Emissions Modeling Technical Support
Document: Tier 3 Motor Vehicle
Emission and Fuel Standards
&EPA
United States
Environmental Protection
Agency
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Emissions Modeling Technical Support
Document: Tier 3 Motor Vehicle
Emission and Fuel Standards
By:
Alexis Zubrow, Rich Mason, Alison Eyth
Emissions Inventory an Analysis Group
Air Quality Assessment Division
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC
&EPA
United States
Environmental Protection
Agency
ERA-454/R-14-003
February 2014
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TABLE OF CONTENTS
ACRONYMS Ill
LIST OF TABLES VI
LIST OF FIGURES VII
1 INTRODUCTION 1
2 2007 EMISSION INVENTORIES AND APPROACHES 4
2.1 2007 NEI POINT SOURCES (PTIPM AND PTNONIPM) 7
2.1.1 IPM sector (ptipm) 7
2.1.2 Non-IPM sector (ptnonipm) 7
2.2 2007 NONPOINT SOURCES (AFDUST, AG, NONPT) 9
2.2.1 Area fugitive dust sector (afdust) 10
2.2.2 Agricultural ammonia sector (ag) 10
2.2.3 Other nonpoint sources (nonpt) 10
2.3 FIRES (AVEFIRE) 11
2.4 BlOGENIC SOURCES (BIOG) 11
2.5 2007 MOBILE SOURCES (ONROAD, ONROAD_RFL, NONROAD, ClC2RAIL, C3MARINE) 12
2.5.1 Onroad non-refueling (onroad) 12
2.5.2 Onroad refueling (onroad_rfl) 14
2.5.3 Nonroad mobile equipment sources: (nonroad) 15
2.5.4 Class I/Class 2 Commercial Marine Vessels and Locomotives and (clc2rail) 16
2.5.5 Class 3 commercial marine vessels (cSmarine) 16
2.6 EMISSIONS FROM CANADA, MEXICO AND OFFSHORE DRILLING PLATFORMS (OTHPT, OTHAR, OTHON) 16
2.7 SMOKE-READY NON-ANTHROPOGENIC INVENTORIES FOR CHLORINE 17
3 EMISSIONS MODELING SUMMARY 18
3.1 EMISSIONS MODELING OVERVIEW 18
3.2 CHEMICAL SPECIATION 21
3.2.1 VOC speciation 23
3.2.2 PM speciation 29
3.3 TEMPORAL ALLOCATION 30
3.3.1 FF10 format and inventory resolution 31
3.3.2 Ptipm Temporalization 32
3.3.3 Meteorologically-based temporalization 32
3.3.4 Onroad and Onroad'_rfl Temporalization 34
3.3.5 Additional sector specific details 35
3.4 SPATIAL ALLOCATION 35
3.4.1 Spatial Surrogates for U.S. emissions 36
3.4.2 Allocation method for airport-related sources in the U.S. 39
3.4.3 Surrogates for Canada andMexico emission inventories 39
4 DEVELOPMENT OF FUTURE YEAR EMISSIONS 43
4.1 STATIONARY SOURCE PROJECTIONS: ECU SECTOR (PTIPM) 47
4.2 STATIONARY SOURCE PROJECTIONS: NON-EGU SECTORS (PTNONIPM, NONPT, AG, AFDUST) 47
4.2.1 RFS2 upstream future year inventories and adjustments (nonpt, ptnonipm) 49
4.2.2 Upstream agricultural and Livestock adjustments (afdust, ag, nonpt, ptnonipm) 60
4.2.3 Fuel sulfur rules (nonpt, ptnonipm) 62
4.2.4 Portland Cement NESHAP projections (ptnonipm) 62
4.2.5 Controls, Closures and consent decrees from CSAPR and NODA Comments (nonpt, ptnonipm) 63
4.2.6 All other PROJECTION and CONTROL packets (ptnonipm, nonpt) 65
4.3 MOBILE SOURCE PROJECTIONS 66
4.3.1 Onroad mobile (onroad and onroad_rfl) 69
4.3.2 Nonroad mobile (nonroad) 73
4.3.3 Locomotives and Class 1 & 2 commercial marine vessels (clc2rail) 74
4.3.4 Class 3 commercial marine vessels (c3marine) 78
4.4 CANADA, MEXICO, AND OFFSHORE SOURCES (OTHAR, OTHON, AND OTHPT) 78
i
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5 EMISSION SUMMARIES 79
6 REFERENCES 99
11
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ACI
AE5
AE6
AEO
AIM
ARW
BAFM
BEIS3.14
BELD3
Bgal
EPS
BTP
C1/C2
C3
CAEP
CAIR
CAMD
CAMX
CAP
CARB
CB05
CBM
CEC
CEM
CEP AM
CISWI
Cl
CMAQ
CMV
CO
CSAPR
EO, E10, E85
EBAFM
ECA
EEZ
EF
ECU
EIS
EISA
EPA
EMFAC
FAA
FAPRI
FASOM
FCCS
FIPS
FHWA
HAP
Acronyms
Activated Carbon Injection
CMAQ Aerosol Module, version 5, introduced in CMAQ v4.7
CMAQ Aerosol Module, version 6, introduced in CMAQ v5.0
Annual Energy Outlook
Architectural and Industrial Maintenance (coatings)
Advanced Research WRF
Benzene, Acetaldehyde, Formaldehyde and Methanol
Biogenic Emissions Inventory System, version 3.14
Biogenic Emissions Land use Database, version 3
Billion gallons
Bulk Plant Storage
Bulk Terminal (Plant) to Pump
Category 1 and 2 commercial marine vessels
Category 3 (commercial marine vessels)
Committee on Aviation Environmental Protection
Clean Air Interstate Rule
The EPA's Clean Air Markets Division
Comprehensive Air Quality Model with Extensions
Criteria Air Pollutant
California Air Resources Board
Carbon Bond 2005 chemical mechanism
Coal-bed methane
North American Commission for Environmental Cooperation
Continuous Emissions Monitoring
California Emissions Projection Analysis Model
Commercial and Industrial Solid Waste Incineration
Chlorine
Community Multiscale Air Quality
Commercial Marine Vessel
Carbon monoxide
Cross-State Air Pollution Rule
0%, 10% and 85% Ethanol blend gasolines, respectively
Ethanol, Benzene, Acetaldehyde, Formaldehyde and Methanol
Emissions Control Area
Exclusive Economic Zone
Emission Factor
Electric Generating Units
Emissions Inventory System
Energy Independence and Security Act of 2007
Environmental Protection Agency
Emission Factor (California's onroad mobile model)
Federal Aviation Administration
Food and Agriculture Policy and Research Institute
Forest and Agricultural Section Optimization Model
Fuel Characteristic Classification System
Federal Information Processing Standards
Federal Highway Administration
Hazardous Air Pollutant
in
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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
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 Liquified 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)
MOBILE6 OTAQ's model for estimation of onroad mobile emissions factors, replaced by
MOVES2010b
MOVES Motor Vehicle Emissions Simulator OTAQ's model for estimation of onroad
mobile emissions - replaces the use of the MOBILE model
MSA Metropolitan Statistical Area
MSAT2 Mobile Source Air Toxics Rule
MTBE Methyl tert-butyl ether
MWRPO Mid-west Regional Planning Organization
NCD National County Database
NEEDS National Electric Energy Database System
NEI National Emission Inventory
NESCAUM Northeast States for Coordinated Air Use Management
NESHAP National Emission Standards for Hazardous Air Pollutants
NH3 Ammonia
NIF NEI Input Format
NLCD National Land Cover Database
NLEV National Low Emission Vehicle program
nm nautical mile
NMIM National Mobile Inventory Model
NOAA National Oceanic and Atmospheric Administration
NODA Notice of Data Availability
NONROAD OTAQ's model for estimation of nonroad mobile emissions
NOx Nitrogen oxides
NSPS New Source Performance Standards
NSR New Source Review
iv
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OAQPS
OHH
OTAQ
ORIS
ORD
ORL
OTC
PADD
PF
PFC
PM25
PM10
ppb, ppm
RCRA
RBT
RFS2
RIA
RICE
RRF
RWC
RPO
RVP
SCC
SEMAP
SESARM
SESQ
SMARTFIRE
SMOKE
SO2
SOA
SI
SIP
SPDPRO
SPPD
TAF
TCEQ
TOG
TSD
ULSD
USDA
VOC
VMT
VPOP
WGA
WRAP
WRF
The EPA's Office of Air Quality Planning and Standards
Outdoor Hydronic Heater
The EPA's Office of Transportation and Air Quality
Office of Regulatory Information System
The EPA's Office of Research and Development
One Record per Line
Ozone Transport Commission
Petroleum Administration for Defense Districts
Projection Factor, can account for growth and/or controls
Portable Fuel Container
Particulate matter less than or equal to 2.5 microns
Particulate matter less than or equal to 10 microns
Parts per billion, parts per million
Resource Conservation and Recovery Act
Refinery to Bulk Terminal
Renewable Fuel Standard
Regulatory Impact Analysis
Reciprocating Internal Combustion Engine
Relative Response Factor
Residential Wood Combustion
Regional Planning Organization
Reid Vapor Pressure
Source Classification Code
Southeastern Modeling, Analysis, and Planning
Southeastern States Air Resource Managers
Sesquiterpenes
Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation
Sparse Matrix Operator Kernel Emissions
Sulfur dioxide
Secondary Organic Aerosol
Spark-ignition
State Implementation Plan
Hourly Speed Profiles for weekday versus weekend
Sector Policies and Programs Division
Terminal Area Forecast
Texas Commission on Environmental Quality
Total Organic Gas
Technical support document
Ultra Low Sulfur Diesel
United States Department of Agriculture
Volatile organic compounds
Vehicle miles traveled
Vehicle Population
Western Governors' Association
Western Regional Air Partnership
Weather Research and Forecasting Model
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List of Tables
Table 1-1. List of base cases run in the Tier 3 FRM Emissions Modeling Platform 2
Table 2-1. Platform sectors starting point for the 2007 platform 5
Table 2-2. Summary of significant changes between 2007v5 platform and 2007 Tier 3 base case by sector.. 6
Table 2-3. Corn Ethanol Plant Criteria Pollutant Emission Factors (grams per gallon produced) 9
Table 2-4. Toxic-to-VOC Ratios for Corn Ethanol Plants 9
Table 2-5. 2007 Platform SCCs representing emissions in the ptfire and avefire modeling sectors 11
Table 3-1. Key emissions modeling steps by sector 19
Table 3-2. Descriptions of the 2007v5 platform grids 20
Table 3-3. Emission model species produced for CB05 with SOAfor CMAQ and CAMx* 22
Table 3-4. Integration approach for BAFM and EBAFM for each platform sector 23
Table 3-5. HAP augmentation for clc2rail 25
Table 3-6. VOC profiles for WRAP Phase III basins 26
Table 3-7. Select VOC profiles 2007. 2018 and 2030 28
Table 3-8. PM model species: AE5 versus AE6 29
Table 3-9. Temporal settings used for the platform sectors in SMOKE 31
Table 3-10. U.S. Surrogates available for the 2007 platform 36
Table 3-11. Spatial Surrogates for WRAP Oil and Gas Data 37
Table 3-12. Counties included in the WRAP Dataset 37
Table 3-13. Spatial Surrogates for Mexico 39
Table 3-14. Canadian Spatial Surrogates for 2007-based platform Canadian Emissions 40
Table 4-1. Control strategies and growth assumptions for creating the 2018 and 2030 emissions inventories
from the 2007 base case 45
Table 4-2. Summary of non-EGU stationary projections subsections 49
Table 4-3. Renewable Fuel Volumes Assumed for Stationary Source Adjustments 50
Table 4-4. 2007 and 2018/2030 corn ethanol plant emissions [tons] 50
Table 4-5. Emission Factors for Biodiesel Plants (Tons/Mgal) 51
Table 4-6. 2018/2030 biodiesel plant emissions [tons] 51
Table 4-7. PFC emissions for 2007, 2018, and 2030 [tons] 52
Table 4-8. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/RIN gallon) 53
Table 4-9. Toxic Emission Factors for Cellulosic Plants (Tons/RIN gallon) 53
Table 4-10. 2018 and 2030 cellulosic plant emissions [tons] 53
Table 4-11. 2018 and 2030 VOC working losses (Emissions) due to ethanol transport [tons] 54
Table 4-12. RVPs Assumed for 2018 ethanol and gasoline volumes with EISA 56
Table 4-13. RVPs Assumed for 2018 ethanol and gasoline volumes without EISA 56
Table 4-14. RVPs Assumed for 2030 ethanol and gasoline volumes with EISA 56
Table 4-15. RVPs Assumed for 2030 ethanol and gasoline volumes without EISA 56
Table 4-16. Storage and Transport Vapor Loss Emission Factors (g/mmBtu) 57
Table 4-17. Adjustment factors applied to storage and transport emissions 58
Table 4-18. Impact of VOC losses from reduced gasoline production due to EISA 58
Table 4-19. 2018 adjustment factors applied to petroleum pipelines and refinery emissions associated with
gasoline and diesel fuel production 59
Table 4-20. 2030 adjustment factors applied to petroleum pipelines and refinery emissions associated with
gasoline and diesel fuel production 59
Table 4-21. Impact of refinery adjustments on 2007 emissions [tons] 60
Table 4-22. Adjustments to modeling platform agricultural emissions for the Tier 3 reference case 61
Table 4-23. Composite NH3 projection factors to year 2018 and 2030 for animal operations 61
Table 4-24. Summary of fuel sulfur rules by state 62
Table 4-25. ISIS-based cement industry change (tons/yr) 63
VI
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Table 4-26. Factors used to project 2008 base-case aircraft emissions to 2020 65
Table 4-27. Overview of Reference and Control Scenarios 67
Table 4-28. Comparison of MOVES runs 70
Table 4-29. CA LEVIII program states 70
Table 4-30. Early NLEV states 71
Table 4-31.LEV2 states and MOVES databases 71
Table 4-32. RVP bins by representative county 72
Table 4-33. Non-California year 2018 and 2030 Projection Factors for locomotives and Class 1 and Class 2
Commercial Marine Vessel Emissions 75
Table 4-34. Scalars Applied to Rail Combustion Emissions in 2030 to account for 2017-2025 LDGHG
emission standards 76
Table 4-35. Scalars Applied to C1/C2 Combustion Emissions in 2030 to account for 2017-2025 LDGHG
emission standards 77
Table 4-36. Cumulative RFS2 and LDGHG adjustments to clc2rail sector emissions 77
Table 4-37. Growth factors to project the 2007 ECA-EVIO inventory to 2018 and 2030 78
Table 5-1. National and non-U.S. CAP emissions by sector for 2007 base case 80
Table 5-2. National and non-U.S. CAP emissions by sector for 2018 reference case 81
Table 5-3. National and non-U.S. CAP emissions by sector for 2018 control case 82
Table 5-4. National and non-U.S. CAP emissions by sector for 2030 reference case 83
Table 5-5. National and non-U.S. CAP emissions by sector for 2030 control case 84
Table 5-6. CO emissions (tons/yr) for each case and state 85
Table 5-7. NHa emissions (tons/yr) for each case and state 87
Table 5-8. NOx emissions (tons/yr) for each case and state 89
Table 5-9. PM2.5 emissions (tons/yr) for each case and state 91
Table 5-10. PMio emissions (tons/yr) for each case and state 93
Table 5-11. SO2 emissions (tons/yr) for each case and state 95
Table 5-12. VOC emissions (tons/yr) for each case and state 97
List of Figures
Figure 3-1. Air quality modeling domains 20
Figure 3-2. Example of new animal NET? emissions temporalization approach, summed to daily emissions 34
Figure 3-3. Example of SMOKE-MOVES temporal variability of NOx emissions 35
Figure 4-1. Map of Petroleum Administration for Defense Districts (PADD) 55
vn
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1 Introduction
The U.S. Environmental Protection Agency (EPA) developed a year 2007 air quality modeling platform in
support of the Tier 3 Motor Vehicle Emission and Fuel Standards. The air quality modeling platform
consists of all of the emissions inventories, ancillary files needed for emissions modeling, and the
meteorological, initial condition, and boundary condition files needed to run the air quality model. This
platform uses all Criteria Air Pollutants (CAPs) and a select set of Hazardous Air Pollutants (HAPs). This
document focuses on the emissions modeling components of the 2007 platform, including the emission
inventories and the ancillary data and the approaches used to transform emission inventories for use in air
quality modeling.
The Tier 3 modeling platform was developed by implementing specific modifications to the "CAP-BAFM
2007-Based Platform, Version 5", also known as the "2007v5" platform. The 2007v5 platform was used to
support the Regulatory Impact Assessment (RIA) for the 2012 Final National Ambient Air Quality Standards
(NAAQS) for particulate matter less than 2.5 microns (PIVb.s). The Technical Support Document (TSD)
"Preparation of Emissions Inventories for the Version 5.0, 2007 Emissions Modeling Platform" contains
many additional details on the aspects of the Tier 3 and 2007v5 platforms that are shared. The TSD is
available from the Emissions Modeling Clearinghouse website, http://www.epa.gov/ttn/chief/emch/, under
the section entitled "Particulate Matter (PM) NAAQS (2007v5) Platform". The appendices available for the
2007v5 TSD that do not reference the specific PM NAAQS modeling cases are also relevant to the "Tier 3"
platform.
Many emissions inventory components of the Tier 3 air quality modeling platform are based on the 2008
National Emissions Inventory version 2, hereafter referred to as the "2008 NEI", with updated inventory data
for some emission sectors. In particular, a version of the Motor Vehicle Emissions Simulator (MOVES)
designed to represent the impacts of the Tier 3 Motor Vehicle Emission and Fuel Standards
(MOVESTier3FRM) was used to generate emission factors for onroad mobile sources. The emissions
modeling tool used to create the air quality model-ready emissions from the emission inventories was the
Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system (http://www.smoke-
model.org/index.cfm) version 3.5 beta. Emissions were created for 36 km and 12 km national grids.
The gridded meteorological model used for Tier 3 is the Weather Research and Forecasting Model (WRF,
http://wrf-model.org) version 3.3, Advanced Research WRF (ARW) core (Skamarock, et al., 2008). The
WRF Model is a mesoscale numerical weather prediction system developed for both operational forecasting
and atmospheric research applications. WRF was run for 2007 over a domain covering the continental
United States at a 36 km and 12 km resolution with 35 vertical layers2. This meteorological run was different
than the one used for the 2007v5 platform.
The air quality model used for the Tier 3 platform is the Community Multiscale Air Quality (CMAQ) model
(http://www.epa.gov/AMD/CMAQ/). CMAQ supports modeling ozone (Os) and particulate matter (PM)
and requires hourly and gridded emissions of chemical species from the following inventory pollutants:
carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOC), sulfur dioxide (802),
ammonia (NFb), particulate matter less than or equal to 10 microns (PMio), and individual component
species for particulate matter less than or equal to 2.5 microns (PM2.s). The CMAQ version used the
chemical mechanism called Carbon Bond 2005 (CB05) with chlorine chemistry, which is part of the "base"
version of CMAQ. CB05 allows explicit treatment of benzene, acetaldehyde, formaldehyde, and methanol
For more details on the meteorological models and the run see section 2.5.1.2.
1
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(BAFM) and includes anthropogenic HAP emissions of hydrochloric acid (HC1) and chlorine (Cl). The
Tier 3 modeling used the most recent multi-pollutant version of CMAQ available at the time (CMAQ MP
version 5.0.1) the modeling was performed, and an additional set of HAPs (from here out referred to as
"CMAQ MP-lite HAPs"): acrolein, 1,3-butadiene and naphthalene were modeled.
The emissions and modeling effort for the Tier 3 platform consists of five emissions cases: 2007 base case,
2018 reference case, 2018 Tier 3 control case, 2030 reference case and 2030 Tier 3 control case. Table 1-1
provides more information on these emissions cases. The purpose of 2007 base case is to provide a 2007
case that is consistent with the methods used in the future-year base cases and ultimately, in the future year
reference and control cases for the Final Tier 3 Rule. For regulatory applications, the 2007 base case is used
with the outputs from the 2018 and 2030 reference cases in the relative response factor (RRF) calculations to
identify future areas of nonattainment. For more information on the use of RRFs and air quality modeling,
see "Guidance on the Use of Models and Other Analyses for Demonstrating
Attainment of Air Quality Goals for Ozone, PM 2.5, and Regional Haze", available from
http://www.epa.gov/ttn/scram/guidance/guide/fmal-03-pm-rh-guidance.pdf
Table 1-1. List of base cases run in the Tier 3 FRM Emissions Modeling Platform
Case Name
2007 base case
2018 reference
case
20 18 Tier 3
control case
2030 reference
case
2030 Tier 3
control case
Internal EPA
Abbreviation
2007rg_v5
2018rg_ref2_v5J
2018rg_ctl_v5
2030rg_ref_v5
2030rg_ctl_v5
Description
2007 case created using average-year wildfires data, smoothed
prescribed fires, and an average-year temporal allocation approach
for Electrical Generating Units (EGUs); used for computing
relative response factors with 2018 and 2030 reference scenario(s).
2018 future year reference scenario with EGU emissions that
represent the implementation of Clean Air Interstate Rule (C AIR)
and final Mercury and Air Toxics (MATS), with upstream
stationary and mobile sources representing the implementation of
the EISA/EPAct fuel supply (RFS2 Rule)
2018 Tier 3 control case scenario sharing many aspects of the
2018 reference case, but also representing national Tier 3 vehicle
and fuel emissions standards.
2030 future year reference scenario with EGU emissions that
represent the implementation of Clean Air Interstate Rule (C AIR)
and final Mercury and Air Toxics (MATS), with upstream
stationary and mobile sources representing the implementation of
the EISA/EPAct fuel supply (RFS2 Rule)
2030 Tier 3 control case scenario sharing many aspects of the
2030 reference case, but also representing national Tier 3 vehicle
and fuel emissions standards.
This document contains five sections. Section 2 describes the inventories input to SMOKE for the 2007 base
case. Section 3 describes the emissions modeling and the ancillary files used to process the emission
inventories into a form that can be used by the air quality model. Section 4, describes the development of the
2018 and 2030 Tier 3 FRM reference and control case inventories (projected from 2007). Data summaries
The case 2018rg_ref2_v5 is identical to an earlier case that was created for Tier 3 FRM (2018rg_ref_v5), except that the "ref2"
case updated the following modeling sectors: nonpt, onroad, othpt, othar, and othon.
2
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comparing the 2007 base case and the 2018 and 2030 reference and control cases are provided in Section 5.
Section 6 provides references.
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2 2007 Emission Inventories and Approaches
This section describes the 2007 emissions data created for input to SMOKE that is part of the 2007 base
case; future year emissions inventory data development is discussed in Section 4. While providing some
background, this section focuses on the differences between the 2007v5 platform and the updated Tier 3
platform. The starting point for the 2007 stationary source emission inputs is the 2008 NEI version 2, for
which a draft TSD is available from http://www.epa.gov/ttn/chief/net/2008inventory.html.
The NEI data are largely compiled from data submitted by state, local and tribal (S/L/T) agencies for CAPs.
HAP emissions data are more often augmented by EPA because they are voluntarily submitted. For fires,
EPA used the SMARTFIRE2 (SF2) system in the 2008 NEI. SF2 assigns all fires as either prescribed
burning or wildfire categories and includes improved emission factor estimates for prescribed burning.
The 2008 NEI includes five data categories: nonpoint (formerly called "stationary area") sources, point
sources, nonroad mobile sources, onroad mobile sources, and fires. The 2008 NEI TSD uses approximately
sixty sectors to further describe the emissions. In addition to the NEI data, 2007 biogenic emissions,
emissions from the Canadian and Mexican inventories, and other non-NEI data are included in the 2007
platform. The non-NEI emissions components of the 2007 platform include primarily year-2007 onroad
mobile and nonroad mobile emissions, a computed average fires inventory, and data received from some
regional planning organizations (RPOs).
In the 2007v5 platform, some data in the 2008NEIv2 were updated with data from RPOs. The RPOs focused
on addressing visibility impairment from a regional perspective and updated related inventory and ancillary
data. A map of these RPOs can be found here: http://www.epa.gov/visibility/regional.html. The RPOs most
involved in providing data were:
Mid-Atlantic Regional Air Management Association (MARAMA): http://www.marama.org/
Midwest Regional Planning Organization (MWRPO): http://www.ladco.org/
Southeastern States Air Resource Managers (SESARM): http://www.metro4-sesarm.org/
Western Regional Air Partnership (WRAP): http://www.wrapair2.org/
For the purposes of preparing the air quality model-ready emissions, the 2007 emissions inventory was split
into inventories for each of the modeling "platform" sectors. The significance of an emissions modeling or
"platform" sector is that the data is run through all of the SMOKE programs except the final merge
(Mrggrid) independently from the other sectors. The final merge program is then used to combine the
sector-specific gridded, speciated, hourly emissions together to create CMAQ-ready emission inputs.
Table 2-1 presents the sectors in the 2007 platform and how they generally relate to the 2008 NEI as a
starting point. The sector abbreviations are provided in italics. These abbreviations are used in the SMOKE
modeling scripts, inventory file names, and throughout the remainder of this document. As discussed in
greater detail in Table 2-2, the Tier 3 platform emissions platform was modified in specific ways from the
original 2007v5 platform.
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Table 2-1. Platform sectors starting point for the 2007 platform
Platform Sector:
abbreviation
ECU (also called
the IPM sector):
ptipm
Non-EGU (non-
IPM sector):
ptnonipm
Agricultural:
as
Area fugitive dust:
afdust
Class 1 & 2 CMV
and locomotives:
clc2rail
C3 commercial
marine:
cSmarine
Remaining
nonpoint:
nonpt
Nonroad:
nonroad
Onroad non-
refueling:
onroad
Onroad refueling:
onroad rfl
Average-fire:
avefire
Other point
sources not from
the NEI:
othpt
Other non-NEI
nonpoint and
nonroad:
othar
2008NEI
Sector
Point
Point
Nonpoint
Nonpoint
Mobile:
Nonroad
Mobile:
Nonroad
Nonpoint
Mobile:
Nonroad
Mobile:
onroad
Mobile:
onroad
N/A
N/A
N/A
Description and resolution of the data input to SMOKE
2008 NEI point source EGUs that could be mapped to the Integrated
Planning Model (IPM) model using the National Electric Energy
Database System (NEEDS) version 4.10. Annual resolution.
All NEI point source records not matched to the ptipm sector.
Includes all aircraft emissions and some rail yard emissions. Annual
resolution.
NH3 emissions from NEI nonpoint livestock and fertilizer application,
county and annual resolution.
PMio and PM2 5 from fugitive dust sources from the NEI nonpoint
inventory. Includes building construction, road construction, paved
roads, unpaved roads and agricultural dust. County and annual
resolution. Processed as a separate sector to allow for the application
of a land use based transport fraction and precipitation adjustments.
Non-rail maintenance locomotives and category 1 and category 2
commercial marine vessel (CMV) emissions sources from the NEI
nonpoint inventory. County and annual resolution.
Non-NEI, year 2007 category 3 (C3) CMV emissions projected from
year 2002. Developed for the rule called "Control of Emissions from
New Marine Compression-Ignition Engines at or Above 30 Liters per
Cylinder", also described as the Emissions Control Area- International
Maritime Organization (ECA-IMO) study:
http://www.epa.gov/otaq/oceanvessels.htm. (EPA-420-F- 10-041,
August 2010). Annual resolution and treated as point sources.
Primarily NEI nonpoint sources not otherwise included in other
SMOKE sectors; county and annual resolution.
Monthly nonroad equipment emissions from the National Mobile
Inventory Model (NMIM) using NONROAD2008 version NROSb.
NMIM was used for all states except California. Monthly emissions
for California created from annual emissions submitted by the
California Air Resources Board (CARB).
Onroad mobile gasoline and diesel vehicles from parking lots and
moving vehicles. Includes the following modes: exhaust, extended
idle, evaporative, permeation, and brake and tire wear. For all states,
based on Motor Vehicle Emissions Simulator (MOVES) emission
factor tables and monthly activity data for 2007.
Onroad mobile gasoline and diesel vehicle refueling emissions for all
states. Based on MOVES emission factor tables and 2007 activity
data.
Average-year wildfire and prescribed fire emissions, county and daily
resolution. This sector is used in all modeling cases.
Point sources from Canada's 2006 inventory and Mexico's Phase III
2008 inventory grown from year 1999. Includes annual U.S. offshore
oil 2008 NEI point source emissions. Annual resolution.
Year 2006 Canada (province resolution) and year 2008 (grown from
1999) Mexico Phase III (municipio resolution) nonpoint and nonroad
mobile inventories, annual resolution.
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Platform Sector:
abbreviation
Other non-NEI
onroad sources:
othon
Biogenic:
beis
2008NEI
Sector
N/A
N/A
Description and resolution of the data input to SMOKE
Year 2006 Canada (province resolution) and year 2008 (grown from
1999) Mexico Phase III (municipio resolution) onroad mobile
inventories, annual resolution.
Year 2007, hour-specific, grid cell-specific emissions generated from
the BEIS3.14 model, including emissions in Canada and Mexico.
Table 2-2 provides a brief by-sector overview of the most significant differences between the Tier 3 platform
and the 2007v5 platform. The specific by-sector updates to the 2007 platform made for Tier 3 are described
in greater detail later in the following subsections. The remainder of Section 2 provides details about the
data contained in each of the 2007 platform sectors. Different levels of detail are provided for different
sectors, depending on the degree of changes or manipulation of the data needed to prepare it for input to
SMOKE, and on whether the Tier 3 2007 platform emissions are significantly different from the original
2007v5 platform.
Table 2-2. Summary of significant changes between 2007v5 platform and 2007 Tier 3 base case by sector
Platform Sector
IPM sector:
ptipm
Non-IPM sector:
ptnonipm
Agricultural:
as
Area fugitive dust:
afdust
Remaining
nonpoint sector:
nonpt
Class 1 & 2 CMV
and locomotives:
clc2rail
C3 commercial
marine:
cSmarine
Nonroad sector:
nonroad
Onroad non-
refueling:
onroad
Onroad non-
refueling:
onroad rfl
Average fires:
avefire
Summary of Significant Inventory Differences
Included additional HAPs
Updated ethanol inventory
Replaced oil and gas emissions with Western Regional Air Partnership
(WRAP) Phase III year 2008 emissions in select oil and gas basins
Temporalized to hours using the Tier 3 WRF output
Performed meteorological adjustments with the Tier 3 WRF
output
Replaced oil and gas emissions with Western Regional Air Partnership
(WRAP) Phase III year 2008 emissions in select oil and gas basins
Augmented HAPs in California and RPO data
Augmented HAPs
Used updated fuels
Augmented HAPs in California
Used MOVESTier3FRM emission factors
Used updated fuels
Used MOVESTier3FRM emission factors
Used updated fuels
Used 2007 through 20 1 0 SMARTFIRE 2 data to generate average fire
emissions
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2.1 2007 NEI point sources (ptipm and ptnonipm)
Point sources are sources of emissions for which specific geographic coordinates (e.g., latitude/longitude) are
specified, as in the case of an individual facility. A facility may have multiple emission points, which may
be characterized as units such as boilers, reactors, spray booths, kilns, etc. A unit may have multiple
processes (e.g., a boiler that sometimes burns residual oil and sometimes burns natural gas). With a couple
of minor exceptions, this section describes only NEI point sources within the contiguous United States. The
offshore oil platform (othpt sector) and category 3 CMV emissions (cSmarine sector) are also point source
formatted inventories that are discussed in Section 2.6 and Section 2.5.5, respectively.
After removing offshore oil platforms into the othpt sector, EPA created an initial version of two platform
sectors from the remaining 2008 NEI point sources for input into SMOKE: the EGU sector - also called the
IPM sector (i.e., ptipm) and the non-EGU sector - also called the non-IPM sector (i.e., ptnonipm). This split
facilitates the use of different SMOKE temporal processing and future-year projection techniques for each of
these sectors. The inventory pollutants processed through SMOKE for both the ptipm and ptnonipm sectors
were: CO, NOX, VOC, SO2, NH3, PMio, and PM2.5 and the following HAPs: HC1 (pollutant code =
7647010), Cl (code = 7782505), and the CMAQ MP-lite HAPs (see Section 3.2 for details). For more
details on the development of these sectors and the differences between the inventories and the 2008 NEI see
the 2007v5 TSD.
2.1.1 IPM sector (ptipm)
The ptipm sector contains emissions from EGUs in the 2008 NEI point inventory that were matched to units
found in the May 2012 version 4.10 of the NEEDS database (http ://www. epa. gov/airmarkets/progsregs/epa-
ipm/BaseCasev410.html#needs). IPM provides future-year emission inventories for the universe of EGUs
contained in the NEEDS database. This matching was done (1) to provide consistency between the 2007
EGU sources and future-year EGU emissions for sources which are forecasted by IPM and (2) to avoid
double counting when projecting point source emissions to future years. A comprehensive description on
how EGU emissions were characterized and estimated in the 2008 NEI can be found in Section 3.10 in the
2008 NEI documentation (EPA, 2012a). The ptipm sector is identical to the 2007v5 platform except for the
inclusion of CMAQ MP-lite HAPs (see Section 3.2 for details) in the 2007 model-ready files4.
2.1.2 Non-IPM sector (ptnonipm)
With several exceptions, the non-IPM (ptnonipm) sector contains the remaining 2008 NEI point sources that
were not included in the IPM (ptipm) sector. The ptnonipm sector contains all sources not reflected in future
year IPM inventories. For the most part, the ptnonipm sector reflects the non-EGU component of the NEI
point inventory; however, as previously discussed, it is likely that some small low-emitting EGUs that are
not reflected in the CEMs database are present in the ptnonipm sector.
There are numerous modifications between the published 2008 NEI and the 2007 ptnonipm inventory used
for modeling. More details on some of these modifications can be found in the 2007v5 TSD. The
differences between the 2007v5 ptnonipm and the 2007 ptnonipm for this base case are limited to the
following:
Integration of BAFM (see Section 3.2.1.1 for details)
Inclusion of the CMAQ MP-lite HAPs (see Section 3.2 for details)
4 Note, these additional HAPs are not in the future year scenarios because IPM does not produce estimates for them.
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Removal of one cement kiln because of unreasonable emissions5
Modification to the South Dakota point inventory to include 2005 NEI emissions for sources missing
in the 2008 NEI.
Updated ethanol facilities (see below)
Updated oil and gas (see below)
Additional Ethanol facilities
An updated set of corn ethanol facilities was developed for year 2008. Any facilities already included in the
2008 NEI were removed from the set prior to including them in the 2007 base case. Locations and FIPS
codes for these ethanol plants were verified using web searches and Google Earth. These emissions are
included in the 2007 case as a separate FFlO-format inventory for HAPs and CAPs. Emission rates for the
facilities were obtained from EPA's spreadsheet model for upstream impacts developed for the Renewable
Fuel Standard (RFS2) rule (EPA, 2010a). The plant emission rates for criteria pollutants used to estimate
impacts are given in Table 2-3. Toxic emission rates were estimated by applying toxic-to-VOC ratios in
Table 2-4 to the VOC emission rates shown in Table 2-3. For air toxics other than ethanol, toxic-to-VOC
ratios were developed using emission inventory data from the 2005 NEI (EPA, 2009a). Emission rates in
Table 2-3 and Table 2-4 were multiplied by facility production estimates for 2007, 2018 (via 2017 emission
factors), and 2030 based on analyses performed for the industry characterization described in Chapter 1 of
the RFS2 final rule regulatory impact analysis (RIA).
WRAP Phase III oil and gas emissions
The Western Regional Air Partnership (WRAP) RPO created year 2008 "Phase III" oil and gas sector point
and non-point format emissions for several major basins in Colorado and Montana, New Mexico, Texas,
Utah and Wyoming. These basins are listed here: Denver-Julesburg, Uinta, San Juan (North and South),
Piceance, Southwest Wyoming (Green River), Powder River, Wind River and Permian. A map showing the
geographic area of these basins is provided at: http ://www. eia. gov/oil gas/rpd/shale gas.jpg.
The WRAP oil and gas Phase III project was co-sponsored by the Independent Petroleum Association of
Mountain States (IPAMS) and is based on survey outreach efforts. Survey coverage varied, and survey data
were generally reflected as point sources in the inventory. Unpermitted sources were based somewhat on
surveys but also on activity and emission factor estimates and were generally reflected as nonpoint (nonpt
sector) sources.
Overall, the Phase III project estimated emissions for a couple dozen source types, including drilling rigs,
compressor stations, heaters and boilers, tank breathing venting and flashing, pneumatic devices, well and
pipeline/compressor fugitive emissions, dehydrators, amine units, truck loading and other miscellaneous
sources. Phase III emissions include basin-specific speciation, surrogates and hence SCCs to account for the
different products extracted: oil, gas and coal-bed methane (CBM). To prevent possible double-counting of
oil and gas sector emissions, all oil and gas emissions were removed from the 2008 NEI for counties that
comprise the 9 basins in the WRAP Phase III inventories. The list of oil and gas SCCs that were removed
from the point (and nonpoint) 2008 NEI are provided in Appendix A of the 2007v5 TSD6.
5 The cement kiln had emissions orders of magnitude higher than in any other year in the NEI. The particular facility is facility ID
4773111, FIPS 26017.
6 The 2007v5 platform used the 2006 WRAP Phase III inventory.
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Table 2-3. Corn Ethanol Plant Criteria Pollutant Emission Factors (grams per gallon produced)
Corn Ethanol Plant Type
Dry Mill Natural Gas (NG)
Dry Mill NG (wet distillers
grains with solubles (DGS))
Dry Mill Biogas
Dry Mill Biogas (wet DGS)
Dry Mill Coal
Dry Mill Coal (wet DGS)
Dry Mill Biomass
Dry Mill Biomass (wet
DGS)
Wet Mill NG
Wet Mill Coal
Year
2005, 2017
2030
2005, 2017
2030
2005, 2017
2030
2005, 2017
2030
2005, 2017
2030
2005, 2017
2030
2005, 2017
2030
2005, 2017
2030
2005, 2017
2030
2005, 2017
2030
voc
2.29
2.29
2.27
2.27
2.29
2.29
2.27
2.27
2.31
2.31
2.31
2.28
2.42
2.42
2.35
2.35
2.35
2.33
2.33
2.33
CO
0.58
0.58
0.37
0.37
0.62
0.62
0.39
0.39
2.65
2.65
2.65
1.68
2.55
2.55
1.62
1.62
1.62
1.04
1.04
3.50
NOX
0.99
0.94
0.63
0.60
1.05
1.00
0.67
0.63
4.17
3.68
2.65
2.34
3.65
3.65
2.32
2.32
1.77
1.68
5.51
4.86
PM10
0.94
0.94
0.91
0.91
0.94
0.94
0.91
0.91
3.81
3.64
2.74
2.62
1.28
1.28
1.12
1.12
1.12
1.00
4.76
4.53
PM25
0.23
0.23
0.20
0.20
0.23
0.23
0.20
0.20
1.71
1.54
1.14
1.03
0.36
0.36
0.28
0.28
0.28
0.29
2.21
1.98
SO2
0.01
0.01
0.00
0.00
0.01
0.01
0.00
0.00
4.52
3.48
2.87
2.21
0.14
0.14
0.09
0.09
0.09
0.01
5.97
4.60
NH3
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Table 2-4. Toxic-to-VOC Ratios for Corn Ethanol Plants
Wet Mill NG
Wet Mill Coal
Dry Mill NG
Dry Mill Coal
Acetaldehyde
0.02580
0.08242
0.01089
0.02328
Acrolein
0.00131
0.00015
0.00131
0.00102
Benzene
0.00060
0.00048
0.00060
0.00017
1,3-Butadiene
2.82371E-08
2.82371E-08
2.82371E-08
2.82371E-08
Formaldehyde
0.00127
0.00108
0.00127
0.00119
2.2 2007 nonpoint sources (afdust, ag, nonpt)
The nonpoint sectors use the 2008 NEI as a starting point. EPA created several sectors from the 2008 NEI
nonpoint inventory, and this section describes the stationary nonpoint sources. Class 1 & Class 2 (clc2) and
Class 3 (c3) commercial marine vessels and locomotives are also in the 2008 NEI nonpoint data category.
However, these mobile sources are included in the mobile documentation in Sections 2.5.4 2.5.5 as the
clc2rail and c3marine sectors, respectively.
The nonpoint tribal-submitted emissions were removed to prevent possible double counting with the county-
level emissions and also because spatial surrogates for tribal data were not available. Because the tribal
nonpoint emissions are small, these omissions will have a limited impact on the results at the 12-km scales
used for this modeling. The documentation for the nonpoint sector of the 2008 NEI is available on the 2008
NEI website (EPA, 2012a).
The following subsections describe the partitioning of the 2008 NEI nonpoint inventory into the 2007v5
modeling platform sectors, and also the differences between the nonpoint emissions in the 2007v5 platform
and the 2007 base case.
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2.2.1 Area fugitive dust sector (afdust)
The area-source fugitive dust (afdust) sector contains PMio and PM2.5 emission estimates for nonpoint SCCs
identified by the EPA staff as dust sources. Categories included in the afdust sector are paved roads,
unpaved roads and airstrips, construction (residential, industrial, road and total), agriculture production, and
mining and quarrying. It does not include fugitive dust from grain elevators because these are elevated point
sources.
This sector is separated from other nonpoint sectors to allow for the application of "transport fraction," and
meteorology/precipitation-based reductions. These adjustments are applied via sector-specific scripts,
beginning with land use-based gridded transport fractions and then subsequent daily zero-outs for days
where at least 0.01 inches of precipitation occurs or days when there is snow cover on the ground. The land
use data used to reduce the NEI emissions explains the amount of emissions that are subject to transport.
This methodology is discussed in (Pouliot, et. al., 2010),
http://www.epa.gov/ttn/chief/conference/ei 19/session9/pouliot_pres.pdf, and in Fugitive Dust Modeling for
the 2008 Emissions Modeling Platform (Adelman, 2012). The precipitation adjustment is then applied to
remove all emissions for days where measureable rain occurs. Both the transport fraction and
meteorological adjustments are based on the gridded resolution of the platform; therefore, different
emissions will result from different grid resolutions. Application of the transport fraction and meteorological
adjustments reduces the overestimation of fugitive dust impacts in the grid modeling as compared to ambient
samples.
For more details on this approach and the differences between the 2007 base case and the 2008 NEI, see the
2007v5 TSD. The afdust sector is identical to the 2007v5 platform except for the fact that the meteorological
adjustments were computed using the update WRF outputs.
2.2.2 Agricultural ammonia sector (ag)
The agricultural NFL? "ag" sector is based on livestock and agricultural fertilizer application emissions from
the 2008 NEI nonpoint inventory. The ag sector is identical to 2007v5 platform (see 2007v5 TSD for
details) except for the temporalization of animal NH3.
An updated temporal allocation methodology for animal NFb that allocates emissions down to the hourly
level by taking into account temperature and wind speed was incorporated into the 2007 platform (see
Section 3.3.3 for more details).
2.2.3 Other nonpoint sources (nonpt)
Stationary nonpoint sources that were not subdivided into the afdust or ag sectors were assigned to the
"nonpt" sector. All fire emissions from the 2008 NEI nonpoint inventory were removed and replaced with
SMARTFIRE emissions, described in Section 2.3. Additionally, locomotives and CMV mobile sources
from the 2008 NEI nonpoint inventory are described in Section 2.5.
For more details on the development of the nonpt sector see the 2007v5 TSD. The differences between the
2007v5 nonpt and the 2007 nonpt for this base case are limited to the following:
Replaced 2008 NEI oil and gas emissions (SCCs beginning with "23100") with year 2008 Phase III
oil and gas emissions for several basins in the WRAP RPO states. These WRAP Phase III emissions
contain point and nonpoint formatted data are discussed in greater detail in Section 2.1.2 and here:
http://www.wrapair2.org/PhaseIII.aspx. These changes were made in counties affected by the WRAP
data.
10
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Updated speciation and spatial surrogates were used to support the addition of the Permian basin (see
Section 3.2.1.3 and3.4.1, respectively).
Included the CMAQ MP-lite HAPs (see Section 3.2 for details)
Updated the temporalization of the residential wood combustion (RWC) inventories to account for
the updated meteorology (see Section 3.3.3 for details).
2.3 Fires (avefire)
Wildfire and prescribed burning emissions are contained in the ptfire and avefire sectors. The ptfire sector
has emissions provided at geographic coordinates (point locations) and has daily emissions values, whereas
the avefire sector contains county-summed inventories also at daily resolution. EPA used the ptfire sector in
the 2007v5 evaluation case but not for this 2007 base case. For the 2007 and future base cases, the avefire
sector was used instead of point fires. The SCCs in Table 2-5 are considered "fires" - note that the complete
SCC description includes "Miscellaneous Area Sources" as the first tier level description.
Table 2-5. 2007 Platform SCCs representing emissions in the ptfire and avefire modeling sectors
SCC
2810001000
2810015000
2811015000
2811090000
SCC Description
Other Combustion; Forest Wildfires; Total
Other Combustion; Prescribed Burning for Forest Management; Total
Other Combustion-as Event; Prescribed Burning for Forest Management; Total
Other Combustion-as Event; Prescribed Forest Burning ;Unspecified
The avefire sector excludes agricultural burning and other open burning sources, which are included in the
nonpt sector. The agricultural burning and other open burning sources are left in the nonpt sector because
these categories were not factored into the development of the average fire sector. Additionally, the
emissions are much lower and their year-to-year variability is much lower than that of wildfires and non-
agricultural prescribed/managed burns.
The purpose of the avefire sector is to represent emissions for a typical year's fires for use in projection year
inventories, since the location and degree of future-year fires are not known. This approach keeps the fires
information constant between the 2007 base case and future-year cases to eliminate large and uncertain
differences between those cases that would be caused by changing the fires. Using an average of multiple
years of data reduces the possibility that a single-year's high or low fire activity would unduly affect future-
year model-predicted concentrations. Emissions are day-specific but aggregated to county-level where
spatial surrogates will allocate the fires to forest and crop/pasture land. The creation of the avefire daily
nonpoint inventory is distinct for prescribed burning and wildfires. For more details on Fire Averaging Tool
and the various smoothing techniques, see the 2007v5 TSD.
For this 2007 base case (and future years), EPA used 4 years (2007 through 2010) of fire data from the
Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation (SMARTFIRE) version 2 for
both prescribed and wildfires and used a 29 day averaging period.
2.4 Biogenic sources (biog)
The biogenic emissions were computed based on 2007 meteorology data using the Biogenic Emission
Inventory System, version 3.14 (BEIS3.14) model within SMOKE. The BEIS3.14 model creates gridded,
hourly, model-species emissions from vegetation and soils. It estimates CO, VOC (most notably isoprene,
terpine, and sesquiterpene), and NO emissions for the U.S., Mexico, and Canada. The BEIS3.14 model is
11
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described further in http://www.cmascenter.org/conference/2008/slides/pouliot_tale_two_cmas08.ppt. The
2007v5 biog sector and the biog sector for this 2007 base case, the meteorology differed between the two
cases. The changes in meteorology will impact both the total emissions and the temporalization and spatial
distribution of these emissions. The BIOSEASONS file was also updated using the meteorology data for this
case.
2.5 2007 mobile sources (onroad, onroad_rfl, nonroad, dc2rail, cSmarine)
For the 2007 base case, as indicated in Table 2-1, EPA separated the 2007 onroad emissions into two sectors:
(1) "onroad" and (2) "onroad_rfl". As discussed in Section 2.5.2, the onroad and onroad_rfl sectors are
processed separately to allow for different spatial allocation to be applied to onroad refueling (using a gas
station surrogate) versus onroad vehicles (using surrogates based on roads and population). All onroad and
onroad refueling emissions are generated using a new SMOKE-MOVES emissions modeling framework that
leverages MOVES generated outputs (http://www.epa.gov/otaq/models/moves/index.htm) and hourly
meteorology.
The nonroad sector is based on NMEVI except for California which uses data provided by the California Air
Resources Board (CARB). All nonroad emissions are compiled at the county/SCC level. NMEVI (EPA,
2005) creates the nonroad emissions on a month-specific basis that accounts for temperature, fuel types, and
other variables that vary by month.
The locomotive and commercial marine vessel (CMV) emissions are divided into two nonroad sectors:
"clc2rail" and "cSmarine". The clc2rail sector includes all railway and most rail yard emissions as well as
the gasoline and diesel-fueled Class 1 and Class 2 CMV emissions. The cSmarine sector emissions contain
the larger residual fueled ocean-going vessel Class 3 CMV emissions and are treated as point emissions with
an elevated release component; all other nonroad emissions are treated as county-specific low-level
emissions (i.e., are in model layer 1).
All tribal data from the mobile sectors have been dropped because EPA does not have spatial surrogate data
for tribal regions, the data may be double-counted with emissions submitted by state and local agencies for
the same areas, and the emissions are small.
2.5.1 Onroad non-refueling (onroad)
For the 2007 base case, EPA estimated emissions for every county in the continental U.S7. EPA used a
modeling framework that took into account the strong temperature sensitivity of the onroad emissions.
Specifically, EPA used county-specific inputs and tools that integrated the MOVES model with the SMOKE8
emission inventory model to take advantage of the gridded hourly temperature information available from
meteorology modeling used for air quality modeling. This integrated "SMOKE-MOVES" tool was
developed by EPA in 2010 and is in use by states and regional planning organizations for regional air quality
modeling. SMOKE-MOVES requires emission rate "lookup" tables generated by MOVES that differentiate
emissions by process (running, start, vapor venting, etc.), vehicle type, road type, temperature, speed, hour of
day, etc. To generate the MOVES emission rates that could be applied across the U.S., an automated process
ran MOVES to produce emission factors by temperature and speed for 146 "representative counties," to
which every other county could be mapped. Using the MOVES emission rates, SMOKE selected
appropriate emissions rates for each county, hourly temperature, SCC, and speed bin and multiplied the
7 EPA estimated California as well, this is different than the approach for the 2007v5 platform.
8 A beta version of SMOKE v3.5 was used for modeling the Tier 3 FRM. The release version is available at: http://www.smoke-
model.org/index.cfm
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emission rate by activity (VMT (vehicle miles travelled) or vehicle population) to produce emissions. These
calculations were done for every county, grid cell, and hour in the continental U.S.
SMOKE-MOVES can be used with different versions of the MOVES model. For the 2007 platform, EPA
used the latest publically released version: MOVES2010b
(http://www.epa.gov/otaq/models/moves/index.htm). However, since the release of MOVES2010b, EPA has
continued to collect and analyze emissions data. In particular, EPA completed two major studies of fuel
effects on emissions in Tier 2 light-duty gasoline vehicles, and an important collection of studies on
evaporative emissions. Because fuels and evaporative emissions are affected by Tier 3 standards, it was very
important to include these data in the modeling. Therefore, in estimating the impact of the Tier 3 vehicle and
fuel standards on air quality for the future years, MOVES2010b was updated to include the results from
these studies, along with numerous other updates. Furthermore, the new model incorporated the changes that
reflect recent EPA rules on light-duty and heavy-duty greenhouse gas emissions. These changes are
documented in the docket for the NPRM9 and in the docket for this rule10.
The following inputs and methodologies are identical to the 2007v5 platform11 and are detailed in the
2007v5 TSD:
Activity data (VMT, VPOP, speed)
Representative counties
Fuel Months
Local MOVES inputs
Procedure for running MOVES to create emission factors
Procedure for running SMOKE to create emissions
The following inputs differed between the 2007 base case and the 2007v5 modeling and are detailed below:
fuels, meteorology, and extended idle adjustments.
2.5.1.1 Fuels
Although state-submitted NMEVI and MOVES input data may have included information about fuel
properties, the MOVES runs for the 2007 base case were run using a set of fuel properties for each county in
2007 generated by EPA. These data were developed using a combination of purchased fuel survey data,
proprietary fuel refinery information, ethanol and other biofuel production levels, and known federal and
local regulatory constraints. The fuel supply used in the Tier 3 FRM varies significantly from that used in
the NPRM, including the introduction of a new approach to aggregating fuels by region. For more
information regarding this new approach to fuels, please refer to the Tier 3 FRM, Chapter 7.1.3.2. The fuel
supplied used in the Tier 3 FRM varies slightly from that used in the 2007v5, including less ethanol overall
with a slightly different regional distribution, which more closely matched the new AEO regions. The fuel
properties themselves are the same in 2007v5 and in Tier 3 FRM (2007 base case).
2.5.1.2 Temperature and humidity
Ambient temperature can have a large impact on emissions. Low temperatures are associated with high start
emissions for many pollutants. High temperatures are associated with greater running emissions due to the
higher engine load of air conditioning. High temperatures also are associated with higher evaporative
emissions.
9 U.S. EPA. 2013. "Memorandum to Docket: Updates to MOVES for the Tier 3 NPRM"
10 U.S. EPA. 2014. "Memorandum to Docket: Updates to MOVES for the Tier 3 FRM Analysis"
11 Tier 3 NPRM was based on the 2005 platform and hence inputs such as the representative counties, activity data, and local
MOVES inputs were updated as part of the development of the 2007 platform (used for Tier 3 FRM).
13
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The 36-km and 12-km gridded meteorological input data for the entire year of 2007 covering the continental
United States were derived from simulations of version 3.3 of the Weather Research and Forecasting Model
(WRF, http://wrf-model.org). Advanced Research WRF (ARW) core (Skamarock, et al., 2008). The WRF
Model is a mesoscale numerical weather prediction system developed for both operational forecasting and
atmospheric research applications. The Meteorology-Chemistry Interface Processor (MCIP) version 4.1.2
(http://www.cmascenter.org/help/model_docs/mcip/4.1/ReleaseNotes) was used as the software for
maintaining dynamic consistency between the meteorological model, the emissions model, and air quality
chemistry model. The meteorology run used for the 2007 base year12 is different than what was used for the
2007v5 platform. Specifically it used newer versions of WRF and MCIP and 25 vertical layers13 instead of
the24usedin2007v5.
The SMOKE-MOVES tool Met4moves reads the gridded, hourly meteorological data (output from MCIP) to
generate a list of the maximum temperature ranges, average relative humidity, and temperature profiles that
are needed for MOVES to create the emission-factor lookup tables. For more details on Met4moves and the
processing of meteorology for SMOKE-MOVES see the 2007v5 TSD.
2.5.1.3 Extended idle adjustments and SMOKE-MOVES
Emissions from the extended idling of long haul trucks are a subset of the exhaust emissions. These
emissions are typically from trucks, which have traveled across county and state boundaries. Federal rules
require that truck drivers may not drive more than 10 hours without rest. These long haul trucks are known
to stop for these rest periods at truck stops along their routes and idle their trucks for hours while they rest.
The MOVES model generates an estimate of the total number of extended idling hours and emissions for
every county. However, when MOVES is run using the County scale, the extended idling rate (in grams per
hour per truck) is not adjusted to account for allocation of the extended idling to counties where interstate
travel occurs.
SMOKE has an optional input that adjusts emissions (CFPRO) by county, SCC, and mode. To account for
the extended idle adjustment, EPA created an adjustment file that applies these allocation factors by county
for extended idle and for the long haul type SCCs (SCC7 2230073 and 2230074) only (see the 201 Ivl NEI
TSD for more details).
SMOKE-MOVES, specifically Movesmrg, uses the adjustment factor file (CFPRO) for extended idle to
estimate 2007 emissions that incorporates these adjustments.
2.5.2 Onroad refueling (onroad_rfl)
Onroad refueling is modeled very similarly to other onroad emissions (see Section2.5.1).
MOVESTier3FRM can produce EFs for refueling. These EFs are at the resolution of the onroad SCCs. The
refueling EFs were run separately from the other onroad mobile sources to allow for different spatial
allocation. To facilitate this, the EFs from the refueling process were separated out into RPD refueling and
RPV refueling tables14. EPA then ran SMOKE-MOVES using these EF tables as inputs and spatially
allocated the results based on a gas stations surrogate (see Section 3.4.1).
Lastly, the Mrggrid SMOKE program combined RPD refueling and RPV refueling into a single onroad_rfl
model ready output for final processing with the other sectors prior to use in CMAQ.
12 Note the meteorology is consistent across all Syears: 2007, 2018 and 2030.
13 WRF was run at 35 layers and MCIP post-processed it to 25 layers.
14 The Moves2smk post-processing script has command line arguments that will either consolidate or split out the refueling EF.
14
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2.5.3 Nonroad mobile equipment sources: (nonroad)
This sector includes monthly exhaust, evaporative and refueling emissions from nonroad engines (not
including commercial marine, aircraft, and locomotives) that are derived from NMEVI for all states except
California. Year-2007 inventories from CARB were used for California after the completion of several
preprocessing steps discussed in the 2007v5 TSD.
NMEVI (non-California) nonroad
NMEVI ran the version of NONROAD, NROSb, which models all in-force nonroad controls, including the
marine spark ignited (SI) and small SI engine final rule, published May 2009 (EPA, 2008). This version of
NONROAD is very similar to the publicly released version, but it can model ethanol blends up to E20. The
NMEVI version is NMIM20090504d, which has the same results as the publicly-released NMEVI version
NMEVI20090504a. The underlying National County Database (NCD) is NCD20101201a, but with 2007
meteorology inserted into the countymonthhour table. NCD20101201a includes state inputs for the 2008
NEI.
The NMEVI run, abs2007basenr, includes the lower 48 states plus Washington D.C. ; it excludes Alaska,
Hawaii, Puerto Rico and the Virgin Islands. To conserve processing time, NMEVI was run using 392 county
groups. The county groups are in the same state and have the same fuels and similar temperature regimes.
The county from each group with the highest VMT was chosen as the representing county. All counties are
mapped to their representing county in the MySQL table countymap392. The fuels database,
countryyearmonth2007_Baseline_0906012, is a conversion to NMEVI format of the MOVES fuels for the
2007 base case (see Section 2.5.1.1).
As with the onroad emissions, NMEVI provides nonroad emissions for VOC by three emission modes:
exhaust, evaporative and refueling. Unlike the onroad sector, refueling emissions from nonroad sources are
not separated into a different sector.
The EPA ran NMEVI to create county-SCC emissions and removed California emissions because they were
replaced with a CARB inventory. Emissions were converted from monthly totals to SMOKE-ready FF10
format (http://www.cmascenter.Org/smoke/documentation/3.5.l/html/ch08s02s04.html) monthly average-day
based on the number of days in each month. EPA retained only CAPs and the necessary HAPs: BAFM,
acrolein, butadiene, and naphthalene.
California nonroad
California year 2007 nonroad emissions were provided by CARB and are documented in a staff report (ARB,
2010a). The nonroad sector emissions in California are developed using a modular approach and include all
rulemakings and updates in place by December 2010. These emissions were developed using Version 1 of
the California Emissions Projection Analysis Model (CEP AM) which support various California off-road
regulations such as in-use diesel retrofits (ARB, 2007), Diesel Risk-Reduction Plan (ARB, 2000) and 2007
State Implementation Plans (SIPS) for the South Coast and San Joaquin Valley air basins (ARB, 201 Ob).
EPA converted the CARB-supplied nonroad annual inventory to monthly emissions values by using the
aforementioned EPA NMEVI monthly inventories to compute monthly ratios by pollutant and SCC. Some
adjustments to the CARB inventory were needed to convert the provided total organic gas (TOG) to VOC
and augment the HAPs. See Section 3.2.1.3 for details on speciation of California nonroad data.
15
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2.5.4 Class 1/Class 2 Commercial Marine Vessels and Locomotives and (c1c2rail)
The clc2rail sector contains locomotive and commercial marine vessel (CMV) sources, except for category
3/residual-fuel (C3) CMV and railway maintenance. The "clc2" portion of this sector name refers to the
Class I/II CMV emissions, not the railway emissions. Railway maintenance emissions are included in the
nonroad sector. The C3 CMV emissions are in the c3marine sector.
For more details on the development of the clc2rail sector see the 2007v5 TSD. The differences between the
2007v5 clc2rail and the 2007 clc2rail for this base case are limited to the following:
Updated an outdated FIPS in the RPO rail inventory. Specifically changed Clifton Forge, VA
(51560) to Alleghany County, VA (51005)
Augmented the CARB clc2rail and RPO rail inventories to include the CMAQ MP-lite HAPs (see
Section 3.2 and 3.2.1.3 for details)
2.5.5 Class 3 commercial marine vessels (cSmarine)
The c3marine sector emissions data were developed based on a 4-km resolution ASCII raster format dataset
used since the Emissions Control Area-International Marine Organization (ECA-EVIO) project began in
2005, then known as the Sulfur Emissions Control Area (SEC A). These emissions consist of large marine
diesel engines (at or above 30 liters/cylinder) that until very recently, were allowed to meet relatively modest
emission requirements, often burning residual fuel. The emissions in this sector are comprised of primarily
foreign-flagged ocean-going vessels, referred to as Category 3 (C3) CMV ships. The c3marine inventory
includes these ships in several intra-port modes (cruising, hoteling, reduced speed zone, maneuvering, and
idling) and underway mode and includes near-port auxiliary engines. This sector is identical to the 2007v5
platform except for the addition of naphthalene (see Section 3.2.1.3). For more details on the development
of this sector's emissions, see the 2007v5 TSD.
2.6 Emissions from Canada, Mexico and offshore drilling platforms (othpt,
othar, othon)
The emissions from Canada, Mexico, and offshore drilling platforms are included as part of three emissions
modeling sectors: othpt, othar, and othon.
The "oth" refers to the fact that these emissions are usually "other" than those in the U.S. state-county
geographic FIPS, and the third and fourth characters provide the SMOKE source types: "pt" for point, "ar"
for "area and nonroad mobile", and "on" for onroad mobile. All "oth" emissions are CAP-only inventories.
For more details on the development of the "oth" sectors see the 2007v5 TSD. The differences between the
2007v5 "oth" and the 2007 "oth" for this base case are limited to the following:
othar has updated spatial surrogates (see Sections 3.4.3)
othon has updated speciation and spatial surrogates (see Sections 3.2.1.3 and 3.4.3, respectively)
othpt has updated speciation (see Sections 3.2.1.3)
16
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2.7 SMOKE-ready non-anthropogenic inventories for chlorine
The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (C12)
concentrations in oceanic air masses (Bullock and Brehme, 2002). Data at 36 km and 12 km resolution were
available and were not modified other than the name "CHLORINE" was changed to "CL2" because that is
the name required by the CMAQ model.
17
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3 Emissions Modeling Summary
Both the CMAQ and CAMx models require hourly emissions of specific gas and particle species for the
horizontal and vertical grid cells contained within the modeled region (i.e., modeling domain). To provide
emissions in the form and format required by the model, it is necessary to "pre-process" the "raw" emissions
(i.e., emissions input to SMOKE) for the sectors described above in Section 2. In brief, the process of
emissions modeling transforms the emissions inventories from their original temporal resolution, pollutant
resolution, and spatial resolution into the hourly, speciated, gridded resolution required by the air quality
model. Emissions modeling includes temporal allocation, spatial allocation, and pollutant speciation. In
some cases, emissions modeling also includes the vertical allocation of point sources, but many air quality
models also perform this task because it greatly reduces the size of the input emissions files if the vertical
layer of the sources does not need to be included.
As seen in Section 2, the temporal resolutions of the emissions inventories input to SMOKE vary across
sectors, and may be hourly, daily, monthly, or annual total emissions. The spatial resolution, which also can
be different for different sectors, may be individual point sources, county/province/municipio totals, or
gridded emissions. This section provides some basic information about the tools and data files used for
emissions modeling as part of the modeling platform. In Section 2, the emissions inventories and how they
differ from the 2007v5 platform were described. In Section 3, the descriptions of data are limited to the
ancillary data SMOKE uses to perform the emissions modeling steps.
SMOKE version 3.5 beta was used to pre-process the emissions inventories into emissions inputs for
CMAQ. For sectors that have plume rise, the in-line emissions capability of the air quality models was used,
thereby creating source-based and two-dimensional gridded emissions files that are much smaller than full
three-dimensional gridded emissions files. For quality assurance of the emissions modeling steps, emissions
totals by specie for the entire model domain are output as reports that are then compared to reports generated
by SMOKE on the input inventories to ensure that mass is not lost or gained during the emissions modeling
process.
3.1 Emissions modeling Overview
When preparing emissions for the air quality model, emissions for each sector are processed separately
through SMOKE, and then the final merge program (Mrggrid) is run to combine the model-ready, sector-
specific emissions across sectors. The SMOKE settings in the run scripts and the data in the SMOKE
ancillary files control the approaches used by the individual SMOKE programs for each sector. Table 3-1
summarizes the major processing steps of each platform sector. The "Spatial" column shows the spatial
approach used: here "point" indicates that SMOKE maps the source from a point location (i.e., latitude and
longitude) to a grid cell; "surrogates" indicates that some or all of the sources use spatial surrogates to
allocate county emissions to grid cells; and "area-to-point" indicates that some of the sources use the
SMOKE area-to-point feature to grid the emissions. 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
18
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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 cSmarine, and othpt sectors are the only
sectors that contain only "in-line" emissions, meaning that all of the emissions are placed in aloft layers and
there are no emissions for those sectors in the two-dimensional, layer-1 files created by SMOKE.
Table 3-1. Key emissions modeling steps by sector.
Platform sector
ag
afdust
avefire
beis
clc2rail
c3 marine
nonpt
nonroad
othar
onroad
onroad rfl
othon
othpt
ptipm
ptnonipm
Spatial
Surrogates
Surrogates
Surrogates
Pre-gridded
land use
Surrogates
Point
Surrogates &
area-to-point
Surrogates &
area-to-point
Surrogates
Surrogates
Surrogates
Surrogates
Point
Point
Point
Speciation
Yes
Yes
Yes
inBEIS3.14
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Inventory
resolution
annual
(some monthly)
Annual
daily
computed hourly
Annual
Annual
annual
(some monthly)
Monthly
Annual
computed hourly
computed hourly
Annual
Annual
Daily
Annual
Plume rise
in-line
in-line
in-line
in-line
SMOKE has the option of grouping sources so that they are treated as a single stack when computing plume
rise. For the 2007v5 platform, no grouping was performed because grouping combined with "in-line"
processing will not give identical results as "offline" (i.e., when SMOKE creates 3-dimensional files). This
occurs when stacks with different stack parameters or lat/lons are grouped, thereby changing the parameters
of one or more sources. The most straightforward way to get the same results between in-line and offline is
to avoid the use of grouping.
EPA ran SMOKE for the 36-km CONtinental United States "CONUS" modeling domain for the boundary
conditions and for the smaller CONUS US 12-km modeling domain (12US2) shown in Figure 3-1 and
described in Table 3-2.
19
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Figure 3-1. Air quality modeling domains
12US l Continental US Domain
J.2US2 Continental US Domain
Both grids use a Lambert-Conformal projection, with Alpha = 33°, Beta = 45° and Gamma = -97°, with a
center of X = -97° and Y = 40°.
Table 3-2. Descriptions of the 2007v5 platform grids
Common
Name
Continental
36km grid
(36US1)
Continental
12km grid
US 12 km or
"smaller"
CONUS-12
Grid
Cell Size
36km
12km
12km
Description
(see Figure 3-1)
Entire conterminous
US plus some of
Mexico/Canada
Entire conterminous
US plus some of
Mexico/Canada
Smaller 12km
CONUS plus some of
Mexico/Canada
Grid name
36US1_148X112
12US1_459X299
12US2
Parameters listed in SMOKE grid
description (GRIDDESC) file:
projection name, xorig, yorig,
xcell, ycell, ncols, nrows, nthik
'LAM 40N97W, -2736000, -
2088000, 36.D3, 36.D3, 148, 112, 1
'LAM 40N97W, -2556000, -1728000,
12.D3, 12.D3, 459, 299, 1
'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.
20
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3.2 Chemical Speciation
The emissions modeling step for chemical speciation creates "model species" needed by the air quality
model for a specific chemical mechanism. These model species are either individual chemical compounds or
groups of species, called "model species." The chemical mechanism used for the 2007 platform is the CB05
mechanism (Yarwood, 2005). The same base chemical mechanism is used with CMAQ and CAMx, but the
implementation differs slightly between the two models. The specific versions of CMAQ and CAMx used in
applications of this platform include secondary organic aerosol (SOA) and HONO enhancements.
From the perspective of emissions preparation, the CB05 with SOA mechanism is the same as was used in
the 2007 platform. Table 3-3 lists the model species produced by SMOKE for use in CMAQ and CAMx. It
should be noted that the BENZENE model species is not part of CB05 in that the concentrations of
BENZENE do not provide any feedback into the chemical reactions (i.e., it is not "inside" the chemical
mechanism). Rather, benzene is used as a reactive tracer and as such is impacted by the CB05 chemistry.
BENZENE, along with several reactive CB05 species (such as TOL and XYL) plays a role in SOA
formation. Unlike the 2007v5 platform, the Tier3 FRM modeling included additional hazardous air
pollutants (HAPs) and used slightly revised speciation. A "lite" version15 of the multi-pollutant CMAQ
(Version 5.0.1) was used that required additional HAP species16 (see Table 3-3 for details): ACROLEIN,
ALD2_PRIMARY, BUTADIENE13, ETOH, FORM_PRIMARY, and NAPHTHALENE .
The approach for speciating PM2.5 emissions supports both CMAQ 4.7.1 and CMAQ 5.0 and includes
speciation of PM2.5 into a larger set of PM model species than is listed above (see the 2007v5 TSD and
Section 3.2.2 for details). The TOG and PM2.5 speciation factors that are the basis of the chemical speciation
approach were developed from the SPECIATE4.3 database (http://www.epa.gov/ttn/chief/software/speciate),
EPA's repository of TOG and PM speciation profiles of air pollution sources. However, a few of the profiles
used in this modeling will be published in later versions of the SPECIATE database after the release of this
documentation.
The approach for speciating NOx into NO, NO2, and HONO is consistent with the 2007v5 platform (see the
2007v5 TSD for details).
The SPECIATE database development and maintenance is a collaboration involving the EPA's ORD,
OTAQ, and the Office of Air Quality Planning and Standards (OAQPS), and 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.
15 Note, that the MP version of CMAQ allows the user to control the list of HAPs that they wish to explicitly model.
16 These additional HAPs are referred to in this document as "CMAQ MP-lite HAPs".
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Table 3-3. Emission model species produced for CB05 with SOA for CMAQ and CAMx*
Inventory Pollutant
CL2
HC1
CO
NOX
SO2
NH3
VOC
VOC species from the biogenics
model that do not map to model
species above
PM10
PM2.518
Sea-salt species (non -
anthropogenic)19
Model Species
CL2
HCL
CO
NO
NO2
HONO
SO2
SULF
NH3
ACROLEIN
ALD2
ALD2 PRIMARY
ALDX
BENZENE
BUTADIENE 13
CH4
ETH
ETHA
ETOH
FORM
FORM PRIMARY
IOLE
ISOP
MEOH
NAPHTHALENE
OLE
PAR
TOL
XYL
SESQ
TERP
PMC
PEC
PNO3
POC
PSO4
PMFINE
PCL
PNA
Model species description
Atomic gas-phase chlorine
Hydrogen Chloride (hydrochloric acid) gas
Carbon monoxide
Nitrogen oxide
Nitrogen dioxide
Nitrous acid
Sulfur dioxide
Sulfuric acid vapor
Ammonia
Acrolein from the HAP inventory
Acetaldehyde for VOC speciation
Acetaldehyde from the HAP inventory
Propionaldehyde and higher aldehydes
Benzene (not part of CB05)
1,3 -butadiene from the HAP inventory
Methane17
Ethene
Ethane
Ethanol
Formaldehyde from VOC speciation
Formaldehyde from the HAP inventory
Internal olefin carbon bond (R-C=C-R)
Isoprene
Methanol
Naphthalene from the HAP inventory
Terminal olefin carbon bond (R-C=C)
Paraffin carbon bond
Toluene and other monoalkyl aromatics
Xylene and other polyalkyl aromatics
Sesquiterpenes
Terpenes
Coarse PM > 2.5 microns and < 10 microns
Particulate elemental carbon < 2.5 microns
Particulate nitrate < 2.5 microns
Particulate organic carbon (carbon only) < 2.5
microns
Particulate Sulfate < 2.5 microns
Other particulate matter < 2.5 microns
Particulate chloride
Particulate sodium
Technically, CH4 is not a VOC but part of TOG. Although we derive emissions of CH4, the AQ models do not use these
emissions because the anthropogenic emissions are dwarfed by the CH4 already in the atmosphere.
18 For CMAQ 5.0, PM25 is speciated into a finer set of PM components. Listed in this table are the AE5 species
These emissions are created outside of SMOKE
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*The same species names are used for the CAMX model with exceptions as follows:
1. CL2 is not used in CAMX
2. CAMX particulate sodium is NA (in CMAQ it is PNA)
3. CAMX uses different names for species that are both in CBO5 and SOA for the following: TOLA=TOL, XYLA=XYL,
ISP=ISOP, TRP=TERP. They are duplicate species in CAMX that are used in the SOA chemistry. CMAQ uses the same
names in CB05 and SOA for these species.
4. CAMX uses a different name for sesquiterpenes: CMAQ SESQ = CAMX SQT
5. CAMX uses paniculate species uses different names for organic carbon, coarse paniculate matter and other paniculate
mass as follows: CMAQ POC = CAMX POA, CMAQ PMC = CAMX CPRM, CMAQ PMFINE= CAMX FCRS, and CMAQ
PMOTHR = CAMx FPRM
3.2.1 VOC speciation
3.2.1.1 The combination of HAP BAFM (benzene, acetaldehyde, formaldehyde and
methanol) and VOC for VOC speciation
The VOC speciation includes HAP emissions from the NEI in the speciation process. Instead of speciating
VOC to generate all of the species listed in Table 3-3, EPA integrated emissions of four specific HAPs,
benzene, acetaldehyde, formaldehyde and methanol (collectively known as "BAFM") from the NEI with the
NEI VOC. The integration process (described in more detail in the 2007v5 TSD) combines these HAPs with
the VOC in a way that does not double count emissions and uses the HAP inventory directly in the speciation
process. The basic process is to subtract the specified HAPs from VOC and to use a special integrated
profile to speciate the remainder of VOC to the model species excluding the specific HAPs. Generally, the
HAP emissions from the NEI are considered to be more representative of emissions of these compounds than
their generation via VOC speciation.
Specific sectors fell into 3 categories: all sources are speciated from VOC directly (no integration), all
sources are speciated with BAFM or EBAFM (ethanol plus BAFM) coming from the inventory (full
integration), or some sources have BAFM and other sources do not (partial integration). See Table 3-4 for
the integration status of each of the modeling sectors.
Table 3-4. Integration approach for BAFM and EBAFM for each platform sector
Platform
Sector
ptipm
ptnonipm
avefire
Ag
Afdust
Nonpt
nonroad
clc2rail
cSmarine
Onroad
Biog
Othpt
Othar
othon
Approach for Integrating NEI emissions of Benzene (B), Acetaldehyde (A),
Formaldehyde (F), Methanol (M), and Ethanol (E)
No integration
Partial integration (BAFM)
No integration
N/A - sector contains no VOC
N/A - sector contains no VOC
Partial integration (BAFM and EBAFM)
Partial integration (BAFM). Except for California: no integration
Partial integration (BAFM)
Full integration (BAFM)
Full integration (EBAFM and BAFM)
N/A - sector contains no inventory pollutant "VOC"; but rather specific VOC species
No integration
No integration
No integration
More details on the integration of specific sectors and additional details of the speciation are provided in
Sections.2.1.3 and the 2007v5 TSD.
23
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3.2.1.2 County specific profile combinations (GSPRO_COMBO)
EPA used the SMOKE feature to compute speciation profiles from mixtures of other profiles in user-
specified proportions. The combinations are specified in the GSPRO_COMBO ancillary file by pollutant
(including pollutant mode, e.g., EXH_VOC), state and county (i.e., state/county FIPS code) and time period
(i.e., month).
EPA used this feature for onroad and nonroad mobile and gasoline-related related stationary sources
whereby the emission sources use fuels with varying ethanol content, and therefore the speciation profiles
require different combinations of gasoline profiles, e.g. EO and E10 profiles. Since the ethanol content varies
spatially (e.g., by state or county), temporally (e.g., by month) and by modeling year (future years have more
ethanol) the feature allows combinations to be specified at various levels for different years. SMOKE
computes the resultant profile using the fraction of each specific profile assigned by county, month and
emission mode.
The GSREF file indicates that a specific source uses a combination file with the profile code "COMBO".
Because the GSPRO_COMBO file does not differentiate by SCC and there are various levels of integration
across sectors, we typically have a sector specific GSPRO_COMBO. For the onroad and onroad_rfl sectors,
the GSPRO_COMBO uses E-profiles (i.e. there is EBAFM integration). Different profile combinations are
specified by the mode (e.g. exhaust, evaporative, refueling, etc.) by changing the pollutant name (e.g.
EXH_NONHAPTOG, EVP_NONHAPTOG, RFL_NONHAPTOG). For the nonpt sector, there is a
combination of BAFM and EBAFM integration. Due to the lack of SCC in the GSPRO_COMBO, the only
way to differentiate the sources that should use BAFM integrated profiles versus E-profiles is by changing
the pollutant name. For example, we changed the pollutant name for the PFC future year inventory so the
integration would use EVP NONHAPVOC to correctly select the E-profile combinations while other
sources used NONHAPVOC to select the typical BAFM profiles.
3.2.1.3 Additional sector specific details
The decision to integrate HAPs into the speciation was made on a sector by sector basis. For some sectors
there is no integration (VOC is speciated directly), for some sectors there is full integration (all sources are
integrated), and for other sectors there is partial integration (some sources are not integrated and other
sources are integrated). The integrated HAPs are either BAFM (ethanol not subtracted from VOC with
BAFM HAPs) or EBAFM (ethanol and BAFM HAPs subtracted from VOC). Table 3-4 summarizes the
integration for each platform sector. The additional CMAQ MP-lite HAPs were evaluated and, where it was
needed, augmented in the mobile sectors, with details provided below.20
For the clc2rail sector, EPA integrated BAFM for most sources from the 2008 NEI. There were a few
sources that had zero BAFM; therefore, they were processed as no integrate. The RPO and CARB
inventories did not include HAPs; therefore, EPA processed all non-NEI source emissions in the clc2rail
sector as no integrate. For California, EPA converted the CARB inventory TOG to VOC by dividing the
inventory TOG by the available VOC-to-TOG speciation factor. For CARB and the RPO inventories, EPA
augmented the inventories to include the CMAQ MP-lite HAPs. In Table 3-5, "geography" indicates
whether the emission factor is applied nationally or to a subset of the country and "speciation base" is the
pollutant value to use in the emissions calculation.
20 EPA analyzed the presence of acroleine, 1,3-butadiene, and naphthalene in the nonpt and point sectors and found that the
coverage was very inconsistent; therefore the decision was made to augment only the mobile sectors where it was needed.
24
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Table 3-5. HAP augmentation for clc2rail
Pollutant
1,3 Butadiene
1,3 Butadiene
Acrolein
Acrolein
Napthalene
Napthalene
Napthalene
Napthalene
Acrolein
Acrolein
1,3 Butadiene
1,3 Butadiene
Acrolein
Acrolein
Naphthalene
Naphthalene
Speciation
Fraction
6.146E-05
6.146E-05
8.547E-05
8.547E-05
1.851E-03
1.851E-03
2.576E-03
2.576E-03
4.594E-03
4.594E-03
4.774E-03
4.774E-03
2.625E-03
2.188E-03
1.051E-03
8.756E-04
Speciation
Base
PM10-PRI
PM10-PRI
PM10-PRI
PM10-PRI
PM10-PRI
PM10-PRI
PM10-PRI
PM10-PRI
PM10-PRI
PM10-PRI
PM10-PRI
PM10-PRI
VOC
VOC
PM25-PRI
PM25-PRI
Geography
California
California
California
California
California
California
49 States
49 States
49 States
49 States
49 States
49 States
National
National
National
National
sec
2285002007
2285002006
2285002007
2285002006
2285002007
2285002006
2285002006
2285002007
2285002007
2285002006
2285002006
2285002007
2280002100
2280002200
2280002100
2280002200
SCC Description
Railroad Equipment; Diesel; Line Haul
Locomotives: Class II / III Operations
Railroad Equipment; Diesel; Line Haul
Locomotives: Class I Operations
Railroad Equipment; Diesel; Line Haul
Locomotives: Class II / III Operations
Railroad Equipment; Diesel; Line Haul
Locomotives: Class I Operations
Railroad Equipment; Diesel; Line Haul
Locomotives: Class II / III Operations
Railroad Equipment; Diesel; Line Haul
Locomotives: Class I Operations
Railroad Equipment; Diesel; Line Haul
Locomotives: Class I Operations
Railroad Equipment; Diesel; Line Haul
Locomotives: Class II / III Operations
Railroad Equipment; Diesel; Line Haul
Locomotives: Class II / III Operations
Railroad Equipment; Diesel; Line Haul
Locomotives: Class I Operations
Railroad Equipment; Diesel; Line Haul
Locomotives: Class I Operations
Railroad Equipment; Diesel; Line Haul
Locomotives: Class II / III Operations
Marine Vessels; Commercial; Diesel; Port
Marine Vessels; Commercial; Diesel;
Underway
Marine Vessels; Commercial; Diesel; Port
Marine Vessels; Commercial; Diesel;
Underway
For the cSmarine sector, EPA computed HAPs directly from the CAP inventory; therefore, the entire sector
is integrated to use the VOC BAFM HAP species directly, rather than VOC speciation profiles. There is no
methanol in the VOC speciation, but the remaining VOC BAF HAPs and the CMAQ MP-lite HAPs21
emissions are derived from the following equations:
Benzene = VOC * 9.795E-06
Acetaldehyde = VOC * 2.286E-04
Formaldehyde = VOC * 1.5672E-03
Naphthalene = PM2.5 * 1.990E-5
For the onroad and onroad_rfl sectors, there are series of unique speciation issues. First, SMOKE-MOVES
(see the 2007v5 TSD) is used to estimate these sectors, meaning that both the MEPROC and INVTABLE
files are involved in controlling which pollutants are ingested and speciated. Second, these sectors have
estimates of TOG as well as VOC; therefore, TOG can be speciated directly. Third, the gasoline sources use
The c3marine sources do not emit acrolein or 1,3-butadiene.
25
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full integration of EBAFM (i.e. use E-profiles) and the diesel sources use full integration of BAFM. Fourth,
the onroad sector utilizes 7 different modes for speciation: exhaust, extended idle, auxiliary power units
(APU), evaporative, permeation (gasoline vehicles only), brake wear, and tire wear. The onroad_rfl sector
utilizes an eighth mode, refueling. Fifth, the gasoline exhaust profiles were updated to 8750a (revision to
Gasoline Exhaust - Reformulated gasoline) and 875la (revision to Gasoline Exhaust - E10 ethanol
gasoline).22 Sixth, the CMAQ MP-lite HAPs are produced directly from the SMOKE-MOVES processing.
For the nonroad sector, CNG or LPG sources (SCC beginning with 2268 or 2267) were not integrated
because NMEVI computed only VOC and no HAPs were available for these SCCs. All other nonroad
sources were integrated. For California, EPA converted the CARB inventory TOG to VOC by dividing the
inventory TOG by the available VOC-to-TOG speciation factor. SMOKE later applies the same VOC-to-
TOG factor prior to computing speciated emissions. The CARB-based nonroad data includes exhaust and
evaporative mode-specific data for VOC, but does not contain refueling. The CARB inventory also does not
include HAP estimates; therefore all California nonroad emissions are processed as no integrate so that the
HAP species are generated by speciating the TOG emissions. The CMAQ MP-lite HAPS are produced
directly by NMEVI. In California, the CMAQ MP-lite HAPs were augmented by applying state-wide SCC
HAP to VOC ratios based on EPA's NMEVI estimates.
For the ptnonipm sector, there is partial integration limited to the 2007 ethanol inventory (SCC 30125010),
which includes BAFM . In the future year, there is also partial integration because both the ethanol and
biodiesel inventories (SCC 30125010) provided by OTAQ include BAFM. See the 2007v5 TSD for
additional details on this sector.
For the oil and gas sources in ptnonipm and nonpt, the WRAP Phase III sources have basin-specific VOC
speciation that takes into account the distinct composition of gas. ENVIRON developed these basin-specific
profiles using gas composition analysis data obtained from operators through surveys. ENVIRON separated
out emissions and speciation from conventional/tight sands/shale gas from coal-bed methane (CBM) gas
sources. Table 3-6 lists the basin and gas composition specific profiles used for the WRAP Phase III
inventory.23
Table 3-6. VOC profiles for WRAP Phase III basins
Profile Code
SSJCB
SSJCO
WRBCO
PRBCB
PRBCO
DJFLA
DJVNT
UNT01
UNT02
UNT03
UNT04
PNC01
Description
South San Juan Basin Produced Gas Composition for CBM Wells
South San Juan Basin Produced Gas Composition for Conventional Wells
Wind River Basin Produced Gas Composition for Conventional Wells
Powder River Basin Produced Gas Composition for CBM Wells
Powder River Basin Produced Gas Composition for Conventional Wells
D-J Basin Flashing Gas Composition for Condensate
D-J Basin Produced Gas Composition
Uinta Basin Gas Composition at CBM Wells
Uinta Basin Gas Composition at Conventional Wells
Uinta Basin Flashing Gas Composition for Oil
Uinta Basin Flashing Gas Composition for Condensate
Piceance Basin Gas Composition at Conventional Wells
These revised profiles are expected to be in the yet to be released SPECIATE 4.4.
23 Profile PRM01 was used in Tier 3 but not in the 2007v5 modeling. All other profiles are the same between the two cases.
26
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Profile Code
PNC02
PNC03
SWFLA
SWVNT
PRM01
SWE01
Description
Piceance Basin Gas Composition at Oil Wells
Piceance Basin Flashing Gas Composition for Condensate
SW Wyoming Basin Flash Gas Composition
SW Wyoming Basin Vented Gas Composition
Permian Basin Produced Gas Composition
Wyoming Flashing Gas Composition
Othon and othpt used a GSPRO_COMBO that included a gasoline exhaust profile updated to 8750a (revision
to Gasoline Exhaust - Reformulated gasoline).
For the remaining sectors, see the 2007v5 TSD for speciation details.
3.2.1.4 Future year speciation
The VOC speciation approach used for the future year case is customized to account for the impact of fuel
changes. These changes affect the onroad, onroad_rfl, nonroad, and parts of the nonpt and ptnonipm sectors.
Speciation profiles for VOC in the nonroad, onroad and onroad_rfl sectors that account for the changes in
ethanol content of fuels across years. The actual fuel formulations used can be found in Sections 2.5.1.1 and
4.3.1.2. For 2007, EPA used "COMBO" profiles to model combinations of profiles for EO and E10 fuel use.
For 2018 and 2030, EPA used "COMBO" profiles to model combinations of E10, E15, and E85 fuel use.
The speciation of onroad exhaust VOC additionally accounts for changes in the fraction of the vehicle fleet
meeting different vehicle standards over time; currently, different exhaust profiles are available for pre-Tier
2 versus Tier 2 and later vehicles. Thus for onroad gasoline, VOC speciation uses different COMBO profiles
to take into account both the increase in ethanol use, and the increase in vehicles meeting Tier 2 and later
standards in the future case.
The speciation changes from fuels in the nonpt sector are for PFCs and fuel distribution operations
associated with the BTP distribution. For these sources, ethanol may be mixed into the fuels; therefore,
speciation is expect to change across years. The speciation changes from fuels in the ptnonipm sector
include BTP distribution operations inventoried as point sources. RBT fuel distribution and BPS speciation
does not change across the modeling cases because this is considered upstream from the introduction of
ethanol into the fuel. For PFC, ethanol was present in the future inventories and therefore EBAFM profiles
were used to integrate ethanol in the speciation. Mapping of fuel distribution SCCs to PFC, BTP, BPS, and
RBT emissions categories can be found in Appendix B of the 2007v5 TSD.
Error! Reference source not found. Table 3-7 summarizes the different profiles utilized for the fuel-related
sources in each of the sectors for 2007 and the future year cases. This table indicates when "E-profiles" were
used instead of BAFM integrated profiles. The term "COMBO" indicates that a combination of the profiles
listed was used to speciate that subcategory using the GSPRO_COMBO file. Note, the speciation for the
Tier3 2018 control case is identical to the 2018 reference case and the speciation for the 2030 control case is
identical to the 2030 reference case. Although many of the profiles making up the COMBO are the same
between 2018 and 2030, the ratio of the profiles changes between the two years.
27
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Table 3-7. Select VOC profiles 2007. 2018 and 2030
Sector
onroad
onroad
onroad
onroad_rfl
onroad
onroad
onroad
onroad
onroad rfl
nonroad
nonroad
nonroad
Sub-
category
gasoline
exhaust
gasoline
evaporative
gasoline
permeation
gasoline
refueling
diesel
exhaust
diesel
extended
idle
auxiliary
power units
diesel
evaporative
diesel
refueling
gasoline
exhaust
gasoline
evaporative
gasoline
refueling
2007
COMBO:
Pre-Tier 2 EO
8750aE exhaust
Pre-Tier 2 E10
875 laE exhaust
Tier2EO
8756E Exhaust
Tier2E10
8757E Exhaust
COMBO:
8753E EO Evap
8754E E10 Evap
COMBO:
8766E EO evap perm
8769E E10 evap perm
COMBO:
8869E EO Headspace
E10
8870E Headspace
Pre-2007 MY
8774 HDD exhaust
Pre-2007 MY
8774 HDD exhaust
N/A
Diesel
4547 Headspace
Diesel
4547 Headspace
COMBO:
Pre-Tier 2 EO
8750a exhaust
Pre-Tier 2 E10
875 la exhaust
COMBO:
8753 EO evap
8754 E10 evap
COMBO:
8869 EO Headspace
8870 E10 Headspace
2018
COMBO:
Pre-Tier 2 E 10
875 laE exhaust
_ Tier 2 E10 Exhaust
o /J IE,
O--OT-, Tier 2 E15 Exhaust
O /JOE,
8855E Tier 2 E85 Exhaust
COMBO:
8754E E10 Evap
8872E E15 Evap
8934E E85 Evap
COMBO:
8769E E10 evap perm
8770E E15 evap perm
8934E E85 Evap
COMBO:
8870E E10 Headspace
887 IE E15 Headspace
8934E E85 Evap
Weighted diesel
877PO exhaust for 20 18
Weighted diesel
877EIT3 extended idle for 20 1 8
Pre-2007 MY HDD
8774 exhaust
4547 Diesel Headspace
4547 Diesel Headspace
Pre-Tier 2 E 10
875 la exhaust
8754 E10 evap
8870 E10 Headspace
2030
COMBO:
875 laE Pre-Tier 2 E10 exhaust
_ Tier 2 E10 Exhaust
o /J ICi
O--OT-, Tier 2 E15 Exhaust
O /JOE,
8855E Tier 2 E85 Exhaust
COMBO:
8754E E10 Evap
8872E E15 Evap
8934E E85 Evap
COMBO:
8769E E10 evap perm
8770E E15 evap perm
8934E E85 Evap
COMBO:
8870E E10 Headspace
887 IE E15 Headspace
8934E E85 Evap
Weighted diesel
87730T3 exhaust for 2030
Weighted diesel
877EIT3 extended idle for 20 1 8
Pre-2007 MY HDD
8774 exhaust
4547 Diesel Headspace
4547 Diesel Headspace
875 la Pre-Tier 2 E10 exhaust
8754 E10 evap
8870 E10 Headspace
28
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Sector
nonroad
nonroad
nonroad
nonpt/
ptnonipm
nonpt/
ptnonipm
nonpt/
ptnonipm
Sub-
category
diesel
exhaust
diesel
evaporative
diesel
refueling
PFC
BTP
BPS/RBT
2007
Pre-2007 MY
8774 HDD exhaust
Diesel
4547 Headspace
Diesel
4547 Headspace
COMBO:
8869E EO Headspace
8870E E10 Headspace
COMBO:
8869 EO Headspace
8870 E10 Headspace
8869 EO Headspace
2018
Pre-2007 MY HDD
8774 exhaust
4547 Diesel Headspace
4547 Diesel Headspace
COMBO:
8870E E10 Headspace
887 IE E15 Headspace
8934E E85 Evap
COMBO:
8870 E10 Headspace
8871 E15 Headspace
8934 E85 Evap
8869 EO Headspace
2030
Pre-2007 MY HDD
8774 exhaust
4547 Diesel Headspace
4547 Diesel Headspace
COMBO:
8870E E10 Headspace
887 IE E15 Headspace
8934E E85 Evap
COMBO:
8870 E10 Headspace
8871 E15 Headspace
8934 E85 Evap
8869 EO Headspace
3.2.2 PM speciation
In addition to VOC profiles, the SPECIATE database also contains the PM2.5 speciated into both individual
chemical compounds (e.g., zinc, potassium, manganese, lead), and into the "simplified" PM2.s components
used in the air quality model. For CMAQ 4.7.1 modeling, these "simplified" components (AE5) are all that
is needed. For CMAQ 5.0.1, there is a new thermodynamic equilibrium aerosol modeling tool
(ISORROPIA) v2 mechanism that needs additional PM components (AE6), which are further subsets of
PMFINE (see Table 3-8). EPA speciated PM2.5 so that it included both AE5 and AE6 PM model species
without causing any double counting. Therefore, emissions from these scenarios can be used with either
CMAQ 4.7.1 or CMAQ 5.0.1.
Table 3-8. PM model species: AE5 versus AE6
species name
POC
PEC
PSO4
PNO3
PMFINE
PNH4
PNCOM
PFE
PAL
PSI
PTI
PCA
PMG
PK
PMN
species description
organic carbon
elemental carbon
sulfate
nitrate
unspeciated PM2.5
ammonium
non-carbon organic matter
iron
aluminum
silica
titanium
calcium
magnesium
potassium
manganese
AE5
Y
Y
Y
Y
Y
N
N
N
N
N
N
N
N
N
N
AE6
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
29
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species name
PNA
PCL
PH2O
PMOTHR
species description
sodium
chloride
water
unspeciated PM2.5
AE5
N
N
N
N
AE6
Y
Y
Y
Y
The majority of the PM profiles come from the 911XX series, which include updated AE6 speciation24.
Unlike the 2007v5 platform, the profile numbers used in the Tier 3 runs are consistent with SPECIATE 4.3.
Although the profile numbers changed, the underlying profiles (namely the percentage of AE6 components)
did not change between 2007v5 and this 2007 base case (see the 2007v5 TSD for details on the earlier profile
names). This change in profile numbers impacts most sectors with PM emissions25.
3.3 Temporal Allocation
Temporal allocation (i.e., temporalization) is the process of distributing aggregated emissions to a finer
temporal resolution, such 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 annual emissions and distributes them to the month, and then distributes
the monthly emissions to the day, and the daily emissions to the hour. This process is typically done by
applying temporal profiles to the inventories in this order: monthly, day of the week, and diurnal.
The temporal profiles and associated cross references used to create the hourly emissions inputs for the air
quality model were similar to those used for the 2007v5 platform. New methodologies introduced in this
platform and updated profiles are discussed in this section. Temporal factors are typically applied to the
inventory by some combination of country, state, county, SCC, and pollutant. The following values are used
in Error! Not a valid bookmark self-reference.: the value "all" means that hourly emissions computed for
every day of the year and that emissions potentially have day-of-year variation. The value "week" means
that hourly emissions computed for all days in one "representative" week, representing all weeks for each
month. This means emissions have day-of-week variation, but not week-to-week variation within the month.
The value "mwdss" means hourly emissions for one representative Monday, representative weekday
(Tuesday through Friday), representative Saturday, and representative Sunday for each month. This means
emissions have variation between Mondays, other weekdays, Saturdays and Sundays within the month, but
not week-to-week variation within the month. The value "aveday" means hourly emissions computed for
one representative day of each month, meaning emissions for all days within a month are the same. Special
situations with respect to temporalization are described in the following subsections.
Table 3-9 summarizes the temporal aspects of emissions modeling by comparing the key approaches used for
temporal processing across the sectors. The temporal aspects of SMOKE processing are controlled through
(a) the L_TYPE (temporal type) and M_TYPE (merge type) settings used, and (b) the temporal profiles
themselves. 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
The exceptions are 5674 (Marine Vessel - Marine Engine - Heavy Fuel Oil) used for cSmarine and 92018 (Draft Cigarette
Smoke - Simplified) used in nonpt.
25 It impacts the profile names (numbers) used in the sector, but it does not impact the fraction of AE6 PM species and hence will
not impact the air quality model-ready files.
30
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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).
The following values are used in Error! Not a valid bookmark self-reference.: the value "all" means that
hourly emissions computed for every day of the year and that emissions potentially have day-of-year
variation. The value "week" means that hourly emissions computed for all days in one "representative"
week, representing all weeks for each month. This means emissions have day-of-week variation, but not
week-to-week variation within the month. The value "mwdss" means hourly emissions for one
representative Monday, representative weekday (Tuesday through Friday), representative Saturday, and
representative Sunday for each month. This means emissions have variation between Mondays, other
weekdays, Saturdays and Sundays within the month, but not week-to-week variation within the month. The
value "aveday" means hourly emissions computed for one representative day of each month, meaning
emissions for all days within a month are the same. Special situations with respect to temporalization are
described in the following subsections.
Table 3-9. Temporal settings used for the platform sectors in SMOKE
Platform
sector short
name
ptipm
ptnonipm
othpt
nonroad
othar
clc2rail
c3 marine
onroad
onroad rfl
othon
nonpt
ag
afdust adj
avefire
biog
Inventory
resolutions
Daily
Annual
Annual
Monthly
Annual
Annual
Annual
annual & monthly1
r\
annual & monthly
annual
annual & monthly
annual & monthly
annual
daily
hourly
Monthly
profiles
used?
yes
yes
yes
yes
yes
yes
yes
yes
yes
Daily
temporal
approach
all
mwdss
mwdss
mwdss
week
mwdss
aveday
all
all
week
all
all
week
all
n/a
Merge
processing
approach
all
mwdss
mwdss
mwdss
week
mwdss
aveday
all
all
week
all
all
all
all
all
Process
Holidays as
separate days
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
1. Note the annual and monthly "inventory" actually refers to the activity data (VMT and VPOP) for
onroad. The actual emissions are computed on an hourly basis.
2. Note the annual and monthly "inventory" actually refers to the activity data (VMT and VPOP) for
onroad rfl. The actual emissions are computed on an hourly basis.
See Section 3.3.5 for more details on the temporalization and inventory resolution of specific sectors.
In addition to the resolution, temporal processing includes a ramp-up period for several days prior to January
1, 2007, which is intended to mitigate the effects of initial condition concentrations. The ramp-up period
was 10 days (December 22-31, 2006). For most non-EGU sectors, our approach used the emissions from
31
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December 2007 to fill in surrogate emissions for the end of December 2006. In particular, we used
December 2007 emissions (representative days) for December 2006. For biogenic emissions, we processed
December 2006 emissions using 2006 meteorology.
3.3.1 FF10 format and inventory resolution
The Flat File 2010 format (FF10) inventory format for SMOKE provides a more consolidated format for
monthly, daily, and hourly emissions inventories than previous formats supported. Previously, to process
monthly inventory data required the use of 12 separate inventory files. With the FF10 format, a single
inventory file can contain emissions for all 12 months and the annual emissions in a single record. This
helps simplify the management of numerous inventories. Similarly, daily and hourly FF10 inventories
contain individual records with data for all days in a month and all hours in a day, respectively.
SMOKE 3.5 prevents the application of temporal profiles on top of the "native" resolution of the inventory.
For example, a monthly inventory should not have annual to month temporalization applied to it; rather, it
should only have month-to-day and diurnal temporalization. This becomes particularly important when
specific sectors have a mix of annual, monthly, daily, and/or hourly inventories (e.g. the nonpt sector). The
flags that control temporalization for a mixed set of inventories are discussed in the SMOKE documentation.
The modeling platform sectors that make use of monthly values in the FF10 files are nonroad, onroad, and
the ag burning inventory within the nonpt sector.
3.3.2 Ptipm Temporalization
The approach for temporalization of the ptipm sector (EGUs) has not changed from the 2005 v4.3 platform,
and is consistent with the method described in the 2007v5 TSD. However, the importance of this sector
warrants a restating of the methodology.
Daily emissions were computed from the annual emissions using a structured query language (SQL) program
and state-average CEM data. To allocate the annual emissions to each month, state-specific, three-year
averages of 2006-2008 CEM data were created. These average annual-to-month factors were assigned to
sources by state. To allocate the monthly emissions to each day, the 2007 CEM data was used to compute
state-specific month-to-day factors, averaged across all units in each state. The factors were applied to the
annual emissions to calculate the daily emissions outside of SMOKE, and the resulting daily inventories
were used as inputs to SMOKE.
The daily-to-hourly allocation was performed in SMOKE using diurnal profiles. The state-specific and
pollutant-specific diurnal profiles were created by using the 2007 CEM data to create state-specific, day-to-
hour factors, averaged over the whole year and all units in each state. The diurnal factors were calculated for
SO2 emissions, NOx emissions, and heat input. The SO2 and NOx-specific factors were used to temporally
allocate those pollutants, and the factors created from the hourly heat input data were used to allocate all
other pollutants. The resulting profiles were assigned by state and pollutant. The same procedures and
factors were used to allocate the base and future year emissions.
3.3.3 Meteorologically-based temporalization
A significant improvement over previous platforms was the introduction of meteorologically-based
temporalization. There are many factors that impact the timing of when emissions occur. The benefits of
considering meteorology in support of temporalization are: (1) a meteorological dataset consistent with the
one used by the AQ model is available (e.g., outputs from WRF); (2) the meteorological model data is highly
resolved in terms of spatial resolution; and (3) the meteorological variables vary at hourly resolution and can
therefore be translated into hour-specific temporalization. Because the WRF output data for this study was
32
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from a different run than that described in the 2007v5 platform TSD, any meteorologically-based
temporalization factors, adjustments, and other meteorological effects were recomputed for the Tier 3 runs.
This included updating the BIOSEASON file used for biogenic emissions.
The SMOKE program GenTPRO provides a method for developing meteorology -based temporalization.
Currently, the program can utilize three types of temporal algorithms: annual -to-day temporalization for
residential wood combustion (RWC), month-to-hour temporalization for agricultural livestock ammonia, and
a generic meteorology -based algorithm for other situations. For the 201 1 platform, meteorological -based
temporalization was used for portions of the rwc sector and for livestock within the ag sector. GenTPRO
reads in gridded meteorological data (output from MCIP) along with spatial surrogates, and uses the
specified algorithm to produce a new temporal profile that can be input into SMOKE. The meteorological
variables and the resolution of the generated temporal profile (hourly, daily, etc.) depend on the selected
algorithm and the run parameters. For more details on the development of these algorithms and running
GenTPRO, see the GenTPRO documentation and the SMOKE documentation at
http://www.cmascenter.Org/smoke/documentation/3.l/GenTPRO Technical Summary Aug20 12 Final.pdf
For the RWC algorithm, GenTPRO uses the daily minimum temperature to determine the temporal
allocation of emissions to days. We ran GenTPRO so that it created annual -to-day temporal profiles for the
RWC sources within the nonpt sector. These generated profiles distribute the annual RWC emissions to the
coldest days of the year. On days where the minimum temperature does not drop below a user defined
threshold, RWC emissions are zero. Conversely, the program temporally allocates the largest percentage of
emissions to the coldest days. Similar to other temporal allocation profiles, the total annual emissions do not
change, only the distribution of the emissions within the year is affected. The default 50 °F threshold was
used for the majority of the states, and a 60 °F threshold for the following states: Alabama, Arizona,
California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and Texas.
For the agricultural livestock NHs algorithm, the GenTPRO algorithm is based on an equation derived by
Jesse Bash of EPA ORD based on the Zhu, Henze, et al. (2013) empirical equation. This equation was
updated from that described in the 2007v5 TSD, and is based on observations from the TES satellite
instrument with the GEOS-Chem model and its adjoint to estimate diurnal NH3 emission variations from
livestock as a function of ambient temperature, aerodynamic resistance, and wind speed. The equations are:
Ei.h = [161500/Ta x e(-1380/V] x ARa
PElrh = Elrh I Sum(E;,/,)
where
PEjh = Percentage of emissions in county /' on hour h
Ei:h = Emission rate in county /' on hour h
T,,/, = Ambient temperature (Kelvin) in county / on hour h
Vt,h = Wind speed (meter/sec) in county /' (minimum wind speed is 0.1 meter/sec)
ARif/, = Aerodynamic resistance in county /
GenTPRO was run using the "BASH_NH3" profile method to create month-to-hour temporal profiles for
these sources. Because these profiles distribute to the hour based on monthly emissions, the monthly
33
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emissions are obtained from a monthly inventory, or from an annual inventory that has been temporalized to
the month26.
Figure 3-2 compares the daily emissions for Minnesota from the "old" approach (i.e., uniform monthly
profiles) with the "new" approach (i.e., GenTPRO generated month-to-hour profiles). Although the
GenTPRO profiles show daily (and hourly variability), the monthly total emissions are the same between the
two approaches.
Figure 3-2. Example of new animal NHs emissions temporalization approach, summed to daily emissions
MN ag NH3 livestock temporal profiles
0.0 L
1/1/2008 2/1/2008 3/1/2008 4/1/2008 5/1/2008 6/1/2008 7/1/2008 8/1/2008 9/1/2008 10/1/2008 11/1/2008 12/1/2008
For the afdust sector, meteorology is not used in the development of the temporal profiles, but it is used to
reduce the total emissions based on meteorological conditions. These adjustments are applied through
sector-specific scripts, beginning with the application of land use-based gridded transport fractions and then
subsequent zero-outs for hours during which precipitation occurs or there is snow cover on the ground. The
land use data used to reduce the NEI emissions explains the amount of emissions that are subject to transport.
This methodology is discussed in (Pouliot, et. al., 2010,
http://www.epa.gov/ttn/chief/conference/eil9/session9/pouliot_pres.pdf, and in Fugitive Dust Modeling for
the 2008 Emissions Modeling Platform (Adelman, 2012). The precipitation adjustment is applied to remove
all emissions for days where measureable rain occurs. Therefore, the afdust emissions vary day-to-day based
on the precipitation and/or snow cover for that grid cell and day. Both the transport fraction and
meteorological adjustments are based on the gridded resolution of the platform; therefore, somewhat
different emissions will result from different grid resolutions. Application of the transport fraction and
meteorological adjustments prevents the overestimation of fugitive dust impacts in the grid modeling as
compared to ambient samples.
3.3.4 Onroad and Onroad_rfl Temporalization
For the onroad and onroad_rfl sectors, meteorology was not used in the development of the temporal
profiles; rather, meteorology impacts the actual calculation of the hourly emissions through the program
Movesmrg. The result is that the emissions vary at the hourly level by grid cell. More specifically, the on-
network (RPD) and the off-network (RPV) exhaust, evaporative, and evaporative permeation modes use the
gridded meteorology (MCIP) directly. Movesmrg determined the temperature for that hour and grid cell and
used it to select the appropriate EF for that SCC/pollutant/mode. For the off-network RPP, Movesmrg used
the Met4moves output for SMOKE (daily minimum and maximum temperatures by county) to determine the
appropriate EF for that hour and SCC/pollutant. The result was that the emissions varied hourly by county.
SMOKE v3.5.1 will correctly read in a monthly inventory and apply GenTPRO ag NH3 month-to-hour temporalization. When
the emissions were developed for this sector, we were using SMOKE v3.1 beta that incorrectly applied an annual-to-month
temporal profile on top of a monthly inventory when temporalizing with GenTPRO ag NH3 profiles. As an interim solution, a flat
monthly profile was applied to the states with a monthly ag NH3 inventory.
34
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The combination of these three processes (RPD, RPV, and RPP) is the total onroad emissions, while the
combination of the two processes (RPD, RPV) for the refueling mode only is the total onroad_rfl emissions.
Both sectors show a strong meteorological influence on their temporal patterns (see Sections 2.5.1.2 and
Error! Reference source not found, for more details).
Figure 3-3 illustrates the difference between temporalization of the onroad sector used in previous platforms
and that from SMOKE-MOVES. In the plot, the "MOVES" inventory is a monthly inventory that is
temporalized by SCC to day-of-week and hour. Similar temporalization is done for the VMT in SMOKE-
MOVES, but the meteorologically varying EFs add an additional variable signal on top of the
temporalization. Note how the MOVES emissions have a repeating pattern within the month, while the
SMOKE-MOVES shows day-to-day (and hour-to-hour) variability. In addition to tracking the
meteorological influence, SMOKE-MOVES does not show the artificial jumps between the months.
Figure 3-3. Example of SMOKE-MOVES temporal variability of NOx emissions
ov
75
70
65
60
50
45
40
J J J J
J
/
MOVE
- SMOK
||fl((jn.
1 II
1
1
s
MW
r'
i^ n
j'rHrnJIJJIJJJJj
I y
1 \
.E-MOVES
If
, / I
V ~i I
1 1
1
J J J
1
o
s
Julian date
For the onroad and onroad_rfl sectors, the "inventories" referred to in The following values are used in
Error! Not a valid bookmark self-reference.: the value "all" means that hourly emissions computed for
every day of the year and that emissions potentially have day-of-year variation. The value "week" means
that hourly emissions computed for all days in one "representative" week, representing all weeks for each
month. This means emissions have day-of-week variation, but not week-to-week variation within the month.
The value "mwdss" means hourly emissions for one representative Monday, representative weekday
(Tuesday through Friday), representative Saturday, and representative Sunday for each month. This means
emissions have variation between Mondays, other weekdays, Saturdays and Sundays within the month, but
not week-to-week variation within the month. The value "aveday" means hourly emissions computed for
one representative day of each month, meaning emissions for all days within a month are the same. Special
situations with respect to temporalization are described in the following subsections.
Table 3-9 are actually the activity data inventories. For RPP and RPV processes, the VPOP inventory is
annual and does not need temporalization. For RPD, the VMT inventory is monthly and is temporalized to
day of the week and then to hourly VMT through temporal profiles. In addition, the RPD processes used a
speed profile (SPDPRO) that had vehicle speed by hour for typical weekday and weekend. In addition,
35
-------
RPD, RPV, and RPP all have additional temporal variability due to the meteorological based emissions
calculated through Movesmrg.
3.3.5 Additional sector specific details
For a discussion of additional sector-specific details related to temporalization, see Section 3.3.4 of the
2007v5 platform TSD. The topics discussed include updates made in the 2007 platform to agricultural
burning and residential wood combustion diurnal profiles.
3.4 Spatial Allocation
The methods used to perform spatial allocation for the 2007 platform are summarized in this section. For the
2007 platform, spatial factors are typically applied by country and SCC. As described in Section 3.1, spatial
allocation was performed for national 12-km and 36-km domains. To do this, SMOKE used national 12-km
and 36-km spatial surrogates and a SMOKE area-to-point data file. For the U.S., the surrogates were
updated to use 2010-based data wherever possible. For Mexico, we used the same spatial surrogates as were
used for the 2005 platform. For Canada we used a set of Canadian surrogates provided by Environment
Canada, also unchanged from the 2005v4.3 platform. The U.S., Mexican, and Canadian 12-km surrogates
cover the entire CONUS domain 12US1 shown in Figure 3-1. When SMOKE runs, it windows the
surrogates to the area needed, such as the 12US2 domain. For the original 2007v5 platform, SMOKE was
run on the 12US1 grid and windowed to 12US2 prior to air quality modeling. For the Tier 3 runs, SMOKE
was actually run on the 12US2 domain. The remainder of this subsection provides further detail on the origin
of the data used for the spatial surrogates and the area-to-point data.
3.4.1 Spatial Surrogates for U.S. emissions
There are 69 spatial surrogates available for spatially allocating U.S. county-level emissions to the 12-km
grid cells used by the air quality model. As described in Section 3.4.2, an area-to-point approach overrides
the use of surrogates for some sources. Table 3-10 lists the codes and descriptions of the surrogates.
Table 3-10. U.S. Surrogates available for the 2007 platform.
Code
N/A
100
110
120
130
137
140
150
160
165
170
180
190
200
210
220
Surrogate Description
Area-to-point approach (see 3.3. 1.2)
Population
Housing
Urban Population
Rural Population
Housing Change
Housing Change and Population
Residential Heating - Natural Gas
Residential Heating - Wood
0.5 Residential Heating - Wood plus 0.5 Low
Intensity Residential
Residential Heating - Distillate Oil
Residential Heating - Coal
Residential Heating - LP Gas
Urban Primary Road Miles
Rural Primary Road Miles
Urban Secondary Road Miles |
Code
520
525
527
530
535
540
545
550
555
560
565
570
575
580
585
590
Surrogate Description
Commercial plus Industrial plus Institutional
Golf Courses + Institutional +Industrial +
Commercial
Single Family Residential
Residential - High Density
Residential + Commercial + Industrial +
Institutional + Government
Retail Trade
Personal Repair
Retail Trade plus Personal Repair
Professional/Technical plus General
Government
Hospital
Medical Office/Clinic
Heavy and High Tech Industrial
Light and High Tech Industrial
Food, Drug, Chemical Industrial
Metals and Minerals Industrial
Heavy Industrial
36
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Code
230
240
250
255
260
270
280
300
310
312
320
330
340
350
400
500
505
510
515
Surrogate Description
Rural Secondary Road Miles
Total Road Miles
Urban Primary plus Rural Primary
0.75 Total Roadway Miles plus 0.25 Population
Total Railroad Miles
Class 1 Railroad Miles
Class 2 and 3 Railroad Miles 1
Low Intensity Residential
Total Agriculture
Orchards/Vineyards
Forest Land
Strip Mines/Quarries
Land
Water
Rural Land Area
Commercial Land
Industrial Land
Commercial plus Industrial
Commercial plus Institutional Land |
Code
595
596
600
650
675
680
700
710
720
800
801
802
807
810
850
860
870
880
890
Surrogate Description
Light Industrial
Industrial plus Institutional plus Hospitals
Gas Stations
Refineries and Tank Farms
Refineries and Tank Farms and Gas Stations
Oil & Gas Wells, IHS Energy, Inc. and
USGS
Airport Areas
Airport Point
Military Alports
Marine Ports
NEI Ports
NEI Shipping Lanes
Navigable Waterway Miles
Navigable Waterway Activity
Golf Courses
Mines
Wastewater Treatment Facilities
Drycleaners
Commercial Timber
The surrogates in bold have been updated with 2010-based data, including 2010 census data at the block
group level, 2010 American Community Survey Data for heating fuels, 2010 TIGER/Line data for railroads
and roads, and 2010 National Transportation Atlas Data for ports and navigable waterways. Not all of the
available surrogates are used to spatially allocate sources in the 2007 platform; that is, some surrogates
shown in Table 3-10 were not assigned to SCCs used in the 2007 platform. Alternative surrogates for ports
(801) and shipping lanes (802) were developed from the 2008 NEI shapefiles: Ports_032310_wrf and
ShippingLanes_l 11309FINAL_wrf These new surrogates were used in the 2007 platform for cl and c2
commercial marine emissions instead of the standard 800 and 810 surrogates, respectively. Note that the 800
surrogate was used for nonpoint SCCs starting with 250502, which are related to the storage and transfer of
petroleum products.
Specific updates made to the surrogates for the Tier 3 runs include updating of the land use-based 300-series
surrogates, gas stations (600), construction and mining (861), and dry cleaners (880). Updates to the oil and
gas surrogates (689-699) were made as described below, and updated surrogates were used for Mexico.
The creation of surrogates and shapefiles for the U.S. was generated via the Surrogate Tool. The tool and
updated documentation for it is available at http://www.ie.unc.edu/cempd/projects/mims/spatial/ and
http://www.cmascenter.org/help/documentation. cfm?MODEL=spatial_allocator&VERSION=3.6&temp_id=
99999. Note that the surrogate methodology "gapfills" surrogates with less spatial coverage in terms of
counties with surrogates that have more spatial coverage. This helps ensure no emissions are dropped during
SMOKE processing. Because the land use-based surrogates were updated and are used to gapfill many
other surrogates, there were some changes to most of the remaining 500 and 600 series surrogates in terms of
using new gapfiling values.
For the onroad sector, the on-network (RPD) emissions were spatially allocated to roadways, which the off-
network (RPP and RPV) emissions were allocated to parking areas. For the onroad_rfl sector, the emissions
were spatially allocated to gas station locations.
37
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For the oil and gas sources in the nonpt sector, the WRAP Phase III sources have detailed basin-specific
spatial surrogates shown in Table 3-11. The remaining oil and gas sources used the 2005-based surrogate
"Oil & Gas Wells, fflS Energy, Inc. and USGS" (680) developed for oil and gas SCCs. The surrogates in
Table 3-11 were applied for the counties listed in Table 3-12. For the Tier 3 runs, surrogate data for the
Permian basin was added.
Table 3-11. Spatial Surrogates for WRAP Oil and Gas Data
Country
USA
USA
USA
USA
USA
USA
USA
USA
USA
USA
USA
Code
699
698
697
696
695
694
693
692
691
690
689
Surrogate Description
Gas production at CBM wells
Well count - gas wells
Oil production at gas wells
Gas production at gas wells
Well count - oil wells
Oil production at Oil wells
Well count - all wells
Spud count
Well count - CBM wells
Oil production at all wells
Gas production at all wells
Table 3-12. Counties included in the WRAP Dataset
FIPS
08001
08005
08007
08013
08014
08029
08031
08039
08043
08045
08051
08059
08063
08067
08069
08073
08075
08077
08081
08087
08095
08097
State
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
County
Adams
Arapahoe
Archuleta
Boulder
Broomfield
Delta
Denver
Elbert
Fremont
Garfield
Gunnison
Jefferson
Kit Carson
La Plata
Larimer
Lincoln
Logan
Mesa
Moffat
Morgan
Phillips
Pitkin
FIPS
08103
08107
08115
08121
08123
08125
30003
30075
35005
35015
35015
35031
35039
35041
35043
35045
48003
48033
48079
48081
48103
48105
State
CO
CO
CO
CO
CO
CO
MT
MT
NM
NM
NM
NM
NM
NM
NM
NM
TX
TX
TX
TX
TX
TX
County
Rio Blanco
Routt
Sedgwick
Washington
Weld
Yuma
Big Horn
Powder River
Chaves
Eddy
Lea
Me Kinley
Rio Arriba
Roosevelt
Sandoval
San Juan
Andrews
Borden
Cochran
Coke
Crane
Crockett
FIPS
48107
48109
48115
48125
48135
48141
48151
48165
48169
48173
48219
48227
48229
48235
48263
48269
48301
48303
48305
48317
48329
48335
State
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
County
Crosby
Culberson
Dawson
Dickens
Ector
El Paso
Fisher
Gaines
Garza
Glasscock
Hockley
Howard
Hudspeth
Irion
Kent
King
Loving
Lubbock
Lynn
Martin
Midland
Mitchell
38
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FIPS
48353
48371
48383
48389
48413
48415
48431
48435
48445
48451
48461
48475
48495
48501
State
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
County
Nolan
Pecos
Reagan
Reeves
Schleicher
Scurry
Sterling
Sutton
Terry
Tom Green
Upton
Ward
Winkler
Yoakum
FIPS
49007
49009
49013
49015
49019
49043
49047
56001
56005
56007
56009
56011
56013
56019
State
UT
UT
UT
UT
UT
UT
UT
WY
WY
WY
WY
WY
WY
WY
County
Carbon
Daggett
Duchesne
Emery
Grand
Summit
Uintah
Albany
Campbell
Carbon
Converse
Crook
Fremont
Johnson
FIPS
56023
56025
56027
56033
56035
56037
56041
56045
State
WY
WY
WY
WY
WY
WY
WY
WY
County
Lincoln
Natrona
Niobrara
Sheridan
Sublette
Sweetwater
Uinta
Weston
3.4.2 Allocation method for airport-related sources in the U.S.
There are numerous airport-related emission sources in the 2008 NEI, such as aircraft, airport ground support
equipment, and jet refueling. The 2007 platform includes the aircraft emissions as point sources. For the
2007 platform, the SMOKE "area-to-point" approach was used for airport ground support equipment
(nonroad sector) and jet refueling (nonpt sector). The approach is described in detail in the 2002 platform
documentation: http://www.epa.gov/scram001/reports/Emissions%20TSD%20Voll_02-28-08.pdf.
The ARTOPNT file that lists the nonpoint sources to locate using point data was unchanged from the 2005-
based platform.
3.4.3 Surrogates for Canada and Mexico emission inventories
The Mexican spatial surrogates were updated from the 207v5 platform to use the surrogates shown in Table
3-13. The same surrogates for Canada were used to spatially allocate the 2006 Canadian emissions as were
used for the 2005v4.2 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). Per
Environment Canada, the surrogates are based on 2001 Canadian census data. The Canadian surrogates used
for this platform are listed in Table 3-14. We added the leading "9" to the surrogate codes to avoid duplicate
surrogate numbers with U.S. surrogates.
Table 3-13. Spatial Surrogates for Mexico
Srg code
22
10
12
14
16
Description
MEX Total Road Miles
MEX Population
MEX Housing
MEX Residential Heating - Wood
MEX Residential Heating - Distillate
Oil
39
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20
22
24
26
28
32
34
36
38
40
42
44
46
48
50
MEX Residential Heating - LP Gas
MEX Total Road Miles
MEX Total Railroads Miles
MEX Total Agriculture
MEX Forest Land
MEX Commercial Land
MEX Industrial Land
MEX Commercial plus Industrial Land
MEX Commercial plus Institutional Land
Residential (RES1-
4)+Comercial+lndustrial+lnstitutional+
Government
MEX Personal Repair (COM3)
MEX Airports Area
MEX Marine Ports
Brick Kilns - Mexico
Mobile sources - Border Crossing - Mexico
Table 3-14. Canadian Spatial Surrogates for 2007-based platform Canadian Emissions
Code
9100
9101
9102
9103
9104
9106
9111
9113
9114
9115
9116
9211
9212
9213
9219
9221
9222
9231
9232
Description
Population
Total dwelling
Urban dwelling
Rural dwelling
Total Employment
ALL INDUST
Farms
Forestry and logging
Fishing hunting and trapping
Agriculture and forestry activities
Total Resources
Oil and Gas Extraction
Mining except oil and gas
Mining and Oil and Gas Extract activities
Mining-unspecified
Total Mining
Utilities
Construction except land subdivision and land
development
Land subdivision and land development
Code
9493
9494
9511
9512
9513
9514
9516
9521
9522
9523
9524
9526
9528
9531
9532
9533
9534
9541
9551
Description
Warehousing and storage
Total Transport and warehouse
Publishing and information services
Motion picture and sound recording industries
Broadcasting and telecommunications
Data processing services
Total Info and culture
Monetary authorities - central bank
Credit intermediation activities
Securities commodity contracts and other
financial investment activities
Insurance carriers and related activities
Funds and other financial vehicles
Total Banks
Real estate
Rental and leasing services
Lessors of non-financial intangible assets
(except copyrighted works)
Total Real estate
Professional scientific and technical services
Management of companies and enterprises
40
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Code
9233
9308
9309
9313
9314
9315
9316
9321
9322
9323
9324
9325
9326
9327
9331
9332
9333
9334
9335
9336
9337
9338
9339
9411
9412
9413
9414
9415
9416
9417
Description
Total Land Development
Food manufacturing
Beverage and tobacco product manufacturing
Textile mills
Textile product mills
Clothing manufacturing
Leather and allied product manufacturing
Wood product manufacturing
Paper manufacturing
Printing and related support activities
Petroleum and coal products manufacturing
Chemical manufacturing
Plastics and rubber products manufacturing
Non-metallic mineral product manufacturing
Primary Metal Manufacturing
Fabricated metal product manufacturing
Machinery manufacturing
Computer and Electronic manufacturing
Electrical equipment appliance and component
manufacturing
Transportation equipment manufacturing
Furniture and related product manufacturing
Miscellaneous manufacturing
Total Manufacturing
Farm product wholesaler-distributors
Petroleum product wholesaler-distributors
Food beverage and tobacco wholesaler-
distributors
Personal and household goods wholesaler-
distributors
Motor vehicle and parts wholesaler-distributors
Building material and supplies wholesaler-
distributors
Machinery equipment and supplies wholesaler-
distributors
Code
9561
9562
9611
9621
9622
9623
9624
9625
9711
9712
9713
9721
9722
9723
9811
9812
9813
9814
9815
9911
9912
9913
9914
9919
9920
9921
9922
9923
9924
9925
Description
Administrative and support services
Waste management and remediation services
Education Services
Ambulatory health care services
Hospitals
Nursing and residential care facilities
Social assistance
Total Service
Performing arts spectator sports and related
industries
Heritage institutions
Amusement gambling and recreation
industries
Accommodation services
Food services and drinking places
Total Tourism
Repair and maintenance
Personal and laundry services
Religious grant-making civic and professional
and similar organizations
Private households
Total other services
Federal government public administration
Provincial and territorial public administration
(9 121 to 9 129)
Local municipal and regional public
administration (9131 to 9139)
Aboriginal public administration
International and other extra-territorial public
administration
Total Government
Commercial Fuel Combustion
TOTAL DISTRIBUTION AND RETAIL
TOTAL INSTITUTIONAL AND
GOVERNEMNT
Primary Industry
Manufacturing and Assembly
41
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Code
9418
9419
9420
9441
9442
9443
9444
9445
9446
9447
9448
9451
9452
9453
9454
9455
9481
9482
9483
9484
9485
9486
9487
9488
9491
9492
Description
Miscellaneous wholesaler-distributors
Wholesale agents and brokers
Total Wholesale
Motor vehicle and parts dealers
Furniture and home furnishings stores
Electronics and appliance stores
Building material and garden equipment and
supplies dealers
Food and beverage stores
Health and personal care stores
Gasoline stations
clothing and clothing accessories stores
Sporting goods hobby book and music stores
General Merchandise stores
Miscellaneous store retailers
Non-store retailers
Total Retail
Air transportation
Rail transportation
Water Transportation
Truck transportation
Transit and ground passenger transportation
Pipeline transportation
Scenic and sightseeing transportation
Support activities for transportation
Postal service
Couriers and messengers
Code
9926
9927
9928
9929
9930
9931
9932
9933
9941
9942
9943
9944
9945
9946
9947
9950
9960
9970
9980
9990
9993
9994
9995
9996
9997
9991
Description
Distribution and Retail (no petroleum)
Commercial Services
Commercial Meat cooking
HIGHJET
LOWMEDJET
OTHERJET
CANRAIL
Forest fires
PAVED ROADS
UNPAVED ROADS
HIGHWAY
ROAD
Commercial Marine Vessels
Construction and mining
Agriculture Construction and mining
Intersection of Forest and Housing
TOTBEEF
TOTPOUL
TOTSWIN
TOTFERT
Trail
ALLROADS
30UNPAVED_70trail
Urban area
CHBOISQC
Traffic
42
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4 Development of Future Year Emissions
This section describes the methods used for developing the emissions for the 2018 and 2030 future-year
scenarios. Within the 2018 and 2030 cases, the future year emissions for the Tier 3 FRM "reference" and
"control" cases are the same for all stationary sources. For 2018 and 2030, the Tier 3 FRM control case
emissions include Tier 3 engine and fuel controls that impact emissions for onroad mobile and nonroad
mobile sources. EPA analyzed emission impacts of the Tier 3 vehicle emissions and fuel standards by
comparing projected emissions for future years without the Tier 3 rule (reference scenario) to projected
emissions for future years with the Tier 3 standards in place (control scenario). For more details on the
differences between the reference and control scenarios, see the onroad and nonroad sections (Section 4.3.1
and 4.3.2, respectively).
The future scenarios' projection methodologies vary by sector. The 2018 and 2030 reference scenarios
represent predicted emissions in the absence of any further controls beyond those Federal and State measures
already promulgated, or under reconsideration before emissions processing began in March, 2013. The
future base-case scenario reflects projected economic changes and fuel usage for EGU and mobile sectors.
The 2020 (used as a surrogate for 2018) and 2030 EGU projected inventories represent demand growth, fuel
resource availability, generating technology cost and performance, and other economic factors affecting
power sector behavior. It also reflects the expected 2020 and 2030 emissions effects due to environmental
rules and regulations, consent decrees and settlements, plant closures, control devices updated since 2007,
and forecast unit construction through the calendar year 2020 and 2030. In this analysis, the projected EGU
emissions include the Final Mercury and Air Toxics (MATS) rule announced on December 21, 2011, the
Clean Air Interstate Rule issued on March 10, 2005, including impacts of electric vehicle penetration
resulting from the Light-duty Greenhouse Gas (LDGHG) Rule.
For the other mobile sources (clc2rail and c3marine sectors), all national measures for which data were
available at the time of modeling have been included.
For nonEGU point (ptnonipm sector) and nonpoint stationary sources (nonpt, ag, and afdust sectors), local
control programs are generally not included in the future base-case projections for most states unless
information was provided by the states. One exception are some NOx and VOC reductions associated with
the New York, Virginia, and Connecticut State Implementation Plans (SIP), that were added as part of a
larger effort to start including more local control information on stationary non-EGU sources; this is
described further in Section 4.2. The following bullets summarize the projection methods used for sources in
the various sectors, while additional details and data sources are given in the following subsections and Table
4-1. For information on control/growth strategies that are not different from the 2007v5 platform, please
reference the 2007v5 TSD, available at:
http://epa.gov/ttn/chief/emch/2007v5/2007v5 2020base EmisMod TSD 13dec2012.pdf
IPM sector (ptipm): Unit-specific estimates from IPM, version 4.10 Final MATS with CAIR and the
penetration of electric vehicles anticipated due to the LDGHG rule.
Non-IPM sector (ptnonipm): Projection factors and percent reductions reflect Cross-State Air
Pollution Rule (CSAPR) comments and emission reductions due to national rules, control programs,
plant closures, consent decrees and settlements, and 1997 and 2001 ozone State Implementation Plans
in NY, CT, and VA. Also used projection approaches for corn ethanol and biodiesel plants, refineries
and upstream impacts from the Energy Independence and Security Act of 2007 (EISA). Terminal
area forecast (TAP) data aggregated to the national level were used for aircraft to account for
projected changes in landing/takeoff activity.
43
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Average fires sector (avefire): No growth or control.
Agricultural sector (ag): Projection factors for livestock estimates based on expected changes in
animal population from 2005 Department of Agriculture data, updated based on personal
communication with EPA experts in July 2012; fertilizer application NHa emissions projections
include upstream impacts EISA.
Area fugitive dust sector (afdust): Projection factors for dust categories related to livestock estimates
based on expected changes in animal population and upstream impacts from EISA.
Remaining Nonpoint sector (nonpt): Projection factors that implement Cross State Air Pollution Rule
comments and reflect emission reductions due to control programs. Residential wood combustion
projections are based on growth in lower-emitting stoves and a reduction in higher emitting stoves.
PFC projection factors reflecting impact of the final Mobile Source Air Toxics (MSAT2) rule.
Upstream impacts from EISA, including post-2007 cellulosic ethanol plants are also reflected.
Nonroad mobile sector (nonroad): Other than for California, this sector uses data from a run of
NMEVI that utilized NONROAD2008b, using future-year equipment population estimates and control
programs to the year 2018 and 2030 and using national level inputs. Fuels are consistent with the
onroad sector. Final controls from the final locomotive-marine and large spark ignition OTAQ rules
are included. California-specific data were provided by CARB.
Locomotive, and non-Class 3 commercial marine sector (clc2rail): For all states except California,
projection factors for Class 1 and Class 2 commercial marine and locomotives which reflect final
locomotive-marine controls. California projected year-2017 (used for 2018) and 2030 inventory data
were provided by CARB. Additional RFS2-related county-level emissions adjustments were applied
to reflect different fuel volume characteristics from increased ethanol fuel transport on rail and
commercial marine vessels.
Class 3 commercial marine vessels (c3marine): Base-year 2007 emissions grown and controlled to
2018 and 2030, incorporating controls based on Emissions Control Area (EGA) and International
Marine Organization (EVIO) global NOx and SO2 controls.
Onroad mobile, not including refueling (onroad): focus of the rule, see Section 4.3.1.
Onroad refueling mode (onroad_rfl): Uses the same projection approach as the onroad sector and
processing as described in Section 2.5.2.
Other onroad (othar): No growth or control for Canada because data are not available from Canada.
Mexico inventory data were grown from 1999 to years 2018 and 2030.
Other nonroad/nonpoint (othon): No growth or control for Canada. Mexico inventory data were
grown from 1999 to years 2018 and 2030.
Other point (othpt): No growth or control for Canada and offshore oil. Mexico inventory data were
grown from 1999 to years 2018 and 2030.
Biogenic: 2007 emissions used for all future-year scenarios.
Table 4-1 summarizes the control strategies and growth assumptions by source type that were used to create
the U.S. 2018 and 2030 scenario emissions from the 2007 base-case inventories. The control, closures,
projection packets (datasets) used to create stationary non-EGU and clc2rail sector 2018 and 2030 future
years scenario inventories from the 2007 base case are described in the following sections. These datasets
(i.e., "packets") were processed through the EPA Control Strategy Tool (CoST) to create future year
inventories. CoST is described here: http://www.epa.gov/ttnecas 1 /cost.htm.
44
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The remainder of this section is organized either by source sector or by specific emissions category within a
source sector for which a distinct set of data were used or developed for the purpose of projections for the
2018 and 2030 scenarios. This organization allows consolidation of the discussion of the emissions
categories that are contained in multiple sectors, because the data and approaches used across the sectors are
consistent and do not need to be repeated. Sector names associated with the emissions categories are
provided in parentheses. If a strategy is identical to the 2007v5 platform, it is noted in the Section as
"2007v5" and is documented in the 2007v5 TSD.
Table 4-1. Control strategies and growth assumptions for creating the 2018 and 2030 emissions inventories
from the 2007 base case
Control Strategies and/or growth assumptions
(grouped by standard and approach used to apply to the inventory)
CAPs
affected
Section
Non-EGU Point (ptnonipm sector) Controls and Growth Assumptions
Ethanol plants that account for increased ethanol production due to EISA mandate
Biodiesel plants producing 1.6 billion gallons of production due to EISA mandate
Ethanol distribution vapor losses adjustments due to EISA mandate
Refinery upstream adjustments from EISA mandate
Livestock emissions growth from year 2008 to 20 18 and 2030, also including upstream RFS2
impacts on agricultural-related activities such as pesticide and fertilizer production
Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsiderations
State fuel sulfur content rules for fuel oil - as of July, 20 12, effective only in Maine, Massachusetts,
New Jersey, New York and Vermont
Industrial/Commercial/Institutional Boilers and Process Heaters MACT with Reconsideration
Amendments
NESHAP: Portland Cement (09/09/10) - plant level based on Industrial Sector Integrated Solutions
(ISIS) policy emissions in 2013. The ISIS results are from the ISIS-Cement model runs for the
NESHAP and NSPS analysis of July 28, 2010 and include closures.
Future baseline inventory improvements received from a 2005 platform NODA and comments from
the CSAPR proposal, including local controls, fuel switching, unit closures and consent decrees
Facility and unit closures obtained from various sources such as states, industry and web posting,
EPA staff and post-2008 inventory submittals: effective prior to spring 2012
Aircraft growth via Itinerant (ITN) operations at airports to 2018 and 2030
Emission reductions resulting from controls put on specific boiler units (not due to MACT) after
2008, identified through analysis of the control data gathered from the Information Collection
Request (ICR) from the Industrial/Commercial/Institutional Boiler NESHAP.
New York ozone SIP controls
Boat Manufacturing MACT rule, VOC: national applied by SCC
Lafarge and Saint Gobain consent decrees
Consent decrees on companies (based on information from the Office of Enforcement and
Compliance Assurance - OECA) apportioned to plants owned/operated by the companies
Refinery Consent Decrees: plant/unit controls
All
All
VOC
All
All
NOX,
CO, PM,
S02
S02
CO, PM,
S02,
VOC
All
All
All
All
S02
NOX
VOC
NOX,
PM, SO2
CO,
NOx,
PM, S02,
VOC
NOx,
Error!
Referen
ce
source
not
found.
4.2.1.2
0
4.2.1.7
4.2.2
2007v5
4.2.3
2007v5
4.2.4
4.2.5
2007v5
4.2.6.1
2007v5
2007v5
2007v5
2007v5
2007v5
2007v5
45
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Control Strategies and/or growth assumptions
(grouped by standard and approach used to apply to the inventory)
Commercial and Industrial Solid Waste Incineration (CISWI) revised NSPS
Hazardous Waste Incineration (HWI), Phase I and II
CAPs
affected
SO2
PM, SO2
PM
Section
2007v5
2007v5
Nonpoint (afdust, ag and nonpt sectors) Controls and Growth Assumptions
MSAT2 and RFS2 impacts on portable fuel container growth and control from 2007 to 2018 and
2030
Cellulosic ethanol and diesel emissions from EISA mandate
Ethanol transport working losses inventory from EISA mandate
Ethanol distribution vapor losses adjustments due to EISA mandate
Livestock emissions growth from year 2008 to 20 18 and 2030, also including upstream RFS2
impacts on agricultural-related activities such as pesticide and fertilizer production
Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsiderations
State fuel sulfur content rules for fuel oil -as of July, 20 12, effective only in Maine, Massachusetts,
New Jersey, New York and Vermont
Residential wood combustion growth and change-outs from year 2008 to 2018
Future baseline inventory improvements received from a 2005 platform NODA and comments from
the CSAPR proposal, reflecting local controls
New York ozone SIP controls
Texas oil and gas projections to year 2018
voc
All
VOC
voc
All
NOX,
CO, PM,
SO2
SO2
All
NOX,
VOC
NOX
All
0
4.2.1.4
0
0
4.2.2
2007v5
4.2.3
2007v5
4.2.5
2007v5Er
ror!
Referen
ce
source
not
found.
4.2.6.2
Onroad Mobile Controls
(All national in-force regulations are modeled. The list includes key recent mobile control strategies but is
not exhaustive.)
National Onroad Rules:
All onroad control programs finalized as of the date of the model run, including most recently:
Light-Duty Greenhouse Gas Rule: October, 2012
Heavy (and Medium) -Duty Greenhouse Gas Rule: September, 2011
Renewable Fuel Standard: March, 2010
Light Duty Greenhouse Gas Rule: May, 2010
Corporate-Average Fuel Economy standards for 2008-201 1, April, 2010
2007 Onroad Heavy-Duty Rule: February, 2009
Final Mobile Source Air Toxics Rule (MSAT2): February, 2007
Tier 2 Rule: Signature date February, 2000
National Low Emission Vehicle Program (NLEV): March, 1998
Local Onroad Programs:
California LEVIII Program
Ozone Transport Commission (OTC) LEV Program: January, 1995
Inspection and Maintenance programs
Fuel programs (also affect gasoline nonroad equipment)
Stage II refueling control programs
All
VOC
4.3
4.3
Nonroad Mobile Controls
(All national in-force regulations are modeled. The list includes recent key mobile control strategies but is
not exhaustive.)
National Nonroad Controls:
All nonroad control programs finalized as of the date of the model run, including most recently:
All
4.3.2
46
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Control Strategies and/or growth assumptions
(grouped by standard and approach used to apply to the inventory)
CAPs
affected
Section
Emissions Standards for New Nonroad Spark-Ignition Engines, Equipment, and Vessels: October,
2008
Control of Emissions from Nonroad Large Spark-Ignition Engines, and Recreational Engines
(Marine and Land-Based), November 8, 200227
Clean Air Nonroad Diesel Final Rule - Tier 4: June, 200428
Locomotives:
Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition Engines
Less than 30 Liters per Cylinder: March, 2008
Clean Air Nonroad Diesel Final Rule - Tier 4: May, 2004
All
4.3.3
Commercial Marine:
Category 3 marine diesel engines Clean Air Act and International Maritime Organization standards:
April, 2010
Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition Engines
Less than 30 Liters per Cylinder: March, 2008
Clean Air Nonroad Diesel Final Rule - Tier 4: May, 2004
All
4.3.4
The ancillary input data in the future-year scenarios are very similar to those used in the 2007 base case
except for the speciation profiles used for gasoline-related sources, which change in the future to account for
increased ethanol usage in gasoline (see Section 3.2.1.4 for details).
4.1 Stationary source projections: EGU sector (ptipm)
The future-year data for the ptipm sector used in the air quality modeling were created by the Integrated
Planning Model (IPM) version 4.10 (v4.10) Final MATS (Mercury and Air Toxics Standards)
http://www.epa.gov/airmarkets/progsregs/epa-ipm/toxics.html). IPM is a multiregional, dynamic,
deterministic linear programming model of the U.S. electric power sector. IPM 4.10 was updated from the
previous version to include adjustments to assumptions regarding the performance of acid gas control
technologies, new costs imposed on fuel-switching (e.g., bituminous to sub-bituminous), correction of lignite
availability to some plants, incorporation of planned retirements, implementation of a scrubber upgrade
option, and the availability of a scrubber retrofit to waste-coal fired fluidized bed combustion units without
an existing scrubber.
The scenario used for this modeling represents CAIR, MATS, and the penetration of electric vehicles
anticipated due to the LDGHG rule. IPM v4.10 Final MATS originally included the Cross-State Air
Pollution Rule (CSAPR), but the rule was stayed by the D.C. circuit court pending judicial review.
Therefore, the original CAIR rule was included in the run for this project. Electric vehicle penetration
expected from the Light-duty Greenhouse Gas Rule was also included.
The Boiler MACT reconsideration was not represented in the 2020 IPM dataset because the rule was not
final at the time the IPM modeling was performed. Further details on the future-year EGU emissions
inventory used for this rule can be found in the incremental documentation of the IPM v.4.10 platform,
available at http://www.epa.gov/airmarkets/progsregs/epa-ipm/transport.html. .
While NOX, 862, and mercury are specific outputs from IPM, directly emitted PM emissions (i.e., PM2.s and
PMio) from the EGU sector are computed via a post processing routine that applies emission factors to the
https://www.federalregister.gov/articles/2002/ll/08/02-23801/control-of-emissions-from-nonroad-large-spark-ignition-engines-
and-recreational-engines-marine-and
http://www.epa.gov/otaq/nonroad-diesel.htm
47
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TPM-estimated fuel throughput based on fuel, configuration and controls to compute the filterable and
condensable components of PM.
4.2 Stationary source projections: non-EGU sectors (ptnonipm, nonpt, ag,
afdust)
To project U.S. stationary sources other than the ptipm sector, EPA applied growth factors and/or controls to
certain categories within the ptnonipm, nonpt, ag and afdust platform sectors. This subsection provides
details on the data and projection methods used for these sectors. In estimating future-year emissions, EPA
assumed that emissions growth does not track with economic growth for many stationary non-IPM sources.
This "no-growth" assumption is based on an examination of historical emissions and economic data. For
more details on the projection methodology and justification for the approaches, see the 2007v5 TSD.
Year-specific projection factors (PROJECTION packets) for year 2018 and 2030 were used for creating the
scenarios unless noted otherwise. The contents of these projection packets (and control reductions) are
provided in the following sections where feasible. However, some sectors used growth or control factors
that varied geographically and their contents could not be provided in the following sections (e.g., facilities
and units subject to the Boiler MACT reconsideration has thousands of records). If the growth or control
factors for a sector are not provided in a table in this document, they are available as a "projection",
"control", or "closures" packet for input to SMOKE on the 2007v5 platform website. This document only
summarizes the impact of controls and projections that differ between Tier 3 and the previous modeling for
PM NAAQS (2007v5 platform). The following projection, control or closure packets had identical or nearly
identical impact in 2018 (as well as 2030) for Tier 3 as in 2020 of PM NAAQS modeling and are detailed in
the2007v5TSD:
RICE NESHAP
Industrial Boiler MACT reconsideration
Residential wood combustion growth
Remaining non-EGU plant closures
Boiler reductions not associated with the MACT rule
NY Ozone SIP controls
Boat manufacturing MACT
Lafarge and St Gobain settlements
OECA consent decrees
Refinery consent decrees
CISWI/HWI controls
This section is divided into several subsections that are summarized in Table 4-2. Note that for some
sources, future year inventories were used rather than projection or control packets.
48
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Table 4-2. Summary of non-EGU stationary projections subsections
Subsection
4.2.1
4.2.2
Error!
Reference
source not
found.
4.2.3
Error!
Reference
source not
found.
4.2.4
Title
RFS2 upstream future year
inventories and
adjustments
Agricultural and livestock
adjustments, including
RFS2
Fuel sulfur rules
Portland cement NESHAP
projections
CS APR and NOD A
comments
All other PROJECTION
and CONTROL packets
Sector(s)
nonpt
ptnonipm
afdust, ag,
nonpt,
ptnonipm
nonpt
ptnonipm
ptnonipm
nonpt
ptnonipm
nonpt
ptnonipm
Brief Description
1) Point and non-point inventories received from
OTAQ that account for the upstream impact of
the RFS2 and the EISA mandate.
2) Point and non-point adjustment factors that we
apply to the 2007 inventory to reflect RFS2
Adjustment factors to all ag-related sources that also
reflect upstream RFS2 impacts on ag-related
processes impacted by increased ethanol use
Control packet reflecting state and local fuel sulfur
rules, including ULSD
Year-2013 ISIS policy case reflecting closures,
controls at existing kilns and an inventory containing
new kilns constructed after 2008 that account for
shifting capacity from some closed units to open units
Post-2008 controls, adjustments, and closures
received in response to preparing the 2005 NEI for a
future year baseline. These are not reflective of
CSAPR; but rather of non-EGU future year
information received from comments.
All other non-EGU stationary source PROJECTION
and CONTROL packets not covered in previous
subsections.
4.2.1 RFS2 upstream future year inventories and adjustments (nonpt, ptnonipm)
EPA incorporated adjustments for some stationary source categories to account for impacts of the Energy
Independence and Security Act (EISA) renewable fuel standards mandate in the Renewable Fuel Standards
Program (RFS2; EPA, 2010a) ), as estimated by Annual Energy Outlook (AEO) 2013, as well as impacts of
recent 2017-2025 light duty vehicle greenhouse gas emission standards and heavy-duty greenhouse gas
standards. These mandates not only impact emissions associated with highway vehicles and nonroad engines,
but also emissions associated with point and nonpoint sources. The "upstream" emission impacts of the
renewable fuels mandate are associated with all stages of biofuel production and distribution, including
biomass production (agriculture, forestry), fertilizer and pesticide production and transport, biomass
transport, biomass refining (corn or cellulosic ethanol production facilities), biofuel transport to
blending/distribution terminals, and distribution of finished fuels to retail outlets. These impacts are
accounted for in the 2018 and 2030 inventories. A portion of these impacts are discussed in this section,
with additional impacts due to transport discussed in the onroad and clc2rail sectors (see Section 4.3.1.1 and
4.3.3, respectively). There are also impacts on domestic crude emissions upstream of petroleum refineries,
due to displacement of gasoline and diesel fuel with biofuels, but these are not accounted for in these
projections as these data were not available. Greenhouse gas standards also affect production and
distribution of gasoline and diesel fuels, but the impacts of these rules will be very small in 2018 and were
not accounted for. Where it was feasible, EPA included the impact of these greenhouse gas standards in the
2030 estimates.
49
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EPA assumed that an unadjusted 2018 inventory, which does not account for the impacts of the EISA
renewable fuel mandate, would have comparable ethanol volumes to 2007, approximately 6.9 billion gallons.
However, analyses done to support the RFS2 rule (EPA, 2010a) suggest a significant increase in renewable
fuel volumes in 2018 and 2030 (see Table 4-3). Adjustments applied to the inventories (described in the
following subsections) reflect the impacts on emissions due to the difference between the 2007 ethanol
volumes and the renewable fuel volumes shown in Table 4-3. In 2018, EPA assumed 1 Bgal (billion gallons)
of ethanol would be used as E85, 10 Bgal as E10, and about 4 Bgal as E15. In 2030, EPA assumed 1.4 Bgal
of ethanol would be used as E85, 6.8 Bgal as E10, and 6.5 Bgal as E15.
Table 4-3. Renewable Fuel Volumes Assumed for Stationary Source Adjustments.
Renewable Fuel
Corn Ethanol
Cellulosic Ethanol
Imported Ethanol
Biodiesel
Renewable Diesel
Cellulosic Diesel
2018 Volume (Bgal)
14.7
0.235
1.061
1.887
0.236
0.290
2030 Volume (Bgal)
14.4
0.235
0.707
1.887
1.179
0.915
4.2.1.1 Corn Ethanol plants inventory (ptnonipm)
Future year inventories: "ethanol_plants_2018_NEI" and "ethanol_plants_2018_OTAQ_revised"
As discussed in Section 2.1.2, EPA supplemented the 2007 NEI with corn ethanol plants that EPA/OTAQ
developed. The 2007 emissions were projected to account for the increased domestic corn ethanol
production assumed in this modeling, specifically an increase from 6.9 Bgal in 2007 to approximately 15
Bgal in 2018 and 2030. An industry characterization (EPA, 2012b) was also used to project the 2007
inventory to future years, based on new plants, changes in production capacity since 2007, or changes in
progress. Table 4-4 provides the summaries of estimated emissions for the corn ethanol plants in year 2007
and 201829. Note, EPA estimated the same amount of ethanol plant production in the future years for 2018
and 2030 and assumed that the control scenarios would have limited to no impact on the ethanol production.
Table 4-4. 2007 and 2018/2030 corn ethanol plant emissions [tons]
pollutant
1,3 -Butadiene
Acrolein
Formaldehyde
Benzene
Acetaldehyde
Naphthalene
CO
NH3
NOX
PM10
PM2.5
SO2
2007
0.0015
30
31
13
746
0.028
12,821
1,036
13,403
11,468
4,452
16,930
2018/2030
0.0021
70
65
26
857
0.030
21,811
1,072
25,618
21,054
7,827
20,235
The 2007 emissions are the sum of the NEI and OTAQ facilities. The same is true for 2018.
50
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voc
19,271
41,146
4.2.1.2 Biodiesel plants inventory (ptnonipm)
New Future year inventory: "Biodiesel_Plants_2018_fflO"
EPA/OTAQ developed an inventory of biodiesel plants for 2018 and 2030. Plant location and production
volume data came from the Tier 3 proposed rule.30'31 The total volume of biodiesel came from the AEO
2013 early release, 1.3 BG for 2018 and 2030. To reach the total volume of biodiesel, plants that had current
production volumes were assumed to be at 100% production and the remaining volume was split among
plants with planned production. These emission factors in Table 4-5 are in tons per million gallons (Mgal)
and were obtained from EPA's spreadsheet model for upstream EISA impacts developed for the RFS2 rule
(EPA, 2010a). Inventories were modeled as point sources with Google Earth and web searching validating
facility coordinates and correcting state-county FIPS. Table 4-6 provides the 2018 and 2030 biodiesel plant
emissions estimates. Emissions in 2007 are assumed to be near zero, and HAP emissions in 2018 and 2030
are nearly zero. Note, EPA estimated the same amount of biodiesel plant production in the future years for
2018 and 2030 and assumed that the control scenarios would have limited to no impact on the biodiesel
production.
Table 4-5. Emission Factors for Biodiesel Plants (Tons/Mgal)
Pollutant
VOC
CO
NOx
PMio
PM2.5
S02
NH3
Acetaldehyde
Acrolein
Benzene
1,3 -Butadiene
Formaldehyde
Ethanol
Emission Factor
4.3981E-02
5.0069E-01
8.0790E-01
6.8240E-02
6.8240E-02
5.9445E-03
0
2.4783E-07
2.1290E-07
3.2458E-08
0
1.5354E-06
0
Table 4-6. 2018/2030 biodiesel plant emissions [tons]
Pollutant
CO
NOX
PMio
PM2.5
SO2
2018/2030
649
1048
89
89
8
30 US EPA 2013. Draft Regulatory Impact Analysis for Tier 3 Vehicle Emission and Fuel Standards Program. EPA-420-D-13-002.
31 Cook, R. 2012. Development of Air Quality Reference Case Upstream and Portable Fuel Container Inventories for Tier 3
Proposal. Memorandum to Docket EPA-HQ-OAR-2010-0162.
51
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voc
57
4.2.1.3 Portable fuel container inventory (nonpt)
Future year inventories: "2018_PFC_inventory_FF 10_revision2", "2030_PFC_inventory_FF 10_revision2"
EPA used future-year VOC emissions from Portable Fuel Containers (PFCs) from inventories developed and
modeled for the EPA's MSAT2 rule (EPA, 2007a). The 10 PFC SCCs are summarized below (note that the
full SCC descriptions for these SCCs include "Storage and Transport; Petroleum and Petroleum Product
Storage" as the beginning of the description).
2501011011 Residential Portable Fuel Containers:
2501011012 Residential Portable Fuel Containers:
2501011013 Residential Portable Fuel Containers:
2501011014 Residential Portable Fuel Containers:
2501011015 Residential Portable Fuel Containers:
2501012011 Commercial Portable Fuel Containers
2501012012 Commercial Portable Fuel Containers
2501012013 Commercial Portable Fuel Containers
2501012014 Commercial Portable Fuel Containers
2501012015 Commercial Portable Fuel Containers
Permeation
Evaporation
Spillage During Transport
Refilling at the Pump: Vapor Displacement
Refilling at the Pump: Spillage
: Permeation
: Evaporation
: Spillage During Transport
: Refilling at the Pump: Vapor Displacement
: Refilling at the Pump: Spillage
The large majority of spillage emissions occur when refueling equipment, and this is already included in the
nonroad equipment inventory. Thus we did not include these emissions in the PFC inventory for this rule.
Vapor displacement for nonroad equipment container refueling was also subtracted from vapor displacement
in the PFC inventory to avoid double counting these emissions. The future-year emissions reflect projected
increases in fuel consumption, state programs to reduce PFC emissions, standards promulgated in the
MSAT2 rule, and impacts of the EISA on gasoline volatility. OTAQ provided year 2018 and 2030 PFC
emissions that include estimated Reid Vapor Pressure (RVP) and oxygenate impacts on VOC emissions, and
more importantly, large increases in ethanol emissions from EISA. Because the future year PFC inventories
contain ethanol in addition to benzene, EPA developed a VOC E-profile that integrated ethanol and benzene;
see Section 3.2.1.1 and Section 3.2.1.4for more details. Emissions for 2007, 2018, and 2030 are provided in
Table 4-7.
Table 4-7. PFC emissions for 2007, 2018, and 2030 [tons]
pollutant
Benzene
Naphthalene
Ethanol
VOC
2007
1,049
0.6
220,472
2018
645
7.1
3,719
29,119
2030
829
9.3
4,969
37,947
4.2.1.4 Cellulosic fuel production inventory (nonpt)
New Future year inventories: "2018_cellulosic_inventory", "2030_cellulosic_inventory"
Depending on available feedstock, cellulosic plants are likely to produce fuel through either a biochemical
process or a thermochemical process. OTAQ developed county-level inventories for biochemical and
thermochemical cellulosic fuel production for 2018 and 2030 to reflect AEO2013 (early release) 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 4-8 and Table 4-9 were used to develop
52
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cellulosic plant inventories. Criteria pollutant emission rates are in tons per REST gallon. Emission factors
from the cellulosic diesel work in the Tier 3 NPRM were used as the emission factors for the
thermochemical plants in the FRM modeling. 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, we developed a VOC E-profile that integrated ethanol, see
Sections 3.2.1.1 and 3.2.1.3 for more details.
Plants were treated as area sources spread across the entire area of whatever county they were considered to
be located in. Cellulosic biofuel refinery siting was based on utilizing the lowest cost feedstock, accounting
for the cost of the feedstock itself as well as feedstock storage and the transportation of the feedstock to the
cellulosic biofuel refinery. The total number of cellulosic biofuel refineries was projected using volumes
from AEO2013 (early release). The methodology used to determine most likely plant locations is described
in Section 1.8.1.3 of the RFS2 RIA (EPA, 2010a). Table 4-10 provides the 2018 and 2030 cellulosic plant
emissions estimates.
Table 4-8. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/RIN gallon)
Cellulosic Plant
Type
Thermochemical
Biochemical
VOC
5.92E-07
1.82E-06
CO
8.7E-06
1.29E-05
NOX
1.31E-05
1.85E-05
PM10
1.56E-06
3.08E-06
PM25
7.81E-07
1.23E-06
SOX
1.17E-06
6.89E-07
NH3
1.44E-10
0
Table 4-9. Toxic Emission Factors for Cellulosic Plants (Tons/RIN gallon)
Plant Type
Thermochemical
Biochemical
Acetaldehyde
2.95E-08
3.98E-07
Acrolein
1.27E-09
1.11E-08
Benzene
9.61E-10
1.39E-08
1,3 -Butadiene
0
0
Formaldehyde
5.07E-09
2.28E-08
Ethanol
2.09E-07
6.41E-07
Table 4-10. 2018 and 2030 cellulosic plant emissions [tons]
pollutant
Acrolein
Formaldehyde
Benzene
Acetaldehyde
CO
Ethanol
NH3
NOX
PM10
PM2.5
SO2
VOC
2018
0.9
3.5
0.7
20.6
6,087
146
0.1
9,199
1,088
547
819
414
2030
3.1
10.3
2.9
86.1
15,196
397
0.2
22,867
2,792
1,372
1,941
1,125
4.2.1.5 Ethanol working loss inventory (nonpt)
New Future year inventories: "Ethanol_transport_vapor_2018rg_ref',
"Ethanol_transport_vapor_2030rg_ref"
53
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These inventories were provided by OTAQ to represent upstream impacts of loading and unloading at
ethanol terminals. Emissions are entirely evaporative and were computed by county for truck, rail and
waterway loading and unloading and intermodal transfers (e.g., highway to rail). Inventory totals are
summarized in Table 4-11. The leading descriptions are "Industrial Processes; Food and Agriculture;
Ethanol Production" for each SCC.
Table 4-11. 2018 and 2030 VOC working losses (Emissions) due to ethanol transport [tons]
SCC
30205031
30205052
30205053
description
Denatured Ethanol Storage Working Loss
Ethanol Loadout to Truck
Ethanol Loadout to Railcar
2018
23,420
14,425
10,484
2030
22,396
13,794
10,025
4.2.1.6 Vapor losses from Ethanol transport and distribution (nonpt, ptnonipm)
Packets: "PROJECTION_2008_2018_distribution_upstream_OTAQ_Tier3FRM",
"PROJECTION_2008_2030_distribution_upstream_OTAQ_Tier3FRM"
OTAQ developed county-level inventories for ethanol transport and distribution for 2018 and 2030 to
account for losses for the processes such as truck, rail and waterways loading/unloading and intermodal
transfers such as highway-to-rail, highways-to-waterways, and all other possible combinations of transfers.
These emissions are entirely evaporative and therefore limited to VOC and HAPs in VOC.
2018 and 2030 inventories which included EISA impacts were developed by adjusting the 2007 platform
inventory. Impacts of the light-duty greenhouse gas rule are small enough to ignore for 2018. EISA
adjustments were made using an updated version of EPA's spreadsheet model for upstream emission
impacts, developed for the RFS2 rule32. The development of emission factors and fuel volumes to make
these adjustments with the RFS2 impacts spreadsheet are described below.
Vapor loss VOC emission factors (EFs) for gasoline were first developed, based on inventory estimates from
the 2005 NEI (EPA, 2009a). Total volume of gasoline was based on gasoline sales as reported by the Energy
Information Administration (2006). Emissions were partitioned into 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.
Total nationwide emissions for these components were divided by the energy content of the total volume of
gasoline distributed in 2005 to obtain emission factors in grams per million metric British Thermal Units
(g/mmBTU). Total volume of gasoline was based on gasoline sales as reported by the Energy Information
Administration.33 In addition to gasoline VOC emission factors for the RBT/BPS components, emission
factors were developed for the BTP component, for 10% ethanol, 15% ethanol, and 85% ethanol. Emission
factors were calculated by applying adjustment factors to the gasoline EFs. The BTP adjustment factors
were based on an algorithm from the 1994 On-Board Refueling Vapor Recovery Rule (EPA, 1994):
EF(g/gal) = exp[-1.2798-0.0049(AT) + 0.0203(Td) + 0.1315(RVP)]
Here delta T is the difference in temperature between the fuel in the tank and the fuel being dispensed, and
Td is the temperature of the gasoline being dispensed. We assumed delta T is zero, and the temperature of
the fuel being dispensed averages 60 °F over the year.
U.S. EPA. 2013. Spreadsheet "upstream_emissions_rev T3.xls.
33 Source: Energy Information Administration. 2006. Annual Energy Outlook 2006. Report #:DOE/EIA-0383(2006) Available at
54
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Average summer RVPs at the Petroleum Administration for Defense Districts (PADD) level was used to
calculate adjustments. The U.S. is broken into five PADDs for petroleum products data collection purposes
via the U.S. Energy Information Administration; see: http://www.eia.gov/oog/info/twip/padddef.html. These
PADD regions are shown in Figure 4-1.
Figure 4-1. Map of Petroleum Administration for Defense Districts (PADD)
PADD 5:
West Coast,
AK, HI
1 : .
NV
CA
AK
:
PADD 4:
Rocky
Mountain
PADD 2:
Midwest
PADD3:GulfCoas
Source: U.S. Energy Information Administration
All counties within a PADD received the same adjustment for BTP emissions. Volumes for each fuel type
and summer RVPs for 2018 with EISA impacts are provided in Table 4-12 while volumes without EISA are
in Table 4-13. Volumes with and without EISA for 2030 are provided in Table 4-14 and Table 4-16. These
volumes and RVPs were obtained from analyses done for the Tier 3 rule. These two sets of volumes were
used to estimate emissions using an updated version of the RFS2 impacts spreadsheet (EPA, 2013a).
55
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Table 4-12. RVPs Assumed for 2018 ethanol and gasoline volumes with EISA
PADD
1
2
3
4
5
Total
Total
Fuel
Volume
4.50E+10
3.46E+10
2.14E+10
5.07E+09
2.35E+10
1.30E+11
Gasoline
Volume
3.99E+10
3.03E+10
1.89E+10
4.48E+09
2.08E+10
1.14E+11
Ethanol
Volume
5.18E+09
4.25E+09
2.48E+09
5.89E+08
2.71E+09
1.52E+10
E10 Only
Volume
3.71E+10
2.31E+10
1.72E+10
4.06E+09
1.91E+10
1.01E+11
E15 Only
Volume
7.43E+09
1.11E+10
3.95E+09
9.53E+08
4.13E+09
2.75E+10
E85(74)
Only
Volume
4.73E+08
3.63E+08
2.25E+08
5.33E+07
2.47E+08
1.36E+09
Weighte
dRVP
8.536
9.051
8.399
9.322
7.906
8.567
Weighted
E10RVP
8.651
9.35
8.515
9.536
7.974
8.696
Weighted
E15RVP
8.056
8.493
7.973
8.536
7.645
8.175
Weighted
E85RVP
7
7
7
7
7
7
Table 4-13. RVPs Assumed for 2018 ethanol and gasoline volumes without EISA
PADD
1
2
3
4
5
Total
Total Fuel
Volume
4.42E+10
3.38E+10
2.09E+10
4.96E+09
2.30E+10
1.27E+11
Ethanol
Volume
2.47E+09
1.92E+09
1.02E+09
2.54E+08
1.27E+09
6.93E-K)9
Gasoline
Volume
4.17E+10
3.19E+10
1.99E+10
4.71E+09
2.17E+10
1.20E+11
EO Only
Volume
1.95E+10
1.47E+10
1.07E+10
2.43E+09
1.03E+10
5.76E+10
E10 Only
Volume
2.47E+10
1.92E+10
1.02E+10
2.54E+09
1.27E+10
6.93E+10
Weighted
RVP
8.63
9.48
8.77
9.18
7.77
8.75
Weighted EO
RVP
8.301
8.925
8.391
8.674
7.602
8.372
Weighted E10
RVP
8.883
9.907
9.166
9.674
7.911
9.059
Table 4-14. RVPs Assumed for 2030 ethanol and gasoline volumes with EISA
PADD
1
2
3
4
5
Total
Total
Fuel
Volume
3.92E+10
2.97E+10
1.95E+10
4.69E+09
2.05E+10
1.14E+11
Gasoline
Volume
3.41E+10
2.58E+10
1.68E+10
4.08E+09
1.81E+10
9.89E+10
Ethanol
Volume
5.02E+09
3.91E+09
2.63E+09
6.14E+08
2.47E+09
1.46E+10
E10 Only
Volume
2.43E+10
1.62E+10
9.32E+09
2.64E+09
1.60E+10
6.84E+10
E15 Only
Volume
1.43E+10
1.31E+10
9.85E+09
1.98E+09
4.24E+09
4.34E+10
E85(74)
Only
Volume
6.02E+08
4.56E+08
2.98E+08
7.20E+07
3.17E+08
1.75E+09
Wtd.
RVP
8.444
8.942
8.245
9.063
7.884
8.464
Wtd.
E10RVP
8.763
9.333
8.644
9.524
7.93
8.716
Wtd.
E15
RVP
7.962
8.525
7.905
8.524
7.774
8.126
Wtd. E85
RVP
7
7
7
7
7
7
Table 4-15. RVPs Assumed for 2030 ethanol and gasoline volumes without EISA
PADD
1
2
3
4
5
Total
Total Fuel
Volume
3.83E+10
2.90E+10
1.89E+10
4.57E+09
2.02E+10
1.11E+11
Ethanol
Volume
2.47E+09
1.92E+09
1.02E+09
2.54E+08
1.27E+09
6.93E+09
Gasoline
Volume
3.59E+10
2.71E+10
1.79E+10
4.32E+09
1.89E+10
1.04E+11
EO Only
Volume
1.36E+10
9.88E+09
8.78E+09
2.04E+09
7.48E+09
4.18E+10
E10 Only
Volume
2.47E+10
1.92E+10
1.02E+10
2.54E+09
1.27E+10
6.93E+10
Weighted
RVP
8.68
9.57
8.81
9.23
7.80
8.80
Weighted
EORVP
8.301
8.925
8.391
8.674
7.602
8.372
Weighted
E10RVP
8.883
9.907
9.166
9.674
7.911
9.059
56
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A benzene g/mmgal emission factor for 2018 and 2030 was based on benzene inventory projections used in
the 2011 Cross-State Air Pollution Rule and projected gasoline volumes obtained from the Annual Energy
Outlook 2011 Early Release Overview. This emission factor was used to estimate g/mmBTU emission
factors based on the energy content of EO, E10, and E15 gasoline. Aside from energy content, we did not
account for the effect of other fuel parameters on emission rates for EO, E10, and E15 blends. Thus, the E10
emission rate is slightly higher than the EO rate due to the lower energy content of E10, and the E15 emission
rate is higher still. The E85 emission rate was estimated for the RFS2 rule. Emission factors are summarized
in Table 4-16.
Table 4-16. Storage and Transport Vapor Loss Emission Factors (g/mmBtu)
Process
BTP
RBT/BPS
Fuel
EO
E10
E15
E85
EO
Benzene
0.250
0.259
0.264
0.023
0.059
These emission factors for VOC and benzene were used in conjunction with an updated version of EPA's
spreadsheet model for upstream emission impacts, developed for the RFS2 rule, to estimate PADD-level
inventory changes of the changes in gasoline volume in 2018 with 2007 ethanol volumes versus projected
volumes with EISA. VOC inventory changes were used to develop nationwide adjustment factors that were
applied to modeling platform inventory SCCs associated with storage and transport processes (see Table
4-17). Benzene emission estimates were obtained either by application of the adjustments in Table 4-17 or
through speciation of VOC in SMOKE.
A similar approach was used to develop adjustment factors for 2030. However, in addition to the impacts of
EISA, the 2017-2025 light-duty greenhouse gas rule will significantly reduce gasoline production. The
impact of this rule was small enough to ignore for 2018, but quite significant in 2030. To account for
impacts of this rule in 2030, an additional scalar of 0.7916 was applied.
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 the EPAct exhaust test program34 and have been submitted for incorporation into EPA's
SPECIATE database. The E85 profile was obtained from data collected as part of the CRC E-80 test
program (Environ, 2008) and has also been submitted for incorporation into EPA's SPECIATE database.
For more details on the change in speciation profiles between 2007 and the future years, see Section
3.2.1.4Error! Reference source not found..
After developing emissions for 2018 with EISA volumes versus 2018 without EISA volumes, and 2030 with
EISA and the light-duty greenhouse gas rule versus 2030 without those rules, EPA created ratios of these
two cases to apply against the 2007 platform emissions. From this, EPA created 2018 and 2030 reference
cases.
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.
57
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Table 4-17. Adjustment factors applied to storage and transport emissions
Year
2018
2030
Process
BTP
RBT/BPS
BTP
RBT/BPS
PADD
1
2
O
4
5
All
1
2
3
4
5
All
Pollutant
voc
benzene
VOC
benzene
VOC
benzene
VOC
benzene
VOC
benzene
VOC
benzene
VOC
benzene
VOC
benzene
VOC
benzene
VOC
benzene
VOC
benzene
VOC
benzene
Adjustment
Factor
0.9515
0.9905
0.9619
0.9882
0.9778
0.9879
0.8983
0.9885
0.9430
0.9901
0.9553
0.9893
0.8005
0.7871
0.8436
0.7865
0.8318
0.7824
0.7905
0.7840
0.7700
0.7888
0.8114
0.7863
It should be noted that these adjustment factors are based on summer RVP, but applied to emissions for the
whole calendar year. However, higher RVPs in winter corresponding to lower temperatures result in roughly
the same vapor pressure of the fuel and roughly the same propensity to evaporate. Significant evaporative
emissions are not expected from storage and transport of biodiesel, renewable or cellulosic diesel fuel due to
their low volatility. The cumulative impacts are a reduction in VOC emissions from 2007 levels (see Table
4-18). See 2007v5 TSD, Appendix B for the complete cross-walk between SCC, and state-SCC for BTP
components, and each type of petroleum transport and storage.
Table 4-18. Impact of VOC losses from reduced gasoline production due to EISA
Sector
ptnonipm
nonpt
Total
20 18 Reductions
2,039
22,082
24,121
2030 Reductions
7,884
97,030
104,914
4.2.1.7 Pipeline and Refinery EISA adjustments (ptnonipm)
Packets: "PROJECTION_2018rg_ref_pipelines_refmeries",
"PROJECTION_2030rg_ref_pipelines_refmeries"
58
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Pipeline usage, refinery, and bulk terminal emissions were adjusted for changes in fuels due to EISA and
reductions in gasoline and diesel volumes due to greenhouse gas emission standards. These adjustments
were developed by EPA/OTAQ and impact processes such as process heaters, catalytic cracking units,
blowdown systems, wastewater treatment, condensers, cooling towers, flares and fugitive emissions.
Calculation of the emission inventory impacts of decreased gasoline and diesel production, due to EISA and
greenhouse gas rules, on nationwide refinery emissions was done in the updated version of EPA's
spreadsheet model for upstream emission impacts. Emission inventory changes reflecting EISA and
greenhouse gas rules implementation were used to develop adjustment factors that were applied to
inventories for each petroleum refinery in the U.S. (Table 4-19 and Table 4-20). These impacts of decreased
production were assumed to be spread evenly across all U. S. refineries. Toxic emissions were estimated in
SMOKE by applying speciation to VOC emissions. It should be noted that the adjustment factors in Table
4-19 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 impact of the EISA and greenhouse gas
rules is shown in
59
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Table 4-21.
Table 4-19. 2018 adjustment factors applied to petroleum pipelines and refinery emissions associated with
gasoline and diesel fuel production.
Pollutant
CO
NOX
PMio
PM2.5
SO2
NH3
VOC
Pipelines
0.996
0.982
0.997
0.998
0.998
n/a
0.999
Refineries
0.978
0.987
0.984
0.979
0.978
0.952
0.972
Both
0.974
0.969
0.981
0.977
0.976
n/a
0.971
Table 4-20. 2030 adjustment factors applied to petroleum pipelines and refinery emissions associated with
gasoline and diesel fuel production.
Pollutant
CO
NOX
PMio
PM2.5
SO2
NH3
VOC
Pipelines
0.981
0.890
0.985
0.989
0.986
n/a
0.994
Refineries
0.773
0.781
0.779
0.775
0.774
0.750
0.769
Both
0.763
0.695
0.767
0.766
0.763
n/a
0.764
60
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Table 4-21. Impact of refinery adjustments on 2007 emissions [tons]
pollutant
CO
NH3
NOX
PM10
PM2.5
SO2
VOC
reductions
2018
2,148
111
2,884
515
555
3,401
1,891
reductions
2030
19,952
572
28,116
6,185
5,503
33,913
15,341
4.2.2 Upstream agricultural and Livestock adjustments (afdust, ag, nonpt, ptnonipm)
Packets: "PROJECTION 2008 2018 ag including upstream OTAQ":
"PROJECTION_2008_2030_ag_including_upstream_OTAQ"
Inventory adjustments were previously developed for 2030 as part of final RFS2 rule modeling35. For the
Tier 3 proposal, adjustments for 2017 were scaled by the ratio of 2017 renewable fuel volumes versus 2030
volumes. Although 2018 was modeled for this rule rather than 2017, EPA continued to use the 2017
adjustments. Impacts on farm equipment emissions were not accounted for, however. Emission rates from
the GREET model (fertilizer and pesticide production)36 or based on the 2002 National Emissions Inventory
(fertilizer and pesticide application, agricultural dust, livestock waste) were combined with estimates of
agricultural impacts from FASOM (Forest and Agricultural Section Optimization Model). Since FASOM
modeling used a reference case of 13.2 billion gallons of ethanol, impacts used in the modeling for this rule
are underestimates.
Adjustment factors are provided in
U. S. Environmental Protection Agency. 2010. Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis.
Assessment and Standards Division, Office of Transportation and Air Quality, Ann Arbor, MI. Report No. EPA-420-R-10-006,
February, 2010. Available at .
' GREET, version 1.8c. Available at < http://greet.es.anl.gov/>.
61
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Table 4-22. These adjustments were applied equally to all counties having any of the affected sources. This
is an area of uncertainty in the inventories, since there would likely be variation from one county to another
depending on how much of the predicted agricultural changes occurred in which counties. By using percent
change adjustments rather than attempting to calculate absolute ton changes in each county, EPA attempted
to minimize the inventory distortions that could occur if the calculated change for a given county was out of
proportion to the reference case emissions for that county. For instance, a different approach could estimate
reductions that were larger than the reference case NEI emissions, since there was no linkage between the
NEI inventories and the FASOM modeling. The specific sources (SCCs) and affected pollutants that these
adjustments were applied to are listed in a docket reference37.
37 U. S. EPA. 2011. Spreadsheet "agricultural sector adjustments.xls." Docket EPA-HQ-OAR-2011-0135.
62
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Table 4-22. Adjustments to modeling platform agricultural emissions for the Tier 3 reference case
Source Description
Nitrogen Fertilizer Application
Fertilizer Production
Pesticide Production
Tilling/Harvesting Dust
Agricultural Burning
Livestock Dust
Livestock Waste
2018 Adjustment
1.0242
1.0603
0.9544
1.0079
1.0000
0.9868
0.9901
2030 Adjustment
1.0573
1.0603
0.9954
1.0265
1.0000
0.9983
0.9983
For the animal waste sources, EPA also estimated animal population growth in ammonia (NFb) and dust
(PMio and PM2.s) emissions from livestock in the ag and afdust and ptnonipm sectors. Therefore, a
composite set of projection factors is needed for animal operations that also reflect the minor 0.99% decrease
resulting from the EISA mandate. These composite projection factors by animal category are provided in
Table 4-23. As discussed below, Dairy Cows and Turkeys are assumed to have no growth in animal
population, and therefore the projection factor for these animals is the same as the upstream agriculture-
related projection factor. The PROJECTION packet used for these sources, including the cross-reference to
the animal categories listed in Table 4-23 and the source categories in
63
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Table 4-22.
Table 4-23. Composite NFL? projection factors to year 2018 and 2030 for animal operations
Animal Category
Dairy Cow
Beef
Pork
Broilers
Turkeys
Layers
Poultry Average
Overall Average
2018 Factors
0.9901
0.9927
1.0798
1.1399
0.9050
1.0974
1.0865
1.0428
2030 Factors
0.9983
0.9460
1.1614
1.1795
0.8953
1.1281
1.1149
1.0505
Except for dairy cows and turkey production, the animal projection factors are derived from national-level
animal population projections from the U.S. Department of Agriculture (USDA) and the Food and
Agriculture Policy and Research Institute (FAPRI). This methodology was initiated in 2005 for the 2005
NEI, but was updated on July 24, 2012 in support of the 2007v5 platform (EPA, 2012). For dairy cows,
EPA assumed that there would be no growth in emissions based on little change in U.S. dairy cow
populations from year 2007 through 2030 according to linear regression analyses of the FAPRI projections.
This assumption was based on an analysis of historical trends in the number of such animals compared to
production rates. Although productions rates have increased, the number of animals has declined. Based on
this analysis, EPA concluded that production forecasts do not provide representative estimates of the future
number of cows and turkeys; therefore, EPA did not use these forecasts for estimating future-year emissions
from these animals. In particular, the dairy cow population is projected to decrease in the future as it has for
the past few decades; however, milk production will be increasing over the same period. Note that the
ammonia emissions from dairies are not directly related to animal population but also nitrogen excretion.
With the cow numbers going down and the production going up, the excretion value will likely change, but
EPA assumed no change because of the lack of a quantitative estimate. Appendix H of the 2007v5 platform
TSD provides the animal population data and regression curves used to derive the growth factors.
4.2.3 Fuel sulfur rules (nonpt, ptnonipm)
Packets: CONTROL_SULF_2020_2007v5; CONTROL_SULF_2030_2007v5
Fuel sulfur rules that were signed by July, 2012 are limited to Maine, Massachusetts, New Jersey, New York
and Vermont. The fuel limits for these states are incremental starting after year 2012, but are fully
implemented by year 2018 in all of these states. In 2030, New York City had additional controls
implemented, mandating that all sources burn ULSD (15 ppm). For more details on these rules, see the
2007v5 TSD. A summary of the sulfur rules by state, with emissions reductions is provided inTable 4-24.
Table 4-24. Summary of fuel sulfur rules by state
State/
Metro
ME
ME
MA
NJ
NJ
NY
Fuel
Distillate
Residual
Distillate
Distillate
Kerosene
Distillate
%
reduction
2018
99.5
75
99.5
99.5
96.25
99.5
%
reduction
2030
99.5
75
99.5
99.5
96.25
99.5
2008
Emissions
12,076
17,265
7,285
54,093
2018
Emissions
1,056
86
45
655
2018
Reductions
11,021
17,178
7,240
53,439
2030
Emissions
1,056
86
45
274
2030
Reductions
11,021
17,178
7,240
53,819
64
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NYC
VT
Residual
Distillate
75
99.5
99.25
99.5
2,018
10
2,008
10
2,008
4.2.4 Portland Cement NESHAP projections (ptnonipm)
As indicated in Table 4-1, the Industrial Sectors Integrated Solutions (ISIS) model (EPA, 201 Ob) was used to
project the cement industry component of the ptnonipm emissions modeling sector to 2013. This approach
provided reductions of criteria and hazardous air pollutants, including mercury (Hg). The ISIS cement
emissions were developed in support for the Portland Cement NESHAPs and the NSPS for the Portland
cement manufacturing industry.
The ISIS model produced a Portland Cement NESHAP policy case of multi-pollutant emissions for
individual cement kilns (emission inventory units) that were relevant for years 2013 through 2017; however,
no additional policy case scenario for later future years (i.e., 2018) are available. Therefore, the 2013 policy
case is used for the 2018 and 2030 base case. These ISIS-based emissions are reflected using CoST packets
and a cement inventory for new kilns:
1) Inventory: "cement_newkilns_ISIS2013_2007v5_POINT_fflO"
Contains information on new cement kilns constructed after year 2008. This inventory was
accidentally not included in projections for all future year scenarios.
2) Packet: "CLOSURES cement ISIS 2007v5 2013policv"
Contains facility and unit-level closures,
3) Packet: "PROJECTION_ISIS2013_cement_2007v5"
Contains updated policy case emissions at existing cement kilns which we include via projection
factors. The units that opened or closed before 2010 were included in the 2020 base case.
The ISIS model results for the future show a continuation of 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. Multiple regulatory requirements such as the NESHAP and NSPS currently apply to
the cement industry to reduce CAP and HAP emissions. Additionally, state and local regulatory
requirements might apply to individual cement facilities depending on their locations relative to ozone and
PM2.5 nonattainment areas. The ISIS model provides the emission reduction strategy that balances: 1)
optimal (least cost) industry operation, 2) cost-effective controls to meet the demand for cement, and 3)
emission reduction requirements over the time period of interest. Table 4-25 shows the magnitude of the
ISIS-based cement industry reductions in the future-year emissions that represent 2018 and 2030, and the
impact that these reductions have on total stationary non-EGU point source (ptnonipm) emissions. The
impact of accidentally not including the inventory of new kilns in future year modeling is quantified below.
This error has the most significant impact on NOx emissions; however, nationally, NOx from cement kilns
would still decrease from 2008 by over 50% had these new kilns been included in the future year scenarios.
Table 4-25. ISIS-based cement industry change (tons/yr)
Pollutant
CO
NH3
NOX
PMio
PM2.5
Cement Industry
emissions in 2008
46,317
270
156,579
6,621
3,689
Cement Industry
projected emissions in
2018 & 2030
8,713
77
57,477
1,005
800
New cement kilns
erroneously dropped
from inventory
0
0
17,699
2
1
% decrease in
Cement
projections
81%
71%
63%
85%
78%
65
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S02
voc
98,277
6,954
22,287
1,131
1,543
135
77%
84%
4.2.5 Controls, Closures and consent decrees from CSAPR and NODA Comments
(nonpt, ptnonipm)
EPA released a Notice of Data Availability (NODA) after the CSAPR proposal to seek comments and
improvements from states and outside agencies. The goal was to improve the future baseline emissions
modeling platform prior to processing the Final CSAPR. EPA received several control programs and other
responses that were used for future year projections. However, this effort was performed on a version of the
2005 modeling platform, which used the NEI2005v2 as a base year starting point for future year projections.
Now with the 2007 platform using the 2008 NEI for most non-EGU point and nonpoint sources, many of
these controls and data improvements were removed from this 2007 base case projection. But for those
controls, closures and consent decree information that are implemented after 2008, EPA used these
controls/data after mapping them to the correct SCCs and/or facilities in the 2008 NEI. This subsection
breaks down the controls used for the nonpt and ptnonipm sectors separately, and also describes the consent
decrees separately. EPA used July 1, 2008 as the cut-off date for assuming whether controls were included
in the 2007 modeling platform (2008 NEI). For example, if a control had a compliance date of December
2008 it was assumed that the 2008 NEI emissions did not reflect this control and would need to reflect this
control in our 2018 or 2030 scenarios. It is important to note that these controls are not comprehensive for
all state/counties and source categories. These only represent post-year 2008 controls for those areas and
categories where EPA received usable feedback from the CSAPR comments and related 2005 platform
NODA.
Nonpoint controls: packet "CONTROL_CSAPR_nonpoint_2018_2007v5"
The remaining nonpt sector CSAPR comments controls with compliance dates after 2008 are limited to state-
level Ozone Transport Commission (OTC) VOC controls in Connecticut and local controls around
Richmond Virginia. These controls target many of the same sources in the previously-discussed NY SIP
ozone control packet: AIM coating, Mobile Equipment Repair and Refmishing, Adhesives and Sealants and
Consumer Products. Cumulatively, these controls reduce VOC by approximately 1,400 tons. This control
packet also impacts Texas oil and gas drill rigs (SCC 2310000220), were though factors for these emissions
were available through year 2021, EPA only projected these to 2018. The impact in TX is a reduction of
NOx by 17,244 tons, PM2.s by 1,158 tons and VOC by 1,470 tons with minor reductions for other pollutants.
The control packet is used for both 2018 and 2030 and has the same impact in both years.
Ptnonipm controls: packet "CONTROL_CSAPR_ptnonipm_2020_2007v5"
EPA created a CONTROL packet for the ptnonipm sector that contains reductions needed to achieve post
year-2008 emissions values from the CSAPR response to comments. These reductions reflect fuel
switching, cleaner fuels, and permit targets via specific information on control equipment and unit and
facility zero-outs in the following states: California, Delaware, Georgia, New Hampshire, New York and
Virginia. Cumulatively, these controls reduce NOx about 1,000 tons and SO2 by approximately 4,100 tons.
The control packet is used for both 2018 and 2030 and has the same impact in both years.
Ptnonipm closures: packet "CLOSURES_TRl_2008NEIv2"
This packet contains observed unit and facility-level closures based on CSAPR comments. This packet
includes only units that reported by states as closed prior to receipt of the CSAPR comments in year 2012 or
sooner. EPA found a couple of units in the 2008 NEI-based inventory that were reported as closed in year
2007; therefore, the compliance dates in this packet range from 2007 to 2012. EPA also retained all year-
2007 closures to allow for this packet to potentially be used on RPO year-2007 point inventories. All
66
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closures were provided for the 2005 NEI facility and unit identifier codes. EPA matched these
units/facilities to the 2008 NEI using the "agy_facility_id" and "agy_point_id" codes in the NEI and
searching the EIS for closure information. Overall, these facility and unit closures reduced NOx, 862 and
PM2.5 emissions by approximately 8,800, 1,300 and 50,000 tons respectively distributed amongst the
following states: Alabama, Arkansas, Delaware, Georgia, Illinois, Maine, Massachusetts, Missouri, New
Hampshire, South Carolina, Texas, Virginia and West Virginia. The closure packet is used for both 2018
and 2030 and has the same impact in both years.
Ptnonipm projection: packet "PROJECTION_CSAPR_WVunit_ptnonipm_2020_2007v5"
This packet contains the only post-2008 unit-level growth projection resulting from CSAPR comments. The
Sunoco Chemicals Neal Plant in Wayne County West Virginia replaced a 155MM Btu/hour coal-fired boiler
with a 96.72 MM Btu/hour natural gas-fired unit in 2010. The shutdown of the coal boiler was included in
the CLOSURES_TRl_2008NEIv2" packet just discussed and simply added the emissions from the new
natural gas unit to an existing unit by computing the new cumulative total from the new and old natural gas
units. The closing of the coal-fired boiler removed 51 tons of NOx and 234 tons of 862 while this packet
resulted in only 28 more tons of NOx and minimal emissions from PM and SO2. The projection packet is
used for both 2018 and 2030 and has the same impact in both years.
Consent decrees (ptnonipm): packet "CONTROLS_CSAPR_consent_2008NEIv2"
These controls reflect consent decree and settlements that were identified in our preparation of the Final
CSAPR emissions modeling platform. These controls generally consist of one or more facilities and target
future year reductions. After removing all consent decrees with compliance dates prior to late-2008, EPA
matched the remaining controls to the 2008 NEI using a combination of EIS facility codes, "agy_facility_id",
"agy_point_id" and searching the EIS. Then, the percent reductions were recomputed such that the future
year emissions would match those for facilities originally projected from the 2005 platform. EPA did not
retain consent decree controls if the emissions in 2007 (2008 NEI) were less than the controlled future year
emissions based on the 2005 platform. The remaining consent decree controls are in sixteen states (AL, CA,
IN, KS, KY, LA, MI, MS, MO, OH, OK, TN, TX, UT, WI, WY) and accounted for 3,835 tons of NOX and
37,368 tons of SO2 cumulative reductions in 2018 and approximately 3,411 tons of NOx and 36,878 tons of
SO2 cumulative reductions in 2030. The control packet is used for both 2018 and 2030, but compliance
dates for 2 facilities are in late-2018; therefore these controls were not applied in 2018 but were applied for
2030.
4.2.6 All other PROJECTION and CONTROL packets (ptnonipm, nonpt)
This section describes all remaining non-EGU stationary sources not already discussed. These control
packets and projection packets generally have lesser national-level impact on future year projections than
those items above. However, some of the consent decrees discussed below have significant local impacts.
4.2.6.1 Aircraft growth (ptnonipm)
Packets: "PROJECTION 2008 2018 aircraft": "PROJECTION 2008 2030 aircraft"
Aircraft emissions are contained in the ptnonipm inventory. These 2008 point-source emissions are
projected to future years by applying activity growth using data on itinerant (ITN) operations at airports.
The ITN operations are defined as aircraft take-offs whereby the aircraft leaves the airport vicinity and lands
at another airport, or aircraft landings whereby the aircraft has arrived from outside the airport vicinity. We
used projected ITN information available from the Federal Aviation Administration's (FAA) Terminal Area
Forecast (TAP) System: http://www.apo.data.faa.gov/main/taf.asp (publication date March, 2012). This
information is available for approximately 3,300 individual airports, for all years up to 2030. EPA
67
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aggregated and applied this information at the national level by summing the airport-specific (U.S. airports
only) ITN operations to national totals by year and by aircraft operation, for each of the four available
operation types: commercial, general, air taxi, military. Growth factors were computed for each operation
type by dividing future-year 2018 and 2030 ITN by 2008-year ITN. EPA assigned factors to inventory SCCs
based on the operation type.
The methods that the FAA used for developing the ITN data in the TAP are documented in:
http://www.faa.gov/about/office_org/headquarters_offices/apl/aviation_forecasts/taf_reports/media/TAF_su
mrnary report FY20112040.pdf. Table 4-26 provides the national growth factors for aircraft; all factors are
applied to year 2008 emissions. For example, year 2018 commercial aircraft emissions are 7.5% higher than
year 2008 emissions.
None of the aircraft emission projections account for any control programs. EPA considered the NOx
standard adopted by the International Civil Aviation Organization's (ICAO) Committee on Aviation
Environmental Protection (CAEP) in February 2004, which is expected to reduce NOx by approximately 3%
in 2020. However, this rule has not yet been adopted as an EPA (or U.S.) rule; therefore, the effects of this
rule were not included in the future-year emissions projections.
Table 4-26. Factors used to project 2008 base-case aircraft emissions to 2020
sec
2270008005
2275000000
2275001000
2275020000
2275050000
2275050011
2275050012
2275060000
2275060011
2275060012
2275070000
27501014
27501015
27502001
27502011
27505001
27505011
27601014
27601015
27602011
Description
Commercial Aircraft: Diesel Airport Ground Support Equipment, Air Ground Support
Equipment
All Aircraft Types and Operations
Military Aircraft, Total
Commercial Aviation, Total
General Aviation, Total
General Aviation, Piston
General Aviation, Turbine
Air Taxi, Total
Air Taxi, Total: Air Taxi, Piston
Air Taxi, Total: Air Taxi, Turbine
Commercial Aircraft: Aircraft Auxiliary Power Units, Total
Military aircraft: Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Military;
Jet Engine : JP -4
Military aircraft, This SCC is in 2005v2: Internal Combustion Engines; Fixed Wing Aircraft L
& TO Exhaust; Military; Jet Engine: JP-5
Commercial Aircraft, Total, This SCC is in 2005v2 NEI: Internal Combustion Engines; Fixed
Wing Aircraft L & TO Exhaust; Commercial; Piston Engine: Aviation Gas
Commercial Aircraft, Total, This SCC is in 2005v2 NEI: Internal Combustion Engines; Fixed
Wing Aircraft L & TO Exhaust; Commercial; Jet Engine: Jet A
General Aviation Total. This SCC is in 2005v2 NEI: Internal Combustion Engines; Fixed
Wing Aircraft L & TO Exhaust; Civil; Piston Engine: Aviation Gas
General Aviation Total. This SCC is in 2002 NEI: Internal Combustion Engines; Fixed Wing
Aircraft L & TO Exhaust; Civil; Jet Engine: Jet A
Military aircraft: Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust;
Military; Jet Engine: JP-4
Military aircraft: Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust;
Military; Jet Engine: JP-5
Commercial aircraft: Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust;
Commercial; Jet Engine: Jet A
2018
1.0750
1.0750
1.0620
1.0750
0.9206
0.9206
0.9206
0.9381
0.9381
0.9381
1.0750
1.0620
1.0620
1.0750
1.0750
0.9206
0.9206
1.0620
1.0620
1.0750
2030
1.3502
1.3502
1.0623
1.3502
0.9709
0.9709
0.9709
1.0965
1.0965
1.0965
1.3502
1.0623
1.0623
1.3502
1.3502
0.9709
0.9709
1.0623
1.0623
1.3502
68
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4.2.6.2 Oil and gas projections in TX, and non-California WRAP states (nonpt)
EPA used a suite of intermediate-year projected WRAP Phase III oil and gas emissions for all future year
scenarios. These intermediate-year inventories are 2008 for the Permian basin, and 2010, 2012 or 2015 for
the remaining WRAP area basins. These point and nonpoint inventories are discussed in the 2007 base case
Sections 2.1.2 and 2.2.3, respectively. Summaries of these mid-term projections are posted on the WRAP
Phase III oil and gas project website: http://www.wrapair2.org/PhaseIII.aspx. As discussed in Section
4.2.6.2, for drilling rig operations in the remaining counties in Texas (non-Permian basin), EPA applied year-
2018 projections from a TCEQ report
(http://www.tceq.state.tx.us/assets/public/implementation/air/am/contracts/reports/ei/5820783985FY0901-
20090715-ergi-Drilling Rig El.pdf).
4.3 Mobile source projections
EPA analyzed emission impacts of the Tier 3 vehicle emissions and fuel standards by comparing projected
emissions for future years without the Tier 3 rule (reference scenario) to projected emissions for future years
with the Tier 3 standards in place (control scenario). Table 4-27 presents an overview of the reference and
control scenarios for calendar years 2018 and 2030. Both scenarios reflect the renewable fuel volumes and
market fractions projected by the Annual Energy Outlook 2013 Report ("AEO2013")38
38 U.S. Energy Information Administration, Annual Energy Outlook 2013 (April 15, 2013)
69
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Table 4-27. Overview of Reference and Control Scenarios
Reference Scenario
Control Scenario
2018
Renewable Fuels: AEO 2013a
17.5 B gallons renewable fuels
(18.3 B ethanol-equivalent gallons):
16.0 B gallons ethanol: E10b, E15C, E85d
Fuel Sulfur Level: 30 ppm (10 ppm California)
Fleet:e
96 percent Tier 2 and older vehicles
4 percent LEV III vehicles
Renewable Fuels: AEO 2013a
17.5 B gallons renewable fuels
(18.3 B ethanol-equivalent gallons):
16.0 B gallons ethanol: E10b, E15C, E85d
Fuel Sulfur Lev el: 10 ppm
Fleet:e
86 percent Tier 2 and older vehicles
14 percent Tier 3/LEV III vehicles
2030
Renewable Fuels: AEO 2013a
17.6 B gallons renewable fuels
(18.6 B ethanol-equivalent gallons):
15.3 B gallons ethanol: E10b, E15C, E85d
Fuel Sulfur Level: 30 ppm (10 ppm California)
Fleet:e
76 percent Tier 2 and older vehicles
24 percent LEV III vehicles
Renewable Fuels: AEO 2013a
17.6 B gallons renewable fuels
(18.6 B ethanol-equivalent gallons):
15.3 B gallons ethanol: E10b, E15C, E85d
Fuel Sulfur Lev el: 10 ppm
Fleet:e
21 percent Tier 2 and older vehicles
79 percent Tier 3/LEV III vehicles
aU.S. Energy Information Administration, Annual Energy Outlook 2013 (April 15, 2013)
b Gasoline containing 10 percent ethanol by volume
0 Gasoline containing 15 percent ethanol by volume
d Gasoline containing up to 85 percent ethanol by volume (74 percent nominal used in this analysis)
e Fraction of the vehicle population
The reference scenarios assumed an average fuel sulfur level of 30 ppm in accordance with the Tier 2
gasoline sulfur standards. Under the Tier 3 program, federal gasoline will contain no more than 10 ppm
sulfur on an annual average basis by January 1, 2017, and therefore EPA assumed a nationwide fuel sulfur
level of 10 ppm for both future year control cases.
EPA assumed a continuation of the existing Tier 2 standards for model years 2017 and later in modeling
emissions for the reference scenario, with the exception of California and Section 177 states that have
adopted the LEV III program. The Tier 3 control scenario modeled the suite of exhaust and evaporative
emission standards for light-duty vehicles (LDVs), light duty trucks (LDTs: 1-4), medium passenger vehicles
(MDPVs) and large pick-ups and vans (Class 2b and 3 trucks) including:
Fleet average Federal Test Procedure (FTP) NMOG+NOx standards of 30 mg/mi for LDVs, LDTs
and MDPVs, phasing in from MYs 2017 to 2025 for the light-duty fleet under 6,000 Ibs. GVWR and
phasing in from MYs 2018 to 2025 for the light-duty fleet over 6,000 Ibs. GVWR, and MDPVs
Fleet average Supplemental Federal Test Procedure (SFTP) NMOG+NOx standards of 50 mg/mi for
LDVs, LDTs and MDPVs, phasing in from MYs 2017 to 2025 for the light-duty fleet under 6,000
Ibs. GVWR and phasing in from MYs 2018 to 2025 for the light-duty fleet over 6,000 Ibs. GVWR,
and MDPVs
Per-vehicle FTP PM standard of 3 mg/mi for LDVs, LDTs and MDPVs, phasing in from MYs 2017
to 2022 for the light-duty fleet under 6,000 Ibs. GVWR and phasing in from MYs 2018 to 2022 for
the light-duty fleet over 6,000 Ibs. GVWR, and MDPVs
70
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Per-vehicle US06-only PM standard of 10 mg/mi for LDVs, LDTs and MDPVs through MY2021
and of 6 mg/mi for MY2022 and later model years
New standards for Class 2b and 3 trucks phasing in by MY 2022 including NMOG+NOx declining
fleet average, and more stringent PM standards
More stringent evaporative emission standards for diurnal plus hot soak emissions, a new canister
bleed test and emission standard, and new requirements addressing evaporative leaks on in-use
vehicles.
New refueling emission control requirements for all complete HDGVs equal to or less than 14,000
Ibs GVWR (i.e., Class 2b/3 HDGVs), starting in the 2018 model year, and for all larger HDGVs by
the 2022 model year
Implementation of the Tier 3 standards is aligned with the model year 2017-2025 Light-Duty GHG
standards39 to achieve significant criteria pollutant and GHG emissions reductions while providing
regulatory certainty and compliance efficiency to the auto and oil industries. Accordingly, the analyses for
the Tier 3 rule include the final LD GHG standards in both the reference and control scenarios, and thus
account for their impacts on the future vehicle fleet and future fuel consumption.
The analysis described here accounts for the following national onroad rules:
Tier 2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control Requirements (65 FR 6698,
February 10, 2000)
Heavy-Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements
(66 FR 5002, January 18, 2001)
Mobile Source Air Toxics Rule (72 FR 8428, February 26, 2007)
Regulation of Fuels and Fuel Additives: Changes to Renewable Fuel Standard Program (75 FR
14670, March 26, 2010)
Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy
Standards for 2012-2016 (75 FR 25324, May 7, 2010)
Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty
Engines and Vehicles (76 FR 57106, September 15, 2011)
2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average
Fuel Economy Standards (77 FR 62623, October 15, 2012)
In addition, the modeling accounts for state and local rules including local fuel standards,
Inspection/Maintenance programs, Stage II refueling controls, the National Low Emission Vehicle Program
(NLEV), and the Ozone Transport Commission (OTC) LEV Program. Furthermore, the Tier 3 emissions
modeling for both the national inventory and air quality analysis includes California's LEV III program and
its associated emission reductions from both California and the states that adopted the LEV III program, in
the baseline scenario.
Onroad mobile sources are comprised of several components and are discussed in the next subsection (4.3.1).
Monthly nonroad mobile emission projections are discussed in subsection 4.3.2. Locomotives and Class 1
and Class 2 commercial marine vessel (C1/C2 CMV) projections are discussed in subsection 4.3.3, and Class
3 (C3) CMV projected emissions are discussed in subsection 4.3.4.
39 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards;
Final Rule (77 FR 62623-63200), October 2012.
71
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4.3.1 Onroad mobile (onroad and onroad_rfl)
The onroad emissions for 2018 and 2030 use the same SMOKE-MOVES system as for the base year (see
Sections 2.5.1 and 2.5.2). Meteorology, speed, spatial and temporal surrogates, representative counties, and
fuel months were the same as for 2007, discussed above.
4.3.1.1 VMT and vehicle population
EPA estimates of total national Vehicle Miles Travelled (VMT) in 2018 and 2030 came from DOE's Annual
Energy Outlook (AEO) 2013 early release (http://www.eia.gov/forecasts/aeo/). The VMT was allocated
between vehicle types using a version of MOVES2010b that had been modified with VMT growth factors
from the AEO and with historical data from FHWA
(http://www.fhwa.dot.gov/policyinformation/statistics.cfm). The growth rates by SCC were applied to the
VMT values from the 2007 base year in each county to generate the future year VMT estimates. These
VMT values were normalized such that the total national VMT from the growth calculations matched the
total VMT estimates in the AEO for 2018 and 2030. Vehicle populations by county, month and vehicle type
were estimated by dividing annual VMT by national estimates of annual VMT per vehicle.
Tank trucks are used to transport ethanol mandated by EISA from production facilities to bulk terminals and
from terminals to bulk plants and dispensing facilities. Impacts of this activity on emissions from tank trucks
transporting ethanol (Class 8 trucks) are accounted for in these inventories by adjusting VMT used in
SMOKE-MOVES. The VMT adjustments were derived from an Oak Ridge National Laboratory analysis of
ethanol transport (Oak Ridge National Laboratory, 2009), scaled to account for the difference in ethanol
volume for AEO 2013 future year projections versus the 2007 platform volume of 8.7 billion gallons. It
should be noted that the Oak Ridge analysis only addressed ethanol transport and did not account for impacts
of other biofuels on transportation activity. Impacts from the 2017-2025 light duty vehicle GHG emission
standards were assumed to be negligible in 2018 and were accounted for with a 0.997 scalar in 2030.
4.3.1.2 Set up and Run MOVES to create EF
Emission factor tables were created by running SMOKE-MOVES using the same procedures and models as
2007 (see Section 2.5.1 and the 2007v5 TSD). The same meteorology and the same representative counties
were used. Changes between 2007, 2018, and 2030 are predominantly VMT, fuels, national and local rules,
and the model-year distribution of the fleet, which is built into MOVES. Fleet turnover resulted in a greater
fraction of newer vehicles meeting stricter emission standards.
A detailed list of the differences between the runs for each case are described in
72
-------
Table 4-28.
73
-------
Table 4-28. Comparison of MOVES runs
Year
Case
Version
Run Name
MOVES
code
MOVES
default
database
CDBs
Early NLEV
LEV rates
VMT and
VPOP
GFRE
hcspeciation
Fuels
TierS
control rates
2007
base
20130130
abs2007base 201
30130
moves20121002f
movesdb20121002
f
2007PFCdb146Re
pCnties201 20402
early_nlev
tierS lev standard
s YYYY
nationaldefaultvmt
pop 20120410
N/A
N/A
2007 Baseline 09
062012
N/A
2018
ref
20130609
tier3frm201 8ref 20
130609
moves20121002f
movesdb20121002
k_truncatedgfre
2018PM146Counti
es20120724
N/A
Iev3 standards SS
20130603
nationaldefaultvmtp
op 20120410
non-LEV:
tier3frm3030gfre
LEV:
tier3frm301 Ogfre 0
53013
tier3frm_ref2018_h
cspec M rvpB
tier3frm201 Sreffuel
s 02252013
N/A
2018
ctl
20130610
tier3frm2018ctl 20
130610
moves20121002f
movesdb20121002
k_truncatedgfre
2018PM146Counti
es20120724
N/A
Iev3 standards SS
20130603
nationaldefaultvmtp
op_201 20410
tier3frm301 Ogfre 0
53013
tier3frm_ref2018_h
cspec_M_rvpB
tier3frm2018ctrlfuel
s 03152013
tier3ctldbs_060313
2030
ref
20130603
tier3frm2030ref 20
130603
moves20121002f
movesdb20121002
k_truncatedgfre
2030146Counties2
0130312
N/A
Iev3 standards SS
20130603
nationaldefaultvmtp
op 20120410
non-LEV:
tier3frm3030gfre
LEV:
tier3frm301 Ogfre 0
53013
tier3frm_ref2030_h
cspec M rvpB
tier3frm2030reffuel
s 03072013
N/A
2030
Ctl
20130604
tier3frm2030ctl 20
130604
moves20121002f
movesdb20121002
k_truncatedgfre
2030146Counties2
0130312
N/A
Iev3 standards S
S 20130603
nationaldefaultvmt
pop_201 20410
tier3frm301 Ogfre 0
53013
tier3frm_ref2030_h
cspec M rvpB
tier3frm2030ctrlfuel
s 03152013
tier3ctldbs_060313
The following states were modeled as having adopted the California LEV II and LEV III programs (see
Table 4-29)
Table 4-29. CA LEVIII program states
FIPS
06
09
10
23
24
25
34
36
41
42
44
50
53
State Name
California
Connecticut
Delaware
Maine
Maryland
Massachusetts
New Jersey
New York
Oregon
Pennsylvania
Rhode Island
Vermont
Washington
74
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Early NLEV refers to states which adopted the California "low emission vehicle" (LEV) standards in the
1990's "early", since the California LEV standards were adopted nationally (NLEV) starting in 2001. The
following states were modeled as using the early NLEV program in 2007 (see Table 4-30).
Table 4-30. Early NLEV states
FIPS
09
10
11
23
24
33
34
42
44
50
51
State Name
CONNECTICUT
DELAWARE
DISTRICT OF COLUMBIA
MAINE
MARYLAND
NEW HAMPSHIRE
NEW JERSEY
PENNSYLVANIA
RHODE ISLAND
VERMONT
VIRGINIA
The following table indicates when the LEV states adopted the LEV2 standards and lists the emission
standards databases used in the 2007 base run (see Table 4-31)
Table 4-31. LEV2 states and MOVES databases
FIPS
6
9
10
23
24
25
34
35
36
41
42
44
50
53
State Name
CALIFORNIA
CONNECTICUT
DELAWARE
MAINE
MARYLAND
MASSACHUSETTS
NEW JERSEY
NEW MEXICO
NEW YORK
OREGON
PENNSYLVANIA
RHODE ISLAND
VERMONT
WASHINGTON
Database
tier3 lev standards 1994
tier3 lev standards 2008
tier3 lev standards 2014
tier3 lev standards 2001
tier3 lev standards 2011
tier3 lev standards 1995
tier3 lev standards 2009
tier3 lev standards 2016
tier3 lev standards 1996
tier3 lev standards 2009
tier3 lev standards 2008
tier3 lev standards 2008
tier3 lev standards 2000
tierS lev standards 2009
The use of RVP bins for specifying hydrocarbon speciation (HCspeciation) database for January and July for
both 2018 and 2030 runs are listed by representative county (see Table 4-32) Because the hydrocarbon
speciation is dependent on the level of RVP, four RVP bins were generated based on RVP of the fuels in
each representative county for each month. The HCspeciation database contains hydrocarbon speciation
profiles forNMOG and VOC and applies only to fuels containing 70% or more ethanol by volume (i.e.,
E85).
75
-------
Table 4-32. RVP bins by representative county
FIPS
1073
4013
4019
4021
6025
6037
8031
8041
8101
9003
10003
10005
11001
12011
12033
12086
13015
13051
13121
16001
16027
17031
17093
18019
18089
18097
19095
19133
19153
19163
20091
21029
21111
21117
22033
22047
23005
Jan
2
2
2
2
2
2
3
3
3
2
2
2
2
2
2
2
2
2
2
3
3
2
2
3
2
3
2
2
2
2
2
2
2
2
2
2
2
Jul
2
1
4
4
1
1
3
4
4
1
1
1
1
3
4
3
2
4
2
4
4
1
1
3
1
4
4
4
4
4
2
1
1
1
3
3
2
FIPS
23019
23031
24005
24015
24029
24033
24043
24045
25017
26081
26163
27003
27053
27111
27137
27145
27165
29095
29189
32003
32005
32007
32009
32027
32031
33009
33015
34003
35001
35013
36029
36103
37051
37081
37119
39023
39035
Jan
2
2
2
2
2
2
2
2
2
3
3
4
4
4
4
4
4
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
3
Jul
3
2
1
1
1
1
4
4
1
4
2
4
4
4
4
4
4
2
1
4
4
4
4
4
3
4
1
1
4
4
3
1
4
3
3
3
4
FIPS
39041
39049
39061
39113
39119
39151
40143
41005
41019
41029
41037
41039
41047
41051
41067
41071
42003
42005
42009
42017
42019
42039
42043
42049
42051
42055
42063
42071
42073
42089
42091
42101
42129
44007
46099
47157
Jan
3
3
3
3
3
3
2
3
3
3
3
3
3
3
3
3
3
3
2
2
3
3
2
3
3
2
3
2
3
2
2
2
3
2
4
3
Jul
4
4
3
3
4
4
4
3
4
4
4
4
3
3
3
4
2
2
4
1
2
4
4
4
2
4
4
4
4
4
1
1
2
1
4
3
FIPS
47163
48001
48003
48005
48011
48039
48047
48139
48141
48143
48221
48251
48439
49005
49009
49011
49017
49035
49043
49045
49049
49057
50007
51036
51041
51059
51087
51107
51683
51740
53033
53063
53067
55025
55079
55117
Jan
2
2
2
2
2
1
2
2
2
2
2
2
1
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
3
3
3
4
2
4
Jul
4
2
4
2
4
1
4
2
1
4
2
2
1
4
4
3
4
3
4
4
4
4
4
1
1
1
1
1
1
1
4
4
4
4
1
4
Fuels were projected into the future using estimates from the AEO2013 (http://www.eia.gov/forecasts/aeo/),
release dates April 15* -May 2n 2013. . The fuel supply used in the Tier 3 FRM varies significantly from
that used in the NPRM, including the introduction of a new approach to aggregating fuels by region. For
more information regarding this new approach to fuels, please refer to the Tier 3 FRM, Chapter 7.1.3.2.
Reference fuels contain E10, E15, and E85 at a market share fraction determined regionally from the
AEO2013 report. Additional fuel properties for the reference cases are based on local fuel surveys as well as
EPA fuel compliance and certification reports. All sulfur levels were set at 30ppm in the reference cases in
order to reduce possible calculation artifacts. Fuel property changes associated with this sulfur adjustment
were also calculated appropriately by region. The Tier 3 control cases adjust this sulfur level to lOppm, as
well as adjusting associated fuel properties with this sulfur reduction. Market shares between E10, E15 and
76
-------
E85 remain unchanged between the reference and the control cases but changed across the years (2018
versus 2030).
4.3.1.3 Long Haul truck adjustments and SMOKE-MOVES
A set of adjustments were calculated for SMOKE-MOVES to estimate 2018 and 2030 emissions in each
county to account for extended idle and the use of auxiliary power units (APU). These adjustments use the
same approach as was used in 2007 (see Section 2.5.1.3 and the 2011 NEI vl TSD for details) except for the
vehicle population (VPOP) was updated to be consistent with 2018 or 2030. These adjustments are by
county, vehicle type (long-haul truck SCCs only), and mode (extended idle or APU only) and impacted the
RPV process only. The set of adjustments created for the extended idle mode are identical for APU because
it was assumed that the distribution of APU usage and emissions would mirror extended idle activity.
SMOKE-MOVES, specifically Movesmrg, uses the adjustment factor file (CFPRO) for extended idle and
APU to estimate 2018 and 2030 emissions that incorporates each of these adjustments.
4.3.2 Nonroad mobile (nonroad)
The nonroad sector includes a wide range of mobile emission sources ranging from construction equipment
to hand-held lawn tools. In the nonroad sector, the only emissions that are directly affected by the Tier 3
regulation are the emissions from gasoline-powered equipment such as lawn-mowers, recreational boats and
all-terrain vehicles. Their SO2 emissions are reduced with the decrease in gasoline sulfur levels. As with
onroad, reference and control case emissions were generated using the fuel supply inputs reflecting the
projected fuel volumes from AEO2013.
Gasoline and land-based diesel nonroad emissions were estimated using EPA's NONROAD2008b model40,
as run by the EPA's consolidated modeling system known as the National Mobile Inventory Model
(NMIM).41 The fuels in the NMEVI database, NCD2010201a, were developed from the reference and control
fuels used for onroad vehicles, as described in Section 4.3.1.2. Onroad and nonroad gasoline formulations
are assumed to be identical for all years. In 2018 and 2030, nonroad equipment is assumed to use E10 only.
For all years, the reference case included the higher sulfur reference gasoline and the control case met the
sulfur limits.
This sector includes monthly exhaust, evaporative and refueling emissions from nonroad engines (not
including commercial marine, aircraft, and locomotives) derived from NMEVI for all states except California.
The version of NONROAD models all in-force nonroad controls (see nonroad section under Table 4-1). Not
included are voluntary local programs such as encouraging either no refueling or evening refueling on Ozone
Action Days.
California nonroad emissions
40 This version of NONROAD is very similar to the publicly released version, but it can model ethanol blends up to E20. The
NMIM version is NMIM20090504d, which has the same results as the publicly-released NMIM version NMIM20090504a. The
underlying National County Database (NCD) is NCD20101201a, but with 2007 meteorology inserted into the countymonthhour
table. NCD20101201a includes state inputs for the 2008 NEI.
41 U.S. EPA. 2005 EPA's National Inventory Model (NMIM), A Consolidated Emissions Modeling System for MOBILE6 and
NONROAD; EPA420-R-05-024; Office of Transportation and Air Quality, Ann Arbor, MI.
http://www.epa.gov/otaq/models/nmim/420r05024.pdf
77
-------
Similar to the 2007 base nonroad mobile, NMIM was not used to generate future-year nonroad emissions for
California, other than for NF^. EPA used NMIM for California future nonroad NFL? emissions because
CARB did not provide these data for any nonroad vehicle types. For the rest of the pollutants, EPA
converted the CARB-supplied 2017 (surrogate for 2018) and 2030 nonroad annual inventories to monthly
emissions values by using the NMIM monthly inventories to compute monthly ratios by pollutant and SCC.
Some adjustments to the CARB inventory were needed to convert the provided TOG to VOC and to augment
the HAPs. See Section 3.2.1.3 for details on speciation of California nonroad data. The CARB nonroad
emissions include nonroad rules reflected in the December 2010 Rulemaking Inventory
(http://www.arb.ca.gov/regact/2010/offroadlsi 10/offroadisor.pdf) and those in the March 2011 Rule
Inventory, the Off-Road Construction Rule Inventory for "In-Use Diesel".
4.3.3 Locomotives and Class 1 & 2 commercial marine vessels (c1c2rail)
Recall from Section 2.5.4 that there are several non-NEI components to the clc2rail sector in the 2007 base
case. There are three distinct approaches used to craft year 2018 and 2030 inventories from the 2007 base
case. The first component of the 2018 and 2030 clc2rail inventories is the non-California data projected
from the 2007 base case. The second component is the CARB-supplied year 2017 (used for 2018 scenarios)
and year 2030 data for California. The third component is a set of OTAQ-provided county-specific
emissions adjustments that account for different fuel transport characteristics resulting from the EISA
(RFS2) mandate. Specifically, these adjustments reduce finished petroleum-based fuel transport by rail and
barge (CMV) and add ethanol-based finished fuel transport by rail and barge.
Step 1: Project non-California CMV and rail emissions
Packet: "PROJECTION_2008 2018 clc2rail": "PROJECTION 2008 2030 clc2rail"
In this step, a projection packet creates an intermediate set of year 2018 and year 2030 emissions,
respectively, for all states except California. This packet does not reflect emission impacts from ethanol
volume impacts from the EISA (RFS2) mandate; the EISA impacts are applied for all states in Step 3. This
packet consists of national projection factors by SCC and pollutant between 2007 and 2018 and 2030 that
reflect the May 2004 "Tier 4 emissions standards and fuel requirements"
(http://www.epa.gov/otaq/documents/nonroad-diesel/420r04007.pdf) as well as the March 2008 "Final
locomotive-marine rule" controls (http://www.epa.gov/otaq/regs/nonroad/420f08004.pdf).
Projection factors are based on year 2008 rather than year 2007 for a couple of reasons. First, many states
with large clc2rail emissions utilize the 2008 NEI emissions; Texas is one example. Second, the year 2007
emissions are mostly lower than the 2008 RIA summaries, and these emissions generally decrease in the
future. By choosing year 2008, the projections are unlikely to overly-reduce emissions by years 2018 and
2030. In addition, the 2007 platform emissions are often much different than the RIA emissions for any
year. EPA OTAQ experts determined that the 2007 platform estimates were more up-to date and likely more
reliable than the RIA estimates in 2007/2008 and 2018 and 2030. However, the controls and hence the
relative reductions in the RIA are expected to be fairly close to what would be expected from the 2007
platform. Therefore, EPA simply apply the ratio of the RIA 2018 or 2030 to 2008 emissions to project the
2007 platform emissions. These projection ratios are provided in
78
-------
Table 4-33.
79
-------
Table 4-33. Non-California year 2018 and 2030 Projection Factors for locomotives and Class 1 and Class 2
Commercial Marine Vessel Emissions
sec
2280002X00
2280002X00
2280002X00
2280002X00
2280002X00
2280002X00
2285002006
2285002006
2285002006
2285002006
2285002006
2285002006
2285002007
2285002007
2285002007
2285002007
2285002007
2285002007
2285002008
2285002008
2285002008
2285002008
2285002008
2285002008
2285002009
2285002009
2285002009
2285002009
2285002009
2285002009
2285002010
2285002010
2285002010
2285002010
2285002010
2285002010
Description
Marine Vessels, Commercial;Diesel;Underway & port emissions
Marine Vessels, Commercial;Diesel;Underway & port emissions
Marine Vessels, Commercial;Diesel;Underway & port emissions
Marine Vessels, Commercial;Diesel;Underway & port emissions
Marine Vessels, Commercial;Diesel;Underway & port emissions
Marine Vessels, Commercial;Diesel;Underway & port emissions
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
Railroad Equipment;Diesel;Yard Locomotives
Railroad Equipment;Diesel;Yard Locomotives
Railroad Equipment;Diesel;Yard Locomotives
Railroad Equipment;Diesel;Yard Locomotives
Railroad Equipment;Diesel;Yard Locomotives
Railroad Equipment;Diesel;Yard Locomotives
Pollutant
CO
NOX
PM10
PM25
S02
voc
CO
NOX
PM10
PM25
S02
VOC
CO
NOX
PM10
PM25
S02
VOC
CO
NOX
PM10
PM25
S02
VOC
CO
NOX
PM10
PM25
S02
VOC
CO
NOX
PM10
PM25
S02
VOC
2018
0.9284
0.7042
0.6429
0.6429
0.1213
0.7529
1.1720
0.7523
0.6168
0.6168
0.0334
0.5453
.1709
.1092
.0700
.0722
0.0349
.1740
.0832
0.5292
0.5457
0.5445
0.0286
0.4735
1.0832
0.5292
0.5444
0.5446
0.0335
0.4730
1.1720
0.9948
0.9562
0.9567
0.0337
0.9511
2030
0.9506
0.3931
0.3696
0.3696
0.0647
0.4065
1.4180
0.4426
0.2939
0.2939
0.0405
0.2914
.4174
.1014
.0250
.0206
0.0349
.4189
.1915
0.2723
0.1926
0.1908
0.0343
0.1527
1.1918
0.2723
0.1918
0.1906
0.0335
0.1535
1.4180
0.7533
0.7271
0.7269
0.0404
0.6851
The future-year locomotive emissions account for increased fuel consumption based on Energy Information
Administration (EIA) fuel consumption projections for freight rail, and emissions reductions resulting from
emissions standards from the Final Locomotive-Marine rule (EPA, 2009d). This rule lowered diesel sulfur
content and tightened emission standards for existing and new locomotives and marine diesel emissions to
lower future-year PM, SC>2, and NOx, and is documented at: http://www.epa.gov/otaq/marine.htmtf2008final.
EPA applied HAP factors for VOC HAPs by using the VOC projection factors to obtain 1,3-butadiene,
acetaldehyde, acrolein, benzene, and formaldehyde. C1/C2 CMV diesel emissions (SCC = 2280002100 and
2280002200) were projected based on the Final Locomotive Marine rule national-level factors provided in
Table 4-33. Similar to locomotives, VOC HAPs were projected based on the VOC factor.
80
-------
Step 2: Intermediate California year 2018 and year 2030 inventories
Obtained from CARB, the locomotive, and class 1 and 2 commercial marine emissions used for California
reflect year 2017 and year 2030 and include nonroad rules reflected in the December 2010 Rulemaking
Inventory (http://www.arb.ca.gov/regact/2010/offroadlsi 10/offroadisor.pdf), those in the March 2011 Rule
Inventory, the Off-Road Construction Rule Inventory for "In-Use Diesel", cargo handling equipment rules in
place as of 2011 (see http://www.arb.ca.gov/ports/cargo/cargo.htm), and the 2007 and 2010 regulations to
reduce emissions diesel engines on commercial harbor craft operated within California waters and 24
nautical miles of the California baseline.
The C1/C2 CMV emissions for both 2017 and 2030 were obtained from the CARB nonroad mobile dataset
"ARMJ_RF#2002_ANMJAL_MOBILE.txt". These emissions were developed using Version 1 of the
CEP AM which supports various California off-road regulations. The locomotive emissions were obtained
from the CARB trains dataset "ARMJ_RF#2002_ANMJAL_TRAINS.txt". Documentation of the CARB
offroad methodology, including clc2rail sector data, is provided here:
http://www.arb.ca.gov/msei/categories.htm#offroad_motor_vehicles. EPA converted the CARB inventory
TOG to VOC by dividing the inventory TOG by the available VOC-to-TOG speciation factor. Note there
was no attempt to modify year 2017 CARB emissions to year 2018 via linear interpolation or other schemes.
EPA simply assigned year 2018 emissions as the 2017 CARB submittal with these medications as well as
those discussed next in step 3.
Step 3: Adjusting intermediate year 2018 and 2030 clc2rail emissions to reflect the EISA mandate and
LD GHG emission standards
Rail category 1 and commercial marine activity are impacted by both the EISA mandate and the 2017-2025
light-duty vehicle greenhouse gas emission standards. The inventories were adjusted to account for both.
Inventories were adjusted to account for (a) differences in C1/C2 CMV and rail emission rates in 2018 and
2030 versus 2007, (b) the difference in ethanol volume impacts for AEO 2013 future year projections versus
the 2007 platform volume of 8.7 billion gallons and (c) impacts on gasoline production and distribution from
the 2017-2025 light duty vehicle GHG emissions standards (EPA, 2013b). Adjustments to C1/C2 CMV and
rail emission rates were calculated using ton/ton-mile emission factors that were obtained from the Tier 3
proposed rule inventory and AEO 2013 projected domestic shipping estimates (EPA, 2013c; Energy
Information Administration, 2013). The ethanol volume impacts were allocated to individual counties using
factors developed from the ORNL analysis (ORNL, 2013). Impacts on C1/C2 CMV and rail emissions from
the 2017-2025 light duty vehicle GHG emission standards were calculated using the RFS2 impacts
spreadsheet. Impacts from the 2017-2025 light duty vehicle GHG emission standards were assumed to be
negligible in 2018 but were accounted for by the scalars in Table 4-34 and Table 4-35 for 2030.
Table 4-34. Scalars Applied to Rail Combustion Emissions in 2030 to account for 2017-2025 LDGHG
emission standards
Pollutant
CO
NOX
PM2.5
PM10
SO2
VOC
Scalar
0.998
0.998
0.997
0.996
0.997
0.997
81
-------
Table 4-35. Scalars Applied to C1/C2 Combustion Emissions in 2030 to account for 2017-2025 LDGHG
emission standards
Pollutant
CO
NOX
PM2.5
PMio
SO2
voc
Scalar
0.996
0.993
0.994
0.993
0.991
0.978
These emissions from updated ethanol volumes via RFS/EISA and LDGHG are not included in the
previously-discussed non-California loco-marine rule-based projections (Step 1) and CARB 2017 and 2030
inventories (Step 2). Nationally, these adjusted emissions are modest.
On average, for year 2018 rail emissions, the impact of the adjustment for transporting more ethanol is an
increase in emissions by 1.24%, and the adjustment for transporting less gasoline is -0.04%, leaving a small
increase in rail emissions in year 2018. For year 2030 rail emissions, the impact of the adjustment for
transporting more ethanol is an increase in emissions by 0.92%, and the adjustment for transporting less
gasoline is -0.06%, and the adjustment for the LD GHG rule is a reduction of 0.24%, leaving a small increase
in emissions in year 2030. These small increases in rail emissions are evident in Table 4-36. For CMV
emissions in year 2018, the impact of transporting more ethanol by 0.21% is offset by the adjustment for
transporting less gasoline by -1.32%. For 2030, as shown in the "2030 Error" column in Table 4-36, we
erroneously misapplied these RFS2 as well as LDGHG decreasing emissions adjustments: increased ethanol
by 0.19%, decreased gasoline by 2.05% and LDGHG rule reductions of-0.78%. In short, in year 2030,
national total NOx emissions from C1/C2 CMV are 2,678 tons larger than intended, a 0.6% error for C1/C2
CMV nationally.
Table 4-36. Cumulative RFS2 and LDGHG adjustments to clc2rail sector emissions
Pollutant
CO
CO
NH3
NH3
NOX
NOX
PM10
PM10
PM25
PM25
SO2
SO2
VOC
VOC
Source
cmv
rail
cmv
rail
cmv
rail
cmv
rail
cmv
rail
cmv
rail
cmv
rail
2018
Adjustment
-855
1,715
-2
5
-3,635
8,346
-139
198
-155
-10
-296
80
-136
357
Final
2018
88,226
152,424
218
358
343,725
683,017
11,584
18,530
11,102
16,857
5,380
482
11,452
28,272
2030
Applied
adjustment
0
1,296
0
5
0
2,678
0
31
0
-36
0
-7
0
91
Final
2030
92,593
185,916
225
357
197,537
430,577
7,060
9,672
6,773
8,826
4,249
478
7,484
17,143
Intended
2030
90,360
185,916
221
357
192,590
430,577
6,869
9,672
6,504
8,826
3,870
478
7,293
17,143
2030
Error
2,233
0
4
0
4,947
0
192
0
269
0
379
0
191
0
82
-------
4.3.4 Class 3 commercial marine vessels (cSmarine)
As discussed in Section 2.5.5, the cSmarine sector emissions data were developed for year 2002 and
projected to year 2007 for the 2007 base case. The ECA-IMO project provides pollutant and geographic-
specific projection factors to year 2007, and also projection factors to years 2018 and 2030 that reflect
assumed growth and final ECA-IMO controls. The ECA-IMO rule, published in December 2009, applies to
Category 3 (C3) diesel engines (engines with per cylinder displacement at or above 30 liters) installed on
U.S. vessels. The ECA-IMO rule includes an implementation of Tier 2 and Tier 3 NOx limits for C3 engines
beginning in 2011 and 2016, respectively. The ECA-IMO rule also imposes fuel sulfur limits of 1,000 ppm
(0.1%) by 2015 in the EGA region -generally within 200 nautical miles of the U.S. and Canadian coastlines,
as well as 5,000 ppm (0.5%) for "global" areas -those areas outside the EGA region. For comparison, with
the exception of some local areas, year 2007 sulfur content limits are as high as 15,000 ppm (1.5%) in U.S.
waters and 45,000 ppm (4.5%) in global areas. More information on the ECA-IMO rule can be found in the
Category 3 marine diesel engines Regulatory Impact Assessment:
http://www.epa.gov/otaq/oceanvessels.htm.
Projection factors for creating the year 2018 and year 2030 cSmarine inventories from the 2007 base case are
provided in Table 4-37. Background on the region and EEZ FIPS is provided in the discussion on the
cSmarine inventory for 2007 -Section 2.5.5. The impact of the Tier 2 and Tier 3 NOx engine standards is
less noticeable because of the inevitable delay in fleet turnover for these new engines; however, the
immediate and drastic cuts in fuel sulfur content are obvious. VOC and CO are mostly unaffected by the
engine and fuel standards, thus providing an idea on how much these emissions would have grown without
ECA-IMO controls. VOC HAPs are assigned the same growth rates as VOC.
Table 4-37. Growth factors to project the 2007 ECA-IMO inventory to 2018 and 2030
Region
foot r^oc.+ rcr*\
r/ast Coast (JiCJ
r\iif r^oc.+ tc^C'\
Lrllll Coast (LrCJ
W>,>-t1-i Do^ifi^ rMT>\
iNorui r acme \r\r )
G/Aii+1-i Do/^i-fi/^ /"QDA
oOUUl r aClIlC (Jf )
rvoo+ T oi^oc. ^r^i \
Lrreat Lakes (LrLj
Ontoirl<=. T7P A
UUISlQe £/l_,/v
EEZ FIPS
ornn/i
OJUU4
ornno
OJUUJ
ornni
OJUU1
oĞf\m
OJUUZ
n/a
Qsooi
Year
2018
2030
2018
2030
2018
2030
2018
2030
2018
2030
2018
2030
NOX
1.166
0.900
0.984
0.633
1.061
0.773
.255
.009
.023
.013
.401
2.083
PM10
0.221
0.373
0.187
0.264
0.197
0.293
0.241
0.456
0.159
0.195
1.606
0.612
PM25
0.219
0.373
0.186
0.265
0.196
0.297
0.239
0.457
0.158
0.195
1.606
0.606
VOC
1.623
2.752
1.370
1.930
1.429
2.128
1.731
3.290
1.204
1.474
1.606
2.778
CO
1.623
2.752
1.370
1.930
1.429
2.126
1.730
3.254
1.204
1.474
1.606
2.778
SO2
0.058
0.098
0.049
0.069
0.055
0.082
0.068
0.128
0.043
0.052
1.606
0.506
4.4 Canada, Mexico, and Offshore sources (othar, othon, and othpt)
Emissions for Canada and offshore sources were not projected to future years, and are therefore the same as
those used in the 2007 base case. Canada did not provide future-year emissions that were consistent with the
base year emissions. The Mexico emissions are based on year 1999 but projected to years 2018 and 2030. A
background on the development of year-2018 and year-2030 Mexico emissions from the 1999 inventory is
available at: http://www.wrapair.org/fonams/ef/inventories/MNEI/index.html.
83
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5 Emission Summaries
The following tables summarize the emissions for the 2007 base case, 2018 reference and control cases, and
the 2030 reference and control cases. These summaries are provided for the contiguous U.S. and for the
portions of Canada and Mexico inside the smaller 12km domain (12US2) discussed in Section 3.1. The
afdust sector emissions represent the summaries after application of both the land use (transport fraction) and
meteorological adjustments (see Section 2.2.1); therefore, this sector is called "afdust-adj" in these
summaries. The onroad and onroad refueling (onroad_rfl) sector totals are post-SMOKE-MOVES totals,
representing air quality model-ready emission totals. Biogenic emissions totals are only given within the
United States. The "c3marine-US" sector represents c3marine sector emissions with U.S. FIPS only; these
extend to roughly 3-5 miles offshore and include all U.S. waters in the Great Lakes along with all U.S. ports.
The Offshore c3marine represents all non-U.S. c3marine emissions outside of U.S. state waters. The
c3marine sector is discussed in Section 2.5.5. The "Off-shore othpt" sector is the non-Canada, non-Mexico
component of the othpt sector, i.e., the offshore oil platform emissions from the 2008 NEI.
National emission totals by air quality model-ready sector are provided for all CAP emissions for the 2007
base case and 2007 evaluation case in Table 5-1. The total of all U.S. emissions in all sectors is provided.
The lower portion of the table provides the non-U.S. emissions including subtotals for Canada, Mexico, and
all non-U.S. emissions. Tables 5-2 through 5-5 provide similar CAP emission totals by sector for the 2018
reference case, 2018 control case, 2030 reference case, and 2030 control case, respectively.
Table 5-6 provides total emissions for CO by state for all five cases: 2007 base case, 2018 reference and
control cases, and 2030 reference and control cases. Tables 5-7 through 5-12 provide the same summaries
for NHs, NOx, PM2.5, PMio, SO2 and VOC, respectively. Note that all of these tables use average fire
emissions and do not include biogenic emissions. Emission totals by state for each modeling platform
sector, for CAPs and air quality model species, can be found in these spreadsheets available in the docket:
2007rg_state_totals.xlsx, 2018rg_ref2_state_totals.xlsx, 2018rg_ctl_state_totals.xlsx,
2030rg_ref_state_totals.xlsx, and 2030rg_ctl_state_totals.xlsx.
84
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Table 5-1. National and non-U.S. CAP emissions by sector for 2007 base case
US Totals
Sector
afdust
ag
biogenic
clc2rail
nonpt
nonroad
onroad
avefire
ptipm
ptnonipm
c3marine
Total
CO
6,522,111
218,827
4,329,593
17,834,128
38,519,970
18,347,571
703,760
2,963,103
12,724
89,451,787
NH3
3,595,613
557
155,297
1,915
140,897
300,999
25,427
67,997
4,288,703
NOX
1,049,976
1,338,134
1,199,385
1,877,955
7,612,080
243,561
3,357,349
2,136,694
138,033
18,953,168
PM10
6,124,268
43,832
768,986
187,707
365,017
1,860,459
437,210
582,397
12,476
10,382,352
PM25
863,738
41,015
677,339
178,342
282,304
1,576,667
329,585
406,476
11,452
4,366,917
SO 2
48,805
406,290
100,652
40,008
135,806
9,136,112
1,586,719
104,822
11,559,214
voc
40,283,861
61,547
6,529,064
2,517,282
4,273,876
4,326,863
38,071
1,101,615
4,902
59,137,081
Non-US Totals
Country/Sector
Canada othar
Canada othon
Canada othpt
Canada Subtotal
Mexico othar
Mexico othon
Mexico othpt
Mexico Subtotal
Offshore othpt
Canada c3marine
Offshore
c3marine
Total
CO
2,809,975
1,207,227
571,728
4,588,930
410,176
2,685,132
100,075
3,195,383
82,133
2,607
55,599
7,924,652
NH3
386,148
6,123
15,546
407,816
109,861
14,114
0
123,975
0
531,791
NOX
463,154
150,856
338,967
952,977
171,735
326,165
343,480
841,380
74,277
31,870
674,615
2,575,119
PM10
810,685
6,402
65,952
883,039
71,082
11,805
120,802
203,690
780
2,633
55,891
1,146,033
PM2.5
248,902
5,199
39,787
293,889
47,115
9,115
89,358
145,588
769
2,402
51,386
494,033
SO 2
61,190
3,679
831,669
896,539
53,424
5,462
731,675
790,560
1,021
19,504
417,293
2,124,917
VOC*
932,086
94,610
155,906
1,182,601
450,935
192,045
77,255
720,235
60,756
1,109
23,635
1,988,337
85
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Table 5-2. National and non-U.S. CAP emissions by sector for 2018 reference case
US Totals:
Sector
afdust
ag
biogenic
clc2rail
nonpt
nonroad
onroad*
avefire
ptipm
ptnonipm
c3marine
Total
CO
6,522,111
236,042
4,613,119
12,702,891
16,144,911
18,347,571
857,784
2,639,792
18,960
62,083,182
NH3
3,717,417
567
157,772
2,276
80,053
300,999
40,255
68,053
4,367,393
NOX
1,049,976
1,008,203
1,185,288
1,067,481
2,495,021
243,561
1,941,441
2,010,662
151,489
11,153,123
PM10
6,139,289
29,525
808,719
106,715
191,678
1,860,459
295,290
536,133
2,530
9,970,338
PM25
866,710
27,390
715,966
100,588
111,049
1,576,667
233,699
368,215
2,304
4,002,588
SO2
5,693
317,310
2,670
26,089
135,806
2,131,278
957,178
5,734
3,581,760
voc
40,283,861
39,272
6,423,764
1,408,935
1,800,164
4,326,863
46,057
1,013,798
7,290
55,350,004
Non-US Totals
Country/Sector
Canada othar
Canada othon
Canada othpt
Canada Subtotal
Mexico othar
Mexico othon
Mexico othpt
Mexico Subtotal
Offshore othpt
Canada c3marine
Offshore
c3marine
Total
CO
2,809,975
1,207,227
571,728
4,588,930
527,942
2,572,443
148,760
3,249,146
82,133
3,750
87,463
8,011,422
NH3
386,148
6,123
15,546
407,816
109,840
15,225
0
125,065
0
532,881
NOX
463,154
150,856
338,967
952,977
226,387
304,436
544,711
1,075,534
74,277
34,364
807,580
2,944,732
PM10
810,685
6,402
65,952
883,039
70,937
13,185
170,913
255,035
780
518
32,180
1,171,552
PM2.5
248,902
5,199
39,787
293,889
47,201
10,317
127,736
185,254
769
469
29,537
509,917
SO2
61,190
3,679
831,669
896,539
19,287
3,177
1,066,523
1,088,987
1,021
1,054
190,969
2,178,569
VOC*
932,086
94,610
155,906
1,182,601
577,084
178,219
94,351
849,654
60,756
1,595
37,173
2,131,779
86
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Table 5-3. National and non-U.S. CAP emissions by sector for 2018 control case
US Totals:
Sector
afdust
ag
biogenic
clc2rail
nonpt
nonroad
onroad*
avefire
ptipm
ptnonipm
c3marine
Total
CO
6,522,111
236,042
4,613,119
12,702,891
15,893,583
18,347,571
857,784
2,639,792
18,960
61,831,854
NH3
3,717,417
567
157,772
2,276
80,053
300,999
40,255
68,053
4,367,393
NOX
1,049,976
1,008,203
1,185,288
1,067,481
2,248,595
243,561
1,941,441
2,010,662
151,489
10,906,697
PM10
6,139,289
29,525
808,719
106,715
191,209
1,860,459
295,290
536,133
2,530
9,969,869
PM25
866,710
27,390
715,966
100,588
110,617
1,576,667
233,699
368,215
2,304
4,002,157
SO2
5,693
317,310
1,912
11,493
135,806
2,131,278
957,178
5,734
3,566,405
voc
40,283,861
39,272
6,423,764
1,408,935
1,757,359
4,326,863
46,057
1,013,798
7,290
55,307,199
Non-US Totals
Country/Sector
Canada othar
Canada othon
Canada othpt
Canada Subtotal
Mexico othar
Mexico othon
Mexico othpt
Mexico Subtotal
Offshore othpt
Canada c3marine
Offshore
c3marine
Total
CO
2,809,975
1,207,227
571,728
4,588,930
527,942
2,572,443
148,760
3,249,146
82,133
3,750
87,463
8,011,422
NH3
386,148
6,123
15,546
407,816
109,840
15,225
0
125,065
0
532,881
NOX
463,154
150,856
338,967
952,977
226,387
304,436
544,711
1,075,534
74,277
34,364
807,580
2,944,732
PM10
810,685
6,402
65,952
883,039
70,937
13,185
170,913
255,035
780
518
32,180
1,171,552
PM2.5
248,902
5,199
39,787
293,889
47,201
10,317
127,736
185,254
769
469
29,537
509,917
SO2
61,190
3,679
831,669
896,539
19,287
3,177
1,066,523
1,088,987
1,021
1,054
190,969
2,178,569
VOC*
932,086
94,610
155,906
1,182,601
577,084
178,219
94,351
849,654
60,756
1,595
37,173
2,131,779
87
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Table 5-4. National and non-U.S. CAP emissions by sector for 2030 reference case
US Totals:
Sector
afdust
ag
biogenic
clc2rail
nonpt
Nonroad
onroad*
Avefire
Ptipm
Ptnonipm
c3marine
Total
CO
6,522,111
273,741
4,622,228
13,926,036
14,434,283
18,347,571
1,064,259
2,693,137
29,906
61,913,273
NH3
3,784,658
574
157,772
2,702
84,796
300,999
49,461
67,596
4,448,559
NOX
1,049,976
617,480
1,198,956
715,617
1,367,429
243,561
1,997,303
2,022,539
116,493
9,329,353
PM10
6,174,969
16,392
809,404
68,626
175,639
1,860,459
300,645
532,186
3,972
9,942,292
PM25
873,719
15,270
716,460
63,839
82,137
1,576,667
238,259
364,598
3,654
3,934,603
SO 2
4,633
318,053
3,045
22,310
135,806
2,188,169
935,044
9,104
3,616,164
voc
40,283,861
24,372
6,353,099
1,182,812
1,260,883
4,326,863
52,061
1,001,777
11,479
54,497,206
Non-US Totals
Country/Sector
Canada othar
Canada othon
Canada othpt
Canada Subtotal
Mexico othar
Mexico othon
Mexico othpt
Mexico Subtotal
Offshore othpt
Canada c3marine
Offshore
c3marine
Total
CO
2,809,975
1,207,227
571,728
4,588,930
794,133
2,673,052
222,044
3,689,230
82,133
5,650
146,574
8,512,517
NH3
386,148
6,123
15,546
407,816
109,861
17,507
0
127,368
0
535,184
NOX
463,154
150,856
338,967
952,977
326,219
293,017
812,593
1,431,829
74,277
26,704
784,389
3,270,176
PM10
810,685
6,402
65,952
883,039
75,903
17,129
249,006
342,038
780
780
23,561
1,250,198
PM2.5
248,902
5,199
39,787
293,889
51,815
13,873
185,682
251,370
769
718
21,600
568,346
SO 2
61,190
3,679
831,669
896,539
9,909
3,499
1,552,126
1,565,534
1,021
1,584
83,949
2,548,627
VOC*
932,086
94,610
155,906
1,182,601
798,874
180,518
119,095
1,098,487
60,756
2,405
62,442
2,406,691
88
-------
Table 5-5. National and non-U.S. CAP emissions by sector for 2030 control case
US Totals:
Sector
afdust
ag
biogenic
clc2rail
nonpt
nonroad
onroad*
avefire
ptipm
ptnonipm
c3marine
Total
CO
6,522,111
273,741
4,622,228
13,926,036
10,780,871
18,347,571
1,064,259
2,693,137
29,906
58,259,861
NH3
3,784,658
574
157,772
2,702
84,796
300,999
49,461
67,596
4,448,559
NOX
1,049,976
617,480
1,198,956
715,617
1,018,962
243,561
1,997,303
2,022,539
116,493
8,980,886
PM10
6,174,969
16,392
809,404
68,626
166,388
1,860,459
300,645
532,186
3,972
9,933,042
PM25
873,719
15,270
716,460
63,839
73,619
1,576,667
238,259
364,598
3,654
3,926,085
SO 2
4,633
318,053
2,176
10,040
135,806
2,188,169
935,044
9,104
3,603,025
voc
40,283,861
24,372
6,353,099
1,182,812
1,079,042
4,326,863
52,061
1,001,777
11,479
54,315,365
Non-US Totals
Country/Sector
Canada othar
Canada othon
Canada othpt
Canada Subtotal
Mexico othar
Mexico othon
Mexico othpt
Mexico Subtotal
Offshore othpt
Canada c3marine
Offshore
c3marine
Total
CO
2,809,975
1,207,227
571,728
4,588,930
794,133
2,673,052
222,044
3,689,230
82,133
5,650
146,574
8,512,517
NH3
386,148
6,123
15,546
407,816
109,861
17,507
0
127,368
0
535,184
NOX
463,154
150,856
338,967
952,977
326,219
293,017
812,593
1,431,829
74,277
26,704
784,389
3,270,176
PM10
810,685
6,402
65,952
883,039
75,903
17,129
249,006
342,038
780
780
23,561
1,250,198
PM2.5
248,902
5,199
39,787
293,889
51,815
13,873
185,682
251,370
769
718
21,600
568,346
SO 2
61,190
3,679
831,669
896,539
9,909
3,499
1,552,126
1,565,534
1,021
1,584
83,949
2,548,627
VOC*
932,086
94,610
155,906
1,182,601
798,874
180,518
119,095
1,098,487
60,756
2,405
62,442
2,406,691
89
-------
Table 5-6. CO emissions (tons/yr) for each case and state
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
2007rg
1,924,241
1,466,047
1,600,242
6,868,040
1,190,074
574,605
162,704
60,932
5,022,400
3,792,250
1,953,101
2,288,411
1,860,666
906,397
1,085,160
1,313,600
1,901,576
417,956
1,054,787
876,162
2,970,746
2,785,008
1,170,077
2,144,179
1,330,031
531,194
724,335
304,620
1,352,241
677,284
2,641,178
3,273,181
293,318
3,087,510
1,686,238
2,201,777
2018rg_ref
1,360,917
957,043
1,212,877
5,236,858
809,875
357,385
105,413
35,900
3,138,544
2,457,784
1,772,944
1,436,945
1,204,836
498,846
754,279
920,767
1,443,889
251,190
708,961
604,516
1,688,062
2,148,692
835,928
1,422,916
1,214,531
310,062
518,145
209,280
902,273
453,892
1,649,663
1,954,088
209,600
1,726,198
1,214,110
1,749,839
2018rg_ctl
1,351,229
944,935
1,206,827
5,217,036
792,847
354,137
104,551
35,291
3,105,671
2,438,401
1,767,029
1,421,535
1,191,445
492,379
748,374
913,119
1,436,282
248,837
704,541
598,218
1,668,056
2,137,462
829,347
1,411,751
1,210,079
306,155
513,004
206,548
893,855
448,885
1,632,656
1,932,231
208,195
1,703,457
1,204,794
1,740,219
2030rg_ref
1,355,227
1,011,463
1,205,866
5,027,026
872,740
333,723
93,324
36,923
3,230,725
2,418,199
1,772,829
1,493,876
1,214,686
498,959
757,743
919,051
1,461,086
230,967
666,789
578,757
1,628,993
2,094,983
836,297
1,422,427
1,213,708
316,475
542,295
215,511
867,908
445,239
1,615,599
1,907,338
206,779
1,707,663
1,221,253
1,670,492
2030rg_ctl
1,255,631
871,117
1,154,433
5,027,026
763,605
332,897
93,129
30,370
2,815,295
2,227,785
1,738,622
1,330,020
1,095,670
455,151
706,892
844,092
1,378,073
230,019
666,962
577,788
1,486,651
2,011,938
765,258
1,311,400
1,193,598
283,905
490,562
194,314
865,761
413,092
1,608,909
1,749,607
195,525
1,536,234
1,133,433
1,665,955
90
-------
State
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
2007rg
2,460,443
151,235
1,424,534
321,482
1,788,332
5,466,088
824,426
188,782
1,837,272
2,215,115
655,801
1,396,496
656,091
82,878,364
2018rg_ref
1,440,750
106,761
898,530
236,051
1,035,150
3,430,870
589,517
147,145
1,146,294
1,341,308
432,831
919,043
562,905
55,764,204
2018rg_ctl
1,427,500
105,767
890,759
234,446
1,022,408
3,388,430
580,111
145,493
1,134,028
1,322,137
429,529
910,135
559,282
55,269,404
2030rg_ref
1,353,066
101,731
906,112
234,663
1,012,970
3,632,059
590,348
140,898
1,174,277
1,175,059
427,734
957,000
562,888
55,361,723
2030rg_ctl
1,350,221
101,681
822,767
220,361
898,967
3,119,557
538,323
140,486
1,039,507
1,166,307
401,919
861,066
546,433
51,708,312
91
-------
Table 5-7. NHa emissions (tons/yr) for each case and state
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
2007rg
78,589
41,818
135,328
393,315
74,561
5,158
14,044
366
63,410
116,050
130,482
119,703
98,051
301,952
161,525
60,996
84,106
7,264
31,585
7,229
69,890
210,960
68,874
139,500
72,677
179,531
10,483
2,303
11,949
43,094
51,428
187,827
86,696
111,991
112,918
64,640
80,223
1,135
39,330
134,157
2018rg_ref
84,806
41,331
142,800
390,595
74,495
4,612
15,331
281
60,707
123,001
130,704
122,450
101,698
316,176
162,607
62,786
85,334
7,353
32,929
6,421
69,713
214,414
73,643
142,734
73,473
182,275
10,706
2,214
1 1 ,042
42,928
48,036
198,254
88,085
114,657
116,127
65,193
81,438
1,097
40,054
136,513
2018rg_ctl
84,806
41,331
142,800
390,595
74,495
4,612
15,331
281
60,707
123,001
130,704
122,450
101,698
316,176
162,607
62,786
85,334
7,353
32,929
6,421
69,713
214,414
73,643
142,734
73,473
182,275
10,706
2,214
11,042
42,928
48,036
198,254
88,085
114,657
116,127
65,193
81,438
1,097
40,054
136,513
2030rg_ref
86,662
42,090
145,796
395,474
74,938
4,691
15,764
308
62,042
125,862
130,326
126,561
104,955
327,804
162,849
63,724
86,389
7,482
34,163
6,700
71,021
220,096
75,479
145,793
74,049
182,928
10,761
2,266
11,530
43,343
49,238
206,640
89,930
117,372
118,004
65,684
83,198
1,123
41,038
139,008
2030rg_ctl
86,662
42,090
145,796
395,474
74,938
4,691
15,764
308
62,042
125,862
130,326
126,561
104,955
327,804
162,849
63,724
86,389
7,482
34,163
6,700
71,021
220,096
75,479
145,793
74,049
182,928
10,761
2,266
11,530
43,343
49,238
206,640
89,930
117,372
118,004
65,684
83,198
1,123
41,038
139,008
92
-------
State
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
2007rg
42,899
320,410
42,676
8,404
51,371
53,330
16,526
120,740
26,768
4,288,260
2018rg_ref
43,506
323,482
43,006
8,395
51,793
53,641
17,102
120,384
26,877
4,367,201
2018rg_ctl
43,506
323,482
43,006
8,395
51,793
53,641
17,102
120,384
26,877
4,367,201
2030rg_ref
43,838
326,720
42,859
8,507
52,776
54,200
17,436
122,140
26,851
4,448,408
2030rg_ctl
43,838
326,720
42,859
8,507
52,776
54,200
17,436
122,140
26,851
4,448,408
93
-------
Table 5-8. NOx emissions (tons/yr) for each case and state
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
2007rg
419,234
321,030
244,131
1,013,990
316,568
100,231
47,738
14,360
924,488
648,360
97,617
706,849
578,412
277,985
313,735
423,640
547,743
73,939
244,074
184,511
596,927
436,436
264,622
484,132
150,854
232,064
113,273
53,845
279,205
242,966
512,170
470,574
158,552
780,928
422,925
191,065
649,142
24,475
260,234
72,894
2018rg_ref
229,845
166,137
156,429
534,773
193,930
47,836
22,292
6,129
415,734
309,555
68,588
374,954
303,868
181,738
187,508
210,206
386,244
45,873
110,763
98,027
322,122
251,574
152,848
250,506
90,351
154,028
68,916
28,002
156,200
214,189
288,870
240,195
109,343
380,073
276,827
117,844
382,391
13,179
147,083
47,905
2018rg_ctl
224,086
162,417
152,943
534,207
188,903
45,256
21,481
5,797
396,122
295,898
66,444
364,591
296,821
178,314
184,433
205,730
382,129
44,558
106,299
94,083
312,688
245,143
148,935
242,841
88,666
152,036
66,421
26,788
150,033
211,475
278,693
229,296
108,580
367,931
271,902
114,051
373,752
12,437
142,073
47,011
2030rg_ref
194,697
131,427
134,335
401,302
169,439
35,480
16,641
4,603
307,302
232,426
58,280
308,293
253,053
147,307
153,618
179,350
313,834
38,031
86,801
78,391
274,422
203,733
128,683
197,412
75,197
114,138
56,506
24,553
126,532
193,579
233,250
189,772
90,989
308,996
246,670
71,781
323,768
9,836
126,278
36,593
2030rg_ctl
186,751
123,261
129,259
401,302
160,990
33,745
16,142
3,930
278,307
215,596
55,122
291,621
242,065
142,454
149,064
172,780
307,329
37,127
84,079
75,973
260,185
195,604
123,054
186,391
72,858
111,001
52,503
21,983
122,477
189,554
225,541
174,568
89,772
292,899
239,147
69,227
318,208
9,418
119,008
35,209
94
-------
State
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
2007rg
453,075
1,643,416
217,960
23,019
409,546
338,856
283,713
313,623
245,053
17,824,174
2018rg_ref
196,382
1,049,545
159,486
13,139
233,194
195,727
133,980
181,705
202,613
10,108,647
2018rg_ctl
188,871
1,027,205
156,268
12,430
225,015
189,118
132,031
176,143
201,270
9,849,620
2030rg_ref
151,166
915,168
142,187
10,261
186,346
120,291
118,343
161,856
185,500
8,268,417
2030rg_ctl
140,425
877,443
137,588
9,800
171,417
115,573
115,759
152,732
183,713
7,919,951
95
-------
Table 5-9. PM2.5 emissions (tons/yr) for each case and state
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
2007rg
108,906
74,270
108,112
399,796
59,418
11,334
6,098
1,606
167,089
167,573
149,633
114,168
1 1 1 ,294
65,220
114,378
76,841
148,650
13,644
33,292
24,024
92,529
182,166
67,639
113,722
114,123
54,712
47,003
11,396
32,028
73,249
67,804
131,243
45,406
130,887
119,448
136,821
121,002
3,551
67,530
34,265
2018rg_ref
106,546
69,748
104,947
378,847
58,602
10,251
4,945
1,283
140,539
160,387
148,313
103,352
91,698
57,874
110,828
76,393
136,251
12,574
24,795
21,679
89,602
176,876
63,514
104,744
115,121
52,061
47,297
1 1 ,424
24,540
73,117
59,517
109,172
47,772
93,624
113,347
134,681
73,505
3,283
54,106
32,640
2018rg_ctl
106,536
69,738
104,941
378,829
58,625
10,243
4,943
1,282
140,509
160,368
148,322
103,280
91,658
57,863
110,820
76,371
136,244
12,570
24,770
21,663
89,545
176,837
63,507
104,718
115,126
52,055
47,291
11,418
24,520
73,111
59,483
109,152
47,767
93,558
113,336
134,692
73,474
3,280
54,098
32,636
2030rg_ref
105,571
68,653
104,175
373,435
57,615
9,806
4,762
1,173
137,516
158,003
147,911
101,102
91,207
56,419
109,983
75,463
132,771
12,117
24,009
20,663
87,489
174,485
62,644
102,905
113,412
50,565
46,785
11,329
23,033
72,514
56,787
107,654
46,969
91,451
112,259
133,082
71,428
3,154
53,836
32,079
2030rg_ctl
105,391
68,436
104,073
373,435
57,347
9,803
4,761
1,156
136,937
157,632
147,826
100,555
90,845
56,278
109,854
75,273
132,630
12,116
23,994
20,656
86,974
174,123
62,521
102,623
113,360
50,466
46,684
11,251
23,023
72,402
56,772
107,324
46,914
90,922
112,075
133,101
71,413
3,153
53,683
32,023
96
-------
State
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
2007rg
70,324
304,810
42,657
8,798
56,025
81,218
55,146
52,086
88,542
4,361,473
2018rg_ref
62,282
287,235
43,349
10,084
51,254
74,509
37,131
53,545
83,455
4,002,642
2018rg_ctl
62,261
287,156
43,361
10,081
51,225
74,529
37,124
53,507
83,459
4,001,882
2030rg_ref
60,406
280,355
42,052
9,934
50,234
72,302
36,655
53,172
82,734
3,934,060
2030rg_ctl
60,155
279,426
41,932
9,933
49,896
72,341
36,583
52,783
82,690
3,925,542
97
-------
Table 5-10. PMio emissions (tons/yr) for each case and state
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
2007rg
183,182
221,304
213,094
767,701
167,205
14,555
8,759
2,263
293,334
293,557
255,419
360,962
249,264
231,578
431,017
127,958
213,933
18,899
47,375
50,126
183,520
379,202
135,088
313,634
240,329
244,975
207,718
13,604
42,034
499,439
110,972
188,502
184,477
228,328
395,287
202,601
155,601
4,623
126,413
120,429
2018rg_ref
178,922
216,564
209,261
746,021
166,169
13,095
8,981
1,917
267,282
280,599
253,805
349,384
232,131
223,006
427,337
127,539
198,283
17,372
37,502
47,953
179,905
370,541
130,000
301,513
242,981
242,296
208,009
13,375
34,824
499,598
100,461
162,820
187,463
190,228
387,332
200,067
104,914
4,348
110,963
119,324
2018rg_ctl
178,911
216,553
209,254
746,001
166,194
13,085
8,979
1,916
267,250
280,578
253,814
349,306
232,088
222,994
427,329
127,514
198,275
17,368
37,474
47,935
179,843
370,499
129,992
301,485
242,987
242,290
208,002
13,369
34,801
499,591
100,425
162,799
187,458
190,156
387,320
200,080
104,881
4,345
110,955
119,319
2030rg_ref
178,468
216,217
209,464
741,688
166,316
12,687
8,814
1,814
266,055
279,207
253,162
349,836
233,654
223,494
429,770
127,013
195,160
16,930
36,883
47,152
178,640
369,565
129,689
300,857
241,375
242,488
207,635
13,332
33,564
499,279
98,415
161,721
188,449
189,335
387,718
198,682
103,180
4,248
111,480
119,870
2030rg_ctl
178,272
215,981
209,354
741,688
166,024
12,683
8,813
1,796
265,425
278,803
253,069
349,241
233,260
223,341
429,630
126,807
195,006
16,928
36,867
47,144
178,081
369,172
129,555
300,552
241,319
242,380
207,525
13,247
33,554
499,157
98,399
161,362
188,389
188,760
387,518
198,702
103,163
4,246
111,314
119,809
98
-------
State
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
2007rg
112,667
1,214,912
134,321
11,723
89,603
155,262
71,742
125,130
328,164
10,371,781
2018rg_ref
104,066
1,197,608
134,782
13,032
83,362
148,701
52,483
123,545
316,276
9,967,938
2018rg_ctl
104,043
1,197,521
134,795
13,028
83,330
148,723
52,475
123,504
316,281
9,967,113
2030rg_ref
102,637
1,195,116
133,513
12,912
82,778
147,304
52,054
124,019
315,609
9,939,248
2030rg_ctl
102,364
1,194,108
133,383
12,912
82,411
147,346
51,975
123,597
315,562
9,929,998
99
-------
Table 5-11. SO2 emissions (tons/yr) for each case and state
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
2007rg
532,060
95,311
96,013
63,232
74,021
20,638
45,503
1,685
405,072
698,480
28,009
409,821
815,212
189,267
138,105
417,969
252,229
24,257
312,223
84,476
427,357
129,765
95,879
418,246
38,639
77,017
18,667
51,031
61,773
34,258
240,983
440,967
149,603
1,111,660
148,867
31,045
1,102,035
4,576
217,175
13,097
2018rg_ref
120,900
63,737
53,257
47,581
24,643
13,765
8,554
906
102,249
135,516
23,180
187,550
227,783
78,977
48,411
174,597
146,547
6,038
49,690
13,227
200,680
83,686
37,150
189,346
20,432
57,286
14,363
9,421
15,013
25,728
58,409
116,993
32,717
206,919
75,536
26,354
189,787
3,727
66,623
14,981
2018rg_ctl
120,556
63,547
53,084
47,579
24,328
13,603
8,506
882
100,910
134,826
23,072
186,932
227,371
78,817
48,244
174,335
146,284
5,953
49,385
12,908
200,117
83,355
36,918
188,954
20,369
57,181
14,206
9,342
14,554
25,580
57,631
116,434
32,676
206,273
75,257
26,152
189,263
3,674
66,343
14,932
2030rg_ref
127,284
65,920
56,039
49,907
25,224
14,143
8,286
898
108,181
132,415
23,168
189,244
237,607
81,301
49,836
179,587
138,417
6,056
50,476
13,390
211,957
88,071
38,561
191,943
18,784
58,990
14,387
9,791
14,664
26,093
58,321
116,411
32,833
211,543
72,933
13,415
200,627
3,733
67,591
15,638
2030rg_ctl
127,008
65,743
55,890
49,907
24,933
14,013
8,248
879
107,040
131,818
23,073
188,731
237,259
81,157
49,688
179,376
138,188
5,987
50,225
13,126
211,496
87,797
38,366
191,609
18,729
58,894
14,245
9,723
14,279
25,967
57,641
115,949
32,797
211,001
72,688
13,244
200,180
3,689
67,357
15,595
100
-------
State
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
2007rg
297,935
651,329
35,663
4,173
258,286
36,638
422,049
207,922
115,330
11,545,544
2018rg_ref
63,903
236,570
28,388
2,346
72,522
16,137
85,518
53,394
51,648
3,582,687
2018rg_ctl
63,502
235,013
28,232
2,292
72,082
15,781
85,418
53,044
51,596
3,567,299
2030rg_ref
64,567
207,330
26,626
2,426
74,142
16,421
91,986
60,050
49,837
3,617,050
2030rg_ctl
64,236
205,952
26,490
2,379
73,757
16,106
91,910
59,760
49,794
3,603,918
101
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Table 5-12. VOC emissions (tons/yr) for each case and state
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
2007rg
394,478
299,405
367,862
1,493,672
359,741
91,124
30,272
1 1 ,564
970,251
731,394
491,700
490,297
348,813
189,536
238,892
284,125
502,713
80,956
172,580
180,220
565,194
622,926
267,564
423,126
293,547
105,934
152,771
57,530
261 ,426
311,387
535,040
620,323
64,768
479,576
512,690
435,314
455,475
27,159
292,949
81,613
2018rg_ref
312,021
226,453
314,065
1,203,729
265,489
56,030
19,775
7,841
685,446
580,549
465,738
358,934
264,712
142,561
199,124
224,140
427,256
56,005
111,856
128,399
398,966
515,939
213,246
323,326
276,893
82,394
132,532
39,034
177,994
280,189
337,635
474,236
52,510
317,531
450,517
387,230
336,441
18,437
220,191
70,455
2018rg_ctl
311,865
229,150
313,618
1,214,353
265,909
55,574
19,737
7,783
685,391
578,596
465,639
357,262
263,731
141,889
198,562
223,611
427,040
55,813
111,432
127,673
397,029
514,442
213,264
322,071
276,801
81,972
134,495
38,770
177,196
280,751
336,195
473,183
52,365
315,292
449,649
386,735
337,248
18,318
219,953
70,331
2030rg_ref
295,361
219,068
301,947
1,155,135
259,229
49,410
17,633
7,516
630,355
541,521
458,300
340,282
247,129
132,241
189,268
210,998
406,847
48,750
101,205
118,733
357,995
481,519
199,541
302,292
272,858
75,541
132,199
35,049
164,288
273,093
307,325
440,230
49,120
283,813
435,508
367,051
312,623
16,762
205,620
67,162
2030rg_ctl
290,621
212,501
299,537
1,151,387
254,687
49,018
17,506
7,224
610,660
532,475
456,974
332,759
241,765
130,142
186,925
207,531
402,894
48,542
100,363
117,986
351,667
477,844
196,050
297,093
272,065
74,071
129,597
33,985
163,145
271,173
305,271
432,671
48,609
276,248
431,452
366,503
311,092
16,627
201,702
66,511
102
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State
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
2007rg
354,294
2,414,946
297,616
30,384
360,687
353,447
117,626
337,259
251,477
18,813,646
2018rg_ref
256,459
2,158,320
287,999
22,924
265,016
277,515
88,643
241,006
260,241
15,013,942
2018rg_ctl
255,849
2,155,608
288,306
22,977
264,007
276,658
88,413
240,083
260,208
15,002,796
2030rg_ref
235,103
2,108,224
279,249
20,511
247,344
239,200
82,210
222,276
256,390
14,201,023
2030rg_ctl
229,819
2,083,716
276,992
20,367
240,525
238,165
81,028
217,957
255,740
14,019,183
103
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