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DRAFT Technical Support Document (TSD):
Preparation of Emissions Inventories for the
Version 6.0, 2011 Emissions Modeling Platform

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EP A-454/D-20-001
February 2014
DRAFT Technical Support Document (TSD): Preparation of Emissions Inventories for the
Version 6.0, 2011 Emissions Modeling Platform
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC

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DRAFT
TABLE OF CONTENTS
LIST OF TABLES	IV
LIST OF FIGURES	VI
LIST OF APPENDICES	VI
ACRONYMS	VII
1	INTRODUCTION	1
2	2011 EMISSION INVENTORIES AND APPROACHES	3
2.1	2011NEI POINT SOURCES (PTEGU, PTEGU_PK, PT_OILGAS AND PTNONIPM)	7
2.1.1	EGU non-peaking units sector (ptegu)	8
2.1.2	EGU peaking units sector (ptegu_pk)	10
2.1.3	Point source oil and gas sector (pt oilgas)	11
2.1.4	Non-IPM sector (ptnonipm)	12
2.2	2011 NONPOINT SOURCES (AFDUST, AG, NP_OILGAS, RWC, NONPT)	14
2.2.1	Area fugitive dust sector (afdust)	15
2.2.2	Agricultural ammonia sector (ag)	20
2.2.3	Nonpoint source oil and gas sector (np oilgas)	21
2.2.4	Residential wood combustion sector (rwc)	21
2.2.5	Other nonpoint sources sector (nonpt)	22
2.3	2011 ONROAD MOBILE SOURCES (ONROAD, ONROAD_RFL)	23
2.3.1	Onroad non-refueling (onroad)	23
2.3.2	Onroad refueling (onroad_rfl)	26
2.4	2011 NONROAD MOBILE SOURCES (C1C2RAIL, C3MARINE, NONROAD)	26
2.4.1	Class 1/Class 2 Commercial Marine Vessels and Locomotives and (clc2rail)	26
2.4.2	Class 3 commercial marine vessels (c3marine)	28
2.4.3	Nonroad mobile equipment sources: (nonroad)	30
2.5	"Other Emissions": Offshore Class 3 commercial marine vessels and drilling platforms and non-U.S.
sources	31
2.5.1	Point sources from offshore C3 CM]' and drilling platforms and Canada and Mexico (othpt)	32
2.5.2	Area and nonroad mobile sources from Canada and Mexico (othar)	32
2.5.3	Onroad mobile sources from Canada and Mexico (othon)	33
2.6	Fires (PTFiRE)	33
2.7	BIOGENIC SOURCES (BIOG)	34
2.8	SMOKE-READY NON-ANTHROPOGENIC INVENTORIES FOR CHLORINE	34
3	EMISSIONS MODELING SUMMARY	36
3.1	Emissions modeling Overview	36
3.2	Chemical Speciation	39
3.2.1	VOC speciation	40
3.2.2	PM speciation	49
3.2.3	NO x speciation	51
3.3	Temporal Allocation	51
3.3.1	Use of FF10 format for finer than annual emissions	53
3.3.2	Electric Generating Utility temporalization (ptegu, ptegu_pk)	54
3.3.3	Residential H ood Combustion Temporalization (rwc)	56
3.3.4	Agricultural Ammonia Temporal Profiles (ag)	60
3.3.5	Onroad mobile temporalization (onroad, onroad rfl)	61
3.3.6	Additional sector specific details (afdust, beis, clc2rail, c3marine, nonpt, ptfire)	67
3.4	Spatial Allocation	68
3.4.1	Spatial Surrogates for U.S. emissions	69
3.4.2	Allocation method for airport-related sources in the U.S.	72
3.4.3	Surrogates for Canada and Mexico emission inventories	73
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DRAFT
4	DEVELOPMENT OF 2018 BASE-CASE EMISSIONS	78
4.1	Stationary source projections: EGU sectors (ptegu. ptegu_pk)	84
4.2	Stationary source projections: non-EGU sectors (aedust. ag. nonpt, np_oilgas, ptnonipm, pt_oilgas, rwc)
84
4.2.1	RFS2 upstream future year inventories and adjustments (nonpt, ptnonipm)	86
4.2.2	Upstream agricultural and Livestock adjustments (afdust, ag, nonpt, ptnonipm)	95
4.2.3	Residential wood comb ustion growth (nonpt)	97
4.2.4	Oil and Gas projections (npoilgas, ptoilgas)	99
4.2.5	RICE NESHAP (nonpt, ptnonipm, np oilgas, pt oilgas)	104
4.2.6	Fuel sulfur rules (nonpt, ptnonipm)	106
4.2.7	Industrial Boiler M.! CT reconsideration (ptnonipm)	108
4.2.8	Portland Cement NESHAP projections (ptnonipm)	110
4.2.9	State comments and consent decrees/settlements (nonpt, ptnonipm)	113
4.2.10	A ircraft projections (ptnonipm)	118
4.2.11	Remaining non-EGU controls and closures (ptnonipm)	119
4.3	Mobile source projections	121
4.3.1 Onroad mobile (onroad and onroad_rfl)	121
4.4	Nonroad mobile source projections (c 1c2rail, C3MARINE, nonroad)	125
4.4.1	Locomotives and Class 1 & 2 commercial marine vessels (clc2rail)	125
4.4.2	Class 3 commercial marine vessels (c3marine)	127
4.4.3	Other nonroad mobile sources (nonroad)	128
4.5	"Other Emissions": Offshore Class 3 commercial marine vessels and drilling platforms, Canada and
Mexico (othpt, othar, and othon)	129
4.5.1	Point sources from offshore C3 CM]' and drilling platforms and Canada and Mexico (othpt)	130
4.5.2	Area, nonroad mobile and onroad mobile sources from Canada and Mexico (other, othon)	130
5	EMISSION SUMMARIES	131
6	REFERENCES	141
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DRAFT
List of Tables
Table 1-1. List of cases in the 2011 Version 6 Emissions Modeling Platform	2
Table 2-1. Platform sectors for the 2011 emissions modeling platform	4
Table 2-2. Summary of significant changes between 2011 platform and 201 INEIvl by sector	6
Table 2-3. Point source oil and gas sector SCCs	11
Table 2-4. Corn Ethanol Plant Criteria Pollutant Emission Factors (grams per gallon produced)	13
Table 2-5. Toxic-to-VOC Ratios for Corn Ethanol Plants	14
Table 2-6. 201 INEIvl nonpoint sources removed from the 2011 platform	15
Table 2-7. SCCs in the afdust platform sector	16
Table 2-8. Total Impact of Fugitive Dust Adjustments to Unadjusted 2011 Inventory	17
Table 2-9. Livestock SCCs extracted from the NEI to create the ag sector	20
Table 2-10. Fertilizer SCCs extracted from the NEI for inclusion in the "ag" sector	21
Table 2-11. SCCs in the Residential Wood Combustion Sector (rwc)*	22
Table 2-12. Onroad emission modes	25
Table 2-13. 201 INEIvl SCCs extracted for the starting point in clc2rail development	26
Table 2-14. Growth factor adjustment factors to project the 2002 ECA-IMO inventory to 2011	29
Table 2-15. 2011 Platform SCCs representing emissions in the ptfire modeling sector	33
Table 3-1. Key emissions modeling steps by sector	37
Table 3-2. Descriptions of the platform grids	38
Table 3-3. Emission model species produced for CB05 with SOA for CMAQ5.0.1 and CAMx*	40
Table 3-4. Integration approach for BAFM and EBAFM for each platform sector	43
Table 3-5. VOC profiles for WRAP Phase III basins	45
Table 3-6. National VOC profiles for oil and gas	45
Table 3-7. Counties included in the WRAP Dataset	46
Table 3-8. Select VOC profiles 2011 versus 2018	48
Table 3-9. PM model species: AE5 versus AE6	49
Table 3-10. MOVES exhaust PM species versus AE5 species	50
Table 3-11. NOx speciation profiles	51
Table 3-12. Temporal settings used for the platform sectors in SMOKE	52
Table 3-13. Mapping of MOVES to SMOKE road types	65
Table 3-14. U.S. Surrogates available for the 2011 modeling platform	69
Table 3-15. Spatial Surrogates for WRAP and Marcellus Shale Oil and Gas Data	70
Table 3-16. Total CAP emissions by sector for U.S. Surrogates	71
Table 3-17. Canadian Spatial Surrogates	73
Table 3-18.CAPs Allocated to Mexican and Canadian Spatial Surrogates	75
Table 4-1. Control strategies and growth assumptions for creating the 2018 base-case emissions inventories
from the 2011 base case	80
Table 4-2. Subset of CoST Packet Matching Hierarchy	83
Table 4-3. Summary of non-EGU stationary projections subsections	85
Table 4-4. Renewable Fuel Volumes Assumed for Stationary Source Adjustments	86
Table 4-5. 2011 and 2018 corn ethanol plant emissions [tons]	87
Table 4-6. Emission Factors for Biodiesel Plants (Tons/Mgal)	87
Table 4-7. 2018 biodiesel plant emissions [tons]	88
Table 4-8. PFC emissions for 2011 and 2018 [tons]	88
Table 4-9. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/RIN gallon)	89
Table 4-10. Toxic Emission Factors for Cellulosic Plants (Tons/RIN gallon)	89
Table 4-11. 2018 cellulosic plant emissions [tons]	90
Table 4-12. 2018 VOC working losses (Emissions) due to ethanol transport [tons]	90
Table 4-13. RVPs Assumed for 2018 ethanol and gasoline volumes with EISA	92
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DRAFT
Table 4-14. RVPs Assumed for 2018 ethanol and gasoline volumes without EISA	92
Table 4-15. Storage and Transport Vapor Loss Emission Factors (g/mmBtu)	93
Table 4-16. Adjustment factors applied to storage and transport emissions	93
Table 4-17. 2018 adjustment factors applied to petroleum pipelines and refinery emissions associated with
gasoline and diesel fuel production	95
Table 4-18. Impact of refinery adjustments on 2011 emissions [tons]	95
Table 4-19. Adjustments to modeling platform agricultural emissions for the Tier 3 reference case	96
Table 4-20. Composite NH3 projection factors to year 2018 for animal operations	96
Table 4-21. Non-West Coast RWC projection factors to year 2018	98
Table 4-22. National RWC impacts for PM2.5 and NOx from 2011 to 2018	99
Table 4-23. AEO-based 2018 Projection Factors	101
Table 4-24. Oil and Gas sector VOC 2018 Projection Factors for NSPS sources	102
Table 4-25. Projected national Oil and Gas sector 2011 and 2018 emissions, summed point and nonpoint 103
Table 4-26. Projected by-state NOx and VOC 2011 and 2018 Oil and Gas sector emissions	103
Table 4-27. Summary RICE NESHAP SI and CI percent reductions prior to 201 INEIvl analysis	105
Table 4-28. National by-sector reductions from RICE Reconsideration Controls	106
Table 4-29. Summary of fuel sulfur rules by state	107
Table 4-30. Facility types potentially subject to Boiler MACT reductions	108
Table 4-31. Default Boiler MACT fuel percent % reductions by ICR fuel type	109
Table 4-32. Summary of Boiler MACT reductions (tons) compared to Reconsideration RIA reductions.... 109
Table 4-33. Locations of new ISIS-generated cement kilns	Ill
Table 4-34. U.S. Census Division ISIS-based projection factors for existing kilns	112
Table 4-35. ISIS-based cement industry change (tons/yr)	112
Table 4-36. Impacts of most non-EGU point source state comments received in 2013	114
Table 4-37. Minor source ptnonipm sector NAICS-level projections for Texas	115
Table 4-38. Minor source nonpt sector projections for Texas	115
Table 4-39. Target company-wide reductions from OECA consent decree information	117
Table 4-40. Default national-level factors used to project 2011 base-case aircraft emissions to 2018	118
Table 4-41. Increases in aircraft emissions by year 2018 from airport-specific and national-level methodsl 19
Table 4-42. Reductions from all ElS-based and remaining information facility/unit-level closures	120
Table 4-43. Projection factors for 2018 VMT (in millions of miles)	121
Table 4-44. Comparison of MOVES runs for 2018	122
Table 4-45. CA LEVIII program states	123
Table 4-46. Non-California year 2018 intermediate projection factors for locomotives and Class 1 and
Class 2 Commercial Marine Vessel Emissions	125
Table 4-47. EISA mandate emission adjustments in 2018	127
Table 4-48. Difference in clc2rail sector emissions between 2011 and 2018	127
Table 4-49. Growth factors to project the 2011 ECA-IMO inventory to 2018	128
Table 5-1. National by-sector CAP emissions summaries for 2011 evaluation case	132
Table 5-2. National by-sector CAP emissions summaries for 2018 base case	133
Table 5-3. National by-sector CO emissions (tons/yr) summaries with differences	134
Table 5-4. National by-sector NH3 emissions (tons/yr) summaries with differences	135
Table 5-5. National by-sector NOx emissions (tons/yr) summaries with differences	136
Table 5-6. National by-sector PM2.5 emissions (tons/yr) summaries with differences	137
Table 5-7. National by-sector PM10 emissions (tons/yr) summaries with differences	138
Table 5-8. National by-sector SO2 emissions (tons/yr) summaries with differences	139
Table 5-9. National by-sector VOC emissions (tons/yr) summaries with differences	140
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DRAFT
List of Figures
Figure 2-1. January PM2.5 afdust emissions: raw 2008 NEI (top), after application of transport fraction
(middle) and final post-meteorological adjusted (bottom)	19
Figure 2-2. Illustration of regional modeling domains in ECA-EVK) study	30
Figure 2-3. Annual NO emissions output from BEIS 3.14 for 2011	35
Figure 2-4. Annual isoprene emissions output from BEIS 3.14 for 2011	35
Figure 3-1. Air quality modeling domains	38
Figure 3-2. Process of integrating BAFM with VOC for use in VOC Speciation	42
Figure 3-3. IPM Regions for EPA Base Case v5.13	55
Figure 3-4. Example of RWC temporalization in 2007 using a 50 versus 60 °F threshold	57
Figure 3-5. RWC diurnal temporal profile	58
Figure 3-6. Diurnal profile for OHH, based on heat load (BTU/hr)	59
Figure 3-7. Day-of-week temporal profiles for OHH and Recreational RWC	59
Figure 3-8. Annual-to-month temporal profiles for OHH and recreational RWC	60
Figure 3-9. Example of new animal NH3 emissions temporalization approach, summed to daily emissions 61
Figure 3-10. Example of SMOKE-MOVES temporal variability of NOx emissions	62
Figure 3-11. Previous onroad diurnal weekday profiles for urban roads	63
Figure 3-12. Variation in MOVES diurnal profiles	63
Figure 3-13. Use of submitted versus new national default profiles	64
Figure 3-14. Updated national default profiles for LDGV vs. HHDDV, urban restricted weekday	66
Figure 3-15. Agricultural burning diurnal temporal profile	68
Figure 4-1. Map of Petroleum Administration for Defense Districts (PADD)	92
Figure 4-2. Oil and Gas NEMS Regions	100
Figure 4-3. Cement sector trends in domestic production versus normalized emissions	Ill
List of Appendices
Appendix A: Nonpoint Oil and Gas NEI SCCs
Appendix B: Mapping of Fuel Distribution SCCs to BTP, BPS and RBT
Appendix C: Crosswalk between AE6 Profile Codes and SPECIATE 4.3 Profile Codes
Appendix D: Memo Describing the Differences in MOVES speciated PM and CMAQ PM
Appendix E: CAP emissions by surrogate and sector
Appendix F: SMOKE Input Data Files and Parameters Used in the 2011 Evaluation and 2018 Base Cases
Appendix G:Future Animal Population Projection Methodology, Updated 07/24/12
Appendix H:Boiler MACT ICR Fuels Cross-Reference to NEI SCCs
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DRAFT
Acronyms
ACI
Activated Carbon Injection
AE5
CMAQ Aerosol Module, version 5, introduced in CMAQ v4.7
AE6
CMAQ Aerosol Module, version 6, introduced in CMAQ v5.0
AEO
Annual Energy Outlook
AIM
Architectural and Industrial Maintenance (coatings)
ARW
Advanced Research WRF
BAFM
Benzene, Acetaldehyde, Formaldehyde and Methanol
BEIS3.14
Biogenic Emissions Inventory System, version 3.14
BELD3
Biogenic Emissions Land use Database, version 3
Bgal
Billion gallons
BPS
Bulk Plant Storage
BTP
Bulk Terminal (Plant) to Pump
C1/C2
Category 1 and 2 commercial marine vessels
C3
Category 3 (commercial marine vessels)
CAEP
Committee on Aviation Environmental Protection
CAIR
Clean Air Interstate Rule
CAMD
EPA's Clean Air Markets Division
CAMx
Comprehensive Air Quality Model with Extensions
CAP
Criteria Air Pollutant
CARB
California Air Resources Board
CB05
Carbon Bond 2005 chemical mechanism
CBM
Coal-bed methane
CEC
North American Commission for Environmental Cooperation
CEMS
Continuous Emissions Monitoring System
CEPAM
California Emissions Projection Analysis Model
CISWI
Commercial and Industrial Solid Waste Incineration
CI
Chlorine
CMAQ
Community Multiscale Air Quality
CMV
Commercial Marine Vessel
CO
Carbon monoxide
CSAPR
Cross-State Air Pollution Rule
EO, E10, E85
0%, 10% and 85% Ethanol blend 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
FF10
Flat File 2010
FIPS
Federal Information Processing Standards
FHWA
Federal Highway Administration
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HAP
Hazardous Air Pollutant
HC1
Hydrochloric acid
HDGHG
Heavy-Duty Vehicle Greenhouse Gas
Hg
Mercury
HMS
Hazard Mapping System
HPMS
Highway Performance Monitoring System
IIWC
Hazardous Waste Combustion
HWI
Hazardous Waste Incineration
ICAO
International Civil Aviation Organization
ICI
Industrial/Commercial/Institutional (boilers and process heaters)
ICR
Information Collection Request
IDA
Inventory Data Analyzer
I/M
Inspection and Maintenance
IMO
International Marine Organization
IPAMS
Independent Petroleum Association of Mountain States
IPM
Integrated Planning Model
ITN
Itinerant
LADCO
Lake Michigan Air Directors Consortium
LDGHG
Light-Duty Vehicle Greenhouse Gas
LPG
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)
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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DRAFT
OAQPS
EPA's Office of Air Quality Planning and Standards
OHH
Outdoor Hydronic Heater
OTAQ
EPA's Office of Transportation and Air Quality
ORIS
Office of Regulatory Information System
ORD
EPA's Office of Research and Development
ORL
One Record per Line
OTC
Ozone Transport Commission
PADD
Petroleum Administration for Defense Districts
PF
Projection Factor, can account for growth and/or controls
PFC
Portable Fuel Container
PM2.5
Particulate matter less than or equal to 2.5 microns
PM10
Particulate matter less than or equal to 10 microns
ppb, ppm
Parts per billion, parts per million
RBT
Refinery to Bulk Terminal
RFS2
Renewable Fuel Standard
RIA
Regulatory Impact Analysis
RICE
Reciprocating Internal Combustion Engine
RRF
Relative Response Factor
RWC
Residential Wood Combustion
RPO
Regional Planning Organization
RVP
Reid Vapor Pressure
see
Source Classification Code
SEMAP
Southeastern Modeling, Analysis, and Planning
SESARM
Southeastern States Air Resource Managers
SESQ
Sesquiterpenes
SMARTFIRE
Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation
SMOKE
Sparse Matrix Operator Kernel Emissions
SO2
Sulfur dioxide
SOA
Secondary Organic Aerosol
SI
Spark-ignition
SIP
State Implementation Plan
SPDPRO
Hourly Speed Profiles for weekday versus weekend
SPPD
Sector Policies and Programs Division
TAF
Terminal Area Forecast
TCEQ
Texas Commission on Environmental Quality
TOG
Total Organic Gas
TSD
Technical support document
ULSD
Ultra Low Sulfur Diesel
USD A
United States Department of Agriculture
VOC
Volatile organic compounds
VMT
Vehicle miles traveled
VPOP
Vehicle Population
WGA
Western Governors' Association
WRAP
Western Regional Air Partnership
WRF
Weather Research and Forecasting Model
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DRAFT
1 Introduction
The U.S. Environmental Protection Agency (EPA) developed an air quality modeling platform for 2011
based on the 2011 National Emissions Inventory, version 1 (201 INEIvl). The air quality modeling
platform consists of all the emissions inventories and ancillary data files used for emissions modeling, as
well as the meteorological, initial condition, and boundary condition files needed to run the air quality
model. This document focuses on the emissions modeling component of the 2011 modeling platform,
which includes the emission inventories, the ancillary data files, and the approaches used to transform
inventories for use in air quality modeling. Many emissions inventory components of this air quality
modeling platform are based on the 201 INEIvl, although there are some differences between the
platform inventories and the 201 INEIvl emissions.
This 2011 modeling platform includes all criteria air pollutants and precursors (CAPs) and the following
hazardous air pollutants (HAPs): chlorine (CI), hydrogen chloride (HC1), benzene, acetaldehyde,
formaldehyde and methanol. The latter four HAPs are also abbreviated as BAFM. This platform is called
the "CAP-BAFM 2011-Based Platform, version 6" because it is primarily a CAP platform with BAFM
species included. Here, "version 6" denotes an evolution from the 2007-based platform, version 5, with
substantial improvements due to the use of newer data and methods. For the rest of this document, the
platform that is described is referred to as the "2011 platform" or "201 lv6". Future updates to the 2011
platform will include a version qualifier such as "2011 Platform v6.1", and so on.
The first use of the 2011 platform is for the proposed rule related to the transport of ozone that will focus
on helping states in the eastern United States meet the 2008 National Ambient Air Quality Standards
(NAAQS) for ozone. The air quality model used for this rule is the Comprehensive Air Quality Model
with Extensions (CAMx) model, version 6.10; however, emissions are first processed for the Community
Multiscale Air Quality (CMAQ) model version 5.0.1 and then converted to CAMx-ready format. Both
CAMx and CMAQ support modeling ozone (O3) and particulate matter (PM), and require as input hourly
and gridded emissions of chemical species that correspond to CAPs and specific HAPs. The chemical
mechanism used by CAMx for this platform is called Carbon Bond 2005 (CB05) with chlorine chemistry.
CB05 allows explicit treatment of BAFM and includes HAP emissions of HC1 and CI.
The 2011 platform consists of two 'complete' emissions cases: the 2011 base case (i.e., 201 led_v6) and
the 2018 base case (i.e., 2018ed_v6). In the case abbreviations, the 2011 and 2018 are the year
represented by the emissions; the "e" stands for evaluation, meaning that year-specific data for fires and
EGUs are used, and the "d" represents that this was the fourth set of emissions modeled for the 201 lv6
platform. Table 1-1 provides more information on these emissions cases. The purpose of the 2011 base
case is to represent the year 2011 in a manner consistent with the methods used in corresponding future-
year cases, including the 2018 future year base case, as well as any additional future year control and
source apportionment cases.
For regulatory applications, the outputs from the 2011 base case are used in conjunction with the outputs
from the 2018 base case in the relative response factor (RRF) calculations to identify future areas of
nonattainment. For more information on the use of RRFs and air quality modeling, "Guidance on the Use
of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone. PM 2.5.
and Regional Haze". This document is available on EPA's Emissions Modeling Clearinghouse website,
under the section entitled "2011-based Modeling Platform (201 lv6 Platform)".
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Table 1-1. List of cases in the 2011 Version 6 Emissions Modeling Platform
Case Name
Abbreviation
Description
2011 base case
2011ed_v6
2011 case relevant for air quality model evaluation purposes
and for computing relative response factors with 2018
scenario(s). Uses 201 INEIvl and some other inventory data,
with hourly 2011 continuous emissions monitoring System
(CEMS) data for Electrical Generating Units (EGUs), hourly
onroad mobile emissions, and 2011 day-specific wild and
prescribed fire data.
2018 base case
2018ed v6
2018 "base case" scenario, representing the best estimate for
the future year that incorporates estimates of the impact of
current "on-the-books" regulations, without including
implementation of controls needed to attain current PM2.5
annual and 24-hour standards (35 ppm and 15 ppm,
respectively) and ozone 8-hour standard (75 ppb).
A brief summary of the emissions data used in the 201 lv6 platform follows:
1)	Point and nonpoint sources are based on the 201 INEIvl.
2)	Onroad mobile sources are based on year 2011 emissions computed using the Sparse Matrix
Operator Kernel Emission (SMOKE) interface to emission factors developed with the version of
Motor Vehicle Emissions Simulator (MOVES) that represents the proposed Tier 3 Vehicle
Emission and Fuel Standards.
3)	Nonroad mobile sources are based on the 201 INEIvl, except for some additions of VOC in
California where there were HAP emissions but no VOCs in 201 INEIvl.
4)	Commercial marine vessels (CMV) are based on the 201 INEIvl except for 2011 California Air
Resources Board (CARB) inventory in California, 2010 regional planning organization (RPO)
inventories in the Midwest, and a separate year-2002-based (projected to 2011) inventory for Class
3 CMV vessels. Additional minor changes were made to point sources as described in Section 2.1.
The primary emissions modeling tool used to create the air quality model-ready emissions was SMOKE
modeling system. SMOKE version 3.5.1 was used to create emissions files for a 12-km national grid that
includes all of the contiguous states "12US2", shown in Figure 3-1. Boundary conditions for this grid
were obtained from a 2011 run of GEOS-Chem. Electronic copies of the data used as input to SMOKE for
the 2011 Platform are available from the Emissions Modeling Clearinghouse website.
The gridded meteorological model used for the emissions modeling was developed using the Weather
Research and Forecasting Model version 3.4, Advanced Research WRF (ARW) core (Skamarock, et al.,
2008). The WRF Model is a mesoscale numerical weather prediction system developed for both
operational forecasting and atmospheric research applications. WRF was run for 2011 over a domain
covering the continental United States at a 12km resolution with 35 vertical layers. The data was
collapsed to 25 layers prior to running the emissions and air quality models.
This document contains five sections and several appendices. Section 2 describes the 2011 inventories
input to SMOKE. Section 3 describes the emissions modeling and the ancillary files used with the
emission inventories. Section 4 describes the development of the 2018 inventory (projected from 2011).
Data summaries comparing the 2011 base case and 2018 base case are provided in Section 5. Section 6
provides references. The Appendices provide additional details about specific technical methods.
2

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2 2011 Emission Inventories and Approaches
This section describes the 2011 emissions data that make up the 2011 platform. The starting point for the
2011 stationary source emission inputs is the 201 INEIvl. Emissions of NOx, SO2, VOC and PM
emissions decrease from values in the 2008 NEI version 3 for most source sectors, with a couple of
notable exceptions including increased industrial NOx, VOC and CO associated with increased oil and
gas sector emissions and improved emission estimates; slightly increased VOC, CO and NH3 from fuel
combustion; and increased wildfire emissions. Documentation for the 201 INEIvl, including a Technical
Support Document (TSD), is available at Air Emissions Inventories.
The NEI data for CAPs are largely compiled from data submitted by state, local and tribal (S/L/T) air
agencies. HAP emissions data are also from the state data, but are often augmented by EPA because they
are voluntarily submitted. EPA uses the Emissions Inventory System (EIS) to compile the NEI. EIS
includes hundreds of automated QA checks to help improve data quality, and also supports tracking
release point (e.g., stack) coordinates separately from facility coordinates. EPA collaborated extensively
with S/L/T agencies to ensure a very high quality of data in the 201 INEIvl. Tangible benefits of this
collaboration are seen in improved data quality from past first version inventories, improved completeness
and avoided duplication between point and nonpoint source categories such as industrial boilers. Onroad
mobile source emissions in the 201 INEIvl were developed using MOVES2010b; however, the 2011
emissions modeling platform used a different version of MOVES, hence forth referred to as
"MOVESTier3NPRM", that facilitated the representation of the proposed Tier 3 standards in future years.
When given the same inputs, these two versions of MOVES produce similar emissions estimates for the
year 2011.
The 2011 NEI includes five data categories: point sources, nonpoint (formerly called "stationary area")
sources, nonroad mobile sources, onroad mobile sources, and events consisting of fires. The 201 INEIvl
uses 60 sectors to further describe the emissions, with an additional biogenic sector generated from a
summation of the gridded, hourly 2011 biogenic data used in the modeling platform. In addition to the
NEI data, emissions from the Canadian and Mexican inventories and several other non-NEI data sources
are included in the 2011 platform. As explained below, the non-NEI emissions component to the 2011
platform primarily includes: different version of MOVES-based onroad mobile source emissions, non-
meteorologically-adjusted road dust, year-2010 commercial marine vessel (CMV) emissions in the
Midwest (Ladco), and Class 3 CMV data developed by EPA.
Fire emissions in 201 1NEIv2 were developed based on Version 2 of the Satellite Mapping Automated
Reanalysis Tool for Fire Incident Reconciliation (SMARTFIRE) system (Sullivan, et al., 2008).
SMARTFIRE 2 was the first version of SMARTFIRE to assign all fires as either prescribed burning or
wildfire categories. In past inventories, a significant number of fires were published as unclassified,
which impacted the emissions values and diurnal emissions pattern. Recent updates to SMARTFIRE
include improved emission factors for prescribed burning.
For the purposes of preparing the air quality model-ready emissions, the 201 INEIvl was split into finer-
grained sectors used for emissions modeling. The significance of an emissions modeling or "platform
sector" is that the data are run through all of the SMOKE programs except the final merge (Mrggrid)
independently from the other sectors. The final merge program then combines the sector-specific gridded,
speciated, hourly emissions together to create CMAQ-ready emission inputs. For CAMx applications, the
CMAQ-ready emissions are then converted into the format needed by CAMx by a converter program.
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Table 2-1 presents the sectors in the 2011 platform and how they generally relate to the 201 INEIvl as a
starting point. As discussed in greater detail in Table 2-2, the emissions in some of these sectors were
modified from the 201 INEIvl emissions for the 2011 modeling platform. The platform sector
abbreviations are provided in italics. These abbreviations are used in the SMOKE modeling scripts,
inventory file names, and throughout the remainder of this document.
Table 2-1. Platform sectors for the 2011 emissions modeling platform
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
EGU non-peaking
units:
ptegu
Point
2011 NEI point source EGUs determined to operate as non-peaking
units based on criteria discussed in Section 2.1. For future year
emissions, these units are mapped to the Integrated Planning Model
(IPM) model using the National Electric Energy Database System
(NEEDS) version 5.13. The 201 INEIvl emissions are replaced with
hourly 2011 CEMS values for NOx and SO: where the units match.
Other pollutants are scaled from 201 INEIvl using CEMS heat input.
Emissions for all non-CEMS sources come from 201 INEIvl. Annual
resolution for non-CEMS sources, hourly for CEMS sources.
EGU peaking
units:
ptegu pk
Point
Same as ptegu sector, but limited to EGUs that are determined to
operate as peaking units, as discussed in Section 2.1. All sources in
this sector have CEMS data for 2011 and are therefore hourly.
Point source oil
and gas:
pt oilgas
Point
201 INEIvl point sources with oil and gas production emissions
processes.
Remaining non-
EGU point:
ptnonipm
Point
All 201 INEIvl point source records not matched to the ptegu,
ptegu_pk, and pt_oilgas sectors, except for offshore point sources that
are in the othpt sector. Includes all aircraft emissions and some rail
yard emissions. Annual resolution.
Agricultural:
ag
Nonpoint
NH3 emissions from 201 INEIvl nonpoint livestock and fertilizer
application, county and annual resolution.
Area fugitive dust:
afdust
Nonpoint
PM10 and PM2.5 from fugitive dust sources from the 201 INEIvl
nonpoint inventory including building construction, road construction,
and agricultural dust, and road dust; however, unpaved and paved road
dust emissions differ from the NEI in that do not have a precipitation
adjustment. Instead, the emissions modeling adjustment applies a
transport fraction and a meteorology-based (precipitation and snow/ice
cover) zero-out. County and annual resolution.
Nonpoint source
oil and gas:
np oilgas
Nonpoint
201 INEIvl nonpoint sources from oil and gas-related processes.
County and annual resolution.
Residential Wood
Combustion:
rwc
Nonpoint
201 INEIvl NEI nonpoint sources with Residential Wood Combustion
(RWC) processes. County and annual resolution.
Class 1 & 2 CMV
and locomotives:
clc2rail
Nonpoint
Locomotives and primarily category 1 (CI) and category 2 (C2)
commercial marine vessel (CMV) emissions sources from the
201 INEIvl nonpoint inventory. Midwestern states" CMV emissions,
including Class 3 sources, are from a separate year 2010 emissions
inventory. County and annual resolution.
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
commercial
marine:
c3marine
Nonpoint
Category 3 (C3) CMV emissions projected to 2011 from year 2002
values. These emissions are not from the NEI, but rather were
developed for the rule called "Control of Emissions from New Marine
Compression-Ignition Engines at or Above 30 Liters per Cylinder',
usually described as the Emissions Control Area- International
Maritime Organization (ECA-IMO) studv: International Standards
to Reduce Emissions from Marine Diesel Engines and Their
Fuels. (EPA-420-F-10-041. August 2010). U.S. states-onlv emissions
(zero in Midwest); see othpt sector for all non-U.S. emissions. Treated
as point sources to reflect shipping lanes, annual resolution.
Remaining
nonpoint:
nonpt
Nonpoint
201 INEIvl nonpoint sources not otherwise removed from modeling or
included in other platform sectors; county and annual resolution.
Nonroad:
non road
Nonroad
201 INEIvl nonroad equipment emissions developed with the National
Mobile Inventory Model (NMIM) using NONROAD2008 version
NR08a. NMIM was used for all states except California and Texas,
which submitted their own emissions to the 201 INEIv 1. County and
monthly resolution.
Onroad non-
refueling:
onroad
Onroad
2011 onroad mobile source gasoline and diesel vehicles from parking
lots and moving vehicles. Includes the following modes: exhaust,
extended idle, evaporative, permeation, and brake and tire wear. For
all states except California and Texas, based on monthly MOVES
emissions tables from MOVESTier3NPRM. Texas emissions are
from the 201 INEIvl based on MOVES 2010b, and California
emissions are based on Emission Factor (EMFAC). MOVES-based
emissions computed for each hour and model grid cell using monthly
and annual activity data (e.g., VMT, vehicle population).
Onroad refueling:
onroadrfl
Onroad
201 INEIvl onroad mobile gasoline and diesel vehicle refueling
emissions for all states. Based on MOVES 2010b emissions tables.
Computed hourly based on temperature and for each model grid cell.
Point source fires:
ptfire
Fires
Point source day-specific wildfires and prescribed fires for 2011
computed using SMARTFIRE 2, except for Georgia-submitted
emissions. Consistent with 201 INEIvl.
Other point
sources not from
the 2011 NEI:
othpt
N/A
Point sources from Canada's 2006 inventory and Mexico's Phase III
2012 inventory, annual resolution. Mexico's inventory is year 2012
and grown from year 1999 (ERG, 2009; Wolf, 2009). Also includes
all non-U.S. C3 CMV and U.S. offshore oil production, which are
unchanged from the 2008 NEI point source annual emissions.
Other non-NEI
nonpoint and
nonroad:
othar
N/A
Annual year 2006 Canada (province resolution) and year 2012 (grown
from 1999) Mexico Phase III (municipio resolution) nonpoint and
nonroad mobile inventories.
Other non-NEI
onroad sources:
othon
N/A
Year 2006 Canada (province resolution) and year 2012 (grown from
1999) Mexico Phase III (municipio resolution) onroad mobile
inventories, annual resolution.
Biogenic:
beis
Nonpoint
Year 2011, hour-specific, grid cell-specific emissions generated from
the BEIS3.14 model with SMOKE, including emissions in Canada and
Mexico.
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Table 2-2 provides a brief by-sector overview of the most significant differences between the 2011
emissions platform and the 201 INEIvl. Only those sectors with significant differences between the
201 INEIvl and the 2011 emissions modeling platform are listed. For some sectors, such as non-EGU
point (ptnonipm), these changes are very minor and localized. In contrast, other sectors such as C3
commercial marine (c3marine) are either completely replaced or have significant and detailed edits based
on review of available alternative data. The specific by-sector updates to the 2011 platform are described
in greater detail later in this section under each by-sector subsection. A spreadsheet quantifying the
differences between the 201 INEIvl and modeling platform is available on CHIEF under
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/reports/2011 emissions/201 INEIvl versus 201 led
differences.xlsx.
Table 2-2. Summary of significant changes between 2011 platform and 201 INEIvl by sector
Platform Sector
Summary of Significant Inventory Differences of 2011 Platform vs.
201 INEIvl
IPM sectors:
ptegu & ptegu_pk
1)	Based on 201 INEIvl and 2011 CEMS data analysis, added ORIS Boiler IDs to
some units (greater than 1,000 tons of NOx or SO2) with missing or incorrect
values to allow for hourly CEMS data processing.
2)	Added CEMS matches to additional units identified as CEMS sources.
3)	Hourly NOx and SO2 CEMS data replaces annual NOx and SO2 NEI data in
the air quality model inputs.
Remaining non-
EGU (IPM)
sector:
ptnonipm
1)	Based on items above (ptegu & ptegu_pk), made additional matches to
IPM_YN codes and ORIS facility codes that caused several sources to move
into the ptegu and ptegu_pk sectors. This edit prevents double counting of
EGU emissions in the future years.
2)	Included 2011 ethanol plant facilities from EPA's Office of Transportation and
Air Quality (OTAQ) that were not identified in the 2011 NEIvl.
Area fugitive dust:
afdust
1)	Replaced EPA-provided emission estimates for paved and unpaved road dust
with ""non-met-adjustcd" emissions; i.e., the meteorology/precipitation
reduction included in the 201 INEIvl is backed-out.
2)	All emissions in this sector are processed (adjusted) to reflect land use
(transport) and meteorological effects such as rain and snow cover that
significantly reduce PM emissions input to the air quality model. These
adjusted emissions are known as the afdust adj emissions.
Remaining
nonpoint sector:
nonpt
1)	Split the 201 INEIvl nonpoint file into the platform sectors afdust, ag,
np_oilgas, rwc, c3marine, and clc2rail.
2)	Used agricultural fires emissions from daily inventory aggregated to monthly
values, whereas the NEI only stores annual values.
Class 1 & 2 CMV
and locomotives:
clc2rail
1)	Replaced Midwest RPO states clc2 CMV emissions with comprehensive year
2010 RPO inventory.
2)	Replaced all California estimates with year-2011 CARB estimates.
C3 commercial
marine:
c3marine
1)	Used non-201 INEIvl -based data. Rather used year-2011 point sources as
projected from 2002 from the ECA-IMO project.
2)	Midwest RPO states replaced with 2010 RPO inventory (see clc2rail sector).
Nonroad sector:
non road
1)	States other than Texas: monthly rather than annual + small VOC adjustments
in California.
2)	Texas: replaced with annual 2011 Texas data apportioned to months using
EPA's 2011 nonroad estimates.
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Platform Sector
Summary of Significant Inventory Differences of 2011 Platform vs.
201 INEIvl
Onroad non-
refueling:
onroad
1)	For all states except California and Texas: Year 2011 emissions for all
pollutants and modes (exhaust, evaporative, permeation, tire and brake wear)2
from all vehicle types are based on emission factors from the version of
MOVESTier3NPRM, as opposed to MOVES 2010b which was used for the
201 INEIvl. Processed with 2011 meteorology using SMOKE-MOVES
(discussed later).
2)	For California and Texas: merged in 2011 California and Texas data to post-
adjust SMOKE-MOVES data (discussed later).
Onroad non-
refueling:
onroadrfl
For all states including California: Year 2011 emissions for all pollutants AND all
vehicle types are based on MOVES2010b emission factor tables processed with
2011 meteorology using SMOKE-MOVES (discussed later). Therefore, these
emissions are identical to the 201 INEIvl for states that did not submit refueling
emissions, but are inconsistent with 201 INEIvl for states that did submit point and
nonpoint refueling since the 201 INEIvl kept state-submissions over EPA data.
The emission inventories in SMOKE input format for the 2011 base case are available from the Emissions
Modeling Clearinghouse website. The inventories "readme" file indicates the particular zipped files
associated with each platform sector. A number of reports were developed for the 2011 platform.
Descriptions of the available data and reports are available from the FTP site at
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/. The types of reports include state summaries of
inventory pollutants and model species by modeling platform sector for 2011 and 2018 in the Microsoft®
Excel® files "201 led_v6_l lf_state_sector_totals.xlsx" and "2018ed_v6_l lf_state_sector_totals.xlsx",
with a comparison of the emissions in two cases in the file "201 led_2018ed_comparison_6jan2014.xlsx".
CAP emission totals by county, month, and modeling platform sector are available in the files
"201 led_county_monthly_report_CAPs.xlsx" and "2018ed_county_monthly_report_CAPs.xlsx".
Summaries by state and source classification code (SCC), including SCC descriptions, by modeling sector
for 2011 and 2018 are available at the FTP site:
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/reports/State-SCC-Summaries/. A comparison of the
complete list of inventory files, ancillary files, and parameter settings for the 2011 and 2018 modeling
cases is available in the file "201 led_2018ed_case_inputs.xlsx".
The remainder of Section 2 provides details about the data contained in each of the 2011 platform sectors.
Different levels of detail are provided for different sectors depending on the availability of reference
information for the data, the degree of changes or manipulation of the data needed to prepare it for input
to SMOKE, and whether the 2011 platform emissions are significantly different from the 201 INEIvl.
2.1 2011 NEI point sources (ptegu, ptegu_pk, pt_oilgas and ptnonipm)
Point sources are sources of emissions for which specific geographic coordinates (e.g.,
latitude/longitude) are specified, as in the case of an individual facility. A facility may have multiple
emission release points, which may be characterized as units such as boilers, reactors, spray booths,
kilns, etc. A unit may have multiple processes (e.g., a boiler that sometimes burns residual oil and
sometimes burns natural gas). With a couple of minor exceptions, this section describes only NEI point
sources within the contiguous United States. The offshore oil platform (othpt sector) and category 3
CMV emissions (c3marine and othpt sectors) are processed by SMOKE as point source inventories, as
described in Section 2.5.1 and Section 2.4.2, respectively. A comprehensive description on how EGU
emissions were characterized and estimated in the 2011 NEI can be found in Section 3.10 in the
201 INEIvl TSD.
2 For the extended idle mode, used MOVES2010b emissions factors. See onroad section below for details.
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The point source file used for the modeling platform is exported from EIS into the Flat File 2010 (FF10)
format that is compatible with SMOKE. After moving offshore oil platforms into the othpt sector, initial
versions of the other four platform point source sectors were created from the remaining 201 INEIvl
point sources. The point sectors are: the EGU sector for non-peaking units (ptegu), the EGU sector for
peaking units (ptegu_pk), point source oil and gas extraction -related emissions (ptoilgas) and the
remaining non-EGU sector also called the non-IPM (ptnonipm) sector. The EGU emissions are split out
to facilitate the use of distinct SMOKE temporal processing and future-year projection techniques. The
EGU sectors are further split into "peaking" (ptegu_pk) and non-peaking units to allow for better
analysis of the impact of peaking units. The oil and gas sector emissions (pt oilgas) were processed
separately for summary tracking purposes and unique future-year projection techniques from the
remaining non-EGU emissions (ptnonipm).
In addition to the emissions summaries described in Section 1, two other specialized point source
summaries are available on the Emissions Modeling Clearinghouse website. A summary report of stack
parameters for the point source sectors, including cross references to CEMS data via ORIS IDs, can be
found in the file
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/reports/2011 emissions/201 lec stack parameter re
port.xlsx. A comparison of the 201 INEIvl EGU emissions with the 2011 CEMS data is available in the
same directory in the file "201 lEGUsNEICEMS.xlsx".
The inventory pollutants processed through SMOKE for both all point source sectors were: CO, NOx,
VOC, SO2, NH3, PM10, and PM2.5 and the following HAPs: HC1 (pollutant code = 7647010), and CI
(code = 7782505). The inventory BAFM from these sectors was not used, instead VOC was speciated
to these pollutants without any use (i.e., integration) and the VOC HAP pollutants from the inventory
were ignored (VOC integration is discussed in detail in Section 3.2.1.1).
The ptnonipm and pt oilgas sector emissions were provided to SMOKE as annual emissions. For those
ptegu and ptegu_pk sources with CEMS data (that could be matched to the 201 INEIvl), 2011 hourly
CEMS NOx and SO2 emissions were used (rather than NEI emissions) and for all other pollutants
annual emissions were used as-is from the NEI, but were allocated to hourly values using heat input
CEMS data. For the non-CEMS sources in the ptegu and ptegu_pk sectors, daily emissions were created
using an approach described in Section 2.1.1, and IPM region- and pollutant-specific diurnal profiles
were applied to create hourly emissions.
Changes made to the point-based sectors from the 201 INEIvl for the 2011 platform were briefly
described in Table 2-2. One of these changes involved splitting the stacks, units and facilities into the
ptnonipm, pt oilgas, ptegu and ptegu_pk sectors. Sources were included in the ptegu or ptegu_pk
sectors only when it was determined that these sources were reflected in the future-year IPM output
data. These changes and other updates to the point source sectors for the 2011 platform are discussed in
the following sections.
2.1.1 EGU non-peaking units sector (ptegu)
The ptegu and ptegu_pk (see Section 2.1.2) sectors contain emissions from EGUs in the 201 INEIvl
point inventory that could be matched to units found in the NEEDS v5.13 database. It was necessary to
put these EGUs into separate sectors in the platform because IPM projects future emissions for the
EGUs defined in the NEEDS database, and emissions for sources in the ptegu and ptegu_pk sectors are
replaced with IPM outputs in the future year modeling case. Sources not matched to units found in
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NEEDS are placed into the pt oilgas (see Section 2.1.3) or ptnonipm sector (see Section 2.1.4) and are
projected to the future year using projection and control factors. It is important that the matching
between the NEI and NEEDS database be as complete as possible because there can be double counting
of emissions in the future year if emissions for units are projected by IPM are not properly matched to
the units in the NEI.
In the SMOKE point flat file, emission records for sources that have been matched to the NEEDS
database have a value filled into the IPMYN column. Many of these matches are stored within EIS. In
some cases, it was difficult to match the sources between the databases due to different facility names in
the two data systems and due to differences in how the units are defined, thereby resulting in matches
that are not always one-to-one. Some additional matches were made in the modeling platform to
accommodate some of these situations as described later in this section. The NEEDS v5.13 database,
along with additional information about IPM.
Some units in the ptegu and ptegu_pk sectors are matched to CEMS data via ORIS facility codes and
boiler ID. For these units, SMOKE replaces the 2011 emissions of NOx and SO2 with the CEMS
emissions, thereby ignoring the annual values specified in the NEI. For other pollutants, the hourly
CEMS heat input data are used to allocate the NEI annual emissions to hourly values. All stack
parameters, stack locations, and SCC codes for these sources come from the NEI. Because these
attributes are obtained from the NEI, the chemical speciation of VOC and PM2.5 for the sources is
selected based on the SCC or in some cases, based on unit-specific data. If CEMS data exists for a unit,
but the unit is not matched to the NEI, the CEMS data for that unit is not used in the modeling platform.
However, if the source exists in the NEI and is just not matched to a CEMS unit, the emissions from that
source would still be modeled using the annual emission value in the NEI. EIS stores many matches
from EIS units to the ORIS facility codes and boiler IDs used to reference the CEMS data. Some
additional matches were made in the modeling platform as described later in this section.
In the SMOKE point flat file, emission records for point sources matched to CEMS data have values
filled into the ORIS FACILITY CODE and ORIS BOILER ID columns. The CEMS data in SMOKE-
ready format is near the bottom of the "Prepackaged Data" tab. Many smaller emitters in the CEMS
program are not identified with ORIS facility or boiler IDs that can be matched to the NEI due to
inconsistencies in the way a unit is defined between the NEI and CAMD datasets, or due to uncertainties
in source identification such as inconsistent plant names in the two data systems. Also, the NEEDS
database of units modeled by IPM includes many smaller emitting EGUs that are not included in the
CAMD hourly CEMS programs. Therefore, there will be more units in the NEEDS database than have
CEMS data.
For sources not matched to CEMS data ("non-CEMS" sources), daily emissions were computed from
the NEI annual emissions using average CEMS data profiles specific to fuel type, pollutant3, and IPM
region. To allocate to hour, diurnal profiles were created using average CEMS data for heat input
specific to fuel type and IPM region. For future-year scenarios, there are no CEMS data available for
specific units, but the shape of the CEMS profiles is preserved for sources that are carried into the future
year. This method keeps the temporal behavior of the base and future year cases as consistent as
possible. See Section 3.3.2 for more details on the temporalization approach for ptegu sources.
Finding additional matches between the NEI. NEEDS, and CEMS data
3 The year to day profiles use NOx and S02 CEMS for NOx and S02, respectively. For all other pollutants, they use heat
input CEMS data.
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Several analytical steps were performed to better link the NEEDS units to the 201 INEIvl, along with
implementing better matching to the CEMS data cross-referenced using "ORIS" facility and boiler IDs.
The steps described in the 201 INEIvl TSD have some detail on how the values in the IPMYN column
were assigned. For the modeling platform, an initial ptipm/ptnonipm split was determined using the
values in the SMOKE point source flat file variable "IPM YN", which is populated based on an EIS
alternative facility identifier. Because EIS expects the matches to be one-to-one for an entire unit, if the
units are not defined in the same way in EIS and NEEDS, it is not possible to store the match in EIS.
Some IPM YN values in the SMOKE input file were therefore manually updated based on units that
had been matched to IPM units in past modeling platforms, but for which the alternative facility IDs in
EIS did not include a code for IPM matching. These additions were usually needed due to one-to-many
or many-to-one relationships between units in the EIS and the NEEDS databases.
Additional matches between the NEI and NEEDS were identified by identifying units in IPM outputs
that were not yet matched to NEI data, and by looking for units identified in the NEI with facility type
codes identifying them as EGUs or facility names that indicated they were EGUs. In each case, priority
was given to units with larger emissions (e.g., > 300TPY of NOx or SO2). The units in each data set that
did not yet have matches within the same county were compared to one another on the basis of their
plant names and locations. In some cases, IDs were similar but were mismatched only due to a missing
leading zero in one of the databases. In other cases, a facility level match was specified, but a unit/boiler
level match was not yet identified and therefore the units at the facility were compared to one another on
the basis of design capacity and naming. For any new matches that were found, values that represented
the NEEDS IDs were filled in to the IPM YN in the modeling platform flat files. When possible, these
matches were loaded into EIS.
A similar matching process was used to identify additional matches between the 201 INEIvl and CEMS
data. To determine whether a NEI unit matched a CEMS unit, the CEMS units were compared to
facilities in the NEI that were not yet identified as a CEMS unit on the basis of their county FIPS codes,
locations, and total emissions of NOx and SO2. Additional CEMS matches that were found were applied
to the FF10 file by specifying values for ORIS FACILITY CODE, ORIS BOILER ID. Because IPM
uses a concatenation of the ORIS facility code and boiler ID, values were also filled in to the IPM YN
field for these units.
As a result of identifying additional matches through this analysis, many EGUs that otherwise would
have remained in the ptnonipm sector were moved to the ptegu sector. Some of the new CEMS
assignments were loaded into EIS for use in future inventories, but some could not be loaded into EIS
because they were not one-to-one matches (e.g. multiple EIS units corresponding to a single CEMS
unit). Note that SMOKE is more flexible when associating CEMS data with the inventory than EIS is,
and can perform matches down to the stack or release point level.
2.1.2 EGU peaking units sector (ptegu_pk)
The ptegu_pk sector includes sources identified by EPA as peaking units. The units were separated into
this sector to facilitate analyses of the impact of peaking units. Aside from their inclusion in this sector,
in all other ways they are treated in the same way as CEMS sources in the ptegu sector because all of
them are matched to CEMS data. To identify units for inclusion in this sector, EPA made sure they
satisfied two tests: (1) the capacity factor was less than 10% over a 3 year average (2010-2012), and (2)
the capacity factor was less than 20% in each of the 3 years. Here, "capacity factor" means either: (1)
The ratio of a unit's actual annual electric output (expressed in MWe/hr) to the unit's nameplate capacity
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(or maximum observed hourly gross load (in MWe/hr) if greater than the nameplate capacity) times
8760 hours; or (2) The ratio of a unit's annual heat input (in million BTUs or equivalent units of
measure) to the unit's maximum rated hourly heat input rate (in million BTUs per hour or equivalent
units of measure) times 8,760 hours. The list of units in the ptegu_pk sector is provided in the file (file
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/reports/2011 emissions/Peakers CAMP 2011.0802
13 NEI IPM match.xls).
2.1.3 Point source oil and gas sector (pt_oilgas)
The ptoilgas sector includes sources with the SCCs specified in the list in Table 2-3. The emissions
and other source characteristics in the pt oilgas sector are submitted by states, while EPA developed a
dataset of nonpoint oil and gas emissions for each county in the U.S. with oil and gas activity that was
available for states to use. The nonpoint emissions can be found in the np oilgas sector. More
information on the development of the 2011 oil and gas emissions can be found in Section 3.21 of the
201 INEIvl TSD.
Table 2-3. Point source oil and gas sector SCCs
see
SCC Description*
31000309
IP;OGP;Natural Gas Processing Facilities;Compressor Seals
31000310
IP;OGP;Natural Gas Processing Facilities;Pump Seals
31000311
IP;OGP;Natural Gas Processing Facilities;Flanges and Connections
31000321
IP;OGP;Natural Gas Processing Facilities;Glycol Dehydrators: Niagaran Formation (Mich.)
31000322
IP;OGP;Natural Gas Processing Facilities;Glycol Dehydrators: Prairie du Cliien Formation (Mich.)
31000323
IP;OGP;Natural Gas Processing Facilities;Glycol Dehydrators: Antrim Formation (Mich.)
31000324
IP;OGP;Natural Gas Processing Facilities;Pneumatic Controllers Low Bleed
31000325
IP;OGP;Natural Gas Processing Facilities;Pneumatic Controllers High Bleed >6 scfm
31000401
IP;OGP;Process Heaters;Distillate Oil (No. 2)
31000402
IP;OGP;Process Heaters;Residual Oil
31000403
IP;OGP;Process Heaters;Crude Oil
31000404
IP;OGP;Process Heaters;Natural Gas
31000405
IP;OGP;Process Heaters;Process Gas
31000406
IP;OGP;Process Heaters;Propane/Butane
31000411
IP;OGP;Process Heaters;Distillate Oil (No. 2): Steam Generators
31000412
IP;OGP;Process Heaters;Residual Oil: Steam Generators
31000413
IP;OGP;Process Heaters;Crude Oil: Steam Generators
31000414
IP;OGP;Process Heaters;Natural Gas: Steam Generators
31000415
IP;OGP;Process Heaters;Process Gas: Steam Generators
31000502
IP;OGP;Liquid Waste Treatment;Liquid - Liquid Separator
31000503
IP;OGP;Liquid Waste Treatment;Oil-Water Separator
31000504
IP;OGP;Liquid Waste Treatment;Oil-Sludge-Waste Water Pit
31000506
IP;OGP;Liquid Waste Treatment;Oil-Water Separation Wastewater Holding Tanks
31088801
IP;OGP;Fugitive Emissions;Specily in Comments Field
31088802
IP;OGP;Fugitive Emissions;Specily in Comments Field
31088803
IP;OGP;Fugitive Emissions;Specily in Comments Field
11

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DRAFT
see
SCC Description*
31088804
IP;OGP;Fugitive Emissions;Specify in Comments Field
31088805
IP;OGP;Fugitive Emissions;Specify in Comments Field
31088811
IP;OGP;Fugitive Emissions;Fugitive Emissions
31700101
Industrial Processes;NGTS;Natural Gas Transmission and Storage Facilities;Pneumatic Controllers
Low Bleed
40400300
PSE;PLS;OGFSWT;Fixed Roof Tank: Flashing Loss
40400301
PSE;PLS;OGFSWT;Fixed Roof Tank: Breathing Loss
40400302
PSE;PLS;OGFSWT;Fixed Roof Tank: Working Loss
40400303
PSE;PLS;OGFSWT;External Floating Roof Tank with Primary Seals: Standing Loss
40400304
PSE;PLS;OGFSWT;External Floating Roof Tank with Secondary Seals: Standing Loss
40400305
PSE;PLS;OGFSWTinternal Floating Roof Tank: Standing Loss
40400306
PSE;PLS;OGFSWT;External Floating Roof Tank: Withdrawal Loss
40400307
PSE;PLS;OGFSWTinternal Floating Roof Tank: Withdrawal Loss
40400311
PSE;PLS;OGFSWT;Fixed Roof Tank, Condensate, working+breathing+flashing losses
40400312
PSE;PLS;OGFSWT;Fixed Roof Tank, Crude Oil, working+breathing+flashing losses
40400313
PSE;PLS;OGFSWT;Fixed Roof Tank, Lube Oil, working+breathing+flashing losses
40400314
PSE;PLS;OGFSWT;Fixed Roof Tank, Specialty Chem-working+breathing+flashing losses
40400315
PSE;PLS;OGFSWT;Fixed Roof Tank, Produced water, working+breathing+flashing losses
40400316
PSE;PLS;OGFSWT;Fixed Roof Tank, Diesel, working+breathing+flashing losses
40400321
PSE;PLS;OGFSWT;External Floating Roof Tank, Condensate, working+breathing+flashing losses
40400322
PSE;PLS;OGFSWT;External Floating Roof Tank, Crude Oil, working+breathing+flashing losses
40400323
PSE;PLS;OGFSWT;External Floating Roof Tank, Lube Oil, working+breathing+flashing losses
40400324
PSE;PLS;OGFSWT;External Floating Roof Tank, Specialty Chem-working+breathing+flashing losses
40400325
PSE;PLS;OGFSWT;External Floating Roof Tank, Produced water, working+breathing+flashing losses
40400326
PSE;PLS;OGFSWT;External Floating Roof Tank, Diesel, working+breathing+flashing losses
40400331
PSEiPLSiOGFSWT; Internal Floating Roof Tank, Condensate, working+breathing+flashing losses
40400332
PSEiPLSiOGFSWT; Internal Floating Roof Tank, Crude Oil, working+breathing+flashing losses
40400334
PSEiPLSiOGFSWT:Internal Floating Roof Tank, Specialty Chem-working+breathing+flashing losses
40400335
PSEiPLSiOGFSWT;Internal Floating Roof Tank, Produced water, working+breathing+flashing losses
*IP;OGP = Industrial Processes;Oil and Gas Production and
PSE;PLS;OGFSWT=Petroleum and Solvent Evaporation;Petroleum Liquids Storage (non-Refinery);Oil and Gas
Field Storage and Working Tanks
2.1.4 Non-IPM sector (ptnonipm)
Except for some minor exceptions, the non-IPM (ptnonipm) sector contains the 201 INEIvl point
sources included in the modeling platform that are not in the ptegu, ptegu_pk, or pt oilgas sectors. For
the most part, the ptnonipm sector reflects the non-EGU sources of the NEI point inventory; however, it
is likely that some small low-emitting EGUs not matched to the NEEDS database or to CEMS data are
present in the ptnonipm sector. The sector also includes some ethanol plants that have been identified
by EPA but are not in 201 INEIvl.
The ptnonipm sector contains a small amount of fugitive dust PM emissions from vehicular traffic on
paved or unpaved roads at industrial facilities, coal handling at coal mines, and grain elevators. Some
point sources in the 201 INEIvl that are not included in any modeling sectors are:
12

-------
DRAFT
•	Sources with state/county FIPS code ending with "777". These sources represent mobile
(temporary) asphalt plants that are only reported for some states, and are generally in a fixed
location for only a part of the year and are therefore difficult to allocate to specific places and
days for modeling. Therefore, these sources are dropped from the point-based sectors in the
modeling platform.
•	Offshore oil records with FIPS=85000 were not updated from the 2008NEIv3 and are processed
in the othpt sector as discussed in Section 2.5.1.
Additional Ethanol facilities
Another difference between the 201 INEIvl data and the modeling platform is the addition of some
ethanol production facilities identified by EPA but were not found in the NEI. For some rule
development work, EPA developed a list of corn ethanol facilities for 2011. Many of these ethanol
facilities were included in the 201 INEIvl, but those that were not matched were added to the ptnonipm
sector in a separate FFlO-format inventory file. Locations and FIPS codes for these ethanol plants were
verified using web searches and Google Earth. EPA believes that some of these sources are not included
in the NEI as point sources because they do not meet the 100 ton/year potential-to-emit threshold for
NEI point sources. In other cases, EPA is following up with states to evaluate whether the state data
should include these point sources.
Emission rates for the ethanol plants were obtained from EPA's updated spreadsheet model for upstream
impacts developed for the Renewable Fuel Standard (RFS2) rule (EPA, 2010a). Plant emission rates for
criteria pollutants used to estimate impacts for years 2011 and 2018 are given in Table 2-4.
Table 2-4. Corn Ethanol Plant Criteria Pollutant Emission Factors (grams per gallon produced)
Corn Ethanol Plant Type
VO
c
CO
NO
X
PMi
0
PMi.
5
SO
2
NH
3
Dry Mill Natural Gas (NG)
2.29
0.5
8
0.99
0.94
0.23
0.0
1
O
o o
Dry Mill NG (wet distillers grains with solubles
(DGS))
2.27
0.3
7
0.63
0.91
0.20
o
o o
o
o o
Dry Mill Biogas
2.29
0.6
2
1.05
0.94
0.23
0.0
1
o
o o
Dry Mill Biogas (wet DGS)
2.27
0.3
9
0.67
0.91
0.20
o
o o
o
o o
Dry Mill Coal
2.31
2.6
5
4.17
3.81
1.71
4.5
2
o
o o
Dry Mill Coal (wet DGS)
2.31
2.6
5
2.65
2.74
1.14
2.8
7
o
o o
Dry Mill Biomass
2.42
2.5
5
3.65
1.28
0.36
0.1
4
o
o o
Dry Mill Biomass (wet DGS)
2.35
1.6
2
2.32
1.12
0.28
VO o
o
o
o o
Wet Mill NG
2.35
1.6
2
1.77
1.12
0.28
VO o
o
o
o o
Wet Mill Coal
2.33
1.0
4
5.51
4.76
2.21
5.9
7
o
o o
13

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DRAFT
Air toxic emission rates were estimated by applying toxic to VOC ratios in Table 2-5 were multiplied by
facility production estimates for 2011 and 2018 based on analyses performed for the industry
characterization described in Chapter 1 of the RFS2 final rule regulatory impact analysis. For air toxics
except ethanol, the toxic-to-VOC ratios were developed using emission inventory data from the 2005
NEI (EPA, 2009a).
Table 2-5. Toxic-to-VOC Ratios for Corn Ethanol Plants

Acetaldehyde
Acrolein
Benzene
1,3-Butadiene
Formaldehyde
Wet Mill NG
0.02580
0.00131
0.00060
2.82371E-08
0.00127
Wet Mill Coal
0.08242
0.00015
0.00048
2.82371E-08
0.00108
Dry Mill NG
0.01089
0.00131
0.00060
2.82371E-08
0.00127
Dry Mill Coal
0.02328
0.00102
0.00017
2.82371E-08
0.00119
2.2 2011 nonpoint sources (afdust, ag, npoilgas, rwc, nonpt)
Several modeling platform sectors were created from the 201 INEIvl nonpoint inventory. This section
describes the stationary nonpoint sources. Locomotives, CI and C2 CMV, and C3 CMV are also
included the 201 INEIvl nonpoint data category, but are mobile sources that are described in Sections
2.4.1 and 2.4.2 as the clc2rail and c3marine sectors, respectively. The 201 INEIvl TSD available from
Air Emissions Inventories includes documentation for the nonpoint sector of the 201 INEIvl.
The nonpoint tribal-submitted emissions are dropped during spatial processing with SMOKE due to the
configuration of the spatial surrogates. Part of the reason for this is to prevent possible double-counting
with county-level emissions and also because spatial surrogates for tribal data are not currently
available. These omissions are not expected to have an impact on the results of the air quality modeling
at the 12-km scales used for this platform.
The emissions modeling sector inventories start with the NEI data. Several source categories were not
included in the modeling platform inventories for the following reasons: 1) these sources are only
reported by a small number of states or agencies, 2) these sources are 'atypical' and have small
emissions, and/or 3) EPA has have other data the Agency believes to be more accurate. Table 2-6
provides a list of SCCs removed from the nonpoint sectors, justification for their removal, and the
national annual NOx, VOC and NH3 emission totals. The following subsections describe how the
remaining sources in the 201 INEIvl nonpoint inventory were separated into 2011 modeling platform
sectors, along with any data that were updated replaced with non-NEI data.
14

-------
DRAFT
Table 2-6. 201 INEIvl nonpoint sources removed from the 2011 platform
see
Description
Reason for
Removal
NOx*
voc*
MI;
2280003100
Marine Vessels, Commercial; Residual; Port
emissions
Replaced with
OTAQ ECA-
IMO dataset -see
Section 2.4.2
62,906
2,411
23
2280003200
Marine Vessels, Commercial; Residual; Underway
emissions
817,367
30,846
151
2294000000
Paved Roads; All Paved Roads; Total: Fugitives
Replaced with
emissions NOT
reduced via
precipitation -
see Section 2.2.1



2294010000
Paved Roads; All Other Public Paved Roads; Total:
Fugitives



2501060100
Gasoline Stage 2 refueling: Total
Replaced with
MOVES 2010b-
based estimates -
see Section2.3.2

154,349

2501060101
Gasoline Stage 2 refueling: Displacement
Loss/Uncontrolled

6,731

2501060102
Gasoline Stage 2 refueling: Displacement
Loss/Controlled

6,890

2501060103
Gasoline Stage 2 refueling: Spillage

2,771

2810005001
Managed Burning, Slash (Logging Debris) ;Pile
Burning
Replaced with
SMARTFIRE 2
estimates -see
Section 2.6
84.5
95

2810005002
Managed Burning, Slash (Logging Debris);Broadcast
Burning
0
0

2810020000
Prescribed Rangeland Burning; Unspecified



2810090000
Open Fire; Not categorized



2275087000
Aircraft; In-flight (non-Landing-Takeoff cycle);Total
Dropped because
they are atypical
and sparsely-
reported
categories with
small emissions



2806010000
Domestic Animals Waste Emissions; Cats; Total


294
2806015000
Domestic Animals Waste Emissions; Dogs; Total


1,674
2807020001
Wild Animals Waste Emissions; Bears; Black Bears


3
2807020002
Wild Animals Waste Emissions; Bears; Grizzly Bears


0
2807025000
Wild Animals Waste Emissions; Elk; Total


1,425
2807030000
Wild Animals Waste Emissions; Deer; Total


1,431
2807040000
Wild Animals Waste Emissions; Birds; Total


0
2810003000
Cigarette Smoke; Total
2
43
4
2810010000
Human Perspiration and Respiration; Total


2,742
2830000000
Catastrophic/Accidental Releases; All; Total
0
167
0
2830010000
Catastrophic/Accidental Releases; Transportation
Accidents; Total

0

2862000000
Swimming Pools; Total (Commercial, Residential,
Public);Total

198

* Emission units are short tons
2.2.1 Area fugitive dust sector (afdust)
The area-source fugitive dust (afdust) sector contains PMio and PM2.5 emission estimates for nonpoint
SCCs identified by EPA staff as dust sources. Categories included in the afdust sector are paved roads,
unpaved roads and airstrips, construction (residential, industrial, road and total), agriculture production,
and mining and quarrying. It does not include fugitive dust from grain elevators, coal handling at coal
mines, or vehicular traffic on paved or unpaved roads at industrial facilities because these are treated as
point sources so they are properly located.
The afdust sector is separated from other nonpoint sectors to allow for the application of a "transport
fraction," and meteorological/precipitation reductions. These adjustments are applied with a script that
applies land use-based gridded transport fractions followed by another script that zeroes out emissions
15

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DRAFT
for days on which at least 0.01 inches of precipitation occurs or there is snow cover on the ground. The
land use data used to reduce the NEI emissions determines the amount of emissions that are subject to
transport. This methodology is discussed in (Pouliot et. al.. 2010). and in "Fugitive Dust Modeling for
the 2008 Emissions Modeling Platform" (Adelman, 2012). The purpose of applying the transport
fraction and meteorological adjustments is to reduce the overestimation of fugitive dust in the grid
modeling as compared to ambient observations. Both the transport fraction and meteorological
adjustments are based on the gridded resolution of the platform (e.g., 12km grid cells); therefore,
different emissions will result if the process were applied to different grid resolutions. A limitation of
the transport fraction approach is the lack of monthly variability that would be expected with seasonal
changes in vegetative cover. While wind speed and direction are not accounted for in the emissions
processing, the hourly variability due to soil moisture, snow cover and precipitation is accounted for in
the subsequent meteorological adjustment.
The sources in the afdust sector are for SCCs and pollutant codes (i.e., PMio and PM2.5) that are
considered to be "fugitive" dust sources. These SCCs are provided in Table 2-7.
Table 2-7. SCCs in the afdust platform sector
see
SCC Description
2275085000
Industrial Processes;Construction: SIC 15 - 17;A11 Processes;Vehicle Traffic
2294000000
Industrial Processes;Construction: SIC 15 - 17;Industrial/Commercial/Institutional;Total
2294005000
Industrial Processes;Construction: SIC 15 - 17;Residential;Total
2294010000
Industrial Processes;Construction: SIC 15 - 17;Road Construction;Total
2296000000
Industrial Processes;Construction: SIC 15 - 17;Special Trade Construction;Total
2296005000
Industrial Processes;Mining and Quarrying: SIC 14;A11 Processes;Total
2296010000
Industrial Processes;Mining and Quarrying: SIC 14;Crushed and Broken Stone;Total
2311000070
Industrial Processes;Mining and Quarrying: SIC 14;Sand and Gravel;Total
2311010000
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture -
Crops;Harvesting
2311020000
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Planting
2311030000
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Tilling
2311040000
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Total
2325000000
Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Transport
2325020000
Miscellaneous Area Sources;Agriculture Production - Livestock;Beef cattle - finishing
operations on feedlots (drylots);Dust Kicked-up by Hooves (use 28-05-020, -001, -002, or
-003 for Waste
2325030000
Mobile Sourccs:A i rc raft: U11 paved Airstrips;Total
2801000000
Mobile Sources;Paved Roads;All Other Public Paved Roads;Total: Fugitives
2801000002
Mobile Sources;Paved Roads;All Paved Roads;Total: Fugitives
2801000003
Mobile Sources;Paved Roads;Interstate/Arterial;Total: Fugitives
2801000005
Mobile Sources;Unpaved Roads;All Unpaved Roads;Total: Fugitives
2801000008
Mobile Sources;Unpaved Roads;Industrial Unpaved Roads;Total: Fugitives
2805001000
Mobile Sources;Unpaved Roads;Public Unpaved Roads;Total: Fugitives
The dust emissions in the modeling platform are not the same as the 201 INEIvl emissions because the
NEI paved and unpaved road dust emissions include a built-in precipitation reduction that is based on
average meteorological data, which is at a coarser temporal and spatial resolution than the modeling
16

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DRAFT
platform meteorological adjustment. Due to this, in the platform paved and unpaved road emissions
data was used that did not include any precipitation-based reduction. This allows the entire sector to be
processed consistently so that the same grid-specific transport fractions and meteorological adjustments
can be applied. Where states submitted afdust data, the state-submitted data was used assuming that it
was not met-adjusted and therefore the meteorological adjustments were still applied. Thus, it is possible
that these sources may have been adjusted twice. Even with that possibility, air quality modeling shows
that in general, dust is frequently overestimated in the air quality modeling results.
The total impacts of the transport fraction and meteorological adjustments for 201 INEIvl are shown in
Table 2-8, where the starting inventory numbers include unadjusted paved and unpaved road dust, so
they do not match the NEI values which include a different type of adjustment. The amount of the
reduction ranges from about 6% in New Hampshire to almost 73% in Nevada. Figure 2-1 shows the
impact of each step of the adjustment for January 2008, using the 2008 NEI as an example. The raw
NEI afdust PM2.5 emissions - prior to transport fraction meteorological adjustments - are shown at the
top of Figure 2-1. The afdust emissions after the application of the transport fraction, but prior to
meteorological adjustments are shown in the middle of Figure 2-1. Finally, the resulting emissions after
both transport fraction and meteorological adjustments are shown at the bottom of Figure 2-1. The top
and middle plots show how the transport fraction has a larger reduction effect in the east where forested
areas are more effective at reducing PM transport than in many western areas. Comparing the bottom
and middle plots shows how the meteorological impacts of precipitation, along with snow cover in the
north, further reduce the dust emissions.
Table 2-8. Total Impact of Fugitive Dust Adjustments to Unadjusted 2011 Inventory
State
Unadjusted
PM10
Unadjusted
PM2 5
Change
in PM10
Change in
PM2 5
PM10
Reduction
PM2 5
Reduction
Alabama
378,873
47,158
-310,412
-38,554
18.1%
18.2%
Arizona
237,361
30,015
-78,365
-9,757
67.0%
67.5%
Arkansas
421,958
58,648
-305,667
-40,779
27.6%
30.5%
California
255,889
38,664
-119,728
-18,039
53.2%
53.3%
Colorado
244,630
40,421
-130,902
-21,038
46.5%
48.0%
Connecticut
29,067
4,393
-26,045
-3,938
10.4%
10.4%
Delaware
11,477
2,046
-8,004
-1,437
30.3%
29.8%
District of Columbia
2,115
337
-1,597
-254
24.5%
24.6%
Florida
292,797
39,636
-181,252
-24,357
38.1%
38.5%
Georgia
733,478
90,041
-593,397
-71,996
19.1%
20.0%
Idaho
432,116
49,294
-295,315
-33,293
31.7%
32.5%
Illinois
763,665
123,680
-478,200
-76,944
37.4%
37.8%
Indiana
603,153
85,151
-440,718
-61,488
26.9%
27.8%
Iowa
590,528
96,070
-342,678
-55,397
42.0%
42.3%
Kansas
748,652
118,902
-353,498
-54,883
52.8%
53.8%
Kentucky
199,744
29,496
-160,887
-23,547
19.5%
20.2%
Louisiana
236,787
35,730
-162,460
-24,039
31.4%
32.7%
Maine
50,547
7,016
-44,059
-6,137
12.8%
12.5%
Maryland
49,225
8,361
-37,212
-6,290
24.4%
24.8%
Massachusetts
205,561
22,444
-179,580
-19,567
12.6%
12.8%
17

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DRAFT
State
Unadjusted
PM10
Unadjusted
PM2 5
Change
in PM10
Change in
PM2 5
PM10
Reduction
PM2 5
Reduction
Michigan
462,324
61,969
-360,229
-48,087
22.1%
22.4%
Minnesota
336,791
64,253
-221,558
-42,026
34.2%
34.6%
Mississippi
956,702
107,965
-781,604
-86,622
18.3%
19.8%
Missouri
1,064,146
130,995
-781,001
-94,632
26.6%
27.8%
Montana
385,541
50,583
-270,346
-34,134
29.9%
32.5%
Nebraska
591,457
85,206
-317,684
-45,301
46.3%
46.8%
Nevada
152,191
19,538
-44,038
-5,350
71.1%
72.6%
New Hampshire
25,540
3,766
-23,951
-3,532
6.2%
6.2%
New Jersey
24,273
5,412
-19,282
-4,270
20.6%
21.1%
New Mexico
924,497
95,871
-352,163
-36,350
61.9%
62.1%
New York
274,114
37,493
-239,314
-32,394
12.7%
13.6%
North Carolina
186,650
33,409
-146,997
-26,199
21.2%
21.6%
North Dakota
354,107
59,113
-224,340
-37,233
36.6%
37.0%
Ohio
414,902
64,609
-324,179
-49,996
21.9%
22.6%
Oklahoma
733,749
87,864
-385,316
-44,579
47.5%
49.3%
Oregon
348,093
40,596
-272,169
-30,935
21.8%
23.8%
Pennsylvania
208,246
30,344
-181,086
-26,319
13.0%
13.3%
Rhode Island
4,765
731
-3,679
-572
22.8%
21.7%
South Carolina
259,350
31,494
-198,329
-24,020
23.5%
23.7%
South Dakota
262,935
44,587
-158,320
-26,623
39.8%
40.3%
Tennessee
139,732
25,357
-108,109
-19,543
22.6%
22.9%
Texas
2,573,682
304,550
1,275,075
-145,799
50.5%
52.1%
Utah
196,554
21,589
-114,478
-12,534
41.8%
41.9%
Vermont
67,690
7,563
-61,971
-6,917
8.4%
8.5%
Virginia
131,797
19,374
-108,734
-15,900
17.5%
17.9%
Washington
174,969
27,999
-101,341
-15,685
42.1%
44.0%
West Virginia
85,956
10,652
-79,843
-9,900
7.1%
7.1%
Wisconsin
239,851
41,669
-166,313
-28,920
30.7%
30.6%
Wyoming
434,090
45,350
-267,536
-27,773
38.4%
38.8%
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Figure 2-1. January PM2.5 afdust emissions: raw 2008 NEI (top), after application of transport fraction
(middle) and final post-meteorological adjusted (bottom)
16.0 299
tons ]
16.0299 r
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2.2.2 Agricultural ammonia sector (ag)
The agricultural NH3 (ag) sector includes livestock and agricultural fertilizer application emissions from
the 201 INEIvl nonpoint inventory. The livestock and fertilizer emissions in this sector are based only
on the SCCs listed in Table 2-9 and Table 2-10. The "ag" sector includes all of the NH3 emissions from
fertilizer from the NEI. However, the "ag" sector does not include all of the livestock ammonia
emissions, as there are also a small amount of NH3 emissions from livestock feedlots in the ptnonipm
inventory (as point sources) in California (175 tons) and Wisconsin (125 tons).
Table 2-9. Livestock SCCs extracted from the NEI to create the ag sector
see
SCC Description*
2805001100
Beef cattle - finishing operations on feedlots (drylots);Confinement
2805001200
Beef cattle - finishing operations on feedlots (drylots) ;Manure handling and storage
2805001300
Beef cattle - finishing operations on feedlots (drylots);Land application of manure
2805002000
Beef cattle production composite;Not Elsewhere Classified
2805003100
Beef cattle - finishing operations on pasture/range Confinement
2805007100
Poultry production - layers with dry manure management systems;Confinement
2805007300
Poultry production - layers with dry manure management systems;Land application of manure
2805008100
Poultry production - layers with wet manure management systems;Confinement
2805008200
Poultry production - layers with wet manure management systems;Manure handling and storage
2805008300
Poultry production - layers with wet manure management systems;Land application of manure
2805009100
Poultry production - broilers;Confinement
2805009200
Poultry production - broilers;Manure handling and storage
2805009300
Poultry production - broilers;Land application of manure
2805010100
Poultry production - turkeys;Confinement
2805010200
Poultry production - turkeys;Manure handling and storage
2805010300
Poultry production - turkeys;Land application of manure
2805018000
Dairy cattle composite;Not Elsewhere Classified
2805019100
Dairy cattle - flush dairy;Confinement
2805019200
Dairy cattle - flush dairy;Manure handling and storage
2805019300
Dairy cattle - flush dairy;Land application of manure
2805020000
Cattle and Calves Waste Emissions;Milk Total
2805021100
Dairy cattle - scrape dairy;Confinement
2805021200
Dairy cattle - scrape dairy;Manure handling and storage
2805021300
Dairy cattle - scrape dairy;Land application of manure
2805022100
Dairy cattle - deep pit dairy;Confinement
2805022200
Dairy cattle - deep pit dairy;Manure handling and storage
2805022300
Dairy cattle - deep pit dairy;Land application of manure
2805023100
Dairy cattle - drylot/pasture dairy;Confinement
2805023200
Dairy cattle - drylot/pasture dairy;Manure handling and storage
2805023300
Dairy cattle - drylot/pasture dairy;Land application of manure
2805025000
Swine production composite;Not Elsewhere Classified (see also 28-05-039, -047, -053)
2805030000
Poultry Waste Emissions;Not Elsewhere Classified (see also 28-05-007, -008, -009)
2805030001
Poultry Waste Emissions;Pullet Chicks and Pullets less than 13 weeks old
2805030002
Poultry Waste Emissions;Pullets 13 weeks old and older but less than 20 weeks old
2805030003
Poultry Waste Emissions;Layers
2805030004
Poultry Waste Emissions;Broilers
2805030007
Poultry Waste Emissions;Ducks
2805030008
Poultry Waste Emissions;Geese
2805030009
Poultry Waste Emissions;Turkeys
2805035000
Horses and Ponies Waste Emissions;Not Elsewhere Classified
2805039100
Swine production - operations with lagoons (unspecified animal age);Confinement
2805039200
Swine production - operations with lagoons (unspecified animal age);Manure handling and storage
2805039300
Swine production - operations with lagoons (unspecified animal age);Land application of manure
20

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SCC
SCC Description*
2805040000
Sheep and Lambs Waste Einissions;Total
2805045000
Goats Waste Einissions;Not Elsewhere Classified
2805045002
Goats Waste Einissions;Angora Goats
2805045003
Goats Waste Einissions;Milk Goats
2805047100
Swine production - deep-pit house operations (unspecified animal age) Confinement
2805047300
Swine production - deep-pit house operations (unspecified animal age);Land application of manure
2805053100
Swine production - outdoor operations (unspecified animal age);Confinement
* All SCC Descriptions begin "Miscellaneous Area Sources;Agriculture Production - Livestock"
Table 2-10. Fertilizer SCCs extracted from the NEI for inclusion in the "ag" sector
SCC
SCC Description*
2801700001
Anhydrous Ammonia
2801700002
Aqueous Ammonia
2801700003
Nitrogen Solutions
2801700004
Urea
2801700005
Ammonium Nitrate
2801700006
Ammonium Sulfate
2801700007
Ammonium Thiosulfate
2801700008
Other Straight Nitrate
2801700009
Ammonium Phosphates
2801700010
N-P-K (multi-grade nutrient fertilizers)
2801700011
Calcium Ammonium Nitrate
2801700012
Potassium Nitrate
2801700013
Diammonium Phosphate
2801700014
Monoammonium Phosphate
2801700015
Liquid Ammonium Polyphosphate
2801700099
Miscellaneous Fertilizers
* All descriptions include "Miscellaneous Area Sources;
Agriculture Production - Crops; Fertilizer Application" as
the beginning of the description.
2.2.3	Nonpoint source oil and gas sector (np_oilgas)
The nonpoint oil and gas (npoilgas) sector contains onshore and offshore oil and gas emissions. EPA
estimated emissions for all counties with 2011 oil and gas activity data with the Oil and Gas Tool, and
many S/L/T agencies also submitted nonpoint oil and gas data. The types of sources covered include
drill rigs, workover rigs, artificial lift, hydraulic fracturing engines, pneumatic pumps and other devices,
storage tanks, flares, truck loading, compressor engines, and dehydrators. For more information on the
development of the oil and gas emissions in the 201 INEIvl, see Section 3.21 of the 201 INEIvl TSD.
A complete list of SCCs for the np oilgas modeling platform sector is provided in Appendix A. See the
pt oilgas sector (section 2.1.3) for more information on point source oil and gas sources.
2.2.4	Residential wood combustion sector (rwc)
The residential wood combustion (rwc) sector includes residential wood burning devices such as
fireplaces, fireplaces with inserts (inserts), free standing woodstoves, pellet stoves, outdoor hydronic
heaters (also known as outdoor wood boilers), indoor furnaces, and outdoor burning in firepots and
chimineas. Free standing woodstoves and inserts are further differentiated into three categories:
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conventional (not EPA certified); EPA certified, catalytic; and EPA certified, noncatalytic. Generally
speaking, the conventional units were constructed prior to 1988. Units constructed after 1988 had to
meet EPA emission standards and they are either catalytic or non-catalytic. For more information on the
development of the residential wood combustion emissions, see Section 3.14 of the 201 INEIvl TSD.
The SCCs in the rwc sector are shown in Table 2-11.
Table 2-11. SCCs in the Residential Wood Combustion Sector (rwc)*
see
SCC Description
2104008100
SSFC;Residential;Wood;Fireplace: general
2104008210
SSFC;Residential;Wood;Woodstove: fireplace inserts; non-EPA certified
2104008220
SSFC;Residential;Wood;Woodstove: fireplace inserts; EPA certified; non-catalytic
2104008230
SSFC;Residential;Wood;Woodstove: fireplace inserts; EPA certified; catalytic
2104008300
SSFC;Residential;Wood;Woodstove: freestanding, general
2104008310
SSFC;Residential;Wood;Woodstove: freestanding, non-EPA certified
2104008320
SSFC;Residential;Wood;Woodstove: freestanding, EPA certified, non-catalytic
2104008330
SSFC;Residential;Wood;Woodstove: freestanding, EPA certified, catalytic
2104008400
SSFC;Residential;Wood;Woodstove: pellet-fired, general (freestanding or FP insert)
2104008510
SSFC;Residential;Wood;Furnace: Indoor, cordwood-fired, non-EPA certified
2104008610
SSFC;Residential;Wood;Hydronic heater: outdoor
2104008700
SSFC;Residential;Wood;Outdoor wood burning device, NEC (fire-pits, chimineas, etc)
2104009000
SSFC;Residential;Firelog;Total: All Combustor Types
* SSFC=Stationary Source Fuel Combustion
2.2.5 Other nonpoint sources sector (nonpt)
Stationary nonpoint sources that were not subdivided into the afdust, ag, np oilgas, or rwc sectors were
assigned to the "nonpt" sector. Locomotives and CMV mobile sources from the 201 INEIvl nonpoint
inventory are described in Section 2.4.1. There are too many SCCs to list all of them individually, but
the types of sources in the nonpt sector include:
•	stationary source fuel combustion, including industrial, commercial, and residential;
•	chemical manufacturing;
•	industrial processes such as commercial cooking, metal production, mineral processes, petroleum
refining, wood products, fabricated metals, and refrigeration;
•	solvent utilization for surface coatings such as architectural coatings, auto refinishing, traffic
marking, textile production, furniture finishing, and coating of paper, plastic, metal, appliances,
and motor vehicles;
•	solvent utilization for degreasing of furniture, metals, auto repair, electronics, and
manufacturing;
•	solvent utilization for dry cleaning, graphic arts, plastics, industrial processes, personal care
products, household products, adhesives and sealants;
•	solvent utilization for asphalt application and roofing, and pesticide application;
•	storage and transport of petroleum for uses such as portable gas cans, bulk terminals, gasoline
service stations, aviation, and marine vessels;
•	storage and transport of chemicals;
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•	waste disposal, treatment, and recovery via incineration, open burning, landfills, and composting;
•	agricultural burning and orchard heating;
•	miscellaneous area sources such as cremation, hospitals, lamp breakage, and automotive repair
shops.
Most sources in this sector have annual emissions that are temporally allocated to hourly values using
temporal profiles. The annual agricultural burning estimates are treated as monthly values. The annual
values in the 201 INEIvl were split into monthly emissions by aggregating the data up to monthly values
from daily estimates of emissions.
2.3 2011 onroad mobile sources (onroad, onroad_rfl)
Onroad mobile sources include emissions from motorized vehicles that are normally operated on public
roadways. These include passenger cars, motorcycles, minivans, sport-utility vehicles, light-duty trucks,
heavy-duty trucks, and buses. The sources are further divided between diesel and gasoline vehicles.
The sector characterizes emissions from off-network processes (e.g. starts, hot soak, and extended idle)
as well as from on-network processes (i.e., from vehicles moving along the roads). For the 2011
platform, as indicated in Table 2-1, the 2011 onroad emissions are separated into two sectors: (1)
"onroad" and (2) "onroadrfl". The onroad and onroadrfl sectors are processed separately to allow for
different spatial allocation to be applied to onroad refueling, which is allocated using a gas station
surrogate, versus onroad vehicles, which are allocated using surrogates based on roads and population.
Except for California and Texas, all onroad and onroad refueling emissions are generated using the
SMOKE-MOVES emissions modeling framework that leverages MOVES generated outputs and hourly
meteorology. All tribal data from the mobile sectors have been dropped because the emissions are
small, the emissions could be double-counted with state-provided onroad emissions, all tribal data was
developed using the older model MOBILE6, and because spatial surrogate data is not currently
available.
2.3.1 Onroad non-refueling (onroad)
For the continental U.S., EPA used a modeling framework that took into account the temperature
sensitivity of the on-road emissions. Specifically, EPA used MOVES inputs for representative counties,
vehicle miles traveled (VMT) and vehicle population (VPOP) data for all counties, along with tools that
integrated the MOVES model with SMOKE. In this way, it was possible to take advantage of the
gridded hourly temperature information available from meteorology modeling used for air quality
modeling. The "SMOKE-MOVES" integration tool was developed by EPA in 2010 and is in use by
states and regional planning organizations for regional air quality modeling of onroad mobile sources.
SMOKE-MOVES requires that emission rate "lookup" tables be generated by MOVES which
differentiate emissions by process (i.e., running, start, vapor venting, etc.), vehicle type, road type,
temperature, speed, hour of day, etc. To generate the MOVES emission rates that could be applied
across the U.S., EPA used an automated process to run MOVES to produce emission factors by
temperature and speed for a series of "representative counties," to which every other county was
mapped. Using the MOVES emission rates, SMOKE selects appropriate emissions rates for each
county, hourly temperature, SCC, and speed bin and multiplied the emission rate by activity (VMT
(vehicle miles travelled) or VPOP (vehicle population)) to produce emissions. These calculations were
done for every county and grid cell, in the continental U.S. for each hour of the year.
Using SMOKE-MOVES for creating the model-ready emissions requires numerous steps:
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1)	Determine which counties will be used to represent other counties in the MOVES runs
2)	Determine which months will be used to represent other month's fuel characteristics
3)	Create MOVES inputs needed only by MOVES. MOVES requires county-specific information
on vehicle populations, age distributions, and inspection-maintenance programs for each of the
representative counties.
4)	Create inputs needed both by MOVES and by SMOKE, including a list of temperatures and
activity data
5)	Run MOVES to create emission factor tables
6)	Run SMOKE to apply the emission factors to activity data (VMT and VPOP) to calculate
emissions
7)	Aggregate the results to the county-SCC level for summaries and quality assurance
The onroad emissions inputs are similar to the emissions in the onroad data category of the 201 INEIvl,
described in more detail in Section 4.6 of the 201 INEIvl TSD. Specifically the platform and
201 INEIvl have identical:
•	MOVES County databases (CDBs)
•	Fuels
•	Representative counties
•	Fuel months
•	Meteorology
•	Activity data (VMT, VPOP, speed)
•	Extended idle adjustments
Despite the commonalities, there are some key differences between the two onroad emission inventories:
•	The 201 INEIvl used MOVES2010b to create the emission factor (EF) tables, while the 2011
platform used the MOVESTier3NPRM (specifically, model "Moves20110414a" and the default
database "movestier3db20110512") for most EFs. The exceptions are that refueling (described
in the next section) and extended idle EFs were generated using the MOVES2010b model for
both inventories because MOVESTier3NPRM did not create these emission rates for these
modes.
•	The 2011 platform used a different post-processor to create EFs for SMOKE because the
pollutants needed for speciation and running CMAQ are different than what is needed for the
NEI. For example, the NEI needs a much larger set of HAPs and the modeling platform requires
emissions for the components of PM2.5.
•	The treatment of Texas and California emissions differs between the two inventories (see below
for more details).
•	The list of emission modes differ between the two inventories. Both SMOKE-MOVES runs
were generated at the same level of detail, but the NEI emissions were aggregated into 4 all-
inclusive modes: exhaust (including extended idle), evaporative (including permeation), brake
wear, and tire wear. The list of modes and the corresponding MOVES processes mapped to
them are listed in Table 2-12.
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Table 2-12. Onroad emission modes
Mode
Description
MOVES process IDs
EXH
Exhaust, including running and starts, excluding extended idle
1 ;2; 15; 16
EXT
Extended idle exhaust from long-haul trucks
17;90
EVP
Evaporative emissions, including vapor venting and fuel leaks, excluding
permeation
12; 13
EPM
Evaporative permeation
11
RFL
Refueling
18; 19
BRK
Brake wear
9
TIR
Tire wear
10
For more detailed information on methods used to develop the onroad emissions and input data sets and
on running SMOKE-MOVES, see the 201 INEIvl TSD.
The California and Texas onroad emissions were created through a hybrid approach of combining state-
supplied annual emissions (from the 201 INEIvl) with EPA developed SMOKE-MOVES runs. Through
this approach, the platform was able to reflect California's unique rules and Texas' detailed modeling,
while leveraging the more detailed SCCs and the highly resolved spatial patterns, temporal patterns, and
speciation from SMOKE-MOVES. The basic steps involved in temporally allocating California's and
Texas' onroad emissions based on SMOKE-MOVES results were:
1)	Run CA/TX using EPA inputs through SMOKE-MOVES to produce hourly 2011 emissions
hereafter known as "EPA estimates". These EPA estimates for CA/TX are run in a separate
sector called "onroadcatx".
2)	Calculate ratios between state-supplied emissions and EPA estimates4. For Texas, these ratios
were calculated for each county/SCC7 (fuel and vehicle type)/pollutant combination. For
California, these were calculated for each county/SCC3 (fuel type)/pollutant combination. These
were not calculated at a greater resolution because California's emissions did not provide data
for all vehicle types.
3)	Create an adjustment factor file (CFPRO) that includes EPA-to-state estimate ratios. For
extended idle adjustments, each specific state ratio (county/SCC Group (7 or 3)/pollutant) was
multiplied by the extended idle adjustment factor (see the 201 INEIvl TSD for details).
4)	Rerun CA/TX through SMOKE-MOVES using EPA inputs and the new adjustment factor file.
Through this process, adjusted model-ready files were created that sum to California's and Texas'
annual totals, but have the temporal and spatial patterns reflecting the highly resolved meteorology and
SMOKE-MOVES. After adjusting the emissions, this sector is called "onroadcatxadj". Note that in
emission summaries, the emissions from the "onroad" and "onroad catx adj" sectors are summed and
designated as the emissions for the onroad sector.
4 These ratios were created for all matching pollutants. These ratios were duplicated for all appropriate modeling species.
For example, EPA used the NOx ratio for NO, NO2, HONO and used the PM2 5 ratio for PEC, PNO3, POC, PSO4, and
PMFINE (For more details on NOx and PM speciation, see Sections 3.2.3 and 3.2.2). For VOC model-species, if there was
an exact match (e.g., BENZENE), EPA used that HAP pollutant ratio. For other VOC-based model-species that didn't exist
in the NEI inventory, EPA used VOC ratios.
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2.3.2 Onroad refueling (onroad_rfl)
Onroad refueling is modeled very similarly to other onroad emissions. As noted in the onroad section
(Section 2.3.2), the refueling emissions were generated via MOVES2010b. The onroadrfl emissions
are spatially allocated to gas station locations (see Section 3.4.1). . Because the refueling emission
factors use the same SCCs as the other onroad models, refueling was run in a separate sector from the
other onroad mobile sources to allow for the different spatial allocation. To facilitate this, the refueling
EFs were separated from the other emission factors into rate-per-distance (RPD) refueling and rate-per-
vehicle (RPV) refueling tables5. SMOKE-MOVES was run using these EF tables as inputs, and
spatially allocated using a gas stations spatial surrogate. Lastly, the SMOKE program Mrggrid combined
RPD refueling and RPV refueling into a single onroad rfl model ready output for final processing with
the other sectors prior to use in CMAQ. EPA SMOKE-MOVES generated emissions for onroad
refueling were used without any adjustments for all states, including California and Texas. These
emissions were used instead of state submissions to provide a consistent approach nationwide and also
because most states did not submit refueling emissions for diesel fuel. Since the 201 INEIvl includes the
state-submitted emissions, the platform and the NEI refueling emissions in the nonpoint category are
consistent for states that did not submit emissions, but are inconsistent for states that submitted refueling
emissions.
2.4 2011 nonroad mobile sources (c1c2rail, c3marine, nonroad)
The nonroad mobile source emission modeling sectors consist of nonroad equipment emissions
(nonroad) and locomotive and commercial marine vessel (CMV) emissions divided into two nonroad
sectors: "clc2rail" and "c3marine".
2.4.1 Class 1/Class 2 Commercial Marine Vessels and Locomotives and
(c1c2rail)
The clc2rail sector contains locomotive and smaller CMV sources, except for railway maintenance
locomotives and C3 CMV sources outside of the Midwest states. The "clc2" portion of this sector name
refers to the Class 1 and 2 CMV emissions, not the railway emissions. Railway maintenance emissions
are included in the nonroad sector. The C3 CMV emissions are in the c3marine sector. All emissions in
this sector are annual and at the county-SCC resolution.
The starting point for the clc2rail sector is the 201 INEIvl nonpoint inventory for all but specific
Midwest states, which are instead derived from the Great Lakes 2010 CMV inventory. As discussed in
Table 2-1 and Table 2-2, the modeling platform emissions for the clc2rail SCCs were extracted from the
NEI nonpoint inventory. For more information on CMV sources in the NEI, see Section 4.3 of the
201 INEIvl TSD. For more information on locomotives, see Section 4.4 of the 201 INEIvl TSD. Table
2-13 lists the NEI SCCs included in the clc2rail sector of the modeling platform.
Table 2-13. 201 INEIvl SCCs extracted for the starting point in clc2rail development
see
Description: Mobile Sources prefix for all
2280002100
Marine Vessels; Commercial; Diesel; Port
2280002200
Marine Vessels; Commercial; Diesel; Underway
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)
5 The Moves2smk post-processing script lias command line arguments that will either consolidate or split out the refueling
EF.
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2285002009
Railroad Equipment; Diesel; Line Haul Locomotives: Commuter Lines
2285002010
Railroad Equipment; Diesel; Yard Locomotives
Some differences exist between the 201 INEIvl and the modeling platform for this sector due to the
availability of alternative data. The differences follow.
Replaced California C1/C2 CMV and rail data with CARB data
As discussed in Section 2.4, the CARB provided year 2011 and corresponding future year emissions for
all mobile sources, including C1/C2 CMV and locomotives. These emissions were documented in a
staff report.
The C1/C2 CMV emissions obtained from the CARB nonroad mobile dataset include the 2011
regulations to reduce emissions from diesel engines on commercial harbor craft operated within
California waters and 24 nautical miles of the California baseline. These emissions were developed
using Version 1 of the California Emissions Projection Analysis Model (CEP AM) that supports various
California off-road regulations. The locomotive emissions were obtained from the CARB trains dataset
"ARMJ_RF#2002_ANNUAL_TRAINS.txt". Documentation of the CARB offroad mobile
methodology, including clc2rail sector data. The TOG in the CARB inventory was mapped to VOC by
dividing the inventory TOG by the available VOC-to-TOG speciation factor according to the SCC of the
source. See Section 3.2.1.3 for more details on clc2rail speciation. The RPO and CARB inventories
did not include HAPs; therefore, all non-NEI source emissions in the clc2rail sector were processed
using VOC speciation only, rather than use the inventory BAFM.
Replaced all CMV in the Midwest RPO
Year-2010 emissions were received from the Lake Michigan Air Directors Consortium for tug boats,
Great Lakes vessels ("Lakers") and inland waterways for states within the Midwest RPO and Minnesota,
hereafter simply referred to as "MWRPO" (LADCO). The states in the MWRPO are: Illinois, Indiana,
Michigan, Minnesota, Ohio and Wisconsin. These MWRPO CMV emissions include coverage for
bordering states/counties along the inland waterways such as the Mississippi and Ohio rivers in Iowa,
Missouri, Kentucky, West Virginia, Pennsylvania and New York. The LADCO 2010 inventory was
used to replace EPA-estimated CMV emissions in the MWRPO states, but was not used to replace the
201 INEIvl emissions in the bordering non-MWRPO states.
Some modifications to the MWRPO CMV data were made prior to SMOKE processing:
•	Emissions provided at the level of NEI Shape IDs were aggregated to county4evel.
•	The 201 INEIvl was used to determine which counties had ports; for those counties that had
ports, 90% of emissions in the MWPRO inventory were assigned as underway
(SCC=2280002200) and 10% were assigned as port emissions (SCC=2280002100).
•	Emissions were converted to short tons and PM2.5 was added by assuming it is equal to 92% of
PM10 at the suggestion of the MWRPO.
•	Tugs were assigned a unique SCC (2280002021) to allow for unique spatial allocation (see
Section 3.4.1).
•	Tugs were assigned from MWRPO total to counties based on 201 INEIvl county-level activity
information for tug vessels.
Because the Great Lakes vessels include all CMV activity on the Great Lakes, EPA-estimated C3 CMV
(c3marine) sector emissions (discussed in the following section) in the MWRPO states were removed to
avoid potential double-counting of C3 CMV with the LADCO inventory in the MWRPO states.
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2.4.2 Class 3 commercial marine vessels (c3marine)
The U.S. C3 CMV inventory was developed based on a 4-km resolution ASCII raster format dataset
used since the Emissions Control Area-International Marine Organization (ECA-IMO) project began in
2005, then known as the Sulfur Emissions Control Area (SECA). The ECA-IMO data are used instead
of the 201 INEIvl data for the modeling platform because accompanying estimates of emission
projections for future years are available. In addition, the inventory preserves shipping lanes in federal
waters while these are not stored within the NEI data. Keeping the sources in this sector separate from
smaller CMV sources allows for the emissions to be elevated above the surface layer within the AQ
model. The ECA-IMO data are used for all states with C3 CMV emissions. For the MWPRO states, the
ECA-IMO C3 CMV emissions in the Great Lakes are assumed to be misclassified as C3 vessels for
which emissions are included in the clc2rail sector as part of the LADCO inventory, therefore the ECA-
IMO emissions are not included in the c3marine sector.
The development of this ECA-IMO-based C3 CMV inventory is discussed below; however, all non-U. S.
emissions (Canadian emissions and emissions farther offshore than U.S. waters) are processed in the
"othpt" sector, discussed later in Section 2.5.1. This splitting of the C3 CMV emissions from the farther
offshore emissions allows for easier summaries of U.S.-only and state or county total emissions.
The ECA-IMO emissions consist of large marine diesel engines (at or above 30 liters/cylinder) that until
recently, were allowed to meet relatively modest emission requirements, and often burn residual fuel.
The emissions in this sector are comprised of primarily foreign-flagged ocean-going vessels, referred to
as C3 CMV ships. The c3marine inventory includes these ships in several intra-port modes (i.e.,
cruising, hoteling, reduced speed zone, maneuvering, and idling) and an underway mode, and includes
near-port auxiliary engine emissions. An overview of the C3 ECA Proposal to the International
Maritime Organization (EPA-420-F-10-041, August 2010) project and future-year goals for reduction of
NOx, SO2, and PM C3 emissions can be found at Vehicles and 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.
The ECA-IMO emissions data were converted to SMOKE point-source ORL input format. As described
in the paper, the ASCII raster dataset was converted to latitude-longitude, mapped to state/county FIPS
codes that extended up to 200 nautical miles (nm) from the coast, assigned stack parameters, and
monthly ASCII raster dataset emissions were used to create monthly temporal profiles. Counties were
assigned as extending up to 200nm from the coast because this was the distance to the edge of the U.S.
Exclusive Economic Zone (EEZ), a distance that defines the outer limits of ECA-IMO controls for these
vessels.
The base year ECA inventory is 2002 and consists of these CAPs: PM10, PM2.5, CO, CO2, NH3, NOx,
SOx (assumed to be SO2), and hydrocarbons (assumed to be VOC). EPA developed regional growth
(activity-based) factors that were applied to create the 2011 inventory from the 2002 data. These growth
factors are provided in The emissions were converted to SMOKE point source inventory format,
allowing for the emissions to be allocated to modeling layers above the surface layer. All non-US, non-
EEZ emissions (i.e., in waters considered outside of the 200 nm EEZ, and hence out of the U.S. and
Canadian ECA-IMO controllable domain) were simply assigned a dummy state/county FIPS
code=98001, and were projected to year 2011 using the "Outside ECA" factors in Error! Not a valid
bookmark self-reference.. The SMOKE-ready data have been cropped from the original ECA-IMO
28

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entire northwestern quarter of the globe to cover only the large continental U.S. 36-km "36US1" air
quality model domain, the largest domain used by EPA in recent years.
. The geographic regions listed in the table are shown in Figure 2-2. The East Coast and Gulf Coast
regions were divided along a line roughly through Key Largo (longitude 80° 26' West).
The Canadian near-shore emissions were assigned to province-level FIPS codes and paired those to
region classifications for British Columbia (North Pacific), Ontario (Great Lakes) and Nova Scotia (East
Coast).
Table 2-14. Growth factor adjustment factors to project the 2002 ECA-IMO inventory to 2011
Region
EEZ
FIPS
NOx
PMio
PM2.5
voc
(HC)
CO
SO2
East Coast (EC)
85004
1.301
0.500
0.496
1.501
1.501
0.536
Gulf Coast (GC)
85003
1.114
0.428
0.423
1.288
1.288
0.461
North Pacific (NP)
85001
1.183
0.467
0.458
1.353
1.353
0.524
South Pacific (SP)
85002
1.367
0.525
0.521
1.565
1.562
0.611
Great Lakes (GL)
n/a
1.072
0.394
0.390
1.177
1.176
0.415
Outside ECA
98001
1.341
1.457
1.457
1.457
1.457
1.457
* Technically, these are not really "FIPS" state-county codes, but are treated as such in the
inventory and emissions processing.
The assignment of U.S. state/county FIPS codes was restricted to state-federal water boundaries data
from the Mineral Management Service (MMS) that extend approximately 3 to 10 nautical miles (nm) off
shore. Emissions outside the 3 to 10 mile MMS boundary, but within the approximately 200 nm EEZ
boundaries in Figure 2-2, were projected to year 2011 using the same regional adjustment factors as the
U.S. emissions; however, the state/county FIPS codes were assigned as "EEZ" codes and these
emissions processed in the "othpt" sector (see Section 2.5.1). Note that state boundaries in the Great
Lakes are an exception, extending through the middle of each lake such that all emissions in the Great
Lakes are assigned to a U.S. county or Ontario. This holds true for MWRPO states and other states such
as Pennsylvania and New York. The classification of emissions to U.S. and Canadian FIPS codes is
needed to avoid double-counting of C3 CMV U.S. emissions in the Great Lakes because, as discussed in
the previous section, all CMV emissions in the Midwest RPO are processed in the "clc2rail" sector.
29

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Figure 2-2. Illustration of regional modeling domains in ECA-IMQ study
SP


'i \ r?
v v J J
.. -
The emissions were converted to SMOKE point source inventory format, allowing for the emissions to
be allocated to modeling layers above the surface layer. All non-US, non-EEZ emissions (i.e., in waters
considered outside of the 200 nm EEZ, and hence out of the U.S. and Canadian ECA-IMO controllable
domain) were simply assigned a dummy state/county FIPS code=98001, and were projected to year
2011 using the "Outside ECA" factors in Error! Not a valid bookmark self-reference.. The SMOKE-
ready data have been cropped from the original ECA-IMO entire northwestern quarter of the globe to
cover only the large continental U.S. 36-km "36US1" air quality model domain, the largest domain used
by EPA in recent years6.
The original ECA-IMO inventory did not delineate between ports and underway emissions (or other C3
modes such as hoteling, maneuvering, reduced-speed zone, and idling). However, a U.S. ports spatial
surrogate dataset was used to assign the ECA-IMO emissions to ports and underway SCCs 2280003100
and 2280003200, respectively. This had no effect on temporal allocation or speciation because all C3
CMV emissions, unclassified/total, port and underway, share the same temporal and speciation profiles.
See Section 3.2.1.3 for more details on c3marine speciation and Section 3.3.6 for details on temporal
allocation.
2.4.3 Nonroad mobile equipment sources: (nonroad)
The nonroad equipment emi ssions are equivalent to the emissions in the nonroad data category of the
201 INEIvl, with the exception that the modeling platform emissions also include monthly totals. All
nonroad emissions are compiled at the county/SCC level. NMIM (EPA, 2005) creates the nonroad
emissions on a month-specific basis that accounts for temperature, fuel types, and other variables that
vary by month. The nonroad sector includes monthly exhaust, evaporative and refueling emissions from
nonroad engines (not including commercial marine, aircraft, and locomotives) that EPA derived from
6 The extent of the "36US1" domain is similar to the full geographic region shown in Figure 3-1. Note that this domain is not
specifically used in this 2011 platform, although spatial surrogates that can be used with it are provided.
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NMIM for all states except California and Texas. Additional details on the development of the
201 INEIvl nonroad emissions are available in Section 4.5 the 201 INEIvl TSD.
California year 2011 nonroad emissions were submitted to the 201 INEIvl and are also documented in a
staff report (ARB, 2010a). The nonroad sector emissions in California were developed using a modular
approach and include all rulemakings and updates in place by December 2010. These emissions were
developed using Version 1 of the CEP AM which supports various California off-road regulations such
as in-use diesel retrofits (ARB, 2007), Diesel Risk-Reduction Plan (ARB, 2000) and 2007 State
Implementation Plans (SIPS) for the South Coast and San Joaquin Valley air basins (ARB, 2010b).
The CARB-supplied 201 INEIvl nonroad annual inventory emissions values were converted to monthly
values by using the aforementioned EPA NMIM monthly inventories to compute monthly ratios by
county, SCC7 (fuel, engine type, and equipment type group), mode, and pollutant. SCC7 ratios were
used because the SCCs in the CARB inventory did not align with many of the SCCs in EPA NMIM
inventory. By aggregating up to SCC7, the two inventories had a more consistent coverage of sources.
Some VOC emissions were added to California to account for situations when VOC HAP emissions
were included in the inventory, but there were no VOC emissions. These additional VOC emissions
were computed by summing benzene, acetaldehyde, and formaldehyde for the specific sources.
Texas year 2011 nonroad emissions were also submitted to the NEI. The 201 INEIvl nonroad annual
inventory emissions values were converted to monthly values by using EPA's NMIM monthly
inventories to compute monthly ratios by county, SCC7, mode, and poll7.
2.5 "Other Emissions": Offshore Class 3 commercial marine vessels and
drilling platforms and non-U.S. sources
The emissions from Canada, Mexico, and non-U.S. offshore Class 3 Commercial Marine Vessels (C3
CMV) and drilling platforms are included as part of three emissions modeling sectors: othpt, othar, and
othon.
The "oth" refers to the fact that these emissions are usually "other" than those in the U.S. state-county
geographic FIPS, and the third and fourth characters provide the SMOKE source types: "pt" for point,
"ar" for "area and nonroad mobile", and "on" for onroad mobile.
For Canada, year-2006 Canadian emissions were the starting point with the addition of several
modifications to these inventories. The SCCs in these inventories were changed to the generic
39999999 and the industrial code information was removed to preserve confidentiality. The Canadian
point sources are split into three inventory files:
•	ptinv_canada_point_2006_orl_13aug2013_v3_orl.txt: contains point sources for all pollutants
except VOC;
•	ptinv_canada_point_cb5_2006_orl_13aug2013_vl_orl.txt: contains VOC emissions split into
CB05 species;
•	ptinv_canada_point_uog_2006_orl_02mar2009_v0_orl.txt: contains oil and gas-related sources.
7 If there was no match at county/SCC7/mode/poll, the allocation would fall back to state/SCC7/mode/poll. If that did not
find a match, then state/SCC7 was used. For a few situations, that would also fail to match and the monthly emissions were
allocated with a similar SCC7.
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For Mexico, emissions for year 2012 are projections of their 1999 inventory originally developed by
Eastern Research Group Inc., (ERG, 2006; ERG, 2009; Wolf, 2009) as part of a partnership between
Mexico's Secretariat of the Environment and Natural Resources (Secretaria de Medio Ambiente y
Recursos Naturales-SEMARNAT) and National Institute of Ecology (Instituto Nacional de Ecologia-
INE), the U.S. EPA, the Western Governors' Association (WGA), and the North American Commission
for Environmental Cooperation (CEC). This inventory includes emissions from all states in Mexico. A
background on the development of year-2012 Mexico emissions from the 1999 inventory is available at:
WRAP.
2.5.1	Point sources from offshore C3 CMV and drilling platforms and Canada and
Mexico (othpt)
As discussed in Section 2.4.2, the ECA-IMO-based C3 CMV emissions for non-U.S. states are
processed in the othpt sector. These C3 CMV emissions include those assigned to Canada, those
assigned to the Exclusive Economic Zone (EEZ, defined as those emissions just beyond U.S. waters
approximately 3-10 miles offshore, extending to about 200 nautical miles from the U.S. coastline), and
all other offshore emissions -far offshore and non-U.S. These emissions are included in the othpt sector
for simplicity of creating U.S.-only emissions summaries. Otherwise, these emissions are developed in
the same way as the U.S. C3 CMV emissions in the c3marine sector.
For Canadian point sources, other than some minor formatting changes, the Canada-provided year-2006
emissions were modified as follows:
i.	Speciated VOC emissions from the Acid Deposition and Oxidant Model (ADOM) chemical
mechanism were not included because EPA modeling uses speciated emissions from the CB5
chemical mechanism, which Canada also provided.
ii.	Excessively high CO emissions were removed from Babine Forest Products Ltd (British
Columbia SMOKE plantid='5188') in the point inventory. This change was made at EPA's
discretion because the value of the emissions was impossibly large.
iii.	The county part of the state/county FIPS code field in the SMOKE inputs were modified in the
point inventory from "000" to "001" to enable matching to existing temporal profiles.
iv.	An update to the 2007 platform version was the removal of three units that closed in 2010: Grand
Lake Generating Station in New Brunswick (PLANTID=' 1708', POINTID=' 130011'),
Raffinerie de Montreal-Est in Quebec (PLANTID='3127', POINITD='53202982') and Kidd
Metallurgical Site in Ontario (PLANTID='2815', POINTID='ON500004').
Mexico point-format year-2012 inventories projected from the 1999 Mexico NEI were used essentially
as-is with only minor formatting changes. The othpt sector also includes point source offshore oil and
gas drilling platforms that are beyond U.S. state-county boundaries in the Gulf of Mexico. For these
offshore emissions, the 2008 NEI version 3 point source inventory data were used because the 2011 data
were not yet available. This is consistent with the 201 INEIvl. Updated offshore oil and gas drilling
emissions are expected to be incorporated into version 2 of the 2011 NEI. The 2008-based offshore
emission sources were provided by the Mineral Management Services (MMS).
2.5.2	Area and nonroad mobile sources from Canada and Mexico (othar)
For Canada, year-2006 emissions provided by Canada and unchanged from EPA 2007 platform were
used. Inventory files were provided for area fugitive dust, agricultural, commercial marine, railroad,
nonroad, aircraft, and other area sources. The following adjustments were made to the original files:
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i.	Wildfires or prescribed burning were not included because Canada does not include these
inventory data in their modeling. Note that SMARTFIRE 2 is used for U.S. sources only.
ii.	In-flight aircraft emissions were not included because these sources are not included in the U.S.
modeling.
iii.	A 75% reduction ("transport fraction") was applied to PM for the road dust, agricultural, and
construction emissions in the Canadian "afdust" inventory. This approach is more simplistic
than the county-specific approach used for the U.S., but a comparable approach was not
available for Canada.
iv.	Wind erosion (SCC=2730100000) and cigarette smoke (SCC=2810060000) emissions were
removed from the nonpoint (nonpt) inventory because these emissions are not modeled in the
U.S. inventory.
v.	Quebec PM2.5 emissions (2,000 tons/yr) were removed for one SCC (2305070000) for Industrial
Processes, Mineral Processes, Gypsum, Plaster Products due to corrupt fields after conversion to
SMOKE input format.
vi.	C3 CMV SCCs (22800030X0) records were removed because, as discussed in Section 2.5.1,
these emissions are included in the (ECA-IMO derived) othpt sector, which covers not only
emissions close to Canada but also emissions far at sea. Canada was involved in the inventory
development of the ECA-IMO C3 CMV inventory.
For Mexico nonpoint-format year-2012 inventories, the only significant modification was the removal of
domestic ammonia (SCC=5555555555) (ERG, 2009; Wolf, 2009).
2.5.3 Onroad mobile sources from Canada and Mexico (othon)
Both year-2006 Canada and year-2012 Mexico inventories (ERG, 2009; Wolf, 2009) were converted
from their original SMOKE One-Record per Line (ORL) and Inventory Data Analyzer (IDA) formats,
respectively, into the SMOKE Flat File 2010 (FF10) inventory format. Otherwise, these inventories
were used as-is. The emission values in the Canada-provided Canadian inventories were unchanged
from the 2007 platform.
2.6 Fires (ptfire)
Wildfire and prescribed burning emissions are contained in the ptfire sector. The ptfire sector has
emissions provided at geographic coordinates (point locations) and has daily emissions values. The
ptfire sector excludes agricultural burning and other open burning sources that are included in the nonpt
sector. Emissions are day-specific and include satellite-derived latitude/longitude of the fire's origin and
other parameters associated with the emissions such as acres burned and fuel load, which allow
estimation of plume rise. Emissions for the SCCs listed in Table 2-15 are treated as point sources and
are consistent with the fires stored in the Events data category of the 201 INEIvl. For more information
on the development of the 201 INEIvl fire inventory, see Section 5.1 of the 201 INEIvl TSD.
Table 2-15. 2011 Platform SCCs representing emissions in the ptfire modeling sector
SCC
SCC Description*
2810001000
Other Combustion; Forest Wildfires; Total
2810015000
Other Combustion; Prescribed Burning for Forest Management; Total
2811015000
Other Combustion-as Event; Prescribed Burning for Forest Management; Total
2811090000
Other Combustion-as Event; Prescribed Forest Burning ;Unspecified
* The first tier level of the SCC Description is "Miscellaneous Area Sources"
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The point source day-specific emission estimates for 2011 fires rely on SMARTFIRE 2 (Sullivan, et al.,
2008), which uses the National Oceanic and Atmospheric Administration's (NOAA's) Hazard Mapping
System (HMS) fire location information as input. Additional inputs include the CONSUMEv3.0
software application (Joint Fire Science Program, 2009) and the Fuel Characteristic Classification
System (FCCS) fuel-loading database to estimate fire emissions from wildfires and prescribed burns on
a daily basis. The method involves the reconciliation of ICS-209 reports (Incident Status Summary
Reports) with satellite-based fire detections to determine spatial and temporal information about the
fires. A functional diagram of the SMARTFIRE 2 process of reconciling fires with ICS-209 reports is
available in the documentation (Raffuse, et al., 2007). Once the fire reconciliation process is completed,
the emissions are calculated using the U.S. Forest Service's CONSUMEv3.0 fuel consumption model
and the FCCS fuel-loading database in the BlueSky Framework (Ottmar, et. al., 2007).
SMARTFIRE 2 estimates were used directly for all states except Georgia and Florida. For Georgia, the
satellite-derived emissions were removed from the ptfire inventory and replaced with a separate state-
supplied ptfire inventory. Adjustments were also made to Florida as described in Section 5.1.4 of the
201 INEIvl TSD. These changes made the data in the ptfire inventory consistent with the data in the
201 INEIvl.
2.7	Biogenic sources (biog)
The biogenic emissions were computed based on 2011 meteorology data using the Biogenic Emission
Inventory System, version 3.14 (BEIS3.14) model within SMOKE. The BEIS3.14 model creates
gridded, hourly, model-species emissions from vegetation and soils. It estimates CO, VOC (most
notably isoprene, terpene, and sesquiterpene), and NO emissions for the U.S., Mexico, and Canada. The
BEIS3.14 model.
The inputs to BEIS include:
•	Temperature data at 2 meters, which were obtained from the meteorological input files to the air
quality model,
•	Land-use data from the Biogenic Emissions Land use Database, version 3 (BELD3). BELD3
data provides data on the 230 vegetation classes at 1-km resolution over most of North America.
To provide a sense of the scope and spatial distribution of the emissions, plots of annual BEIS outputs
for isoprene and NO for 2011 are shown in Figure 2-3 and Figure 2-4, respectively.
2.8	SMOKE-ready non-anthropogenic inventories for chlorine
The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (Cb)
concentrations in oceanic air masses (Bullock and Brehme, 2002). Data at 36 km and 12 km resolution
were available and were not modified other than the model-species name "CHLORINE" was changed to
"CL2" to support CMAQ modeling.
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Figure 2-3. Annual NO emissions output from BEIS 3.14 for 2011
Biogenic NO
2011
1	57	113	169	225	281	337	393
January 1, 2011 00:00:00 UTC
Min (1,1)= 0., Max (192, 3) = 138.
Figure 2-4. Annual isoprene emissions output from BEIS 3.14 for 2011
Biogenic Isoprene
2011		
1	50	99	148	197	246	295	344	393
January 1, 2011 00:00:00 UTC
Min (1,1)= 0., Max (210, 58) = 2258.
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3 Emissions Modeling Summary
Both the CMAQ and CAMx models require hourly emissions of specific gas and particle species for the
horizontal and vertical grid cells contained within the modeled region (i.e., modeling domain). To
provide emissions in the form and format required by the model, it is necessary to "pre-process" the
"raw" emissions (i.e., emissions input to SMOKE) for the sectors described above in Section 2. In brief,
the process of emissions modeling transforms the emissions inventories from their original temporal
resolution, pollutant resolution, and spatial resolution into the hourly, speciated, gridded resolution
required by the air quality model. Emissions modeling includes temporal allocation, spatial allocation,
and pollutant speciation. In some cases, emissions modeling also includes the vertical allocation of point
sources, but many air quality models also perform this task because it greatly reduces the size of the
input emissions files if the vertical layer of the sources does not need to be included.
As seen in Section 2, the temporal resolutions of the emissions inventories input to SMOKE vary across
sectors, and may be hourly, daily, monthly, or annual total emissions. The spatial resolution, which also
can be different for different sectors, may be individual point sources, county/province/municipio totals,
or gridded emissions. This section provides some basic information about the tools and data files used
for emissions modeling as part of the modeling platform. In Section 2, the emissions inventories and
how they differ from the 201 INEIvl were described. In Section 3, the descriptions of data are limited to
the ancillary data SMOKE uses to perform the emissions modeling steps. Note that all SMOKE inputs
for the 201 lv6 platform are available from the CHIEF Emissions Modeling Clearinghouse website (see
Section 1).
SMOKE version 3.5.1 was used to pre-process the raw emissions inventories into emissions inputs for
CMAQ. For projects that used CAMx, the CMAQ emissions were converted into the CAMx formats
using CAMx convertor programs. For sectors that have plume rise, the in-line emissions capability of
the air quality models was used, which allows the creation of source-based and two-dimensional gridded
emissions files that are much smaller than full three-dimensional gridded emissions files. For quality
assurance of the emissions modeling steps, emissions totals by specie for the entire model domain are
output as reports that are then compared to reports generated by SMOKE on the input inventories to
ensure that mass is not lost or gained during the emissions modeling process.
3.1 Emissions modeling Overview
When preparing emissions for the air quality model, emissions for each sector are processed separately
through SMOKE, and then the final merge program (Mrggrid) is run to combine the model-ready,
sector-specific emissions across sectors. The SMOKE settings in the run scripts and the data in the
SMOKE ancillary files control the approaches used by the individual SMOKE programs for each sector.
Table 3-1 summarizes the major processing steps of each platform sector. The "Spatial" column shows
the spatial approach used: here "point" indicates that SMOKE maps the source from a point location
(i.e., latitude and longitude) to a grid cell; "surrogates" indicates that some or all of the sources use
spatial surrogates to allocate county emissions to grid cells; and "area-to-point" indicates that some of
the sources use the SMOKE area-to-point feature to grid the emissions (further described in Section
3.4.2). The "Speciation" column indicates that all sectors use the SMOKE speciation step, though
biogenics speciation is done within the Tmpbeis3 program and not as a separate SMOKE step. The
"Inventory resolution" column shows the inventory temporal resolution from which SMOKE needs to
calculate hourly emissions. Note that for some sectors (e.g., onroad, beis), there is no input inventory;
instead, activity data and emission factors are used in combination with meteorological data to compute
hourly emissions.
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Finally, the "plume rise" column indicates the sectors for which the "in-line" approach is used. These
sectors are the only ones with emissions in aloft layers based on plume rise. The term "in-line" means
that the plume rise calculations are done inside of the air quality model instead of being computed by
SMOKE. The air quality model computes the plume rise using the stack data and the hourly air quality
model inputs found in the SMOKE output files for each model-ready emissions sector. The height of
the plume rise determines the model layer into which the emissions are placed. The c3marine, othpt, and
ptfire sectors are the only sectors with only "in-line" emissions, meaning that all of the emissions are
placed in aloft layers and there are no emissions for those sectors in the two-dimensional, layer-1 files
created by SMOKE.
Table 3-1. Key emissions modeling steps by sector.
Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
afdust
Surrogates
Yes
annual

ag
Surrogates
Yes
annual
(some monthly)

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

clc2rail
Surrogates
Yes
annual

c3 marine
Point
Yes
annual
in-line
nonpt
Surrogates &
area-to-point
Yes
annual
(some monthly)

nonroad
Surrogates &
area-to-point
Yes
monthly

np oilgas
Surrogates
Yes
annual

onroad
Surrogates
Yes
monthly activity,
computed hourly

onroadrfl
Surrogates
Yes
monthly activity,
computed hourly

othar
Surrogates
Yes
annual

othon
Surrogates
Yes
annual

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

SMOKE has the option of grouping sources so that they are treated as a single stack when computing
plume rise. For the 2011 platform, no grouping was performed because grouping combined with "in-
line" processing will not give identical results as "offline" processing (i.e., when SMOKE creates 3-
dimensional files). This occurs when stacks with different stack parameters or lat/lons are grouped,
thereby changing the parameters of one or more sources. The most straightforward way to get the same
results between in-line and offline is to avoid the use of grouping.
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SMOKE was run for the smaller 12-km CONtinental United States "CONUS" modeling domain
(12US2) shown in Figure 3-1 and boundary conditions were obtained from a 2011 run of GEOS-Chem.
Figure 3-1. Air quality modeling domains
12USI <_ oiitiiiental US Domain
12US2 Con(mental ITSDomain
Both grids use a Lambert-Conformal projection, with Alpha = 33°, Beta = 45° and Gamma = -97°, with a
center of X = -97° and Y = 40°. Table 3-2 describes the grids for the two domains.
Table 3-2. Descriptions of the platform grids
Common
Name
Grid
Cell Size
Description
(see Figure 3-1)
Grid name
Parameters listed in SMOKE grid
description (GRIDDESC) file:
projection name, xorig, yorig,
xcell, ycell, ncols, nrows, nthik
Continental
12km gnd
12 km
Entire conterminous
US plus some of
Mexico/Canada
12US1_459X299
'LAM 40N97W, -2556000, -1728000,
12.D3, 12.D3, 459, 299, 1
US 12 km or
"smaller"
CONUS-12
12 km
Smaller 12km
CONUS plus some of
Mexico/Canada
12US2
'LAM 40N97W',-2412000,-
1620000, 12.D3, 12.D3, 396, 246, 1
Section 3.4 provides the details on the spatial surrogates and area-to-point data used to accomplish
spatial allocation with SMOKE.
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3.2 Chemical Speciation
The emissions modeling step for chemical speciation creates the "model species" needed by the air
quality model for a specific chemical mechanism. These model species are either individual chemical
compounds or groups of species, called "model species." The chemical mechanism used for the 2011
platform is the CB05 mechanism (Yarwood, 2005). The same base chemical mechanism is used within
both CMAQ and CAMx, but the implementation differs slightly between the two models. The specific
versions of CMAQ and CAMx used in applications of this platform include secondary organic aerosol
(SOA) and HONO enhancements.
From the perspective of emissions preparation, the CB05 with SOA mechanism is the same as was used
in the 2007 platform. Table 3-3 lists the model species produced by SMOKE for use in CMAQ and
CAMx. It should be noted that the BENZENE model species is not part of CB05 in that the
concentrations of BENZENE do not provide any feedback into the chemical reactions (i.e., it is not
"inside" the chemical mechanism). Rather, benzene is used as a reactive tracer and as such is impacted
by the CB05 chemistry. BENZENE, along with several reactive CB05 species (such as TOL and XYL)
plays a role in SOA formation.
The TOG and PM2.5 speciation factors that are the basis of the chemical speciation approach were
developed from the SPECIATE4.3 database which is EPA's repository of TOG and PM speciation
profiles of air pollution sources. However, a few of the profiles used in the v6 platform will be
published in later versions of the SPECIATE database after the release of this documentation. The
SPECIATE database development and maintenance is a collaboration involving EPA's ORD, OTAQ,
and the Office of Air Quality Planning and Standards (OAQPS), in cooperation with Environment
Canada (EPA, 2006a). The SPECIATE database contains speciation profiles for TOG, speciated into
individual chemical compounds, VOC-to-TOG conversion factors associated with the TOG profiles, and
speciation profiles for PM2.5.
Speciation profiles and cross-references for this platform are available in spreadsheet form from
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/reports/speciation profiles/. The profiles are in the
Excel files "gspro_2011 .xlsx" and "gspro_combo_2011 .xlsx, gsref_2011 .xlsx". The cross reference
information is in "gsref_201 l.xlsx", and differences between 2011 and 2018 speciation profiles are
shown in "201 led_2018ed_gspro_differences.xlsx". A spreadsheet showing emission totals for each
speciation profile by modeling sector is available in the file
"201 led_speciation_profile_CAPs_febl 12014.xlsx".
39

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Table 3-3. Emission model species produced for CB05 with SOA for CMAQ5.0.1 and CAMx*
Inventory Pollutant
Model Species
Model species description
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
so2
S02
Sulfur dioxide
SULF
Sulfuric acid vapor
nh3
NH3
Ammonia
VOC
ALD2
Acetaldehyde
ALDX
Propionaldehyde and higher aldehydes
BENZENE
Benzene (not part of CB05)
CH4
Methane8
ETH
Ethene
ETHA
Ethane
ETOH
Ethanol
FORM
Formaldehyde
IOLE
Internal olefin carbon bond (R-C=C-R)
ISOP
Isoprene
MEOH
Methanol
OLE
Terminal olefin carbon bond (R-C=C)
PAR
Paraffin carbon bond
TOL
Toluene and other monoalkyl aromatics
XYL
Xylene and other polyalkyl aromatics
VOC species from the biogenics
model that do not map to model
species above
SESQ
Sesquiterpenes
TERP
Terpenes
PMio
PMC
Coarse PM > 2.5 microns and < 10 microns
PM2.s9
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 -
anthropogenic)10
PCL
Particulate chloride
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, CMAQ PMFINE= CAMX FCRS, and CMAQ
PMOTHR= CAMx FPRM
3.2.1 VOC speciation
40

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3.2.1.1 The combination of HAP BAFM (benzene, acetaldehyde, formaldehyde and
methanol) and VOC for VOC speciation
The VOC speciation includes HAP emissions from the 201 INEIvl in the speciation process. Instead of
speciating VOC to generate all of the species listed in Table 3-3, emissions of four specific HAPs:
benzene, acetaldehyde, formaldehyde and methanol (collectively known as "BAFM") from the NEI
were "integrated" with the NEI VOC. The integration process (described in more detail below)
combines these HAPs with the VOC in a way that does not double count emissions and uses the HAP
inventory directly in the speciation process. The basic process is to subtract the specified HAPs
emissions mass from VOC emissions mass and to then use a special "integrated" profile to speciate the
remainder of VOC to the model species excluding the specific HAPs. EPA believes that generally, the
HAP emissions from the NEI are more representative of emissions of these compounds than their
generation via VOC speciation.
The BAFM HAPs (benzene, acetaldehyde, formaldehyde and methanol) were chosen because, with the
exception of BENZENE, they are the only explicit VOC HAPs in the base version of CMAQ 5.0.1
(CAPs only with chlorine chemistry) model. Explicit means that they are not lumped chemical groups
like the other CB05 species. These "explicit VOC HAPs" are model species that participate in the
modeled chemistry using the CB05 chemical mechanism. The use of these HAP emission estimates
along with VOC is called "HAP-CAP integration". BENZENE was chosen because it is a model
species in the base version of CMAQ 5.0.1, and there was a desire to keep its emissions consistent
between multi-pollutant and base versions of CMAQ.
For specific sources, especially within the onroad and onroad rfl sectors, the integration included
ethanol. To differentiate when a source was integrating BAFM versus EBAFM (ethanol in addition to
BAFM), the speciation profiles that do not include ethanol are referred to as an "E-profile" (to be used
when the ethanol comes from the inventory pollutant). For example, use E10 headspace gasoline
evaporative speciation profile 8763 when ethanol is speciated from VOC, but use 8763E when ethanol is
obtained directly from the inventory.
The integration of HAP VOC with VOC is a feature available in SMOKE for all inventory formats other
than PTDAY (the format used for the ptfire sector). SMOKE allows the user to specify both the
particular HAPs to integrate via the INVTABLE and the particular sources to integrate via the
NHAPEXCLUDE file (which actually provides the sources to be excluded from integration11). For the
"integrated" sources, SMOKE subtracts the "integrated" HAPs from the VOC (at the source level) to
compute emissions for the new pollutant "NONHAPVOC." The user provides NONHAPVOC-to-
NONHAPTOG factors and NONHAPTOG speciation profiles12. SMOKE computes NONHAPTOG and
then applies the speciation profiles to allocate the NONHAPTOG to the other air quality model VOC
species not including the integrated HAPs. After determining if a sector is to be integrated, if all sources
have the appropriate HAP emissions, then the sector is considered fully integrated and does not need a
8	Technically, CH4 is not a VOC but part of TOG. Although emissions of CH4 are derived, the AQ models do not use these
emissions because the anthropogenic emissions are dwarfed by the CH4 already in the atmosphere.
9	For CMAQ 5.0, PM2.5 is speciated into a finer set of PM components. Listed in this table are the AE5 species
111 These emissions are created outside of SMOKE
11	In SMOKE version 3.5, the options to specify sources for integration are expanded so that a user can specify the particular
sources to include or exclude from integration, and there are settings to include or exclude all sources within a sector. In
addition, the error checking is significantly stricter for integrated sources. If a source is supposed to be integrated, but it is
missing BAFM or VOC, SMOKE will now raise an error.
12	These ratios and profiles are typically generated from the Speciation Tool when it is run with integration of a specified list
of pollutants, for example BAFM.
41

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NHAPEXCLUDE file. If on the other hand, certain sources do not have the necessary HAPs, then an
NHAPEXCLUDE file must be provided based on the evaluation of each source's pollutant mix. EPA
considered CAP-HAP integration for all sectors and developed "integration criteria" for some of them
(see Section 3.2.1.3 for details).
The process of partial integration for BAFM is illustrated in Figure 3-2 that the BAFM records in the
input inventories do not need to be removed from any sources in a partially integrated sector because
SMOKE does this automatically using the INVTABLE configuration. For EBAFM integration, this
process is identical to that shown in the figure except for the addition of ethanol (E) to the list of
subtracted HAP pollutants. For full integration, the process would be very similar except that the
NHAPEXCLUDE file would not be used and all sources in the sector would be integrated.
Figure 3-2. Process of integrating BAFM with VOC for use in VOC Speciation
li st of "no-i integrate"
sources (NHAPEXCLUDE)
Speciation Cross
Reference File(GSREF) ¦
VOC-to-TOG factors
N ON HAPVOC-t o-N ON H APTOG
factors (GSCNV)
TOG and NONHAPTOG
speciati on factors
(GSPRO)
In SMOKE, the INVTABLE allows the user to specify both the particular HAPs to integrate. Two
different types of INVTABLE files are included for use with different sectors of the platform. For
sectors that had no integration across the entire sector (see Table 3-4), EPA created a "no HAP use"
INVTABLE in which the "KEEP" flag is set to "N" for BAFM pollutants. Thus, any BAFM pollutants
in the inventory input into SMOKE are automatically dropped. This approach both avoids double-
counting of these species and assumes that the VOC speciation is the best available approach for these
species for sectors using this approach. The second INVTABLE, used for sectors in which one or more
sources are integrated, causes SMOKE to keep the inventory BAFM pollutants and indicates that they
are to be integrated with VOC. This is done by setting the "VOC or TOG component" field to "V" for
all four HAP pollutants. This type of INVTABLE is further differentiated into a version for those
sectors that integrate BAFM and another for those that integrate EBAFM, such as the onroad and
onroad rfl sectors.
Emissionsreadyfor SMOKE
SMOKE
Compute moles of each CBOS model species.
Use MOMHAPTOG profiles a pplied to NONHAPTOG
emissiorisand B, Fr Ar M emissions for integrate sources.
Use TOG profiles applied to TOG for no-integrate sources
Assign speciation profile code to each emission source
Compute NON HAPVOC= VOC - (B + F + A+M)
emissionsfor each integrate sou ree
Retain VOC emissionsfor each no-integrate source
Compute: NONHAPTOG emissions from NONHAPVOCfor
each integrate source
Compute: TOG emissions from VOC for each no-integrate
sou rce
Speciated Emissionsfor VOC species
42

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DRAFT
Table 3-4. Integration approach for BAFM and EBAFM for each platform sector
Platform
Sector
Approach for Integrating NEI emissions of Benzene (B), Acetaldehyde (A),
Formaldehyde (F), Methanol (M), and Ethanol (E)
ptegu
No integration
ptegu_pk
No integration
ptnonipm
No integration
ptfire
No integration
othar
No integration
othon
No integration
ag
N/A - sector contains no VOC
afdust
N/A - sector contains no VOC
biog
N/A - sector contains no inventory pollutant "VOC"; but rather specific VOC species
nonpt
Partial integration (BAFM and EBAFM)
np oilgas
Partial integration (BAFM)
pt oilgas
Partial integration (BAFM)
rwc
Partial integration (BAFM)
nonroad
Partial integration (BAFM)
clc2rail
Partial integration (BAFM)
othpt
Partial integration (BAFM)
c3marine
Full integration (BAFM)
onroad
Full integration (EBAFM and BAFM)
onroad rfl
Full integration (EBAFM and BAFM)
More details on the integration of specific sectors and additional details of the speciation are provided in
Section 3.2.1.3.
3.2.1.2 County specific profile combinations (GSPRO_COMBO)
SMOKE can compute speciation profiles from mixtures of other profiles in user-specified proportions.
The combinations are specified in the GSPROCOMBO ancillary file by pollutant (including pollutant
mode, e.g., EXH	VOC), state and county (i.e., state/county FIPS code) and time period (i.e.,
month).This feature was used to speciate onroad and nonroad mobile and gasoline-related related
stationary sources that use fuels with varying ethanol content. In these cases, the speciation profiles
require different combinations of gasoline profiles, e.g. EO and E10 profiles. Since the ethanol content
varies spatially (e.g., by state or county), temporally (e.g., by month) and by modeling year (future years
have more ethanol) the GSPRO COMBO feature allows combinations to be specified at various levels
for different years. SMOKE computes the resultant profile using the fraction of each specific profile
assigned by county, month and emission mode.
The GSREF file indicates that a specific source uses a combination file with the profile code
"COMBO". Because the GSPRO COMBO file does not differentiate by SCC and there are various
levels of integration across sectors, sector specific GSPRO COMBO files are used. For the onroad and
onroad rfl sectors, the GSPRO COMBO uses E-profiles (i.e. there is EBAFM integration). Different
profile combinations are specified by the mode (e.g. exhaust, evaporative, refueling, etc.) by changing
the pollutant name (e.g. EXH	NONHAPTOG, EVP	NONFLAPTOG, RFL	NONFLAPTOG). For
the nonpt sector, a combination of BAFM and EBAFM integration is used. Due to the lack of SCC-
specificity in the GSPRO COMBO, the only way to differentiate the sources that should use BAFM
integrated profiles versus E-profiles is by changing the pollutant name. For example, EPA changed the
pollutant name for the PFC future year inventory so the integration would use EVP	NONHAPVOC to
43

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DRAFT
correctly select the E-profile combinations, while other sources used NONHAPVOC to select the typical
BAFM profiles.
3.2.1.3 Additional sector specific details
The decision to integrate HAPs into the speciation was made on a sector by sector basis. For some
sectors there is no integration (VOC is speciated directly), for some sectors there is full integration (all
sources are integrated), and for other sectors there is partial integration (some sources are not integrated
and other sources are integrated). The integrated HAPs are either BAFM (BAFM HAPs subtracted from
VOC) or EBAFM (ethanol and BAFM HAPs subtracted from VOC). Table 3-4 summarizes the
integration for each platform sector.
For the clc2rail sector, EPA integrated BAFM for most sources from the 201 INEIvl. There were a few
sources that had zero BAFM; therefore, they were not integrated. The MWRPO and CARB inventories
(see Section 2.4.1) did not include HAPs; therefore, all non-NEI source emissions in the clc2rail sector
were not integrated. For California, the CARB inventory TOG was converted to VOC by dividing the
inventory TOG by the available VOC-to-TOG speciation factor.
For the othpt sector, the C3 marine sources (see Section 2.4.2) are integrated. HAPs in this sector are
derived identically to the U.S. c3marine sector. The rest of the sources in othpt are not integrated, thus
the sector is partially integrated.
For the onroad and onroadrfl sectors, there are series of unique speciation issues. First, SMOKE-
MOVES (see Sections 2.3.1 and 2.3.2) is used to estimate these sectors, meaning both the MEPROC
and INVTABLE files are involved in controlling which pollutants are ingested and speciated. Second,
these sectors have estimates of TOG as well as VOC; therefore, TOG can be speciated directly. Third,
the gasoline sources use full integration of EBAFM (i.e. use E-profiles) and the diesel sources use full
integration of BAFM. Fourth, the onroad sector utilizes 6 different modes for speciation: exhaust,
extended idle, evaporative, permeation (gasoline vehicles only), brake wear, and tire wear (See Table
2-12 for more details). The onroad rfl sector utilizes an additional mode: refueling. Fifth, the gasoline
exhaust profiles were updated to 8750a (revision to Gasoline Exhaust - Reformulated gasoline) and
8751a (revision to Gasoline Exhaust - E10 ethanol gasoline)13. Sixth, for California and Texas, EPA
applied adjustment factors to SMOKE-MOVES to produce California and Texas adjusted model-ready
files (see Section 2.3.1 for details). By applying the ratios through SMOKE-MOVES, the CARB and
Texas inventories are essentially speciated to match EPA estimated speciation grid cell by grid cell. The
future year CARB inventories did not have BAFM, so EPA estimates of BAFM were adjusted using
VOC adjustment factors for California only.
For the nonroad sector, CNG or LPG sources (SCCs beginning with 2268 or 2267) are not integrated
because NMIM computed only VOC and not any HAPs for these SCCs. All other nonroad sources were
integrated except in California. For California, the CARB inventory TOG was converted to VOC by
dividing the inventory TOG by the available VOC-to-TOG speciation factor. SMOKE later applies the
same VOC-to-TOG factor prior to computing speciated emissions. The CARB-based nonroad data
includes exhaust and evaporative mode-specific data for VOC, but does not contain refueling. The
CARB inventory does not include HAP estimates for all sources; therefore, the sources which have
VOC but do not have BAFM or BAFM is greater than VOC are not integrated. The remaining sources
are integrated. The future year CARB inventories did not have BAFM so all sources for California were
13 These revised profiles are expected to be in the yet to be released SPECIATE 4.4.
44

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DRAFT
not integrated. Similar to onroad, the gasoline exhaust profiles were updated to 8750a and 8751a (this is
true nation-wide).
For the ptnonipm sector, the 2011 and 2018 runs were not integrated. This was an oversight— it should
have been partial integration because the 2011 ethanol inventory (SCC 30125010) provided by OTAQ
includes BAFM. In the future year, ptnonipm should be partially integrated because both the ethanol
and biodiesel inventories (SCC 30125010) provided by OTAQ include BAFM. Aircraft emissions use
the profile 5565b which is chemically equivalent to 5565 (aircraft exhaust) in SPECIATE 4.3 database.
The profile numbers are differentiated from each other because a draft version of 5565 was used in
previous modeling platforms.
For most sources in the rwc sector, the VOC emissions were greater than or equal to BAFM, and BAFM
was not zero, so those sources were integrated, although a few specific sources that did not meet these
criteria could not be integrated.
For the oil and gas sources in np oilgas and pt oilgas, the basins studied in WRAP Phase III have basin-
specific VOC speciation that takes into account the distinct composition of gas. ENVIRON developed
these basin-specific profiles using gas composition analysis data obtained from operators through
surveys. ENVIRON separated out emissions and speciation from conventional/tight sands/shale gas
from coal-bed methane (CBM) gas sources. Table 3-5 lists the basin and gas composition specific
profiles used for the sources in the WRAP Phase III basins. For oil and gas sources outside of the
WRAP Phase III basins, the profiles did not vary by region or basin (see Table 3-6). Table 3-7 lists the
WRAP Phase III counties.
Table 3-5. VOC profiles for WRAP Phase III basins
Profile Code
Description
DJFLA
D-J Basin Flashing Gas Composition for Condensate
DJVNT
D-J Basin Produced Gas Composition
PNC01
Piceance Basin Gas Composition at Conventional Wells
PNC 02
Piceance Basin Gas Composition at Oil Wells
PNC03
Piceance Basin Flashing Gas Composition for Condensate
PRBCO
Powder River Basin Produced Gas Composition for Conventional Wells
PRM01
Permian Basin Produced Gas Composition
SSJCO
South San Juan Basin Produced Gas Composition for Conventional Wells
SWE01
Wyoming Flashing Gas Composition
SWFLA
SW Wyoming Basin Flash Gas Composition
SWVNT
SW Wyoming Basin Vented Gas Composition
UNT02
Uinta Basin Gas Composition at Conventional Wells
UNT03
Uinta Basin Flashing Gas Composition for Oil
UNT04
Uinta Basin Flashing Gas Composition for Condensate
WRBCO
Wind River Basin Produced Gas Composition for Conventional Wells
Table 3-6. National VOC profiles for oil and gas
profile
Description
0000
Over All Average
0001
External Combustion Boiler - Residual Oil
45

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DRAFT
profile
Description
0002
External Combustion Boiler - Distillate Oil
0003
External Combustion Boiler - Natural Gas
0004
External Combustion Boiler - Refinery Gas
0007
Natural Gas Turbine
0008
Reciprocating Diesel Engine
0051
Flares - Natural Gas
0296
Fixed Roof Tank - Crude Oil Production
1001
Internal Combustion Engine - Natural Gas
1010
Oil and Gas Production - Fugitives - Unclassified
1011
Oil and Gas Production - Fugitives - Valves and Fittings - Liquid Service
1012
Oil and Gas Production - Fugitives - Valves and Fittings - Gas Service
1207
Well Heads (Water Flood) Composite
2487
Composite of 7 Emission Profiles from Crude Oil Storage Tanks - 1993
2489
Composite of 15 Fugitive Emission Profiles from Petroleum Storage Facilities - 1993
Table 3-7. Counties included in the WRAP Dataset
MPS
State
County
08001
CO
Adams
08005
CO
Arapahoe
08007
CO
Archuleta
08013
CO
Boulder
08014
CO
Broomfield
08029
CO
Delta
08031
CO
Denver
08039
CO
Elbert
08043
CO
Fremont
08045
CO
Garfield
08051
CO
Gunnison
08059
CO
Jefferson
08063
CO
Kit Carson
08067
CO
La Plata
08069
CO
Larimer
08073
CO
Lincoln
08075
CO
Logan
08077
CO
Mesa
08081
CO
Moffat
08087
CO
Morgan
08095
CO
Phillips
08097
CO
Pitkin
08103
CO
Rio Blanco
08107
CO
Routt
08115
CO
Sedgwick
08121
CO
Washington
MPS
State
County
08123
CO
Weld
08125
CO
Yuma
30003
MT
Big Horn
30075
MT
Powder River
35005
NM
Chaves
35015
NM
Eddy
35015
NM
Lea
35031
NM
Mc Kinley
35039
NM
Rio Arriba
35041
NM
Roosevelt
35043
NM
Sandoval
35045
NM
San Juan
48003
TX
Andrews
48033
TX
Borden
48079
TX
Cochran
48081
TX
Coke
48103
TX
Crane
48105
TX
Crockett
48107
TX
Crosby
48109
TX
Culberson
48115
TX
Dawson
48125
TX
Dickens
48135
TX
Ector
48141
TX
El Paso
48151
TX
Fisher
48165
TX
Gaines
MPS
State
County
48169
TX
Garza
48173
TX
Glasscock
48219
TX
Hockley
48227
TX
Howard
48229
TX
Hudspeth
48235
TX
Irion
48263
TX
Kent
48269
TX
King
48301
TX
Loving
48303
TX
Lubbock
48305
TX
Lynn
48317
TX
Martin
48329
TX
Midland
48335
TX
Mitchell
48353
TX
Nolan
48371
TX
Pecos
48383
TX
Reagan
48389
TX
Reeves
48413
TX
Schleicher
48415
TX
Scurry
48431
TX
Sterling
48435
TX
Sutton
48445
TX
Terry
48451
TX
Tom Green
48461
TX
Upton
48475
TX
Ward
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MPS
State
County
48495
TX
Winkler
48501
TX
Yoakum
49007
UT
Carbon
49009
UT
Daggett
49013
UT
Duchesne
49015
UT
Emery
49019
UT
Grand
49043
UT
Summit
MPS
State
County
49047
UT
Uintah
56001
WY
Albany
56005
WY
Campbell
56007
WY
Carbon
56009
WY
Converse
56011
WY
Crook
56013
WY
Fremont
56019
WY
Johnson
MPS
State
County
56023
WY
Lincoln
56025
WY
Natrona
56027
WY
Niobrara
56033
WY
Sheridan
56035
WY
Sublette
56037
WY
Sweetwater
56041
WY
Uinta
56045
WY
Weston
For the biog sector, the speciation profiles used by BEIS are not included in SPECIATE. The 2011
platform uses BEIS3.14, which includes a new species (SESQ) that was mapped to the model species
SESQT. The profile code associated with BEIS3.14 profiles for use with CB05 uses the profile:
"B10C5."
For the nonpt sector, where VOC emissions were greater than or equal to BAFM and BAFM was not
zero, the sources were integrated. For portable fuel containers (PFCs) and fuel distribution operations
associated with the bulk-plant-to-pump (BTP) distribution, ethanol may be mixed into the fuels; therefore,
county- and month-specific COMBO speciation was used (via the GSPROCOMBO file). Refinery to
bulk terminal (RBT) fuel distribution and bulk plant storage (BPS) speciation are considered upstream
from the introduction of ethanol into the fuel; therefore a single profile is sufficient for these sources. No
refined information on potential VOC speciation differences between cellulosic diesel and cellulosic
ethanol sources was available, therefore cellulosic diesel and cellulosic ethanol sources used the same
SCC (30125010: Industrial Chemical Manufacturing, Ethanol by Fermentation production) for VOC
speciation as was used for corn ethanol plants. For the future year, PFC and the cellulosic sources were
integrated EBAFM (i.e. used E-profiles) because ethanol was present in those inventories.
3.2.1.4 Future year speciation
The VOC speciation approach used for the future year case is customized to account for the impact of fuel
changes. These changes affect the onroad, onroadrfl, nonroad, and parts of the nonpt and ptnonipm
sectors.
Speciation profiles for VOC in the nonroad, onroad and onroad rfl sectors account for the changes in
ethanol content of fuels across years. A description of the actual fuel formulations for 2011 can be found
in the 201 INEIvl TSD, and for 2018 see Section 4.3. For 2011, EPA used "COMBO" profiles to model
combinations of profiles for E0 and E10 fuel use. For 2018, EPA used "COMBO" profiles to model
combinations of E10 and E85 fuel use. The speciation of onroad exhaust VOC also accounts for a portion
of the vehicle fleet meeting Tier 2 standards in that different exhaust profiles are available for pre-Tier 2
versus Tier 2 vehicles. Thus for onroad gasoline, VOC speciation uses different COMBO profiles to take
into account both the increase in ethanol use, and the increase in Tier 2 vehicles in the future case.
The speciation changes from fuels in the nonpt sector are for PFCs and fuel distribution operations
associated with the BTP distribution. For these sources, ethanol may be mixed into the fuels, in which
47

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case speciation would change across years. The speciation changes from fuels in the ptnonipm sector
include BTP distribution operations inventoried as point sources. RBT fuel distribution and BPS
speciation does not change across the modeling cases because this is considered upstream from the
introduction of ethanol into the fuel. For PFCs, ethanol was present in the future inventories and therefore
EBAFM profiles were used to integrate ethanol in the speciation. The mapping of fuel distribution SCCs
to PFC, BTP, BPS, and RBT emissions categories can be found in Appendix B.
Table 3-8 summarizes the different profiles utilized for the fuel-related sources in each of the sectors for
2011 and the future year 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 was used to
speciate that subcategory using the GSPRO COMBO file.
Table 3-8. Select VOC profiles 2011 versus 2018
Sector
su bcategory
2011
2018
onroad
gasoline
exhaust
COMBO:
8750aE Pre-Tier 2 E0 exhaust
COMBO:
8751E Pre-Tier 2 E10 exhaust
8751aE Pre-Tier 2 E10 exhaust
8756E Tier 2 E0 Exhaust
8757E Tier 2 E10 Exhaust
8757E Tier 2 E10 Exhaust
8758E Tier 2 E15 Exhaust
8855E Tier 2 E85 Exhaust
onroad
gasoline
evaporative
COMBO:
8753E E0 Evap
8754E E10 Evap
COMBO
8754E E10 Evap
8872E E15 Evap
8934E E85 Evap
onroad
gasoline
permeation
COMBO:
8766E E0 evap perm
8769E E10 evap perm
COMBO
8769E E10 evap perm
8770E E15 evap perm
8934E E85 Evap
onroad_rfl
gasoline
refueling
COMBO:
8869E E0 Headspace
8870E E10 Headspace
COMBO
8870E E10 Headspace
8871E E15 Headspace
8934E E85 Evap
onroad
diesel
exhaust
Weighted diesel exhaust for
87710 2010
Weighted diesel exhaust
877P0 for 2020
onroad
diesel
extended
idle
Weighted diesel exhaust for
877P0 2020
Weighted diesel extended
877EIT3 idle for 2018
onroad
diesel
evaporative
4547 Diesel Headspace
4547 Diesel Headspace
onroad rfl
diesel
refueling
4547 Diesel Headspace
4547 Diesel Headspace
nonroad
gasoline
exhaust
COMBO:
8750a Pre-Tier 2 E0 exhaust
8751a Pre-Tier 2 E10 exhaust
8751a Pre-Tier 2 E10 exhaust
nonroad

COMBO:
8754 E10 evap
48

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Sector
su bcategory
2011
2018

gasoline
evaporative
8753	E0 evap
8754	E10 evap

nonroad
gasoline
refueling
COMBO:
8869	E0 Headspace
8870	E10 Headspace
8870 E10 Headspace
nonroad
diesel
exhaust
8774 Pre-2007 MY HDD exhaust
8774 Pre-2007 MY HDD exhaust
nonroad
diesel
evaporative
4547 Diesel Headspace
4547 Diesel Headspace
nonroad
diesel
refueling
4547 Diesel Headspace
4547 Diesel Headspace
nonpt/ptnonipm
PFC
COMBO:
8869E E0 Headspace
8870E E10 Headspace
COMBO
8870E E10 Headspace
8871E E15 Headspace
8934E E85 Evap
nonpt/ptnonipm
BTP
COMBO:
8869	E0 Headspace
8870	E10 Headspace
COMBO
8870	E10 Headspace
8871	E15 Headspace
8934 E85 Evap
nonpt/ptnonipm
BPS/RBT
8869 E0 Headspace
8869 E0 Headspace
3.2.2 PM speciation
3.2.2.1 AE5 versus AE6 speciation
In addition to VOC profiles, the SPECIATE database also contains the PM2.5 speciated into both
individual chemical compounds (e.g., zinc, potassium, manganese, lead), and into the "simplified" PM2.5
components used in the air quality model. For CMAQ 4.7.1 modeling, these "simplified" components
(AE5) are all that is needed. For CMAQ 5.0.1, there is a new thermodynamic equilibrium aerosol
modeling tool (ISORROPIA) v2 mechanism that needs additional PM components (AE6), which are
further subsets of PMFINE (see Table 3-9). EPA speciated PM2.5 so that it included both AE5 and AE6
PM model species without causing any double counting. Therefore, emissions from this platform can be
used with either CMAQ 4.7.1 or CMAQ 5.0.1.
Table 3-9. PM model species: AE5 versus AE6
species name
species description
AE5
AE6
POC
organic carbon
Y
Y
PEC
elemental carbon
Y
Y
PS04
sulfate
Y
Y
PN03
nitrate
Y
Y
PMFINE
unspeciated PM2.5
Y
N
PNH4
ammonium
N
Y
PNCOM
non-carbon organic matter
N
Y
PFE
iron
N
Y
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species name
species description
AE5
AE6
PAL
aluminum
N
Y
PSI
silica
N
Y
PTI
titanium
N
Y
PCA
calcium
N
Y
PMG
magnesium
N
Y
PK
potassium
N
Y
PMN
manganese
N
Y
PNA
sodium
N
Y
PCL
chloride
N
Y
PH20
water
N
Y
PMOTHR
PM2.5 not in other AE6 species
N
Y
The majority of the 2011 platform PM profiles come from the 911XX series which include updated AE6
speciation14. Appendix C contains a crosswalk between AE6 profile codes and SPECIATE 4.3 profile
codes. Summaries of 2011 and 2018 emissions by county, month, and sector speciated according to AE5
and AE6 speciation profiles are available from
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/reports/speciation profiles/ in the files
201 led_county_monthly_report_PM_AE5.xlsx, 201 led_county_monthly_report_PM_AE6.xlsx,
2018ed_county_monthly_report_PM_AE5.xlsx, and 2018ed_county_monthly_report_PM_AE6.xlsx. In
addition, the file 201 led_speciation_profiles_6feb2014.xlsx shows the total PM2.5 apportioned to each
sector.
3.2.2.2 Onroad PM speciation
Unlike other sectors, the onroad sector has pre-speciated PM. This speciated PM comes from the
MOVES model and is processed through the SMOKE-MOVES system (see Section 2.3.1).
Unfortunately, the MOVES speciated PM does not map 1-to-l to the AE5 speciation (nor the AE6
speciation) needed for CMAQ modeling. Table 3-10 shows the relationship between MOVES15 exhaust
PM2.5 related species and CMAQ AE5 PM species.
Table 3-10. MOVES exhaust PM species versus AE5 species
MOVES2010b Pollutant Name
Variable name
for Equations
Relation to AE5 model species
Primary Exhaust PM2.5 - Total
PM25 TOTAL

Primary PM2.5 - Organic Carbon
PM250M
Sum of POC, PN03 and PMFINE
Primary PM2.5 - Elemental Carbon
PM25EC
PEC
Primary PM2.5 - Sulfate Particulate
PM25S04
PS04
MOVES species are related as follows:
PM25 TOTAL = PM25EC + PM250M + PS04
14	The exceptions are 5674 (Marine Vessel - Marine Engine - Heavy Fuel Oil) used for c3marine and 92018 (Draft Cigarette
Smoke - Simplified) used in nonpt.
15	The Tier3 NPRM MOVES model lias the same PM components as MOVES2010b. MOVES2014 is expected to have a one-
to-one mapping of PM species to CMAQ PM species.
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The five CMAQ AE5 species also sum to total PM2.5:
PM2.5 = P0C+PEC+PN03+PS04+PMFINE
The basic problem is to differentiate MOVES species "PM250M" into the component AE5 species (POC,
PN03 and PMFINE). The Moves2smkEF post-processor script takes the MOVES species (EF tables)
and calculates the appropriate AE5 PM2.5 species and converts them into a format that is appropriate for
SMOKE. For 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 MOVES. These PM
modes are speciated by SMOKE. PMFINE from onroad exhaust is further speciated by SMOKE into the
component AE6 species.
For California and Texas, adjustment factors were applied to SMOKE-MOVES to produce California and
Texas adjusted model-ready files (see Section 2.3.1 for details). California and Texas did not supply
speciated PM, therefore the adjustment factors applied to PM2.5 were also applied to the speciated PM
components. By applying the ratios through SMOKE-MOVES, the CARB and Texas inventories are
essentially speciated to match EPA estimated speciation grid cell by grid cell.
3.2.3 NOx speciation
NOx can be speciated into NO, N02, and/or HONO. For the non-mobile sources, EPA used a single
profile "NHONO" to split NOx into NO and NO2. For the mobile sources except for onroad (including
nonroad, clc2rail, c3marine, othon sectors) and for specific SCCs in othar and ptnonipm, the profile
"HONO" splits NOx into NO, NO2, and HONO. Table 3-11 gives the split factor for these two profiles.
Table 3-11. NOx speciation profiles
profile
pollutant
species
split factor
HONO
NOX
N02
0.092
HONO
NOX
NO
0.9
HONO
NOX
HONO
0.008
NHONO
NOX
N02
0.1
NHONO
NOX
NO
0.9
The onroad sector does not use the "HONO" profile to speciate NOx. MOVES2010b produces speciated
NO, NO2, and HONO by source, including emission factors for these species in the emission factor tables
used by SMOKE-MOVES. Within MOVES, the HONO fraction is a constant 0.008 of NOx. The NO
fraction varies by heavy duty versus light duty, fuel type, and model year. The NO2 fraction = 1 - NO -
HONO. For more details on the NOx fractions within MOVES. HONO is not calculated directly by the
Tier 3 proposal version of MOVES. For these EF tables, the calculation of HONO and the NO2 fraction
are calculated externally by the moves2smk script16. The SMOKE-MOVES system then models these
species directly without further speciation.
3.3 Temporal Allocation
Temporal allocation (i.e., temporalization) is the process of distributing aggregated emissions to a finer
temporal resolution, thereby converting annual emissions to hourly emissions. While the total emissions
16 A specific version of the moves2smk script was developed to do this calculation of HONO. The typical version assumes that
HONO was calculated directly by MOVES2010b.
51

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are important, the timing of the occurrence of emissions is also essential for accurately simulating ozone,
PM, and other pollutant concentrations in the atmosphere. Many emissions inventories are annual or
monthly in nature. Temporalization takes these aggregated emissions and if needed distributes them to the
month, and then distributes the monthly emissions to the day and the daily emissions to the hour. This
process is typically done by applying temporal profiles to the inventories in this order: monthly, day of
the week, and diurnal.
The temporal profiles and associated cross references used to create the hourly emissions inputs for the
2011 air quality modeling platform were similar to those used for the 2007 platform. The temporal
factors applied to the inventory are selected using some combination of country, state, county, SCC, and
pollutant. Table 3-12 summarizes the temporal aspects of emissions modeling by comparing the key
approaches used for temporal processing across the sectors. In the table, "Daily temporal approach"
refers to the temporal approach for getting daily emissions from the inventory using the SMOKE
Temporal program. The values given are the values of the SMOKE LTYPE setting. The "Merge
processing approach" refers to the days used to represent other days in the month for the merge step. If
this is not "all", then the SMOKE merge step runs only for representative days, which could include
holidays as indicated by the right-most column. The values given are those used for the SMOKE
MTYPE setting (see below for more information).
Table 3-12. Temporal settings used for the platform sectors in SMOKE
Platform
sector short
name
Inventory
resolutions
Monthly
profiles
used?
Daily
temporal
approach
Merge
processing
approach
Process
Holidays as
separate days
ptegu
Daily & hourly

all
all
Yes
ptegu_pk
Daily & hourly

all
all
Yes
ptnonipm
Annual
yes
mwdss
mwdss
Yes
ptoilgas
Annual
yes
mwdss
mwdss
Yes
ptfire
Daily

all
all
Yes
othpt
Annual
yes
mwdss
mwdss

nonroad
Monthly

mwdss
mwdss
Yes
othar
Annual
yes
week
week

clc2rail
Annual
yes
mwdss
mwdss

c3 marine
Annual
yes
aveday
aveday

onroad
Annual & monthly1

all
all
Yes
onroadrfl
Annual & monthly2

all
all
Yes
othon
Annual
yes
week
week

nonpt
Annual & monthly
yes
all
all
Yes
npoilgas
Annual
yes
mwdss
mwdss
Yes
rwc
Annual
no
met-based
All
Yes
ag
Annual
yes
all
all
Yes
afdustadj
Annual
yes
week
all
Yes
beis
Hourly

n/a
all
Yes
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1.	Note the annual and monthly "inventory" actually refers to the activity data (VMT and VPOP) for onroad. The
actual emissions are computed on an hourly basis.
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 table: The value "all" means that hourly emissions computed for
every day of the year and that emissions potentially have day-of-year variation. The value "week" means
that hourly emissions computed for all days in one "representative" week, representing all weeks for each
month. This means emissions have day-of-week variation, but not week-to-week variation within the
month. The value "mwdss" means hourly emissions for one representative Monday, representative
weekday (Tuesday through Friday), representative Saturday, and representative Sunday for each month.
This means emissions have variation between Mondays, other weekdays, Saturdays and Sundays within
the month, but not week-to-week variation within the month. The value "aveday" means hourly
emissions computed for one representative day of each month, meaning emissions for all days within a
month are the same. Special situations with respect to temporalization are described in the following
subsections.
Spreadsheets summarizing the temporal profiles and cross-references used in the modeling are available
with the other 2011 platform reports in
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/reports/temporal profiles/ with the profiles in the files
tpro_201 l.xlsx, tpro_onroad_201 l.xlsx, tpro_rwc_201 l.xlsx, and the cross references in tref_201 l.xlsx.
The file 201 led_temporal_profiles_CAPs_febl 12014.xlsx shows the emissions assigned to each of the
profiles for sectors other than onroad, ptegu, and ptegu_pk.
In addition to the resolution, temporal processing includes a ramp-up period for several days prior to
January 1, 2011, which is intended to mitigate the effects of initial condition concentrations. The ramp-up
period was 10 days (December 22-31, 2010). For most sectors, emissions from December 2011 were
used to fill in surrogate emissions for the end of December 2010. In particular, December 2011 emissions
(representative days) were used for December 2010. For biogenic emissions, December 2010 emissions
were processed using 2010 meteorology.
3.3.1 Use of FF10 format for finer than annual emissions
The Flat File 2010 format (FF10) inventory format for SMOKE provides a more consolidated format for
monthly, daily, and hourly emissions inventories than previous formats supported. Previously, to process
monthly inventory data required the use of 12 separate inventory files. With the FF10 format, a single
inventory file can contain emissions for all 12 months and the annual emissions in a single record. This
helps simplify the management of numerous inventories. Similarly, daily and hourly FF10 inventories
contain individual records with data for all days in a month and all hours in a day, respectively.
SMOKE 3.5.1 prevents the application of temporal profiles on top of the "native" resolution of the
inventory. For example, a monthly inventory should not have annual to month temporalization applied to
it; rather, it should only have month-to-day and diurnal temporalization. This becomes particularly
important when specific sectors have a mix of annual, monthly, daily, and/or hourly inventories (e.g. the
nonpt sector). The flags that control temporalization for a mixed set of inventories are discussed in the
SMOKE documentation. The modeling platform sectors that make use of monthly values in the FF10
files are nonroad, onroad, and the ag burning inventory within the nonpt sector.
53

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3.3.2 Electric Generating Utility temporaiization (ptegu, ptegu_pk)
3.3.2.1 Base year temporal allocation of EGUs
The 201 INEIvl annual EGU emissions are allocated to hourly emissions using the following 3-step
methodology: annual value to month, month to day, and day to hour. The temporal allocation procedure
is differentiated by whether or not the source could be directly matched to a CEMS unit via ORIS facility
code and boiler ID. Prior to temporal allocation, as many sources as possible were matched to CEMS
data via ORIS facility code and boiler ID. EIS stores a base set of previously matched units via alternate
facility and unit IDs. For any units not yet matched, reports were generated by unit to identify potential
matches with the NEI. The reports included FIPS state/county code, facility name, and NOx and SO2
emissions. Units were considered matches if the FIPS state/county code matched, the facility name was
similar, and the NOx and SO2 emissions were similar.
For sources not matched to CEMS measurements, the first two steps of the allocation are done outside of
SMOKE. For sources in the ptegu and ptegu_pk sectors that are matched to CEMS data, annual totals of
the emissions may be different than the annual values in 201 INEIvl because the CEMS data actually
replaces the inventory data. All units in the ptegu_pk sector with non-zero emissions for 2011 were
matched to CEMS data.
For units not matched to CEMS data, the allocation of the inventory annual emissions to months is done
using average fuel-specific season-to-month factors generated for each of the 64 IPM regions shown in
Figure 3-3. These factors are based 2011 CEMS data only. In each region, separate factors were
developed for the fuels coal, natural gas, and "other", where the types of fuels included in "other" vary by
region. Separate profiles were computed for NOx and SO2, and heat input. An overall composite profile
was also computed and was used in a few cases in which the fuel-specific profile was too irregular, or
there were no CEMS units with the specified fuel in the region containing the unit. For both CEMS and
non-CEMS matched units, NOx and SO2 CEMS data are used to allocate NOx and SO2 emissions, while
CEMS heat input data is used to allocate all other pollutants.
Daily "temporal allocation" of units with CEMS was performed using a procedure similar to that in the
first step in that the CEMS data replaces the inventory data for each pollutant. For units without CEMS
data, emissions were allocated from month to day using IPM-region and fuel-specific average month-to-
day factors based on the 2011 CEMS data. Separate allocation factors were computed for NOx, SO2, and
heat input for the fuels coal, natural gas, and other. For both CEMS and non-CEMS matched units, NOx
and SO2 CEMS data are used to allocate NOx and SO2 emissions, while CEMS heat input data is used to
allocate all other pollutants.
For units with associated CEMS data, hourly emissions use the hourly CEMS values as described above
for NOx and S02, while other pollutants are allocated according to heat input values. For units without
CEMS data, temporal profiles from days to hours are computed based on the region- and fuel-specific
average day-to-hour factors derived from the CEMS data for those fuels and regions using data from the
entire year. For non-CEMS matched units, CEMS heat input data is used to allocate all pollutants
(including NOx and SO2). SMOKE then allocates the daily emissions data to hours using the profiles
obtained from the CEMS data for the analysis base year.
54

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Figure 3-3. EPM Regions for EPA Base Case v5.13
EPA Base Case v5.12 U.S. Regions
J/VECCJ>NW
EST
NY_Z
A4BZ-
MSI
WECC_NNV
COMD
NY_Z_J
WECC_CO
WECC
WECC.
ERC_WEST
_D_WOTA
3.3.2.2 Future year temporal allocation of EGUs
IPM provides unit-level emission projections of average winter (representing October through April) and
average summer (representing May through September) values. These annualized emissions are allocated
to hourly emissions using a 3-step methodology: annualized summer/winter value to month, month to
day, and day to hour. The first two steps are done outside of SMOKE and the third step is done by
SMOKE using daily emissions files created from the first two steps. This approach maximizes the use of
the CEMS data from the air quality analysis year (e.g., 2011).
For CEMS matched units, the 2011 based CEMS were scaled so that their seasonal emissions matched
IPM totals. In other words, EPA created a set of artificial CEMS data which had the same temporal
pattern as 2011, but for which the seasonal total emissions matched IPM's predictions for 2018. Except
for the scaling of CEMS data, the procedure for allocating the emissions of CEMS matched units is the
same as the base year (see Section 3.3.2.1). For sources not matched to CEMS units, the allocation of the
IPM seasonal emissions to months was done using average fuel-specific season-to-month factors
generated for each of the 64 IPM regions shown in Figure 3-3. These factors are based on a single year of
CEMS data consistent with the modeling base year, in this case 2011. Similar to the base year, profiles
were created for coal, natural gas, and "other" fuel. For each fuel, separate profiles were computed for
NOx, SO2, and heat input. An overall composite profile was also computed in the event that a fuel-specific
profile was too irregular or in the case when a unit changed fuels between the base and future year and
55

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there were previously no units with that fuel in the specific region. Except for the season-to-month
allocation, the procedure for allocating the emissions of non-CEMS matched units is the same as the base
year.
Units with year-specific impacts in the season-to-month allocations, such as long-duration downtimes for
maintenance or installation of controls that occur only in one year were temporalized using average
profiles instead of using the anomalous profile for the base year. These situations are determined by
analysis of the base and future year data. Note that IPM uses load data (reflecting the shape of demand)
corresponding to the load in each IPM region that occurred in the base year of the air quality modeling
analysis, such as 2011.
Some refinements to the above approach were made in some special cases:
•	When emissions were substantially higher for units with limited hours of operation in the base
year, an averaged profile was used.
•	When a unit switched fuels in the future year to a fuel not used in the base year, the profile was
selected according to the new type of fuel. If the unit was a CEMS unit in the base year, it was
treated as non-CEMS in the future year.
•	When a CEMS unit operated in only one season in the base year, but IPM predicted that there
were emissions in both seasons, an average profile was used for the future year unit during both
seasons.
•	New units coming on line used the appropriate region and fuel-specific profiles
•	Units that are not new but had no emissions in 2011 were treated like new units.
For more information on the development of IPM emission estimates and the temporalization of those, in
particular the Air Quality Modeling Flat File Documentation and accompanying inputs.
3.3.3 Residential Wood Combustion Temporalization (rwc)
There are many factors that impact the timing of when emissions occur, and for some sectors this includes
meteorology. The benefits of utilizing meteorology as method for temporalization are: (1) a
meteorological dataset consistent with that used by the AQ model is available (e.g. outputs from WRF);
(2) the meteorological model data is highly resolved in terms of spatial resolution; and (3) the
meteorological variables vary at hourly resolution and can therefore be translated into hour-specific
temporalization.
The SMOKE program GenTPRO provides a method for developing meteorology-based temporalization.
Currently, the program can utilize three types of temporal algorithms: annual-to-day temporalization for
residential wood combustion (RWC), month-to-hour temporalization for agricultural livestock ammonia,
and a generic meteorology-based algorithm for other situations. For the 2011 platform, meteorological-
based temporalization was used for portions of the rwc sector and for livestock within the ag sector.
GenTPRO reads in gridded meteorological data (output from MCIP) along with spatial surrogates, and
uses the specified algorithm to produce a new temporal profile that can be input into SMOKE. The
meteorological variables and the resolution of the generated temporal profile (hourly, daily, etc.) depend
on the selected algorithm and the run parameters. For more details on the development of these
algorithms and running GenTPRO, see the GenTPRO documentation and the SMOKE documentation.
respectively.
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For the RWC algorithm, GenTPRO uses the daily minimum temperature to determine the temporal
allocation of emissions to days. GenTPRO was used to create an annual-to-day temporal profile for the
RWC sources. These generated profiles distribute annual RWC emissions to the coldest days of the year.
On days where the minimum temperature does not drop below a user-defined threshold, RWC emissions
for most sources in the sector are zero. Conversely, the program temporally allocates the largest
percentage of emissions to the coldest days. Similar to other temporal allocation profiles, the total annual
emissions do not change, only the distribution of the emissions within the year is affected. The
temperature threshold for rwc emissions was 50 °F for most of the country, and 60 °F for the following
states: Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and
Texas.
Figure 3-4 illustrates the impact of changing the temperature threshold for a warm climate county. The
plot shows the temporal fraction by day for Duval County, Florida for the first four months of 2007. The
default 50 °F threshold creates large spikes on a few days, while the 60 °F threshold dampens these spikes
and distributes a small amount of emissions to the days that have a minimum temperature between 50 and
60 °F.
Figure 3-4. Example of RWC temporalization in 2007 using a 50 versus 60 °F threshold
RWC temporal profile, Duval County, FL, Jan - Apr
	60F, alternate formula
	50F, default formula
« 0.015
0.005
The diurnal profile for used for most RWC sources (see Figure 3-5) places more of the RWC emissions in
the morning and the evening when people are typically using these sources. This profile is based on a
2004 MANE-VU survey based temporal profiles. This profile was created by averaging three indoor and
three RWC outdoor temporal profiles from counties in Delaware and aggregating them into a single RWC
diurnal profile. This new profile was compared to a concentration based analysis of aethalometer
measurements in Rochester, NY (Wang et al. 2011) for various seasons and day of the week and found
that the new RWC profile generally tracked the concentration based temporal patterns.
57

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Figure 3-5. RWC diurnal temporal profile
Comparison of RWC diurnal profile
0.12
0.1
c
o
0.08
	NEW
0.06
o
jj" 0.04
	OLD
0.02
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
The temporalization for "Outdoor Hydronic Heaters" (i.e.,"OHH", SCC=2104008610) and "Outdoor
wood burning device, NEC (fire-pits, chimineas, etc)" (i.e., "recreational RWC",SCC=21040087000)
were updated because the meteorological-based temporalization used for the rest of the rwc sector did not
agree with observations for how these appliances are used.
For OHH, the annual-to-month, day-of-week and diurnal profiles were modified based on information in
the New York State Energy Research and Development Authority (NYSERDA) "Environmental, Energy
Market, and Health Characterization of Wood-Fired Hydronic Heater Technologies, Final Report"
(NYSERDA, 2012) as well as a Northeast States for Coordinated Air Use Management (NESCAUM)
report "Assessment of Outdoor Wood-fired Boilers" (NESCAUM, 2006). A Minnesota 2008 Residential
Fuelwood Assessment Survey of individual household responses (MDNR, 2008) provided additional
annual-to-month, day-of-week and diurnal activity information for OHH as well as recreational RWC
usage.
The diurnal profile for OHH, shown in Figure 3-6 is based on a conventional single-stage heat load unit
burning red oak in Syracuse, New York. As shown in Figure 3-7, the NESCAUM report describes how
for individual units, OHH are highly variable day-to-day but that in the aggregate, these emissions have
no day-of-week variation. In contrast, the day-of-week profile for recreational RWC follows a typical
"recreational" profile with emissions peaked on weekends.
Annual-to-month temporalization for OHH as well as recreational RWC were computed from the MN
DNR survey (MDNR, 2008) and are illustrated in Figure 3-8. OHH emissions still exhibit strong
seasonal variability, but do not drop to zero because many units operate year round for water and pool
heating. In contrast to all other RWC appliances, recreational RWC emissions are used far more
frequently during the warm season.
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Figure 3-6. Diurnal profile for OHH, based on heat load (BTU/hr)
Heat Load (BTU/hr)
45,000
40,000
35,000
30,000
25,000
20,000
15,000
10,000
5,000
^	<0^ ^ sjrvi?
Figure 3-7. Day-of-week temporal profiles for OHH and Recreational RWC
Fire Pits/Chimineas Day-of-Week Profile
350
300
250
200
150
100
	Fire Pit/Chimenea
Outdoor Hydronic Heater
59

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Figure 3-8. Annual-to-month temporal profiles for OHH and recreational RWC
Monthly Temporal Activity for OHH & Recreational RWC
Fire Pit/Chimenea
Outdoor Hydronic Heater
3.3.4 Agricultural Ammonia Temporal Profiles (ag)
For the agricultural livestock NH3 algorithm, the GenTPRO algorithm is based on an equation derived by
Jesse Bash of EPA ORD based on the Zhu, Henze, et al. (2013) empirical equation. This equation is based
on observations from the TES satellite instrument with the GEOS-Chem model and its adjoint to estimate
diurnal NH3 emission variations from livestock as a function of ambient temperature, aerodynamic
resistance, and wind speed. The equations are:
Ea = [161500/Ta x e("1380/V] x AR,/;
PE;,/; = Ea, / Sum(E, /,)
where
•	PE;,/; = Percentage of emissions in county i on hour h
•	Eij, = Emission rate in county i on hour h
•	Tin = Ambient temperature (Kelvin) in county i on hour h
•	Vi,/; = Wind speed (meter/sec) in county i (minimum wind speed is 0.1 meter/sec)
•	AR;,/; = Aerodynamic resistance in county i
GenTPRO was run using the "BASH NH3" profile method to create month-to-hour temporal profiles for
these sources. Because these profiles distribute to the hour based on monthly emissions, the monthly
emissions are obtained from a monthly inventory, or from an annual inventory that has been temporalized
to the month17.
17 SMOKE v3.5.1 will correctly read in a monthly inventory and apply GenTPRO ag NH3 month-to-hour temporalization.
However, SMOKE v3.5 beta incorrectly applied an annual-to-month temporal profile on top of a monthly inventory when
60

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Figure 3-9 compares the daily emissions for Minnesota from the "old" approach (uniform monthly
profile) with the "new" approach (GenTPRO generated month-to-hour profiles). Although the GenTPRO
profiles show daily (and hourly variability), the monthly total emissions are the same between the two
approaches.
Figure 3-9. Example of new animal NFb emissions temporalization approach, summed to daily
emissions
MN ag NH3 livestock temporal profiles
0.0
1/1/2008 2/1/2008 3/1/2008 4/1/2008 5/1/2008 6/1/2008 7/1/2008 8/1/2008 9/1/2008 10/1/2008 11/1/2008 12/1/2008
-old
-new
3.3.5 Onroad mobile temporalization (onroad, onroad_rfl)
For the onroad and onroadrfl sectors, the temporal distribution of emissions is a combination of more
traditional temporal profiles and the influence of meteorology. This section will discuss both the
meteorological influences and the updates to the diurnal temporal profiles for the 2011 platform.
Meteorology is not used in the development of the temporal profiles, but rather it impacts the calculation
of the hourly emissions through the program Movesmrg. The result is that the emissions vary at the
hourly level by grid cell. More specifically, the on-network (RPD) and the off-network (RPV and RPP)
processes use the gridded meteorology (MCIP) directly. Movesmrg determines the temperature for each
hour and grid cell and uses that information to select the appropriate emission factor (EF) for the specified
SCC/pollutant/mode combination. In the previous platform, RPP used county level minimum and
maximum temperature ranges for the day to determine the appropriate EF. This potentially overestimated
the temperature range for any particular grid cell, which would result in increased emissions for vapor-
venting. In the 2011 platform (and the 201 INEIvl), RPP was updated to use the gridded minimum and
maximum temperature for the day. This more spatially resolved temperature range produces more
accurate emissions for each grid cell. The combination of these three processes (RPD, RPV, and RPP) is
the total onroad sector emissions, while the combination of the two processes (RPD, RPV) for the
refueling mode only is the total onroad rfl sector emissions. Both sectors show a strong meteorological
influence on their temporal patterns (see the 201 INEIvl TSD for more details).
Figure 3-10 illustrates the difference between temporalization of the onroad sector used in the 2005 and
earlier platforms and the meteorological influence via SMOKE-MOVES. In the plot, the "MOVES"
inventory is a monthly inventory that is temporalized by SCC to day-of-week and hour. Similar
temporalization is done for the VMT in SMOKE-MOVES, but the meteorologically varying EFs add an
additional variation on top of the temporalization. Note, the SMOKE-MOVES run is based on the 2005
platform and previous temporalization of VMT to facilitate the comparison of the results. In the figure,
the MOVES emissions have a repeating pattern within the month, while the SMOKE-MOVES shows
temporalizing with GenTPRO ag NH3 profiles. As an interim solution, a flat monthly profile was applied to the states with a
monthly ag NH3 inventory.
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day-to-day (and hour-to-hour) variability. In addition, the MOVES emissions have an artificial jump
between months which is due to the inventory providing new emissions for each month which are then
temporalized within the month but not between months. The SMOKE-MOVES emissions have a
smoother transition between the months.
Figure 3-10. Example of SMOKE-MOVES temporal variability of NOx emissions
BHM (Jefferson Co., AL) daily NOX
	I II I	
MOVES
SMOKE-MOVES
OrHfN^-inr^ooarHrMrj-inioooc^iHfNjroLnvoooworvimLn'X)
OOOOOOOOfHrHiHHrHrHpHrNIINfNfNfNfNfNmmmmm
LninifitntninmtnintnininLnifiinLnifiininmtnininmininin
ooooooooooooooooooooooooooo
ooooooooooooooooooooooooooo
fNrMfM(N(NfSlfN(Nf\rMr\J(N(Nrs|fNfM(NfNfMrslfM(N(N(NfN(N(N
Julian date
For the onroad and onroad_rfl sectors, the "inventories" referred to in Table 3-12 actually consist of
activity data. For RPP and RPV processes, the VPOP inventory is annual and does not need
temporalization. For RPD, the VMT inventory is monthly and was temporalized to days of the week and
then to hourly VMT through temporal profiles. The RPD processes require a speed profile (SPDPRO)
that consists of vehicle speed by hour for a typical weekday and weekend day. Unlike other sectors, the
temporal profiles and SPDPRO will impact not only the distribution of emissions through time but also
the total emissions. Because SMOKE-MOVES' process RPD calculates emissions from VMT, speed and
meteorology, if one shifted the VMT or speed to different hours, it would align with different
temperatures and hence different EF. In other words, two SMOKE-MOVES runs with identical annual
VMT, meteorology, and MOVES EF, will have different total emissions if the temporalization of VMT
changes.
In previous platforms, the diurnal profile for VMT18 varied by road type but not by vehicle type (see
Figure 3-11). These profiles were used throughout the nation.
18 These same profiles were used for onroad emissions in the 2005 platform.
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Figure 3-11. Previous onroad diurnal weekday profiles for urban roads
Diurnal Weekday profiles - urban
1 2 3 4 5 6
n	1	1	1	1	1	1	1	1	1	1	1	r
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
-SMOKE interstate
-SMOKE other expwy
-SMOKE major art.
-SMOKEminorart.
-SMOKE collector
-SMOKE local
MOVES default
EPA wanted to create new diurnal profiles that could differentiate by vehicle type as well as by road type
and would potentially vary over geography. The 2011NEIvl process provided an opportunity to update
the diurnal profile with information submitted by states. States submitted MOVES county databases
(CDBs) that included information on the distribution of VMT by hour of day and by day of week19 (see
the 2011NEIvl TSD for details on the submittal process for onroad). EPA decided not to update the day
of week profile because MOVES only differentiated weekday versus weekend while the default SMOKE
profiles differentiated each of the 7 days. EPA mined the state submitted MOVES CDBs for non-default
diurnal profiles2". The list of potential diurnal profiles was then analyzed to see whether the profiles
varied by vehicle type, road type, weekday vs. weekend, and by county within a state (see Figure 3-12).
Figure 3-12. Variation in MOVES diurnal profiles
varies by vehicle,
but not road or day
varies bv road and day,
but not vehicle
varies by road,
but not vehicle or day
varies by day,
but not vehicle or road
no variation
(1 profile for county)
MOVES temporal profile diurnal submittals
| California (not MOVES)
varies by vehicle, road, and day
varies by vehicle and road,
but not day
13 The MOVES tables are the hourvmtfraction and the dayvmi Tract ion.
Further QA was done to remove duplicates and profiles that were missing two or more hours. If they were missing a single
hour, the missing hour could be calculated by subtracting all other hours fractions from 1.
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EPA attempted to maximize the use of state and/or county specific diurnal profiles. If a specific state or
county's profiles varied by vehicle type or/and road type, then the submitted profile was used. If the
profile had less variability than the old SMOKE defaults (i.e. neither varied by vehicle type nor road
type), then a new default profile would be used (see below for description of new profiles). This analysis
was done separately for weekdays and for weekends, therefore some areas had submitted profiles for
weekdays but defaults for weekends. The result was a set of profiles that varied geographically
depending on whether or not the profile was submitted and the characteristics of the profiles (see Figure
3-13).
Figure 3-13. Use of submitted versus new national default profiles
MOVES temporal profile diurnal use
ndicates where we are using MOVES-submitted diurnal profiles, and where we are using national defaults.
I
I
S/L/T weekday and weekend
S/L/T weekday,
national weekend
national weekday and weekend
A new set of diurnal profiles was developed from the submitted profiles that varied by both vehicle type
and road type. Before developing the national profiles, there needs to be a mapping between MOVES
road types and SMOKE road types (i.e., the last three digits of the SCC) and between MOVES source
types and SMOKE vehicle types. The mapping between road types is relatively straight forward (see
Table 3-13). Basically the road types are consolidated into 4 types in MOVES, therefore the new profiles
will not differentiate at the level of the SMOKE road type. For example, the SMOKE "urban interstate"
(SCCLAST3=230) will have the same profile as the SMOKE "urban other freeways and expressways"
(SCCLAST3=250). The mapping between MOVES source type and SMOKE vehicle type is more
complicated; it is a many-to-many mapping (see the 201 INEIvl TSD for more details).
64

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Table 3-13. Mapping of MOVES to SMOKE road types
MOVES
roadtype ID
Description
SMOKE
SCCLAST3
Description
2
Rural Restricted Access
110
Rural Interstate: Total
3
Rural Unrestricted Access
130
Rural Other Principal Arterial: Total
150
Rural Minor Arterial: Total
170
Rural Major Collector: Total
190
Rural Minor Collector: Total
210
Rural Local: Total
4
Urban Restricted Access
230
Urban Interstate: Total
250
Urban Other Freeways and Expressways: Total
5
Urban Unrestricted Access
270
Urban Other Principal Arterial: Total
290
Urban Minor Arterial: Total
310
Urban Collector: Total
330
Urban Local: Total
For the purposes of constructing the SMOKE diurnal profiles, all MOVES profiles for the road type and
for any overlapping source types are averaged together to create a single diurnal profile for a specific
county, SMOKE road type, SMOKE vehicle type, and weekday or weekend . This process is also used
for creating SMOKE versions of the submitted profile in the non-default regions (described above). The
states that submitted profiles that varied by vehicle and road types for weekdays were: Idaho, Maine,
Michigan, New Jersey, Ohio, and Pennsylvania. The states that submitted profiles that varied by vehicle
and road types for weekends were: Idaho, Maine, and Michigan. EPA created individual profiles for
each state (averaging over the counties within) to create a single profile by state, vehicle type, road type,
and weekday or weekend. The states individual profiles were averaged together to create a new default
profile21. Figure 3-14 shows two new default profiles for light duty gas vehicles (LDGV, SCC7 2201001)
and heavy, heavy duty diesel vehicles (HHDDV, SCC7 2230074) on restricted urban roadways
(interstates and freeways, SCCLAST3=230 and 250) for weekdays. The grey lines are the individual state
profiles, the black line is the new default profile, and the 2 colored lines are the previous SMOKE default
profiles. Note that there are two previous SMOKE profiles for this road type, but that they don't vary by
vehicle. In contrast, the new default profile does vary by vehicle and places more LDGV VMT (left plot)
in the rush hours while placing HHDDV VMT (right plot) predominately in the middle of the day with a
longer tail into the evening hours and early morning. For a full list of the default profiles, see
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/reports/temporal profiles/
onroad_default_hourly_profile_plots_2011ed.zip.
21 Note that the states were weighted equally in the average independent of the size of the state or the variation in submitted
county data.
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DRAFT
Figure 3-14. Updated national default profiles for LDGV vs. HHDDV, urban restricted weekday
Hourly VMT fraction: multi_comp_2201001_230-250_weekday
0.08
c 0.06
> 0.04
0.02
0.00
hour
Hourly VMT fraction: multi_comp_2230074_230-250_weekday
0.08
c 0.06
multi_comp_2230O74_230-25
0_weekday
Freeways Profile 2008
> 0.04
0.02
0.00
hour
For California, CARB supplied diurnal profiles that varied by vehicle type, day of the week22, and air
basin. These CARB specific profiles were used in developing EPA estimates for California. For Texas,
the profiles used were a combination of state supplied (via MOVES CDBs) and new national defaults.
Although EPA adjusted the total emissions to match California's and Texas' submittals to the
72 California's diurnal profiles varied within the week. Monday, Friday, Saturday, and Sunday had unique profiles and
Tuesday, Wednesday. Thursday had the same profile.
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201 INEIvl, the temporalization of these emissions took into account both the state specific VMT profiles
and the SMOKE-MOVES process of incorporating meteorology. For more details on the adjustments to
California's and Texas' onroad emissions, see the 201 INEIvl TSD.
3.3.6 Additional sector specific details (afdust, beis, c1c2rail, c3marine, nonpt,
ptfire)
For the afdust sector, meteorology is not used in the development of the temporal profiles, but it is used to
reduce the total emissions based on meteorological conditions. These adjustments are applied through
sector-specific scripts, beginning with the application of land use-based gridded transport fractions and
then subsequent zero-outs for hours during which precipitation occurs or there is snow cover on the
ground. The land use data used to reduce the NEI emissions explains the amount of emissions that are
subject to transport. This methodology is discussed in (Pouliot et. al.. 20101 and in Fugitive Dust
Modeling for the 2008 Emissions Modeling Platform (Adelman, 2012). The precipitation adjustment is
applied to remove all emissions for days where measurable rain occurs. Therefore, the afdust emissions
vary day-to-day based on the precipitation and/or snow cover for that grid cell and day. Both the
transport fraction and meteorological adjustments are based on the gridded resolution of the platform;
therefore, somewhat different emissions will result from different grid resolutions. Application of the
transport fraction and meteorological adjustments prevents the overestimation of fugitive dust impacts in
the grid modeling as compared to ambient samples.
Biogenic emissions in the beis sector vary by every day of the year because they are developed using
meteorological data including temperature, surface pressure, and radiation/cloud data. The emissions are
computed using appropriate emission factors according to the vegetation in each model grid cell, while
taking the meteorological data into account.
For the clc2rail and c3marine sectors, emissions are allocated with flat monthly and day of week profiles,
and most emissions are also allocated with flat hourly profiles.
For the nonpt sector, most the inventories are annual except for the agricultural burning (SCC
2801500000) inventory which was allocated to months by adding up the available values for each day of
the month. For all agricultural burning, the diurnal temporal profile used reflected the fact that burning
occurs during the daylight hours - see Figure 3-15 (McCarty et al., 2009). This puts most of the emissions
during the work day and suppresses the emissions during the middle of the night. All states used a
uniform profile for each day of the week for all agricultural burning emissions, except for the following
states that for which EPA used state-specific day of week profiles: Arkansas, Kansas, Louisiana,
Minnesota, Missouri, Nebraska, Oklahoma, and Texas.
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Figure 3-15. Agricultural burning diurnal temporal profile
Comparison of Agricultural Burning Temporal Profiles
0.18
0.16
0.14
	New McCarty Profile
	OLD EPA
0.12
c
o
0.1
10.08
0.06
0.04
0.02
12345678 9 10111213141516171819 20 2122 23 24
For the ptfire sector, the inventories are in the daily point fire format ORL PTDAY. The ptfire sector is
used in the model evaluation case (201 led and in the future base case (2018ed). The 2007 and earlier
platforms had additional regulatory cases that used averaged fires and temporally averaged EGU
emissions, but the 2011 platform uses base year-specific (i.e., 2011) data for both cases.
For the nonroad sector, while the NEI only stores the annual totals, the modeling platform uses monthly
inventories from output from NMIM. For California, a monthly inventory was created from CARB's
annual inventory using EPA-estimated NMIM monthly results to compute monthly ratios by pollutant and
SCC7 and these ratios were applied to the CARB inventory to create a monthly inventory. .
3.4 Spatial Allocation
The methods used to perform spatial allocation are summarized in this section. For the modeling
platform, spatial factors are typically applied by county and SCC. As described in Section 3.1, spatial
allocation was performed for a national 12-km domain. To accomplish this, SMOKE used national 12-
km spatial surrogates and a SMOKE area-to-point data file. For the U.S., EPA updated surrogates to use
circa 2010-2011 data wherever possible. For Mexico, updated spatial surrogates were used as described
below. For Canada surrogates provided by Environment Canada were used and are unchanged from the
2007 platform. The U.S., Mexican, and Canadian 12-km surrogates cover the entire CONUS domain
12US1 shown in Figure 3-1. The remainder of this subsection provides further detail on the origin of the
data used for the spatial surrogates and the area-to-point data.
Additional documentation on the 2011 spatial surrogates is available at
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/reports/spatial surrogates/ in the files
US_SpatialSurrogate_Documentation_v091113.pdf and US_SpatialSurrogate_Workbook_v093013.xlsx.
The spatial cross reference file is in gsref_201 l.xlsx. Plots of the spatial surrogates are available in
all_surrogate_maps_201 lplatform_12USl_v2.pdf. Note that these are plots of the surrogate fractions
summed by grid cell, so grid cells that overlap multiple counties can show values greater than one. These
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maps are only to give an idea of the spatial distribution of the surrogates. Allocations of CAP emissions to
each of the surrogate codes is given in 201 led_spatial_surrogate_CAPs_febl 12014.xlsx.
3.4.1 Spatial Surrogates for U.S. emissions
There are more than 70 spatial surrogates available for spatially allocating U.S. county-level emissions to
the 12-km grid cells used by the air quality model. As described in Section 3.4.2, an area-to-point
approach overrides the use of surrogates for some sources. Table 3-14 lists the codes and descriptions of
the surrogates. The surrogates in bold have been updated with 2010-based data, including 2010 census
data at the block group level, 2010 American Community Survey Data for heating fuels, 2010
TIGER/Line data for railroads and roads, the 2006 National Land Cover Database, 2011 gas station and
dry cleaner data, and the 2012 National Transportation Atlas Data for rail-lines, ports and navigable
waterways. Surrogates for ports (801) and shipping lanes (802) were developed based on the 201 INEIvl
shapefiles: Ports_032310_wrf and ShippingLanes_111309FINAL_wrf, but also included shipping lane
data in the Great Lakes and support vessel activity data in the Gulf of Mexico.
The creation of surrogates and shapefiles for the U.S. was generated via the Surrogate Tool. The tool and
documentation and Spatial Allocator 3.6.
Table 3-14. U.S. Surrogates available for the 2011 modeling platform.
Code
Surrogate Description
Code
Surrogate Description
N/A
Area-to-point approach (see 3.3.1.2)
520
Commercial plus Industrial plus
Institutional
100
Population
525
Golf Courses + Institutional +Industrial +
Commercial
110
Housing
527
Single Family Residential
120
Urban Population
530
Residential - High Density
130
Rural Population
535
Residential + Commercial + Industrial +
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
160
Residential Heating - Wood
555
Professional/Technical plus General
Government
165
0.5 Residential Heating - Wood plus 0.5 Low
Intensity Residential
560
Hospital
170
Residential Heating - Distillate Oil
565
Medical Office/Clinic
180
Residential Heating - Coal
570
Heavy and High Tech Industrial
190
Residential Heating - LP Gas
575
Light and High Tech Industrial
200
Urban Primary Road Miles
580
Food, Drug, Chemical Industrial
210
Rural Primary Road Miles
585
Metals and Minerals Industrial
220
Urban Secondary Road Miles
590
Heavy Industrial
230
Rural Secondary Road Miles
595
Light Industrial
240
Total Road Miles
596
Industrial plus Institutional plus Hospitals
250
Urban Primary plus Rural Primary
600
Gas Stations
255
0.75 Total Roadway Miles plus 0.25 Population
650
Refineries and Tank Farms
260
Total Railroad Miles
675
Refineries and Tank Farms and Gas Stations
270
Class 1 Railroad Miles
680
Oil & Gas Wells, IHS Energy, Inc. and
USGS
261
NTAD Total Railroad Density
700
Airport Areas
271
NTAD Class 1,2,3 Railroad Density
710
Airport Points
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Code
Surrogate Description
Code
Surrogate Description
280
Class 2 and 3 Railroad Miles
720
Military Airports
300
Low Intensity Residential
800
Marine Ports
310
Total Agriculture
801
NEI Ports
312
Orchards/Vineyards
802
NEI Shipping Lanes
320
Forest Land
807
Navigable Waterway Miles
330
Strip Mines/Quarries
808
Gulf Tug Zone Area
340
Land
810
Navigable Waterway Activity
350
Water
812
Midwest Shipping Lanes
400
Rural Land Area
850
Golf Courses
500
Commercial Land
860
Mines
505
Industrial Land
870
Wastewater Treatment Facilities
510
Commercial plus Industrial
880
Drycleaners
515
Commercial plus Institutional Land
890
Commercial Timber
For the onroad sector, the on-network (RPD) emissions were spatially allocated to roadways, and the off-
network (RPP and RPV) emissions were allocated to population. For the onroad rfl sector, the emissions
were spatially allocated to gas station locations. For the oil and gas sources in the nonpt sector, the WRAP
Phase III sources have detailed 2008-based basin-specific spatial surrogates shown in Table 3-15. These
surrogates were also updated in the northeast Marcellus Shale region using 2011 data. Any remaining oil
and gas sources still use the 2005-based surrogate "Oil & Gas Wells, IHS Energy, Inc. and USGS" (680)
developed for oil and gas SCCs. The surrogates in Table 3-15 were applied for the counties listed in
Table 3-7.
Table 3-15. Spatial Surrogates for WRAP and Marcellus Shale Oil and Gas Data
Country
Code
Surrogate Description
USA
689
Gas production at all wells
USA
690
Oil production at all wells
USA
691
Well count - CBM wells
USA
692
Spud count
USA
693
Well count - all wells
USA
694
Oil production at Oil wells
USA
695
Well count - oil wells
USA
696
Gas production at gas wells
USA
697
Oil production at gas wells
USA
698
Well count - gas wells
USA
699
Gas production at CBM wells
Not all of the available surrogates are used to spatially allocate sources in the modeling platform; that is,
some surrogates shown in Table 3-14 were not assigned to any SCCs, although many of the "unused"
surrogates are actually used to "gap fill" other surrogates that are assigned. When the source data for a
surrogate has no values for a particular county, gap filling is used to provide values for the surrogate in
those counties to ensure that no emissions are dropped when the spatial surrogates are applied to the
emission inventories. Table 3-16 shows the total of CAP emissions (i.e., NH3, NOx, PM2.5, SO2, and
VOC) by sector. Appendix E shows the emissions totals by surrogate code and sector for most of the
CAPs. Some sectors use only one surrogate, those are listed below:
• ag: Total Agriculture (#310) with 3,524,607 tons of NH3;
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•	onroad_rfl: Gas Stations (#600) with 157,629 tons of VOC; and
•	rwc: 0.5 Residential Heating - Wood plus 0.5 Low Intensity Residential (#165) with 903,651 tons
of CAPs.
Table 3-16. Total CAP emissions by sector for U.S. Surrogates
Code
Description
afdust
clc2rail
nonpt
nonroad
nP_oilgas
onroad
100
Population


1,221,647
47,453

2,742,952
120
Urban Population





521,778
130
Rural Population
1,102,192




282,583

Housing Change and






140
Population
162,157

224,499
662,641



Residential Heating -






150
Natural Gas


278,120




0.5 Residential Heating -







Wood plus 0.5 Low






165
Intensity Residential







Residential Heating -






170
Distillate Oil


142,700



180
Residential Heating - Coal


12,791



190
Residential Heating - LP Gas


42,196



200
Urban Primary Road Miles





2,534,014
210
Rural Primary Road Miles





1,609,013

Urban Secondary Road






220
Miles





281,011
230
Rural Secondary Road Miles





491,782
240
Total Road Miles
287,531





240
Total Road Miles


6,825




Urban Primary plus Rural






250
Primary


102,793



260
Total Railroad Miles


2,195




NTAD Total Railroad






261
Density

15,271

3,559



NTAD Class 12 3 Railroad






271
Density

802,720




280
Class 2 and 3 Railroad Miles

45,114




300
Low Intensity Residential


162,807
234,134


310
Total Agriculture
896,741

709,177
586,125


312
Orchards/Vineyards


5,720



320
Forest Land


347



330
Strip Mines/Quarries
59,782





350
Water



770,993


400
Rural Land Area
1

1,188
663,518


500
Commercial Land


115,905



505
Industrial Land


747,634
186,522


510
Commercial plus Industrial


225,266
281,481



Commercial plus






515
Institutional Land


294,718




Commercial plus Industrial






520
plus Institutional


11,252
122,236



Golf Courses plus







Institutional plus Industrial






525
plus Commercial


0
220,996


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DRAFT
Code
Description
afdust
clc2rail
nonpt
nonroad
nP_oilgas
onroad
527
Single Family Residential


0




Residential + Commercial +







Industrial + Institutional +






535
Government


334,251



540
Retail Trade (COM1)


1,375



545
Personal Repair (COM3)


63,005




Professional/Technical







(COM4) plus General






555
Government (GOV1)


2,872



560
Hospital (COM6)


9




Light and High Tech






575
Industrial (IND2 + IND5)


2,554




Food, Drug, Chemical






580
Industrial (IND3)


11,626




Metals and Minerals






585
Industrial (IND4)


615



590
Heavy Industrial (IND1)


156,032



595
Light Industrial (IND2)


80,484



600
Gas Stations


413,518



650
Refineries and Tank Farms


130,222




Refineries and Tank Farms






675
and Gas Stations


1,203



680
Oil and Gas Wells




116,568

692
Spud count




77,009

693
Well count - all wells




8,599

694
Oil production at Oil wells




1,241,682

695
Well count - oil wells




221,759

696
Gas production at gas wells




1,130,789

697
Oil production at gas wells




1

698
Well count - gas wells




294,244

700
Airport Areas


32,030



801
Port Areas


12,578



802
Shipping Lanes

571,927




808
Gulf Tug Zone Area

5,503




820
Ports NEI2011 NOx

76,008




850
Golf Courses



9,628


860
Mines



3,874



Wastewater Treatment






870
Facilities


6,018



880
Drycleaners


10,026



890
Commercial Timber



23,202


3.4.2 Allocation method for airport-related sources in the U.S.
There are numerous airport-related emission sources in the NEI, such as aircraft, airport ground support
equipment, and jet refueling. The modeling platform includes the aircraft emissions as point sources. For
the modeling platform, EPA used the SMOKE "area-to-point" approach for only airport ground support
equipment (nonroad sector), and jet refueling (nonpt sector). The approach is described in detail in the
2002 platform documentation. The ARTOPNT file that lists the nonpoint sources to locate using point
data was unchanged from the 2005-based platform.
72

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DRAFT
3.4.3 Surrogates for Canada and Mexico emission inventories
The surrogates for Canada to spatially allocate the 2006 Canadian emissions are unchanged from the 2007
platform. The spatial surrogate data came from Environment Canada, along with cross references. The
surrogates they provided were outputs from the Surrogate Tool (previously referenced). Per Environment
Canada, the surrogates are based on 2001 Canadian census data. The Canadian surrogates used for this
platform are listed in Table 3-17. The leading "9" was added to the surrogate codes to avoid duplicate
surrogate numbers with U.S. surrogates. Some new surrogates for Mexico became available in the 2011
platform. The surrogates are circa 1999 and 2000 and were based on data obtained from the Sistema
Municpal de Bases de Datos (SIMBAD) de INEGI and the Bases de datos del Censo Economico 1999.
Most of the CAPs allocated to the Mexico and Canada surrogates are shown in Table 3-18. The entries in
this table are for the othar sector except for the MEX Total Road Miles and The CAN traffic rows, which
are for the othon sector.
Table 3-17. Canadian Spatial Surrogates
Code
Description
Code
Description
9100
Population
9493
Warehousing and storage
9101
Total dwelling
9494
Total Transport and warehouse
9102
Urban dwelling
9511
Publishing and information services
9103
Rural dwelling
9512
Motion picture and sound recording
industries
9104
Total Employment
9513
Broadcasting and telecommunications
9106
ALL INDUST
9514
Data processing services
9111
Farms
9516
Total Info and culture
9113
Forestry and logging
9521
Monetary authorities - central bank
9114
Fishing hunting and trapping
9522
Credit intermediation activities
9115
Agriculture and forestry activities
9523
Securities commodity contracts and other
financial investment activities
9116
Total Resources
9524
Insurance carriers and related activities
9211
Oil and Gas Extraction
9526
Funds and other financial vehicles
9212
Mining except oil and gas
9528
Total Banks
9213
Mining and Oil and Gas Extract activities
9531
Real estate
9219
Mining-unspecified
9532
Rental and leasing services
9221
Total Mining
9533
Lessors of non-financial intangible assets
(except copyrighted works)
9222
Utilities
9534
Total Real estate
9231
Construction except land subdivision and
land development
9541
Professional scientific and technical
services
9232
Land subdivision and land development
9551
Management of companies and enterprises
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
73

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DRA
FT
Code
Description
Code
Description
9316
Leather and allied product manufacturing
9624
Social assistance
9321
Wood product manufacturing
9625
Total Service
9322
Paper manufacturing
9711
Performing arts spectator sports and related
industries
9323
Printing and related support activities
9712
Heritage institutions
9324
Petroleum and coal products manufacturing
9713
Amusement gambling and recreation
industries
9325
Chemical manufacturing
9721
Accommodation services
9326
Plastics and rubber products manufacturing
9722
Food services and drinking places
9327
Non-metallic mineral product manufacturing
9723
Total Tourism
9331
Primary Metal Manufacturing
9811
Repair and maintenance
9332
Fabricated metal product manufacturing
9812
Personal and laundry services
9333
Machinery manufacturing
9813
Religious grant-making civic and
professional and similar organizations
9334
Computer and Electronic manufacturing
9814
Private households
9335
Electrical equipment appliance and
component manufacturing
9815
Total other services
9336
Transportation equipment manufacturing
9911
Federal government public administration
9337
Furniture and related product manufacturing
9912
Provincial and territorial public
administration (9121 to 9129)
9338
Miscellaneous manufacturing
9913
Local municipal and regional public
administration (9131 to 9139)
9339
Total Manufacturing
9914
Aboriginal public administration
9411
Farm product wholesaler-distributors
9919
International and other extra-territorial
public administration
9412
Petroleum product wholesaler-distributors
9920
Total Government
9413
Food beverage and tobacco wholesaler-
distributors
9921
Commercial Fuel Combustion
9414
Personal and household goods wholesaler-
distributors
9922
TOTAL DISTRIBUTION AND RETAIL
9415
Motor vehicle and parts wholesaler-
distributors
9923
TOTAL INSTITUTIONAL AND
GOVERNEMNT
9416
Building material and supplies wholesaler-
distributors
9924
Primary Industry
9417
Machinery equipment and supplies
wholesaler-distributors
9925
Manufacturing and Assembly
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
74

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DRA
FT
Code
Description
Code
Description
9444
Building material and garden equipment and
supplies dealers
9932
CANRAIL
9445
Food and beverage stores
9933
Forest fires
9446
Health and personal care stores
9941
PAVED ROADS
9447
Gasoline stations
9942
UNPAVED ROADS
9448
clothing and clothing accessories stores
9943
HIGHWAY
9451
Sporting goods hobby book and music stores
9944
ROAD
9452
General Merchandise stores
9945
Commercial Marine Vessels
9453
Miscellaneous store retailers
9946
Construction and mining
9454
Non-store retailers
9947
Agriculture Construction and mining
9455
Total Retail
9950
Intersection of Forest and Housing
9481
Air transportation
9960
TOTBEEF
9482
Rail transportation
9970
TOTPOUL
9483
Water Transportation
9980
TOTSWIN
9484
Truck transportation
9990
TOTFERT
9485
Transit and ground passenger transportation
9993
Trail
9486
Pipeline transportation
9994
ALLROADS
9487
Scenic and sightseeing transportation
9995
3 0UNPA VED7 Otrail
9488
Support activities for transportation
9996
Urban area
9491
Postal service
9997
CHBOISQC
9492
Couriers and messengers
9991
Traffic
Table 3-18.CAPs Allocated to Mexican and Canadian Spatial Surrogates
Srg code
Description
nh3
NOx
pm25
so2
voc
22
MEX Total Road Miles
15,965
370,867
34,396
13,713
375,276
10
MEX Population
0
0
0
0
431,231
12
MEX Housing
0
161,013
17,483
2,123
452,685
14
MEX Residential Heating - Wood
0
20,093
211,525
2,859
380,572
16
MEX Residential Heating - Distillate Oil
0
38
0
11
2
20
MEX Residential Heating - LP Gas
0
25,303
787
63
614
22
MEX Total Road Miles
0
0
0
0
3,513
24
MEX Total Railroads Miles
0
74,969
1,669
663
2,824
26
MEX Total Agriculture
679,212
164,144
72,372
2,127
43,958
28
MEX Forest Land
0
16,224
67,683
660
79,018
32
MEX Commercial Land
0
125,211
7,726
0
286,982
34
MEX Industrial Land
0
45,831
5,684
59,201
133,440
36
MEX Commercial plus Industrial Land
0
0
0
0
332,495
38
MEX Commercial plus Institutional Land
0
6,400
216
84
28,293
40
Residential (RES 1 -
4)+Commercial+Industrial+Institutional+
Government
0
8
20
0
241,710
42
MEX Personal Repair (COM3)
0
0
0
0
33,616
75

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DRAFT
Srg code
Description
nh3
NOx
pm25
so2
voc
44
MEX Airports Area
0
14,639
0
1,149
6,857
46
MEX Marine Ports
0
124,951
2,991
1,482
1,099
48
Brick Kilns - Mexico
0
776
6,691
0
10,244
50
Mobile sources - Border Crossing - Mexico
0
454
0
0
2,668
9100
CAN Population
603
0
276
0
304
9101
CAN total dwelling
643
46,256
12,783
14,698
32,944
9106
CAN ALL INDUST
133
21,526
381
3,921
2
9113
CAN Forestry and logging
1,582
8,561
28,622
1,809
36,114
9115
CAN Agriculture and forestry activities
160
239,553
25,318
9,092
26,526
9116
CAN Total Resources
0
17
0
0
5
9212
CAN Mining except oil and gas
0
0
5,391
0
0
9221
CAN Total Mining
42
2,292
45,374
728
26
9222
CAN Utilities
189
14,882
369
1,124
255
9233
CAN Total Land Development
17
20,789
1,928
981
2,551
9308
CAN Food manufacturing
0
0
0
0
4,535
9323
CAN Printing and related support activities
0
0
0
0
25,203
9324
CAN Petroleum and coal products
manufacturing
0
0
2,402
0
0
9327
CAN Non-metallic mineral product
manufacturing
0
238
7,708
2,941
1,218
9331
CAN Primary Metal Manufacturing
0
98
5,062
12
6
9412
CAN Petroleum product wholesaler-distributors
0
0
0
0
70,125
9416
CAN Building material and supplies
wholesaler-distributors
2
0
1,461
3,259
560
9448
CAN clothing and clothing accessories stores
0
0
0
0
328
9562
CAN Waste management and remediation
services
165
893
1,596
1,998
16,551
9921
CAN Commercial Fuel Combustion
494
33,816
2,750
35,471
850
9924
CAN Primary Industry
0
0
0
0
219,282
9925
CAN Manufacturing and Assembly
0
0
0
0
139,227
9931
CAN OTHERJET
9
14,388
548
1,139
7,629
9932
CAN CAN RAIL
109
122,694
4,093
5,737
3,304
9942
CAN UNPAVED ROADS
40
3,462
3,499
48
152,674
9945
CAN Commercial Marine Vessels
28
45,454
6,404
14,325
61,139
9946
CAN Construction and mining
247
156,770
10,070
5,667
17,180
9947
CAN Agriculture Construction and mining
19
37,452
536
26
32,683
9950
CAN Intersection of Forest and Housing
1,053
11,700
120,045
1,671
173,130
9960
CAN TOTBEEF
176,156
0
7,420
0
317,394
9970
CAN TOTPOUL
74,204
0
2
0
264
9980
CAN TOTS WIN
122,094
0
996
0
3,186
76

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DRAFT
Srg code
Description
nh3
NOx
pm25
so2
voc
9990
CAN TOTFERT
178,791
0
9,279
0
0
9991
CAN traffic
22,294
550,896
10,888
5,548
285,104
9994
CAN ALLROADS
0
0
55,468
0
0
9995
CAN 30UNPAVED 70trail
0
0
106,707
0
0
9996
CAN urban area
0
0
284
0
0
77

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DRAFT
4 Development of 2018 Base-Case Emissions
This section describes the methods used for developing the 2018 future-year base-case emissions. The future
base-case projection methodologies vary by sector. With the exceptions discussed in Section 4.2, the 2018
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
November, 2013. The future base-case scenario reflects projected economic changes and fuel usage for
EGU and mobile sectors. The 2018 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 2018 emissions effects due to environmental rules and regulations,
consent decrees and settlements, plant closures, control devices updated since 2011, and forecast unit
construction through the calendar year 2018. In this analysis, the projected EGU emissions include the Final
Mercury and Air Toxics (MATS) rule announced on December 21. 2011 and the Clean Air Interstate Rule
(CAIR) issued March 10, 2005. More information on the EGU base case.
For mobile sources (onroad, onroad rfl, nonroad, clc2rail and c3marine sectors), all national measures for
which data were available at the time of modeling have been included. The Tier 3 standards proposed in
March, 2013 are represented (Vehicles and Engines). Efforts made to include some regional haze and state-
reported local controls as part of a larger effort to include more local control information on stationary non-
EGU sources are described further in Section 4.2. The following bullets summarize the projection methods
used for sources in the various sectors, while additional details and data sources are given in the following
subsections and Table 4-1.
•	EGU sector (ptegu and ptegu_pk): Unit-specific estimates from IPM version 5.13, including CAIR
and Final MATS.
•	Non-IPM sector (ptnonipm): Projection factors and percent reductions reflect comments received
during the development of the Cross-State Air Pollution Rule (CSAPR) along with emission
reductions due to national and local rules, control programs, plant closures, consent decrees and
settlements. Projection approaches for corn ethanol and biodiesel plants, refineries and upstream
impacts represent the Energy Independence and Security Act of 2007 (EISA). Airport-specific
terminal area forecast (TAF) data were used for aircraft to account for projected changes in
landing/takeoff activity.
•	Point and nonpoint oil and gas sectors (pt oilgas and np oilgas): Regional projection factors by
product type using Annual Energy Outlook (AEO) 2013 projections to year 2018. Cobenefits of
stationary engines CAP-cobenefit reductions (RICE NESHAP) and New Source Performance
Standards (NSPS) VOC controls reflected for select source categories.
•	Fires sector (ptfire): No growth or control - 2011 estimates used directly.
•	Agricultural sector (ag): Projection factors for livestock estimates based on expected changes in
animal population from 2005 Department of Agriculture data, updated according to EPA experts in
July 2012; fertilizer application NH3 emissions projections include upstream impacts EISA.
•	Area fugitive dust sector (afdust): Projection factors for dust categories related to livestock estimates
based on expected changes in animal population and upstream impacts from EISA.
•	Residential Wood Combustion (rwc): Projection factors that reflect assumed growth of wood
burning appliances based on sales data, equipment replacement rates and change outs. These changes
include a growth in lower-emitting stoves and a reduction in higher emitting stoves.
78

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•	Remaining Nonpoint sector (nonpt): Projection factors implement comments received during Cross
State Air Pollution Rule development and emission reductions due to control programs. PFC
projection factors reflecting impact of the final Mobile Source Air Toxics (MSAT2) rule. Upstream
impacts from EISA, including post-2007 cellulosic ethanol plants are reflected.
•	Nonroad mobile sector (nonroad): Other than for California and Texas, this sector uses data from a
run of NMIM that utilized NONROAD2008a with database "NCD20130731_nei2018dvl", using
future-year equipment population estimates and control programs to the year 2018 and inputs that
were either state-supplied as part of the 201 INEIvl process or using national level inputs. Final
controls from the final locomotive-marine and small spark ignition OTAQ rules are included.
California and Texas-specific data were provided by CARB and TCEQ, respectively.
•	Locomotive, and non-Class 3 commercial marine sector (clc2rail): For all states except California,
projection factors for Class 1 and Class 2 commercial marine and locomotives reflect final
locomotive-marine controls and RFS2 adjustments. California projected year-2017 inventory data
were provided by CARB.
•	Class 3 commercial marine vessel (c3marine): Base-year 2011 emissions grown and controlled to
2018, 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 (extended idle mode) and
MOVESTier3NPRM-based emissions factors for year 2018 were developed using the same
representative counties, state-supplied data, meteorology, and procedures that were used to produce
the 2011 emission factors described in Section 2.3.1. California and TCEQ-specific data were
provided by CARB and TCEQ, respectively. This sector includes all non-refueling onroad mobile
emissions (exhaust, extended idle, evaporative, evaporative permeation, brake wear and tire wear
modes).
•	Onroad refueling mode (onroad rfl): the same projection approach is used as for the onroad sector
and processing is described in Section 2.3.2, in that emission factors are from MOVES2010b and that
California and Texas did not include state supplied emissions.
•	Other onroad (othar): No growth or control for Canada because data are not available. Mexico
inventory data were grown from year 1999 to 2018.
•	Other nonroad/nonpoint (othon): No growth or control for Canada. Mexico inventory data were
grown from year 1999 to 2018.
•	Other point (othpt): No growth or control for Canada and offshore oil. Mexico inventory data were
grown from 1999 to year 2018. Non-U.S. C3 CMV data projected using the same methodology as
the c3 marine sector.
•	Biogenic: 2011 emissions used for all future-year scenarios.
Table 4-1 summarizes the control strategies and growth assumptions by source type that were used to create
the U.S. 2018 base-case emissions from the 201 lv6 base-case inventories. Lists of the control, closures,
projection packets (datasets) used to create 2018 future year base-case scenario inventories from the 2011
base case are provided in
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/2018emissions/2Q18ed CoST%20packets.zip in the file
2018ed_Projections_with_CoST_23decl3.xlsx. These packets were processed through EPA Control
Strategy Tool (CoST) to create future year inventories. The CoST packets are formatted in the same way as
those needed for SMOKE and are available on the 201 lv6 web site within the CoST_packets directory
mentioned above in the file 2018ed_CoST packets.zip. Summaries of the emissions changes resulting from
all CoST packets (control programs, projections and closures) can be found in the reports directory of the
79

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DRAFT
2011/2018 emissions modeling platform release:
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/reports/2018 emissions/ in the files
2018ed_CoST_ptnonipm_facility_summary.xlsx and 2018ed_CoST_sector_summaries.xlsx.
Table 4-1. Control strategies and growth assumptions for creating the 2018 base-case emissions inventories
from the 2011 base case
Control Strategies and/or growth assumptions
(grouped by standard and approach used to apply to the inventory)
CAPs
affected
Section
Non-EGU Point (ptnonipm and pt oilgas sectors) 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 2011 to 2018, also including upstream RFS2 impacts on
agricultural-related activities such as pesticide and fertilizer production
All
4.2.2
Oil and gas production AEO-based regional growth factors and VOC NSPS controls
All
4.2.4
Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsiderations
NOx,
CO, PM,
S02
4.2.3
State fuel sulfur content rules for fuel oil - as of July, 2012, effective only in Maine, Massachusetts,
New Jersey, New York and Vermont
S02
4.2.6
Industrial/Commercial/Institutional Boilers and Process Heaters MACT with Reconsideration
Amendments
CO, PM,
S02,
VOC
4.2.7
NESHAP: Portland Cement census-division level based on Industrial Sector Integrated Solutions
(ISIS) policy emissions to year 2018. The ISIS results are from the ISIS-Cement model runs for the
NESHAP and NSPS analysis of August 2013 and include closures.
All
4.2.8
Future baseline inventory improvements received from states and a 2005 platform NODA and
comments from the CSAPR proposal, including local controls, fuel switching, unit closures and
consent decrees
All
4.2.9
Facility and unit closures obtained from various sources such as states, industry and web posting,
EPA staff and post-2011 inventory submittals
All
4.2.10
Aircraft growth via Itinerant (ITN) operations at airports to 2018
All
4.2.10
Boat Manufacturing MACT rule, VOC: national applied by SCC
VOC
4.2.11.1
Lafarge and Saint Gobain consent decrees
NOx,
PM, S02
4.2.9.3
Consent decrees on companies (based on information from the Office of Enforcement and
Compliance Assurance - OECA) apportioned to plants owned/operated by the companies
CO,
NOx,
PM, S02,
VOC
4.2.9.3
Refinery Consent Decrees: plant/unit controls
NOx,
S02
4.2.9.3
Commercial and Industrial Solid Waste Incineration (CISWI) revised NSPS
PM, S02
4.2.11.2
Nonpoint (afdust, ag, nonpt np oilgas, and rwc sectors) Controls and Growth Assumptions
MSAT2 and RFS2 impacts on portable fuel container growth and control from 2011 to 2018
VOC
4.2.1.3
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
Livestock emissions growth from year 2011 to 2018, also including upstream RFS2 impacts on
agricultural-related activities such as pesticide and fertilizer production
All
4.2.2
Oil and gas production AEO-based regional growth factors and VOC NSPS controls
All
4.2.4
80

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DRAFT
Control Strategies and/or growth assumptions
(grouped by standard and approach used to apply to the inventory)
CAPs
affected
Section
Reciprocating Internal Combustion Engines (RICE) NESHAP with reconsiderations
NOx.
CO, PM,
S02
4.2.3
State fuel sulfur content rules for fuel oil - as of July, 2012, effective only in Maine, Massachusetts,
New Jersey, New York and Vermont
S02
4.2.6
Residential wood combustion growth and change-outs from year 2011 to 2018
All
4.2.3
Future baseline inventory improvements received from states
NOx,
VOC
4.2.9
Onroad Mobile Controls
(All national in-force regulations are modeled. The list includes key recent mobile control strategies but is
not exhaustive.)
National Onroad Rules:
All onroad control programs finalized as of the date of the model run, including most recently:
Proposed Tier-3 Standards: March, 2013
Light-Duty Greenhouse Rule: March 2013
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:
California LEVIII Program
Ozone Transport Commission (OTC) LEV Program: January, 1995
Inspection and Maintenance programs
Fuel programs (also affect gasoline nonroad equipment)
Stage II refueling control programs
VOC
4.3
Nonroad Mobile Controls
(All national in-force regulations are modeled. The list includes recent key mobile control strategies but is
not exhaustive.)
National Nonroad Controls:
All nonroad control programs finalized as of the date of the model run including most recently:
Emissions Standards for New Nonroad Spark-Ignition Engines, Equipment, and Vessels: October,
2008
Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition Engines
Less than 30 Liters per Cylinder: March 2008
Clean Air Nonroad Diesel Final Rule - Tier 4: May, 2004
All
4.4
Locomotives:
Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition Engines
Less than 30 Liters per Cylinder: March, 2008
Clean Air Nonroad Diesel Final Rule - Tier 4: May, 2004
All
4.4.1
Commercial Marine:
Category 3 marine diesel engines Clean Air Act and International Maritime Organization standards:
April, 2010
Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition Engines
Less than 30 Liters per Cylinder: March 2008
Clean Air Nonroad Diesel Final Rule - Tier 4: May, 2004
All
4.4.2
81

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A quick background on the Control Strategy Tool (CoST)
CoST is used to apply most non-EGU projection/growth factors, controls and facility/unit/stack-level
closures to the 2011 emissions modeling inventories to create year 2018 inventories for the following
sectors: afdust, ag, clc2rail, nonpt, npoilgas, ptnonipm, pt oilgas and rwc. The CoST training manual.
The CoST development document, which is a more thorough but dated document of how to build and format
CoST input files (packets). The CoST packets used for 2018ed are available here: The CoST packets
themselves can be found in
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/2018emissions/2018ed CoST%20packets.zip.
CoST allows the user to apply projection factors, controls and closures at various geographic and inventory
key field resolutions. CoST provides the user with the ability to perform numerous quality assurance
routines as well as create SMOKE-ready future year inventories. There are also available linkages to
existing and user-defined control measures databases and it is up to the user to determine how control
strategies are developed and applied. EPA typically creates individual CoST datasets or "packets" that
represent specific intended purposes. For example, aircraft projections for airports are in a separate
PROJECTION packet from residential wood combustion sales/appliance turnover-based projections. CoST
uses three packet types as described below:
1.	CLOSURE: Applied first in CoST. This packet can be used to zero-out (close) point source
emissions at resolutions as broad as a facility to as specific as a stack. EPA used these types of
packets for known post-2011 controls as well as information on closures provided by states on
specific facilities, units or stacks. This packet type is only used in the ptnonipm sector in the 2011
platform.
2.	PROJECTION: This packet allows the user to increase or decrease emissions for virtually any
geographic and/or inventory source level. Projection factors are applied as simple scalars to the 2011
emissions inventories prior to the application of any possible subsequent CONTROLS. A
PROJECTION packet is necessary whenever emissions increase from 2011 and is also desirable
when information is based more on activity assumptions rather than known controls. EPA used
PROJECTION packet(s) in every non-EGU modeling sector in the 2011 platform.
3.	CONTROL: These packets are applied after any/all CLOSURE and PROJECTION packet entries.
The user has similar level of control as PROJECTION packets regarding specificity of geographic
and/or inventory source level application. Control factors are expressed as a percent reduction (0 to
100) and can be applied in addition to any pre-existing inventory control, or as a replacement control
where inventory controls are first backed out prior to the application of a more-stringent replacement
control.
As mentioned above, CoST first applies any/all CLOSURE information for point sources, then applies
PROJECTION packet information, followed by CONTROL packets. A hierarchy is used by CoST to
separately apply PROJECTION and CONTROL packets. In short, in a separate process for PROJECTION
and CONTROL packets, more specific information is applied in lieu of less-specific information in ANY
other packets. For example, a facility-level PROJECTION factor will be replaced by a unit-level, or facility
and pollutant-level PROJECTION factor. It is important to note that this hierarchy does not apply intra-
packet types; for example, CONTROL packet entries are applied irrespective of PROJECTION packet
hierarchies. A more specific example: a state/SCC-level PROJECTION factor will be applied before a
stack/pollutant-level CONTROL factor that impacts the same inventory record. However, an inventory
source that is subject to a CLOSURE packet record is removed from consideration of subsequent
PROJECTION and CONTROL packets.
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The implication for this hierarchy and intra-packet independence is important to understand and quality
assure when creating future year strategies. For example, with consent decrees, settlements and state
comments, the goal is typically to achieve a targeted reduction (from the 201 INEIvl) or a targeted 2018
emissions value. Therefore, as encountered with this 2018 base case, consent decrees and state comments
for specific cement kilns (expressed as CONTROL packet entries), needed to be applied instead of (not in
addition to) the more general approach of the PROJECTION packet entries for cement manufacturing. By
processing CoST control strategies with PROJECTION and CONTROL packets separated by type of
measure/program and also by consent decree and state comments, it is possible to show actual changes from
the 2011 inventory to the 2018 inventory for each packet.
Ultimately, CoST concatenates all PROJECTION packets into one PROJECTION dataset and uses a
hierarchal matching approach, a sample subset of which is shown in Table 4-2, to assign PROJECTION
factors to the inventory. For example, a packet entry with Ranking=l will supersede all other potential
inventory matches from other packets. CoST then computes the projected emissions from all PROJECTION
packet matches and then performs a similar routine for all CONTROL packets. Therefore, when
summarizing "emissions reduced" from CONTROL packets, it is important to note that these reductions are
not relative to the 2011 inventory, but rather, to the intermediate inventory after application of any/all
PROJECTION packet matches. It is also important not all 70+ hierarchy options are shown. The fields listed
in Table 4-2 are not necessarily named the same in CoST, but rather are similar to those in the SMOKE FF10
inventories; for example, "REGIONCD" is the county-state-county FIPS code (e.g., Harris county Texas is
48201) and "STATE" would be the 2-digit state FIPS code with three trailing zeros (e.g., Texas is 48000).
Table 4-2. Subset of CoST Packet Matching Hierarchy
Rank
Matching Hierarchy
Inventory Type
1
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID, SCC, POLL
point
2
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID, POLL
point
3
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, SCC, POLL
point
4
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, POLL
point
5
REGION CD, FACILITY ID, UNIT ID, SCC, POLL
point
6
REGION CD, FACILITY ID, UNIT ID, POLL
point
7
REGION CD, FACILITY ID, SCC, POLL
point
8
REGION CD, FACILITY ID, POLL
point
9
REGION CD, FACILITY ID, UNIT ID, REL POINT ID, PROCESS ID
point
10
REGION CD, FACILITY ID, UNIT ID, REL POINT ID
point
11
REGION CD, FACILITY ID, UNIT ID
point
12
REGION CD, FACILITY ID
point
13
REGION CD, NAICS, SCC, POLL
point, nonpoint
14
REGION CD, NAICS, POLL
point, nonpoint
15
STATE, NAICS, POLL
point, nonpoint
16
REGION CD, NAICS
point, nonpoint
17
NAICS
point, nonpoint
18
REGION CD, SCC, POLL
point, nonpoint
19
STATE, SCC, POLL
point, nonpoint
20
SCC, POLL
point, nonpoint
21
REGION CD, SCC
point, nonpoint
22
STATE, SCC
point, nonpoint
23
SCC
point, nonpoint
24
REGION CD, POLL
point, nonpoint
25
REGION CD
point, nonpoint
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26
STATE, POLL
point, nonpoint
27
STATE
point, nonpoint
28
POLL
point, nonpoint
The remainder of this section is organized either by source sector or by specific emissions category within a
source sector for which a distinct set of data were used or developed for the purpose of projections for the
2018 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.
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 2011 base case except for the
speciation profiles used for gasoline-related sources, which change in the future to account for increased
ethanol usage in gasoline. Table G-l of Appendix G also shows the differences between ancillary input data
sets between the 2011 base case and the future-year scenario. The specific speciation profile changes are
discussed in Section 3.2.1. Table G-2 in Appendix G also provides the values for the main parameters used
in the emissions modeling cases.
4.1	Stationary source projections: EGU sectors (ptegu, ptegu_pk)
The future-year data for the ptipm sector used in the air quality modeling were created by the Integrated
Planning Model (IPM) version 5.13 (v5.13) Final MATS (Mercury and Air Toxics Standards) of (Clean Air
Markets). The IPM is a multiregional, dynamic, deterministic linear programming model of the U.S. electric
power sector. Version 5.13 reflects state rules, consent decrees and announced shutdowns through August,
2013. IPM 5.13 was significantly updated from the previous version 4.10 and represents electricity demand
projections for the Annual Energy Outlook (AEO) 2013. The scenario used for this modeling represents the
implementation of the Clean Air Interstate Rule, the Mercury and Air Toxics Standards, and the final actions
EPA has taken to implement the Regional Haze Rule.
Directly emitted PM emissions (i.e., PM2.5 and PM10) from the EGU sector are computed via a post
processing routine that applies emission factors to the IPM-estimated fuel throughput based on fuel,
configuration and controls to compute the filterable and condensable components of PM. This methodology
is documented in the air quality modeling flat file documentation. As part of the development of the flat file,
a cross reference between the 201 INEIvl and IPM is used to help populate stack parameters and other
related information. This cross reference. The emissions in the flat file created from the IPM outputs are
temporalized into the hourly emissions needed by the air quality model as described in Section 3.3.2.
4.2	Stationary source projections: non-EGU sectors (afdust, ag, nonpt,
npoiigas, ptnonipm, pt_oiigas, rwc)
To project U.S. stationary sources other than the ptipm sector, growth factors and/or controls were applied to
certain categories within the afdust, ag, nonpt, np oiigas, ptnonipm, pt oilgas and rwc platform sectors. This
subsection provides details on the data and projection methods used for these sectors. In estimating future-
year emissions, EPA assumed that emissions growth does not track with economic growth for many
stationary non-IPM sources. This "no-growth" assumption is based on an examination of historical
emissions and economic data. While EPA is working toward improving the projection approach in future
emissions platforms, the Agency is still using the no-growth assumption for the 2011 platform unless states
provided specific growth factors for 2018. 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).
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For many sources, EPA applied emissions reduction factors (CONTROL packets) to the 2011 base case
emissions for particular sources in the ptnonipm, nonpt and two oil and gas sectors (npoilgas and ptoilgas)
to reflect the impact of stationary-source national and local-scale control programs including consent
decrees. Information on plant, unit and stack closures (CLOSURE packets) is restricted to the ptnonipm
sector. Some of the controls described in this section were obtained from comments on the Cross-State Air
Pollution Rule (CSAPR) proposal.
The contents of the controls, local adjustments and closures for the 2018 base case are described in the
following subsections. Detailed summaries of the impacts of all control programs, local adjustments and
closures are provided in ftp://newftp.epa.gov/air/emismod/201 l/vlplatform/reports/2018 emissions/. Year-
specific projection factors (PROJECTION packets) for year 2018 were used for creating the 2018 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). Regardless of whether the growth or
control factors for a sector are provided in a table in this document, they are available as "projection",
"control", or "closures" packets for input to SMOKE on the 201 lv6 platform website in
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/2018emissions/2018ed CoST%20packets.zip. This
section is divided into several subsections that are summarized in Table 4-3. Note that future year
inventories were used rather than projection or control packets for some sources.
Table 4-3. Summary of non-EGU stationary projections subsections
Subsection
Title
Sector(s)
Brief Description
4.2.1
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 EPA
applied to the 2011 inventory to reflect RFS2 in
2018.
4.2.2
Upstream agricultural and
livestock adjustments
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
Residential wood
combustion projections
rwc
Adjustment factors that reflect the change in RWC
emissions by appliance type, including wood stove
change-outs and accounting for estimated future
sales and replacement rates.
4.2.4
Oil and Gas projections
npoilgas,
ptoilgas
Projection packet reflecting regional AEO-based
growth for oil and gas production as well as VOC
NSPS controls for select sources.
4.2.3
RICE NESHAP controls
nonpt,
npoilgas,
ptnonipm,
pt oilgas
Control packet reflecting RICE NESHAP with
reconsideration amendments.
4.2.6
Fuel sulfur rule controls
nonpt
ptnonipm
Control packet reflecting state and local fuel sulfur
rules, including ULSD.
4.2.7
Industrial Boiler MACT
reconsideration controls
ptnonipm
Control packet reflecting ICI Boiler MACT
reconsideration reductions.
4.2.8
Portland cement NESHAP
projections
ptnonipm
Year-2018 ISIS policy case reflecting the Portland
Cement NESHAP, including closures, controls at
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Subsection
Title
Sector(s)
Brief Description



existing kilns and an inventory containing new kilns
constructed after 2011 that account for shifting
capacity from some closed units to open units.
4.2.3
State comments and
consent
decrees/settlements
nonpt,
ptnonipm
Projection and control packets reflecting numerous
sources of consent decree/settlement information as
well as state comments and data regarding 2018.
4.2.9
Aircraft projections
ptnonipm
Airport-specific projections to year 2018 based on
FAA itinerary activity estimates.
4.2.10
Remaining non-EGU
controls and closures
ptnonipm
All other controls and plant/unit/stack closures
information not covered in previous subsections
4.2.1 RFS2 upstream future year inventories and adjustments (nonpt, ptnonipm)
EPA incorporated adjustments for some stationary source categories to account for impacts of the Energy
Independence and Security Act (EISA) renewable fuel standards mandate in the Renewable Fuel Standards
Program (RFS2; EPA, 2010a). These mandates not only impact emissions associated with highway vehicles
and nonroad engines, but also emissions associated with point and nonpoint sources. The "upstream"
emission impacts of the renewable fuels mandate are associated with all stages of biofuel production and
distribution, including biomass production (agriculture, forestry), fertilizer and pesticide production and
transport, biomass transport, biomass refining (corn or cellulosic ethanol production facilities), biofuel
transport to blending/distribution terminals, and distribution of finished fuels to retail outlets. These impacts
are accounted for in the 2018 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. Greenhouse gas standards also affect
production and distribution of gasoline and diesel fuels, but the impacts of these rules will be very small in
2018 and were not accounted for.
Based on the Annual Energy Outlook 2013 (early release) energy use of 15.47 quad (1015 BTU) (Department
of Energy, 2012), EPA estimated the 2011 ethanol volume as 11.1 billion gallons (Bgal). EPA assumes that
an unadjusted 2018 inventory, which does not account for the impacts of the EISA renewable fuel mandate,
would have comparable ethanol volumes to 2011. However, analyses done to support the RFS2 rule (EPA,
2010a) suggest a significant increase in renewable fuel volumes in 2018 (see Table 4-4). Adjustments
applied to the inventories (described in the following subsections) reflect the impacts on emissions due to the
difference between the 2011 ethanol volumes and the renewable fuel volumes shown in Table 4-4. In 2018,
EPA assumed 1 Bgal of ethanol would be used as E85, 10 Bgal as E10, and about 4 Bgal as E15.
Table 4-4. Renewable Fuel Volumes Assumed for Stationary Source Adjustments.
Renewable Fuel
Volume (Bgal)
Corn Ethanol
14.7
Cellulosic Ethanol
0.235
Imported Ethanol
1.061
Biodiesel
1.887
Renewable Diesel
0.236
Cellulosic Diesel
0.290
4.2.1.1 Corn Ethanol plants inventory (ptnonipm)
Future year inventories: "ethanol plants 2018ed NET and "ethanol_plants_2018ed_OTAQ"
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As discussed in Section 2.1.4, EPA supplemented the 2011 NEI with corn ethanol plants that EPA OTAQ
identified. The 2011 emissions were projected to account for the increased domestic corn ethanol production
assumed in this modeling, specifically an increase from 13.9 Bgal in 2011 to 14.7 Bgal by 2018. The
projection was applied to all pollutants and all facilities equally. Table 4-5 provides the summaries of
estimated emissions for the corn ethanol plants in year 2011 and 201823.
Table 4-5. 2011 and 2018 corn ethanol plant emissions [tons]
Pollutant
2011
2018
CO
15,934
16,858
nh3
726
768
NOx
18,048
19,095
PMio
10,602
11,217
PM2.5
5,995
6,343
S02
34,608
36,294
voc
19,654
21,115
4.2.1.2 Biodiesel plants inventory (ptnonipm)
New Future year inventory: "Biodiesel_Plants_2018_ffl0"
EPA OTAQ developed an inventory of biodiesel plants for 2018 in support of producing biodiesel fuels for
the EISA mandate. Plant location and production volume data came from the Tier 3 proposed rule.24 25 The
total volume of biodiesel came from the AEO 2013 early release, 1.3 BG for 2018. To reach the total
volume of biodiesel, plants that had current production volumes were assumed to be at 100% production and
the remaining volume was split among plants with planned production. Once facility-level production
capacities were scaled, emission factors were applied based on soybean oil feedstock. These emission
factors in Table 4-6 are in tons per million gallons (Mgal) and were obtained from EPA's spreadsheet model
for upstream EISA impacts developed for the RFS2 rule (EPA, 2010a). Inventories were modeled as point
sources with Google Earth and web searching validating facility coordinates and correcting state-county
FIPS. Table 4-7 provides the 2018 biodiesel plant emissions estimates. Emissions in 2011 are assumed to
be near zero, and HAP emissions in 2018 are nearly zero.
Table 4-6. Emission Factors for Biodiesel Plants (Tons/Mgal)
Pollutant
Emission Factor
VOC
4.3981E-02
CO
5.0069E-01
NOx
8.0790E-01
PM10
6.8240E-02
PM2.5
6.8240E-02
S02
5.9445E-03
nh3
0
Acetaldehyde
2.4783E-07
23	The 2011 emissions are the sum of the NEI and OTAQ facilities. The same is true for 2018.
24	US EPA 2013. Draft Regulatory Impact Analysis for Tier 3 Vehicle Emission and Fuel Standards Program. EPA-420-D-13-002.
25	Cook, R. 2012. Development of Air Quality Reference Case Upstream and Portable Fuel Container Inventories for Tier 3
Proposal. Memorandum to Docket EPA-HQ-OAR-2010-0162.
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Pollutant
Emission Factor
Acrolein
2.1290E-07
Benzene
3.2458E-08
1,3-Butadiene
0
Formaldehyde
1.5354E-06
Ethanol
0
Table 4-7. 2018 biodiesel plant emissions [tons]
Pollutant
2018
CO
649
NOx
1048
PMio
89
PM2.5
89
S02
8
VOC
57
4.2.1.3 Portable fuel container inventory (nonpt)
Future year inventory: "2018_PFC_inventory_FF 10_revision2"
EPA used future-year VOC emissions from Portable Fuel Containers (PFCs) from inventories developed and
modeled for EPA's MSAT2 rule (EPA, 2007a). The 10 PFC SCCs are summarized below (note that the full
SCC descriptions for these SCCs include "Storage and Transport; Petroleum and Petroleum Product Storage"
as the beginning of the description).
2501011011	Residential Portable Fuel Containers:
2501011012	Residential Portable Fuel Containers:
2501011013	Residential Portable Fuel Containers:
2501011014	Residential Portable Fuel Containers:
2501011015	Residential Portable Fuel Containers:
2501012011	Commercial Portable Fuel Containers
2501012012	Commercial Portable Fuel Containers
2501012013	Commercial Portable Fuel Containers
2501012014	Commercial Portable Fuel Containers
2501012015	Commercial Portable Fuel Containers
Permeation
Evaporation
Spillage During Transport
Refilling at the Pump: Vapor Displacement
Refilling at the Pump: Spillage
: Permeation
: Evaporation
: Spillage During Transport
: Refilling at the Pump: Vapor Displacement
: Refilling at the Pump: Spillage
The future-year emissions reflect projected increases in fuel consumption, state programs to reduce PFC
emissions, standards promulgated in the MSAT2 rule, and impacts of the EISA on gasoline volatility.
OTAQ provided year 2018 PFC emissions that include estimated Reid Vapor Pressure (RVP) and oxygenate
impacts on VOC emissions, and more importantly, large increases in ethanol emissions from RFS2. These
emission estimates also include refueling from the NONROAD model for gas can vapor displacement,
changes in tank permeation and diurnal emissions from evaporation. Because the future year PFC
inventories contain ethanol in addition to benzene, EPA developed a VOC E-profile that integrated ethanol
and benzene; see Section 3.2.1.1 for more details. Emissions for 2011 and 2018 are provided in Section 5.
Table 4-8. PFC emissions for 2011 and 2018 [tons]
Pollutant
2011
2018
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VOC
198,395
29,119
Benzene
786
645
Ethanol
0
3,719
4.2.1.4 Cellulosic fuel production inventory (nonpt)
New Future year inventory: "2018_cellulosic_inventory"
Depending on available feedstock, cellulosic plants are likely to produce fuel through either a biochemical
process or a thermochemical process. OTAQ developed county-level inventories for biochemical and
thermochemical cellulosic fuel production for 2018 to reflect AEO2013er renewable fuel volumes.
Emissions factors for each cellulosic biofuel refinery reflect the fuel production technology used rather than
the fuel produced. Emission rates in Table 4-9 and Table 4-10 were used to develop cellulosic plant
inventories. Criteria pollutant emission rates are in tons per RIN gallon. Emission factors from the cellulosic
diesel work in the Tier 3 NPRM were used as the emission factors for the thermochemical plants. Cellulosic
ethanol VOC and related HAP emission factors from the Tier 3 NPRM were used as the biochemical VOC
and related HAP emission factors.26 Because the future year cellulosic inventory contains ethanol, a VOC
E-profile that integrated ethanol was used, see Sections 3.2.1.1 and 3.2.1.3 for more details.
Plants were treated as area sources spread across the entire area of whatever county they were considered to
be located in. Cellulosic biofuel refinery siting was based on utilizing the lowest cost feedstock, accounting
for the cost of the feedstock itself as well as feedstock storage and the transportation of the feedstock to the
cellulosic biofuel refinery. The total number of cellulosic biofuel refineries was projected using volumes
from AEO2013 (early release). The methodology used to determine most likely plant locations is described
in Section 1.8.1.3 of the RFS2 RIA (EPA, 2010a). Table 4-11 provides the year 2018 cellulosic plant
emissions estimates.
Table 4-9. Criteria Pollutant Emission Factors for Cellulosic Plants (Tons/RIN gallon)
Cellulosic Plant
Type
VOC
CO
NOx
PMio
PM2.s
sox
nh3
Thermochemical
5.92E-07
8.7E-06
1.31E-05
1.56E-06
7.81E-07
1.17E-06
1.44E-10
Biochemical
1.82E-06
1.29E-05
1.85E-05
3.08E-06
1.23E-06
6.89E-07
0
Table 4-10. Toxic Emission Factors for Cellulosic Plants (Tons/RIN gallon)
Plant Type
Acetaldehyde
Acrolein
Benzene
1,3-Butadiene
Formaldehyde
Ethanol
Thermochemical
2.95E-08
1.27E-09
9.61E-10
0
5.07E-09
2.09E-07
Biochemical
3.98E-07
1.11E-08
1.39E-08
0
2.28E-08
6.41E-07
26 It should be noted that in the Tier 3 NPRM we meant to use different cellulosic ethanol non-VOC CAP emission factors
depending on which feedstock the plant was using but instead used the same emission factors (based on a forest waste feedstock)
for all the plants. This was corrected by using emission factors for the non-VOC CAPS that were based on a stover feedstock for
the biochemical plants.
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Table 4-1
DRAFT
. 2018 cellulosic plant emissions [tons]
Pollutant
Emissions
Acrolein
1
Formaldehyde
4
Benzene
1
Acetaldehyde
21
CO
6,088
Ethanol
146
nh3
0.1
NOx
9,199
PMio
1,088
PM2.5
547
S02
819
VOC
414
4.2.1.5 Ethanol working loss inventory (nonpt)
New Future year inventory: "Ethanol_transport_vapor_2018rg_ref_v 1"
This inventory was provided by OTAQ to represent upstream impacts of loading and unloading at ethanol
terminals. Emissions are entirely evaporative and were computed by county for truck, rail and waterway
loading and unloading and intermodal transfers (e.g., highway to rail). Inventory totals are summarized in
Table 4-12. The leading descriptions are "Industrial Processes; Food and Agriculture; Ethanol Production"
for each SCC.
Table 4-12. 2018 VOC working losses (Emissions) due to ethanol transport [tons]
SCC
Description
Emissions
30205031
Denatured Ethanol Storage Working Loss
23,420
30205052
Ethanol Loadout to Truck
14,425
30205053
Ethanol Loadout to Railcar
10,484
4.2.1.6 Vapor losses from Ethanol transport and distribution (nonpt, ptnonipm)
Packet: "PROJECTION2011 2018_distribution_upstream_OTAQ_Tier3FRM"
OTAQ developed county-level inventories for ethanol transport and distribution for 2018 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.
A 2018 inventory which included EISA impacts was developed by adjusting the 2007 platform inventory.
These adjustments were made using an updated version of EPA's spreadsheet model for upstream emission
impacts, developed for the RFS2 rule27. Below EPA describes how the Agency developed emission factors
and fuel volumes to make these adjustments with the RFS2 impacts spreadsheet.
27 U.S. EPA. 2013. Spreadsheet "upstreamemissionsrev T3.xls.
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Vapor loss VOC emission factors (EFs) for gasoline were first developed, based on inventory estimates from
the 2005 NEI (EPA, 2009a). Total volume of gasoline was based on gasoline sales as reported by the Energy
Information Administration (2006). Emissions were partitioned into refinery to bulk terminal (RBT), bulk
plant storage (BPS), and bulk terminal to gasoline dispensing pump (BTP) components. Emissions for the
BTP component are greater than the RBT and BPS components.
Total nationwide emissions for these components were divided by the energy content of the total volume of
gasoline distributed in 2005 to obtain emission factors in grams per million metric British Thermal Units
(g/mmBTU). ). Total volume of gasoline was based on gasoline sales as reported by the Energy Information
Administration.28 -In addition to gasoline VOC emission factors for the RBT/BPS components, emission
factors were developed for the BTP component, for 10% ethanol and 15% ethanol, and 85% ethanol.
Emission factors were calculated by applying adjustment factors to the gasoline EFs. The BTP adjustment
factors were based on an algorithm from the 1994 On-Board Refueling Vapor Recovery Rule (EPA, 1994):
EF (g/gal) = exp[-1.2798 - 0.0049(AT) + 0.0203(Td) + 0.1315(RVP)]
Here delta T is the difference in temperature between the fuel in the tank and the fuel being dispensed, and
Td is the temperature of the gasoline being dispensed. EPA assumed delta T is zero, and the temperature of
the fuel being dispensed averages 60 °F over the year.
Average summer RVPs at the Petroleum Administration for Defense Districts (PADD) level was used to
calculate adjustments. The U.S. is broken into five PADDs for petroleum products data collection purposes
via the U.S. Energy Information Administration. These PADD regions are shown in Figure 4-1.
All counties within a PADD received the same adjustment for BTP emissions. Volumes for each fuel type
and summer RVPs for 2018 with EISA impacts are provided in Table 4-13 while volumes without EISA are
in Table 4-14. These volumes and RVPs were obtained from analyses done for the Tier 3 rule. These two
sets of volumes were used to estimate emissions using the RFS2 impacts spreadsheet (see below for details).
28 Source: Energy Information Administration. 2006. Annual Energy Outlook 2006. Report #:DOE/EIA-0383(2006)
91

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Figure 4-1. Map of Petroleum Administration for Defense Districts (PADD)
PADD 5: or
West Coast,
AK, HI
fean
Francisco
Lo* An
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DRAFT
5
2.30E+10
1.27E+09
2.17E+10
1.03E+10
1.27E+10
7.77
7.602
7.911
Total
1.27E+11
6.93E+09
1.20E+11
5.76E+10
6.93E+10
8.75
8.372
9.059
A benzene g/mmgal emission factor for 2018 was based on benzene inventory projections used in the 2011
Cross-State Air Pollution Rule and projected gasoline volumes obtained from the Annual Energy Outlook
2011 Early Release Overview. This emission factor was used to estimate g/mmBTU emission factors based
on the energy content of E0, E10, and El5 gasoline. Aside from energy content, EPA did not account for
the effect of other fuel parameters on emission rates for E0, E10, and E15 blends. Thus, the E10 emission
rate is slightly higher than the E0 rate due to the lower energy content of E10, and the E15 emission rate is
higher still. The E85 emission rate was estimated for the RFS2 rule. Emission factors are summarized in
Table 4-17.
Table 4-15. Storage and Transport Vapor Loss Emission Factors (g/mmBtu)
Process
Fuel
Benzene
BTP
E0
0.250
E10
0.259
E15
0.264
E85
0.023
RBT/BPS
E0
0.059
These emission factors for VOC and benzene were used in conjunction with an updated version of EPA's
spreadsheet model for upstream emission impacts, developed for the RFS2 rule, to estimate PADD level
inventory changes of the changes in gasoline volume in 2018 with 2007 ethanol volumes versus projected
volumes with EISA. VOC inventory changes were used to develop nationwide adjustment factors that were
applied to modeling platform inventory SCCs associated with storage and transport processes (see Table
4-16). Benzene emission estimates were obtained either by application of the adjustments in Table 4-16 or
through speciation of VOC in SMOKE.
Table 4-16. Adjustment factors applied to storage and transport emissions
Year
Process
PADD
Pollutant
Adjustment
Factor
2018
BTP
1
VOC
0.9515



benzene
0.9905


2
VOC
0.9619



benzene
0.9882


3
VOC
0.9778



benzene
0.9879


4
VOC
0.8983



benzene
0.9885


5
VOC
0.9430



benzene
0.9901

RBT/BPS
All
VOC
0.9553



benzene
0.9893
Ethanol emissions were estimated in SMOKE by applying the ethanol to VOC ratios from headspace profiles
to VOC emissions for E10 and E15, and an evaporative emissions profile for E85. These ratios are 0.065 for
E10, 0.272 for E15, and 0.61 for E85. The E10 and E15 profiles were obtained from an ORD analysis of
93
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fuel samples from EPAct exhaust test program29 and have been submitted for incorporation into the EPA's
SPECIATE database. The E85 profile was obtained from data collected as part of the CRC E-80 test
program (Environ, 2008) and has also been submitted for incorporation into EPA's SPECIATE database.
For more details on the change in speciation profiles between 2011 and 2018, see Section 3.2.1.4.
After developing emissions from the 2018 with EISA volumes versus the 2018 without EISA volumes, EPA
created ratios of these two cases to apply against the 2007 platform emissions. This created a 2018 reference
case. For the 2011 platform, EPA scaled the same sources so that their total emissions matched the 2018
reference case.
It should be noted that these adjustment factors are based on summer RVP, but applied to emissions for the
whole calendar year. However, higher RVPs in winter corresponding to lower temperatures result in roughly
the same vapor pressure of the fuel and roughly the same propensity to evaporate. Significant evaporative
emissions are not expected from storage and transport of biodiesel, renewable or cellulosic diesel fuel due to
their low volatility. The cumulative impacts are VOC reductions of approximately 26,075 tons across the
nonpt sector and 2,681 tons in the ptnonipm sector in 2018 for these processes. See Appendix B for the
complete cross-walk between SCC, and state-SCC for BTP components, and each type of petroleum
transport and storage.
4.2.1.7 Pipeline and Refinery EISA adjustments (ptnonipm)
Packet: "PROJECTION_pipelines_refineries_2018ed"
Pipeline usage and 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.
A portion of these impacts are discussed in this section, with additional impacts due to transport discussed in
the onroad and clc2rail sectors (see Sections 4.3.1 and 4.4.1, respectively).
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 4-17). 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 4-17 are estimated relative to that portion of refinery emissions associated with gasoline and
diesel fuel production. Production of jet fuel, still gas and other products also produce emissions. If these
emissions were included, the adjustment factors would not be as large. The impact of the EISA-based
reductions is shown in Table 4-18.
29 U.S. EPA. 2011. Hydrocarbon Composition of Gasoline Vapor Emissions from Enclosed Fuel Tanks. Office of Research and
Development and Office of Transportation and Air Quality. Report No. EPA-420-R-11-018. EPA Docket EPA-HQ-OAR-2011-
0135.
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Table 4-17. 2018 adjustment factors applied to petroleum pipelines and refinery emissions associated with
gasoline and diesel fuel production.
Pollutant
Pipelines
Refineries
Both
CO
0.9964
0.9776
0.9741
NOx
0.9819
0.9867
0.9688
PMio
0.9967
0.9839
0.9806
PM2.5
0.9975
0.9789
0.9765
S02
0.9981
0.9781
0.9763
nh3
n/a
0.9517
n/a
voc
0.999
0.9719
0.9710
Table 4-18. Impact of refinery adjustments on 2011 emissions [tons]
Pollutant
Reductions 2018
CO
1,233
nh3
135
NOx
2,256
PM10
466
PM2.5
495
S02
1,986
VOC
1,515
4.2.2 Upstream agricultural and Livestock adjustments (afdust, ag, nonpt, ptnonipm)
Packet: "PRO JECTION2011 2018_ag_including_upstream_OTAQ_25nov2013_v 1"
Inventory adjustments were previously developed for 2030 as part of final RFS2 rule modeling30. For the
Tier 3 proposal, adjustments for 2017 were scaled by the ratio of 2017 renewable fuel volumes versus 2030
volumes. Although 2018 was modeled for this rule rather than 2017, EPA continued to use the 2017
adjustments. Impacts on farm equipment emissions were not accounted for, however. Emission rates from
the GREET model (fertilizer and pesticide production)31 or based on the 2002 National Emissions Inventory
(fertilizer and pesticide application, agricultural dust, livestock waste) were combined with estimates of
agricultural impacts from FASOM (Forest and Agricultural Section Optimization Model). Since FASOM
modeling used a reference case of 13.2 billion gallons of ethanol, impacts used in the modeling for this rule
are underestimates.
Adjustment factors are provided in Table 4-19. These adjustments were applied equally to all counties
having any of the affected sources. This is an area of uncertainty in the inventories, since there would likely
be variation from one county to another depending on how much of the predicted agricultural changes
occurred in which counties. By using percent change adjustments rather than attempting to calculate
absolute ton changes in each county, EPA has attempted to minimize the inventory distortions that could
occur if the calculated change for a given county was out of proportion to the reference case emissions for
311U. S. Environmental Protection Agency. 2010. Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis.
Assessment and Standards Division Office of Transportation and Air Quality, Ann Arbor, MI. Report No. EPA-420-R-10-006,
February, 2010.
31 GREET, version 1.8c.
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that county. For instance, a different approach could estimate reductions that were larger than the reference
case emissions, since there was no linkage between the 201 INEIvl inventories and the FASOM modeling.
The specific sources (SCCs) and affected pollutants that these adjustments were applied to are listed in a
docket reference32.
Table 4-19. Adjustments to modeling platform agricultural emissions for the Tier 3 reference case
Source Description
Adjustment
Nitrogen fertilizer application
1.0242
Fertilizer production, mixing/blending
1.0603
Pesticide production
0.9544
Agricultural tilling/loading dust
1.0079
Agricultural burning
1.000
Livestock dust
0.9868
Livestock waste
0.9901
For the animal waste sources, EPA also estimated animal population growth in ammonia (NH3) and dust
(PM10 and PM2.5) emissions from livestock in the ag and afdust and ptnonipm sectors. Therefore, a
composite set of projection factors is needed for animal operations that also reflect the minor 0.99% decrease
resulting from the EISA mandate. These composite projection factors by animal category are provided in
Table 4-20. As discussed below, dairy cows and turkeys are assumed to have no growth in animal
population, and therefore the projection factor for these animals is the same as the upstream agriculture-
related projection factor. The PROJECTION packet used for these sources, including the cross-reference to
the animal categories listed in Table 4-20 and the source categories in Table 4-19 is provided on the 201 lv6
platform website in
ftp://newftp.epa.gov/air/emismod/2011/vlplatform/2018emissions/2Q18ed CoST%20packets.zip.
Table 4-20. Composite NH3 projection factors to year 2018 for animal operations
Animal Category
Projection Factor
Dairy Cow
0.9901
Beef
0.9851
Pork
1.0582
Broilers
1.0904
Turkeys
0.9290
Layers
1.0629
Poultry Average
1.0557
Overall Average
1.0310
Except for dairy cows and turkey production, the animal projection factors are derived from national-level
animal population projections from the U.S. Department of Agriculture (USDA) and the Food and
Agriculture Policy and Research Institute (FAPRI). This methodology was initiated in 2005 for the 2005
NEI, but was updated on July 24, 2012 in support of the 2007v5 platform (EPA, 2012) and 2011 to 2018
animal population projections were computed for these 201 lv6 projections to year 2018. For dairy cows,
EPA assumed that there would be no growth in emissions based on little change in U.S. dairy cow
populations from year 2011 through 2018 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
32 U. S. EPA. 2011. Spreadsheet "agricultural sector adjustments.xls." Docket EPA-HQ-OAR-2011-0135.
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this analysis, EPA concluded that production forecasts do not provide representative estimates of the future
number of cows and turkeys; therefore, these forecasts were not used for estimating future-year emissions
from these animals. In particular, the dairy cow population is projected to decrease in the future as it has for
the past few decades; however, milk production will be increasing over the same period. Note that the
ammonia emissions from dairies are not directly related to animal population but also nitrogen excretion.
With the cow numbers going down and the production going up the excretion value will change, but no
change was assumed because a quantitative estimate was not available. Appendix G provides the animal
population data and regression curves used to derive the growth factors.
4.2.3 Residential wood combustion growth (nonpt)
Packet: "PROJECTION2011 v6_2018bau_RWC_25nov2013 .txt"
EPA used a "business as usual" (BAU) approach to Residential Wood Combustion (RWC) projections that
does not account for national New Source Performance Standards (NSPS) for wood stoves, since they are
currently in the comment-seeking process from proposal (EPA, 2013a). EPA projected residential wood
combustion (RWC) emissions to the year 2018 based on expected increases and decreases in various
residential wood burning appliances. As newer, cleaner woodstoves replace some older, higher-polluting
wood stoves, there will be an overall reduction of the emissions from older "dirty" stoves but an overall
increase in total RWC due to population and sales trends in all other types of wood burning devices such as
indoor furnaces and outdoor hydronic heaters (OHH). It is important to note that our RWC projection
methodology does not explicitly account for state or local residential wood control programs. There are a
number state and local rules in place, specifically in California, Oregon and Washington. However, at this
time, EPA does not have enough detailed information to calculate state specific or local area growth rates.
Therefore, with the exception of California, Oregon and Washington, EPA is using national level growth
rates for each RWC SCC category. After discussions with California air districts, regional office contacts
and EPA experts, EPA decided to simply hold RWC emissions flat (unchanged) for all SCCs in California,
Oregon and Washington.
The development of projected growth in RWC emissions to year 2018 starts with the projected growth in
RWC appliances derived from year 2012 appliance shipments reported in the Regulatory Impact Analysis
(RIA) for Proposed Residential Wood Heaters NSPS Revision Final Report (EPA. 2013b). The 2012
shipments are based on 2008 shipment data and revenue forecasts from a Frost & Sullivan Market Report
(Frost & Sullivan, 2010). Next, to be consistent with the RIA (EPA, 2013b), growth rates for new appliances
for certified wood stoves, pellet stoves, indoor furnaces and OHH were based on forecasted revenue (real
GDP) growth rate of 2.0% per year from 2013 through 2018 as predicted by the U.S. Bureau of Economic
Analysis (BEA, 2012). While this approach is not perfectly correlated, in the absence of specific shipment
projections, the RIA assumes the overall trend in the projection is reasonable. The growth rates for
appliances not listed in the RIA (fireplaces, outdoor wood burning devices (not elsewhere classified) and
residential fire logs) are estimated based on the average growth in the number of houses between 2002 and
2012, about 1% (U.S. Census, 2012).
In addition to new appliance sales and forecasts extrapolating beyond 2012, assumptions on the replacement
of older, existing appliances are needed. Based on long lifetimes, no replacement of fireplaces, outdoor
wood burning devices (not elsewhere classified) or residential fire logs is assumed. It is assumed that 95%
of new woodstoves will replace older non-EPA certified freestanding stoves (pre-1988 NSPS) and 5% will
replace existing EPA-certified catalytic and non-catalytic stoves that currently meet the 1988 NSPS (Houck,
2011).
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EPA RWC NSPS experts assume that 10% of new pellet stoves and OHH replace older units and that
because of their short lifespan, that 10% of indoor furnaces are replaced each year. These are the same
assumptions used in the 2007 emissions modeling platform (EPA, 2012d). The resulting growth factors for
these appliance types varies by appliance type and also by pollutant because the emission rates, from EPA
RWC tool (EPA, 2013rwc), vary by appliance type and pollutant. For our non-NSPS projection approach,
the projection factors are the same for all pollutants except for EPA certified woodstoves of all types. For
EPA certified units, the projection factors for PM are lower than those for all other pollutants. The
projection factors also vary because the total number of existing units in 2011 varies greatly between
appliance types.
California did not report detailed SCCs in the 201 INEIvl, simply reporting emissions from general
fireplaces (SCC=2104008100) and general woodstoves (SCC=2104008300). California, Oregon and
Washington also have state-level RWC control programs, including local burn bans in place. Without
appliance counts in California at specific appliance types (e.g., certified versus non-certified), and an
inability to incorporate significant local RWC control programs/burn bans for a future year inventory, EPA
decided to leave all RWC emissions unchanged in the future for all three states. The RWC projections
factors for 2018 for states other than California, Oregon and Washington are provided in Table 4-23. EPA-
certified woodstoves (inserts and freestanding) utilize different projection factors for direct PM than all other
pollutants.
Table 4-21. Non-West Coast RWC projection factors to year 2018
Pollutant
SCC
Description
Projection
Factor
All
2104008100
Fireplace: general
1.072
All
2104008210
Woodstove
fireplace inserts; non-EPA certified
0.897
PM
2104008220
Woodstove
fireplace inserts; EPA certified; non-catalytic
1.076
All other
2104008220
Woodstove
fireplace inserts; EPA certified; non-catalytic
1.181
PM
2104008230
Woodstove
fireplace inserts; EPA certified; catalytic
1.081
All other
2104008230
Woodstove
fireplace inserts; EPA certified; catalytic
1.181
All
2104008300
Woodstove
freestanding, general
1.171
All
2104008310
Woodstove
freestanding, non-EPA certified
0.98
PM
2104008320
Woodstove
freestanding, EPA certified, non-catalytic
1.076
All other
2104008320
Woodstove
freestanding, EPA certified, non-catalytic
1.181
PM
2104008330
Woodstove
freestanding, EPA certified, catalytic
1.081
All other
2104008330
Woodstove
freestanding, EPA certified, catalytic
1.181
All
2104008400
Woodstove
pellet-fired, general (freestanding or FP insert)
1.645
All
2104008510
IF: Indoor Furnaces: cordwood-fired, non-EPA certified
1.103
All
2104008610
OHH: Outdoor Hydronic heaters
1.237
All
2104008700
Outdoor wood burning device, NEC (e.g., fire-pits,
chimineas)
1.072
All
2104009000
Residential firelog total; all combustor types
1.072
The national impact of RWC projections for PM2.5 and NOx by SCC are provided in Table 4-24.
Regionally, these impacts vary considerably depending on the distribution of these appliance types. For
example, RWC emissions increase more in areas with a higher proportion on OHH (Great Lakes and
Northeast).
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Table 4-22. National RWC impacts for PM2.5 and NOx from 2011 to 2018


pm25
NOx
see
Description
2011
2018
Difference
2011
2018
Difference
2104008100
Fireplace: general
66,699
70,422
3,723
7,263
7,673
410

Woodstove: fireplace inserts;
49,336
44,788
-4,548
4,514
4,098
-416
2104008210
non-EPA certified







Woodstove: fireplace inserts;
11,420
12,130
710
1,328
1,525
197
2104008220
EPA certified; non-catalytic







Woodstove: fireplace inserts;
4,205
4,482
277
412
473
61
2104008230
EPA certified; catalytic







Woodstove: freestanding,
9,525
9,525
0
1,079
1,079
0
2104008300
general







Woodstove: freestanding, non-
83,842
82,321
-1,521
7,672
7,533
-139
2104008310
EPA certified







Woodstove: freestanding, EPA
17,998
19,129
1,131
2,094
2,407
313
2104008320
certified, non-catalytic







Woodstove: freestanding, EPA
10,838
11,520
682
1,063
1,212
149
2104008330
certified, catalytic







Woodstove: pellet-fired,
1,747
2,719
972
2,169
3,377
1,208
2104008400
general (freestanding or FP
insert)







IF: Indoor Furnaces:
26,432
29,052
2,620
1,759
1,933
174

cordwood-fired, non-EPA






2104008510
certified






2104008610
OHH: Outdoor Hydronic
heaters
79,163
97,910
18,747
2,290
2,832
542

Outdoor wood burning
20,573
21,916
1,343
2,266
2,414
148

device, NEC (e.g., fire-pits,






2104008700
chimineas)







Residential firelog total; all
6,511
6,939
428
1,762
1,877
115
2104009000
combustor types






Total
Total RWC
388,289
412,853
24,564
35,671
38,433
2,762
4.2.4 Oil and Gas projections (np_oilgas, pt_oilgas)
Packet: PROJECTION 2011 v6_2018_oilgas_27nov2013 .txt
The oil and gas point (pt oilgas) and nonpoint (np oilgas) sectors are modeled separately from the remaining
point (ptnonipm) and nonpoint (nonpt) sector emissions primarily to better track/isolate and summarize the
oil and gas projections from 2011 to future years. EPA is aware that these emissions inventories are subject
to much scrutiny in the base year (2011) as well as growth and control assumptions in the coming years. Our
initial approach at projecting these emissions is a simple regional-level Annual Energy Outlook (AEO) 2013-
based methodology with some associated VOC reduction factors for sources that would be subject to New
Source Performance Standards (NSPS). The methodology EPA describes here was a result of a coordinated
effort between EPA OAQPS and EPA Office of Atmospheric Programs (OAP) Climate Change Division
(CCD).
The AEO-2013 regional growth factors are based on 2011 to 2018 oil production, gas production and
combined oil and gas production trends, available in Supplemental tables for regional detail, Table 131 and
Table 132. These National Energy Modeling System (NEMS) regions are shown in Figure 4-2 and
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demonstrate one of the many limitations of this projection strategy: projections are not based on oil/gas
basin but rather, much larger geographic regions. A county-NEMS region cross-walk was developed to
assign counties in New Mexico and Texas to specific NEMS regions.
Figure 4-2. Oil and Gas NEMS Regions
West Coast
Rocky Mountains
Northeast
Midcontinent
Gulf Coast
Shallow
Mexicol
Deep Gulf of Mexico
Atlantic
Source: U.S. Energy Information Administration, Office of Energy Analysis.
The AEO-2013 provides regional growth factors for oil production and gas production; however, numerous
sources (SCCs) in the 2011 platform are ambiguous regarding the type of product being extracted/produced.
These sources were assigned to a combined oil and gas category set of factors where oil and natural gas
production levels were summed using a barrel-of-oil equivalent of 0.178 barrels of crude oil to 1000 cubic
feet of natural gas. The AEO-based projection factors for each products type and NEMS region, provided in
Table 4-25, are applied to for all pollutants and SCCs in the point and nonpoint oil and gas sector
inventories, with the exception of VOC for select SCCs. The two character region codes (e.g., "NE" for
Northeast region) are relevant in the following discussion on VOC projection factors.
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Table 4-23. AEO-based 2018 Projection Factors
Region
Oil
Gas
Oil/Gas
Northeast (NE)
1.238
1.596
1.572
Gulf Coast (GC)
1.853
1.246
1.368
Midcontinent (MC)
1.165
0.910
0.955
Southwest (SW)
1.391
1.043
1.173
Rocky Mountains (RM)
1.642
1.098
1.243
West Coast (WC)
0.865
0.993
0.888
For select VOC processes, SCCs were identified that were likely to be affected by NSPS and verified with
EPA OAP and OAQPS oil and gas sector experts. NSPS reductions for VOC-only were applied in
composite with AEO-based regional growth factors to create a set of "net" growth factors. These NSPS
VOC reductions are consistent with EPA OAP-led Climate Action Report. The VOC NSPS reductions
specifically, are discussed in Section 2 of the "Methodologies for U.S. Greenhouse Gas Emissions
Projections". These composite projection factors for VOC NSPS sources are provided in Table 4-24.
There were several assumptions in the application of NSPS VOC reductions. NSPS VOC reductions were
only applied to increase (if any) of emissions from 2011 to 2018 as provided by the AEO projection factor.
If AEO-based gas or oil production was projected to decrease in 2018 versus 2011, then NSPS reductions
had no impact. One exception, highlighted in Table 4-24, is for natural gas well completions; these "one-
shot" activities are generally short-term year to year processes and therefore NSPS reductions are applied to
the entire 2018 projected estimates. Other important assumptions are:
•	Emissions change linearly with production-level changes (AEO projections)
•	In the absence of local/state rules, existing equipment will continue to be used and there is no
replacement of capital that would be affected by the NSPS; the NSPS only affects growth for
processes other than natural gas well completions.
•	Engine-related regulatory impacts are accounted for separately (see RICE NESHAP in the following
section)
•	EPA did not attempt to account for or quantify the potential reductions due to the oil and natural gas
NESHAP
•	Secondary emissions related to NSPS reductions were not accounted for (e.g., NOx emissions arising
from the combustion of VOC emissions)
EPA acknowledges that these assumptions are not ideal, particularly the linear scaling of production changes
to emissions for all processes. EPA hopes that future refinement of this methodology, particularly for large
processes with highly-reactive pollutants such as glycol dehydrators, improve this aspect of oil and gas
projections.
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1
3
ll)
m;	
.177
.073
.910
.013
.029
.993
.071
.253
.049
.116
.191
.865
.080
.062
.045
.052
.055
.050
.137
.056
.910
.010
.023
.993
.000
.000
.910
.000
.000
.993
.055
.196
.038
.090
.148
.865
.120
.049
.910
.009
.020
.993
DRAFT
Table 4-24. Oil and Gas sector VOC 2018 Projection Factors for NSPS sources
SCC Level 4
NSPS Source
NSPS
Reduction
Resource
Region
Gas Well Tanks - Flashing &
Standing/Working/Breathing,
Uncontrolled;
Gas Well Water Tank Losses;
Storage Tanks: Condensate
Gas
Storage
Tanks
70.3%
NE
GC
MC
SW
RM
WC
Oil Well Tanks - Flashing &
Standing/Working/Breathing;
Storage Tanks: Crude Oil
Oil
NE
GC
MC
SW
RM
WC
Drilling and Well Completion;
Gas Well Completion: All Processes;
Gas Well Venting - Initial
Completions;
Gas Well Venting - Recompletions
Gas Well
Completions
95.0%
Gas
NE
GC
MC
SW
RM
WC
Gas Well Pneumatic Devices
77.0%
Gas
NE
GC
MC
SW
RM
WC
Pneumatic Controllers High Bleed
>6 scfm;
Pneumatic Controllers Low Bleed
Pneumatic
controllers
100.0%
Gas
NE
GC
MC
SW
RM
WC
Oil Well Pneumatic Devices
77.0%
Oil
NE
GC
MC
SW
RM
WC
Compressor Seals
Compressor
Seals
79.9%
Gas
NE
GC
MC
SW
RM
WC
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DRAFT
The national impact of the AEO-based projections for oil and gas sector point and nonpoint inventories is
shown in Table 4-27. As previously-discussed, these projections vary by product type, NEMS region and for
some processes for VOC NSPS controls. The smaller percent increase in VOC due to NSPS controls is
evident. The larger percent growth in NH3 is an artifact of Virginia containing 77 of the 112 tons; Virginia
being in the Northeast NEMS region, has gas production increasing 59.6% according to AEO projections in
Table 4-23.
Table 4-25. Projected national Oil and Gas sector 2011 and 2018 emissions, summed point and nonpoint
Pollutant
2011
2018
Increase
Percent Difference
CO
662,762
807,868
145,107
22%
nh3
112
159
47
42%
NOx
670,245
817,136
146,891
22%
PM10
23,587
29,248
5,661
24%
PM2.5
19,008
23,537
4,529
24%
S02
72,337
91,054
18,716
26%
VOC
2,360,726
2,664,887
304,161
13%
National, and even NEMS-region projections are not representative of the trends in each state for many of
the reasons already discussed. Emissions projections also vary by state depending on the proportion of gas,
oil and gas/oil (undefined) emissions in the inventory for that state. For this reason, EPA provided VOC and
NOx state-level projections for the oil and gas sector in Table 4-28.
Table 4-26. Projected by-state NOx and VOC 2011 and 2018 Oil and Gas sector emissions

VOC
VOC
VOC
NOx
NOx
NOx
State
2011
2018
Difference
2011
2018
Difference
Alabama
22,445
26,400
3,954
11,309
14,178
2,869
Arizona
78
90
12
15
18
3
Arkansas
9,019
8,140
-879
11,573
10,790
-783
California
15,829
14,203
-1,626
3,179
2,976
-203
Colorado
238,717
209,628
-29,090
29,860
37,636
7,776
Connecticut
1
1
0
0
0
0
Delaware
0
0
0
0
0
0
Florida
3,179
3,846
667
180
237
58
Georgia
3
4
1
16
25
9
Idaho
7
9
2
0
0
0
Illinois
27,313
31,029
3,715
9,293
14,121
4,828
Indiana
9,694
11,574
1,880
6,189
9,648
3,459
Iowa
36
33
-3
2
2
0
Kansas
94,601
96,185
1,584
56,613
53,677
-2,936
Kentucky
24,958
32,322
7,364
24,448
38,803
14,355
Louisiana
117,324
143,754
26,429
49,701
65,351
15,649
Maine
16
26
9
0
0
0
Maryland
8
11
3
12
19
7
Massachusetts
22
35
13
0
0
0
Michigan
28,377
35,758
7,382
17,682
28,002
10,320
Minnesota
0
0
0
0
0
0
Mississippi
26,248
33,407
7,159
3,492
4,558
1,066
Missouri
79
81
2
17
17
0
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DRAFT

VOC
VOC
VOC
NOx
NOx
NOx
State
2011
2018
Difference
2011
2018
Difference
Nebraska
2,374
2,495
121
901
864
-37
Nevada
440
553
113
20
26
6
New






Hampshire
0
0
0
0
0
0
New Jersey
13
21
7
1
2
0
New Mexico
139,402
148,382
8,980
42,277
46,874
4,597
New York
8,222
11,379
3,157
606
944
338
North Dakota
96,873
117,943
21,070
6,375
8,225
1,851
Ohio
10,321
11,996
1,675
322
507
184
Oklahoma
231,536
220,820
-10,717
83,337
77,766
-5,571
Oregon
45
45
-1
41
41
0
Pennsylvania
18,947
25,959
7,012
40,604
64,481
23,877
South






Carolina
9
14
5
0
0
0
South Dakota
1,444
1,718
274
256
304
47
Tennessee
2,700
3,182
482
1,548
2,432
884
Texas
969,231
1,150,990
181,759
193,512
226,694
33,182
Utah
131,497
157,230
25,733
21,128
26,013
4,885
Virginia
6,628
9,509
2,880
9,577
15,285
5,708
Washington
0
0
0
0
0
0
West Virginia
43,422
60,296
16,873
30,346
48,391
18,045
Wisconsin
0
0
0
1
1
0
Wyoming
42,977
53,237
10,260
2,785
3,330
544
Total
2,360,726
2,664,887
304,161
670,245
817,136
146,891
Note, the national and state-level summaries provided in Table 4-25and Table 4-26 do not include reductions
from the RICE NESHAP. EPA is also aware that early release AEO 2014 projections became available in
December 2013. Overall, it appears that oil production increases significantly in the draft AEO 2014
compared to the AEO 2013 projections, about 22% higher by 2019 in the draft AEO 2014 projection versus
the AEO 2013 projection. There appears to be less significant increase, about 11%, in projections for natural
gas in the draft 2014 AEO versus AEO 2013.
4.2.5 RICE NESHAP (nonpt, ptnonipm, np_oilgas, pt_oilgas)
Packet: CONTROL_RICE_incl_SO2_2007v5_27nov2013 .txt
There are three rulemakings for National Emission Standards for Hazardous Air Pollutants (NESHAP) for
Reciprocating Internal Combustion Engines (RICE). These rules reduce HAPs from existing and new RICE
sources. In order to meet the standards, existing sources with certain types of engines will need to install
controls. In addition to reducing HAPs, these controls have co-benefits that also reduce CAPs, specifically,
CO, NOx, VOC, PM, and SO2. In 2014 and beyond, compliance dates have passed for all three rules; thus
all three rules are included in the emissions projection. These RICE reductions also reflect the recent
(proposed January, 2012) Reconsideration Amendments, which results in significantly less stringent NOx
controls (fewer reductions) than the 2010 final rules.
The rules are listed below:
• National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion
Engines; Final Rule (69 FR 33473) published 06/15/04
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•	National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion
Engines; Final Rule (FR 9648) published 03/03/10
•	National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion
Engines; Final Rule (75 FR 51570) published 08/20/2010
The difference among these three rules is that they focus on different types of engines, different facility types
(major for HAPs, versus area for HAPs) and different engine sizes based on horsepower. In addition, they
have different compliance dates, though all are after 2011 and fully implemented prior to 2018. EPA
projects CAPs from the 201 INEIvl RICE sources, based on the requirements of the rule for existing sources
only because the inventory includes only existing sources and the current projection approach does not
estimate emissions from new sources.
The Regulatory Impact Analysis (RIA) for the Reconsideration of the Existing Stationary Compression
Ignition (CI) Engines NESHAP: Final Report (EPA. 2013ci).
The Regulatory Impact Analysis (RIA) for Reconsideration of the Existing Stationary Spark Ignition (SI)
RICE NESHAP: Final Report (EPA. 2013siV
Together, EPA calls these the RICE NESHAP amendment RIA's for SI and CI engines. From these RICE
NESHAP RIA documents, EPA obtained cumulative RICE reductions for all SCCs represented by CI and SI
engines. These aggregate reductions and percent reductions from baseline emissions (not the 201 INEIvl)
are provided in Table 4-27.
Table 4-27. Summary RICE NESHAP SI and CI percent reductions prior to 201 INEIvl analysis

CO
NOx
PM
SOi
VOC
RIA Baseline: SI engines
637,756
932,377


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


9,147
RIA Baseline: CI engines
81,145

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

2,818
5,100
27,142
RIA Cumulative Reductions
36,449
9,638
2,818
5,100
36,289
SI % reduction
3.5%
1.0%
n/a
n/a
7.2%
CI % reduction
17.5%
n/a
14.5%
46.1%
33.9%
These RIA percent reductions were used as an upper-bound for reducing emissions from RICE SCCs in the
201 INEIvl point and nonpoint modeling sectors (ptnonipm, nonpt, pt oilgas and np oilgas). To begin with,
the RIA inventories are based on the 2005 NEI, so EPA wanted to ensure that our 2011 reductions did not
exceed those in the RICE RIA documents. For the 2011 platform EPA worked with EPA RICE NESHAP
experts and developed a fairly simple approach to estimate RICE NESHAP reductions. Most SCCs in the
inventory are not broken down by horsepower size range, mode of operation (e.g., emergency mode), nor
major versus area source type. Therefore, EPA summed NEI emissions nationally by-SCC for RICE sources
and also for sources that were at least partially IC engines (e.g., "Boiler and IC engines"). Then, EPA
applied the RIA percent reductions to the 201 INEIvl for SCCs where national totals exceeded 100 tons;
EPA chose 100 tons as a threshold arbitrarily, assuming there would be little to no application of RICE
NESHAP controls on smaller sources. Next, EPA aggregated these national reductions by engine type (CI
vs. SI) and pollutant and compared these to the RIA reductions. As expected, for most pollutants and engine
types, our cumulative reductions were significantly less than those in the RIA. The only exception was for
S02 CI engines, where EPA opted to scale the RIA percent reduction from 46.1% to 10.2% for four broad
nonpoint SCCs that were not restricted to only RICE engines. These four SCCs were the "Boilers and IC
105
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DRAFT
Engines" or "All processes" that would presumably contain some fraction of non-RICE component.
Reducing the SO2 percent reduction for these four SCCs resulted in slightly less than 5,100 tons of SO2
reductions overall from only RICE NESHAP controls. However, more specific CoST projection packets
would later override these RICE NESHAP reductions. Recall the CoST hierarchy discussed earlier; these
RICE NESHAP reductions are national by pollutant and SCC and thus easily overridden by more-specific
information such as state-level fuel sulfur rules (discussed in the next section). Impacts of the RICE
NESHAP controls on nonpt, ptnonipm, pt oilgas and np oilgas sector emissions are provided in Table 4-28.
Table 4-28. National by-sector reductions from RICE Reconsideration Controls
Pollutant
Nonpoint
Oil & Gas
(np oilgas)
Point
Oil & Gas
(pt oilgas)
Nonpoint
(nonpt)
Point
(ptnonipm)
Total
CO
1,865
95
8,838
7,167
17,964
NOx
1,101
94
1,976
2,033
5,205
PM10
0
0
1,201
300
1,501
PM2.5
0
0
1,120
282
1,402
S02
1,699
0
1,571
1,049
4,319
voc
6,249
52
1,304
4,074
11,679
4.2.6 Fuel sulfur rules (nonpt, ptnonipm)
Packet: CONTROL_SULF_2011 v6_2018_27nov2013 .txt
Fuel sulfur rules that were signed by November, 2013 are limited to Connecticut, Maine, Massachusetts,
New Jersey, New York, Pennsylvania and Vermont. The fuel limits for these states are incremental starting
after year 2012, but are fully implemented before June 30, 2018 in all of these states. Other states in the
Northeast and Mid-Atlantic had pending sulfur rules but were not finalized prior to November, 2013 -the
completion date of the 2011 platform year-2018 projection. Background on most of these enforceable and
pending fuel sulfur rules can be found ILTA. A more recent update to the status of fuel sulfur rules.
Connecticut
A public hearing on proposed regulations on fuel sulfur limits for heating oil via Connecticut State Agencies
section 221-174-19b was held on October 9, 2013 (Today in Energy). Effective July 1, 2018 maximum fuel
sulfur content limits for distillate, residual and kerosene fuels go into effect. For distillate fuel oil or
distillate fuel oil blended with biodiesel, these new limits must not exceed 15 ppm, a 99.5% reduction from
3000 ppm in the baseline and down from 500 ppm effective July 1, 2014. Residual oil or residual fuel oil
blended with biodiesel fuel must not exceed 3000 ppm, a 70% reduction from today's 1% fuel content
assumption for smaller stationary sources. For kerosene, a 15 ppm limit replaces the existing 500 ppm limit,
a 97% reduction.
Maine
The Maine Law Legislative Document (LD) 1662 sets a fuel sulfur rule effective January 1, 2014 that
reduces sulfur to 15 ppm for distillate fuel, resulting in a 99.5% reduction from 3,000 ppm assumed in year
2008. Maine Law LD 1662 also states that #5 and #6 fuel oils must not exceed 0.5% by weight (500 ppm),
which is a 75% reduction from an assumed 2% baseline sulfur content in 2008. Maine sulfur content
reductions.
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DRAFT
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 97% reduction from an
assumed 2,000 ppm baseline. More details on these fuel sulfur limits in New Jersey.
New York
New York also signed a law requiring ULSD to replace distillate heating oil #2, which results in a fuel sulfur
content limit of 15 ppm, a 99.5% reduction from the 3,000 ppm baseline. The ULSD law (A.8642-
A/S.l 145-C) can be found here: NR.DC and here: NY Times Green Blogs. New York City also includes
limits by year 2015 on #4 and #6 residual oils, where fuel sulfur content must not exceed 0.5% by weight
(500 ppm), a 75% reduction from an assumed 2% baseline sulfur content in 2008. By 2030, these sources
must burn ULSD (15 ppm). The NYC updated Air Code, updated from the NY DEP.
Pennsylvania
Legislation has been proposed in Pennsylvania that would reduce allowable sulfur levels to 15 ppm for
distillate oil, a 99.5% reduction from the 3,000 ppm baseline. While EPA typically do not include proposed
rulemakings in our base projection scenarios without direction from state agencies, the existence of similar,
finalized standards in neighboring Northeast census region states such as New Jersey and New York suggest
this will become finalized prior to 2018. EPA can revise this, and potential application to other fuels, based
on state comment or regulatory changes.
Vermont
Vermont ULSD fuel and date requirements for home heating oil are similar to those adopted in
Massachusetts: a 99.5% reduction to 15 ppm from the 3,000 ppm baseline.
A summary of the sulfur rules by state, with emissions reductions is provided in Table 4-31.
Table 4-29. Summary of fuel sulfur rules by state
State/
Fuel
%
2011
2018
2018
Metro

reduction
Emissions
Emissions
Reductions
CT
Distillate
99.5



CT
Kerosene
97
12,535
347
12,188
CT
Residual
70



ME
Distillate
99.5
7,041
706
6,335
ME
Residual
75
MA
Distillate
99.5
19,540
98
19,443
NJ
Distillate
99.5
6,146
31
6,115
NJ
Kerosene
96.25
NY
Distillate
99.5
32,984
1,027
31,957
NYC
Residual
75
PA
Distillate
99.5
14,634
73
14,561
VT
Distillate
99.5
997
5
992
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DRAFT
4.2.7 Industrial Boiler MACT reconsideration (ptnonipm)
Packet: CONTROL_BlrMACT_ptnonipm_20XX_201 lv6
The Industrial/Commercial/Institutional Boilers and Process Heaters MACT Rule hereafter simply referred
to as the "Boiler MACT" was promulgated on January 31, 2013 based on reconsideration. Background
information on the Boiler MACT. The Boiler MACT promulgates national emission standards for the
control of HAPs (NESHAP) for new and existing industrial, commercial, and institutional (ICI) boilers and
process heaters at major sources of HAPs. The expected cobenefit for CAPs at these facilities is significant
and greatest for SO2 with lesser impacts for direct PM, CO and VOC.
Boiler MACT reductions were computed from a non-NEI database of ICI boilers. As seen in the Boiler
MACT Reconsideration RIA. this Boiler MACT Information Collection Request (ICR) dataset computed
over 558,000 tons of SO2 reductions by year 2015. However, the Boiler MACT ICR database and reductions
are based on the assumption that if a unit could burn oil, it did burn oil, and often to capacity. With high oil
prices and many of these units also able to burn cheaper natural gas, the 201 INEIvl inventory has a lot more
gas combustion and a lot less oil combustion than the boiler MACT database. For this reason, EPA decided
to target units that potentially could be subject to the Boiler MACT and compute preliminary reductions for
several CAPs prior to building a control packet.
Step 1: Extract facilities/sources potentially subject to Boiler MACT
EPA did not attempt to map each ICR unit to the NEI units, instead choosing to use a more general approach
to extract NEI sources that would be potentially subject to, and hence have emissions reduced by the Boiler
MACT. The NEI includes a field that indicates whether a facility is a major source of HAPs and/or CAPs.
This field in our FF10 point inventory modeling file is called "FACIL CATEGORY CODE" and the
possible values for that field are shown in Table 4-30. Because the Boiler MACT rule applies to only major
sources of HAPs, EPA restricted the universe of facilities potentially subject to the Boiler MACT to those
classified as HAP major or unknown (UNK). The third column indicates whether the facility was a
candidate for extraction as being potentially subject to the Boiler MACT.
Table 4-30. 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.
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From these facilities EPA extracted records (process level / release point level emissions) from our modeling
file with industrial, commercial, institutional boiler or process heater SCCs. A complete list of these SCCs is
provided in Appendix H. The resultant data are the NEI sources potentially subject to the Boiler MACT.
Step 2: Match fuel types and control reductions to the NEI SCCs
After obtaining the subset of 201 INEIvl sources potentially subject to the Boiler MACT, EPA assigned each
inventory SCC to a fuel type. The reductions are based on the ICR fuel types and associated controls from
an April 2010 "Baseline Memo.pdf' memorandum available on the Regulations.gov website under docket #
EPA-HQ-OAR-2002-0058-0802. These ICR fuel types and associated default controls were mapped to
SCCs in our inventory using the cross-walk provided in Table 4-31. The previously-mentioned Appendix H
also maps the complete list of inventory SCCs to these ICR fuel categories.
Table 4-31. 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
4-32. Controls were applied as "replacement" controls to prevent over-control of units that had existing
controls. However, this assumes that the inventory correctly reflects units with controls, so it is likely that
some units that are not recorded as controlled in the 201 INEIvl but are actually controlled were reduced
more than they should have. Overall, the CO and PM2.5 reductions are reasonably close to the year-2015
expected reductions in the Boiler MACT Reconsideration RIA.
It is worth noting that the SO2 reductions in the preamble were estimated at 442,000 tons; the additional SO2
reductions in the reconsideration are from an additional cobenefit from more stringent HC1 controls. The
201 INEIvl SO2 emissions are actually less than the estimated Boiler MACT reductions, likely a result of
numerous units undergoing fuel switching from coal or oil to natural gas via changing energy prices between
the Boiler MACT RIA analyses and the 201 INEIvl. It is also worth noting that EPA did not attempt to
quantify the reductions of nonpoint ICI boiler emissions from Boiler MACT controls.
Table 4-32. Summary of Boiler MACT reductions (tons) compared to Reconsideration RIA reductions
Pollutant
2011 Emissions
2018 Emissions
Reductions
RIA Reductions
CO
267,685
66,682
201,003
187,000
PM2.5
34,586
10,819
24,654
25,601
S02
301,748
35,553
276,195
558,430
VOC
19,295
6,984
12,311
n/a
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4.2.8 Portland Cement NESHAP projections (ptnonipm)
As indicated in Table 4-1, the Industrial Sectors Integrated Solutions (ISIS) model (EPA, 2010b) was used to
project the cement industry component of the ptnonipm emissions modeling sector to 2018. This approach
provided reductions of criteria and select hazardous air pollutants. The ISIS cement emissions were
developed in support for the Portland Cement NESHAPs and the NSPS for the Portland cement
manufacturing industry.
The ISIS model produced a Portland Cement NESHAP policy case of multi-pollutant emissions for
individual cement kilns (emission inventory units) that were relevant for years 2015 through 2030. These
ISIS-based emissions are reflected using a CoST packet for all existing kilns that are not impacted by more
local information from states (or consent decrees) -see next section- and two cement inventories for new
kilns:
1)	Inventory: "cement_newkilns_year2018_from_ISIS2013_NEI201 lvl"
Contains information on new cement kilns constructed after year 2011,
2)	Inventory: "cement newkilns year 2018_from_ISIS2013_NEI2011 vlNONPOINTvO"
Contains information ISIS-generated, but not-permitted, new cement kilns constructed after year
2011,
3)	Packet: "PROJECTION 201 l_2018_ISIS_cement_by_CENSUS_DIVISION_04dec2013.txt"
Contains U.S. census division level based projection factors for each NEI unit (kiln) based on ISIS
updated policy case emissions at existing cement kilns. The units that closed before 2018 are
included in the 2018 base case but are included in other CoST packets that reflect state comments and
consent decrees (discussed in the next section).
The ISIS model, version August 2013 was used for these projections. Recent data updates include updated
matching of kilns to better capture recent retirements, capacity additions and projections of capacity
additions from Portland Cement Association (PCA) Plant Information Summary of December 31, 2010 and
feedback from Portland Cement NESHAP reconsideration comments. Updated cement consumption
projections are based on a post-recession (July 2012) PCA long-term cement consumption outlook. Updated
emissions controls in 2015 from the NESHAP are also reflected. Overall, as seen in Figure 4-3, domestic
production of cement grows significantly between 2011 and 2015, then more slowly through 2018.
Meanwhile, emissions from NESHAP-regulated pollutants such as PM and SO2 drop significantly based on
regulated emissions rates. Emissions for NOx increase, though not as much as production because the ISIS
model continues the recent trend in the cement sector of the replacement of lower capacity, inefficient wet
and long dry kilns with bigger and more efficient preheater and precalciner kilns.
110
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Figure 4-3. Cement sector trends in domestic production versus normalized emissions
Domestic Production
PM trend
NOX trend
S02 trend
Multiple regulatory requirements such as the NESHAP and NSPS currently apply to the cement industry to
reduce CAP and HAP emissions. Additionally, state and local regulatory requirements might apply to
individual cement facilities depending on their locations relative to ozone and PM2.5 nonattainment areas.
The ISIS model provides the emission reduction strategy that balances: 1) optimal (least cost) industry
operation, 2) cost-effective controls to meet the demand for cement, and 3) emission reduction requirements
over the time period of interest.
The first step in using ISIS 2018 projected emissions the cement industry is matching the kilns in year 2018
to those in the 201 INEIvl. For kilns that were new in 2018, EPA used two different approaches for
modeling. For kilns already permitted, known locations (coordinates) allowed us to process these as point
sources. However, the ISIS model also created "generic" kilns in specific geographically strategic locations
(counties) to cover the need for increased production/capacity in future years. Because these generic kilns
are not permitted and the location in these counties is uncertain, EPA decided to model these as county-level
to avoid placing large emissions sources from a model (ISIS) artifact in one grid cell. These nonpoint source
kilns were then spatially allocated based on industrial land activity in the county. A list of all new point and
nonpoint inventory cement kilns in 2018 are provided in Table 4-33. There are additional new kilns
generated by ISIS beyond year 2018 that are not shown in this table.
Table 4-33. Locations of new ISIS-generated cement kilns
ISIS ID
Permitted?
Facility Name
FIPS
State
County
FLNEW2
Y
Vulcan
12001
FL
Aluchua
GANEW1
Y
Houston American Cement
13153
GA
Houston
NCNEW1
Y
Titan America LLC
37129
NC
New Hanover
NewGA2
N
n/a
13153
GA
Houston
NewPA8
N
n/a
42011
PA
Berks
NewSCl
N
n/a
45035
SC
Dorchester
NewTXl
N
n/a
48029
TX
Bexar
NewTXIO
N
n/a
48091
TX
Comal
NewWAl
N
n/a
53033
WA
King
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While ISIS provides by-kiln emissions for each future year, EPA cement kilns experts preferred that the
Agency project existing cement kilns based on a more-smooth geographic approach to reduce the "on"/"off'
switching that ISIS assigns to each kiln based on production and capacity demands. It would be inefficient
and unrealistic to project existing cement kilns to operate as essentially 0% or 100% capacity based strictly
on ISIS output. Therefore, EPA developed a U.S. Census Division approach where ISIS emissions in 2011
and 2018, that matched the 201 INEIvl (e.g., not new ISIS kilns), were aggregated by pollutant for each year
within each of the 9 census divisions in the contiguous U.S.. These aggregate emissions were used to create
a 2018/2011 emissions ratio for each pollutant and geographic area. The projection ratios, provided in Table
4-34, were then applied to all 201 INEIvl cement kilns -except for kilns where specific local information
(e.g., consent decrees/settlements/local information).
Table 4-34. U.S. Census Division ISIS-based projection factors for existing kilns
Region
Division
NOx
PM2.5
SOI
voc
Midwest
East North Central
2.024
0.106
1.800
0.527
Midwest
West North Central
0.930
0.614
0.695
0.317
Northeast
Middle Atlantic
1.853
0.058
0.904
0.561
Northeast
New England
2.560
0.004
3.563
0.713
South
East South Central
0.999
0.109
0.402
0.323
South
South Atlantic
1.042
0.284
0.911
0.413
South
West South Central
1.220
0.079
0.484
0.225
West
Mountain
1.453
2.542
1.917
0.310
West
Pacific
1.465
0.001
0.300
0.321
For all ISIS future year emissions, PMio is assigned as 0.85 of total PM provided by ISIS, and PM2.5 is
assigned as 0.45 of total PM. All new ISIS-generated kilns, point and nonpoint format, are assigned as
Precalciner kilns (SCC=30500623). While ISIS provides emissions for mercury, EPA did not retain these in
our modeling.
Table 4-35 shows the magnitude of the ISIS-based cement industry emissions changes between the
201 INEIvl and 2018 projection scenario. Kilns that matched the 201 INEIvl were simply projected to year
2018 based on U.S. census division aggregate changes in ISIS predictions. There are some local exceptions
where EPA did not use ISIS-based projections for cement kilns where local information from consent
decrees/settlements and state comments were used instead. Cement kilns projected using these non-ISIS
information are not reflected here in Table 4-35. EPA also split out ISIS-based new kilns in 2018 with
permitted (as of August 2013) kilns modeled as point sources and "generic" ISIS-generated kilns as nonpoint
sources.
Table 4-35. ISIS-based cement industry change (tons/yr)
Pollutant
2011
NEIvl
2018
projected
New kilns in 2018
Total
2018
Difference 2018-2011
Permitted
(point)
ISIS-generated
(nonpoint)
NOx
53,874
71,205
3,751
6,836
81,792
27,919
PM2.5
1,772
722
8
15
745
-1,027
SO2
17,065
18,629
1,775
3,263
23,667
6,602
VOC
2,690
903
91
167
1,161
-1,529
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4.2.9 State comments and consent decrees/settlements (nonpt, ptnonipm)
This subsection describes the numerous (12 in all) CoST PROJECTION and CONTROL packets developed
to reflect a wide range of information on future year non-EGU point and nonpoint source projections. In
general, this information is derived from:
•	comments received from the Cross-State Air Pollution proposal
•	local and state comments over the past several years,
•	consent decrees and settlements, and
•	EPA staff data mining and analyses
4.2.9.1 Comments from Cross-State Air Pollution Rule (2010)
EPA released a Notice of Data Availability (NOD A) after the CSAPR proposal to seek comments and
improvements from states and outside agencies. The goal was to improve the future baseline emissions
modeling platform prior to processing the Final CSAPR. EPA received several control programs and other
responses that were used for future year projections. However, this effort was performed on a version of the
2005 modeling platform, which used the 2005NEIv2 as a base year starting point for future year projections.
Now with the 2011 platform using the 201 INEIvl for most non-EGU point and nonpoint sources, many of
these controls and data improvements were removed from this 2018 base case projection. But for those
controls, closures and consent decree information that are implemented after 2011, EPA used these
controls/data after EPA mapped them to the correct SCCs and/or facilities in the 2011 NEI. This subsection
breaks down the controls used for the nonpt and ptnonipm sectors separately, and also describes the consent
decrees separately. EPA used July 1, 2011 as the cut-off date for assuming whether controls were included
in the 2011 NEI. For example, if a control had a compliance date of December 2011 EPA would assume that
the 2011 NEI emissions did not reflect this control and EPA would need to reflect this control in our 2018
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 2011 controls for those areas and categories where EPA received
usable feedback from the CSAPR comments and related 2005 platform NOD A.
Packet: "CONTROLS_CSAPR_consent_201 lv6.csv"
These controls reflect consent decree and settlements that were identified in our preparation of the Final
CSAPR emissions modeling platform. These controls generally consist of one or more facilities and target
future year reductions. After EPA removed all consent decrees with compliance dates prior to late-2011,
EPA matched the remaining controls to the 2011 NEI using a combination of EIS facility codes,
"agy facility id", "agy_point_id" and searching the EIS. Then, EPA recomputed the percent reductions
such that the future year emissions would match those for facilities originally projected from the 2005 NEI-
based platform -these consent decrees were released from 2007 through 2010, when the 2005 NEI was the
general baseline. EPA did not retain consent decree controls if the emissions in the 2011NEI were less than
the controlled future year emissions based on the 2005 platform. EPA were left with consent decree controls
in twelve states (AL, CA, IN, KS, KY, LA, MI, MS, OH, TN, TX, WY) that accounted for 2,515 tons of
NOx and 11,014 tons of SO2 cumulative reductions.
Packet: "CONTROL_CSAPR_ptnonipm_201 Iv6_22nov2013.txt"
EPA created a CONTROL packet for the ptnonipm sector that contains reductions needed to achieve post
year-2011 emissions values from the CSAPR response to comments. These reductions reflect fuel
switching, cleaner fuels, and permit targets via specific information on control equipment and unit and
facility zero-outs in the following states: Georgia, New Hampshire, New York and Virginia. Cumulatively,
these controls reduce NOx by 655 tons and SO2 by 7,221 tons.
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Packet: "PROJECTION_CSAPR_WVunit_ptnonipm_2012_201 Iv6_21nov2013.txt"
This packet contains the only post-2011 unit-level growth projection resulting from CSAPR comments. The
Sunoco Chemicals Neal Plant in Wayne County West Virginia replaced a 155MM Btu/hour coal-fired boiler
with a 96.72 MM Btu/hour natural gas-fired unit in 2010. This closure is already reflected in the 2011 NEI;
however, in 2012, a new natural gas unit was slated to operate and therefore EPA scaled emissions at an
existing natural gas boiler to match these 2012 emission targets provided to us by West Virginia via CSAPR
comments. This packet simply results in an extra 22.5 tons of NOx and minimal increased emissions for PM
and SO2.
4.2.9.2 State comments since spring of 2013
The following packets were derived from information received from several states since the spring of 2013
regarding point and nonpoint projections to year 2018.
Packets:
"PROJECTION_VA_ME_TCEQ_AL_comments_2011 v6_2018_03dec2013 .txt"
"CONTROL_VA_ME_TCEQ_comments_2011 v6_2018_03 dec2013 .txt"
These packets represent primarily local closures and expected changes in future year emissions, in some
cases, specified as year 2018, but otherwise simply to be used rather than the 2011 NEI values for general
future year modeling. These comments from Alabama, Maine, Texas and Virginia were received in the
spring through early fall of 2013. The CONTROL packet was used for specific stack/unit closures and
emissions reductions. Deciding which packet type to use (PROJECTION or CONTROL) for applying state
comments in CoST is fairly subjective. EPA is forced to use PROJECTION packets when emissions
increase, and if EPA can get away with using only 1 type of packet (PROJECTION or CONTROL) for a
single source of comments, AND, the choice does not result in different final projected values, then the
packet type that best fits was used. For example, if a set of state comments results in emissions increasing
and decreasing at various stacks and other CoST packets do not apply, then the packet type choice does not
matter. If, however, EPA chose to represent emission decreases as a PROJECTION packet entry, and
another CoST CONTROL packet applies to that source, then EPA are applying two different sources of
reductions -not ideal. Our goal is for state comments to pass through to the final future year inventory as-is.
For this reason, EPA does not quantify emission changes for these packets separately. The cumulative
impact of these emissions is shown in Table 4-36. Note that the widespread Texas NAICS-level economic-
based growth factors and impacts are discussed separately.
Table 4-36. Impacts of most non-EGU point source state comments received in 2013
State
Pollutant
2011NEIvl
2018 Projection
Change
Alabama
NOx
2,941
3,062
120
Alabama
S02
1,156
1,168
12
Maine
NOx
178
45
-134
Maine
S02
2,069
666
-1,463
Texas
NOx
3,337
712
-2,625
Texas
S02
8,461
229
-8,233
Texas
VOC
469
65
-404
Virginia
NOx
8,065
4,531
-3,534
Virginia
SO2
1,646
2
-1,644
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Packet: "PROJECTION_TCEQ_ptnonipm_NAICS_comments_2011 v6_2018_04dec2013 .txt"
This packet represents county-specific economic-based NAICS-level projections provided by the Texas
Commission on Environmental Quality (TCEQ) for minor source emissions. Growth factors are based on
projections of gross product for various types of industry, population and various Economic Solution data.
EPA did not apply these projections to oil and gas sources, opting to use the consistent regional/fuel-based
approach discussed in Section 4.2.4. A summary of these minor source ptnonipm sector projection impacts
for Texas are provided in Table 4-37.
Table 4-37. Minor source ptnonipm sector NAICS-level projections for Texas
Pollutant
201 INEIvl
2018 Projection
Change
CO
114,817
136,696
21,879
nh3
2,099
2,619
520
NOx
138,389
157,997
19,609
PMio
21,146
26,044
4,898
PM2.5
17,301
21,384
4,084
S02
21,432
28,033
6,601
voc
62,386
79,671
17,285
Packet: "PROJECTION_TCEQ_AREA_comments_2011 v6_2018_04dec2013 .txt"
This packet represents nonpt sector 2011-based projections for year 2018 for Texas as provided by TCEQ.
These county-level and SCC-specific projections are based on a combination of Economic Solutions and
Annual Energy Outlook (AEO) data. EPA did not apply these projections to oil and gas sources, opting to
use the consistent regional/fuel-based approach discussed in Section 4.2.4. EPA also did not apply these
projections to the Residential Wood Combustion sector which were the same for every RWC SCC and
county, opting instead to use our national-based but SCC-specific approach discussed in Section 4.2.3. A
summary of these nonpt sector changes in Texas is provided in Table 4-38.
Table 4-38. Minor source nonpt sector projections for Texas
Pollutant
201 INEIvl
2018 Projection
Change
CO
68,967
83,299
14,333
nh3
2,659
2,720
60
NOx
32,581
34,329
1,748
PM10
19,999
24,416
4,416
PM2.5
15,520
19,268
3,747
S02
9,099
8,805
-293
VOC
239,657
256,046
16,389
4.2.9.3 Consent decrees and settlements
These packets were derived in prior emissions modeling platforms, dating back to the 2005 NEI and 2008
NEI. EPA updated this information based on information in the 201 INEIvl and analysis for compliance
dates. Many of these consent decrees were already in place in 2011 and therefore removed from
consideration for projections. New information (e.g., Cabot Corporation) has also been obtained since the
spring of 2013 and has been included in our projections. Consent decrees or settlements released after
November are not included. EPA also does not reflect consent decrees that do not have obvious quantifiable
reductions for important emissions modeling pollutants (CAPs).
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Packet: "CONTROL_ConsentDecree_Cabot_BlackPowderPlants_03dec2013_v0.txt"
This Cabot Corporation Clean Air Act settlement (release date of November 19, 2013) targets NOx and SO2
reductions of 1,975 and 12,380 tons, respectively, from three carbon black manufacturing plants in Louisiana
and Texas. More information on this settlement.
Because EPA did not have specific stack-level information on this settlement, the Agency apportioned the
total reductions proportionally to each of the three facilities such that each process in all the facilities was
assigned the same percent reduction and that the cumulative NOx and SO2 reductions would be achieved.
Packet: "CONTROLS_Refineries_additional_consent_201 lNEI_vl_25nov2013_vl .txt"
This packet consists of two settlements. The BP Whiting settlement (released May 23, 2012).
The Marathon Petroleum Company. Detroit Refinery environmental mitigation project (released April 5,
2012).
The initial application of these settlements was to the 2008 NEI. Therefore, to be consistent with previous
future year estimates for these facilities, EPA modified existing computed reductions from the 2008 such that
future year estimates from the 201 INEIvl matched those done with the 2008 NEI. These settlements reduce
NOx by 78 tons at the Detroit Refinery and NOx and SO2 by 780 and 150 tons, respectively, at the Indiana
BP Whiting facility.
Packet: "CONTROL_OECA_201 Iv6_25nov2013.txt"
The Office of Enforcement and Compliance Assurance (OECA) provided emission reduction information for
several consent decrees while EPA was preparing emissions for the 2005 NEI-based modeling platform
(Clearinghouse for Inventories and Emissions Factors (CHIEF)). The press releases for these consent
decrees are available on EPA's enforcement website and some were available with quantitative emission
reductions that EPA was able to convert into a control packet. These petroleum refinery settlements. These
settlements were released in the 2003-2010 time period and include information for a few corporations but
with aggregate reductions over numerous facilities under these companies and subsidiaries. Therefore, EPA
developed an initial table of 2008 NEI emissions summed over all affected facilities for each company.
Then EPA merged the multi-facility expected reductions from each of these consent decrees to develop an
overall future year (post-compliance date) emissions estimate for each company after all controls/reductions
are implemented. Using this methodology, the emissions reductions were apportioned to each plant
owned/operated by each company using the same percent reduction from the 2005 NEI emissions.
Now that EPA is using the 2011 NEI, the Agency201 INEIvl, expected that some of these consent decree
controls/reductions would have already been applied by 2011. EPA did not want to over-control any
particular plant. Therefore, EPA computed facility-specific reductions based on the controlled emissions
from the 2008 NEI. For example, as seen in Table 4-37, SO2 emissions at all Cargill facilities were reduced
about 24% in the 2008 NEI: from 6,921 tons to 5,280 tons. In the 201 INEIvl, SO2 emissions at these same
Cargill facilities totaled 6,263 tons, so only approximately 1,000 tons, a 16% cumulative reduction over all
Cargill facilities, were needed to achieve the 5,280 consent decree target.
The column "2008 NEI Controlled" in Table 4-39 was our target for year 2018 emissions. However, many
of these facilities are ethanol plants and are therefore projected separately using EPA OTAQ's national
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projections for ethanol plants (see Section 4.2.1). This is a biggest issue for the Cargill facilities, a majority
of which are defined as ethanol plants. Note in Table 4-39, the "applicable" (non-ethanol plants) 201 INEIvl
emissions available for OECA consent decree controls is significantly less than the sum of all Cargill facility
emissions. The discrepancies between actual and applicable 2011 NEI emissions for the other OECA
facilities are primarily a result of CoST hierarchy assignments. In short, more-specific (more resolved than
facility/pollutant of the OECA packet) control information from other CoST packets are used for some of
these stacks/units/facilities.
Table 4-39. Target company-wide reductions from OECA consent decree information
Corporation
Pollutant
2008 NEI
2008 NEI
Reductions
201 INEIvl
201 INEIvl
Actual


(tons)
Controlled
from 2008
Emissions
applicable
2018



(tons)
(tons)
(tons)
(tons)
Reductions
Cargill
CO
10,889
262
10,627
6,045
401
394
NOx
2,265
1,478
787
1,714
806
111
S02
6,921
5,280
1,642
6,263
849
172
Conoco Phillips
NOx
14,331
7,334
6,997
9,391
9,070
2,932
Sunoco
NOx
4,506
1,975
2,531
3,235
3,154
1,231
PM2.5
1,030
585
445
1,072
714
379
Valero
NOx
8,212
6,109
2,103
6,676
4,913
966
PM2.5
2,554
1,955
599
2,338
1,883
718
S02
11,479
2,903
8,575
6,040
4,807
3,367
Total
CO
10,889
262
10,627
6,045
401
394
NOx
29,314
16,896
12,418
21,016
17,943
5,240
PM2.5
3,584
2,540
1,044
3,410
2,597
1,097
S02
18,400
8,183
10,217
12,303
5,656
3,539
Packet: "CONTROL_LaFarge_StGobain_ptnonipm_201 Iv6_22nov2013.txt"
This control packet includes settlements for all 15 U.S. plants owned by Saint-Gobain Containers, Inc., the
nation's second largest container glass manufacturer, and all 13 U.S. plants owned by the Lafarge Company
and two subsidiaries, the nation's second largest manufacturer of Portland cement. These settlements,
released January 21, 2010, are the first system-wide settlements for these sectors under the Clean Air Act and
require pollution control upgrades, acceptance of enforceable emission limits, and payment of civil penalties.
The settlements require various NOx and SO2 controls, some of which (SO2 scrubbers) also reduce PM
emissions. A couple of Lafarge kilns were also scheduled to be shut down. One of these units was
shutdown prior to 2011 and as expected, is not in the 201 INEIvl. However, a Lafarge kiln in Joppa, Illinois
was unexpectedly found in the 201 INEIvl and communication with the Illinois DEP indicated that this unit
was not closed as of the summer of 2012. More information on the Lafarge settlement.
More information on the Saint-Gobain settlement. Many of the controls for the units at these facilities were
implemented prior to 2011 and were therefore removed from the CONTROL packet; however, cumulatively,
there is still significant reductions post-2011: 9,210 tons of NOx, 214 tons of PM2.5 and 11,777 tons of SO2.
4.2.9.4 EPA staff data mining
Packet: "CONTROLS_Regional_Haze_201 lv6.csv"
This packet includes a set of NOx and SO2 reductions provided by EPA's OAQPS Air Quality Policy
Division (AQPD) visibility experts. These reductions reflect expected emissions reductions and future year
caps for facilities of various industries (e.g., cement kilns, taconite, steel, pulp and paper and mining
industries) in the following states: Georgia, Idaho, Michigan, Minnesota, Montana, New York, Ohio,
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Tennessee, Virginia and Wisconsin. Cumulatively, 28,589 tons of NOx and 20,698 tons of SO2 are reduced
by these controls.
4.2.10 Aircraft projections (ptnonipm)
Aircraft emissions are contained in the ptnonipm inventory. These 2011 point-source emissions are
projected to future years by applying activity growth using data on itinerant (ITN) operations at airports.
The ITN operations are defined as aircraft take-offs whereby the aircraft leaves the airport vicinity and lands
at another airport, or aircraft landings whereby the aircraft has arrived from outside the airport vicinity. EPA
used projected ITN information available from the Federal Aviation Administration's (FAA) Terminal Area
Forecast (TAF) System (publication date March, 2013). This information is available for approximately
3,300 individual airports, for all years up to 2030. The methods that the FAA used for developing the ITN
data in the TAF.
None of our aircraft emission projections account for any control programs. EPA considered the NOx
standard adopted by the International Civil Aviation Organization's (ICAO) Committee on Aviation
Environmental Protection (CAEP) in February 2004, which is expected to reduce NOx by approximately 3%
by 2020. However, this rule has not yet been adopted as an EPA (or U.S.) rule; therefore, its effects were not
included in the future-year emissions projections.
EPA developed two sets of projection factors for aircraft. The first set was a simple national (U.S.)
aggregation, used primarily for airports with very little activity, by ITN operation type (commercial, general
aviation, military and air taxi) to be used as a default method for projecting from 2011 to 2018. The second
set of projection factors was by airport, where EPA projected project emissions for each individual airport
with significant ITN activity.
Packet: "PROJECTION 201 l_2018_aircraft_21nov2013.txt"
In this case, EPA simply summed the ITN operations to national totals by year and aircraft operation and
computed projection factors as future-year 2018 ITN by 2011-year ITN. EPA assigned factors to inventory
SCCs based on the operation type shown in Table 4-40.
Table 4-40. Default national-level factors used to project 2011 base-case aircraft emissions to 2018
see
Description
Projection
Factor
2265008005
Commercial Aircraft: 4-stroke Airport Ground Support Equipment
1.1741
2267008005
Commercial Aircraft: LPG Airport Ground Support Equipment
1.1741
2268008005
Commercial Aircraft: CNG Airport Ground Support Equipment
1.1741
2270008005
Commercial Aircraft: Diesel Airport Ground Support Equipment
1.1741
2275000000
All Aircraft Types and Operations
1.1741
2275001000
Military Aircraft, Total
0.9972
2275020000
Commercial Aviation, Total
1.1741
2275050000
General Aviation, Total
1.0199
2275050011
General Aviation, Piston
1.0199
2275050012
General Aviation, Turbine
1.0199
2275060000
Air Taxi, Total
0.9417
2275060011
Air Taxi, Total: Air Taxi, Piston
0.9417
2275060012
Air Taxi, Total: Air Taxi, Turbine
0.9417
2275070000
Commercial Aircraft: Aircraft Auxiliary Power Units, Total
1.1741
27501015
Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Military; Jet
Engine: JP-5
0.9972
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SCC
Description
Projection
Factor
27502011
Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Commercial; Jet
Engine: Jet A
1.1741
27505001
Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Civil; Piston
Engine: Aviation Gas
1.0199
27505011
Internal Combustion Engines; Fixed Wing Aircraft L & TO Exhaust; Civil; Jet Engine:
Jet A
1.0199
Packet: "PROJECTION_201 l_2018_aircraft_by_airport_21nov2013.txt"
The second set of projection factors was by airport, where EPA projected emissions for each individual
airport based on the following criteria:
•	ITN activity in year 2011 are greater than 1000 for any of the four available modes: commercial,
general aviation, military and air taxi;
•	ITN airport matched to 201 INEIvl
•	ITN activity is not the same for 2011, 2018 and 2025 AND 2035. The rational here is that these ITN
data add no value if 2011 ITN data are used for all future years. These airports were projected based
on the national default method.
•	A hierarchal assignment was applied when the airport emissions in the NEI did not match the type of
ITN information. For example, if an airport in the 201 INEIvl contained only general aviation
emissions (based on NEI SCC), and the ITN data for that airport did not contain general aviation,
then commercial aviation activity was used to project these emissions. There were 11 of 15 possible
hierarchal assignments used in our projection methodology where EPA assigned a "fall-back" ITN
projection method to an NEI airport SCC, and most of these assignments were linked to very small
NEI emissions.
A summary of the national impact of airport-specific and default national projection approaches for airports
is provided in Table 4-41. Most of the significant airports, and hence increased emissions, are projected via
the airport-specific projection packet. Overall, aircraft NOx emissions increase approximately 17% between
2011 and 2018.
Table 4-41. Increases in aircraft emissions by year 2018 from airport-specific and national-level methods

CO
NOx
PM2.5
SO2
VOC
201 INEIvl total
456,212
111,575
7,362
12,563
29,687
Airport-specific increases
34,334
18,897
420
2,024
2,950
National approach increases
2,254
137
31
12
64
Total Increase in 2018
36,558
19,034
451
2,036
3,014
4.2.11 Remaining non-EGU controls and closures (ptnonipm)
This section describes all remaining non-EGU stationary source reductions and closures not already
discussed. These CONTROL packets and CLOSURE packets generally have lesser national-level impact on
future year projections than many of the items above. However, these impacts can be significant locally -
particularly plant closures.
4.2.11.1 Boat Manufacturing MACT (ptnonipm)
Packet: CONTROL_MACT_BoatManuf_2007v5_03aug2012.txt
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EPA included MACT rules where compliance dates were 2011 or later. 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). The national impact of these reductions is 411 tons of VOC.
4.2.11.2CISWI/HWI controls (ptnonipm)
Packet: CONTROL CISWI 201 Iv6_22nov2013.txt
On March 21, 2011, EPA promulgated the revised NSPS and emission guidelines for Commercial and
Industrial Solid Waste Incineration (CISWI) units. This was a response to the voluntary remand that was
granted in 2001 and the vacatur and remand of the CISWI definition rule in 2007. In addition, the standards
re-development included the 5-year technology review of the new source performance standards and
emission guidelines required under Section 129 of the Clean Air Act (CAA). The history of the CISWI
implementation. Baseline and CISWI rule impacts associated with the CISWI rule. EPA mapped the units
from the CISWI baseline and controlled dataset to the 201 INEIvl inventory and because the baseline CISWI
emissions and the 201 INEIvl emissions were not the same, EPA computed percent reductions such that our
future year emissions matched the CISWI controlled dataset values. CISWI reductions limited to SO2
reductions of 1,427 and 1,413 tons in Arkansas and Louisiana, respectively.
4.2.11.3 Remaining facility closures
Packets:
"CLOSURES_EIS_201 lNEIvl_sep2013_25nov2013_vl.txt" &
"CLOSURES_2008_Merged_12nov2013_v0.txt"
This section describes two CLOSURE packets. The first "EIS" packet is from a September 11, 2013
Emissions Inventory System report of post-2011 permanent facility shutdowns, based on facility status code
"PS". The second "Merged" packet is from a concatenation of previous facility and unit-level closure
information used in the 2008 NEI-based emissions modeling platform. The "EIS" closures impact facilities
in 12 states while the "Merged" packet closures are spread out over 34 states. The cumulative reductions in
emissions from this packet are shown in Table 4-42.
Table 4-42. Reductions from all ElS-based and remaining information facility/unit-level closures
Pollutant
Reductions
CO
1,420
nh3
441
NOx
3,117
PM10
1,858
PM2.5
1,613
S02
26,073
VOC
2,207
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4.3 Mobile source projections
Mobile source monthly inventories of onroad and nonroad mobile emissions were created for 2018 using a
combination of the NMIM and the SMOKE-MOVES models. The 2018 onroad emissions account for
changes in activity data and the impact of on-the-books rules including: the Light-Duty Vehicle Tier 2 Rule
(EPA, 2000), the 2007 Heavy Duty Diesel Rule (Transportation. Air Pollution, and Climate Change), the
Mobile Source Air Toxics (MSAT2) Rule (EPA, 2007a), the Renewable Fuel Standard (RFS2) (EPA,
2010a), the LD GHG/CAFE standards for 2012-2016 (EPA, 2010c), the Heavy-Duty Vehicle Greenhouse
Gas Rule (EPA, 201 la), and the Tier 3 Notice of Proposed Rulemaking (NPRM) (Vehicles and Engines).
Local inspection and maintenance (I/M) and other onroad mobile programs are included such as California
LEVIII, the National Low Emissions Vehicle (LEV) and Ozone Transport Commission (OTC) LEV
regulations(Transportation. Air Pollution, and Climate Change).
Nonroad mobile emissions reductions for these years include reductions to locomotives, various nonroad
engines including diesel engines and various marine engine types, fuel sulfur content, and evaporative
emissions standards.
Onroad mobile sources are comprised of several components and are discussed in the next subsection (4.3.1).
Monthly nonroad mobile emission projections are discussed in subsection 4.4. Locomotives and Class 1 and
Class 2 commercial marine vessel (C1/C2 CMV) projections are discussed in subsection 4.5, and Class 3
(C3) CMV projected emissions are discussed in subsection 4.4.2.
4.3.1 Onroad mobile (onroad and onroad_rfl)
The onroad emissions for 2018 use the same SMOKE-MOVES system as for the base year (see Sections
2.3.1 and 2.3.2). Meteorology, speed, spatial and temporal surrogates, representative counties, and fuel
months were the same as for 2011, discussed above.
4.3.1.1 VMT and vehicle population
Estimates of total national Vehicle Miles Travelled (VMT) in 2018 came from DOE's Annual Energy
Outlook (AEO) 2013 transportation projections, specifically the reference case (release dates April 15th-May
2nd 2013). Trends were developed by calculating ratios between 2011 AEO and 2018 AEO estimates and
renormalizing the trends so that a projection of the 201 INEIvl VMT would match the AEO's 2018 total
VMT (across all vehicle types). These ratios were developed for light versus heavy duty and for gasoline
versus diesel vehicle types. The projection factors, the national 201 INEIvl VMT ("VMT_2011") by vehicle
type (SCC7), and the default future VMT ("VMT_2018) by vehicle type are show in Table 4-43.
Table 4-43. Projection factors for 2018 VMT (in millions of miles)
Classification
SCC7
Description
VMT 2011
ratio
VMT 2018
lightgas
2201001
Light Duty Gasoline Vehicles (LDGV)
1,595,751
1.0226
1,631,840
lightgas
2201020
Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5)
682,930
1.0226
698,375
lightgas
2201040
Light Duty Gasoline Trucks 3 & 4 (M6) = LDGT2 (M5)
351,812
1.0226
359,768
heavygas
2201070
Heavy Duty Gasoline Vehicles 2B thru 8B & Buses (HDGV)
98,334
1.1056
108,714
lightgas
2201080
Motorcycles (MC)
19,744
1.0226
20,190
lightdiesel
2230001
Light Duty Diesel Vehicles (LDDV)
4,764
3.8885
18,526
lightdiesel
2230060
Light Duty Diesel Trucks 1 thru 4 (M6) (LDDT)
13,389
3.8885
52,063
heavydiesel
2230071
Heavy Duty Diesel Vehicles (HDDV) Class 2B
6,080
1.2753
7,753
heavydiesel
2230072
Heavy Duty Diesel Vehicles (HDDV) Class 3, 4, & 5
30,625
1.2753
39,055
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heavydiesel
2230073
Heavy Duty Diesel Vehicles (HDDV) Class 6 & 7
48,998
1.2753
62,486
heavydiesel
2230074
Heavy Duty Diesel Vehicles (HDDV) Class 8A & 8B
131,503
1.2753
167,704
heavydiesel
2230075
Heavy Duty Diesel Buses (School & Transit)
8,938
1.2753
11,399
These national SCC7 ratios were applied to the 201 INEIvl VMT to create an EPA estimate of 2018 VMT at
the county, SCC level. The following states/regional organizations provided projected VMT that supplanted
EPA's projected VMT: Connecticut, Georgia, Maine, Maryland33, Michigan, and SEMCOG. Michigan and
SEMCOG provided county total VMT. For these estimates, EPA's 2018 SCC fraction by county (which
takes into account the AEO growth by vehicle and fuel type) were used to distribute the organization's total
VMT to SCC. For the rest of the states, the VMT was either in MOVES county database (CDB) format or
was already distributed to SMOKE SCC (Georgia only34). For these counties, EPA used the state supplied
distribution of VMT to vehicle type and road type (these distributions tended to match their 2011 submittals).
Vehicle population (VPOP) was developed by creating VMT/VPOP ratios from the 201 INEIvl VMT and
201 INEIvl VPOP at the county, vehicle type (SCC7) level. These ratios were applied to the 2018 VMT
(including state updates) to create a 2018 VPOP. The one exception was Georgia that supplied VPOP for the
20 counties in the Atlanta area.
4.3.1.2 Set up and Run MOVES to create EF
Emission factor tables were created by running SMOKE-MOVES using the same procedures and models as
described for 2011 (see the 201 INEIvl TSD and Section 2.3). The same meteorology and the same
representative counties were used. Changes between 2011 and 2018 are predominantly VMT, fuels, national
and local rules, and the model-year distribution of the fleet, which is built into MOVES. Fleet turnover
resulted in a greater fraction of newer vehicles meeting stricter emission standards.
Similar to the 2011 onroad run, two MOVES runs were needed to model 2018: MOVES2010b was used for
refueling and extended idle modes and Tier3NPRM (version of the MOVES model for the Tier 3 proposal)
was used for all other modes. The similarities and differences between the two runs are described in Table
4-44.
Table 4-44. Comparison of MOVES runs for 2018
Element
T3NPRM
MOVES2010b
Code
MOVES20110414a
MOVES20120410
Default database
movestier3db20110512
movesdb20121030
VMT and VPOP
CDBs and state DBs for 26 states
Same as t3nprm
Hydrocarbon speciation
T3FRM2018_natinv_HCspec_SS_M
Same as t3nprm
Fuels
tier3frm_2018_09192013_NOE85forOAQPS
Same as t3nprm
CA LEVIII
ca_standards_SS_20130617 (16 states)
Same as t3nprm
Tier 3 controls
tier3ctldbs 060313
Default
The following states were modeled as having adopted the California LEV III program (see Table 4-45)
33	Maryland's CDBs were missing VMT projections for Howard County (FIPS 24027). This was discovered at a late date;
therefore EPA estimates of VMT and VPOP were used for this one Maryland county.
34	Georgia supplied VMT for the 20 counties in the Atlanta area. For the rest of Georgia, EPA estimates were used.
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Table 4-45. CA LEVIII program states
FIPS
State Name
06
California
09
Connecticut
10
Delaware
23
Maine
24
Maryland
33
New Hampshire
34
New Jersey
36
New York
41
Oregon
42
Pennsylvania
44
Rhode Island
50
Vermont
53
Washington
Fuels were projected into the future using estimates from the AEO2013, release dates April 15th-May 2nd
2013. The AEO2013 projection includes partial implementation of RFS2 in 2018 and assumes that all fuels
have an ethanol content of E10 or greater. The regional fuels in 2011 were projected to 2018 so that some of
the regional variation is preserved but the totals match AEO2013. E85 fuels were removed from the
database and the other fuels were appropriately adjusted because the T3NPRM version of MOVES is unable
to model E85 directly (see Section 3.2.1.2 for a description of how emissions were adjusted to account for
E85). For details on the 2018 speciation of onroad, which is strongly dependent on the fuels, see Section
3.2.1.4.
4.3.1.3 National, California, and Texas adjustments
A set of adjustments were done in SMOKE-MOVES to create 2018 emissions: HDGHG and E85, extended
idle, California emissions, and Texas emissions.
Because the version of MOVES used in the Tier 3 NPRM analysis does not have the capability to model
Heavy-Duty Vehicle Greenhouse Gas Rule (HDGHG) and E85, an interim version of MOVES incorporating
updates for HDGHG and E85 was used to derive the adjustment factors. The medium and heavy duty
greenhouse gas program set in place by the rule begins with 2014 model year and increases in stringency
through 2018. These changes primarily affect long haul diesel, but other heavy-duty source types are also
affected slightly. For additional details, see the documentation for HDGHG rulemaking.35 The HDGHG
rule was implemented in the interim version of MOVES through three key elements. They are: (a) revised
running emission rates for total energy, (b) new aerodynamic coefficients and vehicle masses for use in
MOVES operating mode generation, (c) auxiliary power units (APUs) that largely replace extended idle in
long haul trucks and are added to MOVES as a new process for combination long haul trucks. The affected
MOVES tables are "EmissionRate" and "SourceUseTypePhysics".
EPA conducted a statistical analysis examining the effect of E85 on emissions in comparison to E10. The
dataset included 21 flex-fuel vehicles that were tested on both E10 and E85 over LA92 drive cycle. The
study found that there were no statistically significant difference in THC, NMOG, VOC, NOx, CO and PM
between E10 and E85 (a=0.05). For toxics, E85 showed a statistically significant decrease in emissions of
35 Final Regulatory Impact Analysis, Chapter 5 (RIA) (PDF) (553 pp, EPA-420-R-11-901, August 2011)
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benzene and 1,3-butadiene, and a statistically significant increase in emissions of acetaldehyde,
formaldehyde, and ethanol. The results from the study were incorporated into the version of MOVES used
to develop the adjustment factors by updating the "GeneralFuelRatioExpressioff and "HCSpecation" tables
in MOVES default database. The modeling of E85 also used updated "AVFT\ "FuelFormulatiotf,
"FuelSupply", and "FuelUsageFractiotf tables, reflecting the renewable fuel volumes and market fractions
projected by the Annual Energy Outlook 2013 (AEO2013) Report.36
The two scenarios modeled to develop the adjustment factors were 'pre-HDGHG/without E85' and 'post-
HDGHG/with E85'. All input databases other than the ones related to HDGHG and E85, described above,
were kept the same between the two runs. The emissions inventories for both scenarios were based on an
inventory run at the national scale. MOVES was run separately for January and July in calendar year 20 1 837.
These adjustment factors were national by vehicle type (SCC7), process, month, pollutant, and mode. These
adjustments were only for the rate-per-distance (RPD) and rate-per-vehicle (RPV) processes38.
The second set of adjustment factors was for extended idle. This uses the same approach as was used in
2011 (see the 201 INEIvl TSD for details) except for the VPOP was updated to be consistent with 2018.
These adjustments were by county, vehicle type (long-haul truck SCCs only), and mode (extended idle only)
and impacted the RPV process only.
The third set of adjustment factors was meant to incorporate 2018 emissions provided by California. The
same approach as was used in 2011 was used to match the emissions totals provided by CARB (see Section
2.3.1). The only differences between the 2011 approach and that applied for 2018 are that the latter uses the
2018 emissions from CARB and the 2018 SMOKE-MOVES output (EPA estimates), where the 2018
"CARB emissions" were created by interpolating between the 2017 and 2020 CARB emissions. The
provided CARB emissions were produced from working draft versions of EMFAC2011-LD and
EMFAC2011-HD and include the following heavy duty regulations: chip reflash, extended idling, public
fleet, trash trucks, drayage trucks, and trucks and buses. It does not include the GHG/smartway regulations
for trucks, or the low carbon fuel standard. These adjustment factors are by county, SCC3, pollutant and
impact all processes (RPD, RPV, RPP).
The fourth set of adjustment factors was meant to incorporate emissions provided by Texas. Conceptually,
EPA used the trend of 2011 to 2018 based on EPA's estimates to project Texas' submitted emissions for
2011. Mathematically, this is equivalent to taking the Texas adjustment factors derived for 2011 (see
Section 2.3.1 for details) and applying them directly to EPA's 2018 run. These adjustment factors are by
county, SCC7, pollutant and impact all processes.
Because these adjustment factors are multiplicative, a single set of adjustment factors may be created by
multiplying the 4 adjustment factors together taking care to match process (RPD, RPV, or RPP), mode,
pollutant, SCC, and county. Movesmrg uses the composite adjustment factor file (CFPRO) to estimate 2018
emissions that incorporates each of these adjustments (or a subset of them depending on county, mode, and
process).
36	U.S. Energy Information Administration, Annual Energy Outlook 2013 (April 15, 2013)
37	The January adjustment factors were used for all the winter fuel months and the July adjustment factors were used for all the
summer fuel months.
38	During QA of the adjustment factors, an error was discovered in extended idle for July and evaporative in RPD, RPV, and RPP.
For extended idle, the adjustment factors were replaced with January's adjustment factors. For evaporative, the adjustment factors
were set to 1 for both months. These modifications to the adjustment factors are expected to have minimum impact on the 2018
emissions.
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4.4 Nonroad mobile source projections (c1c2raii, c3marine, nonroad)
The projection of locomotives and Class 1 and 2 commercial marine vessels to 2018 is described in
Section 4.4.1. These sources are treated in shapes in the NEI but are considered at the county-level in the
modeling platform. The projection of the larger Class 3 commercial marine vessels, treated as point sources
in the modeling platform, is described in Section 4.4.2. Most of the remaining sources in the nonroad sector
are projected by running the NMIM model with fuels and vehicle populations appropriate to 2018, as
described in Section 4.4.3.
4.4.1 Locomotives and Class 1 & 2 commercial marine vessels (c1c2rail)
There are three distinct components used to craft year 2018 inventories from the 2011 base case. The first
component of the 2018 clc2rail inventory is the non-California data projected from the 2011 base case. The
second component is the C ARB-supplied year 2017 data for California. The third component is a set of EPA
OTAQ-provided county-specific emissions adjustments that account for different fuel transport
characteristics resulting from the EISA (RFS2) mandate. Specifically, these adjustments reduce finished
petroleum-based fuel transport by rail and barge (CMV) and add ethanol-based finished fuel transport by rail
and barge.
Step 1: Project non-California CMV and rail emissions
Packet: "PROJECTION 201l_2018_clc2rail_BASE_noRFS2_05dec2013.txt"
This packet creates an intermediate set of year 2018 emissions for all states except California. This packet
does not reflect emission impacts from ethanol volume impacts from the EISA (RFS2) mandate; the EISA
impacts are applied for all states in Step 3. This packet consists of national projection factors by SCC and
pollutant between 2011 and 2018 that reflect the May 2004 "Tier 4 emissions standards and fuel
requirements" (Vehicles and Engines) as well as the March 2008 "Final locomotive-marine rule" controls.
These projection ratios are provided in Table 4-46.
Table 4-46. Non-California year 2018 intermediate projection factors for locomotives and Class 1 and
Class 2 Commercial Marine Vessel Emissions
SCC
Description
Pollutant
Projection
Factor
2280002XXX
Marine Vessels, Commercial;Diesel;Underway & port emissions
CO
0.9525
2280002XXX
Marine Vessels, Commercial;Diesel;Underway & port emissions
NOx
0.7623
2280002XXX
Marine Vessels, Commercial;Diesel;Underway & port emissions
PM
0.6755
2280002XXX
Marine Vessels, Commercial;Diesel;Underway & port emissions
S02
0.1275
2280002XXX
Marine Vessels, Commercial;Diesel;Underway & port emissions
VOC
0.7715
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
CO
1.175
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
NOx
0.8123
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
PM
0.6764
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
S02
0.0319
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
VOC
0.6116
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
CO
1.175
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
NOx
1.0576
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
PM
1.0241
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
S02
0.0319
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
VOC
1.1175
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
CO
1.0574
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
NOx
0.6635
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SCC
Description
Pollutant
Projection
Factor
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
PM
0.6052
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
S02
0.0303
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
VOC
0.5316
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
CO
1.0574
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
NOx
0.6635
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
PM
0.6052
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
S02
0.0303
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
VOC
0.5316
2285002010
Railroad Equipment;Diesel;Yard Locomotives
CO
1.175
2285002010
Railroad Equipment;Diesel;Yard Locomotives
NOx
0.9767
2285002010
Railroad Equipment;Diesel;Yard Locomotives
PM
0.9436
2285002010
Railroad Equipment;Diesel;Yard Locomotives
S02
0.0320
2285002010
Railroad Equipment;Diesel;Yard Locomotives
VOC
0.9388
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, and is documented at: Vehicles and Engines.
EPA applied HAP factors for VOC HAPs by using the VOC projection factors to obtain 1,3-butadiene,
acetaldehyde, acrolein, benzene, and formaldehyde. C1/C2 diesel emissions (SCC = 2280002100 and
2280002200) were projected based on the Final Locomotive Marine rule national-level factors. Similar to
locomotives, VOC HAPs were projected based on the VOC factor.
Step 2: Intermediate California year 2018 inventory
Obtained from CARB, the locomotive, and class 1 and 2 commercial marine emissions used for California
reflect year 2017 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. EPA converted the CARB inventory TOG to VOC by
dividing the inventory TOG by the available VOC-to-TOG speciation factor.
Step 3: Adjusting intermediate 2018 clc2rail emissions to reflect the EISA mandate
Rail and barges are 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 2007 and 2018. There is only a small
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difference, not quantified, between ethanol volumes in 2007 and 2011. Emission factors used to make
adjustments were obtained from an EPA locomotive emission factor fact sheet (EPA, 2009e).
In EPA's RFS2 final rule, impacts of these modes of transport of ethanol on combustion emissions from the
CI and C2 CMV and rail inventories were estimated for 2018, based AEO 2013 projections on the difference
between ethanol volumes mandated by EISA versus RFS1 rule volumes (EPA, 2010a). RFS2 rule impacts
were adjusted to account for (a) differences in rail and barges emission rates in 2018 versus 2022, and (b) the
difference in ethanol volume impacts for 2018 under EISA versus the 8.7 billion gallons assumed for the
intermediate 2018 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 of ethanol
transport (Oak Ridge National Laboratory, 2009). These impacts were then applied to the unadjusted
inventory.
These emissions from updated ethanol volumes are not included in the previously-discussed non-California
loco-marine rule-based projections (Step 1) and CARB 2017 inventory (Step 2). Nationally, these additional
emissions are modest and are shown in Table 4-47. The overall difference between 2011 and 2018 clc2rail
sector emissions are provided in Table 4-48. These sector totals include all U.S. states as well as offshore
and Puerto Rico.
Table 4-47. EISA mandate emission adjustments in 2018
Pollutant
C1/C2 CMV
Locomotives
CO
-855
1,715
nh3
-2
5
NOx
-3,635
8,346
PMio
-139
198
PM2.5
-155
-10
S02
-296
80
voc
-136
357
Table 4-48. Difference in clc2rail sector emissions between 2011 and 2018
Pollutant
2011
2018
Difference
CO
242,771
255,496
-12,725
nh3
707
712
5
NOx
1,392,532
1,129,284
-263,248
PM10
46,142
31,963
-14,179
PM2.5
43,491
29,893
-13,598
S02
23,160
3,161
-19,999
VOC
56,543
33,334
-23,209
4.4.2 Class 3 commercial marine vessels (c3marine)
As discussed in Section 2.4.2, the c3marine sector emissions data were developed for year 2002 and
projected to year 2011 for the 2011 base case. The ECA-IMO project provides pollutant and geographic-
specific projection factors to year 2011, and also projection factors to year 2018 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
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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 EC A region. For comparison, with the
exception of some local areas, year 2011 sulfur content limits are as high as 15,000 ppm (1.5%) in U.S.
waters and 45,000 ppm (4.5%) in global areas. More information on the ECA-IMO rule can be found in the
Category 3 marine diesel engines Regulatory Impact Assessment.
Projection factors for creating the year 2018 c3marine inventory from the 2011 base case are provided in
Table 4-49. Background on the region and Exclusive Economic Zone (EEZ) FIPS is provided in the
discussion on the c3marine inventory for 2011 -Section 2.4.2. The impact of the Tier 2 and Tier 3 NOx
engine standards is less noticeable because of the inevitable delay in fleet turnover for these new engines;
however, the immediate and drastic cuts in fuel sulfur content are obvious. VOC and CO are mostly
unaffected by the engine and fuel standards, thus providing an idea on how much these emissions would
have grown without ECA-IMO controls. VOC HAPs are assigned the same growth rates as VOC.
Table 4-49. Growth factors to project the 2011 ECA-IMO inventory to 2018


2018 Adjustment
s Relative to 2011
Region
EEZ
FIPS
NOx
PMio
PM2.5
VOC
(HC)
CO
SO2
East Coast (EC)
85004
1.068
0.556
0.556
1.361
1.361
0.136
Gulf Coast (GC)
85003
0.960
0.504
0.504
1.222
1.222
0.122
North Pacific (NP)
85001
1.014
0.501
0.501
1.255
1.255
0.126
South Pacific (SP)
85002
1.121
0.593
0.593
1.421
1.420
0.144
Great Lakes (GL)
n/a
1.027
0.444
0.444
1.125
1.125
0.113
Outside ECA
98001
1.217
1.356
1.356
1.356
1.356
1.356
As discussed in Section 2.4.2, emissions outside the 3 to 10 mile coastal boundary but within the
approximately 200 nm EEZ boundaries were projected to year 2018 using the same regional adjustment
factors as the U.S. emissions; however, the FIPS codes were assigned as "EEZ" FIPS and these, as well as
Canada C3 CMV, emissions are processed in the "othpt" sector (see Section 2.5.1 and 4.4.1). Note that state
boundaries in the Great Lakes are an exception, extending through the middle of each lake such that all
emissions in the Great Lakes are assigned to a U.S. county or Ontario. The classification of emissions to
U.S. and Canadian FIPS codes is needed to avoid double-counting of C3 CMV U.S. emissions in the Great
Lakes because, as discussed in Section 2.4.1, all CMV emissions in the Midwest RPO are processed in the
"clc2rail" sector.
4.4.3 Other nonroad mobile sources (nonroad)
This sector includes monthly exhaust, evaporative and refueling emissions from nonroad engines (not
including commercial marine, aircraft, and locomotives) derived from NMIM for all states except California
and Texas. Similar to the onroad emissions, NMIM provides nonroad emissions for VOC by three emission
modes: exhaust, evaporative and refueling.
With the exception of California and Texas, U.S. emissions for the nonroad sector (defined as the equipment
types covered by the NONROAD model) were created using a consistent NMIM-based approach as was
used for 2011. Specifically, NMIM utilized NONROAD2008a including future-year equipment population
estimates, control programs to the year 2018, and inputs either state-supplied as part of the 201 INEIvl
process or national level inputs. Fuels for 2018 were assumed to be E10 everywhere for nonroad equipment.
The fuels were developed from the MOVES fuels, which in turn were developed to be consistent with
128
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AEO2013 projections for 2018. The databases used in this run were NMIM county database
"NCD20130731_nei2018dvl" and fuels database "tier3frm2018ctrlfuels_03152013_ elOfuelsNMIM " The
2018 emissions account for increases in activity (based on NONROAD model default growth estimates of
future-year equipment population), changes in fuels and engines that reflect implementation of national
regulations and local control programs that impact each year differently due to engine turnover. For details
on the 2018 speciation of nonroad, see Section 3.2.1.4.
The version of NONROAD used was the current public release, NR08a, which models all in-force nonroad
controls. Recent rules include:
•	"Clean Air Nonroad Diesel Final Rule - Tier 4", published June, 2004:
•	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 and Texas nonroad emissions
Similar to the 2011 base nonroad mobile, NMIM was not used to generate future-year nonroad emissions for
California, other than for NH3. EPA used NMIM for California future nonroad NH3 emissions because
CARB did not provide these data for any nonroad vehicle types. For the rest of the pollutants, the CARB-
supplied 2017 nonroad annual inventory were distributed to monthly emissions values by using the 2018
NMIM monthly inventories to compute monthly ratios by county, SCC7, mode and pollutant, which was
consistent with the approach in 2011 (see Section 2.4.3). Some adjustments to the CARB inventory were
needed to convert the provided TOG to VOC. See Section 3.2.1.3 for details on speciation of California
nonroad data see Section 3.2.1.3)39. . The CARB nonroad emissions include nonroad rules reflected in the
December 2010 Rulemaking Inventory and those in the March 2011 Rule Inventory, the Off-Road
Construction Rule Inventory for "In-Use Diesel".
For Texas, EPA combined Texas' submitted estimates for 2011 with EPA projections of nonroad emissions
into 2018. Specifically, projections were based on state-wide SCC7, mode, poll ratios40 of 2018 NMIM to
2011 NMIM. These ratios were then applied to Texas' submitted 2011 emissions inventory, which had
already been distributed to a monthly inventory (see Section 2.4.3), to create a 2018 monthly nonroad
inventory.
4.5 "Other Emissions": Offshore Class 3 commercial marine vessels
and drilling platforms, Canada and Mexico (othpt, othar, and othon)
Recall from Section 2.5, that emissions from Canada, Mexico, and non-U.S. offshore Class 3 Commercial
Marine Vessels (C3 CMV) and drilling platforms are included as part of three emissions modeling sectors:
othpt, othar, and othon. Non C3 CMV emissions for Canada and offshore sources were not projected to
future years, and are therefore the same as those used in the 2011 base case. Canada did not provide future-
39 In addition, airport equipment was removed from CARB's inventory because these sources were modeled elsewhere.
411 These ratios were initially attempted by county/SCC7/mode/pollutant but due to significantly different distributions of certain
source types between EPA and TCEQ's emissions, this created unreasonable growth in certain areas. The above approach was
used except in the following, relatively limited conditions. If a state/SCC7/mode/pollutant was in EPA 2018 emissions but not in
EPA's 2011 emissions, 2018 EPA emissions were used in the final inventory. If a state/SCC7/mode/pollutant was in TCEQ's
2011 emissions but was not in EPA's 2018 emissions, then state/SCC3/mode/pollutant ratios were used to project to 2018.
129
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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 is available at WRAP.
4.5.1	Point sources from offshore C3 CMV and drilling platforms and Canada and
Mexico (othpt)
As discussed in Section 2.5.1, the ECA-IMO-based C3 CMV emissions for non-U.S. states are processed in
the othpt sector. These C3 CMV emissions include those assigned to Canada, those assigned to the
Exclusive Economic Zone (defined as those emissions just beyond U.S. waters approximately 3-10 miles
offshore, extending to about 200 nautical miles from the U.S. coastline), and all other offshore emissions -
far offshore and non-U.S. EPA processed these emissions in the othpt sector for simplicity of creating U.S.-
only emissions summaries. Otherwise, these emissions are processed in the same way as the U.S. C3 CMV
emissions in the c3marine sector. The projection factors for the othpt C3 CMV emissions vary by
geographic and region as shown in Table 4-c3. C3 CMV emissions in British Columbia were assigned as
North Pacific, Ontario as Great Lakes, and all other eastern Canada provinces as East Coast.
Mexico point-format year-2018 inventories are used essentially as-is with only minor formatting changes.
The othpt sector also includes point source offshore oil and gas drilling platforms that are beyond U.S. state-
county boundaries in the Gulf of Mexico. EPA used emissions from the 2008NEIv2 point source inventory
for both 2011 and 2018. EPA expects updated offshore oil and gas drilling emissions in the next version of
the 2011 NEI (Version 2).
4.5.2	Area, nonroad mobile and onroad mobile sources from Canada and Mexico
(other, othon)
Both year-2006 Canada and year-2018 Mexico inventories were converted from their original SMOKE One-
Record per Line (ORL) and Inventory Data Analyzer (IDA) formats, respectively, to SMOKE Flat File 10
(FF10) inventory format. Otherwise, these inventories were used as-is.
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5 Emission Summaries
The following tables summarize emissions differences between the 2011 evaluation case and the 2018 base
case. These summaries are provided at the national level by sector for the contiguous U.S. and for the
portions of Canada and Mexico inside the smaller 12km domain (12US2) discussed in Section 3.1. The
afdust sector emissions represent the summaries after application of both the land use (transport fraction) and
meteorological adjustments (see Section 2.2.1); therefore, this sector is called "afdust adj" in these
summaries. The onroad and onroad refueling (onroad rfl) sector totals are post-SMOKE-MOVES totals,
representing air quality model-ready emission totals, and the onroad portion include CARB emissions for
California. The "c3marine-US" sector represents c3marine sector emissions with U.S. FIPS only; these
extend to roughly 3-5 miles offshore and all U.S. waters in the Great Lakes and also include all U.S. ports.
The "c3marine, EEZ component" represents all non-U.S. c3marine emissions that are within the (up to) 200
nautical mile Exclusive Economic Zone (EEZ) boundary but outside of U.S. state waters. Finally, the
"c3marine, non-US non-EEZ component" represents all non-U.S. emissions outside of the (up to) 200nm
offshore boundary, including all Canadian and Mexican c3marine emissions. The c3marine sector is
discussed in Section 2.4.2. The "Off-shore othpt" sector is the non-Canada, 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 2011
evaluation case in Table 5-1. The total of all sectors in the 2011 evaluation case are listed as "Con U.S.
Total w/ ptfire". Table 5-2 provides national emissions totals by sector for all CAPs in the 2018 base case.
Table 5-3 provides national-by sector emission summaries for CO for all the cases: 2011 evaluation case and
2018 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 forNFb, NOx, PM2.5, PM10, SO2 and VOC, respectively. These national tables also include
differences and percent differences for each modeling sector between the 2011 evaluation case and the 2018
base case. Note that the same ptfire emissions are used in both cases.
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Table 5-1. National by-sector CAP emissions summaries for 2011 evaluation case
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj



18,502,317
2,487,403


ag

3,517,371





clc2rail
173,347
484
1,051,169
34,865
32,557
18,975
48,818
c3marine
13,403

131,301
4,384
3,992
40,692
5,110
nonpt
3,046,375
142,323
832,166
715,709
533,248
392,638
3,792,612
np oilgas
642,182
0
653,219
21,756
17,200
17,195
2,273,214
nonroad
13,952,389
2,627
1,630,409
162,420
154,660
4,031
2,024,633
onroad
25,473,866
118,124
5,666,702
285,112
205,145
27,915
2,287,603
onroad rfl






157,629
ptfire
22,580,113
362,910
347,103
2,362,132
2,005,142
177,107
5,174,593
ptegu
724,444
21,944
1,990,884
266,633
193,877
4,614,299
32,376
ptegu pk
8,662
425
21,941
2,159
1,886
28,476
783
ptnonipm
2,568,080
74,847
1,771,835
494,639
339,240
1,071,982
873,159
pt oilgas
22,218
112
22,091
1,887
1,857
55,273
89,755
rwc
2,578,229
20,343
35,672
389,019
388,288
8,986
446,972
Con U.S. Total
71,783,308
4,261,510
14,154,492
23,243,031
6,364,495
6,457,569
17,207,256
Off-shore to EEZ*
130,419
0
610,664
16,961
15,525
133,606
81,286
Non-US SECA C3
17,169
0
202,516
17,199
15,823
127,563
7,297
Canada othar
2,810,350
386,147
462,996
810,747
248,907
61,179
932,322
Canada othon
3,303,239
17,572
392,209
11,075
7,712
4,046
199,939
Canada othpt* *
560,661
15,543
369,993
65,782
39,828
825,675
157,170
Mexico othar
439,901
109,861
189,592
69,523
23,600
26,559
499,145
Mexico othon
423,978
3,247
76,880
7,593
6,970
1,413
73,888
Mexico othpt
116,609
0
414,399
137,512
101,884
828,418
83,838
Non-US Total
7,802,326
532,370
2,719,249
1,136,392
460,249
2,008,459
2,034,885
* "Offshore to EEZ" includes both the offshore point emissions, and the "Offshore to EEZ" c3marine
emissions
** Canadian c3 emissions are included in "Canada othpt"
132
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Table 5-2. National by-sector CAP emissions summaries for 2018 base case
Sector
CO
NH3
NOX
PM10
PM2 5
S02
voc
afdust adj



6,741,452
923,168


ag

3,596,908





clc2rail
189,355
489
869,089
24,346
22,508
2,628
33,334
c3marine
17,518

136,147
2,338
2,129
5,354
6,678
nonpt
3,058,148
142,384
847,975
720,106
536,477
304,514
3,634,506
np oilgas
782,408
0
795,491
27,248
21,565
25,488
2,555,021
nonroad
12,377,375
2,900
1,071,612
107,005
100,949
1,868
1,360,554
onroad
15,469,627
86,826
2,647,482
208,598
125,173
12,418
1,133,928
onroad rfl






74,386
ptfire
22,580,113
362,910
347,103
2,362,132
2,005,142
177,107
5,174,593
ptegu
752,467
39,629
1,486,128
256,679
199,186
1,443,777
39,227
ptegu pk
11,249
439
9,954
247
215
3,432
313
ptnonipm
2,419,986
75,822
1,768,859
466,566
316,328
720,681
870,202
pt oilgas
25,493
159
25,970
2,062
2,026
64,076
106,345
rwc
2,736,854
21,485
38,434
413,597
412,852
10,018
466,259
Con U.S. Total
60,420,596
4,329,951
10,044,245
11,332,376
4,667,720
2,771,361
15,455,347
Off-shore to EEZ*
146,323

635,570
9,630
8,841
18,746
88,045
Non-US SECA C3
23,318

246,579
23,327
21,462
173,124
9,896
Canada othar
2,810,350
386,147
462,996
810,747
248,907
61,179
932,322
Canada othon
3,303,239
17,572
392,209
11,075
7,712
4,046
199,939
Canada othpt
561,438
15,543
370,944
65,276
39,370
818,374
157,501
Mexico othar
527,917
109,840
226,341
70,916
47,191
19,286
577,078
Mexico othon
397,197
4,465
46,794
9,420
8,591
659
62,948
Mexico othpt
148,758

544,690
170,910
127,734
1,066,482
94,351
Non-US Total
7,918,540
533,567
2,926,123
1,171,301
509,808
2,161,896
2,122,080
133
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DRAFT
Table 5-3. National by-sector CO emissions (tons/yr) summaries with differences
Sector
2011 CO
2018 CO
2018-2011
% Change
afdustadj




ag




clc2rail
173,347
189,355
16,008
9%
c3marine-US
13,403
17,518
4,115
31%
nonpt
3,046,375
3,058,148
11,773
0%
np_oilgas
642,182
782,408
140,226
22%
nonroad
13,952,389
12,377,375
-1,575,014
-11%
onroad
25,473,866
15,469,627
-10,004,239
-39%
onroadrfl




ptfire
22,580,113
22,580,113
0
0%
ptegu
724,444
752,467
28,023
4%
ptegu_pk
8,662
11,249
2,587
30%
ptnonipm
2,568,080
2,419,986
-148,094
-6%
pt_oilgas
22,218
25,493
3,275
15%
rwc
2,578,229
2,736,854
158,625
6%
Con U.S. Total
71,783,308
60,420,593
-11,362,715
-16%
Off-shore to EEZ*
130,419
146,323
15,904
12%
Non-US SECA C3
17,169
23,318
6,149
36%
Canada othar
2,810,350
2,810,350
0
0%
Canada othon
3,303,239
3,303,239
0
0%
Canada othpt* *
560,661
561,438
777
0%
Mexico othar
439,901
527,917
88,016
20%
Mexico othon
423,978
397,197
-26,781
-6%
Mexico othpt
116,609
148,758
32,149
28%
Non-US Total
7,802,326
7,918,540
116,214
1%
134
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DRAFT
Table 5-4. National by-sector NH3 emissions (tons/yr) summaries with differences
Sector
2011 NH3
2018 NH3
2018-2011
% Change
afdustadj




ag
3,517,371
3,596,908
79,537
2%
clc2rail
484
489
5
1%
c3marine-US




nonpt
142,323
142,384
61
0%
np_oilgas




nonroad
2,627
2,900
273
10%
onroad
118,124
86,826
-31,298
-26%
onroadrfl




ptfire
362,910
362,910
0
0%
ptegu
21,944
39,629
17,685
81%
ptegu_pk
425
439
14
3%
ptnonipm
74,847
75,822
975
1%
pt_oilgas
112
159
47
42%
rwc
20,343
21,485
1,142
6%
Con U.S. Total
4,261,510
4,329,951
68,441
2%
Off-shore to EEZ*




Non-US SECA C3




Canada othar
386,147
386,147
0
0%
Canada othon
17,572
17,572
0
0%
Canada othpt* *
15,543
15,543
0
0%
Mexico othar
109,861
109,840
-21
0%
Mexico othon
3,247
4,465
1,218
38%
Mexico othpt




Non-US Total
532,370
533,567
1,197
0%
135
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DRAFT
Table 5-5. National by-sector NOx emissions (tons/yr) summaries with differences
Sector
2011 NOx
2018 NOx
2018-2011
% Change
afdustadj




ag




clc2rail
1,051,169
869,089
-182,080
-17%
c3marine-US
131,301
136,147
4,846
4%
nonpt
832,166
847,975
15,809
2%
np_oilgas
653,219
795,491
142,272
22%
nonroad
1,630,409
1,071,612
-558,797
-34%
onroad
5,666,702
2,647,482
-3,019,220
-53%
onroadrfl




ptfire
347,103
347,103
0
0%
ptegu
1,990,884
1,486,128
-504,756
-25%
ptegu_pk
21,941
9,954
-11,987
-55%
ptnonipm
1,771,835
1,768,859
-2,976
0%
pt_oilgas
22,091
25,970
3,879
18%
rwc
35,672
38,434
2,762
8%
Con U.S. Total
14,154,492
10,044,244
-4,110,248
-29%
Off-shore to EEZ*
610,664
635,570
24,906
4%
Non-US SECA C3
202,516
246,579
44,063
22%
Canada othar
462,996
462,996
0
0%
Canada othon
392,209
392,209
0
0%
Canada othpt* *
369,993
370,944
951
0%
Mexico othar
189,592
226,341
36,749
19%
Mexico othon
76,880
46,794
-30,086
-39%
Mexico othpt
414,399
544,690
130,291
31%
Non-US Total
2,719,249
2,926,123
206,874
8%
136
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DRAFT
Table 5-6. National by-sector PM2.5 emissions (tons/yr) summaries with differences
Sector
2011 PM25
2018 PM2.s
2018-2011
% Change
afdustadj
913,525
923,168
9,643
1%
ag




clc2rail
32,557
22,508
-10,049
-31%
c3marine-US
3,992
2,129
-1,863
-47%
nonpt
533,248
536,477
3,229
1%
np_oilgas
17,200
21,565
4,365
25%
nonroad
154,660
100,949
-53,711
-35%
onroad
205,145
125,173
-79,972
-39%
onroadrfl




ptfire
2,005,142
2,005,142
0
0%
ptegu
188,811
199,186
10,375
5%
ptegu_pk
1,886
215
-1,671
-89%
ptnonipm
339,240
316,328
-22,912
-7%
pt_oilgas
1,857
2,026
169
9%
rwc
388,288
412,852
24,564
6%
Con U.S. Total
4,785,551
4,667,718
-117,833
-2%
Off-shore to EEZ*
15,525
8,841
-6,684
-43%
Non-US SECA C3
15,823
21,462
5,639
36%
Canada othar
248,907
248,907
0
0%
Canada othon
7,712
7,712
0
0%
Canada othpt* *
39,828
39,370
-458
-1%
Mexico othar
23,600
47,191
23,591
100%
Mexico othon
6,970
8,591
1,621
23%
Mexico othpt
101,884
127,734
25,850
25%
Non-US Total
460,249
509,808
49,559
11%
137
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DRAFT
Table 5-7. National by-sector PMio emissions (tons/yr) summaries with differences
Sector
2011 PMi„
2018 PMi„
2018-2011
% Change
afdustadj
6,663,357
6,741,452
78,095
1%
ag




clc2rail
34,865
24,346
-10,519
-30%
c3marine-US
4,384
2,338
-2,046
-47%
nonpt
715,709
720,106
4,397
1%
np_oilgas
21,756
27,248
5,492
25%
nonroad
162,420
107,005
-55,415
-34%
onroad
285,112
208,598
-76,514
-27%
onroadrfl




ptfire
2,362,132
2,362,132
0
0%
ptegu
266,633
256,679
-9,954
-4%
ptegu_pk
2,159
247
-1,912
-89%
ptnonipm
494,639
466,566
-28,073
-6%
pt_oilgas
1,887
2,062
175
9%
rwc
389,019
413,597
24,578
6%
Con U.S. Total
11,404,072
11,332,376
-71,696
-1%
Off-shore to EEZ*
16,961
9,630
-7,331
-43%
Non-US SECA C3
17,199
23,327
6,128
36%
Canada othar
810,747
810,747
0
0%
Canada othon
11,075
11,075
0
0%
Canada othpt* *
65,782
65,276
-506
-1%
Mexico othar
69,523
70,916
1,393
2%
Mexico othon
7,593
9,420
1,827
24%
Mexico othpt
137,512
170,910
33,398
24%
138
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DRAFT
Table 5-8. National by-sector SO2 emissions (tons/yr) summaries with differences
Sector
2011 S02
2018 SO2
2018-2011
% Change
afdustadj




ag




clc2rail
18,975
2,628
-16,347
-86%
c3marine-US
40,692
5,354
-35,338
-87%
nonpt
392,638
304,514
-88,124
-22%
np_oilgas
17,195
25,488
8,293
48%
nonroad
4,031
1,868
-2,163
-54%
onroad
27,915
12,418
-15,497
-56%
onroadrfl




ptfire
177,107
177,107
0
0%
ptegu
4,614,299
1,443,777
-3,170,522
-69%
ptegu_pk
28,476
3,432
-25,044
-88%
ptnonipm
1,071,982
720,681
-351,301
-33%
pt_oilgas
55,273
64,076
8,803
16%
rwc
8,986
10,018
1,032
11%
Con U.S. Total
6,457,569
2,771,361
-3,686,208
-57%
Off-shore to EEZ*
133,606
18,746
-114,860
-86%
Non-US SECA C3
127,563
173,124
45,561
36%
Canada othar
61,179
61,179
0
0%
Canada othon
4,046
4,046
0
0%
Canada othpt* *
825,675
818,374
-7,301
-1%
Mexico othar
26,559
19,286
-7,273
-27%
Mexico othon
1,413
659
-754
-53%
Mexico othpt
828,418
1,066,482
238,064
29%
Non-US Total
2,008,459
2,161,896
153,437
8%
139
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DRAFT
Table 5-9. National by-sector VOC emissions (tons/yr) summaries with differences
Sector
2011 VOC
2018 VOC
2018-2011
% Change
afdustadj




ag




clc2rail
48,818
33,334
-15,484
-32%
c3marine-US
5,110
6,678
1,568
31%
nonpt
3,792,612
3,634,506
-158,106
-4%
np_oilgas
2,273,214
2,555,021
281,807
12%
nonroad
2,024,633
1,360,554
-664,079
-33%
onroad
2,287,603
1,133,928
-1,153,675
-50%
onroadrfl
157,629
74,386


ptfire
5,174,593
5,174,593
0
0%
ptegu
32,376
39,227
6,851
21%
ptegu_pk
783
313
-470
-60%
ptnonipm
873,159
870,202
-2,957
0%
pt_oilgas
89,755
106,345
16,590
18%
rwc
446,972
466,259
19,287
4%
Con U.S. Total
17,207,257
15,455,346
-1,751,911
-10%
Off-shore to EEZ*
81,286
88,045
6,759
8%
Non-US SECA C3
7,297
9,896
2,599
36%
Canada othar
932,322
932,322
0
0%
Canada othon
199,939
199,939
0
0%
Canada othpt* *
157,170
157,501
331
0%
Mexico othar
499,145
577,078
77,933
16%
Mexico othon
73,888
62,948
-10,940
-15%
Mexico othpt
83,838
94,351
10,513
13%
Non-US Total
2,034,885
2,122,080
87,195
4%
140
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DRAFT
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United States	Office of Air Quality Planning and Standards	Publication No. EPA-454/D-20-001
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