
430,751
433,724
0.7%
103,465
91,799
-11.3%
122,900
118,689
-3.4%
Yukon
1,355
1,250
-7.7%
524
354
-32.4%
702
664
-5.4%
N W Territories
9,214
8,380
-9.1%
4,736
3,309
-30.1%
2,199
1,645
-25.2%
Nunavut
978
774
-20.8%
1,438
975
-32.2%
658
617
-6.2%
Canada Total
4,151,170
4,260,172
2.6%
667,282
535,990
-19.7%
1,285,976
1,189,878
-7.5%
95
-------
Table 5-12. Canadian province emissions changes from 2011 to 2023 for othpt sector
2023 othpt emissions
(tons)
201 lei
2023el
% diff
(2023el-
201 lei)
201 lei
2023el
% diff
(2023el-
2011el)
201 lei
2023el
% diff
(2023el-
2011el)
Province
CO
CO
CO
NOX
NOX
NOX
voc
VOC
VOC
Newfoundland
13,073
13,073
0.0%
23,646
23,646
0.0%
19,926
19,926
0.0%
Prince Edward Island
49
49
0.0%
321
321
0.0%
417
417
0.0%
Nova Scotia
4,451
4,451
0.0%
25,181
25,181
0.0%
11,346
11,346
0.0%
New Brunswick
28,314
28,310
0.0%
16,900
16,804
-0.6%
4,691
4,691
0.0%
Quebec
472,250
471,057
-0.3%
52,177
50,554
-3.1%
65,053
64,141
-1.4%
Ontario
85,168
79,696
-6.4%
90,405
72,773
-19.5%
121,838
121,747
-0.1%
Manitoba
2,394
2,394
0.0%
3,822
3,822
0.0%
30,505
30,505
0.0%
Saskatchewan
27,496
27,496
0.0%
65,439
65,439
0.0%
169,269
169,269
0.0%
Alberta
496,794
496,794
0.0%
575,981
575,981
0.0%
498,580
498,580
0.0%
British Columbia
196,308
196,308
0.0%
89,526
89,526
0.0%
56,938
56,938
0.0%
Yukon
50
50
0.0%
135
135
0.0%
5
5
0.0%
N W Territories
1,871
1,871
0.0%
9,107
9,107
0.0%
1,037
1,037
0.0%
Nunavut
817
817
0.0%
5,588
5,588
0.0%
326
326
0.0%
Canada Total
1,329,036
1,322,367
-0.5%
958,229
938,876
-2.0%
979,932
978,928
-0.1%
96
-------
Table 5-13. Mexican state emissions changes from 2011 to 2023 for othon sector
2023 othon emissions
(tons)
201 lei
2023el
% diff
(2023el-
201 lei)
201 lei
2023el
% diff
(2023el-
2011el)
201 lei
2023el
% diff
(2023el-
2011el)
State
CO
CO
CO
NOX
NOX
NOX
voc
VOC
VOC
Aguascalientes
74,458
72,499
-2.6%
18,716
19,700
5.3%
7,126
7,314
2.6%
Baja Calif Norte
292,747
316,731
8.2%
74,570
77,577
4.0%
25,233
26,025
3.1%
Baja Calif Sur
83,274
91,452
9.8%
19,961
20,750
4.0%
6,999
7,340
4.9%
Campeche
52,849
58,506
10.7%
9,367
9,834
5.0%
3,948
4,122
4.4%
Coahuila
170,357
165,632
-2.8%
38,217
40,294
5.4%
15,532
16,135
3.9%
Colima
59,533
65,737
10.4%
11,485
12,026
4.7%
4,735
5,004
5.7%
Chiapas
114,015
125,700
10.2%
23,295
24,325
4.4%
9,109
9,519
4.5%
Chihuahua
280,049
271,634
-3.0%
76,676
80,295
4.7%
26,460
27,193
2.8%
Distrito Federal
602,306
602,050
0.0%
143,350
138,120
-3.6%
60,134
60,474
0.6%
Durango
98,318
107,195
9.0%
24,238
25,168
3.8%
8,817
9,370
6.3%
Guanajuato
230,777
224,860
-2.6%
57,800
60,848
5.3%
22,563
23,431
3.8%
Guerrero
156,199
172,474
10.4%
28,815
30,232
4.9%
12,770
13,669
7.0%
Hidalgo
131,136
127,736
-2.6%
34,009
35,730
5.1%
12,794
13,110
2.5%
Jalisco
456,462
433,740
-5.0%
122,360
125,191
2.3%
45,893
47,241
2.9%
Mexico
413,998
448,551
8.3%
102,556
103,470
0.9%
38,111
38,793
1.8%
Michoacan
301,589
330,111
9.5%
68,641
71,574
4.3%
27,435
29,395
7.1%
Morelos
83,388
81,392
-2.4%
19,926
20,997
5.4%
7,929
8,274
4.3%
Nayarit
71,260
78,690
10.4%
13,702
14,352
4.7%
5,947
6,409
7.8%
Nuevo Leon
340,264
353,709
4.0%
86,518
86,734
0.3%
34,033
35,793
5.2%
Oaxaca
98,480
95,690
-2.8%
26,792
27,781
3.7%
8,496
8,625
1.5%
Puebla
196,606
212,743
8.2%
49,244
51,425
4.4%
18,745
19,950
6.4%
Queretaro
71,514
69,650
-2.6%
20,361
21,327
4.7%
6,963
7,164
2.9%
Quintana Roo
67,166
65,537
-2.4%
13,672
14,466
5.8%
5,594
5,739
2.6%
San Luis Potosi
144,504
140,708
-2.6%
32,362
34,138
5.5%
13,518
14,187
4.9%
Sinaloa
203,180
223,769
10.1%
46,984
48,875
4.0%
17,555
18,869
7.5%
Sonora
195,052
214,002
9.7%
46,289
48,130
4.0%
17,094
18,303
7.1%
Tabasco
93,227
103,029
10.5%
17,304
18,148
4.9%
7,343
7,754
5.6%
Tamaulipas
296,180
325,932
10.0%
58,506
61,170
4.6%
24,360
25,872
6.2%
Tlaxcala
33,247
32,217
-3.1%
8,901
9,355
5.1%
3,266
3,321
1.7%
Veracruz
265,631
259,302
-2.4%
68,186
71,617
5.0%
24,046
24,651
2.5%
Yucatan
97,722
95,382
-2.4%
20,606
21,783
5.7%
8,431
8,745
3.7%
Zacatecas
112,450
122,582
9.0%
28,420
29,527
3.9%
10,411
11,130
6.9%
Mexico Total
5,887,937
6,088,942
3.4%
1,411,830
1,454,958
3.1%
541,390
562,919
4.0%
97
-------
Table 5-14. Mexican state emissions changes from 2011 to 2023 for othar sector
2023 othar emissions
(tons)
201 lei
2023el
% diff
(2023el-
201 lei)
201 lei
2023el
% diff
(2023el-
2011el)
201 lei
2023el
% diff
(2023el-
2011el)
State
CO
CO
CO
NOX
NOX
NOX
voc
VOC
VOC
Aguascalientes
4,018
4,901
22.0%
6,605
7,492
13.4%
19,358
23,699
22.4%
Baja Calif Norte
13,589
19,079
40.4%
21,841
28,254
29.4%
61,514
77,009
25.2%
Baja Calif Sur
3,110
4,372
40.6%
4,996
6,085
21.8%
10,889
14,748
35.4%
Campeche
51,137
55,561
8.7%
35,074
34,844
-0.7%
35,129
41,592
18.4%
Coahuila
12,444
14,769
18.7%
15,089
19,367
28.4%
48,687
58,739
20.6%
Colima
8,562
10,303
20.3%
3,883
4,601
18.5%
16,571
20,176
21.8%
Chiapas
305,524
354,916
16.2%
22,097
23,492
6.3%
312,206
365,483
17.1%
Chihuahua
61,301
67,860
10.7%
55,606
59,045
6.2%
99,006
116,057
17.2%
Distrito Federal
10,780
14,230
32.0%
7,966
10,765
35.1%
108,040
112,654
4.3%
Durango
39,499
43,328
9.7%
27,428
28,670
4.5%
51,830
59,027
13.9%
Guanajuato
71,662
83,363
16.3%
41,641
49,568
19.0%
122,993
141,500
15.0%
Guerrero
156,577
167,856
7.2%
5,770
6,172
7.0%
176,647
192,150
8.8%
Hidalgo
98,080
110,966
13.1%
17,781
21,582
21.4%
113,582
128,929
13.5%
Jalisco
61,762
70,602
14.3%
47,329
50,076
5.8%
147,659
174,141
17.9%
Mexico
178,322
219,642
23.2%
32,009
37,849
18.2%
344,893
416,931
20.9%
Michoacan
115,037
132,429
15.1%
21,496
37,382
73.9%
152,964
171,488
12.1%
Morelos
26,857
27,190
1.2%
13,692
5,457
-60.1%
45,963
52,672
14.6%
Nayarit
23,142
26,534
14.7%
13,483
13,091
-2.9%
30,199
36,612
21.2%
Nuevo Leon
31,440
38,770
23.3%
24,518
30,517
24.5%
88,474
108,061
22.1%
Oaxaca
238,829
255,390
6.9%
13,735
14,059
2.4%
250,320
270,763
8.2%
Puebla
202,340
227,306
12.3%
17,744
21,075
18.8%
250,507
283,412
13.1%
Queretaro
26,941
34,278
27.2%
8,463
12,791
51.1%
50,165
61,365
22.3%
Quintana Roo
26,335
35,351
34.2%
5,137
5,773
12.4%
38,633
53,296
38.0%
San Luis Potosi
88,201
98,880
12.1%
22,207
27,521
23.9%
106,283
118,702
11.7%
Sinaloa
54,362
59,869
10.1%
35,373
38,123
7.8%
76,165
85,204
11.9%
Sonora
26,007
30,706
18.1%
23,917
27,984
17.0%
60,018
72,372
20.6%
Tabasco
91,388
102,556
12.2%
14,024
16,009
14.1%
103,490
117,803
13.8%
Tamaulipas
44,743
51,876
15.9%
46,959
54,576
16.2%
70,902
83,656
18.0%
Tlaxcala
21,451
25,104
17.0%
6,672
7,438
11.5%
32,549
38,656
18.8%
Veracruz
357,503
389,550
9.0%
48,159
50,987
5.9%
390,957
432,607
10.7%
Yucatan
97,808
113,125
15.7%
7,176
7,935
10.6%
111,556
131,043
17.5%
Zacatecas
30,865
32,736
6.1%
38,745
40,253
3.9%
36,798
40,838
11.0%
Mexico Total
2,579,614
2,923,397
13.3%
706,612
798,834
13.1%
3,564,949
4,101,385
15.0%
98
-------
Table 5-15. Mexican state emissions changes from 2011 to 2023 for othpt sector
2023 othpt emissions
(tons)
201 lei
2023el
% diff
(2023el-
201 lei)
201 lei
2023el
% diff
(2023el-
2011el)
201 lei
2023el
% diff
(2023el-
2011el)
State
CO
CO
CO
NOX
NOX
NOX
voc
VOC
VOC
Aguascalientes
275
391
42.3%
987
1,407
42.6%
2,151
3,069
42.7%
Baja Calif Norte
8,083
17,500
116.5%
14,498
32,455
123.9%
13,603
19,505
43.4%
Baja Calif Sur
644
173
-73.1%
8,899
2,582
-71.0%
610
771
26.4%
Campeche
9,342
11,361
21.6%
35,616
41,077
15.3%
3,637
4,324
18.9%
Coahuila
31,659
35,549
12.3%
217,689
218,533
0.4%
7,328
10,306
40.6%
Colima
1,496
1,052
-29.7%
15,921
7,294
-54.2%
1,514
2,152
42.1%
Chiapas
2,861
3,919
37.0%
5,503
7,500
36.3%
3,926
5,439
38.5%
Chihuahua
11,318
15,659
38.4%
11,989
13,663
14.0%
5,540
7,803
40.8%
Distrito Federal
887
1,321
49.0%
2,582
3,853
49.2%
25,747
36,748
42.7%
Durango
3,552
4,737
33.4%
6,988
7,371
5.5%
3,727
5,261
41.1%
Guanajuato
78,844
95,712
21.4%
9,566
12,567
31.4%
11,245
14,846
32.0%
Guerrero
3,200
3,184
-0.5%
14,706
14,270
-3.0%
785
952
21.2%
Hidalgo
123,941
218,498
76.3%
35,641
50,270
41.0%
8,325
14,004
68.2%
Jalisco
3,766
5,367
42.5%
7,403
10,547
42.5%
18,313
26,129
42.7%
Mexico
7,294
14,501
98.8%
17,656
35,567
101.4%
56,433
81,136
43.8%
Michoacan
3,341
4,753
42.3%
4,966
6,938
39.7%
6,306
8,997
42.7%
Morelos
1,553
2,216
42.7%
4,249
6,064
42.7%
3,381
4,825
42.7%
Nayarit
553
789
42.8%
375
538
43.2%
1,673
2,387
42.7%
Nuevo Leon
86,971
107,975
24.1%
41,887
57,573
37.4%
15,730
22,180
41.0%
Oaxaca
113,001
135,442
19.9%
10,928
13,944
27.6%
8,267
10,729
29.8%
Puebla
2,994
4,748
58.6%
7,360
11,104
50.9%
4,317
6,168
42.9%
Queretaro
3,184
6,613
107.7%
9,793
22,762
132.4%
7,013
10,332
47.3%
Quintana Roo
410
550
34.1%
616
388
-37.0%
1,016
1,441
41.8%
San Luis Potosi
6,764
14,529
114.8%
22,263
33,743
51.6%
7,563
11,590
53.2%
Sinaloa
1,315
1,098
-16.5%
10,982
2,049
-81.3%
3,641
5,076
39.4%
Sonora
4,299
8,350
94.2%
14,581
18,526
27.1%
4,786
7,018
46.6%
Tabasco
7,682
10,102
31.5%
23,255
29,986
28.9%
6,767
8,468
25.1%
Tamaulipas
71,893
89,752
24.8%
34,020
42,968
26.3%
34,256
46,543
35.9%
Tlaxcala
286
435
52.1%
962
1,531
59.1%
1,425
2,033
42.7%
Veracruz
88,864
108,452
22.0%
48,607
56,892
17.0%
30,199
40,973
35.7%
Yucatan
3,210
3,679
14.6%
11,020
11,529
4.6%
4,454
6,206
39.3%
Zacatecas
3
4
42.0%
11
15
42.4%
226
322
42.7%
Mexico Total
683,482
928,414
35.8%
651,521
775,506
19.0%
303,905
427,730
40.7%
99
-------
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/B-20-012
Environmental Protection Air Quality Assessment Division December 2016
Agency Research Triangle Park, NC
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