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Technical Support Document (TSD):
Preparation of Emissions Inventories for the
Version 5.0, 2007 Emissions Modeling Platform
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EPA-454/B-20-007
December 2012
Technical Support Document (TSD): Preparation of Emissions Inventories for the Version 5.0,
2007 Emissions Modeling Platform
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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TABLE OF CONTENTS
ACRONYMS IV
LIST OF TABLES VII
LIST OF FIGURES VIII
LIST OF APPENDICES IX
1 INTRODUCTION 1
2 2007 EMISSION INVENTORIES AND APPROACHES 4
2.1 2007 NEI POINT SOURCES (PTIPM AND PTNONIPM) 10
2.1.1 1PM sector (ptipm) 10
2.1.2 Non-IPM sector (ptnonipm) 12
2.2 2007 NONPOINT SOURCES (AFDUST, AG, NONPT) 15
2.2.1 Area fugitive dust sector (afdust) 16
2.2.2 Agricultural ammonia sector (ag) 20
2.2.3 Other nonpoint sources (nonpt) 22
2.3 Fires (ptfire, avefire) 27
2.3.1 Day-specific point source fires (ptfire) 28
2.3.2 Average fires (avefire) 29
2.4 Biogenic sources (biog) 31
2.5 2007 MOBILE SOURCES (ONROAD, ONROAD_RFL, NONROAD, C1C2RAIL, C3 MARINE) 32
2.5.1 Onroad non-refueling (onroad) 33
2.5.2 Onroad refueling (onroad rfl) 40
2.5.3 Nonroad mobile equipment sources: (nonroad) 40
2.5.4 Class 1/Class 2 Commercial Marine Vessels and Locomotives and (clc2rail) 41
2.5.5 Class 3 commercial marine vessels (c3marine) 45
2.6 Emissions from Canada, Mexico and offshore drilling platforms (othpt, othar, othon) 47
2.7 SMOKE-ready non-anthropogenic inventories for chlorine 48
3 EMISSIONS MODELING SUMMARY 49
3.1 Emissions modeling Overview 49
3.2 Chemical Speciation 51
3.2.1 VOC speciation 54
3.2.2 PM speciation 61
3.2.3 NO x speciation 62
3.3 Temporal Allocation 63
3.3.1 FF10 format and inventory resolution 65
3.3.2 Ptipm Temporalization 65
3.3.3 Meteorologically based temporalization 65
3.3.4 Additional sector specific details 68
3.4 Spatial Allocation 71
3.4.1 Spatial Surrogates for U.S. emissions 71
3.4.2 Allocation method for airport-related sources in the U.S. 74
3.4.3 Surrogates for Canada and Mexico emission inventories 74
4 DEVELOPMENT OF 2020 BASE-CASE EMISSIONS 78
4.1 Stationary source projections : EGU sector (ptipm) 82
4.2 Stationary source projections: non-EGU sectors (ptnonipm, nonpt, ag, afdust) 82
4.2.1 RFS2 upstream future year inventories and adjustments (nonpt, ptnonipm) 84
4.2.2 Upstream agricultural and Livestock adjustments (afdust, ag, nonpt, ptnonipm) 91
4.2.3 RLCENESHAP (nonpt, ptnonipm) 93
4.2.4 Fuel sulfur rules (nonpt, ptnonipm) 94
4.2.5 Industrial Boiler MACT reconsideration (ptnonipm) 95
4.2.6 Portland Cement NESHAPprojections (ptnonipm) 97
4.2.7 Residential wood combustion growth (nonpt) 98
4.2.8 CSAPR and NODA Controls, Closures and consent decrees (nonpt, ptnonipm) 101
4.2.9 Remaining non-EGU plant closures (ptnonipm) 103
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4.2.10 All other PROJECTION and CONTROL packets (ptnonipm, nonpt) 104
4.3 Mobile source projections 110
4.3.1 Onroad mobile (onroad and onroad rfl) 110
4.3.2 Nonroad mobile (nonroad) 113
4.3.3 Locomotives and Class 1 & 2 commercial marine vessels (clc2rail) 113
4.3.4 Class 3 commercial marine vessels (c3marine) 116
4.4 Canada, Mexico, and Offshore sources (othar, othon, and othpt) 117
5 EMISSION SUMMARIES 117
6 REFERENCES 127
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Acronyms
ACI
Activated Carbon Injection
AE5
CMAQ Aerosol Module, version 5, introduced in CMAQ v4.7
AE6
CMAQ Aerosol Module, version 6, introduced in CMAQ v5.0
AEO
Annual Energy Outlook
AIM
Architectural and Industrial Maintenance (coatings)
ARW
Advanced Research WRF
BAFM
Benzene, Acetaldehyde, Formaldehyde and Methanol
BEIS3.14
Biogenic Emissions Inventory System, version 3.14
BELD3
Biogenic Emissions Land use Database, version 3
Bgal
Billion gallons
BPS
Bulk Plant Storage
BTP
Bulk Terminal (Plant) to Pump
C1/C2
Category 1 and 2 commercial marine vessels
C3
Category 3 (commercial marine vessels)
CAEP
Committee on Aviation Environmental Protection
CAIR
Clean Air Interstate Rule
CAMD
The EPA's Clean Air Markets Division
CAMx
Comprehensive Air Quality Model with Extensions
CAP
Criteria Air Pollutant
CARB
California Air Resources Board
CB05
Carbon Bond 2005 chemical mechanism
CBM
Coal-bed methane
CEC
North American Commission for Environmental Cooperation
CEM
Continuous Emissions Monitoring
CEPAM
California Emissions Projection Analysis Model
CISWI
Commercial and Industrial Solid Waste Incineration
CI
Chlorine
CMAQ
Community Multiscale Air Quality
CMV
Commercial Marine Vessel
CO
Carbon monoxide
CSAPR
Cross-State Air Pollution Rule
EO, E10, E85
0%, 10% and 85% Ethanol blend gasolines, respectively
EBAFM
Ethanol, Benzene, Acetaldehyde, Formaldehyde and Methanol
ECA
Emissions Control Area
EEZ
Exclusive Economic Zone
EF
Emission Factor
EGU
Electric Generating Units
EIS
Emissions Inventory System
EISA
Energy Independence and Security Act of 2007
EPA
Environmental Protection Agency
EMFAC
Emission Factor (California's onroad mobile model)
FAA
Federal Aviation Administration
FAPRI
Food and Agriculture Policy and Research Institute
FASOM
Forest and Agricultural Section Optimization Model
FCCS
Fuel Characteristic Classification System
FIPS
Federal Information Processing Standards
FHWA
Federal Highway Administration
HAP
Hazardous Air Pollutant
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HC1
Hydrochloric acid
HDGHG
Heavy-Duty Vehicle Greenhouse Gas
Hg
Mercury
HMS
Hazard Mapping System
HPMS
Highway Performance Monitoring System
IIWC
Hazardous Waste Combustion
HWI
Hazardous Waste Incineration
ICAO
International Civil Aviation Organization
ICI
Industrial/Commercial/Institutional (boilers and process heaters)
ICR
Information Collection Request
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)
MOBILE 6
OTAQ's model for estimation of onroad mobile emissions factors, replaced by
MOVES2010b
MOVES
Motor Vehicle Emissions Simulator (2010b) — OTAQ's model for estimation
of onroad mobile emissions - replaces the use of the MOBILE model
MSA
Metropolitan Statistical Area
MSAT2
Mobile Source Air Toxics Rule
MTBE
Methyl tert-butyl ether
MWRPO
Mid-west Regional Planning Organization
NCD
National County Database
NEEDS
National Electric Energy Database System
NEI
National Emission Inventory
NESCAUM
Northeast States for Coordinated Air Use Management
NESHAP
National Emission Standards for Hazardous Air Pollutants
NH3
Ammonia
NIF
NEI Input Format
NLCD
National Land Cover Database
NLEV
National Low Emission Vehicle program
nm
nautical mile
NMIM
National Mobile Inventory Model
NO A A
National Oceanic and Atmospheric Administration
NODA
Notice of Data Availability
NONROAD
OTAQ's model for estimation of nonroad mobile emissions
NOx
Nitrogen oxides
NSPS
New Source Performance Standards
NSR
New Source Review
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OAQPS
The EPA's Office of Air Quality Planning and Standards
OHH
Outdoor Hydronic Heater
OTAQ
The EPA's Office of Transportation and Air Quality
ORIS
Office of Regulatory Information System
ORD
The EPA's Office of Research and Development
ORL
One Record per Line
OTC
Ozone Transport Commission
PADD
Petroleum Administration for Defense Districts
PF
Projection Factor, can account for growth and/or controls
PFC
Portable Fuel Container
PM2.5
Particulate matter less than or equal to 2.5 microns
PM10
Particulate matter less than or equal to 10 microns
ppb, ppm
Parts per billion, parts per million
RCRA
Resource Conservation and Recovery Act
RBT
Refinery to Bulk Terminal
RFS2
Renewable Fuel Standard
RIA
Regulatory Impact Analysis
RICE
Reciprocating Internal Combustion Engine
RRF
Relative Response Factor
RWC
Residential Wood Combustion
RPO
Regional Planning Organization
RVP
Reid Vapor Pressure
see
Source Classification Code
SEMAP
Southeastern Modeling, Analysis, and Planning
SESARM
Southeastern States Air Resource Managers
SESQ
Sesquiterpenes
SMARTFIRE
Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation
SMOKE
Sparse Matrix Operator Kernel Emissions
SO2
Sulfur dioxide
SOA
Secondary Organic Aerosol
SI
Spark-ignition
SIP
State Implementation Plan
SPDPRO
Hourly Speed Profiles for weekday versus weekend
SPPD
Sector Policies and Programs Division
TAF
Terminal Area Forecast
TCEQ
Texas Commission on Environmental Quality
TOG
Total Organic Gas
TSD
Technical support document
ULSD
Ultra Low Sulfur Diesel
USD A
United States Department of Agriculture
VOC
Volatile organic compounds
VMT
Vehicle miles traveled
VPOP
Vehicle Population
WGA
Western Governors' Association
WRAP
Western Regional Air Partnership
WRF
Weather Research and Forecasting Model
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List of Tables
Table 1-1. List of base cases run in the 2007 (Version 5) Emissions Modeling Platform 1
Table 2-1. Platform sectors starting point for the 2007 platform 5
Table 2-2. Summary of significant changes between 2007 platform and 2008 NEI by sector 7
Table 2-3. Corn Ethanol Plant Criteria Pollutant Emission Factors (grams per gallon produced) 14
Table 2-4. Toxic-to-VOC Ratios for Corn Ethanol Plants 14
Table 2-5. 2008 NEI nonpoint sources removed from the 2007 platform 16
Table 2-6. SCCs in the afdust platform sector 17
Table 2-7. Livestock SCCs extracted from the 2008 NEI to create the ag sector 20
Table 2-8. Fertilizer SCCs extracted from the 2008 NEI for inclusion in the "ag" sector 21
Table 2-9. Recomputed Outdoor Hydronic Heater Sales for the 2007 Platform 26
Table 2-10. Recomputed Indoor Furnace Units and Emissions Adjustment Factor in MWRPO states 27
Table 2-11. 2007 Platform SCCs representing emissions in the ptfire and avefire modeling sectors 28
Table 2-12. Characteristics for grouping counties 34
Table 2-13. 2008 NEI SCCs extracted for the starting point in clc2rail development 42
Table 2-14. Counties where clc2rail sector rail yard emissions were removed 43
Table 2-15. Growth factors to project the 2002 ECA-IMO inventory to 2007 46
Table 3-1. Key emissions modeling steps by sector 50
Table 3-2. Descriptions of the 2007v5 platform grids 51
Table 3-3. Model species produced by SMOKE for CB05 with SOA for CMAQ4.7.1 and CAMx* 53
Table 3-4. Integration approach for BAFM and EBAFM for each platform sector 56
Table 3-5. VOC profiles for WRAP Phase III basins 58
Table 3-6. Select VOC profiles 2007 versus 2020 60
Table 3-7. PM model species: AE5 versus AE6 61
Table 3-8. MOVES exhaust PM species versus AE5 species 62
Table 3-9. NOx speciation profiles 63
Table 3-10. Temporal settings used for the platform sectors in SMOKE 64
Table 3-11. U.S. Surrogates available for the 2007 platform 72
Table 3-12. Spatial Surrogates for WRAP Oil and Gas Data 73
Table 3-13. Counties included in the WRAP Dataset 74
Table 3-14. Canadian Spatial Surrogates for 2007-based platform Canadian Emissions 75
Table 4-1. Control strategies and growth assumptions for creating the 2020 base-case emissions inventories
from the 2007 base case 80
Table 4-2. Summary of non-EGU stationary projections subsections 84
Table 4-3. Renewable Fuel Volumes Assumed for Stationary Source Adjustments 85
Table 4-4. 2007 and 2020 corn ethanol plant emissions [tons] 85
Table 4-5. Emission Factors for Biodiesel Plants (Tons/Mgal) 86
Table 4-6. 2020 biodiesel plant emissions [tons] 86
Table 4-7. PFC emissions for 2007 and 2020 [tons] 87
Table 4-8. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/Mgal) 88
Table 4-9. Toxic Emission Factors for Cellulosic Plants (Tons/Mgal) 88
Table 4-10. 2020 cellulosic plant emissions [tons] 88
Table 4-11. 2020 VOC working losses (Emissions) due to RFS2 ethanol transport [tons] 89
Table 4-12. Storage and Transport Vapor Loss Emission Factors (g/mmBtu) 90
Table 4-13. Adjustment Factors Applied to Storage and Transport Emissions 90
Table 4-14. Adjustment Factors Applied to Petroleum Refinery Emissions Associated with Gasoline and
Diesel Fuel Production 91
Table 4-15. Impact of refinery adjustments on 2020 emissions [tons] 91
Table 4-16. Adjustments to Agricultural Emissions for post-EPAct/EISA Cases 92
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Table 4-17. Composite Projection factors to year 2020 for Animal Operations 92
Table 4-18. National Impact of RICE Reconsideration Controls on 2020 Non-EGU Projections 94
Table 4-19. Summary of fuel sulfur rules by state 95
Table 4-20. Facility types potentially subject to Boiler MACT reductions 96
Table 4-21. Default Boiler MACT fuel percent % reductions by ICR fuel type 97
Table 4-22. Summary of Boiler MACT reductions (tons) compared to Reconsideration RIA reductions 97
Table 4-23. ISIS-based cement industry change (tons/yr) 98
Table 4-24. Worksheet for computing national RWC projection factors to 2020 100
Table 4-25. Worksheet for creating NY "woodstove" projection factor from 101
Table 4-26. Residential Wood Combustion projection factors to year 2020 101
Table 4-27. Cumulative reductions from facility and unit closures obtained between 2008 and 2010 104
Table 4-28. Cumulative reductions from facility and unit closures obtained from the EIS 104
Table 4-29. Factors used to project 2008 base-case aircraft emissions to 2020 105
Table 4-30. New York Ozone SIP controls reflected in the 2020 base case 106
Table 4-31. Target company-wide reductions from OECA consent decree information 107
Table 4-32. Texas oil and gas missed reductions by EPA 110
Table 4-33. E85 Usage Fraction by Model Year for 2020 112
Table 4-34. Non-California year 2020 Projection Factors for locomotives and Class 1 and Class 2
Commercial Marine Vessel Emissions 114
Table 4-35. Additional clc2rail emissions in 2020 from the EISA mandate 116
Table 4-36. Growth factors to project the 2007 ECA-IMO inventory to 2020 116
Table 5-1. National by-sector CAP emissions summaries for 2007 base and evaluation cases 118
Table 5-2. National by-sector CAP emissions summaries for 2020 base case 119
Table 5-3. National by-sector CO emissions (tons/yr) summaries with differences 120
Table 5-4. National by-sector NH3 emissions (tons/yr) summaries with differences 121
Table 5-5. National by-sector NOx emissions (tons/yr) summaries with differences 122
Table 5-6. National by-sector PM2.5 emissions (tons/yr) summaries with differences 123
Table 5-7. National by-sector PM10 emissions (tons/yr) summaries with differences 124
Table 5-8. National by-sector SO2 emissions (tons/yr) summaries with differences 125
Table 5-9. National by-sector VOC emissions (tons/yr) summaries with differences 126
List of Figures
Figure 2-1. Emissions Components of the 2007 Platform 9
Figure 2-2. January PM2.5 afdust emissions: raw 2008 NEI (top), after application of transport fraction
(middle) and final post-MET adjusted (bottom) 19
Figure 2-3. Examples of Daily RWC PM2.5 emissions changes due to inclusion of new temperature
dependency: old method minus new method 24
Figure 2-4. Illustration of various FAT avefire emissions versus year 2007 fires (top), and with year-2007
fires not shown (bottom) 30
Figure 2-5. NO emissions output from BEIS 3.14 for July, 2007 31
Figure 2-6. Isoprene emissions output from BEIS 3.14 for July, 2007 32
Figure 2-7. NOx rail emissions in 2008 NEI 44
Figure 2-8. Illustration of regional modeling domains in ECA-IMO study 47
Figure 3-1. Air quality modeling domains 51
Figure 3-2. Process of integrating BAFM with VOC for use in VOC Speciation 55
Figure 3-3. Example of RWC temporalization using a 50 versus 60 °F threshold 66
Figure 3-4. Example of new animal NH3 emissions temporalization approach, summed to daily emissions 67
Figure 3-5. Example of SMOKE-MOVES temporal variability of NOx emissions 68
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Figure 3-6. Agricultural burning diurnal temporal profile
Figure 3-7. RWC diurnal temporal profile
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List of Appendices
Appendix A: Oil and Gas SCC NEI SCCs
Appendix B: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT
Appendix C: Crosswalk between 2007 AE6 Profile Codes and SPECIATE 4.3 Profile Codes
Appendix D: Memo Describing the Differences in MOVES speciated PM and CMAQ PM
Appendix E: List of CoST Packets Used to Project Non-EGU Stationary and clc2rail Sectors to 2020
Appendix F: Summary of Future Base Case Non-EGU CoST Packets Containing Control Programs,
Closures and Projections
Appendix G: SMOKE Input Data Files and Parameters Used in the 2007 Evaluation, 2007 Base and 2020
Base Cases
Appendix H:Future Animal Population Projection Methodology, Updated 07/24/12
Appendix I: Approach to Apply RICE reductions to project 2008 Emissions in the 2007v5 modeling
Platform: 2004 and 2010 rules and 2012 RICE NESHAP Reconsideration Amendments
Appendix J: Boiler MACT ICR Fuels Cross-Reference to NEI SCCs
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1 Introduction
The U.S. Environmental Protection Agency (EPA) developed a year 2007, 2008-NEIv2-based air quality
modeling platform. The air quality modeling platform consists of all the emissions inventories and input
ancillary files, along with the meteorological, initial condition, and boundary condition files needed to run
the air quality model. This platform uses all Criteria Air Pollutants (CAPs) and the following select
Hazardous Air Pollutants (HAPs): chlorine (CI), hydrogen chloride (HC1), benzene, acetaldehyde,
formaldehyde and methanol. The latter four HAPs are also denoted BAFM. This platform is called the
"CAP-BAFM 2007-Based Platform, Version 5" platform because it is primarily a CAP platform with BAFM
included. "Version 5" denotes the evolution from the 2005-based platform, version 4, with substantial
improvements using newer data and methods. Many emissions inventory components of this "2007v5" air
quality modeling platform are based on the Version 2 of the 2008 National Emissions Inventory, hereafter
referred to as the "2008 NEI". This document describes only the emissions modeling component of the 2007
platform, which includes the emission inventories and the ancillary data and approaches used to transform
inventories for use in air quality modeling. This document is available from the Emissions Modeling
Clearinghouse website, under the section entitled "CAP-BAFM 2007-Based Platform, Version 5".
From this point on, we refer to this emissions modeling platform as simply the "2007 platform" or "2007v5".
Later updates to the 2007 platform will include a version qualifier such as "2007 Platform V5.1" and so on.
The first use of the 2007 platform is for the Regulatory Impact Assessment of the 2012 Final National
Ambient Air Quality Standards (NAAQS) for particulate matter less than 2.5 microns (PM2.5), hereafter
referred to as the "PM NAAQS". The air quality model used for the PM NAAQS is the Community
Multiscale Air Quality (CMAQ) model version 4.7.1. CMAQ supports modeling ozone (O3) 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 (SO2), ammonia (NH3), particulate matter less than or equal to 10 microns (PM10), and individual
component species for particulate matter less than or equal to 2.5 microns (PM2.5). In addition, 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 BAFM and includes anthropogenic
HAP emissions of HC1 and CI. Applications of the 2007v5 platform to-date have used CMAQ v4.7.1. EPA
is currently evaluating the 2007 platform with CMAQ v5.0. The platform's emissions processing methods
develop emissions that can be used with either CMAQ v4.7.1 or CMAQ v5.0, since extra species are created
that are needed by CMAQ v5.0, but that earlier versions of CMAQ can ignore.
The emissions and modeling effort for the 2007 platform consists of three 'complete' emissions cases: 2007
base case, 2007 evaluation case and the 2020 base 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 baseline, control and sensitivity cases for
the 2012 PM NAAQS. For regulatory applications, the 2007 base case is used with the outputs from the
2020 base case in the relative response factor (RRF) calculations to identify future areas of nonattainment.
For more information on the use of RRFs, please see the PM NAAQS Air Quality Modeling Final Rule TSD.
More information on the use of RRFs and air quality modeling for the PM NAAQS are provided in the Final
PM NAAQS Regulatory Impact Analysis.
Table 1-1. List of base cases run in the 2007 (Version 5) Emissions Modeling Platform
Internal EPA
Case Name
Abbreviation
Description
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2007 base case
2007re v5
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 2020 scenario(s).
2007 evaluation
case
2007ee v5
2007 case created for air quality model performance evaluation
that uses actual 2007 continuous emissions monitoring (CEM)
data for EGUs and actual wild and prescribed fire data.
2020 base case
2020re v5
2020 "base case" scenario, representing the best estimate for the
future year without implementation of controls needed to attain
current PM2.5 annual and 24-hour (35 ppm and 15 ppm
respectively) and Ozone 8-hour (75 ppb) standards.
There are a couple of differences between the 2007 evaluation and 2007 base cases. The evaluation case
uses 2007-specific wildfires and prescribed burning emissions and 2007 hour-specific continuous emissions
monitoring (CEM) data for electric generating units (EGUs). The 2007 base case uses an "average year"
scenario for wildfires and a spatially and temporally-smoothed year 2008 prescribed burning emissions.
Discussed in Section 2.3.2, the recently-developed Fire Averaging Tool (FAT), was used to create the
average year day-specific county-level wildfires and prescribed burning inventory. For EGUs, the base case
uses an illustrative (rather than year-specific) temporal allocation approach for EGUs to allocate annual 2007
emissions to days and hours. This approach to temporal allocation of EGU emissions is described in Section
3.3.2 and is used for both the 2007 base and 2020 base cases to provide temporal consistency between the
years. It is intended to be a conceivable representation of temporal allocation of the emissions without tying
the approach to a single year. For example, each year has different days and different locations with large
fires, unplanned EGU shutdowns, and periods of high electricity demand. By using a base-case approach
such as the one used here in the 2007 base case, the temporal and spatial aspects of the inventory for these
sources are maintained into the future-year modeling. This avoids potentially spurious year-specific artifacts
in the air quality modeling estimates.
This base case EGU temporalization, and many other components in the 2007 platform, are following similar
methodological techniques as the latest (Version 4.2) 2005-based platform. We will not refer to the 2005
platform TSDs in this TSD but much of what we describe in this TSD will be similar; we repeat the
documentation of unchanged components here.
The underlying 2007 inventories used are most significantly defined by: 1) for point and nonpoint sources:
the 2008 NEI, 2) for onroad mobile sources: year 2007 Motor Vehicle Emissions Simulator with database
corrections for diesel toxics (MOVES2Q10b). 3) for nonroad mobile sources: year 2007 National Mobile
Inventory Model (NMIM) EPA-estimated emissions, and 4) numerous year 2007 stationary non-EGU
sources from regional planning organizations (RPOs).
The primary emissions modeling tool used to create the air quality model-ready emissions was the Sparse
Matrix Operator Kernel Emissions (SMOKE) modeling system. We used SMOKE version 3.1 beta to create
emissions files for a 12-km national grid.
This document contains five sections and several appendices. Section 2 describes the 2007 inventories input
to SMOKE for both the evaluation case and base case. Section 3 describes the emissions modeling and the
ancillary files used with the emission inventories. Section 4 describes the development of the 2020 inventory
(projected from 2007). Data summaries comparing the 2007 base case and 2020 base case are provided in
Section 5. Section 6 provides references. The Appendices provide additional details about specific technical
methods.
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Electronic copies of the data used with SMOKE for the 2007 Platform are available from the Emissions
Modeling Clearinghouse website.
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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 platform;
year 2020 emissions data development is discussed in Section 4. The starting point for the 2007 stationary
source emission inputs is the 2008 National Emission Inventory, version 2 (2008 NEI).
There are many similarities between the 2008 NEI version 2 approaches and past versions of the NEI -2008,
2005 and earlier. The 2008 NEI version 2 draft Technical Support Document.
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 a voluntary component. New for
the 2008 NEI is the use of the Emissions Inventory System (EIS) to compile the NEI. The EIS includes
hundreds of automated QA checks to help improve data quality, and also supports release point (stack)
coordinates separately from facility coordinates. Improved EPA collaboration with S/L/T agencies
prevented duplication between point and nonpoint source categories such as industrial boilers. For onroad
mobile sources, the 2008 NEI used the MOVES model for the first time, where emissions were computed
based on hourly meteorology rather than monthly averages used in the MOBILE6 model that was used to
develop 2008 NEI version 1 and prior years of the NEI.
For fires, EPA used the SMARTFIRE2 (SF2) system for the first time in 2008 NEI. SF2 was the first system
to assign all fires as either prescribed burning or wildfire categories and includes improved emission factor
estimates for prescribed burning.
As reflected in the 2008 NEI Technical Support Document, in general, NOx, SO2, VOC and PM emissions
decrease from values in the 2005 NEI, with a couple of notable exceptions: 1) increased onroad NOx and
PM associated with the change to the MOVES model, 2) increased NOx from metals processing and
petroleum and related industries, 3) increased PM from agricultural tilling and paved road dust, and 4)
increased agricultural NH3 from livestock and fertilizer application.
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 Technical Support
Document generally uses 60 sectors to further describe the emissions. In addition to the NEI data, 2007
biogenic emissions, emissions from the Canadian and Mexican inventories, and numerous other non-NEI
data are included in the 2007 platform. As we explain below, the non-NEI emissions component to the 2007
platform reflects primarily year-2007 onroad mobile and nonroad mobile emissions, a computed average
fires inventory and data received from some regional planning organizations (RPOs).
The RPOs focused on addressing visibility impairment from a regional perspective and we relied on a few of
these RPOs to obtain year 2007 inventories to improve the 2007 platform over the 2008 NEI inventories. A
map of these RPOs. The RPOs that were most involved in providing data are listed here:
• Mid-Atlantic Regional Air Management Association (MARAMA)
• Midwest Regional Planning Organization (MWRPO)
• Southeastern States Air Resource Managers (SESARM)
• Western Regional Air Partnership (WRAP)
Virginia year 2007 inventories were provided from both MARAMA and SESARM. Analyses of the RPO
emissions data and conversations with RPOs indicated that MARAMA inventories were preferable to
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SESARM inventories in Virginia for most source categories with the exception of Residential Wood
Combustion (RWC), in which case, we used SESARM RWC emissions.
For the purposes of preparing the air quality model-ready emissions, we split the 2007 emissions inventory
into "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 then combines the sector-specific gridded, speciated and 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. As discussed in greater detail in Table 2-2, the emissions in many of these sectors were
significantly modified for the 2007 platform. 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. We did not use all sectors for all modeling cases. In particular, the ptfire sector is only
used in the 2007 evaluation case; conversely, the avefire sector is only used in the 2007 and 2020 base cases.
Table 2-1. Platform sectors starting point for the 2007 platform
Platform Sector:
abbreviation
2008NEI
Sector
Description and resolution of the data input to SMOKE
EGU (also called
the IPM sector):
ptipm
Point
2008 NEI point source EGUs mapped to the Integrated Planning
Model (IPM) model using the National Electric Energy Database
System (NEEDS) version 4.10. Hourly emissions replaced with 2007
CEM values of NOx and SO2 for 2007 evaluation case only. Other
pollutants are scaled from 2008 NEI using heat input. For 2007 and
2020 base cases, year-2007 CEM data total daily emissions created for
input into SMOKE. Non-CEM sources are 2008 NEI for all cases.
Annual resolution.
Non-EGU (non-
IPM sector):
ptnonipm
Point
All NEI point source records not matched to the ptipm sector.
Includes all aircraft emissions and some rail yard emissions. Annual
resolution.
Agricultural:
Ag
Nonpoint
NH3 emissions from NEI nonpoint livestock and fertilizer application,
county and annual resolution.
Area fugitive dust:
Afdust
Nonpoint
PM10 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. This sector is processed separately to allow for the
application of a land use based transport fraction and precipitation
zero-out.
Class 1 & 2 CMV
and locomotives:
clc2rail
Mobile:
Nonroad
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.
C3 commercial
marine:
c3marine
Mobile:
Nonroad
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
Cvlinder". usuallv described as the Emissions Control Area-
International Maritime Organization (ECA-IMO) studv. (EPA-420-F-
10-041, August 2010). Annual resolution and treated as point sources.
Remaining
nonpoint:
Nonpt
Nonpoint
Primarily NEI nonpoint sources not otherwise included in other
SMOKE sectors; county and annual resolution.
5
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Platform Sector:
abbreviation
2008NEI
Sector
Description and resolution of the data input to SMOKE
Nonroad:
nonroad
Mobile:
Nonroad
Monthly nonroad equipment emissions from the National Mobile
Inventory Model (NMIM) using NONROAD2008 version NR08b.
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 non-
refueling:
onroad
Mobile:
onroad
Onroad mobile gasoline and diesel vehicles from parking lots and
moving vehicles. Includes the following modes: exhaust, evaporative,
permeation, and brake and tire wear. For all states except California,
based on monthly Motor Vehicle Emissions Simulator (MOVES)
emissions tables. For California, based on Emission Factor (EMFAC).
Onroad non-
refueling:
onroad rfl
Mobile:
onroad
Onroad mobile gasoline and diesel vehicle refueling emissions for all
states. Based on monthly MOVES emissions tables.
Point source fires:
ptfire
Fires
Point source day-specific wildfires and prescribed fires for 2007. This
sector used only for the 2007 evaluation case.
Average-fire:
avejire
N/A
Average-year wildfire and prescribed fire emissions, county and daily
resolution. This sector is used in the 2007 base and 2020 base cases,
but not for the 2007 evaluation case.
Other point
sources not from
the NEI:
othpt
N/A
Point sources from Canada's 2006 inventory and Mexico's Phase III
2008 inventory, annual resolution. Mexico's inventory is grown from
year 1999. Also includes annual U.S. offshore oil 2008 NEI point
source emissions.
Other non-NEI
nonpoint and
nonroad:
othar
N/A
Annual year 2006 Canada (province resolution) and year 2008 (grown
from 1999) Mexico Phase III (municipio resolution) nonpoint and
nonroad mobile inventories.
Other non-NEI
onroad sources:
othon
N/A
Year 2006 Canada (province resolution) and year 2008 (grown from
1999) Mexico Phase III (municipio resolution) onroad mobile
inventories, annual resolution.
Biogenic:
beis
N/A
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 2007 emissions
platform and the 2008 NEI. Every modeling sector is different from the 2008 NEI to some degree. For
some sectors, such as ptnonipm (non-EGU point), these changes are very minor and local. In contrast, other
sectors such as nonroad mobile are either completely replaced (2007 NMIM versus 2008 NEI) or have
significant and detailed edits based on review of available alternative data. The specific by-sector updates to
the 2007 platform are described in greater detail later in this section under each by-sector subsection. Figure
2-1 shows how the 2007 platform relates to the underlying 2008 NEI.
The emission inventories in SMOKE input format for the 2007 base case are available at the Emissions
Modeling Clearinghouse website. The inventories "readme" file indicates the particular zipped files
associated with each platform sector.
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 availability of reference
information for the data, the degree of changes or manipulation of the data needed to prepare it for input to
SMOKE, and whether the 2007 platform emissions are significantly different from the 2008 NEI.
6
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Table 2-2. Summary of significant changes between 2007 platform and 2008 NEI by sector
Platform Sector
Summary of Significant Inventory Differences of 2007 Platform vs. 2008 NEI
IPM sector:
ptipm
1) Replaced all NOx and SO2 emissions with 2007 CEM data that were confirmed
to be for the entire year. Other pollutants for these CEM units were scaled
from 2008 NEI values based on 2008 and 2007 heat input ratios.
2) Emission release point type and missing or invalid stack parameters corrected
for several units based on analyses of significant emitters and comparison to
2005 NEI.
3) Added or changed ORIS Boiler IDs to some units with missing or incorrect
values, and for a subset of these, recomputed annual emissions of NOx, SO2 or
both using 2007 CEM data. Only replaced emissions if 2007 CEM data were
confirmed to be for the entire year (since some CEMs only run for the summer
season).
4) Moved several stacks and units from the ptnonipm sector, assigning ORIS
facility and boiler codes and matching stack parameters to those provided in
the future-year IPM emissions. These edits ensure future-year EGUs are not
double counted and that base year and future-year stack parameters are similar.
5) Deleted units from the inventory that were found to be either double counts,
closed or not operational in 2007.
Non-IPM sector:
ptnonipm
1) Moved several sources to the ptipm sector. This edit prevents double counting
of EGU emissions in the future years.
2) Removed onroad refueling for the handful of states that included them;
refueling sources are processed consistently nation-wide in the onroad_rfl
sector.
3) Moved a large California PM source to the afdust sector to allow for transport
factor and meteorology-based reductions.
4) Deleted several units from the inventory that were found to be either double
counts or closed.
5) Corrected miscellaneous SCCs in New Jersey with appropriate values.
6) Updated missing or invalid stack parameters.
7) Replaced oil and gas emissions with Western Regional Air Partnership
(WRAP) Phase III year 2006 emissions in select oil and gas basins.
8) Included plants submitted by Utah and Virginia missing in 2008 NEI
9) Included 2005 South Dakota emissions -not submitted in the 2008 NEI.
10) Included 2008 ethanol plant facilities from EPA's Office of Transportation and
Air Quality (OTAQ) that were not already in the 2008 NEI.
Agricultural:
ag
1) Corrected one New Mexico significant overestimate in NEI.
2) Replaced emissions with monthly-resolution 2007 estimates for states in the
MWRPO.
Area fugitive dust:
afdust
1) Added a large California PM source from the NEI point inventory.
2) Replaced some emissions with year-2007 estimates for states in 3 RPOs.
3) These emissions are adjusted to reflect land use (transport) and meteorological
effects that significantly reduce PM emissions input to the air quality model.
These adjusted emissions are known as the afdust adj emissions.
7
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Platform Sector
Summary of Significant Inventory Differences of 2007 Platform vs. 2008 NEI
Remaining
nonpoint sector:
nonpt
1) Area fugitive dust, agricultural NH3 and clc2rail sources separated out for
processing in different sectors.
2) C3marine removed -see c3marine description.
3) Replaced agricultural fires with daily inventory (aggregated to monthly) from
the SMARTFIRE tool.
4) Replaced oil and gas emissions with WRAP Phase III year 2006 emissions in
select oil and gas basins.
5) Apparent double-counting of EPA and state estimates removed
6) Removed onroad refueling; these are now processed consistently nation-wide
in the onroad_rfl sector.
7) Applied reductions to RWC outdoor hydronic heaters (OHH) based on analysis
of methodology used to create OHH in NEI.
8) Replaced a portion of RWC for states in 3 RPOs with 2007 data.
9) Replaced open burning emissions in select states with RPO 2007 data.
Class 1 & 2 CMV
and locomotives:
clc2rail
1) Removed rail yard emissions for counties that reported them in the point
inventory to remove duplicates.
2) Replaced Texas-reported (NEI) rail emissions with EPA estimates.
3) Replaced all emissions with year-2007 estimates for states in 3 RPOs.
4) Replaced California estimates with year-2007 CARB estimates.
C3 commercial
marine:
c3marine
Not NEI-based, but rather year-2007 as projected from 2002 from the ECA-IMO
project with the following modifications:
1) Canada defined as part of the ECA rather than an "outside the ECA" region,
using region-specific growth rates. For example, British Columbia emissions
were projected the same as "North Pacific" growth and control used in
Washington state.
2) Updated Delaware emissions with data provided by Delaware in Cross-State
Air Pollution Rule (CSAPR) comments.
3) Redefined the spatial extent of state boundaries off-shore from up to 200
nautical miles to under 10 miles based on Mineral Management Service
(MMS) state-federal water boundaries data. This item did not change
emissions but it drastically reduces areas that are assigned to states.
Nonroad sector:
nonroad
1) Non-California: replaced with 2007 NMIM monthly data.
2) California: replaced with annual 2007 CARB data apportioned to months using
2007 NMIM.
Onroad non-
refueling:
onroad
1) For all states except California: Year 2007 emissions for all pollutants and
modes (exhaust, tire and brake wear) from all vehicle types are based on
MOVES2010b monthly emission factor tables. Processed with 2007
meteorology using new SMOKE-MOVES routine (discussed later).
2) For California: merged in year-2007 CARB data to post-adjust SMOKE-
MOVES data via county/pollutant ratios.
Onroad non-
refueling:
onroadrfl
For all states including California: Year 2007 emissions for all pollutants and
modes (exhaust, tire and brake wear) from all vehicle types are based on
MOVES2010b monthly emission factor tables. Processed with 2007 meteorology
using new SMOKE-MOVES routine (discussed later). Replaces all NEI point
(ptnonipm) and nonpoint (nonpt) data.
Point source fires:
ptfire
Used year-2007 SMARTFIRE (Vl)-based emissions
8
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Figure 2-1. Emissions Components of the 2007 Platform
2008 NEI v2
ptipm
(EGU P
oint)
2008 SMOKE-
MOVES
omoad
2008 NEI v2
nonroad
12008 NEI v2
I Alternative Data
2008 NEI v2
ptfire
(fires)
Plattonn
2008 NEI v2
ptnonipm
(non-EGu Point)
2008 NEI v2
afdust, as, nonpt,
clczrail
(Nonpoint)
2006 WRAP Phase III
Oil & Gas:
nonpt & ptnonipm
2007
CEMS (ptipm)
2007 SMOKE-
MOVES
omoad & separate
omoad ill
2007
C3 CMV
(c3marine)
2007 RPO select
sources for:
ag, afdust, clc2rail,
nonpt, ptnonipm
2007 California
omoad, nomoad,
clc2rail
, 2007
ptfire/avefire
2007 nomoad (not
incl. California)
2006 Canada &
2008 Mexico
2007
biogenics
9
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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 (c3marine 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, we 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, PM10, and PM2.5 and the following HAPs: HCl (pollutant code =
7647010), and CI (code = 7782505). We did not utilize BAFM from these sectors because we chose to
speciate VOC without any use (i.e., integration) of VOC HAP pollutants from the inventory (VOC
integration is discussed in detail in Section 3.2.1.1).
The ptnonipm emissions were provided to SMOKE as annual emissions. The ptipm emissions used in 2007
were different for the model evaluation case and for the base case. First, annual NOx and SO2 emissions for
units that match CEM data were replaced with year 2007 CEM data so that there were no changes in total
emissions of CEM pollutants in the base and evaluation cases. Next, annual emissions for other pollutants at
CEM-matched units were scaled to year 2007 using CEMs heat input ratios between year 2008 and year
2007.
For the model evaluation case, those ptipm sources with CEM data (that we could match to the NEI) used
year 2007 hourly NOx and SO2 emissions and for all other pollutants annual emissions were adjusted via
2007-2008 heat input ratios. The hourly data also contained heat input, which was used to allocate the
annual emissions to hourly values. For the non-CEM sources, we created daily emissions using an approach
described in Section 2.1.1, and we applied state-specific diurnal profiles to create hourly emissions. For the
2007 base case, all sources (both CEM and non-CEM) used the daily emissions and diurnal profiles approach
There are several changes made to the ptipm and ptnonipm sectors from the 2008 NEI for the 2007 platform
that were briefly discussed in Table 2-2. One of these changes involved splitting the stacks, units and
facilities into the ptnonipm and ptipm sectors. Sources were placed in the ptipm sector when it was
determined that these sources were reflected in the future-year IPM output data. These changes and other
updates in the ptipm and ptnonipm sectors for the 2007 platform are discussed in the following sections.
2.1.1 IPM sector (ptipm)
The ptipm sector contains emissions from EGUs in the 2008 NEI point inventory that we were able match to
the units found in the year 2007 NEEDS database. We used a May 2012 version 4.10 of NEEDS to split out
the ptipm sector for the 2007 platform. The IPM provides future-year emission inventories for the universe
of EGUs contained in the NEEDS database. As described below, 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
10
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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 2008 NEI point source inventory contains emissions estimates for both EGU and non-EGU sources.
IPM is used to predict the future year emissions for the EGU sources. The remaining non-EGU point
sources are projected by applying projection and control factors to the base year emissions. It was therefore
necessary to identify and separate into two sectors: (1) all sources that are projected via the IPM and (2)
those that are not. While CEM-matched units use year 2007 emissions for NOx and SO2, those sources not
matched to CEMs use the 2008 NEI EGU emissions as-is. In addition, all stack parameters, coordinates and
SCCs use values from the 2008 NEI. The SCC value may be important if a source changed fuel types
between year 2007 and 2008 because speciation of inventory VOC and PM2.5 might differ, but these potential
differences were not accounted for in this version of the platform.
The 2008 NEI point inventory includes EGU ORIS facility IDs and EPA's Clean Air Markets Division
(CAMD) Boiler IDs for most EGUs. However, many smaller emitter's in CAMD's hourly CEM programs
are not identified with ORIS facility or boiler IDs in the NEI due to uncertainties in source identification and
inconsistencies in the way a unit is defined between the NEI and CAMD datasets. In addition, the NEEDS
database includes a larger universe of many smaller emitting EGUs, which are not included in the CAMD
hourly CEM programs.
Methodology to split the EGU from non-EGU sources
Several analytical steps were performed to better link the NEEDS units to the NEI sources that might
potentially be IPM/NEEDS units. The steps described in the 2008 NEI document only detail how IPM and
non-IPM sources were assigned and estimates in the year 2008 inventory. Next we discuss the steps needed
to refine the ptipm/ptnonipm splits and emissions for the 2007 platform.
Ptipm updates from the 2008 NEI used in creating the 2007 platform
• We started with the ptipm/ptnonipm split as determined by the value of the SMOKE input file
variable "IPMYN", which is determined based on the EIS alternative facility identifier. The
SMOKE input was exported from EIS into the SMOKE Flat File 10 (FF10) format. Some IPM YN
values in the SMOKE input file were updated based on units that had previously been matched to
IPM units in past modeling platforms, but for which the alternative facility IDs in EIS did not yet
include a code for IPM matching.
• For NEI units that matched NEEDS units, we recomputed annual emissions for SO2 and NOx using
the year 2007 CEMS data available at the EPA's data and maps website. For other pollutants at these
matched units, we scaled 2008 NEI emissions based on the ratio of 2007 to 2008 heat inputs (i.e.,
2007 emissions = 2008 emissions x 2007 annual CEM heat input / 2008 annual CEM heat input).
• Based on NEI and NEEDS analyses we: 1) removed duplicate emissions for the SIGECO facility in
Indiana (FIPS=18173, facility ID=8183011), 2) reassigned units as EGU (ptipm) from the non-EGU
(ptnonipm) sector, and 3) manually inserted new inventory EGU records for units that existed in the
2007 CEM data but not in the 2008 NEI. The 3rd item listed here made sense in retrospect because of
the temporary and permanent unit closures between 2007 and 2008 due to regulations and the
recession. The importance of reclassifying sources as ptipm versus ptnonipm is that it prevents
potential double-counting of future year EGU emissions because these ptipm emissions are replaced
by the IPM inventory in future years while ptnonipm emissions are projected from the platform
sector.
11
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• Reassigned New Jersey SCCs from 39999999 (miscellaneous) to more-specific values based on
inventory processes from a 2008 state inventory provided by New Jersey. This fix impacts only two
NJ stacks at "North Jersey Energy Assoc": FIPS=34023, facility ID=6719711 and process IDs =
19650514 and 19650814.
• We updated stack parameters for some units with missing or invalid parameter assignments in the
annual inventory. In addition, the emissions release point type flag (SMOKE variable ERPTYPE)
was analyzed for all stacks with any CAP or HAP exceeding 1,000 tons. We found numerous EGU
and non-EGU stacks with an ERPTYPE value indicating a fugitive release (ERPTYPE-'1"). These
stacks were reassigned as vertical stacks (ERPTYPE="2") and assigned sensible stack parameters
from the 2005 NEI when the 2008NEIv2 parameters were invalid or missing.
Creation of temporally resolved emissions for the ptipm sector
Another reason we separated the ptipm sources from the other sources was due to the difference in the
temporal resolution of the data input to SMOKE. For the year 2007 evaluation case, hourly CEM NOx and
SO2 data are directly used for sources that match the CEM data. For other pollutants, hourly CEM heat input
data are used to allocate the NEI annual values. For sources not matching CEM data ("non-CEM" sources),
we computed daily emissions from the NEI annual emissions using state-average CEM data. See Section
3.3.2 for more details on the temporalization approach. For the future-year scenarios, there are no CEM data
available for specific units. Therefore, to keep the base and future year cases consistent, we use the same
procedures as for the "non-CEM" sources to compute daily emissions for the 2007 base case and future year
ptipm sources.
2.1.2 Non-IPM sector (ptnonipm)
With several notable exceptions, the non-IPM (ptnonipm) sector contains the remaining 2008 NEI point
sources that we did not include 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.
The ptnonipm sector contains a very small amount of fugitive dust PM emissions from vehicular traffic on
paved or unpaved roads at industrial facilities or coal handling at coal mines. In previous versions of the
platform, we would reduce these emissions prior to input to SMOKE. However, in the 2007 platform we do
not make this reduction because of a new methodology used to reduce PM dust. This is discussed further in
Section 2.2.1.
There are numerous modifications between the published 2008 NEI and the 2007 ptnonipm inventory we
used for modeling. More details on some of these items will follow; however, these 2007 platform
modifications are summarized here:
Ptnonipm updates from the 2008 NEI used in creating the 2007 platform
• Removed sources with state/county FIPS code ending with "777". These sources represent mobile
(temporary) asphalt plants that are only reported for some states, and are generally in a fixed location
for only a part of the year and are therefore difficult to allocate to certain days for modeling, and
would not be expected to in the same location(s) in any future year projection.
• Reassigned FIPS code for "Lane Construction Corp" facility ID=7945311 from 23009 to 23027.
12
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• Reassigned New Jersey SCCs from 39999999 (miscellaneous) to more-specific values based on
inventory processes from a 2008 inventory provided by New Jersey.
• Moved PM emissions at three California (FIPS=06071) stacks from "US Army National Training
Center" facility ID=706411 to the area fugitive dust sector (afdust sector discussed in Section 2.2.1)
and reassigning SCC from 20200905 (kerosene combustion) to unpaved road dust (2296000000).
These emissions aggregate to 2,072 tons of PM2.5.
• Removed all offshore oil records as reflected by FIPS=85000. These sources are processed in the
othpt sector and discussed in Section 2.6.
• Added South Dakota non-EGU emissions from the 2005 NEI. South Dakota did not submit
emissions for the 2008 NEI.
• Added 2008 ethanol facilities provided by EPA's OTAQ that were not already included in the 2008
NEI.
• Removed oil and gas emissions for counties that are included in the WRAP Phase III inventories.
• Removed onroad refueling emissions. As discussed in Section 2.5.2, these emissions are now
provided by OTAQ's MOVES model and processed in the onroad rfl sector.
• Added the "Meadwestvaco Packaging" facility in Virginia (FIPS=51580) for year 2007 that was
missing in the 2008 NEI.
• Added HAP emissions (HC1 and Chlorine) for the "US Magnesium LLC: Rowley Plant" in Utah
(FIPS=49045) that was inadvertently dropped from the 2008 NEI.
• Corrected stack parameters for some units with missing or invalid parameter assignments.
• As discussed in Section 2.1.1, several sources in the 2008 ptnonipm inventory were found to be EGU
emissions. Therefore, we reassigned these known EGU emissions to the ptipm sector.
Reassigning New Jersey SCCs
It was found that 569 stacks (process IDs) in New Jersey were accidentally assigned as ".. .Miscellaneous
Industrial Processes" with an SCC=3 9999999 in the 2008 NEI. Of these incorrect SCC assignments, only
two are for EGUs (discussed in Section 2.1.1) and the remaining SCCs are non-EGUs. The correct SCCs
were included in the earlier draft version (1.7) of the 2008 NEI based on a prior submission of data from
New Jersey. These correct SCCs were (re)-applied to the (v2) 2008 NEI by inventory process ID (stack).
South Dakota non-EGU emissions
As noted in the 2008 NEI documentation (EPA, 2012a), South Dakota did not provide point source
emissions. Therefore we included South Dakota emissions from the last working version (4.2) of the 2005
platform. These emissions are included in the 2007v5 website as a separate FFlO-format inventory for HAPs
and CAPs.
Ethanol facilities from OTAQ
We added a subset of the ethanol facilities that EPA's OTAQ provided for year 2008. Several of the OTAQ
facilities were already included in the 2008 NEI, and the OTAQ duplicates were removed prior to including
in the 2007 platform. Locations and FIPS codes for these ethanol plants were verified using web searches
and Google Earth. These emissions are included in the 2007v5 website as a separate FFlO-format inventory
for HAPs and CAPs.
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The inventory estimates provided by OTAQ were all for corn ethanol plants. Emission rates were obtained
from EPA's spreadsheet model for upstream impacts developed for the Renewable Fuel Standard (RFS2)
rule (EPA, 2010a). Plant emission rates for criteria pollutants used to estimate impacts are given in Table
2-3. Toxic emission rates were estimated by applying toxic to VOC ratios in Table 2-4 to VOC emission
rates in Table 2-3. For air toxics except 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 and 2020 (via 2017 emission factors) based on analyses done for
the industry characterization described in Chapter 1 of the RFS2 final rule regulatory impact analysis.
Table 2-3. Corn Ethanol Plant Criteria Pollutant Emission Factors (grams per gallon produced)
Corn Ethanol Plant Type
Year
VOC
CO
NOx
PMio
PM2.5
SO2
NH3
Dry Mill Natural Gas (NG)
2005, 2017
2.29
0.58
0.99
0.94
0.23
0.01
0.00
2030
2.29
0.58
0.94
0.94
0.23
0.01
0.00
Dry Mill NG (wet distillers
grains with solubles (DGS))
2005, 2017
2.27
0.37
0.63
0.91
0.20
0.00
0.00
2030
2.27
0.37
0.60
0.91
0.20
0.00
0.00
Dry Mill Biogas
2005, 2017
2.29
0.62
1.05
0.94
0.23
0.01
0.00
2030
2.29
0.62
1.00
0.94
0.23
0.01
0.00
Dry Mill Biogas (wet DGS)
2005, 2017
2.27
0.39
0.67
0.91
0.20
0.00
0.00
2030
2.27
0.39
0.63
0.91
0.20
0.00
0.00
Dry Mill Coal
2005, 2017
2.31
2.65
4.17
3.81
1.71
4.52
0.00
2030
2.31
2.65
3.68
3.64
1.54
3.48
0.00
Dry Mill Coal (wet DGS)
2005, 2017
2.31
2.65
2.65
2.74
1.14
2.87
0.00
2030
2.28
1.68
2.34
2.62
1.03
2.21
0.00
Dry Mill Biomass
2005, 2017
2.42
2.55
3.65
1.28
0.36
0.14
0.00
2030
2.42
2.55
3.65
1.28
0.36
0.14
0.00
Dry Mill Biomass (wet
DGS)
2005, 2017
2.35
1.62
2.32
1.12
0.28
0.09
0.00
2030
2.35
1.62
2.32
1.12
0.28
0.09
0.00
Wet Mill NG
2005, 2017
2.35
1.62
1.77
1.12
0.28
0.09
0.00
2030
2.33
1.04
1.68
1.00
0.29
0.01
0.00
Wet Mill Coal
2005, 2017
2.33
1.04
5.51
4.76
2.21
5.97
0.00
2030
2.33
3.50
4.86
4.53
1.98
4.60
0.00
Table 2-4. Toxic-to-VOC Ratios for Corn Ethanol Plants
Acetaldehyde
Acrolein
Benzene
1,3-Butadiene
Formaldehyde
Wet Mill NG
0.02580
0.00131
0.00060
2.82371E-08
0.00127
Wet Mill Coal
0.08242
0.00015
0.00048
2.82371E-08
0.00108
Dry Mill NG
0.01089
0.00131
0.00060
2.82371E-08
0.00127
Dry Mill Coal
0.02328
0.00102
0.00017
2.82371E-08
0.00119
WRAP Phase III oil and gas emissions
The Western Regional Air Partnership (WRAP) RPO created year 2006 "Phase III" oil and gas sector point
and non-point format emissions for several major basins in Colorado and Montana, New Mexico, Utah and
Wyoming. These basins are listed here: Denver-Julesburg, Uinta, San Juan (North and South), Piceance,
Southwest Wyoming (Green River), Powder River and Wind River. A map showing the geographic area of
these basins.
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
14
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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, we removed all oil and gas emissions
from the 2008 NEI for counties that comprise the 7 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.
Onroad refueling emissions
Most onroad refueling emissions in the 2008 NEI are in the nonpoint sector; however a few states included
(some) gas station point inventory estimates for onroad refueling. These NEI emissions (point and nonpoint)
include VOC and some HAPs and were removed from the ptnonipm sector. These onroad refueling
emissions are now replaced with county-month emission factor estimates from the Motor Vehicle Emissions
Simulator (MQVES2010b) model. These onroad refueling emissions are processed as a new platform sector
"onroad rfl", described in detail in Section 2.5.2.
Corrected stack parameters
Stacks parameters in the 2008 NEI were analyzed for missing or invalid values. A list of stacks with invalid
parameters was developed and alternative values were substituted based on available data from the 2005 NEI
or EIS queries. In addition, similar to the ptipm inventory discussed earlier, emissions release point type flag
corrections and stack parameters reassignments were made to the ptnonipm sector.
2.2 2007 nonpoint sources (afdust, ag, nonpt)
The 2007 platform nonpoint sectors use the 2008 NEI as a starting point. We 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.
We removed the nonpoint tribal-submitted emissions to prevent possible double counting with the county-
level emissions and also because we did not have spatial surrogates for tribal data. Because the tribal
nonpoint emissions are small, we do not anticipate these omissions having an 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 2007 platform emissions modeling sector inventories are initialized with the 2008 NEI by SCC and
sometimes also by pollutant. However, prior to this, we removed several source categories from the 2008
NEI. These sources are dropped from the 2007 platform for a couple of potential reasons: 1) these sources
are only reported by a couple of states or agencies, 2) these sources are 'atypical' and small, and/or 3) we
have other data that we believe to be more accurate. Table 2-5 provides these 2008 NEI SCCs, justification
for removal and national annual NOx, VOC and NH3 emission totals.
15
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Table 2-5. 2008 NEI nonpoint sources removed from the 2007 platform
see
Description
Reason for
Removal
NOx
voc
MI;
2280003100
Marine Vessels, Commercial; Residual; Port
emissions
Replaced with
OTAQ ECA-
IMO dataset -see
Section 2.5.5
70,044
2,412
64
2280003200
Marine Vessels, Commercial; Residual; Underway
emissions
813,907
28,711
323
2294000000
Paved Roads; All Paved Roads; Total: Fugitives
Replaced with
emissions NOT
reduced via
precipitation
2294010000
Paved Roads; All Other Public Paved Roads; Total:
Fugitives
2501060100
Gasoline Stage 2 refueling: Total
Replaced with
MOVES2010b-
based estimates -
see Section 2.5.2
165,389
2501060101
Gasoline Stage 2 refueling: Displacement
Loss/Uncontrolled
20,116
2501060102
Gasoline Stage 2 refueling: Displacement
Loss/Controlled
3,169
2501060103
Gasoline Stage 2 refueling: Spillage
6,276
2801500600
Agricultural Field Burning; Forest Residues
Unspecified
Replaced with
SMARTFIRE
estimates -see
Section 2.2.3
3
116
7
2810005001
Managed Burning, Slash (Logging Debris) ;Pile
Burning
Replaced with
SMARTFIRE
estimates -see
Section 2.3
145
420
2810005002
Managed Burning, Slash (Logging Debris);Broadcast
Burning
3
5
2810020000
Prescribed Rangeland Burning; Unspecified
41
2810090000
Open Fire; Not categorized
210
1,274
0
2275087000
Aircraft; In-flight (non-Landing-Takeoff cycle);Total
Atypical and
sparsely-reported
category with
small emissions
2806010000
Domestic Animals Waste Emissions; Cats; Total
2,994
2806015000
Domestic Animals Waste Emissions; Dogs; Total
8,227
2807020001
Wild Animals Waste Emissions; Bears; Black Bears
3
2807020002
Wild Animals Waste Emissions; Bears; Grizzly Bears
0
2807025000
Wild Animals Waste Emissions; Elk; Total
1,268
2807030000
Wild Animals Waste Emissions; Deer; Total
3,366
2807040000
Wild Animals Waste Emissions; Birds; Total
0
2810003000
Cigarette Smoke; Total
39
171
4
2810010000
Human Perspiration and Respiration; Total
10,882
2830000000
Catastrophic/Accidental Releases; All; Total
0
473
0
2830010000
Catastrophic/Accidental Releases; Transportation
Accidents; Total
0
2862000000
Swimming Pools; Total (Commercial, Residential,
Public);Total
We discuss in each of the following subsections how we separated the remaining portion of the 2008 NEI
nonpoint inventory into 2007v5 modeling platform sectors, and also the changes we made to the NEI data.
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. This sector is separated from other nonpoint sectors to allow for
the application of "transport fraction," and meteorology/precipitation ("MET") 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), and in
16
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Fugitive Dust Modeling for the 2008 Emissions Modeling Platform (A del man. 2012). The precipitation
adjustment is then applied to remove all emissions for days where measureable rain occurs. Both the
transport fraction and MET 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 MET
adjustments reduces the overestimation of fugitive dust impacts in the grid modeling as compared to ambient
samples.
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.
We created the afdust sector from the 2008 NEI based on SCCs and pollutant codes (i.e., PMio and PM2.5)
that are considered "fugitive". The SCCs included in the 2008 NEI nonpoint inventory that comprise the
2007 platform afdust sector are provided in Table 2-6.
Table 2-6. SCCs in the afdust platform sector
see
SCC Description
2275085000
Mobile Sources; Aircraft; Unpaved Airstrips; Total
2294000000
Mobile Sources; Paved Roads; All Paved Roads; Total: Fugitives
2296000000
Mobile Sources; Unpaved Roads; All Unpaved Roads; Total: Fugitives
2296005000
Mobile Sources; Unpaved Roads; Public Unpaved Roads; Total: Fugitives
2311000000
Industrial Processes; Construction: SIC 15 - 17;A11 Processes; Total
2311010000
Industrial Processes; Construction: SIC 15 - 17;Residential;Total
2311020000
Industrial Processes; Construction: SIC 15; Industrial/Commercial/Institutional; Total
2311030000
Industrial Processes; Construction: SIC 15; Road Construction; Total
2325000000
Industrial Processes; Mining and Quarrying: SIC 14;A11 Processes; Total
2801000000
Miscellaneous Area Sources; Agriculture Production - Crops; Agriculture - Crops; Total
2801000002
Miscellaneous Area Sources; Agriculture Production - Crops; Agriculture - Crops; Planting
2801000003
Miscellaneous Area Sources; Agriculture Production - Crops; Agriculture - Crops; Tilling
2801000005
Miscellaneous Area Sources; Agriculture Production - Crops; Agriculture - Crops; Harvesting
2801000008
Miscellaneous Area Sources; Agriculture Production - Crops; Agriculture - Crops; Transport
2805000000
Miscellaneous Area Sources; Agriculture Production - Livestock; Agriculture - Livestock;
Total
2805001000
Miscellaneous Area Sources; Agriculture Production - Livestock; Beef cattle - finishing
operations on feedlots (drylots);Dust Kicked-up by Hooves
A limitation of the transportable fraction approach is the lack of monthly variability, which would be
expected due to seasonal changes in vegetative cover. And while wind speeds are not accounted for, the
variability due to soil moisture, snow cover and precipitation is accounted for in the subsequent MET
adjustment.
Several modifications were included in the 2007 platform after the initial sector emissions were created from
the 2008 NEI. The 2007 platform afdust emissions differ from the 2008 NEI as follows:
• The NEI paved road inventory includes a built-in precipitation reduction. We replaced these
emissions with a paved road emissions inventory not including this MET reduction, thereby allowing
the entire sector to be processed consistently with the same grid-specific transport fractions and MET
adjustments
• A large source of fugitive dust in the 2008 NEI point inventory in California was moved to the afdust
sector to allow transport fraction and MET reductions. This source contains over 2,000 tons of
17
-------
annual PM2.5 and is discussed in Section 2.1.2. We did not use the SMOKE area-to-point
(ARTOPNT) function to assign this source to the correct coordinates. Therefore, these emissions
were spatially allocated to numerous grid cells via the "Rural Population" surrogate in a large
California (San Bernardino) county. We will fix this in later versions of the platform.
• NEI data were replaced with year 2007 RPO inventories for several states and select sources. The
justification for using RPO inventories is that these data are what the RPOs are using for their
modeling and that where different and reasonable, they were used in our 2007 platform.
The 2008 NEI also includes a non-removable precipitation adjustment for unpaved roads and road
construction dust. Therefore, it is possible that there is some double-counting of the MET-based emissions
reductions for these sources. However, air quality modeling shows that in general, we are continuing to
overestimate "dust" in our modeling.
RPO afdust emissions replaced NEI data in the MARAMA and SESARM states with the following
exceptions:
• We retained 2008 NEI "mining and quarrying" (SCC beginning with 2325x) because for many states
in both RPOs we noticed that county emissions were the same in every county. Emissions in the NEI
varied as expected.
• We retained "unpaved" (SCCs beginning with 2296x) road dust because RPO emissions often
appeared to have a built-in transport and/or MET reduction.
• Similarly, as discussed above, we retained our year-2008 "paved" (SCCs beginning with 2294x) road
dust emissions based on 2008 NEI but without transportable fraction or MET-adjustment built-in.
• Massachusetts and North Carolina RPO emissions were missing; therefore 2008 NEI emissions were
used.
• New York "agriculture production, crops" (SCCs beginning with 2801000x) RPO emissions were
missing; therefore 2008 NEI emissions were used.
• Delaware provided more resolved SCCs for "agriculture production, crops" (12 versus 2 NEI SCCs);
however, the county totals were small and we did not find it worthwhile to replace the 2008 NEI
emissions with these more refined but similar totals from the MARAMA inventory.
The impacts of the transport fraction and MET adjustments in January are shown in Figure 2-2. The raw
2008 NEI afdust PM2.5 emissions -prior to transport fraction or MET adjustments- are shown at the top of
Figure 2-2. These afdust emissions after the application of the transport fraction, but prior to MET
adjustments are shown in the middle of Figure 2-2. Finally, the post-MET, and post-transport fraction,
afdust emissions are shown at the bottom of Figure 2-2.
The top and middle plots in Figure 2-2 shows how the transport fraction has a larger reduction effect in the
east where less barren and more forested areas are more effective at reducing PM transport than many
western areas. The bottom versus middle plots show how the MET impacts of precipitation, and especially
snow cover in the north, further reduce these emissions.
18
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Figure 2-2. January PM2.5 afdust emissions: raw 2008 NEI (top), after application of transport fraction
(middle) and final post-MET adjusted (bottom)
16.0299
16.0299
19
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2.2.2 Agricultural ammonia sector (ag)
The agricultural NH3 "ag" sector is based on livestock and agricultural fertilizer application emissions from
the 2008 NEI nonpoint inventory. In building this sector we included livestock and fertilizer emissions
based on only the SCCs listed in Table 2-7 and Table 2-8.
Table 2-7. Livestock SCCs extracted from the 2008 NEI to create the ag sector
see
SCC Description*
2805001100
Beef cattle - finishing operations onfeedlots (drylots);Confinement
2805001200
Beef cattle - finishing operations on feedlots (drylots);Manure handling and storage
2805001300
Beef cattle - finishing operations on feedlots (drylots);Land application of manure
2805002000
Beef cattle production composite;Not Elsewhere Classified
2805003100
Beef cattle - finishing operations onpasture/range;Confinement
2805007100
Poultry production - layers with dry manure management systems;Confinement
2805007300
Poultry production - layers with dry manure management systems;Land application of manure
2805008100
Poultry production - layers with wet manure management systems;Confinement
2805008200
Poultry production - layers with wet manure management systems;Manure handling and storage
2805008300
Poultry production - layers with wet manure management systems;Land application of manure
2805009100
Poultry production - broilers;Confinement
2805009200
Poultry production - broilers;Manure handling and storage
2805009300
Poultry production - broilers;Land application of manure
2805010100
Poultry production - turkeys;Confinement
2805010200
Poultry production - turkeys;Manure handling and storage
2805010300
Poultry production - turkeys;Land application of manure
2805018000
Dairy cattle composite;Not Elsewhere Classified
2805019100
Dairy cattle - flush dairy;Confinement
2805019200
Dairy cattle - flush dairy;Manure handling and storage
2805019300
Dairy cattle - flush dairy;Land application of manure
2805020000
Cattle and Calves Waste Emissions;Milk Total
2805020001
Cattle and Calves Waste Emissions;Milk Cows
2805020002
Cattle and Calves Waste Emissions;Beef Cows
2805020003
Cattle and Calves Waste Emissions;Heifers and Heifer Calves
2805020004
Cattle and Calves Waste Emissions;Steers, Steer Calves, Bulls, and Bull Calves
2805021100
Dairy cattle - scrape dairy;Confinement
2805021200
Dairy cattle - scrape dairy;Manure handling and storage
2805021300
Dairy cattle - scrape dairy;Land application of manure
2805022100
Dairy cattle - deep pit dairy;Confinement
2805022200
Dairy cattle - deep pit dairy;Manure handling and storage
2805022300
Dairy cattle - deep pit dairy;Land application of manure
2805023100
Dairy cattle - drylot/pasture dairy;Confinement
2805023200
Dairy cattle - drylot/pasture dairy;Manure handling and storage
2805023300
Dairy cattle - drylot/pasture dairy;Land application of manure
2805025000
Swine production composite;Not Elsewhere Classified (see also 28-05-039, -047, -053)
2805030000
Poultry Waste Emissions;Not Elsewhere Classified (see also 28-05-007, -008, -009)
2805030001
Poultry Waste Emissions;Pullet Chicks and Pullets less than 13 weeks old
2805030002
Poultry Waste Emissions;Pullets 13 weeks old and older but less than 20 weeks old
2805030003
Poultry Waste Emissions;Layers
2805030004
Poultry Waste Emissions;Broilers
2805030007
Poultry Waste Emissions;Ducks
2805030008
Poultry Waste Emissions;Geese
2805030009
Poultry Waste Emissions;Turkeys
2805035000
Horses and Ponies Waste Emissions;Not Elsewhere Classified
2805039100
Swine production - operations with lagoons (unspecified animal age);Confinement
2805039200
Swine production - operations with lagoons (unspecified animal age);Manure handling and storage
2805039300
Swine production - operations with lagoons (unspecified animal age);Land application of manure
2805040000
Sheep and Lambs Waste Emissions;Total
20
-------
SCC
SCC Description*
2805045000
Goats Waste Emissions;Not Elsewhere Classified
2805045002
Goats Waste Emissions;Angora Goats
2805045003
Goats Waste Emissions;Milk Goats
2805047100
Swine production - deep-pit house operations (unspecified animal age);Confinement
2805047300
Swine production - deep-pit house operations (unspecified animal age);Land application of manure
2805053100
Swine production - outdoor operations (unspecified animal age);Confinement
* All SCC Descriptions begin "Miscellaneous Area Sources;Agriculture Production - Livestock"
Table 2-8. Fertilizer SCCs extracted from the 2008 NEI for inclusion in the "ag" sector
SCC
SCC Description*
2801700001
Anhydrous Ammonia
2801700002
Aqueous Ammonia
2801700003
Nitrogen Solutions
2801700004
Urea
2801700005
Ammonium Nitrate
2801700006
Ammonium Sulfate
2801700007
Ammonium Thiosulfate
2801700008
Other Straight Nitrate
2801700009
Ammonium Phosphates
2801700010
N-P-K (multi-grade nutrient fertilizers)
2801700011
Calcium Ammonium Nitrate
2801700012
Potassium Nitrate
2801700013
Diammonium Phosphate
2801700014
Monoammonium Phosphate
2801700015
Liquid Ammonium Polyphosphate
2801700099
Miscellaneous Fertilizers
* All descriptions include "Miscellaneous Area Sources;
Agriculture Production - Crops; Fertilizer Application" as
the beginning of the description.
The "ag" sector includes all of the NH3 emissions from fertilizer from the NEI. However, the "ag" sector
does not include all of the livestock ammonia emissions, as there are also a very small amount of NH3
emissions -around 38 tons- in California from livestock feedlots in the point source inventory that we
retained from the 2008 NEI.
A significant error in the 2008 NEI was corrected in the 2007 platform ag sector. A fertilizer application
source "N-P-K (multi-grade nutrient fertilizers)" (SCC=2801700010) in Luna county New Mexico
(FIPS=35025), was 6,953 tons of NH3 in the 2008 NEI. However, this source was corrected by a factor of
1,000 to be 6.953 tons in the 2007 platform.
Monthly ag sector NH3 RPO emissions replaced NEI ag sector emissions in the MWRPO (LADCO) states
due to the improved temporal resolution. RPO ag sector emissions in the MARAMA and SESARM RPO
states were either identical or nearly-so to the 2008 NEI; therefore, 2008 NEI (annual) ag sector emissions
were used in all other states. We retained the MWRPO ag sector monthly emissions by creating a SMOKE
FF10 nonpoint format with the monthly values populated. We will discuss the difference of these monthly
MWRPO ag sector emissions versus SMOKE annual-to-month temporal allocation in Section 3.3.4. We also
incorporated a new temporal allocation methodology for animal NH3 (see Section 3.3.3 for more details) that
allocates emissions down to the hourly level by taking into account temperature and wind speed.
21
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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. As discussed in the beginning of Section 2, all fire emissions from the 2008 NEI nonpoint
inventory were removed and replaced with SMARTFIRE emissions; these are described in Section 2.3.
Additionally, locomotives and CMV mobile sources from the 2008 NEI nonpoint inventory are described in
Section 2.5.
Below is a summary of changes made to the 2007 platform nonpt sector beyond what is listed in Table 2-2 at
the beginning of Section 2. Details on these changes not already-discussed are provided following this
summary:
• The 2007 platform replaces 2008 NEI oil and gas emissions (SCCs beginning with "23100") with
year 2006 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. These changes were made in counties affected by the WRAP data.
• 2008 NEI nonpoint agriculture burning emissions were replaced with year 2008 SMARTFIRE day-
specific county-based emissions aggregated to monthly totals in the 2007 platform.
• Replaced open burning "land clearing" (SCC=2610000500) emissions in Florida and Georgia with
SESARM-provided daily point data, but aggregated to county and monthly resolution.
• Replaced all open burning data (SCCs beginning with 261000x) in MARAMA states.
• Replaced, removed and modified much of the residential wood combustion (RWC) emissions in the
MARAMA, MWRPO and SESARM states with RPO data and non-RPO corrections, modified the
outdoor hydronic heater (OHH) emissions in all states and indoor furnaces in MWRPO states.
• Removed industrial coal combustion emissions (SCC=2102002000) in Tennessee.
• Removed EPA-estimated commercial cooking (SCCs 2302002100 and 2302002200) duplicate PM
emissions in California.
• Removed duplicate "Industrial Processes; Food and Kindred Products;.. .Total" source
(SCC=23020000000) in Maricopa county Arizona (FIPS=04013).
The oil and gas changes were discussed in the ptnonipm section. We elaborate on each of the above bullets
below.
Ag burning
2008 NEI agricultural burning estimates were replaced with more specific data from the Fire Characteristic
Classification System (FCCS) module fuel loadings map in the BlueSky Framework. Year 2008-specific fire
locations from SMARTFIRE version 1 (Sullivan, et al., 2008) were read into the FCCS module and
intersected with the FCCS fuel-loading dataset. The module assigned an FCCS code to each fire record that
reflects the ecosystem geography and potential natural vegetation based on remote sensing data. Prescribed
or unclassified fires having an FCCS code equal to zero (0) were assumed to be agricultural fires. Next, Arc
GIS was used to categorize the fires as occurring on rangeland, cropland or other land use via USGS 2006
National Land Cover Database (NLCD). Activity data were analyzed to restrict to cropland fires and assign
state and crop-specific emission factors. Emissions were then appropriately weighted based on known
statistics about each state's crop mix.
These SMARTFIRE-based ag burning emissions were provided in Excel sheets at 1km point source and day-
specific resolution. State-county FIPS codes were assigned using GIS. We aggregated these emissions to
22
-------
county and monthly resolution and converted to SMOKE nonpoint FF10 format. This SMARTFIRE-based
ag burning dataset includes emissions for all but these 7 of the lower 48 states: CT, DC, MA, ME, NH, RI
and VT. These 7 states did not contain any cropland burning estimates for year 2008 based on this
SMARTFIRE approach.
Open burning RPO data
We replaced all 2008 NEI open burning emissions (CAPs only) in the MARAMA states with the 2007
MARAMA open burning inventory. These MARAMA open burning emissions include estimates for
household waste (SCC=2610030000), land clearing (2610000500) and yard waste leaf and brush
(2610000100 and 2610000400 respectively).
We also replaced 2008 NEI land clearing emissions in Georgia and Florida with SESARM-based year-2007
data. The SESARM land clearing emissions are based on daily point emissions from the CONSUME v3.0
model (SESARM, 2012a). These daily point-format emissions were aggregated to county and monthly
resolution as a separate FF10 nonpoint monthly inventory.
Residential Wood Combustion
There are many modifications to the RWC emissions data. We also modified the daily temporalization from
monthly uniform (non-varying) to day-of-year specific. We describe this in more detail in Section 3.3.3, but
believe it is important to mention here because of the large day-to-day impact this change makes on RWC
emissions allocation for some areas. In short, we utilize a new SMOKE program (GenTPRO) to distribute
annual RWC emissions to the coldest days of the year, using maximum temperature thresholds by-state
and/or by-county. On days where the low temperature does not drop below this threshold, RWC emissions
are zero. Conversely, the program temporally allocates the most relative emissions to the coldest days. An
example of the difference between the old method and the new method is reflected in Figure 2-3, where
negative values indicate more emissions in this new method. For example, in the top panel, more RWC
emissions on January 1st in the new method are shown in the northeast and Florida because of colder (than
average) minimum temperatures. However, daily RWC emissions on January 2nd in the bottom panel show
less emissions in many of these same areas, which reflects warmer (than average) daily minimum
temperatures.
23
-------
Figure 2-3. Examples of Daily RWC PM2.5 emissions changes due to inclusion of new temperature
dependency: old method minus new method.
Legend
| <= -2 tons/day
¦ >-2 to <= -1
^ > -1 to <= -0.5
>-0.5 to <=-0.1
~ > -0.1 to <= 0.1
>0.1 to <=0.5
f I > 0.5 to <= 1
¦ >1to<=2
¦ >2
Difference in PM2.5 between old and new - 01/01/2008
Legend
| <= -2 tons/day
¦ > -2 to <= -1
II] >-1 to<= -0.5
>-0.5 to <=-0.1
~ >-0,1 to<= 0.1
>0.1 to <= 0.5
B > 0.5 to <= 1
m > 1 to <= 2
¦ >2
Difference in PM2.5 between old and new -- 01/02/2008
Next, we discuss the modifications to the annual emissions via alternate datasets and in some cases,
recalculations for specific RWC sources.
24
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i. SESARM states: AL, FL, GA, KY, MS, NC, SC, TN, VA, WV
The 2008 NEI nonpoint inventory was the starting point; however, we replaced all emissions in the
SESARM states, including Virginia, with the SESARM year-2007 inventory (SESARM, 2012b). SESARM
updates to the RWC estimates incorporate updated wood burning appliance counts at the sub-MSA
(Metropolitan Statistical Area) level as well as a default urban and overall appliance count profile for other
areas. Urban area RWC were lower than the NEI estimates partially because of the assumptions about
greater penetration of natural gas fireplaces, less access to inexpensive wood supplies and a lower proportion
of housing units with wood burning appliances as primary heating units than rural areas. The specific RWC
updates are referenced in a report.
Overall, the SESARM RWC estimates are considerably lower than the 2008 NEI estimates for several states,
particularly for "uncertified" and "general" wood stoves and insert categories: FL, KY, NC, TN, VA and
WV. However, emissions in Mississippi are only slightly reduced and emissions in AL, GA and SC are very
similar to those in the 2008NEIv2.
ii. MWRPO states and Minnesota: IL, IL, MI, OH, WI, MN
The Midwest RPO (LADCO) states year-2007 RWC inventory was similar to the 2008 NEI for most source
types. However, the pellet stoves (SCC=2104008400), indoor furnaces (2104008510), and outdoor hydronic
heater (OHH, SCC=2104008610) estimates were updated to reallocate the indoor furnaces and OHHs to
non-MSA counties (LADCO, 2012) for several urban areas. Some double counting of appliances was also
fixed in Wisconsin and Michigan. Overall, the MWRPO states totals are very similar to the 2008 NEI;
however, emissions are spatially redistributed from urban to rural areas. Therefore, for the MWRPO states,
the 2008 NEI emissions were used for all RWC sources except the three aforementioned SCCs that use the
2007 MWRPO data.
iii. MARAMA states: CT, DE, DC, ME, MD, MA, NH, NJ, NY, PA, RI, VT
The MARAMA states year 2007 RWC inventory was either unchanged from the 2008 NEI, or was missing
for most states. The exceptions were New York and Pennsylvania which includes significantly revised RWC
estimates compared to the 2008 NEI. For New York, the MARAMA estimates were not split out into the
refined set of 10 RWC appliance types/SCCs in the NEI. New York only reported "general" fireplaces
(SCC=2104008100) and "EPA certified, non-catalytic" woodstoves (SCC=2104008320). However, similar
to the SESARM and MWRPO improvements, the MARAMA NY RWC estimates were spatially reallocated
from urban to more rural areas and were also lower state-wide than the NEI. For Pennsylvania, MARAMA
RWC estimates were not much different state-wide on the aggregate, but were refined by SCC and spatially
compared to the 2008 NEI. Therefore, the MARAMA 2007 RWC data is used for New York and
Pennsylvania and the 2008 NEI emissions are used for all RWC sources in the rest of the MARAMA states.
iv. Adjustments to specific RWC SCCs
We removed all RWC outdoor wood burning devices such as "fire pits and chimineas" (SCC=2104008700)
from the 2007 platform because they were only reported in a couple of states, RPO inventories did not
include them for most states and emissions were generally insignificant.
A market research report (Frost and Sullivan, 2010) developed in support of the potential RWC New Source
Performance Standard (NSPS) indicated slower sales of outdoor hydronic heaters compared to what was
assumed for growth estimates in the 2008 NEI. We therefore recomputed outdoor hydronic heater (OHH)
appliance counts and emissions estimates (SCC=2104008610) for all states. OHH appliance count activity
in the 2008 NEI was based on Northeast States for Coordinated Air Use Management (NESCAUM) sales
survey data in the year 2005 that was extrapolated through year 2008. The Frost and Sullivan report supports
a much smaller amount of OHH sales between 2005 and 2008 and hence much lower OHH emissions in
25
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2008. Table 2-9 details how we modified the NEI-assumed OHH sales between 2005 and 2008, and how
this reduces OHH units by 51% -from 362,333 units to 176,673 cumulative units. We assume that the
63,728 units in 2003 is a correct estimate, and that the NESCAUM-based 24,560 units sold in 2004 is
approximately correct. However, rather than including the sudden spike to 67,546 units sold in year 2005,
we assume, only 25,000 units sold each year between 2005 and 2007. This is still probably a conservatively
high estimate based on only 13,385 units sold in 2008 according to Frost & Sullivan. We applied this 51%
reduction to OHH emissions for all states.
Table 2-9. Recomputed Outdoor Hydronic Heater Sales for the 2007 Platform
Year(s)
2008 NEI
OHH Annual
Sales
Revised
OHH Annual
Sales
Source of Info:
2008 NEI
Source of Info:
2007 Platform
1990-2003 total
63,728
63,728
NESCAUM
NESCAUM
2004
+ 24,560
+ 24,560
NESCAUM
NESCAUM
2005
+ 67,546
+ 25,000
NESCAUM
assumed similar to 2004
NESCAUM
2006
+ 68,833
+ 25,000
extrapolated from
NESCAUM
assumed similar to 2004
NESCAUM
2007
+ 68,833
+ 25,000
extrapolated from
NESCAUM
assumed similar to 2004
NESCAUM
2008
+ 68,833
+ 13,385
extrapolated from
NESCAUM
Frost & Sullivan, 2010
Total Units in
2008
362,333
176,673
sum of 1990-2008
sum of 1990-2008, with
revised 2005-2008
We also recomputed the indoor wood fired furnaces (SCC=2104008510) in several MWRPO states based on
newer, improved survey data from Minnesota. While we used the MWRPO emissions for indoor furnaces
rather than 2008 NEI emissions, as discussed above, the MWRPO emissions primarily redistributed these
emissions from urban to rural counties and for most states did not significantly change the underlying
assumption of the number of indoor furnace units, and hence state-total emissions. The 2008 NEI for these
sources started with an assumption of year 2002 Minnesota wood burning survey data of 38 indoor furnaces
per 100 woodstoves for Illinois, Indiana, Michigan, Ohio, and Wisconsin. Each state had some minor tweaks
from this ratio. However, the calculation of the furnace per woodstove ratio from the 2002 MN survey did
not reflect the number of "combination" devices that were surveyed, such as woodstove/furnace or other
combination of 2 or more wood burning devices. This made the indoor wood furnace ratio from the 2002
MN survey too high. More recent year 2007 MN survey data resulted in the much lower ratio of 7.3 indoor
furnaces per 100 wood stove units, which, as seen in
26
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Table 2-10, is more in line with the 7.6% ratio of indoor furnaces to wood stoves in the 2008 NEI for
Minnesota. Therefore, for the other 5 MWRPO states previously listed, we normalize the indoor furnace
emissions by forcing the indoor furnace count ratio to wood stoves to match the 7.6% reported value in
Minnesota. These adjustment factors reduce the indoor furnace emissions in these states by 67%
(Wisconsin) to as much as 83% in Ohio.
27
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Table 2-10. Recomputed Indoor Furnace Units and Emissions Adjustment Factor in MWRPO states
2008 NEI Indoor
2008 NEI
Indoor Furnaces
Revised Indoor
Furnace
Woodstove
as a % of
Adj
Appliance
State
Appliance Count
Appliance Count
Woodstove
Factor
Count
Ohio
60,795
137,848
44.1%
0.17
10,436
Michigan
58,271
236,129
24.7%
0.31
17,877
Wisconsin
39,072
170,615
22.9%
0.33
12,917
Illinois
34,566
75,185
46.0%
0.16
5,692
Indiana
28,714
61,353
46.8%
0.16
4,645
Minnesota
15,167
200,334
7.h"„
1
15,167
TN coal combustion
Tennessee nonpoint industrial coal combustion (SCC=2102002000) emissions are significantly
overestimated in the 2008 NEI because of incorrect reconciliation with the point source inventory. Nonpoint
industrial coal combustion emissions were estimated by subtracting point source emissions rather than
activity. By not accounting for controlled sources, remaining activity for nonpoint coal combustion is
significantly overestimated. EPA NEI experts determined that it would be more appropriate to completely
remove the nonpoint component of this sector than to leave it as-is. The reality for TN industrial coal
combustion nonpoint sector emissions is likely much closer to zero than the value in the 2008 NEI because
these emissions are accounted for in the point source inventory.
Duplicates removal
Maricopa county Arizona reported the same NH3 emissions value, 1,678.43 tons, for two different but
similar SCCs: 23020000000 "Industrial Processes; Food and Kindred Products: SIC 20; All Processes;
Total" and 23020800000 "Industrial Processes; Food and Kindred Products: SIC 20; Miscellaneous Food
and Kindred Products; Total". We confirmed that this was a duplicate and therefore deleted the more broad
SCC record 2302000000.
We also found numerous "Commercial Cooking" duplicates for PM in California where the California Air
Resources Board (CARB) estimated "Charbroiling Total" emissions (SCC=23020002000 "Industrial
Processes; Food and Kindred Products: SIC 20; Commercial Cooking - Charbroiling; Charbroiling Total")
and EPA provided defaults for "...Conveyorized Charbroiling" (SCC=23020002100) and "... Under-fired
Charbroiling" (SCC=23020002200). At first glance, these are not duplicates because they are different
SCCs; however, it became clear that EPA emissions were "gap-filling" a source that the CARB submittal
already covered for most counties in California and therefore the EPA emission were removed.
2.3 Fires (ptfire, 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. For the 2007 evaluation case,
we modeled 2007 year-specific fires using the emissions from the ptfire sector. For the 2007 and 2020 base
cases, the ptfire sector was replaced by the avefire sector.
For the 2007v5 platform, the following SCCs in
28
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Table 2-11 are considered "fires" - note that the complete SCC description includes "Miscellaneous Area
Sources" as the first tier level description.
29
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Table 2-11. 2007 Platform SCCs representing emissions in the ptfire and avefire modeling sectors
see
SCC Description
2810001000
Other Combustion; Forest Wildfires; Total
2810015000
Other Combustion; Prescribed Burning for Forest Management; Total
2811015000
Other Combustion-as Event; Prescribed Burning for Forest Management; Total
2811090000
Other Combustion-as Event; Prescribed Forest Burning ;Unspecified
Both the ptfire and avefire sectors for the 2007 Platform exclude agricultural burning and other open burning
sources, which are included in the nonpt sector. We chose to keep agricultural burning and other open
burning sources in the nonpt sector because these categories were not factored into the development of the
average fire sector (as described in 2.3.2). 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.
2.3.1 Day-specific point source fires (ptfire)
The ptfire sector includes wildfire and prescribed burning emissions occurring in 2007, which are used in the
2007 model evaluation case and not the 2007 and 2020 base cases. Emissions are day-specific and include
satellite derived latitude/longitude of the fire's origin and other parameters associated with the emissions
such as acres burned and fuel load, which allow estimation of plume rise.
The point source day-specific emission estimates for 2007 fires rely on Version 1 of the Satellite Mapping
Automated Reanalysis Tool for Fire Incident Reconciliation (SMARTFIRE) system (Sullivan, et al., 2008).
This system involves the use the National Oceanic and Atmospheric Administration's (NOAA's) Hazard
Mapping System (HMS) fire location information as input combined with the CONSUMEv3.0 software
application (Joint Fire Science Program, 2009) and the Fuel Characteristic Classification System (FCCS)
fuel-loading database to estimate fire emissions from wildfires and prescribed burns on a daily basis. The
method involves the reconciliation of ICS-209 reports (Incident Status Summary Reports) with satellite-
based fire detections to determine spatial and temporal information about the fires. The ICS-209 reports for
each large wildfire are created daily to enable fire incident commanders to track the status and resources
assigned to each large fire (100 acre timber fire or 300 acre rangeland fire). The SMARTFIRE system of
reconciliation with ICS-209 reports is described in an Air and Waste Management Association report
(Raffuse, et al., 2007). While 2007 data from SMARTFIRE version 2 are available now, they were not
available for use in this version of the platform.
A functional diagram of the SMARTFIRE process is available in the SMARTFIRE documentation (Raffuse,
et al., 2007). Once the fire reconciliation process is completed, the emissions are calculated using the U.S.
Forest Service's CONSUMEv3.0 fuel consumption model and the FCCS fuel-loading database in the
BlueSky Framework (Ottmar, et. al., 2007),
Fires that could be matched in space and time with an ICS-209 report were designated as wildfires; all other
fires were designated as prescribed burning. A limitation of these satellite-based fires compared to ground-
based fires is the distinction between wildfire and prescribed burn is not as precise as with ground-based
methods. Also, the fire size is based on the number of satellite pixels and a nominal fire size of 100
acres/pixel and is assumed for a significant number of fire detections when the first detections were not
matched to ICS 209 reports. This means that the fire size information is not as precise as ground based
methods. In addition, because the HMS satellite product from NOAA is based on daily detections, the
emission inventory represents a time-integrated emission estimate. For example, a large smoldering fire will
show up on satellite for many days and would count as acres burned on a daily basis whereas a ground-based
method would count the area burned only once even it burns over many days.
30
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Additional references for this method are provided in (McKenzie, et al., 2007), (Ottmar, et al., 2003),
(Ottmar, et al., 2006), and (Anderson et al., 2004).
2.3.2 Average fires (avefire)
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.
The avefire sector contains wildfire and prescribed burning emissions. It excludes agricultural burning and
other open burning sources, which are included in the nonpt sector. Generally, their year-to-year impacts are
not as variable as wildfires and non-agricultural prescribed/managed burns.
We use this sector for the 2007 base case, and all future-year cases. 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. We
manually added the pollutant PMC to the avefire inventory prior to processing because the beta version of
SMOKE v3.1 did not support SMKIN VENFORMULA (where PMC = PMio - PM2.5) use for FF10 Daily
Nonpoint inventories. This bug has since been fixed in the public release of SMOKE v3.1.
For prescribed burning, we used a year-2008 specific SMARTFIRE version 2 (SFv2) approach because of
improvements over the SMARTFIRE version 1 approach used in all previous year data. In particular, the
unclassified fires (SCC=2811090000) in SFvl were eliminated in SFv2 and were replaced by either
prescribed burning or wildfire classification. In addition, activity data and emission factors for prescribed
burning were improved significantly in SFv2. However, the wildfire emissions methodology is more stable
between SFvl and SFv2; therefore, we are comfortable using wildfire data from both SFvl and SFv2. We
also know from state and county emissions summaries that prescribed burning emissions are less variable
year-to-year than wildfire emissions. Therefore, we feel comfortable using year 2008 prescribed burning
emissions in the 2007 and future year base case scenarios. Year 2007 data from SFv2 is now available, but
was not available in time to include in this version of the platform.
The EPA developed a new Fire Averaging Tool (FAT) to create avefire inventories from SMARTFIRE
point, day-specific data. The FAT tool is a stand-alone Perl program that reads user options, day-specific
one record per line (ORL) point (PTDAY) source files and an SCC mapping file to a generate day-specific
nonpoint inventory containing averaged fire emissions. The FAT tool allows setting the averaging period
(e.g., one month), the input data years, and the SCC assignments for mapping. The tool calculates the
average emissions for each day and county by using the rolling average to select a set of days from each of
the input PTDAY files for the years being included. For example, if the selected averaging period is 15 days
(+/- 7 days) and the years included were 2006, 2007, and 2008, then for July 15th the tool selects all fires in
that county from July 8th - 22nd for 2006, 2007, and 2008 to compute the average emissions for that day. All
of the fires in the county are included in the average for that county and day. Because many of those days
will have 0 emissions, peaks in the emissions will tend to be smoothed out.
For the 2007 platform, we chose an averaging period of 29 days (+/- 14 days), and included year 2003-2009
wildfires but only year 2008 prescribed burning data. The bottom panel of Figure 2-4 illustrates how the 29-
31
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day averaging period used in the 2007 platform (green line) is smoother than shorter periods of 7 and 15
days; the maximums are lower and the minimums are higher. The top panel in Figure 2-4 shows how the use
of multiple years of fire data greatly smoothes the year-to-year day-specific variability in the ptfire
inventory. The smoothing impact of FAT is seen temporally here, but FAT also smoothes the wildfires
spatially by using multiple years of data. The emissions shown in Figure 2-4 are for the western US only and
therefore mostly wildfire emissions; note the difference in scale from Figure 2-4. It is important to note that
the smoothing of prescribed fires is completely restricted to the 29-day average effect because only year-
2008 SFv2 prescribed burning emissions are used for this component.
Figure 2-4. Illustration of various FAT avefire emissions versus year 2007 fires (top), and with year-2007
fires not shown (bottom)
West: FAT & 2007 fires
160,000
140,000
120,000
| 100,000
+¦»
80,000
N '
60,000
40,000
20,000
0
>
¦c
Q.
>
II
. iJ
111
4? 4? J? 4? 4? 4? ^
y y y
¦7 days
15 days
29 days
2007 ptfire
West: FAT only
16,000
14,000
12,000
10,000
8,000
6,000
4,000
2,000
rS^ ^ ^ ^ ^ ^ ^ ^
4? 4? & ^ 4? J? ^
¦7 days
15 days
29 days
32
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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
described further.
The inputs to BEIS include:
• Temperature data at 2 meters which were obtained from the meteorological input files to the air
quality model,
• Land-use data from the Biogenic Emissions Land use Database, version 3 (BELD3). BELD3 data
provides data on the 230 vegetation classes at 1-km resolution over most of North America.
Plots of BEIS outputs for isoprene and NO for July, 2007 are shown in Figure 2-5 and Figure 2-6,
respectively.
Figure 2-5. NO emissions output from BEIS 3.14 for July, 2007
Monthly BEIS Sector NO
10.00(299
u 0.000
tons/month
July 1,2007 0:00:00
Min= 0.000 at (1,1). Max= 19.057 at (203,21)
33
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Figure 2-6. Isoprene emissions output from BEIS 3.14 for July, 2007
Monthly BEIS Sector ISOP
u 0.000
tons/month
July 1,2007 0:00:00
Min= 0.000 at (1,1), Max= 402.843 at (33,163)
2.5 2007 mobile sources (onroad, onroad_rfl, nonroad, c1c2rail, cSmarine)
For the 2007 platform, as indicated in Table 2-1, we 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). Except for
California, all onroad and onroad refueling emissions are generated using a new SMOKE-MOVES emissions
modeling framework that leverages MOVES2Q1 Ob-generated outputs and hourly meteorology. California
mobile emissions for onroad (including refueling), nonroad and clc2rail sources were provided by the
California Air Resources Board (C ARB).
The nonroad sector is based on NM1M except for California which uses data provided by the California Air
Resources Board (CARB). All nonroad emissions are compiled at the county/SCC level. NV1IM (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 (CMY) emissions are divided into two nonroad sectors:
"clc2rail" and "c3marine". 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 c3marine 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).
The 2008 NEI c3marine emissions were replaced with a set of approximately 4-km resolution point source
format emissions. These data are used for all states, including California, as well as offshore and
international emissions within our air quality modeling doming, and are modeled separately as point sources
34
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in the "c3marine" sector.
All tribal data from the mobile sectors have been dropped because we do not have spatial surrogate data, and
the emissions are small.
2.5.1 Onroad non-refueling (onroad)
For the 2007 platform, EPA estimated emissions for every county in the continental U.S. except for
California. We used a modeling framework that took into account the strong temperature sensitivity of the
onroad emissions. Specifically, we used county-specific inputs and tools that integrated the MOVES model
with the SMOKE2 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., EPA used an automated process to run MOVES to produce emission factors by temperature and
speed for 146 "representative counties," to which every other county could be mapped, as detailed below.
Using the MOVES emission rates, SMOKE selected appropriate emissions rates for each county, hourly
temperature, SCC, and speed bin and multiplied the 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: MOVES2Q10b.
Using SMOKE-MOVES for creating the 2007 and future year emissions requires numerous steps, as
described in the sections below:
• Determine which counties will be used to represent other counties in the MOVES runs (see Section
2.5.1.1)
• Determine which months will be used to represent other month's fuel characteristics (see Section
2.5.1.2)
• Create MOVES inputs needed only for MOVES runs (see Sections 2.5.1.3 and 2.5.1.4). MOVES
requires county-specific information on vehicle populations, age distributions, and inspection-
maintenance programs for each of the representative counties.
• Create inputs needed both by MOVES and by SMOKE, including a list of temperatures and activity
data (see Sections 2.5.1.5 and 2.5.1.6).
• Run MOVES to create emission factor tables (see Section 2.5.1.7).
• Run SMOKE to apply the emission factors to activities to calculate emissions (see Section 2.5.1.8).
• Aggregate the results at the county-SCC level for summaries and quality assurance
The California emissions were post-processed to incorporate both CARB supplied inventories and the
SMOKE-MOVES results (see Section 2.5.1.9).
2 A beta version of SMOKE v3.1 was used for modeling the PM NAAQS.
35
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2.5.1.1 Representative counties
Although EPA compiles county-specific databases for all counties in the nation, actual county-specific data
is rare. Instead, much of our "county" data is based on state-wide estimates or national defaults. For the
modeling platform, rather than explicitly modeling every county in the nation, we have done detailed
modeling for some counties and less detailed estimates for the other counties. This approach dramatically
reduces the number of modeling runs required to generate inventories and still takes into account important
differences between counties.
In this approach, we group counties that have similar properties that would result in similar emission rates.
We explicitly model only one county in the group (the "representative" county) to determine emission rates.
These rates are then used in combination with county-specific activity and meteorology data, to generate
inventories for all of the counties in the group. The grouping of counties was based on several characteristics
as summarized in Table 2-12 below.
Table 2-12. Characteristics for grouping counties
County Grouping Characteristic
Description
PADD
Petroleum Administration for Defense Districts (PADDs).
PADD 1 is divided into three sub-PADD groupings and
each sub-group is treated as a separate PADD (la, lb and
lc). Each state belongs to a PADD and all counties in any
state are within the same PADD.
Fuel Parameters
Weighted average gasoline fuel properties for January and
July 2008, including RVP, sulfur level, ethanol fraction
and percent benzene
Emission Standards
Some states have adopted California highway vehicle
emission standards or plan to adopt them. Since
implementation of the standards varies, each state with
California standards is treated separately.
Inspection/Maintenance Programs
Counties were grouped within a state according to whether
or not they had an inspection and maintenance (I/M)
program. All I/M programs within a state were considered
as a single program, even though each county may be
administered separately and have a different program
design.
Altitude
Counties were categorized as high or low altitude based
on the criteria set forth by EPA certification procedures
(4,000 feet above sea level).
Fleet Age
The weighted average age of passenger cars.
The result is a set of 146 county groups with similar fuel, emission standards, altitude, I/M programs and
fleet age. For each group, the county with the highest total VMT was chosen as the representative county for
the group.
For each county group, SMOKE-MOVES generated a set of emission rates that varied by SCC (vehicle type
and road type), fuel, speed, temperature, and humidity; thus, we did not need to consider the fleet mix, fuel,
speed, temperature range, or humidity in our grouping characteristics. This greatly increased the number of
counties that can be grouped and reduced the number of MOVES runs required.
36
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2.5.1.2 Fuel months
The concept of a fuel month is used to indicate when a particular set of fuel properties should be used in a
MOVES simulation. Similar to the reference county, the fuel month reduces the computational time of
MOVES by using a single month to represent a set of months. Because there are winter fuels and summer
fuels, EPA used January to represent October through April and July to represent May through September.
For example, if the grams/mile exhaust emission rates in January are identical to February's rates for a given
reference county and temperature (as well as other factors), then we use a single fuel month to represent
January and February. In other words, only one of the months needs to be modeled through MOVES. The
hour-specific VMT, temperature and other factors for February are still used to calculate emissions in
February, but the emission factors themselves do not need to be created since one month can represent the
other month sufficiently.
2.5.1.3 Fuels
Although state-submitted NMIM and MOVES input data may have included information about fuel
properties, the MOVES runs for the 2007 platform 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 following list provides a step-by-step outline of the process used by EPA to generate the 2007 county
fuel properties:
1) Fuel properties from proprietary refinery certification data were compiled on a regional basis (based
on typical pipeline delivery areas).
2) Properties within a region for finished fuel batches (e.g. no CBOB, RBOB or OBO fuel batches)
produced in 2007, excluding RFG, were averaged to generate non-ethanol conventional gasoline fuel
properties within that region, for a given month.
3) REG fuel properties were based on REG fuel compliance survey data, and oxygenate levels were
assumed to be 10% ethanol (E10, no MTBE).
4) Refinery modeling results generated for the RFS2 rulemaking were used to adjust the regional
conventional gasoline fuel properties to account for ethanol blending up to E10, for a given month.
5) Additional adjustments to fuel properties were performed on individual counties within a region,
based on refinery modeling, for known local regulatory constraints such as low-RVP or oxygenate
level mandates.
6) Appropriate E10 and conventional gasoline fuel market shares were calculated on a regional basis for
the level of ethanol produced in 2007, after ethanol required for RFG compliance was taken into
account.
7) Gasoline fuel properties and ethanol market shares were applied to each county regionally and
accounting for known local regulatory constraints.
8) Diesel properties were assumed to be 15 ppm nationally with no significant biodiesel penetration.
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2.5.1.4 Other local MOVES inputs
In addition to fuels and the information also needed by SMOKE (in the following sections), MOVES also
required inputs such as age distribution and I/M program descriptions for each of the representative counties.
At the county level, these inputs provide an opportunity to assure that the model properly accounts for the
most recent available local data. When these data were available from the state-supplied NMIM inputs, we
converted the NMIM data (version NCD20101201) for use in MOVES. EPA manually imported the 2008
data from Delaware and Utah into a MOVES format. Only data related to VMT, vehicle populations, speed
distributions and age distributions were imported. Fuel data submitted by states was not used for the 2007
platform in order to use the latest EPA estimates and make selecting representing counties easier. Similarly,
meteorological data from states were not used, since the NEI calculations used the SMOKE generated
meteorological data instead. Other state data from the NMIM data format were not used because of the
project schedule and resource constraints.
In the few cases where MOVES input data were provided, we used that data. When state-supplied data were
not available, we used MOVES defaults. For the continental U.S., all of these MOVES inputs were
organized by representative counties. This means that only the counties used to represent other counties had
specific information for the MOVES runs.
2.5.1.5 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.
The 12-km gridded meteorological input data for the entire year of 2007 covering the continental United
States were derived from simulations of version 3.1 of the Weather Research and Forecasting Model.
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 3.6 was used as the software for
maintaining dynamic consistency between the meteorological model, the emissions model, and air quality
chemistry model.
We applied the SMOKE-MOVES tool Met4moves to 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. "Temperature profiles" are
arrays of 24 temperatures that describe how temperatures change over a day, and they are used by MOVES
to estimate vapor venting emissions. The hourly gridded meteorological data (output from MCIP) was also
used directly by SMOKE (see Section 2.5.1.8).
The temperature lists were organized based on the representative counties and fuel months as described in
Sections 2.5.1.1 and 2.5.1.2, respectively. Temperatures were analyzed for all of the counties that are
mapped to the representative counties, i.e., for the county groups, and for all the months that were mapped to
the fuel months. We used Met4moves to determine the minimum and maximum temperatures in a county
group for the January fuel month and for the July fuel month, and the minimum and maximum temperatures
for each hour of the day. Met4moves also generated idealized temperature profiles using the minimum and
maximum temperatures and 10 degree intervals. In addition to the meteorological data, the representative
counties and the fuel months, Met4moves uses spatial surrogates to determine which grid cells from the
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meteorological data to collect temperature and relative humidity statistics. For example, if a county had a
mountainous area with no roads, this would be excluded from the meteorological statistics.
The treatment of humidity was simpler. Met4moves calculated an average day-time (6 am to 6 pm) relative
humidity for the county group for the months mapped to July and for the months mapped to January. The
humidity was also averaged over the grid cells intersecting the counties in the county group. When the
emission factors are applied by SMOKE (Section 2.5.1.8), the appropriate (July or January) humidity was
used for all runs of the county group.
Met4moves can be run in daily or monthly mode for producing SMOKE input. In monthly mode, the
temperature range is determined by looking at the range of temperatures over the whole month for that
specific county. Therefore, there is one temperature range per county per month. While in daily mode, the
temperature range is determined by evaluating the range of temperatures in that county for each day. The
output for the daily mode is one temperature range per county per day and is a more detailed approach for
modeling the vapor venting (RPP) based emissions. EPA ran Met4moves in daily mode for the 2007
platform.
2.5.1.6 VMT, vehicle population, and speed
SMOKE requires county-specific VMT, vehicle population, and average speed by SCC to calculate the
gridded or county emissions. Unlike the other inputs that are needed just for the representative counties,
these inputs are needed for every county. In some cases, speeds were provided by states. The state-
submitted input data are described in Section 2.5.1.4. If speeds were not provided by states, the average
speeds provided to SMOKE for each county were derived from the default national average speed
distributions found in the default MOVES2010b database AvgSpeedDistribution table. These average
speeds are the average speeds developed for the previous EPA highway vehicle emission factor model,
MOBILE6. EPA used the MOVES distribution of average speeds for each hour of the day for each road
type to calculate an overall average speed for each hour of the day. These hourly average speeds were
weighted together using the default national average hourly VMT distribution found in the MOVES default
database HourlyVMTFraction table, to calculate an average speed for each road type. This average speed by
road type was provided to SMOKE for each county.
SMOKE requires estimates of VMT by county and SCC. The annual VMT values calculated for calendar
year 2007 were estimated using VMT estimates from the Federal Highway Administration (FHWA) for 2007
and 2008, combined with the state-supplied VMT estimates submitted for the 2008 calendar year. The
FHWA estimates can be found in the vehicle miles of travel by functional system table (VM-2).
The VMT data in the VM-2 tables are broken out by state and Highway Performance Monitoring System
(HPMS) road type. We combined the VMT values from both 2007 and 2008 into a single table (matched on
state and road type) and calculated an adjustment factor (2007 VMT / 2008 VMT) for each state and road
type.
FHWA VM-2 table includes Puerto Rico, but not the Virgin Islands. We assumed that the adjustment factor
for VMT for the Virgin Islands is proportional to the small change in human population (approximately
0.2%).
The VMT used for the 2008 NEI is obtained from the by county and SCC FF10 format file used with
SMOKE for Version 2 of the 2008 NEI (VMT_NEI_2008_updated2_18jan2012_v3.csv). These FF10 data
do not include VMT for Alaska, Hawaii, Puerto Rico or the Virgin Islands (AK/HI/PR/VI). VMT data for
these locations were obtained from the original VMT developed for the 2008 NEI in the National Mobile
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Inventory Model (NMIM) National County Database (NCD) version NCD20101201. These data were
aggregated from the MOBILE6 vehicle classes into the SCC vehicle classes and allocated to months using
the MOBILE6 default monthly VMT fractions (NEI2008_VMT_AKIIIPRVI_FF10.csv). Finally, rows with
zero VMT were removed.
The 2007 VMT values were calculated by applying the adjustment factors calculated from the FHWA tables
to the appropriate rows in the 2008 VMT data, matching on state and HPMS road type. This means that the
same adjustment was used for all counties in a state and that all SCC vehicle types use the same adjustment
for each road type. The resulting 2007 VMT includes VMT estimates by county and SCC for all states,
Washington D.C., Puerto Rico and the Virgin Islands.
SMOKE also requires vehicle population estimates for each county by SCC vehicle type. Population
estimates for calendar year 2007 were determined by applying the population to VMT ratio obtained from
running the MOVES2010b emission factor model for calendar year 2007 with results for annual VMT and
population by SCC. These national default values for VMT and vehicle population were used to develop
ratios specific to the 12 SCC vehicle types.
Using the 2007 VMT values calculated previously, the ratios were applied to each appropriate SCC vehicle
type value aggregated across all road types to calculate a corresponding vehicle population value in each
county. The 2007 population results were converted to FF10 format.
2.5.1.7 Run MOVES to create emission factors
EPA used the SMOKE-MOVES driver scripts to run MOVES for each of the representative counties, fuel-
months, and the listed temperatures and temperature profiles. The runspec generator created a series of
runspecs (MOVES jobs) based on the outputs from Met4moves. Specifically, the script used a 5 degree bin
and the minimum and maximum temperature ranges from Met4moves and used the idealized diurnal profiles
from Met4moves to generate a series of MOVES runs that captured the full range of temperatures for each
representative county. The SMOKE-MOVES driver scripts resulted in three emission factors (EF) tables for
each representative county and fuel month: rate per distance (RPD), rate per vehicle (RPV), and rate per
profile (RPP). After the MOVES runs were completed, the post-processor Moves2smk converted the
MySQL tables into EF files that can be read by SMOKE. For more details, see Section 3.2.2.2 or the
SMOKE documentation.
2.5.1.8 Run SMOKE to create emissions
Lastly, we generated air quality model ready emissions at a gridded and hourly resolution. The Movemrg
SMOKE-MOVES program performs this function by combining activity data, meteorological data, and
emission factors to produce gridded, hourly emissions. We ran Movesmrg for each of the three sets of
emission factor tables (RPD, RPV, and RPP). During the Movesmrg run, the program used the hourly,
gridded temperature (for RPD and RPV) or daily temperature profile (for RPP) to select the proper emissions
rates and compute emissions. These calculations were done for all counties and SCCs in the SMOKE inputs,
covering the continental U.S.
The emissions process RPD is for modeling the on-network emissions. This includes the following modes:
vehicle exhaust, evaporation, evaporative permeation, break wear, and tire wear. For RPD, the activity data
is monthly VMT, monthly speed (SPEED), and hourly speed profiles for weekday versus weekend
(SPDPRO)3. The SMOKE program Temporal takes vehicle and roadtype specific temporal profiles and
distributes the monthly VMT to day of the week and hour. Movesmrg reads the speed data for that county
3 If the SPDPRO is available, the hourly speed takes precedence over the average speed in the SPEED inventory.
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and SCC and the temperature from the gridded hourly (MCIP) data and uses these values to look-up the
appropriate emission factors (EFs) from the representative county's EF table. It then multiplies this EF by
temporalized VMT to calculate the emissions for that grid cell and hour. This is repeated for each pollutant
and SCC in that grid cell.
The emission process RPV is for modeling the off-network emissions. This includes the following modes:
vehicle exhaust, evaporative, and evaporative permeation. For RPV, the activity data is vehicle population
(VPOP). Movesmrg reads the temperature from the gridded hourly data and uses the temperature plus SCC
and the hour of the day to look up the appropriate EF from the representative county's EF table. It then
multiplies this EF by the VPOP for that SCC and FIPS to calculate the emissions for that grid cell and hour.
This repeats for each pollutant and SCC in that grid cell.
The emission process RPP is for modeling the off-network emissions for parked vehicles. This includes the
mode vehicle evaporative (fuel vapor venting). For RPP, the activity data is VPOP. Movesmrg reads the
county based diurnal temperature range (Met4moves' output for SMOKE). It uses this temperature range to
determine a similar idealized diurnal profile from the EF table using the temperature min and max, SCC, and
hour of the day. It then multiplies this EF by the VPOP for that SCC and FIPS to calculate the emissions for
that grid cell and hour. This repeats for each pollutant and SCC within the county.
The result of the Movesmrg processing is hourly, gridded data suitable for use in air quality modeling as well
as daily reports for the three processing streams (RPD, RPV, and RPP). The results include emissions for
every county in the continental U.S., rather than just for the representative counties.
After running SMOKE-MOVES for the RPD, RPV and RPP processes have completed, we used the
SMOKE program Mrggrid to combine RPD, RPV and RPP model ready outputs into a single onroad model
ready output.
2.5.1.9 California emissions
The California 2007 onroad emissions were provided by California Air Resources Board (CARB). The 2007
and 2020 onroad emissions were produced from versions of EMFAC2011-LD and EMFAC2011-HD with
default activity assumptions. We did not model the CARB emissions directly because all emissions were
reported as occurring on local roads. We also wanted to take advantage of the temperature dependence in the
SMOKE-MOVES approach. We developed an approach to merge the CARB data with the SMOKE-
MOVES results in order to reflect California's unique rules in the total emissions while leveraging the more
detailed SCCs and the highly resolved spatial patterns, temporal patterns, and speciation from SMOKE-
MOVES.
The basic steps involved in merging CARB onroad emissions with SMOKE-MOVES were:
• Sum CARB emissions to county/pollutant annual totals across all emission modes (excluding
refueling) and SCCs
• Sum SMOKE-MOVES emissions to county/pollutant annual totals across all emission modes
(excluding refueling) and SCCs
• Create county/pollutant ratios by dividing the CARB emissions (county/pollutant totals) by the
appropriate SMOKE-MOVES emissions (county/pollutant totals)4.
4 We created these ratios for all matching pollutants. We also duplicated the ratios for all appropriate modeling species. For
example, we used the NOx ratio for NO, NO2, HONO and use the PM2 5 ratio for PEC, PNO3, POC, PSO4, and PMFINE (For more
details on NOx and PM speciation, see Sections 3.2.3 and 3.2.2). For VOC model-species, if there was an exact match (e.g.
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• Distribute the county/pollutant ratios to grid cells by using the land area spatial surrogate to
determine which grid cells are completely within one county versus those that overlap multiple
counties.5
• Determine the grid cells that fall completely within California, i.e. cells that do not overlap Arizona,
Oregon, or Nevada.
• Multiply the gridded ratios by the SMOKE-MOVES onroad model-ready files (merged combination
of RPD, RPP, RPV but excluding refueling). For all cells that do not fall completely within
California, multiply by a ratio of 1
This process created adjusted model-ready files that approximately sum to CARB annual totals but have the
temporal and spatial patterns reflecting the highly resolved meteorology and SMOKE-MOVES. After
adjusting the California emissions, we call this sector "onroad adj".
2.5.2 Onroad refueling (onroad_rfl)
Onroad refueling is modeled very similarly to other onroad emissions (see Section 2.5.1.8). MOVES2010b
can produce EFs for refueling. These EFs are at the resolution of the onroad SCCs. We ran the refueling
EFs separately from the other onroad mobile sources to allow for different spatial allocation. To facilitate
this, we first separated out the EFs from the refueling process into RPD refueling and RPV refueling tables6.
We 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). For California, we use the SMOKE-MOVES generated emissions
for onroad refueling without any adjustments because we did not have CARB supplied refueling emissions.
Lastly, we used the Mrggrid SMOKE program to combine 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.
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 NMIM for all states except
California. We used year-2007 CARB inventories for California after several preprocessing steps discussed
below.
NMIM (non-California) nonroad
NMIM ran the publically released version of NONROAD, NR08a, which models all in-force nonroad
controls, including the marine spark ignited (SI) and small SI engine final rule, published May 2009 (EPA,
2008). The NMIM version is NMIM20090504d, which has the same results as the publicly-released NMIM
version NMIM20090504a. The underlying National County Database (NCD) is NCD20101201, but with
2007 meteorology inserted into the countymonthhour table. NCD20101201 includes state inputs for the
2008 NEI.
The NMIM run, 2007PfBase2007Nr, only includes states in our emission modeling domain; it excludes
Alaska, Hawaii, Puerto Rico and the Virgin Islands. To conserve processing time, NMIM was run using 392
county groups. The county groups are in the same state and have the same fuels and similar temperature
BENZENE), we used that HAP pollutant ratio. For other VOC based model-species that didn't exist in the CARB inventory, we
used VOC ratios.
5 More specifically, for those grid cells that fall completely within one county, the county /pollutant ratios are used without further
adjustment. For those grid cells that overlap more than one county, the county specific ratios are weighted according to the % of
land area within each county.
6 The Moves2smk post-processing script has command line arguments that will either consolidate or split out the refueling EF.
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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 countymap2007pf. The fuels database,
regionalfuels_2007_20120323fuelsNMIM, is a conversion to NMIM format of the MOVES fuels.
As with the onroad emissions, NMIM 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/OTAQ ran NMIM to create county-SCC emissions and we removed California emissions because
they were replaced with a CARB inventory. Emissions were converted from monthly totals to SMOKE-
ready FF10 monthly average-day based on the number of days in each month. We retained only CAPs and
the necessary HAPs: BAFM, HC1, CI, 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 (CEPAM) 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, 2010b).
We converted the CARB-supplied nonroad annual inventory to monthly emissions values by using the
aforementioned EPA NMIM 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.
See Section 3.2.1.3 for details on speciation of California nonroad data.
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.
The starting point for the clc2rail sector is the 2008 NEI nonpoint inventory. As discussed in Table 2-1 and
Table 2-2, the clc2rail SCCs were extracted from the NEI nonpoint inventory.
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Table 2-13 lists the NEI SCCs included in this sector.
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Table 2-13. 2008 NEI SCCs extracted for the starting point in clc2rail development
see
Description: Mobile Sources prefix for all
2280002100
Marine Vessels; Commercial; Diesel; Port
2280002200
Marine Vessels; Commercial; Diesel; Underway
2285002006
Railroad Equipment; Diesel; Line Haul Locomotives: Class I Operations
2285002007
Railroad Equipment; Diesel; Line Haul Locomotives: Class II / III Operations
2285002008
Railroad Equipment; Diesel; Line Haul Locomotives: Passenger Trains (Amtrak)
2285002009
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines
2285002010
Railroad Equipment; Diesel; Yard Locomotives
We included several modifications to this sector based on the availability of improved data from other
sources and analysis with the NEI point inventory. We describe these modifications here:
Duplicate rail yard emissions removed
The 2008 NEI point inventory contains rail yard emissions for several states and counties. We analyzed the
NEI point and nonpoint inventories for counties with significant rail yard emissions in both inventories. We
assumed that the point inventory contained more accurate information when both inventories contained rail
yard emissions. Therefore, we removed nonpoint rail yards in the clc2rail sector for the states and counties
listed in
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Table 2-14.
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Table 2-14. Counties where clc2rail sector rail yard emissions were removed
FIPSCode
State
County
04013
Arizona
Maricopa
06001
California
Alameda
06013
California
Contra Costa
06019
California
Fresno
06025
California
Imperial
06029
California
Kern
06037
California
Los Angeles
06061
California
Placer
06063
California
Plumas
06067
California
Sacramento
06071
California
San Bernardino
06077
California
San Joaquin
06085
California
Santa Clara
06099
California
Stanislaus
24001
Maryland
Allegheny
24021
Maryland
Frederick
24043
Maryland
Washington
24510
Maryland
Baltimore
41017
Oregon
Deschutes
41035
Oregon
Klamath
41039
Oregon
Lane
41043
Oregon
Linn
41051
Oregon
Multnomah
41059
Oregon
Umatilla
41061
Oregon
Union
Replaced Texas Class I and Class II/III Operations emissions
Analysis of the total rail emissions in the 2008 NEI showed what appeared to be missing rail line emissions
in Texas. We found that line haul emissions from Texas were essentially zero because of challenges faced in
using EIS for the first time in 2008. This error is reflected in Figure 2-7 where rail line emissions are
missing in Texas. Therefore, we removed all line haul emissions from the 2008 NEI (which are zero for
most records) and added information from an EPA default dataset of Texas line haul emissions. These EPA
line haul emissions are restricted to the Class I and Class II/III operations and add approximately 52,000 tons
of NOx to Texas that would otherwise be missing. We consulted Texas on this change and it was agreed that
this was the best solution.
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Figure 2-7. NOx rail emissions in 2008 NEI
clc2rail Sector NOx Rail Sources
2007ea_run1
1000299
750
500
250
tons/year 1
459
January -805,-30:00:00
Min= 0 at (1,1), Max= 2078 at (187,156)
Replaced Texas C1/C2 CMV emissions with improved dataset
For several Texas counties, the C1/C2 CMV emissions in the 2008 NEI included EPA gap filled values
where shape IDs were not populated on submittal. The intended Texas submittal was often much smaller
than the EPA-estimated default value for several counties. An example of this is Harris county
(FIPS=48201) where the Texas submittal was approximately 1,200 tons of NOx for port and underway
emissions but not all shape IDs were included. The NEI methodology used EPA emissions where Texas did
not provide estimates and the resulting double count and overestimate of this top-down method resulted in
over 49,000 tons of NOx in the 2008 NEI in Harris county, Texas. Therefore, we went back to the original
Texas submittal, did not append any EPA emissions, and summed up port and underway for our modeling
files to the county level. Corrections to clc2rail emissions in places where errors similar to this occurred
may be released in a future version of the 208 NEI. Other states were impacted by this error in the 2008 NEI
but for many of these states, alternative data were used as discussed below.
Replaced all California C1/C2 CMV and rail data with CARB data
As discussed in Section 2.5.3, the California Air Resources Board (CARB) provided year 2007 emissions for
all mobile sources, including C1/C2 CMV and rail. California year 2007 emissions were provided by CARB
and are documented in a staff report.
The C1/C2 CMV emissions were obtained from the CARB nonroad mobile dataset that includes the 2007
regulations to reduce emissions from diesel engines on commercial harbor craft operated within California
waters and 24 nautical miles of the California baseline. These emissions were developed using Version 1 of
the CEP AM that supports various California off-road regulations. The locomotive emissions were obtained
from the CARB trains dataset "ARMJ_RF#2002_ANNTJAL_TRAINS.txt". Documentation of the CARB
offroad mobile methodology, including clc2rail sector data. We converted the CARB inventory TOG to
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VOC by dividing the inventory TOG by the available VOC-to-TOG speciation factor. See Section 3.2.1.3
for more details on clc2rail speciation.
The RPO and CARB inventories did not include HAPs; therefore, we processed all non-NEI source
emissions in the clc2rail sector using VOC speciation.
Replaced all C1/C2 CMV and rail data for states in 3 RPOs
As discussed in Section 2.2, we received year-2007 inventories for many sectors from three RPOs:
MARAMA, MWRPO and SESARM. We used the RPO emissions in these areas and removed all 2008 NEI
clc2 CMV and rail emissions for states in these three RPOs to prevent double counting. We used the
emissions data from the MARAMA rather than SESARM dataset for Virginia because the SESARM data
included some rather large emissions for Commuter Lines (SCC=2285002009) that were not reflected in
either the 2008 NEI or the MARAMA dataset. We were unable to confirm that these emissions were reliable
and not potentially reflected in other rail SCCs.
The MWRPO year-2007 clc2rail data were obtained from a subset of their version 7 emissions modeling file
"nrinv.mwrpo_alm.baseCv7.annual.orl.txt", where MWRPO NEI Inventory Format (NIF)-formatted data
were converted to SMOKE ORL format. The MARAMA dataset was obtained from a subset of their version
3.3 January 27, 2012 vintage file "ARINV_2007_MAR_Jan2012.txt". The SESARM dataset was obtained
from a subset of the file "nrinv.alm.semap.base07.v093010.orl.txt" developed for the Southeastern
Modeling, Analysis, and Planning (SEMAP) project. All RPO datasets were edited to remove non-clc2rail
sources. The background and contact information for these RPO datasets can be found via the web links and
contacts provided at the beginning of Section 2.
We made several modifications to the RPO clc2rail data. We changed the county FIPS code field in the
MARAMA RPO dataset from Clifton Forge (FIPS=51560) to Allegheny county (FIPS=51005) because
Clifton Forge is no longer its own county in our SMOKE ancillary input files. We also corrected a
misclassified SCC in several Virginia counties. MARAMA reported an unknown SCC 2283000000 in
Massachusetts that we changed to "diesel-military" (SCC=2280002040) based on analyses of sources in
other counties. We also removed likely duplicate C1/C2 CMV emissions in four New York counties where a
broad SCC (2280002000) was reported alongside more specific SCCs reflecting port (2280002100) and/or
underway (2280002200) processes in the same inventory. These four New York counties (and FIPS) are:
Nassau (36059), Queens (36081), Richmond (36085) and Suffolk (36103).
2.5.5 Class 3 commercial marine vessels (c3marine)
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-IMO) 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. An overview of the C3 EC A Proposal
to the International Maritime Organization (EPA-420-F-10-041, August 2010) project and future-year goals
for reduction of NOx, SO2, and PM C3 emissions. The resulting ECA-IMO coordinated strategy, including
emission standards under the Clean Air Act for new marine diesel engines with per-cylinder displacement at
or above 30 liters, and the establishment of Emission Control Areas. We converted the ECA-IMO emissions
data to SMOKE point-source ORL input format.
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As described in the paper, the ASCII raster dataset was converted to latitude-longitude, mapped to
state/county FIPS codes that extended up to 200 nautical miles (nm) from the coast, assigned stack
parameters, and monthly ASCII raster dataset emissions were used to create monthly temporal profiles.
Counties were assigned as extending up to 200nm from the coast because this was the distance to the edge of
the U.S. Exclusive Economic Zone (EEZ), a distance that defines the outer limits of ECA-IMO controls for
these vessels.
The base year ECA inventory is 2002 and consists of these CAPs: PMio, PM2.5, CO, CO2, NH3, NOx, SOx
(assumed to be SO2), and Hydrocarbons (assumed to be VOC). The EPA developed regional growth
(activity-based) factors that we applied to create the 2007v5 inventory from the 2002 data. These growth
factors are provided in Table 2-15. The geographic regions listed in the table are shown in
50
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Figure 2-8. The East Coast and Gulf Coast regions were divided along a line roughly through Key Largo
(longitude 80° 26' West).
We assigned Canadian near-shore emissions to province-level FIPS codes and paired those to region
classifications for British Columbia (North Pacific), Ontario (Great Lakes) and Nova Scotia (East Coast).
The assignment of U.S. FIPS was also restricted to state-federal water boundaries data from the Mineral
Management Service (MMS) that extended only (approximately) 3 to 10 miles off shore. Emissions outside
the 3 to 10 mile MMS boundary but within the approximately 200 nm EEZ boundary in
51
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Figure 2-8 were projected to year 2007 using the same regional adjustment factors as the U.S. emissions;
however, the FIPS codes were assigned as "EEZ" FIPS. Note that state boundaries in the Great Lakes are an
exception, extending through the middle of each lake such that all emissions in the Great Lakes are assigned
to a U.S. county or Ontario. The classification of emissions to U.S. and Canadian FIPS codes is primarily
needed only for inventory summaries and is irrelevant for air quality modeling except potentially for source
apportionment of states contributions to transport.
Table 2-15. Growth factors to project the 2002 ECA-IMO inventory to 2007
2007 Adjustments Relative to 2002
Region
EEZ
FIPS
NOx
PMio
PM2.5
voc
(HC)
CO
SO2
East Coast (EC)
85004
1.191
1.258
1.260
1.259
1.258
1.258
Gulf Coast (GC)
85003
1.087
1.149
1.146
1.148
1.149
1.149
North Pacific (NP)
85001
1.131
1.188
1.172
1.188
1.188
1.188
South Pacific (SP)
85002
1.221
1.292
1.290
1.284
1.282
1.295
Great Lakes (GL)
n/a
1.076
1.099
1.099
1.100
1.099
1.099
Outside EC A
98001
1.165
1.230
1.230
1.230
1.230
1.230
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Figure 2-8. Illustration of regional modeling domains in ECA-IMO study
Gt
We converted the emissions to SMOKE point source ORL format, allowing for the emissions to be allocated
to modeling layers above the surface layer. We also corrected FIPS code assignments for one county in
Rhode Island. All non-US emissions (i.e., in waters considered outside of the 200 nm EEZ, and hence out of
the U.S. and Canadian ECA-IMO controllable domain) are simply assigned a dummy state/county FIPS
code=98001 and thus projected to year 2007 via the "Outside ECA" factors in Table 2-15. The SMOKE-
ready data have also been cropped from the original ECA-IMO entire northwestern quarter of the globe to
cover only the large continental U.S. 36-km "36US1" air quality model domain, the largest domain we
currently use.
Other modifications to the original ECA-IMO c3marine dataset include updated Delaware county total
emissions that reflect comments received during the Cross-State Air Pollution Rule (CSAPR) emissions
modeling platform development. The original ECA-IMO inventory also did not delineate between ports and
underway (or other C3 modes such as hoteling, maneuvering, reduced-speed zone, and idling) emissions;
however, we used a U.S. ports spatial surrogate dataset to assign the ECA-IMO emissions to ports and
underway SCCs - 2280003100 and 2280003200, respectively. This has no effect on temporal allocation or
speciation because all C3 emissions, unclassified/total, port and underway, share the same temporal and
speciation profiles. See Section 3.2.1.3 for more details on c3marine speciation.
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 Canada we use year-2006 Canadian emissions but applied several modifications to the inventories:
53
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i. We did not include wildfires, or prescribed burning because Canada does not include these inventory
data in their modeling.
ii. We did not include in-flight aircraft emissions because we do not include these for the U.S. and we
do not have a finalized approach to include in our modeling.
iii. We applied a 75% reduction ("transport fraction") to PM for the road dust, agricultural, and
construction emissions in the Canadian "afdust" inventory. This approach is more simplistic than the
county-specific approach used for the U.S., but a comparable approach was not available for Canada.
iv. We did not include speciated VOC emissions from the ADOM chemical mechanism because we use
speciated emissions from the CB5 chemical mechanism that Canada also provided.
v. Residual fuel CMV (C3) SCCs (22800030X0) were removed because these emissions are included in
the c3marine sector, which covers not only emissions close to Canada but also emissions far at sea.
Canada was involved in the inventory development of the c3marine sector emissions.
vi. Wind erosion (SCC=2730100000) and cigarette smoke (SCC=2810060000) emissions were removed
from the nonpoint (nonpt) inventory; these emissions are also absent from our U.S. inventory.
vii. Quebec PM2.5 emissions (2,000 tons/yr) were removed for one SCC (2305070000) for Industrial
Processes, Mineral Processes, Gypsum, Plaster Products due to corrupt fields after conversion to
SMOKE input format. This error should be corrected in a future inventory.
viii. Excessively high CO emissions were removed from Babine Forest Products Ltd (British Columbia
SMOKE plantid='5188') in the point inventory. This change was made at our discretion because the
value of the emissions was impossibly large.
ix. The county part of the state/county FIPS code field in the SMOKE inputs were modified in the point
inventory from "000" to "001" to enable matching to existing temporal profiles.
For Mexico we used emissions for year 2008 that are projections of their 1999 inventory originally
developed by Eastern Research Group Inc., (ERG, 2006) as part of a partnership between Mexico's
Secretariat of the Environment and Natural Resources (Secretaria de Medio Ambiente y Recursos Naturales-
SEMARNAT) and National Institute of Ecology (Instituto Nacional de Ecologia-INE), the U.S. EPA, the
Western Governors' Association (WGA), and the North American Commission for Environmental
Cooperation (CEC). This inventory includes emissions from all states in Mexico. A background on the
development of year-2008 Mexico emissions from the 1999 inventory is available at Western Regional Air
Partnership (WRAP).
The offshore emissions include point source offshore oil and gas drilling platforms. We used emissions from
the 2008 NEI point source inventory. The offshore sources were provided by the Mineral Management
Services (MMS).
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.
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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. The pre-processing steps involving temporal allocation, spatial allocation, pollutant speciation, and
vertical allocation of point sources are referred to as emissions modeling.
As seen in Section 2, the temporal resolution of the emissions inventories input to SMOKE for the 2007
platform varies across sectors, and may be hourly, monthly, or annual total emissions. The spatial resolution,
which also can be different for different sectors, may be individual point sources or county totals with
province totals for Canada and municipio totals for Mexico. This section provides some basic information
about the tools and data files used for emissions modeling as part of the 2007 platform. Since we devoted
Section 2 to describing the emissions inventories, we have limited the descriptions of data in this section to
the ancillary data SMOKE uses to perform the emissions modeling steps. Note that all SMOKE inputs for
the 2007v5 platform emissions are available at the 2007v5 website (see Section 1).
We used SMOKE version 3.1 beta to pre-process the raw emissions to create the emissions inputs for
CMAQ. We utilized the feature in SMOKE to create combination speciation profiles that could vary by
state/county FIPS code and by month; we used this approach for some mobile sources as described in
Section 3.2.1. For sectors that have plume rise, we used the in-line emissions capability of the air quality
model for plume rise, and therefore created source-based emissions files rather than the much larger 3-
dimensional files. Emissions totals by specie for the entire model domain are output as reports that are then
compared to reports generated by SMOKE to ensure mass is not lost or gained during this conversion
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 for 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: "point" indicates that SMOKE maps the source from a point location (i.e., latitude and longitude)
to a grid cell; "surrogates" indicates that some or all of the sources use spatial surrogates to allocate county
emissions to grid cells; and "area-to-point" indicates that some of the sources use the SMOKE area-to-point
feature to grid the emissions (further described in Section3.4.2). The "Speciation" column indicates that all
sectors use the SMOKE speciation step, though biogenics speciation is done within BEIS3 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 which will have emissions in aloft layers, based on plume rise. The term "in-line"
means that the plume rise calculations are done inside of the air quality model instead of being computed by
55
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SMOKE. The air quality model computes the plume rise using the stack data and the hourly air quality
model inputs found in the SMOKE output files for each model-ready emissions sector. The height of the
plume rise determines the model layer into which the emissions are placed. The c3marine and ptfire sectors
are the only sectors with only "in-line" emissions, meaning that all of the emissions are placed in aloft layers
and thus there are no emissions for those sectors in the two-dimensional, layer-1 files created by SMOKE.
Table 3-1. Key emissions modeling steps by sector.
Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
ptipm
Point
Yes
daily & hourly
in-line
ptnonipm
Point
Yes
annual
in-line
othpt
Point
Yes
annual
in-line
nonroad
Surrogates &
area-to-point
Yes
monthly
othar
Surrogates
Yes
annual
c3 marine
Point
Yes
annual
in-line
clc2rail
Surrogates
Yes
annual
onroad
Surrogates
Yes
computed hourly
onroad rfl
Surrogates
Yes
computed hourly
othon
Surrogates
Yes
annual
nonpt
Surrogates &
area-to-point
Yes
annual
(some monthly)
ag
Surrogates
Yes
annual
(some monthly)
afdust
Surrogates
Yes
annual
beis
Pre-gridded
land use
in BEIS3 .14
computed hourly
avefire
Surrogates
Yes
daily
ptfire
Point
Yes
daily
in-line
In addition to the above settings, we used the PELVCONFIG file, which can be optionally used to group
sources so that they are treated as a single stack by SMOKE when computing plume rise. For the 2007v5
platform we chose to have no grouping because grouping done for "in-line" processing will not give
identical results as "offline" (i.e., processing whereby SMOKE creates 3-dimensional files). The only way to
get the same results between in-line and offline is to choose to have no grouping.
We ran SMOKE for the largel2-km CONtinental United States "CONUS" modeling domain for boundary
conditions in the 2007 evaluation case and windowed emissions down to the smaller CONUS US 12-km
modeling domain (12US2) shown in Figure 3-1.
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Figure 3-1. Air quality modeling domains
12US1 Continental US Domain
12US2 Continental US Domain
Both grids use a Lambert-Conformal projection, with Alpha = 33°, Beta = 45° and Gamma = -91°, with a
center of X = -97° and Y = 40°. Table 3-2 describes the grids for the two domains.
Table 3-2. Descriptions of the 2007v5 platform grids
Common
Name
Grid
Cell Size
Description
(see Figure 3-1)
Grid name
Parameters listed in SMOKE grid
description (GRIDDESC) file:
projection name, xorig, yorig,
xcell, ycell, ncols, nrows, nthik
Continental
12km grid
12 km
Entire conterminous
US plus some of
Mexico/Canada
12US1_459X299
'LAM 40N97W', -2556000, -1728000,
12.D3, 12.D3, 459,299, 1
US 12 km or
"smaller"
CONUS-12
12 km
Smaller 12km
CONUS plus some of
Mexico/Canada
12US2
'LAM 40N97W, -2412000, -
1620000, 12.D3, 12.D3, 396, 246, 1
Section 3.4 provides the details on the spatial surrogates and area-to-point data used to accomplish spatial
allocation with SMOKE.
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
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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 2005 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.
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Table 3-3. Model species produced by SMOKE for CB05 with SOA for CMAQ4.7.1 and CAMx*
Inventory Pollutant
Model Species
Model species description
CL2
CL2
Atomic gas-phase chlorine
HC1
HCL
Hydrogen Chloride (hydrochloric acid) gas
CO
CO
Carbon monoxide
NOx
NO
Nitrogen oxide
N02
Nitrogen dioxide
HONO
Nitrous acid
S02
S02
Sulfur dioxide
SULF
Sulfuric acid vapor
nh3
NH3
Ammonia
voc
ALD2
Acetaldehyde
ALDX
Propionaldehyde and higher aldehydes
BENZENE
Benzene (not part of CB05)
CH4
Methane7
ETH
Ethene
ETHA
Ethane
ETOH
Ethanol
FORM
Formaldehyde
IOLE
Internal olefin carbon bond (R-C=C-R)
ISOP
Isoprene
MEOH
Methanol
OLE
Terminal olefin carbon bond (R-C=C)
PAR
Paraffin carbon bond
TOL
Toluene and other monoalkyl aromatics
XYL
Xylene and other polyalkyl aromatics
VOC species from the biogenics
SESQ
Sesquiterpenes
model that do not map to model
species above
TERP
Terpenes
PM10
PMC
Coarse PM >2.5 microns and <10 microns
PM2.5
PEC
Particulate elemental carbon <2.5 microns
PN03
Particulate nitrate <2.5 microns
POC
Particulate organic carbon (carbon only) <2.5 microns
PS04
Particulate Sulfate <2.5 microns
PMFINE
Other particulate matter <2.5 microns
Sea-salt species (non -
PCL
Particulate chloride
anthropogenic)
PNA
Particulate sodium
*The same species names are used for the CAMX model with exceptions as follows:
1. CL2 is not used in CAMx
2. CAMx particulate sodium is NA (in CMAQ it is PNA)
3. CAMx uses different names for species that are both in CB05 and SOA for the following: TOLA=TOL, XYLA=XYL,
ISP=ISOP, TRP=TERP. They are duplicate species in CAMX that are used in the SOA chemistry. CMAQ uses the same
names in CB05 and SOA for these species.
4. CAMx uses a different name for sesquiterpenes: CMAQ SESQ = CAMX SQT
5. CAMx uses particulate species uses different names for organic carbon, coarse particulate matter and other particulate
mass as follows: CMAQ POC = CAMX POA, CMAQ PMC = CAMX CPRM, and CMAQ PMFINE= CAMX FPRM
The approach for speciating PM2.5 emissions supports both CMAQ 4.7.1 and CMAQ 5.0, which includes
additional speciation of PM2.5 into a larger set of PM model species than is listed above (see Section 3.2.2.1
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 which is the EPA's repository of TOG and PM speciation
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profiles of air pollution sources. However, a few of the profiles we used in the v5 platform will be published
in later versions of the SPECIATE database after the release of this documentation.
The SPECIATE database development and maintenance is a collaboration involving the EPA's 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.
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, we 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 below) combines these HAPs with the VOC in
a way that does not double count emissions and uses the HAP inventory directly in the speciation process.
The basic process is to subtract the specified HAPs from VOC and to use a special integrated profile to
speciate the remainder of VOC to the model species excluding the specific HAPs. We believe that generally,
the HAP emissions from the NEI are more representative of emissions of these compounds than their
generation via VOC speciation.
We chose the HAPs benzene, acetaldehyde, formaldehyde and methanol (BAFM) because, with the
exception of BENZENE, they are the only explicit VOC HAPs in the base version of CMAQ 4.7.1 (CAPs
only with chlorine chemistry) model. By "explicit VOC HAPs," we mean model species that participate in
the modeled chemistry using the CB05 chemical mechanism. We denote the use of these HAP emission
estimates along with VOC as "HAP-CAP integration". BENZENE was chosen because it was added as a
model species in the base version of CMAQ 4.7.1, and there was a desire to keep its emissions consistent
between multi-pollutant and base versions of CMAQ.
For specific sources, especially within the onroad and onroad rfl sectors, we included ethanol in our
integration. To differentiate when a source was integrating BAFM versus EBAFM (ethanol in addition to
BAFM), the speciation profiles which do not include ethanol are referred to as an "E-profile", for example
E10 headspace gasoline evaporative speciation profile 8763 where ethanol is speciated from VOC, versus
8763E where ethanol is obtained directly from the inventory.
The integration of HAP VOC with VOC is a feature available in SMOKE for all inventory formats other
than PTDAY (the format used for the ptfire sector). SMOKE allows the user to specify the particular HAPs
to integrate and the particular sources to integrate. The particular HAPs to integrate are specified in the
INVTABLE file, and the particular sources to integrate are based on the NHAPEXCLUDE file (which
actually provides the sources that are excluded from integration8). For the "integrate" sources, SMOKE
subtracts the "integrate" HAPs from the VOC (at the source level) to compute emissions for the new
pollutant "NONHAPVOC." The user provides NONHAPVOC-to-NONHAPTOG factors and
7 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.
8 In SMOKE version 3.1, the options to specify sources for integration are expanded so that a user can specify the particular
sources to include or exclude from integration, and there are settings to include or exclude all sources within a sector.
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NONHAPTOG speciation profiles. SMOKE computes NONHAPTOG and then applies the speciation
profiles to allocate the NONHAPTOG to the other air quality model VOC species not including the
integrated HAPs. After determining if a sector is to be integrated, if all sources have the appropriate HAP
emissions, then the sector is considered fully integrated (full integrate) and does not need a
NHAPEXCLUDE file. If on the other hand, certain sources do not have the necessary HAPs, then one needs
to construct a NHAPEXCLUDE file based on the evaluation of each source's pollutant mix. The process of
partial integration for BAFM is illustrated in Figure 3-2. Note that we did not need to remove BAFM from
any sources in a partially integrated sector, which is different from previous platforms.
Figure 3-2. Process of integrating BAFM with VOC for use in VOC Speciation
j * "i
: Emissions ready for S\1C\E i
SMOKE
Compute NONHAPVOC= VOC - (B+ F+ A+M)
I emissions for each integrate source
Retain VOC emissions for each no-integrate source
Assign speciation profile code to each emission source
-
1APTOG l
erni5s!on5Tfom~NONHAPVOCT5ir
Compute: NONH/
each integrate sou rce
Compute: TOG emissions from VOC for each no-integrate
source
Compute moles of each CB05 model species.
Use NONHAPTOG prcPiesapplied to NONHAPTOG
emissions and B, F, A, M emissions for integrate sources.
Use TOG profiles applied to TOG for no-integrate sources
: 5
: list of "no-integrate" ¦
\ sources (NHAPEXCLUDE) j
Speciati on Cross f
Reference Fil« (GSREF) j
VOC-io-TOG factors
JONHAPVOC-to-NONt- APTDG
fact ors (GSCNVi
TOG end NON-APTOG
speciation factors
(GSPRO)
Specated En-iss'c is fo" VOC species
For EBAFM integration, this process would be identical to the above figure except for the addition of
ethanol (E) to the list of subtracted HAP pollutants. For full integration, the process would be very similar
except that the NHAPEXCLUDE file would not be used and all sources in the sector would be integrated.
We considered CAP-HAP integration for all sectors and developed "integration criteria" for some of them
(see Section 3.2.1.3 for details)
We prepared two different types of INVTABLE files to use with different sectors of the platform. For
sectors in which we chose no integration across the entire sector (see Table 3-4), we created a "no HAP use"
INVTABLE in which the "KEEP" flag is set to "N" for BAFM pollutants. Thus, any BAFM pollutants in
the inventory input into SMOKE are dropped. This approach both avoids double-counting of these species
and assumes that the VOC speciation is the best available approach for these species for the sectors using the
approach. The second INVTABLE, used for sectors in which one or more sources are integrated, causes
SMOKE to keep the BAFM pollutants and indicates that they are to be integrated with VOC (by setting the
"VOC or TOG component" field to "V" for all four HAP pollutants. We further differentiate this integrate
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INVTABLE into those that integrate BAFM versus those that integrate EBAFM (for example for the onroad
and onroadrfl sectors).
Table 3-4. Integration approach for BAFM and EBAFM for each platform sector
Platform
Sector
Approach for Integrating NEI emissions of Benzene (B), Acetaldehyde (A),
Formaldehyde (F), Methanol (M), and Ethanol (E)
ptipm
No integration
ptnonipm
No integration
avefire
No integration
ag
N/A - sector contains no VOC
afdust
N/A - sector contains no VOC
nonpt
Partial integration (BAFM and EBAFM)
nonroad
For other than California: Partial integration (BAFM). For California: no integration
clc2rail
Partial integration (BAFM)
c3marine
Full integration (BAFM)
onroad
Full integration (EBAFM and BAFM)
biog
N/A - sector contains no inventory pollutant "VOC"; but rather specific VOC species
othpt
No integration
othar
No integration I
othon
No integration
More details on the integration of specific sectors and additional details of the speciation are provided in
Section3.2.1.3.
3.2.1.2 County specific profile combinations (GSPRO_COMBO)
We used the SMOKE feature to compute speciation profiles from mixtures of other profiles in user-specified
proportions. The combinations are specified in the GSPROCOMBO ancillary file by pollutant (including
pollutant mode, e.g., EXH VOC), state and county (i.e., state/county FIPS code) and time period (i.e.,
month).
We 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 GSPROCOMBO, 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.
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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.
For the clc2rail sector, we 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, we processed all non-NEI source emissions in the clc2rail sector as no
integrate. For California, we converted the CARB inventory TOG to VOC by dividing the inventory TOG
by the available VOC-to-TOG speciation factor.
For the c3marine sector, we computed HAPs directly from the CAP inventory; therefore, the entire sector
utilizes CAP-HAP VOC integration 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
emissions are derived from the following equations:
Benzene = VOC * 9.795E-06
Acetaldehyde = VOC * 2.286E-04
Formaldehyde = VOC * 1.5672E-03
For the onroad and onroadrfl sectors, there are series of unique speciation issues. First, we are using
SMOKE-MOVES (see Section 2.5.1.7 and Section 2.5.1.8) which means that both the MEPROC and
INVTABLE files are involved in controlling which pollutants are ingested and speciated. Second, we
speciate directly from TOG rather than VOC. Third, for the gasoline sources, we use full integration of
EBAFM (i.e. we use E-profiles). For the diesel sources, we use full integration of BAFM. Fourth, for the
onroad sector we utilize 5 different modes for speciation: exhaust, evaporative, permeation (gasoline vehicles
only), brake wear, and tire wear. For the onroad rfl sector, we utilize a sixth mode, refueling. Fifth, for
California we apply gridded ratios to the SMOKE-MOVES model-ready files to produce California adjusted
model-ready files (see Section 2.5.1.9 for details). By applying the ratios to the model-ready file, we are
essentially speciating the CARB inventory to match the SMOKE-MOVES speciation grid cell by grid cell.
For the nonroad sector, we did not integrate CNG or LPG sources (SCC beginning with 2268 or 2267)
because NMIM computed only VOC and not any HAPs for these SCCs. All other nonroad sources were
integrated. For California, we 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.
For the ptnonipm sector, the 2007 and 2020 runs were no integrate. This was an oversight— it should have
been partial integration because the 2007 ethanol inventory (SCC 30125010) includes BAFM. In the future
year, we should also have partial integration because both the ethanol and biodiesel inventories (SCC
30125010) provided by OTAQ include BAFM. For aircraft emissions, we use the profile 5565b which is
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chemically equivalent to 5565 (aircraft exhaust) in SPECIATE 4.3 database. We differentiate the profile
numbers internally because a draft version of 5565 was used in previous modeling platforms.
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 3-5 lists the basin and gas composition specific profiles used for the WRAP Phase III
inventory.
Table 3-5. VOC profiles for WRAP Phase III basins
Profile Code
Description
SSJCB
South San Juan Basin Produced Gas Composition for CBM Wells
SSJCO
South San Juan Basin Produced Gas Composition for Conventional Wells
WRBCO
Wind River Basin Produced Gas Composition for Conventional Wells
PRBCB
Powder River Basin Produced Gas Composition for CBM Wells
PRBCO
Powder River Basin Produced Gas Composition for Conventional Wells
DJFLA
D-J Basin Flashing Gas Composition for Condensate
DJVNT
D-J Basin Produced Gas Composition
UNT01
Uinta Basin Gas Composition at CBM Wells
UNT02
Uinta Basin Gas Composition at Conventional Wells
UNT03
Uinta Basin Flashing Gas Composition for Oil
UNT04
Uinta Basin Flashing Gas Composition for Condensate
PNC01
Piceance Basin Gas Composition at Conventional Wells
PNC02
Piceance Basin Gas Composition at Oil Wells
PNC03
Piceance Basin Flashing Gas Composition for Condensate
SWFLA
SW Wyoming Basin Flash Gas Composition
SWVNT
SW Wyoming Basin Vented Gas Composition
PRM01
Permian Basin Produced Gas Composition
SWE01
Wyoming Flashing Gas Composition
For the biog sector, the speciation profiles for use with BEIS are not included in SPECIATE. The 2007
platform uses BEIS3.14, which includes a new species (SESQ) that was mapped to the model species
SESQT. The profile code associated with BEIS3.14 profiles for use with CB05 uses the same as in the 2005
platform: "B10C5."
For the nonpt sector, we integrated sources where VOC emissions were greater than or equal to BAFM and
BAFM was not zero. For portable fuel containers (PFCs) and fuel distribution operations associated with
the bulk-plant-to-pump (BTP) distribution, ethanol may be mixed into the fuels; therefore, we used county-
and month-specific COMBO speciation (via the GSPRO COMBO file). Refinery to bulk terminal (RBT)
fuel distribution and bulk plant storage (BPS) speciation are considered upstream from the introduction of
ethanol into the fuel; therefore a single profile is sufficient for these sources. We had no refined information
on potential VOC speciation differences between cellulosic diesel and cellulosic ethanol sources. Therefore,
we summed up cellulosic diesel and cellulosic ethanol sources and used the same SCC (30125010: Industrial
Chemical Manufacturing, Ethanol by Fermentation production) for VOC speciation as was used for corn
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ethanol plants. For future year PFC and the cellulosic inventory, we integrated EBAFM (i.e. we used E-
profiles) because ethanol was in those inventories.
3.2.1.4 Future year speciation
The VOC speciation approach used for the future year case is customized to account for the impact of fuel
changes. These changes affect the onroad, onroadrfl, nonroad, and parts of the nonpt and ptnonipm sectors.
We used 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 Section
2.5.1.3. For 2007, we used "COMBO" profiles to model combinations of profiles for E0 and E10 fuel use.
For 2020, we used "COMBO" profiles to model combinations of E10 and E85 fuel use. The speciation of
onroad exhaust VOC additionally accounts for a portion of the vehicle fleet meeting Tier 2 standards;
different exhaust profiles are available for pre-Tier 2 versus Tier 2 vehicles. Thus for onroad gasoline, VOC
speciation uses different COMBO profiles to take into account both the increase in ethanol use, and the
increase in Tier 2 vehicles in the future case.
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, we
would expect speciation changes 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.
Table 3-6 summarizes the different profiles utilized for the fuel-related sources in each of the sectors for
2007 and the future year case. This table indicates when "E-profiles" were used instead of BAFM integrated
profiles. The term "COMBO" indicates that a combination of the profiles listed were used to speciate that
subcategory using the GSPRO COMBO file. Note, the speciation for the PM NAAQS 2020 control case is
identical to the 2020 base case.
Table 3-6. Select VOC profiles 2007 versus 2020
Sector
Subcategory
2007
2020
onroad
gasoline
exhaust
COMBO:
8750E Pre-Tier 2 E0 exhaust
875 IE Pre-Tier 2 E10 exhaust
8756E Tier 2 E0 Exhaust
8757E Tier 2 E10 Exhaust
COMBO:
Pre-Tier 2 E10
875 IE exhaust
8757E Tier 2 E10 Exhaust
8855E Tier 2 E85 Exhaust
onroad
gasoline
evaporative
COMBO:
8753E E0 Evap
8754E E10 Evap
8754E E10 Evap
onroad
gasoline
permeation
COMBO:
8766E E0 evap perm
8769E E10 evap perm
8769E E10 evap perm
onroadrfl
COMBO:
8870E E10 Headspace
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Sector
Subcategory
2007
2020
gasoline
refueling
8869E
8870E
E0 Headspace
E10 Headspace
onroad
diesel exhaust
8774
Pre-2007 MY HDD
exhaust
877P0
WTD Pre & Post
2007 MY HDD exh
for 2020
onroad
diesel
evaporative
4547
Diesel Headspace
4547
Diesel Headspace
onroad rfl
diesel
refueling
4547
Diesel Headspace
4547
Diesel Headspace
nonroad
gasoline
exhaust
COMBO:
8750
8751
Pre-Tier 2 E0 exhaust
Pre-Tier 2 E10 exhaust
8751
Pre-Tier2 E10
exhaust
nonroad
gasoline
evaporative
COMBO:
8753
8754
E0 evap
E10 evap
8754
E10 evap
nonroad
gasoline
refueling
COMBO:
8869
8870
E0 Headspace
E10 Headspace
8870
E10 Headspace
nonroad
diesel exhaust
8774
Pre-2007 MY HDD
exhaust
8774
Pre-2007 MY HDD
exhaust
nonroad
diesel
evaporative
4547
Diesel Headspace
4547
Diesel Headspace
nonroad
diesel
refueling
4547
Diesel Headspace
4547
Diesel Headspace
nonpt/ptnonipm
PFC
COMBO:
8869
8870
E0 Headspace
E10 Headspace
8870E
E10 Headspace
nonpt/ptnonipm
BTP
COMBO:
8869
8870
E0 Headspace
E10 Headspace
8870
E10 Headspace
nonpt/ptnonipm
BPS/RBT
8869
E0 Headspace
8869
E0 Headspace
3.2.2 PM speciation
3.2.2.1 AE5 versus AE6 speciation
The SPECIATE database also contains the PM2.5 speciated into both individual chemical compounds (e.g.,
zinc, potassium, manganese, lead), and into the "simplified" PM2.5 components used in the air quality model.
For CMAQ 4.7.1 modeling, these "simplified" components (AE5) are all that is needed. For CMAQ 5.0,
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 3-7). Because PMFINE
is used in CMAQ4.7.1 and not in CMAQ 5.0, we were able to speciate PM2.5 so that it included both AE5
and AE6 PM model species without causing a double count. Therefore, the emissions could be modeled
with either CMAQ 4.7.1 or CMAQ 5.09.
9 For PM NAAQS modeling we used CMAQ 4.7.1, therefore only the AE5 species were needed.
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Table 3-7. PM model species: AE5 versus AE6
species name
species description
AE5
AE6
POC
organic carbon
Y
Y
PEC
elemental carbon
Y
Y
PS04
Sulfate
Y
Y
PN03
Nitrate
Y
Y
PMFINE
unspeciated PM2.5
Y
N
PNH4
Ammonium
N
Y
PNCOM
non-carbon organic matter
N
Y
PFE
Iron
N
Y
PAL
Aluminum
N
Y
PSI
Silica
N
Y
PTI
Titanium
N
Y
PCA
Calcium
N
Y
PMG
Magnesium
N
Y
PK
Potassium
N
Y
PMN
Manganese
N
Y
PNA
Sodium
N
Y
PCL
Chloride
N
Y
PH20
Water
N
Y
PMOTHR
unspeciated PM2.5
N
Y
Although we produced AE6 speciation of PM2.5, due to historical data in our GSREF and GSPRO, the profile
numbers are not consistent with SPECIATE 4.3. The profile numbers we used are the 920XX series which
are draft versions of the AE5 speciation. The updated profile numbers are the 911XX series which are the
updated AE6 speciation. Although our profile numbers are inconsistent, the actual profiles themselves
(namely the percentage of AE6 components) are consistent with the updated AE6 profiles (911XX series).
Due to this confusion, we have provided a table that maps our inconsistent profile numbers to the actual
SPECIATE 4.3 AE6 profiles (see Appendix C).
3.2.2.2 Onroad PM speciation
Unlike other sectors, the onroad sector has pre-speciated PM. This speciated PM comes from the MOVES
model and is processed through the SMOKE-MOVES system (see Section 2.5.1). Unfortunately, the
MOVES2010b speciated PM does not map 1-to-l to the AE5 speciation (nor AE6 speciation) needed for
CMAQ modeling. Table 3-8 shows the relationship between MOVES2010b exhaust PM2.5 related species
and CMAQ AE5 PM species.
Table 3-8. MOVES exhaust PM species versus AE5 species
MOVES2010b Pollutant Name
Variable name
for Equations
Relation to AE5 model species
Primary Exhaust PM2.5 - Total
PM25 TOTAL
Primary PM2 5 - Organic Carbon
PM250M
Sum of POC, PN03 and PMFINE
Primary PM2 5 - Elemental Carbon
PM25EC
PEC
Primary PM2 5 - Sulfate Particulate
PM25S04
PS04
MOVES species are related as follows:
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PM25TOTAL = PM25EC + PM250M + PS04
The five CMAQ AE5 species also sum to total PM2.5:
PM2.5 = P0C+PEC+PN03+PS04+PMFINE
The basic problem is to differentiate MOVES species "PM250M" into the component AE5 species (POC,
PN03 and PMFINE). The Moves2smkEF post-processor script takes the MQVES2010b species (EF tables)
and calculates the appropriate AE5 PM2.5 species and converts them into a format that is appropriate for
SMOKE (Moves2smkEF script). For a more detailed discussion of the derivation of these equations, see
Appendix D.
For brake wear and tire wear PM, total PM2.5 (not speciated) comes directly from MOVES2010b. These PM
modes are speciated by SMOKE. PMFINE from onroad exhaust is further speciated by SMOKE into the
component AE6 species.
3.2.3 NOx speciation
NOx can be speciated into NO, N02, and/or HONO. For the non-mobile sources, we use a single profile
"NHONO" to split NOx into NO and NO2. For the mobile sources except for onroad (including nonroad,
clc2rail, c3marine, othon sectors) and for specific SCCs in othar and ptnonipm, the profile "HONO" splits
NOx into NO, N02, and HONO.
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Table 3-9 gives the split factor for these two profiles.
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Table 3-9. NOx speciation profiles
profile
pollutant
species
split factor
HONO
NOX
N02
0.092
HONO
NOX
NO
0.9
HONO
NOX
HONO
0.008
NHONO
NOX
N02
0.1
NHONO
NOX
NO
0.9
The onroad sector does not use the "HONO" profile to speciate NOx. MOVES2010b produces speciated
NO, NO2, and HONO by source, including emission factors for these species in the emission factor tables
used by SMOKE-MOVES. Within MOVES, the HONO fraction is a constant 0.008 of NOx. The NO
fraction varies by heavy duty versus light duty, fuel type, and model year. The NO2 fraction = 1 - NO -
HONO. For more details on the NOx fractions within MOVES. The SMOKE-MOVES system models these
species directly without further speciation.
3.3 Temporal Allocation
Temporal allocation or 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. Typically, emissions inventories are annual or monthly in nature.
Temporalization takes these annual emissions and distributes them to the month, the monthly emissions to
the day, and the daily emissions to the hour. This process is typically done by applying temporal profiles—
monthly, day of the week, and diurnal—to the inventories.
The monthly, weekly, and diurnal temporal profiles and associated cross references used to create the 2007
hourly emissions inputs for the air quality model were similar to those used for the 2005v4.3 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.
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Table 3-10 summarizes the temporal aspects of emissions modeling by comparing the key approaches used
for temporal processing across the sectors. We control the temporal aspects of SMOKE processing through
(a) the LTYPE (temporal type) and MTYPE (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 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 not "all", then the SMOKE merge step runs only for representative days, which
could include holidays as indicated by the right-most column. The values given are the values of the
SMOKE M TYPE setting.
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Table 3-10. Temporal settings used for the platform sectors in SMOKE
Platform
sector short
name
Inventory
resolutions
Monthly
profiles
used?
Daily
temporal
approach
Merge
processing
approach
Process
Holidays as
separate days
Ptipm
daily & hourly
all
all
yes
Ptnonipm
annual
yes
mwdss
mwdss
yes
Ptfire
daily
all
all
yes
Othpt
annual
yes
mwdss
mwdss
Nonroad
monthly
mwdss
mwdss
yes
Othar
annual
yes
week
week
clc2rail
annual
yes
mwdss
mwdss
c3 marine
annual
yes
aveday
aveday
Onroad
annual & monthly1
all
all
yes
onroadrfl
annual & monthly2
all
all
yes
Othon
annual
yes
week
week
Nonpt
annual & monthly
yes
all
all
yes
Ag
annual & monthly
yes
all
all
yes
afdustadj
annual
yes
week
all
yes
Avefire
daily
all
all
yes
Biog
hourly
n/a
all
yes
1. Note the annual and monthly "inventory" actually refers to the activity data (VMT and VPOP) for
onroad. The actual emissions are computed on an hourly basis.
2. Note the annual and monthly "inventory" actually refers to the activity data (VMT and VPOP) for
onroad rfl. The actual emissions are computed on an hourly basis.
The following values are used in the above table: The value "all" means that hourly emissions computed for
every day of the year and that emissions potentially have day-of-year variation. The value "week" means
that hourly emissions computed for all days in one "representative" week, representing all weeks for each
month. This means emissions have day-of-week variation, but not week-to-week variation within the month.
The value "mwdss" means hourly emissions for one representative Monday, representative weekday
(Tuesday through Friday), representative Saturday, and representative Sunday for each month. This means
emissions have variation between Mondays, other weekdays, Saturdays and Sundays within the month, but
not week-to-week variation within the month. The value "aveday" means hourly emissions computed for
one representative day of each month, meaning emissions for all days within a month are the same.
See Section 3.3.4 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
December 2007 to fill in surrogate emissions for the end of December 2006. In particular, we used
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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) is a new inventory format for SMOKE. It provides a more consolidated
format for monthly, daily, and hourly emissions inventories. Previously, if we were going to process a
monthly inventory we would have 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, individual records contain data for all days in a month and
all hours in a day in the daily and hourly FF10 inventories, respectively.
SMOKE 3.1 prevents the application of temporal profiles on top of the "native" resolution of the inventory.
For example, a monthly inventory should not have annual to month temporalization applied; 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.
3.3.2 Ptipm Temporalization
Although the approach for temporalization of the ptipm sector (EGUs) has not changed from the 2005 v4.3
platform, the importance of this sector warrants a restating of the methodology.
For the year 2007 evaluation case (2007ee), hourly CEM NOx and SO2 data are directly used for sources that
match CEMs. For other pollutants, hourly CEM heat input data are used to allocate the NEI annual values.
For sources not matching CEM data ("non-CEM" sources), we computed daily emissions from the NEI
annual emissions using a structured query language (SQL) program and state-average CEM data. To
allocate annual emissions to each month, we created state-specific, three-year averages of 2006-2008 CEM
data. These average annual-to-month factors were assigned to non-CEM sources by state. To allocate the
monthly emissions to each day, we used the 2007 CEM data to compute state-specific month-to-day factors,
averaged across all units in each state. These daily emissions are calculated outside of SMOKE and the
resulting daily inventory is used as an input into SMOKE.
The daily-to-hourly allocation was performed in SMOKE using diurnal profiles. We updated the state-
specific and pollutant-specific diurnal profiles for use in allocating the day-specific emissions for non-CEM
sources in the ptipm sector. We used the 2007 CEM data to create state-specific, day-to-hour factors,
averaged over the whole year and all units in each state. We calculated the diurnal factors using CEM SO2
and NOx emissions and heat input. We computed SO2 and NOx-specific factors from the CEM data for
these pollutants. All other pollutants used factors created from the hourly heat input data. We assigned the
resulting profiles by state and pollutant.
For the 2007 base case (2007re), year-specific CEM data are not used. For future-year scenarios, there are
no CEM data available for specific units. Thus, for the base and future-year cases, we used the same
procedures as for "non-CEM" sources to compute daily emissions for input to SMOKE for all ptipm sources.
3.3.3 Meteorologically based temporalization
A significant improvement over previous platforms is the introduction of meteorologically based
temporalization. We recognize that there are many factors that impact the timing of when emissions occur.
The benefits of utilizing meteorology as method of temporalizing are: (1) we already have consistent
meteorological dataset that is used by the AQ model (e.g. WRF); (2) the meteorological model data is highly
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resolved in terms of spatial resolution; and (3) the meteorological variables vary at hourly resolution which
can translate to hour specific temporalization.
The SMOKE program GenTPRO provides a method for developing meteorology based temporalization.
Currently, the program can utilize three types of temporal algorithms: RWC, agricultural livestock
ammonia, and a generic meteorology based algorithm. For the 2007 platform, we used the RWC and ag NH3
GenTPRO generated profiles. GenTPRO reads in gridded meteorology data (MCIP) and 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 algorithm and the run parameters. For more details on the development of these algorithms and running
GenTPRO, see the GenTPRO documentation and SMOKE documentation.
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 an annual-to-day temporal profile for
the RWC sources within the nonpt sector. These generated profiles will distribute annual RWC emissions to
the coldest days of the year. On days where the minimum temperature does not drop below a user defined
threshold, RWC emissions 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, just the distribution of the emissions within the year. Initially, we ran the RWC algorithm with the
default temperature threshold of 50 °F. For most of the country, this produced a reasonable distribution of
emissions, but for a few Southern counties all of the emissions were compressed into a few days creating
excessively high daily emissions. We made two modifications to GenTPRO to support this work. First, we
added an optional input that defines a county/state specific alternative temperature threshold. Second, we
created an alternative RWC algorithm which avoided negative RWC emissions when the daily minimum
temperature was greater than 53.3 °F. For the 2007 platform, we used the alternative RWC algorithm for the
whole country, the default 50 °F threshold 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.
Figure 3-3 illustrates the impact of changing the temperature threshold for a warm climate county. The plot
shows the temporal fraction by day for Duval County, Florida for the first four months of the year. The
default 50 °F threshold creates large spikes while the 60 °F threshold dampens these spikes and distributes a
small amount of emissions to the days that have a minimum temperature between 50 and 60 °F.
Figure 3-3. Example of RWC temporalization using a 50 versus 60 °F threshold
RWC temporal profile, Duval County, FL, Jan - Apr
5 0.025
60F, alternate formula
50F, default formula
0.015
0.005
For the agricultural livestock NH3 algorithm, GenTPRO algorithm is based on the Russel and Cass (1986)
equation. This algorithm uses county-average hourly temperature and wind speed to calculate the temporal
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profile. We ran GenTPRO so that it created month-to-hour temporal profiles for these sources. Because
these profiles distribute to the hour based on monthly emissions, the emissions will either start from a
monthly inventory or from an annual inventory that has been temporalized already to the month10.
Figure 3-4 compares the daily emissions for Minnesota from the "old" approach (uniform monthly profile)
with the "new" approach (GenTPRO generated month-to-hour profiles). Although the GenTPRO profiles
show daily (and hourly variability), the monthly total emissions are the same between the two approaches.
Figure 3-4. Example of new animal NH3 emissions temporalization approach, summed to daily emissions
MN ag NH3 livestock temporal profiles
1 1 ill
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 onroad and onroadrfl sectors, we are technically not using meteorology 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 will 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 will determine the temperature
for that hour and grid cell and use it to select the appropriate EF for that SCC/pollutant/mode. For the off-
network RPP, Movesmrg uses 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 is that
the emissions will vary hourly by county. 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 will show a strong meteorological influence on their
temporal patterns (see Sections 2.5.1.5 and 2.5.1.8 for more details).
Figure 3-5 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.
111 SMOKE v3.1 will correctly read in a monthly inventory and apply GenTPRO ag NH3 month-to-hour temporalization. When we
developed the emissions 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, we applied a
flat monthly profile to the states with a monthly ag NH3 inventory.
75
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Figure 3-5. Example of SMOKE-MOVES temporal variability of NOx emissions
BHM (Jefferson Co., AL) daily NOX
II II 1
MOVES
SMOKE-MOVES
OrH(N*tir»r«-ooaiHfNminvoooo^OfNminvo
ooooooooHiHHHHiHiHfNjrNfMrjrNrMfNrommroro
ininLnininLnininininininmininininininmintnininLnLnLn
ooooooooooooooooooooooooooo
ooooooooooooooooooooooooooo
(NfNrMfNfNrMrs)fNr\(NrsJfM(NrM(Nr«J(NrMrM(NfSfNfSrM(NfNfM
Julian date
For the afdust sector, we are technically not using meteorology in the development of the temporal profiles;
rather, we are reducing the total emissions by a meteorological factor. 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. 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. Therefore, the afdust emissions will vary day-
to-day based on the precipitation and/or snow cover for that grid cell and day. Both the transport fraction
and MET 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 MET adjustments prevents
the overestimation of fugitive dust impacts in the grid modeling as compared to ambient samples.
3.3.4 Additional sector specific details
For the ptfire and avefire sectors, ptfire inventories are in the daily point fire format PTDAY and avefire
inventories are in the FF10 daily nonpoint format. The ptfire sector is only used in the evaluation case
(2007ee), while the avefire sector is used in the 2007 base case (2007re) and future case.
For the ptipm sector, the evaluation case (2007ee) uses a combination of CEM data and daily inventories.
The 2007 base case (2007re) and the future case uses daily inventories (see Section 3.3.2 for more details).
For the ag sector, the 2008 NEI is annual. We supplemented this with a MWRPO inventory that was
monthly. Only the 2008 NEI portion of the inventory had annual-to-month temporalization. For all
livestock sources, we used the GenTPRO month-to-hour temporalization described in Section 3.3.3.
For the onroad and onroad_rfl sectors, the "inventories" referred to in
76
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Table 3-10 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 we temporalized it
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,
RPD, RPV, and RPP all have additional temporal variability due to the meteorological based emissions
calculated through Movesmrg (see Section 3.3.3 for details). For California we applied gridded ratios to the
SMOKE-MOVES model-ready files to produce California adjusted model-ready files (see Section 2.5.1.9 for
details). By applying the ratios to the model-ready file, we essentially temporalized the CARB annual
inventory to match the SMOKE-MOVES temporalization grid cell by grid cell.
For the nonroad sector, we had monthly inventories from NMIM. For California, we created a monthly
inventory from CARB's annual inventory by using the EPA estimated NMIM monthly results to compute
monthly ratios by pollutant and SCC. For those CARB sources that we did not have an exact match in terms
of SCC, we applied a monthly ratio by pollutant and SCC7.
For the afdustadj sector, we started with the afdust sector's annual inventories which were temporalized to
representative week (L_TYPE=week). The resulting afdust model-ready files were post-processed to take
into account transport fraction and meteorological adjustment (see Section 3.3.3 for details). The post-
processed model-ready files (afdust adj) vary by day because the meteorology varies by day, hence the
M_TYPE=all.
For the nonpt sector, most the inventories are annual except for two monthly inventories: agricultural
burning (SCC 2801500000) inventory and a SESARM-provided open burning, land clearing (SCC
2610000500) inventory for Florida and Georgia. These monthly inventories do not need annual-to-month
temporalization. For all agricultural burning, we used a new diurnal temporal profile - see Figure 3-6
(McCarty et al., 2009). This puts more of the emissions during the actual work day and suppresses the
emissions during the middle of the night. All states used a uniform day of week profile for all agricultural
burning emissions, except for the following states that for which we used state-specific day of week profiles:
Arkansas, Kansas, Louisiana, Minnesota, Missouri, Nebraska, Oklahoma, and Texas.
77
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Figure 3-6. Agricultural burning diurnal temporal profile
Comparison of Agricultural Burning Temporal Profiles
0.18
0.16
0.14
New McCarty Profile
OLD EPA
0.12
0.1
0.08
0.06
0.04
0.02
0
123456789 10111213141516171819 20 2122 23 24
For nonpt RWC sources, we used the GenTPRO annual-to-day temporalization (see Section 3.3.3 for
details). We updated the RWC diurnal profile (see Figure 3-7). This placed more of the RWC emissions in
the morning and the evening when people are typically using these sources. This new profile is based on a
2004 MANE-VU survey based temporal profiles. We took the three indoor and three outdoor temporal
profiles from counties in Delaware for RWC and aggregated them into a single RWC diurnal profile. We
also compared this new profile to a concentration based analysis of aethalometer measurements in Rochester,
NY (Wang el al. 2011) for various seasons and day of the week and found that our new RWC profile
generally tracked the concentration based temporal patterns.
78
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Figure 3-7. RWC diurnal temporal profile
Comparison of RWC diurnal profile
0.12
0.1
c
o
S 0.08
2
"S 0 06
o
g 0.04
£
0.02
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
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, we
performed spatial allocation for a national 12-km domain. To do this, SMOKE used national 12-km spatial
surrogates and a SMOKE area-to-point data file. For the U.S., we updated surrogates 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. 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.
•NEW
¦OLD
79
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Table 3-11 lists the codes and descriptions of the surrogates. The surrogates in bold have been updated with
2010-based data, including 2010 census data at the block group level, 2010 American Community Survey
Data for heating fuels, 2010 TIGER/Line data for railroads and roads, 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
80
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Table 3-11 were not assigned to any SCCs.
81
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Table 3-11. U.S. Surrogates available for the 2007 platform.
Code
Surrogate Description "
1 Code
Surrogate Description
N/A
Area-to-point approach (see 3.3.1.2)
520
Commercial plus Industrial plus Institutional
Golf Courses + Institutional +Industrial +
100
Population
525
Commercial
110
Housing
527
Single Family Residential
120
Urban Population
530
Residential - High Density
f
Residential + Commercial + Industrial +
130
Rural Population
535
Institutional + Government
137
•:
Housing Change
540
Retail Trade
140
Housing Change and Population
545
Personal Repair
150
Residential Heating - Natural Gas
550
Retail Trade plus Personal Repair
Professional/Technical plus General
160
Residential Heating - Wood
555
Government
0.5 Residential Heating - Wood plus 0.5 Low
165
Intensity Residential
560
Hospital
170
Residential Heating - Distillate Oil
565
Medical Office/Clinic
180
Residential Heating - Coal
570
Heavy and High Tech Industrial
190
Residential Heating - LP Gas
575
Light and High Tech Industrial
200
Urban Primary Road Miles
580
Food, Drug, Chemical Industrial
210
Rural Primary Road Miles
585
Metals and Minerals Industrial
220
Urban Secondary Road Miles
590
Heavy Industrial
230
Rural Secondary Road Miles
595
Light Industrial
240
Total Road Miles
596
Industrial plus Institutional plus Hospitals
250
Urban Primary plus Rural Primary
600
Gas Stations
255
0.75 Total Roadway Miles plus 0.25 Population
650
Refineries and Tank Farms
260
Total Railroad Miles
675
Refineries and Tank Farms and Gas Stations
Oil & Gas Wells, IHS Energy, Inc. and
270
Class 1 Railroad Miles j
1 680
USGS
280
Class 2 and 3 Railroad Miles j
700
Airport Areas
300
Low Intensity Residential
710
Airport Points
310
Total Agriculture j
720
Military Airports
312
Orchards/Vineyards 1
800
Marine Ports
320
Forest Land 1
801
NEI Ports
330
Strip Mines/Quarries
802
NEI Shipping Lanes
340
Land
807
Navigable Waterway Miles
350
Water
810
Navigable Waterway Activity
400
Rural Land Area
850
Golf Courses
500
Commercial Land
860
Mines
505
Industrial Land
870
Wastewater Treatment Facilities
510
Commercial plus Industrial
880
Drycleaners
515
Commercial plus Institutional Land i
1 890
Commercial Timber
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.
The creation of surrogates and shapefiles for the U.S. was generated via the Surrogate Tool. The tool and
updated documentation.
82
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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.
For the oil and gas sources in the nonpt sector, the WRAP Phase III sources have detailed basin-specific
spatial surrogates shown in Table 3-12. The remaining oil and gas sources used the 2005-based surrogate
"Oil & Gas Wells, IHS Energy, Inc. and USGS" (680) developed for oil and gas SCCs. The surrogates in
Table 3-12 were applied for the counties listed in
83
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Table 3-13.
Table 3-12. Spatial Surrogates for WRAP Oil and Gas Data
Country
Code
Surrogate Description
USA
699
Gas production at CBM wells
USA
698
Well count - gas wells
USA
697
Oil production at gas wells
USA
696
Gas production at gas wells
USA
695
Well count - oil wells
USA
694
Oil production at Oil wells
USA
693
Well count - all wells
USA
692
Spud count
USA
691
Well count - CBM wells
USA
690
Oil production at all wells
USA
689
Gas production at all wells
84
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Table 3-13. Counties included in the WRAP Dataset
FIPS
State
County
8001
Colorado
Adams
8005
Colorado
Arapahoe
8007
Colorado
Archuleta
8013
Colorado
Boulder
8014
Colorado
Broomfield
8029
Colorado
Delta
8031
Colorado
Denver
8039
Colorado
Elbert
8043
Colorado
Fremont
8045
Colorado
Garfield
8051
Colorado
Gunnison
8063
Colorado
Kit Carson
8067
Colorado
La Plata
8069
Colorado
Larimer
8073
Colorado
Lincoln
8075
Colorado
Logan
8077
Colorado
Mesa
8081
Colorado
Moffat
8087
Colorado
Morgan
8095
Colorado
Phillips
8103
Colorado
Rio Blanco
8107
Colorado
Routt
8115
Colorado
Sedgwick
8121
Colorado
Washington
8123
Colorado
Weld
8125
Colorado
Yuma
30003
Montana
Big Horn
30075
Montana
Powder River
FIPS
State
County
35031
New Mexico
Mc Kinley
35039
New Mexico
Rio Arriba
35043
New Mexico
Sandoval
35045
New Mexico
San Juan
49007
Utah
Carbon
49009
Utah
Daggett
49013
Utah
Duchesne
49015
Utah
Emery
49019
Utah
Grand
49043
Utah
Summit
49047
Utah
Uintah
56001
Wyoming
Albany
56005
Wyoming
Campbell
56007
Wyoming
Carbon
56009
Wyoming
Converse
56011
Wyoming
Crook
56013
Wyoming
Fremont
56019
Wyoming
Johnson
56023
Wyoming
Lincoln
56025
Wyoming
Natrona
56027
Wyoming
Niobrara
56033
Wyoming
Sheridan
56035
Wyoming
Sublette
56037
Wyoming
Sweetwater
56041
Wyoming
Uinta
56045
Wyoming
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, we used the SMOKE "area-to-point" approach for only airport ground support equipment
(nonroad sector), and jet refueling (nonpt sector). The approach is described in detail in the 2002 platform
documentation.
The ARTOPNT file that lists the nonpoint sources to locate using point data was unchanged from the 2005-
based platform.
3.4.3 Surrogates for Canada and Mexico emission inventories
The Mexican single surrogate (population) is the same as was used in the 2005 platform. We used the same
surrogates for Canada to spatially allocate the 2006 Canadian emissions as were used for the 2005v4.2
85
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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 3-14. Canadian Spatial Surrogates for 2007-based platform Canadian Emissions
Code
Description
Code
Description
9100
Population
9493
Warehousing and storage
9101
Total dwelling
9494
Total Transport and warehouse
9102
Urban dwelling
9511
Publishing and information services
9103
Rural dwelling
9512
Motion picture and sound recording
industries
9104
Total Employment
9513
Broadcasting and telecommunications
9106
ALL INDUST
9514
Data processing services
9111
Farms
9516
Total Info and culture
9113
Forestry and logging
9521
Monetary authorities - central bank
9114
Fishing hunting and trapping
9522
Credit intermediation activities
9115
Agriculture and forestry activities
9523
Securities commodity contracts and other
financial investment activities
9116
Total Resources
9524
Insurance carriers and related activities
9211
Oil and Gas Extraction
9526
Funds and other financial vehicles
9212
Mining except oil and gas
9528
Total Banks
9213
Mining and Oil and Gas Extract activities
9531
Real estate
9219
Mining-unspecified
9532
Rental and leasing services
9221
Total Mining
9533
Lessors of non-financial intangible assets
(except copyrighted works)
9222
Utilities
9534
Total Real estate
9231
Construction except land subdivision and land
development
9541
Professional scientific and technical services
9232
Land subdivision and land development
9551
Management of companies and enterprises
9233
Total Land Development
9561
Administrative and support services
9308
Food manufacturing
9562
Waste management and remediation services
9309
Beverage and tobacco product manufacturing
9611
Education Services
9313
Textile mills
9621
Ambulatory health care services
9314
Textile product mills
9622
Hospitals
9315
Clothing manufacturing
9623
Nursing and residential care facilities
9316
Leather and allied product manufacturing
9624
Social assistance
9321
Wood product manufacturing
9625
Total Service
9322
Paper manufacturing
9711
Performing arts spectator sports and related
industries
9323
Printing and related support activities
9712
Heritage institutions
9324
Petroleum and coal products manufacturing
9713
Amusement gambling and recreation
industries
86
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Code
Description
Code
Description
9325
Chemical manufacturing
9721
Accommodation services
9326
Plastics and rubber products manufacturing
9722
Food services and drinking places
9327
Non-metallic mineral product manufacturing
9723
Total Tourism
9331
Primary Metal Manufacturing
9811
Repair and maintenance
9332
Fabricated metal product manufacturing
9812
Personal and laundry services
9333
Machinery manufacturing
9813
Religious grant-making civic and
professional and similar organizations
9334
Computer and Electronic manufacturing
9814
Private households
9335
Electrical equipment appliance and component
manufacturing
9815
Total other services
9336
Transportation equipment manufacturing
9911
Federal government public administration
9337
Furniture and related product manufacturing
9912
Provincial and territorial public
administration (9121 to 9129)
9338
Miscellaneous manufacturing
9913
Local municipal and regional public
administration (9131 to 9139)
9339
Total Manufacturing
9914
Aboriginal public administration
9411
Farm product wholesaler-distributors
9919
International and other extra-territorial
public administration
9412
Petroleum product wholesaler-distributors
9920
Total Government
9413
Food beverage and tobacco wholesaler-
distributors
9921
Commercial Fuel Combustion
9414
Personal and household goods wholesaler-
distributors
9922
TOTAL DISTRIBUTION AND RETAIL
9415
Motor vehicle and parts wholesaler-distributors
9923
TOTAL INSTITUTIONAL AND
GOVERNEMNT
9416
Building material and supplies wholesaler-
distributors
9924
Primary Industry
9417
Machinery equipment and supplies wholesaler-
distributors
9925
Manufacturing and Assembly
9418
Miscellaneous wholesaler-distributors
9926
Distribution and Retail (no petroleum)
9419
Wholesale agents and brokers
9927
Commercial Services
9420
Total Wholesale
9928
Commercial Meat cooking
9441
Motor vehicle and parts dealers
9929
HIGHJET
9442
Furniture and home furnishings stores
9930
LOWMEDJET
9443
Electronics and appliance stores
9931
OTHERJET
9444
Building material and garden equipment and
supplies dealers
9932
CANRAIL
9445
Food and beverage stores
9933
Forest fires
9446
Health and personal care stores
9941
PAVED ROADS
9447
Gasoline stations
9942
UNPAVED ROADS
9448
clothing and clothing accessories stores
9943
HIGHWAY
9451
Sporting goods hobby book and music stores
9944
ROAD
9452
General Merchandise stores
9945
Commercial Marine Vessels
87
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Code
9453
9454
9455
9481
9482
9483
9484
9485
9486
9487
9488
9491
9492
Description
Code
Description
Miscellaneous store retailers
9946
Construction and mining
Non-store retailers
9947
Agriculture Construction and mining
Total Retail
9950
Intersection of Forest and Housing
Air transportation
9960
TOTBEEF
Rail transportation
9970
TOTPOUL
Water Transportation
9980
TOTSWIN
Truck transportation
9990
TOTFERT
Transit and ground passenger transportation
9993
Trail
Pipeline transportation
9994
ALLROADS
Scenic and sightseeing transportation
9995
30UNPAVED 70trail
Support activities for transportation
9996
Urban area
Postal service
9997
CHBOISQC
Couriers and messengers
9991
Traffic
88
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4 Development of 2020 Base-Case Emissions
This section describes the methods we used for developing the 2020 future-year base-case emissions. The
PM NAAQS control case and sensitivity cases are not described in this section, but are discussed in the
Regulatory Impact Assessment.
The future base-case projection methodologies vary by sector. With one exception (described below), the
2020 base case represents 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 July,
2012. The future base-case scenario reflects projected economic changes and fuel usage for EGU and
mobile sectors. The 2020 EGU projected inventory represents demand growth, fuel resource availability,
generating technology cost and performance, and other economic factors affecting power sector behavior. It
also reflects the expected 2020 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. In this analysis, the projected EGU emissions include the Final Mercury and Air
Toxics (MATS) rule announced on December 21, 2011 and the Final Cross-State Air Pollution Rule
(CSAPR) issued on July 6, 2011
For mobile sources (onroad, onroad rfl, nonroad, clc2rail and c3marine sectors), all national measures for
which data were available at the time of modeling have been included with the exception of the 2017 and
Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy
Standards; Final Rule (LDGHG), published October 15, 2012. The LDGHG rule was not included in this
analysis because the rule was not signed at the time the modeling was performed, and it is expected to have
little impact on particulate matter emissions.
For nonEGU point (ptnonipm sector) and nonpoint stationary sources (nonpt, ag, and afdust sectors), local
control programs that might have been necessary for areas to attain the 1997 PM2.5 NAAQS annual standard,
2006 PM NAAQS (24-hour) standard, and the 1997 ozone NAAQS are generally not included in the future
base-case projections for most 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.
• IPM sector (ptipm): Unit-specific estimates from IPM, version 4.10 with CSAPR and Final MATS.
• 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. We 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 (TAF) data aggregated to the national level were used for aircraft to account
for projected changes in landing/takeoff activity.
• 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
89
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communication with EPA experts in July 2012; fertilizer application NH3 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
NMIM that utilized NONROAD2008a, using future-year equipment population estimates and control
programs to the year 2020 and using national level inputs. Final controls from the final locomotive-
marine and small 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-2020 inventory data were provided by CARB.
• Class 3 commercial marine vessel (c3marine): Base-year 2007 emissions grown and controlled to
2020, incorporating controls based on Emissions Control Area (ECA) and International Marine
Organization (IMO) global NOx and SO2 controls.
• Onroad mobile, not including refueling (onroad): MOVES2010b emissions factors for year 2020
were developed using the same representative counties, state-supplied data, meteorology, and
procedures that were used to produce the 2007 emission factors described in Section 2.5.1.
California-specific data were provided by CARB. Other than California, this sector includes all non-
refueling onroad mobile emissions (exhaust, evaporative, evaporative permeation, brake wear and tire
wear modes).
• Onroad refueling mode (onroad rfl): Uses the same projection approach as the onroad sector and
processing as described in Section 2.5.2, except for California where we projected using
MOVES2010b and did not include CARB data.
• Other onroad (othar): No growth or control for Canada because data are not available from Canada.
Mexico inventory data were grown from 1999 to year 2018.
• Other nonroad/nonpoint (othon): No growth or control for Canada. Mexico inventory data were
grown from 1999 to year 2018.
• Other point (othpt): No growth or control for Canada and offshore oil. Mexico inventory data were
grown from 1999 to year 2018.
• 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. 2020 base-case emissions from the 2007v5 base-case inventories. Lists of the control, closures,
projection packets (datasets) used to create 2020 future year base-case scenario inventories from the 2007
base case are provided in Appendix E. These packets were processed through the EPA Control Strategy
Tool (CoST) to create future year inventories. These CoST packets are formatted the same as those needed
for SMOKE and are available on the 2007v5 web site. Summaries on the emissions changes resulting from
all CoST packets (control programs, projections and closures) can be found in Appendix F.
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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
2020 base case. 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.
Table 4-1. Control strategies and growth assumptions for creating the 2020 base-case 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
All
4.2.1.1
Biodiesel plants producing 1.6 billion gallons of production due to EISA mandate
All
4.2.1.2
Ethanol distribution vapor losses adjustments due to EISA mandate
VOC
4.2.1.6
Refinery upstream adjustments from EISA mandate
All
4.2.1.7
Livestock emissions growth from year 2008 to 2020, also including upstream RFS2 impacts on
agricultural-related activities such as pesticide and fertilizer production
All
4.2.2
Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsiderations
NOx,
CO, PM,
S02
4.2.3,
Appendix
I
State fuel sulfur content rules for fuel oil - as of July, 2012, effective only in Maine, Massachusetts,
New Jersey, New York and Vermont
S02
4.2.4
Industrial/Commercial/Institutional Boilers and Process Heaters MACT with Reconsideration
Amendments
CO, PM,
so2,
VOC
4.2.5
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.
All
4.2.6
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
All
4.2.8
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
All
4.2.9
Aircraft growth via Itinerant (ITN) operations at airports to 2020
All
4.2.10.1
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.
S02
4.2.10.2
New York ozone SIP controls
NOx
4.2.10.3
Boat Manufacturing MACT rule, VOC: national applied by SCC
VOC
4.2.10.4
Lafarge and Saint Gobain consent decrees
NOx,
PM, S02
4.2.10.5
Consent decrees on companies (based on information from the Office of Enforcement and
Compliance Assurance - OECA) apportioned to plants owned/operated by the companies
CO,
NOx,
PM, S02,
VOC
4.2.10.6
Refinery Consent Decrees: plant/unit controls
NOx,
so2
4.2.10.7
Commercial and Industrial Solid Waste Incineration (CISWI) revised NSPS
PM, S02
4.2.10.8
1 kizardous Waster Incineration (HWI), Phase I and II
PM
4.2.10.8
Vtnpoinl (al'dust, ag and nonpl sectors) Controls and Growth Assumptions
MSAT2 and RFS2 impacts on portable fuel container growth and control from 2007 to 2020
VOC
4J. 1.3
Cellulosic ethanol and diesel emissions from EISA mandate
All
4.2.1.4
Ethanol transport working losses inventory from EISA mandate
VOC
4.2.1.5
Ethanol distribution vapor losses adjustments due to EISA mandate
VOC
4.2.1.6
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Control Strategies and/or growth assumptions
(grouped by standard and approach used to apply to the inventory)
CAPs
affected
Section
Livestock emissions growth from year 2008 to 2020, also including upstream RFS2 impacts on
agricultural-related activities such as pesticide and fertilizer production
All
4.2.2
Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsiderations
NOx,
CO, PM,
S02
4.2.3,
Appendix
I
State fuel sulfur content rules for fuel oil -as of July, 2012, effective only in Maine, Massachusetts,
New Jersey, New York and Vermont
S02
4.2.4
Residential wood combustion growth and change-outs from year 2008 to 2020
All
4.2.7
Future baseline inventory improvements received from a 2005 platform NODA and comments from
the CSAPR proposal, reflecting local controls
NOx,
voc
4.2.8
New York ozone SIP controls
NOx
4.2.10.3
Texas oil and gas projections to year 2020 -not applied
All
4.2.10.9
Onroad Mobile Controls
(All national in-lorce regulations are modeled. The list includes key recent mobile control strategies but is
not cxhauslnc.)
National Onroad Rules:
Heavy (and Medium)-Duty Greenhouse Gas Rule: August, 2011
Renewable Fuel Standard: February, 2010
Light Duty Greenhouse Gas Rule: April, 2010
Corporate-Average Fuel Economy standards for 2008-2011, 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
All
4.3
Local Onroad Programs:
Ozone Transport Commission (OTC) LEV Program: January, 1995
Inspection and Maintenance programs
Fuel programs (also affect gasoline nonroad equipment)
Stage II refueling control programs
VOC
4.3
Nonroad Mobile Controls
(All national in-lorce regulations are modeled. The list includes recent key mobile control strategies but is
not cxhauslhc.)
National Nonroad Controls:
Emissions Standards for New Nonroad Spark-Ignition Engines, Equipment, and Vessels: October,
2008
Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition Engines
Less than 30 Liters per Cylinder: March, 2008
Clean Air Nonroad Diesel Final Rule - Tier 4: May, 2004
All
4.3.2
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
A list of inventory datasets used for this and all cases is provided in Table G-l in Appendix G. 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). Table G-2 of Appendix G is a table of differences
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between these ancillary input data between the 2007 base case and these future-year scenarios. The specific
speciation profile changes are discussed in Section 3.2.1.4. Table G-3 in Appendix G also provides the
values for the main parameters used in the emissions modeling cases.
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) of. The IPM is
a multiregional, dynamic, deterministic linear programming model of the U.S. electric power sector. Version
4.10 reflects state rules and consent decrees through December 1, 2010 and incorporates information on
existing controls collected through the Information Collection Request (ICR), and information from
comments received on the IPM-related Notice of Data Availability (NOD A) published on September 1,
2010. IPM v4.10 Final included the addition of over 20 GW of existing Activated Carbon Injection (ACI)
reported to the EPA via the MATS Information Collection Request (ICR). Units with SO2 or NOx advanced
controls (e.g., scrubber, SCR) that were not required to run for compliance with Title IV, New Source
Review (NSR), state settlements, or state-specific rules were modeled by IPM to either operate those controls
or not based on economic efficiency parameters.
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 both the Cross-State Air Pollution Rule as it was originally
finalized in July, 2011, and also the Mercury and Air Toxics Standards. On August 21, 2012, the D.C.
Circuit Court of Appeals released an opinion that would vacate CSAPR. However, at the time this document
was written, pending a petition to rehear the case, the Court has not issued a mandate making that opinion
legally effective. As such, CSAPR is still a final rule but remains subject to a stay imposed by the Court on
December 30, 2011. In the interim, the Clean Air Interstate Rule (CAIR) continues to be implemented to
address regional transport of air pollution, as directed by the Court. In light of the still-pending litigation
proceeding on CSAPR and its current status as a final rule (albeit stayed), EPA does not believe it would be
appropriate or possible at this time to adjust emission projections on the basis of speculative alternative
emission reduction requirements in 2020.
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.
Directly emitted PM emissions (i.e., PM2.5 and PM10) from the EGU sector are computed via a post
processing routine which applies emission factors to the IPM-estimated fuel throughput based on fuel,
configuration and controls to compute the filterable and condensable components of PM. This methodology
is documented in the IPM TSD.
4.2 Stationary source projections: non-EGU sectors (ptnonipm, nonpt, ag,
afdust)
To project U.S. stationary sources other than the ptipm sector, we 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, we
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assumed that emissions growth does not track with economic growth for many stationary non-IPM sources.
This "no-growth" assumption is based on an examination of historical emissions and economic data. While
we are working toward improving the projection approach in future emissions platforms, we are still using
the no-growth assumption for the 2007 platform. More details on the rationale for this approach can be
found in Appendix D of the Regulatory Impact Assessment for the PM NAAQS rule (EPA, 2006b).
For many sources, we applied emissions reduction factors (CONTROL packets) to the 2007 base case
emissions for particular sources in the ptnonipm and nonpt sectors to reflect the impact of stationary-source
control programs including consent decrees and plant closures (CLOSURE packets). Some of the controls
described in this section were obtained from comments on the Cross-State Air Pollution Rule (CSAPR)
proposal. Most of the control programs were applied as replacement controls, which means that any existing
percent reductions ("baseline control efficiency") reported in the 2008 NEI were removed prior to the
addition of the new percent reductions due to these control programs. Exceptions to replacement controls are
"additional" controls, which ensure that the controlled emissions match desired reductions regardless of the
baseline control efficiencies in the NEI. We used the "additional controls" approach for many permit limits
and consent decrees where specific plant and multiple-plant-level reductions/targets were desired.
Here we describe the contents of the controls, local adjustments and closures for the 2020 base case.
Detailed summaries of the impacts of all control programs, local adjustments and closures are provided in
Appendix F. All CLOSURE, CONTROL and PROJECTION packets are listed in Appendix E, and these
data are provided on the 2007v5 website. In addition, we note key packets in the relevant sections below.
Year-specific projection factors (PROJECTION packets) for year 2020 were used for creating the 2020 base
case 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 section is divided into several
subsections that are summarized in
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Table 4-2. Note that we used future year inventories rather than projection or control packets for some
sources.
95
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Table 4-2. Summary of non-EGU stationary projections subsections
Subsection
Title
Sector(s)
Brief Description
4.2.1
RFS2 upstream future year
inventories and
adjustments
nonpt
ptnonipm
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
4.2.2
Agricultural and livestock
adjustments, including
RFS2
afdust, ag,
nonpt,
ptnonipm
Adjustment factors to all ag-related sources that also
reflect upstream RFS2 impacts on ag-related
processes impacted by increased ethanol use
4.2.3
RICE NESHAP
nonpt
ptnonipm
Control packet reflecting RICE NESHAP with
reconsideration amendments
4.2.4
Fuel sulfur rules
nonpt
ptnonipm
Control packet reflecting state and local fuel sulfur
rules, including ULSD
4.2.5
Industrial Boiler MACT
reconsideration
ptnonipm
Control packet reflecting ICI Boiler MACT
reconsideration reductions
4.2.6
Portland cement NESHAP
projections
ptnonipm
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
4.2.7
Residential wood
combustion growth
nonpt
Adjustment factors that reflect the change in RWC
emissions by appliance type, including wood stove
change-outs and accounting for estimated future
sales and replacement rates.
4.2.8
CSAPR and NOD A
comments
nonpt
ptnonipm
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.
4.2.9
Remaining non-EGU plant
closures
ptnonipm
All other plant and unit closures information not
covered in previous subsections
4.2.10
All other PROJECTION
and CONTROL packets
nonpt
ptnonipm
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)
We 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). This mandate (EPA, 2010a) not only impacts emissions associated with highway vehicles
and nonroad engines using renewable fuels, but also emissions associated with point and nonpoint sources.
These "upstream" emission impacts 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 2020 inventories. 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.
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Based on the Annual Energy Outlook 2012 (early release) energy use of 14.86 quad (1015 BTU) (Department
of Energy, 2012), we estimated the 2007 ethanol volume as 8.7 billion gallons (Bgal). We assume that an
unadjusted 2020 inventory, which does not account for the impacts of the EISA renewable fuel mandate,
would have comparable ethanol volumes to 2007. However, analyses done to support the RFS2 rule (EPA,
2010a) suggest a significant increase in renewable fuel volumes in 2020 (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 in Table 4-3.
Table 4-3. Renewable Fuel Volumes Assumed for Stationary Source Adjustments.
Renewable Fuel
Volume (Bgal)
Corn Ethanol
15.000
Cellulosic Ethanol
2.536
Imported Ethanol
1.880
Biodiesel
1.280
Renewable Diesel
0.150
Cellulosic Diesel
4.280
We assumed 6.7 Bgal of ethanol would be used in E85 and 8.7 Bgal in E10. While the stationary source
projections do reflect the RFS2, they do not reflect the upstream impacts of the recent Heavy-Duty
Greenhouse Gas (HDGHG) and Light-Duty Greenhouse Gas (LDGHG) rules (EPA, 201 la and EPA,
2012b).
4.2.1.1 Corn Ethanol plants inventory (ptnonipm)
Future year inventory: "Ethanol_plants_2020_POINT_ff 10"
As discussed in Section 2.1.2, for 2007 we supplemented the 2008 NEI with corn ethanol plants that
EPA/OTAQ developed. Additional ethanol plants cited for development in support of increased ethanol
production for the EISA/RFS2 are the cause for the increased number of facilities and emissions in the
future. Table 4-4 provides the summaries of estimated emissions for the corn ethanol plants in year 2007 and
2020.
Table 4-4. 2007 and 2020 corn ethanol plant emissions [tons]
Pollutant
2007
2020
Acrolein
5
34
Formaldehyde
5
35
Benzene
2
16
Acetaldehyde
64
332
CO
1,347
8,038
NOx
1,944
12,662
PMio
2,067
11,982
PM2.5
599
3,082
S02
637
1,547
VOC
4,086
26,990
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4.2.1.2 Biodiesel plants inventory (ptnonipm)
New Future year inventory: "Biodiesel_plants_2020_POINT_ff 10"
EPA/OTAQ developed an inventory of biodiesel plants for 2020 that were sited at existing plant locations in
support of producing biodiesel fuels for the EISA mandate. EISA was estimated to result in 1.6 Bgal of
biodiesel fuel production in year 2020. Only plants with current production capacities were assumed to be
operating in 2020. Total plant capacity at these existing facilities is limited to just over 1 Bgal. There was
no attempt to site future year plants to account for the need to match biodiesel production needed for
RFS2/EISA. Therefore, OTAQ applied scalar adjustments to each individual biodiesel plant to match the
2020 production levels. Once facility-level production capacities were scaled, emission factors were applied
based on soybean oil feedstock. 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 2020 biodiesel plant emissions
estimates. Emissions in 2007 are assumed to be near zero, and HAP emissions in 2020 are nearly zero.
Table 4-5. Emission Factors for Biodiesel Plants (Tons/Mgal)
Pollutant
Emission Factor
voc
4.3981E-02
CO
5.0069E-01
NOx
8.0790E-01
PMio
6.8240E-02
PM2.5
6.8240E-02
S02
5.9445E-03
nh3
0
Acetaldehyde
2.4783E-07
Acrolein
2.1290E-07
Benzene
3.2458E-08
1,3-Butadiene
0
Formaldehyde
1.5354E-06
Ethanol
0
Table 4-6. 2020 biodiesel plant emissions [tons]
Pollutant
2020
CO
801
NOx
1,293
PM10
109
PM2.5
109
S02
10
VOC
70
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4.2.1.3 Portable fuel container inventory (nonpt)
Future year inventory: "pfc_2020_pmnaaqs"
We 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: Permeation
2501011012 Residential Portable Fuel Containers: Evaporation
2501011013 Residential Portable Fuel Containers: Spillage During Transport
2501011014 Residential Portable Fuel Containers: Refilling at the Pump: Vapor Displacement
2501011015 Residential Portable Fuel Containers: Refilling at the Pump: Spillage
2501012011 Commercial Portable Fuel Containers: Permeation
2501012012 Commercial Portable Fuel Containers: Evaporation
2501012013 Commercial Portable Fuel Containers: Spillage During Transport
2501012014 Commercial Portable Fuel Containers: Refilling at the Pump: Vapor Displacement
2501012015 Commercial Portable Fuel Containers: Refilling at the Pump: Spillage
The future-year emissions reflect projected increases in fuel consumption, state programs to reduce PFC
emissions, standards promulgated in the MSAT2 rule, and impacts of the EISA on gasoline volatility.
OTAQ provided year 2020 PFC emissions that include estimated Reid Vapor Pressure (RVP) and oxygenate
impacts on VOC emissions, and more importantly, large increases in ethanol emissions from RFS2. These
emission estimates also include refueling from the NONROAD model for gas can vapor displacement,
changes in tank permeation and diurnal emissions from evaporation. Because the future year PFC
inventories contain ethanol in addition to benzene, we developed a VOC E-profile that integrated ethanol and
benzene; see Sections 3.2.1.1 and 3.2.1.4 for more details. Emissions for 2007 and 2020 are provided in
Table 4-7.
Table 4-7. PFC emissions for 2007 and 2020 [tons]
Pollutant
2007
2020
VOC
220,472
128,588
Benzene
1,049
1,426
Ethanol
0
16,196
4.2.1.4 Cellulosic fuel production inventory (nonpt)
New Future year inventory: "Cellulosic_plants_2020_NONPOINT_ff 10"
OTAQ developed county-level inventories for cellulosic diesel and cellulosic ethanol production for 2020 to
reflect EISA renewable fuel volumes. Emission rates in Table 4-8 and Table 4-9 were used to develop
cellulosic plant inventories. Criteria pollutant emission rates are in tons per Mgal and were obtained from
EPA's spreadsheet model for upstream impacts developed for the RFS2 rule (EPA, 2010a). For air toxics
emitted from cellulosic diesel production, emission rates were obtained from the spreadsheet model, but for
cellulosic ethanol plants, air toxic emission rates were updated from the RFS2 rule using data from five
demonstration plants in the 2005 NEI (EPA, 2009a). 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.
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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 the types of feedstocks that were determined to
be most economical, along with projected volumes from modeling using FASOM. The methodology used to
determined most likely plant locations is described in Section 1.8.1.3 of the RFS2 RIA (EPA, 2010a).
Design capacities for 2022 used in the RFS2 rule air quality modeling were adjusted to account for
differences with the estimated volumes of cellulosic fuel produced for 2020, using final RFS2 rule data.
Since the final RFS2 rule assumed about 57% percent of cellulosic fuel nationwide was cellulosic diesel,
with the remainder cellulosic ethanol, we assumed this split would apply to every plant. In reality, however,
depending on available feedstocks, plants are likely to produce one fuel or the other. Table 4-10 provides the
year 2020 cellulosic plant emissions estimates.
Table 4-8. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/Mgal)
Cellulosic Plant
Year
voc
CO
NOx
PMio
PM2.5
SO2
NH3
Type
Cellulosic Ethanol
2017-2030
1.82
5.68
8.19
0.941
0.480
0.299
0.00
Cellulosic Biodiesel
2017
1.01
14.79
22.35
2.65
1.33
1.99
0.00
2030
1.00
14.73
22.24
2.63
1.32
1.99
0.00
Table 4-9. Toxic Emission Factors for Cellulosic Plants (Tons/Mgal)
Cellulosic
Plant Type
Acetaldehyde
Acrolein
Benzene
1,3-Butadiene
Formaldehyde
Ethanol
Cellulosic
Ethanol
0.398
0.009
0.014
0
0.023
0.645
Cellulosic
Biodiesel
0.050
0.002
0.002
0
0.009
0.355
Table 4-l(
. 2020 cellulosic plant emissions [tons]
Pollutant
Emissions
Acrolein
40
Formaldehyde
111
Benzene
52
Acetaldehyde
1,504
CO
81,876
Ethanol
3,585
nh3
1
NOx
122,437
PM10
14,398
PM2.5
7,255
S02
9,503
VOC
10,204
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4.2.1.5 Ethanol working loss inventory (nonpt)
New Future year inventory: "Ethanol_transport_vapor_2017ct_ref_caphap_25jul2011"
This inventory was provided by OTAQ to represent RFS2 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. 2020 VOC working losses (Emissions) due to RFS2 ethanol transport [tons]
SCC
Description
Emissions
30205031
Denatured Ethanol Storage Working Loss
27,763
30205052
Ethanol Loadout to Truck
19,069
30205053
Ethanol Loadout to Railcar
9,610
4.2.1.6 Vapor losses from Ethanol transport and distribution (nonpt, ptnonipm)
Packet: "PROJECTION_2008_2020_distribution_upstream_OTAQ"
OTAQ developed county-level inventories for ethanol transport and distribution for 2020 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.
To estimate impacts of EISA, 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). In addition to gasoline VOC emission factors for the RBT/BPS components, emission factors
were developed for the BTP component, for 10 percent ethanol and 15 percent 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. Nationwide RVPs for different fuel types (E0, E10
and E85) were used to develop the adjustment factors. We assumed delta T is zero, and the temperature of
the fuel being dispensed averages 60 °F over the year. RVP was assumed to be 8.1 psi for E0 and E10 and
6.2 for E85. These RVPs are based on 2009 refinery compliance data.
E0 and E10 benzene emission factors for 2020 were based on the benzene inventory used in the 2011 Cross-
State Air Pollution Rule (EPA, 201 lb) 2020 gasoline volumes were obtained from the Annual Energy
Outlook 2011 Early Release Overview (Energy Information Administration, 2010) and used to estimate
g/mmBTU emission factors based on the Energy content of E0 and E10 gasoline. Aside from energy
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content, we did not account for the effect of other fuel parameters on emission rates. Thus, the E10 emission
rate is slightly higher than the EO rate due to the lower energy content of E10. The E85 emission rate was
estimated for the RFS2 rule. Emission factors are summarized in Table 4-12.
Table 4-12. Storage and Transport Vapor Loss Emission Factors (g/mmBtu)
Process
Fuel
VOC
Benzene
BTP
E0
25.448
0.260
E10
26.341
0.264
E85
26.827
0.023
RBT/BPS
E0
10.532
0.059
Emission factors for VOC and benzene were used in conjunction with EPA's spreadsheet model for
upstream emission impacts, developed for the RFS2 rule, to estimate national level inventory changes that
reflect EISA implementation (EPA, 2009b, 2012c). VOC inventory changes were used to develop
nationwide adjustment factors that were applied to modeling platform inventory SCCs associated with
storage and transport processes (Table 4-12). Benzene emission estimates were obtained either by
application of the adjustments in Table 4-13 or through speciation of VOC in SMOKE.
Table 4-13. Adjustment Factors Applied to Storage and Transport Emissions
Process
Pollutant
Adjustment Factor
BTP
VOC
1.012
Benzene
0.967
BPS/RBT
VOC
0.944
Benzene
0.944
Ethanol emissions were estimated in SMOKE by applying ethanol to VOC ratios to VOC emissions. These
ratios, obtained from speciation profiles, are 0.065 for E10 and 0.61 for E85. The E0 profile was obtained
from an ORD analysis of fuel samples from the EPAct exhaust test program (EPA, 2009c) and has been
submitted for incorporation into EPA's SPECIATE database. The E85 profile was obtained from evaporative
emission data for E85 vehicles, collected as part of the Auto/Oil emissions research program in the early
1990's (Environ, 2008). For more details on the change in speciation profiles between 2007 and 2020, see
Section Error! Reference source not found..
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. Also, although EISA results in vapor losses from transport of ethanol, they were not
included in this inventory, as the impact of these emissions would be negligible for the modeling in this
action. The cumulative impacts are VOC reductions of approximately 5,415 tons across the nonpt sector and
1,548 tons in the ptnonipm sector in 2020 for these processes. See Appendix B for cross-walk between SCC
and each type of petroleum transport and storage.
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4.2.1.7 Refinery adjustments (ptnonipm)
Packet: "PROJECTION_2008_2020_refineries_upstream_OTAQ"
Refinery emissions were adjusted for changes in fuels due to the EISA. 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, on
nationwide refinery emissions was done in EPA's spreadsheet model for upstream emission impacts (EPA,
2009b). Emission inventory changes reflecting EISA implementation were used to develop adjustment
factors that were applied to inventories for each petroleum refinery in the U.S. (Table 4-14). These impacts
of decreased production were assumed to be spread evenly across all U. S. refineries. Toxic emissions were
estimated in SMOKE by applying speciation to VOC emissions. It should be noted that the adjustment
factors in Table 4-14 are estimated relative to that portion of refinery emissions associated with gasoline and
diesel fuel production. Production of jet fuel, still gas and other products also produce emissions. If these
emissions were included, the adjustment factors would not be as large.
Table 4-14. Adjustment Factors Applied to Petroleum Refinery Emissions Associated with Gasoline and
Diesel Fuel Production.
Pollutant
2020 Adjustment
VOC
0.963
CO
0.971
NOx
0.983
PMio
0.979
PM2.5
0.973
S02
0.972
nh3
0.938
The impact of the EISA-based reductions is shown in Table 4-15.
Table 4-15. Impact of refinery adjustments on 2020 emissions [tons]
Pollutant
Reductions 2020
CO
2,426
nh3
186
NOx
1,608
PM10
562
PM2.5
649
SO2
4,094
VOC
2,386
4.2.2 Upstream agricultural and Livestock adjustments (afdust, ag, nonpt, ptnonipm)
Packet: "PROJECTION_2008_2020_ag_including_upstream_OTAQ"
Impacts of the EISA renewable fuel mandate on criteria pollutant and air toxic emissions from agricultural
operations were quantified for 2022 as part of the RFS2 RIA. Estimates of agricultural impacts were
developed using FASOM (Forest and Agricultural Section Optimization Model). It should be noted that
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FASOM agricultural impacts were estimated relative to a baseline of 13.2 Bgal of ethanol, whereas we
assume a volume of 8.7 Bgal in the unadjusted 2007 modeling platform. Thus, impacts used in the modeling
for this study are likely underestimates.
Adjustments for 2020 were scaled by the ratio of 2020 renewable fuel volumes to 2022 volumes assumed in
the RFS2 RIA. Impacts on farm equipment emissions were not accounted for, however. Adjustment factors
are provided in Table 4-16. 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, we have 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,
using absolute ton changes 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.
Table 4-16. Adjustments to Agricultural Emissions for post-EPAct/EISA Cases
Source Description
Adjustment
Nitrogen fertilizer application
1.0510
Fertilizer production, mixing/blending
1.0537
Pesticide production
0.9959
Agricultural tilling/loading dust
1.0236
Agricultural burning
1.000
Livestock dust
0.9985
Livestock waste
0.9985
For the animal waste sources, we also estimate animal population growth in ammonia (NH3) and dust (PM10
and PM2.5) 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 0.15% decrease resulting
from the EISA mandate. These composite projection factors by animal category are provided in Table 4-17.
As we will discuss 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 in Table 4-16. The PROJECTION packet used for these sources, which includes the cross-
reference to the animal categories listed in Table 4-17 and the source categories in Table 4-16, is provided on
the 2007v5 platform website and is listed in Appendix E.
Table 4-17. Composite Projection factors to year 2020 for Animal Operations
Animal Category
Projection Factor
Dairy Cow
0.9985
Beef
0.9926
Pork
1.0712
Broilers
1.1798
Turkeys
0.9985
Layers
1.1283
Poultry Average
1.168
Overall Average
1.0444
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
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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 this 2007v5 platform. For dairy cows and turkeys, we
assumed that there would be no growth in emissions based on little change in U.S. dairy cow or turkey
populations from year 2007 through 2019 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, we concluded that production forecasts do not provide representative estimates of the future
number of cows and turkeys; therefore, we 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 we suspect the excretion value will be
changing, but we assumed no change because we did not have a quantitative estimate.
The inventory for livestock emissions used 2008 emissions values for all states except the MWRPO states;
therefore, our projection method projected from 2008 rather than from 2007. Appendix H provides the
animal population data and regression curves used to derive the growth factors.
4.2.3 RICE NESHAP (nonpt, ptnonipm)
Packet: CONTROL_RICE_incl_SO2_2007v5
There are three rulemakings for National Emission Standards for Hazardous Air Pollutants (NESHAP) for
Reciprocating Internal Combustion Engines (RICE). These rules reduce HAPs from existing and new RICE
sources. In order to meet the standards, existing sources with certain types of engines will need to install
controls. In addition to reducing HAPs, these controls have co-benefits that also reduce CAPs, specifically,
CO, NOx, VOC, PM, and SO2. In 2014 and beyond, compliance dates have passed for all three rules; thus
all three rules are included in the emissions projection. These RICE reductions also reflect the recent
(proposed January, 2012) Reconsideration Amendments, which results in significantly less stringent NOx
controls (fewer reductions) than the 2010 final rules.
The rules are listed below:
• National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion
Engines; Final Rule (69 FR 33473) published 06/15/04
• National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion
Engines; Final Rule (FR 9648) published 03/03/10
• National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion
Engines; Final Rule (75 FR 51570) published 08/20/2010
The difference among these three rules is that they focus on different types of engines, different facility types
(major for HAPs, versus area for HAPs) and different engine sizes based on horsepower. In addition, they
have different compliance dates. We project CAPs from the 2008 NEI RICE sources, based on the
requirements of the rule for existing sources only because the inventory includes only existing sources and
the current projection approach does not estimate emissions from new sources.
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A complete discussion on the methodology to estimate year 2020 RICE controls, with the new
reconsideration amendments, is provided in Appendix I. Impacts of the RICE controls on nonpt and
ptnonipm sector emissions are provided in Table 4-18.
Table 4-18. National Impact of RICE Reconsideration Controls on 2020 Non-EGU Projections
Pollutant
2008 Emissions
2020 Emissions
2020 Reductions
CO
424,974
399,112
25,862
NOx
614,580
604,973
9,608
PMio
6,840
6,065
775
PM2.5
5,981
5,280
701
S02
58,009
52,741
5,268
voc
68,092
57,462
10,630
4.2.4 Fuel sulfur rules (nonpt, ptnonipm)
Packet: CONTROL_SULF_2020_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. Several other states in the Northeast and Mid-Atlantic had
pending sulfur rules but were not finalized prior to July, 2012 -the completion date of the 2007 platform
year-2020 projection. Background on all these enforceable and pending fuel sulfur rules can be found at
International Liquid Terminals Association CILTA). A more recent update to the status of fuel sulfur rules.
Maine
The Maine Law Legislative Document (LD) 1662 sets a fuel sulfur rule effective January 1, 2014 that
reduces sulfur to 15 ppm for distillate fuel, resulting in a 99.5% reduction from 3,000 ppm assumed in year
2008. Maine Law LD 1662 also states that #5 and #6 fuel oils must not exceed 0.5% by weight (500 ppm),
which is a 75% reduction from an assumed 2% baseline sulfur content in 2008. These Maine sulfur content
reductions.
Massachusetts
The Massachusetts Department of Environmental Protection issued a commitment in their State
Implementation Plan (SIP) to adopt Phase 2 ultra low sulfur diesel (ULSD) limits by year 2016. Similar to
Maine, this will reduce the sulfur content in distillate fuel to 15 ppm, a 99.5% reduction from the 3,000 ppm
baseline. Additional details on the phase-in of ULSD.
New Jersey
The New Jersey Department of Environmental Protection adopted sulfur fuel content rules for kerosene and
home heating distillate oil. For distillate oil, the ULSD limit of 15 ppm yields a 99.5% reduction from the
3,000 ppm baseline. For kerosene, the same 15 ppm limit is adopted, resulting in a 96.25%) reduction from
an assumed 2,000 ppm baseline. More details on these fuel sulfur limits in New Jersey.
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New York
New York also signed a law requiring ULSD to replace distillate heating oil #2, which results in a fuel sulfur
content limit of 15 ppm, a 99.5% reduction from the 3,000 ppm baseline. The ULSD law (A.8642-
A/S. 1145-C) and New York Mandates Cleaner Heating Oil article. New York City also includes limits by
year 2015 on #4 and #6 residual oils, where fuel sulfur content must not exceed 0.5% by weight (500 ppm), a
75%) reduction from an assumed 2%> baseline sulfur content in 2008. By 2030, these sources must burn
ULSD (15 ppm). The NYC updated Air Code, updated from the NY DEP.
Vermont
Vermont ULSD fuel and date requirements for home heating oil are similar to those adopted in
Massachusetts: a 99.5%> reduction to 15 ppm from the 3,000 ppm baseline.
A summary of the sulfur rules by state, with emissions reductions is provided in Table 4-19.
Table 4-19. Summary of fuel sulfur rules by state
State/
Fuel
%
2008
2020
2020
Metro
reduction
Emissions
Emissions
Reductions
ME
Distillate
99.5
12,076
1,056
11,021
ME
Residual
75
MA
Distillate
99.5
17,265
86
17,178
NJ
Distillate
99.5
7,285
45
7,240
NJ
Kerosene
96.25
NY
Distillate
99.5
54,093
655
53,442
NYC
Residual
75
VT
Distillate
99.5
2,018
10
2,008
4.2.5 Industrial Boiler MACT reconsideration (ptnonipm)
Packet: CONTROL BlrMACT ptnonipm 2020 2007v5
The Industrial/Commercial/Institutional Boilers and Process Heaters MACT Rule hereafter simply referred
to as the "Boiler MACT" has been proposed and the reconsideration of the final rule is slated for December
31, 2012. A background on the Boiler MACT. The Boiler MACT promulgates national emission standards
for the control of HAPs (NESHAP) for new and existing industrial, commercial, and institutional (ICI)
boilers and process heaters at major sources of HAPs. The expected cobenefit for CAPs at these facilities is
significant and greatest for SO2 with lesser impacts for direct PM, CO and VOC.
Boiler MACT reductions were computed from a non-NEI database of ICI boilers. As seen in the Boiler
MACT Reconsideration RIA. this Boiler MACT Information Collection Request (ICR) dataset computed
over 558,000 tons of SO2 reductions by year 2015. However, the Boiler MACT ICR database and reductions
are based on the assumption that if a unit could burn oil, it did burn oil, and often to capacity. With high oil
prices and many of these units also able to burn cheaper natural gas, the NEI2008 inventory has a lot more
gas combustion and a lot less oil combustion than the boiler MACT database. For this reason, we decided to
target units that potentially could be subject to the Boiler MACT and compute preliminary reductions for
several CAPs prior to building a control packet.
Step 1: Extract facilities/sources potentially subject to Boiler MACT
We did not attempt to map each ICR unit to the NEI units, instead choosing to use a more general approach
to extract NEI sources that would be potentially subject to, and hence have emissions reduced by the Boiler
MACT. The NEI includes a field that indicates whether a facility is a major source of HAPs and/or CAPs.
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This field in our FF10 point inventory modeling file is called "FACILCATEGORYCODE" and the
possible values for that field are shown in Table 4-20. Because the Boiler MACT rule applies to only major
sources of HAPs, we restricted the universe of facilities potentially subject to the Boiler MACT to those
classified as HAP major or unknown (UNK). The third column indicates whether the facility was a
candidate for extraction as being potentially subject to the Boiler MACT.
Table 4-20. Facility types potentially subject to Boiler MACT reductions
Code
Facility
Category
Subject
to Boiler
MACT?
Description
CAP
CAP Major
N
Facility is Major based upon 40 CFR 70 Major Source definition paragraph
2 (100 tpy any CAP. Also meets paragraph 3 definition, but NOT
paragraph 1 definition).
HAP
HAP Major
Y
Facility is Major based upon only 40 CFR 70 Major Source definition
paragraph 1 (10/25 tpy HAPs).
HAPCAP
HAP and
CAP Major
Y
Facility meets both paragraph 1 and 2 of 40 CFR 70 Major Source
definitions (10/25 tpy HAPs and 100 tpy any CAP).
HAPOZN
HAP and 03
n/a Major
Y
Facility meets both paragraph 1 and 3 of 40 CFR 70 Major Source
definitions (10/25 tpy HAPs and Ozone n/a area lesser tons for NOx or
VOC).
NON
Non-Major
N
Facility's Potential To Emit is below all 40 CFR 70 Major Source threshold
definitions without a FESOP.
OZN
03 n/a Major
N
Facility is Major based upon only 40 CFR 70 Major Source definition
paragraph 3 (Ozone n/a area lesser tons for NOx or VOC).
SYN
Synthetic
non-Major
N
Facility has a FESOP which limits its Potential To Emit below all three 40
CFR 70 Major Source definitions.
UNK
Unknown
N
Facility category per 40 CFR 70 Major Source definitions is unknown.
From these facilities we extracted records (process level / release point level emissions) from our modeling
file with industrial, commercial, institutional boiler or process heater SCCs. A complete list of these SCCs is
provided in Appendix J. The resultant data are the NEI sources potentially subject to the Boiler MACT.
Step 2: Match fuel types and control reductions to the NEI SCCs
After obtaining the subset of NEI sources potentially subject to the Boiler MACT, we assigned each
inventory SCC to a fuel type. The reductions are based on the ICR fuel types and associated controls from
an April 2010 "Baseline Memo.pdf' memorandum available on the Regulations.gov website under docket #
EPA-HQ-OAR-2002-0058-0802. These ICR fuel types and associated default controls were mapped to
SCCs in our inventory using the cross-walk provided in
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Table 4-21. The previously-mentioned Appendix J also maps the complete list of inventory SCCs to these
ICR fuel categories.
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Table 4-21. Default Boiler MACT fuel percent % reductions by ICR fuel type
ICR Fuel Category
SCC Fuel Category(s)
CO
PM2.5
SO2
voc
coal
coal, petroleum coke, waste coal
98.9
95.8
95
98.9
gas 1 (other)
gasified coal, hydrogen, liquified petroleum gas
(LPG), propane/butane, refinery gas
1
1
1
1
gas 2
digester gas, gas, landfill gas, process gas
99.97
0
95
99.97
bagasse
bagasse
95.3
90
95
95.3
dry biomass
wood
95.8
99.1
95
95.8
gas 1 (natural gas)
natural gas, unknown
1
1
1
1
heavy liquid
coal-based Synfuel, crude oil, liquid waste,
methanol, residual oil, waste oil
99.9
98.3
95
99.9
light liquid
distillate oil, gasoline, kerosene, oil, other oil
99.9
93
95
99.9
wet biomass
solid waste, wood/bark waste
85.5
99.2
95
85.5
The impacts of these Boiler MACT reductions on the controllable facilities and units are provided in Table
4-22. Controls were applied as "replacement" controls to prevent over-control of units that had existing
controls. However, this assumes that the inventory correctly reflects units with controls, so it is likely that
some units that are not recorded as controlled in the 2008 NEI but are actually controlled were reduced more
than they should have. Overall, the SO2, CO and PM2.5 reductions are reasonably close to the year-2015
expected reductions in the Boiler MACT Reconsideration RIA. It is worth noting that the SO2 reductions in
the preamble were estimated at 442,000 tons; the additional SO2 reductions in the reconsideration are from
an additional cobenefit from more stringent HC1 controls.
Table 4-22. Summary of Boiler MACT reductions (tons) compared to Reconsideration RIA reductions
Pollutant
2007 Emissions
2020 Emissions
Reductions
RIA Reductions
CO
289,531
69,042
220,489
187,000
PM2.5
36,061
10,311
25,749
25,601
S02
461,167
37,324
423,843
558,430
VOC
19,925
6,817
13,108
n/a
4.2.6 Portland Cement NESHAP projections (ptnonipm)
As indicated in Table 4-1, the Industrial Sectors Integrated Solutions (ISIS) model (EPA, 2010b) was used to
project the cement industry component of the ptnonipm emissions modeling sector to 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., 2020) are available. Therefore, the 2013 policy
case is used for the 2020 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_ffl0"
Contains information on new cement kilns constructed after year 2008,
2) Packet: "CLOSURES cement ISIS 2007v5 2013policv"
Contains facility and unit-level closures,
3) Packet: "PROJECTION_ISIS2013_cement_2007v5"
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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 4-23 shows the magnitude of the
ISIS-based cement industry reductions in the future-year emissions that represent 2020, and the impact that
these reductions have on total stationary non-EGU point source (ptnonipm) emissions.
Table 4-23. ISIS-based cement industry change (tons/yr)
Pollutant
Cement Industry
emissions in 2008
Cement Industry
emissions in 2020
% decrease in
Cement Industry
CO
46,317
8,713
81%
nh3
270
77
71%
NOx
156,579
75,176
52%
PM10
6,621
1,007
85%
PM2.5
3,689
801
78%
S02
98,277
23,830
76%
voc
6,954
1,265
82%
4.2.7 Residential wood combustion growth (nonpt)
Packet: "PROJECTION 2008 2020 RWC"
We projected residential wood combustion (RWC) emissions to the year 2020 based on expected increases
and decreases in various residential wood burning appliances. As newer, cleaner woodstoves replace some
older, higher-polluting wood stoves, there will be an overall reduction of the emissions from older "dirty"
stoves but an overall increase in total RWC due to population and sales trends in all other types of wood
burning devices such as indoor furnaces and outdoor hydronic heaters (OHH). It is important to note that our
RWC projection methodology does not explicitly account for state or local residential wood control
programs. There are many state and local rules in place across the country. However, at this time, we do not
have enough detailed information to calculate state specific or local area growth rates. We are therefore
using national level growth rates for each RWC SCC category. We also do not account for national New
Source Performance Standards (NSPS) for RWC, since they are not currently in place.
We began our projection methodology by obtaining estimates for the future year sales of wood burning
devices through year 202011.
11 The Frost and Sullivan report contained forecasted growth to 2015. Additional growth to 2020 was extrapolated based on the
2008 to 2018 growth rate.
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Table 4-24 provides these new units in 2020 as well as the US total appliance counts for similar groups of
wood burning devices in the 2008 NEI. For wood burning devices that are not expected to be replaced, the
projection factor would simply be the sum total of these new units and existing units from 2008 divided by
the number of units in 2008. However, there are exceptions to this simple ratio for each wood burning
device. The Frost and Sullivan sales for year 2008 are totals for North America. The report estimates that
87% of these units are sold in the U.S. From this beginning point adjustment, future year sales for 2009 to
2020 were summed as they appear in
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Table 4-24. Specific assumptions were then applied to each of the following types of wood burning
equipment:
Fireplaces (2104008100)
The RWC emissions estimates are based on the number of appliances that are actually used to burn wood.
Information collected through local surveys, industry marketing research, and other government publications
has indicated that approximately 42% of homes with usable fireplaces are never used, either for heating or
aesthetic purposes (Kochera, 1997). Therefore the cumulative new units by year 2020 for this category is
only 58% of the expected total number of new units, yielding a projection factor of 1.088, or, 8.8% growth
between 2008 and 2020. We do not assume any change out of these units in the future.
EPA-certified wood stoves (2104008220. 2104008230. 2104008320. 2104008330)
There is no assumption on the removal of existing units. Therefore, the projection factor for these devices is
simply the sum of existing and new units (4,353,690) divided by the number of units in 2008 (2,977,877) =
1.462.
Conventional non-certified woodstoves (2104008210. 2104008310)
EPA NSPS experts assume that 10% of the total new certified wood stoves, inserts and pellet stoves
(2,452,995) are used to replace older, more-polluting units. This 10% change out reduces the existing units
from 5,221,191 to 4,975,892, yielding a projection factor of 0.953.
Pellet stoves (2104008400)
There is no assumption on the removal of existing units. Therefore, the projection factor for these devices is
simply the sum of existing and new units (1,924,113) divided by the number of units in 2008 (846,931) =
2.272.
Indoor Furnaces (2104008510)
We assume that any existing unit in 2008 will be replaced by a new indoor furnace in 202012. This also
assumes that every unit sold between 2009 and 2020 will be in use in 2020. The projection factor for these
devices is therefore simply the sum of the new units (338,734) divided by the number of units in 2008
(197,362)= 1.716.
Outdoor Hydronic Heaters (2104008610)
EPA NSPS experts assume that 10% of the total new OHH (110,584) will replace existing units in 2008
(176,673). This yields a projection factor of 1.563 = 276,199 / 176,673.
12 This is based on the assumption that wood fired furnaces will have a relatively short lifetime. All existing furnaces in 2008 will
be more than 12 years old in 2020.
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Table 4-24. Worksheet for computing national RWC projection factors to 2020
SCC(s)
Description
US total
appliance
count
NEI2008
New units
in 2020
with
fireplace
58%
usage
assumed
US total
units in
2020
US total 2020
with:
1) 10% change
out woodstoves
&OHH
2) 100% 2008
indoor furnaces
replaced by
2020
Ratio
2020/2008 w/
10% change
out for non-
certified
woodstoves &
OHH, and
100% indoor
furnaces
replaced
2104008100
Fireplace: general
9,789,251
862,532
10,651,783
1.088
2104008220
2104008230
2104008320
2104008330
Wood Stoves: inserts
& freestanding EPA
certified, non and
catalytic
2,977,877
1,375,813
4,353,690
1.462
2104008210
2104008310
Conventional non-
certified woodstoves
and inserts
5,221,191
0
5,221,191
4,975,892
0.953
2104008400
Pellet Stoves
846,931
1,077,182
1,924,113
2.272
2104008510
Furnace: indoor,
cordwood
197,362
338,734
536,096
338,734
1.716
2104008610
Outdoor Hydronic
Heating Systems
(including 10% that
may be indoors)
176,673
110,584
287,257
276,199
1.563
New certified woodstoves and pellet stoves in
2020
2,452,995
The ratios in
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Table 4-24 are used as projection factors for RWC for all states except New York. Recall in Section 2.2.3,
that we used MARAMA (RPO) RWC emissions for New York rather than 2008 NEI emissions. New York
was unique in that their RPO RWC emissions were reported in only three SCCs: fireplaces (2104008100),
"woodstoves" (2104008320), and outdoor wood burning devices (2104008700). However, there are two
problems with these SCC assignments for New York RWC:
1 The outdoor wood burning devices actually represent outdoor hydronic heaters (OHH). Therefore,
projections of SCC=2104008700 are assigned the projection factor for OHH (2104008610) in
New York.
New York did not have enough information to split out "wood stoves" into separate categories for
inserts versus freestanding units, catalytic versus non-catalytic, indoor furnaces, and also to
delineate non-EPA certified from EPA-certified units. Therefore, we used the distribution of 2008
NEI PM2.5 emissions for New York wood stoves to create a composite "wood stove"
(2104008320) projection factor. The equations and worksheet for this composite NY woodstove
projection factor are provided in
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2 Table 4-25. The resulting projection factor for NY woodstoves is 1.153, the sum of NEI-based
2020 projected emissions for all woodstove SCCs, divided by those for 2008 (12,373/10,734).
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Table 4-25. Worksheet for creating NY "woodstove" projection factor from
see
Description
NEI
non-NY
NEI-based
NY
pm25
Projection
Factor
Projected
Emissions
composite
Projection
Factor
2104008210
Woodstove: fireplace inserts; non-EPA certified
2,148
0.950
2,041
Woodstove: fireplace inserts; EPA certified; non-
442
1.462
645
2104008220
catalytic
Woodstove: fireplace inserts; EPA certified;
153
1.462
223
2104008230
catalytic
2104008310
Woodstove: freestanding, non-EPA certified
5,211
0.950
4,950
Woodstove: freestanding, EPA certified, non-
1,071
1.462
1,564
n/a
2104008320
catalytic
2104008330
Woodstove: freestanding, EPA certified, catalytic
372
1.462
543
2104008400
Woodstove: pellet-fired, general (freestanding or
FP insert)
193
2.272
438
IF: Indoor Furnaces: cordwood-fired, non-EPA
1,144
1.716
1,968
2104008510
certified
2104008230
Total Wood stoves in New York
10,734
12,373
1.153
California also did not report detailed SCCs in the 2008 NEI, reporting simply 15,373 tons of PM2.5 as
general fireplaces (SCC=2104008100) and 22,456 tons of PM2.5 as general woodstoves (SCC=2104008300).
Without appliance counts at specific appliance types (e.g., certified versus non-certified), and a lack of data
for incorporating significant local RWC control programs in California, we decided to leave the general
woodstoves emissions unchanged in the future and grow the general fireplaces consistent with all other
states. Table 4-26 therefore presents the projection factors used to project all U.S. states in the 2007 base
case for residential wood combustion.
Table 4-26. Residential Wood Combustion projection factors to year 2020
State(s)
sec
Description
Projection
Factor
New York
2104008320
New York only: all woodstoves including indoor furnaces, composite
Projection Factor based on 2008 NEI emissions at all wood stove SCCs
1.153
New York
2104008700
New York only: incorrect SCC assignment, really Outdoor Hydronic
Heaters, so Projection Factor is from OHH
1.563
all other
2104008100
Fireplace: general
1.088
all other
2104008210
Woodstove: fireplace inserts; non-EPA certified
0.950
all other
2104008220
Woodstove: fireplace inserts; EPA certified; non-catalytic
1.462
all other
2104008230
Woodstove: fireplace inserts; EPA certified; catalytic
1.462
all other
2104008310
Woodstove: freestanding, non-EPA certified
0.953
all other
2104008320
Woodstove: freestanding, EPA certified, non-catalytic
1.462
all other
2104008330
Woodstove: freestanding, EPA certified, catalytic
1.462
all other
2104008400
Woodstove: pellet-fired, general (freestanding or FP insert)
2.272
all other
2104008510
IF: Indoor Furnaces: cordwood-fired, non-EPA certified
1.716
all other
2104008610
OHH: Outdoor Hydronic heaters
1.563
4.2.8 CSAPR and NODA Controls, Closures and consent decrees (nonpt, ptnonipm)
We 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. We received several control programs and other
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responses that we 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 2020 base case projection. But for those
controls, closures and consent decree information that are implemented after 2008, we used these
controls/data after we mapped 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. We 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
we would assume that the 2008 NEI emissions did not reflect this control and we would need to reflect this
control in our 2020 base case. 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 we received usable feedback from the CSAPR comments and related 2005 platform
NODA.
Nonpoint controls: packet "CONTROL_CSAPR_nonpoint_2020_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 Refinishing, Adhesives and Sealants and
Consumer Products. Cumulatively, these controls reduce VOC by approximately 1,400 tons.
Ptnonipm controls: packet "CONTROL_CSAPR_ptnonipm_2020_2007v5"
We 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.
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. We found a couple of units in our 2008 NEI-based inventory that were reported as closed in year
2007; therefore, the compliance dates in this packet range from 2007 to 2012. We also retained all year-
2007 closures to allow for this packet to potentially be used on RPO year-2007 point inventories. All
closures were provided for the 2005 NEI facility and unit identifier codes. We 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, SO2 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.
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. We included the shutdown of the coal boiler 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.
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The closing of the coal-fired boiler removed 51 tons of NOx and 234 tons of SO2 while this packet resulted
in only 28 more tons of NOx and minimal emissions from PM and SO2.
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 we removed all consent decrees with compliance dates prior to late-2008, we
matched the remaining controls to the 2008 NEI using a combination of EIS facility codes, "agy facility id",
"agy_p°int_id" and searching the EIS. Then, we recomputed the percent reductions such that the future year
emissions would match those for facilities originally projected from the 2005 platform. We 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. We were left with consent decree controls in sixteen states (AL, CA,
IN, KS, KY, LA, MI, MS, MO, OH, OK, TN, TX, UT, WI, WY) that accounted approximately 4,100 tons of
NOx and 37,000 tons of SO2 cumulative reductions, respectively.
4.2.9 Remaining non-EGU plant closures (ptnonipm)
We have already discussed facility and unit closures at cement facilities and those received from the CSAPR
comments. There are three additional packets that we developed for projecting the 2007 base case to 2020.
For each of these three packets, we relied heavily on the Emissions Inventory System (EIS) to validate
facility and unit IDs, and in the case of the "EIS" packet, the facility status code.
1) Packet: "CLOSURES 2012ck 2008NEIv2"
This packet was developed for the NEI2005-based emissions modeling platform from EPA staff for
projecting emissions through year 2010. This is the first closures packet developed by EPA staff in 2008;
additional closures information was gathered between 2008 and 2010 and that is discussed in the subsequent
packet. For this packet, we translated the original NEI2005-based dataset to the NEI2008 facility identifiers
using the "FACILITY ID" and "UNIT ID" fields in the NEI2005 and the "AGY FACILITY ID" and
AGY POINT ID" in the NEI2008. We also checked the closure status using the EIS. Most of the facilities
in this original dataset were assumed to close during 2007; however, several of these facilities were still
found after our matching procedure. We also retained closures that were from 2007 even if there was not a
match in the NEI2008 data because we want this packet to be useful if users want to project a year-2007
inventory. Therefore, as expected, very few facilities are closed by this packet, with cumulative reductions
of only 117 tons of NOx and less for other pollutants.
2) Packet: "CLOSURES_OAQPS_emv4.2_2008NEIv2"
This packet was also developed for the NEI2005-based emissions modeling platform from EPA staff, but
was created after scouring the web for new closures information in between 2008 and 2010. This packet
includes closures information for facilities and units that were not reflected in the "2012ck" packet just
described. We applied the same matching criteria as the aforementioned "2012ck" packet. This closures
packet impacts much larger facilities in 17 states and is therefore far more detailed, with specific websites
and contact information for each unit and facility. With the exception of a small plant in Georgia that was
closed by 2007, the closures are all implemented in year 2008 through late 2010. The cumulative reductions
in emissions from this packet are fairly significant and are shown in
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Table 4-27.
120
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Table 4-27. Cumulative reductions from facility and unit closures obtained between 2008 and 2010
Pollutant
Reductions
CO
20,517
nh3
297
NOx
5,029
PMio
3,598
PM2.5
2,724
S02
20,364
voc
3,104
3) Packet: "CLOSURES_EIS_2008NEIv2"
This packet was developed specifically for the 2007 platform and is based on a query against the EIS facility
status. The EIS provided information on facilities that closed prior to January 2012. Permanent shutdowns
have a facility status "PS" and temporary shutdowns have a facility status of "TS". Some states provided
additional information to independently confirm closure status and metadata on what happened to the unit or
facility. The cumulative reductions in emissions from this packet are fairly significant and are shown in
Table 4-28.
Table 4-28. Cumulative reductions from facility and unit closures obtained from the EIS
Pollutant
Reductions
CO
6,532
nh3
91
NOx
5,782
PM10
3,399
PM2.5
2,521
S02
4,821
VOC
10,397
4.2.10 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.
The impacts of all packets on the future year emissions are provided in Appendix F.
4.2.10.1 Aircraft growth (ptnonipm)
Packet: "PROJECTION 2008 2020 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 (TAF) System (publication date March, 2012). This information is available for approximately
3,300 individual airports, for all years up to 2030. We 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. We computed growth factors for each operation type by dividing future-year 2020 ITN by 2008-
year ITN. We assigned factors to inventory SCCs based on the operation type.
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The methods that the FAA used for developing the ITN data in the TAF.
Table 4-29 provides the national growth factors for aircraft; all factors are applied to year 2008 emissions.
For example, year 2020 commercial aircraft emissions are 11.6% higher than year 2008 emissions.
Table 4-29. Factors used to project 2008 base-case aircraft emissions to 2020
see
Description
Projection
Factor
2270008005
Commercial Aircraft: Diesel Airport Ground Support Equipment, Air Ground Support
Equipment
1.116
2275000000
All Aircraft Types and Operations
1.116
2275001000
Military Aircraft, Total
1.062
2275020000
Commercial Aviation, Total
1.116
GeneralFactors used to proiect 2008 base-case aircraft emissions to 2020 Aviation.
2275050000
Total
0.928
2275050011
General Aviation, Piston
0.928
2275050012
General Aviation, Turbine
0.928
2275060000
Air Taxi, Total
0.962
2275060011
Air Taxi, Total: Air Taxi, Piston
0.962
2275060012
Air Taxi, Total: Air Taxi, Turbine
0.962
2275070000
Commercial Aircraft: Aircraft Auxiliary Power Units, Total
1.116
27501014
Military aircraft: Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust;
Military; let Engine: IP-4
1.062
27501015
Military aircraft, This SCC is in 2005v2: Internal Combustion Engines; Fixed Wing
Aircraft L & TO Exhaust; Military; let Engine: IP-5
1.062
Commercial Aircraft, Total, This SCC is in 2005v2 NEI: Internal Combustion
27502001
Engines; Fixed Wing Aircraft L & TO Exhaust; Commercial; Piston Engine: Aviation
Gas
1.116
Commercial Aircraft, Total, This SCC is in 2005v2 NEI: Internal Combustion
27502011
Engines; Fixed Wing Aircraft L & TO Exhaust; Commercial; let Engine: let A
1.116
General Aviation Total. This SCC is in 2005v2 NEI: Internal Combustion Engines;
27505001
Fixed Wing Aircraft L & TO Exhaust; Civil; Piston Engine: Aviation Gas
0.928
General Aviation Total. This SCC is in 2002 NEI: Internal Combustion Engines;
27505011
Fixed Wing Aircraft L & TO Exhaust; Civil; let Engine: let A
0.928
27601014
Military aircraft: Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust;
Military; let Engine: IP-4
1.062
27601015
Military aircraft: Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust;
Military; let Engine: IP-5
1.062
Commercial aircraft: Internal Combustion Engines; Rotary Wing Aircraft L & TO
27602011
Exhaust; Commercial; let Engine: let A
1.116
None of our aircraft emission projections account for any control programs. We 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.
4.2.10.2 Boiler reductions not associated with the MACT rule (ptnonipm)
Packet: CONTROL IndBoilers nonMACT bv2008 2007v5
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The Boiler MACT ICR collected data on existing controls. We used an early version of a data base
developed for that rulemaking entitled "survey_database_2008_results2.mdb" (EPA-HQ-OAR-2002-0058-
0788) which is posted under the Technical Information for the Boiler MACT major source rule. This dataset
of controls was originally developed in support of the 2005 NEI-based CSAPR emissions modeling platform.
When using the 2008 NEI, we found only one unit in King William county Virginia that had a control that
was installed during or after 2008. We determined a percent reduction, and verified with the source owner
that the wet scrubber control was actively in use. SO2 emissions at this unit were reduced by 1,484 tons.
4.2.10.3 NY Ozone SIP controls (nonpt, ptnonipm)
Packet: CONTROLS_NYSIP_VOC_2007v5
As part of the CSAPR response to comments, New York state provided 8-hour ozone SIP controls for select
nonpoint and point sources. These sources and reductions are fully implemented by year 2012 and are
described in Appendix J of the NY attainment demonstration document. We mapped the source categories in
this document with SCCs in the 2008 NEI and created the control factor percent reductions based on the
product of the control factor (CF), rule effectiveness (RE) and rule penetration (RP). These controls
impacted VOC and NOx emissions at the sources listed in Table 4-30. We applied the same VOC reductions
to the BAFM VOC HAPs in order to maintain the consistency of our speciation approach. Additional
background on this 2008 NY ozone SIP is found in Section 9 on the NY Department of Environmental
Conservation Ozone Attainment Demonstration website.
Table 4-30. New York Ozone SIP controls reflected in the 2020 base case
Pollutant
Source Category
Sector
Percent Reduction
NOx
Glass Manufacturing
ptnonipm
70%
VOC
Architectural and Industrial Maintenance (AIM) Coatings
nonpt
31%
VOC
Mobile Equipment Repair
nonpt
38%
VOC
Solvent Metal Cleaning
nonpt
66%
VOC
Adhesives and Sealants
nonpt
64.4%
VOC
Consumer Products: Solvent Utilization
nonpt
15.92%
4.2.10.4 Boat Manufacturing MACT (ptnonipm)
Packet: CONTROL_MACT_BoatManuf_2007v5
We include MACT rules where compliance dates were 2008 or later. The EPA OAQPS Sector Policies and
Programs Division (SPPD) provided all controls information related to the MACT rules, and this information
is as consistent as possible with the preamble emissions reduction percentages for these rules.
A 32% reduction to VOC and VOC BAFM HAPs was applied to the Boat Manufacturing SCCs in the
ptnonipm inventory. Compliance with the MACT reduction is expected to occur by use of low HAP resins
and gel coats and use of non-atomized resin spray application systems. Documentation on this control is
provided in the Guidance for Estimating VOC and NOX Emission Changes from MACT Rules document
(EPA, 2007b).
4.2.10.5 Lafarge and St. Gobain settlements (ptnonipm)
Packet: CONTROL LaFarge_StGobain_2007v5
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This control packet impacts the ptnonipm sector and includes settlements for all 15 U.S. plants owned by
Saint-Gobain Containers, Inc., the nation's second largest container glass manufacturer, and all 13 U.S.
plants owned by the Lafarge Company and two subsidiaries, the nation's second largest manufacturer of
Portland cement. These settlements are the first system-wide settlements for these sectors under the Clean
Air Act and require pollution control upgrades, acceptance of enforceable emission limits, and payment of
civil penalties. The settlements require various NOx and SO2 controls, some of which (SO2 scrubbers) also
reduce PM emissions. A couple of Lafarge kilns were also scheduled to be shut down. One of these units
was shutdown prior to 2008 and as expected, is not in the 2007 base case. However, a Lafarge kiln in Joppa
Illinois was unexpectedly found in the 2008 NEI and communication with the Illinois DEP indicated that this
unit was not closed as of the summer of 2012. More information on the Lafarge settlement. More
information on the Saint-Gobain settlement. Many of the controls for the units at these facilities were
implemented prior to 2008; however, cumulatively, there is still significant reductions post-2008:
approximately 6,300 tons of NOx, 300 tons of PM2.5 and 2,100 tons of SO2.
4.2.10.6 OECA consent decrees (ptnonipm)
Packet: CONTROLS_OECA_2008NEIv2
The Office of Enforcement and Compliance Assurance (OECA) provided emission reduction information for
several consent decrees while we were preparing emissions for the NEI2005-based modeling platform. The
press releases for these consent decrees are available on the EPA's enforcement website and some were
available with quantitative emission reductions that we were able to convert into a control packet. The
consent decrees discussed in this section were released in the 2003-2010 time period and include information
for a few corporations but with aggregate reductions over numerous facilities under these companies and
subsidiaries. Therefore, we developed an initial table of NEI2005 emissions summed over all affected
facilities for each company. Then we merged the multi-facility expected reductions from each of these
consent decrees to develop an overall future year (post-compliance date) emissions estimate for each
company after all controls/reductions are implemented. Using this methodology, the emissions reductions
were apportioned to each plant owned/operated by each company using the same percent reduction from the
2005 NEI emissions.
Now that we are using an NEI2008-based inventory, we expected that some of these consent decree
controls/reductions would have already been applied by 2008. We did not want to over-control any
particular plant. Therefore, we computed facility-specific reductions based on the controlled emissions from
the 2005 NEI. For example, as seen in Table 4-31, NOx emissions at all Bunge facilities were reduced about
29.5% in the 2005 NEI: from 914 tons to 644 tons. This roughly matches the 278 tons of reductions in the
consent decree. In the 2008 NEI, NOx emissions at these same Bunge facilities totaled 852 tons, so only 208
tons were needed to achieve the 644 consent decree target. Rather than reducing all Bunge facilities 24.4%,
we applied controls to each individual facility such that the controlled emissions from the 2008 NEI matched
the controlled emissions from the 2005 NEI. If the 2008 NEI emissions for any facility were less than the
controlled emissions based on the 2005 NEI, then we did not apply any further reductions. Actual achieved
reductions in our 2007v5 platform are close, but usually slightly less than the target 2020 reductions because
of other controls or closures already applied at these facilities. We also do not list in Table 4-31 every
company subject to the OECA consent decree controls because the emissions and expected reductions were
very small.
Table 4-31. Target company-wide reductions from OECA consent decree information
Corporation
Pollutant
2005 NEI
Controlled
Reductions
2008 NEI
Target 2020
Actual 2020
Emissions
Emissions,
via 2005 NEI
from 2005
Emissions
Reductions
(Total only)
Reductions
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Bunge
NOx
914
644
270
852
208
PM2.5
416
189
227
265
76
S02
2,918
2,346
572
3,758
1,412
voc
2,627
1,559
1,068
2,539
980
Cargill
CO
10,968
262
10,706
10,889
10,627
NOx
4,173
2,907
1,266
3,466
559
S02
9,639
7,579
2,060
8,790
1,211
Conoco
Phillips
NOx
17,409
7,409
10,000
14,394
6,985
Sunoco
NOx
6,475
1,975
4,500
4,506
2,531
PM2.5
885
585
300
1,030
445
Valero
NOx
13,742
9,742
4,000
10,800
1,058
PM2.5
2,569
2,043
526
2,635
592
S02
19,608
3,608
16,000
11,603
7,995
Total
CO
10,968
262
10,706
10,889
10,627
9,987
NOx
42,712
22,677
20,035
34,017
11,340
12,519
PM2.5
3,870
2,816
1,053
3,929
1,113
1,066
S02
32,166
13,533
18,633
24,151
10,617
9,422
VOC
2,627
1,559
1,068
2,539
980
1,149
4.2.10.7 Refinery consent decrees (ptnonipm)
Packet: CONTROLS_Refineries_additional_consent_2008NEIv2
Two additional refinery consent decrees were obtained from the EPA's Sector Policies and Programs
Division (SPPD). The BP Whiting Settlement consent decree impacts several NOx and SO2 units in Lake
County Indiana. The Marathon Petroleum Detroit consent decree only impacts NOx at its' Wayne County
Michigan facility. Cumulatively, these consent decrees reduce NOx by 900 tons and SO2 by about 160 tons.
It is worth noting that several other facilities are subject to refinery consent decrees but we did not have the
resources to extract and convert these into usable control packets for our projection effort.
4.2.10.8 CISWI/HWI controls (ptnonipm)
Packet: CONTROL_CISWI_2007v5
On March 21, 2011, the EPA promulgated the revised NSPS and emission guidelines for Commercial and
Industrial Solid Waste Incineration (CISWI) units. This was a response to the voluntary remand that was
granted in 2001 and the vacatur and remand of the CISWI definition rule in 2007. In addition, the standards
re-development included the 5-year technology review of the new source performance standards and
emission guidelines required under Section 129 of the Clean Air Act (CAA). The history of the CISWI
implementation. Baseline and CISWI rule impacts associated with the CISWI rule. We mapped the units
from the CISWI baseline and controlled dataset to the NEI2008 inventory and because the baseline CISWI
emissions and the NEI2008 emissions were not the same, we computed percent reductions such that our
future year emissions matched the CISWI controlled dataset values. Cumulatively, CISWI reductions are
applied in five states - Arkansas, Louisiana, Massachusetts, Oklahoma and Tennessee- and reduce PM2.5 and
SO2 by approximately 140 and 3,500 tons, respectively.
Packet: CONTROL HWI 2007v5
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EPA issued the NESHAP for Hazardous Waste Combustors (HWCs) on October 12, 2005. The HWC
category includes combustion units that burn hazardous waste as it is defined under the Resource
Conservation and Recovery Act (RCRA). HWCs burn hazardous waste for various purposes, such as
burning for energy recovery or destruction (treatment) of the hazardous waste. This NESHAP covers the
following categories of combustion units that burn hazardous waste: incinerators, cement kilns, lightweight
aggregate kilns, industrial boilers, and hydrochloric acid production furnaces. In 2005, EPA estimated that
there were 267 hazardous waste combustors operating in the U.S. Of this total, there were 116 industrial
boilers, 107 incinerators, 25 cement kilns, 10 hydrochloric acid production furnaces, and nine lightweight
aggregate kilns. Additional information on the HWC NESHAP.
A control packet developed for the NEI2005 was mapped to the 2008 NEI using EIS facility and unit code
matching. Cumulatively, this packet reduces PM2.5 emissions by about 4,100 tons across 25 states.
4.2.10.9 Oil and gas projections in TX, and non-California WRAP states (nonpt)
We used year 2006 WRAP Phase III oil and gas emissions for both the 2007 and 2020 base cases. These
point and nonpoint inventories are discussed in the 2007 base case Sections 2.1.2 and 2.2.3, respectively.
Only year 2006 baseline inventories were available while we were constructing the 2020 base case during
the summer of 2012. Since then, mid-term projections for years 2010 and 2012 inventories for some basins
have been made available. Summaries of these mid-term projections are posted on the WRAP Phase III oil
and gas project.
We intended to project Texas oil and gas drilling rig emissions to year 2020 based on estimates from the
Texas Commission of Environmental Quality (TCEQ). However, we accidentally applied the national RICE
NESHAP Reconsideration Amendments in precedence over the TCEQ projection target factors. As
illustrated in
126
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Table 4-32 the RICE NESHAP reductions result in lower reductions/higher emissions in 2020 than TCEQ
projections. Future year base cases in subsequent versions of the 2007 platform will include the correct
TCEQ-based emissions as well as more local, detailed, and accurate estimates for Permian Basin emissions
that were received after we completed this 2020 base case.
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Table 4-32. Texas oil and gas missed reductions by EPA
Pollutant
2008
Emissions
2020 TCEQ
Emissions
2020 EPA
Emissions
Missed 2020
Reductions
CO
16,721
6,035
15,738
9,703
NOx
55,238
30,771
54,470
23,699
PMio
2,543
800
2,543
1,743
PM2.5
2,467
776
2,467
1,691
S02
956
35
480
445
voc
4,326
2,205
4,326
2,121
4.3 Mobile source projections
Mobile source monthly inventories of onroad and nonroad mobile emissions were created for 2020 using a
combination of the NMIM and the SMOKE-MOVES models. The 2020 onroad emissions account for
changes in activity data and the impact of on-the-books rules including: the Light-Duty Vehicle Tier 2 Rule
(EPA, 2000), the 2007 Heavy Duty Diesel Rule, the Mobile Source Air Toxics (MSAT2) Rule (EPA,
2007a), the Renewable Fuel Standard (RFS2) (EPA, 2010a), the LD GHG/CAFE standards for 2012-2016
(EPA, 2010c), and the Heavy-Duty Vehicle Greenhouse Gas Rule (EPA, 201 la). The emissions do not
account for the 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate
Average Fuel Economy Standards; Final Rule (LD GHG), published October 15, 2012. The 2017 LD GHG
rule (EPA, 2012b) was not included in this analysis because the rule was not signed at the time the modeling
was performed, and it is expected to have little impact on particulate matter emissions. Local inspection and
maintenance (I/M) and other onroad mobile programs such as the National Low Emissions Vehicle (LEV)
and Ozone Transport Commission (OTC) LEV regulations.
Nonroad mobile emissions reductions for these years include reductions to locomotives, various nonroad
engines including diesel engines and various marine engine types, fuel sulfur content, and evaporative
emissions standards.
Onroad mobile sources are comprised of several components and are discussed in the next subsection (4.3.1).
Monthly nonroad mobile emission projections are discussed in subsection 4.3.2. 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.
4.3.1 Onroad mobile (onroad and onroad_rfl)
The onroad emissions for 2020 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
Our estimate of total national Vehicle Miles Travelled (VMT) in 2020 came from DOE's Annual Energy
Outlook (AEO) 2012. We allocated this VMT between vehicle types using a version of MQVES2010b that
had been modified with VMT growth factors from the AEO 2012 early release and with historical data from
FHWA. The growth was allocated to county and month using information in the NMIM County Database
(NCD20101201), which reflects regional differences in growth based on economic modeling. Details may
be found in "Appendix G: Description of VMT growth approach," EPA Document ID EPA-HQ-OAR-2009-
0491-4198 in Docket ID EPA-HQ-OAR-2009-0491 (Clean Air Transport Rule)Vehicle populations by
county, month and vehicle type were estimated by dividing annual VMT by annual VMT per vehicle.
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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). 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.
4.3.1.2 Fuels
In order for EPA to generate the 2020 fuel supplies used in MOVES modeling, the regional fuel supplies
generated for the 2007 county fuel properties were first updated to refinery certification data produced in
2009. Steps 2 through 5 from the 2007 fuels process outlined in Section 2.5.1.3 above proceeded as normal.
In order to account for additional ethanol required by the RFS2 regulations, all counties are assumed to have
a market share of 100% E10. Diesel fuel is assumed to be at 15 ppm sulfur nationally, with a biodiesel
volume of 3.4% nationally and at 5% in areas with local regulatory constraints encouraging the use of
biodiesel. All counties also contain significant volumes of E85, as shown by vehicle in-use fractions
outlined in
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Table 4-33 below. Usage fractions are zero for years prior to 1998. Vehicle type 21 represents passenger
cars, 31 represents passenger trucks and 32 represents light commercial trucks (Table 3.3). The speciation of
the VOC emissions reflected these changes in fuel composition (see Section Error! Reference source not
found, for details).
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Table 4-33. E85 Usage Fraction by Model Year for 2020
VEHICLE TYPE
21
31/32
1998
0.003451
0.0077
1999
0.006378
0.013743
2000
0.008829
0.018626
2001
0.008979
0.017386
2002
0.013396
0.024916
2003
0.014679
0.024995
2004
0.011625
0.018739
2005
0.012389
0.020728
2006
0.013782
0.016652
2007
0.015127
0.043189
Model
Year
2008
0.021302
0.043784
2009
0.017812
0.047721
2010
0.037429
0.077135
2011
0.040462
0.12271
2012
0.046882
0.163283
2013
0.04512
0.17408
2014
0.044443
0.178874
2015
0.043993
0.182297
2016
0.043378
0.187309
2017
0.042857
0.191911
2018
0.042338
0.196838
2019
0.041817
0.202174
2020
0.041214
0.208914
4.3.1.3 Run MOVES to create EF
Emission factor tables were created by running SMOKE-MOVES using the same procedures and models as
described above for 2007 (see Section 2.5.1.7). The same meteorology and the same representative counties
were used. Changes between 2007 and 2020 are VMT and fuels (described above) 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.
4.3.1.4 California emissions
The adjustment of California onroad emissions for 2020 uses the same approach as 2007 to match the
emissions totals for 2020 to those provided by CARB (see Section 2.5.1.9). The only differences between
the 2007 approach and 2020 is the latter uses the 2020 emissions from CARB and the 2020 SMOKE-
MOVES output. The 2020 CARB emissions were produced from working draft versions of EMFAC2011-
LD and EMFAC2011-HD and include the following heavy duty regulations: chip reflash, extended idling,
public fleet, trash trucks, drayage trucks, and trucks and buses. It does not include the GHG/smartway
regulations for trucks, or the low carbon fuel standard.
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4.3.2 Nonroad mobile (nonroad)
This sector includes monthly exhaust, evaporative and refueling emissions from nonroad engines (not
including commercial marine, aircraft, and locomotives) derived from NMIM for all states except California.
Like the onroad emissions, NMIM provides nonroad emissions for VOC by three emission modes: exhaust,
evaporative and refueling.
With the exception of California, U.S. emissions for the nonroad sector (defined as the equipment types
covered by the NONROAD model) were created using a consistent NMIM-based approach as was used for
2007. Fuels for 2020 were assumed to be E10 everywhere for nonroad equipment. The fuels were
developed from MOVES fuels, and were supplied in the database "RegionalE10_2020_05172012_NMIM."
The only difference between the 2007 and 2020 procedures was that counties were grouped to conserve
computer resources for the 2007 run, but were run individually for 2020. The 2020 emissions account for
increases in activity (based on NONROAD model default growth estimates of future-year equipment
population), changes in fuels and engines that reflect implementation of national regulations and local
control programs that impact each year differently due to engine turnover.
The version of NONROAD used was the current public release, NR08a, which models all in-force nonroad
controls. Recent rules include:
• "Clean Air Nonroad Diesel Final Rule - Tier 4". published June, 2004
• Control of Emissions from Nonroad Large Spark-Ignition Engines, and Recreational Engines (Marine
and Land-Based), November 8, 2002 ("Pentathalon Rule").
• OTAQ's Small Engine Spark Ignition ("Bond") Rule, October, 2008
Not included are voluntary local programs such as encouraging either no refueling or evening refueling on
Ozone Action Days.
California nonroad emissions
Similar to the 2007 base nonroad mobile, NMIM was not used to generate future-year nonroad emissions for
California, other than for NH3. We used NMIM for California future nonroad NH3 emissions because
CARB did not provide these data for any nonroad vehicle types. For the rest of the pollutants, we converted
the CARB-supplied 2020 nonroad annual inventory to monthly emissions values by using the 2020 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. 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 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 2020 inventories from the 2007 base case. The
first component to the 2020 clc2rail inventory is the non-California data projected from the 2007 base case.
The second component is the CARB-supplied year 2020 data for California. The third component is a new
year-2020 inventory from OTAQ that contains clc2 CMV and locomotive emissions above and beyond the
CARB and non-CARB projections that represent additional emissions from the EISA (RFS2) mandate. We
discuss each of these three components below.
Non-California projections from the 2007 base case. Packet: "PROJECTION_2008_2020_clc2rail"
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For all states except California, year 2020 locomotive and Class 1 and Class 2 commercial marine vessel
(CMV) emissions were calculated using projection factors that were computed based on national, annual
summaries in 2008 and 2020. These national summaries were used to create national by-pollutant, by-SCC
projection factors. The national summaries reflect the May 2004 "Tier 4 emissions standards and fuel
requirements" as well as the March 2008 "Final locomotive-marine rule" controls. 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 and 2020, we are potentially being careful not to overly-reduce emissions by year 2020.
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 2020. 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, we
simply apply the ratio of the RIA 2020 to 2008 emissions to project the 2007 platform emissions. These
projection ratios are provided in Table 4-34.
Table 4-34. Non-California year 2020 Projection Factors for locomotives and Class 1 and Class 2
Commercial Marine Vessel Emissions
see
Description
Pollutant
Projection
Factor
2280002X00
Marine Vessels, Commercial;Diesel;Underway & port emissions
CO
0.924
2280002X00
Marine Vessels, Commercial;Diesel;Underway & port emissions
NOx
0.637
2280002X00
Marine Vessels, Commercial;Diesel;Underway & port emissions
PMio
0.583
2280002X00
Marine Vessels, Commercial;Diesel;Underway & port emissions
PM2.5
0.583
2280002X00
Marine Vessels, Commercial;Diesel;Underway & port emissions
S02
0.064
2280002X00
Marine Vessels, Commercial;Diesel;Underway & port emissions
voc
0.675
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
CO
1.210
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
NOx
0.706
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
PM10
0.556
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
PM2.5
0.556
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
S02
0.035
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
VOC
0.488
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
CO
1.210
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
NOx
1.112
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
PM10
1.069
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
PM2.5
1.072
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
S02
0.035
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
VOC
1.211
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
CO
1.100
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
NOx
0.476
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
PM10
0.457
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
PM2.5
0.457
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
S02
0.031
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
VOC
0.371
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
CO
1.100
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
NOx
0.476
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
PM10
0.456
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
PM2.5
0.457
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
S02
0.031
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
VOC
0.371
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SCC
Description
Pollutant
Projection
Factor
2285002010
Railroad Equipment;Diesel;Yard Locomotives
CO
1.210
2285002010
Railroad Equipment;Diesel;Yard Locomotives
NOx
0.958
2285002010
Railroad Equipment;Diesel;Yard Locomotives
PM10
0.923
2285002010
Railroad Equipment;Diesel;Yard Locomotives
PM2.5
0.923
2285002010
Railroad Equipment;Diesel;Yard Locomotives
S02
0.035
2285002010
Railroad Equipment;Diesel;Yard Locomotives
VOC
0.906
The future-year locomotive emissions account for increased fuel consumption based on Energy Information
Administration (EIA) fuel consumption projections for freight rail, and emissions reductions resulting from
emissions standards from the Final Locomotive-Marine rule (EPA, 2009d). This rule lowered diesel sulfur
content and tightened emission standards for existing and new locomotives and marine diesel emissions to
lower future-year PM, SO2, and NOx.
We applied HAP factors for VOC HAPs by using the VOC projection factors to obtain 1,3-butadiene,
acetaldehyde, acrolein, benzene, and formaldehyde. C1/C2 diesel emissions (SCC = 2280002100 and
2280002200) were projected based on the Final Locomotive Marine rule national-level factors provided in
Table 4-34. Similar to locomotives, VOC HAPs were projected based on the VOC factor.
California projections. New inventory: "2020re_california_c 1 c2rail_annual_ff 10"
The locomotive, and class 1 and 2 commercial marine year 2020 emissions used for California were obtained
from CARES, and include nonroad rules reflected in the December 2010 Rulemaking Inventory, those in the
March 2011 Rule Inventory, the Off-Road Construction Rule Inventory for "In-Use Diesel", cargo handling
equipment rules in place as of 2011, and the 2007 and 2010 regulations to reduce emissions diesel engines on
commercial harbor craft operated within California waters and 24 nautical miles of the California baseline.
The C1/C2 CMV emissions were obtained from the CARB nonroad mobile dataset
"ARMJ_RF#2002_ANNUAL_MOBILE.txt". These emissions were developed using Version 1 of the
CEP AM which supports various California off-road regulations. The locomotive emissions were obtained
from the CARB trains dataset "ARMJ_RF#2002_ANNUAL_TRAINS.txt". Documentation of the CARB
offroad methodology, including clc2rail sector data. We converted the CARB inventory TOG to VOC by
dividing the inventory TOG by the available VOC-to-TOG speciation factor.
Additional clc2rail emissions from the EISA mandate. New inventory:
"C1 C2_CMV_RAIL_2020_RFS2_additions_NONPOINT_ffl 0"
Rail is used to transport ethanol from production facilities to bulk terminals. To account for emissions
associated with this transport, 2022 RFS2 rule rail impacts were adjusted to account for differences in
ethanol volumes and locomotive emission rates between 2022 and 2020. Emission factors used to make
adjustments were obtained from an EPA locomotive emission factor fact sheet (EPA, 2009e). The adjusted
national inventory impacts were allocated to individual counties using factors developed from an Oak Ridge
National Laboratory analysis of ethanol transport (Oak Ridge National Laboratory, 2009). These impacts
were then applied to the model platform inventory.
Class 1 and 2 commercial marine vessels are also used to transport ethanol. In EPA's RFS2 final rule,
impacts of water transport of ethanol on combustion emissions from the CI and C2 commercial marine
inventory were estimated for 2022, based on the difference between ethanol volumes mandated by EISA
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versus RFS1 rule volumes (EPA, 2010a). These impacts were based on the Oak Ridge National Laboratory
analysis cited above. For this inventory, RFS2 rule impacts were adjusted to account for (a) differences in
commercial marine vessel emission rates in 2020 versus 2022, and (b) the difference in ethanol volume
impacts for 2020 under EISA versus the 8.7 billion gallons assumed for the unadjusted 2020 inventory.
Emission factors used to make these adjustments were obtained from analyses done to support the 2010
Category 3 Marine Diesel Rule (EPA, 2009f). The adjusted national inventory impacts were allocated to
individual counties using factors developed from the Oak Ridge analysis. These impacts were then applied
to the unadjusted inventory.
These emissions from updated ethanol volumes are not included in the previously-discussed loco-marine
rule-based projections and CARB inventory. These additional emissions are quite small and are shown in
Table 4-35.
Table 4-35. Additional clc2rail emissions in 2020 from the EISA mandate
Pollutant
C1/C2 CMV
Locomotives
CO
148
977
nh3
0
2
NOx
582
3,928
PMio
19
109
PM2.5
18
107
S02
3
2
VOC
14
162
4.3.4 Class 3 commercial marine vessels (c3marine)
As discussed in Section 2.5.5, the c3marine 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 year 2020 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 EC A region -generally within 200 nautical miles of the U.S. and Canadian coastlines, as well as
5,000 ppm (0.5%) for "global" areas -those areas outside the ECA region. For comparison, with the
exception of some local areas, year 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.
Projection factors for creating the year 2020 c3marine inventory from the 2007 base case are provided in
Table 4-36. Background on the region and EEZ FIPS is provided in the discussion on the c3marine
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 4-36. Growth factors to project the 2007 ECA-IMO inventory to 2020
2020 Adjustments Relative to 2007
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EEZ
VOC
Region
FIPS
NOx
PM10
PM2.5
(HC)
CO
SO2
East Coast (EC)
85004
1.108
0.240
0.240
1.772
1.772
0.063
Gulf Coast (GC)
85003
0.909
0.198
0.199
1.449
1.450
0.052
North Pacific (NP)
85001
0.988
0.211
0.214
1.534
1.532
0.059
South Pacific (SP)
85002
1.183
0.263
0.264
1.921
1.903
0.074
Great Lakes (GL)
n/a
1.016
0.160
0.160
1.234
1.241
0.044
Outside ECA
98001
1.399
0.386
0.382
1.754
1.754
0.318
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 year 2018. A
background on the development of year-2018 Mexico emissions from the 1999 inventory.
5 Emission Summaries
The following tables summarize emissions differences between the 2007 evaluation case, the 2007 base case
and the 2020 base case. These summaries are provided at the national-level by sector for the contiguous U.S.
and for the portions of Canada and Mexico inside the smaller 12km domain (12US2) discussed in Section
3.1. The afdust sector emissions represent the summaries after application of both the land use (transport
fraction) and meteorological adjustments (see Section 2.2.1); therefore, we call this sector "afdust-adj" in
these summaries. The onroad and onroad refueling (onroad rfl) sector totals are post-SMOKE-MOVES
totals, representing air quality model-ready emission totals, and the onroad portion include CARB emissions
for California. The "c3marine-US" sector represents c3marine sector emissions with U.S. FIPS only; these
extend to roughly 3-5 miles offshore and all U.S. waters in the Great Lakes and also include all U.S. ports.
The "c3marine, EEZ component" represents all non-U.S. c3marine emissions that are within the (up to) 200
nautical mile Exclusive Economic Zone (EEZ) boundary but outside of U.S. state waters. Finally, the
"c3marine, non-US non-EEZ component" represents all non-U.S. emissions outside of the (up to) 200nm
offshore boundary, including all Canadian and Mexican c3marine emissions. The c3marine sector is
discussed in Section 2.5.5. The "Off-shore othpt" sector is the non-Canada, no-Mexico component of the
othpt sector -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 sectors in the 2007 base case are listed as
"Con U.S. Total w/ avefire" and includes emissions from the avefire sector. Next, we provide the 2007 point
fire (ptfire) emissions, used instead of the avefire emissions for the 2007 evaluation case. Then, the total of
all sectors in the 2007 evaluation case are listed as "Con U.S. Total w/ ptfire". Table 5-2 provides national
emissions totals by sector for all CAPs in the 2020 base case.
Table 5-3 provides national-by sector emission summaries for CO for all three cases: 2007 evaluation, 2007
base case and 2020 base case. Table 5-4, Table 5-5, Table 5-6, Table 5-7, Table 5-8 and Table 5-9 provide
the same summaries for NH3, NOx, PM2.5, PM10, SO2 and VOC, respectively. These national tables also
include differences and percent differences for each modeling sector between the 2007 base case and 2020
base case. Note that ptfire emissions, unique to the 2007 evaluation case, are listed after these comparisons
in each table.
136
-------
Table 5-1. National by-sector CAP emissions summaries for 2007 base and evaluation cases
Sector
CO
NHs
NOx
PMio
PMis
SO2
voc
afdust-ad)
5,853,639
825,331
ag
3,595,429
clc2rail
218,854
557
1,338,370
43,835
41,019
48,814
61,558
c3 marine-US
12,724
138,033
12,476
11,452
104,822
4,902
nonpt
4,336,565
155,317
1,230,624
767,225
676,243
402,633
6,456,455
nonroad
17,794,112
1,920
1,894,569
188,504
179,165
101,735
2,480,715
onroad
36,764,690
145,285
7,562,752
363,551
277,350
40,406
3,222,877
onroad rfl
224,681
ptipm
703,771
25,428
3,357,384
437,096
329,584
9,136,151
38,071
ptnonipm
2,938,024
68,020
2,079,637
586,910
411,085
1,590,091
1,059,429
avefire
15,984,435
262,375
219,611
1,627,425
1,379,174
120,584
3,771,643
Con U.S. Total w/
avefire
78,753,176
4,254,330
17,820,981
9,880,662
4,130,403
11,545,235
17,320,331
ptfire
33,600,784 550,283 397,094 3,363,355 2,850,301
233,739
7,910,324
Con U.S. Total w/
ptfire
96,369,525
4,542,238
17,998,463
11,616,591
5,601,530
11,658,391
21,459,013
c3marine, non-US
EEZ component
41,125
498,850
41,363
38,015
309,370
17,477
c3 marine-non-US,
non-EEZ component
17,125
208,040
17,166
15,770
127,334
7,272
Canada othar
2,833,571
386,690
466,717
812,493
250,089
61,435
938,655
Canada othon
3,304,429
17,579
392,505
11,083
7,718
4,049
200,007
Canada othpt
571,566
15,536
338,722
65,369
39,734
831,520
155,998
Mexico othar
407,882
109,398
170,948
70,853
46,961
53,105
447,730
Mexico othon
579,968
2,629
83,353
7,019
6,500
5,038
85,462
Mexico othpt
100,076
343,485
120,755
89,359
731,692
77,255
Off-shore othpt
82,146
74,285
780
769
1,021
60,823
Non-US Total
7,937,888
531,832
2,576,904
1,146,880
494,914
2,124,563
1,990,679
137
-------
Table 5-2. National by-sector CAP emissions summaries for 2020 base case
Sector
CO
NHs
NOx
PMio
PM2S
SO2
voc
afdust-ad)
5,896,649
833,802
ag
3,764,319
clc2rail
242,208
567
949,823
26,024
24,355
6,972
36,329
c3 marine-US
20,405
143,351
2,708
2,491
6,160
7,848
nonpt
4,672,881
157,793
1,355,270
822,545
724,136
323,646
6,402,307
nonroad
12,769,579
2,355
961,175
95,043
89,422
2,719
1,294,962
onroad
17,302,817
84,304
2,234,887
188,936
102,314
28,284
1,183,159
onroad rfl
65,183
ptipm
862,058
40,416
1,878,795
295,816
233,331
2,098,072
45,885
ptnonipm
2,648,200
68,073
2,043,239
545,193
373,563
996,320
1,042,514
avefire
15,984,435
262,375
219,611
1,627,425
1,379,174
120,584
3,771,643
Con U.S. Total
54,502,582
4,380,203
9,786,151
9,500,338
3,762,588
3,582,757
13,849,831
c3marine, non-US
EEZ component
69,610
528,220
9,564
8,799
19,135
29,656
c3 marine-non-US,
non-EEZ component
29,488
278,988
6,159
5,618
35,400
12,521
Canada othar
2,833,571
386,690
466,717
812,493
250,089
61,435
938,655
Canada othon
3,304,429
17,579
392,505
11,083
7,718
4,049
200,007
Canada othpt
571,566
15,536
338,722
65,369
39,734
831,520
155,998
Mexico othar
524,259
109,378
225,512
70,707
47,045
19,178
573,020
Mexico othon
390,851
4,404
46,128
9,281
8,465
649
62,025
Mexico othpt
148,761
544,720
170,845
127,737
1,066,541
94,352
Off-shore othpt
82,146
74,285
780
769
1,021
60,823
Non-US Total
7,954,682
533,588
2,895,795
1,156,280
495,973
2,038,927
2,127,057
138
-------
Table 5-3. National by-sector CO emissions (tons/yr) summaries with differences
Sector
2007
2020 Base
2020 minus 2007 Base
ptipm
703,771
862,058
158,287
22%
ptnonipm
2,938,024
2,648,200
-289,825
-10%
afdust-adj
as
nonpt
4,336,565
4,672,881
336,316
8%
onroad
36,764,690
17,302,817
-19,461,873
-53%
onroad rfl
0
nonroad
17,794,112
12,769,579
-5,024,533
-28%
clc2rail
218,854
242,208
23,354
11%
c3marine, US
12,724
20,405
7,680
60%
avefire
15,984,435
15,984,435
0
0%
Total CO, All Sources Base Case
78,753,176
54,502,582
-24,250,594
-31%
ptfire
33,600,784
n/a
n/a
n/a
Total CO: 2007 Evaluation Case
96,369,525
n/a
n/a
n/a
c3marine non-US, EEZ
41,125
69,610
28,485
69%
c3marine non-US, non-EEZ
17,125
29,488
12,363
72%
Canada othar
2,833,571
2,833,571
0
0%
Canada othon
3,304,429
3,304,429
0
0%
Canada othpt
571,566
571,566
0
0%
Mexico othar
407,882
524,259
116,378
29%
Mexico othon
579,968
390,851
-189,117
-33%
Mexico othpt
100,076
148,761
48,686
49%
Off-shore othpt
82,146
82,146
0
0%
Total CO: 2007 Non-US
7,937,888
7,954,682
16,794
0%
139
-------
Table 5-4. National by-sector NH3 emissions (tons/yr) summaries with differences
Sector
2007
2020 Base
2020 minus 2007 Base
ptipm
25,428
40,416
14,988
59%
ptnonipm
68,020
68,073
54
0%
afdust-adj
as
3,595,429
3,764,319
168,891
5%
nonpt
155,317
157,793
2,476
2%
onroad
145,285
84,304
-60,981
-42%
onroad rfl
nonroad
1,920
2,355
435
23%
clc2rail
557
567
11
2%
c3marine, US
avefire
262,375
262,375
0
0%
Total NH3, All Sources Base Case
4,254,330
4,380,203
125,873
3%
ptfire
550,283
n/a
n/a
n/a
Total NH3: 2007 Evaluation Case
4,542,239
n/a
n/a
n/a
c3marine non-US, EEZ
c3marine non-US, non-EEZ
Canada othar
386,690
386,690
0
0%
Canada othon
17,579
17,579
0
0%
Canada othpt
15,536
15,536
0
0%
Mexico othar
109,398
109,378
-20
0%
Mexico othon
2,629
4,404
1,776
68%
Mexico othpt
Off-shore othpt
Total NH3: 2007 Non-US
531,832
533,588
1,756
0%
140
-------
Table 5-5. National by-sector NOx emissions (tons/yr) summaries with differences
Sector
2007
2020 Base
2020 minus 2007 Base
ptipm
3,357,384
1,878,795
-1,478,590
-44%
ptnonipm
2,079,637
2,043,239
-36,398
-2%
afdust-adj
as
nonpt
1,230,624
1,355,270
124,646
10%
onroad
7,562,752
2,234,887
-5,327,866
-70%
onroad rfl
nonroad
1,894,569
961,175
-933,394
-49%
clc2rail
1,338,370
949,823
-388,546
-29%
c3marine, US
138,033
143,351
5,317
4%
avefire
219,611
219,611
0
0%
Total NOx, All Sources Base Case
17,820,981
9,786,151
-8,034,830
-45%
ptfire
397,094
n/a
n/a
n/a
Total NOx: 2007 Evaluation Case
17,998,715
n/a
n/a
n/a
c3marine non-US, EEZ
498,850
528,220
29,370
6%
c3marine non-US, non-EEZ
208,040
278,988
70,948
34%
Canada othar
466,717
466,717
0
0%
Canada othon
392,505
392,505
0
0%
Canada othpt
338,722
338,722
0
0%
Mexico othar
170,948
225,512
54,564
32%
Mexico othon
83,353
46,128
-37,226
-45%
Mexico othpt
343,485
544,720
201,235
59%
Off-shore othpt
74,285
74,285
0
0%
Total NOx: 2007 Non-US
2,576,904
2,895,795
318,891
12%
141
-------
Table 5-6. National by-sector PM2.5 emissions (tons/yr) summaries with differences
Sector
2007
2020 Base
2020 minus 2007 Base
ptipm
329,584
233,331
-96,253
-29%
ptnonipm
411,085
373,563
-37,522
-9%
afdust-adj
825,331
833,802
8,471
1%
as
nonpt
676,243
724,136
47,893
7%
onroad
277,350
102,314
-175,036
-63%
onroad rfl
nonroad
179,165
89,422
-89,743
-50%
clc2rail
41,019
24,355
-16,664
-41%
c3marine, US
11,452
2,491
-8,961
-78%
avefire
1,379,174
1,379,174
0
0%
Total PM2.5, All Sources Base Case
4,130,403
3,762,588
-367,815
-9%
ptfire
2,850,301
n/a
n/a
n/a
Total PM2.5: 2007 Evaluation Case
5,601,530
n/a
n/a
n/a
c3marine non-US, EEZ
38,015
8,799
-29,216
-77%
c3marine non-US, non-EEZ
15,770
5,618
-10,152
-64%
Canada othar
250,089
250,089
0
0%
Canada othon
7,718
7,718
0
0%
Canada othpt
39,734
39,734
0
0%
Mexico othar
46,961
47,045
84
0%
Mexico othon
6,500
8,465
1,965
30%
Mexico othpt
89,359
127,737
38,378
43%
Off-shore othpt
769
769
0
0%
Total PM2.5: 2007 Non-US
494,914
495,973
1,059
0%
142
-------
Table 5-7. National by-sector PMio emissions (tons/yr) summaries with differences
Sector
2007
2020 Base
2020 minus 2007 Base
ptipm
437,096
295,816
-141,281
-32%
ptnonipm
586,910
545,193
-41,717
-7%
afdust-adj
5,853,639
5,896,649
43,010
1%
as
nonpt
767,225
822,545
55,320
7%
onroad
363,551
188,936
-174,616
-48%
onroad rfl
nonroad
188,504
95,043
-93,461
-50%
clc2rail
43,835
26,024
-17,811
-41%
c3marine, US
12,476
2,708
-9,768
-78%
avefire
1,627,425
1,627,425
0
0%
Total PMio, All Sources Base Case
9,880,662
9,500,338
-380,324
-4%
ptfire
3,363,355
n/a
n/a
n/a
Total PMio: 2007 Evaluation Case
11,616,592
n/a
n/a
n/a
c3marine non-US, EEZ
41,363
9,564
-31,799
-77%
c3marine non-US, non-EEZ
17,166
6,159
-11,007
-64%
Canada othar
812,493
812,493
0
0%
Canada othon
11,083
11,083
0
0%
Canada othpt
65,369
65,369
0
0%
Mexico othar
70,853
70,707
-146
0%
Mexico othon
7,019
9,281
2,262
32%
Mexico othpt
120,755
170,845
50,090
41%
Off-shore othpt
780
780
0
0%
Total PMio: 2007 Non-US
1,146,880
1,156,280
9,399
1%
143
-------
Table 5-8. National by-sector SO2 emissions (tons/yr) summaries with differences
Sector
2007
2020 Base
2020 minus 2007 Base
ptipm
9,136,151
2,098,072
-7,038,079
-77%
ptnonipm
1,590,091
996,320
-593,770
-37%
afdust-adj
as
nonpt
402,633
323,646
-78,987
-20%
onroad
40,406
28,284
-12,122
-30%
onroad rfl
nonroad
101,735
2,719
-99,016
-97%
clc2rail
48,814
6,972
-41,842
-86%
c3marine, US
104,822
6,160
-98,662
-94%
avefire
120,584
120,584
0
0%
Total SO2, All Sources Base Case
11,545,235
3,582,757
-7,962,478
-69%
ptfire
233,739
n/a
n/a
n/a
Total SO2: 2007 Evaluation Case
11,658,391
n/a
n/a
n/a
c3marine non-US, EEZ
309,370
19,135
-290,235
-94%
c3marine non-US, non-EEZ
127,334
35,400
-91,934
-72%
Canada othar
61,435
61,435
0
0%
Canada othon
4,049
4,049
0
0%
Canada othpt
831,520
831,520
0
0%
Mexico othar
53,105
19,178
-33,927
-64%
Mexico othon
5,038
649
-4,389
-87%
Mexico othpt
731,692
1,066,541
334,849
46%
Off-shore othpt
1,021
1,021
0
0%
Total S02: 2007 Non-US
2,124,563
2,038,927
-85,636
-4%
144
-------
Table 5-9. National by-sector VOC emissions (tons/yr) summaries with differences
Sector
2007
2020 Base
2020 minus 2007 Base
ptipm
38,071
45,885
7,814
21%
ptnonipm
1,059,429
1,042,514
-16,916
-2%
afdust-adj
as
nonpt
6,456,455
6,402,307
-54,148
-1%
onroad
3,222,877
1,183,159
-2,039,718
-63%
onroad rfl
224,681
65,183
-159,498
-71%
nonroad
2,480,715
1,294,962
-1,185,753
-48%
clc2rail
61,558
36,329
-25,228
-41%
c3marine, US
4,902
7,848
2,946
60%
avefire
3,771,643
3,771,643
0
0%
Total VOC, All Sources Base Case
17,320,331
13,849,831
-3,470,500
-20%
ptfire
7,910,324
n/a
n/a
n/a
Total VOC: 2007 Evaluation Case
21,459,013
n/a
n/a
n/a
c3marine non-US, EEZ
17,477
29,656
12,179
70%
c3marine non-US, non-EEZ
7,272
12,521
5,249
72%
Canada othar
938,655
938,655
0
0%
Canada othon
200,007
200,007
0
0%
Canada othpt
155,998
155,998
0
0%
Mexico othar
447,730
573,020
125,290
28%
Mexico othon
85,462
62,025
-23,436
-27%
Mexico othpt
77,255
94,352
17,096
22%
Off-shore othpt
60,823
60,823
0
0%
Total VOC: 2007 Non-US
1,990,679
2,127,057
136,378
7%
145
-------
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